===================================================Date: Tue, 1 Feb 2011 22:05:11 -0500 Reply-To: Leanne Wilkins <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Leanne Wilkins <[log in to unmask]> Subject: Voxel Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> We are currently doing an ROI analysis with several different contrasts. I was wondering if there was an SPM script that would permit me to identify the top voxel activated in each ROI? I am new to SPM, I hope my question makes sense. Leanne ========================================================================Date: Wed, 2 Feb 2011 16:15:32 +1300 Reply-To: Reem Jan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Reem Jan <[log in to unmask]> Subject: Re: Missing condition at first level - what to input in the onset tab? In-Reply-To: A<[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Thank you Cyril, Michael and Donald for all the useful information you have given me regarding this. There are 6 runs/sessions and 4 conditions per subject. I have done the following. If condition A is missing from run 1 and 2 let's say.. I remove condition A from that run (in the first level model specification), and then in the contrast manager when I specify condition A's contrast I type: 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0 And if condition A is missing only from run 2: 0.25 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0 And I also include 6 zeros after each run for the motion regressors (which I haven't typed out here for simplicity's sake). I hope this is right? Kind regards Reem -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Michael Harms Sent: Wednesday, February 02, 2011 7:13 AM To: [log in to unmask] Subject: Re: [SPM] Missing condition at first level - what to input in the onset tab? Well, "reasonably" sure, although happy for someone else to chime in to the contrary. Many different contrasts can be technically valid (i.e., "estimable"). In the example in question, the contrast I proposed would test for an expected difference between the mean of condition A and B of zero, whereas in your original contrast the expected value of the contrast would not be zero under the null hypothesis of no difference between A and B. In effect, I believe that by using a factor of 6/5 on the 5 A conditions, one achieves the desired "upweighting" of the variance associated with the condition A estimate (relative to the condition B estimate). cheers, -MH On Tue, 2011-02-01 at 13:56 +0000, Cyril Pernet wrote: > Hi Michael > > r u sure? ok beta = pinv(X)*Y so we don't really care about the bias (I > was referring to nb of stimuli and variance estimation) > > for the contrast with 5A and 6B using 1/5 1/6 you average 5 sessions vs > 6 which of course works as well but you may want to somehow put this > unbalance in your contrast? I guess it's a matter of choice - note that > C is unchanged by the post multiplication by inv(X'X')X'X using my > contrast so it is still valid too .. contrast don't have to sum up to 0 > (oh yeh my full contrast wasn't right since I copied/pasted 6 times .. > but I'm sure you got the gesture). > > I actually never had this problem - was offering a possible solution but > yours seems good too - anybody out there checked this up before? > > Cyril > > > > Hi Cyril, > > Even if you don't have equal numbers, the estimated betas are themselves > > still unbiased. Thus, if you are going to compare two conditions, it > > seems to me that you still want the contrast to sum to 0. In the example > > you gave, if you wanted to compare the mean level of A to the mean level > > of B, with no estimate of A available from session 3, I think that you > > would want the following contrast: > > 1/5 -1/6 0 1/5 -1/6 0 0 -1/6 0 1/5 -1/6 0 1/5 -1/6 0 1/5 -1/6 0 > > or multiplying by 6: > > 6/5 -1 0 6/5 -1 0 0 -1 0 6/5 -1 0 6/5 -1 0 6/5 -1 0 > > (note the 0 in the "A" position of the 3rd session). > > > > cheers, > > -MH > > > >> Reem > >> > >> It doesn't really matter that in each session you don't have the same > >> number of stimuli per condition as long as across your 6 sessions you > >> end up with equal numbers (will work as well if not but it's not as good > >> because variance estimation can be biased). As for the different number > >> of conditions you can weight your contrast accordingly. Simply create 6 > >> sessions in SPM and your 2 or 3 conditions in each sessions. Let say > >> your design runs like this: > >> > >> Session 1 A B C > >> Session 2 A B > >> Session 3 B C > >> Session 4 A B C > >> Session 5 A B C > >> Session 6 A B > >> = 5*A 6*B 4*C > >> = 30/6*A 36/6*B 26/6*C (I choose to have 6 as denominator because you > >> have 6 sessions) > >> > >> Let say you want to test A - B then use a contrast [30/36 -1 0 30/36 > >> -1 0 30/36 -1 0 30/36 -1 0 30/36 -1 0 30/36 -1 0] > >> How do I end up with 30/36 and -1 --> 1/6 * 30/6 = 30/36 and -1/6 * 36/6 > >> = -1 (I use 1/6 for each session since you have 6 sessions) > >> The sum doesn't end up to 1 but the contrast should still be valid .. > >> (at least using a simple design on my machine with unbalanced design and > >> manual checking the contrast is valid - check in SPM I think it is ok) > >> > >> Good luck > >> Cyril > >> > >> > >> > >>> Dear SPMers > >>> > >>> I am trying run first level analysis on fMRI data in SPM8. > >>> > >>> The experiment design involves 6 runs/sessions, each run/session > >>> consists of 8 stimuli (48 stimuli in total). > >>> > >>> There are 3 experimental conditions spread out equally across stimuli > >>> i.e. There are 16 stimuli of each condition in total. > >>> > >>> Because the stimuli were presented at random and are event-related, I am > >>> now running into a problem because in some of the runs of 8 stimuli, > >>> there happens to be only 2 of the conditions presented and not 3. In > >>> these cases, I'm not sure what to input in the 'onsets' tab of first > >>> level model specification for the 3rd condition. > >>> > >>> Is there a way around this problem. I'd be very appreciative of some > >>> help. > >>> > >>> Kind regards > >>> Reem > >>> > >> > >> -- > >> The University of Edinburgh is a charitable body, registered in > >> Scotland, with registration number SC005336. > >> > > > > > > __________ Information from ESET NOD32 Antivirus, version of virus signature database 5838 (20110201) __________ The message was checked by ESET NOD32 Antivirus. http://www.eset.com __________ Information from ESET NOD32 Antivirus, version of virus signature database 5838 (20110201) __________ The message was checked by ESET NOD32 Antivirus. http://www.eset.com ========================================================================Date: Tue, 1 Feb 2011 22:22:52 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Voxel Comments: To: Leanne Wilkins <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> If you are doing an ROI analysis, then you don't have voxel data anymore. If you are doing a whole brain analysis and using a mask of a specific ROI (not an ROI analysis), then clicking whole brain or current cluster will give you the peak voxel. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Tue, Feb 1, 2011 at 10:05 PM, Leanne Wilkins <[log in to unmask]> wrote: > We are currently doing an ROI analysis with several different contrasts. I was wondering if there was an SPM script that would permit me to identify the top voxel activated in each ROI? > > I am new to SPM, I hope my question makes sense. > > Leanne > ========================================================================Date: Tue, 1 Feb 2011 22:23:45 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Missing condition at first level - what to input in the onset tab? Comments: To: Reem Jan <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The second contrast should use .2 not .25. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Tue, Feb 1, 2011 at 10:15 PM, Reem Jan <[log in to unmask]> wrote: > Thank you Cyril, Michael and Donald for all the useful information you > have given me regarding this. > > There are 6 runs/sessions and 4 conditions per subject. > > I have done the following. > > If condition A is missing from run 1 and 2 let's say.. I remove > condition A from that run (in the first level model specification), and > then in the contrast manager when I specify condition A's contrast I > type: > > 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0 > > And if condition A is missing only from run 2: > > 0.25 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0 > > And I also include 6 zeros after each run for the motion regressors > (which I haven't typed out here for simplicity's sake). > > I hope this is right? > > Kind regards > Reem > > -----Original Message----- > From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] > On Behalf Of Michael Harms > Sent: Wednesday, February 02, 2011 7:13 AM > To: [log in to unmask] > Subject: Re: [SPM] Missing condition at first level - what to input in > the onset tab? > > Well, "reasonably" sure, although happy for someone else to chime in to > the contrary. Many different contrasts can be technically valid (i.e., > "estimable"). In the example in question, the contrast I proposed would > test for an expected difference between the mean of condition A and B of > zero, whereas in your original contrast the expected value of the > contrast would not be zero under the null hypothesis of no difference > between A and B. > > In effect, I believe that by using a factor of 6/5 on the 5 A > conditions, one achieves the desired "upweighting" of the variance > associated with the condition A estimate (relative to the condition B > estimate). > > cheers, > -MH > > > On Tue, 2011-02-01 at 13:56 +0000, Cyril Pernet wrote: >> Hi Michael >> >> r u sure? ok beta = pinv(X)*Y so we don't really care about the bias > (I >> was referring to nb of stimuli and variance estimation) >> >> for the contrast with 5A and 6B using 1/5 1/6 you average 5 sessions > vs >> 6 which of course works as well but you may want to somehow put this >> unbalance in your contrast? I guess it's a matter of choice - note > that >> C is unchanged by the post multiplication by inv(X'X')X'X using my >> contrast so it is still valid too .. contrast don't have to sum up to > 0 >> (oh yeh my full contrast wasn't right since I copied/pasted 6 times .. > >> but I'm sure you got the gesture). >> >> I actually never had this problem - was offering a possible solution > but >> yours seems good too - anybody out there checked this up before? >> >> Cyril >> >> >> > Hi Cyril, >> > Even if you don't have equal numbers, the estimated betas are > themselves >> > still unbiased. Thus, if you are going to compare two conditions, > it >> > seems to me that you still want the contrast to sum to 0. In the > example >> > you gave, if you wanted to compare the mean level of A to the mean > level >> > of B, with no estimate of A available from session 3, I think that > you >> > would want the following contrast: >> > 1/5 -1/6 0 1/5 -1/6 0 0 -1/6 0 1/5 -1/6 0 1/5 -1/6 0 1/5 -1/6 0 >> > or multiplying by 6: >> > 6/5 -1 0 6/5 -1 0 0 -1 0 6/5 -1 0 6/5 -1 0 6/5 -1 0 >> > (note the 0 in the "A" position of the 3rd session). >> > >> > cheers, >> > -MH >> > >> >> Reem >> >> >> >> It doesn't really matter that in each session you don't have the > same >> >> number of stimuli per condition as long as across your 6 sessions > you >> >> end up with equal numbers (will work as well if not but it's not as > good >> >> because variance estimation can be biased). As for the different > number >> >> of conditions you can weight your contrast accordingly. Simply > create 6 >> >> sessions in SPM and your 2 or 3 conditions in each sessions. Let > say >> >> your design runs like this: >> >> >> >> Session 1 A B C >> >> Session 2 A B >> >> Session 3 B C >> >> Session 4 A B C >> >> Session 5 A B C >> >> Session 6 A B >> >> = 5*A 6*B 4*C >> >> = 30/6*A 36/6*B 26/6*C (I choose to have 6 as denominator because > you >> >> have 6 sessions) >> >> >> >> Let say you want to test A - B then use a contrast [30/36 -1 0 > 30/36 >> >> -1 0 30/36 -1 0 30/36 -1 0 30/36 -1 0 30/36 -1 0] >> >> How do I end up with 30/36 and -1 --> 1/6 * 30/6 = 30/36 and -1/6 > * 36/6 >> >> = -1 (I use 1/6 for each session since you have 6 sessions) >> >> The sum doesn't end up to 1 but the contrast should still be valid > .. >> >> (at least using a simple design on my machine with unbalanced > design and >> >> manual checking the contrast is valid - check in SPM I think it is > ok) >> >> >> >> Good luck >> >> Cyril >> >> >> >> >> >> >> >>> Dear SPMers >> >>> >> >>> I am trying run first level analysis on fMRI data in SPM8. >> >>> >> >>> The experiment design involves 6 runs/sessions, each run/session >> >>> consists of 8 stimuli (48 stimuli in total). >> >>> >> >>> There are 3 experimental conditions spread out equally across > stimuli >> >>> i.e. There are 16 stimuli of each condition in total. >> >>> >> >>> Because the stimuli were presented at random and are > event-related, I am >> >>> now running into a problem because in some of the runs of 8 > stimuli, >> >>> there happens to be only 2 of the conditions presented and not 3. > In >> >>> these cases, I'm not sure what to input in the 'onsets' tab of > first >> >>> level model specification for the 3rd condition. >> >>> >> >>> Is there a way around this problem. I'd be very appreciative of > some >> >>> help. >> >>> >> >>> Kind regards >> >>> Reem >> >>> >> >> >> >> -- >> >> The University of Edinburgh is a charitable body, registered in >> >> Scotland, with registration number SC005336. >> >> >> > >> > >> >> > > > __________ Information from ESET NOD32 Antivirus, version of virus > signature database 5838 (20110201) __________ > > The message was checked by ESET NOD32 Antivirus. > > http://www.eset.com > > > > __________ Information from ESET NOD32 Antivirus, version of virus > signature database 5838 (20110201) __________ > > The message was checked by ESET NOD32 Antivirus. > > http://www.eset.com > > ========================================================================Date: Wed, 2 Feb 2011 16:31:03 +1300 Reply-To: Reem Jan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Reem Jan <[log in to unmask]> Subject: Re: Missing condition at first level - what to input in the onset tab? Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Transfer-Encoding: 7bit Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 (iPhone Mail 8C148a) Message-ID: <[log in to unmask]> Oh sorry yes I meant to change that to 0.2. Thank you Donald On 2/02/2011, at 16:23, "MCLAREN, Donald" <[log in to unmask]> wrote: > The second contrast should use .2 not .25. > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Tue, Feb 1, 2011 at 10:15 PM, Reem Jan <[log in to unmask]> wrote: >> Thank you Cyril, Michael and Donald for all the useful information you >> have given me regarding this. >> >> There are 6 runs/sessions and 4 conditions per subject. >> >> I have done the following. >> >> If condition A is missing from run 1 and 2 let's say.. I remove >> condition A from that run (in the first level model specification), and >> then in the contrast manager when I specify condition A's contrast I >> type: >> >> 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0 >> >> And if condition A is missing only from run 2: >> >> 0.25 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0 >> >> And I also include 6 zeros after each run for the motion regressors >> (which I haven't typed out here for simplicity's sake). >> >> I hope this is right? >> >> Kind regards >> Reem >> >> -----Original Message----- >> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] >> On Behalf Of Michael Harms >> Sent: Wednesday, February 02, 2011 7:13 AM >> To: [log in to unmask] >> Subject: Re: [SPM] Missing condition at first level - what to input in >> the onset tab? >> >> Well, "reasonably" sure, although happy for someone else to chime in to >> the contrary. Many different contrasts can be technically valid (i.e., >> "estimable"). In the example in question, the contrast I proposed would >> test for an expected difference between the mean of condition A and B of >> zero, whereas in your original contrast the expected value of the >> contrast would not be zero under the null hypothesis of no difference >> between A and B. >> >> In effect, I believe that by using a factor of 6/5 on the 5 A >> conditions, one achieves the desired "upweighting" of the variance >> associated with the condition A estimate (relative to the condition B >> estimate). >> >> cheers, >> -MH >> >> >> On Tue, 2011-02-01 at 13:56 +0000, Cyril Pernet wrote: >>> Hi Michael >>> >>> r u sure? ok beta = pinv(X)*Y so we don't really care about the bias >> (I >>> was referring to nb of stimuli and variance estimation) >>> >>> for the contrast with 5A and 6B using 1/5 1/6 you average 5 sessions >> vs >>> 6 which of course works as well but you may want to somehow put this >>> unbalance in your contrast? I guess it's a matter of choice - note >> that >>> C is unchanged by the post multiplication by inv(X'X')X'X using my >>> contrast so it is still valid too .. contrast don't have to sum up to >> 0 >>> (oh yeh my full contrast wasn't right since I copied/pasted 6 times .. >> >>> but I'm sure you got the gesture). >>> >>> I actually never had this problem - was offering a possible solution >> but >>> yours seems good too - anybody out there checked this up before? >>> >>> Cyril >>> >>> >>>> Hi Cyril, >>>> Even if you don't have equal numbers, the estimated betas are >> themselves >>>> still unbiased. Thus, if you are going to compare two conditions, >> it >>>> seems to me that you still want the contrast to sum to 0. In the >> example >>>> you gave, if you wanted to compare the mean level of A to the mean >> level >>>> of B, with no estimate of A available from session 3, I think that >> you >>>> would want the following contrast: >>>> 1/5 -1/6 0 1/5 -1/6 0 0 -1/6 0 1/5 -1/6 0 1/5 -1/6 0 1/5 -1/6 0 >>>> or multiplying by 6: >>>> 6/5 -1 0 6/5 -1 0 0 -1 0 6/5 -1 0 6/5 -1 0 6/5 -1 0 >>>> (note the 0 in the "A" position of the 3rd session). >>>> >>>> cheers, >>>> -MH >>>> >>>>> Reem >>>>> >>>>> It doesn't really matter that in each session you don't have the >> same >>>>> number of stimuli per condition as long as across your 6 sessions >> you >>>>> end up with equal numbers (will work as well if not but it's not as >> good >>>>> because variance estimation can be biased). As for the different >> number >>>>> of conditions you can weight your contrast accordingly. Simply >> create 6 >>>>> sessions in SPM and your 2 or 3 conditions in each sessions. Let >> say >>>>> your design runs like this: >>>>> >>>>> Session 1 A B C >>>>> Session 2 A B >>>>> Session 3 B C >>>>> Session 4 A B C >>>>> Session 5 A B C >>>>> Session 6 A B >>>>> = 5*A 6*B 4*C >>>>> = 30/6*A 36/6*B 26/6*C (I choose to have 6 as denominator because >> you >>>>> have 6 sessions) >>>>> >>>>> Let say you want to test A - B then use a contrast [30/36 -1 0 >> 30/36 >>>>> -1 0 30/36 -1 0 30/36 -1 0 30/36 -1 0 30/36 -1 0] >>>>> How do I end up with 30/36 and -1 --> 1/6 * 30/6 = 30/36 and -1/6 >> * 36/6 >>>>> = -1 (I use 1/6 for each session since you have 6 sessions) >>>>> The sum doesn't end up to 1 but the contrast should still be valid >> .. >>>>> (at least using a simple design on my machine with unbalanced >> design and >>>>> manual checking the contrast is valid - check in SPM I think it is >> ok) >>>>> >>>>> Good luck >>>>> Cyril >>>>> >>>>> >>>>> >>>>>> Dear SPMers >>>>>> >>>>>> I am trying run first level analysis on fMRI data in SPM8. >>>>>> >>>>>> The experiment design involves 6 runs/sessions, each run/session >>>>>> consists of 8 stimuli (48 stimuli in total). >>>>>> >>>>>> There are 3 experimental conditions spread out equally across >> stimuli >>>>>> i.e. There are 16 stimuli of each condition in total. >>>>>> >>>>>> Because the stimuli were presented at random and are >> event-related, I am >>>>>> now running into a problem because in some of the runs of 8 >> stimuli, >>>>>> there happens to be only 2 of the conditions presented and not 3. >> In >>>>>> these cases, I'm not sure what to input in the 'onsets' tab of >> first >>>>>> level model specification for the 3rd condition. >>>>>> >>>>>> Is there a way around this problem. I'd be very appreciative of >> some >>>>>> help. >>>>>> >>>>>> Kind regards >>>>>> Reem >>>>>> >>>>> >>>>> -- >>>>> The University of Edinburgh is a charitable body, registered in >>>>> Scotland, with registration number SC005336. >>>>> >>>> >>>> >>> >>> >> >> >> __________ Information from ESET NOD32 Antivirus, version of virus >> signature database 5838 (20110201) __________ >> >> The message was checked by ESET NOD32 Antivirus. >> >> http://www.eset.com >> >> >> >> __________ Information from ESET NOD32 Antivirus, version of virus >> signature database 5838 (20110201) __________ >> >> The message was checked by ESET NOD32 Antivirus. >> >> http://www.eset.com >> >> ========================================================================Date: Tue, 1 Feb 2011 19:40:28 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: Voxel In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00221534d4cfce9be7049b446ae2 Content-Type: text/plain; charset=UTF-8 you could find the peak voxel by doing this in matlab (not spm gui): V = spm_vol('path_to_stat_image.img'); Vd = spm_read_vols(V); M = spm_vol('path_2_stat_image.img'); Md = spm_read_vols(M); mx = max(Vd(find(Md))); %thats it Cheers, Michael On Tue, Feb 1, 2011 at 7:22 PM, MCLAREN, Donald <[log in to unmask]>wrote: > If you are doing an ROI analysis, then you don't have voxel data anymore. > > If you are doing a whole brain analysis and using a mask of a specific > ROI (not an ROI analysis), then clicking whole brain or current > cluster will give you the peak voxel. > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Tue, Feb 1, 2011 at 10:05 PM, Leanne Wilkins > <[log in to unmask]> wrote: > > We are currently doing an ROI analysis with several different contrasts. > I was wondering if there was an SPM script that would permit me to identify > the top voxel activated in each ROI? > > > > I am new to SPM, I hope my question makes sense. > > > > Leanne > > > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --00221534d4cfce9be7049b446ae2 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable you could find the peak voxel by doing this in matlab (not spm gui):

V = spm_vol('path_to_stat_image.img');

Vd = spm_read_vols(V);

M = spm_vol('path_2_stat_image.img');

Md = spm_read_vols(M);

mx = max(Vd(find(Md)));

%thats it

Cheers,

Michael

On Tue, Feb 1, 2011 at 7:22 PM, MCLAREN, Donald <[log in to unmask]> wrote:

If you are doing an ROI analysis, then you don't have voxel data anymore.

If you are doing a whole brain analysis and using a mask of a specific
ROI (not an ROI analysis), then clicking whole brain or current
cluster will give you the peak voxel.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.

On Tue, Feb 1, 2011 at 10:05 PM, Leanne Wilkins
<[log in to unmask]> wrote:
> We are currently doing an ROI analysis with several different contrasts. I was wondering if there was an SPM script that would permit me to identify the top voxel activated in each ROI?
>
> I am new to SPM, I hope my question makes sense.
>
> Leanne
>

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--00221534d4cfce9be7049b446ae2-- ========================================================================Date: Tue, 1 Feb 2011 19:44:24 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: Voxel In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --000325575fded53840049b447827 Content-Type: text/plain; charset=UTF-8 whoops, 4th line should be M = spm_vol('path_2_ROI_image.img'); On Tue, Feb 1, 2011 at 7:40 PM, Michael T Rubens <[log in to unmask]>wrote: > you could find the peak voxel by doing this in matlab (not spm gui): > > V = spm_vol('path_to_stat_image.img'); > Vd = spm_read_vols(V); > > M = spm_vol('path_2_stat_image.img'); > Md = spm_read_vols(M); > > mx = max(Vd(find(Md))); > > %thats it > > Cheers, > Michael > > On Tue, Feb 1, 2011 at 7:22 PM, MCLAREN, Donald <[log in to unmask]>wrote: > >> If you are doing an ROI analysis, then you don't have voxel data anymore. >> >> If you are doing a whole brain analysis and using a mask of a specific >> ROI (not an ROI analysis), then clicking whole brain or current >> cluster will give you the peak voxel. >> >> Best Regards, Donald McLaren >> ================>> D.G. McLaren, Ph.D. >> Postdoctoral Research Fellow, GRECC, Bedford VA >> Research Fellow, Department of Neurology, Massachusetts General >> Hospital and Harvard Medical School >> Office: (773) 406-2464 >> ====================>> This e-mail contains CONFIDENTIAL INFORMATION which may contain >> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED >> and which is intended only for the use of the individual or entity >> named above. If the reader of the e-mail is not the intended recipient >> or the employee or agent responsible for delivering it to the intended >> recipient, you are hereby notified that you are in possession of >> confidential and privileged information. Any unauthorized use, >> disclosure, copying or the taking of any action in reliance on the >> contents of this information is strictly prohibited and may be >> unlawful. If you have received this e-mail unintentionally, please >> immediately notify the sender via telephone at (773) 406-2464 or >> email. >> >> >> >> On Tue, Feb 1, 2011 at 10:05 PM, Leanne Wilkins >> <[log in to unmask]> wrote: >> > We are currently doing an ROI analysis with several different contrasts. >> I was wondering if there was an SPM script that would permit me to identify >> the top voxel activated in each ROI? >> > >> > I am new to SPM, I hope my question makes sense. >> > >> > Leanne >> > >> > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --000325575fded53840049b447827 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable whoops, 4th line should be

M = spm_vol('path_2_ROI_image.img');

On Tue, Feb 1, 2011 at 7:40 PM, Michael T Rubens <[log in to unmask]> wrote:

you could find the peak voxel by doing this in matlab (not spm gui):

V = spm_vol('path_to_stat_image.img');

Vd = spm_read_vols(V);

M = spm_vol('path_2_stat_image.img');

Md = spm_read_vols(M);

mx = max(Vd(find(Md)));

%thats it

Cheers,
Michael

On Tue, Feb 1, 2011 at 7:22 PM, MCLAREN, Donald <[log in to unmask]> wrote:

If you are doing an ROI analysis, then you don't have voxel data anymore.

If you are doing a whole brain analysis and using a mask of a specific
ROI (not an ROI analysis), then clicking whole brain or current
cluster will give you the peak voxel.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.

On Tue, Feb 1, 2011 at 10:05 PM, Leanne Wilkins
<[log in to unmask]> wrote:
> We are currently doing an ROI analysis with several different contrasts. I was wondering if there was an SPM script that would permit me to identify the top voxel activated in each ROI?
>
> I am new to SPM, I hope my question makes sense.
>
> Leanne
>

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--000325575fded53840049b447827-- ========================================================================Date: Wed, 2 Feb 2011 10:19:07 +0530 Reply-To: sarika cherodath <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: sarika cherodath <[log in to unmask]> Subject: Comparing conditions across runs MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a5e3bbc302049b455f89 Message-ID: <[log in to unmask]> --20cf3054a5e3bbc302049b455f89 Content-Type: text/plain; charset=ISO-8859-1 Hi SPMers, How can one compare effect of two conditions in two separate runs? For eg. If run 1 = A R A R A R and so on and run 2 = B R B R B R then, how can one perform A vs. B contrast?? Will it be a problem that both A and B have same onsets?? Many thanks in advance -- Sarika Cherodath Graduate Student National Brain Research Centre Manesar, Gurgaon -122050 India --20cf3054a5e3bbc302049b455f89 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hi SPMers,

How can one compare effect of two conditions in two separate runs? For eg. If run 1 = A R A R A R and so on and run 2 = B R B R B R then, how can one perform A vs. B contrast?? Will it be a problem that both A and B have same onsets??

Many thanks in advance

--
Sarika Cherodath
Graduate Student
National Brain Research Centre
Manesar, Gurgaon -122050
India

--20cf3054a5e3bbc302049b455f89-- ========================================================================Date: Tue, 1 Feb 2011 23:02:31 -0800 Reply-To: Michael Spezio <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Spezio <[log in to unmask]> Subject: Bandpass filtering with wide bands in spm8: Why is there a problem? Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=__PartFAD6F397.0__=" Message-ID: <[log in to unmask]> This is a MIME message. If you are reading this text, you may want to consider changing to a mail reader or gateway that understands how to properly handle MIME multipart messages. --=__PartFAD6F397.0__Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Hi Vladimir, I saw in your post 041502, dated 2010-05-07, that for some reason bandpass filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by the Matlab SP Toolbox and causes displays of the subsequent data to fail. Can you specify why this is occurring? It doesn't seem to make sense given that wide bandpass filters are used in signal processing routinely. Also, for the Multimodal Face tutorial, why isn't filtering done prior to downsampling? Downsampling, especially to 200 Hz or lower, should only be done after filtering out higher frequency contaminating signals, measured at a high sampling rate (500 Hz or above), to avoid aliasing from muscle signals at frequencies of 90 Hz to 200 Hz. Can you help me understand what is going on with the bandpass processing and why filtering is left out of the tutorial? Thanks so much. Best, Michael Michael L. Spezio, Ph.D. Assistant Professor of Psychology Department of Psychology Scripps College Claremont, CA [log in to unmask] 909.607.0914 and Visiting Associate Scientist Psychology & Neuroscience Division of Humanities & Social Sciences Caltech Pasadena, CA 91125 [log in to unmask] --=__PartFAD6F397.0__Content-Type: text/html; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Content-Description: HTML Hi Vladimir,

I saw in your post 041502, dated 2010-05-07, that for some reason bandpass filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by the Matlab SP Toolbox and causes displays of the subsequent data to fail.

Can you specify why this is occurring? It doesn't seem to make sense given that wide bandpass filters are used in signal processing routinely.

Also, for the Multimodal Face tutorial, why isn't filtering done prior to downsampling? Downsampling, especially to 200 Hz or lower, should only be done after filtering out higher frequency contaminating signals, measured at a high sampling rate (500 Hz or above), to avoid aliasing from muscle signals at frequencies of 90 Hz to 200 Hz. Can you help me understand what is going on with the bandpass processing and why filtering is left out of the tutorial?

Thanks so much.

Best,

Michael

Michael L. Spezio, Ph.D.
Assistant Professor of Psychology
Department of Psychology
Scripps College
Claremont, CA
[log in to unmask]
909.607.0914

and

Visiting Associate Scientist
Psychology & Neuroscience
Division of Humanities & Social Sciences
Caltech
Pasadena, CA 91125
[log in to unmask]

--=__PartFAD6F397.0__=-- ========================================================================Date: Wed, 2 Feb 2011 10:13:56 +0100 Reply-To: Ali Gholami <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ali Gholami <[log in to unmask]> Subject: SPM Content-Type: text/plain; charset="UTF-8" Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <1296638036.16391.2.camel@tb06> Hi I've installed a matlab 2010b under "/usr/local/matalab". I also extracted a SPM8 update under"/home/spm8". Now I can't see any information how to use and run SPM. My question is how to run SPM? ========================================================================Date: Wed, 2 Feb 2011 09:28:01 +0000 Reply-To: =?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?= <[log in to unmask]> Subject: Re: SPM Comments: To: =?utf-8?B?QWxpIEdob2xhbWk=?= <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----------=_1296638927-6285-52" Content-Transfer-Encoding: binary Message-ID: <[log in to unmask]> This is a multi-part message in MIME format... ------------=_1296638927-6285-52 Received: from [192.168.0.3] (5ada724d.bb.sky.com [90.218.114.77]) (authenticated user=cpernet mech=LOGIN bits=0) by lmtp1.ucs.ed.ac.uk (8.13.8/8.13.7) with ESMTP id p129SjX0022169 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-SHA bits%6 verify=NOT); Wed, 2 Feb 2011 09:28:46 GMT Message-Id: <[log in to unmask]> To: "=?utf-8?B?QWxpIEdob2xhbWk=?=" <[log in to unmask]>, [log in to unmask] From: "=?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?=" <[log in to unmask]> Subject: =?utf-8?B?UmU6IFtTUE1dIFNQTQ==?Date: Wed, 02 Feb 2011 09:28:01 +0000 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_11_1296638881155" X-Edinburgh-Scanned: at lmtp1.ucs.ed.ac.uk with MIMEDefang 2.52, Sophie, Sophos Anti-Virus X-Scanned-By: MIMEDefang 2.52 on 129.215.149.64 ------=_Part_11_1296638881155 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline SW4gbWF0bGFiIHVzZSB0aGUgdGFicyBGaWxlIC0gYWRkIHBhdGggLSBhbmQg YWRkIHRoZSBTUE0gZm9sZGVyLgpUaGlzIHdheSBtYXRsYWIga25vd3Mgd2hl cmUgaXMgU1BNIGFuZCBjYW4gdXNlIGl0cyBmdW5jdGlvbi4gVHlwZSBTUE0g aW4gdGhlIGNvbW1hbmQgd2luZG93IGFuZCB0aGUgR1VJIHdpbGwgcG9wIHVw LiBBbHNvIGRvd25sb2FkIHRoZSBkYXRhIHNldC4gTWFudWFsIGlzIGluIHlv dXIgU1BNIGZvbGRlciBpbiB0aGUgbWFuIHN1YmZvbGRlci4gCgpFbmpveSB0 aGUgYmVzdCBzb2Z0IGV2ZXIgbG9sCgpEciBDeXJpbCBQZXJuZXQKU0JJUkMg LyBTSU5BUFNFClVuaXZlcnNpdHkgb2YgRWRpbmJ1cmdoCgotLS0tLSBSZXBs eSBtZXNzYWdlIC0tLS0tCkZyb206ICJBbGkgR2hvbGFtaSIgPGdob2xhbWlA UERDLktUSC5TRT4KRGF0ZTogV2VkLCBGZWIgMiwgMjAxMSAwOToxMwpTdWJq ZWN0OiBbU1BNXSBTUE0KVG86IDxTUE1ASklTQ01BSUwuQUMuVUs+CgpIaQoK SSd2ZSBpbnN0YWxsZWQgYSBtYXRsYWIgMjAxMGIgdW5kZXIgIi91c3IvbG9j YWwvbWF0YWxhYiIuIEkgYWxzbwpleHRyYWN0ZWQgYSBTUE04IHVwZGF0ZSB1 bmRlciIvaG9tZS9zcG04Ii4gTm93IEkgY2FuJ3Qgc2VlIGFueQppbmZvcm1h dGlvbiBob3cgdG8gdXNlIGFuZCBydW4gU1BNLiAKCk15IHF1ZXN0aW9uIGlz IGhvdyB0byBydW4gU1BNPwoKCiAgCgo ------=_Part_11_1296638881155 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline SW4gbWF0bGFiIHVzZSB0aGUgdGFicyBGaWxlIC0gYWRkIHBhdGggLSBhbmQg YWRkIHRoZSBTUE0gZm9sZGVyLjxicj5UaGlzIHdheSBtYXRsYWIga25vd3Mg d2hlcmUgaXMgU1BNIGFuZCBjYW4gdXNlIGl0cyBmdW5jdGlvbi4gVHlwZSBT UE0gaW4gdGhlIGNvbW1hbmQgd2luZG93IGFuZCB0aGUgR1VJIHdpbGwgcG9w IHVwLiBBbHNvIGRvd25sb2FkIHRoZSBkYXRhIHNldC4gTWFudWFsIGlzIGlu IHlvdXIgU1BNIGZvbGRlciBpbiB0aGUgbWFuIHN1YmZvbGRlci4gPGJyPjxi cj5FbmpveSB0aGUgYmVzdCBzb2Z0IGV2ZXIgbG9sPGJyPjxicj5EciBDeXJp bCBQZXJuZXQ8YnI+U0JJUkMgLyBTSU5BUFNFPGJyPlVuaXZlcnNpdHkgb2Yg RWRpbmJ1cmdoPGJyPjxicj4tLS0tLSBSZXBseSBtZXNzYWdlIC0tLS0tPGJy PkZyb206ICZxdW90O0FsaSBHaG9sYW1pJnF1b3Q7ICZsdDtnaG9sYW1pQFBE Qy5LVEguU0UmZ3Q7PGJyPkRhdGU6IFdlZCwgRmViIDIsIDIwMTEgMDk6MTM8 YnI+U3ViamVjdDogW1NQTV0gU1BNPGJyPlRvOiAmbHQ7U1BNQEpJU0NNQUlM LkFDLlVLJmd0Ozxicj48YnI+SGk8YnI+PGJyPkkmIzM5O3ZlIGluc3RhbGxl ZCBhIG1hdGxhYiAyMDEwYiB1bmRlciAmcXVvdDsvdXNyL2xvY2FsL21hdGFs YWImcXVvdDsuIEkgYWxzbzxicj5leHRyYWN0ZWQgYSBTUE04IHVwZGF0ZSB1 bmRlciZxdW90Oy9ob21lL3NwbTgmcXVvdDsuIE5vdyBJIGNhbiYjMzk7dCBz ZWUgYW55PGJyPmluZm9ybWF0aW9uIGhvdyB0byB1c2UgYW5kIHJ1biBTUE0u IDxicj48YnI+TXkgcXVlc3Rpb24gaXMgaG93IHRvIHJ1biBTUE0/PGJyPjxi cj48YnI+ICZuYnNwOzxicj48YnI+PGJyPjxicj4 ------=_Part_11_1296638881155-- ------------=_1296638927-6285-52 Content-Type: text/plain Content-Disposition: inline Content-Transfer-Encoding: 7bit MIME-Version: 1.0 X-Mailer: MIME-tools 5.420 (Entity 5.420) Content-Description: Edinburgh University charitable status The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ------------=_1296638927-6285-52-- ========================================================================Date: Wed, 2 Feb 2011 10:22:57 +0100 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Javier Arbizu <[log in to unmask]> Subject: unsubscribe MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; DelSp="Yes"; format="flowed" Content-Disposition: inline Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> ---------------------------------------------------------------- Este mensaje ha sido enviado desde https://webmail.unav.es ========================================================================Date: Wed, 2 Feb 2011 10:36:59 +0100 Reply-To: Ali Gholami <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ali Gholami <[log in to unmask]> Subject: Re: SPM Comments: To: "[log in to unmask]" <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="UTF-8" Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <1296639419.16391.4.camel@tb06> Thank you Cyril for the information. Now, I get this error: >> spm ??? Error using ==> spm_check_installation>check_basic at 77 There appears to be some problem with the installation. The original spm8.zip distribution should be installed and the updates installed on top of this. Unix commands to do this are: unzip spm8.zip unzip -o spm8_updates_r????.zip -d spm8 You may need help from your local network administrator. Error in ==> spm_check_installation at 25 check_basic; Error in ==> spm at 297 spm_check_installation('basic'); Do you know the reason? Best regards Ali Gholami On Wed, 2011-02-02 at 09:28 +0000, [log in to unmask] wrote: > In matlab use the tabs File - add path - and add the SPM folder. > This way matlab knows where is SPM and can use its function. Type SPM > in the command window and the GUI will pop up. Also download the data > set. Manual is in your SPM folder in the man subfolder. > > Enjoy the best soft ever lol > > Dr Cyril Pernet > SBIRC / SINAPSE > University of Edinburgh > > ----- Reply message ----- > From: "Ali Gholami" <[log in to unmask]> > Date: Wed, Feb 2, 2011 09:13 > Subject: [SPM] SPM > To: <[log in to unmask]> > > Hi > > I've installed a matlab 2010b under "/usr/local/matalab". I also > extracted a SPM8 update under"/home/spm8". Now I can't see any > information how to use and run SPM. > > My question is how to run SPM? > > > > > > > The University of Edinburgh is a charitable body, registered in > Scotland, with registration number SC005336. ========================================================================Date: Wed, 2 Feb 2011 23:21:00 +1300 Reply-To: Reem Jan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Reem Jan <[log in to unmask]> Subject: Re: SPM Comments: To: Ali Gholami <[log in to unmask]> In-Reply-To: <1296639419.16391.4.camel@tb06> Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 (iPhone Mail 8C148a) Message-ID: <[log in to unmask]> Hi Ali I think you may not have downloaded spm8 software and instead only downloaded the updates. You will need to download the software first then unpack the latest update and allow it to overwrite the original files. You can then set path to the spm8 folder as Cyril suggested. Cheers Reem On 2/02/2011, at 22:37, "Ali Gholami" <[log in to unmask]> wrote: > Thank you Cyril for the information. Now, I get this error: > >>> spm > ??? Error using ==> spm_check_installation>check_basic at 77 > There appears to be some problem with the installation. > The original spm8.zip distribution should be installed > and the updates installed on top of this. Unix commands > to do this are: > unzip spm8.zip > unzip -o spm8_updates_r????.zip -d spm8 > You may need help from your local network administrator. > > Error in ==> spm_check_installation at 25 > check_basic; > > Error in ==> spm at 297 > spm_check_installation('basic'); > > > > Do you know the reason? > > Best regards > Ali Gholami > > > On Wed, 2011-02-02 at 09:28 +0000, [log in to unmask] wrote: >> In matlab use the tabs File - add path - and add the SPM folder. >> This way matlab knows where is SPM and can use its function. Type SPM >> in the command window and the GUI will pop up. Also download the data >> set. Manual is in your SPM folder in the man subfolder. >> >> Enjoy the best soft ever lol >> >> Dr Cyril Pernet >> SBIRC / SINAPSE >> University of Edinburgh >> >> ----- Reply message ----- >> From: "Ali Gholami" <[log in to unmask]> >> Date: Wed, Feb 2, 2011 09:13 >> Subject: [SPM] SPM >> To: <[log in to unmask]> >> >> Hi >> >> I've installed a matlab 2010b under "/usr/local/matalab". I also >> extracted a SPM8 update under"/home/spm8". Now I can't see any >> information how to use and run SPM. >> >> My question is how to run SPM? >> >> >> >> >> >> >> The University of Edinburgh is a charitable body, registered in >> Scotland, with registration number SC005336. ========================================================================Date: Wed, 2 Feb 2011 11:25:58 +0100 Reply-To: Ali Gholami <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ali Gholami <[log in to unmask]> Subject: Re: SPM Comments: To: Reem Jan <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="UTF-8" Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <1296642358.16391.6.camel@tb06> Hi Reem, Thank you for your answer. You are right, I just installed the updates. It was a little bit strange for me to first install the update but I couldn't find a link to "SPM" itself. Do you know where I get the SPM directly? Regards Ali On Wed, 2011-02-02 at 23:21 +1300, Reem Jan wrote: > Hi Ali > > I think you may not have downloaded spm8 software and instead only downloaded the updates. You will need to download the software first then unpack the latest update and allow it to overwrite the original files. You can then set path to the spm8 folder as Cyril suggested. > > Cheers > Reem > > On 2/02/2011, at 22:37, "Ali Gholami" <[log in to unmask]> wrote: > > > Thank you Cyril for the information. Now, I get this error: > > > >>> spm > > ??? Error using ==> spm_check_installation>check_basic at 77 > > There appears to be some problem with the installation. > > The original spm8.zip distribution should be installed > > and the updates installed on top of this. Unix commands > > to do this are: > > unzip spm8.zip > > unzip -o spm8_updates_r????.zip -d spm8 > > You may need help from your local network administrator. > > > > Error in ==> spm_check_installation at 25 > > check_basic; > > > > Error in ==> spm at 297 > > spm_check_installation('basic'); > > > > > > > > Do you know the reason? > > > > Best regards > > Ali Gholami > > > > > > On Wed, 2011-02-02 at 09:28 +0000, [log in to unmask] wrote: > >> In matlab use the tabs File - add path - and add the SPM folder. > >> This way matlab knows where is SPM and can use its function. Type SPM > >> in the command window and the GUI will pop up. Also download the data > >> set. Manual is in your SPM folder in the man subfolder. > >> > >> Enjoy the best soft ever lol > >> > >> Dr Cyril Pernet > >> SBIRC / SINAPSE > >> University of Edinburgh > >> > >> ----- Reply message ----- > >> From: "Ali Gholami" <[log in to unmask]> > >> Date: Wed, Feb 2, 2011 09:13 > >> Subject: [SPM] SPM > >> To: <[log in to unmask]> > >> > >> Hi > >> > >> I've installed a matlab 2010b under "/usr/local/matalab". I also > >> extracted a SPM8 update under"/home/spm8". Now I can't see any > >> information how to use and run SPM. > >> > >> My question is how to run SPM? > >> > >> > >> > >> > >> > >> > >> The University of Edinburgh is a charitable body, registered in > >> Scotland, with registration number SC005336. > ========================================================================Date: Wed, 2 Feb 2011 23:47:36 +1300 Reply-To: Reem Jan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Reem Jan <[log in to unmask]> Subject: Re: SPM Comments: To: Ali Gholami <[log in to unmask]> In-Reply-To: <1296642358.16391.6.camel@tb06> Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 (iPhone Mail 8C148a) Message-ID: <[log in to unmask]> Hi Ali You can download SPM after filling out a registration form here http://www.fil.ion.ucl.ac.uk/spm/software/download.html Cheers Reem On 2/02/2011, at 23:28, "Ali Gholami" <[log in to unmask]> wrote: > Hi Reem, > > Thank you for your answer. You are right, I just installed the updates. > It was a little bit strange for me to first install the update but I > couldn't find a link to "SPM" itself. Do you know where I get the SPM > directly? > > > Regards > Ali > > > On Wed, 2011-02-02 at 23:21 +1300, Reem Jan wrote: >> Hi Ali >> >> I think you may not have downloaded spm8 software and instead only downloaded the updates. You will need to download the software first then unpack the latest update and allow it to overwrite the original files. You can then set path to the spm8 folder as Cyril suggested. >> >> Cheers >> Reem >> >> On 2/02/2011, at 22:37, "Ali Gholami" <[log in to unmask]> wrote: >> >>> Thank you Cyril for the information. Now, I get this error: >>> >>>>> spm >>> ??? Error using ==> spm_check_installation>check_basic at 77 >>> There appears to be some problem with the installation. >>> The original spm8.zip distribution should be installed >>> and the updates installed on top of this. Unix commands >>> to do this are: >>> unzip spm8.zip >>> unzip -o spm8_updates_r????.zip -d spm8 >>> You may need help from your local network administrator. >>> >>> Error in ==> spm_check_installation at 25 >>> check_basic; >>> >>> Error in ==> spm at 297 >>> spm_check_installation('basic'); >>> >>> >>> >>> Do you know the reason? >>> >>> Best regards >>> Ali Gholami >>> >>> >>> On Wed, 2011-02-02 at 09:28 +0000, [log in to unmask] wrote: >>>> In matlab use the tabs File - add path - and add the SPM folder. >>>> This way matlab knows where is SPM and can use its function. Type SPM >>>> in the command window and the GUI will pop up. Also download the data >>>> set. Manual is in your SPM folder in the man subfolder. >>>> >>>> Enjoy the best soft ever lol >>>> >>>> Dr Cyril Pernet >>>> SBIRC / SINAPSE >>>> University of Edinburgh >>>> >>>> ----- Reply message ----- >>>> From: "Ali Gholami" <[log in to unmask]> >>>> Date: Wed, Feb 2, 2011 09:13 >>>> Subject: [SPM] SPM >>>> To: <[log in to unmask]> >>>> >>>> Hi >>>> >>>> I've installed a matlab 2010b under "/usr/local/matalab". I also >>>> extracted a SPM8 update under"/home/spm8". Now I can't see any >>>> information how to use and run SPM. >>>> >>>> My question is how to run SPM? >>>> >>>> >>>> >>>> >>>> >>>> >>>> The University of Edinburgh is a charitable body, registered in >>>> Scotland, with registration number SC005336. >> ========================================================================Date: Wed, 2 Feb 2011 11:18:12 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Bandpass filtering with wide bands in spm8: Why is there a problem? Comments: To: Michael Spezio <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear Michael, On Wed, Feb 2, 2011 at 7:02 AM, Michael Spezio <[log in to unmask]> wrote: > Hi Vladimir, > > I saw in your post 041502, dated 2010-05-07, that for some reason bandpass > filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by > the Matlab SP Toolbox and causes displays of the subsequent data to fail. > > Can you specify why this is occurring? It doesn't seem to make sense given > that wide bandpass filters are used in signal processing routinely. > It's not a display issue but actual data corruption issue. There are numerical stability problems that I have since also observed in high-pass filters for some combinations of filter settings and sampling rate. These problems are common for wide bandpass filters, especially for ones with low cut-off close to DC (e.g. 0.1-40 Hz). Therefore, I recommended that people use separate high-pass and low-pass in these cases. In the next SPM8 update there is a change in the code that detects automatically when a filter is unstable and gives an error so people will not have to figure it out later when their display crashes. > Also, for the Multimodal Face tutorial, why isn't filtering done prior to > downsampling? Downsampling, especially to 200 Hz or lower, should only be > done after filtering out higher frequency contaminating signals, measured at > a high sampling rate (500 Hz or above), to avoid aliasing from muscle > signals at frequencies of 90 Hz to 200 Hz. Can you help me understand what > is going on with the bandpass processing and why filtering is left out of > the tutorial? > Aliasing can occur when downsampling is done by simple decimation in the time domain (e.g. taking every other sample). SP toolbox downsampling routine is smarter than that and it pre-filters data to avoid aliasing so it is not necessary to do it explicitly. In the absence of SP toolbox we use or own routine that downsamples in the frequency domain by truncating the DFT coefficients. This way there is also no aliasing. The only problem that can occur is that if there are large DC offsets in the data, low-pass filter will cause ringing at the edges. Therefore I'd suggest to apply high-pass filtering before downsampling especially for epoched data. In the case of the faces tutorial this is not a problem because downsampling is done on continuous data (for EEG) or on baseline-corrected data (for MEG). Best, Vladimir > Thanks so much. > > Best, > > Michael > > Michael L. Spezio, Ph.D. > Assistant Professor of Psychology > Department of Psychology > Scripps College > Claremont, CA > [log in to unmask] > 909.607.0914 > > and > > Visiting Associate Scientist > Psychology & Neuroscience > Division of Humanities & Social Sciences > Caltech > Pasadena, CA 91125 > [log in to unmask] > ========================================================================Date: Wed, 2 Feb 2011 12:49:43 +0100 Reply-To: Michel Thiebaut de Schotten <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michel Thiebaut de Schotten <[log in to unmask]> Subject: Natbrainlab Workshop Comments: To: [log in to unmask] Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> Dear all, The 9th Neuroanatomy and Tractography workshop will be held in London (Institute of Psychiatry) on the 23-24-25 March 2011. This workshop intends to help scientists to develop and optimise strategies for DTI datasets analysis and tractography. The number of participants is limited to 12 for allowing 1:1 tutoring. The participants will be able to work on their own datasets and receive advice from the members of the Natbrainlab (http://www.natbrainlab.com). This is also an opportunity to familiarise with fundaments of white matter anatomy and brain hodology. Wednesday 23th March VISUALIZATION OF WHITE MATTER CONNECTIONS 09:30 - 10:00 Registration 10:00 - 10:30 Brain Hodology: From post mortem dissections to diffusion MRI (M Catani) 10:30 - 11:30 Introduction to Diffusion Tensor MRI (F Dell'Acqua) 11:30 - 11:45 Coffee Break 11:45 - 12:45 Diffusion Tensor Tractography (F Dell'Acqua) 12:45 - 14:00 Lunch Break 14:00 - 15:15 DTI data formats: Management and pre-processing (M Thiebaut de Schotten) 15:15 - 15:45 Coffee Break 15:45 - 17:00 Tractography with TrackVis (M Thiebaut de Schotten) Thursday 24th March VIRTUAL IN VIVO DISSECTIONS 09:30 - 10:00 Classification of white matter connections (M Catani) 10:00 - 11:00 Association Pathways 1 (M Catani) 11:00 - 11:15 Coffee Break 11:15 - 12:30 Association Pathways 2 (S Budisavljevic) 12:30 - 14:00 Lunch Break 14:00 - 15:15 Commissural Pathways (S Forkel) 15:15 - 15:45 Coffee Break 15:45 - 17:00 Projections Pathways (S Forkel) Friday 25th March DTI TRACTOGRAPHY: CURRENT APPLICATIONS AND FUTURE DIRECTIONS 09:30 - 10:30 Beyond the limitations of tensor modeling (F Dell'Acqua) 10:30 - 10:45 Coffee Break 10:40 - 11:30 Studying Normal Anatomy with DTI Tractography (M Catani) 11:30 - 12:30 Applications to Neurological and Psychiatric Disorders (M Catani) 12:30 - 14:00 Lunch Break 14:00 - 15:15 Supervised Dissection 1 15:15 - 15:45 Coffee Break 15:45 - 16:30 Supervised Dissection 2 16:30 -17:00 Conclusions & Take Home Messages (M Catani) Michel Thiebaut de Schotten, PhD NATBRAINLABANR-CAFORPFC/ANR-HMTC CRICM-INSERM UMRS 975 Pavillon de l'Enfance et l'Adolescence 47 Bd de l'Hôpital 75651 Paris Cedex 13, France www.natbrainlab.com skype: michel_thiebaut_de_schotten +33 613579133 ========================================================================Date: Wed, 2 Feb 2011 15:25:16 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Bandpass filtering with wide bands in spm8: Why is there a problem? Comments: To: Michael Spezio <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> It's probably an SP toolbox issue, but I have never seen an example of this happening for any setting. It usually only happens for some settings. What is your sampling rate? Is your data epoched or continuous? If it's epoched perhaps your epochs are not long enough? If it's continuous I'd try changing the filter order or using an IIR filter (you can do it if you use batch interface to filtering). Or perhaps downsampling event to a slightly different rate will help. Vladimir On Wed, Feb 2, 2011 at 3:16 PM, Michael Spezio <[log in to unmask]> wrote: > Great, thanks for this very helpful response, Vladimir. > > I have noticed that even with using just highpass filtering, any setting > below 2 Hz results in the data corruption issues. If this is an issue with > the SP toolbox, then the Mathworks should be contacted to fix it. If it's a > problem with SPM8, then may there be an upgrade in the future? > > Best, > > Michael > > > Michael L. Spezio, Ph.D. > Assistant Professor of Psychology > Department of Psychology > Scripps College > Claremont, CA > [log in to unmask] > 909.607.0914 > > and > > Visiting Associate Scientist > Psychology & Neuroscience > Division of Humanities & Social Sciences > Caltech > Pasadena, CA 91125 > [log in to unmask] >>>> Vladimir Litvak <[log in to unmask]> 02/02/11 3:19 AM >>> > Dear Michael, > > On Wed, Feb 2, 2011 at 7:02 AM, Michael Spezio > <[log in to unmask]> wrote: >> Hi Vladimir, >> >> I saw in your post 041502, dated 2010-05-07, that for some reason bandpass >> filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by >> the Matlab SP Toolbox and causes displays of the subsequent data to fail. >> >> Can you specify why this is occurring? It doesn't seem to make sense given >> that wide bandpass filters are used in signal processing routinely. >> > > It's not a display issue but actual data corruption issue. There are > numerical stability problems that I have since also observed in > high-pass filters for some combinations of filter settings and > sampling rate. These problems are common for wide bandpass filters, > especially for ones with low cut-off close to DC (e.g. 0.1-40 Hz). > Therefore, I recommended that people use separate high-pass and > low-pass in these cases. In the next SPM8 update there is a change in > the code that detects automatically when a filter is unstable and > gives an error so people will not have to figure it out later when > their display crashes. > >> Also, for the Multimodal Face tutorial, why isn't filtering done prior to >> downsampling? Downsampling, especially to 200 Hz or lower, should only be >> done after filtering out higher frequency contaminating signals, measured >> at >> a high sampling rate (500 Hz or above), to avoid aliasing from muscle >> signals at frequencies of 90 Hz to 200 Hz. Can you help me understand what >> is going on with the bandpass processing and why filtering is left out of >> the tutorial? >> > > Aliasing can occur when downsampling is done by simple decimation in > the time domain (e.g. taking every other sample). SP toolbox > downsampling routine is smarter than that and it pre-filters data to > avoid aliasing so it is not necessary to do it explicitly. In the > absence of SP toolbox we use or own routine that downsamples in the > frequency domain by truncating the DFT coefficients. This way there is > also no aliasing. The only problem that can occur is that if there are > large DC offsets in the data, low-pass filter will cause ringing at > the edges. Therefore I'd suggest to apply high-pass filtering before > downsampling especially for epoched data. In the case of the faces > tutorial this is not a problem because downsampling is done on > continuous data (for EEG) or on baseline-corrected data (for MEG). > > Best, > > Vladimir > >> Thanks so much. >> >> Best, >> >> Michael >> >> Michael L. Spezio, Ph.D. >> Assistant Professor of Psychology >> Department of Psychology >> Scripps College >> Claremont, CA >> [log in to unmask] >> 909.607.0914 >> >> and >> >> Visiting Associate Scientist >> Psychology & Neuroscience >> Division of Humanities & Social Sciences >> Caltech >> Pasadena, CA 91125 >> [log in to unmask] >> > ========================================================================Date: Wed, 2 Feb 2011 15:18:53 +0000 Reply-To: Gaetan Yvert <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Gaetan Yvert <[log in to unmask]> Subject: eeg source reconstruction and temporal contrast Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPM experts, I have used the group source reconstruction toolbox provided by SPM. It worked very well, but know it's for me to interpret my results ! I have used the both type of temporal contrast (ie gaussian window and morlet wavelet) and I founded slightly different results. I used a time windows for the inversion between 0 to 800 ms and a temporal contrast between 0 and 400 ms to highlight the different sources involved in all my process. I am working on a langage task which is known to imply broca's and wernicke's area and it is what I found ! with the gaussian window I found Broca's area and with the series of wavelet I found broca's and Wernicke's areas. both type of contrast are strongly significant even with FWE correction. And know I have to interpret it! Can I say that using a gaussian windows I highlight more continuous activation whereas using a series of wavelet I will more highlight transient activation in upper frequency band? should I refine my study and doing it in the different well known frequency band? thank you for your attention, bests regards, Gaetan Yvert ========================================================================Date: Wed, 2 Feb 2011 07:35:35 -0800 Reply-To: Michael Spezio <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Spezio <[log in to unmask]> Subject: Re: Bandpass filtering with wide bands in spm8: Why is there a problem? Comments: To: [log in to unmask] Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=__PartC2EECBD7.1__=" Message-ID: <[log in to unmask]> This is a MIME message. If you are reading this text, you may want to consider changing to a mail reader or gateway that understands how to properly handle MIME multipart messages. --=__PartC2EECBD7.1__Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Hi Vladimir, I tried the <2 Hz highpass setting for the sampling rate for the tutorial data, which is 2048 Hz. But I also tried first downsampling to 500 Hz, then applying <2 Hz filtering, and it also failed for that sampling rate. Both times, filtering was applied to continuous data rather than to epoched data. Perhaps I will try the IIR filter via the batch interface. Best, Michael Michael L. Spezio, Ph.D. Assistant Professor of Psychology Department of Psychology Scripps College Claremont, CA [log in to unmask] 909.607.0914 and Visiting Associate Scientist Psychology & Neuroscience Division of Humanities & Social Sciences Caltech Pasadena, CA 91125 [log in to unmask] >>> Vladimir Litvak 02/02/11 7:26 AM >>> It's probably an SP toolbox issue, but I have never seen an example of this happening for any setting. It usually only happens for some settings. What is your sampling rate? Is your data epoched or continuous? If it's epoched perhaps your epochs are not long enough? If it's continuous I'd try changing the filter order or using an IIR filter (you can do it if you use batch interface to filtering). Or perhaps downsampling event to a slightly different rate will help. Vladimir On Wed, Feb 2, 2011 at 3:16 PM, Michael Spezio wrote: > Great, thanks for this very helpful response, Vladimir. > > I have noticed that even with using just highpass filtering, any setting > below 2 Hz results in the data corruption issues. If this is an issue with > the SP toolbox, then the Mathworks should be contacted to fix it. If it's a > problem with SPM8, then may there be an upgrade in the future? > > Best, > > Michael > > > Michael L. Spezio, Ph.D. > Assistant Professor of Psychology > Department of Psychology > Scripps College > Claremont, CA > [log in to unmask] > 909.607.0914 > > and > > Visiting Associate Scientist > Psychology & Neuroscience > Division of Humanities & Social Sciences > Caltech > Pasadena, CA 91125 > [log in to unmask] >>>> Vladimir Litvak 02/02/11 3:19 AM >>> > Dear Michael, > > On Wed, Feb 2, 2011 at 7:02 AM, Michael Spezio > wrote: >> Hi Vladimir, >> >> I saw in your post 041502, dated 2010-05-07, that for some reason bandpass >> filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by >> the Matlab SP Toolbox and causes displays of the subsequent data to fail. >> >> Can you specify why this is occurring? It doesn't seem to make sense given >> that wide bandpass filters are used in signal processing routinely. >> > > It's not a display issue but actual data corruption issue. There are > numerical stability problems that I have since also observed in > high-pass filters for some combinations of filter settings and > sampling rate. These problems are common for wide bandpass filters, > especially for ones with low cut-off close to DC (e.g. 0.1-40 Hz). > Therefore, I recommended that people use separate high-pass and > low-pass in these cases. In the next SPM8 update there is a change in > the code that detects automatically when a filter is unstable and > gives an error so people will not have to figure it out later when > their display crashes. > >> Also, for the Multimodal Face tutorial, why isn't filtering done prior to >> downsampling? Downsampling, especially to 200 Hz or lower, should only be >> done after filtering out higher frequency contaminating signals, measured >> at >> a high sampling rate (500 Hz or above), to avoid aliasing from muscle >> signals at frequencies of 90 Hz to 200 Hz. Can you help me understand what >> is going on with the bandpass processing and why filtering is left out of >> the tutorial? >> > > Aliasing can occur when downsampling is done by simple decimation in > the time domain (e.g. taking every other sample). SP toolbox > downsampling routine is smarter than that and it pre-filters data to > avoid aliasing so it is not necessary to do it explicitly. In the > absence of SP toolbox we use or own routine that downsamples in the > frequency domain by truncating the DFT coefficients. This way there is > also no aliasing. The only problem that can occur is that if there are > large DC offsets in the data, low-pass filter will cause ringing at > the edges. Therefore I'd suggest to apply high-pass filtering before > downsampling especially for epoched data. In the case of the faces > tutorial this is not a problem because downsampling is done on > continuous data (for EEG) or on baseline-corrected data (for MEG). > > Best, > > Vladimir > >> Thanks so much. >> >> Best, >> >> Michael >> >> Michael L. Spezio, Ph.D. >> Assistant Professor of Psychology >> Department of Psychology >> Scripps College >> Claremont, CA >> [log in to unmask] >> 909.607.0914 >> >> and >> >> Visiting Associate Scientist >> Psychology & Neuroscience >> Division of Humanities & Social Sciences >> Caltech >> Pasadena, CA 91125 >> [log in to unmask] >> > --=__PartC2EECBD7.1__Content-Type: text/html; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Content-Description: HTML Hi Vladimir,

I tried the <2 Hz highpass setting for the sampling rate for the tutorial data, which is 2048 Hz. But I also tried first downsampling to 500 Hz, then applying <2 Hz filtering, and it also failed for that sampling rate. Both times, filtering was applied to continuous data rather than to epoched data.

Perhaps I will try the IIR filter via the batch interface.

Best,

Michael

>>> Vladimir Litvak <[log in to unmask]> 02/02/11 7:26 AM >>>
It's probably an SP toolbox issue, but I have never seen an example of
this happening for any setting. It usually only happens for some
settings. What is your sampling rate? Is your data epoched or
continuous? If it's epoched perhaps your epochs are not long enough?
If it's continuous I'd try changing the filter order or using an IIR
filter (you can do it if you use batch interface to filtering). Or
perhaps downsampling event to a slightly different rate will help.

Vladimir

On Wed, Feb 2, 2011 at 3:16 PM, Michael Spezio
<[log in to unmask]> wrote:
> Great, thanks for this very helpful response, Vladimir.
>
> I have noticed that even with using just highpass filtering, any setting
> below 2 Hz results in the data corruption issues. If this is an issue with
> the SP toolbox, then the Mathworks should be contacted to fix it. If it's a
> problem with SPM8, then may there be an upgrade in the future?
>
> Best,
>
> Michael
>
>
> Michael L. Spezio, Ph.D.
> Assistant Professor of Psychology
> Department of Psychology
> Scripps College
> Claremont, CA
> [log in to unmask]
> 909.607.0914
>
> and
>
> Visiting Associate Scientist
> Psychology & Neuroscience
> Division of Humanities & Social Sciences
> Caltech
> Pasadena, CA 91125
> [log in to unmask]
>>>> Vladimir Litvak <[log in to unmask]> 02/02/11 3:19 AM >>>
> Dear Michael,
>
> On Wed, Feb 2, 2011 at 7:02 AM, Michael Spezio
> <[log in to unmask]> wrote:
>> Hi Vladimir,
>>
>> I saw in your post 041502, dated 2010-05-07, that for some reason bandpass
>> filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by
>> the Matlab SP Toolbox and causes displays of the subsequent data to fail.
>>
>> Can you specify why this is occurring? It doesn't seem to make sense given
>> that wide bandpass filters are used in signal processing routinely.
>>
>
> It's not a display issue but actual data corruption issue. There are
> numerical stability problems that I have since also observed in
> high-pass filters for some combinations of filter settings and
> sampling rate. These problems are common for wide bandpass filters,
> especially for ones with low cut-off close to DC (e.g. 0.1-40 Hz).
> Therefore, I recommended that people use separate high-pass and
> low-pass in these cases. In the next SPM8 update there is a change in
> the code that detects automatically when a filter is unstable and
> gives an error so people will not have to figure it out later when
> their display crashes.
>
>> Also, for the Multimodal Face tutorial, why isn't filtering done prior to
>> downsampling? Downsampling, especially to 200 Hz or lower, should only be
>> done after filtering out higher frequency contaminating signals, measured
>> at
>> a high sampling rate (500 Hz or above), to avoid aliasing from muscle
>> signals at frequencies of 90 Hz to 200 Hz. Can you help me understand what
>> is going on with the bandpass processing and why filtering is left out of
>> the tutorial?
>>
>
> Aliasing can occur when downsampling is done by simple decimation in
> the time domain (e.g. taking every other sample). SP toolbox
> downsampling routine is smarter than that and it pre-filters data to
> avoid aliasing so it is not necessary to do it explicitly. In the
> absence of SP toolbox we use or own routine that downsamples in the
> frequency domain by truncating the DFT coefficients. This way there is
> also no aliasing. The only problem that can occur is that if there are
> large DC offsets in the data, low-pass filter will cause ringing at
> the edges. Therefore I'd suggest to apply high-pass filtering before
> downsampling especially for epoched data. In the case of the faces
> tutorial this is not a problem because downsampling is done on
> continuous data (for EEG) or on baseline-corrected data (for MEG).
>
> Best,
>
> Vladimir
>
>> Thanks so much.
>>
>> Best,
>>
>> Michael
>>
>> Michael L. Spezio, Ph.D.
>> Assistant Professor of Psychology
>> Department of Psychology
>> Scripps College
>> Claremont, CA
>> [log in to unmask]
>> 909.607.0914
>>
>> and
>>
>> Visiting Associate Scientist
>> Psychology & Neuroscience
>> Division of Humanities & Social Sciences
>> Caltech
>> Pasadena, CA 91125
>> [log in to unmask]
>>
>
--=__PartC2EECBD7.1__=-- ========================================================================Date: Wed, 2 Feb 2011 15:40:53 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: eeg source reconstruction and temporal contrast Comments: To: Gaetan Yvert <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Gaetan, First it is very good to hear that you got sensible and significant results. It's indeed my feeling that the last version of the routines is really on the right track. Regarding interpretation, I understand that in both cases you used averaged ERP/ERF rather than epoched file as input. In this case the difference between using a Gaussian window and wavelets over your whole bandwidth is like the difference between average and RMS. So if you have some activity that changes polarity in your time window if you average over it, it can get attenuated or even average to zero. So looking at total power retains more and if you can do all your analysis just on that, perhaps that's what you should do. Averaging is just more conceptually similar and standard thing to do. Regarding limiting your analysis to a band, that's a question of whether you can justify it. Also it's perhaps something that makes more sense when looking at induced activity (i.e. inverting an epoched file). Best, Vladimir On Wed, Feb 2, 2011 at 3:18 PM, Gaetan Yvert <[log in to unmask]> wrote: > Dear SPM experts, > > I have used the group source reconstruction toolbox provided by SPM. > It worked very well, but know it's for me to interpret my results ! > > I have used the both type of temporal contrast (ie gaussian window and morlet wavelet) and I founded slightly different results. > I used a time windows for the inversion between 0 to 800 ms and a temporal contrast between 0 and 400 ms to highlight the different sources involved in all my process. > I am working on a langage task which is known to imply broca's and wernicke's area and it is what I found ! > > with the gaussian window I found Broca's area and with the series of wavelet I found broca's and Wernicke's areas. both type of contrast are strongly significant even with FWE correction. > > And know I have to interpret it! > > Can I say that using a gaussian windows I highlight more continuous activation whereas using a series of wavelet I will more highlight transient activation in upper frequency band? > > should I refine my study and doing it in the different well known frequency band? > > thank you for your attention, > > bests regards, > > Gaetan Yvert > ========================================================================Date: Wed, 2 Feb 2011 15:45:17 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Bandpass filtering with wide bands in spm8: Why is there a problem? Comments: To: Michael Spezio <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> That's really strange and should not happen. Perhaps there is a different reason that would be hard for me to pinpoint if I can't reproduce the problem here. Vladimir On Wed, Feb 2, 2011 at 3:35 PM, Michael Spezio <[log in to unmask]> wrote: > Hi Vladimir, > > I tried the <2 Hz highpass setting for the sampling rate for the tutorial > data, which is 2048 Hz. But I also tried first downsampling to 500 Hz, then > applying <2 Hz filtering, and it also failed for that sampling rate. Both > times, filtering was applied to continuous data rather than to epoched data. > > Perhaps I will try the IIR filter via the batch interface. > > Best, > > Michael > > > Michael L. Spezio, Ph.D. > Assistant Professor of Psychology > Department of Psychology > Scripps College > Claremont, CA > [log in to unmask] > 909.607.0914 > > and > > Visiting Associate Scientist > Psychology & Neuroscience > Division of Humanities & Social Sciences > Caltech > Pasadena, CA 91125 > [log in to unmask] >>>> Vladimir Litvak <[log in to unmask]> 02/02/11 7:26 AM >>> > It's probably an SP toolbox issue, but I have never seen an example of > this happening for any setting. It usually only happens for some > settings. What is your sampling rate? Is your data epoched or > continuous? If it's epoched perhaps your epochs are not long enough? > If it's continuous I'd try changing the filter order or using an IIR > filter (you can do it if you use batch interface to filtering). Or > perhaps downsampling event to a slightly different rate will help. > > Vladimir > > On Wed, Feb 2, 2011 at 3:16 PM, Michael Spezio > <[log in to unmask]> wrote: >> Great, thanks for this very helpful response, Vladimir. >> >> I have noticed that even with using just highpass filtering, any setting >> below 2 Hz results in the data corruption issues. If this is an issue with >> the SP toolbox, then the Mathworks should be contacted to fix it. If it's >> a >> problem with SPM8, then may there be an upgrade in the future? >> >> Best, >> >> Michael >> >> >> Michael L. Spezio, Ph.D. >> Assistant Professor of Psychology >> Department of Psychology >> Scripps College >> Claremont, CA >> [log in to unmask] >> 909.607.0914 >> >> and >> >> Visiting Associate Scientist >> Psychology & Neuroscience >> Division of Humanities & Social Sciences >> Caltech >> Pasadena, CA 91125 >> [log in to unmask] >>>>> Vladimir Litvak <[log in to unmask]> 02/02/11 3:19 AM >>> >> Dear Michael, >> >> On Wed, Feb 2, 2011 at 7:02 AM, Michael Spezio >> <[log in to unmask]> wrote: >>> Hi Vladimir, >>> >>> I saw in your post 041502, dated 2010-05-07, that for some reason >>> bandpass >>> filtering with wide bands (e.g., 0.1 to 30 Hz) elicits bad performance by >>> the Matlab SP Toolbox and causes displays of the subsequent data to fail. >>> >>> Can you specify why this is occurring? It doesn't seem to make sense >>> given >>> that wide bandpass filters are used in signal processing routinely. >>> >> >> It's not a display issue but actual data corruption issue. There are >> numerical stability problems that I have since also observed in >> high-pass filters for some combinations of filter settings and >> sampling rate. These problems are common for wide bandpass filters, >> especially for ones with low cut-off close to DC (e.g. 0.1-40 Hz). >> Therefore, I recommended that people use separate high-pass and >> low-pass in these cases. In the next SPM8 update there is a change in >> the code that detects automatically when a filter is unstable and >> gives an error so people will not have to figure it out later when >> their display crashes. >> >>> Also, for the Multimodal Face tutorial, why isn't filtering done prior to >>> downsampling? Downsampling, especially to 200 Hz or lower, should only be >>> done after filtering out higher frequency contaminating signals, measured >>> at >>> a high sampling rate (500 Hz or above), to avoid aliasing from muscle >>> signals at frequencies of 90 Hz to 200 Hz. Can you help me understand >>> what >>> is going on with the bandpass processing and why filtering is left out of >>> the tutorial? >>> >> >> Aliasing can occur when downsampling is done by simple decimation in >> the time domain (e.g. taking every other sample). SP toolbox >> downsampling routine is smarter than that and it pre-filters data to >> avoid aliasing so it is not necessary to do it explicitly. In the >> absence of SP toolbox we use or own routine that downsamples in the >> frequency domain by truncating the DFT coefficients. This way there is >> also no aliasing. The only problem that can occur is that if there are >> large DC offsets in the data, low-pass filter will cause ringing at >> the edges. Therefore I'd suggest to apply high-pass filtering before >> downsampling especially for epoched data. In the case of the faces >> tutorial this is not a problem because downsampling is done on >> continuous data (for EEG) or on baseline-corrected data (for MEG). >> >> Best, >> >> Vladimir >> >>> Thanks so much. >>> >>> Best, >>> >>> Michael >>> >>> Michael L. Spezio, Ph.D. >>> Assistant Professor of Psychology >>> Department of Psychology >>> Scripps College >>> Claremont, CA >>> [log in to unmask] >>> 909.607.0914 >>> >>> and >>> >>> Visiting Associate Scientist >>> Psychology & Neuroscience >>> Division of Humanities & Social Sciences >>> Caltech >>> Pasadena, CA 91125 >>> [log in to unmask] >>> >> > ========================================================================Date: Wed, 2 Feb 2011 09:54:31 -0600 Reply-To: Shashwath Meda <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Shashwath Meda <[log in to unmask]> Subject: Art Repair batch error MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00235444787d264889049b4eaa18 Content-Type: text/plain; charset=ISO-8859-1 Hi All - I am in the process of batching the art_global script over multiple subjects. I seem to get the following error, can someone help. art_global(b,b1,4,0); Generated mask image is written to file ArtifactMask.img. Adaptive Mask Threshold 130533 Mapping files...: ...done Calculating globals...: ...done\n 93415 voxels were in the auto generated mask. ??? Error using ==> cd Cannot CD to (Name is nonexistent or not a directory). Error in ==> art_global at 614 cd(fileparts(P(1,:))); b = all my image files b1 = my realignment params text file Any help would be much appreciated. -- with regards, Shashwath meda *********************************************** Imaging Genetics Analyst III 515-F Light Hall Vanderbilt University Medical Center Cell: (313) 505-6847 Off: (615) 875-3086 *********************************************** --00235444787d264889049b4eaa18 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi All - I am in the process of batching the art_global script over multiple subjects. I seem to get the following error, can someone help.

art_global(b,b1,4,0);

Generated mask image is written to file ArtifactMask.img.

Adaptive Mask Threshold

130533

Mapping files...: ...done

Calculating globals...: ...done\n

93415 voxels were in the auto generated mask.

??? Error using ==> cd

Cannot CD to (Name is nonexistent or not a directory).

Error in ==> art_global at 614

cd(fileparts(P(1,:)));

b = all my image files

b1 = my realignment params text file

Any help would be much appreciated.

--
with regards,

Shashwath meda

***********************************************
Imaging Genetics Analyst III

515-F Light Hall

Vanderbilt University Medical Center
Cell: (313) 505-6847
Off: (615) 875-3086

***********************************************

--00235444787d264889049b4eaa18-- ========================================================================Date: Wed, 2 Feb 2011 18:58:44 +0100 Reply-To: Jokel Meyer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jokel Meyer <[log in to unmask]> Subject: Advice regarding proper programming style MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00163628447e666edd049b5066e9 Content-Type: text/plain; charset=ISO-8859-1 Dear SPM-list! In advance I would like to apologize for this slightly off-topic post. I started extending various SPM functions and adding own scripts to facilitate my analysis of MRI data. However as the number of functions & scripts increased rapidly I start loosing overview which is due to the fact that I do not have proper education in programming. Could you recommend any online resources or books that would introduce me to some principles of "good" programming, how to best organize your function and distribute calculations between .m-files? Many thanks for your help! Jokel --00163628447e666edd049b5066e9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPM-list!

In advance I would like to apologize for this slightly off-topic post.

I started extending various SPM functions and adding own scripts to facilitate my analysis of MRI data. However as the number of functions & scripts increased rapidly I start loosing overview which is due to the fact that I do not have proper education in programming. Could you recommend any online resources or books that would introduce me to some principles of "good" programming, how to best organize your function and distribute calculations between .m-files?

Many thanks for your help!

Jokel

--00163628447e666edd049b5066e9-- ========================================================================Date: Wed, 2 Feb 2011 13:17:39 -0500 Reply-To: "Watson, Christopher" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Watson, Christopher" <[log in to unmask]> Subject: Re: Advice regarding proper programming style Comments: To: Jokel Meyer <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> The Matlab programming fundamentals guide is pretty good. ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Jokel Meyer [[log in to unmask]] Sent: Wednesday, February 02, 2011 12:58 PM To: [log in to unmask] Subject: [SPM] Advice regarding proper programming style Dear SPM-list! In advance I would like to apologize for this slightly off-topic post. I started extending various SPM functions and adding own scripts to facilitate my analysis of MRI data. However as the number of functions & scripts increased rapidly I start loosing overview which is due to the fact that I do not have proper education in programming. Could you recommend any online resources or books that would introduce me to some principles of "good" programming, how to best organize your function and distribute calculations between .m-files? Many thanks for your help! Jokel ========================================================================Date: Wed, 2 Feb 2011 10:28:27 -0800 Reply-To: Michael Spezio <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Spezio <[log in to unmask]> Subject: Re: Advice regarding proper programming style Comments: To: [log in to unmask] Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=__Part95B99C5B.1__=" Message-ID: <[log in to unmask]> This is a MIME message. If you are reading this text, you may want to consider changing to a mail reader or gateway that understands how to properly handle MIME multipart messages. --=__Part95B99C5B.1__Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Hi Jokel, I've found that the book by Stormy Attaway is very good for teaching programming basics to undergraduates: http://www.amazon.com/gp/product/0750687622/ref=pd_lpo_k2_dp_sr_1?pf_rd_p=486539851&pf_rd_s=lpo-top-stripe-1&pf_rd_t=201&pf_rd_i=0495073008&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1XGCACF3TPNKC80VQPXV Best, Michael Michael L. Spezio, Ph.D. Assistant Professor of Psychology Department of Psychology Scripps College Claremont, CA [log in to unmask] 909.607.0914 and Visiting Associate Scientist Psychology & Neuroscience Division of Humanities & Social Sciences Caltech Pasadena, CA 91125 [log in to unmask] >>> "Watson, Christopher" 02/02/11 10:18 AM >>> The Matlab programming fundamentals guide is pretty good. ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Jokel Meyer [[log in to unmask]] Sent: Wednesday, February 02, 2011 12:58 PM To: [log in to unmask] Subject: [SPM] Advice regarding proper programming style Dear SPM-list! In advance I would like to apologize for this slightly off-topic post. I started extending various SPM functions and adding own scripts to facilitate my analysis of MRI data. However as the number of functions & scripts increased rapidly I start loosing overview which is due to the fact that I do not have proper education in programming. Could you recommend any online resources or books that would introduce me to some principles of "good" programming, how to best organize your function and distribute calculations between .m-files? Many thanks for your help! Jokel --=__Part95B99C5B.1__Content-Type: text/html; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Content-Description: HTML Hi Jokel,

I've found that the book by Stormy Attaway is very good for teaching programming basics to undergraduates:

http://www.amazon.com/gp/product/0750687622/ref=pd_lpo_k2_dp_sr_1?pf_rd_p=486539851&pf_rd_s=lpo-top-stripe-1&pf_rd_t=201&pf_rd_i=0495073008&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1XGCACF3TPNKC80VQPXV

Best,

Michael

>>> "Watson, Christopher" <[log in to unmask]> 02/02/11 10:18 AM >>>
The Matlab programming fundamentals guide is pretty good.
________________________________________
From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Jokel Meyer [[log in to unmask]]
Sent: Wednesday, February 02, 2011 12:58 PM
To: [log in to unmask]
Subject: [SPM] Advice regarding proper programming style

Dear SPM-list!

In advance I would like to apologize for this slightly off-topic post.
I started extending various SPM functions and adding own scripts to facilitate my analysis of MRI data. However as the number of functions & scripts increased rapidly I start loosing overview which is due to the fact that I do not have proper education in programming. Could you recommend any online resources or books that would introduce me to some principles of "good" programming, how to best organize your function and distribute calculations between .m-files?

Many thanks for your help!
Jokel
--=__Part95B99C5B.1__=-- ========================================================================Date: Wed, 2 Feb 2011 19:34:04 +0100 Reply-To: Claus Lamm <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Claus Lamm <[log in to unmask]> Organization: University of Vienna Subject: Re: Advice regarding proper programming style In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_00AE_01CBC310.26D0C3E0" Message-ID: <[log in to unmask]> This is a multipart message in MIME format. ------=_NextPart_000_00AE_01CBC310.26D0C3E0 Content-Type: text/plain; charset="US-ASCII" Content-Transfer-Encoding: 7bit This might be useful too: http://www.amazon.com/Matlab-Neuroscientists-Introduction-Scientific-Computi ng/dp/0123745519/ref=sr_1_1?ie=UTF8 &s=books&qid96671614&sr=1-1 From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Michael Spezio Sent: Mittwoch, 02. Februar 2011 19:28 To: [log in to unmask] Subject: Re: [SPM] Advice regarding proper programming style Hi Jokel, I've found that the book by Stormy Attaway is very good for teaching programming basics to undergraduates: http://www.amazon.com/gp/product/0750687622/ref=pd_lpo_k2_dp_sr_1?pf_rd_pH 6539851 &pf_rd_s=lpo-top-stripe-1&pf_rd_t 1&pf_rd_i95073008&pf_rd_m=ATVPDKIKX0D ER&pf_rd_r=1XGCACF3TPNKC80VQPXV Best, Michael Michael L. Spezio, Ph.D. Assistant Professor of Psychology Department of Psychology Scripps College Claremont, CA [log in to unmask] 909.607.0914 and Visiting Associate Scientist Psychology & Neuroscience Division of Humanities & Social Sciences Caltech Pasadena, CA 91125 [log in to unmask] >>> "Watson, Christopher" <[log in to unmask]> 02/02/11 10:18 AM >>> The Matlab programming fundamentals guide is pretty good. ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Jokel Meyer [[log in to unmask]] Sent: Wednesday, February 02, 2011 12:58 PM To: [log in to unmask] Subject: [SPM] Advice regarding proper programming style Dear SPM-list! In advance I would like to apologize for this slightly off-topic post. I started extending various SPM functions and adding own scripts to facilitate my analysis of MRI data. However as the number of functions & scripts increased rapidly I start loosing overview which is due to the fact that I do not have proper education in programming. Could you recommend any online resources or books that would introduce me to some principles of "good" programming, how to best organize your function and distribute calculations between .m-files? Many thanks for your help! Jokel ------=_NextPart_000_00AE_01CBC310.26D0C3E0 Content-Type: text/html; charset="US-ASCII" Content-Transfer-Encoding: quoted-printable

This might be useful too:

http://www.amazon.com/Matlab-Neuroscientists-Introduction-Scientific-Computing/dp/0123745519/ref=sr_1_1?ie=UTF8&s=books&qid=1296671614&sr=1-1

From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Michael Spezio
Sent: Mittwoch, 02. Februar 2011 19:28
To: [log in to unmask]
Subject: Re: [SPM] Advice regarding proper programming style

Hi Jokel,

I've found that the book by Stormy Attaway is very good for teaching programming basics to undergraduates:

http://www.amazon.com/gp/product/0750687622/ref=pd_lpo_k2_dp_sr_1?pf_rd_p=486539851&pf_rd_s=lpo-top-stripe-1&pf_rd_t=201&pf_rd_i=0495073008&pf_rd_m=ATVPDKIKX0DER&pf_rd_r=1XGCACF3TPNKC80VQPXV

Best,

Michael

------=_NextPart_000_00AE_01CBC310.26D0C3E0-- ========================================================================Date: Wed, 2 Feb 2011 13:35:15 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: covariates in multiple regression Comments: To: Sien Hu <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e648be78fd17dc049b50e81d Content-Type: text/plain; charset=ISO-8859-1 Hi Sien What if I would like to look at the effect of only age with bmi > being controlled. Is there any way to do it in SPM? > Sure. To look at the effect of one covariate while controlling for a second, enter both in your model as covariates, but leave a 0 over the column(s) you aren't interested in in your contrast. So the columns in your design matrix might be: BMI age grand_mean and you could then use contrasts of [1 0 0] or [-1 0 0] to look for effects of BMI, with the effects of age controlled for. Hope this helps, Jonathan --0016e648be78fd17dc049b50e81d Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi Sien

What if I would like to look at the effect of only age with bmi
being controlled. Is there any way to do it in SPM?

Sure. To look at the effect of one covariate while controlling for a second, enter both in your model as covariates, but leave a 0 over the column(s) you aren't interested in in your contrast. So the columns in your design matrix might be:

BMI age grand_mean

and you could then use contrasts of [1 0 0] or [-1 0 0] to look for effects of BMI, with the effects of age controlled for.

Hope this helps,

Jonathan

--0016e648be78fd17dc049b50e81d-- ========================================================================Date: Wed, 2 Feb 2011 13:34:12 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Advice regarding proper programming style Comments: To: Jokel Meyer <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> MATLAB for Neuroscientists has alot of information about matlab functions. In terms of general programming, both of the mentioned sources are good. Remember that you don't need a book specifically about MATLAB as the fundamentals of programming are always the same. The only difference is the syntax. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Wed, Feb 2, 2011 at 12:58 PM, Jokel Meyer <[log in to unmask]> wrote: > Dear SPM-list! > In advance I would like to apologize for this slightly off-topic post. > I started extending various SPM functions and adding own scripts to > facilitate my analysis of MRI data. However as the number of functions & > scripts increased rapidly I start loosing overview which is due to the fact > that I do not have proper education in programming. Could you recommend any > online resources or books that would introduce me to some principles of > "good" programming, how to best organize your function and distribute > calculations between .m-files? > Many thanks for your help! > Jokel ========================================================================Date: Wed, 2 Feb 2011 22:33:08 +0000 Reply-To: Thomas Nichols <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Thomas Nichols <[log in to unmask]> Subject: PhD Studentship in Statistics of fMRI Clinical Trials Comments: To: FSL - FMRIB's Software Library <[log in to unmask]>, [log in to unmask] MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001636765b69bc29a3049b543b11 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Apologies for the cross-posting. Please distribute to all interested parties. Thank you! -Tom * * *MRC CASE Studentship in Statistics - University of Warwick & GlaxoSmithKline* * * Applications are invited for a PhD studentship in Statistics at the University of Warwick, funded by a MRC Industrial CASE award with the GlaxoSmithKline's Clinical Unit Cambridge. * * *Title: *Optimal Fixed and Adaptive Designs for fMRI Clinical Trials *Supervisors:* Dr. Thomas Nichols (Warwick) and Dr. Ed Bullmore (GSK) *Funding: *Tuition fees (at UK/EU level) and tax-free stipend for a 4 year PhD. Stipend rises annually as per the UK Research Councils minimum rate (£13,590 for 2010/11), and is topped-up £2,500 by the industrial sponsor. *Start Date:* October 2011 * * *Project Description:* Functional Magnetic Resonance Imaging (fMRI) is now almost 20 years old and is an indispensible tool for cognitive neuroscience. However only recently has fMRI been employed as part of drug development, as pharmaceutical companies seek to develop biomarkers that are more sensitive to disease state. By it's increased sensitivity and specificity, fMRI has the potential to reduce the time require to bring a drug to the marketplace, thus lower the cost of drug development and ultimately improve patient care. However, fMRI for clinical trials requires a different set of statistical tools than that used by conventional brain mapping: clinical trial outcomes must be precisely defined a priori, and the number of outcomes considered minimized. Further, cutting-edge type of fMRI designs, using task-free data to measure "resting state networks" presents yet new challenges for use in a clinical trial, as task-free results are intrinsically multivariate and difficult to define a priori. The goal of this project is to develop novel statistical methodology to maximize information obtained from an fMRI study while still adhering to the stringent inferential requirements of a clinical trial. The project has three broad aims. The first is to develop adaptive designs for task-based fMRI clinical trials, using initial data to customize outcomes to an unknown signal. The second is to develop optimal outcomes for the task-free (or resting-state) fMRI data. The third aim is to develop optimal outcomes from graph-theoretic measures of task-based or task-free fMRI data, understanding the impact of matrix filtering operations on the power of final summary measure. The student will obtain a unique training in statistics and neuroimaging with mentorship from leading researchers in the field. The CASE award also includes three-months of on-site research at the GSK Clinical Unit Cambridge, giving them valuable industrial experience. *Requirements:* The studentship is available to candidates with the equivalent of a first class or upper second class degree in a relevant discipline, and who meet University entry requirements (see http://www2.warwick.ac.uk/services/academicoffice/gsp/prospective). *Eligibility:* UK nationals, or EU nationals who have been resident in the UK in the last three years. Other EU nationals can only receive a fees-only award. For full deatils see http://www.mrc.ac.uk/Fundingopportunities/Applicanthandbook/Studentships/Eligibility/index.htm *To Apply: * http://www2.warwick.ac.uk/fac/sci/statistics/postgrad/research/apply (and please notify Dr. Nichols [log in to unmask] when application is submitted) * * *Deadline*: Applications are considered on rolling basis, up until 31 July 2011, but priority given to applications received before 28 February. *Inquires:* Specific questions on this project should be directed to Thomas Nichols [log in to unmask]; questions on the postgraduate application process at Warwick should be directed to Ms. Kristine Prismall [log in to unmask] *Links:* Warwick Statistics http://go.warwick.ac.uk/stats Thomas Nichols http://go.warwick.ac.uk/tenichols Ed Bullmore's academic page http://www.psychiatry.cam.ac.uk/pages/profiles/bullmore.html --001636765b69bc29a3049b543b11 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Apologies for the cross-posting. Please distribute to all interested parties. Thank you!

-Tom

MRC CASE Studentship in Statistics - University of Warwick & GlaxoSmithKline

Applications are invited for a PhD studentship in Statistics at the University of Warwick, funded by a MRC Industrial CASE award with the GlaxoSmithKline's Clinical Unit Cambridge.

Title: Optimal Fixed and Adaptive Designs for fMRI Clinical Trials

Supervisors: Dr. Thomas Nichols (Warwick) and Dr. Ed Bullmore (GSK)

Funding: Tuition fees (at UK/EU level) and tax-free stipend for a 4 year PhD. Stipend rises annually as per the UK Research Councils minimum rate (£13,590 for 2010/11), and is topped-up £2,500 by the industrial sponsor.

Start Date: October 2011

Project Description: Functional Magnetic Resonance Imaging (fMRI) is now almost 20 years old and is an indispensible tool for cognitive neuroscience. However only recently has fMRI been employed as part of drug development, as pharmaceutical companies seek to develop biomarkers that are more sensitive to disease state. By it's increased sensitivity and specificity, fMRI has the potential to reduce the time require to bring a drug to the marketplace, thus lower the cost of drug development and ultimately improve patient care. However, fMRI for clinical trials requires a different set of statistical tools than that used by conventional brain mapping: clinical trial outcomes must be precisely defined a priori, and the number of outcomes considered minimized. Further, cutting-edge type of fMRI designs, using task-free data to measure "resting state networks" presents yet new challenges for use in a clinical trial, as task-free results are intrinsically multivariate and difficult to define a priori.

The goal of this project is to develop novel statistical methodology to maximize information obtained from an fMRI study while still adhering to the stringent inferential requirements of a clinical trial. The project has three broad aims. The first is to develop adaptive designs for task-based fMRI clinical trials, using initial data to customize outcomes to an unknown signal. The second is to develop optimal outcomes for the task-free (or resting-state) fMRI data. The third aim is to develop optimal outcomes from graph-theoretic measures of task-based or task-free fMRI data, understanding the impact of matrix filtering operations on the power of final summary measure. The student will obtain a unique training in statistics and neuroimaging with mentorship from leading researchers in the field. The CASE award also includes three-months of on-site research at the GSK Clinical Unit Cambridge, giving them valuable industrial experience.

Requirements: The studentship is available to candidates with the equivalent of a first class or upper second class degree in a relevant discipline, and who meet University entry requirements (see http://www2.warwick.ac.uk/services/academicoffice/gsp/prospective).

Eligibility: UK nationals, or EU nationals who have been resident in the UK in the last three years. Other EU nationals can only receive a fees-only award. For full deatils see http://www.mrc.ac.uk/Fundingopportunities/Applicanthandbook/Studentships/Eligibility/index.htm

To Apply: http://www2.warwick.ac.uk/fac/sci/statistics/postgrad/research/apply (and please notify Dr. Nichols [log in to unmask] when application is submitted)

Deadline: Applications are considered on rolling basis, up until 31 July 2011, but priority given to applications received before 28 February.

Inquires: Specific questions on this project should be directed to Thomas Nichols [log in to unmask]; questions on the postgraduate application process at Warwick should be directed to Ms. Kristine Prismall [log in to unmask]

Links:

Warwick Statistics http://go.warwick.ac.uk/stats

Thomas Nichols http://go.warwick.ac.uk/tenichols

Ed Bullmore's academic page http://www.psychiatry.cam.ac.uk/pages/profiles/bullmore.html

--001636765b69bc29a3049b543b11-- ========================================================================Date: Thu, 3 Feb 2011 09:23:36 +0100 Reply-To: Ali Gholami <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ali Gholami <[log in to unmask]> Subject: SPM CLI Content-Type: text/plain; charset="UTF-8" Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <1296721416.28831.4.camel@tb06> Hi Anyone knows if there's a command line mode in SPM? Best regards Ali Gholami ========================================================================Date: Thu, 3 Feb 2011 07:40:53 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: SPM CLI Comments: To: Ali Gholami <[log in to unmask]> In-Reply-To: <1296721416.28831.4.camel@tb06> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]ail.com> spm_jobman However, you have to setup the matlabbatch by hand first if you want to use it. Additionally, you can call each function individually from the commandline as well. Some force a GUI if the input is not correct while others will fail. On Thursday, February 3, 2011, Ali Gholami <[log in to unmask]> wrote: > Hi > > Anyone knows if there's a command line mode in SPM? > > > Best regards > Ali Gholami > -- Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Thu, 3 Feb 2011 13:58:12 +0100 Reply-To: Ali Gholami <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ali Gholami <[log in to unmask]> Subject: Re: SPM CLI Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="UTF-8" Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <1296737892.28831.7.camel@tb06> Thank you for the answer. Is there some documentation about installing the batch and how to use it? Best regards Ali On Thu, 2011-02-03 at 07:40 -0500, MCLAREN, Donald wrote: > spm_jobman > > However, you have to setup the matlabbatch by hand first if you want > to use it. Additionally, you can call each function individually from > the commandline as well. Some force a GUI if the input is not correct > while others will fail. > > On Thursday, February 3, 2011, Ali Gholami <[log in to unmask]> wrote: > > Hi > > > > Anyone knows if there's a command line mode in SPM? > > > > > > Best regards > > Ali Gholami > > > ========================================================================Date: Thu, 3 Feb 2011 08:05:14 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: SPM CLI Comments: To: Ali Gholami <[log in to unmask]> In-Reply-To: <1296737892.28831.7.camel@tb06> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> help spm_jobman will give you a list of all of its options. spm_jobman('initcfg') is needed first in most cases, then spm_jobman('run_nogui',matlabbatch) or spm_jobman('run',matlabbatch) as for matlabbatch, I'm not sure if there is documentation for all of its possible fields. The best way to understand the matlabbatch is to make one subject with the gui and then modify the matlabbatch structure. There is even a function in spm for doing this, but I have never used it. Perhaps others might be able to point to good documentation on the matlabbatch structure. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 3, 2011 at 7:58 AM, Ali Gholami <[log in to unmask]> wrote: > Thank you for the answer. Is there some documentation about installing > the batch and how to use it? > > Best regards > Ali > > > > On Thu, 2011-02-03 at 07:40 -0500, MCLAREN, Donald wrote: >> spm_jobman >> >> However, you have to setup the matlabbatch by hand first if you want >> to use it. Additionally, you can call each function individually from >> the commandline as well. Some force a GUI if the input is not correct >> while others will fail. >> >> On Thursday, February 3, 2011, Ali Gholami <[log in to unmask]> wrote: >> > Hi >> > >> > Anyone knows if there's a command line mode in SPM? >> > >> > >> > Best regards >> > Ali Gholami >> > >> > > > ========================================================================Date: Thu, 3 Feb 2011 14:25:50 +0100 Reply-To: Michael Erb <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Erb <[log in to unmask]> Subject: Re: SPM CLI In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> A good starting point is Part VIII of the manual, describing the Batch Interface. You can also find a file matlabbatch.man in the subdirectory matlabbatch. Michael. Am 03.02.2011 14:05, schrieb MCLAREN, Donald: > help spm_jobman will give you a list of all of its options. > spm_jobman('initcfg') is needed first in most cases, then > spm_jobman('run_nogui',matlabbatch) or spm_jobman('run',matlabbatch) > > as for matlabbatch, I'm not sure if there is documentation for all of > its possible fields. The best way to understand the matlabbatch is to > make one subject with the gui and then modify the matlabbatch > structure. There is even a function in spm for doing this, but I have > never used it. Perhaps others might be able to point to good > documentation on the matlabbatch structure. > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Thu, Feb 3, 2011 at 7:58 AM, Ali Gholami<[log in to unmask]> wrote: >> Thank you for the answer. Is there some documentation about installing >> the batch and how to use it? >> >> Best regards >> Ali >> >> >> >> On Thu, 2011-02-03 at 07:40 -0500, MCLAREN, Donald wrote: >>> spm_jobman >>> >>> However, you have to setup the matlabbatch by hand first if you want >>> to use it. Additionally, you can call each function individually from >>> the commandline as well. Some force a GUI if the input is not correct >>> while others will fail. >>> >>> On Thursday, February 3, 2011, Ali Gholami<[log in to unmask]> wrote: >>>> Hi >>>> >>>> Anyone knows if there's a command line mode in SPM? >>>> >>>> >>>> Best regards >>>> Ali Gholami >>>> >>> >> >> >> > -- <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Dr. Michael Erb Sektion f. experimentelle Kernspinresonanz des ZNS Abteilung Neuroradiologie, Universitaetsklinikum Hoppe-Seyler_Str. 3, D-72076 Tuebingen Tel.: +49(0)7071/2987753 priv. +49(0)7071/61559 Fax.: +49(0)7071/294371 e-mail: <[log in to unmask]> www: http://www.medizin.uni-tuebingen.de/nrad/sektion/ <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< ========================================================================Date: Thu, 3 Feb 2011 14:05:51 +0000 Reply-To: Chris Rorden <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chris Rorden <[log in to unmask]> Subject: \spm8\tpm FWHM? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hello- Short version: What is the FWHM smooth (if any) applied to the spm8\tpm\csf.nii, spm8\tpm\grey.nii and spm8\tpm\white.nii? The corresponding images in the spm8\apriori folder seem very smooth (8mm FWHM?), the the spm8\tpm images appear very sharp. Long version I want to make a set of alternative templates for SPM's unified normalization segmentation. The aim is to build a nice template for large stroke patient studies, and as the average age ~65 I want an age specific template. The aim is to conduct lesion-masked unified segmentation (www.pubmed.com/20542122) with an age specific template derived from a healthy aging sample. I was able to create very nice tissue probability maps from the 50 healthy controls following the instructions for SPM's newseg and DARTEL, essentially following John Ashburner's instructions here (including affine transform of the template to MNI space): http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf Now I want to use this TPM for standard unified segmentation normalization. The question is whether I should apply any smoothing to the resulting Dartel TPM image or not. As this image is based on 50 people, to my eye it looks approximately as sharp as the images in spm8\tpm (which seg-norm uses). Should I use the mean image TPMs directly, or apply a FWHM with a small smoothing kernel? -chris ========================================================================Date: Thu, 3 Feb 2011 14:42:53 +0000 Reply-To: Duncan Astle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Duncan Astle <[log in to unmask]> Subject: PhD Opportunity in the UK Mime-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Content-Disposition: inline Message-ID: <[log in to unmask]> Dear All, please forward this to anyone who might be interested. The Department ofPsychology at Royal Holloway, University of London invitesapplications for PhD studentship for the academic year 2011-12. The Department of Psychology was ranked 7thin the UKin the 2008 Research Assessment Exercise (RAE) and was ranked 4thoverall in the Guardian league tables. The department hasover thirty members of academic staff and some 25 post-graduate research (PhD)students. We offer a vibrant researchcommunity and excellent facilities including a research designated functionalimaging unit. We areseeking talented final year undergraduate or MSc students, who are interestedin developmental and/or cognitive neuroscience. The research would use 'real-time' imaging techniques (e.g. EEG and MEG) to study the neural mechanisms of memory and attention.,in typically developing children and adults. The studentship would be under the supervision of Dr. Duncan Astle, who enjoys strong research links, particularly at Royal Holloway and at Oxford. Potentialcandidates are encouraged to contact Dr. Astle to discuss potential proposals,with there being plenty of scope for students interested in executive control,working memory, visual attention and childhood development to put together anengaging research program. Thestudentship provides a stipend and also covers the full tuition fees for UK and EUstudents. Overseas applicants are alsowelcome to apply but would be required to pay the over-seas tuition fees (inthis case there may be opportunities to compete for scholarships to cover partof these fees). We encourage applications from students with good undergraduatedegrees in Psychology, or related disciplines. Contact: [log in to unmask] Many thanks, Duncan ========================================================================Date: Thu, 3 Feb 2011 17:57:33 +0100 Reply-To: Ali Gholami <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ali Gholami <[log in to unmask]> Subject: Re: SPM CLI Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="UTF-8" Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <1296752253.28831.24.camel@tb06> Hi Maclaren, Thank you for your help! Still one thing I didn't understood. Do I need to install: http://sourceforge.net/projects/matlabbatch/ to have batch functionality? or just manipulating the spm_defaults.m is enough. There is a field there says: defaults.cmdline=0 and if it be turned to true, there should be no GUI running which in my case is not working. As an output when I run this command get the following error in Matlab: >> spm('defaults' , 'frmi') ??? Error using ==> spm at 481 Unknown Modality. Error in ==> spm at 426 Modality = spm('CheckModality',Modality); Best regards Ali On Thu, 2011-02-03 at 08:05 -0500, MCLAREN, Donald wrote: > help spm_jobman will give you a list of all of its options. > spm_jobman('initcfg') is needed first in most cases, then > spm_jobman('run_nogui',matlabbatch) or spm_jobman('run',matlabbatch) > > as for matlabbatch, I'm not sure if there is documentation for all of > its possible fields. The best way to understand the matlabbatch is to > make one subject with the gui and then modify the matlabbatch > structure. There is even a function in spm for doing this, but I have > never used it. Perhaps others might be able to point to good > documentation on the matlabbatch structure. > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Thu, Feb 3, 2011 at 7:58 AM, Ali Gholami <[log in to unmask]> wrote: > > Thank you for the answer. Is there some documentation about installing > > the batch and how to use it? > > > > Best regards > > Ali > > > > > > > > On Thu, 2011-02-03 at 07:40 -0500, MCLAREN, Donald wrote: > >> spm_jobman > >> > >> However, you have to setup the matlabbatch by hand first if you want > >> to use it. Additionally, you can call each function individually from > >> the commandline as well. Some force a GUI if the input is not correct > >> while others will fail. > >> > >> On Thursday, February 3, 2011, Ali Gholami <[log in to unmask]> wrote: > >> > Hi > >> > > >> > Anyone knows if there's a command line mode in SPM? > >> > > >> > > >> > Best regards > >> > Ali Gholami > >> > > >> > > > > > > ========================================================================Date: Thu, 3 Feb 2011 20:28:47 +0100 Reply-To: Michael Erb <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Erb <[log in to unmask]> Subject: Re: SPM CLI MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-15; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> You don't have to install matlabbatch because it is already installed as part of spm8 ( matlabbatch subdirectory of your spm directory). try spm('defaults' , 'fmri') instead of spm('defaults' , 'frmi') Good luck, Micheal. Am 03.02.2011 17:57, schrieb Ali Gholami: > Hi Maclaren, > > Thank you for your help! Still one thing I didn't understood. Do I need > to install: http://sourceforge.net/projects/matlabbatch/ to have batch > functionality? or just manipulating the spm_defaults.m is enough. There > is a field there says: defaults.cmdline=0 and if it be turned to true, > there should be no GUI running which in my case is not working. > > As an output when I run this command get the following error in > Matlab: > >>> spm('defaults' , 'frmi') > ??? Error using ==> spm at 481 > Unknown Modality. > > Error in ==> spm at 426 > Modality = spm('CheckModality',Modality); > > > Best regards > > Ali > > > > > > > On Thu, 2011-02-03 at 08:05 -0500, MCLAREN, Donald wrote: >> help spm_jobman will give you a list of all of its options. >> spm_jobman('initcfg') is needed first in most cases, then >> spm_jobman('run_nogui',matlabbatch) or spm_jobman('run',matlabbatch) >> >> as for matlabbatch, I'm not sure if there is documentation for all of >> its possible fields. The best way to understand the matlabbatch is to >> make one subject with the gui and then modify the matlabbatch >> structure. There is even a function in spm for doing this, but I have >> never used it. Perhaps others might be able to point to good >> documentation on the matlabbatch structure. >> >> Best Regards, Donald McLaren >> ================ >> D.G. McLaren, Ph.D. >> Postdoctoral Research Fellow, GRECC, Bedford VA >> Research Fellow, Department of Neurology, Massachusetts General >> Hospital and Harvard Medical School >> Office: (773) 406-2464 >> ==================== >> This e-mail contains CONFIDENTIAL INFORMATION which may contain >> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED >> and which is intended only for the use of the individual or entity >> named above. If the reader of the e-mail is not the intended recipient >> or the employee or agent responsible for delivering it to the intended >> recipient, you are hereby notified that you are in possession of >> confidential and privileged information. Any unauthorized use, >> disclosure, copying or the taking of any action in reliance on the >> contents of this information is strictly prohibited and may be >> unlawful. If you have received this e-mail unintentionally, please >> immediately notify the sender via telephone at (773) 406-2464 or >> email. >> >> >> >> On Thu, Feb 3, 2011 at 7:58 AM, Ali Gholami<[log in to unmask]> wrote: >>> Thank you for the answer. Is there some documentation about installing >>> the batch and how to use it? >>> >>> Best regards >>> Ali >>> >>> >>> >>> On Thu, 2011-02-03 at 07:40 -0500, MCLAREN, Donald wrote: >>>> spm_jobman >>>> >>>> However, you have to setup the matlabbatch by hand first if you want >>>> to use it. Additionally, you can call each function individually from >>>> the commandline as well. Some force a GUI if the input is not correct >>>> while others will fail. >>>> >>>> On Thursday, February 3, 2011, Ali Gholami<[log in to unmask]> wrote: >>>>> Hi >>>>> >>>>> Anyone knows if there's a command line mode in SPM? >>>>> >>>>> >>>>> Best regards >>>>> Ali Gholami >>>>> >>>> >>> >>> >>> > -- <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Dr. Michael Erb Sektion f. experimentelle Kernspinresonanz des ZNS Abteilung Neuroradiologie, Universitaetsklinikum Hoppe-Seyler_Str. 3, D-72076 Tuebingen Tel.: +49(0)7071/2987753 priv. +49(0)7071/61559 Fax.: +49(0)7071/294371 e-mail: <[log in to unmask]> www: http://www.medizin.uni-tuebingen.de/nrad/sektion/ <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< ========================================================================Date: Thu, 3 Feb 2011 14:34:31 -0500 Reply-To: Andrew Murrin <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Andrew Murrin <[log in to unmask]> Subject: Interpreting SPM Plots MIME-version: 1.0 Content-type: multipart/alternative; boundary=B_3379588477_4569921 Message-ID: <[log in to unmask]> > This message is in MIME format. Since your mail reader does not understand this format, some or all of this message may not be legible. --B_3379588477_4569921 Content-type: text/plain; charset="US-ASCII" Content-transfer-encoding: 7bit Hello! I have a couple basic questions about the plots generated by SPM: 1.) I assume that it is a log-log plot of sorts, but can anyone provide more information about how SPM actually calculates the values? 2.) For the plots at a given voxel, when using Tools > Basic Fitting, and getting the equation for the line of the format y =p1*x + p2, is p1 equal to the beta value at that voxel (and thus p2 is the error term)? Any additional insight for interpretation of plots would also be appreciated. Thanks in advance! --B_3379588477_4569921 Content-type: text/html; charset="US-ASCII" Content-transfer-encoding: quoted-printable Interpreting SPM Plots Hello! I have a couple basic questions about the plots generated by SPM:

1.) I assume that it is a log-log plot of sorts, but can anyone provide more information about how SPM actually calculates the values?

2.) For the plots at a given voxel, when using Tools > Basic Fitting, and getting the equation for the line of the format y =p1*x + p2, is p1 equal to the beta value at that voxel (and thus p2 is the error term)?

Any additional insight for interpretation of plots would also be appreciated. Thanks in advance! --B_3379588477_4569921-- ========================================================================Date: Thu, 3 Feb 2011 14:06:24 -0600 Reply-To: Natalie Zhaoying Han <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Natalie Zhaoying Han <[log in to unmask]> Subject: Re: SPM CLI Comments: To: Ali Gholami <[log in to unmask]> In-Reply-To: <1296721416.28831.4.camel@tb06> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Hi, Ali, Here are the step-by-step using command line in SPM, based on my experience. Step 1: Save an example batch 1.1 Start regular SPM with GUI, open the Batch Editor, and set up an example job you want to do. 1.2 Click Batch Editor->File->Save Batch, this will allow you to save this job, say you give the name of test, you will have the file test.mat. This is the matlabbatch variable you will need to modify in the next step. 1.3 Then click Batch Editor ->File->Save Batch & Scripts, this will allow you to check how to change parameters, say you give the name of test, you will find two files: test.m and test_job.m. If you open test_job.m you will find all the parameters for matlabbatch structure. Step 2: Change the batch parameters and save a new matlabbatch file 2.1 Load in the test.mat: load test.mat; you will find the variable of matlabbatch. If you have multiple jobs set up before you save in 1.2, you will see matlabbatch{1}, matlabbatch{2}, .. matlabbatch{i}. Each matlabbatch{i} is called spm, and you will need to modify this variable. 2.2 To find out how to modify, you can check from test_job.m, it usually has all the information you will need to change. 2.3 Save the new spm into a new matlabbatch as matlabbatch{1}=spm; Step 3: Run the batch in command line. Here are two situation, one is you could have GUI pop-up, one is that you don't have GUI display. 3.1 With GUI: addpath /home/spm8/ spm('default','fmri'); spm_jobman('initcfg'); spm_jobman('run',matlabbatch); 3.2 without GUI you will need to change defaults.cmdline=0 to defaults.cmdline=true in the spm_defaults.m in your SPM folder. addpath /home/spm8/ spm('default','fmri'); spm_jobman('initcfg'); spm_jobman('run_nogui',matlabbatch); Step 4: enjoy the results.:-) Hope this will be helpful. Natalie Han PhD student, Electrical Engineering, Vanderbilt University http://vuiis.vanderbilt.edu/ On Thu, Feb 3, 2011 at 2:23 AM, Ali Gholami <[log in to unmask]> wrote: > Hi > > Anyone knows if there's a command line mode in SPM? > > > Best regards > Ali Gholami > ========================================================================Date: Thu, 3 Feb 2011 16:00:51 -0600 Reply-To: Michael Harms <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Harms <[log in to unmask]> Subject: Re: Antw: Re: [SPM] PPI interpretation questions (with attachments) Comments: To: Darren Gitelman <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hello Darren, I know this thread is a bit stale, but I wanted to mention (and get it included into the thread) that betas are not simply the reciprocal of each other if you swap an "IV" with the "DV". That is not the way that the regression formulas work out. Best, -MH On Mon, 2011-01-24 at 15:20 -0600, Darren Gitelman wrote: > Dear DDW > > I am not surprised that while A to B is "significant", B to A might > not be, because the variance may differ between the areas. However, I > would be surprised if there was absolutely no result at A if you start > with B as the source. You should see some activity at A starting with > B as the source and the beta should be the reciprocal of starting with > A as the source. (When selecting the voxels defining B as the source > they should be the same voxels as the ones that came up as the target > using A as the source.) > > Unfortunately you can neither use the difference in significance nor > the values of the [reciprocal] betas to help you decide on the actual > direction of the influence. You have to decide on the direction based > on your concept or model of the brain's organization, and can then > take the PPI result as a confirmation of an influence existing (and > supporting your model) or not. > > Darren > > > On Mon, Jan 24, 2011 at 3:00 PM, D D W <[log in to unmask]> wrote: > Dear Darren & list, > > > > Exactly. You cannot use PPI to make an independent > "statistical" > > statement about the absolute direction of the influence, but > you > > shouldn't let that trouble you. This is why I brought up the > > Grabenhorst and Rolls paper because they did a nice of > showing how you > > can pick a seed (source) region but have an inference about > the > > directionality that is actually target to source (prefrontal > to > > orbitofrontal) rather than the canonical source to target. > As I said, > > PPI allows an inference about directionality because you > have > > specified the alternative model. Of course someone could > disagree with > > your model but that's ok.' > > > > I don't have much to contribute except to say that I am > flummoxed by how to interpret such a finding. For instance, > I'm sure I'm not alone in having observed a significant PPI > from source to target that is not symmetric (i.e. the target > to source PPI is non-significant). According to the line of > reasoning you outlined above, I would still be allowed to > argue, based on other evidence, that the direction of > influence is from target to source. > > To be more specific, I've run PPI analyses on 4 apriori ROIs > and found that ROI A has a significant PPI to ROI B, but not > vice versa (all at 0.05, so it's not a threshold issue). It > seems odd that I would still be allowed to conclude that the > direction of influence is from B to A. > > I understand that the directionality of the regression > doesn't buy you directionality of causal influence, but > nevertheless it seems rather odd to me that you can infer a > specific direction (based on a biological model) in the > absence of PPI in that same direction. > > If I'm missing something glaringly obvious, apologies. > > Cheers, > ddw > > > > > > > > > > > > > > -- > Darren Gitelman, MD > Northwestern University > 710 N. Lake Shore Dr., 1122 > Chicago, IL 60611 > Ph: (312) 908-8614 > Fax: (312) 908-5073 ========================================================================Date: Thu, 3 Feb 2011 16:23:21 -0600 Reply-To: Darren Gitelman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Darren Gitelman <[log in to unmask]> Subject: Re: Antw: Re: [SPM] PPI interpretation questions (with attachments) Comments: To: Michael Harms <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba3092ee99714b049b683669 Message-ID: <[log in to unmask]> --90e6ba3092ee99714b049b683669 Content-Type: text/plain; charset=ISO-8859-1 Michael You are correct. It only works out to be the reciprocal if there is no error. Thanks for the update. Darren On Thu, Feb 3, 2011 at 4:00 PM, Michael Harms <[log in to unmask]>wrote: > > Hello Darren, > I know this thread is a bit stale, but I wanted to mention (and get it > included into the thread) that betas are not simply the reciprocal of > each other if you swap an "IV" with the "DV". That is not the way that > the regression formulas work out. > > Best, > -MH > > On Mon, 2011-01-24 at 15:20 -0600, Darren Gitelman wrote: > > Dear DDW > > > > I am not surprised that while A to B is "significant", B to A might > > not be, because the variance may differ between the areas. However, I > > would be surprised if there was absolutely no result at A if you start > > with B as the source. You should see some activity at A starting with > > B as the source and the beta should be the reciprocal of starting with > > A as the source. (When selecting the voxels defining B as the source > > they should be the same voxels as the ones that came up as the target > > using A as the source.) > > > > Unfortunately you can neither use the difference in significance nor > > the values of the [reciprocal] betas to help you decide on the actual > > direction of the influence. You have to decide on the direction based > > on your concept or model of the brain's organization, and can then > > take the PPI result as a confirmation of an influence existing (and > > supporting your model) or not. > > > > Darren > > > > > > On Mon, Jan 24, 2011 at 3:00 PM, D D W <[log in to unmask]> wrote: > > Dear Darren & list, > > > > > > > Exactly. You cannot use PPI to make an independent > > "statistical" > > > statement about the absolute direction of the influence, but > > you > > > shouldn't let that trouble you. This is why I brought up the > > > Grabenhorst and Rolls paper because they did a nice of > > showing how you > > > can pick a seed (source) region but have an inference about > > the > > > directionality that is actually target to source (prefrontal > > to > > > orbitofrontal) rather than the canonical source to target. > > As I said, > > > PPI allows an inference about directionality because you > > have > > > specified the alternative model. Of course someone could > > disagree with > > > your model but that's ok.' > > > > > > > > I don't have much to contribute except to say that I am > > flummoxed by how to interpret such a finding. For instance, > > I'm sure I'm not alone in having observed a significant PPI > > from source to target that is not symmetric (i.e. the target > > to source PPI is non-significant). According to the line of > > reasoning you outlined above, I would still be allowed to > > argue, based on other evidence, that the direction of > > influence is from target to source. > > > > To be more specific, I've run PPI analyses on 4 apriori ROIs > > and found that ROI A has a significant PPI to ROI B, but not > > vice versa (all at 0.05, so it's not a threshold issue). It > > seems odd that I would still be allowed to conclude that the > > direction of influence is from B to A. > > > > I understand that the directionality of the regression > > doesn't buy you directionality of causal influence, but > > nevertheless it seems rather odd to me that you can infer a > > specific direction (based on a biological model) in the > > absence of PPI in that same direction. > > > > If I'm missing something glaringly obvious, apologies. > > > > Cheers, > > ddw > > > > > > > > > > > > > > > > > > > > > > > > > > > > -- > > Darren Gitelman, MD > > Northwestern University > > 710 N. Lake Shore Dr., 1122 > > Chicago, IL 60611 > > Ph: (312) 908-8614 > > Fax: (312) 908-5073 > > -- Darren Gitelman, MD Northwestern University 710 N. Lake Shore Dr., 1122 Chicago, IL 60611 Ph: (312) 908-8614 Fax: (312) 908-5073 --90e6ba3092ee99714b049b683669 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Michael

You are correct. It only works out to be the reciprocal if there is no error.

Thanks for the update.

Darren

On Thu, Feb 3, 2011 at 4:00 PM, Michael Harms <[log in to unmask]> wrote:

Hello Darren,
I know this thread is a bit stale, but I wanted to mention (and get it
included into the thread) that betas are not simply the reciprocal of
each other if you swap an "IV" with the "DV". That is not the way that
the regression formulas work out.

Best,
-MH

On Mon, 2011-01-24 at 15:20 -0600, Darren Gitelman wrote:
> Dear DDW
>
> I am not surprised that while A to B is "significant", B to A might
> not be, because the variance may differ between the areas. However, I
> would be surprised if there was absolutely no result at A if you start
> with B as the source. You should see some activity at A starting with
> B as the source and the beta should be the reciprocal of starting with
> A as the source. (When selecting the voxels defining B as the source
> they should be the same voxels as the ones that came up as the target
> using A as the source.)
>
> Unfortunately you can neither use the difference in significance nor
> the values of the [reciprocal] betas to help you decide on the actual
> direction of the influence. You have to decide on the direction based
> on your concept or model of the brain's organization, and can then
> take the PPI result as a confirmation of an influence existing (and
> supporting your model) or not.
>
> Darren
>
>
> On Mon, Jan 24, 2011 at 3:00 PM, D D W <[log in to unmask]> wrote:
> Dear Darren & list,
>
>
> > Exactly. You cannot use PPI to make an independent
> "statistical"
> > statement about the absolute direction of the influence, but
> you
> > shouldn't let that trouble you. This is why I brought up the
> > Grabenhorst and Rolls paper because they did a nice of
> showing how you
> > can pick a seed (source) region but have an inference about
> the
> > directionality that is actually target to source (prefrontal
> to
> > orbitofrontal) rather than the canonical source to target.
> As I said,
> > PPI allows an inference about directionality because you
> have
> > specified the alternative model. Of course someone could
> disagree with
> > your model but that's ok.'
>
>
>
> I don't have much to contribute except to say that I am
> flummoxed by how to interpret such a finding. For instance,
> I'm sure I'm not alone in having observed a significant PPI
> from source to target that is not symmetric (i.e. the target
> to source PPI is non-significant). According to the line of
> reasoning you outlined above, I would still be allowed to
> argue, based on other evidence, that the direction of
> influence is from target to source.
>
> To be more specific, I've run PPI analyses on 4 apriori ROIs
> and found that ROI A has a significant PPI to ROI B, but not
> vice versa (all at 0.05, so it's not a threshold issue). It
> seems odd that I would still be allowed to conclude that the
> direction of influence is from B to A.
>
> I understand that the directionality of the regression
> doesn't buy you directionality of causal influence, but
> nevertheless it seems rather odd to me that you can infer a
> specific direction (based on a biological model) in the
> absence of PPI in that same direction.
>
> If I'm missing something glaringly obvious, apologies.
>
> Cheers,
> ddw
>
>
>
>
>
>
>
>
>
>
>
>
>
> --
> Darren Gitelman, MD
> Northwestern University
> 710 N. Lake Shore Dr., 1122
> Chicago, IL 60611
> Ph: (312) 908-8614
> Fax: (312) 908-5073

--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr., 1122
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073
--90e6ba3092ee99714b049b683669-- ========================================================================Date: Thu, 3 Feb 2011 23:22:27 +0000 Reply-To: Emanuele Tinelli <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Emanuele Tinelli <[log in to unmask]> Subject: tool time course? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> hi, I wanted to know if there is a tool to obtain the values of the time course (over Y) in a group of 30 subj by placing a ROI in the same region. thanks ========================================================================Date: Fri, 4 Feb 2011 12:32:36 +1100 Reply-To: Lucy Elizabeth Vivash <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lucy Elizabeth Vivash <[log in to unmask]> Subject: Normalising FDG-PET MIME-version: 1.0 Content-type: text/plain; charset=UTF-8 Content-transfer-encoding: 8bit Message-ID: <[log in to unmask]> Hi, I am having trouble normalising a couple of FDG-PETs. The majority of images (matrix size 128 x 128 x 56) have normalised fine. However there are a couple of images with differing matrix sizes (128 x 128 x 35, 256 x 256 x 224) which do not normalise (error message: "not enough overlap"). I have tried manually altering the matrix size to 128 x 128 x 56 to no avail. Any suggestions? Regards, Lucy Lucy Vivash MSci (Hons) PhD Student Department of Medicine Royal Melbourne Hospital 3050 Victoria Australia Ph: (03) 8344 0531 Email: [log in to unmask] ========================================================================Date: Fri, 4 Feb 2011 08:40:12 +0100 Reply-To: Christophe Phillips <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Christophe Phillips <[log in to unmask]> Subject: Re: Normalising FDG-PET Comments: To: Lucy Elizabeth Vivash <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Lucy, You shouldn't worry about the "matrix sizes". What is important is the original, i.e. before launching any spatial preprocessing, orientation of the image. You should check it and possibly adjust it with the 'CheckReg' function: - click on 'CheckReg' - select the PET template and your image, - check your image is overlapping the template with a "good" match (same orientation and not too far off) - if not, manually reorient your PET image (right-click on it - reorient - current image - adjust parameters as needed) and apply the parameters on the image. Then try normalising again. Note also that SPM PET template comes from H2O15 images, it could be a good idea to create your own FDG-PET template at some point. Best, Chris Le 4/02/2011 2:32, Lucy Elizabeth Vivash a Ã©crit : > Hi, > > I am having trouble normalising a couple of FDG-PETs. The majority of > images (matrix size 128 x 128 x 56) have normalised fine. However there > are a couple of images with differing matrix sizes (128 x 128 x 35, 256 x > 256 x 224) which do not normalise (error message: "not enough overlap"). I > have tried manually altering the matrix size to 128 x 128 x 56 to no > avail. > > Any suggestions? > > Regards, > Lucy > > > > Lucy Vivash MSci (Hons) > PhD Student > Department of Medicine > Royal Melbourne Hospital > 3050 > Victoria > Australia > Ph: (03) 8344 0531 > Email: [log in to unmask] > ========================================================================Date: Fri, 4 Feb 2011 09:10:15 +0100 Reply-To: Alexander Hammers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Hammers <[log in to unmask]> Subject: Re: Normalising FDG-PET Comments: To: Lucy Elizabeth Vivash <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: multipart/mixed; boundary=Apple-Mail-39-672235824 Message-ID: <[log in to unmask]> --Apple-Mail-39-672235824 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Dear Lucy, Try DISPLAY: they may have funny origins or offsets and SPM then "sees" them in some corner of the matrix. You can then reorient them with the functions below the graphics window - best move them around until the crosshair shows up roughly in the position of the anterior commissure. This will likely solve the issue. If not, do get back to the list as there are some more exotic possible errors. All the best, Alexander ----------------------------- Alexander Hammers, MD PhD --Apple-Mail-39-672235824 Content-Disposition: inline; filename=image.png Content-Type: image/png; name="image.png" Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAALAAAAA6CAIAAACf/kS3AAAgAElEQVR4Ae2ceZxVxbH4+2x3mXtn hhmG2cFhF8GYKLKoEX9KDD81RiCaRY1LNL9sRhOXmBhjjJo8RNREn8a4JmqIAU2UKCQmGhcERRZZ BEEWYdgZmJm7nv19+zZcBh0igx/+ePymxUOfPtXV1VXVVdXVfdHCMBTdpZsDezig76l0/93NAcmB boXo1oN9ONCtEPuwo/ulWyG6dWAfDhxahbCFcAoxqye8vMgI4YpAhME+FBzQixvupLsn+3p+iwh5 5njuv6RzwuW74wvhMS5Pl2p3+VgOaId2lxHaIvSEowsjLjQiFtvVXV4MEf9YyjoCIEtb5ErcuFSF iJ0JI0lNM3xHGJGOYHvreSFiKF9OBGW+KYzAdXTNEDp/9sJ01zrjwKFlkBdGhZ+wY7pt+oL/dMsS UUNEO6PkP7UZvigJ457li0igeVq5VC6RN/ZrInw5giXcMscUaWySjmaYBhR0l4/jwKFVCE1khWVH 83rUMzJRlrcubCvX9WXqaCG+RpNOwNVN08ll9FBE9u97NE9IyxRtdcOg3E9rXlmEJo/W7vIxHDi0 CqHrJakgKiJWxhJWKMycsKMiHhBadK1E9KxvCMM0867t4zTiCTRED639YfEsW9Ah08PXskKLpU0A TRF2zU/tD/nh3X5oFUILvFIvyOs4iTDieU48Gwo7PIgxfR8l8jRhWAlfmFukxxDe/oNT04t6phYm 7BIR+gY14Wh2er/6c3iLuGuzOwjhdGEAR2dtZmOuPWOlVvH0xoqpy5dviWpul2MIT4vSx/QCyzHe 3NEy4p6Xbc8zJfLOCzpnPbCOmNP0S42MqH1izXPbIsm82zl0d2sHDuyXpx1gDr7qC0eYQVqPPrhm +5iq5C0je/cpTQs/2VWMpogGRIREpo6e08o3JPsaETMr3BIix86KJtqTkbwVusIIw4S18cJ+ZY4Q +9mRdIbg/9+2Q2shrCDiibKol3MjsaFlkaElernwXYOV2k4oILyUR5gYsKWk7Cj8JRtEEMjwEQ1w A9eVqQtH93TfbUP8ughzlojkTdFa4hl5iQTn4Ung0GUfKjMVLlBJ1zd1Ox6ElubbUmvYr4r2TGAD ImPL0M+ylQ194eZFaNPCX8LP5an5gXRGQQYAYefe4QkNniTDc3N8OAg7EwTSvfEsbvJ9f/eWp/BF FNu9QuTLaxGAjqooJHveDtXfhzgPIby3t6aO/3eLaK+o19p2mJH8RXVEAjcvdSct3+7aO+Ju4vKB fW8b7bumtmiDOeaVJef0PXLOknVbIvqwMu++0waPrsDI2BvWRi6Zu2GOERUtO8b0a3hj844dFw8o EWJhJn3JjPTytp1GaZkb5F46s+qzFfFfLWz72eKUIP8QMcbU2P8+pd/IlzK/GVk6vIcwRPrW1/L3 Nme2Ou1l0YarBkV/fkxC09x/bzc/9/y8EXVNi1pE1m8/tiQxfXxdXz0jXO3uNbmrBiSERQgkTFNu Yf1QNz7ZOkK0ui5RcNKsqXjI8wzD0NRLQXX2AMgFotRFtRwqRSjiZbBDV/ww2MH6C7NjZm348cJN qL0dOE9vDMxH197/dhDa4fT1zoCn1l87vy0fpt/Y7IiHtn3t5VUbwnCrHx737PIvz94U2rtCNxww fctXZje/F4ZrXW/s06vFg2v9cFvoZUfOWj9hxrowCFNueM2irSf+cxPAOf4PvOTjK9t96uk21yt7 ePnrm1q9MPz1B17vJ5pf2GiH+XDG+63lf3znd6vonXllQ6A/sOXiV1dn7XDFtvyQGYuum7MxDBw3 DH80bwMdqfBHPjybDgdRUAIKHYsVjF/hdTcybANWoSMAkKpFNe6GO8R/fTJVL6rVfip6XuvpaYZn RNyErsXZGERCa+rC1vMH+eceJ+xIZmJjcO3AxL9WtxDLtLmWFYgbj2loFLvK9fSEAf1bNqbzkR7/ 2uR/0N5++wkNg0K3KdS//el+ESuqiTJPj4/uGfvJSRWhJpK6GGYlYtmcpyxxaKTbSpJ6oIlomWG0 a7FcEME8PDF/6zcG1YytJ0XhjetvnHtkr2mrNpDtEppjlKbv+Ey/SMQb3Mv7al3ff6aipEww9PF4 KYvU9RjkExWlB6DADChLsHnz5smTp9x1192zZ8+hvWgAWlpa3n777ZdeeimXy9GIAvCV7p9o+APu fGgVwiF/7AjH1EMErgdhxMSBt8ZS4xvqe9qaLhJOGG1qKl+QskORSJgZt2RXU0Vc+BWhSLZqoRmN xDxnVV6M6lXdOyd8TfNNf1SlKHFSvoiatrhreHR1c9k5M3ZVTltzxdLWwIoxjpk3CRjoG4jAIzCx RdSu6GFEbWHNawvPbCA+KeF7kNUuq+v1ry0R05WRtevZkaQwbJIk5tZIe09T5tEsT+g5QEVMh1oR EKDouIwD5m4HQESLUyg2sPS3bNly5513/vrXv54zZw5SR0uAQfAffPDBWWeddfHFF8+fP59XpT1F dSliOESVQ6sQuFykZ/lkAvRoaJAPSEUCx8+145AD4scgEgSmkxKW5ZCIJBmdMmOsBMONkXbeommx 2nbTyKUyKZEr7Cd8Q/NXtIlWspUi7cTEwGfTN77bfuKR4T9P6/H88OpYoLVGcyLW7uiup7cbwo2F cZTSrmhtzzCcXZIQbWK7JQKfU5WScGc21MO8Y6WF0S6CnqUEnHokFNESrdLbtTUbJ/FRiFgLnh7i pWxoOiieKbmCT614BOw4Do2YAaUNRUuQz3MSI02CaZqA0a4cxyHSgA+hlYvj0JVoGBH5iGYJO26n CNGD8oihHxlN/H5Z68T+pSWoRd58ba05qqKnxQ5R9NRjqcKJVFzG+RVbUtvak6LxrP7h1fP91YFo ClK+Ht+g+SSxObxascNek965/Zz6SktkI+IHK7N+LlIi4qYXN01b8/PCifsRhO9qbY5ZZsRyZadU hk+vTZ1Uo0f9qBGJ/nbtu+OGlhpB0sd0mauFmcCix9xE0tkVjbglhKXIngyrhq8LdV2qA3GFDnAX ixK20oniE4OBvIuxpFILlKBPnz7XXXcdIzQ0NPAEvqNp6eLIXQbv8ty6NIJGVBQnM5TGPleXVnLa yZbzymGNI2c1nz9r4/eOa/hbc9vdK9qeOS1pRe2U4bpa3jbj0UBwEpU1I74W071Ynx4lpzRtP3vm zslHV7V64orX1opY0g/MqpgbOPH/WrLz5L7ajHW5362yhRv926aNE6obAi+q69X/3byrTstNOKI+ tGxbC9yYceMga/S/ORPPXzTYe3x5MGtD2T9ObTQCP94e4/QrDJMB56G6yOtlWTMm/NDDXsQMVwuw 5tJyYR2Ic1DIvbb/gPiBUNVCRwOoK0fAk0BStYBFGQPAevfuffXVV9Oi1EgpinpVynRAQx4sUOfm D1ILRkvu2LFXHZEzDfWqyJV5gkKhXX2ivdiFA0k0wAyT4yo3VFalM2GUFFNTT+fZ8Y1x3b1p/o5N 21MvntbjC3XJvIhWRGNnV8nNf8CRmC8GR40hNVqbyWpOP37agO/0yT08f8l9H2y5+IS+F8Sb23VR nwgf/3yv97e6t853zNZtb57Z9+uDKx5Yks9xamGKrx3p/nXpNi2eIIawIptwDKYmRvaOvXxmXXt2 w42LUy1u6/QvNA7vwxr0/J7ivGQkzcE8CRJXDEkYo5KlfoQIVTdyrsX5CShd/Jbpa75upPPQJ29m FFIaIucFKbyNzIUUSnH66hWGUKiz0HETVPaqBV7T8/XC4S3cQ958Ur14KjUqdqdFZSnUk1e6KMzU i5Wif1Hi4FPXihrvI08fnUCyqt22bVWBFCrMSpFS/Kra1asiiDowduiEeS/0wp1h2EYTvTPgckI3 x1Rwnm2BfIbsAt02ckXU3TwvhZF9tqUAsYeUMBnG9cN04ZNDk+/IXoENmXzK0u5JAHJNobPLlX+T 3Qp3wLKAofnghn5awQAvh5C7STfMZ8PA9X2GlcNl2Vi6rmSzEwBG318sbvFCX24Qc8wIDHJXCz6P nFnopH3HB1qicsBQZAsVxQd4JdHsKdQpasM5e/bsxvre9bUNkyfdUeyoANRT9VV1JQKFRrWAvyPb aVRfi/xXYF197tXHD+kRiFBlxqA9Eonw5ro+Ok67ZVnKdjE2AHylXYVCkKh0nAowBksoKhdHhSvK 1BlniU5MRwSguXYsyJeJgO8hJ6JaGaeRMT8Xmk6UxKOPccpF/IyIsWMIYr6I+3jzIEFIYGMCNMFR J73CiG1kSoQbD4lISRrZEWE7FqnxCAlH0xM9Q65PiLK8CNhC6vQOrDAko2U5XLjCGjlsVjzNJNtk 4DMCuhE3mHmO0qysKdrpqzkxImJ5rcLKaxg7PSS0ELYfagS2RkIYwHhwUV71yDBlxQcqHdc6HKAF 1sEx6gQKVGhRBgPuUadd8ZOKYjtg1NEexWSegFHUV7ApcVChAKm+fmhc2rtU9hdDSEVhYLBDUEED 2ApIz8mora2tb7zxxrJly/ja2Nh4zDHHDB48OBaL8VXNgXkWdIh9BXdaOKXMW8TLmk7dD/2IZixa 9u7i995dv2m94ftjR48ZOvjIkooyvhph3DcCy897Jt47jpVOGaLECrjyxMigx0ob8TgmvEALu0S0 IgEzNDcg1HPZykBEiOX3s5ZRkg7CpK+zV7SiWS0SFzA01Aydy3Tr17UuWbhg3YY1377mCsZNcN7m uNy+8ULT4zoX93oCwkovFw3ao+LGN5vdaE3fhHdCeVu/ml4ldIhg4ZIoIBY/ovu2Zhkax/GmH/iK D5KKwpJ955133nrrLbYS8G306NFHHXVUeXn5Xv7sUQUlYwRMR9SCioKhDvNpVFKgsmXLtqVLly5e vBiY4447DpzgoJ0VqJhP/ROVgnp18oBENaWGht51dQ1nnHEWr2272n9w5Q8b6hrrauoxd7XVdfyp rqoZMWLUzJl/t21sL4ZLYqMu/yrUqeWkvXbSTv7ZZ2d85phjezf0qe9Z36+2qb6qqqGhorynNf68 L86Z+5ZKB2KB6SddFqbfk3a9of6I+tq6O6b8quAcMPJ56TIwmfIjBh2/gNPIv/n6W/WVvavrG+65 fTImPfQw49n1a9+t711dW1v929/+FpQznv/b6BOOr62qqK+o7FNbD434o9bQx19I5+WnwmAXB/UQ INOGufCG+d64WVvEY5vFE1vEQ6s/9dy6V3aELQXq5Lg4QE+SIL2YJJqqfK55f+111/wIRuER+AO7 +EOFiV95xVXvLl0OzOuvzqaxplftnXfcJXlVKBiYuXPn1tbW8+e112bTBj8dB8JleWrqn4cOGQbP 6VhV2QuEJ4w6cck7S9VX9fyQH+n46UDq+3UZSsvQ6xEjRqAcaOXrr7+BPj7++ON8Qm0hHQuh/EVz c/Oll176k5/8hE8YP4+MZMTE28gcDuedrD2hrVn23te/eN63vn7Z5m07bMenr+fahhlzgkRJj6ZX X1149hfO/f6Pr2hr2YYL0YgADM82PdMgiSGCvKWFSVsvcUUsTUaCjayLOzLk8YKM0bDs8cBha+/H TCMWWr7DWjN9w9JEvK5pSEXP3pqI/WXqc/+c8a9vXnb56g3r/NJIu1m4qOOLmCfKfWHnSWuyPBO+ 02NFi3XXisz1y5o5m0uK7cPL7NpEPpLb3kN4y1qSY6Zuu37uph3c0NHy8ozDMPMBTsOTN4iZsCam 3HHnySef/Nhjj8E3VjY8lJMthF88p06deuqpp/7ugQej0SirnK8Mi6gApkKjqtAFJqOUNCrbPG3a 01ddddXOnTtVr3g8jv1YvXr1l7/8ZQwGYKrQUVXAuaetC393rhDQBKEoJphQAuq+F06ccC5Z1ZKS kgsvvPAf//jHtm3bNm5q/utf//rNb36TsdGPadOm3XzzzcyLhAodIUzShBUMxftL3vvqOee9OWeu GWWf3XDfPbevWrvkg+3r1m5YPeell793/iVHlFckQvuJx6ede9HXW9JbPYPY29TwFUJjo2ImbM9u 1fN5y9WSuPl8wHm4iLioSkbkOQvNuLYXMfJJry3YldczWomNgNAXXbhO6Ez8P+OIS95dueCWm3+h OZqZMj53zNhJP5ryl6l/k6wyvNBwSoVDPuhnC9qrZqwc9velV7+x6TfzZX4yr1k/HdG47ktNq84f 9o3jK8u1TUap+eCy9slv7mglGS+PTEVAOktKU6rm1T+8ZsqUKcSAiPbcc8/905/+1LxxA3+2bd8K 0+BVjx49cCu33nrr3Xff/aEYQomwKEiUCYVRYsVT3HjjjQi7oqLi9ttv37ixecWKFeeddx6iQSj3 3HMPykFRgpOkFJRMVbr2ZPhOC6hp5/HGG3NxGbU1jfV1fYYMPmrRgncwd/C/aB4xksuWLR8yZGhN TV2vXjVz5rypEBYQ+I7npjLpAQMHV9fU1dbX3XHnFAwypl5tYjCFrV6ewH37js3nj5/QWNfUo1fD 9y79LktN4lcHSU5YWVvdp7H3pLum4ETwPnxKKQtPf6J49h6+3DjM/fe8QVVN1Q29bp/8y7Rsko6A vyf91501jUdgaXvX9B3Sf+CCN2cTmtK1sLPgmAJ348xtD8yp681Hl4nHlopHdlY8vEL/3SbovHoB mwmISYcpj73Q/Hx44p/Xise2G/cvu3lhypY+Ai+j9iE+xh8XgF8YefyoxYuW7GYRc4Fkxis4lOb1 G8ee+jn8bJ9G/GADboVeyLIoBXYf9fWNVVXVnHHQiLklnH/xxX/BXqYg/Qt48CN5uf8aMXwkSGpq avbwnG90YbCDLJ1biKJOsXsig0/WlgsELMe7fn3n0KOPkuEdl1x55z9ZF0OOGnQX0iKw140H7v8t JkHaPU0eFpMC/P53vp/PZLUgvPfue6++6oeGTkRoytyO3PeLciMaE2ZVz9onnnl65KjhSUuf/rdn Zv19JpsPTy8kj7mpTaznsWvAWoQxOTYRnZBBLP0JuRICEokngUgZjulHdC2WACyiyTsx7A1IN7k4 KXYn6XumPjlsxAhN46wrg7vR8D1u/JHVbSc/tctrJfo3Tq6umnZ8uOorg+3Ly7BSpUFbyChaVCT5 3zxWD188p35k+VbDrL95/uZ1MiNhOkSUuljbvH7y5Mks4tLS0hdmPX/0McMc12GOkkukGtjGyKMQ 0dC7fvoz0z716aNtrmIUIvfiskaGkl72EVxXtyIFC8sZh4/FnT9/HjEjJ2wnnHSi50u2W1GObMRV P/wBLSeccMKmTZvQA/pinPApChXYulr2qxBKwZieKryyoTjttNMgC3VWAW3HUU855ZSBAwdC0KxZ s1AIeilSOMKhhTmPGjVq4pcm8ImikH/0+cgjj+CSAMCigkqy4KAcoRr6o8/Tzxj32eHHS8psGdtA ixXG5+XED99wHDOtW9t+dkrvWeNqxg+tjJfwmUz27oIK7q7x+w7DmHTaUNPfHljRyfO2G6RT+RZ6 v5l0u+LVQw89VFlZCeUEWEy8yAoqNOII2GjgVsCzB/2H/2bigNEKE+iFpBOJhAraCBqUR2aFEH4Q QEyb9tQzzzxTW1urEKoohI5yTXa97FchoAOkIIQyNAC3N3bsWAjtOIyigK+AQQfqQp3GxYuX0MIe jJ9LIWMleEJO6WhBWTglAvlHS1l56Ve+8hUwMG02bBLJvnlSWj5JOWXsqWxnlWHLkqKAxkDcOvv9 Ng/Z5+/+/IBr+pfE/cAI27nlhb8ojrVXL7neoxtj4uLEvmVCM//8AWgkVGhnZkyfCbXDhg0bPXoU LcyOp2JXkWk0EioSOnzqU58aP3687NlZAQBWqy/0hSGsN0RA/ZZbbnnhBRbY3kANb4PGKHlhHuhF Hanx7Az3x7R13kfNn+kxAdQc1O3t7b169VIDQx+UFUXFqyfzSEJlI2jPZrM86WVaBh4RFnBgM2Lk 8Rwhy1/UkVXaTwEJgRKjwA5CMF6LfPmYeRzY5379+smlLu9nihKNzEXwyjYxc4NjaFu+37/misao hWAJZ0mI4OoKEi1o7W7p7lWLMDtxYE3c81O2trZN/kRx4btruMMLwFe/+lUmCH8ovCr6YYUSFePS qJI0Z5999v6ohqV0VBymLzQMGjQIE0vHHTt2fOtb32JxPvDAg+vWrQcDgSfADEedZal6gWF/yP9z e+eJKSgojCSRKguByWL/WRQPs1JDKuGZhsRzxBFHkIFRxPGVpeg6HsEwGDjjJ6RUaJkhMVanZHmO y5QAYImAio4oE0N0CnwQjRyU4sVNdq3czQg4swr+sLbZNau50XnTSeUi34YlTES5P5kwuW5J9nIP ewrH3nsH9K1wMNkBju+Fti6n9UuG8+Yt8SMuRzdS5wrmQU0WRlFBWsyLOp+YGnUajz322L0Y960h VI4OmDvNSrFQhQcffJC9PWkudOv9999n0/GLX/wC/fvud7/bt+8RgIFTiYNeaqx9sR7QW+cWQmGE dPBCFkXpHaN+SAGLigwpEE2hL9qgwCzuRmezEAoGpUBF+E6pQ+0YAki6sLdW4xa1sNMuXWq0dCtt s1mVqfNCqGe91mpj+cdX13EHizQ2iVCXVY1E2Vrs0YZ9hijEEnQuMyzUgi6uvG4TsVMpR2uFbITN MqCieinBMCleqSsAeEVLVVXVPpg7vDBxVoVaCVRgCGujoqL8L395mgs1JDkymYzKQ/z+978/88wz Fy1apNYhHRmCgeiihu6A9YCqnU5a5kGhQ0kX0aKSahgai1iBUaJibLyYogC61eR3Nwqd8Aorx7r5 4he/CEKUTCIhldRZ8V25LMAciVl1dXUMrfSjM9iDadOMaEmUZRfkOPIm4xvq67YS6mdPqi+NEjBY Ii3MSiqml9ZJUQXECx2HkWIuEI5km2V3jZ8g1/GLkVCwq7X0kqBwLsYyoJfioeIYjFLWTumE4tva tWs7Iu9YBzgalaJl+orlBbZwZUY/55xzvvSlCZs3byXx9eSTT5KESKVSF1xwAQmhAQMGwDFGpCMV RNAR5wHWO1cIKFb6BUFK3SCoqA1qMGCKo5LAgVWEwawPRK560YjdHTp0KDcE2Wtce9010ESCQYeV neuD5CzORK4uMkoyLSMLoxzgZD4WLEMSq7AZhldkFjmqkr8aiuY28ZMewge/pBwe2m3CKC8RWHX2 t3JZS0vPIUgH7J6WnLWyhS2EIzJHVUiy65oahF0q9Pzrr78+ctQIaFZSL3Tnco1MO4KggEtGJLBo 1apVHVDuU1W85VlQCGkq4IPaXKgn8dyPf/wj0lxf+9rXFixY4Lo2yoETAUwhKgprH7wH8LLPCugI r0gHL2NQZ4YQpwCKoxYrTJRPummQhmL1EyLIxUT6RPijThiZd3LZfOaFmS9IPSgkMBSbisPxypzl qyaemvbnyezJpkyRGAoclATIbvrrs+coEvZ8kjkSGSTyp5B35sgNOhUvoBYwzrXBCRkqja7FyObo jhZEuDnpZbI6cYJt+VUZPyacMq7VMo+wtJT0hk6zLn+3we+M0QXOxyA9E6Yz3PHhPrAvpq1Pe27p uU11Gr8q9gWnORU9Y2Qb/vTUNKYCzSwMRb9a4kqtoQ1Fpx3yHn74YSZOC3OnImdfmLLspQUwM5PL qr4FPu9WSMUBLlsBRtITJKgaHZcsWVIUENiKdcC6VDpXCKhXxIEX7KooRneKHWAgi/pBhVcaqaj0 Kq9cKKWv4gsV1UXhp67WxMKF71x77bX33nsv64wWNRajY2Z4Ll22uBDWFNeZUTAmGBVuNHEY4Uyf Ph0TxRAUqKWLIolXvJXUOTfE78swh3mb5KbEifUVrpZbtLatPcLZiGlo3L3z0wCQcw+jXKAqEQQ0 /LynDYklRDLhoTbiey9uzgX5wMh/b2AF6gU2UJ0xdizTIUH0yKMPITblcAtkSFkq7ilKIAyr+fLL L1OhBZo78pYWlIaZlpWVKQ5Qb2rqy3HXxInnKp1hefKJWKu+vp4TVPDwqjhGd8hg4qpRYTjwZ+cK ATqEBBaQgp2ReFWqvT/UCpKJUZSYgYQ40lnnn38+3VeuXPntb38bdQaSVwUDMK9qMeFxbrjhBp6E LFTozoiAAXzSSSdhv9va2jgZgi7Q7iajwAhV/+Mf/7h+/XpFJATTUbWDX2kDjXFdpjfldSd5Bs59 S3HeoCoRpOZnWuZudgy597FShpXkl1qMormm3657EW54x3M4F7Q4yNjhFfObp7WWZoQ5riY1ut7k X0KxdXm7+6ofXYEI+f0XCq2SKAxNgX5FCU8YC2Gs5ksuuQRWKBECo6iFeOrAEDBSITiAP0wWjaGR Opt/kDEzZSFAiAkh+lZgii3UAaY7pTjugVc6Vwj6KxIVHYwE9uJJ/0exA6wgC2RIM8iraoQdP/3p T1ELxDxjxgw2TsyKT0W6qSCwefPmEXXiDpn8N77xjeHDhwMDy8DD6JddfqkadNKkSZyzw0elQ5JO jJmuT5/2NIkvIMFGo2Kl/Fq4t6gqUFJIhudZ0MQxMn0uxOnVkVqRIvl8w/w2W4trXAtnZ2jaJrf2 SWNqZa2s/lC0xeOeKVa16OPf/uDe5RYb1Gor9+zpQ9JSOm7UNsmo925suO2Xt8ABjATTJH2kVEF5 BOpKYBg/VgjKnUwmIQ/aitGlMv5AsirgCYWvigMkHsDMmTNHS0xRqQVfH3roEbXGxowZoyDlNAtF sQKYLpXOg0pIBztooZiiRvoPFoKxAWbmas6KBaoR+thM/uEPf5g4cSJHtzNnzoQjZ53xBU4CWU/c tdm+fTsR8ksvvcRAIGFjfeutkq10Z0Rkz+ic/990002/+tWvwEAkNWHChDPOOAO0lqHv2rXr+eef nz79GeDJlCAM7MSrr74KFxiawFDhgWv5HFfsZEzAuiIlxWm1YYnBCXHBUX3ueD9YsMP77qv5e08o j2ttwi31YrYrfC7iJ0rsR1e2L0iVv7Vm+5KcneegO5I5Lt76wGePxY9XcIOKe9r86xci5wiL+J/h SAxs3LjxsssuO/LII/GYXGOJx6NsFEnGYAIlChAAAAVTSURBVOEIdJgR5LGBvPGGn3FzQJkTmMZk FRsLlEu7AgfUqrjooouee+45ADAtpB9YYNTh5C9/+UvMCTD8lAN4JSzFPYWQxi6VzhVCoYAsRbp6 7Vj/6BiQAllQyScqBcrkvHilkWMOfo5y+eWXv/LKK+l0moPyp556Chg1cyag6tdffz3cpBOqRS/w KAZRv+yyS3G4t9xyGzyiO9l7zyWSlDt+YIAllwe7b7vttkcffXT58uUkv/v376/oBDmaCjYiIwwI /oZbcRyOy0gxdL95XNWLG9cvyUeeXNay3nZ+89m+gzAhQdzQU65R2uol567ZOXOnXuZF8mFORLOX 9u95w6h+/WRyluu8CEHnskZKxEsLDeecczYJgyuuuBKdWLNmjTyz1kwrIm/LoSvwEBOIKhMMciL1 9lvz8S/vvfce18+OPvpo6JT6WwADHokqDkDliSeO/s53vnP//fdj/Likr75is7EiTG3SpNvZcypW KySqrqbfpafx85///KMdoAm+K2nt2tnKBqmpqen000+vrun1UWBaYDjbotbWNgIFfk0wbty42toa GuE/1g9xghDqWdkkVZjSiuUr6KVmyygcD+IvuHrzudPHKu8IPGLmE2AwUVks/Mi4cf8XNiHsXNYG BpcC/p49e1555VWslWQyUV1djRgaGhsHDRrIMipg07Zu3drenmps6P25M89orCzXNQs94rYFO3Zc fqVp/L9B5c+927Itkl61Pfvf7+Y/2JXbks/mncr+5cHzm/3tuWxbKje8p3/+sNgjo/pdfESyQidN bXGFJ9R0Iyu8aIr/5bYSTdJ1eHXBBRfyZPW37NjFNDnx5yPC4wjq0ksvve+++1ghtCQTpdBG48BB A2hhmkwZ/qxcuapfv37jxn2+srKC6cNGnDasQ2nwreR1AIOfqMWnP/1pDOeECePxyHBMLVq+ggpK GKKrRS6v/9BHSoJ/uw0QKZo9z046YO0hQwHJzwVxKpnKRiVR1U+2Cu2tN+cxB3jEWX5T3yMKfdhu Se9IHg8VKppQ4JkandB9Jgy9YFy+fBXuht/jcnI4dNgwx3YjBYmAR6Y6zN1JXAJ2g5Si8oCB4Jps nF/8myb/xFGC7aKmcz4dCdpzejQtoje8tPOJDU6O5ELhYjwD+RdW376g9frP1PLb0x6oppvxrYjh WtI0FWwrRqGEfy/RjwsjF9oJjWs7hZvJShgYkVwuLz2CJhMJ7JWwDUU5KT5wuMOJDyfjkn0dEkpy mjJ+lBlCtEFJmpADVqxfj6Y1ww10rrGxnikrRhcrB+cspAik7fyPCqGADv+nawv5D6OJhbvEk0s3 /n1j61onbgfR9EUNz76z4LyjjuUKGLcn+OeMyHx4Fkntg1l8/yvY2K0QUkxcyDd8j9wkq9LTdDYY LP1cVvQoEe+3tw8r5dcCwjEEG1BpgKTNPGxLt0JI0SJoLjTJLSRFuji5C6HVIVuFU+DcVl75IeiX PwfZ6xcl9OFWuhWiIFGSTsQE3PLDAMgGhC/P30h4owBct9f5ISC5GD90I/L34YexUhRCo8NNy7s8 H8SOlH35yxt56M1FS3n6QZPJfX+ZmcZ6GJyERQrWoVshuszg/20dCCBCg1/ykZPk4r+6HUzqmjxF wYkU3AkHJwT+aExhcoet3+i2EEp58QicZGEhCCzxF/JaM6cZAdu9wiF44MrNKt5EXnw+bHcYkhWH 9eSUrA/kaXgut3zIALF/4MxbbsXlDxC40kFv9IBL8VRwJDI5LBsO29IdVB62oj24iXVbiIPj22Hb q1shDlvRHtzEuhXi4Ph22PbqVojDVrQHN7H/AWFrGkuL8nFSAAAAAElFTkSuQmCC --Apple-Mail-39-672235824 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 Chair in Functional Neuroimaging Neurodis Foundation http://www.fondation-neurodis.org/ Postal Address: CERMEP – Imagerie du Vivant Hôpital Neurologique Pierre Wertheimer 59 Boulevard Pinel, 69003 Lyon, France Telephone +33-(0)4-72 68 86 34 Fax +33-(0)4-72 68 86 10 Email [log in to unmask];[log in to unmask] --------------------------------- Other affiliations: Visiting Reader; Honorary Consultant Neurologist Division of Neuroscience and Mental Health, Faculty of Medicine Imperial College London, UK --------------------------------- Honorary Reader in Neurology; Honorary Consultant Neurologist Department of Clinical and Experimental Epilepsy National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK On 4 Feb 2011, at 02:32, Lucy Elizabeth Vivash wrote: > Hi, > > I am having trouble normalising a couple of FDG-PETs. The majority of > images (matrix size 128 x 128 x 56) have normalised fine. However there > are a couple of images with differing matrix sizes (128 x 128 x 35, 256 x > 256 x 224) which do not normalise (error message: "not enough overlap"). I > have tried manually altering the matrix size to 128 x 128 x 56 to no > avail. > > Any suggestions? > > Regards, > Lucy > > > > Lucy Vivash MSci (Hons) > PhD Student > Department of Medicine > Royal Melbourne Hospital > 3050 > Victoria > Australia > Ph: (03) 8344 0531 > Email: [log in to unmask] --Apple-Mail-39-672235824-- ========================================================================Date: Fri, 4 Feb 2011 12:07:25 +0100 Reply-To: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> Subject: Re: tool time course? Comments: To: Emanuele Tinelli <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a2852104e0049b72e355 Message-ID: [log in to unmask]> --20cf3054a2852104e0049b72e355 Content-Type: text/plain; charset=ISO-8859-1 Hi Emanuele, you can try to use RFXplot toolbox. http://rfxplot.sourceforge.net/ Kind regards, Rainer On Fri, Feb 4, 2011 at 12:22 AM, Emanuele Tinelli <[log in to unmask]>wrote: > hi, > I wanted to know if there is a tool to obtain the values of the time course > (over Y) in a group of 30 subj by placing a ROI in the same region. > thanks > --20cf3054a2852104e0049b72e355 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi Emanuele,

you can try to use RFXplot toolbox.

http://rfxplot.sourceforge.net/

Kind regards,
Rainer

On Fri, Feb 4, 2011 at 12:22 AM, Emanuele Tinelli <[log in to unmask]> wrote:

hi,
I wanted to know if there is a tool to obtain the values of the time course (over Y) in a group of 30 subj by placing a ROI in the same region.
thanks

--20cf3054a2852104e0049b72e355-- ========================================================================Date: Fri, 4 Feb 2011 11:52:04 +0000 Reply-To: Kie Woo Nam <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Kie Woo Nam <[log in to unmask]> Subject: VBM8 - What to do after sample homogeneity check Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPM experts, I've run VBM8 using a customized DARTEL-template. Then, I did sample homogeneity check and identified some poor images (i.e. images with severe motion artefacts, enlarged ventricles, etc.). Among 296 images in total, I found 11 "poor" images so far, but there seem to be some more of them. Now, my question is which option I should choose between the following. (-----1-----) Start statistical analysis without those outliers (-----2-----) Start again from the beginning (i.e. re-run VBM8) without those outliers I was originally going to go for option 2 because, to my understanding, a customized DARTEL-template is made from my own images, and therefore outliers would cause bias in the template. However, I'm not sure whether this reasoning is correct. So, could anyone who understands how VBM8 works please advise me on this? I would really appreciate your help. Best wishes, Kie Woo Nam ___________________________ Kie Woo Nam, PhD student Division of Psychological Medicine Institute of Psychiatry, King's College London 16 De Crespigny Park, Denmark Hill London, SE5 8AF, UK ========================================================================Date: Fri, 4 Feb 2011 12:35:35 +0000 Reply-To: Christoph Berger <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Christoph Berger <[log in to unmask]> Subject: case study of deformations Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPMers, I have a question regarding a case study of morphometric abnormalities of a single subject compared to a group of probands. I have measured an t1 image from every subject and segmented every subject with the vbm8 toolbox into grey and white matter and calculated from every subject the map of the Jacobean determinates. I would like to know, whether the Jacobean from a particular subject differs from the group. I would like to use a factorial design specification (one sample t) for easy labeling and visualization of possible effects, but I do not know how. I would think of this posibility: one sample t flex fact model tests, whether a goup differs from 0. In my case, to test, whether the group differes from the particular subject I have to substract the jacobean of the particular subject from the each Jacobean in the group bevor I perform the secondlevel one sample t test over the group. Would that analysis be correct? My actual workaround is as follows: 1. I calculate with IMcalc the maps of the voxelwise standard deviations and voxelwise means of the group of probands and use that to calculate the z transformed Jacobean map of the particular proband 2. I use MRIcroN and look there for regions where the Z value is extending the significant threshold. Is there a better way to look for effects in this case study? Many thanks and kind regards, Christoph Berger ========================================================================Date: Fri, 4 Feb 2011 08:46:18 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: case study of deformations Comments: To: Christoph Berger <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Two sample t-test with equal variance with the single subject as 1 group and the Probands as the other group. Contrast: 1 -1 or -1 1 depending on the direction of the change. A one-sample t-test against 0 won't work with Jacobians because they are centered around 1. Either a log transform or a percent change calculation for longitudinal studies is needed to change them to be centered around 0. On Friday, February 4, 2011, Christoph Berger <[log in to unmask]> wrote: > Dear SPMers, > > I have a question regarding a case study of morphometric abnormalities of a single subject compared to a group of probands. > > I have measured an t1 image from every subject and segmented every subject with the vbm8 toolbox into grey and white matter and calculated from every subject the map of the Jacobean determinates. I would like to know, whether the Jacobean from a particular subject differs from the group. > > I would like to use a factorial design specification (one sample t) for easy labeling and visualization of possible effects, but I do not know how. I would think of this posibility: one sample t flex fact model tests, whether a goup differs from 0. In my case, to test, whether the group differes from the particular subject I have to substract the jacobean of the particular subject from the each Jacobean in the group bevor I perform the secondlevel one sample t test over the group. > Would that analysis be correct? > > My actual workaround is as follows: > 1. I calculate with IMcalc the maps of the voxelwise standard deviations and voxelwise means of the group of probands and use that to calculate the z transformed Jacobean map of the particular proband > 2. I use MRIcroN and look there for regions where the Z value is extending the significant threshold. > Is there a better way to look for effects in this case study? > Many thanks and kind regards, > Christoph Berger > -- Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Fri, 4 Feb 2011 13:47:06 +0000 Reply-To: "Stephen J. Fromm" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Stephen J. Fromm" <[log in to unmask]> Subject: Re: SPM CLI Comments: To: Donald McLaren <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> "There is even a function in spm for doing this, but I have never used it." What function is that? Best, S ========================================================================Date: Fri, 4 Feb 2011 16:05:46 +0100 Reply-To: michel grothe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: michel grothe <[log in to unmask]> Subject: Re: VBM8 - What to do after sample homogeneity check Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_da845b0a-7b5d-4acf-844a-7c616bedcf1b_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_da845b0a-7b5d-4acf-844a-7c616bedcf1b_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable If you didn´t change the default settings of the toolbox, then all of your images are warped independently to the IXI-template provided by the toolbox. This is a DARTEL-compatible average template of healthy adult subjects in MNI space (see also the VBM8-manual). Thus, outliers won´t affect the template and it should be safe to go for option 1 (given that you have good reasons to exclude these images, i.e. your definition of "poor" quality). BTW, it would be of interest to know the average age of the IXI-template sample, but as far as I know this information is not provided by the VBM8-manual. Maybe the author, Christian Gaser, can comment on this. Best regards, Michel > Date: Fri, 4 Feb 2011 11:52:04 +0000 > From: [log in to unmask] > Subject: [SPM] VBM8 - What to do after sample homogeneity check > To: [log in to unmask] > > Dear SPM experts, > > I've run VBM8 using a customized DARTEL-template. Then, I did sample homogeneity check and identified some poor images (i.e. images with severe motion artefacts, enlarged ventricles, etc.). Among 296 images in total, I found 11 "poor" images so far, but there seem to be some more of them. > > Now, my question is which option I should choose between the following. > > (-----1-----) Start statistical analysis without those outliers > (-----2-----) Start again from the beginning (i.e. re-run VBM8) without those outliers > > I was originally going to go for option 2 because, to my understanding, a customized DARTEL-template is made from my own images, and therefore outliers would cause bias in the template. However, I'm not sure whether this reasoning is correct. > > So, could anyone who understands how VBM8 works please advise me on this? I would really appreciate your help. > > Best wishes, > > Kie Woo Nam > > ___________________________ > Kie Woo Nam, PhD student > Division of Psychological Medicine > Institute of Psychiatry, King's College London > 16 De Crespigny Park, Denmark Hill > London, SE5 8AF, UK --_da845b0a-7b5d-4acf-844a-7c616bedcf1b_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable If you didn´t change the default settings of the toolbox, then all of your images are warped independently to the IXI-template provided by the toolbox. This is a DARTEL-compatible average template of healthy adult subjects in MNI space (see also the VBM8-manual). Thus, outliers won´t affect the template and it should be safe to go for option 1 (given that you have good reasons to exclude these images, i.e. your definition of "poor" quality).

BTW, it would be of interest to know the average age of the IXI-template sample, but as far as I know this information is not provided by the VBM8-manual. Maybe the author, Christian Gaser, can comment on this.

Best regards,
Michel

> Date: Fri, 4 Feb 2011 11:52:04 +0000
> From: [log in to unmask]
> Subject: [SPM] VBM8 - What to do after sample homogeneity check
> To: [log in to unmask]
>
> Dear SPM experts,
>
> I've run VBM8 using a customized DARTEL-template. Then, I did sample homogeneity check and identified some poor images (i.e. images with severe motion artefacts, enlarged ventricles, etc.). Among 296 images in total, I found 11 "poor" images so far, but there seem to be some more of them.
>
> Now, my question is which option I should choose between the following.
>
> (-----1-----) Start statistical analysis without those outliers
> (-----2-----) Start again from the beginning (i.e. re-run VBM8) without those outliers
>
> I was originally going to go for option 2 because, to my understanding, a customized DARTEL-template is made from my own images, and therefore outliers would cause bias in the template. However, I'm not sure whether this reasoning is correct.
>
> So, could anyone who understands how VBM8 works please advise me on this? I would really appreciate your help.
>
> Best wishes,
>
> Kie Woo Nam
>
> ___________________________
> Kie Woo Nam, PhD student
> Division of Psychological Medicine
> Institute of Psychiatry, King's College London
> 16 De Crespigny Park, Denmark Hill
> London, SE5 8AF, UK
--_da845b0a-7b5d-4acf-844a-7c616bedcf1b_-- ========================================================================Date: Fri, 4 Feb 2011 17:14:43 +0100 Reply-To: "Dr. Anja Ischebeck" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Dr. Anja Ischebeck" <[log in to unmask]> Subject: Postdoctoral position at Graz University Austria In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="------------050203040904090002020506" Message-ID: <[log in to unmask]> --------------050203040904090002020506 Content-Type: text/plain; charset="UTF-8"; format=flowed Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bofur.jiscmail.ac.uk id p14GEi9l020714 Dear SPM-List members, The Department of General Psychology (Institute of Psychology, University Graz, Austria) has an opening (starting in march 2011) for a full-time postdoctoral position for 6 years. We are looking for a candidate with a strong background in neuroimaging research. As duties also include teaching and some administration, proficiency in German is required. The University Graz is an equal opportunity employer. Please find attached the announcement (in German) for further details. A. Ischebeck ------------------------------ Univ.-Prof. Dr. Anja Ischebeck Department of General Psychology Institute of Psychology University Graz UniversitÃ¤tsplatz 2/DG, 8010 Graz Tel: +43 (0) 316 380-5118 E-Mail: [log in to unmask] http://psychologie.uni-graz.at/ --------------050203040904090002020506 Content-Type: application/msword; name="position_details_university_graz.doc" Content-Disposition: attachment; filename="position_details_university_graz.doc" Content-Transfer-Encoding: base64 0M8R4KGxGuEAAAAAAAAAAAAAAAAAAAAAPgADAP7/CQAGAAAAAAAAAAAAAAABAAAASQAAAAAA AAAAEAAATAAAAAEAAAD+////AAAAAEgAAAD///////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// ///////////////////////////////////spcEAJ2AJBAAA+BK/AAAAAAAAEAAAAAAABgAA 5iYAAA4AYmpiakxVTFUAAAAAAAAAAAAAAAAAAAAAAAAHBBYANDIAAC4/AQAuPwEA3QkAAAAA AAAPAAAAAAAAAAAAAAAAAAAAAgAAAAAAAAD//w8AAAAAAAAAAAD//w8AAAAAAAAAAAD//w8A AAAAAAAAAAAAAAAAAAAAAKQAAAAAADoHAAAAAAAAOgcAADoHAAAAAAAAOgcAAAAAAACKBwAA AAAAAIoHAAAAAAAAigcAABQAAAAAAAAAAAAAAJ4HAAAAAAAAAg8AAAAAAAACDwAAAAAAAAIP AAA4AAAAOg8AAEQAAAB+DwAAJAAAAJ4HAAAAAAAA6CYAAJgBAACuDwAANgEAAOQQAAAAAAAA 5BAAAAAAAADkEAAAAAAAAPwQAAAAAAAAaBIAANwAAABEEwAAZAAAAKgTAAA0AAAAKyYAAAIA AAAtJgAAAAAAAC0mAAAAAAAALSYAAAAAAAAtJgAAAAAAAC0mAAAAAAAALSYAACQAAACAKAAA aAIAAOgqAACkAAAAUSYAAFEAAAAAAAAAAAAAAAAAAAAAAAAAigcAAAAAAAC/FAAAAAAAAAAA AAAAAAAAAAAAAAAAAABkEgAABAAAAGgSAAAAAAAAvxQAAAAAAAC/FAAAAAAAAFEmAAAAAAAA AAAAAAAAAAA6BwAAAAAAADoHAAAAAAAA5BAAAAAAAAAAAAAAAAAAAPwQAABoAQAAoiYAABYA AAAhFwAAAAAAACEXAAAAAAAAIRcAAAAAAAC/FAAAagAAADoHAAA4AAAA5BAAAAAAAACKBwAA AAAAAPwQAAAAAAAAKyYAAAAAAAAAAAAAAAAAACEXAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAvxQAAAAAAAArJgAAAAAAAAAAAAAAAAAA IRcAAAAAAAAhFwAAbgEAAMchAAAIAQAAcgcAABgAAACKBwAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAVyMAAAAAAADkEAAA GAAAAKIPAAAMAAAA0EkD/1e5ywEAAAAAAAAAAAIPAAAAAAAAKRUAAGoAAADPIgAAIAAAAAAA AAAAAAAA9yUAADQAAAC4JgAAMAAAAOgmAAAAAAAA7yIAAGgAAACMKwAAAAAAAJMVAACgAAAA jCsAAEAAAABXIwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIwrAAAAAAAAAAAAAAAAAACKBwAA AAAAAFcjAACgAgAA3BMAACIAAAD+EwAAGAAAACEXAAAAAAAAFhQAABQAAAAqFAAAlQAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA3BMAAAAAAADcEwAAAAAAANwTAAAAAAAA USYAAAAAAABRJgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMxYAAO4A AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANwTAAAAAAAA3BMAAAAAAADcEwAA AAAAAOgmAAAAAAAAvxQAAAAAAAC/FAAAAAAAAL8UAAAAAAAAvxQAAAAAAAAAAAAAAAAAAJ4H AAAAAAAAngcAAAAAAACeBwAAZAcAAAIPAAAAAAAAngcAAAAAAACeBwAAAAAAAJ4HAAAAAAAA Ag8AAAAAAACeBwAAAAAAAJ4HAAAAAAAAngcAAAAAAAA6BwAAAAAAADoHAAAAAAAAOgcAAAAA AAA6BwAAAAAAADoHAAAAAAAAOgcAAAAAAAD/////AAAAAAIADAEAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0NDUlkZWVucmVpY2gsIElubm92YXRpdiwgSW50ZXJu YXRpb25hbCCWIG1pdCAzLjAwMCBNaXRhcmJlaXRlcklubmVuIHVuZCBydW5kIDI3LjAwMCBT dHVkaWVyZW5kZW4gYmlldGV0IGRpZSBLYXJsLUZyYW56ZW5zLVVuaXZlcnNpdOR0IEdyYXog ZWluIHNwYW5uZW5kZXMgdW5kIGFid2VjaHNsdW5nc3JlaWNoZXMgQXJiZWl0c3VtZmVsZC4g TWl0IHVuc2VyZXIgRm9yc2NodW5ncy0gdW5kIExlaHJrb21wZXRlbnogc2luZCB3aXIgZWlu ZSB6ZW50cmFsZSBJbnN0aXR1dGlvbiBm/HIgZGllIFNpY2hlcnVuZyBkZXMgU3RhbmRvcnRz IFN0ZWllcm1hcmsuDSANEyBGT1JNVEVYVCABFERlciBBcmJlaXRzYmVyZWljaCBBbGxnZW1l aW5lIFBzeWNob2xvZ2llIChMZWl0dW5nOiBQcm9mLiBBbmphIElzY2hlYmVjaykgYW0gSW5z dGl0dXQgZvxyIFBzeWNob2xvZ2llIHN1Y2h0IGVpbmUvbhUNEyBGT1JNVEVYVCABFAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAiACIAIg AiACIBUADQANABMAIABGAE8AUgBNAFQARQBYAFQAIAABABQAVQBuAGkAdgBlAHIAcwBpAHQA 5AB0AHMAYQBzAHMAaQBzAHQAZQBuAHQALwBpAG4AIABtAGkAdAAgAEQAbwBrAHQAbwByAGEA dAAVAA0AKAATACAARgBPAFIATQBUAEUAWABUACAAAQAUADQAMAAgAFMAdAB1AG4AZABlAG4A LwBXAG8AYwBoAGUAFQA7ACAAEwAgAEYATwBSAE0AVABFAFgAVAAgAAEAFABiAGUAZgByAGkA cwB0AGUAdAAgAGEAdQBmACAANgAgAEoAYQBoAHIAZQAVADsAIAATACAARgBPAFIATQBUAEUA WABUACAAAQAUAHYAbwByAGEAdQBzAHMAaQBjAGgAdABsAGkAYwBoACAAegB1ACAAYgBlAHMA ZQB0AHoAZQBuACAAYQBiACAAcwBvAGYAbwByAHQAKQAVACkADQANAEEAdQBmAGcAYQBiAGUA bgBiAGUAcgBlAGkAYwBoADoADQATACAARgBPAFIATQBUAEUAWABUACAAAQAUAEQAaQBlACAA UwB0AGUAbABsAGUAbgBpAG4AaABhAGIAZQByAGkAbgAvAGQAZQByACAAUwB0AGUAbABsAGUA bgBpAG4AaABhAGIAZQByACAAcwBvbGwgaW4gZWluZW0gVGVhbSB6dSBUaGVtZW4gZGVyIGV4 cGVyaW1lbnRlbGxlbiBHcnVuZGxhZ2VuZm9yc2NodW5nIGF1cyBkZXIgQWxsZ2VtZWluZW4g UHN5Y2hvbG9naWUgaW4gZGVyIEZvcnNjaHVuZyBtaXRhcmJlaXRlbiB1bmQgc2Nod2VycHVu a3Rt5N9pZyBQcm9qZWt0ZSB1bnRlciBWZXJ3ZW5kdW5nIGRlciBmdW5rdGlvbmVsbGVuIEtl cm5zcGludG9tb2dyYXBoaWUgKGZNUlQpLCBhYmVyIGF1Y2ggYW5kZXJlciBNZXRob2RlbiB6 dXIgTWVzc3VuZyB2b24gSGlybmFrdGl2aXTkdCB1bmQgVmVyaGFsdGVuIGVpZ2Vuc3TkbmRp ZyBwbGFuZW4gdW5kIGR1cmNoZvxocmVuLiAgV2VpdGVycyBzb2xsIGRlciBTdGVsbGVuaW5o YWJlci9kaWUgU3RlbGxlbmluaGFiZXJpbiBhbiBkZW4gTGVoci0gdW5kIFZlcndhbHR1bmdz YXVmZ2FiZW4gZGVzIEluc3RpdHV0cyBm/HIgUHN5Y2hvbG9naWUgbWl0d2lya2VuLiBEaWUg TfZnbGljaGtlaXQgenVyIFdlaXRlcnF1YWxpZmlrYXRpb24gKEhhYmlsaXRhdGlvbikgd2ly ZCB1bnRlcnN0/HR6dC4VDQ1GYWNobGljaGUgUXVhbGlmaWthdGlvbjoNEyBGT1JNVEVYVCAB FJUJQWJnZXNjaGxvc3NlbmVzIERva3RvcmF0c3N0dWRpdW0gZGVyIFBzeWNob2xvZ2llIG9k ZXIgZWluZSBm/HIgZGllIFZlcndlbmR1bmcgaW4gQmV0cmFjaHQga29tbWVuZGUgdW5kIGRl bSBEb2t0b3JhdCBnbGVpY2ggenUgYmV3ZXJ0ZW5kZSB3aXNzZW5zY2hhZnRsaWNoZSBCZWbk aGlndW5nDZUJRWluc2NobORnaWdlIEVyZmFocnVuZyBpbiBLb2duaXRpb25zLSB1bmQvb2Rl ciBOZXVyb3dpc3NlbnNjaGFmdGVuDZUJRnVuZGllcnRlIE1ldGhvZGVuLSB1bmQgU3RhdGlz dGlra2VubnRuaXNzZQ2VCUVyZmFocnVuZyBpbiBkZXIgUGxhbnVuZyB1bmQgQXVzd2VydHVu ZyB2b24gU3R1ZGllbiBtaXR0ZWxzIGZNUlQNlQlFcmZhaHJ1bmcgbWl0IHdlaXRlcmVuIE1l dGhvZGVuICh6LkIuIEVFRywgTklSUywgQmxpY2tiZXdlZ3VuZ2VuKQ2VCVB1Ymxpa2F0aW9u c2VyZmFocnVuZywgUHJvZ3JhbQBtAGkAZQByAGsAZQBuAG4AdABuAGkAcwBzAGUAIAAoAHoA LgBCAC4AIABNAGEAdABsAGEAYgApABUADQANAFAAZQByAHMA9gBuAGwAaQBjAGgAZQAgAEEA bgBmAG8AcgBkAGUAcgB1AG4AZwBlAG4AOgANABMAIABGAE8AUgBNAFQARQBYAFQAIAABABQA SQBuAGkAdABpAGEAdABpAHYAZQAsACAAWgB1AHYAZQByAGwA5ABzAHMAaQBnAGsAZQBpAHQA LAAgAHMAZQBsAGIAcwB0AOQAbgBkAGkAZwBlACAAQQByAGIAZQBpAHQAcwB3AGUAaQBzAGUA LAAgAEYAcgBlAHUAZABlACAAYQBuACAAZQB4AHAAZQByAGkAbQBlAG4AdABlAGwAbABlAHIA IABGAG8AcgBzAGMAaAB1AG4AZwA7ACAATwByAGcAYQBuAGkAcwBhAHQAaQBvAG4AcwAtACAA dQBuAGQAIABUAGUAYQBtAGYA5ABoAGkAZwBrAGUAaQB0ABUADQANABMAIABGAE8AUgBNAFQA RQBYAFQAIAABABQAAiACIAIgAiACIBUADQANAEUAbgBkAGUAIABkAGUAcgAgAEIAZQB3AGUA cgBiAHUAbgBnAHMAZgByAGkAcwB0ADoAIAATACAARgBPAFIATQBUAEUAWAAABgAAAQgAAAII AAADCAAASQgAAE4IAABPCAAAUAgAAMIIAAARCQAAJgkAADoJAAA7CQAAPAkAAD0JAAA+CQAA SAkAAEkJAABKCQAAvAkAAL0JAAC+CQAA/PXq2sraytq2oraTiH1uY1FuRW5jAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAXFWj0B74AFmj0B74AbUgABG5IAAR1CAEjAgiBA2oAAAAA BggBFWh+QG4AFmhtJ4gAVQgBXkoCAGFKFAAUFWh+QG4AFmhtJ4gAXkoCAGFKFAAAHQNqAAAA ABVofkBuABZobSeIAFUIAV5KAgBhShQAFBVofkBuABZouE9MAF5KAgBhShQAABQVaH5AbgAW aBpEIwBeSgIAYUoUAAAcFWh+QG4AFmiqZoMAXkoCAGFKFABuSAcMdEgHDAAnFWh+QG4AFmh4 ErAAXQiBXkoCAGFKFABtSAcEbkgHDHNIBwR0SAcMJxVofkBuABZoqmaDAF0IgV5KAgBhShQA bUgHBG5IBwxzSAcEdEgHDB8VaH5AbgAWaDwZmQBcCIFeSgIAYUoUAG1IBwRzSAcEHxVofkBu ABZoqmaDAFwIgV5KAgBhShQAbUgHBHNIBwQUFWh+QG4AFmi+QnsAXkoCAGFKFAAADBVojSBi ABZofDXKAAAGFmhdIC0AFQAGAAABCAAAAggAAAMIAAA7CQAAPQkAAL4JAAAOCgAAEAoAAHgK AABwCwAAcgsAAJQLAAAYDgAAGQ4AADIOAADnDgAALA8AAFoPAACdDwAA4Q8AAD4QAAD9AAAA AAAAAAAAAAAA/QAAAAAAAAAAAAAAAPQAAAAAAAAAAAAAAADiAAAAAAAAAAAAAAAA1gAAAAAA AAAAAAAAANYAAAAAAAAAAAAAAADWAAAAAAAAAAAAAAAA1gAAAAAAAAAAAAAAAMoAAAAAAAAA AAAAAADKAAAAAAAAAAAAAAAA1gAAAAAAAAAAAAAAANYAAAAAAAAAAAAAAADWAAAAAAAAAAAA AAAA1gAAAAAAAAAAAAAAANYAAAAAAAAAAAAAAADBAAAAAAAAAAAAAAAAwQAAAAAAAAAAAAAA AMEAAAAAAAAAAAAAAADBAAAAAAAAAAAAAAAAwQAAAAAAAAAAAAAAALUAAAAAAAAAAAAAAAAA DAAAAyQDFiQBSWYBAAAAYSQDZ2RuMlMACQAAFiQBSWYBAAAAZ2RuMlMADAAAAyQBFiQBSWYB AAAAYSQBZ2R+QG4ADAAAAyQDFiQBSWYBAAAAYSQDZ2R+QG4AABEAAAMkAw3GCAACBAJAFwAA FiQBSWYBAAAAYSQDZ2R+QG4ACQAAFiQBSWYBAAAAZ2TpQUoAAAEAAAAVAAYAAMImAADgJgAA 5CYAAP39/QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAEBAABAQO+CQAAvwkAAMkJAADKCQAAAAoAAAoKAAAMCgAADgoAABAKAAASCgAA JgoAACgKAAAqCgAAdAoAAHYKAAB4CgAAegoAAHwKAACQCgAA8OXT8Lrwr+Wdj3qdbp1gVUY7 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFBVofkBuABZoXSAtAF5KAgBhShQAAB0DagAAAAAV aH5AbgAWaF0gLQBVCAFeSgIAYUoUABQVaH5AbgAWaJ8IcABeSgIAYUoUAAAaFWh+QG4AFmga RCMANQiBPioBXkoCAGFKFAAAFxVo9Ae+ABZo9Ae+AG1IAARuSAAEdQgBKQIIgQNq6AAAAAYI ARVofkBuABZowgodADUIgT4qAVUIAV5KAgBhShQAGhVofkBuABZobgBlADUIgT4qAV5KAgBh ShQAACMDagAAAAAVaH5AbgAWaG4AZQA1CIE+KgFVCAFeSgIAYUoUABQVaH5AbgAWaG0niABe SgIAYUoUAAAwA2oAAAAAFWh+QG4AFmgBF5EAUEoDAFFKAwBVCAFeSgIAYUoUAG1IAARuSAAE dQgBACMCCIEDanQAAAAGCAEVaH5AbgAWaAEXkQBVCAFeSgIAYUoUABQVaH5AbgAWaAEXkQBe SgIAYUoUAAAdA2oAAAAAFWh+QG4AFmgBF5EAVQgBXkoCAGFKFAAAEpAKAACSCgAAlAoAALQK AAC2CgAAugoAALwKAADQCgAA0goAANQKAAD+CgAAAAsAAAQLAAAGCwAAGgsAABwLAAAeCwAA PgsAAEQLAABqCwAAbAsAAG4LAAByCwAAkAsAAJQLAACWCwAAqgsAAKwLAACuCwAAzQ0AAO3e 0t7H3se13tLex97Ho96aj9LehHltx97HW95PAAAAAAAAAAAAFxVobjJTABZobjJTAG1IAARu SAAEdQgBIwIIgQNq/gIAAAYIARVofkBuABZoXSAtAFUIAV5KAgBhShQAFxVofkBuABZoXSAt AD4qAV5KAgBhShQAFBVofkBuABZoGkQjAF5KAgBhShQAABQVaH5AbgAWaJ8IcABeSgIAYUoU AAAVFmj0B74AUEoDAG1IAARuSAAEdQgBERZoGjJdAG1IAARuSAAEdQgBIwIIgQNqigIAAAYI ARVofkBuABZoXSAtAFUIAV5KAgBhShQAIwIIgQNqFgIAAAYIARVofkBuABZoXSAtAFUIAV5K AgBhShQAFBVofkBuABZoXSAtAF5KAgBhShQAABcVaPQHvgAWaPQHvgBtSAAEbkgABHUIAR0D agAAAAAVaH5AbgAWaF0gLQBVCAFeSgIAYUoUACMCCIEDaqIBAAAGCAEVaH5AbgAWaF0gLQBV CAFeSgIAYUoUAAAdzQ0AABYOAAAXDgAAGQ4AADAOAAAyDgAAMw4AAD0OAAA+DgAAPw4AAJoO AACbDgAAOhAAADwQAABAEAAAWBAAAHIQAAB2EAAAeBAAAIwQAACOEAAAkBAAAIoRAACMEQAA jhEAAJARAACSEQAA9ufc0MW2q5m2jYSNtsXQeMW2q2a2jbZb3EwAAAAAAAAAAAAAAAAAAAAA AAAdA2oAAAAAFWh+QG4AFmhtJ4gAVQgBXkoCAGFKFAAUFWh+QG4AFmi4T0wAXkoCAGFKFAAA IwIIgQNq8gMAAAYIARVofkBuABZoGCDcAFUIAV5KAgBhShQAFxVofkBuABZopXjYAD4qAV5K AgBhShQAERZoKk+bAG1IAARuSAAEdQgBFxVobjJTABZobjJTAG1IAARuSAAEdQgBIwIIgQNq cgMAAAYIARVofkBuABZoGCDcAFUIAV5KAgBhShQAFBVofkBuABZolC4pAF5KAgBhShQAAB0D agAAAAAVaH5AbgAWaJQuKQBVCAFeSgIAYUoUABQVaH5AbgAWaBpEIwBeSgIAYUoUAAAXFWh+ QG4AFmgaRCMAPioBXkoCAGFKFAAUFWh+QG4AFmhdIC0AXkoCAGFKFAAAHQNqAAAAABVofkBu ABZoXSAtAFUIAV5KAgBhShQAERZo2UxlAG1IAARuSAAEdQgBABo+EAAAQBAAAHYQAACOEQAA kBEAALgRAAC6EQAAFiQAAEIkAABDJAAA/SQAAP4kAAAdJQAALSUAAEElAABMJQAAoyUAAKQl AAAQJgAAEiYAAPMAAAAAAAAAAAAAAADzAAAAAAAAAAAAAAAA8wAAAAAAAAAAAAAAAPMAAAAA AAAAAAAAAADzAAAAAAAAAAAAAAAA8wAAAAAAAAAAAAAAAPMAAAAAAAAAAAAAAADzAAAAAAAA AAAAAAAA8wAAAAAAAAAAAAAAAPMAAAAAAAAAAAAAAADzAAAAAAAAAAAAAAAA8wAAAAAAAAAA AAAAAPMAAAAAAAAAAAAAAADzAAAAAAAAAAAAAAAA8wAAAAAAAAAAAAAAAPMAAAAAAAAAAAAA AADzAAAAAAAAAAAAAAAA8wAAAAAAAAAAAAAAAOoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAJAAAWJAFJZgEAAABnZBpEIwAMAAADJAMWJAFJZgEAAABh JANnZH5AbgAAE5IRAACmEQAAqBEAAKoRAAC0EQAAthEAALoRAADuEQAA8BEAAAASAAAAJAAA AiQAAAMkAAAEJAAAFCQAABUkAAAgJAAAISQAACskAAAsJAAALSQAAEAkAABBJAAAHSUAACwl AABBJQAATCUAAPXj1LvU9bCfk5GTfZ95n7Cfk2WfWp+wUrBDHBVoGGxZABZoGkQjAF5KAgBh ShQAbUgJBHNICQQADhZoxT2HAF5KAgBhShQAABUWaBoyXQBQSgMAbUgABG5IAAR1CAEmAgiB A2piBQAABggBFWh+QG4AFmgYINwANQiBVQgBXkoCAGFKFAAABhZo9l5xAAAmAgiBA2rkBAAA BggBFWh+QG4AFmgYINwANQiBVQgBXkoCAGFKFAAAA1UIARcVaH5AbgAWaJQuKQA1CIFeSgIA YUoUACADagAAAAAVaH5AbgAWaJQuKQA1CIFVCAFeSgIAYUoUAAAUFWh+QG4AFmgaRCMAXkoC AGFKFAAAMANqAAAAABVofkBuABZoAReRAFBKAwBRSgMAVQgBXkoCAGFKFABtSAAEbkgABHUI AQAdA2oAAAAAFWh+QG4AFmhtJ4gAVQgBXkoCAGFKFAAjAgiBA2pwBAAABggBFWh+QG4AFmht J4gAVQgBXkoCAGFKFAAUFWh+QG4AFmhtJ4gAXkoCAGFKFAAaAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAABUIAEUMjMuIEZlYnJ1YXIgMjAxMRUNS2VubnphaGw6IBMgRk9STVRFWFQgARRN Qi80MS85OSBleCAyMDEwLzExFQ0NQmVpIEludGVyZXNzZSBzZW5kZW4gU2llIElocmUgQmV3 ZXJidW5nc3VudGVybGFnZW4gaW5uZXJoYWxiIGRlciBhbmdlZ2ViZW5lbiBCZXdlcmJ1bmdz ZnJpc3QgaW5rbC4gTGViZW5zbGF1ZiwgRm90byB1bmQgcmVsZXZhbnRlciAoRGllbnN0LSla ZXVnbmlzc2UgdW50ZXIgQW5nYWJlIGRlciBLZW5uemFobCBiaXR0ZSBhbjoNDUthcmwtRnJh bnplbnMtVW5pdmVyc2l05HQgR3Jheg1QZXJzb25hbHJlc3NvcnQNVW5pdmVyc2l05HRzcGxh dHogMw04MDEwIEdyYXogDW9kZXIgcGVyIEVtYWlsIGFuOiATIEhZUEVSTElOSyAibWFpbHRv OmJld2VyYnVuZ0B1bmktZ3Jhei5hdCIgARRiZXdlcmJ1bmdAdW5pLWdyYXouYXQVDQ0TIEZP Uk1URVhUIAEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIgAiACIAIgAiAVACAADQAHAAcA DQANAA0AUABTAFAALQBQAGwAYQBuAHUAbgBnAHMASQBEADoAIAATACAARgBPAFIATQBUAEUA WABUACAAAQAUAFEARgBOABUAIAAtACAAEwAgAEYATwBSAE0AVABFAFgAVAAgAAEAFABXAEcA VQBNADQAMAAVACAALQAgABMAIABGAE8AUgBNAFQARQBYAFQAIAABABQAMQAwADEAMAAwADEA FQANAA0ADQANAA0AAwANAA0ABAANAA0AAwANAA0ABAANAA0ADQANAA0ADQANAA0AAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAATCUAAF8lAABgJQAAiiUAAIslAACMJQAAlSUAAKElAACiJQAAoyUAAKQlAAClJQAA ryUAALAlAAAAJgAACiYAAAwmAAAOJgAA8eLTweKwn+KQgXJnVXI8cjEAAAAAAAAAAAAUFWh+ QG4AFmiCSBAAXkoCAGFKFAAAMANqAAAAABVofkBuABZoAReRAFBKAwBRSgMAVQgBXkoCAGFK FABtSAAEbkgABHUIAQAjAgiBA2qfBgAABggBFWh+QG4AFmhtJ4gAVQgBXkoCAGFKFAAUFWh+ QG4AFmhtJ4gAXkoCAGFKFAAAHQNqAAAAABVofkBuABZobSeIAFUIAV5KAgBhShQAHBVoGGxZ ABZobSeIAF5KAgBhShQAbUgJBHNICQQAHBVoGGxZABZoGCDcAF5KAgBhShQAbUgJBHNICQQA IBVoGGxZABZoGkQjADBKFgBeSgIAYUoUAG1ICQRzSAkEACAVaBhsWQAWaKkTKgAwShYAXkoC AGFKFABtSAkEc0gJBAAjAgiBA2rcBQAABggBFWgaMl0AFmioW84AVQgBXkoCAGFKFAAcFWgY bFkAFmi8b9QAXkoCAGFKFABtSAkEc0gJBAAdA2oAAAAAFWh+QG4AFmi8b9QAVQgBXkoCAGFK FAAcFWgYbFkAFmipEyoAXkoCAGFKFABtSAkEc0gJBBEOJgAAECYAABImAAAUJgAAFiYAABom AAAiJgAAMiYAADYmAAA4JgAAOiYAADwmAABQJgAAUiYAAFQmAABaJgAAXCYAAGImAABkJgAA eCYAAHomAAB8JgAAiCYAAPXq39PHu7K7opOCdmKCV4JOgnY6glcAACYCCIEDatkHAAAGCAEV aH5AbgAWaBhsWQA1CIFVCAFeSgIAYUoUAAARFmgYbFkANQiBXkoCAGFKFAAVFmgaMl0AUEoD AG1IAARuSAAEdQgBJgIIgQNqXwcAAAYIARVofkBuABZoGGxZADUIgVUIAV5KAgBhShQAABcV aH5AbgAWaBhsWQA1CIFeSgIAYUoUACADagAAAAAVaH5AbgAWaBhsWQA1CIFVCAFeSgIAYUoU AAAcFWgYbFkAFmgYbFkAXkoCAGFKFABtSAkEc0gJBAAfFWgYbFkAFmgYbFkANQiBXkoCAGFK FABtSAkEc0gJBBEWaHZGWQA1CIFeSgIAYUoUABcVaBhsWQAWaBhsWQA1CIFeSgIAYUoUABYW aIJIEABDShYAT0oEAFFKBABhShYAABYWaA55iABDShYAT0oEAFFKBABhShYAABQVaH5AbgAW aBpEIwBeSgIAYUoUAAAUFWh+QG4AFmibB/4AXkoCAGFKFAAAFBVofkBuABZoGCDcAF5KAgBh ShQAFhImAAAUJgAAFiYAABgmAAAaJgAAuiYAALwmAAC+JgAAwCYAAMImAADGJgAAyCYAAMwm AADOJgAA0iYAANQmAADYJgAA2iYAANwmAADeJgAA4CYAAL4AAAAAAAAAAAAAAAC5AAAAAAAA AAAAAAAAuQAAAAAAAAAAAAAAALkAAAAAAAAAAAAAAAC5AAAAAAAAAAAAAAAAuQAAAAAAAAAA AAAAALkAAAAAAAAAAAAAAAC5AAAAAAAAAAAAAAAAuQAAAAAAAAAAAAAAALcAAAAAAAAAAAAA AAC3AAAAAAAAAAAAAAAAtwAAAAAAAAAAAAAAALcAAAAAAAAAAAAAAAC3AAAAAAAAAAAAAAAA twAAAAAAAAAAAAAAALcAAAAAAAAAAAAAAAC3AAAAAAAAAAAAAAAArgAAAAAAAAAAAAAAALcA AAAAAAAAAAAAAAC3AAAAAAAAAAAAAAAAAAAAAAAIEQANxgUAAd4nAmdkpUeVAAABAAAABAAA Z2TmSywAQQAAa2QVBwAAFiQBFyQBSWYBAAAAApZGAAjWGgABlP/cIwAGSCQAAAAAAAAAAAAA AAAAAAAACnQAAOABFPYDSCQVNgEX9gMAABrWBAAAAP8b1gQAAAD/HNYEAAAA/x3WBAAAAP80 1gYAAQUDAAA01gYAAQoDbABh9gMAAHl0fkBuAAAUiCYAAIomAACQJgAAkiYAAKYmAACoJgAA qiYAALYmAAC4JgAAuiYAAMAmAADCJgAAxCYAAMgmAADKJgAAziYAANAmAADUJgAA1iYAANom AADeJgAA4CYAAOImAADkJgAA5iYAAO/m79rG78DvtKiZkY2RjZGNkY2JjYmFmQAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGFmi0ChUAAAYWaLhjLgAABhZonhRsAAAPA2oA AAAAFmieFGwAVQgBHBVojSBiABZoGGxZAENKFgBPSgQAUUoEAGFKFgAAFhZoGGxZAENKFgBP SgQAUUoEAGFKFgAAFhZogkgQAENKFgBPSgQAUUoEAGFKFgAAChZoGjJdAFBKAwAAJgIIgQNq UwgAAAYIARVofkBuABZoGGxZADUIgVUIAV5KAgBhShQAABcVaH5AbgAWaBhsWQA1CIFeSgIA YUoUABEWaBhsWQA1CIFeSgIAYUoUACADagAAAAAVaH5AbgAWaBhsWQA1CIFVCAFeSgIAYUoU ABjgJgAA4iYAAOQmAADmJgAA/QAAAAAAAAAAAAAAAP0AAAAAAAAAAAAAAAD4AAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAQAAGdk5kssAAABAAAAAzIAMZBoATpwAUV1AB+wgi4gsMZBIbCKBSKw igUjkDUEJJBuBCWwAAAXsMUCGLDFAgyQxAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA dAAAAEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAD/////AAAAABQABQBUAGUAeAB0ADgAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAB0AAAARAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AAAAAFAAFAFQAZQB4AHQAOAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAALoAAABEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wBAAAAAABQARgB1AG4AawB0AGkA bwBuAHMAYgBlAHoAZQBpAGMAaABuAHUAbgBnAAAAFABGAFUATgBLAFQASQBPAE4AUwBCAEUA WgBFAEkAQwBIAE4AVQBOAEcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHQAAABEAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA /////wAAAAAUAAUAVABlAHgAdAA0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAdAAAAEQA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAD/////AAAAABQABQBUAGUAeAB0ADUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAB0AAAARAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAP////8AAAAAFAAFAFQAZQB4AHQANgAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAHQAAABEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAAAUAAUAVABlAHgAdAAzAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAgAAAAEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD/////AEAAAAAACwBmAGEAYwBoAGwA XwBRAHUAYQBsAGkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB+AAAARAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP// //8AQAAAAAAKAHAAZQByAHMAXwBRAHUAYQBsAGkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAB0AAAARAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAP////8AAAAAFAAFAFQAZQB4AHQAOQAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAH4AAABEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wBAAAAAAAoARQBuAGQAZQBfAEYAcgBpAHMA dAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHoAAABEAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wBAAAAAAAgA SwBlAG4AbgB6AGEAaABsAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAwwAAAEQAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAA0Mnqefm6zhGMggCqAEupCwIAAAADAAAA4Mnqefm6zhGMggCqAEupC1IAAABtAGEAaQBs AHQAbwA6AGIAZQB3AGUAcgBiAHUAbgBnAEAAdQBuAGkALQBnAHIAYQB6AC4AYQB0AAAAeViB 9Dsdf0ivLIJdxIUnYwAAAAClqwAAdgAAAEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD/////AAAAABQABgBUAGUAeAB0 ADEAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgAFiQBFyQBSWYBAAAAAZba/yF2AAFo ATXWBQABA0gkI3YAAUgkOlYLAAKWRgAKdAAA4AEU9gNIJBU2ATXWBQABA0gkeXR+QG4AegAA AEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAD/////AEAAAAAACABLAGUAbgBuAHoAYQBoAGwAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAB6AAAARAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP////8AQAAAAAAIAEsAZQBuAG4AegBhAGgAbAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHoAAABEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wBAAAAAAAgASwBl AG4AbgB6AGEAaABsAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIYCFwASAAEA nAAPAAQAAAAAAAAAAAAEAAgAAACYAAAAngAAAJ4AAACYAAAAngAAAJ4AAACeAAAAngAAAJgA AAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAB2AgAAdgIAAHYCAAB2AgAA dgIAAHYCAAB2AgAAdgIAAHYCAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA+AgAANgYAADYG AAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAA NgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAAqAAAADYG AAA2BgAAFgAAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAAuAAAADYGAAA2BgAA NgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAAGgBAABIAQAANgYAADYG AAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAA NgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYG AAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAA NgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYG AAA2BgAANgYAADYGAAA2BgAANgYAADYGAAA2BgAANgYAADYGAACwAwAANgYAAEgAAEDx/wIA SAAMEAAALFTDAAAACABTAHQAYQBuAGQAYQByAGQAAAACAAAAHABPSgIAUUoCAF9IAQRhShgA bUgHDHNIBwx0SAcEjAABQAEAAgCMAAwQAABgOTwAAAANANwAYgBlAHIAcwBjAGgAcgBpAGYA dAAgADEAAABMAAEABiQBJGQEAQABJWQEAQAEJmQEAQAGJ2QEAQAEQCYATsYIAAAA/wQBAQBP xggAAAD/BAEEAFDGCAAAAP8EAQYAUcYIAAAA/wQBBAAMAE9KAgBRSgIAXkoCAAAAAABSAARA AQACAFIADBAAAJpEjAAAAA0A3ABiAGUAcgBzAGMAaAByAGkAZgB0ACAANAAAABAABAAGJAET pPAAFKQ8AEAmAw4ANQiBQ0ocAFwIgWFKHAAAAAAAAAAAAGAACUABAAIAYAAMEAAAYDk8AAAA DQDcAGIAZQByAHMAYwBoAHIAaQBmAHQAIAA5AAAADgAJAAMkAQYkAUAmCGEkAR4ANQiBPioD Q0oWAE9KAgBRSgIAYUoUAG1IBwRzSAcESgBBQPL/oQBKAAwBAAAAAAAAAAAZAEEAYgBzAGEA dAB6AC0AUwB0AGEAbgBkAGEAcgBkAHMAYwBoAHIAaQBmAHQAYQByAHQAAAAAAFgAaUDz/7MA WAAMAQAAAAAAAAAADwBOAG8AcgBtAGEAbABlACAAVABhAGIAZQBsAGwAZQAAABwAF/YDAAA0 1gYAAQoDbAA01gYAAQUDAABh9gMAAAIACwAAADAAawD0/8EAMAAAAQAAAAAAAAAACwBLAGUA aQBuAGUAIABMAGkAcwB0AGUAAAACAAwAAAAAAHoAmgCzAPMAegAMAAAAGkQjAAAAEgBUAGEA YgBlAGwAbABlAG4AZwBpAHQAdABlAHIAbgBlAHQAegAAADcAOlYPABPWMAAAAP8EAQAAAAAA /wQBAAAAAAD/BAEAAAAAAP8EAQAAAAAA/wQBAAAAAAD/BAEAAAACAA8AAAA6AB9AAQACAToA DAAAAKVHlQAAAAkASwBvAHAAZgB6AGUAaQBsAGUAAAANABAADcYIAAK4EXAjAQIAAAA4ACBA AQASATgADAAAAKVHlQAAAAgARgB1AN8AegBlAGkAbABlAAAADQARAA3GCAACuBFwIwECAAAA OgBCQAEAIgE6AAwAAABdIC0AAAAKAFQAZQB4AHQAawD2AHIAcABlAHIAAAACABIACgA1CIFc CIFhShQAVABRQAEAMgFUAAwAAABdIC0AAAAMAFQAZQB4AHQAawD2AHIAcABlAHIAIAAzAAAA GQATAAMkAyRkBAEAAU7GCAAAAP8EAQEAYSQDAAgAQ0oYAF5KAgBCAP5PAQACAEIADAAAAF0g LQAAAAMAUABhADAAAAARABQAEmTxAAAANyQAOCQASCQAABAAQ0oYAE9KBQBRSgUAdEgHDDoA /k/y/1EBOgAMAAAAXSAtAAAAAgBBADAAAAAdAENKEABPSgUAUUoFAF5KBQBhShAAbygAcGgA AAAAADYAVUCiAGEBNgAMAAAA6lKMAAAACQBIAHkAcABlAHIAbABpAG4AawAAAAwAPioBQioC cGgAAP8AAAAAAAEAAADvCQAA/////wAAAAABAP////8AAAAAAAAAAAAAAAABAAAAAAD///// AAAAAAAAAAAAAAAAAQAAAAQAAAAAAAAAAAj//wAAAAAAAAAA7wkAAAQAADIAAAAA/////wAA AAABAAAAAgAAAAMAAAA7AQAAPQEAAL4BAADSAQAA0wEAAAcCAACDAgAAhAIAAJUCAADjBAAA 5AQAAP0EAACyBQAA9wUAACUGAABoBgAArAYAAOoGAADrBgAABgcAAJIHAACTBwAApwcAAKgH AADhBwAADQgAAA4IAADICAAAyQgAAOgIAAD4CAAADAkAABcJAABuCQAAbwkAAIQJAACFCQAA hgkAAIcJAACICQAAiQkAANkJAADaCQAA2wkAANwJAADdCQAA3wkAAOAJAADiCQAA4wkAAOYJ AADpCQAA6gkAAOsJAADsCQAA8AkAAJgAAAAAMAAAAAAAAACAAAAAgAAAAAAAAAAAAACYAAAA ADAAAAAAAAAAgAAAAIAAAAAAAAAAAAAAqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAKkA AAAAMAAAAAAAAACAAAAAgAEAANAAAAAAAACpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAAA qQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAakAAAAAMAAAAAAAAACAAAAAgAEAANAAAAAA AACpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAAAqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAA AAAAAakAAAAAMAAAAAAAAACAAAAAgAEAANAAAAAAAAGpAAAAADAAAAAAAAAAgAAAAIABAADQ AAAAAAAAqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAKkAAAAAMAAAAAAAAACAAAAAgAEA ANAAAAAAAAGpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAAAqQAAAAAwAAAAAAAAAIAAAACA AQAA0AAAAAAAAKkAAAAAMAAAAAAAAACAAAAAgAEAAPgAAAAAAAGpAAAAADAAAAAAAAAAgAAA AIABAADQAAAAAAABqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAakAAAAAMAAAAAAAAACA AAAAgAEAANAAAAAAAAGpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAABqQAAAAAwAAAAAAAA AIAAAACAAQAA0AAAAAAAAakAAAAAMAAAAAAAAACAAAAAgAEAANAAAAAAAACpAAAAADAAAAAA AAAAgAAAAIABAADQAAAAAAAAqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAakAAAAAMAAA AAAAAACAAAAAgAEAANAAAAAAAACpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAAAqQAAAAAw AAAAAAAAAIAAAACAAQAA0AAAAAAAAKkAAAAAMAAAAAAAAACAAAAAgAEAANAAAAAAAAGpAAAA ADAAAAAAAAAAgAAAAIABAADQAAAAAAABqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAKkA AAAAMAAAAAAAAACAAAAAgAEAANAAAAAAAACpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAAA qQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAKkAAAAAMAAAAAAAAACAAAAAgAEAANAAAAAA AACpAAAAADAAAAAAAAAAgAAAAIABAADQAAAAAAAAqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAA AAAAAKkAAAAAMAAAAAAAAACAAAAAgAEAANAAAAAAAAGpAAAAADAAAAAAAAAAgAAAAIABAADQ AAAAAAAAqQAAAAAwAAAAAAAAAIAAAACAAQAA0AAAAAAAAKkAAAAAMAAAAAAAAACAAAAAgAEA ANAAAAAAIACZAAAAADAAAAAAAAAAgAAAAIABAADUAAAAACAAmAAAAAAwAAAAAAAAAIAAAACA AAAAAAAAAAAAAJgAAAAAMAAAAAAAAACAAAAAgAAAAAAAAAAAAACYAAAAADAAAAAAAAAAgAAA AIAAAAAAAAAAAAAAmAAAAAAwAAAAAAAAAIAAAACAAAAAAAAAAAAAAJgAAAAAMAAAAAAAAACA AAAAgAAAAAAAAAAAAACYAAAAADAAAAAAAAAAgAAAAIAAAAAAAAAAAAAAmAAAAAAwAAAAAAAA AIAAAACAAAAAAAAAAAAAAJgAAAAAMAAAAAAAAACAAAAAgAAAAAAAAAAAAACo0QAwADAAAAAA AAACAAAAAQAAAAAAAAAAACIHqJEAMAAwAAAAAAAAAQAAAAAAAAAAAAAAAACrB6jRADAAMAAA AAAAAAIAAAABAAAAAAAAAAAAIgeokQAwADAAAAAAAAABAAAAAAAAAAAAAAAAAKsHqJEAMAAw AAAAAAAAAQAAAAAAAAAAAAAAAACrB6iRADAAMAAAAAAAAAEAAAAAAAAAAAAAAAAAqweQQAAA ETAAAAAAAAAAgAAAAIAAAAAAAAAAAAAHqNEAMAUwAAAAAAAAAgAAAAEAAAAGAAAAOIEiB6iR ADAAMAAAAAAAAAEAAAAAAAAAAAAAAFAFqweokQAwAAAAAAAAAAABAAAAAAAAAAAAAADsBKsH AAAAAAMAAAAGAAAABgAAAAkAAAAMAAAADAAAAAwAAAAMAAAADAAAAA4AAAAOAAAADgAAABEA AAAABgAAvgkAAJAKAADNDQAAkhEAAEwlAAAOJgAAiCYAAOYmAAAJAAAADAAAAA0AAAAOAAAA EAAAABQAAAAVAAAAFwAAAAAGAAA+EAAAEiYAAOAmAADmJgAACgAAAA8AAAAWAAAAGAAAAAAG AADkJgAACwAAAD0BAABJAQAAvAEAAL4BAADKAQAA0AEAANMBAADfAQAABQIAAAgCAAAUAgAA JQIAACgCAAA0AgAASgIAAE0CAABZAgAAgAIAAJUCAAChAgAA4QQAAP0EAAAJBQAA6AYAAAYH AAASBwAAkAcAAJMHAACfBwAApQcAAMIHAADOBwAA3wcAAOsHAAD3BwAACwgAACoJAABWCQAA bAkAAG8JAAB7CQAAgQkAAJkJAAClCQAAqQkAAK0JAAC5CQAAwAkAAMQJAADQCQAA1wkAAO8J AAATRhT/lYwTRhT/lYQTRhT/lYwTRhT/lYwTRhT/lYwTRhT/lYwTRhT/lYwTRhT/lYwTRhT/ lYwTRhT/lYQTRhT/lYwTRhT/lYwTWBT/FYQTRhT/lYQTRhT/lYwTRhT/lYwTRhT/lYwBAAAA //////////8AAAAAAAAAAAAAAAAPAADwdAAAAAAABvAgAAAAAgwAAAMAAAAJAAAAAgAAAAIA AAAJAAAAAQAAAAEAAAAfAAHwLAAAAFIAB/AkAAAABQXx4ZCe9tQTHLvvOsxK5u9a/wCmdQAA AAAAAP////8AAAAAQAAe8RAAAAD//wAAAAD/AICAgAD3AAAQAA8AAvCSAAAAIAAI8AgAAAAB AAAACAQAAA8AA/AwAAAADwAE8CgAAAABAAnwEAAAAAAAAAAAAAAAAAAAAAAAAAACAArwCAAA AAAEAAAFAAAADwAE8EIAAAASAArwCAAAAAEEAAAADgAAUwAL8B4AAAC/AQAAEADLAQAAAAD/ AQAACAAEAwkAAAA/AwEAAQAAABHwBAAAAAEAAAABDwAC8EgAAAAQAAjwCAAAAAEAAAAACAAA DwAD8DAAAAAPAATwKAAAAAEACfAQAAAAAAAAAAAAAAAAAAAAAAAAAAIACvAIAAAAAAgAAAUA AADvCQAA//8MAAAABQBUAGUAeAB0ADgAFABGAHUAbgBrAHQAaQBvAG4AcwBiAGUAegBlAGkA YwBoAG4AdQBuAGcABQBUAGUAeAB0ADQABQBUAGUAeAB0ADUABQBUAGUAeAB0ADYABQBUAGUA eAB0ADMACwBmAGEAYwBoAGwAXwBRAHUAYQBsAGkACgBwAGUAcgBzAF8AUQB1AGEAbABpAAUA VABlAHgAdAA5AAoARQBuAGQAZQBfAEYAcgBpAHMAdAAIAEsAZQBuAG4AegBhAGgAbAAGAFQA ZQB4AHQAMQAwAD4BAADUAQAACQIAACkCAABOAgAAlgIAAP4EAAAHBwAAlAcAAMMHAADsBwAA cAkAAPAJAAAAAAAAAQAAAAIAAAADAAAABAAAAAUAAAAGAAAABwAAAAgAAAAJAAAACgAAAAsA AAC9AQAABgIAACYCAABLAgAAgQIAAOIEAADpBgAAkQcAAKYHAADgBwAADAgAAIIJAADwCQAA //8CAAAABgD+mJIAEAABANz9IQAGAP+YkgARAAEAbCIkAhEJAAARCQAA8AkAAAAAAAACAAEA AAACABUJAAAVCQAA8AkAAAAAAAABAAAAAgAAADgAAAABAAAAKoB1cm46c2NoZW1hcy1taWNy b3NvZnQtY29tOm9mZmljZTpzbWFydHRhZ3MEgENpdHkAgDkAAAACAAAAKoB1cm46c2NoZW1h cy1taWNyb3NvZnQtY29tOm9mZmljZTpzbWFydHRhZ3MFgHBsYWNlAIAMAAABqCpQAwAAAAAC AAAAAAABAAAAAAAAAAAA2wcAAOAHAADdCQAA3QkAAN8JAADfCQAA4AkAAOAJAADiCQAA4wkA AOUJAADmCQAA6AkAAOkJAADrCQAA7AkAAPAJAAAHAAQABwAEAAcABAACAAQABwAEAAcABAAH AAQABwACAAcAAAAAANMHAADhBwAA3QkAAN0JAADfCQAA3wkAAOAJAADgCQAA4gkAAOMJAADl CQAA5gkAAOgJAADpCQAA6wkAAOwJAADwCQAABwAEAAcABAAHAAQAAgAEAAcABAAHAAQABwAE AAcAAgAHAAAAAADTAQAABwIAAJkEAADkBAAAqAcAANMHAADhBwAA6AgAAPgIAABvCQAA3AkA AN0JAADdCQAA3wkAAN8JAADgCQAA4AkAAOIJAADjCQAA5QkAAOYJAADoCQAA6QkAAOsJAADs CQAA8AkAAAcABQAHAAUABwAFAAQABwAFAAcABQAHAAQABwAEAAIABAAHAAQABwAEAAcABAAH AAIABwAAAAAA0wEAAPgBAABDAgAARAIAAE0CAABpAgAAagIAAGwCAADCBwAA4AcAAOsHAAAA CAAAAQgAAAMIAAAECAAADAgAAFUJAABtCQAAmQkAAKoJAACtCQAAwQkAAMQJAADYCQAA3QkA AN0JAADfCQAA3wkAAOAJAADgCQAA4gkAAOMJAADlCQAA5gkAAOgJAADpCQAA6wkAAOwJAADw CQAABwADAAcAAwAHAAMABwADAAcABAAHAAMABwADAAcAAwAHAAMABwADAAcAAwAHAAMABwAE AAcABAACAAQABwAEAAcABAAHAAQABwACAAcADQB8////Ho3ACf8P/w//D/8P/w//D/8P/w// DwEAff///3ZZ1rr/D/8P/w//D/8P/w//D/8P/w8BAH7///+4QZBp/w//D/8P/w//D/8P/w// D/8PAQB/////tsJ8Of8P/w//D/8P/w//D/8P/w//DwEAgP///xz7jrb/D/8P/w//D/8P/w// D/8P/w8BAIH///9WMXZC/w//D/8P/w//D/8P/w//D/8PAQCC////yF1uF/8P/w//D/8P/w// D/8P/w//DwEAg////0wXRhz/D/8P/w//D/8P/w//D/8P/w8BAIj///8kL7qi/w//D/8P/w// D/8P/w//D/8PAQCJ////OJhMEP8P/w//D/8P/w//D/8P/w//DwEA1xMcFejfRvr/D/8P/w// D/8P/w//D/8P/w8AAAcRBDHo30b6/w//D/8P/w//D/8P/w//D/8PEABRe1Ndzt8UvP8P/w// D/8P/w//D/8P/w//DxAAAQAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAABgAAA+E1AURhJj+FcYF AAHUBQZehNQFYISY/gIAAAAuAAEAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAYAAAPhLkEEYSY /hXGBQABuQQGXoS5BGCEmP4CAAAALgABAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAGAAAD4Se AxGEmP4VxgUAAZ4DBl6EngNghJj+AgAAAC4AAQAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAABgA AA+EgwIRhJj+FcYFAAGDAgZehIMCYISY/gIAAAAuAAEAAAAXAAAAAAAAAAAAAAAAAAAAAAAA AAsYAAAPhNQFEYSY/hXGBQAB1AUGXoTUBWCEmP5PSgEAUUoBAG8oAAEAt/ABAAAAFwAAAAAA AAAAAAAAAAAAAAAAAAALGAAAD4S5BBGEmP4VxgUAAbkEBl6EuQRghJj+T0oBAFFKAQBvKAAB ALfwAQAAABcAAAAAAAAAAAAAAAAAAAAAAAAACxgAAA+EngMRhJj+FcYFAAGeAwZehJ4DYISY /k9KAQBRSgEAbygAAQC38AEAAAAXAAAAAAAAAAAAAAAAAAAAAAAAAAsYAAAPhIMCEYSY/hXG BQABgwIGXoSDAmCEmP5PSgEAUUoBAG8oAAEAt/ABAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAA GAAAD4RoARGEmP4VxgUAAWgBBl6EaAFghJj+AgAAAC4AAQAAABcAAAAAAAAAAAAAAAAAAAAA AAAACxgAAA+EaAERhJj+FcYFAAFoAQZehGgBYISY/k9KAQBRSgEAbygAAQC38AEAAAAAAAEA AAAAAAAAAAAAAAAAAAAAAAMYAAAPhNACEYSY/hXGBQAB0AIGXoTQAmCEmP5vKAACAAAALgAB AAAABAABAAAAAAAAAAAAAAAAAAAAAAAKGAAAD4SgBRGEmP4VxgUAAaAFBl6EoAVghJj+h2gA AAAAiEgAAAIAAQAuAAEAAAACAgEAAAAAAAAAAAAAAAAAAAAAAAoYAAAPhHAIEYRM/xXGBQAB cAgGXoRwCGCETP+HaAAAAACISAAAAgACAC4AAQAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAChgA AA+EQAsRhJj+FcYFAAFACwZehEALYISY/odoAAAAAIhIAAACAAMALgABAAAABAABAAAAAAAA AAAAAAAAAAAAAAAKGAAAD4QQDhGEmP4VxgUAARAOBl6EEA5ghJj+h2gAAAAAiEgAAAIABAAu AAEAAAACAgEAAAAAAAAAAAAAAAAAAAAAAAoYAAAPhOAQEYRM/xXGBQAB4BAGXoTgEGCETP+H aAAAAACISAAAAgAFAC4AAQAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAChgAAA+EsBMRhJj+FcYF AAGwEwZehLATYISY/odoAAAAAIhIAAACAAYALgABAAAABAABAAAAAAAAAAAAAAAAAAAAAAAK GAAAD4SAFhGEmP4VxgUAAYAWBl6EgBZghJj+h2gAAAAAiEgAAAIABwAuAAEAAAACAgEAAAAA AAAAAAAAAAAAAAAAAAoYAAAPhFAZEYRM/xXGBQABUBkGXoRQGWCETP+HaAAAAACISAAAAgAI AC4AAQAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAAxgAAA+E0AIRhJj+FcYFAAHQAgZehNACYISY /m8oAAIAAAAuAAEAAAAEgAEAAAAAAAAAAAAAAAAAAAAAAAoYAAAPhKAFEYSY/hXGBQABoAUG XoSgBWCEmP6HaAAAAACISAAAAgABAC4AAQAAAAKCAQAAAAAAAAAAAAAAAAAAAAAAChgAAA+E cAgRhEz/FcYFAAFwCAZehHAIYIRM/4doAAAAAIhIAAACAAIALgABAAAAAIABAAAAAAAAAAAA AAAAAAAAAAAKGAAAD4RACxGEmP4VxgUAAUALBl6EQAtghJj+h2gAAAAAiEgAAAIAAwAuAAEA AAAEgAEAAAAAAAAAAAAAAAAAAAAAAAoYAAAPhBAOEYSY/hXGBQABEA4GXoQQDmCEmP6HaAAA AACISAAAAgAEAC4AAQAAAAKCAQAAAAAAAAAAAAAAAAAAAAAAChgAAA+E4BARhEz/FcYFAAHg EAZehOAQYIRM/4doAAAAAIhIAAACAAUALgABAAAAAIABAAAAAAAAAAAAAAAAAAAAAAAKGAAA D4SwExGEmP4VxgUAAbATBl6EsBNghJj+h2gAAAAAiEgAAAIABgAuAAEAAAAEgAEAAAAAAAAA AAAAAAAAAAAAAAoYAAAPhIAWEYSY/hXGBQABgBYGXoSAFmCEmP6HaAAAAACISAAAAgAHAC4A AQAAAAKCAQAAAAAAAAAAAAAAAAAAAAAAChgAAA+EUBkRhEz/FcYFAAFQGQZehFAZYIRM/4do AAAAAIhIAAACAAgALgABAAAAAAABAAAAAAAAAAAAAAAAAAAAAAADGAAAD4TQAhGEmP4VxgUA AdACBl6E0AJghJj+bygAAgAAAC4AAQAAAASAAQAAAAAAAAAAAAAAAAAAAAAAABgAAA+EoAUR hJj+FcYFAAGgBQZehKAFYISY/gIAAQAuAAEAAAACggEAAAAAAAAAAAAAAAAAAAAAAAAYAAAP hHAIEYRM/xXGBQABcAgGXoRwCGCETP8CAAIALgABAAAAAIABAAAAAAAAAAAAAAAAAAAAAAAA GAAAD4RACxGEmP4VxgUAAUALBl6EQAtghJj+AgADAC4AAQAAAASAAQAAAAAAAAAAAAAAAAAA AAAAABgAAA+EEA4RhJj+FcYFAAEQDgZehBAOYISY/gIABAAuAAEAAAACggEAAAAAAAAAAAAA AAAAAAAAAAAYAAAPhOAQEYRM/xXGBQAB4BAGXoTgEGCETP8CAAUALgABAAAAAIABAAAAAAAA AAAAAAAAAAAAAAAAGAAAD4SwExGEmP4VxgUAAbATBl6EsBNghJj+AgAGAC4AAQAAAASAAQAA AAAAAAAAAAAAAAAAAAAAABgAAA+EgBYRhJj+FcYFAAGAFgZehIAWYISY/gIABwAuAAEAAAAC ggEAAAAAAAAAAAAAAAAAAAAAAAAYAAAPhFAZEYRM/xXGBQABUBkGXoRQGWCETP8CAAgALgAN AAAAUXtTXQAAAAAAAAAAAAAAAAcRBDEAAAAAAAAAAAAAAADXExwVAAAAAAAAAAAAAAAAif// /wAAAAAAAAAAAAAAAIP///8AAAAAAAAAAAAAAACC////AAAAAAAAAAAAAAAAgf///wAAAAAA AAAAAAAAAID///8AAAAAAAAAAAAAAACI////AAAAAAAAAAAAAAAAf////wAAAAAAAAAAAAAA AH7///8AAAAAAAAAAAAAAAB9////AAAAAAAAAAAAAAAAfP///wAAAAAAAAAAAAAAAP////// /////////////////////////////////////////////////////////////////w0AAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP//DQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAS APQLbs4ZAAcMGwAHDA8ABwwZAAcMGwAHDA8ABwwZAAcMGwAHDBIADwAHBBkABwQbAAcEDwAH BBkABwQbAAcEDwAHBBkABwQbAAcEogAAAAQAAAAIAAAA5QAAAAAAAAChAAAAMCYAAMMqAgBq KAQA8AQFABBTDgCCSBAA3mAQALQKFQCwaRoANnUaAMIKHQDvIx8AlhghAARrIQAaRCMAFHsn AJQuKQArSSkAhmUpAKkTKgBiGyoA9iwqAFoKLADmSywAXSAtALxwLQBkQS4AuGMuAJAaLwBM ajEAM2sxALJeNwCtTDgAYDk8AGwcPQCpV0YA2RBHALBzSQALC0oA6UFKAARkSgBVeEoAuE9M AJoPUwBuMlMAUDhTAMpuUwCCK1YAdkZZAPBKWQAYbFkAkBxaAEsFXQAaMl0A1CpfADRaXwC5 V2AAXWFhAPgeYgCNIGIAbgBlANlMZQDBbWUAU2xnALkOaACISmgAnhRsAIRsbAB+QG4AAG5u AJ8IcAD2XnEA0xxzAAFFdQAoc3UA+2p5ABx8eQAFB3sAJAh7AKoaewC+QnsAsHB7AMhxewDT LIEAf3KCAKpmgwCzH4cAxT2HAG0niAAOeYgAqT2LAO0DjACaRIwA6lKMAKA9jQB4E48AAReR AERtkQCuL5MAvjKTAKVHlQAzV5YAJguXADwZmQDRQJkAYxaaACpPmwCDUJsAggWcAABCnAAr YZ8AIkGgAAtzpAAQA6YAzimmAGUbqwCeWawALnutAHgSsADVRrYAECG4AF9RuQD0B74ArQS/ ACxUwwB8DMUA3H7GADkHxwDBNMcA+TrHAN1BxwB/SskAfDXKACIDywD7WswAPmXMAKhbzgAO ItIAz0nSADBv0wD4MtQAvG/UAKV42AAYINwAmTjdACcm5QDQMOgAOTzqAMVf6gBpeOwAqw/t AEgw7QCGRO0AmxHwALVF8gDtVPcAAkv4ACRm+QDLbfkAUkX6AJsH/gBsUv4AAAAAAAEAAAAC AAAAhQkAAIYJAADcCQAA8AkAAAAAAAARMxUACAAAAAIBAAAWAQAAAQAAAP9ADYABAN8HAADf BwAA4B56AQEAAQDfBwAAAQAAAN8HAAAAAAAAAkwAAAAAAAAAywEAAMsCAADLBgAAywcAAHwJ AADvCQAAQAAAEABAAABAAAAKAAAAAEAAABgAQAAAQAAAEAAAAABAAABIAEAAAEAAACYAAAAA //8BAAAABwBVAG4AawBuAG8AdwBuAP//AQAIAAAAAAAAAAAAAAD//wEAAAAAAP//AAACAP// AAAAAP//AAACAP//AAAAAAYAAABHFpABAAACAgYDBQQFAgMEh3oAIAAAAIAIAAAAAAAAAP8B AAAAAAAAVABpAG0AZQBzACAATgBlAHcAIABSAG8AbQBhAG4AAAA1FpABAgAFBQECAQcGAgUH AAAAAAAAABAAAAAAAAAAAAAAAIAAAAAAUwB5AG0AYgBvAGwAAAAzJpABAAACCwYEAgICAgIE h3oAIAAAAIAIAAAAAAAAAP8BAAAAAAAAQQByAGkAYQBsAAAARzWQAYAKAgIGCQQCBQgDBL8C AKD7/MdoEAAAAAAAAACfAAIAAAAAAE0AUwAgAE0AaQBuAGMAaABvAAAALf8z/yAADmYdZwAA NyaQAQAAAgsGBAMFBAQCBIcCACAAAAAAAAAAAAAAAACfAQAAAAAAAFYAZQByAGQAYQBuAGEA AABhIJABABcAAAAAAAAAAAAAAwAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAVABKAE4ASwBFAEUA KwBTAHcAaQBzAHMANwAyADEAQgBUAC0AVABoAGkAbgAAAFMAdwBpAHMAcwAAACIABAAxCIwa APDFAgAAqQEAAAAAImzxhuuq8aaRU+ymAwAAAAAAWQEAAIQIAAABAAUAAAAEAAMQEgAAAFkB AACECAAAAQAFAAAAEgAAAAAAAAAhAwDwEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAACKBTUEtAC0AIGBEjQAABAAGQBkAAAAGQAAANgJAADYCQAAAAAAAIMQ JWoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AgAAAAAAAAAAAAgyg3EA8BAACAD//QEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAASFgAAAAA CfD/DwEAAT8AAOQEAAD///9/////f////3////9/////f////3////9/0UCZACIEAAAqAAAA AAAAAAAAAAAAAAAAAAD//xIAAAAAAAAAEgBBAG4AZgBvAHIAZABlAHIAdQBuAGcAcwBwAHIA bwBmAGkAbAAAAAAAAAANAEEAZABtAGkAbgBpAHMAdAByAGEAdABvAHIAHgBLAGEAcgBsAC0A RgByAGEAbgB6AGUAbgBzAC0AVQBuAGkAdgBlAHIAcwBpAHQA5AB0ACAARwByAGEAegAAAAAA AAAAAAAAAAAAAAAAAAAAAEAAAAAGAAAADQAAAAAADAABAAwAAgAMAAMADAAEAAwABQAMAAYA DAAHAAwACAAMAAkADAAKAAwACwAMAAwADAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/v8AAAUBAgAAAAAAAAAAAAAAAAAAAAAAAQAAAOCF n/L5T2gQq5EIACsns9kwAAAAnAEAABEAAAABAAAAkAAAAAIAAACYAAAAAwAAALQAAAAEAAAA wAAAAAUAAADYAAAABgAAAOQAAAAHAAAA8AAAAAgAAAAEAQAACQAAACwBAAASAAAAOAEAAAsA AABYAQAADAAAAGQBAAANAAAAcAEAAA4AAAB8AQAADwAAAIQBAAAQAAAAjAEAABMAAACUAQAA AgAAAOQEAAAeAAAAFAAAAEFuZm9yZGVydW5nc3Byb2ZpbAAAHgAAAAQAAAAAAAAAHgAAABAA AABBZG1pbmlzdHJhdG9yAAAAHgAAAAQAAAAAAAAAHgAAAAQAAAAAAAAAHgAAAAwAAABOb3Jt YWwuZG90AAAeAAAAIAAAAEthcmwtRnJhbnplbnMtVW5pdmVyc2l05HQgR3JhegAAHgAAAAQA AAAzAAAAHgAAABgAAABNaWNyb3NvZnQgT2ZmaWNlIFdvcmQAAABAAAAAAC4Ph2yYywFAAAAA ACSbTDezywFAAAAAAJLw+Fe5ywEDAAAAAQAAAAMAAABZAQAAAwAAAIQIAAADAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAP7/AAAFAQIAAAAAAAAAAAAAAAAAAAAAAAIAAAAC1c3VnC4bEJOX CAArLPmuRAAAAAXVzdWcLhsQk5cIACss+a5AAQAA/AAAAAwAAAABAAAAaAAAAA8AAABwAAAA BQAAAHwAAAAGAAAAhAAAABEAAACMAAAAFwAAAJQAAAALAAAAnAAAABAAAACkAAAAEwAAAKwA AAAWAAAAtAAAAA0AAAC8AAAADAAAANsAAAACAAAA5AQAAB4AAAAEAAAAAAAAAAMAAAASAAAA AwAAAAUAAAADAAAA2AkAAAMAAAAPJwsACwAAAAAAAAALAAAAAAAAAAsAAAAAAAAACwAAAAAA AAAeEAAAAQAAABMAAABBbmZvcmRlcnVuZ3Nwcm9maWwADBAAAAIAAAAeAAAABgAAAFRpdGVs AAMAAAABAAAAAAAAvAAAAAMAAAAAAAAAIAAAAAEAAAA4AAAAAgAAAEAAAAABAAAAAgAAAAwA AABfUElEX0hMSU5LUwACAAAA5AQAAEEAAAB0AAAABgAAAAMAAAAsAFcAAwAAACQAAAADAAAA AAAAAAMAAAAFAAAAHwAAAB0AAABtAGEAaQBsAHQAbwA6AGIAZQB3AGUAcgBiAHUAbgBnAEAA dQBuAGkALQBnAHIAYQB6AC4AYQB0AAAAAAAfAAAAAQAAAAAADwUAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAABAAAAAgAAAAMAAAAEAAAABQAAAAYAAAAHAAAACAAAAAkAAAAKAAAACwAAAAwA AAANAAAADgAAAA8AAAAQAAAAEQAAABIAAAATAAAAFAAAABUAAAAWAAAAFwAAABgAAAAZAAAA /v///xsAAAAcAAAAHQAAAB4AAAAfAAAAIAAAACEAAAD+////IwAAACQAAAAlAAAAJgAAACcA AAAoAAAAKQAAACoAAAArAAAALAAAAC0AAAAuAAAALwAAADAAAAAxAAAAMgAAADMAAAA0AAAA NQAAADYAAAA3AAAA/v///zkAAAA6AAAAOwAAADwAAAA9AAAAPgAAAD8AAAD+////QQAAAEIA AABDAAAARAAAAEUAAABGAAAARwAAAP7////9////SgAAAEsAAABWAAAA/v///04AAABPAAAA UAAAAFEAAABSAAAAUwAAAFQAAABVAAAA/v////7///////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////1IAbwBvAHQAIABFAG4AdAByAHkA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAWAAUB//////// //8DAAAABgkCAAAAAADAAAAAAAAARgAAAAAAAAAAAAAAALChEf9XucsBTQAAAAARAAAAAAAA RABhAHQAYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAoAAgH///////////////8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAaAAAAABAAAAAAAAAxAFQAYQBiAGwAZQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADgACAAEAAAD//////////wAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACIAAADMKwAAAAAAAFcAbwByAGQARABvAGMA dQBtAGUAbgB0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAaAAIB BgAAAAUAAAD/////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADQy AAAAAAAABQBTAHUAbQBtAGEAcgB5AEkAbgBmAG8AcgBtAGEAdABpAG8AbgAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAACgAAgH///////////////8AAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAA4AAAAABAAAAAAAAAFAEQAbwBjAHUAbQBlAG4AdABTAHUAbQBtAGEA cgB5AEkAbgBmAG8AcgBtAGEAdABpAG8AbgAAAAAAAAAAAAAAOAACAQQAAAD//////////wAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAEAAAAAAAAE0AYQBjAHIA bwBzAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAOAAEBAgAAAA0AAAALAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADQSQP/V7nLAdBJA/9XucsB AAAAAAAAAAAAAAAAVgBCAEEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAAQD//////////wkAAAAAAAAAAAAAAAAAAAAAAAAA AAAAANBJA/9XucsB0EkD/1e5ywEAAAAAAAAAAAAAAABkAGkAcgAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAACAP////////// /////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKAgAAAAAAAFQA aABpAHMARABvAGMAdQBtAGUAbgB0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAaAAIBCAAAAAoAAAD/////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAACQAAAJwDAAAAAAAAXwBWAEIAQQBfAFAAUgBPAEoARQBDAFQAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABoAAgD///////////////8AAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYAAAAyggAAAAAAABQAFIATwBKAEUAQwBUAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAACAQcA AAAMAAAA/////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADwAAAA6AQAA AAAAAAEAAAACAAAAAwAAAAQAAAAFAAAABgAAAAcAAAAIAAAA/v///woAAAALAAAADAAAAA0A AAAOAAAADwAAABAAAAARAAAAEgAAABMAAAAUAAAAFQAAABYAAAAXAAAA/v///xkAAAAaAAAA GwAAABwAAAAdAAAAHgAAAB8AAAAgAAAAIQAAACIAAAAjAAAAJAAAACUAAAAmAAAAJwAAACgA AAApAAAAKgAAACsAAAAsAAAALQAAAC4AAAAvAAAAMAAAADEAAAAyAAAAMwAAADQAAAA1AAAA NgAAADcAAAA4AAAAOQAAADoAAAA7AAAA/v///z0AAAA+AAAAPwAAAEAAAAD+/////v///0MA AAD+//////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// ////////////////////////////////////////AQaygAEABAAAAAEAMCoCApAJAHAUBkgD AIICAGTkBAQABwAcAFByb2plY3QFUQAoAABAAhQGAhQ9rQIKBwJsARQIBhIJAhKAVu1nTQoA DAJKEjwCChYAAXJzdGQQb2xlPgIZcwB0AABkAG8AbABlUAANAGgAJV4AAyoAXEd7MDAwMjCw NDMwLQAIBARDAAoDAg4BEjAwNDZ9IwAyLjAjMCNDOgBcV0lORE9XUwBcc3lzdGVtMwAyXFNU RE9MRUAyLlRMQiMACCAAQXV0b21hdGkcb24AYAACg0VOb3IobWFsg0VOgENyAIhtAGGARg4A IIAR4gmAASpcQwMSCgaBbQQIAIMhT2ZmaWMRhGdPAGaAAGkAYxWCZ6CAH5aCIUd7MgBERjhE MDRDLQA1QkZBLTEwMUBCLUJERTWAZ0FqQYBlNIAFMohngLpnAHJhbW1lXEdlAG1laW5zYW1l ACBEYXRlaWVuAFxNaWNyb3NvAGZ0IFNoYXJlAGRcT0ZGSUNFADEyXE1TTy5EmExMI4cQg04g MYCUCCBPYoHkIExpYrByYXJ5ACaAAA8Ce4gBABPCAbAMGcJlAFRoaXNEb2N1gG1lbnRHABjA CYJUQGdpAHMAREBJiGMAdcBJZQBuQG8qGs4LMtoLHMASAACqSEIBMcKJ/0CbHkICRQEFLMIh bYwiQggrBUIBEEIBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAARYBAADwAAAAngIAANQAAADaAQAA/////6UCAAD5AgAAAAAAAAEAAACw DG2MAAD//6MBAACIAAAAtgD//wEBAAAAAP////8AAAAA////////AAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAMAAAAFAAAABwAA AP//////////AQEIAAAA/////3gAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAD//wAAAABNRQAA////////AAAAAP//AAAAAP//AQEAAAAA3wD//wAAAAAMAP////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// ////////////////KAAAAAIAUyL/////AAABAFMQ/////wAAAQBTIv////8AAAAAAjz///// AAD//wEBAAAAAAEAKAAxAE4AbwByAG0AYQBsAC4AVABoAGkAcwBEAG8AYwB1AG0AZQBuAHQA AAAAAP////8BATgAAAACgP7//////xAA//8oAAAAAgH//wAAAAAAAAAA//////////8AAAAA HQAAACUAAAD/////SAAAAAAA//8AAAEAAAAAAAAAAAD///////////////8AAAAA//////// //////////////////8AAAAA//////////////////////////8AAAAAAAAAAP////////// AAAAAP///////////////////////////////////////wAAAAAAAN8AAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD+ygEAAAD/ ////AQEIAAAA/////3gAAAD/////AAABmbAAQXR0cmlidXQAZSBWQl9OYW0AZSA9ICJUaGkA c0RvY3VtZW4QdCINCgqMQmFzAQKMMU5vcm1hbAIuGVZHbG9iYWwhAapTcGFjAWxGYQhsc2UM okNyZWEQdGFibBUfUHJlIGRlY2xhAAZJZBEAnlRydQ1CRXhwCG9zZRQcVGVtcABsYXRlRGVy aQJ2FSRDdXN0b20MaXqEQ4MxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA zGGIAAABAP8HDAAACQQAAOQEAQAAAAAAAAAAAAEABQACAPoAKgBcAEcAewAwADAAMAAyADAA NABFAEYALQAwADAAMAAwAC0AMAAwADAAMAAtAEMAMAAwADAALQAwADAAMAAwADAAMAAwADAA MAAwADQANgB9ACMANAAuADAAIwA5ACMAQwA6AFwAUABSAE8ARwBSAEEAfgAxAFwARwBFAE0A RQBJAE4AfgAxAFwATQBJAEMAUgBPAFMAfgAxAFwAVgBCAEEAXABWAEIAQQA2AFwAVgBCAEUA NgAuAEQATABMACMAVgBpAHMAdQBhAGwAIABCAGEAcwBpAGMAIABGAG8AcgAgAEEAcABwAGwA aQBjAGEAdABpAG8AbgBzAAAAAAAAAAAAAAAAAAgBKgBcAEcAewAwADAAMAAyADAAOQAwADUA LQAwADAAMAAwAC0AMAAwADAAMAAtAEMAMAAwADAALQAwADAAMAAwADAAMAAwADAAMAAwADQA NgB9ACMAOAAuADQAIwAwACMAQwA6AFwAUAByAG8AZwByAGEAbQBtAGUAXABNAGkAYwByAG8A cwBvAGYAdAAgAE8AZgBmAGkAYwBlAFwATwBmAGYAaQBjAGUAMQAyAFwATQBTAFcATwBSAEQA LgBPAEwAQgAjAE0AaQBjAHIAbwBzAG8AZgB0ACAAVwBvAHIAZAAgADEAMgAuADAAIABPAGIA agBlAGMAdAAgAEwAaQBiAHIAYQByAHkAAAAAAAAAAAAAAAAAvAAqAFwARwB7ADAAMAAwADIA MAA0ADMAMAAtADAAMAAwADAALQAwADAAMAAwAC0AQwAwADAAMAAtADAAMAAwADAAMAAwADAA MAAwADAANAA2AH0AIwAyAC4AMAAjADAAIwBDADoAXABXAEkATgBEAE8AVwBTAFwAcwB5AHMA dABlAG0AMwAyAFwAUwBUAEQATwBMAEUAMgAuAFQATABCACMATwBMAEUAIABBAHUAdABvAG0A YQB0AGkAbwBuAAAAAAAAAAAAAAAAABIAKgBcAEMATgBvAHIAbQBhAGwAEgAqAFwAQwBOAG8A cgBtAGEAbABW7WdNCAAAAAAAAAAsASoAXABHAHsAMgBEAEYAOABEADAANABDAC0ANQBCAEYA QQAtADEAMAAxAEIALQBCAEQARQA1AC0AMAAwAEEAQQAwADAANAA0AEQARQA1ADIAfQAjADIA LgA0ACMAMAAjAEMAOgBcAFAAcgBvAGcAcgBhAG0AbQBlAFwARwBlAG0AZQBpAG4AcwBhAG0A ZQAgAEQAYQB0AGUAaQBlAG4AXABNAGkAYwByAG8AcwBvAGYAdAAgAFMAaABhAHIAZQBkAFwA TwBGAEYASQBDAEUAMQAyAFwATQBTAE8ALgBEAEwATAAjAE0AaQBjAHIAbwBzAG8AZgB0ACAA TwBmAGYAaQBjAGUAIAAxADIALgAwACAATwBiAGoAZQBjAHQAIABMAGkAYgByAGEAcgB5AAAA AAAAAAAAAAAAAAEAAgAEAAQCAAAGAgEACAIAAAoCAQAQAv///////wAAAAD//wAAVu1nTQoA //////////////////////////////////////////////////////////////////////// /////wAA/////wEAAAAAAAAAAAAAAAAAAAAAAAAAsAwBABgAVABoAGkAcwBEAG8AYwB1AG0A ZQBuAHQAFAAwADMANABkADYANwBlAGQANQA2AP//EwIYAFQAaABpAHMARABvAGMAdQBtAGUA bgB0AP//bYwAAAAAAAAAAgAAAP8CAAD///////8BASACAAD///////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// /wACAAD///////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////// /////////+95iYPyXMZGkRHDlytdfV7/////AQAAAP////9gAAAAgAAAAAAADQEOAP8AzCUA AAQEV29yZLVrEAADBFZCQffiEAAFBFdpbjE2wX4QAAUEV2luMzIHfxAAAwRNYWOzshAABARW QkE2rSMQAAgEUHJvamVrdDHSQRAABgRzdGRvbGWTYBAABwRQcm9qZWN0La4QAAwEVGhpc0Rv Y3VtZW50PJ4QAAmAAAD/AwMAX0V2YWx1YXRlGNkQAAYATm9ybWFs39gQAAaECAD/AwMAT2Zm aWNlFXUQAAgERG9jdW1lbnRq0xAAAv//AQFUAAAA////////FgIDAP//GAIEAP////////// AAIBAP//AgIAAP//////////////////////////////////////////DgICAP//EAL///// EwIAAA0ADQAOAAAAAQASAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAASUQ9IntEQzAyNTgyOS1CMDhF LTQyMDctODI1QS0xMjU3MDJCRjU4NzN9Ig0KRG9jdW1lbnQ9VGhpc0RvY3VtZW50LyZIMDAw MDAwMDANCk5hbWU9IlByb2plY3QiDQpIZWxwQ29udGV4dElEPSIwIg0KVmVyc2lvbkNvbXBh dGlibGUzMj0iMzkzMjIyMDAwIg0KQ01HPSJBMEEyMzkzRjNEM0YzRDNGM0QzRjNEIg0KRFBC PSI0MDQyRDlEQURBREFEQURBIg0KR0M9IkUwRTI3OTdBN0E3QTdBODUiDQoNCltIb3N0IEV4 dGVuZGVyIEluZm9dDQomSDAwMDAwMDAxPXszODMyRDY0MC1DRjkwLTExQ0YtOEU0My0wMEEw QzkxMTAwNUF9O1ZCRTsmSDAwMDAwMDAwDQoAAAAAAABUaGlzRG9jdW1lbnQAVABoAGkAcwBE AG8AYwB1AG0AZQBuAHQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQD+/wMKAAD///// BgkCAAAAAADAAAAAAAAARh8AAABNaWNyb3NvZnQgT2ZmaWNlIFdvcmQtRG9rdW1lbnQACgAA AE1TV29yZERvYwAQAAAAV29yZC5Eb2N1bWVudC44APQ5snEAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAUABSAE8A SgBFAEMAVAB3AG0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAABQAAgD///////////////8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AABBAAAAKQAAAAAAAAABAEMAbwBtAHAATwBiAGoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgACAP///////////////wAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAEIAAABxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA//////// ////////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAD///////////////8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAA--------------050203040904090002020506-- ========================================================================Date: Fri, 4 Feb 2011 17:17:28 +0000 Reply-To: Karen Lythe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Karen Lythe <[log in to unmask]> Subject: wierd looking images Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="=__PartE5C9E9B8.0__=" Message-ID: <[log in to unmask]> This is a MIME message. If you are reading this text, you may want to consider changing to a mail reader or gateway that understands how to properly handle MIME multipart messages. --=__PartE5C9E9B8.0__Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Content-Disposition: inline Hi, I've ran some analyses using the following steps: Realign: estimate and reslice - mean image written only Coregister: estimate - ref image is mean, and source image is individuals structural Segment Normalise:write - paramter file is segmented structural - images to write are realigned images - writing options - voxel size is [3.75x3.75x3.8] (i changed this to the size of the voxels that we used in our scanning sequence) Smoothing - 4mm All other options are left as default. Each individual person's data looks fine - but when I look at a really low threshold of the group map the BOLD signal doesn't fill the whole brain, as though my data has not been normalised correctly to the MNI brain (see image attached - overlay used is the single_subj_T1 from the canonical folder). Does anyone know what is happening and how to remedy this? Any comments/suggestions would be much appreciated. Many thanks Karen Karen Lythe PhD Wales Institute of Cognitive Neuroscience School of Psychology Cardiff University Tower Building Park Place CF10 3AT UK Tel: 029 208 70716 Email: [log in to unmask] --=__PartE5C9E9B8.0__Content-Type: application/pdf; name="spm_2011Feb04.pdf" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="spm_2011Feb04.pdf" JVBERi0xLjIKJcfsj6IKNSAwIG9iago8PC9MZW5ndGggNiAwIFIvRmlsdGVyIC9GbGF0ZURlY29k ZT4+CnN0cmVhbQp4nM1ZyXIcRRDVETdgjlzrZsl4emrvbsJwkFcZG4xmInywHIRCHksDM9pmZItw cGfnB/gBMIYDB34Bs5458gcsBq5k7dXdo9G0HQZJtjSd/TLz1avM6qrWDsIpQVh9299rw2QnkTlP ieQoE1KmMkNDb+EFw2iQMJ7j0meHHCQbyTW0mewgmhUizZkOSiXTd02UzHhCpvZyjs5uJa/D907C wZbJIk8Zh4SCEOEvB+YyzwR8VDjzyaTCqSwKSgp0IXxch3hEDwvZX2tDtNiFhAUiBHVvJXADteAf RoKlAkksUgaZusNkvnP1ykL3TcCCqwFTBrRh9Bh1bybzw/62vU/s/ZagKTBqEWpwCnUdg+kU6Ao/ WkAMwxe5oRzPdWG8Ru3lC/bD7sGMCUVEqiwGCTmFUNmEIKkEMRXl0yquYpunBclU/utga2UsK1JB 4eON7iWTFyuZpuhDGMSPs1FmskmI5OS527UCAJgqMM/gZkv9UENnRbhtYjEQl1HkAO+o+y2CsWZL gGTOjGrnVwejHlrsDfq9W+hlZC6vbrw96q+tDrx4zaa8NiSZp1wXcya9gjCq3d5obzAevWjJUxUH vAQHljK3k5+213e39rbfWN1cHQCrkR6Jg8CQVAHkGnmx11/fGKPxBsTd2BrcRF30EpQD1l8I3d0+ DbWBVub3NtfSlQUtiXEHJS4n8+f2x73N2P0t5Y5ub+33BqOFE66K1uF/P2EZU52FuJBMNQyhGVbN R3KmigTt9pJbJxOB7gAa0oBuM3h0dFs6oGSFgL6s+kFrYiyqlxoc1gIXXMuvE2TIBMqsJ1PLweJS 0j7TaS9nSfsaIkn7IuI4aS9ePYPypH2+fX6QLJ1F+59+/c2J43NzDx4e+yU5t4Qec/E4rB1cFbja 4ViqOUKcUb9gnO2N+uubaLg63u3v+yJ1alcEjPXp1OQcVuXsHD4BnSjmFP/HS1GPEiw5MUv/gYEk y1TrK8TMQjN4oABLzlNq1h3oFq8tZ7AcldgES2DjbJZNuGzOhuuByJgPCTMd6+nlDTxq0/NIDAp4 TkEPxQwiRbzUJC9UWynx9QPCWwITD+FFLquQBhOkQglHpk5FFBJHPPSlJ2FvOgb+5uzpRaFWcfAM jUhwnURmlpuIh7UEKh7i2USQ5oT8k5JM0ERgmoqYjTEEMg7guQRAcyoceig32tAJ2tDM97JlYy2B jod4PhGkOSHYSbknLp2gDua8XLzOEgh5iCcUQZoTwmqbpvmwukA8h/rOY4GcxfMJEMcnhjTmw2Fh 0Ds4RaguEKwqslzNzhIIeYgnFEGaExJGHV5X5/96oM2+ZJp9J8pwWCLWtjbHu6uj8cr8aGUh2o2v B1pcYDV/btPCC3P0MXuoeO80BdnRO4sq0IllL0tieIi3FFyUH2MeEizMPUe8lywyu8BQTcoaBtXt XOH6SFHSwML1kbdAQnXAiyhGpy4RacYp1SXmlKDc/C7vO2NJpnh0/psdXTgBMkl1rUvYcrjmI6Xa IJRpdrk+MeSwiRVqUSaC68HUt9eHO5hROhzl1M5tyTOjSn+c6x1y3eK8op22yaPHLcx8lqlIc+4m PBy86xwgvqYZ4jsLKA1QCZYQ1lkMhzDZSuDlIAV0hn30WfbWoCqTMj2fRquhuTTJma/cRy2IuPqq 0zIsiwsWlW2iIBXBOxNiVf10X1bizxprGq/pGY/sGKM+ojzX7TCpjeL2ORBX7h6OqX3mVRzjVqkY nM9MvWO9TeuIeusEAq5RQvjQTO5EEEUlMYUDW4fhIoTX7J3FN49laHvHZJdPpHd8ppq0ncl6VOTu 1CNV3eKiqqWbGmkKp6nZjuTg1AOWiFIjCD6pXQ5B2Se/BUX0y24w/dK8s60ZJjSLyhG3SiWYaRUZ WqWeHoKb/akP7iyhMaKo01vFAUOrOO5xq7CCFrYXre7W8CQapqLIsC5vZ7IqolYu1UhVN71NLEef MdIUTlOzHcXB6YZhektc6K0/TLQwW3JS2pDqnjkUaNvG4CjL3dG07Ag1Kgp9FKtbnFP0VxFh/gKi 69amKgU84EWo5BNehN776t79z778/Iv59guthfTkqW9//v7HBz9998PF1165svTqpcvvfvLBR+99 /P6HTz339LPHjifP/PbPn3/9/vcfD7fvjPZ2bu+O5+Zu3H/+18d/iVoq+rI6wwnqdCZoWhOsMyFS 2UtjKsFnjXQwp+npLIbqMmXmBK6uSlzMXaE3Q/5uo5fMjApdCnkO9QFk9OvPcP42LAhhdqYcE2eJ 2UQoyyhGNWZE9J8o7AucGiMmbAc7QsZQ4uMxjk7ANGfDiH+/xWtshHQvbxwdaynxCShHKEI1ZwSD cUdIWWOUZe7drGNkLSVGAeUYRajmjDL/vjivESqKyoQZQ4mOxzg2ATM7GVKo12wE1mw3X6RW0JRi tzGxdJwl5hOhLKEY1ZQRdcf9WjEfqRVMrc//AqCrafdlbmRzdHJlYW0KZW5kb2JqCjYgMCBvYmoK MTg0NQplbmRvYmoKNCAwIG9iago8PC9UeXBlL1BhZ2UvTWVkaWFCb3ggWzAgMCA1OTUgODQyXQov Um90YXRlIDAvUGFyZW50IDMgMCBSCi9SZXNvdXJjZXM8PC9Qcm9jU2V0Wy9QREYgL0ltYWdlQyAv SW1hZ2VJIC9UZXh0XQovQ29sb3JTcGFjZSAxNyAwIFIKL1hPYmplY3QgMTggMCBSCi9Gb250IDE5 IDAgUgo+PgovQ29udGVudHMgNSAwIFIKPj4KZW5kb2JqCjMgMCBvYmoKPDwgL1R5cGUgL1BhZ2Vz IC9LaWRzIFsKNCAwIFIKXSAvQ291bnQgMQo+PgplbmRvYmoKMSAwIG9iago8PC9UeXBlIC9DYXRh bG9nIC9QYWdlcyAzIDAgUgo+PgplbmRvYmoKNyAwIG9iagpbL0luZGV4ZWQKL0RldmljZVJHQgox MjcKPDAwMDAwMDA0MDQwNDA4MDgwODBDMEMwQzEwMTAxMDE0MTQxNDE4MTgxODFDMUMxQzIwMjAy MDI0MjQyNDI4MjgKMjgyQzJDMkMzMDMwMzAzNDM0MzQzODM4MzgzQzNDM0M0MDQwNDA0NDQ0NDQ0 ODQ4NDg0QzRDNEM1MDUwNTA1NQo1NTU1NTk1OTU5NUQ1RDVENjE2MTYxNjU2NTY1Njk2OTY5NkQ2 RDZENzE3MTcxNzU3NTc1Nzk3OTc5N0Q3RDdECjgxODE4MTg1ODU4NTg5ODk4OThEOEQ4RDkxOTE5 MTk1OTU5NTk5OTk5OTlEOUQ5REExQTFBMUE1QTVBNUFBQUEKQUFBRUFFQUVCMkIyQjJCNkI2QjZC QUJBQkFCRUJFQkVDMkMyQzJDNkM2QzZDQUNBQ0FDRUNFQ0VEMkQyRDJENgpENkQ2REFEQURBREVE RURFRTJFMkUyRTZFNkU2RUFFQUVBRUVFRUVFRjJGMkYyRjZGNkY2RkFGQUZBRkZGRkZGCjkwMDAw MDk5MDAwMEExMDAwMEFBMDAwMEIyMDAwMEJCMDAwMEMzMDAwMENDMDAwMEQ0MDAwMEREMDAwMEU1 MDAKMDBFRTAwMDBGNjAwMDBGRjAwMDBGRjA4MDBGRjExMDBGRjE5MDBGRjIyMDBGRjJBMDBGRjMz MDBGRjNCMDBGRgo0NDAwRkY0QzAwRkY1NTAwRkY1RDAwRkY2NjAwRkY2RTAwRkY3NzAwRkY3RjAw RkY4ODAwRkY5MDAwRkY5OTAwCkZGQTEwMEZGQUEwMEZGQjIwMEZGQkIwMEZGQzMwMEZGQ0MwMEZG RDQwMEZGREQwMEZGRTUwMEZGRUUwMEZGRjYKMDBGRkZGMDBGRkZGMENGRkZGMTlGRkZGMjZGRkZG MzNGRkZGM0ZGRkZGNENGRkZGNTlGRkZGNjZGRkZGNzJGRgpGRjdGRkZGRjhDRkZGRjk5RkZGRkE1 RkZGRkIyRkZGRkJGRkZGRkNDRkZGRkQ4RkZGRkU1RkZGRkYyRkZGRkZGPl1lbmRvYmoKMTcgMCBv YmoKPDwvUjcKNyAwIFI+PgplbmRvYmoKMTggMCBvYmoKPDwvUjE2CjE2IDAgUi9SMTUKMTUgMCBS L1IxNAoxNCAwIFIvUjgKOCAwIFI+PgplbmRvYmoKMTYgMCBvYmoKPDwvU3VidHlwZS9JbWFnZQov Q29sb3JTcGFjZSA3IDAgUgovV2lkdGggMTgxCi9IZWlnaHQgMTgxCi9CaXRzUGVyQ29tcG9uZW50 IDgKL0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggMTE5MjM+PnN0cmVhbQp4nO19h3Yk15ElgfJZ 6X2lKe8LBdeWRrNLkRrNcKTVOfv/H7P3RrwsVKGBphFJac/hOxK7GyhzMzJexA3zIr/44o/1x/pj /bH+WH+sf+N1dXV93Wq12u1Op9vtdrDa7Vbr+vrq6l+N7PV11Wp3ur1+f2ANbcdxbHs4tAb9XrcD 4P+GuK+uW51u37Jtx8XyzpfrEv2g321D4v9qnOfrqtXpD90gSmTFcRyFYRCEYRhFsfwgDNzhABL/ t4F91e4Ohq5HkFEEtKd19vfA9yFz2xr0/k2Aty3Hj9I0hXh9YINy+D6uAEKOIG5fdMT3wzhO4tB3 h/3uvxb2FdUCkIEwjkTKqg5pluVFWZZFMcrzLE2TSH+Lawh81xlSv/9VuGEs+naQABR01vcAHXhH AFvV48lssZjPZ7PpZDKuCwCHlE/y9uxBp/UvQX3V6g5sl2pA9QWYLM9HZV1XdT2dzxfL1Xqz3e12 2/ViOqkrSD2nAhkFdx2r/y/Q7qtWb+hDFyJqLXQaeMfjeraa1ZPpYsO1u7m9f3i8v90tF/PZdDaf 1KM08o28Ie5h/3cW93UbJs4PjJVIsqKeLiDc1XJRQ1mSDPqQQq1xAdPZdDpfbXa7/WY5hcAzqEcY UNy0Jr3fUbmvuwMXWy7CF/uEPJ4u1vv9/uZ2M8s9u9/r9eDFe73BEPoTZ+Vsc/Pw8HC7X0NtJmUe B7Qn3LyhM+j+PtIWZfYD0c8IelEB8Ya6C9XNQhemuFkA3reGjhel5YSv2R/2e7ysHMnWVXHbFkzg b4+53bODiNYAUgTkyWJ7uL293S3KyB10wY/ImOS/11z8V6czsGPI+/j28eH2sFlMxxA3twJ8aBTY /c5vjBqQjTIHYQxVhgS3B2orUAwHvW6bywi6/fSPbn8IeVezxXp3s99uVnNoSRKJbsNyD39bJblq DdyQ9xY2IOHuW++Px9tlEUAp2q0GIdTZ6LVQVP5KBG65cbm8PWLtYAhLuVvUbViSXvu3Qn113bM9 lXKYZNDlzRr2YlLE7pAYibcnvBTEVMmdUFP+RmTeG9geLnWy2h8O29V8rNKmm/Qcq9f5TQwJmJEj FtbDrS7G883+uFuOcx8kCID6RNcfDB1qD9w5HDs2mmsL6lNAgBfaQbW5uT/ut2vAzmJaILze/20M SQu316dkQuw/uJHdzW41ySNKGbx/MJD/kP67nq+UTyipDWlLJEBFwf9Bu31eMvbCbrMg7ECFjev7 1WFfdy0/UZuRV9Pl7nh3v6njYb/d7ZH8DwUxl22D6AF4CCcD4yBqAk4qmkP01P2Bk8129/d3eyjJ KAlpSaB1UO1fFzW4EY0GxJzD6G72O2hG5tu9nthigLawBLPos4g7htuGpGVB3LoEeK/vhPl4sdlB t2fVCNcW+JQ2hP0ravZ13w1EIgnv7P54v1/mdh86CjW2uPWITXCbPUgtCZPAljtg4UoobrzWxi8t cOo2pJBOtrDb2+XUeMkgCrxh91ezI52BAzUN4FTK2Wq7269oMvo96Cm2nm2WQ/kqtyZt5t/h8IAZ jFt3pVzMk7pQ2rPVBu59XGQxpe1z5/5KvuZ6AA0NsMuzag7/d7Or3AFsWLffV9lBjAxp6eHSfISV JYHD+BZShab7acZoAErgySsZ/Q5xyW14qni8ucOOntVFGvoOr9kddH4NWbfhUbjDk9F4ub3ZrmDm dGOpZoiAA+HUaQbMBbhzxi3riumwvYg/HSGGkY3smBtjUbdhAHNskc12OQNzDUWz3V9D1ld9ytkJ 4mKyOhzvV2WAuJpxlq3pDfj1KGUIIKsoNM7KQlyMjctyQap4ISMBnsBGDKn6nug27tfALVb7O1KS ilYbmv0ryLrVtYXRpeUUzGE9zbFZxGSIMlPKUXzCDHBlVYE3j2DBoTcATlrFiJGXMkL44kP8tDLE Puh34Se9uMTmhrNhmCA2e/DPmr7uMEhD3OJqvgFrmPgW3Bqt3JDaLMTzJGNCRoRYM7waxdAE2D2a awnDKoM6ENA0gUNmceBPW207nuGzt4sJyG3ghbHT/acU5KplSRyY14vt/mZZRfiWnrgRkTIUJ80b vIUI9ASaRgxqnjI0rxvUAC1G3INuD8Vu01v2hzG2+BaEpB4l+D7f6f0zor7uurhlUVItd7fHQwnK 3u4OxGrZ+N4AUlSdaFaFULwB7fsxtQaYeSH4YamgqVCwcK4FmgVvKimF3jCbb0FIFpMRKWtk/xPm +qqHDRhBn+cIrWeFTwLRuGsH3iNTxJUsxSygRaf9IMlVy/kzBU2zEsLKMLnjDS0hhRYtfs+JK0Tw q8VYZO1ZnV+MuW3DTEVRgbBjt0wRY3ShiZbwInsYZMQkQHn3iZqCRlReivUAulx/zZ8p6ARbOpFd myNusMGlLKPZ7U5QzdZrWJER9TGwfplagz8j4AhhnyfrzXacOAxBLEslDQUBaBEjMQmqymCuRA1s rwFtfk3tSKBquD2q3pAnllASmJG25aX1ZLZY1ggPwsjp/xINQQRrS24lG68Om3kMzevC7g56Fp0G FkFLOknWWBI1ADipS3iXxBfQI/OCsVHp2I+JGZdHWx7glg16jNstq4t4chjl2JCzIkGQH7j9n2/4 riV9C76R1Ms1WKiNYKoHzENEL2S/Q8slKBX15IcffvhvrL9i/QXru+++84ZksaPif2MB9NhImtKn xlRQodQ3ToZcnFFZD+Z1vFpMC0leQtY/V84ko77wuvl+s4g7bVwFBe2EUYK9Dy5ku6E4aMB+AbQL ogTQIwUtoqZHjE0yCv/C3RCxwIzAjA4Yy19bQTFfbhhchEno9n+eXgMhzIOHO1ziQ6DPrExAmx23 2fsBc/6kHEkGO/wCaLj3M9BEXTCHCkHLXi3g/kJJxMMswzdydVpdK4SlWorhCwL7Z8n6qgOi4wFW XMx3G9oNfBw8ge3DmeTY+SRtPojHt1iEBZQG7HdUaElFUz2gH9yIovUAnWZ0j7gC6Ar0G5yFdgQe 1wJ5shwwdOj1BC59QusTw8n8dFlTNxzGQEk1X4DVeT0GgpCz+RrJO0so/QLokdI5h+qRnYOGJcyY WR1PxrJX41DtODUd2gblow1BcDCdIwzLJZ1t936yrLEjmG8LsnqxX01TREED24Ik4AH1ayhr3uv0 BdDEDNC2IaVPoGm/i6ImfNHoGPyvEHoFHoVtx9VD1G4n9Wq1GmchbhVQ/0RZX3eH5DqIYGE1wZ5N FMhsIonGSDHnWZblL4NmPgeSfg4aak0DKBoNKx2nykoodaIeMt0AIzJw08kc4UwmYTqi3Z+EuWMJ 2RmN5a2+1bYCEA1lGirnjKsBTHeBvxLwn7H4I6gOrYcB3Zhys8bjyhi/ETel+KIc/2ZBpgci0un5 cGerZZ2IrIc/SdZi7OAH69kM8UQAg2QBs3eSMyCn56ApsWegoSG+LcEBFeAStHHzSSSCFtCFiQ9I Q5jbGfr5dLlZQK9DRNPD7o/LGs6bZYnRZM4YKLZbna71JGeJm7DOQNMvPwONl4RSIbhwmifXWXKz kmdXarL19iWSZ+rDzXT7MNf7LUxtCEoI8vRjrvG6M4D9VTmPSwTdElq5GukpZlawAPsMdPkcNJU0 ENCj8pmo1WAnYrHFklRSCmM4HAVqsXsdG9HMamtk7f5oJNNCsBkg3KScS2bqyDiUR4h5AmbCA0qq a6m6WVbjJ0RiXLJEXEemjv4SND04XBStH68SG1giNP4U7hFEstfqO3Ex227WNbwYyNOPRTIdscb1 cobtkinjAN/wGVUZ3WhA01wVBvQTbdKvzwhamPNz/dBtSNC45skJtAkRIs8BFeu1un0vq2bL7bzO JGHW/ayor/uu8I3dDNKK/AH8OXOhQXwCnTSgS7nRQoEuQev9iALm81IR9Xj8hFndOX5R4E0n0HVl qCtQS5Q+jFJ641UlTNP6nIJcgdX6wWi+hCPNYO162IZa8GGhkKYO2vwEmj7imaRLMWCZljuZvsnE sD2zHSk+rnwCXZvAMmf6byiMb+DGo8lyvZshkgEL+Zy1pkaH4Xi3nlcA6EhyfwAOInkCAv5PLLoR fFkljjkOJdwWWIaAyqUxN6bvy18GPfrOeKT/0lXpZmRBF6Cxk4K8nKyYtAiYYPuMgnToq0dzvLQE nCGz4CCjkmwWk5Gcga4ntTKIzHg9ZW9iEsndfCWBUJCXQOfFM9Bm+zI5TJbaRkg9Kmeb9WoshY7X aep1D/47n6xv13WRkc+2mXukjBE/E/Y56DFBw7TFZ6CxycSNs2JA5RDb+By0UtTyGWi5k3ib47pD ZkOuh7hJ9Xx9WDAbElivRV9XrQG+qlxtdnN8sD/oQtJDEkxJxD0HLbwtjSUW0TiRoGEY2JNAUxVS o/P8YiNOGr9dVM9Ah9ow4ktCs4+AYODF0PvlYT0vEcnYr5k9bEPc0clhuxzjsp0W6zs27j7twV+f FoF///339CL0CPzzz7rG1YhbMJTMBhspxLmY0LcBzUsroOd8Az8KwQNBEz8Tyr6DUMPCVqRWj0az /W43haq7g1dIKugdODA0elLiu4dMmvc8UgRY0ZdBl1DDc9Clgoato7wJungGWmQtEfALoNlawR4B RmCIlTwEEePFer+o8ghb8RXbAaqUlqvbJbZKHFjXrXbPCgwXexk07/QFaAlbuP3w7eHLoJv1AmiQ XSoiaSo7hxxhiQsoSIGotP+yUtN2jGYrcSyh0wfo/jCkd8DX/vflwpfxS781pAPXYDT9z82PsfhX Y9SfYcbrv+f186P+povA+QYh1xA1tHoo2c3JcrEoYUAGLyq1FDerzXpZ07EMugRth7kmL3430FpJ sGUrMhuYFfUM3JoG+AVRX7VYDpogwKJj8Xrt6zaL2hJgVNWvBNooymdAFw3odovKydi4nlbwlfZL FQLYDlzZ/LiCjYbtwJV2ev2BIzmhovh1QBvL+DnQdKkKGv7Yk9gO1Ax00n3J6F13htiGi8NCOLrd ur7uWqQdUWwsKb/lv55WExQae9vkPLjMNZi/fg8o/MPg/xZ/5fsJ+Aesf/zjH/8Hi+831wydHjIy b7ddSViUMAQjUvuXDJ4d5OOlbMMksq6uoeOOy1rq7w06ZE293zX2I8+r6XRaxJ71gqUG8w6L+XzB bRjBwFzDPwbSafd7g0YcLR1OYj+YeKun8Ir+S5YasXtUrWbjAiwgGHavr1tD5pKxfm/QiKvB6/ot ceVSMZuu6iR4qajRgaUYb+clqaXX71y3Og4sRwMNX1YbWmR23/dm/UURNy88+yn/NBnqZuc1aLH+ B4s78Ay0EQHZYQC/3cKuCqUKNd1M0pcSktBgP57s52SImYs3tOHCs3PQjVf7WaANlfpZoOlRI9+C 0TP2I5uCf0Tep+7lGiqdTbZTiU8csR0+tsEl6PHPBl0qA/xZoKWBObSlXOey1pRPEcHEn9YXSUsR 4KzHzMOk9jVshxNegq5/CWgyF/CjnwU6ISVn9xlQqP2YbmfFS6DhWtJqMqM3BMPDy/tuBCvJbyPK ujJLs1z4CckONxK/3OzNZm8ZDmSY1ffmUgiLL/773/9OsP9jFl7f7GvuQoY9zFqHiAWAAqEAFGS8 nFap/0kgANcS5FMSYoT/oSUG7xy0yQM1sfVvB1qzQYyLQdmury0p7DHXmn8avYBLB8V8LMnFyB9c tRCXM6vUgGasTM0xmZnfDrTJu7FCwiR734nE6I1no2D4nH0wDi+XNeMj0NLeVasjoIsTaDCChBGX xt6/HWgjnCRiu16r1bUCgM6zelEEn4RcLew7BS2uhe2ZvsQmWjhmxErQqRJVqvVfzjyKCfeI0vyo SesxSvn2aZlXne9akwM0XoiESQPzwLG67Wsmfgl6vC4/zX6Al0blopIkrj/oXDEM55dqZbuSpGES a6rzNwUtm4dRaQz96EBNPSZrYT8q335OmeDE44qSVi591YV2fAJa2yCMj/lpoJll+Fmga6myayVG yIRa6smmCpxB5zloL6lXCtoFv4J2JNLfU2i9XtQjzVmbMqFH88WEhn8SMP/J2oxJArO0D0dmGfPb 0Ou/XK6nCkhDY8S+i4L0G0udjVcVjOBlngnq4GdjhDWpCQB6Dg8pZJKVZn8BUywZ8y4/Crpv2QYH K4UegtRfApoZHSZAr69tSSOOl2XkXybH2ModwIRX0No0pRPvu2EqKf9cYDMVmwsd/1HQWAaHJ6Wf Xu+XgWbKzVL3Agjjefmc510xHhuNFwKaUctV34skHSZJIgpbNkgj6J8G2lHMvwy0pjGAxGJedlRN yuc8j6BDgC7ZIRoP8dJBEIu1jJku1c4B/E8w86PNBuS3fas2m/9MA+kq6Jqf8reCuu82kaOB12zT 8SluFJpwxraZDI/huFtXAylXlzW3xyVoJgtG9ayEYBME63Cf0kzChKXmmMva0I7x+HXQ8KU2QZ6B liMNdvAa6NOqG9vYgBb7YcOOkQNRQ9lidAH6GmY5wi0oAJA3hYEOfX6GIMeXvhLTzMHyw2dAp4n7 Cehub+hHr4Ku6yfg4zPQDMpTELvOVY+OXFhPcsmoWdOKcAtGqTAP+Hxb2BWuwA1NcovEVHoePgva Y/v3paRBH5JXQDOTXhRN4uwctDSEIJjtXnVtjV7KKn0O2gKbLkoB7Q5aki3FC5nQj/WAk5BpFkyk fAGUjaR4C4QGSknGP3MfZpt96yVpkwZprlKiN5rxFIZV2MyZtLmn5ZsSH5bZhFwwAuklo2bOIykE dK6gnSTHXmD/Z5xpaXU8MVn8M9DKsgV0LaY8DV8ADTf1Emi6LEl8p2cZ7roBXWsbSxceJFBJvwA6 SGmKCdphz66DTypYpPGjU6nHlFrPQZtePAZjWomNXwAdxi+BZneZNCOEMaljY0Qa0KT2CrovoPMX QNthJg07WR46/VYDmj6NkqatGxvM56AraSFUSdd6R18AHWl59xNJl/KBTL8LEbsAXZ4k3e77EtyW k+yydnsNbc+ZwyNo+wQa1qP5NvNNUmLWro9KWsDKwuyehjD9oKvJ1Ble3VyDkM9Se1IJWvLRkXRe l+OmPy6RiyguQafl9BPQDqLaUkFLW46dEHSefAK68ZCQjMj554N+6pqU0mEqJ+di8JqmezKNY9ou UCYW89u9J9D9Z6CTUoLaLGtAs9aSp89Bp6mhUAQtbbG/ALTpSxXUUg2DYZXdaEAnEf0x/qSdbkBX s5dBCzGNmNshaJrA7Blooxu6/8pfCtr0/2pBXAoWXii8V5sMAZrno3AH3BPoLC1fBD2vJEIEAWbq T1oVi7xhOUxmRtpB39xefGWTkD5Lgxgyz0v57nI1DMv4mKqWOyUgQ7YtZ0Wl4XPKkx6uD3PLM3QG dPKCTrtpZUDDjbOtTwr1xegMNLfLk0bX/yzo8iRpggzYM9D0AvAgrh+wq6bXue70XwXtZQ1oECaA BjWVg9QXoKWjhuXMshA5/XOgTVhFpQ5935OTBXlqTuHxZJvv8ESr2mmAnmXwIM9A14tKYoQkshm5 2EHW6LJB9u3l+s8zVTX6/MKPzGU0wBv+QdBlAzrlp1OrM+0TYbGX+bwI8Xf7WpIIAF08l3Sr5+WI wtjfiGuVekvfS39b0Oxj1oYa+SLpDiHH5Pkwpip4012A7rHCBlTFpybPyxhsiX5okQjc7HcArb1c /HQWpxNtBNCDBrwHLlvvHQVN9XgG2qV68Pg0UDMa7/SG3m8PGsS02c9JAAlDnd2AXTdqR1K309ZE KEBXi/wSdAvWo2ZYmypoqj97gLSZLRPL6XtBFPFLzyDyr6eDDFXVFKFNrYO/NaBrPTSgq2hWaeLL Js3ruS4b43LTsqi7SxKh8JLlM9CwhQANnaYy5bnHdNTAZXm9WYijeOjKfxH0ycF9DrS85gL06BPQ w6HlRWJqc+nO8RFDtaRu+zJoLx2vazl/leW+HFnwpFM6MQv2h8fKXgOthy9eB30mZ2nCM3njZ6At OdIhp7+YOo08xlBDhk4EPcsu2mUJOpusx5JiBOhBV4oXVBXippIkkcszVy+BLo1/1P6KV0CXz0EL sX8O2hNqXZguS6YMeN6IgkzjYppegu4Q9NaAzoJhj35IPDY/lmU5/XTJbjSUwiRzT4JWQkKkT/0V iri+wAzQTaKDO/Bsx1IkjJ8Ec5rCTAO0I/sqIeiLJvArQpzuJiYDTUbNy2AMrvtcZPIZ0I22AvVr oJ/OHRWfBY2Xkq7y9rKjt9V2xRQA9CR1+p+A3k7YqIQXh4wSOw7THYVSm7L4SaBJpF4A3ew9zQn+ iKSlCVxUEsS0K/Ut0VGCti8lPfCpHqaRTXet6pJEgojCXwc9Khsw8v8XQD+ZOAO6+Cxo5mdlQosr J41coVQEnVz2rQN0PiXoWM5WhGpqQkPryM7xDU0dzbRONLtuZIKQs7v/VJj4Ic9HRXGGWg5OlTlA GB7+vdnTDWisXGxWHNo8rtn1cu2aey7pL9oI02c7AzphNo+UCQZI1CUfVZPPgG4O7p2M8AVoHpYy v1PrzNOJOdjnZ0Gzs4812zb2Vq6dfmI9ri5AQz02Y8SYiQTcttSVXLBaFbZ00b0CuqjKJ6VVX3YJ 2hxYVCMneeMqB4jPgJZo1+fx41YXOiDXQPW4TKoDNJzLWI5xML3rELTrBZ7tk49k0mT5Gmj1bqcF /3sGOkubExt6/EHqaxUzWa+DHklTsDvstVjSD9h/iTX6FLSXjDfTUSqSTjPwvNYAPMAdesJs5ZwF Uf7wdHao4fQnEZplhi/ByDpULj3jPGp6KDOpiOBFI34M68t/+9vf+MnNrsWnSgDGiU1SRgm1Gqqg L3TacqN6OyuzWEG77baAdgYOuxljSca9BrrBSv+pB2AMgXdiHSZFbsxj43Lk1kheu6JeB+2Dh/BM KDiekol4NE4uT2KwPDveSa+HOFBXzvY4rj10ZAZCFL8OWkPRRMagRRJGZFLbFtBywTnkHLqeGTDV nH1+HTT0gYesJNga+JHxj8X4ucmDGkwOC2yoXD7W63XbPRlyIJPkEBkb+/x3s0yLl6iwdDtymIc9 lAM8BrTzBLpII3fgMDZJlO1SiZuK7X9q45lJIP+VL9D5TpzVdG1JRYLbqqzj56D9eHpcYovkonde v9sZOM2kDp4bfwW0OSbAGSS+59i2E5Bc8bx+7PZBbmNRCo6fsETRRCSfBQ0B8N38Vm5FKwTolEmy 6hPnMvST+S1A1yP5VA4OGPqhCoYt3K+AHpkzXKIdsDXgw+yK8ThFzAMjjyJPZlLwOJkbRmYrZvln QDNGjOR7WUdsDWka0mJSFfU0vdTpjh0k87sVflHKp/rWgPl7DVvEGLwMOm8OCiQc7ccpEzZtjsz3 4IlEzrXSg7RD24t0X6afB607Qz6U/cckE+TS46KefQo6XdyvysmiktsX4Ht8s2MaExaRbPzDLOA+ P3mmGuJxiAfPh1oy0wNraI5HcjpF0IBO1cj81azvtKrHv/5fLJlfJ3MqpI8JoPMkLBeTYgzQF3a6 50T56mEN0GNCBDkdWr4cUilNiBlrI+Ql6AazijrmeY/TyJRmgoqCHvLwVwNaUz6vgBavMCrUVg86 rP1kSbUE6Hl2ARrWMC7Wj5tqyrkmZNSungs3Rx7ldOxz0ApZdVrDMh5gb2Qrg3cGzd8sngdT0LpN IJBXQGvVshzhI3170HWijN0eywkE+gnopNy82U9ni+mYZTnEsQM/My3beg4kTT+RtAGdnUDHPkfu 9BvUp78AtKfb8IQ6y14BLZEEE+px6OCz3JgxYz2tXwDtpfX27c1qMZuZAqjDrrwsP51kowsZVdJG LVXWSk/hNMrBQw6SZz4DfQbespUONKvRa1bNTEaEteApljT/FkzpeVa3D6uWFZq+ny4vQF8BdDbe Pu7n88WcqXWQJqfryCFgrSDyfCbrfROCnk6aI9VnmA1oGW70CWoOJ6F6XIDOtHG/AT01S0TCwXq+ 0+cQF1Yy5NDP5Ua8urqWwOVhO55B1rXkSZyuLZ2mI220koLheDIV0NOpOeyWPWFW0D4P+H6CeoD9 aDPVEyfpBWip9L0AuiZoz4JrCZipK+VITzmOrSfCJKDz6Q6g5+sVVJ5b1+UgBE1GaxmfRblpA3oq x6hH5jCXqjQrJ66cSlbzoWMOVM6cbsMhR+egcz2QODGgm64/7ePJJEJsD2V2TyVn6+pp8gw0AxeA Xmw2nPmV05FbdqCgKWGumVnTBnXR7EJBTT/OIQEDmZNiyXAmS6YvceCDzAl60o60EXRzjqQUiqhD b6RUl4L/dBztThN1FPV4As2WPYB+3E4Wm+VsPq/wlgBf7SXmOLIBLVhPd1FPxGVy4lZAe/CEHH9B ZfA5k8B5mtDEg0JOYA6AGfuRN8enKICRFJyzXBkgTazT7fU9XGGJ7+Uh14l4xKsz0GEx3z/upovN rJoh7ErgQi3ZimZuhMiZ7x0b2JPTOT6ToRTQfuSKUvjmxHszbZj5UMk7424b0CLDyhxs4BFSPR2m wBm6DNu9IeeZVRxmSMHBuZyDBi0plweAXm6W0/Vhpsm/jh2d5kpoYRIg8T3GLpn5Fw3R4yBLuD1X hBtmOqOVpROfuVAZJRqaBGdu5GlaG6SAbQJBzSbKweKBjBRM0sluid+PZ/NFDpbXDIUW0PX65nE/ W27Xy/3dUg4zDdtDnWbQVCoFHIy10UI5mCwp3JEJAihZIUsk7jKDJZJD1PyZY3syFjfVip50RZ0w y9E1JbhG3AlPuVsywmR+s5pUo/FiuboA3WrbUb05vtlz9Ovm5nELrxhHTs+CxEQmRXMmmFSmOp3B qkwAnmcNN5XpGjKSIGBjv/yNU65c/aGemxOlNQ6gOUpSadmddXIO2uHJ/C5ceJwU0/XNegrQy+Vq BKp6fQINRRhvj28Oy/l0tb97e7OscK88a9Dz1HGpD4Hs4iRnh626LVjT/HTy1kyd1oF9CEo5ikZG WwWcYOLiKqg8kYSM/DBjpJszOzRyGrOEPmNTHg6GIUji8WZ/s4HvmKxWy5E7OAONLTfeHR+PUPnl 9vbN3WGakMpbnSHLcEryZaV6YFJn51Sj07bR+nagGgx7IeYOFlpAOxpp8j5o/VQMSF42bSmTM1eV ahMyY9o+6HKWzzjkdUrQy8XIOwPd7rrJZH98uD2sJgvo9uPjgrs8trF/PS0ECDVP9LxsrepYV7kk d9Tg6YAtT+YiWjKGa9DrcdIzQgOb2KWiGUiYmMj7StNwZTqjTGqEu4nBYUe6ikfl6vbufgv1mKwW 81xAq1ZftUHypofbh7vjllM/7x/fbOsCUmQ/0pCpMaFwaqO05C4H76tRqmlW8YYyEZaTTIZ0LJIA 4OSwoUzIc3WuIV4iNW/pMijNSCG1TXVzdhwW2hUi4HBm7nR7f3uzwq/hqynp9gl0Z+Cls+Pd48P9 AZhvbx/fHdc1VCEEZaGsNewStzup5XCnyFwYgpDkONQJrRy+Aso01EF+nHXK2WkwgZ5oewRa74eR idArHc5kLKo26KklcjlvDSwiSevV4fG4nVeT+WqzXhJ0S0FfXXVB+OfH+zdvHm/Wh4f7u/v7434i rNpq9yw3SgwHITNgIjYKtRVGk7KihTJ60peRMUNhH5zi17rmFH6glol6jOx1/Kq0jlen+kAzJ0uH 1mEvOr0+fCq7hCYb7LTtApxovdlsSrYaNKBZE53f3L999+64Or57c388HO/m1L3Y7vT6AtqMO5CO ZIALZEtWlLqMxXAcylLsHUDLwIt2M623ix1FbQ5k5rpsjVQHbJdmLpnOOCpMtBkHw0635zJSxDa8 fbzdrWDTtuvtrobDa0BLsLW4uXvz7sPd+vju7ZvjenuYS5KYrYG2TPbQjmR2Q4eaS5VWTu2MEUnL DEeau2Gf41cNZqDuQjsVNMfEB6JsI2kIGGm/y5nrEnrrDDp9NkInebXYH26Ot7vlRkEHdvcEGoF3 tTxAlb+83xzfvvtwM1tuZmUGZL6MJAm1YUIsMhkF75y2VpeSSNNB9bo4epyQiflKHizRHShoGhbX 12weR5UQpnQ0iKIYY8+p1DB3lpScC0h4s7p5e7vdH3YAPYbitBX0NdxlNl5B0u+/etgeH959PNTT +aTK40QyD6QtJoDOGFC5ClpmdxQcImDSYhy1zwPUMh35hBmgOW9TLMcFaJnjkJmRaQpanCr8Spf+ gXnS8RxBye7+Zrs/Hra73QT2rAGNl4ym6+P92w9fPe5ubt9+uZOdncsM2OF1p+eewmgOerLB5aQA neaFmW4kM7AAGu6X03k7On1aMF+3erTS4QXojHUaso20CZ01TRFyRijHbttiY/J6DGK5utlv9rc3 +912Ngr6HQXd7jlRMd8cb+/efHiADt3er6C6I6YigUbHv/gyPt1n2zl3tqfM2CR2DVviyM3AG8po WwNaHjvSM1OHGde4+k4paZDXcUeWpyYgJtLNhFkJg3P8YrLaHjaL3e3x5rCbl+Ggo5QJSptU+PEN WMfdARcEfU6wNakMSep0Ou2ekHpaYKorh2dous2EiIYtBTIIUc6Zmk1I1C1JGZOWMtfniPVQ1pSJ X5Qch+YDOb1JBocOLM/ktZNwAhEvJ5u7u9ub3aKKEHCJI+8irKo5LvHm/t39Ybtagi75Q1/YS5w4 HNMsw21lzC7nQYjp9WQzGsiSNNVJ0pyOaCStFg+gdShn2Azc13qA6HCiM0+1MCT6bGEjS2Am6f3Q rzbb3aLePNzdHnaLcWIbntfzoPHL3d3t8fH9/X6FECX3hjDdccqz9o5Mu+qexru3dUw6VCYwsZNg ljy/jqPsEbRBzfHffRkl6kdiQow/VKIf03/kypJU+frgmy5cf8jBEOyy8UYcK1BuHu9lrG/G9juC 7nP24GoHnX58+3C7hkdNQBzkySEccgM/7geuTMqzbRnp2eqI7L1YQWsC3JUAQOYI87EzglouTwaK ypRZWcLzUr1JkXBI2cUS28CXIlDzOXgt4dQKF8QrRWAyKxdQ291uNR95lj4TYRClo8l6d3P38Pj4 8LCGMQo1k8WsbWDzbGWZ+oz/dLI/tpZlwRjbYkPYCOhIrtHmPGTmDfrdVrPaXZncKk+n8UN9RkZs knkMqUJhAcxrO7juQXvgxNhsFvgOzfoAhCsG75wWE4QAm91mWfg6N/nKijOEB2Cm7969e/v2MJ/P Ehm2aomEhpB5Oim0kTLzOYe0y4FuVo87nLURW+bYOjI42JYBvgN9RJEMr++anKlGM77Mzop05h7A csC2LUPF+nK5TpTmfp/2SWc+9wdRPUNoMl8ul+vtZl2FcC/XMNNDmJYZLOGbjx8/fPx4v9sgrEFM x+wFAUAR0gXzVDkYTSSZDAYktjyVIURYxFnq8u14ly2ZaE7RlFHqMhCp3+M4Z+N78D7efMbkHCam Iytln2D1h+EoE+rQ5zfL4PMQIe0StHSxWG232zEYYKeFsMVORvV8e7h79/WXH7/86v3DcV+T+rgm 19LjG0cyCHE0in0TBUIQHuJUjphr0s+E5eItjifDUPTNMpQSEiSbpolxGISxfM208ECG8xM1NH/I aQBJkXGytQ4zZ2ZqmM5ns83NdrXiEzQAmnkn+EMHweNye3P//k9ff/nVN19/9fZ2xhHWEibJjWPB h3NRqIdyTIqJRglTGdDp3F1ovLgf3FTHN0/MgWGH+WLWCaoUqTKZid+ujCnqykMF5HECfM5DzOfB sD+3p0NfJdFTbJez3cPNZg0St99NEg9sSLtMF+vd8eHDn77+CNBfv7tbskM91Jma1GxPntmSZJmZ 30Z3TjugTwvhcmx5SpHMlzaPbBAG7cm08iE4ObPT9NJitOFrApkjNtCdI9knxnScw27LDHFO72fv WA1+d/N4hMujK5/y+E/ni1afLVfb/d2bj/8B0F8T9LoeqeWElCSNIWQ0a8JyeIaEOQ2SVDFiUeTZ OmlfchzyAKBYQljWaZkQYxdGDN5NyGHyVHeEKrmBFOAS8nRW40mYEPAM+b1Qo+nNAdjuDrvt8fH2 MMtkIq72H+wO92+/BOgvv/z44eGwkqS2mE9Xdr3KuUn8Z7lJB0UCmfmXVB4gIMVEVzNJCloGX4m5 o1MJXJm6V+i4NF9TITLfhg6HDZsyL1lMAGkKRJVOD7vNDaJX+GtAn/MhGwBtBfl0u4cL/+p/ffPl x/dvH498AoemFEWS8jyD/NlinJhE5hk0FXgsXL7OUaSrlqjHNLyIBjE3Ta/Jx6QwM5My9yT3KJH+ JGmHlatQR6VT5QNc4nS3XuwQAD48vHl7f5iPEsQ1X3RgZmZbRAAfvvqPrz+8f/uAOHI6LuX0ePPs r0SotGF1ZmnRPox1bBH7+FNDrKUCniSmtUiplI6R0pFeiKukkC4OPVE3njbPZ5JHpMk2EX2L02o+ lbHoD28A+mZRpqHdBeionO8QIH78+k9fUc67uRD0fJTB6UjPbZqXPPylo6CeRC2nJph2YYZPCL2i js6rFIZO6fEHXHiunIM7NRY+3ewT486d82e7gaCnIxDX6fpw94gw8P5mWeeRQ9DxeIF45e3Hr/70 5ds3D4f1VDKh5LsMsTTpyNaH+An0yIAOYwNa8816aOWsIHTKTunDuLJE3bfOxGL+I33a3ZIR07ya 8USi7+xYmyy34q/BjCYjmMQvOnYKK31EUPvVNx/fPNzt5MC7slxpVBTbnEu/zRNobToh6FTTXPJb FatWX07VWc1bSiCcK+FMNceTiEY/mSRamlB6LWkwIy3sa/5svt7fv3///s3dlhNVhgCdjBcIZ9/D Rn94vDsy2UfQhZzfHmnWnC3YWaaGQ0GbIEsLEZrquwB9ErRSbg0ps1iPZKrwqddJ/gSancPs6M3M FceRpqtH1Xi22t9/+PDhzR3igCSwATquZuvbdx++/PpP7+9vb9ZTzmbLTTeSjF6FLuamCykz9YUm ykpPBjAyLVvxU+HtxOaaw7ppZFLmJm4IZdiaAc3Gfj5DqOJsoub+EDQnGC539+BFb+/2q3EaOkY9 7hT03c12wYFyyRNo0eo4e2r5adRDK1UKS2xM3OivNH8mTWmlwfwE2iRhxbObhFtjRuV+mPtjJJAy gbjY3b//8BE2bz3JY++LrpPDid+9gzP80/vb3XI2rstcRy9Bn6kmuRy+y5/6quTvaVMTlD+18KC5 yJO5S0ytrhG0dH2lTfVfTGmqvWj5k8BPb1DVl6xfWc4p1o/vHo+cAx5A0vlys7+npL95Jzaa0ygz k5sRSTMs0mCuQT2SjakbMGl0uUmgJk1WUmKUJG5q4epEUtPeqJebigs4M6RZ0twNA1pTI/Avb9+9 fwPixCQSQSNIJ+ivvnl7sxZ/pcOtm+cscOpmGJ2j1pJZKjWo5GTqokh9RCP3yDiZRtTmEuU4WWMN UzlloWJQ0HGgvuwEWjJyi/3t45u3j/eHDR9aI5Le3jxAYwAatmMy1mOnmX6Q2I9QeqaeUGsKS0+l mDGcasDSk7oyESI5juTUtyR3gTqrd11HNF54Wd4/uHq1U6axQUDXy/3N8e4Bwe1mXhcpdZqmm6C/ Buj5tC45moI3bpSb51qE0n+UakF1pDG/5gGSU+knNTzCCJ3DNllFMKhNzC1nQXI9jZ7rePILVpMx 4IyetFvTOSOC3m0PxyOf2TAuc0g6m29vH1Wnb6HTcrxpJJta/gNAgZTj47NRzuJqUnYrSGVQNiZ/ r86dzoHdS64QUjmOmhurMHoCTQoYmXc3fCYEW4pODUZiN6WlYMaHFx6O+nSgTEzeFp4dgv7mPfzh dKwnMnNlEwTKsdk2YnHzZBbtxKPNyHR8hnlaDvd5LtG5L1kbSxLjJEyRZO9GmemJMqBP1OAEOuGs TV/Uw7jNRPP5xXiBuBYMiU/ZqVI5fB2ZFppCHt7lyqNlmBSQ/4Oq92S+qRbpbelYswbm8U/yX/Mz jeElQjORX888kYuv0c/UMr/pcdLQ2TyXif9mqI+o0jylSYNE+b1rOL1Oa//i/8f1/wCPaGJ8CmVu ZHN0cmVhbQplbmRvYmoKMTUgMCBvYmoKPDwvU3VidHlwZS9JbWFnZQovQ29sb3JTcGFjZSA3IDAg UgovV2lkdGggMjE3Ci9IZWlnaHQgMTgxCi9CaXRzUGVyQ29tcG9uZW50IDgKL0ZpbHRlci9GbGF0 ZURlY29kZS9MZW5ndGggMTMzNzU+PnN0cmVhbQp4nO19aY8k2XUdqyq32NeMyMitsvbqZTb6D0ge iZRN2ZJIyqZtiN5lC4IFw18Mwwb8133OufdFZvVUzXTPdHMIgw+D6equLU7c9+49d30/+ckf1h/W H9Yf1o++zrDOz88vJpPpdDrzNbU1mVxcXJyf4yt+7Kf88HU2wQKgRRQnaZr5SrmSJI4W8/lsCnjn P/ZzvveimC6ACIDiJEmzvCiKUgsfFDmxJUCGFWEtFnPAo+x+7Of+rnV+cUFQCQCVVVU3TdM2XDVX VQKZgCWSYo6VpfGCsvt9RnZ2fjGFoCglCKjSAhxgW7at4wO2QrIr8qI4yjAmut/XjUlYcVYYEIiI uAiwbpZd1y0Jjv+TDCk9frbQRq2IN0uwLX//kJ0Ble2/mmKSeABwuez6Yb3dbbeb9WaDPzfrYcB/ q15QKUKBF0QIL17MoTt/j+CdTeZxXktSePmUQQsxrYY1QF1e39zeXF8drq5u7m6uDpeXh8PlnkhX gLfEN2hLllXD/VqXOHezHxuOL5yteZIVlRQE5dT1gAT57PaHq9v7h8eH+7vbu4eHh/v72+ubG310 d0uwV0C4HhwgtQvFV1ZZHEFZ/ujqBLtwnuEgNZQV/lytt/vLw9U1MNzcv3rz+edfvH31cPfqs7dv Xz/e394/4l8+//wzrLdv+A/XECbg9S1knVNyNV5NU6bR5EfGdY7DlZeUFZRDP2y2l9e3d/eUCfYd 8AEeH3y7323Xqx7SXO/2l1xXN/y6x1evXj8+3F7tsTU77kXTplWRQ5lAU/5ocjufRlmlo9VATewO 17cPr99AHG8e764Puw33WQ8BQK3kWULCMY/TXMqyW++v7h7ffvnVT7/68u2rewhutx4gOJ6ygiYQ HyWLix8JFpRGVvJ0LVebPYRwz5MEUe03Q6/NmdNQgUNF+I8imE7nJCWgJTyVbT9sL7lreRRf3R4k 1q51sUFXUm7kJr9jWoltmEKd1Thaw+U1Ntart2/e3F9tuzpPY8GYkO9OfOmv5L8X/m/gXvMoLet2 fXn/9ouv3lClYOOuO7ySPJembOqC3GTyu+Qm0PFRWuDNNm23wtHCscLuW/dNaZsuUPr5YrGITEqk iAvux/lc0iNPXoAl59Vy2B0g8NevXz/eXe3XvdSkxEYalgDb7wwZcCU5rHFVtKvNbr+/hobbLMuY W+dC3soMj78QyTKWJUNMtqi9SeAmTkpwOkuK1dXjl19+9cVnr+6uAa2D3ArJjZsyjX5XyKQNabza 5bAFLhyP1ZK/fypIC9H71DEZa6zFhl3tlbnob/DPJhB/XvfUqfePMAM3h8sdzylttnyDnNB+B5zk fBZhA/F5+83ucMCxX9XZnERWBwe6wRCJ4Ie11Oq6vl+tVssqTyLbr3Jd6O7MFlk9HB7c9hEcd3ah 41aV6eKTk8mzixm1NgnuanNJafVNkSymE3PF5IeZ2/IOqACNTBGbWPsSfgwAakdOwKPzqoPZuIHc Xj0+YFNuXE0GqX3SDXkBLS+1sRz2h+tLEIcSv5JPBl1gqGojwctOUAIuBykvphU5rMj0oW10NCk2 /pC0Wt+8+uKLL3XeDtuhq2kJawBM55+Sa53PgIvKGEwD4sJ2KeLZKC1tnMZk1NlavrPI6w2Yu6CQ XWI+mV4PKHXTb/bXd2Qmr6AmN70btyKLPx00sI2c8qJR3sMUL3m68K5plDIzP0dMOFB9/wSaHJba gJn/qT9xhkxsdt7IQNvN9cNnX3z12av7Gxg3clHY9CpffKLteDaFUeU2BIfabDarFk/kFinlBiWL dUwuL8onIAgbsBmX6xdSKAUKtCV13pKi6deHuwdYt/vr/dAZk8TG/SQu29lkAT+ZJGpNt3FofRvC 0EJcdKuWJifJCh4J6F9ZmMZ3BHi4SpvQ4yF6FxY/0Eu6uDBsOrPl6vrNFz/94s39Ydu3sBIk3Okn UI9noFHcbcseLheIXZNHc1iuOVWhuYtHxaeAACSBE5QXRYiDGMzafK/KCYYwakPCvrmOlJnHcW6G /e3j2zc4azCVklqRLT62TYO80gI/u4XbtQWwOltMeB7iTJxcsFy7+6YTEU4ViLMQHGhxYVwpzy2Y gA/JpMkMKVIz8+KSxjVn+fLqsy9/+vnr2z13fgn9mH9kmwZcTsshL6iNKgWJ4zZMc/cRlyYlyYF7 zpA4MItt0J0sjfhn9s+KDQCZ7U7oSEpt6jFXUJMobdaH+9efv3m8vVzDC6pwHqEdPyIuBQHKqu2G 7RZqvoxpvTyOU4dtKDlRGhah8RCw8UWLb/DPPESGs1xwSwJz7lXT3AvZjM4OxDadl8PdVz/96duH q02Hb68h1/nHkxmIlBTEshtA5Jo8qI1UrqHhMuXA587F8Xy5xEw4xoYNl0dORcDKQCshNWjIaDGj 9xaJU8Z5t7t98/azx5sd6DH3crr4aDKbRCC+Rc0IFOxKOpucK5pIXMEky720QGhe+la04OgY6S7d G3FgHtkfvyrE77DdUmCK4bVEMt2zqLt9+4++fHu77aGQqrYqFh9JZudz7BsqRKq7Ip5DKZu8yurp PsTzG7DaWN5xEWZpfpYB0zEzoRYeQQ7qshqdHEWKcd6ydnvzCJp1CVLAH5JF049hq8+nMX5TvRoG GBQq3LPz86lYoxkoCJJkyRVyZnI0Q2WoTGWQtdSVM+AsH5WKy9LUpZQJAx/wAWI4Eom86Mkkaa6+ +Pzzh8MGzgF99/hjsBBq+qLodhu4HBnU7TnkNY9Tqo1wvoz8ud+byZhaOsLOjzZgZvHiAEwQKtcq /s3vwGTYhCETaJN50mxvwPuv4SYxTJ7FP1xm2ohFOVzv+prykueFfVg5rm6ktKV5yrJXLwMbcZlh tih+CJu68nF4mXJSRiWni/7+zZev7/Z4CljUHy6zs0mELQK/8nIA25iN8qpGOiuq5GekcKNc+mv3 rajnNf2XnwBrDZj4h95CbRYhz83QYWNnicIlM5605fb24Q0OGnRn+cNldj5L8DDL/Q52JJbLa+fL dqE92qj78HGWSJEHHW/2i2rf7ZnpRLd/eESxD5pnvgEBLIKd43fCsEWmRHDS8v72q7cPh3WTlT9c ZlOxixXtYzKn4oD6TR2XUaeiOOo2SCQo8qDy+OZzqpTS7DOzf05YGnlkhX9UyHg0ISyZ2XfnFp+U 1KKsP9y9vr9aN9wjeTL7AcjOF/gdZbO5WVcJ7NeZEke5scNOcYnCzpWFOoJySBjRcRtcSE6EIvqo z+ROhLkz89Liv4VeV2u7IA/Mq6STyZR2soB+jMrLz948Xg2UWV3/AJnphGXVctgu88g3YpKXzbgN c1Nezgmr0u1UwmOf/BOsLEDPJbk/wfpTrK+//jpED2keuAHzEVg9HtjMvl2RO9q0eVRvrh9vsRtx zur8+58znDA8e7PqltiIcnEZ3K6l5Bv/3cwupyOr9UCNkugElsoQZ7Jc+OJTYKVtNKhLkkyRgHYk 04oc8JUpPsSgnaXkZ9Hq4fHxMNQM2pbR95WZTljVrbs6ncuCgTUarraujpQ2kckN9NdqH7AETJ/x N5/lTyQWlKgDM4m5opVtK81MlMrpmtBms7y/vL+92rQ1deP35CDnCzxm2a52XREpKEHFwbTlclm7 UhjXqC/wb//YFlEAwdd/jFWUoyfGvzZuz22TVvhifqGHsPjOjDmW8uJKsU9pkfn0Yjpb3j68vtm0 /EzxvWRmNqzpV6sKjgqZrxTikfKmJomjT8mFB3gGWKnvkM7gX7XdGDI0vR6AKQUv54cBnEZsPy9H hydlvG46hfV5uLvZSmbfi+vDDcOjdJu+SacTbkT83c5BZbJJ5LhIF/KsmDXDpnkGGGGYJmwNWKND ZMczAJOlpmzCvqzx0zKzhPbSkojscdrePLyhzMqmzL6Hf8YTVjb9ellQI15AceQmr9pMbqZ4h3nM 9mYtOv8cMD6xh/IFjCIrR3PuwEbHJvdgHrOAOqVZOMEMH08nabu9u73ZyKwnsw9NE57PcW6qrh+a dC4qFVPRg8pXOkluZnTOyQGh3P4Mi6rBEfEPAvwjrBbbN4QBrPJDusG0JZU+4X7t34QfoaAXvieX +zJmbsoslswm9eXd62s8Wclw2YfJzE/Y0C9zcXqm+2qdr8Lc/iMy2a/vAmbx7eaYfxEXzIN7+g4w xSglMQBjyVlQTS6zpN7e316vpZujDwsS84QV5XLTVcYR8dea8qpPlGFmDr/O9ncBs9XUgTJL/ReB GL8LzELkNTarsoZRzBcpMpZGstWT5ur+zdWqhsyyDxPZNOEJ61YNnJULakQZUEuD545t9JaN7X4L sHqM39djqCrz90LtWjwHDC/RyCKz2eJigmf54LTZPdxeDTrVH8IawRLhN/cdNMdMVIpZY+qpPCXL yE1gweUArl/84he0yAT3cyyzwl8TWHhm1yOK2MTaXaWlJvQRfg6/4mdY+Hb/4+elrDQdTmBTCV0G v3qhQA904+tDV4MyJO9vzZSCKNphKfILTh/n7mhY9CL4vk0bCo4+DJhK/BxY4V7Mc8DMBSWlisiE ceJOZNbuH28uu/yDrNmFUrIdbMVC+auFqC9xFeY3WUzJsl6qzvkAYMb+MwNWFs7r2+YZYPaJSieL OiTB/3IAgcwm0+ny7v5hU/KUvrc1m2GnVG1HziGOGBeVmEBwDE+Xeb/vDQxGlbuYlMwCPJW5QS8B W8pQ45iRD/DEpXmQWdbtHw5Di1cTv6dmPF/gR/CE5dyIUPWZqi/KzGOJzlb/OdY/O64/9wWMvwC+ n/n6E1snauVrnc4qxLgbj00Stht3fhG/gd9PE5PTmmFfMOoYJxmcjTkDBvMe5GqFU5bO38tMn8MY 013BCVN6B04Yjave8TFx/gOAmbKwGPAxmPxtwBJTVQzMmcwiFY3k/Y4MBGJ4L804UX1vO0jVX1xM FpnrL8Xax2Te9wfmp6q2YlQHVr8ADGe7LoCr1n61E5pTZovpxWzR3e7hneXV+2zGsxl0OelTybgv SGeMTWAFeJVntJofBkymo/X48RgZegkYSw1Yniv4UCRQjxlNN7n+rFhtLnvs6fS7fbMz6MAsrfpl kzFaOZnOEq/gVZTUa7TbX//61/8G619h/TXWL3/5y7/ACgB9y/JD6pSfHxfBVbWfU25vy9hIu+ZW cqpYsEgbQQIbX5XnFZs6NyNYqHTzYp6u7jbaTd+ZpT6f8YS1az9hcJtTYwiZ2ZvW6hu+A5gFq18A Jvs3yixsTMilbAIyo2mnwKwQAXQk0WYEKWE+bTavtutVg6+NvysreME0X9mZ32yqw4MrhWX5rBrg u4AJ2gvATrLuPPm2EQAMDvqomcgqmxNgXmDRqhwtlp+WzGaQWdIMlx226ndqRrNhPY7knF0qUB25 pyiDn7T8K6zf/OY3/x7rP2L9W6y/+Zu/+RdYBPdPsYCAgPiFgPzLX/3qV7906HxIt3IWJahCNFgF K1UolcCpNpD44vBuXOWQGtPJzVUCs0iW+5Uc1Pm37kVFOuiuFNGUkXqo/uD9Z3llG/EjArPdaBka D6AqLRBK3dvmBJh9ppbMmBUvEiV3y926r/D3b400siw2L+iupPOJSriSkMbzTgH86I8pMdt09dhT MWamnwE2mgnGGkmCMvPOmuHQfxdntIqpfrfMFBCYMP9MWJVni7VNPiYwi7fVgZ5ZxkUtCrICTX0C jKextWBtpgNTgFvRmq0edyLL38IZpziXVbvatDiLNGKLVMAsi0BNx2eltvjtb39LUP8Z62+x8OG/ xvpLLPJFj3lQl/CLoWH+JRbw/YrGz8ikVojGjXUtIZuhwBGVB34aXwZfF/80MdaM+TAAlygX0xx2 6xav/1vCcTDO8MOGdQ36yxMWZblX6kIbBml9bGDtaTmSYvpZJtX/AjBp/YzpLNha1qxmoFYrnLKX q3egOmjDehWLXEDIiadQLSeLn/gpgHnycJSagpUs6XsGmJeVkPGDDTe5MtVRsb7fsqYgmj6PiyEc 2LAtTtjUHMzUw5ds8gCwfvlpgFlc2/08DzfkZseeAUYGooxnzqIWpeBX9Kap8p8XGVkHY4lNuriw EA53Ymm1bQLW8QEJAYrjv2D9V6y/tUUdQhQEVlrIl0/DNwG1ws8QGKHj+cgCCYyPHsCEVXrxRFkx XoIv5I/hC3EKYEF2ODAx/bpEPL8+7LcdXsYLMSvWdNRdP8DBPDsHsrlyCqWKA9j9sOz7TwCsCkls z4vmeci9vwisrRgJAbUq6oz+tE7ZAHuUPH/KZpAQ/LBGJwxMcaHIX720aj1K7L2BNe8PzBLvVSgK USmTFWa9BAzESqHUJMnhhDKIGhfDzQYnL3m2FpUSgh/WsqwIfvNMJ64sm44V2ATWfRJgWV6GTWjJ wDIUtr8IjA6nVs5IOZjVbA7PbAV37bkuhLMLAmlXNUw43BUo0li/Y9mvhmHouyV/MFnvv8P6u7/7 u/+O9T+w/hvW3//93xMnIZP5uuPFJyISAONnaMKJEw/Hz/yZe234QiIIsVfPctauIvFZfiH10F/a oq0vFd1MQPNzgOEpq7ab9RJGevFNkTEkwCo31rdN1POQKEzKnrf1erX85MAK34r5CAzreWCMKBeG DOSPzVnpctj1eDnP+NJ0WBiNYkUH5TVn/wD0Bnv51uv+0wNz1pFbEcy3AuNeFTC8+0wFPFHRH9Y4 Zc+4LxPSqa5Rd8IsmsMhUBBO9c2/C2CV+iNKRZvL0WA/D4y7VPEPqg+5L/Oou96toC7jb1DhGTzu umNqdwL3ck6emDO6OGwAbMAp5yOD/P7mP2EB1P/B+r9Y/xPrH/7hH6hA+OikrB63J3MlBHwfD78T 4D89BjcUdWy9Vri0xo+qfCIz/Cj+XqL5a1vUX8pDCVhWypfGBmuv9puu/GYFCOP1UO0sVSH5XUwD MApsWH06YH1Tejmc+WP0pXPrJsFfXgAmXIrmY/NanLG+Olz2gPlO9IN0ihzDTlgEDhYFia3XcDw/ HbD1qlMdAtVcLmJlRbim9Z8HZlVXuVWZ4Fwx6F0M++u1qr2fACOdquD/sHhvlkSw5+RTBgy48Bob PifPCM8ZUPwvrP+N5cD+A9avsYjEQ28/d78M/+Jw/0Q1jtZbzIO109puBpxtsL8489oqJZeEDMj5 veFw4dfzlxAgNy/8Mkg3XSygE5gO3OKlvEOrmIho2LHCE5ZGs0WcqeZuuVpZ72VefQxgy9ABsuwI jK3ThmxlaWBFQBjxzfzjl4FVlZXTAVgUAViUrR/30IvvxKum2K0tQzjwL6NsofgvPaWWZMrq5D8G ML4jc0vEBNdbQ2ZrBU0n81znNL0Kqb8MrFZNK4METFqAfSyG11d9W73TLTID8rZnfgWaM1ngxNGG tV4JpDTrxwA2rELrbW5JXA0m8MXWAWUImACRQvk2YKXXBuXWIArF2D1crpZ1+gQYdCIIXlcspmdT 8GYINieXWsptJ/nAL+DB50HmTwVXpB2jDaMBg2Wj4lCCDi/9Z8flIW5qCjeJK7yuP/7mCoFwj4Wf rADsV7YIjH/myvAzRpIzSQH60d7sh+4pMPJENtWw/Hyu/FMCx2ylVlfa6GH9fsAW3wasZPO+bEf3 UYDlVhdEIhxhI84m1X67eQcYq7Vr+KCM/kayC2nVrgb1bC+pwzYfAqx8HhidvWGz3W710344MGbM CgEr4gmB5asN+OITUsXyh2bF6C+zK8lsERVtvwZHxJGwnrH3BMbahZckxirpBtB22+3HAEad7VVB ADaPZhO2wwxPgU2g3JeMTbHVuUxgnKseVEoTKmCgAQwnnb+E+oCcEdSPlPG3WIzhw2yzwVvAoKnL dmDnEtVDE0dq7raOb7DPbsCPCf6YY2a7Pn7/1muVvn66+MU08Hyh+EV8g8RpFXKqeYSKn0Phw928 XZM6nSr73ICxFxPAYsiPcMB+ud4HGNvTVZzMQ1azxWejGo/Iu9hZKsDMZL18DphiTt0HAaO/xkRZ WjDXwn0WZaubAeb6BNgMLG05VNGUXjSApXXHpirIbEtw7wNMYokSKzkt696+548qk6KXZPL/1TeB RVajUn4gsFrVViw9iafZMsdv72+G4slckHkOldWXAMYC53ieNZ0ERmjvCWzhwLy8HDp1ILDcy2q8 upF26llg/LL8w7YimQezZfjJ8TQFGYzi1f3mieNyvsAOWXaFgLGYs1j263UAJuUBJhwqGzws/2tf SpwubFBClFj7dgJdtFz1XZ2lql5Trbe4n+mxEI539RI5Mr6Jr8fKoz/1dO2pP/ZXtvjX4LiwXiee JmWGnzG82tdFMoZNYcVwKpbwnbkVWYVdwlvx4yWJ4YPhRWBq+zJkmo5mxXgK0ZiXm5gQQx4McnsO GJB9EDDmXOw9pnjwCPtswfQEaNk4WoguS0Pvk8DwlDQ4K2EZzH22j18CxirruSFbSGTxIgxUMJRR ZOfOs5iA9iyw6MOAJdKyeU6VFE3ncLRm8+5+WxX5/NRlYeVUFs0uFmkSpWCNK86SWknZ66O+714A lo4DOwiOe0Py097SSA/VZhShnVO19e8Aix3ZBwGzimJrPAGwCMBmy5sNHJdF8FvosjCdnoEjR2CU oIkrA9N3DL1xWtbSEqieFmZemOc5y01e8wBsToUPy7WwuR5WA0VbIzbtzfpWX3RS/xAHZPhiD3Lw pxPCX9gKcUVfZNp2aFWCi604Z6PItLka8C9jVyDEVCy3HRvkYZqhOtqOsKwJOPTWK+XdfAOYna/o RGrwvgMwPzrxU2DtM8DiDwdWuDJyYJEBYx/FCGwSpQXrBFK+cehEqA4AW9aeAfFNxA/bbwBLtO1Y mabRMtL5Bowfae6gaFY5du1/LGCWe2LV3ykwNhscgYFt9LueLjYPPYD1AMY9s+xCpQx2PyXIp/mZ hZ3Z38CNiJMUZRpRZzKLAonSk0YO3Gtrgujh1ofaG/zwxJCJteBfSEgDGq+r89DJnzsw2bHaZkcI 2GSukriGTfxldAKs6vcCpqFLpaL1DNivVifAOPHhBBj7GGg7Uryzom2U91j4tjSbxkJl05iRgspt 6G1XwO0dYLGxseq9gVkig8GqrAAw1UHXh+2wrGMHdjaN83q174pEyiyKCWzQjL1h/TKwXJXwLI0s 0hIKITcYJ8ASN2qRkJVWoG6JZjhTp8Asd5KSL743MKssVmwREoPSg+NSX+7WXR1fuHmWz7LvVH3L BystXi/i8TIwk1eSVcsadm/ZFjipqUQ2shBxQ9ObkVh4SIGxbuQEWBooZrvavDew2KaHcvIG1H0M jjiHq7nb9nXiwNjd0Q67pQwrLVGpoJuR+xO/KAQDVXOYhw6+pISdYOAuT4o6j2w76oypq7iyxjGi +IU/pP/1pAqJrwsSbb82zcEvDNVLnqb9c49NUmFZa1ZmfU80/mmywKaDyCbVbrdf1cnEgS3wcMOu TWNqNLwBFqYPIlPD6gVgeOZCnQ4p5FvBorMiv1qWrjhM8Wd1y87A9wPGaSHvCawap6NqC6udJ8op t3KzOwy1k0WOmyrhP7UJq6Up2oIT2hSxX70EzLIienNlW4FCkwLWbWlT7BwYS6aZQXk/YG3Xvy+w UMfj0Dgxac6Gx2mx3l0NVTILwFL4tdvG5916t45PsHwBGFw8pjCshpvJwUZDMUsGgkZgzhGr9wJG a/kBwKwJLbe5CxMbcwNgw+6awQEHFqU1gLTWg6iiuUYSG/p+FeIW7hvxQ0v28OezpdEn5jQFyytL /YyF44JXXPv3EknIG/kxDFERvqYwr9ayR+yn+JmTRP/1wSeD0lBJcVN7syt7Aaeab8VwTtZtr+FD zwMFzpoBwEx5sCkhrwFsvV51zwMr1WpVsRVKLWQwvV1dqJuTlFdRPgLLcG6eARaZhjkBFpqp1VqX aWbBtwBTTbkVh1J/LOYTm2015VyCDXzoZAQGbr/etFlsdowqnKQKzLcfngFmZhFKQzQL3KzplwqG c1wk/eQ0olGGElp2/TPAEtvzT4CNdsAmnBTVy8C89FpdUdYIyCJ6zQCfpO1GUY8x9LaEc9JmiZnX SI9ESvU8MDw+PZPc6quaMmsYMW6oAVXF0xSSPZMcffcMMOnpJDkBFprd2UcoilTWLwOrx6QfHRcY X3YHnNlEMgDDVswiB5aWHTRFmycMM0mD5jamZ0lq1VZeuFDa7JsKCpBHNrSiep6LNdYwMQ3/BDWO rTGrCYiOedmvYQD4B1UEvbQyP3bxS2JkICznC/V8X/uLUd7Taq0K7wNkIsmAaSI4gG1vToFV/W67 Zvuc2sStLwH6robcVn1TOLDca2bUHBWTJdWMazgwbMSCMTRuSzAQeuHs2nsGGD4fgNGvLlLv8CzG 6TMwJM8A80qr4smcQ439EDAiS9rt7RpHwYFldb/bDG0hiWmGe6J2XJqWvhuB+VCfSic3jTRrEUIN EmOUD+9C5y3jcGTNMHkGmDuaAgYZ54l5wj6pW6e0egGYN/KHLkoG4OL5VANPDVgDYGUWu5sJYCry Zv9BwuMIfDke3qr56gDMareySsXlBRODeJxm5cBkMoFmxfFoRVIqCNAunwEGx/wILJejWByXPXPz DDBZ5YwVVFUYk10znDG5OLNFYJvbNU5CANb0nPsH8CB4DMbQGZEZbNWdYMMdjMFKzWtEFn9fyCNL OGQgFLG8tkL+SWtlPRbw8j4lviMQlTDpP1NXmA9O8OkszTjXj9ERfk+oXspVtGKjQmTxNDXzCKwG sPIJMOiIPJ7NOTdV1pwqv7XONxuYVYbB/JUmr1T5u8BopKkHzQfIdSBac4CGd4F11p1rA+ItGKl4 pDXnjFPXvwlM4bzMimtrvkrwqBNgsYBlT4Axuz3z+BlZY1zgsNTjbBtrrrQ+7RbPVddPgCmi0cCW qVED/5Kqb2XZPw+sH9vENVA4GVcYW/OSxMIIKG/5wmHmHIQjsGpztxm34hyGiMAgVQ956gqI7DiD Qrj4A0WjPDbt8egwF6GX1BTOqpQMY12v7IUhGwYOFIqCfasFLNW0XQ/5KFKX+7A7awUZSRZfinfk yr3lmH2fGTcJuAAsKtZ3W+jZoO7hPwuY+b0OLMl9YpT3Osu+0Ay/AGxJG635VQLGBgSaiyGsXrNZ KZ7Ch5NYN3okspz6CI0y1HT7BQfOdwmsChORUiucwFdoJx5xCdgGqmu0Y6vLoS1VurMQMI3hiTK9 IhuEZaeWCfeXgOGE4URRZAKmZFJ/xDXA0RariTkSKg/KSP2XUape3URhujr0S3rDhBr61SnhhowV IeChpBC5Rt6cBW0/iYrhdgTGkT+rw5rKYz6fucQUt0jcCobBEwwDdF37PLCuldnTjiQwbKq2PwqM CYrE4lZj62+hGXzcH2zh5vskc6/VhjX2uFgwSr0tJkOb78ERN1CtR52okZoRK02bAGwaF6urDexV cgQWsds4lk71BnqYtyStgl/GZhBwwpL6HN72SpFi1th2bnWSKG+6o7g0btJSFwsj95pv1Xh/UeSp QTUsMlCnmyjG5jmzW343hUUFcvV5JqPqsHGaUb46AjubANhhw1H6igaKeyhKt7AB1RJXYgHdAMyu x9BwQF4isdLZosCWDiyOC7jhARajlCxQXniwx8J0QbnzzVvXd1GbexdmntYGy2tpLSJpVVSUNqsF TgTGgUEEBvIwJpH6wxqUKLUscmIj5TjKJQmUV53IqY2HIbClmP2y1JvrlLMAMOILwJJqPGHKAnB4 nEMKiz+xaldgPdXYzU6+o2kS6lGqzL5YjZ9PqmXvoSlqnbDzI64RWOTJlghbcQutxpJGSyQnVuui YtpCukz+ZzVuRZhhnqlGVy90jK3azNnlCAxe+Wp1ohS5G3l3xCkwdTezEI2KhafOdKJNczWJhZtg xq5rtRDZZJpkPGEaWaueidXVukk9pXkWlcP1ft1BNvFsChc7xOKxGVPWvRgw6kQVZzcGhDmfZWPN BnBLO080uT6Js3q1Go2YMqOb9VB7iC+sWI2JNb6iTkwnujq0ix0UNK7D6NrGRgTpHMp7Hnn9mWa6 zqc4VP3l0EQhP7bAGTtcrtiRtZhPOa4+JqtiD+QiLa0rjkMncJ4EjIV5q57ALBOjPcdzdAIMZthx WeR1qwtclmMgfASWyCbJFEQ2t9FCGketWI/tZWEeoKyPBmCYxNjppueNyx4WeQQ2L7r91d06V0fW lL0ikc+TwzsocxvgyZKtVo9eN1ILBGYjnDvLfz4FVjSOi7U4XGudtjKy7KdvxkT5T7m3TNzBdpZj O9lIPmzQoXqEeU2HDeANM6H9gC2YA4NO6Hd9vQjAZvlye/Ww4UjvMtElAvPYL4S4WKgk0haO0x/L V4aO73sHxsXBkixqOwHGyU+GS8AUpeSqx1MmEgJNmyjQooGs3E9SlqXN9m7HOFuR52ZdU589lmge tF1qJoGRENIB23TVCbBmuHq1U5dY7rNDLbUwxUcKukvt81x3puI6H73dBxusCMFJajNTVnSUl4Bx UzbiNC6tHO6qZwxmUwvJ2EOOGWvfg25v6E6VGu0EhWiT9g0YnzKaLmIOvF6WYw56BhZ8eLwayPZs gDSHovP3cVRB7JyeCrglFPlctKGa0O8JJ6UEj+HtKG+7J/LyHP2mdSCLiOGHXC5InvECiYvzgEzX hiRhBNB40ZeYHiTGdq8mp8TOw3zymQwUeBYORTteSHQ2Aws+PN5erej2sypY1yIwBTjH7lh4t0nF rSi3pINp4KAZuzuIW7FbNk+BMRU1asOwiGxZZJ7chSu6rMg1ONN+MR05uhslu1NDwR1LndmHpHX9 euhLPNv4Ki5UMwBq2HKzj3Ue5zM8xv7+/n4t91VkeDJTmnwxZam61VaLE9jA7b7VTEtrv/K54k+A schsZQrxRFz867KwrDVTH8u+ZbXnqrexeWdPoFkbigU64yTMKlM/WwdgVXwExvH08xlvOui36z4P NcFn2KHFcnf3+Gab2zyBIl1MplPyfAYU5l40rlbXzg5Xo0EI6r+yUfCcMJbFnnuecyr/yqtEtk9w KYW/sERgwxT+Zs+bvEAWPNJ0Cu5cE7FkfQofxq428CWBJQsBGwU8hUJthsOmi8MJ4zzIvN3ePLzZ 1+akyJdjlID+6QWH9CnqVlfVsVRDLrNwda5KjFcYsFSbYh3KlqQ4DNiScWI6KiwXw89Yse7Hxnod gY3/pzBmM7+pJ7ZzBiqO38pWsQCM/b8zZi+79X5ooxNg8KHX1/evD4zlMq0GCeUMdTNoh88m+rdK p4wqo9GwtE7el825119xYuJFyIx1Fg8I+zD8ZbvM08hMcUpFwK0IO6AhNqeiOjqPk6lfG+IDyPHT c7zVgltRyIwm8jYRVuz1R2V/hi9Pm+Hy9vXNphPFpPHnvMYEx4F9xrFd4XecUm9qoz8BppsX6IRb EZwxe5PSelwEucTLUkZHYQExepa+juTItpZho7x8zU4XnofFNpOgSC9koLD795uhyU4qqSbTpO53 N6/uDitmO3xss6WEOPovyqwjwxtDrV9eN7H0foGEeWPw0ESjmZ4/Rt2e4NooQyBHIdNkUt0mOrk4 VR3nbng1EUvjBOy+G6WA7RKpOLObHUdcTEC0m+ttV5xUK2IvwsdYXz3e3w42hU8UlJ2rNlyZpswD cJlfjNGFWxbCVmw1aaxguhe/tu6fBQY1UbOyIGJEJNVk74uA4lRp+L9x1oHy2Qs/ZCelSUr32e0o EwNWdpvLdZueNoDA1QQBunx4fNwYzZRUWqgqkhiestRmzGqCbC1p9V0AFuIB4R6qBWhPG4JuXjrn FYLyvBS80YSw2RNAwnQC7EIX+ERWKOHj5tJg2EQewmUN/GCeVKv17oQAh+BADUv2+GqnQalhTFil 88D7I6I097GUFtDpRjXfifl6IVthE7Q5qnZwf+UpsAGHPtK9PKxjnbyD6ygxu+BrNg8Vq34DgAOz coGF3Wjgm3mR0PnpjgTYgLHhA5bs9R7nXwPnlOdq6gzbWfNeF2lVeNS2HMu9JK9TYDZzdqFoh9sx K3V0YLsV3ROF0VPYyotvogrEQ0qCBieMR7UsTOrI7JZYh4YXwDBGw9vnjjzRkM3xkrc3968uYWZt kp5pv9xezAU1o2ZjY2fkR2Dds8BYeEsPJQAKFm0r3qGMKb/sGHN/BphYR6TWHR8QmCRx2JEeEI+t o0U6E8BqXlBSPAX2k2laLTdXd68OXe2TKi0zz/gWJ9OAO0KRKZrEiN4y3GdCyvEOMEBn4e0gb+wI zFzNJrUYg87+c/vwKLKF3X9rw68VWI2eIrNp1prdh/8W8MMZX569CwxUYX/76rBqOQ0WyEob96Kb Emketd+llZjdGy93AjQ6YD73TWWzDAoMJ8D8sEFeKxa4iPjJm/pWYGT4BOY3inipyhFVEnDNdVEd JQbOWX9DYiDGq+31K7gulUaiZjaqv9J1e3NVUswUCaFOk8yUgSayE2DKrbF+yWJTtgdHYOul4r6J RglOX8blIpsztaiWb6vVPmpGh+W4FhadgfPM8aDfAFYQ2OMV9EounWWOHkVGzxQup/kRicksL23a OX3Bd4EZTwzLt6Laq2rFu1Ix0em7qv4pMrK/hV1TeTpd9ER3SOUbz2EQg44SnKdngDUCtuF1aTbF QxO+QnIJupm6MfadzYyn1Z429Qkw3byVBmVvmtGN83roWE2g8pj3AKa7rZQxrh1ZdlzHIxYKPhf0 AOV1PCuxq/sDgYFn5Fm4HKiWmxFuUqHvl7g/NV6a48Mq9CvxORHggKz3BBkOdqtLMKwGn6n+b0Em Gz3h1Wacwjjei3IC64gMb9tSUbkoQnT2DjDWvx1uAUzZ5Tz1+SQrzQRh1kyFIox6Z6FeyvuLcuVO /PrqiFasXwdB9diqpI3WwJgpbZ/GGXXSt4ns3B3NhYK0uuUgDP22vKePadedy+GApGIV70w0FbDt /mpPzZWyh85OEctYjH8k3GVTu3EsCd6J2uDyMIXbuFyszjPXh8RzjAqmrgSKpszjELz5FonNbRha GW5CCJZ6zFgz9aqCT1qRRDP9iqdXwk7Z9LHhtbnsKygqG8RpMalWE5U5Fih4Z1molrILyAor6/d+ Kryi0D5iyT5vQlKhGmNSCfutq1TRMPOnngXm19OnFvyrxhkLwbL57QexeQoa4MrRCO9YMtB7XhO8 B0v1oe/muzAI1alYj5vC7v+V6EfSHS7jyrzELGa04wgM32v3yIebCxLdtlFXjDlPJ6NL9e7SBaIL u5cz9yuIjtNnvGrA8na6LkCsCFuhb5+yxWlKo7rVBZ3NeNtvq6EXrM1JucUau92N0ZDYN6P1mtrl BBYJTbJmNfor1IQivYpk0w5ynoNiXjnLJ2fTcJHtOwAvHFjipQThhrbxUrZ6zJYVJj87vzDSx5gH 10yPw/vgoL008souP7Lc15KzYlVfJU0ymwevzzsXlSZPLN1Au3ECLJBeZi3V65J4MK/Mw7UsdoNt 8FR0c69iAXMPv50oX59AMxY3+qAnG7vHl1ZBDWenHhljwbKlBNaUXkkSgNE+UE82NulqToK18CCA R/zsKRkGpXket+LSzmeWWuOBxwJs8EOlCdGxdVh4SEOYLPCnXrTc7kLIwli9kxuijpvTzp20VwUi Vz3p+KNraLhAlMos836vZaeuJBV+FWqWt+k7ukE4aAsjAl7azluDR2CrRqGb2O+ZCDy2CLUApVcs R37xsAVr5F9nSRZuKLMO/nD1zXF+lV0SNd6+x5cL9tFV6ck8sQW7nonLyhlU4iiRQVeq5lnbsfCB WPQReVmXQUmOhJRzD5tuHZzLFX6JobFDtpibgc/D5Eu/Q4LOtO3Iqal4u1DIajFOroRybBZvZ5JE VnJlx4dpC0aXwQNOwjmJpRD0Na1KzW3shE2WodA63uLgJR95zvmhc9FQKz0xRkqrw7sAw+oZEFp4 Q5KZA0kuH++zDWPs0jgOLyhOszCXXefpHWDhqkdqD9X9rPkCLR+e6dqT8kirJvhVVs1FyTStjevP bZidj5bhhIBSVYz8yZQZI7TRIjRTSXHoFoPVGAww1eHIKC9VeVjU3i7RbH2KYuGzuYs0XON1vG7Z R5G/IzRlH919Zc2CZ91FBhffAMaspDZIAGZpBwvDLO3qLX27nbOFKwyvXl4wHN/ZSxxMdaQmVIeu PAej9uHqMlvqskgkbY37PN6MF4B5PVfApRKg1eDNHJqZxeCfl+JXbchBi9sbMHIF1flqbnsoPQfZ OuxAj8fr08R2OQfpaUaZObFVH7Q9VUeyCCJdiFpqsotC22EJWJXJAta8d9dGlTwBZpVq4ZIUTrFY yXHdKv9rpRi9XUkPZHVfBlM2xZEPBXj2ZaVJTJVukNh2d0my1Yx3KyYKpc7jICtLvwpYCAiY6jCV YhX9HhoM1xCNyFg6w9ANLGU1Bpsby9NahYcl2b1mjVcBD57XJjLTeesdNxWfsMEJGIGVPjFssOiu QjoW6tY/84J13kBq59lSFgQWGt9szIAclmMMB35CElKXcYh4Jul4f9l4YzSL4hR91/jf41XZjSfZ fUyrjS+lOulCvn6726m1HvLYHg6Xuv6VRYf1yDvUaBiA9Z2oq13IvVT+Dj8BPLILp0yMd66yTZ+d oMqXvHFgEj3EHmyACvGUcVZZUXOUGI+9VNGFXcH8LrI6VMCNRad4251nSnd4Kk7cIbg9gF3uN0r2 t80pMGn2wQLyy1qHuDRgeCVAxqvj28qv+sys64hlExZ10FEqZFVWXq5SZ/rcIiQgzJglqV1sY8Gg TiOA4pk1cSyMb0lGrRUeaRtWXkkQxNhp9gRf9n4PNPxjf3k4HK5vD9iabJr9BrCVkuW9gOU2tZ2J URD/HX6AbUa71M7rDEN9lCdfOwtOsXW1KdyRsaIzq++UFTsBZrMtrbCBt3gnrhPH4o463G1Yec1O K6O8dnkZMv4PuA439zd4ygE/9wkwZqroyg/AXHt1h3wyHM71Ft97yQvkayuiFYU1WURPgA1u6OUR hI3ooQTVk8JrOsYl26ZQNMWWPObxzkC7Ja9SOXx5MoBZIhhx8bkM1uFw++r+5vqwwY47UR6NeKGA 8bZz+dAOTP+20zdv7f7RMXSUFUeRRXAx+xDq8Fo3Z5Kq3YCcFbJWB4UnRlmsPAuOy4VdMe1TJIrx 5mizY1bdZ6dtubKBVgHX5eEKD3d19+r++uqwxTE7sWOKAwRgMuPhHVlJ0RbffHN3tR/8PtBCVj4v 8rE6Kj4BZn6Ysg+SbaqupdxKRhyYBfcWfuf66IEl6dM7bOWmlMEj803K3KXJzOUFYFcBGJTEEZia bnq97DWUhGp2jbpYEdiKIru+vbne4aEDNbddGUQG37nrPVSqcEIUj9l+m1WXsU1Bv8nv+bZo1cWJ z+wzTU7ort8earelBj9zOYRRXYJmwK7vCexyO5wAY5TQLVm/3g3yu/wn+33q2IwAdnt3tRvCZXx+ l0fqVprMYeVGvmFolB5zMt7ep3nuhbaSLjBn1MrukVf2zuJSCoeNt9kegRV+k63DLO028XDQCOzq 6vrm/vX99WEPYOGMnU8TbTpT+ADW6fpOu3HViznwky6vbm7vbq62fecnpNVQsFCDCOLgBUh9V6lD 2k5ho/Q51V+aODD8BBvvflKrEYD5GJpT58uVSRU6/So2qpp1HpFB2d/cP97fgPvhxR4dTd1aQfY0 4FsETE3Ypbr5mL8Eyd/je29vby/xRiyL2WmSW3YMCgRgDOFYbotD5vFK9rtezL1sDVjfqQlN9VDP AMvCIBrHJv/M7kKRiZYlM9MctuOBKuDh7uaSHGsYgV1Y+NMnEaUeY52FuUUyrOHCscLjTYphWF5x 1GnerM6UvJcxzJh/4klkVEo+nFW9aVQ9M2TjM3jI3kMes2MPq/Ga+Uin/eKucbpQuGRVbNIuhf3J /6/r/wGsZJI3CmVuZHN0cmVhbQplbmRvYmoKMTQgMCBvYmoKPDwvU3VidHlwZS9JbWFnZQovQ29s b3JTcGFjZSA3IDAgUgovV2lkdGggMjE3Ci9IZWlnaHQgMTgxCi9CaXRzUGVyQ29tcG9uZW50IDgK L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggMTE5MDg+PnN0cmVhbQp4nO19h5Ycx5Ulu8tnZaX3 tnwbAJTOfsFIICmRkjijWe3+/6fsve9FlOmu6m4QAEntmThA2+rKvPni2Xjmm2/+Z/3P+v9z3dwO hnYNBrc3Nze/9R19mXU7HE9mM0fWbDoZA9y/PzIQaySw5nN3sXBddw5s08lkPMICQqHfGcx/D8w3 w/FUQLnuwvO5PKIjQCyQb6LosAygm38DapJak5njen4YxXGSZlmeZVnKlcRRFAZECQpyTUHG8Qg8 CDremnXzlJi/k3VDWCCWHyZ5WdV103Z913ZcbV2VZS74AM/38d/zgBIguU25uFOHv0NuBLXGUxAr iOKsqJqmqcuqAra+7wUZVlPXdVWVBZeQMonCMCAZF8KKyovDgdDut8ZjF6nlLPwoyYq67Zervqtx +2XVLntSrV8ulyus9Xq15OqJtKnKHBDzjHQMhRnnIkWxRQe3vzWibyxryRbMSqFRv+yaClTJC+xI kIkfQMRG92WH3/fA1fIzvgMpK1Cy5GYFxsBfQMqMh7852W6Ho+l84WMPpkXVrTebFZC1dVlkEB95 noMmZoGC2J41CUg0/Wb/sN/s9rsN1na72e7WPbkxT0LPdQDtN+S3m5tbIRbkYFLUHYnFW6vqSvmI aIoDMm7OUsQKaFSCE1u8uGlJS1JxuVoScg9q52kcgG7E9puAuxkMxzPXgwSI4rLb3O93q7aEUEhz bsPjElBn6HLRAomuNIW8qSlfuiXottutsZWLNPLc2eQ34DaimjrYhCH2YA2Bgf1FQugtx1RcxxVF otbyA1LRcQYbXs1dix9hs9bCieS+usCenM8mo8Htr0m22yH2oOcHIXBV/e7d3bbNEkhFkeMQAR5t DdiKtBcdmFcBoeWkFnARpMAz2hs0x+KbQbWnedUst/u7Lcifhv7CAbRfCdkNqAVYIjEgCPv1dr1s K0gLiu2jVppOjY3BTwAH4xFmVhAKfXIhG3ahPAksynxQOYriJAM07kwKmSqLvF+L224JC7qYGyxr VrvHx8cluD3Go/fdKY3BW9iD8FzGkykE93gyUd3En43GEKHQC8JzgsvsT3zD7RvZjZvgkS3v7h+2 fZVGvutMvraQBLXExABrgeW71RrivWvyKFiAVOSIIQHJ7gN9aDhhv8K8UP1L9Qu0lKNKuSwvjOgk +VSYxLqwJVuxWSAm09BzIEm+IrPdUhAuAlq5eQVB+P5h1yQhTD93PjWG+2A0c32wimyyOCF1DH2i wJ2pzUTB49JQOaqDTD9ycVtiR4oMyuvV3X7XF+S22ehryUixnLAJqYvb1Q4Mvu7KxFNjFrTC9sNe czzDRiodcugulRlgQrErxnylOAILoac4A+Yv1CFQNcAPYnmS4aBIQgqSr0E1GrqwnHDXZbu+e/fH D4+rIvZ0/w0GykMT0DM+lewZZbiYvwZd5M/HI75YAgh4EBBD9HQSlZRKNIMuJdHxq3q9e9jiGQag 2vALU41WxtRZBAm2/moDNbrbLms8xJk4VgbUXARfzFukdQ/DKs9KsS4MMvxArApxqof6Z+C6KcUm qcctfKBbKtoN2xnsRgVeQ/DKbv6SRIOEH5Et0qJZ7d+//4AHWEQQBxN1FnGHo8kcXBPHoR9ZVYtb AbCGfplYugQnAjAUQaP8JiJU+A5Pxg2EJw90MxhjODgZbS+IKX8++YLIwF3knaRo+g2Yeb1sYBe4 1g3WDSW7NCEwVcWARjrVnbgoRW5MR5HsofWnp+KkCD5l0AV8OuzK/AwYLZkEX1egWxF5s/GXkiKM ZbjgHfLW47d7EAuqBW+PWxF+4W8DkWQJBEQYixz/+eef/4H1F6zvsD5+/EgrEshg92OXcavF0L5T A8woP4Bzw+TIa7kyHO2TMMy7zd2ySnyI4C+C7GYINzJMSpGEm2WVBvArhFx06Ml6IFast8vwhnyZ XgImJEitBBQVQOlDDU5NjvdkkEu0v76JLGOexHEG/l5ht0BkfYn9eDucgn2yst89fHi8a2MfEnsk TI9Pt8Oxqzaike+0jqBfo/giMH38ajVaHeCbGI8Dq2s2UoQOdSGXykYrKeGhr/Bo6xR88LnIbm6p kpOiXe/ud9BbEewbDcCMBR5ZL80O7CO2Q/I3LID6O9b3WB918cu/YvHzn3XxL6B7aTBDQ5LtqBJp nzhzuySWRytadnhN360vY8+ZfB6n3Y4cL4Lm2r57925VhNjeSi2V8mMnyFRtlZU4Lapa3wyMhIjh nnB5ECg+tbCNjh8WBCZuIlGi5XW/e1yVJNpncNrNgFI8pyN/v2mzwCVjUWaIOKR8jnMjAiEDqaXo OaZvBiY6GDQjsRYe1wLbUi6i0I4yRYJgfHM6FOt1m0d8Br9wP4KD5jB3m/X+8W5XwXinJNRFy8EJ YtVZEkVs6zJPIrFt3wxM3DQ8johxKuy4hVjPDqWJKHCRlrgSvxqOHaiSBCZCVW3e7UQ2T36Z0Q/t BZKU3Xq/2/Zl5ExHqrbULsTvIMALBhChjKmGqzyN4r+Y9eOPPxLFD1hEUqjgtqBgs/8JC8Dy/8CC dLHRcGgyUXGOxIuxoCJpYdIRms5hiOKaZbtcb7oKdups/AusRxhRC+zsbnf/uOuzBU1zOSAiLkpD uYTCgofRd3Dn0yh6CVh6BFYXBBYegYkIYdjfp19mQ6lYAXzPOJhLMHw4XnBLYM93d7t1iZfNx59s PQ5EHJawNbbrJvPVKjdrNAZ7pRI5FFOnlVBFg714nWL5KbC6zAyw7EAxEYELMWAi+uLmYIOWTJrQ whyTr+diZqdV19NvTwMS8xPpNSO9+vuHhxWE/FRMVj3LozT04kSjS5AatURDGU+EWXEdGJTWAViZ p0+AYfvN8c8XAQFJaWjHKIhqksh3QJ3bwcQJRG9X/YoOdrBwxp9Cs9vhDNZG1W/uN30RLiA2BBc/ 8o5Ue4m3RIrxRxAPREE0BPaDrhwvK+uqVB390ejqj3/SFYlhkabmN1z8I/w5XxQaX7qoJGQXw7YX k2AmLnya191qQ48Q0vETNuKENtvy/v6uywLGHG6pT8SO5x2BXrylgkb868CaqrgMLFajKbkMLFaP Ew8GTwYUT6MFdBcF5XjmgQ/Tcv3+bl3R4n8zzW6hv+Ky3z5s+yIQ19CcLE/nC95RIr5SgmvygOEF YMWLwEyYI7oMLDHuJh8fPYQs8Z2ZHB5SQLqQANVqe7duUs95K5/B3gDTdvcP8JJDGC8DcZiGg9F4 kQhzYH/woCQVV6JtrwMrIQxfBBZTiV0GlhpHmmc4Hb07enKyHW9NrCzKV+8f7prIeyOf3VBwwDyE dVhF7mRkLJvhcEbVy8tTAIDpU9w0b9DoW6uCDTC+sIJlYv7gVEHjtkVB84ws8H18ad+Gf/vTTz/x RQZYQmBU/ynjczCaHZGPDHj5abO5e7eu4sXsLVL/RgRHu9/fLcsY8nB4sNkWT4CBy6qXgdWw4q8D o7aiIXUZmI19EBhUScboJVgggUFJAxyGggtm2P7hflWGMEJeBzacQElVK9CrTjyRh4prMg+eAgNn vwyMJ5nXgblyZB1FL1BMgkdtV+cMqYK1S3F1YIzTYJ25cdVv9/d9Fs7Hr5ogEBxeBMHxuGlTbzYy 9Lodjv6uCxrqR+NmGVb5k7EGieD747JsY54Cf2T/1liQM2O1x4EHI3FBlcbf4unxCYgAgfytOobf 4pDh74LHvXkCgUZzazwK4FVs/7BrUn/2mt0omjlrd7tdi1dPNDjNaL375YE5ji+OJGmw8P1LwCIB 1hSgHQVsJadrEPxw3Kb474Zpubrfbytsxle0GTwVbMT1426ZgyXVeYCVD3f9ywOb05+jhoIg98Lo IjAo4h72tUbEqwYgKzh+UNZ41g7s5IChmA89A5Yvyo8bmkvlcvcggn6ooTXxk9MvD2zhhhK/py0I Y/fiVsyKhsaa4CpVo0kIzwHFHAjIRVI02/cbONXTlzbjzWAKj7m7u39oVDED2WQeZrwQbuons3iD EA8ERVXNzwaBFQJGPPBbk+3BL0803XfmweQS+mDYDqIi8vljXIBSii8mVgBrykyOsWHCVWJPU35M 4X1OR/AK89W7/aoInJdc6tvRPMgkZJIxtALRMZ4uwrR4ARge5evAcGtXgBWl6PowUV67AKxddgIH BGsb6DOqAXiZ4LAZTP6pE5XddrdvQnDOCxw2XcQwVO77nBbiCDSDUwaevQ6MwaNXgMFXa6r8GjBu LbEJCSy4AKxb9tBjkjnSg8HEvIzEj5lKHCGAl7F918fB4gWbEQRLW3jMdSTxjdEEuDJsgevAcsB+ C7DiCrCyVmCZBOOii8DkhFtOqGlYCTAYIDOG/efksqxcP65h518X+TfgsGwpHvNUfOXZIibLdobj rQAgOHPZEhuEEAwwA+qj+bYzS0RZmlo1jr+1YRBjPtL6FHWsxv6Pxp/jqzpN55GD98qkHEgwGuRy YCbCTpJ7hrl+jctuBjNy2L6nRAS9xpNFLAcm7XVg9DJfAdZ3IqSx164Do01hz46eAOsFGN8HFqOE +GLsRAU2ldhIkLfbLXQZxMIVYMN5WK7W6zKY60Z0wkIOTl4AxsSVl4FJaksHLsnz68AgGghM4r6X gDFQuqTFqFl0nmQlMLg6nY2HXlF324c29L3pZZINQKFmt+5zzwG1RsOZn5R0/Ovq84CpvIfNcB2Y ZFPpefszYIqM2R8M1THUA6+AqWV8/FPYjdBH1eb9Ko+vGVZAkvYP6yZZTCHmx4N5kkPX24ucaGSj ej+eiAgsiXrEYUC8Rl/ztwrM7tfvjDbnt1aW4BOEQ3E4A8jEgLL6Hi8UYCA639335awpy5OIUYEZ uGwKC2b97b7Nw8tcdjN2o3L10IuNyBD6QvIP3wysZpA7CvxfAqySbEZJSpJDAIiRM2CUjDCsJGtC skFyPeEckcsmuNP+3d0GFuP0gvsC0eFl7eax8R1JzWDEnBGHNwODASFnes+A9W8CZvL7qtIcbzwF VjMgq1xokihIMgqQydDxa1hLbejNL4iP2/HcL5ardU6bYziWcClcyZLHQUaviOLC7uStG4aytuJ3 Z07/KXTRRc+A8UsTP/jBOG56oKvHG6kEHjpzhf8wS5Oycsk1K+h6MpVgyv9utrr7sIJ+mz5nMiit oNp0lXjNw6kbl7Lv86fA6u7TgR0lzBVgGqGsFZ6QLGJE5Qkwc97LY5BKLLEA9zqBkp7Monr/xy0c mvlz8QGCxfWGPtsYynkeZRUFWfmUYiBY/+kUew2YiSkr4XQzXgDG00KBVTMrUp1pyfSfTjyIjzso YPeZ93JL66ndlL4j8ShfNBgv9AQYEye/PDBerbNk42bMeBb2dCvKg64q3pemeybRAqLDgafvBN2H h10dwmR6LjqgtdrMo3s5gc1cmYf4TyyCgwyxBp61CM2iADAW5F/NMhE4K0DAY/YJGMI80WPGiJH4 a8McERq9jSh3CA7+Vh5qLQm4AkzzRiT3Yyb/K4iPjodL53uRscQMRngEiQkb0Us0TRmX+iLA2gMw SVhvmgvA1Mgx0ACgFVynwGQHNRJ+liRpESBzinwYxOn6/sMK6mZ2vhcHUzfM2yL2ZvSbnTCrzY6v vwSwY1T1Y6s3fwGYbLK2s+DkpEMy2g/AOv1F29p0H56iLqaMf0zGYbP7dpMnoXN+0AlVEBddDmak DnPTAnuegeWy/NWAYfsBWd8dVv8c2GE1RsrkPFugyB8u0vW3uzqLnlgfNLhKCbgxGOnGuQZO8FD+ G4vCA/dq2EXe2YDizf2MhRf9J9bPZqkx+YOVFuZBHJ54Y/dbZcBZOcQNaC1nC2u5tA+yP6ImMiEZ 1fRoAoPY8dt3O8rF8+DpeBHheSXCYRIOKHkJ2uRPgel9/SJguBtFRliym+onwHjodobpDJiY+b26 C5KhRZKlsQuTHRbgrGBQPlw4Z9YHzC28LJxPKDr8KOO5VisVN0+A2b3wS4CZY8IDMKwnwMpK6y3O kR2BnUBrtVwGMt/jaTX+J+vtroGvdioXb6deWhdUzoPBcAHbt4CZ2Mhz+YLAWBKiuAysunkKrNYC g3Nkp8Ds6rtGkWVp4DD+MRkFzfq+i8/kIrSYn9ZZyLOV4QjKGR4vnX4KDsqEU0RmfdRzI0ImIryQ T+AfJg/iR132RAl3bw5wf2yt1LNSt6qOatvGJk9MMYKwUuocWKsaL09DsfIncDiX9z3l4tGsuhnA pakT5nDC6gh4cNPUeHpnwJoTWCLWfjGwTmlmkV0H1iqyS8Ck1knzZiKIvOl8MpzH7d2qzqP5UeBr PFHNXwCrGnkezWWK6dP+PGDtCcnq68A63ZAW2PkGlegXTzpDWEtTWB+OX+1WbX4q8OGywL2ORXSM JgBmZKp1z4+g7C2Rx0w8+m8WDZZFoqdBP/31fNkfibg+A2Z5zIDjt16QNZ0ymDUsDcvyk2Ezk447 g2viTGbzfLPqzgxhxkVBMQDjAW9IHSbrObDG8sdXB+YnpRDsBWCSngsraqGe9GyWLJfnx4AwDuO8 ATCe7joRrQ7hgSfADCbR+58JTHfjQXhcABYWjRWJV4AxwJxJkHHG/F3IxqhdbqrgJEAAgyop2kS2 4tRN6EI0p8BIJ2t6EnGVl58PzCK7QrFUNuLLwEomQyuwMa2PoO63Ffzqg+vC0HbZJYzXjxxoNAKr TciNF1GvgoBoz9V1njBIZs5VJSANUH8z5z+8BUgJ68eZeN2Pz5d9CrhPLb/V0gkgCfCgq6OhSCT2 wZgHaXZilhpgDGaMh17Zwyfz3IPtMXYDmL3YipCJwKgu5lNg+JnkbWD35PGXBkb1YkpCACyE29S8 CKyXuNUBGDMoxXhvtzVtDysUJ3IEF86nAOYyKMA98gwYATHvoiqzLw5MIsW6ACyKS4ZIZV0G1ikw DeRLLRYXxMOW9TYmInwzEGD5ERi9uOoJsJqHUzwXgU39xYE11aHgkeeIadG8Cqzv6jKTSL4UmUmS +wyarM1if3YANg9zC4wHEeKaG2Afj0uSEdLcnnVZnUupAfvQ9BaYO+4iiKPIihRz9GTQ/91gtU/g L5qHI9lS+SEOXMBxsfr6zxo054Vohf5TV8tCVWZIRqEUPSqwiVvCc0mCU2CkGBQAKBYmhcTuLgJj 3m9yGZjnOky5Zobewve8TwKm9DJ56DS/++XLwEx+FVOWIAVnkj52Oxg7+bYvU1iLCmwowIpIgDHN Xo7onwPT6HMUXQa2cKZzP2C+uXT0+CRgtuBD6iJKMfBfBqYBHx7yCrDZWIFNs01XZUdgDHgUEipl eX2kqdnPgfHskQdvl4GRUoEPijlSmflJwGzhAGCx8pPrFWByLC01/TA8HNpMtJom6aZv8lNgUS7+ M0sR51GhobeqsMBMmPA0aogbYrmpK5WzgbAwU/+l8mGqmco8SnUXRkz8eCJP/qKL4PhjXCROFNfJ I/ubWUbK8Mv/woKlTWA8us61W0G4mM0XUy1IGyebJazF+fCEYlULYGKahKVIxfo1YL5p0uH6QaQ9 OwCMNYBTi4y10m8C5pnSvzcDY9ap1spQjZFiI6nfSNYAFp0Cy6ounU9HE2yiEBuxyMpXgS1MebAX h7yvNJSS4IXiUmCuH74JmBQg4z3eDqywVVgUijPYTGNpLhGfAruFmwZgsBVnzFCaeiyJgmhiympR fndc1i8iVtbNLhb4N3f8OGRFEtwHX/IJJWFetuLcC6IA9FQv63ur1sy3Bp9N8uF5Ht8ZcE8tUHxJ HjWMzBVqAZ0UqIYsJ4eomsB45+FtBMFTRuY0SYGVDYBNWUo613pgchlEzxVg2Hae60lNrY+nFrIq bhGmvmNhAdjMDUIeQnpvA4aLvgmYL4VyUoDLyztzlhBi17lgpaDulgdg2Io0gkExR0rNnbkc+VUa k7wCDJvOw/YJAC8AsCiNJEs+mM9OKMZCwDcD4zXfBGzhsYA/0LpBkoulkSAOgflVewpsosCk5I1J IRBzuZaQVte2ogKTQvwwjbwoC6WuJsDTs+UbE6F9CMBvApZmxduAOVJTIYkDpgfWdDSCqzkHMA9e XBm541NgXSoCbYZn7rD0grUr2IpPEtosOHlenjw2C4xlDloPaMU9RcJfVB9SaZ1ERCg4rCVqlhVN 32sqqtV05jz3bwagETjuXBtFaXODiWSYz+1WDOenwKpegDEPEM/charVpM4rwEJPTSeIxigzwKID MJWMUgf8lYA5vDp7Jrm4Wxb16FYM234FYKODrRgkhmITh0kvDlP2qVqi6AowKi6p+HIXYRbBDItA nVgqOB0pmXIEmLv4OsCYbCs9sJgvRhYT4TEDsK4vngMj5zv4qLYEOdRzrwCDEZRI6o8CizMjPPBY FiA11KYh2dcBFni6o1gzSM05VmDjCL740fIwwDKar1OmpLJSi1nxfEfeibnIqZI2zH8iBIAlgC6D +GdpVLgAU9sgx4mCNoFwI0O+o9T2cUteGGmHAfYq6W1M25wf2QAm/8iob7Y2kUr3jIksc0CaQphM J1HXSsj04GgKMDGHeFTtRlrG9UnAmNkbcXuGLI2Kfez514EVki/Km9TiYBbhV28ARkbRDgU8rvUC dyqNUSZRW5epBcbQQABnPGcWoAXGIobwk4CFHls+QMlQfXIvvgUYrVLaELYsmEQ7ZvNfByb5OUUp keAAlwX7iEgPJct7PjgA86NyWUi/K8bnXOnTFkSfBMxTSx8CEoZcEgLY/HVgejpZHltDSHXE68C0 UKmi1SfdC7Qj4MwJ5KE6Z8CUYnKeO4d+YouL/zSLN2g43V5TAxs/mtu2uXHWRTsy/g8mKMi3+IdZ Jrhvn5P2qjpBJrnb9oQP1+KLCYxvZ1xDU4AlqROxKYScwZdMmjJLwiOwhQUGxTodjxy49nQ3fx1g pNop0ZJIolSvAMtY5GWOW3zoXgqQuV+s6jQOHRO8vxnDaihY2weKcbkQAvGvBkzOAk+IxtrotDIn m1eAZYdeIUUuAbjJDOrZTZptm54c/Y0hbgvYWNrUYDLz8RBibOH/g0Xvh+96onSMT0YU1us3aul7 o7G+PxYUGOfNvtDAtocBjN3b+J62xyC0kKdaJl3AxPn4rrwu5KCkGJS1nkOTn9lngimZPPq765gq ZoGN4DjlcNA8UeKOFxeM2OVfEdjxVOIATGLBuh8jqN+s7S4Bgxi1ucgGl6joyWgmBtXmoUvC4yn0 0FkEWZsTOYwTN87hiaWQbl8ZmEl3M8DkKF+h4d5mUd1eAgY6m0Ncfb12UIL3PFvM4D/vHzso/PHh tIWFzXUWkqTDoZsWeBYZ3LmvuhXtEbQFZs6UCy3jATtUl4B1mnwnB7UmTy5kQiaM99k4298/dkz8 sMDYLSEpU1jsrPBbwJtu6jzWYuf/jUVdZhhX31ySvnjLNvymGuYHC8wIDbtORIuWg5/Qi15fbY9z WoMsDxzXz3tz2Gx9QPOZ79j3issUEkty32xaPjw+tqfFIMwuTao8CaSU24XTySMjqf9pLgLr+18K TM6gDxl8FpskiUnQT7Yji9+CMG/NgcslYJ2+UmxFER00DurH+31zeqLJ3LcUSjxaCLCklJ2iDTov Aes/E9iBXgZYqYKOElwOzMUEjPO6uwpMu9kWKjociA58mHutpDyfHK7f8KwWbxcLMCfMTOpDdxkY f/MZW7E9o5dkoWiSmPZ9OqSZVs0LwGzKIuwOl8UgvjP20uV7ZlOdpENofmlRxt4MwKYeD2u5/i8W sxxMBYGA6rALGjlk57e8nkHDTzYY/p06bjYLzwgd4tWUI9Oz1eKyi0kzpKcmgYF8bAKqwKwAMe4Z DUxp6spWhQwbBfNRUG/fb+vsNIHlm5vJggG4lPmlw4kTC7C2vQCskeQWiWC9Dqw+z0/iXZkyft73 gWBHZIVti2l71B4o9hyYScWErJBWa844Xt19WOeH0zFjeyxC+EG570jxdCiZHm1zAZhJHeP6hcDs Ou5EbXqkNLOZpcdEldUrwCDwprTtJ/n+8dslXN2zCpcR5X3PREzmK3pFRTYoL1LMsnf+OrDKJMKc bcUzYIrqBFgpwE5ziy4CU2OMJ5E+o8AMQDkVhP2ZFlMV7UVFn0PesyWJqujioKCNT6aCo1PFmKVi iJrEyD9rjrZ1ogyS0myqP5+Xf1tgIrHPgOFT3XZnOVPq+x2Kw9Wk/kHTHQvJ4x6DeaZjZ9G+fzgX 9qKip25YdKXgZ0FcKGb2JWCSuGAYvnoFmOkf/BSYYTLb1fkITDomScrsWXrpBWBlLWfiLEOFdTue TkZutPxwJ6G3s9RZqOggh96PedI0GkGv8ZjhAjCTLqTcURs74howk4j9DJi+Q2XaSh6Q4WNt02YP yC4DY3NCFjbS6tD2X16++QMrkpzzWombIVQ07jNd8IB9NJxLczlaHnSgftJjf3vrxwwki6szAYOU DbUjKQSzRqVx4KweMzbfsZLlSDPTKbg/R2YU5vdHf5BMnEpnF8Y7pnowNo66PWXis+K42ylUdLsu IPBZTTuPcpN4fwGYFhid5FsfgPEskMXaqdYtXgcmR4tPsEmmsyleOkjG1WVg7DjKfBNpqIDNOJnl +4f3/aV+LGM3TJtNKQnqI0M/rsvA+jNgbWMQMOLNiPCrFKtOlqI67GubZmeSFS8DY2/9gr2t2bZN 4sDV/f1dEz0rARGBH9dMsZLeiVM3rrUV/1VgJwnyWpiOxXMCRvPz/O3AhMsqkyNpcuywzShFXgAW MqqYBJ7kpgwHM799t19XT7PuVS4uwmrTpMF8Iu3WQtUonc1hVpfse96n9fQOef8d6wV41C7APPYm yK0cMS/8kxECxowULWdpVZnKDsu5YikWVfMkS32pmcDiCsQRE05CsRPZtM1N+nebJguc52VWtPCL VVslNKvgbnq5popfB9YfeEGMfVxlbppTB0FSHIF1V4AZmWjakSmlDLlyrUZ6Amyl0PiuerTgY3tx ZsN4EhSrd0sI/9nzIk1Y+BJnyD1HmsI6QSrb4wqwZX+EpsBYecppGNK3Oa8+jWK2csI0xGfT8rw6 BXZ4jAosZzmot2CmIoBN5mm/ve8gSsbPcKlcLPt9JU3kBoORbsarwORiFhk5geWhbIcpLWDqxgJb XgVWnwj7SpW+KXqjM8Lenv0pLlEvFFlS4VOkMXfI1BQ2V/v9vr7WOwdyMevYDknyXIajBQwr8Y8q goMMoZIk5xpCnCRKfGdYW+tH6+ZPphjFYhfXUpjDvJifTAnSoTSTlYqVqfiLIVmPFDvWfFSVsVgY s2d8lww2noft47a7Vi4smcHtXZexbI49SrgZJVG2vAAMN3sB2IkddC4+pQDoMjATHdAqv1JN9jSG gXDgMYtLmv7ppIOiYLsSdj0dYm8t4iWUc3JJdIhcnLhhuVr2OUgmPS+dUOPjxQVguJU3ATOik80G 8gvAqroyRpV0imcWl/RkYfy+NkXrrTEsS623NbW1muTMniMztt56t0zDxeRyewiYVUHWrO8bluFK TzSXHSjr8hIwvPUFYCqgRZbw26MGl4YVF4ApR0kfFjlMsuNceOagybNMuBTohRbCaXd19kZaUDkz YTYp6jUdlktV0Afxkff3K1MyPJRUpEJqIdPMHLP+1RjD2BHWH3sS1TCHRQwiauRHgWVxTCRPoiJV raJd06JYkk4FaIMrRMYkdB2VonfCXsSUUYHP6O5I6gTYIby+XLduxYcPLtttSnakkjWPUu208QxY Ub4NmFZ71UWqfYyeACulkN80yA+l9/o5sBUzZE2jZw0jRiYjzTS/mbppv7nbLF/o5yFF3mHRr3dd 5k81f3gy90D6MIjiJ8CKsnorxQQYOyBcAsZMbK20TyP1C06AUXp0da59nqWVa2anR3mHjZiWzeZh JYbF1T5H9MrirL/b9Kl2wWSzLWchCXtwY+xRjogzdTGMJfhnE4gmKMbr/vWvf5Ev7Ys7SW0P7JMw UQL+AX8rbXjBYpEXmkgDfyv+Uc+WnpmYT7FOPki1FagHI3EiXbPDrinX7/rkSazjqfhgQ7t6vdlU 7INpWtmzA5TLhu+nwEw46Y3A2Ocoj68Ci02Lz+gJMAr8lsH2OE7MSAZ2HJckqpkM+JpMo6Zf7e5b xjpeaEwFknlh3q7vesnp1hbEsELYmSbNToGZOPFbgclmvAKMw1ti6RkTPwFWwcTDVhRJyNP+SBvL MJlwNqG7Mpotkmr9cLerYbu/2LEPgpDhqv2mLxjYsa36pCA1PgOmRYlvBtY1dXkdmHRlxV0/AVY0 HUcKsd285DEQWCTpuTPtCQ7rqGy27ykUXmy49Y00tYPDud3fdXBgpB+9aR7NUts0K46aldHg2t4r 7oSihaAYKMEnYqSkMNXNJgFAnoJpeCQuvswSChahCoiP+oBsgCOvmF9fQqwIMMDHQ2CeEwT9WKAx E2f/7TqDh/lKv77bseNFRbe5Z3vgI80G0lMsTvNzYNVbgLFU9gxYGAYWGOUGUzg1nPAUWCYFsBWU sYxZgmyO6Bxx1A1xjVjb0232sOtf5jBZA/a/yJcP+01tjpXsmnnhERjv9k3AxLRt2lNgcEgtMKYP RhAGgQ5/egKMDUw7yntJp5E5Q7A31BEmMA/yv7/fsS/T621Mb6XPdr3ectABz24HVoSwK0tqgRXS cfZNW1EO2k6BQX1YYMxgI9f4QXQJGHMeenYBkj7WTP50HXGZyWAzNyqa5fY1HXbKZmz2uXm3hzb3 YKcYPiPNfDaCl+x4ALOgTA4EpQVBQXD865///OfPJv+GiLRcXESIAcefsl3/QlLbfbjdkoHFxyYt 0LR5v5SDSDlVwiTChUhDhj1ljIALz2b97lF65byp7+yA7fuzdrvb93LIObI0Y+NxZqnx5rC73g4s l26WhNaeAGOEhLMcqaMCLwiTTICZFBa1RmDja7PIJDTd8CXEIR3wo7Re7d/taEy9sbvz7cSFNut2 7/d9pXxGYDKuY+wAGm+uaT8JGDvUNt1R8fGn7DkbCAJaHR5uNDHAzKwrmsfwo1txeiLgYv/jEbei dOLPi+X94/tVFr3cNPJ0DXnYXnS73X7V5tqL2wwh4ag4TyhWN58GTETIGTBmRIbaZYpbjeymwOwY KAFWSjEKgclUw4FSi3GwdnPHkFsMi/3Nbe9vp5BTRbt7/7DvoCNmOoeKHTJZfhYkcpJllaxJsmYd A8+dCAxf2rIIYUhxTYRmvVLN+MmRDMqTuqKIgo8B8sxOJcvMiY704s5Cd2ZsPEc6BhfF5sOHD5sC fvOnzGAQ0Vh02732T59po/GBTOOifcVkwU8DlqoE6ZXPTFINTVtjtcemg78BJu21Em2hW2oeKXvD jGdsec/88m5797hf14n/ugY7XTcDTkPK683jg2QnzVlTp73hhdN8XPITgMmEHRlzZ+LhEtyRARux NulLdTygJ8DoK8e+8J+kyGeSpDK6HfAYT9qAVfXuj+9h0cb+J9HrGy2fgHBv1rv77ZIihDMSTTN/ GTUZsABAItRkLi2n+W/qMQI0dWOEXsjRlwKThrimvWWjyYbJccWSlRpya7JFtS9DAcUDlZE8I5mM EOski9X+8QNHZnjO6z2dn9GMw02yasUu6pK1MxmY6QvSTn3OChM51HoFmCwzeUv6rVdy0qwBGnrn dkWc4cKmyBICYXrlwg8ZyKaZIHNifAn30DS4/1/fPvTSPuHTx2XcDCnbs6pbc/xjkTBNU+Yh6Wgu 9sRPpWnZy8CsfNNF0WjKxKV7VqEusUSQOVKOVYOaz83hrjILXF0UliLqQJWyWW72D+93crD8Sfx1 WGLqx0m92T/uliWur41MdAYw446xCOaXgR0GJ8nELQ7SK6VxDTmskA4qOm3H5eAPnbojRiFrEmY6 hE26S+sUJg7X69b3f3jctdSwv3Rc40AGqpFom+2qqwsqyakZhTPkKEOd6Cc8kVlQlBomUYdazsoG nXZFs7bKqa4ZItURr4lswfmhWkXq7eYzeyF6g7O5zLGQIULLzd3dflmn/vwzZj+ZPtzKabueVOMI FzOXRrjQzN/K0ivAIpNTL/0OCEx6x1LwFDKpp+RIYSks0omGnmaD2slk1J9wT+LcJK+nHbdhm3P+ x+eM17yBaHf9OKv7NUfk9kK1w7Q4lqpyUCNvOr4CTIeTpWYAJfagdPslJgmUSiq7p2VgsIg93RJm gtCtmflEWchRlElSdhs4VE0W8uTk82Z1sYLTx83R4HzHqURSS3uc78cqizSLw/AKMFMCoUPVaAtL 12CJEprWlgl8CFfFRuzJSc/gOEYOrkaYa64wZxR0dw/v2ezY+fQZOxeQUSBFWdmutvudmfG3YHET n6sMZJSTTIpm04mTp0GHxr+HgX2p1DbUjTn/ShOdBBpRTDjCYK72eZCJETJAVczkxDZszWuZjVvC xvslU5GeIxMZEoI/2vX+3ftv79fsfrcwYXCdviNTFY11JJ3ObSvqwxBv3lxuVDpMWmyvkDOFZR4e 65Zc6YVgRxTJVC7ThjbPdSIqrN4HMaK+2GzGG1bx06JmCiGnW23WHcf3zITZzCgNLQ6O9W7MAUIc 24l+2nA1s7OTY5+TlaUQcTZVD0vGteETJIgaU2Llq2AibRmxWsKb5ri4LzlzcshgNxVM3q5k3l8H Y2Th6rARpRz4QSWJdlCFGRuKSWSqBCLKRB2UKcmTw6Flppsb6xSNYMcl6ckUW1tgEGXd3W7TsHbg C8+IvrFjoEWwrXQOdCrs4bBz91Cjj5zRyhmm4jxygLWU12kFLP3lQMPuM0tpG7dkEIMkD7R+0s7u TTQ8J5NG5Xou52h+4ZG1MnDJlzBYTBn5eL9vYZ76cBHnOrZGlY6Imli6G7DW0cxJk/jmRAdVDo9S T0BxH2JbLqThRib+WWFOVjh2NE6a7e5uqbOrvsZo6JtbkYCend6tLa9SGfnsmYnQOoB25ggLTaVW TcMUKhYkLD0SASFHAnbuoowmjxM7q1YYUoaIxhxI07FhNT2U6VebVH47kGm8OiE9r1f3jJr74iMl WmcxOFoMnKiEfQMOuuGHwQmFdBwhi0d1aLIdNK+MJcNhQymIBLU2ewZvpdjhK07yvrkd2mHHMixm uerbUrYL7ybwDlPXzUTTkdJJ4oBSpX8yCFqbg6RZdlQIKmRibmSmVWgQuYXi9Odfd4o3l3Q6YXsE xsjgoT+usftDSQO0Gsf0w5iaynyZjEwlLNE2cSTPSKSYxANIhGtjmaGa5g2EVMeMKcYAv9IE79NF vcZeHbjNtGjkXJ8FI6kepupo19iIQxublhNW2yU8O5k2nB5Hrev5XsiyUskgrZjWwial069OreO6 pc5ZCKXazQ5aLQ51IJ9mip4vm7138KaxCU3IRsqSA01VEuSJeG3terNbtaXM+2Nc81dCxQVuExlJ e6RdsvFmoSN2T3EdUpiPW08b/tjBwZlxrZNYbY1CCkpleiDnV0px5fDm16KWXbcytRNUS+FNbHet mLb5GahcjRA74dlIcjlyjUyfBqEn+1LJjCCII2Y684TF91wJ+/3KqLhuRB/PZWx51dRyi3bMsN6+ 0Ea+Ogo/zdmQLugS1GHTw1J6YWn6EcOjsXSbGg8Hvzq1juAG0kpjIbPL18u+SmPDM7TdJSlDj8XV Ni61TQiz61ebVQ910fQrzUgiyZjVForRNf5taHWGjBOcXRkkSzl2bE+n3KXEOAtbZ8YH4PdML+IR mYQReeziymQgiMHfjFZHZBxRzsnHASRJ2bTHVGwYXfyvTSe1pVnEEBQzazwN2kgpve1Fw5Y9o8/3 jb/UuhEJSWjSxik1Y+Ct86yST3UUjXvvMEDY2JGyTL7M4Isb75+3bihFSLSFLM/zZXC1NHjS/7ok dijGvoQLZb/diA35u4Jzsm5uJfwhxpPmqC908rEEoBhcm2krncMU65sDE9FG/r3i0nXDvnIzE5jR afCy46RGQez+3zmAq4veydAOMFfWsQGA3ztR/k3W/wPrfF91CmVuZHN0cmVhbQplbmRvYmoKOCAw IG9iago8PC9TdWJ0eXBlL0ltYWdlCi9Db2xvclNwYWNlIDcgMCBSCi9XaWR0aCA0MDAKL0hlaWdo dCAzNjQKL0JpdHNQZXJDb21wb25lbnQgOAovRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCA5ODI0 Pj5zdHJlYW0KeJztnel22zi2hbvrdlffu7qGHlLdSSVVKTtREoOaZTvLv/T+b3Wxcc7BzAGURNkh tlYciaJIAB83MYPHY1VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVdXV tXh6tq9FsuWlxWp4gK2ezqSuEywufpLzxCr88SLYeuEAW6VpVbiFj6M6zqFGnSPdVOKP3mNFn8MI 2D26DjrgJIMDHJx5rIaeQ516otITlp4se4BF7otLBdhqtD+i43T6I4nMc/JHSwT0HtloDDpmcYDd aUep7BwuquPOVn7acxyyIzHPH2B31uQY/VtybhjiDxeVZ+OPjgh0bug65pgABwcuU/k5opiVn7L4 1Oc4WtcVdqZTZFTqj7awDvXH8LNe2h99gezdkBQbp/dH+fGzMSs+b+npz3GoHn8MOs/gAFsV1Td1 GM/46q3dXq5+fr5YXLN+Xn50UtuVVnLusgCc4zBD/NF3rsEBtirIP9KwPkCPj/daaqel/99A2+22 O2ZDaiSXyj9yYdJhVpt7I8RjF+5xgB4f1WNPJKbNPzI/9njsiniMOX8+CMVHze5vQt3H4/Ex+9uu sw0OsNVgfwS/Opc/XGSm8kc+TCf5oz0Ol/NH/sdn8MfwILSGouCQbfuf5o/y87VrmD9kbwR4r6X0 Pw47AzD+cB+N9E74b63W63WIKCSUP+sF/BGGgCNggrjWgeSQ8yZtmM0G3+/3HDW1Q9QPBxNFTSdp dpjMH3Zv4bFPeCDxh/MYE4ink/0R7RTyWPfy2EU8HvtPOTjAVkP84S6EDn8U8UgaU6fwR7DHmf2R j8Nl/OHtfSF/DHXISbFKdjqvP7InHRxgq2GtueCw1Gqgsf4QlsdhLVpn9ke4w1f19Wu/P0x0dbQz /sBnKs08dpy1nMfTsDaSe/1a6lejX5qG+fxg/h7M68H83fELezzqbQ/8Lb2wN74Z0/JgXiX+6Gkh 0TxMeCS0FBO8oxhQeBvzWppvHzlGfkzx6bGrBaicR58/sE/GH/qzvjiw2RQGH3QoDwff1/q6weVj vlXm76HTHyr1+zn9EZ/Q88eObr4I5J5NvjOhvr8XfyCeOr94IB04qok/VByF8+cfvFfMY5/wOBTx GBWWE/KPZIevX2Me9708HiMeu5hHeuLBAbYa0Nth0tPjoW+unMIeACV5B20w+ceBFfLQX+i7QHMH uXBkxjyczx98eM3AhkuZdN2I9NWuv0AUlystxEBvaEgmqJRp7jwpAri3tUUVR+Hc/qBdODGXIpfU Hg+bl9NWG2/TlkJu4ktrS/GMeAwJzlh/yFafx0PMY5fhwVcg89jvk6JWyCM9dTmPAWOrPH8Ijw5/ DOLR4o8wOufyhz18sT9CHirikfXHcVyArXouRxPCzQbFc8vDlQuH+YN53nv3K60vWp8hpTU8QOWx oi1s8uA+GvpDR1Hyi6V+v1KrlR/MKOYZfyBPyp67RB3+OAoPlfBQCY8Of2R4qIRHZkzQefxBG1TM Q8U8VC8PFfGI/YEyW8jjrP44Wh7kDwheXiVXSXDropvzvUiHM+WBg7b54yzdCelvpETB4o9+qIf7 Q9xF8TtE/sicvEA9vYGeP3weKuGhiniohEfmlnUWf/AGFfNQMY8Cf0ixMeahYh7nq5+j5onaqtTJ G/OOXivvtdGvrVrrF/bReaBX45V6ObbSflvzzdYcJeChhTpwz0iHEfVzOp5rI/Br4AgTQo14bE0s XExX/D68rSKeFBeKDR0Pn6S2HoW+nEeLP1AYwsUMP+Bu3yyd9Ca1Wnm3LjhHcc1EbCP3BBSo1Na2 atGdDWUX/NyA0Bee4aHlpeeZ/GE+meyCSne2zKpM5mEMgZBsESQjjpkfayVam4Y4iQznH7bWpXk8 uNCP9kdOx4BHk/BYJTyaIh7bHh6ndieEPzi4CodfAw95bIt5bGMeDzGPs/njeGV/HE/sTgjn0kzt j2N5gL1op+IzIFQLrT+gm5sbvMedFLdUXUESPOwNaMMlMVdnMlFUFEn+xis6czRNfUtSpitkpbHi T3Lpb7yKlFfhkLDpi0oLsfugZWL9UevOibN6woJLzwLEJlx7URWknEfOH2N4qCIein5qftjG45Tu hHDE2Javfp+HinmoMh6wWMxDCY9jcYCtclfhZP7o5pFOcyuMlXy6hD8ohi3+OBYH2CrXW/5M/HFC d0I4nORl+4NHI223CNmt1q+//voGeqv1TkujuflMokDiLz59griszq1yOJLO/U1sENuwqJbj0ZWH lMXKvLuXMWEky4Pe+/7wrj7E0kb5/fv32E5RtVm7+70NPdsOOpYG2CrxB/UGAvwFeYT+4GKWK0lT 0E7oTljEPFTMg0YryKUOB/XwwC0g5KFiHsrxOJ7NHxSHC/NYDuGRhq8sVj6PnD9CHtteHk0z3B/H wgBbZfv9701rwEAeKsvDtELsTFtCwCNoecEny8NriaFWDpW0nZTFyo3E2NgXWjzo3NTKgc9o60B8 V6qThwm500ZaiYgHx0tGdFC4z+APXDecqyEUv2m9fv36F61/QniD0Op8LvQHdgaoG+SFjATeoFrf ZykHcFngjjaBH3640P/zVWb21P9nQ1gUq6PJyG0Dg3iPcm2/Jspm1eF+y9JX4K//0TJR1v+DEV2A EtpPFC+5Ek3cPFhH36JFinuacasdxkOCQLXcC/BI51cUxSrgoXp5qAvwONkfVLZ6JjzSMBbF6nhd fxyLAmzl+4NaY9RGn0RK4AjZq1ev/qVlAqffv9J8XnMW0s7Dy+SEh1d38TKciId+hzzLlP5jIMX+ +OJOdydJRTywxWR3/K12kM4ncLX9G9IMEFO5BMEIofJ4OBASL/0F4v4ewh6n+0Nax54dj+Li/FM5 j+YCPE7zx5EaP9Uuz+Nfr1geD6Q4VeSlWvuBa/G9PO74rmhE9QHcNfI8Sovz3HeV54HgDvOHibbH 4+7uC9+Y7iIeVKIJeBxP9MfxKI3R1+CxHMCjyB+lPHL+OJXHKf44Eg/OP3DnQSgQuB9//PF/oX9o wcDYxudE6KgpEYGywdFBx7ecWUY85P9+f+B4PpAif+BgKI38xgeS1je6dORu+YmSGF9ybCHE8u9a Jto6+sjbcSiOchgh/EU7lz4PUuw/srOuLp7ijyNNB+Ty1dl4SM8O1WO/ZHhwQyT3ZS2pOoOff4h5 FPmjnwebtZAHFcr8AqMObguPdTmPoF6OHnmqfxTwoH7/Nh5uZEAbD/Ptxo6FWKqARzDGoSRWqC1n eagTeaiBPNT6hPq5ef+c/VFWnH/J/nA8kCToCGAeyLd1YH787rvv/gR9r4XPCLG7mZ6FB/XIc/7x kYV85NZP4xJ/oPlL6eT5ryjMkyMeOB3zQBb+Vy0T5T9raS7I51t5INYo9nCV+ScIJ9R7jfbH0eNh Wi0R8jYeKHnowCF9uY22oUZTv7UTx5GOfrWTptVGRJ06jTSLUtW5j0eJPzp43EmrMrXzIgzMA5d2 Gw9cZ4gbBZWjgaN08Bi6BIqnRcJDjeKhiniohEe/P47n8se5eKheHmP9cfR5PFd/HOfjjyPl5QP9 gbPq8yMPLPGH5DKfOGvp9YdpfvGYjPKHVGJfIcneU1+GVA9d7z/OpJMQMUMMYx5cgvF4IOPQQcVG JBMg6tLOz1qGBwDqqI70x5F5yAh8Ph8CiSvmL3/5y/9Af9PCGT0e4Ugk/q3pjZYBcLR6QAuPtT8w nnrruN5rm8N8HkX+wMnfvXvXxYOCir8ZHibK/6elYwweqKC6uLXz+BnFUObxuZzHIuGhRvFQJ/JQ A3gU+OP4LHh82/44FvjjefAY5w/z/4FmpKxxJ+eaAUIQ8/hBC6F+8+YN4tDuD/kGTZSt+UfeH9IK n/Ao8gfCkeOhjykZFAUVuUgXD50dILmRGlRdUpw+3AD5EwtJ84PHAycr5/Hkz5FAXZB4QNK+ixD+ 6J3T42HmP9DLzbHY8TiBNc8F2fG4hqUb+fCZ2kg2ZrTBvf0VtWUKD1M/N/C4zaTEHyYePDTh3e9a +v37iAfP5+BwuWLSL8KFYx3yUNJ/kuPBzRfjebT4YyCPvZVykyTl1rPmMYrsj+Vy6fEwbSQbN3lM 5h/meNiwDo8V+aOHB3lYwjWUR3NRHib7WHoDi6TMenc3jAe1d5FoRkHAw5V3Yx74jB8Qj53jYese wkMagoryDxw+x0OLx7/eUHSl5VxveAY8Fjyr2fFQV+GhBvAo8scL5fGU8LD+QLrwyDAc33SW4WTI HfU2pBgVyCIeexlHbXnspfRFd0Q34wrbaaUAWbpQH8Xy+MijWEb7g296AleneDePtzzk762v3377 DQeRDk83olIfHcWdf3OacB+q+Yj6OUJfzqPDHxfkoRIeagCPYn+8QB7GH+KJYn/olOLKAvctYOaX 3JyW0jKluD4S+MO0VeNbKgQIxLP5g5uWbj218+Di9TAeklSX8cfR49Fws6Ub6Yxg6wABy2sRAqkD SK0/BTxw+fM0SpXwUMzD6yih3kOqqbiwDo1VhgchoToNX3o8RJqLc/ZbGYfEEw14BGPj8cBnNwAb GZROFQz5tck0sn6e8Ggm4NHujy4ep/rjRB7LiMeyl8eY+nnCo/rjqv5A/Zh7kG26Ur7ew8OMr1+r gIeuS66jNR1o25rXMDiYdREgSu2VLT+48kTCQ/+6vH7ew8NGtRnAQ+LSKOkwv6P4XcAfKGjmeKyk mq68UsqNiHJZtFAtySfeBCRrbVz9psx08EWFKJqkRnl7yCP+aBYIpLAOj9XxuKPBEdJxxN0sknnb qFJXDa72z+FoUF9spCWtuvKFN3H/CT7hd5I8chHoPUb54zw81hPwKMo/Qh5NL49lL49lxKPp5dH0 hzRW9ccz8wcSceFpGA+9jb+VkS+cefv5cUORXSu3aGfEgyqiHTzM+1H+4FKF74/GVTa86NqhXrwl 5OEyDKUkV2GOjLiNB7aX81hYHt7JiIdEg7peKMzSNDqGh7cMmDLXLpVaCAKvDbJ1c3gcDxvW4bHi Sdu2P0bxIAQpSXxyaRvw+PTJH2V9p1QbjyVPEqZef+wgPf8e05P8MSWPXcKjGcCj2B9e/9gFeCx7 eVR/uD1fsj/McYp4eOcdwIOWDwkXW8z5g65j+3tJChvW4bEydVw7tqvYHw6CBIIvWRvz5dKf/MyH s2kj6VLOw1zmcgzVJ24KDXj0/qhUAQ//iwJ/DDwVziAG/xKdPdwz5hF+G/PgdBrjD1yrPg9p5ODr lbqJzFWu2nnw/hu7aIEJJd7Afll/yI1dfNLijyCsw2Pl+YMa/tdyKj4Plb4CHhQOx8O2puR4dPtD 2vDLeSx8HnJZNO7+sTVPwsjx+MgjCLmNQVhmeZhbucdDJTxUyMPeNYKwDo8V83CdFXalDj6NWz3U nooCgvjZPKSPh8CzDS2sM/mjhUfeH6fw6PVHlke5P9p5rKbgMbZ9V4Lg89jYx9zw+ocrHmHLkWzj wUtgMcS1+/2G1+t6fHzEiu5gIifA+C2eS7qmCu8n7jdfJ2EdGisvqDGPjVnjJuDh7tQBD9fYYO9q pgnbREi5m1uOhxo3vn0Aj+3ZedwnPHb9PErG7/byWJ3OY9nLY9T4do+HHIbDHqanxwNBEB7v379H a/Pv3Ez9yWtikGJB2P+BLhDlaDM95T628jiDPyRp7eXXzYNuYcP8IQkhiYG7WjkPGV9ieXjtPUgP 3PTdUzjxqXGLaOO0o3hsEh4bZxUBv8wFdWCs6D9p7tThX7q87XN0O/AyMX9tYcWbuSK2dhmqywEl sfyEOI3HIuGhEh7q7Dza/eHxSB5SWOSPkIfq5aF6eaiIh7oEj+qP5+YPdURfpOUhkhYc6pVd8+xw vBqV4WG7KnkMoPRRu/nn5ki0pj6vGmdeS3uGNY/nptoYipPhM+qK/GF+seW1+zGvns4jsfNbRfw1 69yTAlwLAa1ut7ajzWXPxr14WarXr3kwJE9sGDf/w4w9RtLZ2qrHQ4rsbngb18BlRpr+i7O38VjZ +c52ABwNXJDL0K98UMUgqZd7IR0aK/rv0T3WCbbk2oWNnreyqXiDGyA3NMPjC2XgZi+Kqg2xZw4t n4eMSj2WVJisFgN4qISHOpGHSniolEf6fNjhsYp5qF4eqpeHinioXh5j50e9BH+Urc8Q83hJ/nh6 inh8YHmt6bIgNdVDTApzuBuzUlHMw6xUIb367kKS+PPUbVXgj8L1fXweO5p+tWMe0riuQ2eCSbUT CiSH57r+kAvPpJ2iGdXdPJqAh3nQhC55YQdeXLyHB5/IFHJdWKTavmsLZ0Gs/E8yrntFtb08j8bn wW2e7o4gkyeiLkcxNw4ry6p743bHr39l3g/noRIeqoiHSniolEfgj9L1ryIeqpeH6uWhIh6ql8cJ 61+V8ZjcH+PWh3M8Xpo/ZNpBH4+1m78x2B/c9GOL/dxqXeSP8vUTvbQQfzRmucxPZ/OHG2Z2fn8g AraGSkMB5Mrfes9UIEmet5EKcHBNY987UTuPEaEs2z/Y4vGwPXjShuAV8raYP2efiLdzpuDW6b1r hkYq2BsFRdnE1A9GSRxJ/vq7xEMlPFTCQyU8vIt5FI9M03TyyM4xzzeQY/XyUL08VMRD9fE4df3d 5+oPf43Hsl+4LWuqNl3AH3d8mAyPU59vsNSvlX5t7LMS6RmPWztrwLzMfssFPRVxyev6u3Ym7Gt5 6CO05R/Hnpey7Vbjnm/g1oXEMehpBAkPrxWNWum20RoFspoBPn+1z5Ncu3kVxENZHi4G53i+AV9U 8qzTR+rQQ60KrbzSGLuhCbGmsTZ7EdiWW2PsjVjF/D8+hCfEioNiPC/hosF5/ChwmeQrH80TVall 23ymp9MiKWwWT6URzx9h3EoCbJV7Ps4x4qESHsrnYc4b3f97eKS5RVv+4Qdt5PPPzU9jHirmoXp5 qIiHinksfB5lAQ5+lrkCTUr7/qBQ5fxxzLUtDfLHgMsnH7rSWPX6g0H0+0NGx/T4ozjAVuGVhC3m sgx4qEdpmPvKeJRtp/OO03qONjf4P8n6I77IR8YKP41DF55PY1D3kup8BSp+hDY/Mo1+YtKFH4ya BNnVeooDbNVy/WX84fN4THh03S7bhll0X0HpNX6KP7r3t8bo4gH5PE4+a06L5Ofj/MGPhMmeo90f 0VnDqJ3l+edtP72UP0YG2KqFJk8MMB3m3H/e549j6wXf5Y/43Ok1NuZyK/kp4iRjA/Z8Feb9od/5 PPh33lOQxwfYapH82pWvJJAq8WfXFZ+eo7U05V9KaRKe0x9JENwG5qF8HtlQq+jepWIeyQNez+eP gEfb/bLlaMnW1mF6KYNujYpV3y9z/siHOuJxIX8kdVl5ah9etBYfxmf01aa9mvSgfcOad+61SLaM ipX3LML8S1csbFxRC9d3qo4909+1x+fK/mi7LKo/hqsl/wg0cEtrOJ53/jF6Q7rD+ABbnYhz0JGG lK8GaVxYRsZsxOD05LzlR2ipf/SELLOlIyQtvw/2/yb8Mb5BwWo4TtwqvWabjsPEB8tfaZmf9Glc rIp+aUStbvmBLkXnLf19a/tVINri80iGnueu7+j34RHjnS/sj576ebQhz2N6f3Ty7PBHmpbB4TL+ aPtFt0bGquin0Fh/nHZWo/j5zvmrGWXA7dYtrpQMHcxd3zZAyREzez8rf5hoKh4i2/qTy5evWoHG PNYdh0iPmGkrHKexsSr5KZTnMeK0RWc1imuyLTVT1FllNsVa1s/jXuLuWvY5X6Nj1VNDj19LO1dl F4wM6HudoX4+TJ3++Pa0lKH4hf6YTJXH85QpeAxv7n2pWr+US67yeF6qPJ6XKo+qqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqm9JE67WOuF6ry9XQ1aKbt/ijtPydIaWhY0vvT71y1VLgnUIj2/n522bp13SQ6GXoocH PGdm6y3ynkvrcbpqSk2jcn/keah2HoOe9lH9Qeq9JhMV+8O/l5Wfztc1EmhiFfvjONIfVvmkrv4g Dbsw8Rj3t9AfWnicOy0b3kQCj8Nh6wsrbtPTVb2nk43NUa6bVJNomD8SHpsNlqhF+jMKhT94TPUQ Hhd/Ps7L1bALs59HU8Sj4NS+rpVIE6rHH3jG+rt37/6j9QoCE/CgB7qbx5awunjojfTsRH5M8R4P i8XnlrO2bKr+uBSPPfPoD0CgayfWBBrhj4UWP9YLj1pj8QPTNRIgGOgPCkP1h9MIf5yBxz7g0R+M 2fsDd6PPWrg3+Txeazkeugqy8XlQim9j4clY+z3I7AyQA/xhPrpHJaQlruoPTz6P33///Y2W5YHC VsRDp7FO2fvBPPYJj67AVH+U81BFPHL+IFV/PCN/dIVoNv7IvO4Wd3dKtfEw5V21VlvzJELOPdS9 fu3Ma+u91or2xJP88A7f79VGv1bmdeDf7M3vw+f4LebYH5W7DFGo0jy+aCHvvrm5+U3rV8irf+iL 3lRC4Aryxo5ky1Va9CRY89m8xbb9Hr5aQdorO9oUFrdaTHK9ZJpMufxjDA9VxEMlPFTI45hrdKz+ uJo/8mG7SgpNq/gibJpFwzyQb99qxTw+aOm09Hnc3ytJWsfDproKGx/NFvqGNy/p6dbAsndBUzGR OfoDLeecR3s80Kz7HtLvb5gHp/iGeBwOtug0gMcq4bFOeaTBu04STarUH+N4qCIeQ/0RApmbP5Bh fHYCD4bQxYPuU2uR6xlZUk/uSlFWJIc0agh66o9d/AjfGfvjFB7qRB4q5cFV9uoPTxbC7a15CxbY jW8u0In+MNI3OL7fJf7wg3idJJpUHf4o4jHaH8xDpTzssJS5+gOVjYVOtQ8sd7/K8dhZJsiZUUTG F1+axsLEtsakfMQDNZiPqT/2ufzDD+Q1EmhiLbp5oHx1u1gg/ZCH/IE90IjICTeUxx2LuXzkIUNL aXE8HFA/32fzD2iW/jgO57E5O49DyiMN5vTJM7mcP44FPKb2B/exXyOBJlZwr2rlgU8eD1OX4yxE v0MlzwBAclM7fQsP/iLnDy1TfM774zg3f/g8PlM74gJZLyUgeCBXv0GKax5qMA8zJMiWtvyimzQv UluLEh6uXTEZ9jsPf1BPTwkP7kuiniS8a1QrD7X0Xz4P2z91rw78Qj+V3y/lv9D8fu3EmkALPy+v /ri6grx8GI+mMRk3NdNuPB6mbkE8TL6DN2as9VLaEn0efv5BjfQej8dHM+Y3Cul1kmhSLZgHktLy 4AZeboGV4aAMoAl5rGnaAbzANW98GfJY+QOvpSAW8lDylkDgbzQlcUb+OF6fxzblMV9/6DQNeFD/ oJKpHR4PSn3Lg5ThgQ7F33C4dh58VG7HUg4NshQzTAhvvUk8s/HH8RnwWKc8tiGP42z8ceTBb1w+ UnchDzP3iRKLm/8aweN4KGZyR228lgfuaGod7OlOUOSP41z8cezj0SQ8moRHcyKP6g/SQgpXUJ7H Z8mX+a1yX1GtQm5o5neUzfs8TG0ly8PctwgikKy4iKBlDhtWR3RYr5lQE8kWrujebzOLxiXgYB7W NrawZKoUhge1TPEm8ZjHY53yWMY85uGP4xV4qIRH9Qep+uN5yVxzA3h4bSbIFjg/przA/JYb16VU jLRs5YEtsT9WKykVKxEYoW0rCOs3LnPNSX0AacTd2VxlRuoO5+FKYD4Pcy8yAx9kxNxq5S14Qk5Y DeAxG38EPNQEPFTCo/qD9IS+BctDv9z8GfxF70YBDzsDZ236MrbmL33e216OvTlq0C/C83PwzuFQ 5nf4BfeBzNYfLk8FCI0AQ33+C2F+FD58/PiRs3b917azUN4hF30jtz8VjOip/uiSueZk5Jp/szkn D7p38YiFvZthuKY+KfnIeYgtuSEQ/gzD2fgj4HEBfyQ8VMJDpTxUzGMW/kAjkceDGzawgedyYjLn P0Vg8k7r9vaWM42YxypozPV5uBGIS98FUqCzPGyRW3pIbFivmVATaZHwUBPwUAkPlfJQMY/Z+MMb CD3GH6Zh2NUhAx5mijpd/r3+4Ak81R8Bj3J/lPPI+yPiMU9/2FllXASlt6hNYDwi5nBqDD9C4IGF ZTCnM8tDaikfouYTRUU2HmCqEzjjD/GmVGdkuAq22bBeM6Em0iLmsTGz/fp46ET3Onb5gh7Mw3jQ 3afM+JIBPGbpD2p+vTCPVcJjgD/m0T/4cvwxj/7zjD90MqIEi3wba8h8//33f4V+0AITLC6T8ECL CwoAZhYV5y7Y3u0PqnX6BSqZPaV1Q/Oybm8lqLPwB1eZTRKZ8f5qN4yHVMNd11IXDypf8QpyO0Vj fzclPGYy/irhsZuAx66cx1zmf7wQf8xmflTGHzp1kBRoO0Q+/t133/0J+pvWT1q/aOms3vFALsIL Ad0M9oc0WClvOJYyi3SABa/TYXnMZ/6glfI1gIfb2edhpBNURoQOkc+Dm8PMeGwU1Ra+rp1YE0gm c7JNzBU8jAcubmrkaOMh1RllWtV3fHRudm/xR47HcU7+4Ig6HmoCHqqUx3zyD77uTLWMu1N16uR4 6Hz9e3z+t5bOJJAB9/OQ/lrHQ/+1Y4ApM494SP7Bc3vN4svzKV9xLB0PBR5ICtTNf9bSGP4MZXhQ vtz4dXOvfi7dvzy+5N6uXCbjExspXFEfCZfTAh4oFxgec6l/JDz2E/BYJzzWvTxmUj9HLL0uQvR/ DONBU203nI4xjy/cJRLy4BV8MFvki18Co85y7P+J1iAwiw/EPObhj2PEYzUBjybhsRzAYx7+OCb+ WFHSCBOd/H8XgQf6CHVqOR74Ka9MQk2MdikT1z9IK4rn/EG1Suk/93nIHnP3x+V5NAmPZT+PefQP mpnerMbOORvAQ2Yh0ICrUf7g6Qt3PPY3xwMSHrPwR54HGpx4LNzN27dvUQX8F4S8A1PRvNnl0cq6 1PduJOMR+OlEONPXr195fHuGB0772VvUT3jYsF4zoSZSiz+KeagiHmoUj+qP6o+pZfrPZYChJCRV 43p4fPJKtfx8zg4ePPnjwTzeNucPLhz7POQMNqzXTKiJ9BTzkK5tuuyxhdeGM1OYMT4O+a2uQoc8 ZN7gxq0EvpYqv6zlSjy0pFDGP7U8ZOIgjeM2PPDBhvWaCTWREn/g2h3BQxXxUKN4zMMfaqMiHjyj CbOVBvAws6oaM79pa2ZE0epxMs9q782MOqgH88I5aS6UeREP/U62L5XlYT7NaX6U9Ydc7ooaOc7k D7N2NQ304fZ2NECqaBXS6g+Rueb8zFManYI7ipR07F7cYcFjqDZudJUMHeGm4l2eRxPx4LFfBBB/ bXlgLZXB40zyD/wJeKiEhxrAQxXxiP2xWqkBPGbjDzO8Zqw/mAln3jsnmZEmo0N5CvSaVpyTO5K5 V7rO3c3c/YFIBjxK/UE7qwl4zMIflscXt+Tb2nR4b2iou2TtjYhz7gPNk11Lzu3xoP+6eQjePA/v 3HP1h/BQCQ+V8FAJD3V+HirmMQ9/PDkekuJ+X8hKpjCbO5d0WBhgNsW9MtaGU31rp5+tGrfQnDcI LuDBHe7d/phJ/6Ashiw8VMKD1tvzeSjHQy7mMh701mZJAQ8pnCmZ2chBnUn/YMwj54+YxzLhUeiP ITx2MY+Z+OPpZfhjNuOvHA/uA2EegSGy+YfN5ikv5r53X8wDP5Nu3M/UcVLsj9mMTzTxtFcu5oaT LdwFHfJgN4Q8lPDwbl9sH7QupjwaP8em8Ykbee4qPZ5YcR2fH9k5H3+EPJYJj2XCY5PwWJ7IY53y 2Ac85jN+10T02ftjTuPbNZCMP7zKAm7wMvPp9gvV3jZei0rgD1sOcPVBsJD2Q5NbFPtjPvM/6HlF VOrRL/T/UH9UJw+zxt7avnam3wmvLfdHUf8Uf5/y0Gei/empR19Nj9NX74UQoe/KPq1oMSd/PCm/ bMW3kF5/2IvbNbBvU39keTSN3yJPi7R/9aQ/Pnhrkc1sfhTxMI2qwoUyC6Tdzc0NhilijMkvWJvh Dxqc6N9sdjvleICoZDT4ktuHAx5cLJA6zOOjemQ5Hsrx4GfAXTWlptHTmXjsTuTxmPJ4iHnMyR+I bTkPWTKUno92oCZfnwfk9Z9YtfvDBc09P+ppfv6ATuFxOJHHY8ojDeTUiXMFLdp5IN0wu4aXacDU 2p9kfR/NiKsTXyh7N+NLaCho3LCi+JGEEQ+/80v7q8MfTzP1B4+18TvNB/NYhjxsM3GGB3fFhjwO XsGqNYjTJ8/kWiQ81Cge6kQeKuXB/nDhq/54Bv7wA3iNBJpYHf7gvPzmzZs3lgfmd2Kcu87f/QUA PnywoxdlGoJjkuPRhJ0s260pn2X84eOYmz/kGlzTfLXxPCilhUfjnlkQ85DaPA2POKT+SML2rWuR AFFX4ZH3RxiyWfrj6Rn5I+Pdb12pPxZP3JYoy715PH7w1ohjFKaZ8Y8/sGgcFqB5jdoiP5ibM26Z URDykBpjhz+SkF0vmSZT6g9zIUY8UDWHNWIet7e3PTxMRwoN/1zRKHauKtoaPE/T6cs7ZuyPJ2pe nJSHSng8JUGbhz8WuZdaEA+sadnLgxfdJx7Aw7cydJbovENmFcgTogwPfmYVtqE/6oH7oOwrF6rq j9N5NDzTI/GH4QH3oD8q9ocLyPz80SqkG/NAtfwfWmDyM+Y86/IVqu50RwMP7PgOZSzw4Aex+Tw0 CF40ceMWPLE8BobpOkk0qVr8cWEesT8ynR3VHx3+wP3K1j/6eVCW3sJj7eYbpP7oCM7s/SGJoBPZ 8kDegcqI5oHqIq3gAB6/82Kg+qNdSwyjUM38QdtbxUNAbeewCwf80ROQuftDNAmPAeGo/piKR+6s 1R/n8ocdqGV5uFkIGR6DnEG6dmJNoAH+4C2/+NIpzusqaAy3MkqOPhoe+EpM4fHgs0ppquus1R/d OiePwlOLrpVIE+pa/ug/a/VHt87GY/gpY107sSbQcH+ITHJTJzkS3V+rCe0gDIEXB7/HOVzd+zjw HPlN1R85lfMYe6ZE10umyTSJP4rPkd9U/ZFTGY+WtB6nayfWBMr2Rj3X17UTq6qqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqrq29X/A1vgOzkKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8 PC9SMTEKMTEgMCBSL1IxMgoxMiAwIFIvUjkKOSAwIFIvUjEzCjEzIDAgUi9SMTAKMTAgMCBSPj4K ZW5kb2JqCjExIDAgb2JqCjw8L0Jhc2VGb250L1RpbWVzLVJvbWFuL1R5cGUvRm9udAovRW5jb2Rp bmcgMjAgMCBSL1N1YnR5cGUvVHlwZTE+PgplbmRvYmoKMjAgMCBvYmoKPDwvVHlwZS9FbmNvZGlu Zy9EaWZmZXJlbmNlc1sKNDUvbWludXNdPj4KZW5kb2JqCjEyIDAgb2JqCjw8L0Jhc2VGb250L0hl bHZldGljYS9UeXBlL0ZvbnQKL1N1YnR5cGUvVHlwZTE+PgplbmRvYmoKOSAwIG9iago8PC9CYXNl Rm9udC9UaW1lcy1Cb2xkL1R5cGUvRm9udAovU3VidHlwZS9UeXBlMT4+CmVuZG9iagoxMyAwIG9i ago8PC9CYXNlRm9udC9IZWx2ZXRpY2EtQm9sZC9UeXBlL0ZvbnQKL1N1YnR5cGUvVHlwZTE+Pgpl bmRvYmoKMTAgMCBvYmoKPDwvQmFzZUZvbnQvVGltZXMtSXRhbGljL1R5cGUvRm9udAovU3VidHlw ZS9UeXBlMT4+CmVuZG9iagoyIDAgb2JqCjw8L1Byb2R1Y2VyKEVTUCBHaG9zdHNjcmlwdCA4LjE1 KQovQ3JlYXRpb25EYXRlKEQ6MjAxMTAyMDQxNjQ1MjMpCi9Nb2REYXRlKEQ6MjAxMTAyMDQxNjQ1 MjMpPj5lbmRvYmoKeHJlZgowIDIxCjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMjIwMiAwMDAw MCBuIAowMDAwMDUxMjYwIDAwMDAwIG4gCjAwMDAwMDIxNDMgMDAwMDAgbiAKMDAwMDAwMTk1MCAw MDAwMCBuIAowMDAwMDAwMDE1IDAwMDAwIG4gCjAwMDAwMDE5MzAgMDAwMDAgbiAKMDAwMDAwMjI1 MCAwMDAwMCBuIAowMDAwMDQwODA5IDAwMDAwIG4gCjAwMDAwNTEwNTcgMDAwMDAgbiAKMDAwMDA1 MTE5MiAwMDAwMCBuIAowMDAwMDUwODUxIDAwMDAwIG4gCjAwMDAwNTA5OTIgMDAwMDAgbiAKMDAw MDA1MTEyMiAwMDAwMCBuIAowMDAwMDI4NzU1IDAwMDAwIG4gCjAwMDAwMTUyMzQgMDAwMDAgbiAK MDAwMDAwMzE2NSAwMDAwMCBuIAowMDAwMDAzMDcyIDAwMDAwIG4gCjAwMDAwMDMxMDIgMDAwMDAg biAKMDAwMDA1MDc3NyAwMDAwMCBuIAowMDAwMDUwOTM0IDAwMDAwIG4gCnRyYWlsZXIKPDwgL1Np emUgMjEgL1Jvb3QgMSAwIFIgL0luZm8gMiAwIFIKL0lEIFsou+Fa0AVt0+J/a5fSVzR3sSkou+Fa 0AVt0+J/a5fSVzR3sSldCj4+CnN0YXJ0eHJlZgo1MTM2OQolJUVPRgo --=__PartE5C9E9B8.0__=-- ========================================================================Date: Fri, 4 Feb 2011 17:42:01 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: VBM8 - What to do after sample homogeneity check Comments: To: michel grothe <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Most demographic information about these subjects is available from http://www.brain-development.org/ . Age is not explicitly provided, although date of birth is there (but not the date of the scans). Best regards, -John On 4 February 2011 15:05, michel grothe <[log in to unmask]> wrote: > If you didn´t change the default settings of the toolbox, then all of your > images are warped independently to the IXI-template provided by the toolbox. > This is a DARTEL-compatible average template of healthy adult subjects in > MNI space (see also the VBM8-manual). Thus, outliers won´t affect the > template and it should be safe to go for option 1 (given that you have good > reasons to exclude these images, i.e. your definition of "poor" quality). > > BTW, it would be of interest to know the average age of the IXI-template > sample, but as far as I know this information is not provided by the > VBM8-manual. Maybe the author, Christian Gaser, can comment on this. > > Best regards, > Michel > >> Date: Fri, 4 Feb 2011 11:52:04 +0000 >> From: [log in to unmask] >> Subject: [SPM] VBM8 - What to do after sample homogeneity check >> To: [log in to unmask] >> >> Dear SPM experts, >> >> I've run VBM8 using a customized DARTEL-template. Then, I did sample >> homogeneity check and identified some poor images (i.e. images with severe >> motion artefacts, enlarged ventricles, etc.). Among 296 images in total, I >> found 11 "poor" images so far, but there seem to be some more of them. >> >> Now, my question is which option I should choose between the following. >> >> (-----1-----) Start statistical analysis without those outliers >> (-----2-----) Start again from the beginning (i.e. re-run VBM8) without >> those outliers >> >> I was originally going to go for option 2 because, to my understanding, a >> customized DARTEL-template is made from my own images, and therefore >> outliers would cause bias in the template. However, I'm not sure whether >> this reasoning is correct. >> >> So, could anyone who understands how VBM8 works please advise me on this? >> I would really appreciate your help. >> >> Best wishes, >> >> Kie Woo Nam >> >> ___________________________ >> Kie Woo Nam, PhD student >> Division of Psychological Medicine >> Institute of Psychiatry, King's College London >> 16 De Crespigny Park, Denmark Hill >> London, SE5 8AF, UK > ========================================================================Date: Fri, 4 Feb 2011 17:55:02 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: wierd looking images Comments: To: Karen Lythe <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> I'd suggest using the Check Reg button to compare the mask.img file with one of the MNI-space templates to see if they match. Also try check reg with a spatially normalised image from each of your subjects. It's possible that the grey matter masking was a bit too severe. Best regards, -John On 4 February 2011 17:17, Karen Lythe <[log in to unmask]> wrote: > Hi, > > I've ran some analyses using the following steps: > Realign: estimate and reslice > - mean image written only > Coregister: estimate > - ref image is mean, and source image is individuals structural > Segment > Normalise:write > - paramter file is segmented structural > - images to write are realigned images > - writing options - voxel size is [3.75x3.75x3.8] (i changed this to the size of the voxels that we used in our scanning sequence) > Smoothing - 4mm > > All other options are left as default. > > Each individual person's data looks fine - but when I look at a really low threshold of the group map the BOLD signal doesn't fill the whole brain, as though my data has not been normalised correctly to the MNI brain (see image attached - overlay used is the single_subj_T1 from the canonical folder). > > Does anyone know what is happening and how to remedy this? > > Any comments/suggestions would be much appreciated. > > Many thanks > Karen > > > Karen Lythe PhD > Wales Institute of Cognitive Neuroscience > School of Psychology > Cardiff University > Tower Building > Park Place > CF10 3AT > UK > > Tel: 029 208 70716 > Email: [log in to unmask] > > ========================================================================Date: Fri, 4 Feb 2011 12:30:06 -0600 Reply-To: Steffie Tomson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Steffie Tomson <[log in to unmask]> Subject: Anatomical Regions MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CBC499.8BA602C8" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CBC499.8BA602C8 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Everyone - I'd like to pull BOLD data from a set of anatomical regions. For example, I'd like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann's areas? Thanks so much, Steffie ------_=_NextPart_001_01CBC499.8BA602C8 Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Hi Everyone –

I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas?

Thanks so much,

Steffie

------_=_NextPart_001_01CBC499.8BA602C8-- ========================================================================Date: Fri, 4 Feb 2011 10:19:39 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: tool time course? Comments: To: [log in to unmask] In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba6e89aaaedfb9049b78ee59 Message-ID: <[log in to unmask]> --90e6ba6e89aaaedfb9049b78ee59 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Marsbar is very popular among users of spm. http://marsbar.sourceforge.net/ cheers, michael On Fri, Feb 4, 2011 at 3:07 AM, Rainer BÃ¶gle <[log in to unmask]>wrote: > Hi Emanuele, > > you can try to use RFXplot toolbox. > > http://rfxplot.sourceforge.net/ > > Kind regards, > Rainer > > On Fri, Feb 4, 2011 at 12:22 AM, Emanuele Tinelli <[log in to unmask]>wrote: > >> hi, >> I wanted to know if there is a tool to obtain the values of the time >> course (over Y) in a group of 30 subj by placing a ROI in the same region. >> thanks >> > > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --90e6ba6e89aaaedfb9049b78ee59 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Marsbar is very popular among users of spm.Â http://marsbar.sourceforge.net/

cheers,

michael

On Fri, Feb 4, 2011 at 3:07 AM, Rainer BÃ¶gle <[log in to unmask]> wrote:

Hi Emanuele,

you can try to use RFXplot toolbox.

http://rfxplot.sourceforge.net/

Kind regards,
Rainer

On Fri, Feb 4, 2011 at 12:22 AM, Emanuele Tinelli <[log in to unmask]> wrote:

hi,
I wanted to know if there is a tool to obtain the values of the time course (over Y) in a group of 30 subj by placing a ROI in the same region.
thanks

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--90e6ba6e89aaaedfb9049b78ee59-- ========================================================================Date: Fri, 4 Feb 2011 10:38:32 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: Anatomical Regions Comments: To: Steffie Tomson <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba53b5283b59cd049b79329c Message-ID: <[log in to unmask]> --90e6ba53b5283b59cd049b79329c Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable WFU pickatlas http://fmri.wfubmc.edu/software/PickAtlas cheers, Michael On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote: > Hi Everyone â€“ > > > > Iâ€™d like to pull BOLD data from a set of anatomical regions. For example, > Iâ€™d like to look at traces in the anterior cingulate or the left ITG. Does > anyone know how to find a database of MNI-normalized anatomical regions like > this? Ideally, somewhere to download individual masks for anatomical > regions, or even Brodmannâ€™s areas? > > > > Thanks so much, > > Steffie > > > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --90e6ba53b5283b59cd049b79329c Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable WFU pickatlas

http://fmri.wfubmc.edu/software/PickAtlas

cheers,

Michael

On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote:

Hi Everyone â€“

Â
Iâ€™d like to pull BOLD data from a set of anatomical regions.Â For example, Iâ€™d like to look at traces in the anterior cingulate or the left ITG.Â Does anyone know how to find a database of MNI-normalized anatomical regions like this?Â Ideally, somewhere to download individual masks for anatomical regions, or even Brodmannâ€™s areas?

Â
Thanks so much,

Steffie
Â

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--90e6ba53b5283b59cd049b79329c-- ========================================================================Date: Fri, 4 Feb 2011 20:08:51 +0000 Reply-To: Ben Becker <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ben Becker <[log in to unmask]> Subject: DARTEL-VBM: total intracranial volume in second level analysis Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPM-experts, Iâ€™ve computed a VBM analysis using the DARTEL-toolbox to investigate GM density differences in SPM8. The aim of the study was to investigate differences in GM density in two groups of patients. I set up the analysis according to the SPM8 manual (chapter 26 Dartel tools) using unmodulated GM images of the patients. My questions: Do I have to incorporate the individual total intracranial volume in the second-level between-group comparison as a covariate or does the analysis already account for this nuisance variable? So is it possible to analyze the date using an unpaired t-test or do I have to use the total intracranial volume as a covariate in the second-level analysis? Thanks and kind regards Ben ========================================================================Date: Fri, 4 Feb 2011 21:17:16 +0000 Reply-To: Deborah Talmi <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Deborah Talmi <[log in to unmask]> Subject: MEG source localizations and orthogonality Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi there, I ran two group source localizations of MEG data: identical but one with GS and one with MSP. I entered the nifti files to an ANCOVA. The GS gave me nice results, in line with the literature. The design matrix with MSP-localized files showed that the 4 regressors were not orthogonal, even before estimation. There were no significant voxels and the SPM was greyed out. I don't understand why the design matrix showed correlations even before estimation? If this is not an error, can I learn, from this difference between the models, something about my data? Cheers Deborah ========================================================================Date: Fri, 4 Feb 2011 17:06:41 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: results visualization MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: [log in to unmask]> --0016e6d7843fd7b8af049b7c18f5 Content-Type: text/plain; charset=ISO-8859-1 Hello all, I will like to see the results obtained with roi analysis, using wfu_pick_atlas, in others software like MRICro or xjview, but I see that the spmT_0001.hdr contains the same data like I never do the roi analysis. Is possible see the ROI analysis in others software? Thanks John Ochoa --0016e6d7843fd7b8af049b7c18f5 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hello all,

I will like to see the results obtained with roi analysis, using wfu_pick_atlas, in others software like MRICro or xjview, but I see that the spmT_0001.hdr contains the same data like I never do the roi analysis.

Is possible see the ROI analysis in others software?

Thanks

John Ochoa

--0016e6d7843fd7b8af049b7c18f5-- ========================================================================Date: Fri, 4 Feb 2011 17:56:42 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: results visualization Comments: To: John Fredy <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Click the save button to save the thresholded maps. On Friday, February 4, 2011, John Fredy <[log in to unmask]> wrote: > Hello all, > I will like to see the results obtained with roi analysis, using wfu_pick_atlas, in others software like MRICro or xjview, but I see that the spmT_0001.hdr contains the same data like I never do the roi analysis. > > Is possible see the ROI analysis in others software? > Thanks > John Ochoa > -- Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Sat, 5 Feb 2011 15:03:06 +0100 Reply-To: maike tahden <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: maike tahden <[log in to unmask]> Subject: Re: separate BOLD-functions per each single trial Comments: To: "MCLAREN, Donald" <[log in to unmask]>, Michael T Rubens <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hello Mr. McLaren, hello Mr. Rubens, thank you very much for your advice! You gave me some new ideas of modelling the data and also some interesting and helpful literature. Please, excuse my late response. Thanks a lot! Best regards, Maike Tahden -------- Original-Nachricht -------- > Datum: Sun, 30 Jan 2011 16:06:03 -0800 > Von: Michael T Rubens <[log in to unmask]> > An: [log in to unmask] > Betreff: Re: [SPM] separate BOLD-functions per each single trial > Just to note, it is not uncommon to run a GLM with separate regressors for > each trial. This is how the beta series correlation method of functional > connectivity is performed. See Rissman et al. Neuroimage 2004. > > I don't think it has ever been done with using FIR as the bf though. > > cheers, > Michael > > On Sun, Jan 30, 2011 at 3:55 PM, MCLAREN, Donald > <[log in to unmask]>wrote: > > > If I understand you correctly, this is impossible. > > > > It seems like you want a separate response for each trial, this would > > require a separate set of FIR for each trial. Because of this you will > > end up with more regressors than measurements/observations. Since you > > have more predictors than measurements, the model is overspecified and > > there would be multiple solutions. > > > > On the other hand, if you assume the same shape for each individual > > event, then you could obtain a measure of the amplitude of the > > response for each trial. To do this, as Cesar stated, you want to have > > N event types in your model where N is the number of trials. > > > > Chapter 9, I believe, in Functional Magnetic Resonance Imaging by > > Jezzard et al., has a section on estimating the BOLD response and its > > limitations. Also, there were 2 papers from either 2000 or 2001 by > > Ollinger et al. on event-related designs. > > > > Hope this helps. > > > > Best Regards, Donald McLaren > > ================= > > D.G. McLaren, Ph.D. > > Postdoctoral Research Fellow, GRECC, Bedford VA > > Research Fellow, Department of Neurology, Massachusetts General > > Hospital and Harvard Medical School > > Office: (773) 406-2464 > > ===================== > > This e-mail contains CONFIDENTIAL INFORMATION which may contain > > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > > and which is intended only for the use of the individual or entity > > named above. If the reader of the e-mail is not the intended recipient > > or the employee or agent responsible for delivering it to the intended > > recipient, you are hereby notified that you are in possession of > > confidential and privileged information. Any unauthorized use, > > disclosure, copying or the taking of any action in reliance on the > > contents of this information is strictly prohibited and may be > > unlawful. If you have received this e-mail unintentionally, please > > immediately notify the sender via telephone at (773) 406-2464 or > > email. > > > > > > > > On Sun, Jan 30, 2011 at 3:43 PM, Maike Tahden <[log in to unmask]> wrote: > > > Hello Cesar, > > > > > > thank you very much for your response! However, I donâ€™t think Iâ€™ll > get > > the information I need, if I create a different regressor for each > event. As > > I wrote last time, I need separate BOLD-functions per each single trial > and > > till now Iâ€™ve found no way to identify these with the help of SPM, so > that > > Iâ€™m trying to model the data by myself using the Generalized Linear > Model. > > In this way I hope, to get â€“ in whatever form â€“ the knowledge which > > respective proportion of the signal results from the stimulus presented > 2 s > > before, to that one presented 4 s before, to that one presented 6 s > before > > and so on. But till now I donâ€™t know howâ€¦ > > > > > > Or maybe I havenâ€™t understood your tip correctly? > > > > > > Kind regards, > > > Maike > > > > > > > > > > > > > > > > > > -------- Original-Nachricht -------- > > >> Datum: Fri, 28 Jan 2011 10:02:45 +0100 > > >> Von: Cesar Caballero <[log in to unmask]> > > >> An: [log in to unmask], [log in to unmask] > > >> Betreff: Re: [SPM] separate BOLD-functions per each single trial > > > > > >> You need to specify one condition for each trial in your model > (specify > > >> 1st-level in SPM). > > >> Set the duration of each trial equal to zero (event-related) and > define > > >> the onset for each trial. > > >> This will create a different regressor in the design matrix for each > > >> event. > > >> > > >> Then specify an F-test for the columns of the design matrix > > corresponding > > >> to the conditions you want to compare. > > >> > > >> Hope this helps, > > >> Cesar > > >> > > >> You can read chapter > > >> On Jan 27, 2011, at 10:55 PM, Maike Tahden wrote: > > >> > > >> > Hello, > > >> > > > >> > at the moment Iâ€™m writing my bachelor thesis in mathematics using > > >> fMRI-data, which come from a biological psychology group. They > employed > > a rapid > > >> event-related design so that the duration of the inter-stimulus- and > > >> inter-trial-intervals varies between 2 and 4 s. That is why the > > respective > > >> BOLD-responses probably superpose each other in a linear fashion. For > my > > >> bachelor thesis I need separate BOLD-functions per each single trial. > To > > achieve > > >> this aim, I try to model the data using the Generalized Linear Model > > (cf. > > >> first pages of Chapter 10 â€œAnalysis of fMRI Timeseriesâ€ in > â€œHuman Brain > > >> Functionâ€, 2nd Edition). However, I still do not know how I get the > > >> separate functions or rather: If you fix one point at the time > series, > > how do > > >> you know which respective proportion of the signal results from the > > stimulus > > >> presented 2 s before, to that one presented 4 s before, to that one > > >> presented 6 s before and so on? > > >> > > > >> > Does anybody know the answer? > > >> > > > >> > I would be most appreciative of each advice! > > >> > > > >> > Kind regards, > > >> > Maike > > >> > > > >> > -- > > >> > GMX DSL Doppel-Flat ab 19,99 Euro/mtl.! Jetzt mit > > >> > gratis Handy-Flat! http://portal.gmx.net/de/go/dsl > > >> > > > > > > -- > > > Neu: GMX De-Mail - Einfach wie E-Mail, sicher wie ein Brief! > > > Jetzt De-Mail-Adresse reservieren: http://portal.gmx.net/de/go/demail > > > > > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco -- GMX DSL Doppel-Flat ab 19,99 Euro/mtl.! Jetzt mit gratis Handy-Flat! http://portal.gmx.net/de/go/dsl ========================================================================Date: Sat, 5 Feb 2011 13:31:45 -0500 Reply-To: "Watson, Christopher" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Watson, Christopher" <[log in to unmask]> Subject: Re: DARTEL-VBM: total intracranial volume in second level analysis Comments: To: Ben Becker <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> You don't *have* to include TIV. It depends on what you are looking at, your hypotheses, etc. See O'Brien et al. 2006. But yes, if you want TIV as a covariate you have to include it yourself. SPM doesn't automatically do it. ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Ben Becker [[log in to unmask]] Sent: Friday, February 04, 2011 3:08 PM To: [log in to unmask] Subject: [SPM] DARTEL-VBM: total intracranial volume in second level analysis Dear SPM-experts, I’ve computed a VBM analysis using the DARTEL-toolbox to investigate GM density differences in SPM8. The aim of the study was to investigate differences in GM density in two groups of patients. I set up the analysis according to the SPM8 manual (chapter 26 Dartel tools) using unmodulated GM images of the patients. My questions: Do I have to incorporate the individual total intracranial volume in the second-level between-group comparison as a covariate or does the analysis already account for this nuisance variable? So is it possible to analyze the date using an unpaired t-test or do I have to use the total intracranial volume as a covariate in the second-level analysis? Thanks and kind regards Ben ========================================================================Date: Sat, 5 Feb 2011 19:06:28 +0000 Reply-To: Steve Fleming <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Steve Fleming <[log in to unmask]> Subject: Re: DARTEL-VBM: total intracranial volume in second level analysis Comments: To: Chris Watson <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Another option is to select proportional scaling under 'Globals' when setting up the model. This converts volumes into values that are a proportion of total brain/GM volume. See here for more details (and for discussion of the relationship between different covariate approaches): http://en.wikibooks.org/wiki/SPM/VBM#Globals Best Steve ========================================================================Date: Sat, 5 Feb 2011 20:56:42 +0100 Reply-To: Alexander Hammers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Hammers <[log in to unmask]> Subject: Re: Anatomical Regions Content-Type: multipart/alternative; boundary=Apple-Mail-108-801023220 Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> --Apple-Mail-108-801023220 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 Dear Steffie, Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al. They are great but don't exist for all areas. The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007. One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached). There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009). The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-). Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise. Hope this helps, ATB, A PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially). On 4 Feb 2011, at 19:38, Michael T Rubens wrote: > WFU pickatlas > > http://fmri.wfubmc.edu/software/PickAtlas > > cheers, > Michael > > On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote: > Hi Everyone – > > > I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas? > > > Thanks so much, > > Steffie > > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco --Apple-Mail-108-801023220 Content-Type: multipart/mixed; boundary=Apple-Mail-109-801023220 --Apple-Mail-109-801023220 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=iso-8859-1 Dear Steffie,

Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al.

They are great but don't exist for all areas.

The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007.

One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached).

There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009).

The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-).

Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise.

Hope this helps,

ATB, A

PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially).

--Apple-Mail-109-801023220 Content-Disposition: inline; filename=n30r83_SPM5_Licence_AH090625.pdf Content-Type: application/pdf; name="n30r83_SPM5_Licence_AH090625.pdf" Content-Transfer-Encoding: base64 JVBERi0xLjMKJcTl8uXrp/Og0MTGCjQgMCBvYmoKPDwgL0xlbmd0aCA1IDAgUiAvRmlsdGVyIC9G bGF0ZURlY29kZSA+PgpzdHJlYW0KeAG1ndmyJMdxpu/rKepq7NCsu1C51DZz1YQIsSW2TCRaQ5OJ vJAgUJSGABdIgvRIeh1g9D7ze8QXnuFZWVmRPY3TF9W5efjy+xJLRv5x//P9H/ddv++6w01/1/3p 0u2v3SUd7f/05f6X+6/3n3z6Tbf/4pv9Mf375gs9cjz049H+8kk/Ol0O5+Ox218u4+F43X3x1f7H 7/fDJd3Fz/uv9p981h2O+27//jf7v9u/fPaj/evr/uUX+umO+5ef6PfmP3sdjfuXNz/a2clPuedN vufPOOSRd3bY71/e6lfPlJtF4tf793+x/8n7JO0j1rthPHTdMGzinZb/CkYKQ3D9N+n07uVz/U4i FUG56We6KPFnTM8IZ1l2RTmulUT3z2m9EI4aLFoJFHcv79u00l+7w3nsi0X3CxbdRYu+iLn3//JM 2w6U83C4nG63THi3BpWNhE/DYRxutwYMbiQ8DsJJE2GB+7+wDRYrmPx9Nvof8s9/Ct2X/cufdHTa v/yzfob9yz/la7/NP/+qH2EaMoDlK500wEPmS7t1NyPz9/n53+lHRJfZ4CI/iYy1vzNccsSD8P0b UZu4Ad5f6KSkgH2e+zo3/0WWMBITBKsWeBx5eY724IwjOPvP3B6c/Q8dycc4KqCnwX/MLcFazeju hZYia7960ROyxXf5Z0Xfldl2puGo7+/z87/6Uf59pZ+st5aINJzHw/Vyve0vEXLHw/F4TdH3uH// xWp4femPx+SO6c4nEXC43A7X22ne3KpPji3OPlzHw+14HDdRft1G+SKWL3eUfygVjcf+cOuGbTq6 tUgyHpV0u3tjr2r/0ES5Ox7G7nano1XK8qMNQXzoDycF8Y8fa/v+0LcFccXa4vS44H/I2aZ4VGLl v+WwVg4tXiiaewwlBhNp/kFXFQMhyBFRJMWkXQl3xOcpJrU4eDeOh/PtctlfNskJH7AKc3BFNNtL yiQWV2GPOMrJb7KCpIImZm/Hw+XSC/ofwmxsMoZlZxatuwyYptgKGlz+t2AbJPud5FYQnsyQtUD8 jxr71kyvgnPenKtsZ7F6UWXwPykwN0NajBjK3O9SZkomGTLjisuGLQcgiQ/SMEHrmC/dsruTjnsR Er3EJ+ec5vy2eyk6AD1TOy2IGMfzoRuv/UZEuMTXrAH04aeT+nal8KAMgsGIkLlUUXdeUOxahDl1 p8PlfJuEaSl1La83hMnuKtJn9SzwHJHePe8XuUJKMTJZpyqb5DxN4pWOmfX0VL0uSbd//9Uuds2+ k4VUBb3Rj0LkWhD5Pt9pCoGdnXUtH3W2rueD8sVZ3YrIj+Xt3LG8L23m/YxXUpCU31LZ3NSJOR5P qqRoriFTKaE89I7iuIoPf5/ziRlrihagFXyiN24BwrGEJmR8mYk9wXyLvbvL8XAezoJzu8QWi56j ubuo/zOclbTaKUuXMxCjBSTFcf9V4kuJMRKiGrT3wAeS8kHrO5mi63cla/METUANA8Uoi7kqKBv4 Gx2s14jH+VKpZcnDZhCWWv4u4+YXxrNyEVwSvGM8gz1OWtKUrv5dD6rIgUynI+WUQyZKhvl1PkKB fb6Fa/oxNO2e9QxOVod1KiGjuy6XkLs0svOi8P4cTYMGAvrhfF2lPFPbiyRqoXw7XIaLom7g+WMO MRw7i2Ifv+w9K171jaNnxGYQww+2Lqj6Z2Hl0YgCqJo9Acgi5JLv7ArkuEVVR1M8Gs6KR6fLPsuW neMj2uJ8PR5u54aw3hbkyvDU2cLo2EBWrvxn8qzKh/FM1IRhfp8D/B+yE04qLAnlet6//IsuylpE SCIVRWqdgkMhaA/SL5iK3ybDjP3helKi2CJqBB1i0N8ipn6dJaUo5ZaEoFzzGsM/RWM8Uur88usP 7SwOC4st8vT9SUA73iTP6dAThNVrsZHq/DOPJgqaz6NJ31+Fr8sVNbXg91UT3aE7jOfr0K7+NgD3 Gs2+HNWx3WLWt7KHcgrIc0OmnAIACSVT1Giyyfl8uJnvJ29qS4yfixfVnKQyhaemhi5XdajHbpPU fw4KEZv+ExJ6oZAGQxsxOCjjXLqj+dTHxOCgLspx1ARNrcfdWsmsir6ebHnZUDIPvbr7V40ubwHQ z6TKabwE3RH6gJPVLeb7b7J9qfXQ9gS5FmurbDicRjydGB08fS5+k6cPl+5wFYdb5Jarm+NYEjXZ wC5Rnnj8JwmuMIZSiItgDucqz//vrBsuoht+0sldazQcx5PGR3sLWx8TieN4O/T9LXr0ajZXfpwp 6VMJaekSBEgvbvPd446jYqXFy+ACayPgu5fXLWFYXiWyp+hake4sbUigkr7iSFQx4kw+kpks/evd 88nIU9cfLv04PrabY5tquwnbp+58OJ67km3aIvHMbqWoBJ1499SdbbGil1fn42F4PBpRYtcuTxQX faNKxnf+j3CkHhAnS+0wRRILSKb1PAW8OirRHVU79BdFlDTz4qMky0Fli+K7o5UPo3C7IvE9wirV K7y8t/DiEaRIanjTxXfmTpoLTIe7l1/aoarIGIMxFifRUSGkAOO2Wxm66dTRvV2O8n2U1BJ5+xYn 7JS9h+NFoRcltdXdUUk/y0oCnbjmXdViGuPOGXDQDWd5XqWtq2YlOPVjdxjkY66apfLTHXffGa5f hhbVKBQcztfryVWz2XUlcOmgYHd1UFyoFXv3ZznF7Wr2zs7aZhX0SJ4DpH8h48hXOfqxMqJKPDoF WIwfvLoRk4OmuPrupLi2AZNNEXPoBk1t9SqDNkkfMYnYiEbCo4OELj6XZqQLjoqlgB/lAQFvCm1u vhVMDuoYH2+jCWCzyU9i2hZMDldbYdEpZ26KaYg4SWExLcKkxDLuodMbRwdjDBNOKmBFmP1lVi3a x7H/kLunKtFciSs+MF407Xvrppi35Niz8N3m2OPlfBh6KzeDEp/VUihxNqgKshCRe77LdeebrAUQ hUp5ANXgiOjyez2gerUa0d//fAVnJw3wXEfr5AVBYg01j31i8XnX24sFFZ3TUiFNLfgqiN2zVRBd bqhlqkDRbrgcBxmE5trCXfFYh1fdEUKxALjkW27FubGIGU1r0EoffCqr0tlSUzLNiQ9AgaMYJX5n 9HJcMbJ/pcNqjCqhwVcMAQPsDx2oRma5ppMt3tOrmO2HQcXsJpUWVTyMFCbQO6Hbyp5fIFjRUHm4 HKOHdY2lPv5MY5+LsAJzmzYslCX15eleY7AwEBWItQFGox6Hk61QHFUERD3+UCMAp5tc4fahnoCq GQblCGxRjwE48FtMxtkZ9JeQS+2wK96yCblmq9KkQywhoEpGGWK5sl60IE/OQJN7Kj6HHW+KsfeV 8KXaTJy0uJLHwuHWurpRHdXP1IakhQ2gTCaAc2LTNHZsTN1bQidLckYdKAv7Jtq+KAbDcqcpW8+/ bZO0G49aXNALfZtELcE1jrDjZzDJlG1lM7FV/BSZucgPEsTZyiTPrnQjIpbRynSyxbj9oDGaoxbg bRDZgLMhhfa3eu1qlUKfLyTMDTWmUPWkLXDE5pZnC30d+F/nSIsTg0TA6oVKclCMy0lgjY0LACLY o1XT0a6ssI2Y/xtxYRkS7AIZ+WyLBbvz7XDWAPRGyV9lz8BBKg6qNjXOu7zkvtNaiu5ifR+0vdQ1 rAovrbrfKSYgWJylQocepVPtQLyKakKhv1H6lfPA+Y+zBaO3QQwqkyEq2R4uE+lPWrhxs1mXdiRt 9Ahbm9M2hyulDRKQtCGpH9V8CaMoAbELKhdzCNpzlZaE8HrceVgiEGG1CE58JGYZiHIyzsZx7SfZ XMzbrUXMwL4vmUa2xXahDVHYjtGULlj09+gKb7NjIG5EEjRhgmWdHKHmx4pdroTn9QT8YhiiP34Q Ga181gACb/8u75iGJtudaykX7cpKRGRbyEU+pAXDqG2ei5rWi41nTQuPnTwPB1mKKvMer/S9IRdp SHT08YjVDu82lz7droeuZSmAHDoCLPoMbhVfXQCD/GBnFI1JoAIUSVKOyBQviz/hOhG1MXxG76Ld BL7cx1AcigaGbcq70hLtwykNQo2lRB4qUoyDKMJANAIrsu2kS0ZoCvCC2UXrf/bZaC0gszGP5yiz UctLr1HqmvAqyIQGXBsp0WAEBTZmMg+tunFT5EYtUFlHQ3qAJMoDBJ0Ye2YrER8DzvscoKjY302d WmwydUpjtAQaYszlKIKChv5njtyS3mDwbH3ZeLoq2vTRWg+Gjph2EeXnKCjdpdNVr4mMLejaRvZy OZzKGpOqmH4+HnVI3G8qpk+psYbRqLZoeRvym5rNZOUf73P5Mw9p+9da2/xSkEbvKgaH1piWS0kj lxDnPfsPCWqZLYd66do3hSXNkyqCjHodtrJw6L/Mcp+0Y55unG+JlZR6TSnZiuHrqR9WmFKZP1vB XUaIvxNzGs19px8bLkMr60ZKGiMO4dbfZzLlfTbc+9nLnsNxOAzX8w3OGzAsdcawUpQLx0Dwfl7P DACvMVhVMdruQQHgMl3M+dQuUrVyEUairjByPOk5c5eAcLcebwEeNEXmiW6Scs2uLJCgqZT6J7AV r4NJCCAdtiu6iytQyGRoUtRbHON01LKyUZOBzWHDrNEQqI9aULKJbuVw7uJ14fKJkKraiAWX6KUR rx4bbd3auSFtiBlsHx2LI4DAEfB0QGoSVGuGFkzva2MwvdnRbuUQy/FgAsDuhaIlGhdwcG2V2oyr UndEeIErh3tma9EMmWdfjA+z3kp+EuqlMWoObpq18g/qU6mHVY1GmEreZmsXb1jry8KmZGhZiKPp /PyO0+khFnxsJVcmANO4WtTIJwWYCa0TMKNpk5AzNb0x+++LiSMKooEEMffmlTm7cdAY60nvLDQI l4bpbOBIpUsZ8jEZCyjf67Tscl8b2E0LnHtcu+O8TORWGsRlouegOkauCx9RLWiQVDAp27WzMunr UeCkpeuNa8ALAKNQE+TyXFzqIcEMrEWHRQjkNdEUyjgJaU7iDhVSdCc08Ryu4VVQ4RYmWTji9aPK 40QM5hd5ubf29EAckeHOqJdIk2vVvM8k9G+z10cxNxUzCZ0pcKXFkwYyIbkFB0N/0YskWkp9SkBo SAdtFbh3TvQa5bQIbrWPupGw5hNsVxfqraqD8ny0X7pR1t7WQYnNhWrZY2Qe7W+Twx2wnbDi09/m KAmaYie+uCd9lCqJCGo4JBgD9vjCqyno6c5YH1KERv/CPaHCtehYHMFoTn4+tkqK5WJkJsaDOx8E 06srHVVraapJ68lO6HYJ1W40utzyt+eVXH/SwEu3Sve+64R8xCYsEAMkt6AXjxwplBIyUNa2KD8c NbGivp5z3NY7gR26AdE+8P+VYpb1tQiu3CokNQUdLSXsR80FFgO1sQU0wBsK0VFTkxqE0WvKk+0a mmxbXT2c9T7Eeeg3ChM70sV3y6+jJC2r5150HcEipbTIP9oiwstVe2BtijcxbADMGEToeMEjDtvK 1ajsc9NbDtu4QhEAgZZxHmLiP+acGu+kDokhJkoTxfDHU8XK44c2jWtd5WEYrxPichT6mCnQ3gbT JEMLlCXz8/DmSVvv4gzTlidbcusg7WzOrbG5j5hb2wmv5dbuqCo/e6b39arsmi4Du+ghBrR0ldhf Z7M0INPiud1ZLxHfjgqWSLOUzWZZx4bCnpvbFrtftClEJLwKUKmJ8NOUztKbuZ7OUEYZtXPnStWr 66p0lGNRzaJAnNVvvsuKWd8PSFPaUCAR2xBk46hfb28x37S0tSivwQmlvEfJfAaTeQc5gQjuQRrc cxI9RPjpqG0UQFOKJ2WGFXzdj73OTVD1bBK36zVEErAkO6xF6FUNUbrgiRBRflu619Ltw03R14Vq ME9buh/17uktdZg2BReEQ4oiefndlO4zVp5mZl/5OqW0lnBz6mxhpJYGVnBo3c/F4iR2x2rqBjQ1 OmgKy1tssZXg9zzAeR9Lrz4dm3faipEt4pigFHtFqgtahOzU1dYLWPsT3LTE8SYxO4FdffgZ4Wdx vITh7+Rvmjz5a/1o/M2RmE5mfPqSRzQTFVT1+zpNQxZCD8JUcvwYpr7PDJQArALC/d/ofaZjjb0t RPqrlhDBi+zSYgKbZdLg/lxVcTJ2lksVteusYyyBCZaXwRlRDHWkW7zDiwvO6fBkHTqNfOw7J0Xu yv4HyFsHSXtkUoPpat5ObVsPCyX2wDcU+IHCM9um3slaCkpZ4l4myIMDbnJtoRZYcRIxqaPSedWf sMJQnz8Zi7NIZ+oTtACoRJTxptKpbZ/UxlpM2610R71dt4FyazGWfA5FVSoXbB6VIy2q0Fr9g9ZP 9Rs5LmEHKxSvl62qRh+vB9UoZddpmrioaSmI+tCOD+s3Zt8U87gX/nD1pD1fpabiv2L28QJPLQI7 6x27VWZn0aatCOm1B8O5l19FNTwL+Rg/OiyZmmseZpNr34cBgeZp5eG3oEqNbzepSyW19t+6zoR6 FpzNy8WVM56MyMkZ2ALmfIXZw9gpqgTACAZChhemdU7jTrRZhWkRo6GoPwDGtTKshuJpooqEjyQt 0by2mOdt7FBjOc9tiVq8Fnlz1SYJqzSl5wBRZB/pF7lHJQcNzOjx6O8PXeikIQ5tEtxtwUTb+HsZ 6xi1fW+nsY6PPjozaiu561R0bhlEGVUDbR1EmTX38QZRNhBWQno0QdESALRB/eHYaTRwW5MAi9I8 glxxm5ZXZwysgj7148VbXk0rYSpcL4P4+xR3hUuT1KejcKJxwm1Sz0Lb1jyqpan90E+qXhXY8yiq pp7Syo8mAfUWsDb5npTb0r973dK/67Sn90V7S29UHaUQwYoAGGNWXBdtcTDZ+T7JpNMxS6Akj/b1 zjgslkGB3ELT3gqnAfR0tWUwR2P8h+FmBRLh54Fh65V0bRVqf7Xddiw9r1CeVTOtFeo0f+0L3Gzd dlIu2kD1D5SELUlfdbfF3JOXMQgN3ARdPdkCYu0JajuvqT6PYX0tzk49RyDRmPRsVwO9cT45Z4PD qK0H4AyBwkuC5XjxMA+PGrPQS84q9ifjN4wKxY7XhORg2bvlDy320Ncv0qBlxc/d11hmYGyDue37 fD7pLbdI+VlpHesgJAVpgNMzUxkVb5JTW1udL/pESWSnpShO3hOs74nKvehusL3uK87t9QBgiEet OmF8NeN6R1qfSPFV5o+ciS2SYskZ3djw3KJPdR7Vzz6d9mNquM2zkrrU0y0DP5M7v7YOcC7sfc1X rTa7urhAEqXNKSUV70q/fN4uKq7nnqwBMhjmKT2B/Dt/ZYJ755D0fRSSPCgaDUOXGVVcGjoEUdWr G4r6/ppylDLJBhu8zU1UnZDEKrqAR5Zlud6QAIv8USQ0OoYEqCDe4k8yR+THkrcFXmkxgNVyD2E9 C0pT3I5280E8+Fuwmw9HOJNNhU3ZUKwS5+Fwy6jFAurCKOw/lOd+sMXZeTS6VDnN0prKZNey9cWW uJpAPPeohGVXFUiAKvdODlvp5GEq9MilbzD1bfED4wFSvBjXgRUKTp1s4aHT2oJzd5FdjImlEu8e ZsksJz2zeT2xa++/5UDTKwgTQURBj0hL+Yq0yM40LkcMpNTxLDEY9fXQMZ6ujYlBNkKJa26TlPgm kZZZiBkohmMf61Hd+kFq9s9xTFy8zYMlNBRVyezDulQp4LlLNA3DnTRqebla1/ARtu4ng3+WIys6 LQbjMKJjqs0BeluZYBs5+/u0sfrxCMRKPit5ytygbYzc7Ve/6nezrzVctP3VOLXRUNoCb4TjB3zw fuAnlrJ2L+ln+t5CKbnK78Hu8oV0RXdQwqVSEvDaHZPHaL8FI6kAdeTXGIGZiH5Okje1ed0UoqQ8 TbKPtp5vnyYEroqbejVVn0nsd/rC4m/S1tTHdQv0Urw+6aCSe7KAFfM2ZHbKnwJ7+tLhr34kLhvH zDTQo40sjO0MqoYgvnFM8Yf6ZNRo31gpq/W3jCie2rWjd4tVIOv9hNDYo9LcPnPZphzPm7UMa2RV Gj0aTXytDXSmJVkAkzCHK6Ygand9nWMTcJ9frX3JZ5Nrrwtb+1bNen87Dv5Q8/DRQ88JGj8yXmiN R6ZQmK/CG5y6AHdveeW7eRpeCQYcQYkjp0RRi0ZIu9xLPCNmc8s0OZrbRDrCEYyWkOXNRIoTE1PY eLwFsm0DdhlPW9D3bgr3K3RtwNO2F29GteAXcyuCUJCjKFSK+j3TYu957ySFWtcTFkSN36YBL8cr FLkYgeMUuApLfppu0FTetmheS1U18GFRODkoRaUFGb5Z9DQGb9iAXW9D6nVfLZ/bYg4MwJYu5ChO ki+r96Vqh5vbBeNNp1sU5AHMPrnSWPkvpPEStzzlw3P02So2aKmh34tfmqXTaegDD5Zic4TzAg5O 8gDePikgk+tzrIS8L3al1TlPQ7jb3YXxDrCZylQfhTjUxcNjb+31PqK2qVQNmnTd3sFJSnHtfC4G p3cK4YsvkICbhCIP/P5klCEGNL+J0wjqSot3044/hAGwClep5YElBKMXi0ALSNM3PK8W5zZoDhjA DNaOeORkdDobo08aB1wPcDIHaZqhn4M0deYWQeqqO2e8VUl1oXEyb6O63Kf52lEb0mCTEB0VFd2N a27Y1MtETWi9EmfUWyS+1QQowOmgQAJOR7sy8Ac787ZSsllviycZ2otNNZAzyySe4TXJzroKEwWC 4Ap1cdREXksFjE5ddnjdVZcd6S4oEtpoxln8GANKlX38ZXSsSOPeHAFkzgX+U6vCoyzI4Vo1KJKa jbqkVcjxJKtSY9M854wBJHDhp0ECpz/MiQYt4LFltm1ORFSgeIdphIdJxJvj+3H3NYnjCEEtnFwM vN8ogk2bmCE9tpw3m8a+QHp0q0OOzc82Aur1DpY+vDLu1xQ1H80Q92Uw43G+LItZbObQliU2WKCt 0+aE9XGjssVQ1e98WhKepZrGXrn3O7VD9aH0cdf6h20SlAjfSnWt16nl7Tgec5ZABNTy88qiYl5f pQfu/e1ukfzkba91LfZmqwCia6CPeGjA1sl53CsjmS3lQq8tT/SpG0GyMnBQ+mwguW0S1dYnXm56 27CVbGtnK+U09Oz5sywoM2VEdcdwi2ZjJEWJHv+K9owYJ6HCVxtwfUxRRvHgCXOlJnwPVk6WhMUh dKrEcce+GzhJeJ/Y9ABQqBPwVELo+RYQjNqf5jRoI9INPnKHxKaG9G7J6dydtjT0nUCuJRz/pR/b uRaVfMohbGiBR1P79mK4tn18iMpZ8BUqScOeYVMSoGeFQaIF34ozeX8xNktPsHkkE6PHsmTgKv14 L4r2IdpoZQ+E+oTBsfH7m3dWfq2PhZQvgOEWVB0R1Vzj5KusE5PQnn+v3+ktBW5Nypy/AoD0sIHY VWVk1NxzE1Evd9Qz0UWNDaXX1GGEqwQJFyAZFX+clJoI9Mbr7uWYWebnko+U20qgt7be6FC9zdpi Ga920S2fipdFABHPipZ4An64yFG6tvCxUlOruGrxhUHrXE4nc4YNePheIsoZC4tgHc2VswyJoeuo cnwFA2BXnp9QUvH/cGpadebhqI9Awv9SxePe7OsXafeO4TqtYL8izUKZ6vOhEUcAcU3EycSIuDr5 NSona2XyTMSq/Lrb6WSerH8voDSWX6N9XdJeQNmCh5mSHkC27vSinTZdVTh4OB1vOwf0+tp6O9/2 rb6Gkjq9ktisjI3FaPzGRSi4Imr3Uw7CW6hiTPXakb2pItW9Lars9GqrNstSMbiJvRhGMfAURluQ 3ukT3KdRX6IrLa+6s6+/ni1u27imsdM3o05HzQOWRhvmAWUNBLyvxmprMGeDrfCLmBTdgPdJNlHy dpL73Olz//OV3b16rY7US8q9i5b1ubqIsbGy19ypxiPkbpswEv29zmJesj+eSTGUQ2AKoBn837Uh u9c89VWfCp1zrVja5QnmtBnUmh++fPvtt0qtjdF00Mbi16sW521T03s5tVL4j3Ml8WmbcIN9nGpj Wy/K5M9j4CAY6bt8w0YpHNkYjXTrpVkKXLuXmfeSTDc68aCXC67HzTHLnSvpe5tzDaqktYRRaflD XIB6gh8iAyGAzgInUcii6zRGdFvhcLkdtxoQ5miZo+i0aXm+OSZWRQJCHHGvdIJ4FByg+7oW866y Q+f/x0De09n0cR1YR/mwHJVPVwdZqypSX4azsZm2JKsFzzd958qcS6vYW3bvUdaphnw0Of4oCTX1 gjtNsZ/Gs0I4HCwl23kl+aYlXnTqSuib1ZNsDWlnyqgU3qi8ivQmsAULU++zMdbO3p3o1eNaE85L LK8kIm7BQIRCRC/Mel9U01fi8rvcP0IEA7OdxXaxGFgEeGoxT6DYg/gWWGSSkCMY8MiRAtmdw6Xa gaYKOzwyQ9SM50KJ0/+Rx8nx2XJx5ualgcXYxgB/oue7GUAPWTiCXVdtViKMcNGxkgRcwIp1U38q /duYEY8WrssvisTKzAQUGd7kFPxKP4zlmkE4S+v8oM9CNlPY+Uf7CkWam4zREi08lNknghrXHCAu 3B2yBJUgkuenrDVZMKCuUn6gGMjV4TqPAOjOd6bh3mewMLx+WmTTd5209az1OJNwbbHCtKmGe/2o UDr6T1OD0qY2OlJsaNVm22dOu/RNFn3no52uVTnP66/eNoU466WzZsKKpqrsDPz/KysKfYE6oMo3 UXBJgMmdWJ2TWBQsAQWCJbcweczj0KycV9ai9fsuk67RAqQXh0shDfNV2tXjHA0OBIMFMlyFch1x DSplwD4yE4nCE96RRJqP+RByIhXideu7SKNgeO21P0SzeW1w7zls1D/TZ3e1ormdrpTznK5PBlrn pqGz3DYm4lS17vyD1rZeslI2bec71I2tdvuaNONhuia7NlDnFUgamtSQ+G+/zA21SGHvsF1PZ8Wx urlVKRSon9u36/UNdG2P1E5X4QbHxcWJCTggOeOuZ98UrNW50jyNBqOSkC01apOp9CF17SBjSaCi uzowMgkZY6Oa06pYxaA6jvoifSIKI/1+L6EFSoS0uPzLq74UvThyAuib+DM/7cdcd3uk5VWxnRjG /cl5qkBIj+GJK+j6QzEzcOSChNoUUKTWc6mblAg3s2KqFFWEYpQqEi0QGrRJ0yhH2YRn5KwSWGIP kWCgynnp6n1qqh9yLfH0dLdJ8axHM2p/4VNvnaoKsJrCTG9W8PP+q/0nn2nxDW/AvG7x9tGGP7XQ coXurBMoR1i3kqMtVSDcm+ztJSKVhKsE34moBiLCcIuZpxmAOhxa9GXh8t00yTz6bli4XCK9Fk00 LwIuJQdiuScltwAM8YMCST1H1S7okDIDLRE8plngFi1Z3DueNVCxhfPDVER2xo7z9UYXNPiT2PN1 iIgCe3g/QgN9eAYFOJn3TWzHB7WyEIGq07Hkco5iJ5v3DGkHXBITIe9PcpVl5VzlZGI7LA9OHBLF I95LrIpmjvTqwGEyxbgWAyZ6dD6xPm1TKSMhbQIX7sS3ohKcHKfTvbuXgtFXM4u/zcdFtkXBaRxK 0PV2YAbW+EFzkENHdU7TCoVS2wMrJ0gDdauegzkZb7n7/HeLv7ij600FTcK2LKmbCgXQXuVulQnI AVD5cQilBF1pYOrbVPLrZDEUagUk/GACmket6ISWYhNc4wFTsZqAGLsookvaizRhLT7OLRPbLVmu H7UN11WbmPYr+va47eN2tHUfEyTGO0nz/HNwaT0GwiEqKjq0pR/tAm0fNh+d84/eM+r1nb7+h9jn 3V6zuJWPyW7prlylG/UnWjorvqwzNLZcvtAZEgyfd1bcP2sZ1sjKO39piNAL7KA+gge83idiW75D oHLPSKmbN0mI4THfkRlxOnMsI3PnGvXZexDbI57lU92Np3nUSIVWRG8UrqpjjVoSMuwvUrG1zACB CvVEDfxTHmHRtZaQ2l+1I54tOcpQaOjSyWYxQk3dqaYG5Zra1k2uuQEkmBql1oHVjRHfs+IBtPcq x9CZxZdVmyOU18agIxoX+aONqyBu9nN0piSCpWJnL5YWVWKy5xeqEF9riAKW5YG5GTsgxqFS9xhn 6kGsWLdEXkvpkdryWlP8tCDAo0T9dasq0j1dwP7l1+KmMdSpNNSuHBqX6Vs/qta222un1dTnTluy N9OV32BTTBMxZcbs9PKl1iqm9IcZY21GqIkn/cnoHok8nXmjy1W/26GQ4mbkKVIibuIvTiAGThBO uvabGmBM/IAAciPpFFuycoAxVPH5mDMeqDUCeGHezj/9i+cWjEMPhmjL5UNTXHUfVq+p0nlUtT/L Qwg+p+yKSK7K1chMlMkbB0g8Anla9Wk+KDkzc3iAMezpzCSwpCN3e56cLL8pCPAtuoYCTQ6EiHBD w2DEtZn0BZ7RAQ8gU5JZ240Vl+AqxA9TsuhOJxzI7n6vC+oexrRR5XLd7SQXLG60PCmkGgHLNCaF RP7/iof0MeFaSJcHQZD9v/O9fvUxVI3x0oeBBopDWDjF7bh2TzgxNdcIQKMrLw20wMO+gqptZ1Ul bMJHVDt64MfZXdi7wRSA4MXtMVXEP7LUnmZmLbpDw3DBT1Q7ulNjLWoY9XKEFlSfN6oBNhH8VQD0 vCsZIRmf5AjPgnXkmQQxQKJT7rzXePIcFCJDtMjuZcJQekNPehILqWa+YxujDojAy0MYHt6JJohQ BwObVkggsv+8UZaeFvr6aTQG2GlnGVK1qne2/rksakjD1IwTLXictV4At+CsnsYePBoBXViLhFwj yaHrHGyNY/kYB10DD7omNSWiibeSwEErcz28zXopfCa0zutgCKFtBHSGcFYarbVu5lSiNpkwPjch oZOAN9qJtYPfxIwalOooWFsMJp+GiF15y8npR7VDZ85pvCkJmzLYJofT/u5i+Yuvnm3eqoT8TuZ5 Pp5Dzkp2xjzV0p6r4kc0kslsZx9axS7W8PHeM3qtr3kPEdMX4lFVPEFFWQV4a4oYGW3H85Wh7daa AV9BiavwxH1Ytgcj8RmvhFd8q0hQnIKbaZoihzYhiyYTB963fTgWbfzgChAtTcK7oy+9BInuuAaT 3AI3iB6JBtXtXuJWoDwfw1XUeRUrggZTj3umQbiCVegUqRZjVr7oyrr3a2tzDRm0hQYYw0IepJu0 u8lB9ZWzru1DN3JRWCQd0CInsYecsaX9rtfSp+6q6SoYaIsRlSVs3GcBWen0ghFy8Z2uwj4Ym5Pk KjbCe2u3qCih+nt7pnZoAP1AAhMmg3qoieY1tCQC3ItmI0cRfYsrl2m/gnaiGh3nvrGUXks8gHmg VnlZIrVSO+SEmO5q0hJuxL0cobPVr6bMxYsWK3K4DpN0aIZWwHFsGv36cynhuLLSaf/Ulp+etRZZ QYe6ucVBvGTVG/wti7Em70QcOJ/3CJP86LVKTYu+pJNoCqcADQ6KenRpkSZpocKNaL790Drs0Ka6 vh8O56veYbLdD1p0t20hW2fLTn6I6Rp9Xk/LQRm9qAYxn65vuEkxjWOYPl0TGlvrDbUpp8C1mazw Cg6Ia+ZE2mt0Vqilpaw6C7pivcyTuHEVCIxOjOPJ+bzYB9X8lJZxHJqKJQThwG9JblT7j1pcALvO 4vZElxIxS5v3AwjGOzfTV/BGUwxa8DM9kXTg/bVCHa5nGi48QJeb0KI/kiS870kZe4UA9yIhP6Xt t7WX+3AV3MemEXdGAHXyRCEbh7R58lH12RJoR612tc+7b0EuuiLEFtaIcpxVkdbU/Kj3hfTJUNpf KoM0LV5/zkWeU1pcqHOmi6ATFZXURNcSw6JcHOFVNlmh7lBIuKMYwUjJu3alrxXvhLbItCjAI4d9 eaRlXyDJj0zgusou+jBONaPa0rwtitVWNue9Pk2n9tv0T8MRzBHFlS6NKTQc17/XtaALhYJnz0cN pwd9KWosM2JQjEyZZY0bIIoXWoy1s4vlJowjagyKnKxbdKaWG36EyYSwOSbT1l2FZejFwAFzUTkT /CZZfTs3PLdQRRH4UTnLTfejUqYlOl9RL+XJuWKzuiN/SJJilqsLq3MnDMyAfR91Mj9JeXGns8LP qiTvhIYHAy5e2sJQtCayq5EWD3MH3/A1kNm7wyBsHoLqJBx1XGNA4/HkB0oClJJ0pIuJuKfO2cVo lxJEo5lwA68G5E0iu2AtnYUeOEGRkz7TkzUo6Wzqyeg6UMcudbB4fGtSV5E6ZtdV7eFztbd75Odk vMU1IfiLnUgclrEl38XAMpDhFmSkiZmMC4rclSjLEzO1bpAYZ4Ir6ESVR6FocSY4Ll67sdQxLxOS +xazLAQQPULT7EpH0yhranOTM9rHH9omSuntYRRahSNYYSkhiiqv+FcWe9S9hKhJr1tSGPZRMmhj 6IgQ9M1ztcNQ/5jOHuyqpoaqyNeiNNvuLr1YaIusG7UWwYvWEAmpKWUrmIo1nkOkKCCWQPi5syRx o2JmhbrDyCGnBmGGEJvAmYOOrpV4xz1NMqUBepcpMXUPatHmlmg7Wih8026S1Dtv6CQ6570Dqgk8 vvI/nWQ3RtqFyqEtlaVPptz6cZ+AoFrx2YdE2jrP5RW5Tts5nFtK4I1k0/uCHzCu0B3bxxX0yQ2t j5Jm6sZWxxWk8+fLQLVFlpZHaW1UO13h4zldL0pa2VWv428VpTRPG5GT0KjtIYAqKybxTVBZO6Vu XXBxnSU24Icpqjvkix9ykVupWmt4i87bHEvLIx4zUvZ/EIeSz84kqRkhqBYu/YUJd3OFb12Mvgx7 MEu+RxXwQZ6tE4XIcM9MgpjwEbpWsKf/GTmYROs4PFmT55GVW5a5QjjuiX0DxCkarK3vXNFUHdK8 y7VIbabcOCc8Zf2keUdfiuwPpEpfhss8TrvzRDDGLtiarK7kplEOdzctR12YDN6w8L/Tm3JlHw6g HpGK8GXOILoMEnEPPyj6ezmOaJfqxRSVGnuo+HQV04EOQFpZJ93kikxOSLPewLoYuY+2IIZHhyiG k4WzCLjiVDDqj6a+doW4xDYeDJwRsVDwdmjAreDdDLSXIAmwuJeYwCM0i5IquokSN1VRrqbrrTZ1 RUd9B/x21kvH+sBeW/FbcZPkQG/8cNWXL3I6SlkUhpivcnx2wmCFZwpsOYz+OLcPN006sDWFIkxN u7r5pHukLT6+rjrlvuMDrYiH9WidIouT8RZMyrUENo+HSQH68glkkI0fcAnyote6c2m9jZ6/L/Ls rHtcshoMQM4JyLV0a8qQ3uuITdace8laOH80SJF8CUXEdAM5DFYNUhgfcXQjqcWHh2YYKhzgoHgm rJeLtIIqKUyifSK8eCBm6hAOxWUuzf1juEjEM4ScwgGmhS9ujRZOq6ZEFptwD0/AOsQxKRLU8UDP JxX4qFVhIOp7SxtpZnPWxvNwKEYi8IB84Wexz6KLLZ1Qd1htGtO6jBFgwLhxoQ7QnMMSr1uY0JIN 1fnaXaFLXCyFjdlL5dbXfl6Od/3ZlqFoFL6i27hrA/LEkIHlQeBiT/veaxaUk8IHigO51APgkGa5 ZVHhMIF7VaBSe3FLQB4HqKAlwh8maI/gRwtxDLgKc2oI0jDxAApFWoPCs30LBps40dfUg8nW9y1o gsLQ2z4x526F7gxi04RQExTKAEWTkOqa9yd96KDG5bqQhxa8D6fb4XLShqWP6c6E3Nbl50st5+yg q460jTAfIPmQVQpdlzTT9FJpGU2YNfcRxxO2UJa3PA9hJT5vICzgvldMVtGGd5pb3nRY0j1+zeou fBeQ+7WE58qvjQD3xH5lpu5T8SRZmo6uE2MOwXQeQrRqfWK9yjF2lpYJYTN5ZswqbLXknt62Y+0V GIqGG7KPNAwLRMqFr8O5tqrQaiIsDhQWC0EVSQi1MQXVdtq98IJAlS6sDZ6PNihtQGCGjYfzj0bv kd1b9DsetXPSTa+vFP029JDasvtoH0a6XbYQbo3pqXghFc/sx+FMfZ4qfTTE9eZzgMUA3ItS+cGC BdKMr3GxPBk9tpyFHkafCoEW65TwogFRbSrrb/WvRUTpcDaQPjVZFYIsQTImVSfEIhkdEg5QZVyE VfU6Uv30eNaFMBKdBWWgP27hqPpw0lTBVIFmOhmZKLZB38n1fLQEOaNIkQuei0GQ58pnXCOnVcdH TD2ys+mX51KDPovKyflazZQaiDTQBOow4z2S+sXwIr0zrG6udkmFRWw4uwmFp3vmmqo59s1uIYPe CzWYdGOmXhS3omK4is/TBN3R2KDTrOe3wNChret0Ol4Ow6j9eDY4T10W/fz/AfyrUW8KZW5kc3Ry ZWFtCmVuZG9iago1IDAgb2JqCjEyMTEzCmVuZG9iagoyIDAgb2JqCjw8IC9UeXBlIC9QYWdlIC9Q YXJlbnQgMyAwIFIgL1Jlc291cmNlcyA2IDAgUiAvQ29udGVudHMgNCAwIFIgL01lZGlhQm94IFsw IDAgNTk1IDg0Ml0KPj4KZW5kb2JqCjYgMCBvYmoKPDwgL1Byb2NTZXQgWyAvUERGIC9UZXh0IF0g L0NvbG9yU3BhY2UgPDwgL0NzMSA3IDAgUiA+PiAvRm9udCA8PCAvRjEuMCA4IDAgUgo+PiA+Pgpl bmRvYmoKOSAwIG9iago8PCAvTGVuZ3RoIDEwIDAgUiAvTiAzIC9BbHRlcm5hdGUgL0RldmljZVJH QiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeAGFlE1IFGEYx/+zjQSxBtGXCMXQwSRU JgtSAtP1K1O2ZdVMCWKdfXedHGenmd0tRSKE6Jh1jC5WRIeITuGhQ6c6RASZdYmgo0UQBV4itv87 k7tjVL4wM795nv/7fL3DAFWPUo5jRTRgys67yd6Ydnp0TNv8GlWoRhRcKcNzOhKJAZ+plc/1a/Ut FGlZapSx1vs2fKt2mRBQNCp3ZAM+LHk84OOSL+SdPDVnJBsTqTTZITe4Q8lO8i3y1myIx0OcFp4B VLVTkzMcl3EiO8gtRSMrYz4g63batMnvpT3tGVPUsN/INzkL2rjy/UDbHmDTi4ptzAMe3AN211Vs 9TXAzhFg8VDF9j3pz0fZ9crLHGr2wynRGGv6UCp9rwM23wB+Xi+VftwulX7eYQ7W8dQyCm7R17Iw 5SUQ1BvsZvzkGv2Lg558VQuwwDmObAH6rwA3PwL7HwLbHwOJamCoFZHLbDe48uIi5wJ05pxp18xO 5LVmXT+idfBohdZnG00NWsqyNN/laa7whFsU6SZMWQXO2V/beI8Ke3iQT/YXuSS87t+szKVTXZwl mtjWp7To6iY3kO9nzJ4+cj2v9xm3Zzhg5YCZ7xsKOHLKtuI8F6mJ1Njj8ZNkxldUJx+T85A85xUH ZUzffi51IkGupT05meuXml3c2z4zMcQzkqxYMxOd8d/8xi0kZd591Nx1LP+bZ22RZxiFBQETNu82 NCTRixga4cBFDhl6TCpMWqVf0GrCw+RflRYS5V0WFb1Y4Z4Vf895FLhbxj+FWBxzDeUImv5O/6Iv 6wv6Xf3zfG2hvuKZc8+axqtrXxlXZpbVyLhBjTK+rCmIb7DaDnotZGmd4hX05JX1jeHqMvZ8bdmj yRzianw11KUIZWrEOOPJrmX3RbLFN+HnW8v2r+lR+3z2SU0l17K6eGYp+nw2XA1r/7OrYNKyq/Dk jZAuPGuh7lUPqn1qi9oKTT2mtqttahffjqoD5R3DnJWJC6zbZfUp9mBjmt7KSVdmi+Dfwi+G/6Ve YQvXNDT5D024uYxpCd8R3DZwh5T/w1+zAw3eCmVuZHN0cmVhbQplbmRvYmoKMTAgMCBvYmoKNzky CmVuZG9iago3IDAgb2JqClsgL0lDQ0Jhc2VkIDkgMCBSIF0KZW5kb2JqCjMgMCBvYmoKPDwgL1R5 cGUgL1BhZ2VzIC9NZWRpYUJveCBbMCAwIDU5NSA4NDJdIC9Db3VudCAxIC9LaWRzIFsgMiAwIFIg XSA+PgplbmRvYmoKMTEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDMgMCBSID4+CmVu ZG9iagoxMiAwIG9iago8PCAvTGVuZ3RoIDEzIDAgUiAvTGVuZ3RoMSAzNTMzMiAvRmlsdGVyIC9G bGF0ZURlY29kZSA+PgpzdHJlYW0KeAGUvQlgFOXdP/48zxy7O3vN3ley2c0mm4QNBEg4AtGMconc pwSJBLlBhHAJCgqKgIiKtt4XqPVGAgQMqK+ppVoPCp71qEotnjVKLaVWyOb/eZ7ZALZ931//WXbm mWNnnnm+9+f7fQZCCSEOsoZIxJi+YNqiGx9ePgp73iCEeqcvX5q4Y9Fby9E+Qojafdai2QsmPfzX 8wix6oQob86+bOWslW//+U1CnEcJudw6Z+a0Ge03DpxGyFXf4hq952CHd4HjICGrirBdNGfB0hV9 F+fdhe0LcM3yyxZOn3b718+UErJ6KY7ftWDaikXya47bCbnage3E5dMWzDwSnzMJ2+V8e9HCJUst f7HNwfZwbLcsWjxz0djsga8JuUYmROvAPooP/8PvqSpa/+fCPJkRCb9XVIvVptkdTpdb93h9fvHD QDAUjkRjefnxgkSykKRIUXG6pLSMdCEZwvvUtVtF9x49K6t69e7Tt7pf/5pzzq01zjt/wMBB/+dt //3g4H/f9Z/3DLlg6IX/+ch/3qvsJxF8o8pjJCKnSZiQji/x/Yqvs3M7vuLH+Zp9g9+35L6EPE62 07lkO3mRvESP4Vc7yD7STH5HQmQguY+sIr8kG4hKJmPPDWQsPgr2/5JGOppJBdkGXtpGDuLci8jV ZD8J0nDH1+Qacr30Nn51PXGSQnIeGU0Wkpvo8I5lZAr5VL6O9CHDyeVkEV3TManj5o7bOh4hvyL7 pN91tBM7iZLp+Bzs+E55v+OPpCt+cTu5m3xKb7PtIQbusgZn3k8Wk3ukepl2zO74CT1IkivQB5mM IAdpK8vg6jPJlzRMV0kDcJWHO5o6DuCsPFJP5pB7yH7aiw5hSWVKx4iOgySIe6zAVe8mu8hefFrI C+RD6lCOdTzScYxEQPmheJ5m8nvaKmXb12ZrMW4KRqmMVOPIQvI/5BVymKbor9lCxaH0VAzlyo53 iJ/0IBPQ28fwyy/oP9jV+FwjvSwP7jifuDAut/LRJr8lf6JRWkFH0YmsjC1kD0iLiRV37IHPDDIX 430Xrv4JzdC9zMEOSQ/LT8kn1fzskQ4XKJIm95L7ya+pE0+aoEvotfQ9+mc2gE1l97LPpF/KT8hv WabhqS8hC8hN5CnyD+qlfekYejGdQ1fRDfRWejc9SA/Tr9h5bDybz76X5kiN0gvy+fiMk5fI1ynr lRvVr7KTsgeyb2b/0dGzYz0ZA35Yi97fTh7Ak+0jh8gH+HxKPqMKtVMXPgmapBPoVfhcTW+iD9HH 6RO0GXc5TD+jX9Mf6N/pSUbwUVmMJVkhPim2mF3BfsnuY4fwOcy+Zf+UQlKhlJF6STVSnbQQvdog bcFnj/QnOSofkjswzj2VO5QHlceVp5SXlGOqw3KtlVjfOPVwe5f2T7IkuzF7R3ZXtrnjTyQAGkYx CgWkBr2fhs880PsOcNwO8jZ1YOyitAs9lw7HyEyl82gjXYGRXEfvob8SfX+GPo9R+gP9Hn12sjzR 526sFzufjcLnEjaTNbIt7DbWzN5jP0kWyS65pYDURRoi1UszpaXSSukOqUl6Q/pY+kw6IZ3Cp0PW 5AK5UE7LGXmIPFVeJj8gfyl/qUxRXlc+VzV1gbpebVH/aultOdcy2jLGUm+5xbLX8o61Adz5G7KH PHu29NMj0lppkLSH3Mwq5Qj7Pfs9+HkqmSGNYOBU9jjdyFbTZlakrFD7s/50JDkmpzHWL7MH2QnW XxpBh9FxZB7rYV5T9ctPolUj/4a0yc/j2X6PK69QHfRq9r3qILsoYdXQtr+VussZ6XXyofQptcjb yEeyRkO0jT0mjQYXvCCfq0wiSek+8ozUSFeTPQwaUjtp3Qw+HkmfhF4YT3vSH6UOIrGR4KI+0p/J dWQ+e5+0QY43kjvpDHk2uZlU0lXkS/IopKJMuVztogboq2yuvIn5aDNh8hN4umpaRCXFT9bReuke 9Xv2AVlGDska+UR6Gr0/xJ6RRsjHlLF0DiRgNVlPGjvWkpXKJPktOptIdCIplo9Au62SespJrK+B VpkCnbYX0r0feuA8aQT2hME5w8EXE6Ah7sHnLugJGRw0FzJ+EbTY70mzOp61kNmKi0LrECK/nh1L Jnc8Su7umE0u77iNdIU+2NCxCld8nHxObiGP0+uzV5FFJA7J+YQOVwazQ8rgjq5sE/uAjWN3/Jy+ GO1iGibf4PMMGUzOVZ4jm+Q/kHGktmNzx7vg7lJo2LvJpeRCchRP+R3ucIHUSiqzI9nOjsHSIjzv p2RMx2MdBVQjczouI6PI8+RXFoVMs2SMARPGn2fUnntOTf9+1X379Kqq7Nmje0W3ruWZLmWlJeni olRhMlEQz8+LRSPhUDDg93k9utvldNg1m9WiKrLEKCkflBrckGhKNzTJ6dQFF3Tl26lp2DHtrB0N TQnsGvzzc5oS/HfTcOhnZxo4c9a/nGmYZxqnz6R6oobUdC1PDEolmg4OTCVa6OQxk9C+aWCqLtHU JtojRHuLaDvRTibxg8Sg8JyBiSbakBjUNHj5nE2DGgZ2Lac77dqA1ICZWtdyslOzo2lHqymUWrST hs6losFCg/rtZMTqxCM2RVMDBzVFUvgpLiMVD5o2o2n0mEmDBsaSybqu5U10wPTUpU0kdX6TOyNO IQPEbZrUAU0WcZvE3CY8DbkxsbO8ddPmFp1c2pBxzEjNmDZlUpM0DdcY1OTJ4L4Dm0JXHg2f2cTF vQMmbTj7aEzaNCg8N8FP3rRpQ6Jp65hJZ/02luRXqKvDNfBbVjy4YdNg3HozKDVsXAJ3Y9fXTWqi 1+OWCf4k/KnM55uZGsT3NMxLNNlS56fmbJrXANJENzWRsSuTu6JRY1/HERIdlNg0flIq2VQbS9VN G5i30082jV25O2IkIj8/0rV8p+4xB3any51rOJxnN2Zi0M1joiVO561hY0+PLOU9Sg1tMsBR0xPo yaQUnqkvX8zsSzZN7wsC4K+O4ldNM0CRuU22AQ2b9H58Px6RNinFeiqx6e8EHJBq+/bne6bl9qjF +t8JP8j55DSrNdFpne2mTKapSxfOIpYBoCn6eK7Y7tW1fHkLS6UW6QmsMHxkNMZ2Wl2/Cgx/MskJ fGOLQS7FRtOaMZPM7QS5NLaLGBWZuibWwI+0dh4JTOBH1nQeOf3zhhQ4uVm4v4Ema/r0P7ce9A2a 06+JBv+PwzPN48PGpYaNmTwpMWhTQ45rh43/2ZZ5nA8oxg3Hcq0m34BJUoxhH2+xmCSOgimnTD59 CjYmOZrkYvxTBVPPaLFYwZViD00MbtIbLjCXdVoymZOZ/9ePWjqO8V+J1Zmf5R6jqV8m11Gz2039 f7b9s+45NknDxkPlsGHjJ2/apP3sGFjN7OXQ3AocT8ZPSiYGNJEJkMxi/GvpaO3Lv3WxJgNDhiPj IUVid10st/mzE2O5H9Xhj3Nn1/LB0JmbNg1OJQZvatg0raVjzaWphJ7atI+9xF7atGgQtJ3JOC0d +2+MNQ3eXIcRm0P7QTwYOZ+L8YDxk3JPLsjCuRtk4kYcKpk7qAqBx2sh5zczelS1tLC7DR9R5KMS 0SzyUUoiVlU5yqTnYfhtcAO7kXBGP1HTXjNSP14zor2G1KKtn8KiR/ekJ+kpxoLC7J1KSK2nDIWc JAm5ld9hUscnSonyNmxkOelNzzF+d2VgcXBx6MpuV1asDz5a8TGx3pH/cJDdUHFdb3Zd3rokaw7S htC0JAsGjOA8Ij0Z/zDIluQtyWfLootjbBm5Ksg2ha6LsScCzwTZdfFNCbZJuy6PvZ54uYQdDL4U Y/ujL/vZ3N77g2xuaGYlm1lBJ1ZO6c0GV04uYCOC58dY92h1AUvHihKMdO0a79pN00gsGMwPJILB RGK/1tWvaV3TZTqtKov3k+yx9fmpSxp8i3xbfVKFz/Ax3x/zbwnTcAubbORFzo0vTuTT/L59yy7Z 6qTOrT0uSVioZV6fxrv4eNW3Ha9v04/XHz3eVo8V2kdJ7dG22rYNrm4Z12r9gMVVs8HFV3qNaPTo Tuv//Y/kdhWraqqwJN2rqnefNF9W9oTJVWjvPiHVEgxZ0rR3715V6VShGvAHQ5SqfF3Zs7d0sO6t K/+0bv6OZ6aff+j+O17M/oVaukae6z525pqVC7LxZYOmDhk6LZWiI7J7b5t187Vjtm+fPv2uVXdv /Gjc4pvPX/eblrVv/jK7c9LS0tZV6y++ZbB0/aA5tcOmXjKwcFiX9l707otuH1rXOhMsRcmq7BjW AFrr5BxDK3FTonstVl1voZW7yYMuK9aGx/Kg6xIi6VJCkqSnPfdvFsPUfqJNP9EGnqoFO9F6mmae qj69+1SqFnwCOqWf3v77EZOfX7uy5JxUhmayY56nP1LXdx+2nzxct+mO517IFmQTP7v/TMNRykp1 ZtN0Srw23gPtQYli3UwelC5xQUc06zqbgMaPzW63aBxtdjpF41vDrWlsgttV4GKup725Pmbw9y/9 9KWIp6okjU9lEN6PztrX0kym8JySK9c+P3nEoewYeoT+6fl9d2ya/NbJ9g+/y/6QtYpxejL7Cb0O 8ahGRu7RIIRPqS10tJGmUg1jVKM1RGMSNoja19JvFHz1hfA8t0Jgt9q3cbY6Xn/8qN5Wo0MI+VJv 09vbqMdb3aN7Za/KgF+1lPTu3WfvwdEX9awG7Q823pgeEZl2McbnPNrC5rEFkMpyI7KILZLYCDoC t0wRFlUW4YSIvOimcGakfrRe/4JUjGjr0Z000npfr2TgPFZGW/bsQfdx2n4sNqD/Eik2wox3t8bs 5A4ib8XxrbLo5wkwPLpodmv/wYOAfqB6Or5k1eARiYzbR6SOT3b5q1lLxydGwl99p0SZ9KC0Q2LS ckIBuVCoKugj6SvCvgLtnsDt5d1XYgRq9ONtuskvG5RumXpIEuebTCZAKyl9Ykt2UkT59idcgZEJ HV/KHqUVPJlPJ+xkXC8aWjQuK/640xmytXR8JejPG0aEM4DNQxycI0jQ4cDSwfeRChD/IBYH8Tz8 iWI71X+/0nFcSZ2AK30BThKN74yI3Y6Wh+h8D9EdDr7k+05f8sw1m9VERM8Da+5iCfv/wHUK4uvF 191xxLhUVjewjfaN7lddis1iD7NBvuGBCyMDYuN9UwJTImNj8y3z7dN9lwXmRxpiK9kV6nL7le4N 6l2WO/RXwx+y99T37B+5o6cffInNSKaqutsosek2ZttS4FlCYLIMF/YmAKMwsiX+yo1CODOQzfrG DCclf3Ra3wiYpC//o/jW1fl0L9dEQS8EQOgnn861jkeHJrKoE+a/vXX5rqXnz3t72zsrb933xKpV Tzxx9aoL69nbVKbnPD11d7bjw2w2+5vtdz1L78/e+f0xYA/zvpu7nvPKpyDgSdBOIzuMhGQ4PVXz 5WvYLexuq/y0TG1EVZhkU6iD0dc00XuNPxOhCfy2peOIkHA0vjE8gqB5gqAuQVCMshHh5OqkiaBP 1KEYTneV0jkS3RWaAF7DlIh9P62h18MIcuFozEC3c32AP2yY9rA2VE09kEFaT+ozyZRHVS29IIeV 7GTzeW+Pv/OziqXyVeeuKnhmyGtT+bPVgJcteLY4fSXHSzaP7gz7fOoEZ0vH8WaPRzS+M2y6jlbc r8Q5i4b4CfE4PxrPc+FIHAyKZQt7znAwLRRKFOgexhIF0AcV7xzky4Okoo33tJYvDyCAi+XEgN/Q 4fUycUPD5vagZd7niGH3+tiEuJ/v49fehUtzUbHb2QQ0vjXEKP6nu3EZ4ffjdxM3M3r3V/qrzykv qs9ZXrG+mmcZ6qhzjHfNd8xwXem90neD93nv59HPY8eijhftz/pYTM/T8/W4rv4PwDULmN+KtQ3U isY13aqqr+VF/Xl5UWteFNrCGs2TnHG9hT2ye5SHelpoeA9/AiKGw02ZQ1sSehujzXmdPsfWkgTR aV/D4dlTCxBsIbuGyWw/KyIF9JadJrNDr5zIcPUiHJzatvb6ox4vpywWnUbb1LVCBLgE9CX1tH5x XV1xIJnuA4p3GmGuhoWFBifgn2w51YeFih++5/vH777q2vvoPt+Pb7594oLHXnpoSnz79vNqprde feDzWfN/cd8m36EPvtk+6cnnH9k4jaMulEzs+EIOglcytC5HOnskbHA+DucRypk148AGLUtpTrfD Hde0skA8T46X5SllzpTTEY7ACCagfNiEhCXN6chPT1dwlXawgn+It7q2FoakDfzS9rL+srdaP5Dp yb9gF6NUcQadg5zrnfIgz0We5TFpbPAyfZ5/RnCZc6V/vXOT/4bYr5yakpAgdIadg+ayheK+MDeP 7DbwAM8BoCgjTtqr2eEIyOH97BESYXOMEvRSQTed3iVTEwsTLBHmvJxYY1mSFtopTUlaTzP0+Piz /Eh6S9dwC+27K/I23U/7wpS0GvYz+qq8hd6Wo2KmTdCRa63jGWGEQEkQEg+nC4qaBIWwQolBXmlj na9PkGst4T9Z+pxuCmPKtRu3q3xJUoXpic0Ft8+/ZsdDqyuH+732JS3r583d7G9OfvPMitfmz5px 7ZbsV+/9uoNeF757Q9O1q7b5H2ArVk+/dt26xJ5XZu+aMfW+bvEXbm7N/v0L0JYBeSSyruyHjnPS tNHbO8kxx3GP4wnHqw5luDTc+UtZ8oLPiUOVLIpmlyzEAYF/TZL9kiRLTsIcTtkiPceeAyzM6FZD I7KMU8hrmtzCZj2rKJqRX1CldWpDNLhxYhPQ+E5YKa2F9jGcFqMwVWVZk+xl2eKGOca4Ov1VhOks wSRsHxG/QePoXk4HtsfVQjeLsf4WGlAow+NcxdToX+g8NqhFkHCixlPNh7m6ekO3jAwL7Xa7MeAI /PYRJ+y+txp67h3DXlktFXatluT8/Bp+iTqQA+cYfodhr3asGV3tMNLVjsI8rLtW8xMydQg1etFK T2Ug5ZE8lN3Rvo7d/4uXX27O9qJTfyXtPXXhr7LbINi3t88H63H7n1QehZ6daMrOPkLxfE4+CDTP pcUDgTwv1552tyzH85wuSixh2AzhFYiGkDNu+7mccBsINmo/ANngolHmFfrXLZbDoivzN+Xf4XvM 9xvHe46PYlabL+zqEpVs3ZXu9v3QZRLkQ/dpAa/P95rL7Xf5/C63E0Ji+HhHDNdWOJwutxGguU49 65bp21yAoNmMBO+eZ6q+UL9Gv0WXdYhJWIhJmJKwHmborCkm4S0J7/O0F3HT28FUfXe59vwncSn4 ubicEZh67ldCSsSD1nuqK+qhGI5usHbLKKAiAUWF3EByGuFx/UxwIC2+ZCApQe+RgN8CbyA94YXA 3Zdd27x980WbS5+4mX3Q/uyodbe2UuvSm47/rp2u0TfdeOChe3aNqg2yvz6dXT4le+LNV27ddQSi AdkYAdoFoPfySRc6Kqf5Cty0ADC8RGOlcQMRlxOGMaYUxv1OLU5JsY5BMP04PR7SudkPCb0XAoHQ zvlxB985qP+2k5aIyg7Uc1p2nR+hAy1GYGBkYGKyd3xivjTDMsM6zzsjsdS6LO966/q896zvBD2W BJeBElMq1AkpofT4rqQ4YOEHShKpRJIf8PBejnYy9DNG357KSQnFZ+vsM7zavoaX7CleogtSIlrR EZfgKY49y31FfUu5xlVdnFYbwdrQ1NDC0DUhOQTXVJ0QCvKbhlpY0e6M6apBFtu4/RJ6z/TXTG1X Uc8dN26ruABxjVdHLYhbuIOmWriZ8kK/gVzEo/fBVpD6z2hDVTq5O1w+dP7E8yZcys57fnZz+xWH 1/0pe/T+G77a/nF7n1E3j1z8yENXXfmkPM41r/uI7ud+98fpDdl/vLWp7WqkDlbRJ379+EunPq5/ sq7lgbt27ABdKTIsBPbsMeT+FhmuA04q4x+zyjboMy6J3RmVbQ7nEklifFBGCVMtsajbusT2FzIK 1J/KpFqsFtJr4ERGoIyE4gc0Ud9YM+J420j9BPfKeITAYYpqj9BDGIFGEcuoRFItqd5eb59p0p7N 2bZhvd37pGv/doP80/bNt2e92ZMtH22n39BX7uP4xTjwYAQ8GEK6tzsjJhc2O0gs3o3rSfhjbEK3 bt5kXFVK415n3ObgZhZBwHGoSjQybh5rckZEw3SgeEMcdIdhMc1AVDT4WWjkGFgqCji4vxUQVwwI Bg7kGNiMRs4KSaCTMm3VkMxcZPKs6IgIQnhH0OAdOSoiFN4Q+3L3524wbnvKKOQn8tty9uI35Ev+ pGeer1NocC8qdKLZExEbcRnq0ytIy4JDg0PTXzi+7q7YuiO9s5qukpdaG+2LHcucV4ZuJJvoZnm9 da19nWO986bQG56Xfd5CyMquvESUrxKJCr7qmoDdP2LEyxIOEg8TB7qxtRs905P4khdt1NbCZht6 ZonbSMDzB+Lg1t3M3UJv3dszvKQJQTSO7ypaEuh06BMBI8ACW3qcDm1MfIb7CTk3wVtdX8Efjhuu nMxwuYGH10ga6+roGeDltD9AAMX4BNJioi/S2cJD5y267IsXW7+Zv2DDTdkTH3yQPXHrpevnz7n+ hlmzN/YbumXc2se3X3vNY1Ks7K55Wz/8dOusO8vKD2x8voNQ2nrLr+n4Oeuumzp9w7pTHSO2jHp0 zbVPPs71IrdpnCfj0IvPmNHDs/YCmIFiD4zACUFkbg2EgUfjmFHKKRr2CJJ6RBTqCXvKM/bSOEc5 Rrkkl8tPRlMqnEmnjuiCcmsDtaoIih/I1PcEi9W39RQDA8pzRtS5Hv34t5zpRGB9VifO2E+jizCg HsHF/8tdf36vf7kV7nTmRkZVv+jwoJG6OHhRapZ0WXBBdHbqyujq+ObojfF7gk9En49+E/wicSLh Oyf4QHB7UOpXNkNlJdz2psBM4WRCTZTGR7mmckObxx+Pvj3aVMrNvBMF+2k1sUMne35uWreUc03d zBW15zQveQwP82zJ6d560+fkqpdr3tP2s1PxknogKQiWhZt5LutVVcL1LdYE6hZZMx46p2knegdP dNH24Kpp41aP7k17P7dg7ylqefmWtquu/OtDT3/IXv/V0hW7nli1ehsdp195+fBr3l/kCE+cT63v f0r1e7J/Bs70ZXb3My9KVffuPXDfZq50GTLxhK5HzQfHfPvCl1CIarExtUaWaqgqA8GBb0NYAmOx zZpDmRq5/kRMIEguxMEHfEnCdx/AHKnu4MFTjwHUYUTgWeLaqFwwKpbYr7P/wv6w/ZhdwVimtT7a YG2iNlPbo32mWeyay8LvaalRVcUl25+CAzraSCk1sujGWkDTqqVG1vra+ykVcq3MEjKVt7k7u1QD 6AvAM0Av7mW2t7ehyREw0Umiv8qVPFnc2NnR02DYwRwc1tnrTlCM+xmoKiFKHXxwC3HR2XspSn7Y BDi7PzTnGj8KQcKe40YdFySu49UJilhW6N312dY5tgZ9o7RFf1V5WW3Vj+l2q1KHko3R+hx7k/43 x9+cf3PZZIfslF0SUqOKLCNGsqoWiwNtK2oTgIsBiTTcAqFIWBx+HGISlPKPBrQxrEJCdvjxK1tc UaxxVVJb2CLDRqyOrw1GGdtP7VAYdsPrSJCZFmnsaJRAfCpLWzB0LZQa9tGOVsunDmmLgzr4tu62 HLKwayxrLMzyC/d7fxCYYmME1hP/whjMaEQHF4dra6JttUcxvPjHcbYM/L8N3cJiLZgCHv4G/cAB 14EDGxRzjfEf1mQfN6wpjtRRs+yWrJb9COBJx49ci9bRxdxn5H8pIHUpKSn5klK6RLVIrPJNNunj p9rv3fYB/evdgwvzKpX9Pw2mz2cHssn0jn1X3HQj9wkl1GUQ+WvQyiO8Qt8+IoMqQziiJsuDUxNT s1JLbOts6tzoMmWRDZyoXGdXS4I2KVzSJR7Mt9l83niXLmVlBEVcGLkCQCnEGk6rDo4Gq4iOjEpu hVUvV1qqysdetfKrowmaq35uFNXxxWlHHv+FQ+PnOThnBPhZjmh5fjxBuRgl+HFQlavjXIOfiz0/ IQo+3QACxRU0roNWfab/FI65mUNUL9ItfGMEwljzD2yPvfwLdVwDUaiu8FTDVlHgExh7jj1VepJn xasulqLJniYokU4hdOrZh2ufNNp3sPTjry+ZNfv6Wy5a8+vN2V/Qc9b2vXDY4GsfyH5EF1ySHjC5 3/jbN2e3K/vr9s285NHKkufXzN7Z0EMa6wnOGjF0YdnJrRZH3/mDx64ESEHJrI4vleXAdfPJ23um s3n5DKaEuzvi+b4ypvJWgvR0Tkddw9L8NWRd/hZyj/KU9CvnPqnZ+YrzMDma/7d8j8ub78nPl7qo pZ4ueYmCIc6J/osCEyNzlPn5V3lv9N4j3e26J+9x+gh73POuy4cKi6ju16MyZPOTXaXVwnx1La3W 3YTKMV/cIcXisk1Puy8k6QSsW7QglE5YqRV+lTrBGolPx2jDa8zUj+A+I5Yc90GE5xGDCXeaY51w mBfTkCqnCoswcN6iyp4yci3wnVUW8Hs5UiA3v3RO9jeft2X/cO8OOuClP9Ly/i9WvvSLJ/48ZcEX 6x/+jLEe35/8Nb38rc+BQB95vevW2x7Kfn/rc9mvNz3PeZqhUoook8HTboze50ZFooAOsJr86dHj bmJFp220QAA+NsFWNo3zlA1wielqciUBtRQtyNf/a+b7B7hQEOfHTuaL/yvz5RiRe0Y5puvRfcBK o7cUs1hVq2KVrbIaCUfDTLVrkARNUgNBf9AXlNSYFEpSrwuLsDUvSYOaJ0kwjplMF/ytpfWcR0PI mSDsYODQ4mTPHG5WAr58gP7zqclX1y1dMvLKWw9en91Jq2/9VY9BI+68bOT27BvK/kD+8Euzhw48 ls0+Ma3n9t49Bn396Bf/6BIHDz4E3cBrGO3kdiOgKnGr1WJBNScXdM0WtxMrYrNWI1/3VlnGSxcm tISTaVGnbPuvx4xL7s8F1tH/YpOFhHjWcyhYcNLxo5nTg5aTVORBPAiOc9+H5KJTD0iZU+9K65T9 27O1T2ed27kcwcGTr8cz2MhNRkY8wy1IKHY+Bh7hvgSyA4xF7f9Fvw270DSC3aFmsv/WfY2TnEuA +Xem/8hT5pRMPdcyZ/f9cenjU5+zpvbRvN/9trfPAg9TsgDyvw/yX0x9RjTmjwVYQwm9xOqjXqmo iCS9IVZMQAhOgAQfRKQnQ3GXhLjJRmm6pLgICUE8WUmDAJx4oJKzwZzHId4fCqUpbHCM/54tXlNC S/LTCY1qwqHVIunpOVpAkEfo9UKL4onQfSjI09BNBqyMba4z8eVgBlh6oJyK5UXzInmS6kjrxYF0 QdpajHKl4rAzP0mCbl8SJ/t9CQu2CpXiJM2zg7f9HizitmSSFElYgMMFj3OXJDegYHhwOwDGXsWe n2kQpGu7MagQnt/0e2UokT4eaThbcEv28Nb3sw8276ajP3qQ0tvSO5KX7l14/UtXJPtuoOzWq4+d y2qfpu1HFi/ZRy95/z26pHl2yy+7L1ozYsy6URsfPJD9cc20PtQDejwCnVIoZOF9jre1GlFfoEqW 4jZtq3ZYY5rCmN0KGU5YLDB734nxRuMHIH8YcFWAJtiGz8x1pUr5mKv1a4C4MLspL5yUGi76f1m6 HAMKCwoGPEvnBE2D50g4aQIASYNzkVPuXxcGdtFp/qB+QaocHQEtiqKD2hrEZ9gNWwdDB4HCN4Xl Iy+xn156qV1V9rc/yib/NJjtbh8h+PJFMOdajINE3thDUZzFeHJnd99zRJJnd2WVue7a3VyXlpnr VLG5zo+b63BUrBHL6FUJZYuyQwG3wmm7BXnZJiJXIGM2GumqY0TxJrBzC5FEDkmMJcA50xP4ttMT 4Mir6RIYYpxJQtjJh+T3MACdj89R0F1r4NbV1zUurmnPuU3AWCGZXBwrPS++xF0k0LpPx5fSNDyj hzxh6DPZbHUpW6ZudG70qDYhcc12LnAtNGrY5bjbZktrmjVt5yAn75lo8A6hwTWEaJimm+8xBNhk r0/4aALlD6N9DT7ZR9OgJtIIph/zTade+WPOlAzz7u18kja9vtH0Z7gXCevalkH3SX0Ob+/dCw8i oKd0/x2WRdOHzit9qe7X1/76IN0afnzVgCVXSz+cirS8Nu8TrmPg+yljOU/TrBGXCvtUW239SrRe am9tiHaRtF76g2RZrn0gfQBDxD0yYR5Llc3yJuVJ+Rurosm0l/yezADOHDFs3mSVlOALuA67HdUo Eug4shvb1txa5uv8ZBXWrbu9Qb7/E+OcCO5ZXHyO1RaJnAPhtaHOUVMkWU4omh9gu80KgVLhvaso KlGYTJnFjrJjTWJ2VMe0sH6GGznFrUqT0qocUWTlQivfZ+9uoQl4400WCYU46w2HPZHA4/7/8SB/ 6DTijv6Pc3c+p88RhzYC2mgUYVINWKemBs5jDXceuUPPS1CwDgt8HqUaNdYauO9huO8xuO/ct36/ b50JMPCNY7sdHj5ex4wQGqru8lRZdZdeZeMtTYdsCBUIUJiLp/nHsXyPrRDjVh6plvm3MFYN4fhk bxDNYDUo9QmyjdXWQn+1bPir+TDvKUYzYKL95lXq+IVp4+L6DOEBBOd+mqT4Z/Hc8RJ7n1ra72bX dpD2E8egAMrYH9qfOXUX++KbLGZymHwjdwHfKGSB4aAMWlAhVh5XtrDHDLeFQZT/y/E+0cnpp50m 9d+cpi/qTctvCmkygA6+BUH9G8w7+nIX5s640RedHe3MR1g7Tph60upyIt8KGwcFgQZG6TujlLcc Xi5pitsh2VAUYbXZXcRqY5pdFfKL2gIhsz/tFcKrQzS/6Mx9m9Ut2HPK1DkctuFgHa86qG1t1Q8f buWpzQySGZxipLOwocAidJIqlpJYymKpiCWo9IOR4lqLCecCZpPbZBdfmtGxJuIlOFxm8Iwf/GgU cJ2fRsI+oXmr3GKhOCRCXXDNrPDR+IPza4oGv5T2HJtIvBiriYaTmF6MuBGex7ws4SBk5ngF+B2D Ds42HwZwfif/ZUyWjBnXEOa2+lnMKi93rHf8DkPpGOoY6pbK5GJnuWuSdLG83LnCtcFptTPFWu3s 7RrFhklIB1hHOM93aXexu6U7LHdYH5ces6he5na5uisMEs+swNS6K1Y0rY6x7rHUQDhu5VOHoPtd Lp3TqcG7xsu8+9njyMb02KUkUAjVw9AcNi1hOK6xU/t+PKSL2nGEtSCItwHGTLgX6RR57YnPJpQG ZY0Cc8Ie3+3hBjLC63/qa8IwjSJORzt6euNoPaJ2DINARXLLKGJ5LuwbVovgHSvo3zNB+gvE0XES Wfb3AIS8J2L0YU0OaIBSoQGcHT/udGk8cs8l7t7Zm6x2lSdF8m5vn2pXzz6iuacr9uYSdJk6RPmQ U453wULTYKh3H5qEmcYkGM9dqMi/uHswglwdVZ7LTtyRnaTsP/nDrReMvlc69dNg+fWTveQjJxNC VgDAKwWQFRtdvdMLW2T6G9awIyhw8q+MJG9ZAZQkLFYoXSuzSJLVJjNms1hlKQGECbUKwuqi8YPp 2iimLMEZMaKc2ZT6hJ0m7KPtDfZF9jV2xW5FPAAGQ44Qzs3/Qy/k/BtZ2O+f+Te5gF7jJOs06UiV Co+mUajn0x4NoDVUjlRvkAWNOtUtcoXPQstaE1iAh6FSuXMJKjRbjcHV0MKtewdXW42eZrNntQU6 lofBeyNo9jSbfG/KrK+yp6otLj++Pr59fK8PzXyzmY9mgDd/3Hla6ebERwgPiFhJuZ9FPfe9IrH9 r5zKgmRr5WtArjUn14BUZgwrt4NWThImu4zymZ75fjZMH+a/WL/YL9sdwJldJBTmoRixetNWDA8o JyqhoBqOG8Knt0YTUYp/0bAzQf+7ke8Mbf49MoucrZiFKzlSbxRBwQi4IZ0IivAk4aCLgDSOgJ4l kx4Ep7wQQMSirOy2EZfdVvdd9tXsRnrV8w/UD++xLnuDst/lnbl3wXPZ9vanJbr5minXBZzmOGwD zwIywSgU0lNG0mt3UW/vvMkFs6wLCgBFcA1oFUuLWBaBjsKNEkU/XH9zMEnsAcObDW9Lx2e7vdEq rI/tLiypAgL92e78kipkCcUa+RyxxvH3d+enzeM4XxzHmh83hqJR7Low78LEOPuUvAV5i20rXCvd 12sb3Xc6n3C3uL9yfenWob8THrff43F73A6bF/OvokFNBTrtdChhmy0YikbiKP9pNcvaQiGSLBQU DYfdbpc1nnbdB3NuFtShcUKYHDSOGIX8yVSVP71anyhaVLSmSCoqDP+3VFb/V/lKcXfnXwLYXMgQ ORrmwAVXhTlqZzhyXF0h6nnMch6e0YatOO1cw5jAkuArahA0q+Guduv9PN5+2FVHG4UOdMFhiUaq PZA3L74uI69ah/OiFxbge1qAuObrhOGAdPhSUjcGhkoJ5hJlJsltbNOBN6587e0RpROGdxx/acLl F3VNDvsT3Xb9HSPvfDjbXdk/6ncr73svv7ho5LJsI+2xbnNfu6V9mVTZZ+WQOeu5LzEFeaC/IObu zgJGyXRpurxEWirLxSW9pOq8AdJQy/D8QQUDiwaXjJPqLFPyLyq9wedKcVibew1gPLNR3NlIdzaQ 2jYP4WTQ0DzZbOBks4GTzQZOPmEM5ieVOtNFrEgqKe7trkoNLB5UMTkxMTWh+DL7POd81yz/zPBK +5XOK92r9WVFS4rXS5vsNzg3uW/Sry+6rvg25x3uOwJxU/UZXZNpbywdtaXLEGaQsqhX7tkjjQmb jDi7rozdEGOx4qCza7ykmBYrQSj244aJx8e72uLxoCQQvAxi+3ozzOereoTvIVT/mB+k+ouLXE67 kgTKFsNEJcxTUmlxUSH2AXKJdY3iimzCLdBEbZj9KUALYTV0mqCjaQNdRLdQFQFVk+Hrym+p4Nbo 8YW2NCmjZTywcrnYBDSOG05+pbJoTzwTTUNCvxWH0MDwQQWikYP9UXAAkkZ65ECM+hFHwXPA4gEn gR1PQ5OoX8ogBVKfOc6fCEAlnk6gvzAQyDHhN7k/1Gn6+sQZkAVTlxWViNQlz+7zWnGOXwb8oSCK CXhtE7JPRekpzzqn/m71wifHjZ7SP3vZmLmzr/7hlw//c72y3739iaZt1X3pB5PWXLn+5P2vZP92 N/2DfvlNF52/ZOCg2anQtEyfh2cu/PWMuW+sdd1489qLR1VWzi/tv2f5skNLlmJCNufV7sC19kMv WsgNhlNhcQw5wCxMEUMKd8lu4YxT+qyaoKyCp20p3UMpHxDoE8MuAmhrDkf/QWhHHPisM4w+1emV Z7GH43O4onXv3WcMLzAsuEl6+9H6L7g6MNW/mLiA0uakh/my+fKmbExxbt/+09/M/m4DnsXxEz/5 wNDS7knyJOurVjnIlV8QfkGV3N86WL7Qutz9qPKV2+IgzIPShWbV5k/DkJo+Bxo5n4OJMB/bR4w8 rspYfSJIE8HRQdYQXBRcE5SCTgFk8atzSEUToR/cYDN5IBqcW9D4yXQ5NOFyYNuEVNDIRSRafYC7 HGdUImpCAIblgnBu+trMIvsMUCmE3szEtXkULko+PHLDSzOyJ9/5ffanRS8N2b76vb3K/lM7P86e evhm6vxaGnVq14t7Ln1JVGcDoyTKYIyRRs/NVed4FQqIhdt4hL82q0KZUvExcsQHPZWVGPVaMCuv EiiqUGgXUioVaxWO7o4Gxw3WG2xbHK2OY4h8HaMdKN6yW1kusW2jDoQHuGRtrcg54deazZawKn7A V4BHEkzxM6bYcKuvExr87ZlWOpPBqUARW2n1aCtdY91ixTbyXk5mlFZPZfQWzG1l8LWp4UkooxXW HT72FkTkxxQFfvbG3fYGGBXuZzfyWl/+DfMUI4xJNNKGrBj3pnOpMKxyCS8//OVdxA1K/HWXzQud 8dddCDcQ0sCnxl8dTiuFW91buNUoXUTlNLc88JTr65JIhgkvuZKy89p/9xZd3a2gsCvd/HI7IJ6T f1izaMUKuQxQD1cQhFiWc/+CfmSky0jaU+ZNh6tJb0+1t3d4KBniGeodEp5ELvJM8l4U1u+y3uXO DaRRqdNoJBOoUqocA5WBjmGB8cp4x8WBGcoMx/zAUmWp46qAWwnweMwLyMLNBB1BM061kNCg1dUx 4C4yoh68HQF4CtBlG16Q4HZg3qeXvxwhjEKLmt2Y/J7ga4fXw9fG5ABcaqAhLEGJn6JYTbFa44Gw PxAIex02WzzgRdPrQdV9Qvf4dbxvweawhgOKG5UKhKFLihRGKZcN4AomK7Cw1+tB0i4aCkX182x0 DEkQB5YBfA2i0DF7EwmkeiKRFnrjTtM5qI9GRrQjSGqPRtrDIwfNHPjFab+gM1DiPgEUKVem4gt3 fMTZIRNPe54JoKBi+bSdA1jU8IVonb0Asd0gtofzhFfjRRkmBxRjZ5czHJALw1zYs9thKAZO4kyx uB4M4TMZwudF9ORDrhQguWqh9IHsVa98WhTti/nU37w1KpXX9YvfZC9/Lvt6iSXkz74KWa298/a/ FEmftEez3/7txmbpGTjp9ZsTM4ecfFjoYTUnsw46by/gM0nuD+Dry93eEAe4vjRcaMgRLCS+wKH3 d4cFJva+0R8NuRQLb1ous3bRKlzyHDpHnWP/RJUx01dSrRabqtpUyaY54A7YEpodM6nsSFbbEK2c AI6IvcD4KcSVqg67SmECqL2FRQybptkkBq3hamFhw+awjTW0NQCpW+gew4mC4gSRxo5C4T8X2j0G EgYEbJJDUO3CLIjqKGETuL2FBQjvdbpeSnJBzgj4kVsAFP+aK9Cfo2CoUuWsDcKjRDVjhSwrIr3N Wxt4UlvHYlhTCETLA4marQ6bQ97fcRx1nMdFNZ2wuVT4ijaBccEfBF74yc4IB6/qcpY4k0l6zgi4 h/Vvf/1bmhw96PxLaN5n7c+yBdKI7OBVq5ZsoTtO7W7/hWl/bszOZWFhLwcbGVnKUKYraoZYvBgn i/qMrBRTItLT0Hg21HVDNTxtvX8Ot3nwZhFmHucoAPjczBb36O5D4imFotkb6U2oQpprGXP7Pz+4 3bxXSXYubRb3qjVCspKxqLqEl2RQr6pAgz8jS8UWDO23wCz42D5tu3eygBj/w01oshfixF5J2pxd 8sEH9Kbs3NvVEtyFkQs7vpLz5HMxG70P62qU25y2LhFntEuZs0sXgC2BPrF+XYZ2qXfWd5nnnNul ofsm5/qye4L3Rp9wBko7E8lwNTFHh9vPRyNPlu6NPFd6IHKo9K3Ax6XWgUGK6RHHDVQZqRO88NU6 yzN6cZs7gW8XhArCmfIuVdVydflQ+YLyida6zCzr3MxyxwYUW//T+c+Mp0+Vi8p6RVFVqGfSH55a trCMleVVuGpdt7gedHW4lAddO1zfo1ZKzA/C1DETP0cD2X8+S8Ml6qtcKi+oQ3mRhNrMJ/eGb8d8 BT5+x40o7wcZVKL1zJPsZdP0aQQREUa0OAlv/NtOt/xbE6cokvlo48BRMesCjeMCHEPjj9wjUicU iRth2/R/ilrYxYarxOA184l09/SOtFINKyj8Tbjr7+3lPmm6B99nOOMol6turWZbq2k1Irrjxnn8 iqHicGFF0YvqIZUVqLUqU6HcEbcJ4VLDvD+QW5Qk8iUiOUwCwVJk39Qefc+gHICc2zLA/bnkYQpk pxjUtGc+/5z7H0cxO8QsxxeHAE83QulyvSvcdO7I8gOivpg0ilmPfLojUsT8g9Ip7rxaSs6Fcwtf Nhjg0x1TaRR1uhDA84Q8TpJqZuybt+P5IUsu6DX/w9m0ctDGa1bmN4UvP3zDxidH67ZQ4fN5oUsP LJzSc8HcOQ+l86+bMPip60euHel3OaNFxdrlXc+paww33jjMmHZhtxXHTl5/Tl/6cWmeXjqi4oKG i0edcwUEh5H14GmOT/G5ZWuMe6nicBcpvZRBilJb0FTACgpQxZJ3ft6igi0Faj9fTbAGpWvDo/XW euckd33wkug862XOOe7Lg5dHWws+cHwY+jDyme/b0LeRP+cfKegoiCSUCneFv7tS6zaU4e7Ryizl w/y/yz/pDj3gklVGYnkwCFogz2UPFx22U91uAMNaY5fNSgG74FK7SIIASuDItUCJjwkuEuAC51M0 jgj3me8xKjhF7UuB9hDBfkQWDnWlVMxYK0XUs5U20WNULqC1eCcNJlQiY8Q1MRqnjHzOYFQwCxUO L/VyZoH/BmbBGT/iVNE4ZQT5rSk4Cks/vwWNxIf0+ZnbCtZB3gvZW/APAp5OJgKzcBbCP1H3wnkF TtRi0ogpV5UeRDcAcXRM0iiRENyAFcySTNr1sebFOy/d0Whkf3jh+fmsasKty5/+1bLlTyNd8Pdb Rt3y2pLs99n37qd3vDjhxoOvH375oLCVozu+ktqgs6J0cs6/rXJd46ZuO+XpvkXIKcrePLslnCfj zTYBi5U/v0U8vwURKdrAt7DkEHXm4Dsvi8AUteaYV1Mv5tUMsTloQd4A34DQON+4UIOvIXQvu1e6 x/mI/kjUYXVGtHlsrjRPWeZY5FzjfNSxx7ZX2+NwBAFf/5lJrsKp7oXua9ySG9NsnjRWdhc5yAZ0 awuSkkeQi7QRt9uOsKuzj3noepHLyofbVRiDwSiyZwpgS+EtGYJEhqDPBYIqUUGVoXmBokMWWmCp RamYi59k0fhJFqFiLT1iVQdyURbPMQlDW784N+9cTLToW9e2+HimbbF4dmSdMJ1Arz+KfyJaBeXq UFqD4BMTacUswtORKaedVLMz//tnPsz+Y/HXN2z/Y8GOyDWTNz75yLp5N9PrQ88eovlUe5qytTu2 xeZf9pu333vpWi6Tg0GzTyGTqA+jE4xHNCY7i51VzoFOpZe/V95FbLw21j8ubzabocy0Tfc35LUW vKO86/s48rnvc//3ob9EPheyFywoyES5wA6LculFrr7I2S3Yj/VyDmODnIP9Q/Mu0iY6Zzs/V78M /kSPu3QakFx2lB3FwA8eAqGU7OFKXpDrLtb1wx6qo1i0wbPGA+HkPGGKqMfLZQdwHgwXV7QelXOQ R4gs9iJ45CPucfERx/Z3Qk7R+NE4n1PHs9Rb9CIq+T61dFhkTqJRyB/GBcsJXW3BDFfOkIJswjRZ hAWyROJVo8+StfrGEW2n5YuLHTIL8JRQAoJQkX/PSBrH9ZO9QC+OJZgEg9RhtsBpSZP6zjxwzbvL 5r1zXcMdFbvbE08vW/6rx69asW39A5tPPvwglTaNOY+5kJ/3vvHar1/+8I0DnGbDoEfjkLMAaDbO CBWQvAA8xXql3jbBPlOaryy0zbRbEVrwGdpiJI4aY3krP48vS7wfKD/5T0TlHt5+kR5553lHRM/L G+PFnNg8vHAuOi1vhboicIKdCOt4+ZjbGQqNDvKoWwrmubfoWzHZQpdjeZqF7GdP8qlBnfqsFdKA ccfkc3q7DxIeMgDR/lFADmiYk6fQMHPfaLQatpIuVU0okIgWYGt3cbqKr43zuKktoAXBSr3IYhR1 qeqkFBJpoI5JKTwI2qaAYZoqBEzUR3BKna0V6zMj2o8CxoZPK5AeEc7z0oDcZJ2a9sYaESSLOTq8 GFBkTTtFTEetMfH4LUkR6dOkmAGiSpfsL/9u39fZ76n/j+/i/VynvtJ2XT99c/uHbIyj78QbVj1B J4YebsasGwkvwyrNfpL9p57YsX8OvX39gDmPCj3pAxHXAIcMUacR99uoO1IR6R7BFPPIvY77nE84 rVFnqbMp0hqRI3xESqMFVflWp+Rw52k0wDJ+nyypRHvQT/0dPkMOFct479NtUEx8GHv0reJrI5NX ULWF0IjBBSViOCEouSCgVAQAhVx0SLnwp4TocAVM/Jz38XvuqYnGF8gGicZPYn4NeTgceZ7uJ0ly Am8/6owVcrYGo8dDBL0GsX8bkro8ZIA33Yb5JKJoyI86eZtFtcJP0gGWE4/qjlHkaLqsxWsAICmL uStc2QtvMoBZgmLjiFuAz1rb9eCDvuh1y4dPifXtOXbgoUPSPZsb51cNvsh7vza44dLNp2ZBJs7P jpG+gUzwGv+FRoPdrvjL7cX+4fZBftWWH8kvt6f95alqe2//hfbB/omWSfY59p+0vwdc3VLlJeem zi0ZXrKlfGu5pXeyd1lt+WD74OSgsvHJ8WVzLdOT08sayteUf1jyVfK71PclnlBQDbSwnc2leT6L sCV6AnAdtyRrSCs5DMiuha02eip5eW5tUGGeQwsGKosrteJw+HCI6iEj1BBaE5LLAUyxCeWiSjEk FJvwK4ViCwnFxicsienD35iKjZ/FJzDlFBsap4wLuUSHlrppMSksKHrRfcj9qbvDLRe4a92jYOqE zLihxTCdBrNVsBR4mjn9ju9XJ7gjmfKlSa7gMiNzridXcJjf9i86rv3oCcRHbRAdUax/1ITFUPDZ GOKlicKNLIGq40WfnICIbMxClbPneszaYe85YOnqjWEXXd700bHL37zp+SsfnfnR1v/55u5HV696 fPuVKx6fFB1T3HPG5D5NN9Kaj++idPNda07N+/HQiqekLm+2vvjGb17+Dcd1NqC4mdcu+um0fZj4 37o7gBicBy/CyS6We+EdbvudstjVLxSpClk9Do9fAt7mzlMsfhRgFtuMyt5VHTbaaqNBjDCbEIQK QyBeKpZ+LiAI6L81PHzgUI6OQbQhBSr2ogKBi4rNz0mCs37kQQhaKDQV2ydQW4DGSAGAhqp6VzUF jwXZouDWYFOwIygHmR/xKJdTHX04hucBKnMYXoiMnT8JlcobRkhIqelaoigMEtqZOv3J9AkxoRX3 wYv0cHMyMjAEZDwdV/B5bRBAHlicjim4qGKbY3OmS8ijeSGdLtVlKXapjhh1WiGXmDydyawlSMGa hWOgKIBvJKXFlAs14NnQfHXr8meGNS+bP/qmGriFP9xW/8h97VPZtg1Xjbt5dftzkMmNIBQOwe+z kIPGJbbe/AlG2bbYttqabK22T23HbBZiK7Atsq2xPZjbdcTWYdMK8J4FvAcPbytQpasRtyuYcaFa ihUiPyhvlZvkVvmIrLbKx2RG5IR8GFuybPrLbAIauXFD/T9IJqO0AEuh2XDM1GxomNg3Gqd4nI5y +JHWfx09lJEJ7LvWnNLBAy4OQS5uzIj5ErDkG5ubm+W/HDp0MiCnT34Itd7xEN5i0k88s5e8awwC 7qD0lyvx4kclZFUUiywzWfER6rQzye/AmzbsFv6EdtWS53FvgUYHSocZy8WatsVOC+y19lF2CZHG T0Yfzgl2s3RFBAt2EVna4b8gAkE5PpZW/hyYTg5esEd8/u1J/kCnpVp4KogPUHDLIb1GUjuCxwV4 KrO00wTyKis36FaUC6LI02XV3WmrrsWozWWJoVyTcwR/vUllgPbh8i5q0SwQ+fXN2TmFvQv69G6u PO/OofLXb775z6vudg29TZ5ycuuBETO4vIIXpB8xLnY2zYjxCBk2W52oTrZJbufflBNAwTqnFJhJ XODCZgPCZTYgyl8ZIgk8QbpCY1414RNI27Hd3hKOvB1rxtqL/BZ2JMUOYx32qDLQNrWPbQhIoXbV JmlXSMu0D6U/q5ZHVZpS05Zia7Xa11brHOWsk+vUSZY622p5pXK37WX1Lfk99aj6teUf6j+tAa+G wjVJZryKDUVsGuB6a7FZu4Z6tmKznk0Dw8ocfJf5C3EhsQTzyqkbE1jBi8BYCoGru41kQsQHAgiw RLfABbIXE1aMeJHQWrxPkEH2s0YPIfuC4vylG5B9wckEQSJkXQQUeHcel/uIw/mn5JBZZ9OaT+zk GDycH8xv5fMhuMibuV04qMjmApPns8xFOZt4tRLIbq2RxDKXWHQOQxm9bZ3EUDnPiyoQfYD/eYWa ZivPr7ZZMQcd6epPduXzsrR3diXEamfSrETDzHTUtDQiOyzSwGpH666kKL7YFeSrT3bpopgNK7Hl EKuddvPHyB5DU/FbeT+WqdUfxN38/hqxwL1O7ArzH3+7M2aejtIZEwPBZN1Gs9wNL7ZJodxtYzN9 8uvsPPriJ9lt1wDufZ42ZZe3z2AFV2Yv5nx5HRZ9hLz+ea8iFBQ4qHV3n75mIWtVL3PdvYe5LjQL XY1imBs3im0eVD5V5FFYHFOkAmURSo86FBnaHG8kMhU8v5JQ9AF4Ng8S2opAk52t7XmUD9pyGReA QA5IMGlt+mN4JxSo3Kmy0OjoTODldBcZKf9cd4FUiwWqCS9MZE2wxf94IeB1gC7N3AhsqJqGz5Si r/CaZ3P+Iy8vEXEDROp9Y4TdWVUsH5WP2v4U+jyhvKucSLCQNZGyhWMJmySl4nlqgLsUFqqmMAtK O1xMtxRvLWbF0GOu4i14yYgsYjYUPIjIDWAdZ2uPnzM0QjO8oYWrZw/jTO0RagxuIWwojpk1Kjx+ y8UxtN5whIu3xGhMXC7GjbO4XExcDtvfGR5+uZiwkjERemNv1jTOMSA86gRsm/hfrAXXCxJWmSqm hwlkbythBZjUNwr2iv/GpMbZ8ic0LgkK+eNXyZHluOEXTrIwI0T4HyRSVNxCV+z+Vw3M6YI5D0c7 C05BkrMAP2y0i3QL8BnuPMODFkIMceVRSqehRvoo7Xd4YtTrDHQaajGTSdA3wL1n5EKwMM218KPP Ntzbej46b/mdBVe/9sCTu1NTzl30y+ZJM4av7Senbx859dJJ+3fsbS9h9182td/tj7TfyXatWDH6 nlvbP+Cywn2uL8AvQbra8CmS6mOP6y36n6UvfcekEz4VtvSYUQOGWanTu/TD4SPhjrCcsPpd/qAX PhdVg07N6XK4isLCzwoLn8suvC278LZg6HLell2YbnshJ6YA2oS3ZRfeFrb/aRLULrwtbJ/ATD5u +oRDZ6cdKBIciSQSivi55xU+FmaLwlvDTeHWsBzGzLlAUMjmCbw0yJS8MyJ4tsNliuAZhwuuOcTQ dLhMnI/fwvuvDtzIkHj/k5A3vhCwv0B3oZrO/uMvI+N+GGzwaS8sqHpsmlWzoCRbTwPfiFG35s0R mU+NgDqtb4QUB4Q/BixXENYk8YaHln3csG20rjV3mX/Bksfk9J07Bi0a0XN1+xK2/vIF5932Rntu /tRA4AcloKOTROj8vQHgJ+oEH88a8Aaqn74ylvBWRBzwWrSIY4h6gXWiWmedrc61Wqv0ft5+wV7h Qfow77DgoPAUZYptrF7vrQ+ODS9QFthm6Au8C4IzwlfQgE1VnBdLSJxqFzsuk2YqM7XLHFooT7Z4 oDT8RTER/cQEI1jgm5mwjkUAOjkwkNt1LnA4fEz0TzQ4JUSDkx2NVsNXVFyFsnBi0S0JwDo9PoWW 4PuHcjgBbVcRcbigg4iYqYhXvnCDik5gKWCEnNwKDcRfZQZKG7gkVwiM9IhyWEFkinIEbAOoUI8X tp2hp4BZ26BsOebDDZdtnDLOdqlyqU3m1omf6BMvi8CbPgSCd3ZYNPCRG377EQ1e9ZcbP8227du1 Yf2u3ddv2IUXM5fcvDz7p/aDf7mWxqnzjdffePO3r78msPQN2blyEjT04l0Xlxo3O/Su+jn6MF2u TTQlWEGizJHK7xnomX9+/qLEloS1X6hf7MLQhbE668WOKaEpsXnW+Y65+oLQ/Fhr4m3/x+GPo2/H j/qPxo8kOhLBlJzRM4Fecj8dFRv6ZP1z+1/ys7rd4wIExCF0NQgInbgiRYc1qmuG1oDco5wQREwI gsJ3+wLvh8Foa4KU2Oa6PPcWHU5N4d1xIqLxlZHiw60tpb5KVuktJuQ/I+edgLnQyDnAXADGpwHz E0IjC2zdBMxFnRPUJJiZRgoAmNOzCz1MZQzA/F/hckRGXCa5vu1Ey31c5ITMoeQRHm+6xIPXFpxG 8TY80u+2ORsPz1v26VWTb+nmeXT5iqceW7pkZ3au8sKmMWM2d9z1cPbkjcP7tZ+UHjl44PV3X3/t DxzHuyA7VzoCGuokj/Y2brazDOsS7s+GsZUOtTZQGxkW2RLfGleqfFWx2vhA38AYYO/YdN/0WEN8 Tfwd9V3vF+rXjm/CehkrdGRQkdrLMZQNdkxmc9kHjo/Cfw5+Hfkidoq58b4QfxQ4q0v1A5cjrpCr Ei9+0Q+7qe423A3uNW45LsAIvHqFQwQCjIAayKGsbgFGuAUYgb0wppyU7iC3flxZCF9EnF4r9MdS z7+jrEVc0DiaiqXAISxCxCwCNbdE8uM/RyD+A8LafpyHYv9CGLxtEe+NE2g4Jw6HHH6GrZZ3uXPC C9nvF7599W8bH2pPPr1iyaM7li97GKlia/+RtBu1bM1e9+jNPw2Qth88+JtX3nnvFYgW7Nz1IM7L oIuHvGr0r/BRXaYpuUoegJf4z5KXyqrNY7VZbU6fx+YkkpXahVAQzVa6BXNlCzHxx8cKPf97fH/a 4/vR8JwV36NoU1ijs/wKwcU8bQ1tZLr6I71DOjMIQvXApNTAnag/vpjPQORcC7RNeAvVmN2/wSWK 1+sX8xmkJgObuBpm0Xmuf+jcubUXX3Lu+ef3v8Qfl9PbGi/o91jJkNqGxe3vmONQi9zAToxDdylk XCUX+gv72S60DSyaWDizcJXtZtu6okd9T5W/JDltoWg41H1Y+XshJYb5GEzvSbXwFOsU2xRtin2K Y4pznnWebZ42zz7PMc/ZnG4ucZeki0qKynoXTdbq7DPSM0qXppaixPUX2n2O20rvLL+9+yPaE46H Sx4p3Z3+bTqIhLbpkRZ2NlKdjaLOhjiHj5M4hzfEObwhzuGNfAQdhjdePdlaUuzQ5GgiHZDt3fKj PB1UGCnnw18QqY2MikyN7IgciqjuSEFkYeTTiFwQuSXCIi+AOgFwhkC9DXjmDGA3pi/o+J8YUJih 4+2bMDi7/cEqvjb4HB9Ku03Jvyyf5ecFLPCOeEJaABR8ugkQB64mfVwLynnd7AWoniyKGL5wVU/+ 8wqB2wo/l9thYLiQGCwT/JeRBP9VRASQEYF8R5DM3mUp6oKf7smrPtyFovWF0LlomBXGosHHAY1v 9nJR7RIVt0oCh2/o2dqT1fZc05P15Ah+ERH3zL2GM2GOMqomeIN3gDfM90EmitxCCbtF99wJoUF4 MIMuQkuIGS45uLHw087wNtIjB9ND0HPQFH/Zoo4CzsUjc4nwTKbxrHn8/AjAR5xU29aIvBgHMxaL wk6+gnOMf7lkOJALo6RrPAUAOO3RvbpPl9RCZyJGbKWWGFW6YhH3YzPpSsVIIV67Zy0DxFFaYtPU jBwjBXo+97f4S1NrzAWPRDNdMmvXAg7r/ONvJ8GsjdNvwStJl+D/sMCLhk0jcTotx5HREK+T52oq XbvLfcNVq1b0Kv7Fy3ePOq9vl1vHrX5hsqfJsWTuqnnBYEVs3Yt3Tpz78upDH9Bz8uYvnjnwnFS4 uOfQtSOHrCwtyFxw1ezw2Clj+6Ty8n1aUeV5q6ZMfvCip7m+Kur4gXVR7sY7lzB3VQMPptIc/0Au BY01eJkhkswalUhQx1uNNJhvye7WC1Fw7/QWO2iHxTrINqjBsggz6rZYZAL/aSum1rVaDuNtwhxs 5gEcGvx1raLxgyiRwB4el4k9PwpOwx4OXZqeGbf/aAndhQOmb2nZz+ahEq/3TmAVZ2BKkFK8oBdQ 5VGu5ZFBw5RjmF8UQYo3lMDLKg7x8Uv34hkCTx/ospRZf8n06PCaSy8rX7du9549vkxpfNuD+rkz H2LTN1PLZdmbNrf/YkQ5XhmIOB+67Aj/P3zoqH0kirGxIYJnCV+Ql/sfMyq9/qqMjxZZfUEH9QXt yK94MEykMlgcDvGwIipilpCIVkJerraBvyP85CMQEtGKgO9FnBLy81HAdg4VDonAE9sneHmzOqEj RFtDNDQSr29qNQI8RIkei7JF0a3RpmhHVI4CmuZHBDTM3zibsB22HbGh8leUBAj8OWc6cqg0IhUT dTZBYZuIUWwCFLaNjPwMGoDB4FNMM/wVtWf+YEP4uGO2sLAdAv6LyrrL6Xby2kX+8gIEJLIjRpxW jwkF4r0EcI5wiVx+E+/ZBriAIgLuFQloUKpd9e4lD4/S7c12z+Vjxtzcv/m+5gsWjOq1hN3Wvvum HkPGjLtlI6vmsCnoAyJJX4E+Gv0mVzsQUqxEs6pUPV0aW8QZUKnInF0hy5202LO9FEoKPdUa1/BO T7UNAWeVlS9QUPrNbqyhksUaZ7xv2OLJKlKKBba+MmzAdEgQC2x9aFxd2g0ztLFwO8pIKab+VpNe 2gVkiDYR76eps06yzaKz2FzrXNsKcgW9gq20rrBdoW2gG9h66QbLRusm2/3kLtut2tPkIe0F8qxl p/Yq+a32IXlX+5b8WTtJjmvleBwtTIJaKeGv+hlFAKYphjdYpYCZqnLIG97SS/ijE/TpuOHmylrj r1aGHwDcke8TTi0vGBZ7maI47FCBFR9nUD2M78HMwQypOF1A3EcDGlls0/w2m4ZkIbBGUVmqYNYt n4PLCwwtKCkkVKn4/xq7FugoinRdVT2Z7nn2TM/kOZPM5DGTkIkkJoGQEEknJgbJwUSIyPCQlyhC FLzhcXURRhcUF5V7XUTQXVHwCuLZZTLhkYBr0KBglAUFcS8oouJZ3HMVdH0hS3K/qpmg7nLOvZn8 f1VX/13dXfV3Pf8HbN7kKLquY00emr/Us1PHoha0wKlHN/mZTnMsf3uXf71Qprs09dLUjLQvznCN AXyulZdXGJ1iefEn+U8sG6LpFPJJP/FcXI5XiG1CXpP+sb/tlTMByJz9T3f/XYbgpRW3z29dzFYJ 9gB/cDnM3eAPzZA5qAeq8RGqaIHiYmECo8COCXOt6F0hCc8Ntzr9HOMEZLvQlOEEulcec+ri2OyU KAyGyihvFeVhswo5QStMZcKvkFMI8SUWoXAJep5DhxzHDzmOCZXQhLSveD/+avyD8OArdNNCwxAz G+Oc7HwMVjfRLYq5DjcMKjr+eATZntdNvuxyhxf6Sfi+z+u7fXnlBqPV5DJ6TOlakoEYjBboryqa g7gkt+xVPJZMzGUDcqESspeTYXKVMtJeLzUadXms0mS5Vm10jtEmq+O0ebDFeLt2j/FeeaHSbdyj 7tK+NV40FVicBaTAlm8vUPO1YvcIUqEtUR5U1ktPWrfQrWyrBWIzZJdxj/0g1r7/23TWcFb9q/aN 8UeT1yK0kawCOwS2C6wKrCUY12O2qwaNOBUZi+NqwM6nc3ZZslFrAHv+x/UK3lLZwH+FPAJPW26X 0WxxBs0hZ6thnHmKs8251Pkbp9lpNoAbeXXEK4YPbn8uWF0MJda4SofjDP/FRwDAHh2bfFzgWk6C lKyCuYrZAf2sroEmyFlrGLdcr99mVu3+XqcM1XOnpoWwG4iNGTvqOWCzu6GFqmChJ2RWIIyrcCns xLcCc7GyZlBUp9VuE4+noS3nFlP4x6NBn8tOzO7vHDbKTTNEbJKti27Rzf5mM51vXs5lc9lNuglW l+c7l8MMGj+yOJLodLFmDHVVumUn/c71HTpGqCKkj4XzgzToouOff2ZT064sgZ347jDix9f3/xDA 5n4TOHARbA5NUd/4iTtsfqufvQyTbxRgHziyg5SofijanBYyu0IYvylaPh767crAkQ6ZG8YMN0Wz IeVbJkSzlYHTHbI/nqohlZuy6uYZ7cJwEHmjvToSk0t4jjEygnHjcrjT5cxFbvy6VHGdE2YEzH6D n9uLFtLdYvfAPnBsl1ZJigD4wDtcfNk/zD84MQ6M66AJDVouCS4kwF2pQgxcypdoU//ePS/WGMpe 7N447Jpd2/t37H1xyPtoYp4+4+xjd11a/9YhdtvFE2zpzn8cFn2Rir7oK7Q1DvpBoi9KVqnFCEsI EF2wgSdVMS5Xi6FEzbmSWz/y7FY1qkKemW+I6C3plZPUdYZ1CswvqfuS9hn3yW+pJlVPqcyQXKZk W4ZjGK2y3E8fsyjF2s2GsBy2TLQ/Sdeb11t2sy7rQUuf/W3HCek90zu2k47PzNrg52WxEs2pptkw vMB9zmKzGzHViP1fAhcMRr6ayI21oHSEWItHv80IQ8iQeIZzC+xBSRj4QWvRZqOqanPAxALGCBbJ 6jAbYQXS7HiDvGFijgAxuQmBfLntDexMBazYp7RKUCGBeVEj1l5gLNncrFHtetsya45ZnWE0LdMh eO7ZrRtbjBFhau1a3e6XlrGcZrTb1zuXignr1G/iHQb6C8dnsBcutP7j+1Ec8+2oqYkOg7sn4GZz K1X1IUXwaRzvV+KOP5Rq9Cd8G2qHPS2zEsu/p3RLJmwep8Iycqo4xkYTlDmhG5RcSXOyK01QORS8 AhQWS6gon6lhdDplGJ0Pr6jge0VSPlXpiv4NH28e6i0KdL7f/5909Ycnqvo/ZwW0/0JjSV3ZxX7r pT/TMeH+qXivbMibfAkeyaDfJ3gk0+xW4azOm65qRovRpWuQvtCt/gSvpBeHMj7MSDuETRIeiMk6 ZOnBOJ2ql6J9OqXf6a0scE9Qt5thul9HhfgLSsodHMHsnZZiS9PyLfnWfNtw63DbMPsGp6VAK3CN TglrYVc4+Q7tDtcdyfcYF9vucd7rvjd5pe03zke0R1wPu9ebt1pedux17nH/zfxX97e2S44L7gFv 1iBHpbgsXo9BrVdXQFwk/fLjxxcT4oqAXI2lAlorUDTRMHpId7tcAc3sxgGMpzutAYsZk2EzVFqs UFLg70+8Di8r9vZ4mbeL1exUURa6u4u16pYaTdfYNK0H+v1dtG6XSnNIgwdNY2u8tGDqqMTabJVa rANWBtN7dZ3FkMFEHjs8/qVoGlF4l7jNPTARN7mX5vjmTDr3L/BFBhSORAzq/Jg+DHKUUEzg9jr4 BidnKTR61dUK2j072ps0tDd7oc1/llgGzvLmK8FW3cQNtfCKSjMspkBY/ezOZCivxhVVwT1oa2BS A+zjyucjXSFjndA+wTAGowhMVJa7RxZVj051BpMs/Xe+9mEoxxf6dEd/W21eydIJ5f23v+goyPPM UzMNBZc2LLp/6WI27+LB7XXh8Wh2oCrxZdJZyIap8ABURct0/1ptLeT6n856Qdpq2WrrLjmmHUs5 mXEy672y48PtCxR/F3upM/uhvGIelsDg6142He5VR7CZuqM5DwKs8/OW553LM+StVUq7kGghoTWh gZAUWkvgkmdmTLHAbMLMGJGQwczY0KPIb2Zn9irDsD+xmSSf3QpnsF6kGDKqUYMzd1rTR3KPIuij hAm4sfDWw/8TduCEzogwnwIRIagPinlEa1lFoJD6hgS1gD3IAlnBlKA5YAxmBMoKh/lIRQAIOtTZ cCoAy2epJsTSZcTKC0p9dHhwhI+4bI5smikBJVvSsoknCSj+OYuZ+eCXjRBTdHjDKTVwM2lxPT3Y n0vNMXDDc7AZlacNK2d5rlQxF+cC8nyjbFC1k1cm++4VOrfvQP+G/fv7Nxx8i859ZWP/0W1baNHm zbTohRf73239VVbv6ugHt3YvWRn1L8la1Nz+xKMLJq1KOtrb/8Trvf3rDxygc3pfp/N679vc/87z m/vf27KFhjY/T4s3X3Lk3ffEmb4VvbOW35k5pvn5DRt+P4evAY+X/s4mob4tfPauT9mIpSd2Tj7n Yh/JH7nYYfmwi/XIPS62Xd7uYhvljS62Rl7jYsvkZS52UbnoZm1Km5tNUia5mVWxupnbpciYb1qI pF6wSxeY3caotdpGqm3Qv23Ri13z5eVwfynJ1DXCXQ3Xg9UYuuipGeX2RVQeoVTDhUy1JK2B3lh6 2t1bRE3HbYKhOjFl50YYECM13NYU5Av4l8blDHh3g3+sOfIZOyyK3n03BYg/VElyLhfrg98lowyt mstx6n7VXzi5qKJcomsHY4b97/zXg9UtQ65LnXzzTzH0y4w0Sp+zG5LeFGV1Ur9BlNV55bybUQVK Uqfl0y52RD7iYvvkfS4WlaMutkne5GKPy4+72APyAy62QF7gYrOV2W42XhmfKCvVapGI+yUXLx2r DYVmR3FR5SWZJ5RQFCEj1RRGTqutKLF8W+ooyOfwArMtYgxOh1Bo+YRr0M0V5YVNooTNVV5Y2OjF d8DXNISMMEwlxsNfFtflkrqb22KNS9jAGrzwS1QGaZvB+M2v+kKTi6Cr8ZfBiOEHFNHIG4c0pkwb /1OMz6cj0t/pjYKvlunDf6U8ojCbQi/J9JR8TmZ/4vZMn5b/KLNJcpvMFBnsgveEVdRqqozgZsLX cGUFFECyjLzEK4IroIvG0q2/v+cnrsDb8ZebSs8Qzhh4OVjmSJiV5XXPX+fn9V17pVoWY6426XN6 jajbhXrwqPypzDrkXpl9rdDfKs8prF15QIEBjNmYqmKNHTWWqKAsUUHiwcnl2hDV8YsHTdRC/BET 7p/IIKNyTuVCTYPFvPRKpcvLFO2z9dAT+e6UaWr1t4pHQQIhB55bUjkYcukvaJsexTEMD/FU/CGU R/XfQK51kB+395c5qi6fiZ8npMmIJO4HNmkCmYj15aUCKsk2hLVI38PPGdrJTYCPANWACYAMAE8b C5gBgI9hchNouwHbkiYMXEJ+65IOkNsAzyC+yfAp2WqsJHfi+HnQ9BgIqeA0uG6dcRtZj/Tf4fgZ hM8hnAL6kkTcJD8KX9gTiBHXrQbk45oxgAeRRwvC6wBNSHchrAM8RA+QVfTAwCacR0h+jfwf4umA +kQ4Gu+3EudrcF0e0n6NeAbuZ0SoArKTPsNixTYyHtCIPCKANhTacHjGK6JtdB/9BxxujGKLpVbp NcNfYEt4sdyq6Mo602nzj5YCyyrrLbZC20F7m/3PaqH6tWOx46xzivbv2ivu1uRCuMM7k1aV9nh6 XvqpjH7Pu17FW5X5fNZa313+0dkF2e/m9OX+Li8lb1Pgy2Bb/oqCOUPGFBYV/hBaelXLVZ8MjRW/ dvUPZeby4cOGDts/fNLwWIVB1GoTKluCt+0ktFYOeL6dgHqzwbe6hGPOCVqi7o3ETsik0eMn3nBt qPbf7pjRNrZVUIBoIJ/bbbvCXxPSJPCVBV2yitydyM1F3NiTSEHfkQZN5wzigV/mTMhAY4EA3qzz oT046G39KjIUz1NCriZlZBhKsIKMgL/tKvhVGkV0+B9vgC/cRjIaHrjHkCb4+x5LboAkTAs8PI+D b9xW+N6eAO+8E0kYfssnw6vvavgZxkoB2d3RqtbmSKnkHGAAIBEfcDGgGTANsAawEWAkaiJlPsLl gB7AeYCR6FJq7PEyvQvBahF0zm0rFYcz4odTporDzpvD8XDsjfGw/vo4WVWc7OryePLQuniYXxQP tUBpBJl3mm2l+2qhy0COABhZAEzZfrj+oBhvPSslkyiAQbw+nqJLWmdesHRjD3zcQ/0W60S3Et/A PonGbM7SWjMbYOdQCz72JfsifoZ90Wl3lm6sHcM+IdsBPQCJfYLfx+xjspydBgM4gGsAGwE9gMOA cwAjO43fR/idYqeIyj4kxYAawDTARkAP4BxAZh8CO9gHnJ0E5vEaAGMfADvYSbzWSWCVnUDsBDsx sI8djVVUlnaLSKg4EfEFEpFUTyKipWCc+G7swhBfF/u00x/yPVtbwo6RKAA8DOwA+AEtgOmABQAj YscRO04igP8APAuIArBuB+wA+Fkf4G3AcVhgOU50QAtAYUdiuE0XOxwL1vlqU+By+wAY2scOsYMi fJu9IcK32OsifBNhFs73sTdiWT5Sa8F5gmscCB0Ii3E+ib3amaf5BmqdrAeF5AMuBtQAmgHTAGsA RtbDcmK3+jRkspf0oUX3sRj5XIQvkE0K0ef69OC14DE/R8GqaxAD2ujfGGR6cN0GHHIUfOxxxDgK rngEMY6C996PGEfBtsWIcRS8dS5iHAUnTUOMo2BzK2JAXeyZ3Xn5vormedRfq7IlKKUlKKUlKKUl xACP7viRC2gXfezpWGEhSuwpPTSk0BfZQyMv08g4GtlEI7NpZBmN3E8j1TRyC42EaMRLI1k0otPI XjhGwcCA6jt+cVipp9FIH438gUbaaSRIIwEayaMRP63Qu1h27Hp8WAgaRNBZy78rlt15zahSFc+Y jRLNBltn47PvAT4MGBBHOoj8OXHi9Cwe5nQW1sSPh1aVzq8dzXpxYS+qoZd8BDCggnrBRr3IpBfZ qcA1gGmAfYBzgAGAEdQ5eI81AqvAxYAawDTAcsA5gFE8zjk8CiPzgfkjbhcPVgxcA2jmR6wXvxz8 slk2DB17HSHHaGkN5uNZtDlrIItVkJQUNLuaU4GzNNuu720/fG8jploTe4ytQUPrg6P6eLgmdiET HoLWx4J7fbXJ9EmSBSlQH60kQRpAOIK0i+NhxKvw9HJMql5CWBrzTsBlaixYBFcKdn7VLt8F7xnf 55g5I3rWu9f3vr/LQGO+95Dy0i7fMe/DvjeLuxSkvByESY6Yb49fkHZ7R/j+0CdI78eJp2K+ZTzY 5bvP2+ib5xUnZsdP3NKOI131jQtO8o1GfvXemT69HXnu8tV4b/FVx6mG8Wt2+UrwCKF4tBAPO8Qr bpqbJTK8qaKLztGL5HXyRGjTDZdL5SI5W/bJmbJHdiua4lDsilUxKwq2kAywKkIUN9fRCPG+0G10 8IB39rD1KeIOtDBUdIS8XaMK1OBJ1CU1sabxdTARsW8WaZrpj343PreLmm+cFE3KraNRrYk0tdZF R4SauuSBcdGKUFNUbpk8sYPSx8JIjbJVXRT+hLvoAE9a6eHevqFqSZ0rH/XwsGDlo+EwSUtZXJNW o41yVl5XfwU0XSROr//ZXBPS1pf/0kKZ0XVN4ydGt2WGo6U8MpCJJdLfcnfg3fRrer6hvpt+xYPw xG5pFP26YRxPl0bVh8NNXXSCoMOo9SvQgWMQgE7JIn5OR/xKVpzuqThdANeDLo8HoDOZSEDQBUwm QWegnK6jPa+hviMPCDSpftIuaNpT/T+n6QuAJgAEmpQI6RM0fSkRThMdJbLxekGSBQQSmkG8gsRL MwSJePIOQVKcIHn4MsnD4k5S/GkEDUfIxnZ6kMZ2GjSXi/H/isyuw7pd58jwrCkNcKU+PbdhNmB6 dPXiOWnRyEy/v2NWmJ+AR/Pg9Jmz5vBwxuxoOHd2fXRWbr2/Y6S47p9OT+GnR+bWd5ApDa0TO6bo s+tjI/WRDbkz6sOdjS3lFb+418OX71XecoV7tfDMYM3L39Eorvune1Xw0438XhX8XhX8Xo16o7gX ETzeMrFDIXVhLG6KsJNZzODX6Z7scF2KY8Eowbwjs9OWefZgQLKVWODi3JpbF7UBOF9fVXtVLT+F b4qfsiNZTZxKWzYy27OHbk2cciDZmVtHQgsXtS8iaQ131Mf/2/GHpIWLeGXEcYinXfEPJA1RfUZ9 +0JMYqKFWFOrwZpahywjdXp9GGlVg2kWSwNWZOOJQ5FYxQkl6TIhT6vmaSZTgvBfuUE8E5LFjkCE 7e2kehZdSNrDUjSrqZWhKWidhGKA3/Q9GC7xTqIdgpcL26Fk2D6YG38PYbEOCQSv3D4ICxclYoly WJgIxYX8kvbB4hjMKsRLifwvvjwSaQplbmRzdHJlYW0KZW5kb2JqCjEzIDAgb2JqCjI0OTY3CmVu ZG9iagoxNCAwIG9iago8PCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL0FzY2VudCA5MDUgL0NhcEhl aWdodCA3MjMgL0Rlc2NlbnQgLTIxMiAvRmxhZ3MgMzIKL0ZvbnRCQm94IFstNjY1IC0zMjUgMjAy OCAxMDA2XSAvRm9udE5hbWUgL1pIU1hOQytBcmlhbE1UIC9JdGFsaWNBbmdsZSAwIC9TdGVtVgow IC9MZWFkaW5nIDMzIC9NYXhXaWR0aCAyMDAwIC9YSGVpZ2h0IDUyNSAvRm9udEZpbGUyIDEyIDAg UiA+PgplbmRvYmoKMTUgMCBvYmoKWyAyNzggMCAwIDAgMCAwIDY2NyAwIDMzMyAzMzMgMCAwIDI3 OCAzMzMgMjc4IDI3OCA1NTYgNTU2IDU1NiA1NTYgNTU2IDU1Ngo1NTYgNTU2IDU1NiA1NTYgMjc4 IDI3OCAwIDAgMCAwIDAgNjY3IDY2NyA3MjIgNzIyIDY2NyA2MTEgNzc4IDcyMiAyNzggNTAwCjY2 NyA1NTYgODMzIDcyMiAwIDY2NyAwIDcyMiA2NjcgNjExIDcyMiA2NjcgOTQ0IDAgNjY3IDAgMjc4 IDAgMjc4IDAgMCAwIDU1Ngo1NTYgNTAwIDU1NiA1NTYgMjc4IDU1NiA1NTYgMjIyIDIyMiA1MDAg MjIyIDgzMyA1NTYgNTU2IDU1NiA1NTYgMzMzIDUwMCAyNzgKNTU2IDUwMCA3MjIgNTAwIDUwMCAw IDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMAowIDAg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgNzM3IDAgMCAwIDAgMCAwIDAgMCAw IDAgMCAwIDAgMCAwCjAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDAgMzMzIDMzMyAyMjIgMjIyIF0KZW5kb2JqCjggMCBvYmoKPDwgL1R5cGUgL0ZvbnQgL1N1 YnR5cGUgL1RydWVUeXBlIC9CYXNlRm9udCAvWkhTWE5DK0FyaWFsTVQgL0ZvbnREZXNjcmlwdG9y CjE0IDAgUiAvV2lkdGhzIDE1IDAgUiAvRmlyc3RDaGFyIDMyIC9MYXN0Q2hhciAyMTMgL0VuY29k aW5nIC9NYWNSb21hbkVuY29kaW5nCj4+CmVuZG9iagoxIDAgb2JqCjw8IC9UaXRsZSAoTWljcm9z b2Z0IFdvcmQgLSBuMzByODNfU1BNNV9MaWNlbmNlX0FIMDkwNjI1LmRvYykgL0F1dGhvciAoQWxl eCBIYW1tZXJzKQovQ3JlYXRvciAoTWljcm9zb2Z0IFdvcmQpIC9Qcm9kdWNlciAoTWFjIE9TIFgg MTAuNS44IFF1YXJ0eiBQREZDb250ZXh0KSAvQ3JlYXRpb25EYXRlCihEOjIwMTAwMjE3MTAzNjQx WjAwJzAwJykgL01vZERhdGUgKEQ6MjAxMDAyMTcxMDM2NDFaMDAnMDAnKSA+PgplbmRvYmoKeHJl ZgowIDE2CjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAzOTUzMyAwMDAwMCBuIAowMDAwMDEyMjMw IDAwMDAwIG4gCjAwMDAwMTMzODEgMDAwMDAgbiAKMDAwMDAwMDAyMiAwMDAwMCBuIAowMDAwMDEy MjA5IDAwMDAwIG4gCjAwMDAwMTIzMzQgMDAwMDAgbiAKMDAwMDAxMzM0NiAwMDAwMCBuIAowMDAw MDM5MzYxIDAwMDAwIG4gCjAwMDAwMTI0MzIgMDAwMDAgbiAKMDAwMDAxMzMyNiAwMDAwMCBuIAow MDAwMDEzNDY0IDAwMDAwIG4gCjAwMDAwMTM1MTQgMDAwMDAgbiAKMDAwMDAzODU3MiAwMDAwMCBu IAowMDAwMDM4NTk0IDAwMDAwIG4gCjAwMDAwMzg4MjkgMDAwMDAgbiAKdHJhaWxlcgo8PCAvU2l6 ZSAxNiAvUm9vdCAxMSAwIFIgL0luZm8gMSAwIFIgL0lEIFsgPGJhN2QxZDNkMjRlNGVlZjMxZWEy ZDkyZjM5ODYyNDk2Pgo8YmE3ZDFkM2QyNGU0ZWVmMzFlYTJkOTJmMzk4NjI0OTY+IF0gPj4Kc3Rh cnR4cmVmCjM5NzgzCiUlRU9GCg= --Apple-Mail-109-801023220 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252

On 4 Feb 2011, at 19:38, Michael T Rubens wrote:

WFU pickatlas

http://fmri.wfubmc.edu/software/PickAtlas

cheers,
Michael

On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote:
Hi Everyone –

I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas?

Thanks so much,
Steffie

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--Apple-Mail-109-801023220-- --Apple-Mail-108-801023220-- ========================================================================Date: Sat, 5 Feb 2011 16:41:48 -0500 Reply-To: "Watson, Christopher" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Watson, Christopher" <[log in to unmask]> Subject: Re: DARTEL-VBM: total intracranial volume in second level analysis Comments: To: Steve Fleming <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> This may not always be appropriate, however. See e.g. Barnes et al (Neuroimage 2010) who found that volumes of some ROIs had a nonlinear relationship with whole brain volume. ________________________________________ From: Steve Fleming [[log in to unmask]] Sent: Saturday, February 05, 2011 2:06 PM To: [log in to unmask]; Watson, Christopher Cc: Steve Fleming Subject: Re: DARTEL-VBM: total intracranial volume in second level analysis Another option is to select proportional scaling under 'Globals' when setting up the model. This converts volumes into values that are a proportion of total brain/GM volume. See here for more details (and for discussion of the relationship between different covariate approaches): http://en.wikibooks.org/wiki/SPM/VBM#Globals Best Steve ========================================================================Date: Sat, 5 Feb 2011 17:45:43 -0600 Reply-To: Pilar Archila-Suerte <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pilar Archila-Suerte <[log in to unmask]> Subject: Signal change in SPM MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016364c794904d324049b919a8c Content-Type: text/plain; charset=UTF-8 SPM Users, Can somebody please verify this for me: Signal change for an ROI in SPM is found through the following steps: 1. Find ROI, then: "Plot -> fitted responses --> contrast --> adjusted --> plot by scan/time", am I correct? Thanks, Pilar A. --0016364c794904d324049b919a8c Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable SPM Users,Â

Can somebody please verify this for me: Signal change for an ROI in SPM is found through the following steps: 1. Find ROI, then: "Plot -> fitted responses --> contrast --> adjusted --> plot by scan/time", am I correct?

Thanks,

Pilar A.Â

--0016364c794904d324049b919a8c-- ========================================================================Date: Sat, 5 Feb 2011 22:51:23 -0500 Reply-To: Carlton CHU <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carlton CHU <[log in to unmask]> Subject: Re: Anatomical Regions Comments: To: Alexander Hammers <[log in to unmask]> Comments: cc: Carlton CHU <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf300258e468f1cf049b950706 Message-ID: <[log in to unmask]> --20cf300258e468f1cf049b950706 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear Alexander: In your opinion, how does LPBA40 ( http://www.loni.ucla.edu/~shattuck/resources/lpba40/) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps. I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions? Best, Carlton On Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers < [log in to unmask]> wrote: > Dear Steffie, > > > Brodmann areas are histological entities; you can therefore only really get > to them via histology. There is a long-term initiative under way in Jülich, > Germany to provide such maps via histological workup of ten brains and > spatial transfer to MNI space - see > http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by > Zilles K, Amunts K, Eickhoff E et al. > > They are great but don't exist for all areas. > > The next best thing is to define your regions based on multiple brains. The > good news is that for major sulci, there is, on average, actually a > reasonable correspondence between histology and macroscopic landmarks (which > you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp > 2007. > > One such atlas is our maximum probability map in MNI space (Hammers A, > Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 > regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage > 2008 for the additional region definitions)), which you can get by agreeing > to a one-page free academic licence (attached). > > There are many more atlases based on _single_ brains, including AAL > (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain > (Hammers et al. 2002); and digitalized versions based on the single > Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, > single brain / hemisphere atlases are by definition not representative and > are therefore not likely to well represent your subject under study. This > can be, and has been, quantified for AAL and PickAtlas versus the maximum > probability map (Rodionov R et al.Magn Reson Imaging 2009). > > The most exhaustive quantification of the errors involved in single subject > atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial > overlap, for single atlases, you loose five Dice index points compared to > the maxprobmap. You'd need to train a student for several months to improve > that much, and by using a maximum probabilty map you get the same > improvement for free :-). > > Obviously there are other more involved and more accurate forms of atlasing > (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), > but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, > etc.) I think maxprobmaps are an excellent compromise. > > Hope this helps, > > ATB, A > PS: If you want the n30r83 maxprobmap, just email me off-list if the > licence is acceptable to you (it essentially asks to cite our work and not > use it commercially). > > > On 4 Feb 2011, at 19:38, Michael T Rubens wrote: > > WFU pickatlas > > http://fmri.wfubmc.edu/software/PickAtlas > > cheers, > Michael > > On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> > wrote: > >> Hi Everyone – >> >> >> I’d like to pull BOLD data from a set of anatomical regions. For example, >> I’d like to look at traces in the anterior cingulate or the left ITG. Does >> anyone know how to find a database of MNI-normalized anatomical regions like >> this? Ideally, somewhere to download individual masks for anatomical >> regions, or even Brodmann’s areas? >> >> >> Thanks so much, >> >> Steffie >> >> > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > > --20cf300258e468f1cf049b950706 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear Alexander:

In your opinion, how does LPBA40 (http://www.loni.ucla.edu/~shattuck/resources/lpba40/) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps.

I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions?

Best,

Carlton

On Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers <[log in to unmask]> wrote:

Dear Steffie,

Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al.

They are great but don't exist for all areas.

The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007.

One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached).

There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009).

The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-).

Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise.

Hope this helps,

ATB, A
PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially).

On 4 Feb 2011, at 19:38, Michael T Rubens wrote:

WFU pickatlas

http://fmri.wfubmc.edu/software/PickAtlas

cheers,
Michael

On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote:

Hi Everyone –

I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas?

Thanks so much,
Steffie

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--20cf300258e468f1cf049b950706-- ========================================================================Date: Sat, 5 Feb 2011 21:10:44 -0800 Reply-To: Michael Spezio <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Spezio <[log in to unmask]> Subject: Code for saving movies made in spm eeg analyses Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=__Part022E0C44.0__=" Message-ID: <[log in to unmask]> This is a MIME message. If you are reading this text, you may want to consider changing to a mail reader or gateway that understands how to properly handle MIME multipart messages. --=__Part022E0C44.0__Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Does anyone have experience with saving the movies that appear in the Graphics Window in SPM8's EEG analyses (e.g., the movie in EEG/MEG Source Localization)? Is there an easy way to save the scalp topographies in the Graphics Window (e.g., from Display --> M/EEG) as movies over time? Is this better accomplished in FieldTrip directly, or can this be done from within SPM8? Thanks for any guidance. Best, Michael Michael L. Spezio, Ph.D. Assistant Professor of Psychology Department of Psychology Scripps College Claremont, CA [log in to unmask] 909.607.0914 and Visiting Associate Scientist Psychology & Neuroscience Division of Humanities & Social Sciences Caltech Pasadena, CA 91125 [log in to unmask] --=__Part022E0C44.0__Content-Type: text/html; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Content-Description: HTML Does anyone have experience with saving the movies that appear in the Graphics Window in SPM8's EEG analyses (e.g., the movie in EEG/MEG Source Localization)? Is there an easy way to save the scalp topographies in the Graphics Window (e.g., from Display --> M/EEG) as movies over time?

Is this better accomplished in FieldTrip directly, or can this be done from within SPM8?

Thanks for any guidance.

Best,

Michael

--=__Part022E0C44.0__=-- ========================================================================Date: Sun, 6 Feb 2011 10:30:40 +0100 Reply-To: ematine tine <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: ematine tine <[log in to unmask]> Subject: Re: tool time course? In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> 2011/2/4 Michael T Rubens <[log in to unmask]>: > Marsbar is very popular among users of spm. http://marsbar.sourceforge.net/ > cheers, > michael > > On Fri, Feb 4, 2011 at 3:07 AM, Rainer Bögle <[log in to unmask]> > wrote: >> >> Hi Emanuele, >> >> you can try to use RFXplot toolbox. >> >> http://rfxplot.sourceforge.net/ >> >> Kind regards, >> Rainer >> Thanks!! ========================================================================Date: Sun, 6 Feb 2011 19:23:16 +0200 Reply-To: Panagiotis Tsiatsis <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Panagiotis Tsiatsis <[log in to unmask]> Subject: Warning in spm_eeg_convert2scalp (griddata) MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Dear All, When I am converting my CTF MEG epoched data to images, I am getting the following warning: Warning: Duplicate x-y data points detected: using average of the z values. > In griddata at 107 In spm_eeg_convert2scalp at 204 My settings are: S = []; S.n = 32; S.Fname = Concatenated_File_Name_Time; S.interpolate_bad = 1; S.modality = 'MEG'; I also have EOG channels whose chantype has been set to 'EEG' during the conversion (this of course should not interfere since I ve selected my modality to be 'MEG'). By browsing the Mathwoks forum, I suspect that this happens when nearby values get very similar (as is the case with MEG data). Anybody seen that before? Any intuitions? Thanks and best, Panagiotis -- Panagiotis S. Tsiatsis Max Planck Institute for Biological Cybernetics Cognitive NeuroImaging Group Tuebingen, Germany ========================================================================Date: Mon, 7 Feb 2011 13:31:52 +1300 Reply-To: Reem Jan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Reem Jan <[log in to unmask]> Subject: Re: tool time course? Comments: To: =?iso-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> In-Reply-To: A[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CBC65E.6A96BF8B" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CBC65E.6A96BF8B Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi Rainer Do you know whether RFXplot toolbox can be used with SPM8? Many thanks Reem From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Rainer Bögle Sent: Saturday, February 05, 2011 12:07 AM To: [log in to unmask] Subject: Re: [SPM] tool time course? Hi Emanuele, you can try to use RFXplot toolbox. http://rfxplot.sourceforge.net/ Kind regards, Rainer On Fri, Feb 4, 2011 at 12:22 AM, Emanuele Tinelli <[log in to unmask]> wrote: hi, I wanted to know if there is a tool to obtain the values of the time course (over Y) in a group of 30 subj by placing a ROI in the same region. thanks __________ Information from ESET NOD32 Antivirus, version of virus signature database 5851 (20110206) __________ The message was checked by ESET NOD32 Antivirus. http://www.eset.com ------_=_NextPart_001_01CBC65E.6A96BF8B Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Hi Rainer

Do you know whether RFXplot toolbox can be used with SPM8?

Many thanks

Reem

From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Rainer Bögle
Sent: Saturday, February 05, 2011 12:07 AM
To: [log in to unmask]
Subject: Re: [SPM] tool time course?

Hi Emanuele,

you can try to use RFXplot toolbox.

http://rfxplot.sourceforge.net/

Kind regards,
Rainer

On Fri, Feb 4, 2011 at 12:22 AM, Emanuele Tinelli <[log in to unmask]> wrote:

hi,
I wanted to know if there is a tool to obtain the values of the time course (over Y) in a group of 30 subj by placing a ROI in the same region.
thanks

__________ Information from ESET NOD32 Antivirus, version of virus signature database 5851 (20110206) __________

The message was checked by ESET NOD32 Antivirus.

http://www.eset.com

__________ Information from ESET NOD32 Antivirus, version of virus signature database 5851 (20110206) __________

The message was checked by ESET NOD32 Antivirus.

http://www.eset.com
------_=_NextPart_001_01CBC65E.6A96BF8B-- ========================================================================Date: Mon, 7 Feb 2011 03:13:12 +0000 Reply-To: Jared Stokes <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jared Stokes <[log in to unmask]> Subject: LAB MANAGER OF FMRI LABORATORY Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> LAB MANAGER OF FMRI LABORATORY Lab manager position available in the laboratory of Roberto Cabeza (www.cabezalab.org) at the Center for Cognitive Neuroscience of Duke University (www.mind.duke.edu). fMRI scanning is conducted at the nearby Brain Imaging and Analysis Center (www.biac.duke.edu). Duties include recruitment and neuropsychological testing of young and older participants, behavioral testing and MRI scanning, analyzing behavioral and brain imaging data, supervising and training undergraduate students, and general laboratory administration. Research topics include various forms of memory (relational, implicit, emotional) and the effects of healthy and pathological aging. The position includes opportunities for research and co-authorship of abstracts and papers. Qualifications: B.A. or equivalent with background in psychology, neuroscience, or computer science. All candidates should have excellent interpersonal and organizational skills, good computer skills, and some research experience. Experience in computer programming and familiarity with statistic are desirable. Two-year commitment is requested. Send a cover letter and a CV to [log in to unmask] Please enter â€œLab Manager Positionâ€™ as the subject of the e-mail. ========================================================================Date: Mon, 7 Feb 2011 03:14:20 +0000 Reply-To: Jared Stokes <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jared Stokes <[log in to unmask]> Subject: POSTDOCTORAL POSITION IN FMRI LABORATORY Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> POSTDOCTORAL POSITION IN FMRI LABORATORY Postdoctoral position available in the laboratory of Roberto Cabeza (www.cabezalab.org) at the Center for Cognitive Neuroscience of Duke University (www.mind.duke.edu). fMRI scanning is conducted at the nearby Brain Imaging and Analysis Center (www.biac.duke.edu). Research will focus on the neural mechanisms of memory (e.g., relational memory, emotional memory, memory development). Within this general topic, the postdoctoral researcher will design her/his own studies. The position includes RA support and collaborations with graduate students and faculty at CCN and BIAC. The ideal candidate will have several of the following qualifications: (1) background in episodic memory research; (2) experience in fMRI methods; (3) programming skills; (4) expertise in statistics; and (5) a promising publication record. Send a statement of research interests and a CV to [log in to unmask] Please enter "Postdoctoral Position" as the subject of the e-mail. ========================================================================Date: Mon, 7 Feb 2011 13:35:14 +0800 Reply-To: han zhang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: han zhang <[log in to unmask]> Subject: Re: SPM CLI In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba5bc0bda73b76049baa98e6 Message-ID: <[log in to unmask]> --90e6ba5bc0bda73b76049baa98e6 Content-Type: text/plain; charset=ISO-8859-1 I think he means the toolkit "spm-batch". I remember it can be downloaded at SPM website. But I don't know if it still works fine for new version SPM (8?). Best, Han 2011/2/4 Stephen J. Fromm <[log in to unmask]> > "There is even a function in spm for doing this, but I have never used it." > > What function is that? > > Best, > > S > -- Han ZHANG NeuoImage Computing (NIC) group http://psychbrain.bnu.edu.cn/home/chaozhezhu/ http://publicationslist.org/han_zhang State Key Lab of Cognitive Neuroscience and Learning Beijing Normal Univ 19# Xinjiekouwai St. 100875, Beijing, China (Fax) +86-10-5880 6154 (Tel) +86-10-5880 2965 --90e6ba5bc0bda73b76049baa98e6 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

I think he means the toolkit "spm-batch". I remember it can be downloaded at SPM website. But I don't know if it still works fine for new version SPM (8?).

Best,

Han

2011/2/4 Stephen J. Fromm <[log in to unmask]>

"There is even a function in spm for doing this, but I have never used it."

What function is that?

Best,

S

--
Han ZHANG
NeuoImage Computing (NIC) group
http://psychbrain.bnu.edu.cn/home/chaozhezhu/
http://publicationslist.org/han_zhang
State Key Lab of Cognitive Neuroscience and Learning
Beijing Normal Univ 19# Xinjiekouwai St. 100875, Beijing, China
(Fax) +86-10-5880 6154
(Tel) +86-10-5880 2965
--90e6ba5bc0bda73b76049baa98e6-- ========================================================================Date: Mon, 7 Feb 2011 09:25:24 +0000 Reply-To: Karen Lythe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Karen Lythe <[log in to unmask]> Subject: Re: wierd looking images Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a033c9a914049badcf42 Message-ID: <[log in to unmask]> --20cf3054a033c9a914049badcf42 Content-Type: text/plain; charset=ISO-8859-1 John, After using Check Reg it's evident that each of my subjects' normalised data and mask image is slightly smaller than the EPI template, although some to a higher degree than others. Is there a way to remedy this if it is indeed the grey matter masking that is too severe as you suggest. Best Karen On Fri, Feb 4, 2011 at 5:55 PM, John Ashburner <[log in to unmask]> wrote: > I'd suggest using the Check Reg button to compare the mask.img file > with one of the MNI-space templates to see if they match. Also try > check reg with a spatially normalised image from each of your > subjects. It's possible that the grey matter masking was a bit too > severe. > > Best regards, > -John > > On 4 February 2011 17:17, Karen Lythe <[log in to unmask]> wrote: > > Hi, > > > > I've ran some analyses using the following steps: > > Realign: estimate and reslice > > - mean image written only > > Coregister: estimate > > - ref image is mean, and source image is individuals structural > > Segment > > Normalise:write > > - paramter file is segmented structural > > - images to write are realigned images > > - writing options - voxel size is [3.75x3.75x3.8] (i changed this to the > size of the voxels that we used in our scanning sequence) > > Smoothing - 4mm > > > > All other options are left as default. > > > > Each individual person's data looks fine - but when I look at a really > low threshold of the group map the BOLD signal doesn't fill the whole brain, > as though my data has not been normalised correctly to the MNI brain (see > image attached - overlay used is the single_subj_T1 from the canonical > folder). > > > > Does anyone know what is happening and how to remedy this? > > > > Any comments/suggestions would be much appreciated. > > > > Many thanks > > Karen > > > > > > Karen Lythe PhD > > Wales Institute of Cognitive Neuroscience > > School of Psychology > > Cardiff University > > Tower Building > > Park Place > > CF10 3AT > > UK > > > > Tel: 029 208 70716 > > Email: [log in to unmask] > > > > > -- Karen Lythe, PhD School of Psychology Cardiff University Tower Building Park Place CF10 3AT Wales, UK Email: [log in to unmask] Tel: 029 208 70716 --20cf3054a033c9a914049badcf42 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable John,

After using Check Reg it's evident that each of my subjects' normalised data and mask image is slightly smaller than the EPI template, although some to a higher degree than others.

Is there a way to remedy this if it is indeed the grey matter masking that is too severe as you suggest.

Best
Karen

On Fri, Feb 4, 2011 at 5:55 PM, John Ashburner <[log in to unmask]> wrote:

I'd suggest using the Check Reg button to compare the mask.img file
with one of the MNI-space templates to see if they match. Also try
check reg with a spatially normalised image from each of your
subjects. It's possible that the grey matter masking was a bit too
severe.

Best regards,
-John

On 4 February 2011 17:17, Karen Lythe <[log in to unmask]> wrote:
> Hi,
>
> I've ran some analyses using the following steps:
> Realign: estimate and reslice
> - mean image written only
> Coregister: estimate
> - ref image is mean, and source image is individuals structural
> Segment
> Normalise:write
> - paramter file is segmented structural
> - images to write are realigned images
> - writing options - voxel size is [3.75x3.75x3.8] (i changed this to the size of the voxels that we used in our scanning sequence)
> Smoothing - 4mm
>
> All other options are left as default.
>
> Each individual person's data looks fine - but when I look at a really low threshold of the group map the BOLD signal doesn't fill the whole brain, as though my data has not been normalised correctly to the MNI brain (see image attached - overlay used is the single_subj_T1 from the canonical folder).
>
> Does anyone know what is happening and how to remedy this?
>
> Any comments/suggestions would be much appreciated.
>
> Many thanks
> Karen
>
>
> Karen Lythe PhD
> Wales Institute of Cognitive Neuroscience
> School of Psychology
> Cardiff University
> Tower Building
> Park Place
> CF10 3AT
> UK
>
> Tel: 029 208 70716
> Email: [log in to unmask]
>
>

--
Karen Lythe, PhD
School of Psychology
Cardiff University
Tower Building
Park Place
CF10 3AT
Wales, UK
Email: [log in to unmask]
Tel: 029 208 70716
--20cf3054a033c9a914049badcf42-- ========================================================================Date: Mon, 7 Feb 2011 07:41:41 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: Re: results visualization Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001485f85aecbbed82049bb08da1 Content-Type: text/plain; charset=ISO-8859-1 Thanks! On Fri, Feb 4, 2011 at 5:56 PM, MCLAREN, Donald <[log in to unmask]>wrote: > Click the save button to save the thresholded maps. > > On Friday, February 4, 2011, John Fredy <[log in to unmask]> wrote: > > Hello all, > > I will like to see the results obtained with roi analysis, using > wfu_pick_atlas, in others software like MRICro or xjview, but I see that > the spmT_0001.hdr contains the same data like I never do the roi analysis. > > > > Is possible see the ROI analysis in others software? > > Thanks > > John Ochoa > > > > -- > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General Hospital > and > Harvard Medical School > Office: (773) 406-2464 <+17734062464> > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED > HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is > intended only for the use of the individual or entity named above. If the > reader of the e-mail is not the intended recipient or the employee or agent > responsible for delivering it to the intended recipient, you are hereby > notified that you are in possession of confidential and privileged > information. Any unauthorized use, disclosure, copying or the taking of any > action in reliance on the contents of this information is strictly > prohibited and may be unlawful. If you have received this e-mail > unintentionally, please immediately notify the sender via telephone at > (773) > 406-2464 or email. > --001485f85aecbbed82049bb08da1 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thanks!

On Fri, Feb 4, 2011 at 5:56 PM, MCLAREN, Donald <[log in to unmask]> wrote:

Click the save button to save the thresholded maps.

On Friday, February 4, 2011, John Fredy <[log in to unmask]> wrote:
> Hello all,
> I will like to see the results obtained with roi analysis, using wfu_pick_atlas, in others software like MRICro or xjview, but I see that the spmT_0001.hdr contains the same data like I never do the roi analysis.
>
> Is possible see the ROI analysis in others software?
> Thanks
> John Ochoa
>

--

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
notified that you are in possession of confidential and privileged
information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.

--001485f85aecbbed82049bb08da1-- ========================================================================Date: Mon, 7 Feb 2011 12:48:45 +0000 Reply-To: Giancarlo Rossi-Fedele <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Giancarlo Rossi-Fedele <[log in to unmask]> Subject: Hi, I would need the STANDALONE SPM8 for LINUX-32 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi, I would need the STANDALONE SPM8 for LINUX-32... Giancarlo Rossi-Fedele ========================================================================Date: Mon, 7 Feb 2011 14:15:31 +0100 Reply-To: Anders Eklund <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Anders Eklund <[log in to unmask]> Subject: Random permutation test MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Dear SPMers, I'm currently implementing a random permutation test for single subject fMRI analysis but I'm not sure of all the steps. In my opinion it is not obvious what steps that should be done in each permutation. My current algorithm looks like this Slice timing correction Motion compensation Detrending (cubic) BOLD removal (remove best linear fit between detrended time series and GLM basis functions) Estimate AR parameters from GLM residuals Apply spatial smoothing to the estimated AR parameters, to improve the estimates Apply whitening transform, to make sure the time series are white before they are permuted Iterate the whitening procedure 3-5 times, to further improve the AR estimates For each permutation (10 000) Permute the whitened timeseries Apply inverse whitening transform Smooth all the volumes For i = 1:3 ?? Detrending and BOLD removal (smoothed timeseries first iteration, then whitened smoothed timeseries) Estimate AR parameters from GLM residuals Apply whitening to smoothed time series end Detrend whitened timeseries Apply statistical analysis Save max test value End The whitening in each permutation is not necessary for the permutation approach (since the null distribution is estimated), but I want to make an as fair comparison as possible between corrected thresholds from the random permutation test and random field theory / Bonferroni correction. Would you change anything in the algorithm and why? /Anders -- -- ----------------------------------------------------------------------- Anders Eklund Phd student Medical Informatics, Department of Biomedical Engineering CMIV, Center for Medical Image Science and Visualization Tel: +46 73 6003790 mail: [log in to unmask] Fax: +46 13 101902 web: http://www.wanderineconsulting.com/ ----------------------------------------------------------------------- ========================================================================Date: Mon, 7 Feb 2011 11:53:21 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: DARTEL-VBM: total intracranial volume in second level analysis Comments: To: Ben Becker <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> A couple of comments: (1) VBM is not the best measure of density; (2) Unmodulated images will give you a percentage of grey matter per voxel -- not necessarily density (or tissue density) (3) Since you are looking a how much tissue you have per voxel and not modulating, you are looking at a property of the tissue. Since tissue properties (like FA, MD, T2 time, etc.) are independent of brain volume, there is no need to include TBV, or TICV. (4) If you switch to modulated images, then TBV or TICV might need to be included in the model depending on how the modulation was done. See http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/ for more details. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Fri, Feb 4, 2011 at 3:08 PM, Ben Becker <[log in to unmask]> wrote: > Dear SPM-experts, > > I’ve computed a VBM analysis using the DARTEL-toolbox to investigate GM density differences in SPM8. The aim of the study was to investigate differences in GM density in two groups of patients. I set up the analysis according to the SPM8 manual (chapter 26 Dartel tools) using unmodulated GM images of the patients. > > My questions: > Do I have to incorporate the individual total intracranial volume in the second-level between-group comparison as a covariate or does the analysis already account for this nuisance variable? So is it possible to analyze the date using an unpaired t-test or do I have to use the total intracranial volume as a covariate in the second-level analysis? > > Thanks and kind regards > > Ben > ========================================================================Date: Mon, 7 Feb 2011 18:02:25 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Code for saving movies made in spm eeg analyses Comments: To: Michael Spezio <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear Michael, There is no extensive support for making movies in SPM8 and Fieldtrip as far as I know. However, there is quite a simple generic way in Matlab to make movies from anything you can plot in a figure. Try help getframe help movie2avi In the tool invoked by 'render' button in 3D Source Reconstruction someone has already added these commands to the movie displaying code there (see around line 582 of spm_eeg_inv_visu3D_api.m). So if you write global MOVIE in the command line and then run a movie in this tool, you'll get a movie stored in the variable MOVIE and then you can save it using movie2avi. For the movie that you can generate using the 'mip' button see the code starting around line 96 of spm_eeg_invert_display.m. You can modify it in a similar way. One way to make a movie of scalp topographies via the GUI in SPM is to export your sensor data to image, open the image using CheckReg button, right click on it and choose 'Movie tool'. You can then scroll through the Z axis which is basically what you want and save it as a movie. If you want to use the plot from the reviewing tool, you'll have to write your own bit of code using spm_eeg_plotScalpData. Best, Vladimir On Sun, Feb 6, 2011 at 5:10 AM, Michael Spezio <[log in to unmask]> wrote: > Does anyone have experience with saving the movies that appear in the > Graphics Window in SPM8's EEG analyses (e.g., the movie in EEG/MEG Source > Localization)? Is there an easy way to save the scalp topographies in the > Graphics Window (e.g., from Display --> M/EEG) as movies over time? > > Is this better accomplished in FieldTrip directly, or can this be done from > within SPM8? > > Thanks for any guidance. > > Best, > > Michael > > Michael L. Spezio, Ph.D. > Assistant Professor of Psychology > Department of Psychology > Scripps College > Claremont, CA > [log in to unmask] > 909.607.0914 > > and > > Visiting Associate Scientist > Psychology & Neuroscience > Division of Humanities & Social Sciences > Caltech > Pasadena, CA 91125 > [log in to unmask] > ========================================================================Date: Mon, 7 Feb 2011 18:10:08 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Warning in spm_eeg_convert2scalp (griddata) Comments: To: Panagiotis Tsiatsis <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Panagiotis, Try doing: spm_eeg_load D.coor2D(D.meegchannels('MEG')) and look which of the channels have the same 2D coordinates. In principle this should not happen for CTF MEG. You can also look in 'Prepare' tool at the 2D channel layout an modify it if necessary or load a template layout from EEGtemplates. Best, Vladimir On Sun, Feb 6, 2011 at 5:23 PM, Panagiotis Tsiatsis <[log in to unmask]> wrote: > Dear All, > > When I am converting my CTF MEG epoched data to images, I am getting the > following warning: > > Warning: Duplicate x-y data points detected: using average of the z values. >> In griddata at 107 > In spm_eeg_convert2scalp at 204 > > My settings are: > > S = []; > S.n = 32; > S.Fname = Concatenated_File_Name_Time; > S.interpolate_bad = 1; > S.modality = 'MEG'; > > I also have EOG channels whose chantype has been set to 'EEG' during the > conversion (this of course should not interfere since I ve selected my > modality to be 'MEG'). > > By browsing the Mathwoks forum, I suspect that this happens when nearby > values get very similar (as is the case with MEG data). > > Anybody seen that before? Any intuitions? > > Thanks and best, > Panagiotis > > > -- > Panagiotis S. Tsiatsis > Max Planck Institute for Biological Cybernetics > Cognitive NeuroImaging Group > Tuebingen, Germany > ========================================================================Date: Mon, 7 Feb 2011 19:17:57 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: \spm8\tpm FWHM? Comments: To: Chris Rorden <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> The tissue probability maps used for unified segmentation should probably be a bit more blurred than the typical averages obtained via Dartel. There are a couple or main reasons for this: 1) The unified segmentation only samples the original images about every 3mm. Therefore, to prevent strange aliasing effects from sampling the tissue probability maps at frequencies lower than some of the frequenciies in the data, you may need to include some additional smoothing. I'm not sure what would be optimal here. 2) The deformation model in the unified does not have as much freedom as that of Dartel, so will not be able to warp the tissue probability maps quite as far. They may therefore need to be a bit more blurred in order to compensate for the less precise registration. All the best, -John On 3 February 2011 14:05, Chris Rorden <[log in to unmask]> wrote: > Hello- > > Short version: > What is the FWHM smooth (if any) applied to the spm8\tpm\csf.nii, spm8\tpm\grey.nii and spm8\tpm\white.nii? The corresponding images in the spm8\apriori folder seem very smooth (8mm FWHM?), the the spm8\tpm images appear very sharp. > > Long version > I want to make a set of alternative templates for SPM's unified normalization segmentation. The aim is to build a nice template for large stroke patient studies, and as the average age ~65 I want an age specific template. The aim is to conduct lesion-masked unified segmentation (www.pubmed.com/20542122) with an age specific template derived from a healthy aging sample. > I was able to create very nice tissue probability maps from the 50 healthy controls following the instructions for SPM's newseg and DARTEL, essentially following John Ashburner's instructions here (including affine transform of the template to MNI space): > http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf > Now I want to use this TPM for standard unified segmentation normalization. The question is whether I should apply any smoothing to the resulting Dartel TPM image or not. As this image is based on 50 people, to my eye it looks approximately as sharp as the images in spm8\tpm (which seg-norm uses). Should I use the mean image TPMs directly, or apply a FWHM with a small smoothing kernel? > > -chris > ========================================================================Date: Mon, 7 Feb 2011 20:15:41 +0100 Reply-To: Carsten Stahlhut <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carsten Stahlhut <[log in to unmask]> Subject: Code for saving movies made in spm eeg analyses Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a36dd4bf2f049bb60e55 Message-ID: <[log in to unmask]> --20cf3054a36dd4bf2f049bb60e55 Content-Type: text/plain; charset=ISO-8859-1 Dear Michael, In addition to Vladimir's answer you may want to download the mpgwrite package from Matlab Central http://www.mathworks.com/matlabcentral/fileexchange/309 if you want to save your movies in 'mpeg' format rather than in the '.avi' format. Hereby, you reduce the movie files quite a bit. Best, Carsten 2011/2/7 Vladimir Litvak <[log in to unmask]> Dear Michael, > > There is no extensive support for making movies in SPM8 and Fieldtrip > as far as I know. However, there is quite a simple generic way in > Matlab to make movies from anything you can plot in a figure. Try > > help getframe > help movie2avi > > In the tool invoked by 'render' button in 3D Source Reconstruction > someone has already added these commands to the movie displaying code > there (see around line 582 of spm_eeg_inv_visu3D_api.m). So if you > write > > global MOVIE > > in the command line and then run a movie in this tool, you'll get a > movie stored in the variable MOVIE and then you can save it using > movie2avi. > > For the movie that you can generate using the 'mip' button see the > code starting around line 96 of spm_eeg_invert_display.m. You can > modify it in a similar way. > > One way to make a movie of scalp topographies via the GUI in SPM is to > export your sensor data to image, open the image using CheckReg > button, right click on it and choose 'Movie tool'. You can then scroll > through the Z axis which is basically what you want and save it as a > movie. If you want to use the plot from the reviewing tool, you'll > have to write your own bit of code using spm_eeg_plotScalpData. > > Best, > > Vladimir > > > On Sun, Feb 6, 2011 at 5:10 AM, Michael Spezio > <[log in to unmask]> wrote: > > Does anyone have experience with saving the movies that appear in the > > Graphics Window in SPM8's EEG analyses (e.g., the movie in EEG/MEG Source > > Localization)? Is there an easy way to save the scalp topographies in the > > Graphics Window (e.g., from Display --> M/EEG) as movies over time? > > > > Is this better accomplished in FieldTrip directly, or can this be done > from > > within SPM8? > > > > Thanks for any guidance. > > > > Best, > > > > Michael > > > > Michael L. Spezio, Ph.D. > > Assistant Professor of Psychology > > Department of Psychology > > Scripps College > > Claremont, CA > > [log in to unmask] > > 909.607.0914 > > > > and > > > > Visiting Associate Scientist > > Psychology & Neuroscience > > Division of Humanities & Social Sciences > > Caltech > > Pasadena, CA 91125 > > [log in to unmask] > > > -- Carsten Stahlhut Section for Cognitive Systems Department of Informatics and Mathematical Modelling Richard Petersens Plads, Building 321 Technical University of Denmark DK-2800 Kongens Lyngby, Denmark --20cf3054a36dd4bf2f049bb60e55 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Dear Michael,

In addition to Vladimir's answer you may want to download the mpgwrite package from Matlab Central
http://www.mathworks.com/matlabcentral/fileexchange/309
if you want to save your movies in 'mpeg' format rather than in the '.avi' format. Hereby, you reduce the movie files quite a bit.

Best,
Carsten

2011/2/7 Vladimir Litvak <[log in to unmask]>

Dear Michael,

There is no extensive support for making movies in SPM8 and Fieldtrip
as far as I know. However, there is quite a simple generic way in
Matlab to make movies from anything you can plot in a figure. Try

help getframe
help movie2avi

In the tool invoked by 'render' button in 3D Source Reconstruction
someone has already added these commands to the movie displaying code
there (see around line 582 of spm_eeg_inv_visu3D_api.m). So if you
write

global MOVIE

in the command line and then run a movie in this tool, you'll get a
movie stored in the variable MOVIE and then you can save it using
movie2avi.

For the movie that you can generate using the 'mip' button see the
code starting around line 96 of spm_eeg_invert_display.m. You can
modify it in a similar way.

One way to make a movie of scalp topographies via the GUI in SPM is to
export your sensor data to image, open the image using CheckReg
button, right click on it and choose 'Movie tool'. You can then scroll
through the Z axis which is basically what you want and save it as a
movie. If you want to use the plot from the reviewing tool, you'll
have to write your own bit of code using spm_eeg_plotScalpData.

Best,

Vladimir

On Sun, Feb 6, 2011 at 5:10 AM, Michael Spezio
<[log in to unmask]> wrote:

> Does anyone have experience with saving the movies that appear in the
> Graphics Window in SPM8's EEG analyses (e.g., the movie in EEG/MEG Source
> Localization)? Is there an easy way to save the scalp topographies in the
> Graphics Window (e.g., from Display --> M/EEG) as movies over time?
>
> Is this better accomplished in FieldTrip directly, or can this be done from
> within SPM8?
>
> Thanks for any guidance.
>
> Best,
>
> Michael
>
> Michael L. Spezio, Ph.D.
> Assistant Professor of Psychology
> Department of Psychology
> Scripps College
> Claremont, CA
> [log in to unmask]
> 909.607.0914
>
> and
>
> Visiting Associate Scientist
> Psychology & Neuroscience
> Division of Humanities & Social Sciences
> Caltech
> Pasadena, CA 91125
> [log in to unmask]
>

--
Carsten Stahlhut
Section for Cognitive Systems
Department of Informatics and Mathematical Modelling

Richard Petersens Plads, Building 321
Technical University of Denmark
DK-2800 Kongens Lyngby, Denmark
--20cf3054a36dd4bf2f049bb60e55-- ========================================================================Date: Mon, 7 Feb 2011 19:34:36 +0000 Reply-To: Sandro Shakushiya <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Sandro Shakushiya <[log in to unmask]> Subject: Error in "proportional Scaling" from Pet Study Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi When estimating the study is fine, to show the results SPM resturn this error ??? Error using ==> image Error using ==> image Image CData can not be complex. Error in ==> spm_conman at 975 hDesMtxIm = image((varargin{2}.nKX+1)*32); Error in ==> spm_conman at 944 spm_conman('ImDesMtx',xX,H. hDesMtxAx) %-Depict DesMtx Error in ==> spm_conman at 812 [F,cF] = spm_conman('Initialise','on',SPM,STATmode,n,Prompt,Mcstr,OK2chg); Error in ==> spm_getSPM at 267 [Ic,xCon] = spm_conman(SPM,'T&F',Inf,... Error in ==> spm_results_ui at 275 [SPM,xSPM] = spm_getSPM; ??? Error while evaluating uicontrol Callback. The Design of study was Paired t-test Independence: Yes Variance:Unequal Grand mean scaling: No ANCOVA:No Covariates:0 Masking Threshold Masking: Relative Threshold: 0.8 Implicit Mask: Yes Global Calculation: mean Global Normalization: Overall grand mean scaling:Yes Grand mean scaled value:50 Normalization: proportional Sandro S. Shakushiya University of SÃ£o Paulo RibeirÃ£o Preto School of Philosophy, Sciences and Literature - Department of Physics Medical Physics ========================================================================Date: Mon, 7 Feb 2011 20:03:35 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: non isotropic voxels for DARTEL anatomical template? Comments: To: kalina michalska <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> I don't think it would be so straightforward to get Dartel to handle non-isotropic voxel sizes. Usually, everything is done using isotropic voxels, so I really haven't looked into how well anisotropic voxels are handled. I suspect that there would be little or no benefit from having anisotropic voxels in the imported images anyway. Best regards, -John On 25 January 2011 21:31, kalina michalska <[log in to unmask]> wrote: > Dear SPM experts, > I would like to create an anatomical template using the DARTEL Toolbox in > SPM8. We have acquired 102 high resolution 3D T1 images in young and older > children. > I am completely new to using DARTEL. So far, I have used New Segment on each > T1 image and would like to generate a group template using the rc1 and rc2 > images from each subject. > My questions (so far!) are as follows: The voxel size of our T1 weighted > images is 1x.1x1.6mm^3. The SPM8 manual suggests that voxels should be > isotropic (p. 193, 26.1.4). Is it possible to create a template with > non-isotropic voxels or is in neccessary that they be isotropic? If so, is > there any way to make them isotropic in SPM in order to use this toolbox? Is > it okay to reslice the T1 images? > Many thanks in advance for your insights, > > Kalina > --------------------------------------------------- > Kalina J. Michalska, Ph.D. > > Postdoctoral scholar > Social Cognitive Neuroscience Lab > Department of Psychology > University of Chicago > > Phone: (773) 702-4661 > EMail: [log in to unmask] > Web: http://scnl.org > > ========================================================================Date: Mon, 7 Feb 2011 20:13:36 +0000 Reply-To: Avery Rizio <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Avery Rizio <[log in to unmask]> Subject: Using scaling Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> I am currently scanning a project that includes an encoding and retrieval task, for both younger and older adults. I'm finding that in about half of my older adult subjects, basic trial type versus baseline contrasts show little to no activity during retrieval. Despite the fact that behavioral data indicates they are successfully performing the task, the imaging data reveals, in many cases, a complete lack of even visual activity. I've checked for several possible issues (including onset errors, or problems with the scanner), but to no avail. I reanalyzed a subject for whom there was no activity, this time with global scaling. With scaling, there was a minimal improvement, particularly with respect to visual activity. Everything that I've read has strongly advised against using scaling, but are there scenarios (such as this one) in which it acceptable? ========================================================================Date: Mon, 7 Feb 2011 20:25:47 +0000 Reply-To: "G. Sharvit" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "G. Sharvit" <[log in to unmask]> Subject: A problem with SPM and FIR modeling Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> To the SPM experts, I am trying to set a 2nd-level factorial analysis based on contrast images taken from 1st-analysis FIR model. The software being used: SPM8 (version: 8.4010) and Matlab 7.11.0 (R2010b). In the 2nd-analysis model specification, I am using full factorial design with 2 factors: the first is 'experiment_conditions' composed of 3 levels [conditions A,B and C] and the second factor 'time_bins' composed of 32 levels [for each time bin I have]. After setting the correct order of the cells with the relevant con images from the 1st analysis I encountered a strange problem. When I first (mistakenly) set the two factors Independent value to 'Yes', the model specification batch ran and later I could estimate the model and continue the analysis. However later I realized that I need to change both my factors to be dependent so I switched the Independent value to 'No' for both factors and since this moment the model specification refuses to run. In the command window of Matlab I don't get any warnings but only the notification that the factorial design specification process is running - but this keeps forever [and there is not even generation of the SPM.mat file that I would expect in the desired folder]. Is there any solution to this strange problem? or maybe it is related to some lack of my understanding? I hope you can help me with this one, Thank you very much for your time in advance, Gil ========================================================================Date: Mon, 7 Feb 2011 20:51:38 +0000 Reply-To: Amir Tahmasebi <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Amir Tahmasebi <[log in to unmask]> Subject: POSTDOCTORAL POSITION - Neuroimaging and Brain Plasticity Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-LiZWzTlDWILBAyw1ZNVmY89w4C3iG6ZWd5QknrWA8bCBN" Message-ID: <[log in to unmask]> --part-LiZWzTlDWILBAyw1ZNVmY89w4C3iG6ZWd5QknrWA8bCBN Content-Type: multipart/alternative; boundary="part-U6JulmCEACvjpprFCVtiSU224FE1FhCJ2ALlfn2duACAG" This is a multi-part message in MIME format. --part-U6JulmCEACvjpprFCVtiSU224FE1FhCJ2ALlfn2duACAG Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Please see the attached file. If interested, please submit your C.V. and relevant reprints, together with a cover letter describing current research interests and future research goals, and also arrange to have three letters of reference sent independently to: Dr. Sylvain Moreno Rotman Research Institute Baycrest 3560 Bathurst Street Toronto, Ontario, M6A 2E1, Canada E-mail: [log in to unmask] ________________________________ Amir M. Tahmasebi, Ph.D. Postdoctoral Research Fellow Rotman Research Institute, University of Toronto, Toronto, Ontario CANADA ________________________________ Tel: (416) 785-2500, ext. 3130 Web: www.cs.queensu.ca/~tahmaseb --part-U6JulmCEACvjpprFCVtiSU224FE1FhCJ2ALlfn2duACAG Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="UTF-8" --part-U6JulmCEACvjpprFCVtiSU224FE1FhCJ2ALlfn2duACAG-- --part-LiZWzTlDWILBAyw1ZNVmY89w4C3iG6ZWd5QknrWA8bCBN Content-Transfer-Encoding: base64 Content-Type: APPLICATION/PDF; name="Postdoc Fellowship - ad.pdf" JVBERi0xLjUNJeLjz9MNCjUwIDAgb2JqDTw8L0xpbmVhcml6ZWQgMS9MIDI1NDk5L08gNTIv RSAxOTkwOC9OIDEvVCAyNTE5NC9IIFsgNDgzIDE3MF0+Pg1lbmRvYmoNICAgICAgICAgICAg ICAgICAgDQo2NSAwIG9iag08PC9EZWNvZGVQYXJtczw8L0NvbHVtbnMgNC9QcmVkaWN0b3Ig MTI+Pi9GaWx0ZXIvRmxhdGVEZWNvZGUvSURbPDdDM0RGQTI4RTAyMjc4NEE4OTg0RDFFNDlC NDY3MjVBPjw4ODZEMUU1RTUyRTU1MzQxOThCODBCNDBCOEE5NDlEMT5dL0luZGV4WzUwIDMz XS9JbmZvIDQ5IDAgUi9MZW5ndGggODAvUHJldiAyNTE5NS9Sb290IDUxIDAgUi9TaXplIDgz L1R5cGUvWFJlZi9XWzEgMiAxXT4+c3RyZWFtDQpo3mJiZBBgYGJgqgUSjFNARD2QYJsLJJgz QIQpiBADEix2IJYbiLUMxFIAaasAEtyRIG1KQOKiOwMTI8NBIIuBgZFKxH/GdR8BAgwAcjYK KA0KZW5kc3RyZWFtDWVuZG9iag1zdGFydHhyZWYNCjANCiUlRU9GDQogICAgICAgIA0KODIg MCBvYmoNPDwvQyA4Ni9GaWx0ZXIvRmxhdGVEZWNvZGUvSSAxMDkvTGVuZ3RoIDg1L1MgMzg+ PnN0cmVhbQ0KaN5iYGDgZWBgKmcAAq/FDKiAEYhZGDgakMV4oZiBUYmBl+ml3JEeRYNfHAUu TS38H0In5THw7WqA6Z11CUIz/QDTjA1g4xjWJUCEGSUBAgwAHcEPZA0KZW5kc3RyZWFtDWVu ZG9iag01MSAwIG9iag08PC9MYW5nKP7/AEUATgAtAFUAUykvTWFya0luZm88PC9NYXJrZWQg dHJ1ZT4+L01ldGFkYXRhIDIgMCBSL1BhZ2VMYXlvdXQvT25lQ29sdW1uL1BhZ2VzIDQ4IDAg Ui9TdHJ1Y3RUcmVlUm9vdCA2IDAgUi9UeXBlL0NhdGFsb2c+Pg1lbmRvYmoNNTIgMCBvYmoN PDwvQW5ub3RzIDY2IDAgUi9Db250ZW50c1s1NCAwIFIgNTUgMCBSIDU2IDAgUiA1NyAwIFIg NTggMCBSIDU5IDAgUiA2MCAwIFIgNjEgMCBSXS9Dcm9wQm94WzAuMCAwLjAgNjEyLjAgNzky LjBdL01lZGlhQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL1BhcmVudCA0OCAwIFIvUmVzb3Vy Y2VzPDwvQ29sb3JTcGFjZTw8L0NTMCA2OCAwIFIvQ1MxIDY5IDAgUi9DUzIgNjggMCBSPj4v RXh0R1N0YXRlPDwvR1MwIDcwIDAgUj4+L0ZvbnQ8PC9UVDAgNzIgMCBSL1RUMSA3NCAwIFIv VFQyIDc2IDAgUi9UVDMgNzggMCBSL1RUNCA4MCAwIFI+Pi9YT2JqZWN0PDwvSW0wIDY0IDAg Uj4+Pj4vUm90YXRlIDAvU3RydWN0UGFyZW50cyAwL1R5cGUvUGFnZT4+DWVuZG9iag01MyAw IG9iag08PC9GaWx0ZXIvRmxhdGVEZWNvZGUvRmlyc3QgMTE3L0xlbmd0aCAxMzM5L04gMTYv VHlwZS9PYmpTdG0+PnN0cmVhbQ0KaN7sWG1v20YM3k+5jy2wWPd+0lAYs7NmDdCkQZwtA4x8 UBwtEWpLgayizb8febq7nBXbSZ1hL4ARENKRPJLHIx8x1ppQog1JiU4Jk4LojDDNiKGEmYwY RrhRxHAicSVAJlJiJGGphLUigjIQayI4h6chUqQgT4nUjBLYogwopZSoLBMkBbtMczIFh5Sc X717l4yQCe/JeAKrCfxdPNwXybhuboomuSR0OHSLKQU9epV8SI6T82LWTpmgA5YxAjRQTBCu xUBkKeFUDsDLVTJpmy+z9ixviqolLJl8uW7R9sey+tx5GVVV3Q6H0+T48HCcL4sbAsFhYDFH hFCPJ+TPfL6E6E6S07pZ5PPkcETYgCafzpzk09kJehoRcF0kk5N8+RlUq6Lz9/5b++ukzdsi meV2X33f7RsO0f5yZgOlVCeH+f2Hory9azFhyS9FJzoQXCVH8/x2CflOjuqqHY/rb9MDrZWV wdEptQaurPQoX5Tzhzejpsznby3nNF/AsXFtl5Chop3d+dMg67JzKylNjtt8Xs5G1e28IBSy WSx+J2naHQVVMaymvG/rJvnDRatYZs+CuUOVztfJRfK+mtU3ZXWbXJbVqFqWYX1UNsv28C5v wMWqUSw+rIyPuVPgSoVLvID82kBCNGD5pr1bTo2CwvsXiUP1IwmliFLaUppmREPRY7kKIToC nkplpwtrv8/veY6CPtgIBHyGHYnOkAznluyaQfvBBlzbjVAryLcBQgBWF/h2n9ML+9x7JmWw jXwfBLctn1lbeJgQKPiIAw7vGAMQyvGZxnscWTuUPu6Dd+P2eBICEqi7JyZAqM4fPq19Z4fR KJDAwFOEBW7BI+HTb0cK9+VMd8rBpltgYqyGM+tv9cmtaRpiMALcGxcXBu94SnHsP6IAYoN3 tAU4q4RZ4Qmt3AXKQHiKcEk9skGHm40ovknubt9XRJ8wWBPrxFXmKmNT2WIrxFXRL+cXtQEk Zq0Pb5fSqxhSsyx9HlEzHSGqYqZDVCZUhvufB9QfO7j8Tlw9YNwha8Z3QNaDztauACv2ALsH 2D3A7gH2lQALLbEZYY1eO7Py1Mr8zMpeArHjen7zDMCaDYMrE3oXfEWPu6Kr+r+iK9a9NHKl pCyoifRvQ9fQcxG6Ysll9t/NNWXfB1ewYWXA3xVcfdwhjghcffn7J/Jt30eg7NvKfn2czJPN T7TPxhvnB3ENdHhKQy5XwDUC5V3A1YfoIXQ9uDp7LwbX9JXgGl38NnDtF4Cn/wa4bpkVPPWr ISYPrk/4j9XWA1dqInDVmkfgCiIHrjIeX3VqZeEHAbMCrhfloliS0+IrOa8XeRXBrJWAwPJ3 /YmAbgVaKdNVoF3xeTbZFW3NTmi7pj7+SeKq60ZJH1vVohnIWEofmwdlWgZ97hEiGtW2UYAD sOFJQtNnnK3UvkdUj67Gz2fuC2DR1r0bN/75/b5+vQxBJfRWD23tKOuQCOPA9/gZzkxXoYz7 Hu6dz8cY5yQeZdGmhHxyfEcQkywAnnA59Xph7FTroBJP3wdbf0vWSwSlAWwzB/LCfahQQbr0 8Mj9ypVFERiNR9ehDDzPhuyxFnWdPmKtVBHOIp+5tEQfexldUZ/8v1RPPmwxfvsP7wbCWDfK 3MfNJ61P6Hsd/8kcu630ZfZca2yfY9W6OXb1lwK4BjvHMpF91xz7op8LzMafC5iku86zr/vN INtPtfupdj/V7qfaHafaSfLb+THSm0Veztv6p/y2nF/X+c9N3cIAeHCdP8yaYtkO6mowy394 Oxz+JcAAAvdxQQ0KZW5kc3RyZWFtDWVuZG9iag01NCAwIG9iag08PC9GaWx0ZXIvRmxhdGVE ZWNvZGUvTGVuZ3RoIDgwMj4+c3RyZWFtDQpIiYxV226jMBB95yv8iKVibEO4SFWlbbKXrlSp q/BW9YECDVk1Jg2h3f79zowxDW2lXUUEsGfOOTOeGcIr87g1zbot9w07Pw+vl1crptjFxeVq ybwnL/y+lmzTeypXIkvzPMsTJuEXpSKOpZQ6ZnEkZAaPKmNpLIUGqySLWLXzwqudZKvO++V9 vQa0y8IL1/vSTDza8YTLtWJVD7isr4wXFoUEEcWDpzSxwU1HC6GJQMas2Hk+48Vvixv+0BNk 5CADlbBAilyzop4Z30y28URfFMryATrCV+gqgbV4AfZACQWLK+/Wv+TKL1+rA1cLv+mPPPHP uPZZyV54EPvdgaf+IyzW/K746UkEkojhB8g/A5cEngELmtSAXT1yJf2y5yr2e8ZjQmrKGlcb RGZbWDD23+Bq91yC2REU4FqHS7it0Krc2IcNCcStPWJ0z1t8rhuiYRUC4HpDJgjVktmwwfeW lXhjgyGwp6Gh16oDL2N5kWMYdlyBvu6BuDD2gCLDzC3G6N6Y+lfIHLA31gsXS7OhsBB+wxDm gHudtaDkNpgOvEzTQ3qyMR7yIAiy7c/AgiCbfozUuMiQoUQE1lL2BmthKFQbqakZIQ5ov6fz cJ5vOMyeFAnGv4rk4A75UiZLFqd41/9bCdIlC8vsvqkJxQwYyUxma3PS2Lqzmq1+FuFdyk8Z VSSiha002wxzCTFJEDpzxXhPVVfbw1Qij91G2/V7OEmMGw5RY0LnfJGIPxDpd7GeEB1byF3J A+0fx8qZHCZ74F84+63hQYK9Ah4DCBlb7WM0WiRja0EpkJBJohIyOZVITomjTOYS/fvOhZFZ ZzW17JEHGTTJl0lDYi3SKbxqQLlk17xLFAzU/N8nko4mJRzzLIjPnFy2Eke/xP60FbOncfIH B8sSp4VxhW2GcVKc5D9yyYgQMYi00LNuXvJA4aml/uF9WFoop1lYhVMmP53Ci5MprO0UfotF 07jHT4B9UrkUkWbJQsPnBz8Dt/4N9l2Peo5YE3VX8YV9hHGcoEgFYyPCtvS/Ne4Nrw7rF2aL hnkLPu0W1vb8jv0VYAD7vqJ0DQplbmRzdHJlYW0NZW5kb2JqDTU1IDAgb2JqDTw8L0ZpbHRl ci9GbGF0ZURlY29kZS9MZW5ndGggNzUxPj5zdHJlYW0NCkiJlFRNT+MwEL33V/hoS8TEjp20 EuJQ2JVYiRWC3NAesmmgrFpnlbSL+Pc7b5yUbFWQ9tAwHs/HmzfPlN9mqShfhTHaZwtB9mom hSp/zb7cXonZ+Z24uDi/vbq5Frm4vFxeky9JdZpaJCUm0/OFpb/aeGQ+yu8qyWSjEiP3Xfui MrmlX4XzM05B5fJZKBNdYQVziZSOHRwiVCHvlJWbMbPHZ6cSy/f1GEieuXxTP8pxhFxbbz4b oTg9wsLHEZyLM1wdZgBcbsynDmi/th0ALtURZroZQFkZmgPsfoLP6NwVn+Gbj/gAz4iyFgw0 i0C99r6Ycl1S0zX9GnS/R7eWkO1gbJWTVSA3sN7zBD3N0lQd+eo1Xxh5Q1GBAvpdHMTF5P1Y pTkj17AiWtmbMnPQn0tUaShrmK0WcUCvFzmZ6WB5msHPMw1etx+MvBhHPi/LTNDMTzPiKXWx DFsu01b4fAH7wzomnRRKYyFw5w4s8rqNjYXtUNV7neWoCjYTDzpT4vOeuGp3ylgiMoEKyRvY 2/SNMsRtRxyBR+LnhrQaiIycGIRcLBFI8TCwmZS2Qp8nqFUslYlEOll3sBtOFMjsBbE9xNoY 36AN7sIz/KISlFQwiH8xHDo0G4reKEP4X/Ht1xSJCr+5RzhaWSTzBFGZ04WLWsWjpsH3aNei yJaMigFNocXSyVCLSq+49liSGo43SSw+UfIS3Hfx3wLRGPAVd3Bu2AOBkr9+gZLJdu/vfhgk 1z6fPq0TI6XazseJGLjAioaNM2XVzxZDVrx5NnlTosVCxLWC8AupceD2j/KBMHMMaP/DyngB vCBuwRbCmxA1cET8GVCaQmfuv2HX6NPGRb+DjpozloPaIHj9oyLXJB0WJKIqLFBMVMkwe1SN 94OwXNRfH4dNrNMun26N0dQbpOxXEym077wiX/S0N9QO8LPSccSBJV8NXMIheII1XqDhJ2ij 2kJ0HL2+E4+PsveHQSFK+rP6XPTirwADAD67m2wNCmVuZHN0cmVhbQ1lbmRvYmoNNTYgMCBv YmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCA3MjA+PnN0cmVhbQ0KSImMVcFunDAQ ve9X+GhXwcEGdokU5dCkh1aK1AM9RT2w4GSTbvCqLE379515Niy7SascMGAPnjfvvTFJqlNj RfUibKnTTKSiahd3cq+MlRsnvqml7B5p+KVMLt1PtZL9ozIpBSSl/MOTQiX07u95QlQqKaTn MM9R3V5ZevpefVlQnpTyNCKJTy/CpLq8CBmlFqp6WqQckPJaqosiLmHl0+21WJx/FZeX57fX n2+EMeLq6uMNTSbZhc5XIjHaFP+Jt2M8p8+PgVQfqORrqnJg5Hw5IBc7VPJEK67hWnmyFyqX jefXrnF846AuFDnhDyiOcgWajdF2onnNn9bMbyd2W7rV/Z5GnmgizTawfMZwwDTgbWpeXXPg Nn7AoTUP/OK7M1ZQAJXJNOWelLXykBXDA+8VNg8Fo1bXgg0nHC/f02d8uYYBEgfJUq55YQBR NaPovcpjyiRWTvUWOitObXWIAScs4UzBO2QaumbkInhJoOauFXxrwUpPrsvlgPnTaB9gsyu3 yhC1XA1KdadaJa+0igbN9WrSqm9qVYJtJzpwAn5QOSD1BKmUENA9qyQLTqkxut8MF+y6vncc GOoI2jfM3DMtwgQhWGxQF3aqO0i0RtoNDzUaEuWiKc+AArQG1DM2kaPZAnI7U7xGgnY0DzY6 mA1e4stNnTx3FqN1PJx8BrxDOCQawdM1SRYUCHmHOqiglZ25JR5CS52bkfFKFWwW2G4bne5f eKdTk+W6nHQClwPHtu/tSauX5dSSZPggliedctpkag6417930yTL9IV9pQMY7QI/4VANm0dt Z7rO7IWiQfQPjl5KTAThjluJet2uxlo2ZFTs4kHdw2g4cnIONsVuOjEOKsOwUcHoFHCKx250 y+GnMO8Gvwtd0Gzog7ezHtNnC71azg/6N9ow1bY8OkE8dg6ej4fCP1U5+efwUZOZuSp7rxIY UfwVYAD+yKusDQplbmRzdHJlYW0NZW5kb2JqDTU3IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURl Y29kZS9MZW5ndGggNzYwPj5zdHJlYW0NCkiJhJVLb9swDMfv+RQ8SkOtWn4lAYoC6wNbCxQo utyCHlRbnTOkdjA3y/btx4elpOtjF8eWSIn8/UlmcT1JTZoWsKjBGmshhUUzWaonneRqu9aV etaZWuHvk56qvnG0BNqmqvP03P6k5bDvvtMafXX8BnyMpyNa+u6bnvz7aOYH0IUatC3Utm7x FRx/wPCMu3T2tqY3vMbi8fp+cT1JKOCMAh4j38FSreFGW3V3pZNCHdGDY3zUCR721s6lTkp1 d0tPcHhvp5NKNfTgg3j7C1+3v41uyqdmPhVIygDoxY9JKqHM8nGZFy9vzmFyfAsnJ8c351cX YHM4PT27wMUkn5u5hQRplx/YF8E+yhPy3sHiEyq0oBCZqhdajnCJWjYK0LhnbTM0IdoMW3Tj 3T5o6x5IFn+En/DAstIOg9rQqX6EOVOePh19sssfFutBfvbqjKJ8Q5LsP1YE44wos8KU00Nm LzPlM1KTzUaTLLWWrGjTMobMZHmoV0PxHiJB5VMUNJSoC6n2HfQxG7FiQq2HRzTxlBXX6I4o DlST5LYR05Hiy0RsYeZjkH2IMK1CgVaSR1mNJsg/Fg17x657l0JS5KYq9yWzFO4eFT9sCDpr XgQiLOYO412NTdwCPz3L2m9ipSARXhEq6FT3VCYBWMcMD3o60usJLDhZpECWavDoiv62EL8G 3PgrkFdjVzf/IHwrddE4N3YeUopZT0XvM6wvVndwtBMqTYYKm0ndd7AeOUjxA2v7mgvDzEqD zXrA+uOyGGqnZ7wl1+4xxqnn4Zx6jEG4ZhXf4QotOu5diQUJceN5mYEHdfqVbvUuzGM+Fu74 Uh9yrcmpFT26BuIkrtesYCPCO1HbvUzIdTWdAyF4kZau4ub5xeBl9BMkHkAOL2HGw14WmeGc 9G9a3fgOA8SXwZDzq86xsYXfGYNlHJti+7+xWb0amzhCylmcmp9pJG1COdQy+nkGZDIeWdoe //mQSgMt05T0wcEterdUehfobPQ9/BVgAHCrrkUNCmVuZHN0cmVhbQ1lbmRvYmoNNTggMCBv YmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCA3Njg+PnN0cmVhbQ0KSIl8VMlu2zAQ vfsr5kgWESNSm1UEAZq4hwQwEDRCL0EPisRYKRLJtewa/fvOIkuu0fhgmhrO8t7jDIv7mY1N nkIIRT17UhfaKVjqTBncLHTK/9Bt0AI6sKHyvzSuu1c8+q1trMo33Pl2S261X9EhOXvPmcqW DDWs2Ub7Fx3MlR8/28rrWMFe21RRzjf+BWh69sDZXumLS/kWqEwH+kdxPwtNGDooKkK+h0BY BNbYRJiUOnBqPS5vWOYVfxVCmqttD5hpT1nJuG3IBTik5e1SJ8os6NxcwAMyVw2GoQUNQ32q bSOTZqId6lP8nH1d3sLs8gGuri6Xt3cLsBlcX98s0BiI74jwA//5wf+yKBxYKF5mwUTVuCwn utaaMMa64bCLI+MgCjOTo987sv+iXUzEUcA16pmRjBmR3+ogVcjeqr7pdnxU89fu+Z2UZj92 ImtJy62OsA3Q/F3bYcdnLQduKMpzIo/XRPYxXM7WmzFxSw4C4WLI4+S7W3n+b7z0mqXrCWx2 BKgZEVUdV/KcmAxSnr148XQ8nta+ryYMz3TjDGRFy6GbXJySsiHfUIzCL1DGasdxwgN5JQpk 3/uSN1XDOU7ISX/3g9YilKXWT9WOjbsRn58SjraqgVVXMqeejCIVpuFaw0EnH2MQLyWTeVLt ysOIpoOmFL1AdJSKrBHQXBwLx2dcFToBnahp4LJJosSJRJLsZQr2NNMU3w+KiTi1X3tWqPbS IEzjj3boKTCnseKRVp9pQGxiotyeHbF8GjFxPtzgRxEuHCOIVCKjxZs9FJ+Q1GJD4AzPj7A/ HsJBB4vzHB8ezkd8T5gM05JBEOL4lhC7QZtTlpmJ4vk5es6OYHMTOnfM7hsFTMh4F/MVmcxm B2gdy8vTXbb0ilsMjIYezqjb7hhoT7fFz+HUqP4ELj43ORY7h9f9I+4omZNn2pn0mMKTukEo JYNi+aphHLYnhWPjwrMvrYv+X5evKpDwQ99S3ShJQ+DiMhS7qfPl/Xqk1463w7wgJPgrwACZ mKVTDQplbmRzdHJlYW0NZW5kb2JqDTU5IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9M ZW5ndGggODY0Pj5zdHJlYW0NCkiJfFXJbtswEL37K+ZIFhYjarXaIGjq5JCiQYtGt6AH2pKX 1pZcLw3y952Fku0ECQJQ9AzncebNG6b8OgihnAIuT1CYrMhoWw0U6PL34PZ+DIOLH3B5eXE/ vruBKIGrqy83aAxCE4YRRfIuofBA4gNrbGIJ5FGV2lrVbnWQqLbZ6yBT7VAHuYLvtIrFbXWu lvQbfYmCezJmOsDIax2nmEkQWRXd6khZCWbfWMfKNa5yoH+Vp1W8kXrapV5+GKhbOhKaLIt9 uQEZuJScgUxoIwILTRzncuZRrXVQKKeDlNK1aoVLpD6CJHBkxG+e4FHtqP41l88k1E3LhyW7 b8vmzzHBrEvwYvwQwXSHt+KfSQB202ZwzjfD5yYc9bl9poy2rbap2utCrR1S2bxgJjFxMnpV 8HnOZwVP3LNG7qeSO6NhTdw1w81sDDVk6k6Keti45lhUflKUlaKknD6pzBRZ/q7oRr3obGHy uDhR2BsRxWuZ+tZigX34TSfQRQ1V7aoV1cJCbGog+4xrJHmC22xWyCh7p+RzzILItuE2A/+Q raeKpezIwgxOFyhDdmEhyajoaBZVR6F90a/IRLajxopi0zR5j6s47PkuyxCwyNkAhUxKwk8S mwhsmhqbQbl+C8KeQFiB6GZcBoNSG5kwYVjeCHCSoiAJmGjFIUFaf+LQtnsam7Ue4biyod7x ANc4+Kgt3i9It3CH+xBVi3s5smfDEvUsu4N8aj5NyxPBrYhlwWnpGo+PBxS3jftU+BPYNx7f jNuGz82MISkVDgbHAbwQwt8DGgoGmPls0FNXwN+mIsc/RmZDJcclgyFe5c9N8SrGPFRimBM4 Z0vLpEaB2MTHAY+xXMdldhd0wAwxYbwa5pz7YbPDB3FIVAMhyi3y2lD3su7tsKk8TVvNQIna 01LriEjHFIbwpINYtWRYazsitrkpIbqOL92ZGvxMFTJTbtJSSpzBXPIQUmFTk0NqEPqHUqRr KnFmZ16g/jKCNH4B1ZEEN1n2nfISKXx3KNYc6YNr7c/5tsp0cmt98HlvKcjLpZF+VWRyXn6+ S6xf0WLdeP0dyObmiMHpcCOhU+EzduhTl5ZatL2Cpb9MzrD3j9FD/+D4fid89Dz5eaobvgaY ufpEHBV5ujHCguG/AAMAYwzQsA0KZW5kc3RyZWFtDWVuZG9iag02MCAwIG9iag08PC9GaWx0 ZXIvRmxhdGVEZWNvZGUvTGVuZ3RoIDczMD4+c3RyZWFtDQpIiYRVy27bMBC8+yu2NxKIFFGS ExsJAjSPQ1MEyEE3IwfGoqUULeXGcQ3368t90GIebS8CJe4uZ2dmqeZ2UkBmclPV0FxPFmqn M1OpJ52dqO/8mCt4dNA9hcWvsGmU87B+1oaDBl7N1UvYK9Re1yqnFeiHBos3SwiPHczzsprj sp2Evebb5ObuCibH93B+fnx39eUaqhIuLi6vw8esyIuipsy8MJiccTYCredYYqFuCctAIDxY baYKWtfS+5L26FuANVeuBYbn7A89C8eHRNxcCdKw3FAKR9lxb6VDWetbiuH2B+Jl66WiQwI2 oW0KYfr6gcs5QsVpyNFGVsibp71I40ZX9MJFCZxAxvLWM8RaUBHp1AnH9C453KIGsHbDmoA6 sJ3DFpKaAx5FXbYMznqmzMHPrUXJCdRLaH5P31PGgh+oMDZEr46lntZ5XVWs8CI2IsJ4t9Ol 5LmRSkcfYhNsGJZ+BwvqfNh2PcQNtoKck3ohYUGtB6YBdQm1B1IQSznRxOILt9sTOJTXtdvX pgn1sMHBH40eSWYjQqZ8ODAzcJHt6CgQoR9JR3ErxW5pY5n4ToyB5/oufzM/lcnL4t8DVH04 QExnJvlx1t+PEJ133DQ1BJ5XkzI3Zh7VvAzq2f3yWWeB2M1LaOUohhsOH4EGeU5nKc7341zk 5ayOtQfPY8JK0byKn9ldI/NAvu/wgw0sZaVRv3UVHP+KPuFduorWoCO4DFUh56Uz0SfKw6iM 9cn1QCH7WD/eecCjHYbuBgH7eCOKxxhIB1fIIRnxDBvl2B4nr1YxBut/pQ1xMU9M2xH+sxEc Z8schkfXy3XkpRR1dqezU6ndjl18itJVb6Wbmvz0xKTaFf+9teuD6aazfDYzicP+kjGNGaN7 PrDIYcDxr/QZ26U7EnmWWxr9QhY50gdtWWc7/rq45Z6JJ+rEGHh9yjxKPX7hJwZ2YbIT3Vf6 Af4IMAAwC6WnDQplbmRzdHJlYW0NZW5kb2JqDTYxIDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURl Y29kZS9MZW5ndGggNTYyPj5zdHJlYW0NCkiJhFNNb9swDL37V3A3C6gVyZbtGCh66MdhBQrs IGCHYQc1dpBuqR0syYL8+5GiYmnBPm42xffIRz7a50wvpTI1KLB99iUfQOg6X4lC69z9EBoD w0ihnz70Joomp/A0votlPowHUXQ5rPFR5RM9JPgtJXsEUxx81l5U+Y1HUYyRjsh8ZoLZC0OJ +Axu7CFtokOWuZ3DXLwJYcqndHAexUG3pqKDj58wUgXId4b8TyW1gZFrpdQkvzp+Wos6kRef j/2FJtElvtrnrAgbKLTUpuM1+Mlz34NfA090M9A3x2+wEOw2Zxznfh7LKvbvxqj9aoCTn/Ix WcqQEEvf1cJaAxrsOkNnrMgeJ9CtrKuWrYLN22/Z08sDZItPcHu7eHn4+AhVA3d3948UtFYz vlBSKeNJpNLEU+hGtrr5TfFnrF3nm2gC3tQ9jdLR5xnHxvKS4RxoCG6386FUfCKNDEerc9uZ myCUEd0WHE98/n8aaU0QvAWRbhPdB7spjjhYcYaHDU/jFvPPkWsTMBVeVig69h7Tu9Bwsrek rmOtfJPzsoZZBsqvcn+OQ58cZMMkbgRfBD3oPUc7qWkXppWmqebzJ/rkCDz9SZSXk4nrIVng d/Z6ETIFg7vQCWnp2U2Jh5Rs2yb1kJLl0qSBaJjYJiY13aVLKwq0ihvD6Z65+vHDxbjltXGN 7Mr6n75tE98qhusSEfhdQqUaMJ002NP73wiWfzB+qWXHJr+q/GQz+CXAAICZPxYNCmVuZHN0 cmVhbQ1lbmRvYmoNNjIgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAyNTc0 L04gMz4+c3RyZWFtDQpIiZyWeVRTdxbHf2/JnpCVsMNjDVuAsAaQNWxhkR0EUQhJCAESQkjY BUFEBRRFRISqlTLWbXRGT0WdLq5jrQ7WferSA/Uw6ug4tBbXjp0XOEedTmem0+8f7/c593fv 793fvfed8wCgJ6WqtdUwCwCN1qDPSozFFhUUYqQJAAMKIAIRADJ5rS4tOyEH4JLGS7Ba3An8 i55eB5BpvSJMysAw8P+JLdfpDQBAGTgHKJS1cpw7ca6qN+hM9hmceaWVJoZRE+vxBHG2NLFq nr3nfOY52sQKjVaBsylnnUKjMPFpnFfXGZU4I6k4d9WplfU4X8XZpcqoUeP83BSrUcpqAUDp JrtBKS/H2Q9nuj4nS4LzAgDIdNU7XPoOG5QNBtOlJNW6Rr1aVW7A3OUemCg0VIwlKeurlAaD MEMmr5TpFZikWqOTaRsBmL/znDim2mJ4kYNFocHBQn8f0TuF+q+bv1Cm3s7Tk8y5nkH8C29t P+dXPQqAeBavzfq3ttItAIyvBMDy5luby/sAMPG+Hb74zn34pnkpNxh0Yb6+9fX1Pmql3MdU 0Df6nw6/QO+8z8d03JvyYHHKMpmxyoCZ6iavrqo26rFanUyuxIQ/HeJfHfjzeXhnKcuUeqUW j8jDp0ytVeHt1irUBnW1FlNr/1MTf2XYTzQ/17i4Y68Br9gHsC7yAPK3CwDl0gBStA3fgd70 LZWSBzLwNd/h3vzczwn691PhPtOjVq2ai5Nk5WByo75ufs/0WQICoAIm4AErYA+cgTsQAn8Q AsJBNIgHySAd5IACsBTIQTnQAD2oBy2gHXSBHrAebALDYDsYA7vBfnAQjIOPwQnwR3AefAmu gVtgEkyDh2AGPAWvIAgiQQyIC1lBDpAr5AX5Q2IoEoqHUqEsqAAqgVSQFjJCLdAKqAfqh4ah HdBu6PfQUegEdA66BH0FTUEPoO+glzAC02EebAe7wb6wGI6BU+AceAmsgmvgJrgTXgcPwaPw PvgwfAI+D1+DJ+GH8CwCEBrCRxwRISJGJEg6UoiUIXqkFelGBpFRZD9yDDmLXEEmkUfIC5SI clEMFaLhaBKai8rRGrQV7UWH0V3oYfQ0egWdQmfQ1wQGwZbgRQgjSAmLCCpCPaGLMEjYSfiI cIZwjTBNeEokEvlEATGEmEQsIFYQm4m9xK3EA8TjxEvEu8RZEolkRfIiRZDSSTKSgdRF2kLa R/qMdJk0TXpOppEdyP7kBHIhWUvuIA+S95A/JV8m3yO/orAorpQwSjpFQWmk9FHGKMcoFynT lFdUNlVAjaDmUCuo7dQh6n7qGept6hMajeZEC6Vl0tS05bQh2u9on9OmaC/oHLonXUIvohvp 6+gf0o/Tv6I/YTAYboxoRiHDwFjH2M04xfia8dyMa+ZjJjVTmLWZjZgdNrts9phJYboyY5hL mU3MQeYh5kXmIxaF5caSsGSsVtYI6yjrBmuWzWWL2OlsDbuXvYd9jn2fQ+K4ceI5Ck4n5wPO Kc5dLsJ15kq4cu4K7hj3DHeaR+QJeFJeBa+H91veBG/GnGMeaJ5n3mA+Yv6J+SQf4bvxpfwq fh//IP86/6WFnUWMhdJijcV+i8sWzyxtLKMtlZbdlgcsr1m+tMKs4q0qrTZYjVvdsUatPa0z reutt1mfsX5kw7MJt5HbdNsctLlpC9t62mbZNtt+YHvBdtbO3i7RTme3xe6U3SN7vn20fYX9 gP2n9g8cuA6RDmqHAYfPHP6KmWMxWBU2hJ3GZhxtHZMcjY47HCccXzkJnHKdOpwOON1xpjqL ncucB5xPOs+4OLikubS47HW56UpxFbuWu252Pev6zE3glu+2ym3c7b7AUiAVNAn2Cm67M9yj 3GvcR92vehA9xB6VHls9vvSEPYM8yz1HPC96wV7BXmqvrV6XvAneod5a71HvG0K6MEZYJ9wr nPLh+6T6dPiM+zz2dfEt9N3ge9b3tV+QX5XfmN8tEUeULOoQHRN95+/pL/cf8b8awAhICGgL OBLwbaBXoDJwW+Cfg7hBaUGrgk4G/SM4JFgfvD/4QYhLSEnIeyE3xDxxhrhX/HkoITQ2tC30 49AXYcFhhrCDYX8PF4ZXhu8Jv79AsEC5YGzB3QinCFnEjojJSCyyJPL9yMkoxyhZ1GjUN9HO 0YrondH3YjxiKmL2xTyO9YvVx34U+0wSJlkmOR6HxCXGdcdNxHPic+OH479OcEpQJexNmEkM SmxOPJ5ESEpJ2pB0Q2onlUt3S2eSQ5KXJZ9OoadkpwynfJPqmapPPZYGpyWnbUy7vdB1oXbh eDpIl6ZvTL+TIcioyfhDJjEzI3Mk8y9ZoqyWrLPZ3Ozi7D3ZT3Nic/pybuW65xpzT+Yx84ry duc9y4/L78+fXOS7aNmi8wXWBeqCI4WkwrzCnYWzi+MXb1o8XRRU1FV0fYlgScOSc0utl1Yt /aSYWSwrPlRCKMkv2VPygyxdNiqbLZWWvlc6I5fIN8sfKqIVA4oHyghlv/JeWURZf9l9VYRq o+pBeVT5YPkjtUQ9rP62Iqlie8WzyvTKDyt/rMqvOqAha0o0R7UcbaX2dLV9dUP1JZ2Xrks3 WRNWs6lmRp+i31kL1S6pPWLg4T9TF4zuxpXGqbrIupG65/V59Yca2A3ahguNno1rGu81JTT9 phltljefbHFsaW+ZWhazbEcr1FraerLNua2zbXp54vJd7dT2yvY/dfh19Hd8vyJ/xbFOu87l nXdXJq7c22XWpe+6sSp81fbV6Gr16ok1AWu2rHndrej+osevZ7Dnh1557xdrRWuH1v64rmzd RF9w37b1xPXa9dc3RG3Y1c/ub+q/uzFt4+EBbKB74PtNxZvODQYObt9M3WzcPDmU+k8ApAFb /pi4mSSZkJn8mmia1ZtCm6+cHJyJnPedZJ3SnkCerp8dn4uf+qBpoNihR6G2oiailqMGo3aj 5qRWpMelOKWpphqmi6b9p26n4KhSqMSpN6mpqhyqj6sCq3Wr6axcrNCtRK24ri2uoa8Wr4uw ALB1sOqxYLHWskuywrM4s660JbSctRO1irYBtnm28Ldot+C4WbjRuUq5wro7urW7LrunvCG8 m70VvY++Cr6Evv+/er/1wHDA7MFnwePCX8Lbw1jD1MRRxM7FS8XIxkbGw8dBx7/IPci8yTrJ uco4yrfLNsu2zDXMtc01zbXONs62zzfPuNA50LrRPNG+0j/SwdNE08bUSdTL1U7V0dZV1tjX XNfg2GTY6Nls2fHadtr724DcBdyK3RDdlt4c3qLfKd+v4DbgveFE4cziU+Lb42Pj6+Rz5Pzl hOYN5pbnH+ep6DLovOlG6dDqW+rl63Dr++yG7RHtnO4o7rTvQO/M8Fjw5fFy8f/yjPMZ86f0 NPTC9VD13vZt9vv3ivgZ+Kj5OPnH+lf65/t3/Af8mP0p/br+S/7c/23//wIMAPeE8/sNCmVu ZHN0cmVhbQ1lbmRvYmoNNjMgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAy MTYvTiAxPj5zdHJlYW0NCkiJYmBgnOHo4uTKJMDAkJtXUuQe5BgZERmlwH6egY2BmQEMEpOL CxwDAnxA7Lz8vFQGDPDtGgMjiL6sCzILUx4vYE0uKCoB0geA2CgltTgZSH8B4szykgKgOGMC kC2SlA1mg9SJZIcEOQPZHUA2X0lqBUiMwTm/oLIoMz2jRMHQ0tJSwTElPylVIbiyuCQ1t1jB My85v6ggvyixJDUFqBZqBwjwuxclViq4J+bmJioY6RmR6HIiACgsIazPIeAwYhQ7jxBDgOTS ojIok5HJmIEBIMAAScY4Lw0KZW5kc3RyZWFtDWVuZG9iag02NCAwIG9iag08PC9CaXRzUGVy Q29tcG9uZW50IDgvQ29sb3JTcGFjZSA2OCAwIFIvRmlsdGVyL0RDVERlY29kZS9IZWlnaHQg NzgvTGVuZ3RoIDc2NzEvTmFtZS9YL1N1YnR5cGUvSW1hZ2UvVHlwZS9YT2JqZWN0L1dpZHRo IDQwMD4+c3RyZWFtDQr/2P/uAA5BZG9iZQBkgAAAAAH/2wCEAAwICAgICAwICAwQCwsLEBQO DQ0OFBgSExMTEhgUEhQUFBQSFBQbHh4eGxQkJycnJyQyNTU1Mjs7Ozs7Ozs7OzsBDQoKDAoM DgwMDhEODg4RFA8PDw8UFBAREhEQFBQTFBUVFBMUFRUVFRUVFRoaGhoaGh4eHh4eIyMjIycn JywsLP/AABEIAE4BkAMBIgACEQEDEQH/xAFCAAABBQEBAQEBAQAAAAAAAAADAAECBAUGBwgJ CgsBAAEFAQEBAQEBAAAAAAAAAAEAAgMEBQYHCAkKCxAAAQQBAwIEAgUHBggFAwwzAQACEQME IRIxBUFRYRMicYEyBhSRobFCIyQVUsFiMzRygtFDByWSU/Dh8WNzNRaisoMmRJNUZEXCo3Q2 F9JV4mXys4TD03Xj80YnlKSFtJXE1OT0pbXF1eX1VmZ2hpamtsbW5vY3R1dnd4eXp7fH1+f3 EQACAgECBAQDBAUGBwcGAjsBAAIRAyExEgRBUWFxIhMFMoGRFKGxQiPBUtHwMyRi4XKCkkNT FWNzNPElBhaisoMHJjXC0kSTVKMXZEVVNnRl4vKzhMPTdePzRpSkhbSVxNTk9KW1xdXl9VZm doaWprbG1ub2JzdHV2d3h5ent8fX5/f/2gAMAwEAAhEDEQA/AOy63125lzsPCds2aWWDmf3W rAe59ji97i5x5LjJSc4vcXuMlxJJ8zqmWpjxxxRAA8z3eY5nmcnMzMpk1fpj0AUkkknsCkkk klKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklPRfVLjK+LP+/LoVz31S 4yviz/vy28y52Ni25DQCa2FwB4MBZ3Mi80gOtfkHovhxEeTxyOwEifpKSZJYlHU+u5FTb6cO tzHiWnfEj5uWld1DDxjsybmVvDQ4tcdYKZLFKJrQntE8W3kzY+Zx5ImWsY6eqYMAb7GVW2Uk AZuIcf7ULWej/pJ08FWyeqUnFutwLarLKQCdzoaJManRAQkTsd6+q+WbHEWZD5eIAHUgC9B1 dBJVvtlNONXkZdjK97QSZ9pJE+3xQM7qtdHTnZ2IWXgENbB0kmNYSEJEgAbmvC0SzY4RMpSA 4Y8ZH6XD5bugkquL1DEyiK6rmPsDQXNadR4oOJ1BzhlPzH0sros2BzHTA49/gUuCWum1addV e9j9NSBEr1Go9Is2ejoJKtT1HByLPRovZY+J2tOqV3UcHHs9G69jHxO1x1Q4ZXXCb7Un3cdc XHHhursVfZspKFdjLmCytwcxwlrhwQs3L6rlHLdg9NoF9tYmxzjDW+XZGMDIkDpvelIyZoYo iUiTxGo8I4jInsBu6qSo4OfdbRZZn0nFNRhxcYYfMEo2P1DCy3FmNcyxw1IadUjCQvTbcjUf amOaEhGpUZ/KJemR/wAE6thJVR1Tp7rBUMisvc7aGg6z4KTuoYTb/srrmC46bCdZ8EOCX7p7 7K93H+/Hetxv282wkkkgvUkkkkpSSSSSlJJJJKfPEWjEysnccep9u36W0TEoS6L6pfRyviz8 jlp5pnHAyAuq/N5jlMA5jNHFIkCV6jwBLj/srqf/AHFt/wA1QtwM6lu63HsY3xLTC63qHWMX ptjK72vJe3cNgB0mO5CngdTxeotcccmWRua4QRKg+85RHjOMcPfVv/6M5YzOIcwfcH6Ol99t HiElsfWXDqxsqu6loaLwS5o43NjX5yslldljttbXPd4NBJ/BWYTE4CY0Bc7PhlgyyxHUxNad eoYpIz8LMrbusosa0cksMfkQ212WfzbHPjnaCfyIgg7ELDCQNGJB7EMUk7mPYYe0tPg4EflS a1zjtaC4nsBJ/BFFG6WSUnVWsEvY5o4lzSPyp6qbrzFNbrCOdgJ/IhY3Vwm6o32YJIluPkUf z9T6543tI/KotqteJYxzh4taSPwSsb2nhkDRBvtTFJErx8i1xZVU97m8hrSSPiovY+txZY0s cOWuEEfIpWNrVwkCyDXdiki1YuTeN1NNlg8WtJChZVbU7Zax1bvBwIP4pWLqxajGQFkGj1rR ikkjNws17d7KLS3xDD/ckSBuQFRjKXygnyFoUWnFysgF1FT7ADBLATBQ9jw7YWndxtjWfgum +qzHsxrw9rmn1B9IEfmjxTM2T2oGQo7M/J8uOZzDHImIIOo8A81bVZS813NLHjlrhBEqK1Pr BVc/qtrm1vcCGahpI+iPALLToS44xl3AP2sefH7WScNajIxBPWi9F9UuMr4s/wC/LW6r/wAm 5P8AxTvyLJ+qXGV8Wf8Aflvua1zS1wBB0IOoVHmDWcnsR+QdzkI8fIwjtxCY+2Unmuj5PRsJ leRbkPbkbC17DJaJ8AGq1bXRlfWSsWtFjDjh4DhI7wYK1vsuH/oav81v9yJ6VYeLAxu4CA6B MeEoSzAylICVyBGp2vtorHycxCGORx8MJxn6YkXw73cju8vAZ0jqdbdGMyAGjw9wWhn4uNjd AtdTW2svqr3ECJ1bz961jRRtc01s2u1cNognxKkWVvZsc1rmEcEAiPgkc9kGjpPiOu/y/wAE x5HhjIGQJOI4omvlsz1/5zz2Uaa8vptueJxPs7QJEtD47j7ks9/Tn9FyXdObsZ6zdxggEyNR PZdC6mp7PTexrmfukAjTyTehSa/SLG7B+ZA2/ckM49Jo+kjS9NDe3dR5KR9wCUKyRIsxuYJg I/Nfy6OJZRRjdW6Z9nY2vew7tuk6d/vVaR+zusn/AIf/AL8ulNVZc1xa0uZ9EkCR8E3oUw5v pth+rhtEE+aQz1Vgmq6/uy4kS5G+LhkIiXHQrbjxjH+YtwLcejHs6NZSxtbrC3cW6F0hvP3q ta/Dr/alWcB9qc8mkuEkzxtK6j06Xke1jvSMN0B2ny8FV6l037ewNZYKSdHu2Bzi3wBOoToZ xYErHeV6/NxdlmXkpCMpY+GR6QoV/N+2eoF9bZdH/wCS8b/i2rMqyquk9YzBmyxmSRZXZBI7 6afFbdFLMellFf0K2hrfgE9lNVw22sa8Ds4A/lUYyASnYuM78DvbYlgkYYeGQjkxVRI4o/Lw kHbu5GZ1luR0vIyMSouaxwrmxstIOhdHdUMFzD1rDNd9d5LXSamCsCWu9pjldMGVhvpgANiN oAiPgoMx8ZhBrrY0t42tAIlOjmjGMoiO99e4rsx5OUyZJ45yyA8JiToRrGXFoBKtdtbcfoGL j2jJvexrrWZDtjyJLY1EfesljGursxsm+qi02nd6lbjZunneOy7FlddcitobJk7QBJ+SY0Uu sFrq2F44cWgn70RzFSkaOtVrtXmCtn8P4seOAlG4cXFoalx/3ZA39WVYcGNDjuIABPiY5Ukk lA3tlJJiQCB3PCdJSkkkwIMx20SUukkkkp88XRfVL6GV8WfkcudXRfVL6OV8WfkctHmv5mX0 /MPO/DP914/8L/oydDqXRaup2MssscwsbtAbHjPdT6f0rH6Wx5pLnvf9JzuTHYAQs36xdRzM LIpZjWmtrmEkCNTPmCodB6t1DLzPs97vWr2kudABbHGohVvbynDfEOCr4f5B0vf5WPOGHtH3 jLh46sWR59vBoddzLcvMiyt1LahtYx4h2v5x+K08PrfScLAZ6VZZZw6lolxcO5ceR5o/1iwm 5NWOWwLTc2prvJ/P96O3G6b0XF9VzQA2A6wjc9xKdLJjlixx4ZXekYn+W7HDBnxcznn7kAKs 5cgugew02rvTDp/Xas+70DS+pzgS0u1aY80OwV9M6xVZWAyrOBZY0aAWD6LvnKLhddxc/JGN RXYCQXbnAAQPmVS+thIZikaEOeQfOGpsYfreDhMBMEGJN9Cy5Mtcqc3uDNLFMGMxHh/SiCPq DSb6z4frYjcpo91B939R2h/FU/qribrrcxw0rHps+LtT+C2cK5nVOmte/UWsLLB5/RcmwMZn Sen+m8j9GHWWO8e8/ch7pjilhPzcVfTr/LxUeWjk5rHzYr2zDjJ/rAen8D+DU6hV+1ep19PJ Ipxm+rfHcu+i37lcyczA6PSxr4radGVsGpjyWZ9W8n7TlZttn85aWv8AlLtPkq31pqtbmV3O k1urDWnsCCZH4pwx8WSOGRqMR9pqysPMcHLZOcxxEp5JkWf0YiXDH8B9pdR3XOlX4dlrzuaN HUvHuJPA2pvq25rsB7mN2NNzy1kzA00lcmGPcw2BpLWQHOA0E8SV1X1X/wCTD/xrv4J2bDHF ilwkm5D6MfJc3k5rmYe5GI4cctQN9Rq2crqvTum2Gq0xY873NY2Tr3dCo4uHi9X6lf1N36TH aWtra4QHODRMg+CxesEnqmTP7/8AALofqy5p6WGt5a94d8ZlCWP2cQnEnikACfCWq7FzB5zm jgyRj7eKUpRFbmHpF/ba2d9YcXAvOKys2OZo7aQ0N8lYacLruDuiWOka/SY4fxQ8nrXTMa99 F4cLGH3ewlJvXunCo3MbZ6TTtLxWdoJ81HwHhiYY5CWnrvf8GwMsOPJHLzGKcDYOKgOGvG+n W3P+r/TGNycl+Q0PfjP9NoOoB/e/uWjn9co6fkCi6qwiATYB7dfDxWRV1r0eq33YrHXU5JBL AIcSByB4rWo670vNIpLtrn6BlrYBJ7TqE/LCZnxzgZRMRoOmn4asPK5cMcXs4cscUxOQ4pC+ P1GuouxXVz+qdV6c/Nxn1NFhpsbY+9vZo12jxWzgdRx+osfZj7oYdp3CNYlZP1g6RjV4zs3G YKnsI3tbo1wJjhT+qf8ARr/+MH/UhKcccsAnG/T6dfNOHJnx86cWTgrIOP0joBp+WttzL67g 4V7sa7fvbBO1sjUSuOcZcT4klaP1i/5Wu+DP+pCzVY5fHGEBIXcgCXO+IczPNlljlVYpzjGh 41r9j0X1S4yviz/vy1+qf8nZP/FO/Isj6pcZXxZ/35bWdS/IxLqK43WMc1s6CSFVzmuYJ8Y/ kHU5EE8hEDUmM/zk830nC6dnbKba8gW7S51kxWY8Fq5nUs6rqA6dhUstcaw8F5I8edfJBx8X 6x4tDMel2PsrENmSfyK03Byf2wzPft2Cn03Qdd3w8E7JKJmTIxlGpcIsnX8GPBjnDFCEITxz MoDJMQEDw9e913aOX1GzO6HlG1vpW1PFdgaTH0gjYfU86q/FxMyhtdeQwCpzTJ0Gm5Rd0fMO FnY/s35N3qV66RM66Kzf0/Isyen3N27cQfpJOvAGmnkkTiox0omR8vSKr6pjHmeKOQ8fEI44 nQer9bISvT9zXRr9Q6zlYd9jWfZiyv8AMNn6Rw+A4KJl9YyWvxGYVLbDmM3ta8kQfiFUt6J1 AfaaKW0WV5Dy8W2fzgkzA0VtvTMkZHTrfbtxK9lmusxHt0SIwgR+U0D9fTpf1UJc3KUx64gy j0Hp/WVKjWvoXxetP9LL+31iu3D1e1hkGeI+aWH1Dq19lL7sRox79Wua7Vo5BdKY9Ittv6ib iBVmBoYQZII7kfFLBx+uVPppvsqGPRoS0S57QIATT7dSMeG9NydPT+j/AIS+J5kSxiZycIJF xEdayEDjvpwU18bLGJX1XJoqaHVXayXEOMka+CJR1jPF+KMzHZXTmQK3NJJkxE/enPScv7P1 Kr2bsyzdVr2mfdoiZHTcmxnTWt2zhuabZP7ob9HTyTicRJujfWzp6B/3SyMeZiBw8UREXwgC iTmld6fuaosjrOcbck4VDLKMMkWOeSDI5jXyWn0/Idl4dWS8BrrG7iBwFh9Toyumfa3V2VDG zSSQ+d8nlrR46rY6Qx1fTMZjxDhWJB89U3LGAxiUQNSKOt7a39WTlsmU8xOGQy0iTKJrhB4/ Tw104XGzc+zA6xmvqaX22VtZWOQDDSXR5AK90dlNPTH51TvVusa6y17uS5snb8karp9jesZG dYGmuysNZ3cDDQdI8kHF6ZlYdmXRUWnEyGuNYJ1a4iOI4RlOEoCINECF/wBahRH0WY8WaGU5 DEyiZ5REV8nESRP/AAtr8mszr3URRVm3Y1YxbH7C5pO6ZiQJ8lbzOp5n2x2D02htz6mb7C8w BPYcIL+kZbuiU9PGz1a7NztfbEuPMeaJl4HUas52d0xzN1zAyxtnaNJH3JH2idBEfMBvw6Ec N/S0D71GFSOQgjHKRAjxixLjEdOkuFi3rr304uQ2sNZbd6F4Myx3khn6xO9PMc2tpdQ8NoGv vBcW6/cifsR7ejPwQ4Ovc71d3A3yP4IQ+r9gtwXS3bS0faNfpEOL9NNdSiPY18Ca8hr+OyJf fvTV6wiZbaSkOD/m/OUm9/8AzhqNxgjFlwHAP50IP/OO6ftHp1/Zd+yN/wCmj97ar78C1/WB mnb6PomoifdJ8ln09DzMewUirFuo3z6trZft/dKUTikBxUSIjT7b+qZx5qBPt8QByzNgX+7w k/1d21l9UzHZdmH0+ltvos32ucY0In28eKp9P6i/B6LW+tvq3XXOYxrjySeSreT0/qFWfZl9 NdXtyGBljbPzYESPuQR0PLHS6scOY3Jx7TcwzLT5TCUTi4Yg8NExvvsbv6omOaOWcgJ8UY5B HQcIBlHh4O54R16p8Xqmac1+Hn1V1elUbXFhJ0Eeaqn6yZDQMl1NZx3EtawP/SjwJb/sUemm 7P6tkPyHMfNBrsdT9ATAgE909HRM+gigVYltbSSLrWy4jsCjw4okiYiDUdLodbpaMnNZIA4p TMeOYEqBloRw8Wm27zwW79WMrGxm5P2i1lW4s27yBMB3isJJWskBkiYk1bl8vnPL5Y5QATG9 D4gh7Z+d0mwy++hxHG5zT+VM7qvScZpIvqA8K4J+5q4pJQ/c4/vSpun4xk3GKAPfV1+p9edl ZFLsZpbVjvFjd3LnDuVtV9T6V1PH9O57AHD31WkAg/Ncckny5aEhEC48OxDDi+JZoTnKYjkG T5oy2000+j1tWV0Hp1oqx3VsdZo5zTuAA19z9VnfWXMxcpmOMa1tu0v3bTMSAsNXui+h+0az klgrAcT6kbSYgDX4powDGfd4pSMQTr10Xnnp8zH7sIY8cMkox0FcOo+jofVXJcLbsMyWuHqD wBGh+9XPrNl+jhDGaYdkGD/VbqVbbf0jAY59b6KgdTsLZPybqVy3VuoHqOWbhIraNtYPMDv8 1HCPvZvc4TGI117hsZ8n3PkvY9wTySuI4ekTv+CLBzbcDJbk1akaOaeHNPIXVU9X6Vn1bbHs bP0qroH/AFWhXHJKfLgjlomxIdQ0eV57JyoMQBOB3jJ7DIz+i4uO6kuqdW4EGqoAz8mqp0Lq HT8bDeyy1tM2vc1j3e4NMRK5pJMHKx4THikbNn6Mp+KZPcjkGOA4ImIFdD4/RtdUsZb1HItq cHMc+WuHBEI3SOqu6ZcdwL6bI3tHII/OCz0lMYRMOA6iq+xqxzzhl96B4ZcRl9v7HsHX9C6k BZa6l5A09Qhrh5awVDL6v0rCxzRTst0IFNcFvzjSFySShHKRsXKRiP0W4fiuQg8OLHGchRmB r/LzdnoOf07FtsOSwVW2H22ctDT+YOdq1zX0D1ftc4++d27cOfGJhcekjPlxORkJyjehoseH 4gcWMY5YscxE3Gxse/m73Xut0ZNJwsQ72uINlnaBrAQfq71KjCfZRku2MtgteeA4aarHSTvY gMZx60evW1h57KeYHMGuKOgH6PD2/F7S63otjxk3Px3vaNHuLSdFxjtXEjuT+VNCSWLD7V+o yut/BXN82ea4bhGHDfy9Sa3+x6L6pcZXxZ/35bmTeMbHsyHAuFbS4gcmFh/VLjK+LP8Avy1+ qf8AJ2T/AMU78iqZxecg9TH8g63Ikx5GMhuIzI+kpNBv1jD2h7MLIc08ENkFa+9sSTGk6rD6 GTTTVddns9IsIGO4gbTPxTZmLTnfWFlF0mt1AcQ0xMT4JTxwMzEekRBJOp286Vi5jMMUZyrJ LJKEYwPDCuLrceLTz1dfNzK8LFfl2AuawAw3kyYCGM9xvx6RQ8tyGeobB9FmkwVgjczonUce SWU37WTrA3BXT/yv0v8A8L/99KXsgA3qRxa+UQQj75OcokekSGLTQ6zyShLWttHb3NBgkTzE qtd1CurNpwi0l9zS7dptAaD/AHLmn1UZGBm52Q8/bG2kAF0aSBG1XcjGry8/pmPbOx+MN0GC QATEo+xEH1SJoSvTrGN6a6oPPZJgcEACTjMfVdxnPho+n0nTxp37LmV1OucZaxpcY8AJ0Q8H MZnYzMljS0Pkhp5EEjX7lh42LWauq4BLjTju31ieC0OP8FDFbXi9AtzMVxGTYwCyHSWjftmO 2iHsxoizxcUQDWlSF91w52fEJGAEBjySkOLW8cuE1prtp5vTBwMwQY5hNvaO48OVzfScfIZm Y12MxlbHN/TRcHmxpH0iznlAZg0X4nUsuzd6lFr/AEyCQBBnhL2I2Rx6Cul7mu6vv0zEEYdT xbyMRUIiVgmFnttu9Dl3huRj47sc3NtcffAIrI7mVaL2jkgduVgOsfbkdEseSXOaS4+OgQBg UZlvVrrtxdQ95rgwAfcZ/BL2RQuVUNaF/pmPdX3yQlLhhxGUvSCeGh7QybiJenJAEnQeaQIP Gq5iyx2Vi9Joy3kUWlwtMxO07RJ+CtdEqpo6pnU453VMDQ0zu/FCWDhiSZaizVaaS4d18Od9 zJCIh6ZGIsy9Vyx+58tbVpu6XUuo19MqZdaxzw92z2xpoTOvwUsvPoxMM5rvcyAWhvLt3EKh 9Zmh+LjsPDr2g/MFZVzcq2l/TLZDOmtse5373+j/ACp2PDGcYSJrU8X93+Wn1Y+Y5zJhyZYA X6B7em0yL1+ln6O+epk04t9WO+xuUQNI9gPdyul7RyQPiVzt0/Yei/8AGM/gk7Aoz+r9Rbkb iK2hzQDGu3lA4Y73wgcXS9pcPdMebyCgIicpcAAJ4R6sXGdons9GSAJPCqs6hXZnuwGNJcyv 1HP0jWIA+9c+brLej4FVzyKbLzXa6fzAdASrXT8ejH6zlY+I+Kxj+127dtJ29/JL2BES4jZA lWmnpNX/AGKHOyySxiEQIyMOKz6vXAyoCtvF39zSdsifCdVJcpi014GRQ7NqFm60bMum3duJ Om5s8Lq0zLjGMijYPX+RLPy3MHOJcUeCUSLjZJF97jH8LDBlVdQIra1gJkhoAk+Oimkko2cC tnzxJJJa7yKkkkklKSSSSUpJJJJSoCSSSSlJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSSlJJI +LTjWvH2nIFDO/tc53y2tIQJoXr9NV0YmRAFC+5ER9p0d76p1ObRkXEe17mtHntBn/qlt21M vrdVYJY8Frh4goPTxiDErGEQaI9hH8Z7qys3NIyySNEEnbr4PScpCGPloQ4oziIm5A3E6ni1 7Xbnf83+kH/tOP8AOd/5JWW4OM3IblBn6VrPTa6To0dlYSSl7v6XHsd726qx/dP8n7O4+Tg+ b9Hbr2an7MwTVbT6fsyHb7RJ9zpmeVP7DjetVfs/SUN2Vuk6N4hWEkD7nXi69+2v4Lh930r2 +lfL+8eH/nXXi4vVehjKsD8SqpjrSfVtcTI82tGklaLcDGbZTcWzbjs9Ot8nQRCspJ0jlMYg iVAGt9uv4LIQ5UZJyicZlIx4hcdJA6ad+L62grw8emy22tgDsgzadTu+/wCKhj9MwcRtjaKg 0XaPBkgjw1nTVWkk39Zr83S9/wDBv9jJ+ouP83fq4Pl/w6/7pp43SOn4lvr49IZZ2dJMT4SV NvTsNlV1La4ZkEutEn3E891ZSSPu36uO9N7+i2H3bhHB7XD6q4eGtvVt4btYdOxAaHenrjCK dT7fxTswMWv19jI+0ybtT7pnz81YSS/Wf1vx7/x/FcPYvT27/wAH93/vPwadvS8N+GMP0mmu uTW0k6H4zKrdF6Xbgm664MY+6AK65LWNHaStVJG8ohIES4SfVd73/FZwcscuOQMPciPQAYgk EaabkVt0Q5OJj5bWtyG7wxwe0SRDh30SsxMe0Wh7AfXAbb23AaBGSTRx0Kuun2/xZD7Vy4uC /wBK6vY7/wCDf0azunYbmU1mv24xBqEn2kfNSZhYzLrb2siy8RY6TqAjpJeuv0q+vf8Aigex Yr270r5f3dP+bt4NT9l4P2X7F6Q9CdwYSTB8QZlNj9J6fiOc6ioNLm7Haky09jJVxJO/W0fn q/Vvv4rf6LxQ/muKhwfLfD04fDyaFXROmU2i6ugBzTLdSQD5AmFfSSTZ8d+viv8ArX+1dh9m j7HBV6+3w1f+CpJJJNZX/9kNCmVuZHN0cmVhbQ1lbmRvYmoNMSAwIG9iag08PC9GaWx0ZXIv RmxhdGVEZWNvZGUvRmlyc3QgMzIwL0xlbmd0aCA2NjQvTiA0Mi9UeXBlL09ialN0bT4+c3Ry ZWFtDQpo3tSUTU/cQAyG/4qP7Skz9uRLQisBLWpLC6vd7Qn1ECCClCVZhUSCP9/2nTh8VLS+ 9VBpFWviN4/H73g2I0c5FUIFSeqoJMkL8o7SPJD3VISMPFPpcvJCZSjJBypLSFKoBEn8XIYs fq7EAynPiFD6NCd2iAWiBwciZsSAKIgoxSgjriQGT4IjBk9yTxERHBODF0SIY+UskIAXypQE vBRiAS9FHUh8WqAJ8DIUF/AyNDRtsfBoC1tEJwJeHoQEvBw9oqQvXEoBvALiAF6BfiDxBfoM 4JUoHsArM0fRj7L0FFtwaGZvLzmGgY5WybLq63bY9HUNM39/cVLfD8f1A3Gy6rb1l2oHo6Ni 87Crk/XQjxeTbNV1w2IRkWewGYJo8xQyDbmGQsPEiA5PwWtgDaJBKawUVgorhZXCShGliFJE KaIUUYooRea9zBKFicJEYUFhQWFBlUFhQWFBYUFhYaJ8S5Y0rZN1sq4v1IqT8fbuzMWRnKrG oYzSKbfftt1QDU3XJutd1SaH11U/JEfN1djXybumuuqr26dl3+0Oq93j8n17iW/r5CQ+juD7 8+pju23aen1d4Wxm9ek4xHdaBefV3NTdOMzL8fzuom92T8td3b98scHZH3T3yT629rW9rPsn 0mJx1o7bbbxZU2//yZHrCfyTAZhGnzOE0/PvlOXPl+T04NNqsYgTonWT5RWlPA/L56a9me9N /B+ZbcmjOn8lXurobIc3P36+xeX1kRleyV5MwSN6PigLe0zyx+wHntPB/Dg1s5mZzc1sYWZL M+udnfZ2mu202GnbMG875m3LvO2Zt03ztmtsu8a2a2y7xrZrbLvGdmNsNyZ2Y2I3JnZjYjcm dmNij4PY4yD2OMhfXfslwAA+HBacDQplbmRzdHJlYW0NZW5kb2JqDTIgMCBvYmoNPDwvTGVu Z3RoIDQwMjcvU3VidHlwZS9YTUwvVHlwZS9NZXRhZGF0YT4+c3RyZWFtDQo8P3hwYWNrZXQg YmVnaW49Iu+7vyIgaWQ9Ilc1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCI/Pgo8eDp4bXBtZXRh IHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJBZG9iZSBYTVAgQ29yZSA0LjIu MS1jMDQxIDUyLjM0Mjk5NiwgMjAwOC8wNS8wNy0yMDo0ODowMCAgICAgICAgIj4KICAgPHJk ZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50 YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAg ICAgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIj4KICAgICAgICAg PHhtcDpNb2RpZnlEYXRlPjIwMTEtMDItMDdUMTI6MTM6MzctMDU6MDA8L3htcDpNb2RpZnlE YXRlPgogICAgICAgICA8eG1wOkNyZWF0ZURhdGU+MjAxMS0wMi0wN1QxMjoxMzozNy0wNTow MDwveG1wOkNyZWF0ZURhdGU+CiAgICAgICAgIDx4bXA6TWV0YWRhdGFEYXRlPjIwMTEtMDIt MDdUMTI6MTM6MzctMDU6MDA8L3htcDpNZXRhZGF0YURhdGU+CiAgICAgICAgIDx4bXA6Q3Jl YXRvclRvb2w+QWNyb2JhdCBQREZNYWtlciA5LjAgZm9yIFdvcmQ8L3htcDpDcmVhdG9yVG9v bD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRm OmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29t L3hhcC8xLjAvbW0vIj4KICAgICAgICAgPHhtcE1NOkRvY3VtZW50SUQ+dXVpZDo0NzJkOTlj ZC1mZGUyLTQ1ZDctYWIxZS04ZjdlY2FhYjhjNDk8L3htcE1NOkRvY3VtZW50SUQ+CiAgICAg ICAgIDx4bXBNTTpJbnN0YW5jZUlEPnV1aWQ6ZjkwMjY1ZTQtNTRhNC00YjFjLTk2ZDQtZjVl OTU4N2ZiNjViPC94bXBNTTpJbnN0YW5jZUlEPgogICAgICAgICA8eG1wTU06c3ViamVjdD4K ICAgICAgICAgICAgPHJkZjpTZXE+CiAgICAgICAgICAgICAgIDxyZGY6bGk+NTwvcmRmOmxp PgogICAgICAgICAgICA8L3JkZjpTZXE+CiAgICAgICAgIDwveG1wTU06c3ViamVjdD4KICAg ICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0 PSIiCiAgICAgICAgICAgIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMv MS4xLyI+CiAgICAgICAgIDxkYzpmb3JtYXQ+YXBwbGljYXRpb24vcGRmPC9kYzpmb3JtYXQ+ CiAgICAgICAgIDxkYzp0aXRsZT4KICAgICAgICAgICAgPHJkZjpBbHQ+CiAgICAgICAgICAg ICAgIDxyZGY6bGkgeG1sOmxhbmc9IngtZGVmYXVsdCI+IDwvcmRmOmxpPgogICAgICAgICAg ICA8L3JkZjpBbHQ+CiAgICAgICAgIDwvZGM6dGl0bGU+CiAgICAgICAgIDxkYzpjcmVhdG9y PgogICAgICAgICAgICA8cmRmOlNlcT4KICAgICAgICAgICAgICAgPHJkZjpsaT5KdWxpZSBH cmFjZTwvcmRmOmxpPgogICAgICAgICAgICA8L3JkZjpTZXE+CiAgICAgICAgIDwvZGM6Y3Jl YXRvcj4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24g cmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOnBkZj0iaHR0cDovL25zLmFkb2JlLmNv bS9wZGYvMS4zLyI+CiAgICAgICAgIDxwZGY6UHJvZHVjZXI+QWRvYmUgUERGIExpYnJhcnkg OS4wPC9wZGY6UHJvZHVjZXI+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICAgICA8cmRm OkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpwZGZ4PSJodHRw Oi8vbnMuYWRvYmUuY29tL3BkZngvMS4zLyI+CiAgICAgICAgIDxwZGZ4OlNvdXJjZU1vZGlm aWVkPkQ6MjAxMTAyMDcxNzEzMzA8L3BkZng6U291cmNlTW9kaWZpZWQ+CiAgICAgICAgIDxw ZGZ4OkNvbXBhbnk+QmF5Y3Jlc3QgQ2VudHJlIEZvciBHZXJpYXRyaWMgQ2FyZTwvcGRmeDpD b21wYW55PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1w bWV0YT4KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAog ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg IAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAg IAo8P3hwYWNrZXQgZW5kPSJ3Ij8+DQplbmRzdHJlYW0NZW5kb2JqDTMgMCBvYmoNPDwvRmls dGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDUvTGVuZ3RoIDUwL04gMS9UeXBlL09ialN0bT4+c3Ry ZWFtDQpo3jKxUDBQsLHRd84vzStRMNT3zkwpjjY1AgoGxeqHVBak6gckpqcW29kBBBgA6J8L 5Q0KZW5kc3RyZWFtDWVuZG9iag00IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9GaXJz dCA1L0xlbmd0aCAxODgvTiAxL1R5cGUvT2JqU3RtPj5zdHJlYW0NCmjefM3BCoJAEIDhV5lb eihXK8QIwZSESBAKOo+7Ey2VE9N68O1boUOn7j/fv8pAwXYbFYO7sQSH4WEJakFNYVTy84X9 GOxw1EJvByX1Tgj2LFCTWHRiNZQoUyuEznJfoaOg2iQqjlWi0jiJl8t0rtYzpWbfym8KLdyh g7baN3gngWyh4OrZC4sJo4bNf6cVNoMmDxnuaGLgaDtBGScpjE48iCbP2Ksl8+uk3vHB2boH BRDm+UeAAQAQx0y9DQplbmRzdHJlYW0NZW5kb2JqDTUgMCBvYmoNPDwvRGVjb2RlUGFybXM8 PC9Db2x1bW5zIDQvUHJlZGljdG9yIDEyPj4vRmlsdGVyL0ZsYXRlRGVjb2RlL0lEWzw3QzNE RkEyOEUwMjI3ODRBODk4NEQxRTQ5QjQ2NzI1QT48ODg2RDFFNUU1MkU1NTM0MTk4QjgwQjQw QjhBOTQ5RDE+XS9JbmZvIDQ5IDAgUi9MZW5ndGggNTAvUm9vdCA1MSAwIFIvU2l6ZSA1MC9U eXBlL1hSZWYvV1sxIDIgMV0+PnN0cmVhbQ0KaN5iYgACJkbfIwxMDMw/gIQAB5Bg7AMRkkCJ edOALAYGxmFEMF0HEowMAAEGAOdDBWsNCmVuZHN0cmVhbQ1lbmRvYmoNc3RhcnR4cmVmDQox MTYNCiUlRU9GDQo --part-LiZWzTlDWILBAyw1ZNVmY89w4C3iG6ZWd5QknrWA8bCBN-- ========================================================================Date: Mon, 7 Feb 2011 21:11:03 +0000 Reply-To: Lizanne Schweren <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lizanne Schweren <[log in to unmask]> Subject: VBM in large heterogeneous dataset Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all, I am currently integrating structural MRI data from collaborating sites from a.o. Europe and Asia, in order to create a large (Â±1000 subjects) dataset for VBM analyses. I have some questions regarding how to deal with such large and (morphologically) heterogeneous datasets in SPM8. Does anyone have experience segmenting brains from Asian participants? In the 'new segment' module, one can choose to warp to European (default) or Asian (or other) brains. Would it be beneficial to segment the Asian brains seperately from the European ones, and than pool all data at a later stage (e.g. in the Dartel pipeline)? And would that not conflict with the tissue probability maps that are selected by default in SPM? Second question regards Dartel (create template). Would it be beneficial to increase the number of iterations for the template, as a way to deal with the heterogeneity in the dataset? Does anyone have experience and/or specific setting recommendations in dealing with such large and heterogeneous datasets? Thanks. Lizanne ========================================================================Date: Mon, 7 Feb 2011 21:36:33 +0000 Reply-To: Lin Wang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lin Wang <[log in to unmask]> Subject: Re: 'NaN' in cluster level p value Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all, I got the same problem after running the 2nd-level statistical analysis. The cluster-level p values are NaNs, and I get the following warning when I click "whole brain" to show all the clusters: Warning: Returning NaN for out of range arguments > In spm_Pcdf at 88 In spm_P_RF at 106 In spm_P at 48 In spm_list at 487 I searched the documents, but I couldn't find a clear answer for it. Could anyone help me with it? Thanks very much. Best, Lin ========================================================================Date: Tue, 8 Feb 2011 03:07:15 +0000 Reply-To: "Stephen J. Fromm" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Stephen J. Fromm" <[log in to unmask]> Subject: Re: A problem with SPM and FIR modeling Comments: To: "G. Sharvit" <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> I think this might be similar to a problem I had once: https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;36d6a42e.0709 ========================================================================Date: Tue, 8 Feb 2011 08:29:16 +0000 Reply-To: Tuomas Tolvanen <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Tuomas Tolvanen <[log in to unmask]> Subject: Voxel's center Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> In voxel world, is the voxel's center same as the voxel's index? I mean that if I have voxel index=[1 1 1], does that index refer to voxel's center or corner point? Same question to mm-world. When I extract mm co-ordinates with [b xyz] = spm_read_vols(a), does those mm co-ordinates refer to desired voxel's center or corner point? br, Tuomas ========================================================================Date: Tue, 8 Feb 2011 09:45:55 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: VBM in large heterogeneous dataset Comments: To: Lizanne Schweren <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> If you use New Segment, the initial affine registration should be much more robust, so the regularisation used (for penalising unlikely shears and zooms) will have less effect. The affine registration would probably work reasonably well if you use the same form of regularisation throughout (either European or East Asian, or even disabling the regularisation). Best regards, -John On 7 February 2011 21:11, Lizanne Schweren <[log in to unmask]> wrote: > Dear all, > > I am currently integrating structural MRI data from collaborating sites from a.o. Europe and Asia, in order to create a large (±1000 subjects) dataset for VBM analyses. I have some questions regarding how to deal with such large and (morphologically) heterogeneous datasets in SPM8. > > Does anyone have experience segmenting brains from Asian participants? In the 'new segment' module, one can choose to warp to European (default) or Asian (or other) brains. Would it be beneficial to segment the Asian brains seperately from the European ones, and than pool all data at a later stage (e.g. in the Dartel pipeline)? And would that not conflict with the tissue probability maps that are selected by default in SPM? > > Second question regards Dartel (create template). Would it be beneficial to increase the number of iterations for the template, as a way to deal with the heterogeneity in the dataset? Does anyone have experience and/or specific setting recommendations in dealing with such large and heterogeneous datasets? > > Thanks. Lizanne > ========================================================================Date: Tue, 8 Feb 2011 11:29:01 +0100 Reply-To: =?iso-8859-1?Q?André_Schmidt?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?iso-8859-1?Q?André_Schmidt?= <[log in to unmask]> Subject: inverse reconstruction MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_001E_01CBC783.62799710" Message-ID: <001d01cbc77b$00b52f10$021f8d30$@[log in to unmask]> This is a multi-part message in MIME format. ------=_NextPart_000_001E_01CBC783.62799710 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear all I’d like to imaging my eeg data. After preprocessing and forward computation, I get some problems with the inverse reconstruction. If I chose imaging, all conditions (2:deviant, std 6)“ and standard, this error message appeared : ??? Error using ==> fprintf Function is not defined for sparse inputs. Error in ==> spm_eeg_invert at 514 fprintf('Percent variance explained %.2f (%.2f)\n',R2,R2*VE(i)) Error in ==> spm_eeg_invert_ui at 95 D = spm_eeg_invert(D); Error in ==> spm_eeg_inv_imag_api>Inverse_Callback at 94 handles.D = spm_eeg_invert_ui(handles.D); Error in ==> spm_eeg_inv_imag_api at 53 feval(varargin{:}); % FEVAL switchyard ??? Error using ==> pause Error while evaluating uicontrol Callback Does anybody know the problem? Should I better take the Custom model and if yes which model? Thanks for help andré ------=_NextPart_000_001E_01CBC783.62799710 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Dear all

I’d like to imaging my eeg data. After preprocessing and forward computation, I get some problems with the inverse reconstruction.

If I chose imaging, all conditions (2:deviant, std 6)“ and standard, this error message appeared :

??? Error using ==> fprintf

Function is not defined for sparse inputs.

Error in ==> spm_eeg_invert at 514

fprintf('Percent variance explained %.2f (%.2f)\n',R2,R2*VE(i))

Error in ==> spm_eeg_invert_ui at 95

D = spm_eeg_invert(D);

Error in ==> spm_eeg_inv_imag_api>Inverse_Callback at 94

handles.D = spm_eeg_invert_ui(handles.D);

Error in ==> spm_eeg_inv_imag_api at 53

feval(varargin{:}); % FEVAL switchyard

??? Error using ==> pause

Error while evaluating uicontrol Callback

Does anybody know the problem?

Should I better take the Custom model and if yes which model?

Thanks for help

andré

------=_NextPart_000_001E_01CBC783.62799710-- ========================================================================Date: Tue, 8 Feb 2011 10:36:52 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: inverse reconstruction Comments: To: =?ISO-8859-1?Q?André_Schmidt?= <[log in to unmask]> In-Reply-To: <-3958352575239621134@unknownmsgid> MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Andre, This problem should be fixed in the latest SPM update. Please make sure you have the latest version. Best, Vladimir On Tue, Feb 8, 2011 at 10:29 AM, André Schmidt <[log in to unmask]> wrote: > Dear all > > > > I’d like to imaging my eeg data. After preprocessing and forward > computation, I get some problems with the inverse reconstruction. > > If I chose imaging, all conditions (2:deviant, std 6)“ and standard, this > error message appeared : > > > > ??? Error using ==> fprintf > > Function is not defined for sparse inputs. > > > > Error in ==> spm_eeg_invert at 514 > > fprintf('Percent variance explained %.2f (%.2f)\n',R2,R2*VE(i)) > > > > Error in ==> spm_eeg_invert_ui at 95 > > D = spm_eeg_invert(D); > > > > Error in ==> spm_eeg_inv_imag_api>Inverse_Callback at 94 > > handles.D = spm_eeg_invert_ui(handles.D); > > > > Error in ==> spm_eeg_inv_imag_api at 53 > > feval(varargin{:}); % FEVAL switchyard > > ??? Error using ==> pause > > Error while evaluating uicontrol Callback > > > > Does anybody know the problem? > > > > Should I better take the Custom model and if yes which model? > > > > Thanks for help > > andré > > > > ========================================================================Date: Tue, 8 Feb 2011 11:38:06 +0100 Reply-To: =?iso-8859-1?Q?André_Schmidt?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?iso-8859-1?Q?André_Schmidt?= <[log in to unmask]> Subject: inverse reconstruction MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_0027_01CBC784.A73E4E80" Message-ID: <002601cbc77c$4579e680$d06db380$@[log in to unmask]> This is a multi-part message in MIME format. ------=_NextPart_000_0027_01CBC784.A73E4E80 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear all I’d like to imaging my eeg data. After preprocessing and forward computation, I get some problems with the inverse reconstruction. If I chose imaging, all conditions (2:deviant, std 6)“ and standard, this error message appeared : ??? Error using ==> fprintf Function is not defined for sparse inputs. Error in ==> spm_eeg_invert at 514 fprintf('Percent variance explained %.2f (%.2f)\n',R2,R2*VE(i)) Error in ==> spm_eeg_invert_ui at 95 D = spm_eeg_invert(D); Error in ==> spm_eeg_inv_imag_api>Inverse_Callback at 94 handles.D = spm_eeg_invert_ui(handles.D); Error in ==> spm_eeg_inv_imag_api at 53 feval(varargin{:}); % FEVAL switchyard ??? Error using ==> pause Error while evaluating uicontrol Callback Does anybody know the problem? Should I better take the Custom model and if yes which model? Thanks for help andré ------=_NextPart_000_0027_01CBC784.A73E4E80 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Dear all

I’d like to imaging my eeg data. After preprocessing and forward computation, I get some problems with the inverse reconstruction.

If I chose imaging, all conditions (2:deviant, std 6)“ and standard, this error message appeared :

??? Error using ==> fprintf

Function is not defined for sparse inputs.

Error in ==> spm_eeg_invert at 514

fprintf('Percent variance explained %.2f (%.2f)\n',R2,R2*VE(i))

Error in ==> spm_eeg_invert_ui at 95

D = spm_eeg_invert(D);

Error in ==> spm_eeg_inv_imag_api>Inverse_Callback at 94

handles.D = spm_eeg_invert_ui(handles.D);

Error in ==> spm_eeg_inv_imag_api at 53

feval(varargin{:}); % FEVAL switchyard

??? Error using ==> pause

Error while evaluating uicontrol Callback

Does anybody know the problem?

Should I better take the Custom model and if yes which model?

Thanks for help

andré

------=_NextPart_000_0027_01CBC784.A73E4E80-- ========================================================================Date: Tue, 8 Feb 2011 10:43:08 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Voxel's center Comments: To: Tuomas Tolvanen <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> I think of the indices as referring to the centers of the voxels. After transforming into mm coordinates, the coordinates refer to the voxel centers. Best regards, -John On 8 February 2011 08:29, Tuomas Tolvanen <[log in to unmask]> wrote: > In voxel world, is the voxel's center same as the voxel's index? I mean that if I have voxel index=[1 1 1], does that index refer to voxel's center or corner point? Same question to mm-world. When I extract mm co-ordinates with [b xyz] = spm_read_vols(a), does those mm co-ordinates refer to desired voxel's center or corner point? > > br, > > Tuomas > ========================================================================Date: Tue, 8 Feb 2011 13:12:40 +0100 Reply-To: =?ISO-8859-1?Q?Wibeke_Nordhøy?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Wibeke_Nordhøy?= <[log in to unmask]> Subject: Problem using the Fieldmaps module on GE data MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hello SPM-users: We've been trying to calculate B0 maps based on dual gradient echo images from our GE Signa scanner. We used the GRE sequence, with a 128 acquisition matrix reconstructed to a 256 matrix, making Re and Im images for the two echo times (4.9 ms and 7.4 ms). Then we converted these to NIfTI using either MRIcro or SPM8 before running the Fieldmaps toolbox on them. After a while, the analysis will crash and produce the following errors: ??? Error using ==> mrdivide Matrix dimensions must agree. Error in ==> pm_smooth_phasemap at 49 pm = pm_smooth_phasemap_dtj(pm,1/angvar,krnl); Error in ==> pm_make_fieldmap at 201 fm.fpm = pm_smooth_phasemap(fm.upm.*fm.mask,angvar,pxs,flags.fwhm); Error in ==> FieldMap at 1623 IP.fm = pm_make_fieldmap([IP.P{1} IP.P{2} IP.P{3} IP.P{4}],IP.uflags); Error in ==> FieldMap at 699 status=FieldMap('CreateFieldMap',IP); ??? Error while evaluating uicontrol Callback It seems that the Fieldmaps tool is geared towards Siemens scanners; have anyone used it on images from a GE scanner with good results? Any tips or ideas for us? The first error message mentions matrix dimensions. Could our problem be the difference between the acquired and reconstructed matrices? Wibeke and Øystein ========================================================================Date: Tue, 8 Feb 2011 13:14:22 +0100 Reply-To: Helmut Laufs <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Helmut Laufs <[log in to unmask]> Subject: limitations of DCM (fMRI) for subcortical regions? Content-Type: multipart/alternative; boundary="_42eda7d2-88a8-477f-a898-2fbf263ed496_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_42eda7d2-88a8-477f-a898-2fbf263ed496_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear fMRI-DCMers, is there any literature or less formal knowledge about the validity of DCM for fMRI when subcortical regions are involved? I am asking because of two things 1. empirical evidence (potentially due to ROI definition issues, see similar posting on this list [DARTEL-related]) 2. work by Caroline Malherbe on functional connectivity (via sICA): http://www.imt.liu.se/minew/Intern/Onlineproceedings/ISBI_2010/pdfs/0001169.pdf where she describes that - at least in the basal ganglia - subcortical ROI time courses are a mixture of signals some of which correlate with cortical ones but others to more regional activity. Any hints greatly appreciated. Thank you. Bw, Helmut --_42eda7d2-88a8-477f-a898-2fbf263ed496_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear fMRI-DCMers,

is there any literature or less formal knowledge about the validity of DCM for fMRI when subcortical regions are involved?

I am asking because of two things

1. empirical evidence (potentially due to ROI definition issues, see similar posting on this list [DARTEL-related])
2. work by Caroline Malherbe on functional connectivity (via sICA):
http://www.imt.liu.se/minew/Intern/Onlineproceedings/ISBI_2010/pdfs/0001169.pdf
where she describes that - at least in the basal ganglia - subcortical ROI time courses are a mixture of signals some of which correlate with cortical ones but others to more regional activity.

Any hints greatly appreciated.

Thank you.

Bw,

Helmut

--_42eda7d2-88a8-477f-a898-2fbf263ed496_-- ========================================================================Date: Tue, 8 Feb 2011 11:53:08 -0000 Reply-To: Lorelei Howard <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lorelei Howard <[log in to unmask]> Subject: comparing men and women Comments: cc: [log in to unmask] MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Hi everyone, I want to compare men and women in my experiment and am finding it difficult to track down any guidance on how to do this in SPM. Does anyone have any links to useful websites/papers/texts that I could use to work out how to set up this kind of comparison in SPM? I usually use scripts to run all of my analyses so it would be even better if someone had an example script they could pass on. Also, are there any issues I should be aware of when comparing male and female brains? For example, does the difference in head size between the two groups have any detrimental influences? I would be grateful of any help or advice. Many thanks, Lorelei -- Lorelei Howard Ph.D. Student Institute of Behavioural Neuroscience Cognitive, Perceptual and Brain Sciences Department University College London 26 Bedford Way WC1H 0AP +44 (0)20 7679 8553 ========================================================================Date: Tue, 8 Feb 2011 05:19:01 -0800 Reply-To: turki sabrah <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: turki sabrah <[log in to unmask]> Subject: Conjunction analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1483646110-1297171141=:68903" Message-ID: <[log in to unmask]> --0-1483646110-1297171141=:68903 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPM users, I have conducted two experiments and I want to carry out conjunction analysis to look at activations that are common to all subjects in these studies. Any thoughts or suggestions about which approach should I go with and why ? fixed-effect or Random-effects approach (between two studies) ? Thanks in advance Turk --0-1483646110-1297171141=:68903 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

Dear SPM users,

I have conducted two experiments and I want to carry out conjunction analysis to look at activations that are common to all subjects in these studies. Any thoughts or suggestions about which approach should I go with and why ? fixed-effect or Random-effects approach (between two studies) ?

Thanks in advance

Turk

--0-1483646110-1297171141=:68903-- ========================================================================Date: Tue, 8 Feb 2011 22:28:52 +0800 Reply-To: Anqi Qiu <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Anqi Qiu <[log in to unmask]> Subject: Postdoctoral Fellowship on infant neurocognitive and brain development using MRI, Eyetracking, and EEG MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001517573e02e20efb049bc62afc Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable * * *Job description:* National University of Singapore (NUS) is a leading global university centred in Asia. It offers a global approach to education and research, with a focus on Asian perspectives and expertise. Computation functional anatomy laboratory at NUS (http://www.bioeng.nus.edu.sg/cfa) focuses on brain and cognition studies using neuroimaging techniques. Our group is leading a large-scale healthy infant neuroimaging project in Singapore. The project examines neurocognition using EEG and eye trackers and brain development using diffusion tensor imaging (DTI), resting-state fMRI, and structural MRI. We are looking for a research fellow who is interested in multi-modal MRI studies in infants for understanding normal development of brain and cognition trajectory. We offer high salary with medical and other benefits. *Requirements:* · Ph. D · Research background on infant brain or child cognition is preferable but necessary · Knowledge in statistical analysis is necessary · Good communication and writing skills in English language If you are interested in the jobs listed below, please send your CV to Dr. Anqi QIU Division of Bioengineering National University of Singapore Email: [log in to unmask] Phone: +(65) 6516 7002 --001517573e02e20efb049bc62afc Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Job description:

National University of Singapore (NUS) is a leading global university centred in Asia. It offers a global approach to education and research, with a focus on Asian perspectives and expertise. Computation functional anatomy laboratory at NUS (http://www.bioeng.nus.edu.sg/cfa) focuses on brain and cognition studies using neuroimaging techniques. Our group is leading a large-scale healthy infant neuroimaging project in Singapore. The project examines neurocognition using EEG and eye trackers and brain development using diffusion tensor imaging (DTI), resting-state fMRI, and structural MRI. We are looking for a research fellow who is interested in multi-modal MRI studies in infants for understanding normal development of brain and cognition trajectory. We offer high salary with medical and other benefits.

Requirements:

· Ph. D

· Research background on infant brain or child cognition is preferable but necessary

· Knowledge in statistical analysis is necessary

· Good communication and writing skills in English language

If you are interested in the jobs listed below, please send your CV to

Dr. Anqi QIU

Division of Bioengineering

National University of Singapore

Email: [log in to unmask]

Phone: +(65) 6516 7002

--001517573e02e20efb049bc62afc-- ========================================================================Date: Tue, 8 Feb 2011 09:46:25 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Conjunction analysis Comments: To: turki sabrah <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> There are potentially several options; however, based on your wording of the word "common", I'd recommend the following: (1) Threshold each subject and make a binary image for each (imcalc: i1>0); (2) Add up all the subjects in study 1 and threshold (imcalc: X>n, where n is the number of subjects; (3) Repeat for study 2; (4) Now add study 1 and study 2 (imcalc: i1+i2.*2) This will give you regions common in all subjects in study1, study2, and study 1 and 2 combined. ------- Global Conjunction and the Conjunction Null could also be used, but there intrepretations are different from what you explicitly asked for in your email. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Tue, Feb 8, 2011 at 8:19 AM, turki sabrah <[log in to unmask]> wrote: > Dear SPM users, > > I have conducted two experiments and I want to carry out conjunction > analysis to look at activations that are common to all subjects in these > studies. Any thoughts or suggestions about which approach should I go with > and why ? fixed-effect or Random-effects approach (between two studies) ? > > Thanks in advance > > Turk > ========================================================================Date: Tue, 8 Feb 2011 09:46:25 -0500 Reply-To: "Watson, Christopher" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Watson, Christopher" <[log in to unmask]> Subject: Re: limitations of DCM (fMRI) for subcortical regions? Comments: To: Helmut Laufs <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> This doesn't specifically discuss subcortical regions but is related to what you want: http://www.ncbi.nlm.nih.gov/pubmed/19961941 ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Helmut Laufs [[log in to unmask]] Sent: Tuesday, February 08, 2011 7:14 AM To: [log in to unmask] Subject: [SPM] limitations of DCM (fMRI) for subcortical regions? Dear fMRI-DCMers, is there any literature or less formal knowledge about the validity of DCM for fMRI when subcortical regions are involved? I am asking because of two things 1. empirical evidence (potentially due to ROI definition issues, see similar posting on this list [DARTEL-related]) 2. work by Caroline Malherbe on functional connectivity (via sICA): http://www.imt.liu.se/minew/Intern/Onlineproceedings/ISBI_2010/pdfs/0001169.pdf where she describes that - at least in the basal ganglia - subcortical ROI time courses are a mixture of signals some of which correlate with cortical ones but others to more regional activity. Any hints greatly appreciated. Thank you. Bw, Helmut ========================================================================Date: Tue, 8 Feb 2011 10:08:19 -0500 Reply-To: "Watson, Christopher" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Watson, Christopher" <[log in to unmask]> Subject: Re: comparing men and women Comments: To: Lorelei Howard <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Are you doing fMRI? Why not just do a regular second-level analysis? RE: head size issues - I shouldn't think it makes much of a difference in fMRI analysis, but I'm sure a Pubmed or Google Scholar search of "gender covariates fmri" could help. ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Lorelei Howard [[log in to unmask]] Sent: Tuesday, February 08, 2011 6:53 AM To: [log in to unmask] Subject: [SPM] comparing men and women Hi everyone, I want to compare men and women in my experiment and am finding it difficult to track down any guidance on how to do this in SPM. Does anyone have any links to useful websites/papers/texts that I could use to work out how to set up this kind of comparison in SPM? I usually use scripts to run all of my analyses so it would be even better if someone had an example script they could pass on. Also, are there any issues I should be aware of when comparing male and female brains? For example, does the difference in head size between the two groups have any detrimental influences? I would be grateful of any help or advice. Many thanks, Lorelei -- Lorelei Howard Ph.D. Student Institute of Behavioural Neuroscience Cognitive, Perceptual and Brain Sciences Department University College London 26 Bedford Way WC1H 0AP +44 (0)20 7679 8553 ========================================================================Date: Tue, 8 Feb 2011 16:46:05 +0200 Reply-To: Panagiotis Tsiatsis <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Panagiotis Tsiatsis <[log in to unmask]> Subject: Re: Warning in spm_eeg_convert2scalp (griddata) & spm_eeg_crop Comments: To: Vladimir Litvak <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hello dear Vladimir and thanks for your response First of all, apologies in advance for the long e-mail; I hope that it will be at least a bit important for other users of SPM, too It seems that the problem appeared after I used spm_eeg_crop to drop my continuous CTF meg data file straight after reading it in and before applying filtering / downsampling. Without spm_eeg_crop everything works fine. [A small recap : As explained in a previous e-mail of mine, the logic behind this is that the start and the end of a continuous MEG files might be contaminated by artifacts (i.e due to subject's movement at the end of the session if she for whatever reason realized that the session is over and started moving before you press 'stop recording' ) or / and zero values because of padding added by the CTF software. All this should affect any subsequent filtering function if they are not removed (even the padding because of the sudden jump from values of order 10^-12 to zero!!!!) so I decided to crop the edges of my continuous file before continuing. ] I remember that when you send me the spm_eeg_crop file you told me that it has not been tested for continuous data but it should in principle work. I call it by using the following code : Time_Offset_in_ms = 3000; % in ms S = []; S.D = D; S.timewin = [(Time_of_First_Trial_Onset_in_ms - Time_Offset_in_ms) (Time_of_Last_Trial_Onset_in_ms + Time_Offset_in_ms)] S.freqwin = []; S.channels = 'all' D = spm_eeg_crop(S); D.save; The problem seems to appear in spm_eeg_crop when applied on a continuous file to and more specifically somewhere in the creation of the new object: Dnew = coor2D(Dnew, [], coor2D(D, chanind)); as some channels do indeed have the same coordinates : >> size(coor2D(D, chanind)') ans 337 2 >> size(unique(coor2D(D, chanind)','rows')) ans 335 2 The above number shows that spm_eeg_crop indeed puts in the coor2D matrix (or tries to put) the coordinates of all available channels. Up to now (and correct me if I am wrong) I knew that the coor2D matrix contains the coordinates of the MEGGRAD channels only. So I modified the code as follows : MEG_Channels = (D.meegchannels('MEG')); Dnew = coor2D(Dnew, [], coor2D(D, MEG_Channels)); *And indeed the conversion to images of subsequent files was executed without warnings*. So, is this a bug or am I going the wrong direction here? A couple more issues / clarifications : 1. The statistics at the end of the analysis (assuming all other equal) were slightly different depending on whether I cropped the start and the end of the continuous initial files that in many cases had large artifacts or zero values. I suppose this is because the subsequent filtering applied on the data was "smoother", in the sense that the filters did not have to compensate for the artifacts or the jumps because of the zero padding. Does this train of thought sound logical to you? 2.The cropped file (where only a few non-trial seconds from the start and from the and are missing) contains exactly the same number of events as the original continuous file. Why??? It seems that some zombie events are retained after cropping! 3. Please note that there appears to be one more problem with spm_eeg_crop and continuous data : The line Dnew = badchannels(Dnew, [], badchannels(D, chanind)); always results in an error when there are no bad channels, i.e. when isempty(D.badchannels) == 1. so i have substituted the above line with if isempty(D.badchannels) == 0 Dnew = badchannels(Dnew, [], badchannels(D, chanind)); end I suspect I should put an 'else' condition somewhere there, but of course you are the expert on this :) Thanks again and all the best, Panagiotis On 2/7/2011 8:10 PM, Vladimir Litvak wrote: > Dear Panagiotis, > > Try doing: > > spm_eeg_load > D.coor2D(D.meegchannels('MEG')) > > and look which of the channels have the same 2D coordinates. In > principle this should not happen for CTF MEG. You can also look in > 'Prepare' tool at the 2D channel layout an modify it if necessary or > load a template layout from EEGtemplates. > > Best, > > Vladimir > > On Sun, Feb 6, 2011 at 5:23 PM, Panagiotis Tsiatsis > <[log in to unmask]> wrote: >> Dear All, >> >> When I am converting my CTF MEG epoched data to images, I am getting the >> following warning: >> >> Warning: Duplicate x-y data points detected: using average of the z values. >>> In griddata at 107 >> In spm_eeg_convert2scalp at 204 >> >> My settings are: >> >> S = []; >> S.n = 32; >> S.Fname = Concatenated_File_Name_Time; >> S.interpolate_bad = 1; >> S.modality = 'MEG'; >> >> I also have EOG channels whose chantype has been set to 'EEG' during the >> conversion (this of course should not interfere since I ve selected my >> modality to be 'MEG'). >> >> By browsing the Mathwoks forum, I suspect that this happens when nearby >> values get very similar (as is the case with MEG data). >> >> Anybody seen that before? Any intuitions? >> >> Thanks and best, >> Panagiotis >> >> >> -- >> Panagiotis S. Tsiatsis >> Max Planck Institute for Biological Cybernetics >> Cognitive NeuroImaging Group >> Tuebingen, Germany >> -- Panagiotis S. Tsiatsis Max Planck Institute for Biological Cybernetics Cognitive NeuroImaging Group Tuebingen, Germany ========================================================================Date: Tue, 8 Feb 2011 11:28:50 -0500 Reply-To: David Kennedy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: David Kennedy <[log in to unmask]> Subject: INCF Neuroinformatics Congress - Call for Abstracts MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf30433f10f7a3db049bc7d72c Message-ID: <[log in to unmask]> --20cf30433f10f7a3db049bc7d72c Content-Type: text/plain; charset=ISO-8859-1 The INCF Congress of Neuroinformatics will be held from September 4th to 6th at the Seaport Hotel in Boston, MA. They are currently collecting abstracts for poster presentations and computer demos. The annual INCF Neuroinformatics Congress provides a meeting place for researchers in all fields related to neuroinformatics, including data- and knowledge-bases of the nervous system from molecular to behavioral levels; tools for the acquisition, analysis, and visualization of nervous system data; and theoretical, computational, and simulation environments for modeling the brain. There is a current call for abstracts, deadline *April 19th, 2011*. *Submission Information:* The abstract format will be the same as in previous years, i.e. maximum one page (A4 or US letter). The abstract submission form allows for inclusion of one image and we encourage submitters to make use of this option. Accepted abstracts will be published online with Frontiers in Neuroscience and will receive a DOI (Digital Object Identifier). In order to submit an abstract, you need to visit INCF website and log in with your INCF account credentials. If you do not yet have an INCF account, please register at the INCF website before continuing. Submitted abstracts can be modified until the deadline - April 19th, 2011. To submit: http://www.neuroinformatics2011.org/abstracts * Registration Information:* All participants, including invited speakers, will be expected to cover their own expenses and registration fees. A range of housing options will be available. Registration Fees: USD Full fee early 525 Full fee late 740 Postdoc fee early 290 Postdoc fee late 460 Student fee early 155 Student fee late 250 *Early Registration closes on June 1st, 2011* *Important Dates** * Abstract submission February 4 - April 19, 2011* * Abstract decisions Mid May, 2011* * Early registration ends June 1, 2011* * Neuroinformatics 2011 Congress September 4-6, 2011 For more information, please visit http://www.neuroinformatics2011.org/ --20cf30433f10f7a3db049bc7d72c Content-Type: text/html; charset=ISO-8859-1 The INCF Congress of Neuroinformatics will be held from September 4th to 6th
at the Seaport Hotel in Boston, MA. They are currently collecting abstracts for
poster presentations and computer demos.

The annual INCF Neuroinformatics Congress provides a meeting place for
researchers in all fields related to neuroinformatics, including data- and
knowledge-bases of the nervous system from molecular to behavioral levels;
tools for the acquisition, analysis, and visualization of nervous system data; and
theoretical, computational, and simulation environments for modeling the
brain.

There is a current call for abstracts, deadline *April 19th, 2011*.

Submission Information: The abstract format will be the same as in previous
years, i.e. maximum one page (A4 or US letter). The abstract submission form
allows for inclusion of one image and we encourage submitters to make use of
this option. Accepted abstracts will be published online with Frontiers in
Neuroscience and will receive a DOI (Digital Object Identifier). In order to submit
an abstract, you need to visit INCF website and log in with your INCF account
credentials. If you do not yet have an INCF account, please register at the INCF
website before continuing. Submitted abstracts can be modified until the
deadline - April 19th, 2011.
To submit: http://www.neuroinformatics2011.org/abstracts

Registration Information: All participants, including invited speakers, will be
expected to cover their own expenses and registration fees. A range of housing
options will be available.

Registration Fees:

	USD
Full fee early	525
Full fee late	740
Postdoc fee early	290
Postdoc fee late	460
Student fee early	155
Student fee late	250

*Early Registration closes on June 1st, 2011*

Important Dates

Abstract submission
February 4 - April 19, 2011

Abstract decisions
Mid May, 2011

Early registration ends
June 1, 2011

Neuroinformatics 2011 Congress
September 4-6, 2011

For more information, please visit http://www.neuroinformatics2011.org/ --20cf30433f10f7a3db049bc7d72c-- ========================================================================Date: Tue, 8 Feb 2011 16:59:37 +0000 Reply-To: Soha Saleh <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Soha Saleh <[log in to unmask]> Subject: imcalc to do a contrast? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi: I have 4 conditions in my experiment (A, B, C, D).. I am interested in finding the neuro activity that is higher in A than in B, and also the areas where the difference in activity between A and B, are higher than between C and D. Simply it is (A>B) - (C>D). I did each of the contrasts (A>B) and (C>D) in SPM8 and then used imcalc to subtract them to get (A>B)-(C>D). I got huge activity and T value that is very high which made me suspicious about this procedure. My questions are: Is this a valid procedure? Is there any precautions or limitations I need to be aware of? Thank you, I appreciate any assistance or advice you give. Kind regards, Soha ========================================================================Date: Tue, 8 Feb 2011 19:20:31 +0100 Reply-To: "CARLES M. FALCON FALCON" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "CARLES M. FALCON FALCON" <[log in to unmask]> Subject: Re: Problem using the Fieldmaps module on GE data Comments: To: Wibeke =?utf-8?b?Tm9yZGjDuHk=?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-disposition: inline Content-type: text/plain; charset=UTF-8; DelSp=Yes; format=flowed Content-transfer-encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Wibeken, it could happen that voxel size of converted images was wrong due to using a different matrix for acquisition and reconstruction. Voxel size stored in dicom header could be, depending on the manufacturer, both the acquisition or the reconstruction one (one is half size the other in x and y axis). You could check if this is your problem with spm's DISPLAY tool. If brains look deformed, or the total brain size is too large (number of voxels multiplied by the voxel size), you could fix the problem by resizing your images by a factor 0.5 (probably just along x and y axis). Hope this helps Carles Wibeke NordhÃ¸y <[log in to unmask]> ha escrit: > Hello SPM-users: > > We've been trying to calculate B0 maps based on dual gradient echo > images from our GE Signa scanner. We used the GRE sequence, with a > 128 acquisition matrix reconstructed to a 256 matrix, making Re and > Im images for the two echo times (4.9 ms and 7.4 ms). Then we > converted these to NIfTI using either MRIcro or SPM8 before running > the Fieldmaps toolbox on them. After a while, the analysis will > crash and produce the following errors: > > ??? Error using ==> mrdivide > Matrix dimensions must agree. > > Error in ==> pm_smooth_phasemap at 49 > pm = pm_smooth_phasemap_dtj(pm,1/angvar,krnl); > > Error in ==> pm_make_fieldmap at 201 > fm.fpm = pm_smooth_phasemap(fm.upm.*fm.mask,angvar,pxs,flags.fwhm); > > Error in ==> FieldMap at 1623 > IP.fm = pm_make_fieldmap([IP.P{1} IP.P{2} IP.P{3} > IP.P{4}],IP.uflags); > > Error in ==> FieldMap at 699 > status=FieldMap('CreateFieldMap',IP); > > ??? Error while evaluating uicontrol Callback > > It seems that the Fieldmaps tool is geared towards Siemens scanners; > have anyone used it on images from a GE scanner with good results? > Any tips or ideas for us? > > The first error message mentions matrix dimensions. Could our > problem be the difference between the acquired and reconstructed > matrices? > > Wibeke and Ã˜ystein > ========================================================================Date: Tue, 8 Feb 2011 10:29:21 -0800 Reply-To: Matthew Brett <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Matthew Brett <[log in to unmask]> Subject: Re: Question about ROI analysis in marsbar Comments: To: =?GB2312?Q?½ªallen?= <[log in to unmask]> Comments: cc: [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=GB2312 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi, 2011/1/26 ½ªallen <[log in to unmask]>: > When we use Marsbar for a ROI and time course analysis, we usually use PSTH > value for time course analysis, however, there is also a "beta" value > option in the workshop, which each value represents for the beta value of > each parameter in the modeling matrix. > > My question is that, for a typical condition A (like the example I attach in > email), can I use this beta value to represent the mean activation of ROI? > If so, why the magnitude has so big difference compare to the PSTH value? Sorry to take so long to reply. I've Cc'ed the marsbar list - I think it's the one you want. Sorry if I'm being slow, but what exactly are you doing in the marsbar interface to get the PSTH and beta values in your workspace? Best, Matthew ========================================================================Date: Tue, 8 Feb 2011 13:52:48 -0500 Reply-To: Fred Sanders <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Fred Sanders <[log in to unmask]> Subject: ROI image dimensions - Marsbar MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --000e0cd2e07ad4c051049bc9da32 Content-Type: text/plain; charset=ISO-8859-1 Hi All, I created ROI masks with Marsbar but the masks that I create all have the following image dimensions: 91x109x91. These dimensions match the image dimensions of the SPM template brains. Is there a way with Marsbar (or another script) to create ROI masks with different dimensions (say 151x188x154)? Or is there a way to easier convert the 91x109x91 images to 151x188x154? Thanks much!! Fred --000e0cd2e07ad4c051049bc9da32 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi All,

I created ROI masks with Marsbar but the masks that I create all have the following image dimensions: 91x109x91. These dimensions match the image dimensions of the SPM template brains. Is there a way with Marsbar (or another script) to create ROI masks with different dimensions (say 151x188x154)? Or is there a way to easier convert the 91x109x91 images to 151x188x154?

Thanks much!!

Fred

--000e0cd2e07ad4c051049bc9da32-- ========================================================================Date: Tue, 8 Feb 2011 18:55:45 -0000 Reply-To: Lorelei Howard <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lorelei Howard <[log in to unmask]> Subject: Re: comparing men and women Comments: To: "Watson, Christopher" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Hi Chris, Thanks for the reply. Yes, I am doing fMRI. I am aware that I need to do a second level analysis... but I want to compare two groups at the second level, rather than simply running the standard second level analysis. Looking at previous papers and their methods sections has not really helped as they don't report the specifics of how to set this up in SPM. I can't find an example in the manual or any hints in the various help options and was hoping someone may give me a little more insight into how I may set it up. Thanks again, Lorelei > Are you doing fMRI? Why not just do a regular second-level analysis? > RE: head size issues - I shouldn't think it makes much of a difference in > fMRI analysis, but I'm sure a Pubmed or Google Scholar search of "gender > covariates fmri" could help. > > ________________________________________ > From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf > Of Lorelei Howard [[log in to unmask]] > Sent: Tuesday, February 08, 2011 6:53 AM > To: [log in to unmask] > Subject: [SPM] comparing men and women > > Hi everyone, > > I want to compare men and women in my experiment and am finding it > difficult to track down any guidance on how to do this in SPM. > > Does anyone have any links to useful websites/papers/texts that I could > use to work out how to set up this kind of comparison in SPM? > > I usually use scripts to run all of my analyses so it would be even better > if someone had an example script they could pass on. > > Also, are there any issues I should be aware of when comparing male and > female brains? For example, does the difference in head size between the > two groups have any detrimental influences? > > I would be grateful of any help or advice. > > Many thanks, > Lorelei > > > -- > Lorelei Howard > > Ph.D. Student > > Institute of Behavioural Neuroscience > Cognitive, Perceptual and Brain Sciences Department > University College London > 26 Bedford Way > WC1H 0AP > +44 (0)20 7679 8553 > -- Lorelei Howard Ph.D. Student Institute of Behavioural Neuroscience Cognitive, Perceptual and Brain Sciences Department University College London 26 Bedford Way WC1H 0AP +44 (0)20 7679 8553 ========================================================================Date: Tue, 8 Feb 2011 14:03:34 -0500 Reply-To: Chris Watson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chris Watson <[log in to unmask]> Subject: Re: comparing men and women Comments: To: Lorelei Howard <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset="ISO-8859-1"; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> See Ch. 30 of the manual. Although, you'll want to do a 2-sample t-test, since you have two groups. Lorelei Howard wrote: > Hi Chris, > > Thanks for the reply. > > Yes, I am doing fMRI. > > I am aware that I need to do a second level analysis... but I want to > compare two groups at the second level, rather than simply running the > standard second level analysis. Looking at previous papers and their > methods sections has not really helped as they don't report the specifics > of how to set this up in SPM. I can't find an example in the manual or any > hints in the various help options and was hoping someone may give me a > little more insight into how I may set it up. > > Thanks again, > > Lorelei > > > >> Are you doing fMRI? Why not just do a regular second-level analysis? >> RE: head size issues - I shouldn't think it makes much of a difference in >> fMRI analysis, but I'm sure a Pubmed or Google Scholar search of "gender >> covariates fmri" could help. >> >> ________________________________________ >> From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf >> Of Lorelei Howard [[log in to unmask]] >> Sent: Tuesday, February 08, 2011 6:53 AM >> To: [log in to unmask] >> Subject: [SPM] comparing men and women >> >> Hi everyone, >> >> I want to compare men and women in my experiment and am finding it >> difficult to track down any guidance on how to do this in SPM. >> >> Does anyone have any links to useful websites/papers/texts that I could >> use to work out how to set up this kind of comparison in SPM? >> >> I usually use scripts to run all of my analyses so it would be even better >> if someone had an example script they could pass on. >> >> Also, are there any issues I should be aware of when comparing male and >> female brains? For example, does the difference in head size between the >> two groups have any detrimental influences? >> >> I would be grateful of any help or advice. >> >> Many thanks, >> Lorelei >> >> >> -- >> Lorelei Howard >> >> Ph.D. Student >> >> Institute of Behavioural Neuroscience >> Cognitive, Perceptual and Brain Sciences Department >> University College London >> 26 Bedford Way >> WC1H 0AP >> +44 (0)20 7679 8553 >> >> > > > ========================================================================Date: Tue, 8 Feb 2011 19:21:45 -0000 Reply-To: Lorelei Howard <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lorelei Howard <[log in to unmask]> Subject: Re: comparing men and women Comments: To: Chris Watson <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Brilliant... I had only used one-sample t-tests at the second level. Didn't even know you could change the design option to specify 2 sample t-tests etc as I hadn't been using the GUI. It makes much more sense now. Thanks again! Lorelei > See Ch. 30 of the manual. Although, you'll want to do a 2-sample t-test, > since you have two groups. > > Lorelei Howard wrote: >> Hi Chris, >> >> Thanks for the reply. >> >> Yes, I am doing fMRI. >> >> I am aware that I need to do a second level analysis... but I want to >> compare two groups at the second level, rather than simply running the >> standard second level analysis. Looking at previous papers and their >> methods sections has not really helped as they don't report the >> specifics >> of how to set this up in SPM. I can't find an example in the manual or >> any >> hints in the various help options and was hoping someone may give me a >> little more insight into how I may set it up. >> >> Thanks again, >> >> Lorelei >> >> >> >>> Are you doing fMRI? Why not just do a regular second-level analysis? >>> RE: head size issues - I shouldn't think it makes much of a difference >>> in >>> fMRI analysis, but I'm sure a Pubmed or Google Scholar search of >>> "gender >>> covariates fmri" could help. >>> >>> ________________________________________ >>> From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On >>> Behalf >>> Of Lorelei Howard [[log in to unmask]] >>> Sent: Tuesday, February 08, 2011 6:53 AM >>> To: [log in to unmask] >>> Subject: [SPM] comparing men and women >>> >>> Hi everyone, >>> >>> I want to compare men and women in my experiment and am finding it >>> difficult to track down any guidance on how to do this in SPM. >>> >>> Does anyone have any links to useful websites/papers/texts that I could >>> use to work out how to set up this kind of comparison in SPM? >>> >>> I usually use scripts to run all of my analyses so it would be even >>> better >>> if someone had an example script they could pass on. >>> >>> Also, are there any issues I should be aware of when comparing male and >>> female brains? For example, does the difference in head size between >>> the >>> two groups have any detrimental influences? >>> >>> I would be grateful of any help or advice. >>> >>> Many thanks, >>> Lorelei >>> >>> >>> -- >>> Lorelei Howard >>> >>> Ph.D. Student >>> >>> Institute of Behavioural Neuroscience >>> Cognitive, Perceptual and Brain Sciences Department >>> University College London >>> 26 Bedford Way >>> WC1H 0AP >>> +44 (0)20 7679 8553 >>> >>> >> >> >> > -- Lorelei Howard Ph.D. Student Institute of Behavioural Neuroscience Cognitive, Perceptual and Brain Sciences Department University College London 26 Bedford Way WC1H 0AP +44 (0)20 7679 8553 ========================================================================Date: Tue, 8 Feb 2011 14:57:04 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: imcalc to do a contrast? Comments: To: Soha Saleh <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Its unclear what was subtracted - single subject contrasts values or the the group level T-maps of A>B and C>D. You should not subtract T-maps nor use T-maps in second level or third level analysis. Only con images should be used. I wouldn't use imcalc as its more time consuming, but in theory will produce the same results. I'd use the contrast manager: A>B is 1 -1 0 0 (A>B) > (C>D) is 1 -1 -1 1 The enter the conimages into the second-level analysis. If D>C, then the contrast will show more than A>B. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Tue, Feb 8, 2011 at 11:59 AM, Soha Saleh <[log in to unmask]> wrote: > Hi: > > I have 4 conditions in my experiment (A, B, C, D).. I am interested in finding the neuro activity that is higher in A than in B, and also the areas where the difference in activity between A and B, are higher than between C and D. Simply it is (A>B) - (C>D). I did each of the contrasts (A>B) and (C>D) in SPM8 and then used imcalc to subtract them to get (A>B)-(C>D). I got huge activity and T value that is very high which made me suspicious about this procedure. > > My questions are: Is this a valid procedure? Is there any precautions or limitations I need to be aware of? > > Thank you, I appreciate any assistance or advice you give. > > Kind regards, > > Soha > ========================================================================Date: Tue, 8 Feb 2011 16:19:41 -0800 Reply-To: Matthew Brett <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Matthew Brett <[log in to unmask]> Subject: Re: ROI image dimensions - Marsbar Comments: To: Fred Sanders <[log in to unmask]> Comments: cc: [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi, > I created ROI masks with Marsbar but the masks that I create all have the > following image dimensions: 91x109x91. These dimensions match the image > dimensions of the SPM template brains. Is there a way with Marsbar (or > another script) to create ROI masks with different dimensions (say > 151x188x154)? Or is there a way to easier convert the 91x109x91 images > to 151x188x154? You can set the default 'base space' for ROI images in the options menu, by choosing an image with the dimensions and voxel sizes that you want. Does that help? Best, Matthew ========================================================================Date: Wed, 9 Feb 2011 02:03:30 +0000 Reply-To: Richard Morris <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Morris <[log in to unmask]> Subject: Re: SPM8 updates installing error in Mac Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> I also installed the SPM8 4010 update on my mac (snow leopard 10.6.5) and get the same error when I try to run spm8 fmri. Is there a solution to this problem? Ta, Rich ========================================================================Date: Wed, 9 Feb 2011 00:14:26 -0200 Reply-To: Felipe Picon <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Felipe Picon <[log in to unmask]> Subject: Re: SPM8 updates installing error in Mac Comments: To: Richard Morris <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016364d2909048abf049bd008a9 Content-Type: text/plain; charset=ISO-8859-1 Hi Richard, I just received this possible solution (below), but I didn't tried it out yet. 1) reinstalled spm 8, using command line to unzip the folder rather than double clicking on the folder and using mac's archive utility to unzip. 2) re-set my path to only include matlab/spm8 not subfolders. 3) did not install the updates 4010 4) didn't keep spm in my matlab toolbox Hope this helps - Felipe Picon, MD Child and Adolescent Psychiatrist Master's Degree Student - Department of Psychiatry of Federal University of Rio Grande do Sul - UFRGS - www.ufrgs.br Adult ADHD outpatient research program - PRODAH-A - http://www6.ufrgs.br/prodah/prodah/equipe.html Hospital de Clinicas de Porto Alegre - HCPA - www.hcpa.ufrgs.br Member of the World Psychiatric Association (WPA) Early Career Psychiatrists Council Area Coordinator for the Americas - http://www.wpanet.org/detail.php?section_id"&content_id0 Web based CV: http://lattes.cnpq.br/3896757276389918 [log in to unmask] 2011/2/9 Richard Morris <[log in to unmask]> > I also installed the SPM8 4010 update on my mac (snow leopard 10.6.5) and > get the same error when I try to run spm8 fmri. Is there a solution to this > problem? > > Ta, > > Rich > - --0016364d2909048abf049bd008a9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hi Richard,

I just received this possible solution (below), but I didn't tried it out yet.

1) reinstalled spm 8, using command line to unzip the folder rather than double clicking on the folder and using mac's archive utility to unzip.

2) re-set my path to only include matlab/spm8 not subfolders.

3) did not install the updates 4010

4) didn't keep spm in my matlab toolbox

Hope this helps

-
Felipe Picon, MD
Child and Adolescent Psychiatrist
Master's Degree Student - Department of Psychiatry of Federal University of Rio Grande do Sul - UFRGS - www.ufrgs.br
Adult ADHD outpatient research program - PRODAH-A - http://www6.ufrgs.br/prodah/prodah/equipe.html
Hospital de Clinicas de Porto Alegre - HCPA - www.hcpa.ufrgs.br
Member of the World Psychiatric Association (WPA) Early Career Psychiatrists Council
Area Coordinator for the Americas - http://www.wpanet.org/detail.php?section_id=22&content_id=120
Web based CV: http://lattes.cnpq.br/3896757276389918
[log in to unmask]

2011/2/9 Richard Morris <[log in to unmask]>

I also installed the SPM8 4010 update on my mac (snow leopard 10.6.5) and get the same error when I try to run spm8 fmri. Is there a solution to this problem?

Ta,

Rich

-
--0016364d2909048abf049bd008a9-- ========================================================================Date: Wed, 9 Feb 2011 04:44:52 +0000 Reply-To: Paul <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Paul <[log in to unmask]> Subject: Re: Non-Stationary correction in VBM 8 Comments: To: Christian Gaser <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear Christian Gaser Could you help me how to produce statistical table when using non-stationary correction function [located in "Threshold and transform spm-T maps" of newest VBM8 toolbox ] Best ========================================================================Date: Wed, 9 Feb 2011 17:05:53 +1100 Reply-To: Richard Morris <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Morris <[log in to unmask]> Subject: Re: SPM8 updates installing error in Mac In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_149929_502318070.1297231553722" Message-ID: <[log in to unmask]> ------=_Part_149929_502318070.1297231553722 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit Word to the wise, regarding instructions to update SPM. If you use the Mac OS (e.g., Leopard or Snow Leopard), don't copy the update folders to your SPM directory as per Guillaume's instructions. Apple, in their divine wisdom, elected to instantiate a version of 'copy' which overwrites rather than merges common directories. So you will lose your original SPM files in any directories with a common name. I don't know what the best command to use to merge the files in terminal might be, but perhaps someone here does! Rich ----- Original Message ----- From: "Felipe Picon" <[log in to unmask]> To: "Richard Morris" <[log in to unmask]> Cc: [log in to unmask] Sent: Wednesday, 9 February, 2011 1:14:26 PM Subject: Re: [SPM] SPM8 updates installing error in Mac Hi Richard, I just received this possible solution (below), but I didn't tried it out yet. 1) reinstalled spm 8, using command line to unzip the folder rather than double clicking on the folder and using mac's archive utility to unzip. 2) re-set my path to only include matlab/spm8 not subfolders. 3) did not install the updates 4010 4) didn't keep spm in my matlab toolbox Hope this helps - Felipe Picon, MD Child and Adolescent Psychiatrist Master's Degree Student - Department of Psychiatry of Federal University of Rio Grande do Sul - UFRGS - www.ufrgs.br Adult ADHD outpatient research program - PRODAH-A - http://www6.ufrgs.br/prodah/prodah/equipe.html Hospital de Clinicas de Porto Alegre - HCPA - www.hcpa.ufrgs.br Member of the World Psychiatric Association (WPA) Early Career Psychiatrists Council Area Coordinator for the Americas - http://www.wpanet.org/detail.php?section_id"&content_id0 Web based CV: http://lattes.cnpq.br/3896757276389918 [log in to unmask] 2011/2/9 Richard Morris < [log in to unmask] > I also installed the SPM8 4010 update on my mac (snow leopard 10.6.5) and get the same error when I try to run spm8 fmri. Is there a solution to this problem? Ta, Rich - ------=_Part_149929_502318070.1297231553722 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable

Word to the wise, regarding instructions to update SPM. If you use the Mac OS (e.g., Leopard or Snow Leopard), don't copy the update folders to your SPM directory as per Guillaume's instructions. Apple, in their divine wisdom, elected to instantiate a version of 'copy' which overwrites rather than merges common directories. So you will lose your original SPM files in any directories with a common name.

I don't know what the best command to use to merge the files in terminal might be, but perhaps someone here does!

Rich

From: "Felipe Picon" <[log in to unmask]>
To: "Richard Morris" <[log in to unmask]>
Cc: [log in to unmask]
Sent: Wednesday, 9 February, 2011 1:14:26 PM
Subject: Re: [SPM] SPM8 updates installing error in Mac

Hi Richard,

I just received this possible solution (below), but I didn't tried it out yet.

1) reinstalled spm 8, using command line to unzip the folder rather than double clicking on the folder and using mac's archive utility to unzip.

2) re-set my path to only include matlab/spm8 not subfolders.

3) did not install the updates 4010

4) didn't keep spm in my matlab toolbox

Hope this helps

2011/2/9 Richard Morris <[log in to unmask]>

I also installed the SPM8 4010 update on my mac (snow leopard 10.6.5) and get the same error when I try to run spm8 fmri. Is there a solution to this problem?

Ta,

Rich

------=_Part_149929_502318070.1297231553722-- ========================================================================Date: Wed, 9 Feb 2011 01:10:11 -0500 Reply-To: "Robert L. Savoy" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Robert L. Savoy" <[log in to unmask]> Subject: Hands-On SPM8 Training 2011Feb28 and 2011Mar07 in Boston; Future (New) Programs Content-Type: multipart/alternative; boundary=Apple-Mail-298--1050452246 Mime-Version: 1.0 (Apple Message framework v1081) Message-ID: <[log in to unmask]> --Apple-Mail-298--1050452246 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 Upcoming Courses in fMRI Experimental Design and Analysis This year we will be offering our continuing series of functional neuroimaging courses covering various conceptual and practical aspects of fMRI experimental design and analysis. In the SPM Basics and Network Analysis programs the laboratory exercises will focus on the use of SPM8, with extensive discussion of a variety of complementary software tools. Lectures and laboratory exercises will cover preprocessing, statistical modeling, visualization and anatomical labeling of data associated with a broad range of neuroimaging experimental designs. Please note that the SPM Basics and Network Analysis programs require that you bring a laptop with MATLAB installed. These courses are designed for investigators with a modest level of experience with functional MRI and basic statistics. The current schedule includes: SPM8 for Basic and Clinical Investigators - Boston, MA FEB 28 – MAR 4, 2011 http://www.neurometrika.org/BasicSPM Network Analysis - Boston, MA MAR 7 – MAR 11, 2011 http://www.neurometrika.org/NetworkAnalysis SPM8 for Basic and Clinical Investigators – Baltimore, MD MAR 14 – MAR 18, 2011 http://neurometrika.org/SPM_Basics_Baltimore SPM8 for Basic and Clinical Investigators – Guangzhou, P.R. China JUL 11 – JUL 15, 2011 http://www.neurometrika.org/BasicSPM *NEW OFFERING* Functional MRI in Clinical Research and Practice is a novel new course designed to give clinical investigators an intensive introduction to the issues associated with using structural and functional MRI in clinical research studies. The lectures have been specifically designed to help medically trained investigators with an interest in neuroimaging make the sometimes difficult transition from clinical training to medical research. Topics will include the basic principles of MRI measurement, techniques for stimulus presentation and response recording in high magnetic fields, basic statistical methods, individual subject experimental design and analysis techniques, group design and analysis techniques, clinical study design principles and guidelines for reporting clinical neuroimaging studies. As functional neuroimaging is enjoying an expanding role in clinical practice, coverage of current and future applications will be explored. The interactive portion of this course will include opportunities for participants to work in small groups to explore measurement, design and analysis issues associated with the use of neuroimaging as an endpoint in clinical research. Course materials will include guidance and material to facilitate subsequent self-study. Functional MRI in Clinical Research and Practice - Pittsburgh, PA JUN 13 – JUN 17, 2011 http://neurometrika.org/fMRI_Clinical A complete listing of all the 2011 courses can be found at: http://www.neurometrika.org/Courses Please feel free to forward this announcement to any colleagues you think might be interested. --Apple-Mail-298--1050452246 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252

Upcoming Courses in fMRI Experimental Design and Analysis

This year we will be offering our continuing series of functional
neuroimaging courses covering various conceptual and practical
aspects of fMRI experimental design and analysis. In the SPM Basics
and Network Analysis programs the laboratory exercises will focus on
the use of SPM8, with extensive discussion of a variety of
complementary software tools. Lectures and laboratory exercises will
cover preprocessing, statistical modeling, visualization and
anatomical labeling of data associated with a broad range of
neuroimaging experimental designs. Please note that the SPM Basics
and Network Analysis programs require that you bring a laptop with
MATLAB installed. These courses are designed for investigators with
a modest level of experience with functional MRI and basic
statistics. The current schedule includes:

SPM8 for Basic and Clinical Investigators - Boston, MA
FEB 28 – MAR 4, 2011
http://www.neurometrika.org/BasicSPM

Network Analysis - Boston, MA
MAR 7 – MAR 11, 2011
http://www.neurometrika.org/NetworkAnalysis

SPM8 for Basic and Clinical Investigators – Baltimore, MD
MAR 14 – MAR 18, 2011
http://neurometrika.org/SPM_Basics_Baltimore

SPM8 for Basic and Clinical Investigators – Guangzhou, P.R. China
JUL 11 – JUL 15, 2011
http://www.neurometrika.org/BasicSPM

*NEW OFFERING*

Functional MRI in Clinical Research and Practice is a novel new
course designed to give clinical investigators an intensive
introduction to the issues associated with using structural and
functional MRI in clinical research studies. The lectures have been
specifically designed to help medically trained investigators with
an interest in neuroimaging make the sometimes difficult transition
from clinical training to medical research. Topics will include the
basic principles of MRI measurement, techniques for stimulus
presentation and response recording in high magnetic fields, basic
statistical methods, individual subject experimental design and
analysis techniques, group design and analysis techniques, clinical
study design principles and guidelines for reporting clinical
neuroimaging studies. As functional neuroimaging is enjoying an
expanding role in clinical practice, coverage of current and future
applications will be explored. The interactive portion of this
course will include opportunities for participants to work in small
groups to explore measurement, design and analysis issues associated
with the use of neuroimaging as an endpoint in clinical research.
Course materials will include guidance and material to facilitate
subsequent self-study.

Functional MRI in Clinical Research and Practice - Pittsburgh, PA
JUN 13 – JUN 17, 2011
http://neurometrika.org/fMRI_Clinical

A complete listing of all the 2011 courses can be found at:
http://www.neurometrika.org/Courses
Please feel free to forward this announcement to any colleagues you
think might be interested.

--Apple-Mail-298--1050452246-- ========================================================================Date: Wed, 9 Feb 2011 10:37:44 +0100 Reply-To: Martin Jensen Dietz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Martin Jensen Dietz <[log in to unmask]> Subject: Re: imcalc to do a contrast? Comments: To: Soha Saleh <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi Soha, As an alternative to taking to the con images from a [1 -1 -1 1] contrast to a one-sample t-test, you could take your (A>B) and (C>D) con images to a paired t-test at the second level. This may seem more transparent to you. Best wishes, Martin ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Soha Saleh [[log in to unmask]] Sent: 08 February 2011 17:59 To: [log in to unmask] Subject: [SPM] imcalc to do a contrast? Hi: I have 4 conditions in my experiment (A, B, C, D).. I am interested in finding the neuro activity that is higher in A than in B, and also the areas where the difference in activity between A and B, are higher than between C and D. Simply it is (A>B) - (C>D). I did each of the contrasts (A>B) and (C>D) in SPM8 and then used imcalc to subtract them to get (A>B)-(C>D). I got huge activity and T value that is very high which made me suspicious about this procedure. My questions are: Is this a valid procedure? Is there any precautions or limitations I need to be aware of? Thank you, I appreciate any assistance or advice you give. Kind regards, Soha ========================================================================Date: Wed, 9 Feb 2011 14:16:17 +0000 Reply-To: Tatiana Usnich <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Tatiana Usnich <[log in to unmask]> Subject: Re: Question regarding the fMRI model specification Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-sjdkecA9iT1QtS5ApCABsAQynPWOAUQ3r2vaAA1LdSvSh" Message-ID: <[log in to unmask]> --part-sjdkecA9iT1QtS5ApCABsAQynPWOAUQ3r2vaAA1LdSvSh Content-Type: multipart/mixed; boundary="part-CauArZiIs945AXavGX1D6AdtUETknsDjOrnFmeySjA5M6" This is a multi-part message in MIME format. --part-CauArZiIs945AXavGX1D6AdtUETknsDjOrnFmeySjA5M6 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Dear SPMlers, We also have the same problem. Starting to run fMRI model specification in SPM8 we are asked for a vector of onsets for one of the conditions and durations even though the conditions are specified in a .mat file. Conditions file is attached. Id really appreciate your help. Tatiana --part-CauArZiIs945AXavGX1D6AdtUETknsDjOrnFmeySjA5M6-- --part-sjdkecA9iT1QtS5ApCABsAQynPWOAUQ3r2vaAA1LdSvSh Content-Transfer-Encoding: base64 Content-Type: APPLICATION/OCTET-STREAM; name="conditions.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBNb24g SnVuIDA4IDE1OjEyOjQ2IDIwMDkgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgIAABSU0PAAAAjQAAAHic42NgYJBgYmBgA9IcQMzIAAGsSHx2KA0S y0vMTS0G0nxAbMGA0MeCRR+yeQJQfnFqTlp8bmouSL8DAf2caPo5YfrzgAZAxck1Jw/mEBLM 4UIzhwtuDsJBxIQLK5o5IH5qUVF+EZHu4EXTD+LnpKaVxBelFhfkA11EpDl8aOaA+EWZ6RnI BgEAet8ivw8AAABFAAAAeJzjY2BgOMDIwMAGpDmAGMgEA1YkPjuU5gTilNKixJLM/LxiqDo+ IDZgQOhnw6KfEclcJiCLZVQfVn3khicIAAAvygeuDwAAAPoDAAB4nJ1TC0yTVxj9K20RVEBW GU8R49yc4oYBhvjggLyciFAr8hqjQHlIgUJbWlpa+gB84kAmsoempup4JCRmJBsbiyhjjA0I ghnTLWOAzgGbA4OCA3H3p0tmmmUku8mX+8i95/vOd861oSgqk0VRbDIvJ8GgTIP13N7y75m+ k5crFkjEFGVD1nLqn3fsf3ln9RwevQ4nIR60fxjokoXmK0Wdm7h5WLls81GHahH8Zm+kNZzK x97zmo03aiWYdyQ3S+Rwoh8YFMAL/PaBfi2co++4zHfoQeePXSI/yyz/KyTs+oK/n/R2x8rq el30rQKUxQ+0jhqLkbgucGr0ugaKy1v2fCjQLuL7LYHPMMOnz2tz7fVtTeL/XR+1v3TVgZlg dJeEX1NVxSPFLnZ7c0Q2xpneq32bZTg2/cOgNq5oEb9vCXyOGb6BxCJO4na0JRDC7RHYeJBl PFPOBbPjHS/ZWAwUsXU+PbIkpM/c5V+M5COq4lyuvUUqWirYkw48AWwVvo27wzLwVY635pN1 OQgreGI7dlEKv+86cry/KQSRp5ivVOJqVPb7svMqdD/oag86qgZ9vM1QgtCXpMucQzSg287J 0MFCvmJD3YTO5INok65tjP/mtc2MVyXZbDj3rFKt9ETpaxFXrGsCcLDG47S0Igpz/qLeY4U8 Ex9uLNp+u7BzCycOLdlWTwxrktHFu153iZmCuW9JoV+nIvVBfNBu63QYz/gz9qenY9IhPGp+ JAtT02/GlW89At1IZ+3n4zngkbb5++Qi4HDC1Wu2IrQPbBY47RKBZOv/6XY++PTBigLcd/vo 1/B4sYlviAS80csffDkuQXHfx3GP3KQQyonjlheibCL607U+hei6F3BpyE2G+Ectd05EyCCt dmTGdMvRn/e2ZS+zCH+qj7RKxhVwYsbYeHooYeVyIWlTjxLDCk5T2FMlJkZ8ndssVHiY2v3W vlkVKg2zXr8Mq7EgmLk7zNCgc98bxwOdNDhEaEcWaBBXTpQb0yD03cfsoVNa07+b10JP+niC o1v8do2ndfhxUCsOrdfh7C2rzPWuergSOQ956bGW0Gsq1yNtx9CBVb/rEZR8r289u3RRR7sl dHQ10/FnEsFZeZNl772OaWGMqskmEOW0QJJwEFfuiKzaixeJHI4WUdj5BbfGYyEBOtq4M4mg 7XM7KRnWg0bRrrQUqAJ8kj5bkwZ3WlBOJmh7ex7OREMyj/syR2jypbvQ1J8UIXq6ybDOxzOP p4+lW8XodSJOdpYg8mZV4gIlh5tfRnBWkByVBMajoQg+f/iFhI0U4f5c/VT/qyqwtWf3nPRW I50eliXINTJaWSc1cKCNxdKCVH9zdaMWfwG+LK+K --part-sjdkecA9iT1QtS5ApCABsAQynPWOAUQ3r2vaAA1LdSvSh-- ========================================================================Date: Wed, 9 Feb 2011 10:20:06 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Question regarding the fMRI model specification Comments: To: Tatiana Usnich <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> (1) You probably have less data than time in your conditions.mat (this would make it appear that no events and it might ask for conditions; how did you specify the time in the design and how many time points are in each run? (2) Have you updated SPM with the latest updates? I just used your condition.mat file with some random files and it created a design without a problem. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Wed, Feb 9, 2011 at 9:16 AM, Tatiana Usnich <[log in to unmask]> wrote: > Dear SPMlers, > > We also have the same problem. Starting to run fMRI model specification in SPM8 we are asked for a vector of onsets for one of the conditions and durations even though the conditions are specified in a .mat file. > > Conditions file is attached. Id really appreciate your help. > > Tatiana > ========================================================================Date: Wed, 9 Feb 2011 16:53:43 +0000 Reply-To: Scott Hayes <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Scott Hayes <[log in to unmask]> Subject: Research Assistant and Postdoctoral Fellowship Positions at VA Boston Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> We anticipate an opening for a full-time research assistant to begin in June, 2011. We are seeking applicants with previous experiences with behavioral testing and neuroimaging. The RA will assist with two primary projects, one focusing on aging and memory, and the other focusing on the elucidation of neural correlates of Posttraumatic Stress Disorder and Traumatic Brain Injury. Mieke Verfaellie and David Salat also have an opening for a postdoctoral fellow examining the neural correlates of blast-induced TBI. Additional details for both positions are available here: http://www.bu.edu/brainlab/job-openings/ ========================================================================Date: Wed, 9 Feb 2011 17:13:52 +0000 Reply-To: Scott Hayes <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Scott Hayes <[log in to unmask]> Subject: Re: Research Assistant and Postdoctoral Fellowship Positions at VA Boston Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Full description of RA position: Preliminary Announcement for Neuroimaging Research Assistant Position National Center for Posttraumatic Stress Disorder (Boston) The Behavioral Science Division of the National Center for PTSD, located at the VA Boston Healthcare System, anticipates an opening for a research assistant who will work on projects related to neuroimaging of posttraumatic stress disorder and memory disorders. Responsibilities for this position include: Collecting behavioral and neuroimaging data from healthy and clinical subjects, entering data and managing databases, performing statistical analyses, and assisting in the preparation of grants, manuscripts, and professional presentations. Applicants should have a B.A./B.S. (concentration in Cognitive Neuroscience/Psychology is preferred), experience working in an imaging laboratory and running functional magnetic resonance imaging (fMRI) experiments, proficiency with UNIX and MATLAB, FSL, and/or SPM, and the maturity and interpersonal sensitivity necessary for working in a clinical setting. Openings are expected for June 2011. Salary and benefits are highly competitive with similar positions at institutions in the Boston area. A two year commitment typically is required. Official job postings and formal application instructions are expected to appear in early March 2011, but potential candidates are encouraged to submit the following materials prior to formal application: 1) a letter describing their interest in a position and related career goals, 2) a resume, 3) a current transcript, and 4) at least two letters of recommendation. Materials can be sent electronically or by mail to: Andrea Carney National Center for PTSD (116B-2) VA Boston Healthcare System 150 South Huntington Ave. Boston, MA 02130-4817 [log in to unmask] Further information can be obtained via e-mail or by phone at (857) 364-4143, or visit the websites: http://www.bu.edu/traumamemorylab/ http://www.bu.edu/brainlab/ ========================================================================Date: Wed, 9 Feb 2011 17:54:43 +0000 Reply-To: Cyril Pernet <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cyril Pernet <[log in to unmask]> Subject: spm_preproc In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Content-Disposition: inline Message-ID: <[log in to unmask]> Dear SPMers, John silly question to get my transformation matrices after segmenting ... once I run spm_prep2sn, what function shall I call to write down on the disk the transformation matrices - couldn't find out .. cheers cyril -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ========================================================================Date: Wed, 9 Feb 2011 18:10:44 +0000 Reply-To: Mohamed Seghier <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mohamed Seghier <[log in to unmask]> Subject: Re: spm_preproc Comments: To: Cyril Pernet <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hi Cyril, Let's say R = spm_preproc('your anatomical image'); just run PO = spm_prep2sn(R) ; and then, all the variables that you need are: VG = PO.VG ; VF = PO.VF ; Tr = PO.Tr ; Affine = PO.Affine ; flags = PO.flags ; You can save them as an '*_seg_sn.mat' if your wish... I hope this helps, Mohamed On 09/02/2011 17:54, Cyril Pernet wrote: > Dear SPMers, John > > silly question to get my transformation matrices after segmenting ... > once I run spm_prep2sn, what function shall I call to write down on > the disk the transformation matrices - couldn't find out .. > > cheers > cyril > > > ========================================================================Date: Wed, 9 Feb 2011 11:41:10 -0800 Reply-To: kambiz rakhshan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: kambiz rakhshan <[log in to unmask]> Subject: Vbm8 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1928328450-1297280470=:17947" Message-ID: <[log in to unmask]> --0-1928328450-1297280470=:17947 Content-Type: text/plain; charset=us-ascii Dear experts, I have done a vbm analysis on 100 subjects with age range between 25-80. I used vbm8 toolbox and modulated non-linear only option to modulate the normalizad data. My specific question was to find relative volumetric loss that were not induced by total gray matter. I have two question now: 1-should one include TIV as a covariate while using non-linear modulate option? 2- in my analysis, i didn't include TIV as cov but instead age as a cov variable. Negative correlation with age reflects almost whole the brain with a very strong t value( P<0.0001,FWE).what could be the reason for that? I basically used all the default options for preprocessing. Additionally,i have removed all the potential outliers that seem to have an artifact. Any comment will be highly appreciate. Best. Kami --0-1928328450-1297280470=:17947 Content-Type: text/html; charset=us-ascii

Dear experts, I have done a vbm analysis on 100 subjects with age range between 25-80. I used vbm8 toolbox and modulated non-linear only option to modulate the normalizad data. My specific question was to find relative volumetric loss that were not induced by total gray matter. I have two question now: 1-should one include TIV as a covariate while using non-linear modulate option? 2- in my analysis, i didn't include TIV as cov but instead age as a cov variable. Negative correlation with age reflects almost whole the brain with a very strong t value( P<0.0001,FWE).what could be the reason for that? I basically used all the default options for preprocessing. Additionally,i have removed all the potential outliers that seem to have an artifact. Any comment will be highly appreciate. Best. Kami

--0-1928328450-1297280470=:17947-- ========================================================================Date: Wed, 9 Feb 2011 16:34:28 -0500 Reply-To: justine dupont <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: justine dupont <[log in to unmask]> Subject: Re: non isotropic voxels for DARTEL anatomical template? Content-Type: multipart/alternative; boundary="_350ede90-50ed-41d4-8d62-165044775448_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_350ede90-50ed-41d4-8d62-165044775448_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear John and other experts I have inherited a nice T1 dataset but the voxels are anisotropic (-0.97 x 0.97 x 1). I want to do a VBM study using these data, however according to a very recent post by John on the list, DARTEL is not appropriate for anisotropic voxels. So my question is... is it OK to use traditional VBM method (i.e. not using DARTEL) for such data? thank you very much :) Justine --_350ede90-50ed-41d4-8d62-165044775448_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear John and other experts

I have inherited a nice T1 dataset but the voxels are anisotropic (-0.97 x 0.97 x 1). I want to do a VBM study using these data, however according to a very recent post by John on the list, DARTEL is not appropriate for anisotropic voxels. So my question is... is it OK to use traditional VBM method (i.e. not using DARTEL) for such data?

thank you very much :)

Justine

--_350ede90-50ed-41d4-8d62-165044775448_-- ========================================================================Date: Wed, 9 Feb 2011 17:37:58 -0500 Reply-To: =?ISO-8859-1?Q?Véronique_Paradis?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Véronique_Paradis?= <[log in to unmask]> Subject: negative beta values In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e656b59ae16c2b049be11db9 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Bonjour our dear SPMers We have the following non-intuitive problem which can be easily stated: 1 -After making a T test on a simple condition (a beta value greater than 0) we get the coordinates of the activated regions for a given spherical region of interest. 2- We thus expect the mean beta value within those ROI to be positive. 3- We extract the beta values themselves (from the beta and SPM files in the corresponding statistical result directory) of all the voxels in the ROIs 4- When we compute means and stdev from them, we sometimes get negative mean beta values which are statistically significant when compared to their stdev. 5- How does it make sense to have both positive and negative beta values considering that we used the same spherical ROI in each cases?. 6- The other point is that when we plot the beta value for a given ROI using SPM we always get positive and much larger values. How can we make sense of all of this? No doubt that other readers have met the same problem before. Can you shed any light on this, we are still groping in the dark... Merci Pierre and Véronique --0016e656b59ae16c2b049be11db9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Bonjour our dear SPMers

We have the following non-intuitive problem which can be easily stated:

1 -After making a T test on a simple condition (a beta value greater than 0) we get the coordinates of the activated regions for a given spherical region of interest.

2- We thus expect the mean beta value within those ROI to be positive.

3- We extract the beta values themselves (from the beta and SPM files in the corresponding statistical result directory) of all the voxels in the ROIs

4- When we compute means and stdev from them, we sometimes get negative mean beta values which are statistically significant when compared to their stdev.

5- How does it make sense to have both positive and negative beta values considering that we used the same spherical ROI in each cases?.

6- The other point is that when we plot the beta value for a given ROI using SPM we always get positive and much larger values.

How can we make sense of all of this?

No doubt that other readers have met the same problem before.

Can you shed any light on this, we are still groping in the dark...

Merci

Pierre and Véronique

--0016e656b59ae16c2b049be11db9-- ========================================================================Date: Wed, 9 Feb 2011 17:50:58 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: Vbm8 Comments: To: kambiz rakhshan <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00163630ed1965d720049be14c09 Content-Type: text/plain; charset=ISO-8859-1 Dear Kami, Dear experts, I have done a vbm analysis on 100 subjects with age range > between 25-80. I used vbm8 toolbox and modulated non-linear only option to > modulate the normalizad data. My specific question was to find relative > volumetric loss that were not induced by total gray matter. I have two > question now: 1-should one include TIV as a covariate while using non-linear > modulate option? 2- in my analysis, i didn't include TIV as cov but instead > age as a cov variable. Negative correlation with age reflects almost whole > the brain with a very strong t value( P<0.0001,FWE).what could be the reason > for that? I basically used all the default options for preprocessing. > Additionally,i have removed all the potential outliers that seem to have an > artifact. Any comment will be highly appreciate. Best. Kami > 1) I have not used the VBM8 toolbox so will let others comment on the specifics. My understanding is that most people who do the non-linear modulate option would not include TIV in the statistical model, under the assumption that the affine scaling component accurately reflects differences in TIV (Buckner et al., 2004). At the same time, if you have a sufficient number of subjects, I don't think there is any downside to including it, as there may be residual variance you can explain (i.e. affine scaling is probably not *perfectly* correlated with TIV). (I suppose one negative would be that if tissue class segmentation provides a poor estimate of TIV, or one that included systematic biases, than including this in the model might decrease sensitivity to effects of interest...). I would be very interested to hear comments from others on this approach. The most common approach when not using the VBM8 toolbox would be to modulate images (for both linear + nonlinear components) and simply try to address differences in TIV in the statistical model (Barnes et al., 2010). 2) There are multiple types of global effects which may affect your data in different ways. One of course is TIV; another is total gray matter (TGM). If you look at, for example, Good et al. (2001; Fig 3) you can see that these two measures don't always correlate. That is, generally, if you have a bigger head (TIV) you may have a bigger brain (TGM). But in the case of aging, there is also age-related GM loss, so as you age TGM decreases, but your skull doesn't shrink (thus, TIV doesn't necessarily change). (Of course, in cross-sectional studies there may be systematic effects on TIV due to nutrition or health care etc., but I'm leaving those aside for now.) Given that there are reliable decreases in GM in most areas of the brain with age, your findings are sensible. If you want to identify areas that change differently than TGM, you will need to somehow account for TGM in your analysis. An easy of doing this is to include TGM in your statistical model as a covariate (although this is not always appropriate; it really depends on what you are interested in). Hope this helps, Jonathan Barnes J, Ridgway GR, Bartlett J, Henley SMD, Lehmann M, Hobbs N, Clarkson MJ, MacManus DG, Ourselin S, Fox NC (Head size, age and gender adjustment in MRI studies: a necessary nuisance? NeuroImage 53:1244-1255.2010). Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ (A unified approach for morphometric and functional data analysis in young, old, and demented adutls using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 23:724-738.2004). Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14:21-36.2001). --00163630ed1965d720049be14c09 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Kami,

Dear experts, I have done a vbm analysis on 100 subjects with age range between 25-80. I used vbm8 toolbox and modulated non-linear only option to modulate the normalizad data. My specific question was to find relative volumetric loss that were not induced by total gray matter. I have two question now: 1-should one include TIV as a covariate while using non-linear modulate option? 2- in my analysis, i didn't include TIV as cov but instead age as a cov variable. Negative correlation with age reflects almost whole the brain with a very strong t value( P<0.0001,FWE).what could be the reason for that? I basically used all the default options for preprocessing. Additionally,i have removed all the potential outliers that seem to have an artifact. Any comment will be highly appreciate. Best. Kami

1) I have not used the VBM8 toolbox so will let others comment on the specifics. My understanding is that most people who do the non-linear modulate option would not include TIV in the statistical model, under the assumption that the affine scaling component accurately reflects differences in TIV (Buckner et al., 2004). At the same time, if you have a sufficient number of subjects, I don't think there is any downside to including it, as there may be residual variance you can explain (i.e. affine scaling is probably not *perfectly* correlated with TIV). (I suppose one negative would be that if tissue class segmentation provides a poor estimate of TIV, or one that included systematic biases, than including this in the model might decrease sensitivity to effects of interest...). I would be very interested to hear comments from others on this approach.

The most common approach when not using the VBM8 toolbox would be to modulate images (for both linear + nonlinear components) and simply try to address differences in TIV in the statistical model (Barnes et al., 2010).

2) There are multiple types of global effects which may affect your data in different ways. One of course is TIV; another is total gray matter (TGM). If you look at, for example, Good et al. (2001; Fig 3) you can see that these two measures don't always correlate. That is, generally, if you have a bigger head (TIV) you may have a bigger brain (TGM). But in the case of aging, there is also age-related GM loss, so as you age TGM decreases, but your skull doesn't shrink (thus, TIV doesn't necessarily change). (Of course, in cross-sectional studies there may be systematic effects on TIV due to nutrition or health care etc., but I'm leaving those aside for now.) Given that there are reliable decreases in GM in most areas of the brain with age, your findings are sensible. If you want to identify areas that change differently than TGM, you will need to somehow account for TGM in your analysis. An easy of doing this is to include TGM in your statistical model as a covariate (although this is not always appropriate; it really depends on what you are interested in).

Hope this helps,

Jonathan

Barnes J, Ridgway GR, Bartlett J, Henley SMD, Lehmann M, Hobbs N, Clarkson MJ, MacManus DG, Ourselin S, Fox NC (Head size, age and gender adjustment in MRI studies: a necessary nuisance? NeuroImage 53:1244-1255.2010).

Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ (A unified approach for morphometric and functional data analysis in young, old, and demented adutls using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 23:724-738.2004).

Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14:21-36.2001).

--00163630ed1965d720049be14c09-- ========================================================================Date: Wed, 9 Feb 2011 22:03:53 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: negative beta values Comments: To: =?iso-8859-1?Q?Véronique_Paradis?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Véronique and Pierre, Please see inline responses below. I think that the answer to your question depends on the details of your analysis, so if you cannot figure this out, it would be helpful to know a bit more about the analysis you've done. > 1 -After making a T test on a simple condition (a beta value greater than 0) we get the coordinates of the activated regions for a given spherical region of interest. > 2- We thus expect the mean beta value within those ROI to be positive. I understand you to mean that you did a whole-brain analysis in SPM, identified some clusters that were significant, and then wanted to extract the parameter estimates (contrast values) for these regions. You are correct to think that if voxels show a positive effect in a t-test (e.g., a [1] contrast over a condition), that these same voxels should show positive beta values for that condition. I suspect the difference may arise in the details of how you constructed your ROIs. For example, if you create spherical ROIs around peak coordinates, there could be voxels within that sphere that do not show a significant effect in the t-test; the bigger the sphere, the more likely this is to be true. If you want to just look at the voxels that show a significant effect in your t-test, you can build such an ROI using MarsBar (http://marsbar.sourceforge.net/)—that is, an ROI equivalent to an SPM cluster. So, it would be helpful to know the specifics of your analysis (single subject or group design, for example), and the details of how you constructed the ROIs. > 3- We extract the beta values themselves (from the beta and SPM files in the corresponding statistical result directory) of all the voxels in the ROIs Again, it would help to know the specifics—what files you extracted the data from, and the procedure you used to extract the values. > 4- When we compute means and stdev from them, we sometimes get negative mean beta values which are statistically significant when compared to their stdev. > 5- How does it make sense to have both positive and negative beta values considering that we used the same spherical ROI in each cases?. (I'm not sure what cases you mean here, so I may have misunderstood what you are trying to do...) > 6- The other point is that when we plot the beta value for a given ROI using SPM we always get positive and much larger values. It would be good to verify how you are plotting the beta value for an ROI in SPM. However, as indeed you would expect the beta values to be positive, it might suggest that whatever values are extracted by SPM in this way are accurate, whereas the alternate approach (that results in the negative beta values) is not accurate (or at least, not what you want to do). > How can we make sense of all of this? It's seldom easy! ;-) Good luck. Best regards, Jonathan ========================================================================Date: Thu, 10 Feb 2011 08:43:13 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: spm_preproc Comments: To: Mohamed Seghier <[log in to unmask]> Comments: cc: cyril pernet <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Cyril, Mohamed, > Let's say R = spm_preproc('your anatomical image'); > just run PO = spm_prep2sn(R) ; > and then, all the variables that you need are: > VG = PO.VG ; > VF = PO.VF ; > Tr = PO.Tr ; > Affine = PO.Affine ; > flags = PO.flags ; just to add another 2 cents: prep2sn is multifunctional, so if you call it without an output argument, it will save the segmentation parameters appropriately, as in % segment dataset, need to specify options beforehand results = spm_preproc(ana,opts_seg); % process generated spatial normalization parameters [po,pin] = spm_prep2sn(results); % save parameters spm_prep2sn(results); % write out segmented data (better specify these options, too :) spm_preproc_write(po,opts_write); This saved me time and some headache, hope it does for you, too. Cheers, Marko -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Thu, 10 Feb 2011 07:58:14 +0000 Reply-To: =?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?= <[log in to unmask]> Subject: Re: spm_preproc Comments: To: =?utf-8?B?TWFya28gV2lsa2U=?= <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----------=_1297324745-21276-186" Content-Transfer-Encoding: binary Message-ID: <[log in to unmask]> This is a multi-part message in MIME format... ------------=_1297324745-21276-186 Received: from [192.168.0.2] (5ada7242.bb.sky.com [90.218.114.66]) (authenticated user=cpernet mech=LOGIN bits=0) by lmtp1.ucs.ed.ac.uk (8.13.8/8.13.7) with ESMTP id p1A7x4gb015293 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-SHA bits%6 verify=NOT); Thu, 10 Feb 2011 07:59:05 GMT Message-Id: <[log in to unmask]> To: "=?utf-8?B?TWFya28gV2lsa2U=?=" <[log in to unmask]>, [log in to unmask] From: "=?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?=" <[log in to unmask]> Subject: =?utf-8?B?UmU6IFtTUE1dIHNwbV9wcmVwcm9j?Date: Thu, 10 Feb 2011 07:58:14 +0000 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_22_1297324694821" X-Edinburgh-Scanned: at lmtp1.ucs.ed.ac.uk with MIMEDefang 2.52, Sophie, Sophos Anti-Virus X-Scanned-By: MIMEDefang 2.52 on 129.215.149.64 ------=_Part_22_1297324694821 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline VGh4IGd1eXMgaSdsbCBnaXZlIGl0IGEgZ28gLi4gY291bGQgZ2V0IFIgYW5k IHdyaXRlIHNlZ21lbnRlZCBpbWFnZXMgYnV0IHN0dWNrIG9uIG5vcm1hbGl6 YXRpb24gcGFyYW0gLi4gCgpDCgpEciBDeXJpbCBQZXJuZXQKU0JJUkMgLyBT SU5BUFNFClVuaXZlcnNpdHkgb2YgRWRpbmJ1cmdoCgotLS0tLSBSZXBseSBt ZXNzYWdlIC0tLS0tCkZyb206ICJNYXJrbyBXaWxrZSIgPE1hcmtvLldpbGtl QE1FRC5VTkktVFVFQklOR0VOLkRFPgpEYXRlOiBUaHUsIEZlYiAxMCwgMjAx MSAwNzo0MwpTdWJqZWN0OiBbU1BNXSBzcG1fcHJlcHJvYwpUbzogPFNQTUBK SVNDTUFJTC5BQy5VSz4KCkhpIEN5cmlsLCBNb2hhbWVkLAoKPiBMZXQncyBz YXkgUiA9IHNwbV9wcmVwcm9jKCd5b3VyIGFuYXRvbWljYWwgaW1hZ2UnKTsK PiBqdXN0IHJ1biBQTyA9IHNwbV9wcmVwMnNuKFIpIDsKPiBhbmQgdGhlbiwg YWxsIHRoZSB2YXJpYWJsZXMgdGhhdCB5b3UgbmVlZCBhcmU6Cj4gVkcgPSBQ Ty5WRyA7Cj4gVkYgPSBQTy5WRiA7Cj4gVHIgPSBQTy5UciA7Cj4gQWZmaW5l ID0gUE8uQWZmaW5lIDsKPiBmbGFncyA9IFBPLmZsYWdzIDsKCmp1c3QgdG8g YWRkIGFub3RoZXIgMiBjZW50czogcHJlcDJzbiBpcyBtdWx0aWZ1bmN0aW9u YWwsIHNvIGlmIHlvdSBjYWxsIAppdCB3aXRob3V0IGFuIG91dHB1dCBhcmd1 bWVudCwgaXQgd2lsbCBzYXZlIHRoZSBzZWdtZW50YXRpb24gcGFyYW1ldGVy cyAKYXBwcm9wcmlhdGVseSwgYXMgaW4KCiUgc2VnbWVudCBkYXRhc2V0LCBu ZWVkIHRvIHNwZWNpZnkgb3B0aW9ucyBiZWZvcmVoYW5kCiAgIHJlc3VsdHMg PSBzcG1fcHJlcHJvYyhhbmEsb3B0c19zZWcpOwoKJSBwcm9jZXNzIGdlbmVy YXRlZCBzcGF0aWFsIG5vcm1hbGl6YXRpb24gcGFyYW1ldGVycwogICBbcG8s cGluXSA9IHNwbV9wcmVwMnNuKHJlc3VsdHMpOwoKJSBzYXZlIHBhcmFtZXRl cnMKICAgc3BtX3ByZXAyc24ocmVzdWx0cyk7CgolIHdyaXRlIG91dCBzZWdt ZW50ZWQgZGF0YSAoYmV0dGVyIHNwZWNpZnkgdGhlc2Ugb3B0aW9ucywgdG9v IDopCiAgIHNwbV9wcmVwcm9jX3dyaXRlKHBvLG9wdHNfd3JpdGUpOwoKClRo aXMgc2F2ZWQgbWUgdGltZSBhbmQgc29tZSBoZWFkYWNoZSwgaG9wZSBpdCBk b2VzIGZvciB5b3UsIHRvby4KCkNoZWVycywKTWFya28KLS0gCl9fX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX18K UEQgRHIuIG1lZC4gTWFya28gV2lsa2UKICBGYWNoYXJ6dCBmw7xyIEtpbmRl ci0gdW5kIEp1Z2VuZG1lZGl6aW4KICBMZWl0ZXIsIEV4cGVyaW1lbnRlbGxl IFDDpGRpYXRyaXNjaGUgTmV1cm9iaWxkZ2VidW5nCiAgVW5pdmVyc2l0w6R0 cy1LaW5kZXJrbGluaWsKICBBYnQuIElJSSAoTmV1cm9ww6RkaWF0cmllKQoK Ck1hcmtvIFdpbGtlLCBNRCwgUGhECiAgUGVkaWF0cmljaWFuCiAgSGVhZCwg RXhwZXJpbWVudGFsIFBlZGlhdHJpYyBOZXVyb2ltYWdpbmcKICBVbml2ZXJz aXR5IENoaWxkcmVuJ3MgSG9zcGl0YWwKICBEZXB0LiBJSUkgKFBlZGlhdHJp YyBOZXVyb2xvZ3kpCgoKSG9wcGUtU2V5bGVyLVN0ci4gMQogIEQgLSA3MjA3 NiBUw7xiaW5nZW4sIEdlcm1hbnkKICBUZWwuICs0OSA3MDcxIDI5LTgzNDE2 CiAgRmF4ICArNDkgNzA3MSAyOS01NDczCiAgbWFya28ud2lsa2VAbWVkLnVu aS10dWViaW5nZW4uZGUKCiAgaHR0cDovL3d3dy5tZWRpemluLnVuaS10dWVi aW5nZW4uZGUva2luZGVyL2VwbgpfX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fCgo ------=_Part_22_1297324694821 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline VGh4IGd1eXMgaSYjMzk7bGwgZ2l2ZSBpdCBhIGdvIC4uIGNvdWxkIGdldCBS IGFuZCB3cml0ZSBzZWdtZW50ZWQgaW1hZ2VzIGJ1dCBzdHVjayBvbiBub3Jt YWxpemF0aW9uIHBhcmFtIC4uIDxicj48YnI+Qzxicj48YnI+RHIgQ3lyaWwg UGVybmV0PGJyPlNCSVJDIC8gU0lOQVBTRTxicj5Vbml2ZXJzaXR5IG9mIEVk aW5idXJnaDxicj48YnI+LS0tLS0gUmVwbHkgbWVzc2FnZSAtLS0tLTxicj5G cm9tOiAmcXVvdDtNYXJrbyBXaWxrZSZxdW90OyAmbHQ7TWFya28uV2lsa2VA TUVELlVOSS1UVUVCSU5HRU4uREUmZ3Q7PGJyPkRhdGU6IFRodSwgRmViIDEw LCAyMDExIDA3OjQzPGJyPlN1YmplY3Q6IFtTUE1dIHNwbV9wcmVwcm9jPGJy PlRvOiAmbHQ7U1BNQEpJU0NNQUlMLkFDLlVLJmd0Ozxicj48YnI+SGkgQ3ly aWwsIE1vaGFtZWQsPGJyPjxicj4mZ3Q7IExldCYjMzk7cyBzYXkgUiA9IHNw bV9wcmVwcm9jKCYjMzk7eW91ciBhbmF0b21pY2FsIGltYWdlJiMzOTspOzxi cj4mZ3Q7IGp1c3QgcnVuIFBPID0gc3BtX3ByZXAyc24oUikgOzxicj4mZ3Q7 IGFuZCB0aGVuLCBhbGwgdGhlIHZhcmlhYmxlcyB0aGF0IHlvdSBuZWVkIGFy ZTo8YnI+Jmd0OyBWRyA9IFBPLlZHIDs8YnI+Jmd0OyBWRiA9IFBPLlZGIDs8 YnI+Jmd0OyBUciA9IFBPLlRyIDs8YnI+Jmd0OyBBZmZpbmUgPSBQTy5BZmZp bmUgOzxicj4mZ3Q7IGZsYWdzID0gUE8uZmxhZ3MgOzxicj48YnI+anVzdCB0 byBhZGQgYW5vdGhlciAyIGNlbnRzOiBwcmVwMnNuIGlzIG11bHRpZnVuY3Rp b25hbCwgc28gaWYgeW91IGNhbGwgPGJyPml0IHdpdGhvdXQgYW4gb3V0cHV0 IGFyZ3VtZW50LCBpdCB3aWxsIHNhdmUgdGhlIHNlZ21lbnRhdGlvbiBwYXJh bWV0ZXJzIDxicj5hcHByb3ByaWF0ZWx5LCBhcyBpbjxicj48YnI+JSBzZWdt ZW50IGRhdGFzZXQsIG5lZWQgdG8gc3BlY2lmeSBvcHRpb25zIGJlZm9yZWhh bmQ8YnI+ICZuYnNwOyByZXN1bHRzID0gc3BtX3ByZXByb2MoYW5hLG9wdHNf c2VnKTs8YnI+PGJyPiUgcHJvY2VzcyBnZW5lcmF0ZWQgc3BhdGlhbCBub3Jt YWxpemF0aW9uIHBhcmFtZXRlcnM8YnI+ICZuYnNwOyBbcG8scGluXSA9IHNw bV9wcmVwMnNuKHJlc3VsdHMpOzxicj48YnI+JSBzYXZlIHBhcmFtZXRlcnM8 YnI+ICZuYnNwOyBzcG1fcHJlcDJzbihyZXN1bHRzKTs8YnI+PGJyPiUgd3Jp dGUgb3V0IHNlZ21lbnRlZCBkYXRhIChiZXR0ZXIgc3BlY2lmeSB0aGVzZSBv cHRpb25zLCB0b28gOik8YnI+ICZuYnNwOyBzcG1fcHJlcHJvY193cml0ZShw byxvcHRzX3dyaXRlKTs8YnI+PGJyPjxicj5UaGlzIHNhdmVkIG1lIHRpbWUg YW5kIHNvbWUgaGVhZGFjaGUsIGhvcGUgaXQgZG9lcyBmb3IgeW91LCB0b28u PGJyPjxicj5DaGVlcnMsPGJyPk1hcmtvPGJyPi0tIDxicj5fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fPGJy PlBEIERyLiBtZWQuIE1hcmtvIFdpbGtlPGJyPiAmbmJzcDtGYWNoYXJ6dCBm w7xyIEtpbmRlci0gdW5kIEp1Z2VuZG1lZGl6aW48YnI+ICZuYnNwO0xlaXRl ciwgRXhwZXJpbWVudGVsbGUgUMOkZGlhdHJpc2NoZSBOZXVyb2JpbGRnZWJ1 bmc8YnI+ICZuYnNwO1VuaXZlcnNpdMOkdHMtS2luZGVya2xpbmlrPGJyPiAm bmJzcDtBYnQuIElJSSAoTmV1cm9ww6RkaWF0cmllKTxicj48YnI+PGJyPk1h cmtvIFdpbGtlLCBNRCwgUGhEPGJyPiAmbmJzcDtQZWRpYXRyaWNpYW48YnI+ ICZuYnNwO0hlYWQsIEV4cGVyaW1lbnRhbCBQZWRpYXRyaWMgTmV1cm9pbWFn aW5nPGJyPiAmbmJzcDtVbml2ZXJzaXR5IENoaWxkcmVuJiMzOTtzIEhvc3Bp dGFsPGJyPiAmbmJzcDtEZXB0LiBJSUkgKFBlZGlhdHJpYyBOZXVyb2xvZ3kp PGJyPjxicj48YnI+SG9wcGUtU2V5bGVyLVN0ci4gMTxicj4gJm5ic3A7RCAt IDcyMDc2IFTDvGJpbmdlbiwgR2VybWFueTxicj4gJm5ic3A7VGVsLiArNDkg NzA3MSAyOS04MzQxNjxicj4gJm5ic3A7RmF4ICZuYnNwOys0OSA3MDcxIDI5 LTU0NzM8YnI+ICZuYnNwO21hcmtvLndpbGtlQG1lZC51bmktdHVlYmluZ2Vu LmRlPGJyPjxicj4gJm5ic3A7PGEgaHJlZj0iaHR0cDovL3d3dy5tZWRpemlu LnVuaS10dWViaW5nZW4uZGUva2luZGVyL2VwbiI+aHR0cDovL3d3dy5tZWRp emluLnVuaS10dWViaW5nZW4uZGUva2luZGVyL2VwbjwvYT48YnI+X19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f Xzxicj48YnI+PGJyPjxicj4 ------=_Part_22_1297324694821-- ------------=_1297324745-21276-186 Content-Type: text/plain Content-Disposition: inline Content-Transfer-Encoding: 7bit MIME-Version: 1.0 X-Mailer: MIME-tools 5.420 (Entity 5.420) Content-Description: Edinburgh University charitable status The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ------------=_1297324745-21276-186-- ========================================================================Date: Thu, 10 Feb 2011 09:57:38 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: non isotropic voxels for DARTEL anatomical template? Comments: To: justine dupont <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> It is the size of the voxels in the "imported" images that I was referring to. The actual Dartel registration is usually done using "imported" data, which are rigidly aligned versions of the GM and WM, downsampled to 1.5 mm x 1.5 mm x 1.5 mm. Your original voxel sizes should be fine. Best regards, -John On 9 February 2011 21:34, justine dupont <[log in to unmask]> wrote: > Dear John and other experts > I have inherited a nice T1 dataset but the voxels are anisotropic (-0.97 x > 0.97 x 1). I want to do a VBM study using these data, however according to > a very recent post by John on the list, DARTEL is not appropriate > for anisotropic voxels. So my question is... is it OK to use > traditional VBM method (i.e. not using DARTEL) for such data? > thank you very much :) > Justine > > ========================================================================Date: Thu, 10 Feb 2011 12:07:03 +0100 Reply-To: marco tettamanti <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: marco tettamanti <[log in to unmask]> Subject: DCM: mutual interactions between networks MIME-Version: 1.0 Content-Type: text/plain; charset="ISO-8859-1"; format=flowed content-transfer-encoding: 7bit Message-ID: <[log in to unmask]> Dear all, I don't think that this question has been addressed before: I would like to set up a DCM analysis with fMRI data to investigate the following issue: we have identified an activation in a very well known network of 4 brain regions (A,B,C,D), and we believe that, in our task, this network can be gated - depending on which one of 2 different input modalities is involved - by the activity of two other distinct networks, each including 3 brain regions (E,F,G, or H,I,J). That is, we would like to assess how network EFG interacts with network ABCD in modality 1 and how network HIJ interacts with network ABDC in modality 2, in the context of a sort of 2x2 (Modality x network-network interaction) factorial design. I presume that one could address this, by simply treating each brain region independently, and for instance by using BMS to test whether the preferred model fits the expected interaction patterns, but I think that there may be problems with the relatively large number of regions (>8). I also presume that the question could be approached by non-linear DCM, even though I think that non-linear DCM has been only used in the context of the influence of one single region on a system of interconnected regions, and not in the context of network-network interactions. Can anyone suggest a more direct way to approach this issue? Thank you and all the best, Marco -- Marco Tettamanti, Ph.D. San Raffaele Scientific Institute Via Olgettina 58 I-20132 Milano, Italy Tel. ++39-02-26434888 Fax ++39-02-26434892 Email: [log in to unmask] ----------------------------------------------------------------------------------- SOSTIENI ANCHE TU LA RICERCA DEL SAN RAFFAELE. NON C'E' CURA SENZA RICERCA. Per donazioni: ccp 42437681 intestato a Fondazione Arete' Onlus del San Raffaele. Per informazioni: tel. 02.2643.4461 - www.sanraffaele.org ========================================================================Date: Thu, 10 Feb 2011 11:14:52 +0000 Reply-To: "Pettersson-Yeo, William" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Pettersson-Yeo, William" <[log in to unmask]> Subject: New Segmentation Out of Memory Error Content-Type: multipart/alternative; boundary="_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Dear SPMers, I use a MacBookPro with a 4GB hard drive and 2.53 GHz processor. When I try and run the 'New Segment' function in SPM8, I get the 'Out of Memory' error message detailed below. The structural files I'm trying to segment are approx. 80mb (bigger than what I usually work with) I have tried re-installing spm8, and have also tried changing the defaults.stats.maxmem = 2^26 to 2^31 but without success. Can anyone help? Many thanks William (Dept.of Psychosis, Institute of Psychiatry, KCL) ERROR MESSAGE: ------------------------------------------------------------------------ Running job #1 ------------------------------------------------------------------------ Running 'New Segment' Failed 'New Segment' Error using ==> unknown Out of memory. Type HELP MEMORY for your options. In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "affind" at line 428. In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "spm_preproc_write8" at line 241. In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "run_job" at line 112. In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "spm_preproc_run" at line 27. In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/tbx_cfg_preproc8.m" (v3910), function "spm_local_preproc_run" at line 353. The following modules did not run: Failed: New Segment ??? Error using ==> spm_input at 1470 Input object cleared whilst waiting for response: Bailing out! Error in ==> spm_dcm_ui at 88 selected = spm_input(str,1,'m',Actions); ??? Error using ==> spm_dcm_ui; Error using ==> spm_input at 1470 Input object cleared whilst waiting for response: Bailing out! ??? Error while evaluating uicontrol Callback --_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Dear SPMers,

I use a MacBookPro with a 4GB hard drive and 2.53 GHz processor.

When I try and run the 'New Segment' function in SPM8, I get the 'Out of Memory' error message detailed below.

The structural files I'm trying to segment are approx. 80mb (bigger than what I usually work with)

I have tried re-installing spm8, and have also tried changing the defaults.stats.maxmem = 2^26 to 2^31 but without success.

Can anyone help?

Many thanks

William

(Dept.of Psychosis, Institute of Psychiatry, KCL)

ERROR MESSAGE:

------------------------------------------------------------------------

Running job #1

------------------------------------------------------------------------

Running 'New Segment'

Failed 'New Segment'

Error using ==> unknown

Out of memory. Type HELP MEMORY for your options.

In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "affind" at line 428.

In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "spm_preproc_write8" at line 241.

In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "run_job" at line 112.

In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "spm_preproc_run" at line 27.

In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/tbx_cfg_preproc8.m" (v3910), function "spm_local_preproc_run" at line 353.

The following modules did not run:

Failed: New Segment

??? Error using ==> spm_input at 1470

Input object cleared whilst waiting for response: Bailing out!

Error in ==> spm_dcm_ui at 88

selected = spm_input(str,1,'m',Actions);

??? Error using ==> spm_dcm_ui;

Error using ==> spm_input at 1470

Input object cleared whilst waiting for response: Bailing out!

??? Error while evaluating uicontrol Callback

--_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_-- ========================================================================Date: Thu, 10 Feb 2011 11:35:59 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: "Pettersson-Yeo, William" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The stats.maxmem setting won't have any effect here, as it only determines how much data is read at a time when doing the stats. I think you may simply be reaching the limit of how much a 32 bit computer can hold in memory at once for one process. This is only (2^32-1)/1024^2 Mbytes, so once you have a few deformation fields and stuff in memory, then this is easily reached. 64 bit computers don't have this limit. I'm pretty sure that much of your image is not actually brain, so maybe you could crop the data to make the file a bit smaller. Best regards, -John On 10 February 2011 11:14, Pettersson-Yeo, William <[log in to unmask]> wrote: > Dear SPMers, > I use a MacBookPro with a 4GB hard drive and 2.53 GHz processor. > When I try and run the 'New Segment' function in SPM8, I get the 'Out of > Memory' error message detailed below. > The structural files I'm trying to segment are approx. 80mb (bigger than > what I usually work with) > I have tried re-installing spm8, and have also tried changing > the defaults.stats.maxmem = 2^26 to 2^31 but without success. > Can anyone help? > Many thanks > William > (Dept.of Psychosis, Institute of Psychiatry, KCL) > > ERROR MESSAGE: > ------------------------------------------------------------------------ > Running job #1 > ------------------------------------------------------------------------ > Running 'New Segment' > Failed 'New Segment' > Error using ==> unknown > Out of memory. Type HELP MEMORY for your options. > In file > "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" > (v3172), function "affind" at line 428. > In file > "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" > (v3172), function "spm_preproc_write8" at line 241. > In file > "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" > (v3859), function "run_job" at line 112. > In file > "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" > (v3859), function "spm_preproc_run" at line 27. > In file > "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/tbx_cfg_preproc8.m" > (v3910), function "spm_local_preproc_run" at line 353. > The following modules did not run: > Failed: New Segment > ??? Error using ==> spm_input at 1470 > Input object cleared whilst waiting for response: Bailing out! > Error in ==> spm_dcm_ui at 88 > selected = spm_input(str,1,'m',Actions); > ??? Error using ==> spm_dcm_ui; > Error using ==> spm_input at 1470 > Input object cleared whilst waiting for response: Bailing out! > ??? Error while evaluating uicontrol Callback > ========================================================================Date: Thu, 10 Feb 2011 13:52:05 +0100 Reply-To: Tatiana Usnich <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Tatiana Usnich <[log in to unmask]> Subject: Re: Question regarding the fMRI model specification Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain;charset=UTF-8 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Thank you! There was in fact one empty cell in other conditions.mat files, that didn't work. That was most likely causing the problem. We restructured the .mat file and it works now. Best regards, Tatiana > (1) You probably have less data than time in your conditions.mat (this > would make it appear that no events and it might ask for conditions; > how did you specify the time in the design and how many time points > are in each run? > > (2) Have you updated SPM with the latest updates? > > I just used your condition.mat file with some random files and it > created a design without a problem. > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Wed, Feb 9, 2011 at 9:16 AM, Tatiana Usnich > <[log in to unmask]> wrote: >> Dear SPMlers, >> >> We also have the same problem. Starting to run fMRI model specification >> in SPM8 we are asked for a vector of onsets for one of the conditions >> and durations even though the conditions are specified in a .mat file. >> >> Conditions file is attached. Id really appreciate your help. >> >> Tatiana >> > ========================================================================Date: Thu, 10 Feb 2011 12:53:29 +0000 Reply-To: Meng-Chuan Lai <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Meng-Chuan Lai <[log in to unmask]> Subject: registration error under DARTEL and suggested smoothness Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear John and SPM users. I would like to inquire if there is any available data showing the average registration error by DARTEL? I am currently running VBM using DARTEL-processed modulated images. I found that in group comparison, the smaller extra smooth kernel I put on the modulated images (e.g. 8mm --> 4mm --> 2mm --> 1mm --> no extra smooth), the more significant clusters identified using topological FDR. Note that even with no extra smoothing, the effective smoothness of the images is about 3.8mm (voxel size is 1mm isotropic). I am not sure what smoothness should I go for so would like choose it according to the registration error by DARTEL (in mm), if there's any data available. Any idea or suggestion regarding an appropriate smoothness for DARTEL-processed modulated images with 1mm isotropic voxels when running VBM? Thanks so much. Meng-Chuan Lai ========================================================================Date: Thu, 10 Feb 2011 13:36:30 +0000 Reply-To: Neal Hinvest <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Neal Hinvest <[log in to unmask]> Subject: PhD studentship opportunity at the University of Bath, UK (Department of Psychology) MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> The Department of Psychology at the University of Bath, U.K., is inviting applications for a PhD studentship. The PhD is for a fixed 3-year term and is fully funded (all tuition fees paid plus stipend). The successful candidate will start in October 2011. The project investigates internet and video-game addiction. The project is supervised by Dr. Neal Hinvest. The Department of Psychology is highly research active. The Departments has three main areas of research stength; Cognition, Affective Science and Technology Labs (CASTL), Health Psychology and Social and Cultural Psychology. For more details see http://www.bath.ac.uk/psychology/research. The successful candidate will have a 1st or 2:1 and will have relevant experience in psychology. Research experience or advanced training (e.g. Masters level degree) would be advantageous but not necessary. Experience with statistical software packages (e.g. SPSS) would be beneficial. The deadline for applications is the 15th April. Send applications to Dr. Neal Hinvest, Department of Psychology, University of Bath, Bath, BA2 7AY, U.K. Electronic applications are preferred and can be sent to [log in to unmask] -- Dr. Neal Hinvest Lecturer Department of Psychology University of Bath Bath BA2 7AY ========================================================================Date: Thu, 10 Feb 2011 15:36:47 +0100 Reply-To: Cornelia Hummel <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cornelia Hummel <[log in to unmask]> Subject: matlab path In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: [log in to unmask]> --00235444776d4c0b65049bee85bf Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear experts, I encounterd the following problem: I installed SPM8, ran matlab and SPM with the respetive path set, then moved the entire SPM folder to another site, changed the path in matlab, and now, when running segment, got the error message that the file "(previous folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting, and cannot find why matlab still tries to read from the path that no longer exists rather than the correctly set one. (The realign and coregister procedures ran without problems.) Can anybody please help me? Thanks in advance, Cornelia -- Cornelia Hummel Voglerstraße 14 01277 Dresden --00235444776d4c0b65049bee85bf Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear experts,
I encounterd the following problem:
I installed SPM8, ran matlab and SPM with the respetive path set, then moved the entire SPM folder to another site, changed the path in matlab, and now, when running segment, got the error message that the file "(previous folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting, and cannot find why matlab still tries to read from the path that no longer exists rather than the correctly set one. (The realign and coregister procedures ran without problems.)
Can anybody please help me?
Thanks in advance,
Cornelia

--
Cornelia Hummel
Voglerstraße 14
01277 Dresden
--00235444776d4c0b65049bee85bf-- ========================================================================Date: Thu, 10 Feb 2011 16:05:04 +0000 Reply-To: William Pettersson-Yeo <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: William Pettersson-Yeo <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: John Ashburner <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Thanks John. It turns out the problem was due to the version of Matlab I was running. Matlab2007 was only running 32bit, whilst the 2010 version is 64. Having set up with Matlab2010, the new segmentation with DARTEL import now runs without any problem, without me having to alter/crop any of the images. All the best William ========================================================================Date: Thu, 10 Feb 2011 16:47:53 +0000 Reply-To: Joachim Gross <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Joachim Gross <[log in to unmask]> Subject: Chair Position Glasgow Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> UNIVERSITY of GLASGOW College of Medical, Veterinary and Life Sciences Institute of Neuroscience and Psychology (INP) Faculty Position in Formal Models of Cortical Networks - may be appointed at Lecturer/Senior Lecturer/Reader or Professor level Ref: M00075 Salary: Lecturer/Senior Lecturer - Â£31,798 - Â£52,556 per annum Reader/Professor â€“ negotiable â€“ depends on experience Applications are invited for a faculty position (up to chair level) in Formal Models of Cortical Networks from individuals with an outstanding research record. The post is designed to complement existing research strengths in the Institute for Neuroscience and Psychology and to develop a programme for studying and describing networks across the different Centres of the Institute. The Post-holder will provide research leadership in the area of cortical networks and networks in general (connectivity, information theory, graph theory). The Post-holder will be expected to raise external funds to support their research programme, to attract postdoctoral research staff and PhD students, and to contribute to research-led teaching, especially via the development of specialised content for masters and postgraduate degrees. Candidates for this post will have an exceptional international research profile, including a track record of high impact publications and substantial research funding, in the areas of network/connectivity analysis, information theory or graph theory. Informal enquiries may be made to Philippe Schyns (Director of Institute, +44 141 330 4937, [log in to unmask]) or Joachim Gross (Acting Director of Centre for Cognitive Neuroimaging (CCNi), +44 141 330 3947, [log in to unmask]). Apply online at www.glasgow.ac.uk/jobs Closing date: 12 April 2011. ========================================================================Date: Thu, 10 Feb 2011 16:59:04 +0000 Reply-To: Vy Dinh <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vy Dinh <[log in to unmask]> Subject: SPM8 VOI Extraction: Adjusting for effects of no interest Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear all, Excuse me for not getting straight to the question, but I found it difficult to formulate my question otherwise. First of all, according to responses on the forum (links below), "adjusting for effects of (no) interest" removes the variances introduced by confounds in the 1st-level design matrix. SPM does this by mean-centering the data based on the betas obtained from columns that are zeroed in the F-contrast. (adjusted Y = global raw data = [sum (beta x effects of no interest)]); Therefore, if I have a design matrix with 3 conditions, motion parameters, and 1 regressor of no interest and I specified an F-contrast of 1 0 0 < right side padded with 2 zeroes (1 for regressor, 1 for constant) 0 1 0 0 0 1 .. the F-contrast will be padded with 2 zeroes as well and these columns will be accounted for during VOI extraction with adjustment (please correct me if I'm wrong). Motion parameters have often been used as an example of a regressor of no interest in these posts, but HOW DOES SPM'S ADJUSTMENT OPTION INTERPRET A COLUMN FOR DEWEIGHTING A "BAD" SCAN (WHERE BAD SCANS ARE SPECIFIED AS 1S)? Are the data associated with these scans also not included in the VOI extraction? Answers to these questions would help us understand why we are seeing slight differences between results using adjusted and non-adjusted data that do not have any confounds in the first model. Motion was addressed by adjusting the data with ArtRepair Toolbox prior to estimation. If the deweighting columns should have no effect on the VOI extraction, then mathematically speaking, the results should be the same. However, they are not. Also, some of our subjects had zero instances of 1 condition, so we had to define this condition as a zero in the Effects Of Interest F-contrast. In these cases, would specifying adjust or not adjust make a difference? Thanks, Vy Here are the links to the listserv responses regarding adjusting for regressors of no interest: https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;664c9b8.03; https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;f695a1.1004; https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;9945e539.03) ========================================================================Date: Thu, 10 Feb 2011 11:05:48 -0600 Reply-To: Darren Gitelman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Darren Gitelman <[log in to unmask]> Subject: Re: DCM: mutual interactions between networks Comments: To: marco tettamanti <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Marco On Thu, Feb 10, 2011 at 5:07 AM, marco tettamanti <[log in to unmask]> wrote: > Dear all, > I don't think that this question has been addressed before: > > I would like to set up a DCM analysis with fMRI data to investigate the > following issue: we have identified an activation in a very well known > network of 4 brain regions (A,B,C,D), and we believe that, in our task, this > network can be gated - depending on which one of 2 different input > modalities is involved - by the activity of two other distinct networks, > each including 3 brain regions (E,F,G, or H,I,J). > That is, we would like to assess how network EFG interacts with network ABCD > in modality 1 and how network HIJ interacts with network ABDC in modality 2, > in the context of a sort of 2x2 (Modality x network-network interaction) > factorial design. > > I presume that one could address this, by simply treating each brain region > independently, and for instance by using BMS to test whether the preferred > model fits the expected interaction patterns, but I think that there may be > problems with the relatively large number of regions (>8). You can deal with this by setting up the DCM file from the command line. If you set up a simpler DCM using the GUI you will see what fields you need. The 8 region limitation is mostly due to the gui (and memory). > I also presume that the question could be approached by non-linear DCM, even > though I think that non-linear DCM has been only used in the context of the > influence of one single region on a system of interconnected regions, and > not in the context of network-network interactions. I'm not sure how you could use a non-linear DCM in this scenario. You can't really setup a small DCM network as an input, per se. The two "subnetworks" would just be connected into the larger 4 region network perhaps through a common area. This would be through an intrinsic connection. DCM only accepts as inputs the condition-type vectors that it extracts from an SPM.mat file. You would also need other actual inputs into each of the subnetworks in order to drive overall system activity. Non-linear DCM's can be used to modulate an intrinsic connection, so perhaps you are thinking of using the 3 region subnetworks to modulate an external input into the 4 region network? However, I don't think you can modulate an input as DCM is currently set up, and you would still need to drive the subnetworks in some way. Darren > > Can anyone suggest a more direct way to approach this issue? > > Thank you and all the best, > Marco > > -- > Marco Tettamanti, Ph.D. > San Raffaele Scientific Institute > Via Olgettina 58 > I-20132 Milano, Italy > Tel. ++39-02-26434888 > Fax ++39-02-26434892 > Email: [log in to unmask] > > ----------------------------------------------------------------------------------- > SOSTIENI ANCHE TU LA RICERCA DEL SAN RAFFAELE. > NON C'E' CURA SENZA RICERCA. > Per donazioni: ccp 42437681 intestato a Fondazione Arete' Onlus del San > Raffaele. > Per informazioni: tel. 02.2643.4461 - www.sanraffaele.org > -- Darren Gitelman, MD Northwestern University 710 N. Lake Shore Dr., 1122 Chicago, IL 60611 Ph: (312) 908-8614 Fax: (312) 908-5073 ========================================================================Date: Thu, 10 Feb 2011 17:29:41 +0000 Reply-To: Tom Johnstone <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Tom Johnstone <[log in to unmask]> Subject: University of Reading International Studentship Comments: To: "[log in to unmask] [log in to unmask]" <[log in to unmask]>, [log in to unmask] MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Opportunity for PhD Studentship at the Centre for Integrative Neuroscience & Neurodynamics (CINN). The Faculties of Sciences & Life Sciences at the University of Reading are jointly holding a studentship competition for International Students (defined as non-EU/EEA). We encourage applications from candidates wishing to complete a PhD in the Cognitive, Affective or Social Neurosciences at the CINN. Each studentship will provide a subsistence grant (normal UK research council rate) as well as the University tuition fee (full-time international at Reading) for 3 Academic Years. Candidates should possess a 1st class Honours degree or equivalent in Psychology, Neuroscience or a related discipline. An MSc with Distinction or progress towards such an MSc would be an advantage. Applications will be judged in a 2-stage procedure. In the first step, the School of Psychology & Clinical Language Sciences will select the strongest 2 submissions to the school. These will then be submitted to the Faculty Deans, who will select the highest quality candidates who have strong research plans for a topic compatible with the University's Research Strategy. The Centre for Integrative Neuroscience and Neurodynamics ( http://www.reading.ac.uk/cinn/ ) occupies a dedicated wing in the School of Psychology & CLS building at the University of Reading. It houses a 3T Siemens Trio research-dedicated MRI scanner and high-density EEG laboratory. These facilities are complemented by high resolution stimulus display systems with integrated high-speed eye tracking, MRI compatible EEG and TMS systems. Research at CINN involves the neural mechanisms underpinning complex cognitive and affective behaviours, with particular strengths in affective neuroscience and the neural mechanisms of perception and action. Interested candidates should send an email with a brief statement of research interests and a CV, including details of nationality to: Tom Johnstone Centre for Integrative Neuroscience & Neurodynamics School of Psychology and CLS [log in to unmask] http://www.personal.reading.ac.uk/~sxs07itj/index.html Closing date: Friday the 25th of February. ========================================================================Date: Thu, 10 Feb 2011 18:12:34 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: registration error under DARTEL and suggested smoothness Comments: To: Meng-Chuan Lai <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Without knowing the ground truth (which probably does not exist anyway), it is difficult to say what the average registration error is. The closest we have are comparisons of various registration algorithms in terms of how well they can propagate manually defined regions. I suspect that the manually defined regions used for the validations may not be so accurate, but they will probably be accurate enough to compare among models. There is a validation of what was in the SPM5 implementation (with the original Unified Segmentation) within: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration Original Research Article NeuroImage, Volume 46, Issue 3, 1 July 2009, Pages 786-802 Arno Klein, Jesper Andersson, Babak A. Ardekani, John Ashburner, Brian Avants, Ming-Chang Chiang, Gary E. Christensen, D. Louis Collins, James Gee, Pierre Hellier, Joo Hyun Song, Mark Jenkinson, Claude Lepage, Daniel Rueckert, Paul Thompson, Tom Vercauteren, Roger P. Woods, J. John Mann, Ramin V. Parsey For a validation of the SPM8 implementation (with the New Segment), see: Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation NeuroImage, In Press, Corrected Proof, Available online 7 January 2011 John Ashburner, Karl J. Friston Best regards, -John On 10 February 2011 12:53, Meng-Chuan Lai <[log in to unmask]> wrote: > Dear John and SPM users. > > I would like to inquire if there is any available data showing the average registration error by DARTEL? > > I am currently running VBM using DARTEL-processed modulated images. I found that in group comparison, the smaller extra smooth kernel I put on the modulated images (e.g. 8mm --> 4mm --> 2mm --> 1mm --> no extra smooth), the more significant clusters identified using topological FDR. Note that even with no extra smoothing, the effective smoothness of the images is about 3.8mm (voxel size is 1mm isotropic). I am not sure what smoothness should I go for so would like choose it according to the registration error by DARTEL (in mm), if there's any data available. Any idea or suggestion regarding an appropriate smoothness for DARTEL-processed modulated images with 1mm isotropic voxels when running VBM? > > Thanks so much. > > Meng-Chuan Lai > ========================================================================Date: Thu, 10 Feb 2011 18:17:56 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: matlab path Comments: To: Cornelia Hummel <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> Are you trying to run a batch file? If so, the location of the tissue probability maps will be defined by that file. Best regards, -John On 10 February 2011 14:36, Cornelia Hummel <[log in to unmask]> wrote: > Dear experts, > I encounterd the following problem: > I installed SPM8, ran matlab and SPM with the respetive path set, then moved > the entire SPM folder to another site, changed the path in matlab, and now, > when running segment, got the error message that the file "(previous > folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting, > and cannot find why matlab still tries to read from the path that no longer > exists rather than the correctly set one. (The realign and coregister > procedures ran without problems.) > Can anybody please help me? > Thanks in advance, > Cornelia > > > -- > Cornelia Hummel > Voglerstraße 14 > 01277 Dresden > ========================================================================Date: Thu, 10 Feb 2011 13:27:21 -0500 Reply-To: Giulia Dormal <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Giulia Dormal <[log in to unmask]> Subject: SPM8 fMRI model specification MIME-Version: 1.0 Content-Type: multipart/mixed; boundaryMessage-ID: <[log in to unmask]> --00504502c86877f759049bf1bb7b Content-Type: multipart/alternative; boundary --00504502c86877f751049bf1bb79 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPMers, I am trying to run an fMRI model specification batch (in attached), for a single run of a block design containing 3 conditions + baseline. I have specified the different conditions in seconds. When I run the batch, I systematically get this error message in matlab window: Failed 'fMRI model specification' Cell contents reference from a non-cell array object. In file "C:\Program Files\spm8\spm8\spm_Ce.m" (v3527), function "spm_Ce" at line 54. In file "C:\Program Files\spm8\spm8\spm_fmri_spm_ui.m" (v3691), function "spm_fmri_spm_ui" at line 345. In file "C:\Program Files\spm8\spm8\config\spm_run_fmri_spec.m"(v3757), function "spm_run_fmri_spec" at line 301. I would very much appreciate your help! Giulia ---------- Forwarded message ---------- From: Giulia Dormal <[log in to unmask]> Date: 2011/2/10 Subject: To: "giulia.dormal" <[log in to unmask]> -- Giulia Dormal, PhD Student IPSY/IoNS - Université Catholique de Louvain CERNEC - Université de Montréal (001) 514 343 61 11 #1715 -- Giulia Dormal, PhD Student IPSY/IoNS - Université Catholique de Louvain CERNEC - Université de Montréal (001) 514 343 61 11 #1715 --00504502c86877f751049bf1bb79 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPMers,

I am trying to run an fMRI model specification batch (in attached), for a single run of a block design containing 3 conditions + baseline. I have specified the different conditions in seconds. When I run the batch, I systematically get this error message in matlab window:
Failed 'fMRI model specification'
Cell contents reference from a non-cell array object.
In file "C:\Program Files\spm8\spm8\spm_Ce.m" (v3527), function "spm_Ce" at line 54.
In file "C:\Program Files\spm8\spm8\spm_fmri_spm_ui.m" (v3691),

function "spm_fmri_spm_ui" at line 345.
In file "C:\Program Files\spm8\spm8\config\spm_run_fmri_spec.m"(v3757), function "spm_run_fmri_spec" at line 301.

I would very much appreciate your help!

Giulia

---------- Forwarded message ----------
From: Giulia Dormal <[log in to unmask]>
Date: 2011/2/10
Subject:
To: "giulia.dormal" <[log in to unmask]>

--
Giulia Dormal, PhD Student
IPSY/IoNS - Université Catholique de Louvain
CERNEC - Université de Montréal
(001) 514 343 61 11 #1715

--
Giulia Dormal, PhD Student

IPSY/IoNS - Université Catholique de Louvain

CERNEC - Université de Montréal

(001) 514 343 61 11 #1715

--00504502c86877f751049bf1bb79-- --00504502c86877f759049bf1bb7b Content-Type: application/octet-stream; name="fMRImodelSpecific.mat" Content-Disposition: attachment; filename="fMRImodelSpecific.mat" Content-Transfer-Encoding: base64 X-Attachment-Id: file0 TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUaHUgRmVi IDEwIDEyOjA4OjM5IDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0OAAAAGA8AAAYAAAAIAAAAAQAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAA AAsAAABtYXRsYWJiYXRjaAAAAAAADgAAANgOAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAA AQAAAAEAAAAAAAAABQAEAAUAAAABAAAABQAAAHNwbQAAAAAADgAAAJAOAAAGAAAACAAAAAIAAAAA AAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAYAAAABAAAABgAAAHN0YXRzAAAADgAAAEgO AAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAoAAAABAAAACgAA AGZtcmlfc3BlYwAAAAAAAAAOAAAA+A0AAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAABAAAA AQAAAAAAAAAFAAQABwAAAAEAAAA/AAAAZGlyAAAAAHRpbWluZwBzZXNzAAAAZmFjdAAAAGJhc2Vz AAB2b2x0AAAAZ2xvYmFsAG1hc2sAAABjdmkAAAAAAA4AAADAAAAABgAAAAgAAAABAAAAAAAAAAUA AAAIAAAAAQAAAAEAAAABAAAAAAAAAA4AAACQAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAA AC8AAAABAAAAAAAAAAQAAABeAAAAQwA6AFwAUAByAG8AZwByAGEAbQAgAEYAaQBsAGUAcwBcAE0A QQBUAEwAQQBCADcAMQBcADAAMABfAGsAcAByAG8AZwBpAHUAZABvAHIAXwAwADgAMQAxADEAMABc AAAADgAAAEABAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAgA AAABAAAAIAAAAHVuaXRzAAAAUlQAAAAAAABmbXJpX3QAAGZtcmlfdDAADgAAADgAAAAGAAAACAAA AAQAAAAAAAAABQAAAAgAAAABAAAABAAAAAEAAAAAAAAABAAAAAgAAABzAGUAYwBzAA4AAAAwAAAA BgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQADAAAADgAAADAAAAAG AAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAAAgABABAAAAAOAAAAMAAAAAYA AAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAACAAEAAQAAAA4AAAAwCQAABgAA AAgAAAACAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAUABAAKAAAAAQAAADwAAABzY2Fu cwAAAAAAY29uZAAAAAAAAG11bHRpAAAAAAByZWdyZXNzAAAAbXVsdGlfcmVnAGhwZgAAAAAAAAAA AAAADgAAADgBAAAGAAAACAAAAAEAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAADgAAAAgB AAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAbAAAAAEAAAAAAAAABAAAANgAAABDADoAXABQ AHIAbwBnAHIAYQBtACAARgBpAGwAZQBzAFwATQBBAFQATABBAEIANwAxAFwAMAAwAF8AawBwAHIA bwBnAGkAdQBkAG8AcgBfADAAOAAxADEAMQAwAFwAcwB3AEwARQBSAE8AVQBYAF4ARABFAFYARQBM AE8AUABQAEUATQBFAE4AVABfAEQATwBSAE0AQQBMAF8ARgBFAF8ARQBQAEkAXwBGAGEAYwBlAF8A TABvAGMAXwBTAEUATgBTAEUAXwA1AF8AMQAuAG4AaQBpACwAMQAOAAAAGAYAAAYAAAAIAAAAAgAA AAAAAAAFAAAACAAAAAEAAAAEAAAAAQAAAAAAAAAFAAQACQAAAAEAAAAtAAAAbmFtZQAAAAAAb25z ZXQAAAAAZHVyYXRpb24AdG1vZAAAAAAAcG1vZAAAAAAAAAAADgAAADgAAAAGAAAACAAAAAQAAAAA AAAABQAAAAgAAAABAAAABAAAAAEAAAAAAAAABAAAAAgAAABGAGEAYwBlAA4AAABIAAAABgAAAAgA AAAGAAAAAAAAAAUAAAAIAAAACgAAAAEAAAABAAAAAAAAAAQAAAAUAAAAAgBaALIACgFiAboBEgJq AsICGgMAAAAADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAA AgABABQAAAAOAAAAMAAAAAYAAAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAC AAEAAAAAAA4AAABQAAAABgAAAAgAAAACAAAAAAAAAAUAAAAIAAAAAAAAAAAAAAABAAAAAAAAAAUA BAAGAAAAAQAAABIAAABuYW1lAABwYXJhbQBwb2x5AAAAAAAAAAAOAAAAQAAAAAYAAAAIAAAABAAA AAAAAAAFAAAACAAAAAEAAAAFAAAAAQAAAAAAAAAEAAAACgAAAFMARgBhAGMAZQAAAAAAAAAOAAAA SAAAAAYAAAAIAAAABgAAAAAAAAAFAAAACAAAAAoAAAABAAAAAQAAAAAAAAAEAAAAFAAAAB8AdwDP ACcBfwHXAS8ChwLfAjcDAAAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAAAQAAAAEA AAABAAAAAAAAAAIAAQAUAAAADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAAAQAA AAEAAAAAAAAAAgABAAAAAAAOAAAAUAAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAAAAAAAAAAA AQAAAAAAAAAFAAQABgAAAAEAAAASAAAAbmFtZQAAcGFyYW0AcG9seQAAAAAAAAAADgAAADgAAAAG AAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAAwAAAAEAAAAAAAAABAAAAAYAAABDAGEAcgAAAA4A AABIAAAABgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAACgAAAAEAAAABAAAAAAAAAAQAAAAUAAAAPQCV AO0ARQGcAfQBTAKkAvwCVAMAAAAADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAA AQAAAAEAAAAAAAAAAgABABQAAAAOAAAAMAAAAAYAAAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAAB AAAAAQAAAAAAAAACAAEAAAAAAA4AAABQAAAABgAAAAgAAAACAAAAAAAAAAUAAAAIAAAAAAAAAAAA AAABAAAAAAAAAAUABAAGAAAAAQAAABIAAABuYW1lAABwYXJhbQBwb2x5AAAAAAAAAAAOAAAAOAAA AAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAADAAAAAQAAAAAAAAAEAAAABgAAAEYAaQB4AAAA DgAAAHAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAAfAAAAAQAAAAEAAAAAAAAABAAAAD4AAAAA ABYANABRAG4AjACpAMYA5AABAR4BOwFZAXYBkwGxAc4B6wEJAiYCQwJhAn4CmwK5AtYC8wIRAy4D SwNoAwAADgAAAFAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAAfAAAAAQAAAAEAAAAAAAAAAgAA AB8AAAACCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJAA4AAAAwAAAABgAAAAgAAAAGAAAA AAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQAAAAAADgAAAFAAAAAGAAAACAAAAAIAAAAA AAAABQAAAAgAAAAAAAAAAAAAAAEAAAAAAAAABQAEAAYAAAABAAAAEgAAAG5hbWUAAHBhcmFtAHBv bHkAAAAAAAAAAA4AAABgAAAABgAAAAgAAAABAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAA AA4AAAAwAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAAAAAAAAAAABAAAAAAAAAAQAAAAAAAAA DgAAAEgAAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAAAAAAAAAAAAAEAAAAAAAAABQAEAAUAAAAB AAAACgAAAG5hbWUAdmFsAAAAAAAAAAAOAAAAYAAAAAYAAAAIAAAAAQAAAAAAAAAFAAAACAAAAAEA AAABAAAAAQAAAAAAAAAOAAAAMAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAAAAAAAAAAAAQAA AAAAAAAEAAAAAAAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAA AAAAAAIAAQCAAAAADgAAAEgAAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAAAAAAAAAAAAAEAAAAA AAAABQAEAAcAAAABAAAADgAAAG5hbWUAAABsZXZlbHMAAAAOAAAAwAAAAAYAAAAIAAAAAgAAAAAA AAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAFAAQABQAAAAEAAAAFAAAAaHJmAAAAAAAOAAAAeAAA AAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAFAAQABwAAAAEAAAAHAAAA ZGVyaXZzAAAOAAAAMAAAAAYAAAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAACAAAAAQAAAAAAAAAC AAIAAAAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIA AQABAAAADgAAADgAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAABAAAAAEAAAAAAAAABAAA AAgAAABOAG8AbgBlAA4AAABgAAAABgAAAAgAAAABAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAA AAAAAA4AAAAwAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAAAAAAAAAAABAAAAAAAAAAQAAAAA AAAADgAAADgAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAABAAAAAEAAAAAAAAABAAAAAgA AABuAG8AbgBlAA=--00504502c86877f759049bf1bb7b-- ========================================================================Date: Thu, 10 Feb 2011 19:47:50 +0100 Reply-To: maaike rive <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: maaike rive <[log in to unmask]> Subject: regressors of no interest in first level model MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: [log in to unmask]> --0016364ef8feb97f88049bf20402 Content-Type: text/plain; charset=ISO-8859-1 Hi all, I hope you can help me with a question regarding modelling regressors of no interest in a first level model. I assumed it is better to model all time intervals, including the periods of certain ''conditions'' you are not interested in (for example, an instruction period before a specific event), to reduce variance in the signal during the conditions that you will use for further analysis that is actually caused by conditions of no interest. However, now I was told that in doing so, one would reduce the degree of orthogonality of each regressor with respect to the constant regressor. Now I do not know what is the best option: model every condition or only the ones you will use for further analysis. I hope someone can advise me on this! Thanks, Maaike --0016364ef8feb97f88049bf20402 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hi all,

I hope you can help me with a question regarding modelling regressors of no interest in a first level model.

I assumed it is better to model all time intervals, including the periods of certain ''conditions'' you are not interested in (for example, an instruction period before a specific event), to reduce variance in the signal during the conditions that you will use for further analysis that is actually caused by conditions of no interest.

However, now I was told that in doing so, one would reduce the degree of orthogonality of each regressor with respect to the constant regressor.

Now I do not know what is the best option: model every condition or only the ones you will use for further analysis. I hope someone can advise me on this!

Thanks,

Maaike

--0016364ef8feb97f88049bf20402-- ========================================================================Date: Thu, 10 Feb 2011 14:13:51 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: regressors of no interest in first level model Comments: To: maaike rive <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> If you know that the activity is changing from baseline, then you should model it if you can. Otherwise, the condition of interest will be with respective to the constant+instruction. The problem can arise though, that the instruction is too close to the condition of interest or is not properly randomized, then it becomes hard to estimate the effects of each. For example, if the instruction is for 1s followed by your condition, then its hard to separate the two events, especially if the instruction is always the same before that type of condition. Its not the orthogonality with the constant, its the lack of orthogonality/collinearity of each of the condtions that becomes an issue. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 10, 2011 at 1:47 PM, maaike rive <[log in to unmask]> wrote: > Hi all, > > I hope you can help me with a question regarding modelling regressors of no > interest in a first level model. > I assumed it is better to model all time intervals, including the periods of > certain ''conditions'' you are not interested in (for example, an > instruction period before a specific event), to reduce variance in the > signal during the conditions that you will use for further analysis that is > actually caused by conditions of no interest. > > However, now I was told that in doing so, one would reduce the degree > of orthogonality of each regressor with respect to the constant regressor. > > Now I do not know what is the best option: model every condition or only the > ones you will use for further analysis. I hope someone can advise me on > this! > > Thanks, > > Maaike > > > ========================================================================Date: Thu, 10 Feb 2011 19:38:43 +0000 Reply-To: Meng-Chuan Lai <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Meng-Chuan Lai <[log in to unmask]> Subject: Re: registration error under DARTEL and suggested smoothness Comments: To: John Ashburner <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear John, Thank you for the very valuable information. With these in mind, practically, if to run VBM and we expect to find regional differences in small areas in the brain such as hypothalamus, ventral striatum or amygdala, what would be the lowest effective smoothness for DARTEL-processed modulated images with 1mm isotropic voxels, that the registration error should have been taken care of by such smoothness? Thank you! best regards, Meng-chuan Lai ========================================================================Date: Thu, 10 Feb 2011 20:42:47 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: registration error under DARTEL and suggested smoothness Comments: To: Meng-Chuan Lai <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> These regions tend to be pretty well aligned, so you probably need a bit less than you would for the cortex. However, you may need to use some smoothing to suppress some of the aliasing effects that occur. These depend on the resolution of your original scans. At a rough guess, I'd suggest that somewhere between about 4mm and 8mm could be reasonable, although I don't have any evidence to back this up. Best regards, -John On 10 February 2011 19:38, Meng-Chuan Lai <[log in to unmask]> wrote: > Dear John, > > Thank you for the very valuable information. > > With these in mind, practically, if to run VBM and we expect to find regional differences in small areas in the brain such as hypothalamus, ventral striatum or amygdala, what would be the lowest effective smoothness for DARTEL-processed modulated images with 1mm isotropic voxels, that the registration error should have been taken care of by such smoothness? > > Thank you! > > best regards, > Meng-chuan Lai > ========================================================================Date: Thu, 10 Feb 2011 16:31:01 -0500 Reply-To: "Claire (Jung Eun) Han" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Claire (Jung Eun) Han" <[log in to unmask]> Subject: Question regarding FIR and contrasts MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: [log in to unmask]> --000e0cd4cd4455ea25049bf44c95 Content-Type: text/plain; charset=ISO-8859-1 Hello, There are two conditions A and B I would like to compare. In fMRI model specification, I chose to use the Finite Impulse Response such that my design has 9 three-second-bins for each condition. I would like to compare the two conditions in each time bin...is it possible to do this at once? Which of the t- or f-test should I be using and what should I put for contrasts? Thanks a lot, Claire --000e0cd4cd4455ea25049bf44c95 Content-Type: text/html; charset=ISO-8859-1 Hello,

There are two conditions A and B I would like to compare.

In fMRI model specification, I chose to use the Finite Impulse Response such that my design has 9 three-second-bins for each condition.

I would like to compare the two conditions in each time bin...is it possible to do this at once? Which of the t- or f-test should I be using and what should I put for contrasts?

Thanks a lot,
Claire
--000e0cd4cd4455ea25049bf44c95-- ========================================================================Date: Thu, 10 Feb 2011 21:39:29 +0000 Reply-To: Meng-Chuan Lai <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Meng-Chuan Lai <[log in to unmask]> Subject: Re: registration error under DARTEL and suggested smoothness Comments: To: John Ashburner <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi John, Thanks so much. For the 4-8mm you suggested, do you mean the smoothing applied with FWHM or the effective smoothness in the data itself? I have tried a 2mm kernel which gives an effective smoothness of 5.5mm in the data itself, and it seems to pick up quite a few reasonable regions. Would you suspect this to be valid? Thanks so much for the prompt answers! best, Meng-chuan ========================================================================Date: Thu, 10 Feb 2011 17:31:16 -0500 Reply-To: Zipporah Nussbaum <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Zipporah Nussbaum <[log in to unmask]> Subject: Postdoctoral Fellowship in Cognitive Neuroscience of Memory MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The Rotman Research Institute of Baycrest, affiliated with the University of Toronto is offering a postdoctoral fellowship in cognitive neuroscience of memory in the laboratory of Dr. Bradley Buchsbaum. The post-doctoral fellow will collaborate in CIHR-funded research investigating the neural basis of human memory, especially short-term and episodic memory. Projects will involve the use of fMRI and MEG to study how memories are stored and represented in the brain, using both univariate and multivariate “pattern recognition” methods. Candidates should have interest and expertise in some of the following topics: - Cognitive neuroscience of working memory - Aging and memory - Functional neuroimaging (fMRI and MEG) - Multivariate pattern recognition applied to neuroimaging data The Rotman Institute is fully equipped for cognitive neuroscience research, with a _3 T MRI, _151 -channel CTF MEG, several EEG systems. We seek a candidate with a strong background in memory research, good computing skills, and experience with functional neuroimaging. Toronto is consistently ranked as one of the most livable cities in the world, as well as the most multicultural. It is an excellent place to work for those interested in cross-linguistic research, as native speaker populations can be found for dozens of world languages. Applicants should have a recent Ph.D. or M.D. degree, and the potential for successfully obtaining external funding. The postdoctoral position carries a term of 2 years and is potentially renewable. Bursaries are in line with the fellowship scales of the Canadian Institutes of Health Research (CIHR) and include an allowance for travel and research expenses. A minimum of 80% of each fellow’s time will be devoted to research and related activities. Start date is negotiable, but ideally in the spring or summer of 2011. Applicants should submit a C.V. and relevant reprints, together with a cover letter describing current research interests and future research goals, and also arrange to have three letters of reference sent independently to: Bradley Buchsbaum, Ph.D. Rotman Research Institute, Baycrest 3560 Bathurst Street, Toronto, Ontario, M6A 2E1, Canada [log in to unmask] For more information on the institute, see http://www.rotman-baycrest.on.ca/ The Rotman Research Institute welcomes applications from all qualified individuals, including members of visible groups, minorities, women, aboriginal persons, and persons with disabilities. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority. ========================================================================Date: Thu, 10 Feb 2011 22:58:01 +0000 Reply-To: Courtney Kastner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Courtney Kastner <[log in to unmask]> Subject: Extracting ROIs in SPM8 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> I'm using SPM8 and its functions to analyze data collected from a test with two conditions of rotation and baseline in math-gifted and average students. The end goal of my project is to extract regions of interest from the fMRI data. Is it absolutely necessary to create a design matrix and define a contrast, or is there another easier but still effective way to do this? ========================================================================Date: Fri, 11 Feb 2011 01:17:50 +0000 Reply-To: Matt Johnson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Matt Johnson <[log in to unmask]> Subject: Standard error calculation in spm_contrasts.m - r4010 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi SPMers. We have been doing some comparisons between different fMRI data processing methods recently, including sanity checks on the stats returned by SPM vs FSL vs done by hand. While evaluating a T-contrast (on a simple second-level one-sample T-test model), I noticed that the T-statistics returned by SPM8 r4010 were different than those returned by the equivalent FSL model or by simply running a T-test in the Matlab command window (with FSL, Matlab, and earlier SPMs being identical, within reasonable rounding error). Upon further investigation, I see that the difference arises from line 178 of spm_contrasts.m, where the parameter estimate is divided by standard error to yield the T-statistic. The line is now: Z=cB./(SE+exp(-8)*max(SE)); whereas previously it did not contain the (+exp(-8)*max(SE)) bit. I believe this is the change referred to thus in the release notes: "Problems with using SPM to test for between-subject effects in contrasts based on source reconstructed MEEG data have been finessed by lower bounding the standard error of a t-test that forces very low variance and effect-size voxels to shrink to 0. This is hidden (in spm_contrasts.m) because it will not affect normal SPMs (where the scaling of the data is roughly uniform over the image)." While the difference induced by this change is small, I think it may not be entirely inconsequential for some users. For instance, in my sample analysis, it changes the max T in the results from a "true" value of 18.37 to a value of 17.91. For smaller Ts, the change is less drastic, but it still equates to about 35 fewer voxels in the output passing an uncorrected p<.001 threshold, or about 600 fewer passing an uncorrected p<.05 threshold. This probably makes no difference for most analyses, but it might be a bit more than rounding error for those who are sticklers about precision. Of course in our local copy we can just change that line back if we want our SPM Ts to agree with the others, but I thought I'd pass this along just as an FYI to others, and/or in case the folks at the FIL might want to tweak the line in question a bit further (e.g., to only apply the standard error correction when it is needed). Cheers, Matt ========================================================================Date: Fri, 11 Feb 2011 11:00:10 +0100 Reply-To: marco tettamanti <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: marco tettamanti <[log in to unmask]> Subject: Re: DCM: mutual interactions between networks Comments: To: Darren Gitelman <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset="ISO-8859-1"; format=flowed content-transfer-encoding: 7bit Message-ID: <[log in to unmask]> Dear Darren, thank you for your useful comments! On 02/10/2011 06:05 PM, Darren Gitelman wrote: > You can deal with this by setting up the DCM file from the command > line. If you set up a simpler DCM using the GUI you will see what > fields you need. The 8 region limitation is mostly due to the gui (and > memory). I was not aware of this possibility and it is good to know. On the other hand, I would like to stick to the "relevant model space" principle as much as possible, whereby I am much more interested in network-network than in within-networks dynamics. But as you are pointing out, it may not be possible to study such modulations with DCM, at least in its current implementation. > I'm not sure how you could use a non-linear DCM in this scenario. You > can't really setup a small DCM network as an input, per se. The two > "subnetworks" would just be connected into the larger 4 region network > perhaps through a common area. This would be through an intrinsic > connection. DCM only accepts as inputs the condition-type vectors that > it extracts from an SPM.mat file. You would also need other actual > inputs into each of the subnetworks in order to drive overall system > activity. Non-linear DCM's can be used to modulate an intrinsic > connection, so perhaps you are thinking of using the 3 region > subnetworks to modulate an external input into the 4 region network? > However, I don't think you can modulate an input as DCM is currently > set up, and you would still need to drive the subnetworks in some way. One possibility I was wondering about is whether it would be possible to constrain the within-network interactions, by modelling each netwtork as a single VOI, i.e. extracting one VOI for network 1 based on a mask including the activations of areas A,B,C,D, one VOI for network 2 including areas E,F,G and one VOI for network 3 including areas H,I,J, and then study modality-dependent bilinear modulations between networks in a simple model including these three VOIs. Does this make any sense? Or do such multi-regional VOIs violate the biophysical assumptions or any other assumptions in DCM? Thank you again, Marco -- Marco Tettamanti, Ph.D. San Raffaele Scientific Institute Via Olgettina 58 I-20132 Milano, Italy Tel. ++39-02-26434888 Fax ++39-02-26434892 Email: [log in to unmask] ----------------------------------------------------------------------------------- SOSTIENI ANCHE TU LA RICERCA DEL SAN RAFFAELE. NON C'E' CURA SENZA RICERCA. Per donazioni: ccp 42437681 intestato a Fondazione Arete' Onlus del San Raffaele. Per informazioni: tel. 02.2643.4461 - www.sanraffaele.org ========================================================================Date: Fri, 11 Feb 2011 11:34:07 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: registration error under DARTEL and suggested smoothness Comments: To: Meng-Chuan Lai <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> It's really a matter of empiricism (although I don't actually know how to check these things properly using the SPM analysis framework), but my best guess is that between 4 and 8 mm of additional smoothing would work reasonably well. As I say, that is just a guess, and I have no idea at all really. Best regards, -John On 10 February 2011 21:39, Meng-Chuan Lai <[log in to unmask]> wrote: > Hi John, > > Thanks so much. For the 4-8mm you suggested, do you mean the smoothing applied with FWHM or the effective smoothness in the data itself? > > I have tried a 2mm kernel which gives an effective smoothness of 5.5mm in the data itself, and it seems to pick up quite a few reasonable regions. Would you suspect this to be valid? > > Thanks so much for the prompt answers! > > best, > Meng-chuan > > ========================================================================Date: Fri, 11 Feb 2011 13:30:57 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: matlab path Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hello, just to mention another instance where this bugged me some time ago: if you segmented data on another machine (or before moving spm), the location of the priors used is hardcoded in the resulting seg_sn-files. Therefore, if you want to use them to normalize an image later-on, this may also result in the error you mentioned. You can check that if you load the file and check po.VG(1) and see which GM prior that refers to. Hope this helps, Marko John Ashburner wrote: > Are you trying to run a batch file? If so, the location of the tissue > probability maps will be defined by that file. > > Best regards, > -John > > On 10 February 2011 14:36, Cornelia Hummel<[log in to unmask]> wrote: >> Dear experts, >> I encounterd the following problem: >> I installed SPM8, ran matlab and SPM with the respetive path set, then moved >> the entire SPM folder to another site, changed the path in matlab, and now, >> when running segment, got the error message that the file "(previous >> folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting, >> and cannot find why matlab still tries to read from the path that no longer >> exists rather than the correctly set one. (The realign and coregister >> procedures ran without problems.) >> Can anybody please help me? >> Thanks in advance, >> Cornelia >> >> >> -- >> Cornelia Hummel >> Voglerstraße 14 >> 01277 Dresden >> > -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Fri, 11 Feb 2011 14:21:51 +0000 Reply-To: Lena Palaniyappan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lena Palaniyappan <[log in to unmask]> Subject: VBM ROI Marsbar query Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> VBM ROI Marsbar query Hi all

I am trying to calculate the volume of grey matter within a ROI defined using Marsbar (originally the binary ROI was obtained from the ‘activated’ cluster when doing a VBM analysis) in a set of 90 scans. When I extract the mean ‘time series’ value using Marsbar for each image using standard marsbar functions, I presume what I get in structural scans is the Mean VBM ‘density’. If I need the volume, I should multiply the density by total number of voxels within the mask (the mask is 1X1X1 isotropic). Is this a correct assumption?

I undertook a ‘modulated VBM’ - hope the assumptions still hold true

Cheers
Lena

This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham.

This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.

========================================================================Date: Fri, 11 Feb 2011 09:51:12 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: VBM ROI Marsbar query In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset=windows-1252 Mime-Version: 1.0 (Apple Message framework v1082) Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Lena > I am trying to calculate the volume of grey matter within a ROI defined using Marsbar (originally the binary ROI was obtained from the ‘activated’ cluster when doing a VBM analysis) in a set of 90 scans. When I extract the mean ‘time series’ value using Marsbar for each image using standard marsbar functions, I presume what I get in structural scans is the Mean VBM ‘density’. If I need the volume, I should multiply the density by total number of voxels within the mask (the mask is 1X1X1 isotropic). Is this a correct assumption? In the modulated images, each voxel value basically represents the gray matter volume for a voxel. So, typically, this is what you would use—for example, the average values in an ROI would represent average gray matter volume within that ROI. The density/volume issue can be a bit tricky and relates to the modulated/unmodulated analyses, but in most cases the modulated analysis (as you've done) is what people want. Hope this helps! Jonathan ========================================================================Date: Fri, 11 Feb 2011 09:25:34 -0800 Reply-To: Hoameng Ung <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Hoameng Ung <[log in to unmask]> Subject: Research Assistant position at Stanford University MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a48fc6ab4c049c04fe03 Message-ID: <[log in to unmask]> --20cf3054a48fc6ab4c049c04fe03 Content-Type: text/plain; charset=ISO-8859-1 *Stanford University Social Science Research Assistant* Job ID: 41512 Job Location: School of Medicine Job Category : Social Science Research Salary : 1A3 Date Posted : Feb 9, 2011 The Stanford Systems Neuroscience and Pain Lab is recruiting a full-time Social Science Research Assistant (SSRA) to assist with ongoing and new NIH funded research related to neuroimaging and pain. These studies involve structural and functional imaging in healthy individuals and patients with chronic pain, functional imaging of the cervical spinal cord, and using fMRI as a biofeedback tool (real-time fMRI neurofeedback) for pain modulation. The research assistant will work directly with postdoctoral research fellows, principal investigators, and fellow research assistants in a large, collaborative environment. This position will span multiple projects, requiring the applicant to be comfortable working in a variety of settings, prioritizing goals, and working with multiple lab members. The SSRA will assist neuroimaging research, operation of MRI scanner and other study equipment, and data pre-processing and analysis. Assistance at the scanner will include operating scanner and stimuli computers, assisting with participant set-up, and transferring and organizing data. The SSRA will also assist with implementation and maintenance of analysis software and serve as technical support for lab personnel regarding software and analyses. Familiarity with SPM (preferred), AFNI, FSL neuroimaging analysis programs is preferred. Some programming experience (eg Matlab) is preferred. The SSRA will be responsible for maintenance of psychophysical testing equipment and communication with technical support as necessary. Must be self-motivated, independent, and able to work in a fast-paced, changing environment. Applicant may also be responsible for some recruitment, scheduling, and participant procedures. The applicant must have strong interpersonal skills, be organized and detail-oriented, and able to work independently with minimal supervision. Required: Proficient with MS office, including Word and Excel. Ability to multi-task and work independently. Strong organizational skills. Familiarity with neuroscience and computer programming. A two year college degree or equivalent experience. Desired: Previous experience working with MRI scanners, MRI and fMRI processing, and neuroimaging analysis highly desirable. Knowledge of programming (eg Matlab) and statistical analyses. Comfort with multiple operating systems, including Linux. Previous experience with software packages such as SPM, FSL, and AFNI. A four-year degree desired. --20cf3054a48fc6ab4c049c04fe03 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Stanford University Social Science Research Assistant

Job ID: 41512

Job Location: School of Medicine

Job Category : Social Science Research

Salary : 1A3

Date Posted : Feb 9, 2011

The Stanford Systems Neuroscience and Pain Lab is recruiting a full-time Social Science Research Assistant (SSRA) to assist with ongoing and new NIH funded research related to neuroimaging and pain. These studies involve structural and functional imaging in healthy individuals and patients with chronic pain, functional imaging of the cervical spinal cord, and using fMRI as a biofeedback tool (real-time fMRI neurofeedback) for pain modulation. The research assistant will work directly with postdoctoral research fellows, principal investigators, and fellow research assistants in a large, collaborative environment. This position will span multiple projects, requiring the applicant to be comfortable working in a variety of settings, prioritizing goals, and working with multiple lab members.

The SSRA will assist neuroimaging research, operation of MRI scanner and other study equipment, and data pre-processing and analysis. Assistance at the scanner will include operating scanner and stimuli computers, assisting with participant set-up, and transferring and organizing data. The SSRA will also assist with implementation and maintenance of analysis software and serve as technical support for lab personnel regarding software and analyses. Familiarity with SPM (preferred), AFNI, FSL neuroimaging analysis programs is preferred. Some programming experience (eg Matlab) is preferred. The SSRA will be responsible for maintenance of psychophysical testing equipment and communication with technical support as necessary. Must be self-motivated, independent, and able to work in a fast-paced, changing environment. Applicant may also be responsible for some recruitment, scheduling, and participant procedures. The applicant must have strong interpersonal skills, be organized and detail-oriented, and able to work independently with minimal supervision.

Required:
Proficient with MS office, including Word and Excel. Ability to multi-task and work independently. Strong organizational skills. Familiarity with neuroscience and computer programming. A two year college degree or equivalent experience.

Desired:
Previous experience working with MRI scanners, MRI and fMRI processing, and neuroimaging analysis highly desirable. Knowledge of programming (eg Matlab) and statistical analyses. Comfort with multiple operating systems, including Linux. Previous experience with software packages such as SPM, FSL, and AFNI. A four-year degree desired.

--20cf3054a48fc6ab4c049c04fe03-- ========================================================================Date: Fri, 11 Feb 2011 10:13:49 -0800 Reply-To: "Jiang, Zhiguo" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Jiang, Zhiguo" <[log in to unmask]> Subject: naive quesiton about mapping between images in different space Content-Type: multipart/related; boundary="_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_"; type="multipart/alternative" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: multipart/alternative; boundary="_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_" --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable In spm_reslice.m , it states % Assuming that image1 has a transformation matrix M1, and image2 has a % transformation matrix M2, the mapping from image1 to image2 is: M2\M1 % (ie. from the coordinate system of image1 into millimeters, followed % by a mapping from millimeters into the space of image2). So to map a x0,y0,z0 in image1 space to image2, isn't the composite matrix M1\M2 instead of M2\M1... ? Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 ... Not sure where I got it wrong... Thanks, Tony ------------------------------------------------------------------------------------------------------------------------------ [cid:image001.jpg@01CBC9D4.606E1D30] Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095 Tel: (310)206-1423|Email: [log in to unmask] ________________________________ IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

In spm_reslice.m , it states

% Assuming that image1 has a transformation matrix M1, and image2 has a

% transformation matrix M2, the mapping from image1 to image2 is: M2\M1

% (ie. from the coordinate system of image1 into millimeters, followed

% by a mapping from millimeters into the space of image2).

So to map a x0,y0,z0 in image1 space to image2, isn’t the composite matrix

M1\M2 instead of M2\M1… ?

Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 …

Not sure where I got it wrong…

Thanks,

Tony

------------------------------------------------------------------------------------------------------------------------------

Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095

Tel: (310)206-1423|Email: [log in to unmask]

IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer.
--_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_-- --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: image/jpeg; name="image001.jpg" Content-Description: image001.jpg Content-Disposition: inline; filename="image001.jpg"; size30; creation-date="Fri, 11 Feb 2011 10:13:49 GMT"; modification-date="Fri, 11 Feb 2011 10:13:49 GMT" Content-ID: <[log in to unmask]> Content-Transfer-Encoding: base64 /9j/4AAQSkZJRgABAAEAYABgAAD//gAfTEVBRCBUZWNobm9sb2dpZXMgSW5jLiBWMS4wMQD/2wCE AAUFBQgFCAwHBwwMCQkJDA0MDAwMDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0BBQgICgcKDAcHDA0MCgwNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0NDQ0NDQ0NDf/EAaIAAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKCwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoLEAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEU MoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2Rl ZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK 0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+foRAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYS QVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNU VVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5 usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/AABEIADABwAMBEQACEQEDEQH/ 2gAMAwEAAhEDEQA/AOXr6o8gKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAC gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKA CgDobfQ1n0yTUt5BiYrs28HlR1zx9707V8xWzR0cxpZUqaaqRUufms1dTduW2vw9+p60MIp4WeM5 rODty231j1v59hnh/RV1uZ4WcxbE3ZC7s8gY6j1q84zN5RShWjTVTnnyWcuW3ut3vZ9hYLCLGzlT cuW0b3Sv1S7ruQ6Vo02rXBt4cAJku56KoOM+5PZe/sASOjH5lSyzDrE1k25WUILeUmr28kvtS6dm 2k8sPhZ4qo6VPRL4pPZK9vvfRdfS7Oh/4RO1uA0VldpLcRgkoQMHHphiQM8Z+YA4BxXzH+sGJw/L Vx+CnSw82kppyvG+uqcUm7ape42rtXPW/s2lUvDDV4zqx3jpZ/c9PXXzMXRNDOq3T2krGFokZj8u SGV1UqRkd2PftXvZpmiy3C08bRiqsak4xj73KnGcJTUk7PpFdOp52Ewn1qrKhNuDjFt6Xd1JRatd d/wMSePyZGjznYxXPrgkV79KftacKlrc0Yyt2uk7HmzjySlHs2vudjV0LSDrNx5G7y1VS7MBnAGA OMjqSO/rXj5rmKynD/WOVTk5xhGLfLdu7etnsk+m9juweF+uVPZX5Uk5N2va2nl1aKuqWB0y6ktW OfLOAcYyCAVOPcEV2YDFrH4ani4rl543cb35ZJuMlfykn8jDEUXhqsqLd+V6Pa6eqf3MoV6Ryna2 3huysrOG/wBbuXthdgtBBDGJJmjHHmtuZVRCfu5yWHI7gczqScnClFO27bsr9vM1UUknJ2vsluU9 d8OLptvFqNlMLywuWKJKFMbJIMkxSoSdr4BI5wwBIAGM1CpzNwkuWS6eXdMUo8qundHLVuZhQAUA dB/Yi/2L/bO87vtv2Ty9vGPI87fuznOfl24989qx5/3ns7fZ5r/O1i+X3ebzt+Fzn62ICgAoA3/D eh/29dGBpPs8UUUk8sm0uVjiGWIQEFjyABkdc9sVjUn7NXSu20kttWXGPM7bdTN1GK2huGSykaeA bdkjp5bNlQWymWxhsgcnIAPerjdr3lZ9k7idk9NilVkhQAUAdz4g8FSaLplnq8bmaK8jjaQbdvlP LGsirwTuU5YbuOVAI+YVywrKc5U2rOLdvOzsbShypSXUytP0Bb3Sb3VTIVNg0AEe3IfzpAhy2Rt2 5z0OfatJT5Zxp2+K+vayuSo3i5drfic3WxmFABQAUAFABQB0Go6IthplhqQcsdQ+05TbgR/Z5RGM HJ3bs5PAx05rGM+acoW+Hl+d1ctxslLvf8Dn62INPV7W1s7gxWM32qEKhEm0pklQWG08/KxK574z WcW2ryVn2KaSdk7ozK0JOg0bRF1S2vbkuYzYwCYKFzvywXaTkbfXOD9KxnPkcY2+J29C1G6b7I5+ tiAoAKACgAoAKACgAoA6zVfD1to9jBNPcMb27giuY7dYvkEMrEKWl3fe2gnAXg8e554zc5NJe7Ft N31uvI0cVFJ31avbyMPStMm1i7isbYAyzttXPAHcknnAUAscAnAOAa1lJQTlLZEpczsjsl8N6C1x /Zo1GT7Xu8sS/Z/9GMuduzdv37d3y+Zjb/F0rm9pUS5+Rcu9r+9b7rfI15Y35ebX00Myz8JSPdah ZXT+TLpdpcXB2jcHMGzCgkrhXDBg3XGPlrR1UlCUdVKSXpf/ACJUNWnpZN/cchXQZBQAUAFABQAU AFABQB32n/8AIuXH/XQ/+hRV+bYv/kocL/17X/pNU+po/wDItq/4n+cCPwF/x9y/9cf/AGda14s/ 3Wj/ANfv/bJkZN/Gn/g/9uRZ0U/ZtJv7hOHLumRwQAq459vMJHvXJma9vmuXYWprTUKc7PVNucrq 3n7OKfdG+E/d4PFVY6S5pRv10irfdzOxxel6g+lXCXUYDFM/KeAQVKkH8/zr73HYOGYYeeEqNxjO 3vJXacZKSav6W9Gz5zD13haka0Fdxvp0d01r952XhO8N/q09yVCGSFmKjoDviz19Tz+NfCcQYdYL KsPhVJzVOtCKk7JtezrW200WnyPoctq+3xlWs0o81Nuy2+KH57nD3n+vk/66P/6Ea/QsN/Apf9e4 f+ko+aq/xJ/4pfmztPDqPp+l3V/GCZX/AHceBk8cZAH+0+T/ALnPFfBZxKGNzPCZbUaVKF6lW7tH X3uVttbxhZf49NT6PAqWHwtbFQT55e7Cyu+10vWV/wDt0j8aQeYbe/UFRPGAw6YIAYZ75IbH/Aa1 4Zq+zWJy2TTdGq3HW94tuMrW0spRv/2+RmsOZ0sUlZTgk/JrVX87O3yOGr9DPmT0L4nDytZ+yr/q 7W3t4YwOAEWMMAB2GWPFceH+Dm6ttv7zerpK3ZJGBp2qTy2R0BFRo7y5idWbduSTKoCuDj5hgNkE 46VtKKUva9Ypr5bkJu3J3Z6LqF5faDf/ANmadp5fSrZlikRrPzTdAYEskkpjLMzHdtKsFA28EcVx xUZx55ztN6r3rcvZJXNm3F8sY+6vLcNP0q18PatrcDQpcQWlm8scUg3DDeVKiE9cYYKSDu2570Sk 6kKTTs3JJtfNMElGUlbRIzdD1ubxZHe6fqaQSRpZTzwFYIo2gkiAK+WUVTt5wQSSQMFsFgbnBUXG cLp8yT1bun3uTGXPeMrbNrTaw9fEN2ng8MPKyL8WY/cx/wCpFn/u/wCs/wCmv+s/2qXs4+36/Dzb vfm/Ly2HzP2fztt0sW9TOpeE5ItL0eyLxQRRG5kNp532qV1DyBpDGx8td2xVQqVww3eijyVU51JW bb5VzW5VstL7+o3eHuxW2+l7lmy0az0/xNIvkAWlxYSXQt2A+QSRbnjAI+XawcKMAoMAdKlzlKkt feUlG/o9/wCtwSSntpa9vkeW65r8+vSK8yxRJECsUcMaxoiE52gDkgdixJruhBU1ZX13bdzCUnL/ AIB2nw21ae0e8t49mxbK5nG6NGbzERQPmIJ2+qZ2nuK5sRFPlbv8UVv0f9bmtN2uvJsS21E6dpcn iSSOGTUr668iBmiTy4UjjBeVItojDkgKPlOOD3YMOPNNUU2oRjd6u7u9E3vYE7Ln0u3ZeRE13/wl +j3k94kf9oab5cqzxxpG0sLtsdJBGqqdn3lO0dhx8xZ29jOKi3ySurN3s+lr9xX54tvddfIv6/rR 0PTdPgsoYElvNOj8+Yxq0jRkMoRSR8uSXLNjcSV5G2phDnlNybtGbsr6X7lSfKopJax1Z5PXecx7 /LqMP/Es0W+OLLVdHtYWP/POYAmCUZ4BDkD0yVLcLXkqL9+pD4oVJP1XVfcdl/hg9nFL59Dk7TTp tI0DXrK4G2WCazRvQ4uFwR/ssMMp7gg10OSlUpSjs1L8jNLljNPo1+ZYlTUPC1nZw6Nal5rm3S5u rj7L55YzZKwbmjcKkaABlXGS2cg5JlctWUnUlZJtRXNa1uu63HrBJQW6u3a+/Q0INKhTxLpV19nW 3XUYfOltym1EmEciyqsbD5VLAMAR1Jx7Q5P2VSN78rsnfdXVtR29+Lta628zz/wzGr+ILVGAZTeI CpAII8zoR0x7V11NKcrfy/oYx+Jep2WiyQWDa/dSQRT/AGWRWjjkUFAwuZAnHHyhtpZQRuUbehrn nd+yim1datf4Vc1jZc7tt/mUNO1EyWl74pv44bi7R4bW2Vo1ESyMvzOYwApKRgFOOuc8nIqUbONC DajrJ6627X82JPR1Hvol2Cw1J/Gtne2upJE95a2z3dtcJGkUg8kgvC3lqoZXU4UEcHJJztIJR9hK MoXUW1Fq7a12eoJ86ae6V0/ToXZ9dfQvDOkPbJH9qkN6I5pI0kMSLckybFcMgZyY/mKkhVIGM1Kh z1aibfKuW6TtfTS9vmPm5YRtvr8tTC1tl17Ro9caOOO8hujaXBiQIJg0fmxysqgKGHKMVxk8kYwB rD93N0k3yuPMr9NbNES96PP1vZnSaloltqXi1bWZQlpHBFNMqjaCkdssjD5cY3sAGIwcEkHNYxm4 0br4rtL1crFuKc7dLX/AydP8bNqN8lleW9sdKuJBF9lEMaiFHbaGSRVEgdM7t27k5wFyCNJUeWLl FvnSve71a7razEp3dmly9rbE+n6b/Y6+IbEHIt4Cik9Som+Un3K4J96mUuf2Mu7v+AJcvOuy/Up2 9yPCOi2t9apGdQ1N5mE0iLIYYYWEYWNXBUM7fNuwcjIPRdtNe1qSjJvkhbRO129dbdhL3Iprd3+S QmouvijRG1V4401CyuEimeJFjE8UwOxnRAF8wSfLkADGfUAOP7qp7NN8kk2k9bNb28rA/ejzdU9f O5e1/XpPB93/AGLpkduILRIln3wRyG6kKK8jSs6l8EttAUrgD5TjAEQgqsfaTbu72s2uVXsrWHKX I+SNrLfTc6LUJLGDxFpkSwpHZX+mQwmMKuAtwZlj5/vBvLG/72B1rGKk6U3d80Zt39LX/Ut2U4q2 jil99zifB+kraa1Mb5Q8WkJczTqRwfs4KAc8cyFSAc5x0rqqyvTXLvOyXz/4BlBWk7/Zu38jhbid rmV5mwGkZnIAwMsSTgdhk8CupKyS7GW+p6BZTr4V0ODU4I421DUpphFNIiyeTDAQjeWrgqHaQ8sQ flOCK5Gva1HBt8sUrpaXb726WNl7kVJbu/ySJpL86/pL608cS6npFxbs8qRoomilbCebGqiNmWVe u0AqNuOTS5fZzVJN8k09LvRrez32C/NHm+1FosfELW7meDT7Z/L8u4020uHxFGG8xvMztYKGROOE UhB2FTQgk5tXupyS1e2n9XHUk/dX91M4bw3rJ8PajBqKrv8AIYkrnGVZWRwD2JRjg9jiuqpD2kXD a/8Aw5lF8rT7HaS+ErHxBK1z4avY3kdjILO4/dToc7iqE8Sbex6ADmRjzXMqsqa5a0XbbmWq+fb+ tDXkUtab+T0ZqeEtc1SGbV4b4g3ENhd3D+bGjSedEsMah2K5ZAqgeWSY2xnBzmoqwham4bOUUrN2 s7v7/PcqEmuZPezfz0Kun62bDw7LrBhglvZNVdEd4kKxNJbI5dUAC8BWCLjapbOOxcoXqqndqKhq r72bEpWg5WV+b9BIGvtN0yLVrC3M+pavNcTSTrbibyI1kK7ETY0cZlcs/wB37oxjhdrfLKbpzdoQ SSV7Xduut3YNYpSitZN62vb/AIcTVdOhvjpOp6nD9ia7nNvfLsMCtskXEuzClDJGT5jAAcZGMURk 4+0hB3srx1vutvk9hNX5ZSVruz6G3rd/qWg6g8Oo2KP4fDsgiitYjH5HRHWVQCswXDfNIvz5A28E ZQjGcU4Saq93J3v6dvkXJuLs17np0/zMjwnaE6XfXmgxLPqUdyFj81I3ljtCAVdEfcnmMdwbAbO0 gZwK0qu04xqu0La2uk5edtbEwWjcFrf8DmvEurf2haxx6lbNb6zDKwkk8lYBLAV4MijYfNVxgYjC 7cndk4ranHlbcHem1or3s/Lyt5mcndWkrSXlbQ4muoyCgDv/AA28Wo6bPpRcRTOxZM987SMeuGXD Ac4ORX5rnUauBzDD5zGm6lGEVGfL9m3Ond9LxneLejaaZ9VgHDEYapgXJRnJtxv1va1u+q1S1tqa Wj6T/wAIoJby+lj5TaqoSc87uNwUliQAAAfc15OZZh/rG6OBy6jU0nzSlNJct043fK5JRSbbk2tk kjswuG/svnxGJnH4bJRb11vpdJtuyskjH8LX0MyXGm3LCNboEqSQBuYbWGTxuxgr6kEdcCvdz7C1 qU8NmmFi5ywzippJt8sWpRdlry35lK2yae12vPy6tCcauDrNRVW7i/NqzXrs16F7S/C50ac3moSR CCENjkkMSCBkMAOhJAGSWwAO9efj89WaUFgcsp1vb1XG+iTik02ouMm3dpJt8qUbttbHTh8v+p1P rGKlD2cE7ebaa1TS6O9tXe1iPwtcreaxcTxjajxOVGMYXzIgvA4HAGfetM+oSwuUYXD1HzThVpqT u3eXsqrlq9Wrt28iMuqKrjatSKtFwlZbWXPC2noUNR8HXkQmui8JRfMlIDPu2jLYA8vGce+M969H B8R4So6GDUK3PL2dJNxp8vM+WF2/aX5b9bXt06HNXyutH2ldyp8q5p2vK9leX8tr28/mbOqajL4Y srW0ttqylSXyM47tx7uxwfY14WBwVLiDGYzGYrmdFSShytxvq1HX+7Tirr+8j0MRXnllChQo2U7X ldX83p5yb+4hkun8R6LK8uDPbPv4GOF5zj/rmzj6it4YeGQ5zRp0bqhiIcmrvrJ2tf8A6+Rg/JSM 5VJZhgZynb2lOXNpptre3+FyXqjziv1Q+QPVdc0ufxtBbavpIFxOsEcF5ArKJY5Y/l8zaSCUkBGC M4AGf4tvBCSoOVOportxfRp9PVHRJc6Uo72s0Vri2h8G6ZFFdJC+sS3cdxtwjyW8ERDBWkG4o0jq MqDyrHP3SKpN1ptxb9motdUm35eQrezVnbmvf0SOi1e01/V7/wC26Ne3LaXeESJMl26RW6kAyLIv mL5XlHdlMdAABuyBjF04R5akVzx0ty6vtbTW5o1Ju8W+V+exmWM0E91rz2sstzCNPlVJp5DLJIE8 tNxc8sCVOz0TaO1W00qSkknzrRKyV79Pz8yVa87aq3Uwfh//AMfN7/2DLz/0Fa1r7R/xxIp7v/Cw /wCZN/7jP/tlR/y//wC4f/twf8u/+3v0Ov1k694kli1Tw/c3D211HEJI4blo1tp1QLIjp5iCMZG/ dja2S3cE88PZ0k4VYpNN2bje66NO2po+aXvQbs+z2ZDpbxHxFNFDcy33k6fPG88spl3yLCTJ5btz 5YckKORwSCQc05X9km0o3mnZK1lfS/mC+J2d9Hr8jxmvSOU734f/APHze/8AYMvP/QVrkr7R/wAc Tanu/wDCyzpNsfE2gnSbUqb+yuWuIoSwUywyJtcR5IDOrDcRnOMAckClJ+yqe0fwyVm+zT0uNLmj yrdO9vIlTTpfCGi3o1HEN5qYjgggLKZBEr7pZHUE7VI+Vc4OR7ilzKrUjyaxhdt9L9EgtyRd9G9E ij41/wCPbSP+wZD/AOhPVUd6n+NintH/AAo4KusxPRfHpxDo5H/QJtf5Vx0N6n+ORvU+z/hR2N5r EWv+D7y+P/H9ttILs9C7Q3EflyEero/LeoKjhK5lB068Y/Z95x8rp3Ro3zU2+uifyZmXMus+IbGy uvD1xcHybeO1ubeC4aNo5YsqJGjDoNsq4IcZAxgkdBolTpylGslq3KLavdPpe3QXvSScG9rNJ7Mh ivItJ8Raat5dy3UtuqrdyzTmaOKeVWVo0diQqRll3ncQDnOCCA7OVKfLFJP4UlZtLq/N9BX5Zxu7 2316hoPgjUtP16K4u0WG1hulcTs67JMvmNY8NlmlOAoAyM5bGDROtGVNqOrcduq739AjBqSb2vuV oP8Aj38S/wC+n/pW9N70fT/21C6T/rqZnhhF1rSbzQUZUu2liurVXYKJXQFJIwxwN5TGwE4J9ACa 0qfu5xq/Zs4y8k9n95Mfei4dd0X9K0m48G2d7qGqqLaW4tZLS1hZl8yR5sBpAgJ+SMDJLcHPrjMS kq0owp6pSUpPokunzKSdNNy00sl6lm48P3OveGNKFiBLcW5vCYAyiRo5LlgzqrEFgjIoYLkjeDjF SpqnVqc2ifLr0uo7fO4+VyhG26vp8zK1u3bw5oMWjXJC31zdG7liDKxijWPy41fBIDOTvAHOAQcd DpB+0qOpH4VHlT7u93b02JkuSPK927+h02panBpXjAPdnZby28UErdNqy2qpuJ7AMVJPYAmsYxcq Fo7ptr5SLbUamu1rfejA03wDfWOopNehItOtpFlkuzInktCjBsowbJMgACgDcC3IGDWsq8ZRajfm aso21Tff0IVNp67Lr0sW7LUl1f8A4SG9T7k8BdM8HYZvkyPXbjPvUuPJ7GPZ/oNO/O/L9SnHYSeL dCtINPxLfaU06SQblWR4ZXEiyIpIDBD8hA+bue2a5lRqSc9Izs0+ia0s/UVueKUd1fTyYl7bv4S0 JrC6KpqF/cxStCGVmighBZGkwWCs0hyF6levIZQJ+1qc0fgimr7Xb7fIGuSPK929uyRd8TeG7vxV ejWtIQT2t+kbswdAIJFjVZUmywKbCu4k8YPBOKmnUjRj7Kpo436bq+jQ5RcnzR2f4epW+JIWzvdP Fq4ZYdMtfKkToQjzbHX2IAYVWH1jPmW85XXyQqmjjb+VfqbniyWG00y41WAgP4jNrhRwUSOMSXH5 zYVx7jt1ypJuapvalzfO7svwLnonJfat/wAH8TxavSOU9ItbJ/FegW9lY4kvtKln/wBH3BXkgnIk Z4wSA+1xgjkgfVQ3E37Ko5S0jNLXomtLP5G6XPFJbxvp5MLqyfwnoFxZXuI77VZYP9H3BnjggJkV 5ACQm5zgDgkfRgon7WopR+GCevRt6WXyC3JFp7u2nkip48/5hX/YGsv/AGrVUf8Al5/18l+gp/Z/ wowvCw09tTgTV1DWbsUkyzIF3KQjFkZSqq5Usc4C5J4rWpzKD9n8XT+vQiNrrm2N9Ph9rdtdgRJt iRwy3gkQQhAciYPu4AHzY+92xmsvb03HV6/y2d/Sxfs5J6ff09Trm1K31bW9eubQhoTo90odejlI 4EZgRwQWU7SOCuD3rn5XCnSjLf2kdO122a3TlNr+V/ocf/zJv/cZ/wDbKuj/AJf/APcP/wBuMv8A l3/29+huafcalrWhWtvoc80d5pzSpPbwzNE8kUj745QqsocISUPVgTnGOTlJQp1JOqlyys02rpNa NeV9y1dxSg9Ve6TsVdVshu0/Tdevrg3UsjtdiS4M6WiudsIIZmVXPDSHd8q8njBNRfxzpRXKl7tl Zy7/AC7Ca2jNu/XW9ux0XhnT/Enh++WG4c/2PGx82SaVGtTb8/OhZiFDLgqF2nJAcY3VjUlSqRul +86JJ81y4qcHZ/D+FjlbbQ21Dz9R8MSyC5hu5gLeNxFItqzExSR/MrMuMKyjnI6YFdDny2hXSs4r Vq65uqZmo3u6e6b020NPxfPdnQbeHXyp1ZbkmIEoZhamPky7Om5+OcFtqk5Ias6SXtG6PwW13tzX 6fIqd+VKfxX+djyWvQOYKACgBWYvyxJPvzUqKjpFJLyVhtt7u4lUIUsSACSQOnt9KlRUbtJJvey3 9R3ez6CVQgoAKACgAoAcjtGdyEqfUHB/SkMbTEODsoKgkA9Rng46ZFIY2mIKACgBwdkBCkgHg4OM /WkMbTEFABQAAlTkcEUAKzFzliST3PJpbAJTAKACgAoAcrsn3SVyMHBxwe30pDG0xDi7MApJIXoM 8DPXA7UthjaYgoAc7tIcsSx9Scn9aW2wxFYocqSCOhHBoEBJY5PJPJJpgJQA4uxUJk7RyBngfh0p DG0xCqxQ5UkEdCODSACSxyeSeSTTA9FvP+Ef1xxeLdnSGkSMT2i2jtHvRQrNF5OI9rYyAwU7iWPJ IHGvaU/d5ee17S5rOz731N3yy1vy91b8rGD4t1mDWLqL7Grra2dtFaQ+Zje0cIOGYDgElj3PGM4P A1pQcE+bdtydtrsibUnpslZfIs+LtVtrxbOx09/NtbC1SMNtdQ0z/NO4V1VhuYL26gkZFTSi480p qzlK/wAumw5taKOyX49Tjq6TIVWKHKnBHQjg0ADMWOSck9SetGwCUAFADvMbbsydvXGTjPrjpSGN piCgBVYocqSCOhHBpAJTAcXYqEydo5AzwPw6UhiKxQ5UkEdCODQICSxyeSeSTTASgAoAKACgAoAK ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA KACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAP//Z --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_-- ========================================================================Date: Fri, 11 Feb 2011 11:10:54 -0800 Reply-To: "Jiang, Zhiguo" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Jiang, Zhiguo" <[log in to unmask]> Subject: Ignore my previous question Content-Type: multipart/related; boundary="_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_"; type="multipart/alternative" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_ Content-Type: multipart/alternative; boundary="_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_" --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable I mistakenly read '\' as '/' Tony ------------------------------------------------------------------------------------------------------------------------------ [cid:image001.jpg@01CBC9DC.596C77E0] Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095 Tel: (310)206-1423|Email: [log in to unmask] ________________________________ IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

I mistakenly read ‘\’ as ‘/’

Tony

------------------------------------------------------------------------------------------------------------------------------

Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095

Tel: (310)206-1423|Email: [log in to unmask]

IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer.
--_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_-- --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_ Content-Type: image/jpeg; name="image001.jpg" Content-Description: image001.jpg Content-Disposition: inline; filename="image001.jpg"; size30; creation-date="Fri, 11 Feb 2011 11:10:54 GMT"; modification-date="Fri, 11 Feb 2011 11:10:54 GMT" Content-ID: <[log in to unmask]> Content-Transfer-Encoding: base64 /9j/4AAQSkZJRgABAAEAYABgAAD//gAfTEVBRCBUZWNobm9sb2dpZXMgSW5jLiBWMS4wMQD/2wCE AAUFBQgFCAwHBwwMCQkJDA0MDAwMDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0BBQgICgcKDAcHDA0MCgwNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0NDQ0NDQ0NDf/EAaIAAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKCwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoLEAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEU MoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2Rl ZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK 0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+foRAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYS QVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNU VVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5 usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/AABEIADABwAMBEQACEQEDEQH/ 2gAMAwEAAhEDEQA/AOXr6o8gKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAC gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKA CgDobfQ1n0yTUt5BiYrs28HlR1zx9707V8xWzR0cxpZUqaaqRUufms1dTduW2vw9+p60MIp4WeM5 rODty231j1v59hnh/RV1uZ4WcxbE3ZC7s8gY6j1q84zN5RShWjTVTnnyWcuW3ut3vZ9hYLCLGzlT cuW0b3Sv1S7ruQ6Vo02rXBt4cAJku56KoOM+5PZe/sASOjH5lSyzDrE1k25WUILeUmr28kvtS6dm 2k8sPhZ4qo6VPRL4pPZK9vvfRdfS7Oh/4RO1uA0VldpLcRgkoQMHHphiQM8Z+YA4BxXzH+sGJw/L Vx+CnSw82kppyvG+uqcUm7ape42rtXPW/s2lUvDDV4zqx3jpZ/c9PXXzMXRNDOq3T2krGFokZj8u SGV1UqRkd2PftXvZpmiy3C08bRiqsak4xj73KnGcJTUk7PpFdOp52Ewn1qrKhNuDjFt6Xd1JRatd d/wMSePyZGjznYxXPrgkV79KftacKlrc0Yyt2uk7HmzjySlHs2vudjV0LSDrNx5G7y1VS7MBnAGA OMjqSO/rXj5rmKynD/WOVTk5xhGLfLdu7etnsk+m9juweF+uVPZX5Uk5N2va2nl1aKuqWB0y6ktW OfLOAcYyCAVOPcEV2YDFrH4ani4rl543cb35ZJuMlfykn8jDEUXhqsqLd+V6Pa6eqf3MoV6Ryna2 3huysrOG/wBbuXthdgtBBDGJJmjHHmtuZVRCfu5yWHI7gczqScnClFO27bsr9vM1UUknJ2vsluU9 d8OLptvFqNlMLywuWKJKFMbJIMkxSoSdr4BI5wwBIAGM1CpzNwkuWS6eXdMUo8qundHLVuZhQAUA dB/Yi/2L/bO87vtv2Ty9vGPI87fuznOfl24989qx5/3ns7fZ5r/O1i+X3ebzt+Fzn62ICgAoA3/D eh/29dGBpPs8UUUk8sm0uVjiGWIQEFjyABkdc9sVjUn7NXSu20kttWXGPM7bdTN1GK2huGSykaeA bdkjp5bNlQWymWxhsgcnIAPerjdr3lZ9k7idk9NilVkhQAUAdz4g8FSaLplnq8bmaK8jjaQbdvlP LGsirwTuU5YbuOVAI+YVywrKc5U2rOLdvOzsbShypSXUytP0Bb3Sb3VTIVNg0AEe3IfzpAhy2Rt2 5z0OfatJT5Zxp2+K+vayuSo3i5drfic3WxmFABQAUAFABQB0Go6IthplhqQcsdQ+05TbgR/Z5RGM HJ3bs5PAx05rGM+acoW+Hl+d1ctxslLvf8Dn62INPV7W1s7gxWM32qEKhEm0pklQWG08/KxK574z WcW2ryVn2KaSdk7ozK0JOg0bRF1S2vbkuYzYwCYKFzvywXaTkbfXOD9KxnPkcY2+J29C1G6b7I5+ tiAoAKACgAoAKACgAoA6zVfD1to9jBNPcMb27giuY7dYvkEMrEKWl3fe2gnAXg8e554zc5NJe7Ft N31uvI0cVFJ31avbyMPStMm1i7isbYAyzttXPAHcknnAUAscAnAOAa1lJQTlLZEpczsjsl8N6C1x /Zo1GT7Xu8sS/Z/9GMuduzdv37d3y+Zjb/F0rm9pUS5+Rcu9r+9b7rfI15Y35ebX00Myz8JSPdah ZXT+TLpdpcXB2jcHMGzCgkrhXDBg3XGPlrR1UlCUdVKSXpf/ACJUNWnpZN/cchXQZBQAUAFABQAU AFABQB32n/8AIuXH/XQ/+hRV+bYv/kocL/17X/pNU+po/wDItq/4n+cCPwF/x9y/9cf/AGda14s/ 3Wj/ANfv/bJkZN/Gn/g/9uRZ0U/ZtJv7hOHLumRwQAq459vMJHvXJma9vmuXYWprTUKc7PVNucrq 3n7OKfdG+E/d4PFVY6S5pRv10irfdzOxxel6g+lXCXUYDFM/KeAQVKkH8/zr73HYOGYYeeEqNxjO 3vJXacZKSav6W9Gz5zD13haka0Fdxvp0d01r952XhO8N/q09yVCGSFmKjoDviz19Tz+NfCcQYdYL KsPhVJzVOtCKk7JtezrW200WnyPoctq+3xlWs0o81Nuy2+KH57nD3n+vk/66P/6Ea/QsN/Apf9e4 f+ko+aq/xJ/4pfmztPDqPp+l3V/GCZX/AHceBk8cZAH+0+T/ALnPFfBZxKGNzPCZbUaVKF6lW7tH X3uVttbxhZf49NT6PAqWHwtbFQT55e7Cyu+10vWV/wDt0j8aQeYbe/UFRPGAw6YIAYZ75IbH/Aa1 4Zq+zWJy2TTdGq3HW94tuMrW0spRv/2+RmsOZ0sUlZTgk/JrVX87O3yOGr9DPmT0L4nDytZ+yr/q 7W3t4YwOAEWMMAB2GWPFceH+Dm6ttv7zerpK3ZJGBp2qTy2R0BFRo7y5idWbduSTKoCuDj5hgNkE 46VtKKUva9Ypr5bkJu3J3Z6LqF5faDf/ANmadp5fSrZlikRrPzTdAYEskkpjLMzHdtKsFA28EcVx xUZx55ztN6r3rcvZJXNm3F8sY+6vLcNP0q18PatrcDQpcQWlm8scUg3DDeVKiE9cYYKSDu2570Sk 6kKTTs3JJtfNMElGUlbRIzdD1ubxZHe6fqaQSRpZTzwFYIo2gkiAK+WUVTt5wQSSQMFsFgbnBUXG cLp8yT1bun3uTGXPeMrbNrTaw9fEN2ng8MPKyL8WY/cx/wCpFn/u/wCs/wCmv+s/2qXs4+36/Dzb vfm/Ly2HzP2fztt0sW9TOpeE5ItL0eyLxQRRG5kNp532qV1DyBpDGx8td2xVQqVww3eijyVU51JW bb5VzW5VstL7+o3eHuxW2+l7lmy0az0/xNIvkAWlxYSXQt2A+QSRbnjAI+XawcKMAoMAdKlzlKkt feUlG/o9/wCtwSSntpa9vkeW65r8+vSK8yxRJECsUcMaxoiE52gDkgdixJruhBU1ZX13bdzCUnL/ AIB2nw21ae0e8t49mxbK5nG6NGbzERQPmIJ2+qZ2nuK5sRFPlbv8UVv0f9bmtN2uvJsS21E6dpcn iSSOGTUr668iBmiTy4UjjBeVItojDkgKPlOOD3YMOPNNUU2oRjd6u7u9E3vYE7Ln0u3ZeRE13/wl +j3k94kf9oab5cqzxxpG0sLtsdJBGqqdn3lO0dhx8xZ29jOKi3ySurN3s+lr9xX54tvddfIv6/rR 0PTdPgsoYElvNOj8+Yxq0jRkMoRSR8uSXLNjcSV5G2phDnlNybtGbsr6X7lSfKopJax1Z5PXecx7 /LqMP/Es0W+OLLVdHtYWP/POYAmCUZ4BDkD0yVLcLXkqL9+pD4oVJP1XVfcdl/hg9nFL59Dk7TTp tI0DXrK4G2WCazRvQ4uFwR/ssMMp7gg10OSlUpSjs1L8jNLljNPo1+ZYlTUPC1nZw6Nal5rm3S5u rj7L55YzZKwbmjcKkaABlXGS2cg5JlctWUnUlZJtRXNa1uu63HrBJQW6u3a+/Q0INKhTxLpV19nW 3XUYfOltym1EmEciyqsbD5VLAMAR1Jx7Q5P2VSN78rsnfdXVtR29+Lta628zz/wzGr+ILVGAZTeI CpAII8zoR0x7V11NKcrfy/oYx+Jep2WiyQWDa/dSQRT/AGWRWjjkUFAwuZAnHHyhtpZQRuUbehrn nd+yim1datf4Vc1jZc7tt/mUNO1EyWl74pv44bi7R4bW2Vo1ESyMvzOYwApKRgFOOuc8nIqUbONC DajrJ6627X82JPR1Hvol2Cw1J/Gtne2upJE95a2z3dtcJGkUg8kgvC3lqoZXU4UEcHJJztIJR9hK MoXUW1Fq7a12eoJ86ae6V0/ToXZ9dfQvDOkPbJH9qkN6I5pI0kMSLckybFcMgZyY/mKkhVIGM1Kh z1aibfKuW6TtfTS9vmPm5YRtvr8tTC1tl17Ro9caOOO8hujaXBiQIJg0fmxysqgKGHKMVxk8kYwB rD93N0k3yuPMr9NbNES96PP1vZnSaloltqXi1bWZQlpHBFNMqjaCkdssjD5cY3sAGIwcEkHNYxm4 0br4rtL1crFuKc7dLX/AydP8bNqN8lleW9sdKuJBF9lEMaiFHbaGSRVEgdM7t27k5wFyCNJUeWLl FvnSve71a7razEp3dmly9rbE+n6b/Y6+IbEHIt4Cik9Som+Un3K4J96mUuf2Mu7v+AJcvOuy/Up2 9yPCOi2t9apGdQ1N5mE0iLIYYYWEYWNXBUM7fNuwcjIPRdtNe1qSjJvkhbRO129dbdhL3Iprd3+S QmouvijRG1V4401CyuEimeJFjE8UwOxnRAF8wSfLkADGfUAOP7qp7NN8kk2k9bNb28rA/ejzdU9f O5e1/XpPB93/AGLpkduILRIln3wRyG6kKK8jSs6l8EttAUrgD5TjAEQgqsfaTbu72s2uVXsrWHKX I+SNrLfTc6LUJLGDxFpkSwpHZX+mQwmMKuAtwZlj5/vBvLG/72B1rGKk6U3d80Zt39LX/Ut2U4q2 jil99zifB+kraa1Mb5Q8WkJczTqRwfs4KAc8cyFSAc5x0rqqyvTXLvOyXz/4BlBWk7/Zu38jhbid rmV5mwGkZnIAwMsSTgdhk8CupKyS7GW+p6BZTr4V0ODU4I421DUpphFNIiyeTDAQjeWrgqHaQ8sQ flOCK5Gva1HBt8sUrpaXb726WNl7kVJbu/ySJpL86/pL608cS6npFxbs8qRoomilbCebGqiNmWVe u0AqNuOTS5fZzVJN8k09LvRrez32C/NHm+1FosfELW7meDT7Z/L8u4020uHxFGG8xvMztYKGROOE UhB2FTQgk5tXupyS1e2n9XHUk/dX91M4bw3rJ8PajBqKrv8AIYkrnGVZWRwD2JRjg9jiuqpD2kXD a/8Aw5lF8rT7HaS+ErHxBK1z4avY3kdjILO4/dToc7iqE8Sbex6ADmRjzXMqsqa5a0XbbmWq+fb+ tDXkUtab+T0ZqeEtc1SGbV4b4g3ENhd3D+bGjSedEsMah2K5ZAqgeWSY2xnBzmoqwham4bOUUrN2 s7v7/PcqEmuZPezfz0Kun62bDw7LrBhglvZNVdEd4kKxNJbI5dUAC8BWCLjapbOOxcoXqqndqKhq r72bEpWg5WV+b9BIGvtN0yLVrC3M+pavNcTSTrbibyI1kK7ETY0cZlcs/wB37oxjhdrfLKbpzdoQ SSV7Xduut3YNYpSitZN62vb/AIcTVdOhvjpOp6nD9ia7nNvfLsMCtskXEuzClDJGT5jAAcZGMURk 4+0hB3srx1vutvk9hNX5ZSVruz6G3rd/qWg6g8Oo2KP4fDsgiitYjH5HRHWVQCswXDfNIvz5A28E ZQjGcU4Saq93J3v6dvkXJuLs17np0/zMjwnaE6XfXmgxLPqUdyFj81I3ljtCAVdEfcnmMdwbAbO0 gZwK0qu04xqu0La2uk5edtbEwWjcFrf8DmvEurf2haxx6lbNb6zDKwkk8lYBLAV4MijYfNVxgYjC 7cndk4ranHlbcHem1or3s/Lyt5mcndWkrSXlbQ4muoyCgDv/AA28Wo6bPpRcRTOxZM987SMeuGXD Ac4ORX5rnUauBzDD5zGm6lGEVGfL9m3Ond9LxneLejaaZ9VgHDEYapgXJRnJtxv1va1u+q1S1tqa Wj6T/wAIoJby+lj5TaqoSc87uNwUliQAAAfc15OZZh/rG6OBy6jU0nzSlNJct043fK5JRSbbk2tk kjswuG/svnxGJnH4bJRb11vpdJtuyskjH8LX0MyXGm3LCNboEqSQBuYbWGTxuxgr6kEdcCvdz7C1 qU8NmmFi5ywzippJt8sWpRdlry35lK2yae12vPy6tCcauDrNRVW7i/NqzXrs16F7S/C50ac3moSR CCENjkkMSCBkMAOhJAGSWwAO9efj89WaUFgcsp1vb1XG+iTik02ouMm3dpJt8qUbttbHTh8v+p1P rGKlD2cE7ebaa1TS6O9tXe1iPwtcreaxcTxjajxOVGMYXzIgvA4HAGfetM+oSwuUYXD1HzThVpqT u3eXsqrlq9Wrt28iMuqKrjatSKtFwlZbWXPC2noUNR8HXkQmui8JRfMlIDPu2jLYA8vGce+M969H B8R4So6GDUK3PL2dJNxp8vM+WF2/aX5b9bXt06HNXyutH2ldyp8q5p2vK9leX8tr28/mbOqajL4Y srW0ttqylSXyM47tx7uxwfY14WBwVLiDGYzGYrmdFSShytxvq1HX+7Tirr+8j0MRXnllChQo2U7X ldX83p5yb+4hkun8R6LK8uDPbPv4GOF5zj/rmzj6it4YeGQ5zRp0bqhiIcmrvrJ2tf8A6+Rg/JSM 5VJZhgZynb2lOXNpptre3+FyXqjziv1Q+QPVdc0ufxtBbavpIFxOsEcF5ArKJY5Y/l8zaSCUkBGC M4AGf4tvBCSoOVOportxfRp9PVHRJc6Uo72s0Vri2h8G6ZFFdJC+sS3cdxtwjyW8ERDBWkG4o0jq MqDyrHP3SKpN1ptxb9motdUm35eQrezVnbmvf0SOi1e01/V7/wC26Ne3LaXeESJMl26RW6kAyLIv mL5XlHdlMdAABuyBjF04R5akVzx0ty6vtbTW5o1Ju8W+V+exmWM0E91rz2sstzCNPlVJp5DLJIE8 tNxc8sCVOz0TaO1W00qSkknzrRKyV79Pz8yVa87aq3Uwfh//AMfN7/2DLz/0Fa1r7R/xxIp7v/Cw /wCZN/7jP/tlR/y//wC4f/twf8u/+3v0Ov1k694kli1Tw/c3D211HEJI4blo1tp1QLIjp5iCMZG/ dja2S3cE88PZ0k4VYpNN2bje66NO2po+aXvQbs+z2ZDpbxHxFNFDcy33k6fPG88spl3yLCTJ5btz 5YckKORwSCQc05X9km0o3mnZK1lfS/mC+J2d9Hr8jxmvSOU734f/APHze/8AYMvP/QVrkr7R/wAc Tanu/wDCyzpNsfE2gnSbUqb+yuWuIoSwUywyJtcR5IDOrDcRnOMAckClJ+yqe0fwyVm+zT0uNLmj yrdO9vIlTTpfCGi3o1HEN5qYjgggLKZBEr7pZHUE7VI+Vc4OR7ilzKrUjyaxhdt9L9EgtyRd9G9E ij41/wCPbSP+wZD/AOhPVUd6n+NintH/AAo4KusxPRfHpxDo5H/QJtf5Vx0N6n+ORvU+z/hR2N5r EWv+D7y+P/H9ttILs9C7Q3EflyEero/LeoKjhK5lB068Y/Z95x8rp3Ro3zU2+uifyZmXMus+IbGy uvD1xcHybeO1ubeC4aNo5YsqJGjDoNsq4IcZAxgkdBolTpylGslq3KLavdPpe3QXvSScG9rNJ7Mh ivItJ8Raat5dy3UtuqrdyzTmaOKeVWVo0diQqRll3ncQDnOCCA7OVKfLFJP4UlZtLq/N9BX5Zxu7 2316hoPgjUtP16K4u0WG1hulcTs67JMvmNY8NlmlOAoAyM5bGDROtGVNqOrcduq739AjBqSb2vuV oP8Aj38S/wC+n/pW9N70fT/21C6T/rqZnhhF1rSbzQUZUu2liurVXYKJXQFJIwxwN5TGwE4J9ACa 0qfu5xq/Zs4y8k9n95Mfei4dd0X9K0m48G2d7qGqqLaW4tZLS1hZl8yR5sBpAgJ+SMDJLcHPrjMS kq0owp6pSUpPokunzKSdNNy00sl6lm48P3OveGNKFiBLcW5vCYAyiRo5LlgzqrEFgjIoYLkjeDjF SpqnVqc2ifLr0uo7fO4+VyhG26vp8zK1u3bw5oMWjXJC31zdG7liDKxijWPy41fBIDOTvAHOAQcd DpB+0qOpH4VHlT7u93b02JkuSPK927+h02panBpXjAPdnZby28UErdNqy2qpuJ7AMVJPYAmsYxcq Fo7ptr5SLbUamu1rfejA03wDfWOopNehItOtpFlkuzInktCjBsowbJMgACgDcC3IGDWsq8ZRajfm aso21Tff0IVNp67Lr0sW7LUl1f8A4SG9T7k8BdM8HYZvkyPXbjPvUuPJ7GPZ/oNO/O/L9SnHYSeL dCtINPxLfaU06SQblWR4ZXEiyIpIDBD8hA+bue2a5lRqSc9Izs0+ia0s/UVueKUd1fTyYl7bv4S0 JrC6KpqF/cxStCGVmighBZGkwWCs0hyF6levIZQJ+1qc0fgimr7Xb7fIGuSPK929uyRd8TeG7vxV ejWtIQT2t+kbswdAIJFjVZUmywKbCu4k8YPBOKmnUjRj7Kpo436bq+jQ5RcnzR2f4epW+JIWzvdP Fq4ZYdMtfKkToQjzbHX2IAYVWH1jPmW85XXyQqmjjb+VfqbniyWG00y41WAgP4jNrhRwUSOMSXH5 zYVx7jt1ypJuapvalzfO7svwLnonJfat/wAH8TxavSOU9ItbJ/FegW9lY4kvtKln/wBH3BXkgnIk Z4wSA+1xgjkgfVQ3E37Ko5S0jNLXomtLP5G6XPFJbxvp5MLqyfwnoFxZXuI77VZYP9H3BnjggJkV 5ACQm5zgDgkfRgon7WopR+GCevRt6WXyC3JFp7u2nkip48/5hX/YGsv/AGrVUf8Al5/18l+gp/Z/ wowvCw09tTgTV1DWbsUkyzIF3KQjFkZSqq5Usc4C5J4rWpzKD9n8XT+vQiNrrm2N9Ph9rdtdgRJt iRwy3gkQQhAciYPu4AHzY+92xmsvb03HV6/y2d/Sxfs5J6ff09Trm1K31bW9eubQhoTo90odejlI 4EZgRwQWU7SOCuD3rn5XCnSjLf2kdO122a3TlNr+V/ocf/zJv/cZ/wDbKuj/AJf/APcP/wBuMv8A l3/29+huafcalrWhWtvoc80d5pzSpPbwzNE8kUj745QqsocISUPVgTnGOTlJQp1JOqlyys02rpNa NeV9y1dxSg9Ve6TsVdVshu0/Tdevrg3UsjtdiS4M6WiudsIIZmVXPDSHd8q8njBNRfxzpRXKl7tl Zy7/AC7Ca2jNu/XW9ux0XhnT/Enh++WG4c/2PGx82SaVGtTb8/OhZiFDLgqF2nJAcY3VjUlSqRul +86JJ81y4qcHZ/D+FjlbbQ21Dz9R8MSyC5hu5gLeNxFItqzExSR/MrMuMKyjnI6YFdDny2hXSs4r Vq65uqZmo3u6e6b020NPxfPdnQbeHXyp1ZbkmIEoZhamPky7Om5+OcFtqk5Ias6SXtG6PwW13tzX 6fIqd+VKfxX+djyWvQOYKACgBWYvyxJPvzUqKjpFJLyVhtt7u4lUIUsSACSQOnt9KlRUbtJJvey3 9R3ez6CVQgoAKACgAoAcjtGdyEqfUHB/SkMbTEODsoKgkA9Rng46ZFIY2mIKACgBwdkBCkgHg4OM /WkMbTEFABQAAlTkcEUAKzFzliST3PJpbAJTAKACgAoAcrsn3SVyMHBxwe30pDG0xDi7MApJIXoM 8DPXA7UthjaYgoAc7tIcsSx9Scn9aW2wxFYocqSCOhHBoEBJY5PJPJJpgJQA4uxUJk7RyBngfh0p DG0xCqxQ5UkEdCODSACSxyeSeSTTA9FvP+Ef1xxeLdnSGkSMT2i2jtHvRQrNF5OI9rYyAwU7iWPJ IHGvaU/d5ee17S5rOz731N3yy1vy91b8rGD4t1mDWLqL7Grra2dtFaQ+Zje0cIOGYDgElj3PGM4P A1pQcE+bdtydtrsibUnpslZfIs+LtVtrxbOx09/NtbC1SMNtdQ0z/NO4V1VhuYL26gkZFTSi480p qzlK/wAumw5taKOyX49Tjq6TIVWKHKnBHQjg0ADMWOSck9SetGwCUAFADvMbbsydvXGTjPrjpSGN piCgBVYocqSCOhHBpAJTAcXYqEydo5AzwPw6UhiKxQ5UkEdCODQICSxyeSeSTTASgAoAKACgAoAK ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA KACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAP//Z --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_-- ========================================================================Date: Fri, 11 Feb 2011 19:56:43 +0000 Reply-To: Hauke Hillebrandt <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Hauke Hillebrandt <[log in to unmask]> Subject: Modelling time derivative: valid to use t-contrasts? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear SPM Users, in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay: "The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are perfectly valid but assume constant delay/dispersion." am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case? Best wishes, Hauke ========================================================================Date: Fri, 11 Feb 2011 16:07:58 -0500 Reply-To: Jason Steffener <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jason Steffener <[log in to unmask]> Subject: Re: Modelling time derivative: valid to use t-contrasts? Comments: To: Hauke Hillebrandt <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: [log in to unmask]> --001636c5b6bbba64f8049c081780 Content-Type: text/plain; charset=ISO-8859-1 Dear Hauke, The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors. e.g your design is: [canonical derivative] and your contrast is: [1 1] Then you are "assuming" that the canonical and derivative have equal weight. The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors. I hope this helps, Jason. On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt < [log in to unmask]> wrote: > Dear SPM Users, > > in my fMRI model specification I model the time derivative and then I use > t-contrasts on the 1st and 2nd level of my analysis to get the activation > maps. Now I read in the SPM manual on page 66 that this might not be okay: > > "The informed basis set requires an SPMF for inference. T-contrasts over > just the canonical are > perfectly valid but assume constant delay/dispersion." > > am I right that this means that I have to use f-contrasts exclusively on > the 1st and 2nd level of my analysis (+the flexible factorial design option) > if I model the time derivative and that I cannot use t-tests at all in this > case? > > Best wishes, > > Hauke > -- Jason Steffener, Ph.D. Department of Neurology Columbia University http://www.cogneurosci.org/steffener.html --001636c5b6bbba64f8049c081780 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Hauke,
The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.
e.g your design is:
[canonical derivative]
and your contrast is:
[1 1]
Then you are "assuming" that the canonical and derivative have equal weight.
The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.

I hope this helps,
Jason.

On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <[log in to unmask]> wrote:

Dear SPM Users,

in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:

"The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are
perfectly valid but assume constant delay/dispersion."

am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?

Best wishes,

Hauke

--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html
--001636c5b6bbba64f8049c081780-- ========================================================================Date: Fri, 11 Feb 2011 14:03:33 -0800 Reply-To: Manish Dalwani <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Manish Dalwani <[log in to unmask]> Subject: Why should I use SPM8 vs SPM5? MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-466683208-1297461813=:75525" Message-ID: <[log in to unmask]> --0-466683208-1297461813=:75525 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Hello SPM'ers, Can experts please advice on why I should switch from SPM8 from SPM5? Regards,Manish DalwaniSr. PRADept. of PsychiatryUniversity of Colorado --0-466683208-1297461813=:75525 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

Hello SPM'ers,

Can experts please advice on why I should switch from SPM8 from SPM5?

Regards,

Manish Dalwani

Sr. PRA

Dept. of Psychiatry

University of Colorado

--0-466683208-1297461813=:75525-- ========================================================================Date: Fri, 11 Feb 2011 14:33:49 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: Modelling time derivative: valid to use t-contrasts? Comments: To: Hauke Hillebrandt <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf30549f638ad175049c094c9f Message-ID: <[log in to unmask]> --20cf30549f638ad175049c094c9f Content-Type: text/plain; charset=UTF-8 I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e., [A-hrf A-tderiv B-hrf B-tderiv] [ 1 1 -1 -1 ] to my understanding that makes sense, but i'd like to hear other opinions. Cheers, Michael On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]>wrote: > Dear Hauke, > The use of a t-test across the two regressors means that you are testing a > specific relationship between the two regressors. > e.g your design is: > [canonical derivative] > and your contrast is: > [1 1] > Then you are "assuming" that the canonical and derivative have equal > weight. > The F-test allows you to test any arbitrary relationship between the > canonical and derivative regressors. > > > I hope this helps, > Jason. > > > > On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt < > [log in to unmask]> wrote: > >> Dear SPM Users, >> >> in my fMRI model specification I model the time derivative and then I use >> t-contrasts on the 1st and 2nd level of my analysis to get the activation >> maps. Now I read in the SPM manual on page 66 that this might not be okay: >> >> "The informed basis set requires an SPMF for inference. T-contrasts over >> just the canonical are >> perfectly valid but assume constant delay/dispersion." >> >> am I right that this means that I have to use f-contrasts exclusively on >> the 1st and 2nd level of my analysis (+the flexible factorial design option) >> if I model the time derivative and that I cannot use t-tests at all in this >> case? >> >> Best wishes, >> >> Hauke >> > > > > -- > Jason Steffener, Ph.D. > Department of Neurology > Columbia University > http://www.cogneurosci.org/steffener.html > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf30549f638ad175049c094c9f Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e.,

[A-hrf A-tderivÂ B-hrf B-tderiv]

[Â Â 1Â Â Â Â Â Â Â 1Â Â Â Â Â Â Â Â -1Â Â Â Â Â Â -1Â Â ]

to my understanding that makes sense, but i'd like to hear other opinions.

Cheers,
Michael

On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]> wrote:

Dear Hauke,
The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.
e.g your design is:
[canonical derivative]
and your contrast is:
[1 1]
Then you are "assuming" that the canonical and derivative have equal weight.
The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.

I hope this helps,
Jason.

On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <[log in to unmask]> wrote:

Dear SPM Users,

in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:

"The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are
perfectly valid but assume constant delay/dispersion."

am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?

Best wishes,

Hauke

--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--20cf30549f638ad175049c094c9f-- ========================================================================Date: Fri, 11 Feb 2011 18:44:08 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Why should I use SPM8 vs SPM5? Comments: To: Manish Dalwani <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Switching from 5 to 8 as newer algorithms and bug fixes are employed. The newer algorithms address concerns of the field such as registration accuracy. Also, only 8 is really supported. Can't think of any reason to go back to 5. On Friday, February 11, 2011, Manish Dalwani <[log in to unmask]> wrote: > Hello SPM'ers, > Can experts please advice on why I should switch from SPM8 from SPM5? > Regards,Manish DalwaniSr. PRADept. of PsychiatryUniversity of Colorado > -- Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Fri, 11 Feb 2011 20:52:23 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: Modelling time derivative: valid to use t-contrasts? In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset=windows-1252 Mime-Version: 1.0 (Apple Message framework v1082) Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> What we usually want to know when doing fMRI analyses is whether a region is "active" or not. When modeling with just a canonical HRF, this is fairly simple, as a positive parameter estimate (beta value) means that there was a response that matched the canonical HRF, which we can fairly safely interpret as "activation" (i.e., increase in signal that is time-locked to some stimulus category at about the time we expect). The tricky thing about interpreting any other type of basis set is how it relates to "activation" (assuming this is what we're interested in). In general, the effect of the derivatives is really only interpretable in relation to a reliable positive loading on the canonical HRF. I.e., if you have an HRF-like shape, the derivatives can tell you about how the observed response differs from canonical. But you could imagine a situation in which you have a significant weighting on a derivative, but not on the canonical. In these situations it's quite difficult to interpret the result. Getting back to the point at hand, if you model conditions A and B with [Ahrf Atemp_deriv Bhrf Btemp_deriv] then the contrast [1 1 0 0] will give you the effect of the *average* of the canonical and temporal derivative. But this is sort of a meaningless measure; it would give the same result for something that is really well-explained by the canonical only, really well-explained by the temporal derivative only, or explained by equal contributions from the two. The most straightforward way to assess "activation" would be to just look at the canonical HRF: [1 0 0 0] Or, if you want to look for "informed" effects, use an F test: [1 0 0 0 0 1 0 0] see Chapter 30 in the SPM manual, for example (Face Group Data). For comparing across groups, the same logic holds: the most straightforward way to assess "activation" would be to just compare the canonical HRFs: [1 0 -1 0] and if you wanted to compare more, you could use an F test: [1 0 -1 0 0 1 0 -1] but you still run into non-straightforward interpretations, because a significant F value doesn't tell you (a) in which direction the effect is, or (b) whether the difference is on the canonical HRF or the derivative. The following papers are very helpful—surely moreso than my attempt above. :) Henson, R.N.A., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J., 2002. Detecting latency differences in event-related BOLD responses: Application to words versus nonwords and initial versus repeated face presentations. NeuroImage 15, 83-97. Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms. NeuroImage 22, 252-257. Hope this helps, Jonathan -- Dr. Jonathan Peelle Department of Neurology University of Pennsylvania 3 West Gates 3400 Spruce Street Philadelphia, PA 19104 USA http://jonathanpeelle.net/ On Feb 11, 2011, at 5:33 PM, Michael T Rubens wrote: > I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e., > > [A-hrf A-tderiv B-hrf B-tderiv] > > [ 1 1 -1 -1 ] > > to my understanding that makes sense, but i'd like to hear other opinions. > > Cheers, > Michael > > On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]> wrote: > Dear Hauke, > The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors. > e.g your design is: > [canonical derivative] > and your contrast is: > [1 1] > Then you are "assuming" that the canonical and derivative have equal weight. > The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors. > > > I hope this helps, > Jason. > > > > On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <[log in to unmask]> wrote: > Dear SPM Users, > > in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay: > > "The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are > perfectly valid but assume constant delay/dispersion." > > am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case? > > Best wishes, > > Hauke > ========================================================================Date: Sat, 12 Feb 2011 11:20:32 +0800 Reply-To: Xinian Zuo <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Xinian Zuo <[log in to unmask]> Subject: Opening positions Comments: To: fsl <[log in to unmask]>, [log in to unmask] Comments: cc: ZANG Yu-Feng <[log in to unmask]>, Xuchu Weng <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf305644d323d10c049c0d4c9c Message-ID: <[log in to unmask]> --20cf305644d323d10c049c0d4c9c Content-Type: text/plain; charset=ISO-8859-1 Opening positions (postdoc, research assistant, or professorship) for collaborative projects from the Center for Human Brain Research, Hangzhou Normal University (Professor WENG Xuchu, [log in to unmask]), Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (Professor ZANG Yufeng, [log in to unmask]), and Institute of Psychology, Chinese Academy of Science (Professor ZUO Xi-Nian, [log in to unmask]). Any applicant with relevant neuroimaging background is welcome, including neuroscience, medicine, psychology, biology, physics, mathematics, computer and information science, and so on. Salary and compensation are almost the best in China. Please send your CV to any of the three e-mail addresses listed above if you are interested in these jobs. -- Xi-Nian Zuo, Ph.D of Applied Mathematics (http://lfcd.psych.ac.cn) --20cf305644d323d10c049c0d4c9c Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Opening positions (postdoc, research assistant, or professorship) for collaborative projects from the Center for Human Brain Research, Hangzhou Normal University (Professor WENG Xuchu, [log in to unmask]), Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (Professor ZANG Yufeng, [log in to unmask]), and Institute of Psychology, Chinese Academy of Science (Professor ZUO Xi-Nian, [log in to unmask]). Any applicant with relevant neuroimaging background is welcome, including neuroscience, medicine, psychology, biology, physics, mathematics, computer and information science, and so on. Salary and compensation are almost the best in China.

Please send your CV to any of the three e-mail addresses listed above if you are interested in these jobs.
--
Xi-Nian Zuo, Ph.D of Applied Mathematics (http://lfcd.psych.ac.cn)

--20cf305644d323d10c049c0d4c9c-- ========================================================================Date: Fri, 11 Feb 2011 22:57:17 -0500 Reply-To: hekmatyar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: hekmatyar <[log in to unmask]> Subject: Re: coregistration problem In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hi All I try to co register mean functional (EPI) to anatomical (T2 weighted) using spm99, it does coregister, but not good, kind of distorted and did not do properly. When I tried to use spm8, after co-registration, it puts one of my coronal view into saggital plane, it completely screws up. I even tried mutual information selection, it did not work.I am wondering that what might be causing the orientation swap. Please let me know. Thanks Khan Hekmatyar BioImaging Research Center University of Georgia Athens, GA-30606 ========================================================================Date: Sat, 12 Feb 2011 11:54:42 +0100 Reply-To: Lin Wang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lin Wang <[log in to unmask]> Subject: Re: 'NaN' in cluster level p value In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e644c79c5d9991049c13a4c9 Content-Type: text/plain; charset=ISO-8859-1 Dear all, I realized that I incorrectly took the SPM[T] values for the 2nd-level statistical analysis, which leads to the NaNs. Now I know I should use the contrast images for the group analysis. But I still don't get it why the use of SPM[T] values will give me NaNs in the statistical results... Best, Lin 2011/2/7 Lin Wang <[log in to unmask]> > Dear all, > > I got the same problem after running the 2nd-level statistical analysis. > The cluster-level p values are NaNs, and I get the following warning when I > click "whole brain" to show all the clusters: > > Warning: Returning NaN for out of range arguments > > In spm_Pcdf at 88 > In spm_P_RF at 106 > In spm_P at 48 > In spm_list at 487 > > I searched the documents, but I couldn't find a clear answer for it. Could > anyone help me with it? > > Thanks very much. > > Best, > Lin > > --0016e644c79c5d9991049c13a4c9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear all,

I realized that I incorrectly took the SPM[T] values for the 2nd-level statistical analysis, which leads to the NaNs.

Now I know I should use the contrast images for the group analysis. But I still don't get it why the use of SPM[T] values will give me NaNs in the statistical results...

Best,
Lin

2011/2/7 Lin Wang <[log in to unmask]>

Dear all,

I got the same problem after running the 2nd-level statistical analysis. The cluster-level p values are NaNs, and I get the following warning when I click "whole brain" to show all the clusters:

Warning: Returning NaN for out of range arguments
> In spm_Pcdf at 88
In spm_P_RF at 106
In spm_P at 48
In spm_list at 487

I searched the documents, but I couldn't find a clear answer for it. Could anyone help me with it?

Thanks very much.

Best,
Lin

--0016e644c79c5d9991049c13a4c9-- ========================================================================Date: Sat, 12 Feb 2011 17:43:10 +0000 Reply-To: "Stephen J. Fromm" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Stephen J. Fromm" <[log in to unmask]> Subject: spm_api_bmc.m: typo in help comments? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> The beginning of SPM8: spm_api_bmc.m reads: function out=spm_api_bmc(F,N,exp_r,xp,family) % API to select and compare DCMs using Bayesian model comparison % FORMAT out=spm_api_bmc(F,N,alpha,exp_r,xp) % % INPUT: % F - Matrix/Vector of log model evidences % N - vector of model names % alpha - vector of model probabilities % exp_r - expectation of the posterior p(r|y) % xp - exceedance probabilities % % OUTPUT: % out - conditional probability of DCMs (when using fixed effect method) %__________________________________________________________________________ % Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging The actual function prototype and the one in the help under FORMAT don't seem to match. ========================================================================Date: Sat, 12 Feb 2011 17:47:13 +0000 Reply-To: "Stephen J. Fromm" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Stephen J. Fromm" <[log in to unmask]> Subject: DCM theory: def'n of exceedance prob.? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is: -------------------------------------------------- For all j in {1, 2, ..., K | j=/=k}: phi_k = p(r_k > r_j | y; alpha). -------------------------------------------------- This doesn't make sense to me as written. I would assume it's something like -------------------------------------------------- phi_k = p(r_k > r_j for all j=/=k | y; alpha). -------------------------------------------------- ========================================================================Date: Sun, 13 Feb 2011 04:27:28 +0000 Reply-To: Yang Hu <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Yang Hu <[log in to unmask]> Subject: about ROI signal value Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear Experts, Based on whole-brain analysis via SPM8 (1st- and 2nd-level analysis), I want to extract signal value from atlas-based ROIs (e.g. amygdala) in four experimental conditions, compared with rest condition respectively, by AFNI rather than MarsBar. Specific steps are listed as follows: 1. Mask generation: by WFU pickatlas, an file of ROI in amygdala (named as "amy.nii") was generated 2. File conversion: by AFNI command "3dcopy", the "amy.nii" was converted to "amy+orig.BRIK/HEAD", which could be recognized by AFNI 3. ROI calculation: by AFNI command "3dmaskave", the average value within the ROI mask (amygdala) of each subject (sbj**) in certain condition (con_000*) could be obtained; Specifically, the full command is like that "3dmaskave -quiet -mask amy+orig sbj**_con_000*.hdr". The file "sbj**_con_000*.hdr" was generated after 1st-level analysis via SPM8. So I just wonder the meaning of ROI value obtained through those steps mentioned above. Whether does the value mean percent signal change (PSC), contrast value (CV), or parameter estimates (PE) ? And what's the difference between the three terms (namely PSC, CV, PE) always used in ROI analysis? Thanks a lot for your help and comment! Yang Hu ICN, ECNU, Shanghai, China ========================================================================Date: Sun, 13 Feb 2011 09:40:33 +0000 Reply-To: William Sohn <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: William Sohn <[log in to unmask]> Subject: MEG data conversion Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPM community, I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns. I was wondering how I can convert this into a format where I can read in SPM. Thank you, -William Sohn ========================================================================Date: Sun, 13 Feb 2011 17:59:34 +0800 Reply-To: Brahim HAMADICHAREF <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Brahim HAMADICHAREF <[log in to unmask]> Subject: Re: MEG data conversion Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_33766111-2257-41d1-9795-31c473c69fcf_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_33766111-2257-41d1-9795-31c473c69fcf_ Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: 8bit Maybe by using biosig to import the data and save it into a standardand known format by SPM ... GDF for e.g. although I am not sure whatSPM8 recognize ... Brahim > Date: Sun, 13 Feb 2011 09:40:33 +0000 > From: [log in to unmask] > Subject: [SPM] MEG data conversion > To: [log in to unmask] > > Dear SPM community, > > I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns. > > I was wondering how I can convert this into a format where I can read in SPM. > > Thank you, > > -William Sohn --_33766111-2257-41d1-9795-31c473c69fcf_ Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: 8bit Maybe by using biosig to import the data and save it into a standard

and known format by SPM ... GDF for e.g. although I am not sure what

SPM8 recognize ...

Brahim

> Date: Sun, 13 Feb 2011 09:40:33 +0000
> From: [log in to unmask]
> Subject: [SPM] MEG data conversion
> To: [log in to unmask]
>
> Dear SPM community,
>
> I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns.
>
> I was wondering how I can convert this into a format where I can read in SPM.
>
> Thank you,
>
> -William Sohn

--_33766111-2257-41d1-9795-31c473c69fcf_-- ========================================================================Date: Sun, 13 Feb 2011 11:45:19 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: MEG data conversion Comments: To: William Sohn <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear William, In SPM manual you can find 'Converting arbitrary data' section which explains what you should do. There is also an example script mentioned there. This will be sufficient to convert your channel data. However, you also need your sensor locations and for this you will need the data in their original format. For some analyses you might manage without, but it depends on what exactly you want to do. Best, Vladimir On Sun, Feb 13, 2011 at 9:40 AM, William Sohn <[log in to unmask]> wrote: > Dear SPM community, > > I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns. > > I was wondering how I can convert this into a format where I can read in SPM. > > Thank you, > > -William Sohn > ========================================================================Date: Sun, 13 Feb 2011 12:00:23 +0000 Reply-To: Viridiana Mazzola <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Viridiana Mazzola <[log in to unmask]> Subject: extract ROI at the edge of the cerebellum Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all, I am trying to extract a ROI at the edge of the cerebellum following the Brett's FAQ instructions. 1 . I rename the ROI my_roi.mat, then I write in matlab the command: roi_filename = 'my_roi.mat'; my_roi = maroi(roi_filename); my_roi = spm_hold(my_roi, 0); % set NN resampling saveroi(my_roi, roi_filename); 2. then I rerun the extraction of my_roi, but this error appears: Fetching data : 1/1 Error in ==> maroi.getdata at 30 if nargin < 2 ??? Output argument "vXYZ" (and maybe others) not assigned during call to "[log in to unmask] (getdata)". Error in ==> maroi.get_marsy at 76 [y vals vXYZ mat] = getdata(o, VY); Error in ==> marsbar at 985 marsY = get_marsy(o{:}, VY, sumfunc, 'v'); Error in ==> spm at 964 evalin('base',CBs{v-1}) ??? Error while evaluating uicontrol Callback => Where am I wrong? Thank you for your attention! Viridiana NeuroLab IPRA ========================================================================Date: Sun, 13 Feb 2011 12:42:14 +0000 Reply-To: Klaas Enno Stephan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Klaas Enno Stephan <[log in to unmask]> Subject: Re: DCM theory: def'n of exceedance prob.? Comments: To: "Stephen J. Fromm" <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-994732579-1297600934=:1208" Message-ID: <[log in to unmask]> --0-994732579-1297600934=:1208 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Dear Stephen, I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation. Best wishes, Klaas ________________________________ Von: Stephen J. Fromm <[log in to unmask]> An: [log in to unmask] Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr Betreff: [SPM] DCM theory: def'n of exceedance prob.? Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is: -------------------------------------------------- For all j in {1, 2, ..., K | j=/=k}: phi_k = p(r_k > r_j | y; alpha). -------------------------------------------------- This doesn't make sense to me as written. I would assume it's something like -------------------------------------------------- phi_k = p(r_k > r_j for all j=/=k | y; alpha). -------------------------------------------------- --0-994732579-1297600934=:1208 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable

Dear Stephen,

I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation.

Best wishes,
Klaas

Von: Stephen J. Fromm <[log in to unmask]>
An: [log in to unmask]
Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr
Betreff: [SPM] DCM theory: def'n of exceedance prob.?

Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is:

--------------------------------------------------

For all j in {1, 2, ..., K | j=/=k}:

phi_k = p(r_k > r_j | y; alpha).

--------------------------------------------------

This doesn't make sense to me as written. I would assume it's something like

--------------------------------------------------

phi_k = p(r_k > r_j for all j=/=k | y; alpha).

--------------------------------------------------

--0-994732579-1297600934=:1208-- ========================================================================Date: Sun, 13 Feb 2011 08:38:39 -0500 Reply-To: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]> Subject: Re: DCM theory: def'n of exceedance prob.? Comments: To: Klaas Enno Stephan <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Klaas, Thanks for your timely response! Very interesting paper. Cheers, Stephen J. Fromm, PhD Contractor, NIMH/MAP (301) 451--9265 ________________________________ From: Klaas Enno Stephan [[log in to unmask]] Sent: Sunday, February 13, 2011 7:42 AM To: Fromm, Stephen (NIH/NIMH) [C]; [log in to unmask] Subject: RE: [SPM] DCM theory: def'n of exceedance prob.? Dear Stephen, I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation. Best wishes, Klaas ________________________________ Von: Stephen J. Fromm <[log in to unmask]> An: [log in to unmask] Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr Betreff: [SPM] DCM theory: def'n of exceedance prob.? Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is: -------------------------------------------------- For all j in {1, 2, ..., K | j=/=k}: phi_k = p(r_k > r_j | y; alpha). -------------------------------------------------- This doesn't make sense to me as written. I would assume it's something like -------------------------------------------------- phi_k = p(r_k > r_j for all j=/=k | y; alpha). -------------------------------------------------- ========================================================================Date: Sun, 13 Feb 2011 14:39:21 +0000 Reply-To: Klaas Enno Stephan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Klaas Enno Stephan <[log in to unmask]> Subject: Re: DCM theory: def'n of exceedance prob.? Comments: To: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1462288926-1297607961=:62528" Message-ID: <[log in to unmask]> --0-1462288926-1297607961=:62528 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Thank you, Stephen. To prevent any further confusion, let me point out that there is a typo in Eq. A.3 in the appendix. The log's are missing for the first two terms on the right hand side, i.e. the equation should read: KL[q(theta)|p(theta|m)] = 0.5*log(det(C_theta)) - 0.5*log(det(C_theta|y)) + ... There is an erratum at this address: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4WNWRT0-1&_user=10&_coverDate=10%2F15%2F2009&_rdoc=1&_fmt=high&_orig=browse&_origin=browse&_zone=rslt_list_item&_cdi=6968&_sort=d&_docanchor=&view=c&_ct=1&_refLink=Y&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=9790004e41680cc8dbdadb633ac5d207&searchtype=a Best wishes, Klaas ________________________________ Von: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]> An: [log in to unmask] Gesendet: Sonntag, den 13. Februar 2011, 14:38:39 Uhr Betreff: Re: [SPM] DCM theory: def'n of exceedance prob.? Klaas, Thanks for your timely response! Very interesting paper. Cheers, Stephen J. Fromm, PhD Contractor, NIMH/MAP (301) 451--9265 ________________________________ From: Klaas Enno Stephan [[log in to unmask]] Sent: Sunday, February 13, 2011 7:42 AM To: Fromm, Stephen (NIH/NIMH) [C]; [log in to unmask] Subject: RE: [SPM] DCM theory: def'n of exceedance prob.? Dear Stephen, I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation. Best wishes, Klaas ________________________________ Von: Stephen J. Fromm <[log in to unmask]> An: [log in to unmask] Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr Betreff: [SPM] DCM theory: def'n of exceedance prob.? Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is: -------------------------------------------------- For all j in {1, 2, ..., K | j=/=k}: phi_k = p(r_k > r_j | y; alpha). -------------------------------------------------- This doesn't make sense to me as written. I would assume it's something like -------------------------------------------------- phi_k = p(r_k > r_j for all j=/=k | y; alpha). -------------------------------------------------- --0-1462288926-1297607961=:62528 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable

Thank you, Stephen.

To prevent any further confusion, let me point out that there is a typo in Eq. A.3 in the appendix. The log's are missing for the first two terms on the right hand side, i.e. the equation should read:

KL[q(theta)|p(theta|m)] = 0.5*log(det(C_theta)) - 0.5*log(det(C_theta|y)) + ...

There is an erratum at this address:
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4WNWRT0-1&_user=10&_coverDate=10%2F15%2F2009&_rdoc=1&_fmt=high&_orig=browse&_origin=browse&_zone=rslt_list_item&_cdi=6968&_sort=d&_docanchor=&view=c&_ct=1&_refLink=Y&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=9790004e41680cc8dbdadb633ac5d207&searchtype=a

Best wishes,
Klaas

Von: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
An: [log in to unmask]
Gesendet: Sonntag, den 13. Februar 2011, 14:38:39 Uhr
Betreff: Re: [SPM] DCM theory: def'n of exceedance prob.?

Klaas,

Thanks for your timely response! Very interesting paper.

Cheers,

Stephen J. Fromm, PhD
Contractor, NIMH/MAP
(301) 451--9265

________________________________
From: Klaas Enno Stephan [[log in to unmask]]
Sent: Sunday, February 13, 2011 7:42 AM
To: Fromm, Stephen (NIH/NIMH) [C]; [log in to unmask]
Subject: RE: [SPM] DCM theory: def'n of exceedance prob.?

Dear Stephen,

I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation.

Best wishes,
Klaas

________________________________
Von: Stephen J. Fromm <[log in to unmask]>
An: [log in to unmask]
Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr
Betreff: [SPM] DCM theory: def'n of exceedance prob.?

Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is:

--------------------------------------------------

For all j in {1, 2, ..., K | j=/=k}:

phi_k = p(r_k > r_j | y; alpha).

--------------------------------------------------

This doesn't make sense to me as written. I would assume it's something like

--------------------------------------------------

phi_k = p(r_k > r_j for all j=/=k | y; alpha).

--------------------------------------------------

--0-1462288926-1297607961=:62528-- ========================================================================Date: Sun, 13 Feb 2011 15:34:23 -0500 Reply-To: Jason Steffener <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jason Steffener <[log in to unmask]> Subject: Re: Modelling time derivative: valid to use t-contrasts? In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001636c5b6bb4e352c049c2fdb0c Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Hello. I completely agree with everything that Johnathan has said. I also want to suggest reading my own paper. I combine many of the ideas behind the Henson and Calhoun papers and provide a method for bringing the derivative info to the group level. I hope this helps for at least providing another view of some of the issues at hand. Also, all the steps discussed in the paper have accompanying code which I provide a link to in the paper and below. Steffener, J., Tabert, M., Reuben, A., Stern, Y. Investigating hemodynamic response variability at the group level using basis functions (2010) NeuroImage, 49 (3), pp. 2113-2122. http://cumc.columbia.edu/dept/sergievsky/cnd/steffener.html Jason. On Fri, Feb 11, 2011 at 8:52 PM, Jonathan Peelle <[log in to unmask]> wrote: > What we usually want to know when doing fMRI analyses is whether a region > is "active" or not. When modeling with just a canonical HRF, this is fairly > simple, as a positive parameter estimate (beta value) means that there was a > response that matched the canonical HRF, which we can fairly safely > interpret as "activation" (i.e., increase in signal that is time-locked to > some stimulus category at about the time we expect). > > The tricky thing about interpreting any other type of basis set is how it > relates to "activation" (assuming this is what we're interested in). In > general, the effect of the derivatives is really only interpretable in > relation to a reliable positive loading on the canonical HRF. I.e., if you > have an HRF-like shape, the derivatives can tell you about how the observed > response differs from canonical. But you could imagine a situation in which > you have a significant weighting on a derivative, but not on the canonical. > In these situations it's quite difficult to interpret the result. > > Getting back to the point at hand, if you model conditions A and B with > > [Ahrf Atemp_deriv Bhrf Btemp_deriv] > > then the contrast > > [1 1 0 0] > > will give you the effect of the *average* of the canonical and temporal > derivative. But this is sort of a meaningless measure; it would give the > same result for something that is really well-explained by the canonical > only, really well-explained by the temporal derivative only, or explained by > equal contributions from the two. The most straightforward way to assess > "activation" would be to just look at the canonical HRF: > > [1 0 0 0] > > Or, if you want to look for "informed" effects, use an F test: > > [1 0 0 0 > 0 1 0 0] > > see Chapter 30 in the SPM manual, for example (Face Group Data). > > For comparing across groups, the same logic holds: the most straightforward > way to assess "activation" would be to just compare the canonical HRFs: > > [1 0 -1 0] > > and if you wanted to compare more, you could use an F test: > > [1 0 -1 0 > 0 1 0 -1] > > but you still run into non-straightforward interpretations, because a > significant F value doesn't tell you (a) in which direction the effect is, > or (b) whether the difference is on the canonical HRF or the derivative. > > The following papers are very helpful—surely moreso than my attempt above. > :) > > Henson, R.N.A., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J., 2002. > Detecting latency differences in event-related BOLD responses: Application > to words versus nonwords and initial versus repeated face presentations. > NeuroImage 15, 83-97. > > Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI > analysis with the general linear model: removal of latency-induced amplitude > bias by incorporation of hemodynamic derivative terms. NeuroImage 22, > 252-257. > > > Hope this helps, > Jonathan > > -- > Dr. Jonathan Peelle > Department of Neurology > University of Pennsylvania > 3 West Gates > 3400 Spruce Street > Philadelphia, PA 19104 > USA > http://jonathanpeelle.net/ > > > On Feb 11, 2011, at 5:33 PM, Michael T Rubens wrote: > > > I think this is a good question Hauke. If I understand you correctly, > you'd want to do a comparison of 2 conditions, each with an informed basis > set. i.e., > > > > [A-hrf A-tderiv B-hrf B-tderiv] > > > > [ 1 1 -1 -1 ] > > > > to my understanding that makes sense, but i'd like to hear other > opinions. > > > > Cheers, > > Michael > > > > On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]> > wrote: > > Dear Hauke, > > The use of a t-test across the two regressors means that you are testing > a specific relationship between the two regressors. > > e.g your design is: > > [canonical derivative] > > and your contrast is: > > [1 1] > > Then you are "assuming" that the canonical and derivative have equal > weight. > > The F-test allows you to test any arbitrary relationship between the > canonical and derivative regressors. > > > > > > I hope this helps, > > Jason. > > > > > > > > On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt < > [log in to unmask]> wrote: > > Dear SPM Users, > > > > in my fMRI model specification I model the time derivative and then I use > t-contrasts on the 1st and 2nd level of my analysis to get the activation > maps. Now I read in the SPM manual on page 66 that this might not be okay: > > > > "The informed basis set requires an SPMF for inference. T-contrasts over > just the canonical are > > perfectly valid but assume constant delay/dispersion." > > > > am I right that this means that I have to use f-contrasts exclusively on > the 1st and 2nd level of my analysis (+the flexible factorial design option) > if I model the time derivative and that I cannot use t-tests at all in this > case? > > > > Best wishes, > > > > Hauke > > > -- Jason Steffener, Ph.D. Department of Neurology Columbia University http://www.cogneurosci.org/steffener.html --001636c5b6bb4e352c049c2fdb0c Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Hello.
I completely agree with everything that Johnathan has said. I also want to suggest reading my own paper. I combine many of the ideas behind the Henson and Calhoun papers and provide a method for bringing the derivative info to the group level. I hope this helps for at least providing another view of some of the issues at hand. Also, all the steps discussed in the paper have accompanying code which I provide a link to in the paper and below.

Steffener, J., Tabert, M., Reuben, A., Stern, Y.
Investigating hemodynamic response variability at the group level using basis functions
(2010) NeuroImage, 49 (3), pp. 2113-2122. 

http://cumc.columbia.edu/dept/sergievsky/cnd/steffener.html

Jason.

On Fri, Feb 11, 2011 at 8:52 PM, Jonathan Peelle <[log in to unmask]> wrote:

What we usually want to know when doing fMRI analyses is whether a region is "active" or not. When modeling with just a canonical HRF, this is fairly simple, as a positive parameter estimate (beta value) means that there was a response that matched the canonical HRF, which we can fairly safely interpret as "activation" (i.e., increase in signal that is time-locked to some stimulus category at about the time we expect).

The tricky thing about interpreting any other type of basis set is how it relates to "activation" (assuming this is what we're interested in). In general, the effect of the derivatives is really only interpretable in relation to a reliable positive loading on the canonical HRF. I.e., if you have an HRF-like shape, the derivatives can tell you about how the observed response differs from canonical. But you could imagine a situation in which you have a significant weighting on a derivative, but not on the canonical. In these situations it's quite difficult to interpret the result.

Getting back to the point at hand, if you model conditions A and B with

[Ahrf Atemp_deriv Bhrf Btemp_deriv]

then the contrast

[1 1 0 0]

will give you the effect of the *average* of the canonical and temporal derivative. But this is sort of a meaningless measure; it would give the same result for something that is really well-explained by the canonical only, really well-explained by the temporal derivative only, or explained by equal contributions from the two. The most straightforward way to assess "activation" would be to just look at the canonical HRF:

[1 0 0 0]

Or, if you want to look for "informed" effects, use an F test:

[1 0 0 0
0 1 0 0]

see Chapter 30 in the SPM manual, for example (Face Group Data).

For comparing across groups, the same logic holds: the most straightforward way to assess "activation" would be to just compare the canonical HRFs:

[1 0 -1 0]

and if you wanted to compare more, you could use an F test:

[1 0 -1 0
0 1 0 -1]

but you still run into non-straightforward interpretations, because a significant F value doesn't tell you (a) in which direction the effect is, or (b) whether the difference is on the canonical HRF or the derivative.

The following papers are very helpful—surely moreso than my attempt above. :)

Henson, R.N.A., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J., 2002. Detecting latency differences in event-related BOLD responses: Application to words versus nonwords and initial versus repeated face presentations. NeuroImage 15, 83-97.

Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms. NeuroImage 22, 252-257.

Hope this helps,
Jonathan

--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/

On Feb 11, 2011, at 5:33 PM, Michael T Rubens wrote:

> I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e.,
>
> [A-hrf A-tderiv B-hrf B-tderiv]
>
> [ 1 1 -1 -1 ]
>
> to my understanding that makes sense, but i'd like to hear other opinions.
>
> Cheers,
> Michael
>
> On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]> wrote:
> Dear Hauke,
> The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.
> e.g your design is:
> [canonical derivative]
> and your contrast is:
> [1 1]
> Then you are "assuming" that the canonical and derivative have equal weight.
> The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.
>
>
> I hope this helps,
> Jason.
>
>
>
> On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <[log in to unmask]> wrote:
> Dear SPM Users,
>
> in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:
>
> "The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are
> perfectly valid but assume constant delay/dispersion."
>
> am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?
>
> Best wishes,
>
> Hauke
>

--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html
--001636c5b6bb4e352c049c2fdb0c-- ========================================================================Date: Sun, 13 Feb 2011 20:58:37 +0000 Reply-To: eleftherios garyfallidis <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: eleftherios garyfallidis <[log in to unmask]> Subject: First Official Dipy Release :-) Content-Type: multipart/alternative; boundary="_000_84A2B00B70D30141AFE47C6D0CA17777390DB30FWSR21mrccbsuloc_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_84A2B00B70D30141AFE47C6D0CA17777390DB30FWSR21mrccbsuloc_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable ======================= Announcing Dipy 0.5.0 ======================= Dipy is a fast growing python toolbox for analysis of diffusion MR imaging. Just from its first official release dipy provides a great amount of utilities to facilitate diffusion MRI research. Here are just a few of them. - Reconstruction algorithms e.g. GQI, DTI - Tractography generation algorithms e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space e.g. MNI space - Reading many different file formats e.g. Trackvis or Nifti - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing on disk * Website http://www.dipy.org * Docs http://www.dipy.org/documentation.html * Tutorials http://www.dipy.org/examples_index.html * Downloads http://pypi.python.org/pypi/dipy * Code https://github.com/Garyfallidis/dipy * Mailing List http://mail.scipy.org/mailman/listinfo/nipy-devel * Bugs/Issues/Requests https://github.com/Garyfallidis/dipy/issues Be happy to inform us for any problems or join in and contribute with your code. Best wishes, Dipy Developers --_000_84A2B00B70D30141AFE47C6D0CA17777390DB30FWSR21mrccbsuloc_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

=======================
 Announcing Dipy 0.5.0
=======================

Dipy is a fast growing python toolbox for analysis of diffusion MR imaging. 

Just from its first official release dipy provides a great amount of utilities to facilitate diffusion MRI research. Here are just a few of them.

- Reconstruction algorithms e.g. GQI, DTI  
- Tractography generation algorithms e.g. EuDX
- Intelligent downsampling of tracks
- Ultra fast tractography clustering
- Resampling datasets with anisotropic voxels to isotropic

- Visualizing multiple brains simultaneously
- Finding track correspondence between different brains
- Warping tractographies into another space e.g. MNI space
- Reading many different file formats e.g. Trackvis or Nifti

- Dealing with huge tractographies without memory restrictions
- Playing with datasets interactively without storing on disk

* Website
http://www.dipy.org

* Docs
http://www.dipy.org/documentation.html

* Tutorials
http://www.dipy.org/examples_index.html

* Downloads
http://pypi.python.org/pypi/dipy

* Code
https://github.com/Garyfallidis/dipy

* Mailing List
http://mail.scipy.org/mailman/listinfo/nipy-devel

* Bugs/Issues/Requests
https://github.com/Garyfallidis/dipy/issues

Be happy to inform us for any problems or join in and contribute with your code.

Best wishes,
Dipy Developers

--_000_84A2B00B70D30141AFE47C6D0CA17777390DB30FWSR21mrccbsuloc_-- ========================================================================Date: Mon, 14 Feb 2011 17:28:08 +0800 Reply-To: Linling Li <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Linling Li <[log in to unmask]> Subject: How to do the coregistration between the functional MRI images with a high resolution anatomical head image Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="====Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --====Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: base64 RGVhciBhbGwsDQogDQpJIGhhdmUgdHdvIGtpbmRzIG9mIGFuYXRvbWljYWwgaW1hZ2VzIG9mIGVh Y2ggc3ViamVjdCwgb25lIGlzIHJlZ3VsYXIgVDIgaW1hZ2Ugd2hpY2ggaGFzIGxvdyByZXNvbHV0 aW9uLCBhbmQgdGhlIG90aGVyIG9uZSBpcyAzRCBUMSBzdHJ1Y3R1cmFsIGltYWdlICgzREhlYWQp IHdoaWNoIGhhcyB2ZXJ5IGhpZ2ggcmVzb2x1dGlvbi4gQW5kIEkgZG8gdGhlIHByZXByb2Nlc3Np bmcgYXMgZm9sbG93Og0KIA0KMS4gICAgICBSZWFsaWdubWVudChFc3RpbWF0ZSZXcml0ZSkgLT4g RGF0YSB0byBpbnB1dDogYWxsIGZNUkkgaW1hZ2VzOw0KDQoyLiAgICAgIENvcmVnaXN0cmF0aW9u KEVzdGltYXRlKSAtPiBSZWZlcmVuY2UgSW1hZ2U6IG1lYW4gZk1SSSBpbWFnZSBwcm9kdWNlZCBh ZnRlciBsYXN0IHN0ZXANCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg U291cmNlIEltYWdlOiBUMiBhbmF0b21pY2FsIGltYWdlDQpTbywgdGhlIFQyIGFuYXRvbWljYWwg aW1hZ2UgY291bGQgYmUgcmVnaXN0ZXJlZCB0byBmdW5jdGlvbmFsIGltYWdlcyBkdXJpbmcgdGhp cyBzdGVwOw0KDQozLiAgICAgIENvcmVnaXN0cmF0aW9uKEVzdGltYXRlKSAtPiBSZWZlcmVuY2Ug SW1hZ2U6IFQyIGFuYXRvbWljYWwgaW1hZ2UNCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgIFNvdXJjZSBJbWFnZTogM0RIZWFkIGltYWdlDQpIZXJlLCB0aGUgM0RIZWFk IGltYWdlIGNvdWxkIGJlIHJlZ2lzdGVyZWQgdG8gVDIgaW1hZ2Ugd2hpY2ggaGFzIGJlZW4gYWxy ZWFkeSByZWdpc3RlcmVkIHRvIGZ1bmN0aW9uYWwgaW1hZ2VzLiBUaGVyZWZvcmUsIHdlIGhhdmUg ZmluaXNoZWQgdGhlIGNvcmVnaXN0cmF0aW9uIGJldHdlZW4gM0RIZWFkIGltYWdlIGFuZCBmdW5j dGlvbmFsIGltYWdlcyB3aXRoIHRoZSBoZWxwIG9mIFQyIGFuYXRvbWljYWwgaW1hZ2UuIFNpbmNl IHRoZSBsYXJnZSByZXNvbHV0aW9uIGRpZmZlcmVuY2UgYmV0d2VlbjNESGVhZCBpbWFnZSBhbmQg ZnVuY3Rpb25hbCBpbWFnZSwgV2UgdGhpbmsgdGhpcyBjb3JlZ2lzdHJhdGlvbiBtZXRob2QgaXMg bW9yZSBwcm9wZXI7DQoNCjQuICAgICAgU2VnbWVudGF0aW9uIC0+IERhdGE6IDNESGVhZCBpbWFn ZQ0KDQo1LiAgICAgIE5vcm1hbGlzZSh3cml0ZSkgLT4gUGFyYW1ldGVyIGZpbGU6IDNESGVhZF9z ZWdfc24ubWF0IA0KICAgICAgICAgICAgICAgICBJbWFnZXMgdG8gd3JpdGU6IGFsbCB0aGUgcmVh bGlnbmVkIGZ1bmN0aW9uYWwgaW1hZ2VzDQogDQpXb3VsZCB0aGlzIGJlIHJpZ2h0PyANCiANCkFu eSBzdWdnZXN0aW9uIHdvdWxkIGJlIGFwcHJlY2lhdGVkIGFuZCB0aGFuayB5b3UgYWxsIGluIGFk dmFuY2UhDQoNCjIwMTEtMDItMTQgDQoNCg0KDQrA7sHVweENCg0KTGlubGluZyBMaSANClJlc2Vh cmNoIENlbnRyZSBmb3IgTmV1cmFsIEVuZ2luZWVyaW5nDQotLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLQ0KUGhvbmU6ICs4Ni03NTUtODYzOTIyMzINCkVtYWlsOiAgbGwubGkx QHNpYXQuYWMuY24NClNoZW56aGVuIEluc3RpdHV0ZXMgb2YgQWR2YW5jZWQgVGVjaG5vbG9neSwN CkNoaW5lc2UgQWNhZGVteSBvZiBTY2llbmNlcy4NCg= --====Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: base64 PCFET0NUWVBFIEhUTUwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4NCjxIVE1MPjxIRUFEPg0KPE1FVEEgY29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PWdi MjMxMiIgaHR0cC1lcXVpdj1Db250ZW50LVR5cGU+DQo8TUVUQSBuYW1lPUdFTkVSQVRPUiBjb250 ZW50PSJNU0hUTUwgOC4wMC42MDAxLjE4OTk5Ij48TElOSyByZWw9c3R5bGVzaGVldCANCmhyZWY9 IkJMT0NLUVVPVEV7bWFyZ2luLVRvcDogMHB4OyBtYXJnaW4tQm90dG9tOiAwcHg7IG1hcmdpbi1M ZWZ0OiAyZW19Ij48L0hFQUQ+DQo8Qk9EWSBzdHlsZT0iTUFSR0lOOiAxMHB4OyBGT05ULUZBTUlM WTogdmVyZGFuYTsgRk9OVC1TSVpFOiAxMHB0Ij4NCjxESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVy ZGFuYT4NCjxQIHN0eWxlPSJNQVJHSU46IDBjbSAwY20gMHB0IiBjbGFzcz1Nc29Ob3JtYWw+PFNQ QU4gc3R5bGU9IkZPTlQtU0laRTogMTJwdCIgDQpsYW5nPUVOLVVTPjxGT05UIGZhY2U9IlRpbWVz IE5ldyBSb21hbiI+RGVhciBhbGwsPD94bWw6bmFtZXNwYWNlIHByZWZpeCA9IG8gbnMgPSANCiJ1 cm46c2NoZW1hcy1taWNyb3NvZnQtY29tOm9mZmljZTpvZmZpY2UiIC8+PG86cD48L286cD48L0ZP TlQ+PC9TUEFOPjwvUD4NCjxQIHN0eWxlPSJNQVJHSU46IDBjbSAwY20gMHB0IiBjbGFzcz1Nc29O b3JtYWw+PFNQQU4gc3R5bGU9IkZPTlQtU0laRTogMTJwdCIgDQpsYW5nPUVOLVVTPjxvOnA+PEZP TlQgZmFjZT0iVGltZXMgTmV3IFJvbWFuIj4mbmJzcDs8L0ZPTlQ+PC9vOnA+PC9TUEFOPjwvUD4N CjxQIHN0eWxlPSJNQVJHSU46IDBjbSAwY20gMHB0IiBjbGFzcz1Nc29Ob3JtYWw+PFNQQU4gc3R5 bGU9IkZPTlQtU0laRTogMTJwdCIgDQpsYW5nPUVOLVVTPjxGT05UIGZhY2U9IlRpbWVzIE5ldyBS b21hbiI+SSBoYXZlIHR3byBraW5kcyBvZiBhbmF0b21pY2FsIGltYWdlcyBvZiANCmVhY2ggc3Vi amVjdCwgb25lIGlzIHJlZ3VsYXIgVDIgaW1hZ2Ugd2hpY2ggaGFzIGxvdyByZXNvbHV0aW9uLCBh bmQgdGhlIG90aGVyIA0Kb25lIGlzIDNEIFQxIHN0cnVjdHVyYWwgaW1hZ2UgKDNESGVhZCkgd2hp Y2ggaGFzJm5ic3A7dmVyeSBoaWdoIHJlc29sdXRpb24uIEFuZCANCkkgZG8gdGhlIHByZXByb2Nl c3NpbmcgYXMgZm9sbG93OjxvOnA+PC9vOnA+PC9GT05UPjwvU1BBTj48L1A+DQo8UCBzdHlsZT0i TUFSR0lOOiAwY20gMGNtIDBwdCIgY2xhc3M9TXNvTm9ybWFsPjxTUEFOIHN0eWxlPSJGT05ULVNJ WkU6IDEycHQiIA0KbGFuZz1FTi1VUz48bzpwPjxGT05UIGZhY2U9IlRpbWVzIE5ldyBSb21hbiI+ Jm5ic3A7PC9GT05UPjwvbzpwPjwvU1BBTj48L1A+DQo8UCANCnN0eWxlPSJURVhULUlOREVOVDog LTE4cHQ7IE1BUkdJTjogMGNtIDBjbSAwcHQgMThwdDsgbXNvLWNoYXItaW5kZW50LWNvdW50OiAw OyBtc28tbGlzdDogbDAgbGV2ZWwxIGxmbzEiIA0KY2xhc3M9TXNvTGlzdFBhcmFncmFwaD48Rk9O VCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjxTUEFOIA0Kc3R5bGU9IkZPTlQtU0laRTogMTJwdDsg bXNvLWZhcmVhc3QtZm9udC1mYW1pbHk6ICdUaW1lcyBOZXcgUm9tYW4nIiANCmxhbmc9RU4tVVM+ PFNQQU4gc3R5bGU9Im1zby1saXN0OiBJZ25vcmUiPjEuPFNQQU4gDQpzdHlsZT0iRk9OVDogN3B0 ICdUaW1lcyBOZXcgUm9tYW4nIj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgDQo8L1NQ QU4+PC9TUEFOPjwvU1BBTj48U1BBTiBzdHlsZT0iRk9OVC1TSVpFOiAxMnB0IiANCmxhbmc9RU4t VVM+UmVhbGlnbm1lbnQoRXN0aW1hdGUmYW1wO1dyaXRlKSAtJmd0OyBEYXRhIHRvIGlucHV0OiBh bGwgZk1SSSANCmltYWdlczs8L1NQQU4+PC9GT05UPjwvUD4NCjxQIA0Kc3R5bGU9IlRFWFQtSU5E RU5UOiAtMThwdDsgTUFSR0lOOiAwY20gMGNtIDBwdCAxOHB0OyBtc28tY2hhci1pbmRlbnQtY291 bnQ6IDA7IG1zby1saXN0OiBsMCBsZXZlbDEgbGZvMSIgDQpjbGFzcz1Nc29MaXN0UGFyYWdyYXBo PjxGT05UIGZhY2U9IlRpbWVzIE5ldyBSb21hbiI+PFNQQU4gDQpzdHlsZT0iRk9OVC1TSVpFOiAx MnB0IiBsYW5nPUVOLVVTPjxvOnA+PC9vOnA+PC9TUEFOPjwvRk9OVD4mbmJzcDs8L1A+DQo8UCAN CnN0eWxlPSJURVhULUlOREVOVDogLTE4cHQ7IE1BUkdJTjogMGNtIDBjbSAwcHQgMThwdDsgbXNv LWNoYXItaW5kZW50LWNvdW50OiAwOyBtc28tbGlzdDogbDAgbGV2ZWwxIGxmbzEiIA0KY2xhc3M9 TXNvTGlzdFBhcmFncmFwaD48Rk9OVCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjxTUEFOIA0Kc3R5 bGU9IkZPTlQtU0laRTogMTJwdDsgbXNvLWZhcmVhc3QtZm9udC1mYW1pbHk6ICdUaW1lcyBOZXcg Um9tYW4nIiANCmxhbmc9RU4tVVM+PFNQQU4gc3R5bGU9Im1zby1saXN0OiBJZ25vcmUiPjIuPFNQ QU4gDQpzdHlsZT0iRk9OVDogN3B0ICdUaW1lcyBOZXcgUm9tYW4nIj4mbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsgDQo8L1NQQU4+PC9TUEFOPjwvU1BBTj48U1BBTiBzdHlsZT0iRk9OVC1T SVpFOiAxMnB0IiANCmxhbmc9RU4tVVM+Q29yZWdpc3RyYXRpb24oRXN0aW1hdGUpIC0mZ3Q7IFJl ZmVyZW5jZSBJbWFnZTogbWVhbiBmTVJJIGltYWdlIA0KcHJvZHVjZWQgYWZ0ZXImbmJzcDs8L1NQ QU4+PC9GT05UPjxTUEFOIHN0eWxlPSJGT05ULVNJWkU6IDEycHQiIGxhbmc9RU4tVVM+PEZPTlQg DQpmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPmxhc3Qgc3RlcDxvOnA+PC9vOnA+PC9GT05UPjwvU1BB Tj48L1A+DQo8UCBzdHlsZT0iVEVYVC1JTkRFTlQ6IDIxcHQ7IE1BUkdJTjogMGNtIDBjbSAwcHQ7 IG1zby1jaGFyLWluZGVudC1jb3VudDogMS43NSIgDQpjbGFzcz1Nc29Ob3JtYWw+PFNQQU4gc3R5 bGU9IkZPTlQtU0laRTogMTJwdCIgbGFuZz1FTi1VUz48Rk9OVCANCmZhY2U9IlRpbWVzIE5ldyBS b21hbiI+PFNQQU4gDQpzdHlsZT0ibXNvLXNwYWNlcnVuOiB5ZXMiPiZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyANCjwv U1BBTj5Tb3VyY2UgSW1hZ2U6IFQyIGFuYXRvbWljYWwgaW1hZ2U8bzpwPjwvbzpwPjwvRk9OVD48 L1NQQU4+PC9QPg0KPFAgc3R5bGU9Ik1BUkdJTjogMGNtIDBjbSAwcHQgMTguNHB0OyBtc28tcGFy YS1tYXJnaW4tbGVmdDogMS43NWdkIiANCmNsYXNzPU1zb05vcm1hbD48U1BBTiBzdHlsZT0iRk9O VC1TSVpFOiAxMnB0IiBsYW5nPUVOLVVTPjxGT05UIA0KZmFjZT0iVGltZXMgTmV3IFJvbWFuIj5T bywgdGhlIFQyIGFuYXRvbWljYWwgaW1hZ2UgY291bGQgYmUgcmVnaXN0ZXJlZCB0byANCmZ1bmN0 aW9uYWwgaW1hZ2VzIGR1cmluZyB0aGlzIHN0ZXA7PC9GT05UPjwvU1BBTj48L1A+DQo8UCBzdHls ZT0iTUFSR0lOOiAwY20gMGNtIDBwdCAxOC40cHQ7IG1zby1wYXJhLW1hcmdpbi1sZWZ0OiAxLjc1 Z2QiIA0KY2xhc3M9TXNvTm9ybWFsPjxTUEFOIHN0eWxlPSJGT05ULVNJWkU6IDEycHQiIGxhbmc9 RU4tVVM+PEZPTlQgDQpmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjxvOnA+PC9vOnA+PC9GT05UPjwv U1BBTj4mbmJzcDs8L1A+DQo8UCANCnN0eWxlPSJURVhULUlOREVOVDogLTE4cHQ7IE1BUkdJTjog MGNtIDBjbSAwcHQgMThwdDsgbXNvLWNoYXItaW5kZW50LWNvdW50OiAwOyBtc28tbGlzdDogbDAg bGV2ZWwxIGxmbzEiIA0KY2xhc3M9TXNvTGlzdFBhcmFncmFwaD48Rk9OVCBmYWNlPSJUaW1lcyBO ZXcgUm9tYW4iPjxTUEFOIA0Kc3R5bGU9IkZPTlQtU0laRTogMTJwdDsgbXNvLWZhcmVhc3QtZm9u dC1mYW1pbHk6ICdUaW1lcyBOZXcgUm9tYW4nIiANCmxhbmc9RU4tVVM+PFNQQU4gc3R5bGU9Im1z by1saXN0OiBJZ25vcmUiPjMuPFNQQU4gDQpzdHlsZT0iRk9OVDogN3B0ICdUaW1lcyBOZXcgUm9t YW4nIj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgDQo8L1NQQU4+PC9TUEFOPjwvU1BB Tj48U1BBTiBzdHlsZT0iRk9OVC1TSVpFOiAxMnB0IiANCmxhbmc9RU4tVVM+Q29yZWdpc3RyYXRp b24oRXN0aW1hdGUpIC0mZ3Q7IFJlZmVyZW5jZSBJbWFnZTogVDIgYW5hdG9taWNhbCANCmltYWdl PG86cD48L286cD48L1NQQU4+PC9GT05UPjwvUD4NCjxQIHN0eWxlPSJURVhULUlOREVOVDogMGNt OyBNQVJHSU46IDBjbSAwY20gMHB0IDE4cHQ7IG1zby1jaGFyLWluZGVudC1jb3VudDogMCIgDQpj bGFzcz1Nc29MaXN0UGFyYWdyYXBoPjxTUEFOIHN0eWxlPSJGT05ULVNJWkU6IDEycHQiIGxhbmc9 RU4tVVM+PEZPTlQgDQpmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjxTUEFOIA0Kc3R5bGU9Im1zby1z cGFjZXJ1bjogeWVzIj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgDQo8L1NQQU4+U291cmNlIEltYWdlOiAz REhlYWQgaW1hZ2U8bzpwPjwvbzpwPjwvRk9OVD48L1NQQU4+PC9QPg0KPFAgc3R5bGU9IlRFWFQt SU5ERU5UOiAwY207IE1BUkdJTjogMGNtIDBjbSAwcHQgMThwdDsgbXNvLWNoYXItaW5kZW50LWNv dW50OiAwIiANCmNsYXNzPU1zb0xpc3RQYXJhZ3JhcGg+PFNQQU4gc3R5bGU9IkZPTlQtU0laRTog MTJwdCIgbGFuZz1FTi1VUz48Rk9OVCANCmZhY2U9IlRpbWVzIE5ldyBSb21hbiI+SGVyZSwgdGhl IDNESGVhZCBpbWFnZSBjb3VsZCBiZSByZWdpc3RlcmVkIHRvIFQyIGltYWdlIA0Kd2hpY2ggaGFz IGJlZW4gYWxyZWFkeSByZWdpc3RlcmVkIHRvIGZ1bmN0aW9uYWwgaW1hZ2VzLiBUaGVyZWZvcmUs IHdlIGhhdmUgDQpmaW5pc2hlZCB0aGUgY29yZWdpc3RyYXRpb24gYmV0d2VlbiAzREhlYWQgaW1h Z2UgYW5kIGZ1bmN0aW9uYWwgaW1hZ2VzIHdpdGggdGhlIA0KaGVscCBvZiBUMiBhbmF0b21pY2Fs IGltYWdlLiBTaW5jZSB0aGUgbGFyZ2UgcmVzb2x1dGlvbiBkaWZmZXJlbmNlIGJldHdlZW4zREhl YWQgDQppbWFnZSBhbmQgZnVuY3Rpb25hbCBpbWFnZSwgV2UgdGhpbmsgdGhpcyBjb3JlZ2lzdHJh dGlvbiBtZXRob2QgaXMgbW9yZSANCnByb3Blcjs8L0ZPTlQ+PC9TUEFOPjwvUD4NCjxQIHN0eWxl PSJURVhULUlOREVOVDogMGNtOyBNQVJHSU46IDBjbSAwY20gMHB0IDE4cHQ7IG1zby1jaGFyLWlu ZGVudC1jb3VudDogMCIgDQpjbGFzcz1Nc29MaXN0UGFyYWdyYXBoPjxTUEFOIHN0eWxlPSJGT05U LVNJWkU6IDEycHQiIGxhbmc9RU4tVVM+PEZPTlQgDQpmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjxv OnA+PC9vOnA+PC9GT05UPjwvU1BBTj4mbmJzcDs8L1A+DQo8UCANCnN0eWxlPSJURVhULUlOREVO VDogLTE4cHQ7IE1BUkdJTjogMGNtIDBjbSAwcHQgMThwdDsgbXNvLWNoYXItaW5kZW50LWNvdW50 OiAwOyBtc28tbGlzdDogbDAgbGV2ZWwxIGxmbzEiIA0KY2xhc3M9TXNvTGlzdFBhcmFncmFwaD48 Rk9OVCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjxTUEFOIA0Kc3R5bGU9IkZPTlQtU0laRTogMTJw dDsgbXNvLWZhcmVhc3QtZm9udC1mYW1pbHk6ICdUaW1lcyBOZXcgUm9tYW4nIiANCmxhbmc9RU4t VVM+PFNQQU4gc3R5bGU9Im1zby1saXN0OiBJZ25vcmUiPjQuPFNQQU4gDQpzdHlsZT0iRk9OVDog N3B0ICdUaW1lcyBOZXcgUm9tYW4nIj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgDQo8 L1NQQU4+PC9TUEFOPjwvU1BBTj48U1BBTiBzdHlsZT0iRk9OVC1TSVpFOiAxMnB0IiBsYW5nPUVO LVVTPlNlZ21lbnRhdGlvbiAtJmd0OyANCkRhdGE6IDNESGVhZCBpbWFnZTwvU1BBTj48L0ZPTlQ+ PC9QPg0KPFAgDQpzdHlsZT0iVEVYVC1JTkRFTlQ6IC0xOHB0OyBNQVJHSU46IDBjbSAwY20gMHB0 IDE4cHQ7IG1zby1jaGFyLWluZGVudC1jb3VudDogMDsgbXNvLWxpc3Q6IGwwIGxldmVsMSBsZm8x IiANCmNsYXNzPU1zb0xpc3RQYXJhZ3JhcGg+PEZPTlQgZmFjZT0iVGltZXMgTmV3IFJvbWFuIj48 U1BBTiANCnN0eWxlPSJGT05ULVNJWkU6IDEycHQiIGxhbmc9RU4tVVM+PG86cD48L286cD48L1NQ QU4+PC9GT05UPiZuYnNwOzwvUD4NCjxQIA0Kc3R5bGU9IlRFWFQtSU5ERU5UOiAtMThwdDsgTUFS R0lOOiAwY20gMGNtIDBwdCAxOHB0OyBtc28tY2hhci1pbmRlbnQtY291bnQ6IDA7IG1zby1saXN0 OiBsMCBsZXZlbDEgbGZvMSIgDQpjbGFzcz1Nc29MaXN0UGFyYWdyYXBoPjxGT05UIGZhY2U9IlRp bWVzIE5ldyBSb21hbiI+PFNQQU4gDQpzdHlsZT0iRk9OVC1TSVpFOiAxMnB0OyBtc28tZmFyZWFz dC1mb250LWZhbWlseTogJ1RpbWVzIE5ldyBSb21hbiciIA0KbGFuZz1FTi1VUz48U1BBTiBzdHls ZT0ibXNvLWxpc3Q6IElnbm9yZSI+NS48U1BBTiANCnN0eWxlPSJGT05UOiA3cHQgJ1RpbWVzIE5l dyBSb21hbiciPiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyANCjwvU1BBTj48L1NQQU4+ PC9TUEFOPjxTUEFOIHN0eWxlPSJGT05ULVNJWkU6IDEycHQiIGxhbmc9RU4tVVM+Tm9ybWFsaXNl KHdyaXRlKSANCi0mZ3Q7IFBhcmFtZXRlciBmaWxlOiAzREhlYWRfc2VnX3NuLm1hdCA8bzpwPjwv bzpwPjwvU1BBTj48L0ZPTlQ+PC9QPg0KPFAgc3R5bGU9IlRFWFQtSU5ERU5UOiAwY207IE1BUkdJ TjogMGNtIDBjbSAwcHQgMThwdDsgbXNvLWNoYXItaW5kZW50LWNvdW50OiAwIiANCmNsYXNzPU1z b0xpc3RQYXJhZ3JhcGg+PFNQQU4gc3R5bGU9IkZPTlQtU0laRTogMTJwdCIgbGFuZz1FTi1VUz48 Rk9OVCANCmZhY2U9IlRpbWVzIE5ldyBSb21hbiI+PFNQQU4gDQpzdHlsZT0ibXNvLXNwYWNlcnVu OiB5ZXMiPiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyANCjwvU1BBTj5J bWFnZXMgdG8gd3JpdGU6IGFsbCB0aGUgcmVhbGlnbmVkIGZ1bmN0aW9uYWwgDQppbWFnZXM8bzpw PjwvbzpwPjwvRk9OVD48L1NQQU4+PC9QPg0KPFAgc3R5bGU9Ik1BUkdJTjogMGNtIDBjbSAwcHQi IGNsYXNzPU1zb05vcm1hbD48U1BBTiBzdHlsZT0iRk9OVC1TSVpFOiAxMnB0IiANCmxhbmc9RU4t VVM+PG86cD48Rk9OVCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPiZuYnNwOzwvRk9OVD48L286cD48 L1NQQU4+PC9QPg0KPFAgc3R5bGU9Ik1BUkdJTjogMGNtIDBjbSAwcHQiIGNsYXNzPU1zb05vcm1h bD48U1BBTiBzdHlsZT0iRk9OVC1TSVpFOiAxMnB0IiANCmxhbmc9RU4tVVM+PEZPTlQgZmFjZT0i VGltZXMgTmV3IFJvbWFuIj5Xb3VsZCB0aGlzIGJlIHJpZ2h0PyANCjxvOnA+PC9vOnA+PC9GT05U PjwvU1BBTj48L1A+DQo8UCBzdHlsZT0iTUFSR0lOOiAwY20gMGNtIDBwdCIgY2xhc3M9TXNvTm9y bWFsPjxTUEFOIHN0eWxlPSJGT05ULVNJWkU6IDEycHQiIA0KbGFuZz1FTi1VUz48bzpwPjxGT05U IGZhY2U9IlRpbWVzIE5ldyBSb21hbiI+Jm5ic3A7PC9GT05UPjwvbzpwPjwvU1BBTj48L1A+DQo8 UCBzdHlsZT0iTUFSR0lOOiAwY20gMGNtIDBwdCIgY2xhc3M9TXNvTm9ybWFsPjxTUEFOIHN0eWxl PSJGT05ULVNJWkU6IDEycHQiIA0KbGFuZz1FTi1VUz48Rk9OVCBmYWNlPSJUaW1lcyBOZXcgUm9t YW4iPkFueSBzdWdnZXN0aW9uIHdvdWxkIGJlIGFwcHJlY2lhdGVkIGFuZCANCnRoYW5rIHlvdSBh bGwgaW4gYWR2YW5jZSE8bzpwPjwvbzpwPjwvRk9OVD48L1NQQU4+PC9QPjwvRk9OVD48L0RJVj4N CjxESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVyZGFuYT48L0ZPTlQ+Jm5ic3A7PC9ESVY+DQo8RElW IGFsaWduPWxlZnQ+PEZPTlQgY29sb3I9I2MwYzBjMCBzaXplPTIgZmFjZT1WZXJkYW5hPjIwMTEt MDItMTQgDQo8L0ZPTlQ+PC9ESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVyZGFuYT4NCjxIUiBzdHls ZT0iV0lEVEg6IDEyMnB4OyBIRUlHSFQ6IDJweCIgYWxpZ249bGVmdCBTSVpFPTI+DQoNCjxESVY+ PEZPTlQgY29sb3I9I2MwYzBjMCBzaXplPTIgZmFjZT1WZXJkYW5hPjxTUEFOPg0KPERJVj4NCjxE SVY+wO7B1cHhPEJSPjxCUj5MaW5saW5nIExpIDwvRElWPg0KPERJVj5SZXNlYXJjaCBDZW50cmUg Zm9yIE5ldXJhbCANCkVuZ2luZWVyaW5nPEJSPi0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tPEJSPlBob25lOiANCis4Ni03NTUtODYzOTIyMzI8QlI+RW1haWw6Jm5ic3A7Jm5i c3A7PEEgDQpocmVmPSJtYWlsdG86bGwubGkxQHNpYXQuYWMuY24iPmxsLmxpMUBzaWF0LmFjLmNu PC9BPjxCUj5TaGVuemhlbiBJbnN0aXR1dGVzIG9mIA0KQWR2YW5jZWQgVGVjaG5vbG9neSw8QlI+ Q2hpbmVzZSBBY2FkZW15IG9mIA0KU2NpZW5jZXMuPC9ESVY+PC9ESVY+PC9TUEFOPjwvRk9OVD48 L0RJVj48L0ZPTlQ+PC9CT0RZPjwvSFRNTD4NCg= --============================================================================Date: Mon, 14 Feb 2011 09:30:45 +0000 Reply-To: Martin Dietz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Martin Dietz <[log in to unmask]> Subject: M/EEG chantype Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear Vladimir & list, I can successfully change the channel type using spm_eeg_prep. According to the manual chantype is able to do the same job. However chantype doesn't seem to allow any (0/1) flags ? Best wishes Martin ========================================================================Date: Mon, 14 Feb 2011 11:15:52 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: naive quesiton about mapping between images in different space Comments: To: "Jiang, Zhiguo" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/related; boundaryMessage-ID: <[log in to unmask]> --0015175cb154c389f2049c3c2bf3 Content-Type: multipart/alternative; boundary --0015175cb154c389eb049c3c2bf2 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Multiplying by M1 maps from voxels in image1 to mm space. Then multiplying this by the inverse of M2 will map from mm space to voxels in image2. Note that if we want to generate a resliced version of image1 that is aligned with image2, we need the mapping from image2 to image1. This is because reslicing an image usually involves scanning through the voxels image that we wish to create, and then figuring out where the values should be read from in the original. Best regards, -John On 11 February 2011 18:13, Jiang, Zhiguo <[log in to unmask]>wrote: > In spm_reslice.m , it states > > > > % Assuming that image1 has a transformation matrix M1, and image2 has a > > % transformation matrix M2, the mapping from image1 to image2 is: M2\M1 > > % (ie. from the coordinate system of image1 into millimeters, followed > > % by a mapping from millimeters into the space of image2). > > > > > > > > So to map a x0,y0,z0 in image1 space to image2, isn’t the composite matrix > > > > M1\M2 instead of M2\M1… ? > > > > Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 > … > > > > Not sure where I got it wrong… > > > > > > Thanks, > > > > Tony > > > > > ------------------------------------------------------------------------------------------------------------------------------ > > [image: logo] > > Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles > Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los > Angeles,CA 90095 > > Tel: (310)206-1423|Email: [log in to unmask] > > > > > > > > ------------------------------ > IMPORTANT WARNING: This email (and any attachments) is only intended for > the use of the person or entity to which it is addressed, and may contain > information that is privileged and confidential. You, the recipient, are > obligated to maintain it in a safe, secure and confidential manner. > Unauthorized redisclosure or failure to maintain confidentiality may subject > you to federal and state penalties. If you are not the intended recipient, > please immediately notify us by return email, and delete this message from > your computer. > --0015175cb154c389eb049c3c2bf2 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Multiplying by M1 maps from voxels in image1 to mm space. Then multiplying this by the inverse of M2 will map from mm space to voxels in image2.

Note that if we want to generate a resliced version of image1 that is aligned with image2, we need the mapping from image2 to image1. This is because reslicing an image usually involves scanning through the voxels image that we wish to create, and then figuring out where the values should be read from in the original.

Best regards,

-John

On 11 February 2011 18:13, Jiang, Zhiguo <[log in to unmask]> wrote:

In spm_reslice.m , it states

% Assuming that image1 has a transformation matrix M1, and image2 has a

% transformation matrix M2, the mapping from image1 to image2 is: M2\M1

% (ie. from the coordinate system of image1 into millimeters, followed

% by a mapping from millimeters into the space of image2).

So to map a x0,y0,z0 in image1 space to image2, isn’t the composite matrix

M1\M2 instead of M2\M1… ?

Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 …

Not sure where I got it wrong…

Thanks,

Tony

------------------------------------------------------------------------------------------------------------------------------

Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095

Tel: (310)206-1423|Email: [log in to unmask]

IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer.

--0015175cb154c389eb049c3c2bf2-- --0015175cb154c389f2049c3c2bf3 Content-Type: image/jpeg; name="image001.jpg" Content-Transfer-Encoding: base64 Content-ID: <[log in to unmask]> X-Attachment-Id: 69e9c46027359f48_0.1 /9j/4AAQSkZJRgABAAEAYABgAAD//gAfTEVBRCBUZWNobm9sb2dpZXMgSW5jLiBWMS4wMQD/2wCE AAUFBQgFCAwHBwwMCQkJDA0MDAwMDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0BBQgICgcKDAcHDA0MCgwNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0NDQ0NDQ0NDf/EAaIAAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKCwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoLEAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEU MoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2Rl ZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK 0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+foRAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYS QVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNU VVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5 usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/AABEIADABwAMBEQACEQEDEQH/ 2gAMAwEAAhEDEQA/AOXr6o8gKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAC gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKA CgDobfQ1n0yTUt5BiYrs28HlR1zx9707V8xWzR0cxpZUqaaqRUufms1dTduW2vw9+p60MIp4WeM5 rODty231j1v59hnh/RV1uZ4WcxbE3ZC7s8gY6j1q84zN5RShWjTVTnnyWcuW3ut3vZ9hYLCLGzlT cuW0b3Sv1S7ruQ6Vo02rXBt4cAJku56KoOM+5PZe/sASOjH5lSyzDrE1k25WUILeUmr28kvtS6dm 2k8sPhZ4qo6VPRL4pPZK9vvfRdfS7Oh/4RO1uA0VldpLcRgkoQMHHphiQM8Z+YA4BxXzH+sGJw/L Vx+CnSw82kppyvG+uqcUm7ape42rtXPW/s2lUvDDV4zqx3jpZ/c9PXXzMXRNDOq3T2krGFokZj8u SGV1UqRkd2PftXvZpmiy3C08bRiqsak4xj73KnGcJTUk7PpFdOp52Ewn1qrKhNuDjFt6Xd1JRatd d/wMSePyZGjznYxXPrgkV79KftacKlrc0Yyt2uk7HmzjySlHs2vudjV0LSDrNx5G7y1VS7MBnAGA OMjqSO/rXj5rmKynD/WOVTk5xhGLfLdu7etnsk+m9juweF+uVPZX5Uk5N2va2nl1aKuqWB0y6ktW OfLOAcYyCAVOPcEV2YDFrH4ani4rl543cb35ZJuMlfykn8jDEUXhqsqLd+V6Pa6eqf3MoV6Ryna2 3huysrOG/wBbuXthdgtBBDGJJmjHHmtuZVRCfu5yWHI7gczqScnClFO27bsr9vM1UUknJ2vsluU9 d8OLptvFqNlMLywuWKJKFMbJIMkxSoSdr4BI5wwBIAGM1CpzNwkuWS6eXdMUo8qundHLVuZhQAUA dB/Yi/2L/bO87vtv2Ty9vGPI87fuznOfl24989qx5/3ns7fZ5r/O1i+X3ebzt+Fzn62ICgAoA3/D eh/29dGBpPs8UUUk8sm0uVjiGWIQEFjyABkdc9sVjUn7NXSu20kttWXGPM7bdTN1GK2huGSykaeA bdkjp5bNlQWymWxhsgcnIAPerjdr3lZ9k7idk9NilVkhQAUAdz4g8FSaLplnq8bmaK8jjaQbdvlP LGsirwTuU5YbuOVAI+YVywrKc5U2rOLdvOzsbShypSXUytP0Bb3Sb3VTIVNg0AEe3IfzpAhy2Rt2 5z0OfatJT5Zxp2+K+vayuSo3i5drfic3WxmFABQAUAFABQB0Go6IthplhqQcsdQ+05TbgR/Z5RGM HJ3bs5PAx05rGM+acoW+Hl+d1ctxslLvf8Dn62INPV7W1s7gxWM32qEKhEm0pklQWG08/KxK574z WcW2ryVn2KaSdk7ozK0JOg0bRF1S2vbkuYzYwCYKFzvywXaTkbfXOD9KxnPkcY2+J29C1G6b7I5+ tiAoAKACgAoAKACgAoA6zVfD1to9jBNPcMb27giuY7dYvkEMrEKWl3fe2gnAXg8e554zc5NJe7Ft N31uvI0cVFJ31avbyMPStMm1i7isbYAyzttXPAHcknnAUAscAnAOAa1lJQTlLZEpczsjsl8N6C1x /Zo1GT7Xu8sS/Z/9GMuduzdv37d3y+Zjb/F0rm9pUS5+Rcu9r+9b7rfI15Y35ebX00Myz8JSPdah ZXT+TLpdpcXB2jcHMGzCgkrhXDBg3XGPlrR1UlCUdVKSXpf/ACJUNWnpZN/cchXQZBQAUAFABQAU AFABQB32n/8AIuXH/XQ/+hRV+bYv/kocL/17X/pNU+po/wDItq/4n+cCPwF/x9y/9cf/AGda14s/ 3Wj/ANfv/bJkZN/Gn/g/9uRZ0U/ZtJv7hOHLumRwQAq459vMJHvXJma9vmuXYWprTUKc7PVNucrq 3n7OKfdG+E/d4PFVY6S5pRv10irfdzOxxel6g+lXCXUYDFM/KeAQVKkH8/zr73HYOGYYeeEqNxjO 3vJXacZKSav6W9Gz5zD13haka0Fdxvp0d01r952XhO8N/q09yVCGSFmKjoDviz19Tz+NfCcQYdYL KsPhVJzVOtCKk7JtezrW200WnyPoctq+3xlWs0o81Nuy2+KH57nD3n+vk/66P/6Ea/QsN/Apf9e4 f+ko+aq/xJ/4pfmztPDqPp+l3V/GCZX/AHceBk8cZAH+0+T/ALnPFfBZxKGNzPCZbUaVKF6lW7tH X3uVttbxhZf49NT6PAqWHwtbFQT55e7Cyu+10vWV/wDt0j8aQeYbe/UFRPGAw6YIAYZ75IbH/Aa1 4Zq+zWJy2TTdGq3HW94tuMrW0spRv/2+RmsOZ0sUlZTgk/JrVX87O3yOGr9DPmT0L4nDytZ+yr/q 7W3t4YwOAEWMMAB2GWPFceH+Dm6ttv7zerpK3ZJGBp2qTy2R0BFRo7y5idWbduSTKoCuDj5hgNkE 46VtKKUva9Ypr5bkJu3J3Z6LqF5faDf/ANmadp5fSrZlikRrPzTdAYEskkpjLMzHdtKsFA28EcVx xUZx55ztN6r3rcvZJXNm3F8sY+6vLcNP0q18PatrcDQpcQWlm8scUg3DDeVKiE9cYYKSDu2570Sk 6kKTTs3JJtfNMElGUlbRIzdD1ubxZHe6fqaQSRpZTzwFYIo2gkiAK+WUVTt5wQSSQMFsFgbnBUXG cLp8yT1bun3uTGXPeMrbNrTaw9fEN2ng8MPKyL8WY/cx/wCpFn/u/wCs/wCmv+s/2qXs4+36/Dzb vfm/Ly2HzP2fztt0sW9TOpeE5ItL0eyLxQRRG5kNp532qV1DyBpDGx8td2xVQqVww3eijyVU51JW bb5VzW5VstL7+o3eHuxW2+l7lmy0az0/xNIvkAWlxYSXQt2A+QSRbnjAI+XawcKMAoMAdKlzlKkt feUlG/o9/wCtwSSntpa9vkeW65r8+vSK8yxRJECsUcMaxoiE52gDkgdixJruhBU1ZX13bdzCUnL/ AIB2nw21ae0e8t49mxbK5nG6NGbzERQPmIJ2+qZ2nuK5sRFPlbv8UVv0f9bmtN2uvJsS21E6dpcn iSSOGTUr668iBmiTy4UjjBeVItojDkgKPlOOD3YMOPNNUU2oRjd6u7u9E3vYE7Ln0u3ZeRE13/wl +j3k94kf9oab5cqzxxpG0sLtsdJBGqqdn3lO0dhx8xZ29jOKi3ySurN3s+lr9xX54tvddfIv6/rR 0PTdPgsoYElvNOj8+Yxq0jRkMoRSR8uSXLNjcSV5G2phDnlNybtGbsr6X7lSfKopJax1Z5PXecx7 /LqMP/Es0W+OLLVdHtYWP/POYAmCUZ4BDkD0yVLcLXkqL9+pD4oVJP1XVfcdl/hg9nFL59Dk7TTp tI0DXrK4G2WCazRvQ4uFwR/ssMMp7gg10OSlUpSjs1L8jNLljNPo1+ZYlTUPC1nZw6Nal5rm3S5u rj7L55YzZKwbmjcKkaABlXGS2cg5JlctWUnUlZJtRXNa1uu63HrBJQW6u3a+/Q0INKhTxLpV19nW 3XUYfOltym1EmEciyqsbD5VLAMAR1Jx7Q5P2VSN78rsnfdXVtR29+Lta628zz/wzGr+ILVGAZTeI CpAII8zoR0x7V11NKcrfy/oYx+Jep2WiyQWDa/dSQRT/AGWRWjjkUFAwuZAnHHyhtpZQRuUbehrn nd+yim1datf4Vc1jZc7tt/mUNO1EyWl74pv44bi7R4bW2Vo1ESyMvzOYwApKRgFOOuc8nIqUbONC DajrJ6627X82JPR1Hvol2Cw1J/Gtne2upJE95a2z3dtcJGkUg8kgvC3lqoZXU4UEcHJJztIJR9hK MoXUW1Fq7a12eoJ86ae6V0/ToXZ9dfQvDOkPbJH9qkN6I5pI0kMSLckybFcMgZyY/mKkhVIGM1Kh z1aibfKuW6TtfTS9vmPm5YRtvr8tTC1tl17Ro9caOOO8hujaXBiQIJg0fmxysqgKGHKMVxk8kYwB rD93N0k3yuPMr9NbNES96PP1vZnSaloltqXi1bWZQlpHBFNMqjaCkdssjD5cY3sAGIwcEkHNYxm4 0br4rtL1crFuKc7dLX/AydP8bNqN8lleW9sdKuJBF9lEMaiFHbaGSRVEgdM7t27k5wFyCNJUeWLl FvnSve71a7razEp3dmly9rbE+n6b/Y6+IbEHIt4Cik9Som+Un3K4J96mUuf2Mu7v+AJcvOuy/Up2 9yPCOi2t9apGdQ1N5mE0iLIYYYWEYWNXBUM7fNuwcjIPRdtNe1qSjJvkhbRO129dbdhL3Iprd3+S QmouvijRG1V4401CyuEimeJFjE8UwOxnRAF8wSfLkADGfUAOP7qp7NN8kk2k9bNb28rA/ejzdU9f O5e1/XpPB93/AGLpkduILRIln3wRyG6kKK8jSs6l8EttAUrgD5TjAEQgqsfaTbu72s2uVXsrWHKX I+SNrLfTc6LUJLGDxFpkSwpHZX+mQwmMKuAtwZlj5/vBvLG/72B1rGKk6U3d80Zt39LX/Ut2U4q2 jil99zifB+kraa1Mb5Q8WkJczTqRwfs4KAc8cyFSAc5x0rqqyvTXLvOyXz/4BlBWk7/Zu38jhbid rmV5mwGkZnIAwMsSTgdhk8CupKyS7GW+p6BZTr4V0ODU4I421DUpphFNIiyeTDAQjeWrgqHaQ8sQ flOCK5Gva1HBt8sUrpaXb726WNl7kVJbu/ySJpL86/pL608cS6npFxbs8qRoomilbCebGqiNmWVe u0AqNuOTS5fZzVJN8k09LvRrez32C/NHm+1FosfELW7meDT7Z/L8u4020uHxFGG8xvMztYKGROOE UhB2FTQgk5tXupyS1e2n9XHUk/dX91M4bw3rJ8PajBqKrv8AIYkrnGVZWRwD2JRjg9jiuqpD2kXD a/8Aw5lF8rT7HaS+ErHxBK1z4avY3kdjILO4/dToc7iqE8Sbex6ADmRjzXMqsqa5a0XbbmWq+fb+ tDXkUtab+T0ZqeEtc1SGbV4b4g3ENhd3D+bGjSedEsMah2K5ZAqgeWSY2xnBzmoqwham4bOUUrN2 s7v7/PcqEmuZPezfz0Kun62bDw7LrBhglvZNVdEd4kKxNJbI5dUAC8BWCLjapbOOxcoXqqndqKhq r72bEpWg5WV+b9BIGvtN0yLVrC3M+pavNcTSTrbibyI1kK7ETY0cZlcs/wB37oxjhdrfLKbpzdoQ SSV7Xduut3YNYpSitZN62vb/AIcTVdOhvjpOp6nD9ia7nNvfLsMCtskXEuzClDJGT5jAAcZGMURk 4+0hB3srx1vutvk9hNX5ZSVruz6G3rd/qWg6g8Oo2KP4fDsgiitYjH5HRHWVQCswXDfNIvz5A28E ZQjGcU4Saq93J3v6dvkXJuLs17np0/zMjwnaE6XfXmgxLPqUdyFj81I3ljtCAVdEfcnmMdwbAbO0 gZwK0qu04xqu0La2uk5edtbEwWjcFrf8DmvEurf2haxx6lbNb6zDKwkk8lYBLAV4MijYfNVxgYjC 7cndk4ranHlbcHem1or3s/Lyt5mcndWkrSXlbQ4muoyCgDv/AA28Wo6bPpRcRTOxZM987SMeuGXD Ac4ORX5rnUauBzDD5zGm6lGEVGfL9m3Ond9LxneLejaaZ9VgHDEYapgXJRnJtxv1va1u+q1S1tqa Wj6T/wAIoJby+lj5TaqoSc87uNwUliQAAAfc15OZZh/rG6OBy6jU0nzSlNJct043fK5JRSbbk2tk kjswuG/svnxGJnH4bJRb11vpdJtuyskjH8LX0MyXGm3LCNboEqSQBuYbWGTxuxgr6kEdcCvdz7C1 qU8NmmFi5ywzippJt8sWpRdlry35lK2yae12vPy6tCcauDrNRVW7i/NqzXrs16F7S/C50ac3moSR CCENjkkMSCBkMAOhJAGSWwAO9efj89WaUFgcsp1vb1XG+iTik02ouMm3dpJt8qUbttbHTh8v+p1P rGKlD2cE7ebaa1TS6O9tXe1iPwtcreaxcTxjajxOVGMYXzIgvA4HAGfetM+oSwuUYXD1HzThVpqT u3eXsqrlq9Wrt28iMuqKrjatSKtFwlZbWXPC2noUNR8HXkQmui8JRfMlIDPu2jLYA8vGce+M969H B8R4So6GDUK3PL2dJNxp8vM+WF2/aX5b9bXt06HNXyutH2ldyp8q5p2vK9leX8tr28/mbOqajL4Y srW0ttqylSXyM47tx7uxwfY14WBwVLiDGYzGYrmdFSShytxvq1HX+7Tirr+8j0MRXnllChQo2U7X ldX83p5yb+4hkun8R6LK8uDPbPv4GOF5zj/rmzj6it4YeGQ5zRp0bqhiIcmrvrJ2tf8A6+Rg/JSM 5VJZhgZynb2lOXNpptre3+FyXqjziv1Q+QPVdc0ufxtBbavpIFxOsEcF5ArKJY5Y/l8zaSCUkBGC M4AGf4tvBCSoOVOportxfRp9PVHRJc6Uo72s0Vri2h8G6ZFFdJC+sS3cdxtwjyW8ERDBWkG4o0jq MqDyrHP3SKpN1ptxb9motdUm35eQrezVnbmvf0SOi1e01/V7/wC26Ne3LaXeESJMl26RW6kAyLIv mL5XlHdlMdAABuyBjF04R5akVzx0ty6vtbTW5o1Ju8W+V+exmWM0E91rz2sstzCNPlVJp5DLJIE8 tNxc8sCVOz0TaO1W00qSkknzrRKyV79Pz8yVa87aq3Uwfh//AMfN7/2DLz/0Fa1r7R/xxIp7v/Cw /wCZN/7jP/tlR/y//wC4f/twf8u/+3v0Ov1k694kli1Tw/c3D211HEJI4blo1tp1QLIjp5iCMZG/ dja2S3cE88PZ0k4VYpNN2bje66NO2po+aXvQbs+z2ZDpbxHxFNFDcy33k6fPG88spl3yLCTJ5btz 5YckKORwSCQc05X9km0o3mnZK1lfS/mC+J2d9Hr8jxmvSOU734f/APHze/8AYMvP/QVrkr7R/wAc Tanu/wDCyzpNsfE2gnSbUqb+yuWuIoSwUywyJtcR5IDOrDcRnOMAckClJ+yqe0fwyVm+zT0uNLmj yrdO9vIlTTpfCGi3o1HEN5qYjgggLKZBEr7pZHUE7VI+Vc4OR7ilzKrUjyaxhdt9L9EgtyRd9G9E ij41/wCPbSP+wZD/AOhPVUd6n+NintH/AAo4KusxPRfHpxDo5H/QJtf5Vx0N6n+ORvU+z/hR2N5r EWv+D7y+P/H9ttILs9C7Q3EflyEero/LeoKjhK5lB068Y/Z95x8rp3Ro3zU2+uifyZmXMus+IbGy uvD1xcHybeO1ubeC4aNo5YsqJGjDoNsq4IcZAxgkdBolTpylGslq3KLavdPpe3QXvSScG9rNJ7Mh ivItJ8Raat5dy3UtuqrdyzTmaOKeVWVo0diQqRll3ncQDnOCCA7OVKfLFJP4UlZtLq/N9BX5Zxu7 2316hoPgjUtP16K4u0WG1hulcTs67JMvmNY8NlmlOAoAyM5bGDROtGVNqOrcduq739AjBqSb2vuV oP8Aj38S/wC+n/pW9N70fT/21C6T/rqZnhhF1rSbzQUZUu2liurVXYKJXQFJIwxwN5TGwE4J9ACa 0qfu5xq/Zs4y8k9n95Mfei4dd0X9K0m48G2d7qGqqLaW4tZLS1hZl8yR5sBpAgJ+SMDJLcHPrjMS kq0owp6pSUpPokunzKSdNNy00sl6lm48P3OveGNKFiBLcW5vCYAyiRo5LlgzqrEFgjIoYLkjeDjF SpqnVqc2ifLr0uo7fO4+VyhG26vp8zK1u3bw5oMWjXJC31zdG7liDKxijWPy41fBIDOTvAHOAQcd DpB+0qOpH4VHlT7u93b02JkuSPK927+h02panBpXjAPdnZby28UErdNqy2qpuJ7AMVJPYAmsYxcq Fo7ptr5SLbUamu1rfejA03wDfWOopNehItOtpFlkuzInktCjBsowbJMgACgDcC3IGDWsq8ZRajfm aso21Tff0IVNp67Lr0sW7LUl1f8A4SG9T7k8BdM8HYZvkyPXbjPvUuPJ7GPZ/oNO/O/L9SnHYSeL dCtINPxLfaU06SQblWR4ZXEiyIpIDBD8hA+bue2a5lRqSc9Izs0+ia0s/UVueKUd1fTyYl7bv4S0 JrC6KpqF/cxStCGVmighBZGkwWCs0hyF6levIZQJ+1qc0fgimr7Xb7fIGuSPK929uyRd8TeG7vxV ejWtIQT2t+kbswdAIJFjVZUmywKbCu4k8YPBOKmnUjRj7Kpo436bq+jQ5RcnzR2f4epW+JIWzvdP Fq4ZYdMtfKkToQjzbHX2IAYVWH1jPmW85XXyQqmjjb+VfqbniyWG00y41WAgP4jNrhRwUSOMSXH5 zYVx7jt1ypJuapvalzfO7svwLnonJfat/wAH8TxavSOU9ItbJ/FegW9lY4kvtKln/wBH3BXkgnIk Z4wSA+1xgjkgfVQ3E37Ko5S0jNLXomtLP5G6XPFJbxvp5MLqyfwnoFxZXuI77VZYP9H3BnjggJkV 5ACQm5zgDgkfRgon7WopR+GCevRt6WXyC3JFp7u2nkip48/5hX/YGsv/AGrVUf8Al5/18l+gp/Z/ wowvCw09tTgTV1DWbsUkyzIF3KQjFkZSqq5Usc4C5J4rWpzKD9n8XT+vQiNrrm2N9Ph9rdtdgRJt iRwy3gkQQhAciYPu4AHzY+92xmsvb03HV6/y2d/Sxfs5J6ff09Trm1K31bW9eubQhoTo90odejlI 4EZgRwQWU7SOCuD3rn5XCnSjLf2kdO122a3TlNr+V/ocf/zJv/cZ/wDbKuj/AJf/APcP/wBuMv8A l3/29+huafcalrWhWtvoc80d5pzSpPbwzNE8kUj745QqsocISUPVgTnGOTlJQp1JOqlyys02rpNa NeV9y1dxSg9Ve6TsVdVshu0/Tdevrg3UsjtdiS4M6WiudsIIZmVXPDSHd8q8njBNRfxzpRXKl7tl Zy7/AC7Ca2jNu/XW9ux0XhnT/Enh++WG4c/2PGx82SaVGtTb8/OhZiFDLgqF2nJAcY3VjUlSqRul +86JJ81y4qcHZ/D+FjlbbQ21Dz9R8MSyC5hu5gLeNxFItqzExSR/MrMuMKyjnI6YFdDny2hXSs4r Vq65uqZmo3u6e6b020NPxfPdnQbeHXyp1ZbkmIEoZhamPky7Om5+OcFtqk5Ias6SXtG6PwW13tzX 6fIqd+VKfxX+djyWvQOYKACgBWYvyxJPvzUqKjpFJLyVhtt7u4lUIUsSACSQOnt9KlRUbtJJvey3 9R3ez6CVQgoAKACgAoAcjtGdyEqfUHB/SkMbTEODsoKgkA9Rng46ZFIY2mIKACgBwdkBCkgHg4OM /WkMbTEFABQAAlTkcEUAKzFzliST3PJpbAJTAKACgAoAcrsn3SVyMHBxwe30pDG0xDi7MApJIXoM 8DPXA7UthjaYgoAc7tIcsSx9Scn9aW2wxFYocqSCOhHBoEBJY5PJPJJpgJQA4uxUJk7RyBngfh0p DG0xCqxQ5UkEdCODSACSxyeSeSTTA9FvP+Ef1xxeLdnSGkSMT2i2jtHvRQrNF5OI9rYyAwU7iWPJ IHGvaU/d5ee17S5rOz731N3yy1vy91b8rGD4t1mDWLqL7Grra2dtFaQ+Zje0cIOGYDgElj3PGM4P A1pQcE+bdtydtrsibUnpslZfIs+LtVtrxbOx09/NtbC1SMNtdQ0z/NO4V1VhuYL26gkZFTSi480p qzlK/wAumw5taKOyX49Tjq6TIVWKHKnBHQjg0ADMWOSck9SetGwCUAFADvMbbsydvXGTjPrjpSGN piCgBVYocqSCOhHBpAJTAcXYqEydo5AzwPw6UhiKxQ5UkEdCODQICSxyeSeSTTASgAoAKACgAoAK ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA KACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAP//Z --0015175cb154c389f2049c3c2bf3-- ========================================================================Date: Mon, 14 Feb 2011 11:34:12 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: M/EEG chantype Comments: To: Martin Dietz <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear Martin, The syntax of chantype is something like: D = chantype(D, 1:10, 'EEG'); I don't see where you expect 0/1 flags to be. Vladimir On Mon, Feb 14, 2011 at 9:30 AM, Martin Dietz <[log in to unmask]> wrote: > Dear Vladimir & list, > > I can successfully change the channel type using spm_eeg_prep. According to the manual chantype is able to do the same job. However chantype doesn't seem to allow any (0/1) flags ? > > Best wishes > Martin > ========================================================================Date: Mon, 14 Feb 2011 12:56:17 +0100 Reply-To: Martin Jensen Dietz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Martin Jensen Dietz <[log in to unmask]> Subject: Re: M/EEG chantype Comments: To: Vladimir Litvak <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi Vladimir, Thanks. I erroneously assumed a flag based on the example in section 12.7.4 Martin On Feb 14, 2011, at 12:34 PM, Vladimir Litvak wrote: > Dear Martin, > > The syntax of chantype is something like: > > D = chantype(D, 1:10, 'EEG'); > > I don't see where you expect 0/1 flags to be. > > Vladimir > > On Mon, Feb 14, 2011 at 9:30 AM, Martin Dietz <[log in to unmask]> wrote: >> Dear Vladimir & list, >> >> I can successfully change the channel type using spm_eeg_prep. According to the manual chantype is able to do the same job. However chantype doesn't seem to allow any (0/1) flags ? >> >> Best wishes >> Martin >> ========================================================================Date: Mon, 14 Feb 2011 12:29:18 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Question about lead field matrix Comments: To: [log in to unmask] Comments: cc: Robert Oostenveld <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Carsten On Fri, Feb 11, 2011 at 11:56 PM, Carsten Stahlhut <[log in to unmask]> wrote: > Dear Vladimir, > > I have approx three questions (or issues) that I hope you can help me out > with. > > Do you know what the units are for the lead field matrix and the source > estimates in SPM8? These are two different questions. > If you apply one of the EEG imaging methods (as I understood it) then the > source estimates should be current density i.e. the unit should be something > like A/m^2 or uA/mm^2 depending on the scaling of the EEG data and lead > field matrix. > And then the units of the lead field should be something like m^2 * Ohm ( = > m^2 / S ). In theory the forward computation done by the 'forward' toolbox should be unit-blind so the units of the lead field should depend on units of the head model (mm in SPM). In practice this might not be the case. We recently found a difference in scaling between 'single shell' and other MEG models. This will be fixed in the next update. There might be something similar also for EEG. SPM is not sensitive to it but it's still something worth looking into. > Does this also holds for SPM8 or do there exist any type of scaling of the > lead field matrix such as a normalization of the columns or something like > that? > Yes, there are several scaling and normalization steps so the units of the output images are not related to original physical units but are normalize to the total power across sources and conditions. > The second question is related to the lead field matrix as well. If I > compare two different head models a BEM and 3-spheres model then they seem > to be on two very different scales - is that really the case? Or am I > missing an important step? Of course it is two very different head model > assumptions so they should be quite different but I'm a bit surprised that > there exists a scaling factor of 100 between the largest element in the BEM > gain matrix and the 3-spheres gain matrix. See above. > What I just did was to load the mat-files with the gain-matrices and used > the imagesc-function to show the amplitudes of the two lead field matrices. > > The 28th Jul 2010 you sent an answer to a post regarding head models and you > here mentioned that FEM head models from SimBio might be an option in the > very nice MEEGTool. I was wondering what the status is on integrating the > SimBio FEM implementation with SPM, as I'm very interested to try out the > FEM head models. I guess there is no reason that I will try to write the > code my self if the option is almost done and will be part of the MEEGTool. > I am aware of some efforts to integrate SimBio support in the forward toolbox. Even if this is done I don't expect it to become one of the standard options in SPM because SimBio runs only on a particular Unix platform (I'm not even sure it's LINUX) and I suspect has very lengthy computing time. But if you compute a gain matrix for the canonical mesh in SimBio you can just replace the mat file generated by SPM with your file and it will be used by spm_eeg_invert. If there is inconsistency in units, it shouldn't be a problem. There is also a possibility to use another BEM implementation OpenMEEG (http://www-sop.inria.fr/athena/software/OpenMEEG/). That's something that works and I tested it last year, but found it impractical for most users again because of difficulties with installation and long computation time (about 24 h for normal mesh). The differences in the lead fields with our BEM were only for a small number of vertices close to the surface. But I can give you the glue code for this if you want. Best, Vladimir > Best, > Carsten > > > ========================================================================Date: Mon, 14 Feb 2011 12:44:58 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: How to do the coregistration between the functional MRI images with a high resolution anatomical head image Comments: To: Linling Li <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=GB2312 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> It's not really about right or wrong. The important thing is whether it works for your data. This will depend on many factors, but the amount of spatial distortion in your fMRI will be one of these. Best regards, -John 2011/2/14 Linling Li <[log in to unmask]>: > Dear all, > > > > I have two kinds of anatomical images of each subject, one is regular T2 > image which has low resolution, and the other one is 3D T1 structural image > (3DHead) which has very high resolution. And I do the preprocessing as > follow: > > > > 1. Realignment(Estimate&Write) -> Data to input: all fMRI images; > > > > 2. Coregistration(Estimate) -> Reference Image: mean fMRI image > produced after last step > > Source Image: T2 anatomical image > > So, the T2 anatomical image could be registered to functional images during > this step; > > > > 3. Coregistration(Estimate) -> Reference Image: T2 anatomical image > > Source Image: 3DHead image > > Here, the 3DHead image could be registered to T2 image which has been > already registered to functional images. Therefore, we have finished the > coregistration between 3DHead image and functional images with the help of > T2 anatomical image. Since the large resolution difference between3DHead > image and functional image, We think this coregistration method is more > proper; > > > > 4. Segmentation -> Data: 3DHead image > > > > 5. Normalise(write) -> Parameter file: 3DHead_seg_sn.mat > > Images to write: all the realigned functional images > > > > Would this be right? > > > > Any suggestion would be appreciated and thank you all in advance! > > > 2011-02-14 > ________________________________ > ÀîÁÕÁá > > Linling Li > Research Centre for Neural Engineering > -------------------------------------- > Phone: +86-755-86392232 > Email: [log in to unmask] > Shenzhen Institutes of Advanced Technology, > Chinese Academy of Sciences. ========================================================================Date: Mon, 14 Feb 2011 15:38:19 +0100 Reply-To: workshop-networks <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: workshop-networks <[log in to unmask]> Subject: workshop "networks in the human brain" MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit Message-ID: <499626810.8000.1297694299153.JavaMail.root@zimbra> Dear all, please do not try to register anymore for our workshop "networks in the human brain" to be held at the Max-Planck- Institute in Leipzig, March 7-8. 2011. We are booked up ! Thank you for your interest, Gabriele Lohmann, Dirk Goldhahn, Robert Turner ========================================================================Date: Mon, 14 Feb 2011 09:44:47 -0500 Reply-To: justine dupont <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: justine dupont <[log in to unmask]> Subject: dartel misregistration - or not? Content-Type: multipart/mixed; boundary="_eb62bde8-154a-4a7d-9fd9-b2bc8bfd8e0e_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_eb62bde8-154a-4a7d-9fd9-b2bc8bfd8e0e_ Content-Type: multipart/alternative; boundary="_df7cf205-f75b-40c4-8a56-9204a9a37eab_" --_df7cf205-f75b-40c4-8a56-9204a9a37eab_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear experts I have run a DARTEL analysis and comparing the DARTEL template and the regular T1 canonical brain I get the result attached. I was surprised that the two images are so different in shape. My sample is 45 young healthy adults, roughly equal numbers of males and females. Eyeballing the original scans there are no 'outliers' or anomalies. Likewise there were no obvious anomalies looking at the various stages of preprocessing. So I am not sure whether something has gone wrong, or whether my sample, for some reason, had genuinely small brains, compared to the canonical T1, or perhaps whether there is some other explanation. Thank you Justine --_df7cf205-f75b-40c4-8a56-9204a9a37eab_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear experts

I have run a DARTEL analysis and comparing the DARTEL template and the regular T1 canonical brain I get the result attached. I was surprised that the two images are so different in shape. My sample is 45 young healthy adults, roughly equal numbers of males and females. Eyeballing the original scans there are no 'outliers' or anomalies. Likewise there were no obvious anomalies looking at the various stages of preprocessing.

So I am not sure whether something has gone wrong, or whether my sample, for some reason, had genuinely small brains, compared to the canonical T1, or perhaps whether there is some other explanation.

Thank you

Justine

--_df7cf205-f75b-40c4-8a56-9204a9a37eab_-- --_eb62bde8-154a-4a7d-9fd9-b2bc8bfd8e0e_ Content-Type: image/jpeg Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="brains.JPG" /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCANgAigDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDshfm9 h1tAhSO2eSFAW3EgRqck8c5J7dMfWsHSf+QNY/8AXvH/AOgiryaxBofhzxRrDWcd8sWpKojZyodX VB1H1HrXEp8cdKjRUTwXbqijCqLggAenSqUXa5Lkr2O0orkZPjZYwpG8ngqJVkXchNwcMPUcVa/4 W9Z/9Chaf+BTf/E1cKU5/Dr80TOpGHxaHSUVzf8Awt6z/wChQtP/AAKb/wCJo/4W9Z/9Chaf+BTf /E1p9Wq9vyI+s0u50lFc3/wt6z/6FC0/8Cm/+Jo/4W9Z/wDQoWn/AIFN/wDE0fVqvb8g+s0u50lF YEfxZs5Fz/wiVoOcf8fLf/E07/hatn/0Kdp/4Et/8TUuhUXQ1U01dG7Utv8A69fx/lXO/wDC1bP/ AKFO0/8AAlv/AImlHxWtAcjwpag+1y3/AMTS9jU7DujrqK5MfFm2Jx/wi1t/4FN/hT/+FrW3/Qr2 3/gS3+FXHB1pq8Y/kHMjqaK5b/ha1t/0K9t/4Et/hR/wta2/6Fe2/wDAlv8ACq+oYj+X8V/mLmR1 NFct/wALWtv+hXtv/Alv8KP+FrW3/Qr23/gS3+FH1DEfy/iv8w5kdTRXN2/xPtriQp/wjNsuBnP2 lj/SrX/CxLb/AKFy2/7/ALf4VLwlZOzX5ESr04uzZtUVi/8ACxLb/oXLb/v+3+FH/CxLb/oXLb/v +3+FL6rV7fkT9ZpdzaorF/4WJbf9C5bf9/2/wo/4WJbf9C5bf9/2/wAKPqtXt+QfWaXc2qKxf+Fi W3/QuW3/AH/b/Cj/AIWJbf8AQuW3/f8Ab/Cj6rV7fkH1ml3NqisX/hYlt/0Llt/3/b/Cj/hYlt/0 Llt/3/b/AAo+q1e35B9ZpdzaorF/4WJbf9C5bf8Af9v8Kkj8fW0i5/4R62HOP9e3+FJ4aquhUa0J OyZrUVmf8J3bf9C/bf8Af5qP+E7tv+hftv8Av81L2FTsaXRp0Vmf8J3bf9C/bf8Af5qUeOrYnH/C P23/AH+ahYeo3ZILo0qKz/8AhN7b/oAW3/f5qmt/GFtcSFP7CtlwM581jWrwNdK7j+KJc4pXZaop P+Eltv8AoDW3/fxqP+Eltv8AoDW3/fxqj6rV7fkZ/WaXcWik/wCEltv+gNbf9/Go/wCEltv+gNbf 9/Go+q1e35B9ZpdxaKT/AISW2/6A1t/38aj/AISW2/6A1t/38aj6rV7fkH1ml3FopP8AhJbb/oDW 3/fxqP8AhJbb/oDW3/fxqPqtXt+QfWaXcWp7f+L8Kr/8JLbf9Aa2/wC/jUo8TwDpo9uPpI1H1Wr2 /IPrNLuXqKpf8JRD/wBAiD/v61SReJIZM/8AEpgGP+mjf40nhqq6FRr05OyZZoqH+34f+gXB/wB/ G/xo/t+H/oFwf9/G/wAaXsKnY0uiaiof7fh/6BcH/fxv8aP7fh/6BcH/AH8b/Gj2FTsF0TUUW+sQ 3EhT+zYFwM53sf61Z+2w/wDPhB+bf41DpyTs0RKrCLs2VqKtLeQk4+wQfm3+NP8AtMP/AD4wfm3+ NNUpy1SF7en3KVFXftMP/PjB+bf40faYf+fGD82/xqvYVOwe2h3KVFXftMP/AD4wfm3+NH2mH/nx g/Nv8aPYVOwe2h3KVFXftMP/AD4wfm3+NH2mH/nxg/Nv8aPYVOwe2h3EtEi+zzSSRLIVdFG4kYyG z0I9BT90H/PnD/30/wD8VVnY8e5PsdovPzL9oxyPX5/rUF3cxWNnNd3MFmkEKF3bz84A9g2T9BzU cj8vvRfOv6TMuG/hGp6ybmO3jtLKK3Yb2ZVQMJWdiS3+yOpwAPrXJ23xJN7rJtrXw5p09iL0Wxuo tYVm2GYQiXygd2CWU+nI5wc16DLYxOLiObTNPYTr5c6vKD5gAI2sC3IwSMH1NZbaL4e0S3mvh4f0 O0SJN8kkSIDhSHH3Tk4ZVIA5yBjnFHI/L70HOv6TG2Nz9q8V39o8UYtre3jZI1zgsVkJJOc9h37f Wq2oeLtD0fUJrTVrdLAJD58U9wWEdwo+8IyHO5lOAUwG5GARzXQQxKHN1Hp9mkkq4Mnm7WcDI5y2 SOo/Om3FlBd+V9p0zTpvJkEsXmShtjjowy3BHYjmj2bX/DoXOv6TOdn1h7ltDaPTpNPW9d2khnXE oURsQGG5gvODjqOAcciijX7+C6s7Ge1jhBXUYohNHkn/AFmxwCSeDgjI6j1B5Klpp2ZSaauil44s rWw+GGsRWlvFBGZrdisaBQTvHOB9BXg0Xhr7PosesyXNpcoWULbhj8xzyMjv6j0zzxX0X4y0671b wLqdjYxebcyyQbE3Bc4fJ5JA6A14tofw18WaVrNteXOgwXkEbZkt3u4wJBjpkN+P4U5tqjeKu1fT 5LuTFR9r79+XS9vmHi3S4/8AhWvhm9B/0j/SC44GR5nUD8f0rrPB9po2vaBp+j6Xe2Fj4k3XBkjv dJS6F22NyYkdWEahVxx3ydp4zyuteAPHuuak15cabBH/AAxQxXEaxwp2RRu4H+TWtF4Z+IkGnHTo Wv47EqyG2TUVEZVs7htD4wcnI75NTldCdGjKNWSu23v3bdvO1zTMq8K04ypwdldfK+nppp8je1z/ AIR3S/DtlFfQaaPtfheB4LeGyAuWvGxtmMoQfLwc5fnB+U55tXS+ALnx/bkaa8V/Hqn9myaRGgW2 lYShFnJAwFC5JQfebAIxuZuNufA3jW88n7VZzT+TGIYvNvI22IOirl+FGeAOKP8AhBvGv277d9jm +1+Z53n/AGyPzN+c7t2/O7POeua9O0LfH+J5t5/y/gei2+gaMNVstPbSbDOpahrCJB9mRluFjZlQ NKRvtguMARBumeCTXAeC/wDiXeGPFniGDjULG2ht7Zz/AMsvPcozqRyrhRwQRjJ65qRPDPxEjguY Ea/WG6Z3uIxqKhZmYYYuN/zEjqT1p+heGPGugXVxNbaNDKlzbSWlxDNPGUlicYZTtcMOgOQQePrQ nFJrmX3+f9IHzNp8r+4l8R22ladqHg6SaxRbKTR7Ke8jt0CNNkneTjGWIHXOfeu30vRtK1bxNpmp WNxpOpaDPqM8Btho0duYm8iR0QnYDIAO57hTgnpxUGg/EVLOO1M2oiCIp5cX9pDamwgpgb8DBAI9 MCnyaH8Q5rmC5lm1F54N3kytqILR7hhtp35GRwcdazlZ6cyOyHwrQtWcthP4c8T61bR291dWUNpH BJcaTbwrHvmYP+5XdGSQB8xG7tx3reGLqTX9J8VabfLD5A059SQRQpGIpodoUoqgKu4HDEDJHek/ sP4h/bftvnaj9r8vyvP/ALRG/ZnO3dvzjPOOmambR/HL6TqFhLY+cdRkje7uZrlZJpRH9xCzORgH ngZ7ZxxRePdFjfDOiaXL8WbPS2sL4aeGLfZtUjCzZEJcb1HGNwzjoRjOc1d8OeHp/GC6ZLqE+npp lxeSWzrYWcVvcRyLC8gBIiAIwoPUjkdxxjP4S8aTaiNQmhuJLwMri4e8QyBlxtO7fnIwMemKvvpX xFkeN5LrU2aJt8ZbUgSjYIyPn4OCR9Ca6aUlyu00n6/1/SJY3QPC2j6tKLmSS7XSZryCwt55ZhFK 87KC42LHIOvIyVABALHkjVg+Hmk/atJ0yW4vWvNRmv4Bco6LHG1uzKreXtJIO0ZG8fWsOw8M+N9L 8z+z1u7TzMb/ALPerHuxnGcOM4yfzrSubT4gXWiW2lGKWOCLzvMaO8Aa581tzeafMw/JPbueua2n O792orevl/n94reRoa5otlrlhp6SG4ivLHwjDfRyrIpjZU/gKbc5OT827jjg45reL/CukaK+qand C7uY49QisoobdobbGbdZCx2xFe+MBV6ZJJNY3/CM+N/7t3/x7/Zf+P1f9T/zz+/9z/Z6VNHonxAh uZ7mKbUEnn2+dKuoANJtGF3Hfk4HAz0pRcYtWqK3r6B8iWPwxpsfg+PW9Ouvt0ywx/bCt0qm2d3I 2tCUzjjaG38nkDFYdbUXh7xpNBHaXgu5bSJQIoZL1WRMDAwu/AwMj6U7/hENd/58f/Iyf/FVLqQT d5p/M468JOeiMOitz/hENd/58f8AyMn/AMVR/wAIhrv/AD4/+Rk/+Kpe1p/zL7zH2U+zMOitz/hE Nd/58f8AyMn/AMVR/wAIhrv/AD4/+Rk/+Ko9rT/mX3h7KfZmHRW5/wAIhrv/AD4/+Rk/+Ko/4RDX f+fH/wAjJ/8AFUe1p/zL7w9lPszDorc/4RDXf+fH/wAjJ/8AFUf8Ihrv/Pj/AORk/wDiqPa0/wCZ feHsp9mYdWbf/Vn61p/8Ihrv/Pj/AORk/wDiqnh8Ka2iENZYOf8Anqn+NTOrC3xI2oQkp3aMuitj /hFtZ/58/wDyKn+NH/CLaz/z5/8AkVP8ay9pDujusY9Kn3hWv/wi2s/8+f8A5FT/ABpV8L6yGBNn /wCRU/xq6dSCktUJozKt6d/x8N/uf1FXP+EZ1j/nz/8AIqf41YsvDuqxTFntcDbj/WL/AI13zr0n F+8vvRlUi3B2QlFX/wCxdQ/59/8Ax9f8aP7F1D/n3/8AH1/xrk9rT/mX3nD7KfZlCir/APYuof8A Pv8A+Pr/AI0f2LqH/Pv/AOPr/jR7Wn/MvvD2U+zKFFX/AOxdQ/59/wDx9f8AGj+xdQ/59/8Ax9f8 aPa0/wCZfeHsp9mUKKv/ANi6h/z7/wDj6/40f2LqH/Pv/wCPr/jR7Wn/ADL7w9lPsyhRV/8AsXUP +ff/AMfX/Gj+xdQ/59//AB9f8aPa0/5l94eyn2ZQqxbfxfhU/wDYuof8+/8A4+v+NTQaTfJu3QYz /tr/AI0pVYW+Jfea0ac1UTaIaKt/2Zef88f/AB4f40f2Zef88f8Ax4f41j7SHdHfYqUVb/sy8/54 /wDjw/xo/sy8/wCeP/jw/wAaPaQ7oLBp3/Hw3+5/UVp1VsrG5imLPHgbcfeFXvJk/u/rXPVlFy0Z x1oSc9ENT74qWmrDIGBK/rUmxvStKU4qOrIjTl2G0U7Y3pRsb0rT2kO6K5JdhtFO2N6UbG9KPaQ7 oOSXYbRTtjelGxvSj2kO6Dkl2DW9X0zSr2T+0dRtLPzJH2faJ1j3YPONxGcZH51i+I7y2v8AwVf3 NncQ3Fu8J2ywuHVsHBwRweQRUWreHr/W5/N1K40u6IZmRZtKWQR7jkhdznHQfkKifwzqR0l9Lj1G zgsmUr5MGnrGqgnJwA/HJzXn8j8vvR3c6/pM6a+sLO81fUJLu2WcxmNU3Owxlpc/dI/uisLxFa2t t4X15ba3SFfIhOFLHkmTPLEnsPyqaew1y5upbiTUNOZpGJw+lo+3knA3MTjmq93omsXunz2Mmp2S QTjEiw6YkZPXHKsOmTRyPy+9Bzr+kzV8SPZReF3fUb6awtFtyXuYZmieP52wVZec5xgc5PGDnB4/ wtqOr3fiC3TxRcXdrd/ZydNtmj+zpdp/FLIquQ02MZjPCA5A5yOrvYNanlX7LqUFvAi7VQ2m89SS SS/PJPYcY+tVvsPiH/oN2/8A4Aj/AOLqpxbk2rfeiYySik/yZi24z4Z0sH/oLr/6UtRVy7059L0b S7V5RKy6nCxcLtBLTFumTjr60VM9/u/IqGx2C/8AHlL/ANdE/k1c14h8Sf2Je2Vt/wAS5PtMcsnm 3999mQbCg2g7GyT5mcccKa6Vf+PKX/ron8mrNlsPM1q11HzceRbzQeXt+95jRtnOeMeX09/bmCjG i8VxnUo4pXtBbC3kkke3d5suBbFFQ7Ru3faMAKCW+TGCSo3LHULbUYGltmchW2OkkbRujYBwyMAy nBB5AyCD0Irn9S8GRalqF5dS3KMtwxcQyQB03f6KVDDPzLm1GRxkORkYydbQ9ITRrJ4FW0UySGRl s7VbeIHAHyoCT0UZJLHOeQMAAGnXLaD4wXV9POoT/wBmRWohSR1tb83M0bvjZG0YjGGJyAASSwwA a6muatPDmpxaHb6TcapaS29pHALdo7JkYPCyNGzkykMMoMgBSc8EUAVW8cJHPeFrZ2ijukhjRoZo 5yhNqrN5ZQkkNck44JwoAOSR1kMqzwRyoHCuoYB0KMARnlSAQfYjIrlj4PuZ72a+u9The7eQzKYr UogcG1KfKXYkA2q5GQSGOCuM11MIlWCMTOjyhQHZFKqzY5IBJwM9sn6mgCtql/8A2ZYG7MW9EkjW T5toRGdVZyccBVJY9sKeR1rPHia2TULmCcJHBC0ipKGZzNs8kHYoXDHzJjHgHduXABJIXWvLSC/s p7O5Tfb3EbRSpkjcrDBGRyOD2rnLrwZFeWNpBPcpI0MMglMkAZJ5nminZ2TONpkiJKDs5AK4FAGn /wAJLpIgkla5eMRQyzyJJBIjxpEFLlkKhlIDocEZIYEAg1oQXcFxLcxRPue2kEUowRtYor49/ldT x61gW3g62h+xswtEeC4MzpZ2gt4mHyEKqqcj54oXLEsSUIyFbaNLQ9Fg0Oye2gO4NIWzgjCgBI15 J+7GkaZ77cnkmgDTrnbPxWht1OpWrwXEk1xHHDZJNeFlhk8t2OyMEDd6joR3OB0VYGj+HrnT7uG5 ur+G4eP7XgRW5iB+0SpKert0ZWH0YemSAXP+Eg0sy7Fut6CPzWuI42aBF2b8tKBsX5cNyw4KnuMw t4q0eONWmuJoWaQRLFPayxyMxVmUBGUMd2xgvHzFSoyeKyYfAyi2jtJr1EtxYCzkazgMMtwPI8nM rb2WQAElQVypxg4B3WtO8JJY3dtcg6dC8NwJilhp62yOBFLGARuLZ/fE5LEYUAAZJIBpaPrMerrd BYJontriWBt0bhW2SOgKuVCtnZkhSducGtOs/TNPl003MQnSS1kmeaJPLIdGkdnfc2cMNzHGFGBw c9a0KAMNNZvk128s7uzsYLK0hW5luzetkRMZApKmMAH90d2WwAepqYeJNMMDSF7hWVgvkPaSrOxI JG2IrvYEKxyFI+Rv7pwXuhRX8+qmaZxFqNglk6oAGRQZcsCc8kS+nG3vmqb+Hb6a8GpzalbnVI2j MUiWjLAFRZVAaMyFicTychx/DxwdwA9fFFr/AGjcK0m60W3geIRwu8zyO86unlgFiV8nlduVw+eh xuQyrPBHKgcK6hgHQowBGeVIBB9iMiuWfwa+2ZkurSSaba7NcWjOI5PMnlZ49sish3XB2kNlQvUk 5HTWdv8AZLKC286abyY1j82ZtzvgY3Me5PUn1oAh1S//ALMsDdmLeiSRrJ820IjOqs5OOAqkse2F PI61nt4ki8+5hiiSWWK/htI0WUFpVcqGkAx0Q+dkc/6h+Rg41ry0gv7KezuU329xG0UqZI3KwwRk cjg9qxrDwtBZ3tjePN5txBGxmfaV8+clz5uM4Xma4+Ucfvf9lcAD9P8AFemXwt1EriSVYtzrBKYF eRFdV81kC5IdcA4J3AYBOKhPi2zbV4oY3f7EbWaZpmtpQJGV4lTymIxKG8w42bskrjqMs07wl9g0 Uad9u8zFxZz+Z5WP+PdYFxjd/F5HXtu745gXwfc7bOJ9ThNvp1uILFRakMu2SGRDK2/D4MCAhQmc nG3jABrf8JLpIgkla5eMRQyzyJJBIjxpEFLlkKhlIDocEZIYEAg1oQXcFxLcxRPue2kEUowRtYor 49/ldTx61hr4VWW8ivr25Sa7N19onaOIojgLGFRV3EqA0ED5JYkxkdGIq/oeiwaHZPbQHcGkLZwR hQAka8k/djSNM99uTyTQBp1ycfi65ntJri3s9Ok2yQRC3/tE+fE8sqxqs6CM+URuO4ZYgqRz1rrK 5q+8MXOssjaze2lx5exVSGyKIyedFK6urSPu3eSqjoAC2Q3YAj/4S6WHWbXTLvT0jkaZ4buWO4Lp C2IfL25UFwzXMS9FwSeoGaktPFv2vw7rWsCx2x6f5jwr5uftEQhWZHzt+TcrqcEEjPPPFXJfDWny SRRpBDDYJZXNm1pDH5asszRlsbcbfuHp/ezkY5LrQftNh4gtftO3+19/zeXnyt0CQ9M/N9zPbrj3 oAp2Hi2C4YG4utDEfmbGez1M3AQeXJISx8tQvEZ6kDAY5yADftfEmmXd4toj3EdwzKqxXFpLCxLL Iw4dRwRFJz0+XHXFGvaFFr9tDbTTPFEjSF9gG5leCSIgE9DiXOcHp0rDstH13UdSl1e/eGzvIvJF qHthjKCdWLxpM+QVuDgiQHI5GB8wBuL4m0drqS2+3IJY1d5AysoREaRHdiRgKGicFjxnHPzLm1Ya pa6l5gtzMrx43xzwPC4Bzg7HAbBwcHGCQR2NY1l4S8u01WC9vvO/tO3eCZoYvK275Z5GK5Zsf8fB ABz93vni5oOgpov2hgmnI820FbCxW1TC5wSMsxPzHktjGMAckgGzXNf8Jrpmzy/tFp9v/tH7D9i+ 1L5v/Hz5O7b16fPjHTv3rpaxv7B/4lP2H7T/AMxH7dv8v/p6+0bcZ/4Dn8cdqAIbvxRamBDYSeZI 1xbovmwuiyxyTxxs8ZIAkADj5lJHKnoRm5pev6drG37HJMd8Ylj863kh8xOPmTeo3jlclcgblz1G cOx8CQWEccUL2MSwtB5ckGnrHK6RzRyfvXyS7HygMjaMkkqeALXhbQ9QsbLSZdVnhNxZ6ctpHDDF s8sMI94dt7ByDEoBXaPvcHIwAdLRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAY/iFGkj0xFGWbUrYAe/mCipNY/12j/9ha0/9GCigDo9OhS4/dSZ 2mVcgd8K5rX/ALJsv+eH/j7f41maP/rh/wBdB/6A9dBQBS/smy/54f8Aj7f40f2TZf8APD/x9v8A GrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtF AFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/ AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f +Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/ jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/Z Nl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/z w/8AH2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8A H2/xq7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/x q7RQBS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQ BS/smy/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/s my/54f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/5 4f8Aj7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8A j7f40f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f4 0f2TZf8APD/x9v8AGrtFAFL+ybL/AJ4f+Pt/jR/ZNl/zw/8AH2/xq7RQBS/smy/54f8Aj7f40f2T Zf8APD/x9v8AGrtFAHGeMLSC1m0HyU27tWts8k/8tF9aKm8b/wCu8Pf9ha3/APRi0UAXdH/1w/66 D/0B66Cuf0f/AFw/66D/ANAeugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5Lxv/rvD3/YWt/8A0YtFHjf/ AF3h7/sLW/8A6MWigC7o/wDrh/10H/oD10Fc/o/+uH/XQf8AoD10FABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FAHJeN/9d4e/7C1v/wCjFoo8b/67w9/2Frf/ANGLRQBd0f8A1w/66D/0B66Cuf0f/XD/AK6D/wBA eugoAKKKKACiiigAooooAKKKKACiiigDP1vW9O8O6RPquq3H2eyg2+ZLsZ9u5go4UEnkgdK5D/hd fw9/6GD/AMkrj/43R8a/+SRa5/27/wDo+OvkOgD68/4XX8Pf+hg/8krj/wCN0f8AC6/h7/0MH/kl cf8AxuvkOigD68/4XX8Pf+hg/wDJK4/+N0f8Lr+Hv/Qwf+SVx/8AG6+Q6KAPrz/hdfw9/wChg/8A JK4/+N0f8Lr+Hv8A0MH/AJJXH/xuvkOigD68/wCF1/D3/oYP/JK4/wDjdL/wuv4e4z/wkHH/AF5X H/xuvkKnn/UL/vH+QrSEOaMn2V/xS/UR9c/8Lr+Hv/Qwf+SVx/8AG6P+F1/D3/oYP/JK4/8AjdfI dFZjPrz/AIXX8Pf+hg/8krj/AON0f8Lr+Hv/AEMH/klcf/G6+Q6KAPrz/hdfw9/6GD/ySuP/AI3R /wALr+Hv/Qwf+SVx/wDG6+Q6KAPrz/hdfw9/6GD/AMkrj/43R/wuv4e/9DB/5JXH/wAbr5DooA+v P+F1/D3/AKGD/wAkrj/43R/wuv4e/wDQwf8Aklcf/G6+Q6KAPuvRNb07xFpEGq6VcfaLKfd5cuxk 3bWKnhgCOQR0rkP+F1/D3/oYP/JK4/8AjdHwU/5JFof/AG8f+j5K+Q6APrz/AIXX8Pf+hg/8krj/ AON0f8Lr+Hv/AEMH/klcf/G6+Q6KAPrz/hdfw9/6GD/ySuP/AI3R/wALr+Hv/Qwf+SVx/wDG6+Q6 KAPrz/hdfw9/6GD/AMkrj/43R/wuv4e/9DB/5JXH/wAbr5DooA+vP+F1/D3/AKGD/wAkrj/43R/w uv4e/wDQwf8Aklcf/G6+Q6KAPrz/AIXX8Pf+hg/8krj/AON0f8Lr+Hv/AEMH/klcf/G6+Q6KAPrz /hdfw9/6GD/ySuP/AI3R/wALr+Hv/Qwf+SVx/wDG6+Q6KAPr3/hdfw9Of+Kg6f8ATlcf/G6T/hdf w9/6GD/ySuP/AI3XyMn3JP8Ad/qKZWk4csYvur/i1+gkz68/4XX8Pf8AoYP/ACSuP/jdH/C6/h7/ ANDB/wCSVx/8br5DorMZ9ef8Lr+Hv/Qwf+SVx/8AG6P+F1/D3/oYP/JK4/8AjdfIdFAH15/wuv4e /wDQwf8Aklcf/G6P+F1/D3/oYP8AySuP/jdfIdFAH15/wuv4e/8AQwf+SVx/8bo/4XX8Pf8AoYP/ ACSuP/jdfIdFAH3H4c8UaN4t0+S/0O8+120cphZ/KePDgAkYcA9GH51sV5D+zn/yT2//AOwrJ/6K ir16gAooooAKKKKACiiigAooooAKKKKAOS8b/wCu8Pf9ha3/APRi0UeN/wDXeHv+wtb/APoxaKAL uj/64f8AXQf+gPXQVz+j/wCuH/XQf+gPXQUAFFFFABRRRQAUUUUAFFFFABRRRQBwHxr/AOSRa5/2 7/8Ao+OvkOvrz41/8ki1z/t3/wDR8dfIdABRRRQAUUUUAFFFFABTz/qF/wB4/wAhTKef9Qv+8f5C t6PwVPT/ANuiJ9BlFFFYDCiiigAooooAKKKKACiiigD68+Cn/JItD/7eP/R8lfIdfXnwU/5JFof/ AG8f+j5K+Q6ACiiigAooooAKKKKACiiigAooooAKKKKAHp9yT/d/qKZT0+5J/u/1FMret8FP0/8A bmJbsKKKKwGFFFFABRRRQAUUUUAfTv7Of/JPb/8A7Csn/oqKvXq8h/Zz/wCSe3//AGFZP/RUVevU AFFFFABRRRQAUUUUAFFFFABRRRQByXjf/XeHv+wtb/8AoxaKPG/+u8Pf9ha3/wDRi0UAXdH/ANcP +ug/9Aeugrn9H/1w/wCug/8AQHroKACiiigAooooAKKKKACiiigAooooA4H41jPwj1wf9e//AKPj r5F8pvVP++x/jX1z8a/+SRa5/wBu/wD6Pjr5DrSDpr4036O36MTuP8pvVP8Avsf40eU3qn/fY/xp lFXzUP5X96/+RDUf5Teqf99j/Gjym9U/77H+NMoo5qH8r+9f/IhqP8pvVP8Avsf40eU3qn/fY/xq e1sLi7cLHGxyewrfg8K3ACkRlnxnGOc0c1D+V/ev/kQ1MC30+5uX2xRM/wDufN/KtI+HLwL5bQzh kAdswPwDx6f7Jr0nwu50/Q5wIRHNH8xJGOKg0jV5r7Xbqdpl2eUiNnGCMv8A4mt6UqPJP3Xt3X8y /uid9DgR4Vumj8xFuCuM5+zyEfntqq2g3W0lIpmx38psfyr2B3uB5mmxnC3H7wNnGF6/0qhe3Uul +QYZB5B4PvWHNQ/lf3r/AORHqePSWssTFXUKR/eIFM8pvVP++x/jXserabFquHMAbcuQ4Fcdqvgu W3QvHhvTAo5qH8r+9f8AyIanG+U3qn/fY/xo8pvVP++x/jU93YXFm+JY2APQkVVo5qH8r+9f/Ihq P8pvVP8Avsf40eU3qn/fY/xplFHNQ/lf3r/5ENR/lN6p/wB9j/Gjym9U/wC+x/jTKKOah/K/vX/y Ian178FRj4R6GP8Ar4/9HyV8iiNiM5X/AL7FfXPwU/5JFof/AG8f+j5K+Q6iLgn7yb+dv0Yaj/Kb 1T/vsf40eU3qn/fY/wAaZRV81D+V/ev/AJENR/lN6p/32P8AGjym9U/77H+NMoo5qH8r+9f/ACIa j/Kb1T/vsf40eU3qn/fY/wAaZRRzUP5X96/+RDUf5Teqf99j/Gjym9U/77H+NIqM7BVUknsK2tH8 K6vrFwIrawuH75EZwKOah/K/vX/yIamN5Teqf99j/GnC3kJ42/8AfQr1zTfg/dmLdqF1FacZ2scH 9a3tO+Hnh/T7QyzzpPs5Y7uBijmofyv71/8AIhqeFJYTP90ZPoOalXSbhnC7WyewQ5r2XVNV0rQL Yy2dtFt6Kf8AP0rMsNXtfEdu5ARLhT8oGM9KOah/K/vX/wAiGp52PDd6iDdDcAy/KuYH543ccc8A 1QudKu7b78TqO25Sv869OupbxL3TMu4IuQAD/usP5ZrpryG01Df9qC+UgA3N2Nb1ZUOSHuvbuv5n /dEr6ngJhcHB2j/gY/xpPKb1T/vsf417FqXw+tb7y2s5ULuuQvf64rzfWvC9/pNxIrW8mxWIztOK w5qH8r+9f/Ij1MXym9U/77H+NHlN6p/32P8AGmEEHBGDRRzUP5X96/8AkQ1H+U3qn/fY/wAaPKb1 T/vsf40yijmofyv71/8AIhqP8pvVP++x/jR5Teqf99j/ABplFHNQ/lf3r/5ENT6e/Z0G34f6gDj/ AJCsnQ5/5ZRV67XkP7Of/JPb/wD7Csn/AKKir16sXa+gwooopAFFFFABRRRQAUUUUAFFFFAHJeN/ 9d4e/wCwtb/+jFoo8b/67w9/2Frf/wBGLRQBd0f/AFw/66D/ANAeugrn9H/1w/66D/0B66CgAooo oAKKKKACiiigAooooAKKKKAOA+Nf/JItc/7d/wD0fHXyHX158a/+SRa5/wBu/wD6Pjr5DoAKKKKA FVSzBQMknArp9M8G6jdWxu/KBjAzzmt74deBU12Vb28LrbxkOSO4r1v7RaafdrpccaC1ZMZPU0Ae WaZpsdhCN65k9a67TtHM1sblCCy4bFdG+gWDxkqpZCfy/SpXtotEsJrqMErFGWZT0wBQByHizytB 8OTXRYB7gbFx1JP/AOquD8NadfakJDanAwhc+xLf4GoPEXiO98ba/DaABLfzQqRrnA5xmvU9F8Of 2ZqUlhbdUsrZmJ6li82f5VvR+Cp6f+3RE+hmtMsXjfTNNYZzabGPvsNcx4l07UrJXALPbROT7iuq vNB1T/haNi4BICEhj6bTXZS6U0rzRXkCtDKNpwOawGefeC9Rj1zTZbNTi5hHQ+n+TWpqdhNhFjH3 B8wPrXnGsS3XgPx5dpYMRGrAqH/iUgH+teqQawmuWqy2iZ3r83HIoA47WtD+0xIrBRIc7SK8/wBW 0O50+6ZHXHf617dHAHmjEoGSelXF0XSNQuJPt6ZVRgYwKAPm6iu++IXggeHbkz2oka2OCCccZrga ACiiigD68+Cn/JItD/7eP/R8lfIdfXnwU/5JFof/AG8f+j5K+Q6ACiiigAooq9pmlXOp3UcMEZZn YKAOpoAqxQvM21Bk12Phv4baz4hIMcaxJnBeTIH8q9E8N+ANK8LacNW15mL4wsRxjPX+lVtV+LS2 we10u0hjjUYVlB/xoAu6N8NtH8NXC3Oq3UU7RDcyA9xz7VpX3xG0HTLeRdLt8S9AQB0/OvIJPEGr azdyGWZv3jHIHoamstJ33qoN7k9qANybxNq+sXT3G9wuOh4q3a+LY7jRLu0YuLgRsvPerEejSQxr CkRG4da5u68LapBqpNtCxV36/WgDnNQ1WWeE2zliAcjNVtD1Z9IvRKM4ByRXe6t4Bu305Z1tyJie QOhqjonwr1XVZma5R4Il74oAtafrb+I/E2l+XAUgSXv3baT/AI11uq4MMiqxVPNG4/0rQi8K2fh1 vD9tbKTM1yd7vzn/AEeWq3iwWun2cUe4yzyTglR+Nb1vgp+n/tzEt2WrW5WG8jlI3DywoC1NMlpf QTWtwFJkBAYnoTWHc6gLPTzcBQGKZUelVdI1eOeBDdORMxyo6DPasBnF+MPBlxpDeeu1oWbCsOlc QylGKsMEV9IX1imu6MbCQlpSN69OO39a8J17SH0+/lhJBaM4PvQBiUUUUAFFFFAH07+zn/yT2/8A +wrJ/wCioq9eryH9nP8A5J7f/wDYVk/9FRV69QAUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/8AXeHv +wtb/wDoxaKPG/8ArvD3/YWt/wD0YtFAF3R/9cP+ug/9Aeugrn9H/wBcP+ug/wDQHroKACiiigAo oooAKKKKACiiigAooooA4D41/wDJItc/7d//AEfHXyHX158a/wDkkWuf9u//AKPjr5DoAKkt4mnu I4lGSzAAVHW54Pg+0eKLFDjG/PNAH0JpttFoXhTT7OJBHM9svmYHU456V594x8Qvpd9ayKclH5z/ AFr0/Wb23hukheMl1UKMrx6da8q+IVn9vtt6oA4fjFAHR6P4zsruASJMoDfeQnvVXx146tY9Bmsb SUNNPGUbB6ZFeIOskLlWyrD3ppJJyTmgDW8M6pHo+v299ModYzkgjOa9r8M+Jb3xb4ouLmwhEcQg gjcjsFaQg/8Ajx/Kvn2u58BeJNf0u6aHRoRO82FdOOi5I6/7xrej8FT0/wDboifQ+kZYR/wkVmSg LiJhuxznFcf4k8ZXHhTVgL+3zaSk4YjPSu3tbiWSwtLiVAt8bcMYzjIYryK+d/if4g8S3l0trrFs LeDcxiXKnP5fhWAzD+IviC08SeKZL6zUCHYqg4xnAFdN8LfEEFrHPa3UmCc7NxryylBKnIJB9qAP pOG6tkYyoU56O1c/qPiC2fVYdPtbjd837wjvXjFquo6jKlrbGaZzwEU16l4P+GWoQFLzVI2jc8hA wOP1oA9H1yyg17wteWrRpIypuViMnjmvmG+tza3s0JGNjEYr6m0ayeK5wysvylfbGK+fPiHbpb+L rpExjdnigDlKKKKAPrz4Kf8AJItD/wC3j/0fJXyHX158FP8AkkWh/wDbx/6Pkr5DoAKKKVVLsFUZ JoAuaZp8upXqW8KF2Y4AFfQmn6RongHw/FczRRPfGEMCyAkNisj4WaBH4d8Pz63qkQR2f91nkkYH 9a5DxRfz+J9dnMb5TzCBk+9AFDxL4n1LxFfFGnl8jdkJuOKij0qKGyD4Ekh7EZrp9D8M3OxI0tjI 7HBOOn516XongxLaMNcxJ5pPTOcCgDzrwp4TuNQeNhZCNCRlyvavWNK8KaZpsYK20TygcsUFXNW1 7RfDOnlr65jhCJwg5J46YFeE+M/i1d6qGtdKcwwZ5YDBNAHs+qXuhWLb7x4Y2HGMCseTxx4VW5Rf PgOzGDgda+ZJri4upC80jyMf7xJpoglPSNqAPsfT9T0zU7dWhlhZeuOOKlu9S0uwjYz3EKAdsgV8 cILyL7jSL9GqOSact+8kkLe7GgD6A8TeLRrWsabY6SuZFuyFkHHWKROv0Yn8K6GPwMsixPcuZGAy d/PNfO2g+J9Q0S6guIHB+zP5qgjvgr/JjXr3hb40Q3kzx6wRCAhIIUnJz7Ct6vwU/T/25iW7KHxO uo9NSDT4AofALYA9a3/D2g2K6DZ3csYeUxK/I74Brhdcvk8Y+NYZbaNpLZNqkhTjgmvdLG2hi0yB Fj2rHGBkj0FYDOY8m4RJJkUxrjA+lYt54a0rVrGdXiU3uCd5UZNaPjXxDDpekM6zL5rvtAHJ71xu geLI52XzGIYtzgGgDyzWNPfTtQlhddu1yuPTBrPr2T4m+HDd2g1a0jyhXex9K8cIIOCMGgBKKKKA Pp39nP8A5J7f/wDYVk/9FRV69XkP7Of/ACT2/wD+wrJ/6Kir16gAooooAKKKKACiiigAooooAKKK KAOS8b/67w9/2Frf/wBGLRR43/13h7/sLW//AKMWigC7o/8Arh/10H/oD10Fc/o/+uH/AF0H/oD1 0FABRRRQAUUUUAFFFFABRRRQAUUUUAcB8a/+SRa5/wBu/wD6Pjr5Dr68+Nf/ACSLXP8At3/9Hx18 h0AFdd8NrL7b4ytFLbQpzXI16Z8F7OK48TvLJ1jX5frQB6X47vprZ5JFtsiMHDZ+9j8K8i1LxRcX GVktyFB/z2r3658Py6lcubuVZItxwuMcZqtP8NtEuYGVrUBmHUMaAPmTUZoLr94Bscdqzok82VUH c10/xB8Ojwz4nexU/IUDr+ZH9KpeGdGm1W5kESFsKVHHc9KAM5pYifs6xDbnGe9SWl9d6Ncx3FnM 0cqOcMD9K7OT4aanZwfaDbtK3ZVBrl7zw/qqSNHJaSKyncQykYB4H/oJ/Kt6PwVPT/26In0N0fFT xEFB84GQDAfNc5q+u6l4jvUl1C4Mrjhc9qkPhq/EIlwmCM43Dio7TQdTnnHk2skmD/ACawGVhLHA xhMYZehY1WljMUrIe1d1H8O9TvoBObWSNj1BU1geI9Gn0x4nljZSww2R3oA9N+D+h2dtbXGqXDI8 uV2hgOBzXqSa7ps07Q/a1jkHbdXy3pniXVtNgNtaXJSNyOMA13/h7wF4i8QQpqM128Il5z0oA9qi eIzCKK6Rt+eVIzXzx8V9MbTfF0mWLCUbhkfT/GvYtE8FXmk3MNx/aMszo2cO2R6VwXx6gCarpkxU BnjYE+uNtAHj9FFFAH158FP+SRaH/wBvH/o+SvkOvrz4Kf8AJItD/wC3j/0fJXyHQAV03gPQ/wC3 vFFvanIQfMxxnjIrma9b+CFtF/ad5dOMtHEcfmKAO+8WXKWlpFpMEZZVjxketc3pfhu1+VwSJCf4 h1PvXTs1td6o8pkBKnGHHpW/HY2kzRMsYVlIPAxmgCzpWmCztY40ID4yTisvxf42tfB1rt2m4vJB lUz0rpLm4FlYzXWPuISPyr5i8d+IZtR1mWd23SH5RnooBPagDI8TeIL3XdTlubqZmeRiRGDwuT0q Xw94L1HX5wkMbBeudvHWn+DdBbX/ABDZ27IX8yVS/wDu55r6w0jw9p3h7TgIIEjAGCx70AeDj4PX 1tb7jMA3ps/rmuX1LwfLZzGGS9UMDjA6/lmvXPiL8TYdDV7SxKNcsMA4Bx/nFeHR6zd3t295cTF5 ZGLMT9c0Aath4GmvpRHDqB34yAyf/Xqvr/gvU9DA+1xebGRkSIOlbvh7VYrfUIJvPIPQgn3r35NJ 07xDosYmjWSN0wcnpQB8dvF5XmDsV4/MU23jaWdETOWYCvQPHfge907xO1raWxMM7FIdoOM5z/IE /hU/gbwBLqHidLe4Q+XAvmSMD3BHFb1vgp+n/tzEt2es+AvB1roGhQzyBZZ5xvJK9MgVo+I9aNlZ SQ26GSZwUVE7E1tlUtbeG0QcIAAT0ArJvr/RbIyTXLJ5gzyW5zWAzxTW/Duu3hM16SqH5gnXFQeE jY2V28d+CrI3GeM17FBquma0jpAyOx6gkVzuveC7G7h85YSko7qxzQBpTXFrqelPZuAI7qMonfAI wK+fvE+ktpGrSwHoDwcda9gsdOntHhWedysZAQfyzXM/F6wjT7FfouDLwx9etAHldFFFAH07+zn/ AMk9v/8AsKyf+ioq9eryH9nP/knt/wD9hWT/ANFRV69QAUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/ 9d4e/wCwtb/+jFoo8b/67w9/2Frf/wBGLRQBd0f/AFw/66D/ANAeugrn9H/1w/66D/0B66CgAooo oAKKKKACiiigAooooAKKKKAOA+Nf/JItc/7d/wD0fHXyHX158a/+SRa5/wBu/wD6Pjr5DoAK9g+B CRtqd7uxvxx+leP13/wm1pNK8VRxyZCzHGaAPb9V8d6dol49vfBkKsRuxxWfdfGXw7bW5MU4aTHC g81teI9L0bUkkF3b7yy53Y45r581bwfcah4imtNGtyyg/wAXAAoAyvFuvS+K/Est8FJL4RFx2/ya 96+GPhu00PQYJ7sxxzModi7AfzrxP/hDtS0DXrOPVYtkTMGLISeM19LReHdN1LSIFWTdG0K4wR0x QBn658RNI02ZLOykS7uC23ZC24j8q8l+LWu3EmpjcjxNPZQ5UjHR5T/7NXqtn4C0DQr8X4jzKAcD GTXmHxXs7jVvFBu0g2wwW0I2nqQWkAP5g1vR+Cp6f+3RE+h5SLm8EPlgvtPtXo/wd1KeDVrsIHch FJUDPGTVNLvT105LR7A/a/LADbRzWv8ADLStSj12fUbSIeUgAkQ/xDJrAZ63pHxG0a8uWsryeO2u EO0pM23+dZfxG8OWXiPQJvskkLzgbl2sCetTah8PNE8RXR1F0MM0vLDjIPStqz8J6botozSyZRQc sxoA+Rrq2n0vUGhlUrLE3Q1714M+LelpodvZ38ojmjXb8xx3Neca/wCG7rxH4uvV0WMyQq+NzHAq jo/hWaz8Uw2WsW58sEbselAHvNv42sdTu447H98SRjbyB+VcJ8fgu3RWOBIRISO/8NenaJ4f0DT2 R9MVAQM1438c9Vivtfs7aNifIRs/jigDyiiiigD68+Cn/JItD/7eP/R8lfIdfXnwU/5JFof/AG8f +j5K+Q6ACvW/guFaa/UEbzERj8RXkldh8N9Z/sjxPHvbbHKNjfmKAPZhq+lW+oNb3UIilJ6v0NdV p0kexZLZgyMMkD09q4Xxro76paJfWTIdq8nua4/R/Hl7ocqxTLK6RnaVA9KAPc9UT7XpVxHG3zFT x+FfLHjHTZ7DXJRJGyg8jIx3NetyfFe0lTcltch+4Kg5/WuK1/U5fFk37nTpmkxjcQKANr4F3lim veVcSokpwEDMBnrXvXi9Libw7cC0BeQqdu0ZzXz54V+Emvy3cF+tzFZkESJuzkd+wr3bQ01mxjW3 1O5ivEAxvjyD+OaAPkPXoLuDVplvIpI5c8iRSD+tZqsynIJFfQvxXsfCNxq6/aZzFemPkIoOeT1/ WvEbvRNiySW0wkRSSFIOSvrQBQt7hllUlj9c1778OPGaweF7qJmTz4GyEZsZGOtfP0Uas3L7SOxF b2kXsiCWLdgsO1AHqM/jlfEHjOwlUMIoZ2XCjrmKRf616Vo9xbwwiOFEiRuTyBk14B4FsbnU/E9j b242brkjzG6D5HJ/QGvoiz8NW9sFMsm9wME1vV+Cn6f+3MS3ZBqmm/2q6qbxY4hwwVhzXF+K/BIk gWKyheVerOFz9TwK9JOi27psXIz71ZjsGiTar5GOhrAZ4xL4HTSNH/tG2cpKoGe3asjTvGGoXl5H BOjvsOM4PNe1ato63NhLBIAquevauUtfBMdt++R41KnINAGlDEl1YIZAN5UEjuCa8o+LksUVjZ2Y cGRXyRnkda9Y1LUbTSrKWeaUHy4ycL6ivm/xjrh1vV3lydgPyg0Ac7RRRQB9O/s5/wDJPb//ALCs n/oqKvXq8h/Zz/5J7f8A/YVk/wDRUVevUAFFFFABRRRQAUUUUAFFFFABRRRQByXjf/XeHv8AsLW/ /oxaKPG/+u8Pf9ha3/8ARi0UAXdH/wBcP+ug/wDQHroK5/R/9cP+ug/9AeugoAKKKKACiiigAooo oAKKKKACiiigDgPjX/ySLXP+3f8A9Hx18h19efGv/kkWuf8Abv8A+j46+Q6ACp7S6lsruK5hOJI2 3KagooA+ivCXxJ03W7C3tr9kiuhGEJPc4rstHh0OymkntZ42mk4LEivkRWKnKkg+oNWodV1C3IMN 9cxkf3ZWH9aAPrvUtC0vXOb6RWGMDBHSptH0K10zatrc3EiLwqswKgflXz58OtV1HU9aSC91G5lj zjbJKSP519G3l5Fo/h+a5RR+5hLcd8CgCzcLbQt5sxG/oM15v4xmhl1y+GwMWsLQKB/10uP8RXj/ AIi+IOvavq7umo3MEasdipIRjmtPw9rt/eJfS3lzJczeUiCSRssAC5Az/wACNb0fgqen/t0RPoLd FB4stmKAIqbTj1wa9T+H00Nvb3WAA4IH1615BLdqbGSVv9cG4bv1qSfxRf2GjR/Yp5IZX4Z1OG6V gM+lbaW2ujlSFk7gHmotb0uHVrUw3ckyRd/LOM/WvlfTfG2v2F8LhdTumYtlg0hOa+l/DPiePXtJ jmcfM4GVNAC6d4f0vRxmwIXP3tx61U1XS9JnvUupWj3jg8j0rC+I0klrapJpsrQyHORE22vn7Udf 1lrqRJNRu+DyPNNAHvOt+KNI8K6fcTwSxvc4wqA5BJ//AF18/a3rE+uanJeT43MeAO1UZZ5pjmWV 5D6sxNR0AFFFFAH158FP+SRaH/28f+j5K+Q6+vPgp/ySLQ/+3j/0fJXyHQAU6ORopFdDhgcg02ig D1Lwr8SxBYfYNVjWRM4DHsPzrrrPUvCN5IsjpEWc5we1fP8ATlkdfuuw+hoA+mRL4P4xbWzYHXH/ ANatK11XwvYrmBbZCOeK+WBdXC9LiUfRzQ13cuMNcSsPdyaAPqCf4l6NC3lJcxbs7cCsnxH8RX0z TmuLAo80vyhW6Dj2NfOAZg24E7uuc81ZS9maUGaaR19GYmgDV1rUb7WNR+1XLmSeQ5Zh2rpmtrPS /C63JcC6ePPPXOKytNu7XygBFGW9SBmtTUo4dT0WYDBnjQlfXgUAcpeQW7wLcIMSt1AqDTrgQ3I3 j5anhxJbbSPmTgimtCjZ2qOlAHsHgWG10+60WSLkyXrSbvUG2n/xFdB4x+IL6BIEjjDNnof/ANde P6Dq93YG0VJmYQyl0BP3TsYf1p+r382t6jGkjPIxPPet63wU/T/25iW7PTtI+NFnIQL2Hyz612uk eOdP1mSKGxlWSaUgFc/dHrXhup+G7Cy0pZ5flkYdMVzOjz6pp2o/aNKkniZW4aIkHFYDPsCSWPAi Zgz+grL1WMJCdgO7FcZ4R8abrJFurGd7sjDytySfrXdpdedZyXMkXloATz6UAfNvjvxTcLe3VggC ncyOPSvOSSTknJra8YXQvPF+qzKcobl9v0yaxKACiiigD6d/Zz/5J7f/APYVk/8ARUVevV5D+zn/ AMk9v/8AsKyf+ioq9eoAKKKKACiiigAooooAKKKKACiiigDkvG/+u8Pf9ha3/wDRi0UeN/8AXeHv +wtb/wDoxaKALuj/AOuH/XQf+gPXQVz+j/64f9dB/wCgPXQUAFFFFABRRRQAUUUUAFFFFABRRRQB wHxr/wCSRa5/27/+j46+Q6+vPjX/AMki1z/t3/8AR8dfIdABRRRQAUUUUAWrC5mtbgSQyGNwcgg1 6r4e17U9ZSKx1G9KQOAu89xXkKnawPpXf6Brmmxab5dwDvVeCDzQB12sfCvT7tftNlfJ03HB46fS uUs7K30O9uYBcLPGqLuKjocsMfp+tY2o+Jr6KSQWk8qQtkfe7U3w1K13NcJNIS8gJyfbmt6PwVPT /wBuiJ9DVkubH7LNhvnZ8hcds1IdMh1jy0FwkMeMZbuaxnTZdmLH8VSa7M+nwWHlMVbezHH4VgM7 /R/gxHOgnuL35DyAO9bH9iano77LC4ZYlGOoxXGaZ8Urm0t4oJUL4GD8/wD9aurv/G1nJpcksdwn mFeFz3oA5rxTqF3ax/6ReBnI9c15dcSma4eQnJJ61oa3qk2oXbF3JGT3rKoAKKKKACiiigD68+Cn /JItD/7eP/R8lfIdfXnwU/5JFof/AG8f+j5K+Q6ACiiigAooooAKKKKACiiigB8cjRMGU1cGpyKw ZSRiqFFAF9L1UuDKO/UVdWe1kG8TohPZs1h0UAdZpcEE9zBHBeI87ORsAPHynuR+H416rpGhPpOk PdxQLJOUIH1NeE6fcNa3SzIcFCDn/gQr6n8HGDVPD1vKG3gqN31rer8FP0/9uYluzgdA8Baxr2on UNbdkiDfJFuHTr2r1nT/AA/Y2lskcdsgwoGcda1IY0SPCqABxWPrHi/SNBjdrq5VWQH5R1rAZsQW scCZCBQBzxXk/wAXfiFbadatomnyhrmRf3hXPyj6/hWD4v8Ajg95bPaaRbeXk/64yc4+mK8bu7ye +uWuLmRpJG6saAISSSSTknrSUUUAFFFFAH07+zn/AMk9v/8AsKyf+ioq9eryH9nP/knt/wD9hWT/ ANFRV69QAUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/9d4e/wCwtb/+jFoo8b/67w9/2Frf/wBGLRQB d0f/AFw/66D/ANAeugrn9H/1w/66D/0B66CgAooooAKKKKACiiigAooooAKKKKAOA+Nf/JItc/7d /wD0fHXyHX158a/+SRa5/wBu/wD6Pjr5DoAKKKKACiiigApQxU5BI+lJRQA8yuRgsT9asWd3JYvF PEcEM2fpgVUp5/1C/wC8f5Ct6PwVPT/26In0OxWa0li+1tIoJw22ue1jUjf3WeCicKOwFZ3mPjG9 semabWAxWbc2acZpCMF2x9aZRQAUUUUAFFFFABRRRQB9efBT/kkWh/8Abx/6Pkr5Dr68+Cn/ACSL Q/8At4/9HyV8h0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAPT7kn+7/UV33gL4k3nhdjbTsZbMj7 hxwc1wKfck/3f6imVvW+Cn6f+3MS3Z7d4h+Ms09mU0v9yzLyxAyPzrx/UdXvtTuJJru5eRnYk5NU cnGM0VgMKKKKACiiigAooooA+nf2c/8Aknt//wBhWT/0VFXr1eQ/s5/8k9v/APsKyf8AoqKvXqAC iiigAooooAKKKKACiiigAooooA5Lxv8A67w9/wBha3/9GLRR43/13h7/ALC1v/6MWigC7o/+uH/X Qf8AoD10Fc/o/wDrh/10H/oD10FABRRRQAUUUUAFFFFABRRRQAUUUUAY/ijw5Z+LfDl3od/JPHbX Oze8DAONrq4wSCOqjtXnP/DOfhD/AKCOuf8Af+L/AONVv/Gv/kkWuf8Abv8A+j46+Q6APp3/AIZz 8If9BHXP+/8AF/8AGqP+Gc/CH/QR1z/v/F/8ar5iooA+nf8AhnPwh/0Edc/7/wAX/wAao/4Zz8If 9BHXP+/8X/xqvmKigD6d/wCGc/CH/QR1z/v/ABf/ABqj/hnPwh/0Edc/7/xf/Gq+YqKAPp3/AIZz 8If9BHXP+/8AF/8AGqX/AIZ08I7Qv9pa5gHP+vh/+NV8w08geSpxzuP9K0hFuMmnsv1Qj6a/4Zz8 If8AQR1z/v8Axf8Axqj/AIZz8If9BHXP+/8AF/8AGq+YqKzGfTv/AAzn4Q/6COuf9/4v/jVH/DOf hD/oI65/3/i/+NV8xUUAfTv/AAzn4Q/6COuf9/4v/jVH/DOfhD/oI65/3/i/+NV8xUUAfTv/AAzn 4Q/6COuf9/4v/jVH/DOfhD/oI65/3/i/+NV8xUUAfTv/AAzn4Q/6COuf9/4v/jVH/DOfhD/oI65/ 3/i/+NV8xUUAfcfhfw5Z+EvDlpodhJPJbW2/Y87Audzs5yQAOrHtXnP/AAzn4Q/6COuf9/4v/jVb /wAFP+SRaH/28f8Ao+Su/oA8h/4Zz8If9BHXP+/8X/xqj/hnPwh/0Edc/wC/8X/xqvXqKAPIf+Gc /CH/AEEdc/7/AMX/AMao/wCGc/CH/QR1z/v/ABf/ABqvXqKAPIf+Gc/CH/QR1z/v/F/8ao/4Zz8I f9BHXP8Av/F/8ar16igDyH/hnPwh/wBBHXP+/wDF/wDGqP8AhnPwh/0Edc/7/wAX/wAar16igDyH /hnPwh/0Edc/7/xf/GqP+Gc/CH/QR1z/AL/xf/Gq9eooA8h/4Zz8If8AQR1z/v8Axf8Axqj/AIZz 8If9BHXP+/8AF/8AGq9eooA8iH7OnhEAgalrnIwf38P/AMapP+Gc/CH/AEEdc/7/AMX/AMar16im 23uB5D/wzn4Q/wCgjrn/AH/i/wDjVH/DOfhD/oI65/3/AIv/AI1Xr1FIDyH/AIZz8If9BHXP+/8A F/8AGqP+Gc/CH/QR1z/v/F/8ar16igDyH/hnPwh/0Edc/wC/8X/xqj/hnPwh/wBBHXP+/wDF/wDG q9eooA8h/wCGc/CH/QR1z/v/ABf/ABqj/hnPwh/0Edc/7/xf/Gq9eooA5zwV4K03wJo02l6XPdTQ S3DXDNdOrMGKquBtVRjCDt610dFFABRRRQAUUUUAFFFFABRRRQAUUUUAcl43/wBd4e/7C1v/AOjF oo8b/wCu8Pf9ha3/APRi0UAXdH/1w/66D/0B66Cuf0f/AFw/66D/ANAeugoAKKKKACiiigAooooA KKKKACiiigDgPjX/AMki1z/t3/8AR8dfIdfXnxr/AOSRa5/27/8Ao+OvkOgAooooAKKKKACiiigA p5/1C/7x/kKZTz/qF/3j/IVvR+Cp6f8At0RPoMooorAYUUUUAFFFFABRRRQAUUUUAfXnwU/5JFof /bx/6Pkrv64D4Kf8ki0P/t4/9HyV39ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcl43/13h7/ALC1v/6MWijxv/rvD3/Y Wt//AEYtFAF3R/8AXD/roP8A0B66Cuf0f/XD/roP/QHroKACiiigAooooAKKKKACiiigAooooA4D 41/8ki1z/t3/APR8dfIdfXnxr/5JFrn/AG7/APo+OvkOgAooooAKKKKACiiigAp5/wBQv+8f5CmU 8/6hf94/yFb0fgqen/t0RPoMooorAYUUUUAFFFFABRRRQAUUUUAfXnwU/wCSRaH/ANvH/o+Su/rg Pgp/ySLQ/wDt4/8AR8ld/QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/8AXeHv+wtb/wDoxaKPG/8ArvD3/YWt/wD0 YtFAF3R/9cP+ug/9Aeugrn9H/wBcP+ug/wDQHroKACiiigAooooAKKKKACiiigAooooA4D41/wDJ Itc/7d//AEfHXyHX158a/wDkkWuf9u//AKPjr5DoAKKKKACiiigAooooAKef9Qv+8f5CmU8/6hf9 4/yFb0fgqen/ALdET6DKKKKwGFFFFABRRRQAUUUUAFFFFAH158FP+SRaH/28f+j5K7+uA+Cn/JIt D/7eP/R8ld/QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/9d4e/wCwtb/+jFoo8b/67w9/2Frf/wBGLRQBd0f/AFw/ 66D/ANAeugrn9H/1w/66D/0B66CgAooooAKKKKACiiigAooooAKKKKAOA+Nf/JItc/7d/wD0fHXy HX158a/+SRa5/wBu/wD6Pjr5DoAKKKKACiiigAooooAKef8AUL/vH+QplPP+oX/eP8hW9H4Knp/7 dET6DKKKKwGFFFFABRRRQAUUUUAFFFFAH158FP8AkkWh/wDbx/6Pkrv64D4Kf8ki0P8A7eP/AEfJ Xf0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQByXjf/AF3h7/sLW/8A6MWijxv/AK7w9/2Frf8A9GLRQBd0f/XD/roP/QHr oK5/R/8AXD/roP8A0B66CgAooooAKKKKACiiigAooooAKKKKAOA+Nf8AySLXP+3f/wBHx18h19ef Gv8A5JFrn/bv/wCj46+Q6ACiiigAooooAKKKKACnnmFP95v5Cmqu44q3DFmMDr8x/pW9H4Knp/7d ET6FPpRWo1lmEuBxVKSEA4rAZBRQRgkUUAFFFFABRRRQAUUUUAfXnwU/5JFof/bx/wCj5K7+uA+C n/JItD/7eP8A0fJXf0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByXjf8A13h7/sLW/wD6MWijxv8A67w9/wBha3/9GLRQ Bd0f/XD/AK6D/wBAeugrn9H/ANcP+ug/9AeugoAKKKKACiiigAooooAKKKKACiiigDgPjX/ySLXP +3f/ANHx18h19efGv/kkWuf9u/8A6Pjr5DoAKKKKACiiigAooooAntlBYkjNaUMWeMdKo2YZm2qC T6VuWcEqMwljZTgEZHbmt6PwVPT/ANuiJ9DZ/sxRoEku3LbQRXFzIysSa9ahg3+GnVUyTGOgz6V5 vPbsrsCv6VgMxZFHUetR1ZnwrMPwqtQAUUUUAFFFFABRRRQB9efBT/kkWh/9vH/o+Su/rgPgp/yS LQ/+3j/0fJXf0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQByXjf/XeHv8AsLW//oxaKPG/+u8Pf9ha3/8ARi0UAXdH/wBc P+ug/wDQHroK5/R/9cP+ug/9AeugoAKKKKACiiigAooooAKKKKACiiigDgfjWM/CPXB/17/+j46+ RfKb1T/vsf419c/Gv/kkWuf9u/8A6Pjr5DrSDpr4036O36MTuP8AKb1T/vsf40eU3qn/AH2P8aZR V81D+V/ev/kQ1H+U3qn/AH2P8aPKb1T/AL7H+NMoo5qH8r+9f/IhqP8AKb1T/vsf40eU3qn/AH2P 8aZRRzUP5X96/wDkQ1NbRUC3il+Rn+H5v5V2dzcWjyy/LNjyIxzC453P7fSvP7K5a2nDA4rfXV2l +Ykcqqn8CT/Wt6UqPJP3Xt3X8y/uid9DubXVLZNK+zkXAbZgf6NJz/47XJsYwshkEnHTMTD+lSr4 iZFVRjAGKo3WpCUuWGN3pWHNQ/lf3r/5Eepg3wDTNs2492Aqp5Teqf8AfY/xqW8cPOSKr0c1D+V/ ev8A5ENR/lN6p/32P8aPKb1T/vsf40yijmofyv71/wDIhqP8pvVP++x/jR5Teqf99j/GmUUc1D+V /ev/AJENR/lN6p/32P8AGjym9U/77H+NMoo5qH8r+9f/ACIan178FRj4R6GP+vj/ANHyV31cB8FP +SRaH/28f+j5K7+sH5DCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooA5Lxv8A67w9/wBha3/9GLRR43/13h7/ALC1v/6MWigC 7o/+uH/XQf8AoD10Fc/o/wDrh/10H/oD10FABRRRQAUUUUAFFFFABRRRQAUUUUAcB8a/+SRa5/27 /wDo+OvkOvrz41/8ki1z/t3/APR8dfIdABRRRQAUUUUAFFFFABUu9lhUhiPmP9Kip5/1C/7x/kK3 o/BU9P8A26In0FE0gOd5pWuJWAy1RUVgMCSTk0UUUAFFFFABRRRQAUUUUAfXnwU/5JFof/bx/wCj 5K7+uA+Cn/JItD/7eP8A0fJXf0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByXjf8A13h7/sLW/wD6MWijxv8A67w9/wBh a3/9GLRQBd0f/XD/AK6D/wBAeugrn9H/ANcP+ug/9AeugoAKKKKACiiigAooooAKKKKACiiigDgP jX/ySLXP+3f/ANHx18h19efGv/kkWuf9u/8A6Pjr5DoAKKKKACiiigAooooAKef9Qv8AvH+QplPP +oX/AHj/ACFb0fgqen/t0RPoMooorAYUUUUAFFFFABRRRQAUUUUAfXnwU/5JFof/AG8f+j5K7+uA +Cn/ACSLQ/8At4/9HyV39ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcl43/13h7/sLW//AKMWijxv/rvD3/YWt/8A0YtF AF3R/wDXD/roP/QHroK5XwvZvZL5b3dzcsZg2+4YFh8j8DAHHH611VABRRRQAUUUUAFFFFABRRRQ AUUUUAcB8a/+SRa5/wBu/wD6Pjr5Dr68+Nf/ACSLXP8At3/9Hx18h0AFFFFABRRRQAUUUUAFPP8A qF/3j/IUynn/AFC/7x/kK3o/BU9P/boifQZRRRWAwooooAKKKKACiiigAooooA+vPgp/ySLQ/wDt 4/8AR8ld/XAfBT/kkWh/9vH/AKPkrv6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5Lxv/AK7w9/2Frf8A9GLRR43/ANd4 e/7C1v8A+jFooAu6P/rh/wBdB/6A9dBXP6P/AK4f9dB/6A9dBQAUUUUAFFFFABRRRQAUUUUAFFFF AHAfGv8A5JFrn/bv/wCj46+Q6+vPjX/ySLXP+3f/ANHx18h0AFFFFABRRRQAUUUUAFPP+oX/AHj/ ACFMp5/1C/7x/kK3o/BU9P8A26In0GUUUVgMKKKKACiiigAooooAKKKKAPrz4Kf8ki0P/t4/9HyV 39cB8FP+SRaH/wBvH/o+Su/oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigDkvG/+u8Pf9ha3/8ARi0UeN/9d4e/7C1v/wCj FooAu6P/AK4f9dB/6A9dBXP6P/rh/wBdB/6A9dBQAUUUUAFFFFABRRRQAUUUUAFFFFAGB428M/8A CY+Eb7Qftf2T7V5f7/y/M27ZFf7uRnO3HXvXkP8AwzP/ANTd/wCU3/7bXvtFAHgX/DM//U3f+U3/ AO20f8Mz/wDU3f8AlN/+2177RQB4F/wzP/1N3/lN/wDttH/DM/8A1N3/AJTf/tte+0UAeBf8Mz/9 Td/5Tf8A7bR/wzP/ANTd/wCU3/7bXvtFAHgX/DM//U3f+U3/AO20v/DNJ2hf+Et4BJ/5Bv8A9t9q 98opqTSaXUDwL/hmf/qbv/Kb/wDbaP8Ahmf/AKm7/wApv/22vfaKQHgX/DM//U3f+U3/AO20f8Mz /wDU3f8AlN/+2177RQB4F/wzP/1N3/lN/wDttH/DM/8A1N3/AJTf/tte+0UAeBf8Mz/9Td/5Tf8A 7bR/wzP/ANTd/wCU3/7bXvtFAHgX/DM//U3f+U3/AO20f8Mz/wDU3f8AlN/+2177RQBgeCfDP/CH eEbHQftf2v7L5n7/AMvy926Rn+7k4xux17Vv0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByXjf/AF3h7/sLW/8A6MWi jxv/AK7w9/2Frf8A9GLRQBd0f/XD/roP/QHroK5/R/8AXD/roP8A0B66CgAooooAKKKKACiiigAo oooAKKKKACiuI+L1/eaZ8LtZu7C7ntLmPyNk0EhjdczRg4YcjIJH418tf8J14v8A+hq1z/wYS/8A xVAH25RXxH/wnXi//oatc/8ABhL/APFUf8J14v8A+hq1z/wYS/8AxVAH25RXxH/wnXi//oatc/8A BhL/APFUf8J14v8A+hq1z/wYS/8AxVAH25RXxH/wnXi//oatc/8ABhL/APFUf8J14v8A+hq1z/wY S/8AxVAH25RXxKvjfxixwvijXT9NQm/+Kq3F4n8cTqAniTXt+cnOoTdO38X1q4wck2umv4pfqB9n 0V8bf254/wD+hl1z/wAGM3/xVNbXvHyDJ8Sa5j21Gb/4qoA+y6K+KJfGXjWFisnibXlI9b+b/wCK qL/hOvF//Q1a5/4MJf8A4qgD7cor4j/4Trxf/wBDVrn/AIMJf/iqP+E68X/9DVrn/gwl/wDiqAPt yiviP/hOvF//AENWuf8Agwl/+Ko/4Trxf/0NWuf+DCX/AOKoA+3KK+I/+E68X/8AQ1a5/wCDCX/4 qj/hOvF//Q1a5/4MJf8A4qgD7coriPhDf3mp/C7Rru/u57u5k8/fNPIZHbE0gGWPJwAB+FfLX/Cd eL/+hq1z/wAGEv8A8VQB9uUV8R/8J14v/wChq1z/AMGEv/xVH/CdeL/+hq1z/wAGEv8A8VQB9uUV 8R/8J14v/wChq1z/AMGEv/xVH/CdeL/+hq1z/wAGEv8A8VQB9uUV8R/8J14v/wChq1z/AMGEv/xV H/CdeL/+hq1z/wAGEv8A8VQB9uUV8R/8J14v/wChq1z/AMGEv/xVH/CdeL/+hq1z/wAGEv8A8VQB 9uUV8R/8J14v/wChq1z/AMGEv/xVOHjbxk33fFGvH6X83/xVAH21RXxYnivxw/TxNrv/AIMJv/iq lHiTx23TxNrh/wC4jN/8VQB9nUV8YnxN45RTv8S67kjC/wDEwm6/99emarP4y8axnDeJteB/6/5v /iquUHFJvrr+LX6Bc+16K+JP+E68Xj/matc/8GEv/wAVSf8ACdeL/wDoatc/8GEv/wAVUAfblFfE f/CdeL/+hq1z/wAGEv8A8VR/wnXi/wD6GrXP/BhL/wDFUAfblFfEf/CdeL/+hq1z/wAGEv8A8VR/ wnXi/wD6GrXP/BhL/wDFUAfblFfEf/CdeL/+hq1z/wAGEv8A8VR/wnXi/wD6GrXP/BhL/wDFUAfb lFeW/ATVtS1jwNe3GqahdX066lIiyXUzSsF8qI4BYk4ySce5r1KgAooooAKKKKACiiigAooooAKK KKAOS8b/AOu8Pf8AYWt//Ri0UeN/9d4e/wCwtb/+jFooAu6P/rh/10H/AKA9dBXP6P8A64f9dB/6 A9dBQAUUUUAFFFFABRRRQAUUUUAFFFFAHAfGv/kkWuf9u/8A6Pjr5Dr68+Nf/JItc/7d/wD0fHXy HQAUUUUAFFKqs7BVUsx6ACup0Lw08kqyXUJYkfJFtyW/CgDDsNKutQb9zGSvdscV1Wn+ELZtiyAz y9CqnHNej6H4LSKz+16m8On2yn/lqwjGPqcUzUfHfh7RUkt9CsYr+ZMr57RnAPTIYcH60AZ+i/Dq 4ubhVhshFkfxKSa2rPwhY2Wv3lrqN6kJS0gkGSRks0wIH/fI/OuCuvFniPUy0k+p3FvCTgRxNgDP as6GK7v7iQtdzSNtXMjSknq2BnP1rej8FT0/9uiJ9D1S50fw1ErEapFwcfeOetQJpOjXEarbX6Mx 4yGPFcEvh9fIJe4kOB8zbjgVT/sG9TmymuJR/eUnH5isBnean4Bu5F81YkuISOpUmuI1DwlZpuBt Xt39WJx+VEWo+I9Hm2RaleQnrsdjj9a3bH4lM+INZ0uC4jPDTYLMPpQB59feGrmDLQDzU9VFYjo0 blHUqw6g19AWkHhXxPbkaZfRQ3IH+qkKoxz6AnmuV8QeDpLZjHd2eAR8k6pnPpz0oA8noq3f6dc6 fMUmjYDPDEcGqlABRRRQB9efBT/kkWh/9vH/AKPkr5Dr68+Cn/JItD/7eP8A0fJXyHQAUUUUAFFF FABRV+w0i7v3GyJlj7yMML+ddlpHhDzF2xWT3chPJ2EgfiKAOFt7Se6dVhjZiTjgVuWvhSUkNdSr Gv8AdOQa9NXwa+mWYvdR2WMCgHa+EyPxqrda/wCG9LjzF5VzKegUBx+hoA5mx8I2kg2pay3D567u K6S28ESKqv5EcUY5JKngVzVz4u1a8nJ0+D7MgGAIQaRE12+bddX9xtPJXzMfpQB21r4d0cti4voU IPIORWvB4a8MJIFfVIFyP7zcmvNBptvEx87UH3f7/P8AOpPsqEBY5JpPQ8nFAHoWpeFdM+2aVHZ3 8T+ddMnBPA8mVv5qBUN78OrgoXWNZF/3Sa82ntry3kiPmzg7/l+dsj5T0qyL7XbVA8WpajH25LEf rW9b4Kfp/wC3MS3Zp6l4MjgZvtGnNn1TiuXvfCYw7Wj5YZPl8k/SunsPHGu2YMd6Dfxn/npycfgK 6iy1bw14jh8smK0vHGNuAvzfifWsBnhEsUkMhSRSrDsaZXqnijwkyRYmgC5OUmVeG/HpXm1/p1xp 83lzIwHUNjg0AVKKKKACiiigD6d/Zz/5J7f/APYVk/8ARUVevV5D+zn/AMk9v/8AsKyf+ioq9eoA KKKKACiiigAooooAKKKKACiiigDkvG/+u8Pf9ha3/wDRi0UeN/8AXeHv+wtb/wDoxaKALuj/AOuH /XQf+gPXQVz+j/64f9dB/wCgPXQUAFFFFABRRRQAUUUUAFFFFABRRRQBwHxr/wCSRa5/27/+j46+ Q6+vfjWcfCPXD/17/wDo+OvkXzW9E/74H+FaQVN/G2vRX/VCdxlOjjaWQIgyTS+a3on/AHwP8K7H wnoP9obWdCZXbChflwPwq+Wh/M/uX/yQakvhrwxLNPGqx75mI+gNekuNM8Awi71Zhc3r8JBAQSD1 59On61ja7PpXhCIWll9pfUSmA4vpl2vjk4Vx3zXHWlld65qWbi5mnmPMkskjMV/E80ctD+Z/cv8A 5INS74k8Qal4m1BZLlykIULHBEcDHbOOM1DaWLxXNvHNHhARkAdq37fwlDFOhDzMOrYlYHP4Ut9p UEWqRjfcFdwz/pEhP57qOWh/M/uX/wAkGpY8QW8UGkJHDCN0jBFwBnPP+FN0exhs5bmKXGTbQsc+ 7S5rSk0G2nSE7rs4bPN3KcdfVuK57UraKPXWhR7jabdGObmQnO5+5bP4VvSjR5J+89uy/mX94Tvo Qzme8uWtbbO1ZCM+wP8A9auu0Vlji8gphk4ORXHaSLWLW5reaScI33StzIpPJ9DzXYxaLZk+akl5 gj/n8mH/ALNWHLQ/mf3L/wCSHqYuq6TLqmrn7qxx8Y/Ljiqmo+FYFULAw8/jaM/nXRDSLVLlsy3o GM5+2Tf/ABVV00OB5Xk33RYL8v8ApUvH47qOWh/M/uX/AMkGpwNzYvaXBRy0U6dHjbA/Su38N/EC 3SyXQ/EUTSYOI51UYGeRk9e9MbwlayoZLk3DyN1PnOcfma5TWNHi0+4MUiu8R5WQscj29P0o5aH8 z+5f/JBqdP4q8JSR2ZzslgflJI+QM9OfyrynVNLm0yYLJgq33WBru/DWuhLpNK12a7msJSVVhezI FPbgPt7DtWp4p8H2dssTQyTS2s4Jid5C2z8+KOWh/M/uX/yQankNFW76KSyvJIGC/KeCUHP6VX81 vRP++B/hRy0P5n9y/wDkg1Prn4Kf8ki0P/t4/wDR8lfIdfXvwVOfhHoZ/wCvj/0fJXyKJGAxhf8A vgVEVBv3m18r/qg1GUU/zW9E/wC+B/hR5reif98D/Cr5aH8z+5f/ACQajK6XRPDclw8FxcDKOQUj HVuam0HQVuYknu4yzO2I4lAXI9en1rtQfD/huya4vWuLjUVX9xbpdyR7TjjO1gcZx0NHLQ/mf3L/ AOSDU39N8HJZ2H9oaxcQ2lqq/Kh4Y/hVDUPipZ6Xbmy8M2ZyPvTzxghj7DqK4HVdb1PWpAL66ufs 4OUge5kkVfp5jNVNoUZAkUZX+8STn/CjlofzP7l/8kGpLe6xqeu3Uk17dyPlslN5C/gKculMyqQn J961tL8MLMI5Jd4VsHg4OK6MeHLRVGw3P/f9/wDGjlofzP7l/wDJBqYtn4cuJbXerqgH3V7/AJ1f 03RGmhYyzcg44JrZt9DgW3bMl3gel3KP5NWdb2Edvd+VK91tLY4upV/k1HLQ/mf3L/5INTRstGto SAEDMe7c1dks4o9pMcefZaZFo9kx3LLe59Pts3H/AI9UdxpVurqPNvcY73sx/wDZqOWh/M/uX/yQ ajNQtI/tOlsQmGuDnK/9MZDUjpbTQtGFU7RzjGBWN4itoLSOzaOa6P7w5zdytj92/TLcf5FPt9Mh bTDIHud7Ani6lA/LdW9WNHkh7z27L+Z/3hK+phJp8t3d3EcJURI3frWVe6bLaTb+UkDDa6nAzXW6 ZpFvb2rTXElwrOc/LcSL29m5pdT0ez+yNODOy4yQ9xI2R+dYctD+Z/cv/kh6kfh3xgsZGma4pkgI OHAB57c0eI/DMU9oZrSRJbdxlDnJX61xupWyQRq0bF0LYG4klau6LqMkUwsbuWYwyfdK3EiAfgGA 7elHLQ/mf3L/AOSDU426tntbh4n7EgVDXZeINHSMyvDliAWUt824fjXIea3on/fA/wAKOWh/M/uX /wAkGoyin+a3on/fA/wo81vRP++B/hRy0P5n9y/+SDU+mv2c/wDknt//ANhWT/0VFXr1eRfs6Hd8 P9QJx/yFZOgx/wAsoq9drF2voMKKKKQBRRRQAUUUUAFFFFABRRRQByXjf/XeHv8AsLW//oxaKPG/ +u8Pf9ha3/8ARi0UAXdH/wBcP+ug/wDQHroK5/R/9cP+ug/9AeugoAKKKKACiiigAooooAKKKKAC iiigDgPjX/ySLXP+3f8A9Hx18h19efGv/kkWuf8Abv8A+j46+QwCTgdTQBe0iyF/qCQtnZ1bHpXt Ghi10Dw7Nq8yqHXMcKn6da43wDoD3l2ke0Av98nsP84rQ+IuoLd6jb6bYtmCGMAjGPmB564oA50T T6vqc17PmS4nkLDP8IJz/Wuu0eSDTIdhhbzTySFJzVXw9pUtpDDNNHneAeBnArqGjXltuaAI01ZG weVPTlRUs8SXGyTowO4e1ZV1bBzmLoe3TFSaXcTJMLd1LbehoA3gxittxHIHeuNlxPrlxKef3SY+ mXrsL5DJauo71zi2jLqF1Ggy3kQkkn/akrej8FT0/wDboifQybFYY/EUrTAEBQVz+NdlaajE7iNT wPauYa1QakhbAfO0fXpV97SeA715A7rWAzp3jV+SOlULu5W2ZI1+UscVNYTefZo569DWHft5l4Qx IAHUigC7d6kyJsQ73boMfzrLn0+bVHBuRhcdq0bSBIo94O7PerSnd0FAHm2s6U9lL5TE+WT8jd1P X/Cuo8D6o2q2F3ompPuMYAhZuqk+9S+IkjurGWJBvk44U9Of/rVxEE0un3sM5JV422yDOCaALPi3 RDHdS5H7xOh9RiuIr3C+sl1nQkvY1JKr82T2/wD1CvHNUtDZ3roRgHkfjQB9X/BT/kkWh/8Abx/6 Pkr5Dr68+Cn/ACSLQ/8At4/9HyV8h0AFdF4b0b7VOJ54i69ETB5PrWfo+nPfXiEqfKVgWI7+1erR zxeDNJF/cIrXk6+VBAMEqP7x7dKAJrq7svBelJPPCs2sTjdFGwyIx2OBx1B6ivOJpJb++a5kJluZ mLEdlyelPu7u41K9kurh991Kcseyiut8MaTHsjk43nBJoAxItHlhKSXKAqxAJz09sV2FnoNjLbqI 4c8dc81rXumLcW3lgc03S9PmtCfMwAOgBzQBNDZ29pEqEcDgdana3QjgYpbgboifSo4ZVWPDNjnj igBs7GBgy/KCOcVFutpQMgB/UUzUblRESvPGBWbZ2WCJ5GIBO4AmgDfUQou8HjpnNQTOJJMjoOKx ru9eSTyoM47nHWmW999mzHJk9/pQBV15ftmoWVtk7fNIOP8AcatKxSe2kVDzHjvzWcFLalaTkYVr hjk/9cpP8KlgvJ4Z2GN0eOpPet63wU/T/wBuYluy0oN5qUm85jj4A6VS1KWSW8WEN+7DbdoHXmre lNLNdzOY2EZOBnuatNpjTXYn42A5OTWAyvdaRaXVsIXiU8VwOraZJp18IlY7RzEfQ+ma9RmkgXo3 IHNc3rNu2tfuoFG1Ry5H8qAKFjNFrWj7HUC6t0wD64GTXB61Yi0udyjCsf1rZ0+7k0XXUMm4BZAs gHfkVreL9KEsInhIMMnzKR69TQB55RSspRirDBHWkoA+nf2c/wDknt//ANhWT/0VFXr1eQ/s5/8A JPb/AP7Csn/oqKvXqACiiigAooooAKKKKACiiigAooooA5Lxv/rvD3/YWt//AEYtFHjf/XeHv+wt b/8AoxaKALuj/wCuH/XQf+gPXQVz+j/64f8AXQf+gPXQUAFFFFABRRRQAUUUUAFFFFABRRRQBwHx r/5JFrn/AG7/APo+Ovkqzj828hT1YV9a/Gv/AJJFrn/bv/6Pjr5Y8PwCbVIgezD+dAHr/hmD+y9B u78ACRo9ifU4P9DXEXKNqviZkU7ejE+47V6JfgWvh20t+m51ZscHGGGK4PwsFl1G8uXJBWUqO3HN AHXx3bWoSKaMFQODk9KvxBZ0zGwY+lZkmqQSz/ZniBQcbh/OrunW1qkhkhndjjoxoAq3NpN9pBiU 5PWtW3tFjCuwG/A7Va75qFrhV3DnI9aAJSVztJHPY1hToW1m7W3YKfs0HP8AwOWrEF/DdXTxxSh5 VOTiq2Xj1y6wuSLaHP8A31LW9H4Knp/7dET6GVJGRegu5yCRj+ddHaAzImBlPeublYnV/u5yScfn XT2iyFs4CqorAZdVQihVGAKp3Omx3Dbs4NSXF5HbttdlU9ixxVgEEAjoaAIY7VY4TH6+3SnJCqIU UckEEjtUtUb+8ECFR1/nQBTW2to5zmQFznGTXB+NbT7POky4HmA5x611MkjSSb2PzVzviZWntcyP kxcj8aAOu8D6h5+lpBIuVnjbj3GVH8q848W2vlsXxgrIVP0HFdd4Fl3abanJBRmQj6uT/WsTx1EU 88f3ZM/m1AHv/wAFP+SRaH/28f8Ao+SvkRVLMFHU19d/BT/kkWh/9vH/AKPkr5Y0GyN3fD5cqtAH e+EtJhtbMXk6FoLVfPlX+/tG7+lYeuatNq962oTjhv3UUec7QOBXbeNL6PRvC2naDZqvmXUKvO/8 QxwR+RridE006tdlZMiKNflx0zQBd0zwxJPp/wBpeQgvyQF7V0uhWM9tLEqltinByMVLpqzWkiWg XKKe4zXSqqonQLxzQA7tzVW5vo7bg4P41n6jqb28oEbDB45qkiyX8wL8KBgkdqANKO9a8B2oQo4q tqMzRwhIzl2IHHpVyKH5QkY4AxTksoIJPMdmLn15oApW2lyTRK00pAJ5Wrs2nLJCIw+APQYpZbkK PvCNPyqpLf2sQJeePjsGBNAFmHSooMlWyT1JFQto8D3O5mAPoO9Zz+I9NjbaZifoM1ft7mG8i8yG TcPY8igCC+jVLrTYwAALhh/5CkqpHNFDdPj5kGeM1avsi603HP8ApJ6/9cpKmeyglycEE9wa3rfB T9P/AG5iW7Ky6nIX2W8JBPQg1e8ySK2ZmPzBckCkSKO2ibYvvzVCXVl8psod3PCjNYDM+5kfbnPL Hk1qaQD9kI6gHrWcLWbUFZpFCKMlQvetGxikt9MaMZ3HgZ/CgDgfEsQe/ndAcqxBx/OtDRdQbWdL bTZOHiy6n2HWta70lJfNiZTvcHJFcbYSSaPrOXO0MdjD/ZzQBj6zb/Z79hjG4bv1NZ9dZ4ztF+2C 4iHyEZU/7Oa5OgD6d/Zz/wCSe3//AGFZP/RUVevV5D+zn/yT2/8A+wrJ/wCioq9eoAKKKKACiiig AooooAKKKKACiiigDkvG/wDrvD3/AGFrf/0YtFHjf/XeHv8AsLW//oxaKALuj/64f9dB/wCgPXQV z+j/AOuH/XQf+gPXQUAFFFFABRRRQAUUUUAFFFFABRRRQBwHxr/5JFrn/bv/AOj46+afB9t5upRE jhnAJ/Gvpb41/wDJItc/7d//AEfHXgXgG3/0qFj0UhyaAOv8YzPa6YWVziOM4571x/hSwuLyCaeO RkVn7Z5NbPjySRrKSFWDF5wgHfG0mjw4ZdJ01IZI8gksSvbNAGhHps6EjaCf73rT47SeC6RssAeu CcVoQ6ym3ndzzyM1OlxHdSYV1DY6CgC1BKXGDzjvWR4qvvsmlS7WxIylUwecmtZLdlYHcBj0rlvF aNqF1Faxj7jbm9+KAOf8HpMNaDgsAYyTz15Fd2kqNrN4T1+ywD8d0tYeh6YumTNcTSoGKbFQHkDI 6/lS3esQ2+pXbJuJa3iUHHcNJ/jW9H4Knp/7dET6E8qRjUd/GA9dLFInkqdyjj1rzQ6w0l1GmD64 Hrius0+/LIscin/ZNYDMDxlFcPdxOGcpt7E8V0XhHUmvtMEcj7po85yecdqZrdi97ZfuQC46A9xW V4RSWx1h4pFwJlwPwyaAO4J2qSewzWbPYfavnwfY5rTI7dRUMkqxDau3I7UAYzaTIHOMkfSs/VtK hTTLjzUVpGU8kcj/ADxW7Nqnk9VBGccVnahIt9bO6SlTjofegDnvArEWk4B/1c+P51W8fJlrpsYO xG/NhTvB7+U2oRqM/vc/pVrxtD5tk9wAAJEVOeuRQB7X8FP+SRaH/wBvH/o+SvAfhpYJe6iiOud0 2368Cvfvgp/ySLQ/+3j/ANHyV4r8LEEUbXRP+ol3/oKAMfxPem88T6g+8kLKYY1z90dOK2fC04hI iMDKOm7HfNcpfAyavLKBkvcM3H+9XpGhPbzKqtCwkVM4IoA6CNYmCyBVB+nSq95ewxxMpcEkEYzU U13G8ojjysgHGeKxDHJcXZTIDlsc+tADbe0N3edWKjnB5rpobdIoggRRj0HWmWVklqmBy56mo9U1 S30m2M05PTgDqaAJLq7trOFmmmSIAHq2K4/UPFNxLlNMhdmzzIRuz9KyS914l1QySMRbh+FPYeld NBYxW8YUAbQPpQBx162tXRLzzXDZ/g3HH5VWj0XUJyNsEjE84xXexWBmbdlfL9e+K0ljWKIIijAo A88i8L3mN0ylB9KuaTZXtlqAWF5FTPIGcGu0b7p+lZ06yIjvC20nqM9aAL126/adLDAZ+0kEj08m Sp87d1YlxqsD3emwBs3CTEsv/bNx/MitN7yKEAHLv1OO1b1vgp+n/tzEt2LIyx27q8gDsCeTzVC0 8oF3mAwPXvT557a5kyGZXAx0Bqj5M858lCACc5JP68VgM37d0liDIoVegFToRuXcflBzWT5skNqY BC+/1UcfnRBfPGbeB42BPBoA2Gtx5wYKCGPUCvOvG1kqXa3CJtyQDgeg4r0eOYqCDk8cYrmvFmn/ AGvSiynDI278hQBy9mp1fw2UkJea3JQg84XAI/UmuHmQxzuhGCrEYrrfDFw66hJAucT8Ee3+RXP6 3B5Gr3C+rk0AfRv7Of8AyT2//wCwrJ/6Kir16vIf2c/+Se3/AP2FZP8A0VFXr1ABRRRQAUUUUAFF FFABRRRQAUUUUAcl43/13h7/ALC1v/6MWijxv/rvD3/YWt//AEYtFAF3R/8AXD/roP8A0B66Cuf0 f/XD/roP/QHroKACiiigAooooAKKKKACiiigAooooA4D41/8ki1z/t3/APR8deIeB02WUsgGSIFN e3/Gv/kkWuf9u/8A6PjrxbwSrDT19GiA/QUAN1mQzX2npJg75g5/AEVuXCNHHHtGY8Y5Heuf1Zc6 9pKA4yD+ea7CxikMYEhDp3yOn40AQWtpb3KqDhc+lX7fTIbeXzFJJp8dhaCTdGBn2NWz1oAjlkWK JnYgAetcxPIJbmSQfMznqR+grW1aCW52Ikm1OCw9apqbS2cfxOOM5zQBBHZSMCW+QDua5TWInW7m dWwBHH1Pu3+FdbNqoUgYUKezVzd7EL7UZlzlPLjOF78tW9H4Knp/7dET6HNRlnulycEtXZ2TXdiy ZCvH+ormxpxEzlVJ2MQPbBrb03UZoE8q5+ZW+4W71gM7CNxNCGU8EflWTgwyq+PmViVzx/nirtlf 26QhlGAB065pfOtbz7y4PagDXtbkXVusi4z3HpWdd6dNJM0iSkKfU0/Sbd7eabEm6J8EexrReMSL tP4UAYA0/cxDy5J9Kh/soQB283g8nNaV3B9nO9Id79jVJ7wOhWeEgdqAOb8Pwrb+Ir+3X7u4Ec/7 IP8AWrPiNjN4cRjyfMkBP0qDSWRvF94YkwvHT/cWn6qM+HSv/TaT6daAPa/gp/ySLQ/+3j/0fJXj fgFBb+GL+bcQTCGB/EV7L8Fht+EuiD0NwP8AyYkrw/SJfs3gOVw20vDt+vSgDl4LuP8AthCxyFl3 H/vqvTtOvIS6NFGA5HWvGVk2XYbP8ef1r07StasI44sKpkAHegDZ1pGguop1+6evt1qSCKK4minz tIIJwcVeZYdUscuoKn1PSsl9IfcwjlLY6YoA6JWU8gg1ieKdJ/tTTjtLb4xlQD1qCG7vLIhZclO1 a0GoxzICBj2oA5bRbRbW2RCCHAya3rWLzH7e2aZeWhbdNBx1zgf1qvbXojO2QEE8E0Aa8kaIg2sC c81CRmkV1fleh5FOzigCFhyQaoXRZd68bAATn61psMjjrWLrxZbQhZNrEcj1FAGDcYl1q3lh42OU ZiOM7Sf5CtLKM255D+eBWJFK88lvbxcHzDlvfY39KvrZyeaI5X4xkCt63wU/T/25iW7NmNbGVdpd xk9RVy1FtHujiLE4+8a537G/2gKsmFAyeOvNWUuLqKQon3chSQO1YDNiK7ItTIwBIbFWYxFOFmCY PbI5rKgdZZFgc7UB3H3q/cP8m6KTGz09aALbMo+UnBPSs69tp7iOWOZv3QUkbfxqxbyJeR5dVLpx Tby4W1gm81/k2HGfXBoA4DR7dYPFKxjO1CFz+dY/iyLy9Yf0JP8AOtrRpVm1xpmA5OcGqfjhFGol gMZNAHuP7Of/ACT2/wD+wrJ/6Kir16vIf2c/+Se3/wD2FZP/AEVFXr1ABRRRQAUUUUAFFFFABRRR QAUUUUAcl43/ANd4e/7C1v8A+jFoo8b/AOu8Pf8AYWt//Ri0UAXdH/1w/wCug/8AQHroK5/R/wDX D/roP/QHroKACiiigAooooAKKKKACiiigAooooA4H41An4R64B1P2f8A9Hx15P4HiX+yGBAJEXft gV618Zv+SUaz/vW//pRHXlnhgrbQKhwPMQZB9+tAHNeJJjbyadeDny5Np/ImujtWnvoIyCREQM81 zXjUBbeWFcYiuBgj/dNY2neLbm1tvIlTeo6HOKAPSpL9bWLyIDkjjdU/9oSQWSvJzI3SuN03Xra7 Rcvtc9Qx4rbu72KWGI7xlRyOxoA2ZtRaONWZdwIwQKzZWs5XLksmeSAKpx3zSRFFXdggcinxwM8g DDkrke1ADLhVkYogIT1qmIxFf3CBNuYI8DHu9bkFk8YZ2xk9gaykDHW7p5EPyQJkY46vit6PwVPT /wBuiJ9DIhiuILiVGOxmJwpPXmta3gkktzEV2zAfN7GuSvJp21OWXc2TIWHtzmupgvWktrObyzvz tfHT1zWAyREcbCGOOjL6VqxJZSgEuF9RmkjtS5LoAEZcjI71VeFkTcflOcYoA3rM29sCkZJDHPSp 1voi7R5Acc4rn4797dV8xNwPTB5qGXUo1uftCHnjK/hQBrXF6ZCY5gYSD8rDnNUpluVj3eaHT1U0 839tPC3n7VA5BzXG694mFufs1lk4yGYnP5UASeF5yddvHf7xZj+lbupxKnhKSUk4eRsZ+vFcP4cv 2h1B3PJkyPzrtdXnL6etmgxsAfnv60Aex/BkEfCjRgeoa4/9KJK+fQX/AOEBt9vfIP6f/Wr6G+EB z8MNLP8A01uun/XzLXz74Qli1Pw/NYXDAtC5KL6rgf1zQBw80bLJk+tdV4PvrGC5eO8IUFcKxz1z VjVNCRrN9p2SRKdvH3x1/D9a5TyZIGDDk0Ae62E9slriL7nUYFVpr62jkYFNuO5BrzKx8W6lp9mI kVWUdNw5Fadt45f5Re2aup4LK2MfpQB3w1Ox8kB5Syg9xVSXVbUNiIA+uK5dvEujsu4eap/u1XPi bTVJCiQ4/wBmgDop9Q3TIQ23ngZqLUb6BFEg+8RjHqaqLLHLbCfcqkruUZzj8K567mmMzOzeYcYA AwB+FAGlbeIbmK8cumdowI88A1s6TrwklFrd/I7H5M8/hXI2MUqgyMCFB5YjmrtlcfadYt4doC+a oJPUjNAHoAUA5FZHiC3aa2TaOhNbI4FNkjWVCrDINAHHW0W02SKvzGc5H/bN613tXKo2zkHrmqF/ bta6paBckGY4wP8AYauhgBEKZznHOa3rfBT9P/bmJbszDCVYlhtIXnv3pixBx1wd4rZxHICrc5HO KBaxoh2Lzj86wGZDQb1JIBjHOTxk1Xffg+Uc5PPTitG4gdiWlfbH7VWZ0jwkSZGcbmGc0AT22pW8 CBHBU/xHFc14q8SwX9slpayMyq+WP0p2vySWto7A/M/yYx0zXIrbS3biCFcueSaANPw3m41Njgkd KPHBUX5UHoa6fwV4f230CZ3FW3zkDoOmK5Px0wbXZNv3AxH60Ae6/s5/8k9v/wDsKyf+ioq9eryH 9nP/AJJ7f/8AYVk/9FRV69QAUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/9d4e/7C1v/wCjFoo8b/67 w9/2Frf/ANGLRQBd0f8A1w/66D/0B66Cuf0f/XD/AK6D/wBAeugoAKKKKACiiigAooooAKKKKACi iigDhPjJ/wAkq1j/AH7b/wBKI68u060UR28pJA2qePpXqPxkIHwr1cnoHts/+BEdeOajryfYLeO2 woEa8g4zwKAM3x/bRwB2jdiGk4+mDXnlel6ksmqaKI50bzCQykgnAwa85uYTBO0ZHQ0AMRmVwV65 4rcso9QmIYl2Qc4HesOMEuMdjmuq0G/jRysrBVA6mgDqNKmgMGw5jYDDIT1962rTyXGFbcyfpWLG sF38wxjn5h14ptnBNZTvJ5kgRjlcfWgDqKzHQSaxeLnrbQ/+hS1Wu9cEMTeWwaQfwnApmkXxv9Ru pSAD5EQOP96T/Gt6PwVPT/26In0Mu50L/SlIBySeMe1bmlWKwW+yRcgnoRWgUVmBIBI6E1HPOltC 0jkBVrAZbAwMCq140MUfmSsF5wCfWsi28SpKXVwi4OFJOM1T1S5kvSkCSZ3noDnGOaAL9zLBGWee f5V+6AeP5VzV+4zLLAh56EfSta109UDCb9445O89Ppmq+oyxW8TCNgD/AHcDigDibjU7x1MLSHb3 FUCSxyetaUqIsbOcDPSsygDX8PWzXGpIBnHtXpGtaZ9nuFKt5gMYGPTiuV8NwGzsGn8re5B/h6da sab4haWdku235GNznOKAPoD4Rp5Xw002P+7NdL+VzLXyTpGqTaVeCaEgZ4bPpX1x8KWV/h1YOuNr T3ZGPT7TLXxxQB6hbXNtq1opyN7L09KwdU0dosyRFic8g1T8MpctPEVLbAwyR2Fa/iKaWCVDn5Dg EZ4oA5iXeCFI5xURcpGVY5FX5o2ldHRDgjsOKr3FrJjLxsuPagCi23+EH8aFZl6d6tRRZO1VPvgc 1YNqrptK4bPUjFAEdtqEiHLPnjGDW9Z3lvNjecHHBB5rmJLSeMFjE+wfxY4pkTsr/fK0Ady0+5SM BwOQTUPkxxSwXsB5RwzD6Gs7Sbo7cSNnnnce1abwxrE0kTEk/eUenegDvoZBLCkgIO4A8U+uJ0jX /sT+XM5aE9yc4rfl8SadHCHEwYnoo5oAlvv+PzT/APr5b/0VJSz3K72gXdvxn5TWLDrLXuo2glVU UXDMvPby3H9a0UXzLljls5+8vWt63wU/T/25iW7CJjBKC4+UjkVdkv0WNTH82ajkUNEYt3zHnJ61 mISN6nCgHjntWAzVe5gnjHmA88dKgk8mNseWc44zyKy5dQtYELTTouP9oc1zmo6pcalJttneO2Xj d0zQAviXVPtFyLOEq2GySP72au2dkNKsFkALXMxwSewNQ6JohEgvbtDj70e4dfetqRPNlyw+UYAF AHReEYv7P0bUNRZj5h/dfUjBz+teQeJro3Gs3Gcf6wnj617CMWvhUojEBnLH8VFeHalJ5upXL+sh oA+k/wBnP/knt/8A9hWT/wBFRV69XkP7Of8AyT2//wCwrJ/6Kir16gAooooAKKKKACiiigAooooA KKKKAOS8b/67w9/2Frf/ANGLRR43/wBd4e/7C1v/AOjFooAu6P8A64f9dB/6A9dBXP6P/rh/10H/ AKA9dBQAUUUUAFFFFABRRRQAUUUUAFFFFAHA/Gr/AJJJrf1t/wD0ojr5oN6StvHzxtBzX018ZRn4 U6wP9q2/9KI68Dl0CGdEaMhZNoJAFAHURPby6XbSR4BGN24Z9eK808TIqas+zoRz9a73TLaWCAW7 cMrA5P8An3rk/GOn+TqvytuDKDnHegDl0GXUe9alvCRN5RyrUml6XLcalbRkHDOM8dBXcal4bVJo 2gYFyOm3k0AZNjK1soLTjbnpnmtGTXLfCqHJ2npjoKqXPhq7hP720kbA3bhjpUMdlbwxM7JnjG3F AEd7qEUzlo169a0PCMv+nXK5++qnH03f5/Gudkt1ilDRsQrnbirenXZsNURkbqApwfU1vR+Cp6f+ 3RE+h6RXNeLrpooIYUOC+c/St77UhRivVV3Yrh9Wum1a/bHHl8KOuawGZyTHvxW9pq5XIkUkHOM8 1zk0Um7cRwnGPWrdja3JjM8NwUfP3B6UAdLdpNP5fkuEK5znvWJeWqRAvcThj1wpNTKb4oxeZlwO QajtNKl1W88redoBJJHXHWgDk7y5EzkKMLmoIRmVQKmvofJvJUAJAYjOPen2Nuz3SLt6mgD0nTbd YrGORyoijjwffIzXnc8vlX0zrnYW4/E16FdW00WmNAXwrKpVj246VzMGhwPcqZ5+vYDrQB9FfBk7 vhTox9WuT/5MSV8l2Fmb2R41UlscYPQ19d/CaNYvhvp0aDCpPdqB7C5lr5m8A2f2nWdxPyjGRigD q9OtbbTNHRHUC4VQCR0zWLqMbahMTt4XtW7q6yNKIwgAVsKfxqZLGOCETMASeOPpQBzun2iG1w45 Vv6Va8lZgUcApjj1pb6dYI2aGEZ7dsmssXN85OLI5PQigCxFZWttMzfePSpktLSeQknae1VbdL1W JmtnKkd+1aFraQ3IJdiCOqnFADdTfTbfTWUczYxxyD/hWRa6FFqEIlhnVWJ5Vq3ZdLsthBgByPvY 5rAs5xpWrFCcoeBnigDQ/wCEelt41QSIXJ4Knim2qNBdrFO2CTg+hrokZZY1cdDyKo6jpwuhvVtr DqQKAMjVNLMH76B1cHqo5xWXAfNYYwMHua62xtY7ZGDDcrLgg/zrmtTsfs14TAd0bcigC9cOI/sj AglW5x/umt+0vEmjBR8MByM4NcUZT8inqDnHpwalS6eJgyNt9cHrW9b4Kfp/7cxLdndrKqozMW3n vnpXLaxq264+z2xJYfKSOmarG8vLqMBZmXPep9M0rLFy3zBssSOo71gMp22kXV4/mTsNo55Ndjo3 haG/AlmdY7SHhvU+/wCoqshE5CxEMo5J7cVr3F8YdMSCEbUIPGaAKup3CeeVjGIo/kQYxxWfZu8r 5J4UnNRzSHaScmrVmoWEHjJoA2JpvN0PYoIHT+VeK30Ziv7iMnO2Q/zr2KW5/wBDWILjj8DzXkes ALrF0Af+WhoA+j/2c/8Aknt//wBhWT/0VFXr1eQ/s5/8k9v/APsKyf8AoqKvXqACiiigAooooAKK KKACiiigAooooA5Lxv8A67w9/wBha3/9GLRR43/13h7/ALC1v/6MWigC7o/+uH/XQf8AoD10Fc5o Syr5azOjyhlDsiFVZtj5IBJwM9sn6mujoAKKKKACiiigAooooAKKKKACiiigDkfido2o+IPh5qmm aTb/AGi+mMJii3qm7bMjHliAOFJ614nF4D+Jkcoc+F43wAMG+gHT/tpXufxD8R3nhLwLqWuWEcEl zbeVsSdSUO6VEOQCD0Y968J/4aM8X/8AQO0P/vxL/wDHaAL7eE/icZWkHhOEE+l9B/8AHKz9T+Hv xJ1Rw83hWIMBjIvoP/jlL/w0Z4v/AOgdof8A34l/+O0f8NGeL/8AoHaH/wB+Jf8A47QBHZfDf4j2 Th08Kxswxyb6D/45WofCfxPaVZH8KQsV9b+H/wCOVn/8NGeL/wDoHaH/AN+Jf/jtH/DRni//AKB2 h/8AfiX/AOO0AdZJD8U3sTbf8IRZjjG/7bATj/vuuWn8BfEu4bLeFYQD1AvYOf8AyJTP+GjPF/8A 0DtD/wC/Ev8A8do/4aM8X/8AQO0P/vxL/wDHaAKrfDD4jMQf+EYjGDkD7dAf/alR/wDCq/iMHEg8 NLuz/wA/sH/xyr3/AA0Z4v8A+gdof/fiX/47Tj+0V4vCBv7O0Pkkf6iX2/6a+9aQ5uWXLtbX0uv1 sIf/AMIP8UPLKf8ACMx8jGftsH/xys8fC/4kLL5g8NrnOf8Aj+g/+OVb/wCGjPF//QO0P/vxL/8A HaP+GjPF/wD0DtD/AO/Ev/x2sxld/hl8R3csfDEYJ/6fYP8A45UqfDn4kxACPwvGvr/p0Bz/AORK f/w0Z4v/AOgdof8A34l/+O0f8NGeL/8AoHaH/wB+Jf8A47QBbt/A/wAQoSS3gmCQnudRi/8AjlPg 8IfEq2kLxeDrdc9vt0P/AMcqj/w0Z4v/AOgdof8A34l/+O0f8NGeL/8AoHaH/wB+Jf8A47QBTvvh Z8Qr64ab/hFIo2bk7b6D/wCOUtp8LfiJaXAmHhaNyOga+gx/6Mq3/wANGeL/APoHaH/34l/+O0f8 NGeL/wDoHaH/AN+Jf/jtAF1/B/xOfbnwpDhQBg30B/8AalUpPh/8THuxOPDMa4/hF7b4/wDQ6P8A hozxf/0DtD/78S//AB2j/hozxf8A9A7Q/wDvxL/8doA90+HOmalo/gWwstXtRa36yTvLCHVwm+Z3 HKkg8MO9eF+Hfh58RvDxJj8JQzEnOXvoAf0kr3b4eeI7zxb4F03XL+OCO5ufN3pApCDbK6DAJJ6K O9dPQB87TeF/iXPJubwbbfT7fD/8cpreFfiY0ew+D7fb6fb4f/jlfRdFAHzXJ4J+JDgf8UfAMf8A T/Cf/alRf8IL8Tu3heEfS8t//i6+maKAPmhfBHxOHXwtCfrewf8Axynr4K+JYGP+ESg/C+gH/tSv pSigD5qbwR8TGyP+EUhA/wCv6D/45WPc/Cj4j3Nx5x8OKp9Bewf/AByvq2igD5otvA3xNt4FiPha JwvAJvoAf/Q6lPgz4mEf8inD/wCB0H/xyvpKigD5sHgv4lbcHwjCQeo+3w//AByq9x8P/iNOR/xS MCAdAL6E4/OSvpuigD5Uf4U/ER2Lf8IyqnqMXsH/AMcpp+FHxFI58NL/AOBsH/xyvq2iqlNyST6f 53/UD5isPh58RrE/8ijDL/vX0I/lJV6bwb8RJEKr4Kt4wf7uoxf/AByvo+ipA+bv+EN+JQi2L4Qt 1P8Ae+3Q/wDxymy+C/iZKip/wicICjHF9D/8cr6TooA+ZG8A/Ethj/hFoh/2+wf/AByp4/BXxMjQ KPCcHH/T9B/8cr6UooA+az4K+JzLg+FYf/A6D/45XO3fwc+Il3dS3DeHQpkbcQL234/8iV9bUUAe cfBbwvrPhLwdd2GuWf2S5k1B5lTzUkyhjjAOUJHVT+Vej0UUAFFFFABRRRQAUUUUAFFFFABRRRQB yXjf/XeHv+wtb/8AoxaKPG/+u8Pf9ha3/wDRi0UAXtI/1/8A21H/AKC9b9YGkf6//tqP/QXrfpy3 YBRRRSAKKKKACiiigAooooAKKKKAOA+Nf/JItc/7d/8A0fHXyHX158a/+SRa5/27/wDo+OvkOgAo oooAKKKKACiiigAp5/1C/wC8f5CmU8/6hf8AeP8AIVvR+Cp6f+3RE+gyiiisBhRRRQAUUUUAFFFF ABRRRQB9efBT/kkWh/8Abx/6Pkrv64D4Kf8AJItD/wC3j/0fJXf0AFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByXjf/XeH v+wtb/8AoxaKPG/+u8Pf9ha3/wDRi0UAXtI/1/8A21H/AKC9b9YGkf6//tqP/QXrfpy3YBRRRSAK KKKACiiigAooooAKKKKAOA+Nf/JItc/7d/8A0fHXyHX158a/+SRa5/27/wDo+OvkOgAooooAKKKK ACiip7W0mvJhHCuSf0oAgqTBMKADPzH+ldPZ+FNpBuZFLf3VGa0otCt1uniEJIWNGHHclv8ACt6P wVPT/wBuiJ9DhRDKekb/APfJpfIl/wCeT/8AfJr1O38PzGNWiswVI45qR9AuAh3WSAfWsBnkpBU4 IIPoaSvQr/w5A+d8JiY98VyWp6Fcad85w8fqKAMqiiigAooooAKKKKAPrz4Kf8ki0P8A7eP/AEfJ Xf1wHwU/5JFof/bx/wCj5K7+gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOS8b/wCu8Pf9ha3/APRi0UeN/wDXeHv+wtb/ APoxaKAL2kf6/wD7aj/0F636wNI/1/8A21H/AKC9b9OW7AKKKKQBRRRQAUUUUAFFFFABRRRQBwHx r/5JFrn/AG7/APo+OvkOvrz41/8AJItc/wC3f/0fHXyHQAUUUUAFFFWrCye/ulhjHJ60AFjYS38w jjH1Neg6LoS26x28CCSVsBnI6Gl0PRRDttrVMyNyzV6TFYWHhfSTeXpUXHl7lDHBzjNADdH8E28E H2m/uERQMHODz+dZ15rXh7SNdvAqefGbOBVJIGGDzbux7Fa8/wBZ8cavqE00EVyy2xfKKAPT/wCu awWjnuZC07kvgEk/jW9H4Knp/wC3RE+h61b/ABP0i2iEX2HdtUKPm/D0q/ZfEnRbktHJZLt9S4H9 K8W+xKMnG7r0qIQ7eR19KwGfQ7WGh+JbMPaSCOY/wkD/AOtXAa94YuNPWSOdN8J74riNM8Rano86 tBcsqqc4xXtOh+KLDxjZtZ3YBkKZDH1/D6UAeA67ov8AZzLLES0T5PTkViV7J4o0FLSU200P7ts9 ex9a8s1jTW068KAfuzypoAzqKKKACiiigD68+Cn/ACSLQ/8At4/9HyV39cB8FP8AkkWh/wDbx/6P krv6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooA5Lxv/rvD3/YWt/8A0YtFHjf/AF3h7/sLW/8A6MWigC9pH+v/AO2o/wDQ XrfrA0j/AF//AG1H/oL1v05bsAooopAFFFFABRRRQAUUUUAFFFFAHAfGv/kkWuf9u/8A6Pjr5Dr6 8+Nf/JItc/7d/wD0fHXyHQAUUUUAKqlmCjkk4Fd14f0prSEEqfNkFctolobvU4gOisCa9f8ADOnR T3rTzNtijXIwM+lAG7pU1l4T0Fr29OJpPmRSOo/D8K8g8S+IrrX9UmnZmZDISFz2z/hXR/EXXjfX kVpCuyKNAMZ965ayEMTxkoXPegCmJyoUIpNKBPI+HBBPQZ7VsvavOHmEYVM9hV3QNOS5upsgttVT k9uW/wAK3o/BU9P/AG6In0E023xbjf8A3f6Viamyxytg8gmuqu4WtLwovfjFQXXhr7TE04JBbJ6V gM5JJ42j+Zu3anafqZ0/U4biM4KtmtBbCK0dllTew9Kq3NohQlcfhQB7Tpeo2vjTRtjSB7qNeBjk k1wOvaMwaaznj2uudufzqL4d6n9g1YoSdpI4zivQPHWnAMl2i/fVTn8BQB87TRNDKyMMEGmVveJd P+z3PnqDsb2rBoAKKKKAPrz4Kf8AJItD/wC3j/0fJXf1wHwU/wCSRaH/ANvH/o+Su/oAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigDkvG/+u8Pf9ha3/wDRi0UeN/8AXeHv+wtb/wDoxaKAL2kf6/8A7aj/ANBet+sDSP8AX/8A bUf+gvW/TluwCiiikAUUUUAFFFFABRRRQAUUUUAcB8a/+SRa5/27/wDo+OvkOvrz41/8ki1z/t3/ APR8dfIdABRRQBk4FAHX+EbMGJ58Hc3AP416taWcujeGrjUZHUKVwFYH6/0rifClrstNPiC/NIyk jHriux+KN6LDw3bWEcmDKwyoPPRqAPJ7tpNRvpbgjqxFaek6WzybiP0rPt9qRqByepruNGsSbKGY HqA2PXjNAFO4tLhYDHDCSCMdKm8MWstrdXaTLhzFG2Mf7Un+FactxeKSI7bP1U1TgvpYdXuDcRbC beIfgGk/xrej8FT0/wDboifQi1TDaoiEkHcDx9P/AK1bsKq1uAvTFY6S295rRKkMAPr2NbSrHAvG FX3NYDOX1bShDctcBSyNjKmufvrRIcSRkhT/ACr0ieKK4h+Uh1YdeOvSuSms9sMsTj5gMjNAHKLI 2n3kVzC2cNkmvd9PuIfEPg6FmwzrHjjr1NeHXCosboV5PI4rtPhxr0qSDSy+QSeCe3+TQBgeIbEy afOjg7o+R+defV7b4tsVgvZo9o2uuOnXg/8A1q8avrdra7kjIIwfSgCvRRRQB9efBT/kkWh/9vH/ AKPkrv64D4Kf8ki0P/t4/wDR8ld/QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHJeN/wDXeHv+wtb/APoxaKPG/wDrvD3/ AGFrf/0YtFAF7SP9f/21H/oL1v1gaR/r/wDtqP8A0F636ct2AUUUUgCiiigAooooAKKKKACiiigD gPjX/wAki1z/ALd//R8dfIdfXnxr/wCSRa5/27/+j46+Q6ACrFlCZ7tEHrVetfw7D52pL6CgD1Pw tbE6laKoysIUnA9OtZPxL1E6h4gSANhIietdb4OiBNxcYyFQnFeaa/J9r8SXL5zhjj0oAgtYDIwW M7iTiu60nTru0gQGZcHkA5rJ8NWNqVEsgG8E4zXR3t9Hb2sjq6l1U4GaAL1zcJZ2PmPtLAfNgA1m wIt9qV4zLgfZoTg/70tZOm3E/iCRo5m2RpyVHO4/5NbsQFpq93GMYNpAM+nzS1vR+Cp6f+3RE+hz UmmTw6262rDP3hk+ua14rO+ndY7ggKO4NV4bsz+JHEHzIF2sc+ma6uDd5Q3fh9KwGVXKWFtswzHG OOa5270W8vZ2uBIsYIxt5rrNqzDLx4I45rMn1RVuhbsAo+uPegDzrV7GW0crIPmGelUtHvTpeswX QJChhnFdZ4pXzdrKeMEGuImiGzIPIOcigD2zxeIr/SIdRjByduTj6f414v4tt/LvI5lACyZr2XTX Gs+BGVV+dFBz1xgj/wCvXl3im28yyQk/NFmgDiaKKKAPrz4Kf8ki0P8A7eP/AEfJXf1wHwU/5JFo f/bx/wCj5K7+gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKAOS8b/wCu8Pf9ha3/APRi0UeN/wDXeHv+wtb/APoxaKAL2kf6 /wD7aj/0F636wNI/1/8A21H/AKC9b9OW7AKKKKQBRRRQAUUUUAFFFFABRRRQBwHxr/5JFrn/AG7/ APo+OvkOvr341nHwj1w/9e//AKPjr5F81vRP++B/hWkFTfxtr0V/1QncZXQ+F8faH9cH+VYPmt6J /wB8D/CtPQ5h9t2vwCP4Tt/lir5aH8z+5f8AyQanu/hKNU0C4ZQN5hJH1rx+9XOtXe84G4nrXp/h XT7O60942lvQ+w8JfTIPyDivPvEFiuna/KkgcIxOCZCxPTvnNHLQ/mf3L/5INTQ04BYULSE5OAoN dULWzS1jacYLAAgk/wCNcBZNFGzO/nYHQCRl/ka3NLWxvWa4up7j5WOF8+XIx75o5aH8z+5f/JBq awtl0XVIpYARBcNsKnseoP6Grk8C3mrXKuSB9lgzhsH70tRW8Gj6hIESa7yhzg3k39W96ytQ0mSX WZUt5JwqW8ZJa5cn7z9y2e1b0o0eSfvPbsv5l/eE76E2jwRW+uXUcZJAHfnua6G/v5IJEjgXc7ZG CK5HRLa1XU54bmeYH+8ly65OTnkGukistNN0scc15IxHVb2bj/x6sOWh/M/uX/yQ9RTd6iYyWgXP s39K56yjl1jU5HumZGQcAcVJdsbLVHUm+kg6/Jdy5HA/2qu2P9i30jNDLdrNt5zdzA/q1HLQ/mf3 L/5INTn9UEyXEkKs7xqcciuanBCPjpmui1WHyLq4AebYW4zO5yPzrCuyEjVVD5Y85Yn+tHLQ/mf3 L/5INT1fwKzf8IhcA9kYdK4TxCVNhdFsH9K7fT9EtdP8FPcTPdrIR8oS8mRTyP4Q2O/pXmPiV1gg QK0hMmc75Gb+Zo5aH8z+5f8AyQanJUU/zW9E/wC+B/hR5reif98D/CjlofzP7l/8kGp9c/BT/kkW h/8Abx/6Pkrv64H4KnPwj0M/9fH/AKPkrvqwfkMKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDkvG/8ArvD3/YWt/wD0YtFH jf8A13h7/sLW/wD6MWigC9pH+v8A+2o/9Bet+sDSP9f/ANtR/wCgvW/TluwCiiikAUUUUAFFFFAB RRRQAUUUUAcB8a/+SRa5/wBu/wD6Pjr5Dr68+Nf/ACSLXP8At3/9Hx18h0AFT2kvk3KN74qCigD2 TwbqyJdwAn5XIB/Gk+JGmmOVL6MDy1kwOOxBNcP4d1ZopYhuwykCvVYdnijQp9PmPzKu9dvXPT+t AHmYaNguwjBHWtnw4sLXMlvKmT2P1NYF1byaXqD2coIIbIz6VqaPP9m1CN2xy2D9KAN6+0S6t5vt mnEKe69M1S/tLUGvLjNsVl8mNW59C/P6n8q277VhCtu0ZBVpNrA+mDVC8ulOoTsiqd1vD+B3Sf41 vR+Cp6f+3RE+hn6JZ+fqBafk5JP1Oa62ysrezkklQYc8496xLAJayxzHPz9T+Fay3EEkxJl2498C sBj7OxaaSeaQL856Htjj+lc1f+H3sUlu1uBG4OcD3P0963W1tE1BbQPhAM7h6/X8axPEd6HZEWT5 QegPXigDAneQ481izt1J7VBDE2o6pDbxR8AjJFRXMzuRt5d+mK9F8A+G44LU6jc5Dcn5u9AGv4sn js9EhsEYlvlJA6djXiPiO8FzehFOVjyK7rxXrLzedMWHHCj8cV5dNK00rOxyTQAyiiigD68+Cn/J ItD/AO3j/wBHyV39cB8FP+SRaH/28f8Ao+Su/oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDkvG/8ArvD3/YWt/wD0YtFH jf8A13h7/sLW/wD6MWigC9pH+v8A+2o/9Bet+sDSP9f/ANtR/wCgvW/TluwCiiikAUUUUAFFFFAB RRRQAUUUUAcB8a/+SRa5/wBu/wD6Pjr5Dr68+Nf/ACSLXP8At3/9Hx18h0AFFFFAEkEzQyq6sRgj Nek+EteMU0cqyHKj5sHqK8yq7p2oPYXAcE7e4FAHuHiXw5Z+KLNb+yVftAXB29civLpYrzTp2huo JBtbAYrjvXVeG/GSwENG31U11dxc6Dr0GJ0MczDncOMn8aAPMvtjzQKDOWVTnBbpUlvqbx3DuwL/ ACKMk5wAW/xrf1H4f8b7G5jYE+/+Fc5caBqVncy20SbyqK749DnH8jW9H4Knp/7dET6Gnc6xby24 xIFPBGGrPGryGT5ZHcA8ZY1jz6TqIY/uio9KntdL1GUhBFs9yawGaEmr+YDujUMf4gBms555LiXY paR24GDmtew8F6pfHcWQLk9Sf8K63S/Cmn6WRLczIWXuPWgDL8L+GXYNc3icgAqJB0/Ot/WdfEEA sbQ+XEgG4pwCevb61U1jXIliMNuxSIfebpmvPdX18MzRW7MQerUAVfEGpm5mMSNlB15rCoJJOT1N FABRRRQB9efBT/kkWh/9vH/o+Su/rgPgp/ySLQ/+3j/0fJXf0AFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByXjf/XeHv8A sLW//oxaKPG/+u8Pf9ha3/8ARi0UAXtI/wBf/wBtR/6C9b9YGkf6/wD7aj/0F636ct2AUUUUgCii igAooooAKKKKACiiigDgPjX/AMki1z/t3/8AR8dfIdfXnxr/AOSRa5/27/8Ao+OvkOgAooooAKKK KAHxTSQtujYqfatqz8TXFvtV13KO4NYVFAHoWm+OGiZSFyBxhmq/H4tE2pTXBiQiSKNMbv7pc+n+ 1Xl3SpvNkWFcOw+Y9/YVvR+Cp6f+3RE+h6wuvWTcvZo2fSUf4VYi8UWMH3LBc9yXH+FeQre3Kfdm YU46jeHrO1YDPUrrxoyhgsaRA+hrm7zxssikKrMc93rjJLiaX78hNRUAauoa5PfcY8tfQGsqiigA ooooAKKKKAPrz4Kf8ki0P/t4/wDR8ld/XAfBT/kkWh/9vH/o+Su/oAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDkvG/wDr vD3/AGFrf/0YtFHjf/XeHv8AsLW//oxaKALehSrP5cyBwsjqwDoUbBRzypAIPsRkV0Vc14fsE05v JTUbm/BlDebcY3D5HG3gDjjP410tNu+oBRRRSAKKKKACiiigAooooAKKKKAOA+Nf/JItc/7d/wD0 fHXyHX158a/+SRa5/wBu/wD6Pjr5DoAKKKKACiiigAooooAKef8AUL/vH+QplPP+oX/eP8hW9H4K np/7dET6DKKKKwGFFFFABRRRQAUUUUAFFFFAH158FP8AkkWh/wDbx/6Pkrv64D4Kf8ki0P8A7eP/ AEfJXf0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQByXjf/AF3h7/sLW/8A6MWim+OnWN9AdjhV1W3JPt5i0UAX9H/1w/66 D/0B66Cuf0f/AFw/66D/ANAeugoAKKKKACiiigAooooAKKKKACiiigDiPi9YXmp/C7WbSwtJ7u5k 8jZDBGZHbE0ZOFHJwAT+FfLX/CC+L/8AoVdc/wDBfL/8TX25RQB8R/8ACC+L/wDoVdc/8F8v/wAT R/wgvi//AKFXXP8AwXy//E19uUUAfEf/AAgvi/8A6FXXP/BfL/8AE0f8IL4v/wChV1z/AMF8v/xN fblFAHxH/wAIL4v/AOhV1z/wXy//ABNH/CC+L/8AoVdc/wDBfL/8TX25RQB8R/8ACC+L/wDoVdc/ 8F8v/wATTz4G8XeUq/8ACK65kMT/AMg6b2/2a+2aKuM3FNLrp+Kf6AfEf/CC+L/+hV1z/wAF8v8A 8TR/wgvi/wD6FXXP/BfL/wDE19uUVAHxH/wgvi//AKFXXP8AwXy//E0f8IL4v/6FXXP/AAXy/wDx NfblFAHxH/wgvi//AKFXXP8AwXy//E0f8IL4v/6FXXP/AAXy/wDxNfblFAHxH/wgvi//AKFXXP8A wXy//E0f8IL4v/6FXXP/AAXy/wDxNfblFAHxH/wgvi//AKFXXP8AwXy//E0f8IL4v/6FXXP/AAXy /wDxNfblFAHEfCGwvNM+F2jWl/aT2lzH5++GeMxuuZpCMqeRkEH8a7eiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOM+ If8AqNG/7CMP/oa0UfEP/UaN/wBhGH/0NaKAK9/pBvXUrr1zaoB/q7c7VJ9TmMkn8f61T/4Rk/8A Q0aj/wB/B/8AGqqnSNP1jxvLDqNrHcRpp0bqH7HzH9x2/wAjqPJf+FoeGe3w+t/x1A//ABv6fr7Y APZP+EZP/Q0aj/38H/xqj/hGT/0NGo/9/B/8aryW5+IGhWdvbzzfDqFYrhS0THUG5wcH/ln16Z+p 9Rj2f/hBvDI66Pb8euf8R6H079OdiTT2G4tb/wBXKX/CMn/oaNR/7+D/AONUf8Iyf+ho1H/v4P8A 41V3/hBvDI66Pb8euf8AEeh9O/TnYf8ACDeGR10e349c/wCI9D6d+nOxiKX/AAjJ/wCho1H/AL+D /wCNUf8ACMn/AKGjUf8Av4P/AI1V3/hBvDI66Pb8euf8R6H079OdlmDRdO0a0nj0+zjt1keMvtHL EBxyTz6/rwOQADJ/4Rk/9DRqP/fwf/GqP+EZP/Q0aj/38H/xqugurq4W8nVbiUASMAA545rl9b8Y XlnqdrpmnT+ffO5aWOSRgqoEZtu4HhiBxwcDkjkZmc4wi5ydkhxi5NRW7LH/AAjJ/wCho1H/AL+D /wCNUf8ACMn/AKGjUf8Av4P/AI1Wtb6nLdW0VxDdzNFKgdG3MMqRkHB5FV/+Ejtv7S/s3+2Yvt// AD6/ah5vTd9zOenPTpzVCKP/AAjJ/wCho1H/AL+D/wCNUf8ACMn/AKGjUf8Av4P/AI1W19suv+fm b/vs0fbLr/n5m/77NAGL/wAIyf8AoaNR/wC/g/8AjVH/AAjJ/wCho1H/AL+D/wCNVtfbLr/n5m/7 7NZPg6aU+H5ZjK5lZ52L7juJNwcnNAEf/CMn/oaNR/7+D/41R/wjJ/6GjUf+/g/+NV59rnxg1vRt VuLWS4iCrNMsYW0eQhEmeMZbz1yfkz0HWol+L3ieR/KBEMz2kl3CLnT5I1kjSNpMg/aDwQhAIBFA Ho3/AAjJ/wCho1H/AL+D/wCNUf8ACMn/AKGjUf8Av4P/AI1VyTxEllptvealqsdmkqr889x5aliM 4BY+x49qsWmr/b7ZLmz1H7RbvnbLDPvVsHBwQcHkEUAZf/CMn/oaNR/7+D/41R/wjJ/6GjUf+/g/ +NVqT6s9vLbRS3kyvcyGKIbmO5gjPj2+VGPPpU32y6/5+Zv++zQBi/8ACMn/AKGjUf8Av4P/AI1R /wAIyf8AoaNR/wC/g/8AjVHia7uWtLBGuJSjajbBlLnBHmA8/iB+Vbk9zPBbWqwyOu5QMByBkyEZ 4/wppXdkBh/8Iyf+ho1H/v4P/jVH/CMn/oaNR/7+D/41Uup+JLjT9Olvlv7eWONCwUTzBmwH4H7v GT5bjnA4qxNr0tvriac09w6TLm3uU3mOQgEld33QcBscn7rZxxnSVGcVdolTTdkUv+EZP/Q0aj/3 8H/xqj/hGT/0NGo/9/B/8aq9ceI7a0vYrK51mKG7mx5UEl0Fd8nAwpOTk8DHemWPimx1OdodP163 u5VXeUt7xZGC5AzgE8ZI/Osiip/wjJ/6GjUf+/g/+NUf8Iyf+ho1H/v4P/jVbX2y6/5+Zv8Avs0f bLr/AJ+Zv++zQBi/8Iyf+ho1H/v4P/jVH/CMn/oaNR/7+D/41UmkTSz+OtQMsryFUtFXexOBmQ4H 4k/nWjLqF8sipFI7sys3zTlBxj1+tCTewN23Mr/hGT/0NGo/9/B/8ao/4Rk/9DRqP/fwf/GquHW7 xJI1d2w7hMpcE4zxk8fT86tzalLbwSTTXrxRRqXd3lKqqgZJJJ4AFNxa3RMZxl8LuZH/AAjJ/wCh o1H/AL+D/wCNUf8ACMn/AKGjUf8Av4P/AI1WudSlWdYTeuJXUuqGU7mUEAkDPQFlz9R60HUpVnWE 3riV1LqhlO5lBAJAz0BZc/UetIoyP+EZP/Q0aj/38H/xqj/hGT/0NGo/9/B/8ara+2XX/PzN/wB9 moL6+u00+5ZbqdWETEESEEHBoAzP+EZP/Q0aj/38H/xqj/hGT/0NGo/9/B/8aq54Zkki8H2DRuyH 7PbjKnH/ACzNOk1i5WEv/aECMqB3SWaUFQUZ+yH+FWPGelXGEpbCbS3KP/CMn/oaNR/7+D/41R/w jJ/6GjUf+/g/+NUsniyWHSYNSeaWWEzSxXH2dpJPJCOy7+Bnadpb5gvyg9TgG7f+I7bSvL/tHWYr PzM7PtF0I92MZxuIzjI/OicJQdpBGSauij/wjJ/6GjUf+/g/+NUf8Iyf+ho1H/v4P/jVa8OpS3EE c0N68sUih0dJSyspGQQQeQRTG1Z0vYrNryYXEsbyom5uVQqGOenBdfz+tQMy/wDhGT/0NGo/9/B/ 8ao/4Rk/9DRqP/fwf/Gq2vtl1/z8zf8AfZrE1i5uJNd0GOSeVkM8rbWckZEL4OPxP50AL/wjJ/6G jUf+/g/+NUf8Iyf+ho1H/v4P/jVdBLPc/aIYIJXDOiKi7yozsBP04BP+JqKS4v4ZhE0ssrEH/j2k eXGOoIAyD+H9azdWClyt6jUWzE/4Rk/9DRqP/fwf/GqP+EZP/Q0aj/38H/xqtaHVJLjzPKu5WMbm NxvYFWHUEHkGq9x4jtrS9isrnWYobubHlQSXQV3ycDCk5OTwMd6tNNXQmraMo/8ACMn/AKGjUf8A v4P/AI1R/wAIyf8AoaNR/wC/g/8AjVajas6XsVm15MLiWN5UTc3KoVDHPTguv5/Wpvtl1/z8zf8A fZpgYv8AwjJ/6GjUf+/g/wDjVH/CMn/oaNR/7+D/AONVtfbLr/n5m/77NYWiSyTXetvLIzv/AGiw 3McnAjjA/QUAP/4Rk/8AQ0aj/wB/B/8AGqP+EZP/AENGo/8Afwf/ABqiX+z4nvbm/FssayqDLOFw PkTHJ9z+tcrr3jHTdMmAsNM0+/gIGZkkTCtz8pABPQdf8KuFOU3aKFc6r/hGT/0NGo/9/B/8ao/4 Rk/9DRqP/fwf/Gqz/Dt9Z6zpT3s+n2sOzltsYIA2hvTsDWfqWvx214YbXw2JI45fJlmlRVUSYBKA gEbhkZBwR0IqZRcW0xnQf8Iyf+ho1H/v4P8A41R/wjJ/6GjUf+/g/wDjVGm/2dqVoJ47KBSCVdDG uVYdR0p1z/ZNqsnmW0G5FLFREM9M+lYVq8KMeao7LY0p0pVHaCG/8Iyf+ho1H/v4P/jVH/CMn/oa NR/7+D/41XJw+NdOmfYPDy7/AEXacf8AjtdrHY2bxoxsrcFgDjyhx+YBrqnRnTScla5y0sRTqtqD vYrf8Iyf+ho1H/v4P/jVH/CMn/oaNR/7+D/41Trk6JZT28N0dPgluG2QJLsVpWyBhQfvHJHT1FPu 4tJsLZ7m8Syt7dMbpZgiKuTgZJ4HJArM2Iv+EZP/AENGo/8Afwf/ABqj/hGT/wBDRqP/AH8H/wAa pJJ/D8X2TzJdMT7bj7LuaMefnGNn977w6Z6j1qa7i0mwtnubxLK3t0xulmCIq5OBkngckCgCL/hG T/0NGo/9/B/8ao/4Rk/9DRqP/fwf/Gql8rSftv2LZZfa/L83yMJv2Zxu29cZ4z0zRaRaTf2yXNml lcW752ywhHVsHBwRweQRQBD/AMIrE8sLz+IL2dYpUlWOVgVLKQRn936iitaztbeBLlooIo2MQGUQ A43rRQBnaccePbjnH/Esj7/9NW9x/n06j5tg0vSodHjv7fVI59QLLttmjBwc85U8/iePbmvpyysj F4hm1RriJUa0S3WNt+7IdmzwMY5Hv16YBrzbRvgo2iavbalB4qtJJIG3BJtNZkbjGCN3vUzclFuC u+g4qLkufbqcd4vtrQfDvwzGZUGpKk8jRs/7xkMhIOP++vrXrPxMsftFxodx/anh+D7LJM32DXpN ttdbkCbiueSmcDgY3ZyuNo5TU/gncaxqU2oX3jWOa5mbc7nT2H4AbuAOwHSvZftMY6Sw+3L/ANF9 h+XbC7ebB4eVCnyzd22397vb0OnF4iNeScY2t+V21917eiR4TaWUvjHWLOODw5pFrNeeE5dlvMvk Q27G4kUTxDy2wSTvHHRz8wxmvVbPxLo+iLbaBfaw8+pWbWmnzSvDJl55EzHlsHBYITy3HGSDjZ0P 2mMdJYfbl/6L7D8u2F2n2mMdJYfbl/6L7D8u2F29ZylfR9YsNe0qHU9Mn86zm3eXLsZM7WKnhgpH KnsPu9sfJLeDED8Y+ZO3s/sP8+nQP+0xjpLD7cv/AEX2H5dsLthuZY2gYLIjMWXhQegDeoHqP/rD AABieKVvT9oa0eURrI/nJbj9668j5Tz05OB83ocjDeTaFb3H/CU3GqSx3cdpE7oon3M7SH5SOeSx bjHJz9a9SudY1WS7mkj8OXJRnYruuYQcE8ZG6qHn6kb8Xb+Hbt3X7iNeRFI2IwWUbuCRgZ9M4xub PLjKM69L2UXZPf08vM2w1RUavtbXdtO1+5J4UmnjspdLvIporqyb7srbi0T5KHPQj7y4BONnboKd rc3lj4YbRbaC7XX4rJ0WT7K5ikuQhPmecV8s73+bLNklvmAbIrS/tXWP+hcuP/AqH/4qj+1dY/6F y4/8Cof/AIquiEeWKj2IqT55uXcqaRcNa3F3JbQ6tLpZ+zRxLdLM0omaRlkbE/z7ApiJP3QAxHO6 tl9Q2ef/AKHdt5VxHb/LFnfv2fOvPKLv+Y9tj+lUf7V1j/oXLj/wKh/+Ko/tXWP+hcuP/AqH/wCK qiDarL8G/wDIsP8AWb/0oNQ/2rrH/QuXH/gVD/8AFUujG+0fwz5TabLNdO0g8hJIwVDSlwSS2Ono TyR74APN7fw1pepeKr3V9Vlimis7m5CWRUsGIupm3yDIygGSFB+ba2SqK7Ca6u4fE+ka3q01hNBN YW91Gk05G5ma3ddpPGW2gkqoO3gYK7fK3n0Cczzyx6RrsHnzPM6Qa0I03uxZiFD4GSTTJfDk9xC8 M2meIpIpFKOj67lWU8EEGTkVzyoOVTnb228u/wB5108TGnSdNR1e7/pdDV1xJf7N8OSxyX0Sw3Kv JNZWxnkjU20q527H4JYD7p+9261Vv0vbxNTv7SXWXktdIjexLCaAyXKmfJaEBVdiVjypTBBHGCM7 EOoatBBHEvhy5KooUE3UOcAY/vU/+1dY/wChcuP/AAKh/wDiq6DkMRIZpfFGmvcx6m9/Fqdy8pZZ zbJb+XOsRH/LEHaYhx82Sc8lq6m21D7T9j/0O7i+1W5uP3sW3ysbPkk5+V/n6f7LelUf7V1j/oXL j/wKh/8AiqP7V1j/AKFy4/8AAqH/AOKoAh1m4+16Xo9z5M0PnXtpJ5Uy7XTLA7WHYjoR610GovLF bWUsMLzPE0chjQqGZVmywG4gZwD1Irmr59W1RrKJtEmt0jvIZmka4iYBVcE8Bs9M1r6hqd/HLHDa 6LPcpGmDKJ4kDEkngFs9+4HOfrTi+VpiaurHKf8ACPPDaXE7W2pzTTadPawrM9rFG07eYvmHNwSA BIRjB9etdTq0oe68O/NuL3s8mRjGTHKT0JHf1NYry+IyX8uDWYY2ZiI47m0AXJJwMrnv3Oaijh1o X9td3FjrF21szNGk95bFQSpU9AD0Y96654hVF72+pjGm4vQtXbPa+IG/st9R+0z3ELXNubRjayg7 FeQylMAiIcBZAMoBgkkGl4RvH07wxDFOdcubi106N2tJtOaPyyiDMcR8pA5zwAWYnA56mtj+1dY/ 6Fy4/wDAqH/4qj+1dY/6Fy4/8Cof/iq4zcvXOofZvtn+h3cv2W3Fx+6i3ebnf8kfPzP8nT/aX1q5 WL/ausf9C5cf+BUP/wAVR/ausf8AQuXH/gVD/wDFUATaJ/yPOpfS0/8AZ6u3aubWaSNSzxRlwo6n BFZuiG+i1zUNUu9NlgV1gKQ+ZGzOU35Aw2O46kdagnv9cmx5ejX0HBB8q7gG4H1yT+lVCTjJSXQz qw54OPcxW1G5aSJ3UpEchT05IOOe/Izx6V0etRT6t4O1GG2iJuLuwlSOMkAlnjIAyeBye9UIW1CK ZJj4dvZZUOVaW/jfBxjgF8DgkcCrn9q6x/0Llx/4FQ//ABVbYiuqtrK1jnweF+rxave5ia3JNrmo Wc2nQ6mkUSrBO/kT2rhZLq1JCkhWxsjlyV6AckZGSPT5ofFcLXS6m2n2c0sFpIJJ3bLrauodgSzx 7xNkuSgxgkYArb/tXWP+hcuP/AqH/wCKo/tXWP8AoXLj/wACof8A4quc7C8mob/I/wBDu1824kt/ mixs2b/nbnhG2fKe+9PWmTXH2rR7yTyZosLNHtlXaTtLLux6HGQe4INVP7V1j/oXLj/wKh/+KqO4 1DWJ7aWH/hHbhfMQrn7TCcZGP71AGn4d/wCROsP+uFv/AOizXM3OiSanKk97basvk/ZfPWI2yJsS IxMu43A++ScNgEDjHNblrNfaV4ZsLOPTJbq4WKJZFSWNRGVTByWbnk9s9D7Zz7i68QSzmWDT9TtM qFZYLm1wcZ5+YE9/XFb0KzpN26mdSHOTXbsngbxG8sflGRblliDxsUR5pnUEo7DOJBn06c1V8SCW PxDpdwLjU7aJbW5Rp9PszcMGZ4CFYeXJgEKxzgfd61Uu7XXr+2lt7qLXZYZQVdDe2mCD/wABra/t XWP+hcuP/AqH/wCKorTjK1hwi1uZTRXst5farAdTEn9p2i2yM8yp9mZbcSfuSduMNLklcqcngrxT 0lprXVLbULi11me+t9IuDqIeOdla43QMUh3/ALslir4EfynAxxiuh/tXWP8AoXLj/wACof8A4qj+ 1dY/6Fy4/wDAqH/4qsCy9c6h9m+2f6Hdy/ZbcXH7qLd5ud/yR8/M/wAnT/aX1qjqv/Iw6B/12m/9 FNR/ausf9C5cf+BUP/xVVydT1DW9Lmm0iW1htpJGd3njYYaNlHCtnqRQBv6sxWa2AkEW/wAtPMJw EDR7c59s0T+INGjtYLZJbaVU2I6CRCpwR0XOT+VZuqahe3E0sA8OzXNttVMvNCA+ABnaW6ZHGf0r JFrg5HgeL/vu3/xrndKfPKUZW5tHp/wTTmi0lJbeZY02Y33jPVbmBfKs4EFusSptViWznjg8cj2a pNNuRpVxPY3sF2bq4vZZRPHaySpIryHyyZFUgbU2od5G0J/dCkx2c1/YLItt4YuEEj72/wBMiOTg Du3oBVn+1dY/6Fy4/wDAqH/4qro0vZU1DsKcueTkVLj9xJpN9/paJFqs8U0l1/rNkrSoq+pjMph2 jptEZP3cjZTUN/kf6Hdr5txJb/NFjZs3/O3PCNs+U996etZ0uoanOgSXwxLIoZXAe4hIDKQyn73U EAj0IFP/ALV1j/oXLj/wKh/+KrUg1LW4+1RNJ5M0WJHj2yrtJ2uV3Y9DjIPcEGsvw/8A6/Wv+wi/ /oCUf2rrH/QuXH/gVD/8VTvD8F1EuoS3ds1s9xeNMsbOrEKUQdVJHUGgCr4g0ubWtI1LTrf/AF09 xGF4z0VG/kDXHab4StNMN7DrV7bIxGIklUg7xj5h2Iwx716ctgJBOzXiwtJJvQoWDp8oXrtI7H8D VeHw5Zpe/a59Ra7m2bAbjLBR7AIBn39q3p1nCNkTymP4TsYF8Pywo8jxzMylyuzcMbcjHTgVgmw/ su7+z3+plCbrfEZoiRlnH70kccZ3EY5Ix04r0hbeJVCrcwgDoAr/APxNI9pBIMPPbsMY5Vj/AOy1 hNuV/M0hJRkpNXscrZva6JrkGmW88k0t2Xe6RnDqrgDEiMABtYZ47EdqxvENzGmu3D5LbSMrwQSA B2/l/wDqrtbPw5pGn3DXFqLWKVhjcFf/AOJon8OaTczvNOYZJHOWZml5/SuWrh3Up8lysTWqSnzY d8v9eR5Bp0hOtTx7vJj8wMNqgMM5I659q9nRQiKgOQoA6AfyrKPgnQ/MLiRQ2c5Dycdcdu3H/fI9 87vkR/8AP1D+T/8AxNbw9rf35XWlvIqosOqUFSgoyt7zS3emt938zjfFmm3lz9qi02XFzq1kbF0k tHlTau/DeYrKsWPOcktuyANqkjDWtSvDY/apks764n+3qsUktq88duxgUGSNUBbywu4cYy7MpKhi w6jyI/8An6h/J/8A4mjyI/8An6h/J/8A4mrMThJpNPW3XTVstTFldws17dnS52muVaSQtGdseULM 0jNkLtD/ACAbgyafia3vtQvNMsbbTre8td0lxdJdyMkEiqu1Y2wjgktIHAI/5Ze2R1HkR/8AP1D+ T/8AxNHkR/8AP1D+T/8AxNAHm+naJq+oy77o3FnqNvYW9k94JHBlVZbmKZlkK5JZNsq/7XlMcgc9 T4Xs/sGjNbC3+zol7d+XFs2BUNxIVwOw2kEdsYrf8iP/AJ+ofyf/AOJo8iP/AJ+ofyf/AOJoAIP9 Tc/9cx/6GtFPAjihm/fxuXQKAob+8D3HtRQB/9k --_eb62bde8-154a-4a7d-9fd9-b2bc8bfd8e0e_-- ========================================================================Date: Mon, 14 Feb 2011 09:56:02 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: dartel misregistration - or not? Comments: To: justine dupont <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> DARTEL typically produces a smaller template compared to the T1 canonical brain in MNI. In SPM8, you should select the option to warp to MNI space as well. If your using SPM5, there is a program under resources at brainmap.wisc.edu to warp to MNI space. If you selected the warp to MNI space, I am surprised with the misregistration. On Monday, February 14, 2011, justine dupont <[log in to unmask]> wrote: > > > > > > Dear experts > > I have run a DARTEL analysis and comparing the DARTEL template and the regular T1 canonical brain I get the result attached. I was surprised that the two images are so different in shape. My sample is 45 young healthy adults, roughly equal numbers of males and females. Eyeballing the original scans there are no 'outliers' or anomalies. Likewise there were no obvious anomalies looking at the various stages of preprocessing. > > So I am not sure whether something has gone wrong, or whether my sample, for some reason, had genuinely small brains, compared to the canonical T1, or perhaps whether there is some other explanation. > > Thank you > > Justine > > > -- Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Mon, 14 Feb 2011 14:59:34 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: dartel misregistration - or not? Comments: To: justine dupont <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The single subject brain is just that of a single individual, who has been affine aligned to MNI space. The Dartel template is the average in both shape and size of your population of subjects. Unless those subjects have extremely large brains, then it is unlikely that their average will match with MNI space. If you use the option to normalise to MNI space (in the Dartel toolbox of SPM8), then an additional affine registration step is done, which aligns your population average to MNI space. It only uses an affine registration because the official MNI space has only been defined using affine registered brains. Best regards, -John On 14 February 2011 14:44, justine dupont <[log in to unmask]> wrote: > Dear experts > > I have run a DARTEL analysis and comparing the DARTEL template and the > regular T1 canonical brain I get the result attached. I was surprised that > the two images are so different in shape. My sample is 45 young healthy > adults, roughly equal numbers of males and females. Eyeballing the original > scans there are no 'outliers' or anomalies. Likewise there were no obvious > anomalies looking at the various stages of preprocessing. > > So I am not sure whether something has gone wrong, or whether my sample, for > some reason, had genuinely small brains, compared to the canonical T1, or > perhaps whether there is some other explanation. > > Thank you > > Justine > > ========================================================================Date: Mon, 14 Feb 2011 10:08:08 -0500 Reply-To: Hekmatyar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Hekmatyar <[log in to unmask]> Subject: Re: Orientation swap In-Reply-To: <499626810.8000.1297694299153.JavaMail.root@zimbra> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_NextPart_000_01BD_01CBCC2F.14974BE0" Message-ID: <[log in to unmask]> ------=_NextPart_000_01BD_01CBCC2F.14974BE0 Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: quoted-printable Hello All Could you please suggest me why the orientation of the images were swapped after co-registraton (snapshot of coregistration)? Thanks Hekmatyar BIRC UGA ------=_NextPart_000_01BD_01CBCC2F.14974BE0 Content-Type: image/png; name="Screenshot-5.png" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="Screenshot-5.png" iVBORw0KGgoAAAANSUhEUgAABV8AAAObCAYAAABEivGIAAAABHNCSVQICAgIfAhkiAAAIABJREFU eJzsvU2sdcl1nveu2udrCiA8o6AM4oFmFjRIoIyEjChKRIs0O81QABFkHk2cwAgMAwYMEU6mHiWG YU8dQ6DE7rDRTUodSowCwQY8oARkINDQQFAQaSBB0CCQB+7v7FoZrLWqVtWu/XPu7zn3roe4vOfs n9q1a+99vtvveetd9MknnzCCIAheGR99/Lt4/71fxOe+8AsP1uaXf/7z+NGPfvRg7Rk//bd/qrz+ iz//U/zq3/sH+I3v/88AMpj7j/C6jJlBRMhgMMvPjLldz+eyLucMyjMyapvE0p61QVSPxPp62Yd6 7NKOvk5Irm23Q6LymzNhhhwTnJCIcaIkbXTHqsfOyzYHvJ1kg6nZLmOCjBMRlTasbel7XrRFRHUs MTfL2n2X+DGzbcq2nJp1fiwpuY5z0mPJOqaMaZpc+zZm2h7lpi1mBmHSNuRYy2uWVs+h7z8AcKZm P6LUXJMMxkRpsV9mAhNAnEA0IaUEQtJ2JmkLk9xzrOtpkh+ckNIJid7R/d4BEYPwBiklJN0XcP2C 7KtnUMazLls7x+V9IKSV5bdB/xwBACg36+Q+aZflnHWvWRt6CyYgz/WZQCZknHW/GTnn3eejpz4j 02JZSkk+Ftx9nlL7DNm2737x7+L3/+2/w1//5Z/Vc8d/2D3+F37q7+Cv/uLfby7vt/nCT/2d8nq0 711Z68vacUd9fKq+HXnf9+PSNh6ir/249O2vjdfW8n6cR9s9FFtjsnWt9/p/pI27cuSZ2jv2JffP Q1zDx7x2a7/7c7r0Xr3WZ25vXX/Ovr9ry44eb2tMfJt7fRpdh8e8X+77zDzlvwVBcK0c+Qz0ED5f Xtt/g//Df/SP73z80533DIIgCK4eZi7akuFFw73t/Xa9oDpa36/zQh8AMNZFF2ZGZgBEIBCSvSZg BoMAjKWx+5IAbJ9HHRQVeZ0oaiKmiVWGF1WtTROFeoHWfuYzAyYCd6IsJQZnAigPRNIMrAhZ/XUb XbMtEawRf7vrWV6zibR+7Jbiu+tF2YZJ3hEREiUR0YgAmtrjgJAwAXQSURcTCElEVzoh0RsQCOAT QLMKdQngqYyN7NOeazl34rb7/frgEP55kWdjZbyH+y3XXzT+TjDu7/mnvI6PJV7c5bgjRqLMU/Nc Y3SkP1ui+mj7u7x/Co6IR3v9e87rckn/9/ZfEyKv/RqOOHqv9s//c5/PfT531gTq+3L0M/Povs99 v1zSh2vobxA8Jf1nx3Pc8yG+BkEQvGCIqAie9h5YExgBJJVKOIlTUh2pxN2+iYb79wLKlvDXizKA OOlMeJxAMHHO3HLmmnxUEkFU4OOIwJqK+HPE8eq3q2LsclyJaOl4RXYu4FYQXh7Plrf3wVKQuqM4 xVVQ9u0TERJo0aw4XOvrStL77wTSDcSxquI4S1tVCDZ3rTllSbfNdX9O5fikon451sp5eEJ4vT+j L4Bs+dqXMAs3+EP14x5ck0NozanV81ACxVHWnHv3beO+bR497jW3F1zGQ9wr13oNj/br6PP/lM/c XRzm9+ExP7ef4/54yM/za72/g+Cx6L+MAx72mTpCiK9BEAQvjMVUdT89fsW92oinLGIXUbcvqXOV 0Ez/9/v7ZUOh103rH/WBUu2zxR88jfyVAJsmvUPvem2XJ3DORSztr4GJy34ZgBIhYGO+JjpVEZbA XOMFgAz0ArmJraWP6tAFANLrc1CPuly42nHhJioOZ7nfsruP6k9tJSGlU4kZMKFVXk/axkmcxSQh ErZOftSdm1Lpw8hh2d6qo3NYE//zzvrbg9B+cXOU0ZcIm8fpXeSDcRfh/Njt2n/xc2nEwRqX/ofq Y/5B/1L+o/mp/6PnLhwVi14qW+f/FOy5t+31kX2C62DNqfsY99dzOE8fkzV3+lP2IQheAk/95bQR 4msQBMELYigIXiCgMDNYcxxFB615sIw287VsvxFhsOds65lIZLNeiKtO2EeEE0AqAq9s4rMkm10P ipO2/2Iaf74s+3J5vAQ4ecrGbjn1umsvcRF+dfL/oX6swZhBeGdTRINzCGeWWAkRYSdUeS0V16s4 W1GFY2sLPv9Tna+UtR3NhAVJ/itRObfhvcdJn5OHd1wGLSORdUt4BS67K3fd/U/ESxFIH5NbGaOn FIuukUum+j/WsbfWrV2XmEb9sriGuJQ9rqFv19CHILhmnvMLihBfgyAIbhAiKZKE1IqtrM7KxvnK wERJxdMMziJ2ZWZwmaZNyPksDrPJi09VjEoMJCIRYqk63LyLcJ7PjUDpXWfTNOGcJbIgOyHXiuhQ IqR5xokymLSIDjISJSQ9zsxrBY+OMkFcioxMGWYkZZbji6DHQClMlmEFn0SELlegXIdyrphL9ikh FZdqL1D34pAUf1r2tBGL+KTX6LNmP3NaisieS7/qNfCiYwLn1k1sQnO93hupuipOL5a5/QnvlNZl unnrMD1zBtEMmicVRWcAE1I6FfG1ZMimOpZgQs7ANL1TclvFCUuY0gm1kNYJiU5O5NXoipJvwIt7 uy9w1rJ1v6WV17dHvSfre7l8CazFsury9l6WwngA8yyxENB7UYtgZZzFp0yMubtf5JjVze2/QGi2 aZ43bUN3k0gS2S2l1LjLXzvP8R8YDyGQ9G3cgugS3J/HmuJ+1zaf4p57zHv9kuc/nrnt8XqJbtLX dn2DwHjO+z7E1yAIghfIqoPMOWHNDegFi2UxpqVbzLfB3O63zDDdcmse6y8AFXge2rnWC2YsAqrq P3JsESOPuHv7aeve4bmKyyQdsRBqvZjJJizKWDPWnYObXWjEtL2N18+pn87fT/+2zFUrpCb3VgKg zlsfu+BEfXKCcmlDX5vIXDNfK5IJe+loBEc56iQlvdb5UBzB+PNmuCX121zPxR5NZ3vIP/ZHQsBf /cW/Xxx3a1rdc4srjz1GD8VaZuSl/X+O8R7dD0fWeR67erunb39rjI/2y4/7msB49PjXeH969p7/ 5z6fh3jeHyO31cdXrI3XkSnK1/CZdkkfnvt+CILXCH3yySdPOw8rCILgCvjo49/F++/9Ij73hV94 sDa//POfx49+9KMHa8/46b/9U+X1X/z5n+JX/94/wG/+1v8kgpvpcCbOcR7kkLpM1zSDcy1gJdPs Z+SckfO5RA5UdHq4bk+pvs9dkS053lTFQC+66U9eqXwuvxNOdvw0DbfrqcfWolM7/6K9nUSsmzgj o8qviRlwRa2IvXvXsmD9Hp5aJKzPdAXUEYulOGlRAyNxenm+KjJOtZp7jQvQglc0cvyl6uykXJ2q yeVhNs7Pje9k1dVbxNfOMcrMSOlN47xO3fXOZOPwBrVQmGS2pjSpuPq54mKV8zm59xMSnTBNEwhv VHyVgluJJO4gle3fce5qc1y279siW+mg8/W2Xa5b2L3ur2Hms66TL2P8822fQcyz/Oi9Xj5fOOky cZGzU0z7HOq1+799Xe/3FnOtA6DlM2h85Uvv4ff/7b/DX//ln9V+4D9cMkRBELwCwhX4Onkt1/21 nGcQ3BfC58tr+2/wf/iP/vGd2wvnaxAEwY1ylzzXfn/Rxqi4CHNffGvQ/sjxJsKbFn4Cg9mENXEf MuvU4PRmeB5CLu/ZHWcvD/LuJI05PRplMBbdRkJR6waeao4sA27S/0W93crXJeIDtlXf1/W29jsy Gge5zsQo50nDTVs3qoikk+4khd6W28v9yURIPIlY218LtliNBOQ2HqEV4rgKr3fi5QqvR7jonrEv B3ZuyzWh9OjzXlyvj2Ql2HKOPjVbzqvn/g/pI66wa+EluL1uabx7jkzhvub+PwRHp7G/pHG4ps9S 47mdqkeJZyYIXgYhvgZBELwAeueYLdtat3SabYsdXgQdOVuB5fT4PttxdIQm6kAF5cebQJzb1+5A zIxEJz23udnLcmmXy23c8t3FzI7RtP2ttmW95PCuFQR7mD5tR0P0cQEAGjFfxP4Eyyu2MZV1Se+P E0Dq1iXJgSV6g4wM4v5PFhPdEij5bFvN3CUa6NFOyGfvdH3doupdaSNI+KIvg445vvvtl59jFvfx OF/QXNd/0F5TX3quuW89t9TXNW75HG6h70+R9frauMZzvsY+jXiMuIUgCJ6eEF+DIAhuEC98NuLW hvZh6/0mtUSTkBKQs3eaLkXaPst1hImA4xzZ9bbq+scRUvaox1wTrEfbAlW8WxY8G02rPsJjV2ff OtfxdrstSiE1tyRhapzFWZf6okomlErBLYE1oKI4XpMU3eqzYOW1Cq1Mw9vfC8dr09mDy+if6foF Qc0EPurv3hdc19fFNQyCIAiCIAhugRBfgyAIbpxexBwJf345YSnsjdyVo9iB5li6thdQt5ywvCOW 9OLryB33KLBkztbjWFzCMtcWWEYllArsO2LR3nlUh21/PbciB0SA5AM6VO8O7B2t4/Xt/m2/ZNz6 bcXFKnEC/rw5k2TTYpJoAC04Jq5YbYsJRBMIEzImTPQGINZl53IcX4CrGQsQKPGq6ifbt1mvwXG2 7kNm1izj6lIlxqF7s29nb5sgCIIgCIIguBVCfA2CIHihHBMwGLnP4Gym4q/ttxRsOWdQSsP1lil7 zmPXbCnuA26KND286Gq5tHaA6lgFSIoDcUIaRjaMi5E9JHtO4XbY7pYdu2i7v45dYbB+n7E41n4B UIofdeeQtc2TnUuJH6hOa1nWxVckKs5X76quRy9Hq8tG09QfMdDiNbHn5l6LGBkxcsCPxdX2eWu/ mAkXbBAEQRAEQXC9hPgaBEFwgxShIZuAplXGnXOxFyOICDkzkIA0aXX6DDAnMRwyIecMEIP5LLWo 0gk5Z6lhRATiz1RYY0xpAuVcKpyLO7GKY/NZ3IfTJEJcnjNOkwktnagoFlrNBDXRzBXgYskJPfcu x0Qgnqv/tBOS/XGYGSfbkoDEDJD0fQaDE4GyCH+WVdo7iKcyhb53TwJAxsw2JX4txzJp3ukyj5QI mOdabb4VsfV17gVpvdZTKo7mvup7zhmcE8AnWDZtSuawNfHTsmyzLm/PTcaatc91mjlhAmEGq4AK TCJwEkPMpwkZtUCWnNMJMwDipHECJzBngE9677wDQkJKb6T9zCAmJJqQ0hvwfAIlBucJKZ1AOElM Ab+DZDmuTLBicjVyAJJJyhJ7sHQvAz4+4mXSOrX9spEAX57tjnJvUtZvaNo2iqCqQry0MxVRfbW9 jfxXeZpT2c6K/JXtSL5AsfzgIAiCIAiCILgWQnwNgiC4Yfz0737KOA+EWAKJ03HFJFa3NzGtFaHy jGYZJQaV6fpjx+quA5e9+NL248j+2ywF2OFWROK4Ta0I1LtAaz9HcQNTEV3X4xK2HXqjdc20fReL MNwfk4peGQSSKeAqSNY+X8b4XBKIst4nJnoREotzVfowrUqYvbPWXK/t7/b4W/EAki27PbaqBQYH kWv4PMceuq4PxEOMolP2HLH/9x9F4ZEgCIIgCIKg5T//2f/iQdsL8TUIguDGMeHPxNL1IlYu45P6 bVrXWkoiqOYs7kmT2Namulsf5pzBWV1wJO7DIvTSUhxJK0LbsqiXCS8DtyLXIj9H6HNofV/ySqGv Wd2eFkdgLt/iAnXCEDkdetnWMTVr5FrW3pdz8NPvs8vhNccrOInTGUBK4rS195WRoLUtctWcWKr3 no1lEc8nt8eGaFqSD9w0dcrwonb56YT0/T4Oxp/3BLyRMzR4Sux63+VLl0XG9IaT1viFL/5Xd+to EARBEARB8GL567/8swdtL8TXIAiCF4KIDesC5kjMqAJFAqsSltivr1PSEzNosunqWUVHdG0n57DU 6uc+zzNXMZaKFXEplFWn55qbMeF41qnfn1fHJzths3e+JoZOZ84q7LRTqE28ThviKlfNe72nOy49 6c8EInddJTOiFRw5Lc6viqZbQvW4cFhzn3DGSEQWAbUT0vWe7M+vCN/sjlduplqEq25DZZmIvtBx oMGYj93DOljBFbP1WXVX9vJpgyAIgiAIguCxCfE1CILgJukKRykjp1crwk0lG1FcmxkEcY5OTJi1 AJdFAZgDFoC4YIsHVo4veZtzOU5q0k4l5oC84EdTySZFL9QNzxHtNqv6yXg8eobTmQdZl6XVvrhT Tk5BXR5TRJ55GJ/Qj83d6B2Z9T0TkIjB2TteTaSuQvhR1sbKMjXr6tyI/nUpNEdCkmGhRydMImaj OlplVy/MJr3X2gJuRCLE0ujecPmuwcNyK8JliKxBEARBEATBNXL6mZ/9uefuw6Px4z/6w+fuAgDg JY9xEDwm1/IMXyM1aiCjdYnadO9lAZzyniWv0y8X0WoC+KyFkvrjmPg5w/I+i6sVJ4kUwIw8w4lz Nk3cF+FBd1wjNcLJSIvtBdsl+wJse4z6ejZHrE7hT52YWKcxi3WXy3Hac1pzF9+FZfRCex3lertj Z0am3LpJh2LrkViBrbzMrCKojgIBKbfHIjKhOYmTWl2rjUu1OFpNFE4quibtowmvep48yfZIxflL pbBW23d7XR2vESNwF65JyDwqrO59wRIEQRAEQRAET83p7X/8/567D3fCpoXaT865ef3P/8W/wvvv /eJzd7Pwtz6vFcN3foLgtXBrz/D1IkJoFeJMOFNnqgoWxb0q5bFEjGIGMev+EjuQzsvp/9aGORJl uRyPeUYVVxMSJWQ+N8KdfbYRybR0X+yr9o0PFce5LxIP4JfI8SZ4V22d4l7HD23mKyawukup6HoZ DF5EC7TCz0YsAS8jEdZI6VS2y5r/miZgPid1LLcZsbLtOC5gD3/9vZbLaZLrySqWFpHVCm21rmHJ qZ0acbgKpP4LBOfmzQRKKI7Xup8JtSS+WhoL85fdU8fc0y+Za3WO3iUH9ik+T4IgCIIgCILgCKd5 np+7D3diT7i5Nkxctf6Nfm71WgTBXbi1Z/jaqULnVq6rMBIlRFgbF6fx10Nm4Scnmk46zT4DUHGy RBbItvWYAKimguai5C3zSf1rLx5XS+zdhMSZ26np5nAtAmOqWa/+d+nPYPmayNMWndpmdLxRhERK p2bbNs+1ZrLWTFgnZPLlz9XyPnH5vUQAW3TA5GIBoMetrmoiArp7switLO7XpVM1LV2tbDmy6qje ENdGBZeuUVgMKlLybhkpESJqEARBEARBcMucblXk6AWbaxduRv3thddr7HcQPBa39gxfHd4+6pyC Js41gqXsICJGbuMI5I2JqQmEdyAFtjJM4KzORyn2xFkFVJrdNhOYT8jnLLmuZGKfFqxK4irkNJXj JyeuLuIAMhUhWRy11ody0gBMGE7FkZl1LCQ6IS+Em9QLkGrmtOGYu9UiIVqfASuy1fQXk+qAExif gaakQmNuWkopYZ5bobZeB2lvmiZ4UbWPSPDXzfJoJyLwzJiZkZJlstaiZz4rNTW6lhc8x0JxzhZj QGAk0GTPbgbxhJRY78VcnNC5FFkDEjsXcdJzc8c1N22JGtBiW5l07CmB+KRjMGGiBKSkblcysy3A bYT9QmgllfW6WI7ubLEXyfDSYcxF8O6/EDg65b//coCZD39P0guv1k5/bOL2IxDQezVrfAnlhWCb 0uu+tkEQBEEQBMHzcbPiqxdqRqLmtbHmeJ3n+ar7HQSPxa09w7eDCJ4melbRwhyRy0gBj0wjtxzO VERUouo0pcTgbI7LCXUuOks9LxXf2OVsZtaiXDkvhEV7zTp9XhrOTmgVhyMRMKk4k91U+FVNiJPL Zr0HNG6jiIfkhM6s47fIgZV720+pt/VNlweu1n79cDkBybU9chC3eEfsuqhGckHr+akQW5y1vQLm 2k28PDdKKrB2+bDlN8sxEyx+QNvjBAabNrvi3l6Ka40rV04oeAKey626dcxwPQdBEARBEATPxc3H DvSuuWsVbnpX35r7NQheC7f2DF87vXvUWAqw86pAIUKVuSBd1AARZs3UJEihJzmoCWPmAAUombBa s2CZGeBp4aDrCzuJw3QZP1CpxcWIEyzAIDtxdLLMWxP3OLXibB/Ieg+88Nrkorr3dexNvG5zUIF2 PNamxo+u6+h9Hy2wFKfHAq3vcz1oGoqVNRqAJOO2uEnFdThxe4xWXLWYAYt/II0fkHVURH8nDkPu wZo3XF3TqXPs7gt+Nj7hgnwsnjsiQJ7L8RcXQRAEQRAEQfAcvAjnq89TZearFDH7fkbBreC1c2vP 8DVzybTgNouzjnM/7d+2YWZwIhBPmvFZowQsq9XrHJSsH5MTAuU44mqtAlp7Eq0Y1k+9t+P1sQPM jMQyTX3Ubu40mPtKbotIABOiiQBiJJLp7ym10Q+kmab+Y34cP7DtWh0Ks+X/R25DKmN2kfjES4eu RQb4olteeAWAjGTyctf/1P4uhbWskFuNbiCoEJsmjReoURXMUkyMiEq+cHtPD86jXKKwvR7lLpED tt3aOPefFY+FjznY6k8QBEEQBEEQPBUvyvnqX18b/fTqkev1Vq9FENyFW3uGrw3LMwXQKowHdAbv hJX3c7c8a56mTP2fmDCrIzFhBoORi2jWuyXfmB8ViSxvdFKHJCHzWbrZib21DNfIsTrVE6Nzcy4m FE+AiMR5XSyiItbdnTZGlEp/TYhNyUTHBGBeuHy3XMd9vy/fp3fH9gJqFTHXnLOywTgawTJW67LL pOzqSJxQsnNRBXkTqJFTcVD7fYmS5uuSZg8sXcSln8whvD4jq8/ffR/AA8h9Gi7XIAiCIAiC4Hq4 Wefr2vT9axVutoTXcL4Gr5Fbe4avFRE5vJBpglRuhL/tqewSR9A4xnxWKjEmdXhKBugschmTGj+9 CInSDyJWYdeJdFydr60gxlrIayrHtHiDOnXdTydenlPJ9UQV2yZ07lfeEQx7jY7RRBXsiXjl3lWh yfpoxcNGsQO+/4vurIikRXj1rxvhtjpN167/EE7bzsW0fv6lAJZGHSxcu0jD7wYWOcSo15yKm7eO W9uuZdB2LmJOg22DPTRY5GHbdKL9Y3P0uQqCIAiCIAiCp+Rmxdct0eYaz6mfVr0mxAbBa+HWnuFb wU/R97EC7bq1fYDMWhTKZYdKlEDdvrRBDBP3Slu5zT5lqsWVmBlTWk5nLuJIYiStWs9oK6SbM9af UUIVfy1/lbgWA7O+Tv54gzHbxQm2Vg+sTqH2nczI87ZAu5bNO8rBXexLWbJuN5ywJsZrb1ePv8bd 10lRsZROi1jdhElFvepULrdQyXqV6IEiuEJvL6qC60QJbGKrywZeOIQ3IiyCfS6NGthq46lhy2De +5IlCIIgCIIgCJ6Qm44d8ILNPM9XLdwccb7e6rUIgrtwa8/wNbImkpjYtOd49EWiilORbX8TL7Lk c2qGaZW99HPNRDB1rKaJwdkVS+L2WP7Yo/6YunpXsYyIgMQaW3AXAcid94A6tl6Etm3bPFIv+tny 87lty/ps47PWZ3ZFxS4VE/fGoc2YbYuttYwFrU1R1mnBPtZi5Iwt27DFVYycrrZhatrdcusG9+PS MeRy/ZbZxXE9giAIgiAIgtfIi3K+XrODdCvb0oTXa+z3c/PFL36xef97v/d7z9STfb74xS9edf+u jVt7hq8N8lPhJyd4dPGfdZJ+nbLvRT4/TT/njFpAqVaFZwassBQTg3HCrHmuk3OkMks8QU5ebDEn K0ATAfObrg9SREuOTcDUioxerGFm0PQ57ZX0qdwrnECZS1wCJTld5rd6DLnfUkrluHbeRFM5VmoE UXHfepE7c8Zk2aRcYwZo0un98wwQMEOm6Nt4WuyAF20bkZyyjGSmRnAs7SMhpQT7jm5NwqJpWllT NkDuXLbevZvV8ZzUhbookFasvzXWwN9JxMDMrrAaEU4k20zTqYirUljrcyLs4426XSeAJ7kfMKsT Npe+zpzVOS1XmSAid0ongOW8Evz5E+CzkbW/QUWuq4snsbxn59Am8qJpBmiWZ6+pvJYhBfb0ywer z2fPsN0vTO7LCqiInrUPGePr4/89sMJu8kVQzhkppfKcWJE/ahzq4XoOgiAIgiAInpebFl9719w1 CzdeaArB6Rhf+tKX8MMf/rBZ9sUvfnGx7JqIa3icW3uGr421aet3b1AyWvfn5asgmdsNE2fMMOFQ lskUchMfMxhZKtSjrrd+i6bHwDQhlabTQnzNdK7rCSK8ufVFXKUZzFQdvASApyK8VgF26c4zwY5I snBLpAEAZI06IBEa7XjlR9f5aNQ1Z3LzHqpLrWS81vPbFlf374IMX4hrWdRrPdLgUPsrxboqW5my M2wAZZP1tnphmFSODZ4fy47ecnIHQRAEQRAEwWviZmMH1pyjJuJcG1vCq/X9Gvv93PRj8oMf/OAq xunLX/4yfvCDHyyWX0PfboVbe4avjbuKGqvT2lkLXBXrbMJw+nkJgCURQYsgpq5RSqrQpSZyQMSy qYivFlcg2a61OFhGdgWyqhMuaTsJp8aNSo3IKW5VcdwRRCieyrlQIuR8RuMHHoqdEwiWVavuSWj0 AjGQ1WVcXKAoU+UtXgBopcDWSVgFTy96EldBepQBm3NGoh1n6y5bwqs4bOv7pVDKjbi65iKtruJU clunsoywfg6l0NaqjmrHdIW42IpEhfj6VNjj1ePvcf8lWjhQgyAIgiAIgtfMi3C+mnBj/3F6jcJN n/m65n4NWrbG5N1338Wnn36Kd999tyz79NNPy7p+Wb/v2rq9bWz5l7/85cX6nPPusQPh1p7hW0Cm wg8KNXXu0a31LUsBljWvsxUus06nV5GTpjL9V9pPTpCkbvo/GgWVshdosk47XwqGYwiYTLyUHFuG RS6IMJxwamIEvGAn/bLXdg+yCLqpRgRk2DRpn42r7SVbTi4Ggrr2a+zBGr1Iex8HYeueHW0xGt9l 0SrZYLlPcy9wFV5tu4XTduVUmCUz17bzP3KU+wrPwR5791nNed7I+SUqz7+P1giCIAiCIAiC18qL cb5ee+EqL7quuV+vsd/Pyfe///0iZH7/+98fbvPuu+8260bb99t89asLlBjkAAAgAElEQVRfLe/7 dUe2+f73v9+s99dt1J+1vr92bu0Zfun0TstKmwGbZxVPNVSViEA8WSQsOJc69Cg5oE58SUzgkj9b 8ybt95Qs4xPD/pQM1K6blk2JbAKRFyyTODpB4ppFLehU9gdr7mpS96+Nh2XKinBJ6az7yTnIkex8 3ZmYwKvbEtaFLb90TaLaEmB5sNMRsYs1e7W99ttFtXixfim8GpNbt3QYT+Impkn3I3GwmujsCmq1 pKUwGzPbH4X2S4rxuj3KlxAX7BMEQRAEQRAEL40X5Xy9ZuFmTXQN5+s2n3zyCQARQ/17v74ft9Ey e/+1r32tWf/JJ5/gq1/9atPukW18m0ePHbTc2jN8C5jI0S/boy8ANWbCsngRQKzL1N3IEPNnVn3V pp6b+EJ0AqxYDrXHBkQmtegDX1SsHnD9vIkIebbCQZbDOumUdyvEkwHvrrVjgdyUekJ1/Lb9q2Kj cxOTiLoTptZaSjZeVLyCXM4pa84utfEE1jJRlaWdQ7hxsZpA3Y1FOzhLsbSHUOMTKLXn2ztwW6Fz XXiV82qdq77T0m6S/VR8tciG8lPiH8RVnXja0VkzEO7YR6XkGm+sHwruobsGQRAEQRAEr5jTrToR mgIng59rY01svfZ+Xwsff/wxABFG7TVw3JFz6bTrh9pmbVlwe8/wtVPci900+j0undKe0kncrcx6 LBW8GMhZ4gR0S81oFUGvCDJZxFdw67Fk0qn+XKMOmGvldZnST6DMbTUrtC7YabJzn0sbcnrslqvr lVM7XmAw3mqbp9qPEjOQYdXZsxNlJxORJwL7Lw7M0ek62mrXnYpFGZw7gdW5UhfO1OFl0/PWdnqh zMvzIlKTG8Cl65Wtghg0l5dOzf71POv+RKSuV8vbpaY9E12ZCWKgNsE1FcHe70NsGvl6sa7al+Hq 4CCbDmsVXvu4jrKvxnxIHnKfFbz35U4QBEEQBEEQvFxejPP12h2kawLTtff7GvHjNBqzvWVH9nmo bdaWBbf3DF8zzfTgHXFjVPjJlktG6c7+UIHMsk3ZhDkpsHNKb8Ak/sNRMauUTk7ccbmwmMUPmwAv ApbCPlqMqhd1rN2FYEQi7kndLHHTcgZmLAtuyf4ydHOe25xRYuR8RsZbACxCLidQsmMyZgYSJZwo Yc6zHI7a868OX3XgYmpdv9Te833hLd/GJs6B2t8P4iZdK7ilzuXsxzG5+AdRtbe0Tcm0NXHW3LTq /C3nwdqHZQ5oFZpHbt31jNwQ9J4fL7wCKE50I8cXakEQBEEQBMErJpyvT0Q4Xy/n61//Or773e8u ll/qYvXLvvvd7+L9998v7dox/D5HtrnLsYOWW3uGr42lI1LIeRkN4BkJXibESjGptBACKwl8rgJZ Fe8mTARMJ9nGhFJ0hbbAQG7EwdoniyNY9FcjC0wvLYV84AXNtbNlERNJXLqUMk54p5y3HwPA3LUM YELGrMvfAjiBpgmcCUmdsdYuKBfROrNmmaal6JpzBjIjnU7NuNtYMlexV8RKAJgwTdVpysxFxBLx 21Z456k6X4sR1K4BaSREdfT64a73BaMIsTT5DeQXulxWPa4I64Q8kziDMyElE28TSCMYMiUR2SlJ 4SY9d38vgRKkyBlhBoETg3HCREDGjKnEEbAK6wBw6s4DqDEZ28XNXhdJ3d9zeW9kPrf3K0bCthXU 85/TWRyxhFJIL88McIYX+O9bOM7Tf+YFQRAEQRAEwTVz087XWxJu1sTWcPut8+GHH+LrX//6Ytl9 na++3b69o9v06y/tT3B7z/CtsCW8HnJRrgmvpYp9uz9Rm7HZuilpsMz3IS0cuMN+UaMSLrdZceuW e4kYNT82Le4v7zKl9E45TjO9mjKQxNmqjesvAlIWUZQTJnP2duOYUhJdmr2zdnSf77s4TXhdilkZ KSXkEi6Q1I0IACzZu2llyngRsvU6o81X7e8dcwu37xlpknsi5eTOs4yCXu/ufWnXu4F9zizVLi8K cakIO8TWZYQAezcaV/0O/p6w96O2giAIgiAIguC1cdPO1y0n6bWxJraG4LTNBx980Lz3Y/TBBx8s xuySZX17a8de26Zff/TYgXBrz/C1szdmR9ePYweccOXyQUfT1gniXKzL0RR0Ar/TtCwSrbWj+6w6 b4G+oJI4dUc9zQA5AVYFUSKJN2BUQZaZUf5njlY+g5mQ6ayirGyb6CeKgChjNosBUx3HOWVQnnXa NUDIfY2qZtz8dPxG4LTz5GZXIGUnUC5FZKRJJEeWpFqCFDyTDfYzU7NdRzMBt0cHJ9K81uWIWzv+ p7Zt55OaKATZv8+F7QV525daId6LsrvYPRUi7BZbn7/ypcJyG3nfXZsgCIIgCIIgCAo3Lb6Ofq7V QbomMPXLguC1cGvP8C0gLsKlGrX12eLjC4DeQ9gJVexE1oHwWt4Tt9tx3R7EA3GtOchqXwEgpeqo rT00cTchq3BrPbJIAYkHSK3L1YmYzBIzMLuCWUSs7Uzg/Lacmwi34vIzp2hGBhF0mvwE4qznNYF8 ti1n1/9agKu/RIx56Bz00/6LQGttEIrGmElEV+nzuugq51kdp4SExNAiY2UL14ml2Cp9I0zqtDXR 1vpZfzsnLKci0vt25GiTCuNJhXu/ncRaSHRBdTOXNuKf0Udm3WncOMWDIAiCIAiCICjcdOzA1rTl a6Pv66j/t3otguAu3NozfK3cx/E6WleXdSKLVr6qYiFr9qOKpybK0eR2s8JK1a0qgqEUXRrHDaw4 E8t0+N41iUYQnIpztyiA4ngFoeSzApI3ylA3LIrzFeks+6rjFciaTfqOvOYZYHPNcnHrEs6iOauI CzLHa0bCSYVBBk3+Hidwrupr6Xm5BgQvEIMZTS0qyi4OQlygMzyto3RMG8NAmbXfFotAzbXJRbSu 128y/zIRkKk4bqkI7TIOpHmvHl94rBdgi7O1RA0kHfP12ITa7+ChMYe4f98UzeP+6qLbNgiCIAiC IAheJzfvfF2bwn9trPU1ploHr5Vbe4ZvkbuN48jturEeaAVAsOpf5kw0O2brclxOS7f1/ksoH3dg 0/6n5To7PhNmHhUKmlxfah8kTkCLSzEj57OKqeLKFLEpq9tUHaskIqjsRmWmtRXZIp6QmRv5T0RJ 7X9C45hlLUSFPBYUl0Ijt+OWpZAWU5JR1oJHqctGZWZkQnXD6hgWB7C5grtM3opd46npk+TIVnGd cxWCRaC1Ym5yHaQyU+t6lffuvDrReZkba65i61d8XjwURz4zVj+jB/fcluM6CIIgCIIgCF4LN+t8 3ZqufI3CTS8wrYmwQfBauLVn+Nrpx+wujlguNlZb0Akn5IU5E8omFddMgCN1mSY0Dknuna6+nRoH kMgyYQdT5XnFyVmEvIREs7bbn1+pDqXnmt3sf3G3UnoDos904QR2patAEyTXso1dMPdfYhbXKxiE GUyTioQac0CMzBknnqvzFiJwEhG4ZK7WvrbiqzhtU5rLccVpKC5iRi+WtuQy3oMCaZ1othTMlsJ5 G1dRxfU0kQrv9qO9Z2puLYtNWMRS7PalpRR2K5mj8dnxEPTF3Irg2lyO3K7DUpgNATYIgiAIgiAI btj5ulW06hpFzDW3azhfg9fKrT3Dt8S9P0tGRbeozYa11+RiBUwIEyGQVQzU36Qi4WJKfWqcsKkR EGt2rPXBYgt8O2U7YlBWcZDyQPTxopCKqInBnCROAATiqQiKzBo5gAxmySydTdzlKlDlnFXAzaA0 ydR9ZiDJuWXMyFmzYmFjZceWPs0aV0B0Kn1MKmTXJIUEYG5jAogAnmQ7JOR81szWer04ubCG7taw a1Z/9/fOUtC1408DIdxcr+ZezcTVbctjl2pxTKO9v0oRJ42HGPfd2l40G9yBu352xN8vQRAEQRAE QbDOzYqvvVBz7XmRa27X/nUQvBZu7Rm+VooDUoWomdSdlpdilm0PyDR1kInc5lwkELMKXiYSvgFw BnBS55tzThLJeyLIbH9CSictkgSXxZokH5ZI5qVzEsenz/vkSZyvpzc7Z2zO05XxUHEzjYpVmUsS zhnLGZKUqoKijknmtyBOIDqLCIi3crr8Tr0/NePyNLmCXfkzgKQwmAi6GcSMBMY7KYFwhoi5Ktqq wEhTBljakXhYKXklWa/ap8QAmzNYpvxnV1DrRIQz+XxUlaVdpIEXOmUDc/ACAMGKknknqh9J5tMy o5Xt/YS3fAZhQiIZm0QTmICcCcxnJPocEv2Ejn+GOIxniSvAhNMbBvLnNFeXkPIJKWURrWkGZUJK GmcAydotpmY4pzVsDOz1a/tCZ3y+i8/Wkm0MWKQFc5vjKs5tvVUzly8kZI8ZWV3khAl5blOHrQ0r Ose9uE9++zXn9rqjew173sN1GwRBEARBEFwDNxs7sFfA6tpYc7v2r4PgtXBrz/CtsSY6SN2sXszY Ejfaz6U1UaNd1ma82jLSfNB+KjJhQkpHikO1x5MOpX5Fs75ku5b3tn0temXOTz+VnjDp+4ScZT9p JzVilfx/FZBSquKfF8Zz1pxSJnXdzlJ0LImzVbJSM4hU5Mbs+i3tTe6Y0lETom26/SRyowqs5rJt czih5+ucymXKeBunIC7WXNphtqxbL9ZNsIxfKapF4tj17ViRtS4v1jtWzUXNzJ3YG58Fj0F/vx8Z 595hzKxu7uJqvi4i8iAIgiAIgiC4Fm7a+TqasnytDtI1sfXa+x0Ej8WtPcPXTCNomSmzZG321ckB zq4oFeVGhHET1JcCKyYAJ50GDnG4qphXXYbV9SpCZxUjbTq/5Zzab+msiXVLUXf1fLlvOzXn2zs0 xUVa4wbEYUp6XhoxQCZGSfEpxiwuWusi1XxuIrtvCYwJTDO86Cx+15NeFBWxkxTssmsAIlBmTClJ O1nazUjSP8ww2VVYKUim5ze52IJRfISJ37K9dSHpWHZiJwPElicrd4ZFTBQRvjhszcFsURHunnCx Eo1I515Le4vuBhdj98eaGFozg835Xu8H+zIC5X1dN2vrc7O+L7B1LYyE12vqXxAEQRAEQfC6OP3+ v/nD5+7Dq+Bf//pHz92FIAheGCMxoYglg+2JCJxpZS0AJ7QJCX2xpUbT0RxXNPuI67UKn+LKLG5M mopoWt2yGwLroI81OqD+7gtJrbVBWriqDIEKwiIS5uqMJdYqVaRJC7bct2YFtewLNqgzVLvKKga7 qdYmUqbkRFIVZyW+VdqZtF8ZvUDZC65OWO7OmUfXOc8qonrVzMYgob9+te92DU14duPENSs2USoO X9hYl1xgE2ypu2+oLu9uz3AuPiz+OfV3hwmvPnIAZctBlABm901PusoZC6NnIgiCIAiCIAieg9N/ +fP/2XP34U70DtJ5nst7e30t/L//zx/jv/nm13A+n5Fzxvl8xjzP5bd/HQSvhVt6hq+RhyiMI6+d gILeLeYzW20qvstpLSLoVNoQUc5lbxZ3ai5tgBKYAeaMRCedAu+dmOvuV3biq4l2QpbZ77nu005f RykCVV22rNqhCoFIOv8/AzypI9RaMXEpq4BrYqg6aOktEiYkd9DMKqhyRrL97Vz1PWlepolXKc2u yFdCskFJ6r7lMzoFvBO+qgg9yr3lwdgxs4ryQEqWKduL6taGtV9zgj0nOoEziW7t4g3KuJdsWcsX NQfy0vk6ug+uTN+7QtYLpAEoY23Caetu5cXng2noZbnlRJfIZNmgRGw8+Pncj5ETPgiCIAiCIAie mtNP/uRPPncfXjx/8zd/07wnIpxOJ5xOp5U9giAIHgY/1V6XtCKEmw5epupyFebKVHGxxKK4RGWt Cq9Zsz99Rqk6HhN3x2yLcVHybQG9mLco7tS/t+3KOWbXh0Z+LePRtsnuvACGi2TQNloH3aT7eAeo iMhMc3GAAqxFt9qp3EmF3sziPCS1ytqXEPW8pmUMgBs+QIXmEpNQrLxVL17ERkjEQR0PO68kcQjw 4zoeZ++MrFmz9l6F+dQ6out4l14s2jb3MlHryAzB7BJ81nIbKVC2yHrvdrfV0EVPNW6g33YUZ3Kt eKE/CIIgCIIgCJ6DUP+CIAheEF5cbLMc220ANNuVQk1OeAWn4pCrAtpU3lvOanG0AmjySRcuVeea pXbaueAiDrSdKtRpP9LS2UeM4tr1hbt6cU+cnycXraACKkuuKnNGmrTglYnONoWeMsSNSiqKzlaq CxImkEF4U8SqKkzWHyKoICvtMWbt1wyAMSUg89tOfu4yNhMXodkLt+WSqfuQMIljcaFdjqdie0ds Ga9cnaY1JqK9lxZxD5xqrIGJ9l3b/nwsb1ZU5L6vwTEumCVAx7a1Ylr2ullu7zmh+VJjsy/23D7P DJ8QXoMgCIIgCILnJMTXIAiCF8TCKagCSTt1v1ayT+gFSsDHDYAZbZGl3AimIsa56evkJFTKEAcn lYzTIuIhFfGNtUiTxBqY2tedmAqOaaTQmbDKA6csRo5YuHUZoAnkMk/JchBAICYgWaZlKhEBVYS2 TFMTpCZIdmwVfH2EhvUhFeF07vqk44RcxqXpf+kbpG8rFNFaj1n8pNSK7tKnqYi3ZozNKkCnJtqh iuCj6e0SKZGrq7nZbz2T17tjR18UhPv17rRZzDXaw+hjB/zy5aOWO+G1PQ4N2g+CIAiCIAiCIMTX IAiC24SrIOr5jGdMMNHMim8R3mqebkqExFXUymRV7G3ZCZyl7ZQsp9NcqIA4RM2ZWp2vmcSJKSkD CUQTOFuxKiDRqbRQ612pCxQaewACpgmUtTCTzHsuoiPz7Kbmd9EIGl+Q1DFbJtcXATO59bmIkVX0 S6DkcmI5gzm7HFyNAUB1iUqkQ40pYJ5LDAO76f1JB9jchPY/UUKtEJUIvBNlAIzMZxDlktvKzJjB mPg0nPK9cDuXDVI7jd/ieYubF0XMrQ7a5BypADu3JNMb35qlFcDug5lnJE6gJmO36nhMb9Q9LG7X 4qZlQs4ZJ57AlEF0kutEIuhOILlGQ/csaZxDKlm67rReGEecq7nkEcN9yZH53GzVxwfIZ4DdQ9lq qcnzneW+7Pe18acS27EU5eU+eyjH6/b5m5Cfc673HICUEvrPyiAIgiAIgiB4KkJ8DYIguGH6LMPJ BK9OZ5hAmM3dSPK+wVyUlIEmRgAoDlF3TCrCmUmykwhjmutZtjsigKlYK05TrKpmRJPLiO26rkJh 7k98IAYtt3Huy94FqFPiq5Djs1JFBK7j7wuUmUuwdRZDj7OsDm/RBEniDyipSFmPfEKWyvPeUYpW dJ2mpcO0PfesA1bzZS0+or1pvEvYOVrJu3EBJhFgpSnWRFfrN9cYC+ruH/LnBnFh05umC7Y+WfyD WzZyw4a4ts5a9Ihbsrgnc/2WBMzzihu2jb0oDOMNfCbt41CF4vZuzjkXt3kQBEEQBEEQPDWnP/iD P3juPjwa3/rWt/C9733vubsRBEHw4AwL5DDLtPxiujShStadKGEeOMd8cSSb/m+V7Et2p2W7Nrmt 5oidSrYnM6mYalmwyYmUbWZonbpvLto6bdmOJWQQy9R5c4IW52o5trEtsDDlzj3p+uPeEU3qZLV+ 5bJNEaS8IMi1EFeJUfCOUT2xhJ8o+Zjif52d6xgAneUcuBWubZuBblz7BXHwbpHSrNEJnYhZ9isj 0AltCSCf4VuXs95y4la2zFkVdEvMgToSG1G3jRwgkMi3JVu2E2g3KE7iV4+5mX1hvG3k3vRfOuQm 89V/8bIWA9Esp9Yl27fxmPiIDxNbbZlfFwRBEARBEARPyelb3/rWc/fhUfgn/+SfPHcXCj/zsz/3 3F0Igpvkx3/0h8/dhaunF2G9E7Y4PDULNYEWk3/NJWuiap2y7UXSOoW+EWWRRSAtWasJYu6s4qsI IKM2nXDr++JcpdQIOAxiBluhrsVI3M/VNpKpfNZtI9LSctxluU2Nh4qwabCPOE5NdJXs27kIrX3h qx7v3hv1IU17gltS4bUX0bwYO2rDnMXLZc2SJne33jdVxK7vR0Je7040l/UaXrgFvYaK9q0LfQt2 d1NZNoisMNcrIF8oFIcrd8ehLGZmzVceu2n7Iz6+29VjnyA+ZiCl1MQQBEEQBEEQBMFTc/rxj3/8 3H24E1nzC/2PX3Zt/K3PnzDPc+lnzhnzPJc+2/sgeC30z2//HP/zf/Gv8P57v/jc3bxZcpN92coO UyO0QfJSiw5z0rxNEUZJM19l6jiXdTX3VQXKXB2uKNPYCTkDlmfaTzc34bUt4JX0uEssj7QVn1ph p52qb/v592PhyvrV/PvBCdACYyWDNNeMV+mkiceWf2mFjZJ7Xa+DFQUjWHYri/PYtp0nALOOudmA 9d+2DDTapAm2TZ+Hp+fO8yTH7Kbxi+PUbbhw0Jo7elv4Sz4T1nfLGWq3+1fvzYk05YDWt3mdXCDA rvw91IuwtdDdsl1JG9ZtWIvPYW6vy9Bx7YXXmPIfBEEQBEEQvF5O5/N5f6srZE2wsd/Xhu+f/fj+ mhAbBK+FrS9P4lk4jhegvHvNHGoj4bVkcHK7rgqXVkRHRa5kwqdNBzchJaO4YEmExEQn14b10fZn EE7OKet/QwpsqaO2/xSv0+OBdSFHnbd9bqgXAYu45PNZu5zXsnN/H1qW6+jY7VTrWiSMu2vU9s1n y1JigGY3Ptz0gRK7KAbfju/7fkGifl/tgPZPr2/JeNXfyMP9t5zXzfZkRaCk8Jq//nKv1eiJKtJD jz1or4Plht8895fFugBbnavOJT0oeNU7uv3HwfDvqJ1IC2aGq8CG5xBcy1PgMl5HGbBBEARBEARB 8JScbtVt2Ttfe0Hz2ugFYu989e+D4LVwa1+gXBsj0bWIoBYzkGqWK7GKqEwi8sG2pSK4MTMmFVlh mZ2A5pmaIGbLXNs0ufdecBkts89ny45NkBr2kg/KfBoLbHpOPJBQ6va06ZTUZpb7+8OsZFrWUxKR j7kXMQc5mCtN+GPI2Pj3ubgLJXbV2rUM1HnRlh1/t++lr17AtetBACekxFVks+tGfus29qA/2prr mDABBMwu/9VEV0mm6Iu8+f01RoMypsE2wd1hZvmqowj4LoKAa4YxZ6rXe3DdGShfnAj9lxxPg0UM AMuM1yi4FQRBEARBEDwXN+187Z1y1yy+9sKSF139TxC8FkbPcDhfL2eYPWoziMu89Lou0bJoU5us OpcCXDZteCTokTrcyBx4ZEKsz37MjUhJxM5dm1EE2LLN+Fi2Tk7VCzxVWLZ9e3F2MUV94Zyt7t01 8XJPvAWcCNpts3SC+qiFenxpIyOBkRPUxdm2ZoK6F8d8+7Ju9CVef77t81ULosk1ElHeimv1246y BGpfvHZr5yQv6vmX6AV1V5ur2jui7yKTLWIjXjNDl+pybIpL1r+HjyJo3x+Lehh9fj+NGMvMi4zX PgM2CIIgCIIgCJ6aF+F8HU3lvza2BFdzvd7qtQiCu7DmeI0vIo5jrlMvKmTMqm90IgeLeJn5jJS0 GBYI4JNMSeaERCd1sL0Bs1S3T+kE4E2dmq/ZrqLLJFCqxbUAgPiNCrhvQGCkNGnhrCS17DkVka0W +3JCJInLzs6vjTiA7O9F2kYLXBdXikCJN92aCX7GOlH7hWQuLlc9Pt6oJFo/r8URKI7djAzwrN7h yaSrup2LEZD39VhECZlPsi/ZM6J5uXr8+VydozYuPjmAcPK913brQeaZqxPaxpch7RPAuXeWThvv aPFS7g/nxEbrniUTBbPdAwl0npAmNdqyXJ/EFhfBQEogJEwMUGod3/U+SCq8r0dS3DYj8bI+G637 3WcNi4hfRVWfjZ+lCTcDAfxWMqDRRhIA+lXAyjNm9yfzpA7YPgbDu+jh4jR8e31xrv1/B6TProBb kmfZ+sqQOA+Of1OCIAiCIAiCZ+RFOF978fUaRcxRREI4X4PXzOgZDvH1OMNp7sygtDItWwWxxKkR PkFZK8VrXEECwBmULPPVnJJUtrUYAUoozsUqgmmBKsuNdUIMM6mw6N2wrdDKyJvuulHcwt64XESZ ci/3YMKkCbR7RY583oGch+/Tel/HTt02mkAcvcRV5KpRBW2BMXt0yOUv+PXJxEu7/M6RusfSxYvF MWaxtWoPfWzCMtKBKOlUcBMI6whmMJJ3Rjci9T2vcQBAnkceuKAzc3Odzcn+IM7iJke5F16P0T8b Ixc5NMZEYhIIGXOTBR0EQRAEQRAET8nNO1978fJaxdctx2s4X4PXSIiv98MLIY2gtVJOvk6Ln9Tp WLM46/4nZJ5BNOlyE790CjoDRG/cFPLO9UricfXCLnNSsdammWNFOBHRk7NNQ99mWNQJ28LcsWnT KrJasanRugFyPWr8gjhW57KOnCC5di7teCyOoPq3OndpXACrHoDKdjZU4/O37er1WTs/acMvXbpJ UxfzMBJ1vZjXnzN58UxjJvaub7nfhsd52WJtHzPRF9by7ld7z524apjw2u9fY0SA3fiAxTOz6PFg 2fHP++GXLyUCpUaHcNOPrG71UF+DIAiCIAiC5+Hmna9ecL1mB2k4X4OgpY8bmOc5xNeLaKvAA9si UxVrbWpvFmFMRVITGxNJPAElVvcqIAKYCjDNtHlbjuqGbcS3pUA67uNSyPFFtDycj7Y5HoNjOJGV MpK6YX0+po1nK2j2fZvAPJdtzKnanA8nHeM8bGsxDqWa/Mq5UD409kQk8Q60vs1Q3G/G0N9/5hhW hzRBRfvcCLCS+2t5sq7bJAW3JCNW3dlYirP9Pkc4nlV6C9Sp+UdiNup77wrVZVS3XTpd70O//17k w/GoAUo+ZgDNMyjnY1Ef7eeU7n2g70EQBEEQBEHw8Nyc8/XLX/7y5voPPvjgKh2ko1zayHwNXjOj L1BCfL0MP0W9R5ZVQbS410ohHiuwlJwKc9IogRMkEfaNumSnms3K1Al1IjJmFUUTuVgDTKCuwngv hC1zT7cEkgyiU7PkiKh2ifBWBWrfgLlMvfMzAeg/s5eiJ1Fb6BvcCysAACAASURBVGc9NqGKsNZW KVZVjociPK0JSVNT7Ko6At3SskzyMXsxd91lKvfRcllT3Emvu3fg+tNMaWriCqwNwgQZAy2ApukF a+LryxFT78IoL1WXDERUn/fq1/k8YhP/vTv0MlF2rU+98Nrnul5Gf06A3Qv6jKblOUehrSAIgiAI guC5uTnx9bd/+7fxy7/8y8N1v/mbv4nz+XyVImbvevWvvWs3CF4LW19GxLNwjLXcV1mnhbi00NaW qGbLRNMTYc8EDSKS4khJpi9LHEE5mrabF85X7x6VNlQ0XBy7FWLavmmbJQd2XXQcnc/KGvd6TZRx 0QNlt3ouVcxpBVNy6mzVKEVQlKJZUNen9dEXRhqdw9LdPOq7d6i2EQbWjtura84LuVuZnpsuY6pj lZzYvhR2NbdW7xUbI2rKeLWu13qPtQ7rS3kpEQSXCYljIZaZwRcMAzODDgmvR7h/4bPlGOTh546c 670PFwRBEARBEAT35iZjBz755BN87Wtfa5Z9+9vfxvl8LmLmtbFWIKx3v752vvSlLzXvf/jDHz5T T4LHZu95CPbwU7fbIkgmuhKwqA6PNInqRQxy03GJxOnKKqQSTTIt3QpsWc5rOulxqshr7kfSKedV tJQ+MjOSContFPY+DzQDaJcR13OwqcZb3Fdcq25iE3G2MmCNVmgWsWoswHrnr0z776eHU9PWcnq+ bTe45lh3ito+W+KlHHttXz0TeuOO7SIFvLu69IvKvVHEa99+ZoAmwPWJODnhFUWAXev3rYupS7bu MWDpth7Ti61+WX8si8bwebFrubDb/b1U6bzgc758UdEWstNXpb+pOF+zxF8wI+MlxU4EQRAEQRAE t8jpVqdjffTRR3j//fcBiOPVpiwf/w+Gp+WI8PraBadf+qVfwu/8zu80y770pS8tlgUvg1GhvBBf L6N384m7UpypDHbiZs0T9Z+ONc/VRLJl5XNzIVrhHhNdq8OxyxEFVCiZ6v5c1/tt22Uy3bz2L6lL sroe9z7bLxdY2piAcg7O2Vp/69hwKkqkCaqe9T5KTIG/JhbNAJq7azmVcbY2fW5suZZOyLXfW8/O peOzn7Vq51L7MI1EUvKirIxljURYtt2f013drsES5hnsvkxp141FSuIjublVkBfWrtkdPtsHX3r4 zOW2D+4zhhMYc7n95MukIAiCIAiCIHh6bi52wGBmfPDBBzfjmjsSOXCr1+Ih6cfg008/fRHj8u67 7+LTTz997m5cFVvi60u45o8Nw0/vtldUXYSkTkbS7FfTHc4mtAIgEucq1A3LpKJqUvfmSdtEWV40 SM3pLIJMyuKcS++ANP+VtD5VEc9SFWJZp5d7sdUX8JIGsrN66vF3M0n3vnxbzzgFdsRTTgCd3TT+ 2n/mVBy+pY3cOlgJJzCZYNSEs5Ys1xmMxAA3BYP0WFxfl+MvulvHqD0X68vIbZxaAd/cqrx00vom iatwz51wz6jZvMsoidxEIYhAb7EVNb7iiFhcv3DYv/bX7X40Z6c/77xYP4RcsTa26778UqDGDWQA dh92TlK9j8u9Rlkf4qzS5qkcs9nPBPhhxqt717zVe2GwXfNlhDsVJu/K7XJficE5g7XPmeUDiAFw PjfO3iAIgiAIgiB4Sk7XKFQe4damLFt/14ptXWu/n5ojY/CVr3wFAPBbv/Vbu+u/8pWvNNvZe9vG tzNaduS4W236/d59993Nfr82tp6DEF/36UU0EysSuunfTbgjFzGTTHhdK2gEv25F9PTiqK3LziE3 CJYswuNileWnVmGSmsJNPit1fVr9PscEuFUxj3txqQpYRAQmLk5h784zl/H4WBMAcb9ORFL0yE/V d8LoEcbbLZ2IxXmL6gqkzhV5PCuV3JjV67u33yhL9rpF0sdlLLweYxQxMJoRVN/nbpulE7wns0ZV OVf6+n25dw5LwZXJhFSyw7QRG10LZR1lU1n1DPJ4fRAEQRAEQRA8AzcbO7AmvF5r7MCWUGxi02sX nD755JMiVH7yySfDbb72ta+VdV/5ylcW2/XrgaWbtt9vdMx+m73jjtq095ZRbO9f+3U2Qny9H6lz N1J5L++adW76NqV3yrKy3MTONIk+0QmMVUxNgLpVxfVoQl1aCGapF1hL9mxqxJXhmXErLlfx8WmF uUNuSoZM4zZXHScdkiyFyqgTjxhFnDWZyIRwE3Kb3FnKBx29tc9Hz6feOxNSEpGdee7a6a+tczWr WG5NMtl9qPfIcAp7Vk1ei26x3svdpr0Yu1kIjHGV/+7fnbFoOTxHGjtG7fVCkO3G2W/DzKDEdXp+ abtzmJZlW2N+mXi8/KyyZ2CG/0KmH4OSM63HZKJybMYsX2Qs+h4EQRAEQRAET8tNO19Hwuu1OkjX RNf+P3xeOx9//DEAlIJq9h4A3nvvPXz88cdD8WC0/uOPP8Z77723GNe+jbVl9n7vuHv7r71/7fT3 fjwLl9I7Bvvp9LY4FWGWQJKs2mW/1lnsVkBJhDgy4c0q2TvR1EQPqVQvTluZPm6RBuj6p32hwfGd KzMRY83luqadeMHmoT//l8JfL0wD4BmgqQiXdVMqAqwbZNeSTNevawi5tG+ia9Lp37wpQq7SCOlU cgOWjueabztyowIqNNt17oW35Pep09B7ZN8kRZCQ3D7s9m23t9/eTVzWLZzIt87y/t275j4TWJeM Ha8EwLleR+2KQKsCbHGiN1tc3P8hTjTmbG2m0r49S7WPteDWtvNd98dczjMIgiAIgiAInpubFV9H Yo0XNK+NNYHp2uMSnouPPvoIgAif9tree/oxG43hXbbplz3WcV8zW07wGKu7MBJdAUq9UCHi6kJg U5GPNNtVRLKlCJdSzfK03NgivFJTy94dYyruyOKcbbapbjtKoxzT1v3aLyvd4ftXNR/9+3GZ6KnF uVxswEiIbMfG/btVNnVFvoAiMu6enSsCZjmYZd/isK1f/K0JrYCNhc/prfdTu71zuu4Mk2SaVrd1 cq7sKsBi2KcjEQZX+M//xRy+14au14GgSoDck6PogadFrmAbAVDrYGV1hW/MfOAZ9uWQ3I/LqAXv hPXLr/FvwyAIgiAIguB1cNPi6y0JN+F8vTt+XL773e+urhu9v+s2T3Xc10w4X+/HIgeUU7Pcq3SN eMoiqhaZtAy1uVg9CSR2x4UD0bdbs1pNRPPHnqond0VMIyIgVedu74AcTl9/tCnEyyn2drzdCIIi /jkBNuVSLd7TCsU1L9ViCuR1AlH7heLeeefsx25tDKsDeXEOg5zellrYyZNAyMygDaFZekT1/xfC 7/41fdmu151/JyjXc6a8uX0RHE14dcuZ58V+5fMEqLEDBSnK9jAs/z7z98em8Oq25XL+7X6Mubk3 TZCOf1KCIAiCIAiC5+RmxddbLLg1Eluvvd9PyTe+8Q18+OGHi+U2Lh9++CG+/vWvD7cZrf/GN77R 7N+3d3TZ3nGPtvnar2/P6HkI8fU4Jt4VwXRFTDPqukmkLx6tl8JapNuYCNqLZL3AKj7GNm7ApqEX M5rLEJX3S/GH2Vya68W1njO3cU2AlRiF9XuWRWnUfFib2k1uuvWynTZz1RdG2nd/tvsvXarD/by7 tRP2/fsqfkFzf6ltw7sY1WVbb7aElKZVMXnLhbvH7eR5rv07YPf8ivioIvzoLP1nZh+Pwy6bdRFD 0N/LZfv1sUxpW4Td/exeXb3172Mq7aYiEvtz1pzixABXodgKiNXttrsWBEEQBEEQBI/FixNfbdm1 sSa2huBU+eCDD4pg6pf5cem3+eCDD1bb+OCDD/Arv/IrD+J83TvuJfv3+75W1r48iWfhGL3zz4ti wFI4KxmsiUt2ait4qiCq08JHx7Mfm45umMgjDlYnzvRu3BXa6237j/pBqwLo4yDizUPB6ogt55AI yK0YRoBZiVfciXtjWQtZjR2l9u+jXptyCNkmpXrsfqzXhHBz7B7RP9ccwEciBfz2tuylfVYw+Wui dO7nLderF2FVexyOefl80AJba8Lupew/6/5dG60xvp5p0H9xmLMrTid51esCa/+ZFQRBEARBEARP yelP/uRPnrsPr4I1galf9tr5zne+07wfjYnfZmv9yAn0ne98Z7HPJctGx710/7jOwprjNVzgx/iV r/x9fPi9/6URRL0gyyyFjTizuNVsuYmzBKdUnEA0g/AOMhicT0iTxRCkRhzjnFTo8JEDpMWjfASC 7VevbcIEIs2M9VqSKxTUirrt+ocQ2/am8C9zZEfrGYy38h6TyEejbplbUa8LM4NM9KoNlp2JtGCY nXfJ650WfV9jzbVqy/pTZgKQuVxH5uy27a49c9HKzKUrubKTCss9GYmoinuUwJo3aw7KMp5FhB07 K+u9dSrCMjfT6Y+Jt89HK3qvsRBeAYBTcXxmPmsu6qCgVndvM2YAM0Cz3lc1ssDiLfKsxdOYkXlW 83vvvq33Qc7nwTm0Ampzv3DrCSdUYVgc3fVvHxGA/dYZzOfysDAzznku5yDnb+2S/tsh52xjQ4ml nQf8EiUIgiAIgiAILuX0rW9967n78CroXa4j52sITvfjm9/8Jn7jN35jsTzG9TpZc3+H8/UY//Kf /VN84+/+D/jfv/+/AjgiLm3nNtYcz+pEHIlhjLkrxFUzQNeLNkGF132xc2v9ddwXXmDGtpmOa2QA oILYIGpgzWHrXZ5l/4NjsD7W6xmhixzh7piNyG/vd8TEUkiNpiIuX1OUxK2wGRfgltV1GYxZfnZc s5LVm3fjM4Qd8Zio+Qwf3t2pJgMvzkXtqyassmZO94xntCzjFapAew2fHUEQBEEQBMFr5fS9733v ufvwKhi5XEcibHB3vv3tb+Ob3/zmYlmM63Wy5v6OZ+FyepGusCJcLDbrczdNX1RBw4tu/ke2b6MF sk5fXma8Ei4t2vOYotz925Yp0yJGA+KGTVgMeFcgaaNHKEK2X9RvdSDCYeR6bfu9vf/o4H00gOS5 rrd3JCJhsNeLjBJ4SEbjU4VGFVApw2mRdd+kgqYXakk2ZPDYdTs4fsXu6S6CpHd3o35GZXDJkx5d 5/LvAGb3ZcDs+ts+R15sBhiZz7Lc3NRFkN0u5BUEQRAEQRAEj8npuTvwWhjl0o5E2OB+/Pqv/3rz Psb0etmKHAjxZZ8v/Cf/Kf7lP/un+K+/+t8X96tgDtcMYOrmMR+rWt6Lsd7p6AvuNAWSGJCa98tj SSGm5XGX4iBtrHsYRgWe7nK/iXhUMyuJSF4SQ87bCku1lea3z6v1CFqBrpa9zzQfLTDKuWyvg1yb KpT6nFh/fYfjdmAKvZzzNGyrHYujma+bm71MqL2H7HfrhlVHK3WzCdBv54RXjcKwzwimtc+HDPOw iug+FujXhFegFspC88y193Lju+Uk15rq55k4c9vz6I9v52SRFBJHkXdjKYIgCIIgCILgMQnx9YlY c7uG8zV4rWwVngvRfB/O1cm1L+odx3JaiQhJMz+b9d5VyfW3FfdZZqayZsfuH/ehz+UhGJ2ProGc eCrimBeHynsTaC2CYDC9fxQvUJYvBNi9ad8XnNvwn5xl8SvvWibKKrpuOV49aWd9u2707+A13Q8P ze6/+zvCq7x2UQIlH3Uu4mtzvGwi6JG/OUafwy7WYLR/uZdzc9+aED8+Lpf/Z8xAyYNt3a5VsG0j BuQAmutKup7bz8dj5xsEQRAEQRAEj0OIr0/Emtu1fx0Er4VebI3M18voBepWwDOnmv3extyIftjv nsvZVjBPoKGwutbeYwtta1mol99zXMfMBFiyYlnO9evHlZOKo06sHQiwfWwAuWX3NvDl5iLbi2G2 br0vXPzEXm6wvWZxHfau2eU+bWzFapucFuL2S2E1JoKWfxcMHZ9UhVe5V3SaPubFvgSJBpFvSvq2 /LXd+gLMub21n+UYWT5z+vs0g61zfe+LwOozkeW+aIVneUbWROjctLcYtwxcHoMRBEEQBEEQBA9D iK9PxJrbtX8dBK+FvSJ0wTa8+3mhsQOegdVxUdBpIG+NC2mhCCE1E3Z5zJGjs2vJ1q6dyJWjghVL pmYVl3x1+17UaoUlz5ogW5bn7Wdjr0BZKzS3Qnh/PJ/bW4XYLZG0is7r07y3xdjhufB+VMb1sPZc 7heq6hkJr32EAHPWyyjHzdniBpYZp7I9IMmrd/2MHWWuuqiM5ssVv+3G5zplEJkgbJmtZS9TTkEE 5DyrKD2X9U27NCMXIdYdn8bPQxAEQRAEQRA8BSG+PhFrYms4X4PXykhwjWfhOH6MRPR7uNiB0es6 vbcV7IqYV4TYux6ZiwvyNq7/OB9z6aJt3cdV4ExYKwI0duJuO4Z9+7adb2dPBO8F37Xc33F/22ng CZMUcBq0c/TSrvfxVkX6dVbPlbr38grN/dSJiqPr3TpEpZCVxFn4LwiAQ5nQlIsYvpa7Ktd5Lvff 2vNsX/VIX/t4AZVOKYNy73St5yLL3H2efBzBclyCIAiCIAiC4DkI8fWJ+Ne//tFzdyEIghdF74A7 i36BNwDqdG4pkKXiLBOyul+l4jgDPDXThsFWpEddZ2jFvDlbHqxECjAzmKC5rknFl6Qm29SEkI7F N2p+55x3owkeg8uLb6Vu2wTOcI7COqbFwMkMJC6uUKKpCLDVPTw6d5v2L87GbbosTFRxaymsjs45 wWeImhuayj3yjju3DCnAVDNht6Q7uzeW59fHZljvJteH5vQu4mnuoyNO9C1HsCDnOqurvF0uz4Zc nyJEui8+AIDSLLNodF8v2mYC0smuEIEWVyuB87kIov6eLF+QUZLcZ8qgtBRXM1hLc9V+W2SFHRcA 5vxZiejIpSiW9FHSCc4gEJjPYI31kE+mt/JxZuOpcQY+I5Z5BmNG1me0ziyK2UVBEARBEATB8xDi 6xMwv/0b/I9//7/D+XzGPM84n8/Nj18WBK8FX3Ar54x5nhevg3XWNUI/9btmdV7KkWnQR/c7us1L dqeVc3NT6Mvk/wuuj4jp6+Q8HxSS17JvW/GbNXKgOqv9c5ka0ZQ6sfBB6UXYF86RrNdKFcO9g7n/ TWx5r+ushkX4a0wEiRdZfkaXYlvOKN/GWrjt1LEqAqoU2tKvipzTvn6ONedENfdalmvsgoq0OcM5 y7Pu//Jc00EQBEEQBMFtEOLrE/DHf/zHz92FILg6Ukq7QlKwxcpU3uI0q1OJmUXS4AxgqpmsVoCn b3VNeF0V1DgBtHTRuV7VqcWrfb49tgp1+enfy236ad57XzTY+NTK9pf00b+W97S5zZKdvFI3DX29 rbtf41ZIvM175SEY3Us1OqAWfFtO9V+6oVtyt+WxL0hKEauiumf4Z1zu1c7hjBngVK4lW+KARhWY y7sUG+Nccl+ZSTJhXcSJ5d5mzYFlnsFZxilDRVlicdG+Guk+CIIgCIIguDZCfA2CILhBtpxwy+JH M4Ck4kNbEKvPaa1ZiUdFrnVhbk/oG72/ftZFZMDpUJyANNouNa8Zs07/zgfGgnYzdb3wNm7v+HiL 63Xa2CIhDdprRdg9YXfj+C/YCb1Gn/Vqy0a5rkx9vMByuybyYpjpuuZeHfQL9dq295nl/npxWLd3 hb+IVVrNeXiOMxhNzbruuG37dR8my9WH/mTboBx0UZgrCIIgCIIgCJ6QEF+DIAheEK1wkt1UW82L vKCt/QJaNkm4FXSoq04/EuJulTa3loduxLIt40AdtCT5ld5AuMF+5uvYbXo0DoKZnPtxWqwHWlev v6H6Al+ST7olBG/A4Yofuc1LHm8jvM6SwbqIFWB3r+YDz3N7ncrxyabty7O+vKZjYXNR9AoAsua3 qss182d6rSUWIOOzIhSLED3LQ0QMYAZnOdfZMmj9j0ixYBe/gO78gyAIgiAIguA5CPE1CILgBtmb 7g60RXOE3Gw30mEWuYwYi0B1h9TofaTTniVSYr+40G2zdMG2451AnA8L3nxAgH2MMattJi2uVQto Nf1rCnalYV978e6x+vxaWD7nvrDUlvsdsOfPsk/TgczcxbUin6vqFpfezPoI6LV2EQeEeu9XMdSi M6RIIIPVCZtBAHK2VsSlK0W3SB2tjJkkPqW6XL3bVbdD+5klY+UcsUEQBEEQBEHwxIT4GgRB8ILY FEqxFCXIL3+QHqRdd92t07tdt98nwE29HpM06WFfHNpz7x2JdWiKZDnhFQAo7dl1x6Krb3cUN3E/ vBD83O7FtWuUdtYfZ++LFV5o/gnEDOZ54T62ZbZ/m7M9iiFYP67FBjR9ZJn2LxnT43P355PZCoua W9XcubMunVUsPct5pSxiqzpXM2eYi1WiBqrrVY7l+6BFuPKMzOfdYmNBEARBEARB8FicPvr4d5+7 D0EQBMGF5Lwu6PlcRmBbjD2awbot6lr8wA5E4hXdLEZ1W8jU+m3BbX/sjgt2e0XqrAL86HhEVNYv +zBm6eSVV72rtY0b8NEMnkuyhF8nNnbbz0UtZNVmotr+9Rpf+owddSt7YbdqwfWLBiJenEcm67sd y1ytFk/g+ynxBpyr+JpBKr6K27URX/XLCynCVXoJICNjxpyPCc1BEARBEARB8Bic3n/vF5+7D0EQ BMGFeBGNcgZN6lx0mYy6pdSdMacj3oL4HVmVJq2WXqeZJxIhNWcG0RmJTuAkIknmjMkEjMzIeAui hGQCHBKY1EFLBMJUxBVSVaRJpCwi0TKb9Lmmqvdi4rHt/FT7VtxkZsnALfplK5a5FQAfyOTlt5ur t8TZ9VzO6o4Enxbrq4CXwTTBikKNim3VNmlwDam5vltCHxcRr8+VbQuWHaF3Jd+PvWOO4xoq20J7 5m2XtEzzT+V6gLMUu1KxUz4XTA7VY1EGIWl+6xmcO6c2NENWOiC/COWZLV/gUAJmc6jquZXhlCiB bHEIdAYzaSGuhMyfgTMDk3xe5ZzBJPmtcPfYfP4MlADQWYVURk7i6p3nWZ2x6mhl73x9C3DGlN5I P3IGz5JXyzoGOZvrNgiCIAiCIAieltPnvvALz92HIAiC4EJ46GAU8WUkYBax5RLxqctz7WmFMEML SBU3m5+Gftsu15fCmgCZmYa3R19hvgq1a23u32O37ni+FmTcnYjdOGDv224vvs8AJpTc2c17xYpo sfutn085u3gBnynrXxPmsk110bJkHIA5t/ccTyXrNc/mqpUIjYzZOWyDIAiCIAiC4OmJzNcgCIIb ZC12wASGlNKmi5Opl8jEuVaXVmE1aVGbTDJ1l5mRSZa3DsYMEWdS25QKr+sORHbbodnmJQgmR920 l7T1oPsnAnMbHbE19ttFwe7mXH7IMdpiLxP5MY73GPTiql2vZebr5ccXl2t9Xk0AJRIhU8Yvd22z uFhtPxNVMasgrO/nz1Q8nfXHnLJ6rKQRA/ncrLefOX9Wjg/KRVQ1MXfOGQzNhvVuXubi6g1eB8Mv H++wzS2xV/DwOc/xaD8u6e+1nNtRHmMMjh7vvm1dM9d4H6zl7d8q1zjGR7jVfgcvlxBfgyAIbpBx duc6RdxaNMQgnWIsemkGY2o2MSdaGkR2rolZUhSIderweiGepQh7O38oXdo/GePrOicmSCwCtv9I LW7qlWJcx+IiljfQU4muTS+eWIB9TLwIa85Xc8NWYROgxOBMom6O7kFOOkU/iRDqHKzVvZoBPomz HeJmL3EUZXvS9SqmgjVupF7nGtVhfZkl15UZmRiZZ3W6anssnyE5Z+RsQm5WYbfGrGSgfN54Vy2y G4vgVfJSvsgLHpaXLMy8lH/jPLdwvY7UWLjWvj8mj33tbuHeCAIgxNcgCIKbZD6P8wtrwSNuRKZ+ qviwIBMwqKQOzWXU3V2bItzVzeSY59q25nW2OZ/bToytP1yvVTTbc7Bc8x+C1Anti/UHxvv4NRkL r8/BU9xL/XVn/TLiMY5j55L065O7QmnZ5/pb3a1OXC2Odspgzav1ma8SVUBFEK1CbS6O17OJpZiR 57N+jszFucrMpbgW6G1ZL20CSP7zRZyvDK790WJe1/bFRxAE22zOwLjCvwWulWv+G+QlseU0jvs1 CAIgxNcgCIKb5N/8X//HcPnojz9bNhFh3vwjPAM81deYGsGIEiPPnVjaOVVJC9zIfzRpZiybaEM4 +s/OtQqtxvI/Zm7rP27EhSuOVxnrqS6/4D/UnrtA2mtjlOXau8eJWb4YoQnAjMsVX+9kbR2jRYR1 66rLtN3Olvl9rK0iuGaf+0rqbs2lD1J4zbll7Tsk0hSB4rCnRqTt82RLXmzwarn2L8Iei9d4zsHr 4hrv8fGXr/6Lwuvr8xa31l/jVvsdXDcffvjhnfc9/ce/+j8fsCtBEAS3xa0WHfzGf/ur+Is//1P8 bx/8zuo2XnQF3B8hyRXTcfKpRQ/UfbMKNybOscxOdn/MZDASp+K4BXTWcplojFqYR+cS71W6R9nc uWyv5g+oUTTCjqB9RcIsEYEJIoyXZdX9OnJqVHdzGrqp6zXcn2639r707Wqu8/0Y/YfXQ+HzXRdK rK0f3XMksQE21X+xDoBls0okgL32wqnez2ROVEBiAyRjVVKhcxU+Mes2JqZ+BrmPRGS1rNZyLDo3 xbiYZ7Aei2hCzp81ffF5sLZ8a1nwurnrl0vGaN9+umtfrG60rm/v0uNsbbfXv702t9o9miH6kFmj o2t2ZGp3f9y9a7F13fbaXuvTQ3/u+3aP3kt+2V6fr/n+vsv1Wtv3kvPc2mfrGHfhvtfj0rbWthuN 530+Ry65dqPzufV7I3h5/Nqv/dqd9z39zM/+3AN2JQiC4Hb48R/94XN34UFhLkGJpeCWCTRFfKDU 7FOEHGSApsW6+qatRG5/gJBGC9j7stwE2NJEAiNvyHP1HMZ/2BRZ+PB4BEuYINmfuMRZXDNh1//I HLXFK8ufj6d0VD/Gsdacr0dFmLqZF1f9Nq2ea3EBch7b5yKfL+pA5bNEWmhRLO+gnTFLriu4uFyF 3LQ1m5tWP7tyfousUQLWn5oFKz/TNC0E2BBfg0ufxbVt3y/pQAAAIABJREFU974MPCoKHjnOnvgw 2u4S9vp61+Nfcp4vhce4Ppce75JjvYb7e69d4LLzvKQva9tdOq537efato/xrF7yOXKXtl7avRG8 DP76r3/6zvueXpr4EARBcISPPv5dvP/eLz53Nx4ESlym+hPVqeT2m4iQyLJgCZQzkAiUVKigDKJ3 ADoDeQKQAFaxlAicROQAgBNZbEB2goaIrUQi+hYBNtXcxfrHyVuAJ4jbcikkSbtVXHFnuT8OB/4A 2lvv+7/cdjyF2m/HzECSokNlqev6kf5t/THLB2MbVp0Iedw+FXurc8RyK9TnnJGSLaMDf6S32/gx PeKmGLNWaC6tLO96tDhe7s7jWDtrrP0HZX1dz7vcZ8X9OR4Dv0zESnvGCYRWWCRKyHkGJdJzS7Dp /PYFinxOMKxmH0MEeUqsBa1EdCV1r8sdP7duWhVlaxbrGQAjz2+1fYsLmMFMYEhGdebPAGYQZ2Qm EGWQCqvMjFl/g2eciJAJmDUHloiQ+TMQJvz/7L3NjuzIlaW79jaPONnAxR1opkdo9KxfQTUQBGUi BdUL3Hk9Qz9Fo9HjnmYKEjIFoQBBk4s7rpnQj1DjAi6qMo/T9u7Btn8a6fQI9/g5sb/EyXDnj9FI Gunui8uWqaJsm1hBbIb+s55tH/PxHP5znGt/KO85Cq/lkhPw0jZm95en/PDfuu8e2cexrnvbv+Wx uzVHXHFPKTPznPNzzbaOtKW97b+X9v3c83Xtfr5W273n+bj1tXr0PnKLa83bhvOl4JmvjuM4XwhE BJUjXzrYxInxe4NuC0/2JScPqBPTlw4TgogIOXsxi7G2XBM9kL8MUeuUvVTP65yTt/7xtCZLUTsU 0etpP7he78vc3AnZUuv2ZX3hfEk37L3YFNubHwm7LZvWbbt4zVmbhyRV/LWZOVaj/wGUH1KYQKv1 QY4qJAKiAggBJIhiMQWtAC0iUKZG5I31mqIIFUKMEYoITvURWdJtSCHau1eKSOx8WI5e51s/4tv1 t+7vR0Sua9bZWuZe96tZXY4cj6cse7Q+RNQd7/Ye81bu2y91fra2dw0fpX3faz9vzS3r+ZrX6l69 nrPeR24bzpeJi6+O4zhfIO0XiG56c9snFdDE6UdJIM2DbrXTuxHNgSSgJtGkmFXNBmtu2FHQC1Bq LHeljlvC73UC7Eux5VTMwvR6+tO/sN3yR+Z+t67k+lWgzYHt15m3qyPbuc2X1q12Ihfm79O3Z6cw Xr8k9oCnxAmkQbVgDtcSTwKBikD0jEWQBs4CSM9FTDWXrQm0i1rWKzMj4gyBpBG10LhhxURb2PmK EotQm534rRMmLwe4+Or0vMduovcS9K65V78VsfMt4sfmefjxuy0vfTzv953P24bzZeHiq+M4zjvH nKfHRLEMw2Q0wdCNObtKkIVZASmVWINOjFGbr41YV4XCXjTtxa20vWqLRVZi5/Vvn0wP+31jjjg7 toTXLADuCa+X6iwydoO/7xfP1ba0xlZkx9HxurxNofxLJT8k2ROSeofHfFlzk8pgfF1HbNiDBXsg o2pu1tb1mjOd7bW5ZWP6m7v8S6wxCSaaKhZNzlfYAFsxxrJtc7E2rtgyGBd1wutqf4b3rSDrfFze klvyGi51472WS91m36M4/Zrc+vx8NL6047d1/dw65uK1eYn7yJfWNhzHxVfHcZwvkO0vJ1zFUWUw CQRiA2SVVQTA8KXJFu+dgSRQ5eKIbQVYAKCmjFKW9t2biahsV1XBg7i690VrNu9eX2iPCq9b6229 H4kpZ5OIumPxEqgqmPgZx/BIDuxrM56n3in71n8QNUZzgOzRSWowjWv5csTA3m7Ohcr6YKEKoFqy XPM9Q5KzVTVCCFA5W91kSe7VJQmqSxJo08BbukBJoXFZCap5UC3br5oz2wq0+V4gMrsOXXx1KlsP lt4q9xRsru3S+xrX0Ox8vaVr+a3W673wpRy/tyYKvuSxvEc0wFjOe24bjtPi4qvjOM4XxuFcO0VS VAWKCNJQjItWxlCOKBCGp9mNADuimgf8OZITdkykfCmxdW8bY0ZUyXgdYjP79Y53iy9dpe8swK7P GXfT19EVr//D4hoX8ZeICZCCWT4zIQCpe/8e29fM4CBFLA9QqsCZy0juVGqdpQIQEBUQyY7adn0C mrJUCJEsA1ahULFBwHK0QK5PdsISEQTSRQhU92vdt9n+qc4fjjjOSCuizLJG8zKvyXPvfdfsyzXH 48iyr8GtcyJfYnv34iO0b+D193N0fV6bM/qUer70tfoabeZLaBvOx8bFV8dxnHdOFkEAIGx8MRml h2HYnIH5HFUt3YqL+EPrLr/FsQrq3+9+abIatV+C2nXztJfmsuMVTdbtej0jjwgvrb340HZfTvBk jAJ4XxfC3uF/yz82L/HeBN3sXN17QKE7DzP2nK+2pnb3lP7uwdAcE6C9E97crpblKpocsVpdsiKx OF+X7HyV5HzN+a26JHE1ZyfHTmyVxvma7w1ZfGXm8rp1w0Z3vjoD17jU3tI9ocQC3dBld21W42uI kO3+XnMdP6Wuz3Hx3eP83Ju3VM9rjt+17eEl9nOs91O2ect6vuS1es195DnX8nttG46TedrIFI7j OM67o37BMIFCyP4Bqcv5sHwrWHTd/rR3sxL3X4aqUNm/t9dx5wuRICU/TsXOlxFQRlfrZImhHuNx bSnzqA4gtMd4HNcu2+fSu1tnjPvW/huWXK37kt15b9kW3qMwd2uXxhHRoXXB5vNPzaB87fzW8Soi iLFmveZ/Vk6TM4wAFSrrtP9ijNP3820qhIBF67IxXnYFOx+HvWtm20H98iL+ke1de8+9tO+zaUen by33Ulyz/WumX1tGy1sTd95r+762bq+xn3tlX1ufp9TzJa/Va+4jTzneX1rbcBzAna+O4zjvHqYT CPkLSCsw1DgAUnOtBrYBcKAKQSxiGXN2y1k3eoBBzIBWByoxQApYN3vYsqmskv1ID2XrShFIObAm PDJQMiKTXZQJbRdqIi6zFZZhWWMNrF//9hcjAvNGzEFx6u4LMLbq+kujIjuMpXa1zruRFhdRMM/E 07TvBMR43t3+KUx+pGneOwB4WM9PtC7hVjTvllGAKEClOgeZ87FpYwbsvChk58vtcfHv6h+fekrl 5vVz+2n3S1DP1dFnyZPu+lcJxkO2xOAKXU2jdUTA6OoeXaotoxCueUA00m5auTqYAAEYJmAiPcwg CiAEiH4uu0Hc56YCXAe3osWOPS1QEBRLcqjm6zw5WpP4GSFQiojxZzABhOxkPUNFAF0QGIiIUBYg RsS4lAc3ohHn5QyifDzWLnhVgtLZnLXZ7RrqcY8xdi5XlV6MdT4WR875pWWeW8YtRL+t6c+Ztjd9 j6PrPEfUuZcg+tIC7K2Pwa3a0i2Xeen2fe2yR+dfu70j3Ov6eu51dOvlnivWPuXcvfe24XxsXHx1 HMf5AIxdoaJe2S2O1oLk5fVXXtq2wOblPMNyjWLru04WbfPAPHV7jfMUR74s9fs0ipmzMkahc28b dCHENct2RDQe8ovc/Isg3SYn8625ft416ZxY/Ec/Kz8g4CFr7ha0AmYraFr8gGWx2tMFa8OiWXzN blUbgEtkAdH6ulpfU7EMvKXaD7LViuUqSHWwe0PErI7zbTiO4zjOR2XWe8ZzTx3nvrj46jiO8wVS naIVc7QBQlqcsnMaJyoDR3NKbRvZWUrmsMwiLVOjva7zXIEIKK9yRW25tUhDnUuzn95/YZRuucv1 X2fYpir37yfMnKfV+XqdCGnnKruWD692c1w4fZswEaLma6BmChPIWpvOroXLFLGytPfx/Oe81+R8 hSACUESbhpzxmmIFEBEhtpQCIrGrl0QUF621tRxNsPSRAkmE1diKrQpNjn1pBNp2P2YirOM4juM4 Ff+u5zjH+e///f958rouvjqO47xzrgn4F0pDbZECOutuPXGgUnWQmkiSBUVaLz+4WEeHbKMpTrYj QMqP7ERPjRizSkchZXu/8/Ibszc40n1p630Veo66bddldAONbXTvf40vy9du07/QX8comO4JqJwe Yhx/NjI+2WAAYwxH6/CmErFh78eIBE75zJIiCeoWVJGcqASV/j6Rr48u95Vkex6QBgGLELUBtMqg W+ke1q6Xy2nLcPHVcRzHcSozt2s73XGcOf/tv/23J6/r4qvjOM4XRC/WWJfg1iWanaQ2oemWn5Y3 2uzKgB6ZrLMWYC2ntV9WSVJ+pIKIoVDwqBzRWIdsvE3TtYqw/RdG25fLXxqvdwLmtbpjt7lsrv/T xJ5WSMoI5azW9Rfke4qb0/iGJ63v3Ir+4UOxp6bpoRMyx5iPrj1qys+dCrC9Y7u8J7F+/siOV4Ui AqRQOQMUIXFJcQNLmn9OrtVUJ8oxAr3QWrJfBetBtLIATEBMy8cYh/ZPZXqubxZf6/unH3fHcRzH +RJxsdVxruPvf//7k9d18dVxHOcLYvwSZWMpNQJoMy938yUwAAErIxahM4ucWQjsRzS/CAnygD9V JKkDJm3mvxYmoi5g8QXmt0tFcCcS1roNA009YUTU3vG3nj57v5cv2Xbnvoa8fQuS6I/LvQXYzDXb cNH1eey6X7OrPMcMkOmheRgwIgIoD56X198YhG6+deRr09aJVQRtB7NSy1jtpjWOVNEFkgboMhds iv9gE0ktE5aKqGuD1XGJGyiuVUIaOCuJqM32+pgBc+rGuJS6W/n716TjOI7jOI7jvAQuvjqO43xB ZDGud7uaYFfFv1ZcNbElO1AVEYSwWmbW/bluS4vDtpMpSVJZl+pbVqiuPWXMBNicKUtEUAJIJUUK jANuZVGyCsBHyEbcLvcS1sUbAET37XMzIbYTIy8MLKao5Y+CcXYj0sQdfGt612v+e1lUdeH13th1 YdeaQUTWrvK1rQywApKWkAui/4b71a7F3i2r6cFHRBJPsziLBZb5erZ7AEWoSHoII+W+oALEGIsj lRhp0CxN05dO4BXtXbBCdf3e1apJfLWIElu+zYx18dVxHMdxHMd5PVx8dRzHee80OatmfFtnhGqT m5rdokWglRTEOsQErGIIDoZLtl3niQUqNvhWX+ZMlJ0JfFIG7uqWbOIAbECqiN4FO3adTtvlfTF4 VaPBNafQToyeZc/Oclvze7nY91lXouusPq+V9+oC1uvQH/v8YKKdt7++qoIQDoVuqCrAMpjTBVBK Qml2oNYAApNX27ZJ03rFGEtkQAhhyGtdUkTBUB9EiAokosl77eubBVjmUy/WiiAO+bGO4ziO4ziO 89K4+Oo4jvPFsRYx2fIHoFTFCc49mYlLl2Ub3MnE3BwVYEmtEUoKkgCFWHnaiqomkIDsY6WIuylb UmRJ00MRRbN7Fa1YGZMjLscdpJiB7GYjekArPFn986t1l3wg56Vyeb175PI66X0Z7Koxgs66hNf9 613C5bhPhNoZhACVvnx7Iam8/mP7kgjbOqHbOrTriUip57rM7BxOO/8STNzBOcKivtd0TnNbuL8b +BYQaXGPXqsF9qI8p4ZvealgRXMpJZdqFvIVAQSRmK4fpIG0BKPjVdVctaIKlQClnyGRkiiqAJ0h CNBIyeV6hookl2vEoufmQUXsRFC7nhWnE6dpETFmYXRJTlWLHogq5T6hQpAInCV2D0Oy4Gpu9wBQ zYuNMeLzuV1esCzLtafLcRzHcRzHcW6Ci6+O4zhfGHs5oG23/axa5uVrtmjvDs3jchWnbBa4SEt5 VhB1mm/OjJwJol2dCIBoI6ih1FEaMdTqGC8Ijq0jMNcTT9YN1/WdZ9G2gussemBe1nx7xO1ybXxC 3n5mnuW5N3LtOG92LLfctS+VL/slozcTsJts1jwi27hEGWDveseyxRrIME2BHElCkrJY68MHbYTc VnjN5Znze6m5sGVgrbpsLAN2WeRB2956l6ykAbaqSEtEZcAtadyuuX55nuM4juM4juO8NC6+Oo7j vHNMYKnRA/vLxiRiMIRMSCzuzm5ArLo8wCDdSB3IDlXNA2oJVpEClN2s1GizWlx2mkRbIoKWzFaa lmHr8SACbu13FY806VMXBzwfxeNxn3c0rFncwyhWXko92BNK+xgFILsWq/t4e9ujcDouU9zQPBOW rxmwydnj9scxXXPKzTVSeVJUxKQcIF8PnO4JzbWVcpAFsRrES9f/7D6tDvBRFG0H2MrxBWWArWH5 dpplv/aO8hgjkOI9VCOWJdUtOWQdx3Ecx3Ec5zU4/ef/8l9fuw6O4zj433//l9euwrtmFGBnYtvo IivzINXNOlk2C6omwCahNamZ1fkahuWB9WBZ7eBabR1qrAAXMVBAxJ04WJ18UiVI5WEAqrkzFTgW WXtZqFrvU96XIyKXXDDfNcbfuUt4Is627uN6THneBiZ13hJln+qadO7LXsbr1vkq5xI6LJsX4DQ4 lrnVNaW5VsHT2lhcBAJJA2PVNkKsICVIjEW8bdtlLqd1u7aiqkIA0u5ZRyu8igggsnsNRxFI08Zz /EAp68CAcY7jOI7jOI7zHH7729/iz3/+82r66fNP//YK1XEc5yPRDoAyDoaiqvgf//N/4dtv/uG1 q/nuyeLapWXSq/Q3Wg4r0kg6NBdhu/WIbVFQXZ4UUBuwq643EUI1De61Vb8hbrTrTd0437os1JIt 2+Z/3jf781B+a45puEbAbMTzsZxZHWaDehnHj0GbCzsrO+eG5nPb4jEEr0V77vsBuCpb0zdK7PJU m7bbzI9d7m4sEQEWOaD2Ny0yuqqre7VxvmoSegdn63w9JKerJBcr2aWvipicsLm9ZmdsjDYYmA+4 5TiO4ziO47wmJ++G5TjOvdkSXYujybkZVUir74Fx4Kaxm3psuq5LGoyHV87ITlQlxjoaYD3Qlw2c 1UzjNIiOKvKgXlXcmzhl0W+7zX3shL8cbaD8rFTNvbzUfTgd8zEnk7q/l8rbEkL3yhzzZo/EDbSv u8G3cs6m1pzdpnZY5zA4T2EUzbdiKm65PZH0oGL3Chkd7zkuxOrLzBAJIAogUogu5mJVc8TO2pvd 5/O9vne7VhG25ri267XCKwAsy9J9bthrVKFV7J4TY8SyLFgkdrEGjuM4juM4jvMcfvvb3wIA/vzn P3cu1zw9v27dr7/97W9x8gEIHMe5N1viq7uR7s362AYQBFmUS4PnpPecMlGJtkRIhqqA2f4S82b3 5369RtyTRtCZ5rbWCAKbXVIkbT2lmlFbLXl9pMJ0z5/G9WLYPNqhTjwiAA1iazOw1p4wOxXJYUL6 Zn3GcshGnIfWWri79b487x44tHZlAP33utlgcKtTSmKCO5ndXJb14HmqbQby0KaY7NpsojDqPT/V Ubm6XBvhNbYP43Tp3N/jw7r8vhVfozQxA6pQsYG7zss5OWBtnWVZnnGcHcdxHMdxnI/OltgKrMXY dh3Ana+O47wAM7erO19viCZxji6nGvYCSoZTjms/aE8vCrWDcuWRzxcAoWYFTEZcb92sa4cmunk2 HUgS8IU9mW8nu3i3BgijaR0reiEYdk+INHchYGJYFZ3rfmuTT3uZVhAtxuUhm7duO6TtRFSBrY0g SIkPw/kYt6fQKr4ncT6fq72sUec67v3Q6SlZvXmgO9EaMUHNIHw5JsC6/teHIuaMDZbZ2lzHVTCt D2H6B3ExRQjotK55H7LYmp237T1E0JbPEMmCa/SHe47jOI7jOM5NGYXV0d26tY47Xx3HeRG2HK/e FfQ2EDejjMcIEzlMfKSSh5oEisDg1P0XugBgCCLAAbHkSC4AWTlEBDChuOliQIy2HDMDWCyblUKK EkDacKobSRX5VJuu7H0epeYR24u4a9MFAgaXfWIGxuzR1uUJejCpaJWIwHU7O3CKZFCq7bYVspiz kLkWYWeRAX0dqwt1i36/2jmXrhMpItRWHax8YM8XzAhptkDVBjZrRdc26qDGRQBE3B2vlrUj94L4 SLl91vbQltUL+PfN9l0z1nvc/nCehvNtIqVM2m9a/IJYuI6gAGarCGJ3DeQoEgJBhcx5Kgzmk92H Y6oTESCfwOFniASoLoOYv4CYoLIgLoQYFcADlAjEJ5AGLHJGlAiR3G7YtkcCBUMJEIk4azChl8na mioEbBEGjeCqRFiSg1bkZ0gEzklcBcxNHxeLHwABy1lwPivOKcZahRAjIHLaPbYfmUti/Wz+Sw7G d00czKVlr42WObLvTzl+e8serd/RZZ9Tv3vu6y3XucV53zrXW9y6/T/3PF1Tt7asvVx5x3Ec5zpm A22589VxnBdjJri6+Ho7WlGr+xKt61xW1Two1yRflZBUR5T1VNV6JDdRBKOAlvNii0g3ZL+uRbfq pqvC7FxEswF/ah/4UTycRxdcRyd+yRBhsBGPUPXkp7gLt9fd/hG0Fvlm2ZrMTxeYLv0Qmy2bl987 Ds/7AXdk4LC9e8hLi7NvmWOBHESzpexhiV3r47mXJOa2btf2X3PPV1nNB7CKFmhdszavEd2HOAT7 HFGLF8Dks0byAFwuJGwxPmRquSRy3ZtrhN9Ly14rIs8eJM3ufdccvz1uua+z+s/KOFqXS8fxyPae u/zWes8973v7erRtPJdbnqutaXtludDqOI5zHVsu1y3+/Oc/u/PVcZz74+Lr/SlfnIdu9cW5WKYE mAjTHHfKeilV3bUVOGADXxEAGYTULLyOjsf2x2j3lyz3EalexSGbuw5v/L5Q0uSA3aLNjG3fb7P+ ASJQCuV45EGGyA5CWqkXA9dC9KUfMLcRA0cxlij/u/CjuJm15UQdRdgtUbae2ypG7y0/buc2XHYE 93zhYmzKbh0jRI4zW6eeR2ZGjOtoCxOgIrTLm23npft9I76WLbYu13ZZqZEERYRtxK72cySq5b0u Z2nWTf8isEi8Q9v7spgJiLP7yTUPad4z7edWZnzQOd43r3WJ3pujzsate/VsX8djslXmLeo3Y3Ze nsveed2rw624tE+33Od7HD/HcZyPyhg5sOd6Bdz56jjOCzDGDeQf7y6+3goTBPucU65xA82o5VUQ sb9CAtKQilFoym3UVpstjlYAlLqip+75bR3st0hyXpImUa4XYDH8kEVTlonEc/E0O2CpyZps5pZj YOLTWmBTEpAyuFeVO4GKiCBY1y/Pa/e1L5ybQcAuuFe4/QG7nq8lu3be9fHIj9X9H8b9/tbt0KpO M+Fh3M4oONz0B91wLmlwVBt9fMXFGICPRD52V4iw5VpXBqVruHWpK6qYalQXuw2UtwziaBPf0awb S1brOv97JsSqWhbtslj5OWZANCIKyrby/CWvG4HYOG+dfWYPzT4il9ymW8foSzp+e/v6WkLzvbZx 6bw+pw6Xln/ufOC44P3e26TjOM5boRVUR9F1JsJ65qvjOC/G6Hh18fW22I8FKTmlRQCjVnSVJC4C EdxrWBSTUGoCpn0357ReXVbLIFIMRQQhQLVmw+Z6GLwSYG1bk/ojVvGyc7BuuRTXP3bLruh2x+om irY7PsBcNOzKLcd0sgMX3a6TVTadSP38LRG2X+cKp1ETH9GupSqW5bnj9Gq31f9ItbDNrW6c1wjH I7bsETfx1n1kdEQ7l9mOAqnCK6fTEtMaXFyv/f2+3oMUMNEVuQztymyFnfFfTA7Y+tmRsl4VaXCt 6qCNTRk5hsA/Z47zJQiHz2Fv37dcwOO0L+H4HdnXl+Sex/Tovt4jauC5wustt9Xy2ufbcRznrdKK q0eE13a6O18dx7k7s6iB9p/zTEigyo3IRRMHYxVVR/L8/BowjXHLnZmF0Sy22Lpt7quiOlGrANuX v9G9Dq3UJqXcprYb9a9RCttynXVbrlTRrhV+rIw+37ZubO3I7Wu3/0CzdQxPnVWj2Dpm9k6yZq/p Atp2rWyXKeeHssjeirT1fdaZ95y5t4obILLw0b6usyVn95Av9b6yI0Y/OWqgKaJztMZ034jpumao EAgBNpCexV+Ye5VS48zu19EhuxZUq8O1cbrCHgStM2BNZM11zMtEiYgxWt6rCIQIkgbjkpg/e4BF ANHlWcfGuZ5L3d3fGkfFrrda/3uwJ0S/lMv3Uvlbnzt787eE80u8pBh67ba2eqocOX632L7jOM5H oBVXt15vLe/O13fAr371q9W0v/3tbzct/5blOc7InvDq4uuNIIFI/uJtblSgFcVsjlJyrY0kkYuK G626DrUTwB7S38bRpgpQHl1drLAibCYBFppsl/l8U7OdNjd2JsDOHbCjaHzrDDjm2XbzgFtb4vE1 maKzHzyX1jin5eYi+rq8rUza9ofZ1g/RtfCZxeP1D7v+PHWC7oar6PrzlYazX0VOtGXX7zRj+aVL /RdFPh6z+IWn3VvH40TENhBdCoMm4iTAKjTWDNbWFUtE5f5hrlQTRBXr3G9VhegCARW3bBVgq8gq AsQlxxUookoRXs8xIg/KZYNrRSzRtn2OEcuyIMr5icf4Y3FLQe1W4tF74ujx+xL2FTh2H3+Jfd3r JTI7F7eOD9ja9vj+nuf6GsHVcRzHeXnc+fpO+Otf/9q9/9WvfrWa9pyyvR0496QVWnPXURdfb0nO ZtzuLt4tvSF+kSg0O1jT/0o5ZLEFKtUhauub47R13ubpqy/+pGAOkx8ErahWt3/5Z8P1A0esHaPr bunmuNSu7FHEPlrDI/VoubgvZbV1Nu7zfmxtXYejuMk7dVQ85ZwcwQT+9K9z5jb7Ozo/2x/fQ/v6 shgEWGXMBdkj1OOpJafDXLAAgfkEEYHFizQOVkQokgO2LU0VIppE0ggNg/MVsRlUK30epHWyw7Us r7FzyPYkB65Y+1uKGxZVjF38s+YSl3I9n1LejKPd+l+DrQiaI3W75vi9hX19LkfbyC329Tnn5R7b 3uOoCHppn265z695/BzHcRzDna/vhNl58nPnvBf2xFdvx8+ndKclgOlUf/whj0RPgHJyc9qiTNnj atOVCUKAyAIiMtenBkhyk0EFzAQmTuUHIIstpElNqJo0AAAgAElEQVSsBCiUfvFdHfOX/C33SYSC KICwpB90AVSGx5IiCWnr+Mybgqbu8FQGrJoPwmRiVP8jRMBFRE6CkwI0db22et7lH9Rz5+Vl1+eu W0pPKxdNp3cNDzOKeFyWlW56LiO7jy+hacA1EEDKaIUGAI1bmCci7Jb4XuvarbMS9EPXdXxdTuwH gtPZy+cKcJedzdMftGT3vvVAdet1gUtiRT6mJkSnZwUAALELHgTtzg3Bzo1ILHVRCFQUqhHMDBvU aknXe3Wuq0rjbkaaZ4JsCI8gVhM6cUYUAWAiaN0ntYHmSHD+/LmrlwhBJCZnvAJqDtYQAs4x4vP5 s7UDBIsTgDnshQJEFqtzrA7aczxjWQQiDIENICi6+EO+A8za7XMF2Pcm7Dyn+/U9jp9jXDovL3GM b13+pX26pi0+N1bA26jjOM7L4M7Xd8LsPM2m/eY3vwEA/OUvf+mmte/H6XvzL5V16b3jAL34mgVX F1/vgPYDaXVfpouoZaKKlC/d9sVbkuhhZWgSTwTIglgScrUTdfpE0u4LfFN+V8VBkOvfSxWLJ/Nt YK5t8cuWtYHHZoMFvWQO6FNzTy/l0s0EXWkGNdoT76gI7ntcOkZNtAChyONrcT27mdvtA3lwrhnj j05KWcb2ZjI4WnOOL2XtfhzaBw2K7IQ1TzIA9LEMvTjU56zm+4nNtwcUAVQfhejaWdtGFrRlZadr 207mPR+SQKwWVQAAxNXtap8XjIiluGljFCxx6dpfbMqPi0UU6IHW77w/PpJw5Pv6cesx8lbr5TiO 42zjztd3Qnuevv76a/z4448r0SpPB0wEza9//PHH7v2sjKNlzZa99N5xXHy9N5K+hEcoTkXbIkLp hktkjjiRxnlJgpKRSQFEgGgE4ZTELjH3aRIzCQRBch4qgUihOIGgIA5pudz1OLsQpbgqTfzr3a9j dqnAXHom1OWR1M0DWxNptzCJyVywMrhgr8li7bmmy+GlZa/tvnhExOUc8UCXu/kSX9r+NccpC6k1 B7cX86rkB2R3cY0NqNPWjs9aRmqf3XOEtYP0kqP0o8AISRxtYymyE35sHzkmolmfGTkqgIjKAHX5 oYhoH/VARIAwFDy0yz7/N//L2689IbILtwq+KoKosZlP5bOCKNi6mgbUApBNtgKFKiGi5skuy5Ly Xl2k2ONS9/jXFHrWDvr92IK9Za8p6xpudfxuua8fiXuf92vjM566nafy3P3ztuQ4jvMynPzm+j74 +uuvy+sffvhh9aH4zTffrKbPuj9tvW9f75X1ww8/4Ouvv8YPP/xQlhvfe5tyRkYH1PjPeR71GI4C CAD0bsjWeda6SKmsZ8uymnxq3ZrrtHMRN5PQmU2ISYfT4sJsu5dXgV2b+uQpM7er6oJ2IK61cDQ/ Bu0itBJg7Rjdk1nmX55+TRktoyA5cwaPP+6vEUueI6zYEc7d0+efKftd6W3QtNnn01ZGXVrN2cgd JjVX8loMb9tWzom+gDLWC3KKhJDB5Zqu7Ykj1rZvUQVZGG3v/3Vaf/3UB3X2mgNbumysAznWjRDO EtODiCrARk3LekevTS5d/7P5L/nZvbetS991rynrqcvfcpu33Nejyz3nXN6iHTylfd37vD93vXts 65rfeNduy7+LO47j3B+PHXgn/OlPfyqvv/nmm+59O72lPbd/+tOfynrffvst/vSnP3Xzx3awV1b7 fuuv47SMztfxn/M8iMXUllbkLEGgNlBOzfSMvUA5dBPOg2wpAkg1ucoIIUUQIJejsHiC0o08u+iy uKldt/FRMOynVQG2FwKrYFqEGhrXbffjNg6m5zCKjs8tL5ezN689nvl6GrddHId36J4vOXt0VWdJ bY2wFzfQRhRcOl5FlCXp3K7+w7FijnTAjv9aFM9u0znZKdvHE+SAEdIAShEQK4Fdm9fAuv1pfRBn 69dy2l4Qgpjab3avZgd8KANnlQG8lGxaFJwlFmf/+IAv58I6juM4juM4zmvgztd3Qnue/vjHP+Lb b7/FH//4x26Z8f3ek+RLTzwvlTWWs1Wu4wDufL03pmGYs6wVNA0G0AqTrXiZclyFksDCAM4ALMAA JRKAEAEE7QU2Te+t7Dy4V7LdIQmvGw7IrW7mfW5pctxyux2symjLH6f37tfrOSKe7gvLL8trbRew rF1qc1i1jRoAWrfl6MqctY0s6I+UdkCNkHx0t6dZwO+Zufs1P7gY768MgqyOdUD2uctKBM/3jiZD tl1GA4gYRKG43WcPIPK/meM1i68iScYfrqHiyQ9AXGhYTzsHbJ5uIm208efSfSS69dVxHMdxHMd5 Jdz5+k4Yz9Mf/vAH/O53v8Mf/vCH6fsZ7TJbTtYjZY3l7JXrOAC6H9wuvt4BytbV1l2YBZNhEKsS OwDk0eSJbURzFYEGIAsxRNaVOK+vChv0isgcsgSg5LJmUatGBRBNBNML2lcV5FpB09Je64BR+T5T 80bHMmxXbf4oR9+K3W7xN2QUs/aumZkQ3Aua0s1fifV04R6up0N1bYXVtl6zuo/L9cvMBdgtXk98 fitYm2eqg+oB62M8MmtSrRO+vE/rM59g95YIBYPFRN3j9/dGdNXFHgAhlEG18rVr+r090GljCewf QSRC1XJfVRkKKVmv5/NS7l8Cd746juM4juM4r4c7X98Js/P0/fff4/e//z2+//777n07/2hZ47Qj ZXlekHOU1tnU/nPx9UZoK6bkbsZzR6FKHeSorF7OQxIzFVCKgIYkvAYrjwnQPHBOWjetVYUZ1NBJ AMTa1W/ueuW+jtoLbhaDMB8Qop00c8O2XOOC3S7r+gzXW7OV77py/U4yP8GtAFWzdLNQThcuR70w eNnM+Vin20j1M4dyu/5sH9sajIO05elpi/s78AGwY2f5qsY6g3VNu/zaRV6cq0RAO2gfGmcrevE1 074fHw6ILkkU5TTYV85qBXI+rL2Pyc3KzWCNKX5AFFElDQaGxvm62H5RjUJxHMdxHMdxnKfyy1/+ 8snruvj6Dvjuu+82Bapx3nfffVdez9aZlbVV/l5Zs+16W3K22HK8eubrbVD6Kb3glPEoyTlq76lk plrOYs3jDJbumAbKIQaYstAZUDM7I0AE0WaUeV3AeACXbuQCaMCiZyT7LAgP5oilBcgZjoudbw5N LqkyAOu2DJUkCLZ7yNYdWoGQ8ybLvPSe2Fy9IqkbtO2/FAGJAZxAFMva5u4dxTrrxs2cs2pHoU+H vyO3F/96wapmura3XFtGp/Uy92BWy6tztZSq9b2mruWK2HiG87bbKIqNuob2nULRVbKfRmT/snG7 Eenr8W+E4XIem/qgioDttM367c1bifvtexMFmXsh0R4MNKJlcm9qaWNhLYZqOrK0zl59yudoL3Tm QbBSJECpfXKwMwCl7n6gWjNVA5EJmWLXNOEBFBS6nAERBIpgRCAgCaAKxQlRlrSOnaNAjChI0wIC A/Hz5ySqMhTp/oITCBGi5oAVVYTwgPP5jGVZIOV+ZVjOa37QoKDAIBUEDaAYscgZIhGEAD4JJJLV bRGQbkU0OI7jOI7jOM5lfvGLXzx5XY8dcBzn7oxOV48duB92PHM+Y6V1ElZnqb1nUMpajLDYgLVT dS4S5eUFQCiDc03rJTDlhwRQNgduEt+qizYiDdOeROJJpMBkf+eO2Cwwt2Uc+bx76+1xLiDd+jpq B1C6JyboEkjX4meZ37ymnTY2shd1sFWXbUczgxqx9C3FG+xFUZT6cnKw6urOgCxoK82PLCsgFOzh SbpnZy9tm+ea3ausjKh2HyG7KZRs1t4Jrd1ng4AQ4/ZnRR40KztbRQRRrewYFVocsREx5kG8AFHv ZeE4juM4juO8Lu58dRzn7ow/tMcf187zKCPJqyY33ZD7mXM8CZDkguXkjssiJSMNllPWDMgyzSwq ICMlI1STa7WJJNCzWSEVIJyqeEbW/VxT5ixIrDu7MEwIyrmxWTy1bskzkWk+6FZT5yT2fim0Ls8x U3dz+Sy60/WC5FjW7a5WtuZK2giwfdvdFmO3c2Bnmbfd+sl9WpYb2ka/zriNeff9Szm8t2W27707 eKsuFjsCMBG0i6cIpbu/tstiPB5crq8cT0IkYBBO/ADFghgfQIiW0YwHJM033Se4EWr7uBOLINAU FTDkhAsgSVxt18uZryLm4hWM02tGbB2U6+2I5o7jOI7jOM774u9///uT13Xnq+M4d2cvcsDF1+cj qau9KRB1sKo9ca6KVNz81Ub8qkJF28W9lS667r+Uy7Bc2dLtv3RF1mS7s5HRbZlUP6nbtEVjcuGF Ug8iE91Kd+4SV5BFpzaLdCay7GeVbi/3tgWbaa7r1jJN/O7W8uVc65Gc0KeQj29/nLPwR2mguMvt tma87j0c6NepVAdr3UdKDyCO0EUPHFrjGJfE4+22SOnc9edsnQ/cxonUY5Yfwqg2cSDDfGZOmcmM KBHjwHgMQiQBIACl+0LKJWE1V2xsHowQhXQPWMp2ahSCNmIq0ohanO41uX7b7n5wsAdMWkVb+77b 5WI4juM4juM4zovgzlfHce7OluM1i7DO87D7uKRuwVm0BKp4UpflqqnABqNJXf2L9BXruto63Wx5 VQKxlm7pvYttASikMihv3dbPy/MjFJYB2ySvonfVoZajYdrVPAuuVutQ3ytNxTZDVuVsL9eKNG9b gM1cEmCBY0kC5oSMGzOXJ9bOUNV0JE2A1aYNAVkcTsJsE5OxfU5tAK49R3QvPk5ybMvMiF54nwj2 G07qe7hfn1rmKMhTzmsu7/OSOlmezM3Kdq3n+0GdD5jMGsAqgzxPKeM2Z80uqBnDdk+QCHvwomsX bOtOLa5YrYJyH0Fh0wetOZXRH4u6jn/fdRzHcRzHcV4HF18dx7k7o9g6CrDOczFR0RyLD6nrfxY1 qnvV3veZlSrZITsRN8u76jBlbnNjYxXPVEH0UIejSt3Ds9PONiplm8BaUFNp3GwabV0yIbREK4BK eW1swVoQa0Tfm/A+BNjLVEHxolh7h0uTVKEUBwd1677OalpMLsuUSbx7n8jrNPnBmh3Tx89ZcVDr KMCul6FWnFeu0R43Zu1cvXQsekfoanmyQIB6/bX3YDv+ds0m0VWSCF6c7wGUnOjMjCARQgHMgOKM EwUoLB9WF0aM51K+DaKWHfYxHeeYYgJkcHATSAmi1l6VrD6zh3htvIDFF6Qqq6ZIAv+ccRzHcRzH cV4Xjx1wHOfuzH4st9Oc51EdogTFkgRJQhraPDnITCyq0kwr0tRBd0L6a0kA5mTNApRprFZm7pbc O8siwNL0bYdFDUChwmn9c6oXkrs1iZpZrE2iFilg+bXt9hStGGZRBDQIxfvHiXDaX+idZ8Qey3TN x5AvdtevonxqA8+8XDWVoRSL0NY6YWf1LAM7KaBpHW4HitK2fXSybteNfrzfbAmz1k6OdU9/Dbfr EQF2jBwZY0YoH2+K6fhx5zDOAmx9n/+mY56iQpgZjHo9IwKBBBQCRARnRAAKJkZgQghnAIBIclBn ByyfwEpQOpc62zbq4IFKQFSBJndrLxqnu1pgkGrJfBWVtC1CCaB1HMdxHMdxnBfGna+O49ydmeDa dzN1nkvOWLVBqrKgVrvgFldZEk2UqSxrk8xZdsIJvQiWXaaznMUsbjVTkpBjImbt5q8lE9Zcj0QP VaRFFfiKm7XbuSzCrrsRl6xXupDnSHIXJ+db47bd3xvha7fr/zUkEU9R8nsVsRFg55RM2LRrguRA 1V6AXdezCvSzMjdjCJIIPzue2dVd9ogotc+NqIYruU6AbR+gXF7f9rleJ6TNA42UqZqXq9EQbVlN JnA6fidiLAyAHhBjGtCsxA9YG+JgdTqp3V9i5JRVTWA6gVOUiS7RylCGarSU58Gt3+5nfs6UYWZE 9C5ZwETZoC6+Oo7jOI7jOK/D6f/9//7ltevgOI7jPAOGpm76AsKSXK5p8CA1RxuHJIpw7sqP5O5j kFYxRumntF4SSMXE2SJ8kGU3aspyNCGH0vQ0D0m4Qo2cMBFHADxCabGhubKbjrNY+wjExcoOaTAv hCTwpjqWjNDquiXSZI/U4sxD2jZriilQS6RUOYOoFXgbISqLViRlH8bs19sIkGu2BKYjtGJbW85c gMvHJw2M1E0b6kQCAhfBzsrcFxhbQbPP6Jw4a9sB3UigGkGa36MbuK2WU895PldlHgDS3sFp67QO zn5ft7JiV7Ea7UaAJhsZaIswF3r/fjV/KGuvPuO80Ym6lW+arw8pT0fsODGf+nNIASG7gkNEHE4v UUj5r/l4xxQfkLr0yyOi2v0HesLp9AgiwrIsOIWv8NVXwPn8M6IsYHrAiRjKj2DSJMz+XIReVYtz kGhu3NPphKiC8zkiRrXB+5QhqsgxJIEtAuF8FizLUjTovI8m0J6KeO84juM4juM4r8Hp22/+4bXr 4DjOF0474JaIIMa4eu08l3GkcrGuxGlk805EKt3qk+uty0YNqG7HPABRdRei5K5mQasOmJUFnzJA jq0EoAqmRbxaiYSanHR5X5Lgh5zpKkM9TLAp+8Qpl3I3NmDWtd2Zoc25qILl5WO3FWPQurHTlN6t mv6TPF9zakYWDvs65Pbci9YCJd6NRth38M5zfd+LO79eF40LFoNXvXG0VlL2qowxIq2rNg/cRWV5 SzZRnDR5fgWAMkTqPagtS0Tx8PAIQcTpFCECaH64EQVgQCSA+WSfDSpd74h8b6v3HU1CbM2UjWeL MzidTpZDq4oYY/qcWbDrjnccx3Ecx3GcO3Eh/M5xHOf5MHNySzn3ohcz02BVpoY0y5hYYvAgXMEG ONKxLAVBoNlxV2IJkshDYgP46AmUM1wJSSwV5HzXPHhSEfIoppqZaAuFuXcHcSSLgHUAL6wE1jIY l0YQnZr56y7LrTjYH6v3I7K9FFkUNfaF18uO221XbBsL0AmqAgARGAdtU3sgULvQS9M+xFyzQHLR 9ud2Hk0ATG2ob5ijsQT1Gs7XX41w6MtgMKOI3yZu52sox4coVFImc8pyDkQgVZASziRgMIgiiBSM RzCLRQpETet/tuckE0GVNUD1bK/5BJEk8DZqOhEnUdUe6kEVORKFmRGTazbXscau3CYSwnEcx3Ec x3GegouvjuM475yxezcRQWRpsh1bB1o/iE1Lm8NZnazpXZe1mZ1nzbaT+FWzYXMZ7d9UgubYAJRo BKQu7oLYCX4KTe5H616cu3UT5TzbKqoKFNSIqfNBmK6ldV1+HGzwttr9fsZMAJQsvs+Wq8bnlPs7 umExREJkAbgOzmVqXM3v7WMBuLi1S3QBIki3Bxar6yZxcXP+vrD8WhypE7exFHmiUnKV1zOiihIx YPcQSW5YWyZntAJ2zuwllf8CCMKKkAbbsvKTizVaudk5azEW0oiv9r51ujLX8yaSYgXyPqWHeZIe zhQBlzn1pGhzxk2ED6ER8R3HcRzHcRznBXHx1XEc571D2a2WM1K1iGajw0/0c816pSqsAAxiguoZ tVty60JNLtcUPZDFORukBwAtScjhxtEqJvKkQbFMKDonN2ywSASEJp7gXMWkHFVptTbhTrNonOrW ORiryGrHYZu18/GSwDpGHjRH5U4ZsO+VPddrR2o/pP0xnGXftnm2WZjP2amWK1wdruN5KnmyebMa ShubCZfj6Zw92Hhr9Meqn75yt6+OrwmwZb00kJY9DDFBFBoAikkYT8dBH2xxWNZrUurBQUBRwaRJ bGecAkMCQFBE/QwR4KSWwzqGzD48POB8PltMgAqYGafTqYiyP5/P3T7kDNssFj88BkgMIFpQr1uZ PohyKpdE/Pm18nIPIzYHxnvCsnvzt67vS/v+lON3Dbfa/yP7d2Rb99z/lzrXW+Udaed7nwPPvSaO 1vkW5/La4+M4juM8DxdfHcdx3jm7olAeXCv/gBCG5u77QHKqpq7ZUp1wRNmm2Ix4rlRcjSrZjTq4 YNsBjZSabWTXrTldFXVadtCV5ZJou6ggoOk2rpqE3H7f18eiPybrQ9O7YWu+abvIKMZ+TAfsc9j7 EVvEz9TMOJ0PQXJXUhVm68OEsUwtMQSd43Uz3gBA6UKPsvyszk/5YfuaXM7aXYuwdVqeHoqQaaZy aZyklFyx6X7CaX2t9xdOcdFEinCy6zkPrBdCALNi+dmiIgICIiuCRkDrOf706T+BiCyn9RxLezid TiAi/PtP564djW1EVUGM5HIFiCJipBRH8HbO11tkSyAb2/lLt/trhN9Ly16af2n/t0THrYc5e3U9 yi33H7gsZl5z/C4d2zz9KC95ri/Vc29frxVEj3JNna9tq+O0136g4jiO8xFx8dVxHOcLoQqmMFFq EEZNMEXKfV2gmsWqXEJMg+XkL+fmni1lSHa/AkD+sZkyW7tBuACAixCbOrAju87IQh/Te4GCQQyo CGzsNU7boRSDkMRSMsdr/oHAeVskKK7YiZg2d1G2bri9g5oHgMpu4I8uwOr09apb+2zNlchdHZdC /XJtWW1EQbsBIk7v21iNtfN1Xo8cM9ALkjMR8y2JrFsccXS1y9UHEznOIQ2ipQxtHtiALM8VGkBQ CNm1SBqANNCeZbASAIHoAuYTIEBUEzztGAcQ1ViD08m+fp7JnK+iltUaiEAPj8XpGmNM0SPmyP30 6VMzgJaAmZLBnqEUEeiUBFi7x2i5z/hAW3tccnXPxLv3cF08l5lwubp2hnnXOELfG1v7f037eU32 RNPZd4Wtc71Xxmvx2tt3HMdxLuPiq+M4znsnCaCqCub5DyIVm54dYVlELW5UAICA9DGZU6tYWgbF QrK2bQiQRMHKlVymDfSjSag98mOdWKGC4r7LIlm3u6rdtOq+PSYGjG6l7QUbl+QwyNdHo3VJtu+v JSIidM5TpHY4O8co7lhMztVaBLosjo8CyaZDdrKdfluXBYe3QbtfozCU3aH5gUoSYJu1TJhVcI4T GQ5TEScYIBDiZ7UBtdL0muFqDzvoFHAie7BDRNAUO6BYQKRYlgUhBHz69Akigp9//hlL1CK4fvr0 CefzuakA2z0DBMQA5VCyXq09EIRPOJ1yXX++wTH9MvkI4uFzaUW62fHam3e07Odw73O1t//vtf1c ctYeOZ+vca6fy2tv33Ec5yPi4qvjOM47JwubrUsDmkRW7bvely/c2gpWDfwToFyG0OmWoTMgn2Bu WAaRAHSGKED0ABUBBGB+MNes5piA0GyXATqnOjJAn0EgcASgwMIMgkCjIFBIokwWfQXgkERmtvxa CkmsVUABoQBChKrAxuRhc98RACwgZnNHFpMwzSXbJkcXyuX4jr9Xtpy1M1HuSJfHa9Y/Khheovux 3NpNO5dp3vbcCVRE8s3t2vTQOBCPOejSD3sCtHHK5nUYQ8wAzqn6lmu82gZrclObM1wBkFgbySJ+ FfP3unFmwWHiIp1wROTdWu4a99WkxK4cbh5maHKb5h0hAAgnAALWkI6rDXonasefOUAiUOM/FMAJ IgtEHhBYIRIQ5YxAJ3w6PeJEgmU5I0bBCf8JdCIQFkj8CQ90wukUwfQVfpJ/h+h/4MSETw8nQAgE wefzGTEoRAP+7f//CSEEPDw+AhLx+fOCZQGUA4gDHlTx+fPnNDhYQDgFUBFjHec4s+vt0vs87S2I a8B21/KWp35WXLvuazH7fG7nbe3D0X19Cw/gbtHe3kqbdRzH+ZI5/ef/8l9fuw6O4zj433//l9eu whfBLbuETp19xQGaXKFa4wVW626VCU4iWnXGRgCcui7Psjj77e/tW9PFuHOqtkpidbFe415sxcfV Pl2IO6jrH+PS+rcSXs3tONoZ50LV1o9Ype12d+SHbSeeXtl+lRrBGPa6No9ZtESNj8jCqUUZ7w3C NRNO5+1gs54Hz8lRIWCLa8SEcT1g1RIapM4lQU74sHzmsoWmwDzYXvvPslizG5aZQaQQJZwUeAwn 6IPFBkT5DGbg8dMncAj4fAY+f/6M04M9bIkxQmMEqyAEKu7pGG1fssM/34ey093Z5znOzRlb1/F7 Pxfvof5HxOOt5S6xd4+5Zft5LntO3KP1u9Uyb5m3cK4cx3E+AqfPP/3ba9fBcZwvnPzDd/yXf4T/ j//5v/DtN//w2tX8Ysk/hA4JWp0o2XZHTrOxpHLSoFkIYBWTYsnELlVbJjtL68BbWtyGNidAEUGp E4aUbv6SFLWspB13re3tI1EzMApiGs39wDEhSV2uGVuLHxVgr2Fr/dsIr302aqE4fvtR6KcOryy6 6v5ydYHanuYirTQ/2mubu5glmLMLMqIARauftMcwn78swLb7YmLhXpfZezicSpn5ukPcWfp4mdvt YN/5NXN35+lEhBizEzxdS3iAZT2n8yYKgpp7HQKi5KKnCGbghK9wljOYCBQeoBoR5QxlxqfHE+hE OJ/PkLMt/3giPDx8AgDEhfD4GLEsC/Rs4m0IIYmuFkugbNEqrWBug22pu1+v4FZt/VpBZ8+l+JI8 R4y6Rnwcj/Ot9v+1xbQj7eclzvW9j8E1x/mtnuvXbiuO4zgfiZN/GXUc595sia7+g/j2jM4T4Lof 0nNXiMUM2IzsGOy3xwoTr1iTizBrYtp07NfejUqAjVSfCyMgJDFPqzvW6hPQCWYXHbCZDVcuZVG3 OuTyfm9Cgj6tZ+Yqum8XxOf/SNrPRR1F1716aPMak9frH5fUiWJrR6m5cIsrerLsuJ3cTougr003 fU37qwBS+1Fd16uLJMgJGwSM5/Jad+/h6y5dE0QEkfV1+5RzflT4mS/DUF3K6zyQnmodyMoOOJX2 QkRpMC0CSBAiQYlAxFBWsFoExMPpoQjtRIQoivizgIPi9PAAOi9gBUQtPkKTyHs6nfDpMSLGUITg fK7y54mIIAROx1HKvpnDlhDj84Xtj8AtXYvXOl/fuwjUHrcjx3HvYc8t6vAaHG0/b+Fcz9rn0eN3 7QOKe5zr5/JW6uE4jvNROPmXUcdx7s2W+C1JrxsAACAASURBVNr+QHZuw6awdCXmKDSBpMYMVMdg Pm3EClVuxFHLae27/C+1btTW0fInKY14DjXP3DaXBrxKbtmOYaTzJG6VGpBA23IvirCX2mvfNX0U 7J7avXP/PF7RLfIJzSFft52w0Ijvs7pYffddo1tdYLfqYH/njqE8TVLuLGNwems0VzYtxcVs64Wu jKOiwZHr6shy9nCi35d2f56y3baMcV+OiEHr7TTtGAHWiFJIM8H+JsGcOUCVoUQ4qUCIgQCIAEoR DMKJGBweoSQQIajY/YHpBKIFp1PAgwpUPiMKoCI48SfwpweI/FzEVBFBjBESIxQCDgQggJltehNv 0LphnZfjPX6+P1WQ2urm/5IC16VtudhW2fr8ueb4vPaxvIVD23Ecx3k53PnqOM7dmbld3fl6X2YO 2KeINz0C6w6eu43nbuJpunKnc9Yv9lS7Ka823/wAGJyjqmqCSvf7YBBTNysvxVk726fuRzE3GbDt tp8hgNUfNk8/9qOjdL7+3HXa1zmNKE/j9Hb5+XWYr9PyflN0PS42HmFbrF0L2rNypZHxsxtWNeZQ C2Q3Z21v6fyTmLsbANS6zc/a7SgCp6ll+bFeu9EJiMiDus325zk/kNc/sHuBeawWARBCus4lLROA Lg5B7aBKW98kXqfjmQVShmBh1AxWnMHMacC8BYEYgYGY4kUIjBAIX+HR2t45P7hRACeoEkh/AiTi fAYkte1c5vlzEnKZsIhCVNKpPRi74rw7PrqI9JH2/63s62vV463sv+M4jvM03PnqOM7d2XK8io9C fTf23IFH15t26ydB8hcO5SWxkxQqCiCmPEgAtNh0AqyvcHbTchJVuCm7ySKlJF6SdVM2uFvu8g7l HNm8fOvivCCKTcXEset6W4/q9twSJ49y7fp7wuvsNI7Ca3E6D/NnTq5JaQBqzERb7h57buB2Uu2G D/TW3Zlzidd1z47r1FXeZMU8rx98zUgxG9ruw5jXNxNgrxvsqluXpBP+ZzxVPNx3wGYxtjlXoshS aHfdQWF5zgHpBgBSQdQsjgKMR4gKmAFVgiohSITwKb3/BHDu8RBwCoyvPhE+Lz9DVXA6pS0T4Ssl nBg4R0WMZ8QY8UgCDSfgQXBixgObo3VZLGdaWOpDJ1gkgahCiRFvkKfrvDyz+/Dedba37DVlXcOe KHbU/frUfZrVZavs1zo+R3kP5zpzbaTGkWVuWecjZR3tkeI4juPcBne+Oo5zd2aCq4uvL8O1jssy 4JI0TjGq5yiPLK+UREdtM2Dbc8k2qBVlAWcQ2pKbMIskFal2vCR+5RxJOiS4tttbi7TtD4tLh6X3 4eaVcuzCetCo1+LYj6UL1xklQWwQFNvXIjI5IAe23bQfFbLc0HZNauZN2lzv29xvA/l+QtkdTU20 CeXpAoYN/Da2v84pTliJ0bMfs8+K9hjYc/M+hyPdkfM2+2Xr4FqqCqHed966u4kI2rSz7CxmDjhJ hBKly9EGwIIADw9kQi0JluUMppN9PmDB6fQIDgIW4PNnxSIKVkIgxqeHR+ABWB7tHvOT/oQYIwII TAwQwAoslM64SK/oO7s8R0C6B9c8eHqqyHl0/kuVeXT9a13y967rc8t4yXP91OXv2fZfav9dZHUc x3l53PnqOM7dcfH1/sy+lLfZiF0EQREQhy/fyp3btSw/dN03ITQNZKScRr1PGZqRQHgAUQT0webz Z9M95ATmlBmZHKk2gE8EEQMazaWmDGKAEZNewsVpGM+M0wM1It6Stmfu1rpPBIim7Uly3aGIi6p5 /wBENaUGACdpaRTUqpsupEiDVFg5hOYMbOS64e9tuPRjqtY7neNyrltxrTqJKYtlCusy3rhXtX2t mnYtf2fYErWl1CO3tZWLtWljbbskbsX5ucgquqBkEef1Jb8OQBmoqw4ElXdQ1dbvu90TFM00eij7 YfMUSKLwWhht2lrdo7K9/H4mzh4RbJlHR+4txYRxwLMsOEeAGCoKJoZGe+hBQLpGk/M97T/xCawK lRNUIpgfQUERo12LKoDqAugDThShSiAECD5DKUCFwKx4CP8XAp9xPv8M0Z8hMYCgYPoKJGeoBjwE gpDgkQmLnCGRocuCx0cGYANqPT4KlmXBz8sZyoSAANZ0iYfTxrFwHMdxHMdxnPvizlfHce7OGDcQ Y3Tx9QWYCi8z4bURukaB6Ug3NVJM80Rn8lIRbchEVqTlVAVQAjUJAa2IV7qckyQBp3UjTne/iIdK VRgkygPv9E5Gza83tKpNEat0W88u2/s5nC6tV8XXlKlMQC9I52VjFUZlW9hrz3/+awMqzeoozfbb 8tbLzsTEow5SE0RHh2YrOLfvuWkjlGuJ0pSGqtkxqV3t8wMFUE4ylSJij/u5rvv+/D0nbdnPZ4iu l9k61nkgsvV+tvVaOWnJHKdScmJtPWYG5ATwApEmmiIdW2ZAlGw5ZTw+PoKjgoMNxiVqnxVf4StE VdhwaecklFsucyDg4eGEEBjLYqJ7hH3OZE7pIZTjOI7jOI7jvAbufHUc5+6MjlcXX1+XInjJDQah SeIjqSSxL4szGP5Wt2vNTTWxzAaE4vQ+WOakjZKUpLT1iPSUuo3b/kQQndALbwDAkOxuLd2hRxFr XMdE1C0xsIrHs+N2RRbtDZgJ45KdqbSxTHG6RjPutssRLgp+2UVdl4krIXFv4Cg7r/OyNSpA+4Op WTsaym7FwDz4GwAbVCt0y+ZViMj2PYmwVVAcHbNpEyTW5pp6XxZgsZq/JaruxRm8aPdQrar07AHK 5j6SgLQ+vKjLBTADkBOIbQAsIgYhpHgAAaX9W8ScsSG9F2bEeIYwASEgaITGB4svCAQmgOkMpgeE 5TNEBMzVxbxwFd+FBerfdx3HcRzHcZxXwp2vjuPcnVnUQPvPuR0zkacTccrAOvNl2szX7Y0w2kzO KsDm9ZO4mmazWRUBKBCkWT80Ak9ytJIkAczcrlUQoyLkmsgWB4EqWvmaYwOa6gqV91viUS905aza bSGwO275WOjaZXovRldyEROH+q3X6wWozU7pF/dDps7o3g1Z81bzsquu9NK7KJn27wfFqctjPmwr ssaS+WrCfH8e14J0PfeaYwkQ0sOA7GpOwjaf0nW0Fk2PCLBPmffiaL3WzKHeRDw0i3VRJrCIBirH qyUgD8hFKUOWmUGqiCCA7JgHYsQU90DJSQs8lLYTo60XgpWnJ4YIg/kMYvssochl/VOMEBEs6Z4n 4uKr4ziO4ziO8zq48/UN8atf/Wpz3t/+9rcnlfeU9Rzn1uwJry6+3p4uc7OZNiyVRCstomHnyqPa NfgQJc4ASbxR9EImULMiY1L9YhJ0oom3g2s0r1brbgKuTuvVd5e2bTFaIQ7o4xGymEREdSCxpsQj XeH74xpRna/3ccCO10sWXbV0ld8X8WZtYldAHd7beV1vq4riTf10XVbnnJX19g/XPwn6Woo6d21+ FOZr2+Zmvr0WaEo1bdy7pf1oIzLWOihyXMZ1A25tOV039xPor8u7k8TTcqHsR2nUerYPcji1kfV6 Jr5bhrOIglSwNLm4J35orkVGYCDwA6A/Q+Q/cOKvAF1grnZKYixDlaCsUPkJSNmuIdjDnVNccE4l Oo7jOI7jOM5r4M7XN8Zf//rX6fSnnKe//vWvLmw5b4JWaM2xAy6+vg65i396ZwJUGc28F1q6YYRG sWh0v662o8nF2ufJ9iJSI6omZ6zKYjEDiBDhJJAlb6wIQLU8G3SLQUEboU+KqNbVsWSztl3M2+3P 9sIclb0ofW17rfmy96AOijUXUit5gC1Ml9sT9zrBNgnMc+GWAand9ttu69kt2mUDT3JXLyHDscyn g1Ner+b5oysYMZ2Kpv1ojYlojd5FiEXWeNcRAAyaCq+qWVyeias14uKoCPua7GUg9wva4HtEBAhZ RMNwDFqXLDMhi7wiNgAXlTxmBQeACeneYdNPDwzRB2hI1+5nhTZt7XQywXVZPpvgqgGa7m1Lcknz 8nKRII7jOI7jOI7T4s7XN8ZTz8evf/1r/PM///ONa/N2t+u8L/bEV78HvTTjKPU5h9VoXaKt0Kiz +WVAorpcKzW2AmwVmfpuycUJi+yIDcgOV6Q82LZb+EoDVi0RB61YVLuGp39anYq1rFyujehe81x5 Kri1r/eyL69yDF/JfEC0fUGYVscbh9+PWBf09TrmOtWieW66WZXLMsTrbY3i6kjt+D6248a6zGv3 6/b+rLenpvxZ7EXyxdqyrUN63T56163Vdqz91vbfTOYr2ms5i5/UXTddHXlsC7HVmK0cIuSB7nLO s4mwbOtHBkIA8QOW5QymE04MLHKGiLWpwJ/w6TEgCkBBgKCIOGHRM6CMqACU8dXjJyzLgoUW+4wh ATODiUygdxzHcRzHcZwn8oc//OHJ67rz9Y1x6Xz85je/Ka//8pe/dNN+/etfr6bn1+37vTLaaXvb 3NvuOH+c7nw8xpgBF19fm/Y+k92u+4NvVZFoNrOKsKpqiQWr5bLoxkmYmd/riCjlemrJfO27r+e/ NTPWZuX6VTdjFcEsfmDcTl1mHCiqVY/mg2iZq7MK0aU8PK89XxY/G/FrOMaz7ulUdqWPCtiKHzjS 7X+aN5tiRQjaiLHrAapEmu0rQ5Nwnwe9mjewGf15ad3M1gQGEby87tcb9ydn0oIpOSutvR4Vcmft ao8tgfXowFz3ZjwlU7F62m76+pas3zw7i7BI0QF4AEQQSEEhIJBCzrHktIZAOJ0e8R//8e9g2OsQ FEGAMwDRBQrBp0+fyraWeAYpQ1RKVqzjOI7jOI7jvAbufH1jtEJn5scffwQAfP311+U1UF2yP/74 YzevPafj+f3Nb37TlZG3N07b2mY7b2+77fRxm87Hw8XXlyNnamZHWjGmEVIuYmyEqNqlv3eeZnGx OkCzayw/IMrbKCKTMqAnK42W5JqLgJh7TnWxcil1M6YAos9gPkHAUJxTHT6D6DFVyaIQCA/JURqh EpKXdQFwQuATEE8QPSOEB2h2yxKB0sjnoP+AKkOxgOmTzSvikO270lr8qlm4eSr3mbBFaOWmm3t2 27btuhVvl81zZ6fpgmuWWwGsdSla129uBiWzfR+EO05iI/IgREtxJeZl2kG5OhGRmpiQMY/X8g+A kLv/J3coE2rWr5acAc2FNIbVMRZgTuvU1pLZi5Tfyynn02ZHyxVtsmApcGfczfm1Np8nwrKCm0Hq yuVEoffolizaUylvFExn4vhMxJ4xPiDoxOZnZMJ2wvt8w6VFA7BrTO3+ne8xlK5pEQFzaj9k571m wSqEcqQDg8HpWl3sGMsCwiPwYN8lVBgn+gQKjCgC0QVxAT59+r+xLAvw+Scsi4Ag4HDCJyXEGBFl AfMjHh4UFOy+IGe7J4adAfQcx3Ecx3Ec5xL/9E//9OR1Ty/bnc25xA8//LCadnQwki1Hylj+kWn5 /d68rffffPPNaj1vZx+bLGhs/XOeSerq3jsOTTCKAEIZTKgdrKrmvj5lW7Yqm9P02m72lAQ0MqFU kUXC1KW9E5by8rnalMRVrqJT6v4sIiauNVEHWUS2/a/ClfVOLyMoravYvK5ttM3LvYbWbbm/vtLo Sh7mT4RXaJyexSKsZV0Q4zWnIArdsiauWoQA0IqSEdBeUOy6opOmczi736+dta1w2NaXrszU7Z2g Yg8YcvshlMiK7phMXKmqBCJAU/hr251ecsRA52y1tlBE+rxPVF28dROvER/w2qxF4QCC6NzR22Lr 1cHOGAEKBbPZcJkZp9MpCeMKjooFp7SypngbRtBo135QG1Lt2oBhx3Ecx3Ecx2n413/91yev67ED b4y98/GnP/0J33zzTXl9ZN1x2pFlxmnffvvt1WXmeu4t43wcRufr+M95HiYwGm333iKOoRWpbpxH 2gxiBcR1t+hJL2lVzgqVCYKUrZI5G1ZSd2cyh6YCJjaaGCOq4NRl2fZtgTnsYhLIsqBoumrvDlyL f60AVATZIigPrsJpr+8jbfgp7Xzdtb4XXfv3rbBJZGmlJja289cPz7Io1QmGKULCRP2lzM/HZ0vG Uo1pO203/75+8/X04jJ531BiIfq84rx9Iw2UBq2RBgAkynQgOeaU+UsxlZOXaI5/s7XcToEAkIA0 uZ9ZktjNjVjb7tNetEfdn62YgZd8qHn0nIzk455fAzULeNwfE68DmNNngfbLmEteEIihYIgudo8L AcRk4isrsqOclXDWCAS7duyZQHoQ4x81juM4juM4zivhztc3xqXz8cc//hGACaL59d66l1yql6b9 7ne/67bzu9/97lCZR+rmfBzc+XpfihCkE2EDgDZiyIWSjm0rhbpu51ruuTyzWJu3p90I9Zq7r9sE UHK9FU00b1MJkkaWtwGy6sBANoAWNQJrK6DFJA4BOTN22MHkZMxSWxLtUudrPtR1Oas8TxG699yu ADRuz2umaRM/ALRic16nFaOzm5hKDEgdrb4Kqbbu2bYxqXlfl5wxO++C367TxgJcuh/M3bLr/Nos 1GchsGxfgexg7mqb4jraa2AU6Kfd+yla9m/jPrd1++zhtsyNPdvd79XSnQt3vu5burfWus6nI2e/ 6gOinLvl8zUOZUh+KMIEEgKTubKZ2QbwgoCIEcXOJamJt7rYfdBxHMdxHMdxXgN3vr4xrjkfT3G1 PsX5eqmM8f0f/vAH/O53v3vWSHDOl4V1A3Xx9V4oYM67YbCj1XKbYuno9ry4ta5MkLSWQJgQbFmv lYmoCDFnW6kXpyxSc6+qLmhGjDIlRsREQADQYFEKSTxk4lI9m08r0bZ3scZu0KyyP6oWntodoRpx sMvQDf561oLdFjMXZNHfG7dnu5zo504ktPlITlcBJQeoDuUCvaNx1Aq36tsPYtavuJ43KXhdYica j1TXbV/OKiYByRZNta1aV/dWfG7yj8v6feaqLZxzlGO/DupxmQml/bU4Fwa3r9k94fnGPLO7fu9k tYcbSG715sYBAGAGQAGq5oTlAEikdK7UsmJVU5Z0PrahDKYVJcerKB5YcJaf7ZEJew8cx3Ecx3Ec 5/Vw5+sb4/e///10+vfff49//Md/7N635+77778v637//fdl+nOdr225bR3zNra2O67XznM+Hqo6 jRtw8fVWcMm3VMTi3sy5la3QbS7QfMwbUeeq3Fbtc1i7MkahaNv9mgdiqtpMEsBUAcRuwK9WzKqO VzFXY8rptFHNAQineVJcutQYajsxjWK370ohGT5HkSw7bmcCzu2iHGaC6paQnpHGPVxE4ma57Aa2 97kb+HjdVadvPp5W4LqstXs1lvmzeraLb4mJR+8D/XL59Xj8JZ2+tUtZoKmJVqcrsSbVWpq2UV3P M8frKKZS4/S1+aEcF3vdi/+zzNu9fX4RkXXYJoAyMNu1bNZXW9E0i69Z6I6WT51crLZ9e6Aiqf0G YhvbL32mEGnyMNsgfrrYOUyr44GsXA4BPraj4ziO4ziO81q4+PqG+O677zbnqWo3f3be8vw877vv vuuWG98fnTar12z+rJy9+jofhy3Hq2e+3gYhoOhHrc4hVETS3g379O631j09YMt5uBZl+/eUXZcc bB6l+iSHq2IB0SNAlLocB2gSki1O4JRtrVAKUKkD6ahScrLGVLcTclZsrWuuTzvQlAmwRNR0P68m VlVz3G1zPGbgcrf62fbrMex9tTOxM4uAOkxL05Ozsz91Qy4r8TCvirX2ZzuHtL5vRcZ2GVqZh+eC 6jHq4HJU3ucyVZfmWGZnZY2aKNeDpDYA67auZUdn1wsX0bAXRXsRForUzZ3LNWGHjpsBuWYC7LbL tatzt/+3+3wdzuza4Xzh1pHP9zhtb4uU7wEIEFnAKTKknCsWQE52rFgRlKFsD5KyUz7Qgz1w0Qf7 TDk9ps8XhlJEjOeVm91xHMdxHMdxXgqPHXAc5+6MTlePHbgteSR2VUXIjjFdi2Bm3JwIhCRVNL3g gM1l9gMJVWdqWX8UYbvpmoTV3EW8LJT+pgG3wEnszfMs2xUabb5m1ZnN0UsEKnqRmBBGkjSkaMJt 2Y/qSpztY9dVWtFk5k5cw4UIotNkOup+0nljfl6qj2sYu6+XWgxCe0ZIwKtzmAbPIklCY0WT8FVH gp+f/15k3L5uNQvjmy7Y3hG55SjdohWn52VK1zbb+aNQWV4XR7SJtdMoilK/HE9g2aLteRkHA1Pl lP3a1s8yMa5xvrbcWmzdYjzOT6MfMA5ozm/negX6Y6RgJUhpmwEh2IMRTWJt9yApuf4BBocseOdz yHhAfmjk4qvjOI7jOI7zdH75y18+eV13vjqOc3dGp+soxDrPg8UEP2JBpKW4YK0LehY/kiONqjOS UjxBFkZbYbX9l88dADDPxEUFswlORUSZiLiq2Z5rkQHWA1ygJIAGAGcTSSVFPCaXoRTxNK0HSV3F Tya6kKaohYgzGCQBRA9gUmBhEM4gjkV8NgddFq1tnyx/tgpiBIJKOkb5OKXjCFTxp3daKlTOaf9M BNJyXFNUQiNG9aJejk4wR58JrnE1PzYDi/VlKCIUrDFFTzTzSC0FVQHl9TVn57cZoEr7NjJep+M+ t9P3rudesC9b31x+i3k3/ImoOqxjdbA2Wp3gmlzW5vgVjVCxHFhmc1yTtvucBdXQOJIZSyr/dLIM Y7v6LNO4CN9AekhQHch2Sebrj0p3+wxR/3DiSI77HtndPR6b0oq0OuXbNlbaQVqSAyCSyktJJ5bP CoAJTIS8mc61S2IPVUp9ABErVyLAnKNG7PhqybK2KIiyfRAiFIQHCOyeEEgBBHPK6smWSg94Hp9w rD4ae+L07Jq654OA2cB8R9bZqudWWXt5yntlH7nXPefYXLP/l5Z96/t/y329tP0j6+/FzMx4ynl+ Tv2OlvmcduE4juNs84tf/OLJ67rz1XGcu7MXOeBf+G5DEQmLuNcLHtmNSJocYV236lrG5fOx9SMk iVNCzTbzprKwh8YBm0Xa7CyNqe69+GTCVX6dBjQyW28RKEkDhBTohMkI0UZgU8uKNVW633eRJYle Jt50x0C5c3NSyFmUmuJQqxC5Pj7m/OuFwurA63/7VKFUm7dt2Upzt2h2KZKYgNhFBJAUcU3TcRiP b1ueSJzOmwm27d8qzsVhf7nMtwcAvaNyzE69xPRHcNvWhC78qMz5v21+rQl0dVkFMaVjl+ZqasfF WZ5iCopYncTzNrZCASCWByGVQfwszw+eeu3tM3P7zkT1l0DTtdtGW9hDkGCu4mYaFZE8idmpDTGb EBvAEIgJtlIdsMyWG53Prz0Yul0u85fKnpC19/7WbIlfR+t3TVmXBLAt0bF9IHFkm9dwzf5fWvZI WdeIqJfKztOPcst9vbTta4/VpXZyaXvXLn9tu98r9xbtwnEcx7k97nx1HOfubDleswjrPA/lYEKP MjiHMqoiuy3zbb79er71I+ApP6zXLkQgO2jb+W1d6spptHlRKMUkMKbuyHQ28UUBwQJAoZK6e6dR za3spYhbigXEAbDOxuYH1Vi8dlYVgYiaSEPB6pXrXsTBlAkL+T/svc+OJEty3X3MIvve0YdZDbTh Iwja6QkIcdaDeZB5HkFr7QcguCWphaA1dxd6BG60EUCAM7cyzL6Fmbmbe3hkZv3prqpuO+SdqsyI 8PDwiMiu/MXxY4iCSWh9iyO0MQ1mpqq9UFADpglGJofxesxw4GuqOuRsnv2bbffW7lw6rzPBVU2x Bzr2FSAonvKWy30ObNrPsx6WB3QVB9hWeIt4XfWoD8cdSJaLo0V+rcY1Jg1erwBv6/HQhl9/2NGL 0XUg3OAw9U72L7ARLZAdvekLOwza5shhpaObOdpfsdf5WnmuVtB8dtf2DukA/ePL+HD/vrgnc5/U 4h1IHIY7cKUL1O/bCxhCiiun+wkX/7eDoUogvUJIoXqBbjtY2AB4QNh2PR0f7JTuawVkMsD5CHpu f54DGw/X/2LZcxyhn01nx3/vc+m9xuA11+atc73SS871W16rcz8+yv1YKpVK37t++eWXF29b8LVU Kn11zbB1BrCl1yuKbrU/xNmJj/Y/9AU76E6m610oO1XcMddafn3+xfV8pzYVmWyjtm1rWHfLd4U2 9+f4xccdsa3Q1pa275mpVrCrF2GKivaqEWdgm+TXvbhYP6bV8Ryv49FtGcc5Ht+0hWqHyOpO10XR o3GfcQ+1Od6+9w4mc3yBygwix2PL4LKDXLrxRXME6v38p7zPFnXRK92vviwSEUSu64Nte/sytNmg K9bnxfaV3LCancGxTdDR3Kfoc7SyxdHa+611u+ZsO05jZG2597kft58XQi78ZWvZ5dgdtdFOgMmX aPXF/OjmigcLuvTWqj+wmA78xf1B/JcgPWJcWj97vy/YcMVuDlcYXLV4AoIIgZWgzB6DYA9TyB2x vegev3gMf1R9byDxJcr3zwq23lr2aNsfWbeO/3uDz/fO9Wrd99ZZP+717SP0vVQqlX5EVexAqVT6 6pqB6+yCLb1OrZCUxrRomwYdMK25NakXVnpk3FfT+SRVkI82x41eCDgC0ImBtICeMb3bV+r2QDLY qsoA2TrhmLWszScoLrBiXBeb1o0dqj+36cwRWWC/B5zx8WuQeiqYhJ+O4NWnO6sqxIFvbMJwW19z RK7g4MI1GZthBct6bwbwmsdSre+He4w63Aw37/i6T/1W/7/mDD4hbx3W6lDUrI9dh7B5Wv4KCt4D ZGPhpPHctGPHPFZ7Wo+7g9mhogHnzY5ROqQb++bXWIK8Zw8BxumdrWPjcWD3a7PHZPTib9lR+/jY nGloJ7XX31PLWV5sq3PhvFcqw9fV9T33IdzI7O7z+PyJz4dY3iIK/CdTz441XWqWxTP0vYHq05kG 0zpnLt9b274EvH4k3XqQ+Mjxfy969Fi/9r3xyLUa633P56NUKpW+R5XztVQqfXWtgGt2wZZeL1a0 Oc72x/sIuPo49+ngqy9ZGYyttAZTctMX9AAAIABJREFUihVQW61zhFKHI/EN2A+BkbHQ4NbzJd3l SiANQOZuQ1WLKGAxDEiey+mQDF6Qx7JILU823jdwY2Np/Z8dk+MUc8ucHNeRE9fh6Rf9mGKtx7y5 uG/Gae5juwb1ZldsXk6prfF8H095HHt/vYKAHaIhDKJTv9ZT7WP5o182fY1Dv5fbJ3iZj43aNTXe Dzbm+f2uFextfVAAlHOCw4XdJVC7NR3EKwWEdPCLDeGaJS84parNyR7X9iOflfcc2Yc2PGqgX98p kuF4mb1aAV/Vfz82H+dg93sk3tvbPWs/1c+xXVusG5S0xQ2EQxYgbNuGwYldekhv8W/zGaj6lv/u v4U783v9O2V1XI9A6Pz+S52/31qPPsB67oPpt9Sj1+qjY33v39bn/dtbKpVKpdfq8r/+97+8dx9K pVKp9ColYAnxokfuCGt/WweQ7O6vEYYeoevZl5XVdPH8e3O3Te0YXIo31yCEoe62y65GbrCLdLdJ 5g6LWoEn/WLbkFeYB6yqOnNzgRIRRBkEbgDIzJ+7AzBzQ2oAXwdmA3zEenzW0DoXZ4lxnnNWj9vk 99pPMVef4npYliGr8SZp+wynaYazo+tRogXE1PP52PI108F29H28FkTma2OisUvlbW5/AVzHCqRt 2qx1addgbKIaZ28f+tW2pwCh+f1wV/p5SebrNkba4xo6bB4LSmn0RYN32vXdwUW02bed781H9Fyn moqdU9vDfE++vVPU+pPAecSjgH0Mwhnd4wcAwqYBba1gGhNDFC2mI6As0wWqVmzPogncHY8xPqR0 rreEaB8B6DwKqV7a1+fAx1v/dn6EsXqpHvmM+gjHOu/3uS7Wt4KeZ3rrcbkHcl/7UKJUKpVKz9Pl j3/4/Xv3oVQqfefKBbdEBPu+H34vvVwBgIBwbwLhNgwWRmpg8h5/WEHEBlQBQLfTNjpMCRehbw80 dx1vsW7f5tAHNeeakdrImuQGCg0h+37SJPm2NyUo+bLGcKivZXbZ8bii/8svY70fQzX7xZfu3oZB ot7eEZTOY5zXyoqcVtV+PAF1ezZpylBWanBv3FefNt8doubADOV7MUPj3vepbzo6CltBqXb+5yOS xRi/wTTOyICNLFW64ap3V3XcG5zGKY6/P7wY7wciu/ZIx3ul3x8Z8F7iLcR53xy+k8Kv0XzsDNWr XzeORIe2uWf5nqgfcQDcaZimTOb+/tedStt36s5eaKPYcb8YLI3+bFZIiwQkMU4eL8ACy/FFi/vo kQO94Jbdl5EFS2D+Rsf4ifXW18FHcL5+Ta0++28d2/cIux4Fz5/9WF/zEOxr6OzB+Gcf51KpVPqe dbm/SqlUKr1OzHxeYbv0ahF+Arg7GEXNScYANo33N6hog2M7DNxtARLRnX6AGEQNkNeIiCDY3Awg 2++N3UVW6J7WBUT6tP54L8PFti6ugF5hjjhpjllRo7eMJ8OLntUJ2r2vfwXhZ4dXP3kMwAUiOxQ/ mTNWGcTeBRWAfnJovWEDQ2UDsVdjdzdxg9oa+aHkiagGgMT7vbWp1AaShMYvSSzpn9303alB0QY/ vQiTOmZujtK9nQsDpzZG9n5k5MLhY3dQhiNT2NrWAQJ3oCeKBMgTlPX1edd2Pwc02yE+rR54etqx bdTud9En2863kV5zzIdgG66fe9rpqa+b3dMN2u3juM5Op3AE7+n6bRB1B3Ef5+iPJNAaIA9sLsyA iI7+oAFXhygCteuDeiABURSY88vK7z223AkA5u7sbmS7L5m+3PlyHZm+5nDmOYYgDQ55xAW1sIys HhEzAmh/COG3QbjZVePBDg1tzG5a9vEWYb/uRvjfwLmI3fc+KnY8BOyMjQm87dih0N3yX/2U4HoF GFuH1PQFDPVu3C7mVjK9Jbz5DCDopcDqzEn4mQDYZ+rra/UWx/pRxupeFMG9Y/2RznupVCp9JBV8 LZVKpU8uwtZgpmIHcTgU1xED8RrYgAZnHQY5oDj7w3yOGLi1/Gyd12k8ppCKAaCWkenASkX8GLtT lqBWU8shKqkBNAPX4lP8exGf7LBVkf4gIcyjHFAvxxJ49XZlCOW82NvT8Pu4x359R4dCaXaO45gP bloP7JwdxgFaD47YBth3B+wdIigdr4cAc3v8TpQyStfHZf/5bmK9XNDpuc6ihVP50Wm/t1xD967Z W+t0+LKD+dL3JQpleyhi6wHNBZwc6TrEcSyOhcUeWJy6esfjuXcc9/TVHbFTQS+ingeL9rlG7T97 MJDXYb+3LJbAAL+0glv2ftzLj0Rg/Lj6LEDms/Tza+lHOv6PcqwfoR8foQ+lUqlUep0KvpZKpdIn 15CxqhcYoFQIGIwO55gZ4rBua87HscAOk2ecKieIGytwAz8zhM3u2EGHbNdx2rTK2M5RU0X7Nm2a waCGYrXltSqAJ3PB4sm4ZUBUUsj+7wBfgCiwBThovYJwwa4E6GbTyqEOZqXBVdDeoG3vorlArafz P6s78sx9wa+LY1yPR3tPu0tV9Oq09wpVg3zm8rX352npCu1tktj20/L80/Y3Ol4jMoKIHCD2PglS ASyFg+k1sDOoG23T9NN+vwtPJ8CrHgPxbEhIsrg2vc3kXjvEDrTYhgD6aOee/drxNyFyBfOltyMK 4fH8NMiP3cH4l9SPY9/2MHQ+83j7/XmMsDjqGNlwOC/zfb7Uo7MdJgCLrUWMAHGs8VAF/vlljnfm cOhaxm+7Frx4GsX7oJp98YA+QkzA6uHGS/f/lm1l3QJhr52G/5w+31v3tcu/tt7yWN9yXyt97Xvj W173733eS6VS6UdVwddSqVT65DIoGK6ua3cXsuVGBrAa/+DOrs74wzuAh031h+cq2uo9e/URx2u0 2eMM2m77/k7g17Kd1j4P70d785dd1R4KAMT0aYGAgchtjWJR0SfSlpVKSpaQGs5SHxtzM07wNV4r IAP8XLks730hXx9//y/OGaX3AlCPx9/HyMfas07bOkKtnVggdAUxtfGLsW/jq8BOAGseYzvWW0Aw +j2vsgKc99TXi7HeHt52nJppADZP1bS4jekaW7YB9PHmHl/g50KFQMzY9yds25fxfAB9H2SD2c9A 1lS064FjvPelOu5HgkJlXFdVrS95/Xm/7XNgvX86X/SQbh1nhuxxzebPNfXlBrwBkWt7MGBO2K/s 4v3kei58+Zqw5lbbt+Dcc9t6ZPm3avPR7e8+GHlmX97iPL6mjbc81nvrvQZmvqVe0r/nnsdvcd5L pVKp9DwVfC2VSqVPLsUXhMOSwCC6AugwNaaNewqnbTQVJKI0vdf4hrkqVaOI1lt3+gheH3ETHSqw x3T8RVEqURkiGYJwKW1eqCcBa3fLWUamT83HBaDL4MJTfAGrwZwubrmhq+OyQ3rUdTc7R9mzZeP/ IjagO2BtnScHftrGII+FIqBiOucccLV/EeswLl8fW182QcRYn5m8cFWHxSMEFH+PD9u/9EtgB3G3 i1DNwM4U8HMRe+DvrVzI4cQeoeXV22VQRDiwFe+yafDXNkXeYj1SMa8EchUExtPhQQNhQ4Drlk97 gjgPGa+HL+Ry8r6evv8sd9nDa66dsxHDoPNtniCrSLhd8zL7PXKFWS22QHn3hwTPdwuXSqVSqVQq lUpvpcvf/8M/vXcfSqVSqfQKtdgBgjs2L1DaB6BizjCAdIOBDwM6BmGjMI5Y9mtjVNocsCsdgd2i XzpNEwcOEPBWG0exH4/v27M0Y/tdYVEBGqWF+lR8BQCxSlvSpie7V2+PzNbeNRsXQa4QD1UI2TJy qGoV2Tu0RDvGhZbvj5DSdhOgUhIA4w7PUq6tlU8z1y5hhJv59xE+pigDdFhL0ANwG9yvmM/5DFmP EO8eVB9G4s51IClmYLXtvN95H6NrlqZtYxvujuFDh1d9Zt/WnM8x7b0VtGr92kF0Gd2v4bZ1tyw4 31c3h+JwnNG/47Gm11FQzB9kNIhLfHqOuh6IGngojuDO9meuWv88ic+PjYA9x1coA2SZuLRZ8TsJ Z7P4dV4qlUqlUqlUKr2DLn/8w+/fuw+lUqlUeoUIP8PiBhTmrPPIAAeP2dloiorsAlVz6xk0Mbin 5B5Y2hwqadsmuwyXUG2Gi3NBHZ+evz6Qc9DbJd7vDgFJMqgyCuYo1mCZqDfLCVQTyKutK744WHxy nnm1NFm9pDHwFuUJzAyS6Tg5IgYWU+CXUQ2ju7UdXc70bBBY0zoBX3eEk1JVIeQYzcvQ9z4EDIUD 1gTlp+vCQOHFroHhEKxNSoCuLfc2Ns/c7f2NKITI4czLRifjMFSz5XEeNYq4hWPWa7ghb8UYzDEH GSKb2I/3JA82nKnZnUp5TOK4/N4iQ37cYiLydrvD/h5/odpdsc1Rm+4h1T5hqR9HGh93KZ+D1LPx jUJe0/G2Nx6DqtbvOPc935fadRsPeuDHdbzfDXyvM3kJG4ikXYsXADsxVK3QlnoBLnOmKwgBu693 r61SqVQqlUqlUulr6fLzf/yv792HUqlUKr1CHZYGsKMGUoZMRDWXaAAVTgW0gj0JXUFiZYCYAk51 YEQDCFpkRiadAbZ5/ZhqfHp8qY3sUOzT2zvwVHeDBn6lTv0AJuh+BfHFHL9kOagULkj1XEiVCDU1 iO05oDZ9WSAajmDu+9j1wI1z4R9/J/0eGl3AIgaeokhYc6HmfSGmne+AKq4q3c8aU9hbk3nfDFWD tWH47efM4g5ExvNr+xxfr85PzoddbRt9n6e352vzEfdzQNe4Jo/u61Xkwbj/NYCN1xErkCBhal91 3me4yB2ItyiL8djnPo3xDkDLjT04d8OF3AHtuP30emXWfSA24Lm5u7YyL+7bI6ylGdzedMea21w1 O7t7VrBlum7NsasKXCgArD9coHCs2zWt4lmwlflaKpVKpVKpVHonVeZrqVQqfXKxXgBcoLiCdgNx yu5iJGlTycPdBp82DyJoK9BlYOKiPzuH29vUaxVqhXoUUc29200bsCECcMUICAmaIBZ7USvqRkmg 5ZF25+MKVEV2Y1+QC07Zej/xBao7dihEoz8bmAhECt20AWTRvzrwInMM6sXB0OaO4CsUf/GoBpte zg20CSTgqprbjkCAbK1okbl8NzCzo1KBerl6Qoepw7Fu3I5TxJySVggqnLdHcMUQREjmHlm0ydXa gCO2PvX6wNiOWaA8ZP1qu05UYccOdsdrkLGoOB/tjC5MogT7p3Pb17tduItYhjZadMKheNSxfYDQ hqexWk9C9nFt+cYez0Et25fcYXyxsSLxhxeEIVqjgeqA6g7R4/3dC7jx3s59znSVgLRk9w9ZlgHC DUu4xulox29uz5TLCxkMpS2HFmjOW6Jx/ObfzxRJDJat2guWxTB1yKx9nQzdvV+8BciO6zmyh20F posf/0/u8iYICUgJkU8djnARAtTHE9rzXgPccjxoejR3uVQqlUqlUqlUelsVfC2VSqVPLmZu4Aqb w6GIBxACKDI72ZlNdryycZpWlGoVI5CgJ41w9Ogi3Mbt3cnWddt9dt/9mCDhtGrkatoydtAbuak5 C3U9HT2cruG2C+A1OIcjc9UGDc6ZDKaqQS0ZwKA5ZVV9wvXkUMz5qjFd3dRjBjRctwtQFhBN4ud+ LD7VnLOkd1yHqWPW+nExufNUDfLtsGxN68vRwbqe2o+2/mr/6ziC7qSdwet5W1PLybHa9kEd/nlL U9syvM8t09eWqY7O5VU32vUDHzZ7ktFXoN371IGt5qgKBiDJ/ZrFBhyRcpsP+x8M1/l4+rT/2RH8 sEhan5e224e0dsw/tPsWc+BmdTKQLZ7fQZTHtGIHSqVSqVQqlUrvo4KvpVKp9MnF9B+84NIV4F8N MsjVqq2rQDwH0abdB5wL6OrFqBrr8OUOFC3jMpyyaJmSrU6Pb2XOSjQHZuQ2EsU/M5HTykfAC3QI tpgaPMQOxHuLcQgXogaQsqRNc+c5aBIH00RbB2LQ7nRMLloFe8ZkgDFAsYGUWywAedapRRi4K3RR yEm9INe+ORRK0/0baFKGJEdsizoY4gHmwUEHwHQETPm8Gq+e4fg0houNB2cuxzgIepZtRAHoeGrj nDYndDh94/ro0BYzXD7t2H3QeeOI2mGNBbZyW36NRq6wJvcuSatX1fugAK4AdUc0WtYrp/07vOX1 n10tJ7advigIZtcf2IqdqXRnqcWNcGpjT/fbJCaQAkrX4bj6eH4rV+g9AJriCiao35XfT+8yQC1z N6JI5LBeqVQqlUqlUqn0rVXwtVQqlT65evZkTAFnqE9plrbcUKL6cmB2yjn0a++HQzNiBm5NT+5T wZurkLNTb3ZzPqZhP3PWLBT+/6n9DVe1Iltby6dl7NihSlbbPQAwRthn1EsHiMbRh+RuFBUzkNJm 0QkRYQB3zZJCEWPcnakBWXXvMC7gkK0b87Z3dCArUMk5qdnlfGOcNAO5w6h6v0aH5y3H4yqX1eC0 TSm3aww+Dlji3RU8zu0+opVr+Z5Tcz6+830FQM73BMEK1s3RCG3vMHd0PJDgYb12T6AXbRO9en+2 ad2xIFfvb0DY2OXmDxLQco3thV2/vSBevtf8mkIvVDc4oDWOWZHP3vlYrVy4ed3nOEz1ofO/yri1 Bxp7i2kAjn3O19ezXb2lUqlUKpVKpdIb6fLX//s/37sPpVKp9G76HooOEr7AYMsTVH8yZycIStcO eSSmFV8QhXKsEE0AooCvQAAoW9+hYURaSiCf7HANCOXrEGC5qRkEudMWT48fV4CTVBV9hisdKPc+ 2cYBiLyAEitIGVe9msNQx1YCUPVp/2KT6tVAtDWnAP3Gc3StUr05XS3ewLJjd2SYSpHb6cvF80L7 fvNPSlAJA4zzxQBdFlPvO1QLx29rt50TcpAbU9zHsW5M7hAbEcvX8P0IUDu+P8YCzOOe+nYy7X2G s6tYg3Efa62iCsb9MJIZ19+LdY+O3OxM9ncQILA5LyHmNI58VQDQL+lhCWCwM13frS1CLt4192mM RSC4F7tnxh7kU/MbpexL2n2qEdPxLXQE6eerMlrcRYyVZkhusnMSOb0JEEcmc7HXUqlUKpVKpdI7 6fKf/vN/ee8+lEql0rvo//zyL+/dhTdRuCEVF+zyqwEgMtiokM4hAwSS9inXjUiYyy+yLIFwN9o2 rBYfG8oO1zZrfgA/MoGzDByPuaS53VDP5Uw7bhXWc8Ykpz10KNzzWX16PMkpXIw2BpiWwjLV/ycy W3eV1ldVhbS4BXtfaAcHuPVCP238Bodknq6vrfhSz3gdHbQNzCbwtHLC0jRmg8n3BrC8tewRlyqx O4BfoLn42Et0t3+tSFscDy+u0xT9sOijFVmLfcU10gE6QP5gwyMp0nns2azRX2l9AuD3bJDwvV// DfTOENwfm3hXuTmvv6bO2pcby0Ln5yfnOD90reWHCB6my3xpxdPyet1pXiqVSqVSqVQqfXtdvhf4 UCqVSs/R3//DP+GPf/j9e3fjTRRwASAwbY7wDKaKXt0oZsCJsSHyWdWdoS3/Ela4iZOrjEihkqfZ dzhpDkxGK+4VuaJt+jbcZZqBTDhD709NNrC7mGKv1KvSp/clrSugJQeiDC2HJQFPge5cjXzWvNZf 0z7jF+674g27XgEFdhA23SBiztQdOsQQxLZj1mkqiJT5Mgwei1wxjt2U8erwMGCuqcNuwk/2cwFW 7Zg6HFy5TJnvOyMVOwRbK8TVt6fDNPDzXM+pzRPn7XNiC9btxDUbUD4G/QgSO+zzazibSBuI3Xxd SS7W2AYg/IyA7X1sx/uhPywZ+4m43xBj5w8VJrA+vXV6/Eu4fnpf9gJwX1vxUIcwRWzo+YObdv3m J0QtSiL+K5VKpVKpVCqV3keV+VoqlUqfXMwXm9KuAtrYCutoB6E9GsCn74ItyxKcplF3IETJzacA yGtkWYzrcfp/c8E62FXPj+ygMUcUtF5jBXLmjM1ZfVl3HZ7lieZW+68OkcjLYkUhLfJ+6m7ra4J6 lJ2LhDkvk8ENBu8xKzqmuSOiAwxs7fuTQzODnEQ2JnFc16d9BGv5eJQc5EYld2pj/LZ5lrcAbIZf 5w7RlTJonX+uIgdi/+O1dnR+RgzDIwzWruXx/Nr+rQhb39fYWHZ05yJpY5+1jdwqJzcfa97Gl8RB +at+n+VxElnELmgeS+sjKZ8YkLU5sdv26eezGeUSiJ48XCE5gOLV77ck5J9D1uBhv1a0jB2m79aP ab+lUqlUKpVKpdK3VsHXUqlU+uRS+f8g+gTVL1C9wlxyP4P4r6CAk169Xa4B9a6WlUq7TdPVHSoE JssytcJT1Jx24MgVNTdtSxVlIPJMdyjYnZOEDlQIaFOwBdeDqy9AsbUXVeKTi0/VoSOB+DyyYAuw pQQhtOnHRAKRgEwOqSZHokqGYArgCcQZuHl/d7aq6mTuSAVhB4M0xlXAigS4Oui6ABASmCe22yYV 3ADaxp7NyRcHvQxx2EdER0dgcz9ae6KU8lb35ugMN7LIU3MDztP8zTUY7lSk7WIFO/ZbUomCZWjT 51cZrRlCZsCYwbL9tNZGSBidyHBcIbuCt9uULadKzKxXs0OYLD239Su21S9+7vfeENiyVrGBaW/v rYDyLn9Nx0cg2npOqzKILaoiPzzJ5yTOZ2T9xiBJ/C7R218H4DiegxmM9utA6czZ6g9QRIZzYw9x JJ0fGR3r8XDAIXGPRIg8afGfPp4k7rAngNyhr7u5rqPInu/DXtj6FvshUDz5/WkuYfWcYdVUbKxU KpVKpVKpVPrGKvhaKpVKn1zM3KevqxWbUXeGzg5V3mDLxbyY0FS0irNbtbv2BmAGMXDY4E8GlBmM ybDfe9OV+364uR1Hx+A2QKMZ3o2NhSPyOCX9ZQ7RRBwd9AVgSx7FWMH7lBy6MVWcteXzzlrHK9j2 Sl6tXrW5M/uxheNzVeV+bvs+fBoLSB2dr48qO1TH99cxAbPD82y7IefzqyjAf9vz2A+gjbv1hdBB 4t5e23sdlPbjy+31a8HAd3b5joA5zn2PzZjiKtJ5a+Ooq7EcsiyerbNrIecfhxs2zlnEnIzHtXiI MoHfAMzd/azNuW/bz/fay/peKpVKpVKpVCp9bVXBrWeqMnJLpdJH00YXc5+1ajVAuPGUdocXV1tE F3ORNUbH7ix1J2BrImAqtZ8BgAwIKngASQTWBG8lptZPU9UP/CMyZ601kSfv5zreYFZeHlO+fa/L bc7gX97f7f1cYWMRLkR31xrKHqErgJwdq4KWmTpAbeesIgJlK26mqu7GtSJhQpEZ2guGzREMqtoK guXl45T58djGschjMK7zHHBlzt+5j7PD9djm2X7G6fsB/RcW3BPgu4otWMJu9PPTwf50DQ0pAxky +lt48oU8uERjY6KLr4fmdo52icgymjXunRir2F66i7xlMmdn83S8ZAXz8n26dn+m8bmbO2DF/Ho7 64cqdp3eeuCSIbA6eD0vIpchNLADGgX12K41d2XvMm5/K9KiVCqVSqVSqVT6Vrr8+pf/9959+DAK d8X8XxRL+W///X98NwV6SqXS9yNiBYlN02VlIKbzDoVmHPYxGeuYMi+ZGRJ+WQU0iOAE7Gw6sBj8 AIEkQxjt6/p+bJt5qvQtcDM7/tbwdIausUyWcClLWpTBKrczprlnRR/CwZcdqH2dGK/bkCdAtG3K HSbB4gMGfujFzoC9xxpQhtl8CpXuwaZ1ZunrxYpDMbTVfp+rDm3jGhBkkDvC2XF/ksbskhyjUZvJ Yjl8Pws39Qhvbb+Wl5wdyAFh8/W7A9rhqJk3M8iNc5AcskJ+DdIBqJKqxT5EH9P/5jEatlnA+Fu6 d34E3ZFtmiH4OUDN43x03/aHMNkB2xyviPtv83gI+1xqcJWtDVY7rzku4uu7pUulUqlUKpVKpdu6 RCZe6QhfA7rG76VSqfQhpRdz9ClADqUUey/2FABDqQEqVXJnXqISbb14TW5QI6eSao487VOkrf6U 2raqAzgBbYCM7feCWopWICfBTOb4DAZ6tfIE2JTcZeoZtnkYbhCWpTMwvb4NplJeZIM64TwMgtej BazNRvY6sAYajIt2O42SAegaLPSMVQ3HpiZ4qz52a/fjuI8OyO4BuFVxqFtjd9y+H9O9GIFbuud4 HrNoc9Gt0fFogLVfJSLSoOtq9n0Dsif9YrWCadrGdHS2NiAeERkeGYEUE9H67+GkcS21+6D5tzOA VTwyvX4F1cXdr3a/3D5/fOe8dIq5Bt1nTtO1U9Y/L/TsIcKJq3aAqZ4Zm5Zv3rf8UOYY+VAqlUql UqlUKn07Xfb9vHjJj6Yz+Bo/S6VS6SOKKBynG7jN+SYDLiAHgwJQz51U2kDMIFxhDkxzV3axuV+z dHS5zXmw9nNyuXLKZvU2tEFVA7ADkEpTuTFNXR6cjeo5tQm4Gbydp43PIKgX8Rp5z63P+L5Ny9al eUo5EugGMugZ95WdlQlCA4DuA4DNU9K7z9GgOih8xhnm2vJzmNW1imMAjg7BEaAS7rG51bT1W1PA 52Nd/Vs7btODJZb7T+10Z6s5ZQFA7kRXnLWZ3bKi8dCCEfW9cgavNZrf2wH0+6K/H2DcAe2iT61v fu+Jn9+uyD7dxvUH1zuwN2fp8frMknvn99CGTsvWxxCO8Wbtbg9x5h3cPreHomjqDyo8tiCiQIgs BkUoIkLK/VoqlUqlUqlUej+V8zVp5XYt52upVProItoQhbaMqRjMsZn/BGUvDERhWqVU+MmgDQug TF41HNaWESSHnLsBSK9mHySDACjvIHFAKVG1HJiL63T3WUDVAHuT07FNLe5txHTvDutyfMGoe67J 1fv3XLMdAF7bfm0TanEAaMWv0Nbpubfx9giCbcPueh1h5FhcyWIjRtiK5jS14l89E3M7cZ4ep6Gv nYHHbW2902HKh/2Q1lP7j87Ned0OYPu1ocNU+HE729a2mQuBDcAUgOgI99p60S9cEjjfsRNFL3xF RgeheYytv7GtgUTYusrtAYoGsh5yAAAgAElEQVQQgf28S4LOBhVzTAVSG0bi8/40Pg+e6fa8D6KB 9TiPD/J1Kqh1s10S76oM8H51TgN6twcFZNnI9hliMDge0uTrW/WBa7dUKpVKpVKpVLqhP//5zy/e tpyvSWeOVxEp+FoqlT6FRohG7ozslddFtz4hVxnEkZe5gZmgV4d7tKMVbsfseOsKsJXjAwBASBrY WjEPlTQVHyM0Mifb+H680fvQC1nlNjR3OvXxViGn1Xo9MzL2YeuxMnbAYU6MdQZGfZq4AdW8RwGw T25VBVGHb/MQR14pO3ijk3539+QtwnQsVHXLIXsAr15U7ZYeZa8rQJ7hc9tnOtZ8fkY38fFaWIsB uo59SDCRiCD76gj6ORZYUTNtAJQhyl5sjiAirZhduC37dbmn9pITNo/H9CAiQ2miDjl72+6UbgXZ Lr48ICWGtpbnOztpbzhPrY2IvciRCNK3a58pN/a30mG/gvGzLNazQlsdgMPzXoH4HMpX4QZ66Moo lUqlUqlkOvu3+7kzgc++O7zV8nvrrJZlQ8Vr910qPVflfE1aAdeCr6VS6cNLN3eGBsDyqfvYsGGH kEL1ChEB0RdzxKpASSHyq7tirwAIG/3kuZAEVrL66qQQCAgCFXP+EZvjktnw6q47RK+4NEgJAJvD motltArAW3dntuJE7t5UVSgTWMNd2B1xAXuUFVHPadMNe8pCDSA3TD235FuQWCSBNieqtqnhANJU /pjePw1x+8XRKcHzXRXG68KFmWHz9bA96WVo3SezN+iqPu+b2XJF2R2NQgJsAGOHSmAnbkgQYKhw A395nxnYzYqoBnPvKnQnh569IVa4I3GM4LkHbldRAmd/BM/bzeB3XpdoS+1HTIWB94gc6MfdjyOu OcU+uF47LBxGZ+ozYXOUx9GXfQezNoarnrC6ef/iOos+arij9QkMsocVMdbKwP4rRAmEy/QgQKyN jaH7FwOvbPe1XZH9SmhT7Kf4iXnq/TyeaC3kjGBu6wKASHd0R5SDfebEkO0AK4jUr0W7j2kLx7zF nOw77AGP7nYcotP1Evfh3h/UKHt/dvscEQewSh5lolBcHWiHc9x89GBqfS/92Hp01sPX+GJeIKJU Kn0WfYT7/Wv24SMcX+lz6k9/+tOLty3na1LB11Kp9BnVpx+n1w2+iM/fZV/v6oCpO+cMdGwOj6J4 EYPI8J4IQUmgxD2uwPIGYJAXIBXwlNE6/GGTinRZH7fmllXPgQXQwKrJp5cr+5xqnZbn4+3ux9lx R0RgnzLfCyV1oDa+PlM4E4OS9vY54Oih8vtK878lsxPVzpUdg5dKIvcv+7hbEa6AjR2w0dCUnY9c dGjcT3br5vNyae8B5hyc173nZjzLb11tuwIBtzS7X4c2Brf08XwK+Soxzf1hxbkNd7J2R7Ry+/uA KTJILVvWOP90fbVxJgf96mC2PzCxFTRtm4CkLHJzPWbC1B9qzPm7Adpn3RpzkciUjXVjPOIYdutX 3A/tBuUGnA8O5mG/Y0SAta1pLQGxxww4gI6xaOZXMtg6Rx+USrNuwcdZ976Yv8cX9wIRpVLpvTQ/ qJn/Fpv/VnxJ+/P2j+7ztQ/DHmn37PjGGVnH/h7+Zjtp7+xv59LH07/+67++eNtyvibNcQP7vhd8 LZVKn0MBXOjSnV8OJja9QOkJOwTQL7Y+7RntoDnNSEEQEFMr2M6sICXz9LG5UA1AuTvV92tFsJ5a i+yRB+ZazRXdY8/c30f8wRGwr0OcBiwTYA3ZtOL4A6eDpfbHTWKsjMa+OkvF+Pp8fKfXpNYf38E9 AHuKt0iAqWhWHK86PFdk96Htg9rBxB+Iag5hGadqG6ddu0jXMQR9jGbwCmAJ78btb0PeM/jRdRvu ni1rII8MJG8g7G/i5JLhpzSLsu+TpF0aexgxIZDmxu3+5Gyr7Xv1hwue+yv65CBW04OCoMYKUbjD EwY83RGqXqRO5QIrWMcIR/nwJSKNryL6C0SRu3Cl2wqLImDthsqRIbDQ6HFtP9wApdf+0AbirvGj OkiPez6NU3p4k2X9jkzoZbOl0l2dfWmOZcBjX64LRBSIKJW+B936DJs/C/JD/Hn5o/u6t/3ZPh9x 7T+677N2z/p39pk4b3N2bKv2a5bB961yvibNjteCr6VS6TMqYESbCs/mTNtwwR7ZoAoHnzb1OeCI /fGwwchrd6nCAgh82wxTe1EtAyqeQana0F5AkdZ2U6r2ntytxz86GPPfTmcQZnaWBsi987fXM7QA rGTjawD2ke3zphzVzry/CRBDGoCNP+pE+3RqDE5jTf1S5Oni5D+hPOw9xkaTi5fUQDtRb2N5zId2 fM96fP+RP1Kfq/tQdU+RDbe2fcD5nPJIVbrrs2/LrU0BY1NzojO74zMggiho2o9qLjIV0/fjYUgA 0eTWdthrIBX+kCWuH07HNscHECAKnQqOnX65Uj4sb33wY16/n53vbS+AZ+VK2k92sR7bwXAsuW99 m3K7lt5Wj3zhv7ddgYgCEaXS96Bb993Zstf+bXdLL/0cuP3A/+0e9pw9WDrbb+nHVDlfk1ZRA/m/ UqlU+qjq/6hv/gfAxV9/sddejInZ3JEGcNin64YjTe3/NXJL48uUgSZyOKTNgcbJlGfTjG0acp5m nIFQFJaCf6FLTk4HjL2QUWw/FvIanLCL4z/+3TQ5KSnDSiQ34r0/uPSx1e4pFSYyCUgDonKrcD8f x+qPOvIs33aO3AkZU9cz/Mw/Y79xnoj6OaKpgNjocDy6rW47UXtfj/s/W3906b70D+G8L1YdAawX hcr9e86+bMzGMRLq10eUPhMHqxTXoCqU5vGgDtd1NxjvDl60c7E317dNwWdHj1t/qEFi27C6I5sA 2uLxSbue7FpbF4sbD7I70vt7x9Vs7GxN+xyRYf34bLEHMoDON9BUaOvWNdU2IfvQUf+8iu1Kpdfq 3nV0dn0WiFjvp0BEqfTj6C0fsH9rfe2HPZ95bEpvq3K+Jt0CrwVfS6XSx9UKSAa8EXcwugMUGyIj 0dThIxEgQanYilTFdqCYWk9Q2R16BPlgAE+TG9CzZHV2CnICNuGcTYAPW8/TjPeyM6YdFzWQSzj+ 4bT6kiygDj3bH1c0vT7RreX6gj+kqLtdl4vdAUtT9qfJxsxcfzF+c3RBtJPA641DaHECkrcbwWv+ Of9+tu/ZsbhyTN3r97h+PFwYPLx9mxOHWADYtj+lm9fZrWNa9TniLzbqDyZ0uMYcQso8HrBlDl3D Xd7GqlHjRhnRYgEIDbSL3QTmXJ76thr3fA+uNblm23Z2zfX2wq0b7l7qMHjuhw/DfWjvbatFJwzn xF3mXQJ9yf1XKn1lfeYv2wUiSqXS19JHc7O/VX/qs6z0iMr5mpRBa8QOFHwtlUqfU31KdP8ZU8kV DIZoTGHWBOZiivtmU5QdsjreAvMO4Ceo/OpVyH1v9AWWX2mvrbCXTbk2DjNNN3QS012eHcKST6c3 ADkXyDqK3W1IZNEK11a0x44lWn7TP/aU5upgAPiB6IE0HXwFYFMGbIC+gT1Rz7dssRLU3ZJ93QzP DOAS0TTFPb78+hZ+uilqS6V9AufQLIPS+Z/KFfTL2xz3cWh+0Hr78z+cR7fubCeOXNQHpQxlMf63 ALx5fI/dYX9/3l94zP3BCAPacmDNFWv3WTimtRVWUwUi11U9doC8MFUU0GtdTxzYzKgbsiP2KDmc k7j2euxFtN1dr/FwpMcr6HDNOrodxu30vhzOTc+Mjs+kERABLcKkVPpK+hpfrgtElEql70GPzoxa uevvOeXv6TWfW7fc/meGjlW/n3PM9fn446qcr0m34GuNU6lU+rhaAaTufO1gM7sXM9gjsP8hYBAz /gdWgEtsPWZAmUEq2MNBSzGtWMDK2LGPU43T3xfn0w7JH3BFvIC09ztBIuyq2Dj/gZWA7fQH0rwv W7b6QvjMP4BO/7brsHMGsJPHFrcyVFcO1XxsSO5NW3cz0OUBp6ccy52shIijyOPVOfJj0QDr1zbm 4x+ns1N0/qOz/THqA0YcywK2PdKf553DDGR1UcTtoBsO5baKqkHF1pXxHMdYi5PTzu2pnQO0azSO 24Fsc6wLRDxHmcf7ibBDoFANB7RifbFGW8fzBcwu1zEPWFXAvP7CoTA3fBi12/L0MCGOKbe/vtxy vwMup/ta53WyY3bVXqlkWn3pfU7UwHO+NBeIKBBRKn1WrT4/8n1+tu7Z789p/zX7fGmbj/TnbD/P PZZ7bX+kh3Glt1c5X5PmmIGCr6VS6fPInXV4gnnLDLIRXZorDS0GgABlbMRgnyatau60jX82SCQG EFU3d/oJdAfoYlCJmfp22KEgXLGDWUHKUCWDSAxzv6pPqpZ/731VheoOEMNmvXOHjDEVu4FGdT/s BRGBoLIDHlMQ7ZvLsxcOIyKIF+VRUqh0UGN9yAWCFBfuYDMcjsafvG90tdFmtuMkgDZAxae1+7Ry PgF2u24tb9TAtQK4wjJz1XM8k1NTzXW8sS0Xd/jtZMfLYKgCAgGrgnnz0mhqfXY3ZMQ/fNnSv/n6 xX/2PN32JZhw+EK8/kLPw3sRX2CQjJsTNNoSXIet27atEBQPP4nGP0Rb91IxsLxc9gCgcY778V6I AXkCSCEBEMIKGv1Jh8yLhxoCMbC+YAUtz7Qt3w+ZqXFdxLoGW7deyMuL3RFfYef5r21fBtt/hipB rpH528ecsEN8OzumPtYGwRmsAtA19UvAfEEU7HpSASvALTM6dT9cs/m68M8Vcieq+nXZC4MpVJ/8 QY1dh+L3b74PRATEmu7H7nSN90QETA6O2/J0vWH32JIrjvD7eig2Vvox9egX6Ee/mBeIeHwfZ+8V iCiVSp9Vr3lQVvrxVM7XpIKvpVLp0+uOS6+Dx7F4UrxnEQTmdg3PnbqdTXlcv0FddchCBFE15OFT 6g3EAkhOPNW9wUVzx2X+5dOdKaZPdzfgPHX8mAdKvr6e/gEUcPZsbEY34bkbR/WYrUniMDUVEjvs owEn6gBRGQpKYDHbZmN8AuzZuLKSg8Lu/CWHp61olsZ+MmzOx3c8/tXv93QeRXD2/vN05rCyZQs3 VoOvHRaO2+kAXHv743UxnsG3fVC9AttE5M9GFBETomldA9prh1lrRyLf2Yp0tetM7d3x/DvIlLiv FJtufl/HOhiItMyOWqXU3z31J9bp96O97w8x0jLrnEBaHm6KGDgMnB6ugaye47u49+o7UemFqi/X j6vGqlQq/Uiqz7nSc3SpC6Yrf0FY/VcqlUofU+P0+4MChAIA7aN7zaGPzYLgNs2Z07RmIkBkh9kh GeRwhjlAirk+GYDugJLALJoOfZQAd4wKscNEhlKAEgHoYu5axFRl+HrmjJUEbMfjDrfb7kDqdSN5 a7pmxDRYP9hAVLy+AXXnNobX2CzvkzosbNI8ZX/MvmXvizrMZSUIGJuST923PpEXUuMTkLyCpOcw FUvg92i7Z21nvebf2uzabQ8HpmUGMI9dO573rzMrqN0z7d4KCG+ubI1ryq8JAtn903p1tc4n92fO CIYoQE/2tjKQnNSqAlHbH9EGkBfOa/fhbu+bj9XbGMcuYiGs/94vTfmwETsSzwqog1JzxtIBvqrK gYzOgLWfm8hzBo6fBatzdizMVio9V/Ud4HHVWJVKpVKptFbFDiTNztf5v1KpVPr46tOxx59dOfOy g5vdf5q7TkVsSnvwnAw+OQMhau5XwMArwR2eqlAICNxzT6k7ZglkcNW2tGVIQLZNgw8nnoLvFHSK 945jsjvM6lp+SVRqcG5Y3hytUQjppZl6yU/pjtcV/OxpCAsHH5FVtWea0kqtnS1lXzKnvqYCU3O2 7KMQNo6hT/sfYezopj7qMWh7e/1xncW5T87XMwhrbJqSqfNWv97u3//5YW5Ml7drnZP7M9ykhFbu bspI7O1kJ6oO0H52oebIj2PnesxHP589OkIVdt0drv3uphd5mpxv/TzYrdwd4/M42HY5RuLogD2f vizdiY/5GsrAtlQqlUqlUqlU+vYq52tSOV9LpdL3JoMfGUbcckACHtIKdvcb4dLca+ZU8yxI3S2n ki4Ge8RnINPW8lc15QmYo9YcfpHjqWaT9Q5E5mXsKYsBffJ+zgDGpzs7WOzHO0fFsJvrOki7BVAt Gza9dlctoYOt1TT9QzH7MzXwui5A1Y9tFDksD3jNukF5S/grj6DBrADsqz5n6HoGTY/v9/ZaGoIX hBog7HzIquDtdizGfZC+1q318nlmvqCDOjuAuaYXD87Yt33wag8beHovpt7vEKJWjEvVXK6CDscJ cT/lc9ZdqopoPy8nv3Hcde77JfK8Xt3a6x1iWyfHtaQ4B40CWJDBpc00HlOGvyOQH8ezZbwqY4wU OI96Gt2wCdb6Z0jEnwDAVlkDpVKpVCqVSqUPoHK+JolIwddSqfTdaAWydHh/BH3GV67uCDQCpSoO aVIbqlbIiTSBHAetTB1oqQBsRbkI3R1r6zvhIll8vs7uuj6tGaodGvm61neBOoC1CcgGAwNk7Q3a zpBo3P/Rudmnhy+VyZ329c/FQCoStGw3Kt3fAYpEDsnbLjtonZ2sdu6zYzWDWizgGC/3f8wqzePJ N2eJxNT3lyhP08+/B3I+nsPVvrVHlI6t2zqsuCjD7NU3HKKvUIyB6jz2dv3yEDXhcNsjJlhxOC6d rhUNABkFy+DctT306E5oO1diUQKRFS3SogH8DbRicHPsR8qXPnOnHmMDjn9PKXZQK+C3H7Ydj3c/ LqPukCViMICrastXznsqlUqlUqlUKpXeQ+V8TYovI/N/BV9LpdLn0QNwa5p+bkDGPusYFwOv+gWA ApsCQpYBK+SxAgzBtQHY2CURQ1RabqWh2QsIDDg0IVySQ1V8mnT+fO0uPps9TlYQyDNjj1OICcF9 c4xCLzgUTsG0h5Vzdrmsw66ATx0CD7bYlHl5p+CZZ7wG7O77iezdeB3Hccyy5FSciwEoKURpcCxy g29mdTRX8gj2ViyqLz9z495WZJpG+wdX752mDmBuAK33FY7Ws7gCBaCUrkfy68de+LT/r/fvvarl I4MUnQ2qn2/FOjvYrgWBbaOqbZyZGVbEylflPnWfsPn7ZOtgtwAQewLT4gxy3rMIWjxHFHBT3S2N Q83h2h7EcAer83Db6/Fe2FXBAV8bJA736jnAvSmS5eliiD1E8oVb8wyXSqVSqVQqlUrfXgVfk84c r5X5WiqVPp9sir8pfX4pN9iUp89rK8gV9C9Xj09Tl2P6s3rBHjKwAzLYIdrzVW3PbLBWBQQ5AMeg cb1g1iIKgNafv9k5SrRBvSiXqmIbinDZVG4ZHIVn//blcTsHhrNr9dF/SwOyjX2n9B8OIGtsQADd GuTdAJ8WDgAEjsrzDXy6g5Wk7eMINOcDTO7YZ4DXfCyAwfrDew9sf2ssVzB2BWwf6fbgns5REnf6 8CptZPHB6TjCUZpdqWeKv0XiZ0QWxHYcDwU0Hq5sgBCIYzsvnAeAvRiXWWoBexiSzhMJCFuDsfY3 0eiezhrzZQnAbqA5Ob3Fi3NF/6lFDcjUDkaLsveL2noOfkVb38jXIwfY8VpVQdxdwaVSqVQqlUql 0rdWxQ4kzU7Xih0olUqfR8dCWx1AUVpuoIOwQUUBCtjoDku9uJPTvHCyP7Wpy2Bg14sX4IKBDWEQ X6FqbliiL97G1XgtrmFhBegnQJ7MQasOgYyQmDsWAPDUoKFG8avmogU2+qlPTW5V2h3J0AW7t7uB wBSxBu5yFIK6W88cgwF6NsuIJMGXbZyuD6Bl2ALArxCrQJbXyqufZFuGRP4dRFt3Okb/HVQyfTFQ 1Jy1DtTCTEoE4p4buqvB1a05gHcbVt4G0knKgEo7/lxwq+dzCoguy37flMM1YoWxvYiJ6JmypxEL c1OLqIz1/lbja+A/zi0inzRBfSICZLeHD40hxpR3cjdnLgRn7ewOOL9oj6lYAeA9/U3FCarDz1Pv doL6eUq+iF23g5Iz+/LFN3GIiVRAjQCRnO9rLnE1S7S/mfpOO0hTvIT68gDAukEpjQN1J/XQ7b74 8PcSTcvbefAHI/3O6g+KIh7B3hpnH4kDVJ7vUTLXsIq0Bz0AQYmhUIjoKTQulUqlUin03IfOpVLp 6+p74nDlfE2ana4ziC2VSqXvQXkKfceIMRU+nK8GB5m3YVpxuDM7VunTebtr0IGNaMyLb9Pos9tS oiAU2fRgzbQQURwopg47VNLRxZtdsQPga+7CAMvaDLx5/QbSAKgXJLolauucPbi8/UCTaANaGx18 BXBSiE/3tn4zJEE69oJHWwN6GM5FgqrUX3e4evvYnqsGVtsxd3hpLyOD9vEdP/Rv7amD8eUPk+1e 6K7QF22f3cw4+QIXfdcMAvN6R/g8wMxwtLq7GUCLWiAijHzR7lsDpus4B2i4sLNjtZ+/nE270gpE 52Jbh/Wx253u9+Yq77n3vccaRH8O/R9ea/soIyKLSpD8BKLMBmcq2FAqfRzVd85SqVT6PlXO16Rb kQP1D2GpVPocOnN3dUA2TK1vAJPae0SbOeDaMlvfQM4XMBEUT1Bxd10AHiIoPYH4ApUrAO2sySGo 6O4c6OLcMApyXSEQsEPaXhV+B8EjDmBOP5suvSd43I/IcF9UTu+AqvtIr8OoZGj22MT4eZxleu/4 b+rgkCQFBSjS3DPbtzlPdxBtYNVxOSnI3aQ2JjPw8uxOcojLm8NqA1GgrR1vm6Z9MhZnx7CKW1iB t3RIzwI799b99v8Wu9NSLXMVwDiG07Fvi9gAe70DuoE8u7hTUv8Z7mF3gmcZmJ/c1kgQVzsyJbqm df1eatv0rFvr1/gwxNyul952u+/SaLRjo+a2jbv7cG0s76Vre9CyvtYSYJ1uXoPMex/fKE5GAXJ3 d5VPwFV5UXyrVCqVSqVSqVT6dirna9KZ4zUgbKlUKn12jTmfMT18S64/n44vsV64Ri0rdk/wJuAn sYKVLTyAveJ9iw7o06LNlMnutjO4atCmg5bIMlUK11t2d9pkYyF0ABu5lU55txsFr87yNAfoeOff xONU8+P+RqA0AVnlPgV94E42vltAbw3o3Vr1eAED3eJQKZbZfrf2HiPODW7y5Bw/cOz78b2XOOQ0 Yioe0HPbD4D4eOZu78wIByVdB30K/HGHUcgMayelpgnxh+Xxeg1eUy9Pfl/IowUQrnPhtk07vgZZ BRr3C8brvrlMHRKbdvQCdn4EIotzFI894AXLpi4u4hnWgDramt+b1yN/2DOu14vUwQuEhXt2B+Hx a6RUKpVKpVKpVHprFXxNmmFrZb6WSqXvUR22dDDYIWZ2xjngGKbjb0Y2WhirRwJouC4vYDY3q7hx 0wBPtMueNUutnciXVewQffL+RCeABoTVqsKrF/KhnP3p0KsX8InjHB2qGwhCR7DTYhh2ujk9meji oHoNlUYtwCxkcuH1/lkhImqwjKgD2B6nYBmWDGn5l7bOMavV1gynLKDPgJStvw+6YNeFvOyIs+/4 rWX7fQ6ANTe1dac7J9vM9slsDBjAVrHHApEbai5Rc4af5tnGtUe9OBYNkRUR8ZGvk1TsLEU2GCM/ wlBblt2m++H9KOhleppAuD0AgeY2pD1YGbNvAWI/9+Lj3q5lmfbT1bBqAttE87Vir4cH3elejnMV BbeU+zU49O/mJVAP0UulUqlUKpVK76OKHUiagevsgi2VSqXvRR2gGcAcq9G7y5RseTZHEpHNwW6w xh13AcB8HWUCQ32asrYGIpdSVXxdNZdtyyeg1uwAynxbbn0GhAUkllObHXUrEKga05a9+wrIKVjc zgFsxCwsYF9koNr+qEGuMydsc+oFpFP/H89KDVi3hFoN6BFW3C9ryLXNfV38s3YvduD43rkr1qIR kNYx9+Ujfb2lyLvNEHw+749o7bKNCI4AkPH6uG0DsOhT4ediWRnSmh4o+pSLYE3v34lgnWIDaPhp x7MA/zpfn8ffOwQd+5+vl/l3+7kPf0MxX/z9fv3kTOkhziMVmpvvoRb9HA55iWPUyfU69b9UKpVK pVKpVHoHlfM1aQVcswu2VCqVvnd1oBYwg9tUXwCAsMOap+4WhYIZnuOYXKEbgeXin6Gxh5guH7DM 4G4U+AqHrA6OzwT58kexMpQFqlaYagawbbUBwkQF+qiqPgKavt0CwEZfT6SqYKYFeM2d3qffKbmD yQop+TgZZIyiVXBnrI+bhJt16sQQR8DmKD7p9OiMPI7bcyMADtBbgRlcvvbf0rcpDDRCfRvEBMUb obaM3Vt9OUzbn6A/9QvBf7+umpoajtiOeX8A4cvpGNr1cB03QHaezrx9dp8yVK8+HgQbI15sEdvE A4D+8GaIHSAvokdAFPITycdP7XMhF21Din1YJhzkInStKbkJp1tBrlKpVCqVSqVS6R10+V//+1/e uw+lUqlUeifFtO1bUCw7HC1vdZ6bHa9pglUEZQVpONx8KjO56zWBq+ZU480BLjWnaneKEvT6PIAi ieVEe+EoZXX8mQlPjjLImhx/K5elTdM+d0kapGKwg2ciwr4Y+w62aIBc3WXMvv1mcMpjIVauVVVN wEyS4/Bxna3fnIuRD0wn0+/fUGtn7gRTv/n+M8BOOcTp/bF43S2lYlcv6Fs710ju0+YKJYCPfe/g +ArCJR0jnx5vj1AAcrbzeC3HdR4RJ8B1H+FsH59wCfftzo4xnM85ziHuz5XzvR6el0qlUqlUKpXe Qn/zN3/z4m0vf/zD79+wK59bueCWiGDf98PvpVKp9Dm1gj4daobUi+0QOizKcOMLNne42vvKT4D+ BNVfofITmDf/rNw6KCGC6JMlkOpPjmevPu3/6nvbodih+hdAtwSEBEpeaksZO3a06uXi08MBKO0+ W/xXBy4xFVwBYig7zNqvzZWpav7anAG7896Ol9DzXc2ZJ2DEtOnjdGsCwNyBFRF5xIFaV8FgB7CE DVBHbOTQVm17a/TL4ZnBsgAAACAASURBVJxpWm6sS7BhS/EJ7pIl8WgGL5TWprFLbx8A6GoOWg5Q xaBW6MwjJMADsA3ncL6GLEaCWv8aAIMk8HcEeeSAsY2xKnIExkrs14Qd6+7Jw9Km0xP71HNVg+45 CzgGkQjMX9rMltinJYra8TI2e2jggI8dWtIuo+myhcRGvmuch8hzlX7/ZPBNNjZxndgYUYf3J7xw 90zkM1HLj+0NxFsNRE6ObkrOaiBD1X7codwtWUDN8bOkO1t9iwZcoy8i2o6ZmSF0RcuhXUiwg9Vd uYoWMcCsIGXsJ3EDLU6kVCqVSqVSqVR6oX73u9+9eNtjhY4fWMw8fjEtlUql0k1Rg3RbAyvMbCWh xKdPJ8DExBC5GhwDeUkog27W4NYqycc68brDq4BEdIA0lqN6fFAWrHbOpLSmtU1XNljYAdm4g6P7 dX49txvHvgEQJnvvhnI+62pfR+h7s7l+HA+tGNA2ZX169mbf77kLNudsHp2H1tfh6CmKvd1vv/Un 2p+cruO50OZ27mMlw3iN/evr9nYOZwBrR+bz/2aw69rgeHv9Zg7N8zZW7Z9l/95rI4/Vebbq6n1G jxsgX6cfPx3Gc3ai2wOG+XqDqF8TX9d5XSqVSqVSqVQqvUQFX0ulUqnUNECgNLU350I2t6MwFARS dgdgZESa04/Jp8MPlcl/BVE4W7lP5yeFkoL0CyIm1VhcL8hjcGYbigatoZUV4bKiTNcDpJzBIJE5 b1dzG/I6s1qh95RHKw6lVpCVFQ9C0GMfsoYM3ru6s+5hKvxiqnm4YaHuhD1m6lqGLjcXZXN8kngf thQjcXaMcjc8gCdgC9IDPCTtrkxzXneIvjqXGeh30OfQbyiWRcvraNR4Fd2P8zjv18NKTlZdxGOc rXvc33z8x36dZynfcMGmfOVWYG9Yz/a9HoPprqTo42YPWvQ69TOvXw/TS6VSqVQqlUpvp19++eXF 2xZ8LZVKpR9S5zEqBwfnAFx8u1Z8agNDfZr6BqUdqgJiTZBTbWq7A1eCO2ZToR6N/XrfiLRlstp+ zdlGDl2OrId8Syu+1fvHrR+sgAaYXRwr+/bz2JwBJyFrc3Zd5nZX7sAzDc7Lk9XPnZkryBrFuu7s OxXpstgBa2sJA1NTx2OSBsvzegHFrLkVYFyfj5X6NdXh/HAo2q+ldmx5+RJwnuT8tp3yoZ3c1mOA dQ0wb/dr1eDtPOJD1uxL94MO5Vewfdh9vieSO1ixp/Ng4F1bVECPGoAXyxNVbG2c1uA0nMpxt7c+ 0OrKeiRjt1QqlUqlUqlU+voq+FoqlUo/nJ6RX92cqTMgC1jXnXKqAsIXkBeLMq/kteVdQhUWvboB AohKy1Kl+OeIDMxCxUCo7kA4V1t2qa8IhnpeZ+ZBDcDmY6AO7ZAKIh1yb9WOJYDQYTgm16y4TXd7 5XTnr1eoagRQpOk8eu4s5cPU3TNinZLlRRoAbQ2YbZ3A5WsH5hLYJnGbjr6GmnE5qi8nATjtX9wJ aTtdgVddvOfPBaY8W83F1xrgPHpz8zYi2T2KNIYO+JkPx/Wcc7/BHeNjA+3XW85hc47fU4b4I4CN pbk9230Cy5BxLRrXsVxnIHKEOyh3l3S7GFOEwASXCeZmtp7GQx4GeAMknf92DKVSqVQqlUql0vuq 4GupVCr9UHoMRhwciA5AmCMCYB/WNafkBqWru177+1GIKYoWEinABKILSH/thXlEG1dSchcjCSL5 lWjrRX54s/UBm8bcCg3ZPsKV2vunw/v9+BhwR+1hfAIw93yBBhtnYCYjYztkn2bdymodM1/FplYj A67kJAw4SNFXbdtoc71aOxpQedixYARtsX8HcGqO5jFjVtpynaHYoW20/ofCBfuoE/gMVOYiZ73f 6feFQ1RhoDafm4OjM6ctTFC/t3tevM5i42+7LV8P2sdogOPr9X5W8NkWyAA4+zrzNP7uWJ3vgeYe 5/G1maHd8moLQO2aUn/Qov4oJffhxKGsEf0QBNd+JyhIxX31pVKpVCqVSqXSx9Ll7//hn967D6VS qVT6gLo1rbpPJ6Y2LZuZINg8TsChKBRWgV3cFbcDSmDazPlKF1gxJGnuQ2CzbFHPkLXtyU1xAQmv MEDYCxdBj9BrBmjAEcBGka7xOLvrkcMJCoC2dcX04LNX7dmVfLLfR/q4lgHV2/mg4bykVoCIblZ5 X8FEL7zlIFbdcUykyBmrYz8yyOWhrbzuqu9nx3MOrimB2YC8af0GXhM45MbpDv1thZvofkxEQMMO 7t/TWXmEsETTuTxkNj9HxygLbs7U6YFE3moRS9DPzROIt/aZMeRLgw3GTu13F6wgyvvFMwcAaAhe 9+SCrriBUqlUKpVKpdLH0eWPf/j9e/ehVCqVSh9YowMxOc/AQCuA5Q5SIptaDQUxQ8HulrP3GxYj +4/S9GOAwSRQISuUReHUVJB6JXQiQLzwE9SzHjd34yooinv5PgLBdPhjP3YFNqIG7eaM2zO4luHp uvBUclayAWOLJwBUFPwAA7tVgX5w1Lap4Mfp5EM/sKNn4D6wb/sNGHJfY5K3m4wbCA8YHC2cZXUa yM1Ox35QcVr6eJ8BPb6M0+Dn5avjUUIbqyFmICDzAIiXzazbTREOAYI5rJ8LAD3381Z8w/OUI0E2 h+6jxoJUi3MUwPrwAGMGsMf+j12hNYiNY3UITuQOeom++nanQ2D94AiOjetTY1nuy43jLJVKpVKp VCqV3kGXn//jf33vPpRKpVLpm+l8yvS4fAJGuco8FOSO1YCuAffIYazqblATBOju0QIKot/YeipQ UgesAt02iAhk9/YCrOjF4CV8qrOFRHqsgRWTilgDg06XAdwJ/golMSirBA5nKjL8SlmyJBCRDlmb o5ISvPJFi2ndm/3iA+cjyj6hmoDdSaNiVaxrfJ1dpY1zuWPQgLVNFzfgF5Arxrm7INu6ZCXLok/A 5ibCvY2t5vgGCjgZYncvs7tEgUgZVfgscO0wzbSl+IkOyYgoAb9Ytrf3+zB0eKeqEEkgUfaxuBZg 2zb45u+Ltn6rZ8ISYup8sDxzXIv+OvS+Pxiw30WemvN2jllQxRitMZ1HIpoyadMRnvP229J+jq2t HWeQtcPlW27d1edDBq7H97K7NhfRA6RD11jVyb053QMI23tEOyLOpMdV8ACE14B6fi/6disBt/Re +u1vf4t/+7d/e+9ulEqlUqlUKn1TVeZrqVQqlW4qF9jSofAWNwBLAerICmjFdqzkRalMCUkB0NR2 QBcHnQ5R+xRynz5PUzyAZ0C6oXJpnCPaAL0FnLwpVXfxojkYVTskfI4zsu/7sQ1W+aY9YzWveCwi tZicjzMLYTiN7WdfvcHoySE6KufI3tYAjpFAp/Rp/eZkluGYiLm5mOP6OPbBHLSNR6e4gfBdHk2Z E6S8kck7r2NX6nxct4752K9VJMabaGo7HMamqdjaWebr3N4t5ZgPwPOQ1zESZ7ERx+iIPHbPceWW 3luXywXX61zk7LYKvJZKpVKpVPoRVfC1VCqVfngd3a6zzmAKEO7BXLiKAdqT03Hzn14MSggtx1Fh v+vFiuaQOVgV5kBU/eIuxR2AQMRclCuIZJB1977GOmc5sGvIGBEJMXWZ3BFpVdr12fznwOrUEm19 6FwjGLQXcS7WsPNsmv2cwzoW7BIHrRfPP91BtLU+5qn0pLkA2TETNvd7rRUA1gZF2zmIymqp/3oH lDufH69JCiflOJV/nubfl0+FpPz8nkHn2wh9nPYf+cEGxneHyVfQnVaij7cUDyDGNyVdaP166W7i fA29DmDmc7MCrx1cZ9fpMXYhj3U8cJnbGLflrwuxSy/SS+BrqVQqlUql0o+ogq+lUqlUcvHJ76Y2 RXt4L6bE98JVxA7VbA2wRiojt/iANBEZ0A6UtOWdUje8acDaDGYMsNr+qTlWx6nG4a4jqAaEmtvp 68Yx2k5jvwZgyQHyKk9zpTOH5HYAcGtHZkgb0LZ11xB8BFTaCoTFcU9gi0YH6xgHkEC8Zih/L64i jiWm94/O1ZwZHK/HMTqHyVmp3ld6cy6WdtvROvY1erX5A4Md67xWHV6rrs/ZCB3P9v0K3XOmtn50 h2m/9h/RHfBNC/h7kmm7am8+73kMj1EOq3b4uP8Csu+mv/zlL+/dhVKpVCqVSqVPoYKvpVKpVEp6 xNWYoQm7izCAm2c0ttxVy4LVyDqNbICYvhxToRUNfhF+MrcgFMCv1ifdoHqBFeqJ6f9qjttxJnTr Xy+apQ0+EcaCRNkhORTsEpmgJLXp+o+My6MBnueO4q5eMCvlmMa+hk1t+en0d9uqtWlQdwarZ5EF AR1vXRd7+jmC4lUOqY337EzNQDPnh47T54/rWh+PDwfmY7ke9tViMhAPF2bgqq2vhH5MBzC8cOIO y7EuhnW7v/PywzvWF49zwNb7buuOx3jf+Xrnvp/crkfYfASxbwOevV80uywTjC0IWyqVSqVSqVT6 oLr89f/+z/fuQ6lUKr2bqujgqEeqrs/OtbnoUUzvztmPx4ruDhLbnHeFSrQnDrnEimXBi2mpV66P nFjPRFWPK7B9r/vbi647kCVZHmcHaKkY04PjcqZ52+hDhnWPtB/QOfdrzHwNoB3O1g7KZlg5OmBv g7kBInJyHS5gVz+mERS3cZycx+Gazvvqrs3csrXXCkqFs5rENxcIFHw6tb/n9ub9+0GlPg9Hvmxp BcyjcFTEZRw2VW4PIL6K0pT8sW8dmncA/hodz3m/ttZtz2O+Aufdcb3e33i9ZCWgX7EEpVKpVCqV SqUPqst/+s//5b37UCqVSu+i//PLv7x3Fz6g2KdUr0HK/YJD5mS1SIAO0Ro4RIDQgI/sjsOtZWIS CQQ7mBmKnwBxh2erEmX5skLmriV1aEepfwSQOJj0PFptbty18nJmKyYWGbRQRs+DPVeGSONU+wyb zqbe32o4n49wC/OyO6tiVT1f8+iKzTmdRBfM7tcBHK/6dBd48cMxA2fKLuTYt6bjbxG/CcZlR2zb jAJ234aQbTl1J6kKA+38rh2sGTSOGb7qAPatlc6VR3Cs++UZyw862s/E0wFYnq33gACR/lCjBYSk LI+h/bjftCNzWew+bzPv37eKpa9Gy6Xn6be//W0V0CqVSqVSqVR6QJeCD6VS6UfU3//DP+GPf/j9 e3fjw+o5YJAQmasBnqKNcYp4h2HxOrVBaI5XgMDEELHK9omwYqczAAMwNoj+2vdxAIjmoh06ieRk VEZ2DUYf577e0q0IASJqcCrHHRzcwA8oRw6s8jL7sc8xAiOcpGX+a3vxIrfv7diDMft11iq79eiq Tq5X22Ha9zYA0GNEQPw8FpnrBbp8G+x2+Z2MwcpFO0Pu8Xgeywt+vs4durkPRzfxSvfw5Qm8XcUs tGtcluOSPxfid26xDtyv6XxJLvN4+2t+ZUGx0vNU4LVUKpVKpVLpMVXma6lUKpWaOkg6K9BzLKAz ODsbYMowsIOvDV+gUKhezbnqTkJRBuNiwJUAoguYFSKARtCqEmgHQAQCYWN2R58C9Ks76X4CMaD6 hJYt61EFwKUjSd37sXgEwXZJSHAAaxuUdgBP0ORsfSSvNY+TqoK5u/TmqdaDa1EZuUhSZGtq+l+L XAA2bPaWKpgi5zafJ27jB+4xA+3cpfOkbXuAWCAt32ADsM68JdbeP/1pWijdnQqA6IsvkHGddNyx 79bPtI4C2N2VSg5GyRcQCCJX3x6AKCjyXMnALMRczQAc7g+dheCarluH8QoAmx9H5NluCWQ6eKQn P2/U4DczQfQ6wN/Y/2o6/nPVoSYWbWpzc7cs45OCYH0E7tBZemr7MTiKdE8ptm28prsbfu5fd4nT 1p3Ndq6zQzna8zHj1YOKuL75kOcc75dKpVKpVCqVSu+pgq+lUqlUerEOAFLZYdx9kMQgCEWxI7Ko AlafyjyDKWCZ00oBxzLoye7GfXD+hff1mAO71rhshqUvmUTO089b6i7VtylaNLofV23m5ZJiFgKt j+vl+ITNwN4qrkJ7UaQl/FNKEQaeWTtlwd52zMq07pnG3Nf590NUwGLbeyL28dMEVL1Y2suul+9Z 3ZVtwHqDaC+IBuSzLXaN3WpNe8G8UqlUKpVKpVLpI6kyX0ul70AVH1L6KIpiSCvYZHCLIR4/aa48 aVOTDcdu7XfbxopIkW7+OhyW7ib1/xuklm25LIqkHWk2YJjgYBOTOyejQr1n1L566vhjLrwRAr5d kuUqDzbHFtwD0WdoKwDscVo/YAjNzl87V2eZwpxjEvLekn0WVtpKp4JiYJ+iTgpSOoC66P8q2mDe zblunD+/jqy9HZTd4547fEuvKer2WXSMauju94h4WF2DD40LCaDb8dqqQlylUqlUKpVKpXfW5de/ /L/37kOpVLqhXP07/ydiX/L/23//H5VdWnpXrR2U3e3XgUo4YnM2KQCyjFdVheiTAbhWzT5nReYc V3dlqsFZbVPt85R7+51oA0jd6WpgR1p2LLfc0xmstrzaxbJHxuDFGgppxbT55AB+U+X8V3I4uQ+R B9YBsTWTy3iVx9q1o4PKNHZ3hmlVJMuXeG95eD1sIwq06eqKiAOYc4fn2IGeIbuGfrcdq0eI3LZJ xbrix/2Cdd+3jg8V2B+2kLueb23TH8qct33yoOIE9pdKpVKpVCqVSo/qz3/+84u3vRwzz0ql0kfS GXSN30ulb6s5T3TUDORG8AUQvqSoAAexarBV9AqiC4AMYRy6OnyFT0u292K5/bTogugjgyhlRjoM 7lkDbJEHRL1uE3oequWfRhyCOuQlgI4Q9hxAHnUK2E6dedzyL99ClODkWWGs5g6lcdmcB7oCsKNm WCZHqNvWG/twux0gIiVm9YJNM1iVHow66xnjeyyQNvU5rpND3xR5mv176WsD3kev/7P1BmfsdE/M 12Rv7/H7r/Q++s1vfoO//OUv792NUqlUKpVKpXfTZd+/VvXdUqn0FjqDr/GzVPr2ug1gQzcdoyTN 3UpEACk2BUTgkMzhCwUQpN6mqrtqO+QiIpBuDhdtmvGYSwqHOem+8YxZA4Lz/eQFiwgDoB0OgdYw 8lkaANMMWjnBpa/1oCXch2P2KQAvVtWnhBsn3ye4zg8A2Fv7nn+/HbcQYPUA+OM9nfJfW5ZsWjcB Pk3n/RHIbeucgMMpaiC7vXOsw9lYvSx24Fg07DPIxmIsPJbv5VZ47ZnFyG6tX/9evp8KvJZKpVKp VPoe9Kc//enF25bztVT64Fq5Xcv5Wvo0SlmXQ0EsdQdscypuUHG4FatAAbBBUhBUBdu22z2AyBe1 n5ElGlCHmp21O2OPeZMwxy0JuE0Jj2zSvfcVsDxRAJAEhYZCX7Yq0zOzJQdWpgnA8QTi3iazklOR sV4MXlruZgfTu+VncgBYi3fo23b3Z3YgK92eGt5zE9axAvdBc3KbxrUCHFy5My3P0HQGdDFdnYgg cizqNjqb58PRlPMaDxLGgmEzKL6le8uPBcvm9WeIrdPrj5N9OsNo+52H5fF+V44UAY7XS78/C7aW SqVSqVQqlT6KyvlaKn1wnTleRaTga+kdtXa/LqfgDwWtJhhE7lR08OL1kuxnc8dlp2KaZjyBXZsu H/0IKNv/jYvog6xwJ/bj6ceVAWU+7lsw7f6061ge8GgeQ5u2/rWmh6+KkOnQj7RfEpBuFr/gsQ9z hupaMyBbLZt1Nh6TIjZh6G1yu/r1tIoHaE0E1IM4tBUcIebJ7hPUG3Nd4W3u3uaYSfsj5Lm+VM1p ne7PFTwlVpyXfDtROMsr87VUKpVKpVKp9Er97ne/e/G25XwtlT64VsC14Gvpo8qiAMa8TQMpHbaq drek6gUSU8VZQLh4xmoANC+oJZ45Sr+CmSFyMVerAqAdKjtEFURfLJIAT94jtpnoDu2Yv3g/A+ha sS1RgUKxRV6sZ4pmp6FNHVcvEDTmTS5dtWm7kMgMVnlat7tQl+0lmEREUCGAcl8emTo/thmFx6z5 FAURU78VTrXJDcDNPps7NhxNh9q+T2yPuRFpxKpddh7yJ15k05JfLznXNYPXcPuKCGizqIEB2KY+ 34ak/TOXWP0a2Xx/aNf2fIzMHdiLXCEyxhCo9mvrHnw+g/052uDW+m8VX0H5OnAx22nImbu5Dwb6 rx7N0K/xcX2PsWDy8fVei/RznJRh7VxsL+JMfI1DhmypVCqVSqVSqfStVM7XUumDq+Br6ePqseJb j0z/bS5XGmHnCiYGyM3OWsJmELaBXW8Dlhur3teb/VE+ML+cW6uqYObmRl8VFrsH8O47IOes2nOo ++10nNZ91Bk0jaUe4/DwYazPEZ1cb8RzMasZ0j1vCvr6PN643k+clfdg/EuUr4nsQv4I7tr5YcVx hbjH5niKxQOHNKbEzzl/62v0eW2USqVSqVQqlUqjfvnllxdvW87XUumDa44b2Pe94Gvpg2ucup+1 gmAM6qhNaMButj674zQgE5pbsGeNErBHwapwwYWjLkcKrB84mttUsAd0I4BhhbigW1ovHJ3H/FDr 1wwBMbweXbN5/ytH47eHaWPhLXnALThGDIzbt9/O9rZ4L4D76rOtX1dDi+3FHG0RsS2rfa+vBbvO HgSZMT6HSI1o6xjB8b1HEMTDidAqoqK/DvcxH9aP6IgcwUFEkFak77QHb3k4pVKpVCqVSqXSm6ic r6XSB9fseC34WvpYuu1+XWntPo0iWQRWQKJqPEkCrYA6iG2ZrhFnkKYZB+Ds/dM0hTkX/wLggLXD zu6mHQtx+b1IHb32vNSO3ThhwUcg23GdE+douB3vtvhSGZxmFchQfGsf+6hn53uEsONxPQc85kzc 2b26N+h2dASvr8F8/h5VANjYz7pQm0PXB8Drqn9jJMd9x3TbrR5zdz8SzL3l9M330bGI1hHem0aQ zgFf5/bV2pHnFrwrlUqlUqlUKpW+gcr5Wip9cK2iBvJ/pdL768FiSZOiwI4Vy7IJ5ZHxSjhmZxoQ 82BJmOuQGvwyt2pA0Y5nAvh4D+ciPq3/BmWzc88gUWRxhvNOsHIxUqeC4+tJzGs4dIC/aYy+VVZl wOaWkboEnbfO8Vk/M6DcTtYZ92WawKr3b186Zo+wlhVQ0kTqvpabeHR6jup9OgOsj0ZzfHci8fxX IM5LhtE9wmBzyMyQyS+wzi8ulUqlUqlUKpU+li5/+7d/i3/+539+eIO/+7u/G9a/97pUKr1Ot8Br wdfSx1JM9Y/f71+fufI8I1U7BxbxAx3QMDEU1Kqfq/i+w5Go7G2McQNE26EgUPTXurKPrsLWG7sP SXQArAeU9kIXItHo/Osw923Ba4zYqmr8Q+BqlW2qR/ie3cpt04fGZs4C9TEhAXQMChjb8zgCPz/h giZkYHwP/przer7WsrtW8UgUw9T31M5Z/MCj7tXVNh8jF/hcGSyPx53PhZ33cbys+F3fVtzxXiqV SqVSqVQqfS5dADwb4Mzr59f/+I//WECoVHpDZdAasQMFX0ufQ49HEmTgOcKk8T8NGKM9dxXosNag m6+rsyORoSweKco+jX0NrKIdJQd67f0OmG/BrpWTMRcQGyFUB62UiwwNDl5Y//VtooIICju4eUmH wKTmfr0L9VI27PnU8rN2bkUYpO1odT7X249V7n0tuodex4zi8fzcuoZvEevb1/55FuoNUeSkIoH+ 9PobuaTPdObgfSyCY4x7yA8kvkbhslKpVCqVSqVS6VvpAgDPzX2d16/c2FLp6+kWfK17r/Q9qGen 5mJUBKj2bNXEdJY5nA36HdsG6PD+sg+TQy+gFqd4BKYNEpmyeoSNvZ0jGFIFiI/5npT6Tti6uzSB tEeP474Ez5v6n/tBa+crkN5ft92dnw8+MKIpdiL9ehO88goAPjeXmPFWXG8AhCQGvN+wzQyLbdmd 4/xGcPaRQnvxfr+HrbjdKsvW3vv/2Xv3KMmu+r73W9U1MyVNaVSSSpqSVEItGMSAwGCJR4KDZZ42 xCMQ2LGdFZKblazETrATY+N7rx0sE3DudWyTOLGNV3Jzc53EjogQVjThYYEw2Ni8JLDBMiMsmRa0 UEmURCNaqDSq7rp/9HxP/86ufd6n6tTj+1mrV1edOmfvfV67an/Pd/9+Y9RqjYR67QMVxX8VQggh hBDzQaTz9TWveQ0A4P3vf//EZ3HO19e85jWhbfie5UWV6dbpliPEquKGGZD4KuaZyfiVrgASvmbD 0+tN+qrx2pkp8ntTkeu1BlADdsZPntlwjPFuDeNxDePd+t50cMaO3a3tqXAYY7y7dsYJecbZSAsk zkxrrjGkwM4Z5+BeAi4r+O7UdlGrr51p/S5QrwX7Fezpmd3Y2T2NWt033X5v/d09FTfklwySgdWB 3fHOvqhb25uKbcXp/ZixZ5ymocM7OWV/fznO6Nlr+yEBatifoo99kdu6kOvjMWoUQo11NCyQ7TsU x2fO79iJgzoe700h3x0/GSrfpVbfj/lp2a8v7Hj27mmt7hGQ1xigwrTLJlsj8YIdr8O9Ntkp8e7D AJ4Lk4hsvB/+wt0uaP94FOnoHI/HqCMsPgbeaccEG0mSOJuWGBF3/1isTSxDbRQcN3v9MGlWrbZ/ /QUPMc6cu92dXawlRB3Y23Y/xEO9vn/tjEsQvoUQQgghhMiD1/l64sQJnDx5EsCeIMrXJMn56r53 y/CV6dbpK0eIVUTiq1heopWiWq2292CvFp6OHAiFaGC3thejcy+z0pkSKR6ynPoZsS7QdfcE2sAx CIp5k+5IfyIkV3RykjzVG8HysCvRf6+6MSyLufaKOf2iHLt58AmIWZJKuce+vCnm+wK1dTfv15m+ JDdExr7AaEIDALDnJXk/oj8v5RgUdb4WFW8T6o8OOxKdrE4IIYQQQoh5pwGEB0TXXXcdbr31Vs/0 S+R+75aXVOett96K6667LtNATYhlZT/zs/9PiGUjVgQ744gN4ruOAWAcTOdH7QDG4xH2nK10XO6t 44uxuRf3lMIrm3PxKgAAIABJREFUY666AtBaEGs1rQC2Jxb7diJJiEtyFGbbxkcWEc+6YoGwo3Va uM7Y8LkoVn+0EM5rwOcezpY8bnJbuw9JDazvH/N6OKTAXPT3qcXb8Hq+mM5cLyqeay2wdWcTncPX bIo4ukIIIYQQQkwZb9iB6667LvQ+LsxAnvd51xFiFXGdr+6fEMtHOOnUnlbDeLD77tYzawBgsi0A tbBoV6vvhSfgOmv1eig+695y1/laPzPVnlvtxWLdE3fj44n6prCnS6pEocl1366Z1wAmYqamE8Mm jpcTV9MVB92YtGmFP7t/4f1OtXmoLbNivz43wZnFHmf3+vS1NywU7x+L+H2r1/dDJox3wzF26/X6 5OmfQ9yHJ0VdzNHCrX89IYQQQggh5o0J5ysA/N7v/V7ofVHnq+/HcJ51hFhF5HwVq8debMz98AE2 VuYZsXCXsUbHGO/iTMzXvYQ9tVoN4921kOJ3xisL4HRkrXsCz34bJgTYSOdltDPSFZrC733C6aRg O0vKEMpc6NjdE6/jt1+MPs0vlBOKpq7zcu/6Wovf1o0deyYBWygG6txTnxDxJ8Vn/0MDK4SPx+O9 hx4p3M5u3GI/C6BcLyGNRgOj0Sh2nVarhe3t7Rm1SAghhBBi9kw4X2+++WZcf/31uPnmmyM3Ktv5 6tb5hje8IXI7IVaN3d1dia9iCfEImeO6E1PSrjMp1uwleBrDalJe111tjNp40onoE7pqGAdiV1iA TYKiqV22u9/OM1nYa7UDsaVkEzuzul7zk8dV6K5vY/Gm2W5WTsaJUBSp2RPdXffw3pv6GRf2ftgL v/A+GWd4sqz9KfizCPtQjKRrMs3DhRowrgdO8zSO1zD+xHMKP1ANacRXCa9CCCGEWHa8ztf3vOc9 gQDK95ZpOF9tne95z3vwAz/wAxKWhMDeveILNyDxVSw+1g03mQl+T6xzv0/2sp+Px6f3thmv7SXk Qj3QVmugw5CuWIo4OFPHXqKlvfdnYrxSRBuPz8TddAXYPPfaviM2yfHI/U0X03Q6oqu7fnSIhHh8 2zF5UtbD6MZ/LYoN48A4vmUxIRKO606IDAA1t87wuYyKLbyIfX3YjZruHNZq9SBMyN65in8IHxfS o1abrYgv/AyHw6qbIIQQQghROY2bbrrJ+6P0pptuCl7bz931s76PWmbrnKvkEkJUTJTjVTFfxfJA 0dGKlRRM9qdu22nItdqaiQO5e0aA3XViooYF3b1wBO5DizPuRdQnhLP9besIRNGE76UxnjT1rcGd gh0l+KUXF/NnfN9LMBaO9Zql/rTfyUVDF/jKmwYU9+J/c+zHAfbjhJyo7QIYexJT1Z2wFn7iYpuW KUJPh3Lcpnv7Pxk3OK5s93rTz0chhBBCCDFPNOZBvPmhH/ohvPvd7wYA/My/+JcAgMuffhV++md/ ocJWCbE4fPijH666CQtJ2cft8qdfhTf8yN8rtcworv4b3xe8Xr/y2TOpUwghFoVPfeoXYUN/CCGE EEIIURWNefhBeuONNwai6xt/7WMVt0YIIYQQQiwyL3rRtQCAT3/6HQo9IIQQQgghKmUunK//5w2/ GIiu99z/SMWtEUIIIYQQiwx/V77whdfiU596e8WtEUIIIYQQi87NN9+ce9vKna8/+wv/Cm/8tY/h 7q98vdJ2CCGEEEKsCvd96LdybXf5K390Icrj78rLTvwsXvSit+LTn35HrvKEEEIIIYQoSuXiK9kx Dtz3/pOrCpX1+t+8q2hzhBBCCCGWks3b/wNe9Q/emmvb2/7T2/Gqf/BW/MXGgwtTHjC9xG1CCCGE EGI1eNOb3pR720rDDrz17b+EN/7ax/CFex/IvO2lz/oO/OyF38IvfPpr+PrjT4Q+29mpPpSCEEII IcS88cBH/x8cP/ET+MpD38y1/cXf8w8DgfML9z4w9+VdcMV3ACd+Fi94wc/hKRe2cpUphBBCCCHE Aw9k1y7JXDhfdzIKwAcPt/DMSy7F6/7hX+KRb5yPt34+fACylieEEEIIsUrkfVDN31g7O7uh31vz Xp6YH1qtFra3t6tuhhBCCCHEzGj84cc/W3UbMv3ArtVqaJ94I6767K048pSz8PpnHMY7v3QQ3xie zlWeEEIIIcSqkXfmE39j7eyOQ7+35r08MT9IeBVCCCHEqtF43XWvqKzyD3/0wwDS/8BeazRw5GXX oTMe4amHnsDB4dk4/0LgFRe1cNNXHgnWqzKUghBienzwp64OvR8+uYv7vzHE//zc13Hbnz+cqaxf /eErAQA3fqqPz3z50cxt+NUP3ocP3ZW+Trft5OY7HsR//Nj9kdu98qoL8FPfdzkA4Pt+9bPeNuTd FyHE6pLXFcrfWDu7u6HfW7a8d/+j47Fl/NB/OJWrPLtd1PKo8oQQQgghhKiKRtUNAIA0oQ9aL3oZ zvmuV+Low5s4/ifvw9ELD6L29TWcdf4Yr+wcDomv8xBKQQgxPT7xmc/hrza+imde+TRc/dyr8Obv vRxf/MLn8BVclrqMqy7di/3X2PwTDPsHcejo0zO1Yeuzv4dvP9jCWZd9Z662k0999s/waH8N51z1 vRFb7PdnX7vl53Hxa9+G3/vsQ/j2vZ/EnR/6KL591rNw1aVXF9oXH416DaNd9aVCLCt5hUn+xtrZ HYd+b2Upz66bpbx3/6Pj+MHf+gsAwE0/+qxM5Yn5od1uY2trq+pmCCGEEEJk4uabb8697dyLr7V6 HUePPQNPa9Zw8Pb/gQPNs9B7/BGc1zoXu9+s4cDhMa45p4GLW008sD1MLE8Isfi8/0Mfw/s+/mdo tS/AH/7urwAAztu+G3d/81E0L3kWjl10Nn78lZfj0vMOodVcwxNP7uLehx7Hb3/8fvzZV7+F297y /KCsG37i7+KGM69f9ct3AABecuV5eMPzj+JpF52NtbUaHth6Av/6fV/G3f3Hgu0uPnoh/q9XPx8v eM7T8e3Tu/hvf/I13Pq5h1K3vX5oP/FLo9XBEw9/FYcu6OGNL74E3/+8i3DWwTo+d9+jOPXAY6Ht H/vyZ3D91dcAV1+He/qP4nd//O/E7svLnnk+rn/+Uax3zkINwMbgcbz3jgfxkS/uP7Di8bjp0318 x2Xn4GlHz8a//9B9+MCfPgjU1xL3SQixeOR1vvI31u7ubuj3llve63/zz73bv/efPDu0bpryXv+b f473/pNnAwiLrr56osoT84OEVyGEEEIsIj//8z+fe9u5FV/POnIuLn3pdbjgpdfh3HOP4MAnP4gn P/5hnNvfwEVnN3DkcAPjIVA7WEPrrBpe0TmM/yrxVYiV4DWvvBbPvvqFeMaxpwIAHvnGFj7zuS/g idZjOHDB5Tj37CN48vQQt//h53D68W0cf/o6nnvVcbz9+ivwQ7/0QfzenQ/i+muOAgg7Ufu3/gf8 3Z/99/jpV18BALjjC1/CvffeiyufdjkOfPnDOD28HMCeUPmjf/9HcPsnP48v/uUGnvvMp+Kfvvwy fPzjf4SHz74yVdvrZ7VRq9dx6yfuwcbgMdQPHcYPvqCLv/PiS7A7HuO2j/4JMB7jjde+OLKs0bcG ePeHPoMfeuULvPvy9//Fr+Mnv3cdu+Mxbv/jO7Fz+nG86nv+Bv6P738q6tsP4ENfORgq7wdf2MUn Pn8v7vr813DvH3wU3/7WhTjrKVcDtVrGMySEmHem7XyNKz+P8/V1v/553PKm75hYlrZ9QgghhBBC FOGRR67Ive1ciK8u3edcg2f93Lvw5MGzMD79OOqPDXDwGc/Bk3d/AZc/uoH22WfjnLPrwAjAWg2H z63jNRcexu/c9wh29UNbiKXnr7/gO/HXzfsHv/4wzj58Dk4fvgDjJ4e448vfxLe/fh+ecc42jhw5 gru/8hCee9VxNA8dxFNP34V/f0sd11/z/QDCTtSDnSvww88/HwBw8vc/gp97xzuxds6FWDt4GHjy MTQufBTAawEAt7z/Q/iFX/p1XPCUZ+APfudXUKvVcHnta/jaQzUcuih62r/b9k/e8af46qEjWDu7 jRPPuxAAcNMt78cv/pt3oXHuJdh+Ygc/8OprI8t753++JRBf3X35W9fs7ct7/ucH8Y53/gYOnHsp tp/YxQ+8+lr8yEueilt+4b/j8NNfEpT1+x/5I7zlhl9C49wu6o1DaJw7xuixh9FoddKeGiHEgrBb MKyI63R1y4sr3/dZUnlpy4kqTwghhBBCiKqYC/HVdSb0v3AnRu/4MRx7079Es3UOaqMn8Y3PfxZX 3v1pHD3nAC48cgCtVg0YA7V6DWedV8Mzmmt45nln465HHpPTQYgl563/6t/if37gwzj3yDm44Wd+ HK+49sX4lbf9DP7hr9yKWvMcvOkVT8Frr36Bd9vDZzfx7S/eCeD7g2UH2pfg4IVPBxoNXHzBEQDA nX96Fw6e10PzsuehduAQgDow3h/Mf/6uL+HA+ZfhyfaxYNnBAwfwRP+LONC+7Mw20W23tJ75MtQP tdA5Z8+J+qV7N3Dw/HU0L3027tv2lxOF3Zfu+eecKe+vcPC8y9HsPSco7+KjF+LbX/4UDnb3E+N8 5nOfx4H2JTjrKVejduAs1BsHgXpDfaoQS8jOTkHn607YWeqWF1e+/Sxtee/7ycn42rf+xHPxN//N 51K1b1WoxcxUWMXjIYRYXdQfCiHmicYtt344ea0p4+v8Bn9+J7710z+Mp33/D+OJR76Ol971MVzR WsPF5zXRu+gAzjmnju1RDT9zyyP4yBcfwxuPtvC67hGJr0KsCGdd9lyMz7sMX3p4jFcAOLZ+Gb69 cQcOXfad+L7v2HOQ/tf/cQt+7bd+G+ceOQe33/JfAAA11DDeeRLj8d5s+np974fZ2pGjWDt8Ph58 9Alc0m7i6u94Fj7wuQdQP/t8HLjgcqzVa9jZtcLADhpntbF2+IJww3Z3sXv6sT3hMqbtB84LJwfb HQ0x+NZpHD33EK582jrqd2yi3urgyjOhFeKI2peHHj2Ni9uHcOXTrkD9M5tYa3Xw9GN7UyUeePDr 2D39OHYe7QflPPHEk1g7fAHqZ7dx4IIrTPnqU4VYNuy0/g/81DWx6776V+8MXu9P698tOexAdHm2 fWwLl73vJ78zVftWhfF4jFqtFux71GshhFh21B8KIeaJxuuue0VllX/4o3vCb1TH98T2t/AXN/5H AMDTLz6CN6yfi6c+5RAuOG8NjcN1/OBvP4jb/uJhAMA7Bt/C69c7aDcPqiMVYsnZi5v6OM7rXo5r r7oYAPC5L9yFnce/iScf3sDgWy/Epec18T3f9SKcfW4Hz3/uVU4JY3z90SEuOreJv/cjr8dV37mJ +pGL8Zsf7eO/f+Jr+KlXPxXXvfrl6F5yKe4fnoWnXnwe3vPpB/DHX3o4XEy9htrBsyfaNx5HD/rZ 9rVzL0Gt0cTn7vsm/uQvvwEA+F9/+hD+wbWX4Qdf+xqc1+mi0b4Y3/PsCxOPR9S+vPtTX8M//94r 8IOvfTU6Ry/B2pEuvvuqvVi3v33je1E/eBbGO0+GyqrV66gdPEf9qBBLTpZp+f4EWY5T1SkvTtDN Ux4AvOqXPxN6fdtbXpC6vFXBFRRc8UEIIVYF9YdCiHmicajzsqrbkIrPPvYkvvvvtFBfA2rfAn7+ 5kcD4ZW8d2OAtXq9ohYKIWYF46bu7u7i4a1t/PGnPoN/+67/jLWzzwdOP45/des9+GcvOYKnXX4p vuvAAbzr3R/A2378b4fK+LX33YV/9N2XYP2yS/HUy/dcqP/6N/8L3odX4Rtf/jP8rWufiecefyqu XlvD5kPfwH2fvAWnR10ATgKsjMmo3Jivw69/GR998C9x8OiV+N1P3I/6N/4Kr/ue5+Glf+25uOue +/Dbt/wB/v718f101L7cildh677P429dexW+65pnoVar4+6/uh//9XffjZO//xE0L30OavUDE+XV 6kqwJcSyY52lr/ilT2XefjJG6+zLi6tHMV+FEEIIIcS8MB8xX3dHies89OjjuK1/Gn/zrx3Az/3G Nn75w/d719vZ3U1VnhBi8XjpL34cD3/w/3aW1lA72ETj8AU4+/JnonbobPzF5hb+t1/5Qzz+lx/H zhPfQv1AEx/8zJfw+JfvALA37f/jdz2A2z/yBxhu3IHdJx4DsPcUvHnxM/Hxx8/Bh37pPTj94N3Y GX4TGAP15mGcffmLcO1bP4BvfPQ3gnLGu7uhdnGZ2w/5277HoYufibVzOqg3j+D/u+Mx/MbvvB1P Dv4K450R1g6fj9/4nf+F0w9+yVtW0r589PFz8P5f/B082f8SdofbAMaoN4+gednzcPC8HuqtTqr2 CyGWi9zi5O7Ome3HoX5i3stbFejscpfZ/1lpNBoYjVbvWAohFptp9IdCCJGXORFfd1Kt98//7f34 3w+t4Usbj5VSnhBi8Tj3hX8b4+Gjkx/U11A/1EL9nIsw3t3BgYuuxNpZ52F8ehvY3QXW6jhwzRXA k48D2AsNcOiS56Bx5GLg9GNBMq3dxx5G/ZyL0Fy/BgcvvALj09/e2x5j1NYOALVaqA3j8S7Guzve ZanbDmD3icdQO3gYtUNn4+wrr8X40mdj/ORpALvA2kGcdelzMB49sVe+p764fTlr/YU41Hkaxk8+ Dox3gHENaBxAvdVBrXlu6vYLIZaHuJiscdQCcTPcT8x7eatE2aKCxFchxKIikVUIMS8slPh63wPD UssTQiwe9SNd4Eg38vMxsOd8qtVRP3IUwNHY8tbOvXiyjN0dYO0Q6kcmP/O1Yby7412Wue1ntqkd PIzawcOx6/nKit2Xcy+JrTNN+4UQi0/zOdfh1Ml/h4PPeGWu7U/f/SFc8aofwwNfu38hyltFfBm+ iwgQw2G6399CCDFvlN0fCiFEXuZSfH3Jz99WanlCCCGEEGKPy1/5o7jvQ7+Ve9uv3f+VhSpvlVAy mekjJ7AQi4H6QyFE2dx447/Lve1ciq9CCCGEEGI6PLC5gctf+aO5t1208oQoE4mvQogitFotbG9v V90MIUQO3vKWn8i97VyIr/j2I1W3QAghhBBiZXjgS+X+9pr38lYFX4IZLhfloDAMQiwG89ofZulD 2u02tra2ptgaIUQWvvrVO3NvW6n4+pQLW/jAb/5MlU0QQgghhBBLzlMubFXdhJkwD9Ns5QwVQswD 89Af+sjSP0p4FWJ5qNz5esfJ/4RHHtoM3o/xWIWtEUIIIYQQi04N+0kLH7x/A//4TT9dYWtWi6rF V03pFUIIIcS8Ubn4KoQQQgghhCiHqqfZVj0tX8KrEIJU3R8KIQSpV90AIYQQQgghRHHG47H3T/hp tVYjHIUQq4j6QyHEPNF41V8/nLzWlHjJi19UWd1CCCGEEEIsA4xt6HN5Acvt9Go2m7ndtlEu2SJl CiGqZZX7QyHE/NK44447Kqv8J3/in1ZWtxBCCCGEEMsAxYRVFBWmIZIqYZgQi8sq94dCiPlFYQeE EEIIIYRYUqLcX0WY9+n6Rdsn8VWI5WQa/aEQQqRB4qsQQgghhBBLyjTcX/Oe1Gre2yeEqAa5YYUQ VdGougF56Bw9Hno/ePBUsNz3epZtcdvkW38WbYwqu4pjlIa4c+pbzs+S9nGabStSXlIZvnVsO+bl vAkhhBBCCCGEEEKIaBZSfAX84lNVglTeetMIidNkXgS8pH2PO06+99OgzHOVZ9uk/RZCCCGEYKKZ pGVpUNIpIcQiU2Z/KIQQRVmqsANxwlvn6PHgb5bE1ctl7v8yGDx4aqK8JKHS11bfOnHvV5Gkc5zm mMZdmz6h1Xd+hRBCiGUlzffpKlOr1YJYhnxtl+VBwqsQYhGZRn8ohBBFWVjnaxaqcg2mdWVSSKvS yVils7LI/ttt7f9ZEXfc8oYXEEIIIcQ+7ve7vjsnoZNLri4hxKqj/lAIMY9EOl/vvPPOVAXkXS/t dlFU5WRNaktce6oIK1B0oOIrYxqDH5ab5B6tarAV1QYN/oQQQojpwu/aWYQZWnR8QkMVbq9msxn7 ftFotVpVN0EIkZF56Q+FEAIoyfl655134pprron9vGyyil7T/JG+LAJc1QOZKMfoPBzfPHFmrVA9 D/sghBBCLDoKu5OMKy5U4fwaDodotVrY3t4GAIxGo5m3oUy4H0KIxWIe+kMhhABmEHZgGsJrHiR+ JbOox6jKsA1JQmyWEARCCCGEiEaiazKcZmun21Y19dYKlosuvgohFo956g+FEKLuE0eTnKxZ1wMQ Wi/LdstEFYOGMoRJt4x5iFG7SqRJniaEEEIsO4MHT038CSGEEEIIMe+U5nz1CapcVrX71SdelfmD 3SeqclBgP4ubKle1oDntYxRHGfVOu63uebSvo4TRqG0sZa8nhBBCCEG3F1+vCjbUgRBCAKvbHwoh 5otSxFefwDpNd2ucQBW1zrTEqqRy49pRdRuz1D/NtmapN81nZR/HrOc463LfemmXCSGEEEIQKyys osgg4VUIQVa9PxRCzBdTj/kaRV43bJxzdNbEhRGoWihLCnFQdfss83ROXeb5HAshhBBCuPiyeRcV HuQoFUIsItPoD4UQIg+liq82zECS6/Waa67Bf/vt/zdzHfMkeM1TW1zmuW0u89zWeW6bEEIIIYRl WslkJLwKIRYNJdcSQswT9bIKoti6qsm0hBBCCCGEWDVarVbVTZgJzWaz6iYIIYQQYkGpRyXJSiLv ehJnhRBCCCGEKB+bWGZWzNoVm1bsLVssHQ6HpZYnhJguVfSHQggRRWnOVyJhVQghhBBCiNlDoaFW q4X+FhlXRE0r9o5Go1LqXxVnrxDLxjL2h0KIxaWeN/GVDwmvQgghhBBCVMN4PJ74K5t2u116mXHk FVHLEl8V71aIxWQW/aEQQqSl4QqmaQXUvOslbfdndynBkRBCCCGEyM/zrlpdQ4Dr7CpbcNja2iq1 vCSKiqiNRqM0IVYIsVhMuz8UQoi0NKpugMvLXvraqpsghBBCCCEWmEce2qy6CZXA7N42y3eejN/L JFgu074IIdJTVn8ohBDkxhv/OPe2cye+PvzQ3VU3QQghhBBCLDA1HK66CQvNMgmWWRNlNZtNJdcS QgghxARvectP5N527sRXIYQQQgghRH5slu88Lq9piI+tVmsh4qdKeBViuSjaHwohBPnqV/NvWy+v GWLW/Pq7bsQFFz2j6mYIIYQQQog5wAoLboKZqrN8L4LwKoRYHua5PxRCrB6N5z//+ZVV/pIXv6iy uoUQQgghhBDlomn7QgghhBBhGrd94rHKKn/7T72ssrqFEEIIIYQQ5VJ2rNhlij8rhBBCiNVEMV9X gM4/O7734kJg8C9OVdsYIYQQQggx97RaLQDZwwVIfBVCCCGECKOYr6vAMwD8dwCbVTdECCGEEEIs Atvb29je3kajUa1XQyEMhBBCCLHoLIzztXP0eOzngwen4+h0681TT1Lboyh9n94LdHAceD0w+C05 YIUQQgghhB86TuU8FUIIIYQoxsKIr0A4Y6GlVquhc/R46WKlr0wuy1rfm98MvPOdyYKqLX9qSIQV QgghhFgpon5HR0HRVc5TIcSykbU/FEKIoixN2IHxeFy6YOkTSqcujCK/UzYz7wU6Fx1H50dnVJ8Q QgghhCidWq028Z5/ebGia7PZTFyfMWLTLhdCiGkwjf5QCCGKsjTiKzvTPMJl5+jx4C/NutPmzW+e ehVhJMIKIYQQQiwFtVoN4/E4+CtDcEjjfvUl5mq1WqkSdlUdV1YIsZxMoz8UQog8LMUvHTttIGuH 2jl6HHubjwHUUKtFC5CDB09NLbbsXKBwBEIIIYQQogBWcE0jvAIIYsqmFWuFEEIIIWbNAw/8r9zb Nv7k93+5xKYsKnvCKzDGeFwLvef/lXpKdgXQectxDH5ZAqwQQgghxCIwL79Vk8TTOIF1lYVXCc9C lMe89IdCiOXiqquuyr1t44YbbiixKdm4/bYPVFZ3GL/gGv6/AvzImf8bAO4GIF1eCCGEEGLuiUtK WySxzDQEQQmMfnRchCiHafWHQghRhMYXv/jFqtswB4SF1r0HZRRc94VXX7zXLGEI3vzm5JixM4/3 CkyKrt/Itl9CCCGEEGL+KCo0JAmCzWYzVTzYLEyjTCGEkPAqhCjKXXfdlXvbBmMsrTZh4TWNAPrO d+YTKCsRV6OQ6CqEEEIIsTTM2tlV5jiCLluNTYQQZSCnqxBinmjs7OxU3YY5YDbCK7dNYuoCrURX IYQQQoilg9m8ZyU4lCmUbm9vo9FoSHwVQpTCrPtDIYSIQ85XALMSXuNgmUlhCQoh0VUIIYQQYmlh khk32UwW8aHKaf+zEl/z7KOEYSEWizL6Q7F6KPmhmBZyvgKVC69T527sCa8bkOgqhBBCCLGklCEq lC28ZhEtZyX65qlHwqsQi4VEVpEHxRwX02KhnK/uU6sySRMOYGEFyw1IdBVCCCGEWAGqdHn5HENZ xhpyHAkhykSuV5GVedDH4r4LNQtjcVkY5+u0RMNZiJGDB08lhhOwn6cRgjOxfubvkZLLFUIIIYQQ c4MvvmHWmIftdhtbW1uZ6240GoWFUwmvQoiyKKM/FKIK4r4LJbwuLo1rr70Wt99+e7Dg5S9/OW6/ /fbgvyiHKh2ng1+T21UIIYQQQiSTd2BHN06Z7tUq488KIYQQQpRF/bbbbsPLX/5y7OzsgC7Yl7/8 5bjtttuCZdP6E0IIIYQQQswPaYXTZrMZek+RtEz3ahkOn1arVUJLhBBCCCHyE4QdsGLoBz7wAYmj QgghhBBCLBDj8XhmMQ5nMfWxjDoUykCI1WSW/aEQ00Kx0JeHxqtf/WoA4R83iiMhhBBCCCHEYkCB YTweFxYX0g70NF4QQswjZfaHQlSNhNfloXHrrbcCCD8FUiclhBBCCCHEYsDf7mW4vNIO9KJE2rQJ u2adsTmpvryJxoQQ80WZ/aEQFn1PiCLUfXFY3WXXXXddpveK+Tob3vRjP4yHH7q76mYIIYQQQog5 gE4v/tW8jS3EAAAgAElEQVRqtQkBIo4s8VGjRNq0A9NGo5G6rjJIqk8DaiGWi6L94TRRLOrFRN8T ogj166+/Htdffz12d3exu7sLAMHrqGVJ79P+CSGEEEIIIaZD1mm3s5zeyARd81rfsoojy7pfQiQx T2EIZt3/CSHK48SJE4mvfTRuuukmAAjE0JtuumlCGHWXJb0vwp/ddaqUcoQQQgghxGryvKuuqboJ C03WkAB2/SLTMucpsci8tKNslnW/hFgkFDNbJDHr0Dxi+jTmzYH6spe+tuomCCGEEEKIBeaRhzar bsJCU0R8LTIt0xUG84qxGrQKIYRYZPQdNr+cPHky8bWPuRNfFcNUCCGEEEIUoYbDVTdhpiTFMcw6 1TZrPNZpTaHN69KU+CoszWZT07xXiLL7QyGWDfWJ1TB34qsQQgghhBAiPUwmU5aosL29HQzOyhyk zWrAp0Hl9FmkwbuE+NWi7P6wCEXCsAghlguJr0IIIYQQQiw4ZQsNFKyyCmxxolwWEWye4r+KSRZF eAUkvq4i8yC8AsXCsIjVoYhIn2fbReq/l4nGvffeW3Ubpk7n6HEAwODByWRecZ8VLbuM9dNuW3Q/ xOLCc08W8RpYlns0rjwyr+fHbScpu68SQggx31D0pGDV7XbR7/cBhIXVZrOJ0Wg0IWzFDeq4bhph VcLr8rBILlkhhJgGcSKpuzxN6ByWV5bAL5f29GnccMMNlVV++20fmGl9naPHQ2JAlNiwiGQROSSM LA++c+le51WT5Xpbtnt0Ec6PS9a2+fZxmvun/ksIIaLxTbXNMv3WF+uVwisQdhAyJEEeV6GE1dVC zlNRBUX7Q1EOmsWwRxZhs9lsJh4zlpdVNI1aX8Lr9Gl86JPfrroNc0eSUy1OEHKFgTRCgS2vTHef bz/ssnkXgUQ8UdeW7xqIuh7te14PvmV23bg6osrn61m5SWdxjyZtl+b8+Noad2yTzoWvPLt9XHuT yNKfJF1nSe2KOiZx11MZ/agQQiwqNsFMUrKZOBqNRqRD0TeA5roaXC8es3KjVu16VfK11aOs/tAH +zq5BNOziK73qs9vlu/TNO1sNptoNBrY3t7WdVuQm2++Ofe29RLbMdf4BIqkKc5RA3738yLEiVVl lWvbOit3mlgsfPdDGjEt7fruax+rdo+6Zec9ttPc96hy8/YnPsE0SlD2HZOo+qZ5joQQYhEYj8eB m4uv7V9ahsMh2u2297O4weAiDq5XnVkJklULnz43t1huyuoPfbAf9AlYutb8VN0H5MF3fqO+G7NQ Rhl5GA6HekA6B6xcD7Fqbs9V21+RD981kvW6KdPVugjX7CzbWEZdWWPe+rYpem6SXMBCCCGK4RMW skyzzetg9U2RTFtWnDNSrsXpMW/i67Tc03owsLoU7Q+zor5quSnDMSrX6eLzhje8Ife2Kye+zitl O7XsdN5FEbPE/LPKjsJ52/dptSdKiJ91f5Jn/+btHAkhRBW402yzCA15xS/fdmnLihMsrPiq0AbL jc6tmAZF+sNlpupwICI9SSEQsp7LqkMqLDoXX/z9ubddKfGVAsI8ipHTaI8EWFE2076GVu0eLcKs 2zPL/iQqRmwS83aOhBBi1tDVZd1dWZxe7XYbw+Ew00CuqDs1LrGIbYfEudVGYpHIStH+sErk+l9c yhY3bVllXBcSXqtjZWK+ToMkUWAeXFgSI5aXqPiWUdfdPFyPs2aa9yhF4iiynp+y8dVbtG71J0II sdxsbW1hNBplil3IdVutVq46JaqKNEiIEqtEs9mcavl6kDE90oqbeeK/+q4LncvFYeXE16RENFYw iUvyEuX8Svo8qb404ohdN05oS7MPqyjILRO+6yfL9Vqknrzbpll/0e/RuG2T7sW0icl87SnjfPv6 l1n3J0nJxNz6ipwjIYRYRuj2yuryajabGI1GXqHLCrKdTid4zYGfRNTVY9oCkUXiq8hL3v6wStSf LidWcI0TadMkvqwqeZfIT+22TzxWWQ/09p96Gf7wjz+JRx7aDJaN8VhVzRFCiKUiraArhBDLRg2H g9cP3r+Bf/ymnwaAid+d51/Um3nb5g0bE5FTGjnFu9vtot/vA9gT2hqNBra3twtPq1T81uVg3qZG L0NogkURB5cVN0ZsEZbhehSzoci1kmfbRYr7WmWfaH8v8rfkv/l3n8xd3krFfBVCiGXHdX1KeBVC iNXCJx6kHbxQSON/O6Czr4sO2vIIrxJs5495El6B9O2ZN9FYTI8i/aEQy8qiCK/LxsqFHRBCiGWG YRuSwjcIIYRYPjit1v3Lymg0QqvVyjVASxP7tdvtZi53FsLrLKfRi/LJIr6K5aes/jAvw+Ewc5+i qeTRLPOxGQ6HwXen+x2a9J2a1fWaNz67KI7EVyGEEEIIIQSAPWG02Wwmip1RooJvO3ewx1AG84am CK8GOs9iWrh9XdZrTY7EaBb12KQVO/nd6X6Hlv3g0S1PD6Nmh8RXIYQQQgghlgAmlilCv98P4r5S YKXjyA4irajA5WkE2W63q8FeDpbBrSRnsZglZfSHWVFoFOFS5sOetN+dvu+LKOew+uXZIfFVCCGE EEKIJYBCAzN78y8ro9Eo+Gu1WsGAL0pY4PKkad+tVgv9fj+T+Cqhdo9lEHWmEWc1SThYBtFa5KOs /jCJNNPh84RaEctBmf1es9n09mnu96RP8I1yDi/Dd8ui0Hhi8JGq2zA1fv1dN1bdBLHC3HDDDXj4 obtLK0/XsxBCiGWk7O/LVcYXzzCv2GAHjHZwFpesKGmQyXKyOIHa7TYGg0Hq9UU0SYmmiiSiSpNx O2vZacpM+lzCwupSZn8Yh0/Ucq9dXYfTo91uL2xIgjTY/dve3g4Jreyz3VBBaft5JbKcLY1nXnV1 1W2YKm/6sR+uugliQfn1d92IG264Ide2b3vb20puzR552yOEEEJMi7e97W1z9325yrjiQpYEM51O B4PBIBiQ2fccsEUN6ujG8Q3k0ohoUUh4LY8k8dP9PMt5y3N+k8pPU2ZW8SBp/SLXqpg/ivSHWXCv m263i42NjeC9BK7psczCKxDeP7f/Yngg+71N+P3tYoVaia95uDP3lo3Tw2+W2BAhlo9vfuO+vS/q 8Rjj8S7GGAPjXYzHY4xGO9jZGWE02tkbkOzsYGe0U3WThRBCiJmj78vqYXZv/rfL0mAdNVFOmiiR NW4AN43p5mUwrYGnz0E6L4PcLO1IEiGL7pN7jPI4b5Pqd9uYtP68XqsiO0X7wyLMa1LBZaKIU3/a lOHG9X3Xun3y9vZ2sN5wOAzVa4+NPVa2PF2n2Tn//PNzb1vf2dlBVX9CCCGEEEKI+WA0GqHRaGB7 exuj0SgY6LnilX3viz/nLkszQC4jtmtcfE/fZ244hbLqbjQaE7FIyxJei8YwTdOOpDq4b1FltVqt VO30ia9F2+aStL/ueZpXMUdUR5qYrmlibopymVaiqDLiRLvCq72G3Ospaj+63e5E/8X+yZZhY67b evm63W5P7JNiYVdDQyKoEEIIIYQQy4PN8p3F5TUYDNBsNgMR1sZbjXI5usso3vqIc0qW4WLKU29U /Vldna47iWJMq9UKCdnE54xKM+Xd1ybfVNQ84g/b5Irrbp3D4dBbR5wom/YcJEGnV5Zz4x5re64l ti4/eftDksbByHte19P0Yd+zvb09lXiveYXzuNA7Vgi17e31et6QAQBwzz33eOvp9Xqp+j/WtbW1 FUqcmfb7SPi56667cm/bWMUOonP0eOj94MFTFbUkO27bLXn2o3P0+ELtv9gjbspMluk0s5p6Uzaz it80DeIC/efZj0U9h0IIIcrHfh+43w1pvi/sALDZbAaDMZ+g6q7LwVycABE3YJyWUyytUOebzlkE Hgc3QQpxB7qtViv1MXCFz7Jcd77Bd9Rx8J3j0WgU2g+KscPhMFaU4jlKEn2BeHHfh32YwP9x4is/ 9x1DiWuLRdH+MAvTSFQ3L6FK5g3b35UhGLrnIO+55LmKirUK7LeXQu3m5mboPUXQdruN4XCI4XAY XAeM7zocDkP7bR9IsW5bjn2gxv4wql1iuqyU85XCpSs2LpoAWWZbF2m/54ne5c/D448/jv79+Z98 iOxEPbVeNAGyzLYu0n4LIVYPfV8uFnbwxtADdG4yBqxPKHPFNjJN8SCubPtZVeJF1sF8lEhr4cDb FWuKDJzTCIrtdju4HkiU+GrbYtexAkeaOKxRTt5Go4FWqxV6MBCXBK7RaGBrayskwsaJ0wwZ4Usu J/F1NYnra5Jc5kmOwqIJ5xaFMr8Lqjgu3W43dXxU276o68POjrDb8Fphn2W3t2IsEO6PuA5FXxuK wO4DBVk+GCyCkhNmZ+VivvrExlURIONcs8tY77R4+jNeFLzuXnpVhS1ZTXxi46oIkHGu2WWsVwix 2Oj7cjGxAhmwHwc2Kc4nt7GDPZsMpGyKhBiw686auDqTxNfRaBQZfzJNXMq4OqPaZUXLrCQJmCyz 0+kklsP1uI1tD/fd7gMFY14LViSIOweczuzbXwkNq4l73q1LMQn7kCAryyT0l3nv+I5L0rmI+zwq fIp9bwX0pP4qzfcPH2j62meXu9+pbruazWYgpgJ7IQnYxlarhW63G6w/GAyCkDFyVFdDJeLri1/8 4pmLr1ndrZ2jxyNFQy6PWiduW/dzd70yhEqWb8uKanNU/VFlJO1zUnlR2y0Kz3netRPLLn3Kcyto STZqtVoqAY3rpF0/bR1uub71kurL6m7N0p4s27qfu+uVIVT6jlNUm6PqjyojaZ+TyovaTgghLIv6 fSn2B6KuiMrBmk+44mBxMBgE29kp50WIS6qSlEQriSoGoFlCL2RJXOYLY5BGXPYlVbOiKB1SSecx qi4utyKUdW751o/bb7prffELfdOR6d4G9q+lNCK3hFZB3PvOuhR5LTWbzYnryl7H00oOtSjkedCV 5Zgluf/Tzg6gCMq6KV76YqSmgdv51o+aJeBLpmVhCAL7MO7YsWMA9sMYUGi14Q9cF2ye/bGoj8xO ZWEH5jncgRVqo0TbNMt967if522fS1J7Bw+eSi1Au+v5wjXE7ad9H1dvmuM8T7zgr70q8rPLn3o1 7v3Sp2fYmvQinxUt0wiYWddPu427fJqhAvK0J+227ud52+eS1F4mC8h6Pmx9Ucc/7tzE1ZvnWhFC LD/z9n0p0uNOcbQuUjpmRqMRut0uRqNRMLCLmlYJ+AVD33RFK6b5yvGxDO6duKnsvn1PEhFsrNmk dTglutPpBI5PAIEzyiZgS4JuUZZpy3fDBtgpsc1mMyQQuO222zIxTavV8l6rTCzjPihw3a/utc11 3Sm8vmMmhL0OXEe1G4vT93oVcPv4vKFRoqa257kf40JAuLFUKb5ubW2FBFT2bbav8ZVLMXNrawvr 6+vY2NiY+Nz203Z7hjfgd66vncB+nHC2hWXah0e+JJKDwSB4COarP+0xE8CJEydw8uTJ1OtLfHVw RcAo8TCLIJtUdlK5LtMWKbOGZliVRF+f+eRtGI93McYYGO9iPB5jNNrBzs4Io9HOzH+QxSXcsq+z CmJucPqkMtzPo7ZJakdZwl2R9uTd16RyXaYtUmYNzaBEX0KIMpm370uRHjvIbbfb3qQhjUYD/X4/ CEWQ1v1iB3pR18AyC1xR+xa3z/wsSygFtzz3HPkG3PY8u1NSrYiahBXkbflRYpWtK+pacsUDJu9y nWKsy7Y9Lrahu8yGGvC1ZVmvS1EM6z6Mmw5v7595FbTmpV02tnWU+zXP1Pks+8a67YMaG4+V60SV 64rwbsxYuqZtX8MkW+zf+J602200m030+/1gvW63i1arFThemTCQ69uHXfYBpw0lY2NjFzlmIpk6 D/4s/4Dl/QKLm5o/Cyjozrr+PPtd9bFaFeKmmotJqj5eFHRnXX+e/a76WAkhhEhPmgdmjGXX6/VC Ihrdhu5Azv09z+19029dQc+HTSbC7ZYFO93e4hMcOYXZFxKAn0cRJRq6LtAobDxWukGLjNsogDI+ oVueDUFAoardbk+ECrBtcvfTbZ8NdeAL5WCn2NrXdryaJ0anWBzKNBBsbW0FGeXd65afuesTt0/I G7u5DMoU2ty+PO9+RT3cS+rHovrIqHbY9rrnxHXIk9FohPX19cg28PvQTURot7dwHYYE4MMmYr9/ uf1gMEC/3w89ALDl8nvc9q+WtOFpRDmsjPM1y7T7ovVUzayn8keFKUhiHo7VKrAs7sQs0+6L1lM1 s57KHxWmIIl5OFZCCCHi+/EsfbV1plJ4swKgHZy608Xt9nQl0dUTJ9gS33T5RZmqS3E5awiBKCj+ ZXXKAvuuMTtF1h20RzmdrBPKnl93W+6r6z5jnVYk9RlvfEKBdbnZ68BOj+X0WhcbZsBuZ9tmQyvQ EWaFVjeubNpwC2L+KKs/zIMvnErcdeSuP22n4Swz1Nv7L2m/3BA3SUSFqUnCDU1i8YUncUOnsM9j PGxfOAHWwTZy3/mZ7Sd91wvbsbm5GaxL5ywTbNEZzDLYRpZhQygACByz7E/ZjuFwGHLkimKcOHEi 9N4NSVBJwi2gurADUUmfiBsKIIuA6QsjEPd5UluWmaRjJcrBNy0+iaxhC9w68gqGWWLYxi0r0p6k 4+UrO037lpE815YQQojpw+89/mXpq+2g005ddAVYxo0DEBJk3YFkVFzXuLrnJTFNFjeQO62zaHlu wie7bZKYw6moduDttstOobVwyio/d7Nuc1vAf55YN9tINyCw74C1225vbwdCJ8UMtw5XvOcUXuvi olPM7qdtHwUTK4Zwe7vcsswu7FWiSH+YhM9J6CYa9LmobbKjaQn8cQ7PWZK2P8/6oC3KkW+PP+9Z eyx8wqvbRk75tw+X3LjTg8Fgohx7nm3YHp5vxtd2+0H7mn2v7Z+4rcXG0O52u8HDJfaXrKPT6aDZ bIZCtthwM26ixXn5/p1nXIHVXX7y5MlAdHXXbVTlXKqi3qhEV754rr4kU2nrcMXcuLLzCJBR22St uyx8++FLuuXWm9ReUQ7uD40sYmoe0XKa93ZUoitfPNe87Uk6Xm7ZeX7ERW2Tte6y8O2HL+mWW2/W a0sIIcT80+l0QoNG1xVjBVi+5/+kZE9pBv5ZxIhpxohNcrL6SJscLKlc10mbRpiw4qh7zpKOuz3n VjSybbROZrvMbstBv00Iw+VWmOA0Wd+2dn/ckAMUam2WeZZnk9rQoWbDJriCtnXARZ2LRXRhi9lh XYzEXie9Xg+bm5sTcZOLXEtp4z+vUqxON24q+z+fcJlWfKZQCiBIPMhtu91u0G+57lI+tNrc3AzO Ffs729+4SbismOq68Pm5r7+KiyVsH4j6rge3Py8aYkaEna6+RFyN3d3dWbYnoKp6geJJrZK2L/L5 NMv2fZ4mkViaZXnbJcE1H1mTJ0Wtn3X5NNuUpc6iSa2KJv+qqmzf52mOYxnHv8xrRQghRPmU5eiy A3sKWG5GZ5vV3p3qbadx26mhvozPPuKm3Lu462VJTGWJ2i6uHb7pu3nb7RIXpsF9z/PAc0TSxHh1 xd0kYcIKnlbYsIlprPDKNrVarUAQYTxbd5rs9vY2er1esC/84/ZsJ69JX8gLe03yOhoOh+h0OqH6 3aRdy5zobVWZ1eysOJGTSZDc9YqIr7N2rhYlqr15+2pg/2GRrw9OmkIf1X+67aHz1e1n2LdwG7bB PkhiP8g+yc4CsA+VfAnOuL7ta+lIZTggLnPFf7cs97vcl/xNzJaVFF+rYlYxWEV5vO1tb6u6CWKO mFUMViGEWDT0fVktUd9NWb+3OLCj49WKpXbA5rp9XPGODkW+73Q62NjYCESupIF31HTSuLAGbozP LORx6vo+s+0uEv+VA+qkOoDo8+LuE4+5Lz6tdVW5uO5Udz0KA51OJxBUKZxa56mNM2u32dzcDIQG uw1DINg4r6zbhqiw04Ftm6w4YgUZK2rYMBtieSirP0yLT0QrmySX9qJRRIB2j0Hc/ZsU49b2PRYm SqMIy/5kOBwG34sUV/kwx/ZPNsyKjaVuhdKohGTutcR96Ha7IUHftt/FfWjqi2Nr3cI+EVgibfks fdiBCy56xkzqSUMWR6KYD17y3c/Ltd0f/eGfltyS2aDrMR7dw0II4WfVvi8XhazfURStOFhkrDrX TcSBpU2C5A5e3SneAIJEH9Y5mxbfANoKiDaOnismJg3Aiyah8e2/K75mqWMag16fEGCx+2Bds1bU dNehGMSs2wACkYDJXWxiGZtQq9FooN/vo9PpBMfKxnelMGvb7MYj9AkPNpGYjTfLZW6SMLE6lPmb 3d4LaQRR22fygUaWPnCaDwji2jFvrnCbCC/pmPBBTprP+XAoalo/+zC3T2dfZr/jgMlEg/Z64fm3 QijrZagDABNOVSu88vvEdeaSqFixvmVp1hH5OHHiRCj8QCXO15tuumklna9iMXnuVdnj8mowKYQQ YtXQ92X1FHV20blIpw8djIQCgnUT+hLKuHAAGjU9EsgngMZNy7ftThIPiooLPjEvKflYFjhw9x2j tNN340IkNBqNkBhkp8fyWvBhp/BT8G632xPtccUSirMMKbCxsREs438rZjBmJv+7MR3tcXCzoLvt jxIrxPIxzRlrUTE6ibvcxil2+0MSJ4JOUwCNE9qKPKwoElogrsy4e9j2kVYstefDOlTtsn6/7+1H rHDui/Fr95NlMukh+y/rhrWhddwHk3bfkgTQMh5ginKxYqsvMVd9d3cXVf0JIYQQQgghyqFoNm9X RKXQShivM27qe1S5cdni7RTNLFnl00zLj1qW5fNpkXZf4xx2Ua5QG1uQri5bpz3XdHDZDNmcYmud X1Zo7/V6wXmlW4vXy/b2NtbX14NtrZuLjle+dhO3ucIq297v9zEajSZc2O60YbsfrVbLu79ZUQbw xaRofwiEp4NbeK1T7HPXc5PFcRsgur+cR7dhHvGU+zsNsS/LMXJdqsRNCGiX2f7Uxqe2yzkjhC5/ G2+bdfb7/ZAzlX0nRVbbHvaV6+vraLfbkdccsH892r7ZLs+Cr09UX5cOn7BqRVd+7ibdqizmqxBC CCGEEKI8KDS4gkNa95dNbETsAJrCF8U068i0QpovLmjcIL6sZDR2P6LKyeLGKjuBl0vWsjmwdx1P PkHcurd4jrgtXV/czjpTGWvVJo9hwior4N5zzz3BayvQ8hrZ2NgICZ/W9cX6WIebqA0IT6tlva4Q zNcUj9l+K6TEJS9Lwo0xKxaLov0hkCz2+dyQXE54H8yjuDoN+AAm6/2W5ORM4/S0U/ktSX2tFdOt 2Or25XS6AvsJKePaxe3phGVyLlvmxsYG2u128D8qlA+QXrhPc6wUgiUfVlB1xVX3vUt9PB6jqj8h RD5qtdrEX9Fyloll259VpcrrM229utbC5Dkeac5z0esgqY60/WnZba36+lnG/n/VKfqb2zcV04dN wAQgiNtpywHCDqhut5vZhWjXy+KIjRtQZnFA2oF2Ena9qPqLuC9t+e6gOkpYtMsoIDJuYLvdDjlc gX0369bW1sR0XZtUhgKqjXkI7B8vum4pfjJ5jRVefcfFOqRtXa6z0HWsUeix++IStTzO6SVhYrGZ tgbBaypJ5LLTyy32gUiW/i0vPndkHsdkEnnvm6Tj6DpG05QT5e7s9Xqh9WyySIuNf06xlQJqq9XC 5uZmyOFP52qr1UK32w2Fp2i1WlhfX/f2gTZOtX2A1Gw2g3PU6/US9913DKKICpdjZyqIclHYASEW FPeHRNYBNOMg6WGImEeqvD6nGSNs2cl63Nzz7OvHip6PpDrcz5PaUWZbq7zOdJ2vDll+HzADPV8T CgNRU2ttjE4LB750TtoESGnwZaa3WAdSWtzEKEmkFRKipv9b4jJT+16nbUeaxDNAePo/p61ah9fG xsbElFy6tSgkWLcUhU/bZl+iGSs28BhYwcnGjbWigz2WdINFxQZ2HdsuUcdertbVosgDxyjxy4b1 8OFzwdrXNh5sElnFUvceclk0N27a+7XdboeScxG6Tm3yKiAcZsCeZ34nsm8aDAZBn8iHPnT28zM+ ZGIMWWA/rjbFWhsShRw7dmxidoANL2DbbMPKsJ15cbdVXOzpIfFVCCGEMKQVpCReFcN3/HyiZhnC a1wdacqfRVtnzSK1VRQj67m2083d6Y/uII1JluwA1yZq8iWHsglTkgQHTiOPWtcKdS5J4moRV2Oc ABI31TZKOLBhAfK6ZtM4OK1Qaqf3s3zrVKYDi+eR7xl+gIITp+naY2L3xwpTVly17bLnyrpoeX3Z 9W0cYvccxx2jIq5jQLEQl4W83310OQKYcGHzoUQUjBEKTCYCpFPSN23cx6KJpVUQdzxdYZEPpOjQ 52tCwdV+B9qkggyn4obO4TXBfnZjYyN43+l0QgIr2+YKwm7b7TVh+2u+z4seQM2Oxsf+6M6q2yCE mCIUB+yPDTcGkvtDJG65FRzybudbJ6mMpM/iiKs/rk1Z2xslxuVt96oSdX36jq+7rKxrrShJ13Ge 6zFNuVHblnFcku5/u45IR5brN8u1ofMg8sL4hRQU6OZxYRzQjY2NQJTjVEm+b7fbwQB0MBiERFy+ ThMTkE5L3wCRg1nrZnUTOPm2sWXxfdqYoDbGqY+8ybuSBsBJ5drtfRm7+Z/n1WYEt/tkxViKtfyM yWVsGAIKEZz2v729jV6vF2xrE2TZGMF0+9l4wD7Bnu8pqNjzbTONu+fPfWiQ5RwnHV+xeljHty9G tevq5zq2D+U1TGdllNjm1kGyZLfnusty3WbZd/Ytdt+jtmUfxm2sYNpqtdDr9XDq1CkcO3YMp06d Cj0QYp/C/qXb7QYO1+FwiF6vF3wnunXygRXLsH0yYR/m9s2+/WG/msUZnPVaEsVpvO66V1RW+b++ 65OV1S3EMhEn+vmEqyShMIvYlXe7pHXi1s8jLPjKAyZF6TT1R62fVK8EkXQUFY+KXmtFSVN21usx qWkbn4UAACAASURBVNys+5xmnVkft3mh6v0q49oQwpL1mmbiDzdbd6fTCUSEZrMZEg7cWJ4U5zjF 000iksV1Greu+1macl3xzZ0mX6Q9Wcmb0CsJipI8D6yLYijrdPeZLi46VPmeg3pOo7UDfBv/leVu bm4GQq3bLrbFDWEAIOQgtAm0ogR2W2Yax3Fe4VUsD3m/49NOxbb3DnGvYVd0tQ+q4h5KZbl+F0Es Syvq2fXSCoy2b/HVw3Ls8mazGfRxvV4Pm5ubOHXqFAAE/7vdLjY3N4PP7WwC6/jnQ0df3G0AWF9f x2AwCNbjNhZ3P8v8rshyfSzCtbQoNA51XlZh9e+osG4hFps0LqiiAqWPvHUlfe6WkSQSZcW3fVyZ edobFUtSzJYi11pRoq4L35R1l7h2JF1LafYh63FJKmMZHZbzsD9Frw1fXyRWm6zXNAefVrijQ5Ei G19TkOXUciZEsQ5YGzrAHQzHDYpd6DAqOgC1Tie+t3WULdDF7c80hNwogdsKn3bAbz+jkEq3K4Uk JlSzDmTXvcyYsBQcuL1tl60rCivmAvuCLNvhuqiJXT9NPWI1mdZ3vK8v87nJo5ItdbvdUIzQqGRI y0QWJ2sUUf2rPX72u4bH13Xl2/M3HA6xubnpPV9c7ib1Yt/E/ouhBWz/xXqZgBBAEE+W9doyo5J/ RZH3+sjimBXAzTffnHvbeontEELMkPE4OdlWrZY9W3yebYpsNy/lT6vOKtotpk8VIl3StaTrtRjz ILwKUTUUXTnIs7E96ephIhFgPzOzFeVstmSfsMrlvmQowKS7yyYsSRp8JsXltAlufGSJm+fWFRVj 1o1bGlVX2rp9ZVGMpNM4qj6KsnYdxqO04QNcIXMwGEyEMLCxYxlGwMaJpcjL9a0gbJe52EQzrmOV gnGUMCVnq5gm1s3qxmS1wp0r9PE65r3WarUmtrf3RJQQNo/Xd9YEYEXLi0u8GLec3zl2BoCt0xVT gck+nu+73a73/PG7st1uo9/vB/2m64K2r214Cl9dSfslFotikceFEHNBFnddmrLytmGaVCF0+ab+ ZkVijiiLLO5HXa/ZkPAqxB7utEkmx2ICLcbzZAISm1TEde1YbFw8u8xOj+eANcrtY52PUSSJE2md Q1EONbsP7v5kCY9g6yK+On0OT/c9BXLfVHy20xW97bRoJtCieMCpt2676Oaiq7nf74fKomjPkAJW eHDPe1RswyjSik7zKE6J5cEKdO50dT6AsJ/57lX7kMOKfrZfS9MP2P6H92gVsTnLri+uPN/+2fA2 Lr4+1g014Kuz2+1iY2Njom9lX9bv99Fut4PvI8Z57XQ6QVxqAMEsEQBBmILhcIj19fUgtI8bRzhN aJ6yXKq8huR6zcab3vSm3NvK+SrEkuBL0pJVgMk7XbWMaa5uGUX3ZdrEtTdqnWXHF3Yh7n3Z9aUl zbnLi6/sMkS9WVxLaeooc99mEaojTR1pwy2U0dZpnMM01/Mq9UOiGK1WC91uN5hCThGOU8iZZMu6 G6Om0VrcOIZ2O35mBT8XDmCTBvpZxbcod1GUC9WWX/a0djdhVlwdbjxeX9xauo+73W7IlUrnF9dl SAm68dz4g25SLN9AnWEGuJ51yTJMgG2vbV8RkpzOQswCnyjYbreD69Mm4UqTaNB1NroxZF3sw5Uq iGtbmc5Y3zEG/PF42cc1m81QG9wyeKy5jhWybZgdYO9BFR9gMcTAsWPHMBgM0G63g23dJIFbW1tB nF8+vHIFddafRgh1v0ujiDr2NvSCyMM1uf8kvgqxREQJsFmmEefZpsh2UWX4XLyznBLtq8+tN669 VbV7mXGPZRnCX1RSKXvOs4puaa6Lom2Ouxajrteidbiflykqx5WZdD7KrCPtvk+rrXnJc16qaquY f7a3t4NszXYgubW1FRJj6XS0Ip0Vw+zrXq8XDGhZR5LL1MLt3PisaYgT6Nwp9CTNtF9fm7OELaDL 1EeauKjEdd+6U1w3NjZCoRs4+Od6dHPZ5DCcPsttOF2azj33eFnXFsVaeyzcpFqsx14PLnHH0ibl su+FmAY+kcqG67CxWoH9+8T2na4rkrE/gf3rlw8kbL8K7D/csCS57rP0RVGkFU7jRN8sgnDUNH8f FDbdhzm+PoUxX90HXM1mE71eL/gOsi5/O0vAbmP7ba7Hc9/v94Prgu/tfrTb7eABl3vObB+ahbjv iqhjz23SJpET5SHxVYgFJEviHRsbNksZWbfJul2cWJn0WdbkXmnrjzt2UfWmbU+a9RadpOMeJXL6 BK+018A0rrU05z2JpOu4yLI87c5zXPLeo5a0omba+963XtE6fOUXPTZp+8Ky+ipbd1rKuM7F8rG9 vR0SCIB9N6zrlrExPwGE4nu6Uxk5yEszEPcNPn2DxLTiQpywa92fPrKKenSQpoEOuF6vN/FZkujh 7pNPRKbgwGnOdGYROrF4bG2iK+u0owOa4hGnxDKhjMWNDxzX/sFgMCE+WeLOr40FG+W+FqIsfAIX hTVep7a/G41G2NjYADD5oACYDCPgSxLnPgDK6iDN+0DC3ndZhNNpul99++ImTGRiRzeGqhXAbTn8 jjp16hS63W6oz+J54DIbu5cPJ1k3nf3sb+lqbbVa6HQ66PV6QZvYvrjjUUWiQDlgZ4fEVyGEyIFc YsWR+LP8zOIcr/p1lCZUxKodE1EMinB0sjK+J6eT8zM3QzMdjXR92QGmT4xzxVs6l9IKaWkTr6Qp L0pksHWkFTOiBs8+MXE0GgUiaFx7rJPUhnyw5VjXFtfjlFfGJPTFEuz1eqFp0YRTaVkX4/va93GC kk3a5h47V6T2Hee0cQjtNG57jCUoiFlghTgL7yn2axQHbQxmF1uGu07WkAJ5Rby8om1c+6L64DzJ tYgbLsB1/bM9NrwJv4cYUoDbsw/mQ6Vms4n19fXggSKF206nEzzE6vV6QVmMm83wPJzZwX6ZDAaD kBjrOxburIBZoJivs0PiqxBC5CCLk1AsF6t6nld1v+edpL5I501kYX19PXjd7XZDg2YKcRQRgLAL 1sb5pOjHKZg2SzTFO1ccoIgxHA6DqZ02O7gvbqj73idwFp1+a51trqAXF67AJzhGEReCAEBILLCh A/gZhXErRNLFym2ixEg6UK2wzjpYpo1VyXWsuGr3kXXynLl106kaJQ4VEQKmGZNXCB+uSMYHT27y LQCBWGevTXtvuDFiLUn9Q1R7suLeN3liirpE3dOuGzWqXN/27sM/+9rOJrCzC2zCM2IfbLFvss78 7e3t4HuI/WSz2cTm5mZwnvndaOP82pjmFt/DNhuCwh6bWaGZA7ND4qsQQgghhBAiJDC6MUBtNnsK ahy0UpizCZ0ajUZoai6TatmEI1HQOURHkevYjBJfkwasrojrfubDF7s0LlYpcQUDK1q79dkYuHFt 8sUgdOuzInir1UKv1wuOuRU6GErC3Y71APuxKimaW/GV61lxmmIF94n7ZZNuZRFFk9yrs3SHCQFM XpNJopm9PziLwJbjc4vTQZl2Kr+ts2zRLk9MUZes4l6acm0MccI+jseY4iiX8zPbD/LB17Fjx0IP DrnfFGIZpsB+F/EcUcClmOwmWEvqp/SgaHWQ+CqEEEIIIYQIBpYU1ejyoQBqEze5SWCsI5LTN+26 brKsKPeXi88Z5RM2fVA0tkJH1EA3Kk6pW3az2QzF8HPrc1/bMn3u1yQRGpic2mxjE/riv1Jk7vf7 wVRXfsbptFaQpQDhy8BNEZzTbK3r1rrGeI7dabNsSx53VZIoUVV2d7G6pBXKWq1WyAFrH1bZewgI 9xGuqOrGLmXCOze2qUuZbtgy8bla04Qa4XZuTFrfjAT2NRRD3ZjXPPadTgcbGxshUdziulhZD5N0 sV10ydrXliz9VBmJ0rJQJDzLrNu6DEh8FUIIIYQQQgDYdwLZGK/M6GwdPj7h0Q6erevVhh3gMkuW qa7uemnEy7zl+6C42el0YvfDOqeIL0N5GkevK2Ba15e7b65YMxqNgtfNZhMbGxsh4dwKutZ512q1 QonOfKICrwmfuMN1bbiCqP0UYpGw17svljUdj/Z+sSJj3EObOOGO8ULtPR8l7E3TDVuEuHinFjcR oZucz27jCz1gP7ehWhi3FZjsR+lktXFiWRa/34bDITY3N9Hr9SbCp9iHcrYfziJ+5425m5ciYV5m 3dZloPHE4CNVt0EIkQM3ycqixvVTMhixzKS9vqOSJi3KvcH2J7W3zPt90Y8ZqboPTHvuxGpghdWo BEq+bSjSAgimp/M1B7VWnE0a8PnqdafcR7WJ6/niyqbZlyQ44LaiZtL6LDvO/dloNNDpdEIOLJv0 zDpJWb8VZG3GdCt42/MBIEgow6m07vkD9mL/Mls767GiiRUieDzo8mP7bJluGUnHYt6Ju7bEckPn e9Q1ENUn2HAd7v2UVhz1xQv1UfT65BT9NO3q9Xqh+KdJ7QL2kzMSn+vUtwwIh2YgvsR//JyiN/tX zn7odrtBHfa7ZWtrKyT8Mo7rYDBAt9sNymY4FR/uscsifhftV9KcB0uRflh9YHYaz7zq6qrbIITI iG+wXvUAXghRjEW9f9n3RImh02RRj5mlauG1qnMn5hff9HOKfMPhMHB82iRXNknTaDRCp9PB1tbW xHTNOKeMrdNXf1qHThFXZdJg0rYrqj02TIPdxk7Pt3S7XW9yHh5PHjuK4rZ+HmsrAlgxlEKDFT1s QjO6shivleeNwiu3scIvYyBSqOdyGwqB7bfiq+sCXGQkOqwmvFeA/fvcvZ/pUvcJpfY+t4JflAAW J6L6HkZFbeOLFe0rl8KdvbeT6uV+phH8fHW6D+7cB2d87Wszl9kYq3ygZAVuiqGj0SgQUPv9frC/ VqBlMi17PFiOXc7t2afa/tTOFPGFhpkm03I564FTOdRPD7+Jqv6EEOWxDCKEEMvEsj0QKeo0Xbbj kYZ5FzVX7XyIZCiw2kEwEI4tur29HbgmKYra0AR2Kianb3L7KDcT1/FlA3frB8Jin28fgHih1/0s 7fTJNHFq3amoViy1zjfS7/eD4+6bmm/FZF9SLJ8gYR3MdHvxMzrwOEinkEqhgFNsLdaxauP+2na5 7aBA41tfiEWEQh2w/0DBve63trYCJyjpdDpYX19PFe7Ebucm+LOvox5gpHmwEbWOdYtmJW9cWV9Y Fl8YAd+xsA962G+xj3NFyGaziV6vF9rGfo8wLEGcI5dhc3wCp3XOEorsy4CE13Jo7OzsVN0GIcSU iJtOmsY967qifOvHlR/1WVKbXScWy/Atc+uLq9O2KUoI0hTc+aLI+ch7faa57tPeU1nLLkLS/RF1 X6Upg8undX8UOR9l1Osrd1bHLKnftdumbUtcHydEHBx4bm5uBoIahVgr5FG843RyG7+UTiYmaiIs J2pKKx231sXpOsvstq6YZ11Mdkqpj7iEXRR/k9xKPocnj2HU1E9XSOC2jUYD3W43diprp9MJObRc By5dW66IOxgM0G63g3PCpDBMQMP1eeztsWB5dNdSTLCuXMZzpTvN1m/bmcf9lXUKrRCzIM116T4o scItkCyAMq70xsZG4hR212XuulPduqLuRTc5Yha3Y1Qs16z3L0OecFu3rSzv2LFjuOeee4L+zIZB AfbDIXC/3FAR9jvIttNtr40h685MiBNXo+KNT4uifaX62izcmXtLia9CLCBphAg78C4yCE8jNEWJ B3nr9pUHTAobaeqPWj+pXgkX1VPkfBS9Pqd5LSQJ/0D+hxZxdSStE/d+mmJe1vPhLifLdMyy9oHu +3l33Ir5hQLfYDCYcFZSdLOCoS/ZDAfBQDhLNAVc+7ml0WhMCI8+YTMqbqhtS5FBJAfUUQKFFZG5 T64Ibev3DWrtMYwKZWCdVJxCmxQzlmI5sXFhKYxTFIoSv61YwDYw3iEFJF4nTGDD8+YKDUWn20oM EPOI77q0Yh+AoA+lIGhFTffhUFQ867TCnU2WVwRbTlRYgyzkuX/TbMN28jiyzxuNRoG7td/vTxxn G5vazgYAEIQjsA7bKNHbzgiZpaM/TiAt2le6+yenazTnn39+7m3rtjOY9Z8QIj/j8TgY3LsDbXdQ XiSmXxohJEnAKKPOuDKzJvnJ61oUs6PMBwZx12fUvRG3jm+bLO2Nur7tX9b7NU3dSetUcf3nOR92 2bIes6x9YJo+Tog0UDxwB190CXE6LZNoAQgJfnRYEg5S6bqkw9N+xtAEPjHXHS+4U1TtNN4oxyhh vW4max9WdHAH19ZhZv+7wqevLLK+vh58xuPS7/cn9olYJ66Nv2rXtYJ3s9lEt9sNjbuseG5x48HS EWs/Y7lcPhgMgusj6bjPG2mmfguRBRsn1CfG9fv9VMkLXVqtlnc6v3sNR4X8iMO204qJFp9eU0a8 5rh70HWgdrvdibALrVYrdEz5OWOxbm1tBUKl7c+ssGz3h99Ptl1x58iKtHa7vKEX0jKrh1GLHpN7 npHzVYgFZx6dmlW4rsqoU26x+WKRz0fR+zGPa3Ke74FZ9E/LdsyEqAJ3SjtDDdgp5kzGZAVaTrXf 3NwMOabcKec2WY07LdPniE0ya1gBwRc70K7ntslXlq8+n/vW9z+qvXZwzqzZvoRW9hi54oDFDsCt 89RNckbHF88nnXk2lITrDLNTad3YtXS/st48zIOjqur6xXJj+7aoJFdpxVI3hAjv2zLEsah2RrWp zGnpvtAmUfgcwDY0g03GaNvH7yQ+3LLJs2y/Z02Bth3dbheDwSAIxxL1/UDh1q1/kZFJMp677ror 97b1nZ0dVPUnhFhOXEfatKHgUrS+WbdbxDPP58M6LefhwUcZ90BZ99GioGMmxCQccAL7CUls4ief eGqTOHGAyinsxLomraBAsZfQ6UXx0RfWwGJj7tnP3PWisndbp5LPecv2piVq0EoBwYrRbiIquont cfa1tdFoYH19PUhOZrfnOXMF4WaziX6/HwhAbkgDd19dAZi4Dr6s5N12WZLWiMUlztVo71fbL9rk dW4CPZu8LqoOX3/izi6wZLlP7L2YRmwrGk8U2E8G6NbnOnzt94cvoaANibO9vY2trS3vjAbGqeY6 NlkZv2co0Lrr0Mlsv/OikFgp0iLxVYgFJMlp5U7BTZsIKC2+8qM+mwfi2hu1jigX3zT9uPdFQ2Wk vT7TxBKdhbhaJIzBIpOnr7Lruu8X4ZhNo59J08epfxNpsAJep9MJOSUp1K2vrwcD1PX19WAgywEw nUjuoJmDV+vOtK4hDqrtMndQy7a4wi4/s+tF4YZFaDabgbvXDrQpkuQR/tg+V1yxwjQTVFlh2BW4 rXjD7TqdTiDe2tiHNu6sFTIodnM/uF8UK6KmNkfF07XMKt5hVeKGRN/Vxl7frvjIe8YV/azAZ5Ns uSKe++DJTpOPgmWmDZ3hlpVlWnyZ1z6PnX2oY48Thc+o+m27bVJA+0DLdyzZb9DtakMa2NjXtnzb Dvs9lvfB0bRDEVTJLOPdLgsSX4VYQGysV/65ooNdJyp+YNz2WdoQV/csBvy++qLEvLh9nXW7RTxF zkfa6zPNtZC0jvt50fupjO3ZjjLLiIsxbdfPKoqmOdZp2rtIx2wa5DmOSedOrB42I7RNlsXpl0BY QNzY2Agco6PRCP1+P+Tq7HQ66Ha7IaHBCopMkMI63CmmUdM87YDbttPiE2jd14PBIEji1Wq10O12 A+eoTeJCfHESfVBYdQfsrpuYLlQrRNiptNZp2u/3A1Fhc3MzECF43Cni2nit9ji7Cbe4Pd1h7rFL CqeQdAzKpCrxVY621SZKgGQCPLpQ+dDJToF3w5K4YU/cvs6XrC+qTVFCYFyIkriybd8cB8OOuDFY 02L77jihmSIzED7W9ruGn7Fdth91RUHfrAYb7sD2m/ZzX4zsrCxKKII8IrEeTmVH4qsQC0qaKdlJ U2Hd7X0iVZo2pCk7roykOtMsc49HXIKepPZoCnH5JJ3nrNeR+/Ahys2c995Iu05WstyPLkn7HHcP ZLmv4u6juGVF7p2kc5W03SIdszz3Qta2pKWMcyeWi9FoFIQd4MDWZrLnQBhAyBnKmKMsg8utIMv4 pG4sUTqU4ga6UUKqncLrChJ2+q91y1JkBfZdoRRKKXYyXqC7P3YQz+NB3MGrm02b7YwKgcA6XPev LdtNWuxOpWUdnGprhVgKt268Rbt/tu1R8XTJPMRvnTYSX1ebNNc37ymKqXR38r50Q6hEhWNJ+yCD 969bvsXtc4h1nfrub9f16ZbFOKl2vTRisSuK8j37UNc9zD6b/Tr7ND4oY9gV66Td3NwM4lj7EjXa 2NZc313Hwu8Gd7lPpFwGB2gekXjZ+/80nDhxItP6dZsFc9Z/QggxC+TmWj5WUTRaxX0uyqocs6Q+ blFCMojq6fV6wWsmhaJb0yZc4Wd0sdopuBy0WpGRg2m6W4kVBK0oa12bAIKp9K5A4QtPYKfTc1vr MmP8U76OEkPt4Nwd6HOZ6ybtdruhgfjx48dDAot12VJo5TFg2XZaLWFIh36/H7iJuY+M5Uoh1Yoy Vli1dbjHj469JMHVYt3RQiwz7n3jujd9MZp5v9mHInZb9+FOVuLEsqjZA9bpyeU+MdENWxJHr9eb OD5R26+vr4eWsW9kQsB2u412ux0SsYHJ47+xsRF8Z9gY4gzb4hMFfc5XVzRN4/70OXZnNQNAzDdp hFg5X4UQS09a15gQQLZrQ9dWMjoe0yfpOtQ5EGmh6MnBpBXy7GCXoQSA/SmbVljlwJnr2AGxG7uU yygoUsx0Y/nZZWkGyVGuHDcjdlR8VntMrHjqJtChYMDjwPfAnkhgXb0UahjWwAoCrjmFn/FY22Nr nXaE7bFZvK1IZMXYqGMlMVWIbFjnvuvwtA+z3ARbVsRj/2HXTzsN3CeOZokJyzja7j6RqLArhA+p fHFT2dfz/ebmJo4dOxY6Vnz45sZ+Zf/Fvq/dbk88WGO99hhYYdmuaxN1cTs3fnha92facA6LTB5h WiQj8VUIIYQQQgiBU6dOBa/toNW6We3UewqOjFUK7McFJHTDApgQHK2QaafTcj07SLcOsrhYc4PB wBub0BV86VKNS6hC4dXGa6RQbBNm2dAMg8Eg5NKlU7XT6YRETpsozBfigAKtFUesU5guMgoobvvd mK02xEKS4FwlRdsUd20sw/RgMXvcqf4+XCdls9kMZggA/riiVmgE9vou3tdFBD3XbRuFL3mX+94X 59o3A8G2193GOlgZEsXdPxu6ptlsBkI0+6tGo4FOpxPE2bV1sQ91H87Zdd0wAwwxk+TsXVXcflR9 ZxjX5cr3vuX8A4C6b0rerP6EEEIIIYQQ8wFFRQp+Np4hRVg3SRPFUA5k+/1+IB5ySr8VU7leo9EI ptHbwTvrpCuJU+ltYhvWAfjFOooK7iDdigCMRctyKcZy4G4T6FhBmbFh+doKGFZQsMfMxpIF9l1d 1qVlQxfYeIM8H3zP/YpL0OOK3FY0Ynm+OLnzQNE2xbl3JSCIvNiwKz54j1r3qnst2hkF1iVLAXA4 HIYE2yjcPs8VynxxnG27XKwA6YqRbj9rk4pFwb7bfh/QAWvb5EuMRYcrP6NYy+8cW4Z9mGax3yW2 DttuO5vBbYOY7If5gFH4OXnyZOg/sCe8njx5Mvg7ceIE6ru7u6jqTwghhBBCCDE/cJBNgdQNF2Bj 9HE5EB7cW6HWioZ8b4UM6+y0Sa3cOK1sE11OVogkFDXo9LRJsWx8UzvdlC5VCsF097rTXjkoty5e e5xYdqfTwcbGRlC+ry3cnkKDFWGt2DEajYKwA9YpaxOj+XBdeL4ppDZh2jIRJ76mcblpeq1Ig+86 2drawubmZshRae/5zc3NUIgSIBybOa4uew+nfUBh14vqL1xB2Q19YuNw2xALcf2G3X/2xzYxFpfb eOHcjsdwOBxObNvv90MxshlDO0oYt25YixuCwLJs/aGoFuuEPXnypMRXIRaduGzvcdvMCiW7EkII IRYDCo/MZM9BNkVXukS5LrDv4hwMBt4pq51OJySu2in7QDimqo33yqzVVmCl8OiL68f1+Ee3qnWb 0oFK8YNOKwoT3O9OpxO4qmzMQbbRFXDdsATcJ7aT7RsMBqFYrm5WciYlY1kUJ7i9Lz6jD/f4+GIU +pKVlcU8hjNIS5Hp3hJulhdXxEt7nbghC7idvc/b7TaOHTsWWYZP7HTjcXM993NbhkuWqeU2SWFU 7FX+Z+gY9oH8frAwiWBUzFU7G8LG1+ax8MWYdfch6hz5wrTYz1YN9VvTgS5YhR0QYgmg4Grvqawi rBBCCCEE4XR/Cq90rwJh4bPdbuOee+7BsWPH0Gq1gqny1hHb6/VCg2YOhDmIb7fbgcBqB9mse2tr K/jcCoQ2MZd1udrYssQO0N04shQtbKxBipJWNKXgbIVYYpOC0aFq/2wMQyuW2ONAUYJxc235jBs5 GAwm4rUSvvcl8PIlCnO3n4b4uooCBpDOWSsWE5/Q6L7me3u/u05/W56dWs9+wBVA+UAmjfhrHwjZ cC+A/5701WX3x8byZl/Gh202NIxtj43nagVlW+6zn/1stNttbG5uBuvyuHH2gQ0v4DtO/X4/8hjM mqJu+UV+WCXiYcgBAAo7IMSi4z7E8D3UkBgrhBBCiDRQyKTjkgNiCq82LADFzsFgEAiJdHNy4ExB kmVTBLRJquzA0wq9FAv6/X4oziBjCFLQoDOUoqp1o7pTfyleUnh1E2pxME+xlYN9n4hJF6pN6sJw AO12G/1+PyjbxnXlfllnFwVldxBOkcPd3jqJbRIvN6ahdRTbxGIa7AuRDSv0uQ7N48ePT4ie7vq9 Xi9039kY0L1ebyL5FjAphvqcqfaet85Q9+GZ7+GL66Rnm9rtNtbX10N9CPfTJriyTn4LHyKdOnUq aAvFWwCB6MoHdsePHw8lHeNrK/7aY8D43L5+LM2DHzeMQ1GKuOWB2T+scvc97UMjN6GcSA8FzcKF RgAAIABJREFUWImvQiwgdLz6oAOW6/G/T4RNE7Igbtu4z+PKi6o7rj1Z2xq1XlwZeUI4CCGEEMsE B7RMcMIBuHWx0p1q/+gS3dzcDGKoUkRlrEMOtm0yrs3NzSCLdafTCdy01gVqk2IBe2IFy2Y72SaK tVY0pVhMAdgmzLJhCtrtdiCEuOEOKEizXraZIgcFCYqqPAZ0BW9ubgZZvG3YAgq1ALCxsYFerxfs p5sIhwIr48zaz10Ha9xnsx7sS+wVi47P3Wgd87zH+PDJ3Y6xYIHJ5FeMg2rhAyc7XT/qIQ2XWdHX xrQGEHr4YpfbfpUPc7a2trCxsRE8gGJbbJJAPoyzD6RYLwXcXq8XCKgbGxvB8XLFPntsbNtse4mN xZ3VaU7B0A3LsmrkFU4luO5h47hmXa8hEVSI5YVCbJQj1i73rWeXJX2eBV/dbG+autO0NaneNGUK IYQQqwQFUQ6YmfSEUzytk5Xrc1osBQUOjoH9MAYckNsp8hR2KVg2m01sbm4GsWVt/FXWRdcTHaGd TidwX1kXlk2AZWMV0ulqB+4Udrk9E2+R9fV1DAaD4HPrBnYTfnF/6M4lNiYjwxwwczeFVOswY138 zIYQsHWx/RZXtJ4Hsoi9bugFIeYBipBun0Ln//r6eiiWMoCQK57vXUGVy133JB8m2e0p0tr+xPaV 9nPXze+KlVyexrXpls9ZDTYMgE2ERewDO1cwtmEZ7PeFr03u+7hkg7PCPTeLRN7+1W63yiFWTp48 6RVW7XKGG5hIuKWYr0KsJkn3YBoxMu997NsurqysbU0jNgshhBAiDJ2djLvX6XQCd+ixY8eCAThd lzZuoZ3KT6xoyM85YOV0VLqk7HK6uLhNv98PnFcUbm0swmazifX19UCwACaFVwAhMZMDyW63G4QK sIPp0WgUuGC5H9yWggcFVMa3pTPVxn10k2pxHVe07fV6gQjMNvMY8b07+PVNnbXrzFocKMPhGiUM yD0rqsaGQ+n1eiHHKvsCPoDhMrpK+Z4OfyDsnAXCSauA/X7LXc4HRr1ebyLUim+avi0rbp/Y5/jK cKecc3/tewqwfIi3sbER9Hmbm5sYDodYX18P+i5XrPbVVSZlCoZJLtBp7UMVFI1pu4wwjAD/2+V2 Gd8r7IAQIpF5moZfRlviylDCMiGEEKuOjQdqk18dP348cJr2er1AvKQDlS4oCpOcxu/GGex2u4F7 loNTO/XfJvtys2FzYE9B1TqPbJKXXq8XEj4ZH5CuLCtI9nq9IDwCxQPr4GVIA8a/pahqp9seO3Ys EB/ceI3D4RAbGxuhZfZYExs7101gQ0HFN9iftvso6zTTaTpW5YYVVWJjPQP7rn6GH7FucytWWWco sBezmmFYXLHTJq0Cwomm7H/Ww7AA7BPZLgvrtcujHmTYPof9HVlfXw+JwL64qRRT7TGwff1gMAj6 Qx4X+4DOHpM0VPlAJqk/WtQp+j7RuGhMW7GPxFchFhAb19WlTIfnvLjVuU9F25G0P1wuAVYIIcQq wkF1v98PXlNgBfaFUk7/t4N+rksRknH87FRV1+lk3Z3WcUvXab/fD8VNBfbdnBQfKBR0u130ej1s bGwETlkO5ikG20Q0bLNtuxU0KAazLiaJcaf0U/ygE5ZuYevUpUhgXcNMWGbb5LpbbcKsIlNt84oU qzy1VAiLey9Yh7o73Z59Ax9KcXvrZGW8a/YPdMgyLAqQLHr5YsUy/jbLdKf0AwjiU9tt+LDKxpZm /zUYDELb870VdvnQiuXY5IW+dlrs/lonbpLjcp77pyofFvH8xxF1bBdVNF4UGh/7ozurboMQIidR sVPLIC5e7LzhtjXO2boI+yOEEEJUAR2YFPsoXFpBAAiLlvzcCotu4imKmtatap2mNn7q/8/e/QRH kp53fv+9mVlAdXc1p3qI5mBIDNWUMGRrOXSMRFLLFa31am2vL7K9oePaZ/kgxYYd4T0zGNqTj47Y qy8Ob+zBCkU4HMHwrtbLpUivVqJESRySQxLkgDNNTpODmUY3qhsFVOb7+lB4Cm9lZ1ZlFQpAFfD9 RHQ0kJX/6g8SyF8++bzxIFYWmlq4kWWZtre3R70GLVywddg+xtWr8S3+FpJub2+PAgRrLWDzxbcN W8hs062XYl2gYduMK3it6jYWtz2Ie86Wg2mbpyqEiUNraXKvVKpGgflYUFrVwiO+kGOBps0b94Mu 94Yufx3/fNvFm7hHa9z3OT5+xvtoF48kjV2oKle8lo8pdhyxdgHxc7Pt2e+BuDK3fEyJ1xv/rrBK 13mOT9PC51XtuXremoTSda9t1XtBH+7Fyf7xf/NfXNrG/5fv/NmlbRtYdVVBY101Z9WAVk23MW39 F6GqGrWqz2v8PCcFsPH38fqqpgMAcF3YSbaFl/1+X9vb23rzzTf12muvjYULVmG1s7MzWtaCUwtw bT0WLNo8FrxaQGvri4ML0+l0Rstb2GEVphYK93o97e3tjd2eb4FvXL1rIadVBsXVaBaQWOCwsbEx qgC252P7Zq0Wdnd3R8/ZHn/48OHoe3tO5ZPXquCiHCRMCxbKwUp8q7IFJousDKsbYKZq+ioPRgPE 6kKq+FhYDj/tGBC3KokH4YsvmJQDU6uovXfv3tixNQ5g457XdreBHWPsmBb35I4rScvHnvIAWXEr F1vGjidVA4MZG0TQnv8qmvT8Lss8x9KzHHvrBn/DYmTrG//wEjf/zy9x28DV0CQoLM8zy4BXs05f 1LabLDfPPp31+QAAcFXt7u5qY2NjdJu93QprIao0XoUlaTTQlVXN2sl8+aTeRsje3d0dLWNVrRsb G9rd3dVrr72mBw8ejFV/xQGHBQtWhbW1tTUKRuPWBd1ud/Rcdnd3tb29ParMjat02+32aH8szLRw 10LauL2BNAxMrDVDHNTu7OyMhbs2v70O1qah3FMvroItT5eqT4bjYNX2MR5MbNHBa7w/TaYvMnit qro6ayUWlVyYRzyQlh2j4ip5OwZtb29rZ2dn9DmzgbfsWGUXdmxQQLsYZAGuHUfsZz+uorevy60D pNPPdXzhxy6SWQuY+DnE/9vzs6pW23b8ePwzY+0F7PlZUFxmx6JpP29Ngs/zvqizDMFr+Tle9EWs Rb8GXIgbd3WGYQNwbdFOAACAs7MqVzuxjqtY7UTeTuYtJLVpFkBakCkNT6h3dnZGoahVjFowuLu7 Owp4LUS0yi+reJU0WrbX62l3d3fUKkA6rQqzwPPhw4djgWlcyWX7af9bsFtuJWADfVmgEFfPxn1v 7Xt7XlbxZc/XglULkaXx233jAHXSrbflMDU+mbXnFU9vGrye9cT4IkLMqvWfdZsEr5iH/ezahSAb VPDNN98cq9a3nz+rnLce1Falb9NtfeWfw7gXtIkr+a2StioYtYtcdqEn7rNdNUhWvB92rI+fqwXB 5Z+ZcjB7Vk3WVXesWsaK1XldtaCy3Mf8KvijP/qjuZclfAVWyLN/eeeyd2EpPf0/us+9NlXTAADn 4X+87B3AgljQZ8GnBYjWe7Xf74+qp+yx+Db++/fvj8LMdrutnZ2dseAyvg02HtDLBp6xaljpdMRt W6ZcWWX7FYehksaqT6XTwapsZHKbPw5BrcrMgkxbvjwQllXdxtsze3t7owpbq6C118iCawtF4spY aTwMtDC7Lmyt+t5aI8TKPWHL4lYJ87pqJ9VAlfiYYpWekkbHODv+2cUXG2DKqlatXUm8nKTRMdEu Etkt/pN+rqxli+1XuWespMpjZRxQxhWz8bJxW5V4WlNVIehFDIq1yOB1lmPirHcYzHKxatp+TAqc F1Vtuoj18DtiHOErsGL+02/835e9CwAASJK+/sXfuexdwILFlaRxb0ILG+PRrC2otfAgDkStJYA0 vOXeTlTj3oEWZlqvVgtcd3d3lee57t27Nwo+44qvOEy4d+/eWGWWrdPCUgtX4yA5vrU2HlBGOh0A yyrHqoKIcr/VchC8sbEh6XSwmXieOICtup3e5ovZ93Un+3VtCyadOJefG4a4TRax+CJOXMUXtwmQ NBo0MO6raj/vcfWp/Yxb2xRb1oI0W4cFuHYsLlfd2vqtij9uUxCzoLVpCBZX2scV+02XXbS6n8fz qnY9z0rNWdY7bT8mPfdFPYerWLW6CH/wB38w97KErwAAAADU7Xb1xhtvjEJNaVhFZcGrVY/aAC92 Umy36duJ++uvv64HDx6MAk67zTYOHK1qNg5G43DRvrZg1yo1456Fcc9VC4FtXzc3N0fhiFXt2jbi FgNxpZntb1ytZr1spfEgIN5fC3ptUJ3y6Og2X13/xHhaeZCcWF0oWBWilkPlJstUmbW1wKL6zZ5H 39omCF8Rs+NFp9MZqzR9+PChNjY2RsfE+PhiA+9tbW2NHWPi4FQaVstvbW2NVcqW2bpNPE+5krVq +biS336Om/RWXRZ1AeB5tRmYpTL1PI9P8cXEWbezqMCU4LXOZ+deMlngXgAAAABYURYyWN/WOMi0 8NACRguo4tvkrTpsb29vrC/q1tbWqOI1Ppm2CtH4RHp3d3dU/RX3ZI0HlbL54+pOq9i1IDYOSeNt WDsAWyZ+7haOVIUU8XOMp9mycb/VuP9jvA9xsFeeJ96POrOGgosIB2Y9AV9UcHlZVblUAyNmP/d5 nmt3d3cUttrxLR7wTtLootP29vaoejS+OBQPXmhtC2IWpNrxzKr642OVzbexsTG6GBYvF4src8vb KLPjorVRmPVnoe6YdhbLGgCWX2egieW5rAGgsb/6F1+sfezXf/8bc61vnuXO6jK2G792i9z2Zb2G AAAsilVqWUVWPMCWnYjboCzWZ9ROQuOTZAtY44rZuIJM0qj/qgWzFmBsb2+PQsq43UC87nibcZib ZdnoVn+7tT+uHLNbfeNBvSwstSA3fq72vVXXWr/bcn9Vm9eUq7KqBsIq374cVzg1GYjrMsWjqpcr 0+w1W8b9BmZlF3TyPB8dE+24ZHcAlKvw48H57Bh679497ezsjKr5Y51OR1tbW3rzzTdHQWo86Jbd URArB651laBV0+vmjS9UzRq8xu0Wrqq41cFFPNfzOo5e1l0FIHwFVtYig77LCg0vcrsWusbbrJo2 L4JXAMCqi9sEWGXX1taW9vf3tbe3p83NzbFq0/hWfwsf7H+r8rKAzipm4zDB1lUejCsOH7Ms08OH D7W5uam9vT3du3dvLAS1frHx7b5xf1r730IPCy1sO/Zc4mnSaVWr7ZPdGhyftJYH2rHnbgGzvR4W Ote1GrB5TV0Lhnmcx230cdg96TkBq8wGHrTjVvzzbYMAWk9XC2h3dnZGPaM3NzdHxypp+PNixxH7 3o4Lb7755qh3qx0fywPxSc/3Oi0Hs+WWAU17o8bzzXO8qeo1e16tAZpa9D5c9POpew8WHcouw3t1 XdB2ALhmJlXNXsXtxsoB6byB6TI8FwAAFs1Cha2trdGt9HZiv7m5qSzLtLm5ObqNttvtjlVf2Um7 rcMChLjHqoUZceWndBr8Wgi6ubk5Cn5t/ffu3dPDhw9H+2aVuv1+f1RVZi0TrCLNBrOx3q8W5koa tUfo9/va3d0dq+q1fYiDSzvptX1++PDhWBVsfItv3Ec2/j+u3I2VA9I4GJ4WhEy6BXae4LXpLbWz BDTL1EcSaMIGALRgtd/vj8JTa4/y4MGD0XHFjlXxhSq7ALW3t/fcRSWbz0z6GbF128UjC4NtP40d n+xnOK6QrVMO32x98XKz/vxOCvOm7c+kZWbZj2mB4rK3Dqg7dp81eLXPtSF4vTiEr8AV9Vf/4ouj f/G0+LHy9Lr5yvOWp017rOl2y+upe2zaflQtUxe0/vrvf6PRa3SZz2WW5woAwLzi2+03NjbGWg7E 1aNxiBD3PNzc3HxucCv73050bZCauNWAPWZVX3ZiaIFFuXrTqs0shLUKWKvEtFt/7XFbPq7YtQBZ Om1fYIFtXHkWn+iW+ybGIYd9X/V6lqct+pbPuKfsIsyyf037PBK+YtXELTTu3bv33PHPjit7e3t6 4403xo4v1qv1wYMHo2NonufPDbplPV/jCnr7vs60W9/L08sDc5WVf97jNi22TJOLOE2OBXaMbSoO kZvuhzQ54LV1lp9302PUrOGx/R5dJgSul4PfgsCKqgriLGCsCxstaGxS8Vmer+62/bptxt/Pu926 ZSZtaxEm7cNFPxd6yQIALooFBNYqwL63vqnx7bf7+/tjt9zGt/HbbbdWKWu9D8uDwpTDTtuG9Xu1 QLfX643d2i+d3tJfZvsVbycOK+JWAOWQIq7GtZCl6hZP29dyr9NyH9eyRdwuWtVGoGpAr4vS9Plc RI9Behli0eJjkF20kU6PM3bhJ54WDxBoF4Wk52/tj39W7QJXOVi1aeW2LvHdA1WfezuWxv1gmwZu Vf2nY9NaqEwy6/Fv0s9zXYg6aTtx+F2u+C23eqgzS3DZ7XbH+pfb77p4sEbpco7duHhUvgIr6td/ /xvP/Vv0+ptMa/JYU3VB53lsa5Im+zDNsjwXAACaspNCCwwsMLXv7cQ1PnGM2w2U2w5YmwG7dTce iMZONONqr/K+xPsRBwLWT1HSKPyI+6PaNvM8H7Uh2NzcHO1HPFiOBSf2vC1YqBo4Kq6UrQtuy1/X zdNUuWJqUnXWdQoeq14Hwgsskh1Dut2uNjc3R+GcVe/HA1TZoFlxdawds+x4ZO0CylXy1tYgDvXs 8x2HtfH/VceSuIWBmbfCMe55XRYfZ8oX1KTm1fCL0qSNSyyu7JVOn8N59Kq2OzTi7dm2yoNUxvuy jJZ531ZF9o/+3q1L2/hv/ebfvbRtA1dZHPRdxqBWWJzLei8BANePVbhKw5PBeFCreGCrfr8/mtcC ha2trVFQEVfISsMT+Lh9gFXC2nwWPkinFaRWOWb7YhVnVrVqIWu73R6NFG6Bqe3nw4cPR+GuDfhl oYgNbhOHrOURu8vBa9y7NraoAVCqKtjK1bRNA9am+1Su/jqvEbYXbVIFMLAI+/v72t7eHvV1tc/X 7u7uqOrfAtl79+6NjnNx1agdJ/f390d9sK1ytdwOJb4oFP+cxxWZdkwtH4+2trbG+k+fZRCluOK/ 6lgQ/5xVbWPSMWqRFZ7W47vuAl6Vqv2tOv7NU0W/ubnZaF8mVerG+7JsA2FV7cuq/L5YFtk3v/nN S9v4//RPf//Stg1cddNud1+0ujYFy2BSq4BVuK3/ot9LAMD1ZGFqq9XSYDBQq9UaG9zFpmdZpjt3 7ujg4GB0O66FopLGerDG/V7jylkLKqxay0YCt/DUwlcLdS3IsDYH1o8wDhzi0MNCjLhyNg4rqkJW W2f5JLPqdtR4nbOefNad2Dep1mpq0j5NOmGum970JPuiTsavU5UvLo9d9IlD0rji9f79+9rZ2Rkb oHBra2s0MFc8zY4V1t/VflbKVaaTfn6q+r12u93R9kz88zHtZ7LqNvyztAeYdBwrH0urjoVNQ8du t6vd3d2Z9nOa+LWYJdSVZq8yLj9PuyAoaaxdxDIjeJ0NPV8BLI1ySHqevVzt+2nzzbsP5/1cAABY tHa7rdu3b4+qsuKg1L63qiwLYO/evat33313tJz1fLWwwsKHuGdsXPklDcPara2t0T5YFap9bbex WpVqlmV6+PDhqI2AtSiwqtn4tlfrk1i+fbauv19dpVc5wIjD5lnVhROTQotFVozF+x332226zCLm A1aB/dxZCNvpdPTgwYPRMcuq/vf29kaDFPZ6vdFFJTsexeFoHLJaawP7vsq0n037Ga4L7JpeqKgL +2apwJx2nFpkT9Wq+ezuhfI+NL0oNK0dTp1p6696Xcr7v6yV+8tWgXvZ/tW/+l/nXpbwFVhRdcFh ubdoOfA7r9vYq3qaVg26NW2757l/tk/labPuw0U+lyb7CwDAWb3++uu6e/euNjY2RkGmsa/LfV3t 9sy7d++O9Uo9PDyUdHrCmee5XnnlFR0cHIxaE1hgYP0UbR/29/eV57kePHige/fujW61b7fb2t3d 1b1790ZBh1Wq2v6UK1fjk+FydVdcMWtsf8sn0nGoW9d6YBbznGSfZ6Xnsp70A5ct7oFtwd729rZ2 dnZGF5Da7ba2t7claRS85nmuN998U/fv3x+1R7GLU3GQVdXndV7xuppWos663mmaDlplbD8XVTFv v1fKz/ksx88m4eO09Td5Xerev1n3K66gXURwSvA67p/9s38697JjP+F/+Zd/KUn67Gc/q6bTy9Mm zQ9gMaaFcLM+Pu37ptPOut0m65l1ILB5512W50LgCgC4CN/61rd0cHAwdgutBZ+DwWBsmk23ATji 0cB7vZ7u3LkzCmal4cn/wcGBpPFKsgcPHoxOFOMRwe1xa19gQayFGlaF1m63R4/Zfjx8+HDUZ9Fu 6Y3XVx4xvBxSTOrbN28vwGVH+AqMy7JMr732ml555ZXR8cSOf3Zciy8olav87ZgT94C1linWIza+ Zf6sIdmyHJPmDVDn3f+qitJZW7pM0+R9mbb+8xrMa9q+NP1M0bu1uXfe+cu5lx2Fr3GQ2vTrKnXz A1iMr3/xdy57F660Z/9S+vrytKsFAOBCvPXWW9rf39e3v/3tUcBqgeve3t4oRBgMBmO30ForAhuU xk724h6J5Z6qkkZ9Dzc3N0frsyqzqlvhbUAtmy9ug7CzszP2XOKqVlvGplVVw8am3W66LCEHgPMz GAz06NEjffOb3xwdMw4PD0eVq/fv3x8dE/v9/uj4Yn2wrcJfOg0Id3d3RxeZbABAaxVw3asL5w1H uXD0vHk+SwSvF2MUvs4akn72s5+tDWABnI+b/+TRZe8CAADjvvzly94DLMDv/d7vaWdnRw8ePBg7 oW3Sa9TmKQ/6UsVODO3/eJn4ltxyyFlugzCtTyIAzOuHP/yhdnd39a1vfWtURR9X/h8eHo5daLIw 1XpR7+zsjKrt46pWu7gTDw4I4Hp4rrEI1aoAAADA9fInf/InldObVBZRfQTgKvnd3/1dvfHGG3Mt a1Ww9v91r2oFMJTE3xC8AgAAAACA62re4BUA6lT2fAUAAABw9YUQLnsXAGApcDwEcF7G2g7EPVwt iI17u8bhrE0rh7Z18wNYDs650ddVf2A456b+4dFknrM4z/XHzz922X9snfdrel1Meh2X+TUu79tl /4yd9fGLdF77Ou1YuYyW6X0BAAAAMNRowK2qx2adH8DlsiChHPCUpzVx3if3q77+eSzjPq2qqxBA LcP+172OdRcwrpJV/Qyt4j4DAAAAV10yfRYAV0X5xHwZTtSvQ5BTdh2f80UKIfAaLyHek8Xi9QQA AABWA+ErcA1MquKqC6qcc7Un95OmTwoEyo/b13XTp21rlm03VfdaVH1v21z0c560/Lz7gWqzvL51 08uP1a1zlvdpnuVn/SxOU3VsmKUadt7P/Vlfu2mm/dzU7Wt5X5qsu8k25/nMcBwB5rcqn/Gmvysm HT9m2VbdsWaW6dO2uyqvPXCdrMrPJcdErLps+iwArps4YKkLW2ZdpupxC3cmrb9qnknfN9nf8i+6 eftBTuvROe9znrT8PPtxHTV5ned5feum22eq6edy1vfprM+l/PhFm+VzX3Yen/FJ65y0r8vymeE4 Alx9s/bfn/c4X7eeWac32S7HFQDz4piIZfCJV16ae9nsLAsDuJriXwSzhlhNHl/kL5p5QoJFbH/W PwBm3WbV8lXPlV/a9ZqGaJPmnVQx3mRak8fOap6fv6Z/hMavy2UFcuexzUWEwKvwmeE4AoyrOymu m1Z3kWTSMvF2mi5fNe9ZjrmzHOcnrWPR2y0/1/Iyda8TF4SA88ExcbZ1LHq7HBOvFypfASzEeVbT TQuALqOSD5jVWT6ndX+ILWLdq+CqP7861/V5A+dtWmV5eVrV3yD2dZPq9UnLT/r6KpvldQJwvjgm Xj6OiVcf4StwDcxSAXiWbVyW6/ALGfNZlqvDVX/UzmrSH6Hn+fwu+zVcxGu3iq7r8waWXdXPYtW0 umPmLPMug0kX/i7CIirWAJwfjokXi2Pi5fr5T3fnXjY7y8IAlkPTYKTpyfysVxunhTOTrmw2Nel2 2fMMhub95XbW57yI1wxDVX+kXKXXd56fv/M27zaW+Y/JVfzMrOI+A8uu6u6bWY6vq/QzyDEDwDQc E3Gd/A9/8D/PvWx2loUBrI5JfXiq5p31ql5Vv5om65xnW7Nu+6zrOmsAW96nps95Ea8NhiYFsPb1 RW9/lj/gpn2+Z/35m/Uz3fQCTN33Vfswz3Lmsv74XabPDMcRYDaLPm6cdX3LfmFkWfZpmS/KAauM Y+JslmWfOCZerv/tj/776DuvEILSMPwbu3y+P/qbXbmCd8q+9o0/u+DdPfX3v/iFS9s2cJXMWlHZ 5PF5frk0Xfe06U3nm2Xbs8w3bX/qwq9Ztndez3kZ/ii4TIt8vWZ5T5pMm2VbsyzbdJ7yHwSLWlfd tEW+Tmf52Z9n/fNu7yI/M7PMz3EE1820k/h5L9pOu4g07eLWIi9EL+rC86T1VF0Mm2e78979U7V/ AGbHMbE5jomYJDl525ykEJwkJ69CLnFyIZVzTkVRyAcv5xJJUgheSuj5CgAAAOAKqbrYNOuFmCZf L+rxpvs3bZlZl5807yIvQpXfg3lfZwDz4ZjIMRGL4Vwq6eT9OMnAXZCcnIJOw3j7N/x+GMISvgKY GQd/AACA81dX4TTr32KLWs9FWVQ7KQBXC8fE5d3H6+A0UD19P5xOKpnj4DUZBq4+BEmppITwFQAA AAAuw6ztRc5rO8tm1fYXwGJwTKy2avt71Q3D1ySqgA3D9gKyqlcphEJy7qRANhC+AgAAAAAAAEAd F6QgC8OL4bSTKtjkpNr1tCo2leQl0XYAAAAAAAAAACYKTsP0dUwiqahsZTEcgCuXRPgKXBtVozOu qllHiASAVdOkt1fTY+G04z99xAAAAIDJvJLRQFshBLkkyIdCcqd/T4cQTtsRpJmSMJwgkkk3AAAg AElEQVRO+ApcA1Un6KscYK7qfgPALCYd6+oGiqiab9rx375uuk4AAADg2kkSjUpfgxTkFE7aDxh/ Er7a4FutVksh0PMVuLZWJcBc5ZAYAM5D1UirdTh+AgAAAGfngmR/WttQWnKZXNAwjA1BiRKFYTIr J6fjYhjOEr4CGIlL5cvT4xP9SbfAmlnWUbecTS8vUxfITtq/qrCCUAIAAAAAADTxXPFDTa5hkiSh 8hW4LpoGp5PCzVkebzrPtOVsv5v2NGwSzk7bRwBYZmc9bnHcAwAAAGZXFa5OKgqLvyd8Ba6Jck+/ WUPIcn/Aup6BTdcxy3LTVO1L1XMicACwygheAQAAgMvhXCqpnKWE56aNL+OofAWuo7rq0EUMtDLv OhjkBQCaqbqSPuvdAQAAAABmU30X7+nXVfPa/4SvACSdvSp03jD3PEJgALiKmrRyqULwCgAAAJxd 0zFkTu84dpI84StwFUw7sZ72eJPeqk16ql6W8v4v2/4BwGXheAgAAACcXeYSSXFVq+RPWhHE08d5 OZcSvgLXQTzgVjxt0jyTHq/r3TrP7bDTlmsyWNgs8wHAddN0cAAAAAAA1SynsL+tnXPyPv5be7zV QHCnPWEJX4ErYN7Brmad5yyPz/tY1eN188+6DcIHAKtsUcd+AAAAAJOFUEgaH8Q8zVIl4eRv7pMM 1jsn770UgryGkwlfAQAAltBFVfPTaxsAAACYzJ20GDitgJXclD/Tk5O/swlfAUxF5RQAXKyLPO5y jAcAAAAmcy49+bvZyVoMSD6aY9gTNglSiOZJRPgKAAAAAAAAALXicWlOJVEgG08dth9ITr4nfAUA AAAAAACABiyArRo83KSSgh9+nwgAAAAAAAAAMBMLXOPgtRzKUvkKXHFNBlJp2u/PGksDwDJZ1MBU 09ZTPp7Os73zOo42WS/HcAAAAGA+8bnCpIrXeFpIEyWB8BW48son2px8A7hKTkcbnX6h6SzrqTp2 cjwFAAAArof4PCFuOxCfD5x+70++T+VE+AoAAFbYosLPaeshZAUAAACusZCdhq4nk5yChpnssKtr CGFs6K1Q5ArRwFsAIGl4pWbWCrK6ZeLeJ1Vl+U2Xm7Q/kx5v8thZq+UALLdJx5iL3Idp25x2vGoy rem2FrEMAAAAcN08/zdzong4LecSDaNZ+zdE5SuAkbhkvunttNOWaTJtEfNMmr/JugBcTdZKYNZj 2zTzHiObzDPv/p3HMRwAAACA5Jz9nRz/vfz8387DAHYc4SsASc+fdJcDiybLVKl6vMnJ/awDxxCu AqgT93K9rOC1vB/T5plnP8/jGA4AAABAsgrXcj3FpL+nk2QYuxK+AjiTeW9VPe9bXCetf9FBDIDr 46oFlrQbAAAAAKarGnCrPL38uD1G+ArgTOYJIaraACxa04rcqxakADg/V/F4cdWeDwAAAHDephVz lR8nfAWugEUEAvP0RTzL7bFnNWl/L3O/ACyfRfQ1nWe5quNUk3matF2Zto/LfgwHAAAAVkpIn5tk f5XXh7CFJMJXAJF5bscv9zBs2s91Wt/DWbddXnbSfs26vwCWV/zzXHU8aNpTddp6yvPE65ukfJya FMDWra/qeDbPeqbt3yzLAQAAANed/S09bawHwlfgCpjlZHmeAbKmPTbr9HkeW+Q2CBeAq2NRg/6d 5dg4Td3ggGfZft33HBMBAACA81W+s62q4CKEoMQx4BYAAAAAAAAATFS+86zu7loLXql8BQAAAAAA AIAZnQarSTTV185P+AoAAAAAAAAANUKIK1/9SYVr9bzlMRWyv//FL5zv3gEAAAAAAADAihpvM5A8 F7w+P/BtIgtps699488uZi8rEPwCAAAAAAAAWGbOxWmrKz3qR9Os56t9LUnZ/vvvnv8eArhU8Q// pMcmzXedTGqkvWyavGe8r8DqKd+qVJ4Wm/Tz3WSZqm0BAAAAmMwG1ir/zT383kka/m2deV9c/N4B WEqcdFcHlaseXq7yvgPXWflnt+pnuS5cnWWZaeEuAAAAcN2NtxXwigfbshB2/OvTAbiy4OtH4wKA OqseSM5iWZ7ndXrNAUw3zzGB4wgAAAAwBz+scD0tV0gkZ5lqakWuw8dPvvanla+ErwCG6qo+Tbk6 6vmG0po4vW6e8nbt+6r1TLo9trxc1T5P269pzrKNWV+XqunTHq97bNLrO2mfJr1PAAAAAACgzjBz zQJtB4BrYZ7bSevCtklBXJMeslUNqJtsuy5EnDaP7fO05xU/t/IyZ93GPK9LPE/T13za85tlmSbv E4DLQdUrAAAAcHmGf1uPfy9VF49R+QpcE9MqGxehSWBYN0+T/W0SGsy7XNX8dSHsrNs4y+sy73M+ 6zJN3ycAAAAAAK666efD1gP2+SJXer4CqNWkCvSiXWQAOK1SFQAuA1WvAAAAwMUKURThnFPQqLXr 6PuTb6K/u4eBbMYf4gAmWaYAsu52fwAAAAAAgPPix8JXSWEYyA5ziiSaM5ESrxCCkpP4IjvNaQGs qmUIRqXnb5Wv2q+qeZbJebyW874ul/meLvv7BFxXVL0CAAAAqyXjb3EAdeLArS4sLD/WpFVBeZ6m wV7VvIsOFaq2sYj1z/O6zLOORZr3fQIAAAAA4CqxKlbnXFTH6qprWsPwsXDyIJWvwBUwLYib9Hg5 OF3Ees86SNQiH5tnAK7z2sYiBs+aZ/CvswwYRrUcsFzOY4A9AAAAAJN5pZIkp7hIbdhu4PkCruH0 9GReer4CuFDc/roaeJ+Ay3dRle5UtQMAAACTWeWrxu4QHU4q/zU9+vP95IHsvHcOAGLzVoTiYvE+ AZfrIn/e+NkGAAAA5jetRSCVrwAAAAAAAAAwRXzXWBy4xvlqeewUKl8BAAAAAAAAoEZITgLVqgfL PV9tchgGsJn3xbnuHAAAAAAAAABcJXWtBmy6ZbKZ9/5CdwwAAAAAAAAAVltdK1ebnkiSskD4CgAA AAAAAAAL45yj7QAAAAAAAAAATOLd89OSUeFrMprmohmPk1xytB0AAAAAAAAAgFrexWHruBAKWQAb NAxfE0khSZUEKSvy/GL2EgAAAAAAAABWzFqRSDotYHVjQaw7+SfF/V5bJ5Fr9vWv/j/nvoMAAAAA AAAAsIpCCBobYCsMe7qe8orbD0heRUiUyin73X/yexezlxX+9//z31zatgEAAAAAAABgmtRLUiK5 YfWrCzotdh3xUkhOglppTcNxtrKL2kkAAAAAAAAAWDWJcyd1r+nJBF+qfB0KUUWshbCZQk23WAAA AAAAAAC45iw99U4KTpISOVeRqaZBo8G3Ql/OOWVErwAAAAAAAABQz7vxrysKX5UEyQbmSpJhCJuN NYsFAAAAAAAAAIz0T3q9Khkmrs45ueCG3/ugInilieRDUHBSK13T8dGx1m6uKyN7BQAAAAAAAIDJ Qghyzg3bDxSF5BIlSoZ9Xl2iEAbKi0I+D0pSqTg6VhZIXwEAAAAAAACgUuKcfAhyQQoKUhF0I12X QqKiKOQKyWVOmc/klSpLUgVJraOEylcAAAAAAAAAqBNCoRBOWg4olSQ9y/vKlMl7r1aQXJIqdamy ltPNbE17+49VHKdUvgIAAAAAAABAnVYeFFxQ4jI5JXJByvNE62mmLDi5EOSPJHd8rEGe60h9veic Bgc9Kl8BAAAAAAAAoE6SOAXnlJz0ePVByrJMaeLkfJC8l0JQnuc6PDzUUS51MulnP3+oTKSvAAAA AAAAAFDpplJJiZxSFUXQ8eFAN1yqJPFyhZQqkXOJ8v5ATz840uMnT/WLRz/S7o/fUhbIXgEAAAAA AACgUpo6KUkUvFNR5Do+Pla3+6J86pQUGta29o/13geP9Lff/Z52f/KOtu54fft736fyFQAAAAAA AADq/PibX1PhnQ57hfI81d/9/BflD4OS2x+Sf/Su3vubv1X3RqKHP/yOvvpH/5dur3l96N4vaUOP CV8BAAAAAAAAoM6//tqf6tbNF/TizRe1sfER5f1DZW5dxQ++q8fvPdLDDz7QB8VTPX60rw+9cEPd 9ZZ6z54qpI62AwAAAAAAAABQ56PrqdqZ13rxTP7xnna/8y1t/9I9/dv/92va+f6PdHt9Tfde+Yiy delDnbbe39+TilSdW4HKVwAAAAAAAACo0711W1nrho6Pgh4f9LT/+Afa+dFP9O/+/Z/qRztv694r L2ut7bSeDPT0WU9P+0fKXCqXBGVkrwAAAAAAAABQ7Qff+g/KfZD3ibxrKcsy7T851lpypJc2pGLw gd5657vaf/JYz3p9feyjL+i9/In6hZQF0lcAAAAAAAAAqPSzn3+gp0dBRS6FNNWdF27r1V/+hNbT jn70zlt69OiR8jzX0dGRfEjlvVQUTq3MU/kKAAAAAAAAAHVevOO0kbUUQtDB4UAffWlNn/jEC3rv F/s6DvtK20dK1lN1P9KWz3O9//ixHq9LL2+Inq8AAAAAAAAAUOfOnRd08/ZNFUWh4uePdKtzU2+/ /bbefbiv3tGh2mtrerJ/oPV2qhdeeEG9Z+/r6Fi6deumMqJXAAAAAAAAAKj2yPXVOz5SUKL0TqKH /T0lcmrfvaVbYU37Tw91u7uuw/xIPu+p1Un04cQryQ6VKRC/AgAAAAAAAECVLElUFLm8H8g5pzRp KUky5flAWZbo1o11pYnkCqdETkniVBRSp9Oh8hUAAAAAAAAA6hzfkApfaDAoJB+UBKmtm3p63JfP pDRJdTQ40rELSpNCR2mh9k3p4798l8pXAAAAAAAAAJjEhUSJJC+vEJzyPNdgUKiVthS801FeKEkS hVDIB+lDtxO9/PIdZT9750eXve8AAAAAAAAAsJTy3Mv7IKdUWbomp0yH/Vw+yZS2b6no51JyrHSt pcPjIx37VC4r5JNjZZ//+79zibv+zy9x2wAAAAAAAAAwjVeaJHJqyfugogg6Pj5WK2ur1Wrp+DhX tr6m1lqi9x8XkqQXPrSuo/5TJZe85wAAAAAAAACwtLLQUqY1tdRSWmTyx0Fp0lY7uyWnNSmkWmvd lNTWwRNJzumVT9xVnjwjfAUAAAAAAACAOkV+rHwwUJ7nCirkJN28eVPt9vqwz6vzylqSc05HhbR+ M9FHNl5SCI7wFQAAAAAAAADqPBs4Fa6tZ4UU0o6++Ftf1N+5/ys6ONrXw/feVl4MlKap7t5+Ueu5 FJ6u64c/2tHHXn5J2WXvPAAAAAAAAAAsq2wtVbaWqjgKcknQYd7X2nqiQTHQk/1CWx9L9Tu//Z/p w3c29N7eI735/ff053/1TB/7+C+ofAUAAAAAAACAOrmCjn1fg0Ffz54d6O133lEabugjH/6o1tpB D999pnzg1G639clf2dKrn/qQbtxw+u733qXyFQAAAAAAAADqHB8fy+epEtdS4Qs9+Nl7ev+9x1pb v6W11rp+unekH/9sV+lapk9v/6o+8+m/o58d/I2+/ld/S/gKAAAAAAAAAHXyvFDalm7cbMv5lj74 YKAH77yvzY+sqdP+qD77mZv65C+9Jq8jJa1CrXXJP7opP2DALQAAAAAAAAColUhKk0xyiQZF0OHR M91qp0qSRHc+3NV/+dtf0Gu/+qrW1lMdF7keP36if/eNP9PN9UDlKwAAAAAAAADUSTOp1Rro6bOB ek+lZ/2g9aSlVnZTrbWuXn5pW6FY0+31F/Rip6MnT/fUzm7rc5/5NOErAAAAAAAAANRxSUvrazd1 cPBYh30pWZM+9MJtvfziS7rduaFWq6X93lO1skxJmuidX/xC//V/9Z/r86//CuErAAAAYJxzl70L uGJCCLWP8XnDRZv0eazCZxTnoepzyGcNl2GWY6JbL9QPT7R9v6sfv72vB+9In/7Ur2h9raPvf/cd 7X3wnlKf6C+/+x1539c/+OJn1Gq1dLB/RPgKAAAAoIkgiZNjXCY+g1gmfB5xmfj8XbR/8PnP6u5L N7W2lmmt9aae7r+rb73xt/r4y5/Ux176iN558Attf/yjunHjhn781gO99c5P9fGPbqq1FhhwCwAA AAAAAADqtG/1td55qoF7pFt3BrrzkldIjrR38JbS1pG+t/OG1m9lemXrozo6OtZ33nxH7/58X/1B TuUrAAAAAAAAANTJ82N9sP9MT3vHerF7W7/x6y/oJ28/1U92e3rn5w/19Omh3n3vfXlfqL3e0cHB Y73xnTe1PdgkfAUAAAAAAACayrJMeZ5f9m6shKvyWt1+MZfWcvWO93T7zovafPmO0tax3vnpE/3o F0/04fZN/YdvflW3b7yg27dbyrKP6f0PfqIf/dDTdgAAAAAAAABoalKYmGWT6xynPX7VXIXgVZKO B30Vvq80der3B+o9faKPvvySXnihpf3HTkeDXD/48U/0waMnunXzQ2plmT7zyU8pSUX4CgAAAAAA ADQxLTydFjZelTDyuhkUj9Q/fk+t9WMVyYEGYV+dTq5P3u/q1VedfBjoqDjQjZuZ0jTVkydPVCho 484m4SsAAAAAAADQxHWrXMVQcIUOj7xcmkgu19papoODx/rUJz+uL37+vpJW0NGxdNQv1O8fazAY 6N/+6X+UV0HPVwAAAAAAAGAagtezWeX+ryFt6dbamlxwci5oMCj0ZPCW7rRu6/5nbuvQZfrmn+f6 af/7+slbff3gRzu6e9Pp9dc+Q+UrAAAAAAAAMM2qBofLYpVfv6IoVHgvH4IkL8nrQ7c6GgyCCgVt fWxTn/rkHd198QXtf/BYz3pB/91/+4/lC1H5CgAAAAAAADTR7/cvexdwCbzP5UMqJylxTiFxyotD 9Z4N1ErW1H3hBX36U10VRx/Rg91va+ClN3/yfT3zB1S+AgAAAAAAAEAdHwoVxUAhFAoqJHkVRaEs S+RdocFgoI/cvaPf/I1f0yc+cU+5C/r+W2/r8ZNDKl8BAAAAAAAAYJoQgqSgEBKlaSrvCuUh19HT x2rpqQq9r/Xbe3ryRDrSQIc+o/IVAAAAAAAAAOokieSSIJcESZL3Xs6lOj7yyn1fypz6gwPtvv0z dW7e0It3pO/94FDrrTXCVwAAAAAAAKBKltXfND7pMVwtzjk555Qkibwbhq8hCcqcU/vGmto3B8pu 5MrdI73+uQ/r059O9c4H0tP8DuErAAAAAAAAUCXP87kew9VjAax9fXx8rDSTkiTR4eGhBseFspbX p37ll/Vid13HT5z+zdf+P3q+AgAAAAAAAECdQk6JSzXwA4UQlLg1eS/duN3W4bMD3ezc1OHT9/X0 Waq//eGf69HTZ0rWpe98720qXwEAAAAAAICL1Ol01G63aV2wIvI8V1EUw3YDPmpBMHDyhRSClKap lDrdvnVTf++zH9eHu1J7nZ6vAAAAAAAAwIXodrtqt9vq9XrK85zWBSvCFYmSkCj4VCE4heCUuJYK fyTnJLlCaetYRXimpPVEv/H5l/Sbv72h0M4JXwEAAAAAAIDztLm5qa2tLfX7feV5rizLRlWvcfUr lbDLKcsSZVmmJEkUglNeFAoqhiFsazgAV6JUR4O+9h8/0wsf6uj+9qt64UOenq8AAAAAAADALLIs a1y12m63lee5er3eWLhqy9v/BK/Ly7mWEpfKB6cgL6dEvnByCkpcoaIolCWFgjvSk6eHenLrw/r0 5/pKb20QvgIAAAAAAACzaBK8Zlk26utqbQYsiLXHbV1VYW78OC5XHnLlIVEIQQqJnHPyPpdSr8QP K1+zteF7NhgEHRw+1Udfelnd9Y8SvgIAAAAAAACLlGWZut2uer3e6HsLWOMA1h6z/+PphK5LJGSS EhWhkPdOrpDkUilxCkUhFxINilxJJmXZDR3njyS/r+JWQc9XAAAAAAAAYJFsUK0q/X5/aosBWhAs lyRJ5NJhjFoUhfI8VwhBPhzLSXIu6DjkGvigRKny44He+8X7WmslVL4CAAAAAAAAi9DtdtVut7W3 t6dOp9Oon6s91u/3L2QfMbus1ZaTUwjHUnIol2QKCkr8mvJBoiT1ShXkQqHgnihJE910a3r/YY/w FQAAAAAAXC+zDJa0iG0ZbiNfXU0+M51OR5LU6/XUbrdHy2VZNlbtausqfzb4rCyvwWAgl6RKkkRp ksl7rzRxCqFQCEEhDPu+Ji5IGn7t0nUliaftAAAAAAAAuF4uMtgiRLsapr2PWZap0+mMqlergtXz CFhpT3AxnHPK81y+GH4dnE5CVzf85528l7yXQnAqgpd3R0ozT+UrAAAAAADAWUwL1Ahgr7Zut6tO p6P9/f1Rpat9JuK2A1WDbJ3188Jn62Ksr6/r6PiZ8nygNAuSnKSgEAp5L8l5yUnBB7kkyEkqjnOt rTnCVwAAAAAAgLMgALu+LHi1ite43UC532tc/Zrn+ejxeFp5WSwP772KopBLguScfHBKXCofpOCT YXsB5yR5yTkVXvJUvgIAAAAAAACzs1YDvV7vuZYC8TzSaUDfbrenDsJFCLt8nj49lA/FsOVAGFa2 +kSS83LDHgQKQQrBy0lKkqA0C/KeylcAAACc0Z9+7a/1p1/768veDQAALtWk28inPUbAtpq63a76 /f4oeI0rXMviate46lV6/nMx6fOCy3F4eKS1tURJmsr7XEmSKQnDbgNeGlbCeidJcgpyIWi9laoo AuErAAAAzuZLX/rSXMt9+ctfXvCeAABweSYFZXXh2rTlsJyyLFO321W73db+/n5tNWvcZsDaEsxS 1VpuU4DL471XkmRyLijPJScnl3qFoFE1bAhBwflhKOuCBrmU57QdAAAAwAI8fvQTheG9VgrBKyhI wSuEoDwvVBS58rwYVnoUhYq8uOxdBgBgYaoqHasGV8LVsLm5qSzLtLe3N9bjtS54PWuASvB6+RKt 6+i4kJxXmrYUlMppTbn3CietB5xyOZcN/xYOw56wWZIRvgIAAAAAAMyqSaDadB7CtdWRZZna7bZ6 vV7t47Fy5Wt5sK14ufI0PhfLoygKOVcoSYeDaSWpUxGCnHPDyldJRfBSkJy8UpfqOM+VJAnhKwAA AAAAwKyaVLY2ua28vDyB2/LKskz37t1Tnufq9/ujqte6wbbsvbTgddJ67X9bZlp/YPrCXqwiz5S4 RE5Oyrzk15W7vtI0VaJUQUFeiSQpUyKFRMGl8mLALQAAAAAAgEZOA6/x6ZMCsCbVrwRoyy/LMm1u bmpjY0M7OzujNgNxCGviyta6lgNV4em0z9HpuvjMXDTvc7kQFOTkfaG8CCpcMax8TZNhxetJFazS k56wLkgS4SsAAAAW5/Nf+Ec6PDwc+xf70Q/+/JL2DACAs6sLvKpuNZ91GSy3TqejTqejvb29seC0 7r2vq2I9a7uBWQbswuL4sKbEFwo+kS8kBSdlmbwyBR+GA28lmZxLpBA08F5SkBRO6mEBAACABfiL P/vXtY/95Md/dYF7AgDAxbAKyHa7rXa7/dzX5X/GqiMJ0JZflmX63Oc+p+3tbe3v7z/3WNX38Xtb Nc8srSroB3v5iqJQCE4hFAohqNCwqjWEoKIoVBSFVHip8PI+16A4lvde3nvCVwAAACzWt//63z83 7adv/80l7AkAALObtSI1DlGtEtLC1ziAjddfF8hiOW1tbenll1/Ww4cPR9Pqws+q0DWukK0KZ820 QLYu6MX5K3KnIg8qiqAiT5XnhQaDQsVxIT/w8gOv/vFAR4Ncee5PglraDgAAAOCc/PD7/1FbH/9P JEkPf/odFQXVGQCA1TBPRWE5SJt3AC4sp83NTUnDgbPKVah1/VzrKl+rBs2yr6sG5op7vZbxebo4 RRGU515KCoWQyBeFlHqtra0pUVARvPLg5RXUShO5IPkgKSF8BQAAwDl58JO/VghBeV5c9q4AAHCh CMWujm63q1dffVXvvvuuer3eaHqe52q328/1dS0HqvH8VV/bfJMqaauWwcUaHHsViZR6L7UKZS1p LV3Ts8PiJHDNlGaZ3CAoH0guS6WsUCgcbQcAAAAAAACAsizLtLW1NdbrtVyJWtdmoGpdkiqrW6sC VlpTLJeBL4ZtB8KwCrYoggbHhXJfKASvIgw/B0f5QIOTaaNWJJe98wAAAAAAAMumbhT7+PHy7eW4 WjqdjjqdjvI8H6t6NXUVq+Xpkypdp1W2Vrc5mP254Gx8kagIiRKfKCRBPjgNjiUvpzRz8j5R4Z0U gpIkDAfmSqUQPOErAAAAAABAWbm6sepxm6dpZSIh7Wq5d++eXnnlFb355pujgdT29/dH77lVsVb1 cbXPRtyeoKpnrFRdDWvi9RL0X56iCHJ+2NfVea+iCEqSRCFI+clj8kFBQc5LXl5ykoJoOwAAAAAA AFDlrGGXhXTtdpvbx1dMlmW6d++e7t+/L0mjADUOVU1VUF9uSxBXuMZB6qTgtW6/Jn2P83F0FDQ4 TpQPEhV5pnyQKgSnwktFLhWFV3AaBq5OKryTzxMVPqHyFQAAAGfz5S9/+bJ3AQCAc1EXbMV5bN1I 9PZY1f9Yfu12Wzdu3FC/39ejR49GIWm/31en0xmbt/z+lwPVfr8/CuD39/eV5/monUHcQ7auVYGt vy54nTRgFxZjMBjIFUGJLyQfJBd0PBg+5p2UuuHAWmmajKYV3ksS4SuwSji5BQAAAIDlUNUTlirE q8PC0l6vNxaUWvhaV/1aVR3bbrfV6/XGQti4ZYFV1dp6ysqtB6oeMwSx58OqXbMkVVCQL7xc5uVc kD9pLyA5FUFKEsk5pxCCvALhK7AqvvSlL132LgAAAADAtTIpxJoUkhnC2NXV7Xb16NEjtdvtsarX drst6fn2AvZ43G6g1+uN9QW2x7a3t0frjXvB2vcW1FYFrTbgVl3FLM7HYDCQBl5JKmWJU5IGJYWk LChNnCTJe68QgkKQlEqpS+Q84SuwMt7/xfcvexcAAAAA4EqYFIpWhWrx9EnrK4ds8XK0HlgdWZZp c3NzFIT2ej1tbm7q4cOHYz1c44pVY6FpXPXabre1vb2tTqcz1pLAlo8/P/GgXA73psYAACAASURB VPv7+6N5q3rHxv/H82HxiuOW0laikDkVCvKJV8ikTE6hSOWSXM4lSlKvkAYpT+STIImerwAAAAAA 4JppElA1DbHisLUq1LVKSQvlqFRcftZu4OWXX1a329Vbb72lfr8/CkbLg2vF/8cVqxsbG8qyTFtb W6NKV1t20kBb8UBtFvLGoS4fn4vnvddgEJQcOaUhyGXF8AEXlKVOiQtS4hRcGHYgCEHhZBbCVwAA AAAAcK2Uw7N5WKhaVe1q4krFeBqWX6fTGfVqLffzjXu7litSpeFno9vtant7W91uV71e77nQNQ7h 4xYC5V6x8TbtsV6P3q4XzR87yUkDFQohUeKdlDilIZFa6wo+V+KdnM8kF+S9V5oVovIVAAAAAABc O9N6s1Y9Hgesvd7zwdi0bRGUrYYsy0YDavX7fW1sbCjPc21tbWl/f39UhTpeiXoaom5ubqrb7Y7a FpSrnSeFplUhb3n6sCpXYy0PJlXRTnqefCabOy5yZbnkEsknQWkIStecvJe8z6WkkPdOiSvkXFAI Qc4PB+MifAUAAAAAANdK3Uj18ePx9LrK1nj5uulx+ErYtfziwPTg4GA0/fbt22M9Wi3wjD87nU5n FL7a9+V5pg3UVhXWx5/XRYX5fBZndJwMe7n6oFAkKnxQmqYKeargWkpCIpcEBe/k5ORcrrzI5Qhf AQAAAADAdTMtALOgLG4tEP8fq6uarbodHcuv3W6Pws5XX31VWZbp29/+tl5//XVlWab9/f3RQFjx e9vtdvX666+PVUTbPHGbgnh61eejqkq26jNk25kW7GIxiuClIBVBSryXc0FFcHI+kYpcLuRyLihJ g5IkkVwh5yTnPOErAAAAcF3UnZzZ7ZXdblcbGxvqdDqjQUPMN78pfeELXxhV+1j/uqqTzHj9dmvm /v5+5SjNk/Zr2VVVwlVVLy2TcmVf/C8+kY9vp+10OqPPiDQcQGZzc1Of+cxnRtOrKgZN/Fkq36Zb vnW33+/r4ODgudt4+/2+vv516bXXXhuNPF71WarqrTnr+1A1Sj2up6qK1/Lne1I1bPmzNO3ztKrH wqvGjofxcSbLMv3qr/6qfuu3fkuf+9zn9Id/+IcnvVeHvwvv37+vra2t597zcg9Xmx4f92xa/H95 f+LHq45RfHbOny8kHTsVTsNQNZHyo0IhTxRaXkq8XBKUZVKaSnJBSSqljp6vAAAAACLlk8K6MKvp 7bdXvepr0q3Gq8JG0p7U/y8OaLvd7uj2W1tu0rrLoUK5qqscGlQFFVWVhFWfTWARpl1MqHqsrpK2 7tbxqu3Zz0Ov15t/53Fm9juu3W7rvffekzSsMu10Orp3757++I//eGw+6/MaX8CSxgfRKl90avq7 sRyuxtPK27D9nKf/a4xesPXyIijJJZdKrjUMZIMLUiHJW6WrJCWSk+SkQOUrAAAAcH3UhQbGKlrj E8Ty400qHMsnmbPu27KpOxGtC6GX+bmUxc+hrnLKwgXrZfjKK6+o2+2q2+2O3ZJt73287rKqW7fL wZN9XfU5KlfKVj2fRZm0LqrMrpa6VgKTLixI0wftiudrGmjxmVoeWZap1Wrp8PBwdJGq1+vpK1/5 ythx6P79+7pz544kaTAYPHd8sP/j41hs0rGs6uLUtGXPis9gtXzQUppLIRRSSJW4lpzrK1FQkJck +VQKh05JJqVuTWFtIKVUvgIAAAALtQph3KSAYFK4ZeFrVbWifR0HsOUwrs602yyX2Srso6k6aS9X ocaPlZftdDp6+eWXtbW1pU6no7t37z63jaqq6arqv6r2B3EVWbwfVeHrWSu7pmkSvOLqKF9Iafoe TzqWNjWplywuT3zcuXHjhvI81w9/+EN95Stf0cOHD0cXKjc3N7W9vT1artVqaTAYjK1H0tiFTRu0 q+pYOUk8yNek/cb58N4ryRL54JT7QjoOSuSVeimEYRuCEDSsdlWQd15Kg5JQEL4CAAAAi1J38rxs QWJdzzhJo/51deFrefCZadupCjWqKkarqnKX4XW77oGI3XprfV83Nja0sbFR2W4gDubLo4DX9cmc 1LYhDl6resbW7e95q6t4xmqpam9Rrr6e5X2e1rJD0tjPhT0maSyMw/Lo9/ujHuiS9NWvflV/8Rd/ MXZhcXt7Wzdu3BgtM6kdSt2Fy6rvq6ZXVVBThX9xci85BUlBwXv5I69W4hRCUCKnkAYpOBXOycnJ Oae0SOULEb4CAAAA85g1fFmGIHGSco+6quDUgrB4kK2qqsUmlVzT+nzG8836ujUNhlGtKhiysDV+ 36reu/J736THZbzNeN3lNhbl7c6K277RVJNj16TpVe03ylWOMT5zyyfLhoNQ9no9HR4eqt1u6913 3x0NHilp1AO76thSVdFaDuHL26t7LF5Hk88Pgez5KILkiqAQpDQMw9bUBzl3EsB6ybsgSQreyzmn oigkEb4CAAAAjTXp/7eIwHHRqqpMm1TZlL+3tgN222RV0FoecKRc6VVVXTapQrZu/yc9v7pp8fJ1 Qcki2kYse9AuVd/qXA5U46rXbrerO3fujFW8HhwcjOZrtVqjf7Z8eX22jbh9Rbm6taridZbbcps+ 36afrTrL/v5iuqbHwEntCOqO+U3Dr6qqyCxjwK3LFlfwx7/bdnd3tb+/P5q+ubmp27dvP7d8OWgv H2fjuwOqLmjZ9/Hj037Pxb9Hz7sty3Xli6BcTsEFuURaS1MFH+SdpMJJ8vInFa8KQYmk3J28f5e5 4wAAAMAqWPQtxk16Cy4q3IlDrjpVwVbVcnZCZwFrXQsCu4U2Xn9dyDDpZHPSc7L5605Mm75n8W2c s7zPVa/pst+KPu05VgUGFr7euXNnbICt+ETf1nnjxo2xoKLf7496H9pjcSgQf+7i23jt+16vN9Y/ ON7upOdg+z7Nsr9fuFhV7QamtR6YFoiVQ9j4gkJsls8tLo4d53u9nrrdrnZ2dkbvVbfb1fb29tTq fnus0+mMXUwqX2CMj8/x47P+jiqvB4sTgpMfDPu4Zj6Rd4l8PlAqKQzzVvk0KFGQkpNpyuVF+AoA AABM1fSk56zzTKvAnOQ8TrKqAoKqSpu4OqgqTDVNQufy/Gd9XuWAtuoEd971rpqmoXb8v1W9bmxs qNPpjFW+2uvXbrdH4eqk1zbPcw0Gg9owPg5/4yB2WuA6ySq+T7gYdRdsyoHrrMHotM9c3c/ItKAX F8feB/vd1ul0JI1f/NnY2NCdO3dG89sFIjvGVR1Py6FreZvTqrGn3QFQV0WLxQihkJOTlAyDVi95 DUNW55yyEBS8pNQpdamcnELuFRQIXwEAAICyuiCxqlpz1pPlclVVfDJVdcIfK1ca1t3WWF5fXQg6 7QSuSjkMswG6yq9NucJr0q2Tk7Zbd0I6LRhuso2qyqOq9TcNgVfpZLfq9Sq/Np1OR5ubm3r11Vf1 2muvjQaVsWrWdrut27dvjwLYunXlea7Dw8NRUF/+XNjy8XxV/5qqer/qwuBpP3O4usqfwfJnYdrn pcm6TVUQh+Vlx6Qsy/To0SP1er3RwFv2b2trq7JS1ZY35R7peZ6P7gwwdX9TlP8+sGUnofL1HDnJ nQStCk7eewXn5Isg56TCS0qlVImUOCVJKnmvIE/4CgAAANSZpfKyXL1XN0+5V6pU3+stFgev5dv6 y/PZtprs86Tq1Lp9qNrv8nOKQ7aqcCNe36RqoKr9rArFJ6mqQqp6vpOCvqt4MlsXPtlnrNPp6OWX X9b29rZ+7dd+TXmea29vT71ebxSU2mBck0Zrt9A1Dl9jVRcU4mVmDV7neQ1mWf9V+gxcZ3WVrmaW Y2PVcnUXdrD87NgTH9fsuGfu3r1b+ftkMBg89/um1Wrp8PBwNK0ckE66UFr+/RjfaWLL2L+qC8Tx eprg8zpBkIoQ5BKn4BLlg2FLgdaa01qQ0iSRvBScU8idjguvtSxVEqh8BQAAAM6srpo1fjyer6pS tImqyteLrtyrCyjLFTnl+cono+cRepVfn0nrqlq2yXZW/cS06f7HAeydO3e0sbExCiBsMKBZP8uT PhN1Fd2zVL42CeEX4SoG8RiqqkCcVq1fDlvLX2P1xH3N645BdRdBW63W6O4A+z4WB6VNwtbyY7aO eHqv13uuwjaeb9a/M1DNOacQgkII8l4KIcglQSGc9Hv1Qc4NGxM455RKci5IcoSvAAAAQGxSuFkV 7JUfn1SJadV8VYFV+cS9KtyMHyvfjlj3XMrrbloR+/y+Vw+wFQdp5Yqcuv0vTyt/beubdGJatb/l 96LueU2qNJr3xHNVArm6z01caSqNtwOIK1vjQWOmVbNO2w9b1irKyuvJsmzsVt/9/frXueozMWme ed+nZX9/MZvyMX3axZvy+x+PWB8f06dV8letC8vDjn3l32/S+IXGqvfZgtc4dLVptl67gFVeR/mz EX8Wy8fG8vR5glbMJiSSvOS9V1F4SUGpy6QiyCfDGZJEcj6TknTYnsAfSYHwFQAAABhTdfJdDi+r TpbKoW38WDytfHtg1TaqqgOrHp8UwJaDgPJjVeJ5y//3+3qu11xdeFoVSFdV7ZaXq9u3cmha9X5M C98mvaf2fZOT1qp56vZ52U6C64L6OAi176XT8LMufJVO20sMBoPRoFvl9cZVYFXbtPXF/V5t+6ef v9OQq+nr2uRCA66vJp+Pus9a/PmfZ71YDVmWaX9/X9L0UDMOaC14teNVq9Ua+/1V7vlad6G07gKk TbcLVwT6FyQJUiE5SSE4hSAlSTEceMsPB+SSUgUVUpAkp+L4JHi/xN0GAAAAllJVcFdVBTtp+ap1 NanuLKt6PA5f45C0KmysCyurtlEX4JbX2SSoLIfU5edS/rppRW7Tyteq7+u2Vw6y60wKxJtUvC2z 8nsah9rx5yD+2gIEC03jwGGS+PWL+7pW7UN5fVWfq0WHXav6HmLxph0r6y6OEcCuvvjY1Ol0tLe3 VxuOzlJVH4e05el1F/cmfabix+JQd9K6MJ80k4KX5INcCAqSvHfKXVDIndLESQpK8mEfAucSOe8U HOErAAAAUKmuinXa9HKVZtXJThzSTQpX4/XF26xal8076XubVq5ArdqPqorSunBzUhVYuTp3UuXo tOrVKnXL1s1XFbKWn3/d11XrnFaptGyahMum7jNQHlzLwoSDg4PR/OU+h/Hyk6qO68Leqp+HswRc 0yqhcX3UXXyapOriVlXF9izbx/Kw41z8fdXvt/jx8vtovV/Lx0I7flqbniYX/srbmlY1O61iu26f MVmaOuXpsJ+rXBgOrhWCfHHa21VOcr44WSIocV7BE74CAAAAz6mrqmt6slJ38l1XwTLt5H9SxWVZ uS9rVfDabrfHnoudyNVVzJSDhngb8YlkXZAbb3uSqte3bpm6itx4W3XVQlUnqbOchFYFyVflJDau oCqH9FXzdjqd0Tx7e3ujQCHuFRvPH1+UiD+P/X5fN27ceK4KuS58rQthq352m/7M4nqpO642vRMg nm7/Tzq+Y7W0223dvXtXvV5vFHjae1nVgsfuAuj1eqNe1Tdu3NBgMHjugmvdhdRJ4uXLn7lykNsE n8vZuXT4L8gPQ9YwbD8ghWjArSDvEw1CLhcSJamT84HwFQAAAGhi2km3NB4W2ffxiXz5pKkqwJvl xD2ev7ztun21QKwuVC4PmlW1zjg0s5PMXq836o1XNzBIVbVY+fu6E8hJFbN1r0k5lCuf+J7l5HPS +7ZqQV7d+9tqtZ77nJbfN+sJa5+Bvb290fSNjQ1J0u3bt5/r2VquoI0/U/FnsWqQt6qQK5426f0o /8xUIZS4HqaFrWbSz3YcwsXrq/sMrdqx4bqyi0oHBwfPBafSeMsUMxgMRu9vPKDW7du3leenva+r Kv3LvzPr/m4of67sd3nTvrRVqIBtLoQg//+zd3cxkmTZfdj/50bUMsvOBrKFaroaqiF76B54FpqB ZokdeNc0bRAQBftJXsAwzAcCfjClB5mAARsC/OLxvNiAP54kgbDgF5sPywcDBAEBJMSFFyYpcSQS 9BI7BIfikNPkFN2l7aQ6hUqzcqci7vFD5Im6eeveyMisrMrMqv8PKFR+xFdmREV3/vPEuYqmjYAA EIF6S2EFXpoQ1nnfTO89DkRZ+UpERERElBIHTOt+YF6lkjNXpbnq+ro+SMUVh6tWjIbLsRDXBmAK q2a7LhFPBX3h73U/BKYqJpdNn1vfOvvhPlzOuazSr6uiOQxgbR4LZnPzpKpgDw8P2y8AcsdMbrv7 Hrvx7X3cV7R5fc8by/4mbroO2r7wSg4g/8Wq3Q/D07jv6rJ/G+Kgtev53Dlr2Tlv2Ze41JMXCBRf khLQAqgqSKVNGwInEKdwBVB7gSsUIkB9WaIoCoavRERERESh2exia+te5zNQVV3NFxTb3IoXLz5b afpw2/bJTbf5an7pMbXebGU30LV/Tk+Bjz4CvvnN+JnHHUv8Dze0ZXlnZy/b2/t4bG1Xn+MxZXvH 6F0Lj6k+x9d4/Or2Nube6joOd+NYe/Gi+cmpqtS58e71Of54nuyy2jnRFYDUgrqqIR5w88c9FKIA VKDaDLalXpo2BU4hWjN8JSIiIiIKDQaHyXYAQL71QHzpf/icPZ6qVLEKmVSlS64fYa4isE+/uFTV a7ic8Lm46ufTT/8Qz569mZzHXkNYLZSqmszdj9/D6XTa+Z7nWizEy7Ntym1D+L4tu0x42fubakPQ 70PvuoHYZoSX9YdVrMfHxzg5OcHXv/51vP/++3jvvfcANJfT2v4JW1TMZjN89tln7eWvwNWlu4eH h7i4uGjnCY/5sBXEbDZrlx1Oe3l5ibOzM3z00W/i+PjpwjRdUvs6V2m97O/9YVeHbfcYvQu5c0l4 Oz7njMevcHT05Nqy4uM7tZzcek34d/SwbP9YK8sSz58/x2g0wnA4xHg8BgCcnp5iMpm0+3Q4HOKd d97B+++/305j7Hw3nU4xGo0AND2xR6MRHj9+jNevX7etCcLznd038b/D4e2zs5c4Pn7aPh7+uxm/ nod9/tqcohBcQqGqUO8h6qCiaGJYexzQGrA+sO7AoYYwfCUiIiIi6pIKZUyfSwjDaVLh3zqXPndd qr8stAxvxyGABW9h+No1wnOq710uqOjz+m7rctw+l2h2bV/XPkrt532y7DLo3BcHqcest2G43HCk 71S7i1TLgD7tA/puf86y/cZLwx+mrhA2JexbDKx+OTjtHtufw+FwIXyNz09VVWEymSz8mxl+GRmf C8MBCR8/frzwBVbq39mu/zfE0/BYuhuqCl83Uas4QKsmcBVpKl4FzQBczs+rYUVQSQXn2HaAiIiI iGipdT7grFqV13fZffpgpqpCw3ni0Ckc2d5uhwMkmVTQbB8044rc8ENqbntT4dcqFaap5cfVxmEl bsoqoWnfaff1g3D83lklqlWg9gmiLIC9uFhs33FwcNAOOBOuL9w3Ya9EOwZzgW3XFwnLXh9RSte5 tetLs9SXTyYOZXPnZtoddn6ZTqcYDofXHjcnJyd4++23cXZ2trCf44G1bH+/++67ba/0sII2nGbZ F0Fd/wat+jitrq4VdQ2UhYMoUHvftBkAIAKoAlCF9zJvoKGQWuBKZfhKRERERJQTV7Ga+EN6qsoz /tCdCyhzH87DiqpVAj+7jDwe6KgrRE0FXPHrCO/ngofUexE+nnpP+gTbfapSb7KMrgrIVEVb6rjY 5w+4VVW1obvdt/YC0+kUr1+/xmQy6ay0tn0ZthcIWxrEwWvqfUu1r0hdimvLzAWwy8KtZftt2RcX dD/l9m2qErsrcI3nSX1htO65iu6O7Qdrn3J6eto+V5Yl3nnnHTx69OjaPAcHB+15azAYLFT/v379 un0uHpSr69+aXEV1WIVLd0NEmkpXCNT7JnB1iqZlhkI8oE6BGvAC1KoQYc9XIiIiIqJr+gQyqaBn WYCXW1Y8bRyE9V12GKKmQtA+lZthYJD7MNhVRdpX32rXPuvOVdWGz69bLdRVmXvfPvDa+xiGoOGP ham54zA8XsKq1TigTYXwqWXlWl4sq/rua53gfJVKadofXcFrPN2yS8DDefseK6t8eUC3z/abnQvD L5Ls9+Hh4bV5Li8vF85d4Zeg5+fnC8vuIw7xc1/q8px0N4pCUB4otFbUlUAFUK/Qej6w1rxlsXhA BRAFDlQgl+z5SkRERES0kly1a9j3LTfgSjhf1+WnZVliOBxeC6viapfUfLYdYYAbzhtvX7xNXeJe dilxz9jU+k38+lLbkqq+jd+L+D1ZFqotC3TjasxwPfHzuVC+a/27KK62nk6n7eWx9mOXzHZVMcdh rVWC5YTVX3Hoe3FxgcPDw6Vh/ypfbph1gtdw+fu0b6mf1PkyPi8uq3IFcO38mlt27nkeW9s3HA7b 89dwOMTjx48BXO2b0WiUveLFBtKyilkAuLy8bJc3GAzawbFSV5uE/37aT9fxwcrXu+NKRXkAfFF5 1JcOBZpWAxa6zjsQNLfRPC4F4D3bDhARERERLVh2SWg4Xcgq/XIVo+F8to6wKib8IBcvp091bSpA jAOB8PF4vtQHuDhUsA+PXUFCuJywcrFPcBzLfaiMtzm1zD5VaOsGcKn37D5dlh7ui/Pz82To3hVG 24+FqBZG2LRxONX1BUW8vtTfwrIwNPV3FC+DHp6+labh8ZM6lnLnqVW+FGDl6+6J95/9+wcAx8fH 2S8s7d92++LIvoCy1ivhfOGXe/EXWeHywu2Jj5Ww0pZuV+EcKlGIVygqeO/a4BU6b0fgbRAuwBUO X1x6AGD4SkRERESU0ieQyX247vthvOvDeS4MjefPBaFxKJX6YBduby7IigOHZRWgfd63rsCrb/Cw 6nu3yrL7uu/BXVjNmjsm7H5u/vC3BbJ9K7VSFbO3EUzd9/1IaX2+1Fr2ZVNq+mXn7mWVjLRdZVli NBq1Fa8vX75cCE6tGrarIj8MXoGm8vXg4ODawIOpfzdXOR/ljrd4e3iMbYbXIFh1ggIFfAWoalAB K800AOCbFgQAw1ciIiIiomtW+eBjv22k9mVVK2EvuLDqJfyJLzkMp4m3L1Wpm6rojAcu6goRchVY Xa0QwnXHFTqpbY/D3nidufWE67Nq4/h9WCUYXKYrNEkt7z5UwIb7azqdthWsZVkmQ4dwP4fz27z2 9xFWaXVV+VnlWK5yOt7OPlWvyx5bFQON/Zarqu4KRXPHUq5ye5VzDI+l3ZO6WsR+p9rrxP+O22PG 2qikql1N6gvTUPxvUPjFWNe/PTzGNsNCVi9XA28BDqq+DWWb6ZrBt1QVWjfTMXwlIiIiIlpBqqI0 9eGsa34AbU/M1KWsudAx92Et/N3FPqiF6wkHUQrbIOSWGX/gjJfXpwonns/eh2WhRXyppS0nfix8 PPc6+sgFusvm2Vep43A6nWIymeDVq1eoqgpHR0e9PuSn9lXXOnPHTVVdDWKT0rX8VfYFw4mHK/V3 nju24+dSXxD0OY66KmNpu+xcEP77bK1Tlp0n4mr9+Hx4cXHRPp5qFWDnQZs+/KIqnMaE/WVzrQe6 Al5ajTgA8+DVqmCBJpT1XiGi80C26f9a1wqZ94HlXzgRERERUaDPh+K4+mTZh+7cJYXx9KkPT3El Vrys3AezOJSNqwltuul0urCcOIANt8luWyVk6vXF257aRtuWuHLYpg3X02d/hAFyvD1dVchd2557 fp1Adh+kAv+qqjCZTPD5559jNBrh5OQEJycn16qzY7Y/RqNRNkzIha5WGVtVFS4uLtrbFjKkQvdc 8B7/nXZVLq/qPu37hy73JVOfx1LHXd+rCvpU1tLds3+PBoMBhsNhO/hWKowN54mDVnvcfoe34+rX +LwWnjeXfdHYtd7c66P1FEUB5xSAh+o8dLUQ1gPiBIDAo6mQdWhCWigH3CIiIiIi6rQswFnnsue+ z4fBZVclVqqaNLWc8MNcrsow9QEv98GwSyp4jV9LuE3hTzgqdBxILwvT7uKD5X388JoLXwG01a/W diIVqBvraWj7MV526guA1DbY7biVR1dlYu519Q0l6OFY9qXOsgAtfi6+1DxXSRsHaqltoe2yfTSZ TPDmm28mz1eTyaTzy9bcv8Phl51d4XufL3LjZXd9IRZPR+txDihc09tVFfDeNy0GVCAybzOgCo8m hG06vzalrwxfiYiIiIiW6PrAHf6+ybLDD2zxB3l7Pp52WSWWVeqEYWaqgjC1vnB5Nm2q8rWrQiyu cA2fj6t1rJdoXPnaJQ54uz68rrqPHuIHVKtKDd/P6XSKs7Ozdpquyl973AaWCZd1fn6+UPEVBxpx wHV5eYnZbIbpdLowXxwu5Kqrw8oxoi5dXybY8ya8MmA2a85b8TThlwa0f8IvAYGrXufh1Rmnp6fX 5ln2xWxY9R8HpfG/813hbO54TJ2XGfRvlnOAiKJAE7ZCg36v6gAAXj28n/cacIKqGXqL4SsRERER 0bq6LgMMp+n7wT68HweeqcCp7/Z1DQQWTht/8EtdEtmnsiYMV5cFqDZPVxVQ12uLl2PP5QJCfhBd 1LcSz6aZTCYYj8fX9m08z8XFBcqybHsgxoFD3594+ascF7cVvPK4uR/s2I+/dAC6v1hL7f/UsZpa Biux90MYvgKLrXHsSyl7LD7nnJ+f4+DgIBmWdlVPd/2bu4xNZ+0R4l7EqZC367XzuMywFgPSVL+K CCoPOJ2XwqIZbKvp/wqoFzRPeYavRERERESrumkVZVe1aNd8y6btWkbuQ1zXB604BFvnA9kqryHe zmUhR07qw+Y6y3kocoFQ6j0K+7DmQoRwgKw4UOgKWcP5u7YVSH95kHqcqEvq3LzsGEqFtKnQP3VO jp9jELt77Esm61ltLQiOjo4AXL/EP7xaI9Z1LjPh/KlzatcXjeEXpX3C/tQ6YjwO837gS4rvz4Ba gboCnFYQBRQOtQou6xp13YSwrgScKEpfwLkDhq9ERERERH1t8tL1+INSL+UrJAAAIABJREFU7gNS fHl/1yXW4XS5D3HLwqvUPLnqW3ssDiPsfmok8NR7EC/7Jh8Ob/JB8yEHd2EAGg9gFk5jla92P9fy IR7ILVy+ta1Iha+px+P5uwKr1PG4yutf93nab11fQHVVwXbJhas3+TKLbl9ZlhgOh3j8+DFGoxE+ ++yz9rFwn7169Qpf/vKXMZvNrlWbXl5eLlS/2nl1Mpm091PnuNTj8fEXPj8cDtvzaerfXZs+dZtW 55xDUSiKQuEKhehVT1ePphK2GZCruW2UA24REREREa0nFzzF9/uEe32qQHPTdn2Yiqtew0qdriAh t/19qwvjSydt3lwAG1ZHrvKa4uXGgXBXoJJ7/KEHsMuOC+vD2je8j2/Hx2Lcy9WqZuMQNt7fXX9/ ps8xvkr7Drr/4uOib4uX3N9DfH6Kz1+rfllEt89aDpyfn7eh5mAwwBtvvNEGrdPpFJ9++imePn3a flll+/fw8BAXFxftwIPxuc9up9q2rBrMh/+eL/tSksfWzYkIDooCB8UlvnCAV4UHoFB4P29JgCak tbYEtfgmlN3uphMRERER7Y9lQeqmPtx0BUO56pi+cpfBdm1Hn2nD51PVPPHteB3rvKZ1PqjSasL9 ErcTCJ83FgSEP33WkVrmsmMi99yy4yJ3fNLDscqXLH2Okfh4Tx3/Nzlv092J2wBYZamFrNb7dTKZ 4NWrV9e+2DSpL43stq2n7xe18bG06VYCD/lLx1WICFwBuAKAoA1bdd4Lth1oa05V4aBw4IBbRERE REQrSQWjuQrPm4SCXdV98TasUikar6Or72AqLOi65LsraOhTVbrsA2Wugij1QXXZexdPm3v+IcmF pakwtGtfHhwcLFyiG++3+PLZuPrVwo7pdHqt5cWqX3rcdH8+5OPhPltlv96kOjp1HrTfqTDN/gb7 DFRIt2c4HOLw8BCj0aitfH3+/DlOTk7afTeZTPDxxx/j6dOn7Xy2f636NdyPYXuC8FwYHyO5AeBS /06lgtxwuaF1rgahRYUInHNwrhlwSzUxkRNYmauqQh3gtWL4SkRERES0aatWb6YqYsLfqQ/uq2xH 1/RWzdPVJ66P8NLHrqrFXHgbfjBcFgbHz4Xz5T50rvOaHqpcn9Y+YXoY6F9cXCQD81B4nFhYEYav XdWCq4SuqWC/b7UXjx8C8n2rlx2H4RcM8XPh7T6Xj9Pti78cqqqmh/VP/uRPYjwe45vf/CYmkwlm sxnOzs7w8uVLPHnypJ3H9mHc8zV83qTObfZvcrg9ZVm258PcNod9X7vOl6lWP9RP005A5rcB5676 ukJ0IXSFCOAUhQA1WPlKRERERLSWdT60bKLC0j4IbkOuGstey7LtyoVoN7Wt9+M+CfdN/H6Ox2OM x2N8/PHH+JVf+ZVtbF5r3YrAVcPbdaaj+23ZMZSrzk7N1xX88xLw7QoD87C/9dHREb785S/j5OQE 4/G4PU+Ox2M8efIk2yog3v9x1Ws4ffxlbJ9WBamewsteX/ibVuO9h6pCROBF59WvAhFBE8Vqc98p nCsAqSHKnq9ERERERERE9IB09SZOVdh3VfOHj/UNTru+iGIotl22DweDQdsmAADOzs7w3nvv4f33 31+4YuSTTz7JXrVycHBwLUwNf2wdqfthBWzu6pfw2F325SZD/Zvz3sN7j0qBGk3PAdF5L1jn4Nw8 hG3v28BbyspXIiIiIiIiIno4lrWlSFnWbzjuu5laT2p+trXYPXYJfxi+/uqv/iq+/e1v49NPP124 OmA6neK73/0u3n333ezyLEjNHReDwaB3GJ9rvxNKVdXSBjiHup6hFIeyBL7vPLwDVD0gTQg7L4RF BYWox8EBADiGr0RERERERET0MHUNMpcbhC5lWVuWPutmALt9tg8eP3680K/3l3/5l/Hxxx+3/a9N WZb49NNPMRwOMRqN2vnsORMPpBUeL6kvA3JtLnLHY27gydTyaT1Ny4Hmtsi88lUEogoVBwcBpBmY q5pXxqoAgGf4SkREREREREQPU1eFYO5y8pQ+gwmm1p1aNsOy7bHL/oGmqtWqXyeTSbu/rFI1rEId j8coyxKPHj1qlxUfB+GgWPaYTddVOd2nX/pt9VSnK02v12awraa9ACCwgbY8BA6lEwA1ChuLaz5A F8NXIiIiIiIiInowuioLU6qqWujBmRvkaJ3QjHZP2IN1Op1iOBxeC19t31t17IsXLzAejzGbzXB0 dITDw0MAwOXlZXsMhO0FwgG2cm0HwuMr1Z4i3Aa6GzJvLwAoIE0YC3UQJ3AQ1A5wKkAb0CpU2fOV iIiIiIiIiB6YVftg2mXi8SBH8TLXDcIYoO0GayswGo1wdnbWPj6ZTBb2fRyaWgj7ySef4O2338Zg MMBwOEwO5pY7Tqw61qYNpaYPp80tLzcvrce5At7CVzRtBQQe4gSYV8Mq/DykbSYQsPKViIiIiIiI iB6QZX1YV30+vny8z3pWmYbulgWwg8GgbTvQ1W81nKaqKozHYxwdHbUhLrDYwiIM8rsC/L5VrX0G 3aJNcAA8VJtqVqAJWFUBr0ABhYrASQE4bQbg8h4Khq9ERERERERERACWh6Hh88uqYVl5uH/CffXq 1au23cBsNsNoNEoObAVc9YqdTqcYj8cYj8cYjUaYTqcAgKOjo3b5cbuAOGSN+8nG09l9W3aMwevt 8L750WYUrbb/KwD4QiEKiAMgHq5wEAF8MwwXw1ciIiIiIiIiethyg23Fz8fha1z1yoGz9ltVVZhO p5jNZhiPx+3jfQdbs97ANr8NxGW/h8MhRqNRe+ykQtfZbNYGq9baYDabZVsSpF4DbZ6qwtdYqHoV BSAOTgGR5nGHAhCgEAd/IBBl+EpERERERERED1w8EFLXNPFjYTWjVUAuWxbtrqqqMBwOATS9Xq39 QPh8uL8tFAWugtjhcNgGptaKwAbuskG54vAeQBu8hm0M4orXGAP/uyFS40sl8BeXAg+Fc4LLEnBw UGDejqBGpXXTErYAxBcAGL4SERERERER0QOVGhAp9/zitOlqyFQlI+0PC1I///zzNhyN+7Da/fB3 ahoL4uPK1fF4jBcvXrTPW9Bq84ZyrQXMbQz+RmlNywEFIBBpftoesOIhcHAOTa/XeTsCuGYahq9E RERERERERJHcpeZxyNY1Le2f6XSK6XTahqZxO4mcMAiNe/+Gy7AQdlnP1lwv4eY+2sdT7S4YvG6e 9x7apqoLzwBoQlcUDoU41FIB8PNWBMLwlYiIiIiIiIho1QArDF5TwRsDsP1k4WvYTgC4XvUaCitN U/2Dw/YEVhU7nU47K1TjfsLh8uL1xIN40eaJSNP31WtQAYumxQCanq+uEDgAfj6tiIcIw1ciIiIi IiIieqBygVmupcBVIHZ9GXQ/zGaztjdrWLXaFZLGoXsc0ob9XcP7qXYBfdpZxOtJ3afNasJXPw9f rx5rRt5qbjsncBAIaqgC4hSAIlUvS0RERERERET0YKQGN+rq/xrPGw6QRPvN+rPaIFvD4XBh34bH wWAwuHbMxIGoiecLB2eL1x8GurnjcJXqW9qEOEJ1UJX5bX/VA3Ze7drmssIBt4iIiIiIiIiIkrrC 1FS/zXAeBrH7aTabtf1Yy7Js2wNYuGmBa6ofa5/K0zjIzVW1xo/npk2tk8fe5tV1DQBwrgRQwfug 12sw+FYTwgLOXYW1rHwlIiIiIiIiIsLqFYOsdr2frOerha9WBWvCytS4H+uyYyhsZbDK8ZY7zvpW aNPNNGGra6ta1XoPAG3gqqptANv8NLcZvhIRERERERERYf2KwWUtC2i/WBuJ4XAIADg+Pm7bD9hz OalK2DC8DStWU0HpTfu28ti7PSIKDwXgoKIQ0abNgAJSAIVzaO54QDy8AChY+UpERERERERERLSg qiqMx2MAwNnZGcqyXOjxGgpD1LAnayqojZcTV9Xa9GFf2FWrZGnzBK6tbLWqV/t9VenaVLs2dD5I l7LnKxERERERERFRrCvs6iouTPUCpf0TDog1nU4xHA7bqtQ4bJ3NZu1tE/YETvVwXcaWFQe+PK62 xUHVQ731dBVceoVzAszH3bL+ryJNP1h1Au89w1ciIiIiIiIiolgq5EpVOObc9PJx2q7pdNpWps5m M4xGozZMtX6wxgJS4Cp0tUA2HKTLhAFuOK2JB/cK19G0PuDxtQ1W9eq9QsRh3up14Xlx0g625ec9 Ytl2gIiIiIiIiIioQ6qCMdXjlZeI3y/W8xUATk9PASz2ao37u9rvsJ1AqvI1nNemjY+beECvdapn aXPC1gIAoOrnLQb8PJD1Cy0JrOUARFj5SkRERERERETUJbyEvLmfrnztO/I8g7P9ELcYmM1mGA6H bVVs3IYgdT9eTi5Ajauqw7A1rLSN1xViNeztsdBVXNNqoFLFPFttQlevEN+0I5B5HwL18+m3ueFE RERERERERPsgrnKNH0/JVTPSfghDVgCYTCY4OTnBaDRqq2JzA2st69Ha9Xz4XNi+YFnvVx5bt0tE ICgAdVDF/EeuTWcVsBbYMnwlIiIiIiIiIlpDXMW4LJhlW4L9UlUVptMpyrLEYDDAYDDAxx9/jJ/4 iZ/AV77ylYUA1qYP93GfvsFxReyy44MtLrZDVeGcg3NNH1fMQ9e2IrZtSaBtG4KiKNjzlYiIiIiI iIhoXWEAtkoQxgrF/RFWn5rPP/8cz58/b/u6pnoBA+kB2nKBbNfzucdz66LNK7yDqxWuruCqCqVX FB4ovG8muFRUlwqpHUotUOoBfF1B64rhKxERERERERHRqlJtCHKtCcLnAVYt7pvxeIzBYIDRaISy LPHRRx/hF3/xF/GNb3wDx8fHyX0Z7+P4mBgMBiu1KoiXn5uXbo+qwqlVuVobAsA1Y2whbkBQgG0H iIiIiIiIiIhurKvlQPgTj1zPwGx/nJ2dYTKZ4OjoCGVZYjqd4pvf/CbeffddPHv2LLlvU5Wpywbe smniQbd4zGyX9XG1HwCQed9XAHDz9gPWB9amKSDgVyxERERERERERHcgd4k4A7XdFw6q9fz5cwDA ixcv8O1vf7utih2PxwDSFax2O25hsOrAWU0oe4MXQmuz/q7AVTWrU4EPBtcKw9nmNnu+EhERERER ERFtVN9esGw7sF9sAK6zszOUZYnnz59jNpu1j/UZgA3ov9/DY2dZT1i6XVcDal1VvDo0AavoPF5V B/EC723QreY3w1ciIiIiIiIiog3JXX4eSg3ORPthNpvh9PQU3/nOd3B2doYf//EfX+jfGgar8WBd 9lxYRdsHj5PdY+0G1Au8923Fq/cevp5XvXqBKth2gIiIiIiIiIhoU/qGZAzT9peFqqenp5hMJnjn nXdwcnKC09PTdnCuuMdvKNXPtYtNT7tBBHAO+EIVAml6vHoArgljVTzEOaj3EGHlKxERERERERHR xrCVwMNgAex0OsUnn3yyMBiXVbva71zlc591xHh8bUfYy/XqMaCGQiWervnt1EGVA24RERERERER EW0MKxQfltlshslkgslkguPjY3z1q1/FZDLBJ598kqxwjdtSpAZfC/u8hscTK2C356rnq6JWwNcO zingFV4VhXOAOqjUEDgUENRaQ9h2gIiIiIiIiIjo7qVCN9pPVuF6enqK2WyGo6MjvP322/j0008x nU4XqmFDYZga3rYANn6Ox8j2qCogHiIOzgmcc9DKQxQQNw9mRQBIMNhWE9qy7QARERERERER0R1K XTrOy8n3X1VVmEwmePHiRRugdvV/7eoLy6B1t3jvAVxVwIoIxOvCY/H0qk0Qy/CViIiIiIiIiGgL 4kvQaf9ZH9iPPvoIADAajTAcDq+FrrnbZVm2P6lpaEtkMXy1HrACwCkAv9gTVr0AXgHPnq9ERERE RERERGtbpw9n31Huab+dnp4CuApgZ7NZNmgPWw3k2hHQ9jjnoPNKV1VFXXsUzkEUADyawtir9gMC oEYT2LLylYiIiIiIiIhoTQzGaJnpdIrBYNBWs1orgpxcZSxtkTqol6aiFYCItj+AA7zA103Fq1XF GoavRERERERERER3LLy8PL7MnO6Xqqrawbhs8K0wgI2rXfviMXO3mj6u2gyi5eaRqmvCWA9dmNYC WFVl2wEiIiIiIiIiorvGisbtWzXwvGkLgNlsBgBtC4Jweessl8fQ3QnDVADzvq8eIgUgAHTebkAE EEBRQ7yDc2D4SkRERERERERED08uvLQWAValan1aNxV2TqfTdhAuq3rt2h7qdhd9cWvn8YU4fOEc KghqVKgcADTrlQIAHFQ9UHuIlKhdBXEFw1ciIiIiIiIiIiIAC71Zb9N0Om0DXoauN3MX75/3HoBr KltxVf3aanu8+vldhc7nY/hKREREREREREQPUthz1/qwVlXVtgi4zXCUoev+UFUUEFQi8wG0HJxU zUBc86BVBdC296tCvaAC2w4QEREREREREdEDNRwOMRwOUZYlxuPxQug6HA4xGAwwm80wnU7b5+jh cc7Bz3u6OudQlA51XQOQtg+sae42j1UVB9wiIiIiIiIiIqIHaDAYYDgcYjQatb1drc/r8fExjo6O 2srXFy9ebLTvK+0XazHgvUeNGq4o5oNuBdMoADh49YACtRf4mpWvRERERERERET0wAwGA7z99tsY DodtZetgMGh/WzD71ltvYTabYTKZoKoqTKfTbW86bUmlHlprE7iqQkShkGvTOQhUm8pYVczbFBAR ERERERERET0Aw+EQz58/x7NnzzAYDACg7fNaliVGoxGGwyHef/99PHv2DEdHRxgOh1veatomBw8p gEIEqjVU64XnFwbfgkBVAC+ACitfiYiIiIiIiIjo4Tg6OsJoNMLBwUHbbsBaDlgbgqOjI1RV1VbB suXAwyZBv1e4qz6vi7/l+jxgz1ciIiIiIiIiInpArJL1/Pwc4/EY0+m0bScwGo0wGo3w1ltvtT1f J5MJzs7OOODWA/ZFLfCq8E4gKOC1hvdNC4ImeHUQ9fC1g6DAgXOYwaOqGb4SEREREREREdEDYi0G JpMJxuPxQuVr+Pvs7Azj8Rjf/va3MZlMtr3ZtEVNWwEFoHAF4L5UwleXgDp47+d9YJvKV1VFpb6Z Dxxwi4iIiIiIiIiIHhALX63adTabtVWtp6enbaXrdDpNDrJl4Sw9HM45qFbw3sN7D1EH54C6Ung/ bz0ggIpA53dUFSIMX4mIiIiIiIiI6AGZTCaYTqdtABu2E7AgNn48xOD14VFcDbClqkDtodq0HfAe EAGcExTOwddoKmFrhRPAbW+ziYiIiIiIiIiI7lbY49XaDMTY35VCqgpXCMqyRFmWKIqieUKa6lYb jEtQLMwnIqx8JSIiIiIiIiKih2MymWAwGKAsSwwGg4VK1lWrWtmC4GHQ8LYq1CtEBCL2WyB+/pwq tFYURRPIsvKViIiIiIiIiIgelNls1oamVvm6ToiamidVSUv7TaTp4VrX9XyALYU4G4gLbS9YVYVr q2DnFbHb3XQiIiIiIiIiIqK7N5vN2upXC0yHw2F7e90QlZWw909b3SqCoihwcHDQPtdWu6oCqnDO 4aAsUVUK7z3bDhARERERERER0cM0mUwW7lsvWIAhKl0RrYBKUOD78N6hqgBUAq08Cgig1hfWQ7XG F/X38aUvHeLi4vusfCUiIiIiIiIiIiLKcc41rQQEADxcR6JqrQhU531f72QLiYiIiIiIiIiIiPaQ k2Ieqrr5z1UzAVUbjstB1aYRVFUF5xzbDhARERERERERERHleK0BCfu71tEUDkATwloYq6gBLVj5 SkREREREfci2N4AePB6DtEt4PNI28fi7a+rd/KcA4ODkAGhjVdsfTVWstR0oCoGqsvKViIiIiMhc XTZGdPt4vNGu4zFKd4XHGu065wCReTUrrnq6QpuKV5ECV5WvAlVBURTwtbLylYiIiIiIiIiIiCin KA7gnAO0hK+ButZ5yKpoK2D1KmZVVXgPOGH4SkRERERERERERJQlohCReajqUdc6f1yaHxTtb+Mr hTB8JSIiIiIiIiIiIsrzHlAFvnRQwB2UcOohInCugHMOiksoanitmj6vZYlaKxwUBXu+EhERERER EREREeWo1vBeUdeA9x5ACe9rqAq8V6gHVP3CPM41Na8MX4mIiIiI5mx0WqJN4SAytM94TqTbwPMi 7SNxzWBbTbzatBNoWg0oBG4+EFejOcY9HAqoF7YdICIiIiKiPvhhmYjoCs+JRA+Ncw5OSogUELHe rk20Gn5ZZX1gAaCGsvKViIiIiIiIiIiIKMehanq8Sg1AoVpDtekDCzRBq4gCbZ2rtAN0sfKViIiI iIiIiIiIKEcdANdWvHrv560GPCC++W2Tat2Erq6EKtsOEBEREREREREREWUVRTGvbPUQB7hisb3A 1e15V9h5b2OvbDtA1OnXf+07+PVf+862N4OIiIiIiIiIiLakLEs4J4C1HygKOKfw2vSCBWp4r7AW BIBCIFAVhq9EXT744IO15vvwww83vCVERERERERERLQNl8UXqKWGL4DSKSqt4ARQFPD+EtADiBRQ 9fDqUSng/QxFCYavRMv8q9d/0pSLq0LVQ6GAeqgqqqpGXVeoqhpVVaGqa9RVve1NJiIiIiIiIiKi W9C0F3AoCoGvm0G1fN1kQd57eFVABAdOcKlgz1ciIiIiIiIiIiKiHNEmdC2dg6BoAljf9HW1fq+q Cq/a9nt1zkHAylciIiIiIiIiIiKiPBU4CFSkCVUFqHyFpgK2QOEOUNc1UFWovYdoUwWrqqx8JSIi IiIiIiIiIlqmiWABEQdxDgprQ4C24hWqEACqAoFj5StRX+9/7a/j4uJi4Sf0R//8n21py4iIiIiI iIiI6Pa4ZjAt7wE4OOdQ/sCXoF5RVxWqy0vUdT2vdgUARY0mmGXlK1FPv/XRP8o+9yd//Dt3uCVE RERERERERHRnnMBDUaPp6wrovP1A0+vVql5FBFCBKlArIGw7QLSa737n/7722J/96e9uYUuIiIiI iIiIiOguWGuBpvJ1/lgw4FbhDlAUBWQeyAJAXSm89wxfiVb1h3/wT9vbZ3/2e1vcEiIiIiIiIiIi um16qSgA/GvuByBlAXWC79ceAsDJQTORF8ALBB7wwKEDoOz5SrSW0z/5DlQVVVVve1OIiIiIiIiI iOi2aVPRKiJwzsEVHqgUQDDglioABxFF7QGvYOUrERERERERERERUY6qQOTqx/q9WouBZnithtP5 jflzDF+JiIiIiIiIiIiIMrRpMAAvTZQqXtvwVeWqJ6wFr07RBrQMX4mIiIiIiIiIiIh6UJV5i4F5 ECsC0XgigfcOqgxfiYiIiIiIiIiIiLJEm56utfrmN7QJYH0TxGrQD9Z6wxoOuEXU4cMPP9z2JhAR ERERERER0VY5iLfyVg+gmFe/No+ICNBWwDaDbqkqRPT+h68Mz4iIiIiIiIiIiGiTVBWCpsq1gKAW BxGPZogtxWXt4XDPw9cPPvhg25tAREREREREREREe6yuLyEiKKSEqqIoSlSoUInCAagKhXhFJR6Q CqrAlwqHyy/8/Q5f//x7f7DtTSAiIiIiIiIiIqI91vR1VajWQK3w3kFEISJQ9fC+6QULzAfgEgAK iLDnKxEREREREREREdESDqrNAFveezhXQsRDVeA9mlawKgAcBE3wWrimCQERERERERERERERZTQV rTr/LfAAvAAiCmdVsGIjcCkKAZxj5SsRERERERERERFRlkgBkQIeRVPtKgW8Vzh18DhA4Qp4p1B1 EN/0fC1EAQjDVyIiIiIiIiIiIqKcAoJaFYoalVd4r1AFVB2cKNQJpHBQD6Dt+eqhULYdICIiIiIi IiLqqyxZx0b00HjvoL6A1E1PV8ChQAlBUxHr5o9BXVslq6occIuIiIiIiIiIaBVVVW17E4jojmnd DKwl86pW5xzUCVTmA3DNpxORpgpWAXElHCpWvhIRERER0WbcRjXYrleY7fr2ERER0c3V3we++Isa F9+vUNcK8YLSlRAR1KihWgAARB2cL6Fe4MUD8Kx8JSIiIiKimwkDyLIsH1xV2EN8zUQPDf/OiR42 EUFRONQKVJceWtVArbi89KiqCr5W1LXC14D3Hqg9IDWkYNsBIiIiIqK19Kl4fAgf1MuyvPXqz30I PeL3YNe3l4hWw79poofN1w61FlDfVL1WFeCg7fOqClUFRCFOIQC8L6G+ZvhKRERERBSyoC8O01YN AHf5g3ouLO0KEKsKGAwG2Xm6AlhbTjiNPdbnfUrtj9Q2bssubAMRERHdnqqqUSvavq9F4aA1IACc q+FFIOIBzPvCooZzDpWy8pWIiIiI6JpU8Gq/c0GbTbOPQVwqQI0Dz/hlxfNYBWzq9ecC7fy6+r+H 266K3cf9TUR3Y5//XSCiReoP0ASrRVPdKg5V/QW8F3jvoergoYB6qCqcAuoKKMNXIiIiIqLNWhY0 9pULJJdd3t613lyVqgWnYZBZVdW1ZacqX8P5B4NBdv64PUFX2BpXxcbbdddS72lqHzNgIbpfUudK Ow9OJsDJycm1c1uu0j88J+Z+23xlWWI8Ht/CKyKidYm4eSsBRV1foqoEdV3De9e0G5iz26o1Litl z1ciIiIioi4WsIW/l00LdFeBxvPk5Kpv7XYujO2aPwwJwucHg8FChVb8Ey8jFfgOBoM2fJ3NZkur hOMgNxXahr9tebnK2tSylk0XT7ssME+976nH+hwvRLQbjo6O2vOXnZu6znXmo4+A58+fZ8+rOfG5 zs6Z0+m03Y7hcIhvfetbG3yVRHRTtRbwvkJ1Kagrj6qSq9DVCdQDgAfQBK8CoFagUGX4SkRERETU ZZUgzabNXcYf67PcZeFqV3gYh4GpwbHi7Y1fb6paNX4+1YKga3tSz8frTr0vffdFn3A63qa+QXhq efEybnsAMiLanK997WsA8l/OhD9hMAsAw+GwnbYsSxwcHCTPKfF+0RtpAAAgAElEQVR88XIvLi4W ztMHBwe39nqJaD1VVTUVr5cOWtcAStR1DfWAwkNV5oNuoWlD4JuBt7x6hq9ERERERKFlwVlc4Wq/ w8AwFWjavKnKzq7t6FNRlVtfuI7cpbFVVWE6na4V5tpjYeVsWAEbBxO5S2xTVbBhRZjdXhZmp7Y1 dznwqlWpufc3niZcx3Q6XWkdRHT33n33XYzHY7x69eraOaerDQuAhdYAqfNuqoWKnSOtwvXw8BBP nz7FcDhcOI8S0W4RV+FAC4hzuLz08DUAOACAr+eBqwpqqVC7Eio1vvAC54v7Hb7+vZ/7hW1vAhER ERF1+OCDD/Dn3/uDbW/GAvvQG1/yHt82cagZVpjGl67OZrN2uq7LWVPBa+rDeFgpFQagtq4wQIir ruJlpKpjw/tnZ02VVypktm2Lq7vi12fbNJvNFt6LeN7U5bhd72FX5Wnuub7VqvG+jKfN7UeGr0S7 76d+6qfw6aef4uLiYuE8E0udB4Cr81HuS6vU+cbOf2V5va8rW5UQ7a66agbSUg+oAFAHoAYg8x9A RCAQzHsQQETg6/p+h68A8Evf+2vb3gQiIiKie+tv/OC38MEHH6w174cffrjhrdmMMIi0+/Fz4f11 lm23U8tLfcDPha+5+eLL87sqbeNeh2GIG4a6H3/c9EaMA90wNLX7h4eHqKoKl5eX14Le+CcV9Npr tYAifg2pEDx+fzela5/EwTwR7ZfBYNCGrvGPic894RdAZ2dnK/3tx/+u2PlzOBxiMBgsfEFFRLul 0grqr1oKeGAewM5DV5lPOG8Dq6pwzqHGAwhfiYiIiOj2/avXf9IMOqAKVQ+FAtpUCFRVjbquUFV1 8+G1rlFX9bY3OSsXugL9RqgOpYLCVIsAW04cgtoycgFgzOYLK7isrUD82sJ1HR8fYzAY4OnTpxiN Rm27gKqqMBqNMBgM8A//IfCVr3wFk8kEs9kMFxcXODw8bEODcP3WdmA6nbbbEla72nNhuPrs2TMA wKNHj9rLb+29ef36ddsaoSsUye23eJ90BaapyubwcuDcMlO3iWi3/eZv/mayGj/1d5w674/H42RV fdcXXqkqejsXn5yc4OjoCG+99RY++eSTTbxEItoQVUVde6gX1KoQdfNer24+8NY8fZ3f91CIbx5n +EpEREREFMkFbatUuub6i9r9MKQMn4vDV3vc7i/bhlQgab0Fy7LEaDTC0dERHj9+jMFg0N632/a4 LSMMV7/61a+2oelsNmuXa0GuDTZjFVzhpbyz2QxnZ2eYzWZtf8XJZIKqqjCZTBaqbnOVv/a7T8CZ aw2QC2dT1c5xdfJtVNYS0faE57Nl4WtOeK5OtWUJlxdX/YfrsRYrAPDbv/3b678oIrolFSACRQ0H wKOG6tWz6mV+3wGqgDr4efUrw1ciIiIioozUYCmpQM6E1Zq5XqBxIJqaJgxfbX2Hh4fJytpwntev X7eVqXb5qgWuJycnGA6HePPNN/H8+XO88cYbGA6HbdVr3HIgFSB8/etfvxYehOFsPG/cd3Y8HmM2 m+HVq1cYj8c4PT3FZDLBZ599dq3aN7zEN3wtYQAbt1gItyvcllRFWiwVtNtjFjSnWh/kls2Qlmj3 nZ2dtbdT57xQ7m86PBeG56dURW1XVSzPGUQ7rnAovMCJQy0KEQGkqYjVNoV1we0Cqh5Fcc8H3Ar9 zt//sexzP/q3//Fay1tnvpvaxnrD926T697We0hERES0TKpaNfwdP263c5ezp6otc20E4scHg0Fb UZpbfxwUhiGChbxW7Xp8fIzj4+M2jLWq11zbhPCx0Wh0rbIrDorjiltbji3LgszDw0MAzSBe4TaH r60rnAiD11j8WlJBbfj+5ZYfPpZbV2peItoPo9Fo4UsiIH/+tvPk1ZdLwDvvvLNwZUFZlnj06BEu Li7aZU2nU5ydnWE6nWI8Hl87T8a62qIQ0fa4Qpter7U242xBIFJAIQDc/Efb/q8CaYPZBxO+ApsN DrcVGt7lei10DdeZemxdDF6JiIjut5Mffg8XFxc4+7Pf2/amrKQrFM1dvh4/FldEhj1R42lSl9rb Jfyp7QnXaxVWs9kMk8kE4/EYZ2dnC+0EHj9+jJOTExwfH2M4HOLp06ft7bIsr/VeDVl7hMZhcjTw eGCY3GX9QBO02s/R0RGOj48xm83w9ttv45NPPmkDivg9ubi4uFY9Fv5etl/C15ObJhX0Lgt24yro eJvikcyJaLcs+xsdjUYYjUbtFQRHR0d44403MBgM8Hf/LvDuu+8mQ1I7fx8cHODw8BBPnz5tn7Me 1pPJpG29Eg+0xeCVaAeJAGjCVO8Vlx74kpRt2NrUu0owucDN7z+o8HVdD6nKNRav/0f/9j/urCLO 2YXXQkRERHfnrX/r325vH//lv4I/+9Pf3eLWrC4XduaqlVIhY7iceCAtC+ysCjRsK5Bab6rC1pZn PQKt5YANTHV8fIy33noLR0dHeP78OZ48edJWaIUDanWNqm3hq60/Fb7mpk+Fxhbk2vpPTk4Wgoey LHF6errwHtlrTIW8uTYCXRWsue1e9viy6uX4kmWGJ0S77+23314YGHA0GuH4+Li9IuDw8HDhvG3n qslkAqD/vwmhN954oz0fTiYTvH79Gi9evMDZ2RmOjo7acwgH3CLaLeJqSCFwAwFcCbksUM1qVF5Q wMEVJRQeuHTw+gVEHWopIGDP1wWpy+vtsbjiMw4T7X7XMsLHuta5ynrj6XPP5bYtpysstWXF25Tb hm28lttqlUBERETLvfvev3/tsb/8Q38Vf/LHv7OFrVldrt+fPZe6H4apcTVrfNtCVgsT47YCqaAV SPcjtHVaFRXQBJuj0QhPnjzB0dERRqPRtWA3V+kaB83x7bilgT2XCiPjZYTTLlbUNp48eXJtPeHl vmF7gjiI7bpkeNUgNvd+5F5HvB4Gr0T7oyxLPH/+HMPhsD1P2jnUztPheSc+z9jvy8vLtto1Xn4o PlcNBgM8fvwYwPUv7Ri+Eu2WohBo7SAKiAJOMa96FahXABoUvjYtCJoqWf+wwtdUxeayIDAOGpct f9kl+qnQMvXcuuvtCjRz69qErm2469fCKlsiIqLtef9rfz373A//yI/ij/75P7vDrbm53KX+wFUA Zz9WgToYDHB8fIzHjx8vhK/2nA1QdXZ2lryEPQ41bX0WOFo4ELYbePXqFT799NO2l+vTp0/xzjvv 4M0332ynD7cjXFb4emwb49DBxIPHhO9JOIBY+BrigDJ+3+x1PH/+HM+fP8fp6Sl+67d+C6enp22V blVVC9W6qYC1a7CrXDAatxZIVenG4tAlnp/BK9H++MY3vpHs+RpWttpv+7F+rkATugJIBq/x/OFj 4fl2NBrh2bNnGA6HbQXuZDLBt771rQ2/WiK6CRGBOIeiVqgoIIKicPAA4JtWBE3ganNcDb71oMLX 2w7kcuHtKtOvqm/QeZevfZWwNbQrr4WIiIjW91sf/SOoeigUUA9VRVXVqOsKVVXvTTCVqmwE8pWT g8GgrSYNq1wHgwGOjo4W5rdwczQaAbheTZoLAy2EDMPYs7MzvHr1CpPJpF2mtRo4OjpaGAQmfG1x tWoYiKamiyttU68nVeEVv4YwiIh7G9oyjo6O8Oabb7Zhazh9GP6GYYgtu08Lgr7HYPgepfq/5o6R 1HqJaDcdHR21vVfDc1uuwrUsmwG14r9vC2Hj+7lQNr4yAkB7FcNsNmO/aKIdJCKoxUOdawpbxQNw TRksPCA6H2ALADycKyBSAHhg4WuXrsvmb9M6/VOp27b2JREREd0Py0I1m8akqmPDD+o2KFZqHRao xsu0ebtaEMxmM5yfn7eVUuEgW3bJbG6bY7lpU+FrajvDAKHrteRC3HDZ1ndxMBjg/Py87YsLLFbY WlDR1VtxWQC6yry5KtnUfF3LJaLdkeonHQq/nMqdq1OP2TkgbEcQfzEXfjnWdf4kot0g8HCuhA8H 2BLfhK2qUK2b6ZxAfNOOoBAHD2X4Glp2ufum5doU7IKu6tV9uKz/rvclERER3R9hMLis36oFq/a4 fZiOLytNXbpqlapVVbUBatyGIPwQbgGnhQXT6RSnp6eYTCZthe3Tp0/x7Nmztrds/LpyFa7ha7XX boPQhNthga959OjRwnKs9UK8zan3Luzraq/L1nN8fNy2Zjg7O2u3aTgc4vHjxzg8PMTr168X3k/b vq4wu684fI51Ba+p20S0m+x8GrdYAXDt/BJWt9pjds6Lpw0HWTw8PGy/UApbwIR9rMNBv2x+Itot RVFA4eBqQeUUItbg1WMexc7pvAq2bipf9YH1fL2PcoNfbUrfgHgT23Dbr4WIiIioj1RP0FRYGVe3 xmGp9WM9OztLriccQTv8bcu2H2tnYGHveDzGq1ev2g/rtqzj4+O2lUFqW3MVumFgGq53Op3i9evX GI/H8+36Mn7/93+/DXsHg8FCaFxVFc7PzxcGFQv7zYbbFoeXFibHIfRsNsPl5WU7oNh0Ol3oAxsG uPHAOHGAnlp3KmyxadZpT5BbLhHtJjvnpMLXPuKrAcJKVjsvXFxcLISqYSV/ajAvVs4T7SYbXEvm la9ow1cHwLdhrAKAClQFIgr1D6zyNRcchpep2/3c85sMA+P12jbGg24tW+9tbp9tU/zYqttwl6+l z/YSERER5XRdNp77cH5wcNBeWnp5eYnZbIaLi4u2ojNXPZnqi2rCQWDCgNFCSqvYsuD28ePH1y7/ j7c5vITWnovDZlvv69ev2z6INv14PMZkMmkDUOCqQsvC5nCkcHtPbJ02baq61wJTe99C9rotgA6D 69xryr3nqWC1K/SIl516PpyXwSvRfvjZn/3ZZCsVk3o8HnQwN7+da+IrEMIvheJzvFXWxv1jiWg3 uHlxawFBKQIVwaUCIgpxDl5l3vP1qverB6BaP5zwdVkIt+rzfQaB6vPYTdfbZzmrDgS27rS78loY uBIREdFNhB+mwyrMXAVlfFl9qrI01xM1XpdVoYZ9CG1kbQtxLRS1INL6vJ6cnLSDbMXrCe/H25EK X8N544owu3TW1mPbbKGEtV4Itz0Mj1M9ZMPg1UJWe31lWWI8HmM8HuP8/BxPnjxZaPcQ7o+wQjiu 6I33Z1dImgtnbT2p9gIMXYn2U1xFHz4eVq3aY+E0VtG/rFo1DFTtvBjOZ1/g2XQ8nxDtHi8KJ5dQ lFAoKpQonaKuPGpvExXQSqHq4JyDiMA5DrhFRERERDfwS9/7a/ilNb/4/Bs/uOGN2ZCwEims2MyF fbFUtVPqcvb40vjwsnkbaCqc19h01jP25OQET548wbNnzzAajfD48eM2TLi4uFioPg2XYY+FA8IA TUhgr3U0GrVBL9D0YgWuAtiwIhVogoh4GyxMmEwmC+9bGMBOp9OFoDZsMWCtFl6+fNlW4oYVtOF+ sbDWQuC4fUMc/tr64j69tux4mtT+sMd4yTDRfgm/OAq/RAkDUDs/xVX3ds60c71d8WDzxT207fFY eF6Jg1gi2i2qgG9KWZtQtWgqW51zqOHbilfnHEQFKlfz3vv/Gfz837z3L3Hrfu93/ynfZyIiogfq p/9BtfagoR9++K0Nb83t6hO8poRhIZDuGWsf8q3KKlWBGn9wt36qjx49akPKMGi1EHXZB36T+rAf B6XxIDL2+sKQ+vDwsO1lGEpVn9r9OCQN+9kCTaWYhaepwcnsvvWajdeba/3QByvQiO6n+G/bzplx tX/qCyN73gYgDM/Z8TypSvll28UvcYh2j0jTYgBOIQI4B9S46gVbVTXUAw4lRAReq7YVAf+iiYiI iOjGvvviNbxXeFXU89/eY/5bUdfNh9V6/qG1rnc30AqrKi1QtHATWAwPLYy0SqlUkJq71D6cP1Wd mhuMJdyep0+ftqNohwNWhT8mbI8QDvoSfsgPA1h7PHzt1lIgHFArZMFrGCyn2i1cXFy04a0FxePx GNPpFKenp3j58iWm0ymePHmCo6OjherasCVDGHDYttn2hvvBprX5lgXb8WNx8N6FoQnRfnj16hWA 661VllW5h+e1R48eted9+5KobyuSXLV87kszItquoigBKLRy87G2HBSXAASqV5WvKoCqoq7rec9X hq9ERERERAvCS9ktzLOAMwxObVpgcRCV8EO4VWqGH64tSLSQMBw4KhS3JwjD13C7LHQMB+IK5w9f l81vPVvDADgODMJgwILSR48etY+FIas9FocW8f3w0ty4Ssz6un722WdtEGutE8KBaVLBa/w+xhWx 8X7rqkTrah8QP9YVtDKEJdp9XecVe95+25UG4bnv5cuX7XPD4RBPnjxBWZY4Pz9PXsEQrje3Lbnn iWi7inLeesApgKa4QAF43wSvIgJxDgKB6rwiFh6AMHwlIiIiIgrFl3zGl5PaY3HbAAta7TEA14LN MES0XqpxCGpBqoWutu64UjW+nD6sfs0FoeFjVpGbes2hVDAbvgdWuXpwcJBcRlgBbMFzahCvs7Mz vH79eqGNwWw2Wwhiw/fPwuvwNVjgEVb2hq8/7Nlo+yP1emNxy4T48dS8DE+IdlvuKoFcq5jw/GrC 1gKpL79SX9Z13Q63i4h2i6rObzg0DQccxFXwqKEqcK6AkxK+FogARVFA/LxFwTY3/K68897XbvT8 TZe/73bp9e3Stqxr319Dn+1f5TXu+/uxS7b5Xt7Gut9572vXfrYpXv++v9+3vf3b3l/mrrdj0+dI 2o7ch95c8GhhYhwOAlgIVcNlhGFk+BMuLw4cbXnxNoUVtuH6c1VUqQqsMBBN/VhIGY7YHQav4e9w nanXY691MplgPB5jMpm0A2mFYTOApa8rtY+6qsfiEGSd6tSu7eh6n4lot8Tn61B8Ho9/4ulS/w7E y8+dV3PPE9FuEakBqQGpIE5RClAcHEBEAPFwDoATeChUazjnAPEAHsCAWwDw8Xc+wjvvfQ0ff+ej a8/lHl91+ffZLr2+XdqWdd2H17DMQ3iNdDfiY2kT5+z7YBPvwW2/jw91Pz3U133fdFU+pu53fUgO K6bCD+fhB3OrpAqrUcO+pnG1ZapFQWr74x6zcZgQL6tPMBlWpR4cHFwb4TsVUIevMwxg41D55cuX C9WxcSCRet/tJ9zW2WyG4XDYThMOYpbaV/GAODm5IJchCdF+y7V8sedSYWtq2i7xeaprfrYsIdpd bfUrAIhHeXAA5xwU1vPVo649tPYQEagXAOz5Shu2yWDkPoQs9+E10P2xqeORx/Xd2of3ex+2cVv4 3uyvVT4oh9PHl/UDuBYodlWepqRaA8QVWGHv09S2x60PbDCvVOAa304FnmEf1TjUTVV32W2rcD0/ P79WzWptGFLBbCoMsWmNvfa43YC95rgyLff+xPN27ZN4v/SZl4h2Q+6c2/UlzSaqUlPnmniZv/Eb v3GjdRDR5okInBPUAGp4eCfwsy8AAEUp0EoBX8MJ4Aug0i/gtYJz8jDaDqyi6/JWeyx+Pnf5YNdl sl3L77s9fbatz7bEz/e5tDa1Xcven9Q25F7fbbzX61y6vMpy+z5/m69h3de4yv0+61r3Na6y7ev8 rfbZjlWW03fbl21rn3mXrXuZu3pP+uzHVY/RPtvU5zhc9di+7e0Lt+Gm55Guc8Sy82xqW1d5bhfO 1zc9v29iW+PHu6bps93r7FO6G30/fIdBZ+5yfiDf629ZlWVXj8CuytW4mrSr1cCy15/6CcPN3M/5 +fm1CthUT9dll+Iu246u5cTvx02lAnUi2g+pc0zXuWLZ1Qepc2J4u2uermmIaLsUHioeItr+ONeE sk0fWAGApserCASueQ4PpO0AkG49sOr93GMp4XR95+laxk2mX7Yt8fPrrmfV9g5d73fXsrqW3Xf9 fZa96nLjae76Nax7nK1j0+/nqsu7yd/qqn+bq/z9rPpaVnmdN/3b7fv3uWybVvk777PNNz1m78Py buM8YvdtGblpuuZftuxdOV9v4ny0qW2NbfLvJdzWbZ37H5rUgFu5Cky7HVev5iqa4vWkKlvj9aTm sQGpwnWG/WXjVgXh8uOq19TtuJo3nC9VVWrzhLfDqt/pdIpXr15dq2ztG0zH22DCbbF2A7Ew2Eg9 l7rdpU9lbN92BkS0HXHPVROeX+IBtrp0VbSmzum5dgc8bxDtKKcQ74ACcLWDiMOla8JWkQIQBaQZ bKuZ3AG4hKqy8jXU58PLOh+A7cPXbVvng2FqW+9q227j8ufce73qutZZ7rr7+bZeQ1/h+mxb4vs3 3ZZlx9mqy1v3b3WdkGLZvrmt/XJXf7ubPF+tEnitsn3hT1foFG/7qsf2pkOsuzyPxMvpemxTy17V bZ3rNn0+yk2z6fPyuucxBq13q2tglVyFaJ+q2Fw4OhgMFn7iaVOjY19cXOD8/Bzn5+d4/fo1Xr9+ nawiDefJVajmHo/7z4ZBZjhYmA2clXrs5cuX+Pzzz3F2dtb+xNPnBhdLvZ+pYCO3L3L7bFm16rLq tnhaItov8TknZueL8JwWzxOfJ+Pb8XkyV/Ef9wInot3jnIMUTaWrVb6q1vDz27AqWACAQpw+vMpX YLECpasSZVeEH/Zu8mFr069pU9sF7Nb7ve82uV/2wbrHzm0cc13L7Novy/bZffz7WPU13fWxfB/f 8772+Xjc5W2j/dVVpdT1Qd2mi0PPXGWVVZeGVaZW0ZrqKxpWrsbrtp/Ly0tcXl7i8PCw92uzeZeF iKnL/uP1h6857O/a53LaPiFmKhS18Dq3vauE4qti8Eq0n7rO0TddpkldgZCajoh2n1eFKuYDa8n8 twLwTdCqDnXTnAAKBdA8LyIPK3xdJnd53zZt4vLX2wgvNrFdu/h+77tNXi69y25y7Gz6femzLV37 pc9z98muv6Zd377bljsed/l8vcvbRvsr1VrALucPw8NUCJgKMXMVlHEgOBgMMBwOOwdvsm2wS2Hj 8NQqqezxMIDNVZB2tTXIBc/hJfxxNWy4nefn5+0gW/Y7fo/C+/H6LLANtydsrRBubzjQVm77chXL ffQZVKurXQQR7ZbwfGXiFi2xODxd1sM1XH7ui6/4MSLaTToPX7338N5DfQGgqXb1osGEAu/rhXkf XNuBVXrT3XQdpm//ubvYlmXP79uH1nXe600udxPrvq3XEFq2X+O/i03/nezKcbbOJd23vW9y7upv 9y6Ov9y6Nr28XIDd99jexvbZ48umuQ/u8lhbdTv6TLMP+2Xf/g3fdamer10DZ9l0sfDDdyosTU0b 309VmMaXssaXr9rPxcUFLi4uFipiLy4u2t9xq4Fw+fGlsAAW1pV6X2wbp9MpXr9+vdBaIG4pEIcT Xe9R6rlwX1kgmwqJU/srFd7G6+iyzv4kot2Uq+LP/Q0v+0ItPm+Fz6emDc+76345RES3zwUBq4hA BU2fVwCCoun7CqCGR62KWpvqV+CBtR1YJvXBexOBWp8P+bl1h8+vK7Wurm1d9uFtlWWtsl227Dgk Wbas27rcvs9yV9nPt/0aNnW83JZN7qeb/K0u+3vomr5vYBduS9e6lm3Hpv92c69l1X1zk3256vt/ m9uybHmbWOZdnkduqu+xFk6/C+frdc4Hff52NrWtd7X/aLNSPVm7PhB3ha59po+rQONgMjddvKyq qhYuve8KBG1Zl5eXyW3MtVjIvQ9h2BC2GghD1673JA4zu9oTxMFrWDW7LAxPvcbcslPP9ZknPH4Y ohDtrq4K15Rl1bCrzs/zA9F+EZGmgrUQSNX0gAXqeQ/YAj6ogBVxUDioVs3z2jQo2Ip/78e+hl/7 xx/hX37vtH1M8f9tbPl/7+d+Af/xf/QfbGx5D8W+VPhs2z69T/u0rbuE7xvdNh5ju4H74WZ++h9U +J2//2P47ovX8F7hVVHPf3uP+W9FXTfhWz0P4eq6wu/8X/8nPvjgA/z59/5go9sk+Nfb2//iz17g b/0X/zUAXPt/51/6wZPr84rg6OgIQPoy81w7ga5KqFCq92m4DGs7YBWm4TQ2EFcYNKYqQMNpchWh uQrSsixxcHCQDKB//uf/D/zMz/zNa683fo3j8RivXr3CdDrFeDxuX8dsNltoGZCreo0rauNp4jYF g8EAo9EIg8Fg4TLicD0WAk8mk+Q2h/trWZi+LEAJ37vZbIYtftwiurGrwWPun9FoBCA/oGHuizjz 4sVnePbszWvzxdP1EU9/enqamfJ+4HmRdl34/0X7v+RX3vsO6lqhXwB/cVGjninqqgC+KFFdCi6/ ALwH6rqAVjVq9Zj570NrVr4+ePzAef9xHxMRXeE58Xb91R/5S/h//ujP2/tfe/sJ/snvf6+9/+98 +Qfx69/9f7exaStJhWmpKsYwBAzvx1I9VQeDQfaS+tz8Yf/T1PNx9WxYBZsLE8LXFW57WBF7cHDQ 3o57JKaqg63Pa6oKNTV/6lLcMHiNq3VTy5xOp0srk+09iUPdrnlsmr7VcTcJX4jobvX5G819ybTu 8pZNw/MG0Y5TB6hCpIBzHpfeA97DewVQtl8sqACqaNoSOIavD17uEldabl/ep33Zzl3E945uG4+x u8d/927X7/7xv8T/8At/ir/zn7wBAPjok1f4r/7XP8L/9DM/AgD4J7//Pfyd/+2P8d//Zz+0zc1c qu9l7an5clWQKXHVbBzixmFjOIBUar5wW8PBp+Jpw+m6+pTGgandDueLq1EteLUwNNcGwFokpNos pPot5gLY+L0xufYLy6pWc/u3q29jahoi2n2584ZV6KeuLoj1DUs3Fd4S0fbYlQAWsAoKKGroPHxV 9VDReR9YhcLDOYHXBzjgFhERERHdnv/mP/0hfPWto/b+//K3/k38u3/l32jv/4//+Y9sY7PWkgoE l4lDzlRlaCpkDC+1j6s+uwKArsvk42WHv+PbXa87dHl5eW3Z9nNxcXFtYK0uy0KNVfsr5sLa3Gvp kqtGXidcJ6LdlDv3xT+56YH0Fzp9v9Qhon3jFn6LCIqimOl+yAQAACAASURBVPd+BSAegEK1hvce 3ntAHZwIK1+JiIiIaLN++w/H+G//9xf47376GQDgN37vX+C//Lk/wv/8Mz+83Q1bQdxOoM+0XX0D w0rQVGVtV8i77AN/OG9Y8RlXdYVVq+HrCytQ4+XZMlOtFcqyGbDr8PAQ5+fnmE6nODs7uxa+xhW2 cXuEcJpUL9zUe5CrgA2nC8PluIo3VdXWZdl0DFqJ9k/fXthdUl/I9Altly2HiHaVA+Cb6lfV+WBb gCsE/z977xYj2Zbmd/2+b+2IzKo6tz49fTlz6enpnovtaTyNha0G2wMPyPCCzAMPIyTLEjIaLPsN C2TJUmvmBcmSkZGwsJDggSceLMASSMgCJM9g0R7Lpu0Zg2cAe8YzfT99OefUqcyMvdf6eFhr7dgR lZEZmZV16vb/lVKZEfu29rUi/uu//l8p4BaEBWYBFMCICImvQgghhBDiblhmvv7yn/4sf+Snt5mv f/XPfp4/8lNvvxCZr52bCG6HvoD3acc4WPfXc0hgPNTWy/JT99t0WcGqq5ykXRDt7Xj33Xd31gXb LNpeJOyQqHlZTuxl81x2LI6JfFj+PhTjcEgEfhJu6tAVQjwfXPVMuuyZ2t+/7PWhEQj7fx+T2S2E eE6JKrxWqtu1RhBsCxNWMdYwDzycHHV+ia9CCCGEEOJOeFkyXx8+/OBZN2Hmefse/pWv/B/Pugkv GC9vpXjxKvFyVqbfGxxwK775zW88+UpeOfRcFC8m0Zyu7gPumTAjYsLMMRwHcpQ5E9YdLIOj2AEh hBBCCHGH/MVf+Az/4uc/zq/91neAmvn6r/z+T/K3/9HXgJr5+rw7e05P7x097yHn1HWu1j7P6enp zvD4y4b3X7XdQ9s+5Dw9lEd7Wfv2+b3f+10+/el3HltXjy04Pz/nBz/4wWPRBIfae9U83UF7lav0 qn1YFszZ37frnGuH2nfZsofaJsTLxcsplh2T6bxf1HD5+913v8Nbb33sYO7roZEOx7jlHz58eNPd EUI8Zar4mjA3jCDoYqzjDsWCyDbnvZYIjIRZkfgqhBBCCCHulhc98/UmRZUOCXfHclku6WVtuK6N 1027qnL3UqS8TshdCgKXDZe9Stjcjzk4JtbhmGI2l03bP5bHCqJXCSQSXoV4uTj2ObsfZ7LfMXRV xMtl0w/FxCg7Wojnm7QayDmYSiFHpiYKOKVAUCgt49W84BEQAyUmLFziqxBCCCGEuBtelszXY78E X5XzunzvquWOEfiuynK9Sji9SiC4LBv1smX6e1c5SC9bfl/svSrP8FC24qH9v4zLxNabFtVabu9Q W49tgxDi5eCq5+v+66ueq5dNO+bZJoR4fohcKCUoJXAzwo2IAhEQhlkCmzAcLDACy0GJ0hJihRBC CCGEeEJ65munZ752eubri8C+4HiMiHeZMHndslcJsZcNd12+f9VQ18u2f8hhe+i9y6YvX3fXbi+4 dd4CFA85YK9qy2Wu3EP7fJmwsX/c9t8/JPoec06vm+82Aq8Q4sWiP+t6YcE+CqD/vT+KYf/ZcZUg 26cf+3+NEOLZYB5znmv/XfJihnDMrE3v81TZVc5XIYQQQghxZ7wMma9LDjlR+++bulj3p1+1rutc s9e1+Srn1f70Q9m1VwkBN23fVSLp/t+H3LjXOZKvEjeOve4ODRUWQoh9Dv0fcOi5deiZeuiZL4R4 fqj5rkamZbpOpbpdw4AqvHbx1cwgYPBElvNVCCGEEELcNT3ztdMzX18WbuKYvAuOdYBe1oZDbblp 8ZenzTHbOmafb7rO65aVACuEOJZDjv7l6IBD7tZDoxOEEM8TBSizyFpjBgrd3YoVCK8/MAuxIOer EEIIIYS4I16WzNerWLpHr/qCfIwr9jrR85js0aucufuvr4o+uGqe/VzZq4TJy9ysx7R9WcTmkAP4 KjH5Jhw6L8dm9l62HiGE6Bz7jLrJ80YI8eyJCCjgGCkl0mpgnDZtqgMZMMCICCKcXDIRIeerEEII IYS4G16mzNfrOMaldNkX6euG9x/KY70Loe8q1+hVWYWH9uUQt3GN7i9znTtsn2MjH24aPXDsayGE eFLkfBXieSeaqLr9WUYNdMdrpf4dYUSYnK9CCCGEEOLueNkyX4/hUHbp/rT++7oc2c5l2a23bdd1 2zk2euAyt+pV02/C0vl6TEGtY9Z33fw3dRVfhiqWCyGEEC8/7o6nQgFyzsRU3yvUuIGITEQBUhNi M4XqhZXzVQghhBBC3CkvW+brVeLddQWqjnFyLn9fNu3QezcRBy9r613wtIbJHnLC3mb7H4UwqmI5 QgghxMvNxSY4Py9MxbG0xtPAuAnCnBic5DVuwLKxMmeNk6LGFEh8FUIIIYQQd8LPfe7t+e9f/tOf 5Uu/7xPz67/6Z6sD9mXiGLHtSQS5q2ILDsUT3Ga9NxGXbzsPHCeCHipWc9n05ToPCbP7ouh1sQuH 5ju0nARXIYQQ4tVgvR5Yn6yAwjRN5By493iBIAqYtdfFKIUWS6DMVyGEEEIIcUe8rJmvt3VO7ot+ h8TU635usp39dvefJxnGv2zHVSLkVTm2V2W6XuYGvqwy+GXC7GXL3+aY3lSAvuy9ZcyBRFkhhBDi 5SKlREqJiKCUgpEwq27XnMfHM2AXSHwVQgghhBB3xl/8hc/wL/3UD82v/8ovfp4/9rOfml//5T/z uWfRrOeW24h0V+WsHhNLcJUI/FFwFzmr1yEBVAghhBB3Sc6ZnDNmVYRNg83iaykFcMy2gmwUa2Js kvgqhBBCCCHulpct8/UQ1+WR3mSY/XWC5KH1X/X+Va7PQy7Q6wTLy+IOrlv+Jo7em7hWL3PFdtfs dcf0MpfuoXmOFbF7+1StXAghhHj5yDko2XD39hmgbIXWqL8B2q/2urpgJb4KIYQQQog74VXLfIXr xchDItwxYu2hfNOr1tt5kiiDmzhGrxqSf922lu05FB1waBuHsl4PCbJXidxXHdelAHvTbF0hxMuF 3PRCvNp0V2vPec05E7GMGHCIbQasmTdh9hXJfP3CF7907U+f75h1PUk7nlee57bdlI96X572dSOE EEK8KLysma+HOFbYvEy0u4nD9Jht7087pg3HbvvQck9aqOuq+W8STXAbUfQm897UHSyEeDlRx4sQ rzbujplRclAKLP2sy6zXiNJcr/UnInglPjn8xle/svP6C1/80mPv3XZd4vnjozxHx4qqum6EEEK8 KvzFX/gMucT8+q/84ucpi9d/+c987oX8AnuMW/Q2+3XVeg+JtYecose0c7nem8x7k2WuW9cx0/s+ XnVcL3OsXhcVcBn729gvULZc73W8iNe2EEIIIY6hEJGZcpAzrNM9xoupuV0zwOx0ncXYMgGviPNV vLg8z47RJxHxn+f9EkIIIcTNuQvhdd/BeVXO6XIdl813TLzAdcPyrxJ/nwaH9vdQW5fvX8WhOIhj owiOQXEEQgghxMtNzsGYjZQSq7RmmqZWaAuMbdxAzXk1cq5FuNyHV8P5elOWwtgxrtmr5r9q/ZfN 29d/2TzHtOuYtly3/Zvsy7HrO2Zf9+fbj4O46bE4djvHrPNJWbbpqv267v1D+3Hd8bhs2pOIx0II IcSrzGXOyCdZz12Ldk/atmMKVV0mkN52nXe17FVFvO5qu7dx1gohhBDixaeUQimFRAK2UQOBETHN rtcotq261ZD4usch4e7Y+W+y/kPLHhJ4r2vXTee5bv5j9u3Y9R27r8vXXWi8zX7eZDvHruOq9d2E Q/u1v95j972v89h1CCGEEOJmXCaQHiNQHsOh9d5WMHwWw+MPbfNQUay74FDswtNGoqsQQgjxamJ4 LbY1bR2uPecVoGSIsBo7QI0iiAAzFDuwz9MWqpbr33cxXtWGY9p1UwFyf/6b7vtV67ts2rH7eh0f 1TKHeFqC5jHrve21cey6hBBCCPE4T1L86rbbua3Id12hqpsM079NEaubLnfbZW663v2oAiGEEEKI m5BSmotuRVQn7LbQ1jJ2AGoYQZdcTc7XJ+VQRMDTQDmhW+76WNz0PO5v/64E2We9X0IIIYS4GU/T cXlMvMFlDtCrhstftewx2zjUpttkpF71/nVu2k7f37sUboUQQggh9umxAjTXq7u3Mlv7VNermWGR iKLYgTvhoxjifWi4+avI0zoWx57HY3KAn7QNd4kiCIQQQoinx13lvz7t9d+l6HiTNh3a7lVZtzcR dyWmCiGEEOKjIOdMKY5FYOZ4WnFh4zx964KtlPZeRCh24KNmKRR+1GLY/tD/JxUtr1rfZdMk/F3N oWiGp8WrLOALIYQQd8mh4e3XzXNsbultC04ds82r9umq96+KSLhNZMFt2iKEEEII8VFhlh6LGCgF MrEtthVBDx2ICNwHwOV8fVKuqjJ/GbcdBn6ZMHcbQfOuh6Fftb672NZl67irY7G//HKbT5tDx2Z/ 3560LR/1fgkhhBCisu8O7X8/TSHxLofeXybkLv8+5Fy9LnP2GMesxFYhhBBCPG8Mw8BQIG8mSimQ 847o2vNegZ2/ASz23/kI+fk/+iV+5e98he99+/fm94IP72z9//l/8d/y7/zb/+adrU8IIYQQQuzy p/7LiX/w1/4ov/7b36eUoESQ2+9SaL+DnKsTMjdHZM4T/+B/+xt8+ctf5rvf/s07bZPxYP77W1/7 bX7xz/8FgMc+d779yR99fNnFcLEn5Sa5pYemXbeuj4rarmAYVvN7x7T7ummvAs/w65YQT8xdPhNf Pmruo7g5ei6K553l58X+WfKLf/Afc7HJ2MWaMRvjh8E0JvJ5MI2FaTRyzuQpUWKqmbD5gmRyvgoh hBBCCPHcs+8aXRamWoqbT1ukPbTd6+YXQgghhHiRKaWQc8ZLARLuPcm1xgxgi99ROxl6B5bEVyGE EEIIIT4ibuN6vW76kwquNy3q9axduEIIIYQQHzU977UYRAkiIKLsTreMRQIK1eDtQEh8FUIIIYQQ 4q65KjN1n4/aHXpZZuuxAuxNhFe5XoUQQgjxspBSIiUokxER5OgCLIDVIBIzZifsAomvQgghhBBC 3DF3Wfzqtstf5aq9qljWdctdJsBKaBVCCCHEy4yZYSSqm7UKrDW/2AgygTUnbJ1uAWE1mkDiqxBC CCGEEC8IT+qoPSbm4LbxB0IIIYQQLyvTlJmmQpSBMFrma35svl5QzswICvYqFtz6whe/xG989Ss3 mr9z2XLHrO+m27wpT3P9y/1f8jT35xie9jEVQgghxPH8oT/3d2613J/85B03RFzLTfNdhRBCCCFE /QxVisMcN1BF1ohC7LwXjy37ynzyWoqI/e+rxLvL5jlmuct42iLhi77+2/A8tkkIIYR4FfmTn/xf +OM//8VbLfurv3LHjXkFuAvXqZyrQgghhBA3IyJwd8KMUjLjmMEMsFl87dSIAuuBsK+O+PobX/3K 7JY81jW5P09f9lnyKjo+X8V9FkIIIV40fu5nf9+Nl/nVX/nqU2iJEEIIIYQQd0wMdKGVSJyuEx98 8AjDcU94GBGGDYUchRKZ4glyTYEVe1wl9h0SYL/wxS8dFGavev8qMXd/ev/70PvXbesm2z6WQ8fi std9m3e9z1ctf9t2CCGEEEIIIYQQQggBNHdrzH/nnHEbMNI8rZRCKbUYl7ViW2bx6jhfYetkvWsX 5VKsPdaled0yl00/xrV72TxXvT42s3Z/GzflujYdatdNnMrH7Ncx7RBCCCGEEEIIIYQQYotDOFEg wokSdE9rRKGUTCkBUacDGAHYqyW+Pi2W4t0xYuFNp9+lOHgbsfEutn/TomQ33eZly1+2rxJahRBC CCGEEEIIIcRNMLP2A1AAo4qvBcIxM9xrwa0S1pywE+4ria/Piqc55P26fFsNtxdCCCGEEEIIIYQQ 4kgiAYmIUt2vubT818AsYRTMDCwIys6iEl8v4Sr36l0NU3+WDky5P4UQQgghhBBCCCGEOI6a65qr +Bq+zYBtLlezBE18Neo0dwcrKrh1FYcKR1013zHi7KGiXYem38apetWw+6fpfL3tup90ny9bXiKz EEIIIYQQQgghhHhyHPAqsrbXRo0bAFoxrrI3b50m5+sBloWb9t+7bN4+37GC375YeJVIelmW6U22 ddNtP+m67kKAvc0+38WxEUIIIYQQQgghhBBiSc10dcJKzX21gllqxbYmSgksHIvuc3Ww6oyV+HoN x7hYj5nvSdZ93fvHzneTbd9kvuvac9k6btrmp7XPEmmFEEIIIYQQQgghxFXkmIhYkXMmj4GTiMhs olDcMHemaSIYcV9RbCKXFUEodkAIIYQQQgghhBBCCCEOkXNuBbUMM5szX6Ms/o4ArP0Gs8BN4utH gtyVQgghhBBCCCGEEEK8mPTYAXcHN6BgTYhd/nS6AAvKfBVCCCGEEEIIIYQQQoiDuHt1t5KJACJw T5gFZr4zj5kTkSgFKBJfhRBCCCGEEEIIIYQQ4iARQSkjeYpaXMv2wwQcyHXeOYrAISS+CiGEEEII IYQQQgghxEGWea/eC2yNhQia2xUirAqzBKWUGj0Q8eqJr1/44peOymD9whe/tPP6Rc5tPXafhRBC CCFuw6/+ylf51V/56rNuhhBCCCGEEE+JmDNfzSYIJ6JFEFAFWA8goNALbiWwV0h8XYqp/e9DguRl YuWLLGC+qO0WQgghxIvBl7/85Vst90u/9Et33BIhhBBCCCHunlpAq0YJ5CjENLUiW30Oo5fYSiSg kEkE5dURX3/jq1+ZBdTbCKkvioD5IovEQgghhHhxee/7vzMPrYooBAFRh1tNUybniWnKTNPElDN5 ys+6yUIIIYQQQhyHJ3LQYgecDEwlMEuUkik5cEskcyJKdclmY4rCfjqsuAFf+OKXHosn6O9fNX1/ +Zuu49Byh5Y51Iar2re/rqv2QwghhBBCCCGEEEKIl5WIoJtcrWW/RsscsBLQJFYzq3+HUwjiVct8 7Y7Q65yh3R171bxLh+l1MQXHxBgcG3Vw1XI3cfVe175j2yiEEEIIIYQQQgghxMuMWS2mtXxtpDlq wEoQFkQxSimUqK5YsyTn6yF+46tfmcXMyxymxwi4y79vU8DrsnnuQvzcb/9l7burbQkhhBBCCCGE EEII8SITEbPTtVOLb1lzu7IVXksh51zdsmavlvP1Nhxyh97FMPzbrkMRAEIIIYR4XvnDX/oTnJ2d 7fws+f9+69eeUcuEEEIIIYS4HUvx1bZVtgBwM3aqGYRjAdM0AUh8vS1P6gq9rZj7NERgIYQQQoi7 4u995W/xhZ/745dO+51/+g/mD6FCCCGEEEK8qPTMVyvUiAEg6AW5bC5Ea2aKHbiM6wTNQ8P0D63j ectK3W//89Y+IYQQQrzY/PpX//Zj733tn//DZ9ASIYQQQggh7ob92IHLZ9oW3uo/cr5ewjEZrfvz XDX9UHbrbbNkr1rumGJhN5lPCCGEEOI2/D+/+Xf50c/8QQC++bV/TM5yvAohhBBCiBeTZdTA/Hdy bIpt7msTZ3tEQZkyllzi6yFuWxDrrqbfdtpl0w/Nf9NtSKQVQgghxE34vd/5KhHBNOXrZxZCCCGE EOI5xVOQwsmbkQjDfSAVKGQmAiJjZLCMAe4ZZ8CKMl+FEEIIIYQQQgghhBDiIJdFDhTy7HK1KrlC BB4QbOeX+PoUkENUCCGEEEIIIYQQQoiXgwijlPq7F9OKCEpLIJijCMzAoORtTIEKbgkhhBBCCCGE EEIIIcQ1mAXhhhnttxG2dMY6NYAg5vckvgohhBBCCCGEEEIIIcSVlCaolia8RjW6toJbgVOI9tOd sEWxA0IIIYQQ4sn4pV/6pWfdBCGEEEIIIZ4aZoa7EQmsGO7g7uCGu9fYgYAwwBJBBjfC4uUXX//G //A/P+smCCGEEEIIIYQQQgghXmBqcS2vWa+lLAptPY5VSyzwkouvX/7yl591E4QQQgghhBBCCCGE EC8wNVrAq55KEFGFVQCL5ow1uvWVYqlPeLnF1+9++zefdROEEEIIIYQQQgghhBAvMJsYKVPCo8YN xDBik5EGI0diM42UAPMV1lyxQcZ4ycVXIYQQQgghhBBCCCGEeBK8OOaOJ8MKBIE75DxRWgYs4eRc IEoVaMMAFdwSQgghhBBCCCGEEEKIw9hEMmqBLYLJCjasiU0hAtwTANMUzQFreBgRgT/blgshhBBC CCGEEEIIIcTzi7VCW7kApRfZKoQbnlptLcAsMIcSQUSW+CqEEEIIIYQQQgghhBBXMXihhg2MmBdS qq7WYXBSSpSYiMgMg2MJSkzzj8RXIYQQQgghhBBCCCGEOETymuNqhntiGNZA1BgChwDCwMzqT1Cd snK+CiGEEEIIIYQQQgghxFUUwjOeDLxgTLg7WKGUgidwr27YKIb7AO6YmcRXIYQQQgghhBBCCCGE OESOAlRnK0AphSEZpUyUkqvI6k7OuU6zGkfgPjA8y4YLIYQQQgghhBBCCCHE88x65UwUvARTMiwP nMeEjzBFQMmk7AwE7iNTDkqeSO4SX4UQQgghhBBCCCGEEOIQEYGVqMGuDXeHIRgiUaaCh+OpQHES AAUzk/gqhBBCCCGEEEIIIYQQh8hRyAbJoBiYG2EBZrgFYVbnKYWco4q1LaJA4qsQQgghhBBCCCGE EEJcgVPFVA8jIjMVSGaE1UJbmcwUhSgBQLSc2OdOfDUePOsmCCGEEEIIIYQQQgghRMWdGAzCyQWC AHdIjmejpMAL+FBdsh6QcyYi8GfddiGEEEIIIYQQQgghhHheMTMGMwZ3zIyaOFCdsIWa/2p9Gols EG6Em8RXIYQQQgghhBBCCCGEOEQYZIcwg+TgVYAtZapZrwRYLbAVBpnnLPP1W1/77WfdBCGEEEII IcSV2PWzCCHEK4OeiUK8ipgZZoBtnwERgZuBgbsBGYBCEPYciK8//0e/9KybIIQQQgghBFA/PAsh hKjomSiEEJVkTmDkmLCSKJaIoWB5xVAMRsgliAjSEEQxYgyixLMVX3/l73zlWW5eCPEc8cv/8X/A X/pP/hp5GtvPRCkTUYKIQmkVA0uZmMaRadwwTRv+m//qr/Pn/8O/NK/nW1/7bX7xz/8F/r3/6O/N 75UwSqkfHqP/jtpL1T9Q9ukppfk999qrVeevPVZMMAxOWIEI3J3T1Yo0GKUUcs4162WxXKe+nwk3 IObt26LHzDwgHKy03xNmCbPa5lJqtUTCd5br24nwetza/hmJHIVSChFByam+b7s99Tv72NvSmm5m uDs1yYZLl5uzbkohonYC1mWY2+zuRCn1d1vO3THqMS+lEMbc9tLm7fOXUnbPR9T/0KDl6pgRASXT 3kv1epnqeTGc0RJlNHK0dk7ONAYlg8VAPg9iWlE2sDmDDx9d8OjDiXHM5I0zvv8aH3y44dGjc6ax HR8G3Id2nCCs4CkzrAJPAIWwgARvvAYn9+DB6yveeGPN6jRjaYOtC76G0/srsInV2hkGxzxwL5jX Yz0MQ9vXjFkCm9rFVY+vEzvnprfLrB4jP4Hz8w2BY2w4uffDePww5w9f4//8h9/mv/7r/xeM9Vyb tevEgoKBBwz1uoioQfNgYCvwAbN+7xTMg/DArBBe6rIUmF6DElCmeqKszO2vF1+pF0+kej77xxTr r0fwXNsVA2H9XgnAWHmQN0GxkbQuuAGe6725MfhewT/5Jn/1P/23+MmfSvzG//0/kS++y4PXBsom s/KTet3YBrOguJExcjkhIlj7OUQQVki+IjG163/3Wu33o9HPS72m05Dn+ziXfhy3N10OoER9HhWb 7z8zA7edZ9KUN0RJ7b203W7Uv/OUmPKGPAWl9B8YhsQwGMOwYrV2kq8wz0TU4zpNE6X9DbVIQMn9 ORksXT5mQRqclOr5GXz7/IzFOvrvKW+2x8b29o+2z1Ywi3m41hKPsvN87c8V2D4fcGMNJIJizsoT vhpIKfGf/fLvRwghhBBCiNsQze3qXp2vTv2aE7Tv9W2e9jEeiyBTP6M/c+erEEI8LfqXeouoWSzW 5MOAyIUu2gG4R9VvFqJT/5LfhcsoBlHIUSCqBFGXN6IYJcOYAyuFwRJtjiocGlCCYVjj4W2EQiGi kMvUtmtNpIl5BENdf2lti60QGbmJflXgjGAWH8wS0Hra6EJzFSigC5l1XSlVMTbnTCmZtromum3/ jsjt77R3lPfbynz8zHzn/YitILsv/s5rW6gt+wL2UqSp8/ZpW+HF3atYmAswzWKwt3aaBZ4CSiFK wlKrVmmlCkzrc8JG3BKr5Jx4sInCdAbmhTWJNYmznCiteiXhDO168DKS1oalNsQkF1Kq/zlbGHlK jBewWSc25wlbDax9wMhY3pAzpDSQpyoork/q8S454z4w5Uxyb0NZYhburP2nHr4V9fu5pl1TEcE9 O2HMZ0wpcX/9Mcp0H3ibv//r3+S//+/+CTwEUoGTgVjfI9IAcQLTPbAMfLNt0rEUVcS1RGCEBVba NW8OUWoeUljva6idD4xA3slDqqIrVNmsXkRhQDThNoKg4IwUq9slBxZey4liWKnicBXwBvJmIsdU xWsz0gQlQfnuB/zN//Fv8uf+/X+Z+w9GNmlFpMTKByKdELkwMZE88AGSGVMujHliOWAoImqF03YG dmiisu29P1/fUQXuKpZur3cLyKXM4mt/BlWxefe+qffo9hmz7QRZdFCUpUiZKCVTnzVD+9BotcOn tcWTkQafNfF5nbbtVOn9SXXaroi6cwguuccvm2f5k8tu5wG2f/wed2Btxd7WeWZ1xoj+/Iu5I0cI IYQQQojbUg1RLD4fR/u8XzA3ihnRxNkSZfs59XmIHRBCiKdB/Z4dTSxowk4TErbf3ZtA0jNb6OLE 445S2rvdgViX24oH1VVWZhFw3BEug8jNRRYXuDurVJ1Y7g4FgiqKlNLEjmIL3WHrOu3iRUpp4Zjr gu1i36KKrVXs2QqYbgMlmsBMrcJY37dm9Z0uFVMiWmYcAwAAIABJREFUfBZblvvVj9H+Mo+JRNs1 7Yqq4dhC8I22//sO151juSPG7opB2/2sgrhT3YJdVV77wBgBeUOE4ebYkDDLFDMSJ0yTEQ7DsCJO V0ybRMkXTJaI0RgGZ5Vg47melmkim5HcKZ4YLDBv10JUYT9hFIJxnMDg4iJzcZJJeWB14iSvxyEy kJhdwCtLEEHOgaXmXm6dCVtBfXvFdmN0d7p2cboflw/GR3g6xazw4MHrXJyv+Oa3vsP//pVf571/ atgphDusqK7aCKw4sK4u1Fm/qqJuv36sdvdCakJ/5HZZNlW6LB3eUUVVmIXc2vgg9rV9aF3H1Q1b PEOZsNgQubYLHwhvvcznGRsMH4yYIM5yvaeGKsThYGPhm995yO9+/TuU1QYvRsmZsUwMnGwd1l4F xzAHT1ipQ4ZoomtYXlz/9ZhXwbBg2OLZUeqx6u7d+f16X287TXpHTxPWra6r9fLMwup8X5EobvUK CCcsk2r/+87hCzdK7s+N1gHSPixGZCKqmN87Y5adHN213gXMeQTBfM9BydE+iG7F6H7d1WvR5mdE H1lwmfv1smGt+yMDktePrVGiPTOZBWpvrtto7e4dbQXDwyFPl1xcQgghhBBCHMc80rL0kaX1+1j/ nJ+wKsIujAzVPJUlvgohXma2zshZNMRwr8JRlO2w6maXwr2LAIsh/vRhvYWUUnU0RsG9PmC7MDGW 0tbr1ZnH1unWXWibszqkdr2Ge6eJ1WqFW7QhusGUL5qqG5j7Il6giRoltsIa1cG61Syqi5OognOE bYWS3g4ynnqbClPOs9hSoxOWbrate3L7k/cE7H5smxjXswrYtmtHfI0+nNr6Ijsibhdb9l2ul8U4 WOtVXLplIzLuiTreHCzSzvrX7ljecEHGooq3gxmrVAhLbCxTpsw4FaJkzBPDCoaVkyeY0kNimLCT wtC11xJYBG6Ffii8uT09wLPhTRabSsFKMCbjfJVJp84wDPhgWKwordeUlHALSjbME4W8u5+zu6/M 16aZkSdaVMFiKPdi/zfvB2++lthcXFCm4JMf/yRf+1rmt/9ZOxnnp3DisKrVO7ECPmKcY7aitGHh hBFMs7hqtXdjvu6rou5gaXZ2dmc5Tczc7ogDqemKs4zc1tMdpO3cpxFKwlgRyYiUwT6EkrEWJRGT tSYG3IPVfXjzzcLJCZyP8Ol3jF/4kz9Jzt/n4qIKeqcpcUFpnS9tnwKYwCgYGS9NWuyCcmtndXiX ttzymbF0/G4jA+YbYTHf9lw1lzQJvPeW1224G8VrlEaJ0oTO3XtkylUgLpHJ4WR2RdNVWnSOWHf/ 90b2bW0d9r2DyCzIGTa5HeOdc7oVZ92X19zjYmo9vltR9XHB9Xp36m7cwLIzzObOib6/eHO9RwCX KftCCCGEEEIch0WNeSstKjC8GVWsmn7q153Aw5pNoC1nJvFVCPFy0sWoXRHPKNlrbABQumACVTgq Syfr7pf6jnmNFVhsqQmds8oyP3QjtjmtJapg+egRGJlprMusc8Y8SMlYJYdsbIf3dpVj63xdOsG2 vW5VrLlMMC0sXWrVDWoL59s0TbPIUl1p3V23K35WthEHW5YCyq4zb5v1ujh+e7ECdZ1tfwzMtvtZ l/UdEdHZbVPM66hMEbi3Y9Hcum42C9HJrQlKbVi7OZTqjLPUHJ6riTJOjLm6AodhxTAUxlSHLg/D wPpkouQWxxBe/5MlUXykhFPacJSe+WPd3RjbYzNu4OJ8ZH1iDLk6ZsmFUgxPdd9KKbgFKbXh5xiO 1xiN2HYa1IgJq9EROGG7QnU/fkMKLAqbKXj46AM+88OvE9O3mB4BDwqRH8BJwLrlZy7EsogJqzbt RqrH3/p5MMhNDPYmyHbn6NyIFl/Rr6X5fPYOhYV4GYvrvrsom6G2pOaezQXGgKmvy0inhTc/lnjn nTf54Xfe5kc+/THeeO2UMTaM00Puv535F77w0/zmP/8qw7CuPdXAMExVYLXm6PUgSqnnj6g5thG1 7S0ywRau86vYPovqIY0mFBNbh/tSeLXmaO2dMv2Hfk01gdhtqO7V5cbciFzTf6N9COzJDzVupN4j 9Z7vTthMFHZcuLvtrvs4jWO7fhdDrtjer0unbBd1l0Jrid3c6F3h+XGxttOn5ZJnB20a6nL9GVbb XiOFc+w/jdjpTBNCCCGEEOKmWPRvL3XUo1kwEqQ+crNHwS0MGD3CUOKrEOKlpDu3YCmeLofE54VL E8Cr4DE7Ly8RA8LbMN3tW6UXBLM2vectlrq9kqGU6kjLuToZCdg0IeLRow3uzr3TAb+/xmKowmiZ qtASuwKy+bRwobLYnz7swedpbYQ0u/mfzZEYTpSx/S7kJvSW6IWD4jERpgrNlznTtmLw0rm6PLb1 WBWSLQuF7TrWds/Vdt+W55LFOZrzTBfLrKzn4NacnVqIqra9lMI6guwZa9mg8wDwUjNFzUd85fip Q67HxQenrAZYFSIuOImay5nHJqTl5s6MidSF1rEWZksWUHx22U7tuBJOngoX54X1ycTq1EhueEqU UhhswJORc40pWPu6in7d8Rj1uM8Fysp2CHY9F4sCRDQnZQTrk1POLs5Y24rzs8z5xciH5yNz5EP+ gBhXwAWsJlgZaQXuZzDdZ3x00q/89gEjt7Z0p+oIVnBSdZ7iNXChWLtAClhaZMFGC2Tw+l7pfcRt WLmN9fqxqQq5+aQO94+xGtY3NfvVH8C9N4033ij8+Gce8HO//8f56c99mrc/4axPYLO54Acf/gAf 3mU6v+Cf/e73mc4ece/+KcWMs3GDMTKs2m5EjfVI4ZRYkZprckrvVTmz3XOPx2Nsr+N6nvtN2jNt B2Aiz/fK1iFfz1cTnJuwu3XVsrOdPoSpRxFUITTV7ZR6PrbO9zqPu9VCZzCLqbNw3NrrdWLbj6Wr nXmbW2F1917t4mend4Bt2S0SuOwc297THGS+3xfL999b0Xf7U93LW2E3uGLlQgghhBBCXEMm4+Gz CcGNOdrQ3dtoxe33dMeY2lcCia9CiJeSHdFwHgrL3PO0nK/+VLFlmqad5fGaC9pdaaVsHbL9C311 mLbCQyU3h9cs9ZKjkHMwTZBaYa8wZ3NR2ORcBY/IrFfOarVqulqp8kcvFGPdMbgVYWArtpXSK5HH YyINi2V8Fj8WzmCvy+XSRDLqcGT3mOetxwRy3i1cs4wK6O8v3ar99X52477IOsdcLsSsfXGmb+9x anuh4D7M2Y91UhWwutCUm+ieUqpZoQyUyYiS2xBtWvYlTWCqFexXqxV5HWxKJq9gGMZ5HrNoLkVI yYgpyO0/2HBrelgh4eRcSKUK8uPGKENwspmYJmM1GBaFPGU8jFU4U64iPENqLljqvkaAOUaiF1XL OVitHz9W1ob0uzsln/Pho4m33nqND3NwPm6IgNW9YPpBoqzXMDisnfDqap1KwGbELj6E1et1vaW6 LS1aHEIfbu9p64KlVqePsCq6lqguWEv1JwqQmV2vZltX7RwFsngZrfMhZyhVLKYUODU++c4DPvPj H+MLP/Vj/NiPvclPfvaT3Ls38Z0Pfo/vfP97RBkJzrF1Zrg38N1vfY+Pf/xtxnFivRqYvNSs2zzN xfHwIDE0EbW7uL01rczXFGw7Hryfmks6EZb3yPYa372P+v1bt9VimK0XyStYrSfXEgKi5gQv76MI SkyUnMnZmKZcnfFhmA/z9kop5DzO6+6Zr0NzWBeCnPPOtbR0ji4jMPr6InrhwqVwuyR2Ygn2n031 hc/LWXNVL9k+7woxbd22O8+WuXNCCCGEEEKIu8PMKL0WSslESXi0742+GHEWgQcUq1rCZHK+CiFe VqxGClQBs7nCgPUAOTehcjZNBlOujtLV0KrLlz5cPYBMSo6vnCmnNtS/frsfrQ43rxEBU3WqRRUu a9GXjLuxOjVWwDTWXNhSgjIV7iUwd6YcvPu9D0nurNdrHjy4z717J+ScOTs7w1ixXq/xk4lSxuoi jbqHpQ21davDzktz5pUyQfQiP1UUGvMZyVdghWmEXAKz9eweS+aMFy2KYKjFpdx9Lm4TkZsIU/eT 7s6rehw5rOadUuYiSyWXlkPqRG6Ffmw7DLg67qqQZsmrYy7asPUS1f0JDO5ktkV1iOqO3A7/rqJR DepMBE4umRJNXGcgR66OTA9KnmqjDWIwLBIWwTiNTFawdcLyyMWUKQ7D6SnDZsUYMKwmXn+wIscF 0zlkjPurU8bNiAGeUhVGc2EqI4ODDRNeYFWckoOYghHYrBLl9B4+3GO0Dc4FpQl+aX2KRzCNxpCg eC2aVAhKzhgZa67RiCCmrThlHqShDi+nX3v+GoUL3n3/IR/72Ju8d/ZdPvfZdxjPqpCKn0KOajRN DuUcXznJ32RaJ8y+T5RCu/iqO3PO1XWCoblZqyM816NRz4wFqWxqjq3Vzgq3gSlalMEUwMiwWsFq YNpkGANfA+vXsClInDO+D7wW3PsEPEjwMz8b/JE//Ak+9abzQ586J8c3+MH53+db742M40XtwLCB ZEYuZ1w8LNy/74wX363HJjsrMyIHNlCHDVULKMYGYzO7Jj0vXdcByWqHSZQ6/L3U/N1x2oq1gydy diKcMkHJqXWiFII+pL/GGpScMWMhUvae9dohMLVOibQaiAhybNp8UEoN5cgFyly5rFSXdSmMMXEv AbRCf5Fap1F16Cc33Kr72qOKr+M4knO/921+XtKeOw5MUzTh1rg4z6Sh17frzl1alnSQ546r+qE0 NUHaU91eGrr7tf3uImq/RoaydeBaFcY9th0OU8nN7boVxaepi8YquCWEEEIIIW6P52oAKSTCCyWq FpBKjceC+s0nJa9fbSYjbQorTiS+CiFeTnaGqsOcLVmFjT78fsvSEVZdantZo76sHl/n76Lf/lDg On3pcrO5B2x90qIBclDS1p1YqALFew8n1sNESok3X38DPylsLi54eHbOJhfs/EPWq8QwDGQW7tAo bDabNtwhKLm6OInuVN3uy5Sr27FmltaMy75/3RlcSqmmRLbZjVVAZRaeZg/tIkrRm3OO4m2TPRvT q0g4D1ffOmHdqTmmZkxlG02+H0UQEUzjWEU7d8wcN1/MW2MhSgRRMlNu+xHRjnA9rssc0uhtmYdl r0kJyJBTIVIv6lXthqtknLdz7wlWKTEMpYqpTaCu+7utfploMRalCm15cewocDEWzjYjqzFhMRIp 48XJU5BtIlthGKqo6asqUuHV7VpLXEW7rr1mXVoXtyGmmjcM1CJNY/Da/ft86wePeCvD+++d8fEH D/j4J+Dd79WoBV+vODlN5OSMeYRSyLkQ+RGsc7tX6vEPavEtswFjIE9TzaPtMQQB0DJJo8YL9Hun s2rFtyKCnKqoWPKErwd8fQ8oTJsJzi4oBOvXB/7AH/ghPvf5dzi5f8an3hn4sXdeJ+wh7733fcI+ oEQVDXPOVQz2Fozfz485KbXrfnkP95ziNl9xWm92O4Yl12UWOcb1nuk3QXeBb++TQo0C6GJjWbhb azzA9v5Ji4JY9Xmz62QOtn8v81W7892idi7lHHMn05xfPd9H1Q27GlY7rvR+jRYriwquvXG1U6S/ rNcb0NqXcy0250691+3xglo1L3d3VIItnkv77trlcYDtyIN+zG0R+7DsyDHmwQ47217cdUIIIYQQ QtyY/ei/lBLjZvEd0La6ws4IUVPsgBDiJaUWoFoOU68PxdJEVbM+VL0yeFCKzQJBFRG2olD9ow3d Xoge2xzVhaBkpblCmYWDYCtwOE4kZmdtdSxWoSMl52ITvPvuQ87OL1ithipseN1mnhJp/YD79+4x DAPusE71Uf5ovOD99x7hUd2cJabmHnMiM7tz8xTkMs1tjGJMrcI5xZtoY9hklFRdgL3ozrBaxAcs xJx+PAZf031nVcjeitLL/Mnq1At6xfVo+YxRurDanbGGe3cjF5KXJvyUqorRxO1ShbGUqkhZSiuE FUM1zTXxpWq7CQunFyDrQhuRgE0VYQdYhWG5u+kyYwTrkxNWm1wzcg1O7xl5U88v06YO72+iXjCS CHyo11s9/jRnqWFDPf/TRXDx4YbzE6pD2hexEaVUwTpNJBwrqRbacqqQ3IbqG1VMy2XzWEdBPVXV ATvxiNX6AfdPnfPzR7zxema9Gvniz32M//Xr3yfy9wjgoqyrgzACSytOkpPzCVNzMdeKWoUoqQ7T hxqNYdX13AXNucJYvdAoljAfWkxDkJlInppgO3GaVuRyRmbCY2Qwp3jh3j1Yve780MeD0zcLP/v7 7vO5zwxM6ZR798+ADzjffIeyuQB/BDZVkT51kT7RC1bNnQGzCFxq7a4oJB5nKRTXglXLDoHSrtPY mb+7O/t5yFDPpY87xfzqAk0U3hvK3z/c7cSIpPYca0X+tj9te2zFyN7vsH1GGZDbtNrGoYnEc5sN Sq5i6jS1yIhFAb/eoVLb1/e3PhOnkVl0Xmi9O/uT2r28f30u57nsuM9xJD1aONXOoP487csaYF47 P+ZjSH1GZHt8e0IIIYQQQhzL/udzgCDT66K4O8W2n9PryNL6OVbiqxDipWTpllw6xvpw2fplfleA 3S8UtSu6FKYopFSHuvY8xGg5h+79/a0za8ed1p2WObcoS5vFz55XauGcDkGs4NEZfP0bI9iGBw/g wT0njxNpBeebDTykDtEv0+xYHaeJPA+Lpg3Xb8VoYit+5jyR83Zoc85BjjLnNeacW/ZprRrexdD6 n8mEez1OacfBWF2eu7mQ2+Jk1e/pLXM2qhAXtVjT7BjuURBuTY/qTrutyJt8Ta00H7P6U12rQzuu UUXdUoVOr4o5ear7tpm6OJmgVDHWIs9O5ZJ7lfk+1DuRklF8glwdqKuVM44jxsDJqRMXkKeLJgw1 p7FXoah4jVA1c3IOwgpTgVR1ZbzAxQjDRcYvNrw+rPEU2FRIQ2BeHbZQowpocRYOrFar6ugszLEO U3Es+dxJUMXmWR3l5OSEsWTe/thbfP1b3+MTHx/4vW9+i5/5iR/j1978AQ/PjBKJkzSQ1ivGcUVM I6NPeHK8i67tvBYGorS7yzI9p3Xp7NxaOzMMa9Ic7QFWorp6S0CmurdTzfqMyZiscP8+/OiPvs1n f/xT/Mgn38JOLvj42/e5d88525wzbTIXbKBMNS7AEubgNuDNBWzWOwCoObzWitI1B2V1o24zRWdB 3spSVyWlhdC387ywx54fsPxwNtV7L5ojeXbOL3Cbi7f19e8/o+q6cjul222W3vZa+a8KvbNrtr5X c6cgUfe7Ftbrz6xCnVKfD2OeHsvH7mx79aOe+4Ubtl+LrWbYYv7dY7N08/ZjVV0BQxN5y8683Vm8 jYLZrjv21tmO/GPt3s+pFUIIIYQQ4iZYs7Z2oxFlv8Dslv6ZNlqtComvQoiXmlmELXuFbraD5tk6 V3fjCGol9914gi5IdLGu/93XPS/bhNdlj9jufG3ovlkbtl2Ht+dp4vTklPXaWA8TZxcTJQcffFiI nLh3L/PhwzNSOmdohaWmKVfpxOpwX0/V2RcRWEoka1EHkcmRKVPTw3oWaK6FtLpLN09LcaZW+Olu ypS789QpwzaugKh5k2MXNz0wS1v3W0CU2p5+XrayVS12FgQWpYqiMA+Xn5qgXMII2/RDh0UXRxMr r7/f++BDci4tCiGaC3ZkyoWSjZwTxRKpZQCXTBOsqnvRrWbmWqS6fII0wDgFXgKLERsyvhoJnBSw PjVWm+r8q/pVFSeDgoVRukBmhaFpxpGDVGobPBfKBUznwbSugmt4Fa8Gc5I7kRNjjAwe1a3rBgwQ RsnN4WyZPNbMy2hO7u7KdG//3ds5YxkZhjdZpzURhbP8bX7ypz7Bv/av3+dv/a0POf+gcFFG3lqt OF07G5+wKPgAZVXziqPn/daS8jXHtTgXUxt2Uxbiq9HcnUb21narDtqYRmCDESQPLqbC6eCsA4Yh eP1N51PvwM/8ZPD5n5zI9s9IA9x/sCYZ+PpRC7Y/xxmx1UCJCShzcbmIqD7IVszJSlRvbndld+dp Hw7f9Upmc/XMMAw7MQPLCIWIgOKLWAFrBexqfICZ1cgPa0euC5JGHRIfMY+Xn9vC1nlq/Xhjs/hK eHu2pH6b7TyDqqO3vpugPmu8Xff9DJXCMGxjWnLZ70TZDpm6TIyd2xsxD7VauoO3TuDqCtgdguWX rq87q3feCqrrmyry9g6wknefy/X3ssCfsXxOCyGEEEIIcRvq6MZq4NmOmHWs9BGtu67Y7WfTIvFV CPFyMnhLmmxDt+dhq7NNK7ZDo1kKKLTf3VXZXrthxbgYx1aFvOaNbof45vZgTTsCxSxA+Da7sDRx o7qzHJpLrVYeh2maWK9PefMN52QTnJ8VNlOQVsHmvAYorFJw/56TUmAp41G11HHTMh5pxagm5mHi UWooeESrkt4EllyiujLLtHVMluqw68V26j7AULq4Uki5ujKXrrbIBffciujQXLIx7/tyfdtjtFC7 aELKYvpSCJrPx8JVTDieluutBcB6tmxpmaW1tpZDgcliHhFfCKyU6s51r9mszXFodVxzO98DU841 0qIGCgC1SNlqGIhU8FQoFtUw2Ubn52jD8A0KTmkFxaYcJK9CWi5wvoHTMYNPhCc8ZVJp0QntOs7T 0tmcgamKULkWfAIIm/DwJkg14bcd9/FsYnUC77//Ph9762P84OE5b7zxNifpLf7Ev/rH+O53/y6/ 9U/e4zv/PPj+xYSf9uJGI7aB9WkreJZ7QSnDcnVslghyHqvDMpp72epVbtYKwk0XbKbzdi77cP/e cWC89uAe91cJ531eu2989rNv8iM/+hpvvb2qsSArA0+1WBwbSA42tsxd4/X1mmmCUqa50FiUbSxG fRYk3NgR5NyMMK/O9Nbe1M79kuX9UU2yqT0z2r3UHK59e0vHeXWo1+uyRlH0AlY2X9g1X3h7jUcr HtVFxrSGiCrwEqne56U78cFjRZRMj+LICzf6nIOdEmXK7fpYDJHKtRhdifxYR9J8rKK6cWvnQteK t8/KYeXzfh1yuvafbSZ2zOvY/bC6Pe5VXG6O30XKQzSnbzuJtYBg9IJfC8H4MVeyEEIIIYQQN6Oa B4xs3TzlrZ5MNd70EZT9s3NKiTKNGCbxVQjxcrLNJ9ynDpev88Djhbe2QknPOY2yFVO6qGDz8Pnt 9gqBx9b1tnTG9hGvpdRh/b3qN1SXpkX1tJ2crpjG4OzsDB8SxMD6ZGJ9UgWSs0cZa0LL+UUtPlWK k3MBCqenK0r0IjvWRI6+7drOHhWay9Ta2iITrDo1+/73Y1NKdVZaqWmeAImt63UrMPfh+jFXPE/J GJIzDEN13LWYhKprNRffouiOkWrsATGLZznn+T+vmt9a6tBrgikHeRrZ4BgBvqrHvonCNTa2CnSY YwyUoPVWNsdgCXKpopfTsmWL1/88cyzcizBxAb7CUoZpxCKDOZ6qe/Ak13iBQuBuON5choGZM1h1 +uZSKFPBBkg4kQv5LJhO6zXpQyJPxnhhDG5YdsIHClUgt8kgSt21SND+s8cSJacd0Q/bFm4aTgI3 b85B44P332N4M/HB+D3efPAx/tS/+zP8+ld/l3/0D7/N179eePghTJS5kNPgteBWtNdls6lZny2m gQzR43jbAV0KcatU80q7O7Kfp5Nh4OTkhJXDm6/fp6TM6YMz3nkn+JEfnvD1B5T4gLfeOGGVBqZ8 zsW4waJei4MnhvWKwUcsBTnVIfVRDMuZHFONpsiBpe2Qe2+F1qpQ76RoDvC5w2DX6Tk7LFvMxn7P di0I1VR3+nH3Ko6W2ulRP6D150u7OW03N3ab09ofMFXo38aobDuTapt6Ab08i8PFrC1el633EqzC 546V3n/h7uRpYtqLG1iKpf35VZ8PdT9qB0Z7JqR+rrmUnvm6X1RrBys7yy/drBFGStuOGrs8obd1 9iydt4c3J4QQQgghxLH00WiHYrCWRbd2jQhyvgohXlK2hVa6eFD/9i4WLNyYWwdcwX2YRcp5uCxb p2VKPj9ISylzMSfcq5jYRdVelKgtm5tAYlGH1NcH8m5kgQFpWIFlxjEzTRtWac29k/uM48j5+Tnr 9ZoyWRNwA7OJVRoYhoEx1+X6cOMSUQXJoQ5dd2/CYkqYB1PeFhBydyyiDlH2FiMwty1VvcNjrjae o2AlSC3XsmtI2xzZgidIKcNqwCwDiSGleZ75vNjWJVgiM00TYczi7tIpN15sqpjiWwF8KcgOw5oo LXrBbS5iZGbNARxNOJzmISBmqbahRItcaG5mJshOzj4PLan7WHNV+/Xh7qRk+GogYoRNE9Pbf7xR Ym5vFdsyGcNzgQTebITTFGw2I+EwZMOm6kR0S63AUK5GT+sV5nMdem+O26rmEueFy3M+bi1HsxVl OnsED+6d8v4HjzhZr9hsNoxT4Wxzwdv33uKLX/hZfvqzf4hvfvOC3/mdd/nwww+498B5cPoGv/6P /19SWhGRuTgvnJ0/4vwstyiEFednG8CxaPcDy3MFQ1rV3N1Uh/CnZDiJ+6f3uH//Pt/4xjd47eQB fuL4aXC6Grh/ekI6qYrvw7P3eP30PhgM6ZQhVZdnNvBVL+RWI2TrkPYmIuZSM5CtzI7W7oDtQ+RL WC3+1cTXyjaDtV7XW1eokSglU0or/OQ+/20t36k+X7ogCoNvhdAeedKzo+jRA8F8/1m/B9xa7nRe PNt2HaVd0IXtB75t1m191jTDci0ANrvHfXahbrOsLxdf+z3b74O0EIHr+mLeLqSt87Tfg8vOmlkc 3j5vZ3fu4ph3Mbg9Zls8CTsu+jJ3hLVns9HLJWL2eAE6IYQQQgghbsp21GurHTHrDMvPurA1VUSr MaHMVyHES0ox5iJLtIJRQSZTRZk+JD15HYreK4BXl2cXFLzll9ZczmTBJjdHo2/XUUfNlraNrWN0 SfVlBiVi9rPVelP1wZzcMCuMmzPMEushASu7t2cXAAAgAElEQVQijM1mAxROTlZsxokcGUvGalhj ZkzTRCkjaUg8erQd8p96kS8g0ystVtHCowqGyZ08ZcKcVUq4O5uLsbpNvefaNNE1IFjhxVsJn3o8 Sy1VvxW3E6TBqzg7Gnky8mis1wOxHslNQLJwCJvFFcdaga+T6s6c0vyfG8C0CdxP6vEukFp7q7W3 iTalDp+epgkrXWhNtVgVMMZEiVIdvGFE6cKME+EMXs/TlCFvJkp2coZpXDHmwGOglA3WBaJa4anm 9Q4Gm8BTy6Qs9XjUNlZRaCrVFjpQxb/qgM5EARuNi4cDr50m7tkJm/MzzjYjQxjre+t6vKeMlyD7 irSGcSpkd4bq22RIQyu+VmqJs94pEC3j9qwerrOzVXWhjoWz8/dgdcbZ+ff4+qNvEPl1Hvgn+InP fJKf+LFPc3H2Ft/5/rd499u/y7/x85/i/ffOeP9h7Qi4Z5/gfAQuINuKiw+nWvRrcqZciFxqXEKp N+XDR++xPjllGNY8/OCMDz78gJOTE9I6+PDht3n99Iz3PvinrCf4/DtrPv0jp6xOP+AiPuTk/sRr lihxDgHJHTPHPWEE41mBtZF8xWDWrp2pxm804Z1Uh+yXaarO58GxqCLhQHVXllqFbSEi1g9TmWix GtuOCcJrXwVVYF0N63Y/WsuHhWmsUQbD4E3st/bBzWrFtehibO/gGVphKWOcpiZs13zjabPBPdWI jEgtl3qNUcilVXHDa7uijdCPPD+R7q2BMrJO1nKZ67V7fr5hs5ma/ltvsJ4jW++T6ratnTU1W9oI Ss++7S7v2dXqswPV5xzbYe4cgK1zoB/fiKjPbLYF8MxsXmdK3UEQ8z9rx6kLz7UTxiAH0fOF2XUe CCGEEEIIcRty1CLZyZzsNbKsFCP5UEcALowKsDVLuLvEVyHEq8W+m7IntCzmWLxuX+QbVRBgZ/n+ Xp++rRz++Jf9nh9a7Xl1U/3hPLu9mmOtu0EjepEZA6tu0l60xkomfNd5VodxVyFzdnuydYF1AeP/ b+/tluRIkuzMT9UsIoFGoX6mOWxyKCsjsiJ8/3fixV6Q3CXZXQ1khpup8kLVzNwjs2a7Z+6KdkQg QEZ4+q+5I+Kzo0fPv+PzuHSW969zIFFeLMONJnjxaKg08mzcyaRMel95kABN4OYds8gmLT1A2Mg8 BYncU+Doy1kabsl2cd0B2NEYJ8q0o1oZEQnuzuvRaK2F85YxA1kmfDl6z0tgGT+gKDrX40R+bzTo ctrh9A6PFjBfExaPXElVxUq4ij0blE2Hnts8R56O4dgnFlzSzEKVgFLfHwePh3NrhW4dc6e1hjZF 1Kg3B8kZVAOXFg7MLLfup05KVRQ/u7TdqQUc4+g9m3/53D9pzl9//Qvt0fgrwqebUsoLVYWvP37m y6d/4v/5L/+VdsQY0egcxb3egh824ccfv/Lt9eD1L28cR8OagznNHO8j99U5PABbrRVLaCZFqb1g OJ8/Kz99/cqXT5+ptdPsG8INkWNeyzMEVRImjpF+LgWiRD5zuiMtoR12dXRyBoFPcQM+Jk1O27zc Q7ncYUc+FwrhKI1JglHOX06V8ssBWohmVLG3M3LA8njk42ZX52iV6dI/PU/MAo6GM31lsUYTPZvO +4jfSFg6c1jltJ4xATJcwHba96uLtZR1r0Wkis2x/pHmMXk4ws/H8Hwdz+d8XqfpNlivxXLnD78y o0u2tra2tra2tra2/rXyYXNNaZq8fmvZ0cQa2PB1a2vr/yydv4BHiayng06IYtxrjuBznoucX/cr gHjejmNzmXf7MGxpY32EnysgaOSZRifzEdzdiSZSBVFbD/2+Oq+T7tZwtBEl1icXbieONbaXjjVk /ocR5dGRS1nSceaWkamkQe/pfJAvxeEEwAkj8KRCdHesC00cfxj3GqXXjxYg8tMNcOVxGKXqOtcT xKxS5Vv9jDjpUCzTpdzd6L3z7dWz4VI47m5lgOWAcO0Mxzv0Bp2IZSgquEczp+MN7Ij3jy4T5ph1 sHApizqULDPXTlGn1IB4o9mVmQUAG5dIFHGQknmkKrg3vIGr41053ir2uMUYkAOrAuWOFMe004sH BHSQongtWLF0RL6dro+ilMyzHY7ASnPABE/4ZhLNscQ7//jzD3z/dnB8/+98+/6db792vHdutXOv lVoP7rcXXprz9gq9/QXkE/1wvv3V+Vz/wOPbG9/+1xuPRzQnU8CaY71zvz/oh+Mo1ZS7d/zR6Pad mzd6EUrtfP1H+Pnfw/2HV0zfcP+OU2M8JlAfjsc1XsjJhgFUIwc5vK7Zgs59pQzPcR8O5RF1EfbP 8UHq1GzqNObf3c/EOIiZbTLmI2ZY7i+FMmfCE457ulO1gFhOqFhuL66Xci3DD5A5XN75O0a6xzPf d8JSp7nPcTccqAO+io5193V8skr0x3l5fn6NhATR8z3q89l4bXT1t8POCZLF3kUR5E9cI2KGRvbu 2r93683Xd+zA1tbW1tbW1tbWv0Vz8v/Uw6CUAj2+DwCzCZeITder28583dra+p3K3QPE2MpsFZHM fA3MOhyIY/mPlRZVYtmizIwXfP3+aHrz7Hh9B2vODi31QV2XA0+VAMCKiWPz1wdEPTXmySY9w6da EPQlAcVo/HPKrOwJVidAGcduUWrtTpRNJLCMBj7p3hQZLZ0AGXG24X4cP5Awh47NbUS5Mh7NfXoD b+GG6wmsHh7n7+hO8WzuJXl48zzG/r4dww2bzl5GNIHRu9HaAE3pSsXp1nKvO3YYwxvqTv5H2aPp lgpqcByN1oxugnlJIKV52uO8qmu6IPMaeTgVSykJwySzaAcEjMZdJgYqqFk4n6PCPaIbOsjd6P3g 8QAtjpeOmdKsUwXsODBzaq1QAtSXPuBiS8ftasQ0HMuk+9ckwbMYzR21uE80DLV8P/7K2wGtlekg tvbALSDsy/0LbhWzV/rROVqM4ceb8/rqHO68vTWaOaKVIulM9hZZys3pdmDZiArC6draAxe43eHL T4V/+tMXfvnxJ1QbINzLHTIWZIyHs4YjfY3FcZsMJ2dMUoxS9XU7xr3yzAkHUA336Rgs6zY8ryMc 5gEzrS9Hbnwmc0q5ZZZ04zBfWbhj+5dtnzJPpUz46u40v+bPPiuc9zYzq+frJ1A63PDP52BMxox7 af6+jE6tMfGDLAfpc4arpot2XZNrlUHv/fJ4dBdWe4Lrcbw7vzlBcF767HoNuLqWVzldww/WubW1 tbW1tbW1tfX3SlXp2QTXzAMMUOZ3r2Eamg19Pb5/47Lh69bW1u9f7gOgrMYzs7kL8O5b/W9oPEjH OqJS+VoC+7z8cL/Ohy86QUcsQwCJ6Yw0NOGihi81HJHT/fZ+G1p8Ok1L4gw7H1BSzMEe3jEIGVBn lV9PSMNwjeYuypHrSCB9AtCgCXLHNlb8QvdwmrrA2xGwpNZwGb4+Vtf1cACOf19LjEWE4/u6fiPz 1U05rNNanONaldutUjKr0nrss1ZBqPjDYh/ynNVShmUPl57wajTeagEwXWE2LzJQQ8wQbWgRtDpe QIlmVGoBNs09nbpxslziRLsPYB8TAloCaL9Q8Ifw+ObUKuhLxbXiopiC3QtaASlo07ziErZkM+w2 yucHiBwDbJTj1ygN587hnWaNiiB2Byl8//U7j1eQ1rj5g/opsofFw2n6eBw8Hq8cbw9aKwHkEvTf S+V4e6MiaI37Q7zlcR6INx7eERW6Kb1XTCuuHZeGYdw/w5/+A/zH/yTcf/ozh7whdIre6CpUS/f3 qbQ9RwsiytFfF2ibdm0jW0xRx7glAJ2egd2pNN4x1NfEzXrj/f13/oCFRKxGTHZYXgOb8FF0NNwT TIjMVhsA9Glb8/IFqJ49wLwAkVfsznyuWYfWwvVqEscoJUeI+IxHwTUb5Om854dD9gxfn92+YQpe TQTHvg7IKjn5dIlByfd6j0xcKR/HD8iYETqd07l+slHdyYk8JmDO+zfcrR/Mgb27vltbW1tbW1tb W1t/ry6Ghvl5cxkCnj0SIpIu2d1wa2tr63eq5dwK+Hm2/ItIlEKfKaR4ZkJey+pH85kFFSCa0QTc GQ1dnHB5Loh46mSeMEbGz74A7IRISYHEhVFKPwiCTEILpG9zuErHNjz379wBfJyHUXJ8E0clHvtu q7wXRml+QLT429IBu5x4NbdhLEdZQF7N3Q8ApqqIOyL1BH4FU6MKvPVYNhqCGYdB73Ar4Ywd++1Z Tj2OV7MMOkqsjXZEczGT3Mce5f1OpwmMsv/eex5fnY7E4+i0Bjcd5dInl54NeDmud5TMx3kyKJKx Bsqt3PGqeG/YLZtsFaEY6YIEt2jS1iNwNL3LAbYrUUI/ylNKEQ6LUnz3wu3m9C48GlQ1rFg4Xi3g vOasq9BAnP7WKUWREutL2ot4T6exUlzToRjRCYoiLlh3Xj7dkdY4uuGtZVZtpZvxeDzoVnh7M/ph eX0LvTuPxwE9oKp7AEbvHe9x/vvRaNZxMQqCDSesKO4NV+N+hx9+gD/+8TM///AV5cHrcVDkgXJD 1Kn1nuOunwBgQOwB+MZ4ExmTLWWObfdzwyw/jbVztrAzGk+9d5kO+AhnV3lEhmhmi9a8D5hO3AE1 pdZ5z7vEOJhO19zugKxzBl0z9iJfIx28z/e5Wad3pxnLGS6RVRtRAz7/vKOT40nw5Ig9vyYis8HW c/l/PN/CkT2uwxninie91nrXpNe/BF/Hh9vI3ZVRLJBZwdf9MMuAibne67FsbW1tbW1tbW1t/Ws1 q2lVw4gzDFlPy0wz0zRg7czXra2t36nOX8rdO5LQ5OJue/qGLhKg7Fyi+uxoDWg1nKELCjzrowdx NLkZ8DPgzxkwJOKdANi9T6fZKFuPh/1YYf7Dn920z/u+jkmI5j5nIMIZspTIlBT3KCMObx5ANguK WAAT0uXq+BnIaDbveTp0TacdrhQNONSbYVnOr+JYdpo/R0G4Z7QBgbqaacC+Y7hlw9k4DuN+E6xB x6gecQEd4zicoxtqldeHcLRo/BQgPpyjUjQbasX+uXge8ynTRwM2ahGKKr0IuNDNeTRD032rRdBi SA/+FuDoEvM78dkAvoZhLYB2Q7iLYq3TGN3eFVOLJmsimHoAToVSnYHkXAQVhYxLCGCnEU+hxsgp imt8Q0qh94K1zrfXVzgETPFu0B2tBaWiduO1GUY08jKBozn9rfH6AHk0XG4I57L1hktHi1HUeUVo KIfdMAqOcjTgDn/4Aj//Eb7+3Cmf/hddv6HacYNmULjBrafLswXYT4f1OJdazs5LTfi5ngVm1wzX cf57BjeUGc7r8EGZv1xG9rqiK+YhnzcDvua6JK+/55gM2JguVbPLmB8HM153G7mm6xkAGQMQIwO8 xz3l654fkyHRSMtRLcstnvB95SispnCXZ56vbUX0Rp/LzA+eEr54tzX5M9ZxheHvn6kXd+0TfB1u 2vGcPSvei+eTndywMhy/c3IqryXX5/jW1tbW1tbW1tbW36uIHrxWXw2FySd7ftj5M3low9etra3f teZDUSwAQr5+BgPnDt3PZe4fRQmMUu5VOu5Yn/jntPQ1L/a5lNct4EE2jSfDP1mlC8EjwimpGJZu 0bG+BZrGtkWuoMMzQzW2fVqOgHnjmIdbrdxK/ns45OIwpNTIgXWyOZPTRyWwjSOV2cTsGUzl2cPE qKpYd448Z0XCndgRmKh3/P6wrp1hWfzHNn5fVbmpz7zT3gEt3EqlFMEbtH5ESfbrG33sj4YD+GhO d5AmVBkl4+m2LQVxUA14JpJ5wYCaUqj0kl3pCVel6wJq0VEeBvyZ1x/w3FYzD6il4eB0nCJOK4Yd HbOOIeit0ouhRRBp4ZIUo1TBtFOcbBo2xnYEvQcI9Jx4KBzWoMcMbLnF2D9apz0e+APutVJehOO7 8fZo9P5AmvH9m3FoAT5hwNE6bwdoj/FWi/D90dAe8UdihvcGbuHKNKfcK70J5umqLiAPp6jw5bPw x1++8uXLC6oPRArldsN652id54r1MREhUucYKyUcsL1fTOzv5HmDxT323u1JOpPfa9x/Y4LkvC8r n3VByPX+/X7nrY11RCSG95zw0fFMiJs+1tMmYD2DevBo1HZ67ZLTejnOGMgrqiP+mK3jmI7vswP4 EoJ7bYJ3Bq9jMms4To+jUYpe4gbWtSnh9n2C3wNEr3gDvbx3LusaFQwjGzf+rGNX1WxUBtdIih07 sLW1tbW1tbW19W+TmSHhrMl/nz+rrqq6YfgYP0f43dbW1tbvUNN5NcpdAfNGyUBsd6fqgo8BTwsU aD1clfdbRYtAsKoAYKbLEerRQEalgEbDJy1jNkwTNBjmWW7eoZTIniwKkVgQcMXMZu5i7D+4SpYh O2rpHs1AwwEdRAQpCxj1bit/EjJ/MkGiFqBNKNIHBJFY971WvDv3W50wJ8qoHThQUXQ4KRGKS5ZE R96ku9HIJl0JIidkSmgUcQ/RbOp2AsXu0fSq1AVJWoM+oLaHv/FOgbeeLleoNxA1rENvwsstXave OL41uCsv9zulwuvrgdUb0jpiPRDYLdySJYIbYryocNOz+3hcK7hrITyqIKXRW8PuCs04Hs53adw/ A73QLVybn2ulNefba+BlSYeeDrefdUwV9WidpiIUEx5vHbpSbspnCl1v6NsNmiGfS4zL+ogPABil 35H7jU4Ph2xe16qVLo2HPwISS2SRqgj9gGavePO49lrw5hyH07sjpeJNeJjiRagPxeiId6zfeemG WY0mar3w4oqhmFtA3szV7Wo4HTGh90ap4L1ztIP+yfn6s/Cnf/4D//GfP/HDPxRcf+X7a6ekqxn/ FK5t3kDgVjXjDIzb7QUR4fv377zcY8wpGUVLNB2zEYlBAEeXuMks40aqdcIvncPNnMNiDJciOZNt aOZizOmEMclhQifiM4bDdDxbRgRA70KphtnjNAeezbn6pLRYlwTmgrlzbmIlRWdEdfeIjHBrAZKt 4H5QRaLRoBulQC3OTQ3xI+cznKq3eCa5IFKhW/bvi4gNZzlYVePe7a2jUommegMytzkhpVq53aIB XmujuyvzXIgI3jtFV/Z2PFVlAWK3SMX2hs5mcfEAK4QL9rCO2cGIEIlnfADfW7oMcmoB88atVFQq 7bhGsmxtbW1tbW1tbW39ParlTi/LaKOqHI+GinLTghRBexiOpIN3xb3SdTtft7a2fueaDqvTzNNz xuOlzJ0sUp2Zjxqd6AE9ZQlG9qnPMuR4+C7QKOLTtCkJekplUpvY9spTPe/viAJwfqtUF3jy5a3l rs7XoeFSa22URg9gUk7rLWhZv3M+P6Nc+ZopG47VcbxuGsc/9ikWCWCb7rMeaY1jr+a+xn9gTik1 nYuGezTlElFaa9gD6t243aHUsf+5J9Xxu2CHpxtyufUgAND9Do+/NsDRMnJePfC3BCAfs5ZGANdS 1rkTif9M3T3jEgo4lFL59FLoh/J4e4Tz1oxPL8rhzvG90RrcS6GZzw7vl5LrVGsBzFwzT1gcrNMO 4aGd+4vRxKB1ijtSHM0IARdo7QElYx88AFz3cJ+6C2jPivS4Tt2i4ZM1o5vTesZGZNOxmDeI64cK nY5M/GyYNQ7TzJ2N6+ReZjm9E85bSYLdj4Z4lPc/PJrF/fhF+dOffuCf/9OfEPlrjj1h+KnXv0+D 8wM9u8uf3wtX9ipxPzsqz2P++bqcZ63fQdcP3abrA9mIzpivZ17rXKflRM1Yc05ioOscPD+nxrp0 7FPGEcxcVSQypOeky2n86vsIgHNEitMTzq/tjQqByKw978M6xqHz8yF+/9ws8H2Mw2zWNfZjuFRF L5EF0UjQr+vOSYfzNYwc3TGxYbOpnnvHrL27VltbW1tbW1tbW1t/q4a5azaZ9vj+6m0YusZ3KM/v 0Ddaf0N9w9etra3fsT4CJM8wcwKGdHPacJOyyvFjueFQ9cs6w921ymVbe++uEhFklOee3v6tDMLx epXVVGY4+Nav+NMxjFzX9d7QAi+rDH5AmChBXsDYJ/DwC6gZvzMA1shXhJ4Al2BjXU/nDJBw/qmu YHKbwDphp4fL1xCkCt6zjVchLadOKYrfDMUSvI7TkC66hLBdFyhurdG7cRxvuMe1LTWWL3k8yyUc wLvWmvCsTxi7zgUUa7gYlHDbSgnac7srnz4rxyH89ddw7X6qcPtUeDzCkVxUwVtksk64dYVXY3yo KjLHitAPo0mP7vVuGJ16K2gxVATpiml4N4soVUvkYXafYBuUfhzh5hSleWTG9h6OVAzMsrM8ih/Q jnittxpZt1boXrAu2FHABTFBvEAf429BtxF/YGYYRpdwc5p3XruAwpcXR+9vPPwvfBKj9xgLo3ld jJEDJ13nT/fNuAfCMX1uZOencXpaF1FIP0M4PKZRMjD3eV6D0fjNHUbM6hmgru2REzIJUPMae6wk fm/EA/gomz+nAEs+h+KD3HrWWD5/JCB7HshHcQMTED+/Nn++Tsycn2NmFnDYr8+4j+JXeMpRHdfg DEzP2x7r0Cfoe54sGvdDPH8rUuIGiAZ+sdXmI7M4r6+MJoq8l2vGOGh+CP5ooa2tra2tra2tra2/ VWdzQcQIWgc3sm/I+ft6fiZWQWXHDmxtbf3ONb5uf4Q5lytW5hLhulpQwXyBySKKp6PVTys9w4Wx yuHiGvLZqOb82H3vXnV/3tdrI61n590CKQMAlwtcObvc3Fe+7ZqNW7EH4+8otV7rGZ3LI4KgzOOc Tru5L4V6cbIOQHQ9xrHNuf8ey5dSsG7p1CMzNOM9V3h5uWGPBYXH7KJIwbOpUDntu+C4H/EfYV7m KIuWWX4+yspLuaECWgXMMStRgm1Roj+PsUZucLESAMiFiNUM+PcPP/8Bt1f+/D87x2vkoH56KYgb x7EanZms+IExhsx8Oo97AmgMynAwduAIvFckmmzFvvo8TyYN7UpP6O30vEYB+swclxbl9g7WPN2W noArc2LNac3pjfwjEQFhjvVCs4555A6LCC7jXsmxZT4/fMR2je4dE9Ci80PJlz/ALz994ibw//63 b/xfX/8Q8D7PSzRgA/0XXK/uPSDwbzhe3Q1HE2Ke8kT1eqdlPOoFVl5cr+mEX6+fIea4FwfoW3D9 vJ7exjbtCpcZwFZZLe7G62tyZd5z7unmdKzHujrjfg0APCc3Uqqrcdc4zqGZXT3dqaxzqus+txPo PEcHjN95dh4/TxaNSJHx75E5++zEzTOz9k+IT7SXZ2qAcqEw4ljOxzVcvHEPVFbm9dbW1tbW1tbW 1tbfLxFBqbi0+bn1OA6kK+7Re8BOJpB+MiZs+Lq1tfV/hBYItAtgiGfjKA8WSg3Io75cVgaoOx17 AgsLiIyfR9Odsc3x+oAQV+feqdv4XOcCRKOs4bzMR2678+sDKF32K3+23L+1jwGSkch4NDe0EPBN T41vJHuKm82O6LGNLI2fYQ0NQSjq2RzJJ2ic7l090aC5IkUGZPZzJECcD50AMpyTAD2vTTQqOpc9 N2y4DtWpt/P1IfIsEw6KgBeLbB4JSGn0dOmOtmSx3dl0yDwAqCVwxBEL912pnc9/+JlfJJpk/fl/ Gr0Zt5tSEd56diZ7gu5nd2DVG+493X2xDx1DJeIXXCLYXVWhR/SBmkfGZxdco3HZgISSll3znmBf 53hwF6x7Oldz/GZesZlgh9EP8F7CQdsENaAr0h2OaJxmB3CAWOFobc78LrgoSFGKB3x0FNPO7ZPx H/5J+L//84/I7Tvf21/Q8pKwXRFeiNCDnjDeAhCHcTTzhuMaVGy6Ks/3xzNEPWuw1wDIp2uRXeXm 5ENPGCtrkmKs//k6fuT4/HgZzQmZ07YtHOJ4NG+7jhO5LHvehlvEQAzgq9kI76bZYCtzV2ut4McF dH4cm8DpmeioLFgasRTXha8TQf1yvuczFhjVAwu8Xp2vwFNzgjV5AKQr97RpIR2w63wMa3JMAuQE SSmoVMpHz56tra2tra2tra2tv1FRtVYAPfU1WZ9n39WKnb6LbPi6tbX1u1RBsCdQcQEhLr/phArg upx7egIzoyz2vcPLn0BGALsJJnSV9l6ddZ3n7FdnNO0KQNSzUF+ypHtu99QhfHnF1mP97AKT4Qqz yJIdm/Msl3ATRAu3m/IMrM6wpNlyql2hydU5N96L3zN8xAN8AIzdbYJOvdULuJpZs81orUUsgTvS ZYJmzaZfbhLNhjzPjQqk0zjchMJDa/bvifZNJrEsmVFay7pmRfRpfwWVhPI9HLNdDU/3rkiArp++ fkWs4u0b3/5seAu4WasmlIz8VVdBT+PJu9O94RKxCK5xjOJgh/EwyyZlRjHHvQQMRuY6rXhEO+TY MM2YCcCt40XXeLFT8b0YgobT1LL0vw3KCeKK4DTvdIt9bS2W8+60XhDrOW7PY/d6zasozYyuzqcX 5ZeffuSXX77iUpGj0b3RDkeKrXtB7DKp4R5Zy6p6gaHnyIFrKT4RE2ASDukJSLPRG+Dpbh4xCXGv jPt6wMXrPfFbZflXt/l5ogXimTAiB+IIrxEESliRlSDi8fPzhE5c73DzPvPTsyP9Gh0i0xn/W88w PO/E86PSwOX07OrMmI/3gPu63uG+HZEQq/Q/s4BFECnTATvKteY9wVr3iGEQCrORYf48zu1yKI8/ HXfFPqx92Nra2tra2tra2vrbFeao9X3ezKi1xrf1kfua309UlYLw6NFwe8PXra2t363eQxhHsyv2 rGsWZ5S9B8RqgGYuZYCDUsqEZuGCfHKO5TqeAckFwKbDq3uW7Z7JMFdo4WSGae5jccc0XIzSR9Og 942B4t+/BRmy7Ll3RE/ZrloQ7yhOqZn7esmhHPAmLIdaFlQdYGeW9ZtnLmyco3DwpuNVBiQ9OweX 69DF5l7WGvmWrTXwjsgdLQ69h2MVZqOtcCgGgHIx5LYaCMX6F7iKLu9HZO6IrnMlCcPNeXmpCaH6 BHPq0CcMV25udDdaEcotGk11heqdowKrN90AAA/qSURBVP+ZIp/49OXGH//xTilvfPtLpzvUOxzH AHN5rUWQUbpPRggk+KtdGEZjEUFdo7v8IZQDelG0Jah1QTKTlwolx6SoYBpOSyQgqrvTEtZGCXtk 9ro73gqYcBwNOwruCl7pzSN24KHQhfbo0VisKZ5u2N5sDftpZM7jccOt0bVweOfTZ+GnPzqfvj54 +H/j4a9QweUR2bKUiCcwxdRAeroa8z45jfuPYgKu43cO/wvYi5+jbP65QVYA+hHn4DMf+AKA3wHW 93B2OOclS/et2xyf7nGdJZtq9R6ZxmMsR+ZwOlo1j7GUGQ8x3NHn+7R3C0evjG3qZT9lxpLYWofH tY0/dokDGADUMs93HKn3PK/TPjxnqMYS071+hr0Dwp7jH9Z1uJ7HOJcJoecbaetVgT6eMzLHXR/Z vPMcF8yi0eAV1G9tbW1tbW1tbW39fVqf+0ecV37P4vxZ+Pw9P75P2na+bm1t/d71EYAdf4+ydQZ4 8oSIp2Y6KkLJhi926jw+gOlztEApp4iAk2uLZ+et2HRsXfZPQCeYOGU8ylrrABoTQK7WQdMN+Jyj OLbz7Fhdy6+GOQOS6NrA6fWSjtRz6bBNeCe1gklmfI668/xLBTnxj9gfnxAQIuv05eWFWitvr688 Hg9oD5B0wD7t2zx3uvYzmmjl+wnNdexvHyBzAWwtki7XvEaZ+er0dJZqOGohmoFNcB7bKHrHboqq 8fbrdxpG1a/8+OPPiH2nPf7M0ZyqlV4GDF4Qe4w/0iVoRE5ua9H1PiYAYsxYhy6Z8dk70uLY6HGa 4/yAJmDvFmXbWqOkvXuLMnWP9wMsk7EPGttIENhbZpy60przODqlKb0H5DMLcDxd1GOmdzo5Y5Y3 rpWAVo5H5Ln+8Bn+9O++8POPPyB8o7U3Pn36EuMx741S8hqpUMojs4KvzuqPdHWbniZKTvfCdbnY P3ciC5fBFGU6NT8uz7/e08s5e73nLN3zMRlwetbY+WfP3NY+J3QExTWWUQTX9/swt+1xvDnPkznG hVJ0NpV7druOCJb4E+Op91jPdMyqTwcwgJYFqHOOYpyN2I95LsYz6vqMjNt23GvXe9jsOev2eZ2S Df/WPuTT+vT8ODXpOgHw3m2+t7W1tbW1tbW1tfWvkYgilGh4HF8saa3jvYCXMByJANHUuNuK5Nrw dWtr63ep9eU8AUgHiGZaARIHFB0l+YUBitSjIrv1KLGmBbB6X44Pk/qpU1UvTjTNbohIACV3n82m FqgaTW0CuvYjOo4P4Kolm9w44B4QbgR49wU5IIEbbZYET0xsZ+dbYuN01pq1k4O1pXs1CM4oqRgw t1Th8XZMV9mIBFgAx0EPEKg3jSiDE5ACp1TN8x+AJ5pjFXrvWHeKOyodlZL7Fa7JyG916gBysqBR XIuRSenEDKNOSCy15HFEA54iitYAfKLRRAw3RDX2r3VuanmtCq31CW6kgmaJvNMpKrg1mh10gU8/ Vr7/2tF6UGrh9qnz0z98Rl/e+Mv/17h9fsG/vWE9zpU3pSOoKE16ZNsGdkMMHIncVqKxlzYB7/TX Egm7pYaztXdEDa03jEJTWfALobdwL3oJwKYOVW9oqVjziHToOc4fYE0D6PZKA9oBPCreS7qcDVpA XHrFu9EtG2MVoXHQH5HRa/0NFxAT9Afnxxv88A/w0y8V/fyd7+1XUKW8vNL7DSg4jXYoqg0BWhfM D4DMLlVai/J8VQVRRKMMf+QBg84JEBGn1MihXWCuz7HjbvTulBKxCWsCZjVF6y0M66uMfuUCe7pn RzxCNKcTalVUCGjeYl1mGYGQgNfacF5Dz/cHcNfTZIlKpfcHKjdAUZwu4ayNmNqcmIkACUbubykV uRnqEr91vqfLLe+lA3eodT1TIuc5H2ED3mLB0ZX5rJtufYGq52Z+cj0Xt7MLN+/rnLwpROxJO7mY a42PqPGcTLdzcVrvua8Vt2gEF6sNt/douDeer9Y9r89vw+utra2tra2tra2t/1+VrPaz4AdaC8dh eHNa69Ggm/xulZ9X+yGI24avW1tbv08Np9uzY21lAV5fG/4qM0O4hmVHbICcHKnn3/tYH7nixvov +8EqHXaV6QwDJsg5l6nz5MT7yMV63l78cHa9jh0cwOQp+1Hel1U/u2hVI+90AI6VayPRgGoAowTF o2FPrD6OcZa8D7CUpc73+306HANeDVjdsc4EK+vP9TqIepQmM5x10cJ+ZGOWWgKYeXjmRvl6LTWh rOKi6YwdkHmdysM6Vcns2WgxBgHV9Sa4KC6dt+MVpHL79MK9ObdHQ2/G8Wj0jGlIxs3IpZwl7xd3 cpSyjKZPnhbXcCk6tWu4dDXSfo/jQHtHSoDAUhMUZkfO6UIeldrWMIsSbuvQvWMJtKIyvWFesyFX /I66UsTpkXMAY6Igz394NpVab+Hwdg1frRrl/sKPX+/8+DXA/XE8sIR54kHbvUTsh7rTPUBv0VUi 31p7gv62gCoLav7W/Xl9BvicfFAdpUMfN9Ua92Gcg3Vvnd+f5fIu6747nbu4vsR5mfflyrPtzaYD +uo+N9x7RI/k8yh2f5TXG33cU/P6nnJhMxvkXO4f+9rmBEYpy/0tmV0wHLnnaIdx3Cv3NYBvjKdV GTCgq6rOJnrnB+u4ZrGnhrKeYesarazZOBeRgXvNzT09A07Ln/O2nh7dW1tbW1tbW1tbW3+3ViUo NLPokXFpoB3fP1U1jAzuGB0X2fB1a2vr96kzKBnyST7+ZdkFqCw4e4aQywU7muSs5d47ZNM+RgBJ N5md2c0Mn411ojR/OcLew88znFvH+fFr78/H9ZR8uJxeKcW5XHkA2Shr17X/6RDUkR+rcs3HZLnp wtWXUHTug12gKji9BWyqVRNChzv2DGziXEQ58WAv4baLa+LeJkaPeAHH5Hpt3EHFwqVaFNcWubbZ gMt6j7J3Cfd0uJEN0ciBdTHMNfI1lYgcuDnHo+E8qLdP3F6M+mJ8+aHyP/5HYzB0kSglj+ER0C3c wgmpRecEQpR4G8UzM7Mofmi6QwXhRisZG9BBTJFSZgk/IpljHG5REXASdD2c3hXMaabhaO0OHYpV pBe8gTTB0oF7doIvICYcIytXBakd90e4zkXxAj/88MYf/13lx18KWt942DdKDVcqCKIWQBenOejI Yh3w+dz46TQmz2OHmb/EvMbjvhmu8QW6OY1rDeg8iarPG2bcAxo++XAky9r+c4b0cNVPYJ1rGlh3 jEdPR/sEnZ0ElStXOaB7OOJv3BGP+I15LBYuc8+mesNNGnmv1/v7OYJlwOuAvZLXYEUCjEiS6TBn TQTN+RpZ99J5e8sBWyg179m8ngMsx73dI2bD4jlwvrZniBrnAURymZOjfxxXkYKZzz8T2Lrw/mm3 tbW1tbW1tbW19bdrfW8Q8GhCHLFpJwMT0XB6fP8Kbefr1tbW71gXJ9rpq/d7B6B8WJKqwz1LuCTx zF78jW/xmsmZ8FvO1/Rais38SYXlenwXbRDl8+vnBXPOx/cMR9f2xoLX/wyGzu7Z8fNw2z6/t47p CpgHSFk6Q9T3Wo3CVi5oRD4YqgXzBZvIXMix/uWQlcs+no8lnLcnq6ovkDYdfXa+LgsAj0DaKMV2 zA6gpxu55JCJazLcllWyKZRAN8G7cft0A40GWdaZDsAvP3zmL3/5FWpcEi35nzYBW0cB+wCFA+BN 565IjJGRqdsdmiGazudu3F+U6B6fB2jjXAbMjh5F4UZ2O6IRUXN6c9SgAdqil5FawLLuknmkCi2u vyWAnThOiIZnSDZ8qvGho0e5vlbj04vyww9/4NOXF2otdB6Z7WpINYbp9AwFXR1NiBml42Q0QChc 2GXGedi810cu6QCccS9ruV775/FznVx5P5HiIxs43l3AV07rmc5UZzTam7DQxxPlDIsHNI3okAFe r7mouW5fUQpnQNnn8T5POS2JRMOvs7N3vD4dqN54fsCJEHDcFRtrlzWJsu71fEaeniHhvpblaF2P 3jyGfrmX53V/eq7N4x1/27jGY23ybh1X7WZbW1tbW1tbW1tb/zaN6tr8KT8PF0Ys3rPi82kYIjZ8 3dra+l2qM5xnjqPhqHOfYGHCrZPcfYKssQzu0ZwogVh3e+ceDaelhNvvqfv4+e/YyGoCNCMCEpzM ZjKcwW3ko07nnqwGM3M5ucKjj1y/63je799HoPW3NJxp56ZeE9yYvVvnu9JvGWXqY7YwAAzEeqOh D6ymTUZr63xoSXjljvXIcBVdxzUaD8VmFxQbTmOtAUWdnh3tIcLS07mnMVOpGs5ETo5Ar46/gXWn Y1Cc6goqHA16MbBOqeG8tccRTlPplFtDzXm5K/LWOTLXc4A0m47FK1i7xEKYYyKICdUUzzzLdsRx mI4S+jxPI35hwGYRuldElCaGtULvjrVwNpr12JGuqDveFXpFrCAd1ApHf42mVO6YJ2jNkpoesxO4 CpSOETm4XuD+B/j68wtff+6U+195dNB6cCsSubN03Cqopcuzs2rFs7EbCjwmhD+7Xt2jzD2u94By 58E+GtVdf2+d6+HgfL4H3t9LbgI64gLs9CzRd+5PWC5xVz81qBqXZC1sFpnIcQ39sozKymqFgKTO KrsfERVmRk7Ez+X7uE/O+Qeps9P0X7p/43wpeBtXBE1wPO89ypzAmMclBpS5HxqzTfN6nbcnIuli vx77OL9mhiGnZ0Oc83ldPCYRIiYhJ4EouHlO6rC1tbW1tbW1tbX1b5J3ZrUicjZxrc+lQjnFDkRs 24avW1tbv0utkvcAUH56PRygH4PGc27qWtf79Z4dnKFVdhxGyo/dnyOE21jvT84yAOvIlUzAtEr4 r1m1vxU18Oy6vbq+Tq7QD/S8z2cI8wyBzttZ5cvvu6pPcKo6m4mtDVpCwmyIY3o5B3FasjmWrO7z ZgOwjXzX2GY4IBesEV+OWdWKvkSeq9PxbvikYbH/pWgAoHfnI8u4KXhCcBGNBlNSUHWkOKWDqdKx BKqFl5c7TuX1V+P+UrFXox+O4LTnIaKSGaGeubN5DnKYmRnqTjOhNMNrMMrSPSBwN0pCRpQF9VUQ amYaZ2OqlhMAVtJVGRMN4hIl/zlZ0YHusb+9d1wH2BNcSjhmT03XIlO30engcCvCl88v/PDlC58/ G6IPuj1QV7TWiNqg07xxpzJykgCqFqoWShnAtFyiJ8bY7L3n+Auwfh4/w1X6W2P96o7/aNJCJww9 8n5QwmUbAJa8ie3irg0X6pXGqg5Ie57wCIBZMoZgxnqI4ThFb3OCxbLJWERiREKDDfOtLLdvPMLG xE3sG6JULad9ec5/dWo2pxtjbR6HO0LEbZwneNZ1WMc0z1vmxrbW5jZK9I7DPng+nuMkztfHMjxE RDK71k/LrX1wE7q1iMXwzKC2E0T+wI2wtbW1tbW1tbW19TfL1+fnguCqHG2xBXe5fJYVkYiNE/jf u80kIZ/f+rgAAAAASUVORK5CYII ------=_NextPart_000_01BD_01CBCC2F.14974BE0-- ========================================================================Date: Mon, 14 Feb 2011 15:18:52 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Orientation swap Comments: To: Hekmatyar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> I'm not sure that any orientation has been swapped. The images are simply displayed in a way that uses the orientation images in the headers. If you show one of the images with eg the Display button, then the orientation in which the image is displayed will be determined by the orientation encoded in your image headers. As these do not appear to be human brains, I would perhaps suggest changing the values in the "Separation" option. When estimating the parameters, the algorithm only looks at the image every few mm (to save time). In the case of these images, every few mm is likely to mean that most of the information is unused. I notice that the joint histograms are very noisy. This should be fixed by using more of the data. Best regards, -John On 14 February 2011 15:08, Hekmatyar <[log in to unmask]> wrote: > Hello All > Could you please suggest me why the orientation of the images were swapped after co-registraton (snapshot of coregistration)? Thanks > > Hekmatyar > BIRC > UGA > > ========================================================================Date: Mon, 14 Feb 2011 10:11:58 -0500 Reply-To: Alexandre Gramfort <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexandre Gramfort <[log in to unmask]> Subject: Re: Question about lead field matrix Comments: To: Vladimir Litvak <[log in to unmask]> Comments: cc: Sharing information on OpenMEEG <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> Hi Vladimir, > There is also a possibility to use another BEM implementation OpenMEEG > (http://www-sop.inria.fr/athena/software/OpenMEEG/). That's something > that works and I tested it last year, but found it impractical for > most users again because of difficulties with installation we're working on this. New windows 64, linux 64 and Mac OS X installers will be available shortly with the 2.1 release that adds speed improvements using the adjoint approach. > and long > computation time (about 24 h for normal mesh). we will be presenting at HBM this year our improvements using the adjoint approach that brings computation on the SPM canonical mesh to 1h40 on a Linux 64 with 8 cores machine (3h45 with non-adjoint method). > The differences in the > lead fields with our BEM were only for a small number of vertices > close to the surface. That is the whole point of the symmetric BEM. Give precision for dipoles close to the inner skull (sources on top of gyri). Note that OpenMEEG features also lead fields computation for EIT and Internal Potential calculation (see. "Forward field computation with OpenMEEG" Alexandre Gramfort, Théodore Papadopoulo, Emmanuel Olivi, and M. Clerc to appear in the special issue: "Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG") Best, Alex and the OpenMEEG developers. -- Alexandre Gramfort, PhD [log in to unmask] Dept. of Radiology MGH Martinos Center / Harvard Medical School http://www-sop.inria.fr/members/Alexandre.Gramfort/ ========================================================================Date: Mon, 14 Feb 2011 11:31:56 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: about ROI signal value Comments: To: Yang Hu <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> These are CV as they come from the contrasts. Contrasts are simply a linear combination (addition or subtraction) of the PE or beta images. PSC requires dividing by the constant term. I always use the CV and I also use the above AFNI commands. The one cautionary note is that AFNI/SPM have the L and R on opposite sides, so you want to make sure your extracting from the correct voxels. Additionally, you don't need step 2 as AFNI can read .nii files. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Sat, Feb 12, 2011 at 11:27 PM, Yang Hu <[log in to unmask]> wrote: > Dear Experts, > > Based on whole-brain analysis via SPM8 (1st- and 2nd-level analysis), I want to extract signal value from atlas-based ROIs (e.g. amygdala) in four experimental conditions, compared with rest condition respectively, by AFNI rather than MarsBar. Specific steps are listed as follows: > > 1. Mask generation: by WFU pickatlas, an file of ROI in amygdala (named as "amy.nii") was generated > 2. File conversion: by AFNI command "3dcopy", the "amy.nii" was converted to "amy+orig.BRIK/HEAD", which could be recognized by AFNI > 3. ROI calculation: by AFNI command "3dmaskave", the average value within the ROI mask (amygdala) of each subject (sbj**) in certain condition (con_000*) could be obtained; Specifically, the full command is like that "3dmaskave -quiet -mask amy+orig sbj**_con_000*.hdr". The file "sbj**_con_000*.hdr" was generated after 1st-level analysis via SPM8. > > So I just wonder the meaning of ROI value obtained through those steps mentioned above. Whether does the value mean percent signal change (PSC), contrast value (CV), or parameter estimates (PE) ? And what's the difference between the three terms (namely PSC, CV, PE) always used in ROI analysis? > > Thanks a lot for your help and comment! > > Yang Hu > > ICN, ECNU, Shanghai, China > ========================================================================Date: Mon, 14 Feb 2011 11:35:04 -0500 Reply-To: Hekmatyar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Hekmatyar <[log in to unmask]> Subject: Re: Orientation swap Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_NextPart_000_023B_01CBCC3B.393DA870" Message-ID: <[log in to unmask]> ------=_NextPart_000_023B_01CBCC3B.393DA870 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: 7bit Thanks Dr. John I will check that, The orientation of anatomical before coregistration is same as orientation in the functional. I have attached some snapshots of display and checkreg. Those are from rat brains, I will try changing separation values as well. Thanks Hek -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of John Ashburner Sent: Monday, February 14, 2011 10:19 AM To: [log in to unmask] Subject: Re: [SPM] Orientation swap I'm not sure that any orientation has been swapped. The images are simply displayed in a way that uses the orientation images in the headers. If you show one of the images with eg the Display button, then the orientation in which the image is displayed will be determined by the orientation encoded in your image headers. As these do not appear to be human brains, I would perhaps suggest changing the values in the "Separation" option. When estimating the parameters, the algorithm only looks at the image every few mm (to save time). In the case of these images, every few mm is likely to mean that most of the information is unused. I notice that the joint histograms are very noisy. This should be fixed by using more of the data. Best regards, -John On 14 February 2011 15:08, Hekmatyar <[log in to unmask]> wrote: > Hello All > Could you please suggest me why the orientation of the images were swapped after co-registraton (snapshot of coregistration)? Thanks > > Hekmatyar > BIRC > UGA > > ------=_NextPart_000_023B_01CBCC3B.393DA870 Content-Type: image/png; name="Screenshot-2.png" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="Screenshot-2.png" iVBORw0KGgoAAAANSUhEUgAAAqgAAAPlCAYAAACuCR9aAAAABHNCSVQICAgIfAhkiAAAIABJREFU eJzsvXl8HEeZ//+pnh5pJI0uW5Z832ds53DIAQkJDkkgQMhBAlmuwDd8OcMuy27Y/cJy7BKWJRy7 bAIEll3u/CAE4oQlN7lIiHM5h+PcThzfsmVb1jnSzHT9/uipnuqaqj5mRtJIft6vl+ye7uqq6uo6 nnqep6rZge4dHDFpmT4djLHCLyYdwz3mlhRaPnYKYRJxk5zCONKxpTnnwrnhNTHHeM10njH3mvgT 74/zPMDctLl3v+XLD3ccMCbHy+GAu/eK+AvPUDzvfy7OGWTk+sM5h2UVwvvqkRQeOa8OMcbgRcct 97fvud1zjBfrqa7+yXmQ4/Kjz08wahyTi5K2DYA7we23tNzU+lxZ++cs7/vNSqp56XvyhbHG/50Y 228ZyO1PG69UPuKyLxzLem1LnOfIe8cOcuA8D4fnCn2E+7/4zZBDPp9F3hkFWA6cO+DIwn3PDiDu Qx6A6J94sa9iWTc+x4HjFO6R+hnOcmAAmMPd92Yl3PicQv1LZKWHtUrKQO7TxG/5nOPkjGUr+jzG mNQv+stb9DFqGm45AnCYph+Cd46HVD/xejmT4pXiChs/LWZ7z6ymDQBWoX3o2jYAOBD3lYYrnPCe n1kis6K/tLxrl1303eAHJYgQ7InOADH+qB2XGf9AX+yUwwdbdyAqxuHeaw4r8mFZljcQAVybP8uy wHXjslUYCHkCxcmQKpyWxqctA+bAL0iVI5xOLdRBOTr+SU6U+hMHznRC6pGM/I7cgvEJTOrEjzlg KE5SLTA4jIFBCFoFIZBbcBwHDBwJ2wLPWeCuKFn8YwCcBIB8oY1aYMxxj7kFcA7G3bs4E8Ie4Am3 uqfhDsAswOJS3yD6sfgv3psAy6Xk61BKBU8TOkG0Urw2xkr7JlfwDL7frJgw51Wcj9K+w/qBj1z8 nwCA+x/cGBoXUX1OO+VkAFOj/LUCat/gCDZv3YfWdD3WLO6sMAkxOyZqCZ3WwYwsYPgHEb8gqh9g imGZT/sjaym8QZMDlk/DVaqBB2y/sMuF5l7cYXn3MTCAqdrQYj5VbQRDoqD5kbF8IaIPirpOnBvO Tw6iDMb6CZAVcn1sEVqr8RZkqy28xI1TFkqKGkG5PnOpOucL7cGBxRkYAxzOwayCNs6BO3FjAON5 2AkGh9vgPAuOBDjPwWFZcOTAmQU4VmGiJwmp4J6wajHuJs0cz6pS1La6fYOs12MFIdXxVJCldSdI OCuWR2m9k7WijmOul0HnPW0nAzi4T8B0Nco8UtP3+iQpbPEdApwH97Ny+LAw5bQ/n9XNKWpROef4 yMXf9QSj3gN7YsdNVMY733nBlCp/rYDa0lSPWx96GddueAxf+/gZ+Nh5x8eLlTkG82y4EEOUUu1B LrqQYeq8dGYnVhj4HDjIu52xbFX0OmqhAS3V5oQPCkKYzCnn/XHIw5rQ+DIm1z0G06SJI1/MkzY7 Ud5FUKc/uYTT8rWmxfsB87vVXQ+tn6z8CS9n4/cGxkI4BeC54ZhcYEqCK6Zq+TdjBYGxoEX1W0kY LABcCHcWh8UAhzsA47BgudM17sDhjivMFoRLgAEJy/MxcPuGPBhzrR/McqeBblJueq7gxcGQKMio rlDHuJhYOGBAyfQxLuZ+RvQveV956cpNC7fAjf1D9DqbFwKoFFWcuqQLG6Q5LWl7IQ3EYQDjvsyB AfjIxdfg/gc34uD+nZHzSlSP88+/aMqVv/3r3/wWl7znYgCu5vSqX/4Fn3v/G3Dlx9Zj89ZuXPXL B/G21y/FvM7WCc7qkcmYDXKhaZQrBFjCWFj4t3Q4YUhA7n112gw5tP8ahyeoFgYUS3bq4n6tqF9D axUHdw1FLYh49sklTI4VJn87EzoBIOyeigdgFLWjYQK1zjdwUhFRMFWRJ4nit7BqCK2qGyLnHnEp cEH45NyBhVTB1O+455kNwAZ3LPfYyoPxPDiykrY276ZlcTgs4WpICy4ArmcA8wQ84WOc54Alufl4 fpUBryvI99M8SSq6FBXP6fsnxpinNfRpZR0xES7VWLsa5OhmdNcvNyYx6oRaRuVMQvPgSBhcIbiU /+ld82PFq3Kge3tF9x+J8HLqT43C5s+by5947CEAwDOv7MN5//BrzO9qxU3fuASbt7q/P/f+U/C5 973Buyl0kRQA3UKpSTcQjAuli6TkBUehlLFISjURFcM5ngDncLWjdVxh0/EPGIDf6d8955TGLeXT YrYUr+aRNJpR/3XFiZ8b6qCHofPm/nrpLWLgZn/VaEzxes6DXdfHup2bFplEFVABv7BQ7fyO/aQy 4gCknYxxbf58AgayhXZQ6IdYHg4vLpIC8nActz9w/UMdODwLxxl1zfveAqssHO4ulILDvYVYHBm4 bTIHzhk4cq7RjecAWMgXFleJZ/DW4RQEqayTD5ww6QQub1EPY94iKTGR1S1EMgmTwhcXgG+BUHGB GZfuTZTeq4lTJWwCVSLAaoRTk7aXMcvo5uLd4yWnH7dzkl+zVdC+f/Td1+L+Bzdi/55tvrCdsxd5 x285+2wsW7IEp59xBtYdeywWL12qz4iEGh+h510XXaIt/23bXsOTTz0DZiXcSbwQz7gDx+EA58jn 88hlR5HLO8jnc8jncpg1swtvectZSCQmdkG7nc3l8cyWZ7Fm9VFYs7gT937vUrzpUz/D5d++BT// 0gVYu6QTv75zs09ADUN0YqzC1bqEy1guAql8MGXg3Cnx8Sp2boAr+BYEAe3qeHj3AnBtSG7uCucU DYfo+DUCZgle5y114kx1EXCvu/mO42NK6AjzMXUCtOcVpRujnYzF4paaQHav4iaLQUiZc7sQomhJ cDWZQgBLuNpTiF0/XHO/W5o2GCus5GdJAKOuoGsByDtgzHUHAAqtTJj3WXFzBZ7PwxEuOdyCUxBS Heb2J0EWlzAfVPieq3itxK/Se3JFewupr+HMFdLltJmFSvuPMHeYMmONLhhHCFeczPuDc8esXLn9 jjtwO4BrfvAD3/nOae04Z9VyvOfYxRh804W46KKLIsVHlKKW15NPPoWz3nI2+g4fct3cXFU+HCeP XC6H7OgoRkZGMDg4iMHBQQwMDACc46GHH8Ypp5yMlubmCXoSF3vu3Lm45dbb0TpjDn544yZc+bH1 uOazb8MH/uVGPPPKPlxy1hp84dq78cwr+6qwYIqIi5jN1spK5XAtgKMIlbKVkCsDnxy+CDP6GBZX FevyYDZVBZu/GEsoPrdqQatxRn0RIlyVNXQV3j+e+t245sNKBcc4PqaT16Jj3o4OiOArqYQthuOa c1KcPAGrYILnhTbuBbMSYDzh2uUTCQC5Yj/A/WsPLKuwDZLjSGlyz9fV4hYY58gX1iwwJMAtB4wD eQewAp4vSv2JIqgJbWtREDP7nRYrnOUTdvUL1Mqr23Hv87c72bqUAOQtActsb26fmffiEJTjnnDi iW/AwsyjWL5gD8697LfgnOPiiy8uO74jGbW88o47vv7jF74Cy07Ctt22lxnJYHh4GIODgxgdHUU2 m8Xw8DAOHzqM7/zbVzA0NIxMJoN0U9MEPYmLPXfuXGx+Zgs+19mKw4MZ/NMP78GVH1uP+V0t+PWd W3DO6101vCqg+macRnOImLW5KznFnpiTd2AYL4odOiuYUFQzTrHs3cUH4lxQZxrmKM+YqxdxTdxi myWnKLwxgFk2GBzfO7cK28eIcEzRIriLHiRtOpc7TLuoGPUUqIV4NBp4juIA5dN8SLsAqIumSkka ykU8Uxwfv6C6bNCExIg9jBJBAgjXgFSaaIAfb7T09JpvUXfDBk2nENbT7/FCHAUVnOyPao5Byp8X drLsNiJ2RtEIqupCHY35VxbwuCParAPAL1B5rj08CbEaH+AAZ0gwB5wn4DDu7YFqWQk4LAvwLBIJ IJ/PgsECS2QLk1Pmmv/hmhZdVx1XBeemmwOcfGFpFpCA69/K4e7bmuccFhg4z3r5V+GcI5FIeHVJ vzCsVFj0CZMWd3XGkulfTGKL5wCLu72Ua/Ap9J/cXQjm1W3Rh3H3JgZgVHFREGlbhXrs6JqvFN5y pHckVtA7rLC4iiEhaTdlzakFBl5IW+yxqu42wLi7c4NIUtUuM+b2y0I4dRh8q9bi+j+uXXUUdu7b j4+dnsKCtRl8dEGD7/pU8qccD0rKizvIjo4ilarHuy5+D0azozjcdxg9PT3Yt38f+vv7MTo6iqGh IRw6eAi5bA7Dw8PI5XJw8s6El7+1aNEiHD7cBwC4+rPn4JaHXkTf4Aje9vrl2Ly1G6ccPQ8AsL27 byLzSUj4BnTIm3IHzPSjxhfrfi79yXGFxcFLwhS1rszXwWvzi4Q7EDvMO3aRB+2pj2mx0NjjVOWP 87z3v/qnOy/uEy4CIibOULKpufAFLLdN1D7lDxwlgps04ZA1hy6WfwIohYHFYFkWLMuC8Le0mA2G BBgsWJbtXXP/GBhLwEIxDYYEGGewCnsCMJaAU0jOXbzlX1EvI+dV/FmWu1erycc4tC5wS/+shdqm 2ybPry0t3WNW/WOwvD9wBl6oxO6yMyZVaP2f73kc5u0Na0nCqa6s5DKI0ybiKJTkNheWRnt7O96+ /gwcfOUFzMnUgzUBcxv9Gjs1PvrT/5nLC8hkMuCcY0/3HmzfsR379u/D8Mgw6urrkG5Oo7GpEfX1 9bASrjVgcGgQ+Vx+wp+Jcw57xowZvgpxyVlrsXnrPqxZMgPXbngscsUkxhe5UoaaoSLEAfh9A4Pv 8w+O6iw7CKP5UO7YSzRaqsar6ELAudqBFgXksdfU6+LnhvPRMZWlKLcwDWNYPZhoC4baoYYuAlHO c8/h310JzhmDEA18cReiZVxtL6ZJjEi3tic5pabb4ocpgJjmYGl7qdJ0rEJbF5pT7mnoXZcj4buT AFi+YLrPg7OkuyVVzi54rybg8MIiS3BYCRtOHuCwwC0Ozl2NHOcAuANWWPHvuO5yhby5z8iYBY7i l6B8/p/GclLDyNvNyQEsJZwl9TXK18s8oRVeWKFl9faNlfIg58+WPhSgelhG6Uctxl0rE2deny0v EOTSsQ7dIkORLlf0C2p+3OdQ2qvvZ7S69+nP/CP2v/Ic7nriWbytsxHNbaOAzTBtmoV0vVwqU3Fy OZao5cUxMNAPzjl27NwBy7KQbk6jvakd+XzeM/NzztHf34+ElcDgwCDy+Rx0iqTxxm5ra8MzW7aU XKBtpWoXjrzr2hVTIDUR3w9JFQj1g4Gug9RrWP2fNmWeRqfg2sCU+KVtYVThdHwWv8Q368eK3Tiw 6n/HpdL7Iy+2MIdQwpfebyoDzjkcR9IisYLrSdCgqmS3Vh2M4vspyh9BkdyCwjSGhfbF3F1LvXt0 E6CikCrSAHjBLxRwLTiuX2PB1Yi5mk/3vbgr9QEGy7ILEwzL1ZoyxxVsvc3rXfN08Y8XNvHngG9C HE1zHNSGisdyn1LcCqrUJ971ey+eLmrw5XIB85ZUeXEW3Sakd1uYIIlFX/7JU/izcea4H0FAcVM/ MR6Y6g7nXCtryO9aJ4zGncxGqbqtzc24+X+uQV+yAc7AYZy1ugV23gZ4Hq3tDvp+9zM473i38YuB hJnSvhQYHBhANpsFOJAZyaA+VY9EQkwa3U8O53I55LI5OI6DoeEhOPl8YXI4Mc8hsJubmzF/3ryS C32DIxOQnSOTYqcSVYNpDscYc/cojNRBF3+7HWe0/MrfMi+Zr3HumfDc/Gj8SFke8mp+Lw5v4JSe x9JrJBkrhvQ/axTtaa2KKH7GXtAuj7HKl19zpV4V7xrulkWqklMafydaQ1wp4fkX1+XPg+p9UoMm bFx1s0FhsWCJA69ozwWNiixIF3YK8NojTxRM83bBl7SorbSsghac1Rfaet4VfoXajnO4u7/UAywP dzFPoV/iDByFsCzrKwW9RUZfT30fCfEKQjbpJ7wJMuf+fqx4X9Y75/tCGtz+ijvFDxSIZ5fz5m3T JPVflhQ8HzIOuAu1uW8vVtfMr/9Kloqq3ZXzJv7XlZ0QWC3ueFrT0rT0eV+0aBEufO9lWLRwIfbt 3omr//PbmDu9HR31DjqRR6KeYehloH1xE7pv2xgaH2GiVIPa19+HbC6LWTNn4cQTT8TuPbvx3AvP Acxtj55LDAOYxTA8PIy8t4BxgjWojY2NaGlp8U7c+tBL+Pj5x+PaDY/jlLXzsGPf4QnM3tRHJzjG n7WWzpzl2W8cgUI2EZkTNJtA3RSLmh3tLFyz1ZQcThfen0d9vkU8k51KniGK1qPyMgrbFSEs/eCt Y4ImU0Dhe0KOf6W1BYY8gITkc2gy+3KH+37XAuXlQ/0EsWnf4GCrgmvil4WTEBcei0PsZsMsDnDx eWD3I8Owcu4+yhYKexS7QqHDXR9RDguMcTiwwKzCBv4FUzoHK8jAkkAsCV4cHMz70pt+P2etEAqp jI39l1V4TiV8wcWhWK/8wqlXPhZXzOvFSZWbn+JHC7ySZ4WvdcnWoJCFKfKbFgsEHeUd69uAmr4f k1Aq4jO6DAS01/bpM3DFl76J1mnt7vVMP6ZNn4EPvPe92HTTbzEjZWF1Ko+GpANWb6G5OYORujRe /f1PsOSiy6ZEfz6e6Mqr99BBZDIZNDQ0YNu2bdj05CZ0dHSgq7MLz2x5Brt27UJ/Xz/6+vqQHc0i kxlGzskDARr58cLevr34pYYf3vQ41izuLHzq9CWcsnY+ntm6HwBwytpSLStROapprRyCzDph5mJx ztvOSuNjVILhU4vFAQBwF7mw0sEN+uf0+6GqmgEgTCjSl0GpJsnNS/BG8xNNFB9U033RGFsfy/Ct YczPVvhVct7vW2r5LAWW5W7u7gkFSvK1IoTqqDxv/i2cyoFLftNigqoz9QrBlYEVJ4meICYcBtzF Ua53DncFX84Ali8IoXk4TuFTpoW8W8y1qnBug3Gxk0je9RJgjruxPnN3AGBg0gy14PMp8ijy64hc FvPv/i89c6gLknnze1VzWiw3dzcE7ugm2YDngiFZoBgv6rGjjgPyVQfiuaUFW7Liy3tmy30NzN8+ RFi5/zf1PbqJjl/wLuXQgf345r9cgb/7wlfR1pxGfjQDJJK48TfX4cwOB2+Z1oiBlIXhRBNa6kYx 2s7RmMjhlV9djYUXfCiwHIhoHD58GP19/fjRf/0Itm0jkUiAgSHv5JHL5pDNZjFa2A91aGgIIyMj E756X2B3d3dj7ZrV2LHvMG558CX84ssX4MGnd2Dz1n24+rPn4IcbHgcArF1Ce6BOBlR/oqhCi6w5 iIy8+lfaEkoIDrIZPlyzJ209JMx5EPdbKHbLenMepMVRJdkcMwFF48tWBYKEU845LMZKnlSnPTFR 6aQ4rE5FvW7ylXSY/3lLrxeEDwZY3iy/UN8RPMgKFxT5XC0LsPEwa1FlXFEybiWQ+xMOd9NvsTVV wRfSsuDkUXDxseAwDsfh4Ix5X3tzFzm5/qgWh7v3KeeFjfiZKzHlC3WjIPy6QpeDot99Qqojoi6V 1pWi1k+nCSoK5a4wLE+gZXN+sV/ynp8nwCyTa0EC8rZ/cn7ckxwJVtwvlXNeMr0Ie4MsgaJ7BUR7 dgVVizH3C0HqPYwZuylxTfc8xUm9/mbV4qZr+4cO7Mc/ffbj+OD7LkWypQ133XQ91ne1ojNxCD35 Abyuaxqmtfdh+/Y8Lr96EJdf8c/I/vxqjO7fO+EavMmGbgLR19eHi857KxJ2AtzhyOXdDfpHR0cx UtgDNZcdRd5xkEjY2Nu9D0B5C6+rjf3UU0/h7W89E/M6W3HTVZegb3AEn/7OLThl7TzM72rFLQ+9 hLe9filamuql26Kp+90v9iS82XdxFn7kfWFKNYO45WfaaJv7Gn2QSd3kf2Uyc5qE1qJJSnwKTwzj 8v6l7rHDsyVaVC5pHayEqCv+Va5c6npFGajubp7eQ7MZfzHfBleAwvmiiU98ylB+zpJHr4CgTrs6 DVuI3b5ygA3xgYNiJ1KsT/m8qoH0+yR7+2BK6XDuL2chAMhpi/QSyqMFuZfoyiLHNCZGyawqbxOk XgcAu5B5YfIFZ0ggAVbY05OzLIp9lPBntYoCi2Nqe8LEW1jMw5WECyVm+tRqVKovEKv9g077J/9y REsBUKwjAAoLMBO+lf3uZMApvldeD3AOBrug1QRcbaYFywLyTmHPUOQAbrsCHzgYyxcmCCNgrA6c ZcF5A/JsGA63kXdGwcGQsDg4dxdsOIXPnxbrB5BHBo7jCpbuV6/cT6Yyy/HCiOfi3EGCccgL6bK+ BU+AuqsD58U+Q3V3AOBuxwMhErr7hrKCkAgAlp0AzzvIi0Wb4OAJBuZw5MFhOUCeiTT8liWLw92l IlA4KPTJPqtXAQembslr8Em4q/8dx0EOwq/UdZNJWAnXvAsU2gEvuiE4DizGkC3pk6P1dT//1c8A AG31dXjTqgTasnVYsrIRTa1Z7No9ihVXHgAAXDZtEd5x40PY8j/fQdfFH44UN2HmwIED7hei4Nbn XD6PXDaLXDaHXD6HXD6PfD4Py7IwODgM206gPpUGs8bW0hYF1tHRwR996H60tDRjx77D+OC/bMD2 7sN44qcfw7UbHsdVv3wQ1/zdObjkzDXeTS3TO9yblY5WazLxBFSORSuOGdunIQiCIAiCICYd9/3x V77f9oknHI+WFvd7q61NKczrbMHPv3Q+tncfxg83PIa1Szp9wmk5LFx+NADgA9+9r6J4CIIgCIIg iKnH6W8/HQBwzx9+AQBgr770LBcCqqBvcATn/cOvsXnrPtz7vUt9nzgFihpUIFyLunjFOk8wfXnX weo8BUEQBEEQBDFlWDpnGgDgF39zOu6+6WdgB7p3lDiQ7Nh3GA8+vQNrFneWCKdAdAFVCKcvbN9f tQcgCIIgCIIgKuO1O68t674FZ318TOPb8+Im7PjDv0K73868zlZccma0L0mpK/x0K/7y0pYFv//k 6siZ1nHh90u/ekUQBEEQBEFEY+effoSzL/tiWffe8d9fxdmXfRHPbuses/gA6DWoYcgaVECvRRXa 081b9/iu3fTptaHxzznqaHx+Rj++8shu7B/2f9HqvKs3x80uQRAEQRAEAWDPvT/GynP/uuz7D/cP Yc+9P8bZl30Rm7fuqXp8AHDg1af1GtRqko+54WtdUxqrZs/B+R95CQcPTcMXn/YLuHHjIwiCIAiC IPzk8+XJU0IOy+cdn0xW7fjsB+66KXZk57z7/wCItp9fnAwzxtB27gewetPNaJnfgAtXNOE7L9bh UGa0rPgIgiAIgiCIUsK/+qdHyGF5h/tksmrHZ5918Xu8H1bhqyAQn2KTvpluMQaGRMDmwXqiZjhh 22g5453o4Dksrh9BXaYR02YAZ3am8dvtxdX/5RYAQRxppFMJfPjUOTh5SSvam2xkcxwHBrN4rSeD r/3vK8jlOW77u3W+e/IOx77+Ufz5hV788qE9GM257U0O9/U/vor7nj8EAKhPWvjVR9cinXI3D//2 ba/hzi3uhttz2uvxoVPnYO3cNFoaEhjNcWw/kMGGTftw93PBO3qsnZvGVe9Zjs07+vG561/y5UFO oxyqFU+5fPuS5QCAXz+8F4++2uedP2v1dPzdWxfg9mcO4N9vf63s+BMWwzlHd2D9ynYs6GhAY52F 4VEHu3pH8PT2fvz4/l0VP0NUopT1RL8PgjhSKdciLeSwvOP4ZDI5vt98dCUA4D0/et53r+68Kb5Y Jv4433wPu0cmfdIZaD7lLHQd2ImVf/kjumbUge1PoGEax1kdTT4BdaI/vUUQk4XPnbMQJy5uRW// EG6+40HkRjJYunghTli5DAOP34C6ted5YR969Am8sm0H2tta8ObT3oB3n9iFdn4A37q3H0j4u4lz F2dx212bkZqzBmevnuYJpwDQu+lGDHWn0TDvOHz5vCWYPz2FQ4cH8Pu7N2Lpwjk4ds0qfO5tC7H1 +WewzZmlzbfFGD595nwwANf8+1U48OohTDvlw9o0KqFa8cRl9Zw0AMDe+Rdk9tahvmsZAOCuLQfw vhOn4y1rpuOGG36H7enjY8fdkLTwrxcvw1Gz08jmHDz0xBbs3rULrelGrF29Ahed0IVvf/8naFl9 lvZ+22LIaT6VWSlBZX3jpn0Y2roRj995L4Yajhr390EQRyrlKvyEHJZ3/F8808X3m4+uxMXXPgsA +O3Hj9KGNcVnB36LmjngvPjdYOZ93jL6p0qDBEpmWehaugJLUgx1f7oeyVQD5g4fRHu6Fc5hhmQT x/HNNmalU9gzkAmNjyCIIusWtAAA/uun1+GXv78FyZYuAAxNSQeHe/agpXMlgBMBALfceR/++MBT sOrT2Lr7ID596YU47dil+H/fuALTTn6fF+f+AwexZtVyLGa/wfbB2Th/3WocODyE6a2NXpiR/a/C bpuHaekkAOBHP70O1224He2zFuHe674FAGg6+DSGhw4hNWtVSb5PXd6O+dNT6N5/EA8/9iQaFp2E 4T3PA3AFtlldM/D1c16HE9Yuw9Cog1/+ZTdufmKfd/85R3fgguO7MLc9hUzWwSOv9OKH9+7EocFs SVoj+1/FZee/CZeuX4rDQzn8vxtexMvdQ7jjitcBAK7buAdHz23GillN2NYzjG/d+ipOWdaO847r RH3Swj3PHcR/3vka8gWhbmlnIz591gLMaa9HOpXASNbB1n3D+NkDu/DUjn4vXgD48l9/EF8uHJ/9 zceQ5xx3bdmP978xjQ++ZR3+3y8eQeOiEwDAu+9bt27DHc/0GN/5+14/C0fNTqOvfxCXfvIKbN2+ E3ZzJyy7ATw/iuVz2jD48hakZq7EPV8/HwDw20f24uh5zVjS1Yir73wNtz7ZjaUzmwOfQ87T7x7r xnHzmzF/egNe2T+Eb926Ddt6hn35CnpnF6zrBNa9Ey/v7cO2J3Yi29+D9euW4l2v68KSzkYkEgx7 ekdw1R9fxQt7B3HUnDQuO20OlnY2oj5poT+Tx86DGfzXfTvx7K4BY9mqaAs0AAAgAElEQVQQBOGn XA2qkMPUz1LL8V34/Wfw+0+6H3mSBVNxLUp8JRpU91vL+u9NR9lSyvQgMg0trZiz/p2Yvv6daG1t QXLjbcg+cBda925DZ6ONliYbPAOwOoZ0A8OZHU34BQmoBBGLPb0jmDc9hU9e9j4cfdzx2LJnCE9v O4Stuw+iqXMATmbIFz7ZNhszFq3Fca9zhdbDff0YPfAqRnq2QQiyN9x0Gz72oUvw/vPPxJ2b92NO ewrX/PR6XP6hd/vi4pzj+3dtx+VnzMJlH7gYy1Yfg2WLFwIA7rz3Qdz3l0dgzx5Bsn0erPom371n HOVu1rzxsU1gdgp2ejqSLTO96x//8F/hTxufxnMvbcMxqxbjU2+ehwce+DMONC7Hees6cfmZC9Bz eBA333k/lszrwhmrV2Je0yg++YsXgbpGX1qfvORsXLp+KboP9uEjl1+BHd29aJcE8ouO78Bdj7yA 6XWzsaxrGv7jkqU4NJTHX556EW89eRXOOboDmx5+APcenAUwC62NNrKjGfzp/icwOjyAlcsW4pjV K/HVCxbhPd+4DTc+3o0Lju8CUNRaA8Dem3+ErnO/jCd3juD9AE45aR3q/vPnyM9a5Sufw09swNCe JjTMO1b7zk9f6Zbd9RtuwSs796Jp2RuRqG8GSzYAVgI78qNoWt6BbN9e756LT5yJh57eii1P78bW e+7FUP8MtC5aH/gcQ6mi9vsdR7fjjo3PIrFkFpbP6cKX39aFD33/EaBpRqR3Jsj194BzB+e8bgGu OHcJAOCxzS9i69atWL5kAZKv3oXRzAL80yfOQUe6Dq/s2INHHn8S09tasHb1SrR0b8TowRYk2+dp y4YgCD9jrUE9/5qnseHyo0vORY1Pa+LnnIOB+YRUDiBIFI0irALAzLXH46gv/ADZugbw0WFYgz2o W7EW2Rc2Y0HfNrQ1NqK50QJyABIMTa0W3jajCb967SAcEk4JIjJf/9+t+NvTWrBs0Ty85dTj8JbC +Ydf3Icvb3gVOSfvhf3q5z+Dr0r3Opzje//9S7BEHfJDvd753Xv34e6NT+OMU0/GwoV7kBnN4Te/ vbFEQAWAJ7f3YdMLOZx67DJceOZJAIC9+w/ijnseAG+cgWRLF3h+FIBfQD1qtmsCf/mV15BId4Al 6mGlWrzrG265E1/5xjWYPn8F7vnVt8AYwwK2G7v3MbzrdW5nuGXLFgwPDeKlHftxzOqVWLZgFhb1 /RRb20/34vnw+96FxQvmYdvu/fi/l1+B/YeH0LjoROT6itrY3264FVdd/WN84IMfxN9/5CI0pOrx ocs/ixe378Xia7+BFYvnYUVXCv/74N1IH/VmPPbqYQztfw0rmgfQ0tKCF7bvwzGrVyJVX4fFo1tw 9QYLFxz/DgB+rXVdxyIMv/Y4Xm1wy8myLKycOw2bD2xDavZq/J//3owD93wP+3sOIT9tKZLt82Cn p5eUudBa79y9F3XT5sNOteGGr30QXa31Xpgzv/ZngBcHktvv/jOu+PI3YLfOhGXXw27l2PjMKxjq 2W58jqeylnf/b268Bd/+3k+wat1J+PV/fB5zujqwaPBxbE2cGumdAScU693IIN57ynwAwB9uvxtf uPI7SDTPQKKuCcgOwp7Rh3r77W79eHkr7nlwE7b1DGL//p+CjfQhOXsNEo3tsOrTJWVDEIQfp0J3 HlUDGyW+oDBqfBoBlSNIHHWF1+ioGs+9mx9H7spPYOnl/4JUuhksl8Whpzdh+QuPoKs5iRktSaTT rhaXWQwN7QwrUgmsam/EloODpEEliIi8uHcQH/7WA+gYfhFHL5uHt775NLzhxHU4aXknlo/+Hs/a RS2c0Obl8nn0HDiIBzY+jq2vbUdqzjFglt+l57rbNuLMNxyLJfNn4/o/3IXDA0Nq0gA4vnLBUiyf 2YTb7n8EX/7qVTjx+KNx9b99CVd95XN43z9+D7ucaWCp1pI23dLgdkv9A0OwUs2wUs3gKIZ5esuL SE6bh2zbUu9cXTKJkb3PobOlDgBw+htOLMnRjNZ6bH75QQBvAAAsXuBq2m69/U7s7x9B05LXw2po QV3nMu+el155DcnpCzBaX9z7+cWXt6JhyRsw7Nhe2tlDO5DrP4DPXLAO560rClwyTY0pDD33OIB3 eOeSbbNRN2MZYNuwkg0YzOS8a20tzci+tB31ncuxvYdjuOVYoH4AyWQ9AK7tCw8NZjGjuQ6zumbA Su5BoqUDt23uwYrGgzh5nbsH9b7bvoHOd3zJu+fRJ55Gsm02GuavA0s2wLLr8DfvWIPzT9RrItXn 2LZ9J5LTF6Abxa8OTm9twOZXHwawHkDwO1OZ2dYAAHj8yS2oa5+L1LxjwZL1ACyAO/jmTVvw8dNn 4ez1p+Ls9a4Q/PQr3bjiB3dgMJOBk82A1TWVxEsQhJ9yd0XyNJ55fz+kxvfHvy31J7/5r4/B2//9 iUjxxVskhWAtqvYeTSfa88zj6P/7S7DkHZdg5OB+rN9yHxalE5jVnsLcziSamy0M5Bg+t+Eg7n5u EB/oSuP8mS0koBJEDNYtbMGm7CocPNyKP72yGw9uvQV/OtFdMV2fPYyhVx8AcDIAvzaPMQbYaTQt Px12fTPsVv9ipi0v78DmV/ZgzaKZ+MX/91utSZVzYHGna05/5umnkWvowMu52QBcN6K5DcPY2ecU wvrbdF8mh9YGG83pRliWDZZMKZ1gHnZDGxJNigbRcbC/L4OZbQ34wpXfwR9uv9u7NHtmJ3bv3ecz jf/htrtx9hmn4hMffi9GrCb8btMhJDuXAomkL61EfTOsVNp3zqpvcc3m3vM6yI8O4q1Hu2btX1y/ Ad+99mdobWnGnzb83H1uMPB8FpwDjAFWYbeUREsXEk2uab6xrqiZPNzfD57PIz8yiIVzu8BbT0BP 7wAGsxwsUaftC//8wkFc+LqZuOidb8X/bnwR/Yl6/PyBnTi9bacnoAJAZvsmCLeNkZEsEk3TYTW2 ITl9EQDgnHVzQp9DsHD+XFibdmPRwoXeuf09h+Bkis8S9M5UunuHMXtaI9YdfRRufWIPrMZpSE5f gITFkHc4Htk+gjs+/1M09W/FMSsW4DtXfh5HL+7CsR3DeDS7CsxO0jhBEBGQTfK3/l3wosxzvv24 d1w0yTtGE78cn7hXnPvj3x4XKT7b/eEUFkJZ0g0OGCzfzQAAxsBYFuAWwBy3p+V2wbwvwvn9VHWM DPTj2V//FwBg2awWvGthKxbPr8f09gTsJgsX/6wbdzzrbjlyZU8/LlzYgbaUvlMmCKKUb16yCvsP L8DmXUPoGxrFqtmuVimTGcGmp7Yg37TQF76ozUsCsGDVpWA1tIMlUyVxX/7jR9G/6ffIDR5AesXp JdcBjs07+nHcghZ87EN/hWVHrcWqFa6/YS6XwzPPvYDRZD8SbbNL4n9+9wBOWtKGxQvnw7nvBYBZ pe3eYmCKPykA/H7jNnzyravwxb//FE5b/2ZkeBKLZ0/D6kVdOPWj/wGrrihUPrLpadz5yLP4zuc/ gc9ceh6QvAM3v2Zr05KFVgBuv8cs/znHQU//KOa0p/CmU05CY2sHXneM+mlnjv19GXS2pnDpX12I 1cfthNUyC9+/1/UJXdjRUIiKY/OzL4K1rwR3svjJ/3VdF/7td0/h9i2H9GUC4Kd/3om1MziWLZiF X3/nCmx8oRt9+RRWzlxZEtb3OJYFVtfsxRnlOQTvOf9taOuYieOOcQXg3Xv3YdPTW1C/4HX+Wwzv TOW6P2/F35+3Fu88582YOXsOdmUasHhWO254ZA8efPEAfv/Zk7D5tcXY1X0AXa113n09u17FUN8B NC47HbCsgBQIggDiLZLKa1bdO4rPqC6+s7/5qO/4jitOiByfpEG1vJm9ewMAiJX7VmQfUzmxqGwa zOK096dhJQDWD3zpd32ecCr4/bYeJKjTIYjIXP/wHhzVMoAT5s9BU9N0DGdG8ehTz+OaH/4P9h86 jHTXtJJ7Ei2dsBpLz6uwhI30MeeCDx0C5zltmCtvegkfWj2C1x+3Cue+6QRkRrN4YstLuPa/f46d e/YhveIo8NxwiYD6py09OGlJG046/mjgv24EeF4bPzT90fUPvoq+PVvxrtPX4IwTV4MD2L7nAH55 450YfOFuTDv7Cl/4h5/fgy/+4CZ87VPn4zPvPRu47k7c/FqzLrHQMgGAf735ZfzNG1uwZMEcnJJM 4ge/uRX//On3+sJ8949b8NHTZmPhvDmem8FV3/850kedjeMXtQIAHnp0E3r7+pCe2wZYxW568IV7 kNmdQGreuhLXCwAYyOTwqR8/ivOXjODMN56AU4+aDdu2cahvEA88+jTuue/Pxrwzq/iMUZ5D8D/X 3YAT1x2DmW0pPP/ya/ji176FvJVEQlePIowh//vYDvTuehnvPn0Vjlm5GOsSCezcdwivbdyA0dxM PPbqCixr4zh+sesu8NquffjtjTfj0Sc2o3Hx6+GM9MFqaAtNhyCOdGSN55nfeDj2/aU+qOHxBaWj xscGMt3c1aAmwLirBXW1qa4pjjEGLgmojDFYjHkaVObToHrRYsnK4/GB796HBzb5fYzu/ac3ajN2 4zeW4O0nJ/GF7w3gm9fvND7Am640d7AEQfgZeHIDcn174IwMgTtZMCsJq6EN9V3LkEx3wO5cjgN3 uls/Ncw7BqlFr4fVqB/cD9z2b4HhdNezPdsw/OpfkD/cDSc3CmZZsBrbUD9jKeyWTtgzloHZdb54 EhbD/3x4FebPnIaPfPaf8WJiBRJtc41pqOdgJTC89SGM7n8ZzugAGBis+jTs5k6kZq6EPXu17576 +cdjdO/zGNr6EACOxsWvR8Py00viHd3/MgafuwsA0HL0O5CcvQaH//IT5Pq6kWyfi6bl65Fon4OR XU9j+KUHkB/ph5VMoX7Oagy/+pgvrpHu55HZ9hickUEIbeS0Uz6E6z9/Ljpb6nHpJz+Hzdt60LTo JNgzVwJWIrT8PbiD/KEdGN7+BHK9u5AfHXR9+u06JBpaYTd3om76AiRnrwmMM+w5Hr7uawCAL/7r f+C2TduRGziA/PAhWPXNaJh7DOx0B+zOZThw+zdC39mh+6/1/WYJG8PbHsNo9wvIZw4DHLBSTWhc cBKSM1dg4Kk/IDe4Hzw7AgCw6ptQN20u6jqWwO5YRD6oBBHA0BM3YOW5f43tu7vLi8DJY+iJG7D0 nE9h997uqscHALmD28U+qG4YzgDGhQbUP9Mt2V5KOTZpWLlj0H4ofOY/duEf6hN4cdtgYLio8REE ATQsPx188KDrM+jkITzJWV0KVroLnDG0nvhe8Iz7RSPOHWMbCwunu55om4PGFWeCD/cCTs5dPc4Y mF0Pq7kTsBIl8eQc4Du3vIh///BJ+Oj7L8DfXH0LrOZZANOnoZ6z6pqRWnwy6ruWA9khcO643ZmV BEu1lNwDy0bd/ONht88BRt39O/MD+0viretagURjO5B1w3Anj/Sac+AMuc/GOQd38kh2LkeioR18 dMD1sUxYSB6/qHgfd1A/ey3sllnA6KC3ov7s1dPR2VKPm267F09sfhaNS94A1De64qvmOYP6Qqt1 NhqWtYAPHXR3ShCaCWaB2XWwGlq1ZSfHGfYcvvTqGpBedSaQy4A7eff9ts4B506kd6b+ZskGpBYe j7oZi8BHhwr552CJJMAYmo46E3yoF1zUKQBIJGE1tgN2isYJgohAudtMsUL7yjv+PqPa8bH+4b2c MQ7AghBKXUM6KwqcrGhKEucYt8AsXgiZ9F2TNaj3P/KULyP3f+XNZT2A4LSv/Kmi+wmCmByM7t6M wWfvQiI9Dc3rLo7kvzjZGd29BYPP3o7ktHlomHsMEm3zAMlvtpa478tnAHA1qHc8sx/1C04Ak7YD Iwiidslsvhl1K/RflAtj9IU7sejsT2DPnuJnk6sdn9O31xVQhYm/KKAWhNCCwMkZwJDwaUiDBFTG LCxesQ4f+O59uG/jprIyTBDEkQ3PZ4GBHvDsEFgyBdY6Z6KzNPbkMnAGDwLZYbBUC1h6Rvg9E0jv 3d8F4Jrm6+YfD1av898lCKLWmDl7Hl6789qy7l1w1sexd/eOMY2PD+wXAio8v1MASEirUxlj7irW widPi1iwmHuDxeoUE39Rg3rvXx4FQRAEQRAEUTvMmruwrPv27Nw29vENHSyu4tf5nQbBOQdHhJX9 Qwcjx0kQBEEQBEGMPXterK58Vu34tBpUsVqfMQbm2wJV+KQmAbjOsAlmQ92GijGG9W98E4amqXvn EQRBEARBEEQwRg2q2MuUKedUjakbTr/v6WN/+G8c3FfcMoojeIU+QRAEQRAEceTBUNwernvXNnjO prLwadpo39WyCgFV85UpL65qZZcgCIIgCII40rA4L35ayiSYyueDfE7pM6QEQRAEQRBEpXga1DDt qRw8KAwJqQRBEARBEEQl+D5uL2tSwwRN97oVcI0gCIIgCIIg4mPJQqlYuS/wFkoxRlpTgiAIgiAI YlywdKvydX6mJt/TKNpWgiAIgiAIgoiK5Tg5OI4Dx3HAuQPG/P6oOSePPHcgFu+7e6NagMPcvwh+ qXHo6Frp+5PP647HEjUvap504XXHY5GviUw/LrWUl3Iw1YNqPNd4lM1Y5FvEO1lQ82r6Xckz1Vp5 BPWlproQpW+Jm9ZkpNxyiBuOIAgzdngQF/HlKHEsC7GWZZWcr4Se7ucjnRsPyk1Xvq+ja+W453+i yqsWqHZ5T/S7rAZqnsf6OaZKOU1Wwso/qE7rfkehlvptgiAmP9pVTjptqOufygPDjaW5P0xzWU3N UFSC0lU1MtXMW0/381rNR9CgEkW7EVWbUglyeZjKzaTZCdL4qNcnQoMRVh+i1NHxqsumOlRJ/sPq fNx3WE3k5xVtRf2t5rncuhp0fSLa3EQSVAeilovpPUxUvy8Tt11MdH4JYjyp1Kqi1aCqny2VtaWM OQAScBzHu1a6mGr8fFLDZv/jma4OMRBOpCZhosooSn5Mx6bw5fweD4LyEDV/E/le4uQ/7H5dnZ8M 71BH1LoaZcI4ns9TSb8j36sK85UQtc+Meu9E15c4eaiF/BLEeBKkCIhKLBM/gyuAOuDuV1EZPLM/ Y8w9ZvIHU8tD7rgmuhGrnagpP9XqxKOiG0SqEUelcUZNt5bjI+JRjbpSq+8war6itv/xbHNynnQu HnK4ajCW/fZE1I9q9ue1Wr8JYqxQFRZA/DZl1KAC/pX7jDGAB1xDUfPKuRMrEypxG/NYCoZTpWOZ DGalqAPqVCXo+ceDMCuAOI5yD1EbmDS+Y1G/JkKDOZaYrBzjmQeCmAqUq8ALNfHrceDXkyZiJ1xN pooQOZZMljIazwG1FoljVh+rtIOumd4LmSynFrXgmhRGLeStFvJAELVMJZM4rYAq+58KOOc+11Kd ECt8UK3KF/JPOiZiJl2NQUSNYzIMTETljJU5udw4x6POjWVdj9P+qc2FL3qdahxp75cgBJXUe09A lbeKCts2qvScMOlbsIRz6jihUx1XsyPQdZY93c+XpBukwp7oAWisy6hamHzY4uZ/IspbVx+iXJMZ Sx++sPILKuOo+ZLL3SSERU2/FuunTFj7n+jnqUZ7Hws/UtlVxFReUcyBtdCnxcnDRNcHgpiMsN6B HRywPKG0cNq3el/8bxUET64oXv0CqwXGgbPWn4n7H9yIg/t2elc4BsfkIQiCmLyQdunI5Eh570fK cxJEpTA0ecfdu7YFL5ICFHN/pN2jHBi2Vw0lSAM53gTN4Ce6s4miXagVpoLWYDKVt0rUleVTmagm 46lUDrXUlwomWuMZFWozBFEbaDWojsM9Dar4syxXMwqEaVABxi3SoBIEQRAEQRCRiKRBLbmpsPiJ GXxL1a9HMVS2zRRBEARBEARx5KK1xasaUSGAFgVRByAhlCAIgiAIghgDjM6igUIqcwq/9UKqqlEl CIIgCIIgiKgYNajynzgHFLae4gCzSr8oRRAEQRAEQRCVYssyKucAuPvb4RwJm3naULHFlGVZcFDw R2UOuLdqX4rHILM+tYVWPhIEQRAEQRB+jl19vO+3cZFUiYmfAYwXNKgAOMI+h1rKGevPixWeIAiC IAiCmPrIuz4BQRuWMvMiKGHmh0O+pgRBEARBEER1ibyjPuccnBXN995CKBJSCYIgCIIgiCpin3v2 hROdB4IgCIIgCILwsO9/cOOYRHzaKSePSbwEQRAEQRDE1MbuPbBnovNAEARBEARBEB624+QnOg8E QRAEQRAE4WFzhz5ZShAEQRAEQdQOtkMCKkEQBEEQBFFD2JxM/ARBEARBEEQNQRpUgiAIgiAIoqYg H1SCIAiCIAiiprC9L0IRBEEQBEEQRA1gAySgEgRBEARBELWDTQpUgiAIgiAIopYgDSpBEARBEARR U5APKkEQBEEQBFFTWBOdAYIgCIIgCIKQIQ0qQRAEQRAEUVOQBpUgCIIgCIKoKWyHPnVKEARBEARB 1BD0qVOCIAiCIAiipqBPnRIEQRAEQRA1BZn4CYIgCIIgiJqCTPwEQRAEQRBETWHnc7mJzgNBEARB EARBeNgP3Hv7ROeBIAiCIAiCIDzsC9/3/jGJ+Bc33Dkm8RIEQRAEQRBTG9qonyAIgiAIgqgpSEAl CIIgCIIgagoSUAmCIAiCIIiaggRUgiAIgiAIoqYgAZUgCIIgCIKoKUhAJQiCIAiCIGoKElAJgiAI giCImoIEVIIgCIIgCKKmIAGVIAiCIAiCqClIQCUIgiAIgiBqChJQCYIgCIIgiJqCBFSCIAiCIAii piABlSAIgiAIgqgpSEAlCIIgCIIgagoSUAmCIAiCIIiaggRUgiAIgiAIoqYgAZUgCIIgCIKoKUhA JQiCIAiCIGoKElAJgiAIgiCImoIEVIIgCIIgCKKmIAGVIAiCIAiCqClIQCUIgiAIgiBqCvupLc9P dB4IgiAIgiAIwsM+Y/15YxLxV/D1MYmXIAiCIAiCmNrYB/a9MNF5IAiCIAiCIAgP8kElCIIgCIIg agoSUAmCIAiCIIiaggRUgiAIgiAIoqYgAZUgCIIgCIKoKUhAJQiCIAiCIGoKElAJgiAIgiCImoIE VIIgCIIgCKKmIAGVIAiCIAiCqClIQCUIgiAIgiBqChJQCYIgCIIgiJqCBFSCIAiCIAiipiABlSAI giAIgqgpSEAlCIIgCIIgagoSUAmCIAiCIIiawp7oDKgwxiY6CwRBEFWDcx4rPPWBBEFMJeL2gYKa E1DLwbZt5HK5kt/qeV3YONcAeNfFb/k4lUph7ty56Onp8a7lcjmkUimkUilkMhkAwMDAgHeut7cX qVTKCxuUBxNqnsLuV8NlMhnfPXJ84ljkEQDS6bT3LOl02rs2c+ZM2LaNVCrlPbf6XHI6IpycTiaT 8cUhl7MII5+3bdt3v+793HbbrTjzzLNKnl9XhnLedNfl/0UYkVeRf/leuaxzuVzJ9VQqhWQyiWw2 68uDKN+9e/eit7cXANDT04N0Oo2BgQHkcjmk02kAQG9vLzo6OjAwMFDyXGo56a4HlZ3629SeUqmU Ly3xvLrwahnIZWPbtvcc8nvW1elyrpnasa7Ol9MWicqwbRttbW3o6OhAR0cH2traAACrVq1CKpXC Qw89hL1793p1ZObMmbj44ovxk5/8BNu2bfPeZ29vr7b/mjt3LmbOnIlVq1bBtm2vbQHA8PAwAGDG jBneOVE3Dx06pO3TBBs23Ii3vvUcXz8g6rN4LjlOAN4ziP5D7QNFGqIvUPMUVjfV/kRt12q7F/nU jUPUDoiJYEoIqHEIamiVNEIhVKnxqR1ZlHjiCqo6AV3Nh+68Gl4NJ+dFvlc8p3hmnbAelFc5LRO6 ztNUlkFx6QYIOQ9h+VHT1+UnTr0xvRtdODGoyBMgMdHR5SVuXQtDN3AFvYO4acvvuJL7414LCkeD 8sQjC2DqxE+tI+l0Gr29vZ4QZ3pnsmCpCnyAW7+TyaQxfJS6qfYrcts1KUt016KmVUm/QxCTAfJB JQiCIAiCIGqKKTGtiqL9Gos0Va2iqkFVwweZItXjqMhazqAw1UDW5OmO42qK5WPVfG/STprMxroy 0Gl9TdoQNU2TBjfMFKyeK1eTJ5ep7EqgmsbVY9kkGPV96Mo4qqa3XOJoKquZNmlFax+dCdvUllKp FHp6enxmcfkeORxQNL0L1HamWoxMmkq5fQjNq2rGN7U/U3y681GsQ+XUabWf1cVDbYWYaKaEgFoL 6MzdpgYf5geni1tcN5mKyhEqdPGazE2qqVmEEX6QapgowlGQ64Gpsw7zo5LjlJ9FDCKZTMY7NnXO cVwJdINp2OBmiksXRmfijxqf6V2EuXwE5SdOuDj1vRz3DWLqIbcn2TfeVGebm5vR29urnVDKiHYj h5VRJ8xyO5P7DJVkMundq+tXggiaUOuE6KDxIa65n9oVMRmYkgLqWDa+oBm1ThAod5Yb5rNUTpym eNX41bCqP5UsoMrHgiBhQ3de7vzDfAKDhHGTZlTkEwD6+/u1gltUDUZUgVMsfDLlx/RMarnKg2Uu lwvVoOryFaQhUTXBumeKMqCZtMtBz67LOw2ehCycirqg+o3K7XrHjh1l+aBGsbjI58PatNzOTMqE sPuqgS4+alfEZGRKCqhjQZjWSTYRmQQHWfgwCUlB8esE1iChLCpRO0jZ1CyfM2kty0U2U4dpmYM0 J/KAopazHMZ0v5yG7rnktOV8BgncUZHNhlEF1LjvXUV2S4gzwaiUcjW6xNREZ5Uw1Ut5kVSQi1UU E798Tl4Iqu4IIKP2wUFWi7FqP3LaujGCBFNiskIjQkyCOikxOzfNqHXxRNWyBmn3TMKESQMbFrec fyFM67YRCiKudi+Oa0BU5K2b5EFJNwCqeY3aqZeT/6BJjBq3quG0bdvnVgGUapdM+TNpOKMMsKZz uvjCUONRff9kgVz+nzjyUOuvWtflFfxByNviiQmwqiXVTTjFNTa5Dw4AACAASURBVHmbQKDYt4it qcR9Qfk3hZko4o43BDHe0Cp+giAIgiAIoqYgDWoFyH5QsnlUvm4yV8n3666ZtHsmU3MUTWyYSVjW Yon8yVot1YQWpLlVNRJhmsU4fqG6NNWwarnL13RaD5MJTn2GSjQLQaZzQG8ulM+pLhY6jU9Us79J a6rTaqvXTOd1z6PGo9PcqnELDbH4IAFB6PpXADh06FCo/6l8ra2tzdiOTS47svuWQPQhcljVP1Vt ezprS9T+bTwgdwCi1pgSAupEN6woAqqOqHku59niCIem8KpwGtUUrMtzUDlks9nYpvKwMlXrhG5A MU0M1DJTzeM6woSxsGdR481ms16Zq3kRZkrVfSEsvajCqm6iErUeRY1bDaOa98XzRH22sDzQwDt5 UPsfgSo4AuZV+SoiTDqd9k1egyaM4lgWUOWvtYlwct+i3h+1z6y0fsZ1MapGGIIYa6aEgDpRqB2p WHkapIXT3a+G04WVz8fpPHQCs0kLq+ZLFk51X3HR/Q7S/AblT7eXYBRMmuMgrbJJQA3TQuueL0qZ qGnqBlodpkFH9UEVn2vUxSnyrC5mC8qzKS9B9c4kxEfV1Iq6JgupUXwLialFkNZR1/cNDAz4JjG6 9ivXTfGpYN1qfIFOg6paM+Q05ImiWt/lfk1tH6ZxIYhqCY5B+SGIWsF+asvzE52HSY9sctWZSMO0 T1FntFG1ipVovGRUATUqUbWnurIqR9ulG5jUeHTfs46S9yhpqsR9BlNY9ZmEkKma+Ct9z6a0gwTK KOmrA7p6j+7dq/tPynthEkcuQgGgCoHlaFDlOqWrn6qVRdRLOW1dnxY0QdO1BTn+sHwHEbW/iSqI UnsjagX7jPXnjUnEX8HXxyRegiAIgiAIYmpjH9j3wkTnYdKizo6DNKgyQWbiMHO4yTSj89VSZ9ZB mlWTNtOkQZX9FMPM57o0dH5ealmoRNUgi2NdvkymQjUfYemb3mHQu4xS9jLqHq0i76oPapBrhZw/ XZ7DNMFB6FwJooTX/a8zo1aiVSemBmEmfvUrUkF9J1DcNzWsD1bRuTiJsLo+T1eHk8lkVSxbujzH ccEhiMkC+aCWSdhnTeVzOr+qSsyzUe6Ja+aXhQN5k+ow4SWKoBaWb5NJLCiv8nGYoBaVOO9CFa5M G+hHiT+onIPiEMeiLup8NuOUSTnCapT4wyZo6gAvP1cqlcLAwAANsEcYav+iCoJx60JHRwcAoKGh wbeoNY7Z2+RuEtVEL+/LHCVNEXe59V6ntCCIycQRKaBWQ5CRBVSdRlMNr56v1ixaJkgglq+bnl1d NS4LqkHpqWmZhNMonXtDQ4NvG6iw+8PiCwtbDeHWtm00NDQAKN3CKkxQDypb0/e/dXHI2uywQXc8 hb2gAVmtj7JWHjBrreLmnwbmyUMULWiUa2odaWtrA+Dv43T1McjKEVfIC7LUVEI57beSsiOIiWJK CKjj3ZjiCqjyfZWkWe3nVONTzflR8qvr0KNqSk0CazKZHFdtaFRMJryGhgZvZf3w8HDgCmFViDRp jONMZuTV7/I2OLq0q0nYe9e5mYjzut8mDWpULRcxNTBZpkS7KkfzKDSo8gRIN5lT+zNxLLSnunZp yk8ul/P1BUECa1zht9pCaiXxEsRY4bXEjq6VAICeblrVH4Zq1jXNvE3aOd1vEX4sBCsVk2CoE1DD zFe6Dj1IcA/r1IM+BhAk1IaVW5R4wq6Zwqt1wRSPLIyq71/8RdlaSX1W3X6pprCVEGYh0KHToKqC q/pbLiedsECD6NRHvGPVrB9mBdLFAwAzZswAUJz0CH/QqCZ7YeIPSkMOKz+Dei1oLJDvj0tQ3yqn Ox5jDEFUAwsoCqfqsfgt/+mu6e4jCIIgCIIgiHKwAVdrqtOgqueEQKpqWXXnpjKqiV/MrFUtmhxe F0dc358oJlX1vMlcpiOVSnkrxMXvcjWNOo1lFA2f6k8Zlu54adN0pni5vNXN81VMpsMgE382my3x QzXFIb83nSYlqg9alPKMooFRNWByOmpZ5HLFDwnIWlNVm09a0yOPKO/cZMmRET6oav8SVXspTPy6 z5ma2o+p7o8X1GaIyU5VdP0TKZyWI+hVEr88eMrIg6tpIDbFHzcfY2GikU38UfOm6+DlAUCNJ6oP 1ESYck3Cf5gQrpr4xT1x3AjKeZ/yPaYvfVUSv84dI25bC/JB1ZlrdT6oQZOVsSbsHQLkFzueqOZ+ 3XWde4ht254P6sDAABoaGmDbNoaHh0v6cfmzy3K8QjiN875NeSyXcu6VBfE4rjm6OAhivJn0zihj 3XBMApg6O66kA6gWOq2lLozoyIXmV9YIj4UfY9ggL4eRfb2qoSlVJxRRJg1hcenyY9riST3WaUyT yWSJZsY0CMvXdVrIar6rKNqlIOFUd86UP6FBnWitk0xUrTMx9gT5iwZZNwD9bhC6uiVPOuT2nMvl fJ9UldMNm2hOtN+nqczk47C6THWdmCgmvYCqYywanKrVMWmA4sRdrU7LJFhGTU/VAFYjP3GviXIT nzNUw0fRZgWdj6INDiLIFC/yrQtbiabNJNCFaRxNGs8wYbLcSUAcod8kGMgCqnBrkPedpEHyyEK1 xESd/KttwrZd9xeT+5XuXnWSnMlkMDw8XFLP1TzorGpheY5Tv+MKk0HpmyxcZBkgaokpKaCOBarQ odP2hJmDq6ERDIpTPR+1c1c1qFHNmmEdtul+3W+hQcxkMujv7w+MI2q5mfIWJlDFMX2J+MJW38ed jMhaeZNmSI5b1qCGaTqDBF+17ujiiDOIRR30xDfSw+IijhxUATXOBFUOK/tny+fFSn41Dp2AOjAw 4KvLtm0b/cPjWBXKpZK44mpQCWKisEwLocLOEQRBEARBEMRYYMsCp074DBJIjyRhVTWj6syP5fqg yrP4uDPaII2Xmo+geE2Lo8JMaXEJi294eBiZTEarbQ0yUYVpV9Tv2seNRxevzgdV1bKE3ReFMI2o rEFNpVIl/nNR4ww6X04ew5DLyfT5STLxH7noTPxR60FUH1Tdb+FzCriWnYGBgZLP7YqPiaj5FfVX /SxqXAtKkHUsLqZ+qBpxE8RYQjUyIqrQYfJBlcOY4ijHcT6qj1JQWJ2vkTgfZOKP6p5QSSes+n+J bZuChL0o8av3hwmIOoE/SOjUmR51ZkBTmKDzOhO/6R4RLuqEQuc2IMejCophpv9yXBjke4RwTYIo YaLcPifIB1W4Fsnmenm1fiaTQW9vrzcBlNNVJ4NqWy/Xp1PXL0bBNJGNO1EmQXXyMtVcNqyJzsBk QQz+6iIpuSMpV4Nabl5M6QQJKibUr0eZOrUovpxR0tb5l4k/3Sr+sLjC0pC/IBMHWdgMei51cZcp HlP6uvNBz6+rf2rdrLT+Bfn9BV0P0s6ahIzm5mZt56obqKdSB0yYkfsieZcLXR+lm8SJMMIHVTdZ FWSzWW/fYdEHCd9T8afr39V4Vc1qWBsaS8LyK86pYQiiVqDaGIGwDkc3Y5bPm36bzoXlRdXA6tKN O2OPMmvXaYpNmjZdGmqeg/Ij/y8/U5h2WiBMbEJoFP/L+xyqHbVO4xGmPRRh1G2iTJjCxfnOuJxf +fd4Ue6Aq2qx5XjEwrhcLofh4WEA8Fw9hKaKBNMjG5OFJwzVHUvXz8lbvZmsCzKib5EXdMpCcTlK ApUwy0mc+6PmZSIFaqJyptq7IwE1AnLjNmn3gjqAamizguLW5UcXJkoegoTTaj9DHBOT2nGaZv7q QCTelxB6oqSpCuum/ESJJww5TDKZ1G5XFeXesHxFuVe9Pp6dnfD5k49F+kG7ExBTE7U9lyPwyZNL U92JYlHRCaxioisLv9ls1rPS6DSVUSbwar7jEHYPaUeJyQaZ+AmCIAiCIIiawr7mB7+e6DzUPEFm exWdmTjIty5KnLp8hOWhEqKajnVay6DrUTBpStTyCtNUqhpU+atZ4rq66EH37qIQxbwva2rK1WSE abHDtLqVaH3j1qko4eX2Icz56jsTJn7S/hw56OqObtW8KayK6h4Sp24C+vYjXFFMH5MQ9VYOrxsH 4mhKo5j8o2hoqw1ZN4ixwr78E5eMScR/uuPWMYl3oqlGQ4xiLo9jUo9jEgoy90cRvmXUNMt1A1Dv kxdsyWHC4ldN87KwI+dTt6VWQ0ODT0iS09XlUffbdC0Mk4BrekemPIYRRVCNWwfC0ovqejIwMIC2 tjbkcjmfid+2Sxe50GB45BBFoItSZ6NM3sQkUp5MmpQM4lgVUE0+qEHKCvW8ehz1OYOeTY3LFI58 UIlagtQSAchaNdGI5f3wdDPasRAqg+IwaS91YaJoQ+W4yhU2TfdGjU8VNHX/m7R+ameralBV4VSE F//rtoWKKmip52y7uPq4oaFBK4jKizN06cYh6nuLG2+1NCRB8ahf7hHI258RRw66yUgllgc5nmw2 i4aGhsgaSFH/1HjESn91D2Tbtn1fqTLFq0tbjkccx4kjLG6CmExMCh/UqILVWKWt0+TJmARTVZtn um4i6mxWTSeK0Bo1/qDrYTNvnQAYFrfpnrBV3MlkskTzIWvjxEAia0rk+2RBSKdBiaq1CaqP6qAV dG9QuYnztSa46epB0ERFDitrTcW2PrpJRVC81aCWypMoErW+6/oh0e51gmqUOHVh5AmwTkC1bRsN DQ0l96rPEdbOdXnRxWOKIyhtHaQ9JWqJSdEbx9VejXUeyhE2o4Sp5uw4TlpCKChXkI4SttJnK0fr p+uMZa0mYN7eSTeAhA0GuvtlTJrSsAlFVHRCvJz/KEJ1tbSlQUStI7IQIPaxBeDTsI5X3ojxJeqE UMUkSIoJkDrZiarVNF1TrT2yr3uUvrRabd+UP90xQUwWJoUGlSAIgiAIgjhyoGlVCDpfIEG1tYVB 6DRu1ZwVq9+ajpIfnbYiTvg4lOsqEGQi1/l6qWUb14wepU6YjtWv5cRNcyw1iyIdU76q4Suo5l/V SJEW6MgjjuY0yL9Zp0EN2gNV19+EmeaFBUOckz+ZWo6FLaoVrFyrB7UnotZhPd3P87GI+IILLsX9 D27EwX07vXPTOueGZ4gxAGOSJYIgiHGGgfN4/Rn1gQRBTB2i94GyvNi9a1utalDZRGcAQHEvu1Qq hd7eXu+cSi6X0y7kCAofxdlfXZwV5g8Vpr0Tn4wUYYVWoa2tDalUCh0dHWhra9PmXacttW1bq7XL ZDJIp9Pe71QqFUuDKuIV98nPIBDxm565p6cH6XQaO3fuxPPPP4djjz0OADB37lzvPvm9NTQ0YHh4 2FskJX9uU5f/MC12kKZR1aCK9yE/b5R4ZJ/MgYEB9PT0oKenx6d1lPNuyrOsRZY1y/IiJZHvqP6q cjwmjbEcv9BODQwM+J4rnU5710Ub7OnpKYlr6lEbfeBEYNs2Ojo6MHfuXK+di//37t3r+csDQG9v r6dp12kgRf997rnnAgCWLl2KZ555xusL1XRFHOITpnK8cr8m52fbtm3o6dmPpUuXYc2aNWhvbwcA HDp0qGQXEZEv8X+QZSWoP5evm9qaQPT5YefEwsTx8EMniCiQD2pE5A6hmo03aLGM2hHp8hIlv7q0 1PNBi6TiIOc5qAMOyo8O2WQf5FogC1Ims7Bq2s/lciVbQEVxETCZ/9S8BNUbU1mVa+IfK/eSKBOg MMKeSV4NLQsdYtAkM/+RR1gfYjqnmt77+/vR398fu/6IyZJu4qjWR1N/La6pbcjUX+ieZSLrPbU5 YqIgAZUgCIIgCIKoKUhADSCOWTpKXCqqFs8UTo5Dp4VU4wmLQ5eOvFdgWPph++hF0d7G0brp/nTp qvfJ/6t5D1vsI/ZHHSvNXRTtalA+dfHpTJa6+4PKoJLnDNIeRXFd0GnL5PcmzLWk0ZmcVLLYsBLr grAQye4jYej6GzVd0T/IFgx1m6lyrVKVWELi9FfUlohahmpnAOoK72rEFeVakLASNNDH8Vkthyjl IXeO5ZqedSY92c8qKG/qb/F/1IFJELZnaZjfV5BbRphJMige3TV1s3Bdmrr7oppLw9IXVCKA6JDL thL3EKI2iFu3ZD9J2W1HnWhG8ZkU/vZCSI3TH4tzahpio/90Oo3e3qLvvMi3/LWqsLYRpZ1Hyaca PqxcyhWgCWI8IAE1hDChsNw444QrJ32T4CriiyroRUknzDdR7gSjdKq6546iYTblDwgXUOPGGyQ4 md5buc+gi1tGLG6IouEuF1kYN00GTKhCRlzBmAbRIxPTxND0f5BgpgqoQXVRzYM68ZfPC82+yEMm k8Hw8DDS6XSoJjNsoh93UlZpGwnrxwlivCEBNYQog7KqVRurPFQjniAqTcOkVQwaaEz5UOMp12QV ZtLXoS6YUvMl4osiVOncOKLkNwhV0JU/42oKKx8HaewrFaJ1cUYNK5tFVXSrjompiWwaD9MeqhpU Ux0XFgYhoAqBVU1XjT+KgCpW9AsBVW77Ufqfctqamq8ggdakZDEJ6SSQErUC+aBWAZ1pPc5gqvo6 qXGGpSt3RGOh3Y2iIdOFUQeauJrgKKaqMI2h0G7In8o0DRrqewxKP2gAVe8rpxx0cZjSl79bX4kW JKomNCrVGpRJg3rkkcvltFszqYKpqS2r4YUP6sDAgG8LvKiofZxcJ+U+RvVDle+fCKK0G2pfRK1C AipBEARBEARRU9jTO1dMdB6qSrXN7FGc6aNqp6LmK672Nch0HGYiE8SdRQf5sKrxqIsdTOmHxR3H v1J1zShXgxHF/GUyDZpM/DrXhXI017KJX41fXA/SzprcVyolal3XWR5UxHOJDyYQRwaqtcFkdYha d4VWs7+/3/dhCFM9FZ9BVfsuXT5UE79oj6lUyhdP2POqBLm8xI0rKCxpT4lahZy6QohiDtb9Np2L klaYr2I1Ta1B16MIGqqwJQQN2SeynA42zAxu8qnS5Q9wvxQFlPqXhpnXg84HCZxqGPWZgsrclAeT P1xQWUfJoy6NqO8sioCrCsxBkyZTGcWdPFVr0I0zCSWqh0lA1Zn2o7xvdZGUQPQHQpDUoRsDRN5U 1yFZQFXzqWuj5bY1kbYaX9x4yrmfIMYLElBDiOr7WK4mqhodRbkaMJ3gUO2BXcQfhEmwNE0CdJ26 biCTw4iBSH1GU3g1zXK1GGFCaZjgbYpb9tEzpaEOrLXgBxf0fLp3ahrYo6ZHTF5MbVVGFVh1cchk Mhn09vZ69wjBNKz9J5NJbR9vmnhms1ntwsUwX9kgKvVpVcuS2glR65CAGkJUs2nUeColbkdmumbK j2m1dNBzmzrOoM43annE6ZR1g1nQ/6YBJ8pzmsJGEcaCBtSwewWmVftRNPumtKLW67DwpjIot+2I e8vVyFcCDeITRzmTyTDrx8DAAHp6emDbtreoSYc8qZM/2CFrLXVpydfFllNy+rp0wvrkSrX4JmsM QdQ6JKCGEGSWNGnx1HujpAGMjYZL1wHKpnj5vOhUy1nlKscjp607HyffqoAalEbQOwoSXsPyoQsb RciMmkY5qBrUciYuUTWUUbW65WiDg8KH/SamNkGTErkvi9NvCg3qzJkzI98TNEk29Qtioh82idQJ 3WFuMLp8lAu1KaKWoVX8BEEQBEEQRE1BGtQQhGZIp22cKJ++StBpA+RZtLpaOq4ZXE2j3DzqNAXC D0wXPq4GNcg8XemzmUzccbU9pnhVH1RT/ipNK6pfX5S4wu4Nyqv6vKT1OTKIauIPqzviy1GA278d OnQIc+fONZrX5bov+6jKC6kGBgYCtaOivob5oMrnolqL1ONyLHUmzXSQ9pYgxptJp0GtdJCPg0no iWPKLNe8HYWguHXlpPutdnTqlkVxiZKuSpDQkkwmvT95e5j/n703CI1ky9I0zytc4AIXuAbXoAA9 iIAIiISXkAlZTL6ZyqEKJqFqmYtc1KK23Uz3rnZFLYqahmm6FwndNTDLXNSiBmqRDLVomMzhwWQP +SAXmfAeZAwREILQEJqSZuQgBznI4c0i8piOH51z7Zq7K2QufR8IuZtfu3bvNbNr/z3n3GtRHm3n wbsMa1zI2exe/1DJHqalfUuf2/Kx9bGDqFLbdw1L6Hod1LZp23G75N8XtnGwug3Yhe/9/eWNBzXn wMag1gzm7H1l+yNfDk2rfdRicfMCjbYlptrKvomYdZ+2FNbDtQx9YeuvxLse8fk4vU3mKbJawP86 ZBYD/V/Tmdbk2bWTi+Jk/fYaK5vPs9QR+4dLlm5nZ2eldrH5eQGZCcnamLWu5yqzFnVhXa+B3z/y Svj00edNg9Won0SDPr2OsxjUtmtcY1BFpBGTNr3m7fud6P5V8RkdP3qblE9jv0fGghKrXLO+rwPo O1t3pbZZaO7iQVOyYrV9tt/bLH+rCAjfmWo+q7porfDpupZpyQpRS8k6nYnMTRKdA792qk/rP9ce oxZbFrvOYnRdlcrkBxA1D8F1yt2WX5sYX7V94eGR9WddRZfeN9PpdGkNU10ntbSfP54XlqUBujV0 2DyyPram36vtD2ueXzXbAT429PoteEuVd6uum7f/XhKVm7CARd+9tcAK1NLxVmmD2geK5m3FoRXe 3lKRlbFNpHe1+tZYRKLfal2KbWW14jSzNteWrZSmNNjwA6G7BnH6eKlxQa/SB52dnclkMgnvyega z+6RGve7Pa4Xnm3udr+/Lafdzj0CD5Gti0EFAAAAgIcNw64KVo31Ecnd9jWj3tKI2eft45dqYzej Mvn1NbuWPbLsrWtpWywWzaLX2fFq86mxeCqZe9+mzazbdnvJytmFyIJqZyl3sZiWLKelNs2sTm3p ++o67Gu5YNl7sEr4R9R3zudzOT8/l8VisfSaUnu8rBw235L11Pd5mZW0ZD0tufHbwrRq05b2Bbhv EKiGTEi1xT/5zzWxdhlR+EBbZ5SVrS1cIJtkY138mfgp1T3rzDMyl13Wwfu4r5qwi5pzkMWTte0b lb/kmsyOk2HTaKycd/F3DVVoIxrw1F7HNYOiLvn1XeCK9Lts20zUl3ii/qV0fS0WH2JQF4tF9UtJ /DWdvfq0rQyl77V9wTp93DrpAT42CFRp78y6UmPNaktTEspdrGW15bQB+7rdvg2lqxW0JPbb9msr r9+WxXGVhHFWp52dHbm+vu583jPxvgnLqa1bNIEti3PVbbVtfhcPrNp7IBP0eg3eVbzrx4qjhfXY 1DnSvsJbUEUk7euya9Pfb5F49PdWm2D122qfS23ts077cY/AfYFAraTkNmkTQX7/mvQ1v5VcQJsQ RCULatv+tS642rKUOupNubD0gWPd+m3pI4uuT2Pz9ttr8rfMZrNWi2lXN33tsWv2K5Vh1QfdJizC sJ3UDPbaPDRRnipQ/T1Z2/8OBoPGiprdJ5GIjJ4jqxgpsm01RgHEJmwL9Pyy2g37sUeVqzyk1y1f jes8InPDlcpTY1GuEfv+IRLlOxwOw3dlLxYf4lzXsZpnZalJnz3Q7uINSlG71Jzn2jKsYj2PyoY4 BZEbj4FaPK3lU+S2RbOtrxkMBjKdTpfWK133/oqO3zbAXrVfz4SqP1atBRagjzCLHwAAAAB6BeaJ NagZ/WbWoFVdrDVWrtLIOrNuZmVSN/+maLOC2hiwLBasFOtVsz2yzNVYHbL6lFxs655n/6pHG1Jg j9nFTVjz+6asyDXpStbzu4yPvct8YbNkYVVdPC+es7MzOTo6avbP+iT9LesjfB9zV9dsjQcqKuO6 1mHuEbgvtlag3oWLfRP5dXGHt8Uvelapc6k8Ne7cTcR4Zq7arGxtHXEkAmvDCqLJDW3HtPtm5c8e DNm+NbS5+EvhDX67L19b+MGmHmrruOjvWpzC9uBd8ZFA7XqtWRe/0vWay8KIMsHY5b7qOrgs9dVd w7QA+gBX7Aqs8gBfN4ZzE2lL+2QdmJ0tXnvMLpblUvpMVEVWXf/gstv8MfRhllnyItosNl0faJre TkKL8AK1a7uV6DqYWpXI8l2yOvs6IlDBr1qh93/NQCi7fqxAjfqT2vztPr7PavMObOratsdq66+5 n2BbQKBuCZE1blX3bZR3m0vd/l9VFK/rNs46YWs9qbEW6wMueg/2YrFYK6QhK1epbKW8vIvfUuPi rz1OlH+Wtk1k2mupdrBScu9rGh6sjxc9/9l1taoFVdcVLq2HWtt32Xshs25mXo8sfCCiZCX137Ga wjbDJCkAAAAA6BWPdnh1H/FtfYupq7E22jjUdUfjXWMhM7zrrBSDGlksdnZ2lr77PLuWy1tMSpYS /dzFDW/j5Gpc/Jtg3Ws0i8Gzn2vdppsiihG8KwhNuM06bVITf+3v97ZjTafT5vXJ1gNTCj/xx7Rr Jte4+Dft1m/7DLDNPFqBeh838aoCKKOLG7Umryg/W2Zf/ra4qsjtvo5Ij/Kpde1Hv6ub35e9a3uW xHH0ILXH0MW+RW63ceQqvEtRmsXmbpou+W/Stf8x73lEwm3WbRMNydnd3W3uA43h1rCdqH+J+jPd pi5+zcunj7bZvOxgV9O1DVCjfHyZ2yiFYm3yOAD3yaMVqOuwyRu8qyCqTRt1lG1izu7ny6gL25fK 5Dv/qJ2yjru2Q/fCrxTjGVlK2/bJRKXWpzbGNctLt7fFkWnsaWQtLnEfcWebiplTVonLg4eP3g8q RkWWX7oxGo1kOp2KyO3BVnRN2QGqzbOm3yqJ0Oyez36LjlnTR7RtK20H2Aa2VqBuw423rvXLj/xr Oz7bKdd2fCXRpgLVduClspTK1qVTz7ZH+akYtS43uz2ydFi8ddg/2EplK5Xdl2tnZ0eur6+rhKZf Aqd0zJL1c1P3ymAwuDVQ6Soiu1iFsfiAxb5NSvui3d3d5pq0FtS2+0vvF3uP2f6tyzVa683J8lrF clobhgCwzWytQO0DmYip7TBWtXTVCFebdlULre0M7Wxy6/byHWXJcus/2zxqy1YSaN7llonbzA3X VqaS4I/y2tnZSbf7PESWH4zWglpzrCy/Lmza8rqqwOTBRvglUQAAIABJREFUCxHWk+O9FLu7uzIa jVLvRdYXqIvfpvdhPzo4b+sT9L5e5T66S88HAz3YVpjFDwAAAAC9AgvqitSORmtcxdE+bQu4K12t gPa3KLYycxFvYrJKFt8ZHT+zxFq3eZbO51/z32InLqlL3ufnLZveKprl3WZt91br6FibYlN5Re1f ulZq7wksPmCJXPzq1h8Oh0sWVJHlviHqd/wkSe1zo8mTGf4323d0ZdX7om/3SRevGEAJBOqGqbk5 13Hn1Oy3imiN9rdpo0Xt247h40P956iskSC1373b3AtNL+YiQRq1f2m2bls92+qxCvP5XGazWfqG m6idV30wZPtv2tWePVj9ZBVc/BCRTZLS/youNa2lxsWvefi46mjfUuxp6d6vyc+XuSauPIpNBdh2 Pvnmm2++uYuM/9v/5vvyf/yfX8r/988nzbb/4r88ai/QJ5/cRXEAAO6Frl0sfSAAPCRq+0CrF/+f //u4fxbUO9LLAABbAX0gAACTpAAAAACgZyBQAQAAAKBXIFABAAAAoFcgUAEAAACgVyBQAQAAAKBX IFABAAAAoFf0bpkp1gAEgIcEy0YBAHQHCyo8AhAIAAAA2wQCFQAAAAB6BQIVAAAAAHoFAhUAAAAA egUCFQAAAAB6BQIVAAAAAHoFAhUAAAAAegUCFQAAAAB6BQIVAAAAAHoFAhUAAAAAegUCFQAAAAB6 BQIVAAAAAHoFAhW2isFgcN9FAAAAgDsGgQpbxWKxuO8iAAAAwB2DQAUAAACAXoFABQAAAIBegUAF AAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUA AACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAA AIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFAhZTBYHDfRQAAAIBH CAIVAAAAAHoFAhVSFovFfRcBAAAAHiEIVAAAAADoFQhUAAAAAOgVCFQAAAAA6BUIVAAAAADoFQhU AAAAAOgVawnUTz75ZFPlAAAAAAAQESyoAAAAANAzNiZQsaYCAAAAwCb4g0xY6vZPPvmk+fN88803 YVoAAAAAgFX5g2+++SYVlZ988ol88803zV+WToWqpgMAAAAAWJU/EJFUfCI2AQAAAOBjwyQpAAAA AOgVCFQAAAAA6BUDkZtYUwAA+MBgMGg+LxaLeywJAMDjY7ApcWrjWBG7ALCNDIdDGY1GslgsZDgc ynw+F5EPYnU+n8t8PpfFYtGIV4QrAMDdMMjEZLS9TXgiTAEAAABgXQbtSQAAtpvBYJBaO4fDoRwd HYmIyGQykcPDQ5nP57K/vy+Xl5ci8sFSOp/P5fz8XGazmcxmMxERmc1mjZUVAAA2BwIVAB40kTgd DoeyWCzk8PBQnj9/3rjsZ7OZ7OzsyN7eniwWCzk4OBARkbdv38p8PpfRaCTz+VyGw+FS/ipYAQBg MyBQAeDBYWNEVZz6mNLvfe97jXhVwfnixQsZj8cyHo9lsVg0wvPg4EAuLi5kOp028aiav81Xj1Wy 2AIAQDsIVAB4MGSTl9Ty+fz58+b7aDRqBOizZ8+atIvFQs7Pz0VEZDqdisiNuJ3P5zKbzWQ0GomI NN+ZNAUAsFkQqABbQler3GOw4tk66me7PJTdfnR0JC9fvhQRkZOTEzk/P5fJZCLD4bDZ5/z8vLGK vn79usnbWk6tIBWRRuhaSyxxqQAA68FC/QAAAADQK7CgwoNhkxbDPrpsu5alT2W/C/z5juI/rfVU Z+eLSDPh6eDgQN6/f9+49M/Ozpq1TqfTaePin8/nYVyrMhwOZTqdNq7/KA0AANSDQIUHwyYFQR/E hXdV2zLZ3+ykncdEdo5sWwwGAxmNRjIej2U0GsnJyYmIiIzHYxkMBnJxcSEiIu/evWv2VTf+fD5v BKrFv2FKRbAu8C9yE7Pah+sIAGAbQaBCio3pi96e4y1YHyPmMTtGJOZuynvze1ud2mhLF1nvRG6W NYpiJEVkSdzod7XaecudzUuPo3/W0udF62MSSxoLOhwO5fDwUIbDocxms6XtItKsaaptY0WpWlVF lgcBfnCga6T6pacAAGB16EUfEZGgFJEl0WO3i0gzgSQTVl6Q+VdDah4qDrxI0v11uxVmlsxS5cVC lPf5ucjh4aGI3Lh2/UQXW0d7/JKALbmUrWC0+ej2tok94/FYhsOhnJ+fy3g8bsq+WCxkMpnI+fn5 kjtZf9PP2u6Kr6/W8SGKVn1dqYg0k6B0ySjfZiKyJFCn0+nS9ej/+88iy+fUrqf6UNsXAOBjgEB9 ZOjD2lrmRG6EoYohxaa1ljl1Yep+mnckfOxak7qsj902nU6XZj+riPDlGI/HtyxVmpeW3S6Yrguo n5/LUr0Gg4EMh8MlwaxlG4/HS+LPH8da2rS+Wi5bFs3bllXT23aYTCa32k/z2d/fb9bjtOdIBw3W GmitfVZs2XawFlU9vt32EMSUXqPaNipMtW0nk4mI3MzWt+JeibZlxxFZjk+1AhgrKgDA6jCLHwAA AAB6BUP8B4x3U1t3/dHRUWN109nN+upHb1X07mq16qlVUv+rC1Vd6GpNmk6nS5ZCWyY9vmJdqnZ9 ShuGYC2Nmpcvk1pih8OhHB/fWLas5dZbYrXOo9Foqc1seILmr6EGPk7UWmd9mIPIh3Ls7e01k3NE RJ48eSKXl5eyt7cnV1dXS/Wy2/b29mRnZ2cpP91uLbpalslk0linbWzleDyW09PTxnptF6M/Pz/f Wtd0ZLHUSU96TvSazyZAafo2spAPzX8ymfD6UwCANeilQPWzZO32bXxwfmys+9eKLf2ss5o1jT5M VSjp9p2dnUaIWcGpwkbd4Zpe8/ZuVhV9kfvei197PFsPReNPveD0sZ0iIru7u424UxG8u7vbHOf6 +rr5fnl52YhPPxtb6+XjO208q58gE8WuagjB5eXl0varq6um7CpA7UDi9PS0KZcNc1B39WKxkKdP nzahCZr3eDxuzpOIyPHxcZP3kydPGtH9+vXrpk4nJye3QgJqRVsf0LZXcegnwmnbqItf9/H182Jf 9685tshyCAgAAHSntwI1moCCOP1AJH6sRVA/D4fDZjkdkQ+WuqurK3nx4oWcnp7K3t7e0v7j8Vgu Li4a0XZxcdEINPvwnc1mjUXUClKdkGLLqNutRdPWw1sgtSyj0Uh2d3eXLJOax8XFRVN2FSBa7rOz MxER2d/fb4SfbavLy8umfvP5vPmsAnw+n8vBwYFcXl42bba3t9dYaW0MrZbHWuL8NWqF7nA4lJ2d nSWRbidmPX36tBGRk8lE3r59K4eHh2HbqFXang+1jH722WciIo2V9Pz8XJ4/f74Ul6n11/20TkdH RzIej+X4+LhJP51OG5Hc56WT7Lm2wtCKTTsQ0n18nTQfe++IyK04XpveTuLTbZGFHgAA6uilQJ1M Js0DMZoxG21/TPgZ7Cpajo6Omu8iIp9++qlcXl7K/v6+iEgjvtTyqelPT0/l6OhI5vO57O/vL1kn Dw8P5fj4WJ48edKIv2fPnslkMpHj4+Mla6bI8uxlFZGDwUAuLy9lZ2cndMPu7e0tCcuLiwsZjUaN SFTm83njGrcLqKsVczabNdt1Zra1mGnbWVGs15nm5QdDb9++bSxy4/F4SRj7CV7a9tb6+fXXX4uI yMuXL2/NvNe20fxtCMFgMJDd3d0lF/xgMGheuWnrqmWYzWZycXHRWER1RvpsNpNf//rXTRlPT0+X BLa1CqvVVeQmdOPw8LAJF5jP57fa8j6JBrDWaupFa9ukMDuI8iEmWV19WAoAAKxPLwXqy5cvG0uV XQJG5HELU8VaKdUCdnh4KM+ePRMRaVzOKoZU1O3u7spsNpM3b97IxcVF8/C9vLxc+uwFp/+8v78v JycncnV1JYvFYilGUvMdDodL8ZTZsjsqCu1xptNpeN6tsPSucz9r2sbO6nF0u8arWtFh87eiVdvZ ikPdlq17adOryH7z5k1jpbXHvb6+ltlsJvv7+3J2dnZr9vm7d+9u1SFaB9UuI/XmzZtmu3XT21hh f041vYpXLYMebz6fy+Hh4dJx1KLoz9/HpuSG97PtI0upPY8aQ63bo6W6sj7ItrcV/gAA0B1m8QMA AABAr+ilBXV/f1+eP38uIiKvXr3qhStxHTY1uUstcxp7+PTpU5lMJvLs2bMld+R8Pm9cynZWvlqH 3r17J9PptHHVDgaD5hWQItJYEO2s+bOzs1tWbT8RxK6x6t2s3g1rJ1qdnJw0liq14Nkya5203vZ3 H/Oq/9XamcUcjsfjpTVGNU+/SkDp2tOy2vR2rVKdEe/r5WMVB4NBE0pg217rYUMx9DxH7333k330 u1qGbViE4hevVxaLxVJZ/BqtIjeT67Q9bSzux7KolqyZ0e92u51wZ9vAW1F1e3T+SuWK1tIFAIA6 eilQLy4uGnf1+fn5LdGybaxbbn2oapypCrVPP/20ecjOZjN5+/atiHxos7OzsybG0MYM6ncvRnS7 dU1at7cVnCpWfCysdxfbyTi+Lv7NUtb9bMW2j+nzk11sKIB1e/uF6P2SQj7OUoWmClQrTKIVCHwa H1vrxZz9PhwOG/Gn7aTuYy90B4NBM3HKhjhMJpOmPHaRfv+2Lo2ptG2jE9u03LaMNp1tKxWgVhjb 8oxGo6VF8G1c7X2jdfKTCq0I1etABza6HJi2jYbJ+PMcDb5E+j2ZDABgG+ilQJ3P50384meffbY0 2eMh0Sa67WSN8XgsL1++lCdPnjTW5Tdv3sjbt2+bSTJ2GSG1iHqrmrUA2bc36QPVCjkrQOySOz6G 0b89x8d/6mzoyWRyy/Kpx/VLGun59mWPxIY9tm9fWxefjy6rZLdFeVjxakXNYDBo6mYHEVlbab1O T0+bfA4PD5s3RomIvH//XkQ+xPNq/KpOctP00+l0yaLt2yyyrGrZdLstt12u6vT0VGaz2a1YVHsu 7DViLfT2fGs72MHQJr0gbbGg9jxGyz2plVTbyL4adbFYNG1u78Go/KUyROusAgBAHb0VqPow2N/f l6Ojo6Vlb0RuT+Dwk1S8a7eP+Ae7t/Coa1fd+PpQ1kkwv/vd70IBYNfojCaFWDGox1XLmBdho9Go ObZu19ACvyTTwcFBM2nKhhC8evVKLi8vf//a0ZuF0v1am1oW767P2s2miSyVVsjqca2FMVr7Mjqu tXDaRe39+qeKFTua17e+9S15+vSpHB4eypMnT+Tp06ciIk2ogZblyy+/FBGRs7MzefXqlbx9+7b5 7ezsTF6/fi3n5+dLwteGW3jLuF4L3kWvgtaHJzx9+lR2dnbk8vKyabOTk5OltrSDC7WWnp6eNiJb lxvTc2ktvNYivw7R/tG5sOLcW5F14DccDpvVGVRwHx4eyunp6a1rxQ/4suPp5z73PwAAfaaXAtUu 3XN9fS3f+ta3GquOfzjYWcUiqy99c58PEy9UR6ORHB4eNpbSg4MDGQwG8qtf/UpEll26PoZS8QJN 06rQ9L9Zgegt1W/evGmsYiIiX331lfzwhz+UP/zDPxQRke985zsiInJ0dCTT6VSOj4/lZz/7WbO0 URRH7IWoFRfWfW/d6FldLV6o+u1Ru2X1jvb3IQr+OIvFohFtatEWEfntb38rh4eHcnR0JD/84Q9v WeZUsOv24+Nj+cd//MelEA37wgQ7KPOizwpJbTff9ipQfTzvycmJTCYTmUwm8u1vf1tERF68eCHv 37+X4+PjpcGjnje1JNt8tF52LVFdpmrTlkUbDuJFtE+j17DeY4PBoBmAiUgTOjGff1iXVsv61Vdf hZb7UpkQpwAAq8MsfgAAAADoFb20oKq1VOQmXk5db9ZFbK1f3lqRzeLN6IO1Qy1R+tYftVRpnKmd FS5y42bWNxxFFrUoBjWynkZuYhs+8ebNm6VYRRGRv/iLv5A3b9401qqf/exn8otf/EJ+9atfLa0K oHn5Gf9tk2gyd2q0j4+rzdas9FZDtajp5JhSebw12lt19b/9s+lPT0/l5OREfvvb3zbhG59//rm8 fPlSfvzjH8tgMJA/+7M/ExGRn//85421206o0hhfe0zbtn5ymu7jy67/7fnW+ujERD2Hk8lEnjx5 Ik+ePJHf/e53S8fXdvNvXVJLqT0n6jZXC+Wq91zkLbBY67yGT3irs7bZkydP5OjoaGnil76Ry05m 01fAtpXZtmUWogIAAO30sgeNRMlgMGji9kRuFnPP6IPgrMVOmDk8PGwejPqAswIlWu7HumuVyLWr ab27Wh/WfnKTTWPjJF++fCknJyfyxRdfyPn5ufz0pz8VEZHXr183ZdUliERk6a1HXc5LFLLg8WX1 QtG7ve1nL/ZsG2bljPKxnzPhpAMtnUGv7fTzn/9cvvjiC/n5z38un376aRPH+bOf/awRp174aR62 rP5tVva4vu1sTKpvI5teB4MatvH06VP5wz/8Q/nqq6+afD777LPm/GpIwNXVlXz66afNZC7r0lch a6+JrkSDAi2PyIf7yb++dTabyeHhYXOv7e/vy+HhoYzH46VYYXX3a/l+97vficjtN5N1KScAAHSn lwI1eouQTiaxa2FueikX+7C2gqVksVsXG9upa2dqPKQvi4rEbJKPt974iVLWsueFlFrBbD762cda Hh8fy2w2k7/+679uhK3IzUSUaPZ9RCSEVTzpJJbotZMisnRMtdbZ+EjbDotFbGWzgtSKPp0449Pb c2LXgvXCNor/Fbmx0qpl1Kb/+uuv5euvv27qpevEatk0bfQWLGvJjKylbe3v34jlJ65p27x69WpJ iL57907m87m8ePFCfvnLX8r19bWIfFiBYD6fy/Pnz+Xo6KiJRbYWeh9zHC1FVhtzbOuhHgXNX2NK nz17dmu7XlP29b7WavrrX/96aa3gbIIX8aYAAJunlwJVZFlsRZZSneTQZkltw842jibWZA+jdR9I KlLU1Suy/IpPK2JUrKjLVNEHpp8o5ieEKCXB7d2+9rNOzLHvflehZI9rZz1H1s/IwqnWLh14jEYj 2d3dbcRpjctUBZqINMuTzWYzOT4+/n2Zbovh4XDYLNulZLP4Nb0/77PZTC4uLpauQZ1IFJXRimJr Xda6+vU4VYT6sI7oc2T5jQZb/nvJKm2FsYrL8/Nz+eUvfykiIn/yJ38iZ2dnIiKNlVLkw6Q+tUDq wFJkeTUAbVOth32JQFtYht2eXSOHh4dN3s+ePWsGPNYjoJMPNURG5EM4zcnJibx69Up++9vf3lpa q3TvIFQBADZHLwVqJsIWi8Uty5OfkV568Ircflhr/pE1MhJEXR5GmbtYhYhaRP16lipWdH/93VtO bRnUWmrRvGw5vHu0VFfNP4oX1UX9bQyqz98eVy3FKkQPDw9lb29vackiy+XlZSpIohns2q4qUMfj sfzwhz+Ur776Sr78clmA//Ef/7H84Ac/WIqTvbi4uCV2RG5Emo9p1WtRxZfy3e9+V96/f9+IVj13 auH1Zda81aVuY2htXKSINEt7+TaILOda9kywKn7QYO8H70JXwanfv/jiC/n8889lOBzKixcv5N27 d03ZJ5NJc43oIECtvHaxf62frlBQs95oZCUWuRHsOjtfRJo1ZtUqb8W37vf69esmr+PjY/nqq6/k 66+/Xro3o4Gq3444BQDYHMziBwAAAIBe0UsLqsiNNcKvhWljD0VuXOU+NrAt3yzWNLI2ajqbR8mi Y9OpK1MtXyLSuLR1ofNoXUlbV+8ezly+0bEzq2lGZoGLwhr860S9Nc4uWH90dLT0RqTr6+s0rMLm 5c9H5vJeLBZyeXkpu7u7IvLBAjudTuXzzz+XL78U+d73viciH6x4P/rRj5q1WbVOOzs7S/lF7nNv efSWe93+rW99q2kfrd/V1ZWcnJw0LyvwbWdd/rZ9bRtZa5695vx5jSzanuiFBf78KZpOXeG2jX71 q1/J559/vvQK3t/85jcyHA4b97+62588eSJnZ2dLr1/VY0Uv3PBxqVH4iI859hZYvY80jMBa7HXG /ps3bxoX/m9+85ul9Zb9PejbBAAA7obeClRLKUbUYyft1JI9/LKy1Gyzv+mDU93Bh4eHjUixMZ/q 5vT5Rm5zW+5SuIEX3ZFr0v8eTZqpaXvdrq58FSyTyUR2dnaWyrm7u3srJtLXW931yu7u7i1Rlokw FcNv374VEZEf//jHIvIhTvbrr79u3Mx22St//KidvDs9ilu1qyHoOR8Oh7K/vy+z2awJARD5ECph Y4tL11IUxqHb7bXhV3koiU4/CLB1821t3zamxxkOh81M988++0xERH7wgx/Ib37zGxmNRvLu3bvm OlA3+2QyWRKLeo35N2HZpaFKcb02Hy2fDafQSX0+7fn5uVxeXsqrV6/CWNOaiX4AAHA3bI1A9Q9L b/WxD9doQkvJyln6PaIk8qLti8VCnj9/3mxXC42NN22rczZJJMNaW60IiwStF4q+jSPBHx1bxcfT p09lNBo1r49sa6doBrefte9/j9p4Z2enmUl+fX0tOzs7jWVU40118s5gsDy5zAokPwiwbefL4q1+ 9lxFVtjRaCQvXryQi4sLEZFmzc3oLWlaT91Xy6uxy5EAjSzMJSuqtYi23QP+uFqmxWLRDARERP7o j/5IfvSjHzVvwlIB++1vf1vOzs6afGy8rYpXH19sBxFtQlpFpU4otPv4PsFa5CNL7ToTLwEAYH22 QqBaIpHmBUK0HEybEO1iIYmO6bfZ354+fRouUaQWOCvUSi55b0HNrKNZOX15rSvYbrMPbLuMUtSG g8GgsZA9ffq0WVNysVgsWUCt9dOXwddDRabIjRV1b2/vlujT9CKytI+ILIlVEVlaLkjz9RbZaAKS lrPt3NjyRBZ/GxIwGAwa8b67u9u8iOL4+HjpRRReVOlKCva1ojZ/f93bJZ387HjdZte3jc6NbePF YrFk5bfXrr4iVUTkn/7pn+Qv//Iv5cc//rH89Kc/bfJ5+/atHBwcyOvXr+XZs2eNcD08PJSTk5NG jForsZbDtqctg62Tva+8iM3QeyryXERpsaQCAHwcmCQFAAAAAL1i6yyoShZ7mVlOSjF+mWXEusij 2MfI5ej319co+vUxrSs4ihmsKXsUJ+jbxsfdeeua3SeywGnZImu0Wv50LVe1dFnXuuItltZSen19 3Xy2+w4Gg2bSU1RHm7fua/OMzqnWZ2dnRy4vL6ss4dF58e5in9bn4+NEfZ4aP6mLxJ+enjbLTtkJ VYPBoInf9OcyCtHI6mR/85ZS2xa+vf3yVnbfV69eiciHWNSf/OQn8pOf/ESm06l88cUXt/bVdYxF ZCkeOHpTmuLX541CIqJwgCzExFpOo9ARD9ZTAICPx9YKVJFYNGXuvCzu0j/AfP6ZAC5hX106GAya BcojceLzi8SkjaPLypKV04oiL0C9e9uuRanps/rqzHy75qT+1/hPy87Ozq28VLD6tLYOUVt5cRqV UYWun2g1GAzk6uqqmbTlj+ePGwk/n589vl+j1pc/y2N/f1/m83nj+p9MJvLq1aultVH1HE2nUzk8 PLx1/qJ8fR10e2mmvwpAv2KGvx7tBKbxeNyEJ5ycnMh4PJa/+7u/k7/6q79q8vniiy+avM7OzpbW R7Xufetuz94wZWNTo3MRhaPY7aXPAABw/2y1QFWyB00p9mwTD6Qoj+FwuPRWpDdv3qTlsTGJNXlH QsJaSn05/MM6mjxmy+aFjE4msjGQIh/E0/Pnz29NZFKhqUtI+XLbvNRq6mNFrfXUWykHg4FcXl6K yI1wUREdleXq6iq0jM3n88YyawWstSJbYbmzs3PrFZ2RRVHT2/JmVlY/cU0/6/b9/X35zne+06w4 YOus50ZXCPATvCILsLek2zK1WYBtm9hzomJZRaVeH8fHx/Ly5Ut5/fq1/P3f/738+Z//uYiIfP31 17JYLOTJkyfy7t07OTg4EBFp4pZ1Aps9H/YeseUsLepvQXgCAGwnWyVQI4tjaeJCSaxGwmXdSRCD wWDJoqgzx70I9e53K1Bs+ray2Dz9upK15VWyiSeaTusm8mEyVLTygJ05r1ZK3a6C0LryI6xgjN6W 5D9boZrN/Lf57u3tLVl6FbtmqW/X+XzehAOoQFbh6slc5j5NJGT9dbK/vy+fffZZcx3pcknRWp+2 3NF17wcM0SoA0XmPjmGFqxWnev3o2qLf+9735Oc//7l897vfFRGRP/3TP5Vf/OIX8vTpU7m4uGjy fPbsWbM+rB9Y2YFAG5sSo+v2AwAAsD5bJVBrrIpt+5ZizUriN3pY+7S6WLlaTbP1KFUcRMsaZVbZ UihCtli/r4MKkyz+1LqQ7b6DwUCeP3/eCFRda1ItgeqW9rGkFl9265r31jp7bO/G1rQ+FMLO8BaR RlDq/loeFZh2Hz2unUHuy+Pb0qYRWT4Harm1bWAtkKXz7a3Ne3t78uLFi+b34+PjRgTrecpegWqv dS/evdD0x4/K6a9Db3X31+HJyYkcHh7KP/zDP4jIzVq0IiIvXrxYWppKX0eq7nsRaeJxVxGLpfu8 LT3iFADg/mEWPwAAAAD0iq2yoG6CLOYzS1uzTd8SNZlM5OTkJHRHWresd+WXJrNERDGEPm4yWnje Lnzuj2MnvHgL3GeffdZYuHxbDAaDW2uQisSz6CNLnZZJj2VfXBBZtKM3C1nrqnUzW6ueuvh3d3eX 3vRkZ8hHb5PSdlKrcdYGUaiI32bfpmXbuDY0Q8NHXr16tdQG1sVuJw9Za7Otn4/t9G1s0+g2zcO6 8n28q194//T0VCaTibx+/VpEPrxG9OXLl/LmzZvGGi/yYcLUZDKR0Wgk5+fnaThHF7qG7mA1BQDo Fw9CoHaNGVsl7rRtn6Ojo8a1b2dd28/eZeqFpj5OAPGpAAAgAElEQVTwo4k40XdbFrufX17Iiy1f H8W6jZXnz5/fEqd+IpGv02KxWFoeSoVjKXzBCjZdlD8S0Xt7e3J9fd2Ux4cj2DLqbHebbm9vrxGo Nu7Uhizo5CNNr/mXxKTmo2LdC0AbXmFXNIgGFUokZsfjsbx8+VK+/vrrJUGoIj+6riIRHRGVyeIn itly2jcy2f10AX4RkV//+tfy+eefN2XWtn379q2cnJzIkydPmnhbPZ5921cXd30XkUvMKQBA/3gQ AnWdh8uqMWpWoL18+bJZ59S/aUcknsATxZ9q2ihW0ZbBfs8EibfS+vz9qya13DbtZDJpZljrvrbs WblEli2o0Tqy/r+KlVKe2uZ2lQD7bnhr9dN6+ldWvn//XkQ+CFZbB81Hxaktjx04dIkDzmJWdU1Y LbM9H17k2eWedILXYPDh7WQao6lpojcxZdeSL2dUXv1sY08jke7bxqaxg6mTk5PG+qtLUmkavV6O jo6WfrNt1Fb+0uCrJEIRpwAA/aOXAnUTFo2ueUTWwNL3weBmVvt8Pm/cmlbkWeFhy2NdrpnAjI7p t6kQ9RZW76pX/OLsVgzZV3mqSLPLZfn1Uf1rNKN6WXEYWeXUmujxoRC+TXZ3dxuXvc3Xur2zNvQD DLu/nfxly23zzoSSFcyazi9zpWW367PqWrA+fy23bffFYtGskmDXQZ1Op811589rJN79YMkLUVsf +3skdkuiz0+qmk6n8v79ezk4OJDLy8ulss3nc7m4uJCDg4PmutMXEpTW4y2VoWsaAADoD70UqKvQ 1Q3o6brfeDxuhNzx8fEtASqyLOT89pK7vc1ipGSiRn+LBG8kLuwi6/YNP+pSjhZE98ew+avgiCy+ Xmzp0lS+DXxcq138fzAYLK1jagWeL6NvAys4M4u2DU/QfbNzlVl6VYzaPK6vr+Xy8lL29vYaC61/ m5XPI7LCqphWISdyswpBZsn09fVeAN9WUZkyq34kam05NLxCl586ODi4FbpwcnIiz58/l7Ozs+bN ZKenp0sDJwAAeDwwix8AAAAAekUvLairWEvaXPIZpVCA7Dedta8TOrzrPLIq2jKp5Sqy7kVWrJIb 1aYpxaJqua21UWMVNR5T34Kl+/h3n2fWUn9cbyH2rn+tq1pJrStcid4wpVZU//pSm7+GFfh4Wu/q VmtuZBG01tro3PjvNRZHzXc4HC5ZTff29ppF622b+BhV3+Z7e3tLLylQK3iWXsvg8dbxaFKfvY58 G0Tr6kaTvexvr1+/Xlov+PDwUE5OTpo3l+l1qXVa1zsCAADbRy8F6sekNrbNPmTH47HMZrOl5Xas 8POvlNS8fDyizzs7ns8jyidz4WZi2ec7HA5lNBrdWvLJz3KP2ieKc/T1jV6zORgMmpjK6HWndnu0 bJVFBad9b7yNXfSi25ZFt0fxsLYNrXD1v0Xt4j/b7/r/6upKDg4O5OzsbKlMWR46wcq2qdZ7Op0u rULgl/XSvH1cdHY9edHpt0cDFy1PFHah98vx8fGtwcRoNJKTkxN5+vRps/3w8LAJXUCcAgA8Lh69 QO2KCiBv2YliTXWCS2TJstvaYkeV6E0+mi7a7vETs7zQ8GuH2hjXUv61r6GMBHMbXjRGllgVuR67 JqjGa9q1SCNRHJVRVw3QfW19/D5+m3+zlheF8/lcdnZ2bgk9FcO2jLu7u0v5qpVV147VyWyRyPSf I8Fp6+yJBmBRGl9HO3Cy19Lx8XGz33Q6laOjIzk9PW1ed6roW7JqrrH7pOSJAQCA7iBQW7Cue5Hl ZX8iseVntZdc4TZ/j7dYZhZXb1H1edj/mta7wweDQWN1s27vaNKRL1smHHy5fBvYunvBGy38ryLU C3r97MWcF+1aVx1geAFoJ2t54eonO9my+9eL2nRRPaI2sMe2147fX0MbooGLFagl8anbs0FMm0fB ittS3bNrV9tMraYiHwZ7o9GoWYdXBxOLxaJ5AcZisbi1/FSfQJwCAGwWBGoL+rC18ZqldJFLPbOI ZoIt+pylt/m2xR5m4s5auqyL31orIyGprtcstjFa0D36XPNwt25pay29vLxcqrP+plY3+6YoZX9/ XxaLxa3Z+ioUraX06upKLi4ubtUxCqmwv/k6aZyttUpH+9WEVERoiIMdeOigoxQuYn/PrNtabn8d +BCAUn5RPez1p3HQdlktEWniUrOlsQAA4GHCLH4AAAAA6BU9taB+c98FWGKxEPn9S3seDNYzv1iI mJDAB8l//s+/vO8ifHS26Zq13vvESbGlfHLfBQAA2Ep6KlD70amry1SkfSJQ5EbNXPCWzEUe7d+W PjuOj49Vd62dLT0ajWQ8Hqev7IzyjFy1Pl7XlzsKYcjia20edn+7hJT+rhOi9DzNZrNm4s2XX/5K vv/9D++AH4/Hsr+/L5eXl0txkHt7e3J1dSU7OzvNsk9aHvsaWF9+X69oBQdfX+vKt+1j20uPl8Xy WobDoZyenspisZCTk5OlGGlbniyO2FMbF+3Lbs9nlrfFx7LqSyFs2+jkPbtdl9MCAICHS08Faj9Q IREJAivQ7GSo0sSUNrI4xiheMMq/TVSoOB0MBkuxfl4IRWW29bXizKazE5MyMaf4iTw+j0yc2/fM 23z861EPDw9lf39f9vb25MsvZUmIDofDWxOOzs7Obk0c0//+9am2Lf3se21XXx7b9tFgx06Qstv8 92gQ4tfh9cuf2dUMfNuVJq1Fg4Xs2vYxqPac20FGlL+WTV9t6ttZVyawgxG7WgEAADw8HpVAzURc hF8v08/M98Kv9B7zaHvbDOvSe989JQutt8BFIkxEbgm/KN/IkmeFlc9D94+WqtL8SmI++j0SK/b8 2Dq9e/dO9vf3RUTk4OBARD5YUN+/f9+8vz4SgSqAojJ40Zadc1teu6+3UGcTk+w2b52N0uo6qKPR SE5/79vXdhmNRqFVM7teslfx+mNaQVuaMObPTXa9euupprPLtYlIY1FFoAIAPFwelUCteaBZ17F9 CJasSd4SGT3IRZaXOvJud03nxa8/do0r1QpItZbaMqtr25ahLR+/PWpLL6q9JcyLrTYRHolZb3Wz 5bP5eUucbn///v2S9VjTWBFnRZK3PPq2tO3kyx61hRdbvg6WaGDgz62WUZdpsqsnZOve2vyjMkai vHS+SwOzzNIbeQiiQUx0rekLJbLrEAAAth9m8QMAAABAr3hUFtQarPWp5L6NrIqlEILB4GYtVW+p iuItfd42fWTp8vnYmERLFItoLW+R5S+y6GYxkzav7Fi2jG1hF94yaScP2fpk1mRdJ1V/293dvZWH 5mst1L4to2NkFkf/u0+brYNq2yey4lqrt28jbz22eftr01t/bXo/kS4qoz1udN3rtixmNcozs0Zb y7O9fwaDDy+X0PVTAQDgYYFANdgYSi+uvJvdUhIu+j9ymWYxfTYPfxyPd//afKLj2okoPpwhOkZU By9avSvf/i+VXfOxeUVubZ9HSaT7evgXK1xeXi6FG2SubP8/E2GZ0NJ9bCiCDS2I4kkXi8WtmNUo rMGXXY+jb2LybnWN2fTHtCEfvozZ+cpc+tF2L3R9+2T7+nz0uL5tSrGvAACw3SBQf4+PE/UxkrXv m7f/Sw/qiCxGz1sGS2LYbo/Elsab6jJMUZ5Zndost7YObeXW73Z2uy1j1paeLA7St+HFxUWzPXs9 Z4lIlHuBZNN6cSmyvNqDvc78flndtH7Z4GOxWJ6xr+Xzol3/R+2gZSvVzX8uXe8+H/92sihfb0X2 3gAbQ10aXAEAwPaCQJWb2dXerZq5Yf2DN7IE2nT2OG3lyISf/90e17t0vTiywsG+MlQtxn4VAl9X X/ZM8Ph9ItGo27IQgJKQLwkQf65K1sfovJbOTclimA0mfBtk9bX7+fMfnZPSOfJi39bd51UqT6kt agZakTjXcugxImt0NNiIRKxv+8gT0JXsXgcAgPsBgWqoeTjZh2PmMs0WqM/Ea2Yl9EtbRWX0a7Da cqo1TIWodf96gVpqB19+L6Lt9sgqZwVStCZmNgCIBGbJYum3+aXCfFov4m35I0usL39mKY7a0C6e b/Px1kYlEqe+rFE9dF+7j19BIRN+Vkxn1s1s5YVIcNa0k8832jca8ET3Q2SZrQVhCgDQL5jFDwAA AAC9AguqLE9msdtEYstKZt3KXNtKm7vWW5u8WzqyMEVWSB8LaOtmYxW9JdSHKeii/plbWUMFIpd/ ZKVrs4ZFbRAd1/+WhRBkVsDMMmvXg/VWw6isJZe1/u4n1Nl29sdps7D7tNk1WHKf6zadLBdRin21 5cgs67bu2TVQyj8679Hxo7Ac+yIKAADYXhCokgu9knjw+2uaNmHhKYnYKJ/S++3t9uztTfrdrlhg RWYUaxp9twIvCjtoW6Yr+14Sad71bssetWNUF62/L5u2WRQuoOn8b17k+mNnwtV+9oI6qr8tayTk Sm/qio5l6+Nd5NF+0dJbvr5Re9njZPWOaItNtmELUf7rxKICAEA/ePQCNRJ4IrmFz26L0mSWoEhs idyejBXF0WmebZNmsrhJ/3skrErpo/29NUzzWywWt2aBZ8ImspRmwsWn8dba7JxEgtYSWc6jNiit xJC1WZY+qpcXW9lMd9/evh6RiI/qbY/ZVg/f1pHIzMS1XwkjWsu1dK/537O8fRmyaw4AALYDBGpB RERWs9IDL3tIa1423yi9F4DRg9n+9yImeo+5P7Z9uI/H41v5RhNsIiuVbZtI5GXLTWXWtuh97Xa7 rWskdDPrZ/aqUnsMS0k8Rb97q+YqQjYb0LRda74e/vrw14M9HzUW3hprtb8Wo/LYOkfHi8ri98u2 Z/ccAhUAYLthkhQAAAAA9IpHa0GNrEOR9azGEpNZobwr3B87s96pBSg6tv2srvSS299vj15laX8r 7W/LE1lPbexprWVSJLf0+WNmRJOYSsePXNR2/5I73Oe/ytuSStdVjeU1a9+o7aJXoCpq9czWtbUx qdm1rNeNtYD667hk7S21gb1PSjGl0f627FhRAQC2j0crUJWSWFkln9oYwEhk+Ad6JrZs+i5vuBK5 EXN2fVT7WzSz27vWI+Gh+2s66/KNBORoNAoFdiaEtBzZwCJy7Zdc7VHb23b3ruvsXPjjW6Fot/vJ ZG3u70gMRyLQ52GPE01q0zoNBh/WyI0muSl+hYMozyzEw4cU2DJn59f+VkpXIzoRpQAA282jFKil B1ybZbUUO9i2r42nzPIpxW7WCtK2OulnWx5b3kgQRcK5ZO2z++oxrACOJmhF+diyWLGYWVn9ZKG2 gUbNcbM62byjFQ/8pLbSwMXXKYonjsRxaV8fL6oDksViIaenp9KG3VcHE75O2cL9tQONqI6RkI+u vyi9b+M2rwAAAPSTRylQS0RioIsb3e7r09S8Ycr/pvmtI07bQgWscMzc5Nm+meXZ5xO9xjLL3+9v y2ndt3a/yGoXWRz92rDZQMHXsWRx9W1sj53lqcxms/CNV1Gb2N/9Ml62PjoRTI85HA6XhOV0Or2V bxuRYM/Km4lTWx5vXfbnyx/T0maB9d8RpwAA28ejFKg1Dyz/oIyso1l+JQFn8/RprLjyy0+t85DN rFBt4snnEVl2a0Ss3eYtWpFIz8S4ngdf9uh4mmf0uxWpkWveH7PUHtG5iQSUTWct6CoeozpEZbdL eFmBp+nm83nadvZ1q7Yt/TnMLJV2X7u9ZNWO6hENwKJ6W2z+9nh+uy1TdCwAANgOmMUPAAAAAL3i 0VlQM2tKycrSdbslWnDdulujOFO/vY0sBtCX0ddRXb2TyaRokYrysRbIaBZ+ZLFUC5/Nx8dqlmIX fTns98wamLWHDxWwaaI3Q0XHi8IEsjRaT38Mb4H0MbSl0Ipo8pm3gEbhIYPBYOltS9FkqpKF09ev zSre5naPrpe249r6lTwYAACwnTw6gRq5Ubu40DOXps3fbldxFm2L8o7KWON+z8pREj2ReMrapyQ6 IyGRuY1L6aL6+M8l4RrFPEZu8KhMWchAaXDiX+nq989m0fvv3uWetUW2PauT/03LMx6PZTabpefa lsnm4Se6+TCHyPVfMyCMBLVNk+3v2yES1V3ubQAA6A+PTqCWrFJZ2uhh6mmziGb5Zg/8LhbUDM3H zpyPXpcqIksTa6K3OkX5Rr9FRGJhsVg0r730+UX5Z2LFnyPfbl6YttFm3csEqN2/7Xj+tywGNVu+ yVtbdV+/bJQfFNnjZCLfHyf67i23fgCm4l0/R1Ztfw9GngZbb1++NhClAADbzaMTqF0eXCVrUvTQ jcRM6aHqxW+NEO5KJi7t9ul0KsPhUKbTqUwmk+L+bXlllq0oHMDOYM/aKfoeWVWj8xMt2ZVZTm1e Pn107qzoL1mXbfqS1Twqi8eKU1+P8/PzW+XVz3ZilcjNRKvScfx33T+7XqPyl6zBfiBUuq90ezZo KbUZAABsJ49OoG6CNqHht7c9ONtcoLX5ZHl4ERgJBz8TXORGVGUhCSVxk5XVi6coBKJU1yi9FUO1 ljsvbLMlvLwYsulLgxQr6HydopCIrLy+zaJBgU2jAw273V4D/vg115UXp35/LVub293XuYu3wWNj aGvSAwDAdsEsfgAAAADoFVhQEzL3YckiFFFjPWpzY5di7jJrWmStK1md7FuGrCWsZp3OrH7eopa5 07N2jsqZHSuLj/T52rxqLMDeWltKaz/718ZmFsLMQhuFe+j5iMqt58+GTWRtEYUIePz5K1l3S+c3 C8fwa9C25e1ps3jX5AEAAP0FgZrQRXCKxA/CtvhGv59/qNcsg7TKwzeLP9RJSxadPOX3i0SwFSBt YlbFcpdwiVK8Y0ZbXplb2qfLXOtRGaK3ctWUSSRfYin6PTsfpbdk2e3ZgMWj4lf3bbv+NI0Xtv5V tdGgLyvzqiITcQoAsJ0gUB2lySMl2gRMFHeYWUpL8ayZ5SnLOxPApfjMtrJnsaOaRxZfWBIc3trn X4ep+0TW3Brh6NsgOq7dFr1O1ecbCatohrvP3x/LWjQzgZbVK6pjm0W+67VthWVJcGdtbI8bndvs +mu7zrGOAgA8XBCojnUeeF3Faba/fzDbSSrWxVsrYLJy+O1RuUrC24uqkgVVjxMJ8ihtFopQIx7b aBOc0YsU/HJVmSWxLewhSh99zsobDTyi8kTHiV4OkFFT3rYy+u3ZbzUW1KyMAADwMEGgdqSL1cYL gUj8Zekz62TXsgyHw6U1TjPLammxfi9mvJCLhHUUxhC5e/2STJGAVby1MUrj03qsyPXiOipDtt0K Vn+ufGiGzSvKv0Rk8S2dI3+uslUNtC26irxSeivmI8uxr3dpkLZq+dbZDwAA+gOz+AEAAACgV2BB 7UhbPFzJvZm5STNXd2QxLeUfYSc52X1qJsdEZbVka4Fm3zU/u280K73GBe0tn9GEsijOUWR5glY2 oSiyrHo3emahbYvBzPYVaZ/gFNWp7ZrLyun3jcrb9lnL3NVqmbn1NwHWUwCA7QeBugG6xM1pmlq3 tt9vnYdvJlbs75Ho9HGZ0TYtexQfafNSfJxnWzyixZexNubVlqFt4lCXAUQkkHV7Njkqok182gGK Px9ty2tlbVEjDjVd9grcLKShLc/oc5YGAAAeFwjUDdBm6SuJtUhQtMXmtZFZ8SJLpW7PrI2RJTGr V5tlN4ql1O0+ZjES4zZutM26nFlhfZxsTdtHdfIxrG1tXSuUfVl8mbL6a6yxt4xn4jorVylN27Uc DSzaBioAAAARCNQ7pu1hnAnJSGypS7zWPe/LULIaepHq02Rub823VKfM2urLZvETsOxxo3y8m9mu RVpbj6jsFiukIytm1B7eyuqX58pWJchEXiY4S9dEdk7briVNV1o5wdcrShPlGZWx1goOAAAPHwTq hsiseiL1y+Vkbv8ad2iWrxVDbWWtEZ41x/O0hTNkwtgfT9+SFLn3Iwts9l3zzuJWfTltWVZdhN+L WU2XvRmqhrbrou36q40dLcW4WnFq27g2X82zxmoflQcAAB4mzOIHAAAAgF6BBXVD1Lg0a+IBM6ti m1UpOmYpnjJz97e5uKP9M7d05PJtm3AUlcf+V5d021qj/o1Tvi3bJotZF7zuM5/Pm1UR1JKr20tu ff0cWcrbrMgWay3OwjNqJj35/NpQF382ga40Mct/r7H61l7fAADwcEGgbpC2B3DNg9UKjezB3jV0 wO8X5Wm3edEULXrfthxSzfGztPa4mfi1xx+NRqEo8nXxdfS/2zL42MzZbBaKct82dr/IfR+FLbTF m/rfsnAJX2e/b1v4RYnSAMaL1LYwi4goxGTTEBoAALA9IFA3SJcHck0+9ntkPVslftBbBiO8+Mws c5mAqhFCJWFbmgjkJz+VhE3JmhjFX2Zi2KbJ3syUtXFmIbWW3tL5KVm0/XWQCdeoLUr5ZseJrKVR fqsOzNrY9H0FAAD9BYF6B6z7IPTWPc2z66QlnybK127PBJpfUqmUl883KkNUNuu6t+JNXeremmtF dCakMyHlj2PLplZbL/a8JTmaHOTbJqpj1NZ2W0lcesFbqqf9rc3i2QUv1qOBTm3eXQVnqZ3bjoM4 BQDYLpgkBQAAAAC9Agtqj/BxhJmrvzYfT2TVa/tu84qsllkcZHTsktUvsxKqS700Ocq2l7fwZZYz b4m1eWWW0uztV74+uhyWx1pLfftF5zZadL/2HNr0kfU7yrMr3oLcJdykJn0bq1peAQCg/yBQe0Tb w7vrhJaMLqLEusSzY2R5lY6RTWby+1qhWDpmyZUdbfOTsLIwBL9IfRQjmtUrmjjkhbYtoz8vNXUu lSFrC/3c5aUPpUFTVPYsD1tGhCMAAGQgUHtImwV03bxrsaIke4Wm/2xjVrOYVt0WWVv9ZLBMoGXx o5nYi95Vby2ovqy+nNaK6tvGt1n03cec+rjZaKWEUp5tFlObt9/Ht0GXeFGfPjpPNflHVmQAAAAF gdpDNv3AjkRKl7L4yTpe4FlhZWfZR27sSLT6PO13X49M0GZl92RCO5v05fOIVjjIxFaUny+zFcm2 LaNZ/NEkq6isNizCn7uIGqHY1saZq3+VPAEAABCoj4BaC1mbCIlEZGSlUyuqz8NbXa3g8gvr+2N7 y2pUt5JFU1cD8GXP6hjhRVhb/GUUNrBYLG6tfWrTaDktVqxmeWaxwrYs0YChrZ6lbVldAQAA1oVZ /AAAAADQK7CgbhF3NbEkyzeyGOpnO3koslpGlsC2eNO29Nm2rAzrpIvSZG+NapuUZPPwk7OyY5XW WY2OWWrHqAz2e5Yu27fNal3zGwAAQAkE6hax6sM+cgXXLA/UdQJQdlz9XBObWNq+acHTJa9s8pSN IdXtJQFo32tfIlsloFT20nldxW3v6Zr+vsQpwhgAYPtBoD4Cook72cSejJoZ67XHbcvXpq891ip0 ETKluntrZrRiwCrHrKVmElxU/i4rOuhxatrhvsXhfR8fAADWB4H6CKmZEJTts4njipRn8dfOAt9k ee4ijzZXeZSmVuTVtlPXVQ5K+/iQhlXCJQAAAGpAoPaYvlik7oIa0fQQ663UxJW2EV0fNcK1LVyi Tbg+5PMCAAD9gFn8AAAAANArEKg95mNaq7pa72A1Su2s57stPnQV62cpnZ3YlR2vS94AAADrgkAF EWlfoL5mW5TmMQvfbPmpGmriQ7MBTNTuNcK4a1kAAADuCgQqtNJlPVKf5jGIm0z8bSLOtCZPn2/U 7m0DEL8k2GMeWAAAwP2DQH0EfEzBUeMq/ljHXuV4q+zTdTUEPR8156W2PKsMBLLlqR7LwAIAAPoL ZpJHwMcUG/fhKvbWv3WPV8ojmzlv6bI+bNdy3deLCiCGlwIAANwNWFABAAAAoFdgQYUlSu9+X8dS 1Lb/OjPFN/kaVD+Dvm1d0bZ30Wf7rVqeVcDKd3fQrgAAdwMCFZa4C3Ga5dvl93Xz75pXrWBeNaSh iyDv+laprvsDAAD0DVz8kGJFVDSpZxOWvXX2vctJV7UThdomPGXb/Xqn69TlMQhQVhUAAHhcIFAh JZrZvclXkda4xkv73qcw8xbQbIH92jVGs8lYXcvzUHkMIhwAAG5AoMISH1vorLOY/X0SCcpNxnp2 sd7WpgcAANgWEKgAAAAA0CsQqLDEx7bEZZZIzyZjX7uSHc8vdL+Om77tBQdR/e87zEF56OEFAADw 8eHJAr0iE1ybjH2twbvOI1d622z/rm+YisrQ9bWl90HfygMAANsPAhXWJhJz2y5auohCPzmqNPlr XdHq86tJBwAAsG3g4odW2ly4m57dXzrmfbiTuwjOUsjCpoXkfbv4ce0DAMBdgUCFVu5DBNW4+vtE JhbXjU3tMzUvIvgY3PWauAAA8PFBoAIAAABAr8DsALAiq1pz+2oF3iQfs46PoT0BAB4bg7/927+9 7zLAI+EuJk89hAlZAAAAsMzgb/7mb+4k4//9f/tPd5IvbC93ISS3SZwipgEAAOoY/L///H/ddxkA HgUPRZyyvBUAANw1TJICAAAAgF6BQH3APPald9ap/yaWLnqo7X/f668CAMDD52E+QUFEcMGuU/9N tN1jb38AAIBVwYIKj5aHauEEAADYdhCo8GjBwgkAANBPEKgAAAAA0CsQqAAAAADQKxCosBLEbwIA AMBdgUCFlSB+EwAAAO4KBCoAAAAA9AoEKgBshE283AAAAECEhfoBYEMQ9gEAAJsCCyoAAAAA9AoE KgAAAAD0CgQqAAAAAPQKBOoWwmQUAAAAeMigcrYQJqMAAADAQwYLKgAAAAD0CgQqAAAAAPQKBCoA AAAA9IpHJ1CZXAQAAADQbx6dQGWCEQAAAEC/eXQC9TGAlRgAAAC2GQTqAwQrMQAAAGwzCFQAAAAA 6BUI1A2Cax0AAABgfRCoGwTXOgAAAMD6IFABAAAAoFcgUAEAAACgVyBQAQAAAKBXIFABAAAAoFcg UAEeAKwgAQAADwkEKsADgBUkAADgIYFAhUggV5IAACAASURBVK0ACyEAAMDjAYEKWwEWQgAAgMcD AhUAAAAAegUCFQAAAAB6BQL1kUJMJwAAAPQVBOojhZhOAAAA6CsIVAAAAADoFQhUAAAAAOgVCFQA AAAA6BUIVAAAAADoFQhUAAAAAOgVCFQAAAAA6BUIVAAAAADoFQhUANgKBoMBL5gAAHgkIFABAAAA oFdgjgCArYC3nwEAPB6woMLK4G4FAACAuwCBCiuDRQsAAADuAgQqAAAAAPQKBCoAAAAA9AoEKgAA AAD0CgQqAAAAAPQKBCoAAAAA9AoEKgAAAAD0CgTq72FNTwAAAIB+gED9PazpCQAAANAPEKgAAAAA 0CsQqAAAAADQKxCoAAAAANArEKgAAAAA0CsQqAAAAADQKxCoAAAAANArEKgAAAAA0CsQqAAAAADQ KxCoAAAAANArEKgAAAAA0CsQqAAAAADQKxCoAAAAANArEKgAAAAA0CsQqAAAAADQKxCoAAAAANAr EKjwYBkMBvddBAAAAFgBBCo8WBaLxX0XAQAAAFYAgQoAAAAAvQKBCgAhg8GAMAkAALgXePoAQAgh EgAAcF9gQQUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFgEcBS2YBAGwP CFQAeBSwbBYAwPaAQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUowMQaAACAjw8CFaAAE2sAAAA+ PghUAAAAAOgVCFQAAAAA6BUIVAAAAADoFQhUAAAAAOgVCFQAAAAA6BUIVAAAAADoFQhUAAAAAOgV CFQAAAAA6BUIVIAtgbdaAQDAYwGBCgAAAAC9AoEKsCXw2lUAAHgsIFABYCUIOQAAgLsCgQoAK4FF FwAA7goEKgAAAAD0CgQqAAAAAPQKBCoAAAAA9AoEKgAAAAD0CgTqFjIYDJhBDQAAAA8WVM4Wwuxp AAAAeMhgQQUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUA AACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFAhUfAJ/ddAAAAAOjA4L4L4Pnmm2/uuwgAAAAA cI9gQQUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACA XoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBe gUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6B QAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFA BQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF4x+J/+5//lvssAAAAAANAw+F// +b+7k4xH8p/uJF8AAAAAeNjg4gcAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBe gUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6BQAUAAACAXoFABQAAAIBegUAFAAAAgF6B QAUAAACAXjG47wJ4Pvnkk/suAgDAxvjmm286pacPBICHRNc+UOmdQBURmUwmMhjcFG2xWNxKE22r xebt88v++32j4w8GAxkOh02awWCwtK1tu37f2dlZKmO0ry2L3SYiMp/PZbFYyHw+l/l8LmdnZyIi Mp1Om79SHX1d/f/hcLj0Nx6PRURkf39fxuOxjEaj5jfbLjV5a3mi82A/+7pH+Sj/8l/+C/kP/+E/ hseO2nlnZ0eur6/DNtnZ2bmV3udj6xF9XywWcnFxISI352Q2m8l0Ol06V7PZrDmHup/9bPOP6mTP w3A4XEoftav9HqXNiO6J6Nqq+Wypucf9+etKdn+3Hc+mOz8/73xc+EDW3qXtvs8UuX3d6v+on9H7 wfcbuv3TTz+Vo6MjERE5PDyUg4ODsFxXV1fN/fmv//W/CvuYiLZnTem/9gFZH2n/i4hcX1+n/ZB9 Tuh2m3e2PapjVt/afmSd57lllT5gXTZVdrjN4O//xd2c0P/+X62+r7/IBoNBJ6GolPYpPVSz77Xl 9p1eJlyj7bu7u0XxVtMR+O9Ze7YNAtrIOlD/sIjOnx7f/o/SRvWNRGUmEG1n7PfJ8hS5EaJWqFpx ao9h887QB4Wmn81mIiIym80acTqbzVLhGOHbwV5rIh8e3lak1gjI6Py1DWSyspXObemce3ER0TZo bXtQrXpvw91T09b+2i8Ncvy1ZveLBt02/WKxkKurq2L/4vuYrDy1A7no3snqXmKxWCz1WdH9lpVF BalPUzpWzeCu7b5dh1Xv0Zqyl/aFu4MYVAAAAADoFVtvFqixpCo1ltO2fDSPzNIXjdDt6Nz/plZT vy2zmtaM9DIrnM87Kr9vi8zaGo3wr6+vZT6fh+3jy5blbz+XLOmRS9vnb79bd5UPPygRWU3tfldX V7esjbZs9vj+T0QaN/5sNpPLy8ulckbtlWGvsdFo1GwbjUayu7ubWlB9KIO1nkThBDWWem+pyTwX /nPJCuYppfdlKOXjy962De6XzNpVY0WN8P2I9sWK3quR18V6RPS7/R8dx/+elXlV62nUV5dClqL9 Iw9K5NFZxWqaWW/vC1+WLtZpuHu2pgdueyDVCtXoBo7+r1pG25FlAtX+buNNo7gom3etG6tUj5I7 vO04GVFHlglRuy0So9n/qB6+PlH+kUBVVMhZMkGalUUfUJq/fZjZ9OrK93Gl8/l8KZatRhRG5dLr bDQaNfVScapu/pprXetj3Zpt59XnVXLl2/3WGVT6/KLttcfqKk67Dhihnq7tmQ2G20Spv0dLLn69 p204lv4W9TFZfWqv+8z4oXmU8o5QkRrlEaX1wru0T3R/RoaFtuN+LGr0gQj3dV/odBY++8735evf fnlXZVmi5uGjZA9a/1uWZzQ6tL9FI+dM6Nnj2E7NClTtLCJBG1n2vEWuVN+oflE+2b4lIqHhhZQK G2+x83lYIeZFjBXqXQSqr4stm8Z8euEUdUhdRtHakevEs+xhZidCXV5eLrWDzad2wOTbzArU/f19 EbmZJNVFoGpbDYfD5hypldhPlCgJzkyY+t+i77Z9S/lkdcgGofDwyQZQEb6/VU+D3UfvgdFoFA7Q /GAzOob+L/W7majz6aK0nqhPaxOl/n/pmbgqqxg/NnnMLsduu3bg47BVZ6AkVrKHnyUTm6tcwFEZ IsFpxal15ft9su8lIqFd+m6PYX9fZYRrxYMVW3ZbaSCgnzV9jZUg++5n1ts8/MNjsVg0Ym2V8x1Z UNQyKnIj7nydZrOZnJ+fy8XFRSNmbR7RNVhjaVGs9VRXVbDbSgI1ssTYiVyDwaAZeFgRbdOXLCm1 30vUCFpfHl+3Wngw9YPSec76TE8mNHwfq+I0spJGYUt+kC5SFqh+FRPdN0qrn7P+uzRwq7GqtonV kkDN8qs5bm0ZNsFd3MP3IbAfOw+iJ44e6tFDs9RRtf2efbfbM8Gp+ZeEV1SfSHTXiFC73S4zYjse b62z9bedqbVmWmuD/VOXsu3kfUiDf4iUHii+rBG6vw2RuL6+vtX2WjYVbYvFYmlme/SgK3XqkfDJ Bhk+zXA4lL29vaV8oryjdvCffVlGo1Ej1iMrT3ScrG1VnFoLqrWeZvlk4jq7Zn2amnxL5da2sP9h e2m796PtXfp7iw+PqTEa6D2tefmQoaxfqBl4tom8bEBYeh7YONRIiGYD5ey4pe1t5b9rsbeJvKM6 wcfl1iz+z77z/eavFpu+y34AAAAAAJ6lIY6PMa2JObVpPoY4XXUU0+Y+yUat2cg5O4a1HFpLpB/B Zta6qH4lS2JEZoX01tNotOytin6xd1t2tV5ay6vur/tldW2zDmQW1NJ+3tVv21+tG4vF4tb6oJre 1ic7btRmvg2i+uoxo/PoLRv+c/Q/sgBlkzi6WFA1vU6UEpFb1tMu1t3Scfz3VS2x8HCosbx1JfOK 2Py0v4isoFEe3qskIo0Xo7S/71va7pPSc8t7c7JnmqfNc7EqWXmy8m2Stutik8em3/l4LJ3VrhOg vID9+rdfbkyk1jzU2n4ruRy8K0j/R2m6iNPIraNlU6GQzRRf5QFc6yrXzyVXrV/oXV1eo9FI9vb2 ljrvqOO2nbbNx7eLL3cmVqy48sfxE82y+tu62GP7MllxWjNoiNrBlieqrz4As4dYrSvcLvpv8/BL 5Gj76YSnkuD1+6hbXyQXqJo+2xZRuq/bhHNbPhkld+Iqggf6T9R3l9J5F78n68NKAjUTtW0Dzi6D cp++1iVdGnC23VteeLb93vU51icB+DFFNtzm1hW2bS762gumJCyjUXskvLK8rLj1+0XlvL6+XrLc laxJft9oRJ3VTS1zvgOKllyyeVpRNx6Pl15h6tNGZcraIRsgRHWMBFgJ//vu7u5S/K3GoPpyllZU 8OUqHbtNoEZr4EZlr7V42PPoy+Lz8OlKVpPFYrG07JXunw14fJl8mhJtorOLyN0UXa8zWI+o/yr1 aV3IBtEWvf/tGsKZldLua40Mdlk3fxzbv+h/e5/4N8z5Y2ZkIrZmAJhtr71v7f5tZSz9vs457rLv JvoLhOn90Ori7zNdrSjZvrbTiISG357lna132mZh8umiB74tZ61I9fmUXPvW+mk73P39fZlMJjKZ TJYmGkWURHwprc9T6xNtE1meGJVZUP0DQicnlcpr8+oycrb7ZS5+K/xLYrWEPY9WNLYt7l8aAPmB kz4oa/IvXa+rdOi1lps2sushe6hD/6gRaTVk96L/PBgMGk+Rn1hq87HCdGdnZ+n73t5eKobb+ufL y8tQxPqy633p03QVlqX7tmteHj/gbhuAZ/vXPGdqygPby1qvOvUu/b4LWgAAAADoP7diUL3IbJso ZffZZAxqG5nbQqQ+ZtTmFVlKM3dtNMLLXNuRu8iPjjOLlE5Ssemt9dCX3W7LXLLeihq1i1r49vb2 ZDwey2QykcPDw2YB+KgunugVmv442W/RNi1ztJ6gzy/aZpePKVmQS5bdEpE11m73a6Nmlvo2rIXT u+DtNl92/9mGf/jfordaZflElhi/VmpEZlHqQpsL0R+nq6UJto+uFlfv/VIXv177ak311tPIg6Mu /rZ72vc/pT45evZ4r1mNtyd71WnmTVmHLt4ngBK37qA2Mdpl+6aJ3Bo1ArQmX9tR2W3R60lF4psu cvFrB2CFQ+TSj/Cvp/Pi1FLrOim5n8fjcRNzKiJN3KmfHe7zzLbbMu3s7DSCW+RGsOkkHtsmUTm1 Xf3KAW2Dh8jdFpXdH7+tfn6QkbkNM4FUK1Cj7Soe7UL62StSVYyWBkrRACmLO7Xp/T7RoKKrYOhC 2zndxD6b6F+gTO11khkB7J8fdEYTNvVvPB4vDXjn87lcXFwU+9Ls3q6pVzRAiu5RP9jzg/lMUEYu ezvh0ebpw3i2hVKbb1tdoJ21etmP+epTxQsJ/8DP0us+0e+204k6suz4Pt/ojVFqCc3iDjNrZ6ke NQKqbV+/nJRaD1SkiiwLVL/yQJe213S7u7tLIlXL4dsgE6ht1omaMvl0pTS+PNEDQdP645VEq/9v Z99H+fjPs9lsKX/7Gld/bV1fX9+yrNqyR9df9KDz+9nvm3owZIOLVfNZN+0qAhj6ge0nfFy9XZXE TvyczWahqPR56XaR7qLI9yPZwLFmQOi3+c9WnHqPSGk1l03wse8TxOnDZK2rKBKn9yVYs+/+t5JI HQxu3jxklwSK8oxubBWnXtTqjHJFxV52U/nt2ZJKvly2PJFLx4/+bWdrZ+xPJhMREZlMJkvra7YJ iJJQtSJVRJZmr/oONxKi2TJQtk724bJKh1UjSKJy+mP5QU9U1mz1gCifTPxGZbPWev1s3woVpfff swdkJtCjvOz2rg+rTQrTVfKqGeyukz+sh7+/ov5CrztvaNCJn9GqJHqf+L7Rvi0quq9tubrQZphY Zbv/b8VpJlA3RZf6R33mpvKGh8PWnPVIHHmxUJtPJDJsDJKINK+lzGIe7c2tYkvFqRdNJWtvyTrV ZpXz+5UERFZfratdTspaUNXC54VW1Pa+3v64pWWRonPiZ+tHbZCVq6bDqxFPJRGeWVBLZc1CFLIH n7+eRG5bULUsut6p327DATyZ9aXmWqodYG2amnu9ywOtS348KO+GrK/yn2sHa/Y326/rAFwFqo3j VtFmBWn0OmTdvgnLY+axqLnf2jwf3nraZkFdh3XEabYNYK1Z/AAAAAAAm2YrzAHRSDqzfpVGcpnV L7Iq7u/vL72u0+dhXalqjfOvm7TWQW/ZyspVM5JsG6225WHrK3ITl6XWU7Wg7u/vFy0X/rNvU18O b3XQNNGbtSLLSI0VK7NOt6W1+GurdP3t7Oyk6e02tUJHFlMf12bzsdeTTecton6ymYgsxZ/69Pba jCxB92UdLVG6BjeRX1sarKf3S+aZsL/786V9sq6DrP3c4eGhDIdDmU6nIiJLoTD+rXMlb5HI+veE fzNcyZPRRpRPZkGN8t9UaFRWtlXgvnu8FM/8fUyCqmWTHUepI1NR4B/eVnD613FaIaG/WXGmebR1 Qpk7K2qDkqsmc1H5uCyt88HBgYjI0sL8tW3pJ4PZ+tbU0Zcz65xqxP46HVs0gLHn3LZ9aaDk8/Hx vNHrcW0eXqBmD2d9wEau/9lstiRQ/e/RNRQdo+0cZgK3Nuwiy7d2e9c0q5SFh+X9kt1XpX7EGhp0 AH5wcNCExCiz2ezWLH7fn9t70fb7q1By5ZeeD/54Pr3OP/Du/UigfgzWEfCbut9W6c9q0sLdUjz7 fRWnIqsJkcgaYoPos+WUai5Om8avRdkm8NZ94GYdVlQ227GrpUBntJaWkyodu/bhnbWt71yjCVH+ vxc9vjP3/32HXBrQeOFvByI2Zs2vU+vztTG0Vlx2aWd7fGsR8WmidU1tfTYh6lZ9WGxKmCqZ98Sn WeXYbcfkQXW/tN3nNYN0O99A+0Bd79kLWs1TB3+av105w/Yxmibqt2qtovZ+rYnXtH1otnSUFdrq bfF59e0a3+RgsEvd+lJ/2BIX/6bxVim7PFSb2Cq5QTNXqReMpVH/qkTiOxPGtpMWuXHxRyK1yyiz rXyRkNMyWrGnnUn2StOsfJE1UL9fXl426fxEpVKe9rt1lV9dXaWDJO/O199Lr0H1edjjZg+kaFsX a3dU7y7nteY4Pr9ts0D6QUufHuCPlWgQ1uW6twNF9ZjNZrNGoPr7VoXfdDpd8ogpGqLkPSz2WrF5 Ru51f8ySoSS691WY2n4pG6RFS/vZum4D0fOk1H917RehH3Ry8et3+7Yo/T3atipdH5Rd8syEQ7TI fimvbFvUefiF0u06qTX13KQ1yApzLUcmTrvk1zWtbS+NlRS56eTtQ0Tkw0OgrXzWumhFr+Zr0/mV CTKLrP19Nps1edo1E6M1br2ltM16WnO9ZeLbposePFn+peuqy8CkizDYRBp7zE1YW7uQDV5gs9Ra yP3ArNY66Vdt0ZeTeOHjLZP25Rg+5Obq6qoZWNvnjebj+xZf1kig1hhBrDgtWVDtZ1svn/d903bu s9+3bfAL7XQ+o5FoFZFb237wX/9XGygeAAAAADw2OgvU+1icv6sVJIv3iSxzOgq2blmbtrZ80We/ GL3md3193RpKYPMquS3a9s/cIL4NrAW1tt5tdYhc1pkFLrOg+vxKedj6qjVBsZOEonq2WSw05lTz 0XhUfXe37q/hA/4Vi5H1vobMWt/Fxe/djRmrWDhLlta+WGRq7hNLm7U5u9/h45BZI0vnwvfrem+q BdWeXxtrrp6SaFKRt8JGMaht7vPFYnFrJRAtm83P7+M9JdZ66kMIMo9Nln9bG3aB+wPWYets4l1c j1k6KxKySVJt7piss4neqRzF+nQpe81NHomtaN+oc7YufvvazZrjZ8fNOsWow/YPBFtGe+xoOSpf Tj+D1R7Dl8XGuHoR48+hilP/4IqErncj+u3ZQ6f0PfotujYzF7+WwW/PxJgv4yoPp5pB2Lqs68LP 7uPSoDDbDzZDzflsE22lQbOGC9k+UH8bDodLM/kHg8GSSLXH9a9K9YPr6P5p68NKZc/CGbw4za7R aMnEUjhBTZk2SddjlJ5z8DDYOoHaRs1F7oWDWk+7xmB6YaCxlJlAtce3nUMXS15EJgRKQsW+klU7 2mjyUJRfm8AutWFW18iCulgslqy8Ppa3VEbfWftllvR8+dm6vv110oFfT7Q2BjUSqL7utXWy6UsP 6Ow1t35wUDpvbb9Hx67hviyrXYVslh6Bev9kgwr/PRtg2fvTbrP3qx+I6uBUJ1suFgvZ39+/dW/q f3+/+98UK1h93xA9H6L66Wc70LfponyjNto0bXlvUvS2PbNgO9kagVp7oWUPFm/BikRdrZXU5hl9 9h2HJ3q/vc8jKn+bSLUCJOrcbLmsC9x2bKsIiBqrhU3nf/OWDF8H3dcK2agMl5eXSwIysyZYEdxW p8wy40Won/hWM6Er2uYfeF3yyNp3NBotndeStWkwGNxyEZbK40VvdHz/uXSN1QrJaDDR9YHX1WLE w+7j0GbBrjnPWR9YEn02jQ9/0u22HH6pJu1Xstdj+2N6cRoNIG1Z7dqrfh3W6Lj+WeXvv9KguVTu Wkr9/qp5weOit2e95qFTM2rKOqdMoHYRwha/6HOpfGq9jMrQ1jHXlk/dWJHo0f/e2thVnNYKU/s9 6vys5aKUh52tGh3ThwpEM1S1DJHgLVnHovOt/6Nly6LrLBJotiz+nOj/SBRG59T/ZoWyPaYfkCjR gzDKP2uTLoKxTaT6PKPfN2UVhe2g5vxF1ke/XF3b/oPBYGmFk+g6tUtK6e+z2exWTHpWvhqiPsSL VF8mHxbl87JlyMT6KkaKtnp4uuS/yj0b9U0MMLePPyj96Cc/1U6Q6vMC/wAAAADQb3ppTvDuu2gE VXI1RrSNGtcZXVnLU015vYWt1irQZkWN6mjzj2KWRG4sat7SVuOKzaxo2ajc10WtFTXWtJJberFY fqWnj8eK8vFWkMiCHtXDfvbutazt2yw4kQXVX0ul6yQru7bNcDhcisMVuR3qoP9tu2je/lglSm3W lciVX/r9Y1lIS2WC9fHnstZ6mt0D0X1U8nDYPt1PpLL/tWwit+Pns3s3iju1K7vYvtWXRa2ktm+z Ft+oD/H5lJ6RH/N6zp7ZNfcwFtHHQS8F6jq0xZX5DsvPqszcvG1CpSZtJiCz/WrqVKIkxG1Hpq7z LiEOmkdtZ9JWRr96QOTGLglUrYefxGT3t5+9i1u3eWFoxWdUbo0/9cuWrXJ+/TnJKJ0n30ZR22o6 HwftH7ptoQ4110o2ULkr2gZvNeEHXfhYgvixUnu+/HVVEp+l/PyA098Hdn+/rFPJxV5Thyy93S8S qfpZB/r2BSW+Xj7fqJ3aDCF3Sc15ztIiWB8eW9G71sT0rGpVtMfw/7OOqTRCjsrZJlZqRIwvp36O jqvHymbla6dqXy9qO9daYRK1gy1LjTjRh4HdZ7FYfruUHtOKyOgYNu7UWoWjMnsLqqbX/K3gtP/9 dRAtT5a90jQisnbY714cexHp28dfs1Y8R21Z++DyZYvKkJUz2xbdu9E9V7oe7TXVRQBkVmEEZ39Y xSIe9YN2e809GQ1Ksz7NDoS1/yiJ1Mh6qtujazo6ZiZc/f1vhWrNfX7flMpRU8Y2UX1fohtWpx9X ZgUlIZRREovaUWRWIzsSLeVjP0cP1EysRDfKpjqKNiHuxYwVZ37ZpRJesKwyiNCHge+c7WQn3Vct pLacto7egnp9fb3k+rL/vTjTfK0V1ZYxGpCo9dQ+0Lq+7EDLZEWj3ebPSbZ/hE6U84uR20XAbdnt ecrKX2Nlb7tPax8UqwiUVfKKBk1taeB+aDNW+L6o673o+81oYGrLYv9HfUd2jJpy+M+2P7D9gu8/ NP3u7m6zTJ7FPvuy59gm772o/6/tAzYtntuewdAveilQS9aXWhHU9cL2nU20lqTmW+r0uliUfFlr yu47QOuOsfmowI7WZdXPVgTaNx/pWn+lske/efGX1bctH/3uXdPa7jZ/L3DtMWwn7mfdeyGpMZre gqovMIjc4TViNKtzmyjS85dZXGwe9rqxKyJEdRT58OCKZvH78vjBSmSlzSgNSqJzFX1vu+7u6gGz yYczbJ4u10nmmWi7jqM+2fZHi8WimbGvaXQ5t8hAYPfP7uMaIdcmHu19q25/f0z/hsPsftwUXZ6J XX7PjoPwfDgUZ/EDAAAAAHxsemsmWNWC0bZf19GVjlYjN8iqZfQWUIuOkLNRf6mMmZXAjyy9O1kt reoi9+6lKJZ1VTdo2yjXWvuitoksqCW3jbWEanpvFRWRJdeZtaBaq6Q/lrr313Ub+XOn/70130/o 0+Pq71oWtZpkr+/11lGLvd79ufAuxK7UWl8jIotpV9dtl7yhf2SWQ29h9L/ZFT1WsRpaq6W3onqP RbYOsu0ruoQARGlL3jv7m11VZrGI3zAX5XHfrNK/9KHcsHlWFqiffef7d7be6fn52Z3kCzecnt53 CT4u//bf/o/3XQToyMN45nyy4n7fbLQU24peA6tcC8fHGy1KK3/3d//x4x7wgfEw7ne4zap9YCJQ I/Fpt332ne8v/d+0UD08fJKOIiNrkCWyJOr28Xgso9FIxuNxuPSOyI01yu67t7d3KybRk43gbGyn tdbpyN6PeqN629hOb4WKrLmz2UwuLy9lOp0uzWzXdUIXi4WMx2MZj8dNncbjsezv7y9tH4/HzSQb Oxu8ZoTrLXelmMQMb2nOloFSzs7OZDqdiog0dR8Oh/Jv/s3/IP/u3/37plyj0Uj29/eXzqVaWS4v L2WxWNx6dWlmMbfn15anZOXItmuZJtpurQAAIABJREFURUQuLi5urVtqr+XonPvz7a87O4FsNps1 Eyi8dSkqm29/SxZTJyJLMdDZ/m3W9E3Eg9Z6Vvwxa+Lnzs/P1y7fbVbv1B8SWX/v+8zod9vfHxwc iIjI8+fP5enTp/LixQs5PDxMvU52NZDsnri4uJDpdCrT/5+9d+mxZdvuvP7zsfJxTu57cl/va1+7 7LKtsltQYKgGBRISH4AWHRr+AAhUwh1alFz+DhRfoPoIITqUkKCBqoGEZBAFEgUNLPy2T3nnvTvP zkdEzEEjYq4cOXLMR8Ra+djnjJ+UyrViRcyY8f7Hf4w559UV/uE//M/x+7//j3BxcbH/k/dMOTSq XKd89tQiYPK/dm3J3/P1nyNm+V4h+47m5fUgr50ttKJzrXWXvq9Z1nh7xC1O6P/xv/3Pz+qgaqJz zTKlMErrhMzJ43leTQhtEVulsFQr5KndbOTvJUGr3WxL02OM+9B+6WavCXq5H6Qw6Wl9XjvWctv4 +rTjzfdnrbuod+/e7R9cUqDKwRb4PimF23rTHWrhOm1flh5QWoOtXGe+z7VQPS8bwKNhY0uh0hhj sYNw/lk+qHh4UdsHb+kBoYnsYwhj43ng55omkPg5yEP8a/t8LglhXq427LF8OdTuEz0vP9q2audl bZp8lvEyt1yDtZfsHo51XR0iTo0vA5/FpmEYhmEYhmG8Bd6sRdDjUpbCPZk1zk0pgVwL/ckGPFvf 3Epv8TXXqVQ/OU1zCqTDqbmXuUFU/p63dxiGpiPKt0nuo5IDyX/nn2vOoja/Fprj83GXI4fgcggQ eNoPquYia8ek1xWRv7Ucfy1sx48nb5zBy+GuTZ4mnRd+Du92uycDIpRSGrTzaO3+0faF5CWcy9b5 CJgj8yVQc7r5+coHJSndE4HyecHvhXm53M0UH9EpzyMblnJnld+b+LpyI0j5LCpFjrR5tOm8jnzf bHEgj3FNrI1O2LX6w+VNCtSecL2cVxM0tZtNC34B81GNeEvv0k2OP5z5cjL8LJflQksrr3Zz5Z9l P3fa9mviYxxHNc1BK6vnoSAF8Rpa4WFt/lz/2vrzMck3bRkG0/YZr4f2/5iCip8jXDDnaVmcynC+ ts3ymmjlmmr14MvLzz2/5frmMksvkNq6n1Ootra/5wWZz2e8DrWXKH6Na9cVz/GWx1s+R0opOLwj fH7/4PdRvpz8rH3X6tOzH7R5a/e00jqeU+xtuV6eqz49wtd4XbpyUN96CoDmuG09+eSNRIpG6TZp /7UyS2KotRz/LOfnYkzehGvCVLshaQKtlK+liWptO1rikjt7mijhgqa0j1oPKDldNgLoaSDE61p6 OdBuvHK6lo8mt5cfU/4wBR6GVZQOZ8tVkfuQH9eSW6Ptg9I8tYeOrKd0c7XyXxvtmPW8nBnPR+la 4p/5PFyYyhxU/tdyJ2vPEe1+ye8Trftfqdzdbveo67g1IpWX2yNOte3hZcny1tSjl9I9vFbOW7tn GM9D1MSpzEvV8lT5tOdqLCWpuVWth/WWdeX/UphoN5/SDUgOKynLKImwkogEHgvTTMn5lGg3Hlkv 6frK8lriQoqe0g2nR9j13ohax4S/WNzc3Dw6Lvl3bb/1vlRIIatNl398HbJc7vLI1rfyXJfD9kpB WHpwbaH0MqHNx+sgP5f28da6HIva/cUeim+PkrgBHl/zUqB++vQJl5eX6jXTU752X+T3T+0+IF9y ZX05u92u+35bQ7unyfSDlgDtPfdf6sWt9Fzbcn3K5d7yi/MPkeIZJUVnSci+FKW3QO1tUJtWcoFa ojD/z6JLc1C1ZTNyzOTeZbXfNWGqhc5bLVXl/pE3W+niyem8nBLDMDzpYL52DFpv0aWbiLYNsgz+ mW9Tb4hfroN/bp1nvM55/pKrqtUzP0wBPOlCipehXQ/aAAvatmh11bal5DDVKAlUWRcN/lA/5OHX eqk9tBzjZdHu95rI0q5p2b2SHBZYroOfgxxeLo/CcHHK75f8ftEShNp2adNK56MmorWya8uupedl dS1bjIot2Evp28WGOjUMwzAMwzDeFF+cJVBycUrhTU7P25AWBuVvyTn0UnNApXOohfjlOmr1lM5p j0u45q2ZLyMd35Kz2ONsafXl8w/D8KjXgFoZNUr5UiWn+fb2dh/m15zkGpo7UXMjpBOjhRJ56I87 Pdk9zSH+q6urYmMzOcxiLjf/yWFSNXqdjjWOZM1BNXfC2Ip2HWn3ZR6JAJ52Us/L49codym58ymj avKa1a5lXk7vtsn1b0E6vLz80j1TzqdNb3HMSMNr3yNewsE1yrxZgdp7YrRyiA5F3mx42Ebrmkre IGN86OReCxNp07VQfu2zLKcntFMTtvyGqwnUmjgttaLXQmWHhH54C3Zte0svEbKRlLbvaustvcC0 hFdpu+U+4LlyOf9UjgCllZ+7z+LdT+UHkdblF/A4z61WX75MzzHT0g/k55599pq0hIGF+18H7b5T CpvLnlfyNJk6k8uQg1zI9Wp10e4JrftR6SVaXjelrg9L52YtFUGiGQMtw6HED+FasHD/6/Bmz6zW g1NzI3uXl4KTT+P5RHw9fPncyrIkGiU3NzfFG4KkJk57t5GLktINVG5bXoYP/5k/599qwiojW6Dm 9WoPla1o+1FuU96WjBRtmiNyCC1RWntw8ZtfrvPl5aXadyEvqzSqE6e2n0sPwLzcVpHKf+fimI/a pYnTtQ+BnnkPeQkyvhxKkQlAvw5rQ4PKz7V18hdCeY/k0ajcDytHXn9a49LafaXneaBF7mrbwz9r 4tmuC+MleZMClQuaXoen980xT+cdN5fGCC+FJmVop/ei5TcgHnIttXrvEalafflnLmRq4ytzt+3d u3cAsO/QXg6tKVuNr0HWsdaQp0bp+HOByqdLgSqX6715a/Us1UVrJKE1csq/cRfn8vKyWK7sIqeH nnrn+bY6hyWRUBrgQe6L2rn5lnhr9TFm+LlWcuj5/b7UgFVS+i3fu/gLvUwRkJ3y19xPbb09z5me e5d8pvJ7U+ne9pYjHJK3Xj9jG1132lpfqVofqcdo3b/2hNMedqU3ZD5ddoguRWnpYaQ5kxLpIPY8 2EritOXQ8fXx7yVXQAvDnp2d4fz8fO/i8ZBxFpN8/rWUxKk2T+9NvDSvFEXcTZXnSK2ctc6BFKey P0PtvOTHKaeD5FFq+Ly8U3C+rjXOiJy/R4y2zjNtOu+5QRtQ4LlFXu+1ZmLzbSPd9dq5J+/fWjk1 B7WnHnx9POoEPH4J5uvi00pRqJ77e2teeb/gYrkmjGXd1ryglp4ppXr3RF/eqth8y3X7vnKUu7MU pP/q7/x9/Lv/zr91jKINwzAMwzCMHxhNgZrd054Rp45Fr7Ox5W1GvmVKB1UmymtuoXSupPsqt+PQ PLgex0tzROV6c1hY9irA3QDuCPAQfylM24PmGvAUB62u8m2+1z3lIX7+xqsdV235UmhQW3/pjbqU R6y5p3IduZ7v379/5LoCT4fI1bZBc22Ax7nBcr1ye9Y6BaVjCsyOcE6nkb1DbHVuXtPFMMf17dJy Tfln7qLyebWoU+2eK0P8rWuc13NN2pA2vec6kCkHtW3qcVt7oymtOpfup/y/uZXG0e+2Wcj+x//J 7x2lvFIIofUgrd1Y8s0JeDxiD5+f5xPV8oP459rFVwo/tcSvnC4/a2XxcH3exlK9+LIxxr0oBbAf aeXs7OxROF6K2603s5IwXRs24mSRJAWZJrBLjYRq51ypnloZ/Heet1xr3CRzZXkXORcXF49epEpd TnFq529p/lI4s3Y91F448nkoB4A49NiXWLPcmjC/idO3gTxmteMi76fjOO67mloT4udl8PXmcxt4 3FuAvM758vIlulT+mnqVhHFeX+/LX0mkHju83XMMa8+WXMZzsea+YDwf1SPAXdOaiyqHRT0GPSef diG1lpMXcb6R8HHOgaf5RPJGUnK2eH34hbTlZNccgB7yjZO3LpXbIOfny3AHNcanDZlK/3vqqDWw koJm7c2w5E7wFuT8QQI8zRGV9Bw7Pk9ej+wVgteRd3sj6y7FH3dP5cvUzc2NejxqxPi025qWm3Gs 48mn5+3gD/PaunpeYrR1rOEYoth4fVpGAv9eclBLlF7a+LWcP+dniTQx8n9tmZrLWDIxWvTcU9eK 00Puzcdgy8vk2jqYc/t2OHoO6rFSAbZelLX5S8Kh5G72rl97gMrvpQetVlZL0PaKd+2mmkWbJnCG YcD19TWurq72y/BuprLIk9uiPeCHYVglXNfcANeUdwxaTqmkdFxLwo2XI8/Njx8/FvtBlU6M7G1B rqvUWf8ah1WSB1soOf7jOD7pG5en1NTW9ZwOTglzTt4WPceidP/WXsCk27jW0JBll67/tcJTMzbk crU6aNskxbgcrKB0HWrPSLl9tXr1Iu/5x7zuDqnbS91rjDbNM0JrpV8jO62HNJKquS/HovcE1C7O 0k1rjTCtCeLaBdsriPK8mrMlQ0uZ7CRngZrf9nluVS7n/Py86Hxykcqn99S7R6xI4XKsG2amZ9Sl nvXx7espU55XvLN+YBaouSW/5t7I8433vlB6yBziOMrjrDEMw5OwZ0kcrH0ReC4xqZVrovXLpHTv ab0k1URpbR5+DWqDieR5+Pzyc889kH9vCVQeeeHb3hqs5LVF2pr1H1tU2vX+NigehVo4/6UaSxmG YRiGYRg/PL6XrwlrQsRbUgdKblTpe54mXauWeyrfuKWzU6t7DrFLt7E1nN/Nzc0TB3Ucx72LCjzk TWUXtbXtNYdN2y75m/zM55fhvd5jq7Vqb9Uxr6MHzQXn7krJ1eHb+OnTp0cOag73y7rIBn18f2j1 5Q3Jere9x+2Wjip3cPL3Umix5Z6+1bCbOS2vw5brkS97rBC//JzXJYcXXvNMat3HesviEQseyeDX X8lFXrvOl+K5oia96zZellgK4ZdcUq2xlCzj0Fb8vTk4a+aR4ZOeNII1N5hW3qEWDi8tW7sAS/XQ xIg2r9aaXRMSwHwj48K0tU75fc0FrYlx7Xf5v0fYlqiN5tVbL61MOdSrVk9ZXj4GfISzLE5lDmpJ iNbOMe17z/auQbteeO6bfCiuCSVufTgcsj01TJi+fUovNVmw8d9a6Uh82dr30ssyn1ebrpkJUhyv DfHn64z3WpCn10L82rqOQe1FVzNmDCNuCdfzZd5CuH/LCd37cGy9PQP1BHc+j/xcmjeLnK3iqyYo gXLL0SyKsqC4vr5WxVb+npcp5VH15Ciu2bb8YAGeun/ajW3NenN5sg/TLUJEcyZLbiaHPyy4OP34 8eOjaTkvuNRLQ48z2qqLHApxi5jkjilQdm5q5fa8iLy0WDRx+nYoHf/SPVkTdT3r0Ka17rMlMSbr oEVbtGujdo1oApWL09J12BLBL81rr7/EW63X9503eaeVF2pJDNXoEZaldfaW17O8Ftovzc+FRUlk yLfsGpqQzsvwPvr4mzX/3tomzcnjvx/zotZcD7lNNUrCVlLb71uESa+LmbePvxzUHFRelky10NxZ WYc128K3vee66hGomnOzxrnK29E7f+34addUTfgYb4vasS05mfIlqfWCVEO+CGtDOHN4lIQP8rLm ftkSq/yzFKh8vaVW/Jw1+7an3nYNGb28yTOl9DZZeiPV6A3JlsrJF2/t9xK8nrnPR219pZB86cLX XKfWTaJ1g87fa52+a/2g5r/ePEaNQwSs7H9Tbqf20GjBl+FjyWvlZ6QDkbtd4nWT88uypKADHrfg ly8O8jMvtzVfa5vy9mjuc02IauvVhGjLeeo5J0oveq0XwJ7y5LStZRrHofWyLOeT0zSHkP/xF8Kr q6v9Ocivud6yM8MwPLoPyN+4IaC9qGn11Lax97rS6q6VV5rWKkd+74kiHsIxjQ/jbeNfuwKGYRiG YRiGwXmzlsBrvSVp4RFZn17XlofTSy4qn69WvvZGnaeXnKeWUyff5GVdSqH8FrVjtyYvVNuumrOl bW9vnWW9eEpAz75tjUjVk1/Gw3DZPc05ZMBTx0WyJdxWW6bnnC+dk9pvh17TpXSJYzqb5pi+bVrn UCu9JU+T9/fb21t8/PixeM+sIZ3S1vku3dmtDqqGdk23IiuHXp+l++z3wen8PmzDl8z3/g689iEj BUNv2FRLyh/Hh2Ht5NjjJcHSqluPOJV1kTcg7ebMbzI1kSqn1ep6KKUy5Ljv+X9veLgmLOUxlCMl aQ9ALTe2lIemHTP+IJL5prxbqbWhPG3d2j6Q5cnt6QktaudmS7yuoTeXt5Tac6jYXHueGcel9rJZ O6/zX+m6lS+EtfSc3npq/0vzHEOglu4LrZ5JamUC9Xz52vKHXttaWVtSPA7FrvPX53svUHto3UTk PL2CMMa42j1t1a8kFiQlYQo87mYkf89iWhOn+TNvLb7lBs4fFGuc1IwmTOXnEnL+1o2VH5+am7rG Oa2tJ3/mD8xPnz49adzQelhpebS19WvLbnFstHzt3gcsp+aay+810XqIIG3ltZpIfX34/i+9lJRe jPg5K3NQc3n8WK/Ns2+9SLauj7XXz9pzsedFd4uxc2jed6v8LcsZXzZv8mhrb7zPVb72W+2zVh/5 4JJll0RqqbytlIRz68YnBZD24Jc3bW26vInUROBaB7klGnppidPSNnChqi2rPTBLaA/S7ObkY/Hp 0ydcXV09aiQl003y/1K9Wj0X1BpCrRGn2gOv5gDVXvDk9NL35wrF95b1nPcnQyffm+Q9qsdZlPPn vxyx4C/nZ2dnT7pwA9YNbCFf2uTvcl4+vSZQa6lCLVFcMzqOwbFFZOk+fUj5PXW0a/rtYI2kDMMw DMMwjDfFm3RQgX4HB9CdltbyvWGTmvujuWwyxK/loPa+Afa4Si1KzgF/w+9xTrUc1DX1bs1TWqbl 5mrz8DIPzSXL5WuuX56vVnct901bjzwmuZspGeLX6lequ8yjXdN1VGsdnFJ3OSVntcVaF/MYZbWW tdDi20BzEWtRkdo5OY4PI7fx/oUvLi4elZPdVe360RpI5eu49Wxp1VF+lsvWptUolb1m2dp6Wy7l Ma+lNfXX6i7rYu7p2+KLveuWLhQt91L7rZUbuGa6dsNszVPjmBdJS0DJumnLSvHEUwd4+bXwUwsu 5I51A6uVIx9qPHSoibYtN7KS6O+pqybuSnXoPW97H26t5fJ3OTJUq15bxG+NnnOltZ41L4tr01KM t4F27PI5y0fV631J5sj+TrUXaL7eXkr3IY3SC2HeRq33j63Cbg2l67N13fY8H9fmgdv1+mXyJgVq za3S5ut9cPD5ay5PrS6tepfEaq+gWbuOjLYPei/kNUIVeHCDx/Fph+58XVsaQpUEHR8sQLq5h9yo +BCnLZGq1a+X1nIlJ0YrY61Y5i8UGi2Rqjk8wNMxvbXlD3kw9lAT8GuW71l2y4PReBto57AUc62X 7hLyPqcJVH7uHJq/XLvWeH1K23SsZ1FvXXvE+tpr9znvI8bb4U0KVEnvAzSz5iHSe+G2fiu5pi9x Myi5gVrZtTB97UZSu5nLeZ/rIV5zJ1poDxxZjhSpfDlZTl6+522fr6vmoJZenGovDlod5fb1PphK 11TvQ71WXqserWO6RkBuofSy23oZsAfbl0frvD0G8nqW628t23qZrF2r8vos9Z98yHNzDT1i/7Wu o0PuGcbz88UdmdKFuTWEfqg41eZd+4ZcunGsuXB6tl8Tsq1ySm/gPcLkmG5jTVhrlDrYL92Q8rRa S/0e57KVVlCapxTil/Pw7Zfn7jHEVOkByKdrY3rXHqi1l0k+vTcdoqf+h5RRKu9Q58t4fUr3tNz3 7zGObeuFci3ymqxdU3IoVX59lpaXdX+u87v17Hku7Hr9crFW/IZhGIZhGMab4k06qGucnjXL1Mo5 9C1Lc07X5saWPq9p7KOVWwtr82m9OajZOavlNrbWWULOW+qcv7W8dORkZ/vSic3bkNfXctkPcYdr x0BzOVru8VanvjVfyTHnDg13UVv7pHatrT1fekNztW1+7bw343j0HsstUSGep36seuR1rkWrI6+X tk0yT/wYz7pDWXMv7b3Wje8fX9xRb4UL1+a5HPOhIy8keRG2hCT/X8uF04SLJpBr+2dNmDmHjVqt +GscmmNYE2lrjqHW6nZLWdoxaFHLodXqIQd4KIX4S2VtfYmrCVT+oNPCh1odWtcs/23LA/6QvFNt v9vD8PuFJobWhL0zpa7aNA55keyh1jhVGgm99+jXonYdv+V6G8/Pm7wTf/vtt69dBeN7xe/id3/3 tevw/eeQh8mx3B3j+88hguv6+no/etRxsXvMofRGGV9yvcbr8uYEKhG9dhUMwzBeDbsHGoZhWCMp wzAMwzAM441hAtUwDMMwDMN4U5hANQzDMAzDMN4UJlANwzAMwzCMN4UJVMMwDMMwDONNEf+r/+af vnYdDMMwDMMwDGNP/IM/+INnKfh/+O//u03LOeeOXBPDMIzXw7qNMgzDWE/8l3/1L167DobxzBAA e/ExDMMwjC8Fy0E1DMMwDMMw3hQmUA3DMAzDMIw3hQlUwzAMwzAM401hAtUwDMMwDMN4U5hANQzD MAzDMN4UJlANwzAMwzCMN4UJVMMwDMMwDONNYQLVMAzDMAzDeFOYQDUMwzAMwzDeFCZQDcMwDMMw jDeFCVTDMAzDMAzjTREdvn7tOhiGYRiGYRjGHnNQDcMwDMMwjDeFCVTDMAzDMAzjTRH/8k//6LXr YBiGYRiGYRh74n/0D/6z166DYRiGYRiGYeyJ/+0//Qe4wYRhAqLzSM7DIyAQEBLgCHDOAQCICCkl TJHUwogepv+H//5/+SIbYBiGYRiGYXy/iJ/vR0TnsfMelBxSSnBuFpqJ/CxOyYFoAgCQmzBRAMCE KyYQ0f4vTzcMwzAMwzCMtcSsJWdxmScnAH7viDoHOBdmkUoe0ftHQpTIIXmCSwBcwATdYTUMwzAM wzCMFtE5AvbuZ57skFwCAhZ1mgAARAlEBD8kOADwe3U7z0qz2+qSCVTDMAzDMAxjGxHwmEDw5JAW vSkj9JSyU+qREuAoze7pIkQfZk+zmLUIv2EYhmEYhrGR6JwDEZAc4BAwf5+FJ2FaZhsfckwxwfsT ALTMt3SlSh4Ofi7Delc1DMMwDMMwNhKBubHTnE9KgPNwNIf0QdkKdUseqpvzTSfsBSswgZKDc7QY p9PTtRiGYRiGYRhGJzH6H4HIsRb5QAhLI6hEc8MoR0vL/jmEP4QBDotITQ6gAE8e7lEjK8MwDMMw DMNYTzw9PcHskDqQm/NNd24Wn9M0AQiLQJ3g/CxST8MpgCxQA1xyADwwAQ6Ek5P4iptkGIZhGIZh fMnEi5NfANwOD7mkBEoDnN+B0gC4Ec55APeYxhEhAoQbOBdBcMA4wE0TKDjAA24AaLAwv2EYhmEY hrGN+Md/8mcYkwOlsM8rjR4IJwE0TnDuDt47jMMNbm/vEX3Cn//1n2D4fA8XPM5PTxDIYxwSUkqg lODJQvyGYRiGYRjGNuLv/Nv/Ae4HYJgASnMO6ngLIM7dTf3Sj4Ff+ek3uDghDIlwGjx+cXcP3I94 /5Nv8Ou/+mu4/PodTiLwo4tzfHXxDf78//uz194uwzAMwzAM4wslEoCJ5i5NnXcIcPBfA84Rpgm4 +Q74k7/8GX7lF3b4N37n7+Lf+3v/On79G+D+ZoTfAV+dniAgYAePy2/e4Ztv3uHs7/89/JP/6b9+ 7W0zDMMwDMMwvkDir/z4DHcDIU0OIQTEODdwOoke3t8B9yM+fXePz98mfP6bbxHSHf7Nf+Xv4q// 8i/xf/6L/wv/9//7x/j0+TuMd/e4vfuMkUacxxMAv/26W2YYhmEYhmF8kcS722EO7y99nnrvcXd3 g7swh/PPosfZucPddxP++M/+FH/4v/9zfDgd8Td/+af4w3/+/+Dbv/pr/PjDOU7h8Wd/do2rj8Bv /Z3wyptlGIZhGIZhfKnEEzch+LmbqOATdj7h9NwDIIzDgM+f5xkvzoFf+tEp3p9H/I//yz8D3X/G n3z6OS5+BPzqb/8YX4VTnP/YwRPggwO+fc3NMgzDMAzDML5UYjwJcOQB+DnE7z3uhwQfT3HqR8QL h4tTh+gmXF3d4p/94f+Kr3bA2YnDx+8I7gz4+DdX+PYWuP58jb/9Sx/wox/96LW3yzAMwzAMw/hC iXE3h/GBBNAIIOH9ucPle+Drr77B/e0tbj7fIXjg9D0hRmC6A37tN07xr31zic83V5jub/CjX/oa v375U5z4Hb779DMAP3nlTTMMwzAMwzC+ROLJ2Rmi9zjZOXz91Q5ffX2Kr04Cvr44w/lpQJrukMYR ceexCwSihJ27xm/+7V/Ch194j9vbG9x+voaLE4IDpvsBP/rm/LW3yzAMwzAMw/hCiV+9G3ESPS7f XeCXf/Eb/OKPf4STs1M4R0g0YLgnEO3gfcQ0DRjub3DqvkLYnSLEM5x8nQBPmECYhu9wP004Pz19 7e0yDMMwDMMwvlBiCAG7eIIYI2I4gfM7jOOIcbzH/XCDYbyBowTvPQgTpmnEyc7hu+++g6MJie6Q UoKLDg53oOQw2VCnhmEYhmEYxkYipc+4G27x8dN3uBl/jj/99gQJIxwAIoIHYXcCfHXm8dX5OXbx FJ/SNaZPt/ib724xTRMII+IuwWEC3TucnsbX3i7DMAzDMAzjCyXejwnOJ9yNIz59d4eJgOgdnCdE B4ToEAPh9Ay4+PoO5+fnuDgByN8gOgDwgBswThOIJrgR+O7mZwB+85U3zTAMwzAMw/gSifeDR3Ae zjkQEVJKmHYAJcIAwE8A4OBugJ9dj9jtbnEaJ5z6CTEG7E4CvE8gciDcI5LH6Yk5qIZhGIZhGMY2 YpoIYxrhHOD8PJFGj5QA54C4I4Qwi9f7+3HOT90BtwkIfoIPAc5PSAAcASc+4fRsfNWNMgzDMAzD ML5cooseJ87BwYNobo2PRICqJqJJAAAgAElEQVQPcN4hkUOaEghASgkAcHdL8AGIHtiFhBA8fJgF 6mdKIFgrfsMwDMMwDGMbMYQADwDkkNwsMl30CJgd1JkAuLklPwB4EEBAWtIAxkQIowOQ5v7+p/vX 2BbDMAzDMAzje0CMJxOICJQATwQAcA/KFITZNXVsIcIJHBEoTZjmnzF5AuDgEmEc/QtV3zAMwzAM w/i+EUNwSGmWoQ5ubiw1dzIFEAHkHi0wi9cApARyNItbIhA5uCU7IIBeY1sMwzAMwzCM7wHROYL3 DzrUuwBg7gMVi6NKxDveJ5AbQZ7gCZi9VQfChJQILgE0eTyWtYZhGIZhGIbRRySXADfnnAIezs3i dBaYbrZEEWbBCgBIIPh59CheEgV4R4AnJCI4G0zKMAzDMAzD2EAEzS3unQsAeVAiODeH+nM//ACW MP6cr+rjPVxKwPTwm1uyAhyA6DzG4RW2xjAMwzAMw/jiiUQE7yPg3Jw5ytJHs1AFwHJNCS5g32qK iODIM5FKQLAAv2EYhmEYhrGN6BHmrqUAALQ4pnOXUUSAc0v/qFOO2Sek24iUUwEWAeuXflTnEaUM wzAMwzAMYxsReHBH82fn0v5zSmn/H1ha8XsHMHEa4JD7ASCaHnVTZRiGYRiGYRhriPnDQyOoOR91 /j73bUoJoJTD/W4J6xNml5UWN/XBQXVkAtUwDMMwDMPYRszC9FHn/CzfdMY95J+6uUspl5bvlNNW /X6e5CzIbxiGYRiGYWxjEagexDXlMowpLa2miBJocUWdc/Bws6NK+0b+AHlMS+f+RLB+UA3DMAzD MIxNRJcciHLOaRahCUgJjpaRpR767McsTyfk8D/HEzDBz3moL7gRhmEYhmEYxveHuG95T/tYPQgO RB5zx/1L5/1p+Y0cPPnFKSWkBw91XoYIbun23zAMwzAMwzDWEh/E6YPnOY4jgGW40kW0zq35lxmW DvvnAP9Di//c2t+7nXWFahiGYRiGYWwiusFhTh1dmjp5jx3iQyOpNCFND42mJhDg/EMn/kSYUppH RYWD8xHDRAhmohqGYRiGYRgbYN1MzZbn3N3pkne6hPWTAwh+6b8/IaWkjjCV3CJqJ4JF+Q3DMAzD MIwtRKKHPk9paYE/UULuNmompwF4UHKzW7p04J+ZQKCl66mUla1hGIZhGIZhrCSmNOebPhKbEwGY 9qL0gdk99Z4eCdS05JumlFj3VIZhGIZhGIaxnpgmh9wSyrk5Lk807GfgQ6Dm//dTYh35p6Urqnmw 0yQ1rWEYhmEYhmGsIObRoQCAltb5nuYO+VNKcCBMuaU/ZoE6pWmfDgAAjvXY7z0QrAm/8YURY1Sn zz1aHH9dxyiX1/k56vlDIu9L24+GYRhvgzi3k8oNpNI+HzWlOYwPzJ3zU3pwTecUgHmx3F2/BxCC h/ceIb7NHNSSCAEeHkwvKVSM16d2TvDfx3FcdW70ltsqx3h++LGovTyYiDUMw3g5Im+Nn5YW+g/f 82zzdEoOKc3jmObl4AjOAd47hBAQQsDJzgPD05UdVNGND/SWUFg7n/H9QDvetXOsdn4c69w5pJxj ubKGjt0fDMMwXpZI04iEB7fUAxjTwLqOmmckByAAFADvcih/GWnKp7n/1J1DjB7h1GP4+GrbZBiG YRiGYXzBxGkiTJT2+aeOgLSMDpXS3EJ/b5a6uXtTFzyC3yHuPGKMcH5uKBUCzd/DcQ3UkntRC7ke 0/EwZ+plObZbNY59jmlpnkOOf09Y+Fjnl7moh1Paf7ZfDcMwXpY4TdMiUOcW+QAedRP1SJyGAOcc /A4IAdjtHHY7D+fnkL/3CQgE5w/LQT1UoJg4fRscGrLW6D0ejxsQ9Yf0e6e16qKJX23+tS9Tre3n 65Hl2rmsY/vFMAzj7RETPbS4n0eTShhpDuH7ZfCoWXw6nMQ5x5SCRwhhDun7HRDmG3xyCR4PI1A9 a8WP/PDVnC57cLU5lsPY89shznivC7+m/EMEOBeRtUZW8rcesbwm4mDn+GGYa20YhvE8xNk59SAa 911H+TB3FZUdU4DgvYePESFGuJjgHMF5QnL3cDQBAMhNSORAy/cvhddsAPGWew3oadEsP9fKWvPb McXpmvX1rmONe3oIvaJyq1AygWUYhmG8ReJICZ4AAu37Ot3twtxdVPBwzs0OasjdSAHOA/PQqEsH /m5YOutPyF1WPTfHFCylsnq6pepdpsfhKpXT05J8i9PWs+61+3jtflwrDIFtvTf07vNjphbUfpPd V5XOQbneY6cEyLp8ybxkao+16jcMw3he4jRNSG52SGNwQPA4iR7kFgfVz79loTq7rItDmkeU8tM8 D+bW/N7bUFKGYRiGYRjGNuJEhOiWsP4uzC6SX0aNWsSp9x7wD6NJpWHumsr7BPIBbumSyieCA4Ec 1dZ5dI7h/GRnak1IdQ3P0WBoS/m10YeOkd+5xn3l7mnvfj92Dw2yDqV6HCvXVrqVNfd0DbwM6dav cX+P7aJuiUKsLec52NonrmEYhnEcYlxGforRwfmEkW5BS5jekQORRyIA0zyiVAg7pEDwLgIu7QP6 RAREDxcCxnT8HFTt4fBSIcmtYd+3EDrtzSPtLav1eytkr4XeNWHYKwp7RfaaED9f76HiVAp2bf/U Gub17oNj5TIfQ1BueVkqlX+oGOy9BnvmM2FqGIbxcsSw84g7Bx8CnJ8AIpD3syO6jC6VRejcon8E MDfvDyEAwJJ7+jAaFdHLOqjHQj7oWw/rYz6I19ByG2v5l8+RW9fjQpaWazmXfF5NLB4iSGXdtf+5 Hr3OZ60+pe3k21TLPe4Ry7W6HopW7jHpPbY95fRMqy37pefjGoZhfOnEeDIhBIL3NA9bCoe09Is6 Ye7/NAtU7wnOYw7le7cMceoBFxZhOg+J6vxhArX0kD7mQ1ZSEqc1wVEKQZd4zu6YtorlVtk1pFBZ Uw++vCyLu5favFvq3BKyNfe3JI4PqY/k7OxMna7tA23/1HjJsH3Pcs8l/nrO7WO7wGvKNAzDMPqJ c1dSc0jeL63zU3poie8c9g2k8h9A8I8683dw3gOYJ4bgcUiQXzpKx3JsWiJPm9azTE84eGsXQD3T D3E91yzfEolyf/Vs8ziOquu3JtR6qOub/2vHW863Ngzd61iX1isFaslFrol5WX5LZB9LxNV6x6jV 4dC0hLXXucZzpBsYhmEY/cSTUwfvlvFMc+OmNHch5fbDnBIAWoQoAZSQfEJwbulzipbRpOaberBW /IZhGIZhGMZG4tfnX8H5aQ7p0wDCgBPskNKca5ockKYlF9U5OHi4XV48gWhuXOXneP8yzxEqduS8 zd6yNUdL+1yqz1o3sKd+rWmtdARetzyf3KZa6kP+33LheDmlVuW18nvY4nCW5i/tg5ojV3KRtXl6 zwVt3TUHVbqovU41X18rZ7k3lUE7b3oiHs+VLrHFXV/jlpqLahiG8TLE3QnB+YREDp4cpikAIQEJ ABI8eUwO8BSWIU894HIAn+BcmnNTHeZW/c7hUAO1Nwd1S07bGkFTEi29QpDPv7Z1cM+DVtavtU01 QdYjUGW5x0i9qB3XXlFeEnP8P8/x5OF87biWpvUKKm1fyTr1Hs/SNvF1yO+l9cp1lerBf69t56Fi bcv1WyqDf157ra7dDhOphmEYz09MmEP2RBPI5874J7gl3O/mYaNmp3TpvJ8wLS3106Mc1SUfAC4c plD5w5E/qHtdjZaQWOMYaawVm3J67cFfesjWyu11UNesk9dVa5yjTV+zH1tO5tnZWVVoZMEpBert 7S0A4Pb2dl+XP/oj4PLy8sk6SiJdW+9ut8MwDE3XstSQKddH5tzyemjb3DomfLvz9LwP8udSGa3z 5VAh1lrHsaIiJfdZfq4tu+Ul1zAMw3g+IuItToPH7TSAAETvkRKQJgdCAmGE80BwZ5hD+gmDG+EJ D0OgOj+7p+TmVICl26mDKlYQkbWQorZsLZSpldtyEltl9D74S/UqiadSOXyZNZTcME3sZ/EjH+al Bk49zmf+fHZ2tv+T087Pz5tOYxZ1MUZcX1/j+voaAPDp06e9UAOeCtS83G63e1K/kkiU4i9vLxeu 2v7hn6W459shv8u65LoOw/BkPWdnZ7i9vX0kSK+vr1efG8cSjS23eA215bXrpCZQe+8NvakihmEY xvMQiTzu0iw44TwmSvAuIETA+wDQbh7ONHkQORAIO784pW4O7c/dShEIlE3UwyoV9Zb7WxzUHodE c/5aQlWrm8wN7H2IaW5ayd2rLduqe0895LJZ8Mh9pImtXIYUJS1xenFxgYuLCwCzMHv37t1+ekmg ZkHHxe3V1RWurq7207NYBbAvHwDOz8/3y2nipiRQuSubxaA85lmslsQ8L0PbJ3I7d7udWjd5LG5u bva/cxHN1y3L5ss/J8dIBelFO36lddf2Qa+zakLVMAzjeYg+3AEOiAHwAAbn4N20DHEKACOmacI0 3e0XIoSHEjzBEQGY+0WFB/CFdtRvGIZhGIZhvD7x4vwELgA0zQ2cToJHSgnee7ilA37Aw4EW0eox TCMbLcrB+blvVKL5zx3BRj2GIwiU3Zu1jmlPHWohfunWaE4rd065u5c/lxq7tFIB1iBdujVlatuR v8uQdZ52cXGB9+/f7x3Oi4uLR65qqQN77qCen58/WjZPv76+3u+zX/7lX360LHdwtW2X2yLzO7mD Kp3VjDxet7e3+1C8RDsfd7vdE5e35KBqZZ2dne2PYe38bh3v3mvlOegJ72v7Z+01Udpuc0gNwzBe h3j+9YBdIHy+ByZyONsBIy2henJICXCBMCVgdklnMQoiOBfgaN82CinNDa68P0ZHU0sFNzwgtAfu oQ/ZWtpAKxwoH6SlusgHrWwIVBK+ax/Gax68pVC+Vi8uRKVY5HmlfBoXpHJ6KQc1z8fXmae9e/cO APDx48dHIf7f+I3feLRsKcQvc1Llvri5uXm0X3jjJGAWoTkXNn8GgJubm72olYKWC0pZH+1YaQ2x tLxW7Rj10puGUFumdZ6tfWlsfefbvzZtoTek/9zpEIZhGMZMDHHuqD9GAk0ED4+vTiNSAkZK8MnD +wRPmEP9ieD9viPUfd/+wDzsKZFDCIcJ1GO5FppL2UvtQbRG4EmxKetSypcrOUO1ZbK4KpEb18h5 +cN5GIYnDYGkAJKunBSi2f18//49AOy/c9GaP2fRysvQHNeMXLesR6775eXlI/H327/92/sy5DpK +1TS0+jp5ubmkTjNAjUL0/zbp0+fAGD/PburJce4VAf+X37m23Msh7AlKvM8xyi7Z96aSM3fW4K1 R9CWXGrDMAzjeYg//WkCyCPsTkDJYZoIw+Rxezfh7m7COExIEQi0wzBNcBNAISGlOZyP9KBQsyz1 rziSVK8TIufPSBem1niiFRrN82uCs1SHkmAqTeeOX01YldbDP9/e3u5FbF6u1hgq/+fh+fz3k5/8 BB8+fAAwi8XLy0tcXFzsHU4uWDW3WNtP2rbweS4uLp40Ysr81m/91qO6aykH/Hft/OlpJMZF6PX1 9V6Iyum5MdfV1dXe7e0J5/NpXKDy48bnLTmotXNWE3XafipdA7LsY4i50vXQqresQ8kZXkPrhcAw DMM4nPibv/aruE8Tfvyjb+DdCX7++RZ//lcf4T/dYxpGjEtfpz4ACA5h8pjGiGlKSOOEBD4sqgO8 w3HGkjIMwzAMwzB+iMS/9bcIw33E7iTg8/09Pv/Ln+H601/j/jYASDg5CRiJkKY57zR4D/gRPnik 6DBN2AtUpHlYVBcOb8V/qBMqw4wtl1PSWm/N9eQuH3frtDC/tu6WiyrD8zX3VJbD9wP/r+XrauHk PD/fptzHaHZILy8v8eHDh72D+uHDB/zkJz951J1U/txyl9eQGwXxuufvuS55vt71tkK70kEdx/FR 2J6H+K+vr3Fzc4Orq6tHObfSQQbmY7w2TN4KY/P5tuznWlidO5cl93XtunuuQe14lOqiOZ9b0xEM wzCM5yW6GODJI9GAz9c/x88+/g3IO5CfW+M7OEQ4jN7D0Q6AByLmBlRwcOT2LffJuXnoU94N1aEV PNKDY404rf2uhWB7wvu1UYJq68hoOaO99eBkIaSFqzVRlefj/YjKxk0ybJ9D+lm45lb6ch/wPlZ7 tqHVcIfXVxOOtfUeEy1dgbfgf/fu3SOhfnl5+UjU8m3hZfByS7mopbSUY4S25bJc3GkvOK3l16CJ SE141pDCVTvvSmWZgDUMw3hZItwE5xxu727ws083+HwzAH4WnQSCQwI8sHNu6XIqYecCiAhpcpgw d94PAgACeY8hTYdVqtMNzNQeKr0PnFY+Hp9PCpuayNIEba0cAE8aO2nzaOW3tpOvLwtS/jk38OEC VYpsAI+6hrq8vHw0nTeQkm4pR4pH6eb2IrepR6Bq6y3N03ueyN9zPix/IeDdUuX98f79e3z69AnX 19f49ttv97mp3377rZpvK0Uh8HCctO1rbcehOZRafd4yWvRA+x3Qr33DMAzjZYjD/YjhfsL19R0+ 396AHObGTyyNdHZEA5wDiBxi8AA8ppDgRkJiI5smePjheB319zwUaiJj7fLabyVhKYVDy5UrlaO5 o73CqOcBWnL0+GcuTrmLx7tkyqIqN4DijZ/yerI7KLuO0twv7hCWxE1LNG4VqK31lV4yar/L+shy +Z/sTzU3kspcXV1hGIYnDadyGgOvP+/vVHNQS/WU29RLy6Hl8z2XqFsrimvXU8mZrR1jwzAM43mJ U4r4fH+Hn1/f4vZmRHAeaUqIzgM+gSjBOZrD9z6BiBBoB2CCT4D3wESEabZQ4YlA/ng5qKXvklL+ W0/Zpd8093ONQC0JBilM17g6vdtRKlcT87J/Tu6sZlGUW+YD2OeXZpGaQ/xczPIuk7gILeVwlkSC Rim3uJQzK7+3ytb2XQnuIMrppW3ShPTV1dWj9IrLy8u9A1s6D3kZt7e3j4ZF1eqp1U1zkeU29YjA QxxUTRD2sna9a+8rpXkPqbNhGIbRJhI53N0NuP7uFrd3hLQ0eAohwDk3N46C348ilRyQprkbqZjm VICRElzuccoB3h0vB9UwDMMwDMP4YRE/Xw+4/vmAm++A+1vA7QCPWaB6D0yOlqFPE+JubgyVfJiH QR0JlBwozS5qcB4TCM6n5oqrleoIp/W6oK3fpeOWf6s5V1ooP8a4H/mIz5tdyWEYHq2Du10lh7NU 75bbWCsvO3a8L87cD2euKw815zB+qV9TPtqT3C+aa9kbWi+FyUvz822Tn2vr5chjV6uPdBpLjm1v WD3GuT9ZPiRrdlG17ZPl5TA/z/XVhmLV9kfJ2ZVsTcPoXUY7B0pOZeseUVq/3H+y/B7n/kvItTUM w/jSiVeffo7Pt/eYiOCcR3SEKRHgErz3mKZptkZdgoObO+EnD+c84Ane+7m1v3PA0gPqMUY67X3o bAnRlUQN8HQITa1Dd204zDx//p/RhrfM868VqLV5SmkEJTHORzO6urp6NCwoF+lZnP70pz991J1U br3PQ/lyX5XEfw0eWi6FnkvbXhOovNN+uZ9q/2v1bolTuZ7SUKb5e96/wLwvc+Op/CKRXyK0FwHZ CC2XeX19XRVVtX1W2p6eFJo1+6ZVlvaZfy+9oLVYkxZkGIZhvCzx+ufA/e0Jbj/fADFhoh3C2QCH e7gExOAQkseUAEceESeY4oBpusOEBLiEEBMwAkQTovdIB/aD2ivUOK0hIvkyfFmeb8l/4yKslL/X I1CzQODrqomimviUwqT0YJUur3TLsjjNojQLVJk7enl5iffv3z/KOQXwaHhSLddU1luj5LRqv8vy avmRPK+2JFC1OpYccz7/GvEmp/OGTNK5y5/5ULD5OP3FX/xFcRs0sSvXWxLmvL4tgVrb3tI0TaTW HO21L5o9LxCtOvJ61tbVU45hGIZxXOLd7QDCKU5OTpD8CEoJIMB5IETgxHsEd4JhcEj3AJFHStMy 3wRgDvt7P7fwB4B4DAt1zUYUHlY9riLw0KKdCx/eIl2KgZKTqwnUUl14XUtlayHPNS6rFENZ9PAh NwHsW+/n+WUfnfnv/fv3+32T560NzykFidaav0Wv4JXr7A3xa65c6XzSxH7phUILx+dppWOsHf98 zADsG1LxfS/XK79nod4rrmvb1DON03K85bTaubH2vOmlVyiv2S+GYRjG4cQxTUh0P4tOlxCiQ3QO wTt4B4QYsQsRzjuMlEBpmruhWvDeA3CgRCBKmDNYD6PH0egJz5XcrdJDSHNLuOsF6F0mtdYrhVxL PGkObYmSm8vLzgInC9HsnvL9wfswLXWwX3t41+gRMqXvJUeytry2Xu3Yl9zwUhml9fY6eJpgLH2O Me6PWxaiHz58eDLiVGuduYcFuS2tfSPnqU1bi3bN1l7MNFqCdm19eiilLRiGYRjHJyY4jEPC/dK1 lIsBIY4IAUAiOBAcAcF5UHCA9xiH2SF1zi2dSy05qs4BIKT0sg6qYRiGYRiG8f0h3o8EQsRuF+F9 gieCQ4R3Hs6NgBvhEUGOEBzmflG9R0oDEhEmmrumImDfaCqlw1rxA32ujszlk7/L75rrUgqbj+Pj lvd8fm24UK2upfmlk9br4Gjb2HIBpYP68ePHfahYhpeze5pD+bmlPs8zlfXpyQc9Flqomqcx9LqK rfSEXnocVHn+1Vy40ry8AdS7d+/25yU/n7SQf15uGAYAT/Ot+bKlevRM38LafS3Ps7W5o1vnK91f zEU1DMN4XmKMEcN9gvcBAQEhEIgGwDmE6OCch3ce0wRgyU8lTPuhUIG531R47PtKjTgs9FYTPazi XQ+JVu6hTBXQcgD5/Fms9dSRrysPfQmg2bJa1rFFSWDnv5xr+vHjR3z77bf773zbcmf8796924f4 P3z4sBeocvjVElse3L3Ckn+upQVoy5WEbUmgatuhpVFoOaulhkna+VSrb/7MR+TKLxz5DwBubm6K dc5hft6tGe9VoifH91AxtiaPuEYrrWdtmsDadfde74ZhGMbhREoBKd3ChwTnA3zwoCWkv3M7JDeP euqRMLkRi1cKIHfo70DkHrmmMR7PQX1U2cKDruak9jzUWg5YySktld1yVrPQWANfRytnlYtTLmRy wyjeICrXJwvU/Aeg6p7m9dSEtiaaSy5vaboUbHxbct35/snz8+3m88v51uS7ynpKR6+03bz+tfOa bx93UEtOrXTAtflLDfa0877n+urlGKJ0y/lSWvcWx7ZUlvxsGIZhHJ94/ekWPgBfn53OnfCnAbuw Q/AR3hOQEogAIsAFD9DccT95B7+02p9HmwJSSnCU4DrdthKtB9C+8opAKD1ESmFx4LFglGIgh1Ol MOoNZ5cejJrwKKUb5PJLAuf29vbRNnCHLDeKArBvGCVDwlmc5q6jNHew5fi1trl2fDQBJL/LYVi5 G5i3nfcZ+unTp0cCNe+D0gtMr/iXQlRLFdBcUL5MaVvly0RJXPH9wKflfaB1ncaFaj6nSy9O2rkm Xxbk/nkueu4FtRSFkuvZ64aaEDUMw3gd4u7E4fyriCHdI0zAV18FwE/zsKX+BNF7jMPc12lywOfr O7hwApDHhGkO78e5i6nggLgj+HhYI6naQ2GNUOLTS25RzZnkwiHPz1u/99RDCphSHfL/2sNYEw65 fOkWZqHC+zvlAkg6qNwt7Qmft5zAkqOoLVcTp/wYZGHGy8/bnsXZx48fATzukgnAfh+UxAwvm5fP Xxo0cZrPH611/ZqXFul2a+eZdMj5snl5LlD5cjyPVXOm+edS+S0HuJdji77SfpK/HSvVwDAMw3h+ 4m4X4H3A+ekOjhJ8cEgYQZMDQgKWkaIS0dy9lHdL/ukEYA7zEwHOEfwyopSzRvyGYRiGYRjGRiL8 iDSOcLsdXBhwOwCn0WNKA8YR2O1OQUQYJsIwZdeBHglR5xx8IDgiOA84Px1Uqb/4iz8/bKsMQ/B7 v/efvnYVjB8k9rZuGIaxhXiao15uCYV7j+iAIc05pSCP5CaktOSezl2dLp3yA/kGHAA47+A9PerI fwsfPvyk2cCm1biJL5fnz90mXV5ePurAPP8OPOSYDsPwqCHO+fn5/rMW7pXry/DRpbRGTLUcu1Lo Vcun5WXwzvjzX962vE2Xl5f7oUs/fPiA9+/f70eMko2kYow4Pz+vpinIaTKFQO6fUri4NM/t7e3+ mGjnwjiOuLq6Uset/8f/+L/A7//+P9rXJW/XxcXFvneCnLOay+f5ufzY8d4M8nHVhnttnZ9aCD0P OQvMPS6UegOQ5Y/jiG+//RZXV1ePemmQaRty5LCbmxs1nUIL8csUC16X1rbJhl/aPKXt5J9bYfnW /WBtqkptXj5PPucMwzCM4xEvL8/gwwQgIbiI07jD58+3s8gMAIKfx4Zy4xzeTx5petxKf++gOppd VXf8Vvw1MfdkoxTx2GrsI+cfhkEVJWvR8lhLOYB8mZJAlX2zxhgfdSEEPBVbUihmsc5HjMpdSslW /K1t78nlK/W/2WoAo+0bbX4u5Hifs7xxEO+qqZTLydchc1DzC0opN5O/zMgcVo1WjifPb5X7QxPA vIeG3CAsC+287Zo41IRfKQc5r7v1YiHrXXrZlPPX6DnPWkK3td3HWJdhGIZxHOLp6Q4nJwHjOCLQ Dl+dn+L29hbee5BzSwt+AuBBSwv+TB45ag75A95j+X/8RlJrG5xov61pzCGFWe+ypVbaXLSU3KOS U8rdXM1BlfUrjb+eG8vk/7xDft4pfxY0WZRpda3BRZTcL3J7tfr3CFRNvPPf8zaUvsvtkctzUa+5 x3z9uScBAGoDJe1z6bhLNAdSc1CzOP348eMjx5wLZl7n3Hm/rGueR3ZPxY9f7fyV07TvNVH8HMJP exFq1VHjOetoGIZhPCam9BlT8oAjJBpxc38PwoTkADiHcZrm0D4RJgImSnCzpzoPdUoEuFmYzn8O IRwuUDUhsmrDGuJUCtiJJHwAACAASURBVBL5ECt9Xguvd60D9/x/HMd96FUKE+2hWnPoNJcyu4o5 pA88CNSzszO8e/fuUXi7Jp60bSzVTROowzDs3V/NTZPl8pcGTeDlOvN1agJVewngy/Bydrvd3nXV 6p+R3V9JpGCX26i9eMhrILfsz79xt5inc/CBGLRzoIb2wtASp/m/1vWVtk9q51JJxGvlrEGrS49r 25rfxKphGMbz4AFCmoDoPJwj3I3zQ9expvhEBHJ+EaRzq/3MQ0MpLA2nHLw1DDAMwzAMwzA2Ei8u LjDSHaZpgvcOX4eI+92Ic7dDmgLubgZgGjBNCW4g7Aggf4oxTZiGCfAeJ97BBYfoAR8cAh3uKpTC iKs3UOQSbllWq1fLvax9LjlKsiFQj4PaqqfMp5SjRgHYf9ZyG/n6tbBwabtL9SrNq/3G18fd091u p7qsvO6yMRtvFMcbB2mhfS0HNbuIPWkaGlpYPv/n+1dzhnnn/TmXWY5Glvu8zbmocnnZvy3fplJd 5T7Px6MV1pe5z8dyGWvnO59n63mnzbPFBTYMwzAOJ4ZdBNKE6AO8B05ixDkCbu4m3NzPD827ccKU gBA8Ygj42ecR5BK8I4ToEQMQo0dwBAf3yH3dVCklPM9Dm3laaQjO/LucXyu7R3Rowkw2TKqFpuV2 aekFpe2V1ISqLEOGgrM4lSNGAQ+NuHKKgVxfK8zP52/tc15H4CHUX6M0DxdrmrDMy/A65Gk8d7Rn Oa3u2jb2CFZ5ruQXE/5SksP1smcC2XNA3hYtBM9ffjSBumbbtPOVi2q5D0v16Qmt9/yu1b+1TYei XZsmVg3DMI5PnKaElAAkDyAhTR7jOOD+fsJEDrvTM7iQcH8zYRwngDyI7ua8Uw94n+C9h/NpCfcn hHDECoqHIxcUskW2tuwhDip/+JYcO/6/NY8USflBpzmGa91ZjhSneZ/l/FLumPKyNOHbcm9LdZVu ppyPb3fvceEiVZYlP2svEBmt2yspSrVju0YQ8bK17yUXMgvU3OUUzyn9+PEjxnHcv2Tw0aFkl1G8 bCneeb1LopXvv9bLnXR/a25j6yVujdjrOX+2CNPWMscSu4ZhGEaZeHd3B6IRRAQ3AHduwO3NMA9d Gs9wenqGk11Amj7j/n7ANN7h5DSAiBCwNJJKBCQAgRCW1vwHVUpxUPN/7gZqIVO5nBSIrYcLF4tc nJVaUvN1aWKOC2leFy54uDCtCT8paFtObV53Dm/zbqRk/bkDp4nymvOlHS9+rPj+KonGXrJIzWKy 1NtCS1zxhk+8znLeXvEjhaz20qEhxWkWo7lPVz5U7dXVFcZxxMXFxT4dJO8D2T8pL5vXS9a3x0kt RSrkeSGPd2n+1r4o/dZ6aes5br3nXEsom0g1DMN4XuI0Ls3vKWBMA9JECLuI4X7CNIwY0j2m0eH+ /h5TIiQPeD/BOSDAzTmnnhACcBpmVzVu7zb0aQULLigXfqUwfe0hUhIOPM9OcxD5d01AyodwLfzM hUwvWthU217poua6yI7neRm871Rte3oEakmIaiKn9nJRE3Yy3F8SoWsFS+sFRtvX2r7J+0w68DHG 4nZJgcoHW+Ah/pJLL4+drAuvs7xmtP1R25caa93lNchtfglxaALUMAzjdYkJ8xClIEIiwpgSoj+D c8A0JAz3dxgnwjAmJMwt9cdEiAHYxYCwA2Jw2J1EnHqPGBzowKFODcMwDMMwjB8ukZIHkQfRPEqU cw739yMoAeTmfk8ncgDmjvsdALjZQXV+mh3T4LE7IZydBOxCxDDcNFa7jppbyb9ry0lyYxHNQawt 2+PGae4Vb5yiOailckuurWxIU6uPrJdMV9D2qXRQe8rV6q/NWzqGrWOxhZqz3kpZ6EFLgwCepobU wu65nnl6bgQFPHWz5bzyOGopGnyd0q0vRSX4MvK8KTnM2jZp5zj/vGbf1xzfElr5WyIWWhml8g3D MIzjEXPfprkzfu8cHMIyrGlCooT7cUJKAJFDSg7x1MHBgRKQEiEPLuU9EOIEjIclofbmm5UeEiXB 2SMMNUq5hqV1yge7XLbnAS6FYilHVHt4a0KQl1ObT663V4zX5nkLD/WWQC2dTzXxXArlawJVTpOi MAvUHOK/ublRQ/Z83fxY8q7JSudmTzpD3g98qF/ZA4Dcfm3flATw1peRHnGqXWul37eU3yrDMAzD OB4xpQmOArwjJHJIExCCQ0rANM3i0yWCS4B3gIvAMBCSI8QTh10I8J4wDSPuHAEuYDpCP6gZ6f6s ETstocrRXCT+WeZQ1h7Ocv2aGOINp3grd02gSte0Jga0Osi82l4HVVuHtn2teTUHr1bW2vy/kpDu ccxKOaG9LyJa91CaQJV922rHTDqopRccvp7aOrU6y3J6HNTSsqXyZdlSoMrj9Ryib4tDW/rtWOsx DMMw+one+7kFPuZO9p1bnFIC0jJkFHmA4EAAQG4/etTsniaQ90hpwjQB4+AeDzV1ZDQ3qDZfSzxy IaIJ4bXCpyYc5PKyJTlvvFQTqFp9epD1KDmCLQHceihrx6gmSrTf1orUErUQv+wYvwc+PxenJYEq BafsxJ4LuNwwKs8vj3nphUc757R6r3UVc3+4MUacn58/mb+0rhov7ab3uKa1a6p0vzFhahiG8bxE uBHJAaC5yyjnPIZpwjQShjQh0ZyX6r2fhzxFws45TCAEP4f1fUjw3sFRAmjC6e7AfqYMwzAMwzCM HyzRw80NodIIR4BDBE0jpmkJ8U8J8IADLe6qhwPhJHjECOxCwM4TfKBlRKmA05OTgyvWckp7wsZr KIU5tc/5O3etao1USsj5rq+v946a1lhpi1tVcyJb7jBPh+h1kmv77FBKx6YnBUQLzdfqWusOin+W oXg5jbugrQZuOe8UaDuoWl207ShtQ81l1ULzAJ4MksDP91oebs+6X4q1uaZvIX/aMAzjh0j0gRBo Wlryzx3uu8UAnXufImACnHfAIlJTSoje4SSGWZQGwMUJJ9FjFwlwx+1mqhaeXNPAqbVcLQSqhTZL D2RtGSnmpAiUdWiF42t1lpSGCG2VUWrcUqpTbXvzd/7g59veeiHRyivVQ5tfhua3vEiUzoHSuVAK /ZfEL28kVWoUV6pzSaCWUlVq558UqGdnZ49emOQxk/uztW+0eh6bWth+aznHSjsxDMMw2sQQ4hzC d8A0TUg055o65xAAJMxDoBIRskD1HgghIMaIk1OPk0CIu4hdAGL0mKaX7wf1kAdSzaXN/3sFqqxL 7QHX+7AuibdaXlxNgLcahNXW0fOb3F+tnN01D/6tjhbPpyy5e7Je8nvrHODTS7mpmjjWBC3vZko7 D3vOpdJLAF9GE/6aax7j3Jjv5uamK/+79sK2RaT2zrs1qtJ6QVpTB8MwDONwYgizGKXkZoGaEoY0 931K3sETADiQW/7DIUaa804DIUaHs7OAuCN4jABGIL2sQNUEodZauLQMoLfSz/9r7hCfViq7t+58 2lbHWNJbTk3c9u7D0j5Ys42131utrTX3sCWOatsg5ysJSzlfFpd85Kda/6glgdpyIFsucu08Ki2X hSm/Hm5vb/cuamvY09a+OUQ8HntebZmee4Y5qYZhGM9PdC7AubnV/TRNGAaH+xFzPqpz8N6BkAB4 JDfnqHo/hyhz36khBIRAS+7qBKTjtuJf85AtOXdr3McWNQdu7QNyy8OuFLZtld3zIO6pT20fH2Of lMoqiVb+W2ldLUe0VteSQCy9rOTuonhOaY9Qll18bXXs5DlfE+7asnx43t518c892/AlupG9ediG YRjG4USHeyQaMS0uqY8BbkhzF1PkMBEhpTk/NSDAB4c7T7g4I5ydneH0ZEJKIzwFwBMwOUywG7Zh GIZhGIaxjTh3LRUwt4uaQ/wA4MgjAXBzH1QgcpjcnIf6dXDYRczuqndzisAiaGfv9PBupnrCzLVc tlooWKPlgvSsq1bGmtbDa/Is1ziwsp6lELrMPVwT2mzlO2rlHpNS2Lvk9K1xUUsOquyz9tOnT09y UEtll1IISudUT1hffu/JReXly7q01lkqs+e6aJV7TDRXuLZ/a1iY3zAM43mJzgUEECZPCJ4wuQTn CAlzi34C5v9L1J4ICC7ATYREAKWAyWMe7tQB5ByWhNXtlao03tHClz3C8Ji5ZccKY2vURAQffWor mlAbhgG73U5tGNMK37fq3KIlnnppCV653bVwdE2EyZB8/suNsHj+qSZQte1cExpfgxSnPeKyNW9r fVp5vcut/W0Lxyivp3GhYRiGcRhx5wNGSnOH+z4gRo8dDXADMI0OjgDMJirIzW7pdD9icIAPd3DO wyMgwsOHZeSpZxxJKtN62EohsEao8vlr6zqULa6qtnyrHNmtUUlgxxj3QjWXe4wH8HM6pZr7p33X BOoacQo8Ht2LC9Sbm5tH3UNpArXHPW7tp7XCcUsOaa1urZxm/rn24njo+fAc16Y5p4ZhGG+LeajT KQEgeE+IEdjBw6UJnoDkPHxKGCfCBMB5D6LZMU0TYRrnUaUmpNlFBT10pLqRnrBby1k9dF0tV7Yk ZkvLavNrYe/8vRYa1kSYVoeWu9zrbtWE8CGunyYge14kWmXW6iZD6vx3/ltpW2XH+1mc1rqHkmWu cZrXpqRo7l7t+tHK4UPutta55vhvEZNvTRBuPS8NwzCMdcTcEp8Icy6qj4hLqB8AAgjwgJ9mG9Uv bqtzABzN6QCOMMEjLH2lHop8CHDx0gqvSaGz9gHfEro9D+7ePEFZR759uVsfbdmSO6yJ1NvbW3Wf 1UQSL1fr5L+0nPZdcmgoP5exJn2Ar69XoGqCmU/nrfRr/ZfyclouJF9fjV5By8+n3vM5L7PGpVzz uzbvc+Uj57Jr695C7WXDMAzDOA5x3yjKAUACuRHeJ3jv5r5O/dw5/zyCFOAD4KJHCB7RO3i/m39b Rp3y8PCHt5EyDMMwDMMwfqBEAPDeIwSHhABMAd57pIng3IQQAe8ixkBIaXZPRxoW19XBYx5ZipyD g4MnD3eEHNRaCD1PK4WDt7gapdxVWYc1qQQ8p69WXskVzp2icydVllVzT9c6X1rZ4zji/Py8u5xa 6FkrWyur5va1HLg1IX75v5U3yZ3S7J7KXNOSg8rrKbdDq/sWJ7XkyGu5xLXUjda6S8dNc6B7r8Vj u6jHDsOvue4NwzCMw4lDmjvYdy4g+gByDuM4YbebEKMDvANogvdp3/hp52YRG2MELc7qLiQ4AkZM cO6wVvwHb9QBeag9D6ItIWatbE2cllIbasKpFDaW/7kgWZM7WBo7Xltem3e3220WH2tDqa3UjZ6Q v9w3/CWDt9bn4lQbAUqrw5p9r227JsZrL3OaOJXTay85Wh21c7SWytJLzwvImt8OmXfN9WYYhmEc nzhNE5yjJac0wcHDLzH6hKWP0wRg6et0Hl3K70eZmsUozY7q0jWVC0eoWEfuaI8bpeUAHuKw1upa Ejhyvto6attUq0NPfmNrmhQqh+wvThata4Tq1pcA3mpey0HlnzXhpW1zFp25f1MAT8SpHDGqlHOq 1Z9/XpM7XTq3WtGE0gtS7bee3GtN7Gvr7rmWaus5hJbDvmZ5wzAM4/mI0zQtXUwlBAckR3A0wLs5 n9Q5B/JzOB+YO+cPzjOXdAKQ9h38A3Swg1p6aPOHZku0ldzKXgekJQZL9d7y0NOcUvmQ73FiW+s7 lqMFQG08lYVoyYXm7mrue7V3/WtfMORx1sRUbzlZoGbXFHgQqPx3vp6Sc6ntGxmS17af17lU9x5R W5ufi/qWa99D73q3li/Le+7lTJwahmG8HDG34M+t+Wf80jBqdkuJHGhK8H52VwNmNzU5IKW5o9QJ sygNDhimw3JQa87N2odJSeBsfdhorpV8oK8VD61yDnGQel3Urftlt9s9Ep5rBLq2Pumybg3xlwRW ad/W0h/GcXzUYh/AI7HKBWPJeWyJNU2cbmHt9aIJ6bWuubZPDzl3twpOrU7HEpUmTg3DMF6W6JyD c3ML/JQmEBFCXNxS5+YUVADJJwRH8I6WsH8CUgIt4f0c6p/gMB0oUA3DMAzDMIwfLtEhwC19nmYH dRacAODgF63pWdh+dl0JU849Xf6A3B1VwqFsDQOWcuike7rGYelxoUq5jmudl63h1d5wdaleW+oa Yyy6pz11KW3nIY2qZF00N650rLT5pXuqNYbaGsrW5qulBbTWp9FTt0PcxjVOqTzerVzZtay9rg3D MIy3S5wbPM0C03sP52kJ6y/9o/rsmM4Np5wD7qfxkTDNOaduGQqVjtCKv/QgO6QMLcS/Jde0xhZR 0qImSmoiq7aOQ9IcZBmH5hpqQqVXpNa2o9VISisr/ya7k+LdR7XKKNWtlnJQS0noCZ1vFa23t7f7 7sxq21a7duT8Mm3gGHXu5aWEqQlhwzCM5yXCP4hJ5wkxRiQkuOQAJCQEROfgQnZGA8Y0wYkofhan 3vslL3U7mrDMnw8pc01Dm7Wia03eqfxd/m+JIM39LAl6rQ5afu5WatvdW/4W57VFq8FaS6RyoVoa vrRU3hbhtabcrS56SSzzbW2J/dJ+1USqNv0YL57HZM0+MwzDMF6OCDjQ/A9wQCJCSoRdjHCJ4CbA +QgiwjAlTNM9QHNeau6OKnc5lT+fhXrr7BbSpVnTaGLLA6dHXKypQ2/DmFJ91lBz5Urra4k8Lizz X3bZ+Dxa45rSb7X6rp1H9gBQWt+aELJ0KXl/pz0CtVZersuWfSLXWTtXSy9e2vnYatSn0eug9tSp d71rXtZa1/aa33qvy+dwgQ3DMIylFf/cVRQAIqTEHFXnQEj7fNOUEkB+n7Oa5+GfPQHOHy/Ef2ir +1K5PdO18GuPsOult6wsUloCp/UQP2Rfcnet5JDKsg/JI+51uoHH6QCllwPeP2qpLvlvGIZVuaa1 bTrETZbHdYuDWjrm2rldQp7/hzioL03vOWQYhmG8LSIl99AJPwAHD8K09ImagGlu2T8SYRwSAA8f eN7pLEwD5qFOAWCkwxtJGYZhGIZhGD9MImECpRFwaR4xigDyCQkER4QEYEoJ00hICXCOkCYgxId+ UqPzj3oAmI7Qij8jc0efo6xjhQ/XhH81Z0dzTUvLt+rbU4etlELFpYZovTmjW+oQY9yH/Fv7M/8v HcubmxuM4/hkZKi1LmpP47SSo3rsdBOtrC37upRyozmoa1MHnqPOWhmSVkpED+bQGoZhPA+RUsAs Uj2AWVj6sDR4QgQt4X/nJrhAiN5jnPK0OQ/VLwI1d/b/0OH/NkoPpmPljPU8mNY8HHsfyL3bUirv WKkOrXB4qzFNaV4uSKUIa+WI9vxWE2y8uysNuR2yfNlanwvUkjDVzqNecartt7Uir+d80oRwSXDW 0EL8tdSDNSK1N/2hlzWCUR7D3jpY4ynDMIznJRJNIDy4n8CElJYupNiM3nsgLeH8wFrtzwNJCY4b 4u95iG4RlHzeUoOf0veScOsVe616leZf+/Bds77WvC3RyIUpF6Xye563VGZtPS3BVRKqudFTaZ9k UcqHMM1l15zTtW5mr6Ctra9XLJfK64lIaPUsnY81gbqmnsd0XNewVmyaODUMw3h+ItGcgzrjQS4B CZimCbQMaTr3ler2eacxhn3eaXZOgaUDf+/2ba6OSa/zmedtIcXTWndSipbaQ3dNY6hjsbYx1xo3 rrQsF6JaiL8mSDWHTlt3zVVe40rL80m6p6X5S7Tc6B53vdcxP3ZYudflrKWlPEeI3zAMw/jhEvdu J3kA8wPFkQfgARDgEnzwcAjLIh4xzmLUM4HqloZWhIQQgrIqwzAMwzAMw2gTh4Hg/AgfAsLS3+np WZjFZgIAD5ccQnCIu1l4pjTAO4dEhHFxTwMcfPCIPh5lqNNMj0O2JYdMOnYtd/YYbpAsqzeMuoWa 49eTJ8k/H6NOsj6y7Faov7bP8v9aPa+vr5/Mzyn1d7rVDdRSRHpD8Hn+Wm5qaZ0vHSavOdul8mp5 n8dKYXlO93bL/cYwDMNYR/TeY0pz7mnwHicnO6S0NIJCehi+lCbs2z65pbU+aww1d0CVR5t6/hzU LQ+4tTl3tfJKuXelda5tfNEb7q39viVtoSfc3ltO6bfM2oZTh+SB5kZPfH6+Li5O+byl/FO+zto2 ldIbZJ1L50lvCgMvt7Wf5OdDxFztGui9FrX82Bat8/qYL5Y1LI3BMAzjeYhEuUW+QwgBMUYMI2to 4hISyzclIiAmUHr4nnNTs5sa/fErusZ90ijlmW7NGTzE6ZEtobe4vlo9am7kFo7t5q4VImtF1HM4 qL3rltSEak/dz87OivmdveKsZ5m1ApiXv9Zl3hKleC6Hcm3jNMMwDONliSklhOhxenqK3S4CIHiP RXg+jBQ1IWHWnQRPwLSMJuXgmMs6953q3DMo1APhIqnXpWs9xLaGfUvhUOlwbV2PFqZfs+yxqAnm LS5Zj7grvXD0OKhZmGqu6bH2S62hEQ/r8/oduu6SWJbr7y2rV5jW0hZ61r9FnG55EaotzzHxahiG 8XJE77F3Tne7gOA97sdh7tM0zT33Ez2E+uc/QsjdS+07AXAgAIf1gPq8aE5eiTUOX4/bKh/sh4ig Xjex1bL8WLRSArY4u73HqbUP8/e1AlWrT209cj65za16tvadVo9eh1mry9pUitKya0L7ax1aOe0Y 58PauhiGYRivQ4wxwrkEooTgA05OPAgR0zRhpATe5/6+qymX9l1PAWCd88+u66Ed9RuGYRiGYRg/ XCIXmdM0YZrcI7d0Fp/THLZ3s0O6i3EeQcp7EE2YpnlZYBaxh7biP2bIj5fZcmGkM7R1fdJR7Ekt qJWzla35tb3UXMU1+YSlsG/P/qqFmXkZ3EGtrXNL7rHmKGr7em3+ZY3eHNeag7gm1N/rkj8HmuP9 Ug6oOa2GYRivQ6Sl9b73AdNEuPt8h3hWziHNHfeHEBCCQ0pzt1NzY6u83PPkoB4q8nrCsoeKVK1B ixbib9XpOTh2qH9LSkJPjqU2fW0+sJzeI1D5/K1cyZJgWys4t+RL1gRbTw5lTx1rx6TUSOoQWtfm 2lzS56qPYRiG8TJEQgSBcD9MCH7C6ckJbu/uASQ4zP2bOjhEcoiegPD/t3dHXY0rSbqG34hMU7v7 rLmbi/P//96sMz3T3XtXFVZGnIuUZJkyxpZlMNT39KIBI8uyoTZBZESk46XRorHfH2dOzZy0wq78 svfpVc4FJtfUM14TJLxVO3hLNvVenciXPPbSuYzZtV3Nl9QwXlqbe86l3e+X1qKey6S/lqE7l7m7 9Oflksz9W9f02sfT51sFVW/VEb/3H1jv+e9HgamIyGOoPbCEiB5s7vd7bO6ACtKgYqQbYb1Lfxj6 EP9ea9qzpe7j7lMbLPEvvRbEXJINvOWX9rlmkDXnfe15nAt+Lv3FvEVJxJrnMz3GJZnpU9fz1utx yeOf+/jcNb12zmu6zC+9vrWuDczOlZJc84fSqe/pR/2RBe8TNCowFRF5LJXsu0NlJJF7INnZDghw +namNDKcaAluENN/zHtwOtWxTrcMse1/7C/JYt6S6bx2ufetbN5r3lrKviTYO3XOrWtLr3HNkvTL j8/ddu72e2bwzgVmly6JrykLuSZ4vPTrL0tMrnFtremlj7HFH3bXlBasKWcQEZGPVyMCi55BTZJK mXeDcksSaAaZQTSwAPMpKO3d+tOw/kP3/uPNQRURERGRz6EaO4zELOlblFbasMfcmJbsM5MY558C OH3EVKFnTnvnfv9ag16regeXZBdfZly26Pz9iOXOt7KOy69dmj1e07hzyXlP3e+S5fxrbz+XOb1X Fmzr5f01mdJrft5OZRvXlrpc+30/9XN4rwaqa8/73k1WIiJym1p3jnsSMRDR+nsGPHqGNGEOTDN7 bWnS56AG4wB/ytFJW97WJHWtt+pFz7kkqLum4WWNa5forykNOHW/ax5rOvaW2sx7B6drfFSN5RY1 oq85V4e9VdB4yX3vHQi+bHjb4lwiIvJYKun0zClkWn/DSJJsgDnpgVHwcevTjErk1Eg17SS1qEW1 7ZqkLrWme/pWl9RIrq3LO/fL997Zw0snGNyaldqyKemaa1n7em8VxF4SpJ6qLV7z+NfUa156ro+y 5o+KWzLIIiLycWpEkAwQiWPj8P3jJfppl6iw7Mv6PoawCUmDLHMdKhZke98M6tLWwcQat4ykmt6f K1t4L5c85q2ZuS2f2yWByFvNMu/1B84lwdZrmc9L/3B5NGv/bd76vLYoERARkfdV3Rql7mg20J4b z8NPat0RMYAlRhAtcQ+cwj4GWti41amT6UDOdalQ5nrUj3TqF9Al3eRrf3G9tUz/WiB3Lhv6cgn/ I4PuS6x9/bbOpJ6675ru8ff0MtN3KpP68tjJW8/5o7Knt3b8r33c9zyfiIjcR8+gDgMRvZ60lm9g A4dl/+gd/BGAExG0lmSWeUnfMhkWWVczdfGLiIiIyDoVL2RrRGuYQ3EncgckOQWlrfWmqDQik2xP RNi4e5SNQWwf3g9Qyu4jn9Or7pWF3GJJ/9TXXmbRtuqS/oi63Gt9VKbrrVrUe2ax36pBhceYDXuv +4iIiExqrzkFSKI1mvWa1LZfZFEJyMAwhmGaj9prUJkTp8HUNLUffvzyQI/grXq+rZaBT9UXXhtA XLIpwRrvFTicGjN1SfnDy9s+ItC55jrvfR3nxkadeo3u8Xq99vN7y+txLuh+r7pfERF5XHXYO45B Mu4WVWiRtCHHDGn25X3vNabZGsGuZ0wzSdp4XP8lk7HtVqf39h6jaq75xfjWDj5bBQXXuKY+9Fyw eU3Dz5rHvuS6Hj1IOVWDOnkto/6Wa35mrvlZPvdv59apCpd6q9ZcREQ+p/r9+3d2pcI403Ti7pQy zTfdA8xL+tF6TeoUoPoUvGaS0bdG3fxC75hlubV7+15jnl57rEf4BfzWBgGXBJ5bfw/v6RFe80ds mnvP67gm4L1lgyI5EgAAHTFJREFUc4NrH09ERLZXn38m4TEGmQk7B3ZYJk4FS4aAloGFEQHZgogc G6ucjD4TNRpENN5jq9NrA5xLBvJf45Ljzx2zRSb00s0JbvlFe65b/LXvwaUTEs69Bp8l43kvl9TC 3lKK8Nrru+UfDpee661jXmbgt/yZeOtn8Hf9+RMR+Wg1M8fO/P2cBTVfLNFbst/vGWIPOK01HKMN QZK4j41SY3f/EAl3yKCKiIiIyO+hPn+v43ipJ9qQ/PPnnm9/G7MKFuOQ/jK+BVB5jj0ZFfNehxrN KWWH4bR94+df3ze/0NeGlr/lXsuPl2RXLs1kXZuluWaG6xZDzs9lrraeZ3mquerl/c/Vsl77Pdlq 2fiSTvtrnXu9L82YX7t8fS4ru2Y5/5J/s+eyueeu5a3Hu+Z+IiLyWOqPH73jPmLcMSob+3bYTapn VA93yMxxAb83UJkZrQXuQeI8Pw/Yneb0bxHIbXn/W5ccTzUPLV2zJL7F9bzmnl3tl57jXDnBFiUM ly5Fb91Qd+35zpV3vNZQtvaPiJePcarE49z9X3rre7X2tb3HH6EKZEVEPlb98d2IaAxDX5YvpRwF p8CieSqxNErpu0WV4mBBGxIcMvcMA5TycVudbuXaJost62HXXMOt3qvZ5ZbHuKRucm2QvlW95HTM pdMHrg0g3+ruX36+5npenvPUY5zLbL51/o/OYl7y86HgVETk49Uf358BJ7Mv4Q/7HqiajW/OmCkd 72GwbwMR0LIf24bpuH5Iax+/1el722rJ/yNM131JY85b3ivIffQgYm1JyqnznDvva7e9dZ5LHveS 6QxvBblbBKnv2Rz16D9XIiK/i/rvP8Es2D0VMo39c8+MmhnmywxqjGOmIFohIvBxClVEUAo8Veb7 fHbnlk1vPefW513j0hrE18oQTnX3v+Wjn/NbtgqErqkRXnOutx5nK/cK1u4xMWMrW07AEBGR9erP 5575nLKhw97YjVtEHZb6+8E2FpdGJq0lpdCD1IDdE1Q3ijm73dcIUkVERETk/dX/+A+jtcMS/tOO cZcoyJiC00MNKtCboAJqsV5vWoOnaj1I3SVPT/efg3qJrZpa7tmEces81LVNNmucy6K+deyWbnkO l7xmj16ucc334TX3bHxba20D2T1WO05dg7KpIiLvp/7f//xPfu6fGYYBx6nliT+//3veyrSNwWnm YbbpkxeG1qi7Qq2ViKBWePojedrZe8zpv9hbQ+VfurUT+lq3BpnXjD+6xRZL/KfOec313RqYLj9e OxHhHk1e195/7XVsOTR/S+fKTa4ZNfXIf1SIiMh1qu3+xVOBGsEwBMPwb7wmXjjUopqR2XePAtgV CBKzPbUmdZd9W1RrEMnf/8/TBz+tX20xDxSu+yV36X2uPe4a92go2io4nd5v1T1/yeO91/22POdb GdP3bPi5ZWzV1o+35g8cBakiIp9D7f/B7nNPd7u+janXQmtt3MrUqG7UWimlzAFra43IPWngHrgZ mQY+BqsP4KPnI27VPHTryKRLruOS8UEf0YDz1nG/w7LrqRFa1y49X5ttfOkeEwmuud8to61OHbfm GkRE5P3U8vQn0DOlRN/q9OnpqW9vOgyYGbtd4ds3o9Ypm9porTG0fW+ksmEMXHtZQFGTlIiIiIis VB0DDDcnPcl0ooFR2O16QFpr3w41IgBIBiKDvvWp4e4U32HeMDOc9xnU/9mW605lobaoHz2VYdpq 84BLs69r77vmvNfWEg/DNuODHml25lsza+/xeHB9xvLS79UlmfJHmcMrIiL3V8suxub8wHAyA/Nn CuA+ZlZ5pvXJU/3/gnlOqmG4g5eB4r1WFbv/L+wtmlXgfYKLe8+rvOZxtz7XJTvybNnQ89puTJOP mMl6jzrfU6/dvYb/n3vMS++79rjJPYbxb+HRrkdE5HdRSymQU9t9r0U1Owzqz0wieuDpHALTUioQ ZBrmQTHHLHqA+8A7nd6aaTzl3C/2tYHQpTWD9wx0tzr3tYHq2uD0Iz1qgLV06c/Utc1EW086uCWA 3sojfx9FRH4H1X0MSpkaoJykj5TK7AFpKYZZxbIv8Zv52Ahl87amfatTAwvsjj1Sj9js8FbGaYuM 1FvlAVt1KD/y/NJTLp3F+tnmWT7SnNV7329yS0nLVj7Dz4aIyO+gZgvSwDx6EGqGe5nnoPYAteLe tzQlg2F/mInaj+nBqZcEK3fb7vSaeshHqkU79Qt1y4H5r53z1nFGH2mLWaFvHbNlMLJFLealj7PF 1IdTx7xnYLrVqsUlPkN2W0REjlXqNCJqIHKAhJKOu1OfCu5O5DMZhvvYre9gtu9B6Q6SBOsjp2p1 WqqLX0RERETWqdNc0z5mKjEr2Lil6TQHtZRCegI9s1rKWLNqNu6FOp2jD/Dv3f0bX+iVTTpbZoi2 8FGd3nBbh/57u2fm9NR93mqwuqQJ7JLr2TKD/lqJx7X3f+3a7u29/y0ocyoi8vnU3tC0qCMlcbdx iX8aIwX9K9lHUFn2sgBjPK53RZmN57Ft9zrdapzOZ9gK8b12u/lKQclWEx1e+/qjNmc9wjWIiIjc w/zbuTdKOeZ9rikwD+CfAljSexDq1qenWj92qlXNHJuraNzbe2baTt3/nrMm36OO9t4Z5i1rLuGy 0UdbTx346HN8RXpdRETkEtU8xm59MAIvhpfhsOxPI7L1UVQWPYCtPaB1dw650pwbqyK2rUFdZhXv 0Vy09X2u9SjZxa0Cva2D0+njS5qDbrn2jyzDmDxCFv+cNX/YKCgVEZFr1b6c35foizm1OkYbh/T3 EtPIhKlbn8KUYHWmILaLgMyglO0Hod77l9wjBga3BoxbzWC95ry31EJeevw9sqlrBstvXWLy8ph7 /UzesjvYpceqc15ERG5Re91oz3h66XWoZB/AjyVJYpZj5z6YtXGH0yDHkVRl3No0CNKSeOBB/SIi IiLy2GqfCDXVmY5NTpR55qm7j2+lN0dlHrJUO7BIvJajetTn9rmyJo+YPV16r8apS127L/u1x6w5 x6lreaRNB9Y87y1ra695PVSDKyIiH63WxXK8WRIMfQl/vDktSHMy90zjTXdP032SxkCljMEp/ByG wxiqT+Iz/DLdKuDacgn5kV631zZD2LI+8iOe75alCp9hioWIiAjMc1CzZ0g95+aoiJibnoBF0xRk tqPa00NzVMz3e0SPuMsUXF6vd+se6a99/dFej7Wu3bHrmq01b7WmZnXL77WIiMhnUiEwc0o1SunL +UTSSFrrY/ctYyxO7YHqMvxcBqrT5xHbD+rf0tYjlt4zM3UuSL3nfvPXDKX/SJcG3ZcG71u6JkhV cCoiIr+zCr05qpTCUy2UUhjaM9OuUdNuUsDR+2nnqOVOVFMd6sug9TO4Nmi9dmn40j3ir6nj3HLu 5y2bIVz62r1nIPXyNXr0pfxr3WsO7yP9sSEiIr+vambjAP4eXJZSsCxQG5HMI6iAeSD/xMwolKPb +o6o5T2fg4iIiIh8ISfTJaX0Lv49gCXkcUZ0Guzv2XeeyuwlATnWn1p77CX+LazZt/3e2akt9nff yqNkId+j2/7l/e7ZdX/v1/VRvm8iIvJ7q7idXJKfb0sjbW7q7w1RJOCLJX8/qkt91Capa10bVK5p hPlMHnX4+kftgPXa8VsFl4/2OouIiLyX3sU/du8HvebUyV+CzGWQOtWZmvmh5tQAM7zQt0X9TZ0L Uj9z1/zymh85UL2llvbax7rl6yIiIvK6+q0aDlQaJaHtBxr0QLW38PcjM+el/eqGe2IeJEFEkm0a UZW0eP7Ap7TeZ8uAbr33/LXnesTX695bhT7a8xUREfmKaimFbG3u2M/MPmpqYTkDdQpCXzrMSD1d MvAZfMYddG4JUr/yKKPPmKUWERGRzp9qJT0Z8jCYfx9tMah/qilNzKD4cmD/r0P8RURERERuUadk 6dSJ70BrDYsefBYzsOwlpuOdzOxol6llxvWrNEjBtttMXnKuWzrIz9VefvVs4lRq8MhTEkRERORy 9bXs59T85HYqU5pzkLpc+v9KweklttgVaKugZxmgvTznZ27OutRXfm4iIiK/mwpQMNoYo85L/YDT u/OXwWdmHmVSJwpO3/f+73XOe3nUoPkzvYYiIiJfVZ22Mi0JAYfaUyAt+hzURfB5atvTpWn81Fcw ZSUfMRg9ZcutT7c+5yWP9R6Pd46CUxERkcdQ29jB7xgZfWSUu+HmmB0HoNOyf+QeKL/Unh6ap77O TlJfMWi51770W+2gdO5cr13PW8d/xe+jiIjIVzVHmGmQbrgbVoywPApOCz2T6gnulYhgGIZ5LFUp ZT42Ux39IiIiIrLO3CQ1bWHqbvBqOWmMy/1+FLxOZQHQs6wv56jK5e49/P6zZBK1LaiIiMjvq06B ZW+OijPzTA/L9stg9LCs316tS5XfwyWjnhRIioiIyFvqMNaLRjQywf1Ud36QSd/ndLxt2nmqB6n5 4nhZ65YA7tR933s70t9h7qqIiIjcVz2XNV2OlmqAjUFqAC16YLsDSukNU5kN6IP+5TF8RMbytc58 ZU9FRETkEnOqq5RyPOt0rkuFPhE1CKYtUY0pUdr6XqgA2Hj7vimLKiIiIiLr1GU96bRsDz1IjaM4 08jxfxGHADXTaC3HZf+eOY2vM2Xqt/BySX7rTKcypyIiInKNutzGdKotPZ5puoxSDRa7Sk33iXF+ KoC7Ea+PARAREREROavWWnGCJOYRUfNuUtnG5fwkEwxoGXyrTzy3gcjELHEzpslSZoYpQP1UlOEU ERGRR1J7A1T2Sf3jKKmC0SyAw1I+TLWpsI/Ws6Z5qE5dHEX5IludioiIiMj7G4sPHR8DUTPDPClp vXN/DFSX0/t7hrV/PAWtx1SEKiIiIiLrVBgH7kfOO0Alfeh+AdKMHqB2ZtmboKzfambgPZM6zUPV LFQRERERWWtu3562PAXGhqeYb+/Z0x6AmvUaVbexKmD82nSs9w/e8SmIiIiIyFdyNF9o6uCHw25S BWMYm6HMjGJJm7Y0pQezYX2Zf9ptylWCKiIiIiIr1VIKAG42Lu0zpkanLGhS5oxoX9uftkPtHf5t TKL27GrPxL778xARERGRL+IQoCZ9DP+ifvRULWlmUuuufxJBRC8BcLdey9qPuvuFi4iIiMjXVLPt e9az9DX6iIZZGXeFSjIPtahThjSzN1T5bkcpwTAMfezUMPTzoBSqiIiIiKxTbV6Pd6DXkrpD5tgM NRaUTk1U0/HTcYBqTkVERERkM9UX0aWZjbtJHZb3+/vAvcw7TQ3DsAhU7TCeSuOlRERERORG1d2P AsseePblfbNpCH8PTKegdJlVhaCUaTyVKUgVERERkZv4y4ByqjGd3vquUflLEGu/tOr7L8eJiIiI iFzrRYAaQPwSgC4D1knfNao3UE1B7PJ4EREREZE1/FBr2hZD+suiHvV4eT+iB7H9uGUQ2/oJ1TEl IiIiIjfwU9nRHpAWzPqM1F9qVC0xZ35LAiVNRURERGQLNTNprVFKGXeIyrkOdRgGANx7oDo3SdkO xvmoGdOHp+pSRURERESuU5fjovpsU5sH709a68v3tVZKKWT2bU2npf7J8WgqEREREZHr1YjeFLUM SMEopVBKISKOAtaXY6mAsRSgLQLUd7p6EREREfly5o6m5Uip5a5Rp7r5D7f7fIrpOAWnIiIiInKL 2mtPfQw8E3ejtePZp9P7iKC1Rq316CTLMgFQhCoiIiIi6x1FmtNyf2avO11mS6dxU8BcEnBqAgBo 1JSIiIiIrOdTbenLYPO1Zf7lsVNG9ThYRd38IiIiIrJazTE7OmVO3aG1Q5A6BaIRffl/ap56uQ3q IbCddpkSEREREbledQ5L+wDDMIwB6KGzvy/xT5nRoMVzv90TJ0kSAxizp5Fx8sFERERERN5Sj0dM 9fc9M9pvmZb4D3WmOY+fmm4rxQ/3iwSt8IuIiIjISkdNUj0YdVo7LNkflv8Py/6l9J2lpszrsua0 ZWCKUEVERERkpQrLMVEAeTTvdLLs5l/ettS/bmiFX0RERETW8mWdaWbSWpuzpnC8bam7z8v70+cv j1veJiIiIiJyLYfjsVGnlu3hOFu6rD99mWV1d7woQBURERGRdeoUmE6B5rSr1CEATXrz1GG0FPDL zNTl0r8G9YuIiIjIWkeRZCmF3W53clg/8Ett6qGLvxwt7StAFREREZG16lRXmpnsW+DpcxDaA00D XgSqLfCx4x+AOOwg5VRae/6wJyQiIiIin5sva0jL4uOpYWoKVpdNVNPXl8v8UwZVDVIiIiIicgs/ 1XU/LdG/XOqfbpuC1WWH/3Tcy9tERERERK5Rp2B0WW+a2cg8BKRTE1UPPgOzupidOh0X8/aoClJF REREZK3q7r+MlmotmPYrfTl6yox5mf84qO2BbJ+jqmV+EREREVmnvgwyYaov7cHpMH5crS/9u0O0 mIf2n5qDqjJUEREREVnL4bjGtM859TEj2oPTSW+SSiKOm6Raa3NWtZTy7k9CRERERL6Oumx6gh54 TmOnAIyeFS1+qEctxeau/akEoB9v1FrVyS8iIiIiq9VSjOfngcycg87WBgDcDcsko9GyL/HXWiEb rQWtBWY9A1vKGJi2YKcsqoiIiIisVPf7PWb+S1f+KdNyvtth/NRUAjBtgdoAH+56zSIiIiLyhdX9 fs8ff/wdgP1+/8sBU+f+cvZpwlEj1BScTvbHn4qIiIiIXKxGHILP1trYmX9ojjpseTqNnEq8HGak 9vfj2dLmCQAiIiIiImvUqfHp5balS8sxVO7MTVSH+x0yqr1EQBGqiIiIiKxTM5NhmJqifFFXGthc a5qYQSmGmeM+HWNE5FgC0D/XLlIiIiIicgsHjmaYTkv9y0BzmV1dzjmdlv/dx8ypxXj8u12/iIiI iHwxPi3d91FRhcz2SxZ0Ws6Hcbk/Gz3Daovdo8Zh/5Y0BagiIiIislIdAnalMIwD980Mr3+QbQ/Z 5i1N5+H9LWj2BPQdp4jAbI97obWgkKS/9bAiIiIiIqfVaUnfss8wNTPwcWg/h7mocwYVxm7/sbvf koi+7N8zqGAKUEVERERkJYfDAP55y9IxGD00TR2+HnYYL1X80OEPvVGq3+eDno2IiIiIfHo+NT0Z 5WjmqSVkGL6YhzoppeB26Nif7lfNjwJWEREREZFr1eUnxqFDvyVYNMCgHu8ktSs7kjZ2+8fYye+Q cbiziIiIiMgKdaotbQaRgTXrc08zIRIIzO1oud/diYDMYT5RwWgWmoMqIiIiIjfx5TL9VGs61ZtO oh2apEopJG2+T+/m52g3qlIREREREVnFDw1O/X0PUg+Z0IYdBazFd/PH09K+BUdzUmstLx9HRERE ROQiPgyN/jaMwWUlE1r2BqlCUsqOyhPGjswegCbDGJR6n3s6Lu+XamSoSUpERERE1qlz1755HyMV vekJNwIojGOlXsScy059T1jOlqpVa/wiIiIiss4cSU5L+pnZ60wNyCTTSHLe7tTd8YSIxCJ7XOo5 j5cyM1yT+kVERERkpRovhvK79y1Nk15XGgAJgeHp0AbCDk1RcEiuTgFqLLr7RURERESuMS/xv+zm j8xxlFSCgzOOnqIwde6/JrPd+bJFRERE5KuqU73oMAzzMr5ZH4HaxtLTZflpRFA43uJ0Gi/FfPv7 PgkRERER+TrqcszUtNyfeXjDwHIRiJLj13pQumyW6kFqvJVgFRERERF5Vc2hL8f3zOkh2DQSXtkU apqL+jJAnbKnrs2kRERERGSlmlZ6c1TavEw/RBuzplNnPwzDlBYdyChE9NFUnvR9Uq13+JdSSaVQ RURERGSlOmVOp/pTGDOhgL+oJe1L/H170xybqMAo9TCiqrWG9pESERERkbWqeZ9l6hy6+Q0wN4yY b0sg02iW4xzUmLOohbFhqiWkMaAxUyIiIiKyToVxtNR4w7TMD5AnilAzjYhetzoP5g/vW53Ox9z1 mkVERETkC6uttb5c3zhqfOrvl01Qtvhar08txXD3HtSmgSXmCYN2khIRERGRdRwgY1Fs6kbLJOIw Tgp4Med0bKxyqAZmpQetqcBURERERG7jdrKlqY+L6m/TnNTFV20MVsOAaYvUnkltw6HZSkRERETk WrW1w7akMc8+7dFoGGP4mpj5vPTvnhRsLgkoxcaM67Q96gc8ExERERH5EjxI0iCYsqFjSJoGrb83 60v38/amLciMcazUfhw35bjX8Rgt9YuIiIjIOnXKii7rTadl/cOWpgBGaw0zo3jBbBxJZb6oU230 fU7t5IOJiIiIiLyl9ncO5Lw0bycG9E+394+nutVf1/L7gH9lUEVERERknbrMnvYAtH/BzPC5SWrZ wT9lXKesac+gOoZTxlIBEREREZF1ap9petytP+0c9TLWnLr1LVnMSu21q/3gXgIQi8YrEREREZFr 1ONAs4tI8sSS/5Q1dT8s4R/qVZO5VAAFqCIiIiKyTi3mJEmQ89K9Gdg8C3XewJRpN6mXW6Bm5pxV NY2YEhEREZEb1GnA/jQ2CnqG1Ok7RfWufOizUPsSfxsOGdIpg+rY4lg1SYmIiIjIOjXpc0xjHBnl 7uxiypQaUIgc2JWKW2XYN0oNMg3PwL2SnrRxUH+hnOjtFxERERG5TCUdiKPRUml9Qd/NwGLc0rTP OY0Yeqt/JAF4BjXAwokWDNloClFFREREZKU+ZsqcjPhl/ikA6WQkmI/jqIyMSkTQN0ctuB3GTjUL omnUlDwS/TyKiIh8Jn3MlIFRmGZLHQby0zvyLUgaZhWzJFsAjnlvihoIPGzcZcop5dxDnjc9roiI iIj8nmoaZBwyTJkJVsaPjczSv14KbpXiRoTjOCUhIhjoy/qlGDsK356ePur5iIiIiMgnV/tS/ZhB BcDncVOHt0amE1NtaRaKOZkDre0ZMnAHL0aEllNFREREZL06zTedB/UHY0f+tFsUZBYinGGIXnva 9oTZOD3VcHOK7yCMFsaff8bZBxUREREReU3tJZ99+P4UpLZxq1J3n7c0zUxaC1prFHZEBGG9Ueqp Fp5KBeA5g7/+/Otjno2IiIiIfHq11kqG0VqOXfwJaZRSMLOeMU0jWt9vKiKpu2A/7MnmfPujsts9 YbGjtYQo7Gz30c9LRERERD6pPmZqKi09fEBE31lqqlF1d2zcIKq1RjEoT4VdqWQaP/bP/PjxzP45 cPvjA56KiIiIiHwFNech/IdZpsUKETm/uTvgkDZOlEyKgUey/x4EO1p7Ito3aMaf3zUqSkRERETW GTOoU+e9zUFqRMzZ04P+ebFxKH9rDEP2CQBmZBr7Ifl///M/73T5IiIiIvLVjGOmfL4hM2lDEtG7 980Mo5CRRPZdTtMKZgn8wa78AfmN55/GP//xnX/8Y8///ksZVBERERFZp/aM6aGDP8NoMZDRg1F3 I2LcVSoTrAe0MUar0ZI2DPz7X3v++x//5L//0UhNmRIRERGRler0QWaS4UQ0PI1py1MzH2ehjvNS cci/kS1pw479c/Lnv37wP/+7569/Q0YhtF2piIiIiKzksOjep29v2uefHrKqE7OklMJ+nJNazCGS v37+4J//+s6fP9o8L1VEREREZI36ZH+wz8awH/Bs/OH0ANTAclyuz4KXb1g6sU9s54Qlf/0Y+K// +ou//l3Y1b+z233nX//OedNUEREREZFr1cyGmVHNSfrcU/e+9aklRADZd5RiXOq3hJ/7gZ/7hnvF 3fjz5zPPz1Ac/v53e/OBRUREREROqRGBm5EOrUUfJDU2TjHP7Tey7WmDMQyB2zf+/NfAX38G0SqN 4PuPgdbg2xM8ffMzDykiIiIi8rqKJcnYAGWGex8x5UCfym8kPXM6DI3nnw0v3/nrz5/89WfS8gc/ n2FoUNz5298LuzJ85HMSERERkU+smvVB/UngxTBzaGA+DutvRimFTOfp6Qkj+PHjG8Ozs3/+ix9D st/DrjjfviV/+6NRd1riFxEREZF1/j/ltKHJdSYWQAAAAABJRU5ErkJggg= ------=_NextPart_000_023B_01CBCC3B.393DA870 Content-Type: image/png; name="Screenshot-3.png" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="Screenshot-3.png" iVBORw0KGgoAAAANSUhEUgAAAtEAAAPzCAYAAABx2lCOAAAABHNCSVQICAgIfAhkiAAAIABJREFU eJzsvXmcH0Wd//+q/nw+M5+5Z5LJTO475A6QcAkIBgOCiAKKsCBe7Koooi7ifpVF3FXcRRGWBZHF dcWLnwhyqVzhFiQGCCQhkARyn5NMzrnn8+mq3x/d1V3dn+pr5jNn3s95fB7z+XTX1dV1vOtd76pi a1a+JnbvbsIHP3QumnZsQjqdRhBvrXkbX7jyanzta19DNptFWVkZSkpKsHbtWowZMwZjx47Fjh07 UFdXh5/c/GM8+tAfAACLr/oVHrnpEtz18Ot4eeVW3PHNc3DsZ+7Gtz51Cr512ckAgOqRIwEAjDEA zImTMQbGGIQQ1gWRtt1wAIbz37pGAIAQpvLdyhc3z2wYB4ThuoPrx/UrHL+ccyUc957rlsOPEAKC cTAm7HRwCJHypSPvSUcQzHnf8KRDwoUouCaUa4a/fPjiZCzl+808zyo04ftTOJCIqPsiykW436j6 1ZvwLcLLQHT8heXXe58p3wvTmodbT+THsK+lGINhGIF+GWMAF97fDrKsR5fxYtPzNlGty4bmmoUn L5j3PgPz5nlI+wIICKMb3LSv2W2TgAkhOJgAOOsCFzkIzgCRA1geQghwYVr1E13g3IQpOCByVjgi ByHy4DABkQcYh7DvCXAIDoAJu37nPM8h0yb/G4bhtAOe55RtBA/Ka8OuP8L5rV6XcN5tt0GGE64Q AjDs9Dnh+8ux5T5v569bdr31Ns8L358HzmEAYIaAASsMLhg4d/sAXdvP7HaU++IPbg8MjzsneiEg H1H6v/Lin+HFl5dh+8Y1nhAWnLAY1373x6gZUWelp7MF+a4ubFy3CiseuR9j0zn84+gcjj27Etmj utGxxcBLj6Sx4Bvfw7RPXFEQHqHn0suv0Ob/o39+HGVlJfjlb3+Pj190MaqqqrDizRWor69HY0Mj 3lrzFrZt34aWwy04ePAgdm3fhY+c9X7s2nMQN37vetTXjxygJ+ob0qNHN2L37qZYjufNnYOFxy7A bbfdhmOPPRann346SktL0dnZifXr12Pv3r0YNWoUtm7d6vj5n0dex7ypDaiuKMXjr7yLU+ZPxFsb 9gIATpk/oU8e6khHbcR62pEGNYI6gUrn1hKg1fsR6ZCdcIFwKwMBLCHcbaTVdOie07omwxOaDi9c sNHngdsZyXRYaQkefA4GggThqHISXzjuWyGRRwkBmvLlTbsouK7eF8xwBovMFppNmO6g3hf9YB60 9z5tUjnRcwTcOm/VE2+6ZHmUg28GBue2o0hhYLAERcNIAxwQhj0gFwxgJphgAExwngIEwOy0GwwQ zIQQaTBhDcGFMMEYwBgH53mAMesaGNzIU1baZBplerlMpZt+67/yzAF1zLrmCryF9+QgvrDts9o7 S+AvCNtuEwGAGUpbKIRT2uP2A95hn3xud0DJBNwq5DyzYb0G5q0f0q3a/ge1PTqBWx0c6Diwby9+ /O/X4prrvo/aqkqY3Z1AKoOH7rsXS+o5PjSiHK1ZAx2pClSXdKO7TqA8lcfG392OyRd8NjQfiHgc OnQILYdbcPfP70Y6nUYqlQIDg8lN5HN55HI5dHd3o6urC+3t7ejq6oKIbMOHJol7/jtuuwWPP/EU vvOvN2DEiBGYMWMGysrK0Nra6rhpamrC/HlzsW3PITz28rv4zQ0X4OVV27B6wx7c/s/n4H8efh0A MH9aQ/GehOgzVKEjXMtQ6C9xf65otBhc7bAUbqzwXI1IeOfgaj0gGITT0kvh2ukSvUlwe/OCe4Vu io2Mr7jhhwnQQggYjBU8qUfjFKkJ7l36ospU3Pt+QU3CWaHw7L1vC0gMMByNo13eES4ICCEcrTVC 3A5NvDNAQVjibtJCoLYnAowZdv23hG4DDDAMcBO2dtQAZwKcCwjGwIQU8gwIpME5hyEAwRggBAxh vVcwAZh22bAFdEsw5IBTLlJKGZFlqbCsuBrUQq00lIGDJbCrg/yU4t6rjbW05ikwo7CcW2GkAEXT q6bHuiiQsgeBsjz6xZWoN8hSdphCzrjY7wfWLB7nhe/Wys+A8Ox7uueJmtmTgy6Jru4f2LcX//rP X8KnL/sMMtW1ePqRP2BxYw0aUgfQbLbiuMYRGFF3GFu3mrjq9jZcde2/Iffr29G9d3cRZs2OLHSD nMOHD+MTHzsbqXQKggvkzTxy3d2W4NzdjVwuh3yuGybnSKXS2N20B1a91tWboU2P1GfnnH0WJkwY j0/+w6dx6aWXYsqUKdi+fTtM00Q+n8fKlStx7tlLMKGhBo/86BIcbuvCV295DKfMn4CJjTV47JV3 8eH3TUd1RakSKgusVN4GIw8g5WgxXG1GSut3OOPRpDmNkn60Z5lUqJ6Dm9WgKcsgU44gwdo1zTEs 7YQ7Qa5EZn3nIlegjRaK9sZIybLinWoWSvcg84D5kuLoj4QiVBc8Kyu4p16X07ky7Z4iWVR5Kaxj KU7jI4cGnnxAGgD3dHKWSyu/TdOvyeUe/9JcxlPEfCYUUkhR45bxpXyPpmu4/X5U8kxjzqFMD3PO C/NPyeq0nXhLaLbUaCmkwLiVD4Ll4LZRhv3+DVeo4kF1T07n22ZnwhexnWOil2Wo+EK7v33QaVHV X1zWFABuGQFgmXKwlG3iYQujgG1eYb9XUQoIAYa0rR0GLK2wAcMATJ6yy04elklfCgICjJn2IKYL jJVAsByEKIPJOsBFGibvhgBDyhAQIg/OObjIW+l0ygdgohOcW8IvRBoMeUsLbHDHjXwuIThSTAC2 fA4AOWF48oMxb34J4bYZOtM3IyXbPCsQLoQdtm1Okk5BmBymXQYFBESKgXEBEwIGB0wm4/DO0BkC EEYqQoCx22TP7KENR1Cz5FT4DBg4BDjnyNuDI0NYJlEpI4U8l4MADjl4MgAIzmEwhlxBmxyvrfv1 734FAKgtLcEHZqdQmyvBtFnlqKjJYcfObsz8wT4AwBUjpuAjD72CNf93Cxov+lyssIlg9u3b5yhO heDImybyuRzyuTzyZh5504RpmjAMA21tHUinUyjNVoIZ/W/W1tekAeCYYxZgX9O2RB7nzZ2D7173 L7jh33+ICy+8EKlUCt3d3ejs7MTy5cvx09tuBgBs23MIn/73h3GorQvP33AB7nr4dRxq7cKHT55R EObw0uD0L26++adirc47ifa4N/d7RQzbaBfFPlrYU5CBU6m9Z6BHz8WM3yvoCkXIQ2BH6xdi1V7V hIDB3DLmaPSYAAuwFVVnNfzmPsEDssKBo5NmIzh/hBBacxCdUK6GbzIBg1v2rFIraU2vS60kt4Uj w9JqKmszCrX4rrDsHeQFz3YMFxiYNb0f8ZyqmZYrbKfsmRIBzjhg579rLqJIwvZ9QCAFAxwmhGEP tLnpi8cZSsLVPNvhMes64645g7/8uOmL3774lRFRFJYVX1hMDkC89sR+OIMjwRdzMO6JQ8nDlFqX GSw7dflbnf2BazYTpongPHy9AwAc7OpGS3kOF55jIFvdAgDIfmofGuvrcdrixVh07LEoaxiP4/7f T7Dl3ZVJH++Ixp//HzjtFLyxchW27dgLMKCsrAylpVlwnkaeA/m8QHe3ic6uHEwzDwDo7OK44Pyz UFqaifU+hxK9MuS85OKLMG/eXFz+mStQWzcCM2fORD6fxwnHL0J1dRUAoKYiiwkN1fj1d8/H1qZD +J+HX8P8aQ24ZMm8ojzAkUiwra67aMW9pv5X8C2gcS4H2qglE+Rk4w7E07IFdyzyeuECLaYI3aoG 2tFIx+is+qpT6S/kM0pzFz9Jn63QvTso8w9yVRMJKWRwWyZxygyT0+qGTLBbLlSBFXnlobwpYPZ0 sjNFLAVSKZQxaBd2eTpsnU2pM50OqHXELRMmTHv2xC1ruvw0YRiWVk0Itw5KYdta9KhqKtUwpCA4 dMtgNAbAzMJBsjA8gzdA5r1hFxPFhEGkLM0lLJ23M+/B5P+MHb4dFzK2ptMAZwaY0W3bFVvpYIY0 o7BMSAzBIMAs8w9pj23AKSN+OAwY4E7bxmIOAsPcuM+vDBSZNX/HhYBQtNvMcIckqh//WNKtFmr5 DqrnwemIgsM1aZL/hTYe+7l8bUmYAi16PYRF+5wPovqMpwEAh8pfgRBTexUeYeHPr7KyLL7x1S9h 1+49OHT4sGPrrHvVlgUVQ3V1NcaMbkBpScmwy/9er4aaN3cO3njtFfz+vvvxl8efBAD89L9vde5X V5Ti19+9AIfbuiyNdGsXbr/pnMDwklSuI4s4q/yD7RetKUB1147eCldAVMfvaJb6QD5g/gWIIQJ0 VBmKU74GazlUhQ/d9Th+o90XCtLc10HKztofTgrMIxQHDdCi0hmkHXcJt7dT7+lnLPRhC5G3yrGR 8bm3F6U5mmdbbWibeahz4NIe1tGyhmgYhwt+u9bAWSbhH/CrGmnXjpgZeUAw23QDABO2AYltbmNY 9hKGMGxzDebMhFiaa1hCsdwlSEkjFwJwRPTC5zAEnJZTfa4gI8Igs7ckqGFIpXnQINmAlT7GLFFe 9asXmOMJyUkEacOnfZZpkelQ06u2z2pdCGpj/QvS5s6ejdPf/358YMkSfOiss1BdU+O63XsDOiu/ jdqybGBah+sCt75Cl19l2SwmTxwfS8Em37dhGECoSdHQpGhbClxy8UW45OKLAu8fauvEF89fhHlT GzBvavEXFOo0qkOLeBU7aisvLYw7i6OCw01asOO5712HEmSXGTyQ0Gmg1d+9KRcDLUjHycu4mh+/ e52AGSqQorDxZFwUTG7LaV4OARhM6w/CAA8p/5Ym2qtBU4UZIQRgdDtaat2zGKlUwbMKAbcYK1sc GoZvhkM1L3DegekxP+DCWpwJe0tHSzi0hDOdEHGkCNJJ3br5bHjKrBBpQFimNZYG2co/q0xI84yc rUNOAcKEHL8ZwtJGC7mjBxgg5HsR1m4WloRtD3+878sUDAyGszBZFfLt+ZfYzx/dDgpPXqhxBfmX s36MWxnir5NBM4FxBek46TaMtDNQtJzKSG3bZ48wr5jh2PegtAv+fPNP/z98/z3O9z3b12HPdvXu ucCeVaFpJZIRZn4hX1V0XRfDzoxD0m/7ck1oqMElS2qiHaLn2ugwTdNQR+6zHI3eTXFHf8m1icnQ CcnKNLkvOrV89PW7H2hBWk2H53dMP3E08lHvk2vspy1trb2ISaOh5pbKLyD97lS/XrNRuJpfXSjI GIM7tnQ7aFXIVs0G/EKKGp+0f/Xnk8nkYinboKBgoJZyd3+R8Ql3H3sVVZC2hDgMe6LLllt/Cwcs 6m/mcS9NPwyDg3NrBx7maKgN67oQMGBAsLS11R0Az140TMBa4GwJoYYQ4ODWtoeCF8QthD9txX6B VgrVQZqMy8kLmXT7XpoZ9kLLwvxLM4a8ZjGjdFOU9psLa9DCvLtvGPZIhtt7cgelQZ220NV1YuCg /A9ncG9um5ChbN8qYi3PDz74IPSekBN+/jgLtfdJp9uDOryeoxOgOYC0VnhWiRJuiyX8DpQgHaWB 7onmy/WjrzvuNY6g8ySEcBfuFZQ+IbVQXCsMSEwz50uPPxjf7jJWku24o8ucR/Gt6bC5Y39r7wZj eHdysWaAmDVtDubsJmKZHMjnc7fEsyI0wZCCe9gQCuIF0OudOQYHuvZHrcteLa6Kk2fqb1Wbypgb lshACruW7GVCCIYUE5adsNSGImXtOCNyYIzB5NaOHMzeLcjSPJtgsv0yLPMcOVNiyL2lbU23Abce +NNeoFkN0BaHtRuFflxB2vMbgNwG0PXrKlj8ZVv+NyJ2YxJG7/pOa5GgOxByyrZ0wPw7/4S3x2pa zHw+0B3R91D+hzOshGhgeGqhe0TA4SWScIEpHv6Ori8HMFHauv5+737td2+fvbfGNFGmGnHzx+9W PpsqQGuFII15gjOFKwI6d3h3TNClW5cunZAVJDxIP5ybBXngMdVwvFsCCuPCWnMWMDjkBkMKrsmB u4jQmd+0N/0yLc0lvDbVbvpkuMNv66dQlPaJQ4AJ/84m3kG/wdJ2eXHzVy4KtFWglikG4wBPAVC3 JGQwWAqCCUBYBzZZgzIGIfLWeIcxKw4BcMEtoU8ZuAnrJWoxDAOqsi6ozMQxlVLv6+osY8ydQ7dz w9K5pxwzFL+QaoAh79u3uqD+oHftmKXtt/JCtY/mEDCtvQMLnidOu/TzW6/vUXqI4kD5H036pacf Geg0AADO+eTnAcQThgRMMFv+d+3GfG4SaikHO/7N+128GiCrQTSV57f9+fInrMHUaaNl1EF+oqZ8 ohvMIE2W9VG1dUx4OwpLk2hNnTuClnSb8L0HPV9QOAMhvKvfXSEuOH3Wuy40cVCn0KXwWHi8sV2u hLV9mLqA0JNXQli6KEe7KuxyqMTBuLVPr8e/jM+r7dAK0PKS8v49Jh2i0OzDtXkuzBfrshQ0UnYa LU85mACHc6BHKpVytOEMDIIbMJml5TSYgRS3zVEMAUO4gp0QwsrTgMMqXA21XzvHPb+jZqriaPf6 h0Lts/e6rKO2jTpz95eW5cHKYa95D4d9LLezkbG0wU0BjENwWx/EpTFHBoDchcMAQ95aWGimwI1u WDbRgDC6wHgJOGuF4HY/Yrd3hpB7m3PASFnPo+wHLusK59IcxKWwDQ3PNcZMCEXBHDQotrTs1gyI YZdXxjmEwd3t4px1AQyW3bawdo9RTFNk8JzDMvUQ1iJgpsRtynCEARYxxpOCuKzf6vWUYTjmV9qB MBdghv6ch3/6xvexXzF63rQt3unKRO/5zQNLC/J/qM709yXpxReeZ391O1Udns5Q/lcOr1Cn3NQG wIDdMMljSRWNg8GYsw9oUgo1TwPdefSWcCE0uHP07V8aw2yjpxWhNxUoeefu3QdalSH8AnRBXD1J YA8Z6EYlSoDWoS8PVh0KHCT5dtkoDBNQR2qups+6Zpr2QjxwmB4BmHvuB6U3asAX7cbdYcQ/tW79 l+G4/vzmJ6rwYQ3kpZabIyUPBRKAs9ODR6APto3WD2jtJW4JZhGGGtZz+6Wz6F2I1Pzya2atBW6W GY3McyZPA4R97LewdgmwNKYpZY9l4fRVhaYJtrkEAwzmFfDVxqnH7YFwy6Aeb/n1x+cOsqwyynzt JWPq7iOqvbVSToOi95mPaJ3I/z4tfFh+JNHME8RgJe3VhIQVZLdyuBVQLp4BejodGdbxDdfOQ6Uv Go/emGrobfM09qgAvA1rb58jfB9o67dOgGY+/4r7IpYf//TnYCCJiUbY63cHoX5H+kMS3N+u4Gpa e2wBsG1TuXWym6NJlDMVzFro5Z0tsabQQ/faFf7dGoI7aY9NppDHINuHpojCgYdlilL4ftWyZvIc DJZWBGnFjTAgmNytQ5ZZKYjJ7dQE3O3v5H+ZP66ArWv3hq6SIGwNh0WUCYF/wFOocbe12sxUcsjO e5ECGGwtrAEhOAxm5bdgKQhuhWcwAwxdEBAw7BMQ7dINxqVGutsRRp3USs1wyKLvJDME1oBA3c1F t7+/q6xy6gCz14z4tLzuBzAMa+GkW5ZkfbdsmQ1RKKCrW/tFIdcQ6N4lF+6uPbryzSMUeMQgggY7 BTg20eGmAiGNAZMHDHinVQMbDdu9RxAHCqbDIvEI8EOPwunqIoTHeK9kWW8DGK1JSBpZEkE0jgDN FI2QOg3Z1wyUQJ10wOVPZ7IZCe9CQq85gis8O/+tVX4yYE8nDlhTwwC3wrGPe/ZrkXWCsRu+DFuf flWrC2EJBpaZjxWXAVvTphPaTGGZnwj5PhVxTApN3AC3F0elUpZAYg0MLAHKZCl7Op3DMAxbDyos swQhrVWthWyMycWGMu3MU//d9nPotm9ejIDvFtYr84trXs19sD2tMzXgUQMZsA9OsQVpucjT2eLO nlmRYch37+7eIQVjZr1TyL3Rlb3BnSmy4BmD+O2D7llTnnzxmv+4+STNGVUFlz8NYcJ8qBa6h6jx MMB7GNKRsB3NMIXeXCGOEK23uSusjJ67suEoEIxZoT/Z3vhj8LmPq4GWglNcdA1LXxIUX5DmzDpl KyxAX+fDfFpCZ0rSm5dhgpLwbHuk036FYS2+kY1jvN1FvOEXvoPgWY0wAVpeH070doYiqJwFCdbu wjgReHCKvCbLjbxvwJafmdqhu4Ky1DrLgZ6lpfYKzFKPWKg1l9cjVogLt+3xa6GFEGCGpkwpC9VE wCBebnfH1O/MtR1nSl4JBmuLNcFgMvX0OMUkgHFrKzwnPn05Hn4zceFmGmEa6SDBz3IftHAaALNP RBQpAAYMZpslCQ55ZLvBmL2wsRRyVoExe7GhZZRk140MDJjgMBxBmjFAGAKM63c/SkKwEO7uD+3u 9FLYt8h9tfX34i0YDM/ncOQWwCzAeNoaKNl1nlv2JswYnm338IbEaD/p3lQwQGn8I/wLWzvksW2N 8BfZkSTURve3wKXGF5nHSbTqfgFaE6funvd6ob8w21o3LHsa0NYmQp4GFrHRi7rwMHzWQ78PtPQX JED35yBpMDb6uilvnZug36bcY1a5rtUIKyYazD7O2ipNchZEEcY5d2z03Q7eq3WW5cJUhACdpp9p 0qS6Ec7uG+pHKSO+6WapbSzQdGtQy5fzXIrALmzhSs6WcGbCEClnMMAZkBJSq83dbco0+0j73+Ng LGs9IepZ9HVZaTMcLYF/8K8p18x2b2v9OTMdG3Z3AMTsUxAFDCEgkAYzTAh7waJcYGqlwnTNgIRp 1xFFaDYMMN/+j0kHwWF5w5i9d7niTghRsFYkqv7rBtG2h0AB2hC6VTbR+DXRXJcmpc+LWrhIDBJI hi4gwpyjWEihLVgQjxO7VmudIBVxBwzFJCpf3c6j9/HohB9dXFFpsUOEKhAVuOXMms4EABieU76i 0qcnWHiW/tXp22TvMWq4Fs1gF2aiBBTG9GXEyUfmCrQ6YRa2Bs4rQOsFbsA9DMXaTsy07KClJlq4 IqszNLPU187HTZf1XNKaxP/apeabwXC8W8+qTtXb22zZz+Hkl2/v6rCpcDV3pUba2ckEwhbu3Lyw RI+UI+zA3ofYE2ZMQdpJ75DGsN9NuH20/F844GNO2dH5kzMpTNgnE8odvRmzF7enHO0nk7MWIgXO 8+DIW+8SHIaQ7ZphHdvCDEAYEKb1rpitkQZSzoDTKh9FGsQzfdvG7CasUFA2rMEZl3nGPINDOQvM pDmTMjsCyMFJTxe1u0TZRFumTV7iDPz7h0UDGPfQIkzZcKSSBuJVkihzBFMIW9uScFofycUbTyOb UBvdn51RHM1LT8LS+fQLM/73FUsTHtLBBVO4I4s+fP1pWjJtLKIU+N2r14IEoP5gcHQChfjzRK9V 9l7zCIE+Qdrz7gIGVaqQIwTA7cWF3DlsRADCABd5j0ANAAKGNl530MR81y2cQ1FCdhaR/u1v7kVm uMKPsLXEvnLstjO2AAw5va7mmeE8v6WthC3wyOf27rRhnZ9nCS/ygJChvL4jCXG16/76LAeCYe4t d+7giTG7TRPWNmtCCMDZ05m7mmlmQMBw1mFYZZHBYCa4YGAsDWF2O/E4R5IDMO2BomEUtk89Jagt E4yDw3A6TGGPGDlDoDZZltFUqnAXrDhtphVm9PM4MWvqj6vZD3m2QdZ+EgHQayrAablVW0L5W/2u 68BUfwbyEMgpnaAJ2XG4HSu3psXg66gBgOUAmNZ/lgMgG013+KwTWDzaLa0GDbBG2uqnUFDo/cf0 fArj9H6s083cj3wdjrAA0/0IAS7yzif4Ob15ZE29Wx83H9389IRjdLl573zyzocjZ3+6wZGzV4QX 5m1QPghmfWAIuzMwnY/6W7oTzC1b3s7Br6FydZquBlItN/FqvT93/B8uhL2rRLzriT+h5ReFKlhf HnDOC47Clv8ZYxCGsI/rdsueYcC57heshRCwlspZ+3TneTdMYSLPTeQERx4M3YyhCxxd4MizDpis GybLIY9u5ESn81vAhDC6YaIbObMbpmlaaeUMgjNwE+BmDoLnAWECwgQDB4QJbubAzRwgcmDIw2Cm 80kZHAx5MLvtYYYJZpgAyzv/5Xd/nZR5IJj13apXpqWZ5Hmn/MrfMi9lnQTcPDdNEzC7wUwT4G6d ywvTzvO8/W4YOLc02abIw+QcJge4SFn21OonoJ2xsJ9B/RTUk8GFW5ct+2T/x0275hrjvhMkXSFZ fgyRAeNppFjaltcMACkIblgfAFDiZoyBGWmwVBrMKIHBKwGeBRNlEKLC2nealwJ5BmbKdDOnrsn/ EAIMHFww5wPD/RS814CPZSnv1l3DMKxnFgYET0EYGQBpGMKAIayt/Ix0xkpXnjnmEJEDYKWcOAI2 GPIcMAWzhHTnY5GyrLGtfaS5vn3qRh7dyCPn++SZCdPg4IzDNDjyzARPCZgGdz48JaC29YO9LB/J iIi/N1auQsO4KZg8Y67nc/8fH8LkGXPR1d3luOns6owMr6d/Qem46B8uj/STy+ex6q23nPS+u2ED PnL+J5A381p/HkNWnZZQ0rMRpJzCk26t12BN6Xm3rIp8eSFug9MQpFkNOza7r4gOX2q1LHp7Xn2A MOaJK+Ce7nznmGYpSeIpjEKKlAaYfbKYe0+vZelvkpTZouKJ013cZN3y31OFLQt5xYSAIVBwaIpO iDaZNfANWhAMLhw7YIFwjbcQzJ5yLhTY5Xf/YF29H2ZTr7Mh9gsQLOC+fYaM3OUZunqnFUog972W WjZbk85dgc/2bRkWMLmNmJz2Zsr7s1TXnpkDBs/Jb0cqVh4rbaNyfLTfnfVF3aHIW2a8/ZvfPthW 2nDVzEcufpPbFbofqfyRH88wXxRnZszyb4fTU+uQmLOhac3CW0DuqgN7QB0Wj7sAUhe/97qqtZe/ ScU5JIj5mtaufh2lpaWea5+48PzCsPr4tevSERmnAObPnYtN698CALS2tmH5q69Dab49sMMd2wqC 1BZ8Od0YMeXPkLH9czCWArOncR3bRakZ8xyZy6zTl4TbSEKkfQ2ATmByzsSRAAAgAElEQVQAps5c GJoegiAIgiAI4shj+XOPaq/vbmrCV79+LV59bQUmThiPT19+Ka67/t+wY/N6vPX22/jQh8/H1g1v 46i5C9HZ2Yny8nIAwKb1qz3heDTRBcKz0GiLVdu9AIHa8mN/Z1D2+NVrkBw/vu86rZ+8JoXny297 QZsGgiAIgiAI4sjlhMWnAwD+/qxXmP7K1deguqoKa95cjta2Nlz2mSsA2LMjUgQWwCN//D0+dO75 WL/mTaTT7q5LksCFhdaUvjutql0tHqGddqYm4dWC66anhJ3iODPlU2cudITn93bsj/ZAEARBEARB HFFIWfHEM07HtHEjce9v/g979u7FSy+/gheffQLl5WUoLy/DVVd+AV/88tfgtTMJ+u6SBjS2pcoJ hH5Uu0VH+Nbs7CAKBGKfRtk+QSqufamMd9qsRbj8thewbuveWP4IgiAIgiCIvmfL0rt65G/SmV/q k/CkrDjhvO9gw59+CEBgzx7r2vhxYyFlXes70CMh2k/QAsKwhX2Ft3z7RvoW/8j9Kz3hMFfU1m1x pGIqC40e/PJcbbricuGda3rlnyAIgiAI4khm+zN346wrru+R36d+8X2cdcX1eHtzU5+FB1gmGqNG 1Vvh79iJGdOnOd/lfaGYc7jbrKLAlAOwTyy0HLgrisME2CTbz+jsoKPcCwQL61ILvXrDrthhSsbN WYDvjGrB95bvxN6OLs890+ztThgEQRAEQRBHJrue/1/MOu9qbN1zqEf+x3zgHx3Bd/WGXUUPb+SU BcB538FJHzwfy555CCe/70TcdPOtuP3WH6G1rQ133vVz26cqKQtUV1cBAHbu2omJE8YXxJNmSEE9 mtePs8gPhcK0c58xyJNY1eM75Y4cgLUPpkcDzQXkqVKW4wwY47b9dMq3L6p3hy9VCx2HkopKzB47 Duf/47vYf2AErl/lFcKThkcQBEEQBEF46alSUsphpsk9MlmxwwMACOCOW3+Mr13zL5h7zEkY3diA Sy+5CCtXvVVgwTF54kRceP5HsfjMc5FOpbHurdc9QRXaRCu7b8TZFzpo9wzXrXcHDgCe0+mCduAI 28wvSaYyxlB73uWYu+JRVE8sw4UzK3DL+hIc6OzuUXgEQRAEQRBEIbyHSkkph5lceGSyYocHAAIC o8c04r577/Fc/8qV/wQAWLBgLnZuWee4veO2H3v8qnhtooXPjlleVk9MUa75zT5UcxDpxjDc43yD D3FhdvDq8bhed2qS4mZqKp1G9RkfRb3IY2ppF0o6yzFiFLCkoRL3b3V39ejpSyKII43KbAqfO3Uc TppWg7qKNHJ5gX1tOWxp7sSNf96IvCnwxDXevdtNLrCnpRt/XXcQv31lF7rzVn1T3f3HXzbhhbUH AAClGQO/+8J8VGatAxx+8sQWLF2zDwAwrq4Unz11HOaPr0R1WQrdeYGt+zrx8Io9ePad8J165o+v xI8uPgqrt7XgW39415MGNY6eUKxwespPLjkKAPD7v+/Gq5sOO9fPnDsS15w9CU++tQ+3Prmlx+Gn DIZzFtRj8aw6TKovQ3mJgY5ujh0Hu7Bqawv+98UdvX6GuMTJ64F+HwRxpNLTmX0ph5n2SbC68O77 wiwAwMV3r/X41V0PCg9AUQ95Sat7QgPKtnT+U8MCNcYWrkAdviBQva4/cS3YXVSYKpUnnoGqU85E 477tmPW3v6BxVAnY3hTKRgicWV/hEaIH4uQ7ghiKfOucyThhag0OtrTj0adeRr6rE9OnTsbxs2ag 9fUHUDL/Y47bV159Axs3b0NdbTU+eNrJ+OQJjagT+3Dz8y1Ayjt+P29qDk88vRrZcfNw1twRjgAN AAdXPIT2pkqUTTgWN3xsGiaOzOLAoVY8+OwyTJ88DsfMm41vfXgyNqx9C5v5GG26Dcbw1SUTwQDc ceuPsG/TAYw45XPaOHpDscJJytxxlQCA9Pa/oXN3CUobZwAAnl6zD5edMBIfmjcSDzzwR2ytXJQ4 7LKMgR9eNANzxlYil+d45Y012LljB2oqyzF/7kx84vhG/OTOX6J67pla/2mDIc+L38aG5fVDK/ag fcMyvL70ebSXzen390EQRyo9VUpKOcz0HS2vC+++L8zCRXe9DQC4/0tztG6DwrPv9iiNOrS7cwQR Zsoh7xlG4VHN4UclG5Fu/PfChF5mGGicPhPTsgwlz/wBmWwZxnfsR11lDfghhkyFwKKqNMZUZrGr tTMyPIIgXBZOqgYA/Pyee/HbBx9DproRAENFhuNQ8y5UN8wCcAIA4LGlL+AvL62EUVqJDTv346uf uRCnHTMd377pWow46TInzL379mPe7KMwld2HrW1jcf7Cudh3qB0ja8odN117NyFdOwEjKjMAgLvv uRf3Pvwk6sZMwfP33gwAqNi/Ch3tB5AdM7sg3aceVYeJI7No2rsff3/tTZRNOREdu9YCsITKMY2j 8B/nHIfj589AezfHb/+2E4++scfxf86CelywqBHj67LozHEs33gQ//P8dhxoyxXE1bV3E644/wP4 zOLpONSex7cfWI/3mtrx1LXHAQDuXbYLC8ZXYeaYCmxu7sDNj2/CKTPq8LFjG1CaMfDcO/vx30u3 wLQFz+kN5fjqmZMwrq4UldkUunIcG/Z04Fcv7cDKbS1OuABww9Wfxg3297N+/BpMIfD0mr341Psr 8ekPLcS3f7Mc5VOOBwDH382Pb8ZTbzUHvvPL3jcGc8ZW4nBLGz7z5WuxYet2pKsaYKTLIMxuHDWu Fm3vrUF29Cw89x/W0b73L9+NBROqMK2xHLcv3YLH32zC9NFVoc+hpumPrzXh2IlVmDiyDBv3tuPm xzdjc3OHJ11h7+yChQ3Awo/ivd2HsfmN7ci1NGPxwun4+HGNmNZQjlSKYdfBLvzoL5uwbncb5oyr xBWnjcP0hnKUZgy0dJrYvr8TP39hO97e0RqYNwRBeOmpJlrKYZxzj0ymhnfhnW/hwS/PA+AVnuW9 OOEBxT1t3CNEB2qhfQSZcgS5Vf9Ha501h5P70qJLV1l1DcYt/ihGLv4oamqqkVn2BHIvPY2a3ZvR UJ5GdUUaohNgJQyVZQxL6ivwGxKiCSIRuw52YcLILL58xWVYcOwirNnVjlWbD2DDzv2oaGgF72z3 uM/UjsWoKfNx7HGWYH3ocAu6921CV/NmSGH7gUeewBc/ewk+df4SLF29F+Pqsrjjnj/gqs9+0hOW EAJ3Pr0VV50xBldcfhFmzD0aM6ZOBgAsff5lvPC35UiP7UKmbgKM0gqP3zPmjAAALHttBVg6i3Tl SGSqRzv3v/S5f8Azy1bhnXc34+jZU/GVD07ASy/9FfvKj8LHFjbgqiWT0HyoDY8ufRHTJjTijLmz MKGiG1/+zXqgpNwT15cvOQufWTwdTfsP4x+vuhbbmg6iThk0fGJRPZ5evg4jS8ZiRuMI/Ncl03Gg 3cTfVq7H2SfNxjkL6rHi7y/h+f1jAGagpjyNXHcnnnnxDXR3tGLWjMk4eu4sfP+CKbj4pifw0OtN uGBRIwBX+w8Aux+9G43n3YA3t3fhUwBOOXEhSv771zDHzPbkz6E3Hkb7rgqUTThG+85Pn2Xl3R8e fgwbt+9GxYz3I1VaBZYpA4wUtpndqDiqHrnDux0/F50wGq+s2oA1q3Ziw3PPo71lFGqmLA59jvas O4vwkQV1eGrZ20hNG4OjxjXihg834rN3LgcqRsV6Z5J8SzOE4DjnuEm49jxrK6vXVq/Hhg0bcNS0 SchsehrdnZPwr1eeg/rKEmzctgvLX38TI2urMX/uLFQ3LUP3/mpk6iZo84YgCC99rYk+/45VePiq BQXX4oZn3+xRGnWk5emBWjtozW91iztVHPbv4OHaR2vMROww9PtLu/HF3Upv9PxFmHPdz5ArKYPo 7oDR1oySmfORW7cakw5vRm15OarKDSAPIMVQUWPgw6Mq8Lst+8FJgCaI2PzHnzfgG6dVY8aUCfjQ qcfiQ/b1v6/fgxse3oQ8Nx233//O1/F9xS8XAj/9xW/BUiUw2w8613fu3oNnl63CGaeehMmTd6Gz O4/77n+oQIgGgDe3HsaKdXmceswMXLjkRADA7r378dRzL0GUj0KmuhHC7AbgFaLnjLXMHd7buAWp ynqwVCmMbLVz/+HHluJ7N92BkRNn4rnf3QzGGCaxndi5h+Hjx1kN9po1a9DR3oZ3t+3F0XNnYcak MZhy+B5sqDvdCedzl30cUydNwOade/FPV12LvYfaUT7lBOQPu1rt+x9+HD+6/X9x+ac/jW/+4ydQ li3FZ6/6Z6zfuhtT77oJM6dOwMzGLP788rOonPNBvLbpENr3bsHMqlZUV1dj3dY9OHruLGRLSzC1 ew1uf9jABYs+AsCr/S+pn4KOLa9jU5mVT4ZhYNb4EVi9bzOyY+fi879YjX3P/RR7mw/AHDEdmboJ SFeOLMhzqf3fvnM3SkZMRDpbiwdu/DQaa0odN0tu/CucLZoAPPnsX3HtDTchXTMaRroU6RqBZW9t RHvz1sDnWJlzFSj3PfQYfvLTX2L2whPx+//6DsY11mNK2+vYkDo11jsDjnfLXVcbLj1lIgDgT08+ i+t+cAtSVaOQKqkAcm1IjzqM0vS5Vvl4bwOee3kFNje3Ye/ee8C6DiMzdh5S5XUwSisL8oYgCC+8 l6Zbfk12nPDC3Og0432miQbUHTT0EUUJt3IxYRw/4drrcPMOld2rX0f+B1di+lX/jmxlFVg+hwOr VuCodcvRWJXBqOoMKiutrfWYwVBWxzAzm8LsunKs2d9GmmiCiMn63W343M0vob5jPRbMmICzP3ga Tj5hIU48qgFHdT+It9OuNlNqRfOmieZ9+/HSstexYctWZMcdDWakPOHe+8QyLDn5GEybOBZ/+NPT ONTa7o8agMD3LpiOo0ZX4IkXl+OG7/8IJyxagNv/87v40fe+hcv+30+xg48Ay9YU1OnqMqupa2lt h5GtgpGt8qyyXrVmPTIjJiBXO925VpLJoGv3O2ioLgEAnH7yCQUpGlVTitXvvQzgZADA1EmWxvLx J5dib0sXKqa9D0ZZNUoaZjh+3t24BZmRk9BdWu/m63sbUDbtZHTwtBN37sA25Fv24esXLMTHFrpC oUpFeRbt77wO4CPOtUztWJSMmgGk0zAyZWjrzDv3aqurkHt3K0objsLWZoGO6mOA0lZkMqUAdLaD wIG2HEZVlWBM4ygYmV1IVdfjidXNmFm+HyctnA8A2PPETWj4yHcdP6++sQqZ2rEom7gQLFMGI12C r31kHs4/Qa/R9T/H5q3bkRk5CU1ocNyMrCnD6k1/B7AYQPg78zO6tgwA8Pqba1BSNx7ZCceAZUoB GIDg+PEja/Cl08fgrMWn4qzFlqC+amMTrv3ZU2jr7ATPdYKVVBSESxCEl57uduZojk1vO+QP7y/f KFzf8OjVR+PcW9+IFZ59s0dp1JFmjBXsAQ1ESOrS1sR3jTEGg8mVkJYgbRiu9lru0pEXpuXWMMBg C9PcUHYBCbel1jX0zW+9jpZvXoJpH7kEXfv3YvGaFzClMoUxdVmMb8igqspAa57hWw/vx7PvtOHy xkqcP7qahGiCSMDCydVYkZuN/Ydq8MzGnXh5w2N45gRrJ4TS3CG0b3oJwEkAvFpRxhiQrkTFUacj XVqFdI13AeCa97Zh9cZdmDdlNH7z/92vnT4XApjaYJlOvLVqFfJl9Xgvbx3VyhjD+LIObD/Mbbfe On24M4+asjSqKsthGGmwTNbXUJtIl9UiVeHTxHKOvYc7Mbq2DNf94Bb86clnnVtjRzdg5+49HjOI Pz3xLM4641Rc+blL0WVU4I8rDiDTMB1IZTxxpUqrYGQrPdeM0mrLRMJ5Xg6zuw1nL7BMGH7zh4dx 212/Qk11FZ55+NfWc4NBmDnIplOuSUlVNyJVYZlhlJe4So1DLS0Qpgmzqw2TxzdC1ByP5oOtaMsJ sFSJti3867r9uPC40fjER8/Gn5etR0uqFL9+aTtOr93uCNEA0Ll1BaSJTldXDqmKkTDKa5EZOQUA cM7CcZHPIZk8cTyMFTsxZfJk59re5gPgne6zhL0zP00HOzB2RDkWLpiDx9/YBaN8BDIjJyFlMJhc YPnWLjz1nXtQ0bIBR8+chFt+8B0smNqIY+o78GpuNlg6Q/0EQcRANb94/Jrwhczn/MTdc9k1v/Da MAeFJ/3Ka3/5xrGxwgOAnds2xHuYGCRaWCgpVlMiTyiU3/1b4/mvq/50dLW24O3fW6fOzBhTjY9P rsHUiaUYWZdCusLARb9qwlNvW9sd/aC5BRdOrkdtVt9xEARRyI8vmY29hyZh9Y52HG7vxuyxlnau s7MLK1augVkx2ePe1YpmABgwSrIwyurAMtmCsK/631fRsuJB5Nv2oXLm6QX3AYHV21pw7KRqfPGz /4AZc+Zj9kzL/jWfz+Otd9ahO9OCVO3YgvDX7mzFidNqMXXyRPAX1gHMKKz3BgPz2TcDwIPLNuPL Z8/G9d/8Ck5b/EF0igymjh2BuVMaceoX/gtGiSv4Ll+xCkuXv41bvnMlvv6ZjwGZp/DolrQ2LlWw BmBJwf41IZyjuaUb4+qy+MApJ6K8ph7HHT23IF/2Hu5EQ00Wn/mHCzH32O0wqsfgzuctG+XJ9WV2 UAKr314PVjcLgufwy3+yzFT+848r8eSaA/o8AXDPX7dj/iiBGZPG4Pe3XItl65pw2Mxi1uhZBW49 j2MYYCVVTphxnkNy8fkfRm39aBx7tCWk79y9BytWrUHppOO8XgLemZ97/7oB3/zYfHz0nA9i9Nhx 2NFZhqlj6vDA8l14ef0+PPjPJ2L1lqnY0bQPjTUljr/mHZvQfngfymecbmmECIIIJcnCQlOzmwb3 2TDrwjvrx696vj917fGxwwOA40/7CIqFJUQLa3/muMRZJBjkR4UxBksPHrzzRk/iAoAVbTmc9qlK GCmAtQDf/eNhR4CWPLi5GSlqGAkiNn/4+y7MqW7F8RPHoaJiJDo6u/HqyrW443/+D3sPHEJl44gC P6nqBhjlhdf9sFQalUefB9F+AELktW5+8Mi7+OzcLrzv2Nk47wPHo7M7hzfWvIu7fvFrbN+1B5Uz 50DkOwqE6GfWNOPEabU4cdEC4OcPAcLUhq9bpPGHlzfh8K4N+Pjp83DGCXMhAGzdtQ+/fWgp2tY9 ixFnXetx//e1u3D9zx7BjV85H1+/9Czg3qV4dEuVLrLIPAGAHz76Hr72/mpMmzQOp2Qy+Nl9j+Pf vnqpx81tf1mDL5w2FpMnjHNMSn50569ROecsLJpSAwB45dUVOHj4MCrH1wKGqz9pW/ccOnemkJ2w sMDMBgBaO/P4yv++ivOndWHJ+4/HqXPGIp1O48DhNrz06io898JfA9POlN2a4jyH5P/ufQAnLDwa o2uzWPveFlx/480wjQxSunIUo2/482vbcHDHe/jk6bNx9KypWJhKYfueA9iy7GF050fjtU0zMaNW YNFUyzRky449uP+hR/HqG6tRPvV94F2HYZTVRsZDEEc6quZ4yU1/T+y/0CY6OrywePr6RGqtJjpq kWFP8O8L7d3+jgNIgXPuMf2Qbv0nGAqu72BV9hzuwFO7u3HuSRlc99NW/Php/WEAJuexwiMIAvjZ 0xvQ+ubDyB/eBd7VDsFzYEYGRlktyqeciFSmHIv//XnsW2ptO1c24WiIgDq2+MaXsO+J//S4S1U3 AtWNgff3t3Tgpj9vRsftv4V5qAk83w1mGDDKa1E++Xhr14lUaUF8z65pwqdOGIGJoxtx9NRGrO9u B+N5bRy6a49vTOHBpf+H7r3vgXe3goFZi/dGTUd+1ztYfKPh8fPyDuDkz/8n2je8AkCgfOr7sPjG vMfN469vxSNPPIu2d5520vn136zCob/9EvnDTcjUjQeEwNvbD+KzN7+IjndfgtnVAiOTxROvrkfH ptecsF5aswvPPPscOje/Bt7VBtlelo+bgzPnWTaEd//qPqQrG5BKlYKlSwuek7cfgFGuFxRzpbX4 /ept+OVf7kD+4A6Y3W3WGpN0CVJlNciOmx/4zuS7iPMckp279uBL3/8F8q37YHYcgFFahfJJx4Ol s1j8g79i35M3hb6z06/7Mw68eJfHzUu7y7D0pgfQ3bQOZuchQABGtgLlk07E9b9djtaVf0K+bS9E rgsAYJRWIDt2trXYkjHqJwgiBj0WWu1F6SYXnrpW7PCKTaQ5R5hAnWQHjaCwmX04C4e1SwiYe6yi Za9tXVdjETxAi+Tj6/+1A/9SmsL6zW3h6YgZHkEQQNlRp0O07bdsWLkJ2LNJrCQLo7IRgjHUnHAp RKd1cp4QPLCORbnT3U/VjkP5zCUQHQcBnrd2hWAMLF0Ko6oBMFIF4eQ5cMtj63Hr507EFz51Ab52 +2MwqsYATB+H/5pRUoXs1JNQ2ngUkGuHENxqlIwMWLa6wA+MNEomLkK6bhzQbe1vbLbuLQi3pHEm UuV1QM5yI7iJynnngLdbzyaEgOAmMg1HIVVWB9Hdatn8pgxkFk1x/QmO0rHzka4eA3S3OTtlnDV3 JBqqS/HIE8/jjdVvo3zayUBpudXCap4zrC00asaibEY1RPt+awcU2bkxAyxdAqOsRpt3aphRz+GJ r6QMlbOXAPlOCG5a77dmHITgsd6Z/zfLlCE7eRFKRk2B6G630y/AUhmAMVTMWQLRftDqcOVOI6kM jPI6IJ2lfoIgYtDTLe6YI/R624xih1dsWEvHTiEEA1ck9bB9ouXiQMCrUZa/DXuhoruw0ABjDJwL x73nI6RtdElBWDI+6W7mvJNw+W0v4MXlKz1pevF7H+xVJpz2vWd65Z8giKFB987VaHv7aaQqR6Bq 4UWx7GmHOt0716Dt7SeRGTEBZeOPRqp2AqDYcQ8mXrjhDADA9T/8Lzz11l6UTjoeTNmKkCCIwUvn 6kdRMlN/cmkU3euWYspZV2LXLtdqoNjh8cO7se1PP8Tm9W+F+ExG2hJ2g801eqttjh8Oh1ffXGib 54TlG1W8/7tP9S5tpGEgiCOC9KgZqFlUB5Frh2hrBtLjBjpJfU6mfjKqF10E5DosrXm6xJnqHGyc dsNSHHz2NgC2GYbggzatBEF4mXTml7Bl6V099rtzx9Y+Da8vCNwnOoiemnOo9tCesJTodOFJm2hl fQoJvQRB9AxmAFUNznD9iGhLjAxYVaPzc7A/c80HrvL8HuzpJQjCYtf2zZh05pd67Levw+sL0kKY HpsT/6I+2dlw30I/v7mF/M7hCsrqNnVhW9YB0FyTaTJgSGNpSfv+nj0tQRAEQRAE0SfsWl9c+azY 4RUb25xDb7hdzP2THdtmjaYZCDMo4RDMAITAxFGVePzObxUtTQRBEARBEMSRwcRRldGOEmCbc7iC dDF331BRw/WYdsSS090TEF/70y+wf892N1yE77xBEARBEARBHHkwVDjfm3Zsxhev+mZRw0/rlM19 cYJf0GErBEEQBEEQBDHUsDXRLFDI7asjsR3b6oBTu/zxsgQnKhIEQRAEQRBEXxJ55nWxhGi/1rlw L2qOJEePEwRBEARBEMRAYQCA4L5t5+AeqlJMk4tQQZpx+3ffL3IkCIIgCIIgiN5gCK43qCi2KYf/ pEJ5DbAXMAqAGcJznSAIgiAIgiAGI2kwE2CAodhFW5phhlQq5TnmW6LTKBuGem6LqtkGICyrES4E Umk3HgPuftIctn004xDObhyutYkguZogCIIgCIIYJHhsooXyAYJNKHqqoS4QvpkbHhN9Z7JR3zjL 81Gv6773Jf60+NOkc6/73hfpGsj4kzKY0tITgspBMZ6rP/KmL9Itwx0q+NMa9Ls3zzTY8iOsLQ0q C3HalqRxDUV6mg9J3REE0X846mNua5xVbXSYSYdqLx3b/IK5+z1rwwOzVNdG8dXOzU1rY13rD3oa r+qvvnFWv6d/oPJrMFDs/B7od1kM/Gnu6+cYLvk0VInK/7Ayrfsdh8HUbhMEQfjxaqI1grQOnV1z TxBCQDCvRhoAwPtnEWGUBriYGra4hMXr12wVM23NTWu1GqSwji+OliiuVqo3qPkRlG9BGrIwzZn/ /kBogqLKQ5wy2l9lOagM9Sb9UWU+6TssJurzyrri/+1Pc0/Latj9gahzA0lYGYibL0HvYaDafZWk 9WKg00sQ/clgm51Kn3fWhQMW+WAlSovSn/HqkJ31QGpkBiqP4qQn6HuQ+5787g/C0hA3fQP5XpKk P8q/rswPhXeoI25ZjTOo7c/n6U27o/r1Dzh6Q9w2M67fgS4vSdIwGNJLEP1JmLJioEi/+PKyAU1A Ek475aQe+1Ub14HOdH9DH5SeYnU0cdF1dMUIo7dhxo13MIdHJKMYZWWwvsO46Ypb//uzzqlp0pnz qO6KQV+22wNRPorZng/W8k0QfYVfqQIM/Exb+uC+XQOagP4iaYPTly9muDR+A1144xC30x+uhD1/ fxA1myK/x/FDDA6CNOd9Ub4GQhPclwTNFvVnGghiONDfSsYg0pybA52GQclwEXT7kqGSR/3Z6Q9G kphQ9FXcYfeC3stgmKojisdgMEOLYjCkbTCkgSAGM4NBeJakBaejtgcrA1FQitHR+cMYCp0n0Xv6 ynSgp2H2R5nry7KepP5TnYteKD7cONLeL0FIBlO5T3MSogvQTRMU86XpGvTmprUF8YZNVwx0J9nX eVQsgmwqk6Z/IPJbVx7i3FPpS5vSqPwLy+O46VLzPUhQjBv/YCyfKlH1f6Cfpxj1vS/smlWzoKD8 ijP1OxjatCRpGOjyQBAEwJq2v9c/+8kVgU988jK8+PIy7N+z3bkm0DaAKSIIYjBCWrojkyPlvR8p z0kQvYWhwvnetGMzvnjVN1HMDTWGvSY6TJPb34RpQga6QYyjpRksDAfty1DKbz9xd4wYzsQ1DxhO +TCY2lLJQGuO40J1hiCGJ8PeJnowNUyDKS1+BnPa/AyltAYxlNLMX/cAACAASURBVJ9hKKS9P2yh jzQG4zMPxjTpGA7b8xEEUUg66FRCgiAIgiAIgiD0pAESogmCIAiCIAgiCWlSRBMEQRAEQRBEMkgT TRAEQRAEQRAJGfI20SvX0AILgiAIgiAIwssxcxf1afjpPg29Hzhj8ccGOgkEQRAEQRDEIEM9V6Qv GPKaaIIgCIIgCILob4yBTgBBEARBEARBDDXSnJsDnQaCIAiCIAiCGFIM+2O/CYIgCIIgCKLYDPtj vwmCIAiCIAii2JA5B0EQBEEQBEEkhMw5CIIgCIIgCCIhaTOfH+g0EARBEARBEMSQIv3S808OdBoI giAIgiAIYkiRvvCyTw10GmLzmweWDnQSCIIgCIIgCIIOWyEIgiAIgiCIpJAQTRAEQRAEQRAJISGa IAiCIAiCIBJCQjRBEARBEARBJISEaIIgCIIgCIJICAnRBEEQBEEQBJEQEqIJgiAIgiAIIiEkRBME QRAEQRBEQkiIJgiCIAiCIIiEkBBNEARBEARBEAkhIZogCIIgCIIgEkJCNEEQBEEQBEEkhIRogiAI giAIgkgICdEEQRAEQRAEkRASogmCIAiCIAgiISREEwRBEARBEERCSIgmCIIgCIIgiISQEE0QBEEQ BEEQCSEhmiAIgiAIgiASQkI0QRAEQRAEQSSEhGiCIAiCIAiCSAgJ0QRBEARBEASRkPTKNWsHOg0E QRAEQRAEMaRIn7H4YwOdhth8D/8x0EkgCIIgCIIgCKT37Vk30GkgCIIgCIIgiCEF2UQTBEEQBEEQ REJIiCYIgiAIgiCIhJAQTRAEQRAEQRAJISGaIAiCIAiCIBJCQjRBEARBEARBJISEaIIgCIIgCIJI CAnRBEEQBEEQBAFg3759uOCCC1BbW4u6ujosXLgQmzdvdu6fdNJJ+PjHP44TTjgB6YFLJkEQBEEQ BEEMHm655Ra0tLRg586dKCsrw8qVK1FeXu5xs2HDBixbtow00QRBEARBEAQBACUlJWhubsa6desg hMAxxxyDhoYGj5uLL74Y2WyWhGiCIAiCIAiCAIBrrrkGixcvxqWXXorq6mp88pOfxMGDBz1uRo4c CYBsogmCIAiCIAgCAFBZWYlbb70V77zzDtauXYu1a9fizjvv9LjZuXMnABKiCYIgCIIgCAIAsHTp UmzYsAEAUFFRAcYYstmsx80999yD7du3kxBNEARBEARBEADwzjvvYMmSJaiursaMGTNw/PHH48or r/S4Ofvss3HmmWfS7hwEQRAEQRAEAQBXX301rr766lA3xx13HO666y7SRBMEQRAEQRBEUkiIJgiC IAiCIIiEkDkHQRAEQRAEQcRg2bJlznfSRBMEQRAEQRBEQkiIJgiCIAiCIIiEkBBNEARBEARBEAkh IZogCIIgCIIgEkJCNEEQBEEQBEEkhIRogiAIgiAIgkgICdEEQRAEQRAEkRASogmCIAiCIAgiIXTY CkEQBEEQBHFEUF9fr71+7733YtOmTbjuuuti3ychmiAIgiAIgjgiePPNN7XX6+vrcfLJJ+Pcc8+N fT+9cs3aPkkkQRAEQRAEQQwm5s2bp71+3333YePGjfj2t78d+z4TQog+SWUfcNopJ+HFl5dh/57t zrURDeMHMEUEQRAEQRDEYESVF5t2bMYXr/omfv2732vdNjY2IpfLYf/+/bHvp/ftWVfcFBMEQRAE QRDEIGTy5MmB98rKylBdXR37Pu3OQRAEQRAEQRAJISGaIAiCIAiCIBKSWIiub5yF+sZZfZEWgiAI giAIghgS9KkmmgRugiAIgiAIYjiSVoXc5qa1jtDb3GRtfef/7ccvJPv9ye+q/yA//vj8/giCIAiC IAhiMGA0N62F/PQEv3+d0B0kJOsEbtUdCdAEQRAEQRDEYGTQLiwkAZogCIIgCIIYrBTl2G+yeyYI giAIgiCOJHqtiY6ymSYIgiAIgiCI4UaoEE0aZoIgCIIgCIIoxJDb0Ol25YijXQ7ajcN/L841giAI giAIghgKpHUCbNDOGrrfQdeK6Z4gCIIgCIIgBhODdncOgiAIgiAIghiskBBNEARBEARBEAkhIZog CIIgCIIgEkJCNEEQBEEQBEEkhIRogiAIgiAIgkhI+o6f/X6g00AQBEEQBEEQQ4r0VVdeMtBpiM0z Tz1ecI0xNgApIQiCGDiEEIncUztJEMSRyL6mbX0afrpPQye0pNNppNNpZLNZAEA2m0Vra6vzGwBq a2uRzWaxe/du51o+n3f8y+/+6/K3/O5H3lPdyv8yfl3Y2WwWnZ2dnrg7Ozsdd2F+KysrPeHI//J7 bW2tJ1/UZ9E9n/yuxq/6l+n0P58atnwONb3+uP33nn56KT7wgcXObzXMoHfjD1vF7059/4Cbv/68 VeOQ783//vwfAGhtbQUANDc3e675y598rqAy5n8W1a2atzp0ZU+XP7qylU6nnWfwlwWdPzUv1Ph0 z5eU3von+p9sNovKykrU1tZi/PjxAKy2Z8aMGWhtbcXq1asBWHWis7MTlZWVOPPMM/HQQw8BAHbv 3o18Po/W1taCMlxZWYnp06c732fMmOEprwDQ0tKCsrIyx6+sd+l0Gi0tLY47eT+TyQAAcrmc407G J9siWd7VtkuWeTVuf9viD099Fn+b4a+bav3x13td/dN9D7tGEEMJEqIHCFWIlPiFMn8D428o/dd7 g04winKjS4uKKvyoHYf8L4W/OOmPkx5dWGGNeFQDHpUuvyAXFp7uffmvBeWFbiAQFodf2PYPDoIE 2agOT0dPyl5YZxrn+VTUOhQ0ePHnS5BbYvjjVwDIdlY3OMxmszh48KBHMAwbHIYN8IKEVbWNVO9l MpmCcOK2gUH3/P7i1Iu4/YDOL0EcCdDCQoIgCIIgCIJICGmi+xmpUVA1C/4pMECviU4ahy5M/z3/ VHxYmHG1hX7Np6pt8X+Pq4lWww9Kg998IShdunCCpvjDNMR+/2FpjnIXJ2/9boN++9PnDzvILCZp eeuJRipK2y7LRJCfoP9h8faFhoy0bkMTfxsRpInO5/OorKzEwYMHA7XIKtLcDXDNpnQzTblczjHT kNf95hz5fB5lZWWe8KPMpPzPqPOruxeHJCZqUeH0NA0EMVghIXqA0DXafqKEI+kmbErP7yfsXjEI EqJlp6QzKSgWcYRn9VqQQBdkbxvmLkka/Z2JzoQnjpmC/17Uu9XlvW4AF0WQWVFcgsxNotzr0qHm U0+eRQ2LOvcjA7WedXZ2FrRNkmw2iwMHDmgHoTpB1W+fHBS3Gr6si1KwjjLHCDK7izPw7A/8+UD1 ihjukBDdz6iCpRQADh48WOAuriZa11AFNb5hwoiucQ7SyvqvqQv5dGHqhGhpD6jzG9e2WMbjv9cT bY1OEx0lQPck3br3mkRzG6bJ1QnnQZpo9d3oNGd+ojrisEFdVNqj4g4b9ESF5193EJc4Gm5i6BGm ifZTVVWF3bt3e4TCILfZbBbNzc2OO7/boMGuHAjq6rh/MJ/JZLTa7SiSlOVia7GD0kN1ihguODbR 9Y2znP/yO9F3yMYpqIHTCVth2tswITbKXZw0+hv0ONoNOVAI+qgdSJhAGtQR6fwEaaP9H394uudW 4+6NUBX1XqPC9ac7LJ6wQYA/3/2ddxhBA6046AYN/rSGCTO6MGT8QeUnjn/iyEInPKvlzj/olAsL w+qe6q+zsxOdnZ2eXTFUd/54ZRxhbbqqqY7TFvvrv85PWHp0eRX0LHHpT004QfQHGzZswLnnnouq qirvwsL6xlloblqL5qa1A5U2giAIgiAIghh0CCFw7rnnYtKkSdi4caPXnIOE576nGBrNoOn8ONeS uAnS2kaFq9MOhmkxg8LWaVV0mtY4tohx8Gtl4mjyexufGqYuT+KUlzDTlKhnUDXSMh/DbIv907s6 E5Kw+NTfOlOToBmIoHevXpNmQ7rpcmn7ShzZBNXf1tbWgvIhr8U1E1NNh+T+ymFtoM5vPp9HR0eH J7583lqQGPYs/j3i/feTmmr1xoSDNM/EcGbv3r1Yt24dHnzwQYwaNYpsovsDf2Pnb5SjBOD+aJTi 2OnGaYhVoUwnHMZJQ9i0vr9TSmoaERSWzk+QCYHufpLONsyNbuCgGyT0RiBUO3spZMa134xDmEAc JAyHmSOp/8PemXp4RdQALAoSuI8MZLlQB1nyf0tLS+x2JJ/3HnbU0tKCqqqqxEK0in8va128/roW ZvrV2zKdVNiOqnNUx4ihSH19PaZOnYqrr74a11xzDQnR/YVfIIgSosIEpijhT3ctqQARFkdYevx2 t/J+0sY0TJD2x6vmWU+1+7prxdRER80i6PLbb88YNMjQaWpVN2GzAOoC17i7W0RpjXXxBMUvf0cN 0HRu/MKCThBRNdFhUId+ZOEvg/KUQvW+XzsdVIdVBYIMq7W1FWVlZR7//t9qHfXvMqNqpNW4/cit 8OT9XC5XcLJs3LIdpTQgCAIwDAPPPvssbrzxRlx55ZUkRPcHfq1aUOOWVGMbx12UwB1lVuEXUIPC 8wtmcaYydXFEhe0n6F4cvzo/QYOPqEGQ7p2qwq3u2aIE6yQL/5IIgf734j81LahMxElLHI1zWGcd Jvz73cs8VAURnR9/eSSBmZDIMuHXREsh2m8GFDUgBVwhurKysmDWJywNMgw56NOdlOivu/4BZtRs UhQ9UUTowggLj+ofMdSZNGkS7r77bgB0YiFBEARBEARBJMYZMtKiwr5F1Zb57dbianiTmgRE+fG7 CSOuZsO/hZoat05joosjzLxBJZPJIJfLxbZb1BGlffejnjbmf4dB6ewJMhy5tZU/rXH8BqGaN6ia aL/GLUgrHGXOEdcMJylR71Atb2q6/bMixJFJWNns6OjQaqKD2j2dttW/sDCOGZ7fnEOG49eAy7am srLSuZbJZLQmTJlMBplMxmMOoiPKhCrKTbHaOoIYanzjG9/A5z//ecyaNYvMOSTFmMYKC9vfqQPx bFD7c+orKi6/7a2uEfXvAw3Et4nW2fn6CRPm4g44op4vLAw1zjgmHf57SdICWPmpHgmsCy/KbCfM VEJ9V+rz6t5Z1Pvw/9YJGknCjTJF8l8LOlhFZ15EU8oE4JbLsN05gvyov9X/ql9/nVLRlV21juj6 B3+8/rqqMxNTTZ8Igug9Bw4cwIc//GHs2rUL6ZENMwc6PcOeYnfecTQAOgGvN1qDIPte2YjrtIBR aVLDCPsdhhq3P61hfpKidlJB2u/eDMT8GlOpcdJph4P8RqU9LG4VnR1nb8tt0vIX173qTh5yoeaH rlySMEHEIcx23++utrYWtbW1AODYQ+uE2ziKijClgN9tZ2enZ8cnXThJBeigQW3cGUsarBLDnXvu ucf5Tppom77UQvdlPEmEk2JoSFVUwU+njQlLQ5DGMihNOkFbPnvQvWKh5rFfgI5rShL2TLpZiiDB 2D+QiRNuUJqC0lhZWVmwrZ46iOqJlr8v8Gu2Vc2d35RIPd68L2ediKGFrh71pFyrQvTo0aML6q+6 97O8Lnfr0O3MEbUvNOC2Rf5dRdR7YSStB2EzWknaxLBZKYIYatDCQoIgCIIgCIJICGmi+5ioqXbp pjf0doFIb+KS5hRSmxJm1hFlztETDadfe9tXWlK/vXPS/Ix6DzIfo7THOnMa3feepE2imx5OQtjs SJJw49jRq99V7bm/TKh5S9ovoieElena2lrU1dU5v6V7qVGWsyCq5lmnidXN9ATNPEktdJKZwJ6Q xJQj7swoaaGJ4QIJ0X2MbuFHnAYkzE3U9WI1qH7btnQ67TTa8n46nXbs/4LiDJrq0wmFOndBacvl cgULGJM8WxIbP50Q3ZP8DROA0+m0s/tHOp0uWF0f1yxE9wxhcfuvBZlzBLkvZmeYtHNV3QaZc/jN fvx1kjhyCTOZijOIk/7r6upQX1/vud/Z2enUYXXfZ+nXb3Ik7yWxie7o6HAOXPHfCzOxKHbZ17WR BHEkQEJ0HxKkje2NsBfU+UfZyUaloSd+ZHp6KsiGEVfICdLC6Gx544QVRJwOoqeCmXwGtTNUt0KM 8qubHfCnOyoM1a3ufepsouNqrIMGd8WeHQlaWCgXX/WFlo4YWhRjhkTnV7WJBgp35+js7HTKZ1i8 0iY6aNCqE4Z1grQujKh+Q0fYYFv9rmsf/e7UdoBsoonhAvUqCUla8XWa6Lhh96bT76mGN8yvvOc3 2VAFlLAjlnUNu26gEZQGXUMMuEffBmlv/e6TotPoFKPx1z2fRF0IF+Tef10npMYZHPmJmlmIImzA FWbOkjQO+V/9rjMrkr/7cso7jCRlry+0+0QhYWUhasZHhypEZ7NZNDc3e7bIk6cYquHozB+CzDl0 SLfZbLZgIWKQRjuKOHU0btkMmxklAZoYLtDCQuL/Z+/tYyO9yvvvr+k4jDfjdJyMG5s6ijfdpRuU 0LQkNKGAQkTUDWlCIpIGIirUFhWloLQQ+ghEJYQqqlJVSfvwK29SS2hSCC2QqK0KYnkSFBSxbfRr lm6q3e3uZi3WZA128KQe4QFPu88f7nXvNZevc+5zz9x+ne9HWu34vs/7/fY917nOOYQQQgghpCC0 RBekqBXai2MthNbCGkqnaL5FzuXl4VkT9ITCssmzVOjhQG2F8dq6XwtkyAJuw6SUO5S+lFUIbbSg h0JTKTq6oS26XjopvqIxl6HQsHJq+WKEnjV7H2ykJaxIPrTObSwxv2Fg7f0Ye/a851ieI73ToF3S rtlsdv0t7zNvhCn07Gi3kV6+UTqtsqD7FBkEeJeXTIpw6PdFVYYYTDmel39oJ7heRHxqnl77StmH h4f7rl+vnYwQMd8/K8yXl5eziYW2c5Cal1dWrwMQEwfyO6/zYF0phNRh31S/yDzhEvP9DInoVJ9z svMp6p7gYd+BckxczUIiutPpdIlocUerVM5NMgbQNaFbx7XPnyfCi7z7YkYID889L7XTTpFNdgK8 i9eJkK8v0LuIzhMcZVmgU31XdR1j/r3e356F2Hsh5xETUCFsHULxNlJgyQdRi+h+0+snrt6G2B7X qwxYIRy6nzwRsF5YceGN/PADPpik+hsXIfYO1AYHGx7oXrVD/hbx7RknvPtW3992TojNMzQCasOG 5rXosoTYrLkHhGwGvMvXidBkETmnj+UJ2bwJGb1akouk4cUJrXiQ+vKMlSO1nnpnrpRdumJlCX1Q Uts35QPtCUpJXx/36hKy9tryheJZa7M9psN71zbmjlEEW9YyP7Yxa7l15yAEQGa5jb2PhZiY1c+W pKXdOexqNpXK6kRo2+nzlr7Lu1+LPpve96WX5zr2jvas84TsNPglKZk862romI0vvz3xnSLqUsoR ixsSZ9a6sl4+0bGy6bLELCa9pl30nA0Xu+ahD4v3EbTtnXrtY4SEgv54y3nbviGBmvfhzLsHPXEf Kneq0LFltCMfFNGDRz+d+yLoe1TSGh0dze5DLdo7nVV3DhunUqlkz2O73e5y7fDyEuzmK14YTa9t EjNy5I16liWoy06PkF7g6hyEEEIIIYQk8sY3vhFDQ0O0RK8X3tCeHO/X9SIlzyJxUqx7XrlDQ/52 2LKXIfvU8DHXhVDZ+7Hm6riee4VXvry6aPcea9m3VtqYFTVmZS6CVw5tlS7LrcPmKfl450KjIinp 2rbV9yetWYOHHd3px1Kr0wq5vEk+8r88R0tLS9nqHNr9w07YzsvDPhe9bixU9L0Yclfznq2Q+0ue 6xufS7JV+cY3vgGA7hyl4g2hleE/2qugTMl/PV5SqXVPEe86bKrvr04zdCw25GhdakJ/512bmPuA J+xT6hDKw1K20NXtr+u1nh+5XsWNFiqShuf2Q5/NwSIm5vT/+njeO1V+641VtDD3XItiZdLPvJ3A 65VbOoSp76Q8UuIWff5j7RiLz2eSbAcooteRXl4CKdZNL85m+Xj2mrcW0N5HSb9gPTHpibqYKM/z 09Npal9EG0bO2/VYdbljHzTPqhS6T4q0bcxSW9a9kdJhSA3fa/6pz5S9jzxhQ7/owSPlPRALF8Kb O5D3Xsu7D7WI9pa7TH2+U0cbdZi8NPux3pcFRTbZCvArUiJWkG3lhzzvpZrysg2JvCJD7Xl5xdJN HQJMeeFrVwlrQdaTeuR3P/X2Pn4p7V2UkPUnxd1D7t+YhTwWP2RpD9HrhzdU9jxLtLbikcFDv8Ni z3RefLnPdadad9rs8xN6N8WeKVnBI9Qht2mGjufRi3D2DBgp+fK5IzsFTiwkhBBCCCGkILRErxN5 1lrdYy86RF1W2fTfZftzh+KGXDN6STdWzph7QZ7bh97wxFosJU7q0nrW6qxdUPL8AUPW7ry8dLiy XH06HX8rcC/vWJhe7ql+3IX0/9o9J2XImuxM+nU98u7hVqvVdT70blpZWVmzXJ2+F7VFWzZO0e8a O3o1PDzctaZ8URewUL16mS9Q9Hnq9V1AKzbZSvAr0gd5Q1mx46nuDt6LzR7zfOxShWWK2C9a7hAp bgAxQmK/lzLF/KlFLOoteCWc+EOnfIBC1yPmy9jLByLFzaUMbJvZc73kHfso9np9Q+4c3oojhKTO UfDQ95fnzmHDafcR+dvr0GlhrN83Otzw8HAWV7+r8uppy+SFCX1jCCHd8CtSIlbQFhWdNn4RX+Gi /p39CNC8fFLKbttJyhKzwKbmmyoqrVVHPnD6g6X/lmOpVsxQuJifsdfZstcqtQPm1TMFXZYyOk1l WcNT8vMs0PK/CGtvRzgy2BQVn/bY0tJS1/nY+99utqKXpNPPjNyvegMVuyGSvPdHRkaCZStab8+4 YON4k65T85b4FOZkJ8CvSB+k9ta946nHUstQxgspViZPEGnrXlkuA/p3L2I9RTCHqFQqWFlZ6bJi ateOWNms0LZxUj7EKYTy9UYtUkcjvDw2aifIlPMhvI5gqtXMa0d+1AcH770ZEo4pBod2u90lLFPu Jd3J9u5HK6xj+XtW7xTyBHOIfp4XPmdkJ8GJhYQQQgghhBSEluiSKOJ6kUqv1uqN6um3Wq01O3Lp MsRcKryh9xDayprqhpJihc1zGwlZd2TJKVvHfqzxRdxYbLvGrNq9lsn6FIcokn7ofvZcVvol7/p7 144Wsp1PaIQrNNcixcIrIzfe6I3nnqWJTdb1yual3+l0ulxEpNwhi7r9u6xR0Y1gq5aLDC5DZ8+e PbvZhUjl9b9yLZ586iB++IPZ7NhFF09tYokIIWSjGULR1/bQ0BCAbfOqJ4SQEhjCC98/nf31/e/N 4F3veT+efOpgX6mePHkS9957L5588smdYIke2uwCAABqtVrQauhZZb1l00Kkzqou4gPr5R3zD6xW q9nWtnKuVqtheno6aI2N+QHLxBLPImjz9iwrnp9gajtK+3vtYY+Nj4+jXq+j1WrhC1/4PH7zN38L Z86cySxPuhxyTSVtsdTHLLD2b8/yZesdKnuofXQ72vtRx5V0Z2dn0W63MTc315VHrVbr8oMPWcBD E0Rj/uqxey/kYx67h61V0LvPqtVqVpeFhYUBmOy0Nd6Vm0mlUkG9Xkej0QAA1Ot1XHLJJVheXsbC wgKAc1t4t1ottNttNJvNrjT0vSST5Pbt24c777wTANBsNtFsNtc8Z3JvaauxvEf0c1ir1TA+Pt61 xJ28a+R+/drXvoo77/z1bLUgmdio71+7i6JnibbvZH3Oho3F9d5b9rnWo1tlzrkgZCM5e/Ysbr75 Ztxwww147rnndoKI3hp4QiD0YkmhrOFtT4j0MwTvvXRjrgiSd8ydw0vbHi9T3KQMj3rHY2E8gRpK v9f21x9aTwT3mofXSUl159go8j7qIUKdy1CnjOxs5Lp7z68OU5QUdw7JR2/hrd8bltQJhd6W4Hlu KLoN+nl3FDHcxOIQsl2Yn5/HsWPH8JWvfAXj4+OcWEgIIYQQQkgejUYDl112Ge6991589atfpYgu i34tWUXjhywqsfC9nIshw569oi2gsX+6jHl1LlKekNXbG2LV1p6QJUvWai2TUDt4ZdFhU6xYsTzt tU1dBsub9NhL/r0Sm2BpXYWknfRxMhjoZ8Wz5MZG1jzknVHUTSF0P3phUkbQQu9PGy5UpyIjPGU+ M3z+yHbhJS95CR5//HFcdtlluOeee+jOsVGk+ofmxbPHiwqOkA+rpeiwXq+k5FFUFHplT33p63Dy O/Zh1GmurKy4/t1FOjopYUNhvCHpUNt5rjYhoR7KI4/N+DDGRIFXP/pmDh6e213quyGG3cwnFftu 8oR9aF5AKv1+b3pJr8i3iS4eZDtx6aWX4jOf+QwALnG3KaQIwtSXWZHJdP2yHqLI8x3vNZ9UK2le HnLObv/thUnJLxQ2xQrkTT704pQxEtJqtQB0C8vUeua1vSfaU8Lb32Xi+UXzY77zCXVW7fNq/88b AZPnp1arJZXDdrh1HvZZD5VhPYR0v3Hy0uIzRnYSdOfYYvQyTGYtpxtVnphI7KccRd0A8oYobXli H0U9vG8nUNrz+u9QPkWHhVPwXDx6meBjy2OHpD2rV6iu/RBqD0/ker9Trn8sb37UBw/v+bHPtUes Q7y8vOx2uG3c4eHhbCdUOS4rxXgubhJGn7Ppxcrn3d9e57sfkeu5lNnzfNbITuG9730vDh8+jJWV FYpoQgghhBBCUlhcXMSb3vQmjIyM0J1ju9Kvm0ZRK2Ivk8xSy7EeQ4ahvIqEE3cOvTayDeO1o7YM SZi8oWD5P8/NxHN/Kct/PWaBjpVJ/19GWXpNo+h95Fn1yM7Hs5LGRtPs+y5kvRV3jqKkzEmxVuhQ vF7o5bkpEo7PFtlpPPjgg9lv3t0lo1+8sQlg+iWZ559X9CXXrzBdT9/QWF1TfHFjL+TQcGSvk+Fk tQ07+Sx03eyHT8R0yO82RVgXoRf/SH0vareO0CYxKXmEho9jYiQvvdj90I//tFzbWq3Gj/0A4d0z /bgqdTrFNxCxnW2dVkgse++SMp7JFMpwQ9NwDgLZCfCrQFBLCwAAIABJREFUUSKhl8JW+jivx4ur 1zTLsGiHrImeYNMfrZTrJPXyPo7WNzrWYcqzgFsRGyqXJ75jfoZFr4vUU1vey7x3Pf9xveujJtRB 6VUwpKQvZUpNg2xPQj701vIcGm2yz1XsPWHJ+0ZYn2g5Njw8nGyJDnXSiwjuvHBljX4Rst3hU1AS oY9yr/HXi7LzKXOySJ4rQ78dFG89WI39MHjb4HrbScfyWVlZKdRGKaMTqceLCuj1uDeAuOW+7DRj 8coS4GT7o59nWU2j32uvO6Gh91VKHrazGNoZVp5ZK7i9kc5eR+OK4uVV5jeCkK0GJxYSQgghhBBS EFqiS6KsnnaKpS3FUuBZQmITaFLdG+wxO+kuZWg+j9jQfa/tkuqXrNtIrMjWX1jHzXPVkOOxNggN G8ewPs1eeqE4obJ5ftIp5chzGyn72QilWdTiX4ZlnGxv8u5zfZ/kvdtCFuMihN4n3r0t+eWNsK2X C18I7znls0V2KhTRJVHkxZn6YtmKfmehobqiQ4fex2mjJprkCT490dFeK7sOrPeB7aUORcVw6iSo 0PCq/R3y+85LKzXPWN69UubzwSHnwSJ232tCcxq8e0W7c3gbrqS832Ii2p5fXl7OfKVj6YTKGzue Cp8ZMuhsLYW2gwhZudZLFIcERZGXnCfMvAl6noWkV+t4SpiYRdo7X0TAWQu1PSd10x/IkBD02sb7 nUIZ90nqSATg16topzB0vJe65PmUltU+W62TSjYG7/nv12qqhXm9XgfQ20RVPblQpx2yRPcy+tJr R7+XuF7ZN8pgQsh6Q5/okihzyLrfoXFPaJQlFkKWaM8toKh1L8V9JLZCQ+ycLU+KO4AXPma5sm3h /QvVNUTKNcyrt1dG/TfQPUmpF1LjxoSxsBFW7Lz0yc5G7tcUS3Qqstvn8vJyX+4c3sRC3aHXI2Up E4KLvB+8uKl47/2YsYKQnQBFdEmkiIOdSOqqDilDjiGKvnTz/ApTOyneFth2xQ4vvZh4jpXX+4CG yqjT7fejJGLCfvx6GdnI64TkpWU/tiltUaR8RcpCdj6pz2YK8r6Q1TlSR+dCx/LeI3Z1Dv0+8N4l vb5vi8yx6CccIdsRimhCCCGEEEIKwnGVEglZuKxlNHVyVuowdyh+v5PdUvOxw6Kh3e5S6MVXLrU9 U/LKs8KELDuxcofO9WJBtkO6IUtWnrVdW4hDOzJ65JW5X1/HMiZoWmLpcGh5sNFuEoKd6Fz0HhEL cd5zn/dOiD3joeMhl6ciI0JluU314x5GyHaBX5CSSX1J6RdsrxM2bPopQ9ueaOxH8EqaMR+4WHls OrGwKUP73kTIWJ46vJ3Q412jWJnsRzf2AU5p8zLcNELpaPcU+7ELfbx7dasJtUOeSLBhy/4gU0AT T+hVq9WsY+k933n3YUxEp7xT5FyR50MTKl+qq1PepF7vPV30+aS4JjsFfkVKIsX6J+Hk75h4zJsg t1Wwlui8+qUQE8K91D1PxIXy8ixRIV9HHTaUVhFi4tt+YFPvPf23tUSnWuq3EmV8uPvtwJLtjffu KjrKY+8hLaJtGI/h4WF3redeRHQRg0oobtFzZeZDyHaDIrpEUtw5ekmzrA+99wEoYi21IlEsNtZ6 m0JM2G7USzblI5dqyZbfRSz9KVbz9bKW6iX7Utw5Op1O5qYTK3fK9dsI4ZraphTRpNPpYHh4OPu7 yAiMd07eiXoTql7Q75giwjyv05wSXv9OeQfRdYMMKpxYSAghhBBCSEEoojcYaxVI8V9L6eXH0snL sx8Lgk4/ZPHo1Z2j7LA23vDw8Br3jLzfnpXKS8P7u1+XDm1dtf+KoK+Vt9Zs3nB20XJvdNiizw4Z XLz7w7pKpaDD9WOJthZxW4a8+9m+571380bD54/sZCiicyhjOD1VAKeI2816IXnD3vqYFWNllL3M F38oneHh4a4PlyXFHSAmvFOGXIuIVe0T3c+9qa+X586xXgI6bzh6vdxXgLX3k23HosP2ZPtjXdG8 ez/1HtEdU/uchty/Qmn242pWRDyndjhD7/M8VxFb3vV6jvh8ks2Cd942Y71eFkV8t/M+JHm7gMXS Dfltl02n08HKykrXB0Dy1h9Vr11Srfqxcue1tfU/D8VL/bjr33mdnVB5U8IWGTEJ+V4XTbvIRzwW lxazwaTT6XTtMqgFMND9jKU+A0XvJ68jnyekvXu3jHs49A5LyYPWbjJoUETnUMbDmfLy7cVKK2n3 Uh77dy/11AJTx08V0f2IzH6QctvdB71w9ndINPfahnnlDJWtSBrSltry7KWZOsnO1jm147CZHzp7 fbyyx+BHeudiJ+1ZAV30fSmivMj663l4z2bs/bAe7yNL6nNPyE6G7hyEEEIIIYQUhJbokgj1xEP+ biH3CTukmEoRX9TQ8TxLZIqlUiyd/VjIrRVFuzaUZaG2Vpw8q0reEGcR+rES9eNX2Ol00Gq13OHf MkYj9LGyyXNriRG792lBI/LOqtfr2TF9b/TizhEa7Uklb+1oL8+ibMQoDJ8vstOhiN5g+pm8VIaI LOMlnCe82+02arWaG89+mMoUxkXwRHTIH1uXL+YDHcsr1WfZO6fbLKWtJI6tj0wmTF2NYz0/zDqs 175F7omy/UDJYCHPhPglr6ysdN1/sspGkQ5szFUo7xtgxbPON8+dI/b89DLfJPZetO+m1DQJ2UnQ nWMTKTr5pIz81jtvbYXpJw/7Ui6SVkpnRMqpRWXMv9cT3WW2Z+gjlUqquI7Vt0hZi1rjUz7WRcsS i5MqYjb6GSRbD93prFTOLX0J5C9PGRq1sit0eKSuzqHL4ZW77Ps35VntNV0+a2SnQRG9RdgOL5fQ xBtL6lBmP0P0RYlZUmL/isT1wobota72Y2p/x6zJMQFdtptMXpiY+E1Jp5dOljdhjB92Is9F6L1m hXRKJ72oO4dO367U4a1n7+UZy6/o8130ncZniAwqFNGEEEIIIYQUhD7RW4g839myCE2Q6WcYz/OJ 00s8Fdm9y7OmprRLqr95aNmqarXqWsdDll/9dxFfXtv+RSaYplrDJK5YxXQZ8/JMTd+jyATWIvEt RSzVvcQng4W2HOt3lX7+qtXqGot1nstQbFKivHNC8a1FOu9dpMvcC3kTJz33NfveC/lJE7JT4V2+ ARR5qaUI2V5FpiZvaLDfF6AWb5VKxZ1sKC/tFOFVNvYjp8sh/9sPppzzJkjGyptaj34m2IUm/+iV UrTvp04/1DEoUodePtx5H22bfi/3Qy+Tqcjg0emsru0MAGNjY9m7amlpKQtTq9XWrCkthJ4ZLZLl WdTPXmxNfftuCXWobT28spV5z6e6efA5I4MARfQ6k2Lxzev99yMmUy2WvQinkLVUjmkRLVbevI9P SEiW8WHIs4B61qOQiE5Js5+ylZFuyF+7F2t5KP1QuFj8ou1nLWDe7xi0iJEYImb18yHid2lpKftd rVaDvsneO0Kn0+l01lidQ6NfEl6nKVZpnUbo/etZh/X/tgy9iF0KZEJW4ddlgylDMKdO9Ngo8RCy iLRaLQDnLDjesncp6YbEX7+dC/vhSRHR9iMWG9a1Vuui5bPkdXa8IVXP0hWypHt5e9awXsoew+tU 9mJBLpIvRQDRyORCYPUZl3dVpVLByMgIgNX3mH0nxDqKtmOujQr2WIzU597+LtoJLjratJHfGEK2 KpxYSAghhBBCSEHYjdxiaKuANwxfpPdfti9cUWLuHEV9ofMsvkIRS6lnwQkNy9pj1noTshzHyhor W0rYPELuHKF8yhgF8dK1170MUv0yU9IgxLpzaEu0586RMiqm4wJrLb9yzrNGe89qp9PpmmwYem5D luWy5i2kuJ8QMijwrt9GrOdHv4gPrBfH87/Tw6Mp/rGxspWBJ4b1LmWCHJP62nhyXm/KoMVqCjqs N0O/F3HuXQs9TK3rlDeE228HLMUnupdJhV4nM5ZHSARQQBONfV/VarVMtMrzOTo6GvSJtng+0Z6L Ryi9lGNFOr82TpH5ER69zLMgZCcydPbs2bObXYhUXv8r1+LJpw7ihz+YzY5ddPElm1giQgjZeIq+ toeGhtapJIQQsnV54funs9/f/94M3vWe9+PJpw6Wlv627z5uoz4AIYRsCnxPEkIGEW10XQ84sZAQ QgghhJCCUEQTQgghhBBSEIpoQgghhBBCCkIRTQghhBBCSEEoogkhhBBCCCkIRTQhhBBCCCEF2fZL 3HH9U0LIoMEl6wghZPOhJZqQZChcCCGEELIKRTQhhBBCCCEFoYgmhBBCCCEEwAsvvIDbb78d9Xod Y2Nj+KVf+iXMzMxk56+99lq85S1vwatf/ert7xNNCCGEEEJIGdx///1YWlrC888/j5GREXznO9/B rl27usKcPHkSBw8epCWaEEIIIYQQADjvvPOwsLCAY8eO4ezZs7jqqqvwMz/zM11h7rrrLlSrVYpo QgghhBBCAOC+++7DG97wBtx999244IIL8Ou//utoNptdYS666CIA9IkmhBBCCCEEAFCr1fDAAw/g yJEjOHr0KI4ePYpPfOITXWGef/55ABTRhBBCCCGEAAAOHDiAkydPAgDOP/98DA0NoVqtdoV58MEH MTs7u/VENDdPIYQQQgghm8GRI0fwxje+ERdccAH27t2La665Bvfcc09XmP379+PGG2/k6hyEEEII IYQAwL333ot77703Gubqq6/Gpz71qa1nidbQKk0IIYQQQrYipYvooaGhoPiV4xLGC3f27Fk3LCGE EEIIIVuFUt05hoaGukSw/A6FiYU7e/Zs8BwhhBBCCCEbzcGDB7PfpVmireAVEWyhKCabRaVSQaXC aQCEEEII6R8qCjIwdDqdzS4CIYQQQnYIW3piISGEEEIIIVuRoIi2rhh5f1v3DfozE0IIIYSQnUqp 7hxaSJchoMtOjxBCCCGEkDIIimgrWvP+zjseOpcnjimeCSGEEELIVoM+0YQQQgghhBSEIpoQQggh hJCCcIk7QgghhBAyEDQaDff45z//eZw6dQof+tCHks9TRBNCCCGEkIHg0KFD7vFGo4HXvOY1uPnm m5PPU0QTQgghhJCB4IorrnCPf/GLX8Rzzz2HD37wg8nnKaIJIYQQQshAELJEX3zxxbjuuutw0003 JZ+niCaEEEIIIQPB9PR08NzIyAguuOCC5PNcnYMQQgghhJCCUEQTQgghhBBSEIpoQgghhBBCCkIR TQghhBBCSEEoogkhhBBCCCkIRTQhhBBCCCEFoYgmhBBCCCGkIBTRhBBCCCGEFIQimhBCCCGEkAL8 8z//M0U0IYQQQgghRfjDP/xDimhCCCGEEDIYPPfcc3jta1+LWq2Gq666CnfddRfe/va3J8d/61vf iunpaRw+fJgimhBCCCGEDAZ33303Lr/8ciwuLuLhhx/G1772tULxH3nkEczMzODnfu7nUFmnMpIB plKpoNPpbHYxCCGEEEK6+Jd/+Rc88sgjGB4exhVXXIFbb70VZ8+e7SktWqJJ6VBAE0IIIWSr8rKX vSz7PTEx0XM6FNGEEEIIIWRgmJubc38XhSKaEEIIIYQMBNdccw3+5E/+BJ1OB0ePHsU//dM/9ZwW faIJIYQQQshA8PnPfx7veMc7cOGFF+LKK6/ELbfcQp9oQgghhBBCYuzZswdPPfUU/uu//gtPPfUU du3ahYsuuqhwOkePHqWIJoQQQgghg8EzzzyDY8eOAQBmZ2fxD//wD7jpppt6SovuHIQQQgghZCCY nZ3Fu9/9biwvL2NkZATve9/78Ku/+qs9pUURTQghhBBCBoJbbrkFt9xySylp0Z2DEEIIIYSQglBE E0IIIYQQUhCKaEIIIYQQQgpCEU0IIYQQQkhBKKIJIYQQQgj5X5555hn82q/9WjTMBz7wAYpoQggh hBBChBdffBEHDx6Mhnnzm99MEU0IIYQQQgaDv/qrv8Lll1+O888/H41GA7/927+NH/3oRwCA//zP /8T09DTe+ta3YnFxEdPT05iensaHP/zhLP6XvvQlTE9PY//+/RTRhBCyUVQqFVQqXJ6fEEI2i127 duFv//Zv0Wq1cPToUXznO9/BRz/6UQDAy1/+cszMzOCRRx7B2NgYZmZmMDMzg4985CNZ/DvuuAMz MzO45557uNkKIYSsF9VqFbVaDZ1OJ/u73W6jUqmg3W4DANrtNjqdDiqVShaOEELI+vC2t70t+91o NHDXXXfh8ccf7yktimhCCClITPBWq1UAwNTUFBqNBiYmJjLBPDY2hqWlJXQ6nezYwsICWq1W9g9A do4QQki5fP3rX8cf//Ef49SpUxgaGsLS0hJe+cpX9pQWRTQhhBTAE9DVahWdTgcTExPYs2dPFq7V amF4eBijo6MAgE6ng/HxcZw6dSoTyrVaDe12OxPfOi4hhJByufXWW/HQQw/hjjvuwNDQED72sY/h q1/9aleYoaEhnD17Njct+kQTQgghhJCB4Cc/+Qle+cpXYmhoCM1mEw899NCaMOPj42g2m5iZmYmm RUs0IYTkoK3P1r9Zzl999dVd4arVKvbu3Yt6vY56vZ7FbbVaGB8fx+LiIgCg2WxmPtJe2nKMPtOE ENI/DzzwAG6++WZMTEzgvPPOwy//8i/j5MmTXWFe8YpX4J3vfCde9apXYWRkBL/5m7+JP/qjPwIA XH/99ZiZmcHi4iKGzqbYq7cIr/+Va/HkUwfxwx/MZscuuviSTSwRGSzOAhja7EKQDURW0rDiVVww pqens78nJia6XDCuvPJKN00RzadOncr+1kIaAFqtVpanFupybBu9tgkhZNPQevH735vBu97zfjz5 VHz95yLQEk3IAFHUmhkSkTsVaR9db7sknYSZmprCvn37AACzs7NYWFhAo9HIfJsrlQoWFhbQ6XSy Y8ePH0en08lEM7AqklutVlc+tVotE9ISl5MNCSFka0ERTcj/UvZw+VYcfi9anq1W/vXEc9nQx7Ww npqa6lp1o91uo1arYXx8HGfOnAGwuurG/Px8l5uGWJ1lmTuh0+m4kxWbzSZqtVowDCGEkM2DEwsJ IYQQQggpCC3RhPwvZVv5toLV0Loi2DLJee2PO6iErpf2SQZWXS3q9TpqtRpmZ1f97er1OiqVSjZZ EABOnz6NdruduWtIWs1mc00e1m1GrN/eRi1b4b4ihBBCEU3WgZBfqTdELr8l3HqXK5SPHVq3x1aP n1sP2KtjKrGwITEFnMvb89MF0DXsL39rtwFJS/vZ6vSsoA4J60EUcdVqNWuziYkJVKtVtFqt7Jj8 rzdMkQ1VtGheWFgAsLbTottex7VrRxNCCNk68K1MongiUR8TYWfPVavV6OQsCavPaYufFhAiVjwr qvYTtaJRsCJerHvehDEvfQk/O7saVyZ96fC6LroeMZGd53cr8a0l1KZr4wr1eh3VajUTbvV6PbNk NhoNAKuiTuqnl1TzRJxcC52PtPdOFtaydbe0mdyLrVarq+OiV9aQY81mc40vs+fb7N3b9rpK2+/k tiaEkO0ERTRJQltg7SoC7XY7WwdX0B9/LQb1SgN2pzYryKxY1wJOjjWbzTVpWjqdTiYgpS6CFvD1 er1riTLZNU6WLpudXbVCViqVzDKprcStVgv1ej0TrV5ZpH623lI+XR5JX5dXhvR1B6PRaHRZNSVM pVLB2NhY1xrFts11XaTcut20FVXaQ+flbVO9k0Se7hRKvaRT1263uzojuvMBnGsH71gsLwBd94IW 6rRGE0LI1oETCwkhhBBCCAFw8uRJ3HzzzRgdHcWuXbtw9dVX48CBA9n566+/HkNDQxgaGqIlmqxF W5C1xVKW9hKLpSzx1el0MDExAcC3GosVul6vZ5Y4sY5q6692lajVapkVVFtarSVO8pW8dRl0ua3V 2KYnZdMW61qtlg3lCxJO+8hqdHhpP+07K3mIVRg4Z3XUVn5JX7tzSBq1Wg2jo6MAkE1km5ycxNLS EgBgdHQUy8vLWRkk7PLyMkZHRzE8PNxVXomj85EyiaVVLP7ax7der2Nubi4bEdDhFhYWtvUa057F Vy9nZ633oQmDMrEwhZDLj+TRaDS6RkoIIYSUy9mzZ3HzzTfjhhtuwHPPPYdarYZvf/vba8K9733v w8c+9rHtL6K9CWH6+Hb8gG8GWjADq0KtUql0CUJZlUDCyUddC7hqtYrh4eEukSiiWIstcXvQvtOS p6SjRaPnqmF9hiVPLeBtnXQ46zpi/U1HRkawvLzc1Q6XXHJJlsbKygpGRkYAAEtLS1nHQOcj9bO+ s1rIS5mtm4l3D4vLiLS3nJNyyrHh4eGujg8AzM3NZddQC+ZGo4FOp5Ptvqf9pKVOzWYz+z0zM5OV cXJysquDcvz4cVSrVczOznb58oqY3G4rgMh10DsI6naWY+LOoc9bFxs5VuTdpO8De98QQggpl/n5 eRw7dgxf+cpXMD4+DgC44YYb1oQbGhpafT9vdAHLxgohbY0k3XiTkkSsatFarVazJbsmJycBrIq0 vXv3Ym5uLrNs6rBiER0ZGcHi4mKXONZCRCybIoq1tVNbjwGsCWtFprbg6vLUarVM3GqhLUJ2cXER o6OjXT6mi4uLGBkZwfz8PMbGxrI62zZbWlrK0m6329lv6Si02+3swVtaWsLk5CRGR0e7LN4i8rWI ls5FbAKiXKPh4eGuDoWd0Dg9PY2ZmRk0Gg2cOnUqs9brjoXEbzabWWdI2lsszFdccQXm5uayOAsL C9izZ0+XVV3aQcetVCqYmprqEt3SgZL0JN5WfU5t5zzkc259nUPHBGkfwPcn1/nbCbG2o0sIIaRc Go0GLrvsMtx7772477778OpXvxoXXXRRMPy2F9GNRiP7MIeWLtuqH+qNRotGbeWdmprq+jhfcskl WFpawtjYWPaBHx8fz6zH2rI5NTWFdrudCU8RxhMTE5nFcnJyEvPz89i9e3cmtmZmZrqs1UD36gNa eC4tLWWWVcvo6Cjm5+cBAGNjY1hcXEStVssstUK73e5aw1d2jdPW8larhWaz2bW6gp1MOTc31yXe Y6Kw0+ng1KlTWQcCWBVRIuClzfXkSM995tlnnwUA7Nu3L7MS22uq8xALcqVSwcjISJerRaVS6dpy Wuqsy9BqtbC4uIjjx493iftWq4Wnn346y3tubm5NB0WspfV6vcu9oV6vY2JiAo1GIzvebrczC+5W sa5Ku9vraDtrUua8CZW28yedy7w6e65JhBBC1peXvOQlePzxx/HRj34U99xzD7773e/i9a9/PT71 qU9h3759WbhPfvKTePjhhzmxkBBCCCGEEAC49NJL8ZnPfAYzMzP47ne/i3q9jrvvvrsrzDve8Q4c OnRo+1ui9+3bl1ngtN+iQCv0OcQK1mg0sqH2iYkJ7N69u8snV7s9aHeFEydOYHFxsctCrH/ruHYC VKvVwtjYWLbD2/LyMjqdTjbZTfKTIWuZGCdxvREFsfzqvMSSbC2J3qYjupx2PV4JJ5Zb7f4gEwPt 0mWSh7ZO63QkvvxtLYza0qnzlrY9ceJE5jaiXSlWVlay9gVWfbr0WsanT5/O0rSTFLW1Xddb8rP+ vXo5Q93udmlAsVLrayBuEOJeItdVL5tnr+dmkDfPQrAjDyE/fD1yoN2mdHuH3lPW7WerWOwJIWQQ mJqawrvf/W7cfPPNXcd37dq1uuTtJpWrNMbGxrBnzx4AwNGjR7fU0HA/lOWGooVjo9HA9PQ0Go0G du/eDeCcsG6329lQxcLCAiYmJrrcLWq1Gk6fPo1ms9m165qIYkFWa9DD0PPz85m7hBZ/ds1j7QPq TayzLhD1eh2zs7PZMRFuut1EeIgbiW5bb71oEa7yW4SS9Xmt1+tdq41oNwA9iSx2P4rYFkHtuUXo VS50/XTbSFudOnUqS0+ug7QVsPoykONa1Iso9/x6tYiWToJ2xxD0CiteR1bytRPj5L7U/sHWFWQj RXVMzIbcxSSe1F0/M/K3FdLeNcx73q37EyGEkPJ573vfi9/6rd/Cvn37cObMGXziE5/Aq171Kjfs thfRi4uLmSBcWFhwhdR2pN/yywdaPrpTU1NoNBq45JJLuoROq9XCqVOnsLCwkPkWi5jTKw7Yv6WM clyLP2v5FZGgBZQWbFYkhXxGdVi9IoS2jGqBKOEEuyOiZ7G2llArouWcXmVE0rSdB/lbWxClnewS eVpUeeW1f3sdB23Z1JvZSPyZmZk1kzEbjUZ2DTV2h0jx7dVtoVc40Z0Q22aCdHy0j7a3NbnsDqg7 a94o02YhddPl0ZM15dpIXaVOADJff+2fb0cgJA/9P7C1J2ISQshOYXFxEW9605tw5swZnH/++bj+ +uvxhS98wQ277UV0u93Ohv6vuOKKbJvdzR4SXi9SOggi0Or1emZdnpycxJ49e3DixAmcOnUqEzLV ahUzMzNrrMraGgl0W8u0pVU+7NpyaIWa/O25eGhBL2EkrhaosgybFcVabOvy2JUPtOjxBJ4nYq0w 1HlKWaTNtFuETUOLXhHVWuzLJEAttrSw9ES9WLFrtRomJia6diYEgDNnzmB0dLRrgqW4e0xMTGTX S0YJ9CQ5aT99bXTd9HJ9uuwAsus0NzeXldsKfX1fSTn0OuPSbnrJQD0JUdqhTPJcKmzHzOYv1mbb yZD2kLaX+KGl6mLPth4FIIQQsj48+OCD0fPf/OY3s9+cWEgIIYQQQkhBdoQlWiw6Y2NjmJqaytal FcS64214YIfKt/pwqbbU6b+Bc/UT143du3d3hTtx4gSOHDmyxi3D2/zD5iPh9P9igdbh9S5/4mYj Flbxx5Y0xsfHsbS0lK2rLBMNxe/66NGjAFYnLbZaLSwsLGR5a4umLo+9riltqH9bNxRJU+cbsoba da51ftrVQpaT064pFrH2NhqNLL3LL78c09PTmJiYyNbvnp6ezvyzdXkOHjyI+fl5HD16NPOVbrfb mJ+fx/HjxzNXCT2xUk86tO4E2n/dusXY0Qm51rIdpnK+AAAgAElEQVQz4tLSEhYWFjA7O+u6QYiV WdKcm5tDvV7vWj9croN2O5FrVcYza9Pwrom+rp7VWUZ/9C6QMrdALO1zc3NrXJXsqE8s353gqkYI ITuFbS+i9fq2KysruPzyy9FqtbLhbm/ykxZJvQ4Lb/bHzApBGdYHgD179mB8fByVSqVru0oRILrc uh5WPNp8rHAA1nY+tOvBiRMnAKy6K9RqNRw+fBg33ngjAOCaa64BAFx11VXZutPNZhMzMzN49NFH 8fTTT2ciWl8nW1btKqHLY32nQ3W2hATNapxzf3ttFnMhskJIr4birTUsEwplrW0AOHToECYmJjA1 NZW1o3YN0HUXN52///u/zwS5+K6Lq4bkLXWOTeSUNrRlFRGt44qbi4jgRqOBK6+8Env37sWZM2ey OunJiroDordWlzrKhEM9IbLd9rfa7hfrfiNlsGGAc50def4qlUpX51F8zqXdpqen0Ww2cfjwYdfv Oa9cFNCEELJ12PYiWgtm8dEU6w+AzHqpRYznE+tZdmNslY+ZiA69k1y73c78nrWFD0BmAdWT8TwR 5flEewJahJQWHXpHN2BVTItvqPAbv/EbOHHiBKrVKh599FEAwDe+8Q18+9vfXrPihxaKenmwPP9R KaPFi6f9vWNLj2nfcOCciJIJZHn3heeXrQW57hxYX1zxNZ6dncWhQ4cArI46XHfdddi3bx/uvPPO LO5NN92EAwcOdPlZi2+y9U8X0WhHarRolni6fjGx3el0sntPytxoNDA5OZlZ0Y8cOZLdf3pFFC2W pTwy10HymZiYyLYx72cUyXvuvZEE7aPtTdqU9pucnMw6hRJGdoCUcNIeqe8czy+dEELI5rPt38gh q+H09HT2t96FLsRWEcWpiDCamJjAxMRE9nEGzg2Jy28Aa4St5wKhP9a2XT0XGBEOIoT0Of2xF1eD ffv2ZQL5iSeewMLCAj772c/i+PHjAM4t+SZLntmVMIpcoxTLc2gSoK6DFbpa/GoxKmGt6I2VzRPo oc6dnBervhyfm5vDgQMH8MQTT+DAgQMAVnecrNfrePTRR1Gr1dbcC3opPS2OrbXVWz0k5AZkRzHs fbWwsJCNNMizec011+Dw4cNot9u44oorAJzbQfHKK6/MJgxfcsklaDabmJ+fX7MLor4GvVilPfFs O5C1Wq1L2E9NTaHVamFiYiJrx7GxMUxMTHS5oYhV2pZROg9FLdFFwxJCCFlftr2I9pYpEx9R4JxP 6XosD2Utt54g9cL2iwgpANlawtqdQJdHW89C7itW9IjI0sc8S6Qc06tCeNZJyXNmZiYr44c+9KFM gOvVHuzGGyFC/szij61dAeyyd8C5lUEkL3EPCK1IIu4cnr80cG65N90enl++3K/Wt9qKVPlt20Bv 1iL5SlzZHvzZZ5/NXDq0sJSwen1ruSesNdizOsdcYATtlqHbUu6pSqWSuemIWD59+nQWdu/evfjW t76FlZWVbBOedruNPXv2YGpqKtt2XK61Ff/2Guq2DP1tw2p3Dhm1qVarmTgWf3/t/9xoNLL7TFbi kPtQb77z9NNPZ6462srvvS8omAkhZGvD1TkIIYQQQggpyLa3RAPd1k/PbUMm/YilrZ81pPVKAXkW L32sDKuS+LeKzyXQvc21tk6KFdK6Qtg1mz33C70rmrWMWuyQtLYO6l3spCxipdXl1T7tNk8d1rpa yDC7thCOjIx0+fdai7HXptKOwOp25FLumZmZru2rxRItlsp9+/Zl/r1Sd9uW8rctD3DuPlxcXMyu o55055XV1kfcXnSdZZUI8SO2m7aE3DY8VxTPgmvdXVLcZcQKrv3aFxYW8K1vfQtveMMbso1+gHPu ErJqi7h46BEmvdKHdS+ya5bn+bXr43YiqqQtLlMAsHv3bnetb5nMq+cbnDp1CrOzs5n1/dChQ2sm RMaeK1qkCSFk67LtRbT10RSBKMf1cLd2G/D8Nj2scNCrGdhyeELNE4QhQv60egc08S/Wm4mIMNFi SLt22Paxx+xWwpKe5xNsy+jV2frY6ushk81qtVo2sdDLQ+ct7iuNRiNbgWR0dNRd3QJYXVItJJB0 O9gJp9VqFcvLy5kIuvHGG3H48GEcOnSoqxNy/fXX43Wve13XBMjFxcU1u/x1Oue2aba+0/qcdof5 xV/8RQCrm6WIsNb+tHZZPy2mdRtbVxmJ02g0uoSsd0/oTqJ+ZvR5nbf+LdfO+t/rDpDe7bFSqeCJ J57Addddl12DvXv34vTp01m9ZffCWq2WdVrErUWWWNR1lBVIJL3Y5FLvubQuIrLqhrhpiBuH9k3X yzyKjz+w2hE7fPhw5mojz26o0+Hdt4QQQrYm215EA93+pnZpKi0k7AcvZSthT+Cl+q9a/1l9PGR9 0lY1ETvav1vW1NUiygpemehnLWohS6Stq2fxzMPzp9bp2vrqToC1hkuHRyzMU1NTmYBZWVnJwngj Abauui4xi2yn08HS0hJGRkayXf6azSauu+46XHnllTh8+DAOHgT27duH22+/HUePHu2ql6yJrNP0 LLy2XJK3/C/nL7/88qydpNzLy8uYnZ3N1sy25/UIi/zWkz5158u7ny1eBycWRvLQ7a/rrDtFUjbp /MlSjNddd122zvkzzzyT5SGWaulETU5OYn5+vsv/Xeph17KWcmlCz6z1x9dp2feMXl5TOrP1ej1b 2rHZbOKZZ57B3NzcmqU2KZgJIWTrctttt+Gyyy7DyZMn8e///u/48Y9/jL/+67/G/v37sbS0hPvu uw+PPvrozhDRmpirhUW7HBTBs9zFypNyzJ4Ta5gs2QesWhhF+MvHW1vddBrW0uYNwcfK4nUUbHjr bgH4E8vsxhIhtHiWf8CqQJWyjIyMuPWx7ScrOwgjIyNuR8arv96i+dSpU7jmmmuwb98+HDwI3HLL LXj22WczC6jeoMSWwf62+XptJnjbn1erVYyNjaHVauHMmTMAkC1jKK4boXwFb+RBjtt7xVrq7Xmd T6xzYO85sUR7Lh5HjhwBAFxxxRV43eteBwB45plnUKvVcPr06a4NWORe0VvYy31nO9P6WnnvB11O 7aahy2gnyOrnsFJZ3QhmaWkpc93Qbht6JEP/TQghZGvyN3/zN3jsscfw2te+Fj/60Y/wwx/+EADw jne8Ay+++CKOHTu280Q0IYQQQggh/XDrrbfita99LQBg165d2LVrF+bm5vDoo4/i3/7t33DhhRfu PBHtuU7YoV1tDfQmfMWstNa6lkLMchc63ul0sGfPHnQ6na7dF63/s5eGWP2KDhlb1w9r0bPpedZH 3d6eld/LX29xPT09jVqthtHR0aB7jE7Hs3J7S9qF4uv2Hh4exsrKSuYyMjw8jOHhYTz99NPZLnQy QVL8zrXVVk869cpk6+9ZfG2b6uusraN79+4FsOqLLZt5eLt02rpKmfXSh577hecvHnPp0D7Iec+G zRvo3n5ctimXl9ftt9+e7bwoluorr7wS8/PzXdudSznESq3z0Bb40NJy+h7X95e+5rY99DNnLdY6 j34mMxNCCNl45Luv+d73vgdg1d1jaGho54loizfErYWK90FN9V8uUgYrUkJ+qHJsenq6a01hnY4W NLL6gCfObF29snjliJXNdjzsMe3C4Q3n23KKcAbOrend6XS63DG0K4auhxWtnU4nE8DC8vJyJsht x0OXRccT/2Y5Njo6umbL9OXl5TXl0puPyDGpjx7GD7WvTt9zS/JcQUZHRzEyMtLl9jMzM5P5zVu/ ZlkpRa+SotMHuicbejtR6jhyTAtjm56+VjpPcUWyAl12N/zHf/xHAMD73vc+3HnnnfjsZz+blefU qVMYHx/H8ePHsxfdkSNHMDExgdnZ2S7BbCdHarcPO/FTwnhrroc6wBrdIfEm99qwsfOEEEI2j6Gh oTXHXvaylwEA/vVf/xUXX3zxzhfRQHxJKxvGO24JCWv9AdUbaOh0QmJdzuktg2dmZtaIEivKtEj2 LK0hn1z9t1d/6xuqBYhtH7tjnV0RwSuLiL6pqakuv1Kx/mq0YJU0ROB6lmNdL+tDbcukxbq1ROuO iu0kDA8PY2lpKalzZMN4/tieQLNphazG+h+AbJfCubm5bKUO4NwkxEql0uVHbEWuroftZNq62dU9 PIt16J73xLqEOXr0aLaL4f3334/7778fzWYTTzzxRFdcqS+AbBlL66tureqCLmtoFY+UTrb+bbdT Txn5IYQQsj2YnJzELbfcgt/7vd/DX/7lXw6GiAbWfrBi1kBPTFuBGcojJtRjyFrWko+sg+uJJpum nZim6xazvtq/bX3zLPXammeXFAvVW6+4IfXV9VtZWVkjooeHh9ekJ6LahrV10aLVnlteXnbLKWkO Dw9jeXl5jUvF8vJyNtnRyzNmgY9Z+IG1llx9LfNGMGRCZLvdxujoKBqNBo4ePZpZ0eXaiDUaWBWe ntVbp6vrZ+ti3VS8drbL7dn7zC6HV6/XsbCwkC0hWK/X8fGPfxwf/OAHs/vsiSeeyNKTlTsmJyfX iGh9b8bWdddxdL3tyECojva39zchhJDtz8MPP4w/+IM/wCte8YrBEdGEEEIIIYTk8dhjjwXPXXDB Bfj0pz+NT3/604MpomOuDnlxyspbU61W0Wg0sg1IYmWK+dR6aYcs8F4beFZFb+KlLZ+10skkLB1e liPbs2dPlpfE0dbklZWV4EhAp9PpcuMQ9wtBu3LoOkj5ZP1n4NwkTesnq9NZXl5eYykV9xVxE9Hu INa1Razgw8PDa3yLPbcGiaPTy3MjsH7PUpd2u42xsTFcddVV2UYferMWQfyjvUmknluKvidillmv HtYdRa6NtoyL5VjcmYDVtbmPHz+Ohx56CG9729sAAM8++yw6nQ4mJydx+vRpAMD4+HjmV683qNF5 ee0KnFv72bPG27oRQgghAHauiM5zyfDIG3Ivml5qGcWtQe+AZ4fx5ZgVYVb45JXH1s1bUSK13MDa daq99qlUKpiYmMD09LS7qoj1aRZ3CTkugtVz9dBoQRtagUGXUQsmvUujV19JW4tALeD1pic6DRFm IuBFzIu4tnjX1SPmMqKv59jYWOZbPDs7m61xbH288+7rkMj31pIOdba8NLVLEHBuc6RWq5VNVjxx 4gSuvvpqHDhwINvRcf/+/fjGN76B6elpLC4uZmnv3r27azMavYqG7bjEKFMw5/lGE0II2Z7sWBGd YpVNiRsTpp4lOzVupXJuEmGj0cCJEydcn0zrl+yJ3pB1O+a/Hdq10NZFC87QpEItgrQVWsq4Z88e TExMdO1kKPUaHR3tsi4DWCMudZ4iZj3BGPLflrysL6/ni1yr1boEr5yXMsk5qbtg20HX0WtX+a3R 10Ss4LottH933vXXwnp0dBQAsmXxZmZmusR6s9nMdsOUY94kRunAWdGs//ZEue3g2dEN2yY6jDA7 O4uJiQl84QtfAADceeed2TmplyyNJ9tySx4y0TLWXnn0IoTtM0QIIWRnsWNFdFkUsdKmCnfZiVAm 2c3OzrqWMb31ty2PFiUh8W7xXAMkLS34tFDX4tKuxKCH6O2ybgAyC6gWNbYedkk6YFVM23bzxJmI VslPr5/tdXC8Heu0AO90Ol0WUC3mRLwPDw937SaoJ62F6ihCzqbphdPltQJMRLW9BqnXHzg36iG7 6klb6Il/8tvreFi3Dy2EbXvr+kkY7TIhFmfPdcSuPT03N4dGo4Hjx48DWN3FcN++fThx4kS2tB+w OslQXKOA1V0+PXeYonjXpkgcQgghO4+XbHYBCCGEEEII2W4MpCW66NBsL1YoHc+LMzU11TWJ0LM6 W39Tz+9ZrHjWWh2y3NryaOugZ922+YUs6zKBS9izZ09m0dX+tyHfWl0u8YGW47LpjJev9R2XjVVs XcR1BFi1dNsye64dzWYTExMTOHPmTFcZxXqq/aB1PlJvAFmeuoyh0Y12u91lmbdWXrmu1Wo1c/Ow 9Zd09OQ/e6/U63Xs27cPwOoEPbsEnednrfPKu/89dxXrJy7hrFVe2tBb21lvovL000/juuuuA3Bu U5PR0VGcOnUKs7OzmJyczOJInhIu5mbl4bVDXvgi6RNCCNmeDKSILmtYNw89JK+Fzr59+7o2UtE7 twneRLfQJEDPzUKXwSuTN9zuuY3YPLRA05PAdNhGo5GtkmDr47lZWMSdw3YE7LC//BahGktTx5UV QLSPtm4H3dmwAv7MmTMAVgW2Pi5phQS0djkJ3UOen3rIVUPcS2S9bOuOo0Wovl6jo6Nd5Zmensbc 3FyXyBRXDuvfneLa5LmXAN2rkthOk72m3k6UQPdkwNnZWRw9ehSVSiWbRChhtDvQ1NRU13lJL1aP FNEc60xQPBNCyGCw7UV0LxN+ykjHfoTz/KEl/MTEROaDqsWN55csZdKW3JDAyPvo63DejoKen7Dk KcdsXImjhZH29dY+tp4/rT2m62d3frPl0tZYjW4/r+7AqvCUiXs6D12fUFvqTpEWo1qUe+lqK3FI wEkaOpwup+Q9MjLStQyfFtI6rrbkS5t0Op2unR1ls5Vms9llqfVGJ3QZJD3vObAdBW/0RP5Osdp6 ArzZbOLMmTMYHx/vmuwpnSBZsUOWvWs2m133WV5+eVAoE0II2fYiulf6Fd9F44qFsl6vY2ZmZk3+ nsjUx22ZPXGXZyWUNDw3ETmn/7ZWQxtedpYDurdeFpcBb2UK/dvWRY55QlNbKIFzW3R7uxnayYp2 abxKpZIJaZ2314nwXBisMAa6RwnEHUWnERPmIcu5CGYdb2VlBUtLS5mVe3R0dM324zYNT8TKMVm/ GzjXAYl11my9dYfC1s+7d0LHQ0Ib6LbeiwtOvV7HiRMnMD4+vqZss7Oz2Xrk8/PzmJqawtzcXNfI BkUwIYSQfuHEQkIIIYQQQgqy7S3RvVqUPEtuCjELduicLGkHnFvOTofVlrbYkHfMehs6ZrEWa8/S ql1I7GYudk1oCSvWTG+ZuVD9vLx128TcWMTabN0yALg7GWprtN6UxebRarWiE9DEFUAs457FXK93 HbLye23uWZPt9ZdJkXot69HRUSwuLnalvbKysmaCptfmo6Oj2UQ+uUdlWTgdzv622FEHb4Ksvqa2 XHZ5RZ2mjmNdjY4fP57de7Lc3ezsbGaxFhcZPWqS4kJCCCGE5LHtRfRGU8SXUj7W9Xo9E2bi/2x9 WEP+pSEXDk8IhESYnJO0PNcEGzZvSF7OVatV1Gq1rpUdQj7XXrl0efT/tpNhy1WpVDLfXm/rb/l/ ZWXFXXfaIuXWkw2176x2YdFxhJCPthXLWszHxFzM/cOeW15exvj4OObn54P56njiRy3HpOOjO0ci qq04Dt239t6x95gVxjast76314ET5HmamZnpmlsgk11lVY7p6Wm0Wq1sLoKUgQKaEEJIv1BErzN6 0wege4KeFQx6sqEWktYqHRMpEk6nK8c8H1d73OJtwKLjipVdC17rdx3LI2/7ZVtvr44xtLD16mJF t0ZWqZA4wOo22lIWT7x75ZTVQCScva46nnfM7uRoRWu73cbw8PAaISqCXZdzZGSkK75YrPUIgqy4 4t0vXt7yW4ezv/UxK8I9vPted/i0dXtmZiaL12w2Mx9oYPW5kzy073fKfbfZxEaUCCGEbD4U0SWj P/wiTPRKE55VWU8qzHN30OcsIdcBGy80Ecwro3Yl0C4PcsyuBW0thrY8djjeQ1vNbbm9umi8HRDF Yu1ZvHUYiWs7GLrOeotyHUdPcrTi2ptoaC3t3mRKm5aNr9tCW7g7nXPrSdv4dtt0wRPR3uocGnsf 2TKGsCLcTp71wnnp2mtTq9WwsLCAWq2W1UW2NO90zi0/KH/bpe+2GhTQhBCyteHEQkIIIYQQQgpC S3TJaOtZrVZbs5OfDue5VnjHQvEkH88C7Q3D2zA2jRTfaHte/mmfaGtV9yzGeoKexRvmt+W3ZYlh fc7FYiuT83QZh4eHu3xnxa1Br3k9NjaGTqezxsIs1mDtdrG8vIzFxcU15fasuDEXHeDcJErrRuGN OITaN9ZeElaPoIR8wHV6ofvR5mlHGEIjLWIBj42WeMclPb0RjnaR0pMN7RwEWn0JIYQUhSK6ZOyQ uIeeSAXkrxYQEiE2Tmhbbx3Wxg8JZVtOu6KI1NGKHW9r75AAF3cJXRa9+keKD3WeCBcxaXeEHBsb c/1xV1ZWuvzXFxYW0Gq1MDU1lR1bWlpa4+c9OjqK5eXlzOcZWBXpzWaza7KiN4HOu1aeG8fIyEg2 STLk267z8MRoCE8Ma7HuraoR+9tzx/Hqa/P16iZh7THrmqQ39dGdHnFNEZ9oEdH1er1LWFNIE0II KQJFdInY5d1CH+WQdTDkD2qPhyy4Wrx6/r8hK2EoPS99XS9tuYyl6dXLs1JrC3Zox0ErpuxqD3JO 79In/2vrpLV2yyRCnd7ExAQAdO12B6wK8KWlpWwlCJu/rJIh9dB+u7pt9OYf0rah5dxsW8Q6KNZX WKfn3QdiaZfJeHZ5OptGSPTaMuSNqtg4uuz2PvPi2vtLC3993aUd9EYtoXITQgghqVBEl4gVRoJ1 bbBrHueJhBgha51nWQyV2cvbCj0RKNqiq905YuXW5bGCyh7T4Wx9AH+JtFi9ddpa8Nq0tMVVxPPY 2Fi2jrJsIz06Oopqtbpmgt78/Pwa4WqtuTq8tthbgS/HrBVYW5a9SZleG4U6TbqMtjxahOtjerUS 246eS0fIzcOzxutw1oKuO2+h1WZ0XOCcYPbWnRYXHb2snxwjhBBCUuHEQkIIIYQQQgqyAyzRZze7 ABkBF+g1x0PhtgtisPvf/SwGir//+7/b7CJsGtv9vhUCc323EUObXQBCCCHYESJ68z8olUolG+oO DQnbTVRs/FA8PUlKh8+bRCjoIXTPrUIP19vf2rdUJtnpSXJTU1Ndk7V0nrGy6L+tW0to8qQ3nO/5 XHtYf+NQe4kbgXafGB8fz3bue+ihv8Ftt92euTno663bJVRfnY/nR+65wVh3F+uHruviuVp4Lj2h a603Bmq329kW9RLXbhCk0w3589u/Y65DNk17D3j3WOy4/O3lqScAA6vXRK/qQQghhOSxA0T05iP+ qyH/ZP2/4E28sog4t+mmxPWETGhSmZemFXmeQAttEqPz08d03p7frk4vlJ9OM9b5ELTA0pvEeL7K nhiXZfBEbI2MjGQi2qat/d11fUMdDO+6Wuy1krCxJe4AuG0oQjnmiyy/JY3QvWLP22N2A5WQmLV1 CPnNhwS8bSP7d6i9ZVKozkNW66CQJoQQkgJFdB9oC3HICugJ0pjV0caV3xLGfuA90WHT9cqlz3vW aZu3rFBhraHeREqbhxZRISslcK4NbVuG6uVZsr362TRiHQrbGbIrcywtLWVh7PUIWdC9+obEndeO WozapfpCS+bpDoMnvq2lVp+rVqtoNptd7dhutzOLvF05xU6MtOUMiVyvkxE67i2xGLq3Y51OiSdl 1OF0x4hCmhBCSB4U0T1irZDe1s2hba2F2HC1Jxo8dDhvuN4rs2cx18e9oXDZnEKvmWzjx+oWs4Lb unjpen/bVRysoIu1hc0v1IHQglTChbbojmHFsyf+bXhPANqOjBXVts6pddTl6HTOrcShy+gJcJuH oEWqtdp7bWKPedZ2m463qY9NT9rMW9XFbhmuLegU0YQQQvLg6hyEEEIIIYQUhJboHhAfTbvOsGfV ld/WLzg01J9ngfXK4vmQ2vjWumyH5D2LpLYG6q2z9WSskK+qZ+32/KBDQ/3eELwud8x9xiPPsuhd A2vR9c7b+Hl52/tAp+nVWf8f8hX3rptur1Abh45pC3/M8ptXpiL3buh8KC19PbxnzbNie+1gXaWk zrH7LJWUkSRCCCFbixdeeAHvfOc78cQTT2BoaAi7d+/GV77yFUxPTwMArr32Wvzsz/4sTp8+TRHd DykfSC1ogG4f1tBvHc/z5bThLJ7YscJHx/d8imXild4WWf7XIjrWJlYEeWJHHw+5w+jJap7wtPmH /IBjLgWeePWuj1cvOWZdPTwBp+vhdXZCbQlgza6HUkbr+iNpeQLaXuuQgNf1l/DaF1zXz5bZaz+v Pp4biBc+1N4hrAuL5+5k0/M6LLY+RaGAJoSQ7cf999+PpaUlPP/88xgZGcF3vvMd7Nq1qyvMyZMn cfDgQYroXtDCTB8D/A9nSMyErNWaPCuiJyxE7Fh/YS3oY2JcRIjnR6pXNLBpSjjxNY1ZPMWqbf1P Y4LbCppYG+a1pRfOWrtDnRDPQi319sS41472uGdRlTDW59mK8pBQ9jphefehPhay8Or07cRLTWgZ xtj19p6jUKcwJI5tHt694NVRW9V1B85un04IIWTnct5552FhYQHHjh3DL/zCL+Cqq65aE+auu+5a /eZvQvm2PSEhGhMzNr4974mLEFYUxISRZ1kLWRBjS5LJ33pFkpCVN7Q1tBfOlkNP7MqrZ6i99Tkt tDyrvlcu7/pocWU7DTpMqDOUZ031ViQJXQcbLnSNQ789kWnFsde58u7zkBj14noW+LxrqtMJPSN5 Ft+Y64/X8bR5hFxWCCGE7Dzuu+8+NJtN3H333Th9+jTe9KY34TOf+Qzq9XoW5qKLLgLAiYWEEEII IYQAWF2t6YEHHsCRI0dw9OhRHD16FJ/4xCe6wjz//PMAKKIL41lxgW7rpbVEehbQImlaS5+2hnoW RDvUba2FOpx1zfDcMzx3EW1FlnLY47aOnl+xzq/T6axZFi1kzY+5Y8Qs1DqcdbOw+Xi/bVq6ztZ6 bK+ZnQzn/fPaLXbelt1eT++4twmLLaN3L+jjsWsTqpO9//JGauzfsYm8Nm/vOfTC6N/2nrb1JoQQ svM5cOAATp48CQA4//zzMTQ0tMZt8cEHH8Ts7CzdOYoSErz2XMxHWvDC6o91SCiGwoQElI6rt/8W UjZM0UJDhjR0ft4kNM/dxdbV8y236z/b9s/kelsAACAASURBVPLC6/LaYXpbZ08chVwxbFnsmtGp LicxFwYtWPN2IgxdI69OMeFt0/PuGU9w63Ahoe21gfdsePcIgDXXy2uLkEuSVx5LSBBbFw+dZuy5 J4QQsnM4cuQIfud3fgcvvPACzjvvPNx222245557usLs378fN954I0V0KvZjH/uI6789YkIr9NuW w4sb8h3V4WRHuTzrnz3urQIh5+1yeR6ehVtP3gLCQjYm0PK27s4TP96kv5CF0/4OCbtYfC9ML7vx xe7DUPy8e8sT1J7gtT1yPenPWnN1GCtGvfpoAe1ZwG0bpbSb1/GM+Th7z4aeNEoxTQghO5d7770X 9957bzTM1VdfjU996lMU0UWJCahe0pG4IWubjeOJxJC48MgTEKEyShxZ+k7jrSCh4+shc0+IWDcH EVKh1UGkHLbeWqCmdHBCYjK0qkQoLRvWWug9QejF9UYTrFU91vnJ6+ilWI0lnOQVWoUG6N7FMjSa IYgI1/eJdR3Rx2x9rKiPWdA1XjvG2iwEhTMhhBALfaIJIYQQQggpCC3RCeS5BHgWS8+CKcetVU0f 99wVQkuGyTE7ySrkG1yUkEXXumF4Q/S9WECt5VoQVxJvqbE81xQ5723UIudt3azPb2yUISVvW+/Q veFN+LPrI6dYU0PXPDRCEXJDsRMNBRkN6HQ6mJubW5Oeh8SXEQRgrRuIzccrX94IgxyzcUPHQy45 ttxyD3l5EEIIGRwOHjyY/aaILoG84X0JEyMk1EPbc8fEuXad6HV925CLiXXDsAI/Jmpiaek0PDcG e8zrUOh0vM6IHPM2U9Hp67rIMe23rds11LGx9c67P0L+wlZ0e/dIq9VyN2Tx2saWWfuhW1cKLXhF RGrhK7tZFsG2V0pn0xPQXudIdyjtPRe6X/La1gtLAU0IIQSgiE4i5aPpCbmYT20ofp7F24bXojK2 WUoveH6kKaIulI4nJHXaOryXhmeVtQI11HEIiWMvT10/ry21kI6lqevo1cfG9c6HrNi68yLi1quL V0ctjr3OlljkQ+2otx8P3eehkQdv9MWL542s2HBeO4Ys2RbbvraDGrNap973hBBCdjYU0TmEPph5 H9LQubyPr7aShiyneUIkhTyxEbKqAucskI1GY43oiFmvtZD1tha3adlOgbWUWneH2LC/rYP92xPk ITEq50LWX8+Nwv7tWX69ODqc1Ndakj2Rp5fm88Ss/ju0OohN0yuzdbHxLPSh0ROvLVJ2XbTxveuo ia0p7T1LKZ06QgghhBMLCSGEEEIIKQgt0Tl4bhpFXCU8K6GXh7ViWqttKG4Rl4i8857VUVu5rYVY W0ZD7RSKG7JOhlxIvLKG2tL7O2bN1OetVdNawT23Fn085PPr5WfXx/bix6zV+pgdkYhZn205bJiQ e4ktU71eR6vVil57XTY7sqJHEvLcOWIjDJ4Lib2XretGLI3Q37H4hBBCBg+K6ByKCBJPOKa4daT6 dOqhbzvsHYrbC5KWHqq3/siC3qYbCO9C59UvT4x4glTi1Gq1NYLLE2C6fVKEvm1Dz9UhRop7geee ouN7v2PhgLUTUHX5vbw9dxE5Jus+h9rDcznyyuZdFxvHtq/uPFrRHbp/bJvZlVgsec+bB4UzIYQQ C0V0DkU+njHrW4q/sWcJ9fIIWXnL/NCHBLA+3mw2Ua1W0Ww20Wg0ovG99EP1te3j+U/rFSm8NrZp eulb6609Zi3xXt28a67zCwk1u+mIbQvvmPWH9upo62cJdTwAYGFhYU1ddNvKhESJa1fv8PKyf+v4 MQtzqmVc199rs1D5rHValydUH0IIIURDEb0BpAgfezxFhObFLWJBtelYi56Xp7amyjnt4hFzQ4mJ rlCZrajTk+e8MBbPVcbmaScq2vJYAa4FnIeXli67DWvDeaJTp6fjxsqt49t89HlBOkc2rhb/sQ6K h62P14nRx2MjB7butgx5IzoWbw3yWJ6EEEIIRTQhhBBCCBkI7Mi58PnPfx6nTp3Chz70oeTzFNEl ERqSzrOepRz3rI/W0pbiChIrayi+ngTmpaN3r9PpxNYujpVPW2BDFmAd3wuTavn3LLKeT3cvVlFb l1BdQ/WTsnib7YTS8azYnltEyL9driVwzl3GTmz03B/yLLW6LCHXk9DIgK5PqG3tZNVQ+rFy5o0k eGUkhBCy/Th06JB7vNFo4DWveQ1uvvnm5PMU0SVRRBQD8Q++/tsTYdZ30/Od1WEF63pRlJCIkEl+ GplwaMscE6ie8LN1sDsHhspoCbV1igC0YtaW36bj+U+niDnvGobCxzpJQLh95JxOJ3RtQi4ntgwh VwiLFukSP3ZPalcZK8ClI+C5wKQI316eAYpnQgjZ/lxxxRXu8S9+8Yt47rnn8MEPfjD5PEV0CfTi NxnyS7XpenE8kZDnY523ykRIjFjxFxOIeeW3VsOQX29MaHt5WUtkyK/Zs4znXQPb5lZYen7f3sYh oXRtWTzLr3eNbX5exypWL10OGy5k0Q+NDqSihW8ovi53bGTBs6rH7hPvvvXyplAmhJCdTcgSffHF F+O6667DTTfdlHyeIroEev3wxuJ5H/2UoXw5ZidxWQGTWp48txCdn00rJuS0YM6zRMu51KH1kOtJ irj1CHUevDBarOt43s6M3s6GsfaOtbPnZuHFt+ftb1tWL27R7eVD90JKOfXfnjC2or7IfRLKlxBC yM5leno6eG5kZAQXXHBB8nnuWEgIIYQQQkhBaIleZ4oMEXvD0TGfVB0nNOyfZ70LUa1Wu9YB1q4F uox6STubX8wf2PNplbC2HvqYtvJ6bgy6nho7YTBm9bUuGtaNIrbTntcGXpt4lmnP99sSc78I4Vma 866XLXdowxNJtxcrbt5ITOw6pLif2HO9Wprp5kEIIcSDInqdSfG/jA2Lx4bCQ+l74VKH3gU7MdAT fzHyhEdsZ8PQMSusvFUmUutpw3mTMb22F3cNuwFMyP3GliUUJ5a3xkvPIzYpMNRxiQnzUHm8Do9H yCXD/l10h0ibd8wdplcooAkhhHhQRG8CedZli2dNSxFSOlyvQiBm5dNhrDC0AtVaN0OT//L8ZL1d CmP+vR7eToQh0R4SfSmT7LyyhOKIOPfSCE0oDGHb0bZvaEQgtpxdzKKbKl4lXCyfkAU+L13vdygM IYQQUgaVj3zkI5tdhoEjbxg7FCYm4GJD2SmEBGhIXMo5z6rppeEJOi+PmOUe8F0tPEFs0wmt4qDT 1y4rofrFBH9MTOaVR/8fihMbqUg5F7OwS5mA1VEIO+KQammOEbsHbBivLVNHLAghhJCNoPLhD394 s8uQzP/39a9udhEIIYQQQghB5YUfHNvsMhBFqkXPs9J5VlI9IS/VnzmUpldGz1rrhQn5KufVQ8oe s5LbNHR4/b89puNpX9yQf7QOZ9P0yu+hLd7WNSXkoqPPa39sCRfaDCZ0TUIjDXn3Ryi9lHtL3EVC m9bYtPJGQ2LHQ5b5PGs6IYQQUgT6RG8SIZcCIe9D77kmxNwiiggH7TubUmYrJou6loTEsDepzzvn iTubn2xjreN6rg62jiEfXs+HOeRHbMsjq5nEhJ6H1zkITbAsgte+XnlCeeRNEtVphe5RuxOlvq6p Ew1jHTXvuJzLKzchhBDiQRG9SaRa1/IEhGe59XxsQyLCyzfm1xsTirG4nr+xLb9XJ5t2il+sZykH ui2tsSXkYjsbyu+UyZxagGufbhH01pocsz7busT8m2PCN3YddPyUiYI2zTzEEh2bhBrqFHp1TOko pjw/hBBCBofp6Wn85Cc/wZkzZzA/P49Go+GG+8AHPoDZ2Vk8/PDDwbQoojeRmKVVSPnIW8uxJ3Yl v9T0bBljaViLruQf2xJbH0+pd2jynQ1r8w6JdBGttVqtKw0r4rT7hc3DilFbZl1XWTLQs4pqAW3d Ijwrs2dlT3Hd0Me9jpCtS6itveOpFl2vHb30Yx2pvHxCQrxsUp8nQgghW4eZmRnMzc1hcnIyGu7N b37zmuV+LdyxkBBCCCGEEABf+tKXMD09jf379+Nzn/vcmvMvvPACbr/9dtTrdYrozcQbnu83LWuB s8dS8/GsktY/2KPdbq+JE7NcemX3wobOhyzWnrXXhtPpev9ibaUt3V5b6/KlWJd1/FC5Q3FCkwol rXa7nV0XD1v+0N9A8TXOQ/XLG32x18amkZdPCmU8c4QQQnYWd9xxB2ZmZnDPPfe45++//34sLS3h +eefpzvHVqHfD7I3SS40XF6kPLG0YpMKtbjTcWPphepj07b1EKGpxale89hzLxEXipivc0y0h3ZL 1GLV6yRJvtY3OOYi47l+xFxOQp2zPN/rGHmuF72g29LrnBWd9Fe0LLE2LzMfQgghO4fzzjsPCwsL OHbsGEX0dkcLWU90pFq6UybteVbWUHibnidaQ764XpoxsWcFqg6nBVpoR0BdXp1+bNULu+23Ts9u Ta7LJWnmTYzTq4nYuno+6LYuupw2fuiaxtrWs857v3sRl951y0sndWJhEYp2MAkhhAwe9913H5rN Ju6++26K6O1OERGUmk6IIhY4K0ZD+eS5GHhl0GHyrO0iZPPyjVlavWOe1dkro7c2shXfobhWbHuT D7Uo12mELOZ5nRRbTi+sHU0osgZ5XmfPlj+Wji0nxS0hhJAUXvKSVW/ms2fPFo5bq9XwwAMP4IEH HqBP9E4hZGlO8SHtJ/1Qnjpv7ScdSzcksGy6Om2blhWUtiyhfwDW+A3r81b0aoFr0/HybrVaXa4l Ul5PpIf+iW+zjisiVpbMs//y2i+146XT8+of8n0Ppe39tvl67jYeoc4UIYQQ4nHhhRfiggsuwJNP Plk47oEDB3Dy5EkAXJ2DEEIIIYQMEJVKBZ/85Cfxu7/7u6jVavj93//97Nz111+P6elpfPKTn8Rj jz2G6elp/PzP/3x2/siRI3jjG9+ICy64AEML3z9a3Ja9Sdx++zvw5FMH8cMfzGbHLrr4kk0s0c6m 3wlU1uJqrbB2kluK5bToZEQbPxRely9UplZrCdXqSDB/zwoecsnw/MND5ffqbLcmt22lXUZ03t7E xBDexih5dc5LMwVO3MunlyFIQggZNLRe/P73ZvCu97wfTz51sLT0Of5JgvTrJ+0JO/u3t6pEaMk2 yQ/oXqVCrwLilcEK+JCAjE1W8/7vZZWL2ETQPBcWHV5v3uLl4S0QH3KrCbWP7eTIudRJgCG3kCL+ zoQQQshWhSJ6h7Oek65illNPQGvf4pDw1QLRHvfyCJUh9Zh3zssrdC6lXXWY0O6EeZP4dDqeldmL a1cGybNyW6t8Xn3y7q2UTkFqWrROE0II2WpQRO9w+nXH8Nwd8iyQKQI21Yppy5EqhO3xfkRYkXqG yFt1w7pNxESlXvEjtipGntAOnbOTBm09Uo7F6KVjt5kCmgKeEEKIBycWEkIIIYQQUhBaokmQkEtG UUtizNe4aN556erwqXn1ShELZV79rTtH6m6GZeFNiPTw6lF0aTnv/tmIOvbKVikHIYSQrQVFNEkm dfJcKF4ZeYd8h+VcLz7L/ZZpPdMp4lesSRWhqa4yqRNIbRzvvPUFL+q/TgghhGwFKKJ3IFvJilc2 vU4O3KnkTRZMIcXanec7nVo2Xb6YxZsQQgjZ6tAnegeykcKEu8RtPHltLuI0b03tXgRwXri8PHX5 CCGEkK3KBz7wAbz97W+PhqECIoQQQgghRPHmN7/ZXXJXQ0s06Ys8P1prmfSOheKSuA94HnlrLhdZ ZjB2LLVstD4TQgjZ6nzpS1/C9PQ09u/fj8997nNd5x577DHs2bMHl19+OS699FKKaLJ+eMP2qUP5 gya4Yttqp4ZNJSaa81Y1ybsudn3v1E4TIYQQshW44447MDMzg3vuucc9/6Mf/QiHDh3C3r17KaJJ mI0WPyn+tBuZfy959lrOohuPaIGal2eqkO3VVzkkvCW9QesQEUII2blceumleOlLX4o9e/bQJ5qE 2WjxsxmuADHh3GueeWmEVsPQFFlHu2jZym5LiuTy6GU3R0IIIRvHT/3UTwFYfV/TEk0IIYQQQkhB aIkmpRCzoPVr/cyL34/1LrZRSy/YpeVSNoCJ5V2GZTy2UU0RdvL641sFti8hhGwfKKJJKcQmwPUr DNbLtaHsNGxaqaKzHzeWfrYeL5oOBR4hhJCdzvXXX4+ZmRksLi7iv//7vzE9PY2XvvSlOHbs2Jqw FNGkdOwSap6YXm/r9HrHTyE1/RQf6tA5vWtgGRb5QYFWdUIIIR7f/OY3g+duu+023HbbbQCA//N/ /g99okn5pCxrt17W6VSXhc0WUJ5w1qtuaPJWuNDxbUelaJkGZTm6zb7+hBBCtj8U0YQQQgghhBSE IpqUwmZYMfvZzW+ziU3ALKsORd1JuKYzIYQQkg5FNCmFzRBg3ioXKVtVbwWxL8fzBGyRsuZ1ZLzd BCXvrcKguJMQQgjZ/vCLRXYMITFYtj92KnryWsgHWp/rZZvtvLC6DHY3wa3IVi0XIYQQYqElmmwq m20lXk+KbIFtt/IOUbR9UkTpTmpzQgghZKOgiCaEEEIIIaQgFNFk3UiZbLgerhYpPsgbSWw5Prte sWetXm//5c12oaAlnBBCyFbh8ccfxw033ICf/umfxtDQkBvmz/7sz3DZZZdRRJP1Y7NWe4i5TGy2 YIxRpNw7aU3nlF0ZN5Kd0q6EEEKKs2vXLrzzne/En/7pn7rn//zP/xwf//jH8eCDD3JiISGbRT+C fit3Bspms1d9IYQQMjhce+21uPbaa/G1r33NPX///ffjgQcewOtf/3paosn2YD2sg7Q4EkIIISSV H/zgBzh9+jTm5+fx8pe/nCKaEEIIIYSQPF588UUAwN/93d/h4MGDFNFke7ATJ9T1Aq3nhBBCyOZw /vnnAwDe/e5348ILL6SIJmQ7sR2Ffwx2CgghhGwXJicnMTo6mq3aQRFN1kBhs0q/7VBGO+70a7HT OgWEEEK2N//zP/+DdruNlZUVAEC73caPf/xjAMDQ0BDuuusu/MVf/AVefPFFimiyFgqbVfpthzLa kdeCEEII2Ti+/vWvY2RkBLfeeisAYGRkJHPjAFZX57jkkkuwe/duLnFHCCGEEEIIAOzfvx9nz54N nh8dHcXDDz8MgO4chPTMTne1IIQQQkgYimhCeoSuFoQQQsjgQhFNCCGEEEJIQSiiyYZSqVToBkEI IYSQbQ/VDNlQ6AJBCCGEkM3ihz+YLS0tWqIJIYQQQggpCEU0IYQQQgghBaE7ByFky1GpVOj6Qwgh pHS+/72Z0tKiiCaEbDkooAkhhKwH73rP+0tLiyKaEEIIIYQMBP/PB/+wtLToE00IIYQQQkhBKKIJ IYQQQggpCN05BghO1iKEEELIIPPlL3+5tLRoiR4gKKAJIYQQQsqBlmhCCCGEEDIQvOUtbyktLYpo QgghhBAyEExO/lppadGdgxBCCCGEkIJQROdQqdBYTwghhBBCuqGIzoGT8QghhBBCiIUimgCgxZ0Q QgghpAgU0YQQQgghhBSEIpoAoNsKIYQQQkgRKKI3kUqlQjcKQgghhJBtCBXcJkLrLyGEEELI9oSW aEIIIYQQQgpCEU0IIYQQQkhB6M5BCCGEEEIGhP9bWkoU0YQQQgghZCC48MILS0uL7hyEEABcLYYQ QggpAr+YhBAAXC2GEELIzuc//uM/SkuLlmhCCCGEEEIKQhFNdjx0USCEEEJI2VBEkx0P3RQIIYQQ UjYU0YQQQgghhBSEIpoUgq4RhBBCCCEU0YQQQgghhBSGIpoUgv7FhBBCCNmu3PJrt5SWFkU0IYQQ QggZGMoS0hTRhBBCCCFkIPjHf/rH0tKiiCaEEEIIIaQgFNGEEEIIIYQUhCKaEEIIIYSQglBEE0II IYSQgYCrcxBCCCGEENIDZU0upIgmhAwk3H2TEEIGD67OQQghfcKNgwghhPQDRTQhhBBCCCEFoYgm mwKH0gkhhBCynaGIJpsCh9IJIYQQsp2hiCaEEEIIIaQgHFMnhBBCCCEDwZe//OXS0qIlmhBCCCGE kILQEk0IIYQQQgaC97znPaWlRRFNCCGEEEIGhFeVlhLdOQghhBBCCCkIRXSPcJ1jQgghhJDBhSKa EEIIIYSQglBE9wg3CyGEEEIIGVwoogkhhBBCCCkIRTQhhBBCCCEFoYgmhBBCCCGkIBTRhBBCCCGE FIQimhBCCCGEkIJwsWNCCCGEEDIQPPLI/1taWhTRhBBCCCFkIPiDP7i3tLQoogkhhBBCyEBw+vT/ LS0t+kQTQgghhBBSEIpoQgghhBBCCkIRTQghhBBCSEEoogkhhBBCCCkIRTQhhBBCCCEFoYgmhBBC CCGkIBTRhBBCCCGEFIQimpAAlQqXUSeEEEKID0U0IQE6nc5mF4EQQgghWxSa2ggh606lUmGnhBBC yKaz+5KLS0uLlmhCCCGEEEIKQhFNCFl3aIUmhBCy06A7ByGEEEIIGQi+/72Z0tKiiCaEEEIIIQPB u97z/tLSoogm/z97dx8ke3bf9f39Pef8umfmPu29q93Vw+rRFrCsBPLaGGEjkI0wRZTrsl2qUDjh IeXCxi6neChTRaoSTAWSSgIJhLjKVYRQJlQCBBEwG8fY2OAoVljjiiTLi20JW17JuxL7vHfvnYfu /p3zzR/n1z09vfPQPXdmumfm86rqujPdvz6/07/d6fn2me/5fkVEREQuhb/9D/4Q7o67A+C0eDHM bM9xZoa7UxgfW3AypXvc3RREi4iIiMjl4ASKF8zqtkD3UncI2u42QXfHAcwoOVO/DBiB2D1eSlEQ LSIiIiKXg5lNbu4+CabHj+2qK9UppcnK9fhmZsQYFUSLiByHal+LiJw/wQwLNXAu7kBkUqzOx8ka mS7bg2AJLFOD6nobB9sKokVEjkEBtIjIOWQRM3DPYEYNh3384CRXGmqqR/ZRFzQ7IRjuTFakFUSL iIiIyKWwm7IRgcJsy5QaLwfqinMXbDtY8KnnO+7KiRYRERGRS8jMyLmdfD0OsM26YBqwELqV57oy PV6pVk60iIiIiFwaxWo6BgbERPDdzYTjIHo6pWM33cOpq9bdijQqcSdyqrT5TEREZHWUqQobZkbT NDPVN8abCKucdzcShpD2HKcgWuQUKYAWERFZHYGE4zXPGWM4qQNtuBlO3Wxo7lCcGHZXqMskuC6Y aSVaRERERC6J2c6Eoct5NjMMp1hN4TDAgmHWdTYcp4Cw281QQbSIiIiIXBr7tfgeC2aY765EE6CU 0q1el+7YLr3jLCctIiIiIrIqditydJ0Mfer+4G/YbDhdxUMr0SIiIiJyKcwGxdP/jmtA7xrXi7ap Fegw+V5BtIiIiIhcKuNg+o9/x/987DEURIuIiIjIJVEzmf/Yt/9NAD7xyaeOPZKCaBERERG5FIzI H/32H54Ez6+9/JVjj6UgWkREREQuhXEA/cqLz973WAqiRURERORS8VImXz/4yDuONYaCaJFLRG3I RUTkMvvEJ5/ixa88s+e+6TrRf+BbvoX3ftVX8Xu/+Zt54gMf4D1f/dUHjqUgWuQSUQAtIiKXnZd8 4GM/8ZM/yU8AP/TDP7zn/odv3eQPPvab+EMfeA+bH/4OPvaxjymIFpHl0uq4iIicpTKVyjGvr//6 b+BdOz/Pb3rnV7j9Xf8Qd1fHQhERERG5PLyUPbejvP+x38qzL7zI133NGu98/w7f/c51QG2/RWTJ tAotIiJnyd333A5z8+ZNPvpN38wrX/gcb9vpY1fg0Y0rgHKiRURERORSOTxwHvtP/vSf58Uv/DI/ 9elf4t97eINrDwwhGbduBa72s4JoEREREbk8jlh8BuDGtWv807/9Q7zerFPu3eH3P36dlBN45sbN wuv/6O8oiBYRERGRy2T/KPrd73433/Gd38W73/UuXvjys/yPf+O/49EHb/KmfuFhMrFvbP0q3HzP FZ7/Z08piBYRERGRy2M2D/rmgw/x5/7CX+HGrZv18Z273HrwIf7Id34nn/rRf8hDa4HH1zLrTcH6 gWvXdhj0rmpj4WWSkj4ziYiIiEx79eUX+Sv/xZ/j1Re/gu/cJQ93ICb+8T/433jv1QHf8dY17m30 2Y5X6PUg3nQ2Yqsg+jJRFQQRERG57ParzvHqyy/yn/3ZP8m//pl/zmef/kX+2l/683zTIzd4uAcv 5Xu89ZGr3Lr5Ol/6UuY7/5s7jP7DP0P6oR/++0t+KSIiIiIiy/e//K9/B4AH+j0+/FjkgVGPr/ot G1y5MeK5Lw/5zX/5ZQC+69a7ST/6wkeWOdeFXOXHlz0FERERETnHyiFtv8deGwy5uzHiO/5gYO36 XQDW/qOXeeRNb+L3fNM38bVf8zXaWCgiIiIil8e8bb+3fuvv4/o3/xQAdzb+Fe7v2fO4gmgRERER uTRmW30//thj/N4PfYgPf+Qj/IFv+Rau37ixe+yLP8jO1f+UB9bX3jCOgmgRERERuTRm0zn+yT/8 kcnXLzz7OV54dvrRj8ILn913HAXRIiIiInJpzJvOcRQF0SIiIiJyaeQTKvmrIFpERERELoX/6a/9 5yc2loJoEREREbkU/sSf+Uu8MpX0/Mmf+/Sxx1LHQhERERGRBSmIFpmbLXsCIiIisiLOfTrHuOe5 iIiIiMhZ0Uq0iIiIiMiCFESLiIiIiCxIQbSIiIiIyIIURIuIiIiILEhBtIiIiIjIgtLf/e7zU6Dj e79v2TMQEREREdFKtIiIiIjIwhREi4iIiIgsSEG0iIiIiMiC7juIft8HPrjU54uIiIiInDWtRIuI iIiILEhBtIiIiIjIgvYNot/3gQ8emmZx1OMHHX+c54y/FhERERFZFW8oEv2+D3yQpz/z1Bu+Pujx o8yOsd+YR81BRERERGSV7FmJPirAnX38qGB43mPu9xwiIiIiImdp35Xok6bVZBERERG5SN4QRJ/0 yu9+6RwiIiIiIufZnnSOpz/z1KFBDhd+5gAAIABJREFU7uzjpxEQn8U5RERERETux74r0dOB6+zK 9PTjRwXdBx1zVO71oucQERERETlLbwii4eiUjpPcXHjQY9pQKCIiIiKrauWarWjVWURERERW3coF 0futQGtVWkRERERWycoF0SIiIiIiq05BtIiIiIjIghREi4iIiIgs6MyDaG0cFBEREZHz7syDaG0S FBEREZHzTukcIiIiIiILekOzlcO6FY47DR52zFFmuxUeNub9nEdERERE5LTsCaIPCnAXPWZR+40J vOG+D33D77yv84iIiIiInIQ96Rz328L7uNRgRURERETOk0PTOURERERE5I2OTOcQEREREZG9VJ1D RERERGRBe1aiZ6tkwMlsHBQRERERuUjekBN9WMB8EhsAZ4+fd8ynP/MU3/t9f2qhc4mIiIiInAal c4iIiIiILEhBtIiIiIjIghREi4iIiIgsSEG0iIiIiMiCFESLiIiIiCxIQbSIiIiIyIIURIuIiIiI LGjhIPo0W4GrzbiIiIiInAd7guj9gtjp+8Zfv+8DH5zr/pMYW0RERERk1Sy0Ej3uJPj0Z556Q1fB cXvw/VqH3+/YIiIiIiKr5MRyoqcD3+MG0iIiIiIi54E2FoqIiIiILEhBtIiIiIjIgg4NohdJyZjd JHhUXrPSPURERETkvErT38zmMu+X2zx930F50PsF0PcztoiIiIjIKkmzd8wGrwcFxPs5KvC9n7FF RERERFaFcqJFRERERBZ0IkG0Vo9FRERE5DLRSrSIiIiIyIIURIuIiIiILEhBtIiIiIjIgt5QneOg Gs/z1HUel6ibJ0f6sOP2O5fyrkVERERkVbwhiD7IbBA7b7B8XPud70Pf8DtP7XwiIiIiIvPak84x DoxPu5vgWZ1HREREROQ0nIuc6NNe9RYRERERWcQkiJ4OVE9zlXje87zvAx+c3BRAi4iIiMgqmTsn +qxNB84KpEVERERklewJos8qR3nR84xXrLWxUERERERWQYLDy9qd5ArwWZ3nMjGzZU9B5MJy92VP 4b7pPUJEzqPz8P67sukcMr8nn3xy2VMQuXBu37697CmcGL1HiMh5cl7ef9Nhq8CLNE+ZdlC6xiLn mR3j6c88xfd+359aaB4iIiIiIqchHRUgHxb4LnL/Uaafp9QOEREREVll56JOtIiIiIjIKlEQLSIi IiKyIAXRIiIiIiILUnUOERE5M/vtuh9XD7l9+/aeSiKz359X87yOw6oRnKdrcBL/zS7CtRi/hvMy XzkeBdEiInKmDgosLmLAsUhQed5f/0l+6Dnv1+K8z1/mo3QOEREROdLt27fPTf3e06ZrIaCVaBER WRGLpj0cN/VjNviZHWd832HnOu759xt/Op3loHnN435f12FjTN9/mtf6olyLg17PQec86v+12edq pXs1KIi+RPSpWaTSL6DlOixwOep595szvV9ws9840/cddMyi8z3ssf3Ov2iwOnttFn1dh41xVAB3 0Lmmj59nTvuNf96uxbxzm17N3m8eR32wkOVTEH3JKHiQy06/iJbvOO9DRwVRJ/3edtS5ZoOdRc9/ 1PGz57jf8eY5blm/Hy7Ttdhv3EXmq/ev1aIgWkREzgUFEIc7ietzUa6xroWcBQXRIiJyLpynv6Sd dXm+k/jT/3HHWLVShMu8FnK5qDqHiIisvKP+lD272Wv8/XGrKBz1nINyZ49r9nyXeQPZebwWZ1Gt Q4H86tFKtIiInAv7BdLHyUWeDXgOylM97gbI47jf17bf64LF61QfNsbs44vkMk/fN+88Zu+b1zKu xWnY73orkF4t9vRnnvJlT2Je3/t9f4pPfPIpXnnh2cl9tx5+dIkzWj4zO/aO5VV2v78oT3Iexz33 /VQOmHZSq1xHjXOe/v+4H/O+ztu3b+N+bt4eD7TIe4Qstup50LGn9bN0Wu8P92NZ7xureC3OwnlY lT8JJ/X+Ox0vPv/cM3zP9//AG+LIT/7cp489vlaiZeXMUwLpPDjufE/jtZ/kjnYRWQ79jO66LNfi PP7uu0wURMtKOegNQ28iJ0tvzCL357AVwcv0s3WZXusynEQKk5weBdFy7hxUm/WovLvDHp/3uYs8 b7/5Hbem7KLznH18tsD/9L+HbZCa5/Vdlj8vyuVwEjWGRU6S/l9bXQqi5Vw6KECd9/vDxjrs8YO+ nmeceY+Z97nHOT8wdyA/z/iHXQ8REZGLTCXu5Fw6Krg7yV3MBwWGxwkY592sNH07KOg9zZ3a855P QbOIiFxWWomWS+GwtIP7SUk4jSD2fjYkKrVCRETkbCiIlkvjsLSD46QkHJaTvSxKrRARETkbSueQ lXJQisJhAep+z7loQeRZv8bLcE1FRETuh1aiZeUcJ/d29jmLVpmY9zxHzfMsA9v7eR3zpH4cdT4R EZHLzP7iX/yL56Yl10//5I+rY+GMi9qxUOS0qGOhiMhqOzcdC3/wB3/wviZ4ln76J3982VMQEZH7 dFB61nnpVLrseR31l7qj0t8WGet+nae5zp7nqFr8pzkHOR/Syy98btlzEBGRS+agAGX6/lUNUJYd QM9uaD5ss/RRY80ee5IfEM7TXKfPcdjj87weuTy0sVBERFbCadY+vyhOOmA7TkB73LFPerzTGP+w MRUsyywF0SIispL227g7/nf6sdnvZ5+z3+MHjbXIc+c59jTONY/Zpk0HzWnR8e53nMPGPg9zFZmm 6hzCY48/sewpiJy4X/43n1r2FOQUzLaah4Proy/y/Txt7ueZ00HPPalzzWO/cx92jnmq8Iz/SnDY 67jocxWZpSBaAEhhRM5531spZfK1yCpwd0opk3/Ht/H3//vH/y++7Vs/suxpyimY90/68wRNJxFU HZTje1L53Yc9b94OpfsFldNjTAeah1236XHmOe95nes8TmtcOV8URAsAm5ubtG1Lzpm2bffcpu8T WQXTgfP4Q9701yJwf3++n6eW+kk5zrlOam5nEQSep7nOQwG0jCmIFgD++7/+N5c9BRGRE3VSQdtZ pAIscq7zFMSdp7nO46K9Hrk/CqKFPLrHn/3T362VaDk3jlqJLqUse4pyDCcZrO6XUnEa9jvPaZeJ G5/3fp5zWNrEPOPO89zzNNd5x5ydm1xuCqKFz3/+88uegshCQgiEoOJC59l+qRanUbJs9jyLpkvM 85zZ8yz6Oo7z3MNe17yvezZv+LDz75fjPW9wel7melBllP1ys4+ar1wO9tLzv3Ju+tp++7f/MbX9 nnFR236P/IFlT0EugMZee8N98/4c3L6ttt8iIstwUu+/p972+75mJ3KKfuzJjy97CnKOffT2x5Y9 BRERucAURMtKU5kyOY7HHn+Czz79hWVPQ0RELrBkXFn2HEREREREzhXtzBERERERWZCCaDnQeAfy 7I7o2e/v93gRERGR8yY9/9wzy56DrLDZWptweFOARY8/T+atEXrQh4P7ee3HKam0SE3T0x5fRETk oknf8/0/sOw5yAqbDZDmqZc67/HnKfgafwCYd/X8JF/bUR9WjnrOIseexvgiIiIXUfrxf/lnMS+U cI172/d489VbsPk69Pq88stf4L/8r/4qb+pf49Ebb+bzz/06r770Mi9u3aMNcONm4Hd8+Lfz3f/x HyW85VG2Xt9k6CMs9NgmM8qQLFAsEIhEh1jAvNYuBXD32mUs7V8PcLpO4B/693/oTC6KyKxlBonH adywX6OBg8Y57fFFREQuovT5X/oV3v/YbyU2kavpQfLrLxPjDXj5LoNnX+bmMHJjo8+/+ZVf4IEH HuDpu/fYxvkT3/Mf8Lt/3zfy+WeeYXDtBjaCNjYMhoXStiQLNCHgxSilYFaD4eKhBtBuuGcA3DLZ IzAVXJNx98ltfL+InC4FxCIiIkdLf+8f/Rg/+47P8PBDb6VY4YNf9T7e9Ujk1z79C/ytv/UjDF7a 4aHrD/LQIw/zr/7VL3MH+M4/8s189CO/m/5bH+bqjQ36qcdW23bBsk0C5hoAj09VgDBZWTYDs1gD aQ+kEPYEy+5GCY4VwCKZ8985TPa6yMHa/bQAnmfs0xhz7KL+NxERETlJ6W3vfBfPPf88/8Nf/sdc b+APf+s38u2//yN8+hd/iZ0W3vSet/G5117gU7/6HI898SiPvfMtfN3v+z2sveXN3NncJsXEve0R ozCkLQWoATKTVeTxqYxiBWJ3gNVj3QvuThgVDCB0K87u9VCvq9ZWFETL+bBofvEiTuuDx/SYF/nD jYiIyElJH/vYbd569UH+6+/6k/ztH/l7PPPMM/yjf/Lj3HnlDm955M2sra3x9L/9VR662eeH/8Zf hVvXGb34Es+/9CK9tQ222oz1EsO27VahDQhknOBG6WLi2WwML+MV50ApYF5XsemC5d3DSw24lc1x 5o7aJHjS319UR+UMz1vN46yCW+U4y3lyGtVwVt3sX47OqlLOftd6v/epwx4XuUjS266/Hd/e5hc+ /jN88Wc+wyNvfoi77Q7vfvujfOH55/jkv/7/+NSvGv/tX/nD+FsfYXuwxXN5xC9/7tf5+t/xBNeu rXN3a5MQMhCm0jGgGBgRM5ukcTi5O3W7m/NMJoQe4N1xXflqDxihjqGK1nJBzVtOTr+MRPa338/G Rf2ZWVZpyYOq+BxWqWf2cZGLJhASP/rxf8qP/P2P8/zLL7I1HPHsr/8GP/fpX+Tzv/Ylvvic8c3f 8CDf+33fhY1GvP7aqzx48zpxvYe3mTIsNNEIIWC2m9Ns1t0XHIJhwettsiRtk2NCSJQCOXt3y7Sj Qs65fp2H5Ha4vKskcojZlZf9vr/fGtFHlZubNluKb9Hnn/T4IsuwSEnKRRs/XfRGUfs1yJrnZ1zv A3LZpL//F/4aP/vT/y9vXb/G2gMP8dKzL3Pn9bsMYoJ+j17fefx9j0Ho8eKdV6C5Qkk93vyW97A5 TFgsbPQeALbw0qVzmOFuU5U2IMZu42DxupnQvNuAWNM1RnFETaX2murhkeAB27MxUWQ5ZoNGOPgX xuwv75P4xTLPn1EPmsM8tb0X/YV5ln9CFjkJB/1MTndanX5skeMP+iA5XV/+qJ+Zw86335yO+vk7 6mf6oMfn/YCsn38RSP/0n/0L3rxxhc2tu3zp2S/y6pbz0NtvsnbjOj/z6S9y57VAvwmUnLnW79Ns XOfl11/lN7/nXWxtb3O9lxjsZFKTAAiWuuC5BtFuNf+5sRog55yB2AXRua5UU+jHPjAOoiNWam41 GQyn10vLukYiCzeNOclfLIs2uDnOHI7TFEe/POW82C/NYPz9fkHwoscvOodFzjdt3g+v+30gmDft 4rBzKN9ZZK/0vpu3uHr1KmUt8pWtAetXMs986VXWrr1OTEZD4R233kr0PtbU4Pbh67fwAlc2NiAU er1AcKtdVHDwANawm9vseBlhocHLCKzFLABDctsSEzjbmCUcg3aE5YxHgwA2Ah/lQ16GiIjI/hYN 9o5z/FGB8EENio4KyE+7Gs9xn3dUvrNWquUySC/feZ3P/Mqv8dueeC/f+PUf4Kc/8RSj1rj3stPr O9/9x7+BO69v8vM//X/z0Fsf5sG3vZlmfY22bWnblqbps7HR59kvP08IAayQW6Mthpc42TyYAsRe xNuM2YAQjHa0zc7OkBQKX3nxWUZbQywG1vs9ogfaUaGUgpdCcKVziIjI8axy3vMycqzv95zjDwAH bTgcHyNykaXXwha///aH2EyRB9/6Vh7bvstbHn6Ef/fCizzzxa/w4MYNPvGz/5of/Yl/DjHw6vaA lzfbWrouAsFIjbN9F2KsOdCjkTMcwSiDl5oT3e4AqZa6e+QWvPXNN7jac0bF6cfAw80Qhi03H7rB Ox99Ow9cuUYvwfWr62xcvcFXvvTlJV4mERE5r/ZbJT7J42Gx1eX9nnuWDsv/PumxRS6y9Ae+6Rv5 8Dd+mGsPP8CvP/Mb/K6v/Rpu3rjBy6+9wosv3uXzX/oNvuHrfhvWv8Jzr3yFp37hc3zxy68yMgh9 aLu6ztfWdgd1IHst+WzBiBjhCpg5OcP2Jjz7/B3e+mDD13zg/Xz4a38777wBw+2W0MBGv0ck0hB4 4MY1bty4xtoHv5a/84n/YzlXSZbmscefWPYUROQcWrWqMQflJM9TR34VXsdBHyyOuk/kIkv/8rO/ xE/8wi/yoQ99Iy8+/xzvf+wxXn1pk2trG7zlzW/mmWe/xDAHXrnzFawJvOsd7yD2ewzaTQoBPLLe 73Hv3ut14yDGqHUGI6dkI8ZISnVTYC8FQhjAsOXu5pCtlwpbr7xELAOeePz9vPj88/ybz/0Kn//1 3+Du1ibtYMjOYIvWW9ZTD3jvcq+WnKmP3v4Yn336C8uehoisuKOq1+xXgWa/QHb89aLHz55n0U2B x6mQc5jpVIv9xtvvcdh9jbOPH3RNjlsZROSiSJ/8+Wd58Bb81P/zcR55CP7Pn3qKMoAHrq0TrOGL X77D5iAy8MyDD0f6G32cQGwaAtC2I+6VlratdZ1HudBmq6kcXtM7QggMBtsMYk3dWEuBtXVjsJn5 jS8/x6c++4u8qd/yyvPP8alf/Le89MKL3HrTOn0CX/7yPV57Fb76q+Jyr5SIiKyceQO0RavcHLcq znGfM28VnHmrAd3v/E9jTJGLJr3nPWvk3PK+3wJN6vPCS5v0Iry6tc2rr22xdgWu3TDWCpSY2Wl3 sBhZC0YTDW8zO1tDQiyMizoHgxhqiboYCk0o9NcD4LSjEVtb9eRX1+GR631urif+xc//LD7c4tm7 r3P1Ojz63ltsxD7rt4zgEKLBS0u7TrIEP/bkx5c9BTnHPnr7Y8uegoiIXGDJLPLgjau8eO8Or29t 8+aHNtjZbtlqh7z7ndcZ5ZbtNhPWr2IWcSuMRpmd7QFDd0IIpAZqJjR46lp9ewBCTecIgeGoEFKf fmhJV42rfSNZ5rXXdvjZT32ajQbWesarm46twauvvMZLO3Bv6x7veORNXL9+fZnXSZbk2771I8ue gpxDjz3+hFKB5MytSv6yiJyN1HtwnUEIXNm4zlUzCEaPSA1ZA+5wo+siOBgM2NlpIbfQ81p+zjOl FEq8Sm5bCi1NNNZSAAp4CxRurhsP3IQrGzcY7uywvTUgBujfdFKCPIC3v6vPb7vxAFvbr5GH21x/ 5ArvfODN9ELD5t07wEPLvFYiIiIHUmMikcslEQNuRq9Zn7TprmqjFPfaRXDz3jY515rPZkYKgWyG d6vRFhuaEIEGIqQQ6DXGlY2GjSt9NnqRK1fXWO9HSh5Q2pbUBJrouBcau8e73/EIb3rwJjs72+xs 3cNSJhrk4YjrN9aXcoFERERERGalV+9tApC74BjYE0x72b0v50ymdhIsqbb27qJscvsqoR9omlqN o5cCD1y7ylsevsHDt67TW+tj5hQfMRo67g0hJHIeMRpu07cNYtMnpjV6VwoEJ+Pk0SbDnFnv98/8 4siug/5MeVhJo8PazK6Co+Y+bdHXfthYh425yPiHzXHR3fFHVTdY9PxHjT3P+CIiIqssvXZnE3cI TQ2Ux7doe4NpPOJu3Uo0BOpxDmBW60aHmgMdY6RJPVJKpNjDQtN1OBwyHG0zarcxL4QQcDI5t/Qa Y3NzE/NM8QGlFCwZxgAvRlbb76U6qq3tUS1vV810U4SDHjvsvkU/ICx6LQ6b31HnnGf+R81v0WYU R1nl/xdERESOIw0ArDYfxB0Dghkt9etxIJ3LsD6jC5bH94duoLUNCKEQQouXIYPRDq/e3WS7fZ3n XupRaDFqakjAaXqwsRbYWF+nSX3ulnvkuzu8srlDzhmnJTUFI+NDo99PZ3ldZEFHdetatSDqOKWm xmZf33E6lN3P/BYNiBd11Pir/gFJRETkLKReqivKg1EBuq6C5O5rA+tWnrto2bo86LHx+rCb0ZZS 2xTiWCgM2pa7mwOyQwqGBScZxGSk6PTX4OqVAevr61ztgYdtkgEEsBFtzrhnrIXN7TvAu8/qusg+ 5gmUL2KAdRFfk4iIiNyfZF0Q3Y97V3proFwAw4zJhsLuUbAyOdbMGAyd3XVpI1qYBNylFHIDXpwR EHI9xrbhzr2WptmhnzL9kEkp0vQiIZSaPsKQ5IF+TyvRq+6wAPu8OajT16qMNz3moivnJ3nsPOef Pnbe40VERFZdSsHAjZYy81BdbR7HzWG8FA1vCKBDCHjx3aA5O21pMQPrnuZtoJQ6XmqcGOuxw2Fb 86Ub2CkQQybEiIVcQ3iHXij019rTuwoyt4OC5P1a4p6nHOlZJzn3RfOT5zFvTvZpXfdFcsJP4/WL iIgsW2gI9GNkrenRT7VMXbJAL0XWm4b1psdGr08vhd1bTPRioBcDTTAiThMDZjWQthTo9RO9Xo8U GywmcIMQsZgo3jDKgVGJDFpje2jcuwt3d+DeDmzvFAaDQG7rbWsAOzuqzrGqDkvtuAhO+rWcxHj7 5WTPWjRYXeT4ec5/2HMvyv8bshpW+f+nk5zb7du3D91sfJoOO/c8x6zyfyOR4wopJYiBlFLtLpgS TdPQpB6xSYQUIdT0jMktQAxhz83MiLHBuuoc482HHrqKH2nv/UacrGKPb3igFGOUYTByhgMYDAvD AWxtDZd9raQz76bB2UBLzo5We0VWw2lsOJ4NSE/z532e4HlsGfMTWaaQQqQh0JjRC92qdEqsN5G1 GOmHQD8EYrLdWxcoT99C05KaEU2/kHoZa1pII0IcEVM7uYXYYnGIxSEhtMSYiTFDSLWteDFya7Qj Y9g6w9YYtbC9E45+NSIn4KhfGLMrqfulrhw23kmkhxx1/qOqa+x33yKr0Mt8/SL3a5mroid97tP+ eXryySePNf74dernXS6yFGMEarYF7NaKDoHJpsBSCo3FQwfawcECwQCMUny8LbGrJ22Ad81ZbM9z 64bFCKXgVrsi1pthXgt+RPyNJxU5ptkgEHZXUfZLNzgsXWWeGtGLbqw7bH7znP84zVMWcdqvX+R+ zNZZ3+9D3mGP7Xf/Ij9zh9WUnz33QUHmQRu1x/etanCqdC25TFJqAu5OG7rNggaEgk82CWZKKeCH rwS7g4VMsNS1At+NlUMXgHvX3bB+Pd08xXFr8eAEH0/CcDKlOFbAc2Bv6C1yfCfRHGWeNJZFxjvN 8x/1+HF+GZ/m6xe5Xwdtfp1nc/RR3x934/RJVS86L4H0qs5P5KSkEEKtxTzV8tsItTiHO8EMrOA+ W71jr5CMgOEUvFuVrqFz2C2RV8/Q1ZKOU/WmC06oXQqnB/VIMIfgFHdMTQtFRGQOJ72p9rSCwaOq GZ10ic2D5iAii0vFnTzVYKUGz7sHuDuOU8IR6RS2Xv8tu6vNZhF8t/xdDdCZlJOuKRu5rlynIVbK pHvLuL04XtelkwXa0cm8aBERkWn3k4KwSHrTMpz1nKZTUbQaLRdZyjljwSnBJrnIb2Td7eDV6BAC 1gXcNZ0jgVmNx6eGHAfTwFTus2OxO0V3v3mYCqQdopI5RETkdJxEigUcbyPd9Gr0aQSdy16JViAt F1XaySPIEFOXbtHlPrvvtvd2dyxFIExymUOgawlej8nDMm7PQiBiPo6JvVt5rkG4O5jVADnncX5G oewkykxnxECYbDDUtsLL6bHHn1j2FETkgjut4PUknFQO9VnZL/9c5KJK44C1tF07QQo16t1t420G 5FKrbFhtqlJzpL3L3CjE2OxZWZ5e1a6BcZl8XUqZ/FvHNwgGUwF07PKrwfbkbMvl8dHbH+OzT39h 2dMQkQtmv/SLearyHGSR6jMHpX6saiB/UMnKwyqKTFNah1xkafJVLngwnBokl7oODNTW24RQV5Ed CgZG16HQsa4ix6zp+8zGGwkdMLyAl3Fqh3UpHE5drfZuVXp3JdpcQfRl0thry56CiJwTRzWAmrdi zLwVZ+ZtOLXoeeZ9/qJj3I/zNFeRs5Y8d8EwsQbJgGN1Hdh3g+gQpqp3WFfu2bsVaXOMNwbS06vH s6vT48B5nL7hZKx03/s4jTpMjimmhA4RERERWQ2pBrVGodT0Za8Vmot1AbXVAnQ2qRNtXb7zblCN QzvJdR7nOQf2xNQOpYzTP2rw7b47RsDqyrRPineAB3LXoGUc4MvZWuU/wa3y3OZx3N38B9W4nXXU MYd1GTysmcR5vuYiq+q8v5+JXEaJXLAQaEvuStLVlemA4aGGyh5ssoHwjfWiuzVj73KaJ6vH4xzo caBcoBTMuw6Gu5XwqCF0ZpzqMS045G5Do4JomXaef+Ec1rjhqOft56iA+bBzLNJ4QpuERE7HeX4/ E7msQimFnDNWrOZFjztzO7VTYAEylFG9eWuUMq7eYZQSKCXsaRE+zmMu3fO9QMmGl4B7BBJmDXgC T3gJWK43MpQc6q1AKfU+K4e3HZfVsMwg67wEeAe18V30efMed9Tz7qc7ooiIyGUVQpeeMWm17U52 I7tRMrX5SSm1BvQ4p7nsBswlew2SZwLo2uJ7d+24bVtydkpm91YKOdcgfnwrU2PV+0a07ZCSlRO9 CsbB3ni39SKPLfqceZ970DiHjXfY45ed/qwsIiJytJS7jX3jJt1QMAuEEIBA8SGjXEiWajqFdbnT 0yXnulXoGBKMg2/frc4RQqDpNh7WIDzX4Lv7PndtwieNWNzJpdQO4RgWEqPsRC1Gr4SDUhEOKtF0 1MroYakN8wZ0q1oe6qQcdxV69jE4PN/5oMdFRERkr+S51ocuYVztzmqc7LnWhyaQwqRcxqQZSynj hObSfQ/QdRgct1npVqLrY10edJdSXQyc0PVgqSvP+3UyLNYF3tlBQfRKWCTImif4O62gbb/Aejbo Pw9O4sPBUTnYx/ngIiIicpml0npXvq6riVELQeN03QhDXZkem22iMn5emar7PF0T2rvKGtlrE5fZ End4wLtSION0kLGM413Zu/KGDY1yXtxP2sRBjQkum3k2Ch4W/C7S1OGir+qLiIichOSTVIzdpt3T ZejMwcPsKvH46/qMKky+d28Km7zkAAAgAElEQVTZGzBDzg7kSeC8q65Ch+B7gujSDVvzo8el8eQ8 OqmWtccJ7KYDwvMaGC4SLIuIiMjZCF4iXiJt20429O2uIDsFr+kY7ngeV+UYB9Khy2E2jAQeKdkp 2SYbDvEIHt+wgj1bzWOYC6Pi3S3vbix0JzvkcMirkJW1ymXRljWv2WuyX8rJouZZWT7J84mIiFx2 aZyL7LngIWBWNwISan7zuFFgyVB3CwLT6R2FPa27gUkXwvp1XX0OXo8rpWDUCiDTx+eSJ6kfQJdK Ur8OAWJUlejzYL/0i/0C6ePURZ4nt3q/1I9VXIU+6zSVw843z3+f/SqhrNL1FBEROWv29U9Er223 a8Rq5K46h02C6Tc8yeoOP3efBNRmzZ4NgeMgerfs3Xj1ue1GCXseG+Wu06F1cbpBiBBjrRQSU2GN 38EnPvkUr7zw7GQutx5+9HSuzDlhZgsFpJc18LnMr132WqTSiV+APLJF3iPkdBx3EeGimOeDtyoE ybSTev+djheff+4Zvuf7f+ANceQnf+7Txx4/RQyjVucopZC9xUqe5D2Py2lYCF2nQWO3VMf4ccPZ TdeYzp/eTREZfz9+YpfKUaxW+rDd52HerYgbMUZijPSaAKNjv04REZEzt18AqQ/1e+mvW3JepdYL hjEYDbsgNhLNcGpwO660sRZ6ePHaBry8MWAOobblrpU0xrnUNWIOQFtGU2Xr6sm9ng6PEGycthEw C1gotb50Y6QUiP3A6NWzvjxyEegXlogsw2EpVOeBgluRw6XdYLimZUQbl6mzGsyaYQZtl24xm5k8 DpSxOMlpLsXJXib50LVq3m496WIwWXS2Wv7ZYiCGhtQEUkpYVxEkRq/fRy1Ey/HoF4CIrLLD0j2m A9mDUh7mWe0+zjmOM9bs40e9/x603+Kgr4/7WvZ7rn43yP0Ku10Ed1t7TzOvt5yN0holB7xE8MS4 skfJgVFuyTl37b3Hbby9SxEpXTvvbszpADpGUko0vUDTh17f6K+Fya3XN0LPCTGf8aURERE5XdNB 30FB4vi+o445yXPMbs6ed6zZx4+a5+x5DirpOXuu41yv+7mGIvsJ7uOSc3n3lls8F0qbJwGx5zLZ GLj3Vu/PrdNmJxcovrtePS6J13pX+zkyle8c6KXIWq+hSf16axpSaIgxEkLYTf1QrxUREbmkDls1 nQ0Kj5vCdhorsyfVJ+AknqeVZzlpXa/vwLiCnLG7KXDcQMU9k0K/21gYulzpqRJ1BdpQV4p3i3kE 3NtJikettFE3CtY8aieEQEiJmBKWCmaOBafYEPM6nlumuOGulWgREZHjOsmVV63iikAadydkOhca wG0SQOOOW2a8ag1Qyu5qs5lhpW4YbLvHg7OnYkfT1JXlunGwnme3hN24Up7vnsNGXaBeq3+IiIjI 8Z3kSqxWdUUgjPOTKYaXUgPmfXKjxznN9ZButboAXrsTQsCKUQo1N9pb3GpljdRP9FIkxlBL1iUj NWGyKl0remSKZ3Ie0bZDRjnTTnU0nJS/ExEROQcOyr09jVXc6XOtUoCrFWu5yNL4ixo0142A49Xo cZ1nrEBbV5xr/By7Z9nkmBAiuav9XHDSuFlKUzcOpi7Pw0OXxhEChN2UkDJqKTghFDzEySbHULxW oba9Qb2IiMiqG2+uO6gqxFGPn9Yc5jnP7POmNwseNNZ+zznJQPosrpfIvOxrf3uoBejKbs1nMwMP e1ekM6SU8BDJ43J3ZhgBd2MUhnVTIg5eOww2TSREcCuTtt1mtRPiuHO4EYixYeQ7BEs1YAegxcxI Tc2jbksmv/K16lg4Qx0LRRajjoUiIqvt3HQsrIHz3i6EXhOTASbpFh6sVsrwvcflXCt0DCwTE8QI IQRSY4QYsdDlVIcalIdu5XqcnGGAWQsE6Ere1bGnuxyWC/HLTEREREQuhuTuhGDsSTn2MHNYwGJN 5XDPuDkBo1bgKLUMnlGD6AZizMTohFCLTBtG8a4UHrXE3fh0ITgWqGkb3TxCCGCxC55riogFBdEi IiIishoSMNm45zOrzABmsQbY4xXpcVk7Ayi4OW5gCVIyUurSNawreddV3dhbzYOproi2W/JuKk42 MywExivkMQZU5E5EREREVkHX9ns3X3k2bcI9d0FunLTxdgNKTc/I7uQAaz3orTm9fm2kEqzr7T3e EFhq+brx3TU49i5YdvBCCYVo1tW7qyvUNdUDYphdHZfj0E5pERERkfv3hpVoI+LslpQrtdrzVPOT Lk85Z9ouKI6BrmV3ZG2tRxN7WMhdkD3CGdGjoZSa+1wMSp6uBBKwZjylgrthodRAPITd2tVyX7S5 SERERORkpHGw7KVuMPTAJHUDymQjYek2/gULlAylZLCawtHv9+mttfT7gV7fSMGxUChuBDdyjhAL lG4kD2SD4HFSrQMbJ2s4ZqXmShtgpTvmzK+NiIiIiMi+khGx4JNOg5OqGeaTQhzu0FIIAZKlSUpG BPpNw8Zaj3SloZfqRsFCTc9wz3iwbpU7YzbOsw7duPWxGCNOngTu0znT4x2PFhVFi4iIiMhqSJYy pfhu9QtPQMA84GQImQKkAE5g1NYNgL4OTTL6G0OuP9jDbYfBoKW0sLbW0I+BnTzCgRRC7XaYjRpi t1iAaGvU9I3CyFqCs9sO3EJdhXaraR9eDn4VIiIiIiJnKMF41bfbYNg1WanJHbUWXQxWA20CMQbI hZyhn5wYYdRu1i6DCRIB98Cg1KAYC2QvBIvEBCFE8KZuUCy1UYvjNGG8ubGmcdSgvsvHVkK0iIiI iKyQFDAyTkzgBbx4rc2MTcrMmRlDMua19nMbDCuQEly91ufqtYat9l4Nfksh+6DmS0cIwMiMYLlr 9w3Q1trSeTCZiE9aiQPBsa5qiNVy1HuavIiIiIiILNNkJRqo5ei6jXxM1cPwcZ4yTlsKkUi/gZQa 1q/0eMejj3Bv5w6bm9sMBoMafEfwXMfqxUAppasfXZuoQMDwLrAOjHI7VV7PsFBrR49rU5uWo0VE RERkRaRx+TgzAzfMEsS66a9QK3C4QxMi7jWIDsFIPXBGNE3Dww9t8EAZ8pWvvMarPqLXM5robA0h u7HWQOtdWoYbpYBFJxeA3WYruNfmLj7ZT0gpdZNiCAqiRURERGQ1JAtOINQ23iF0daJrfWjzWjUD jGiRQW5JIWFeaHPgyppxfeMKvWDEmFjr9VnrjbBUm62k5Hiu42/0E6VA64VQAiEUglPTOooTwqRQ 9KQ/C9QW4O5GjAqiRURERGQ1pBC6xtrepXF4l2phRi03FzCrgTBDp7/eYzja5N4WvPNdDe/9qjex dmWLlpd429udm4PE9k4LHohNDy9Gzs4oB3YGmcEg044yJUH0hlHOWAaPhVK6tuJlN4oeh85BhaJF REREZEXUINqg5NriG494VyO6GLUNN7XVd85OEyLbBXaG8OZHbvH4b/5N3B08y3Bwl2s3NigkXnl1 i2HJ3Lp+g2A9Xt/a4SsvvEq4OySPWtouxzpEIBoxB3KbyLlQ2ly7JE5ahBsEQz0LRURERGRVJMzx Muo28tVV6dA1QwnuBKv1mWMI9NcC9+7d4/q1dW7c2uHu1ovc23qBFze/yEb/JUbhOlevXONtGzAa JppeZGs4ZOvlO9y7+yLDnQgUer1I607JNQ86hgChJcRASUbOTIJoSm0RblHVOURG/sCyp7CyGntt 2VMQEZFLJI07Azq1k2Ag4JOk5K77IDAajlhbW+PKBozykM3Xnc278OIrz7O20efKxga9mGoOsxnB A8VHbN17nTuvvoIHw0OtsmEYCaMNAfMGCLVOiFstrec2qcjhZrUN+HQJPJFL7Mee/Piyp7ByPnr7 Y8uegoiIXDKJSS3oAhSMTFdEAy8Fo9TqHE0BM4ZtZlQyjz4aeeJr3sT6FSf2N0n9jMUd3CLQw8zY GWxz5+42W9sjCDUwdhyjQIDGrCt3V2isVv8ouc7A8XGyNh4Co5KXeZ1EVsq3fetHlj2FlfHY40/w 2ae/sOxpiIjIJZOw2k0QapttvOZAG0AYdzqpK9SjtmU4KKQ1eO9XP8zv+rr3c2fnWYaj18hkrBg2 GkEJjIaZe/cGbO1s14Dcfbr0dLeyHDGr5fRSrOfJsWCtU6a6fBcCYaR0DhERERFZDamWtnPcCuZl NxGZ2lyFru32cJBJIbCxnqDXEq1lMHqNrcG/o59aQukTQx+joS2JreGA1+/tsLPdEi1QciFZgFBw L5h5TdUIBXcnegNkQoEQILuTu1SS4I4HBdEiIiIishpSrRxnZApuoavRHICCObgZXgwzx4LjlonA aDTitVe38JjxlIm9xNraGk5gNILBYMS9zR12Bk7pNgnGGDGzuqGQMOlWWAxKrpsZU6lpH63X85fa /btbLRcRERERWb4UY23DXQtyFIwaSNd220BxCpnUGJ6dnaFx84pz7UbCbciVq4WtUctavA6s0Y6c rXtb3Ht9xPYmDHfAmhqWxxjrKrN51wa8kJq6gbCEWFuCt44Xw0tdjY4WyDgWyqEvRETOzu3btwF4 8sknlzwTERGR5QgpBKIZIdQgN6XU3Xq7DU4KjHJdjd7ow80b11nb2GBzuEMIEeuapIxGmXtbW7x2 93W2dgZkd8wCyawG5JSpMWs7b4PJivT0bVw1ZHyLpjrR59k46Dqt45c97rLPdZZu377Nk08+yZNP PnlhX6OIiMhRQk5DcmopIZHNaK2ljaN6Xwrk2NCGQFo3dgyaK8aVG1us9zO9UHjuyztQNnjw6k3W m8S9Vwbcex2G2z12tqCEQvZEXAPrDbE4IK1l1jcCsQHzQKJHTC3OFplNsAExZWJwgmX6EVKjnOjz bBVWLMfB31m5iEHm7DW8iK9RRERkHsHMupXf2kVw6iGAmgttDhmCw2BY6Pd6rPU3aL1uCtzcHuEG WztDNre3GOyMcAK9Xo+Uan4147zoBGu9wNp6j16vhxFxD5Sc8VLAM7XduBGCTVauU9BKtOxS4CYi IiLLVLcVmhNDrYoRQtdqG7pAFmI0UjSanlGycevGAzz8pltYDKQUaUeFf/trL/DlL99hcyvTlkyb h5TS4l6IqZDMaEIghUBKiV4v0esnUhOAXEvgjSc1ldIxrhQi58c4wJ0OdPcLem/fvr3vsQcds9/4 9xNMHzTOQeMueryIiIhcXAmrmwljBPfApFehW91kaAYxkGLdWDjKzs2bN7h16yYvbT5XK3Zk59nn XsEi5FzrOrejwrAra2cpElNLjEBxDMccogU8GoRAO9oN3OscHKx0gbRTilaiz5OjUiemHz8sgJ4+ Zvz1OIXgJFIz9pvHfuc87vEiIiJyMYXaRbA2Shmz4HVVOu6uCrsVRrmQC6SUiMnw1hkOWnLOpLVE LkabYdg6mUjT9GtFDneMRLCGaAGsJZAJ1hItE2ix7hzFneyF7F3D8eCEZFjUivR5Mm8AfdixJxmQ HhTgzt531DmPc7xWqUVERC6eYFZzlUspjANq94yTgb2b+drWCbGwNRgy2BmRUq+uPJfaGGVUnJAa UkrUShwQicQYcc9gTkxGCnGqSkfNg/YupcO7c5qBBYgpEFPNrxY5rmUFs1qdFhERuZgC0eqGPwtE fE9ZOaC2Au9K05nBWh9ev7vJ5s42N65fJaVAwRlsw2AbvCS8xJoPzQgLmdD1SYkWaKxPsF4tbUcB a8FGkwnVpixGjIFxnA2QklaiRZZt9sOIPiSIiMhlFWoFDCM1gZisC2Bjt8HQJyvCFpwQod+Dra0t BoNtrl3pE5tIWwpra2u0Ldy9u829uzu0LfRjnxgaSimk0BBDqikilLra7Y7FgMXatdC7uYRYzw3d Cnlud4N6Off2C8Tk/Bj/91MALSIil1m6enWDV+/co0cNVA3A68bCugoNVgrDYabfT2wOCqEfGY0K r74+ZJRh1AYMo+kntjZb1vrG+kZiVIbEDBsbEUKuLbxDjxTqxsOYCsVg694Aiz3wQCbXUnupziFa rREdkoLoi2Q6kD5OqsXs8+Vs6ZqLiMhllwajll7TYLkGr+MV31D7gBOCkd0YjupGwF4Pyqgll8Jo tFNXrENhmJ2rG+v00pCcMyFE1vsN5oUQjUKLZ4NYoCtfV7x2OiTYJB8bakqHe12NDpP0kqVdI1nQ PBv4DrrvoMcWff48jjrH/X4vImfjoL+KLPOvJavwl5qzWGg47XPMLrAs+5qKTEuDwQ5rvTWyt11J O3AMvAABD06gNlpJIRBTTbEoFiil0Ov1WOsNeeWVTW5eu0qT4PXNltK2WNNgccTOCPopkMuItoWm 6dc24dkZ5babiu8Jls2MEB1z79JJ8pIukZy0Zf1yOcnSePNYhV+iIrIcy/zZP4sUubM8x/S11Puq rJIULAG1lF2p4fOkTrOXTPEuqu3+HbXOMLcMB4XQMwqGWyB2K8YxRPqpG93a7iSBZDAqNQDHA8Uy pXjNee5O6j7ePFjPFQELRgi+pxmLnG/7pW+c1ZviWbf9FpHTt98H5MscbB1Vg/+8nGP6PAd9L7JM 6eqVDYbDHYIlghWyO8EK7oab4R7AA+4t2Z2tLRi1LVs726yFlq3BgJ3BiFvX+qRezat+oLdGiLV9 d7REPzVsbe3UQDgCMXStEtuaylECJe+tvjFZie42N47zs+Vi0Bvh/Xns8SeWPQWRc2k66Jsn6J6n cdQ84+0XbB70+Fl+GDgoHeMkz3ucc2jDuZwHySzQFic5gE/Vau5Wlt3BnF4/AU6MmaYXgVpXOuda /3mt15C7ihr9fqTXi7RtS/SGjfU+Ozs7XdMWw51uZTngXlejx8YdCmt6x7gNec3NFhH46O2P8dmn v7DsaYicO4etVh9nNXuRQHO/AHKeeZ22/artnPT57/ccB33AEFm2MBjUzYFYmaRMmEXGAW4NclvM IIXIxkag3+/TNJFh2xIiNaeaHYKNCGFIKVvkMgTLFLbZHt7FyRQruBXanMldc5fskH02iAa6JjDj zY4xKogWEZH9TaeJ7Reg7XffbGrZUWOclEWCybMIGqfnclqv+7jnGB97VukjIotIOzs7XL16Fc8D IFIAtwBu5Jzx4DiFnVGLR+j1wLy2Ah9sD4gpkGKkDYH1fg8vI9rcUjI0MVCsZdDWZipmNumB6O64 1Sod7r6nOeLu5kImjV8CCqJFAH7syY8vewor56O3P7bsKcgFscgG5OOU2jwowJ/XQcde1BXai/q6 5GJIN66tMxjcgyZQSksZXe9WfiH2B2ADAMIg0FtfY2dni63NEQTngRtrvHrnFa48sIEPtsnesr7W Z82u0vqgK3VnXImJYdOybg0lRwbbI8gjci7YyGkcPPRpSyaPMoRALxgWjRQgRCN6e8RLEbk8vu1b P7LsKayMxx5/QuktcmLGQe4igfT08+YZ+6Ax5qGgUmR1hJxHtVtgl1IxXgUupZBbJ2enbQtmkbYd 4r6bn2xmNL1ECAkHYoxYDMQmkVKi3/To9/usra1x4+oNmtSnZBiNRgzaTFucGANrvYbhsCXnjJmT UiA1RkqBGOoqtDoWiojIYQ4LfverCjRPjvRJWWRes887bWeRMrHoOZS+IedBKl6brNS9feOc6Bqw 1jQLxx16vR47eavLVTZ2dnZoyoicR+xsjyglkEugFAdKHa+E+nUOtO2I4TCT3Wj6a1gsDLczbZu7 6h+DOnaAEAohBCyULqgvxLiU6yMiIhfEfjnQY4flTM9TQWKeFeKDSnuedNnP/T4sHDTm7OvbbxPg ss4xfdz0fSKrwr7tD+PFIWdqsxW/0TVcGYEN8Fjzmdf617m3dZdo8I5Hb/KOt/cJzT1G+S4pRWJj NL1Ik2rNafcW91p32szY2R7VNt5pjV6zjpfIvXtbbN3bIrfOyCLuTsTw4PSCExOk6LX1d4J/8nd/ J5/45FO88sKzkxdw6+FHl3TpVoOZ6U3lEhn5A/zYkx9XOseUcTpHY6+d6Li3b9++EPXp9R4hIufN Sb3/TseLzz/3DN/z/T/whjjykz/36WOPH0IIlNKVkTPDxq24Kd0NzAKj0YjhoL6glGq3wlIKKTWk lEihBzmQWyO3RimGl8SodXZ2CrGpKR951DIaDBls7zAcDsnFKQFCzKSmkJpCvwep56TG6TfQ9KDp H/s1ygkbrwychz+zHWeOZ/m6zsM13M95+e8vIiJyWpIRcS+kEGpedFfqrn4NpasfjbcMB9DvOSEE QuzSPix2VTR8tyQetV037hR32lJIYQ0zyKPCaDigzc6oLRTqOG1xUoQmRWIDKdZ8634IpGi42n6v hIveBeysX99ZtyI/CadZT1ZEROS8CLkYoUulmDQ+ce/yousqdc5OCE1Xbg5SE2iahJmT84jRaNTl VAec2HUgDORseA6YRYbDllJq9/Dshezd8V1N6kld6JCxkGtA3XPWNiLrGz0a5USLLN1BuY0iIiKX TSgFiDXoxRN4wIjE2NRqG11RjBTXiDESYySlQK+X6PUbLGRKtyExpNg9pz6pdA1VAkYgEkPCLFLc GLaZUck1cM+QkmEW8GKUMt7oWDsWxpRpLCzh8si06SYE+20qOehP/NPPm/539vH9vl9k3NnnnISD XvM8c5rn+P+fvbeLkeW67v1+a+1dPXPO4bc+TF87lqAY8SUIRQ4T24ShGwKmQIAgDiXFfMtbXozg GshDDCN+CSEgwAWS4CIBgpuXXOQlQJCIIiVRygGIyAAFEle2JRkWdG04DggnkONLfdAUec7MdFft vfKwq6qre7p7emZ6zvRMr99BY6aqq3bt7unp+Z/V//1fjuM4juNcTaLIo2ieMOHnSBYEqFPCmoa9 vT1ujR5g0hg/+8k94ugmH374AXVdc/feXW48KOzv30RSIlYBMCwZmYxYQMXIJuQEIUhf1c4ZJBuS QQUkQl0bWYw4EqoQUDVS3TAWAwkkz4m+dJZZD9ZpWXuWj/3PMu681WATLBpzlZ3htMc7juM4jnP1 0NxMGI8bmtraeLqSrhFjZDQaoRqpG3jvvSPee+8e4zGMRvvcvLXPKFZU0drjSvdBVe2/F5HSKCUI ZlIEdbva0hRMhSyQTAb51NZWsKVkVadEU+drsUr+OrJOK91u36L7h40N5sc7TaODRXM5r2hfNs5J 457leK9SO47jOM7VIqYJiO6hOkEwUoK6ATSiohweJX7y03tMahinxEOPwC987BE+/rFDGg6Y1IdE jRxNxliW1ledyAJY2RZR6pRKlF1OZJsKbrPSVrwSIWEELRYODRlVQSyDJfYqt3PsIvdTXF7WIj+v TjuO4zjO1SOmlHjw1j4f3hOanKgnB9SNojGiqhwcNLz/8yOCCuMjQTXz2KMP89DDgbsHh2B7xBip U0OmtA5XpCwezA1iIEQsNaTU2jlSbtcSWsmkNkUwRkGJEaoQqNTQUBI7YgzsjUaX/Vw595lFVg3H cRzHcZxtQCf1ESojUqPUE6WZBHJToXKDKj7IaPQIN/Ye4e494efvZyZHJc4u25i6OSDZPTIfEmJD iA0aazQYQROqWkzPlE6EACX5rnin2z3teBnVzChKEc6hLCgcVcYoGohH3G0j67TSXXXefMeu81Rk 5+fionvzLHqOvYruOI7j7CLx6HDCwd33AMid/UKNnBsmkwlNHejaeKcEQYUqhuJXbko3QYCogWyx bLQZ0iqQUirjUvaV0Uo78OJzLtVoVdrkj8hoTxkFI1aRKpTmLim5iN5WVrXS3eS4cLJoG57jXuOL Yf45dhzHcZxdJOYk3L17QIpKqg1LRfA2KTM5ajg8EI4OApUGYhT2bwh7exWQkTAmxoSJEUQQSZAT UGLuLEsR0TlT50wywVRQAxBM2q8IMVrxQQcr19kPxMpQGqBpx3Uum2Wi6SRhu87+dRb1nXbc84q8 k+Z43u2rynV5HI7jOI5zVmKoymK/yVEiTyh5zpHSuTAbYhACjI9qmlpBjGxKSpmUJlRRyDlRxT2w SM4ZkYBIsWuklKhrYdJQ/NEiqApGBpQsxTOtWgPTbOmSSW2tlzp1rRMd50K534sL3Q7hONvDqlSh Veec5nd4k7/zqz4RWvQp3KI4zlX3n/XaJ4296hPCk+Zwnjk7zqaJSYTJuOLwsGa0Z4Sg1GmCTn4R 0UjWQ3SvYfRA4L3/L/HAA9DYT5BmD2se5ZADqkhr0DhCMYREtobUVps1BqTOJd7OhGRGziWZIxDQ IIzVeGDf2N/fZ2+UygJFC6AGSUh4TrRzf7jfbb8dx7l8zppvf9rf4U38zp9kU1v2WOYz7OeF7TqP dx2L3CKBfd4I0nXHdZz7iR4dGFgmZWhqqCdFrGqoEFEaK1nNR4eJjz8Ov/aPP0luMuPDMZaVB27e wixjljBLfSVZJKCU1YS5bT8oppiB5GKkNiuxdgljLwhVbKvUWjKmzazE5pUZXcLT4ziO41x3VmXE b+O6itdff/1MVrfTHnOWay8bf5NrZS5iXMc5C/G99yoeefAWZu/TJBAzwigTo5JzJtsh2Q6JFXzq Vx/mE7/yOBoOOTq4h4QJMe5hRwmzQM4lfUMkEDCSGkGNJLkkcGCQKaK4PRbK1yABSUY2sBxI2rb+ FjARWgO14zjAE08+ddlTcJydY9gc6qSupbDYHrZse5k9YhOV1vu5EHjd/3Sc1paxjf+ZcZz4s58m bu01ZIpfuahY7bsMmgkShBs34aOPPQK5JlNTp8yNPePg6JAQRuTcABnVQCDQWC5NUzQQo1JZjdSQ GkEMEMCKQBYR0qShFtAwRkRRAhFFQ9vh0DsWOg4AL9x+iR/88J3Lnobj7CQnidp5gX3aMTdlT9gG 7/Cy1KaTrCZnHddx7jd68IExPhyRkwDaCmdFtAEptxAzKRuxukeyuxyMP4BwhKowPjJMEpMGctIi gLWzXhiqVhqoVEoVjCoYsRKqyggxI5oQzZiVynNORmqE1BipyTQ1pMYwcTuH4ziOc7msK6BPOnbd Mc9ru1hH0F9Epbp7LqAUGIkAACAASURBVNa5/mn+03GacR3noolSGfeODrlRGYQiZFNjGHWpTAOW y3LBRx95gI995KP87OAfGFUVKdWgDZNxAst9dB1SfNGdtUM1EltbB0DAQEFTKUdrW7UWAaRtviJG QgltlrTjOIVvvv7KZU9h63jh9kuXPQXH2Vo6u8hJ1pNNX/Mi8Mqzs03Ej3wE7h7UjB4IYBUpNUwE QBGBlDPjOjPah8cf3+eRR2/xs4OMUSLpLAs5V6jUxfucgVYsFw2eMWlQzahKyYLW0mBFpIh2DSBR CUGJKqhW9M4SMxRFvRDtOD1fePFzlz2FreGJJ59ye4vjnAFvmuQ450MfeqTi4LAh59JZMKVEnRqS GaZGtoa6hljB/qgi1Q2gHB0dUGnFZNIwGo1QjX2iBoCqEkJAY6lEV1XV3iKjvcjNvX1u7O1xY6/i xqg0b+kSPpTSwdBUQAWVgJiraMdxHGfzLEvhOK0/eX6cTVgNzjLGok6vsDir+aIq0OvYR04zF7dv ONtI/Bf/3X/E7//n/xv/9zvw73wqcGPvQd7/8BBLiTi6wb3DBjWoVKj2Ax9+eLecGCMfHh6wpw9A ijTNPWKsQIU6lyYpIoGoAROhaRJVlYixCGMsoZp70V1JQFWJMWJthboKGTFoSL21xHEcx3E2zSIh fRaBOZ+0cRGi7yQReprHct6UjEUCeGgfWTTuWZ/rk8Z1nPtN/MSv/BL/9D/9An/4X36VD8aHZEsg ELTixh7cunWTu/cOacyABrOIZUGDEKsKIWAZVCNmpXthziBircc5I0wXG2baDOgSB9JmSguq2ncz LILZiq+6jcWTcInPkuM4M/jHwM515CyNVdbdt+y+026vM891jjnPgsVNHHfR13ec+4HeSz+kqv4f mgyT2hjpHjdGN/ng4IAfv/chk8MJBx8aezdgb18JIxBLCEaIxbaRUgIgJ2ia0pylNFjJBAFRQ7RG Q0MMqdxiSf2IlVGNYFQpo0qJQVBJQEPOTcmqJmOtz9pxTuJ+fty3ix8tDlfH7+Ljd5xl+O+D4+wW 8cc/fY8f/b//ho8+Cof3QNKYGzf2+ODwHnt7I27evEnOd7l5S9m/MUI0UqLwbHATxLStQhexWyLr 8iDfWdvFhKXqbCZYyqiWKnWgVKWzQM4lSDpRLBxBoE6e0OGczP1uAbuomcJ1ZlGE1y49fsdZxaYs IY7jXA1ilg/5x5/+OP/hP3mUb7/9l9z9AGQ0pkqBGyGSm8Q45TYr+ohxU5GbGo0NKSewCHN2ZRFB pCRr5JwwM0IsB6lIsUQDWTNBDBVrLR4ZcsZaK0dn60gIyUW04ziOs+W4aHac3UEPD+/x8EM30RC5 dw9kBHv7+4gI4/GY9z84xAz2KqjHR9y7d0hKCZEidDtx21WlRQQhTDOmbdhxsE3uKJHUxMFiwe78 xLTCnXMmZ2tvbue4DixblDK/Pb945LzXmx9v2finPd5xHMdxnN0kHtxrsMkBsRpz8D5UDwpGJmhi /8aI+ijzyGMf5fHHJ4z2lEkzJluDhkxOYLkhVsWeIaJgtIsEixBWVUSttXC0+dHaVZ7LYkMRmKRm xiLSiXBp24Kbp3PsDJtugbuoDe+q8U97vOM4juM4u4eKCA8/+gj/9D/5j/l3P/Mw1QjuHkxoJsae jmiaxNHhPT7xb/0ijz78UCuGDRNIbavurtosbUwdOhW8okaMEZRWGGdKMIeggb4xS9P6qace6qmA 7pI7nN3gpCikZSwTuOusdj/v8V6ldhzHcZzdQlUOifFdDuV7fPLXEoc/Mm7q4ySg5gOOJvCxj/4C n/7047x/8G+4d/AjNGbS+FFCfgTCEZPmgMYyWTIWBBCMCFKBBLIZORkhRKJUaFKECBaoExxOJpBp 24Rrf+vEs4iwH6rLfq6cLeeyxKxXpx3HcRxn99CShpEZj2tu7T2AiXBwcBcBcp3JKSNZqY/GrRe6 iNqcG1JuyNaA5Lm0Duu7DxZv8/SCxZox9T/nnMGmYrk7Zni8Gl6JdpwtYFFHNv8PhOM4jrOLxJzg aFIT4z/w+OP7fPxXjKT3+Ee/qKhk9kYQ45jJpMaSojYiJ2OStHQlDKUroY5aIWyGItNGKoCgGIkg kCVDKuK6MaOpM6BoGPqgi2gOCNJGfzTmCwuvG8uqxpv2RDubZb4jm+M4juPsIrHJNYdHB0hzl488 9Aif+MQ/4if/8D6f+sSjfPDzu/x9/JCmMURzqSKbkVJTvNCpQWNCqoyYUDI3DJOM5bZCnQED00zG EDMykHImNdZ3N8wJQpzmSEfRmWSP5Okc14J1WuK6SNt+/OfiOI7j7DoxJZjUE3L+MbduPsKjjyV+ 9vMDbt74GAeHNZYhpZ9RjR6nSZF6UjKbcxIgolnJWbEsiCqIYbmkO5f0jSJ+NbSLBIkYpcOhSEKC EVVpUrevpHtoK6K7xYbDBYfO1WZ+4eBJx2zqWhex7TiO4zjObhInkwmTo8T+aMStB/bYq/YRg/F4 TBDl5q3MRx57kJwzKWVS095SmwmthiQjGGCKtnHQxjAfOpFz65UeXFxVIbfWjTBI4ygNC+fwSrRz Mve7g57bTRxn+1lmHVsVb3nea1zG+8JpPsEbznfZ8Zs6ZhNzuYh57frcnfOjR+PE0Tgh0djbV0Z7 96hGUDd3Ge0FHnks8IlPPsqHH77P0dEhk1RT54bGcknkyMWakRPlljNmxQldbooJbUU7kdK0Nfhw MWGMgRAG7b8HFWhTX1TorM/9bvvtOM728/rrrx+7bUr4duJ71dgXyWmbQS3Kv7+oYzYxF5/75ufu bAb5r/4HrKmVB249yMMPfII7/8ff83/9zU/45Kf22Nvb4913P+SZ3/4tbj72t5gdkfJB20kQIDOK QqwgRiFEIWorjKWkbhgTTEARqlFAMYxEjBEhkFIClGpUqtadiO4aruRcovNEhP/5v/0P+Pbb3+G9 H/+ofwCPffyXL+u52wpExIXcDlHbI3zz9Vf4oz/6w8ueylbxgx++QyXvb3TM27dvXwsbmb9HrK4w n/fTpG36NGqdSvSiY5aJsvMec5b5rhrD576ZuV8FNvX+O9SL7/7d3/J7v/8Hx3Tk23/y52ceP8ao HNzNpFvGjb2bxKp4k0d7kclkQqyMd99/h195ZAxSY2LUJCQbEgTBsAQpKjQCCmCINmgIBI2YGXv7 oYjjDKBIFkIQYhUAyLlGRchmNG2lOiBoUKJGb/vtOC0v3H6JH/zwncuehuNcO+YFcbe9qc6pHatE 1vz92yTSHceZJYpAVSlqymQyAWnQChAhxsiNG8peiJhNaMyw1JDNCG3FOVupOKc6ESolhNCmaRQv dFBlNKrIuV04SJ628rZE/x8NaVM4Bv/zKOF3tALaRbTjOI5zf9mUgF4ljF0oO87VJE4aIZnSWOTD wwlZDrAgHNQ1N0eBB/ZucuvRRAhCykYSwEqKRidsZ0vuGbOp5zmEQIyRuqmnh0gmtznQffJGzFie tXIAfVU66sU/GY5zFfjm669c9hS2jhduv3TZU3CuKZsW0N2Y6wpnF9eOs73EnBNNSuQMdT1mf/8m Ue9y98Mx+488wEc/8gCPPvQQjf4UclGyXfNAM8EwRCAEIedEXdeIBEJU9vb2qKoIGKq04njafTCR i/sDQw2StOIbGVSrS7a0iKtox+n4woufu+wpbA1PPPmU21ucK41n4zvO1STGuIdKw0FzRNwzHv3Y DW48/AFHRwIhsv8AsJeY3M1kM6yNsctmYEZWJYRIzhO0Fceq9BXoqgoEVSZNXRI3cum+Yja1dZRb G5PXhnqUoYQ2Mc9xHMdxri3epdVxrh4aYyTGyMH4iKAVjz32MDduQkpGiIkYlaOjI8b1EXUzJrUt u3POkAxrLR2TMYASR3sleUPALBMURiOlipEYQl/F7ug6FA6tHzHGdp8hMq1iO47jOM6m2IRgXRZn t6oz62mue974svn4u/mUh0UV8E0dcxInjeFzv5i5O5sjBko0nTVG2KuJtTJu4N4EJO5TVZFJGpMt kJMiKgQJoGVhYLZAbiLIBMiIaG+9KC3CEynJTNW5+J5TOU5KpblqhbOqYpZIydr4O/qoO8dxHMc5 C4vE6CY7o67KnJ6/f1GTl/PMaZFIXzVWN59Vx23qmJM47Rg+983M3dkM8s//5UN2dJi4m+CXHv+3 ee/nh3z1a3/DT34Kv/30L/Brv/ogFg44Gn9IMznAyOyFqlSKU8YsUWlFdeuIUQjEKlPFSAhKVY0I aqhl4r6SUqJpbR2QUVWilvbe+/sjQgittzpT1zV1XQPaimj4n/6bf99zoufwDNjdosuJvmxP9Da9 eXeeaM+JXoy/RziOc9W4KjnRGmPDgw/BrZuJd3/61zTN3/FPflv5jX8Pgv2EDz/8e+79/C7USqUV e2EfELI1JM3kYEw0YSimirCHETGESZ2Y1DWmgaNxTd00QMl/jggjE0Zq7I0UDYmUJxwdHXF4eEjT NL3VA41UVXXmB+lsH5v4iPKy57BqrOvcQar7KPp+d2VzHMdxnG1CVYv14uEHb/HYQw/w0M0bPPjA DT7+MXj4ESEGIbQZzk1qkzyw3hdd2nMnmiYfa+fd2Tnqum4tHG1L7zahw1TI3dhNJvVjKKCoRkSC 2zmuIZddGbvoxTvXVWDOP2/X9XE6juM4zkmoagaZYFoW9tUpgQoPPTLixs1I0y4kRGqMhJEAw4CU yw0UMcCUYnIOQMRyICWoG8NyheWAtXVoQ7Gs5KSkrL2ANisrD2WwArE0XGnu93Pj3EdciDmO4ziO c5VQKII1TcY0dc348JDJ+JAgihJoUo2SSqdBK2JZFVQMIaMCqhkRUPJMhTrnEosnhEHVulSxE5As 0yQjNUbO9JXq4RjFR910U3WuCMOVw6tWrp90bLdvmchedd9Z53u/r+04juM4ztUjGhM0JMiKBgix IcRMJUaORiRTVcYk16VRipZ86GnFWBEJgIFFhIq2FyEiRulqGElNjagACtLZOqQ9BrRdQBiYVqJz LvclIOrVX+Czaww/+l9ln1jWves0rXI33Zr3tNd2HMdxHGe30O6LSAAR9vdvcmP/ZutbNqoQSQhq EFqLRVclLmTEEmQDyWiAWCkhlJxns0TODcmameryoopzzpk61zTW0FhuzSOl3UoyWfgAnO3lPN7Z Za1yV22fZ+z58VaNvcgXvAj3CzuO4zjO9SUGzWTLRBIpN+yFEaZwULqnsB/3mUwmIJkQSgvvnCEn SuU6BEwUFUMlIoTWG11EdrFoCEZpEW4JEMU0IwS0W2SYY+mC2Feiab923/jCQmczLKt8z7OJBJHL XkDpOI7jOM7FoCKl24lIoE5GV5yuJ0XIjvYiqJXEjRgIoW2kIqDtYkSR0rBFA311OaUGsqFI331w SF+J7pp6q/XHGGnWW20NqXE7h3P/GMa4uRCeMl9d9/8oOI7jOLuK7lUVoV34d2v0IJYaPrx3CEmJ FrBJppJSH7Z6DDkTFSo1NJfbftgjqqAYKgmVRIwVoQoYyqQZI1KRTShtVjIpFZFsOVDXifFRZjwR 6iZQ1xWTWkhNKLe6oqnDZT9Xzik5j9ha1ip3G1gkJHeJYbcsF9CO4zjOrhLVAqqGJWFSTxiPx6SU S1KdKdDQWTM6RNob0lo3lBAqQqgQ0SKOm+KBFhFi2AOZjmOWMZtmQpfKtWEWevuGmNEMutV0rcSd q8NQbK7bDnV47Emtci+SZSJ+0dx20fvs4tlxHMfZdWJqFMl7qMHhpOboMENSqpDbZidSvuYaa8M2 sgFZMAkQtAhcFTQGVJWcBUuJnBKiEFTJVgGGdcI5pXKsCdkMSyNylkGjljYOr9XvIXjHwqvIqkV3 Z923aP95Rd1prr3ofheVjuNcJqctOKxz/KaOWYd1Cy6bnNcm5n7Vn3fnfGidMilnLCnjoyKic84l C7rtTAi0vmYAKQLapIjqYvQgJ+aODRSxbKWRSqpJdUPTlFu2RLaGbA1Nk0EMI5OtLHBMlin5HBkj UzdHl/MMOdeSi64eu9XBcZz7xbwAPem9bZ3jN3XMOnM/bXLTJua1qbmfZoxtmruzGTQ3FZb3mYyN o3sN4yPINUguHQdz21VQTYkSUVVCFEIoCwYBcjaaBibjcmtqxXIAi1iuyCnS1IHURHLaw/KI3IzI zR652StV6KYi1ZFmEqgn0NRCaiIpB5omUrsn2tkwF93223Ec56JZZIMb7j/L8Zs6Zh1Os3h7m+Z+ 1Z93ZzPExjI5NTTjhpTanUEJElARMGkr0oKoUUkii5Aa6aPuGsByDdDbMaoQgTzbvluVEDoxPHt8 ToOOhqS2mt12MMzStht3rgouIi+WJ5586rKn4DiO4zg7TWyOYFw3pDqDKVWlVDESJWIIiKEqlL6B mZQhmdA0mZyEGEHVSI2WhYYAGFnzNNquUqBCzFAiiNHk0vZbchHjljI5W7sYUbFcMqNzgpwT3vbb cQov3H6JH/zwncuehuM4juPsNHE8OeTgqCbkkrKhClGLiC0L/QwziJXQNEbTZOpJZtLQHp/7SrKZ 9dXjkrhR99uig4QPMeq6psk1oKSUUITUZIwi2ksDlpLa0WQDr0Q7juM4juM4W0JMrTg9qHNZOGjC 4XiMmQCKEBDR4lGuI5OxUtepXXyo1JNAE0CrAPuxFdTWRtuNSI3xwbhm70YsV5TcLkAM7S0DkUmu sRwRbZuwpBKbJyipTowPDi/lCXKcbeObr79y2VPYOl64/dJlT8FxHMfZMWL3jVlZIFii5cBS8TsD GNpG04FlaTOdISWhqVNp/d1MmEwmqOpgwWGb2GGJOmmf3lEq09NJmFlr1iiVbxEhpYxqLs1aJg3i DQsdp+cLL37usqewNTzx5FNub3Ecx3HuO1qPI5MjoZlU1GNtb4HJRKgbZTxWxkdw7wAOxzCulUkj jGvhaCKMG2HcGJMxHB7A4T3l6FA4uJe5+2HNvbsNR0fKwT3j8ID+6+E9aW8wvidMjiJHB8rkqGJ8 pIwPhcNDODyoGR8ZdSMnPxrHcRzH2SHm0xkWxZ8tyhRedvwmjzkv2zz36/y8O+ujhwcNR4dGkxMp JZomk1IiZ9pFhJkmZ0RLFbppMuMa6gQ5FetFuZWKcpNqjg4nTMbWtu0WxkeJybh4qVNj/QLCvqGK GHVqqFNi0tRM6sSkztR16hNDUvJStOM4juPMMy+s1m0Uter4TR1zEvNi86Tc6G2a+1V+3p3NIP/Z f1FZagy0LPDrRDGm7fdSBK8KTZ1pmiKuAYJSkjtM0VA81Tkb9aS0Ba9GATOjnmRCKJF1okUMl+i7 3EbcQU6h+KzbBLycMyHAKNKf81d/+lt8++3v8N6Pf9Q/gMc+/sv38/naOkTEf4F2iNoe4Zuvv3Lp do5tevPu7ByVvL/RcW/fvt1b0K4y/h7hOM5VY1Pvv0O9+O7f/S2/9/t/cExHvv0nf37m8XU8qUsV eJJIjZFSJif6anSX3TwZW9tIhbIWsM2IridwdJg4ODCOxsZ4Qn87GifGk8ykLskedZ0ZH5Xb4UHi 8MA4PMgc3MscHDbcO0gcHCSOxonJ2GgaI2dK9F50O8cuMl+ROE9XqU2wiflcdbpujBfd9dFxHMdx thmdNJAaSjMTadt9BxAFEUMVQgSNRrUPezfLbf/W4PsHYH9f2N8Tbt0SHnxQuHEDRlW53bwJsTJC NEKgjBm6rofttkIQiEGoglKNYBSlfN039m54TrRzei66/fauCcn553PXHr/jOI7jdEQzyFIErLQ3 sL7ToIigKoyqChXry+siobdkoEKUPTRQkjpyYFxPaJoGRYlhxL3Du71VJLVNWcym2c8jDTQpEatA jJGcMzEWAT2qxHutOI7jOI7jOFtDfOBWqQqLWutbni4W7KvRgGqNSCCEQF3XiBhVVdE0xe5xY3+P pmlock1qMqMAMWeaJtM0d9ForcjWvtW3WelSCFAFyBgiNTFaqVyHAJIgGzdvjS73mXLOTVfFnK9m LlrBfD/ncNL1t8n/6ziO4zjOdhAfe/QhTCDlgzbjuazsExGEUPaJ9N0HYxyVDoOqxBip67osCJSA mWCSkCK7MTOqqrT01hhan3VZTBhViDESQuhFdUqJbDUmoJpRkdL0RVtB7Vx5FgnoVdsXPYfTzMdt C47jOI7jdMQHHs5l8aDo1J5B2ySFBqEI6pDK16oqbbxVjRgDctS04niMSiI0GQkGtOPlcvxoNCqt vpumHSewtyfE2FWl24i9VJdrS9OK61IVD9XVXyXvcKJgXlSpPivLxlmUubnO+cv8v5ucs+M4juM4 V4MYo5KSIVLsEta2AS8iumvRbYRQAcULbZYBJeciwFOTMTLWRt9pkd6oKKaGmZITCIGqkrai3bYI 7zoh0pCtjf1AUFWCVoim4svG0zmc07GuuD1vhdkFtOM4juPsHhFpEM0g41YwlwqyirSCuRxoua04 W0PKiWwBNSXlMajR6mp614UBZATFLCM6IQCqXcV7QrLuOINMnyMttKkdoSFo8U4jzf1+bpwdYJU/ 2znOIj+5/wfCcRzH2UXU2li7EIUQi3gtIlfbpitCytb7mXPObWfDpm/O0vmjYwy9z/nYTStiGPUe 6NLExSAbYuW6MUaqEIgaCCoEKQsdS1OWy36qnE2zyB6xTaJsfn4usAvd87JNPyvHcRzHud/EbHVZ PEgAjKnzOBeh27bmbg0bBA1IaO0eEhA1YlWBDVuAt+kedIsGdWATaT3WQRCJSOsBEdF28aD0Lb6l E/SSEV9XeC2ZF6r3W5SdJOSH93sm8hQXz47jOM6uE+tJoopCaBcMgg0sF5DbgOamLvurqiR2lLbc ikjFaDQiNQ2WGlLO5GSYgGguQlkE1TAV1yKEEFEtHRGxTFNPM6PLMW1GdZiKdedqs0x4rRJkixYe bnoOJ4257kJEx3Ecx3F2hxi0WCZEjaDQ2TjMukWFhZxKjrTlmqCGkMlpjKpgVjPai0wmqSwOFGvj 6RqyNWAQTIvtY9SKcJtgWVBtUzgUREqMnlZtTVxK3F2MStpAD3Vn97jo5Ay3NDiOsw2c9hO9dY7f 1DGbmMtFzGtTn4KeppfAts3dOR9avMyxrxIvQkQYjQIxKjroHKgaCaFqkzsUkdBXsUv1uV2kqHFa 2c5FnIcQig+79UiH0J47OL+7ZYov2nHOwkW3/XYcx7lM5kXcSbazdY7f1DHXfe6nOW+b5u5sBvnn /zIaZEJsd4hMEzkGolq1a9Xd7jBt92u/ULBpGupmtoLdht21KRuGiJVFiFU7DE2JvxMpFhCRGUFf FhYW/vf/8Tf49tvf4b0f/6jf99jHf3mDT8fVQ0RcyO0QtT3CN19/hT/6oz+87KlsFT/44TtU8v5G x7x9+/bSwsJVwt8jrjeLqqCrKqPrHL+pY67z3E+a11WZ+7ayqfffoV589+/+lt/7/T84piPf/pM/ P/P4sUvckDjMYjYY5DIPhXXXxVDalX7l/HK8mcw86LK4UBEtVekyQNNWpTul3iZwqPSNXVS1906b tQsSmXqmHWeXeeH2S/zgh+9c9jQcx3EcZ6eJqvR62aR4kduac2/BaO8tB0ouolhakaypJHNkgDZz mi6FA4SMBkFD09szIJEtlWp2N16kj8vTwTW7qnQR6o7jOI7jOI5z+cTSDVDbLoRtpJwKAxdFK34D lltfcwI0Fy+zVpgZqa0cT20f5WsQJUZFSG0FuwjsbAZdCgeh91orMiPecwazTAgeFO04AN98/ZXL nsLW8cLtly57Co7jOM6OEY0E0nmdrfcky1ybbVXBZJADTS7V5r5unUu3wb5iXL5qKHnPWMmdRqxU u8XaRA4QSW2374y1cXiBLmIvY2Jk19CO0/OFFz932VPYGp548im3tziO4zj3nShSFgcmy30FWESK eWNQjU4ptfd33QOt7TrYLSScqtwiyDvfc7tAkNBnQqtqewtIK8ybprT1jhVINjSGGX/0JHnbb8dx HMdxHGc70K5xSlOXHX06Rsq9R3naqrsIZ5FACBUxxl7kmhgoBBViEGKAGEq6RqbBtIGQQcEkF/+1 1eTcYJaoRkI1EkSMRENp+lLsIY0lQtCVD8RxHMdxdo351IZFaQ6LMoWXHb/JY67z3E/iKs/dWR8N QQixVI9bW/Qg47nkM6eU5vZ3vudyv3btuaFfPKgKMQZipVQxEGMkBO0908PxOiE+ZLqgsKSHdJVq x3Ecx3GmrIo/O+vxmzrmOs99XiiflBu9TXN3NoP8i/81WM6Jw0MIQdB2AZ8Y/WK/TuCWqnQF5GPN WZrcbudi91BVYqVlTFXIRkqJlFKf3DFszGJMo+9UhUorQigWkElqSCnxv/z3v+U50XN4Buxu0eVE X7YnepvevDtPtOdEL8bfIxzHuWpclZxo1XZxXzVSREvlNzVl0WCXsAHDynCpCM93FTw2cIAQAqNY sVeN+u6EXULHsHI9HE9Vjo2tqsQYz/wgHQc229Vpfqxd6hjVtTrvWqo7juM4zi6iQ2tG31BFu0px f9igM+Ggm6AaRiJbM+gw2No9THsBHEKg0tLmWwMzonv+fxoiQiDMHKNWBLnjnJVO+F0UuyIo55/H XXncjuM4jjNP7Kq9TV0ymzUU8Ry0CF7LipkSwkDwSi65G7nzLIO0VWsTWJRG11kzaiheEZs9qrd4 WOlwaGYkDGvTPyTli3weHMdxHMdxHGdtVKSrIE+tFMPoulJJLlXlENpqclui7qvPzFk7dLHFo99n MqOhp9VtGdg7ZtM4roM3cVdZVqkcri5etCDjtNtnmdOiRSGr5utVV8dxHMdxACJtTvPefijV3waa JpOFEmVXleYnKdW9PSPnrrW3lW6DQWYSPESkNF4RIVOq1YodE8LDqvV0keHUOoIAUirimEfcXUfm 7QEXbbtYdu2T5jF/rOM4juM4u43mVIRtCMVCgWRi1fqhpeQ7Jw4QTYRoRBVGKgSBqELQjEpib6Ro MIJm9qJQCUQS9XjQ+AAAIABJREFUwTKpHlPXNSml4m8WI2IEy5ATYomoRlRDNGM0pDwh1WOMVLKm bXK5z5RzZhb5ZoeL09Y5rzt+fnsdlh27KHtznfOXHev+YMdxHMfZHaJqJMZMk6ZVYpFucaGgSOnI LUKQskgQETRPPcqd7WM0SNOwlPqc5+7+IcNUj/L9cbvGdNHjYnuIcz24aOHZiduTRPd553E/q+iO 4ziO41wusetAaFbsGJ0vmmxT4ZoNCWV/VCWZEVWpB/aMTiTHGFHgyBoaMyorFe46J8h5kAJifdRd 0Kmvw8xmBLZ7oa8HQyF7ko3iMtiWeWw7J/38HMdxHGdX0Jy7joQZEesrzqJD8TpdSNh1EByKWxFp fc8Q2m6F3fGJclxKqfdSBxE63SyDMbpzhkLaK9C7zbxoW7eqfBFz6Nh1gd09Hy6gHcdxnF0mDkVr IWNMEztEQtskpb23a8PdCuLjFeM8zYqeo1swqDJI9uiFsvU51EObh1eirw+LBPAqv/RlzG3ZPIb3 u/d5OzoVOo7jOM5lElW1bcU9K2qn8XWhFbZd2sYwkq4V0ZJLDB5TARwQUqujG2ttHIBSStDDa5SU D6bjtbiA3g1OEmTz929CwJ200PCkc1xEOo7jOM5uo52YzXna5nvGk0zxSnfC2cxIg46FSGvRoG2Q 0to2SufBMkbuvdCGkWcWHM7bQhZZOLrFio5zVi66euzWBsdxHMfZLWJKiZQy2RQwaFtuz4hZaYWv zXYNHArrgNBYJmFUVqrXimA5k7OhKqhov5iwo7N4ZKuBMCOWh9YOM+9YeNW5bKF50W2/HcdxHMfZ HWLXqbBOmdhVpZMgOq0iB4kgqa0kJwxDg8xEz5kZ+9WoX2QIpZmKdZ0Qg5DNCINOhIF2HwIh9lXs EIoPu7t+Gd8XGF51XGhujieefOqyp+A4juM4O020PFy8V6rRQ48ygFlCKKK4XXN4zK8846Me+KqN UoVmqb25S/qYXYw4TABRVbdzOE7LC7df4gc/fOeyp+E4juM4O000s9JSuxW8tAv/ZhI7tKjncuxi NZwFSGnasptuQWFeEVM327AFmDnfLM0kdTiO4ziO4zjONhBzziXUrk3P6GPmTOei76bMJHMM9tU5 I5YHVoyEWWkhfjx1Ixc9PpcPnXNuhbQdu57jOPDN11+57ClsHS/cfumyp+A4juPsGNEELA98Gtkw sV5IQ1eVpv++o1SZO5vFMLJuefV5GGuXAGmFdAZS63+uoM2m1r6TYmkI4zgOwBde/NxlT2FreOLJ p9ze4jiO49x3Ym6MlHLXlLCnqwojXUvv4/nNMmf7mPcthxBms6B7nzSUxOhSBS+2D+mdImlgG5F2 f528Gu04juM4juNsBxGKXg2DjoWlqiztOkNthfDJnuSyoHDW39yLcVphPqOFBWv/5TwV0WZCSl18 XqlAZ0+4cxzHcRzHcbaEmHM+lpzR2TfMisidl89mXRW6tzT3XubufJj6q+dbi896nAXmkj064Z1z J8aFvDzew3Ecx3Ecx3HuKxpCIEZBEYKABhAtnQqFBg2ZEIycaoTcV5tz62Xu4puLPi5CN8ZIFQQo wrqqKmKMpTKthqmQBRogAbVlRqFCRMkGuW0frtpmTLfXdJyL7Dp4nmvdz3ltA7dv3965x+w4juM4 Q3Qm03ngdR4uJOzaeHdYa73IGcj09w8rzaltJV4qypmAIHI87aO7Zp1TX322PNcq3PJMkxZnd7lf DVtO213xotuKbxOX3XnScRzHcbaBOL+jd1q0Vo7ODy1FBrfttxM5U0QxneiWOYGsaDuWiCBqBJOS yCHFdz30kRTR3M3BFlSe3RTtONuAC2jHcRzHaZutzHiUF7TXNhOy5ZlGKFDEs0pAdFph7qraqopk m/qkKY1TAqUV+HChokipWrd9XspYWvI7urxoz4q+unSVy/kK5rByOy/Mlt23aoxlxy+7xmnnv+5+ x3Ecx3GuP3OVaJ3JeBYGCwUHlWYRQbEicnUaXTfPsDpdFgnmfj9td8Ri8dCyMFE6DT9NAynXggWl aecKsUj8Lts+jThdJb5Puua6LPsPgAtox3Ecx9ldWn067BoorZe53c4CaLvQT/vbUNPKjH95VkwP I+46UR16MW7ttvX3KdL6rGfHU7dEX2nuhwA9qxBfNU7H0PO8bOxd8kU7juM4zq4TS/tta/3I0n+v CtZ1LMyCScmOloHghWkTFRlYPToLh4q0Ng7aEnOnvO1YLnXXGrw0XUltMbpUqT2d4/qxSmwOxei6 QniRsD2roF1UeV61f9UcHMdxHMe5nsRp5Vja6m/ZKk0DSxMU1fJ1mAU9FcyCiiJBe3Hd32cMFid2 4x63fZgZMVZlI2dy24a8RNxpK73dE32dOElsnqaivKoyvEmWebsdx3Ecx9k9FG07EyqITiu+NkjW UFVybtoEjYQGCFFKprQICUOyESjWDEs15AbRhEjpOliEdcnEK10IMyJl4WEIAbFSna6qiht7FaOo iBm5aTBLvrDwGrFJ28P9slYMr+O2DcdxHMdxYhG3Ss6piOj2jk48FxEMo9GotVskrPc400begWpE dNp1sKBAiavr7CHz1exh5bo7rox3Hx69c2ksEqLD6vP8vlUsG2fVNU7DWawd15n5xZvgsXeO4zjO 7tGnc5SsZ0NmBG6boKFGjEUA26BroYgSOtktmeECxOm43YLEqZWjfM2ohn6hYtM0s+kfcy3EnavL MoF12v2L7lvXFnIezjLP68yuPm7HcRzHGaKgWJ5NkJvpXjiMsJNSRY4iRFEqDZS24fGYAJ7Nk55a OYqFg0HKx3Qx4rQybYQwXWzoOPeb01o2drUq7TiO4zi7SoQuYWO28jtM7BARcjMhhCKae10rRX1r CKTETJTdkGFb8e7W7RuK7+No74d2Me3cb07b9ttxHMdxnN0hQkYGFV8bpGDMt/IWEUKbwpGtaRuo dC27p3nQs5XoEmE3jMTrvg4FdRl/WgEftgEfnuc4juM4juM4l02c3zGtEBuIAQHIbQVa5nzNtKkd UKrGRUh34rukcHRiPAAJVZ1aRfpuhnnqv565RqJkSKuLaMcZ8MSTT132FBzHcRxnp4ndIsG+acog KQOsFbJTAd1bNlpfc865rRqXanQneOerzOXYbsymvY/2WgKSEe3ajYOl2Uq04ziFF26/xA9++M5l T8NxHMdxdppoCYIqyerSz0RLp8E+ro5ZO0fO1sbUCVoFMpFJUxPV+sWCZkZKiRBCuzhw6oFumgYA 1dCPCaBSQRuXZ7n7VtwL7TiOs2E859xxnG3iqq4ril3FOISBWFYtiwaztQ1Y5JiXuY+364Sz5On5 g8WCJftZaJqmr2IDpFSsHjFGQgiDxY3TY7rrDb86zq7zzddfuewpbB0v3H7psqdw5biqf7Qcx7le XOX/1Meh3WKZUB2K6LI9fz+klMkCsbV0lKr1UBBLn+6Rc54R1Yuu3XmopyL6fA/Uca4TX3jxc5c9 ha3hiSefcnuL4ziOc9+JJdd5usPMUBEwGOpWU0Gn/QwByNaQ8jTaLmcwsZmxcu681jpToR4eM/RO m5Uuh7RNXaZRe47jOI7jOI6zHcSSw6wwaLDS0XmcS/fA43aNsqiwfI2xrSartl5o7VM+VIWU8kwT l2GUXUqJGGeDQmYFt6tox3Ecx3EcZ3voPdHCNBd6WDnurRkpI0jvcwZIJphNI+26vOghXeSdWfFB D1NApgKdmfNtiaB3HGc76Dxs7qu9Xnzve9+77Ck4W8jLL7/MN77xjcuehnOFua5dfSMcr/Z228sE LbRxd1bi7bJNq9KQ2/SOYdRdOWdVd8Lu2K4yPSuovf23M+V+/jKue63r+gaxiO6xXuXFIM5iXn75 5cuegrNlfOlLXzrTeZ5l73T8s3/2X1/2FC6M2MfSDRbxdQxzoYMqA7vzVPCalTi6QbqHDSrYrcOD lKYNVrpzcy5Wj27BYbd/XryL0DZjcZz7V/08jTDuROUuCOldeIy7zLvvvtvn/6+6OdeD+b+583+H z8Nf/evvb2iWjrOdxH7xHtOc5/IGmQdC2EjZyOUEQGjMaFphHFQARTURVVGmNg6ApmlakTxN7OgW NJZjMilPyn41lDKftt9L22bc37Qdx3Eumnmx3H0yOL/PuR4sE9CL7JmO48zSe6I1dOkY04rx0EIx 75kux9Kf03mdj8fbdckd0w6E07bg3S+o9dF33b4QdHpeNnA3x5VmaAEYVjKHloD5Cuey+1aNsez4 Zdc47fzX3e84V5VFAnoopLvvnevBfCV62oXY/OfsOCcQoWv5Pa06dy25O7GcUgIxoigEJTH7y9WJ 5hCOdxjsFimmNP1oaFjh7o4JoXQw7CrYw3GS5ZnOic7VZJH4XbZ9WivF/HXWvabjOLMsqjrPi2iv RF8f5u0b2ySiV71fn+bvxSbe9+eLNZf1d+Q8j8X//m2eCFNRu4qhsO38zJ3fGREs26Al+DATGoaL FGeq24OUjuG++euqCu7muPrcDzF7ViG+ahxY7Hle5xjHuWp04ulTn/rUsfu+//3vryWin376ab7z ne8s3V517DrnnGYcZzWLBPS2iOh1uej33EXv61fxvf6qzfcqEKeC1451JuzoYu2GiwKn1ozyVXWa 6FGq0qE9PpNzQjX0v5BDMa1trnTOTb89v6hBVcnuzbp2rEp3GKY/rPuLv+yN7iycRRBfxTdVx5ln 6Hn+y7/8y140p5RommbtSnTTNCu317lv1TmbON6ZrUTPi2j/xGH5+7q/1zvQiWi1gSjuKsHTynEn stNAJEOpQgdVVItlw2z6CzdsplKascQF7cOnVed5e8hxb/bV+B+xsz4nvQmdpqJ8P97ohsLaBbNz XRmmbyyydKwrouePWXbOm2++yWc/+1nefPPNft8zzzzDm2++eWoR56Lv9CwS0FdNRK8qoKyyg3Rs wupx2jU/J92/7rmnOW/Zp6mnnbczJSL5mIAeLjIY7pveVwR0qWLTiufQD9r9Qg6rzbORdaWtN4QZ UT57zKzNw5utXC82KURXCWgXu5tn3nMO/kZ7nRiK6GEVenh75pln+Na3vtWf8+yzz85sw+kq0ese /+yzz/bfz19v/pz5OS3b7sbs7pvfPs31rxLDx7KIr371q1th5zjLp4nztr5V9y/aPgunWfNz0jVP 4/FeZWE8SwHK/2aejsHCwmkjlC7fuUNaz/PQ5zztNmht6++ECIQw62kuLcADdZ1mxp8X7KIyI9y7 hYZ9eoiL6GvHooYdi9741m12smicVdc4Cy7MXTBfd4Yi+jOf+Uy//6233uotHXBypXndSvS6xz/3 3HO88cYb/fazzz47s32WOQzH6ETlcHs4/jrXv0q88cYbPPfccwvve/XVV3vrzmWzTiV5fv+itSrL 7t8UJwnRTf7tWDbGWcb29/PzEXOCWIW+JTd0wlXaBim5LB40JScQnV/8VzKiq0r6MTqfs5lRp4ya zkTgtYF5s2I6ZbRN8gAgTzsVKpGUJvfvWXE2zml/6Vf9Yi96czrLtR3HOc5QRH/3u9/thfO8H3qT IvrOnTs899xz3Llzh+eff547d+7MHL9o3/Ccs85h0ZjD7e77Rdc/6TFdBbrne8grr7xC0zTXOg98 G7qtrlr3c5Y1QR3nfWznufYuErtvhoJ2tipdKs/Z8kwEXknnGC5GFGK7SHEosoNI31C881iV6vQ0 5i7nTNDldg6Pt3Mug9NUDna9Ou1cH+Y90YvsHHCy/eK0do7hMYuOXWfx4XnntGp7XmyumtNV4mtf +xqf//znAfjyl7/cC+jrLKK35b161bqfs6RMrdND4bzzcmaJZVHh8ei57msXUdeLYxFynl0c2Ilh bW0Y8znQMJu6MX9ed/3u/vm5WPZkDudyWDfWyN9onOvCooWF8w05gJn397NsL2LZ2KvOH+4/75xW bX/9619fe05XCTPj1VdfXfhzvoqPb774scjOd9o+BKeNuFsnEtW5HsSgVcndWPILU/ZNPcndEapF MMfWfpHyBImxF9Ew6382S5hNfdDdwsMyfkYkHquEm2W6VuFX8ZfZuX5sy5vgE08+ddlTcK4py0T0 fCW6+/rFL35xZrvjtNaH1157jRdffJHXXnvt2LHD+zq++MUvHjt22TXPOsdue9H1rwuLkjmGt6vI vCVhmZCe37fOeOscv+ic06ZorHudk+Z5lqjWs1x7V4kiQk4CMq0AzC7+mxWv3S9V3+IbmdnubvOd B1PKdL27u1/O6XWmb1azwtv6N++SQ+04zgu3X+IHP3znsqfhXFOGQnmZsPrKV77SC9OvfOUr/O7v /u4xwXXS9jKWHTe8Zre96hqbmOOy8Yb7rjrLIu62QUSvuzbmpE8HN/Hp4WnW6ZznnNPed5o1Qusc 68L5dMSUWk9ylJnFf1NLxbSJSofZwL/cWTnaxYQ68EUPLR1dNF7Omab9PvYpH5BT7seYt3N0UXqO 4zjOxdKJp7fffrtfTLhIXH35y1/uj//yl788I7hO2l7GScd11+yue9K555njqvGWzeEqMt+hcJtE tONsO3G4OHDejzaTEW1F8MYQ+xSOskiwiOUQ41y771nrBmjbvdBmattlnDavQ6Y+rO6XWjW0HQ2v 5wIHxzkt33z9lcuewtbxwu2XLnsK14ZFonnRPud64CLacc5OBIgxUjfjfmfOeZDL3C78o3igQyyi ua7r8ounVvaHaZrGbGpH2e4i76Ckbahqn8iRc+7P7xYYTn3Q0nc7dByn8IUXP3fZU9gannjyKbe3 bJD5DoXLhLRzPZhfPOoi2nHWJwKkZKTGELWZZI1SCW4tFVJ8zrnOvSgOyLEmKCGURi2TSdPbPESE lEoUkKogZlhOpK66HSNYIqVMSrmN1RNCaMVzylQhHJ+94ziOs1GGYnleTA+/d64HFymiv/r1/3ND s3Sc7SQOf0nmq73DhYUppTbirhw/65ue5mXm3LTnHrd2LKKzhahMLSSd/bqbWwL06sdxOo7jbD3L KtHzQtq5HlyUiP7VT/3SBmfpONtJXBZrNy98Z8S2Fv9yEoPOdiGpTeUoand//yYAdV0fG3/axGVa 8TaYWTw4/8tb+3u24zjOhfKlL33psqfgXBM85cHZBeJ0AeBscPzMokLmPc6DKnG7L0YhitBp3+74 rkNhWTQ4va+zgZT/7RoahtejPxbrhPaFPH7Hcc6At4V1HGcZ3/ve9y57Cs4W8vLLL/ONb3zjsqex Ufp0js66Mb8osGjpNoYuZ3KXrWFdB8KymdJUWA/TNbpx5r3TM50OlX7h4fS8aWV6UV61s7vcz85P 3vb7ON1jPWtLWWd7efnlly97Cs6WcZZPJ/x15MxzXT/lilDEr+hUEM/bOcwMy4a1WtYMEhlBkN4z bTRNg7SDdB7p6UJFKJ0JO3FuiNBG5Cmq3TGlrXjbYRyYbR/uONsmoOH0rWSvMrvwGHeZd999d2nj DU9tuH4MC17zEbPn+dvrr6Pd4qJeR9tOLF8Wv5DbGjKZIo6NzvZBWym23tusYv3iwyDS2zi6CvN8 O9XhkxoGyRtTq0fpcGjk1tKxyYftOI7jLGKdjOiTWng7V4dlwmcaM3s2/HW0W1zU62jbicVWoRjT F3P3JMx3DqSvDOdeSJcIPIgxYDZt3d2dH0KgrseLq9uDCDyxpl1cWGwfqjqtjIuRru/PYCcYWgCG lcyhJWC+wrnsvlVjLDt+2TVOw0nXcZzrwCLhsyjmzrkezFcQ55M6zjOuv452h4t6HW07UcTI2aC1 TwzbfveeZQKZjGVDJCOqpKYI6aqK3Lp1g8n4kJSlKOsIVQg07ZMoImjcx1INlvr23n0DlpRJMqJU nxVyRqRGNZBSJmCYnvRQnG1nkfhdtn1aK8X8dda95mlYdR3HuS4sqhYuyo52rgfzH7tfhIj219H1 56JeR9tOnN+xqP23mRG0IuUaMJDcLjQs+dHj8RjLuY/U6OwbYiXjWUSga+TCNPmje2IF+hbiqgpi 5FwsHl0FXFxEX3k2JWZXcVYhvmqcde/fJV+0c30xMz75yU/yN3/zNzMi6NOf/jTf//73Z8TP008/ zXe+850zXeekc9cZ++mnn+6/P+s8dp1FwmcT4qc7/1Of+hR//dd/vbAqnVI69nM+afu0nPd8Zz0u 6nW07fQieho/N11YOKxGz1s7YiXkBFhmMplQxWn1uoxnbffBtrKdMygzrcG7KvU0DUQICjkPrwvS ne9cK1ZVcofpD+uK0kUC9qzV4lWC+LT7Hecq0f3BGwqdTjQ3TXOsgtgtIj8LJ5276v7PfvazvPXW W/32008/PbPtrMe8f3Uofs5TKR56noevo/nbW2+9deznPNxedP9pOO/5znpc1Oto24lQhG222c6F naWjE71N3cXXQcBQCWgwcp6Ka1Vtz2uFOCUfuvibDTGwLASxaYW6JYSAyrT63Yv4bGTJK7seOleT kwTnaSrKy47ZtKh1oexcd4Yiurv9+q//On/6p3/Kb/7mb/LWW2/N/FE8zx/Ik849zf1vvvnmtf5j fVEsEj6bEtHzr6WhpWOVneMyf47PPPMMb7755qVd/6pyUa+jbSfOZkIfb9M9H0+i2nUbtL61t4hg uZnJm+4QpskbyUByAgTibMfCKlQY5ZfKLPeCnE7c+8rCa8UmrQ+rBPQmRa8L6MK85xx8geV1IufM X/3VX/HEE0/wF3/xFzPVxO5r0zQ8++yzfOtb3+qrfM8++2w/xre+9a2ZMbtju68d8xXCbozumLNW qofXWzafZXNd57GsOveqMHwci/jqV7+6EU80lNfMZz7zGQD+1b/6V72AHr6Ohgx/rvP3n/Sznec0 53f7nnnmmaX3Ldo//9o+7RyvMhf9Otp2+kq0EGY80L1fuVtcKBGVQfScTePtRIQ8ONda4ZsEsmUk SRHdZpANyIjKIEPa2pbhYDb95QkISa53PMous6hhx7D6PL9vFcvGWXWNs7DJsa4qu/iYd4n5CuK8 iO4sHcN9zz33HG+88UY/xrPPPjuzPdy3rIo9HKP7w7yqgvXGG2/0x81fa34Ow+9Pmuuq+9d5nFeF N954g+eee27hfa+++urMz/ksDF9Hn/nMZ/izP/uz3gP95ptvLnwddZy0vexnu4x1z++ek2573df3 otf2aed4Vbno19G2Ezshm3LJeO58yh29x7mNwJtWracr/RbF1w29zmVMQ4GytJDipyZPUzqKwaM9 JwHafxRgZoRjSyCdq8Qy4XXa/YvuW9cWcl5cPDq7wHwF8Td+4zd4++23aZqGP/7jP+Z3fud3uHPn Tn//888/z507d2b+UN65c4fnnnuuP67bt0wczY9x584dnn/++RP/+Hbjz19r/nrD+ZxmHsNxFt03 PPcq0j3PQ1555RWapjl3jvPwdfTd7363rz6/+eabPPPMMzOi87Qiev7ncFpb0EnnD7fX+bkvun/Z 6+86cpGvo22nbfsdjlV7OxHce6Kbpl/817dhab3OOeeBH1pmbCHD1ZqiJUcvIVhK0OZId8kfQpj6 pzNkchu9J4To8RzO/eU0dhC3ejjXhflKNMwuDIPpx+3zX+cZ7l90zGnuX8Xrr7/O888/P/M7uGq8 +U+UTppHx7xQOM0ct5Wvfe1rfP7znwfgy1/+ci98NimiFy1S7ewccPw5PO/2POcd76Sf+7qv3av+ WlnFRb2Otp3YVX5jjMd8KzOrLVOJmSteaCFj5NT04jsMBHX3hJkZIQRijEwmE5IJFWUMCxURRYiY CTGG/kkXUUxBLLdVaMGyLyx07j9nzap2nKvKIhE9/FQQWPp1nuH+Rcec5v51WGe8F198ka9//ev9 vhdffPHE8zqG5511jtuGmfHqq6/OLAQb3s7KooWF3fjddZe9fs67Pc95xzvp577ua/eqv1ZWcVGv o22nN0nUdb1wgeHsYsPlL5qmaY4leoho/4SqKqiQgdCdP6eLh5YQLe0LpxON7udwnI4nnnzqsqfg XFO69+wujePb3/72TLRd9wnNa6+9RkqJ1157jRdffJHXXnutH+OLX/xif3/HompUt29+jC9+8YtL z5m/xqLxgGPjDeez7KP5VY9l0X3XgUWJCsPbWRme//TTT/Ptb3+bnDPPPvssd+7cOZcn+rSWmvOM t87PfdH1V73+riMX9TradnplOh8hN8yJLp7pOQE9d7zZ8v/NdRVpk3KgmWBMbR6qihrkbEguCxbR 2axq9W4rjgPAC7df4gc/fOeyp+FcU4YxZHC889x8lRrgK1/5/9s72x85jjOx/6qqe2aWR1KCbFli TpZPspzgTqLks2RLoWkLsCRe5PNSXtGEozPyNQmQIEiCA/KR0H+QD/kQ5OADDFkwbGolS9SFPvFo wApPR8GQLQewfZGl0xtpW6LEFy2X3N3pqsqH6u7p6el525ndnZ15fovBTndVV1d1V1c9/cxTz7OY C77ZdnnirJpIu5WxuLjIoUOHek6+/c5ZLi9LKx8HQchZXFzsW27Vsdlx25VurslGFX6KphunTp1i //79APzoRz/K+1dVXxrHdplhji/3m/K+Yr5e5+/W/6aVjepHk06ULQLMQnBXaaO7/SzR5udZB3nc O58vQmxpptOySQdkAA8OhfYabIJTrZsALSV1y/vH9NoSCcKgxOriVldBmHKKdoyZF4XyhHj06NGO ifHo0aNtZZTTBt2XHV+VXqbXObMyutWnqt2DtqXbcduR4s/vG6GJzhalnjx5MreD7tWXRt0us57j q/pNt/ve7fy9+t80slH9aNKJMqHVuZYdc7Xg3F8TnB1TjEoILUHceY9z6X4NmtTtHYbgOq9X2dP7 M4ggCMKkUNQSFgXq7TYpboc6TgIbJfxMSz9aL9PctipmWogOAm57I8shvLuRpWUu8owxmCgcl3n0 cM6hVHARbVNT6KIxiHMOU3HO9uiF42mwIAiC0J1M2CkLPOXtSWc71HESKN7TKgFolHKnoR+tl2lu WxUb1Y+jTYI6AAAbSElEQVQmnVQTrTt8Qxf9PIf/3Qtpub5rHZ/9zy5esJkOHxQoXxCW8blNdZXP aXD9FNWCIAjCGCgKOWUhqGzLOqk8+eSTE1/HSWGjhJ9p6EfrZRb734wL0R6tOzXAba5JvM61wUqp Ds8amQmHcw6f+Px7m3COLzr4aCN7qMpCdKaF1tPrIUUQBGFi6KZBLAtAwnSw2Zpo6UfTycwK0c4F v83WkWucvfeY1FWdNh5rbfDV7CGE/KYlDHsXvkfhwmmt8SoKCwp9y7Y6cTbVPmcCOiRJdmETvDOE ugRPHVgFKnjuMCbCiypaEARhQ3n88ce3ugrCFPD8889X7tdaU6vVNrk2wiRwyy23dO0X25kI0sWE lPxD066ZLr9JtOyXg6bYuYL2GdcW8rtVJuiSBjuYcygge3sBUJiopQ231mI2ovUziPgXFsr8+pc/ A2anb2TtFTo5cuTIVldBmDDW+2I1K+OJMDjTOPbmQrTRQUzt5dIuo2xu4b0nW5eoDCgdfD1rWl46 FKC0QuHyfR7wXmGVT/1Eu1wbbUiFdOvBKxLExd242LnDdP15LXtpmeafX2aJctSo7Ke27Pv//F/f 5esHH8zzb/e+MWx7hU7ee++9yj4wKz/PzhrFZ6TquRmF7T6eCIMzq2NvVPSIAVVhXH0a7CRKg6y4 trw+lZ51SfhWSrUsPorhMSuMor1XZC728uAqToNyhTJGbKmQUzWIVa2gFrY/3Qa27H+Z7d43hm2v 0Ek329Xt0geE4djIZ2a7jyfC4Mzq2BsFTbLFe93mHaNrhECv2wRpCAKuy/1Nkz80zrbecluePooL B4veP4K9tDHBFtv7oIFGeZT2kEjEwnHRbZIs/xe2P1UageLgVma7941h2yt00uv+b4c+IAxHWRM9 zmdmu48nwuDM6tibB1spPkTtwVI6PXUUfTYrZQBLatacLzj0rrWIEB3ChisPoehCQJdcEx7K0RqC m2mT2ldrUNP7FrMVlAe1KvdDoh2YDsoagfL3Mtu9bwzbXqGTKsFnO/UBYTg2UvjZ7uOJMDizOva2 2UT3bKhybcJzUbvsvcKYlq9oVbkMUKVa6OICxkKqCr6ilVOQhwvXOJfgkul+k9lsqga14uCWfd+3 b1/HsS+99NKG1Wvfvn0bWv5Wn28rqBrYBtFEl/vFXXfd1ZF3Eq/dsO0VOsmuVVHYuf3223n11Vc3 XfC57777OH369MD7qtJGPd+0UyVAj1uI7jfXbDaDjv398g2SXibLP+z8M+nz1ayOvRG0B1fJtnON cmbekZlY0LJxbj18QcPsCdrm4kPhct/QoWynSEVsj1I6F8S19hhUXqYxCueyB1tsosdJN21Ati9J kvwevvjii23H7tu3r2PfuHjxxRdJks1bQLrZ59sKylqB3NSqi3agl6bo5ZdfbusbG9kX1suw7RU6 Kf/cnt3vshC0WVQ9o932nTp1auRnetrHhDK9hJ9R7/Mwc81mM+h97pevX3qvOXTYvjbJfXNWx97I GIVSEUmhI3vvcxd3mZBrE4/H5gJ1MRqhMZl22ed/LTvnLPKhC9vW47XKg7vk9tHW4tOIhtY20boO aLSO8N4CYhM9LnppB7JP9rAOOoFNGl/5ylf48Y9/vNXV2HKqBrZeE2S3vgHkE17x/m9kX1jPPRy2 vUInZXOOvXv38sorr3D33Xdz+vTpoYTo+++/n5/85Ccj1ad4rqy8/fv3d5Q7rvs7a/2kSoAe1zMz zFyzWWTjype//OWBxpdRhehec+i0C9GzMPZG3RLy1ZReQyo8F7XB5dWW7TbTmZkHbftCPvI0UFgb yjbaoFSm+S5qvS3B2LrkYFpYN2U7tfIgV9QOVHX+4r6HHnoo/37ixIm2fP3STpw4kf8v7hukjOLx VWkQBsuqc5fL6Fdm1Tn6ta983DBtG6TsQamaFIv7euUv9g2go29k+0ZtT1XaoPdw1PYKnZT7ALTu c/lF6oEHHuDkyZP5scXtBx54AAiCL9CRL6O4v8zJkye5//772/KUBZDsnEmSdK1Pdr6qc5XTyoJK t7r2anvV9qRRbFcVP/zhD8duE91rrtlMes1v5fG+nKdferdzZceeOHGi6/n7jfvW2q7z0SDHbySz OvZ2FaIzwuI/gkeOkolHukXRy0YLDfhc8FYlGbi4SDF8z+yoO+02QpAW0USPi+JAVvyeDWjdNNEP P/wwx48fz/dl2xkPPfRQvt0rrbyvm2azXxnF7eL348ePtx3b6+29nFYuMytv2PYV07NyBm1bOW0U 7UNZu1T8ia2bJrqbzWJRgBqlLwxyHYa5h6O0V+ikeO3uuusufvrTn2Kt5dSpU+zfv5+TJ092fZEq br/wwgscOHCAF154oW1/cR8EYa64XUXV+brVoVyfYvnlcxXrkgmVxeP71bVb27ttTxLZ/ani6aef HouAO8xcsxkUx63jx4+PNG5XpVdRFGxHme/K+3rVfZB6jZtZHXsjSB/0tkWDClUlzJZ8Sue21L5d 9C1qoYOQ3MqvVcv0o1hGUZOdudvTKDQGJ1rosVLu6FU/rxWFo4xjx47l++fn59u2M5IkqUw7duwY Dz/8MMeOHassr3h8v/K7Hd8rbzfKearqXXWOYep+7Ngx5ufnh2rbuAbAqoGtrGXslr9szpFphbM2 jdqejbgOw7ZX6KSsPepmG50xjCCZTfTFfcePH+fAgQMdAkO5jOKx5WN61ad8vux7uS7Zi1u39HJd i9+zvOXtSe9zWV2LPPXUUyRJMpZnZpi5ZrMY17hdTu9Gcc4rz4G9xtB+c2ZV+laae8zq2BtBGqrb VGt6VXCnkX8vRjTUKJTSqQBedLTdbu+caZsL6wvbytdao3zLDCQcp9PMwdzDTfFN2GzKnb34E0zx A/Dcc88BcPDgwQ4Tnvn5+Y6yy4tPu6V3y1Pc16v8quP7bferz7DbBw8eHLhu5X292vbcc8/l6dn1 Xy/le1p1j4t06xsQNFdJkvDVr351qL7Qqz3D3uNxt1foJLvnn/3sZwG4995729IPHDjQc+Iedrvf /mJat7KGeZnu95I2yEtcVZ5u/yedZ599lkceeQSAo0eP5gL0uIXofnPNRpON1+UxZ35+Ph+X+o3b /dKrKKZnY2HV+UadM8c5b6yHWR17o0xoLQrHqqBa7iVEh21yrXHmVdqpTvMO59IlhyXzjlwg1y0h vmU/7cnNQhAhelx0G9jKWgJoaW2eeeYZDh48yDPPPJOXU/ye0cvWrLy/n711r/Krjl/PT6nDlpFt LywstNVvYWFhrG3L0svXfFiqFgwNahNdzpf1i2H7Qq/2DHuPx91eoZPitTp9+nSuLczu/4EDB0Z6 mR1EWOiW1q2s9dZnPUJTtzzd/k863nuefvrpDRFwh5lrNoOq8aY4dvcbt/ulV9HrmEHGuvXMK6PO G+thVsdeXRR2K001un18dR6tNUaFD7QubOYfOnzyYN55nvKn+EBLrJXxUrRR69fpi9uLi4ssLCx0 fC9/qtIWFhZYXFzsWnZ5X6/yq47vtz2OMorbvdLKdV9YWBiqbb3qUHVde233u8+D9o319oVx3uNB 2vvoo48O1V6hk27Xv5tJR/Y90/RVvYiXX8CK+7IJv+rn/vJLW7/9/c5f3C7XpVz/QeparkO3uk7y Z5Bndr0Meo7N+JT7c3n/sON2OX3Qc2b7epWdlV+eM8vnL6YP0taN/Aw710wLkfceYww+94zRLkiH ICthX1lbnGmiIdVGaw3ogjDcipmutQ42zhqCtw3IIhdqrbGJbT8vwVyklVcWFo6L8qTYrfNneYsc PXqUQ4cOcfTo0fx7Ob2Yr7i/XFbVg1Xc16v8quO7HVs8ptf5htmuqlt2XarOf/jw4YHbdvjw4bZ9 661j9t25ahdW3a5/Vd8oppWvQb++0Ks9g/ahQe87hIl70PYKnVhrc3d2xZ/2s0/28//i4mI++QMs Li5y6NChtutcTi/vy7YHuTfd8ozyfAxT/151HWRsm1R6jQ+jtmOYuWYjOXz4cOVYCt3HsX7jdlV6 FVXjW9X8OsicWT5/Mb3fvLHRDDvXTAvqf3zXeK01zto8oEoQYlPhWWeaZxciCPokF4yNMUEITrXS TRcekGDDDNa3AqrELjUDSf1DO58Qm4goirDWYqIE7xXau+AbWmdBVjwGg3fwV//9c7z496c5//6Z vAHXfeKmrbhuE4NSqs0+sR9/fPvn+PDcmXxAy36qLa+W3i72fNuFb37zm3z/+9/f9POWB7biJGat 5aev/JqvH3wQmI6+MUx7+zE/P79tfpLvRdUYkS1kKvO1r32NV155pc0TS/H7dugDwnD0e2Z++9vf cuTIEZ5//vmhyp2G8UQYnFHG3qrxaFzjb1FefO/sW/y7//iXHXLk37/883WXH2UNhnYbriygSm4H nWmNlc7zK6UwhLSMEO1QE4Tw1ol8GgRRZ9ptl9k9W5xLggsP53GA9o7IgXIaZx2Jt9gKbyHC+ihq Bnr9HCOsj8cee4zvfe97Hfu34poW72eVhqAq/3buG8O2V+ikSltYtU+YDjZSg7jdxxNhcGZ17I0y 0wtdWjzYbsPcijyoiFAmmF5kXjUAktR0w7s0cqHSeNcuSOd4jXcelMb74FLPuyi90A6PQavWogar HM6Km7txUX5DLG9Pe6ffaJ588kkee+yxjn3bQYje7n1jVgfycSKCz2yxkc/Mdh9PhMGZ1bE3986R 2Tu3wnr7ggBd9hFd8LqhAOcoeor2XoEChSHza9cKqkLwtKFcGkY8QimPtw7QKB0WLSY4tFNpNEON MQhjotskWR7chPXzxBNPtG1v1fUclyZ6u/SNWR3Ix4kIPrPFRj4z2308EQZnVsfeCECpIKG2BOh2 TTSkLupy1z0qFYAz+2ZH0XTFK1KNdLrtPaTn8F7hvQnpxqBVhNEK5zQajfHhZiQEEw5jFDGGeq22 8VdjRvjOE4tbXQVhQpG+IYjgM1tspPAj44kw7URBA+3IAqQEW+ZMkDa5Rtq5lvcO731uBx0eutRv NAbvHc63tgNFjx3Zx+K9xuWxxA1GabxPsLZJ4h1agzYK58SUY1zY5mX+63/+tx2LhMoLiGSxx3TQ TQCqEoamoW8M016hk8cff3yrqyBMCdMwngiDM6tjb5QJxZkwrVS7K7kgXLc7ls+DoBQdOPs0cqFW YB2ZDTUADqwLgVbC+cB7g3OaJEnfdm0zBGnB4QkLGI2OwSmsUywvT+cN2Gxee+21ra6CsIlordtc U/ZiGvrGMO0VOjly5MhWV0GYEqZhPBEGZ1bH3oIQDZDZQrcCpQA41x4lJ49YmG4HTXVBS+0hRBls +Z22trAYMc/nsTa8pRji8NaiwuLCWmSomQiANe+4snxlAy+DIAiCMKwbM0EQhFkmcs4RxboQahu8 d6k5h8OgUNqQFEKARlEQbl2w8UAByngSa/E+pHunsNan3jk8eJX7lQ7HKZz1eBzOeaLY0UyaeKup NyLiuIZyMdZ6cIZYxVtzhQRBEARBEAShRGSi1DNHwTIj0xYDOBw63ZctPCiG5i5GKgSLUrqgjS74 ni7kz2xjtNZk1iPWWowCUzPEJsJ7xUpzjZWVNZprDq0am3JBBEEQBEEQBKEfUaZVDj6dVW7XEqIX WpyDxLkQGry0ODAThsNCw+Dlo+Urumg/DUYZnPP5Jxe8vUrNQjxGgXae5lWHI8baGs7WwSqWr0qw FUEQBEEQBGEyiHINsyp6fyb1nuELdtEtFzhFm+aQNwjRbcFWfMundLmMdsK2SRcphhW7Pnj2UArv Fc3E8+HFixtzBQRBEGaQ+fn5ra6CIAjCtibK3I9oDeSL/WzBRjoIzdbatrDgeTjwzK8kIdx3m/eO FO89NvGpmzyTRj40eOdxPrXDTt3pQYPYNMDXWVtVfHThKhcuNLm0JJpoQRCEcXDs2LGtroIgCMK2 J0qSBO89RgMKnCtGJgwUbaGBgr20Ss04FD5dZNjK1/LM4Z3CugQfsqC1yn1Lh0AsIVqhSyVqZz02 Sbi81OT8hY84f8HixcOdIAiCIAiCMCFEQPDHTDPVOus2IToTitsXFip85nWjQFF7nR/rNM5ZtFdk 4b/D4sMsn0Khwc/hrccmMc01z/LSChcvNblyGbwzeQAXQRAEQRAEQdhqNJAvGsSXA62kWmLlaIUA 1yGsd0EzDal5h/YdgnT4noUT9x1abqU8xhiaqR9pozQ4z5XVFT5ausryis39SQuCIAiCIAjCJBAB NJNVjK7jlcUH6+Zgn6w01iusBe2bqdcOk/t5VqnQ7ZzHO0+sNVrFGBXT9JakmaC9paEJQrIC5VPT DG/Qpo7yGtf0qFjjlOfKSsK5c1e4ctkQRzuI46ssXfZ5AHGhE1kgJAhCL2SMEARBGD8RkC8QRJGH bfTehaArQGQU3qZ20KmHDaVM6gbPo5xKg6Y4vEqCgK0UkdJ4wsJDrcM+5cE5wKdmI6lZh/Kw2kxY bVq0jtBasby6xtoaGA07dqhubZhpvJi5CILQAxkjBEEQNoYoXyToHLqg7g32zyr3Ha1Sgdl6F8J5 e48umWYETx0WhUYrhddgrQtO7NLFhpkZtfcKb5vYRJEkDq3qLC8lXFl2OBthcVxdSbAW6jWo1Wcv JrsgCIIgCIIwmbQ00SoIzcHdXLZI0Af/G67lFzrXWpcLSrXOpJ80/EoqhAf3dhpSZ9Qq9yudJJa1 VYs2V7myvMqVZY/1K6yuQWLBaM3cDkNsks26JoIgCIIgCILQkyj7UtRIoxVaGZRyabhui7PBNV2w 9tCkdhjhWO1Tr9CmEA48mHhoo1BKgwWlU3MQq9LFjJparYbCsbJSJ1nTNNeusJJ4mk2IjaZe98w1 LFEs5hyCIAiCIAjCZBBBK6BKiCboUWHdIFq1O7ELQVI63djlaQp8vtgQNMEUBK1SATpJNdoahSLW Bh3HGG1YWr4aohU6sKnSeW6Ho1HTRFGNuTlZWigIgiAIgiBMBlEWdjvIwgXB2HmczgTkoIXO0pR3 qWeOECQl8Q4dPNNBKjgblaWDcxZUKgT7ENlQo/EolPM4CytLNa6urJA0w/nqRjNXV9TqHhOtYGKx iRYEQRAEQRAmg+jff9NudR0A+A//qc7K6hrNRBNHjnrNEUU1jLEYE1MO7CIIgiAIgiAIW0V0/v0z W10HANZWLVjYNeeYm6uDXiWKEup18D6hHokQLQiCIAiCIEwGUf8sm8PqiiWKNXEUE8cxOnIo5dDG EkWKKBKbaEEQBEEQBGEyiE793bMDZdz/4CMbWxMF9VgT1yCqNWnUFMpYTARxQxOJcw5BEARBEARh QtgQTfRas8n5D8+ztLSEUopdu3Zx3XXXEcfdTze3Q7PjD2Li2KC1xRhQxhBFnrlaDErMOQRBEARB EITJYGxC9IWLF/nVr/6R998/h/Me5zxXV1aBENlQ4bn3C/fw6U/fmvukLtKoQxQ3UdFVFMGZRxQT oigqizZuXFUVBEEQBEEQhJEYWYi+cPEiP/rbE1xdWWPnzl1obTj3wQdc+miJ3R/bwydv/RfsqEdc OneG737vB3z1Xz3EFz5/T0c52jhIIyVGUQjGEkUepROSpEmsxcWdIAiCIAiCMBmMJERfuXKF7/9g kZtu/iPmdsJbb77B66+/zsLCAjfccCOnXvoHnv3BEywtX+WO2/+E+/Z9ib/69ndo1OvceefetrLi WggPXqt7ajXQURMTaUyUBm7RYhQtCIIgCIIgTAYjCdGvv/4GS5evsLSm+N9/8zf8xTcOopSi0Whw 66235ILy+fPn+dnPfs5bb73JY3/xLf76O9/lv/3lf2HPnhvzsuJYo7SnFimiKEapVYyJqdchSRIi Ld45BEEQBEEQhMlgJCF6bm6O5atXiZXl47sbvPPOO1hrqdVq/OIXvwDg2muvZffu3XzpS/v5zW9e 41e//jV7997J62+80SZEm8gTmxAR0SWORlzDKI1truGsJdoxWkMFQRAEQRAEYVyMZGh8ww038OGF S/y/3y3zwJ/9OY1GA2MMjUYD7z3eey5cuMCZM2c4e/YsO3bs4Nrrruetd89wze7dbWUp7/Deo1FE sSKKIiITQohHMSjxziEIgiAIgiBMCCNponfv3sV1u3fy+99/yMc+cRO3/dFn+OO5iLm5OZQKArBH ce7Di/zsl6/zkd/J7373Ee+//yGf+tSn2srSOgjKxkTUahH1WoTSCSiN0ppGozFSQwVBEARBEARh XIzsnePmm/4Ze/QcFy59xKtrO9mhltj1zjmwTX5//iMuXl6jSUTiDWuJ5eqF31P3TXbv3tVWTq2u Ud4TRYZaXRNFBk+CxxNFuqePaUEQBEEQBEHYTEb2G3fbbbdy/r2zLNx/OzXWOHcF/vF9x8/fWebt D1f54OISVy59wPX1FR787B9y963Xcsstn+rwFd2oQ72hiGueyDiUbub+oU0E1tpRqyoIgiAIgiAI Y2Fk9e4//8xneOrp5/jYNTv51p/dwz+9c5Z/evNtzp+3GN3gums+zs4/mGNubo5rrrmGn7/8f/ji fZ/vKCeOY5QOWmcICwwBtIowHlyyNmpVBUEQBEEQBGEsjCxE79lzI5cvX8Z7TxxH/OENHyMi4YNd 9TbtsdYa7z1nz/6Wm2/+ZGdBejX4hY4tKPAq2ElHRqNNlAvVgiAIgiAIgrDVjCUM4J4bb+Ddd9/t mp4tMmw2m5w58y43f/Kmzkxeo7VGa0AbvPfB5ENr8KoyVLggCIIgCIIgbAVjEaJvu+3T/OqXv+ya rlQQgi9cuMCNN3yCHTs6nT43dni0SVBYjG4SxxpjNEZ5PAkosYkWBEEQBEEQJoOxCNF77/gTXnrp JZaXlyvTMy3ya6+9xp177+iSxxMZiGNDFEXhGOXCfxPMQQRBEARBEARhEhiLZPr5e+7mc396J3/9 7W/z7rvv5rbQzjkuXbrE22+/zenTp/m/v3iVf3nfFyrLUECw3tBoA0p7DAqvPcZIyG9BEARBEARh chib8+V/861/zVNP/5Afn/w7li4vc+nSJXbt3Em9XqPRqDPXqPONRx/hC5+/p/L4EGwlaJ9BY1CA w3uNtU001QsLz79/ZlxNEARBEARBEISBGJsQ3Wg0+MajX+c3r7/Bm2++xcrqKrt27uT66z/Onj03 cv3111OL467HxzWde+AwKLTRKKVJvMM5h9aysFAQBEEQBEGYDMYaBrDRaLD3jtvZe8ftQx8bxzFY l3vl0FqjlEd7CbQiCIIgCIIgTBbR/gcf2eo6ABApjStYaHvv8R58asZR9BP93tm3Nrl2giAIgiAI gtBirJroUSgHU8kXJyoX/rvw/8tfvG9zKyYIgiAIgiBMJV+890/Xfez/ByXOf9lNmd9bAAAAAElF TkSuQmCC ------=_NextPart_000_023B_01CBCC3B.393DA870-- ========================================================================Date: Mon, 14 Feb 2011 11:46:32 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Orientation swap Comments: To: Hekmatyar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> A couple other things to check: (1) That the voxel sizes are on the same scale between scans; (2) Use a smaller smoothing kernel as well. (3) Set the voxel size to the actual size, rather than multiplying by 10 or 20. (4) Make sure the origin is the same in both images. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Mon, Feb 14, 2011 at 11:35 AM, Hekmatyar <[log in to unmask]> wrote: > Thanks Dr. John > I will check that, The orientation of anatomical before coregistration is > same as orientation in the functional. I have attached some snapshots of > display and checkreg. > Those are from rat brains, I will try changing separation values as well. > Thanks > Hek > > -----Original Message----- > From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On > Behalf Of John Ashburner > Sent: Monday, February 14, 2011 10:19 AM > To: [log in to unmask] > Subject: Re: [SPM] Orientation swap > > I'm not sure that any orientation has been swapped. The images are > simply displayed in a way that uses the orientation images in the > headers. If you show one of the images with eg the Display button, > then the orientation in which the image is displayed will be > determined by the orientation encoded in your image headers. > > As these do not appear to be human brains, I would perhaps suggest > changing the values in the "Separation" option. When estimating the > parameters, the algorithm only looks at the image every few mm (to > save time). In the case of these images, every few mm is likely to > mean that most of the information is unused. I notice that the joint > histograms are very noisy. This should be fixed by using more of the > data. > > Best regards, > -John > > On 14 February 2011 15:08, Hekmatyar <[log in to unmask]> wrote: >> Hello All >> Could you please suggest me why the orientation of the images were swapped > after co-registraton (snapshot of coregistration)? Thanks >> >> Hekmatyar >> BIRC >> UGA >> >> > ========================================================================Date: Mon, 14 Feb 2011 13:02:36 -0500 Reply-To: "Claire (Jung Eun) Han" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Claire (Jung Eun) Han" <[log in to unmask]> Subject: Putting a non-zero value for the time derivative MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --000e0cd2e6e258a66c049c41da0a Content-Type: text/plain; charset=ISO-8859-1 Hello, What does the result mean if I put a non-zero value for the time derivative (e.g. putting '1' for both hrf and the time derivative)? Thanks in advance, Claire --000e0cd2e6e258a66c049c41da0a Content-Type: text/html; charset=ISO-8859-1 Hello,

What does the result mean if I put a non-zero value for the time derivative (e.g. putting '1' for both hrf and the time derivative)?

Thanks in advance,
Claire
--000e0cd2e6e258a66c049c41da0a-- ========================================================================Date: Mon, 14 Feb 2011 12:20:10 -0600 Reply-To: Steffie Tomson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Steffie Tomson <[log in to unmask]> Subject: Resizing mask MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CBCC73.D0E75523" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CBCC73.D0E75523 Content-Type: text/plain; charset="US-ASCII" Content-Transfer-Encoding: quoted-printable Hi Everyone - probably a silly question, but how do I resize a mask? I have masks from the AAL atlas that are 181x217x181 and I need them in the 46x56x34 size. Conceptually it should be a simple interpolation, but is there a function that does this? Thanks again, Steffie ------_=_NextPart_001_01CBCC73.D0E75523 Content-Type: text/html; charset="US-ASCII" Content-Transfer-Encoding: quoted-printable

Hi Everyone – probably a silly question, but how do I resize a mask? I have masks from the AAL atlas that are 181x217x181 and I need them in the 46x56x34 size. Conceptually it should be a simple interpolation, but is there a function that does this?

Thanks again,

Steffie

------_=_NextPart_001_01CBCC73.D0E75523-- ========================================================================Date: Mon, 14 Feb 2011 18:37:57 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Resizing mask Comments: To: Steffie Tomson <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> You can reslice the mask to match some other image using either the realign (reslice only) or coregister (reslice only) options. Best regards, -John On 14 February 2011 18:20, Steffie Tomson <[log in to unmask]> wrote: > Hi Everyone – probably a silly question, but how do I resize a mask? I have > masks from the AAL atlas that are 181x217x181 and I need them in the > 46x56x34 size. Conceptually it should be a simple interpolation, but is > there a function that does this? > > > > Thanks again, > > Steffie > > ========================================================================Date: Mon, 14 Feb 2011 13:52:37 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Putting a non-zero value for the time derivative Comments: To: "Claire (Jung Eun) Han" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> It means that you would average the weight of the HRF and the derivative. This is completelly uninformative as you would get the same answer for when the the time derivative is 0 and HRF is 1 or when the canonical HRF is 0 and time derivative is 1, which are clearly very different in their interpretation. There been several posts this week on this topic. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Mon, Feb 14, 2011 at 1:02 PM, Claire (Jung Eun) Han <[log in to unmask]> wrote: > Hello, > > What does the result mean if I put a non-zero value for the time derivative > (e.g. putting '1' for both hrf and the time derivative)? > > Thanks in advance, > Claire > ========================================================================Date: Mon, 14 Feb 2011 17:16:57 -0500 Reply-To: cliff <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: cliff <[log in to unmask]> Subject: spm_create_vol &&& spm_write_vol MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --000e0cd5d004f9ac20049c456771 Content-Type: text/plain; charset=ISO-8859-1 Hi, all, I thought before that "spm_create_vol " is used to create the ".hdr" files of the images. However, today I found that "spm_write_vol" can produce both of the ".hdr" and ".img" files. then, what is the "spm_create_vol " exactly used for? Thanks~ --000e0cd5d004f9ac20049c456771 Content-Type: text/html; charset=ISO-8859-1 Hi, all,
I thought before that "spm_create_vol " is used to create the ".hdr" files of the images.
However, today I found that "spm_write_vol" can produce both of the ".hdr" and ".img" files.

then, what is the "spm_create_vol " exactly used for?

Thanks~
--000e0cd5d004f9ac20049c456771-- ========================================================================Date: Mon, 14 Feb 2011 22:32:05 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: spm_create_vol &&& spm_write_vol Comments: To: cliff <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> spm_write_vol calls spm_create_vol. Sometimes, for example in the fMRI stats, we may wish to create headers but only write out a slice of data at a time (as there is not enough memory to hold all the images). best regards, -John On 14 February 2011 22:16, cliff <[log in to unmask]> wrote: > Hi, all, > I thought before that "spm_create_vol " is used to create the ".hdr" files > of the images. > However, today I found that "spm_write_vol" can produce both of the ".hdr" > and ".img" files. > > then, what is the "spm_create_vol " exactly used for? > > Thanks~ > ========================================================================Date: Mon, 14 Feb 2011 17:20:56 -0800 Reply-To: Matthew Brett <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Matthew Brett <[log in to unmask]> Subject: Re: extract ROI at the edge of the cerebellum Comments: To: Viridiana Mazzola <[log in to unmask]> Comments: cc: [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi, On Sun, Feb 13, 2011 at 4:00 AM, Viridiana Mazzola <[log in to unmask]> wrote: > Dear all, > I am trying to extract a ROI at the edge of the cerebellum following the Brett's FAQ instructions. > > 1 . I rename the ROI my_roi.mat, then I write in matlab the command: > roi_filename = 'my_roi.mat'; > my_roi = maroi(roi_filename); > my_roi = spm_hold(my_roi, 0); % set NN resampling > saveroi(my_roi, roi_filename); > > 2. then I rerun the extraction of my_roi, but this error appears: > Fetching data : 1/1 Error in ==> maroi.getdata at 30 > if nargin < 2 > ??? Output argument "vXYZ" (and maybe others) not assigned during call to Could you have a try of the fixes suggested here and let us know they work? http://marsbar.sourceforge.net/faq.html#why-do-i-get-a-no-valid-data-for-roi-warning-when-extracting-data The marsbar is the best list for these questions - I've Cc'ed it... Best, Matthew ========================================================================Date: Mon, 14 Feb 2011 20:25:30 -0500 Reply-To: MP <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: MP <[log in to unmask]> Subject: Re: EEG significant clusters outside the MIP glass brain Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundarye6ba476b13485c07049c480aa2 Message-ID: <[log in to unmask]> --90e6ba476b13485c07049c480aa2 Content-Type: multipart/alternative; boundarye6ba476b13485bfb049c480aa0 --90e6ba476b13485bfb049c480aa0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello Vladimir, I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here? P.S. I include the iskull mask as an implicit mask in the flexible factorial design. Thanks - Muhammad On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote: > Dear Muhammad, > > Threshold masking will not necessarily work. It depends whether the values > outside the brain are really low. You can use an explicit inner skull mask. > There should be one in EEGTemplates as far as I remember or you can make > your own. > > You can either re-run your stats or use small volume correction for inner > skull but the latter will not fix the graphics. > > Best, > > Vladimir > > Sent from my BlackBerry® smartphone from orange > ------------------------------ > *From: * MP <[log in to unmask]> > *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]> > *Date: *Thu, 30 Dec 2010 21:33:40 -0500 > *To: *<[log in to unmask]> > *ReplyTo: * MP <[log in to unmask]> > *Subject: *[SPM] EEG significant clusters outside the MIP glass brain > > Hello all, > > I am doing EEG analysis using SPM8 and while viewing the results, I see > that some of the significant clusters appear outside the brain. I understand > that it could be due to smoothing of the images, but is there a way to > restrict these clusters inside the MIP? I think Threshold masking may be the > way to go, but I am not sure what threshold value to use? > > If indeed threshold masking is the way to go, would I need to re-do all the > analyses? > > Any help would be much appreciated. > > Thanks > - Muhammad > --90e6ba476b13485bfb049c480aa0 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello Vladimir,

I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?

P.S. I include the iskull mask as an implicit mask in the flexible factorial design.

Thanks

- Muhammad

On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:

Dear Muhammad,

Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.

You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.

Best,

Vladimir
Sent from my BlackBerry® smartphone from orange
From: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
Date: Thu, 30 Dec 2010 21:33:40 -0500
To: <[log in to unmask]>
ReplyTo: MP <[log in to unmask]>
Subject: [SPM] EEG significant clusters outside the MIP glass brain

Hello all,

I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?

If indeed threshold masking is the way to go, would I need to re-do all the analyses?

Any help would be much appreciated.

Thanks
- Muhammad

--90e6ba476b13485bfb049c480aa0-- --90e6ba476b13485c07049c480aa2 Content-Type: image/jpeg; name="Results_with_iskull_mask.jpg" Content-Disposition: attachment; filename="Results_with_iskull_mask.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gk64sk150 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAMxAjgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKK5fxP4vl0XUbXR9J0efWtbu4nnjs4ZkiWONSAXl dj+7U5IUkEMy7etAHUUVzfhDxVN4mt75L3RrvSNS0+4Nvd2swLqrYyCkmArgqQePUHoVZukoAKKK y/D2v2PijRIdY0xpGs53kWJ3XaXCOybsdQCVJGcHBGQDxQBqUUVz+r+J/wCy/F/hzQPsfm/2z9p/ f+bt8nyYw/3cHdnOOox70AdBRRXk9j8W/EV34cj8TH4fTnQOXlu4NTjldI1cq7CLaGO3DdcDjJIH NAHrFFRwTw3VvFcW8sc0EqB45I2DK6kZBBHBBHOa5Ox+IFndfEzVPBM1v9nurSJJbeczAi5yiuyh TghgHyAN2QrHjFAHYUVz/hXxP/wk39t/6H9m/szVZ9N/1u/zfL2/P0GM7unOMdTXQUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5vrN/ Z+HvjhY6rrF3BY2F94fksre4nkCo0yTiRlJP3flIOWwDnAJPFekVXvrCz1Ozks7+0gu7WTG+GeMS I2CCMqeDggH8KAPO/GPjtdY+G/i268HXV29xpjrbm8t4WKkbk8xonxghUL5YcrjdwCrHiNLGkaeP GeleDdSu73wufCUlzcbyXjS8MeAdxUbXaMElRjncMDYFX3uxsLPTLOOzsLSC0tY87IYIxGi5JJwo 4GSSfxqva6Fo9jp0+nWelWNvYz7vOtobdEjk3Da25QMHIABz1FAHieieE9MsdY+G6qbuU+I9Huot VaW6kJuYjaIViOCAEUEBQMYCr1IBrA8OaZoDfDzwY9nJH/al74os01TyLthKoElwsWQrZiO0nBG0 nr719HrpOmq9k66faB7BClmwhXNupUKRHx8gKgDAxwMVXHhrQVuHuF0TTRO9wt08gtU3NMpJWQnG S4LMQ3Ubj60AeP8AjTQ/C+meKtK8K67dXeleCrfR57mziSR/LN4Xcsd5DF3VWLBSTyVAB37Wk8M3 2qalqfweu9ZknlvpItVLSTrh3QR4jY+uUCncfvZzk5zXsmpaTpus262+qafaX0CuHWO6hWVQ2CMg MCM4JGfc0XumW188c7xxreQJItteCJGlti67WaMspAOPYg45BHFAFyvB/DXivw/p/wCzfPp11rVj Hftp97CLTz1MxeSSUIPLB3c7l7cA5PHNeuQaFqMNxFK/izWZ0RwzRSRWYVwD907YAcHpwQfQipLH wn4b0y8jvLDw/pVpdR52TQWUcbrkEHDAZGQSPxoA8Q8S2Os+EoINHtY/sl54m8NWWmGLckn2m/je KB0ychMQSMucqpzkHcM1v2nhx/8AhMfHlr4fTyr7Rf7In0pDI2N8NswWJjvXKum6M7m6OSa9cvNJ 03ULi1uL3T7S5ntH320k0Ku0LZByhIypyoOR6D0qSGws7e8ubyG0gjurrb9omSMB5dowu5hy2BwM 9KAPI/hrL4c8S6Nqeqap5ccE3jKW+01bqfyW+0FUaIDDYZ8MflyQeeDXslZ/9haP/wBAqx/4+/t3 /Hun/Hx/z26f6z/a6+9aFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVE277XHj7nlvn73XK49vXrz6d6 AJaKKih3ebPu6eYNv3um1fXjrnpx+OaAJaKKitd32SHf9/y13fe64/2ufz59aAJaKKqaj/x7J/13 h/8ARi0AW6KKibd9rjx9zy3z97rlce3r159O9AEtFFRQ7vNn3dPMG373Tavrx1z04/HNAEtFFRW+ 7yjv6+Y/97puOOvPT8PTjFAEtFRXG7yhs6+Yn97puGenPT8PXjNS0AFFFFABRUV1u+yTbPv+W237 3XH+zz+XPpUtABRUTbvtcePueW+fvdcrj29evPp3qWgAooqK63fZJtn3/Lbb97rj/Z5/Ln0oAloo qpJ/yFrf/rhL/wChR0AW6KKKACiorjd5Q2dfMT+903DPTnp+HrxmpaACiol3fa5M/c8tMfe65bPt 6dOfXtUtABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFRn/AI+EOT9xuMHHUd+n58+nepKiZFN3G+Pm EbgfKehK9+g6Djr6dDQBLUcf35uSfn7gjHyjpnr+HH45qSooUVZZyBy0gJ+Ujnao79enUcfiDQBL UcHFvEMk/IOSCCePQ8/nzUlRWqLHaQoowqxqB8pXjHoeR9DzQBLVTUf+PZP+u8P/AKMWrdVNR/49 k/67w/8AoxaALdRn/j4Q5P3G4wcdR36fnz6d6kqJkU3cb4+YRuB8p6Er36DoOOvp0NAEtRx/fm5J +fuCMfKOmev4cfjmpKihRVlnIHLSAn5SOdqjv16dRx+INAEtRw8IeSfnbqCP4j6//q9OMVJUVuip EQowPMc/dI5LEnr/AD6HtxQAs3KDkj516An+Ien/AOr14zUlRXCK8QDDI8xD90nkMCOn8+g78VLQ AUUUUARz828oyR8h5AJI49Bz+XNSVFdIslpMjDKtGwPyluMeg5P0HNS0ARn/AI+EOT9xuMHHUd+n 58+nepKiZFN3G+PmEbgfKehK9+g6Djr6dDUtABUc/NvKMkfIeQCSOPQc/lzUlRXSLJaTIwyrRsD8 pbjHoOT9BzQBLVST/kLW/wD1wl/9Cjq3VST/AJC1v/1wl/8AQo6ALdFFFAEc3KDkj516An+Ien/6 vXjNSVFcIrxAMMjzEP3SeQwI6fz6DvxUtAEY/wCPhzk/cXjBx1Pfp+XPr2qSolRRdyPj5jGgPyno C3foep46+vUVLQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUTIpu43x8wjcD5T0JXv0HQcdfToalq Jj/pcY2Z/dv8+zOOV43ds+nfHtQBLUUKKss5A5aQE/KRztUd+vTqOPxBqWooT+9n+Tb+8HOzG75V 5z39M+2O1AEtRWqLHaQoowqxqB8pXjHoeR9DzUtRWpzaQnZs/dr8mzZt46be30oAlqpqP/Hsn/Xe H/0YtW6qaj/x7J/13h/9GLQBbqJkU3cb4+YRuB8p6Er36DoOOvp0NS1Ex/0uMbM/u3+fZnHK8bu2 fTvj2oAlqKFFWWcgctICflI52qO/Xp1HH4g1LUUJ/ez/ACbf3g52Y3fKvOe/pn2x2oAlqK3RUiIU YHmOfukcliT1/n0PbipaitzmI/Js/ePxs2/xHnHv1z3znvQAXCK8QDDI8xD90nkMCOn8+g78VLUV wcRD5N/7xONm7+Ic49uue2M9qloAKKKKAIrpFktJkYZVo2B+Utxj0HJ+g5qWoro4tJjs3/u2+TZv 3cdNvf6VLQBEyKbuN8fMI3A+U9CV79B0HHX06GpaiY/6XGNmf3b/AD7M45Xjd2z6d8e1S0AFRXSL JaTIwyrRsD8pbjHoOT9BzUtRXRxaTHZv/dt8mzfu46be/wBKAJaqSf8AIWt/+uEv/oUdW6qSf8ha 3/64S/8AoUdAFuiiigCK4RXiAYZHmIfuk8hgR0/n0HfipaiuDiIfJv8A3icbN38Q5x7dc9sZ7VLQ BEqKLuR8fMY0B+U9AW79D1PHX16ipaiU/wClyDZj92nz7MZ5bjd3x6ds+9S0AFFFFABRRRQAUUUU AFFFFABRRRQAVT1TUodJsGu5lkcB0jSOMAtJI7hEQZIGWZlXJIAzkkDJq5WfrWmf2vphtRN5Miyx XEUhXcFkikWRNy5GV3IuQCCRkAg8gAy7rxlbWiM8mm6kBb2/2q/zGimxi3Ou+QM4LjMUv+q8zITI yGUtl678REsdD1S8stKvnltvtcFvLNEqwyXMHmbl5cMyhYnkLDgqpUHzPkq5qHhG+1BLpZNZjY6l ZCx1Nns8l4t0pxBhwIiBPIAW8zgJncQxaPVfAv8Aafh46T/aPlZu9QufN8jd/wAfSXK7cbv4ftOc 552ds8AFyx8XjUbCW5ttB1lpI7h7XyDFGGaZHdZFDF9mF8skuWCHIUMXyoI/GVtcEJZ6bqV3MqE3 EMMaFrZ/MeII+XAJMsUiblLKNhZmVMPVd/B003hiPS7i9tLidb2e9k86yL2s7SySSFJIC+WQGXIG /hkRsnGDX07wG1jFpURvrQJY3EtwPs+nrEYi87TFbdtxMKNuEbqS4aNQo25JIBHpnjq4u9H0a8uL Ly5JtPgu72NVBYvOwit44hvwPNk3FWZjtVMOFLZXcj1yb+1NNSa1kt7TUUkijWeMpLDdR7mMbYJD BkWQhh8v7okM4kSufsfC19pCW2kyXMd1Hc6PDpZuxZbolNszsiyxFmykscrq2WUfuyAytIm3QttE mtr/AMP6YnmPBpTzX8tx5ZSIM6SRJBGp4VAJpNqgt5aQop+8rUAdZRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFRMf9LjGzP7t/n2ZxyvG7tn0749qlqMqftCNsBARh uwMjkcZz3+nbtjkAkqKE/vZ/k2/vBzsxu+Vec9/TPtjtUtRxqQ8xKBcvkEADd8o5PPPp26fiQCSo rU5tITs2fu1+TZs28dNvb6VLUcClbeJSgQhACoAAHHTAJx+ZoAkqpqP/AB7J/wBd4f8A0YtW6qaj /wAeyf8AXeH/ANGLQBbqJj/pcY2Z/dv8+zOOV43ds+nfHtTpZY4IXmmkWOKNSzu5wqgckknoKQqf tCNsBARhuwMjkcZz3+nbtjkC3UkqKE/vZ/k2/vBzsxu+Vec9/TPtjtUtRxqQ8xKBcvkEADd8o5PP Pp26fiQCSorc5iPybP3j8bNv8R5x79c98571LUcKlUIKBfnY4AA/iPPBPXr/AIdKAEuDiIfJv/eJ xs3fxDnHt1z2xntUtRzKWQAIG+dTggH+Ic8kdOv+PSpKACiiigCK6OLSY7N/7tvk2b93HTb3+lS1 HOpa3lUIHJQgKQCDx0wSM/mKkoAiY/6XGNmf3b/PszjleN3bPp3x7VLUZU/aEbYCAjDdgZHI4znv 9O3bHMlABUV0cWkx2b/3bfJs37uOm3v9KlqOdS1vKoQOShAUgEHjpgkZ/MUASVUk/wCQtb/9cJf/ AEKOrdVJP+Qtb/8AXCX/ANCjoAt0UwSxmZoRIplVQzID8wByASPQ7T+R9KfQDViK4OIh8m/94nGz d/EOce3XPbGe1S1HMpZAAgb51OCAf4hzyR06/wCPSpKAIlP+lyDZj92nz7MZ5bjd3x6ds+9S1GFP 2h22AAoo3YGTyeM57fTv3zxJQAUUUUAFFFFABUc8y21vLO4kKRoXYRxs7EAZ4VQSx9gCT2qSigDh 7P4reHJo5ftbX1rNHcTQmL+zrpyAkjICSIuCQoJXqpODyDVj/haHhP8A5/L7/wAFV3/8ary6b/kK av8A9hW+/wDSmSkoA9KsPit4buNOtprxr60upIkea3/s66k8pyAWTcIsNg5GR1xRf/Fbw3b6dczW bX13dRxO8Nv/AGddR+a4BKpuMWFycDJ6ZrzWigD1L/haHhP/AJ/L7/wVXf8A8aqve/Fbw3BAr2rX 1zIZY0Kf2ddJhGdQ75MX8Klmx324HJrzWigD1L/haHhP/n8vv/BVd/8Axqq978VvDcECvatfXMhl jQp/Z10mEZ1DvkxfwqWbHfbgcmvNaKAPUv8AhaHhP/n8vv8AwVXf/wAaqvcfFbw3HPaJA19NHLKU nf8As66XyU2MQ+DF83zBVwP7+egNea0UAepf8LQ8J/8AP5ff+Cq7/wDjVV3+K3htdRhhVr5rV4pH kuP7Ouh5bgoFTb5WTuDOc9tnuK81ooA9S/4Wh4T/AOfy+/8ABVd//Gqrp8VvDbajNCzXy2qRRvHc f2ddHzHJcMm3ysjaFQ577/Y15rRQB6l/wtDwn/z+X3/gqu//AI1Ve3+K3huSe7Sdr6GOKUJA/wDZ 103nJsUl8CL5fmLLg/3M9CK81ooA9S/4Wh4T/wCfy+/8FV3/APGqr6p8VfDmnWUNyrXziV1K+Zp1 1EpiEirLIGaLBEasWIHoB1IrzWl8df8AIn6F/wBgrVv/AEZFQB77BMtzbxToJAkiB1EkbIwBGeVY AqfYgEd6koooAKKKKACiiigAooooAKKKKACiiigAooooAKKqfZJ/+gjc/wDfMf8A8RR9kn/6CNz/ AN8x/wDxFAFuq86OZg6BuInHyKucnbjBPQ8dOnr0FM+yT/8AQRuf++Y//iKQ2MpcOb+43AEA7Isg Hr/B7D8qAKrPfDpHqh+n2b+ppy3d3GZ3XSLtmdtyruhX+EDBIfnp1/wqz9kn/wCgjc/98x//ABFI LGVSxW/uAWOWISLk4xz8noBQBh6lr/ie28r7D4Lnvd2d/wDxMYI9nTHU855/KqOmeIfGexILvwBJ AscYHmLqtuQxGB90dO9dX9kn/wCgjc/98x//ABFItjKiKiX9wqqMABIgAP8AvigA0+6u7m1je806 SzmbO6NpUfZyccqecjB/Gl1H/j2T/rvD/wCjFo+yT/8AQRuf++Y//iKbJp8kqhXv7kgMG6R9QQR/ B6gUAcNZFta1pLbx2WguVZXs9OljEVo0mQMo4Y+e3K/Kx43sMHnHoLIxu43x8ojcH5R1JXv1HQ8d PXoKztS0C31eza01CaS5gbPyyRxHBwRkHZkHBPI5GatGxlLhzf3G4AgHZFkA9f4PYflWdODhf+n8 ztxeKjiOVpWt0Xwr/D69b69bsuVFCjLLOSOGkBHygcbVHbr06nn8AKh+yT/9BG5/75j/APiKQWMq lit/cAscsQkXJxjn5PQCtDiLlRW6MkRDDB8xz90DgsSOn8+p781D9kn/AOgjc/8AfMf/AMRSLYyo MLf3CjJOAkQ5Jyf4PWgCe4RniAUZPmIfug8BgT1/n1HbmpaqfY5Cy772eRQwbayx4ODkfwVboAKK KKAIrpGktJkUZZo2A+UNzj0PB+h4qWmSxiWF426OpU8A9fY8fnVf7JP/ANBG5/75j/8AiKAJmRjd xvj5RG4PyjqSvfqOh46evQVLVM2MpcOb+43AEA7IsgHr/B7D8qX7JP8A9BG5/wC+Y/8A4igC3UV0 jSWkyKMs0bAfKG5x6Hg/Q8VD9kn/AOgjc/8AfMf/AMRSNYyujI9/cMrDBBSIgj/vigB32Wb/AKCF z/3zH/8AEVzfjW71u0msm0aObBjlE8ttbLcTRrujwUjZgGyeD1wCT2ro/sk//QRuf++Y/wD4imnT 5DMspv7neqlQcR8A4z/B7CpnHmja9jbD1VRqqo4qVuj2/r8O6Zm+E7PQYNNe40KZblbhg9xdGUyS yyYBzITyG5yV4wWPAzW/WTbaBb2V5c3drNJDPdY89o44l8wgsQSAmCcs3PU556Crf2Sf/oI3P/fM f/xFEFyxsPE1FUquabd++/8AX9WQ698zyF8uF5T5inagUnAIP8RA7Y9RnNcrc+J/Gce37P8AD2eb Od2dWt1x+tdO1jK4w1/cMMg4KRHkHI/g9aX7JP8A9BG5/wC+Y/8A4iqMDntJ1vxPc3pfUfBk9jG4 CErfW8pAGTnhgTknGO3XvXTQzySqpe1mhJ6hyhx/3yxqH7DKHLi/uNxABOyLJA6fwe5/Ol+yT/8A QRuf++Y//iKALdFIoKoqlixAwWOMn34paACiiigAooooA+fpv+Qpq/8A2Fb7/wBKZKSlm/5Cmr/9 hW+/9KZKSgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKXx1/wAifoX/AGCtW/8ARkVJS+Ov +RP0L/sFat/6MioA+gaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC ubbQtdl1S9kfxZqUVm7h7aOCK1zGDndGQ0DEgEZDbskNgjK7m6SigCvY28tpZxwTXs97IuczzhA7 5JPIRVXjpwB09easUUUAfP03/IU1f/sK33/pTJSUs3/IU1f/ALCt9/6UyUlABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUvjr/kT9C/7BWrf+jIqSl8df8AIn6F/wBgrVv/AEZFQB9A0UUUAFFF FABRRRQAUUUUAFQ3ZAspyZTEPLbMgBynHXj0qOW4mW68mOBXAQPkybS3JBCjGCRx3HUVMrR3NuGG HikXIyOGUj0NAFDyv+nPUf8AwL/+2VZtIkXM8M0jwyopUO7Njqc/Me4I/Kj7BD/fuf8AwJk/+Kqy qqiBVUKqjAAGABQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVHPI0NvLKkMk7ohZYoyoZyB90bi Bk9OSB6kUAeBTf8AIU1f/sK33/pTJSVdTw94purnULmPwpqQSfULuVRJNbIwDTyMMgyj16jg9QSC CZP+EY8Wf9Ctff8AgTaf/HqAM6irsHh7xVc28U6eFNRCSIHUSTWyMARnlWlBU+xAI70T+HvFVtby zv4U1EpGhdhHNbOxAGeFWUlj7AEntQBSorR/4RjxZ/0K19/4E2n/AMeqObw94qgQO/hTUSC6p8k1 s5yzBRwspOMnk9AMk4AJoApUVo/8Ix4s/wChWvv/AAJtP/j1RzeHvFUCB38KaiQXVPkmtnOWYKOF lJxk8noBknABNAFKitH/AIRjxZ/0K19/4E2n/wAeqOTw94qieFG8KaiTK+xds1swB2lvmIlwowp5 OBnA6kAgFKitH/hGPFn/AEK19/4E2n/x6oz4e8VLcJAfCmo73RnBE1sVwpAOW83APzDAJyecZwcA FKitH/hGPFn/AEK19/4E2n/x6ox4e8VNcPAPCmo70RXJM1sFwxIGG83BPynIByOM4yMgFKitH/hG PFn/AEK19/4E2n/x6o4/D3iqV5kXwpqIMT7G3TWygnaG+UmXDDDDkZGcjqCAAUqXx1/yJ+hf9grV v/RkVaH/AAjHiz/oVr7/AMCbT/49TPFXh/xTqPh7TLSLwpqQktrW7tHJmtmBe4kj2EBZSdox8xIG 0c9ASAD3Sio4JGmt4pXhkgd0DNFIVLISPunaSMjpwSPQmpKACiiigAooooAKKKKAMu78oXwE0NrJ KVG1mmCSY3NgID3HrkZPp2fAPsVg90pYxC3VkgMm4JgE4Ddwc9fb0wBTvb+2N+WW6UxeSA5Vo2Q8 nhgTlx14HI7ferW3mGw3xx7ykWVjVCmcDoB1H0oAYbp5GP2aNZkTG9t+M5AOF45OCD2HI564fZx2 8drH9lGIWUMg5xgj36f571AnnwRNJGLaRJMuXhjIOT/FjJ3+vUHjjNT2T+ZYW8m1V3RKdqjAGQOA PSgCeiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPnTxN8e/FWjeKdW0y3sNGeCzvZreNpIZSxVH KjJEgGcAdBWX/wANHeMP+gbof/fib/47XB+Niv8AwsbxKsn3Dqt0CcdP3rc/h1/CucdSjsjDDKcE e9AHr/8Aw0d4w/6Buh/9+Jv/AI7R/wANHeMP+gbof/fib/47Xj9FAHsH/DR3jD/oG6H/AN+Jv/jt H/DR3jD/AKBuh/8Afib/AOO14/RQB7B/w0d4w/6Buh/9+Jv/AI7R/wANHeMP+gbof/fib/47Xj9F AHsH/DR3jD/oG6H/AN+Jv/jtH/DR3jD/AKBuh/8Afib/AOO14/RQB7B/w0d4w/6Buh/9+Jv/AI7R /wANHeMP+gbof/fib/47Xj9FAHsH/DR3jD/oG6H/AN+Jv/jtH/DR3jD/AKBuh/8Afib/AOO14/RQ B7B/w0d4w/6Buh/9+Jv/AI7R/wANHeMP+gbof/fib/47Xj9FAHs6/tC+MCoZrDQxuj3r/o8wz823 HMoz36Z/nhjftEeMACf7P0PgZ/1Ev/x2vOJ7aKPR47iNCrl44mZSQGUxByDjgndzyc9OBjjMf7jf SgD71ooooAKKKKACiiigAooooA5pYI5Ti0sEa0wS8t3EsahOxRgN3Izycnoa6RtwQlQC2OATgE/W si3s4T9oh1CysEKIHLwpgbTkdSMgjaea1pJEhieWQ4RFLMfQCgCsst+yAm0gUkZKm4OR7cJU8Bka 3jMyhZSoLqOgbHIqH7Zt4ltpoyfuhtpDH0yCQD0HJGc1LbSCa1hlBYh0VgWxk5HfHGaAJaKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigDLj8QafNrc2kRfa3u4X2SlbKYxI2wSYaXZ5YO1lOC3cDqcVY03VbH V7drjT7mO4iVyhZPXAI/AqVYHoysrDIYE8HNoN/dWeu6LpUV9bS3/wDaKXw1GaR7XExlMLxNjaGL OrYiBCqXEmZAmeo0ZLi88Q6lrUlnPaQz2ltaJFcgLJvieZnOASNuZgoOfmKMRlSrMAfIHjv/AJKH 4l/7Ct1/6NasI5kGckuByPYD/D+Vbvjv/kofiX/sK3X/AKNasGNzHIrjBIPQ9D7H2oAbRT5VVXOz JQ8qT6f40ygAooooAKKKKACiiigAooooAKKKKACiiigDTPnLAnnbwrQrjfnBOeOuedgGPu8e33qz /cb6Vpz3Mb6PHbqxD745tpBUFREEJGcAncCOATweSKzHHyHkjigD71ooooAKKKKACiiigAooooA5 a5tdMWSSGHTmlMKbriSKc4h9cE8MRzx7fXHUMquhVlDKwwQRkEVVXTbRLUWyRFIg2/COwO71JByf /rD0qeeVYLeSZgSsaliB1wBmgB3lp5XlEbk27SG+bI989aI40hiSKMYRFCqPQCq/mX3/AD7W3/gQ 3/xFOsUgWyiNshSJ1DKpPQED/P1yaALFFFFABRWC9tbyKVeCJlOeGQEcnJ/MgH60pghIIMSEEMCN o5DHLfn39aAN2isA2dsSSbaEkliTsHJYYb8+/rUcsFs96gkhiaRkc5YckfKp4xg8YHPOOPWgDo6K yrfTV+X9ykaqcjCgEcbePw4+lPtdMsEnnMcCb0kUEjqCEGOwxwcccY+poA0qKrmwszszaQfJt2/u x8uOmOOMZOPrUVlZ2P2KAxWsCxtGhUKmRj7w5IyeTnJ5zzQBdoqsunWKY2WVuu0YGIlGBnPp68/W qeqadaSWq5gQZmjBwo5DSruH49/WgDVoqi2lWzKQEHIYcqCMMct+fes+fTbeO9RDao7usjgqgxzt DZHqcj9aAN6isGS1gdyZII2bnJZATyMH9Bio7eOASTLHEg8uQdOx2KPTj5SBx2/GgDoqKwUtreNt yQRKwxyqAHgYH5AkfSooILaSMEQxEI5C8Z2lWYDGRxjnHYduKAOjornxBbQeSBDEiebEAFG0Z8wY 6D1OfTJ9zXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZd7r9jY63p2j yNI95fOVRI13CMBJHDOeigiJwueWIOAQrEAGpRWOfE+lpeTQyz+TDFvH2uX5IHeMEyosh4LIAS30 fBJjkCWNM1i31XzVjSeCaLBeC5iMcgRs7H2nnawBI9CGUgMrKoB8YeO/+Sh+Jf8AsK3X/o1q5+ug 8d/8lD8S/wDYVuv/AEa1c/QA+NTIwjHVjhc+vpTKKlmdZiJAu1v4+c7m9fxoAiooooAKKKKACiii gAooooAKKKKACiiigDXFpJDpwuWKhGCwYXJyxxIMkADoehJOR06EUn+430q/Jdi401IBH8yhZmfI OAqiPbwCR0B5IHOMdCaD/cb6UAfetFFFABRRRQAUUUUAFFFFABUc5kW3kMKhpQpKKehbHAqSigDM +z2//QD/APHIf/iquQSzyyv5lu0UYVdu8qSTznoTx0/Wp6KACiiigDBe4RFLFZSOfuxMTwcdAP8A 9Y56UpmUAnD8Bj/q27HB7fl69s1JVuzg3HzW6A8D1oArQFJCS6zBAWGRCxzt69vy9e2as7IILlT5 crEBvm2OcYAPQDB+vXPA71eqJt32uPH3PLfP3uuVx7evXn070AKsyvtwJOTgZjYds9xxx+vHXio4 Z1aVxtlG5gQWR8crnuMDoePXrycVYqKHd5s+7p5g2/e6bV9eOuenH45oADcINnyy/Ptx+6bvnGeO OnOenfFMt51NvH8soIVQQyOSMnHVhk8jqe3J61YqK13fZId/3/LXd97rj/a5/Pn1oAat1G+3Czcj IzC4747jjn9OenNV9SmUWqnEnE0Z/wBW3aVQe35evbNX6qaj/wAeyf8AXeH/ANGLQBOZlAJxJwGP +rbscHt+Xr2zUTXKCcHZNhQ6k+XJ1GD0xg+x/AZyas1E277XHj7nlvn73XK49vXrz6d6AGyzRZKS JI3XpExHAz1A9D+fA5qsltE5cr5q73G3cGP8IPQj5fT0z7kitCood3mz7unmDb97ptX14656cfjm gDKYlWxslxx8xiYDkZ6kf5PHWq8VwmxcpMNznGY3PVjjORx0+g47Yroqo/ZmliZukgd/73I3HHX2 /D04xQBQimWVoiiy/wCsQ4ZHQ/fx6Z7dPTrwc1u1iOHDxBRhhNHn73TeM9Oen4evGa26ACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsfWbG4u9V8PTQR747TUHmnO4DYhtZ4weev zOo49fTNbFFAHB6j4MvtVsp9Ckkjgs1uL+7S+HzlzdpcKE8vIIKG5ckk4IRMZLsI+g0ezvn1m+1j ULeO0luLeC0Fskvm4ELSsX3YHDNK2BjO1VJwWKJuUUAfEHjv/kofiX/sK3X/AKNaufroPHf/ACUP xL/2Fbr/ANGtXP0AFOjKhxvzsPDY64ptFAEk0Rhk2llYYyGU5B+lR0/cXjCnnbyPp3/z9aZQAUUU UAFFFFABRRRQAUUUUAFFFFAG3cWscWmQzqzb/ki2sQflZBISM8jk9hjHc5Oc5xlD16etWvtEz2iQ u58oxiUIeBuB2Agcc7Rj+Lv74qv9xvpQB960UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ1rB 5r7m+6p/OtGoreLyoQp69T9aloAKibd9rjx9zy3z97rlce3r159O9S1Gf+PhDk/cbjBx1Hfp+fPp 3oAkqKHd5s+7p5g2/e6bV9eOuenH45qWo4/vzck/P3BGPlHTPX8OPxzQBJUVru+yQ7/v+Wu773XH +1z+fPrUtRwcW8QyT8g5IIJ49Dz+fNAElVNR/wCPZP8ArvD/AOjFq3VTUf8Aj2T/AK7w/wDoxaAL dRNu+1x4+55b5+91yuPb168+nepajP8Ax8Icn7jcYOOo79Pz59O9AElRQ7vNn3dPMG373Tavrx1z 04/HNS1HH9+bkn5+4Ix8o6Z6/hx+OaAJKit93lHf18x/73Tccdeen4enGKlqOHhDyT87dQR/EfX/ APV6cYoAqahGwaCROpmjB69Nw9Oemfb14zV+q94u6KM5I2yxngE/xD0//V68ZqxQAUUUUAFFFfOF umnQ3VnL4rn1zwp4/a7eT/hI7uFmtp3WXbsA3bGj8t1XgKgAHO3hgD6Por548X/8ID/ws7x1/wAJ t5/neVaf2f8AZ/M8zd9mG/Zt+TdnZjzPlz7ZrL8aaJq954F+H2na55lpf2+mapKVkjG5Y4IhJEhU YwTHGi88jPIJBFAH03RXz54s1iHxd8TfC2s2k93HaWF7o0S2s6jG66L3G8YYgHYsanjkjrhRnX1n S7TW/EnxS164SS6Ok6YbXTrnzXMcLGzkE8a4O0kFvmXnaW7E0Ae2UV4v8I9F8KyT6Te2vgzXLDV7 fT1mOq3ccqW0zsgRzGTIVbdvYj5RxkjGK9ooAKKKKACis/W9E07xHo8+k6tb/aLGfb5kW9k3bWDD lSCOQDwa5P4OTzSfDDS7e7lka8snmtbiOViZIGSVgI3B5UquwBTjAx2xQB3lFFFABRRRQAVj6jr3 2DXtJ0oWU8v2+Vo2uMbY4cRSyDk/eY+Uw2joOWIyobYrL1XTZr7UdDuImjCWF61xKGJyVNvNFheO u6RTzjgH6EAx7fxotxf606Lpv9j6M8i3tyL5muIxGhLMbdYyQN6uoywLBCwBBGdjStXa/uLizurO SyvoESZ7d3V/3Uhby23LkZ+R1IzwyNgsu12w7nwhcat9psdRlgisP9NEc1kBHPMl1v3I427VVd+T 9/zHSORipUq2xpOmXkOo3eqalNA99cRRWxFspWMRxGQq2GJO5jK7EZwoKr820u4B8ceO/wDkofiX /sK3X/o1q5+ug8d/8lD8S/8AYVuv/RrVz9ABRRRQAqMyOrqcMpyD71ZubeY20V+Y2EM7vGHxwZFC lx9cOh/4EKq1saFJZT/aNK1K7js7O62v9reMuIJEyVYhVLsCrOm0YGXDH7ooAx6Kkmhkt5TFKu1x jvkEHkEHuCOQRwRUdABRRRQAUUUUAFFFFABRRRQB0K41HTobS1Hm3RkRgmMMVWEA8keoPG78KoX1 nc2ErW93C8MwRXKOMHayhlP4gg/jVvw46x61Zu/3VyT8ucAKe2D/AJ9OtWPF5ifXpTEY2QW9uAUx jIgQHGFUdc9vxPUgH21RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAAgjIOQaKyrTV4CgjMd3u Hf7LKf8A2WrX9owf3Ln/AMBZP/iaALdU78mNJJ4+JY7eXYdpOOh69Ow4PX8DS/2jB/cuf/AWT/4m onuraSZZGinYBGQg2kpyGxntjHHp/wDXAMSw8SyW5kGotLKTjZsReOufT2rpoUVZZyBy0gJ+Ujna o79enUcfiDVAjSmOTp5P1sH/APialjvII3mYR3H7x9xxay/3QOePb2/rQBfqK1RY7SFFGFWNQPlK 8Y9DyPoeah/tGD+5c/8AgLJ/8TUcF5BBbxQiO4xGgUYtZccDHcH+dAF+qmo/8eyf9d4f/Ri0f2jB /cuf/AWT/wCJqtf38LW6AJcf66I820g6SKe60AalRMim7jfHzCNwPlPQle/QdBx19OhrhLbxTdeN pJYdKnn0rTYsedPHH51zKG42hVyIej/MSTwpHeuxN5AbhJvLuMqjL/x6y55IPp7en/14hNT1Wx04 nCyw7UKj97quq9eny6dbMv1FCirLOQOWkBPykc7VHfr06jj8Qah/tGD+5c/+Asn/AMTUcd5BG8zC O4/ePuOLWX+6Bzx7e39as5i/UVuipEQowPMc/dI5LEnr/Poe3FQ/2jB/cuf/AAFk/wDiajhvLeCM qI7nG5n4tJe7E+h9f/1dKAJ71FeBQwyPNiP3SeQ6kdP/ANQ78VYrIudQgu5YbZY7gHzo2y9tIo4Y Edh+vH1rXoAKKKKACvJ774d+NdTs5PCt/wCJ4LvwlJdiV7qfdJqTQghxEWI2nDgfNnPGen7uvWKK AOX0Pw3eaZ478V65NJA1rq/2P7OiMS6+VEUbcCMDJPGCfwqPxJ4Um13xn4Y1RhaSafpqXqXkE+SZ VniEYAXBDDrkEjj1rrKKAPH9F+FniC00XSYtRutKl1K08QWV/LcRFhvtLaFYkjz5YJZQDgEY5Jzk mpIEhuvGvxS8KQXtpaT6pbwiytJJRGrzS2jea6oOSSdrOVBPGTmvXKpyaTps2qQ6pLp9o+oQpsiu 2hUyovPCvjIHzNwD3PrQBX8NabNo3hXSNLuGjaeysobeRoySpZECkjIBxkegrm/BHjx/GXiPxJbx W/l6ZYfZTZPJC0csqSox3sGP3W2hl4HysMjmu4rzeddZ8I+I/H/ildH+2w3n9nCxhFykZuiqeW6r 1IYFsBcZY4CgkigCO/8AG3i/TfHlnpl1pmjDT7zUxaQWSXDSag1uQ3+l4UkCL5WY5UEYKnH3qk0r x34kuPGmiWepaJBY6Rr32sWUMwkjvoPIGd0ysNvzYyFXoGHzHHzV9T0bxdrnxA017zQbGzt9O1UX EPiGymTzHs1Vytu6FvM+YsVbB25J+Ujk1/BNh4yT4i3+u+JfC86SahvhS9fVoXSwtR86QrEn3vmC gt+OAdxYA2PCvxKt9f1jxRJdPBYaFpn2Q2t1dqbdnSVW+dy7Y2sQpThcqy5GTR8Ff3nwt028k+e6 u5bie5mbl5pDO4Lu3VmwAMnngVY0vwrLJ478cXWsaZBPpGrfYPs4nCSpN5URDZQ5xhsfeA9RXaQQ Q2tvFb28UcMESBI441CqigYAAHAAHGKAJKKKKACiiigAoorn9e1bVNJllvI7aA6ZaxI8it80147s y+VDhhtkGE2gq3mNIqApy1AHQUVx9543/s6KbU7u1zpAlu7aIRHM/mWqzNIzAkLtbyJQoByNqE58 wiPY0nU7ybUbvS9ShgS+t4orkm2YtGY5TIFXLAHcpidScYYBW+XcUQA+OPHf/JQ/Ev8A2Fbr/wBG tXP10Hjv/kofiX/sK3X/AKNaufoAKKKKACiiigDT+zy6jpP2mJd81niOVVILmMglXx1IXayluijy x3rMq9pOrXejXv2i1kYBhsnh3sqXERILRSBSCyNjBXPIqK9t0gkjeEsYJkEkZY5OOQQfowZc4GcZ xg0AVqKKKACiiigAooooAKKKKANnRIjNqdtEoXc4KjcQB90+oP8AnpzU/ieNo9ZnDMrExRNlXDA5 iU9QTzz6kjvzUWgTfZ9WtZuuwE43Yz8p4zkf57HpU/iu4e41uV5cB/IhTG5j92FF/iJPboenoOlA H27RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBFbxCKIDHzHlqlqJ2uAp2RRM3PDSEDrx/Ce2T 9eOetKTNg4jjzhsfOeufl7dx19PegCSomP8ApcY2Z/dv8+zOOV43ds+nfHtTS11k4hhxlsfvT0x8 v8Pc9fT3qOVrgXsYS3iZfLk+diQR93AzjjJzx6DPbFAFuooT+9n+Tb+8HOzG75V5z39M+2O1KpmO 3fHGOecOTgY+nrx9OfaoLZrgzzh7eJF8xfmUnLfIMnp82DgZ44HtyAW6itTm0hOzZ+7X5Nmzbx02 9vpQWuPkxFFzt3fvDx1zj5eccY6Z9qhsmuGsoC9vFE3lp8ikgL6jGOMDHHrx70AW6qaj/wAeyf8A XeH/ANGLUitdHbvhhHHOJScHP+76c/Xj3qvqRm+yriOPPnR4+c9fNXb27jr6e9AFLV/DFvqNwL60 nl03VF5F7a4DPwMLIOkiZVflP93GRzWux/0uMbM/u3+fZnHK8bu2fTvj2pSZsHEcecNj5z1z8vbu Ovp71Xd7r7YgFtCRskw+48D5dozt4yeo9s5OMUlFJto0nWnOMYyd0ti5UUJ/ez/Jt/eDnZjd8q85 7+mfbHah2uAx2RRMvPLSEHpx/Ce+R9OeelR27TGSbfBGn7wZIzyNi85x83PHbge1MzLNRW5zEfk2 fvH42bf4jzj36575z3oRrgsN8USrxyshJ6c/wjvgfTnjpUNu1x5QxbxJmQ7hkrj5myQMHPYg8bs5 4oAS/QFIXEe8iaMH5d2BvHOPbrnt1q5VO4aY26+dBHnzYuEJk58weoHQYOe3PHHNygAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn9W0bWLvXINRsdUsYo7eLbFBeWDziKQ7g0il Zk+YqQuSCQAwBAdgegooA5ubwZY3bzQXckk2lu88yWJ+UJLOrrM28EMQRLJgZ+Uyvyf3Yj0NK0hr C4uLy6vJL2+nRIXuHRU/dRlvLXauBn53YnHLO2Aq7UXUooA+IPHf/JQ/Ev8A2Fbr/wBGtXP10Hjv /kofiX/sK3X/AKNaufoAKKKKACiiigArb0NbbUoJdFlt5JL24b/iXSrIQI5iPubApLmQqiDJAUnJ 4zWJRQAUVpXcf26zGpLIhlGEuY8/PuHHmY/un5csTy5PrWbQAUUUUAFFFFABRRRQBu+HIxNrNrE2 7a4ZTtJB5U+n+fXirHi63W212WNVZR9mt2wwIPzQIe6rxz6Y9Mjk0tFLLqEDJkMFYgjqPlPsf89x 1EniHA1SYLjHlR4x6eWvuf5n6mgD7jooooAKKKKACiiigAooooAKKKKACiiigAooooAgtH324z1X ip6xozJGTtuJVBzkqFz1z3GOBwPY+vNagUumVnfBDYIC8ZPB6dug/XNAEtRlT9oRtgICMN2Bkcjj Oe/07dscsMEhJP2uYZLHGE4yOB93t2/XNNkgke7R1mkVdjjIVDjO0AZIz2J9z16AUAWajjUh5iUC 5fIIAG75RyeefTt0/Eixsu3M0jYOTkLzxjHA9efr7cVnX6y2ml6nceax2RM4DIgDYixk4GTk/ToA OOoBq1HApW3iUoEIQAqAABx0wCcfma+e/Fvje9sPseBZneH++D22/wC0PWrPg34n3k2oNE8umoq2 +QR14ZeOW/CgD3+qmo/8eyf9d4f/AEYtUvD93LqujW1894ZDIGB8sJtOHPPT0GP/AK/NWdSjY2qj zpBmaMZwvGZVwenbt+uaAL9RlT9oRtgICMN2BkcjjOe/07dschjYgjzpBkMM4XjJ4PTt2/XNQNbS m5VhcS42yfNsT5c7cAHGcDGehzjk8YIBbqONSHmJQLl8ggAbvlHJ559O3T8SjwuzEi4lQc8KFxyM dx26/U+nFMghdHk3SMf3gOSqjeNgHOOvPOeDxjp1ALFRwqVQgoF+djgAD+I88E9ev+HSkSF1YE3E rjjhguOBjsO/X6j04qGC3kjiAe4kXa5Y/Ii7huY84HfIyeDxng5oAW9JKRKFDEzJkEA8Bgc8kdOu e3v0q1WLKssk0amd5tsseNyoeA4JPQDp39gRz12qACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACuf17VtU0mWW8jtoDplrEjyK3zTXjuzL5UOGG2QYTaCreY0ioCnLV0Fc/q2jaxd 65BqNjqljFHbxbYoLywecRSHcGkUrMnzFSFyQSAGAIDsCAY8XjOdrzXL6W5gj0rSvtTmyOnyi5nj twUkZJWkEbYlU/dDAAoGKs2B0Gk6neTajd6XqUMCX1vFFck2zFozHKZAq5YA7lMTqTjDAK3y7iiU 5vCS6i80GsXsl/pu+d4LZwyuhnV1k3yBssAssiIAFCq2DuIVl0NK0hrC4uLy6vJL2+nRIXuHRU/d RlvLXauBn53YnHLO2Aq7UUA+NPHf/JQ/Ev8A2Fbr/wBGtXP10Hjv/kofiX/sK3X/AKNaufoAKKKK ACiiigAooooA0NGvo7K+xc+YbG4QwXaIMlom6kLuAZlIDqCcb0UnpVe+s5LC+ntJWjd4nK74mDI/ oysOGUjkEcEEGq9SAh49uPmUZB9vT+ZoAjooooAKKKKACiiigDe8NOseuWTu4RQTlicAfKe+R/n1 6Va8avnxFNufLC2tlOX3HIgjGM7m6dMZ46YHQYKfcX6VeeUXOliBCzvBGw2HJxlg2VBLfwqxJUJj HJOcEA+56KKKACiiigAooooAKKKKACiiigAooooAKKKKAMF7m3jUs88SqM8s4A4OD+RIH1qzb38U GQ0sYj+ZmORxt4Y/gevpUdFAGiuoWbZxdwEgkEeYMggZP5Dr6VDd3UUE/mGWMNHDL8p2k5AVsdd3 TnA/HtVZXZG3KSD7U64njug8DgLJJbyIpOMc4B/2vTpx69qAKsHiqwywurhU6bdsb8/oazdX8RWc thqkEEyus8TgZiKkkxhevf6n+QFU7/wbfuGlhntlRY8/fb3PpWZZ+E9Tu5pIhc2/yjndI3t7e9AH j3jzT5Ln+z9kUr7fMzsXOPu+1ct4Ss7tNVlJtph+4PWM/wB5a+kp/htNchPONoxXp+9cfyFUdC+H FulyZVitwGi7TOepFAG78Or2WLwtplnKqxjEh+YENgytj9SBXValPCLVSZYwBNGSdw4CyqG/I9fS l0fT10vS4bJQoEe7hSSOWJ6nnvT9R/49k/67w/8AoxaAJzPCASZYwAGJO4cBThvyPX0qu9zb/bEY 3EICpIrfMvBG0kZzkYA5H59BVyom2/a48/f8t8fd6ZXPv6dOPXtQAPdW8bFXniVhnhnAPAyfyBB+ lR28kZkmYSxsJJBsxt5+RT2PPAzzzj2xUrzRx/ecA+neqSX6ebceWnO8ZJK9dq+nPp15/DFAFtbu 2c4W4iY8cBweoyP0BP0rLa/iEflefCFeQlTlRv3MxXGDznsepxn1qWWZ5jljx6DpVa32+WdnTe/p 13HPTjr+PrzmgBEkjujF5EqSfvY2+Ta/Acc9fY8+3HIrfrEbbui39PNj9Ou8Y68dfx9OcVt0AFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFc/4iuNRsJbW8tb7aplit4rMxKIp5 HbB86U5KLjAUrtIbtKWWMgHQUVweo+M77SrKfXZI457Nri/tEsR8hQ2iXDB/MwSS5tnBBGAHTGCj GToNHvL5NZvtH1C4ju5be3guxcpF5WRM0qlNuTwrRNg5ztZQclS7gHx547/5KH4l/wCwrdf+jWrn 66Dx3/yUPxL/ANhW6/8ARrVz9ABRRRQAUUUUAFFFFABSglWBHUHIpKKAHuowHXG0np6fWmU9CMlS eG4+h7GmEEHBGDQAUUUUAFFFFAFpPuL9KueU1tpzTOdyXGWVCCo+UMmcsNrEFwflDEDPKY5pp9xf pVpp0bTmXYyBAYpHVc7ix3r90L3THzs3XKjjgA+66KKKACiiigAooooAKKKKACiiigAooooAKKKK AMd4rpVJFpK554Vkzwcd279foPXilMFyAT9mc4DHG5ecHgde/b9cVr0UAYxjuwSPsUxwWGdyc4HB +937frikktrkTq4tJXKqygqY8dAe5zyRj69exraooAyo47sAf6PIobhgzIRjGeefw+vtzUlqZo5W J01o95BLDyxgFenBycYx9T6dNGigCubmX5P9Cn+bbn5k+XPXPzdu+PXjNRWxMUKBbCVCFVeRED1x /C2O2ePw9Ku0UAVlupmxmwuFyMnLR8c4xw34/T34qvfvdSQKsVjI5EiPgugPyyKfXuBkfritGigC lJPeEHZaMOGP3lJ4PHfv2/XFU5UvZJxI9pOxUOo+aPB6HP3s84wP17Vs0UAY7xXSsQLSVxzyrJjg Z7t36fUenNJFa3AZm+zOpkcE5KcfKPQ89Mdzn2xWzRQBjpFdM2DaSoOOWZMcjPZu3T6n05pqW1zE qqtpKQzbjzGNu4knOD274z1781tUUAY6W9xKYzJaSoA6MQxjJGG+pHGM/Q8c1sUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFY+oeG7PUtRN3NJOFliWC7t1YeXdxoWZEkBBO0 F34UqGDFX3LxWxRQBjnwxpb3k00sHnQy7z9kl+eBHkBErrGeAzgkN9XwAZJC9jTNHt9K81o3nnml wHnuZTJIUXOxNx52qCQPUlmJLMzNoUUAfEHjv/kofiX/ALCt1/6NaufroPHf/JQ/Ev8A2Fbr/wBG tXP0AFFFFABRRRQAUUUUAFFFFABTyd6En7y9T6j/AD/OmUqNtYHGR3HqKAEopzqFbg7lIyDjGabQ AUUUUAWk+4v0q86omlh4Qqy+WzSPGfn+8EwxBYgFXIwRGDkfewM0U+4v0q1C7zQSRyO0ghDeUjHd sBjcttXnHIBJC9gdydSAfddFFFABRRRQAUUUUAFFFFABRRRQAVR1W8ezto9jKjSyrF5jdI8/xY74 xV6qep+cLMmG2juQGBeJxnco649+lAFNJ7+K1u3aeO5hELSQ3SbR8wH3cD3/AM9hBHdX9u2mzT3q zx3TKDFsVCNw6jA5Az7dvWo4bV3OozW1lLbW72zIInBDO55yF7dcfy71oaVpVrbQW9wLfZcmIbix OQSOeD0NAFWw1G5bWLpLqdRaq0qx7to5UjPvwDSaVq05S8m1KQLHGI2AC8KHyR057iqlzaTrDcTL bTPJ9rnVUCnlHTG7p06VJdWMsdvq0MUMrrtt1jO0kuFABx6/hQBsNq9gluZzcDyhJ5RYKSN2M44H 69KdPqdnbW8U8s4WOUAocElhjPTrVLW7eR5rOdRclI2cN9m/1gyOCPbjn61Vltnt9O04x212Hj3M rxHe8RbnBXA3A9+nTGfUA2kv7aQ24SUN9oBMWAfmx1+n41UvdQ/49/ssv/L6tvL8v5jkfqKpAXME ek3Ulk/7kSCSOCMZG4cHaOmepqOOC6aJGktpEc6qJWXBO1fXPce/SgC9Z69a3H2kySKixMSvB5jG Bu+pJ6daspq9hJayXKXAMUZAc7TkZ6cYzWYY5Bb6raSWM8u+Z5l2/KrKSuMN698Y7Y9qm02a4giv J5ra5dAyiMtCBM46YIH3scc/X6AA2JJEiUM5wCwX8ScD9SKry6hbwtIrmT9398rE7BeM8kDHQin3 iGe1mt14eWJlUkHA4xyfxrPkimnttRlXz0EqkrAUHzZiUemc544PUUAa9RT3EduFMm75m2qFQsSc E9AD2BqggufPuTK85GWwsaHj5vkwScdPQY67qc0U8otgzSpMJyZHUZCny2+7kEbegH155zQBoRyC WMOoYA/3lKn8jzS7lDhSw3EEgZ5IHX+Y/Osm5+2pdkW5lLlSoJBYN+7OGz9xfmAGMA9+hqSGNf7R t5VF35eyRVMm/A+71zyB9773p7LQBp1FNcRwbd+4luiohYn3wATjpz7iqV8t6bhhbb8GMFcHjcA/ 5fMY/r7gGkIkCRSyfaUJaXEsa72Cl8qpXB4Ix24244zQBfimjm3GNw4UgEjpyAR9eCKSW4ihdEkb DP8AdGDzyF/mw/OspYrwRTgRNGzrhEi+ULJ5K85/u5BA5wD68EEonnvfM8mbZ5o27lPC7oD+A4Y/ nnvQBpm9tlglnMyiOJirn+6QcEfWp65+e0uGt7uFYXMczSysuPvMDJg+vURYHf8AOrLfaRd3Cjz0 jZXJCq7Y+YYIJODkEnCYIHHUCgDVEiGVogfnVQxHsc4/kadWRB5zXCy7JxFtjVlYHcfncDkgEgZB OcEjBPcHXoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuX8USfZ5VvYNSnhurXyiQJ8QWcb M26eeJeXjIVlO7IG3KtFh5V6is+60TTr3UYL+4t99xDtwd7BX2ncm9Qdr7WJZdwO1iSuCc0AcHea re6MNc8RagJDc77/APsox6rLNalreOULFJbqwRCUhZz975hJkxttSus0Z7iz8Q6losl5PdwwWltd pLckNJvleZXGQANuYQwGPlLsBhQqroWuiadZajPf29vsuJt2TvYqm47n2KTtTcwDNtA3MAWyRmpN N0qx0i3a30+2jt4mcuVT1wAPwChVA6KqqowFAAB8WeO/+Sh+Jf8AsK3X/o1q5+ug8d/8lD8S/wDY Vuv/AEa1c/QAUUUUAFFFFABRRRQAUUUUAFFFFAD0+ZSnfqv1/wA/0phBBwRg0AkHIODT3+ZQ/fo3 1oAZRRRQBaT7i/Sr0jIdIJjK5WMpKVGOTIpUMcLnIUkAmToeFxkUU+4v0q1EhhtZpyVO4HHlsGdV 2shLBeVG5lHLAHI+V+wB910UUUAFFFFABRRRQAUUUUAFFFFABTXkRGRWOC7bV9zgn+QNOqpexPLJ aBGdMTEl0AJUbG9QR7fjQBZSRHZ1U5KNtb2OAf5EUqsGGRnqRyCOlY0sN5HNKElm2mUkSeXks2xN vC7Rj7w5+Xjmlka6SRS7ThfPUKQeMGcg59sbMfpxuoA1UmjkjikVxtlAKZ43ZGf5UedGZvJDgyYJ 2jtjGf8A0IfnWNb28yPp4lFyURYifvfKxRwQQOgGEHt36nJNbzJqF5JCLkOqSOpG4hjtjKgZ4PIP A9MdOKAN2mySJEoZzgFgv4k4H6kVkSx3vDLNOu6SX+Etgh/kAAI4xk5b5emeMYdcLLJNLEPtWWkR g20kKBIvqCvA5GO2dwyM0Aa9RLcRMkLq2Vm/1ZweeC38gazl+1+agPnfK4WLrjaJGDbux+Tbgt17 c1HZx3IksBIkwCbeCPlVfJxz6HduH8/4aANOW8hhl8pvMLhQxCRM+Ac46A+hqeqZhkbVJJBJJGgi j+6Bh8M+Qcg/pjrRdwSyyho49w24z9qki/RRj8aAJp7mO38sOSWkYKoAyeSBn6cjn/EUR3Mc1xLC hJaMAsccckjAPfof85pt6rPAoVST50RwB2DqTTRlL+4lZW2eTHyFJzgvkDHU8jj3oAQ6hErMNr8E hOB85DBSBz/eIHOOvpzQNQiZlG1+SA/A+QlioB5/vAjjPT05qnma3d3WFy8QnH3CRl5AVPHUY5OM 4x60G3Vlt/LWcyFkZnKMiuRJuYsD06scHA+bjOBgAuC/RoRIsMrbivlqAMyA9CvOOmTzggDJApTL FcsbeS3Zl4Dh1DKGwG2nrzjBz0981QtoAttHHeQSzRRLGCrxltsgDBsLjkAbRwCO/qadYxXUNxGJ DLvYqZAeV2+UATnoTvA7549CcgGiEt7KFikaRR5yQi4yenQdT0FTVVvv9THn7vnRZHc/OMfrj+Xu KNj9umsVKSSAtFGd0pGS2DnBweMbOx798kAGjLdxxTiJgxPy7mA4XccLn6njjPviiW7jinETBifl 3MBwu44XP1PHGffFUGhm80K0chZ/J5J3ZKSFmJI4AwcjOPQDsJLhHkuyVjchzCFO04+SQls+nHr1 7ZoAlOqW627zMHCqodRjJdSCQQPfB64Iwc4q3JIkMTyyHCIpZj6AVkRwS/2bcr5T7hYrBgqcl1D5 A9eo5HB7Vfv/APj1B7LLGzH0AdSSfYAZoAtUVjRLeSy28Un2pFVY0kbJG4hZN3P1wM/QjsansWuH kja58/zSqnphNuwZyOmd+f8Aa6dqALj3UKEhmPEgiOFJwxxgfqOenNTVl3LCSaZ1zgi2UEgjDeYS Mj2ypI4OD2zWpQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV5/8QfFaaXqmkaZHrUGmsl3aXd4 ZJ1jaSA3KR+WNxBKkea7MpO1YSGGJKAPQKK831bxPqmk6TP4iin8yaS71O0+yy8wKlrHdNEVUYIb NsCxz83mODwIxH1GjPcWfiHUtFkvJ7uGC0trtJbkhpN8rzK4yABtzCGAx8pdgMKFVQD5A8d/8lD8 S/8AYVuv/RrVz9dB47/5KH4l/wCwrdf+jWrn6ACiiigAooooAKKKKACiiigAooooAKcjBW5G5SME Zxmm0UAK67WIzkdj6ikqRR5i7cjco49x6fWo6ALSfcX6VeaUXOnJbqWZoo5AQ2Ts535UEseiEEqq YyNxI60U+4v0q9LEINJWRQymYM5L5KuVOzADAL0cnILkEfw80Afc9FFFABRRRQAUUUUAFFFFABRR RQAVHLPFAgaaVI1JwC7ADP41JVHUpPKNo/mxxYm+/IMqPkfryP50AXI5I5oxJE6uh6MpyD+NNMET TLMYkMqjAcqNwH1/E1Se6fahW7jdWX95JGmVjXJ+ccnHpySO/RWqnaXFxHc+QlwrIJnyHIyxMjBh gLyQMN2xuyeOAAbtJuUOFLDcQSBnkgdf5j86w4555ZtONxc9fLkyFVQS6Scf+Ogf8CPthsryQape yR3GXiSSQowB4CxEDjsen0HrzQBv0jMqDLMAMgZJ7ngVjy3V6uGWZAGklC7hgfK+FXgEsTzwME44 wc5W6uHeWSAXCl/NjKoV+7iRQDjgjrznrkEEcgAGxTRJGQhDqQ/3CD97jPHrxzWWt5cGVEMnIcIq 4H7394yMT7hQG4xjPPHFQWU0jNpsbNhV27U2dvIJ3Z+pYe+OOhoA3AysWAYEqcEA9D1/qKWs6GR1 1OdCdsbzYBHO5vKU4PoMAn8O3Qyyef8AbBt+1+XuH3fK2Y79fmx/kUATvcRRyrGzYZsdjgZ4GT2y eBnr2qWsy8/4/mX+KT7PsHdtspLY9cDk+lTXk08UoSMn98oRCF+424DPvw2ceiH3wAXaKzBdymaV Wn2Y37hsB8shwEGOvzDt1PbFRz3l0EjKuiOxYur8BHG3EfQls5JwOW6ggcUAaysrjKsCMkZB7jg0 tZEVyYZI0M4RWnkAUqPmJlYd+v4HgkE5B416AEZVdCrKGVhggjIIoVVRAqqFVRgADAApaKACiiig ApGVXQqyhlYYIIyCKWigAooooAjaCJzlokJ3B8lR94cA/X3qSiigAooooAKKKKACiiigAooooAKK KKACiiigAqve2NvqECw3UfmRrLHMBuIw8brIh49GVT7454qxRQBTj0qxi1SbU47aNbyZNjyjqRxn 2BIVASOSEQHIRcGm6VY6Rbtb6fbR28TOXKp64AH4BQqgdFVVUYCgC5RQB8QeO/8AkofiX/sK3X/o 1q5+ug8d/wDJQ/Ev/YVuv/RrVz9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAACQcg4NPcDIYDhuf oe4plPRh91vun26e9AE6fcX6VaicyWsyuFCICmQoXdlWbDEY3fMikbm7cK3SqwBVQD1AwausqR6d 5sIVZjC4kZDlsF1XnBYjgkdI8g4y2cEA+56KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAw5/BfhW6uJbi48NaNNPK5eSSSwiZnYnJJJXJJPOaj/AOEE8H/9 Cpof/guh/wDia6CigDl4fCfgO4vLmzh8P+HJLq12/aIUsoC8W4ZXcoGVyORnrUkPgvwTcoXg8NeH 5UDshZLCFgGVirDheoYEEdiCK5fUrrTdXfU9M8N6naWuqWSailnbw3ypeXN5IshlABbckQkJbBxl 0VhtSNS/SeGhby65qV1pUHk6M1paQxKkJhQTx+asgCEDDKnkIeOPL2HmMqoBY/4QTwf/ANCpof8A 4Lof/iajHgvwS1w9uvhrw+Z0RXeMWEO5VYkKSNuQCVYA99p9K6SvH7W31abWPEhvtDvoby+i0y81 JC0UoaFbuXfEFjdjIv2dTGAFzIImyoZwGAO8g8F+Cbq3iuLfw14fmglQPHJHYQsrqRkEELggjnNS f8IJ4P8A+hU0P/wXQ/8AxNHhb942s3cHOm3eoGexZeEeMxRB3Qf3WlErZ6NuLjIYMegoA5eHwn4D uLy5s4fD/hyS6tdv2iFLKAvFuGV3KBlcjkZ61JB4L8E3VvFcW/hrw/NBKgeOSOwhZXUjIIIXBBHO a4vT7XVG8NW+nXNv9vntYoDqrLabZLY/aIWurc9TP50aySOBuMhG75lniUdx4W/eNrN3Bzpt3qBn sWXhHjMUQd0H91pRK2ejbi4yGDEAP+EE8H/9Cpof/guh/wDiarnwn4DWzmvD4f8ADgtYd/mzGyg2 R7CQ+5sYG0qwOemDnpXQX679OuV8qeXMTjy7eTy5H4PCNuXax7HcuDzkda830qPybeyvZ4/+Jba6 hA99OieXbLHHauiBotkeGhlEQd9mFKKT5YiMUAB2H/CCeD/+hU0P/wAF0P8A8TUcngvwTC8KS+Gv D6PM+yJWsIQXbaWwvy8narHA7AntVjwjBNbeHY45YpIU+0XLW8TqVMdu07tCu08oBEUAQgFQAuBj Ap+IbG3/AOEs8J35jzdDUJIVdmJ2p9kuiQoPC5OM4xu2rnO1cAEg8F+CWuHt18NeHzOiK7xiwh3K rEhSRtyASrAHvtPpRD4L8E3KF4PDXh+VA7IWSwhYBlYqw4XqGBBHYgiuT1Sx1S4tbmy0uOdfEqXe qzSyRt5cnkSxXC25MvAKk/ZAvzdYQP8Alg2zqPDQt5dc1K60qDydGa0tIYlSEwoJ4/NWQBCBhlTy EPHHl7DzGVUAkk8F+CYXhSXw14fR5n2RK1hCC7bS2F+Xk7VY4HYE9qjh8J+A7i8ubOHw/wCHJLq1 2/aIUsoC8W4ZXcoGVyORnrR4hsbf/hLPCd+Y83Q1CSFXZidqfZLokKDwuTjOMbtq5ztXHN6ldabq 76npnhvU7S11SyTUUs7eG+VLy5vJFkMoALbkiEhLYOMuisNqRqXAPSIZ4blC8EscqB2QsjBgGVir DjuGBBHYgipK5fw0LeXXNSutKg8nRmtLSGJUhMKCePzVkAQgYZU8hDxx5ew8xlV6igAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK8jtXh8I/D 3wzrGgWVpb6hJo7XN4sUQAuY47B5N8yrjcBMIRvPIMm0MPMIYA9corztdT8To8+ntJqSEvAyC6l0 4alIGWcssKo3k4/dIR5gyVE+CSq4r6Z4iu9R1i8S31q0tLR0fUjqCWaRpII7SwYGRX58o/aGLZYS AKqiRQtAHplFeX+HfEfiK90WHWbrVvNWO70q2+zfZo1WT7TDZ+azsBngzsyhduGzncuFXY8R6xrt p4kuYtOv4IbeCLTAsM1sJFaS5upYCzEMrbQuG2ggllT5gNwcA7iivL9S8Wa3Dot9dWt1fXN1o8V3 JLHbQWwRlhnnjSS5aQgsri3OVgCsMSHjcgGXZTaxa6F41gOsyXFlY2WoXQgubSB/Mla5v0O47ACh 8oOylTlsYKplGAPZKK5Ox1e+m8b3Givfxtb2rzzDEeJJBsgYRMduMJ9oySMEhoMMxEwrrKACiiig AooooAKKKKACiiigAooooAKKKKACiis/Xbq8sfD2p3mnW/2i+gtJZbeHYX8yRUJVdo5OSAMDk0Aa FFeL+HPG/iWHwXqnieTxHY+JreLSlnNpFaKlzZXZJG2SNNv7kfMWckEhCVAAydDRvjZpceh6cdWs Nce4itLY6perYYhtnkwA8hBGFf767QcqwwM5UAHrFFeN678TdTj1Lx5aFtS0q30ZLVbW6j02OYxH zQjsyyMAxk3gpkgGNdwAIOe4u/iFpFp4xPhtrbUpZ43hjuLuK1LW9tJN/qkkfqCxKgHBGWHPBwAd ZRXjek/E3U9ZdmuW1LSh/wAJbBp8SnTYyGgdSBbPvbKvlCZGGWUuMDBAHcW3xD0W68byeFES7+1h 5Io7nyw1vLLGivJErqT86q3zAgYIweSMgHWUVxeifEzSPEKXz6fp2sulvbzXNszWRA1COJtrm35+ chio2nacuBjrjrLC6+3adbXn2ee38+JJfJuE2SR7gDtdezDOCOxoAsUVlw+JdBudUOlwa3psuoB2 Q2iXSNKGXO4bAc5GDkY4waJ/Eug2tvLcXGt6bDBFcG1kkkukVUmAyYyScBwOdvWgDUorj9W+Imj2 k+jxaZdWOqf2hdvE8kN6gjt4Y08yeVpOV/doVYqSCQ3Fbh8S6CLe4uDrem+RbpE88n2pNsSyAGMs c4AYEFSeueM0AalFZ6a7o8l5a2ceq2L3V3EJ7aFbhC80ZBIdFzllwCcjjg0f27o/9sf2R/atj/af /Pl9oTzvu7vuZ3fd56dOaANCivP9Y+J8Frr6aRo1rY6tJcael3ZSLqsUKXchuPJ8lGYbSwwzcEk7 SMZrrIfEug3KF4Nb02VBbtdFkukYCFWKtJwfuBgQW6AgigDUormx4+8KyX9vZ2+vabcySpLIxhvI mWKONCzu53YAA7cnqcbVYrJq3jHR9P8AD0+rW2oWN7/olxc2kUd2n+l+ShZ1QjOcbcEgHHegDoKK 4O/+JUMFl4US1tbSbWPESQSRWE98IRDHIm7e0mw8BsKBgFznaCRiuog8S6DdW8Vxb63ps0EtwLWO SO6RleYjIjBBwXI529aANSis+613R7HToNRvNVsbexn2+TczXCJHJuG5drE4OQCRjqKjufEug2Vx c293remwT2qB7iOW6RWhUlQC4JyoJdACf7w9RQBqUVlweJdBujELfW9NmMqB4xHdI29TJ5QIweQZ Pkz/AHuOvFYfiD4iaPov9gNb3VjeQ6vdmJLj7akcMcKf62XzTlDsOBtyCxOBzQB2FFV5r+zt7y2s 5ruCO6ut32eF5AHl2jLbVPLYHJx0qnbeJdBvbi2t7TW9NnnukL28cV0jNMoLAlADlgCjgkf3T6Gg DUorj/DvxE0fW9MutRurqx021WWVrZp71AZ7VZPKW4KttMas+Vww6jGeRWw/izw3HLdRSeINKSS0 z9pRr2MGHDBDvGfl+Yhee5A60AbFFU5NW02HVIdLl1C0TUJk3xWjTKJXXnlUzkj5W5A7H0q5QAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVn6ZoWj6J5v9k6VY2HnY8z7JbpFvxn GdoGcZPX1NaFFAGXH4a0GHS5tLi0TTU0+Z98totqgiduOWTGCflXkjsPSq+taA1+8M1idNhnjuBc t9s09blGlCqqy4DIwlUKArhuBkEH5Su5RQBl6X4e03SdGtdLhto5ILdIRulRS0jRKixu+AAXAjTB xxtGMYFXJbCznlaWW0gkkby9zvGCT5bF48n/AGWJYehORzViigDLvfDWg6kkaX+iabdJG8jos9qj hWdtzkZHBZuSe55NSRaFo8P27ytKsY/7Qz9t226D7TnOfM4+fO5uufvH1rQooArrYWaeXttIF8qV 548RgbJH3bnHox3vk9Tub1NWKKKACiiigAooooAKKKKACiiigAooooAKKKKACq9/DcXGnXMNndfZ LqSJ0huPLEnlOQQr7Tw2Dg4PXFWKyfE1hqOp+Hrqz0q7+yX0mzy5vMaPbhwT8y8jIBH40pOybRpS gpzjGTsm1r28zgdM+HfibUNU1vU/FF/oyXmoaE2jebpsLEylus8u4KC4AUYHBGB8oUZ5fU/gv4x1 jS0ttQ1XRrh4LKCCzV5brZZtHtjIiGdoDxorMxQ5bgKPvV0f/CCfEP8A6Gv/AMqNx/8AE0f8IJ8Q /wDoa/8Ayo3H/wATXN7ef8jPY/srC/8AQVD8SXxf8Nte1q98YnTrzTfsniK3szi43o8U1u6YGVBB QornPXJUYABJua78P9Y1P4mad4gtrjSraztruO6e5jheO+2qgRoCVwssbbBy/wAwDsB8ow2VH4M8 ezPMkXjCN3hfZKq6nOSjbQ2G+Xg7WU4PYg96k/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4kr /DbXl1SUR3mmvp//AAlsPiKMtvWXb83moRgjI+QLjrhiSMgDY0Hwl4j0LxvqM9rqtpH4Xu72fUZL bZvuJ5pUQFWJUBEVgWXac44Oc5HPHwZ49W4S3bxhGJ3RnSM6nPuZVIDEDbkgFlBPbcPWpP8AhBPi H/0Nf/lRuP8A4mj28/5GH9lYX/oKh+JZ8I/D/wASeGNc1vVkuNDjkm0/7La29nDJDBczL/q554h8 sbcAERjHztjBzu9IsPtn9nW39o+R9u8pPtH2fPl+Zgbtmeduc4zzivLv+EE+If8A0Nf/AJUbj/4m j/hBPiH/ANDX/wCVG4/+Jo9vP+Rh/ZWF/wCgqH4mR4F8Aa/rl5Y+IvEcf9mRtqGo6lJbwyz21z5l wEj2YG1olHllgQ7EggEYJNSD4ReKra9168sNctIJWuLmbRCZpWeBrh0WWSSUqW3+Smz+PltwII3H T/4QT4h/9DX/AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+JiD4OeKrl9T83U9Ns 0d7+5sTFPLcuJbpUjeOZ5EBZDErLvHzgtu56VqL8JdTfSZXkaxi1Jdbtb2GKG8mCfY7ePy4bfzgo ZGVGcCQKWOATycif/hBPiH/0Nf8A5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4l3 wf8ADrUPC3ixL6xTTdK0spJ9pt7K5uJjdgqvlxuJcgGNvMIlUgsDjYu4is/U/hr4q1/xdPf6rq1o 1mz6lHDJ9olkaG3uLfyYkWAqEUp1YhgWzySQCX/8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDK jcf/ABNHt5/yMP7Kwv8A0FQ/E5WP4e+KrfxrpWlXBjggnS0uP7QsIZbhbSaytHigdneNY8M/JTk9 BkZBbb0r4RatbaZdW9/baHdL9ktLGC1e7uNjQJJ5twDIqoyM82JQ2H2/dxtq2PBnj1rh7dfGEZnR Fd4xqc+5VYkKSNuQCVYA99p9Kk/4QT4h/wDQ1/8AlRuP/iaPbz/kYf2Vhf8AoKh+JQuvhR4s1qx1 GLXdasbq8k0SHTrW9DvuJSZZyJVKfN8w2CQNnaoYozMcR+IPgzqF4L640WLTbGfVbI299DPqNxOF lFyk3nCV0LSFhGAQQuDzlq0/+EE+If8A0Nf/AJUbj/4moz4M8ercJbt4wjE7ozpGdTn3MqkBiBty QCygntuHrR7ef8jD+ysL/wBBUPxOl8XeCLzxd4jgnmuoLfTbXSry2t2QEzC4uE8pmZSMNGE5ABB3 e1cfpfwf1h9VsG1ubSpNMEtk95bRM8jP9jtfJiwWQKyuzPvQqMLgBjk1f/4QT4h/9DX/AOVG4/8A iajm8GePbZA8/jCOJC6oGfU51BZmCqOV6liAB3JAo9vP+Rh/ZWF/6CofidL458Ka34h1bSrvSLmx tJrHJgvnaVZ7N2kQu6qpKTK0ashjcD/ewSK5PUPhRri+IJtQ06W0ZzrsmqJcnVbm1k8iUDzbcJGj BC3QyqdxAHHYW/8AhBPiH/0Nf/lRuP8A4mj/AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6CofiYml/ BPXh4dvrXUdR02LUItM/s/S5rRnZVVp3ml8wsoILbzHlf4HcFW79B4q+Heva7cLc2cfh+ykWyvMJ Erxh7q7IjmaQhTvAg4D8M0gBKhTtEX/CCfEP/oa//Kjcf/E0f8IJ8Q/+hr/8qNx/8TR7ef8AIw/s rC/9BUPxLPjb4ZaprVr4VsNA1OC0tdItJrCaa7+eQwSRJC2FC4Ztgf8Au8kYI6inq/wp1VPET3fh 6402GzW40+8tkumkJheygkjjiKgEujEpl9wIGeGPV/8AwgnxD/6Gv/yo3H/xNH/CCfEP/oa//Kjc f/E0e3n/ACMP7Kwv/QVD8TIt/gprFsun2Ml7Y3emmK0gvRLK6OtusrT3NvHtj+ZXlIdXJVxtC9Mk yXHwm8VXKXFw93owvp01C4YCSXyhdXjCKUD5M+ULcZX+ISdcrxWn/wAIJ8Q/+hr/APKjcf8AxNH/ AAgnxD/6Gv8A8qNx/wDE0e3n/Iw/srC/9BUPxNXQPhufDvjk6pD5d5p4t7WOB5ruSOW2aG2a3yY1 Xy5iykfMxXbubA9fRK8m/wCEE+If/Q1/+VG4/wDiaP8AhBPiH/0Nf/lRuP8A4mj28/5GH9lYX/oK h+J6zRXk3/CCfEP/AKGv/wAqNx/8TR/wgnxD/wChr/8AKjcf/E0e3n/Iw/srC/8AQVD8T1mivJv+ EE+If/Q1/wDlRuP/AImj/hBPiH/0Nf8A5Ubj/wCJo9vP+Rh/ZWF/6Cofies0V5N/wgnxD/6Gv/yo 3H/xNH/CCfEP/oa//Kjcf/E0e3n/ACMP7Kwv/QVD8T1mivJv+EE+If8A0Nf/AJUbj/4mj/hBPiH/ ANDX/wCVG4/+Jo9vP+Rh/ZWF/wCgqH4nrNFeTf8ACCfEP/oa/wDyo3H/AMTR/wAIJ8Q/+hr/APKj cf8AxNHt5/yMP7Kwv/QVD8T1mivJv+EE+If/AENf/lRuP/iaP+EE+If/AENf/lRuP/iaPbz/AJGH 9lYX/oKh+J6zRXkQ8GePWuHt18YRmdEV3jGpz7lViQpI25AJVgD32n0oHgzx61w9uvjCMzoiu8Y1 OfcqsSFJG3IBKsAe+0+lHt5/yMP7Kwv/AEFQ/E9doryb/hBPiH/0Nf8A5Ubj/wCJqOTwZ49heFJf GEaPM+yJW1OcF22lsL8vJ2qxwOwJ7Ue3n/Iw/srC/wDQVD8T12ivJv8AhBPiH/0Nf/lRuP8A4mj/ AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6Cofies0V5N/wgnxD/wChr/8AKjcf/E0f8IJ8Q/8Aoa// ACo3H/xNHt5/yMP7Kwv/AEFQ/E9Zoryb/hBPiH/0Nf8A5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPb z/kYf2Vhf+gqH4nrNFeTf8IJ8Q/+hr/8qNx/8TR/wgnxD/6Gv/yo3H/xNHt5/wAjD+ysL/0FQ/E9 Zoryb/hBPiH/ANDX/wCVG4/+Jo/4QT4h/wDQ1/8AlRuP/iaPbz/kYf2Vhf8AoKh+J6zRXk3/AAgn xD/6Gv8A8qNx/wDE0f8ACCfEP/oa/wDyo3H/AMTR7ef8jD+ysL/0FQ/E9Zoryb/hBPiH/wBDX/5U bj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6Cofies0V5N/wgnxD/AOhr/wDKjcf/ABNH/CCf EP8A6Gv/AMqNx/8AE0e3n/Iw/srC/wDQVD8T1mivJv8AhBPiH/0Nf/lRuP8A4muh8G+GvFWjavLc a5rf261aAosf2qWXD7lIOHAHQEZ96qNabdnBoyr5dh6dNzjiIya6K92dxRRRXQeQFFFFABRRRQAU UVDdLcPbutpLFFOcbXljMijnnKhlJ4z3FA0ruxzPiHXW0vxVpFvJq8dnBcOkS27RKfPd324+bmQd F/dMDEzIzq6N8vD2uta/qt54dvNT1a0jlTU0K2yTKFjupLW5zbSuYB5ZDKsfl5eRPNbcWJjI9O+z +If+gppf/gtk/wDj9H2fxD/0FNL/APBbJ/8AH6nmfb8v8zb2MP8An4v/ACb/ACPObzXfE2naxqyw /wBm6dPNqbvKWuWeJ5ktLIRQK3kFpS4Zj5SIsjbCEYbTu6DUNe8R211qlhZxSXF3piXt6rG33C6j 8oNbQ7FAIBeYoGBy5s3HVm29N9n8Q/8AQU0v/wAFsn/x+j7P4h/6Cml/+C2T/wCP0cz7fl/mHsYf 8/F/5N/kcj4V1C+1HxVpkl9f2l/sstRSG6tZfOWRN9keZBFGjkMWGUQAYCnLK1eiVk/Z/EP/AEFN L/8ABbJ/8fo+z+If+gppf/gtk/8Aj9HM+35f5h7GH/Pxf+Tf5GtRWXFBromQzalpzxBhvVLB1Yju ATMcH3wfpWpTTv0M5xUdpJ+l/wBUgoqvepevCBY3EEEu7lp4DKpHpgOvPTnP4VR+z+If+gppf/gt k/8Aj9JtroVCnGSu5pff+iZrUVk/Z/EP/QU0v/wWyf8Ax+j7P4h/6Cml/wDgtk/+P0cz7fl/mV7G H/Pxf+Tf5GtWP4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWBAIp32fxD/0FNL/8Fsn/ AMfo+z+If+gppf8A4LZP/j9HM+35f5h7GH/Pxf8Ak3+RxMeuTWfjGG9XUZLywe3s47m8S0P7+H/i ZCM7QDktIIQGQASMy7Bh1WsPWfE17qPgp7e+8ReXc6h4aM32ZII1aZvsZmkypXd82G/epmPaJI9q SKrn1L7P4h/6Cml/+C2T/wCP0fZ/EP8A0FNL/wDBbJ/8fo5n2/L/ADD2MP8An4v/ACb/ACPPYPFP iWY3QttbsZVN3BBC7BZ3RGvYYYpXjSNFRZoWZ+ZD5m4NFsUEC54ov/Eela5ayCO0EkGmXMUF411u ZomurJJJ5f3ISIqhLkhXRcEkFVwe2+z+If8AoKaX/wCC2T/4/R9n8Q/9BTS//BbJ/wDH6OZ9vy/z D2MP+fi/8m/yORXX/EaadZIbqO4m1e3NtaXVtHvSCYXAjEwJjXziYZfOOFVGFq7KAjErn3mta1f6 neWt9PafZotYtVForFpbcJqUCQsyiMeWJI8v88jl8hkCqGA777P4h/6Cml/+C2T/AOP0fZ/EP/QU 0v8A8Fsn/wAfo5n2/L/MPYw/5+L/AMm/yNaisn7P4h/6Cml/+C2T/wCP1XeXWY9RhsG1jSxdTRST Rp/ZkvzIhQMc+djgyJ+fsaOZ9vy/zD2MP+fi/wDJv8jeorJ+z+If+gppf/gtk/8Aj9H2fxD/ANBT S/8AwWyf/H6OZ9vy/wAw9jD/AJ+L/wAm/wAjWorJ+z+If+gppf8A4LZP/j9H2fxD/wBBTS//AAWy f/H6OZ9vy/zD2MP+fi/8m/yNaisn7P4h/wCgppf/AILZP/j9Oig10TIZtS054gw3qlg6sR3AJmOD 74P0o5n2/IHSh/Ov/Jv8jUoooqjAKKzrqHWXuHa0v7CKA42pLZPIw45ywlUHnPYVF9n8Q/8AQU0v /wAFsn/x+p5n2NlSg1fnX4/5GtRWT9n8Q/8AQU0v/wAFsn/x+j7P4h/6Cml/+C2T/wCP0cz7fl/m P2MP+fi/8m/yNaisn7P4h/6Cml/+C2T/AOP0fZ/EP/QU0v8A8Fsn/wAfo5n2/L/MPYw/5+L/AMm/ yNaisn7P4h/6Cml/+C2T/wCP0fZ/EP8A0FNL/wDBbJ/8fo5n2/L/ADD2MP8An4v/ACb/ACNaisn7 P4h/6Cml/wDgtk/+P0fZ/EP/AEFNL/8ABbJ/8fo5n2/L/MPYw/5+L/yb/I53x34g1TRdRso9Ou/K Mmn3s6w/ZfO+0zxmDyYsj7m9n2f7W/auHZGGW/iXX2u/EAuL6O3igeVFit3XzrMLcLHFJIHgZYIm jJkdpDISmXjUBSB1z6TrMmow37ahpZuoYpIY3/s+X5UcoWGPPxyY0/L3NWPs/iH/AKCml/8Agtk/ +P0cz7fl/mHsYf8APxf+Tf5HnvhrxJqN9rov7m6g+zyxWdpc3tqyzb9t9eww7TtCssrBA0iqPvfK iBt8Umuax4jsPEuoSSLpulSzW+nxT3K3+YoIc3zB2mkgKxkuqp80bA7gAQWBXrtW1LVNEigkvdWs v9Il8mFINEuJ3d9rPgJHKzfdRj07VcgXX7m3inTU9PCSIHUSaVMjAEZ5VpgVPsQCO9HM+35f5h7G H/Pxf+Tf5GDb614gfU9P0u4bFxqH2S7+0RWzIkEQjZriNUdQxXfCFLPhk+2ICAVUNh+H9a1rWdT8 Oy6xPaSP/aauYoWLNazNZXnmQP8Au0CFMKPKJeRMnex3LXffZ/EP/QU0v/wWyf8Ax+j7P4h/6Cml /wDgtk/+P0cz7fl/mHsYf8/F/wCTf5GtRWT9n8Q/9BTS/wDwWyf/AB+o5BrcLwpLrOkI8z7Ilawc F22lsL+/5O1WOB2BPajmfb8v8w9jD/n4v/Jv8jaorkdP16/1PURYQanDHdNE0ypd+Hby23IpUMQZ ZFBwXXp6itSEa3coXg1nSJUDshZLB2AZWKsOJ+oYEEdiCKOZ9vy/zD2MP+fi/wDJv8jaorJ+z+If +gppf/gtk/8Aj9H2fxD/ANBTS/8AwWyf/H6OZ9vy/wAw9jD/AJ+L/wAm/wAjWorJ+z+If+gppf8A 4LZP/j9H2fxD/wBBTS//AAWyf/H6OZ9vy/zD2MP+fi/8m/yNaisn7P4h/wCgppf/AILZP/j9Oig1 0TIZtS054gw3qlg6sR3AJmOD74P0o5n2/IHSh/Ov/Jv8jUoooqjAKK878daB4m1XVp59Hm1JIlsg kItdSa3XzfJvDyodQT5ps+SPTsGo0vQPE0HjWPUbibUjZm9leRH1Jni8pmv8fui5XG02OAF44xgh 6APRKK8n1Dw141N5qd1Z3Gq+ZL9s8kDVmCDeNQEe1DJtXrYY4G3g8EPjUstA8TWOk+LYI5tSaW5s pk00zak0rCXzrsR7GdyUPlG15yO2eQ2AD0SivLx4c8WWttq1tby6rJ9plUWpn1R5EUC5u8F2Myyp H5RtcmJg/C5D4dT6hQAUUUUAFFFFABRRRQAUUVDdTPb27yx20ty64xFEVDNz23ED35PagaV3Ymrz /wCHOl3Elnp2vyWUFi93p/m3jwuGOpTzlJjO+AMbfmABHymWRVARVL9V/a99/wBC5qn/AH8tv/j1 H9r33/Quap/38tv/AI9U86/pM2+rz7r/AMCj/ma1FZP9r33/AELmqf8Afy2/+PUf2vff9C5qn/fy 2/8Aj1HOv6TD6vPuv/Ao/wCZNrtreX3h7U7PTrj7PfT2ksVvNvKeXIyEK24cjBIORyKp+F9MttNs rk2mgR6HHcXBm+yKyZzsRNzLGSiE7OiEjADEhmYCb+177/oXNU/7+W3/AMeo/te+/wChc1T/AL+W 3/x6jnX9Jh9Xn3X/AIFH/M1qKyf7Xvv+hc1T/v5bf/HqP7Xvv+hc1T/v5bf/AB6jnX9Jh9Xn3X/g Uf8AM1q8/Ol3Gt+O9WuPsUAmsdQtYodW3jzbeCOKGd4FXAP7wzOuQeVkk3HCIj9V/a99/wBC5qn/ AH8tv/j1H9r33/Quap/38tv/AI9Rzr+kw+rz7r/wKP8Amc7pXxR0nWNJ1HUrWwvmt9PtFvLjElu5 SIxyuMhJThv3RUocMCy5AGSNSPxpp8vjObwwsUgvIn2M7XFuoJ8oS/LGZPNYbWAyEIznsCRkpoMa aWmnLoWvi3OmRaVOPPtM3FvH91XPmcHa0ikptOJW7hCseo+HItXvr6TUdC1u6sb6Xz59Om+wNCZP JEIdWLeYjBVGCrgg9OpFHOv6TD6vPuv/AAKP+Z1Q8S6C1g9+ut6abNEV3uBdJ5aqzlFJbOACysoP cqR1FXLG/s9Ts47ywu4Lu1kzsmgkEiNgkHDDg4II/CuFt/C8cUzXE+leJru7e4W5e4uLq0LtIJop SeJAAD9ngTaAAFiUKFJYt0GkzXGjaNY6Xb+HtXaCyt47eNpJbUsVRQoJxKBnA9BRzr+kw+rz7r/w KP8AmdFXldpfDwxq9xrGuWd3HfaXpkMV/L9ojZdQmu51Tz03SBI0DW+AW2HYVUhFiVa77+177/oX NU/7+W3/AMeqnqc1xqtqlvP4e1dUS4guAUltQd0UqyqOZTxuQA+2enWjnX9Jh9Xn3X/gUf8AMov8 RdMit7q5lsb5LW3iL+ephkSVxai6MSFJDlvKJO77h2kBjxnU03xZpWoeH21uS4jsrFHKPNdTRiNT kAYlVmjYEkDKsRnKnDAgc7qXh1dVudUkutI8RGHUN7NbrNZBIpXtvsxlQ7927ysjDMy/MTtzjBee GrW5iHk+Hdbs7qLctne2stms9lGylTFC5kJEeHkwpyF3/Lt2ptOdf0mH1efdf+BR/wAzqr3xLoOm pG9/rem2qSPIiNPdIgZkba4GTyVbgjseDWpXnsPhOztjamDw9r8f2Z1ZALi1OQkls6Kcy9FWzgQd yoOSWJaus/te+/6FzVP+/lt/8eo51/SYfV591/4FH/M1q4vxDoWr6p4vgu/KjuNHgt4YHtThTcLL M32hS24YRVS3kZSp3iPYMBnDb39r33/Quap/38tv/j1H9r33/Quap/38tv8A49Rzr+kw+rz7r/wK P+ZR0Txpp+va9faRaxSJPZPMkhe4tySY5PLb92shlAz0LIBjHqM9JXI6RBf6PdXLxWPiKa1mlmmF nM9h5cbyymRipVg/3mbGWPB+mNj+177/AKFzVP8Av5bf/HqOdf0mH1efdf8AgUf8zB8Y27al4l0P TZNHtNYtfs93dmzunVU81DDGkhypBCid8g9mLAMyop1LPWdH0HQ7O11TxNYySWkUdtNd3V0iGWRd yFm3McMWil4JJyjjkqatf2vff9C5qn/fy2/+PVxc/gu3un1BptL8TOL15mK+fYgRCRboMqYbOM3s xG7Jzt5wME51/SYfV591/wCBR/zO+Graa1+9guoWhvEdUe3Ey+YrMhdQVzkEqrMB3Ck9BUema7o+ t+b/AGTqtjf+TjzPslwkuzOcZ2k4zg9fQ1yP/CNwnWv7SbQ/EThZfNjtWurXyYyZvtDbR5ufmnCS E5zmNVyEyh0PDlm3hiwezstA1uSNvKyZprQn93BHAvSUfwwqT7k9uAc6/pMPq8+6/wDAo/5nXUVk /wBr33/Quap/38tv/j1H9r33/Quap/38tv8A49Rzr+kw+rz7r/wKP+Y3xZfXGmeDdcv7OTy7q10+ 4mhfaDtdY2KnB4OCB1rk7e4svhvZC7uNKj0yxu3hsINOtbmIIssaSs07yyvGmXA27mO9hHGW+Zti dd/a99/0Lmqf9/Lb/wCPVTvZri+utOuJfD2rh7C4NxEFltcFjFJFhv3vTbIx4xyB9Cc6/pMPq8+6 /wDAo/5kP/CdWC6THqUllfJby6VLq8WVjJeCOOJ2wA5w374KAccq3bBNjRvGOk6xpyXv2iC0jkux ZxCa9t5PMlIBCK0UjqWOeFzu46YxnFfQY30t9ObQtfNuNMl0qAefaZt7eT7yofM5O1Y1BfccRL3L lqd34Ri1DzpL7SfEU93c/u7u8V7CCa6tzs3W8jRMm6M+WvP38ZAYKSCc6/pMPq8+6/8AAo/5nZP4 l0GO4tLd9b01Z71Ee1ja6QNOrnCFBnLBjwCM57VqVwtnoMdlqlrqKaFr7XEDiQl57TEsn+k7nYCT qzXkzELgZ24AAwek/te+/wChc1T/AL+W3/x6jnX9Jh9Xn3X/AIFH/Mh8UW13dWVskNrJe2i3AN9Y xMivdQ7HGwbyqkbzGWBYBlVlOQdrcf4e8aaV4a8P2VlPFdu82mS6+QbiNmWKYz3HlgySCSZ1CspY A5wGbbk47b+177/oXNU/7+W3/wAerH1KzbVP7X8/QNbX+1dPXT59k1oNsY83BXMpw375uTkcDjrk 51/SYfV591/4FH/Mbd/EXTLC8tbK8sb6C8mleGS3cw74WURNtIEn7xis8bBIvMY5wBuBFdJNq2m2 16LKfULSK7KK4geZVcqziNTtJzguQoPckDrXG/8ACOq15HfzaR4il1OPzWW/M1ksolcRL5oCuFDB II0AChSu4MrbmzXufBmmzzt5PhnW7OybeW0+zmtIrYu6CKR9gk+80QMfB4DMVCuS9HOv6TD6vPuv /Ao/5nbWuu6PfajPp1nqtjcX0G7zraG4R5I9p2tuUHIwSAc9DWhXI6RZtot/e3ltoGtvJd58wSTW hAzPPPxiUfxXDj6Be+Sdj+177/oXNU/7+W3/AMeo51/SYfV591/4FH/Mq65or63rmlx3Cz/2ZbxT zM8Fy0Dpc/u0iIeNlf8A1b3A4OOee1bkEK21vFAhkKRoEUySM7EAY5ZiSx9yST3rN/te+/6FzVP+ /lt/8eo/te+/6FzVP+/lt/8AHqOdf0mH1efdf+BR/wAzWorJ/te+/wChc1T/AL+W3/x6j+177/oX NU/7+W3/AMeo51/SYfV591/4FH/M1q5fXZriz8W6bd21r9pmGlahHbwmQRiefdbukIc8BmEbkeys eimtL+177/oXNU/7+W3/AMeo/te+/wChc1T/AL+W3/x6jnX9Jh9Xn3X/AIFH/M51PEA8NadNquua TfQ3lzLHFNcXdzYw+ccOQse652rGmDhN2fmJ+djI50PBt4mpya9qkAza3uoJLC4dXVgLS3RsOhKt tdXQlSRuVhniptSlm1W3WC48P6+iK4cG1v47ds4I5aO4Ukc9M46egqxBqN3bW8UCeHdXKRoEUyXF u7EAY5ZpyWPuSSe9HOv6TD6vPuv/AAKP+ZtUVk/2vff9C5qn/fy2/wDj1H9r33/Quap/38tv/j1H Ov6TD6vPuv8AwKP+ZxvgWJLPTtN8SaitjpAvNPaW7m89R/ac8oW4adzhcbFWQgH7u+UKFRAz9o3i XQVS9dtb00JYOEvGN0mLdixUCTn5CWBGDjkYrB8U6fJ4s02OxutG1+1RHdt9rLZhiHikiZTvkYYK SuOmehBFZ83heOQ3UkWleJra5ndnS5gurRZIGaS5djGfMwCReTLkg4BUjDDdRzr+kw+rz7r/AMCj /mdde+JdB01I3v8AW9NtUkeREae6RAzI21wMnkq3BHY8GtSvPYfCdnbG1MHh7X4/szqyAXFqchJL Z0U5l6KtnAg7lQcksS1dZ/a99/0Lmqf9/Lb/AOPUc6/pMPq8+6/8Cj/ma1FZP9r33/Quap/38tv/ AI9R/a99/wBC5qn/AH8tv/j1HOv6TD6vPuv/AAKP+ZrUVk/2vff9C5qn/fy2/wDj1H9r33/Quap/ 38tv/j1HOv6TD6vPuv8AwKP+ZrUVk/2vff8AQuap/wB/Lb/49R/a99/0Lmqf9/Lb/wCPUc6/pMPq 8+6/8Cj/AJmtRWT/AGvff9C5qn/fy2/+PVYs7+5upik2k3lmoXPmTvCVJ9PkkY5/DHFCkmKVCUVd tfev8y9RRRVGIUUUUAFFFFABRRRQBn6nrenaN5X2+48rzckYRn2quN0jbQdka5G52wq5GSMitCuf 8S+GP+Eg27bz7PvtLiwnzFv3W8+zzAvI2yfu12sdwHOVbIxuATfaHZpIzAUUIgQhg2TuJbOCCNuB gYweTngAy4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxqvfeNdB02zguri6n8maKWYGKz mlKJEQJTIqITHsLANvA2nIOCDWfa+CHtNHubJL+CWQ2lrYWrzWzbYoLdmMRYLIrGYb2PmKyYYIVV SvNyDwksHhGLQBeyOhuBPcSSBn88NcedNGQzElHy6YZmO1vmL85AOgnnhtbeW4uJY4YIkLySSMFV FAySSeAAOc1X03VLTVrdprR5CEcpIksTxSRtgHDo4DKcEEAgZDA9CDVicTNbyrbyRxzlCI3kQuqt jglQQSM9sjPqKy/Dek3mj6dJDqF7BfXcspllu4rYwtMxAG5wXfLcY4IUKFVVVVAoA2KKKKACiiig AooooAKKKKACiiigAooooAKKKKAI554bW3luLiWOGCJC8kkjBVRQMkkngADnNZf/AAlGk/2d9t86 fb5vkeR9kl+0eZjds8jb5m7b8+Nudnzfd5rUnEzW8q28kcc5QiN5ELqrY4JUEEjPbIz6iuT/AOEN vJ/D32DUNRsb27N39qlmm08tDeNs2/6RCZT5nqAGVVKRbVCxhaANx/EOmLcWkEc8ly92iSRG0gku F2OcI7NGrBEbnDMQDtbB+U41K5OXwPC2s6ZqK30jz2dvb273NxGJLp1hZmUpNkFC5dhLw29Tj5et dZQAUUUUAFFFFABRRRQAVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCub0HwvNoNv OsN/HJOtlDp9o7252xwwB/J8xQ+XfMjbiCgbjCp3ANTU9b07RvK+33HlebkjCM+1VxukbaDsjXI3 O2FXIyRkUf21ZDWP7Ldp47o8IZbaRI5Dt3bUkKhHbbk7VYnCtx8pxn+JfDH/AAkG3befZ99pcWE+ Yt+63n2eYF5G2T92u1juA5yrZGC88NvqHiMalcXMEcKRNCn2SBoLpkZCpje4EmWjy7OFVVwwRs5X kA1LDVbHU3vEsbmOc2VwbW42chJQqsUz0JAYZx0OQeQQLlcv4Q8HjwlPqogu/Ns7uWN7e3/fH7Oi II1TMkr7vlVRkBemPuhVXqKAK99fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3V rJ5kL5AJUqQQSGVlOCrAggqQCCCCARVfWtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5 Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd2FGDkndQBJ/benf2x/ZX2j/AEvpt2Ns3bd/ l78bfM2fPszu2/NjbzVeHxRpM9nc3iTT/ZYNv75rSVUm3HCeSxXE244C+Xu3blxncMxyaPqU3iyH VJtRtJNPgTEFk1m2+JipDOsnmY3nOMlDhcquNzlsfTPAK6P4fvdKsrm0VJUhjhBtGWNBEcrK6pIp a4PGZlZCSkZAXZyAdZY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFWKy/D2ir4f0SHTlnkn KPJK8rliWeR2kc5Ys2NznG5mOMZZjknUoAw18YaBLpd1qdtqUd3Z2twlrJLaI04Mr7NqLsBLkmVB 8ueTjqDjQ0vVLTWbBb2yeRoGd0/eRPEwZHKMCrgMCGUjBA6Vl6Do2saZqep3V/qljdx38ouHjgsH gKyCOOMYYzP8u2IcYzk5zjirmj6VNo+jabp6XMcpt0AupniO65bad7/e4dpDvJO7OWzkncACwdVs V1lNHNzH/aD27XQtxy3lKwUufQbmAGevOM4OI9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsH BODWGngiO28eReJbS9kiQpN9otnknkM0kgUFsmbYoAjjAXYRhR6IUseF/Cp8P3F7dS3Uc090kUQS BJI4IYoy5RY0eSQoAZG+VWCAbQFGCSAdJVe+vYtPs5LqZJ3jTGRBA8znJA4RAWPXsOOvSrFU9VtZ r7S7i0ga0DzJsP2u3M8RU8MGjDLuBXIxkde/QgEljfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAgg ggEVX/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mo9I0qbSNLsrGK5jdIXYy5iIXa247I l3fu0VmUKDu2ooXn7wz5PCpn8Zw69LdRrFA/mxW8CSIXlMRi3S/vDG52sw3CNXwEG7C4IBqaZrVl rHmmxaeSOPBEzW0kccgOcNG7KFkU4zuQkYIOcEZ0K5vwj4Rh8JW8tvbzRmApHFHHDAIl2oCBI4BI edgcPJ8u7anyjbz0lAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8 gEqVIIJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm 0mwgtkuY5Cbia4unaIjzGld5HCDd8g8x8jO7CjByTuoAk/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs +fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/wCEY/4qX+1Ptn+j/a/t /keV8/2j7P8AZs7848vy/wCHbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIK BhwAh5oA6ixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6Lpn9kaYLUzedI0stxLIF2h pJZGkfauThdztgEkgYBJPJ0KACqZ1WxXWU0c3Mf9oPbtdC3HLeUrBS59BuYAZ684zg4uVyaeCI7b x5F4ltL2SJCk32i2eSeQzSSBQWyZtigCOMBdhGFHohQA3NM1qy1fzRatOskWC8NzbSW8ig5w2yRV bacMA2MEqwByDjQrD0DQZtJuL+6uLi0ee9cPIljaG2gLAsTIULuTKxb5n3fMFQY+XJuano1rq/lf aZb6Pys7fsl/PbZzjOfKdd3TvnHOOpoAP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qP RPEGn+Ibf7Rp32toNiOsk1lNAsisMqUMiKHBAzlc9R6iq8mj6lN4sh1SbUbSTT4ExBZNZtviYqQz rJ5mN5zjJQ4XKrjc5aPwx4XTw39qEUsHlzbFWG1tVtowFzh2RPlMzbsO6hQwVAFULQB0FZ/9t6d/ bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY280ano1rq/lfaZb6Pys7fsl/PbZzjOfKdd3TvnHOOp qnJo+pTeLIdUm1G0k0+BMQWTWbb4mKkM6yeZjec4yUOFyq43OWALmma3p2s+b9guPN8rBOUZNytn bIu4DfG2Dtdcq2DgnBrQrn/DHhj/AIRz7V/pnn+dsHyxeXv25/ey8nzLh937yXjftX5RjnoKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiq9/fW+madc395 J5draxPNM+0naigljgcnAB6VHpupwarbtPbx3aIrlCLq0lt2zgHhZFUkc9cY6+hoAuUUUUAFFFFA BRRRQAUUUUAFcX4O8R3d7b3lxqt9G0ENlb3V1JIERbK5YSG4tiQAFEQRDtfLrv8AmY5GO0rP0zW9 O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NAGH4v8T2WlaTpV7D4gtLMXF7aNGxmi23Vu00ay 43A5QRyFiy4IwDkDOQz6hJ4z0trTxBJcaffpLeLapFD9n+ypEifLIFLs5lljcHcF2lh2G7c1PW9O 0byvt9x5Xm5IwjPtVcbpG2g7I1yNzthVyMkZFRx+INPm1ubSIvtb3cL7JStlMYkbYJMNLs8sHayn BbuB1OKAOb+HHiG98SWd5fXt79p87ZcRpBJHLBbJIXZYN6xo3nIu0OrFsfI2RvIHcVj6H4o0nxH5 n9mTTvsijn/fWksG6OTdsdfMVdytsbBGRxWxQBj+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3 ywKjblgQCKr6FriNoNncarfwI1xdy2trPM6x/bAJXWFl6BmkRVcbRht2VAGBWxfX1vp1nJdXUnlw pgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEUAc3Pql3/w si3sF1GQ2Ztwp0+JUDiTbIxmkVo9xgI8tRIjgCQBCCScZ8NzrlhpvilJ/EslzJY28VrFe3ltEiwX hi3tLtijwsAE0BJfdt2yE/KMnqI/EGnza3NpEX2t7uF9kpWymMSNsEmGl2eWDtZTgt3A6nFZ9v48 8PXVhNew3N2YIkt3O7T7hWdZ3KQlFKbpA7AgFQaALnha7lvvDlpcTTz3EjbwZpihMuHYb1ZERWjO MowVdyFWxk1sVXsb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0qxQBwem+L7K/HiaCLxdaSC3vQ0Fz GYpmgtWjgLOAgxsRpJF8xwypjL7gpB3NB1xH8Oadd6rfwRveSmG3kmdYzOS7CIcYV5GQAny8oxyU yhU1Yt/FWhXmnXeoWmpwXVpaSiCSa3JlUyEKQibc72O9AAuSWbaPm4rQsb631GzjurWTzIXyASpU ggkMrKcFWBBBUgEEEEAigDi9f8U6rpXj7RLCeOS2027vRaxAS2228VoWLOd7iQFZTGAqqOh5cyIg 1PCerXGpXmpxvqX9oW8PlYlMAiKTMGMkYUD5FX5AI5CZkO7fkFCdw6rYrrKaObmP+0Ht2uhbjlvK Vgpc+g3MAM9ecZwcR6Trml69FPLpN/BewwS+TJLA+9N+1WwGHDcOvQn06gigDQrP1u4+y6PPMb77 Ao2h7vyvM8lSwDPjoMAk7mBVfvMCoIrQqvfXsWn2cl1Mk7xpjIggeZzkgcIgLHr2HHXpQBj6DriP 4c0671W/gje8lMNvJM6xmcl2EQ4wryMgBPl5RjkplCprD1/xTqulePtEsJ45LbTbu9FrEBLbbbxW hYs53uJAVlMYCqo6HlzIiDtLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqv/bVkNY/st2n jujwhltpEjkO3dtSQqEdtuTtVicK3HynAByfhHxTquo+NdW0fWI5La4jsoLr7C0tswtWLOGRfLdn YFTEdzd8tiMOinvKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQBj+KL6407QZbi2 k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKr6FriNoNncarfwI1xdy2trPM6x/bAJXWFl6BmkRVc bRht2VAGBWxfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhl ZTgqwIIKkAggggEUAcv/AG3qP/Ce/wBnfaPk+1+R9h2L/wAen2XzftXTf/r/ANzvz5f8ON/NY+n+ KdZn0G8uJLzcwisHup/KT/iWyzSlbuLgYX7OgD4kDMmcyFhxXcf23p39sf2V9o/0vpt2Ns3bd/l7 8bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2jXGZFc8IyBg54UsaADwvfXGo6DFcXMnn MZZkjn2gefEsrrFLxgHeio+VAU7sqACBWxVexvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCK sUAFcnPql3/wsi3sF1GQ2Ztwp0+JUDiTbIxmkVo9xgI8tRIjgCQBCCScdZRQByfg7VLvUr3Whc6j JfIlwWhKKgigjZ5NsJHlo6TooUSRvuK/IcjcQNzU5tYi8r+ybGxus58z7XePb7emMbYnz364xgdc 8Gma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoA4PX/FOq6V4+0SwnjkttNu70WsQEtt tvFaFizne4kBWUxgKqjoeXMiIJPhx4hvfElneX17e/afO2XEaQSRywWySF2WDesaN5yLtDqxbHyN kbyB0kfiDT5tbm0iL7W93C+yUrZTGJG2CTDS7PLB2spwW7gdTipNM1vTtZ837Bceb5WCcoyblbO2 RdwG+NsHa65VsHBODQAanNrEXlf2TY2N1nPmfa7x7fb0xjbE+e/XGMDrnjHm1a4Xx3badFqXmB93 nWBgCiKERbhIDguzGQgCTIhwGQ4kC7uorP8A7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt 5oAw/A+vDX7e8uE1601SMurxrGYxJEGB5ZE5jRiCUjfdIoHzuWJVOsrP0zW9O1nzfsFx5vlYJyjJ uVs7ZF3Ab42wdrrlWwcE4NaFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFAFe/s01DTrmylOI7iJ4mOxXwGBB+VwVPXowIPcEVn+G9D/sDTpLbzID5kpl8q1g8 i3hyANsUW5tinbuI3HLs7fxYGxRQAUUUUAFFFFABRRRQAUUUUAFc3oPhebQbedYb+OSdbKHT7R3t ztjhgD+T5ih8u+ZG3EFA3GFTv0lFAGH4l0fUtat4Laz1G0trbeTdQ3Nm063K44RtsiEJnlh/FgA/ KWVo/wDhF0Hi/wDt5JYI2PzP5VqqTyHy/L2PMMF4cYbYwJ3qp3YUKOgooAz7HTPsmp6pfPN50l7K jKSvMUaxqojBzyoYSOBwAZW4ySToUUUAZ+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQC DyI9J0qbSbCC2S5jkJuJri6doiPMaV3kcIN3yDzHyM7sKMHJO6tSigDn/wDhF0Hi/wDt5JYI2PzP 5VqqTyHy/L2PMMF4cYbYwJ3qp3YUKC98LpfRa2J5YJ21KWJ0F1arOkaRqm2JlbO+Pervgbcea+0q x3V0FFAGfommf2No8Fh53m+VuOQuxF3MW2IuTsjXO1FydqhRk4zWhRRQBydt4f8AE1sNbdPEWmpc am4mWaPSWHkS+XFFuAadgwCRdD/EckkDadjSNNudL0uys/PtCYnYztFbuolB3HI3SMwcsVZnZnLH cTy2RqUUAcmngiO28eReJbS9kiQpN9otnknkM0kgUFsmbYoAjjAXYRhR6IUuaDo2saZqep3V/qlj dx38ouHjgsHgKyCOOMYYzP8ALtiHGM5Oc44roKKACqeq2s19pdxaQNaB5k2H7XbmeIqeGDRhl3Ar kYyOvfoblFAGXpGlTaRpdlYxXMbpC7GXMRC7W3HZEu792isyhQd21FC8/eFeTQZp/FkOsy3FoqQJ siEFoUuHUqR5cs2874tzM+wKvzBDn5edyigDn/DXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtz DaDxhVwc9BRRQBn61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIj0nSptJsILZLmOQm 4muLp2iI8xpXeRwg3fIPMfIzuwowck7q1KKAOf8A+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/ L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIea7CigDP0XTP7I0w Wpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToUUUAFZ/wDY1r/bH9qebffaP7n2+fyfu7f9Tv8A L6f7PXnrzWhRQBzfhfwqfD9xe3Ut1HNPdJFEEgSSOCGKMuUWNHkkKAGRvlVggG0BRgk6mp6Na6v5 X2mW+j8rO37Jfz22c4znynXd075xzjqa0KKAOf8A+EXQeL/7eSWCNj8z+Vaqk8h8vy9jzDBeHGG2 MCd6qd2FCiTQNH1LTri/udV1G0v7m6cHzobNoGVQWwnMjjYobCgbcfMTuZ2Y7lFABXP/APCMf8Vf /bv2z32+V+9/1fl+V5mf+Pf/AJaeVt/1vz7u1dBRQBz/AIa8Mf8ACP7t159o2WlvYQYi2bbeDf5Y bk7pP3jbmG0HjCrg56CiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKhurq3srd7i7uIoIExuklcIq5OBknjqQKBpNuyJqKyf+Ep8Pf9B7S//AyP /Gj/AISnw9/0HtL/APAyP/Gp549zb6rX/kf3M1qKyf8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8A wMj/AMaOePcPqtf+R/czWorJ/wCEp8Pf9B7S/wDwMj/xo/4Snw9/0HtL/wDAyP8Axo549w+q1/5H 9zNaisn/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGjnj3D6rX/kf3M1qKyf8AhKfD3/Qe 0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMaOePcPqtf+R/czWorJ/wCEp8Pf9B7S/wDwMj/xo/4Snw9/ 0HtL/wDAyP8Axo549w+q1/5H9zNaisn/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGjnj3 D6rX/kf3M1qKyf8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMaOePcPqtf+R/czWorJ/wCE p8Pf9B7S/wDwMj/xo/4Snw9/0HtL/wDAyP8Axo549w+q1/5H9zONg8Ya3Z6LC895Y399PdztCI7G UCeIzmO1QFSUiWY/cnkbbjbxJ8z1T0zxNqWojRLG31GO6i169N/azwsweyhW5Nx5E7h2yXhSaMKA oBhZPmALL1Sy+AFSyRX8MhLBy9moMGLdiwYmP+4SwByMcjNWINT8GWoiFvfaBCInDxiOWFdjCPyg Rg8ER/Jn+7x04o549w+q1/5H9zKPhDxbe6zp1zf6zawada/ZIdQid5o12wSBz8wEjnaoT/WN5e7n 5F2msebxL4guZLATtY6deW+oSNeQSyMq2cQtJGfz9rMsscfm27+YGVXaRFIhbmuqtdb8JWPn/Y9T 0S38+Vp5vJuIk8yRvvO2DyxwMk8mq8V54Ggs1s4rnw7HarFJAsKPAEEchBkQL02sQCR0OOaOePcP qtf+R/czmV+IGvf8IhDrr2NpHLJZXZaxkhcSwzW0MhllcbvliE0flbMZ/eIS4LbKLD4j6vrV/qdv pem2kpifFpEZAJJY5Uhe2kG90DlovtUxjypAiCkofmPVNqfgxnvXa+0AvfoEvGMsObhQpUCTn5wF JGDng4oudT8GXtvc293faBPBdOHuI5ZYWWZgFALgnDEBEAJ/uj0FHPHuH1Wv/I/uZasNR/tDwvbX s+oQQfbYk8q8t/kVvMIETosoO1m3IQjBsM235up4+fWn0X4e2skl1fNNHqs/mOZ2kdUtriaeaLzG bc6+VbSxKWxv+XcEDHb10/iLwxdW8tvcazpE0EqFJI5LqJldSMEEE4II4xUaa34SjitYo9T0RI7T H2ZFuIgIcKUGwZ+X5SV47EjpRzx7h9Vr/wAj+5mTJ4i15NWXRWGmi8uLiKJLuOJ5ILZnhnmaFl3K ZHVYF+bdHkTo2xQMNh6r401O804ada3djNcahaC0S501ZhHHPIEi+0x3JwjRpcTRxlE3OoYNuyCg 6r7Z4G/sf+yPtPh3+zP+fLfB5P3t33Pu/e56deaGvPAz6jJqLXPh1r6XZ5lyXgMj7CrLlupwUQj0 Kr6Cjnj3D6rX/kf3Mh8T+ItS03Ukg0sWkgt7dZrmKaJi0sksqxW0KMGUIZWEq7yGCbQWAB5y7nxt qdtA0KtY3N3DdvAGitZtt4wcRJHtBYW2+bzYRJK5XdCWCsrfL0Uut+Ep5Wll1PRJJG8vc73ERJ8t i8eTn+FiWHoTkc1XmvPA1xLbSzXPh2SS1lae3d3gJikZt7OpP3WLfMSOSeetHPHuH1Wv/I/uZi6t 4yntbbUJLuaxWHTdQjjkFu0pklj+0hi6BGDFY4I5d+NwZ4pwQFiYOWvizxBaNANabSkaOWCG9RIm iCSvF50sYkaVlXyIQZTIcrIDsXawJO0954GkiWJ7nw60aRCBULwELGFdAgH90LJIoHTDsOhNWP7b 8Jef5/8Aaeied5vn+Z9oi3eZs8vfnP3tny567eOlHPHuH1Wv/I/uZR0PxZLc2Ws32qiOKz01BLNM trNCYDsMjwtHIBIxRPLbzNqhxIMKCCKw5fG/iDRrBrvWobGVrPyxe21pbsjmVoDPLCjNKVDQwqZS 5ysm4Iu1gSeitbzwNY6dPp1nc+Hbexn3edbQvAkcm4bW3KODkAA56irH9t+EvP8AP/tPRPO83z/M +0RbvM2eXvzn72z5c9dvHSjnj3D6rX/kf3MxX8SeJG8Rr4egj0qW/bE8soWTybaEo+9Cc/vJIma2 J+4ZFnX5Ywd4k8L+LNT1vRr2+1GC00pDZRX9tNO0ZWOKVXKmRFlJKLszvYxb/mG1NprSs9T8Gack CWN9oFqkCOkKwSwoI1dgzhcHgMwBIHUgE0JqfgyO3u7dL7QFgvXd7qNZYQs7OMOXGcMWHBJznvRz x7h9Vr/yP7mcbY+JfE1gbO81K7tGzZaUmo/aY2j8uV5JWnQR7gqSpA4lkYn5VjBKBWGzQj8a6zdy i3im021P2g/aLm6sp0WziWB5ZVeKRo3Ij/0cGY7Eb7QvyqeD0UGp+DLURC3vtAhETh4xHLCuxhH5 QIweCI/kz/d46cVXtpfAFnbyW9q/hmCCRJEeOIwKrK4UOCBwQwRAR32jPQUc8e4fVa/8j+5kOo6n rF54N0KMQ/Zdb1n7PG8RZ7dY28szTxlsM8WY45UDAFlYr0IyK93qv27wf4Ta61SeO11XyXvL8P8A Y5fLW2kuWbKNiLcYQGwcBWYAjgjavtb8JanZyWd/qeiXdrJjfDPcRSI2CCMqTg4IB/Co7nU/Bl7b 3Nvd32gTwXTh7iOWWFlmYBQC4JwxARACf7o9BRzx7h9Vr/yP7mcjpPjPxJLottdXtzY26m0d5ZLn T5DJCttDH9rnkVZFztuC8PlBVbLBhlVINz/hMtekEtzKdN062KTuUltnuGtI7aNPtUrssi+aEnLQ BFCk5DgsoOeifU/BkiOj32gMjpKjKZYSGWVt0oPPIdhlh/EeTmh9T8GSI6PfaAyOkqMplhIZZW3S g88h2GWH8R5OaOePcPqtf+R/cyHXvE0+keHrO6ujBpl9NF51xHLDLe/ZFVN0rbIRl1UlVL5RRuBJ 6K2HaePtSkAvbiLTUjkuJrM6WHZri0mijw/myIX3AzhYlCR5InhIyzBD0Wpan4M1m3W31S+0C+gV w6x3UsMqhsEZAYkZwSM+5qvZXPgyytbu3/tbSJ0vHmNwZ7iFjKssskpRvVA00gAPZj1ySTnj3D6r X/kf3M5m98beJLvR9STSWsTNa2k7G9a1khdpCwjtxHbyEsJGmS4g8tiTujDdGWN/TIBMtvEtxJHJ OEAkeNCis2OSFJJAz2yceprmRL4AWwewV/DIs3RUe3Bg8tlVy6gr0IDMzAdixPU1qf8ACU+Hv+g9 pf8A4GR/40c8e4fVa/8AI/uZrUVk/wDCU+Hv+g9pf/gZH/jR/wAJT4e/6D2l/wDgZH/jRzx7h9Vr /wAj+5mtRWT/AMJT4e/6D2l/+Bkf+NH/AAlPh7/oPaX/AOBkf+NHPHuH1Wv/ACP7ma1FZP8AwlPh 7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/40c8e4fVa/8AI/uZrUVk/wDCU+Hv+g9pf/gZH/jR/wAJ T4e/6D2l/wDgZH/jRzx7h9Vr/wAj+5mtRWT/AMJT4e/6D2l/+Bkf+NH/AAlPh7/oPaX/AOBkf+NH PHuH1Wv/ACP7ma1FZP8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/40c8e4fVa/8AI/uZrUVk /wDCU+Hv+g9pf/gZH/jR/wAJT4e/6D2l/wDgZH/jRzx7h9Vr/wAj+5mtRWT/AMJT4e/6D2l/+Bkf +NH/AAlPh7/oPaX/AOBkf+NHPHuH1Wv/ACP7ma1FZP8AwlPh7/oPaX/4GR/41Ys9a0rUZjDY6nZ3 UqruKQTq7AdM4B6cj86FOL2YpYetFXlBpejL1FFFUYhRRRQAUUUUAFY/ifWH0HRRfxpvxd2sTqIm kOySeONtqr8zNtc4Azzjg9K2Kz9aOlpphl1meCCxhlimaWebykR0kVoyWyMfOF6nnpznFAHJyfFH SLCW+bU2kt4EvTDbiWE20ghSCCSR3SYqxIabhVBdgRtRsE1oR+OrGOQQTR3c0pvTas0NthYi149t Fv8AmOAWjf5s4PlnIUuiG4mjeHdfibU7OXzlupTN9t0+/kQu21ImAlicHbiFAVB2kxgkZGaz5/B2 natf6dqFnLY/2asqXytHE0kssnnm4DrN5m3aXY4+RiFklClRIaAMvSPiRd6pL4YtU0S7M+opbveT GNI4gssEz5jDyB8B4H5KnKIxG7chY0X4kXdzqt1a6pol3BGlxHBH5caHZvvZ7YNIwkKgApEpAO4t 5hUMgyOkXwv4dgvNNiSHyrqzih+xol3IriO3DImAGyyqLhlOcg+Zhs5FR6Z4f8Lzm9utO8u5F3cQ 3M7x3ryr5iyfaoyPnIQbpfM2rgHfnBBoAz7P4n6Dd3GlQtHd2zamkL24ugkbFZTtjIQvvYM+5Mor Y2ljhCrtXvPiXbW9x50Vhdz2MVvfSTpEiPOGtzASSA+IgElclZdjgqF2hiFbpI/C+kwf2f8AZ4Z7 f7BFHBB5F3LHmOP7iSbWHmqvOA+4fM3945rr4X8Ov9osDD50zWksNwJLuSSZ4bjarGRixc7hAqhi cgR4UgDFAFPUPGsMcggs4JPMa9it4pZkHlzqLyK2uCmG3AxtLt+YLk4K71yak0Pxd/b2uT29pZzm wFpa3EUzxbCVl88iQ7m+aNliixtBYF8MAQwS5N4e0BL0SzwRrPdXCvErzsB5quJ/3S7sKS0QkYIB vKbm3Yo0rw9oGn39xPpUEcNwjpHcCCdhgqjFEdQ2MKk3yqRgL5eAAiYAM/wv4g1DWdX1GK6ljEEF xdQxxJpVxGAIp2jU/aWYxSEhclVAOSf7pq54Y8XWPir7V9ij2fZtm/8A0u2nzuzj/USyY+6fvYz2 zg41NLhsYbBW00xtaTu90jxyb1cyuZGcNk5DM5PHHPHFSX1/Z6ZZyXl/dwWlrHjfNPII0XJAGWPA ySB+NAHLp4s1F/DFrqZ07ZPNrY0+SJtuLeP7cbfL4flgoAypYbyDjbnFjWfEl5p+vi1hjgNrb/Yf tAdSXl+13DQLtYEBNhTccht2cfLjNbH9iad9h+xfZ/8AR/tf23Zvb/Xed5+7Oc/6z5sdO2McVHJ4 e0ya9hvJYJJJ4n3hnnkYMd5dd4LYcIzFkDAiMn5NtAFPw9rF/f6jqVlqCQCS12NmKKSLaWLgptkw 0irs4nAVJMnaAUapNK8VWOr6lNYQw3cUsb3CK00W1ZTBL5Uuw9wrFOeh3gDJVwsa+H9O0WeO/t7j +z7G13z3AMrbSqoyohZmwlvGHkYRABVbaRtwQ0mheF7HQ5rq6jjje8ubi4mecJtOJZmlIxkjOCil hywiTPCqFAMu2+INjf8Ah7XdWsrXzP7JtGuXi+3W0vmYR2C5glk252EZYDrxnBxI3iLUrbS9TjnF pLqFlqdvponSJkidp/I2yeWWJAX7QMrv+bYfmXd8vUTwQ3VvLb3EUc0EqFJI5FDK6kYIIPBBHGKy 08L6Slm1r5M7xvkuZLuV3diUIkZyxZpF8tNrk7k2gKVFAHN2/jXUpbkI0Fpss7iG1vMI2bhpb6Wy Dxnd+6AaEybTvyG25GN51G8U3Fvpfi+/uLHC6FLKIoSwVpUS2jmyWBYDcXOD2UrkAgirC6L4ahgj 1ALAsGn7y85uW8vMbszNMd2JGSTe+6TcVcs2QxJovLbw/qFhead9ugj/AOEnikIMVyu+6BgWNniy SDiNU+6COASOeQCnB4i1JfEkWn3AtJIBejS5DHEyM032P7UZgSxATHyeXgkfe3n7tY+qfE2GPxQN G02bTW3u1mpmnBlF19pgg5iBB2L5sjYJDP5TY2qA7dJa6Xo2qzz3nnwXd/HutryaxuHjRLgJsdgi ufLmCnbuz5iqQu7FSXMXhzX72BJLi0urtrJpIFiufn+zu8beam1s43xxlZByCoKkGgDHt9f168vd ESGbTQlxe3NneQ/ZHZ9ts8ySToRKNiM0cagENsMy5ZuAeg8P6lNquk+fcLGJ47i4tZDGCFdoZniL gEkqGKbtuTjOMnGTGtloWiy28rPBayWlpcFHlnIIhLI88jFj83zCNmdsnJyTljmnpGpaDolrZabL 4h02S7vna7iBuEQ3LXErSbok3ElGd2CgFuMDLHkgHSUVHBPDdW8VxbyxzQSoHjkjYMrqRkEEcEEc 5qSgAooooAKKKKACiiigArD8RX+pWsuj2mly2kM+oXpt2luoGmVFEE0uQquhJzEB17mtysPxRHoD WVtL4hvo7GCG4DwTtftZlZdjr8siupyVZxjPIzQBlxfEbR1v7DTr3/R7+5la3eISoRHIJ3txtyVe RWljcAopIGGcIDQnxG0ueeGG3sNVnkuPJNuEtv8AXiZJpImXJHyssBO44C7hvKlX2aEdp4avJ9Pu LK5gRofLgtvsF60KOFTzY4isTASKEJdUYEBWJAwxzj+GvDmhWE7ay9/pVzN5tusUtgDDBG2x1hGD I+WKXZVQW27GiVVAVcgAnxX8MS6dNeRXG9UljjjTzoV80SB2Rt7SBI8rHIdsjI424Kgsobc0zxXp usapHY2Jkk8yyS+jlbageJ9pUqjESEYYfME2A5UtuBUV4NA8NzW8VvbTSNFdILq0WHUpvkRRgPbY f90gWUL+62ja4XoQK1LPRbKyvDdxrPJceUsIkuLmSYogAGF3sdudqlsY3FQWyRmgDl5/Hkt7qehW ej6fOV1C7iLyzxoQ1m8crpKg81SN4hcjPzKEbcmSitT034nBItUbXdNu7VrW4kIKxRrHHD58kEYe TzmTeZIihJKjLZA2K0g2II/BGl2VnrSX2mw2YuN1pePfgxBwkkYjjYvtCKrzBY1+VcthQc1YutM8 KaZM1reSWlrPq77ESa7KPO4meUeXlshxLOzApggsuMYXABTh+Jfhu4utLt0ucvqO0J+8j+RmlaFR jfmTMiMuYt6jG4kIQxjtPiBDeapLFbWN3PDJb2r2ESQhJbppftD5VnZU2GKASAkjHzKfn+QaB07w 3a6zp9g2oyR6sib4IW1eYXE6BmcbwZN8yA+ZgPuAG4DAJFFt4Q8Luk8ljaRp5jqjTWlw6OjQtIgC ujAoU3yR4UjC/u/ugKADPHxL0qXVLe0trDUriC4eFY71EjEREv2fa2GcPgfa4M/Ln5jwcHGp/wAJ dY/8Jf8A8I15f+l/3vtdt/zz8z/V+b53T/pn7/d5qvPovg60gfVplsba1t5VDT/afKhieN4FC8MF Xa9pAu3jBjxjlgeg+32f9o/2d9rg+3eV5/2bzB5nl5279vXbnjPTNAHL2XibVJbzS45oIHhv9bv7 HzI49ohigE4QHLkmRjBncF243DAOCbH/AAkl5/wln2Dy4PsP9of2Zt2nzPM+yfavN3Zxtx8mzbnP zbv4ak0XS7GK4uLGXULS+u9O1OfUI44vkktDcGVgJFDnJ2zSgEgAgg4yM1JfeErK4ikNrJPa3bxC EXQnkd0G0RtKuWx9o8vKCY5ccZLKNpADXdYv9L1bTkiSA2lxLHEweKQly8gQ5l4jh2hgwDbjKfkU K2Ccu88Ua1YeGvEupNaWl5Jptu8sMlqCIhKobfCS7AyCIqN0i7d2WUKroVrpLrRNOvdRgv7i333E O3B3sFfadyb1B2vtYll3A7WJK4JzVOz8I6PY6NdaPDHdtp9zb/ZZLea+nlURbSuxN7nYNpI+XHb0 GACn4u8SXmhf8eccDeTp93qc3nKW8yO38vdEuCNrN5ow53Bdv3WzxoeGNQvNT0Uz35ga6ju7q2do IzGjeVPJEGClmIyEBxk9akv/AA9pmqPvvYJJiXLHM8gBUqqtGQGwYmCLuj+4xGWUnmjw/pU2jaT9 kuLmO5na4uLiSWOIxKWlmeUgKWYgAvjqelAGpRVf7fZ/2j/Z32uD7d5Xn/ZvMHmeXnbv29dueM9M 1HHq2mzapNpcWoWj6hCm+W0WZTKi8csmcgfMvJHcetAFyiq9rf2d95/2O7guPIlaCbyZA/lyL95G x0YZGQeRUem6tpus27XGl6haX0CuUaS1mWVQ2AcEqSM4IOPcUAXKKz5dd0eH7D5uq2Mf9oY+xbrh B9pzjHl8/PncvTP3h60f27o/9sf2R/atj/af/Pl9oTzvu7vuZ3fd56dOaANCiq8N/Z3F5c2cN3BJ dWu37RCkgLxbhldyjlcjkZ61YoAKKjnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1HdX9nY+R9su4L fz5Vgh86QJ5kjfdRc9WODgDk0AWKKKKACiiigAooooAKKKKACiiigAooooAK5/xpY3uoeGnh06Oe S6W7tJlFu0YkAjuI5GKeb8m4KpI3cEjmugrD8Xavc6H4dkvrPy/P+0W0IL27zhRJOkZPloQzkByQ oOSQBQBhtp2vX11pnm/24LAxNHqSTX0MFxKTKfKI+znaPL+Zn8toyyMgJkKmOuT0vw548sNDWyhG pW2zR3toIluEfy3FqVCFzc4Qm4BdWWNiqlF3quUT0Cw8THOnWV3Fd3N/eozxPFp0lqrqsm2QmOVi 0QjBQkuRu3DZuJ2jP/4WfoI0aLUpI7uATpA8EN0Et2lWVXZSGkdUUfuphlmGfLO3cGjLgFODwxqg 8dx3N8dVu9PSK7tYrv8AtDYUSSK2bcwR1I3Sfavur8p8vAVUj28vo/gvxdpvhN1s01Wyv/njW2Op JgY0vyd+VkIObhI9u5jsCqVCDdXaap8QIRYRXGhWN3qYe4sk8xIQsZWd4SRl2TD+XPGQDjmVeoST ZJp3xK8P6rE72Lz3DCVESKBVmeRWV3DhUY4+SKVtjYk+TGzcyqwBnnSPFVh4qmeO/wBVutN8p/Ll iaJ3YGE5VvNmWNZDPl1IgIGUXeseUSnZeF/EyXMN7NLdxTomnDZDetiRUvpnlEheR3YrBJgoZGQl 2C79qEdBY+NYRonhu41GCT7XrFlBcYt0GxWke3iI+Zsgb7lD34DdwAZI/HmlvZ/a2t75Lf8AcOri HeXhnLLBMqIWYrI67AuPMBI3Io5oA5v+wfGksdoySyR3kDlvPu7hZVF59ju45LlAS22BpJINsYAx hv3SjOdzwdpeo6ffapLqNpfR/abt5rdpr1ZtsPk2yKsmG+aT92VyQ5Gx/nbdukr678SLDTE1OxiX y9atdPluFt7hoztmW3M4idUfccIMl1Hl/wAIfdxWxL4vsIovEErRT7dCiMtyAY97AKzHbHu3rwhw XChuCpZTuoAw/CvhzXofhyug6jPJa6hGkKLctM5G1Y4jhRDKhQJgxYVxuMZc53kHQn0bUtO03QHU SarPpV69xKkTsJJlaKeMBDcSscgzKTvk6KcdlqPUfiV4f0qJHvnnt2Mro8U6rC8aqqOXKuwz8ksT bFzJ8+Nm5WVdCLxdZy2N9eLZ3whtLs2ILRBfPuBMYBHHlucuFG44T5xlgVcKAY6+HfEFlpnhGwtL vaun2kFre+TcMqExyWzs2ONylIJoxxn98AQFZytOHSvGg0uSOKe7i1Avbfvpb1ShuF3/AGqcZ8wL BIhVY0KEK2G8pMbq1F8eW8Fzrgu7efybCKa7i2QlHMEVtayuGVyCJM3OACB0wcEc19a+IsGma1Ha JYzta2920Wo3BMWYYxBNKWEfmCUY8osCyYdFYpuymQDH13Q/F19JqcFst8dPutKlt7a2eVJNqtaF RDM73GPM875jII3Y5AMu0kL0ml3F9YeLL6O/W7ePUrjZZLLJvdUjV2eQosjIkAzEisqoxLqJFLEO xpXxB0rWriztrC3u7ie5eVCsPlyrEYzFv3yI7R4Czo2QxHVfv4Q6moa+lnrlho0cHmXd5llMkqxI EXlyCx3SMFDHbGrY+XfsVg1AHP8Agxddn1jULy6uZ5NN+16hEPOuBIG23bLCqLgFNgSYHOch4/mY KEi2PDGgX2h/avtup/bvO2bPmuTsxnP+vuJeuR93b05zxjH0f4hJeeEtE165tM29/sgkkidUJuCo 3CKJm3SKHEq7RmQmMbUcMGrY1XxdY6R4hstFnjzcXnl7G+120eN7lB8kkqyNyP4UbPQZPFAGPFoW o2nhFdJisp5JIdVkv033CuXjj1EXCoXZ8mSSPkFjjP32U1n23hbWY7ti1nhdRu7e6ZvNTFoItSnv CsvPLFJlQbN43hskLhj1l7q99D4qt9ItrON0l0y5u1kmfYryxvEqpuGSo/eHcSh+8uM4YVy83jrV YLK5YR2kkljb3d7NI9tJAbiK3SBzH5Dtvt3bz8AuWwED7WWRcAFdPDmr2ngXxBbzWMgnTwvFo8Ua ESNcSwRTgvGFJJRjKoUHDnByq8Z1P7PvZPEf9qWdt9tsG1D+2obi3mjKTRnT/syxJlhmQthgThNp zvzxW5q2palZ6zp9vaLaSxzuoNsQxnlXcBI4OQsaRKQxJDbywT5GKlubu/Get2s76d9lge/lu0t4 TFaSny2ZJ32eU7I0qj7PtE+Y428wt8oicUAU9c8GeI5fFsOrxXFpfxq7TyH7P5c5iS7tp47YM0u3 O2Jgp2qmQxbDSlxuaTomordaN9rt/Kt4LvUdUfDqXimmlfyomwSD+7uZt23I3IMNj73SaTeLqOjW N8lxHcJc28cyzRxNGsgZQdwRiSoOc4JJHQmrlAHP+DPm8PvKOY5tQvp4nHSSN7uV0dT3VlZWBHBB BHBroKKKACiiigAooooAKKKKACuf8T6Te6rc6D9iuZ7X7NqDTTXMHl74k+zTpkCRWU5Z1X7p+9nj GR0FZet6lNpSWNwqxtbvew29yCCX2yt5SFOQM+a8ROf4d+MnAoA4vUPB2snxDdX6f6XHHLDdK7ui yXJV9PJUABVEh+wSddqZkjwQN2yvpfgXXtP0lbZ72f8AcahpLG0haEwTpBHZrJLlk8wYMMmBuXOw fLzzoN8R/wDilLzUktv9I+yT3FrIU/c7/Ia6hgcbt/mfZ9juQNmdwD5wK2Lzx1YWkcRWyvp5JZZ4 Y4oljDO8V3HaEfM4AzJKpGSPlyTg8UAcv8PPCXiXQ9W0iXWZJ3t7LRJLALJOrhHaSGYYAY4xueHj taqc4ZQOo0fw1faXp2qQXt1Bq/2qLYkFw9z5bcMCrmeafCtuAO0DjqG4Ay1+LXh5dNgvruO7sklf hLowo3l+VFKZB+8IYbJ4jsUmQ7iAhwcdJrOoXlro73MBgsWWUo0l7GZtq7iqlIomzIznaFQMrfvB /ENhAOHm8M+J2n/tW1F9FfTfakR5JoFuBJIlssUtzsPl+WjW2SsPJRYwVfdKD0mraffXviPT9RtN Nu4LuJ1hW8N9+7igE4M6vCH2kSxqNhCu2Su4RlFIy7/x5qmmQ3N7eaXBHHa77eaw8zMizrYG9Y+c PlKjBiwE6/Pux8tR6x461XQob+O6jtJZNMeYzXEdtJsujHDBMIUQMxjdlnb5izhRA7lSudgBuPpd 4njtdRtbSeFZMC8uzemSG4t1icLEIi3ySCVgw2pjaGO/Lshr6foWqW3hObQraaewmku7yRLx38/b E12zqrHzBIWkif7wYMOSWVgMxxeKtVuNS1O1hhtB5SXDRZikfyhDKsZYbc/aiVJcpEFMbKInIZgw pr48v0uNNWe3gVJpYYGAhkzdGS6a2LLz+48vajujhipmSJir4LAEmlaDrGjeDBZNaRtc2+p3M1sN KhghltonlkKtCsxeLJDngldschXJZfmLHQtT0y80ZRpEYh0y3SWSOzljMEki2rQ5jL7ZTPyIx5rG PygCWD427n/CXWP/AAl//CNeX/pf977Xbf8APPzP9X5vndP+mfv93msO5+IK3HiqfRdKn01vKt7o EyyNJP58LxhwIE+chVMpCcNIYzjaoDsAamm6Xep4ynvZLLyLOKKeOLLxmPMskbkwhAG+fYWl80Eh 8bCV3E8XpnhPxlElul1c6yRKlsl1u1qRiBu09pdp83KnK3/K4OMgcFAdxfHl+lxpqz28CpNLDAwE MmboyXTWxZef3Hl7Ud0cMVMyRMVfBaOH4gak/hrxFffYY5L3TdMj1BYVhaMQtIJcwyb2HmGJomDu pXJDKEV1KkANG0DxNF4Kl06/m1I3kt7p7u7akzSiLba/atsofco3Lc8Bh328EZx7Hwn4ya306C9u dZ2I8Lzka1IGyw08TZYS5I+W+wM4HO3GVz6Jq/iSz0W/srO5jneS7x5ZjUEDM8EHOSP4rhD9A3fA OfH480t7P7W1vfJb/uHVxDvLwzllgmVELMVkddgXHmAkbkUc0AYd7oHia+0nwlBJNqSy21lCmpGH UmiYy+daCTeyOC58oXXOT3xyVyaTHrWmXGlNr39pOpsrKyZGuyZJLzMRYxiObDou2V5WdN+FfDPG Sq3Nd+JFhpianYxL5etWuny3C29w0Z2zLbmcROqPuOEGS6jy/wCEPu4rcTxTZSPrirHIP7HTdPvl ijLfKx+6zgoPlOGkCKwwykr81AGPPpGpw+NZdRSwkns0uDfKsckYR2Fp5GRuYP8AaCflwSIfL5OJ Oak8M6PrOl6/eid5/wCz2lvJW8yVGikM1x5sPkqPmXYrShywBLOAC6qu25YeKotd8NXmqaY1pbCB yjS3d3DLFEAFZpGaCR1wqsW2llJxglQQ1Za+LtYjv9Nt7mzgiMvk+arxOpmWWdolckt/ovyqsgSX LOX8pfnQmgDL07wrrB0HXNP+zXcUd9o8ei2S3zQBrdIo7gI7+U7AoRJGCQWYuXOxUwB1nh+y1F9J vodWe+WO4lbyEuZ1+0xxmNQ254ThWMnmuNjYUMoXaAFWOfV9Xh8b29hNbx22izII4J5IQ7XVxskk ZFZZcxgKoOWjwdjjIyuc+++J+g6aWN7Hd28a3E8HmzBI1byJCkzJucGQKdpwgZm3gKGZXVADL0nw z4i06Xw9JEJ4JI9K0yzuBHNH5ULW7OZxMM5fdHI6Jt3LuYsQpVHHWQW13eeKotUubWS2gg0wR26s yFvMmfdMj7SRlRDBgg4+ZuW4xn6l8QtG0p79LkSI9pcJbbXmgiMkjK7Y/eSL5Y2xuQ0uwOAChfK5 jsvHtnc3LZHm2tzqENrYSoBEGSS2glDMZWUbszHCD5yBwh2sQAZbadrVx4xvxY3MkONTSW7VZzEH iP2J0l2dSnlW08GctukL/dVnEZDpXjQaXJHFPdxagXtv30t6pQ3C7/tU4z5gWCRCqxoUIVsN5SY3 V6JRQB5fruh+Lr6TU4LZb46fdaVLb21s8qSbVa0KiGZ3uMeZ53zGQRuxyAZdpIXQ1S11QPqpufP2 3mq2KaYtxLukMkdwHLiNZGQwqqh8KInZI334I8w+gUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FZfiGLSJtEmXXbiO309Xjd5nuTbhGV1ZD5gZSpDhcEEc4rUrn/Glje6h4aeHTo55Lpbu0mUW7RiQ CO4jkYp5vybgqkjdwSOaADTtG8O6hBY6lZS/2isH/Hpe/b5Llkw5LbJS7Hk5VsH5lARsqAor6P4D 0bSvD1ppfkbpIYoUa6gd4JC8aFd6Mjbo87pCQrDJlkzku5anFaeIZdS0edhrJs40xqEd1dW8c0re bmI7YfkwnzM4Qx70ZATKVMdcvpFl431HwLp9zHe3cyXFvZzSq90ZJ7gGKQs0bCSEqDutcjzIzmKX JfcWmAO41Dwj4bkIub6ORHZIrbz2vpo2ZvMiMZ3hwTKXigG/O9tiDJwBVgeENDFu9s1pJJbM6stv LcSyRRYBG2NGYrGhVmQogClCVIK8Vxer6H4q1KBIr+DVdQVfsNzut72K1yYnti8ZjSQATZF2+VfY NyfMxWPy5NN0nx5HZTRXl3dvOLiJpSsqRLK+yUNJFIZZSE80wOVKRrsjIEJ3NGwB2lromhT6do5t reCe0sYo/wCz3Vy6iMBdmGyd6/LGwySNyI33lUiMeD9AFu9s2mxy27urNBM7SRlVBCR7GJHlLuJW PGxScqoPNcu+g+KIPCvh+2sJbu1utO8OSxtDDcIqtfKkHkI+ThgGRzkfKcEMSrMrF/o/i+5vktM3 f2BbiYyXEGpNG0kcmoQSgDDBlCW3mIDnIxIoAG0uAdJc+GNBnna1nM5kurR4pLf+0Jh9piCCJ2kQ P+9YKyKZGBYfJzkLiTUvDFpeaNrtlC8kU+r28sEtzK7zsu9WAA3NkIpdiEBCjJwBk153Z+FvHMCW UgkvvtcdpKgnn1MSNHNJb2OCSxctGLmOV2QEAqjfeUiOTYn0jxtLrGpr9vvo4Z7uNo57ZowgiF3E wKl5mwyW+9SohRX+bd5h2lgDrD4Q0P7OkMNpJahHZlezuJbeQbgAV3xsrbMIgCZ2gRxgABFAksrL QtU0ForB4LrTLmWScSW85dTI0rSM6SKchhISwKkFWHGMDGPrWjaxNq1pb2Ut99gEVvDHLHfuggVZ D9pMx3h5Gkh2qjfOyuC2Yz85p6Zo2v6N4f8ABlnbxXbJZ28UV/bx3i7o5SYtzszkh4kT7QuwE/eT YBtV4wDUj8E+Eb/RUt49PgudOniXYVmd1kj8mOJcNu5XZFERzjdGj/eUMLFz4X8O6heSxXUP2m6E q3kqS3cjuAwkRQwLZ8khplEZ/dnc428muT0bRvGGlaZo9i0V2IIEikma2vIy8Srpvk+SiOdjEToX 5+TLRt82XKZb+FPGFvdG6givvOuLSAXLR6jNJvVZbg+UA92HWQB7ZjiUKNsoV2DFZAD0Cbwnaizk jsru+gunilt1vJbye4mijlMfm+W0jkqxES7TyFYA4PIbcmghuUCTxRyoHVwrqGAZWDKee4YAg9iA a87m0PxdNpd7JJearNqM8tvHbo1wkMKr9mh8x5VhlVkUzI27ynLKR8gZXkEnUXiv4u8OB9Mvp7BZ JWMcp3ATIjlQ2Y3RzG4AdWR1yCpJKkqQC5pPhzSNDAGnWMcJVFRWJLsqiOOPAZiSBthiBHfYCcnm tSuHOj67LeXdg9hAlhLaRrbBLkCzs9ggCIiKqyeZG4uJFdPLyPLG4Hb5Wpf+HNQuvDVnpketSLcw OGe6drgFwAw2/up43wMgDc7nCjcXb5qANDVYdF+0W8+rG0V5kfT4RdSALMJipaIKxw5by14wTwcd TWXe6N4RsfsVhqMsEU1zKwhF3fv514zbVaNmZ986kCNSjFlIVAQQqgZ/ijw5ql3p1vbWqfbJH0S7 0d5BJjZJOIQJm8xyxjHlNu+Z35GA5yauatp99e+I9P1G0027gu4nWFbw337uKATgzq8IfaRLGo2E K7ZK7hGUUgAuW+j6DqOrSanaXk813bS+RO9tqs2A6SNJ5UirJtO1pG+RgcBtuAuBUg8IaGlu8Mdp JGGdWV47iVJIdoIVYnDbokUM4CIVUB3AADMDT8IW90v2m61DQ59Ku3ihhERaDyY4U3+XDEInb5U3 NlmCli5IAXCJ1FAEcEENrbxW9vFHDBEgSOONQqooGAABwABxipKKKACiiigAooooAKKKKACiiigA qnqUNjeW62N+Y9ly4SNGk2MzqC42EEEOuwuCvI27hjGauVj+JbG4vNMieyj33dpdwXUQVgrkJIpk VGOAGePzI+SAd5BIUmgCufC3hrUYpjHZwSW7xPZvHBKywgBTA2EUhVkCAxbwA4UbMgDFSSeD9Ak1 I6i2mxi7Lh/MV2XkSpN0BxjzY1fHTcXPV33cvpXh/wAR6Wbe2uvtdykVv/pMlpe7EuIzbYkjUF1P 2h7otL5xCnDcyj7tV7/SfHn9jaeou7t9QieQ3s1vKkvnT7Y/KmiUy26CJQHBjb5d33o5c+ZQB1Fr 4R8NtZLHYRyRxxP5Yls76aN1MaJAU8xHDYAgjVlzgmMEjcM1cudG0nXLOJHlnmjt7uSaOWC/lR45 syI4EiOGGN8ibc4H3cAAAc/eaRrqeDprCxjnjv5tVu5UEc4RNklzNIjSukiSLHhkJ8tt44+VhuQ0 30rxXJqT+UNSt7hE1BrW+N+Ht98ktz9nWaIyEbFRomGI3OSgyojKsAdZ/wAItozT+dLZ+exi8plu JXlSQbPLLOrkh5CnyGRgXK/KSRxVf/hGNBk/4l5M8jRfvp4G1CZmmD/KDcAvmZSI9g8zcNqFRwCK p+DbTV7W32axHqXmK8vkm4kG2KMiL5SPtMzOSwYqzMxX51+RdobL0fwxruk65q736WOtQ6lp8UE0 zW4hF1MPtBJn3SOQuGVDtRhiSNVVVjIoA3E0zwpe2F3rEUlpNY3CPI93HdkwxAPvd42DbYjvQOzJ tO9AxO5QRJp2jeHb+Cxv9Ol+12kPMBhv5JbeRlcsZGUOUkk8zLF2BbeMk7gCMu20O7uPCutw6pp1 3dnUbgSSW812kF1cRBIkYSPAREHIjICoVRlCByCzmpDo+u6n4cg0ue4+yssplk+2xi6Se3Ly+XbS 7ZVZmVPK3ncQ+MFpAz5AOkfVtNjt7S4fULRYL10S1kaZQs7OMoEOcMWHIAzntVPVrLQr3UbSDU3g +3TRSw2kbTmOR1zHI5jAIO5TFG25fmUqCCK5NNH1i28FeG9FvtCkvyNMNnqT2XkLLHFtjBtwZJQA HAAd1Y58s4CllZNDV9I1PUfELzxWEiwXT6epkeSPEIs7ySZmcBicSKw2bQxyfnCUAbieF9JjltXE M5W1wY4mu5TEzBi/mPGW2ySbyX3sC27DZyAaIvC+kx6dfWLQzzw38RguWubuWaSSMgjZ5jsXCgM2 ADgFmIwSTXJ6poHiafxrJqNvNqQsxexPGiakyReUrWGf3QcLjaL7IK885ySlHgXQPE2latBPrE2p PE1kUmF1qTXC+b5NmeFLsAfNF5yB69itAHcXmlWOoXFrcXdtHLLavvhZv4TkH8RuVGwcjciN1VSM 8eD9AFu9s2mxy27urNBM7SRlVBCR7GJHlLuJWPGxScqoPNef6Z4T8ZRJbpdXOskSpbJdbtakYgbt PaXafNypyt/yuDjIHBQHY0bQPE0XgqXTr+bUjeS3unu7tqTNKIttr9q2yh9yjctzwGHfbwRkA6if wjo9yJVmju2Sa3NtOgvpws6GPyyZFD4kfZgb2y3C8/KMXLey07S7ySVX2XWoS43zzs7yMAziNC5J CgeYwRflX5iAOa8/0nSvFOlaXp/9p3N8iwXcF3dzXupF4Y4ltrczmSQTBx+8FxtX54s53JtKsuhr q3l3Fpr6XFfWt1deGtQt7FbqQpOlw627xxs7NkTYRzy2f3bnPyk0Adpqml2ms2DWV6kjQM6P+7le JgyOHUhkIYEMoOQR0qmnhfSUltZWhnlktsbHnu5ZS+GLr5hZj5uxiWTfu2E5XBrlzpHiqw8VTPHf 6rdab5T+XLE0TuwMJyrebMsayGfLqRAQMou9Y8ole10jxrb3+gPJPOLWPb5qQSNKY8zu0nm+bdc7 omjU5NxsKv5e3ClgDsLDRdGae21azXz2MSNDP9peVJBsCrLyxDyFML5py5XjcRxUd34f0Mpb28vm WzvcTNA0F7LbytJKzTSqro6uQxDOUBx8gOPlGMOHwnq1z4N8M2Av/wCzbqw0+KG4TzLjlxGgIzb3 EQOCp67uvGOc7mt6XNea74bvYUkYWN7K82JSqrG1tMmSucMdzIAcEjJxgFqAKdza+EXs9SvbjUIB HHd4ur46m6vbTAkeWJt+6HHmMoRWUDzGGAHIMdongubVre2ttVtLi/d0uktv7VaVp2EMeyRozIfN IjijdWYHG3eOSWPPz+GNYluo7/8As+7hjtLiFRbWlxAk5hSK5jMVuy7EWAfaF2M3lzbfMJIYRgdJ YWOsw6jbX+rR/bLrT9ESEPasg+1XMhBuAA20DmCHbnYPnbP+yAbjatpqveo2oWgewQPeKZlzbqVL Ayc/ICoJyccDNWIJ4bq3iuLeWOaCVA8ckbBldSMggjggjnNefvouuWOoX93Dosd95L3DWkMjxNEG mu45g8ILqWI2+bIJWX94iiJkUki5qei6zP4NtLbQlvtOvDLctMtxcokxeaOdTK7RMU/10yzEKeAv yruCpQB3FFcHc6Z4rabV/Iku1nkScQzG7HlPK0ymzeNN3yJDGCJV2rv5+WbNRyab4uXWtEmSW+W0 j2LcL5yTsJPOYzu5M0StHIhXb+7coudscLAKQDuBf2bWcN4LuA2s2zyphINkm8gJtbodxZQMdcjH WrFeTy6RruneCL+2vY544Y/Ci6asM04Ia7aFEWOBI5Cr5fcuWQSF2AVnVgq+sUAFFFFABRRRQAUU UUAFFFFABRRRQAVh+LtXudD8OyX1n5fn/aLaEF7d5woknSMny0IZyA5IUHJIArcqve2NvqECw3Uf mRrLHMBuIw8brIh49GVT7454oA8/1L4ganpEUnmQwXbR2iXO97KayLYW9ldTFIxZcpZhAT0L7/mG FNiw8Zatf+KW04vBBbxyspEek3FyZAL25gwZEfbD8kC/M4xlmPQYHYXmh6XqF4Lq8sILibymhJlT cGQgghlPDcO4GQcCRwMB2zTi8I6PBewXkEd3BPCiJuhvp0EgV2ceYFcCUlpHYlwxYu2c5NAEegeL rPxJFBJZWd8gliiuMTxCMpDIrlJCC33S0bpxkkjcAUIc5+ueLrzSr/WrcWe2Ozi0/wAiQxGUu1zO 8RcIjFnVcLhAFZirAcFTWh4a8I6d4a0mwtLZf31tFEkk8ZaMTMkZTJUNjaSzvs5Xe7P94ljoXeia dfS3Utxb75LqKKGVw7A7Y2Z4ypB+VlZ2YMuGBwc5AwAYcmta/N4MhvNJtY9R1hrjyJVW3WJISkpS XdG84BKBXX5ZSGYAg7TkZ9p46uZtYsLdI472C7RVgWC2eJ7kG0NytwrSNtRHKvEsb4OUc7zsYDpH 8L6S9mtr5M6RpgoY7uVHRgXJkVwwZZG8x9zg7n3EMWFH/CLaMZ/Mez8yMReSlrLK720abPLwkBJj T5Mr8qjhmHRjkAr+ENT1TU9Jl/twQRatbyiK6toYfLFu5jR9mfMkD8OGDBuQwBCkEVy+i+L/ABFr Ol6HJHdWMd1qssSkzaHdRRwBraaZgpeUCbmIAMjAAc87hXYaf4fi0zURPbXE624iZTC8ryNLI5Xd JLI7M0jBY0VCeVXcMkEBSfwvpNxpOnaYYZ47XTdn2QQXcsLxbYzGMOjBvuMw5POeaAOPu/iBqZ1R 4bWGCLyYkU2rWU0/2i6NzPbmEXCsqQq0kIVHkXnfkjgqOosPF1nqV/LZW1nfGZJZolDxBBJ5M4hm ZSWAKoWRie4bC7mVlW5J4c0iW3u7c2MawXdkunzRxkopt1DhYwFICgCRx8uDz7DFPSPCOnaU9zOF 3XdxdzXL3EZaJv3lwZtvDf7isf41jQNlVCgAz7nx5b6XrWq2Oo285S1lkKSW0JcJBFBbSyvIc8bf tBPTkLtALlQ2h4Q1nUdd0y8vNRtPsjLqFzbwxFFBEcchQbisjhmypBPyjIOBgBmsXPhbRrye+mns 98l/FLDcnzXG9JUijccHjKwRDjGNvGCTnQs7G30+BobWPy42lkmI3E5eR2kc8+rMx9s8cUAWKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igD/2Q=--90e6ba476b13485c07049c480aa2 Content-Type: image/jpeg; name="Results_without_mask.jpg" Content-Disposition: attachment; filename="Results_without_mask.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gk64sk1b1 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAMxAjgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiuD1j4ganHf6la+GPCV3r6aU5jvriO6jijV wgbZH94yuMkMoGVIAwcius0PWLfxBodlq9ok8dveRLLGs8RjcA+oP8xkHqCQQSAaFFFZfiPX7Hwt 4fvNa1JpBaWqBn8tdzMSQqqB6liBzgc8kDmgDUooooAKK5/SPE/9qeL/ABHoH2Pyv7G+zfv/ADd3 nedGX+7gbcYx1OfasfxJ431nTPGUfhrQ/C39tXTaeL9j/aCW21PMMZ++uDggd+/TigDuKK5vwh4r bxNb30V5psmlatp9wbe80+WVZGiOMqwIxuRlOQ2ADg4yBk1/iB44h8A6NZ6pcWMl3BPepayLHIFZ FZWYuMjDEBPu5Gc9RQB1lFc3f+LobXxP4Y0m3hju4NeS4eO7jnG1FijEgIABDhgeuR6810lABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFAHlfhDxJovg+/8c2HiHVLTTrtNdub/wAmaUBnglRXjZAPvkqPurlhwCASBVPx9reneJovAj6j cX1j4N1iWSS/81GgDMFVoElbHyqWz32kAuDhQ49Q1PQtH1vyv7W0qxv/ACc+X9rt0l2ZxnG4HGcD p6CrF9YWep2clnf2kF3ayY3wzxiRGwQRlTwcEA/hQB8+IJtW+H3h/T11LUn0tfHEVlpN8xMc4swG WN0cqDkEtg4+UjGBtwK/i7w9pmk6N8TdHsoJItP0m40m6sbczyMsMsi7XcbmOSyuQc57egx9DvpO myW9pbvp9o0Fk6PaxtCpWBkGEKDGFKjgEYx2obSdNZ7120+0L36BLxjCubhQpUCTj5wFJGDng4oA 8ftvCPg/WPidbaFZRx3XhseFy8UdrfSNG5F8xOXV8uA5JwSQCB3AxzHia9X+2fE+sTatqUfj+w11 LXQ7VQ277NuxGiR7cMjozk9jhc/6w7/oOx0LR9MljlsNKsbSSOIwI8FukZWMsXKAgcKWJbHTJz1q STSdNm1SHVJdPtH1CFNkV20KmVF54V8ZA+ZuAe59aAPM47LQtQ8d/FC18SvAmkP/AGV9oM85hQYi yuXBGPmC9+elXNS1bTdG+Pq3GqahaWMDeFwiyXUyxKW+1E4BYgZwCcexrvLjQtHu/tn2nSrGb7ds +1+Zbo32jZ9zzMj5tvbOcdqj1Lw1oOs3C3GqaJpt9OqBFkurVJWC5JwCwJxkk49zQB4vrkLa8PiP 4t0Ax3SWN7p91pt7HIu1Z7SMec4BOGCo7dQVbPy7uKjf/iaf2f4rf97/AGz8QLX7Ldfd86zh3xw/ JxtxhxyAx6nPFe8fYLP+zv7O+yQfYfK8j7N5Y8vy8bdm3ptxxjpiq6aFo8dna2celWKWtpKJ7aFb dAkMgJIdFxhWyScjnk0AeRxRzaN8Z/B3hSUSGDS31CXT5GJbNnNATGmS7MTGySx84+VEOOa9sqvN YWdxeW15NaQSXVru+zzPGC8W4Ybax5XI4OOtWKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqJt32uPH3 PLfP3uuVx7evXn070AS0UVFDu82fd08wbfvdNq+vHXPTj8c0AS0UVFa7vskO/wC/5a7vvdcf7XP5 8+tAEtFFVNR/49k/67w/+jFoAt0UVE277XHj7nlvn73XK49vXrz6d6AJaKKih3ebPu6eYNv3um1f Xjrnpx+OaAJaKKit93lHf18x/wC903HHXnp+HpxigCWiorjd5Q2dfMT+903DPTnp+HrxmpaACiii gAoqK63fZJtn3/Lbb97rj/Z5/Ln0qWgAoqJt32uPH3PLfP3uuVx7evXn071LQAUUVFdbvsk2z7/l tt+91x/s8/lz6UAS0UVUk/5C1v8A9cJf/Qo6ALdFFFABRUVxu8obOvmJ/e6bhnpz0/D14zUtABRU S7vtcmfueWmPvdctn29OnPr2qWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACoz/x8Icn7jcYOOo79 Pz59O9SVEyKbuN8fMI3A+U9CV79B0HHX06GgCWo4/vzck/P3BGPlHTPX8OPxzUlRQoqyzkDlpAT8 pHO1R369Oo4/EGgCWo4OLeIZJ+QckEE8eh5/PmpKitUWO0hRRhVjUD5SvGPQ8j6HmgCWqmo/8eyf 9d4f/Ri1bqpqP/Hsn/XeH/0YtAFuoz/x8Icn7jcYOOo79Pz59O9SVEyKbuN8fMI3A+U9CV79B0HH X06GgCWo4/vzck/P3BGPlHTPX8OPxzUlRQoqyzkDlpAT8pHO1R369Oo4/EGgCWo4eEPJPzt1BH8R 9f8A9XpxipKit0VIiFGB5jn7pHJYk9f59D24oAWblByR869AT/EPT/8AV68ZqSorhFeIBhkeYh+6 TyGBHT+fQd+KloAKKKKAI5+beUZI+Q8gEkceg5/LmpKiukWS0mRhlWjYH5S3GPQcn6DmpaAIz/x8 Icn7jcYOOo79Pz59O9SVEyKbuN8fMI3A+U9CV79B0HHX06GpaACo5+beUZI+Q8gEkceg5/LmpKiu kWS0mRhlWjYH5S3GPQcn6DmgCWqkn/IWt/8ArhL/AOhR1bqpJ/yFrf8A64S/+hR0AW6KKKAI5uUH JHzr0BP8Q9P/ANXrxmpKiuEV4gGGR5iH7pPIYEdP59B34qWgCMf8fDnJ+4vGDjqe/T8ufXtUlRKi i7kfHzGNAflPQFu/Q9Tx19eoqWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKRmVV3MQAO5NAC1EyKbuN 8fMI3A+U9CV79B0HHX06Gg3MA6zxj/gYqFru3+1xjzIj+7f5+DjleN2eM+nfHtQBbqKFFWWcgctI CflI52qO/Xp1HH4g0vnxbGcSKVQZYg5wKoJq9iks26ULlwc+URu+Uc57+meOmO2aANOorVFjtIUU YVY1A+Urxj0PI+h5qp/benf8/H/jjf4UlrqtkbaICQKQg+URldvHTHOPpk0AaNVNR/49k/67w/8A oxajOqIzBYYy+c8swUfrUd3PNLbxZgCoZ4vmEgP/AC0X0oA06iZFN3G+PmEbgfKehK9+g6Djr6dD UtRMf9LjGzP7t/n2ZxyvG7tn0749qAJaihRVlnIHLSAn5SOdqjv16dRx+INS1FCf3s/ybf3g52Y3 fKvOe/pn2x2oAlqK3RUiIUYHmOfukcliT1/n0PbipaitzmI/Js/ePxs2/wAR5x79c98570AFwivE AwyPMQ/dJ5DAjp/PoO/FS1FcHEQ+Tf8AvE42bv4hzj2657Yz2qWgAooooAiukWS0mRhlWjYH5S3G PQcn6Dmpaiuji0mOzf8Au2+TZv3cdNvf6VLQBEyKbuN8fMI3A+U9CV79B0HHX06GpaiY/wClxjZn 92/z7M45Xjd2z6d8e1S0AFRXSLJaTIwyrRsD8pbjHoOT9BzUtZmo6kLdZomhDjaRz8wOR029/pQB p1Uk/wCQtb/9cJf/AEKOo11mxKgtNgkcjaTj9KrPq9gdSgcT/KIZFJ2t1JTHb2NAGxRVaHULW4dU il3M3QbSP6VZoAiuEV4gGGR5iH7pPIYEdP59B34qWorg4iHyb/3icbN38Q5x7dc9sZ7VLQBEqKLu R8fMY0B+U9AW79D1PHX16ipaiU/6XINmP3afPsxnluN3fHp2z709nVBl2Cj1JxQA6imJLHLny5Ef HXac4p9ABRRRQAUUUUAFFFFABRRRQAVT1TUodJsGu5lkcB0jSOMAtJI7hEQZIGWZlXJIAzkkDJq5 WfrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8gAy7rxlbWiM8mm6kBb2/2q/wAxopsY tzrvkDOC4zFL/qvMyEyMhlLZeu/ERLHQ9UvLLSr55bb7XBbyzRKsMlzB5m5eXDMoWJ5Cw4KqVB8z 5Kuah4RvtQS6WTWY2OpWQsdTZ7PJeLdKcQYcCIgTyAFvM4CZ3EMWj1XwL/afh46T/aPlZu9QufN8 jd/x9Jcrtxu/h+05znnZ2zwAXLHxeNRsJbm20HWWkjuHtfIMUYZpkd1kUMX2YXyyS5YIchQxfKgj 8ZW1wQlnpupXcyoTcQwxoWtn8x4gj5cAkyxSJuUso2FmZUw9V38HTTeGI9LuL20uJ1vZ72TzrIva ztLJJIUkgL5ZAZcgb+GRGycYNfTvAbWMWlRG+tAljcS3A+z6esRiLztMVt23Ewo24RupLho1Cjbk kgEemeOri70fRry4svLkm0+C7vY1UFi87CK3jiG/A82TcVZmO1Uw4UtldyPXJv7U01JrWS3tNRSS KNZ4yksN1HuYxtgkMGRZCGHy/uiQziRK5+x8LX2kJbaTJcx3Udzo8Olm7FluiU2zOyLLEWbKSxyu rZZR+7IDK0ibdC20Sa2v/D+mJ5jwaU81/LceWUiDOkkSQRqeFQCaTaoLeWkKKfvK1AHWUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAecatdTrqN4q3M2BO4CiTAHzGsqS9n5B eY/9tP8A61Vte1fSIdb1JZGBlS5kDASd95zxmsZvEGmNJtSG4bkDKZI/nQBtNdyEnJl/7+GlS5fn mQ/8DNZkWp2LqT9mu+vdG/xrdsW06UvmC4wMddw/rQBfgklYP+/lT2Ddf0pzm7jkbAY+uW/zmult fD+jXQfyorhduM7nIqvfeHI0FxJHHcsFUlWEwGfl9OSefp9O5AOauL+8g29BnP8AEahh16ZMDcgA GANrVBq41WyMJsLC6m3bt+XPHTHb61xb+L/iPaRqbfRb1SPl+SNjx/3zQB6NFrkr4/epz/sGtWDV riS2giNwmzzUONh4w4PpXkMfxD+J6sM6RqZxnj7FI3/stacHxE+IcsUMdxoOosPMU86bJkkMCP4f XFAHu9hM08DM8qyENjKgjsPUCpWP+lxjZn92/wA+zOOV43ds+nfHtXjh+IPjqK3kmuNA1OKOMFnd tMdVVQMkkkcCp08Z+O3cP/wj+pBhlQW0xgcHGf4fYflQFup7DUUJ/ez/ACbf3g52Y3fKvOe/pn2x 2rypfGXjvvoOon/uHN/hV628VeLAZDJoWoAsQcrYkZOMc/L6AUAelswRGdjhVGSfasSTW0gO2JY9 pLHlCv8AEecZ79c98546Vy8nijxO6MF0bUlJGAfsZ/wrn77WfFizAJo2rsNucpp7kZJJPb15/GgD rrjxJekKBsYZBwVB5BBHauvspWnsLeZ/vyRKzfUgGvBX1/xeMZ8PeIj9NHc/0r3HQZJpvDumS3Ec sU72kTSJKmx1YoMhl7EHqO1AGhRRRQBFdHFpMdm/923ybN+7jpt7/SvP9SuWGq3g+3zridxtBOB8 x4r0GdS1vKoQOShAUgEHjpgkZ/MVy2qeF9Mmu3nRiZZXZ5M3AGCTnpn60AYcE/8ApCN/aU4I6Nk8 cj/P4V0+nzQsgaTV7hyQflyeOfWuRudKtIDlIJ2wM8OT3qkXmgz5NhfMM4+WItxQB6HqWtJZ2Z8h 9zBVw7ZPfHPFcXf+I7ma5kyY2zjOUPPFY7a9rkI2w6VrDZAACWLN/SqV54m8SxJ+80TXwpOPm0ps Hj6UAdTFqN66BiI8EAjkikFzcNKrMASOOH7cV55J4611Mb7DWRnpu00j+lA8b3JG6aHUgRxzasOK APT4dau4GVAZNg7eYf8AGupsNYiuLYCUmNgi/NyxJx16V4MPiP8AZ5ceTqZ2/wDTscfzp7fFTWh8 thb6mcqAAtseT27UA1Y98a4ifYqzvMTInyGIN/EOeg6dc9sZ5xitCvnFfiF4/ukHkaVqsmGH37Vi Mjn+6eehqzB49+LkikL4fv3wB/y4E/8AslAHt2q3sltJKscXJjQ+YPlP3jxnv9O2feuN1bUZ3lYt 5n3h/wAtPauNg8Z/Fxp9zeG7kZGGY6eQSBnH8Pv+taC+JfiRchftWgXJ5yR9kkA/QUAdx4BlaT+0 Nxbjy/vNn+9XZ1xXw/u9buhqH9s6dJZ7fK8rfE6bvvZ+91xx09a7WgAooooAKjnmW2t5Z3EhSNC7 CONnYgDPCqCWPsASe1SUUAcPZ/Fbw5NHL9ra+tZo7iaExf2ddOQEkZASRFwSFBK9VJweQasf8LQ8 J/8AP5ff+Cq7/wDjVeXTf8hTV/8AsK33/pTJSUAelWHxW8N3GnW01419aXUkSPNb/wBnXUnlOQCy bhFhsHIyOuKL/wCK3hu3065ms2vru6jid4bf+zrqPzXAJVNxiwuTgZPTNea0UAepf8LQ8J/8/l9/ 4Krv/wCNVXvfit4bggV7Vr65kMsaFP7OukwjOod8mL+FSzY77cDk15rRQB6l/wALQ8J/8/l9/wCC q7/+NVXvfit4bggV7Vr65kMsaFP7OukwjOod8mL+FSzY77cDk15rRQB6l/wtDwn/AM/l9/4Krv8A +NVXuPit4bjntEga+mjllKTv/Z10vkpsYh8GL5vmCrgf389Aa81ooA9S/wCFoeE/+fy+/wDBVd// ABqq7/Fbw2uowwq181q8UjyXH9nXQ8twUCpt8rJ3BnOe2z3Fea0UAepf8LQ8J/8AP5ff+Cq7/wDj VV0+K3httRmhZr5bVIo3juP7Ouj5jkuGTb5WRtCoc99/sa81ooA9S/4Wh4T/AOfy+/8ABVd//Gqr 2/xW8NyT3aTtfQxxShIH/s66bzk2KS+BF8vzFlwf7mehFea0UAepf8LQ8J/8/l9/4Krv/wCNVX1T 4q+HNOsoblWvnErqV8zTrqJTEJFWWQM0WCI1YsQPQDqRXmtL46/5E/Qv+wVq3/oyKgD32CZbm3in QSBJEDqJI2RgCM8qwBU+xAI71JRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBztz4C8LXt1Nc3O iWsk0ztJI7A5Zicknn1pkfw/8JxY2aDZjBz909fzra+yT/8AQRuf++Y//iKPsk//AEEbn/vmP/4i gDM/4Qzw+M7NMtkB7CJf6ilHhbTUnXy7dUjKndtjQc5GO317fl30vsk//QRuf++Y/wD4ikNjKXDm /uNwBAOyLIB6/wAHsPyoAjXSY487Lm5TPXa+P6UyPSfnn3Xd3hn/AL4G4bQOfX05xVg2cxBH9o3X PoI//iKhGlMCxGo3gLHLEMgycY/u+gFACf2LbnrLOfqw/wAKjt9CthBH5gffsG4EKOcegyP1NTf2 W/8A0E77/vtf/iaWLTHgULHqF2FUbQvyEAew20ASxafaRRhBbxtjuyAn+VQ39tAlshWCMHz4eiD/ AJ6LUv2Sf/oI3P8A3zH/APEVHPpjXMYSa+umUMGAxGORyOi0AcPp6jV9ZW18cg29wrK9npskQitG kyBlHDHz2wU+VjxvYYPOPQZFYXUcgUsFjYHCr1JXuTkdDx0/IVkX3hSx1O2a3vZJp4j/AAuIzg4I yDt4OCeRzVj+woiwb7Xc5HAPyf8AxNZ04OF/6fzO3F4qOI5Wla3RfCv8Pr1vr1uzTVi2coy/XH9D TIUZZZyRw0gI+UDjao7denU8/gBVD+xU/wCfy6/8c/8Aiamj054i5TULoFzliRGcnGP7vpj8q0OI vVFboyREMMHzHP3QOCxI6fz6nvzUP2Sf/oI3P/fMf/xFItjKgwt/cKMk4CRDknJ/g9aAJ7hGeIBR k+Yh+6DwGBPX+fUdualqp9jkLLvvZ5FDBtrLHg4OR/BVugAooooAiukaS0mRRlmjYD5Q3OPQ8H6H imLawNI7NbjJOSXwc/SpZYxLC8bdHUqeAevsePzrPbRlZ2Y3t1ljk/c/+JoAnawtzdRt9mh2BGB/ dL1yuOevr/nFTC0tl6W8Q+iCq66c6vvGoXRYDALCMkDv1X2H5U/7JP8A9BG5/wC+Y/8A4igCb7NB /wA8Y/8AvkVFdWkUlrKqwRlyh2/u1bBxxweD+NRf2bJ/0Er3/vpP/iacbCQxmNr+5ZCCCGWM5B9c pQBVl8LaHPjzNMgbHTIrhvG3g6xtjaLpGgRmN0kNzJb2ouJowCm0pGzDdyxB64BJ7V6L9kn/AOgj c/8AfMf/AMRTTp8hmWU39zvVSoOI+AcZ/g9hUzjzRtexth6qo1VUcVK3R7f1+HdM5bQ/BXgS90uG bT7G01CFQB5zuZGztH3gfutjBK4GM9BW9beE/D1n/qNEsFPHPkKcY6dRTrbQLeyvLm7tZpIZ7rHn tHHEvmEFiCQEwTlm56nPPQVb+yT/APQRuf8AvmP/AOIoguWNh4moqlVzTbv33/r+rIY+m2kcQW3s 4Iz5it8kKf3hn9O/X05q6AAAAAAOgFU5NPkmUK+oXRAIbpGORyP4KZ/Zb/8AQTvv++1/+JqjAstC z3Epyyq0aAMoAOQWPXr3HHT06mmNZbv+Xq5H0kqAaSQ5caje7iACdy5IGcfw+5/OpVsplXA1G6x7 iM/+yUAS21t9n3fv5pd2P9a+7H0qekUFUVSxYgYLHGT78UtABRRRQAUUUUAfP03/ACFNX/7Ct9/6 UyUlLN/yFNX/AOwrff8ApTJSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABS+Ov+RP0L/sF at/6MipKXx1/yJ+hf9grVv8A0ZFQB9A0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFcnpvhK706K5ls9Vk0+8nvbq6kECJJBcb55JI/NR1ySFZVYoUYgbd2FQr1lFAFex S8js41v54J7oZ3yQQmJG5OMKWYjjH8R9eOlWKKKAPn6b/kKav/2Fb7/0pkpKWb/kKav/ANhW+/8A SmSkoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACl8df8ifoX/YK1b/0ZFSUvjr/AJE/Qv8A sFat/wCjIqAPoGiiigAooooAKKKKACiiigAqG7IFlOTKYh5bZkAOU468elRy3Ey3XkxwK4CB8mTa W5IIUYwSOO46iplaO5twww8Ui5GRwykehoAoeV/056j/AOBf/wBsqzaRIuZ4ZpHhlRSod2bHU5+Y 9wR+VH2CH+/c/wDgTJ/8VVlVVECqoVVGAAMACgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqOeR obeWVIZJ3RCyxRlQzkD7o3EDJ6ckD1IoA8Cm/wCQpq//AGFb7/0pkpKup4e8U3VzqFzH4U1IJPqF 3KokmtkYBp5GGQZR69RweoJBBMn/AAjHiz/oVr7/AMCbT/49QBnUVdg8PeKrm3inTwpqISRA6iSa 2RgCM8q0oKn2IBHeifw94qtreWd/CmolI0LsI5rZ2IAzwqyksfYAk9qAKVFaP/CMeLP+hWvv/Am0 /wDj1RzeHvFUCB38KaiQXVPkmtnOWYKOFlJxk8noBknABNAFKitH/hGPFn/QrX3/AIE2n/x6o5vD 3iqBA7+FNRILqnyTWznLMFHCyk4yeT0AyTgAmgClRWj/AMIx4s/6Fa+/8CbT/wCPVHJ4e8VRPCje FNRJlfYu2a2YA7S3zES4UYU8nAzgdSAQClRWj/wjHiz/AKFa+/8AAm0/+PVGfD3ipbhID4U1He6M 4ImtiuFIBy3m4B+YYBOTzjODgApUVo/8Ix4s/wChWvv/AAJtP/j1Rjw94qa4eAeFNR3oiuSZrYLh iQMN5uCflOQDkcZxkZAKVFaP/CMeLP8AoVr7/wACbT/49Ucfh7xVK8yL4U1EGJ9jbprZQTtDfKTL hhhhyMjOR1BAAKVL46/5E/Qv+wVq3/oyKtD/AIRjxZ/0K19/4E2n/wAepnirw/4p1Hw9plpF4U1I SW1rd2jkzWzAvcSR7CAspO0Y+YkDaOegJAB7pRUcEjTW8UrwyQO6BmikKlkJH3TtJGR04JHoTUlA BRRRQAUUUUAFFFFAGXd+UL4CaG1klKjazTBJMbmwEB7j1yMn07PgH2Kwe6UsYhbqyQGTcEwCcBu4 Oevt6YAp3t/bG/LLdKYvJAcq0bIeTwwJy468DkdvvVrbzDYb4495SLKxqhTOB0A6j6UAMN08jH7N GsyJje2/GcgHC8cnBB7Dkc9cPs47eO1j+yjELKGQc4wR79P896gTz4ImkjFtIkmXLwxkHJ/ixk7/ AF6g8cZqeyfzLC3k2qu6JTtUYAyBwB6UAT0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8/eLfj d4v8O67d20Fjoctmt3cQwOYpWYCKZo9rkSAB8KrEejqeNwrB/wCGjvGH/QN0P/vxN/8AHa5bxFqE Y+IviPTNRkX+yp9YvA5dN32ZmkZfOXgsNpCMwTBcRhTxjHH3lpPp99cWV1GYrm3kaKWMnJV1OCOP Qg0Aes/8NHeMP+gbof8A34m/+O0f8NHeMP8AoG6H/wB+Jv8A47Xj9FAHsH/DR3jD/oG6H/34m/8A jtH/AA0d4w/6Buh/9+Jv/jteP0UAewf8NHeMP+gbof8A34m/+O0f8NHeMP8AoG6H/wB+Jv8A47Xj 9FAHsH/DR3jD/oG6H/34m/8AjtH/AA0d4w/6Buh/9+Jv/jteP0UAewf8NHeMP+gbof8A34m/+O0f 8NHeMP8AoG6H/wB+Jv8A47Xj9FAHsH/DR3jD/oG6H/34m/8AjtH/AA0d4w/6Buh/9+Jv/jteP0UA ewf8NHeMP+gbof8A34m/+O0f8NHeMP8AoG6H/wB+Jv8A47Xj9FAHuyfHPxcdNF3JbaGpe38+NPss o3fvfLx80wJ6E5UN0x2bbRf9oTxgoJ+waGQAT/x7y/8Ax2uLmg2eE1uYPNjTMNsxhLCJ1MaykPtV lZt5zzLuGANigADnJf8AVP8A7poA+7qKKKACiiigAooooAKKKKAOaWCOU4tLBGtMEvLdxLGoTsUY DdyM8nJ6GukbcEJUAtjgE4BP1rIt7OE/aIdQsrBCiBy8KYG05HUjII2nmtaSRIYnlkOERSzH0AoA rLLfsgJtIFJGSpuDke3CVPAZGt4zMoWUqC6joGxyKh+2beJbaaMn7obaQx9MgkA9ByRnNS20gmtY ZQWIdFYFsZOR3xxmgCWiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAy4/EGnza3NpEX2t7uF9kpWymMSN sEmGl2eWDtZTgt3A6nFWNN1Wx1e3a40+5juIlcoWT1wCPwKlWB6MrKwyGBPBzaDf3Vnrui6VFfW0 t/8A2il8NRmke1xMZTC8TY2hizq2IgQqlxJmQJnqNGS4vPEOpa1JZz2kM9pbWiRXICyb4nmZzgEj bmYKDn5ijEZUqzAHyB47/wCSh+Jf+wrdf+jWp11Hba3oMF/Fcr/bFpGtvPZBDulgjXAnBwF+VNiF BlsRtIeNxDfHf/JQ/Ev/AGFbr/0a1ZOm6hLpeowXsKq7RNkxvnZIp4ZHAIJVlJVhnkEjvQBVorT1 vSv7OuxJbrK+m3P7yyuHAPmRkAgFh8pdQwVwCdrAjtWZQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QB1jlI9HjF0VF1JYJt83lz+9GzGQxH7oLjmP5QMAg/PiS/6p/wDdNdHNOG8JrbI7KCYbkLkojqI1 iZlG9FZt4YHERPDHewBNc5MCYm5I4PTvQB93UUUUAFFFFABRRRQAUUUUActc2umLJJDDpzSmFN1x JFOcQ+uCeGI549vrjqGVXQqyhlYYIIyCKqrptolqLZIikQbfhHYHd6kg5P8A9YelTzyrBbyTMCVj UsQOuAM0AO8tPK8ojcm3aQ3zZHvnrRHGkMSRRjCIoVR6AVX8y+/59rb/AMCG/wDiKdYpAtlEbZCk TqGVSegIH+frk0AWKKKKACivHdXluRqF4nnyeUJpAE3nABfOMfUA/hVFtQvCCpup8EMD+8PRuW/P v60Ae4UV4Z9suCSTPKSSxJLnqww359/WrIvbiVizzSs3PLOSeRg/mAB9KAPaqK8kguLlfuzyDaQR hzwcY/lx9Ku2/mLKWR2VuOQx7DA/IHFAHp1FecCKVtnz/c27eT8uOmPTGTj61fsLZkRcFfLKAgDp wcj8iSfqaAO4orkY4YIyoWCMFRgEKBgZ3fz5+tLdwxy2qho1wZFyMDnMgJ/M9aAOtormDEhypRSC GBGOobk/n39age3t/tSgwoWYO2cDHOA2fc8frQB11Fcu1vFKxZ4kZucllBPIwfzAA+lTW8cAkmWO JB5cg6djsUenHykDjt+NAHRUVgpbW8bbkgiVhjlUAPAwPyBI+lRQQW0kYIhiIRyF4ztKswGMjjHO Ow7cUAdHRXNTC2skiIt4wokjwF+QcOCOg9Tn/wDXXRRP5kSPjG5QcemaAH0UUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVl3uv2NjrenaPI0j3l85VEjXcIwEkcM56KCInC55Yg4B CsQAalFY58T6Wl5NDLP5MMW8fa5fkgd4wTKiyHgsgBLfR8EmOQJY0zWLfVfNWNJ4JosF4LmIxyBG zsfaedrAEj0IZSAysqgHxh47/wCSh+Jf+wrdf+jWrn66Dx3/AMlD8S/9hW6/9GtXP0AdBpFxNrVh /wAI3PNLK7Mp0lXLMI5y3MS8kIsm5s4HLiIkhQxrn6K6LXmt9asE8QWtsYZ/NEGprv3B7hgXE3QB RIRLhFGF8o/3hQBztFFFABRRRQAUUUUAFFFFABRRRQAUUUUAdnFA0Pg5rgsVUxpaERo7K7l1mBLp iP7pA2uXYFcYHGznZf8AVP8A7proWdbzwqp8vMyRxzvJtD7VXbAE3bWcdFb5nRecBPuluel/1T/7 poA+7qKKKACiiigAooooAKKKKACo5zItvIYVDShSUU9C2OBUlFAGZ9nt/wDoB/8AjkP/AMVVyCWe WV/Mt2ijCrt3lSSec9CeOn61PRQAUUUUAeSaoM6heD/ps/8A6FWPNFhmO79K39RlgOoXSbzu85hj HfdVJ4Q4OM0AY5GDVuCLdu5x07Va+y/X8xWrY2fzuPmxj1oAtWNiH3/vCOg+59a6GDRlEj5uHA4w WgcDBXOTngdD9OM8nFblrZR2m/y2c7sZ3H0pUVnNyj/dZ8DGem1fXjrnpx+OaAMV7ldNaJoJBKZE wcxsMA9Pp0/DvWL9qYxqNvTHUH+tdBqVkkXlbCxznOSPasdbUmFC2c4Gf8nmgCKG43beDz/smriy gCBiG4mQ8KT0kH+feoktyGHX86vLayLDby7fk8+LnI/56AUAb5mUAnEnAY/6tuxwe35evbNZ1zEk 14ZMygYYH9y5wVA9sH2P4DOa1aibd9rjx9zy3z97rlce3r159O9AFWKOK2LpJ5km70hYgYGeoB7H 8+BzSJbROXK+au9xt3Bj/CD0I+X09M+5IrQqKHd5s+7p5g2/e6bV9eOuenH45oAymJVsbJccfMYm A5GepH+Tx1qtHdxKi7hIpdjgMjnqxxnI46fQcdsVtXd3HbxuC+2TYSvGfpXJ3N+zSZlly3POD03H 1/z6cUAU77UlZY8EDnPzBl7/AP1v8g13GnNv0y0bj5oUPH+6K8e1TUv9VmX1/h+ntXrehtv8P6a+ c7rWI5/4AKAL9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFY+s2Nxd6r4emgj3 x2moPNOdwGxDazxg89fmdRx6+ma2KKAOD1HwZfarZT6FJJHBZrcX92l8PnLm7S4UJ5eQQUNy5JJw QiYyXYR9Bo9nfPrN9rGoW8dpLcW8FoLZJfNwIWlYvuwOGaVsDGdqqTgsUTcooA+IPHf/ACUPxL/2 Fbr/ANGtXP10Hjv/AJKH4l/7Ct1/6NaufoAK0dHv4rK6eO6DNp92ogvFjUFzFvViUyRhgVVhyBlQ DlSQc6igDQ1rSJdE1FrOW4trkBQyXFpL5kMoPdHHDAHKnHQqR2rPrct/+Jt4bltCc3mmZnt8nl7c n97GMnJ2sRIFUAANOxNYdABRRRQAUUUUAFFFFABRRRQAUUUUAdhcxrD4dgYSBWWKK3EMgRmKMonL jewkX5mP3IwuP4myS2BMAYmPPAPQ1vrK7eGFikkZYpLVZ1h37EMiyiIOqZQFti4JCyZ+Yk5zswJf 9U/+6aAPu6iiigAooooAKKKKACiiigAooooAKKKKACiiigDxTVpWXWL7BPFxJ3/2jVZL2QcZY8/3 qZrrFNb1EEci5kB5/wBs1jNdIsmDt/76oA6Jb18dG/76rbsLp9z9eg/iNcXBdpsPK9f71dPpzhvM wR27/WgD03SLh5/O3sx27cbmz61eh3ebPu6eYNv3um1fXjrnpx+Oay9AGPtH/Af61cuZ4oYbzfKo baSFYkfwjpk8/hx+OaADUJI4/L8yLzM5x8xGOlc6k5EKblydozyfT35/OsrVtST9zgKfvfx/SuaF 9k/6v/x6gD0CNw5GFxn3q1LfeXYxw+XnbNGc7vSQGvP7bUBGyMUHfq1Ok1lCxHlr1/56e/0oA9Wt bj7TEX2bcNjGc1DfNIuGQkARtkgt13Ljpx69efTvXnFnrce9AUUfOP8Alp/9auv0bVo5HjQFfmcr gOTydv4f19O+QCc3MwP+tk/76NX7W8aQXchTAQ7lG48jaPX6Hpx+OafPYedM0nm7c9tuaoysLBbu LJk3r1IK4+Xtnr+HH45oAz9YvzIWcIVxEeN31rhtS1KRbhR833f759TWvrd2qpLwP9Sf4vrXmura ki3SjC/cH8fuaAKmqalL+65fv/Gfavovwsxfwjornq1hAf8AyGtfIl/qCN5fCjr/ABfSvrXwY27w L4fYd9Ntj/5CWgDcooooAKKK+cLdNOhurOXxXPrnhTx+128n/CR3cLNbTusu3YBu2NH5bqvAVAAO dvDAH0fRXzx4v/4QH/hZ3jr/AITbz/O8q0/s/wCz+Z5m77MN+zb8m7OzHmfLn2zWX400TV7zwL8P tO1zzLS/t9M1SUrJGNyxwRCSJCoxgmONF55GeQSCKAPpuivnzxZrEPi74m+FtZtJ7uO0sL3RoltZ 1GN10XuN4wxAOxY1PHJHXCjOvrOl2mt+JPilr1wkl0dJ0w2unXPmuY4WNnIJ41wdpILfMvO0t2Jo A9sorxf4R6L4Vkn0m9tfBmuWGr2+nrMdVu45UtpnZAjmMmQq27exHyjjJGMV7RQAUUUUAFFZ+t6J p3iPR59J1a3+0WM+3zIt7Ju2sGHKkEcgHg1yfwcnmk+GGl293LI15ZPNa3EcrEyQMkrARuDypVdg CnGBjtigDvKKKKACiiigArH1HXvsGvaTpQsp5ft8rRtcY2xw4ilkHJ+8x8phtHQcsRlQ2xWXqumz X2o6HcRNGEsL1riUMTkqbeaLC8dd0innHAP0IBj2/jRbi/1p0XTf7H0Z5FvbkXzNcRiNCWY26xkg b1dRlgWCFgCCM7Glau1/cXFndWcllfQIkz27ur/upC3ltuXIz8jqRnhkbBZdrth3PhC41b7TY6jL BFYf6aI5rICOeZLrfuRxt2qq78n7/mOkcjFSpVtjSdMvIdRu9U1KaB764iitiLZSsYjiMhVsMSdz GV2IzhQVX5tpdwD448d/8lD8S/8AYVuv/RrVz9dB47/5KH4l/wCwrdf+jWrn6ACiiigCezu59Pvr e9tnCT28iyxsVDAMpyDggg8joeK1PEC/bRba8g+XUd3n4GAt0mPNHQD5tySYA2gShR901iVueHZr Cb7To+rXgstPvNrm7MTSfZ5Y8lH2qNzZVpE2ggfvNx+6KAMOip7u0nsbp7a4TZKmMgMGBBGQQRwy kEEEEggggkGoKACiiigAooooAKKKKACiiigDsGuLa+8OrFHJE12HjOJMLJsS3UHBZCcZUgDzRnjE YyBWJdQy26vHNG0b7A21hg4IyD9CCD+NWNFZk1CzlUSEQssreWCWVU+ZiMEHgAngjp1HWna6kYvZ UUwv5cSITGVZdwjUNgqoB5B5Gc+rfeIB9wUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeGeJ7 2BtW1WNQ+77VIM8Y++a4i7mb7W4UnHHb2qz4j1A/8JHq2HmC/bJcA5H8Z7HpWfauLqRTySXC5agC 7avKYjz39q7jRxMfO/4D6e9c/Zac3kniP73+HtXc6VaCMyllU5x91CfX0FAHZWVwbK3umIYMUypG DggGufv795JJnLEtIuWO0DPGP6Vs6zcxbmEKSoDEePIdOefUCuPuXO8nJwRzQBBOrXBUDJxmkNmi IGJbJ68ilSZVOSDWdNqOUHzS9fX/AOvQBJPgEAZ4JqjIoBJ55NZ17qyxyDLTck9D/wDXrKl1sbj8 0/X1/wDr0AdGr7ZBjPBFdJoV4yTQsCdwmGDge1ea/wBoSSzKgllRSQCQea7Tw5MZGiYlji4Hv6VE Jqeq2OnE4WWHahUfvdV1Xr0+XTrZnr2l3ct15vmNu24xwB6+lUdVjAnuSo5dcn5SOdoHfr0HI4/E GjRrqOHz9yznO37sDn19BVuSS1dbk+TMXm5BNpLwdoHPyn07Y/xs5jzjX1Iim4/5YH+teSa6zLfI On7sfzNe3+IrXEcw2/8ALufvROvr6qK8h12yZr5D8n+rH8zQB5hdTSHZ83r2r7V8CnPw98NE/wDQ Ktf/AEUtfHF3ZEbOE719keCBt8A+HF9NLth/5CWgDeooooAK8nvvh3411Ozk8K3/AIngu/CUl2JX up90mpNCCHERYjacOB82c8Z6fu69YooA5fQ/Dd5pnjvxXrk0kDWur/Y/s6IxLr5URRtwIwMk8YJ/ Co/EnhSbXfGfhjVGFpJp+mpepeQT5JlWeIRgBcEMOuQSOPWusooA8f0X4WeILTRdJi1G60qXUrTx BZX8txEWG+0toViSPPlgllAOARjknOSakgSG68a/FLwpBe2lpPqlvCLK0klEavNLaN5rqg5JJ2s5 UE8ZOa9cqnJpOmzapDqkun2j6hCmyK7aFTKi88K+MgfM3APc+tAFfw1ps2jeFdI0u4aNp7Kyht5G jJKlkQKSMgHGR6Cub8EePH8ZeI/ElvFb+Xplh9lNk8kLRyypKjHewY/dbaGXgfKwyOa7ivN511nw j4j8f+KV0f7bDef2cLGEXKRm6Kp5bqvUhgWwFxljgKCSKAI7/wAbeL9N8eWemXWmaMNPvNTFpBZJ cNJqDW5Df6XhSQIvlZjlQRgqcfeqTSvHfiS48aaJZ6lokFjpGvfaxZQzCSO+g8gZ3TKw2/NjIVeg YfMcfNX1PRvF2ufEDTXvNBsbO307VRcQ+IbKZPMezVXK27oW8z5ixVsHbkn5SOTX8E2HjJPiLf67 4l8LzpJqG+FL19WhdLC1HzpCsSfe+YKC344B3FgDY8K/Eq31/WPFEl08FhoWmfZDa3V2pt2dJVb5 3LtjaxClOFyrLkZNHwV/efC3TbyT57q7luJ7mZuXmkM7gu7dWbAAyeeBVjS/CssnjvxxdaxpkE+k at9g+zicJKk3lRENlDnGGx94D1FdpBBDa28VvbxRwwRIEjjjUKqKBgAAcAAcYoAkooooAKKKKACi iuf17VtU0mWW8jtoDplrEjyK3zTXjuzL5UOGG2QYTaCreY0ioCnLUAdBRXH3njf+zoptTu7XOkCW 7tohEcz+ZarM0jMCQu1vIlCgHI2oTnzCI9jSdTvJtRu9L1KGBL63iiuSbZi0ZjlMgVcsAdymJ1Jx hgFb5dxRAD448d/8lD8S/wDYVuv/AEa1c/XQeO/+Sh+Jf+wrdf8Ao1q5+gAooooAKKKKAN6SGXXN AiuoY3mvdNXyblUG52t8FklI5JCYZGY4VV8kVg1f0jV7vRb9bq0c8/LNCzMI7iPILRSAEbkbABXo RS6vYR2U8ElsztZ3cC3Fuz/e2klWU8DJV1dM4GdmQACKAM+iiigAooooAKKKKACiiigDd0dfNvLe ELuaZTCvy7sM6lQcbWJwSDwM8cYODTtdbzNSvJd+8Skyht24kMNwydzc4PILEg5BOaboxjW8t5Je UiUylfk+bapbb8/HOMcg9fut90rrrOuoXEU5AkiUQHJfnYoTo/zDp0OMdMDoAD7hooooAKKKKACi iigAooooAKKKKACiiigAooooA+R/F5K+LtcUjkahOD/38aq+kTBHjJHSUHr9K6PxXpkkvi7WiFb5 r+cj5h/z0as2DR5UdSUbhgfvCgDqLLUkWEgov3v79dppGpIzynagwBzu/rXn9tp8hjPynr6ius0u zuEMvydcdx70AdxrGpC4Zj5YX91j72fX2rkrr52J6fLXXzaDeysCYO2OJF/z1qg2kXETyLLEyY/2 hzxnj1/z6UAcZcHZt75zXI3V6EiBKjr/AHq9F1mwI8jIPRv4h7V5Pq8ixWiMWx84HT2NAGDq2qL5 +Ng4Zv4/esj7fmXiPq396oL+dJbh/mzh27e9Ps7ZZTEctgt2+tAG5p1+2UXbxvAGTnFemeEr9F8r IX/j5H8X+7Xm9vaRxEHc3DZ5rpdGvVt3jAkwPOB+7n0pKKTbRpOtOcYxk7pbH0J4fvoH+0bpI1xt 6uOetbcJ/ez/ACbf3g52Y3fKvOe/pn2x2ryLRta/1+J/7v8AB9favU7C7+1PPsSMYkG4rkZBQc5I +bkEduBTMyvrmmpd2txKzKCsDDBTPYmvGvEGmxrfoAV/1Q/g9zXupE8qtHLHGqMMErISeQc/w+vH 68dK4XXdFae8ify3U+WQwyBg726Z6/Xv1wM4oA+YNYuRF5OI853d8elfYXgdt/w/8NtjGdLtT/5C WvmTWfBcr+TmObjd/wAtE9q+n/B8BtfBOg25zmLTrdOTnpGooA2qKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAK5/VtG1i71yDUbHVLGKO3i2xQXlg84ikO4NIpWZPmKkLkgkAMAQHY HoKKAObm8GWN280F3JJNpbvPMliflCSzq6zNvBDEESyYGflMr8n92I9DStIawuLi8uryS9vp0SF7 h0VP3UZby12rgZ+d2JxyztgKu1F1KKAPiDx3/wAlD8S/9hW6/wDRrVz9dB47/wCSh+Jf+wrdf+jW rn6ACiiigAooooAK6LQHt9VsZPDdxbGS6uJC2mThyPJuGAym0D5/NKRR5YgJ971rnaKACiuh1S2/ tfR08QxTQmddsF/Bv/e+YOPP28nYw8vc7HmV29RXPUAFFFFABRRRQAUUUUAbujNi9tkLbVlBhZt2 3arqUJzuUcBj1IHrxmna4my8mOzaZIklI27RlowxwNq4GTwAMYxgkck0RnS6SSIsJI4JHRlHKlY2 IYfKcEYznjGM5XG4N1hY0v71IgojV2CBDkAdsfM3H/Am+p60AfcVFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFAHiWsaXE2r30vk5Zrh2zuP94+9ZpskV8eXjH+1XUartF7d8gnzn/9CrHkQs5wOvoK AI7aFBGfl7+tdPp6oWk+XjArAitnKnCsef7tdPpdhPI8hSOUgDkrHmgD0Ss+9gj+z3szxAMqsVYD GcIOevPpzjp+JuLGy7czSNg5OQvPGMcD15+vtxWbfRmKx1HfcNlo2IUhBv8A3eM8DJyfp0AHHUA8 58Tap5H2XG/5t/QD2rxzVrlbq1VEVshwecehrvvGyt/oPB/5adv92vEbh5VjB2d/Q0ANubaQTudn Vj3HrVux3oIR0w39aisi0skasgwc9vrW3Bp5coVBGT2T3oAkMzgdf0q7p877kOf+Wg7fSpItIdly S3XvHUyWggOOODn7uKAOk0q+aPzuTzjsPevX/DOtxg3ZlWQ52EYUe/vXhVo4XfyO3Wu50m/EfnYe Pnb1P1oA9uqmbCCf5poBuDNjHy8bic4B75znvnt0qhpWpG5aQ3F6mE24GVAPBB7d+D9fbir9tC6w KTcyYDlshYwGG5jk4GOQRkjHTPBzQB5lq2hr+5/0f+9/H9PevTNGTytD0+PGNttGuPTCise8037V IiCXdh1UExIeN3J4AHT+XHPXoYIhBbxxA5CKFzjHQYoAkooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigArn9e1bVNJllvI7aA6ZaxI8it80147sy+VDhhtkGE2gq3mNIqApy1dBXP6to 2sXeuQajY6pYxR28W2KC8sHnEUh3BpFKzJ8xUhckEgBgCA7AgGPF4zna81y+luYI9K0r7U5sjp8o uZ47cFJGSVpBG2JVP3QwAKBirNgdBpOp3k2o3el6lDAl9bxRXJNsxaMxymQKuWAO5TE6k4wwCt8u 4olObwkuovNBrF7Jf6bvneC2cMroZ1dZN8gbLALLIiABQqtg7iFZdDStIawuLi8uryS9vp0SF7h0 VP3UZby12rgZ+d2JxyztgKu1FAPjTx3/AMlD8S/9hW6/9GtXP10Hjv8A5KH4l/7Ct1/6NaufoAKK KKACiiigAooooA0tD1CLT9RBuw72Fwht7yNBktC3BIBIBZeHXPAdFPaoNS0+bStSnspyjPC2BJGc pIvVXQ/xKwIZT3BB71UrftZIdZ0BtNa3/wCJlYxyTW1xvPzwLmR4dvAGN0su8kn5do6igDAooooA KKKKACiiigDb0hkS/sXlKiNZELlhkAZGc/K3H/AT9D0qbxCGiv5IpsrKkESMrn5lIiUFTlmwRjGM 8YxhfujPi/1Sf7oronuV1LwotnE0ks1jbzDyXDEqWkWUmJWaTICRyMzIkAUD5mbIDAH2ZRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQB5FqRJ1S7BHy+e/P/AjTIrZWILbgM8n2HWtu6sd2oTOQuDK x6n1pd0NuhGxsg5yKAIba0gCMPMOQxB+YdhzXVaULa1vXYzov7tgd5Xtg9evTn09egrlm1EBjhpQ Pr/9ertrqoSYtvmDbSAQR0yM98+n+cUAduLmApuE0ZXsQwOeN38ufpXO6lcwyteNHKjBlyMAD+D2 5PQ8n+QFbkV7DdxTEK+1F+YEdRz7+1c9qJiZrj7PuVSvzBiOu329sdefwxQB5n4yUyfYtoJxv6D/ AHa8RvUdYQSjD5u49jX0NrNiJ/IyFON3Un2ry3U/D8ZtlwiffH8behoA4S0nMc6dOM9q6vTLlWjg yRkt6H1rJfRxFO2AnDH+I1o2Vu0ZhGRww7+9AHWQ4aBiPWqN595/wqe2LCMjJ+960TxByxx1oAz0 dlzwK3rG8ZPMyF5x2PvWSYAD0H51dtwBu/CgD0TS9bT97uZB0/hPvXXafrUbWkdtJLEgIyPl2k5O QP8APJ/OvIIrhkzh3GfQ10ej6wIWAZpsCPGAe/HvQB6Qksd0YfIkST95G/y7W43jnk+x59jjkVv1 y9hfQ3UVqMP1ibLbeu5cdffHv6c4rqKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiuf8RXGo2EtreWt9tUyxW8VmYlEU8jtg+dKclFxgKV2kN2lLLGQDoKK4PUfGd9pVlPrskc c9m1xf2iWI+QobRLhg/mYJJc2zggjADpjBRjJ0Gj3l8ms32j6hcR3ctvbwXYuUi8rImaVSm3J4Vo mwc52soOSpdwD488d/8AJQ/Ev/YVuv8A0a1c/XQeO/8AkofiX/sK3X/o1q5+gAooooAKKKKACiii gAqazu57C9t7y2fZcW8iyxPtB2spyDg8Hkd6hooA2dY0yBbaPV9PaL7BdyPttlk3yWZycRyenRtp Jy6qWwOQMatbQpYpJ5dLupEjtb9RFvkYBYZRzFISeFAbhm5IR5MdazJoZbeaSGaN45Y2KOjqQysD ggg9CKAGUUUUAFFFFAGjF/qk/wB0VvJavpvhh7yb95FqAeSOIoyD5FkhLAyjy5GDTKQI1kcDcd0R BJwYv9Un+6K2jewv4akjEUkKwo1rcTJGCZWkJlj5RUyMwhSJpJMAlkX5eAD7UooooAKKKKACiiig AooooAKKKKACiiigAooooA841O5MdzdKYjlZWB5HZsViXFxvZjsxn3r2KigDxXzfb9aek2CTt/Wv Z6KAPO9Nui3mDyjzgdRx1q0sxJYGFwGxkkr6e30x/wDWruqKAPPri3E/l/uSOmeFOM/j2x+tcPq2 nxpaIcL98fwD0Ne80UAfKF7bIJ2wF+838PvUcUKhlPHB9K+s6KAPmCADZ0709h8+K+nKKAPl91GS eKfCcbq+naKAPm4HFPU45r6OooA8X8M3HmXhj8s/LEDwQejL617DZHNhbn/pkv8AIVPRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVj6h4bs9S1E3c0k4WWJYLu3Vh5d3GhZkSQ EE7QXfhSoYMVfcvFbFFAGOfDGlveTTSwedDLvP2SX54EeQESusZ4DOCQ31fABkkL2NM0e30rzWje eeaXAee5lMkhRc7E3HnaoJA9SWYkszM2hRQB8QeO/wDkofiX/sK3X/o1q5+ug8d/8lD8S/8AYVuv /RrVz9ABRRRQAUUUUAFFFFABRRRQAVuXEra7o895KFbUrJg88oQK08LkLvcgAFlkIyxy7+dycJWH V3Sb/wDszU4bpovOiG5Jot23zYmBV0zg7dyswyORnI5FAFKitPXNLh0u9RbO9+3WM8YltrvyjF5y 5Kk7DyuHV15/u56EVmUAFFFFAGjF/qk/3RXROkUPhRZrQJHdfZ5pLiW3I83/AFixAOVeRlQpK67S turZGTIQoPOxf6pP90Vt6fPNe6XcW1xLJcJZxy/ZYZGMghVoJ2kKId20EqrMRGcEAmSLhiAfadFF FABRRRQAUUUUAFFFFABRRRQAVR1W8ezto9jKjSyrF5jdI8/xY74xV6qep+cLMmG2juQGBeJxnco6 49+lAFNJ7+K1u3aeO5hELSQ3SbR8wH3cD3/z2EEd1f27abNPerPHdMoMWxUI3DqMDkDPt29ajhtX c6jNbWUttbvbMgicEM7nnIXt1x/LvWhpWlWttBb3At9lyYhuLE5BI54PQ0AVbDUbltYukup1FqrS rHu2jlSM+/ANJpWrTlLybUpAscYjYALwofJHTnuKqXNpOsNxMttM8n2udVQKeUdMbunTpUl1Yyx2 +rQxQyuu23WM7SS4UAHHr+FAGw2r2CW5nNwPKEnlFgpI3Yzjgfr0p0+p2dtbxTyzhY5QChwSWGM9 OtUtbt5Hms51FyUjZw32b/WDI4I9uOfrVWW2e307TjHbXYePcyvEd7xFucFcDcD36dMZ9QDaS/tp DbhJQ32gExYB+bHX6fjVS91D/j3+yy/8vq28vy/mOR+oqkBcwR6TdSWT/uRIJI4Ixkbhwdo6Z6mo 44LpokaS2kRzqolZcE7V9c9x79KAL1nr1rcfaTJIqLExK8HmMYG76knp1qymr2ElrJcpcAxRkBzt ORnpxjNZhjkFvqtpJYzy75nmXb8qspK4w3r3xjtj2qbTZriCK8nmtrl0DKIy0IEzjpggfexxz9fo ADYkkSJQznALBfxJwP1IqvLqFvC0iuZP3f3ysTsF4zyQMdCKfeIZ7Wa3Xh5YmVSQcDjHJ/Gs+SKa e21GVfPQSqSsBQfNmJR6Zznjg9RQBr1FPcR24UybvmbaoVCxJwT0APYGqCC58+5MrzkZbCxoePm+ TBJx09BjrupzRTyi2DNKkwnJkdRkKfLb7uQRt6AfXnnNAGhHIJYw6hgD/eUqfyPNLuUOFLDcQSBn kgdf5j86ybn7al2RbmUuVKgkFg37s4bP3F+YAYwD36GpIY1/tG3lUXfl7JFUyb8D7vXPIH3vvens tAF9po0mjhZwJJASin+LHX+dR/boDNHErFnkZlGB0I3Zz6fdI98VFdW7T31uy5UpG5WTGQrbkx+e CMdxmobZJmis2khaN/tUruvXbnzO/pyOfegCwNQidWMSvIfM8tQoA3nbu4yQMY5z3xx2zZjkSaJJ YzlHUMp9Qaz3R0unmMblEut5KqSdvkhcgDk8nHFW7KN4bC3ikGHSJVYehAFAE9FFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcv4ok+zyrewalPDdWvlEgT4gs42Zt088S8vGQrKd2Q NuVaLDyr1FZ91omnXuowX9xb77iHbg72CvtO5N6g7X2sSy7gdrElcE5oA4O81W90Ya54i1ASG533 /wDZRj1WWa1LW8coWKS3VgiEpCzn73zCTJjbaldZoz3Fn4h1LRZLye7hgtLa7SW5IaTfK8yuMgAb cwhgMfKXYDChVXQtdE06y1Ge/t7fZcTbsnexVNx3PsUnam5gGbaBuYAtkjNSabpVjpFu1vp9tHbx M5cqnrgAfgFCqB0VVVRgKAAD4s8d/wDJQ/Ev/YVuv/RrVz9dB47/AOSh+Jf+wrdf+jWrn6ACiiig AooooAKKKKACiiigAooooA2dI/4mNlc6K3Mkn+kWWe0yjlB/10XIwBlnWIdBWTNDLbzSQzRvHLGx R0dSGVgcEEHoRRDNLbTxzwSvFNGwdJEYqysDkEEdCD3rW1Y/2pZx60mTL8kF/nj9/htr/wC1vVCx OSS4kJwCuQDGooooA0Yv9Un+6K6KWSF/BrG3Kb4rd4roxgKQzzxtGrkJHuJCMwUvccISFj25XnYv 9Un+6K27GJrLRb28YowlR9v2eQSSxp5csRLqhDxIXkjX53QNuHyTDgAH2nRRRQAUUUUAFFFFABRR RQAUUUUAFNeREZFY4LttX3OCf5A06ql7E8sloEZ0xMSXQAlRsb1BHt+NAFlJEdnVTko21vY4B/kR SqwYZGepHII6VjSw3kc0oSWbaZSRJ5eSzbE28LtGPvDn5eOaWRrpJFLtOF89QpB4wZyDn2xsx+nG 6gDVSaOSOKRXG2UApnjdkZ/lR50Zm8kODJgnaO2MZ/8AQh+dY1vbzI+niUXJRFiJ+98rFHBBA6AY Qe3fqck1vMmoXkkIuQ6pI6kbiGO2MqBng8g8D0x04oA3abJIkShnOAWC/iTgfqRWRLHe8Ms067pJ f4S2CH+QAAjjGTlvl6Z4xh1wssk0sQ+1ZaRGDbSQoEi+oK8DkY7Z3DIzQBr1EtxEyQurZWb/AFZw eeC38gazl+1+agPnfK4WLrjaJGDbux+Tbgt17c1HZx3IksBIkwCbeCPlVfJxz6HduH8/4aANOW8h hl8pvMLhQxCRM+Ac46A+hqeqZhkbVJJBJJGgij+6Bh8M+Qcg/pjrRdwSyyho49w24z9qki/RRj8a AJp7mO38sOSWkYKoAyeSBn6cjn/EUR3Mc1xLChJaMAsccckjAPfof85pt6rPAoVST50RwB2DqTTR lL+4lZW2eTHyFJzgvkDHU8jj3oAQ6hErMNr8EhOB85DBSBz/AHiBzjr6c0DUImZRtfkgPwPkJYqA ef7wI4z09Oap5mt3d1hcvEJx9wkZeQFTx1GOTjOMetBt1Zbfy1nMhZGZyjIrkSbmLA9OrHBwPm4z gYALgv0aESLDK24r5agDMgPQrzjpk84IAyQKUyxXLG3kt2ZeA4dQyhsBtp684wc9PfNULaALbRx3 kEs0USxgq8ZbbIAwbC45AG0cAjv6mnWMV1DcRiQy72KmQHldvlAE56E7wO+ePQnIBohLeyhYpGkU eckIuMnp0HU9BU1Vb7/Ux5+750WR3PzjH64/l7ijY/bprFSkkgLRRndKRktg5wcHjGzse/fJABoy 3ccU4iYMT8u5gOF3HC5+p44z74olu44pxEwYn5dzAcLuOFz9Txxn3xVBoZvNCtHIWfyeSd2SkhZi SOAMHIzj0A7CS4R5LslY3IcwhTtOPkkJbPpx69e2aAJTqlutu8zBwqqHUYyXUgkED3weuCMHOKty SJDE8shwiKWY+gFZEcEv9m3K+U+4WKwYKnJdQ+QPXqORwe1X7/8A49Qeyyxsx9AHUkn2AGaALVFY 0S3kstvFJ9qRVWNJGyRuIWTdz9cDP0I7Gp7Frh5I2ufP80qp6YTbsGcjpnfn/a6dqALj3UKEhmPE giOFJwxxgfqOenNTVl3LCSaZ1zgi2UEgjDeYSMj2ypI4OD2zWpQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUV5/wDEHxWml6ppGmR61BprJd2l3eGSdY2kgNykfljcQSpHmuzKTtWEhhiSgD0CivN9 W8T6ppOkz+Iop/Mmku9TtPssvMCpax3TRFVGCGzbAsc/N5jg8CMR9Roz3Fn4h1LRZLye7hgtLa7S W5IaTfK8yuMgAbcwhgMfKXYDChVUA+QPHf8AyUPxL/2Fbr/0a1c/XQeO/wDkofiX/sK3X/o1q5+g AooooAKKKKACiiigAooooAKKKKACtPQ9Tg0y9dryz+22M0ZiubXzTF5q5DAbwCVw6o3HXbjoTWZR QBd1bT/7M1Oa1Enmxja8Mu3b5sTKGjfHUblZWweRnB5qlW/p9v8A8JDpn2AzQx39hGTZiRtvnxbm ZoVHV5C7goAMnc4J4UDAoA0Yv9Un+6K6JrldT8MwafG8kklnb3AZZAxERJ8/KKWkJBWFgzRxwhSw 3sQTnnYv9Un+6K6Ka2Ww8HRXMaPG98kkzNNlo5TG3kgIsiohIWZzuQzupUEeV81AH2ZRRRQAUUUU AFFFFABRRRQAUUUUAFRyzxQIGmlSNScAuwAz+NSVR1KTyjaP5scWJvvyDKj5H68j+dAFyOSOaMSR OroejKcg/jTTBE0yzGJDKowHKjcB9fxNUnun2oVu43Vl/eSRplY1yfnHJx6ckjv0Vqp2lxcR3PkJ cKyCZ8hyMsTIwYYC8kDDdsbsnjgAG7SblDhSw3EEgZ5IHX+Y/OsOOeeWbTjcXPXy5MhVUEuknH/j oH/Aj7YbK8kGqXskdxl4kkkKMAeAsRA47Hp9B680Ab9IzKgyzADIGSe54FY8t1erhlmQBpJQu4YH yvhV4BLE88DBOOMHOVurh3lkgFwpfzYyqFfu4kUA44I68565BBHIABsU0SRkIQ6kP9wg/e4zx68c 1lreXBlRDJyHCKuB+9/eMjE+4UBuMYzzxxUFlNIzabGzYVdu1NnbyCd2fqWHvjjoaANwMrFgGBKn BAPQ9f6ilrOhkddTnQnbG82ARzubylOD6DAJ/Dt0Msnn/bBt+1+XuH3fK2Y79fmx/kUATvcRRyrG zYZsdjgZ4GT2yeBnr2qWsy8/4/mX+KT7PsHdtspLY9cDk+lTXk08UoSMn98oRCF+424DPvw2ceiH 3wAXaKzBdymaVWn2Y37hsB8shwEGOvzDt1PbFRz3l0EjKuiOxYur8BHG3EfQls5JwOW6ggcUAays rjKsCMkZB7jg0tZEVyYZI0M4RWnkAUqPmJlYd+v4HgkE5B416AEZVdCrKGVhggjIIoVVRAqqFVRg ADAApaKACiiigApGVXQqyhlYYIIyCKWigAooooAjaCJzlokJ3B8lR94cA/X3qSiigAooooAKKKKA CiiigAooooAKKKKACiiigAqve2NvqECw3UfmRrLHMBuIw8brIh49GVT7454qxRQBTj0qxi1SbU47 aNbyZNjyjqRxn2BIVASOSEQHIRcGm6VY6Rbtb6fbR28TOXKp64AH4BQqgdFVVUYCgC5RQB8QeO/+ Sh+Jf+wrdf8Ao1q5+ug8d/8AJQ/Ev/YVuv8A0a1c/QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA+ GaW3njngkeKaNg6SIxVlYHIII6EGtPXYYnnh1O1jSO1v1MojjACwy5xJGAOFAblVySEaMnrWTWto +oRow03UHT+zLhiJC8Yc27MNvmpwWBUhWIUjeECnIoAii/1Sf7orbsZmuNFvUlEaw26PDlEWISB4 5ZAsrLs8w74oyoeTIK/KknKjLntJ7C4ks7lPLuIGMUqZB2spwRkcdRW2VitvDIubIIl49jOLiSFg ZNpmjT58PIUBVivKW+QxG6QHDAH2ZRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFAGHP4L8K3VxLcXHhrRpp5XLySSWETM7E5JJK5JJ5zUf8Awgng/wD6FTQ/ /BdD/wDE10FFAHLw+E/AdxeXNnD4f8OSXVrt+0QpZQF4twyu5QMrkcjPWpIfBfgm5QvB4a8PyoHZ CyWELAMrFWHC9QwII7EEVy+pXWm6u+p6Z4b1O0tdUsk1FLO3hvlS8ubyRZDKAC25IhIS2DjLorDa kal+k8NC3l1zUrrSoPJ0ZrS0hiVITCgnj81ZAEIGGVPIQ8ceXsPMZVQCx/wgng//AKFTQ/8AwXQ/ /E1GPBfglrh7dfDXh8zoiu8YsIdyqxIUkbcgEqwB77T6V0leP2tvq02seJDfaHfQ3l9Fpl5qSFop Q0K3cu+ILG7GRfs6mMALmQRNlQzgMAd5B4L8E3VvFcW/hrw/NBKgeOSOwhZXUjIIIXBBHOak/wCE E8H/APQqaH/4Lof/AImjwt+8bWbuDnTbvUDPYsvCPGYog7oP7rSiVs9G3FxkMGPQUAcvD4T8B3F5 c2cPh/w5JdWu37RCllAXi3DK7lAyuRyM9akg8F+Cbq3iuLfw14fmglQPHJHYQsrqRkEELggjnNcX p9rqjeGrfTrm3+3z2sUB1VltNslsftELXVuepn86NZJHA3GQjd8yzxKO48LfvG1m7g50271Az2LL wjxmKIO6D+60olbPRtxcZDBiAH/CCeD/APoVND/8F0P/AMTVc+E/Aa2c14fD/hwWsO/zZjZQbI9h Ifc2MDaVYHPTBz0roL9d+nXK+VPLmJx5dvJ5cj8HhG3LtY9juXB5yOteb6VH5NvZXs8f/EttdQge +nRPLtljjtXRA0WyPDQyiIO+zClFJ8sRGKAA7D/hBPB//QqaH/4Lof8A4mo5PBfgmF4Ul8NeH0eZ 9kStYQgu20thfl5O1WOB2BParHhGCa28OxxyxSQp9ouWt4nUqY7dp3aFdp5QCIoAhAKgBcDGBT8Q 2Nv/AMJZ4TvzHm6GoSQq7MTtT7JdEhQeFycZxjdtXOdq4AJB4L8EtcPbr4a8PmdEV3jFhDuVWJCk jbkAlWAPfafSiHwX4JuULweGvD8qB2QslhCwDKxVhwvUMCCOxBFcnqljqlxa3Nlpcc6+JUu9Vmlk jby5PIliuFtyZeAVJ+yBfm6wgf8ALBtnUeGhby65qV1pUHk6M1paQxKkJhQTx+asgCEDDKnkIeOP L2HmMqoBJJ4L8EwvCkvhrw+jzPsiVrCEF22lsL8vJ2qxwOwJ7VHD4T8B3F5c2cPh/wAOSXVrt+0Q pZQF4twyu5QMrkcjPWjxDY2//CWeE78x5uhqEkKuzE7U+yXRIUHhcnGcY3bVznauOb1K603V31PT PDep2lrqlkmopZ28N8qXlzeSLIZQAW3JEJCWwcZdFYbUjUuAekQzw3KF4JY5UDshZGDAMrFWHHcM CCOxBFSVy/hoW8uualdaVB5OjNaWkMSpCYUE8fmrIAhAwyp5CHjjy9h5jKr1FABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV5HavD4R+HvhnW NAsrS31CTR2ubxYogBcxx2Dyb5lXG4CYQjeeQZNoYeYQwB65RXna6n4nR59PaTUkJeBkF1Lpw1KQ Ms5ZYVRvJx+6QjzBkqJ8ElVxX0zxFd6jrF4lvrVpaWjo+pHUEs0jSQR2lgwMivz5R+0MWywkAVVE ihaAPTKK8v8ADviPxFe6LDrN1q3mrHd6VbfZvs0arJ9phs/NZ2AzwZ2ZQu3DZzuXCrseI9Y1208S XMWnX8ENvBFpgWGa2EitJc3UsBZiGVtoXDbQQSyp8wG4OAdxRXl+peLNbh0W+urW6vrm60eK7klj toLYIywzzxpJctIQWVxbnKwBWGJDxuQDLsptYtdC8awHWZLiysbLULoQXNpA/mStc36HcdgBQ+UH ZSpy2MFUyjAHslFcnY6vfTeN7jRXv42t7V55hiPEkg2QMImO3GE+0ZJGCQ0GGYiYV1lABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRWfrt1eWPh7U7zTrf7RfQWkstvDsL+ZIqEqu0cnJAGByaANCiv F/DnjfxLD4L1TxPJ4jsfE1vFpSzm0itFS5srskjbJGm39yPmLOSCQhKgAZOho3xs0uPQ9OOrWGuP cRWlsdUvVsMQ2zyYAeQgjCv99doOVYYGcqAD1iivG9d+Jupx6l48tC2paVb6MlqtrdR6bHMYj5oR 2ZZGAYybwUyQDGu4AEHPcXfxC0i08Ynw21tqUs8bwx3F3Falre2km/1SSP1BYlQDgjLDng4AOsor xvSfibqesuzXLalpQ/4S2DT4lOmxkNA6kC2fe2VfKEyMMspcYGCAO4tviHot143k8KIl39rDyRR3 PlhreWWNFeSJXUn51VvmBAwRg8kZAOsori9E+JmkeIUvn0/TtZdLe3mubZmsiBqEcTbXNvz85DFR tO05cDHXHWWF19u062vPs89v58SS+TcJskj3AHa69mGcEdjQBYorLh8S6Dc6odLg1vTZdQDshtEu kaUMudw2A5yMHIxxg0T+JdBtbeW4uNb02GCK4NrJJJdIqpMBkxkk4Dgc7etAGpRXH6t8RNHtJ9Hi 0y6sdU/tC7eJ5Ib1BHbwxp5k8rScr+7QqxUkEhuK3D4l0EW9xcHW9N8i3SJ55PtSbYlkAMZY5wAw IKk9c8ZoA1KKz013R5Ly1s49VsXuruIT20K3CF5oyCQ6LnLLgE5HHBo/t3R/7Y/sj+1bH+0/+fL7 Qnnfd3fczu+7z06c0AaFFef6x8T4LXX00jRrWx1aS409LuykXVYoUu5DceT5KMw2lhhm4JJ2kYzX WQ+JdBuULwa3psqC3a6LJdIwEKsVaTg/cDAgt0BBFAGpRXNjx94Vkv7ezt9e025klSWRjDeRMsUc aFndzuwAB25PU42qxWTVvGOj6f4en1a21Cxvf9EuLm0iju0/0vyULOqEZzjbgkA470AdBRXB3/xK hgsvCiWtraTax4iSCSKwnvhCIY5E3b2k2HgNhQMAuc7QSMV1EHiXQbq3iuLfW9NmgluBaxyR3SMr zEZEYIOC5HO3rQBqUVn3Wu6PY6dBqN5qtjb2M+3ybma4RI5Nw3LtYnByASMdRUdz4l0GyuLm3u9b 02Ce1QPcRy3SK0KkqAXBOVBLoAT/AHh6igDUorLg8S6DdGIW+t6bMZUDxiO6Rt6mTygRg8gyfJn+ 9x14rD8QfETR9F/sBre6sbyHV7sxJcfbUjhjhT/Wy+acodhwNuQWJwOaAOwoqvNf2dveW1nNdwR3 V1u+zwvIA8u0Zbap5bA5OOlU7bxLoN7cW1vaa3ps890he3jiukZplBYEoAcsAUcEj+6fQ0AalFcf 4d+Imj63pl1qN1dWOm2qyytbNPeoDParJ5S3BVtpjVnyuGHUYzyK2H8WeG45bqKTxBpSSWmftKNe xgw4YId4z8vzELz3IHWgDYoqnJq2mw6pDpcuoWiahMm+K0aZRK688qmckfK3IHY+lXKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACs/TNC0fRPN/snSrGw87HmfZLdIt+M4ztAzj J6+prQooAy4/DWgw6XNpcWiaamnzPvltFtUETtxyyYwT8q8kdh6VX1rQGv3hmsTpsM8dwLlvtmnr co0oVVWXAZGEqhQFcNwMgg/KV3KKAMvS/D2m6To1rpcNtHJBbpCN0qKWkaJUWN3wAC4EaYOONoxj Aq5LYWc8rSy2kEkjeXud4wSfLYvHk/7LEsPQnI5qxRQBl3vhrQdSSNL/AETTbpI3kdFntUcKztuc jI4LNyT3PJqSLQtHh+3eVpVjH/aGftu23Qfac5z5nHz53N1z94+taFFAFdbCzTy9tpAvlSvPHiMD ZI+7c49GO98nqdzepqxRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVXv4bi4065hs7r7JdSROk Nx5Yk8pyCFfaeGwcHB64qxWT4msNR1Pw9dWelXf2S+k2eXN5jR7cOCfmXkZAI/GlJ2TaNKUFOcYy dk2te3mcDpnw78Tahqmt6n4ov9GS81DQm0bzdNhYmUt1nl3BQXACjA4IwPlCjPL6n8F/GOsaWltq Gq6NcPBZQQWavLdbLNo9sZEQztAeNFZmKHLcBR96uj/4QT4h/wDQ1/8AlRuP/iaP+EE+If8A0Nf/ AJUbj/4mub28/wCRnsf2Vhf+gqH4kvi/4ba9rV74xOnXmm/ZPEVvZnFxvR4prd0wMqCChRXOeuSo wACTc134f6xqfxM07xBbXGlW1nbXcd09zHC8d9tVAjQErhZY22Dl/mAdgPlGGyo/Bnj2Z5ki8YRu 8L7JVXU5yUbaGw3y8HaynB7EHvUn/CCfEP8A6Gv/AMqNx/8AE0e3n/Iw/srC/wDQVD8SV/htry6p KI7zTX0//hLYfEUZbesu35vNQjBGR8gXHXDEkZAGxoPhLxHoXjfUZ7XVbSPwvd3s+oyW2zfcTzSo gKsSoCIrAsu05xwc5yOePgzx6twlu3jCMTujOkZ1OfcyqQGIG3JALKCe24etSf8ACCfEP/oa/wDy o3H/AMTR7ef8jD+ysL/0FQ/Es+Efh/4k8Ma5rerJcaHHJNp/2W1t7OGSGC5mX/VzzxD5Y24AIjGP nbGDnd6RYfbP7Otv7R8j7d5SfaPs+fL8zA3bM87c5xnnFeXf8IJ8Q/8Aoa//ACo3H/xNH/CCfEP/ AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxMjwL4A1/XLyx8ReI4/wCzI21DUdSkt4ZZ7a58y4CR7MDa 0SjyywIdiQQCMEmpB8IvFVte69eWGuWkErXFzNohM0rPA1w6LLJJKVLb/JTZ/Hy24EEbjp/8IJ8Q /wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/ExB8HPFVy+p+bqem2aO9 /c2JinluXEt0qRvHM8iAshiVl3j5wW3c9K1F+EupvpMryNYxakut2t7DFDeTBPsdvH5cNv5wUMjK jOBIFLHAJ5ORP/wgnxD/AOhr/wDKjcf/ABNH/CCfEP8A6Gv/AMqNx/8AE0e3n/Iw/srC/wDQVD8S 74P+HWoeFvFiX1imm6VpZST7Tb2VzcTG7BVfLjcS5AMbeYRKpBYHGxdxFZ+p/DXxVr/i6e/1XVrR rNn1KOGT7RLI0NvcW/kxIsBUIpTqxDAtnkkgEv8A+EE+If8A0Nf/AJUbj/4mj/hBPiH/ANDX/wCV G4/+Jo9vP+Rh/ZWF/wCgqH4nKx/D3xVb+NdK0q4McEE6Wlx/aFhDLcLaTWVo8UDs7xrHhn5KcnoM jILbelfCLVrbTLq3v7bQ7pfslpYwWr3dxsaBJPNuAZFVGRnmxKGw+37uNtWx4M8etcPbr4wjM6Ir vGNTn3KrEhSRtyASrAHvtPpUn/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxKF18KPFmtWOox a7rVjdXkmiQ6da3od9xKTLORKpT5vmGwSBs7VDFGZjiPxB8GdQvBfXGixabYz6rZG3voZ9RuJwso uUm84SuhaQsIwCCFwectWn/wgnxD/wChr/8AKjcf/E1GfBnj1bhLdvGEYndGdIzqc+5lUgMQNuSA WUE9tw9aPbz/AJGH9lYX/oKh+J0vi7wReeLvEcE811Bb6ba6VeW1uyAmYXFwnlMzKRhownIAIO72 rj9L+D+sPqtg2tzaVJpglsnvLaJnkZ/sdr5MWCyBWV2Z96FRhcAMcmr/APwgnxD/AOhr/wDKjcf/ ABNRzeDPHtsgefxhHEhdUDPqc6gszBVHK9SxAA7kgUe3n/Iw/srC/wDQVD8TpfHPhTW/EOraVd6R c2NpNY5MF87SrPZu0iF3VVJSZWjVkMbgf72CRXJ6h8KNcXxBNqGnS2jOddk1RLk6rc2snkSgebbh I0YIW6GVTuIA47C3/wAIJ8Q/+hr/APKjcf8AxNH/AAgnxD/6Gv8A8qNx/wDE0e3n/Iw/srC/9BUP xMTS/gnrw8O31rqOo6bFqEWmf2fpc1ozsqq07zS+YWUEFt5jyv8AA7gq3foPFXw717Xbhbmzj8P2 Ui2V5hIleMPdXZEczSEKd4EHAfhmkAJUKdoi/wCEE+If/Q1/+VG4/wDiaP8AhBPiH/0Nf/lRuP8A 4mj28/5GH9lYX/oKh+JZ8bfDLVNatfCthoGpwWlrpFpNYTTXfzyGCSJIWwoXDNsD/wB3kjBHUU9X +FOqp4ie78PXGmw2a3Gn3lsl00hML2UEkccRUAl0YlMvuBAzwx6v/wCEE+If/Q1/+VG4/wDiaP8A hBPiH/0Nf/lRuP8A4mj28/5GH9lYX/oKh+JkW/wU1i2XT7GS9sbvTTFaQXolldHW3WVp7m3j2x/M rykOrkq42hemSZLj4TeKrlLi4e70YX06ahcMBJL5QurxhFKB8mfKFuMr/EJOuV4rT/4QT4h/9DX/ AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+Jq6B8Nz4d8cnVIfLvNPFvaxwPNdyR y2zQ2zW+TGq+XMWUj5mK7dzYHr6JXk3/AAgnxD/6Gv8A8qNx/wDE0f8ACCfEP/oa/wDyo3H/AMTR 7ef8jD+ysL/0FQ/E9Zoryb/hBPiH/wBDX/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6C ofies0V5N/wgnxD/AOhr/wDKjcf/ABNH/CCfEP8A6Gv/AMqNx/8AE0e3n/Iw/srC/wDQVD8T1miv Jv8AhBPiH/0Nf/lRuP8A4mj/AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6Cofies0V5N/wgnxD/wCh r/8AKjcf/E0f8IJ8Q/8Aoa//ACo3H/xNHt5/yMP7Kwv/AEFQ/E9Zoryb/hBPiH/0Nf8A5Ubj/wCJ o/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4nrNFeTf8IJ8Q/+hr/8qNx/8TR/wgnxD/6Gv/yo 3H/xNHt5/wAjD+ysL/0FQ/E9ZoryIeDPHrXD26+MIzOiK7xjU59yqxIUkbcgEqwB77T6UDwZ49a4 e3XxhGZ0RXeManPuVWJCkjbkAlWAPfafSj28/wCRh/ZWF/6Cofieu0V5N/wgnxD/AOhr/wDKjcf/ ABNRyeDPHsLwpL4wjR5n2RK2pzgu20thfl5O1WOB2BPaj28/5GH9lYX/AKCofieu0V5N/wAIJ8Q/ +hr/APKjcf8AxNH/AAgnxD/6Gv8A8qNx/wDE0e3n/Iw/srC/9BUPxPWaK8m/4QT4h/8AQ1/+VG4/ +Jo/4QT4h/8AQ1/+VG4/+Jo9vP8AkYf2Vhf+gqH4nrNFeTf8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/ AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/E9Zoryb/AIQT4h/9DX/5Ubj/AOJo/wCEE+If/Q1/+VG4 /wDiaPbz/kYf2Vhf+gqH4nrNFeTf8IJ8Q/8Aoa//ACo3H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD +ysL/wBBUPxPWaK8m/4QT4h/9DX/AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+J 6zRXk3/CCfEP/oa//Kjcf/E0f8IJ8Q/+hr/8qNx/8TR7ef8AIw/srC/9BUPxPWaK8m/4QT4h/wDQ 1/8AlRuP/iaP+EE+If8A0Nf/AJUbj/4mj28/5GH9lYX/AKCofies0V5N/wAIJ8Q/+hr/APKjcf8A xNdD4N8NeKtG1eW41zW/t1q0BRY/tUsuH3KQcOAOgIz71Ua027ODRlXy7D06bnHERk10V7s7iiii ug8gKKKKACiiigAooqG6W4e3dbSWKKc42vLGZFHPOVDKTxnuKBpXdjmfEOutpfirSLeTV47OC4dI lt2iU+e7vtx83Mg6L+6YGJmRnV0b5eHtda1/Vbzw7eanq1pHKmpoVtkmULHdSWtzm2lcwDyyGVY/ Ly8iea24sTGR6d9n8Q/9BTS//BbJ/wDH6Ps/iH/oKaX/AOC2T/4/U8z7fl/mbexh/wA/F/5N/kec 3mu+JtO1jVlh/s3Tp5tTd5S1yzxPMlpZCKBW8gtKXDMfKRFkbYQjDad3Qahr3iO2utUsLOKS4u9M S9vVY2+4XUflBraHYoBALzFAwOXNm46s23pvs/iH/oKaX/4LZP8A4/R9n8Q/9BTS/wDwWyf/AB+j mfb8v8w9jD/n4v8Ayb/I5HwrqF9qPirTJL6/tL/ZZaikN1ay+csib7I8yCKNHIYsMogAwFOWVq9E rJ+z+If+gppf/gtk/wDj9H2fxD/0FNL/APBbJ/8AH6OZ9vy/zD2MP+fi/wDJv8jWorLig10TIZtS 054gw3qlg6sR3AJmOD74P0rUpp36Gc4qO0k/S/6pBRVe9S9eECxuIIJd3LTwGVSPTAdeenOfwqj9 n8Q/9BTS/wDwWyf/AB+k210KhTjJXc0vv/RM1qKyfs/iH/oKaX/4LZP/AI/R9n8Q/wDQU0v/AMFs n/x+jmfb8v8AMr2MP+fi/wDJv8jWrH8UX1xp2gy3FtJ5LCWFJJ9oPkRNKiyy85A2IzvlgVG3LAgE U77P4h/6Cml/+C2T/wCP0fZ/EP8A0FNL/wDBbJ/8fo5n2/L/ADD2MP8An4v/ACb/ACOJj1yaz8Yw 3q6jJeWD29nHc3iWh/fw/wDEyEZ2gHJaQQgMgAkZl2DDqtYes+Jr3UfBT2994i8u51Dw0ZvsyQRq 0zfYzNJlSu75sN+9TMe0SR7UkVXPqX2fxD/0FNL/APBbJ/8AH6Ps/iH/AKCml/8Agtk/+P0cz7fl /mHsYf8APxf+Tf5HnsHinxLMboW2t2Mqm7gghdgs7ojXsMMUrxpGios0LM/Mh8zcGi2KCBc8UX/i PStctZBHaCSDTLmKC8a63M0TXVkkk8v7kJEVQlyQrouCSCq4PbfZ/EP/AEFNL/8ABbJ/8fo+z+If +gppf/gtk/8Aj9HM+35f5h7GH/Pxf+Tf5HIrr/iNNOskN1HcTavbm2tLq2j3pBMLgRiYExr5xMMv nHCqjC1dlARiVz7zWtav9TvLW+ntPs0WsWqi0Vi0tuE1KBIWZRGPLEkeX+eRy+QyBVDAd99n8Q/9 BTS//BbJ/wDH6Ps/iH/oKaX/AOC2T/4/RzPt+X+Yexh/z8X/AJN/ka1FZP2fxD/0FNL/APBbJ/8A H6rvLrMeow2DaxpYupopJo0/syX5kQoGOfOxwZE/P2NHM+35f5h7GH/Pxf8Ak3+RvUVk/Z/EP/QU 0v8A8Fsn/wAfo+z+If8AoKaX/wCC2T/4/RzPt+X+Yexh/wA/F/5N/ka1FZP2fxD/ANBTS/8AwWyf /H6Ps/iH/oKaX/4LZP8A4/RzPt+X+Yexh/z8X/k3+RrUVk/Z/EP/AEFNL/8ABbJ/8fp0UGuiZDNq WnPEGG9UsHViO4BMxwffB+lHM+35A6UP51/5N/kalFFFUYBRWddQ6y9w7Wl/YRQHG1JbJ5GHHOWE qg857Covs/iH/oKaX/4LZP8A4/U8z7GypQavzr8f8jWorJ+z+If+gppf/gtk/wDj9H2fxD/0FNL/ APBbJ/8AH6OZ9vy/zH7GH/Pxf+Tf5GtRWT9n8Q/9BTS//BbJ/wDH6Ps/iH/oKaX/AOC2T/4/RzPt +X+Yexh/z8X/AJN/ka1FZP2fxD/0FNL/APBbJ/8AH6Ps/iH/AKCml/8Agtk/+P0cz7fl/mHsYf8A Pxf+Tf5GtRWT9n8Q/wDQU0v/AMFsn/x+j7P4h/6Cml/+C2T/AOP0cz7fl/mHsYf8/F/5N/kc7478 Qapouo2UenXflGTT72dYfsvnfaZ4zB5MWR9zez7P9rftXDsjDLfxLr7XfiAXF9HbxQPKixW7r51m FuFjikkDwMsETRkyO0hkJTLxqApA659J1mTUYb9tQ0s3UMUkMb/2fL8qOULDHn45Mafl7mrH2fxD /wBBTS//AAWyf/H6OZ9vy/zD2MP+fi/8m/yPPfDXiTUb7XRf3N1B9nlis7S5vbVlm37b69hh2naF ZZWCBpFUfe+VEDb4pNc1jxHYeJdQkkXTdKlmt9PinuVv8xQQ5vmDtNJAVjJdVT5o2B3AAgsCvXat qWqaJFBJe6tZf6RL5MKQaJcTu77WfASOVm+6jHp2q5Auv3NvFOmp6eEkQOok0qZGAIzyrTAqfYgE d6OZ9vy/zD2MP+fi/wDJv8jBt9a8QPqen6XcNi41D7Jd/aIrZkSCIRs1xGqOoYrvhClnwyfbEBAK qGw/D+ta1rOp+HZdYntJH/tNXMULFmtZmsrzzIH/AHaBCmFHlEvImTvY7lrvvs/iH/oKaX/4LZP/ AI/R9n8Q/wDQU0v/AMFsn/x+jmfb8v8AMPYw/wCfi/8AJv8AI1qKyfs/iH/oKaX/AOC2T/4/Ucg1 uF4Ul1nSEeZ9kStYOC7bS2F/f8narHA7AntRzPt+X+Yexh/z8X/k3+RtUVyOn69f6nqIsINThjum iaZUu/Dt5bbkUqGIMsig4Lr09RWpCNbuULwazpEqB2Qslg7AMrFWHE/UMCCOxBFHM+35f5h7GH/P xf8Ak3+RtUVk/Z/EP/QU0v8A8Fsn/wAfo+z+If8AoKaX/wCC2T/4/RzPt+X+Yexh/wA/F/5N/ka1 FZP2fxD/ANBTS/8AwWyf/H6Ps/iH/oKaX/4LZP8A4/RzPt+X+Yexh/z8X/k3+RrUVk/Z/EP/AEFN L/8ABbJ/8fp0UGuiZDNqWnPEGG9UsHViO4BMxwffB+lHM+35A6UP51/5N/kalFFFUYBRXnfjrQPE 2q6tPPo82pJEtkEhFrqTW6+b5N4eVDqCfNNnyR6dg1Gl6B4mg8ax6jcTakbM3sryI+pM8XlM1/j9 0XK42mxwAvHGMEPQB6JUc88Nrby3FxLHDBEheSSRgqooGSSTwABzmvK9Q8NeNTeandWdxqvmS/bP JA1Zgg3jUBHtQybV62GOBt4PBD40IfDHiCPQfGVhIb64W+0+4hsUu9Qafc5luwgBkc7cxNbdcds8 hqAPRBPC1w9ussZnRFd4ww3KrEhSR1AJVgD32n0qSvN9b8Na9HqN+0U2uarHdxWcX2y3vIbW5hCG 6L7BGYVbG9AA+RmTcQ4TZXpFABRRRQAUUUUAFFFFABRRUN1M9vbvLHbS3LrjEURUM3PbcQPfk9qB pXdiavP/AIc6XcSWena/JZQWL3en+bePC4Y6lPOUmM74Axt+YAEfKZZFUBFUv1X9r33/AELmqf8A fy2/+PUf2vff9C5qn/fy2/8Aj1Tzr+kzb6vPuv8AwKP+ZrUVk/2vff8AQuap/wB/Lb/49R/a99/0 Lmqf9/Lb/wCPUc6/pMPq8+6/8Cj/AJk2u2t5feHtTs9OuPs99PaSxW828p5cjIQrbhyMEg5HIqn4 X0y202yuTaaBHocdxcGb7IrJnOxE3MsZKITs6ISMAMSGZgJv7Xvv+hc1T/v5bf8Ax6j+177/AKFz VP8Av5bf/HqOdf0mH1efdf8AgUf8zWorJ/te+/6FzVP+/lt/8eo/te+/6FzVP+/lt/8AHqOdf0mH 1efdf+BR/wAzWrz86Xca3471a4+xQCax1C1ih1bePNt4I4oZ3gVcA/vDM65B5WSTccIiP1X9r33/ AELmqf8Afy2/+PUf2vff9C5qn/fy2/8Aj1HOv6TD6vPuv/Ao/wCZzulfFHSdY0nUdStbC+a30+0W 8uMSW7lIjHK4yElOG/dFShwwLLkAZI1I/Gmny+M5vDCxSC8ifYztcW6gnyhL8sZk81htYDIQjOew JGSmgxppaacuha+Lc6ZFpU48+0zcW8f3Vc+ZwdrSKSm04lbuEKx6j4ci1e+vpNR0LW7qxvpfPn06 b7A0Jk8kQh1Yt5iMFUYKuCD06kUc6/pMPq8+6/8AAo/5nVDxLoLWD3663pps0RXe4F0nlqrOUUls 4ALKyg9ypHUVcsb+z1OzjvLC7gu7WTOyaCQSI2CQcMODggj8K4W38LxxTNcT6V4mu7t7hbl7i4ur Qu0gmilJ4kAAP2eBNoAAWJQoUli3QaTNcaNo1jpdv4e1doLK3jt42kltSxVFCgnEoGcD0FHOv6TD 6vPuv/Ao/wCZ0VeV2l8PDGr3Gsa5Z3cd9pemQxX8v2iNl1Ca7nVPPTdIEjQNb4BbYdhVSEWJVrvv 7Xvv+hc1T/v5bf8Ax6qepzXGq2qW8/h7V1RLiC4BSW1B3RSrKo5lPG5AD7Z6daOdf0mH1efdf+BR /wAyi/xF0yK3urmWxvktbeIv56mGRJXFqLoxIUkOW8ok7vuHaQGPGdTTfFmlah4fbW5LiOysUco8 11NGI1OQBiVWaNgSQMqxGcqcMCBzupeHV1W51SS60jxEYdQ3s1us1kEile2+zGVDv3bvKyMMzL8x O3OMF54atbmIeT4d1uzuoty2d7ay2az2UbKVMULmQkR4eTCnIXf8u3am051/SYfV591/4FH/ADOq vfEug6akb3+t6bapI8iI090iBmRtrgZPJVuCOx4Naleew+E7O2NqYPD2vx/ZnVkAuLU5CSWzopzL 0VbOBB3Kg5JYlq6z+177/oXNU/7+W3/x6jnX9Jh9Xn3X/gUf8zWri/EOhavqni+C78qO40eC3hge 1OFNwsszfaFLbhhFVLeRlKneI9gwGcNvf2vff9C5qn/fy2/+PUf2vff9C5qn/fy2/wDj1HOv6TD6 vPuv/Ao/5lHRPGmn69r19pFrFIk9k8ySF7i3JJjk8tv3ayGUDPQsgGMeoz0lcjpEF/o91cvFY+Ip rWaWaYWcz2HlxvLKZGKlWD/eZsZY8H6Y2P7Xvv8AoXNU/wC/lt/8eo51/SYfV591/wCBR/zMHxjb tqXiXQ9Nk0e01i1+z3d2bO6dVTzUMMaSHKkEKJ3yD2YsAzKinUs9Z0fQdDs7XVPE1jJJaRR2013d XSIZZF3IWbcxwxaKXgknKOOSpq1/a99/0Lmqf9/Lb/49XFz+C7e6fUGm0vxM4vXmYr59iBEJFugy phs4zezEbsnO3nAwTnX9Jh9Xn3X/AIFH/M74atprX72C6haG8R1R7cTL5isyF1BXOQSqswHcKT0F R6Zruj635v8AZOq2N/5OPM+yXCS7M5xnaTjOD19DXI/8I3Cda/tJtD8ROFl82O1a6tfJjJm+0NtH m5+acJITnOY1XITKHQ8OWbeGLB7Oy0DW5I28rJmmtCf3cEcC9JR/DCpPuT24Bzr+kw+rz7r/AMCj /mddRWT/AGvff9C5qn/fy2/+PUf2vff9C5qn/fy2/wDj1HOv6TD6vPuv/Ao/5jfFl9caZ4N1y/s5 PLurXT7iaF9oO11jYqcHg4IHWuTt7iy+G9kLu40qPTLG7eGwg061uYgiyxpKzTvLK8aZcDbuY72E cZb5m2J139r33/Quap/38tv/AI9VO9muL66064l8PauHsLg3EQWW1wWMUkWG/e9NsjHjHIH0Jzr+ kw+rz7r/AMCj/mQ/8J1YLpMepSWV8lvLpUurxZWMl4I44nbADnDfvgoBxyrdsE2NG8Y6TrGnJe/a ILSOS7FnEJr23k8yUgEIrRSOpY54XO7jpjGcV9BjfS305tC18240yXSoB59pm3t5PvKh8zk7VjUF 9xxEvcuWp3fhGLUPOkvtJ8RT3dz+7u7xXsIJrq3OzdbyNEyboz5a8/fxkBgpIJzr+kw+rz7r/wAC j/mdk/iXQY7i0t31vTVnvUR7WNrpA06ucIUGcsGPAIzntWpXC2egx2WqWuopoWvtcQOJCXntMSyf 6TudgJOrNeTMQuBnbgADB6T+177/AKFzVP8Av5bf/HqOdf0mH1efdf8AgUf8yHxRbXd1ZWyQ2sl7 aLcA31jEyK91DscbBvKqRvMZYFgGVWU5B2tx/h7xppXhrw/ZWU8V27zaZLr5BuI2ZYpjPceWDJIJ JnUKylgDnAZtuTjtv7Xvv+hc1T/v5bf/AB6sfUrNtU/tfz9A1tf7V09dPn2TWg2xjzcFcynDfvm5 ORwOOuTnX9Jh9Xn3X/gUf8xt38RdMsLy1sryxvoLyaV4ZLdzDvhZRE20gSfvGKzxsEi8xjnAG4EV 0k2rabbXosp9QtIrsoriB5lVyrOI1O0nOC5Cg9yQOtcb/wAI6rXkd/NpHiKXU4/NZb8zWSyiVxEv mgK4UMEgjQAKFK7gytubNe58GabPO3k+Gdbs7Jt5bT7Oa0iti7oIpH2CT7zRAx8HgMxUK5L0c6/p MPq8+6/8Cj/mdta67o99qM+nWeq2NxfQbvOtobhHkj2na25QcjBIBz0NaFcjpFm2i397eW2ga28l 3nzBJNaEDM88/GJR/FcOPoF75J2P7Xvv+hc1T/v5bf8Ax6jnX9Jh9Xn3X/gUf8yrrmivreuaXHcL P/ZlvFPMzwXLQOlz+7SIh42V/wDVvcDg4557VuQQrbW8UCGQpGgRTJIzsQBjlmJLH3JJPes3+177 /oXNU/7+W3/x6j+177/oXNU/7+W3/wAeo51/SYfV591/4FH/ADNaisn+177/AKFzVP8Av5bf/HqP 7Xvv+hc1T/v5bf8Ax6jnX9Jh9Xn3X/gUf8zWrl9dmuLPxbpt3bWv2mYaVqEdvCZBGJ591u6QhzwG YRuR7Kx6Ka0v7Xvv+hc1T/v5bf8Ax6j+177/AKFzVP8Av5bf/HqOdf0mH1efdf8AgUf8znU8QDw1 p02q65pN9DeXMscU1xd3NjD5xw5Cx7rnasaYOE3Z+Yn52MjnQ8G3ianJr2qQDNre6gksLh1dWAtL dGw6Eq211dCVJG5WGeKm1KWbVbdYLjw/r6IrhwbW/jt2zgjlo7hSRz0zjp6CrEGo3dtbxQJ4d1cp GgRTJcW7sQBjlmnJY+5JJ70c6/pMPq8+6/8AAo/5m1RWT/a99/0Lmqf9/Lb/AOPUf2vff9C5qn/f y2/+PUc6/pMPq8+6/wDAo/5nG+BYks9O03xJqK2OkC809pbubz1H9pzyhbhp3OFxsVZCAfu75QoV EDP2jeJdBVL121vTQlg4S8Y3SYt2LFQJOfkJYEYOORisHxTp8nizTY7G60bX7VEd232stmGIeKSJ lO+RhgpK46Z6EEVnzeF45DdSRaV4mtrmd2dLmC6tFkgZpLl2MZ8zAJF5MuSDgFSMMN1HOv6TD6vP uv8AwKP+Z1174l0HTUje/wBb021SR5ERp7pEDMjbXAyeSrcEdjwa1K89h8J2dsbUweHtfj+zOrIB cWpyEktnRTmXoq2cCDuVBySxLV1n9r33/Quap/38tv8A49Rzr+kw+rz7r/wKP+ZrUVk/2vff9C5q n/fy2/8Aj1H9r33/AELmqf8Afy2/+PUc6/pMPq8+6/8AAo/5mtRWT/a99/0Lmqf9/Lb/AOPUf2vf f9C5qn/fy2/+PUc6/pMPq8+6/wDAo/5mtRWT/a99/wBC5qn/AH8tv/j1H9r33/Quap/38tv/AI9R zr+kw+rz7r/wKP8Ama1FZP8Aa99/0Lmqf9/Lb/49Vizv7m6mKTaTeWahc+ZO8JUn0+SRjn8McUKS YpUJRV2196/zL1FFFUYhRRRQAUUUUAFFFFAGfqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZ IyK0K5/xL4Y/4SDbtvPs++0uLCfMW/dbz7PMC8jbJ+7Xax3Ac5VsjG4BN9odmkjMBRQiBCGDZO4l s4II24GBjB5OeADLh8U6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGibxTo0FnbXUl5iG43F T5TkoEOJGkXGY1Q8OzhQh4Yqax7TwL9l0x7U6jukiisrezk8jAjjs5DJb+Yu79424neQUDDgBDzR d+BftWmJajUdsksV7b3knkZEkd5IJLjy13fu23AbCS4UcEOeaAOsnnhtbeW4uJY4YIkLySSMFVFA ySSeAAOc1lr4p0aTR49VgvPtNnNK8ML20TzNM6sysI0QFnxsc/KD8qlvujNak4ma3lW3kjjnKERv IhdVbHBKggkZ7ZGfUVx6+C9Sl0STTr/VdNu0+2yXqRvpjeQ7SPK7pNEZj5qBpcqMrtZEJLYoA7CC eG6t4ri3ljmglQPHJGwZXUjIII4II5zUlV7C1+w6dbWf2ie48iJIvOuH3ySbQBudu7HGSe5qxQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQBHPPDa28txcSxwwRIXkkkYKqKBkkk8AAc5rL/4SjSf7O+2 +dPt83yPI+yS/aPMxu2eRt8zdt+fG3Oz5vu81oX9t9s065tdsDedE8eLiLzYzkEfOmRuXnlcjI4y K4//AIV1E+i/Zbm7gvbs6h/aDNf273Vu8nk+QA0csrOyiPkAykhwCDtASgDqG1vTo59Mt5bjyrjU 932SCVGSSTahdvkIDLhRzuAwSAcEgUWut6de6jPYW9xvuId2RsYK+07X2MRtfaxCttJ2sQGwTiub uvh9G+t6Hqltql2LjTnhNxLdTzzPdrEhRQcSqinDyEnaclzkYZ1fYsNH1KHxLeapf6jaXcUiGO2i WzZHto8qdgcyEEHbljtBY7cnCKoANyiiigAooooAKKKKACs/TNb07WfN+wXHm+VgnKMm5WztkXcB vjbB2uuVbBwTg1oVzeg+F5tBt51hv45J1sodPtHe3O2OGAP5PmKHy75kbcQUDcYVO4BqanrenaN5 X2+48rzckYRn2quN0jbQdka5G52wq5GSMij+29O/tj+yvtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG 3ms/xL4Y/wCEg27bz7PvtLiwnzFv3W8+zzAvI2yfu12sdwHOVbIwf8Ix/wAVL/an2z/R/tf2/wAj yvn+0fZ/s2d+ceX5f8O3O7ndj5aANDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/ AIa8Mf8ACP7t159o2WlvYQYi2bbeDf5Ybk7pP3jbmG0HjCrg56CgCvfX1vp1nJdXUnlwpgEhSxJJ AVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w2om8mRZY riKQruCyRSLIm5cjK7kXIBBIyAQeRHpOlTaTYQWyXMchNxNcXTtER5jSu8jhBu+QeY+RndhRg5J3 UASf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2jXG ZFc8IyBg54Usar/8Ix/xUv8Aan2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTq O6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQC CCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQBh2Xi3StR0uTUrRdSltE SNw40u5BkV/umNTHmQd8oDgcnArUsb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAiub03wa2k 6XqVnZz6aBeIsQhbTFW1KDILSQI6q8rqxV2BQMFT5QFwdjSNKm0jS7KxiuY3SF2MuYiF2tuOyJd3 7tFZlCg7tqKF5+8ACP8A4SjSf7e/sTzp/t3m+Rj7JL5fmeV52zzduzd5fzY3ZxVjTNb07WfN+wXH m+VgnKMm5WztkXcBvjbB2uuVbBwTg0XOmfadcsNRebMdnFMqwFcgyPsAkBzwyqsig4ziVhkAkHL8 L+FT4fuL26luo5p7pIogkCSRwQxRlyixo8khQAyN8qsEA2gKMEkA6Sq99fW+nWcl1dSeXCmASFLE kkBVVRksxJACgEkkAAk1YrL8Q6JD4h0SbTZ/L2O8cgEsYkQtG6yKHQ43IWUBlyMgkZGcgAuWN7Fq FnHdQpOkb5wJ4HhcYJHKOAw6dxz16VX/ALb07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qP SNKm0jS7KxiuY3SF2MuYiF2tuOyJd37tFZlCg7tqKF5+8M+TwqZ/GcOvS3UaxQP5sVvAkiF5TEYt 0v7wxudrMNwjV8BBuwuCAaGieINP8Q2/2jTvtbQbEdZJrKaBZFYZUoZEUOCBnK56j1Falc/4Y8Lp 4b+1CKWDy5tirDa2q20YC5w7InymZt2HdQoYKgCqFroKAK99fW+nWcl1dSeXCmASFLEkkBVVRksx JACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVfWtM/tfTDaibyZFliuIpCu4L JFIsiblyMruRcgEEjIBB5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd2FGDkndQBJ/ben f2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIG DnhSxqv/AMIx/wAVL/an2z/R/tf2/wAjyvn+0fZ/s2d+ceX5f8O3O7ndj5az7TwL9l0x7U6jukii srezk8jAjjs5DJb+Yu79424neQUDDgBDzQB1FjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggE VYrP0XTP7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToUAFFFFAGfpmt6drPm/YLjzfKwT lGTcrZ2yLuA3xtg7XXKtg4Jwa0K5vwv4VPh+4vbqW6jmnukiiCQJJHBDFGXKLGjySFADI3yqwQDa AowSekoAx/8AhKNJ/t7+xPOn+3eb5GPskvl+Z5XnbPN27N3l/NjdnFWNM1vTtZ837Bceb5WCcoyb lbO2RdwG+NsHa65VsHBODRc6Z9p1yw1F5sx2cUyrAVyDI+wCQHPDKqyKDjOJWGQCQaegaPqWnXF/ c6rqNpf3N04PnQ2bQMqgthOZHGxQ2FA24+YnczsxALmp63p2jeV9vuPK83JGEZ9qrjdI20HZGuRu dsKuRkjIoTXNLk1xtEjv4JNTSIzSWqPueNBs5YD7v+sTGcZzkZwap+JdH1LWreC2s9RtLa23k3UN zZtOtyuOEbbIhCZ5YfxYAPyllavqWgarf+JYdRXVbSKzjt5bX7OLSQSmKUxGTEyzKVfMQ2sFG3PQ kZoA2LDVbHU3vEsbmOc2VwbW42chJQqsUz0JAYZx0OQeQQLlcv4Q8HjwlPqogu/Ns7uWN7e3/fH7 OiII1TMkr7vlVRkBemPuhVXqKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK4vwd4ju723vLjVb6NoIbK3urqSQIi2Vywk NxbEgAKIgiHa+XXf8zHIx2lZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBoA5vxj4ju7 K3s7jSr6NYJrK4urWSMI63tyojNvbAkEMJQ7namHbZ8rDBzJ/beo/wDCe/2d9o+T7X5H2HYv/Hp9 l837V03/AOv/AHO/Pl/w43810Gp63p2jeV9vuPK83JGEZ9qrjdI20HZGuRudsKuRkjIo/tvTv7Y/ sr7R/pfTbsbZu27/AC9+NvmbPn2Z3bfmxt5oA5/wPreo6x5/224+0YtLaeb5FX7Ldv5nn2vygY8v bH8j5kXf8xORjsKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQBj+KL6407QZbi2k 8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKr6FriNoNncarfwI1xdy2trPM6x/bAJXWFl6BmkRVcb Rht2VAGBWxfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZ TgqwIIKkAggggEUAcv8A23qP/Ce/2d9o+T7X5H2HYv8Ax6fZfN+1dN/+v/c78+X/AA4381j6f4p1 mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHFdx/benf2x/ZX2j/S+m3Y2zdt3+Xvxt 8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxoAPC99cajoMVxcyecxl mSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFV7G+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqxQ Bwek65d3Fr4hlutfkEUdwCl4kCPFZQvK4CFfLRo540x5gl3iP5Hbgstbmg64j+HNOu9Vv4I3vJTD byTOsZnJdhEOMK8jIAT5eUY5KZQqauWfiPSL63uriG+jEFqnmSySgxqIsEiUFgA0RCsRIMo204Jw asaXqlprNgt7ZPI0DO6fvIniYMjlGBVwGBDKRggdKAOfvPE9lZ/Eax0mTxBaIk1lKslg80QK3HmQ eV23h3WR8KTggAgcE1T8Aa5qWvXmsS3N/wDbbSDyYRLG9u8H2rDNMIGi+Zodrw7fMJbqD8wYV1H9 t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY280aZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11 yrYOCcGgDQrn/G2rXGieEL69s5oILo+XBDcXEgSOB5ZFiWVyQRtQuGOQeFroKr399b6Zp1zf3knl 2trE80z7SdqKCWOBycAHpQBj6DrUA8OadcalqWJLqU28c15LEDcyF2C+WyqiyK2MxlVBdNrYyTWP NrmpSfFK20S3v/MhTdNcWtu9u0cdqIOswP75ZvOkjxjClCpGSHrsLG9i1CzjuoUnSN84E8DwuMEj lHAYdO4569KrvrVkmuLo26dr5ohMVS2kZEQ7wpaQLsTJjcDcRnHFAGH4S8T2Ws6tr9lD4gtNTMF6 WtVjmiZhbmGFjjywNyLI7ruOTxgkkV1lY+h+KNJ8R+Z/Zk077Io5/wB9aSwbo5N2x18xV3K2xsEZ HFbFAGP4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWBAIqvoWuI2g2dxqt/AjXF3La2s 8zrH9sAldYWXoGaRFVxtGG3ZUAYFbF9fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6j Zx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARQBy/8Abeo/8J7/AGd9o+T7X5H2HYv/AB6fZfN+1dN/ +v8A3O/Pl/w4381j6f4p1mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHFdx/benf2x /ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnh SxoAPC99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFV7G+t9Rs47q1k8yF8gEqVII JDKynBVgQQVIBBBBAIqxQAVn+drH9seV9hsf7M/5+Ptj+d93/nl5W373H3+nPtWhRQBw/gDXNS16 81iW5v8A7baQeTCJY3t3g+1YZphA0XzNDteHb5hLdQfmDCu4rL0rxDpmtXFxBYTySvAiSMWgkRWR ywR0ZlAkRtjYZSQccGtSgDl7n+1ofHdhCmtzva3XnXLWJgiEKW8cSRlQ20yGQzSxuDuA27h2G6Pw Prw1+3vLhNetNUjLq8axmMSRBgeWROY0YglI33SKB87liVTU/wCEo0n+3v7E86f7d5vkY+yS+X5n leds83bs3eX82N2cVY0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NAGH4y14aRcaXbjXr TSZLh3dWujGkbBCm4u8nBQBsGNMSOWXDxqHYF54nsrP4jWOkyeILREmspVksHmiBW48yDyu28O6y PhScEAEDgmtzU9b07RvK+33HlebkjCM+1VxukbaDsjXI3O2FXIyRkVHJ4h0yLW4dHaeQ3kr+WoWC RkD7DJsaQLsV9iltpIOMHGCMgHL+EfFOq6j411bR9YjktriOyguvsLS2zC1Ys4ZF8t2dgVMR3N3y 2Iw6Ke8rP03WrLV5buOyad/ssrQyO9tJGhdWZGCuyhXwyMDtJxj3FaFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRVe/m+z6dcz/aYLXy4nfz7gZjiwCdzj K/KOp5HA6jrXn/8Awlzv4N/tODxLBLYHUPJS9+0Wkd48Pl7gv7zbAsxfnYwUiHqBJQB6RRXn58Ta 611of2o/Yru4tNOl/s/yQv2mSaUrdrtYF/3EYD4UgpnL7l4qwmvXsOueIYbTVP7XhtrSeeOG3WO7 e2lTGImSNYyjEllWNmdpNh+eMqdwB3FFc34K1K71PRpnu7yO+MVw0cd3E6SRzLtVspKiosoBYqWC JgqVwSpZukoAKKKKACiiigArm9B8LzaDbzrDfxyTrZQ6faO9udscMAfyfMUPl3zI24goG4wqd+kr i/B3iO7vbe8uNVvo2ghsre6upJAiLZXLCQ3FsSAAoiCIdr5dd/zMcjABoa94Xm163gWa/jjnaym0 +7dLc7ZIZwnneWpfKPmNdpJcLzlX7Sf8Ix/xUv8Aan2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud 2PlrP8ca3qOj+R9iuPs+bS5nh+RW+1XaeX5Fr8wOfM3SfImJG2fKRg5P7b1H/hPf7O+0fJ9r8j7D sX/j0+y+b9q6b/8AX/ud+fL/AIcb+aANDw14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ+8bcw2g8Y VcHPQVx/gfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzIu/wCYnIx2FAGfrWmf2vphtRN5 MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k3ml6Zb2treweZ9rkubuSW2LCTzZHklWNQ42ZZzt JL7RgHceaPFF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFR+G9Va70S0lvrmNpJ7ie C2lbapu0R5PLkXGAxeJBJlQAQSwAXoAR/wDCMf8AFS/2p9s/0f7X9v8AI8r5/tH2f7NnfnHl+X/D tzu53Y+Ws+08C/ZdMe1Oo7pIorK3s5PIwI47OQyW/mLu/eNuJ3kFAw4AQ80f23qP/Ce/2d9o+T7X 5H2HYv8Ax6fZfN+1dN/+v/c78+X/AA4381j6f4p1mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6AP iQMyZzIWHFAHcaLpn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYBJPJ0Kx/C99cajoMVxcyec xlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFAHNxeEluE1BdavZNQN28H7xA1u6rC26I70bcHDfMdh RM5KopZy0fhnwzqfhrRxYx6vBcM+oSXUsk0Ez7o3YsyLvnYq2ScNkjuVZizHn9A8Q3sPhfVrnXvE +2SzihNzqOyOSG3JJDoiiCJkuODmKRXKFoshiSp6zw1qwvfD+n3F1qFpPJdO6QyRzRt52C5UEodj ShEJcJ8u5X2/KKAI/wDhGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy0eGPDH/ AAjn2r/TPP8AO2D5YvL37c/vZeT5lw+795Lxv2r8oxzXuf7Wh8d2EKa3O9rdedctYmCIQpbxxJGV DbTIZDNLG4O4DbuHYbjwr/ayanq9rqGtz6pHZfZ7ZpJ4Ioj9o8vzJWRY1GIyssOAxJBDDpgkA6iq eq2TajpdxZpLHGZk25lhWVGHdXRuGRh8rDIJBOCpwRcrD8X399pnhi6utOMa3CvEnmSNtWJGkVZJ CxVgoRCzbirBduSpAIIBJp2k3mk6PY2FnewfuZd0xktiyGMsWaOJQ48pRkKgJYIqgYbGajk8PtL4 sh1trqMCJMKq26rMRtK+U0oOWgyxk8sgnzMNuwAoPDWpC48P6fLd3kkk9w7xxyXDx5uGBc5jKKiu hVCyMFG6MBsDmsv+29R/4WP/AGZ9o/0T/V/ZNi79vk+Z5+3G7y9/yedv27v3flbv3lAHQWOmfZNT 1S+ebzpL2VGUleYo1jVRGDnlQwkcDgAytxkknQri/AniHUNcuNSS6uY7yCBIT9rheGSBp2MnmpC8 RI8pQsZUSfvQHy/Va7SgDP1rTP7X0w2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9M t7W1vYPM+1yXN3JLbFhJ5sjySrGocbMs52kl9owDuPNHii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnI GxGd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL0AI/+EY/4qX+1 Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fv G3E7yCgYcAIeaP7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/APX/ALnfny/4cb+ax9P8U6zPoN5c SXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDigDuNF0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJw u52wCSQMAknk6FY/he+uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtigArP/sLR/wC2 P7X/ALKsf7T/AOf37Onnfd2/fxu+7x16cVoVycmqzxfEOHT4NYjuoJUxcaeskUj2p8ssGaNUDxoc J+8aRhmQLs+ZWUAseFPCp8OvqE811HcXF26BfJSSOKCFFwkMcbyPsRWaRgAQBvIAAAA6SuP8C63q Osfb/t9x5+zy5BhFHkM+7dA20Dy5E2jdC29o9wzLJuG3oNT1G6sPK+zaNfalvzu+yPAvl4xjPmyJ 1z2z0OccZAC50z7TrlhqLzZjs4plWArkGR9gEgOeGVVkUHGcSsMgEg0/DWj6lo9vPHqOo2l/JK4k a4is2hklkxhnkJkcMSAoAAUKFCgBQoGXceKEs/iVb6Nc+IdKW1mtJQtjlUmSfdbiNXYuSzMHkKqF XIzw2ARH4E8Q6hrlxqSXVzHeQQJCftcLwyQNOxk81IXiJHlKFjKiT96A+X6rQBsa/o+papcWEllq NpBHauZHt7uzaeOWTKmNyFkQ5QgkAkjJDY3KpFc+FTL43TxBNdR+RCjGG0iSRMzFBH50h8wo7hN6 A7AdrgEnauK/jjXhodvZldetNMuZ3ZbaK4MarcSgAjzHfhYFGS+0BzlQrBiFevHrWpv8SptNnnkj s0fZb2sDR7pE8gOZ5UaMv5W8vH5qyAb1RNudxIBc8FeEP+EQ042v2qCX91FDi1tfs8beWCPNZNzZ mbPzvn5gqDA289RXJ+DdeGt3GqCHXrTWLaB0USxGMMsuXEm1E5WDKgR7yXO1zuddjHrKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhXN6D4Xm0G3nWG/jknWyh0+ 0d7c7Y4YA/k+YofLvmRtxBQNxhU7gGpqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZIyKP7b 07+2P7K+0f6X027G2btu/wAvfjb5mz59md235sbeay9e8Lza9bwLNfxxztZTafduludskM4TzvLU vlHzGu0kuF5yr9pP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLQBoaZrenaz5v2 C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCuf8NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MN oPGFXBz0FAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJD KynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k3ml6Zb2tr eweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaALH9t6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8zZ8+ zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxqv8A8Ix/xUv9qfbP9H+1/b/I 8r5/tH2f7NnfnHl+X/Dtzu53Y+Ws+08C/ZdMe1Oo7pIorK3s5PIwI47OQyW/mLu/eNuJ3kFAw4AQ 80AdRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFWKz9F0z+yNMFqZvOkaWW4lkC7Q0ksjS PtXJwu52wCSQMAknk6FAGPF4o0mWC+nWacQ2Mpglka0lVWkDmPZGSv71t6lcJuJbA6kZ0LG+t9Rs 47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIrm4/CN9bWGrR2msx213fumZobPy1IVyXd0R1zPIrMry oYycIVCFRWxaWWpWml2FpFc6bG8Dqsvk2DJEYRkbI4/N/dnbtAJLAYPy84ABJ/benf2x/ZX2j/S+ m3Y2zdt3+Xvxt8zZ8+zO7b82NvNGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBqnHo+pJ 4sm1Z9RtJbR08uOCSzYywR7RlEkEm0BnG9jsJbgEkKm2TRtGuNOvL67vNQ+23Fz5aeYYQh8uMELu 5OW5JO3amSSsaFmLAGxVe/vrfTNOub+8k8u1tYnmmfaTtRQSxwOTgA9KsVj+KND/AOEj0GXTPMgT fLDLm4g8+NvLlSTa6bl3K2zBGRwaANCxvYtQs47qFJ0jfOBPA8LjBI5RwGHTuOevSq/9t6d/bH9l faP9L6bdjbN23f5e/G3zNnz7M7tvzY281X07SbzSdHsbCzvYP3Mu6YyWxZDGWLNHEoceUoyFQEsE VQMNjNV/+EY/4q/+3ftnvt8r97/q/L8rzM/8e/8Ay08rb/rfn3dqANDTdastXlu47Jp3+yytDI72 0kaF1ZkYK7KFfDIwO0nGPcVoVh6B4fbRbi/ne6jme7cFhDbrArEFj5rqpw077vnkAUNtTCrtrcoA r319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpA IIIIBFV9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkV9P0m80vTLe1tb2DzPtclzdy S2xYSebI8kqxqHGzLOdpJfaMA7jzQBY/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8P inRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/O PL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5oA6ixvrfUbOO 6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6Lpn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgY BJPJ0KACs/8AtvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mtCsOPR9STxZNqz6jaS2jp5c cElmxlgj2jKJIJNoDON7HYS3AJIVNoBc03WrLV5buOyad/ssrQyO9tJGhdWZGCuyhXwyMDtJxj3F aFcv4K8If8IhpxtftUEv7qKHFra/Z428sEeaybmzM2fnfPzBUGBt56igArL0rxDpmtXFxBYTySvA iSMWgkRWRywR0ZlAkRtjYZSQccGpP7C0f+2P7X/sqx/tP/n9+zp533dv38bvu8denFZfhTwqfDr6 hPNdR3FxdugXyUkjighRcJDHG8j7EVmkYAEAbyAAAAADU1PW9O0byvt9x5Xm5IwjPtVcbpG2g7I1 yNzthVyMkZFH9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY281T1/R9S1S4sJLLUbSCO1cyP b3dm08csmVMbkLIhyhBIBJGSGxuVSCPR9STxZNqz6jaS2jp5ccElmxlgj2jKJIJNoDON7HYS3AJI VNoBc0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NaFc/4a8Mf8I/u3Xn2jZaW9hBiLZtt 4N/lhuTuk/eNuYbQeMKuDnoKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACio554bW3luLiWOGCJC8kkjBVRQMkkngADnNZa+KNJl0ePVrWae9sZZXhSWxtJb ncysykgRqx25RhuxtPGDyMgGxRWe2uaWk+mQ/b4Gk1TcbEI+77QAhkLLjqoUZ3dORzkjJa63p17q M9hb3G+4h3ZGxgr7TtfYxG19rEK20naxAbBOKANCiiigAooooAKKKKACuL8HeI7u9t7y41W+jaCG yt7q6kkCItlcsJDcWxIACiIIh2vl13/MxyMdpWfpmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKt g4JwaAOb8Y+I7uyt7O40q+jWCayuLq1kjCOt7cqIzb2wJBDCUO52ph22fKwwcyf23qP/AAnv9nfa Pk+1+R9h2L/x6fZfN+1dN/8Ar/3O/Pl/w43810Gp63p2jeV9vuPK83JGEZ9qrjdI20HZGuRudsKu RkjIo/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mgDn/A+t6jrHn/AG24+0YtLaeb5FX7 Ldv5nn2vygY8vbH8j5kXf8xORjsKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQBj +KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKr6FriNoNncarfwI1xdy2trPM6x/bA JXWFl6BmkRVcbRht2VAGBWxfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1aye ZC+QCVKkEEhlZTgqwIIKkAggggEUAcv/AG3qP/Ce/wBnfaPk+1+R9h2L/wAen2XzftXTf/r/ANzv z5f8ON/NY+n+KdZn0G8uJLzcwisHup/KT/iWyzSlbuLgYX7OgD4kDMmcyFhxXcf23p39sf2V9o/0 vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2jXGZFc8IyBg54UsaADwv fXGo6DFcXMnnMZZkjn2gefEsrrFLxgHeio+VAU7sqACBWxVexvrfUbOO6tZPMhfIBKlSCCQyspwV YEEFSAQQQQCKsUAcPoevmWDxFNqviLZaxynyr0tDHDZI7uI4yXiUpcINgeOXfglP7xWtDwn4rsNT 8G6Hf3mtWMl1dRW8Ez+fGN120alosA4EhJPyDn2rYg1zS7mLUZYr+BodNleG8lL4SF0UM4Zjx8oY Z545B5BAsWF9b6np1tf2cnmWt1Ek0L7SNyMAVODyMgjrQBzd54nsrP4jWOkyeILREmspVksHmiBW 48yDyu28O6yPhScEAEDgmqfw48Q3viSzvL69vftPnbLiNIJI5YLZJC7LBvWNG85F2h1Ytj5GyN5A 6D/hKNJ/t7+xPOn+3eb5GPskvl+Z5XnbPN27N3l/NjdnFWNM1vTtZ837Bceb5WCcoyblbO2RdwG+ NsHa65VsHBODQBoVz/jbVrjRPCF9e2c0EF0fLghuLiQJHA8sixLK5II2oXDHIPC10FV76+t9Os5L q6k8uFMAkKWJJICqqjJZiSAFAJJIABJoAx9B1qAeHNOuNS1LEl1KbeOa8liBuZC7BfLZVRZFbGYy qgum1sZJqvc/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaWNwdwG3cOw3dBY3sWoWcd1Ck6Rv nAngeFxgkco4DDp3HPXpVf8AtqyGsf2W7Tx3R4Qy20iRyHbu2pIVCO23J2qxOFbj5TgA5fwBrmpa 9eaxLc3/ANttIPJhEsb27wfasM0wgaL5mh2vDt8wluoPzBhXcVn6ZrVlrHmmxaeSOPBEzW0kccgO cNG7KFkU4zuQkYIOcEZ0KAMfxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARVfQtcR tBs7jVb+BGuLuW1tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwK2L6+t9Os5Lq6k8uFMAkKWJJICqqjJZi SAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAigDl/7b1H/AIT3+zvtHyfa/I+w 7F/49Psvm/aum/8A1/7nfny/4cb+ax9P8U6zPoN5cSXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM 5kLDiu4/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcR tGuMyK54RkDBzwpY0AHhe+uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtiq9jfW+o2c d1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYoAK5O88T2Vn8RrHSZPEFoiTWUqyWDzRArceZB5XbeH dZHwpOCACBwTXWUUAcv4T1a41K81ON9S/tC3h8rEpgERSZgxkjCgfIq/IBHITMh3b8goT1FZ+ma3 p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoAz/ADtY/tjyvsNj/Zn/AD8fbH877v8Azy8r b97j7/Tn2rl/AGualr15rEtzf/bbSDyYRLG9u8H2rDNMIGi+Zodrw7fMJbqD8wYV1H9t6d/bH9lf aP8AS+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBo Aw/GWvDSLjS7ca9aaTJcO7q10Y0jYIU3F3k4KANgxpiRyy4eNQ7AvPE9lZ/Eax0mTxBaIk1lKslg 80QK3HmQeV23h3WR8KTggAgcE1ualrml6PLaRahfwW815KsNtE7/ADzOzKoCr1bl1zgcZycDmj+2 rIax/ZbtPHdHhDLbSJHIdu7akhUI7bcnarE4VuPlOADk/CPinVdR8a6to+sRyW1xHZQXX2FpbZha sWcMi+W7OwKmI7m75bEYdFPeVn6ZrVlrHmmxaeSOPBEzW0kccgOcNG7KFkU4zuQkYIOcEZ0KACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI5xM1vKtvJHHOUI jeRC6q2OCVBBIz2yM+ork4PCmsLoN1pN1qOh3sNxdyXJS60Z5Iz5krzOrIbj5vndSpzwF53E5HUX 832fTrmf7TBa+XE7+fcDMcWATucZX5R1PI4HUda4Ox8TSat4IvNRHiyNp9IuLyfUJdIigeSS3jec RqiPvVAyqjKWySF6nJagDQuvh9G+t6Hqltql2LjTnhNxLdTzzPdrEhRQcSqinDyEnaclzkYZ1fQ0 zwqbHxVda5LdRsXSWOC3t0kjjjWV1dyytI67yUUlkWPcSxYMSMc/e+KfEOh+IvCGjarHGEuXjt7u 8EtuBfzGBg5jVnVlRZDGfuAsTtGPlWSQa7rt1da5a2t7i7W01E+T9nD/ANnyRShLQ7FQv+9jJkww cvtyg2/KQD0Ciub8FahPqOjTSXF/JfulwyC6EsU0Mg2qf3MkcUYdBnBOwEOHXnaK6SgAooooAKKK KACub0HwvNoNvOsN/HJOtlDp9o7252xwwB/J8xQ+XfMjbiCgbjCp36SuL8HeI7u9t7y41W+jaCGy t7q6kkCItlcsJDcWxIACiIIh2vl13/MxyMAGp4l8Mf8ACQbdt59n32lxYT5i37refZ5gXkbZP3a7 WO4DnKtkYP8AhGP+Kl/tT7Z/o/2v7f5HlfP9o+z/AGbO/OPL8v8Ah253c7sfLWf441vUdH8j7Fcf Z82lzPD8it9qu08vyLX5gc+Zuk+RMSNs+UjByf23qP8Awnv9nfaPk+1+R9h2L/x6fZfN+1dN/wDr /wBzvz5f8ON/NAGh4a8Mf8I/u3Xn2jZaW9hBiLZtt4N/lhuTuk/eNuYbQeMKuDnoK4/wPreo6x5/ 224+0YtLaeb5FX7Ldv5nn2vygY8vbH8j5kXf8xORjsKAM/WtM/tfTDaibyZFliuIpCu4LJFIsibl yMruRcgEEjIBB5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd2FGDkndUfii+uNO0GW4tp PJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgky oAIJYAL0AI/+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLWfaeBfsumPanUd0kUV lb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIeaP7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/APX/ALnf ny/4cb+ax9P8U6zPoN5cSXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDigDuNF0z+yNMFqZvO kaWW4lkC7Q0ksjSPtXJwu52wCSQMAknk3J4VubeWBzIEkQoxjkZGAIxwykFT7ggjtWX4XvrjUdBi uLmTzmMsyRz7QPPiWV1il4wDvRUfKgKd2VABArYoA4Ow+GdtY2Wr2X22RrS5uIrixhWa7QWjRIEi DEXGXC7YzwU5UdAqBOg8M6Hc+HPDWk6OL6O4FinlSStE+ZUAbaF3SMUIJXuRgEBVBG3l7XxHqA0T X3ttcj1OK3eEW+pRvDOiK7lWLyxxpGhRQHcGN/KBDkyghB1HhrVWu/D+nzX9zG1xcO8UcjbV+0FS +GQjAkDIhcMoUOvzhUB2gAuXOmfadcsNRebMdnFMqwFcgyPsAkBzwyqsig4ziVhkAkHP8MeGP+Ec +1f6Z5/nbB8sXl79uf3svJ8y4fd+8l437V+UY5y5PEOoL8SodGiuY7iF3xLbwPC628HkF98q585J TKFXJHlbHQffarHg3Xhrdxqgh1601i2gdFEsRjDLLlxJtROVgyoEe8lztc7nXYxAOsrL8Q6JD4h0 SbTZ/L2O8cgEsYkQtG6yKHQ43IWUBlyMgkZGcjUrD8Xz31t4YuptOvY7K4R4j9ok4WNPMXzCSY5A o2bvnKEL944AJABY0jSptI0uysYrmN0hdjLmIhdrbjsiXd+7RWZQoO7aihefvCvJoM0/iyHWZbi0 VIE2RCC0KXDqVI8uWbed8W5mfYFX5ghz8vJ4a1IXHh/T5bu8kknuHeOOS4ePNwwLnMZRUV0KoWRg o3RgNgc1h3niXUrf4hDS7dvt6fMXsrSS3YxQi3MgZ1LCVJjKFUM2IdkiDIc5oA1PCPhGHwlby29v NGYCkcUccMAiXagIEjgEh52Bw8ny7tqfKNvPSVxfgLxTqHiK68Q2+qRxw3djeqn2dJYXFurRJ+6z G7FiriQFzjJzgKQyJ2lAGfrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwg tkuY5Cbia4unaIjzGld5HCDd8g8x8jO7CjByTuqPxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO +WBUbcsCARUfhvVWu9EtJb65jaSe4ngtpW2qbtEeTy5FxgMXiQSZUAEEsAF6AEf/AAjH/FS/2p9s /wBH+1/b/I8r5/tH2f7NnfnHl+X/AA7c7ud2PlrPtPAv2XTHtTqO6SKKyt7OTyMCOOzkMlv5i7v3 jbid5BQMOAEPNH9t6j/wnv8AZ32j5PtfkfYdi/8AHp9l837V03/6/wDc78+X/DjfzWPp/inWZ9Bv LiS83MIrB7qfyk/4lss0pW7i4GF+zoA+JAzJnMhYcUAdxoumf2RpgtTN50jSy3EsgXaGklkaR9q5 OF3O2ASSBgEk8nQrH8L31xqOgxXFzJ5zGWZI59oHnxLK6xS8YB3oqPlQFO7KgAgVsUAFFFZ/9o3X 9sfYv7Gvvs//AD/74PJ+7np5nmdfl+519uaAMvwv4VPh+4vbqW6jmnukiiCQJJHBDFGXKLGjySFA DI3yqwQDaAowSdTU9GtdX8r7TLfR+Vnb9kv57bOcZz5Tru6d845x1Nc34E8Q6hrlxqSXVzHeQQJC ftcLwyQNOxk81IXiJHlKFjKiT96A+X6rXSanqN1YeV9m0a+1Lfnd9keBfLxjGfNkTrntnoc44yAZ /wDwjH/FX/279s99vlfvf9X5fleZn/j3/wCWnlbf9b8+7tUfhfwqfD9xe3Ut1HNPdJFEEgSSOCGK MuUWNHkkKAGRvlVggG0BRgk58+v6nD4+lsUaSe3RCq6fGsYldRD5nmhWwdhf935xkCb/AN2Ywf3t HgLxTqHiK68Q2+qRxw3djeqn2dJYXFurRJ+6zG7FiriQFzjJzgKQyIAbHibR9S1q1t7ew1G0s0ju IrhzPZtOWaKVJUAxImBuTB65B4x1qNNAvJfEdrrF7d2Jkt4gga0sTDNL8hDI8hkbdDuZnEeBhghy SuWp+ONeGh29mV1600y5ndltorgxqtxKACPMd+FgUZL7QHOVCsGIV47jxQln8SrfRrnxDpS2s1pK FscqkyT7rcRq7FyWZg8hVQq5GeGwCAC54R8Iw+EreW3t5ozAUjijjhgES7UBAkcAkPOwOHk+XdtT 5Rt56SuL+H2tanrNvdy6tPJJd7InliRo2gtXYNugGI0dJUIw8TlygMfzEsa7SgAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVzeg+F5tBt51hv45J1sodPtHe3O2O GAP5PmKHy75kbcQUDcYVO4BqanrenaN5X2+48rzckYRn2quN0jbQdka5G52wq5GSMij+29O/tj+y vtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG3msvXvC82vW8CzX8cc7WU2n3bpbnbJDOE87y1L5R8xrt JLhecq/aT/hGP+Kl/tT7Z/o/2v7f5HlfP9o+z/Zs7848vy/4dud3O7Hy0AaGma3p2s+b9guPN8rB OUZNytnbIu4DfG2Dtdcq2DgnBrQrn/DXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtzDaDxhVwc 9BQBXvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYE EFSAQQQQCKr61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIr6fpN5pemW9ra3sHmfa5 Lm7kltiwk82R5JVjUONmWc7SS+0YB3HmgCx/benf2x/ZX2j/AEvpt2Ns3bd/l78bfM2fPszu2/Nj bzVeHxTo09nc3Ud5mG32lj5TguHOI2jXGZFc8IyBg54Usar/APCMf8VL/an2z/R/tf2/yPK+f7R9 n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTqO6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNAHUWN 9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLu dsAkkDAJJ5OhQBjxeKdGm06+v0vM2tjEZ53MTj90ASJVBGXjIVtrrlW2naTg1oWN7FqFnHdQpOkb 5wJ4HhcYJHKOAw6dxz16Vhx+Gbv7PqwudUjubm/RIvNls0ZTEgICyoSQ4YFg6qUQ7mKLGzMxuadp N5pOj2NhZ3sH7mXdMZLYshjLFmjiUOPKUZCoCWCKoGGxmgC4dUtF1lNJZ5BePbtcopifa0asFYh8 bSQWXK5yNwOMGq+leIdM1q4uILCeSV4ESRi0EiKyOWCOjMoEiNsbDKSDjg1Tu9G1ifxba6xFqljH a20T262z2Ds5jkaJpMyecBuzCMHbgZ5DVH4U8Knw6+oTzXUdxcXboF8lJI4oIUXCQxxvI+xFZpGA BAG8gAAAAA6Sq9/fW+madc395J5draxPNM+0naigljgcnAB6VYqnqtk2o6XcWaSxxmZNuZYVlRh3 V0bhkYfKwyCQTgqcEAEljexahZx3UKTpG+cCeB4XGCRyjgMOncc9elV31qyTXF0bdO180QmKpbSM iId4UtIF2JkxuBuIzjiq+naTeaTo9jYWd7B+5l3TGS2LIYyxZo4lDjylGQqAlgiqBhsZrPt/CHke NrvxF9qgHnyibbFa7JziBYfKebcd8Py79m0fPtOfl5ANjTdastXlu47Jp3+yytDI720kaF1ZkYK7 KFfDIwO0nGPcVoVy/grwh/wiGnG1+1QS/uoocWtr9njbywR5rJubMzZ+d8/MFQYG3nqKAK99fW+n Wcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARV fWtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z7XJc3cktsWEnm yPJKsahxsyznaSX2jAO480AWP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qvD4p0aezu bqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNV/+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h2 53c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIeaAOosb631GzjurWTzIX yASpUggkMrKcFWBBBUgEEEEAirFZ+i6Z/ZGmC1M3nSNLLcSyBdoaSWRpH2rk4Xc7YBJIGASTydCg AqmdUtF1lNJZ5BePbtcopifa0asFYh8bSQWXK5yNwOMGrlc/d6NrE/i211iLVLGO1tont1tnsHZz HI0TSZk84DdmEYO3AzyGoA0NM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhWPo2jXGnX l9d3mofbbi58tPMMIQ+XGCF3cnLcknbtTJJWNCzFrGp6Fo+t+V/a2lWN/wCTny/tdukuzOM43A4z gdPQUAH9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY280WWt6dqOo3thaXHm3Flt88BGCjcW UYYja3zRup2k4ZGBwQRWf/wjH/FX/wBu/bPfb5X73/V+X5XmZ/49/wDlp5W3/W/Pu7VT8P8AgiPw 54ludQsr2T+z3sorOCyeSeQwrGSV+eSZgR8zADYNoI24y28A3NT1vTtG8r7fceV5uSMIz7VXG6Rt oOyNcjc7YVcjJGRUh1S0XWU0lnkF49u1yimJ9rRqwViHxtJBZcrnI3A4waz9f0fUtUuLCSy1G0gj tXMj293ZtPHLJlTG5CyIcoQSASRkhsblUiO70bWJ/FtrrEWqWMdrbRPbrbPYOzmORomkzJ5wG7MI wduBnkNQBoabrVlq8t3HZNO/2WVoZHe2kjQurMjBXZQr4ZGB2k4x7itCuX8FeEP+EQ042v2qCX91 FDi1tfs8beWCPNZNzZmbPzvn5gqDA289RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUVHPMttbyzuJCkaF2EcbOxAGeFUEsfYAk9qy/+Eo0n+zvtvnT7fN8 jyPskv2jzMbtnkbfM3bfnxtzs+b7vNAGxRWO3ijSf7Rt7GKae5muIop42tLSWePy5CwRzIilFUlW 5JAwCenNWLXW9OvdRnsLe433EO7I2MFfadr7GI2vtYhW2k7WIDYJxQBoUUUUAFFFFABRRRQAVxfg 7xHd3tveXGq30bQQ2VvdXUkgRFsrlhIbi2JAAURBEO18uu/5mORjtKz9M1vTtZ837Bceb5WCcoyb lbO2RdwG+NsHa65VsHBODQBzfjHxHd2VvZ3GlX0awTWVxdWskYR1vblRGbe2BIIYSh3O1MO2z5WG DmT+29R/4T3+zvtHyfa/I+w7F/49Psvm/aum/wD1/wC5358v+HG/mug1PW9O0byvt9x5Xm5IwjPt VcbpG2g7I1yNzthVyMkZFCa5pcmuNokd/BJqaRGaS1R9zxoNnLAfd/1iYzjOcjODQBh+DtUu9Svd aFzqMl8iXBaEoqCKCNnk2wkeWjpOihRJG+4r8hyNxA6ys/Sdc0vXop5dJv4L2GCXyZJYH3pv2q2A w4bh16E+nUEVoUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ3Gq3 8CNcXctrazzOsf2wCV1hZegZpEVXG0YbdlQBgVsX19b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQ ACTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFAHL/ANt6j/wnv9nfaPk+1+R9h2L/AMen 2XzftXTf/r/3O/Pl/wAON/NZ+heI9RvtHvpr7VvIVbS0mubv7Mr/ANnXEjOLi3wBhPKCof3gYx79 0hZRiuw/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcR tGuMyK54RkDBzwpY0AHha+uNS8OWl3dSea8m8rIVALxh2CM235SxQKSyfIxJKEoVNbFV7G9i1Czj uoUnSN84E8DwuMEjlHAYdO4569KsUAcP4b1S68V6PfnT/F0EjPLE8c9tHA80CFgW/d8iNXCsESQO 6Dl2Zsomp4Z1gf8ACJ6Te6xqsbPqT5tZrpo4nmWVmeBCFCqZfLKghR1BxnrVg+L9D+wXF6LuR4IH iU7LeVmfzHCRNGoXdIjscK6Aq2DgnBxqWN7FqFnHdQpOkb5wJ4HhcYJHKOAw6dxz16UAcfNrmpSf FK20S3v/ADIU3TXFrbvbtHHaiDrMD++WbzpI8YwpQqRkh6j8I+KdV1Hxrq2j6xHJbXEdlBdfYWlt mFqxZwyL5bs7AqYjubvlsRh0U9Z/benf2x/ZX2j/AEvpt2Ns3bd/l78bfM2fPszu2/NjbzRpmt6d rPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4JwaANCsvxJczWfhrUpra6jtLgW7iG5lUskEhGFd+Gw isQWJBAAJPANalRzzw2tvLcXEscMESF5JJGCqigZJJPAAHOaAMPQdagHhzTrjUtSxJdSm3jmvJYg bmQuwXy2VUWRWxmMqoLptbGSaz/7b1H/AIT3+zvtHyfa/I+w7F/49Psvm/aum/8A1/7nfny/4cb+ a6iwvrfU9Otr+zk8y1uokmhfaRuRgCpweRkEdar/ANt6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7 tvzY280Acn4R8U6rqPjXVtH1iOS2uI7KC6+wtLbMLVizhkXy3Z2BUxHc3fLYjDop7ysvRPEGn+Ib f7Rp32toNiOsk1lNAsisMqUMiKHBAzlc9R6itSgDH8UX1xp2gy3FtJ5LCWFJJ9oPkRNKiyy85A2I zvlgVG3LAgEVX0LXEbQbO41W/gRri7ltbWeZ1j+2ASusLL0DNIiq42jDbsqAMCti+vrfTrOS6upP LhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIoA5f+29R /wCE9/s77R8n2vyPsOxf+PT7L5v2rpv/ANf+5358v+HG/msfT/FOsz6DeXEl5uYRWD3U/lJ/xLZZ pSt3FwML9nQB8SBmTOZCw4ruP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qvD4p0aezu bqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNAB4XvrjUdBiuLmTzmMsyRz7QPPiWV1il4wDvRUfKgK d2VABArYqvY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFWKAM/U5tYi8r+ybGxus58z7XeP b7emMbYnz364xgdc8Yc+qXf/AAsi3sF1GQ2Ztwp0+JUDiTbIxmkVo9xgI8tRIjgCQBCCScdZWf8A 21ZHWP7LRp5LocOYraR44zt3bXkClEbbg7WYHDLx8wyAYfg7VLvUr3Whc6jJfIlwWhKKgigjZ5Ns JHlo6TooUSRvuK/IcjcQNzU5tYi8r+ybGxus58z7XePb7emMbYnz364xgdc8Gma3p2s+b9guPN8r BOUZNytnbIu4DfG2Dtdcq2DgnBrQoA5e5/taHx3YQprc72t151y1iYIhClvHEkZUNtMhkM0sbg7g Nu4dhuz/AABrmpa9eaxLc3/220g8mESxvbvB9qwzTCBovmaHa8O3zCW6g/MGFdB/wlGk/wBvf2J5 0/27zfIx9kl8vzPK87Z5u3Zu8v5sbs4qxpmtWWr+aLVp1kiwXhubaS3kUHOG2SKrbThgGxglWAOQ cAGP401a40qzgktdS+yXDbvs8RgDC6myoSNmIOV5JMaDznAPl8qQS5/taHx3YQprc72t151y1iYI hClvHEkZUNtMhkM0sbg7gNu4dhu2NT1qy0jyhdNO0kuSkNtbSXEjAYy2yNWbaMqC2MAsoJyRk/tv Tv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mgDl/hx4hvfElneX17e/afO2XEaQSRywWySF2W DesaN5yLtDqxbHyNkbyB3FZ+ma1Zax5psWnkjjwRM1tJHHIDnDRuyhZFOM7kJGCDnBGdCgAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCOcTNbyrbyRxzlCI3k QuqtjglQQSM9sjPqK5P/AIQf/inv7N+0WP8Ax9/avs32D/iX/c2eX9l3/wCr/wCWm3f/AK35/wDZ rsKKAOXm8GpJqOkXQvd8lhFDE11PAsl44iORtuBhl35IkBDBlJACFmJsaV4Y/szxDe6r9s8z7R5n yiLbJJvcP++fJ83ZjZFwuxCV+bOa6CigAooooAKKKKACiiigArm9B8LzaDbzrDfxyTrZQ6faO9ud scMAfyfMUPl3zI24goG4wqd+kri/B3iO7vbe8uNVvo2ghsre6upJAiLZXLCQ3FsSAAoiCIdr5dd/ zMcjABqeJfDH/CQbdt59n32lxYT5i37refZ5gXkbZP3a7WO4DnKtkYLvRtYn8W2usRapYx2ttE9u ts9g7OY5GiaTMnnAbswjB24GeQ1ZfjHxHd2VvZ3GlX0awTWVxdWskYR1vblRGbe2BIIYSh3O1MO2 z5WGDmxeeJ7Kz+I1jpMniC0RJrKVZLB5ogVuPMg8rtvDusj4UnBABA4JoAuaDo2saZqep3V/qljd x38ouHjgsHgKyCOOMYYzP8u2IcYzk5zjiugrk/CXiey1nVtfsofEFpqZgvS1qsc0TMLcwwsceWBu RZHddxyeMEkiusoAz9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkR6TpU2k2EFslzH ITcTXF07REeY0rvI4QbvkHmPkZ3YUYOSd1R+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKj blgQCKr6FriNoNncarfwI1xdy2trPM6x/bAJXWFl6BmkRVcbRht2VAGBQAf8Ix/xUv8Aan2z/R/t f2/yPK+f7R9n+zZ35x5fl/w7c7ud2PlrLT4fQnRLjTbm8juENvZ2luJLYFBDaOZIVlQsfMJZiHIK BhwAnWpP7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/8AX/ud+fL/AIcb+a59fGWsp4Q1W9N9Abr7 JZD7RcbIo9NvrmRopYXIUhVgJjYo4ZwD8xORgA9A0TTP7G0eCw87zfK3HIXYi7mLbEXJ2Rrnai5O 1QoycZrQrH8LXct94ctLiaee4kbeDNMUJlw7DerIiK0ZxlGCruQq2MmtigDk4/Cd9c2GrQ6zqGm6 jPfujiVtL+UBHLJG6PI4aIcAKuwjLNu3sXrY0jSptI0uysYrmN0hdjLmIhdrbjsiXd+7RWZQoO7a ihefvDk9K8US6n4S16/tPFFjcyCIm1n2oTZs6kLJKiBvKh3fMFkDMiKxkcncqdBoOtQDw5p1xqWp YkupTbxzXksQNzIXYL5bKqLIrYzGVUF02tjJNAB/wjH/ABV/9u/bPfb5X73/AFfl+V5mf+Pf/lp5 W3/W/Pu7VH4X8Knw/cXt1LdRzT3SRRBIEkjghijLlFjR5JCgBkb5VYIBtAUYJNP+29R/4WP/AGZ9 o/0T/V/ZNi79vk+Z5+3G7y9/yedv27v3flbv3lWPCerXGpXmpxvqX9oW8PlYlMAiKTMGMkYUD5FX 5AI5CZkO7fkFCQDqKy9f0ODxBpbWc7SKVcTQss0sYWVeUZvLdGYK2GxuHIByCARqVn63caja6PPN pNl9svhtEcOVHVgC3zMoO0EttLLu243DOQAU/DOh3Phzw1pOji+juBYp5UkrRPmVAG2hd0jFCCV7 kYBAVQRtj/4Rj/ir/wC3ftnvt8r97/q/L8rzM/8AHv8A8tPK2/63593asvwL4vt9V8MJc6jqcZkO pz2EU11PbBp28xvKUeS2wuU2jAxuwSAVIYyf23qP/Cx/7M+0f6J/q/smxd+3yfM8/bjd5e/5PO37 d37vyt37ygDQ8MeF08N/ahFLB5c2xVhtbVbaMBc4dkT5TM27DuoUMFQBVC10FcH4R8U6rqPjXVtH 1iOS2uI7KC6+wtLbMLVizhkXy3Z2BUxHc3fLYjDop7ygDP1rTP7X0w2om8mRZYriKQruCyRSLIm5 cjK7kXIBBIyAQeRHpOlTaTYQWyXMchNxNcXTtER5jSu8jhBu+QeY+RndhRg5J3VH4ovrjTtBluLa TyWEsKST7QfIiaVFll5yBsRnfLAqNuWBAIqvoWuI2g2dxqt/AjXF3La2s8zrH9sAldYWXoGaRFVx tGG3ZUAYFAB/wjH/ABUv9qfbP9H+1/b/ACPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTqO6 SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNH9t6j/wnv9nfaPk+1+R9h2L/AMen2XzftXTf/r/3 O/Pl/wAON/NY+n+KdZn0G8uJLzcwisHup/KT/iWyzSlbuLgYX7OgD4kDMmcyFhxQB3Gi6Z/ZGmC1 M3nSNLLcSyBdoaSWRpH2rk4Xc7YBJIGASTydCsfwvfXGo6DFcXMnnMZZkjn2gefEsrrFLxgHeio+ VAU7sqACBWxQAVzcfhGGHxnN4himjR5n82XbABM7eUIvLaXPMG1Vfyyv+sAbdxtrpK4eHxDe3XxS udK+27bG1224sbeSN5GfyPOaeZDHvWHEiIGVx86qCDv4ANjwx4Y/4Rz7V/pnn+dsHyxeXv25/ey8 nzLh937yXjftX5RjnQ1PRrXV/K+0y30flZ2/ZL+e2znGc+U67unfOOcdTXP+Bdb1HWPt/wBvuPP2 eXIMIo8hn3boG2geXIm0boW3tHuGZZNw29Bqc2sReV/ZNjY3Wc+Z9rvHt9vTGNsT579cYwOueAAu dM+065Yai82Y7OKZVgK5BkfYBIDnhlVZFBxnErDIBINPQNBm0m4v7q4uLR571w8iWNobaAsCxMhQ u5MrFvmfd8wVBj5cnDh8Q3t18UrnSvtu2xtdtuLG3kjeRn8jzmnmQx71hxIiBlcfOqgg7+LHgXW9 R1j7f9vuPP2eXIMIo8hn3boG2geXIm0boW3tHuGZZNw2gFzxR4Rh8S3FlcNNHDPapLEkjwCRolkK FpITkGOdTGpSTnbz8pzxYk0fUpvFkOqTajaSafAmILJrNt8TFSGdZPMxvOcZKHC5VcbnLY/xK13W NA8NXF3piSQxpbzSS3yNBmB1A8pdszAEOxwSA5wpULudWUt/EE+oeN9OWDUJP7PubJHGnxGJnV2R 5DNJ8jboCpjQSxS7fMAX5snABoeEfCMPhK3lt7eaMwFI4o44YBEu1AQJHAJDzsDh5Pl3bU+Ubeek rg/CPinVdR8a6to+sRyW1xHZQXX2FpbZhasWcMi+W7OwKmI7m75bEYdFPeUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV n6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCub0HwvNoNvOsN/HJOtlDp9o7252xwwB /J8xQ+XfMjbiCgbjCp3ANTU9b07RvK+33HlebkjCM+1VxukbaDsjXI3O2FXIyRkVIdUtF1lNJZ5B ePbtcopifa0asFYh8bSQWXK5yNwOMGsfXvC82vW8CzX8cc7WU2n3bpbnbJDOE87y1L5R8xrtJLhe cq/aS70bWJ/FtrrEWqWMdrbRPbrbPYOzmORomkzJ5wG7MIwduBnkNQBoaZrenaz5v2C483ysE5Rk 3K2dsi7gN8bYO11yrYOCcGtCsPw1o+paPbzx6jqNpfySuJGuIrNoZJZMYZ5CZHDEgKAAFChQoAUK BuUAV76+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWB BBUgEEEEAiq+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQCDyK+n6TeaXplva2t7B5n2u S5u5JbYsJPNkeSVY1DjZlnO0kvtGAdx5oAsf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/Njb zVeLxTo02nX1+l5m1sYjPO5icfugCRKoIy8ZCttdcq207ScGq/8AwjH/ABUv9qfbP9H+1/b/ACPK +f7R9n+zZ35x5fl/w7c7ud2PlrHf4ebtBn0xtQgl/dWNrAZ7PfGLe0l8yNJU3jzGbLByCoIIwq4O QDsLG9i1CzjuoUnSN84E8DwuMEjlHAYdO4569KsVn6Jpn9jaPBYed5vlbjkLsRdzFtiLk7I1ztRc naoUZOM1oUAY8XinRptOvr9LzNrYxGedzE4/dAEiVQRl4yFba65Vtp2k4NaFjexahZx3UKTpG+cC eB4XGCRyjgMOncc9elc/p/hrVINDv9Pv9Vsbua7wz3A03Bmc8OZ1eRxIrAKhVdmEG1doC7dDTtJv NJ0exsLO9g/cy7pjJbFkMZYs0cShx5SjIVASwRVAw2M0AWH1qyTXF0bdO180QmKpbSMiId4UtIF2 JkxuBuIzjijTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1j2/hDyPG134i+1QDz5RNtit dk5xAsPlPNuO+H5d+zaPn2nPy86GjaNcadeX13eah9tuLny08wwhD5cYIXdyctySdu1MklY0LMWA Nio554bW3luLiWOGCJC8kkjBVRQMkkngADnNSVT1TS7TWbBrK9SRoGdH/dyvEwZHDqQyEMCGUHII 6UAGl6paazYLe2TyNAzun7yJ4mDI5RgVcBgQykYIHSo/7b07+2P7K+0f6X027G2btu/y9+NvmbPn 2Z3bfmxt5rH8M+GdT8NaOLGPV4Lhn1CS6lkmgmfdG7FmRd87FWyThskdyrMWY2P+EY/4q/8At37Z 77fK/e/6vy/K8zP/AB7/APLTytv+t+fd2oA0NM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHB ODWhWPo2jXGnXl9d3mofbbi58tPMMIQ+XGCF3cnLcknbtTJJWNCzFtigCvfX1vp1nJdXUnlwpgEh SxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w2om8 mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vYPM+1yXN3JLbFhJ5sjySrGocbMs52 kl9owDuPNAFj+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbearw+KdGns7m6jvMw2+0sfKc Fw5xG0a4zIrnhGQMHPCljVf/AIRj/ipf7U+2f6P9r+3+R5Xz/aPs/wBmzvzjy/L/AIdud3O7Hy1n 2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmgDqLG+t9Rs47q1k8yF8gEqVIIJD KynBVgQQVIBBBBAIqxWfoumf2RpgtTN50jSy3EsgXaGklkaR9q5OF3O2ASSBgEk8nQoAKz31qyTX F0bdO180QmKpbSMiId4UtIF2JkxuBuIzjitCuXt/CHkeNrvxF9qgHnyibbFa7JziBYfKebcd8Py7 9m0fPtOfl5ANjTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/hjwx/wjn2r/TPP87Y Pli8vftz+9l5PmXD7v3kvG/avyjHOhqehaPrflf2tpVjf+Tny/tdukuzOM43A4zgdPQUAV/+Eo0n +3v7E86f7d5vkY+yS+X5nleds83bs3eX82N2cVY0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlW wcE4NFzpn2nXLDUXmzHZxTKsBXIMj7AJAc8MqrIoOM4lYZAJBr6No1xp15fXd5qH224ufLTzDCEP lxghd3Jy3JJ27UySVjQsxYAsatrVlokUEl60/wDpEvkwpBbSTu77WfASNWb7qMenaj+29O/tj+yv tH+l9Nuxtm7bv8vfjb5mz59md235sbeap+JfD7eIbeCAXUcCI5JL26yMhIwJYWyDFOvOyQEhdxyr cYkm0a4uPEdtqUuob7W23PDamEZSRk2HDZxtxkj5d4LMBIEZkIBYstb07UdRvbC0uPNuLLb54CMF G4sowxG1vmjdTtJwyMDggitCuT8P+CI/DniW51CyvZP7Peyis4LJ5J5DCsZJX55JmBHzMANg2gjb jLb+soAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAz9da8 Tw9qbadL5V8LSU28nlmTZJsO07QrFsHHAViemD0rj7bU7+fwReapJ4jvhHpsstxc3lmLW6FzCkJY pbyeSkbLkjLFMh0dM4GR6BRQBweo+JLvw5qPhOy13xPo0LyIqapE+xHkYW8xMu9mAWIyooGEXnA3 c7a7yiigAooooAKKKKACiiigAri/B3iO7vbe8uNVvo2ghsre6upJAiLZXLCQ3FsSAAoiCIdr5dd/ zMcjHaVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGgDD8carPpNvZzWesR2l3vYpZNJ FuvQACVSNkZ5nztAjRoy2/G9TgiS5/taHx3YQprc72t151y1iYIhClvHEkZUNtMhkM0sbg7gNu4d hu2NW1qy0SKCS9af/SJfJhSC2knd32s+Akas33UY9O1RyeIdMi1uHR2nkN5K/lqFgkZA+wybGkC7 FfYpbaSDjBxgjIBh+C/FCa1fa1ZT+IdK1S6t7sGD7AVRTB5MJLKgdztDuwLFj82RkcAdhWfputWW ry3cdk07/ZZWhkd7aSNC6syMFdlCvhkYHaTjHuK0KAMfxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzk DYjO+WBUbcsCARUfhvVWu9EtJb65jaSe4ngtpW2qbtEeTy5FxgMXiQSZUAEEsAF6al9fW+nWcl1d SeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARQBy9x4 oSz+JVvo1z4h0pbWa0lC2OVSZJ91uI1di5LMweQqoVcjPDYBGfp/iUx6P4gnuvFcEum2ex01lTDc qiMzAqHjREM21VxHsba0iEmUOEHYf23p39sf2V9o/wBL6bdjbN23f5e/G3zNnz7M7tvzY281Xh8U 6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGgA8LX8up+HLS8mu4LtpN+2aGRJAyh2C7mj+Qy BQA+z5d4bbxitiq9jfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYoA8zi8b3c+jeJEtPEOj anqVrewrBJaMkccdqy2wkmAzKVRDJKTI29VKkkEDbXYeGtSFx4f0+W7vJJJ7h3jjkuHjzcMC5zGU VFdCqFkYKN0YDYHNXINb064i1GUXHlx6bK8N286NCImVQ5JLgfLtZW3D5SDkHFSaXqlprNgt7ZPI 0DO6fvIniYMjlGBVwGBDKRggdKAOXk8Q6gvxKh0aK5juIXfEtvA8LrbweQX3yrnzklMoVckeVsdB 99qPAniHUNcuNSS6uY7yCBIT9rheGSBp2MnmpC8RI8pQsZUSfvQHy/Va3P8AhKNJ/t7+xPOn+3eb 5GPskvl+Z5XnbPN27N3l/NjdnFWNM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODQBoVl+I 7gWvh+8nOqR6YUQFbuSaOJUbI2gvIjqoY4XJVvvcDOK1Kr319b6dZyXV1J5cKYBIUsSSQFVVGSzE kAKASSQACTQBl+GtVa78P6fNf3MbXFw7xRyNtX7QVL4ZCMCQMiFwyhQ6/OFQHaMeTxDqC/EqHRor mO4hd8S28DwutvB5BffKufOSUyhVyR5Wx0H32rrLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBB BAIqu+tWSa4ujbp2vmiExVLaRkRDvClpAuxMmNwNxGccUAc/4H1vUdY8/wC23H2jFpbTzfIq/Zbt /M8+1+UDHl7Y/kfMi7/mJyMdhWfpmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwa0KAMfxR fXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARUfhvVWu9EtJb65jaSe4ngtpW2qbtEeTy 5FxgMXiQSZUAEEsAF6al9fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5 AJUqQQSGVlOCrAggqQCCCCARQBy/9t6j/wAJ7/Z32j5PtfkfYdi/8en2XzftXTf/AK/9zvz5f8ON /NY+n+KdZn0G8uJLzcwisHup/KT/AIlss0pW7i4GF+zoA+JAzJnMhYcV3H9t6d/bH9lfaP8AS+m3 Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxoAPC99ca joMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFV7G+t9Rs47q1k8yF8gEqVIIJDKynBVgQQ VIBBBBAIqxQBn6nqN1YeV9m0a+1Lfnd9keBfLxjGfNkTrntnoc44zzcepamnxKmsZ9Ukks3f/R7C Ax7o08gEvKjQh/K3hx5qykb2RMZ3AdpRQBx/gvxQmtX2tWU/iHStUure7Bg+wFUUweTCSyoHc7Q7 sCxY/NkZHAHQanqN1YeV9m0a+1Lfnd9keBfLxjGfNkTrntnoc44zHpXiHTNauLiCwnkleBEkYtBI isjlgjozKBIjbGwykg44NalAHH3HihLP4lW+jXPiHSltZrSULY5VJkn3W4jV2LkszB5CqhVyM8Ng ER+BPEOoa5cakl1cx3kECQn7XC8MkDTsZPNSF4iR5ShYyok/egPl+q12lZeleIdM1q4uILCeSV4E SRi0EiKyOWCOjMoEiNsbDKSDjg0AY/jjxC3h6LTJV1vTdOSW9gSWK6VfMniM8SyeWzOAoVGYsdrY Bz8uM1n23jS8uvibZaO0UcOl3dlPJaYuLdzdAGNkuAVkLbCBKFULkj5jn5hF2Go6paaUlu948iJP cR2yMsTuPMkbagbaDtBYgbjgZIGeRUf9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY280AYf g3Xhrdxqgh1601i2gdFEsRjDLLlxJtROVgyoEe8lztc7nXYx6ys/TdastXlu47Jp3+yytDI720ka F1ZkYK7KFfDIwO0nGPcVoUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFV7+b7Pp1zP9pgtfLid/PuBmOLAJ3OMr8o6nkcDqOtcHH4vt28FTapJ4ujFib3yb a+U2xu5lKgrG2QIY5Wc5+ZRtixvCNuZQD0SiuLvJ9ckfw3dx+II1fUXtIjb2MUT2krBXnuH8x1aQ o8SMqbSMEKSfmJHaUAFFFFABRRRQAUUUUAFc3oPhebQbedYb+OSdbKHT7R3tztjhgD+T5ih8u+ZG 3EFA3GFTv0lcX4O8R3d7b3lxqt9G0ENlb3V1JIERbK5YSG4tiQAFEQRDtfLrv+ZjkYALHiLwdN4j uNKuLi9tEns7eWGSQWRZg0hiJmtyXzBKpjyjfPt3d8c2D4VMvjdPEE11H5EKMYbSJJEzMUEfnSHz CjuE3oDsB2uASdq4z/HepanptxpstnqklnaFJvOihMaz3L5j2JD5kMiPKR5gWLKFyeDhTiNfFupD 4q2/h67tfsdjNaXBgjea3LzlShWY4kLhSBKoQLnjcc/MIgDc0Dw+2i3F/O91HM924LCG3WBWILHz XVThp33fPIAobamFXbW5XJ+DdVn1C41SFtYj1i0gdDb3sUkUysGLjaZIkjTeAqkx7SU3g73DgL1l AGfrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k3ml6Zb2treweZ9rkubuSW2LC TzZHklWNQ42ZZztJL7RgHceaPFF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFR+G9V a70S0lvrmNpJ7ieC2lbapu0R5PLkXGAxeJBJlQAQSwAXoASTaNcXHiO21KXUN9rbbnhtTCMpIybD hs424yR8u8FmAkCMyHHtPAv2XTHtTqO6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNSSa8B8Q4 dGh160eXZun01jGvlxeWWXb/AMtHnLDdwdgiB3KCUZ8/w3qOueJLC6g/tiS3nNvZzzXMdvEzWVy7 sbm0AK7QUVFXa4Z08zLFuBQB1mi6Z/ZGmC1M3nSNLLcSyBdoaSWRpH2rk4Xc7YBJIGASTydCsPwf c3d94TsL28upLp7pGuIppVRXMLsXiDhAFDiNkDbRjIOM9TuUAcna+Er2Z9VTXdRtL201G4ju3jtL aW0dJo1hVGWQTsQB5CnA5zznHFSeGfDOp+GtHFjHq8Fwz6hJdSyTQTPujdizIu+dirZJw2SO5VmL Mebi8X3eo6N4kgtPF2jT6laXsLQXNoUSNLXbbGRwMTFUBeVTIwdVOScBcL2HhrUhceH9Plu7ySSe 4d445Lh483DAucxlFRXQqhZGCjdGA2BzQBcudM+065Yai82Y7OKZVgK5BkfYBIDnhlVZFBxnErDI BINfRtGuNOvL67vNQ+23Fz5aeYYQh8uMELu5OW5JO3amSSsaFmLZeoSaxZ+M7UQ6pd3ME6T3f9mJ DAqeRFEiFFdgGMpmljYEuq4LA4wM1/AXinUPEV14ht9Ujjhu7G9VPs6SwuLdWiT91mN2LFXEgLnG TnAUhkQA7Sq99DcT2ckdpdfZbjgxymMOAQQcMp6qcYIBBwTgqcEWKy/EdwLXw/eTnVI9MKICt3JN HEqNkbQXkR1UMcLkq33uBnFAEenaTeaVo9jY2l7AWil33Mk1sW81WYs4XDghiW4dy7d23sSxz7fw h5Hja78RfaoB58om2xWuyc4gWHynm3HfD8u/ZtHz7Tn5ebnhrVWu/D+nzX9zG1xcO8UcjbV+0FS+ GQjAkDIhcMoUOvzhUB2inc/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaWNwdwG3cOw3AFzw1 o+paPbzx6jqNpfySuJGuIrNoZJZMYZ5CZHDEgKAAFChQoAUKBuVxfw+1LU763u4tW1STUruFIvNl QxmBHIbcgxDE8coIy8TglAY+ck12lAGfrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8 ivp+k3ml6Zb2treweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaPFF9cadoMtxbSeSwlhSSfaD 5ETSossvOQNiM75YFRtywIBFR+G9Va70S0lvrmNpJ7ieC2lbapu0R5PLkXGAxeJBJlQAQSwAXoAR /wDCMf8AFS/2p9s/0f7X9v8AI8r5/tH2f7NnfnHl+X/Dtzu53Y+Ws+08C/ZdMe1Oo7pIorK3s5PI wI47OQyW/mLu/eNuJ3kFAw4AQ80f23qP/Ce/2d9o+T7X5H2HYv8Ax6fZfN+1dN/+v/c78+X/AA43 81j6f4p1mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHFAHcaLpn9kaYLUzedI0stxL IF2hpJZGkfauThdztgEkgYBJPJ0Kx/C99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIF bFABWf8A2Fo/9sf2v/ZVj/af/P79nTzvu7fv43fd469OK0K4uPWtTf4lTabPPJHZo+y3tYGj3SJ5 Aczyo0Zfyt5ePzVkA3qibc7iQDQ8KeFT4dfUJ5rqO4uLt0C+SkkcUEKLhIY43kfYis0jAAgDeQAA AB0lcf4F1vUdY+3/AG+48/Z5cgwijyGfdugbaB5cibRuhbe0e4Zlk3Db0Gp6jdWHlfZtGvtS353f ZHgXy8YxnzZE657Z6HOOMgB/YWj/ANsf2v8A2VY/2n/z+/Z0877u37+N33eOvTisvwp4VPh19Qnm uo7i4u3QL5KSRxQQouEhjjeR9iKzSMACAN5AAAAGWvi3Uh8Vbfw9d2v2OxmtLgwRvNbl5ypQrMcS FwpAlUIFzxuOfmEUnw+1rU9Zt7uXVp5JLvZE8sSNG0Fq7Bt0AxGjpKhGHicuUBj+YljQBseJtH1L WrW3t7DUbSzSO4iuHM9m05ZopUlQDEiYG5MHrkHjHWhdFvpdbstQvtTjnjtULLAlttAmZAjFSWIC YyQCDICzASbGZDuVxcniHUF+JUOjRXMdxC74lt4HhdbeDyC++Vc+ckplCrkjytjoPvtQBc8FeEP+ EQ042v2qCX91FDi1tfs8beWCPNZNzZmbPzvn5gqDA289RXH+B9b1HWPP+23H2jFpbTzfIq/Zbt/M 8+1+UDHl7Y/kfMi7/mJyMdhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQBHPMttbyzuJCkaF2EcbOxAGeFUEsfYAk9q5u58e6PaeHItblE8cE93JZwxzhLd3k R5FYHzWRU/1Tn52U8YxuIWuknjaa3liSaSB3QqssYUshI+8NwIyOvII9Qa5u18Fra2fkDXtVkkju 3vbadxb77aZzIZGUCIKd/nSAhwwG75QpANAGhc+IEspbNLrT76FbjyhLKyKUtnlYJGjsGO5i52/u 9+3gsVUhjsVz58JwJeaNLaajfWlrpESw21lH5Tw7QNuT5iM24p8m4MGClgCNzZ6CgAooooAKKKKA CiiigArH0PxJZ6/5n2aOePEUdzH5ygedby7vKmXBOFbY+A2HG35lGRnYrD0rwvbaNb3EVld3avJb paQysULW0EYbyo0yuCE3sQXDMc/MW4oANf8AFNl4fuLC0mjknvL9yttbRyxRtJgqDgyuik5dBtB3 HdwCASI7fxba3fi+78NwWs73Vpgzy+dAFQGNXDbDJ5pX51XcExuOM9TVjXPDdnr/AJf2mSePEUlt J5LAedby7fNhbIOFbYmSuHG35WGTkvPDdnqGpi6vZJ7mAbmFjMweAO0ZiZ9pGeY2ZSmdh3E7dx3U ASaLr9j4gF6+ntI8VpcfZzKy4WU+WkgZD/EhWRcN0bqMggnUrn/Dng/TPC15qlxpo2LqMqyvCsEM aRbQQFTy0U7QOzE9z1ZiegoAp6pqUOk2DXcyyOA6RpHGAWkkdwiIMkDLMyrkkAZySBk0aXqUOrWC 3cKyIC7xvHIAGjkRyjocEjKsrLkEg4yCRg0appsOrWDWkzSIC6SJJGQGjkRw6OMgjKsqtggg4wQR kVHp+kpplnb21vczhY5ZJpWbaTcO5dnL/Lxl3L/JtwQAML8tAEcmuQw63Dpktrdp5z+VFdNGBE82 wyeWvO4nYrNuC7OCu7cNtZ+n+Mra6S6a+03UtJNvbwXJjvo03ukzOsYVI3dt5ZCuwgNkgAEnFXP7 BP8AwlH9uHVb4sIvJW0YQmFUxyFzHvXLYY4Ybiq5yFUCO98J6VqEOpxXlvHcDULgXR+0QxzCGUQp CHRXUqCFQHkHknscUAaGk6lDrOjWOqW6yLBe28dxGsgAYK6hgDgkZwfU1crL8OaFbeGfD9no1m8j 29ohSNpFQMRknnYqgnnrjJ6nJJJ1KAOXn8eaXBoep6ube+Fvp13HaSrND9mcu/lYOJim1f3y5L7e hPTBO5pWoDVdLt79YJIEnTeiPJG52nod0bMhBGCCGPBFY9l4Sks31Fz4j1mZ75xLIzGBCsyrGqyL siXkLEg2nKEZDK2TWpp+kpplnb21vczhY5ZJpWbaTcO5dnL/AC8Zdy/ybcEADC/LQBX/AOEks/7e /sny593m/ZvtG0eX9o8rzvJ67t3lfPnbsxxu3fLRofiSz1/zPs0c8eIo7mPzlA863l3eVMuCcK2x 8BsONvzKMjJ/wjdn/b39reZPu837T9n3Dy/tHleT53Tdu8r5MbtmOdu75qND8N2egeZ9mknkzFHb R+cwPk28W7yoVwBlV3vgtlzu+ZjgYANiqeq6gNK0u4v2gknSBN7okkaHaOp3SMqAAZJJYcA1cqvf W8t3ZyQQ3s9lI2MTwBC6YIPAdWXnpyD19eaAI9K1KHV9Lt9Qt1kWKdNyhwM/mCQw9GUlWGCpIIJz 4/FNlP4sm8O20ck13bpuuWWWICDKhhuRnEhGGT5lRly4GchsXNP0lNMs7e2t7mcLHLJNKzbSbh3L s5f5eMu5f5NuCABhflqv/wAI3Z/29/a3mT7vN+0/Z9w8v7R5Xk+d03bvK+TG7Zjnbu+agCv4T8W2 vjDTmv7G1nitRtCvLNA+4kZK4ikcqwBXKvtI3DjrjoKy9K0OHSri4uBdXd1PMiReZdSB2SFCxjjB ABYKXc7m3Odx3M3GNSgCnqmpQ6TYNdzLI4DpGkcYBaSR3CIgyQMszKuSQBnJIGTRpepQ6tYLdwrI gLvG8cgAaORHKOhwSMqysuQSDjIJGDRqmmw6tYNaTNIgLpIkkZAaORHDo4yCMqyq2CCDjBBGRUen 6SmmWdvbW9zOFjlkmlZtpNw7l2cv8vGXcv8AJtwQAML8tAFf/hJLP+3v7J8ufd5v2b7RtHl/aPK8 7yeu7d5Xz527Mcbt3y1Tt/Gum3NhNdpBdgKlvJBGyLuuo7hylu6fNgCRgVAcqV6uEHNXP+Ebs/7e /tbzJ93m/afs+4eX9o8ryfO6bt3lfJjdsxzt3fNVO38FabbWE1ok92QyW8cEjOu61jt3L26J8uCI 2JYFwxbo5ccUAbGl6lDq1gt3CsiAu8bxyABo5Eco6HBIyrKy5BIOMgkYNXKp6XpsOk2C2kLSOA7y PJIQWkkdy7ucADLMzNgAAZwABgVcoAKKKKAMfQ/Elnr/AJn2aOePEUdzH5ygedby7vKmXBOFbY+A 2HG35lGRnYrD0DwtZeH7i/u4ZJJ7y/cNc3MkUUbSYLEZESIpOXc7iNx3ckgAC5qehaPrflf2tpVj f+Tny/tdukuzOM43A4zgdPQUAV/+Eks/7e/sny593m/ZvtG0eX9o8rzvJ67t3lfPnbsxxu3fLVfw n4ttfGGnNf2NrPFajaFeWaB9xIyVxFI5VgCuVfaRuHHXEknhayn8WQ+IrmSSa7t022ytFEBBlSp2 uqCQjDP8rOy5cnGQuLGlaHDpVxcXAuru6nmRIvMupA7JChYxxggAsFLudzbnO47mbjAAaxr9joZs 0u2kaW8uI7eGKJdzEvIke4jsgaRMseBuA5LKDTt/Ftrd+L7vw3BazvdWmDPL50AVAY1cNsMnmlfn VdwTG44z1NR+KvA+jeLkiN/BHHcRuhF0ltA8u1GLBN0sb/JuOSMc8g8Eg3Lzw3Z6hqYur2Se5gG5 hYzMHgDtGYmfaRnmNmUpnYdxO3cd1AEmi6/Y+IBevp7SPFaXH2cysuFlPlpIGQ/xIVkXDdG6jIIJ 1K5/w54P0zwteapcaaNi6jKsrwrBDGkW0EBU8tFO0DsxPc9WYnoKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAK9+2zTrlvNnixE58y3j8yRODyi7W3MOw2tk 8YPSvO7y61CT4aarLY3PieyuJHmFhbLZXEt3ETD8kLvJG0hBbLGQHCs2xZMKCfTKKAOL1PVft2va I9kfEEJmdWEhsLpLeFBJh1kTywC8g+T97xGo8wFTjzO0oooAKKKKACiiigAooooAK4vwdNqttb3k +rDUikdlbvdCeOR2N8BIbrylIJZP9VtEQMZ/5Z967SsfQ/Elnr/mfZo548RR3MfnKB51vLu8qZcE 4Vtj4DYcbfmUZGQDH8df2x/oH9l/bht8xx9k3nfONvlRnZ93d8/7yTdCuD5iPlcSSXErfEOG3hk1 lAiZmkkt5jaOhjOIUIXygQ2JGlY7s4jBYMVTQ8R+KLbw1bmWa0u7t1t5rt4rUJuWCIL5kh3sowu9 BgEsdwwDg4k/4SSz/t7+yfLn3eb9m+0bR5f2jyvO8nru3eV8+duzHG7d8tAGX4NFwtxqirJqUuml 0a2e/S5jYEl9ybbktKSFEeXyI2z8qqQ+7rKz9M1ZNTn1KFbaeCTT7s2sgm2/MdiSBl2sflKyKRnB 55ANaFAGP4o+2f2DL9i8/d5sPnfZ8+Z9n81PP2bfm3eV5mNnz5+782Kj8NzXMWiWiX4uzJLcTpb+ dG7OIN8jQ+YSNynyVTJkw2cBvnODoapqUOk2DXcyyOA6RpHGAWkkdwiIMkDLMyrkkAZySBk0aXqU OrWC3cKyIC7xvHIAGjkRyjocEjKsrLkEg4yCRg0Acmt74nT4q28F5B/xJpbS4SIWxmeFBlCjyN5I TziUfgvgKcDB/wBbn2mo6nNpPiWziuPEd1M13HJbXV5p80D/AGby7dZCuIox8p84+XHtkbDbCGIe uwfxFAvi1fDiWs8l0bQXjyh4gkcZZ1BILhz8yYO1SBuXJGarjxdZ/wBk6zqMlnfQx6VE080ckQDs gj8wEDd8rFedj7XXK7lUMuQCx4W+2f8ACOWgvvPMw3gNPneyB2CMQ3zLlNp2uS65w7MwZjsVT0rU Bqul29+sEkCTpvRHkjc7T0O6NmQgjBBDHgirlAHnem37z6b4ms7678TzBrgS2l3c6ZeRSeX5UCja II4jgS7som0kBieCzV1Hhqa5i8P6fHqAuzPI7xo0sblmQFyjPkFowUUECQlhkK7M+S0kXiBJotVK 6ffGbTrv7I0CorPM5VGQptYjawlQ5YrtyS20AkXNL1KHVrBbuFZEBd43jkADRyI5R0OCRlWVlyCQ cZBIwaAOb/4nH/Cx93+nfYvubPn8nyPJz5m7/Vf635fLx52fm3+V8lHgvUZ5b7WrS5uNcus3Ymtb jU9Plt90PkwqQCYkQfvN/wAoAJwWwcknoH1ZI/EMOjNbTiSa0kuo5/l8tgjorL97duHmIfu4weuQ RVfQ/Elnr/mfZo548RR3MfnKB51vLu8qZcE4Vtj4DYcbfmUZGQDYrL8RiNvD94skl3GCgAe0Sd5F bI2kLARKRnGQpGRnJAzWpVe+uvsVnJcC3nuGXAWGBNzuxIAA7DJI5JCjqSACQAZfhqa5i8P6fHqA uzPI7xo0sblmQFyjPkFowUUECQlhkK7M+S2Gt74nT4q28F5B/wASaW0uEiFsZnhQZQo8jeSE84lH 4L4CnAwf9b1ml6lDq1gt3CsiAu8bxyABo5Eco6HBIyrKy5BIOMgkYNV5Nchh1uHTJbW7Tzn8qK6a MCJ5thk8tedxOxWbcF2cFd24baAOf8CSazJcakb83clmEhEU90J0a4nzJ50gjmRWiDDyj5a5jXkI Sd1dpWHoPii28QXE8VvaXcKJbw3cUswQLcQTF/LkTaxIB8tjhwrDjIFblAGP4o+2f2DL9i8/d5sP nfZ8+Z9n81PP2bfm3eV5mNnz5+782Kj8NzXMWiWiX4uzJLcTpb+dG7OIN8jQ+YSNynyVTJkw2cBv nODoapqUOk2DXcyyOA6RpHGAWkkdwiIMkDLMyrkkAZySBk0aXqUOrWC3cKyIC7xvHIAGjkRyjocE jKsrLkEg4yCRg0Ac3/xOP+E9/wCX7yvtf+39m+wfZf8Av35n2r/trj/pnWPp/wDwkn9g3nm/2ru8 qw+37/M8z7R5p/tDyc/Nt8rbt8n5M/6r5s12H/CSWf8Ab39k+XPu837N9o2jy/tHled5PXdu8r58 7dmON275ap2/jXTbmwmu0guwFS3kgjZF3XUdw5S3dPmwBIwKgOVK9XCDmgC54X+2f2DF9t8/d5s3 k/aM+Z9n81/I37vm3eV5ed/z5+982a2Kp6XqUOrWC3cKyIC7xvHIAGjkRyjocEjKsrLkEg4yCRg1 coAKz/O1j+2PK+w2P9mf8/H2x/O+7/zy8rb97j7/AE59q0KKAOL8CSazJcakb83clmEhEU90J0a4 nzJ50gjmRWiDDyj5a5jXkISd1dJqc2sReV/ZNjY3Wc+Z9rvHt9vTGNsT579cYwOueK+k+JLPWtRu 7S0jn228UU6XDKBHcRyGRVeM5yVzE/JABGGXcpBNjU9d0fRPK/tbVbGw87Pl/a7hIt+MZxuIzjI6 eooA5uSTWf8AhZUMdsbuWyD5uXYTxxQweQcRqrJ5Mh87Y/mK5k+YpjYrUfD601Cxt7u3v31K5eNI la7v2uFZ5AGDp5cssgyvBMkR8t9+FzszWxJ4otk8RQ6OtpdyF7j7I10oTyo5/IM/ltlg+fLAbIUr 8wGc5Ak0LxFBr8uorbWs8cdhdyWbyyPEQ8iMVYAK7MuMA/OFJDKQCDQBh+O49ZkuNNFgLuSzCTGW C1M6NcT5j8mMyQurRBh5o8xsxrwXBO2pP+Jx/wAJ7/y/eV9r/wBv7N9g+y/9+/M+1f8AbXH/AEzr Y8QeIoPD0Vi0trPcyX12tnBFC8SlpGVmAzI6LzsIHOSSAASaP+Eks/8AhKP+Ef8ALn+1eV5u7aNu MZ6Z37cf8tNvl7vk37/loAx/A/8AbH7/APtL7d/x6W32n7Xv/wCP/wDefafL3/8ALP8A1WPL/ddd n8VdhWXpGuQ6w92kdrd272zqGW4jCkhl3KeCdpKkHY22RQVLKu5c6lABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYeleF 7bRre4isru7V5LdLSGViha2gjDeVGmVwQm9iC4Zjn5i3FblFAHP674StfElnp1pqt1Pc29nLHM8c kMDC6dCCDJmM4zgg7NmQ7DocVY/4Ruz/ALe/tbzJ93m/afs+4eX9o8ryfO6bt3lfJjdsxzt3fNWx RQBh6L4dk0a/vbttb1K+N6/mTR3SwBTJsRA48uJSCFjVcZx1OM81uUUUAU9U02HVrBrSZpEBdJEk jIDRyI4dHGQRlWVWwQQcYIIyKr22iixsLa0tL+7gEVwbiWRRGzXLM7PIH3IQA7MzHYFxnC7RxWpR QBlyaHDNrcOpy3V2/kv5sVq0gMSTbDH5i8bgdjMu0Ns5Lbdx3VT0zwsdM0eXTV13VZo2wYpZGhWS FwxYuGSNd7Mx3N5m/ec7gQzA9BRQBT0vTYdJsFtIWkcB3keSQgtJI7l3c4AGWZmbAAAzgADAq5RR QBzdl4Sks31Fz4j1mZ75xLIzGBCsyrGqyLsiXkLEg2nKEZDK2TWhaaKLDS7DT7K/u4IrR1OVEZMq jOUYMhCoc9ECbQAF2gAVqUUAYdz4dkufEsGtjW9SieBGjS2RYPKEbGMunMRfDGJSTuyOcEVJofhu z0DzPs0k8mYo7aPzmB8m3i3eVCuAMqu98Fsud3zMcDGxRQAVXvrX7bZyW4uJ7dmwVmgfa6MCCCOx wQOCCp6EEEg2KKAMu00UWGl2Gn2V/dwRWjqcqIyZVGcowZCFQ56IE2gALtAAoOhwt4gTV2ursyIj BIDIPLUsArEcbgCFXKbvLJUNt3jdWpRQBh+HfC1l4cfUZoJJLi71G4+0XVzNFEjyNtAwfKRBjgnp 1Zj1Y1uUUUAU9U02HVrBrSZpEBdJEkjIDRyI4dHGQRlWVWwQQcYIIyKr22iixsLa0tL+7gEVwbiW RRGzXLM7PIH3IQA7MzHYFxnC7RxWpRQBj/8ACN2f9vf2t5k+7zftP2fcPL+0eV5PndN27yvkxu2Y 527vmqnb+CtNtrCa0Se7IZLeOCRnXdax27l7dE+XBEbEsC4Yt0cuOK6SigCnpemw6TYLaQtI4DvI 8khBaSR3Lu5wAMszM2AABnAAGBVyiigAooooA5vSPA+jaD4lu9b0yCO1e5t1tzbQW0EcSKDnK7Yw +Sck5Y54z91dvSUUUAYZ8LWUnjFPE08kk95Dbtb26SRRbYFbGSrBA+eG+8xxvcDAY1Y0rQ4dKuLi 4F1d3U8yJF5l1IHZIULGOMEAFgpdzubc53HczcY1KKAMvW9Dh123+z3F1dxQOjxTxwyALcQuMPG4 IIwQANww452suTmP/hG7P+3v7W8yfd5v2n7PuHl/aPK8nzum7d5XyY3bMc7d3zVsUUAY/h7QT4fs zajVb6+h6oLsQ5U5JZt0calmYsSzMWJPOck52KKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooqG6ureyt3uLu4iggTG6SVwirk4GSeOpAoGk27ImorJ/4Snw9/0HtL/8DI/8aP8A hKfD3/Qe0v8A8DI/8annj3Nvqtf+R/czWorJ/wCEp8Pf9B7S/wDwMj/xo/4Snw9/0HtL/wDAyP8A xo549w+q1/5H9zNaisn/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGjnj3D6rX/kf3M1qK yf8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMaOePcPqtf+R/czWorJ/wCEp8Pf9B7S/wDw Mj/xo/4Snw9/0HtL/wDAyP8Axo549w+q1/5H9zNaisn/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/ AMDI/wDGjnj3D6rX/kf3M1qKyf8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMaOePcPqtf+ R/czWorJ/wCEp8Pf9B7S/wDwMj/xo/4Snw9/0HtL/wDAyP8Axo549w+q1/5H9zNaisn/AISnw9/0 HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGjnj3D6rX/kf3M42DxhrdnosLz3ljf3093O0IjsZQJ4jO Y7VAVJSJZj9yeRtuNvEnzPVPTPE2paiNEsbfUY7qLXr039rPCzB7KFbk3HkTuHbJeFJowoCgGFk+ YAsvVLL4AVLJFfwyEsHL2agwYt2LBiY/7hLAHIxyM1Yg1PwZaiIW99oEIicPGI5YV2MI/KBGDwRH 8mf7vHTijnj3D6rX/kf3Mo+EPFt7rOnXN/rNrBp1r9kh1CJ3mjXbBIHPzASOdqhP9Y3l7ufkXaax 5vEviC5ksBO1jp15b6hI15BLIyrZxC0kZ/P2syyxx+bbv5gZVdpEUiFua6q11vwlY+f9j1PRLfz5 Wnm8m4iTzJG+87YPLHAyTyarxXngaCzWziufDsdqsUkCwo8AQRyEGRAvTaxAJHQ45o549w+q1/5H 9zOZX4ga9/wiEOuvY2kcslldlrGSFxLDNbQyGWVxu+WITR+Vsxn94hLgtsosPiPq+tX+p2+l6baS mJ8WkRkAkljlSF7aQb3QOWi+1TGPKkCIKSh+Y9U2p+DGe9dr7QC9+gS8Yyw5uFClQJOfnAUkYOeD ii51PwZe29zb3d9oE8F04e4jllhZZmAUAuCcMQEQAn+6PQUc8e4fVa/8j+5lqw1H+0PC9tez6hBB 9tiTyry3+RW8wgROiyg7WbchCMGwzbfm6nj59afRfh7aySXV800eqz+Y5naR1S2uJp5ovMZtzr5V tLEpbG/5dwQMdvXT+IvDF1by29xrOkTQSoUkjkuomV1IwQQTggjjFRprfhKOK1ij1PREjtMfZkW4 iAhwpQbBn5flJXjsSOlHPHuH1Wv/ACP7mZMniLXk1ZdFYaaLy4uIoku44nkgtmeGeZoWXcpkdVgX 5t0eROjbFAw2HqvjTU7zThp1rd2M1xqFoLRLnTVmEcc8gSL7THcnCNGlxNHGUTc6hg27IKDqvtng b+x/7I+0+Hf7M/58t8Hk/e3fc+797np15oa88DPqMmotc+HWvpdnmXJeAyPsKsuW6nBRCPQqvoKO ePcPqtf+R/cyHxP4i1LTdSSDSxaSC3t1muYpomLSySyrFbQowZQhlYSrvIYJtBYAHnLufG2p20DQ q1jc3cN28AaK1m23jBxEke0Fhbb5vNhEkrld0JYKyt8vRS634SnlaWXU9Ekkby9zvcREny2Lx5Of 4WJYehORzVea88DXEttLNc+HZJLWVp7d3eAmKRm3s6k/dYt8xI5J560c8e4fVa/8j+5mLq3jKe1t tQku5rFYdN1COOQW7SmSWP7SGLoEYMVjgjl343BninBAWJg5a+LPEFo0A1ptKRo5YIb1EiaIJK8X nSxiRpWVfIhBlMhysgOxdrAk7T3ngaSJYnufDrRpEIFQvAQsYV0CAf3QskigdMOw6E1Y/tvwl5/n /wBp6J53m+f5n2iLd5mzy9+c/e2fLnrt46Uc8e4fVa/8j+5lHQ/FktzZazfaqI4rPTUEs0y2s0Jg OwyPC0cgEjFE8tvM2qHEgwoIIrDl8b+INGsGu9ahsZWs/LF7bWluyOZWgM8sKM0pUNDCplLnKybg i7WBJ6K1vPA1jp0+nWdz4dt7Gfd51tC8CRybhtbco4OQADnqKsf234S8/wA/+09E87zfP8z7RFu8 zZ5e/OfvbPlz128dKOePcPqtf+R/czFfxJ4kbxGvh6CPSpb9sTyyhZPJtoSj70Jz+8kiZrYn7hkW dfljB3iTwv4s1PW9Gvb7UYLTSkNlFf2007RlY4pVcqZEWUkouzO9jFv+YbU2mtKz1PwZpyQJY32g WqQI6QrBLCgjV2DOFweAzAEgdSATQmp+DI7e7t0vtAWC9d3uo1lhCzs4w5cZwxYcEnOe9HPHuH1W v/I/uZxtj4l8TWBs7zUru0bNlpSaj9pjaPy5XkladBHuCpKkDiWRiflWMEoFYbNCPxrrN3KLeKbT bU/aD9oubqynRbOJYHllV4pGjciP/RwZjsRvtC/Kp4PRQan4MtRELe+0CEROHjEcsK7GEflAjB4I j+TP93jpxVe2l8AWdvJb2r+GYIJEkR44jAqsrhQ4IHBDBEBHfaM9BRzx7h9Vr/yP7mQ6jqesXng3 QoxD9l1vWfs8bxFnt1jbyzNPGWwzxZjjlQMAWVivQjIr3eq/bvB/hNrrVJ47XVfJe8vw/wBjl8tb aS5Zso2ItxhAbBwFZgCOCNq+1vwlqdnJZ3+p6Jd2smN8M9xFIjYIIypODggH8KjudT8GXtvc293f aBPBdOHuI5ZYWWZgFALgnDEBEAJ/uj0FHPHuH1Wv/I/uZyOk+M/Ekui211e3NjbqbR3lkudPkMkK 20Mf2ueRVkXO24Lw+UFVssGGVUg3P+Ey16QS3Mp03TrYpO5SW2e4a0jto0+1SuyyL5oSctAEUKTk OCyg56J9T8GSI6PfaAyOkqMplhIZZW3Sg88h2GWH8R5OaH1PwZIjo99oDI6SoymWEhllbdKDzyHY ZYfxHk5o549w+q1/5H9zIde8TT6R4es7q6MGmX00XnXEcsMt79kVU3StshGXVSVUvlFG4EnorYdp 4+1KQC9uItNSOS4mszpYdmuLSaKPD+bIhfcDOFiUJHkieEjLMEPRalqfgzWbdbfVL7QL6BXDrHdS wyqGwRkBiRnBIz7mq9lc+DLK1u7f+1tInS8eY3BnuIWMqyyySlG9UDTSAA9mPXJJOePcPqtf+R/c zmb3xt4ku9H1JNJaxM1raTsb1rWSF2kLCO3EdvISwkaZLiDy2JO6MN0ZY39MgEy28S3Ekck4QCR4 0KKzY5IUkkDPbJx6muZEvgBbB7BX8MizdFR7cGDy2VXLqCvQgMzMB2LE9TWp/wAJT4e/6D2l/wDg ZH/jRzx7h9Vr/wAj+5mtRWT/AMJT4e/6D2l/+Bkf+NH/AAlPh7/oPaX/AOBkf+NHPHuH1Wv/ACP7 ma1FZP8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/40c8e4fVa/8AI/uZrUVk/wDCU+Hv+g9p f/gZH/jR/wAJT4e/6D2l/wDgZH/jRzx7h9Vr/wAj+5mtRWT/AMJT4e/6D2l/+Bkf+NH/AAlPh7/o PaX/AOBkf+NHPHuH1Wv/ACP7ma1FZP8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/40c8e4fV a/8AI/uZrUVk/wDCU+Hv+g9pf/gZH/jR/wAJT4e/6D2l/wDgZH/jRzx7h9Vr/wAj+5mtRWT/AMJT 4e/6D2l/+Bkf+NH/AAlPh7/oPaX/AOBkf+NHPHuH1Wv/ACP7ma1FZP8AwlPh7/oPaX/4GR/40f8A CU+Hv+g9pf8A4GR/40c8e4fVa/8AI/uZrUVk/wDCU+Hv+g9pf/gZH/jViz1rStRmMNjqdndSqu4p BOrsB0zgHpyPzoU4vZilh60VeUGl6MvUUUVRiFFFFABRRRQAVj+J9YfQdFF/Gm/F3axOoiaQ7JJ4 422qvzM21zgDPOOD0rYrP1o6WmmGXWZ4ILGGWKZpZ5vKRHSRWjJbIx84XqeenOcUAcnJ8UdIsJb5 tTaS3gS9MNuJYTbSCFIIJJHdJirEhpuFUF2BG1GwTWhH46sY5BBNHdzSm9NqzQ22FiLXj20W/wCY 4BaN/mzg+WchS6IbiaN4d1+JtTs5fOW6lM323T7+RC7bUiYCWJwduIUBUHaTGCRkZrPn8Hadq1/p 2oWctj/ZqypfK0cTSSyyeebgOs3mbdpdjj5GIWSUKVEhoAy9I+JF3qkvhi1TRLsz6ilu95MY0jiC ywTPmMPIHwHgfkqcojEbtyFjRfiRd3Oq3VrqmiXcEaXEcEflxodm+9ntg0jCQqACkSkA7i3mFQyD I6RfC/h2C802JIfKurOKH7GiXciuI7cMiYAbLKouGU5yD5mGzkVHpnh/wvOb2607y7kXdxDczvHe vKvmLJ9qjI+chBul8zauAd+cEGgDPs/ifoN3caVC0d3bNqaQvbi6CRsVlO2MhC+9gz7kyitjaWOE Ku1e8+Jdtb3HnRWF3PYxW99JOkSI84a3MBJID4iASVyVl2OCoXaGIVukj8L6TB/Z/wBnhnt/sEUc EHkXcseY4/uJJtYeaq84D7h8zf3jmuvhfw6/2iwMPnTNaSw3Aku5JJnhuNqsZGLFzuECqGJyBHhS AMUAU9Q8awxyCCzgk8xr2K3ilmQeXOovIra4KYbcDG0u35guTgrvXJqTQ/F39va5Pb2lnObAWlrc RTPFsJWXzyJDub5o2WKLG0FgXwwBDBLk3h7QEvRLPBGs91cK8SvOwHmq4n/dLuwpLRCRggG8pubd ijSvD2gaff3E+lQRw3COkdwIJ2GCqMUR1DYwqTfKpGAvl4ACJgAz/C/iDUNZ1fUYrqWMQQXF1DHE mlXEYAinaNT9pZjFISFyVUA5J/umrnhjxdY+KvtX2KPZ9m2b/wDS7afO7OP9RLJj7p+9jPbODjU0 uGxhsFbTTG1pO73SPHJvVzK5kZw2TkMzk8cc8cVJfX9nplnJeX93BaWseN808gjRckAZY8DJIH40 AcunizUX8MWupnTtk82tjT5Im24t4/txt8vh+WCgDKlhvIONucWNZ8SXmn6+LWGOA2tv9h+0B1Je X7XcNAu1gQE2FNxyG3Zx8uM1sf2Jp32H7F9n/wBH+1/bdm9v9d53n7s5z/rPmx07YxxUcnh7TJr2 G8lgkknifeGeeRgx3l13gthwjMWQMCIyfk20AU/D2sX9/qOpWWoJAJLXY2YopItpYuCm2TDSKuzi cBUkydoBRqk0rxVY6vqU1hDDdxSxvcIrTRbVlMEvlS7D3CsU56HeAMlXCxr4f07RZ47+3uP7PsbX fPcAyttKqjKiFmbCW8YeRhEAFVtpG3BDSaF4XsdDmurqOON7y5uLiZ5wm04lmaUjGSM4KKWHLCJM 8KoUAy7b4g2N/wCHtd1aytfM/sm0a5eL7dbS+ZhHYLmCWTbnYRlgOvGcHEjeItSttL1OOcWkuoWW p2+midImSJ2n8jbJ5ZYkBftAyu/5th+Zd3y9RPBDdW8tvcRRzQSoUkjkUMrqRggg8EEcYrLTwvpK WbWvkzvG+S5ku5Xd2JQiRnLFmkXy02uTuTaApUUAc3b+NdSluQjQWmyzuIbW8wjZuGlvpbIPGd37 oBoTJtO/IbbkY3nUbxTcW+l+L7+4scLoUsoihLBWlRLaObJYFgNxc4PZSuQCCKsLovhqGCPUAsCw afvLzm5by8xuzM0x3YkZJN77pNxVyzZDEmi8tvD+oWF5p326CP8A4SeKQgxXK77oGBY2eLJIOI1T 7oI4BI55AKcHiLUl8SRafcC0kgF6NLkMcTIzTfY/tRmBLEBMfJ5eCR97efu1l3Pjy/t9UvrM28Cs vm7d8MgFikdzFAZZmJxIpSUXBA2bYwMnDrJXQWul6Nqs89558F3fx7ra8msbh40S4CbHYIrny5gp 27s+YqkLuxUceneG769mSHUZJ7y+t/tS+Xq8zSiBnDb4SJN0cTMFz5e1WwoOQAKAMe18X61ejRbl bSO30uS4ktb6+ltTgyi5+zxosRlWWIuVOdyvsLoDnDEdR4f1KbVdJ8+4WMTx3FxayGMEK7QzPEXA JJUMU3bcnGcZOMnPGmeFIbhyJLRZdHRZLpTdn92MmVXuRu+chi0gaXJDFnBySak0m60fQtM0+1m1 uxaTUpZLi3kaZEF3JNIZG8lSxypaX5QCxwVGSeSAdBRUcE8N1bxXFvLHNBKgeOSNgyupGQQRwQRz mpKACiiigAooooAKKKKACsPxFf6lay6PaaXLaQz6hem3aW6gaZUUQTS5Cq6EnMQHXua3Kw/FEegN ZW0viG+jsYIbgPBO1+1mVl2OvyyK6nJVnGM8jNAGXF8RtHW/sNOvf9Hv7mVrd4hKhEcgne3G3JV5 FaWNwCikgYZwgNCfEbS554Ybew1WeS48k24S2/14mSaSJlyR8rLATuOAu4bypV9mhHaeGryfT7iy uYEaHy4Lb7BetCjhU82OIrEwEihCXVGBAViQMMc4/hrw5oVhO2svf6VczebbrFLYAwwRtsdYRgyP lil2VUFtuxolVQFXIAJ8V/DEunTXkVxvVJY44086FfNEgdkbe0gSPKxyHbIyONuCoLKG3NM8V6br GqR2NiZJPMskvo5W2oHifaVKoxEhGGHzBNgOVLbgVFeDQPDc1vFb200jRXSC6tFh1Kb5EUYD22H/ AHSBZQv7raNrhehArUs9FsrK8N3Gs8lx5SwiS4uZJiiAAYXex252qWxjcVBbJGaAOXn8eS3up6FZ 6Pp85XULuIvLPGhDWbxyukqDzVI3iFyM/MoRtyZKK1PTficEi1Rtd027tWtbiQgrFGsccPnyQRh5 POZN5kiKEkqMtkDYrSDUifwFpMVhqS6ppUELytLZXEmojYSivFtiLPjy0EsiiNfkTecAE1oSaL4a tbp7KVYIbrVdxWJrllkmKSyTlowWyGV5nfcmCpIIIwuADPh+Jfhu4utLt0ucvqO0J+8j+RmlaFRj fmTMiMuYt6jG4kIQxjtPiBDeapLFbWN3PDJb2r2ESQhJbppftD5VnZU2GKASAkjHzKfn+QbB0DR7 W409mmu47hH8uB21KcSTkFpAjsXzMB+8IV9wA3YABNZ9t4f8Fvpc+q2P2RLKRFdr+0vWRUWHzACs yONgRXkTKkYT5PuAKACuPiXpUuqW9pbWGpXEFw8Kx3qJGIiJfs+1sM4fA+1wZ+XPzHg4ONT/AIS6 x/4S/wD4Rry/9L/vfa7b/nn5n+r83zun/TP3+7zVebRfB1rZprci2NtYjypkuhc+XAozb+WVIYIF zbW2McfKB/Ec7kGrabdXEVvb6haTTy24uo445lZnhJwJAAclCeN3SgDm7LxNqkt5pcc0EDw3+t39 j5kce0QxQCcIDlyTIxgzuC7cbhgHBNj/AISS8/4Sz7B5cH2H+0P7M27T5nmfZPtXm7s424+TZtzn 5t38NSaLpdjFcXFjLqFpfXenanPqEccXySWhuDKwEihzk7ZpQCQAQQcZGakvvCVlcRSG1kntbt4h CLoTyO6DaI2lXLY+0eXlBMcuOMllG0gBrusX+l6tpyRJAbS4ljiYPFIS5eQIcy8Rw7QwYBtxlPyK FbBOXeeKNasPDXiXUmtLS8k023eWGS1BEQlUNvhJdgZBEVG6Rdu7LKFV0K10l1omnXuowX9xb77i Hbg72CvtO5N6g7X2sSy7gdrElcE5qnZ+EdHsdGutHhju20+5t/sslvNfTyqItpXYm9zsG0kfLjt6 DABT8XeJLzQv+POOBvJ0+71ObzlLeZHb+XuiXBG1m80Yc7gu37rZ40PDGoXmp6KZ78wNdR3d1bO0 EZjRvKnkiDBSzEZCA4yetGo6Lo2pXiRXy+bcSb5Via5cF4wEWRdu7mE4j3x42MSCwJOaPC9l9h0G Jft8F/58s159pt02RyedK82UG5vl/ecfMcjnNAGxRVf7fZ/2j/Z32uD7d5Xn/ZvMHmeXnbv29due M9M0Q39ncXlzZw3cEl1a7ftEKSAvFuGV3KOVyORnrQBYoqva39nfef8AY7uC48iVoJvJkD+XIv3k bHRhkZB5FBv7NbOa8N3ALWHf5sxkGyPYSH3N0G0qwOemDnpQBYoquL+zazhvBdwG1m2eVMJBsk3k BNrdDuLKBjrkY61X/t3R/wC2P7I/tWx/tP8A58vtCed93d9zO77vPTpzQBoUVXhv7O4vLmzhu4JL q12/aIUkBeLcMruUcrkcjPWrFABRUc88Nrby3FxLHDBEheSSRgqooGSSTwABzmo7q/s7HyPtl3Bb +fKsEPnSBPMkb7qLnqxwcAcmgCxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXP+NLG91Dw08OnRzy XS3dpMot2jEgEdxHIxTzfk3BVJG7gkc10FYfi7V7nQ/Dsl9Z+X5/2i2hBe3ecKJJ0jJ8tCGcgOSF BySAKAMNtO16+utM83+3BYGJo9SSa+hguJSZT5RH2c7R5fzM/ltGWRkBMhUx1yel+HPHlhoa2UI1 K22aO9tBEtwj+W4tSoQubnCE3ALqyxsVUou9VyiegWHiY506yu4ru5v71GeJ4tOktVdVk2yExysW iEYKElyN24bNxO0Z/wDws/QRo0WpSR3cAnSB4IboJbtKsquykNI6oo/dTDLMM+Wdu4NGXAKcHhjV B47jub46rd6ekV3axXf9obCiSRWzbmCOpG6T7V91flPl4Cqke3l9H8F+LtN8Jutmmq2V/wDPGtsd STAxpfk78rIQc3CR7dzHYFUqEG6u01T4gQiwiuNCsbvUw9xZJ5iQhYys7wkjLsmH8ueMgHHMq9Qk myTTviV4f1WJ3sXnuGEqIkUCrM8isruHCoxx8kUrbGxJ8mNm5lVgDPOkeKrDxVM8d/qt1pvlP5cs TRO7AwnKt5syxrIZ8upEBAyi71jyiU7Lwv4mS5hvZpbuKdE04bIb1sSKl9M8okLyO7FYJMFDIyEu wXftQjoLHxrCNE8N3GowSfa9YsoLjFug2K0j28RHzNkDfcoe/AbuADJH480t7P7W1vfJb/uHVxDv LwzllgmVELMVkddgXHmAkbkUc0Ac3/YPjSWO0ZJZI7yBy3n3dwsqi8+x3cclygJbbA0kkG2MAYw3 7pRnO54O0vUdPvtUl1G0vo/tN281u016s22HybZFWTDfNJ+7K5IcjY/ztu3SV9d+JFhpianYxL5e tWuny3C29w0Z2zLbmcROqPuOEGS6jy/4Q+7itiXxfYRReIJWin26FEZbkAx72AVmO2PdvXhDguFD cFSyndQBh+FfDmvQ/DldB1GeS11CNIUW5aZyNqxxHCiGVCgTBiwrjcYy5zvIOhPo2padpugOok1W fSr17iVInYSTK0U8YCG4lY5BmUnfJ0U47LUeo/Erw/pUSPfPPbsZXR4p1WF41VUcuVdhn5JYm2Lm T58bNysq6EXi6zlsb68WzvhDaXZsQWiC+fcCYwCOPLc5cKNxwnzjLAq4UAx18O+ILLTPCNhaXe1d PtILW98m4ZUJjktnZscblKQTRjjP74AgKzlacOleNBpckcU93FqBe2/fS3qlDcLv+1TjPmBYJEKr GhQhWw3lJjdWovjy3gudcF3bz+TYRTXcWyEo5gitrWVwyuQRJm5wAQOmDgjmvrXxFg0zWo7RLGdr W3u2i1G4JizDGIJpSwj8wSjHlFgWTDorFN2UyAY+u6H4uvpNTgtlvjp91pUtvbWzypJtVrQqIZne 4x5nnfMZBG7HIBl2khek0u4vrDxZfR363bx6lcbLJZZN7qkauzyFFkZEgGYkVlVGJdRIpYh2NK+I Ola1cWdtYW93cT3LyoVh8uVYjGYt++RHaPAWdGyGI6r9/CHU1DX0s9csNGjg8y7vMspklWJAi8uQ WO6RgoY7Y1bHy79isGoA5/wYuuz6xqF5dXM8mm/a9QiHnXAkDbbtlhVFwCmwJMDnOQ8fzMFCRbHh jQL7Q/tX23U/t3nbNnzXJ2Yzn/X3EvXI+7t6c54xj6P8QkvPCWia9c2mbe/2QSSROqE3BUbhFEzb pFDiVdozITGNqOGDVsar4usdI8Q2Wizx5uLzy9jfa7aPG9yg+SSVZG5H8KNnoMnigDHi0LUbTwiu kxWU8kkOqyX6b7hXLxx6iLhULs+TJJHyCxxn77Kaz7bwtrMd2xazwuo3dvdM3mpi0EWpT3hWXnli kyoNm8bw2SFwx3PEXiXUtBur2ZLe0vLS0spbuS1jLCVI0iZxK8h+VQzp5Sx7STy4YhXVc+XxZrNv oM98ZLSeW0uHWdX02e3PEayRxspdxCHRg5nkYLGGUOmdwUAz08OavaeBfEFvNYyCdPC8WjxRoRI1 xLBFOC8YUklGMqhQcOcHKrxnU/s+9k8R/wBqWdt9tsG1D+2obi3mjKTRnT/syxJlhmQthgThNpzv zxWpr2r6vp+vaVFBbxxaPI6peX0sIlAkeREiiUCVXUsSQX2MAWTjG4jD07xprGqNa2kaWMN1fyxC GZ4XZLcPFPKVKbwZ1At9onRljk35X/VsCAU7vwvqr6rd3MVpqRtbJ55LOFhbEyTSXsN0HQeaN6eZ ExYO0R8vYqjduY2NO8M6vZSaVP8AZrs6ktxLL9ve+GIYZbxp5o54VIjLvEQuY1ceZ3QIj12mhan/ AG34e0zVvJ8n7daRXPlbt2zegbbnAzjOM4FaFAHP+DPm8PvKOY5tQvp4nHSSN7uV0dT3VlZWBHBB BHBroKKKACiiigAooooAKKKKACuf8T6Te6rc6D9iuZ7X7NqDTTXMHl74k+zTpkCRWU5Z1X7p+9nj GR0FZet6lNpSWNwqxtbvew29yCCX2yt5SFOQM+a8ROf4d+MnAoA4vUPB2snxDdX6f6XHHLDdK7ui yXJV9PJUABVEh+wSddqZkjwQN2yvpfgXXtP0lbZ72f8AcahpLG0haEwTpBHZrJLlk8wYMMmBuXOw fLzzoN8R/wDilLzUktv9I+yT3FrIU/c7/Ia6hgcbt/mfZ9juQNmdwD5wK2Lzx1YWkcRWyvp5JZZ4 Y4oljDO8V3HaEfM4AzJKpGSPlyTg8UAcv8PPCXiXQ9W0iXWZJ3t7LRJLALJOrhHaSGYYAY4xueHj taqc4ZQOo0fw1faXp2qQXt1Bq/2qLYkFw9z5bcMCrmeafCtuAO0DjqG4Ay1+LXh5dNgvruO7sklf hLowo3l+VFKZB+8IYbJ4jsUmQ7iAhwcdBr9/rkGiNLoekyT6g1wIljl8pgkYfDSEGVAQUBKjeDll yB82ADDstE1ae1iju7ecJea3Fe3Uly8X2oRwxIyNJ5Z8rcZYIkxGMeWRkB9zC5rdhqGp6vYtDpkk cX2iFppGeHZIsE+8edj95gAb4tjHLORIqjIIvjBYbjTpJ3jfSZdCm1abUVhZFcRmHlE3F1G2RmKs CfmUAkhq5cfFSa9ury3tJdNh8h5pzNCp1AQ2scVu5aQRSKAf30hLBioMLRr5jEEgGh408Ga9resR 3lpcabdxM8saQXtu4FtC9pLGyF0lU7HduSi78upJYRKBYTS9V/4R/UVn07Upru5vY9Qid2tmuDJC bfy/PVZEiJ3RdIyoMcfLLIedC98VzaLe6nNqhtG0y0Qbnt8g20jOiwxPI5CM8gcP/AIhjeSrK5w9 G+I15q5Mkf8AZUkdpdw292lpKZg/n3sttHskyNu1USQkqfMDjAjBBoAsN4Z1aPwvo6uL6a4tdVub +4jt5oo7uSOY3HyBsrGsmLhd5VlAw5RshTUdpoXiG31+wvLqK7uXtHXUL50+ziK8uvsJtn8k7g+S dm0Msaj96SxygXU0DxfcXunXV7qMUAjXSrfWEEJEeyKYSkRM0jbSy+UcyEop3fdQDnU0PxPb+IdG udSsLaSQQuyCGO5tpmkYKGwGjlZATkDDMPfAwaAKem6Xep4ynvZLLyLOKKeOLLxmPMskbkwhAG+f YWl80Eh8bCV3E8XpnhPxlElul1c6yRKlsl1u1qRiBu09pdp83KnK3/K4OMgcFAewsPFF9daf4Jne 0jzrqK11Ig+SIm0ebaoLbgSy8cMMK2SDjOfe+LdetR9nW3tLie4vVs7e4tLd5o0m8uZ3hCF1MpQw hDLujTMhLBPKcUAV9G0DxNF4Kl06/m1I3kt7p7u7akzSiLba/atsofco3Lc8Bh328EZx7Hwn4ya3 06C9udZ2I8Lzka1IGyw08TZYS5I+W+wM4HO3GVzoa18W7HSo7I211pt7tt/Ou2eX7M0pNm88YiiY l1DkRjc2QPMVRvYtsuX/AI01jTW1K0kSxmutIiurq5mWF0S5jgit5SiJvJiZhdBdxZwPLJ2ndhQC xa+H9euf+ETkudQvrG6s9EkgvrqF4ZZDO32bKsZFcNkxuSwHVevPOX4ds77Qn0uHWbHWbcR2Vhp8 McGo74Z5wsG8+WJsgoUbIRAvlxzMxcPgdZq/jHS9D1NrK/8APj2RLNJME3IiGO4kycHccLaSZwD1 XGcnEZ8ZW0YRJdN1JLtrhrP7II0eRbjyxKsR2OVy0bbw27YoB3shGKAM+fSNTh8ay6ilhJPZpcG+ VY5IwjsLTyMjcwf7QT8uCRD5fJxJzUmg6JqNn4rubm4t9lrH9s8pi6mMefOsv7kA7/mC5l83o4Aj wmRUd18RNNk1C40zTZI5L63vbe3dXZWDKbuK3mO1W3oVMmBvC7jgrvUE1oDxpp7eH9Q1kRSC3srj 7PIslxbxNuyo53yARn5x8kpRx0Kg4BAMvwVo2paPeSyXNpfJataWum28d0bcSQRW4mKs3luwKkOg zuLFy52omALmm6Ld6b4KudP+xyTTpe3U0KPMkku03ckkcivLvVpQpV13/wAYXcV5IsSeIptQ8Jw6 zpAjjSV8F2iN+Y1DFSVjtWbzTuAGFcbQSxPylTn6T4xvtS12xt3trSKC5SNWijk87azWwn3C4U+W 5BOzyQN5X98DsoAx73wZ4jutM02WG4tFkgvYrpbS6t/NaKQ3pme4LLKqeaUYbwoI4kWIqr89hBbX d54qi1S5tZLaCDTBHbqzIW8yZ90yPtJGVEMGCDj5m5bjFO117WdQ8E32qWeleZq6y3cNtZEoMPHP JEgcmTbxtUvh+cNt7CiDxX9m0PTLi9invb29u5LERWVpsIuE80tGUMjBcGFkLbymRu3BPmABhtp2 tXHjG/FjcyQ41NJbtVnMQeI/YnSXZ1KeVbTwZy26Qv8AdVnEZDpXjQaXJHFPdxagXtv30t6pQ3C7 /tU4z5gWCRCqxoUIVsN5SY3VoQfFDw9d6XeX9q8k8ds8YIWSEApJu2OzmQJEDsb5ZWRgQAVDMgYu viJpsmoXGmabJHJfW97b27q7KwZTdxW8x2q29CpkwN4XccFd6gmgDD13Q/F19JqcFst8dPutKlt7 a2eVJNqtaFRDM73GPM875jII3Y5AMu0kLoapa6oH1U3Pn7bzVbFNMW4l3SGSO4DlxGsjIYVVQ+FE Tskb78EeYes0PXbbX7e5ntUkRILhrdhIybtygHlVYlDyPkcK4/iUVqUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFZfiGLSJtEmXXbiO309Xjd5nuTbhGV1ZD5gZSpDhcEEc4rUrn/Glje6h4aeHTo55 Lpbu0mUW7RiQCO4jkYp5vybgqkjdwSOaADTtG8O6hBY6lZS/2isH/Hpe/b5Llkw5LbJS7Hk5VsH5 lARsqAor6P4D0bSvD1ppfkbpIYoUa6gd4JC8aFd6Mjbo87pCQrDJlkzku5anFaeIZdS0edhrJs40 xqEd1dW8c0rebmI7YfkwnzM4Qx70ZATKVMdcvpFl431HwLp9zHe3cyXFvZzSq90ZJ7gGKQs0bCSE qDutcjzIzmKXJfcWmAO41Dwj4bkIub6ORHZIrbz2vpo2ZvMiMZ3hwTKXigG/O9tiDJwBVgeENDFu 9s1pJJbM6stvLcSyRRYBG2NGYrGhVmQogClCVIK8Vxer6H4q1KBIr+DVdQVfsNzut72K1yYnti8Z jSQATZF2+VfYNyfMxWPy5NN0nx5HZTRXl3dvOLiJpSsqRLK+yUNJFIZZSE80wOVKRrsjIEJ3NGwB 2lromhT6do5treCe0sYo/wCz3Vy6iMBdmGyd6/LGwySNyI33lUiMeD9AFu9s2mxy27urNBM7SRlV BCR7GJHlLuJWPGxScqoPNcu+g+KIPCvh+2sJbu1utO8OSxtDDcIqtfKkHkI+ThgGRzkfKcEMSrMr F/o/i+5vktM3f2BbiYyXEGpNG0kcmoQSgDDBlCW3mIDnIxIoAG0uAdJc+GNBnna1nM5kurR4pLf+ 0Jh9piCCJ2kQP+9YKyKZGBYfJzkLiTUvDFpeaNrtlC8kU+r28sEtzK7zsu9WAA3NkIpdiEBCjJwB k153Z+FvHMCWUgkvvtcdpKgnn1MSNHNJb2OCSxctGLmOV2QEAqjfeUiOTYn0jxtLrGpr9vvo4Z7u No57ZowgiF3EwKl5mwyW+9SohRX+bd5h2lgDrD4Q0P7OkMNpJahHZlezuJbeQbgAV3xsrbMIgCZ2 gRxgABFAksrLQtU0ForB4LrTLmWScSW85dTI0rSM6SKchhISwKkFWHGMDGPrWjaxNq1pb2Ut99gE VvDHLHfuggVZD9pMx3h5Gkh2qjfOyuC2Yz85p6Zo2v6N4f8ABlnbxXbJZ28UV/bx3i7o5SYtzszk h4kT7QuwE/eTYBtV4wDUj8E+Eb/RUt49PgudOniXYVmd1kj8mOJcNu5XZFERzjdGj/eUMLFz4X8O 6heSxXUP2m6Eq3kqS3cjuAwkRQwLZ8khplEZ/dnc428muT0bRvGGlaZo9i0V2IIEikma2vIy8Srp vk+SiOdjEToX5+TLRt82XKZb+FPGFvdG6givvOuLSAXLR6jNJvVZbg+UA92HWQB7ZjiUKNsoV2DF ZAD0Cbwnaizkjsru+gunilt1vJbye4mijlMfm+W0jkqxES7TyFYA4PIbcmghuUCTxRyoHVwrqGAZ WDKee4YAg9iAa87m0PxdNpd7JJearNqM8tvHbo1wkMKr9mh8x5VhlVkUzI27ynLKR8gZXkEnUXiv 4u8OB9Mvp7BZJWMcp3ATIjlQ2Y3RzG4AdWR1yCpJKkqQC5pPhzSNDAGnWMcJVFRWJLsqiOOPAZiS BthiBHfYCcnmtSuHOj67LeXdg9hAlhLaRrbBLkCzs9ggCIiKqyeZG4uJFdPLyPLG4Hb5Wpf+HNQu vDVnpketSLcwOGe6drgFwAw2/up43wMgDc7nCjcXb5qADVdN8L6XdXGr6xdR2iXr7Jxd6i8drcMY vLw0TOImJjXGCp4XPbNE/h/wvE9npt15f2i4eRoknvXM938q+YrMz7502qgZGLKVRARhVxT8R6Hf zaTpcVpBPdanZRMtpexXskQtroxhEnkR5D5kf3t24yNgkbZN7VJrdhqGp6vYtDpkkcX2iFppGeHZ IsE+8edj95gAb4tjHLORIqjIIBoQ6ZoGqXpvIJI7yezuGjlZLtpAsquX2SjcQxjZiyq4Plk/KFoH hDQ0t3hjtJIwzqyvHcSpJDtBCrE4bdEihnARCqgO4AAZgafhC3ul+03WoaHPpV28UMIiLQeTHCm/ y4YhE7fKm5sswUsXJAC4ROooAjgghtbeK3t4o4YIkCRxxqFVFAwAAOAAOMVJRRQAUUUUAFFFFABR RRQAUUUUAFU9Shsby3WxvzHsuXCRo0mxmdQXGwggh12FwV5G3cMYzVysfxLY3F5pkT2Ue+7tLuC6 iCsFchJFMioxwAzx+ZHyQDvIJCk0AVz4W8NajFMY7OCS3eJ7N44JWWEAKYGwikKsgQGLeAHCjZkA YqSTwfoEmpHUW02MXZcP5iuy8iVJugOMebGr46bi56u+7l9K8P8AiPSzb2119ruUit/9JktL3Ylx GbbEkagup+0PdFpfOIU4bmUfdqvf6T48/sbT1F3dvqETyG9mt5Ul86fbH5U0SmW3QRKA4MbfLu+9 HLnzKAOotfCPhtrJY7COSOOJ/LEtnfTRupjRICnmI4bAEEasucExgkbhmtCXTNM1ew8kySTxJcSO s0V3IJIpQ7B9kqtvQgl0IUjAynA4rm7zSNdTwdNYWMc8d/Nqt3KgjnCJskuZpEaV0kSRY8MhPltv HHysNyGm+leK5NSfyhqVvcImoNa3xvw9vvklufs6zRGQjYqNEwxG5yUGVEZVgDuI9KsYbi0nhto4 ns7drW3EfyrHExQlAo4x+7Ttxt4xzWfc+HtA1C/uVmgjku3cXNyizsGdXRY8OobmJ1gUFD8j+XyC RWf4NtNXtbfZrEepeYry+SbiQbYoyIvlI+0zM5LBirMzFfnX5F2hsvR/DGu6TrmrvfpY61DqWnxQ TTNbiEXUw+0EmfdI5C4ZUO1GGJI1VVWMigDoNItfDtnBc6/pmobrGbzp5JxqcktoMuXlcKXMa/MG JIAx83TmjT9F8NagtvqmnLBdQ+bJMk1vcs8UrmV5CWwxWTbKzsu7OxiSu01l22h3dx4V1uHVNOu7 s6jcCSS3mu0guriIJEjCR4CIg5EZAVCqMoQOQWc1IdH13U/DkGlz3H2VllMsn22MXST25eXy7aXb KrMyp5W87iHxgtIGfIBqafF4c0K3huLS4tIINReOO2ke53LLkExRQlmI2YLFI0+UZO0DJrUmv7O3 vLazmu4I7q63fZ4XkAeXaMttU8tgcnHSuDTR9YtvBXhvRb7QpL8jTDZ6k9l5CyxxbYwbcGSUABwA HdWOfLOApZWTQ1fSNT1HxC88VhIsF0+nqZHkjxCLO8kmZnAYnEisNm0Mcn5wlAHSQ6Jp1va6XbRW +2HStv2Jd7HysRNEOc/N8jsOc9c9eaz7bwXolppy2EKXwt02eSDqVyzW+wFV8pjJui+Vip2FcqSD kcVzeqaB4mn8ayajbzakLMXsTxompMkXlK1hn90HC42i+yCvPOckpR4F0DxNpWrQT6xNqTxNZFJh dak1wvm+TZnhS7AHzRecgevYrQB2lxoel3draWs9hA9raZEEGzEaAxNEV2Dgrsdl2kYwenSs+48L +HYbONLuHEYlw0txdyF52kKp5csjNulV8RpscsrbUXBCqBwemeE/GUSW6XVzrJEqWyXW7WpGIG7T 2l2nzcqcrf8AK4OMgcFAdTTPDXiU+Bn02/mvvt76hps6yzXi3EkYQWjTsrSFx8siTsFORkcAggEA 7SfStF1W/lnntrS7uIENrMGw+0Mmdjr0zskbG4ZCytjAkbNObwv4dSK2tbmHezysYDcXcjyvOVz5 gdmLNMqR/K+d6KpClRmuXk0bVtK1qWe8l1Waw/tX7dPqq38UBWFILUfvkV4ozGTG4YlGISNgFzIH FjXVvLuLTX0uK+tbq68Nahb2K3UhSdLh1t3jjZ2bImwjnls/u3OflJoA6hvC+ktdSTmGf95Kk7Qi 7lEIkWVZQ4iDbFbzEDEhQSS2c7mzc03S7TSbdobRJAHcvI8sryySNgDLu5LMcAAEk4CgdABXFnSP FVh4qmeO/wBVutN8p/LliaJ3YGE5VvNmWNZDPl1IgIGUXeseUSva6R41t7/QHknnFrHt81IJGlMe Z3aTzfNuud0TRqcm42FX8vbhSwB3F1Zad4g06Bnfz7dts9vcW07IwyOHjljIYZViMqeVYjoTmmul +HNN1uyKpaWt9IhWzthLsVtiBS0cOdpdY8LvC7gny528Vhw+E9WufBvhmwF//Zt1YafFDcJ5lxy4 jQEZt7iIHBU9d3XjHOdDWre6uvE1pEdDnn00/Z5Zru2aAF5I5S0ayF3VxHE2JMKGLFscAMsgBGl3 4Iu9Ju2i1nTZNPgd5Ll49THlwvPN5m9mD4RzKMo3BU/cIrQsovDkj6ZZ2dxaSy2yHUbONLne7K6u hn+8TIG818u2dxYkknmuf1Hw9qdxCNQaC7+0nXZr64t7GeOOWSAQyWkYiZmAUtEImYl1YZcqVbaB HYaJ4is9ctr3Ubf7dLDsvbh7d4xFPOLIQMyAlWFwz5UA7YfKOcCTmgDY/sfwrHFqFkbzFvZ7Jb2z bVZfJtowrFVeIybY4Shb92QEKgZBCjFyLw/od6ft8HmSwXLx3SiK9lNuzCRZlkSMP5YJdVcso+Yk k53Nnm77TL+/uNQll8OX0E1pKX0w2xtTCcXUc27BmUu0rxpK+7Z8qlQQ2Wcm8O+JU8DW9lYXc8Wp Nd3s0jG4WOTFyLnYXMeF3I88Tvt4BjYpuIUEA7TTdLtNJt2htEkAdy8jyyvLJI2AMu7ksxwAASTg KB0AFXK4O50zxW02r+RJdrPIk4hmN2PKeVplNm8abvkSGMESrtXfz8s2ajk03xcutaJMkt8tpHsW 4XzknYSecxndyZolaORCu3925Rc7Y4WAUgHcC/s2s4bwXcBtZtnlTCQbJN5ATa3Q7iygY65GOtWK 8nl0jXdO8EX9texzxwx+FF01YZpwQ120KIscCRyFXy+5csgkLsArOrBV9YoAKKKKACiiigAopCQO pA+tJvT++v50AOopu9P76/nRvT++v50AOoo60UAFYfi7V7nQ/Dsl9Z+X5/2i2hBe3ecKJJ0jJ8tC GcgOSFBySAK3Kr3tjb6hAsN1H5kayxzAbiMPG6yIePRlU++OeKAPP9S+IGp6RFJ5kMF20dolzvey msi2FvZXUxSMWXKWYQE9C+/5hhTYsPGWrX/iltOLwQW8crKRHpNxcmQC9uYMGRH2w/JAvzOMZZj0 GB2F5oel6heC6vLCC4m8poSZU3BkIIIZTw3DuBkHAkcDAds04vCOjwXsF5BHdwTwoibob6dBIFdn HmBXAlJaR2JcMWLtnOTQBHoHi6z8SRQSWVnfIJYorjE8QjKQyK5SQgt90tG6cZJI3AFCHNebWdYj 1HVIQ+lLb20TO1w5dY7DBXHnOSBIzRlpSg2bAoVmxIslWPDXhHTvDWk2FpbL++tookknjLRiZkjK ZKhsbSWd9nK73Z/vEsR/BeiSLdIUvhHdSmeSNdSuQiyGUTb0USYjbzAGyoB69iaAI5NX1UeE4b+X 7JYXJfa8tzbyMNm4qjLbghy8nyYhLBlMmMsy7WF1rV5tZ07S2sI7Ce70ea8dpiJ1huFaFRH8rguF Mh3cAN8u1uuLE/hLSrm3t4Zm1JxbuHhc6pc+YjASDIfzN2cSuCc8ggHIVQNCPS7SK4tLgJI09pbt bQySSu7CNihYEsSWJMaHc2Tx15OQDP8ACd/qWp6IbzU5bSV5LiYQSWsDRJJCrlY3AZ3yHVQ4IOCH GM9Tyei+L/EWs6Xockd1Yx3WqyxKTNod1FHAGtppmCl5QJuYgAyMABzzuFdpZaKun3FktrPJFp9j ZCztrJS20DK/M5JO4hUQL0Iy+S27iOfwvpNxpOnaYYZ47XTdn2QQXcsLxbYzGMOjBvuMw5POeaAO Pu/iBqZ1R4bWGCLyYkU2rWU0/wBoujcz25hFwrKkKtJCFR5F535I4KjqLDxdZ6lfy2VtZ3xmSWaJ Q8QQSeTOIZmUlgCqFkYnuGwu5lZVuSeHNIlt7u3NjGsF3ZLp80cZKKbdQ4WMBSAoAkcfLg8+wxT0 jwjp2lPczhd13cXc1y9xGWib95cGbbw3+4rH+NY0DZVQoAI9c8RX2l6y1pbaXJdouj3eoKq/euJY miCxJgkgnec5U/eXGcMKp6f4m1TUvDOuT6ZBBqup2ErW9q8Efkw3bmKORHCyPxH+9HO87lXcpO5R XSXWl2l5cLcTJJ5628tsskcrxsschQuAVIIJMaHcORjgiqaeF9JSza18md43yXMl3K7uxKESM5Ys 0i+Wm1ydybQFKigDl7bxtfzapY2Mk0CBPK+3T/2ZIV3yXMtv5XExETLJEI87pdzPuA2IzVseEPEl 5r/2n7XHAu2KG4TylK7BJv8A3fJPmquzidMRyZO0fKa0E8LaNHLayLZ4a2wV/eviQhi4aUZ/esHZ nDPuIdmYEMSasaZomnaN5v2C38rzcA5dn2qudsa7idka5O1Fwq5OAMmgDQooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACsPxhqk2j+E7+7tXkS7KLBbOkRlKTSsI422AEsA7qSAGJAOAx4O5 Ve9sbfUIFhuo/MjWWOYDcRh43WRDx6Mqn3xzxQBw8Xiy90G1la+ln1CO4u3aw/tIx2d3PAkUW5I4 BGjyTGVpAibF3YGWAKFrF18RPIXz4tGnmspbRLq1uEk3GRZZY4YN0aKzp5jO7AbS2xOFZ98adJqP h7TNVvbe8u4JGngeN1aOeSMOY33x7wrASBW+ZQ4IBJx1OcfRfA8Fgmo/2hNHcm9SGLy7dZYYoEiZ mjEStK7RFWckbGULhdqqRkgGhDrVze+EzqqwR6ZPsYv/AGkHjSAKxV5GDBWKAAuoYIWGM7MkrY8N TX1z4V0ifVBINQksoXuhJHsYSlAXyuBtO7PGBipDomnNoc2im3zYTRPDLGXbMivneWbO4s25iWJ3 EkknJzWhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFAFLVFD2ioc7XljVsHGQWAI/I1mxC0ub6SCGytQkBxKXjXccjgBeo9ctjIHGQcjXvLdrq3 8tX2MGDK3oQcj9QKqvY3byxytLa+ZHnawiYEZ6j7/I6cdMgHsKAMK8t0uLiG1fymRIbtoluEMsYd ZERCyZG4AMeMj6isqG+0bU9I1c2+lWtnqOnJIHe2EbBJUUnKSx+jL91trgYLIu4Z6W+8OzXcKBLz ZKEljdyGw6yEFvulWU5AIKsCMVXHhi6+xfYVubGC08vySltaMhWPGCq5cheOOnFAHSp/q1+gopVG FA9BRQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGfp1reW99q0tzcebDc3ay2qby3lRiGJCuD9350ds Dj5s9Sa0KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//2Q=--90e6ba476b13485c07049c480aa2-- ========================================================================Date: Mon, 14 Feb 2011 21:04:16 -0500 Reply-To: cliff <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: cliff <[log in to unmask]> Subject: Re: spm_create_vol &&& spm_write_vol Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --000e0cd5d004ec29fe049c48948b Content-Type: text/plain; charset=ISO-8859-1 Thanks~~ Does that mean if I use "spm_create_vol" to create headers followed by "spm_write_vol" (if it is necessary), then the headers created by "spm_create_vol" will be overwrited? On Mon, Feb 14, 2011 at 5:32 PM, John Ashburner <[log in to unmask]>wrote: > spm_write_vol calls spm_create_vol. Sometimes, for example in the > fMRI stats, we may wish to create headers but only write out a slice > of data at a time (as there is not enough memory to hold all the > images). > > best regards, > -John > > On 14 February 2011 22:16, cliff <[log in to unmask]> wrote: > > Hi, all, > > I thought before that "spm_create_vol " is used to create the ".hdr" > files > > of the images. > > However, today I found that "spm_write_vol" can produce both of the > ".hdr" > > and ".img" files. > > > > then, what is the "spm_create_vol " exactly used for? > > > > Thanks~ > > > -- Sincerely, Senhua Zhu --000e0cd5d004ec29fe049c48948b Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Thanks~~

Does that mean if I use "spm_create_vol" to create headers followed by "spm_write_vol" (if it is necessary), then the headers created by "spm_create_vol" will be overwrited?

On Mon, Feb 14, 2011 at 5:32 PM, John Ashburner <[log in to unmask]> wrote:

spm_write_vol calls spm_create_vol. Sometimes, for example in the
fMRI stats, we may wish to create headers but only write out a slice
of data at a time (as there is not enough memory to hold all the
images).

best regards,
-John

On 14 February 2011 22:16, cliff <[log in to unmask]> wrote:
> Hi, all,
> I thought before that "spm_create_vol " is used to create the ".hdr" files
> of the images.
> However, today I found that "spm_write_vol" can produce both of the ".hdr"
> and ".img" files.
>
> then, what is the "spm_create_vol " exactly used for?
>
> Thanks~
>

Sincerely,

Senhua Zhu

--000e0cd5d004ec29fe049c48948b-- ========================================================================Date: Mon, 14 Feb 2011 18:09:10 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: EEG significant clusters outside the MIP glass brain Comments: To: MP <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00248c6a5726382f06049c48a80a Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid. Cheers, Michael On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote: > Hello Vladimir, > > I tried to use the inner skull mask as you suggested to avoid getting > significant cluster outside the glass brain, but it is drastically changing > the results. Attached the figures of results with and without applying the > mask. Could you please tell me if I am doing something wrong here? > > P.S. I include the iskull mask as an implicit mask in the flexible > factorial design. > > Thanks > - Muhammad > > > On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote: > >> Dear Muhammad, >> >> Threshold masking will not necessarily work. It depends whether the values >> outside the brain are really low. You can use an explicit inner skull mask. >> There should be one in EEGTemplates as far as I remember or you can make >> your own. >> >> You can either re-run your stats or use small volume correction for inner >> skull but the latter will not fix the graphics. >> >> Best, >> >> Vladimir >> >> Sent from my BlackBerryÂ® smartphone from orange >> ------------------------------ >> *From: * MP <[log in to unmask]> >> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]> >> *Date: *Thu, 30 Dec 2010 21:33:40 -0500 >> *To: *<[log in to unmask]> >> *ReplyTo: * MP <[log in to unmask]> >> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain >> >> Hello all, >> >> I am doing EEG analysis using SPM8 and while viewing the results, I see >> that some of the significant clusters appear outside the brain. I understand >> that it could be due to smoothing of the images, but is there a way to >> restrict these clusters inside the MIP? I think Threshold masking may be the >> way to go, but I am not sure what threshold value to use? >> >> If indeed threshold masking is the way to go, would I need to re-do all >> the analyses? >> >> Any help would be much appreciated. >> >> Thanks >> - Muhammad >> > > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --00248c6a5726382f06049c48a80a Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.

Cheers,
Michael

On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:

Hello Vladimir,

I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?

P.S. I include the iskull mask as an implicit mask in the flexible factorial design.

Thanks
- Muhammad Â

On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:

Dear Muhammad,

Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.

You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.

Best,

Vladimir
Sent from my BlackBerryÂ® smartphone from orange
From: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
Date: Thu, 30 Dec 2010 21:33:40 -0500
To: <[log in to unmask]>
ReplyTo: MP <[log in to unmask]>
Subject: [SPM] EEG significant clusters outside the MIP glass brain

Hello all,

I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?

If indeed threshold masking is the way to go, would I need to re-do all the analyses?

Any help would be much appreciated.

Thanks
- MuhammadÂ

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--00248c6a5726382f06049c48a80a-- ========================================================================Date: Mon, 14 Feb 2011 21:27:10 -0500 Reply-To: MP <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: MP <[log in to unmask]> Subject: Re: EEG significant clusters outside the MIP glass brain Comments: To: Michael T Rubens <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundarye6ba6e8f6ed086e6049c48e60a Message-ID: <[log in to unmask]> --90e6ba6e8f6ed086e6049c48e60a Content-Type: multipart/alternative; boundarye6ba6e8f6ed086de049c48e608 --90e6ba6e8f6ed086de049c48e608 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii mask on stat map. See attached. What do you think is going on? On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>wrote: > that is not right, the results should change due to the mask. Why don't you > just run the analysis and mask out the resulting stat map. You're obviously > not interested in anything off the brain (i.e., noise) anyway, so its valid. > > Cheers, > Michael > > > On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote: > >> Hello Vladimir, >> >> I tried to use the inner skull mask as you suggested to avoid getting >> significant cluster outside the glass brain, but it is drastically changing >> the results. Attached the figures of results with and without applying the >> mask. Could you please tell me if I am doing something wrong here? >> >> P.S. I include the iskull mask as an implicit mask in the flexible >> factorial design. >> >> Thanks >> - Muhammad >> >> >> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote: >> >>> Dear Muhammad, >>> >>> Threshold masking will not necessarily work. It depends whether the >>> values outside the brain are really low. You can use an explicit inner skull >>> mask. There should be one in EEGTemplates as far as I remember or you can >>> make your own. >>> >>> You can either re-run your stats or use small volume correction for inner >>> skull but the latter will not fix the graphics. >>> >>> Best, >>> >>> Vladimir >>> >>> Sent from my BlackBerry® smartphone from orange >>> ------------------------------ >>> *From: * MP <[log in to unmask]> >>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]> >>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500 >>> *To: *<[log in to unmask]> >>> *ReplyTo: * MP <[log in to unmask]> >>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain >>> >>> Hello all, >>> >>> I am doing EEG analysis using SPM8 and while viewing the results, I see >>> that some of the significant clusters appear outside the brain. I understand >>> that it could be due to smoothing of the images, but is there a way to >>> restrict these clusters inside the MIP? I think Threshold masking may be the >>> way to go, but I am not sure what threshold value to use? >>> >>> If indeed threshold masking is the way to go, would I need to re-do all >>> the analyses? >>> >>> Any help would be much appreciated. >>> >>> Thanks >>> - Muhammad >>> >> >> > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > --90e6ba6e8f6ed086de049c48e608 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii mask on stat map. See attached.

What do you think is going on?

On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]> wrote:

that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.

Cheers,
Michael

On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:

Hello Vladimir,

I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?

P.S. I include the iskull mask as an implicit mask in the flexible factorial design.

Thanks
- Muhammad

On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:

Dear Muhammad,

Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.

You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.

Best,

Vladimir
Sent from my BlackBerry® smartphone from orange
From: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
Date: Thu, 30 Dec 2010 21:33:40 -0500
To: <[log in to unmask]>
ReplyTo: MP <[log in to unmask]>
Subject: [SPM] EEG significant clusters outside the MIP glass brain

Hello all,

I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?

If indeed threshold masking is the way to go, would I need to re-do all the analyses?

Any help would be much appreciated.

Thanks
- Muhammad

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--90e6ba6e8f6ed086de049c48e608-- --90e6ba6e8f6ed086e6049c48e60a Content-Type: image/jpeg; name="Results_with_inclusive_mask.jpg" Content-Disposition: attachment; filename="Results_with_inclusive_mask.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gk66zxad0 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAMxAjgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiuD1j4ganHf6la+GPCV3r6aU5jvriO6jijVwgbZH94yuMkMoGVIAwcius0PWLfxBodl q9ok8dveRLLGs8RjcA+oP8xkHqCQQSAaFFFZfiPX7Hwt4fvNa1JpBaWqBn8tdzMSQqqB6liBzgc8 kDmgDUooooAKK5/SPE/9qeL/ABHoH2Pyv7G+zfv/ADd3nedGX+7gbcYx1OfasfxJ431nTPGUfhrQ /C39tXTaeL9j/aCW21PMMZ++uDggd+/TigDuKK5vwh4rbxNb30V5psmlatp9wbe80+WVZGiOMqwI xuRlOQ2ADg4yBk1/iB44h8A6NZ6pcWMl3BPepayLHIFZFZWYuMjDEBPu5Gc9RQB1lFc3f+LobXxP 4Y0m3hju4NeS4eO7jnG1FijEgIABDhgeuR681c8ReIbfw7Z2skq77i9u4rGziJIEk8hwgZgDtXgk tg4AOATgEA2KKKKACiiuf8VeJ/8AhGf7E/0P7T/aeqwab/rdnleZu+foc429OM56igDoKKKKACiu f0jxP/ani/xHoH2Pyv7G+zfv/N3ed50Zf7uBtxjHU59qx38eaj/wm2t6HbeGJ7yx0aJZbu8t7lWk +aAyoqQkAuzEbAAevJxQB3FFV7C6+3adbXn2ee38+JJfJuE2SR7gDtdezDOCOxqxQAUVz+r+J/7L 8X+HNA+x+b/bP2n9/wCbt8nyYw/3cHdnOOox71n+KvGWo+GfEOiWX9gfadM1O7gsv7Q+2KnlTSOw 2+XtLHCruzwDnGRQB2FFc/4q8T/8Iz/Yn+h/af7T1WDTf9bs8rzN3z9DnG3pxnPUV0FABRRWfaar 9r1jUdO+wX0P2Hyv9Jmh2w3G9d37ps/Nt6N0waANCiuf8beJ/wDhDvCF9r/2P7Z9l8v9x5vl7t0i p97Bxjdnp2roKACis/XdT/sTw9qereT532G0lufK3bd+xC23ODjOMZwa5/QvHX9t+IdM0n+zvJ+3 eH4tb83z92ze4XysbRnGc7sj6UAdhRRWfd6r9k1jTtO+wX0327zf9Jhh3Q2+xd371s/Lu6L1yaAN CiuX0/xtb3mo6PaXWnX2myazFO9nDexFJt8JPmJInOz5drqcncM52kLu6igAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigDyvwh4k0Xwff+ObDxDqlpp12mu3N/5M0oDPBKivGyAffJUfdXLDgEAkCqfj7W9O8TReBH1G4v rHwbrEskl/5qNAGYKrQJK2PlUtnvtIBcHChx6hqehaPrflf2tpVjf+Tny/tdukuzOM43A4zgdPQV YvrCz1Ozks7+0gu7WTG+GeMSI2CCMqeDggH8KAPnxBNq3w+8P6eupak+lr44istJvmJjnFmAyxuj lQcglsHHykYwNuBX8XeHtM0nRvibo9lBJFp+k3Gk3VjbmeRlhlkXa7jcxyWVyDnPb0GPod9J02S3 tLd9PtGgsnR7WNoVKwMgwhQYwpUcAjGO1DaTprPeu2n2he/QJeMYVzcKFKgScfOApIwc8HFAHj9t 4R8H6x8TrbQrKOO68NjwuXijtb6Ro3IvmJy6vlwHJOCSAQO4GOY8TXq/2z4n1ibVtSj8f2Gupa6H aqG3fZt2I0SPbhkdGcnscLn/AFh3/QdjoWj6ZLHLYaVY2kkcRgR4LdIysZYuUBA4UsS2OmTnrUkm k6bNqkOqS6faPqEKbIrtoVMqLzwr4yB8zcA9z60AeZx2Whah47+KFr4leBNIf+yvtBnnMKDEWVy4 Ix8wXvz0q5qWrabo3x9W41TULSxgbwuEWS6mWJS32onALEDOATj2Nd5caFo939s+06VYzfbtn2vz LdG+0bPueZkfNt7ZzjtUepeGtB1m4W41TRNNvp1QIsl1apKwXJOAWBOMknHuaAPF9chbXh8R/Fug GO6SxvdPutNvY5F2rPaRjznAJwwVHbqCrZ+XdxUb/wDE0/s/xW/73+2fiBa/Zbr7vnWcO+OH5ONu MOOQGPU54r3j7BZ/2d/Z32SD7D5XkfZvLHl+Xjbs29NuOMdMVXTQtHjs7Wzj0qxS1tJRPbQrboEh kBJDouMK2STkc8mgDyOKObRvjP4O8KSiQwaW+oS6fIxLZs5oCY0yXZiY2SWPnHyohxzXYeLJ5l+K 3w9t1lkEDvqLvGGO1mW3wpI6EgMwB7bj612k1hZ3F5bXk1pBJdWu77PM8YLxbhhtrHlcjg461l+J 9Dm1q3sJLS6kt7zTb2O+t9shRZWQEGJ2AJCOrOpIBxuzhgCpAPEH0yBtUfU1ku4rxviRLYebBdyx FYJf9Yq7WAUsAAWGGIAGcCrG3SLHwHq2iagbs6TD44k0+0tf7QMEAjBBEc0hDFYB8znAJ3AHg5Ye 5/2Fo/8A0CrH/j7+3f8AHun/AB8f89un+s/2uvvQ+haPJZ3VnJpVi9rdyme5ha3QpNISCXdcYZsg HJ54FAHz5YW8dz8IbzS7rXdNs7eLxabeBHnnfT51Cq3keYpLrASWkDE/wgkgncLCXEM/w+8Pp4bg ktp4PHEUcNrfXou7e3mwxVI5E5aDLKcjklmOWzuPvb6Fo8lndWcmlWL2t3KZ7mFrdCk0hIJd1xhm yAcnngUJoWjx2drZx6VYpa2kontoVt0CQyAkh0XGFbJJyOeTQB4RrH2w/D+90678hvEL+JYbXxNc 3uVjmj3M0MkjDDx22PKCsNgAB24zzbtBe6d8K7DT5dStJdBl8ULaXkunGVLWPT3c+annOqsYvMLK ZAxBDbd55Fe2XGhaPd/bPtOlWM327Z9r8y3RvtGz7nmZHzbe2c47Uf2Fo/8AY/8AZH9lWP8AZn/P l9nTyfvbvuY2/e56deaAPN/hSdG/4Tvx3H4en87SYf7Pht3EzyjCROhCu5JKgqQOSMAY4xVe+/5D nxr/AOwVb/8ApFJXqllpOm6a8j2Gn2lq8iRo7QQqhZUXagOByFXgDsOBXH2vhTXrb4la/qjDRrjw 3riQpeQT73nKxwGMALjZgsTkEnK+9AHB6R4Z07xX4y8LadqwnksR4FtJZIIpmjWfEgAVypBKgkNg EfMqntiqmjXK6j4D+G2neI7yQeF7t7+PUnuLhoomMRf7Mjy5BABX5V3AHYODtGPd4NJ021uIri30 +0hnitxaxyRwqrJCDkRggZCA87elR/2Fo/8AY/8AZH9lWP8AZn/Pl9nTyfvbvuY2/e56deaAPK9O GkL4n+E6aDqV3qOlomqpbXN2T5jKseMHKqcKQVAwMBRW58Rr+z1Oz8DXlhdwXdrJ4rstk0EgkRsG QHDDg4II/Cu8XSdNV7J10+0D2CFLNhCubdSoUiPj5AVAGBjgYri/E/g3WNQ1bw1Z6JDodj4a0nUL fUHhVXimEiSOWCKq7NpVs44JbPNAFP40z2lro3hi4v5ZIbOLxHavPJGzqyRhZCxBT5gQMnK8+nNc XfaRFe+GfHlj4auv+JF4fltNR0KeK6d4reZIjJceVKCSWwW+XJCs4Pyk5r3i6sLO+8j7ZaQXHkSr PD50Yfy5F+6656MMnBHIrLudJm0bw/c2/g3T9GsbxnDxRywmK3LZUMXEQBztBAI7gdqAOL+Gt5b+ MvF/iXx5AJ/ssvk6bYJcuRJAiRo8q7ASoVnKsME87umTnn/EyxSan8YY5tW/sqN4tKQ3ZR2C5jxt YIC2187DgHhjwRxXpHw98J/8IV4LsdGkeCS6TdJczQptEkjEk+7YGFDHkhRwOg3G0nTWe9dtPtC9 +gS8Ywrm4UKVAk4+cBSRg54OKAPnSRtLh+FHxB0/Tofs8ltLYefFaah9rsGJkQCW3Y5Yb9pLBixG FBOQQNDxd9sj8K/EGDxV5Eni0fY5Enjz5TWLTRFUtw2D5aOGDYH3iNxY817nH4a0GHS5tLi0TTU0 +Z98totqgiduOWTGCflXkjsPSrE+k6bdXEtxcafaTTy25tZJJIVZnhJyYySMlCedvSgDxvxLrml6 94+8Wy6TfwXsMHgW6hklgfem/fuwGHDcOvQn06girngueG18deHri4ljhgi+Hts8kkjBVRRKpJJP AAHOa9Qh8NaDbIUg0TTYkNu1qVS1RQYWYs0fA+4WJJXoSSak/sLR/wDoFWP/AB6fYf8Aj3T/AI9/ +ePT/V/7PT2oA8z1zQdI8a/Gy2t72WS50uTwutxstrgrHdL9pO0MyHLJlg4weqqe3PP+BZ5rl/g4 88skrhNYQM7FiFVWVRz2CgADsABXuEGk6ba3EVxb6faQzxW4tY5I4VVkhByIwQMhAedvSo7fQtHt Psf2bSrGH7Dv+yeXbov2ff8Af2YHy7u+MZ70AeJ+H55rb4SfC54JZInPiiNCyMVJVprhWHHYqSCO 4JFe+VzbeFIR4g0W4tBHYaXoyTPb2VphI5ZpQVJaMAKAqlyCOSZW+7g7+koAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiom3fa48fc8t8/e65XHt6 9efTvQBLRRUUO7zZ93TzBt+902r68dc9OPxzQBLRRUVru+yQ7/v+Wu773XH+1z+fPrQBLRRVTUf+ PZP+u8P/AKMWgC3RRUTbvtcePueW+fvdcrj29evPp3oAlooqKHd5s+7p5g2/e6bV9eOuenH45oAl ooqK33eUd/XzH/vdNxx156fh6cYoAloqK43eUNnXzE/vdNwz056fh68ZqWgAooooAKKiut32SbZ9 /wAttv3uuP8AZ5/Ln0qWgAoqJt32uPH3PLfP3uuVx7evXn071LQAUUVFdbvsk2z7/ltt+91x/s8/ lz6UAS0UVUk/5C1v/wBcJf8A0KOgC3RRRQAUVFcbvKGzr5if3um4Z6c9Pw9eM1LQAUVEu77XJn7n lpj73XLZ9vTpz69qloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqM/wDHwhyfuNxg46jv0/Pn071J UTIpu43x8wjcD5T0JXv0HQcdfToaAJajj+/NyT8/cEY+UdM9fw4/HNSVFCirLOQOWkBPykc7VHfr 06jj8QaAJajg4t4hkn5ByQQTx6Hn8+akqK1RY7SFFGFWNQPlK8Y9DyPoeaAJaqaj/wAeyf8AXeH/ ANGLVuqmo/8AHsn/AF3h/wDRi0AW6jP/AB8Icn7jcYOOo79Pz59O9SVEyKbuN8fMI3A+U9CV79B0 HHX06GgCWo4/vzck/P3BGPlHTPX8OPxzUlRQoqyzkDlpAT8pHO1R369Oo4/EGgCWo4eEPJPzt1BH 8R9f/wBXpxipKit0VIiFGB5jn7pHJYk9f59D24oAWblByR869AT/ABD0/wD1evGakqK4RXiAYZHm Ifuk8hgR0/n0HfipaACiiigCOfm3lGSPkPIBJHHoOfy5qSorpFktJkYZVo2B+Utxj0HJ+g5qWgCM /wDHwhyfuNxg46jv0/Pn071JUTIpu43x8wjcD5T0JXv0HQcdfToaloAKjn5t5Rkj5DyASRx6Dn8u akqK6RZLSZGGVaNgflLcY9ByfoOaAJaqSf8AIWt/+uEv/oUdW6qSf8ha3/64S/8AoUdAFuiiigCO blByR869AT/EPT/9XrxmpKiuEV4gGGR5iH7pPIYEdP59B34qWgCMf8fDnJ+4vGDjqe/T8ufXtUlR Kii7kfHzGNAflPQFu/Q9Tx19eoqWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAComRTdxvj5hG4Hyn oSvfoOg46+nQ1LUTH/S4xsz+7f59mccrxu7Z9O+PagCWooUVZZyBy0gJ+Ujnao79enUcfiDUtRQn 97P8m394OdmN3yrznv6Z9sdqAJaitUWO0hRRhVjUD5SvGPQ8j6HmpaitTm0hOzZ+7X5Nmzbx029v pQBLVTUf+PZP+u8P/oxat1U1H/j2T/rvD/6MWgC3UTIpu43x8wjcD5T0JXv0HQcdfToalqJj/pcY 2Z/dv8+zOOV43ds+nfHtQBLUUKKss5A5aQE/KRztUd+vTqOPxBqWooT+9n+Tb+8HOzG75V5z39M+ 2O1AEtRW6KkRCjA8xz90jksSev8APoe3FS1FbnMR+TZ+8fjZt/iPOPfrnvnPegAuEV4gGGR5iH7p PIYEdP59B34qWorg4iHyb/3icbN38Q5x7dc9sZ7VLQAUUUUARXSLJaTIwyrRsD8pbjHoOT9BzUtR XRxaTHZv/dt8mzfu46be/wBKloAiZFN3G+PmEbgfKehK9+g6Djr6dDUtRMf9LjGzP7t/n2ZxyvG7 tn0749qloAKiukWS0mRhlWjYH5S3GPQcn6Dmpaiuji0mOzf+7b5Nm/dx029/pQBLVST/AJC1v/1w l/8AQo6t1Uk/5C1v/wBcJf8A0KOgC3RRRQBFcIrxAMMjzEP3SeQwI6fz6DvxUtRXBxEPk3/vE42b v4hzj2657Yz2qWgCJUUXcj4+YxoD8p6At36HqeOvr1FS1Ep/0uQbMfu0+fZjPLcbu+PTtn3qWgAo oooAKKKKACiiigAooooAKKKKACqeqalDpNg13MsjgOkaRxgFpJHcIiDJAyzMq5JAGckgZNXKz9a0 z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkAGXdeMra0Rnk03UgLe3+1X+Y0U2MW513yB nBcZil/1XmZCZGQylsvXfiIljoeqXllpV88tt9rgt5ZolWGS5g8zcvLhmULE8hYcFVKg+Z8lXNQ8 I32oJdLJrMbHUrIWOps9nkvFulOIMOBEQJ5AC3mcBM7iGLR6r4F/tPw8dJ/tHys3eoXPm+Ru/wCP pLlduN38P2nOc87O2eAC5Y+LxqNhLc22g6y0kdw9r5BijDNMjusihi+zC+WSXLBDkKGL5UEfjK2u CEs9N1K7mVCbiGGNC1s/mPEEfLgEmWKRNyllGwszKmHqu/g6abwxHpdxe2lxOt7PeyedZF7WdpZJ JCkkBfLIDLkDfwyI2TjBr6d4Daxi0qI31oEsbiW4H2fT1iMRedpitu24mFG3CN1JcNGoUbckkAj0 zx1cXej6NeXFl5ck2nwXd7GqgsXnYRW8cQ34HmybirMx2qmHClsruR65N/ammpNayW9pqKSRRrPG Ulhuo9zGNsEhgyLIQw+X90SGcSJXP2Pha+0hLbSZLmO6judHh0s3Yst0Sm2Z2RZYizZSWOV1bLKP 3ZAZWkTboW2iTW1/4f0xPMeDSnmv5bjyykQZ0kiSCNTwqATSbVBby0hRT95WoA6yiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKiY/6XGNmf3b/PszjleN3bPp3x7VLU ZU/aEbYCAjDdgZHI4znv9O3bHIBJUUJ/ez/Jt/eDnZjd8q857+mfbHapajjUh5iUC5fIIAG75Rye efTt0/EgElRWpzaQnZs/dr8mzZt46be30qWo4FK28SlAhCAFQAAOOmATj8zQBJVTUf8Aj2T/AK7w /wDoxat1U1H/AI9k/wCu8P8A6MWgC3UTH/S4xsz+7f59mccrxu7Z9O+PanSyxwQvNNIscUalndzh VA5JJPQUhU/aEbYCAjDdgZHI4znv9O3bHIFupJUUJ/ez/Jt/eDnZjd8q857+mfbHapajjUh5iUC5 fIIAG75RyeefTt0/EgElRW5zEfk2fvH42bf4jzj36575z3qWo4VKoQUC/OxwAB/EeeCevX/DpQAl wcRD5N/7xONm7+Ic49uue2M9qlqOZSyABA3zqcEA/wAQ55I6df8AHpUlABRRRQBFdHFpMdm/923y bN+7jpt7/SpajnUtbyqEDkoQFIBB46YJGfzFSUARMf8AS4xsz+7f59mccrxu7Z9O+PapajKn7Qjb AQEYbsDI5HGc9/p27Y5koAKiuji0mOzf+7b5Nm/dx029/pUtRzqWt5VCByUICkAg8dMEjP5igCSq kn/IWt/+uEv/AKFHVuqkn/IWt/8ArhL/AOhR0AW6KYJYzM0IkUyqoZkB+YA5AJHodp/I+lPoBqxF cHEQ+Tf+8TjZu/iHOPbrntjPapajmUsgAQN86nBAP8Q55I6df8elSUARKf8AS5Bsx+7T59mM8txu 749O2fepajCn7Q7bAAUUbsDJ5PGc9vp3754koAKKKKACiiigAqOeZba3lncSFI0LsI42diAM8KoJ Y+wBJ7VJRQBw9n8VvDk0cv2tr61mjuJoTF/Z105ASRkBJEXBIUEr1UnB5Bqx/wALQ8J/8/l9/wCC q7/+NV5dN/yFNX/7Ct9/6UyUlAHpVh8VvDdxp1tNeNfWl1JEjzW/9nXUnlOQCybhFhsHIyOuKL/4 reG7fTrmaza+u7qOJ3ht/wCzrqPzXAJVNxiwuTgZPTNea0UAepf8LQ8J/wDP5ff+Cq7/APjVV734 reG4IFe1a+uZDLGhT+zrpMIzqHfJi/hUs2O+3A5Nea0UAepf8LQ8J/8AP5ff+Cq7/wDjVV734reG 4IFe1a+uZDLGhT+zrpMIzqHfJi/hUs2O+3A5Nea0UAepf8LQ8J/8/l9/4Krv/wCNVXuPit4bjntE ga+mjllKTv8A2ddL5KbGIfBi+b5gq4H9/PQGvNaKAPUv+FoeE/8An8vv/BVd/wDxqq7/ABW8NrqM MKtfNavFI8lx/Z10PLcFAqbfKydwZznts9xXmtFAHqX/AAtDwn/z+X3/AIKrv/41VdPit4bbUZoW a+W1SKN47j+zro+Y5Lhk2+VkbQqHPff7GvNaKAPUv+FoeE/+fy+/8FV3/wDGqr2/xW8NyT3aTtfQ xxShIH/s66bzk2KS+BF8vzFlwf7mehFea0UAepf8LQ8J/wDP5ff+Cq7/APjVV9U+KvhzTrKG5Vr5 xK6lfM066iUxCRVlkDNFgiNWLED0A6kV5rS+Ov8AkT9C/wCwVq3/AKMioA99gmW5t4p0EgSRA6iS NkYAjPKsAVPsQCO9SUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAIxKqSFLH0GM/rSIxYZKMns2 P6Gq32Sf/oI3P/fMf/xFH2Sf/oI3P/fMf/xFAFuoJQ4uFlVC4SJxtULknK4AJIx0Pt64wKj+yT/9 BG5/75j/APiKQ2MpcOb+43AEA7IsgHr/AAew/KgCOTUbpDhdFv5OOqvB/WQVDBf3glmLaJfqHbeM m3H8IGOJeTx1P07Vb+yT/wDQRuf++Y//AIikFjKpYrf3ALHLEJFycY5+T0AoAz7/AFnVrfy/snhe /u92d2Lm3Tb0x1k5zz+VZ2ma94m2JBdeCbuBY4wPMF9akMRgfdV+O9dF9kn/AOgjc/8AfMf/AMRS LYyoiol/cKqjAASIAD/vigCWzmmuLVJbi1e1lbO6F2VivPqpI9+vesrWtQvIbcCHRbyfFxGAVkhU HDjBGXzyQB071o/ZJ/8AoI3P/fMf/wARTZNPklUK9/ckBg3SPqCCP4PUCgDzpLy81LxDb2fjbTrh 5TIrWtl+6htFfKgsrNJ+/b5l+XLY3MMHPHojzT/akkFhcFVRl48rnJU9S+R06d/wqvqWgW+r2bWm oTSXMDZ+WSOI4OCMg7Mg4J5HIzVo2MpcOb+43AEA7IsgHr/B7D8qzpwcL/0/mduLxUcRytK1ui+F f4fXrfXrdk8Mzy7t9vLDjp5hU5/75JohRllnJHDSAj5QONqjt16dTz+AFQ/ZJ/8AoI3P/fMf/wAR SCxlUsVv7gFjliEi5OMc/J6AVocRcqK3RkiIYYPmOfugcFiR0/n1Pfmofsk//QRuf++Y/wD4ikWx lQYW/uFGScBIhyTk/wAHrQBPcIzxAKMnzEP3QeAwJ6/z6jtzUtVPschZd97PIoYNtZY8HByP4Kt0 AFFFFAEV0jSWkyKMs0bAfKG5x6Hg/Q8Vm3GqalBM6R+H7u4QMQrxTwjIHfDOCM1qSxiWF426OpU8 A9fY8fnVf7JP/wBBG5/75j/+IoAjWeeS7ic2FxGoUoS3lHqRzkOTgY6Dr+FX6pmxlLhzf3G4AgHZ FkA9f4PYflS/ZJ/+gjc/98x//EUAW6iukaS0mRRlmjYD5Q3OPQ8H6Hiofsk//QRuf++Y/wD4ikax ldGR7+4ZWGCCkRBH/fFAFyuR8a3et2k1k2jRzYMconltrZbiaNd0eCkbMA2TweuASe1dH9kn/wCg jc/98x//ABFNOnyGZZTf3O9VKg4j4Bxn+D2FTOPNG17G2HqqjVVRxUrdHt/X4d0zN8J2egwaa9xo Uy3K3DB7i6MpkllkwDmQnkNzkrxgseBmtxnYOFEbMD/EMYH61mW2gW9leXN3azSQz3WPPaOOJfMI LEEgJgnLNz1Oeegq39kn/wCgjc/98x//ABFEFyxsPE1FUquabd++/wDX9WRJdB2iUIhY+YhwAp4D A/xEDt16jqKwr3X9fgCfZPBt9c5zuze20e306yc962GsZXGGv7hhkHBSI8g5H8HrS/ZJ/wDoI3P/ AHzH/wDEVRgYOn634gmumkvPBt7bKwCYW7tXIxk5yJBnOcY7Yz3roba4lnRWks57cnOVlKEr9drE flUf2GUOXF/cbiACdkWSB0/g9z+dL9kn/wCgjc/98x//ABFAFuikUFUVSxYgYLHGT78UtABRRRQA UUUUAfP03/IU1f8A7Ct9/wClMlJSzf8AIU1f/sK33/pTJSUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABS+Ov8AkT9C/wCwVq3/AKMipKXx1/yJ+hf9grVv/RkVAH0DRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVyem+ErvTormWz1WTT7ye9urqQQIkkFxvnkkj81 HXJIVlVihRiBt3YVCvWUUAV7FLyOzjW/ngnuhnfJBCYkbk4wpZiOMfxH146VYoooA+fpv+Qpq/8A 2Fb7/wBKZKSlm/5Cmr/9hW+/9KZKSgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKXx1/wAi foX/AGCtW/8ARkVJS+Ov+RP0L/sFat/6MioA+gaKKKACiiigAooooAKKKKACobsgWU5MpiHltmQA 5Tjrx6VHLcTLdeTHArgIHyZNpbkghRjBI47jqKmVo7m3DDDxSLkZHDKR6GgCh5X/AE56j/4F/wD2 yrNpEi5nhmkeGVFKh3ZsdTn5j3BH5UfYIf79z/4Eyf8AxVWVVUQKqhVUYAAwAKAFooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiio55Ght5ZUhkndELLFGVDOQPujcQMnpyQPUigDwKb/kKav8A9hW+/wDS mSkq6nh7xTdXOoXMfhTUgk+oXcqiSa2RgGnkYZBlHr1HB6gkEEyf8Ix4s/6Fa+/8CbT/AOPUAZ1F XYPD3iq5t4p08KaiEkQOokmtkYAjPKtKCp9iAR3on8PeKra3lnfwpqJSNC7COa2diAM8KspLH2AJ PagClRWj/wAIx4s/6Fa+/wDAm0/+PVHN4e8VQIHfwpqJBdU+Sa2c5Zgo4WUnGTyegGScAE0AUqK0 f+EY8Wf9Ctff+BNp/wDHqjm8PeKoEDv4U1EguqfJNbOcswUcLKTjJ5PQDJOACaAKVFaP/CMeLP8A oVr7/wACbT/49Ucnh7xVE8KN4U1EmV9i7ZrZgDtLfMRLhRhTycDOB1IBAKVFaP8AwjHiz/oVr7/w JtP/AI9UZ8PeKluEgPhTUd7ozgia2K4UgHLebgH5hgE5POM4OAClRWj/AMIx4s/6Fa+/8CbT/wCP VGPD3iprh4B4U1HeiK5JmtguGJAw3m4J+U5AORxnGRkApUVo/wDCMeLP+hWvv/Am0/8Aj1Rx+HvF UrzIvhTUQYn2NumtlBO0N8pMuGGGHIyM5HUEAApUvjr/AJE/Qv8AsFat/wCjIq0P+EY8Wf8AQrX3 /gTaf/HqZ4q8P+KdR8PaZaReFNSElta3do5M1swL3EkewgLKTtGPmJA2jnoCQAe6UVHBI01vFK8M kDugZopCpZCR907SRkdOCR6E1JQAUUUUAFFFFABRRRQBl3flC+AmhtZJSo2s0wSTG5sBAe49cjJ9 Oz4B9isHulLGIW6skBk3BMAnAbuDnr7emAKd7f2xvyy3SmLyQHKtGyHk8MCcuOvA5Hb71a28w2G+ OPeUiysaoUzgdAOo+lADDdPIx+zRrMiY3tvxnIBwvHJwQew5HPXD7OO3jtY/soxCyhkHOMEe/T/P eoE8+CJpIxbSJJly8MZByf4sZO/16g8cZqeyfzLC3k2qu6JTtUYAyBwB6UAT0UUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQB8/eLfjd4v8O67d20Fjoctmt3cQwOYpWYCKZo9rkSAB8KrEejqeNwrB/4 aO8Yf9A3Q/8AvxN/8drlvEWoRj4i+I9M1GRf7Kn1i8Dl03fZmaRl85eCw2kIzBMFxGFPGMcfeWk+ n31xZXUZiubeRopYyclXU4I49CDQB6z/AMNHeMP+gbof/fib/wCO0f8ADR3jD/oG6H/34m/+O14/ RQB7B/w0d4w/6Buh/wDfib/47R/w0d4w/wCgbof/AH4m/wDjteP0UAewf8NHeMP+gbof/fib/wCO 0f8ADR3jD/oG6H/34m/+O14/RQB7B/w0d4w/6Buh/wDfib/47R/w0d4w/wCgbof/AH4m/wDjteP0 UAewf8NHeMP+gbof/fib/wCO0f8ADR3jD/oG6H/34m/+O14/RQB7B/w0d4w/6Buh/wDfib/47R/w 0d4w/wCgbof/AH4m/wDjteP0UAewf8NHeMP+gbof/fib/wCO0f8ADR3jD/oG6H/34m/+O14/RQB7 snxz8XHTRdyW2hqXt/PjT7LKN373y8fNMCehOVDdMdm20X/aE8YKCfsGhkAE/wDHvL/8dri5oNnh NbmDzY0zDbMYSwidTGspD7VZWbec8y7hgDYoAA5yX/VP/umgD7uooooAKKKKACiiigAooooA5pYI 5Ti0sEa0wS8t3EsahOxRgN3Izycnoa6RtwQlQC2OATgE/Wsi3s4T9oh1CysEKIHLwpgbTkdSMgja ea1pJEhieWQ4RFLMfQCgCsst+yAm0gUkZKm4OR7cJU8Bka3jMyhZSoLqOgbHIqH7Zt4ltpoyfuht pDH0yCQD0HJGc1LbSCa1hlBYh0VgWxk5HfHGaAJaKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDLj8Qaf Nrc2kRfa3u4X2SlbKYxI2wSYaXZ5YO1lOC3cDqcVY03VbHV7drjT7mO4iVyhZPXAI/AqVYHoysrD IYE8HNoN/dWeu6LpUV9bS3/9opfDUZpHtcTGUwvE2NoYs6tiIEKpcSZkCZ6jRkuLzxDqWtSWc9pD PaW1okVyAsm+J5mc4BI25mCg5+YoxGVKswB8geO/+Sh+Jf8AsK3X/o1qddR22t6DBfxXK/2xaRrb z2QQ7pYI1wJwcBflTYhQZbEbSHjcQ3x3/wAlD8S/9hW6/wDRrVk6bqEul6jBewqrtE2TG+dkinhk cAglWUlWGeQSO9AFWitPW9K/s67Elusr6bc/vLK4cA+ZGQCAWHyl1DBXAJ2sCO1ZlABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFAHWOUj0eMXRUXUlgm3zeXP70bMZDEfuguOY/lAwCD8+JL/qn/3TXRzT hvCa2yOygmG5C5KI6iNYmZRvRWbeGBxETwx3sATXOTAmJuSOD070Afd1FFFABRRRQAUUUUAFFFFA HLXNrpiySQw6c0phTdcSRTnEPrgnhiOePb646hlV0KsoZWGCCMgiqq6baJai2SIpEG34R2B3epIO T/8AWHpU88qwW8kzAlY1LEDrgDNADvLTyvKI3Jt2kN82R7560RxpDEkUYwiKFUegFV/Mvv8An2tv /Ahv/iKdYpAtlEbZCkTqGVSegIH+frk0AWKKKKACisF7a3kUq8ETKc8MgI5OT+ZAP1pTBCQQYkII YEbRyGOW/Pv60AbtFYBs7Ykk20JJLEnYOSww359/Wo5YLZ71BJDE0jI5yw5I+VTxjB4wOeccetAH R0VlW+mr8v7lI1U5GFAI428fhx9Kfa6ZYJPOY4E3pIoJHUEIMdhjg444x9TQBpUVXNhZnZm0g+Tb t/dj5cdMccYycfWorKzsfsUBitYFjaNCoVMjH3hyRk8nOTznmgC7RVZdOsUxssrddowMRKMDOfT1 5+teN+L9XuNJvrx8vLD9skjEXmlVALFunTqooA9torxTwv8AEFbu7tdLn0oyvd3Qh857jdsEjKDw U555IyM16bPptvHeohtUd3WRwVQY52hsj1OR+tAG9RWDJawO5MkEbNzksgJ5GD+gxUdvHAJJljiQ eXIOnY7FHpx8pA47fjQB0VFYKW1vG25IIlYY5VADwMD8gSPpUUEFtJGCIYiEcheM7SrMBjI4xzjs O3FAHR0Vz4gtoPJAhiRPNiACjaM+YMdB6nPpk+5roKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiisu91+xsdb07R5Gke8vnKoka7hGAkjhnPRQRE4XPLEHAIViADUorHPifS0v JoZZ/Jhi3j7XL8kDvGCZUWQ8FkAJb6PgkxyBLGmaxb6r5qxpPBNFgvBcxGOQI2dj7TztYAkehDKQ GVlUA+MPHf8AyUPxL/2Fbr/0a1c/XQeO/wDkofiX/sK3X/o1q5+gDoNIuJtasP8AhG55pZXZlOkq 5ZhHOW5iXkhFk3NnA5cREkKGNc/RXRa81vrVgniC1tjDP5og1Nd+4PcMC4m6AKJCJcIowvlH+8KA OdooooAKKKKACiiigAooooAKKKKACiiigDs4oGh8HNcFiqmNLQiNHZXcuswJdMR/dIG1y7ArjA42 c7L/AKp/9010LOt54VU+XmZI453k2h9qrtgCbtrOOit8zovOAn3S3PS/6p/900Afd1FFFABRRRQA UUUUAFFFFABUc5kW3kMKhpQpKKehbHAqSigDM+z2/wD0A/8AxyH/AOKq5BLPLK/mW7RRhV27ypJP OehPHT9anooAKKKKAMF7hEUsVlI5+7ExPBx0A/8A1jnpSmZQCcPwGP8Aq27HB7fl69s1JVuzg3Hz W6A8D1oArQFJCS6zBAWGRCxzt69vy9e2as7IILlT5crEBvm2OcYAPQDB+vXPA71eqJt32uPH3PLf P3uuVx7evXn070AKsyvtwJOTgZjYds9xxx+vHXio4Z1aVxtlG5gQWR8crnuMDoePXrycVYqKHd5s +7p5g2/e6bV9eOuenH45oADcINnyy/Ptx+6bvnGeOOnOenfFMt51NvH8soIVQQyOSMnHVhk8jqe3 J61YqK13fZId/wB/y13fe64/2ufz59aAGrdRvtws3IyMwuO+O445/TnpzWB4g0lby23efImZw3+o Y9W2n/0LI9ccV01VNR/49k/67w/+jFoA5iHwasDq/wDaEjbCXx9lbnaee/5evbNRz6zdaJfzWcCx yKjfekRgTkLnjOO3B/LrXbVE277XHj7nlvn73XK49vXrz6d6AMTRtbfVPPW7hXCAFfKic9jnPXHT itBLaJy5XzV3uNu4Mf4QehHy+npn3JFaFRQ7vNn3dPMG373Tavrx1z04/HNAGUxKtjZLjj5jEwHI z1I/yeOtV4rhNi5SYbnOMxuerHGcjjp9Bx2xXRVR+zNLEzdJA7/3uRuOOvt+HpxigChFMsrRFFl/ 1iHDI6H7+PTPbp6deDmt2sRw4eIKMMJo8/e6bxnpz0/D14zW3QAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAVj6zY3F3qvh6aCPfHaag8053AbENrPGDz1+Z1HHr6ZrYooA4PUfBl 9qtlPoUkkcFmtxf3aXw+cubtLhQnl5BBQ3LkknBCJjJdhH0Gj2d8+s32sahbx2ktxbwWgtkl83Ah aVi+7A4ZpWwMZ2qpOCxRNyigD4g8d/8AJQ/Ev/YVuv8A0a1c/XQeO/8AkofiX/sK3X/o1q5+gArR 0e/isrp47oM2n3aiC8WNQXMW9WJTJGGBVWHIGVAOVJBzqKANDWtIl0TUWs5bi2uQFDJcWkvmQyg9 0ccMAcqcdCpHas+ty3/4m3huW0JzeaZme3yeXtyf3sYycnaxEgVQAA07E1h0AFFFFABRRRQAUUUU AFFFFABRRRQB2FzGsPh2BhIFZYorcQyBGYoyicuN7CRfmY/cjC4/ibJLYEwBiY88A9DW+srt4YWK SRliktVnWHfsQyLKIg6plAW2LgkLJn5iTnOzAl/1T/7poA+7qKKKACiiigAooooAKKKKACiiigAo oooAKKKKAM61g819zfdU/nWjUVvF5UIU9ep+tS0AFRNu+1x4+55b5+91yuPb168+nepajP8Ax8Ic n7jcYOOo79Pz59O9AElRQ7vNn3dPMG373Tavrx1z04/HNS1HH9+bkn5+4Ix8o6Z6/hx+OaAJKitd 32SHf9/y13fe64/2ufz59alqODi3iGSfkHJBBPHoefz5oAkqpqP/AB7J/wBd4f8A0YtW6qaj/wAe yf8AXeH/ANGLQBbqJt32uPH3PLfP3uuVx7evXn071LUZ/wCPhDk/cbjBx1Hfp+fPp3oAkqKHd5s+ 7p5g2/e6bV9eOuenH45qWo4/vzck/P3BGPlHTPX8OPxzQBJUVvu8o7+vmP8A3um44689Pw9OMVLU cPCHkn526gj+I+v/AOr04xQBU1CNg0EidTNGD16bh6c9M+3rxmr9V7xd0UZyRtljPAJ/iHp/+r14 zVigAooooAKKK+cLdNOhurOXxXPrnhTx+128n/CR3cLNbTusu3YBu2NH5bqvAVAAOdvDAH0fRXzx 4v8A+EB/4Wd46/4Tbz/O8q0/s/7P5nmbvsw37Nvybs7MeZ8ufbNZfjTRNXvPAvw+07XPMtL+30zV JSskY3LHBEJIkKjGCY40XnkZ5BIIoA+m6K+fPFmsQ+Lvib4W1m0nu47SwvdGiW1nUY3XRe43jDEA 7FjU8ckdcKM6+s6Xaa34k+KWvXCSXR0nTDa6dc+a5jhY2cgnjXB2kgt8y87S3YmgD2yivF/hHovh WSfSb218Ga5Yavb6esx1W7jlS2mdkCOYyZCrbt7EfKOMkYxXtFABRRRQAUVn63omneI9Hn0nVrf7 RYz7fMi3sm7awYcqQRyAeDXJ/ByeaT4YaXb3csjXlk81rcRysTJAySsBG4PKlV2AKcYGO2KAO8oo ooAKKKKACsfUde+wa9pOlCynl+3ytG1xjbHDiKWQcn7zHymG0dByxGVDbFZeq6bNfajodxE0YSwv WuJQxOSpt5osLx13SKeccA/QgGPb+NFuL/WnRdN/sfRnkW9uRfM1xGI0JZjbrGSBvV1GWBYIWAII zsaVq7X9xcWd1ZyWV9AiTPbu6v8AupC3ltuXIz8jqRnhkbBZdrth3PhC41b7TY6jLBFYf6aI5rIC OeZLrfuRxt2qq78n7/mOkcjFSpVtjSdMvIdRu9U1KaB764iitiLZSsYjiMhVsMSdzGV2IzhQVX5t pdwD448d/wDJQ/Ev/YVuv/RrVz9dB47/AOSh+Jf+wrdf+jWrn6ACiiigCezu59Pvre9tnCT28iyx sVDAMpyDggg8joeK1PEC/bRba8g+XUd3n4GAt0mPNHQD5tySYA2gShR901iVueHZrCb7To+rXgst PvNrm7MTSfZ5Y8lH2qNzZVpE2ggfvNx+6KAMOip7u0nsbp7a4TZKmMgMGBBGQQRwykEEEEggggkG oKACiiigAooooAKKKKACiiigDsGuLa+8OrFHJE12HjOJMLJsS3UHBZCcZUgDzRnjEYyBWJdQy26v HNG0b7A21hg4IyD9CCD+NWNFZk1CzlUSEQssreWCWVU+ZiMEHgAngjp1HWna6kYvZUUwv5cSITGV ZdwjUNgqoB5B5Gc+rfeIB9wUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAAIIyDkGisq01eAoI zHd7h3+yyn/2WrX9owf3Ln/wFk/+JoAt1EyKbuN8fMI3A+U9CV79B0HHX06Gof7Rg/uXP/gLJ/8A E1GbyA3CTeXcZVGX/j1lzyQfT29P/rgF+ooUVZZyBy0gJ+Ujnao79enUcfiDUP8AaMH9y5/8BZP/ AImo47yCN5mEdx+8fccWsv8AdA549vb+tAF+orVFjtIUUYVY1A+Urxj0PI+h5qH+0YP7lz/4Cyf/ ABNRwXkEFvFCI7jEaBRi1lxwMdwf50AX6qaj/wAeyf8AXeH/ANGLR/aMH9y5/wDAWT/4mq1/fwtb oAlx/rojzbSDpIp7rQBqVEyKbuN8fMI3A+U9CV79B0HHX06GuEtvFN142klh0qefStNix508cfnX MobjaFXIh6P8xJPCkd67E3kBuEm8u4yqMv8Ax6y55IPp7en/ANeITU9VsdOJwssO1Co/e6rqvXp8 unWzL9RQoqyzkDlpAT8pHO1R369Oo4/EGof7Rg/uXP8A4Cyf/E1HHeQRvMwjuP3j7ji1l/ugc8e3 t/WrOYv1FboqREKMDzHP3SOSxJ6/z6HtxUP9owf3Ln/wFk/+JqOG8t4Iyojucbmfi0l7sT6H1/8A 1dKAJ71FeBQwyPNiP3SeQ6kdP/1DvxVisi51CC7lhtljuAfOjbL20ijhgR2H68fWtegAooooAK8n vvh3411Ozk8K3/ieC78JSXYle6n3Sak0IIcRFiNpw4HzZzxnp+7r1iigDl9D8N3mmeO/FeuTSQNa 6v8AY/s6IxLr5URRtwIwMk8YJ/Co/EnhSbXfGfhjVGFpJp+mpepeQT5JlWeIRgBcEMOuQSOPWuso oA8f0X4WeILTRdJi1G60qXUrTxBZX8txEWG+0toViSPPlgllAOARjknOSakgSG68a/FLwpBe2lpP qlvCLK0klEavNLaN5rqg5JJ2s5UE8ZOa9cqnJpOmzapDqkun2j6hCmyK7aFTKi88K+MgfM3APc+t AFfw1ps2jeFdI0u4aNp7Kyht5GjJKlkQKSMgHGR6Cub8EePH8ZeI/ElvFb+Xplh9lNk8kLRyypKj HewY/dbaGXgfKwyOa7ivN511nwj4j8f+KV0f7bDef2cLGEXKRm6Kp5bqvUhgWwFxljgKCSKAI7/x t4v03x5Z6ZdaZow0+81MWkFklw0moNbkN/peFJAi+VmOVBGCpx96pNK8d+JLjxpolnqWiQWOka99 rFlDMJI76DyBndMrDb82MhV6Bh8xx81fU9G8Xa58QNNe80Gxs7fTtVFxD4hspk8x7NVcrbuhbzPm LFWwduSflI5NfwTYeMk+It/rviXwvOkmob4UvX1aF0sLUfOkKxJ975goLfjgHcWANjwr8SrfX9Y8 USXTwWGhaZ9kNrdXam3Z0lVvncu2NrEKU4XKsuRk0fBX958LdNvJPnuruW4nuZm5eaQzuC7t1ZsA DJ54FWNL8KyyeO/HF1rGmQT6Rq32D7OJwkqTeVEQ2UOcYbH3gPUV2kEENrbxW9vFHDBEgSOONQqo oGAABwABxigCSiiigAooooAKKK5/XtW1TSZZbyO2gOmWsSPIrfNNeO7MvlQ4YbZBhNoKt5jSKgKc tQB0FFcfeeN/7Oim1O7tc6QJbu2iERzP5lqszSMwJC7W8iUKAcjahOfMIj2NJ1O8m1G70vUoYEvr eKK5JtmLRmOUyBVywB3KYnUnGGAVvl3FEAPjjx3/AMlD8S/9hW6/9GtXP10Hjv8A5KH4l/7Ct1/6 NaufoAKKKKACiiigDekhl1zQIrqGN5r3TV8m5VBudrfBZJSOSQmGRmOFVfJFYNX9I1e70W/W6tHP PyzQszCO4jyC0UgBG5GwAV6EUur2EdlPBJbM7Wd3Atxbs/3tpJVlPAyVdXTOBnZkAAigDPooooAK KKKACiiigAooooA3dHXzby3hC7mmUwr8u7DOpUHG1icEg8DPHGDg07XW8zUryXfvEpMobduJDDcM nc3ODyCxIOQTmm6MY1vLeSXlIlMpX5Pm2qW2/PxzjHIPX7rfdK66zrqFxFOQJIlEByX52KE6P8w6 dDjHTA6AA+4aKKKACiiigAooooAKKKKACiiigAooooAKKKKAIreIRRAY+Y8tUtV55pYYWk2RcZ+9 IQOoA6Ke35HjnrRFcNPGzwrGwG4feI5/hHTuOvp70AWKiY/6XGNmf3b/AD7M45Xjd2z6d8e1NLXW TiGHGWx+9PTHy/w9z19Peo5WuBexhLeJl8uT52JBH3cDOOMnPHoM9sUAW6ihP72f5Nv7wc7MbvlX nPf0z7Y7UqmY7d8cY55w5OBj6evH059qgtmuDPOHt4kXzF+ZSct8gyenzYOBnjge3IBbqK1ObSE7 Nn7tfk2bNvHTb2+lBa4+TEUXO3d+8PHXOPl5xxjpn2qGya4aygL28UTeWnyKSAvqMY4wMcevHvQB bqpqP/Hsn/XeH/0YtSK10du+GEcc4lJwc/7vpz9ePeq+pGb7KuI48+dHj5z181dvbuOvp70AUtX8 MW+o3AvrSeXTdUXkXtrgM/Awsg6SJlV+U/3cZHNa7H/S4xsz+7f59mccrxu7Z9O+PalJmwcRx5w2 PnPXPy9u46+nvVd3uvtiAW0JGyTD7jwPl2jO3jJ6j2zk4xSUUm2jSdac4xjJ3S2LlRQn97P8m394 OdmN3yrznv6Z9sdqHa4DHZFEy88tIQenH8J75H0556VHbtMZJt8EafvBkjPI2LznHzc8duB7UzMs 1FbnMR+TZ+8fjZt/iPOPfrnvnPehGuCw3xRKvHKyEnpz/CO+B9OeOlQ27XHlDFvEmZDuGSuPmbJA wc9iDxuznigBL9AUhcR7yJowfl3YG8c49uue3WrlU7hpjbr50EefNi4QmTnzB6gdBg57c8cc3KAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuf1bRtYu9cg1Gx1Sxijt4tsUF5YP OIpDuDSKVmT5ipC5IJADAEB2B6CigDm5vBljdvNBdySTaW7zzJYn5Qks6uszbwQxBEsmBn5TK/J/ diPQ0rSGsLi4vLq8kvb6dEhe4dFT91GW8tdq4Gfndiccs7YCrtRdSigD4g8d/wDJQ/Ev/YVuv/Rr Vz9dB47/AOSh+Jf+wrdf+jWrn6ACiiigAooooAK6LQHt9VsZPDdxbGS6uJC2mThyPJuGAym0D5/N KRR5YgJ971rnaKACiuh1S2/tfR08QxTQmddsF/Bv/e+YOPP28nYw8vc7HmV29RXPUAFFFFABRRRQ AUUUUAbujNi9tkLbVlBhZt23arqUJzuUcBj1IHrxmna4my8mOzaZIklI27RlowxwNq4GTwAMYxgk ck0RnS6SSIsJI4JHRlHKlY2IYfKcEYznjGM5XG4N1hY0v71IgojV2CBDkAdsfM3H/Am+p60AfcVF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFaELc2nlyDcvQjp06f0qWGCOBCsS7VJzjOayozJGT tuJVBzkqFz1z3GOBwPY+vNagUumVnfBDYIC8ZPB6dug/XNAEtRlT9oRtgICMN2BkcjjOe/07dscs MEhJP2uYZLHGE4yOB93t2/XNNkgke7R1mkVdjjIVDjO0AZIz2J9z16AUAWajjUh5iUC5fIIAG75R yeefTt0/Eixsu3M0jYOTkLzxjHA9efr7cVSn86xtL6685iY0Mih0QBtsfU45OT16dABx1ANGo4FK 28SlAhCAFQAAOOmATj8zXj/ij4h39n9jzNY8hgd4xnGMnr3zz9BjFWvCHxLl1O6+xXN9pqxxW4ZS jKD8rDjrjGDj8B0PNAHrVVNR/wCPZP8ArvD/AOjFqLTJGvbKG5W+aZWBG5NhVsMecge2P/r80upR sbVR50gzNGM4XjMq4PTt2/XNAF+oyp+0I2wEBGG7AyORxnPf6du2OQxsQR50gyGGcLxk8Hp27frm oGtpTcqwuJcbZPm2J8uduADjOBjPQ5xyeMEAt1HGpDzEoFy+QQAN3yjk88+nbp+JR4XZiRcSoOeF C45GO47dfqfTimQQujybpGP7wHJVRvGwDnHXnnPB4x06gFio4VKoQUC/OxwAB/EeeCevX/DpSJC6 sCbiVxxwwXHAx2Hfr9R6cVDBbyRxAPcSLtcsfkRdw3MecDvkZPB4zwc0ALeklIlChiZkyCAeAwOe SOnXPb36VarFlWWSaNTO822WPG5UPAcEnoB07+wI567VABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABXP69q2qaTLLeR20B0y1iR5Fb5prx3Zl8qHDDbIMJtBVvMaRUBTlq6Cuf1b RtYu9cg1Gx1Sxijt4tsUF5YPOIpDuDSKVmT5ipC5IJADAEB2BAMeLxnO15rl9LcwR6VpX2pzZHT5 Rczx24KSMkrSCNsSqfuhgAUDFWbA6DSdTvJtRu9L1KGBL63iiuSbZi0ZjlMgVcsAdymJ1JxhgFb5 dxRKc3hJdReaDWL2S/03fO8Fs4ZXQzq6yb5A2WAWWREAChVbB3EKy6GlaQ1hcXF5dXkl7fTokL3D oqfuoy3lrtXAz87sTjlnbAVdqKAfGnjv/kofiX/sK3X/AKNaufroPHf/ACUPxL/2Fbr/ANGtXP0A FFFFABRRRQAUUUUAaWh6hFp+og3Yd7C4Q295GgyWhbgkAkAsvDrngOintUGpafNpWpT2U5RnhbAk jOUkXqrof4lYEMp7gg96qVv2skOs6A2mtb/8TKxjkmtrjefngXMjw7eAMbpZd5JPy7R1FAGBRRRQ AUUUUAFFFFAG3pDIl/YvKVEayIXLDIAyM5+VuP8AgJ+h6VN4hDRX8kU2VlSCJGVz8ykRKCpyzYIx jGeMYwv3Rnxf6pP90V0T3K6l4UWziaSWaxt5h5LhiVLSLKTErNJkBI5GZkSAKB8zNkBgD7MooooA KKKKACiiigAooooAKKKKACiiigAooooAwXubeNSzzxKozyzgDg4P5EgfWrNvfxQZDSxiP5mY5HG3 hj+B6+lR0UAaK6hZtnF3ASCQR5gyCBk/kOvpUN3dQ28/mtNGDHDLlTgnICt67unOAPr2qsrsjblJ B9qLuZLuOW3bCSyW0qKTjHOAf9r06cevagCnH400hGZbq8VDgFcROc/oaytU8W2Fxa6lb2dwkizx MBmFlPKBevf6n+QFY2rfD3Wbh/Pt7yzWNYv+erjPU9lqho3gTWmvJQ95aMAhBBlc+h/u+9AHnHjS ylvPsOyGV9nmZ2KTjO3/AArlPBhu4NYmY27DNuR8yH+8tfRMnw5vZceZJZnHT94//wATXPad8PYb aTzhHb4ZMcTOeuDQB6B4Auoz4L01ZZI0lIkOwtg4MrAcfXA+tbepTwi1UmWMATRkncOAsqhvyPX0 qDwzZjT/AA9a2oAATf0JI5cnv9at6j/x7J/13h/9GLQBOZ4QCTLGAAxJ3DgKcN+R6+lV3ubf7YjG 4hAVJFb5l4I2kjOcjAHI/PoKuVE237XHn7/lvj7vTK59/Tpx69qAB7q3jYq88SsM8M4B4GT+QIP0 qO3kjMkzCWNhJINmNvPyKex54Geece2KleaOP7zgH071SS/Tzbjy053jJJXrtX059OvP4YoAtrd2 znC3ETHjgOD1GR+gJ+lZbX8Qj8rz4QryEqcqN+5mK4wec9j1OM+tSyzPMcsePQdKrW+3yzs6b39O u456cdfx9ec0AIkkd0YvIlST97G3ybX4Djnr7Hn245Fb9Yjbd0W/p5sfp13jHXjr+PpzitugAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorn/EVxqNhLa3lrfbVMsVvFZmJRFPI 7YPnSnJRcYCldpDdpSyxkA6CiuD1HxnfaVZT67JHHPZtcX9oliPkKG0S4YP5mCSXNs4IIwA6YwUY ydBo95fJrN9o+oXEd3Lb28F2LlIvKyJmlUptyeFaJsHOdrKDkqXcA+PPHf8AyUPxL/2Fbr/0a1c/ XQeO/wDkofiX/sK3X/o1q5+gAooooAKKKKACiiigAqazu57C9t7y2fZcW8iyxPtB2spyDg8Hkd6h ooA2dY0yBbaPV9PaL7BdyPttlk3yWZycRyenRtpJy6qWwOQMatbQpYpJ5dLupEjtb9RFvkYBYZRz FISeFAbhm5IR5MdazJoZbeaSGaN45Y2KOjqQysDggg9CKAGUUUUAFFFFAGjF/qk/3RW8lq+m+GHv Jv3kWoB5I4ijIPkWSEsDKPLkYNMpAjWRwNx3REEnBi/1Sf7oraN7C/hqSMRSQrCjWtxMkYJlaQmW PlFTIzCFImkkwCWRfl4APtSiiigAooooAKKKKACiiigAooooAKKKKACiiigDHeK6VSRaSueeFZM8 HHdu/X6D14pTBcgE/ZnOAxxuXnB4HXv2/XFa9FAGMY7sEj7FMcFhncnOBwfvd+364pJLa5E6uLSV yqsoKmPHQHuc8kY+vXsa2qKAMqOO7AH+jyJu4IZkIxjPPP4fX25qS1M0crE6cyb2BLAxjA29ODk4 xj6n06aNFAFc3Mvyf6FP823PzJ8ueufm7d8evGarwxoIUU6Y4ICj5liz1x2OPfj149K0KKAKkU8i qqrp08a4zjMeF5xjhvx+nvxUN+91JAqxWMjkSI+C6A/LIp9e4GR+uK0aKAKUk94Qdlow4Y/eUng8 d+/b9cVTlS9knEj2k7FQ6j5o8Hoc/ezzjA/XtWzRQBjvFdKxAtJXHPKsmOBnu3fp9R6c0kVrcBmb 7M6mRwTkpx8o9Dz0x3OfbFbNFAGOkV0zYNpKg45ZkxyM9m7dPqfTmmpbXMSqq2kpDNuPMY27iSc4 PbvjPXvzW1RQBjpb3EpjMlpKgDoxDGMkYb6kcYz9DxzWxRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAVj6h4bs9S1E3c0k4WWJYLu3Vh5d3GhZkSQEE7QXfhSoYMVfcvFbFFAG OfDGlveTTSwedDLvP2SX54EeQESusZ4DOCQ31fABkkL2NM0e30rzWjeeeaXAee5lMkhRc7E3Hnao JA9SWYkszM2hRQB8QeO/+Sh+Jf8AsK3X/o1q5+ug8d/8lD8S/wDYVuv/AEa1c/QAUUUUAFFFFABR RRQAUUUUAFblxK2u6PPeShW1KyYPPKECtPC5C73IABZZCMscu/ncnCVh1d0m/wD7M1OG6aLzohuS aLdt82JgVdM4O3crMMjkZyORQBSorT1zS4dLvUWzvft1jPGJba78oxecuSpOw8rh1def7uehFZlA BRRRQBoxf6pP90V0TpFD4UWa0CR3X2eaS4ltyPN/1ixAOVeRlQpK67SturZGTIQoPOxf6pP90Vt6 fPNe6XcW1xLJcJZxy/ZYZGMghVoJ2kKId20EqrMRGcEAmSLhiAfadFFFABRRRQAUUUUAFFFFABRR RQAVR1W8ezto9jKjSyrF5jdI8/xY74xV6qep+cLMmG2juQGBeJxnco649+lAFNJ7+K1u3aeO5hEL SQ3SbR8wH3cD3/z2EEd1f27abNPerPHdMoMWxUI3DqMDkDPt29ajhtXc6jNbWUttbvbMgicEM7nn IXt1x/LvWhpWlWttBb3At9lyYhuLE5BI54PQ0AVbDUbltYukup1FqrSrHu2jlSM+/ANJpWrTlLyb UpAscYjYALwofJHTnuKqXNpOsNxMttM8n2udVQKeUdMbunTpUl1Yyx2+rQxQyuu23WM7SS4UAHHr +FAGw2r2CW5nNwPKEnlFgpI3Yzjgfr0p0+p2dtbxTyzhY5QChwSWGM9OtUtbt5Hms51FyUjZw32b /WDI4I9uOfrVWW2e307TjHbXYePcyvEd7xFucFcDcD36dMZ9QDaS/tpDbhJQ32gExYB+bHX6fjVS 91D/AI9/ssv/AC+rby/L+Y5H6iqQFzBHpN1JZP8AuRIJI4Ixkbhwdo6Z6mo44LpokaS2kRzqolZc E7V9c9x79KAL1nr1rcfaTJIqLExK8HmMYG76knp1qymr2ElrJcpcAxRkBztORnpxjNZhjkFvqtpJ Yzy75nmXb8qspK4w3r3xjtj2qbTZriCK8nmtrl0DKIy0IEzjpggfexxz9foADYkkSJQznALBfxJw P1IqvLqFvC0iuZP3f3ysTsF4zyQMdCKfeIZ7Wa3Xh5YmVSQcDjHJ/Gs+SKae21GVfPQSqSsBQfNm JR6Zznjg9RQBr1FPcR24UybvmbaoVCxJwT0APYGqCC58+5MrzkZbCxoePm+TBJx09BjrupzRTyi2 DNKkwnJkdRkKfLb7uQRt6AfXnnNAGhHIJYw6hgD/AHlKn8jzS7lDhSw3EEgZ5IHX+Y/Osm5+2pdk W5lLlSoJBYN+7OGz9xfmAGMA9+hqSGNf7Rt5VF35eyRVMm/A+71zyB9773p7LQBfaaNJo4WcCSQE op/ix1/nUf26AzRxKxZ5GZRgdCN2c+n3SPfFRXVu099bsuVKRuVkxkK25MfngjHcZqG2SZorNpIW jf7VK7r1258zv6cjn3oAsDUInVjEryHzPLUKAN527uMkDGOc98cds2Y5EmiSWM5R1DKfUGs90dLp 5jG5RLreSqknb5IXIA5PJxxVuyjeGwt4pBh0iVWHoQBQBPRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABXL+KJPs8q3sGpTw3Vr5RIE+ILONmbdPPEvLxkKyndkDblWiw8q9RWfdaJp 17qMF/cW++4h24O9gr7TuTeoO19rEsu4HaxJXBOaAODvNVvdGGueItQEhud9/wD2UY9VlmtS1vHK Fikt1YIhKQs5+98wkyY22pXWaM9xZ+IdS0WS8nu4YLS2u0luSGk3yvMrjIAG3MIYDHyl2AwoVV0L XRNOstRnv7e32XE27J3sVTcdz7FJ2puYBm2gbmALZIzUmm6VY6Rbtb6fbR28TOXKp64AH4BQqgdF VVUYCgAA+LPHf/JQ/Ev/AGFbr/0a1c/XQeO/+Sh+Jf8AsK3X/o1q5+gAooooAKKKKACiiigAoooo AKKKKANnSP8AiY2VzorcySf6RZZ7TKOUH/XRcjAGWdYh0FZM0MtvNJDNG8csbFHR1IZWBwQQehFE M0ttPHPBK8U0bB0kRirKwOQQR0IPetbVj/alnHrSZMvyQX+eP3+G2v8A7W9ULE5JLiQnAK5AMaii igDRi/1Sf7oropZIX8Gsbcpvit3iujGApDPPG0auQke4kIzBS9xwhIWPbledi/1Sf7orbsYmstFv bxijCVH2/Z5BJLGnlyxEuqEPEheSNfndA24fJMOAAfadFFFABRRRQAUUUUAFFFFABRRRQAU15ERk Vjgu21fc4J/kDTqqXsTyyWgRnTExJdACVGxvUEe340AWUkR2dVOSjbW9jgH+RFKrBhkZ6kcgjpWN LDeRzShJZtplJEnl5LNsTbwu0Y+8Ofl45pZGukkUu04Xz1CkHjBnIOfbGzH6cbqANVJo5I4pFcbZ QCmeN2Rn+VHnRmbyQ4MmCdo7Yxn/ANCH51jW9vMj6eJRclEWIn73ysUcEEDoBhB7d+pyTW8yaheS Qi5DqkjqRuIY7YyoGeDyDwPTHTigDdpskiRKGc4BYL+JOB+pFZEsd7wyzTrukl/hLYIf5AACOMZO W+XpnjGHXCyyTSxD7VlpEYNtJCgSL6grwORjtncMjNAGvUS3ETJC6tlZv9WcHngt/IGs5ftfmoD5 3yuFi642iRg27sfk24Lde3NR2cdyJLASJMAm3gj5VXycc+h3bh/P+GgDTlvIYZfKbzC4UMQkTPgH OOgPoanqmYZG1SSQSSRoIo/ugYfDPkHIP6Y60XcEssoaOPcNuM/apIv0UY/GgCaa5WF1XY7swLYQ ZIUYyfwyOBzzwKS6ufsse/yZJF77NvHoOSMk9gKq6kkxljkh8wMIpFQoM5c7SoPoMjr0454ODPc5 laGIKxXzl3naeNo3g59MhRn3x1oAkjuY5riWFCS0YBY445JGAe/Q/wCc1Cup2zW0NwpYpNL5UfHJ O4j8uCajuoJZpbxI1H72CNMsSARufcM4POD79RVI298VWQ28fyzHavmNnmcMSfl6cDn0yfYAGiuo xOBsjkZmwY0AGXBBII5wBhWPODx9Kn/c3dspKrLDIoYBlyCOo4NZURuLa2bYkiOyRxl/JZ9jjdvI A/hAxjHy5PHetaBUS3jWJSsaqAoIIIGOOvP50ANtkt0R1to0RAxBCLgbu/19Pwx2p00qwRF2BPIA A6kk4A/EkVj27Tm7KRtKSGBIU/JnzpN5I7ZAbH4ck7asyw3eGM26QB0cFGyAA6k/KADnA4HzHgjP qAXPtcf2bz8Njds2453btu30znjrj3xTFv4WkhjG7fKxXbj7pGc59OVI98cZqvscWAXy33GczY2n Ozzt354OcdfalijkxaEowH2qWQ5HRWEhBPp1HX1oAvvIiMiscF22r7nBP8gadWdqHmLd2zpuGFOS vUjfGxA9TtVuO+DUEX2wytK32lcOuEbkYMzg5+iY9sYPYEAGxUMd1DK8aoxJkj81PlIBXjnP4jjr zUFizrDmXz/Mwok3gkeYfvbe+M+ny+neobTm5gI/i+0MD2KGQEY+uVIPp9cgA06KKKACiiigAooo oAKKKKACiiigAooooAKKKKACiivP/iD4rTS9U0jTI9ag01ku7S7vDJOsbSQG5SPyxuIJUjzXZlJ2 rCQwxJQB6BRXm+reJ9U0nSZ/EUU/mTSXep2n2WXmBUtY7poiqjBDZtgWOfm8xweBGI+o0Z7iz8Q6 losl5PdwwWltdpLckNJvleZXGQANuYQwGPlLsBhQqqAfIHjv/kofiX/sK3X/AKNaufroPHf/ACUP xL/2Fbr/ANGtXP0AFFFFABRRRQAUUUUAFFFFABRRRQAVp6HqcGmXrteWf22xmjMVza+aYvNXIYDe ASuHVG467cdCazKKALuraf8A2Zqc1qJPNjG14Zdu3zYmUNG+Oo3Kytg8jODzVKt/T7f/AISHTPsB mhjv7CMmzEjbfPi3MzQqOryF3BQAZO5wTwoGBQBoxf6pP90V0TXK6n4Zg0+N5JJLO3uAyyBiIiT5 +UUtISCsLBmjjhClhvYgnPOxf6pP90V0U1sth4OiuY0eN75JJmabLRymNvJARZFRCQsznchndSoI 8r5qAPsyiiigAooooAKKKKACiiigAooooAKjlnigQNNKkak4BdgBn8akqjqUnlG0fzY4sTffkGVH yP15H86ALkckc0YkidXQ9GU5B/GmmCJplmMSGVRgOVG4D6/iapPdPtQrdxurL+8kjTKxrk/OOTj0 5JHforVTtLi4jufIS4VkEz5DkZYmRgwwF5IGG7Y3ZPHAAN2k3KHClhuIJAzyQOv8x+dYcc88s2nG 4uevlyZCqoJdJOP/AB0D/gR9sNleSDVL2SO4y8SSSFGAPAWIgcdj0+g9eaAN+kZlQZZgBkDJPc8C seW6vVwyzIA0koXcMD5Xwq8AlieeBgnHGDnK3Vw7yyQC4Uv5sZVCv3cSKAccEdec9cggjkAA2KaJ IyEIdSH+4QfvcZ49eOay1vLgyohk5DhFXA/e/vGRifcKA3GMZ544qCymkZtNjZsKu3amzt5BO7P1 LD3xx0NAG4GViwDAlTggHoev9RS1nQyOupzoTtjebAI53N5SnB9BgE/h26GWTz/tg2/a/L3D7vlb Md+vzY/yKAJ3uIo5VjZsM2OxwM8DJ7ZPAz17VLWZef8AH8y/xSfZ9g7ttlJbHrgcn0qa8mnilCRk /vlCIQv3G3AZ9+Gzj0Q++AC7RWYLuUzSq0+zG/cNgPlkOAgx1+Ydup7YqOe8ugkZV0R2LF1fgI42 4j6EtnJOBy3UEDigDWVlcZVgRkjIPccGlrIiuTDJGhnCK08gClR8xMrDv1/A8EgnIPGvQA1Y41ke RUUO+NzActjpk96dRRQAUUUUAIVVipKglTkEjoen9TS0UUAFRrBEjhliRWVdgIUAhfT6e1SUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVe9sbfUIFhuo/MjWWOYDcRh43WRDx6Mqn3xzxVii gCnHpVjFqk2px20a3kybHlHUjjPsCQqAkckIgOQi4NN0qx0i3a30+2jt4mcuVT1wAPwChVA6Kqqo wFAFyigD4g8d/wDJQ/Ev/YVuv/RrVz9dB47/AOSh+Jf+wrdf+jWrn6ACiiigAooooAKKKKACiiig AooooAKKKKAHwzS288c8EjxTRsHSRGKsrA5BBHQg1p67DE88Op2saR2t+plEcYAWGXOJIwBwoDcq uSQjRk9aya1tH1CNGGm6g6f2ZcMRIXjDm3Zht81OCwKkKxCkbwgU5FAEUX+qT/dFbdjM1xot6koj WG3R4coixCQPHLIFlZdnmHfFGVDyZBX5Uk5UZc9pPYXElncp5dxAxilTIO1lOCMjjqK2ysVt4ZFz ZBEvHsZxcSQsDJtM0afPh5CgKsV5S3yGI3SA4YA+zKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAw5/BfhW6uJbi48NaNNPK5eSSSwiZnYnJJJXJJPOaj/4 QTwf/wBCpof/AILof/ia6CigDl4fCfgO4vLmzh8P+HJLq12/aIUsoC8W4ZXcoGVyORnrUkPgvwTc oXg8NeH5UDshZLCFgGVirDheoYEEdiCK5fUrrTdXfU9M8N6naWuqWSailnbw3ypeXN5IshlABbck QkJbBxl0VhtSNS/SeGhby65qV1pUHk6M1paQxKkJhQTx+asgCEDDKnkIeOPL2HmMqoBY/wCEE8H/ APQqaH/4Lof/AImox4L8EtcPbr4a8PmdEV3jFhDuVWJCkjbkAlWAPfafSukrx+1t9Wm1jxIb7Q76 G8votMvNSQtFKGhW7l3xBY3YyL9nUxgBcyCJsqGcBgDvIPBfgm6t4ri38NeH5oJUDxyR2ELK6kZB BC4II5zUn/CCeD/+hU0P/wAF0P8A8TR4W/eNrN3Bzpt3qBnsWXhHjMUQd0H91pRK2ejbi4yGDHoK AOXh8J+A7i8ubOHw/wCHJLq12/aIUsoC8W4ZXcoGVyORnrUkHgvwTdW8Vxb+GvD80EqB45I7CFld SMgghcEEc5ri9PtdUbw1b6dc2/2+e1igOqstptktj9oha6tz1M/nRrJI4G4yEbvmWeJR3Hhb942s 3cHOm3eoGexZeEeMxRB3Qf3WlErZ6NuLjIYMQA/4QTwf/wBCpof/AILof/iarnwn4DWzmvD4f8OC 1h3+bMbKDZHsJD7mxgbSrA56YOeldBfrv065Xyp5cxOPLt5PLkfg8I25drHsdy4POR1rzfSo/Jt7 K9nj/wCJba6hA99OieXbLHHauiBotkeGhlEQd9mFKKT5YiMUAB2H/CCeD/8AoVND/wDBdD/8TUcn gvwTC8KS+GvD6PM+yJWsIQXbaWwvy8narHA7AntVjwjBNbeHY45YpIU+0XLW8TqVMdu07tCu08oB EUAQgFQAuBjAp+IbG3/4SzwnfmPN0NQkhV2Ynan2S6JCg8Lk4zjG7auc7VwASDwX4Ja4e3Xw14fM 6IrvGLCHcqsSFJG3IBKsAe+0+lEPgvwTcoXg8NeH5UDshZLCFgGVirDheoYEEdiCK5PVLHVLi1ub LS4518Spd6rNLJG3lyeRLFcLbky8AqT9kC/N1hA/5YNs6jw0LeXXNSutKg8nRmtLSGJUhMKCePzV kAQgYZU8hDxx5ew8xlVAJJPBfgmF4Ul8NeH0eZ9kStYQgu20thfl5O1WOB2BPao4fCfgO4vLmzh8 P+HJLq12/aIUsoC8W4ZXcoGVyORnrR4hsbf/AISzwnfmPN0NQkhV2Ynan2S6JCg8Lk4zjG7auc7V xzepXWm6u+p6Z4b1O0tdUsk1FLO3hvlS8ubyRZDKAC25IhIS2DjLorDakalwD0iGeG5QvBLHKgdk LIwYBlYqw47hgQR2IIqSuX8NC3l1zUrrSoPJ0ZrS0hiVITCgnj81ZAEIGGVPIQ8ceXsPMZVeooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiv I7V4fCPw98M6xoFlaW+oSaO1zeLFEALmOOweTfMq43ATCEbzyDJtDDzCGAPXKK87XU/E6PPp7Sak hLwMgupdOGpSBlnLLCqN5OP3SEeYMlRPgkquK+meIrvUdYvEt9atLS0dH1I6glmkaSCO0sGBkV+f KP2hi2WEgCqokULQB6ZRXl/h3xH4ivdFh1m61bzVju9Ktvs32aNVk+0w2fms7AZ4M7MoXbhs53Lh V2PEesa7aeJLmLTr+CG3gi0wLDNbCRWkubqWAsxDK20LhtoIJZU+YDcHAO4ory/UvFmtw6LfXVrd X1zdaPFdySx20FsEZYZ540kuWkILK4tzlYArDEh43IBl2U2sWuheNYDrMlxZWNlqF0ILm0gfzJWu b9DuOwAofKDspU5bGCqZRgD2SiuTsdXvpvG9xor38bW9q88wxHiSQbIGETHbjCfaMkjBIaDDMRMK 6ygAooooAKKKKACiiigAooooAKKKKACiiigAoorP126vLHw9qd5p1v8AaL6C0llt4dhfzJFQlV2j k5IAwOTQBoUV4v4c8b+JYfBeqeJ5PEdj4mt4tKWc2kVoqXNldkkbZI02/uR8xZyQSEJUADJ0NG+N mlx6Hpx1aw1x7iK0tjql6thiG2eTADyEEYV/vrtByrDAzlQAesUV43rvxN1OPUvHloW1LSrfRktV tbqPTY5jEfNCOzLIwDGTeCmSAY13AAg57i7+IWkWnjE+G2ttSlnjeGO4u4rUtb20k3+qSR+oLEqA cEZYc8HAB1lFeN6T8TdT1l2a5bUtKH/CWwafEp02MhoHUgWz72yr5QmRhllLjAwQB3Ft8Q9FuvG8 nhREu/tYeSKO58sNbyyxoryRK6k/Oqt8wIGCMHkjIB1lFcXonxM0jxCl8+n6drLpb281zbM1kQNQ jiba5t+fnIYqNp2nLgY646ywuvt2nW159nnt/PiSXybhNkke4A7XXswzgjsaALFFZcPiXQbnVDpc Gt6bLqAdkNol0jShlzuGwHORg5GOMGifxLoNrby3FxremwwRXBtZJJLpFVJgMmMknAcDnb1oA1KK 4/VviJo9pPo8WmXVjqn9oXbxPJDeoI7eGNPMnlaTlf3aFWKkgkNxW4fEugi3uLg63pvkW6RPPJ9q TbEsgBjLHOAGBBUnrnjNAGpRWemu6PJeWtnHqti91dxCe2hW4QvNGQSHRc5ZcAnI44NH9u6P/bH9 kf2rY/2n/wA+X2hPO+7u+5nd93np05oA0KK8/wBY+J8Frr6aRo1rY6tJcael3ZSLqsUKXchuPJ8l GYbSwwzcEk7SMZrrIfEug3KF4Nb02VBbtdFkukYCFWKtJwfuBgQW6AgigDUormx4+8KyX9vZ2+va bcySpLIxhvImWKONCzu53YAA7cnqcbVYrJq3jHR9P8PT6tbahY3v+iXFzaRR3af6X5KFnVCM5xtw SAcd6AOgorg7/wCJUMFl4US1tbSbWPESQSRWE98IRDHIm7e0mw8BsKBgFznaCRiuog8S6DdW8Vxb 63ps0EtwLWOSO6RleYjIjBBwXI529aANSis+613R7HToNRvNVsbexn2+TczXCJHJuG5drE4OQCRj qKjufEug2Vxc293remwT2qB7iOW6RWhUlQC4JyoJdACf7w9RQBqUVlweJdBujELfW9NmMqB4xHdI 29TJ5QIweQZPkz/e468Vh+IPiJo+i/2A1vdWN5Dq92YkuPtqRwxwp/rZfNOUOw4G3ILE4HNAHYUV Xmv7O3vLazmu4I7q63fZ4XkAeXaMttU8tgcnHSqdt4l0G9uLa3tNb02ee6QvbxxXSM0ygsCUAOWA KOCR/dPoaANSiuP8O/ETR9b0y61G6urHTbVZZWtmnvUBntVk8pbgq20xqz5XDDqMZ5FbD+LPDcct 1FJ4g0pJLTP2lGvYwYcMEO8Z+X5iF57kDrQBsUVTk1bTYdUh0uXULRNQmTfFaNMoldeeVTOSPlbk DsfSrlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWfpmhaPonm/2TpVjYedj zPslukW/GcZ2gZxk9fU1oUUAZcfhrQYdLm0uLRNNTT5n3y2i2qCJ245ZMYJ+VeSOw9Kr61oDX7wz WJ02GeO4Fy32zT1uUaUKqrLgMjCVQoCuG4GQQflK7lFAGXpfh7TdJ0a10uG2jkgt0hG6VFLSNEqL G74ABcCNMHHG0YxgVclsLOeVpZbSCSRvL3O8YJPlsXjyf9liWHoTkc1YooAy73w1oOpJGl/omm3S RvI6LPao4Vnbc5GRwWbknueTUkWhaPD9u8rSrGP+0M/bdtug+05znzOPnzubrn7x9a0KKAK62Fmn l7bSBfKleePEYGyR925x6Md75PU7m9TViiigAooooAKKKKACiiigAooooAKKKKACiiigAqvfw3Fx p1zDZ3X2S6kidIbjyxJ5TkEK+08Ng4OD1xVisnxNYajqfh66s9Ku/sl9Js8ubzGj24cE/MvIyAR+ NKTsm0aUoKc4xk7Jta9vM4HTPh34m1DVNb1PxRf6Ml5qGhNo3m6bCxMpbrPLuCguAFGBwRgfKFGe X1P4L+MdY0tLbUNV0a4eCyggs1eW62WbR7YyIhnaA8aKzMUOW4Cj71dH/wAIJ8Q/+hr/APKjcf8A xNH/AAgnxD/6Gv8A8qNx/wDE1ze3n/Iz2P7Kwv8A0FQ/El8X/DbXtavfGJ068037J4it7M4uN6PF NbumBlQQUKK5z1yVGAASbmu/D/WNT+JmneILa40q2s7a7junuY4XjvtqoEaAlcLLG2wcv8wDsB8o w2VH4M8ezPMkXjCN3hfZKq6nOSjbQ2G+Xg7WU4PYg96k/wCEE+If/Q1/+VG4/wDiaPbz/kYf2Vhf +gqH4kr/AA215dUlEd5pr6f/AMJbD4ijLb1l2/N5qEYIyPkC464YkjIA2NB8JeI9C8b6jPa6raR+ F7u9n1GS22b7ieaVEBViVARFYFl2nOODnORzx8GePVuEt28YRid0Z0jOpz7mVSAxA25IBZQT23D1 qT/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6CofiWfCPw/wDEnhjXNb1ZLjQ45JtP+y2tvZwyQwXM y/6ueeIfLG3ABEYx87Ywc7vSLD7Z/Z1t/aPkfbvKT7R9nz5fmYG7ZnnbnOM84ry7/hBPiH/0Nf8A 5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4mR4F8Aa/rl5Y+IvEcf9mRtqGo6lJbw yz21z5lwEj2YG1olHllgQ7EggEYJNSD4ReKra9168sNctIJWuLmbRCZpWeBrh0WWSSUqW3+Smz+P ltwII3HT/wCEE+If/Q1/+VG4/wDiaP8AhBPiH/0Nf/lRuP8A4mj28/5GH9lYX/oKh+JiD4OeKrl9 T83U9Ns0d7+5sTFPLcuJbpUjeOZ5EBZDErLvHzgtu56VqL8JdTfSZXkaxi1Jdbtb2GKG8mCfY7eP y4bfzgoZGVGcCQKWOATycif/AIQT4h/9DX/5Ubj/AOJo/wCEE+If/Q1/+VG4/wDiaPbz/kYf2Vhf +gqH4l3wf8OtQ8LeLEvrFNN0rSykn2m3srm4mN2Cq+XG4lyAY28wiVSCwONi7iKz9T+GvirX/F09 /qurWjWbPqUcMn2iWRobe4t/JiRYCoRSnViGBbPJJAJf/wAIJ8Q/+hr/APKjcf8AxNH/AAgnxD/6 Gv8A8qNx/wDE0e3n/Iw/srC/9BUPxOVj+Hviq38a6VpVwY4IJ0tLj+0LCGW4W0msrR4oHZ3jWPDP yU5PQZGQW29K+EWrW2mXVvf22h3S/ZLSxgtXu7jY0CSebcAyKqMjPNiUNh9v3cbatjwZ49a4e3Xx hGZ0RXeManPuVWJCkjbkAlWAPfafSpP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+JQuvhR4s1 qx1GLXdasbq8k0SHTrW9DvuJSZZyJVKfN8w2CQNnaoYozMcR+IPgzqF4L640WLTbGfVbI299DPqN xOFlFyk3nCV0LSFhGAQQuDzlq0/+EE+If/Q1/wDlRuP/AImoz4M8ercJbt4wjE7ozpGdTn3MqkBi BtyQCygntuHrR7ef8jD+ysL/ANBUPxOl8XeCLzxd4jgnmuoLfTbXSry2t2QEzC4uE8pmZSMNGE5A BB3e1cfpfwf1h9VsG1ubSpNMEtk95bRM8jP9jtfJiwWQKyuzPvQqMLgBjk1f/wCEE+If/Q1/+VG4 /wDiajm8GePbZA8/jCOJC6oGfU51BZmCqOV6liAB3JAo9vP+Rh/ZWF/6CofidL458Ka34h1bSrvS LmxtJrHJgvnaVZ7N2kQu6qpKTK0ashjcD/ewSK5PUPhRri+IJtQ06W0ZzrsmqJcnVbm1k8iUDzbc JGjBC3QyqdxAHHYW/wDhBPiH/wBDX/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6CofiY ml/BPXh4dvrXUdR02LUItM/s/S5rRnZVVp3ml8wsoILbzHlf4HcFW79B4q+Heva7cLc2cfh+ykWy vMJErxh7q7IjmaQhTvAg4D8M0gBKhTtEX/CCfEP/AKGv/wAqNx/8TR/wgnxD/wChr/8AKjcf/E0e 3n/Iw/srC/8AQVD8Sz42+GWqa1a+FbDQNTgtLXSLSawmmu/nkMEkSQthQuGbYH/u8kYI6inq/wAK dVTxE934euNNhs1uNPvLZLppCYXsoJI44ioBLoxKZfcCBnhj1f8A8IJ8Q/8Aoa//ACo3H/xNH/CC fEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxMi3+CmsWy6fYyXtjd6aYrSC9Esro626ytPc28e2P5 leUh1clXG0L0yTJcfCbxVcpcXD3ejC+nTULhgJJfKF1eMIpQPkz5Qtxlf4hJ1yvFaf8AwgnxD/6G v/yo3H/xNH/CCfEP/oa//Kjcf/E0e3n/ACMP7Kwv/QVD8TV0D4bnw745OqQ+XeaeLe1jgea7kjlt mhtmt8mNV8uYspHzMV27mwPX0SvJv+EE+If/AENf/lRuP/iaP+EE+If/AENf/lRuP/iaPbz/AJGH 9lYX/oKh+J6zRXk3/CCfEP8A6Gv/AMqNx/8AE0f8IJ8Q/wDoa/8Ayo3H/wATR7ef8jD+ysL/ANBU PxPWaK8m/wCEE+If/Q1/+VG4/wDiaP8AhBPiH/0Nf/lRuP8A4mj28/5GH9lYX/oKh+J6zRXk3/CC fEP/AKGv/wAqNx/8TR/wgnxD/wChr/8AKjcf/E0e3n/Iw/srC/8AQVD8T1mivJv+EE+If/Q1/wDl RuP/AImj/hBPiH/0Nf8A5Ubj/wCJo9vP+Rh/ZWF/6Cofies0V5N/wgnxD/6Gv/yo3H/xNH/CCfEP /oa//Kjcf/E0e3n/ACMP7Kwv/QVD8T1mivJv+EE+If8A0Nf/AJUbj/4mj/hBPiH/ANDX/wCVG4/+ Jo9vP+Rh/ZWF/wCgqH4nrNFeRDwZ49a4e3XxhGZ0RXeManPuVWJCkjbkAlWAPfafSgeDPHrXD26+ MIzOiK7xjU59yqxIUkbcgEqwB77T6Ue3n/Iw/srC/wDQVD8T12ivJv8AhBPiH/0Nf/lRuP8A4mo5 PBnj2F4Ul8YRo8z7IlbU5wXbaWwvy8narHA7AntR7ef8jD+ysL/0FQ/E9doryb/hBPiH/wBDX/5U bj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6Cofies0V5N/wgnxD/AOhr/wDKjcf/ABNH/CCf EP8A6Gv/AMqNx/8AE0e3n/Iw/srC/wDQVD8T1mivJv8AhBPiH/0Nf/lRuP8A4mj/AIQT4h/9DX/5 Ubj/AOJo9vP+Rh/ZWF/6Cofies0V5N/wgnxD/wChr/8AKjcf/E0f8IJ8Q/8Aoa//ACo3H/xNHt5/ yMP7Kwv/AEFQ/E9Zoryb/hBPiH/0Nf8A5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gq H4nrNFeTf8IJ8Q/+hr/8qNx/8TR/wgnxD/6Gv/yo3H/xNHt5/wAjD+ysL/0FQ/E9Zoryb/hBPiH/ ANDX/wCVG4/+Jo/4QT4h/wDQ1/8AlRuP/iaPbz/kYf2Vhf8AoKh+J6zRXk3/AAgnxD/6Gv8A8qNx /wDE0f8ACCfEP/oa/wDyo3H/AMTR7ef8jD+ysL/0FQ/E9Zoryb/hBPiH/wBDX/5Ubj/4muh8G+Gv FWjavLca5rf261aAosf2qWXD7lIOHAHQEZ96qNabdnBoyr5dh6dNzjiIya6K92dxRRRXQeQFFFFA BRRRQAUUVDdLcPbutpLFFOcbXljMijnnKhlJ4z3FA0ruxzPiHXW0vxVpFvJq8dnBcOkS27RKfPd3 24+bmQdF/dMDEzIzq6N8vD2uta/qt54dvNT1a0jlTU0K2yTKFjupLW5zbSuYB5ZDKsfl5eRPNbcW JjI9O+z+If8AoKaX/wCC2T/4/R9n8Q/9BTS//BbJ/wDH6nmfb8v8zb2MP+fi/wDJv8jzm813xNp2 sassP9m6dPNqbvKWuWeJ5ktLIRQK3kFpS4Zj5SIsjbCEYbTu6DUNe8R211qlhZxSXF3piXt6rG33 C6j8oNbQ7FAIBeYoGBy5s3HVm29N9n8Q/wDQU0v/AMFsn/x+j7P4h/6Cml/+C2T/AOP0cz7fl/mH sYf8/F/5N/kcj4V1C+1HxVpkl9f2l/sstRSG6tZfOWRN9keZBFGjkMWGUQAYCnLK1eiVk/Z/EP8A 0FNL/wDBbJ/8fo+z+If+gppf/gtk/wDj9HM+35f5h7GH/Pxf+Tf5GtRWXFBromQzalpzxBhvVLB1 YjuATMcH3wfpWpTTv0M5xUdpJ+l/1SCiq96l68IFjcQQS7uWngMqkemA689Oc/hVH7P4h/6Cml/+ C2T/AOP0m2uhUKcZK7ml9/6JmtRWT9n8Q/8AQU0v/wAFsn/x+j7P4h/6Cml/+C2T/wCP0cz7fl/m V7GH/Pxf+Tf5GtWP4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWBAIp32fxD/wBBTS// AAWyf/H6Ps/iH/oKaX/4LZP/AI/RzPt+X+Yexh/z8X/k3+RxMeuTWfjGG9XUZLywe3s47m8S0P7+ H/iZCM7QDktIIQGQASMy7Bh1WsPWfE17qPgp7e+8ReXc6h4aM32ZII1aZvsZmkypXd82G/epmPaJ I9qSKrn1L7P4h/6Cml/+C2T/AOP0fZ/EP/QU0v8A8Fsn/wAfo5n2/L/MPYw/5+L/AMm/yPPYPFPi WY3QttbsZVN3BBC7BZ3RGvYYYpXjSNFRZoWZ+ZD5m4NFsUEC54ov/Eela5ayCO0EkGmXMUF411uZ omurJJJ5f3ISIqhLkhXRcEkFVwe2+z+If+gppf8A4LZP/j9H2fxD/wBBTS//AAWyf/H6OZ9vy/zD 2MP+fi/8m/yORXX/ABGmnWSG6juJtXtzbWl1bR70gmFwIxMCY184mGXzjhVRhauygIxK595rWtX+ p3lrfT2n2aLWLVRaKxaW3CalAkLMojHliSPL/PI5fIZAqhgO++z+If8AoKaX/wCC2T/4/R9n8Q/9 BTS//BbJ/wDH6OZ9vy/zD2MP+fi/8m/yNaisn7P4h/6Cml/+C2T/AOP1XeXWY9RhsG1jSxdTRSTR p/ZkvzIhQMc+djgyJ+fsaOZ9vy/zD2MP+fi/8m/yN6isn7P4h/6Cml/+C2T/AOP0fZ/EP/QU0v8A 8Fsn/wAfo5n2/L/MPYw/5+L/AMm/yNaisn7P4h/6Cml/+C2T/wCP0fZ/EP8A0FNL/wDBbJ/8fo5n 2/L/ADD2MP8An4v/ACb/ACNaisn7P4h/6Cml/wDgtk/+P06KDXRMhm1LTniDDeqWDqxHcAmY4Pvg /Sjmfb8gdKH86/8AJv8AI1KKKKowCis66h1l7h2tL+wigONqS2TyMOOcsJVB5z2FRfZ/EP8A0FNL /wDBbJ/8fqeZ9jZUoNX51+P+RrUVk/Z/EP8A0FNL/wDBbJ/8fo+z+If+gppf/gtk/wDj9HM+35f5 j9jD/n4v/Jv8jWorJ+z+If8AoKaX/wCC2T/4/R9n8Q/9BTS//BbJ/wDH6OZ9vy/zD2MP+fi/8m/y Naisn7P4h/6Cml/+C2T/AOP0fZ/EP/QU0v8A8Fsn/wAfo5n2/L/MPYw/5+L/AMm/yNaisn7P4h/6 Cml/+C2T/wCP0fZ/EP8A0FNL/wDBbJ/8fo5n2/L/ADD2MP8An4v/ACb/ACOd8d+INU0XUbKPTrvy jJp97OsP2XzvtM8Zg8mLI+5vZ9n+1v2rh2Rhlv4l19rvxALi+jt4oHlRYrd186zC3CxxSSB4GWCJ oyZHaQyEpl41AUgdc+k6zJqMN+2oaWbqGKSGN/7Pl+VHKFhjz8cmNPy9zVj7P4h/6Cml/wDgtk/+ P0cz7fl/mHsYf8/F/wCTf5HnvhrxJqN9rov7m6g+zyxWdpc3tqyzb9t9eww7TtCssrBA0iqPvfKi Bt8Umuax4jsPEuoSSLpulSzW+nxT3K3+YoIc3zB2mkgKxkuqp80bA7gAQWBXrtW1LVNEigkvdWsv 9Il8mFINEuJ3d9rPgJHKzfdRj07VcgXX7m3inTU9PCSIHUSaVMjAEZ5VpgVPsQCO9HM+35f5h7GH /Pxf+Tf5GDb614gfU9P0u4bFxqH2S7+0RWzIkEQjZriNUdQxXfCFLPhk+2ICAVUNh+H9a1rWdT8O y6xPaSP/AGmrmKFizWszWV55kD/u0CFMKPKJeRMnex3LXffZ/EP/AEFNL/8ABbJ/8fo+z+If+gpp f/gtk/8Aj9HM+35f5h7GH/Pxf+Tf5GtRWT9n8Q/9BTS//BbJ/wDH6jkGtwvCkus6QjzPsiVrBwXb aWwv7/k7VY4HYE9qOZ9vy/zD2MP+fi/8m/yNqiuR0/Xr/U9RFhBqcMd00TTKl34dvLbcilQxBlkU HBdenqK1IRrdyheDWdIlQOyFksHYBlYqw4n6hgQR2IIo5n2/L/MPYw/5+L/yb/I2qKyfs/iH/oKa X/4LZP8A4/R9n8Q/9BTS/wDwWyf/AB+jmfb8v8w9jD/n4v8Ayb/I1qKyfs/iH/oKaX/4LZP/AI/R 9n8Q/wDQU0v/AMFsn/x+jmfb8v8AMPYw/wCfi/8AJv8AI1qKyfs/iH/oKaX/AOC2T/4/TooNdEyG bUtOeIMN6pYOrEdwCZjg++D9KOZ9vyB0ofzr/wAm/wAjUoooqjAKK878daB4m1XVp59Hm1JIlsgk ItdSa3XzfJvDyodQT5ps+SPTsGo0vQPE0HjWPUbibUjZm9leRH1Jni8pmv8AH7ouVxtNjgBeOMYI egD0SivJ9Q8NeNTeandWdxqvmS/bPJA1Zgg3jUBHtQybV62GOBt4PBD41LLQPE1jpPi2CObUmlub KZNNM2pNKwl867EexnclD5RtecjtnkNgA9Eory8eHPFlrbatbW8uqyfaZVFqZ9UeRFAubvBdjMsq R+UbXJiYPwuQ+HU+oUAFFFFABRRRQAUUUUAFFFQ3Uz29u8sdtLcuuMRRFQzc9txA9+T2oGld2Jq8 /wDhzpdxJZ6dr8llBYvd6f5t48LhjqU85SYzvgDG35gAR8plkVQEVS/Vf2vff9C5qn/fy2/+PUf2 vff9C5qn/fy2/wDj1Tzr+kzb6vPuv/Ao/wCZrUVk/wBr33/Quap/38tv/j1H9r33/Quap/38tv8A 49Rzr+kw+rz7r/wKP+ZNrtreX3h7U7PTrj7PfT2ksVvNvKeXIyEK24cjBIORyKp+F9MttNsrk2mg R6HHcXBm+yKyZzsRNzLGSiE7OiEjADEhmYCb+177/oXNU/7+W3/x6j+177/oXNU/7+W3/wAeo51/ SYfV591/4FH/ADNaisn+177/AKFzVP8Av5bf/HqP7Xvv+hc1T/v5bf8Ax6jnX9Jh9Xn3X/gUf8zW rz86Xca3471a4+xQCax1C1ih1bePNt4I4oZ3gVcA/vDM65B5WSTccIiP1X9r33/Quap/38tv/j1H 9r33/Quap/38tv8A49Rzr+kw+rz7r/wKP+ZzulfFHSdY0nUdStbC+a30+0W8uMSW7lIjHK4yElOG /dFShwwLLkAZI1I/Gmny+M5vDCxSC8ifYztcW6gnyhL8sZk81htYDIQjOewJGSmgxppaacuha+Lc 6ZFpU48+0zcW8f3Vc+ZwdrSKSm04lbuEKx6j4ci1e+vpNR0LW7qxvpfPn06b7A0Jk8kQh1Yt5iMF UYKuCD06kUc6/pMPq8+6/wDAo/5nVDxLoLWD3663pps0RXe4F0nlqrOUUls4ALKyg9ypHUVcsb+z 1OzjvLC7gu7WTOyaCQSI2CQcMODggj8K4W38LxxTNcT6V4mu7t7hbl7i4urQu0gmilJ4kAAP2eBN oAAWJQoUli3QaTNcaNo1jpdv4e1doLK3jt42kltSxVFCgnEoGcD0FHOv6TD6vPuv/Ao/5nRV5XaX w8Mavcaxrlndx32l6ZDFfy/aI2XUJrudU89N0gSNA1vgFth2FVIRYlWu+/te+/6FzVP+/lt/8eqn qc1xqtqlvP4e1dUS4guAUltQd0UqyqOZTxuQA+2enWjnX9Jh9Xn3X/gUf8yi/wARdMit7q5lsb5L W3iL+ephkSVxai6MSFJDlvKJO77h2kBjxnU03xZpWoeH21uS4jsrFHKPNdTRiNTkAYlVmjYEkDKs RnKnDAgc7qXh1dVudUkutI8RGHUN7NbrNZBIpXtvsxlQ7927ysjDMy/MTtzjBeeGrW5iHk+Hdbs7 qLctne2stms9lGylTFC5kJEeHkwpyF3/AC7dqbTnX9Jh9Xn3X/gUf8zqr3xLoOmpG9/rem2qSPIi NPdIgZkba4GTyVbgjseDWpXnsPhOztjamDw9r8f2Z1ZALi1OQkls6Kcy9FWzgQdyoOSWJaus/te+ /wChc1T/AL+W3/x6jnX9Jh9Xn3X/AIFH/M1q4vxDoWr6p4vgu/KjuNHgt4YHtThTcLLM32hS24YR VS3kZSp3iPYMBnDb39r33/Quap/38tv/AI9R/a99/wBC5qn/AH8tv/j1HOv6TD6vPuv/AAKP+ZR0 Txpp+va9faRaxSJPZPMkhe4tySY5PLb92shlAz0LIBjHqM9JXI6RBf6PdXLxWPiKa1mlmmFnM9h5 cbyymRipVg/3mbGWPB+mNj+177/oXNU/7+W3/wAeo51/SYfV591/4FH/ADMHxjbtqXiXQ9Nk0e01 i1+z3d2bO6dVTzUMMaSHKkEKJ3yD2YsAzKinUs9Z0fQdDs7XVPE1jJJaRR2013dXSIZZF3IWbcxw xaKXgknKOOSpq1/a99/0Lmqf9/Lb/wCPVxc/gu3un1BptL8TOL15mK+fYgRCRboMqYbOM3sxG7Jz t5wME51/SYfV591/4FH/ADO+Graa1+9guoWhvEdUe3Ey+YrMhdQVzkEqrMB3Ck9BUema7o+t+b/Z Oq2N/wCTjzPslwkuzOcZ2k4zg9fQ1yP/AAjcJ1r+0m0PxE4WXzY7Vrq18mMmb7Q20ebn5pwkhOc5 jVchModDw5Zt4YsHs7LQNbkjbysmaa0J/dwRwL0lH8MKk+5PbgHOv6TD6vPuv/Ao/wCZ11FZP9r3 3/Quap/38tv/AI9R/a99/wBC5qn/AH8tv/j1HOv6TD6vPuv/AAKP+Y3xZfXGmeDdcv7OTy7q10+4 mhfaDtdY2KnB4OCB1rk7e4svhvZC7uNKj0yxu3hsINOtbmIIssaSs07yyvGmXA27mO9hHGW+Ztid d/a99/0Lmqf9/Lb/AOPVTvZri+utOuJfD2rh7C4NxEFltcFjFJFhv3vTbIx4xyB9Cc6/pMPq8+6/ 8Cj/AJkP/CdWC6THqUllfJby6VLq8WVjJeCOOJ2wA5w374KAccq3bBNjRvGOk6xpyXv2iC0jkuxZ xCa9t5PMlIBCK0UjqWOeFzu46YxnFfQY30t9ObQtfNuNMl0qAefaZt7eT7yofM5O1Y1BfccRL3Ll qd34Ri1DzpL7SfEU93c/u7u8V7CCa6tzs3W8jRMm6M+WvP38ZAYKSCc6/pMPq8+6/wDAo/5nZP4l 0GO4tLd9b01Z71Ee1ja6QNOrnCFBnLBjwCM57VqVwtnoMdlqlrqKaFr7XEDiQl57TEsn+k7nYCTq zXkzELgZ24AAwek/te+/6FzVP+/lt/8AHqOdf0mH1efdf+BR/wAyHxRbXd1ZWyQ2sl7aLcA31jEy K91DscbBvKqRvMZYFgGVWU5B2tx/h7xppXhrw/ZWU8V27zaZLr5BuI2ZYpjPceWDJIJJnUKylgDn AZtuTjtv7Xvv+hc1T/v5bf8Ax6sfUrNtU/tfz9A1tf7V09dPn2TWg2xjzcFcynDfvm5ORwOOuTnX 9Jh9Xn3X/gUf8xt38RdMsLy1sryxvoLyaV4ZLdzDvhZRE20gSfvGKzxsEi8xjnAG4EV0k2rabbXo sp9QtIrsoriB5lVyrOI1O0nOC5Cg9yQOtcb/AMI6rXkd/NpHiKXU4/NZb8zWSyiVxEvmgK4UMEgj QAKFK7gytubNe58GabPO3k+Gdbs7Jt5bT7Oa0iti7oIpH2CT7zRAx8HgMxUK5L0c6/pMPq8+6/8A Ao/5nbWuu6PfajPp1nqtjcX0G7zraG4R5I9p2tuUHIwSAc9DWhXI6RZtot/e3ltoGtvJd58wSTWh AzPPPxiUfxXDj6Be+Sdj+177/oXNU/7+W3/x6jnX9Jh9Xn3X/gUf8yrrmivreuaXHcLP/ZlvFPMz wXLQOlz+7SIh42V/9W9wODjnntW5BCttbxQIZCkaBFMkjOxAGOWYksfckk96zf7Xvv8AoXNU/wC/ lt/8eo/te+/6FzVP+/lt/wDHqOdf0mH1efdf+BR/zNaisn+177/oXNU/7+W3/wAeo/te+/6FzVP+ /lt/8eo51/SYfV591/4FH/M1q5fXZriz8W6bd21r9pmGlahHbwmQRiefdbukIc8BmEbkeyseimtL +177/oXNU/7+W3/x6j+177/oXNU/7+W3/wAeo51/SYfV591/4FH/ADOdTxAPDWnTarrmk30N5cyx xTXF3c2MPnHDkLHuudqxpg4Tdn5ifnYyOdDwbeJqcmvapAM2t7qCSwuHV1YC0t0bDoSrbXV0JUkb lYZ4qbUpZtVt1guPD+voiuHBtb+O3bOCOWjuFJHPTOOnoKsQajd21vFAnh3VykaBFMlxbuxAGOWa clj7kknvRzr+kw+rz7r/AMCj/mbVFZP9r33/AELmqf8Afy2/+PUf2vff9C5qn/fy2/8Aj1HOv6TD 6vPuv/Ao/wCZxvgWJLPTtN8SaitjpAvNPaW7m89R/ac8oW4adzhcbFWQgH7u+UKFRAz9o3iXQVS9 dtb00JYOEvGN0mLdixUCTn5CWBGDjkYrB8U6fJ4s02OxutG1+1RHdt9rLZhiHikiZTvkYYKSuOme hBFZ83heOQ3UkWleJra5ndnS5gurRZIGaS5djGfMwCReTLkg4BUjDDdRzr+kw+rz7r/wKP8Amdde +JdB01I3v9b021SR5ERp7pEDMjbXAyeSrcEdjwa1K89h8J2dsbUweHtfj+zOrIBcWpyEktnRTmXo q2cCDuVBySxLV1n9r33/AELmqf8Afy2/+PUc6/pMPq8+6/8AAo/5mtRWT/a99/0Lmqf9/Lb/AOPU f2vff9C5qn/fy2/+PUc6/pMPq8+6/wDAo/5mtRWT/a99/wBC5qn/AH8tv/j1H9r33/Quap/38tv/ AI9Rzr+kw+rz7r/wKP8Ama1FZP8Aa99/0Lmqf9/Lb/49R/a99/0Lmqf9/Lb/AOPUc6/pMPq8+6/8 Cj/ma1FZP9r33/Quap/38tv/AI9Vizv7m6mKTaTeWahc+ZO8JUn0+SRjn8McUKSYpUJRV2196/zL 1FFFUYhRRRQAUUUUAFFFFAGfqWuaXo8tpFqF/BbzXkqw20Tv88zsyqAq9W5dc4HGcnA5rQrD8TaP qWtWtvb2Go2lmkdxFcOZ7NpyzRSpKgGJEwNyYPXIPGOtalsl4u37VPBL+6QHyoSmZBne3LN8p+XC 9Rg5Zs8AGfD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNE3inRoLO2upLzENxuKnynJQI cSNIuMxqh4dnChDwxU1j2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmi78C/at MS1Go7ZJYr23vJPIyJI7yQSXHlru/dtuA2Elwo4Ic80AdZPMttbyzuJCkaF2EcbOxAGeFUEsfYAk 9qr6bqlpq1u01o8hCOUkSWJ4pI2wDh0cBlOCCAQMhgehBqxOJmt5Vt5I45yhEbyIXVWxwSoIJGe2 Rn1FZfhvQ/7A06S28yA+ZKZfKtYPIt4cgDbFFubYp27iNxy7O38WAAbFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFAEc88Nrby3FxLHDBEheSSRgqooGSSTwABzmsv/hKNJ/s77b50+3zfI8j7JL9o 8zG7Z5G3zN2358bc7Pm+7zWpOJmt5Vt5I45yhEbyIXVWxwSoIJGe2Rn1FcvbeFNSh02283VrSbWL a9N6l8bFgs0jRNExmTzcsdrsBtdAuEAAVNpANh/EekJcWkP26NzdojxPGC8e1ziMs6gqoc8IWI3n hcnitSuTHgeGG40Q299IINKt4LePzYw8yrCcgxSAgRmQfLL8p8xAF+XGa6ygAooooAKKKKACiiig ArP0nXNL16KeXSb+C9hgl8mSWB96b9qtgMOG4dehPp1BFaFc3pGia9p11q9xPrGmzvqD+eAmmvGI 5hFHEp5nbcgWIErwSSfmHSgDU1PW9O0byvt9x5Xm5IwjPtVcbpG2g7I1yNzthVyMkZFH9t6d/bH9 lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY281n+JfDH/CQbdt59n32lxYT5i37refZ5gXkbZP3a7WO4 DnKtkYP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLQBoaZrenaz5v2C483ysE5R k3K2dsi7gN8bYO11yrYOCcGtCuf8NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MNoPGFXBz0F AFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQV IBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4una IjzGld5HCDd8g8x8jO7CjByTuoAsHVbFdZTRzcx/2g9u10Lcct5SsFLn0G5gBnrzjODinb+KtCvN Ou9QtNTgurS0lEEk1uTKpkIUhE253sd6ABcks20fNxWWngiO28eReJbS9kiQpN9otnknkM0kgUFs mbYoAjjAXYRhR6IUrv4L1K+t9Zt9W1XTbuDU7iK8aNNMZVE0QhCBg0zB4iIFDJwW3NhloA6yxvYt Qs47qFJ0jfOBPA8LjBI5RwGHTuOevSrFZ+iaZ/Y2jwWHneb5W45C7EXcxbYi5OyNc7UXJ2qFGTjN aFAGPb+KtCvNOu9QtNTgurS0lEEk1uTKpkIUhE253sd6ABcks20fNxWhY31vqNnHdWsnmQvkAlSp BBIZWU4KsCCCpAIIIIBFcvH4T1iT+1Pt2tWMv227hv18jTnj8u4h8nyyczNuj/0ddy8E5OGXitzS dKm0mwgtkuY5Cbia4unaIjzGld5HCDd8g8x8jO7CjByTuoAk/tvTv7Y/sr7R/pfTbsbZu27/AC9+ NvmbPn2Z3bfmxt5o0nXNL16KeXSb+C9hgl8mSWB96b9qtgMOG4dehPp1BFZ//CMf8VL/AGp9s/0f 7X9v8jyvn+0fZ/s2d+ceX5f8O3O7ndj5aNB0bWNM1PU7q/1Sxu47+UXDxwWDwFZBHHGMMZn+XbEO MZyc5xxQB0FV76+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJqxVPVYL660u4g0y9jsbyRNsV y8HnCI/3tmQCcZxk4zjII4IBJY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFV/7b07+2P7K +0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qPSNNudL0uys/PtCYnYztFbuolB3HI3SMwcsVZnZnLHc Ty2RnyeFTP4zh16W6jWKB/Nit4EkQvKYjFul/eGNztZhuEavgIN2FwQDU0zWrLV/NFq06yRYLw3N tJbyKDnDbJFVtpwwDYwSrAHIONCsPQNBm0m4v7q4uLR571w8iWNobaAsCxMhQu5MrFvmfd8wVBj5 cncoAr319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBIZWU4Ks CCCpAIIIIBFV9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkR6TpU2k2EFslzHITcTX F07REeY0rvI4QbvkHmPkZ3YUYOSd1AEn9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY281Xh 8U6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGq//AAjH/FS/2p9s/wBH+1/b/I8r5/tH2f7N nfnHl+X/AA7c7ud2Plqnp/hG+0jS7qDTtZjhvDZQadZ3L2e8QQQ7/LLIXw8uJHy2VUnb8gAIIB0l jfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYrP0TTn0rR4LKV4JJI9xZ4ImjVyWJLEO7sWO cszMSzEknJrQoAKpnVbFdZTRzcx/2g9u10Lcct5SsFLn0G5gBnrzjODi5XJp4IjtvHkXiW0vZIkK TfaLZ5J5DNJIFBbJm2KAI4wF2EYUeiFADU0PxRpPiPzP7MmnfZFHP++tJYN0cm7Y6+Yq7lbY2CMj itis+x0z7JqeqXzzedJeyoykrzFGsaqIwc8qGEjgcAGVuMkkmp6Na6v5X2mW+j8rO37Jfz22c4zn ynXd075xzjqaAD+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbeaj0TxBp/iG3+0ad9raDYj rJNZTQLIrDKlDIihwQM5XPUeoqvJo+pTeLIdUm1G0k0+BMQWTWbb4mKkM6yeZjec4yUOFyq43OWj 8MeF08N/ahFLB5c2xVhtbVbaMBc4dkT5TM27DuoUMFQBVC0AdBWf/bVkNY/st2njujwhltpEjkO3 dtSQqEdtuTtVicK3HynBqejWur+V9plvo/Kzt+yX89tnOM58p13dO+cc46mqcmgzT+LIdZluLRUg TZEILQpcOpUjy5Zt53xbmZ9gVfmCHPy8gFzTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg 1oVh6Bo+padcX9zquo2l/c3Tg+dDZtAyqC2E5kcbFDYUDbj5idzOzHcoAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKr399b6Zp1zf3knl2trE80z7SdqKCW OBycAHpWf/wkth/Z3277Pqvk+b5O3+ybrzN2M58vy9+3H8WNueM54oA2KKjgnhureK4t5Y5oJUDx yRsGV1IyCCOCCOc1JQAUUUUAFFFFABRRRQAVx/hnxdpl9feI4n8S2N7DZ3bTQv8AaIf3dr5MLs2U xmNXd13nOMYJJFdhWfpOuaXr0U8uk38F7DBL5MksD7037VbAYcNw69CfTqCKAOb8Y+I7uyt7O40q +jWCayuLq1kjCOt7cqIzb2wJBDCUO52ph22fKwwcyf23qP8Awnv9nfaPk+1+R9h2L/x6fZfN+1dN /wDr/wBzvz5f8ON/NdBqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZIyKP7b07+2P7K+0f6X 027G2btu/wAvfjb5mz59md235sbeaAOf8D63qOsef9tuPtGLS2nm+RV+y3b+Z59r8oGPL2x/I+ZF 3/MTkY7Cs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAY/ii+uNO0GW4tpPJYSwpJ PtB8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ3Gq38CNcXctrazzOsf2wCV1hZegZpEVXG0YbdlQB gVsX19b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCC pAIIIIBFAHHw+Ib26+KVzpX23bY2u23FjbyRvIz+R5zTzIY96w4kRAyuPnVQQd/EfhzxNPd6Xfz6 zrMdtAtlbS3F4/lRjT7yXzFltskbVMREWEkDOC43lsgV1EfiDT5tbm0iL7W93C+yUrZTGJG2CTDS 7PLB2spwW7gdTiq58XaOthcXjyXcSQPEkkUtjOk+ZHCR4hZBIwZjtBCkEggdDgAj8Ea4niHwbpV/ 9vgvbprSEXjwup2zmNS6sF4VsnleMZ6V0FV7G9i1CzjuoUnSN84E8DwuMEjlHAYdO4569KsUAY/i m+uNO8OXdzayeVMuxRKVBEQZ1VnYnIRVBLFyGCAFirhdpr6DriP4c0651a/gSa5lNtFLK6oLl97K nlngSbwu5WULvBDBEB2jYvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMh fIBKlSCCQyspwVYEEFSAQQQQCKAOPh8Q3t18UrnSvtu2xtdtuLG3kjeRn8jzmnmQx71hxIiBlcfO qgg7+LHgfW9R1jz/ALbcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv+YnIx0H9t6d/bH9lfaP9L6b djbN23f5e/G3zNnz7M7tvzY280aZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGgDQrH8U6 omjeHLu+kvfsSpsXzwisVLOqgLvIQMS2AznYpIZsqCK2Kr319b6dZyXV1J5cKYBIUsSSQFVVGSzE kAKASSQACTQBl6PrNsPD+m3F/rmm3L3TiCO6iuEMc8pJARGGA78EHaF3FSQi/dGHD4hvbr4pXOlf bdtja7bcWNvJG8jP5HnNPMhj3rDiREDK4+dVBB38dhY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpA IIIIBFZ//CUaT/b39iedP9u83yMfZJfL8zyvO2ebt2bvL+bG7OKAOf8AAGualr15rEtzf/bbSDyY RLG9u8H2rDNMIGi+Zodrw7fMJbqD8wYV3FZ+ma1Zax5psWnkjjwRM1tJHHIDnDRuyhZFOM7kJGCD nBGdCgDH8UX1xp2gy3FtJ5LCWFJJ9oPkRNKiyy85A2IzvlgVG3LAgEVX0LXEbQbO41W/gRri7ltb WeZ1j+2ASusLL0DNIiq42jDbsqAMCti+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9 Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIoA5f+29R/4T3+zvtHyfa/I+w7F/49Psvm/aum/wD1 /wC5358v+HG/ms/QvEniK90e+mto/wC1L5bS0laHbGv2W7lZxcW/VB+5ARvLdhJzhn+YEdh/benf 2x/ZX2j/AEvpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxRpNzZ3N1bzT3EdvtLCC0lkdlY4SREVS0kb YOHQFTtYgkA4AMv4d+IZPEfhiS5muZLmSC9ubczSPAzOqyNsJ8glM7Cg4xnGRlSrHrKr2N9b6jZx 3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVigArk59Uu/+FkW9guoyGzNuFOnxKgcSbZGM0itHuMBH lqJEcASAIQSTjrKz/wC2rI6x/ZaNPJdDhzFbSPHGdu7a8gUojbcHazA4ZePmGQDD8Hapd6le60Ln UZL5EuC0JRUEUEbPJthI8tHSdFCiSN9xX5DkbiBuanNrEXlf2TY2N1nPmfa7x7fb0xjbE+e/XGMD rng0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NaFAHB6/4p1XSvH2iWE8cltpt3ei1iAl ttt4rQsWc73EgKymMBVUdDy5kRBJ8OPEN74ks7y+vb37T52y4jSCSOWC2SQuywb1jRvORdodWLY+ RsjeQO4rP0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NABqc2sReV/ZNjY3Wc+Z9rvHt9 vTGNsT579cYwOueMebVrhfHdtp0WpeYH3edYGAKIoRFuEgOC7MZCAJMiHAZDiQLu6is/+2rIax/Z btPHdHhDLbSJHIdu7akhUI7bcnarE4VuPlOADn/Aut6jrH2/7fcefs8uQYRR5DPu3QNtA8uRNo3Q tvaPcMyybht7Cqdhqtjqb3iWNzHObK4NrcbOQkoVWKZ6EgMM46HIPIIFygAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCvf2aahp1zZSnEdxE8THYr4DAg/K4K nr0YEHuCK5uPwhNB4am0mKTRmSe482W3n0ovZIoA+SK380bBuVX5ZvnLtj5uOsooAr2Fr9h062s/ tE9x5ESRedcPvkk2gDc7d2OMk9zViiigAooooAKKKKACiiigArm9I0TXtOutXuJ9Y02d9QfzwE01 4xHMIo4lPM7bkCxAleCST8w6V0lcf4Z8XaZfX3iOJ/Etjew2d200L/aIf3dr5MLs2UxmNXd13nOM YJJFAGh4l8Mf8JBt23n2ffaXFhPmLfut59nmBeRtk/drtY7gOcq2Rg/4Rj/ipf7U+2f6P9r+3+R5 Xz/aPs/2bO/OPL8v+Hbndzux8tZfjHxHd2VvZ3GlX0awTWVxdWskYR1vblRGbe2BIIYSh3O1MO2z 5WGDmT+29R/4T3+zvtHyfa/I+w7F/wCPT7L5v2rpv/1/7nfny/4cb+aANDw14Y/4R/duvPtGy0t7 CDEWzbbwb/LDcndJ+8bcw2g8YVcHPQVx/gfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzI u/5icjHYUAZ+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQCDyI9J0qbSbCC2S5jkJuJri 6doiPMaV3kcIN3yDzHyM7sKMHJO6o/FF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBF V9C1xG0GzuNVv4Ea4u5bW1nmdY/tgErrCy9AzSIquNow27KgDAoAP+EXQeL/AO3klgjY/M/lWqpP IfL8vY8wwXhxhtjAneqndhQopx+DWksNWivJ9Nkl1F0kkjh0xVtGdHL73gd33u7HDtuBZVQAqVDV Jc/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaWNwdwG3cOw3c/eatr2g6H4meTxD9tms/senx 3d+kNulvdy7d83yR4EKi4hbDbj8jg8YJAO40TTP7G0eCw87zfK3HIXYi7mLbEXJ2Rrnai5O1Qoyc ZrQrH8LXct94ctLiaee4kbeDNMUJlw7DerIiK0ZxlGCruQq2MmtigDP1vTn1XR57KJ4I5JNpV54m kVCGBDAI6MGGMqysCrAEHIqvoWiPoOj2enwXfmrHLLLcSTKztKZGd22kuSPnfILFztGCSTuq5qlr d3lg0FlqEmnzs6H7THEkjKocFgA4K5Kgrkg4znBxisfwzrA/4RPSb3WNVjZ9SfNrNdNHE8yyszwI QoVTL5ZUEKOoOM9aAJP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLUmgaPqWnXF /c6rqNpf3N04PnQ2bQMqgthOZHGxQ2FA24+Ynczsxp3P9rQ+O7CFNbne1uvOuWsTBEIUt44kjKht pkMhmljcHcBt3DsN0fg7VLvUr3Whc6jJfIlwWhKKgigjZ5NsJHlo6TooUSRvuK/IcjcQADrKp6rB fXWl3EGmXsdjeSJtiuXg84RH+9syATjOMnGcZBHBuVj+KdUTRvDl3fSXv2JU2L54RWKlnVQF3kIG JbAZzsUkM2VBFAEmkabc6XpdlZ+faExOxnaK3dRKDuORukZg5YqzOzOWO4nlsiS50z7TrlhqLzZj s4plWArkGR9gEgOeGVVkUHGcSsMgEg09H1m2Hh/Tbi/1zTbl7pxBHdRXCGOeUkgIjDAd+CDtC7ip IRfujPn1S7/4WRb2C6jIbM24U6fEqBxJtkYzSK0e4wEeWokRwBIAhBJOACx4R8Iw+EreW3t5ozAU jijjhgES7UBAkcAkPOwOHk+XdtT5Rt56SuX8J6tcaleanG+pf2hbw+ViUwCIpMwYyRhQPkVfkAjk JmQ7t+QUJ6igDP1rTP7X0w2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRHpOlTaTYQWyXMchNx NcXTtER5jSu8jhBu+QeY+RndhRg5J3VH4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWB AIqvoWuI2g2dxqt/AjXF3La2s8zrH9sAldYWXoGaRFVxtGG3ZUAYFAB/wjH/ABUv9qfbP9H+1/b/ ACPK+f7R9n+zZ35x5fl/w7c7ud2Plqnp/g6bTdLurSK9tJ3eyg02H7XZGSIWsO8Ksse8eY5WRwzA qpyPlGCDH/beo/8ACe/2d9o+T7X5H2HYv/Hp9l837V03/wCv/c78+X/DjfzXPr4y1lPCGq3pvoDd fZLIfaLjZFHpt9cyNFLC5CkKsBMbFHDOAfmJyMAHoGiaZ/Y2jwWHneb5W45C7EXcxbYi5OyNc7UX J2qFGTjNaFY/ha7lvvDlpcTTz3EjbwZpihMuHYb1ZERWjOMowVdyFWxk1sUAZ+p6Na6v5X2mW+j8 rO37Jfz22c4znynXd075xzjqay4/CMMPjObxDFNGjzP5su2ACZ28oReW0ueYNqq/llf9YA27jbWp qc2sReV/ZNjY3Wc+Z9rvHt9vTGNsT579cYwOueOXh8Q3t18UrnSvtu2xtdtuLG3kjeRn8jzmnmQx 71hxIiBlcfOqgg7+ADY8NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MNoPGFXBz0FcH4R8U6r qPjXVtH1iOS2uI7KC6+wtLbMLVizhkXy3Z2BUxHc3fLYjDop7ygArD0DR9S064v7nVdRtL+5unB8 6GzaBlUFsJzI42KGwoG3HzE7mdmO5XJ+B9eGv295cJr1pqkZdXjWMxiSIMDyyJzGjEEpG+6RQPnc sSqAG5qejWur+V9plvo/Kzt+yX89tnOM58p13dO+cc46ms+88NvqHiMalcXMEcKRNCn2SBoLpkZC pje4EmWjy7OFVVwwRs5XnQ1ObWIvK/smxsbrOfM+13j2+3pjG2J89+uMYHXPGHHrwm+Ic2kpr1oP JTZJp0hjV2JjDgIp/eO4B3tJkRhSFCM290AJPCHg8eEp9VEF35tndyxvb2/74/Z0RBGqZklfd8qq MgL0x90Kq9RXJ+B9eGv295cJr1pqkZdXjWMxiSIMDyyJzGjEEpG+6RQPncsSqdZQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABVOw1S01J7xLV5C9ncG2nV4njKyBVbHzAZG1lIYZBBBBNXK5vSNE17TrrV7ifWNNnfUH88BNN eMRzCKOJTzO25AsQJXgkk/MOlAGpqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZIyKP7b07+ 2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5rL17wvNr1vAs1/HHO1lNp926W52yQzhPO8tS+UfM a7SS4XnKv2k/4Rj/AIqX+1Ptn+j/AGv7f5HlfP8AaPs/2bO/OPL8v+Hbndzux8tAGhpmt6drPm/Y LjzfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwa0K5/w14Y/wCEf3brz7RstLewgxFs228G/wAsNyd0n7xt zDaDxhVwc9BQBXvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSC CQyspwVYEEFSAQQQQCKr61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIr6fpN5pemW9 ra3sHmfa5Lm7kltiwk82R5JVjUONmWc7SS+0YB3HmgCx/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8 +zO7b82NvNVz4p0Zf7WP2zK6TEZr1hE5WNBvyQcYfBikU7c4ZGU4IxVf/hGP+Kv/ALd+2e+3yv3v +r8vyvMz/wAe/wDy08rb/rfn3dqx7T4djTdR1GTT9R8uwudPisYbK4Wa6SNEOQriWZleM7nUoFXC thWQlmYA7CxvYtQs47qFJ0jfOBPA8LjBI5RwGHTuOevSrFZ+iaZ/Y2jwWHneb5W45C7EXcxbYi5O yNc7UXJ2qFGTjNaFAFPVNUtNGsGvb15FgV0T93E8rFncIoCoCxJZgMAHrRpeqWms2C3tk8jQM7p+ 8ieJgyOUYFXAYEMpGCB0q5WPpuk3mk6HpenWt7A0ltsF3PNbFjdd5XwHG2R2JYsS3JJIYnNAFh9a sk1xdG3TtfNEJiqW0jIiHeFLSBdiZMbgbiM44qvofijSfEfmf2ZNO+yKOf8AfWksG6OTdsdfMVdy tsbBGRxUcnh9pfFkOttdRgRJhVW3VZiNpXymlBy0GWMnlkE+Zht2AFFyx0z7JqeqXzzedJeyoykr zFGsaqIwc8qGEjgcAGVuMkkgGhVe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASasVn63ZXm o6PPaWF/9huJdoE+wvhdwLDCsrDcuVyrKwzkEEA0AWLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVI BBBBAIqu+tWSa4ujbp2vmiExVLaRkRDvClpAuxMmNwNxGccVHaWWpWml2FpFc6bG8Dqsvk2DJEYR kbI4/N/dnbtAJLAYPy84FeTw+0viyHW2uowIkwqrbqsxG0r5TSg5aDLGTyyCfMw27ACgAuaZrena z5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCuf8NeGP+Ef3brz7RstLewgxFs228G/yw3J3Sfv G3MNoPGFXBz0FAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqV IIJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k3ml6Z b2treweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaALH9t6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8 zZ8+zO7b82NvNV4vFOjTadfX6XmbWxiM87mJx+6AJEqgjLxkK211yrbTtJwar/8ACMf8VL/an2z/ AEf7X9v8jyvn+0fZ/s2d+ceX5f8ADtzu53Y+Wsd/h5u0GfTG1CCX91Y2sBns98Yt7SXzI0lTePMZ ssHIKggjCrg5AOwsb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0qxWfommf2No8Fh53m+VuOQuxF3M W2IuTsjXO1FydqhRk4zWhQAVnvrVkmuLo26dr5ohMVS2kZEQ7wpaQLsTJjcDcRnHFGp6Fo+t+V/a 2lWN/wCTny/tdukuzOM43A4zgdPQVTk8PtL4sh1trqMCJMKq26rMRtK+U0oOWgyxk8sgnzMNuwAo ANCw1S01J7xLV5C9ncG2nV4njKyBVbHzAZG1lIYZBBBBNXK5/QdG1jTNT1O6v9UsbuO/lFw8cFg8 BWQRxxjDGZ/l2xDjGcnOccV0FAFM6paLrKaSzyC8e3a5RTE+1o1YKxD42kgsuVzkbgcYNR6Zrena z5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGs+70bWJ/FtrrEWqWMdrbRPbrbPYOzmORomkzJ5wG 7MIwduBnkNR4Y8Mf8I59q/0zz/O2D5YvL37c/vZeT5lw+795Lxv2r8oxyAdBWf8A23p39sf2V9o/ 0vpt2Ns3bd/l78bfM2fPszu2/NjbzRqehaPrflf2tpVjf+Tny/tdukuzOM43A4zgdPQVXm0a4uPE dtqUuob7W23PDamEZSRk2HDZxtxkj5d4LMBIEZkIBY03WrLV5buOyad/ssrQyO9tJGhdWZGCuyhX wyMDtJxj3FaFcv4K8If8IhpxtftUEv7qKHFra/Z428sEeaybmzM2fnfPzBUGBt56igAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCvfukenXMkt59ijWJy11lR 5IAOXy4KjHX5gRxyMVh+CtSu9T0aZ7u8jvjFcNHHdxOkkcy7VbKSoqLKAWKlgiYKlcEqWbpKKACi iigAooooAKKKKACiiigAri/CXjC21CXX4r/xNo1+9lcGRJbR0ijW2EELM4Xex2B2cFixGcjIxgdp RQBx/jjW9R0fyPsVx9nzaXM8PyK32q7Ty/ItfmBz5m6T5ExI2z5SMHJ/beo/8J7/AGd9o+T7X5H2 HYv/AB6fZfN+1dN/+v8A3O/Pl/w43812FFAHH+B9b1HWPP8Attx9oxaW083yKv2W7fzPPtflAx5e 2P5HzIu/5icjHYUUUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAio/Deqtd6Ja S31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL03KKAOLk8Q6gvxKh0aK5juIXfEtvA8LrbweQX 3yrnzklMoVckeVsdB99qy9G8YXVxo+vXWpa5BBY2UUDyatH5E8KSsziZLcxkgqAqeWsgMoMil1fK qfSKKAMfwtfy6n4ctLya7gu2k37ZoZEkDKHYLuaP5DIFAD7Pl3htvGK2KKKAMPxff32meGLq604x rcK8SeZI21YkaRVkkLFWChELNuKsF25KkAgnhrUhceH9Plu7ySSe4d445Lh483DAucxlFRXQqhZG CjdGA2BzW5RQBx/9t6j/AMLH/sz7R/on+r+ybF37fJ8zz9uN3l7/AJPO37d37vyt37yjwLreo6x9 v+33Hn7PLkGEUeQz7t0DbQPLkTaN0Lb2j3DMsm4bewooAKy/Ed/Dpfh+8vbjU49MghQNJeOgfyly AdqnguRwvDfMV+VvunUooAw/DWrC98P6fcXWoWk8l07pDJHNG3nYLlQSh2NKEQlwny7lfb8orPk1 WeL4hw6fBrEd1BKmLjT1kike1PllgzRqgeNDhP3jSMMyBdnzKy9ZRQBxfw+1rU9Zt7uXVp5JLvZE 8sSNG0Fq7Bt0AxGjpKhGHicuUBj+YljXaUUUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8 sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL03KKAOP/tvUf8AhPf7 O+0fJ9r8j7DsX/j0+y+b9q6b/wDX/ud+fL/hxv5rHsPFWrS+HNSvJ9TgRorSznuJpjFELK4kdhc2 qs2EWSNQoRJckPIvmEg16RRQBh+ENSu9W8MWt7erIJHeUI0gTdJEsjLG52EoSyBWynyHdlflIrco ooAK5OTXgPiHDo0OvWjy7N0+msY18uLyyy7f+Wjzlhu4OwRA7lBKM/WUUAcf4L8UJrV9rVlP4h0r VLq3uwYPsBVFMHkwksqB3O0O7AsWPzZGRwB0Gp6jdWHlfZtGvtS353fZHgXy8YxnzZE657Z6HOOM 6FFAHH/23qP/AAsf+zPtH+if6v7JsXft8nzPP243eXv+Tzt+3d+78rd+8qPwJ4h1DXLjUkurmO8g gSE/a4XhkgadjJ5qQvESPKULGVEn70B8v1Wu0ooAz9T1G6sPK+zaNfalvzu+yPAvl4xjPmyJ1z2z 0OccZ5dfFupD4q2/h67tfsdjNaXBgjea3LzlShWY4kLhSBKoQLnjcc/MIu4ooA5Pwbrw1u41QQ69 aaxbQOiiWIxhlly4k2onKwZUCPeS52udzrsY9ZRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFQ3V1b2Vu9xd3EUECY3SSuEVcnAyTx1IFA0m3ZE1FZP /CU+Hv8AoPaX/wCBkf8AjR/wlPh7/oPaX/4GR/41PPHubfVa/wDI/uZrUVk/8JT4e/6D2l/+Bkf+ NH/CU+Hv+g9pf/gZH/jRzx7h9Vr/AMj+5mtRWT/wlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NHP HuH1Wv8AyP7ma1FZP/CU+Hv+g9pf/gZH/jR/wlPh7/oPaX/4GR/40c8e4fVa/wDI/uZrUVk/8JT4 e/6D2l/+Bkf+NH/CU+Hv+g9pf/gZH/jRzx7h9Vr/AMj+5mtRWT/wlPh7/oPaX/4GR/40f8JT4e/6 D2l/+Bkf+NHPHuH1Wv8AyP7ma1FZP/CU+Hv+g9pf/gZH/jR/wlPh7/oPaX/4GR/40c8e4fVa/wDI /uZrUVk/8JT4e/6D2l/+Bkf+NH/CU+Hv+g9pf/gZH/jRzx7h9Vr/AMj+5mtRWT/wlPh7/oPaX/4G R/40f8JT4e/6D2l/+Bkf+NHPHuH1Wv8AyP7mcbB4w1uz0WF57yxv76e7naER2MoE8RnMdqgKkpEs x+5PI23G3iT5nqnpnibUtRGiWNvqMd1Fr16b+1nhZg9lCtybjyJ3DtkvCk0YUBQDCyfMAWXqll8A Klkiv4ZCWDl7NQYMW7FgxMf9wlgDkY5GasQan4MtRELe+0CEROHjEcsK7GEflAjB4Ij+TP8Ad46c Uc8e4fVa/wDI/uZR8IeLb3WdOub/AFm1g061+yQ6hE7zRrtgkDn5gJHO1Qn+sby93PyLtNY83iXx BcyWAnax068t9Qka8glkZVs4haSM/n7WZZY4/Nt38wMqu0iKRC3NdVa634SsfP8Asep6Jb+fK083 k3ESeZI33nbB5Y4GSeTVeK88DQWa2cVz4djtVikgWFHgCCOQgyIF6bWIBI6HHNHPHuH1Wv8AyP7m cyvxA17/AIRCHXXsbSOWSyuy1jJC4lhmtoZDLK43fLEJo/K2Yz+8QlwW2UWHxH1fWr/U7fS9NtJT E+LSIyASSxypC9tIN7oHLRfapjHlSBEFJQ/MeqbU/BjPeu19oBe/QJeMZYc3ChSoEnPzgKSMHPBx Rc6n4Mvbe5t7u+0CeC6cPcRyywsszAKAXBOGICIAT/dHoKOePcPqtf8Akf3MtWGo/wBoeF7a9n1C CD7bEnlXlv8AIreYQInRZQdrNuQhGDYZtvzdTx8+tPovw9tZJLq+aaPVZ/McztI6pbXE080XmM25 18q2liUtjf8ALuCBjt66fxF4YureW3uNZ0iaCVCkkcl1EyupGCCCcEEcYqNNb8JRxWsUep6Ikdpj 7Mi3EQEOFKDYM/L8pK8diR0o549w+q1/5H9zMmTxFryasuisNNF5cXEUSXccTyQWzPDPM0LLuUyO qwL826PInRtigYbD1Xxpqd5pw061u7Ga41C0FolzpqzCOOeQJF9pjuThGjS4mjjKJudQwbdkFB1X 2zwN/Y/9kfafDv8AZn/Plvg8n7277n3fvc9OvNDXngZ9Rk1Frnw619Ls8y5LwGR9hVly3U4KIR6F V9BRzx7h9Vr/AMj+5kPifxFqWm6kkGli0kFvbrNcxTRMWlkllWK2hRgyhDKwlXeQwTaCwAPOXc+N tTtoGhVrG5u4bt4A0VrNtvGDiJI9oLC23zebCJJXK7oSwVlb5eil1vwlPK0sup6JJI3l7ne4iJPl sXjyc/wsSw9Ccjmq8154GuJbaWa58OySWsrT27u8BMUjNvZ1J+6xb5iRyTz1o549w+q1/wCR/czF 1bxlPa22oSXc1isOm6hHHILdpTJLH9pDF0CMGKxwRy78bgzxTggLEwctfFniC0aAa02lI0csEN6i RNEEleLzpYxI0rKvkQgymQ5WQHYu1gSdp7zwNJEsT3Ph1o0iECoXgIWMK6BAP7oWSRQOmHYdCasf 234S8/z/AO09E87zfP8AM+0RbvM2eXvzn72z5c9dvHSjnj3D6rX/AJH9zKOh+LJbmy1m+1URxWem oJZpltZoTAdhkeFo5AJGKJ5beZtUOJBhQQRWHL438QaNYNd61DYytZ+WL22tLdkcytAZ5YUZpSoa GFTKXOVk3BF2sCT0VreeBrHTp9Os7nw7b2M+7zraF4Ejk3Da25RwcgAHPUVY/tvwl5/n/wBp6J53 m+f5n2iLd5mzy9+c/e2fLnrt46Uc8e4fVa/8j+5mK/iTxI3iNfD0EelS37YnllCyeTbQlH3oTn95 JEzWxP3DIs6/LGDvEnhfxZqet6Ne32owWmlIbKK/tpp2jKxxSq5UyIspJRdmd7GLf8w2ptNaVnqf gzTkgSxvtAtUgR0hWCWFBGrsGcLg8BmAJA6kAmhNT8GR293bpfaAsF67vdRrLCFnZxhy4zhiw4JO c96OePcPqtf+R/czjbHxL4msDZ3mpXdo2bLSk1H7TG0flyvJK06CPcFSVIHEsjE/KsYJQKw2aEfj XWbuUW8U2m2p+0H7Rc3VlOi2cSwPLKrxSNG5Ef8Ao4Mx2I32hflU8HooNT8GWoiFvfaBCInDxiOW FdjCPygRg8ER/Jn+7x04qvbS+ALO3kt7V/DMEEiSI8cRgVWVwocEDghgiAjvtGego549w+q1/wCR /cyHUdT1i88G6FGIfsut6z9njeIs9usbeWZp4y2GeLMccqBgCysV6EZFe71X7d4P8JtdapPHa6r5 L3l+H+xy+WttJcs2UbEW4wgNg4CswBHBG1fa34S1Ozks7/U9Eu7WTG+Ge4ikRsEEZUnBwQD+FR3O p+DL23ube7vtAngunD3EcssLLMwCgFwThiAiAE/3R6Cjnj3D6rX/AJH9zOR0nxn4kl0W2ur25sbd TaO8slzp8hkhW2hj+1zyKsi523BeHygqtlgwyqkG5/wmWvSCW5lOm6dbFJ3KS2z3DWkdtGn2qV2W RfNCTloAihSchwWUHPRPqfgyRHR77QGR0lRlMsJDLK26UHnkOwyw/iPJzQ+p+DJEdHvtAZHSVGUy wkMsrbpQeeQ7DLD+I8nNHPHuH1Wv/I/uZDr3iafSPD1ndXRg0y+mi864jlhlvfsiqm6VtkIy6qSq l8oo3Ak9FbDtPH2pSAXtxFpqRyXE1mdLDs1xaTRR4fzZEL7gZwsShI8kTwkZZgh6LUtT8Gazbrb6 pfaBfQK4dY7qWGVQ2CMgMSM4JGfc1XsrnwZZWt3b/wBraROl48xuDPcQsZVllklKN6oGmkAB7Meu SSc8e4fVa/8AI/uZzN7428SXej6kmktYma1tJ2N61rJC7SFhHbiO3kJYSNMlxB5bEndGG6Msb+mQ CZbeJbiSOScIBI8aFFZsckKSSBntk49TXMiXwAtg9gr+GRZuio9uDB5bKrl1BXoQGZmA7FieprU/ 4Snw9/0HtL/8DI/8aOePcPqtf+R/czWorJ/4Snw9/wBB7S//AAMj/wAaP+Ep8Pf9B7S//AyP/Gjn j3D6rX/kf3M1qKyf+Ep8Pf8AQe0v/wADI/8AGj/hKfD3/Qe0v/wMj/xo549w+q1/5H9zNaisn/hK fD3/AEHtL/8AAyP/ABo/4Snw9/0HtL/8DI/8aOePcPqtf+R/czWorJ/4Snw9/wBB7S//AAMj/wAa P+Ep8Pf9B7S//AyP/Gjnj3D6rX/kf3M1qKyf+Ep8Pf8AQe0v/wADI/8AGj/hKfD3/Qe0v/wMj/xo 549w+q1/5H9zNaisn/hKfD3/AEHtL/8AAyP/ABo/4Snw9/0HtL/8DI/8aOePcPqtf+R/czWorJ/4 Snw9/wBB7S//AAMj/wAaP+Ep8Pf9B7S//AyP/Gjnj3D6rX/kf3M1qKyf+Ep8Pf8AQe0v/wADI/8A Gj/hKfD3/Qe0v/wMj/xo549w+q1/5H9zNaisn/hKfD3/AEHtL/8AAyP/ABqxZ61pWozGGx1OzupV XcUgnV2A6ZwD05H50KcXsxSw9aKvKDS9GXqKKKoxCiiigAooooAKx/E+sPoOii/jTfi7tYnURNId kk8cbbVX5mba5wBnnHB6VsVn60dLTTDLrM8EFjDLFM0s83lIjpIrRktkY+cL1PPTnOKAOTk+KOkW Et82ptJbwJemG3EsJtpBCkEEkjukxViQ03CqC7AjajYJrQj8dWMcggmju5pTem1ZobbCxFrx7aLf 8xwC0b/NnB8s5Cl0Q3E0bw7r8TanZy+ct1KZvtun38iF22pEwEsTg7cQoCoO0mMEjIzWfP4O07Vr /TtQs5bH+zVlS+Vo4mkllk883AdZvM27S7HHyMQskoUqJDQBl6R8SLvVJfDFqmiXZn1FLd7yYxpH EFlgmfMYeQPgPA/JU5RGI3bkLGi/Ei7udVurXVNEu4I0uI4I/LjQ7N97PbBpGEhUAFIlIB3FvMKh kGR0i+F/DsF5psSQ+VdWcUP2NEu5FcR24ZEwA2WVRcMpzkHzMNnIqPTPD/hec3t1p3l3Iu7iG5ne O9eVfMWT7VGR85CDdL5m1cA784INAGfZ/E/Qbu40qFo7u2bU0he3F0EjYrKdsZCF97Bn3JlFbG0s cIVdq958S7a3uPOisLuexit76SdIkR5w1uYCSQHxEAkrkrLscFQu0MQrdJH4X0mD+z/s8M9v9gij gg8i7ljzHH9xJNrDzVXnAfcPmb+8c118L+HX+0WBh86ZrSWG4El3JJM8NxtVjIxYudwgVQxOQI8K QBigCnqHjWGOQQWcEnmNexW8UsyDy51F5FbXBTDbgY2l2/MFycFd65NSaH4u/t7XJ7e0s5zYC0tb iKZ4thKy+eRIdzfNGyxRY2gsC+GAIYJcm8PaAl6JZ4I1nurhXiV52A81XE/7pd2FJaISMEA3lNzb sUaV4e0DT7+4n0qCOG4R0juBBOwwVRiiOobGFSb5VIwF8vAARMAGf4X8QahrOr6jFdSxiCC4uoY4 k0q4jAEU7RqftLMYpCQuSqgHJP8AdNR23xBsb/w9rurWVr5n9k2jXLxfbraXzMI7BcwSybc7CMsB 14zg46TS4bGGwVtNMbWk7vdI8cm9XMrmRnDZOQzOTxxzxxRqs+m22l3D6xLaRaeU2TtdsqxFW+XD buMHOMHrnFAGHpmsX974e1KebVLG3vLOUiSe502SCO3QIkmZImmzzGwfJddocBgGVlqOy1/WltPB 66lZRrd6w5F6VhMS25+zyTCPYzlw+VAycj5XztJUVqL4X0kadcWBhne3uYpYbgSXcrtOsgVWMjFi zttVVDEllUYUgcVc1CwW9EMqiMXdq7TWkkisyxSmN4wxUMu4bZGGMjr1BwQAZ+veKrHw7cQQ3kN2 /m2810ZIYt6xRQlPMdz2AWTd77SBliqtHqvi6x0jxDZaLPHm4vPL2N9rto8b3KD5JJVkbkfwo2eg yeKr6h4U0uT+zLrWLn7TZ6Pp80DyX7ZZw3lFpZJMj+GFg+RhlkcH5SQeooA5f/hJLz/hLPsHlwfY f7Q/szbtPmeZ9k+1ebuzjbj5Nm3Ofm3fw1Hp3ii+utXlhe0jmglS/a2ggG2VTaTrAylmYK5kLbh9 wJjBLfeGxP4e0y5v5b54JBcSIVZ455EwxTZ5gCsAsuz5PMGHC/KGxxUlromnWWoz39vb7Libdk72 KpuO59ik7U3MAzbQNzAFskZoA5Oz8a6lc2uj3TQWgSWy0u4vECNmRr6UxARnd8gRgW5D7gcfLjcd DWfFF9YaR4wv7W0jlOiJshixku4gSYyOSyjYBKuVHOI2ILFgouJovhqCztbtVgS00mIQrJ9pbykS 3JAEvzbX8plYjfnYwYjDZNRi00WeXULafUrS4tPFCebHbGYBrgeQschjZWBZDEsZ+UZHzHcQwCgE fi7xJeaF/wAeccDeTp93qc3nKW8yO38vdEuCNrN5ow53Bdv3Wzxl3HjXUorkosFpsvLia1s8o2bd or6KyLyHd+9BaYSbRswF25Od46CXTNA8RPO5kjvjBcPFP5d2zBH2qskDhWwEIVN8J+ViAWUnmo00 Xw1eStJGsEralEZ4tlyxBTcjtLAA37vLmNy8e3L7GJ3YNAFfQ/El5f6pa2V1HAfOivQXiUriS0uR A7YJPyvvVgvVNpBZ85HUVzdtaaLYazJcRalaRJotlJFLB5wLQCZllklnZmJy3lK25sE/vGJbd8uw uraaz2SLqFoXv0L2aiZc3ChQxMfPzgKQcjPBzQBcoqOCeG6t4ri3ljmglQPHJGwZXUjIII4II5zU lABRRRQAUUUUAFFFFABWH4iv9StZdHtNLltIZ9QvTbtLdQNMqKIJpchVdCTmIDr3NblYfiiPQGsr aXxDfR2MENwHgna/azKy7HX5ZFdTkqzjGeRmgDLi+I2jrf2GnXv+j39zK1u8QlQiOQTvbjbkq8it LG4BRSQMM4QGhPiNpc88MNvYarPJceSbcJbf68TJNJEy5I+VlgJ3HAXcN5Uq+zQjtPDV5Pp9xZXM CND5cFt9gvWhRwqebHEViYCRQhLqjAgKxIGGOcfw14c0KwnbWXv9KuZvNt1ilsAYYI22OsIwZHyx S7KqC23Y0SqoCrkAE+K/hiXTpryK43qksccaedCvmiQOyNvaQJHlY5DtkZHG3BUFlDbmmeK9N1jV I7GxMknmWSX0crbUDxPtKlUYiQjDD5gmwHKltwKivBoHhua3it7aaRorpBdWiw6lN8iKMB7bD/uk Cyhf3W0bXC9CBWpZ6LZWV4buNZ5LjylhElxcyTFEAAwu9jtztUtjG4qC2SM0AcvP48lvdT0Kz0fT 5yuoXcReWeNCGs3jldJUHmqRvELkZ+ZQjbkyUVqem/E4JFqja7pt3ata3EhBWKNY44fPkgjDyecy bzJEUJJUZbIGxWkGpE/gLSYrDUl1TSoIXlaWyuJNRGwlFeLbEWfHloJZFEa/Im84AJrQk0Xw1a3T 2UqwQ3Wq7isTXLLJMUlknLRgtkMrzO+5MFSQQRhcAGfD8S/DdxdaXbpc5fUdoT95H8jNK0KjG/Mm ZEZcxb1GNxIQhjHafECG81SWK2sbueGS3tXsIkhCS3TS/aHyrOypsMUAkBJGPmU/P8g2DoGj2txp 7NNdx3CP5cDtqU4knILSBHYvmYD94Qr7gBuwACaz7bw/4LfS59VsfsiWUiK7X9pesiosPmAFZkcb AivImVIwnyfcAUAFcfEvSpdUt7S2sNSuILh4VjvUSMRES/Z9rYZw+B9rgz8ufmPBwcan/CXWP/CX /wDCNeX/AKX/AHvtdt/zz8z/AFfm+d0/6Z+/3earzaL4OtbNNbkWxtrEeVMl0Lny4FGbfyypDBAu ba2xjj5QP4jncg1bTbq4it7fULSaeW3F1HHHMrM8JOBIADkoTxu6UAc3ZeJtUlvNLjmggeG/1u/s fMjj2iGKAThAcuSZGMGdwXbjcMA4Jsf8JJef8JZ9g8uD7D/aH9mbdp8zzPsn2rzd2cbcfJs25z82 7+GpNF0uxiuLixl1C0vrvTtTn1COOL5JLQ3BlYCRQ5yds0oBIAIIOMjNSX3hKyuIpDayT2t28QhF 0J5HdBtEbSrlsfaPLygmOXHGSyjaQA13WL/S9W05IkgNpcSxxMHikJcvIEOZeI4doYMA24yn5FCt gnLvPFGtWHhrxLqTWlpeSabbvLDJagiISqG3wkuwMgiKjdIu3dllCq6Fa6S60TTr3UYL+4t99xDt wd7BX2ncm9Qdr7WJZdwO1iSuCc1Ts/COj2OjXWjwx3bafc2/2WS3mvp5VEW0rsTe52DaSPlx29Bg Ap+LvEl5oX/HnHA3k6fd6nN5ylvMjt/L3RLgjazeaMOdwXb91s8aHhjULzU9FM9+YGuo7u6tnaCM xo3lTyRBgpZiMhAcZPWpL/w9pmqPvvYJJiXLHM8gBUqqtGQGwYmCLuj+4xGWUnmjw/pU2jaT9kuL mO5na4uLiSWOIxKWlmeUgKWYgAvjqelAGpRVf7fZ/wBo/wBnfa4Pt3lef9m8weZ5edu/b1254z0z RDf2dxeXNnDdwSXVrt+0QpIC8W4ZXco5XI5GetAFiiq9rf2d95/2O7guPIlaCbyZA/lyL95Gx0YZ GQeRQb+zWzmvDdwC1h3+bMZBsj2Eh9zdBtKsDnpg56UAWKKri/s2s4bwXcBtZtnlTCQbJN5ATa3Q 7iygY65GOtV/7d0f+2P7I/tWx/tP/ny+0J533d33M7vu89OnNAGhRVeG/s7i8ubOG7gkurXb9ohS QF4twyu5RyuRyM9asUAFFRzzw2tvLcXEscMESF5JJGCqigZJJPAAHOajur+zsfI+2XcFv58qwQ+d IE8yRvuouerHBwByaALFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/40sb3UPDTw6dHPJdLd2kyi 3aMSAR3EcjFPN+TcFUkbuCRzXQVh+LtXudD8OyX1n5fn/aLaEF7d5woknSMny0IZyA5IUHJIAoAw 207Xr660zzf7cFgYmj1JJr6GC4lJlPlEfZztHl/Mz+W0ZZGQEyFTHXJ6X4c8eWGhrZQjUrbZo720 ES3CP5bi1KhC5ucITcAurLGxVSi71XKJ6BYeJjnTrK7iu7m/vUZ4ni06S1V1WTbITHKxaIRgoSXI 3bhs3E7Rn/8ACz9BGjRalJHdwCdIHghuglu0qyq7KQ0jqij91MMswz5Z27g0ZcApweGNUHjuO5vj qt3p6RXdrFd/2hsKJJFbNuYI6kbpPtX3V+U+XgKqR7eX0fwX4u03wm62aarZX/zxrbHUkwMaX5O/ KyEHNwke3cx2BVKhBurtNU+IEIsIrjQrG71MPcWSeYkIWMrO8JIy7Jh/LnjIBxzKvUJJsk074leH 9Vid7F57hhKiJFAqzPIrK7hwqMcfJFK2xsSfJjZuZVYAzzpHiqw8VTPHf6rdab5T+XLE0TuwMJyr ebMsayGfLqRAQMou9Y8olOy8L+JkuYb2aW7inRNOGyG9bEipfTPKJC8juxWCTBQyMhLsF37UI6Cx 8awjRPDdxqMEn2vWLKC4xboNitI9vER8zZA33KHvwG7gAyR+PNLez+1tb3yW/wC4dXEO8vDOWWCZ UQsxWR12BceYCRuRRzQBzf8AYPjSWO0ZJZI7yBy3n3dwsqi8+x3cclygJbbA0kkG2MAYw37pRnO5 4O0vUdPvtUl1G0vo/tN281u016s22HybZFWTDfNJ+7K5IcjY/wA7bt0lfXfiRYaYmp2MS+XrVrp8 twtvcNGdsy25nETqj7jhBkuo8v8AhD7uK2JfF9hFF4glaKfboURluQDHvYBWY7Y929eEOC4UNwVL Kd1AGH4V8Oa9D8OV0HUZ5LXUI0hRblpnI2rHEcKIZUKBMGLCuNxjLnO8g6E+jalp2m6A6iTVZ9Kv XuJUidhJMrRTxgIbiVjkGZSd8nRTjstR6j8SvD+lRI9889uxldHinVYXjVVRy5V2GfklibYuZPnx s3KyroReLrOWxvrxbO+ENpdmxBaIL59wJjAI48tzlwo3HCfOMsCrhQDHXw74gstM8I2Fpd7V0+0g tb3ybhlQmOS2dmxxuUpBNGOM/vgCArOVpw6V40GlyRxT3cWoF7b99LeqUNwu/wC1TjPmBYJEKrGh QhWw3lJjdWovjy3gudcF3bz+TYRTXcWyEo5gitrWVwyuQRJm5wAQOmDgjmvrXxFg0zWo7RLGdrW3 u2i1G4JizDGIJpSwj8wSjHlFgWTDorFN2UyAY+u6H4uvpNTgtlvjp91pUtvbWzypJtVrQqIZne4x 5nnfMZBG7HIBl2khek0u4vrDxZfR363bx6lcbLJZZN7qkauzyFFkZEgGYkVlVGJdRIpYh2NK+IOl a1cWdtYW93cT3LyoVh8uVYjGYt++RHaPAWdGyGI6r9/CHU1DX0s9csNGjg8y7vMspklWJAi8uQWO 6RgoY7Y1bHy79isGoA5/wYuuz6xqF5dXM8mm/a9QiHnXAkDbbtlhVFwCmwJMDnOQ8fzMFCRbHhjQ L7Q/tX23U/t3nbNnzXJ2Yzn/AF9xL1yPu7enOeMY+j/EJLzwlomvXNpm3v8AZBJJE6oTcFRuEUTN ukUOJV2jMhMY2o4YNWxqvi6x0jxDZaLPHm4vPL2N9rto8b3KD5JJVkbkfwo2egyeKAMeLQtRtPCK 6TFZTySQ6rJfpvuFcvHHqIuFQuz5MkkfILHGfvsprPtvC2sx3bFrPC6jd290zeamLQRalPeFZeeW KTKg2bxvDZIXDHoL7Vtdj8XppyRwWWmTxeVaXVxbCb7RdeXI5UbJgyqqqG+ZBu2ONy5UnDbxlr1r on2yc6bO62+o6kJIrZ0Se1tXRVVAZGwZVcOsuWUAj5XzmgCunhzV7TwL4gt5rGQTp4Xi0eKNCJGu JYIpwXjCkkoxlUKDhzg5VeM9RPbtN4q0/wAR2Ecl9aDR7mMNBcKyyFngeIRhmCZYK/zDAOF3NgLR r2r6vp+vaVFBbxxaPI6peX0sIlAkeREiiUCVXUsSQX2MAWTjG4jD07xtqepWd7lrGzmgitriSSa1 mYW3nGRfs/lAg3EyPGIjtZMu5G0MhRgCvc+FtZku1K2eV067uLpW81MXYl1KC8Cxc8MEhZDv2DeV wSuWBbeFtZju2LWeF1G7t7pm81MWgi1Ke8Ky88sUmVBs3jeGyQuGPoFhNcXGnW015a/ZLqSJHmt/ MEnlOQCybhw2DkZHXFWKAOf8GfN4feUcxzahfTxOOkkb3cro6nurKysCOCCCODXQUUUAFFFFABRR RQAUUUUAFc/4n0m91W50H7Fcz2v2bUGmmuYPL3xJ9mnTIEispyzqv3T97PGMjoKy9b1KbSksbhVj a3e9ht7kEEvtlbykKcgZ814ic/w78ZOBQBxeoeDtZPiG6v0/0uOOWG6V3dFkuSr6eSoACqJD9gk6 7UzJHggbtlfS/AuvafpK2z3s/wC41DSWNpC0JgnSCOzWSXLJ5gwYZMDcudg+XnnQb4j/APFKXmpJ bf6R9knuLWQp+53+Q11DA43b/M+z7HcgbM7gHzgVsXnjqwtI4itlfTySyzwxxRLGGd4ruO0I+ZwB mSVSMkfLknB4oA5f4eeEvEuh6tpEusyTvb2WiSWAWSdXCO0kMwwAxxjc8PHa1U5wygdRo/hq+0vT tUgvbqDV/tUWxILh7ny24YFXM80+FbcAdoHHUNwBlr8WvDy6bBfXcd3ZJK/CXRhRvL8qKUyD94Qw 2TxHYpMh3EBDg46DX7/XINEaXQ9Jkn1BrgRLHL5TBIw+GkIMqAgoCVG8HLLkD5sAGHZaJq09rFHd 284S81uK9upLl4vtQjhiRkaTyz5W4ywRJiMY8sjID7mFzW7DUNT1exaHTJI4vtELTSM8OyRYJ948 7H7zAA3xbGOWciRVGQRfGCw3GnSTvG+ky6FNq02orCyK4jMPKJuLqNsjMVYE/MoBJDVy4+Kk17dX lvaS6bD5DzTmaFTqAhtY4rdy0gikUA/vpCWDFQYWjXzGIJANDxp4M17W9YjvLS4027iZ5Y0gvbdw LaF7SWNkLpKp2O7clF35dSSwiUCwml6r/wAI/qKz6dqU13c3seoRO7WzXBkhNv5fnqsiRE7oukZU GOPllkPOhe+K5tFvdTm1Q2jaZaINz2+QbaRnRYYnkchGeQOH/gEQxvJVlc4ejfEa81cmSP8AsqSO 0u4be7S0lMwfz72W2j2SZG3aqJISVPmBxgRgg0AWG8M6tH4X0dXF9NcWuq3N/cR280Ud3JHMbj5A 2VjWTFwu8qygYco2QpqO00LxDb6/YXl1Fd3L2jrqF86fZxFeXX2E2z+SdwfJOzaGWNR+9JY5QLqa B4vuL3Trq91GKARrpVvrCCEiPZFMJSImaRtpZfKOZCUU7vuoBzqaH4nt/EOjXOpWFtJIIXZBDHc2 0zSMFDYDRysgJyBhmHvgYNAFPTdLvU8ZT3sll5FnFFPHFl4zHmWSNyYQgDfPsLS+aCQ+NhK7ieL0 zwn4yiS3S6udZIlS2S63a1IxA3ae0u0+blTlb/lcHGQOCgPYWHii+utP8EzvaR511Fa6kQfJETaP NtUFtwJZeOGGFbJBxnPvfFuvWo+zrb2lxPcXq2dvcWlu80aTeXM7whC6mUoYQhl3RpmQlgnlOKAK +jaB4mi8FS6dfzakbyW9093dtSZpRFttftW2UPuUblueAw77eCM49j4T8ZNb6dBe3Os7EeF5yNak DZYaeJssJckfLfYGcDnbjK50Na+LdjpUdkba602922/nXbPL9maUmzeeMRRMS6hyIxubIHmKo3sW 2XL/AMaaxpralaSJYzXWkRXV1czLC6JcxwRW8pRE3kxMwugu4s4Hlk7TuwoBHe6B4mvtJ8JQSTak sttZQpqRh1JomMvnWgk3sjgufKF1zk98clcmkx61plxpTa9/aTqbKysmRrsmSS8zEWMYjmw6Ltle VnTfhXwzxkqvSav4x0vQ9Tayv/Pj2RLNJME3IiGO4kycHccLaSZwD1XGcnEZ8ZW0YRJdN1JLtrhr P7II0eRbjyxKsR2OVy0bbw27YoB3shGKAM+fSNTh8ay6ilhJPZpcG+VY5IwjsLTyMjcwf7QT8uCR D5fJxJzUmg6JqNn4rubm4t9lrH9s8pi6mMefOsv7kA7/AJguZfN6OAI8JkVHdfETTZNQuNM02SOS +t723t3V2Vgym7it5jtVt6FTJgbwu44K71BNaA8aae3h/UNZEUgt7K4+zyLJcW8TbsqOd8gEZ+cf JKUcdCoOAQDL8FaNqWj3kslzaXyWrWlrptvHdG3EkEVuJirN5bsCpDoM7ixcudqJgC5pui3em+Cr nT/sck06Xt1NCjzJJLtN3JJHIry71aUKVdd/8YXcV5IsSeIptQ8Jw6zpAjjSV8F2iN+Y1DFSVjtW bzTuAGFcbQSxPylTn6T4xvtS12xt3trSKC5SNWijk87azWwn3C4U+W5BOzyQN5X98DsoAx73wZ4j utM02WG4tFkgvYrpbS6t/NaKQ3pme4LLKqeaUYbwoI4kWIqr89hBbXd54qi1S5tZLaCDTBHbqzIW 8yZ90yPtJGVEMGCDj5m5bjFO117WdQ8E32qWeleZq6y3cNtZEoMPHPJEgcmTbxtUvh+cNt7CiDxX 9m0PTLi9invb29u5LERWVpsIuE80tGUMjBcGFkLbymRu3BPmABhtp2tXHjG/FjcyQ41NJbtVnMQe I/YnSXZ1KeVbTwZy26Qv91WcRkOleNBpckcU93FqBe2/fS3qlDcLv+1TjPmBYJEKrGhQhWw3lJjd WhB8UPD13pd5f2ryTx2zxghZIQCkm7Y7OZAkQOxvllZGBABUMyBi6+ImmyahcaZpskcl9b3tvbur srBlN3FbzHarb0KmTA3hdxwV3qCaAMPXdD8XX0mpwWy3x0+60qW3trZ5Uk2q1oVEMzvcY8zzvmMg jdjkAy7SQuhqlrqgfVTc+ftvNVsU0xbiXdIZI7gOXEayMhhVVD4UROyRvvwR5h6zQ9dttft7me1S REguGt2EjJu3KAeVViUPI+Rwrj+JRWpQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVl+IYtIm0SZ dduI7fT1eN3me5NuEZXVkPmBlKkOFwQRzitSuf8AGlje6h4aeHTo55Lpbu0mUW7RiQCO4jkYp5vy bgqkjdwSOaADTtG8O6hBY6lZS/2isH/Hpe/b5Llkw5LbJS7Hk5VsH5lARsqAor6P4D0bSvD1ppfk bpIYoUa6gd4JC8aFd6Mjbo87pCQrDJlkzku5anFaeIZdS0edhrJs40xqEd1dW8c0rebmI7Yfkwnz M4Qx70ZATKVMdcvpFl431HwLp9zHe3cyXFvZzSq90ZJ7gGKQs0bCSEqDutcjzIzmKXJfcWmAO41D wj4bkIub6ORHZIrbz2vpo2ZvMiMZ3hwTKXigG/O9tiDJwBVgeENDFu9s1pJJbM6stvLcSyRRYBG2 NGYrGhVmQogClCVIK8Vxer6H4q1KBIr+DVdQVfsNzut72K1yYnti8ZjSQATZF2+VfYNyfMxWPy5N N0nx5HZTRXl3dvOLiJpSsqRLK+yUNJFIZZSE80wOVKRrsjIEJ3NGwB2lromhT6do5treCe0sYo/7 PdXLqIwF2YbJ3r8sbDJI3IjfeVSIx4P0AW72zabHLbu6s0EztJGVUEJHsYkeUu4lY8bFJyqg81y7 6D4og8K+H7awlu7W607w5LG0MNwiq18qQeQj5OGAZHOR8pwQxKsysX+j+L7m+S0zd/YFuJjJcQak 0bSRyahBKAMMGUJbeYgOcjEigAbS4B0lz4Y0GedrWczmS6tHikt/7QmH2mIIInaRA/71grIpkYFh 8nOQuJNS8MWl5o2u2ULyRT6vbywS3MrvOy71YADc2Qil2IQEKMnAGTXndn4W8cwJZSCS++1x2kqC efUxI0c0lvY4JLFy0YuY5XZAQCqN95SI5NifSPG0usamv2++jhnu42jntmjCCIXcTAqXmbDJb71K iFFf5t3mHaWAOsPhDQ/s6Qw2klqEdmV7O4lt5BuABXfGytswiAJnaBHGAAEUCSystC1TQWisHgut MuZZJxJbzl1MjStIzpIpyGEhLAqQVYcYwMY+taNrE2rWlvZS332ARW8Mcsd+6CBVkP2kzHeHkaSH aqN87K4LZjPzmnpmja/o3h/wZZ28V2yWdvFFf28d4u6OUmLc7M5IeJE+0LsBP3k2AbVeMA1I/BPh G/0VLePT4LnTp4l2FZndZI/JjiXDbuV2RREc43Ro/wB5QwsXPhfw7qF5LFdQ/aboSreSpLdyO4DC RFDAtnySGmURn92dzjbya5PRtG8YaVpmj2LRXYggSKSZra8jLxKum+T5KI52MROhfn5MtG3zZcpl v4U8YW90bqCK+864tIBctHqM0m9VluD5QD3YdZAHtmOJQo2yhXYMVkAPQJvCdqLOSOyu76C6eKW3 W8lvJ7iaKOUx+b5bSOSrERLtPIVgDg8htyaCG5QJPFHKgdXCuoYBlYMp57hgCD2IBrzubQ/F02l3 skl5qs2ozy28dujXCQwqv2aHzHlWGVWRTMjbvKcspHyBleQSdReK/i7w4H0y+nsFklYxyncBMiOV DZjdHMbgB1ZHXIKkkqSpALmk+HNI0MAadYxwlUVFYkuyqI448BmJIG2GIEd9gJyea1K4c6Prst5d 2D2ECWEtpGtsEuQLOz2CAIiIqrJ5kbi4kV08vI8sbgdvlal/4c1C68NWemR61ItzA4Z7p2uAXADD b+6njfAyANzucKNxdvmoAuXVloVjrEGq3jwW93PKsMLTTlEknZdilUJ2mYoCgYDeV+XO3iqf9meF Lt7bw4slpJPpdusa2KXZ82ODag2SKG3NEw8vcr5Vvl3A8VT8Q6fenTtN0z+x59Vge0e1v7m1aMTG IhA0SmeUMqy4+Zt7MAmPvEOsfh/S9c0bXLwyJdy2D3F07L5sRjfz7oSRGJcgqI1eYyFsMS2F8wKg UAuXj+Do5Rql7qliv2W7aB5p9R+QThjIIpMvtZkYsyI2fL6qFqSLwl4evrKCaJru4R3S5hvU1S4e U/IyqVn8zfs2yPhQ2352OPmOeX07wrrA0TVbNra7S2vLKx0WOC7aBZorRHkSRwY3ZciGYsCWJMiu dgXalemUARwQrbW8UCGQpGgRTJIzsQBjlmJLH3JJPepKKKACiiigAooooAKKKKACiiigAqnqUNje W62N+Y9ly4SNGk2MzqC42EEEOuwuCvI27hjGauVj+JbG4vNMieyj33dpdwXUQVgrkJIpkVGOAGeP zI+SAd5BIUmgCufC3hrUYpjHZwSW7xPZvHBKywgBTA2EUhVkCAxbwA4UbMgDFSSeD9Ak1I6i2mxi 7Lh/MV2XkSpN0BxjzY1fHTcXPV33cvpXh/xHpZt7a6+13KRW/wDpMlpe7EuIzbYkjUF1P2h7otL5 xCnDcyj7tV7/AEnx5/Y2nqLu7fUInkN7NbypL50+2PypolMtugiUBwY2+Xd96OXPmUAdRa+EfDbW Sx2EckccT+WJbO+mjdTGiQFPMRw2AII1Zc4JjBI3DNaEumaZq9h5JkkniS4kdZoruQSRSh2D7JVb ehBLoQpGBlOBxXN3mka6ng6awsY547+bVbuVBHOETZJczSI0rpIkix4ZCfLbeOPlYbkNN9K8Vyak /lDUre4RNQa1vjfh7ffJLc/Z1miMhGxUaJhiNzkoMqIyrAHcR6VYw3FpPDbRxPZ27WtuI/lWOJih KBRxj92nbjbxjms+58PaBqF/crNBHJdu4ublFnYM6uix4dQ3MTrAoKH5H8vkEis/wbaava2+zWI9 S8xXl8k3Eg2xRkRfKR9pmZyWDFWZmK/OvyLtDZej+GNd0nXNXe/Sx1qHUtPigmma3EIuph9oJM+6 RyFwyodqMMSRqqqsZFAHQaRa+HbOC51/TNQ3WM3nTyTjU5JbQZcvK4UuY1+YMSQBj5unNGn6L4a1 BbfVNOWC6h82SZJre5Z4pXMryEthism2VnZd2djEldprLttDu7jwrrcOqadd3Z1G4EklvNdpBdXE QSJGEjwERByIyAqFUZQgcgs5qQ6Prup+HINLnuPsrLKZZPtsYuknty8vl20u2VWZlTyt53EPjBaQ M+QDU0+Lw5oVvDcWlxaQQai8cdtI9zuWXIJiihLMRswWKRp8oydoGTWpNf2dveW1nNdwR3V1u+zw vIA8u0Zbap5bA5OOlcGmj6xbeCvDei32hSX5GmGz1J7LyFlji2xg24MkoADgAO6sc+WcBSysmhq+ kanqPiF54rCRYLp9PUyPJHiEWd5JMzOAxOJFYbNoY5PzhKAOkh0TTre10u2it9sOlbfsS72PlYia Ic5+b5HYc565681n23gvRLTTlsIUvhbps8kHUrlmt9gKr5TGTdF8rFTsK5UkHI4rm9U0DxNP41k1 G3m1IWYvYnjRNSZIvKVrDP7oOFxtF9kFeec5JSjwLoHibStWgn1ibUniayKTC61JrhfN8mzPCl2A Pmi85A9exWgDtLjQ9Lu7W0tZ7CB7W0yIINmI0BiaIrsHBXY7LtIxg9OlUz4Q0N7dIZLSSQK7MzyX EryTbgAyyuW3SowVAUcspCICCFUDz/TPCfjKJLdLq51kiVLZLrdrUjEDdp7S7T5uVOVv+VwcZA4K A7GjaB4mi8FS6dfzakbyW9093dtSZpRFttftW2UPuUblueAw77eCMgHaXmh6XqF4Lq8sILibymhJ lTcGQgghlPDcO4GQcCRwMB2znzeF/DqRW1rcw72eVjAbi7keV5yufMDsxZplSP5XzvRVIUqM1x+k 6V4p0rS9P/tO5vkWC7gu7ua91IvDHEttbmcySCYOP3guNq/PFnO5NpVl0NdW8u4tNfS4r61urrw1 qFvYrdSFJ0uHW3eONnZsibCOeWz+7c5+UmgDqG8L6S11JOYZ/wB5Kk7Qi7lEIkWVZQ4iDbFbzEDE hQSS2c7mzc03S7TSbdobRJAHcvI8sryySNgDLu5LMcAAEk4CgdABXFnSPFVh4qmeO/1W603yn8uW JondgYTlW82ZY1kM+XUiAgZRd6x5RK9rpHjW3v8AQHknnFrHt81IJGlMeZ3aTzfNuud0TRqcm42F X8vbhSwB3F1Zad4g06Bnfz7dts9vcW07IwyOHjljIYZViMqeVYjoTmmul+HNN1uyKpaWt9IhWzth LsVtiBS0cOdpdY8LvC7gny528Vhw+E9WufBvhmwF/wD2bdWGnxQ3CeZccuI0BGbe4iBwVPXd14xz nQ1q3urrxNaRHQ559NP2eWa7tmgBeSOUtGshd1cRxNiTChixbHADLIARpd+CLvSbtotZ02TT4HeS 5ePUx5cLzzeZvZg+EcyjKNwVP3CK0LKLw5I+mWdncWkstsh1GzjS53uyuroZ/vEyBvNfLtncWJJJ 5rn9R8PancQjUGgu/tJ12a+uLexnjjlkgEMlpGImZgFLRCJmJdWGXKlW2gR2GieIrPXLa91G3+3S w7L24e3eMRTziyEDMgJVhcM+VAO2HyjnAk5oA2P7H8KxxahZG8xb2eyW9s21WXybaMKxVXiMm2OE oW/dkBCoGQQoxci8P6Hen7fB5ksFy8d0oivZTbswkWZZEjD+WCXVXLKPmJJOdzZ5u+0y/v7jUJZf Dl9BNaSl9MNsbUwnF1HNuwZlLtK8aSvu2fKpUENlnJvDviVPA1vZWF3PFqTXd7NIxuFjkxci52Fz HhdyPPE77eAY2KbiFBAO003S7TSbdobRJAHcvI8sryySNgDLu5LMcAAEk4CgdABVyuDudM8VtNq/ kSXazyJOIZjdjynlaZTZvGm75EhjBEq7V38/LNmo5NN8XLrWiTJLfLaR7FuF85J2EnnMZ3cmaJWj kQrt/duUXO2OFgFIB3Av7NrOG8F3AbWbZ5UwkGyTeQE2t0O4soGOuRjrVivJ5dI13TvBF/bXsc8c MfhRdNWGacENdtCiLHAkchV8vuXLIJC7AKzqwVfWKACiiigAooooAKKKKACiiigAooooAKw/F2r3 Oh+HZL6z8vz/ALRbQgvbvOFEk6Rk+WhDOQHJCg5JAFblV72xt9QgWG6j8yNZY5gNxGHjdZEPHoyq ffHPFAHn+pfEDU9Iik8yGC7aO0S53vZTWRbC3srqYpGLLlLMICehff8AMMKbFh4y1a/8UtpxeCC3 jlZSI9JuLkyAXtzBgyI+2H5IF+ZxjLMegwOwvND0vULwXV5YQXE3lNCTKm4MhBBDKeG4dwMg4Ejg YDtmnF4R0eC9gvII7uCeFETdDfToJArs48wK4EpLSOxLhixds5yaAI9A8XWfiSKCSys75BLFFcYn iEZSGRXKSEFvulo3TjJJG4AoQ5rzazrEeo6pCH0pbe2iZ2uHLrHYYK485yQJGaMtKUGzYFCs2JFk qx4a8I6d4a0mwtLZf31tFEkk8ZaMTMkZTJUNjaSzvs5Xe7P94liP4L0SRbpCl8I7qUzyRrqVyEWQ yibeiiTEbeYA2VAPXsTQBHJq+qjwnDfy/ZLC5L7XlubeRhs3FUZbcEOXk+TEJYMpkxlmXawutavN rOnaW1hHYT3ejzXjtMROsNwrQqI/lcFwpkO7gBvl2t1xYn8JaVc29vDM2pOLdw8LnVLnzEYCQZD+ ZuziVwTnkEA5CqBoR6XaRXFpcBJGntLdraGSSV3YRsULAliSxJjQ7myeOvJyAZ/hO/1LU9EN5qct pK8lxMIJLWBokkhVysbgM75DqocEHBDjGep5PRfF/iLWdL0OSO6sY7rVZYlJm0O6ijgDW00zBS8o E3MQAZGAA553Cu0stFXT7iyW1nki0+xshZ21kpbaBlfmckncQqIF6EZfJbdxHP4X0m40nTtMMM8d rpuz7IILuWF4tsZjGHRg33GYcnnPNAHH3fxA1M6o8NrDBF5MSKbVrKaf7RdG5ntzCLhWVIVaSEKj yLzvyRwVHUWHi6z1K/lsrazvjMks0Sh4ggk8mcQzMpLAFULIxPcNhdzKyrck8OaRLb3dubGNYLuy XT5o4yUU26hwsYCkBQBI4+XB59hinpHhHTtKe5nC7ru4u5rl7iMtE37y4M23hv8AcVj/ABrGgbKq FABX8R+KbjQdRmQWPm2sGiXupszMF814TFiNSCSOHOSV/iXBOGFGma1qd34e1KczQS6haylGB02a Dyl2I4JhDyO7eW4kCAqx3Kh2NuxuXWlWN9cLPd20c7i3ltcSfMpilKGRCp4IPlp1Hb3OaaeF9JSz a18md43yXMl3K7uxKESM5Ys0i+Wm1ydybQFKigDDttf169uPD5gm00R3txNDdQm0dpRHCZN8w/eg xAlIlKOpMTyhWJYbTY8HeKrnxDcXlvdwxxyw29vclUiePyDKZAbdt2d7xtEVZxgFty7VKEVuWmia dYy2stvb7JLWKWGJy7E7ZGV5CxJ+ZmZFYs2WJyc5JyaZotlpHmm1WdpJcB5rm5kuJGAzhd8jM20Z Yhc4BZiBknIBoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAf/2Q=--90e6ba6e8f6ed086e6049c48e60a-- ========================================================================Date: Mon, 14 Feb 2011 18:39:53 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: EEG significant clusters outside the MIP glass brain Comments: To: MP <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054ac0b0f9a2b049c491673 Message-ID: <[log in to unmask]> --20cf3054ac0b0f9a2b049c491673 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable no idea, did you look at the mask to see what it looks like? what kind of threshold/correction are you using? if you're using a cluster based threshold like friston's cluster fdr then it could be that off brain stuff is inflating clusters that don't pass correction with the mask. Cheers, Michael On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote: > Thanks Michael, but even then the results ARE very different when I use the > "inclusive" iskull.nii mask on stat map. See attached. > > What do you think is going on? > > On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>wrote: > >> that is not right, the results should change due to the mask. Why don't >> you just run the analysis and mask out the resulting stat map. You're >> obviously not interested in anything off the brain (i.e., noise) anyway, so >> its valid. >> >> Cheers, >> Michael >> >> >> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote: >> >>> Hello Vladimir, >>> >>> I tried to use the inner skull mask as you suggested to avoid getting >>> significant cluster outside the glass brain, but it is drastically changing >>> the results. Attached the figures of results with and without applying the >>> mask. Could you please tell me if I am doing something wrong here? >>> >>> P.S. I include the iskull mask as an implicit mask in the flexible >>> factorial design. >>> >>> Thanks >>> - Muhammad >>> >>> >>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote: >>> >>>> Dear Muhammad, >>>> >>>> Threshold masking will not necessarily work. It depends whether the >>>> values outside the brain are really low. You can use an explicit inner skull >>>> mask. There should be one in EEGTemplates as far as I remember or you can >>>> make your own. >>>> >>>> You can either re-run your stats or use small volume correction for >>>> inner skull but the latter will not fix the graphics. >>>> >>>> Best, >>>> >>>> Vladimir >>>> >>>> Sent from my BlackBerryÂ® smartphone from orange >>>> ------------------------------ >>>> *From: * MP <[log in to unmask]> >>>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]> >>>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500 >>>> *To: *<[log in to unmask]> >>>> *ReplyTo: * MP <[log in to unmask]> >>>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain >>>> >>>> Hello all, >>>> >>>> I am doing EEG analysis using SPM8 and while viewing the results, I see >>>> that some of the significant clusters appear outside the brain. I understand >>>> that it could be due to smoothing of the images, but is there a way to >>>> restrict these clusters inside the MIP? I think Threshold masking may be the >>>> way to go, but I am not sure what threshold value to use? >>>> >>>> If indeed threshold masking is the way to go, would I need to re-do all >>>> the analyses? >>>> >>>> Any help would be much appreciated. >>>> >>>> Thanks >>>> - Muhammad >>>> >>> >>> >> >> >> -- >> Research Associate >> Gazzaley Lab >> Department of Neurology >> University of California, San Francisco >> > > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf3054ac0b0f9a2b049c491673 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable no idea, did you look at the mask to see what it looks like?

what kind of threshold/correction are you using? if you're using a cluster based threshold like friston's cluster fdr then it could be that off brain stuff is inflating clusters that don't pass correction with the mask.

Cheers,
Michael

On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote:

Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii Â mask on stat map. See attached.

What do you think is going on?

On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]> wrote:
that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.

Cheers,
Michael

On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:

Hello Vladimir,

I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?

P.S. I include the iskull mask as an implicit mask in the flexible factorial design.

Thanks
- Muhammad Â

On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:

Dear Muhammad,

Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.

You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.

Best,

Vladimir
Sent from my BlackBerryÂ® smartphone from orange
From: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
Date: Thu, 30 Dec 2010 21:33:40 -0500
To: <[log in to unmask]>
ReplyTo: MP <[log in to unmask]>
Subject: [SPM] EEG significant clusters outside the MIP glass brain

Hello all,

I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?

If indeed threshold masking is the way to go, would I need to re-do all the analyses?

Any help would be much appreciated.

Thanks
- MuhammadÂ

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--20cf3054ac0b0f9a2b049c491673-- ========================================================================Date: Mon, 14 Feb 2011 21:52:53 -0500 Reply-To: MP <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: MP <[log in to unmask]> Subject: Re: EEG significant clusters outside the MIP glass brain Comments: To: Michael T Rubens <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba476b13bf211e049c494270 Message-ID: <[log in to unmask]> --90e6ba476b13bf211e049c494270 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable hi Michael, The mask looks fine to me. Could it be due to different dimensions of the mask and the stat map? - M On Mon, Feb 14, 2011 at 9:39 PM, Michael T Rubens <[log in to unmask]>wrote: > no idea, did you look at the mask to see what it looks like? > > what kind of threshold/correction are you using? if you're using a cluster > based threshold like friston's cluster fdr then it could be that off brain > stuff is inflating clusters that don't pass correction with the mask. > > Cheers, > Michael > > > On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote: > >> Thanks Michael, but even then the results ARE very different when I use >> the "inclusive" iskull.nii mask on stat map. See attached. >> >> What do you think is going on? >> >> On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>wrote: >> >>> that is not right, the results should change due to the mask. Why don't >>> you just run the analysis and mask out the resulting stat map. You're >>> obviously not interested in anything off the brain (i.e., noise) anyway, so >>> its valid. >>> >>> Cheers, >>> Michael >>> >>> >>> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote: >>> >>>> Hello Vladimir, >>>> >>>> I tried to use the inner skull mask as you suggested to avoid getting >>>> significant cluster outside the glass brain, but it is drastically changing >>>> the results. Attached the figures of results with and without applying the >>>> mask. Could you please tell me if I am doing something wrong here? >>>> >>>> P.S. I include the iskull mask as an implicit mask in the flexible >>>> factorial design. >>>> >>>> Thanks >>>> - Muhammad >>>> >>>> >>>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote: >>>> >>>>> Dear Muhammad, >>>>> >>>>> Threshold masking will not necessarily work. It depends whether the >>>>> values outside the brain are really low. You can use an explicit inner skull >>>>> mask. There should be one in EEGTemplates as far as I remember or you can >>>>> make your own. >>>>> >>>>> You can either re-run your stats or use small volume correction for >>>>> inner skull but the latter will not fix the graphics. >>>>> >>>>> Best, >>>>> >>>>> Vladimir >>>>> >>>>> Sent from my BlackBerry® smartphone from orange >>>>> ------------------------------ >>>>> *From: * MP <[log in to unmask]> >>>>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]> >>>>> >>>>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500 >>>>> *To: *<[log in to unmask]> >>>>> *ReplyTo: * MP <[log in to unmask]> >>>>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain >>>>> >>>>> Hello all, >>>>> >>>>> I am doing EEG analysis using SPM8 and while viewing the results, I see >>>>> that some of the significant clusters appear outside the brain. I understand >>>>> that it could be due to smoothing of the images, but is there a way to >>>>> restrict these clusters inside the MIP? I think Threshold masking may be the >>>>> way to go, but I am not sure what threshold value to use? >>>>> >>>>> If indeed threshold masking is the way to go, would I need to re-do all >>>>> the analyses? >>>>> >>>>> Any help would be much appreciated. >>>>> >>>>> Thanks >>>>> - Muhammad >>>>> >>>> >>>> >>> >>> >>> -- >>> Research Associate >>> Gazzaley Lab >>> Department of Neurology >>> University of California, San Francisco >>> >> >> > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > --90e6ba476b13bf211e049c494270 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable hi Michael,

The mask looks fine to me. Could it be due to different dimensions of the mask and the stat map?

- M

On Mon, Feb 14, 2011 at 9:39 PM, Michael T Rubens <[log in to unmask]> wrote:

no idea, did you look at the mask to see what it looks like?

what kind of threshold/correction are you using? if you're using a cluster based threshold like friston's cluster fdr then it could be that off brain stuff is inflating clusters that don't pass correction with the mask.

Cheers,
Michael

On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote:

Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii mask on stat map. See attached.

What do you think is going on?

On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]> wrote:
that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.

Cheers,
Michael

On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:

Hello Vladimir,

I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?

P.S. I include the iskull mask as an implicit mask in the flexible factorial design.

Thanks
- Muhammad

On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:

Dear Muhammad,

Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.

You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.

Best,

Vladimir
Sent from my BlackBerry® smartphone from orange
From: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
Date: Thu, 30 Dec 2010 21:33:40 -0500
To: <[log in to unmask]>
ReplyTo: MP <[log in to unmask]>
Subject: [SPM] EEG significant clusters outside the MIP glass brain

Hello all,

I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?

If indeed threshold masking is the way to go, would I need to re-do all the analyses?

Any help would be much appreciated.

Thanks
- Muhammad

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--90e6ba476b13bf211e049c494270-- ========================================================================Date: Tue, 15 Feb 2011 08:43:31 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: spm_create_vol &&& spm_write_vol Comments: To: cliff <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> If spm_create_vol provides a different set of information about headers from what was in spm_create_vol, then the header will contain the most recent information. Best regards, -John On 15 February 2011 02:04, cliff <[log in to unmask]> wrote: > Thanks~~ > > Does that mean if I use "spm_create_vol" to create headers followed by > "spm_write_vol" (if it is necessary), then the headers created by > "spm_create_vol" will be overwrited? > > On Mon, Feb 14, 2011 at 5:32 PM, John Ashburner <[log in to unmask]> > wrote: >> >> spm_write_vol calls spm_create_vol. Sometimes, for example in the >> fMRI stats, we may wish to create headers but only write out a slice >> of data at a time (as there is not enough memory to hold all the >> images). >> >> best regards, >> -John >> >> On 14 February 2011 22:16, cliff <[log in to unmask]> wrote: >> > Hi, all, >> > I thought before that "spm_create_vol " is used to create the ".hdr" >> > files >> > of the images. >> > However, today I found that "spm_write_vol" can produce both of the >> > ".hdr" >> > and ".img" files. >> > >> > then, what is the "spm_create_vol " exactly used for? >> > >> > Thanks~ >> > > > > > -- > Sincerely, > Senhua Zhu > > > ========================================================================Date: Tue, 15 Feb 2011 12:09:03 +0100 Reply-To: vgarcia <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: vgarcia <[log in to unmask]> Subject: Paired t-test + Covariate MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear List, Our design is a paired t-test (13 subjects) with a covariate (its value is the same in both images of the same subject). We would like to test the hypothesis of zero regression slope against the alternative of a positive slope with the following contrast (value 1 in the column of the covariate): zeros(1,13) 1 0 0 But the contrast is invalid. What are we doing wrong? Thanks a million Verónica ========================================================================Date: Tue, 15 Feb 2011 11:20:54 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: EEG significant clusters outside the MIP glass brain Comments: To: MP <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Muhammad, It looks like the analysis you are doing is at the sensor level, but you use a brain mask. This does not make sense. In your original message you said that you are getting clusters outside the brain so I assumed that you are talking about source images. If you are doing sensor level analysis and you smooth your images you can possibly get clusters extending outside the scalp. To avoid that you can generate a mask using Other/Mask Images. You should provide an original unsmoothed image as input. Best, Vladimir On Tue, Feb 15, 2011 at 2:52 AM, MP <[log in to unmask]> wrote: > hi Michael, > The mask looks fine to me. Could it be due to different dimensions of the > mask and the stat map? > - M > > On Mon, Feb 14, 2011 at 9:39 PM, Michael T Rubens <[log in to unmask]> > wrote: >> >> no idea, did you look at the mask to see what it looks like? >> >> what kind of threshold/correction are you using? if you're using a cluster >> based threshold like friston's cluster fdr then it could be that off brain >> stuff is inflating clusters that don't pass correction with the mask. >> >> Cheers, >> Michael >> >> On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote: >>> >>> Thanks Michael, but even then the results ARE very different when I use >>> the "inclusive" iskull.nii mask on stat map. See attached. >>> What do you think is going on? >>> >>> On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]> >>> wrote: >>>> >>>> that is not right, the results should change due to the mask. Why don't >>>> you just run the analysis and mask out the resulting stat map. You're >>>> obviously not interested in anything off the brain (i.e., noise) anyway, so >>>> its valid. >>>> >>>> Cheers, >>>> Michael >>>> >>>> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote: >>>>> >>>>> Hello Vladimir, >>>>> I tried to use the inner skull mask as you suggested to avoid getting >>>>> significant cluster outside the glass brain, but it is drastically changing >>>>> the results. Attached the figures of results with and without applying the >>>>> mask. Could you please tell me if I am doing something wrong here? >>>>> P.S. I include the iskull mask as an implicit mask in the flexible >>>>> factorial design. >>>>> Thanks >>>>> - Muhammad >>>>> >>>>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote: >>>>>> >>>>>> Dear Muhammad, >>>>>> >>>>>> Threshold masking will not necessarily work. It depends whether the >>>>>> values outside the brain are really low. You can use an explicit inner skull >>>>>> mask. There should be one in EEGTemplates as far as I remember or you can >>>>>> make your own. >>>>>> >>>>>> You can either re-run your stats or use small volume correction for >>>>>> inner skull but the latter will not fix the graphics. >>>>>> >>>>>> Best, >>>>>> >>>>>> Vladimir >>>>>> >>>>>> Sent from my BlackBerry® smartphone from orange >>>>>> >>>>>> ________________________________ >>>>>> From: MP <[log in to unmask]> >>>>>> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> >>>>>> Date: Thu, 30 Dec 2010 21:33:40 -0500 >>>>>> To: <[log in to unmask]> >>>>>> ReplyTo: MP <[log in to unmask]> >>>>>> Subject: [SPM] EEG significant clusters outside the MIP glass brain >>>>>> Hello all, >>>>>> I am doing EEG analysis using SPM8 and while viewing the results, I >>>>>> see that some of the significant clusters appear outside the brain. I >>>>>> understand that it could be due to smoothing of the images, but is there a >>>>>> way to restrict these clusters inside the MIP? I think Threshold masking may >>>>>> be the way to go, but I am not sure what threshold value to use? >>>>>> If indeed threshold masking is the way to go, would I need to re-do >>>>>> all the analyses? >>>>>> Any help would be much appreciated. >>>>>> Thanks >>>>>> - Muhammad >>>> >>>> >>>> >>>> -- >>>> Research Associate >>>> Gazzaley Lab >>>> Department of Neurology >>>> University of California, San Francisco >>> >> >> >> >> -- >> Research Associate >> Gazzaley Lab >> Department of Neurology >> University of California, San Francisco > > ========================================================================Date: Tue, 15 Feb 2011 11:29:30 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Question about lead field matrix Comments: To: Carsten Stahlhut <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> Dear Carsten, I actually see now that the option to use OpenMEEG is already in the code of the present version 4010. If you look in spm_eeg_inv_forward around line 88, you'll find it there. It's just not in the GUI, which is easy to fix by adding 'OpenMEEG BEM' to the list in spm_eeg_inv_forward_ui. I'm sure Alexandre will be glad to help you with installation. I suspect on LINUX it's more straightforward than on Windows. Best, Vladimir On Mon, Feb 14, 2011 at 9:58 PM, Carsten Stahlhut <[log in to unmask]> wrote: > Dear Vladimir, > > Thanks for clarifying the issue with the physical units - that confirmed a > little suspicion on my that it would probably be better for me to leave out > the units in some plots that I did of reconstructed sources. > > Yes, indeed the platform is also one my concerns - however, I think it > should work with Linux. > Regarding the computation time - my impression is that it is very fast > compared to other FEM implementations and almost just as fast as computing > BEM solutions. I guess the reason why I suddenly found the SimBio's FEM head > model very appealing was that in one of their recent papers (see e.g. the > attached paper page 1926), they claim that with a grid of 2 * 2 * 2 mm^3 > they can build a FEM with 71 electrodes in less than 22 min and less than 38 > min for a sensor configuration of 258 electrodes! That's quite an > improvement. > All of this is performed just on a Linux-workstation - Intel Core 2 Duo > E6600 processor (2.4 GHz) with 4 GB memory. > > Thanks for bringing the OpenMEEG software to my attention. I think that's a > really good alternative. Yes, that would be really great if I would send me > your code for using the OpenMEEG BEM implementation. > > Best, > Carsten > > > > > 2011/2/14 Vladimir Litvak <[log in to unmask]> >> >> Dear Carsten >> >> On Fri, Feb 11, 2011 at 11:56 PM, Carsten Stahlhut <[log in to unmask]> wrote: >> > Dear Vladimir, >> > >> > I have approx three questions (or issues) that I hope you can help me >> > out >> > with. >> > >> > Do you know what the units are for the lead field matrix and the source >> > estimates in SPM8? >> >> These are two different questions. >> >> > If you apply one of the EEG imaging methods (as I understood it) then >> > the >> > source estimates should be current density i.e. the unit should be >> > something >> > like A/m^2 or uA/mm^2 depending on the scaling of the EEG data and lead >> > field matrix. >> > And then the units of the lead field should be something like m^2 * Ohm >> > ( = >> > m^2 / S ). >> >> In theory the forward computation done by the 'forward' toolbox should >> be unit-blind so the units of the lead field should depend on units of >> the head model (mm in SPM). In practice this might not be the case. We >> recently found a difference in scaling between 'single shell' and >> other MEG models. This will be fixed in the next update. There might >> be something similar also for EEG. SPM is not sensitive to it but it's >> still something worth looking into. >> >> > Does this also holds for SPM8 or do there exist any type of scaling of >> > the >> > lead field matrix such as a normalization of the columns or something >> > like >> > that? >> > >> >> Yes, there are several scaling and normalization steps so the units of >> the output images are not related to original physical units but are >> normalize to the total power across sources and conditions. >> >> > The second question is related to the lead field matrix as well. If I >> > compare two different head models a BEM and 3-spheres model then they >> > seem >> > to be on two very different scales - is that really the case? Or am I >> > missing an important step? Of course it is two very different head model >> > assumptions so they should be quite different but I'm a bit surprised >> > that >> > there exists a scaling factor of 100 between the largest element in the >> > BEM >> > gain matrix and the 3-spheres gain matrix. >> >> See above. >> >> > What I just did was to load the mat-files with the gain-matrices and >> > used >> > the imagesc-function to show the amplitudes of the two lead field >> > matrices. >> > >> > The 28th Jul 2010 you sent an answer to a post regarding head models and >> > you >> > here mentioned that FEM head models from SimBio might be an option in >> > the >> > very nice MEEGTool. I was wondering what the status is on integrating >> > the >> > SimBio FEM implementation with SPM, as I'm very interested to try out >> > the >> > FEM head models. I guess there is no reason that I will try to write the >> > code my self if the option is almost done and will be part of the >> > MEEGTool. >> > >> >> I am aware of some efforts to integrate SimBio support in the forward >> toolbox. Even if this is done I don't expect it to become one of the >> standard options in SPM because SimBio runs only on a particular Unix >> platform (I'm not even sure it's LINUX) and I suspect has very lengthy >> computing time. But if you compute a gain matrix for the canonical >> mesh in SimBio you can just replace the mat file generated by SPM with >> your file and it will be used by spm_eeg_invert. If there is >> inconsistency in units, it shouldn't be a problem. >> >> There is also a possibility to use another BEM implementation OpenMEEG >> (http://www-sop.inria.fr/athena/software/OpenMEEG/). That's something >> that works and I tested it last year, but found it impractical for >> most users again because of difficulties with installation and long >> computation time (about 24 h for normal mesh). The differences in the >> lead fields with our BEM were only for a small number of vertices >> close to the surface. But I can give you the glue code for this if you >> want. >> >> Best, >> >> Vladimir >> >> >> >> >> > Best, >> > Carsten >> > >> > >> > > > > > -- > Carsten Stahlhut > Section for Cognitive Systems > Department of Informatics and Mathematical Modelling > > Richard Petersens Plads, Building 321 > Technical University of Denmark > DK-2800 Kongens Lyngby, Denmark > ========================================================================Date: Tue, 15 Feb 2011 12:59:06 +0000 Reply-To: "B. de Haan" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "B. de Haan" <[log in to unmask]> Subject: Lesion analysis workshop Tuebingen 27th of May 2011 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> We would like to announce a lesion analysis workshop on the 27th of May 2011 in Tuebingen. The workshop will provide an overview on the background, rationale and methods of modern lesion analysis as well as a demonstration of their practical application. At the end of the workshop, participants should be able to independently conduct modern lesion analyses. Further information including program and registration form can be found on: http://homepages.uni-tuebingen.de/karnath/LesionAnalysisWorkshop2011/LesionAnalysisWorkshopHome.html ========================================================================Date: Tue, 15 Feb 2011 14:07:20 +0000 Reply-To: Joseph Whittaker <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Joseph Whittaker <[log in to unmask]> Subject: Normalization after New Segment MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001636c59ab9c984c4049c52ae52 Content-Type: text/plain; charset=ISO-8859-1 Dear SPM experts, I have some structural images that will not segment properly using the default segment option in SPM8, however the New Segment option available in the tools does work. The only problem is that it does not output a parameter file that can be used in the normalization procedure. Has anyone found away around this problem? Perhaps I could copy the relevant parameters from the seg8.mat file (produced by New Segment) to the seg_sn.mat file (produced by default Segment) as a sort of work around, or maybe just change the seg8.mat file into an acceptable format. Any advice or information would be appreciated. Thanks. Regards joe -- Joseph Whittaker, M.Sc Ph.D Student G.803 Neuroscience and Psychiatry Unit Stopford Building University of Manchester Oxford Road Manchester, UK Tel: 0161 275 1729 --001636c59ab9c984c4049c52ae52 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable
Dear SPM experts,

I have some structural images that will not segment properly using the default segment option in SPM8, however the New Segment option available in the tools does work. The only problem is that it does not output a parameter file that can be used in the normalization procedure. Has anyone found away around this problem? Perhaps I could copy the relevant parameters from the seg8.mat file (produced by New Segment) to the seg_sn.mat file (produced by default Segment) as a sort of work around, or maybe just change the seg8.mat file into an acceptable format. Any advice or information would be appreciated. Thanks.

Regards

joe
--
Joseph Whittaker, M.Sc
Ph.D Student
G.803 Neuroscience and Psychiatry Unit
Stopford Building
University of Manchester
Oxford Road
Manchester, UK
Tel: 0161 275 1729
--001636c59ab9c984c4049c52ae52-- ========================================================================Date: Tue, 15 Feb 2011 14:42:35 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Normalization after New Segment Comments: To: Joseph Whittaker <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: [log in to unmask]> The new segment algorithm can be set up to generate a deformation field, which can be used for spatially normalising images. You'll see these options down the bottom of the new segment UI. The deformation fields can be used to warp images via the Deformations Utility, although it may not be so friendly for those who don't know what a composition is. Best regards, -John On 15 February 2011 14:07, Joseph Whittaker <[log in to unmask]> wrote: > > Dear SPM experts, > > I have some structural images that will not segment properly using the > default segment option in SPM8, however the New Segment option available in > the tools does work. The only problem is that it does not output a parameter > file that can be used in the normalization procedure. Has anyone found away > around this problem? Perhaps I could copy the relevant parameters from the > seg8.mat file (produced by New Segment) to the seg_sn.mat file (produced by > default Segment) as a sort of work around, or maybe just change the seg8.mat > file into an acceptable format. Any advice or information would be > appreciated. Thanks. > > Regards > > joe > -- > Joseph Whittaker, M.Sc > Ph.D Student > G.803 Neuroscience and Psychiatry Unit > Stopford Building > University of Manchester > Oxford Road > Manchester, UK > Tel: 0161 275 1729 > ========================================================================Date: Tue, 15 Feb 2011 16:42:06 +0100 Reply-To: vgarcia <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: vgarcia <[log in to unmask]> Subject: Re: Paired t-test + Covariate In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="------------080802090806010109080809" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --------------080802090806010109080809 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bifur.jiscmail.ac.uk id p1FFjeGB011923 Dear List Our design matrix is the following one: Thanks a lot, Verónica On 14 February 2011 15:08, vgarcia<[log in to unmask]> wrote: > Dear List, > > Our design is a paired t-test (13 subjects) with a covariate (its value > is the same in both images of the same subject). We would like to test > the hypothesis of zero regression slope against the alternative of a > positive slope with the following contrast (value 1 in the column of the > covariate): > > zeros(1,13) 1 0 0 > > But the contrast is invalid. What are we doing wrong? > > Thanks a million > > Verónica > > > > --------------080802090806010109080809 Content-Type: multipart/related; boundary="------------060905000103080704000403" --------------060905000103080704000403 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit

Dear List

Our design matrix is the following one:




Thanks a lot,

Verónica


On 14 February 2011 15:08, vgarcia <[log in to unmask]> wrote:

[log in to unmask]" type="cite">Dear List,

Our design is a paired t-test (13 subjects) with a covariate (its value
is the same in both images of the same subject). We would like to test
the hypothesis of zero regression slope against the alternative of a
positive slope with the following contrast (value 1 in the column of the
covariate):

zeros(1,13) 1 0 0

But the contrast is invalid. What are we doing wrong?

Thanks a million

Verónica

--------------060905000103080704000403 Content-Type: image/png; name="moz-screenshot-11.png" Content-ID: <[log in to unmask]> Content-Disposition: attachment; filename="moz-screenshot-11.png" Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAAlEAAAMmCAIAAABaTkNEAAAgAElEQVR4nO3dsZXsONCeYYQl Q2koEAWgEBSGHIUhQ2HIUAhtyIWM3b+HDVQVQbAAgqj3OffMIZtgAeCQ+Lbn9uxNGQCAGNLT AwAAYBIyDwAQBZkHAIiCzAMAREHmAQCiIPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiC zAMAREHmAQCiIPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZmH2dKv+mh7naLmuLOMajcrTO7O ZcBJcrOm4/AAA7cXprKXy0vR1XFid3eNBUdbZMDiN9HlOpB5GI3bC/NoIfd9hcwb3Z1j5tWv jKgM+OL2wjziiuaSeXcGcMfrMm/cMBzf7QHjcINiHntNFH9Q1vii+Abu6llZX7jFgn0zOp4l Hm0/Uezu0oC1ZvbUtEMjruexTT3BrF9DQMRdgnnstUlcB1NFbGmf0nKWNgBjGHUFe0b2CI2R 1N0ZpxhXoPFCnU5NO2TPwpidOLyWlsb4ARG3CKayVyh72T1dHO8fSlKitBdsnEs9/Y7uhlaw aS1HDK9x99L4ERm3CB6QfhWv22eJLe3VUKvfFwDGWQb7rI7u7l8Bo1n7XFqGemd4fUcBDbcI nlQsf/ZSeHVJrdfW07MaR+t+lljHPvF4SGx2egWMZi0zsl+vi/cNz67fMirgiFsE84irUstC pq2bWuWOs+wVs7Fg91k3x3/zutXNbFrLuiOteMvw7Kt0eg0BEbcI5hFXpZYVubGl1mPLWaeH GtfljsF3d/fd7RuG0cwmtmzsqP2slqt0OiqgwC2CeeqF1V4ojd1Bh44R0lGwnmzjWXfGr13V 0wr2WQaxpfv3TvwuNF5DQMMtgqmSRDxqNxZbnnZhnFWf2DLmomXjTOuzLo1f7KWja6NZx9Tq xnbllgm2t7QHDHxxi2C2xoWy3tUWROOQtjKKZ9W9n55lr7PtZzVOrWPAYuX2C2XPSzvXa3gd l0IcBvDFLQK8mB05m4kzU4zDDQS82MYxwDs5jMANBLzS6c8V3679J6hAO+4h4JUixACBB3fc RgCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPMAAFGQeQCAKMg8AEAUZB4AIAoy DwAQBZkHAIiCzAMAREHmAQCiIPNwUUr84c+2f7A7vse46PFViT/8GfcHu+N7jIseX5X4w59x f7A7vse46PFViT/8GfcHu+N7DACIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8A EAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEA oiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABA FGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCI gswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABR kHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gFolVJKKf2z8fRYgB7cuACaFGlH7OGN uGsBNCHzsAHuWgBNyDxsgLsWQKt08PRYgB7cuACAKMg8AE2K93bdb/XSrzvjKeocC/JOFCJu CwAnkqKv1Gnx7jrHDTIPIm4LAE28UsSIK686/I0jNNwWAFq5vM9z+dmmVx1Ew40CCBZcTL3+ sqr+q6+rJ94cAPAgblyglMZ8WON+UOXbkVOcfqnOnXOBRXDXAgKv93mOweD1l1Xddbwyz/Ha rvZeHOvjXpmHJxNv1/1z0aKCtju/DqJ54EYZetN3/J2HVif3/ofwcbfvp1tinXp7Ziltau0j 0epkv6udf6/5nTodHFfhBYfkZeOp4RWm3ihiBnQEw+mJx5W0u05Rra+UV50HS53WOe6uMLXs cQOIZUfXWXNIWfrvifZz7bH1VfCtg1Cef59nvHinztXn+X4drVQ+PJx36nwreJW6WadvoTGu 9tXFy75K7SOcc0M+UspxSMAenrn7r65Kb6nDkGbWWXBIG0/tZoXiPyK736J51UFYz7/P675Z V6vDkGbWWXBIG0/NsQLwLDJv86VqtSExtZcOKTu9tVpzaojjsZ9tuvxEYrU6DGlmnQWHtPHU 7kuKp+ogJm4UAK3ux4xXOBFy6MN9A6BJ+o+P1+brkfNtf/P9mVcdhPXkzzaz018wrFOHIc2s s+CQNp7at8IxdW4WBOZ77DMs95+c1eowpJl1FhzSxlMbUQ14BJm3+VK12pCY2kuHVBS8Wirp HqmDsJ782eb9O3W1OgxpZp0Fh7Tx1Bwlpwz2qoNQuFcANPHKcmN3fh1EM+9GOf5HmeildRgS U9t1alpxlwrr1EEoq9wrXnftanUcS208JKY2s5RLHZIGL8VdC+AC0g6vxmdYPOswpJl1FhzS xlPLfmmXfj1eB6E8cKMUd2f3zbpaHYY0s86CQ9p4ao68xrDCXPBGj73PKzb2qMOQZtZZcEgb T+1bxOX92f2RONZBKFM/t6l5dR2GNLPOgkPaeGp1WWO3e2zdg3GcGuLgRgHQxCXzgGdx1wJo QuZhA898hmWpH244/pCEIU2rs+CQNp6aWPDZIo51EMrD94rXzbpaHcdSGw+Jqc0stUgwJKc3 i151EM3zN8pqj/Teq9VqQ2JqM0vdfHPmUmq1Oojm+Z9tblOHIc2ss+CQ9p5a44tXR9U9JMc6 CIV7BcAJl8wrwqk7q7zqIKap98rx7rzzH2ir1WFIM+ssOKSNp/at0PjipQpXizjWQVjzbpTj Taltv7EOQ5pZZ8EhbTw1rebNIa1QB2GReXfrMKSZdRYc0sZTq8t2v2Uk87AIMu9uHYY0s86C Q9p4ar7q3vvG41UHMU29V4r/SOz7D8YF6zCkmXUWHNLGU/OVfj1eBwFxuwAAoiDzAABRPJx5 Xj+XWK2OY6mNh8TUZpbaeEj8eBPtyLwhdRxLbTwkpjaz1MZDIvPQjswbUsex1MZDYmozS208 JDIP7ci8IXUcS208JKY2s9TGQyLz0G7gvZIAYCXjlju8xdjM86oDYI7/5MTl2XdfARxHhZci 8wD8IfPuD7VxN/NT2SeQeQD+xMy876Fvs7px0aYueGxwupulCBT7hS8yD8AfMu/YaTGGoo22 nRoyLynJitHIPAB/YmZelpIpjcy8ehtzkHkA/kTOvGOzov1xN43MvCS9C6x7t+cCg/OFa7/D umsCGGfvzPsSe8lKUNWHxDriudpuvV00rjfqo+jA+zwAf/bOPLuX3Jx5xYb4ur0rVjjtS+sa 7cg8AH/IvHpXPGRsG6WM0+3GRhFcQuYB+BM2874dfbfFr3mBzGuZCzRkHoA/0TIv/SZQUsJG 3NCG2rhb957PMq9ugKvIPAB/omXeS+00l8nIPAB/yLzu4Y0o/mBfuxqbeR8Pjnc8ANt/duI4 JJdl5J+VZNxyh7cg8wD8IfOwNzIPwB8y7+ail5o/w1LsYg4yD8CfmJmXDp+KTMoHI4s235Zi g6u7dREMQuYB+EPmfTeS8p6seF0rcmkX05B5AP7EzLwsRVEamXliBUxA5gH4EznzvvJzmXds nKQALg7hKucLN+JmdXx4ANj2zrwvce3KSlDVh8Q64rnabr1dNK436qPowPs8AH/2zjx7vcrN mVdsiK/bu1mJLrsvrWu0I/MA/CHz6l3xkLFtlNLq2MOwx4BLyDwAf8JmXtb/2iwNyDxtMFpf xUhO5wINmQfgT7TMS7+hlfR3Y/WGWE0rWNf/OpZNZubVDXAVmQfgT7TMe6md5jIZmQfgD5nX sdD9Y0TxB/vaFZkH4A+Zh72NzTwXCw4J2NV/cbLmY+s4KrwUmQfgD5mHvZF5AP6QefeH2rib +SjKE8g8AH9iZl76/RWCLKVR0n/NoG5wupulCBT7hS8yD8AfMu/YaTGGoo22nZp/J90YEgYh 8wD8iZl5WUqmNDLz6m3MQeYB+BM5847NivbH3TQy85L0LrDu3Z4LDM4XzvHubLlNVxgesJO9 M+9L7CUrQVUfEuuI52q79XbRuN6oj6ID7/MA/Nk78+xecnPmFRvi6/auWOG0L61rtCPzAPwh 8+pd8ZCxbZQyTrcbG0VwCZkH4E/YzPt29N0Wv+YFMq9lLtCQeQD+RMu89JtASQkbcUMbauNu 3Xs+y7y6Aa4i8wD8iZZ5L7XTXCYj8wD8IfO6hzei+IN97eoFmef1L4l8+GeJgDNemec4JMfH f9xyh7cg8wD8IfOwNzIPwB8y7/6i17eLOcg8AH9iZl46fCoyKR+MLNp8W4oNru7WRTAImQfg D5l3XL6K1axoo22nG5mH0cg8AH9iZl6WoiiNzDyxAiYg8wD8iZx5X/m5zDs2TlIAF4dwlfOF c7zR3e/4D5kHnNk7877EtSsrQVUfEuuI52q79XbRuN6oj6ID7/MA/Nk78+z1KjdnXrEhvm7v ZiW67L60rtGOzAPwh8yrd8VDxrZRSqtjD8MeAy4h8wD8CZt5+feNVxqcedpgtL6KkZzOBRoy D8CfaJmXfkMr6e/G6g1t0WvZPU7wWDaZmVc3wFVkHoA/0TLvpXaay2RkHoA/ZF73Qjei+IN9 7YrMA/CHzMPeXpB5jjaeGrAUr2fN93FzHBVeiszbZGrAUryeNd/HzXFUxlAbdzN/LfcEMm+T qQFL8XrWfB83o4tjX1lKo6R/5LJucLqbpQgU+4UvMm+TqQFL8XrWfB83o4t6Iylv0YrXtSIt u74XCi3IvE2mBizF61nzfdzsXvLEzKu3MQeZt8nUgKV4PWu+j1tjL/m5zDs2TlIAF4dwlfOF c7w7RwgyTeBxXs/aoMdN7CUrQVUfEuuI52q79XbRuN6oj6ID7/M2mRqwFK9nzfdxs3vJzZlX bIiv27tihdO+tK7RjszbZGrAUryeNd/Hze4lX8w8bdsoZZxuNzaK4BIyb5OpAUvxetZ8H7eW jr7b4te8QOa5X+FQyLxNpgYsxetZ833cxOJFR8XrxoY21Mbduvd8lnl1A1xF5m0yNWApXs+a 7+PmOKpn7TSXyci8TaYGLMXrWfN93BxHVQ9vRPEH+9oVmbfJ1ICleD1rvo+b46jwUrEyz/Ef JQEwh+NjO265w1uQeZ0PD4A5HB/bccsd3oLM63x4AMzh+NiOW+6Oi17fLuYg8zofHgBzOD62 xmJ1XLWy9MHIos23pdjg6m5dBIOQeZ0PD4A5HB9bY7GqN5Lynqx4XStyaRfTkHmdDw+AORwf W3u9yhMzT6yACci8zocHwByOj23jepWfy7xj4yQFcHEIVzlfuKG3/n2ODw+AOUY8tuLalZWg qg+JdcRztd16u2hcb9RH0YH3eXcfHgBDOT629nqVmzOv2BBft3ezEl12X1rXaEfmdT48AOZw fGzt9SpfzDxt2yil1bGHYY8Bl5B5nQ8PgDkcH9uWJeu7LX7NTpmnDUbrqxjJ6VygIfM6Hx4A czg+tuIyVSxZxevGhrboteweJ3gsm8zMqxvgKjKv8+EBMIfjYztuuZtsp7lMRuZ1PjwA5nB8 bIcudCOKP9jXrsi8zocHwByOj+245Q5vESvzvHCJsKv/6sTrGfF9TBxHhZci83pwibArMu/+ UBt3M38t9wQyrweXCLuKmXnp9+OUWUqjpH/ksm5wupulCBT7hS8yrweXCLsi846dFmMo2mjb qfn384whYRAyrweXCLuKmXlZSqY0MvPqbcxB5vXgEmFXkTPv2Kxof9xNIzMvSe8C697tucDg fOEc786Vcbmwq70z70vsJStBVR8S64jnarv1dtG43qiPogPv83pwibCrvTPP7iU3Z16xIb5u 74oVTvvSukY7Mq8Hlwi7IvPqXfGQsW2UMk63GxtFcAmZ14NLhF2FzbxvR99t8WteIPNa5gIN mdeDS4RdRcu89JtASQkbcUMbauNu3Xs+y7y6Aa4i83pwibCraJn3UjvNZTIyrweXCLsi87qH N6L4g33tiszrwSXCrsg87I3M6+H4j5sAS/HKPMchOT5u45Y7vAWZ18PxIQSWQuZhb2ReD8eH EFgKmXd/0evbxRxkXg/HhxBYSszMS4ffBEjKLwMUbb4txQZXd+siGITM6+H4EAJLIfOOy1ex mhVttO10I/MwGpnXw/EhBJYSM/OyFEVpZOaJFTABmdfD8SEElhI5877yc5l3bJykAC4O4Srn C+d4o6/M8SEElrJ35n2Ja1dWgqo+JNYRz9V26+2icb1RH0UH3uf1GPEQAivYO/Ps9So3Z16x Ib5u72Yluuy+tK7Rjszr4fgQAksh8+pd8ZCxbZTS6tjDsMeAS8i8Ho4PIbCUsJmXf994pcGZ pw1G66sYyelcoCHzejg+hMBSomVe+g2tpL8bqze0Ra9l9zjBY9lkZl7dAFeReT0cH0JgKdEy 76V2mstkZF4Px4cQWAqZ173QjSj+YF+7IvOexNXGav6vkzXvbcdR4aXIvCdxtbEaMg97I/Oe xNXGasi8+0Nt3M38tdwTyLwncbWxmpiZl34/TpmlNEr6Ry7rBqe7WYpAsV/4IvOexNXGasi8 Y6fFGIo22nZq/v08Y0gYhMx7Elcbq4mZeVlKpjQy8+ptzEHmPYmrjdVEzrxjs6L9cTeNzLwk vQuse7fnAoPzhXO8OyPgymM1e2fel9hLVoKqPiTWEc/VduvtonG9UR9FB97nPYmrjdXsnXl2 L7k584oN8XV7V6xw2pfWNdqReU/iamM1ZF69Kx4yto1Sxul2Y6MILiHznsTVxmrCZt63o++2 +DUvkHktc4GGzHsSVxuriZZ56TeBkhI24oY21Mbduvd8lnl1A1xF5j2Jq43VRMu8l9ppLpOR eU/iamM1ZF738EYUf7CvXZF5T+JqYzVkHvZG5j3J6x8G+/BP8cHJ/3HiOCTHZ2Tccoe3IPOe 5PUwf8g8OCHzsDcy70leD/OHzIMTMu/+ote3iznIvCd5PcwfMg9OYmZeOvwmQFJ+GaBo820p Nri6WxfBIGTek7we5g+ZBydk3nH5Klazoo22nW5kHkYj857k9TB/yDw4iZl5WYqiNDLzxAqY gMx7ktfD/CHz4CRy5n3l5zLv2DhJAVwcwlXOF87xRo/A62H+kHlwsnfmfYlrV1aCqj4k1hHP 1Xbr7aJxvVEfRQfe5z3J62H+kHlwsnfm2etVbs68YkN83d7NSnTZfWldox2Z9ySvh/lD5sEJ mVfvioeMbaOUVscehj0GXELmPcnrYf6QeXASNvPy7xuvNDjztMFofRUjOZ0LNGTek7we5g+Z ByfRMi/9hlbS343VG9qi17J7nOCxbDIzr26Aq8i8J3k9zB8yD06iZd5L7TSXyci8J3k9zB8y D07IvO6FbkTxB/vaFZm3Cb5rcOF1Iznae3aYjMzbBN81uPC6kRztPTtMRuZtgu8aXHjdSI5e NLuiF3s389dyTyDzNsF3DS68biRHE2aXfj9OmaU0KtrUBZPy0UpxN0sRKPYLX2TeJviuwYXX jeRowuzSWSAdd4vXtSItu2te8L2ReZvguwYXXjeSozmzS1UypZGZV29jDjJvE3zX4MLrRnI0 Z3ZFs6L9cTeNzLxj4yQFcHEIVzlfOMe7E5fwHYQLrxvJ0ZxpprOfQKbfBKrriOdqu/V20bje qI+iA+/zNsF3DS68biRHc2aXrmResSG+bu+KFU770rpGOzJvE3zX4MLrRnI0Z3bpeuZp20Yp 43S7sVEEl5B5m+C7BhdeN5KjabM7tkkLZ96y36lXIPM2wXcNLrxuJEdDZ5d+EygpYSNuaENt 3K17z2eZVzfAVWTeJviuwYXXjeRo79n12Wkuk5F5m+C7BhdeN5KjxWc389Kt/G16CzJvE3zX 4MLrRnK09+wwGZm3Ccd/YwyR/T8njkNyvLfHLXd4CzJvE47rAiIj8+4ven27mIPM24TjuoDI YmZeOnwqMikfjCzafFuKDa7u1kUwCJm3Ccd1AZGRecflq1jNijbadrqReRiNzNuE47qAyGJm XpaiKI3MPLECJiDzNuG4LiCyyJn3lZ/LvGPjJAVwcQhXOV84xxsdlziuC4hs78z7EteurARV fUisI56r7dbbReN6oz6KDrzP28SIdQEB7Z159nqVmzOv2BBft3ezEl12X1rXaEfmbcJxXUBk ZF69Kx4yto1SWh17GPYYcAmZtwnHdQGRhc28/PvGKw3OPG0wWl/FSE7nAg2ZtwnHdQGRRcu8 9BtaSX83Vm9oi17L7nGCx7LJzLy6Aa4i8zbhuC4gsmiZ91I7zWUyMm8TjusCIiPzuhe6EcUf 7GtX1s+U870fHDve9DjluC4gMjIPe7N+wF1vXCuNF+q+k7gB9vCfnKx5IzmOCi9F5uFH953E DbAHMg97U3+2me4FXmbJe6fO+4gbYBdk3v2hNu5mPoryBD7Dgh/cAMHFzLxUfYKhbly0qQum 3/cJ9m6WIlDsF76s93na9761NF6o/1biBtgCmXfstBhD0UbbTs2/k24MCYPw93n40X0ncQPs IWbmZSmZ0sjMq7cxB5mHH913EjfAHiJn3rFZ0f64m0Zm3rFxkgK4OISrnDPP8e7EI1zuKm6G 99o7877EXrISVPUhsY54rrZbbxeN6436KDqc/H3enYvrfJNiihv3EjfADvbOPLuX3Jx5xYb4 ur0rVjjtS+sa7QZeOMc7FdNwAwRH5tW74iFj2yhlnG43NorgEvk9fscdc6kOluV0X3EDvFXY zPt29N0Wv+YFMq9lLtCoP9s83T4vjRfqvpO4AfYQLfPS7/qWlLARN7ShNu7WveezzKsb4KqT m6B45dKFdrxTMU33ncQNsIdomfdSO81lMjIPP7rvJG6APZB53cMbUfzBvnZ14XObVy+0452K abz+3ZYP/yzRO/1nJ45Dcrwh766XeD8+t4kfXuvLh8x7JzIPe2t6n9d3rzje9JjGa335kHnv RObd8c+A+3Yxh/X3eeneB4Qcb3pM47W+fMi8d4qZeemw6CVl3SvafFuerpwtu3URDHKeebn3 2+B402Mar/XlQ+a9E5l3XL6K1axeD8XtdCPzMBqZhx9e68uHzHunmJmXpShKIzNPrIAJTn4n vfjGXyuNF/JaXz5k3jtFzryveuk77qaRmXdsnKR1uDiEq5wvnOONjkd4rS8fMu+d9s68L3Ht ykpQ1YfEOuK52m69XTSuN+qj6MDvKuCH1/ryIfPeae/Ms9er3Jx5xYb4ur2bleiy+9K6Rjv1 Z5uFntJ4Ia/15UPmvROZV++Kh4xto5RWxx6GPQZcYn2G5W5pvJDX+vIh894pbOZ9l6zjMlh/ zU6Zpw1G66sYyelcoCHz8MNrffmQee8ULfPSb2glJWzEDW3Ra9k9TvBYNpmZVzfAVfxsEz+8 1pcPmfdO0TLvpXaay2S8z8MPr/XlQ+a9E5nXvdCNKP5gX7si8/DDa335kHnvROZhb3Lm1XpK I7bbNyc30gP49/OwN34/D6NwI70RmYe9Cb9T6XWvON6peCOfO5QbaS4y7/5QG3czH0V5Au/z MAo30hvFzLx0+E2ApPwyQNGmLpiUXycQd7MUgWK/8EXmYRRupDci846dFmMo2mjbqfl30o0h YRAyD6NwI71RzMzLUjKlkZlXb2MOMg+jcCO9UeTMOzYr2h9308jMS9K7wLp3ey4wOF84x7sT b8dN9UZ7Z96X2EtWgqo+JNYRz9V26+2icb1RH0UH3udhFG6kN9o78+xecnPmFRvi6/auWOG0 L61rtCPzMAo30huRefWueMjYNkoZp9uNjSK4hMzDKNxIbxQ2874dfbfFr3mBzGuZCzRkHkbh RnqjaJmXfhMoKWEjbmhDbdyte89nmVc3wFVkHkbhRnqjaJn3UjvNZTIyD6NwI70Rmdc9vBHF H+xrV2QeRnH8J2AwDf+WEPZG5mEUx6UK05B52BuZh1EclypMQ+bdX/T6djEHmYdRHJcqTBMz 89LhU5FJ+WBk0ebbUmxwdbcugkHIPIziuFRhGjLvuHwVq1nRRttONzIPo5F5GMVxqcI0MTMv S1GURmaeWAETkHkYxXGpwjSRM+8rP5d5x8ZJCuDiEK5yvnCONzreznGpwjR7Z96XuHZlJajq Q2Id8Vxtt94uGtcb9VF04H0eRhmxVGG0vTPPXq9yc+YVG+Lr9m5WosvuS+sa7cg8jOK4VGEa Mq/eFQ8Z20YprY49DHsMuITMwyiOSxWmCZt5+feNVxqcedpgtL6KkZzOBRoyD6M4LlWYJlrm pd/QSvq7sXpDW/Rado8TPJZNZubVDXAVmYdRHJcqTBMt815qp7lMRuZhFMelCtOQed0L3Yji D/a1KzIPozguVZiGzMPeyDysjhtypv/uxOu75vuNcxwVXorMw+q4IWci8+4PtXE389dyTyDz sDpuyJliZl76/ThlltIo6R+5rBuc7mYpAsV+4YvMw+q4IWci846dFmMo2mjbqfn384whYRAy D6vjhpwpZuZlKZnSyMyrtzEHmYfVcUPOFDnzjs2K9sfdNDLzkvQusO7dngsMzhfO8e4E/sHN OdPemfcl9pKVoKoPiXXEc7XdertoXG/UR9GB93lYHTfkTHtnnt1Lbs68YkN83d4VK5z2pXWN dmQeVscNOROZV++Kh4xto5Rxut3YKIJLyDysjhtyprCZ9+3ouy1+zQtkXstcoCHzsDpuyJmi ZV76TaCkhI24oQ21cbfuPZ9lXt0AV5F5WB035EzRMu+ldprLZGQeVscNOROZ1z28EcUf7GtX ZB5Wxw05E5mHvZF5WJ3jPyWDU16Z5zgkxxtg3HKHtyDzsDrHJQ+nyDzsjczD6hyXPJwi8+4v en27mIPMw+oclzycipl56fCbAEn5ZYCizbel2ODqbl0Eg5B5WJ3jkodTZN5x+SpWs6KNtp1u ZB5GI/OwOsclD6diZl6WoiiNzDyxAiYg8zZt38sAAB/bSURBVLA6xyUPpyJn3ld+LvOOjZMU wMUhXOV84RxvdOAfjkseTu2deV/i2pWVoKoPiXXEc7XdertoXG/UR9GB93lY3YglD5q9M89e r3Jz5hUb4uv2blaiy+5L6xrtyDysznHJwykyr94VDxnbRimtjj0Mewy4hMzD6hyXPJwKm3n5 941XGpx52mC0voqRnM4FGjIPq3Nc8nAqWual39BK+ruxekNb9Fp2jxM8lk1m5tUNcBWZh9U5 Lnk4FS3zXmqnuUxG5mF1jkseTpF53QvdiOIP9rUrMg+rc1zycIrMw97IPETBvd3CK/PWvNqO o8JLkXmIgnu7BZl3f6iNu5m/lnsCmYcouLdbxMy89PtxyiylUdI/clk3ON3NUgSK/cIXmYco uLdbkHnHTosxFG207dT8+3nGkDAImYcouLdbxMy8LCVTGpl59TbmIPMQBfd2i8iZd2xWtD/u ppGZl6R3gXXv9lxgcL5wjncn4Iv7vMXemfcl9pKVoKoPiXXEc7XdertoXG/UR9GB93mIgnu7 xd6ZZ/eSmzOv2BBft3fFCqd9aV2jHZmHKLi3W5B59a54yNg2Shmn242NIriEzEMU3Nstwmbe t6Pvtvg1L5B5LXOBhsxDFNzbLaJlXvpNoKSEjbihDbVxt+49n2Ve3QBXkXmIgnu7RbTMe6md 5jIZmYcouLdbkHndwxtR/MG+dkXmIQru7RZkHvZG5iEKr3+S5rP1P0vklXmOQ3L8ro1b7vAW ZB6i8Fo6P2QemYfXIvMQhdfS+SHzyDxz0evbxRxkHqLwWjo/ZN52mZcOvwmQlF8GKNp8W4oN ru7WRTAImYcovJbOD5m3deYdl69iNSvaaNvpRuZhNDIPUXgtnR8yb7vMy1IUpZGZJ1bABGQe ovBaOj9k3qaZ95Wfy7xj4yQFcHEIVzlfOMcbHfDltXR+yLzXZt6XuHZlJajqQ2Id8Vxtt94u Gtcb9VF04H0eovBaOj9k3mszz16vcnPmFRvi6/ZuVqLL7kvrGu3IPEThtXR+yDwy7/esYtso pdWxh2GPAZeQeYjCa+n8kHk7Zl7+feOVBmeeNhitr2Ikp3OBhsxDFF5L54fM2yjz0m9oJf3d WL2hLXotu8cJHssmM/PqBriKzEMUXkvnh8zbKPNeaqe5TEbmIQqvpfND5pF50kI3oviDfe2K zAMu2/gZ+d9OvC6R71VyHBVeiswDLtv4GSHzsDcyD7hs42eEzLs/1MbdzF/LPYHMAy7b+BmJ mXnp9+OUWUqjpH/ksm5wupulCBT7hS8yD7hs42eEzDt2WoyhaKNtp+bfzzOGhEHIPOCyjZ+R mJmXpWRKIzOv3sYcZB5w2cbPSOTMOzYr2h9308jMS9K7wLp3ey4wOF84x7sTWNbGz8vemfcl 9pKVoKoPiXXEc7XdertoXG/UR9GB93nAZRs/I3tnnt1Lbs68YkN83d4VK5z2pXWNdmQecNnG zwiZV++Kh4xto5Rxut3YKIJLyDzgso2fkbCZ9+3ouy1+zQtkXstcoCHzgMs2fkaiZV76TaCk hI24oQ21cbfuPZ9lXt0AV5F5wGUbPyPRMu+ldprLZGQecNnGzwiZ1z28EcUf7GtXZB5w2cbP CJmHvZF5wGWO/6Lbav6XE8chOV7tccsd3oLMAy5zXIVXQ+Zhb2QecJnjKrwaMu/+ote3iznI POAyx1V4NTEzLx1+EyApvwxQtPm2FBtc3a2LYBAyD7jMcRVeDZl3XL6K1axoo22nG5mH0cg8 4DLHVXg1MTMvS1GURmaeWAETkHnAZY6r8GoiZ95Xfi7zjo2TFMDFIVzlfOEcb3RgWY6r8Gr2 zrwvce3KSlDVh8Q64rnabr1dNK436qPowPs84LIRq/Ai9s48e73KzZlXbIiv27tZiS67L61r tCPzgMscV+HVkHn1rnjI2DZKaXXsYdhjwCVkHnCZ4yq8mrCZl3/feKXBmacNRuurGMnpXKAh 84DLHFfh1UTLvPQbWkl/N1ZvaItey+5xgseyycy8ugGuIvOAyxxX4dVEy7yX2mkuk5F5wGWO q/BqyLzuhW5E8Qf72hWZBzxmwWftvzvxmprv7BxHhZci84DHLPiskXnYG5kHPGbBZ43Muz/U xt3MX8s9gcwDHrPgsxYz89LvxymzlEZJ/8hl3eB0N0sRKPYLX2Qe8JgFnzUy79hpMYaijbad mn8/zxgSBiHzgMcs+KzFzLwsJVMamXn1NuYg84DHLPisRc68Y7Oi/XE3jcy8JL0LrHu35wKD 84VzvDuB7S343O2deV9iL1kJqvqQWEc8V9utt4vG9UZ9FB14nwc8ZsFnbe/Ms3vJzZlXbIiv 27tihdO+tK7RjswDHrPgs0bm1bviIWPbKGWcbjc2iuASMg94zILPWtjM+3b03Ra/5gUyr2Uu 0JB5wGMWfNaiZV76TaCkhI24oQ21cbfuPZ9lXt0AV5F5wGMWfNaiZd5L7TSXycg84DELPmtk XvfwRhR/sK9dkXnAYxZ81sg87I3MAx7j+C/DefHKPMchOV6lccsd3oLMAx7juJp7IfPuL3p9 u5iDzAMe47iae4mZeenwqcikfDCyaPNtKTa4ulsXwSBkHvAYx9XcC5l3XL6K1axoo22nG5mH 0cg84DGOq7mXmJmXpShKIzNPrIAJyDzgMY6ruZfImfeVn8u8Y+MkBXBxCFc5XzjHGx3YnuNq 7mXvzPsS166sBFV9SKwjnqvt1ttF43qjPooOvM8DHjNiNb9p78yz16vcnHnFhvi6vZuV6LL7 0rpGOzIPeIzjau6FzKt3xUPGtlFKq2MPwx4DLiHzgMc4ruZewmZe/n3jlQZnnjYYra9iJKdz gYbMAx7juJp7iZZ56Te0kv5urN7QFr2W3eMEj2WTmXl1A1xF5gGPcVzNvUTLvJfaaS6TkXnA YxxXcy9kXvdCN6L4g33tiswDHuO4mnsh87A3Mg94PcfH9n86cRzSmhcKL0XmAa/n+NiSedgb mQe8nuNjS+bdH2rjbuajKE8g84DXc3xsY2Ze+v0VgiylUdJ/zaBucLqbpQgU+4UvMg94PcfH lsw7dlqMoWijbafm30n3/d6hBZkHvJ7jYxsz87KUTGlk5tXbmIPMA17P8bGNnHnHZkX7424a mXlJehdY9+77TQ/F+cI53p0AGjk+wntnnnHF0tlPINNvAtV1xHO13Xq7aFxv1EfRgfd5wOs5 PrZ7Z57dS27OvGJDfN3eFSuc9qV1jXZkHvB6jo8tmVfvioeMbaOUcbrd2CiCS8g84PUcH9uw mfft6Lstfs0LZJ77Nz0UMg94PcfHNlrmpd8ESkrYiBvaUBt3697zWebVDXAVmQe8nuNjGy3z XmqnuUxG5gGv5/jYknndwxtR/MG+dkXmAa/n+NiSedgbmQe8nte/MPf5fP6HkwVnl8g8kHnA BrxS4UPmeSx6fbuYg8wDXs8rFT5RMy8dPhWZlA9GFm2+LcUGV3frIhiEzANezysVPmReFX71 bvG6VuTSLqYh84DX80qFT9TMy1IUpZGZJ1bABGQe8HpeqfCJnXlf+bnMOzZOUgAXh3CV84Vz vNEBNPJKhc/umfclrl1ZCar6kFhHPFfbrbeLxvVGfRQdeJ8HvJ5XKnx2zzx7vcrNmVdsiK/b u1mJLrsvrWu0I/OA1/NKhQ+ZdyXztG2jlFbHHoY9BlxC5gGv55UKn8CZl3/feKXBmacNRuur GMnpXKAh84DX80qFT7zMS7+hlfR3Y/WGtui17B4neCybzMyrG+AqMg94Pa9U+MTLvJfaaS6T kXnA63mlwofMu7HQjSj+YF+7IvOA1/NKhQ+Zh92ReQCiGLfc4S0evgm87sLV6jiW2nhITG1m qY2HRJihHZk3pI5jqY2HxNRmltp4SGQe2pF5Q+o4ltp4SExtZqmNh0TmoR33CgAgCjIPABAF mQcAiILMAwBEQeYBAKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIPABAFmQcAiILMw1vxf1kEcBWr Bt6KzANwFasGPP2TQ8W/z1n8i53fNo1HxTratlZ/zHQBvAxrATwZ2ZOlRDSOHje0lu31iT0A mcyDrzqB6qNa/Gjv8DoyzxgDgMhYEeBJzJt0kKsc0o7WmSe21F4v6g+ZLYC3YS2Ap5Z3Xdr7 sPb3eS2vt7wIIBoWAnhq/Pu84hXxqO/PNsk8AJnMg6/6UyTf3Zs/2zy2PO6Kr9f1necJ4J1Y C+CJdAGwMlYoeCLzAKyMFQqeyDwAK2OFAgBEQeYBAKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIP ABAFmQcAiILMAwBEQeYBAKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIPABAFmQcAiILMAwBEQeYB AKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIPABAFmQcAiILMAwBEQeYBAKIg8wAAUZB5AIAoyDwA QBQDMy8BwErGLXd4i7GZ9988rFbHsdRqdRxLbTwkpjazlO+Qxi13eAsy78lSq9VxLLXxkJja zFJkHnyReU+WWq2OY6mNh8TUZpYi8+CLzHuy1Gp1HEttPCSmNrMUmQdfZN6TpVar41hq4yEx tZmlyDz4IvOeLLVaHcdSGw+Jqc0sRebBF5n3ZKnV6jiW2nhITG1mqacyb+OA7Jja0KvxT/H0 H1pa3uruzsknpRe76Td+njeemmOp1eo4llqtjmOpaJl3uu5rZ40YzLT6jV2Qec/UcSy1Wh3H UhsPianNLPVI5j0VId/Xrw4gVOY1NlaLdJ95Xnqxm37j53njqTmWWq2OY6nV6jiWejbzjm8s ih/BievvzVg11vT6Lc53DPVPBY3xXzoqtjRGaLfXdtMvrb19fdqReU+WWq2OY6mNh8TUZpZ6 MPOOiZKrVTgr4XFphTT6rQ8ZEXU6MDG860On9bURtvRo7562rzO1D5n3ZKnV6jiW2nhITG1m qcczr97N1Vur06wy1P3m3/W9PmSPrbHxaZvjCBtnagxArKaNyu5dG0AjMu/JUqvVcSy18ZCY 2sxSC2aetnF1Ia7bi7Fh9zUu81oKauNvrKaNyu7dGEALMu/JUqvVcSy18ZCY2sxSj2de47uN +4F37Ktu0BFR2siN2dmn2CM3um7PYLsBmfdMHcdSq9VxLLXxkJjazFIPZl5WFuJ8+OFbfcql +uLrRWVxSLnKAC32tOm07F5K9+M10a6YuKsNte6UzHumjmOp1eo4ltp4SExtZqlHMi9ffw93 ZxX2LTK602lXY1BxMu/JUqvVcSy18ZCY2sxSD2Ze/WbOaHxjpfSvM67TxsZk3t07dak6jqVW q+NYauMhMbWZpZ7KPOyKzHuy1Gp1HEttPCSmNrMUmQdfZN6TpVar41hq4yExtZmlyDz4IvOe LLVaHcdSGw+Jqc0sRebBF5n3ZKnV6jiW2nhITG1mKTIPvsZmHgCsY9xyh7fgJgAAREHmAQCi IPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPPwAN//C9Q/1a7WrNsf 6/TVBLA4Hmk8YETmeZ11fJ3MAzbDI41/fd/ZFIv+8ZXi3c/pUbGOtl1X0IZxHGfxfxBuHIM4 36KC+P8mJgWBV+MBxr/s7NESQjyqJVCucsWoXxwSB1ZXuzQGu45xLoCX4gHGv+yVXXsHJh5t yU7j9dOBFc3sd3gtczlNO3v6AN6CZxj/EjOv+PleHTbi0ToqxJba6/XAimbfF4tejDHYcyHz gCB4hvEv++2U9op49DQq7NdbXqzrtI+BzAPC4hnGv7R3TvYr4tHGOnb99o66M++0jjZfAC/F Y4x/1Unw3dV+9qgdNX6ueNwVX9fGJg6sPtoyhiLV7OQ7HRuAF+Exxr9Y021cH2ADPMb4F2u6 jesDbIDHGP9iTdcUP1YF8F48yQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPNw UUr84c+2f7A7vse46PFViT/8GfcHu+N7jIseX5X4w59xf7A7vse46PFViT/8GfcHu+N7DACI gswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABR kHkAgCjIPABAFGQeACAKMg8AEAWZN1X6dafIiIH51rxU+dtm3EgAgMVlqlTpLjJiYL412ysf 25B5AMZhcZmqXtwXWd+fHck61wHA3lhopioWd3G3WP3rF7Vd8d2SHSfam87GkdjDaxxVMQBt JKdTBoBTrBdTGeklxo/xotjg9PV6MJc61eJHa9kyWqO9WL9xdgBQY7GYSly7i23xxax8yuPq tjaYmyMxursz2juzA4Aai8VUp3nwVbyoFRmXeVdH0j7HcdsAYGOxmKoxD8Sz3FOhI0WMkVyd I5kHYD4Wi6nqxbpeu4sXtRP7tu3BdI/E6I7MA7AOFoupUkU7dPqiXbAlFexzW14sXhe7u1RE O/20Zvs2gMhYCKbSFv3iqP2ittux4reEkNfwtKFmMg/ALCwE+1h/ZV9/hAD2xgL0bu96N7P+ CAHsjQXo9cSf/q3pFYMEsDEWIABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkA gCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5oXAP2gAAJnMe5fff9X8wvduaOa1DOlq76n6 19j7CpL0AI5YEd5kzX8h9jgMryGReQBGYEV4Ey3z6rdZxSti4++Lp28f29/DaUP6dmS0OXZk zKV4xS6yzn8ZAFgBy8GbiDHWt3H64qVRFdvaK3UKimfVY6iHZHckNgAAVoQ3uZp59YmDMs94 R1VkXvF63aY+Wu8ap5yeCCAyVoQ3MRLrmDraK3aFuov2UWlDPXaRq4gS24g1i3nVr2hTIPMA HLEivMlpYmntJ2denV6nqWYf1RobQz1tACAgVoQ3sRPr0sbpi5dGZbzSknnF0XoMLZlXFxFP BBAZK8KbpF/169or9faxwWnm2UEoHqq7qFtqw9Zaiq8YRU5HDiAaloO4yAMA0bDkxcJ7IACR seSFI/50FAAiYNULgYQDgEzmZel9z+lHNlryoy9mrp7V+OmSFTJP/HAKAMwUfQESo26PzFsh 52oLDglAHNEXoNPM094F1n8l1vJm8dJZp4eM9sUI+2YkXiXt6LFaPmSb0S8ATBZ99TGSKTfH jL1hlO3bcGl/+mL7BSy2xZEUXwHgEdEXoGKB7o4NraB46HQYp6e3NHsw84pDZB6ARURfgOwM uJQQ6SA3Z552VkfmGQMg8wAgk3lemXc1tG6e3nIimQcAhegL0COZd3PDpf3pi+0XsNgm8wAs iwWo/JFgljJAe6VY8Y8viuFx6Sy7sdG+JdXs+YrDPr2A311xw7gsADAHqw8AIAoyDwAQBZkH AIiCzAMAREHmhcAnRwAgx8k88ZOW9wu6lBrH/hDpfsRPnJ6+MsHxk6sAHhTiITz9LP7Nmsua PMjHL8jpb1k89V272unjVxLYVYhHy/iVtXr32Kx+a6i93l6wbn9aubGCVlObdeO8cltUHDut Tzmd1/HEoloxI6348Sxt5OIF1wYp9tVS2SjVPoXTCw6gQ4jn6nQdP6449ZJkv1gXvLkxtHjH vK5e3qLa9+vxlXpXG0zR0igujlwbm1j5tC9x2MZu8crVKXR8LwDYQjxRxXpaLz1ay5YXjXON jcYe62bGK5dG0t61TbyGxXb9SmPjuuWl4kWb+mvLWafDNsbc2HXLYAC4CPE4nS7039072XCa NMf6jT2KwxPHb9e8M69Llzd7Z149o/biRZvia8t3xK58PFE8WpS6OoWO7wUAW4gnqn2hv5MN RpFLjcX2xomXBtAxr0bHCsWL2pp+qfHVwKjb1F/7RqLVPy11aQp93wUAthDPVftCfycbLkVO Y4/uxTvmdek6f7/ar9S7x8GIjU+LiyM/tqm/ugzb2L3atXgRADiK8milg+MrWgOtZfpVdyFu HE+82mPdvuUVbSQd89LGo0nVmi4OtS6epOW+npFWvD63aHM6pJa+6mEbu+LUrk4BgCMeMPy4 FG/uXT/S71K4CMBQPGBw+9lmX9fidihcBGAaHjDkLP3sd37Xk/tdChcBmINn7E26l0XWUwDI G2de/cbFXvcbU+HBd0IdvT/1E8s7tHE2fne8h2N11NgdH04B1rHncyhG3QaZN+GsO1y6e1Hm jWjc0R5Auz2frtPM094F1n+tcvpm8fRErYvjUa1lyxTEoYr1r85dvJ7GNT9uGGPTih+/ihfh tGyqvjWNp2gDaDnL7sK42vYg6wsF4L49Hy1xeS2WoUsvis3qvny78Nroe7H7ahfVvl+Pr9S7 x8GIp7SUtb81LWXra2h0VBy91NiYVz14AF72fKiKxas7DLSC4qG+LloqtzS2h3pn7ra6sbis a+21xpeKiG0ai4hl6w1jkMblshu3zwuAlz2fqPZ0OV3300FxqKWv9hdbOi02jm3sofbNveNq 57mZV0+5pXhL2ZaOtItmNG6/OB3fCACn9nyo7JW9IwzEZi193eniUu8u0xS7aL/mxcZ3u35F 3D02biliv9JdpN5oGWRj46sDAOBrz0erMV3aX2xJHfcuvDb6Xuy77N+v9iv17nEwWpFj49OO Gkcijq2xo+LopcbtswDgaNunKx0cX7GPFi/WLetIuNpFsS1W1joV27RMuWNg2hxPpWoRF0db F0+/K349yHok9YW6OhKtjXaWMVRjVOL1NwYAYBCeMayCFR/AaKwyeBLvcgDMxCqDhxk/9gQA Xyw0AIAoyLyp0q85Pc7vZdDs6s+PjOtlNK2Xm70bHxTq66h7PPNPBFpwe01Vf35vWnfTehE/ kejVkbjt28Wza+6d3o0rPznzxv0XCXATN+VUYuZpn2W/c7RuebWUNuz6UNZXN3GpLaKrGJLW pjh6OjV72OIgT+dyOvK6oDgqrU3jt8Mef72dpQuiDdJoNnQYWbkyxagabxVtagB3w1TiMl08 vXWb+xsdR6/Oq+XFesEqDomLWj1C8atdp33w9lyMHlteaaxTv94+fm1SYtfGsLVmp8MwBt9x BfpuFXsYiIxbYSoxZopdMXWMxGrZaD/aPa/TV+oXu5ND/GqfJQ6m1j1ysetLo7Jn1D5+o3hL R/VM7RvDGIZYzehOG5g4l5aRa8NAZNwKU2krVLHmFo+reFTb0EpdPXp1Xi0vdq+A9Sva+qi9 0j74lrmI9cWuiwsunlW3ufTtuDr+9kunvWgPw7j4l77jxdxPL2bd151bGrviVpiqfvyOz3Cx YR+1T6y7s4/aL7bM63Tb3m1ZyOrFuv2sxsFr7U9HXnd9f3aNWibb0pGWHI3jMSZ+OoxLA7h0 IlDgtphqTuZdLaW9eGle9XZdR4uHlleOIxS/2nX65tI+cnHA7bM7nVH3yL+HxHHaF1ybTuNI jBtArC8OrO9WERsAmcybTEyUdFC30Y4a0VWfLh7VOmocdtGgsb3dqbhOiauhOJfTOsYUtLk0 jlw8t/7eabPTvq3iKcbIi2rFueIlEjsSmzUOw2jW/v3N+gU/vZj1KS0DRhDcDYji7WvfIuNf ZBhAH25fhPD2lXqR8S8yDKAbdzAAIAoyDwAQBZkHAIiCzIOD+q95xM/LGR+JdHnd1v13Uacf QXTsy5HxaczugrcHBTyMmxh3aZ+Mz9Uq+Wzm9dHC+xWZZ+zeLwi8ETcxbjF+WapuefzlqtNt u1RW3seI59a//qW9BxJzoj3z6jr1r4gVbRrbt7xjs69kcbSjO+2s+mg9NmAR3J1woAWM2MxO o9MiWfqlaftcLe3EpdyenTG2uo4xqiKc6o5aKmiDFEd4vzujTb2RgSVxa8KB/bagaNaSbaeZ J24Xr4gh0bLQG92djvlqX1rvHf8dYLe83117DJN5WBa3JhzYK2mxe/pm4vTtgthFca5Y3O5d 61Hr7rRZ+tXSe91enNfVxO3ujszDZrg14cAl805XbaMLI5bcM68OoZZRXRq5PR7xUF/mnXZH 5mEz3JpwcLq8aq8vlXni+ydtGEY8tLxJMtq0j1YbT0fm3RkwmYcX4daEg8Y3KMd18xgw9etX 1/f6XK2lca4x7PYXxagoOtWS3mgvVhB7ty/ane60SZF5eBFuTbzAtDV05mJNMADz8dRhdaOz QXtf+Pa+ANR46gDhB3p79AWgwIMHAIiCzMMtd96vdJ8rfhBjUF+OGj+Kcqmgy0juDKCuo714 vzvgPm5E3DJ/LauTo2UkK6y54gcpHQtOOFEs0vHtAJ7CfYlbtM+v16/UK2Px1f58fH2iVrZo b3/Ovm7T2L7lHduxZX01iqMd3Wln1UfFUd0pkqUrL16EuoFYs2OaWb/ygIbbBbfUi93VV+x1 VssSYzD1br16Gn3V42mcl9a7eK4x/fbujDb1RkfXdhGxoNbMrtk9zXrwgI17Bbdo61Hdpm7c no5aQfvFO0ncV6G9d5fu2sPgTten0WLn09WafRMHGnHT4Jbjf7Z/fQ/Vr+RqvXPJvNNm2jhP e6/bazNt6b1ucLW7S2GgRYt9nRuLaCe29ChunE6z8coDBm4X3CKu5llZ/sSFr2URN4prbU4r t7Sx27ePqj3zTrtbKvNOp9xY89KkasQe2nGv4JZLuXU187T/ij8Nxbrf9r4ak0CbTuO5RsH2 7p7NPONbo6VjR+ZdmiZwinsFtxRrU7EwiS8W21o6Gv91r/UiLrXiGlpXEMdjz6uuIPbeHjxX u9MmZWy0d91S5PRbrPUobtQDaP/eAY24XbC0mSsaq+ea+L7AETcTlnP6puGlfaEd3xcMws2E FdU/xdqjL7Tj+4IRuJ8AAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPMA AFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMARPH/AZwx0Q9AabJoAAAAAElFTkSuQmCC --------------060905000103080704000403-- --------------080802090806010109080809-- ========================================================================Date: Tue, 15 Feb 2011 10:55:03 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Paired t-test + Covariate Comments: To: vgarcia <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/related; boundary cf3054a28506dee0049c54307c Message-ID: <[log in to unmask]> --20cf3054a28506dee0049c54307c Content-Type: multipart/alternative; boundary cf3054a28506deda049c54307b --20cf3054a28506deda049c54307b Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Veronica, A couple of points: (1) Conditions should be set to equal variance, you have them set to unequal; (2) This is not a valid contrast, however, there are two ways around this depending on your question: (a) You want to know where you covariate correlates to the average value of condition1 and condition2; form a new contrast at the single subject level for the average of condition1 and condition 2 (1/2 1/2) and then then use a one-sample t-test with your covariate. (b) You want to know where the covariate correlates with the difference in condition 1 and condition2; form a new contrast at the single subject level for condition1-condition2 (1 -1) and then then a one-sample t-test with your covariate. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Tue, Feb 15, 2011 at 10:42 AM, vgarcia <[log in to unmask]> wrote: > Dear List > > Our design matrix is the following one: > > > > Thanks a lot, > > Verónica > > > On 14 February 2011 15:08, vgarcia <[log in to unmask]> <[log in to unmask]> wrote: > > Dear List, > > Our design is a paired t-test (13 subjects) with a covariate (its value > is the same in both images of the same subject). We would like to test > the hypothesis of zero regression slope against the alternative of a > positive slope with the following contrast (value 1 in the column of the > covariate): > > zeros(1,13) 1 0 0 > > But the contrast is invalid. What are we doing wrong? > > Thanks a million > > Verónica > > > > > > --20cf3054a28506deda049c54307b Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Veronica,

A couple of points:
(1) Conditions should be set to equal variance, you have them set to unequal;
(2) This is not a valid contrast, however, there are two ways around this depending on your question:
(a) You want to know where you covariate correlates to the average value of condition1 and condition2; form a new contrast at the single subject level for the average of condition1 and condition 2 (1/2 1/2) and then then use a one-sample t-test with your covariate.
(b) You want to know where the covariate correlates with the difference in condition 1 and condition2; form a new contrast at the single subject level for condition1-condition2 (1 -1) and then then a one-sample t-test with your covariate.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.

On Tue, Feb 15, 2011 at 10:42 AM, vgarcia <[log in to unmask]> wrote:

Dear List

Our design matrix is the following one:




Thanks a lot,

Verónica


On 14 February 2011 15:08, vgarcia <[log in to unmask]> wrote:
Dear List,

Our design is a paired t-test (13 subjects) with a covariate (its value
is the same in both images of the same subject). We would like to test
the hypothesis of zero regression slope against the alternative of a
positive slope with the following contrast (value 1 in the column of the
covariate):

zeros(1,13) 1 0 0

But the contrast is invalid. What are we doing wrong?

Thanks a million

Verónica

--20cf3054a28506deda049c54307b-- --20cf3054a28506dee0049c54307c Content-Type: image/png; name="moz-screenshot-11.png" Content-Transfer-Encoding: base64 Content-ID: <[log in to unmask]> X-Attachment-Id: 1179950fe0314f89_0.1.1 iVBORw0KGgoAAAANSUhEUgAAAlEAAAMmCAIAAABaTkNEAAAgAElEQVR4nO3dsZXsONCeYYQlQ2ko EAWgEBSGHIUhQ2HIUAhtyIWM3b+HDVQVQbAAgqj3OffMIZtgAeCQ+Lbn9uxNGQCAGNLTAwAAYBIy DwAQBZkHAIiCzAMAREHmAQCiIPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCi IPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZmH2dKv+mh7naLmuLOMajcrTO7OZcBJcrOm4/AAA7cX prKXy0vR1XFid3eNBUdbZMDiN9HlOpB5GI3bC/NoIfd9hcwb3Z1j5tWvjKgM+OL2wjziiuaSeXcG cMfrMm/cMBzf7QHjcINiHntNFH9Q1vii+Abu6llZX7jFgn0zOp4lHm0/Uezu0oC1ZvbUtEMjruex TT3BrF9DQMRdgnnstUlcB1NFbGmf0nKWNgBjGHUFe0b2CI2R1N0ZpxhXoPFCnU5NO2TPwpidOLyW lsb4ARG3CKayVyh72T1dHO8fSlKitBdsnEs9/Y7uhlawaS1HDK9x99L4ERm3CB6QfhWv22eJLe3V UKvfFwDGWQb7rI7u7l8Bo1n7XFqGemd4fUcBDbcInlQsf/ZSeHVJrdfW07MaR+t+lljHPvF4SGx2 egWMZi0zsl+vi/cNz67fMirgiFsE84irUstCpq2bWuWOs+wVs7Fg91k3x3/zutXNbFrLuiOteMvw 7Kt0eg0BEbcI5hFXpZYVubGl1mPLWaeHGtfljsF3d/fd7RuG0cwmtmzsqP2slqt0OiqgwC2CeeqF 1V4ojd1Bh44R0lGwnmzjWXfGr13V0wr2WQaxpfv3TvwuNF5DQMMtgqmSRDxqNxZbnnZhnFWf2DLm omXjTOuzLo1f7KWja6NZx9Tqxnbllgm2t7QHDHxxi2C2xoWy3tUWROOQtjKKZ9W9n55lr7PtZzVO rWPAYuX2C2XPSzvXa3gdl0IcBvDFLQK8mB05m4kzU4zDDQS82MYxwDs5jMANBLzS6c8V3679J6hA O+4h4JUixACBB3fcRgCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPMAAFGQeQCAKMg8 AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPNwUUr84c+2f7A7vse46PFViT/8GfcHu+N7jIse X5X4w59xf7A7vse46PFViT/8GfcHu+N7DACIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAK Mg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEA oiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQe ACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB 5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABA FGQeACAKMg8AEAWZBwCIgswDAERB5gFolVJKKf2z8fRYgB7cuACaFGlH7OGNuGsBNCHzsAHuWgBN yDxsgLsWQKt08PRYgB7cuACAKMg8AE2K93bdb/XSrzvjKeocC/JOFCJuCwAnkqKv1Gnx7jrHDTIP Im4LAE28UsSIK686/I0jNNwWAFq5vM9z+dmmVx1Ew40CCBZcTL3+sqr+q6+rJ94cAPAgblyglMZ8 WON+UOXbkVOcfqnOnXOBRXDXAgKv93mOweD1l1Xddbwyz/HarvZeHOvjXpmHJxNv1/1z0aKCtju/ DqJ54EYZetN3/J2HVif3/ofwcbfvp1tinXp7Ziltau0j0epkv6udf6/5nTodHFfhBYfkZeOp4RWm 3ihiBnQEw+mJx5W0u05Rra+UV50HS53WOe6uMLXscQOIZUfXWXNIWfrvifZz7bH1VfCtg1Cef59n vHinztXn+X4drVQ+PJx36nwreJW6WadvoTGu9tXFy75K7SOcc0M+UspxSMAenrn7r65Kb6nDkGbW WXBIG0/tZoXiPyK736J51UFYz7/P675ZV6vDkGbWWXBIG0/NsQLwLDJv86VqtSExtZcOKTu9tVpz aojjsZ9tuvxEYrU6DGlmnQWHtPHU7kuKp+ogJm4UAK3ux4xXOBFy6MN9A6BJ+o+P1+brkfNtf/P9 mVcdhPXkzzaz018wrFOHIc2ss+CQNp7at8IxdW4WBOZ77DMs95+c1eowpJl1FhzSxlMbUQ14BJm3 +VK12pCY2kuHVBS8WirpHqmDsJ782eb9O3W1OgxpZp0Fh7Tx1Bwlpwz2qoNQuFcANPHKcmN3fh1E M+9GOf5HmeildRgSU9t1alpxlwrr1EEoq9wrXnftanUcS208JKY2s5RLHZIGL8VdC+AC0g6vxmdY POswpJl1FhzSxlPLfmmXfj1eB6E8cKMUd2f3zbpaHYY0s86CQ9p4ao68xrDCXPBGj73PKzb2qMOQ ZtZZcEgbT+1bxOX92f2RONZBKFM/t6l5dR2GNLPOgkPaeGp1WWO3e2zdg3GcGuLgRgHQxCXzgGdx 1wJoQuZhA898hmWpH244/pCEIU2rs+CQNp6aWPDZIo51EMrD94rXzbpaHcdSGw+Jqc0stUgwJKc3 i151EM3zN8pqj/Teq9VqQ2JqM0vdfHPmUmq1Oojm+Z9tblOHIc2ss+CQ9p5a44tXR9U9JMc6CIV7 BcAJl8wrwqk7q7zqIKap98rx7rzzH2ir1WFIM+ssOKSNp/at0PjipQpXizjWQVjzbpTjTaltv7EO Q5pZZ8EhbTw1rebNIa1QB2GReXfrMKSZdRYc0sZTq8t2v2Uk87AIMu9uHYY0s86CQ9p4ar7q3vvG 41UHMU29V4r/SOz7D8YF6zCkmXUWHNLGU/OVfj1eBwFxuwAAoiDzAABRPJx5Xj+XWK2OY6mNh8TU ZpbaeEj8eBPtyLwhdRxLbTwkpjaz1MZDIvPQjswbUsex1MZDYmozS208JDIP7ci8IXUcS208JKY2 s9TGQyLz0G7gvZIAYCXjlju8xdjM86oDYI7/5MTl2XdfARxHhZci8wD8IfPuD7VxN/NT2SeQeQD+ xMy876Fvs7px0aYueGxwupulCBT7hS8yD8AfMu/YaTGGoo22nRoyLynJitHIPAB/YmZelpIpjcy8 ehtzkHkA/kTOvGOzov1xN43MvCS9C6x7t+cCg/OFa7/DumsCGGfvzPsSe8lKUNWHxDriudpuvV00 rjfqo+jA+zwAf/bOPLuX3Jx5xYb4ur0rVjjtS+sa7cg8AH/IvHpXPGRsG6WM0+3GRhFcQuYB+BM2 874dfbfFr3mBzGuZCzRkHoA/0TIv/SZQUsJG3NCG2rhb957PMq9ugKvIPAB/omXeS+00l8nIPAB/ yLzu4Y0o/mBfuxqbeR8Pjnc8ANt/duI4JJdl5J+VZNxyh7cg8wD8IfOwNzIPwB8y7+ail5o/w1Ls Yg4yD8CfmJmXDp+KTMoHI4s235Zig6u7dREMQuYB+EPmfTeS8p6seF0rcmkX05B5AP7EzLwsRVEa mXliBUxA5gH4EznzvvJzmXdsnKQALg7hKucLN+JmdXx4ANj2zrwvce3KSlDVh8Q64rnabr1dNK43 6qPowPs8AH/2zjx7vcrNmVdsiK/bu1mJLrsvrWu0I/MA/CHz6l3xkLFtlNLq2MOwx4BLyDwAf8Jm Xtb/2iwNyDxtMFpfxUhO5wINmQfgT7TMS7+hlfR3Y/WGWE0rWNf/OpZNZubVDXAVmQfgT7TMe6md 5jIZmQfgD5nXsdD9Y0TxB/vaFZkH4A+Zh72NzTwXCw4J2NV/cbLmY+s4KrwUmQfgD5mHvZF5AP6Q efeH2rib+SjKE8g8AH9iZl76/RWCLKVR0n/NoG5wupulCBT7hS8yD8AfMu/YaTGGoo22nZp/J90Y EgYh8wD8iZl5WUqmNDLz6m3MQeYB+BM5847NivbH3TQy85L0LrDu3Z4LDM4XzvHubLlNVxgesJO9 M+9L7CUrQVUfEuuI52q79XbRuN6oj6ID7/MA/Nk78+xecnPmFRvi6/auWOG0L61rtCPzAPwh8+pd 8ZCxbZQyTrcbG0VwCZkH4E/YzPt29N0Wv+YFMq9lLtCQeQD+RMu89JtASQkbcUMbauNu3Xs+y7y6 Aa4i8wD8iZZ5L7XTXCYj8wD8IfO6hzei+IN97eoFmef1L4l8+GeJgDNemec4JMfHf9xyh7cg8wD8 IfOwNzIPwB8y7/6i17eLOcg8AH9iZl46fCoyKR+MLNp8W4oNru7WRTAImQfgD5l3XL6K1axoo22n G5mH0cg8AH9iZl6WoiiNzDyxAiYg8wD8iZx5X/m5zDs2TlIAF4dwlfOFc7zR3e/4D5kHnNk7877E tSsrQVUfEuuI52q79XbRuN6oj6ID7/MA/Nk78+z1KjdnXrEhvm7vZiW67L60rtGOzAPwh8yrd8VD xrZRSqtjD8MeAy4h8wD8CZt5+feNVxqcedpgtL6KkZzOBRoyD8CfaJmXfkMr6e/G6g1t0WvZPU7w WDaZmVc3wFVkHoA/0TLvpXaay2RkHoA/ZF73Qjei+IN97YrMA/CHzMPeXpB5jjaeGrAUr2fN93Fz HBVeiszbZGrAUryeNd/HzXFUxlAbdzN/LfcEMm+TqQFL8XrWfB83o4tjX1lKo6R/5LJucLqbpQgU +4UvMm+TqQFL8XrWfB83o4t6Iylv0YrXtSItu74XCi3IvE2mBizF61nzfdzsXvLEzKu3MQeZt8nU gKV4PWu+j1tjL/m5zDs2TlIAF4dwlfOFc7w7RwgyTeBxXs/aoMdN7CUrQVUfEuuI52q79XbRuN6o j6ID7/M2mRqwFK9nzfdxs3vJzZlXbIiv27tihdO+tK7RjszbZGrAUryeNd/Hze4lX8w8bdsoZZxu NzaK4BIyb5OpAUvxetZ8H7eWjr7b4te8QOa5X+FQyLxNpgYsxetZ833cxOJFR8XrxoY21Mbduvd8 lnl1A1xF5m0yNWApXs+a7+PmOKpn7TSXyci8TaYGLMXrWfN93BxHVQ9vRPEH+9oVmbfJ1ICleD1r vo+b46jwUrEyz/EfJQEwh+NjO265w1uQeZ0PD4A5HB/bccsd3oLM63x4AMzh+NiOW+6Oi17fLuYg 8zofHgBzOD62xmJ1XLWy9MHIos23pdjg6m5dBIOQeZ0PD4A5HB9bY7GqN5Lynqx4XStyaRfTkHmd Dw+AORwfW3u9yhMzT6yACci8zocHwByOj23jepWfy7xj4yQFcHEIVzlfuKG3/n2ODw+AOUY8tuLa lZWgqg+JdcRztd16u2hcb9RH0YH3eXcfHgBDOT629nqVmzOv2BBft3ezEl12X1rXaEfmdT48AOZw fGzt9SpfzDxt2yil1bGHYY8Bl5B5nQ8PgDkcH9uWJeu7LX7NTpmnDUbrqxjJ6VygIfM6Hx4Aczg+ tuIyVSxZxevGhrboteweJ3gsm8zMqxvgKjKv8+EBMIfjYztuuZtsp7lMRuZ1PjwA5nB8bIcudCOK P9jXrsi8zocHwByOj+245Q5vESvzvHCJsKv/6sTrGfF9TBxHhZci83pwibArMu/+UBt3M38t9wQy rweXCLuKmXnp9+OUWUqjpH/ksm5wupulCBT7hS8yrweXCLsi846dFmMo2mjbqfn384whYRAyrweX CLuKmXlZSqY0MvPqbcxB5vXgEmFXkTPv2Kxof9xNIzMvSe8C697tucDgfOEc786Vcbmwq70z70vs JStBVR8S64jnarv1dtG43qiPogPv83pwibCrvTPP7iU3Z16xIb5u74oVTvvSukY7Mq8Hlwi7IvPq XfGQsW2UMk63GxtFcAmZ14NLhF2FzbxvR99t8WteIPNa5gINmdeDS4RdRcu89JtASQkbcUMbauNu 3Xs+y7y6Aa4i83pwibCraJn3UjvNZTIyrweXCLsi87qHN6L4g33tiszrwSXCrsg87I3M6+H4j5sA S/HKPMchOT5u45Y7vAWZ18PxIQSWQuZhb2ReD8eHEFgKmXd/0evbxRxkXg/HhxBYSszMS4ffBEjK LwMUbb4txQZXd+siGITM6+H4EAJLIfOOy1exmhVttO10I/MwGpnXw/EhBJYSM/OyFEVpZOaJFTAB mdfD8SEElhI5877yc5l3bJykAC4O4SrnC+d4o6/M8SEElrJ35n2Ja1dWgqo+JNYRz9V26+2icb1R H0UH3uf1GPEQAivYO/Ps9So3Z16xIb5u72Yluuy+tK7Rjszr4fgQAksh8+pd8ZCxbZTS6tjDsMeA S8i8Ho4PIbCUsJmXf994pcGZpw1G66sYyelcoCHzejg+hMBSomVe+g2tpL8bqze0Ra9l9zjBY9lk Zl7dAFeReT0cH0JgKdEy76V2mstkZF4Px4cQWAqZ173QjSj+YF+7IvOexNXGav6vkzXvbcdR4aXI vCdxtbEaMg97I/OexNXGasi8+0Nt3M38tdwTyLwncbWxmpiZl34/TpmlNEr6Ry7rBqe7WYpAsV/4 IvOexNXGasi8Y6fFGIo22nZq/v08Y0gYhMx7Elcbq4mZeVlKpjQy8+ptzEHmPYmrjdVEzrxjs6L9 cTeNzLwkvQuse7fnAoPzhXO8OyPgymM1e2fel9hLVoKqPiTWEc/VduvtonG9UR9FB97nPYmrjdXs nXl2L7k584oN8XV7V6xw2pfWNdqReU/iamM1ZF69Kx4yto1Sxul2Y6MILiHznsTVxmrCZt63o++2 +DUvkHktc4GGzHsSVxuriZZ56TeBkhI24oY21Mbduvd8lnl1A1xF5j2Jq43VRMu8l9ppLpOReU/i amM1ZF738EYUf7CvXZF5T+JqYzVkHvZG5j3J6x8G+/BP8cHJ/3HiOCTHZ2Tccoe3IPOe5PUwf8g8 OCHzsDcy70leD/OHzIMTMu/+ote3iznIvCd5PcwfMg9OYmZeOvwmQFJ+GaBo820pNri6WxfBIGTe k7we5g+ZBydk3nH5Klazoo22nW5kHkYj857k9TB/yDw4iZl5WYqiNDLzxAqYgMx7ktfD/CHz4CRy 5n3l5zLv2DhJAVwcwlXOF87xRo/A62H+kHlwsnfmfYlrV1aCqj4k1hHP1Xbr7aJxvVEfRQfe5z3J 62H+kHlwsnfm2etVbs68YkN83d7NSnTZfWldox2Z9ySvh/lD5sEJmVfvioeMbaOUVscehj0GXELm PcnrYf6QeXASNvPy7xuvNDjztMFofRUjOZ0LNGTek7we5g+ZByfRMi/9hlbS343VG9qi17J7nOCx bDIzr26Aq8i8J3k9zB8yD06iZd5L7TSXyci8J3k9zB8yD07IvO6FbkTxB/vaFZm3Cb5rcOF1Izna e3aYjMzbBN81uPC6kRztPTtMRuZtgu8aXHjdSI5eNLuiF3s389dyTyDzNsF3DS68biRHE2aXfj9O maU0KtrUBZPy0UpxN0sRKPYLX2TeJviuwYXXjeRowuzSWSAdd4vXtSItu2te8L2ReZvguwYXXjeS ozmzS1UypZGZV29jDjJvE3zX4MLrRnI0Z3ZFs6L9cTeNzLxj4yQFcHEIVzlfOMe7E5fwHYQLrxvJ 0ZxpprOfQKbfBKrriOdqu/V20bjeqI+iA+/zNsF3DS68biRHc2aXrmResSG+bu+KFU770rpGOzJv E3zX4MLrRnI0Z3bpeuZp20Yp43S7sVEEl5B5m+C7BhdeN5KjabM7tkkLZ96y36lXIPM2wXcNLrxu JEdDZ5d+EygpYSNuaENt3K17z2eZVzfAVWTeJviuwYXXjeRo79n12Wkuk5F5m+C7BhdeN5KjxWc3 89Kt/G16CzJvE3zX4MLrRnK09+wwGZm3Ccd/YwyR/T8njkNyvLfHLXd4CzJvE47rAiIj8+4ven27 mIPM24TjuoDIYmZeOnwqMikfjCzafFuKDa7u1kUwCJm3Ccd1AZGRecflq1jNijbadrqReRiNzNuE 47qAyGJmXpaiKI3MPLECJiDzNuG4LiCyyJn3lZ/LvGPjJAVwcQhXOV84xxsdlziuC4hs78z7Eteu rARVfUisI56r7dbbReN6oz6KDrzP28SIdQEB7Z159nqVmzOv2BBft3ezEl12X1rXaEfmbcJxXUBk ZF69Kx4yto1SWh17GPYYcAmZtwnHdQGRhc28/PvGKw3OPG0wWl/FSE7nAg2ZtwnHdQGRRcu89Bta SX83Vm9oi17L7nGCx7LJzLy6Aa4i8zbhuC4gsmiZ91I7zWUyMm8TjusCIiPzuhe6EcUf7GtX1s+U 870fHDve9DjluC4gMjIPe7N+wF1vXCuNF+q+k7gB9vCfnKx5IzmOCi9F5uFH953EDbAHMg97U3+2 me4FXmbJe6fO+4gbYBdk3v2hNu5mPoryBD7Dgh/cAMHFzLxUfYKhbly0qQum3/cJ9m6WIlDsF76s 93na9761NF6o/1biBtgCmXfstBhD0UbbTs2/k24MCYPw93n40X0ncQPsIWbmZSmZ0sjMq7cxB5mH H913EjfAHiJn3rFZ0f64m0Zm3rFxkgK4OISrnDPP8e7EI1zuKm6G99o7877EXrISVPUhsY54rrZb bxeN6436KDqc/H3enYvrfJNiihv3EjfADvbOPLuX3Jx5xYb4ur0rVjjtS+sa7QZeOMc7FdNwAwRH 5tW74iFj2yhlnG43NorgEvk9fscdc6kOluV0X3EDvFXYzPt29N0Wv+YFMq9lLtCoP9s83T4vjRfq vpO4AfYQLfPS7/qWlLARN7ShNu7WveezzKsb4KqTm6B45dKFdrxTMU33ncQNsIdomfdSO81lMjIP P7rvJG6APZB53cMbUfzBvnZ14XObVy+0452Kabz+3ZYP/yzRO/1nJ45Dcrwh766XeD8+t4kfXuvL h8x7JzIPe2t6n9d3rzje9JjGa335kHnvRObd8c+A+3Yxh/X3eeneB4Qcb3pM47W+fMi8d4qZeemw 6CVl3SvafFuerpwtu3URDHKeebn32+B402Mar/XlQ+a9E5l3XL6K1axeD8XtdCPzMBqZhx9e68uH zHunmJmXpShKIzNPrIAJTn4nvfjGXyuNF/JaXz5k3jtFzryveuk77qaRmXdsnKR1uDiEq5wvnOON jkd4rS8fMu+d9s68L3HtykpQ1YfEOuK52m69XTSuN+qj6MDvKuCH1/ryIfPeae/Ms9er3Jx5xYb4 ur2bleiy+9K6Rjv1Z5uFntJ4Ia/15UPmvROZV++Kh4xto5RWxx6GPQZcYn2G5W5pvJDX+vIh894p bOZ9l6zjMlh/zU6Zpw1G66sYyelcoCHz8MNrffmQee8ULfPSb2glJWzEDW3Ra9k9TvBYNpmZVzfA VfxsEz+81pcPmfdO0TLvpXaay2S8z8MPr/XlQ+a9E5nXvdCNKP5gX7si8/DDa335kHnvROZhb3Lm 1XpKI7bbNyc30gP49/OwN34/D6NwI70RmYe9Cb9T6XWvON6peCOfO5QbaS4y7/5QG3czH0V5Au/z MAo30hvFzLx0+E2ApPwyQNGmLpiUXycQd7MUgWK/8EXmYRRupDci846dFmMo2mjbqfl30o0hYRAy D6NwI71RzMzLUjKlkZlXb2MOMg+jcCO9UeTMOzYr2h9308jMS9K7wLp3ey4wOF84x7sTb8dN9UZ7 Z96X2EtWgqo+JNYRz9V26+2icb1RH0UH3udhFG6kN9o78+xecnPmFRvi6/auWOG0L61rtCPzMAo3 0huRefWueMjYNkoZp9uNjSK4hMzDKNxIbxQ2874dfbfFr3mBzGuZCzRkHkbhRnqjaJmXfhMoKWEj bmhDbdyte89nmVc3wFVkHkbhRnqjaJn3UjvNZTIyD6NwI70Rmdc9vBHFH+xrV2QeRnH8J2AwDf+W EPZG5mEUx6UK05B52BuZh1EclypMQ+bdX/T6djEHmYdRHJcqTBMz89LhU5FJ+WBk0ebbUmxwdbcu gkHIPIziuFRhGjLvuHwVq1nRRttONzIPo5F5GMVxqcI0MTMvS1GURmaeWAETkHkYxXGpwjSRM+8r P5d5x8ZJCuDiEK5yvnCONzreznGpwjR7Z96XuHZlJajqQ2Id8Vxtt94uGtcb9VF04H0eRhmxVGG0 vTPPXq9yc+YVG+Lr9m5WosvuS+sa7cg8jOK4VGEaMq/eFQ8Z20YprY49DHsMuITMwyiOSxWmCZt5 +feNVxqcedpgtL6KkZzOBRoyD6M4LlWYJlrmpd/QSvq7sXpDW/Rado8TPJZNZubVDXAVmYdRHJcq TBMt815qp7lMRuZhFMelCtOQed0L3YjiD/a1KzIPozguVZiGzMPeyDysjhtypv/uxOu75vuNcxwV XorMw+q4IWci8+4PtXE389dyTyDzsDpuyJliZl76/ThlltIo6R+5rBuc7mYpAsV+4YvMw+q4IWci 846dFmMo2mjbqfn384whYRAyD6vjhpwpZuZlKZnSyMyrtzEHmYfVcUPOFDnzjs2K9sfdNDLzkvQu sO7dngsMzhfO8e4E/sHNOdPemfcl9pKVoKoPiXXEc7XdertoXG/UR9GB93lYHTfkTHtnnt1Lbs68 YkN83d4VK5z2pXWNdmQeVscNOROZV++Kh4xto5Rxut3YKIJLyDysjhtyprCZ9+3ouy1+zQtkXstc oCHzsDpuyJmiZV76TaCkhI24oQ21cbfuPZ9lXt0AV5F5WB035EzRMu+ldprLZGQeVscNOROZ1z28 EcUf7GtXZB5Wxw05E5mHvZF5WJ3jPyWDU16Z5zgkxxtg3HKHtyDzsDrHJQ+nyDzsjczD6hyXPJwi 8+4ven27mIPMw+oclzycipl56fCbAEn5ZYCizbel2ODqbl0Eg5B5WJ3jkodTZN5x+SpWs6KNtp1u ZB5GI/OwOsclD6diZl6WoiiNzDyxAiYg8zZt38sAAB/bSURBVLA6xyUPpyJn3ld+LvOOjZMUwMUh XOV84RxvdOAfjkseTu2deV/i2pWVoKoPiXXEc7XdertoXG/UR9GB93lY3YglD5q9M89er3Jz5hUb 4uv2blaiy+5L6xrtyDysznHJwykyr94VDxnbRimtjj0Mewy4hMzD6hyXPJwKm3n5941XGpx52mC0 voqRnM4FGjIPq3Nc8nAqWual39BK+ruxekNb9Fp2jxM8lk1m5tUNcBWZh9U5Lnk4FS3zXmqnuUxG 5mF1jkseTpF53QvdiOIP9rUrMg+rc1zycIrMw97IPETBvd3CK/PWvNqOo8JLkXmIgnu7BZl3f6iN u5m/lnsCmYcouLdbxMy89PtxyiylUdI/clk3ON3NUgSK/cIXmYcouLdbkHnHTosxFG207dT8+3nG kDAImYcouLdbxMy8LCVTGpl59TbmIPMQBfd2i8iZd2xWtD/uppGZl6R3gXXv9lxgcL5wjncn4Iv7 vMXemfcl9pKVoKoPiXXEc7XdertoXG/UR9GB93mIgnu7xd6ZZ/eSmzOv2BBft3fFCqd9aV2jHZmH KLi3W5B59a54yNg2Shmn242NIriEzEMU3Nstwmbet6Pvtvg1L5B5LXOBhsxDFNzbLaJlXvpNoKSE jbihDbVxt+49n2Ve3QBXkXmIgnu7RbTMe6md5jIZmYcouLdbkHndwxtR/MG+dkXmIQru7RZkHvZG 5iEKr3+S5rP1P0vklXmOQ3L8ro1b7vAWZB6i8Fo6P2QemYfXIvMQhdfS+SHzyDxz0evbxRxkHqLw Wjo/ZN52mZcOvwmQlF8GKNp8W4oNru7WRTAImYcovJbOD5m3deYdl69iNSvaaNvpRuZhNDIPUXgt nR8yb7vMy1IUpZGZJ1bABGQeovBaOj9k3qaZ95Wfy7xj4yQFcHEIVzlfOMcbHfDltXR+yLzXZt6X uHZlJajqQ2Id8Vxtt94uGtcb9VF04H0eovBaOj9k3mszz16vcnPmFRvi6/ZuVqLL7kvrGu3IPETh tXR+yDwy7/esYtsopdWxh2GPAZeQeYjCa+n8kHk7Zl7+feOVBmeeNhitr2Ikp3OBhsxDFF5L54fM 2yjz0m9oJf3dWL2hLXotu8cJHssmM/PqBriKzEMUXkvnh8zbKPNeaqe5TEbmIQqvpfND5pF50kI3 oviDfe2KzAMu2/gZ+d9OvC6R71VyHBVeiswDLtv4GSHzsDcyD7hs42eEzLs/1MbdzF/LPYHMAy7b +BmJmXnp9+OUWUqjpH/ksm5wupulCBT7hS8yD7hs42eEzDt2WoyhaKNtp+bfzzOGhEHIPOCyjZ+R mJmXpWRKIzOv3sYcZB5w2cbPSOTMOzYr2h9308jMS9K7wLp3ey4wOF84x7sTWNbGz8vemfcl9pKV oKoPiXXEc7XdertoXG/UR9GB93nAZRs/I3tnnt1Lbs68YkN83d4VK5z2pXWNdmQecNnGzwiZV++K h4xto5Rxut3YKIJLyDzgso2fkbCZ9+3ouy1+zQtkXstcoCHzgMs2fkaiZV76TaCkhI24oQ21cbfu PZ9lXt0AV5F5wGUbPyPRMu+ldprLZGQecNnGzwiZ1z28EcUf7GtXZB5w2cbPCJmHvZF5wGWO/6Lb av6XE8chOV7tccsd3oLMAy5zXIVXQ+Zhb2QecJnjKrwaMu/+ote3iznIPOAyx1V4NTEzLx1+EyAp vwxQtPm2FBtc3a2LYBAyD7jMcRVeDZl3XL6K1axoo22nG5mH0cg84DLHVXg1MTMvS1GURmaeWAET kHnAZY6r8GoiZ95Xfi7zjo2TFMDFIVzlfOEcb3RgWY6r8Gr2zrwvce3KSlDVh8Q64rnabr1dNK43 6qPowPs84LIRq/Ai9s48e73KzZlXbIiv27tZiS67L61rtCPzgMscV+HVkHn1rnjI2DZKaXXsYdhj wCVkHnCZ4yq8mrCZl3/feKXBmacNRuurGMnpXKAh84DLHFfh1UTLvPQbWkl/N1ZvaItey+5xgsey ycy8ugGuIvOAyxxX4dVEy7yX2mkuk5F5wGWOq/BqyLzuhW5E8Qf72hWZBzxmwWftvzvxmprv7BxH hZci84DHLPiskXnYG5kHPGbBZ43Muz/Uxt3MX8s9gcwDHrPgsxYz89LvxymzlEZJ/8hl3eB0N0sR KPYLX2Qe8JgFnzUy79hpMYaijbadmn8/zxgSBiHzgMcs+KzFzLwsJVMamXn1NuYg84DHLPisRc68 Y7Oi/XE3jcy8JL0LrHu35wKD84VzvDuB7S343O2deV9iL1kJqvqQWEc8V9utt4vG9UZ9FB14nwc8 ZsFnbe/Ms3vJzZlXbIiv27tihdO+tK7RjswDHrPgs0bm1bviIWPbKGWcbjc2iuASMg94zILPWtjM +3b03Ra/5gUyr2Uu0JB5wGMWfNaiZV76TaCkhI24oQ21cbfuPZ9lXt0AV5F5wGMWfNaiZd5L7TSX ycg84DELPmtkXvfwRhR/sK9dkXnAYxZ81sg87I3MAx7j+C/DefHKPMchOV6lccsd3oLMAx7juJp7 IfPuL3p9u5iDzAMe47iae4mZeenwqcikfDCyaPNtKTa4ulsXwSBkHvAYx9XcC5l3XL6K1axoo22n G5mH0cg84DGOq7mXmJmXpShKIzNPrIAJyDzgMY6ruZfImfeVn8u8Y+MkBXBxCFc5XzjHGx3YnuNq 7mXvzPsS166sBFV9SKwjnqvt1ttF43qjPooOvM8DHjNiNb9p78yz16vcnHnFhvi6vZuV6LL70rpG OzIPeIzjau6FzKt3xUPGtlFKq2MPwx4DLiHzgMc4ruZewmZe/n3jlQZnnjYYra9iJKdzgYbMAx7j uJp7iZZ56Te0kv5urN7QFr2W3eMEj2WTmXl1A1xF5gGPcVzNvUTLvJfaaS6TkXnAYxxXcy9kXvdC N6L4g33tiswDHuO4mnsh87A3Mg94PcfH9n86cRzSmhcKL0XmAa/n+NiSedgbmQe8nuNjS+bdH2rj buajKE8g84DXc3xsY2Ze+v0VgiylUdJ/zaBucLqbpQgU+4UvMg94PcfHlsw7dlqMoWijbafm30n3 /d6hBZkHvJ7jYxsz87KUTGlk5tXbmIPMA17P8bGNnHnHZkX7424amXlJehdY9+77TQ/F+cI53p0A Gjk+wntnnnHF0tlPINNvAtV1xHO13Xq7aFxv1EfRgfd5wOs5PrZ7Z57dS27OvGJDfN3eFSuc9qV1 jXZkHvB6jo8tmVfvioeMbaOUcbrd2CiCS8g84PUcH9uwmfft6Lstfs0LZJ77Nz0UMg94PcfHNlrm pd8ESkrYiBvaUBt3697zWebVDXAVmQe8nuNjGy3zXmqnuUxG5gGv5/jYknndwxtR/MG+dkXmAa/n +NiSedgbmQe8nte/MPf5fP6HkwVnl8g8kHnABrxS4UPmeSx6fbuYg8wDXs8rFT5RMy8dPhWZlA9G Fm2+LcUGV3frIhiEzANezysVPmReFX71bvG6VuTSLqYh84DX80qFT9TMy1IUpZGZJ1bABGQe8Hpe qfCJnXlf+bnMOzZOUgAXh3CV84VzvNEBNPJKhc/umfclrl1ZCar6kFhHPFfbrbeLxvVGfRQdeJ8H vJ5XKnx2zzx7vcrNmVdsiK/bu1mJLrsvrWu0I/OA1/NKhQ+ZdyXztG2jlFbHHoY9BlxC5gGv55UK n8CZl3/feKXBmacNRuurGMnpXKAh84DX80qFT7zMS7+hlfR3Y/WGtui17B4neCybzMyrG+AqMg94 Pa9U+MTLvJfaaS6TkXnA63mlwofMu7HQjSj+YF+7IvOA1/NKhQ+Zh92ReQCiGLfc4S0evgm87sLV 6jiW2nhITG1mqY2HRJihHZk3pI5jqY2HxNRmltp4SGQe2pF5Q+o4ltp4SExtZqmNh0TmoR33CgAg CjIPABAFmQcAiILMAwBEQeYBAKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIPABAFmQcAiILMw1vxf1kE cBWrBt6KzANwFasGPP2TQ8W/z1n8i53fNo1HxTratlZ/zHQBvAxrATwZ2ZOlRDSOHje0lu31iT0A mcyDrzqB6qNa/Gjv8DoyzxgDgMhYEeBJzJt0kKsc0o7WmSe21F4v6g+ZLYC3YS2Ap5Z3Xdr7sPb3 eS2vt7wIIBoWAnhq/Pu84hXxqO/PNsk8AJnMg6/6UyTf3Zs/2zy2PO6Kr9f1necJ4J1YC+CJdAGw MlYoeCLzAKyMFQqeyDwAK2OFAgBEQeYBAKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIPABAFmQcAiILM AwBEQeYBAKIg8wAAUZB5AIAoyDwAQBRkHgAgCjIPABAFmQcAiILMAwBEQeYBAKIg8wAAUZB5AIAo yDwAQBRkHgAgCjIPABAFmQcAiILMAwBEQeYBAKIg8wAAUZB5AIAoyDwAQBQDMy8BwErGLXd4i7GZ 9988rFbHsdRqdRxLbTwkpjazlO+Qxi13eAsy78lSq9VxLLXxkJjazFJkHnyReU+WWq2OY6mNh8TU ZpYi8+CLzHuy1Gp1HEttPCSmNrMUmQdfZN6TpVar41hq4yExtZmlyDz4IvOeLLVaHcdSGw+Jqc0s RebBF5n3ZKnV6jiW2nhITG1mqacyb+OA7Jja0KvxT/H0H1pa3uruzsknpRe76Td+njeemmOp1eo4 llqtjmOpaJl3uu5rZ40YzLT6jV2Qec/UcSy1Wh3HUhsPianNLPVI5j0VId/Xrw4gVOY1NlaLdJ95 Xnqxm37j53njqTmWWq2OY6nV6jiWejbzjm8sih/BievvzVg11vT6Lc53DPVPBY3xXzoqtjRGaLfX dtMvrb19fdqReU+WWq2OY6mNh8TUZpZ6MPOOiZKrVTgr4XFphTT6rQ8ZEXU6MDG860On9bURtvRo 7562rzO1D5n3ZKnV6jiW2nhITG1mqcczr97N1Vur06wy1P3m3/W9PmSPrbHxaZvjCBtnagxArKaN yu5dG0AjMu/JUqvVcSy18ZCY2sxSC2aetnF1Ia7bi7Fh9zUu81oKauNvrKaNyu7dGEALMu/JUqvV cSy18ZCY2sxSj2de47uN+4F37Ktu0BFR2siN2dmn2CM3um7PYLsBmfdMHcdSq9VxLLXxkJjazFIP Zl5WFuJ8+OFbfcql+uLrRWVxSLnKAC32tOm07F5K9+M10a6YuKsNte6UzHumjmOp1eo4ltp4SExt ZqlHMi9ffw93ZxX2LTK602lXY1BxMu/JUqvVcSy18ZCY2sxSD2Ze/WbOaHxjpfSvM67TxsZk3t07 dak6jqVWq+NYauMhMbWZpZ7KPOyKzHuy1Gp1HEttPCSmNrMUmQdfZN6TpVar41hq4yExtZmlyDz4 IvOeLLVaHcdSGw+Jqc0sRebBF5n3ZKnV6jiW2nhITG1mKTIPvsZmHgCsY9xyh7fgJgAAREHmAQCi IPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPPwAN//C9Q/1a7WrNsf6/TV BLA4Hmk8YETmeZ11fJ3MAzbDI41/fd/ZFIv+8ZXi3c/pUbGOtl1X0IZxHGfxfxBuHIM436KC+P8m JgWBV+MBxr/s7NESQjyqJVCucsWoXxwSB1ZXuzQGu45xLoCX4gHGv+yVXXsHJh5tyU7j9dOBFc3s d3gtczlNO3v6AN6CZxj/EjOv+PleHTbi0ToqxJba6/XAimbfF4tejDHYcyHzgCB4hvEv++2U9op4 9DQq7NdbXqzrtI+BzAPC4hnGv7R3TvYr4tHGOnb99o66M++0jjZfAC/FY4x/1Unw3dV+9qgdNX6u eNwVX9fGJg6sPtoyhiLV7OQ7HRuAF+Exxr9Y021cH2ADPMb4F2u6jesDbIDHGP9iTdcUP1YF8F48 yQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCiIPNwUUr84c+2f7A7vse46PFViT/8GfcH u+N7jIseX5X4w59xf7A7vse46PFViT/8GfcHu+N7DACIgswDAERB5gEAoiDzAABRkHkAgCjIPABA FGQeACAKMg8AEAWZBwCIgswDAERB5gEAoiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZN1X6dafI iIH51rxU+dtm3EgAgMVlqlTpLjJiYL412ysf25B5AMZhcZmqXtwXWd+fHck61wHA3lhopioWd3G3 WP3rF7Vd8d2SHSfam87GkdjDaxxVMQBtJKdTBoBTrBdTGeklxo/xotjg9PV6MJc61eJHa9kyWqO9 WL9xdgBQY7GYSly7i23xxax8yuPqtjaYmyMxursz2juzA4Aai8VUp3nwVbyoFRmXeVdH0j7HcdsA YGOxmKoxD8Sz3FOhI0WMkVydI5kHYD4Wi6nqxbpeu4sXtRP7tu3BdI/E6I7MA7AOFoupUkU7dPqi XbAlFexzW14sXhe7u1REO/20Zvs2gMhYCKbSFv3iqP2ittux4reEkNfwtKFmMg/ALCwE+1h/ZV9/ hAD2xgL0bu96N7P+CAHsjQXo9cSf/q3pFYMEsDEWIABAFGQeACAKMg8AEAWZBwCIgswDAERB5gEA oiDzAABRkHkAgCjIPABAFGQeACAKMg8AEAWZBwCIgswDAERB5oXAP2gAAJnMe5fff9X8wvduaOa1 DOlq76n619j7CpL0AI5YEd5kzX8h9jgMryGReQBGYEV4Ey3z6rdZxSti4++Lp28f29/DaUP6dmS0 OXZkzKV4xS6yzn8ZAFgBy8GbiDHWt3H64qVRFdvaK3UKimfVY6iHZHckNgAAVoQ3uZp59YmDMs94 R1VkXvF63aY+Wu8ap5yeCCAyVoQ3MRLrmDraK3aFuov2UWlDPXaRq4gS24g1i3nVr2hTIPMAHLEi vMlpYmntJ2denV6nqWYf1RobQz1tACAgVoQ3sRPr0sbpi5dGZbzSknnF0XoMLZlXFxFPBBAZK8Kb pF/169or9faxwWnm2UEoHqq7qFtqw9Zaiq8YRU5HDiAaloO4yAMA0bDkxcJ7IACRseSFI/50FAAi YNULgYQDgEzmZel9z+lHNlryoy9mrp7V+OmSFTJP/HAKAMwUfQESo26PzFsh52oLDglAHNEXoNPM 094F1n8l1vJm8dJZp4eM9sUI+2YkXiXt6LFaPmSb0S8ATBZ99TGSKTfHjL1hlO3bcGl/+mL7BSy2 xZEUXwHgEdEXoGKB7o4NraB46HQYp6e3NHsw84pDZB6ARURfgOwMuJQQ6SA3Z552VkfmGQMg8wAg k3lemXc1tG6e3nIimQcAhegL0COZd3PDpf3pi+0XsNgm8wAsiwWo/JFgljJAe6VY8Y8viuFx6Sy7 sdG+JdXs+YrDPr2A311xw7gsADAHqw8AIAoyDwAQBZkHAIiCzAMAREHmhcAnRwAgx8k88ZOW9wu6 lBrH/hDpfsRPnJ6+MsHxk6sAHhTiITz9LP7NmsuaPMjHL8jpb1k89V272unjVxLYVYhHy/iVtXr3 2Kx+a6i93l6wbn9aubGCVlObdeO8cltUHDutTzmd1/HEoloxI6348Sxt5OIF1wYp9tVS2SjVPoXT Cw6gQ4jn6nQdP6449ZJkv1gXvLkxtHjHvK5e3qLa9+vxlXpXG0zR0igujlwbm1j5tC9x2MZu8crV KXR8LwDYQjxRxXpaLz1ay5YXjXONjcYe62bGK5dG0t61TbyGxXb9SmPjuuWl4kWb+mvLWafDNsbc 2HXLYAC4CPE4nS7039072XCaNMf6jT2KwxPHb9e8M69Llzd7Z149o/biRZvia8t3xK58PFE8WpS6 OoWO7wUAW4gnqn2hv5MNRpFLjcX2xomXBtAxr0bHCsWL2pp+qfHVwKjb1F/7RqLVPy11aQp93wUA thDPVftCfycbLkVOY4/uxTvmdek6f7/ar9S7x8GIjU+LiyM/tqm/ugzb2L3atXgRADiK8milg+Mr WgOtZfpVdyFuHE+82mPdvuUVbSQd89LGo0nVmi4OtS6epOW+npFWvD63aHM6pJa+6mEbu+LUrk4B gCMeMPy4FG/uXT/S71K4CMBQPGBw+9lmX9fidihcBGAaHjDkLP3sd37Xk/tdChcBmINn7E26l0XW UwDIG2de/cbFXvcbU+HBd0IdvT/1E8s7tHE2fne8h2N11NgdH04B1rHncyhG3QaZN+GsO1y6e1Hm jWjc0R5Auz2frtPM094F1n+tcvpm8fRErYvjUa1lyxTEoYr1r85dvJ7GNT9uGGPTih+/ihfhtGyq vjWNp2gDaDnL7sK42vYg6wsF4L49Hy1xeS2WoUsvis3qvny78Nroe7H7ahfVvl+Pr9S7x8GIp7SU tb81LWXra2h0VBy91NiYVz14AF72fKiKxas7DLSC4qG+LloqtzS2h3pn7ra6sbisa+21xpeKiG0a i4hl6w1jkMblshu3zwuAlz2fqPZ0OV3300FxqKWv9hdbOi02jm3sofbNveNq57mZV0+5pXhL2ZaO tItmNG6/OB3fCACn9nyo7JW9IwzEZi193eniUu8u0xS7aL/mxcZ3u35F3D02biliv9JdpN5oGWRj 46sDAOBrz0erMV3aX2xJHfcuvDb6Xuy77N+v9iv17nEwWpFj49OOGkcijq2xo+LopcbtswDgaNun Kx0cX7GPFi/WLetIuNpFsS1W1joV27RMuWNg2hxPpWoRF0dbF0+/K349yHok9YW6OhKtjXaWMVRj VOL1NwYAYBCeMayCFR/AaKwyeBLvcgDMxCqDhxk/9gQAXyw0AIAoyLyp0q85Pc7vZdDs6s+PjOtl NK2Xm70bHxTq66h7PPNPBFpwe01Vf35vWnfTehE/kejVkbjt28Wza+6d3o0rPznzxv0XCXATN+VU YuZpn2W/c7RuebWUNuz6UNZXN3GpLaKrGJLWpjh6OjV72OIgT+dyOvK6oDgqrU3jt8Mef72dpQui DdJoNnQYWbkyxagabxVtagB3w1TiMl08vXWb+xsdR6/Oq+XFesEqDomLWj1C8atdp33w9lyMHlte aaxTv94+fm1SYtfGsLVmp8MwBt9xBfpuFXsYiIxbYSoxZopdMXWMxGrZaD/aPa/TV+oXu5ND/Gqf JQ6m1j1ysetLo7Jn1D5+o3hLR/VM7RvDGIZYzehOG5g4l5aRa8NAZNwKU2krVLHmFo+reFTb0Epd PXp1Xi0vdq+A9Sva+qi90j74lrmI9cWuiwsunlW3ufTtuDr+9kunvWgPw7j4l77jxdxPL2bd151b GrviVpiqfvyOz3CxYR+1T6y7s4/aL7bM63Tb3m1ZyOrFuv2sxsFr7U9HXnd9f3aNWibb0pGWHI3j MSZ+OoxLA7h0IlDgtphqTuZdLaW9eGle9XZdR4uHlleOIxS/2nX65tI+cnHA7bM7nVH3yL+HxHHa F1ybTuNIjBtArC8OrO9WERsAmcybTEyUdFC30Y4a0VWfLh7VOmocdtGgsb3dqbhOiauhOJfTOsYU tLk0jlw8t/7eabPTvq3iKcbIi2rFueIlEjsSmzUOw2jW/v3N+gU/vZj1KS0DRhDcDYji7WvfIuNf ZBhAH25fhPD2lXqR8S8yDKAbdzAAIAoyDwAQBZkHAIiCzIOD+q95xM/LGR+JdHnd1v13UacfQXTs y5HxaczugrcHBTyMmxh3aZ+Mz9Uq+Wzm9dHC+xWZZ+zeLwi8ETcxbjF+WapuefzlqtNtu1RW3seI 59a//qW9BxJzoj3z6jr1r4gVbRrbt7xjs69kcbSjO+2s+mg9NmAR3J1woAWM2MxOo9MiWfqlaftc Le3EpdyenTG2uo4xqiKc6o5aKmiDFEd4vzujTb2RgSVxa8KB/bagaNaSbaeZJ24Xr4gh0bLQG92d jvlqX1rvHf8dYLe83117DJN5WBa3JhzYK2mxe/pm4vTtgthFca5Y3O5d61Hr7rRZ+tXSe91enNfV xO3ujszDZrg14cAl805XbaMLI5bcM68OoZZRXRq5PR7xUF/mnXZH5mEz3JpwcLq8aq8vlXni+ydt GEY8tLxJMtq0j1YbT0fm3RkwmYcX4daEg8Y3KMd18xgw9etX1/f6XK2lca4x7PYXxagoOtWS3mgv VhB7ty/ane60SZF5eBFuTbzAtDV05mJNMADz8dRhdaOzQXtf+Pa+ANR46gDhB3p79AWgwIMHAIiC zMMtd96vdJ8rfhBjUF+OGj+Kcqmgy0juDKCuo714vzvgPm5E3DJ/LauTo2UkK6y54gcpHQtOOFEs 0vHtAJ7CfYlbtM+v16/UK2Px1f58fH2iVrZob3/Ovm7T2L7lHduxZX01iqMd3Wln1UfFUd0pkqUr L16EuoFYs2OaWb/ygIbbBbfUi93VV+x1VssSYzD1br16Gn3V42mcl9a7eK4x/fbujDb1RkfXdhGx oNbMrtk9zXrwgI17Bbdo61Hdpm7cno5aQfvFO0ncV6G9d5fu2sPgTten0WLn09WafRMHGnHT4Jbj f7Z/fQ/Vr+RqvXPJvNNm2jhPe6/bazNt6b1ucLW7S2GgRYt9nRuLaCe29ChunE6z8coDBm4X3CKu 5llZ/sSFr2URN4prbU4rt7Sx27ePqj3zTrtbKvNOp9xY89KkasQe2nGv4JZLuXU187T/ij8Nxbrf 9r4ak0CbTuO5RsH27p7NPONbo6VjR+ZdmiZwinsFtxRrU7EwiS8W21o6Gv91r/UiLrXiGlpXEMdj z6uuIPbeHjxXu9MmZWy0d91S5PRbrPUobtQDaP/eAY24XbC0mSsaq+ea+L7AETcTlnP6puGlfaEd 3xcMws2EFdU/xdqjL7Tj+4IRuJ8AAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMAREHmAQCi IPMAAFGQeQCAKMg8AEAUZB4AIAoyDwAQBZkHAIiCzAMARPH/AZwx0Q9AabJoAAAAAElFTkSuQmCC --20cf3054a28506dee0049c54307c-- ========================================================================Date: Tue, 15 Feb 2011 08:36:13 -0800 Reply-To: Jose Soares <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jose Soares <[log in to unmask]> Subject: fMRI-EEG Post-Doc Open Position MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1499706008-1297787773=:89678" Message-ID: <[log in to unmask]> --0-1499706008-1297787773=:89678 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Hi all, there is an open position to fMRI-EEG, http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=22187 Best, Jose --0-1499706008-1297787773=:89678 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

Hi all,

there is an open position to fMRI-EEG,

http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=22187

Best,

Jose

--0-1499706008-1297787773=:89678-- ========================================================================Date: Tue, 15 Feb 2011 09:03:39 -0800 Reply-To: "Jiang, Zhiguo" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Jiang, Zhiguo" <[log in to unmask]> Subject: AR(1) Content-Type: multipart/related; boundary="_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_"; type="multipart/alternative" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_ Content-Type: multipart/alternative; boundary="_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_" --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Can someone point me to some useful example/scripts/tutorial on how SPM take care of the serial correlations. It seems to me that SPM uses 0.2 as a default AR coefficient. (but in Statistical Parametric Maping book, p. 122. It says SPM uses a 1/e as coefficient). I am trying to figure out how to get the whitening matrix SPM uses without running SPM8 model specification. I tracked it down to spm_Ce.m and spm_Q.m but I got stuck on these codes Q = spm_Q(a,v); dQda = spm_diff('spm_Q',a,v,1); C{1} = Q - dQda{1}*a; C{2} = Q + dQda{1}*a; Any comments? Thanks, Tony ------------------------------------------------------------------------------------------------------------------------------ [cid:image001.jpg@01CBCCEF.04F7F480] Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095 Tel: (310)206-1423|Email: [log in to unmask] ________________________________ IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Can someone point me to some useful example/scripts/tutorial on how SPM take care of the serial correlations.

It seems to me that SPM uses 0.2 as a default AR coefficient. (but in Statistical Parametric Maping book, p. 122. It says SPM uses a 1/e as coefficient).

I am trying to figure out how to get the whitening matrix SPM uses without running SPM8 model specification. I tracked it down to

spm_Ce.m and spm_Q.m but I got stuck on these codes

Q = spm_Q(a,v);

dQda = spm_diff('spm_Q',a,v,1);

C{1} = Q - dQda{1}*a;

C{2} = Q + dQda{1}*a;

Any comments?

Thanks,

Tony

------------------------------------------------------------------------------------------------------------------------------

Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095

Tel: (310)206-1423|Email: [log in to unmask]

IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer.
--_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_-- --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_ Content-Type: image/jpeg; name="image001.jpg" Content-Description: image001.jpg Content-Disposition: inline; filename="image001.jpg"; size30; creation-date="Tue, 15 Feb 2011 09:03:39 GMT"; modification-date="Tue, 15 Feb 2011 09:03:39 GMT" Content-ID: <[log in to unmask]> Content-Transfer-Encoding: base64 /9j/4AAQSkZJRgABAAEAYABgAAD//gAfTEVBRCBUZWNobm9sb2dpZXMgSW5jLiBWMS4wMQD/2wCE AAUFBQgFCAwHBwwMCQkJDA0MDAwMDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0BBQgICgcKDAcHDA0MCgwNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N DQ0NDQ0NDQ0NDQ0NDQ0NDf/EAaIAAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKCwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoLEAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEU MoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2Rl ZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK 0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+foRAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYS QVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNU VVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5 usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/AABEIADABwAMBEQACEQEDEQH/ 2gAMAwEAAhEDEQA/AOXr6o8gKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAC gAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKA CgDobfQ1n0yTUt5BiYrs28HlR1zx9707V8xWzR0cxpZUqaaqRUufms1dTduW2vw9+p60MIp4WeM5 rODty231j1v59hnh/RV1uZ4WcxbE3ZC7s8gY6j1q84zN5RShWjTVTnnyWcuW3ut3vZ9hYLCLGzlT cuW0b3Sv1S7ruQ6Vo02rXBt4cAJku56KoOM+5PZe/sASOjH5lSyzDrE1k25WUILeUmr28kvtS6dm 2k8sPhZ4qo6VPRL4pPZK9vvfRdfS7Oh/4RO1uA0VldpLcRgkoQMHHphiQM8Z+YA4BxXzH+sGJw/L Vx+CnSw82kppyvG+uqcUm7ape42rtXPW/s2lUvDDV4zqx3jpZ/c9PXXzMXRNDOq3T2krGFokZj8u SGV1UqRkd2PftXvZpmiy3C08bRiqsak4xj73KnGcJTUk7PpFdOp52Ewn1qrKhNuDjFt6Xd1JRatd d/wMSePyZGjznYxXPrgkV79KftacKlrc0Yyt2uk7HmzjySlHs2vudjV0LSDrNx5G7y1VS7MBnAGA OMjqSO/rXj5rmKynD/WOVTk5xhGLfLdu7etnsk+m9juweF+uVPZX5Uk5N2va2nl1aKuqWB0y6ktW OfLOAcYyCAVOPcEV2YDFrH4ani4rl543cb35ZJuMlfykn8jDEUXhqsqLd+V6Pa6eqf3MoV6Ryna2 3huysrOG/wBbuXthdgtBBDGJJmjHHmtuZVRCfu5yWHI7gczqScnClFO27bsr9vM1UUknJ2vsluU9 d8OLptvFqNlMLywuWKJKFMbJIMkxSoSdr4BI5wwBIAGM1CpzNwkuWS6eXdMUo8qundHLVuZhQAUA dB/Yi/2L/bO87vtv2Ty9vGPI87fuznOfl24989qx5/3ns7fZ5r/O1i+X3ebzt+Fzn62ICgAoA3/D eh/29dGBpPs8UUUk8sm0uVjiGWIQEFjyABkdc9sVjUn7NXSu20kttWXGPM7bdTN1GK2huGSykaeA bdkjp5bNlQWymWxhsgcnIAPerjdr3lZ9k7idk9NilVkhQAUAdz4g8FSaLplnq8bmaK8jjaQbdvlP LGsirwTuU5YbuOVAI+YVywrKc5U2rOLdvOzsbShypSXUytP0Bb3Sb3VTIVNg0AEe3IfzpAhy2Rt2 5z0OfatJT5Zxp2+K+vayuSo3i5drfic3WxmFABQAUAFABQB0Go6IthplhqQcsdQ+05TbgR/Z5RGM HJ3bs5PAx05rGM+acoW+Hl+d1ctxslLvf8Dn62INPV7W1s7gxWM32qEKhEm0pklQWG08/KxK574z WcW2ryVn2KaSdk7ozK0JOg0bRF1S2vbkuYzYwCYKFzvywXaTkbfXOD9KxnPkcY2+J29C1G6b7I5+ tiAoAKACgAoAKACgAoA6zVfD1to9jBNPcMb27giuY7dYvkEMrEKWl3fe2gnAXg8e554zc5NJe7Ft N31uvI0cVFJ31avbyMPStMm1i7isbYAyzttXPAHcknnAUAscAnAOAa1lJQTlLZEpczsjsl8N6C1x /Zo1GT7Xu8sS/Z/9GMuduzdv37d3y+Zjb/F0rm9pUS5+Rcu9r+9b7rfI15Y35ebX00Myz8JSPdah ZXT+TLpdpcXB2jcHMGzCgkrhXDBg3XGPlrR1UlCUdVKSXpf/ACJUNWnpZN/cchXQZBQAUAFABQAU AFABQB32n/8AIuXH/XQ/+hRV+bYv/kocL/17X/pNU+po/wDItq/4n+cCPwF/x9y/9cf/AGda14s/ 3Wj/ANfv/bJkZN/Gn/g/9uRZ0U/ZtJv7hOHLumRwQAq459vMJHvXJma9vmuXYWprTUKc7PVNucrq 3n7OKfdG+E/d4PFVY6S5pRv10irfdzOxxel6g+lXCXUYDFM/KeAQVKkH8/zr73HYOGYYeeEqNxjO 3vJXacZKSav6W9Gz5zD13haka0Fdxvp0d01r952XhO8N/q09yVCGSFmKjoDviz19Tz+NfCcQYdYL KsPhVJzVOtCKk7JtezrW200WnyPoctq+3xlWs0o81Nuy2+KH57nD3n+vk/66P/6Ea/QsN/Apf9e4 f+ko+aq/xJ/4pfmztPDqPp+l3V/GCZX/AHceBk8cZAH+0+T/ALnPFfBZxKGNzPCZbUaVKF6lW7tH X3uVttbxhZf49NT6PAqWHwtbFQT55e7Cyu+10vWV/wDt0j8aQeYbe/UFRPGAw6YIAYZ75IbH/Aa1 4Zq+zWJy2TTdGq3HW94tuMrW0spRv/2+RmsOZ0sUlZTgk/JrVX87O3yOGr9DPmT0L4nDytZ+yr/q 7W3t4YwOAEWMMAB2GWPFceH+Dm6ttv7zerpK3ZJGBp2qTy2R0BFRo7y5idWbduSTKoCuDj5hgNkE 46VtKKUva9Ypr5bkJu3J3Z6LqF5faDf/ANmadp5fSrZlikRrPzTdAYEskkpjLMzHdtKsFA28EcVx xUZx55ztN6r3rcvZJXNm3F8sY+6vLcNP0q18PatrcDQpcQWlm8scUg3DDeVKiE9cYYKSDu2570Sk 6kKTTs3JJtfNMElGUlbRIzdD1ubxZHe6fqaQSRpZTzwFYIo2gkiAK+WUVTt5wQSSQMFsFgbnBUXG cLp8yT1bun3uTGXPeMrbNrTaw9fEN2ng8MPKyL8WY/cx/wCpFn/u/wCs/wCmv+s/2qXs4+36/Dzb vfm/Ly2HzP2fztt0sW9TOpeE5ItL0eyLxQRRG5kNp532qV1DyBpDGx8td2xVQqVww3eijyVU51JW bb5VzW5VstL7+o3eHuxW2+l7lmy0az0/xNIvkAWlxYSXQt2A+QSRbnjAI+XawcKMAoMAdKlzlKkt feUlG/o9/wCtwSSntpa9vkeW65r8+vSK8yxRJECsUcMaxoiE52gDkgdixJruhBU1ZX13bdzCUnL/ AIB2nw21ae0e8t49mxbK5nG6NGbzERQPmIJ2+qZ2nuK5sRFPlbv8UVv0f9bmtN2uvJsS21E6dpcn iSSOGTUr668iBmiTy4UjjBeVItojDkgKPlOOD3YMOPNNUU2oRjd6u7u9E3vYE7Ln0u3ZeRE13/wl +j3k94kf9oab5cqzxxpG0sLtsdJBGqqdn3lO0dhx8xZ29jOKi3ySurN3s+lr9xX54tvddfIv6/rR 0PTdPgsoYElvNOj8+Yxq0jRkMoRSR8uSXLNjcSV5G2phDnlNybtGbsr6X7lSfKopJax1Z5PXecx7 /LqMP/Es0W+OLLVdHtYWP/POYAmCUZ4BDkD0yVLcLXkqL9+pD4oVJP1XVfcdl/hg9nFL59Dk7TTp tI0DXrK4G2WCazRvQ4uFwR/ssMMp7gg10OSlUpSjs1L8jNLljNPo1+ZYlTUPC1nZw6Nal5rm3S5u rj7L55YzZKwbmjcKkaABlXGS2cg5JlctWUnUlZJtRXNa1uu63HrBJQW6u3a+/Q0INKhTxLpV19nW 3XUYfOltym1EmEciyqsbD5VLAMAR1Jx7Q5P2VSN78rsnfdXVtR29+Lta628zz/wzGr+ILVGAZTeI CpAII8zoR0x7V11NKcrfy/oYx+Jep2WiyQWDa/dSQRT/AGWRWjjkUFAwuZAnHHyhtpZQRuUbehrn nd+yim1datf4Vc1jZc7tt/mUNO1EyWl74pv44bi7R4bW2Vo1ESyMvzOYwApKRgFOOuc8nIqUbONC DajrJ6627X82JPR1Hvol2Cw1J/Gtne2upJE95a2z3dtcJGkUg8kgvC3lqoZXU4UEcHJJztIJR9hK MoXUW1Fq7a12eoJ86ae6V0/ToXZ9dfQvDOkPbJH9qkN6I5pI0kMSLckybFcMgZyY/mKkhVIGM1Kh z1aibfKuW6TtfTS9vmPm5YRtvr8tTC1tl17Ro9caOOO8hujaXBiQIJg0fmxysqgKGHKMVxk8kYwB rD93N0k3yuPMr9NbNES96PP1vZnSaloltqXi1bWZQlpHBFNMqjaCkdssjD5cY3sAGIwcEkHNYxm4 0br4rtL1crFuKc7dLX/AydP8bNqN8lleW9sdKuJBF9lEMaiFHbaGSRVEgdM7t27k5wFyCNJUeWLl FvnSve71a7razEp3dmly9rbE+n6b/Y6+IbEHIt4Cik9Som+Un3K4J96mUuf2Mu7v+AJcvOuy/Up2 9yPCOi2t9apGdQ1N5mE0iLIYYYWEYWNXBUM7fNuwcjIPRdtNe1qSjJvkhbRO129dbdhL3Iprd3+S QmouvijRG1V4401CyuEimeJFjE8UwOxnRAF8wSfLkADGfUAOP7qp7NN8kk2k9bNb28rA/ejzdU9f O5e1/XpPB93/AGLpkduILRIln3wRyG6kKK8jSs6l8EttAUrgD5TjAEQgqsfaTbu72s2uVXsrWHKX I+SNrLfTc6LUJLGDxFpkSwpHZX+mQwmMKuAtwZlj5/vBvLG/72B1rGKk6U3d80Zt39LX/Ut2U4q2 jil99zifB+kraa1Mb5Q8WkJczTqRwfs4KAc8cyFSAc5x0rqqyvTXLvOyXz/4BlBWk7/Zu38jhbid rmV5mwGkZnIAwMsSTgdhk8CupKyS7GW+p6BZTr4V0ODU4I421DUpphFNIiyeTDAQjeWrgqHaQ8sQ flOCK5Gva1HBt8sUrpaXb726WNl7kVJbu/ySJpL86/pL608cS6npFxbs8qRoomilbCebGqiNmWVe u0AqNuOTS5fZzVJN8k09LvRrez32C/NHm+1FosfELW7meDT7Z/L8u4020uHxFGG8xvMztYKGROOE UhB2FTQgk5tXupyS1e2n9XHUk/dX91M4bw3rJ8PajBqKrv8AIYkrnGVZWRwD2JRjg9jiuqpD2kXD a/8Aw5lF8rT7HaS+ErHxBK1z4avY3kdjILO4/dToc7iqE8Sbex6ADmRjzXMqsqa5a0XbbmWq+fb+ tDXkUtab+T0ZqeEtc1SGbV4b4g3ENhd3D+bGjSedEsMah2K5ZAqgeWSY2xnBzmoqwham4bOUUrN2 s7v7/PcqEmuZPezfz0Kun62bDw7LrBhglvZNVdEd4kKxNJbI5dUAC8BWCLjapbOOxcoXqqndqKhq r72bEpWg5WV+b9BIGvtN0yLVrC3M+pavNcTSTrbibyI1kK7ETY0cZlcs/wB37oxjhdrfLKbpzdoQ SSV7Xduut3YNYpSitZN62vb/AIcTVdOhvjpOp6nD9ia7nNvfLsMCtskXEuzClDJGT5jAAcZGMURk 4+0hB3srx1vutvk9hNX5ZSVruz6G3rd/qWg6g8Oo2KP4fDsgiitYjH5HRHWVQCswXDfNIvz5A28E ZQjGcU4Saq93J3v6dvkXJuLs17np0/zMjwnaE6XfXmgxLPqUdyFj81I3ljtCAVdEfcnmMdwbAbO0 gZwK0qu04xqu0La2uk5edtbEwWjcFrf8DmvEurf2haxx6lbNb6zDKwkk8lYBLAV4MijYfNVxgYjC 7cndk4ranHlbcHem1or3s/Lyt5mcndWkrSXlbQ4muoyCgDv/AA28Wo6bPpRcRTOxZM987SMeuGXD Ac4ORX5rnUauBzDD5zGm6lGEVGfL9m3Ond9LxneLejaaZ9VgHDEYapgXJRnJtxv1va1u+q1S1tqa Wj6T/wAIoJby+lj5TaqoSc87uNwUliQAAAfc15OZZh/rG6OBy6jU0nzSlNJct043fK5JRSbbk2tk kjswuG/svnxGJnH4bJRb11vpdJtuyskjH8LX0MyXGm3LCNboEqSQBuYbWGTxuxgr6kEdcCvdz7C1 qU8NmmFi5ywzippJt8sWpRdlry35lK2yae12vPy6tCcauDrNRVW7i/NqzXrs16F7S/C50ac3moSR CCENjkkMSCBkMAOhJAGSWwAO9efj89WaUFgcsp1vb1XG+iTik02ouMm3dpJt8qUbttbHTh8v+p1P rGKlD2cE7ebaa1TS6O9tXe1iPwtcreaxcTxjajxOVGMYXzIgvA4HAGfetM+oSwuUYXD1HzThVpqT u3eXsqrlq9Wrt28iMuqKrjatSKtFwlZbWXPC2noUNR8HXkQmui8JRfMlIDPu2jLYA8vGce+M969H B8R4So6GDUK3PL2dJNxp8vM+WF2/aX5b9bXt06HNXyutH2ldyp8q5p2vK9leX8tr28/mbOqajL4Y srW0ttqylSXyM47tx7uxwfY14WBwVLiDGYzGYrmdFSShytxvq1HX+7Tirr+8j0MRXnllChQo2U7X ldX83p5yb+4hkun8R6LK8uDPbPv4GOF5zj/rmzj6it4YeGQ5zRp0bqhiIcmrvrJ2tf8A6+Rg/JSM 5VJZhgZynb2lOXNpptre3+FyXqjziv1Q+QPVdc0ufxtBbavpIFxOsEcF5ArKJY5Y/l8zaSCUkBGC M4AGf4tvBCSoOVOportxfRp9PVHRJc6Uo72s0Vri2h8G6ZFFdJC+sS3cdxtwjyW8ERDBWkG4o0jq MqDyrHP3SKpN1ptxb9motdUm35eQrezVnbmvf0SOi1e01/V7/wC26Ne3LaXeESJMl26RW6kAyLIv mL5XlHdlMdAABuyBjF04R5akVzx0ty6vtbTW5o1Ju8W+V+exmWM0E91rz2sstzCNPlVJp5DLJIE8 tNxc8sCVOz0TaO1W00qSkknzrRKyV79Pz8yVa87aq3Uwfh//AMfN7/2DLz/0Fa1r7R/xxIp7v/Cw /wCZN/7jP/tlR/y//wC4f/twf8u/+3v0Ov1k694kli1Tw/c3D211HEJI4blo1tp1QLIjp5iCMZG/ dja2S3cE88PZ0k4VYpNN2bje66NO2po+aXvQbs+z2ZDpbxHxFNFDcy33k6fPG88spl3yLCTJ5btz 5YckKORwSCQc05X9km0o3mnZK1lfS/mC+J2d9Hr8jxmvSOU734f/APHze/8AYMvP/QVrkr7R/wAc Tanu/wDCyzpNsfE2gnSbUqb+yuWuIoSwUywyJtcR5IDOrDcRnOMAckClJ+yqe0fwyVm+zT0uNLmj yrdO9vIlTTpfCGi3o1HEN5qYjgggLKZBEr7pZHUE7VI+Vc4OR7ilzKrUjyaxhdt9L9EgtyRd9G9E ij41/wCPbSP+wZD/AOhPVUd6n+NintH/AAo4KusxPRfHpxDo5H/QJtf5Vx0N6n+ORvU+z/hR2N5r EWv+D7y+P/H9ttILs9C7Q3EflyEero/LeoKjhK5lB068Y/Z95x8rp3Ro3zU2+uifyZmXMus+IbGy uvD1xcHybeO1ubeC4aNo5YsqJGjDoNsq4IcZAxgkdBolTpylGslq3KLavdPpe3QXvSScG9rNJ7Mh ivItJ8Raat5dy3UtuqrdyzTmaOKeVWVo0diQqRll3ncQDnOCCA7OVKfLFJP4UlZtLq/N9BX5Zxu7 2316hoPgjUtP16K4u0WG1hulcTs67JMvmNY8NlmlOAoAyM5bGDROtGVNqOrcduq739AjBqSb2vuV oP8Aj38S/wC+n/pW9N70fT/21C6T/rqZnhhF1rSbzQUZUu2liurVXYKJXQFJIwxwN5TGwE4J9ACa 0qfu5xq/Zs4y8k9n95Mfei4dd0X9K0m48G2d7qGqqLaW4tZLS1hZl8yR5sBpAgJ+SMDJLcHPrjMS kq0owp6pSUpPokunzKSdNNy00sl6lm48P3OveGNKFiBLcW5vCYAyiRo5LlgzqrEFgjIoYLkjeDjF SpqnVqc2ifLr0uo7fO4+VyhG26vp8zK1u3bw5oMWjXJC31zdG7liDKxijWPy41fBIDOTvAHOAQcd DpB+0qOpH4VHlT7u93b02JkuSPK927+h02panBpXjAPdnZby28UErdNqy2qpuJ7AMVJPYAmsYxcq Fo7ptr5SLbUamu1rfejA03wDfWOopNehItOtpFlkuzInktCjBsowbJMgACgDcC3IGDWsq8ZRajfm aso21Tff0IVNp67Lr0sW7LUl1f8A4SG9T7k8BdM8HYZvkyPXbjPvUuPJ7GPZ/oNO/O/L9SnHYSeL dCtINPxLfaU06SQblWR4ZXEiyIpIDBD8hA+bue2a5lRqSc9Izs0+ia0s/UVueKUd1fTyYl7bv4S0 JrC6KpqF/cxStCGVmighBZGkwWCs0hyF6levIZQJ+1qc0fgimr7Xb7fIGuSPK929uyRd8TeG7vxV ejWtIQT2t+kbswdAIJFjVZUmywKbCu4k8YPBOKmnUjRj7Kpo436bq+jQ5RcnzR2f4epW+JIWzvdP Fq4ZYdMtfKkToQjzbHX2IAYVWH1jPmW85XXyQqmjjb+VfqbniyWG00y41WAgP4jNrhRwUSOMSXH5 zYVx7jt1ypJuapvalzfO7svwLnonJfat/wAH8TxavSOU9ItbJ/FegW9lY4kvtKln/wBH3BXkgnIk Z4wSA+1xgjkgfVQ3E37Ko5S0jNLXomtLP5G6XPFJbxvp5MLqyfwnoFxZXuI77VZYP9H3BnjggJkV 5ACQm5zgDgkfRgon7WopR+GCevRt6WXyC3JFp7u2nkip48/5hX/YGsv/AGrVUf8Al5/18l+gp/Z/ wowvCw09tTgTV1DWbsUkyzIF3KQjFkZSqq5Usc4C5J4rWpzKD9n8XT+vQiNrrm2N9Ph9rdtdgRJt iRwy3gkQQhAciYPu4AHzY+92xmsvb03HV6/y2d/Sxfs5J6ff09Trm1K31bW9eubQhoTo90odejlI 4EZgRwQWU7SOCuD3rn5XCnSjLf2kdO122a3TlNr+V/ocf/zJv/cZ/wDbKuj/AJf/APcP/wBuMv8A l3/29+huafcalrWhWtvoc80d5pzSpPbwzNE8kUj745QqsocISUPVgTnGOTlJQp1JOqlyys02rpNa NeV9y1dxSg9Ve6TsVdVshu0/Tdevrg3UsjtdiS4M6WiudsIIZmVXPDSHd8q8njBNRfxzpRXKl7tl Zy7/AC7Ca2jNu/XW9ux0XhnT/Enh++WG4c/2PGx82SaVGtTb8/OhZiFDLgqF2nJAcY3VjUlSqRul +86JJ81y4qcHZ/D+FjlbbQ21Dz9R8MSyC5hu5gLeNxFItqzExSR/MrMuMKyjnI6YFdDny2hXSs4r Vq65uqZmo3u6e6b020NPxfPdnQbeHXyp1ZbkmIEoZhamPky7Om5+OcFtqk5Ias6SXtG6PwW13tzX 6fIqd+VKfxX+djyWvQOYKACgBWYvyxJPvzUqKjpFJLyVhtt7u4lUIUsSACSQOnt9KlRUbtJJvey3 9R3ez6CVQgoAKACgAoAcjtGdyEqfUHB/SkMbTEODsoKgkA9Rng46ZFIY2mIKACgBwdkBCkgHg4OM /WkMbTEFABQAAlTkcEUAKzFzliST3PJpbAJTAKACgAoAcrsn3SVyMHBxwe30pDG0xDi7MApJIXoM 8DPXA7UthjaYgoAc7tIcsSx9Scn9aW2wxFYocqSCOhHBoEBJY5PJPJJpgJQA4uxUJk7RyBngfh0p DG0xCqxQ5UkEdCODSACSxyeSeSTTA9FvP+Ef1xxeLdnSGkSMT2i2jtHvRQrNF5OI9rYyAwU7iWPJ IHGvaU/d5ee17S5rOz731N3yy1vy91b8rGD4t1mDWLqL7Grra2dtFaQ+Zje0cIOGYDgElj3PGM4P A1pQcE+bdtydtrsibUnpslZfIs+LtVtrxbOx09/NtbC1SMNtdQ0z/NO4V1VhuYL26gkZFTSi480p qzlK/wAumw5taKOyX49Tjq6TIVWKHKnBHQjg0ADMWOSck9SetGwCUAFADvMbbsydvXGTjPrjpSGN piCgBVYocqSCOhHBpAJTAcXYqEydo5AzwPw6UhiKxQ5UkEdCODQICSxyeSeSTTASgAoAKACgAoAK ACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoA KACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAP//Z --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_-- ========================================================================Date: Tue, 15 Feb 2011 17:29:28 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a163abc1b8049c5581a3 Message-ID: <[log in to unmask]> --20cf3054a163abc1b8049c5581a3 Content-Type: text/plain; charset=ISO-8859-1 Dear John (and other knowledgeable types), I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a functional MRI timeseries. That worked fine - the anatomical images that I additionally warped in this way look great - but I now cannot do anything with the timeseries. It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI data can result in massive files which are problematic (and thus a work around is to apply the warp to contrast images), but I didn't expect the segment deformation field to do the same. Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5 x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake somewhere? so I'm supposing my inability to use the warped timeseries is a memory issue - I am running a 64-bit machine with 8Gb memory (I can see all this being used in the task manager so doesn't seem to be a matlab version problem) but maybe this file is just way too big for the SPM platform.....???? I could crop the image, etc I suppose but maybe the biggest problem is the shear number of volumes. Thanks in advance for your thoughts Richard On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo < [log in to unmask]> wrote: > Thanks John. > > It turns out the problem was due to the version of Matlab I was running. > > Matlab2007 was only running 32bit, whilst the 2010 version is 64. > > Having set up with Matlab2010, the new segmentation with DARTEL import now > runs without any problem, without me having to alter/crop any of the images. > > All the best > William > --20cf3054a163abc1b8049c5581a3 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Dear John (and other knowledgeable types),

I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a functional MRI timeseries. That worked fine - the anatomical images that I additionally warped in this way look great - but I now cannot do anything with the timeseries.

It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI data can result in massive files which are problematic (and thus a work around is to apply the warp to contrast images), but I didn't expect the segment deformation field to do the same.

Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5 x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake somewhere?

so I'm supposing my inability to use the warped timeseries is a memory issue - I am running a 64-bit machine with 8Gb memory (I can see all this being used in the task manager so doesn't seem to be a matlab version problem) but maybe this file is just way too big for the SPM platform.....???? I could crop the image, etc I suppose but maybe the biggest problem is the shear number of volumes.

Thanks in advance for your thoughts

Richard

On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo <[log in to unmask]> wrote:

Thanks John.

It turns out the problem was due to the version of Matlab I was running.

Matlab2007 was only running 32bit, whilst the 2010 version is 64.

Having set up with Matlab2010, the new segmentation with DARTEL import now runs without any problem, without me having to alter/crop any of the images.

All the best
William

--20cf3054a163abc1b8049c5581a3-- ========================================================================Date: Tue, 15 Feb 2011 17:42:49 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: Richard Binney <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> You may wish to change the voxel sizes and bounding box in order to obtain slightly lower resolution versions (other than the default 1.5mm isotropic with a large bounding box). See the email I sent out recently. Best regards, -John On 15 February 2011 17:29, Richard Binney <[log in to unmask]> wrote: > Dear John (and other knowledgeable types), > > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a > functional MRI timeseries. That worked fine - the anatomical images that > I additionally warped in this way look great - but I now cannot do anything > with the timeseries. > > It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI > data can result in massive files which are problematic (and thus a work > around is to apply the warp to contrast images), but I didn't expect the > segment deformation field to do the same. > > Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5 > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake > somewhere? > > so I'm supposing my inability to use the warped timeseries is a memory > issue - I am running a 64-bit machine with 8Gb memory (I can see all this > being used in the task manager so doesn't seem to be a matlab version > problem) but maybe this file is just way too big for the SPM > platform.....???? I could crop the image, etc I suppose but maybe the > biggest problem is the shear number of volumes. > > Thanks in advance for your thoughts > > Richard > > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo > <[log in to unmask]> wrote: >> >> Thanks John. >> >> It turns out the problem was due to the version of Matlab I was running. >> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. >> >> Having set up with Matlab2010, the new segmentation with DARTEL import now >> runs without any problem, without me having to alter/crop any of the images. >> >> All the best >> William > > ========================================================================Date: Tue, 15 Feb 2011 17:36:09 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: Question regarding FIR and contrasts Comments: To: "Claire (Jung Eun) Han" <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016363b959676d552049c59ca34 Content-Type: text/plain; charset=ISO-8859-1 Hi Claire > There are two conditions A and B I would like to compare. > > In fMRI model specification, I chose to use the Finite Impulse Response > such that my design has 9 three-second-bins for each condition. > > I would like to compare the two conditions in each time bin...is it > possible to do this at once? Which of the t- or f-test should I be using and > what should I put for contrasts? > Two ways to compare A and B in your design are: 1) Use an F test, where each row of the F test compares A vs. B at one time point. For example: 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1 or alternatively, use some Matlab functions: [eye(9) 0-eye(9)] The trick is that if you get a significant difference, you don't know which time bin caused it, or which direction the difference was in (A>B or B>A), so you would need to investigate further to find out (e.g., plotting effects for each region). 2) A simpler approach is to simply look at the total "area under the curve" for each condition in a t-test. This effectively discards the timing information: [1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1] for example would give you the sum of condition A compared to the sum of condition B (i.e., A > B). Alternatively you could write this [ones(1,9) ones(1,9)*-1]. Because this is a t-test it can give you information about directionality. However, you lose the temporal detail, which may be one of the reasons to choose an FIR model in the first place. Ultimately it will depend on what you are most interested in. There is some discussion of FIR models in Chapter 30 of the SPM manual (Face Group fMRI Data), which may also be of interest. Hope this helps, Jonathan -- Dr. Jonathan Peelle Department of Neurology University of Pennsylvania 3 West Gates 3400 Spruce Street Philadelphia, PA 19104 USA http://jonathanpeelle.net/ --0016363b959676d552049c59ca34 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi Claire

There are two conditions A and B I would like to compare.

In fMRI model specification, I chose to use the Finite Impulse Response such that my design has 9 three-second-bins for each condition.

I would like to compare the two conditions in each time bin...is it possible to do this at once? Which of the t- or f-test should I be using and what should I put for contrasts?

Two ways to compare A and B in your design are:

1) Use an F test, where each row of the F test compares A vs. B at one time point. For example:

1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1

or alternatively, use some Matlab functions:

[eye(9) 0-eye(9)]

The trick is that if you get a significant difference, you don't know which time bin caused it, or which direction the difference was in (A>B or B>A), so you would need to investigate further to find out (e.g., plotting effects for each region).

2) A simpler approach is to simply look at the total "area under the curve" for each condition in a t-test. This effectively discards the timing information:

[1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]

for example would give you the sum of condition A compared to the sum of condition B (i.e., A > B). Alternatively you could write this [ones(1,9) ones(1,9)*-1]. Because this is a t-test it can give you information about directionality. However, you lose the temporal detail, which may be one of the reasons to choose an FIR model in the first place.

Ultimately it will depend on what you are most interested in. There is some discussion of FIR models in Chapter 30 of the SPM manual (Face Group fMRI Data), which may also be of interest.

Hope this helps,

Jonathan

--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/

--0016363b959676d552049c59ca34-- ========================================================================Date: Tue, 15 Feb 2011 18:58:35 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Fwd: Urge your Representative to Oppose Science Funding Cuts Comments: To: [log in to unmask], Gazz Lab <[log in to unmask]> In-Reply-To: <12.37.01488.7160B5D4@dc1bhmta04> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba6e8e7cc867ec049c5d7622 Message-ID: <[log in to unmask]> --90e6ba6e8e7cc867ec049c5d7622 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable ---------- Forwarded message ---------- From: Society for Neuroscience <[log in to unmask]> Date: Tue, Feb 15, 2011 at 2:55 PM Subject: Urge your Representative to Oppose Science Funding Cuts To: Michael Rubens <[log in to unmask]> [image: Society for Neuroscience banner graphic] Urge Your Representative to Oppose $2.4 Billion in Proposed Cuts for NIH and NSF SfN urges members to contact their representatives *today* to support National Institutes of Health (NIH) and National Science Foundation (NSF) funding. The House of Representatives is set to debate and vote *this week*on legislation to reduce FY2011 funding for science, including a $1.6 billion cut for NIH and a $826 million cut for NSF. Such cuts, proposed by the House Republican leadership, would reduce the NIH and NSF budgets to FY2008 levels. Contact your representatives *now* through the SfN Legislative Action Centerand ask them to *oppose cuts* that will delay treatments, eliminate jobs, and jeopardize Americaâ€™s standing as a global leader in scientific discovery. House votes are expected Thursday â€“ contact your member of Congresstoday! * Please urge five colleagues to add their voices* in opposition to cuts that will hurt science and hurt America. Society for Neuroscience | 1121 14th St NW | Suite 1010 | Washington | DC | 20005 This message was sent to: [log in to unmask] as part of an established business relationship with the Society for Neuroscience. You were added to the system August 5, 2010. For more information click here[log in to unmask]&cid=963dd964631f5b9b1df45416d6fa1fbc> . -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --90e6ba6e8e7cc867ec049c5d7622 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

---------- Forwarded message ----------
From: Society for Neuroscience <[log in to unmask]>
Date: Tue, Feb 15, 2011 at 2:55 PM
Subject: Urge your Representative to Oppose Science Funding Cuts
To: Michael Rubens <[log in to unmask]>

Urge Your Representative to Oppose $2.4 Billion in
Proposed Cuts for NIH and NSF

SfN urges members to contact their representatives today to support National Institutes of Health (NIH) and National Science Foundation (NSF) funding. The House of Representatives is set to debate and vote this week on legislation to reduce FY2011 funding for science, including a $1.6 billion cut for NIH and a $826 million cut for NSF. Such cuts, proposed by the House Republican leadership, would reduce the NIH and NSF budgets to FY2008 levels.

Contact your representatives now through the SfN Legislative Action Center and ask them to oppose cuts that will delay treatments, eliminate jobs, and jeopardize Americaâ€™s standing as a global leader in scientific discovery.

House votes are expected Thursday â€“ contact your member of Congress today!Â Please urge five colleagues to add their voices in opposition to cuts that will hurt science and hurt America.

This message wasÂ sentÂ to: [log in to unmask] as part of an established business relationship with the Society for Neuroscience.

You were added to the system August 5, 2010. For more information [log in to unmask]&cid=963dd964631f5b9b1df45416d6fa1fbc" target="_blank">click here.

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--90e6ba6e8e7cc867ec049c5d7622-- ========================================================================Date: Wed, 16 Feb 2011 14:57:13 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Warning in spm_eeg_convert2scalp (griddata) & spm_eeg_crop Comments: To: Panagiotis Tsiatsis <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Panagiotis, Sorry for the late reply. I only now found time to look into it more carefully. On Tue, Feb 8, 2011 at 2:46 PM, Panagiotis Tsiatsis <[log in to unmask]> wrote: > Hello dear Vladimir and thanks for your response > > First of all, apologies in advance for the long e-mail; I hope that it will > be at least a bit important for other users of SPM, too > > It seems that the problem appeared after I used spm_eeg_crop to drop my > continuous CTF meg data file straight after reading it in and before > applying filtering / downsampling. Without spm_eeg_crop everything works > fine. > > [A small recap : > As explained in a previous e-mail of mine, the logic behind this is that the > start and the end of a continuous MEG files might be contaminated by > artifacts (i.e due to subject's movement at the end of the session if she > for whatever reason realized that the session is over and started moving > before you press 'stop recording' ) or / and zero values because of > padding added by the CTF software. All this should affect any subsequent > filtering function if they are not removed (even the padding because of the > sudden jump from values of order 10^-12 to zero!!!!) so I decided to crop > the edges of my continuous file before continuing. ] > > I remember that when you send me the spm_eeg_crop file you told me that it > has not been tested for continuous data but it should in principle work. > > I call it by using the following code : > > Time_Offset_in_ms = 3000; % in ms > S = []; > S.D = D; > S.timewin = [(Time_of_First_Trial_Onset_in_ms - > Time_Offset_in_ms) (Time_of_Last_Trial_Onset_in_ms + Time_Offset_in_ms)] > S.freqwin = []; > S.channels = 'all' > > D = spm_eeg_crop(S); > D.save; > > > The problem seems to appear in spm_eeg_crop when applied on a continuous > file to and more specifically somewhere in the creation of the new object: > > Dnew = coor2D(Dnew, [], coor2D(D, chanind)); > > as some channels do indeed have the same coordinates : > >>> size(coor2D(D, chanind)') > > ans = > > 337 2 > >>> size(unique(coor2D(D, chanind)','rows')) > > ans = > > 335 2 > > > The above number shows that spm_eeg_crop indeed puts in the coor2D matrix > (or tries to put) the coordinates of all available channels. Up to now (and > correct me if I am wrong) I knew that the coor2D matrix contains the > coordinates of the MEGGRAD channels only. So I modified the code as follows > : > > MEG_Channels = (D.meegchannels('MEG')); > Dnew = coor2D(Dnew, [], coor2D(D, MEG_Channels)); > > > *And indeed the conversion to images of subsequent files was executed > without warnings*. So, is this a bug or am I going the wrong direction here? > I tried this on my CTF dataset and I don't get identical 2D coordinates. So this is something that happens for your data and I'm not sure why. If you send me an example I can look. In general this code should work as it is because 2D coordinates are defined for all channels (for non-scalp channels these are just points on a square grid). > A couple more issues / clarifications : > > 1. The statistics at the end of the analysis (assuming all other equal) were > slightly different depending on whether I cropped the start and the end of > the continuous initial files that in many cases had large artifacts or zero > values. I suppose this is because the subsequent filtering applied on the > data was "smoother", in the sense that the filters did not have to > compensate for the artifacts or the jumps because of the zero padding. Does > this train of thought sound logical to you? Yes that's a reasonable explanation, although how reasonable it is depends on how far your trials were from the edges and how low was your high-pass cutoff. It's not that the filters 'compensate' for something it's just that due to filtering artefacts from the edges can contaminate data slightly away from the edges. > > 2.The cropped file (where only a few non-trial seconds from the start and > from the and are missing) contains exactly the same number of events as the > original continuous file. Why??? It seems that some zombie events are > retained after cropping! > > That's indeed a bug, due to the fact that I wasn't planning this function to be used on continuous data. I now fixed it and it'll be in the next update. > 3. Please note that there appears to be one more problem with spm_eeg_crop > and continuous data : > > The line Dnew = badchannels(Dnew, [], badchannels(D, chanind)); always > results in an error when there are no bad channels, i.e. when > isempty(D.badchannels) == 1. so i have substituted the above line with > > if isempty(D.badchannels) == 0 > Dnew = badchannels(Dnew, [], badchannels(D, chanind)); > end > I can't reproduce this problem either. The code should work also for no bad channels because badchannels(D, chanind) always returns a vector of 0/1 Best, Vladimir > > I suspect I should put an 'else' condition somewhere there, but of course > you are the expert on this :) > > Thanks again and all the best, > Panagiotis > > On 2/7/2011 8:10 PM, Vladimir Litvak wrote: >> >> Dear Panagiotis, >> >> Try doing: >> >> spm_eeg_load >> D.coor2D(D.meegchannels('MEG')) >> >> and look which of the channels have the same 2D coordinates. In >> principle this should not happen for CTF MEG. You can also look in >> 'Prepare' tool at the 2D channel layout an modify it if necessary or >> load a template layout from EEGtemplates. >> >> Best, >> >> Vladimir >> >> On Sun, Feb 6, 2011 at 5:23 PM, Panagiotis Tsiatsis >> <[log in to unmask]> wrote: >>> >>> Dear All, >>> >>> When I am converting my CTF MEG epoched data to images, I am getting the >>> following warning: >>> >>> Warning: Duplicate x-y data points detected: using average of the z >>> values. >>>> >>>> In griddata at 107 >>> >>> In spm_eeg_convert2scalp at 204 >>> >>> My settings are: >>> >>> S = []; >>> S.n = 32; >>> S.Fname = Concatenated_File_Name_Time; >>> S.interpolate_bad = 1; >>> S.modality = 'MEG'; >>> >>> I also have EOG channels whose chantype has been set to 'EEG' during the >>> conversion (this of course should not interfere since I ve selected my >>> modality to be 'MEG'). >>> >>> By browsing the Mathwoks forum, I suspect that this happens when nearby >>> values get very similar (as is the case with MEG data). >>> >>> Anybody seen that before? Any intuitions? >>> >>> Thanks and best, >>> Panagiotis >>> >>> >>> -- >>> Panagiotis S. Tsiatsis >>> Max Planck Institute for Biological Cybernetics >>> Cognitive NeuroImaging Group >>> Tuebingen, Germany >>> > > > -- > Panagiotis S. Tsiatsis > Max Planck Institute for Biological Cybernetics > Cognitive NeuroImaging Group > Tuebingen, Germany > > ========================================================================Date: Wed, 16 Feb 2011 15:39:18 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054962583e57b049c681574 Message-ID: <[log in to unmask]> --20cf3054962583e57b049c681574 Content-Type: text/plain; charset=ISO-8859-1 Ah, yes. I couldn't find the email you were refferring to, but I've done it and it makes a huge difference. Pretty obvious really. One more thing, John (or anybody else for that matter) - is there now a way in which one can easily create an inverse of the DARTEL normalise-to-MNI deformation composition (so from MNI --> study average --> individual subject space)? Is it possible (i.e., is there script) to have this (forward and/or backwards) written out for each subject or, alternatively, to have the affine template-to-TPM transform outputted such that one can combine it with a flow field, and subsequently inverse the composition using the deformations tool? All the best, Richard On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>wrote: > You may wish to change the voxel sizes and bounding box in order to > obtain slightly lower resolution versions (other than the default > 1.5mm isotropic with a large bounding box). See the email I sent out > recently. > > Best regards, > -John > > On 15 February 2011 17:29, Richard Binney <[log in to unmask]> > wrote: > > Dear John (and other knowledgeable types), > > > > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a > > functional MRI timeseries. That worked fine - the anatomical images that > > I additionally warped in this way look great - but I now cannot do > anything > > with the timeseries. > > > > It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI > > data can result in massive files which are problematic (and thus a work > > around is to apply the warp to contrast images), but I didn't expect the > > segment deformation field to do the same. > > > > Does this sound about right for a timeseries with 465 volumes (voxel dim > 2.5 > > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake > > somewhere? > > > > so I'm supposing my inability to use the warped timeseries is a memory > > issue - I am running a 64-bit machine with 8Gb memory (I can see all this > > being used in the task manager so doesn't seem to be a matlab version > > problem) but maybe this file is just way too big for the SPM > > platform.....???? I could crop the image, etc I suppose but maybe the > > biggest problem is the shear number of volumes. > > > > Thanks in advance for your thoughts > > > > Richard > > > > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo > > <[log in to unmask]> wrote: > >> > >> Thanks John. > >> > >> It turns out the problem was due to the version of Matlab I was running. > >> > >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. > >> > >> Having set up with Matlab2010, the new segmentation with DARTEL import > now > >> runs without any problem, without me having to alter/crop any of the > images. > >> > >> All the best > >> William > > > > > --20cf3054962583e57b049c681574 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Ah, yes. I couldn't find the email you were refferring to, but I've done it and it makes a huge difference. Pretty obvious really.

One more thing, John (or anybody else for that matter) - is there now a way in which one can easily create an inverse of the DARTEL normalise-to-MNI deformation composition (so from MNI --> study average --> individual subject space)? Is it possible (i.e., is there script) to have this (forward and/or backwards) written out for each subject or, alternatively, to have the affine template-to-TPM transform outputted such that one can combine it with a flow field, and subsequently inverse the composition using the deformations tool?

All the best,

Richard

On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> wrote:

You may wish to change the voxel sizes and bounding box in order to
obtain slightly lower resolution versions (other than the default
1.5mm isotropic with a large bounding box). See the email I sent out
recently.

Best regards,
-John

On 15 February 2011 17:29, Richard Binney <[log in to unmask]> wrote:
> Dear John (and other knowledgeable types),
>
> I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a
> functional MRI timeseries. That worked fine - the anatomical images that
> I additionally warped in this way look great - but I now cannot do anything
> with the timeseries.
>
> It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI
> data can result in massive files which are problematic (and thus a work
> around is to apply the warp to contrast images), but I didn't expect the
> segment deformation field to do the same.
>
> Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5
> x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> somewhere?
>
> so I'm supposing my inability to use the warped timeseries is a memory
> issue - I am running a 64-bit machine with 8Gb memory (I can see all this
> being used in the task manager so doesn't seem to be a matlab version
> problem) but maybe this file is just way too big for the SPM
> platform.....???? I could crop the image, etc I suppose but maybe the
> biggest problem is the shear number of volumes.
>
> Thanks in advance for your thoughts
>
> Richard
>
> On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> <[log in to unmask]> wrote:
>>
>> Thanks John.
>>
>> It turns out the problem was due to the version of Matlab I was running.
>>
>> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>>
>> Having set up with Matlab2010, the new segmentation with DARTEL import now
>> runs without any problem, without me having to alter/crop any of the images.
>>
>> All the best
>> William
>
>

--20cf3054962583e57b049c681574-- ========================================================================Date: Wed, 16 Feb 2011 15:44:35 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf305641c971f168049c6828fc Message-ID: <[log in to unmask]> --20cf305641c971f168049c6828fc Content-Type: text/plain; charset=ISO-8859-1 ......I've just spotted that running normalise-to-mni now spits out a 'Template_6_2mni.mat' file. I don't recall seeing this before - would this happen to be the affine transform I spoke of? R On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney < [log in to unmask]> wrote: > Ah, yes. I couldn't find the email you were refferring to, but I've done it > and it makes a huge difference. Pretty obvious really. > > One more thing, John (or anybody else for that matter) - is there now a way > in which one can easily create an inverse of the DARTEL normalise-to-MNI > deformation composition (so from MNI --> study average --> individual > subject space)? Is it possible (i.e., is there script) to have this (forward > and/or backwards) written out for each subject or, alternatively, to have > the affine template-to-TPM transform outputted such that one can combine it > with a flow field, and subsequently inverse the composition using the > deformations tool? > > All the best, > > Richard > > On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>wrote: > >> You may wish to change the voxel sizes and bounding box in order to >> obtain slightly lower resolution versions (other than the default >> 1.5mm isotropic with a large bounding box). See the email I sent out >> recently. >> >> Best regards, >> -John >> >> On 15 February 2011 17:29, Richard Binney <[log in to unmask]> >> wrote: >> > Dear John (and other knowledgeable types), >> > >> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a >> > functional MRI timeseries. That worked fine - the anatomical images that >> > I additionally warped in this way look great - but I now cannot do >> anything >> > with the timeseries. >> > >> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp >> fMRI >> > data can result in massive files which are problematic (and thus a work >> > around is to apply the warp to contrast images), but I didn't expect the >> > segment deformation field to do the same. >> > >> > Does this sound about right for a timeseries with 465 volumes (voxel dim >> 2.5 >> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake >> > somewhere? >> > >> > so I'm supposing my inability to use the warped timeseries is a memory >> > issue - I am running a 64-bit machine with 8Gb memory (I can see all >> this >> > being used in the task manager so doesn't seem to be a matlab version >> > problem) but maybe this file is just way too big for the SPM >> > platform.....???? I could crop the image, etc I suppose but maybe the >> > biggest problem is the shear number of volumes. >> > >> > Thanks in advance for your thoughts >> > >> > Richard >> > >> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo >> > <[log in to unmask]> wrote: >> >> >> >> Thanks John. >> >> >> >> It turns out the problem was due to the version of Matlab I was >> running. >> >> >> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. >> >> >> >> Having set up with Matlab2010, the new segmentation with DARTEL import >> now >> >> runs without any problem, without me having to alter/crop any of the >> images. >> >> >> >> All the best >> >> William >> > >> > >> > > --20cf305641c971f168049c6828fc Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

......I've just spotted that running normalise-to-mni now spits out a 'Template_6_2mni.mat' file. I don't recall seeing this before - would this happen to be the affine transform I spoke of?

On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney <[log in to unmask]> wrote:

Ah, yes. I couldn't find the email you were refferring to, but I've done it and it makes a huge difference. Pretty obvious really.

One more thing, John (or anybody else for that matter) - is there now a way in which one can easily create an inverse of the DARTEL normalise-to-MNI deformation composition (so from MNI --> study average --> individual subject space)? Is it possible (i.e., is there script) to have this (forward and/or backwards) written out for each subject or, alternatively, to have the affine template-to-TPM transform outputted such that one can combine it with a flow field, and subsequently inverse the composition using the deformations tool?

All the best,

Richard

On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> wrote:

You may wish to change the voxel sizes and bounding box in order to
obtain slightly lower resolution versions (other than the default
1.5mm isotropic with a large bounding box). See the email I sent out
recently.

Best regards,
-John

On 15 February 2011 17:29, Richard Binney <[log in to unmask]> wrote:
> Dear John (and other knowledgeable types),
>
> I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a
> functional MRI timeseries. That worked fine - the anatomical images that
> I additionally warped in this way look great - but I now cannot do anything
> with the timeseries.
>
> It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI
> data can result in massive files which are problematic (and thus a work
> around is to apply the warp to contrast images), but I didn't expect the
> segment deformation field to do the same.
>
> Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5
> x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> somewhere?
>
> so I'm supposing my inability to use the warped timeseries is a memory
> issue - I am running a 64-bit machine with 8Gb memory (I can see all this
> being used in the task manager so doesn't seem to be a matlab version
> problem) but maybe this file is just way too big for the SPM
> platform.....???? I could crop the image, etc I suppose but maybe the
> biggest problem is the shear number of volumes.
>
> Thanks in advance for your thoughts
>
> Richard
>
> On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> <[log in to unmask]> wrote:
>>
>> Thanks John.
>>
>> It turns out the problem was due to the version of Matlab I was running.
>>
>> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>>
>> Having set up with Matlab2010, the new segmentation with DARTEL import now
>> runs without any problem, without me having to alter/crop any of the images.
>>
>> All the best
>> William
>
>

--20cf305641c971f168049c6828fc-- ========================================================================Date: Wed, 16 Feb 2011 15:58:16 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: Richard Binney <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> This file does indeed contain an affine transform, and is generated when the affine registration between population average and MNI space is estimated. There's no script to do what you're after yet, but if the demand is high enough I could introduce the option. Can I ask how you plan to use the transforms? Best regards, -John On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote: > ......I've just spotted that running normalise-to-mni now spits out a > 'Template_6_2mni.mat' file. I don't recall seeing this before - would this > happen to be the affine transform I spoke of? > > R > > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney > <[log in to unmask]> wrote: >> >> Ah, yes. I couldn't find the email you were refferring to, but I've done >> it and it makes a huge difference. Pretty obvious really. >> >> One more thing, John (or anybody else for that matter) - is there now a >> way in which one can easily create an inverse of the DARTEL normalise-to-MNI >> deformation composition (so from MNI --> study average --> individual >> subject space)? Is it possible (i.e., is there script) to have this (forward >> and/or backwards) written out for each subject or, alternatively, to have >> the affine template-to-TPM transform outputted such that one can combine it >> with a flow field, and subsequently inverse the composition using the >> deformations tool? >> >> All the best, >> >> Richard >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> >> wrote: >>> >>> You may wish to change the voxel sizes and bounding box in order to >>> obtain slightly lower resolution versions (other than the default >>> 1.5mm isotropic with a large bounding box). See the email I sent out >>> recently. >>> >>> Best regards, >>> -John >>> >>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]> >>> wrote: >>> > Dear John (and other knowledgeable types), >>> > >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise >>> > a >>> > functional MRI timeseries. That worked fine - the anatomical >>> > images that >>> > I additionally warped in this way look great - but I now cannot do >>> > anything >>> > with the timeseries. >>> > >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp >>> > fMRI >>> > data can result in massive files which are problematic (and thus a work >>> > around is to apply the warp to contrast images), but I didn't expect >>> > the >>> > segment deformation field to do the same. >>> > >>> > Does this sound about right for a timeseries with 465 volumes (voxel >>> > dim 2.5 >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake >>> > somewhere? >>> > >>> > so I'm supposing my inability to use the warped timeseries is a memory >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all >>> > this >>> > being used in the task manager so doesn't seem to be a matlab version >>> > problem) but maybe this file is just way too big for the SPM >>> > platform.....???? I could crop the image, etc I suppose but maybe the >>> > biggest problem is the shear number of volumes. >>> > >>> > Thanks in advance for your thoughts >>> > >>> > Richard >>> > >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo >>> > <[log in to unmask]> wrote: >>> >> >>> >> Thanks John. >>> >> >>> >> It turns out the problem was due to the version of Matlab I was >>> >> running. >>> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. >>> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL import >>> >> now >>> >> runs without any problem, without me having to alter/crop any of the >>> >> images. >>> >> >>> >> All the best >>> >> William >>> > >>> > >> > > ========================================================================Date: Wed, 16 Feb 2011 17:48:33 +0100 Reply-To: michel grothe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: michel grothe <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi, I haven´t seen such a file in my version of SPM8 (the penultimate update, run on win32). Is it possible that this file is just generated during estimation of the affine transform and not stored permanently on disk? Or does it come with the latest update? It would be very helpful to have such a file, mainly for the reasons Richard mentioned, i.e. use it to compose the complete transform 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used to warp Atlas-labels in MNI space to native space. Best regards, Michel > Date: Wed, 16 Feb 2011 15:58:16 +0000 > From: [log in to unmask] > Subject: Re: [SPM] New Segmentation Out of Memory Error > To: [log in to unmask] > > This file does indeed contain an affine transform, and is generated > when the affine registration between population average and MNI space > is estimated. > > There's no script to do what you're after yet, but if the demand is > high enough I could introduce the option. Can I ask how you plan to > use the transforms? > > Best regards, > -John > > > On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote: > > ......I've just spotted that running normalise-to-mni now spits out a > > 'Template_6_2mni.mat' file. I don't recall seeing this before - would this > > happen to be the affine transform I spoke of? > > > > R > > > > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney > > <[log in to unmask]> wrote: > >> > >> Ah, yes. I couldn't find the email you were refferring to, but I've done > >> it and it makes a huge difference. Pretty obvious really. > >> > >> One more thing, John (or anybody else for that matter) - is there now a > >> way in which one can easily create an inverse of the DARTEL normalise-to-MNI > >> deformation composition (so from MNI --> study average --> individual > >> subject space)? Is it possible (i.e., is there script) to have this (forward > >> and/or backwards) written out for each subject or, alternatively, to have > >> the affine template-to-TPM transform outputted such that one can combine it > >> with a flow field, and subsequently inverse the composition using the > >> deformations tool? > >> > >> All the best, > >> > >> Richard > >> > >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> > >> wrote: > >>> > >>> You may wish to change the voxel sizes and bounding box in order to > >>> obtain slightly lower resolution versions (other than the default > >>> 1.5mm isotropic with a large bounding box). See the email I sent out > >>> recently. > >>> > >>> Best regards, > >>> -John > >>> > >>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]> > >>> wrote: > >>> > Dear John (and other knowledgeable types), > >>> > > >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise > >>> > a > >>> > functional MRI timeseries. That worked fine - the anatomical > >>> > images that > >>> > I additionally warped in this way look great - but I now cannot do > >>> > anything > >>> > with the timeseries. > >>> > > >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp > >>> > fMRI > >>> > data can result in massive files which are problematic (and thus a work > >>> > around is to apply the warp to contrast images), but I didn't expect > >>> > the > >>> > segment deformation field to do the same. > >>> > > >>> > Does this sound about right for a timeseries with 465 volumes (voxel > >>> > dim 2.5 > >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake > >>> > somewhere? > >>> > > >>> > so I'm supposing my inability to use the warped timeseries is a memory > >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all > >>> > this > >>> > being used in the task manager so doesn't seem to be a matlab version > >>> > problem) but maybe this file is just way too big for the SPM > >>> > platform.....???? I could crop the image, etc I suppose but maybe the > >>> > biggest problem is the shear number of volumes. > >>> > > >>> > Thanks in advance for your thoughts > >>> > > >>> > Richard > >>> > > >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo > >>> > <[log in to unmask]> wrote: > >>> >> > >>> >> Thanks John. > >>> >> > >>> >> It turns out the problem was due to the version of Matlab I was > >>> >> running. > >>> >> > >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. > >>> >> > >>> >> Having set up with Matlab2010, the new segmentation with DARTEL import > >>> >> now > >>> >> runs without any problem, without me having to alter/crop any of the > >>> >> images. > >>> >> > >>> >> All the best > >>> >> William > >>> > > >>> > > >> > > > > --_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi,
I haven´t seen such a file in my version of SPM8 (the penultimate update, run on win32). Is it possible that this file is just generated during estimation of the affine transform and not stored permanently on disk? Or does it come with the latest update?
It would be very helpful to have such a file, mainly for the reasons Richard mentioned, i.e. use it to compose the complete transform 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used to warp Atlas-labels in MNI space to native space.

Best regards,
Michel

> Date: Wed, 16 Feb 2011 15:58:16 +0000
> From: [log in to unmask]
> Subject: Re: [SPM] New Segmentation Out of Memory Error
> To: [log in to unmask]
>
> This file does indeed contain an affine transform, and is generated
> when the affine registration between population average and MNI space
> is estimated.
>
> There's no script to do what you're after yet, but if the demand is
> high enough I could introduce the option. Can I ask how you plan to
> use the transforms?
>
> Best regards,
> -John
>
>
> On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote:
> > ......I've just spotted that running normalise-to-mni now spits out a
> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would this
> > happen to be the affine transform I spoke of?
> >
> > R
> >
> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> > <[log in to unmask]> wrote:
> >>
> >> Ah, yes. I couldn't find the email you were refferring to, but I've done
> >> it and it makes a huge difference. Pretty obvious really.
> >>
> >> One more thing, John (or anybody else for that matter) - is there now a
> >> way in which one can easily create an inverse of the DARTEL normalise-to-MNI
> >> deformation composition (so from MNI --> study average --> individual
> >> subject space)? Is it possible (i.e., is there script) to have this (forward
> >> and/or backwards) written out for each subject or, alternatively, to have
> >> the affine template-to-TPM transform outputted such that one can combine it
> >> with a flow field, and subsequently inverse the composition using the
> >> deformations tool?
> >>
> >> All the best,
> >>
> >> Richard
> >>
> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
> >> wrote:
> >>>
> >>> You may wish to change the voxel sizes and bounding box in order to
> >>> obtain slightly lower resolution versions (other than the default
> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
> >>> recently.
> >>>
> >>> Best regards,
> >>> -John
> >>>
> >>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
> >>> wrote:
> >>> > Dear John (and other knowledgeable types),
> >>> >
> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise
> >>> > a
> >>> > functional MRI timeseries. That worked fine - the anatomical
> >>> > images that
> >>> > I additionally warped in this way look great - but I now cannot do
> >>> > anything
> >>> > with the timeseries.
> >>> >
> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
> >>> > fMRI
> >>> > data can result in massive files which are problematic (and thus a work
> >>> > around is to apply the warp to contrast images), but I didn't expect
> >>> > the
> >>> > segment deformation field to do the same.
> >>> >
> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
> >>> > dim 2.5
> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> >>> > somewhere?
> >>> >
> >>> > so I'm supposing my inability to use the warped timeseries is a memory
> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
> >>> > this
> >>> > being used in the task manager so doesn't seem to be a matlab version
> >>> > problem) but maybe this file is just way too big for the SPM
> >>> > platform.....???? I could crop the image, etc I suppose but maybe the
> >>> > biggest problem is the shear number of volumes.
> >>> >
> >>> > Thanks in advance for your thoughts
> >>> >
> >>> > Richard
> >>> >
> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> >>> > <[log in to unmask]> wrote:
> >>> >>
> >>> >> Thanks John.
> >>> >>
> >>> >> It turns out the problem was due to the version of Matlab I was
> >>> >> running.
> >>> >>
> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
> >>> >>
> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL import
> >>> >> now
> >>> >> runs without any problem, without me having to alter/crop any of the
> >>> >> images.
> >>> >>
> >>> >> All the best
> >>> >> William
> >>> >
> >>> >
> >>
> >
> >
--_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_-- ========================================================================Date: Wed, 16 Feb 2011 17:10:35 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: Fwd: [SPM] New Segmentation Out of Memory Error In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba6e85e6fd0d09049c695bdd Message-ID: <[log in to unmask]> --90e6ba6e85e6fd0d09049c695bdd Content-Type: text/plain; charset=ISO-8859-1 ---------- Forwarded message ---------- From: Richard Binney <[log in to unmask]> Date: Wed, Feb 16, 2011 at 5:10 PM Subject: Re: [SPM] New Segmentation Out of Memory Error To: John Ashburner <[log in to unmask]> Hi John, Thanks for the quick response. So for example - I may have ROIs defined on the basis of coordinates taken from functional data or probabilistic anatomical maps in MNI space. Say I wanted to use these ROIs for DWI tractography which is of course performed in the native diffusion space, and so I would require a transform from the MNI space to the T1 - which would of course have been coreg'd to the diffusion dataset prior to any transform estimation (alternatively I could use the b0 image but the T1 or multispectral classification is better). I can easily do this with the other normalisation methods (e.g., segment and new segment), however, for interpreting the tractography maps (say in averaging to get group maps) I require the best possible inter-subject registration available to me. I believe that is where DARTEL comes in. So I would need an MNI-to-native option in DARTEL or a file containing the affine transform that I can combine with the DARTEL flowfield and subsequently invert using the deformations utility (I can't use the current .mat file in 'deformations', can I?). I had previously thought I would need this option for performing functional connectivity (etc) analyses seeded from ROIs defined on the basis of standard group-level (in MNI) GLM analyses. This was because I had been performing fixed effects (1st level) analyses in subject space and normalising the contrast images and I ideally wanted to use the same transform in the forward and backwards direction of course. I was doing this because I found that normalising the whole time-series with DARTEL resulted in enormous files that SPM couldn't deal with at the stage of the GLM. I've now of course (quite embarrasingly) realised with your help, that changing the voxel size and bounding box size circumvents this issue. This may remain an issue for people who find themselves with datasets larger than mine. Other people may know of other potential applications beyond DWI tractography...... Any thoughts? Kind regards, Richard On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <[log in to unmask]>wrote: > This file does indeed contain an affine transform, and is generated > when the affine registration between population average and MNI space > is estimated. > > There's no script to do what you're after yet, but if the demand is > high enough I could introduce the option. Can I ask how you plan to > use the transforms? > > Best regards, > -John > > > On 16 February 2011 15:44, Richard Binney <[log in to unmask]> > wrote: > > ......I've just spotted that running normalise-to-mni now spits out a > > 'Template_6_2mni.mat' file. I don't recall seeing this before - would > this > > happen to be the affine transform I spoke of? > > > > R > > > > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney > > <[log in to unmask]> wrote: > >> > >> Ah, yes. I couldn't find the email you were refferring to, but I've done > >> it and it makes a huge difference. Pretty obvious really. > >> > >> One more thing, John (or anybody else for that matter) - is there now a > >> way in which one can easily create an inverse of the DARTEL > normalise-to-MNI > >> deformation composition (so from MNI --> study average --> individual > >> subject space)? Is it possible (i.e., is there script) to have this > (forward > >> and/or backwards) written out for each subject or, alternatively, to > have > >> the affine template-to-TPM transform outputted such that one can combine > it > >> with a flow field, and subsequently inverse the composition using the > >> deformations tool? > >> > >> All the best, > >> > >> Richard > >> > >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> > >> wrote: > >>> > >>> You may wish to change the voxel sizes and bounding box in order to > >>> obtain slightly lower resolution versions (other than the default > >>> 1.5mm isotropic with a large bounding box). See the email I sent out > >>> recently. > >>> > >>> Best regards, > >>> -John > >>> > >>> On 15 February 2011 17:29, Richard Binney < > [log in to unmask]> > >>> wrote: > >>> > Dear John (and other knowledgeable types), > >>> > > >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to > normalise > >>> > a > >>> > functional MRI timeseries. That worked fine - the anatomical > >>> > images that > >>> > I additionally warped in this way look great - but I now cannot do > >>> > anything > >>> > with the timeseries. > >>> > > >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp > >>> > fMRI > >>> > data can result in massive files which are problematic (and thus a > work > >>> > around is to apply the warp to contrast images), but I didn't expect > >>> > the > >>> > segment deformation field to do the same. > >>> > > >>> > Does this sound about right for a timeseries with 465 volumes (voxel > >>> > dim 2.5 > >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake > >>> > somewhere? > >>> > > >>> > so I'm supposing my inability to use the warped timeseries is a > memory > >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all > >>> > this > >>> > being used in the task manager so doesn't seem to be a matlab version > >>> > problem) but maybe this file is just way too big for the SPM > >>> > platform.....???? I could crop the image, etc I suppose but maybe the > >>> > biggest problem is the shear number of volumes. > >>> > > >>> > Thanks in advance for your thoughts > >>> > > >>> > Richard > >>> > > >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo > >>> > <[log in to unmask]> wrote: > >>> >> > >>> >> Thanks John. > >>> >> > >>> >> It turns out the problem was due to the version of Matlab I was > >>> >> running. > >>> >> > >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. > >>> >> > >>> >> Having set up with Matlab2010, the new segmentation with DARTEL > import > >>> >> now > >>> >> runs without any problem, without me having to alter/crop any of the > >>> >> images. > >>> >> > >>> >> All the best > >>> >> William > >>> > > >>> > > >> > > > > > --90e6ba6e85e6fd0d09049c695bdd Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

---------- Forwarded message ----------
From: Richard Binney <[log in to unmask]>
Date: Wed, Feb 16, 2011 at 5:10 PM
Subject: Re: [SPM] New Segmentation Out of Memory Error
To: John Ashburner <[log in to unmask]>

Hi John,

Thanks for the quick response.

So for example - I may have ROIs defined on the basis of coordinates taken from functional data or probabilistic anatomical maps in MNI space. Say I wanted to use these ROIs for DWI tractography which is of course performed in the native diffusion space, and so I would require a transform from the MNI space to the T1 - which would of course have been coreg'd to the diffusion dataset prior to any transform estimation (alternatively I could use the b0 image but the T1 or multispectral classification is better). I can easily do this with the other normalisation methods (e.g., segment and new segment), however, for interpreting the tractography maps (say in averaging to get group maps) I require the best possible inter-subject registration available to me. I believe that is where DARTEL comes in.

So I would need an MNI-to-native option in DARTEL or a file containing the affine transform that I can combine with the DARTEL flowfield and subsequently invert using the deformations utility (I can't use the current .mat file in 'deformations', can I?).

I had previously thought I would need this option for performing functional connectivity (etc) analyses seeded from ROIs defined on the basis of standard group-level (in MNI) GLM analyses. This was because I had been performing fixed effects (1st level) analyses in subject space and normalising the contrast images and I ideally wanted to use the same transform in the forward and backwards direction of course. I was doing this because I found that normalising the whole time-series with DARTEL resulted in enormous files that SPM couldn't deal with at the stage of the GLM. I've now of course (quite embarrasingly) realised with your help, that changing the voxel size and bounding box size circumvents this issue. This may remain an issue for people who find themselves with datasets larger than mine.

Other people may know of other potential applications beyond DWI tractography......

Any thoughts?

Kind regards,

Richard

On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <[log in to unmask]> wrote:

This file does indeed contain an affine transform, and is generated
when the affine registration between population average and MNI space
is estimated.

There's no script to do what you're after yet, but if the demand is
high enough I could introduce the option. Can I ask how you plan to
use the transforms?

Best regards,
-John

On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote:
> ......I've just spotted that running normalise-to-mni now spits out a
> 'Template_6_2mni.mat' file. I don't recall seeing this before - would this
> happen to be the affine transform I spoke of?
>
> R
>
> On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> <[log in to unmask]> wrote:
>>
>> Ah, yes. I couldn't find the email you were refferring to, but I've done
>> it and it makes a huge difference. Pretty obvious really.
>>
>> One more thing, John (or anybody else for that matter) - is there now a
>> way in which one can easily create an inverse of the DARTEL normalise-to-MNI
>> deformation composition (so from MNI --> study average --> individual
>> subject space)? Is it possible (i.e., is there script) to have this (forward
>> and/or backwards) written out for each subject or, alternatively, to have
>> the affine template-to-TPM transform outputted such that one can combine it
>> with a flow field, and subsequently inverse the composition using the
>> deformations tool?
>>
>> All the best,
>>
>> Richard
>>
>> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
>> wrote:
>>>
>>> You may wish to change the voxel sizes and bounding box in order to
>>> obtain slightly lower resolution versions (other than the default
>>> 1.5mm isotropic with a large bounding box). See the email I sent out
>>> recently.
>>>
>>> Best regards,
>>> -John
>>>
>>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
>>> wrote:
>>> > Dear John (and other knowledgeable types),
>>> >
>>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise
>>> > a
>>> > functional MRI timeseries. That worked fine - the anatomical
>>> > images that
>>> > I additionally warped in this way look great - but I now cannot do
>>> > anything
>>> > with the timeseries.
>>> >
>>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
>>> > fMRI
>>> > data can result in massive files which are problematic (and thus a work
>>> > around is to apply the warp to contrast images), but I didn't expect
>>> > the
>>> > segment deformation field to do the same.
>>> >
>>> > Does this sound about right for a timeseries with 465 volumes (voxel
>>> > dim 2.5
>>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
>>> > somewhere?
>>> >
>>> > so I'm supposing my inability to use the warped timeseries is a memory
>>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
>>> > this
>>> > being used in the task manager so doesn't seem to be a matlab version
>>> > problem) but maybe this file is just way too big for the SPM
>>> > platform.....???? I could crop the image, etc I suppose but maybe the
>>> > biggest problem is the shear number of volumes.
>>> >
>>> > Thanks in advance for your thoughts
>>> >
>>> > Richard
>>> >
>>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>>> > <[log in to unmask]> wrote:
>>> >>
>>> >> Thanks John.
>>> >>
>>> >> It turns out the problem was due to the version of Matlab I was
>>> >> running.
>>> >>
>>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>>> >>
>>> >> Having set up with Matlab2010, the new segmentation with DARTEL import
>>> >> now
>>> >> runs without any problem, without me having to alter/crop any of the
>>> >> images.
>>> >>
>>> >> All the best
>>> >> William
>>> >
>>> >
>>
>
>

--90e6ba6e85e6fd0d09049c695bdd-- ========================================================================Date: Wed, 16 Feb 2011 17:15:28 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba6e8b88716639049c696db4 Message-ID: <[log in to unmask]> --90e6ba6e8b88716639049c696db4 Content-Type: text/plain; charset=ISO-8859-1 Dear Michel, I updated my SPM8 yesterday - today is the first time I have seen this file. I ran DARTEL's normalise-to-MNI in the usual way and this file was stored to disk permanently. Richard On Wed, Feb 16, 2011 at 5:10 PM, Richard Binney < [log in to unmask]> wrote: > > > ---------- Forwarded message ---------- > From: Richard Binney <[log in to unmask]> > Date: Wed, Feb 16, 2011 at 5:10 PM > Subject: Re: [SPM] New Segmentation Out of Memory Error > To: John Ashburner <[log in to unmask]> > > > Hi John, > > Thanks for the quick response. > > So for example - I may have ROIs defined on the basis of coordinates taken > from functional data or probabilistic anatomical maps in MNI space. Say I > wanted to use these ROIs for DWI tractography which is of course performed > in the native diffusion space, and so I would require a transform from the > MNI space to the T1 - which would of course have been coreg'd to the > diffusion dataset prior to any transform estimation (alternatively I could > use the b0 image but the T1 or multispectral classification is better). I > can easily do this with the other normalisation methods (e.g., segment and > new segment), however, for interpreting the tractography maps (say in > averaging to get group maps) I require the best possible inter-subject > registration available to me. I believe that is where DARTEL comes in. > > So I would need an MNI-to-native option in DARTEL or a file containing the > affine transform that I can combine with the DARTEL flowfield and > subsequently invert using the deformations utility (I can't use the current > .mat file in 'deformations', can I?). > > I had previously thought I would need this option for performing functional > connectivity (etc) analyses seeded from ROIs defined on the basis of > standard group-level (in MNI) GLM analyses. This was because I had been > performing fixed effects (1st level) analyses in subject space > and normalising the contrast images and I ideally wanted to use the same > transform in the forward and backwards direction of course. I was doing this > because I found that normalising the whole time-series with DARTEL resulted > in enormous files that SPM couldn't deal with at the stage of the GLM. I've > now of course (quite embarrasingly) realised with your help, that changing > the voxel size and bounding box size circumvents this issue. This may remain > an issue for people who find themselves with datasets larger than mine. > > Other people may know of other potential applications beyond DWI > tractography...... > > Any thoughts? > > Kind regards, > > Richard > > On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <[log in to unmask]>wrote: > >> This file does indeed contain an affine transform, and is generated >> when the affine registration between population average and MNI space >> is estimated. >> >> There's no script to do what you're after yet, but if the demand is >> high enough I could introduce the option. Can I ask how you plan to >> use the transforms? >> >> Best regards, >> -John >> >> >> On 16 February 2011 15:44, Richard Binney <[log in to unmask]> >> wrote: >> > ......I've just spotted that running normalise-to-mni now spits out a >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would >> this >> > happen to be the affine transform I spoke of? >> > >> > R >> > >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney >> > <[log in to unmask]> wrote: >> >> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've >> done >> >> it and it makes a huge difference. Pretty obvious really. >> >> >> >> One more thing, John (or anybody else for that matter) - is there now a >> >> way in which one can easily create an inverse of the DARTEL >> normalise-to-MNI >> >> deformation composition (so from MNI --> study average --> individual >> >> subject space)? Is it possible (i.e., is there script) to have this >> (forward >> >> and/or backwards) written out for each subject or, alternatively, to >> have >> >> the affine template-to-TPM transform outputted such that one can >> combine it >> >> with a flow field, and subsequently inverse the composition using the >> >> deformations tool? >> >> >> >> All the best, >> >> >> >> Richard >> >> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> >> >> wrote: >> >>> >> >>> You may wish to change the voxel sizes and bounding box in order to >> >>> obtain slightly lower resolution versions (other than the default >> >>> 1.5mm isotropic with a large bounding box). See the email I sent out >> >>> recently. >> >>> >> >>> Best regards, >> >>> -John >> >>> >> >>> On 15 February 2011 17:29, Richard Binney < >> [log in to unmask]> >> >>> wrote: >> >>> > Dear John (and other knowledgeable types), >> >>> > >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to >> normalise >> >>> > a >> >>> > functional MRI timeseries. That worked fine - the anatomical >> >>> > images that >> >>> > I additionally warped in this way look great - but I now cannot do >> >>> > anything >> >>> > with the timeseries. >> >>> > >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp >> >>> > fMRI >> >>> > data can result in massive files which are problematic (and thus a >> work >> >>> > around is to apply the warp to contrast images), but I didn't expect >> >>> > the >> >>> > segment deformation field to do the same. >> >>> > >> >>> > Does this sound about right for a timeseries with 465 volumes (voxel >> >>> > dim 2.5 >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake >> >>> > somewhere? >> >>> > >> >>> > so I'm supposing my inability to use the warped timeseries is a >> memory >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all >> >>> > this >> >>> > being used in the task manager so doesn't seem to be a matlab >> version >> >>> > problem) but maybe this file is just way too big for the SPM >> >>> > platform.....???? I could crop the image, etc I suppose but maybe >> the >> >>> > biggest problem is the shear number of volumes. >> >>> > >> >>> > Thanks in advance for your thoughts >> >>> > >> >>> > Richard >> >>> > >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo >> >>> > <[log in to unmask]> wrote: >> >>> >> >> >>> >> Thanks John. >> >>> >> >> >>> >> It turns out the problem was due to the version of Matlab I was >> >>> >> running. >> >>> >> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. >> >>> >> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL >> import >> >>> >> now >> >>> >> runs without any problem, without me having to alter/crop any of >> the >> >>> >> images. >> >>> >> >> >>> >> All the best >> >>> >> William >> >>> > >> >>> > >> >> >> > >> > >> > > > --90e6ba6e8b88716639049c696db4 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Dear Michel,

I updated my SPM8 yesterday - today is the first time I have seen this file. I ran DARTEL's normalise-to-MNI in the usual way and this file was stored to disk permanently.

Richard

On Wed, Feb 16, 2011 at 5:10 PM, Richard Binney <[log in to unmask]> wrote:

---------- Forwarded message ----------
From: Richard Binney <[log in to unmask]>
Date: Wed, Feb 16, 2011 at 5:10 PM
Subject: Re: [SPM] New Segmentation Out of Memory Error

To: John Ashburner <[log in to unmask]>

Hi John,

Thanks for the quick response.

So for example - I may have ROIs defined on the basis of coordinates taken from functional data or probabilistic anatomical maps in MNI space. Say I wanted to use these ROIs for DWI tractography which is of course performed in the native diffusion space, and so I would require a transform from the MNI space to the T1 - which would of course have been coreg'd to the diffusion dataset prior to any transform estimation (alternatively I could use the b0 image but the T1 or multispectral classification is better). I can easily do this with the other normalisation methods (e.g., segment and new segment), however, for interpreting the tractography maps (say in averaging to get group maps) I require the best possible inter-subject registration available to me. I believe that is where DARTEL comes in.

So I would need an MNI-to-native option in DARTEL or a file containing the affine transform that I can combine with the DARTEL flowfield and subsequently invert using the deformations utility (I can't use the current .mat file in 'deformations', can I?).

I had previously thought I would need this option for performing functional connectivity (etc) analyses seeded from ROIs defined on the basis of standard group-level (in MNI) GLM analyses. This was because I had been performing fixed effects (1st level) analyses in subject space and normalising the contrast images and I ideally wanted to use the same transform in the forward and backwards direction of course. I was doing this because I found that normalising the whole time-series with DARTEL resulted in enormous files that SPM couldn't deal with at the stage of the GLM. I've now of course (quite embarrasingly) realised with your help, that changing the voxel size and bounding box size circumvents this issue. This may remain an issue for people who find themselves with datasets larger than mine.

Other people may know of other potential applications beyond DWI tractography......

Any thoughts?

Kind regards,

Richard

On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <[log in to unmask]> wrote:

This file does indeed contain an affine transform, and is generated
when the affine registration between population average and MNI space
is estimated.

There's no script to do what you're after yet, but if the demand is
high enough I could introduce the option. Can I ask how you plan to
use the transforms?

Best regards,
-John

On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote:
> ......I've just spotted that running normalise-to-mni now spits out a
> 'Template_6_2mni.mat' file. I don't recall seeing this before - would this
> happen to be the affine transform I spoke of?
>
> R
>
> On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> <[log in to unmask]> wrote:
>>
>> Ah, yes. I couldn't find the email you were refferring to, but I've done
>> it and it makes a huge difference. Pretty obvious really.
>>
>> One more thing, John (or anybody else for that matter) - is there now a
>> way in which one can easily create an inverse of the DARTEL normalise-to-MNI
>> deformation composition (so from MNI --> study average --> individual
>> subject space)? Is it possible (i.e., is there script) to have this (forward
>> and/or backwards) written out for each subject or, alternatively, to have
>> the affine template-to-TPM transform outputted such that one can combine it
>> with a flow field, and subsequently inverse the composition using the
>> deformations tool?
>>
>> All the best,
>>
>> Richard
>>
>> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
>> wrote:
>>>
>>> You may wish to change the voxel sizes and bounding box in order to
>>> obtain slightly lower resolution versions (other than the default
>>> 1.5mm isotropic with a large bounding box). See the email I sent out
>>> recently.
>>>
>>> Best regards,
>>> -John
>>>
>>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
>>> wrote:
>>> > Dear John (and other knowledgeable types),
>>> >
>>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise
>>> > a
>>> > functional MRI timeseries. That worked fine - the anatomical
>>> > images that
>>> > I additionally warped in this way look great - but I now cannot do
>>> > anything
>>> > with the timeseries.
>>> >
>>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
>>> > fMRI
>>> > data can result in massive files which are problematic (and thus a work
>>> > around is to apply the warp to contrast images), but I didn't expect
>>> > the
>>> > segment deformation field to do the same.
>>> >
>>> > Does this sound about right for a timeseries with 465 volumes (voxel
>>> > dim 2.5
>>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
>>> > somewhere?
>>> >
>>> > so I'm supposing my inability to use the warped timeseries is a memory
>>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
>>> > this
>>> > being used in the task manager so doesn't seem to be a matlab version
>>> > problem) but maybe this file is just way too big for the SPM
>>> > platform.....???? I could crop the image, etc I suppose but maybe the
>>> > biggest problem is the shear number of volumes.
>>> >
>>> > Thanks in advance for your thoughts
>>> >
>>> > Richard
>>> >
>>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>>> > <[log in to unmask]> wrote:
>>> >>
>>> >> Thanks John.
>>> >>
>>> >> It turns out the problem was due to the version of Matlab I was
>>> >> running.
>>> >>
>>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>>> >>
>>> >> Having set up with Matlab2010, the new segmentation with DARTEL import
>>> >> now
>>> >> runs without any problem, without me having to alter/crop any of the
>>> >> images.
>>> >>
>>> >> All the best
>>> >> William
>>> >
>>> >
>>
>
>

--90e6ba6e8b88716639049c696db4-- ========================================================================Date: Wed, 16 Feb 2011 17:16:00 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: michel grothe <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> > I haven´t seen such a file in my version of SPM8 (the penultimate update, > run on win32). Is it possible that this file is just generated during > estimation of the affine transform and not stored permanently on disk? Or > does it come with the latest update? This change occurred in SPM8 r3727, so should be in the current updates, which are to r4010. > It would be very helpful to have such a file, mainly for the reasons Richard > mentioned, i.e. use it to compose the complete transform > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used > to warp Atlas-labels in MNI space to native space. There's no easy way to use the file yet, but I'll try to code something up for a future version of SPM8. Best regards, -John >> Date: Wed, 16 Feb 2011 15:58:16 +0000 >> From: [log in to unmask] >> Subject: Re: [SPM] New Segmentation Out of Memory Error >> To: [log in to unmask] >> >> This file does indeed contain an affine transform, and is generated >> when the affine registration between population average and MNI space >> is estimated. >> >> There's no script to do what you're after yet, but if the demand is >> high enough I could introduce the option. Can I ask how you plan to >> use the transforms? >> >> Best regards, >> -John >> >> >> On 16 February 2011 15:44, Richard Binney <[log in to unmask]> >> wrote: >> > ......I've just spotted that running normalise-to-mni now spits out a >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would >> > this >> > happen to be the affine transform I spoke of? >> > >> > R >> > >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney >> > <[log in to unmask]> wrote: >> >> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've >> >> done >> >> it and it makes a huge difference. Pretty obvious really. >> >> >> >> One more thing, John (or anybody else for that matter) - is there now a >> >> way in which one can easily create an inverse of the DARTEL >> >> normalise-to-MNI >> >> deformation composition (so from MNI --> study average --> individual >> >> subject space)? Is it possible (i.e., is there script) to have this >> >> (forward >> >> and/or backwards) written out for each subject or, alternatively, to >> >> have >> >> the affine template-to-TPM transform outputted such that one can >> >> combine it >> >> with a flow field, and subsequently inverse the composition using the >> >> deformations tool? >> >> >> >> All the best, >> >> >> >> Richard >> >> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> >> >> wrote: >> >>> >> >>> You may wish to change the voxel sizes and bounding box in order to >> >>> obtain slightly lower resolution versions (other than the default >> >>> 1.5mm isotropic with a large bounding box). See the email I sent out >> >>> recently. >> >>> >> >>> Best regards, >> >>> -John >> >>> >> >>> On 15 February 2011 17:29, Richard Binney >> >>> <[log in to unmask]> >> >>> wrote: >> >>> > Dear John (and other knowledgeable types), >> >>> > >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to >> >>> > normalise >> >>> > a >> >>> > functional MRI timeseries. That worked fine - the anatomical >> >>> > images that >> >>> > I additionally warped in this way look great - but I now cannot do >> >>> > anything >> >>> > with the timeseries. >> >>> > >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp >> >>> > fMRI >> >>> > data can result in massive files which are problematic (and thus a >> >>> > work >> >>> > around is to apply the warp to contrast images), but I didn't expect >> >>> > the >> >>> > segment deformation field to do the same. >> >>> > >> >>> > Does this sound about right for a timeseries with 465 volumes (voxel >> >>> > dim 2.5 >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake >> >>> > somewhere? >> >>> > >> >>> > so I'm supposing my inability to use the warped timeseries is a >> >>> > memory >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all >> >>> > this >> >>> > being used in the task manager so doesn't seem to be a matlab >> >>> > version >> >>> > problem) but maybe this file is just way too big for the SPM >> >>> > platform.....???? I could crop the image, etc I suppose but maybe >> >>> > the >> >>> > biggest problem is the shear number of volumes. >> >>> > >> >>> > Thanks in advance for your thoughts >> >>> > >> >>> > Richard >> >>> > >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo >> >>> > <[log in to unmask]> wrote: >> >>> >> >> >>> >> Thanks John. >> >>> >> >> >>> >> It turns out the problem was due to the version of Matlab I was >> >>> >> running. >> >>> >> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. >> >>> >> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL >> >>> >> import >> >>> >> now >> >>> >> runs without any problem, without me having to alter/crop any of >> >>> >> the >> >>> >> images. >> >>> >> >> >>> >> All the best >> >>> >> William >> >>> > >> >>> > >> >> >> > >> > > ========================================================================Date: Wed, 16 Feb 2011 11:24:12 -0600 Reply-To: Prabhakaran Vivek <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Prabhakaran Vivek <[log in to unmask]> Subject: Postdoctoral position in neuroimaging applications in translational and clinical research MIME-Version: 1.0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> PhDs and/or MDs who are U.S. Citizens or Permanent Residents interested in postdoctoral traineeships in the Computation and Informatics in Biology and Medicine (CIBM) Training Program, funded by the NLM Grant 5T15LM007359 are asked to apply. The postdoctoral traineeship is for a one- to two-year period. One is to begin by June, 2011, and the other to begin Summer 2011 or later. (The last year is contingent on renewal of the NLM Grant supporting the CIBM Training Program.) These traineeships are designed for cross-disciplinary training - the candidate will have a secondary mentor in addition to the primary mentor (one from computational / informatics areas, and the other from biological areas). If interested in a postdoctoral position in neuroimaging applications in translational and clinical research, please email a CV to Vivek Prabhakaran M.D., Ph.D., Director of Functional Neuroimaging in Radiology. Vivek Prabhakaran M.D., Ph.D. Assistant Professor, Director of Functional Neuroimaging in Radiology UWHealth UW Hospital and Clinics University of Wisconsin-Madison Department of Radiology HealthEmotions Research Institute 600 Highland Avenue 6001 Research Park Blvd rm 1003 Madison, WI 53792 Madison, WI 53719 Office: 608-265-5269 Office: 608-232-3342 ========================================================================Date: Wed, 16 Feb 2011 13:51:45 -0500 Reply-To: cliff <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: cliff <[log in to unmask]> Subject: T1 & functional image reorigin MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --000e0cd6a8ccc443dd049c6ac5ce Content-Type: text/plain; charset=ISO-8859-1 Now I always use SPM99's Head-edit funtion to do these stuff (set the origin first and then apply to images). In this way, the new origin can be read correctly both in SPM99 and SPM8. However, it is quite a boring job. Is there any independent funtion in SPM that I can use to reorigin images in a modified batch way? Or is there any assistant toolbox I can use? Thanks! --000e0cd6a8ccc443dd049c6ac5ce Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Now I always use SPM99's Head-edit funtion to do these stuff (set the origin first and then apply to images).

In this way, the new origin can be read correctly both in SPM99 and SPM8.

However, it is quite a boring job.

Is there any independent funtion in SPM that I can use to reorigin images in a modified batch way?

Or is there any assistant toolbox I can use?

Thanks!

--000e0cd6a8ccc443dd049c6ac5ce-- ========================================================================Date: Wed, 16 Feb 2011 20:45:14 +0100 Reply-To: michel grothe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: michel grothe <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_d114b127-7ddd-48ec-b028-6c3509a53daf_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_d114b127-7ddd-48ec-b028-6c3509a53daf_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Thanks, this would be great. Meanwhile, I have some questions regarding a possible work-around: Would it be OK to use an affine transform calculated with the Normalise-Button to approximate the affine transform from 'DARTEL-Normalise to MNI' in order to be able to generate the mentioned composition 'native->DARTEL->MNI'. Does the affine alignment step in 'DARTEL-Normalise to MNI' register the DARTEL-GM template to the GM-apriori map (or the GM map in TPM.nii,1?) or does it also use the respective WM-maps? How much does the affine alignment in 'DARTEL-Normalise to MNI' differ from the one in 'Normalise', when registering DARTEL-GM to GM-apriori map (or TPM.nii,1) and setting non-linear iterations to 0? Thanks again for your help. Best regards, Michel > Date: Wed, 16 Feb 2011 17:16:00 +0000 > From: [log in to unmask] > Subject: Re: [SPM] New Segmentation Out of Memory Error > To: [log in to unmask] > > > I haven´t seen such a file in my version of SPM8 (the penultimate update, > > run on win32). Is it possible that this file is just generated during > > estimation of the affine transform and not stored permanently on disk? Or > > does it come with the latest update? > > This change occurred in SPM8 r3727, so should be in the current > updates, which are to r4010. > > > > It would be very helpful to have such a file, mainly for the reasons Richard > > mentioned, i.e. use it to compose the complete transform > > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used > > to warp Atlas-labels in MNI space to native space. > > There's no easy way to use the file yet, but I'll try to code > something up for a future version of SPM8. > > Best regards, > -John > > > >> Date: Wed, 16 Feb 2011 15:58:16 +0000 > >> From: [log in to unmask] > >> Subject: Re: [SPM] New Segmentation Out of Memory Error > >> To: [log in to unmask] > >> > >> This file does indeed contain an affine transform, and is generated > >> when the affine registration between population average and MNI space > >> is estimated. > >> > >> There's no script to do what you're after yet, but if the demand is > >> high enough I could introduce the option. Can I ask how you plan to > >> use the transforms? > >> > >> Best regards, > >> -John > >> > >> > >> On 16 February 2011 15:44, Richard Binney <[log in to unmask]> > >> wrote: > >> > ......I've just spotted that running normalise-to-mni now spits out a > >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would > >> > this > >> > happen to be the affine transform I spoke of? > >> > > >> > R > >> > > >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney > >> > <[log in to unmask]> wrote: > >> >> > >> >> Ah, yes. I couldn't find the email you were refferring to, but I've > >> >> done > >> >> it and it makes a huge difference. Pretty obvious really. > >> >> > >> >> One more thing, John (or anybody else for that matter) - is there now a > >> >> way in which one can easily create an inverse of the DARTEL > >> >> normalise-to-MNI > >> >> deformation composition (so from MNI --> study average --> individual > >> >> subject space)? Is it possible (i.e., is there script) to have this > >> >> (forward > >> >> and/or backwards) written out for each subject or, alternatively, to > >> >> have > >> >> the affine template-to-TPM transform outputted such that one can > >> >> combine it > >> >> with a flow field, and subsequently inverse the composition using the > >> >> deformations tool? > >> >> > >> >> All the best, > >> >> > >> >> Richard > >> >> > >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]> > >> >> wrote: > >> >>> > >> >>> You may wish to change the voxel sizes and bounding box in order to > >> >>> obtain slightly lower resolution versions (other than the default > >> >>> 1.5mm isotropic with a large bounding box). See the email I sent out > >> >>> recently. > >> >>> > >> >>> Best regards, > >> >>> -John > >> >>> > >> >>> On 15 February 2011 17:29, Richard Binney > >> >>> <[log in to unmask]> > >> >>> wrote: > >> >>> > Dear John (and other knowledgeable types), > >> >>> > > >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to > >> >>> > normalise > >> >>> > a > >> >>> > functional MRI timeseries. That worked fine - the anatomical > >> >>> > images that > >> >>> > I additionally warped in this way look great - but I now cannot do > >> >>> > anything > >> >>> > with the timeseries. > >> >>> > > >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp > >> >>> > fMRI > >> >>> > data can result in massive files which are problematic (and thus a > >> >>> > work > >> >>> > around is to apply the warp to contrast images), but I didn't expect > >> >>> > the > >> >>> > segment deformation field to do the same. > >> >>> > > >> >>> > Does this sound about right for a timeseries with 465 volumes (voxel > >> >>> > dim 2.5 > >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake > >> >>> > somewhere? > >> >>> > > >> >>> > so I'm supposing my inability to use the warped timeseries is a > >> >>> > memory > >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all > >> >>> > this > >> >>> > being used in the task manager so doesn't seem to be a matlab > >> >>> > version > >> >>> > problem) but maybe this file is just way too big for the SPM > >> >>> > platform.....???? I could crop the image, etc I suppose but maybe > >> >>> > the > >> >>> > biggest problem is the shear number of volumes. > >> >>> > > >> >>> > Thanks in advance for your thoughts > >> >>> > > >> >>> > Richard > >> >>> > > >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo > >> >>> > <[log in to unmask]> wrote: > >> >>> >> > >> >>> >> Thanks John. > >> >>> >> > >> >>> >> It turns out the problem was due to the version of Matlab I was > >> >>> >> running. > >> >>> >> > >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64. > >> >>> >> > >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL > >> >>> >> import > >> >>> >> now > >> >>> >> runs without any problem, without me having to alter/crop any of > >> >>> >> the > >> >>> >> images. > >> >>> >> > >> >>> >> All the best > >> >>> >> William > >> >>> > > >> >>> > > >> >> > >> > > >> > > > --_d114b127-7ddd-48ec-b028-6c3509a53daf_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Thanks, this would be great.

Meanwhile, I have some questions regarding a possible work-around:

Would it be OK to use an affine transform calculated with the Normalise-Button to approximate the affine transform from 'DARTEL-Normalise to MNI' in order to be able to generate the mentioned composition 'native->DARTEL->MNI'. Does the affine alignment step in 'DARTEL-Normalise to MNI' register the DARTEL-GM template to the GM-apriori map (or the GM map in TPM.nii,1?) or does it also use the respective WM-maps?
How much does the affine alignment in 'DARTEL-Normalise to MNI' differ from the one in 'Normalise', when registering DARTEL-GM to GM-apriori map (or TPM.nii,1) and setting non-linear iterations to 0?

Thanks again for your help.
Best regards,
Michel

> Date: Wed, 16 Feb 2011 17:16:00 +0000
> From: [log in to unmask]
> Subject: Re: [SPM] New Segmentation Out of Memory Error
> To: [log in to unmask]
>
> > I haven´t seen such a file in my version of SPM8 (the penultimate update,
> > run on win32). Is it possible that this file is just generated during
> > estimation of the affine transform and not stored permanently on disk? Or
> > does it come with the latest update?
>
> This change occurred in SPM8 r3727, so should be in the current
> updates, which are to r4010.
>
>
> > It would be very helpful to have such a file, mainly for the reasons Richard
> > mentioned, i.e. use it to compose the complete transform
> > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used
> > to warp Atlas-labels in MNI space to native space.
>
> There's no easy way to use the file yet, but I'll try to code
> something up for a future version of SPM8.
>
> Best regards,
> -John
>
>
> >> Date: Wed, 16 Feb 2011 15:58:16 +0000
> >> From: [log in to unmask]
> >> Subject: Re: [SPM] New Segmentation Out of Memory Error
> >> To: [log in to unmask]
> >>
> >> This file does indeed contain an affine transform, and is generated
> >> when the affine registration between population average and MNI space
> >> is estimated.
> >>
> >> There's no script to do what you're after yet, but if the demand is
> >> high enough I could introduce the option. Can I ask how you plan to
> >> use the transforms?
> >>
> >> Best regards,
> >> -John
> >>
> >>
> >> On 16 February 2011 15:44, Richard Binney <[log in to unmask]>
> >> wrote:
> >> > ......I've just spotted that running normalise-to-mni now spits out a
> >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would
> >> > this
> >> > happen to be the affine transform I spoke of?
> >> >
> >> > R
> >> >
> >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> >> > <[log in to unmask]> wrote:
> >> >>
> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've
> >> >> done
> >> >> it and it makes a huge difference. Pretty obvious really.
> >> >>
> >> >> One more thing, John (or anybody else for that matter) - is there now a
> >> >> way in which one can easily create an inverse of the DARTEL
> >> >> normalise-to-MNI
> >> >> deformation composition (so from MNI --> study average --> individual
> >> >> subject space)? Is it possible (i.e., is there script) to have this
> >> >> (forward
> >> >> and/or backwards) written out for each subject or, alternatively, to
> >> >> have
> >> >> the affine template-to-TPM transform outputted such that one can
> >> >> combine it
> >> >> with a flow field, and subsequently inverse the composition using the
> >> >> deformations tool?
> >> >>
> >> >> All the best,
> >> >>
> >> >> Richard
> >> >>
> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
> >> >> wrote:
> >> >>>
> >> >>> You may wish to change the voxel sizes and bounding box in order to
> >> >>> obtain slightly lower resolution versions (other than the default
> >> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
> >> >>> recently.
> >> >>>
> >> >>> Best regards,
> >> >>> -John
> >> >>>
> >> >>> On 15 February 2011 17:29, Richard Binney
> >> >>> <[log in to unmask]>
> >> >>> wrote:
> >> >>> > Dear John (and other knowledgeable types),
> >> >>> >
> >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to
> >> >>> > normalise
> >> >>> > a
> >> >>> > functional MRI timeseries. That worked fine - the anatomical
> >> >>> > images that
> >> >>> > I additionally warped in this way look great - but I now cannot do
> >> >>> > anything
> >> >>> > with the timeseries.
> >> >>> >
> >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
> >> >>> > fMRI
> >> >>> > data can result in massive files which are problematic (and thus a
> >> >>> > work
> >> >>> > around is to apply the warp to contrast images), but I didn't expect
> >> >>> > the
> >> >>> > segment deformation field to do the same.
> >> >>> >
> >> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
> >> >>> > dim 2.5
> >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> >> >>> > somewhere?
> >> >>> >
> >> >>> > so I'm supposing my inability to use the warped timeseries is a
> >> >>> > memory
> >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
> >> >>> > this
> >> >>> > being used in the task manager so doesn't seem to be a matlab
> >> >>> > version
> >> >>> > problem) but maybe this file is just way too big for the SPM
> >> >>> > platform.....???? I could crop the image, etc I suppose but maybe
> >> >>> > the
> >> >>> > biggest problem is the shear number of volumes.
> >> >>> >
> >> >>> > Thanks in advance for your thoughts
> >> >>> >
> >> >>> > Richard
> >> >>> >
> >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> >> >>> > <[log in to unmask]> wrote:
> >> >>> >>
> >> >>> >> Thanks John.
> >> >>> >>
> >> >>> >> It turns out the problem was due to the version of Matlab I was
> >> >>> >> running.
> >> >>> >>
> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
> >> >>> >>
> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL
> >> >>> >> import
> >> >>> >> now
> >> >>> >> runs without any problem, without me having to alter/crop any of
> >> >>> >> the
> >> >>> >> images.
> >> >>> >>
> >> >>> >> All the best
> >> >>> >> William
> >> >>> >
> >> >>> >
> >> >>
> >> >
> >> >
> >
--_d114b127-7ddd-48ec-b028-6c3509a53daf_-- ========================================================================Date: Wed, 16 Feb 2011 19:47:46 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: T1 & functional image reorigin Comments: To: cliff <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> If all subjects have similar positioning in the scanner, then you can use Display to adjust the positioning of one scan of one subject, and then select all scans of all subjects when you actually do the reorienting. I had no idea that anyone was still seriously using SPM99. It's really just a museum piece now. Although it may have some archaeological interest, it probably isn't so great for doing science. Best regards, -John On 16 February 2011 18:51, cliff <[log in to unmask]> wrote: > Now I always use SPM99's Head-edit funtion to do these stuff (set the origin > first and then apply to images). > In this way, the new origin can be read correctly both in SPM99 and SPM8. > However, it is quite a boring job. > > Is there any independent funtion in SPM that I can use to reorigin images in > a modified batch way? > Or is there any assistant toolbox I can use? > > Thanks! ========================================================================Date: Wed, 16 Feb 2011 22:59:07 +0100 Reply-To: SPM-Workshop Hannover <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: SPM-Workshop Hannover <[log in to unmask]> Subject: SPM-Workshop Hannover (Germany) MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001636c5b4d8d76261049c6d6369 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable SPM-Workshop (German) Hannover, Germany May 11-13, 2011 Hands-on workshop for new users at Hannover Medical School in Germany focussing on the practical use of SPM8. This three-day course is designed for researchers with little or no experience in the analysis of functional imaging data. The course will be held in German. The aim of this workshop is to explain the basic principles of fMRI analysis with SPM and Matlab, and to apply this knowledge right away. All steps of data analysis in SPM8 will be covered, from importing the raw data, logfile extraction, preprocessing, 1st and 2nd level analysis to results inspection, extraction and visualization. For more information please visit www.spmworkshop-hannover.de or mail to [log in to unmask] -- °°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°° *SPM-Workshop Hannover* ...learn how to do it! Email: [log in to unmask] Web: www.spmworkshop-hannover.de °°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°° --001636c5b4d8d76261049c6d6369 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable SPM-Workshop (German)
Hannover, Germany
May 11-13, 2011

Hands-on workshop for new users at Hannover Medical School in Germany focussing on the practical use of SPM8. This three-day course is designed for researchers with little or no experience in the analysis of functional imaging data. The course will be held in German.

The aim of this workshop is to explain the basic principles of fMRI analysis with SPM and Matlab, and to apply this knowledge right away. All steps of data analysis in SPM8 will be covered, from importing the raw data, logfile extraction, preprocessing, 1st and 2nd level analysis to results inspection, extraction and visualization.

For more information please visit www.spmworkshop-hannover.de or mail to [log in to unmask]

--
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
SPM-Workshop Hannover
...learn how to do it!

Email: [log in to unmask]
Web: www.spmworkshop-hannover.de
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°

--001636c5b4d8d76261049c6d6369-- ========================================================================Date: Wed, 16 Feb 2011 14:34:00 -0800 Reply-To: kambiz rakhshan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: kambiz rakhshan <[log in to unmask]> Subject: DARTEL template in MNI space MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-778413605-1297895640=:53278" Message-ID: <[log in to unmask]> --0-778413605-1297895640=:53278 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear all, I am wondering how to use spm_get_space to affine normalize the DARTEL template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M? Isn't it possible to use normalize_write function instead? My second question concerns the use of suitable mask after applying normalize to MNI function for fMRI data to get ride of potential aliasing effects.Is it better to use normalized template of dartel as an explicit mask (while doing second level analysis) or to apply the deformation to the first level subject-specific mask and then multiply with the smoothed normalized images created by normalize to MNI function? Thanks in advance /Kami --0-778413605-1297895640=:53278 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

Dear all,

I am wondering how to use spm_get_space to affine normalize the DARTEL template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M?

Isn't it possible to use normalize_write function instead?

My second question concerns the use of suitable mask after applying normalize to MNI function for fMRI data to get ride of potential aliasing effects.Is it better to use normalized template of dartel as an explicit mask (while doing second level analysis) or to apply the deformation to the first level subject-specific mask and then multiply with the smoothed normalized images created by normalize to MNI function?

Thanks in advance

/Kami

--0-778413605-1297895640=:53278-- ========================================================================Date: Wed, 16 Feb 2011 20:02:00 -0500 Reply-To: James Bursley <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: James Bursley <[log in to unmask]> Subject: VOI Batch Module Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: quoted-printable Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> Hi SPMers, I'm having some trouble using the Volume of Interest batch module. I'm trying to extract a sphere from a single-subject .mat file, and I want to adjust the extraction to an F-contrast I've run for that subject. When I enter a '1' as the parameter for "Adjust data," though, to select that contrast, I get this error: Error with file beta_0001.img Failed 'Volume of Interest' Error using --> spm_sample_vol Cant open image file. In file "/usr/local/analysistools/fmritools/matlab/toolbox/spm8/spm_get_data.m" (v3950), function "spm_get_data" at line 44. In file "/usr/local/analysistools/fmritools/matlab/toolbox/spm8/spm_regions.m" (v3812), function "spm_regions" at line 147. In file "/usr/local/analysistools/fmritools/matlab/toolbox/spm8/config/spm_run_voi.m" (v3780), function "spm_run_voi" at line 68. Any idea what's going on? Thanks very much! James========================================================================Date: Thu, 17 Feb 2011 02:21:17 +0000 Reply-To: James Bursley <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: James Bursley <[log in to unmask]> Subject: Re: VOI Batch Module Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Two other pieces of information regarding this problem: 1. No errors come up when I extract a VOI using the GUI; I only encounter this error when using the batch script. 2. The error only appears when "Adjust data" is set to something other than '0', i.e., when data IS being adjusted. Otherwise, the batch runs fine. ========================================================================Date: Thu, 17 Feb 2011 02:41:30 +0000 Reply-To: Toni Pitcher <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Toni Pitcher <[log in to unmask]> Subject: multiple conditions/regressors in SPM8 batch Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi All I have just converted to SPM8 and have constructed a batch template for processing fMRI model specification, estimation, contrast manager and results report for chosen study, whereby each subject has 4 sessions. After assigning dependencies etc and using the 'save and script' option, the following was produced. % List of open inputs % fMRI model specification: Directory - cfg_files % fMRI model specification: Scans - cfg_files % fMRI model specification: Scans - cfg_files % fMRI model specification: Scans - cfg_files % fMRI model specification: Scans - cfg_files nrun = X; % enter the number of runs here jobfile = {'/Users/stats_job.m'}; jobs = repmat(jobfile, 1, nrun); inputs = cell(5, nrun); for crun = 1:nrun inputs{1, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Directory - cfg_files inputs{2, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files inputs{3, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files inputs{4, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files inputs{5, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files end spm('defaults', 'FMRI'); spm_jobman('serial', jobs, '', inputs{:}); Required inputs being directory and scans for each of the 4 sessions. What I would also like to do is specify multiple conditions and multiple regressors files for each session. I would like to do this in a script so that the batch template can be applied across subjects. These are not listed as required inputs (<-X) in the batch, so am unsure how to specify. Thanks Toni ========================================================================Date: Thu, 17 Feb 2011 09:32:54 +0000 Reply-To: Rik Henson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Rik Henson <[log in to unmask]> Subject: Research Assistant Position in Cambridge Content-Type: multipart/alternative; boundary="_000_3822A22C3FB6974099045A8DB62D39F93919E5D1WSR21mrccbsuloc_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_3822A22C3FB6974099045A8DB62D39F93919E5D1WSR21mrccbsuloc_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear colleagues - Please see Research Assistant position available below: MRC Cognition and Brain Sciences Unit - Cambridge Research Assistant Ref: IRC14207 The MRC Cognition and Brain Sciences Unit (CBSU) is an internationally renowned research institute with state-of-the-art cognitive neuroscience facilities, including on-site fMRI, MEG, and EEG laboratories, and neuropsychological patient panel and large panel of healthy volunteers. Applications are invited for a Research Assistant to provide scientific support for Dr Richard Henson's MRC programme on the systems neuroscience of human memory and perception, particularly for acquiring behavioural, EEG/MEG and/or fMRI data during cognitive experiments on volunteers. You will be highly motivated and proactive, with a degree in psychology or a related discipline. Experience in scientific research, particularly with human volunteers, is important, as is familiarity with computer programming and statistics. Experience with EEG, MEG or MRI acquisition and/or analysis would be an advantage. Good communication skills plus the ability to work as part of a large, multi-disciplinary team are essential. The starting salary will be in the range of £20,074 - £27,271 per annum, depending upon qualifications and experience. We offer a flexible pay and reward policy, 30 days annual leave entitlement, and an optional MRC final salary Pension Scheme. On site car and bicycle parking is available. Applications are handled by the RCUK Shared Services Centre; to apply please visit our job board at https://ext.ssc.rcuk.ac.uk and complete an online application form. If you are unable to apply online please contact us on 01793 867003 quoting reference IRC14207. Closing date: 21st March 2011 This position is subject to pre-employment screening The Medical Research Council is an Equal Opportunities Employer ------------------------------------------------------- Dr Richard Henson Assistant Director, Neuroimaging MRC Cognition & Brain Sciences Unit 15 Chaucer Road Cambridge, CB2 7EF, UK Office: +44 (0)1223 355 294 x522 Mob: +44 (0)794 1377 345 Fax: +44 (0)1223 359 062 http://www.mrc-cbu.cam.ac.uk/people/rik.henson ------------------------------------------------------- --_000_3822A22C3FB6974099045A8DB62D39F93919E5D1WSR21mrccbsuloc_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Dear colleagues –

Please see Research Assistant position available below:

MRC Cognition and Brain Sciences Unit – Cambridge

Research Assistant

Ref: IRC14207

The MRC Cognition and Brain Sciences Unit (CBSU) is an internationally renowned research institute with state-of-the-art cognitive neuroscience facilities, including on-site fMRI, MEG, and EEG laboratories, and neuropsychological patient panel and large panel of healthy volunteers. Applications are invited for a Research Assistant to provide scientific support for Dr Richard Henson’s MRC programme on the systems neuroscience of human memory and perception, particularly for acquiring behavioural, EEG/MEG and/or fMRI data during cognitive experiments on volunteers.

You will be highly motivated and proactive, with a degree in psychology or a related discipline. Experience in scientific research, particularly with human volunteers, is important, as is familiarity with computer programming and statistics. Experience with EEG, MEG or MRI acquisition and/or analysis would be an advantage. Good communication skills plus the ability to work as part of a large, multi-disciplinary team are essential.

The starting salary will be in the range of £20,074 - £27,271 per annum, depending upon qualifications and experience. We offer a flexible pay and reward policy, 30 days annual leave entitlement, and an optional MRC final salary Pension Scheme. On site car and bicycle parking is available.

Applications are handled by the RCUK Shared Services Centre; to apply please visit our job board at https://ext.ssc.rcuk.ac.uk and complete an online application form. If you are unable to apply online please contact us on 01793 867003 quoting reference IRC14207.

Closing date: 21^st March 2011

This position is subject to pre-employment screening

The Medical Research Council is an Equal Opportunities Employer

-------------------------------------------------------

Dr Richard Henson

Assistant Director, Neuroimaging

MRC Cognition & Brain Sciences Unit

15 Chaucer Road

Cambridge, CB2 7EF, UK

Office: +44 (0)1223 355 294 x522

Mob: +44 (0)794 1377 345

Fax: +44 (0)1223 359 062

http://www.mrc-cbu.cam.ac.uk/people/rik.henson

-------------------------------------------------------

--_000_3822A22C3FB6974099045A8DB62D39F93919E5D1WSR21mrccbsuloc_-- ========================================================================Date: Thu, 17 Feb 2011 11:49:09 +0100 Reply-To: Michael Erb <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Erb <[log in to unmask]> Subject: Re: multiple conditions/regressors in SPM8 batch Comments: To: Toni Pitcher <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hi Toni, if you right click on the 'multiple conditions' or 'multiple regressors' in the batch editor, you can apply 'Clear Value' to get the <-X. Michael. Am 17.02.2011 03:41, schrieb Toni Pitcher: > Hi All > > I have just converted to SPM8 and have constructed a batch template for processing fMRI model specification, estimation, contrast manager and results report for chosen study, whereby each subject has 4 sessions. > > After assigning dependencies etc and using the 'save and script' option, the following was produced. > > % List of open inputs > % fMRI model specification: Directory - cfg_files > % fMRI model specification: Scans - cfg_files > % fMRI model specification: Scans - cfg_files > % fMRI model specification: Scans - cfg_files > % fMRI model specification: Scans - cfg_files > nrun = X; % enter the number of runs here > jobfile = {'/Users/stats_job.m'}; > jobs = repmat(jobfile, 1, nrun); > inputs = cell(5, nrun); > for crun = 1:nrun > inputs{1, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Directory - cfg_files > inputs{2, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files > inputs{3, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files > inputs{4, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files > inputs{5, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files > end > spm('defaults', 'FMRI'); > spm_jobman('serial', jobs, '', inputs{:}); > > > Required inputs being directory and scans for each of the 4 sessions. > > What I would also like to do is specify multiple conditions and multiple regressors files for each session. I would like to do this in a script so that the batch template can be applied across subjects. These are not listed as required inputs (<-X) in the batch, so am unsure how to specify. > > > > Thanks > > Toni > -- <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Dr. Michael Erb Sektion f. experimentelle Kernspinresonanz des ZNS Abteilung Neuroradiologie, Universitaetsklinikum Hoppe-Seyler_Str. 3, D-72076 Tuebingen Tel.: +49(0)7071/2987753 priv. +49(0)7071/61559 Fax.: +49(0)7071/294371 e-mail: <[log in to unmask]> www: http://www.medizin.uni-tuebingen.de/nrad/sektion/ <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< ========================================================================Date: Thu, 17 Feb 2011 13:11:05 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: DARTEL template in MNI space Comments: To: kambiz rakhshan <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00163630f77d500ad5049c7a214f Content-Type: text/plain; charset=ISO-8859-1 > I am wondering how to use spm_get_space to affine normalize the DARTEL > template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M? > Isn't it possible to use normalize_write function instead? > You could write the matrix in *_2mni.mat into the header of the Template_6.nii image. The spm_get_space function would do that. > My second question concerns the use of suitable mask after applying > normalize to MNI function for fMRI data to get ride of potential aliasing > effects.Is it better to use normalized template of dartel as an > explicit mask (while doing second level analysis) or to apply the > deformation to the first level subject-specific mask and then multiply with > the smoothed normalized images created by normalize to MNI function? > These are not really aliasing effects. It's the effect of missing data in the images, where SPM tries to guess about what values should be there. Usually, these regions contain zeros, which cause even greater artifacts after smoothing, as some subjects' scans will have data, whereas others have zeros. Because this seems to be a concern for so many, I'll have a go at including a fix for the data so that those voxels with very little signal are set to zero. Best regards, -John --00163630f77d500ad5049c7a214f Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

I am wondering how to use spm_get_space to affine normalize the DARTEL template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M?

Isn't it possible to use normalize_write function instead?

You could write the matrix in *_2mni.mat into the header of the Template_6.nii image. The spm_get_space function would do that.

My second question concerns the use of suitable mask after applying normalize to MNI function for fMRI data to get ride of potential aliasing effects.Is it better to use normalized template of dartel as an explicit mask (while doing second level analysis) or to apply the deformation to the first level subject-specific mask and then multiply with the smoothed normalized images created by normalize to MNI function?

These are not really aliasing effects. It's the effect of missing data in the images, where SPM tries to guess about what values should be there. Usually, these regions contain zeros, which cause even greater artifacts after smoothing, as some subjects' scans will have data, whereas others have zeros.

Because this seems to be a concern for so many, I'll have a go at including a fix for the data so that those voxels with very little signal are set to zero.

Best regards,

-John

--00163630f77d500ad5049c7a214f-- ========================================================================Date: Thu, 17 Feb 2011 13:54:19 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: methodological question Comments: To: Pierre Larigneux <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear Gaetan, You should contrast similar things so if you have 20 ms activation time window you should compare it to a 20 ms baseline time window. You can indeed generate multiple windows from the baseline and put them all in the same design matrix as in your option (3). You could also compare your activation windows to each other across trials/subjects, then you could talk about sources being more strongly activated at time A vs. time B. Note that for these comparisons you need to specify all the time windows together (separated by ';' ) when you export to images. Best, Vladimir On Thu, Feb 17, 2011 at 11:42 AM, Pierre Larigneux <[log in to unmask]> wrote: > Dear Vladimir, > > I changed my methodology. I do not work anymore on the amplitude. > I come back to you to have some advise about the way I should look onto my > data. > > Now, to bring to light the region activated by my task I do a paired t-test > between 20 ms time windows post stimulus against baseline. > I reconstruct the sources on the global time window -200 to 600 ms and I use > gaussian windows as contrast. > > I am wondering about the most proper way to do it, I have got three main > ideas: > > 1. contrasting my 20 ms time windows against the global mean on the baseline > -200 to 0. > 2. constrating my 20 ms time windows against the mean energy of ten (or a > bit less) 20 ms time windows on the baseline. > 3. constrating my 20 ms time windows against each 20 ms time windows in a > big design matrix. > > I do not know what is the best way to do it. I tried the different things > and it gave me really close results in the localisation of the activity. > it change mainly the T of the statistics from significant to very > significant. > > Thanks for your attention, > > bests regards, > > Gaetan Yvert ========================================================================Date: Thu, 17 Feb 2011 15:02:45 +0000 Reply-To: Nick Davis <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nick Davis <[log in to unmask]> Organization: Bangor University Subject: Creating an "average" structural image MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hello SPM nerds. I would like to project my activation blobs onto some kind of average image of the brains of the subjects in my experiment. I can't see a way to create this average image in SPM8, so is there another method I could use? Thanks! Nick -- [log in to unmask] -- Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi, gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi, rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu 100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa Cyllid Prifysgol Bangor. www.bangor.ac.uk This email and any attachments may contain confidential material and is solely for the use of the intended recipient(s). If you have received this email in error, please notify the sender immediately and delete this email. If you are not the intended recipient(s), you must not use, retain or disclose any information contained in this email. Any views or opinions are solely those of the sender and do not necessarily represent those of the Bangor University. Bangor University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of the text of the email, this email is not intended to form a binding contract - a list of authorised signatories is available from the Bangor University Finance Office. www.bangor.ac.uk ========================================================================Date: Thu, 17 Feb 2011 15:19:16 +0000 Reply-To: Michael O'Sullivan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael O'Sullivan <[log in to unmask]> Subject: Re: Creating an "average" structural image Comments: To: Nick Davis <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="=_alternative 0054298F8025783A_=" Message-ID: <[log in to unmask]> This is a multipart message in MIME format. --=_alternative 0054298F8025783A_Content-Type: text/plain; charset="ISO-8859-1" Content-Transfer-Encoding: quoted-printable Dear Nick Assuming they are all in the same space.... you should be able to just add them all up with ImCalc. You will probably need to smooth the final sum/average to make it look nicer. If the results are in MNI space an alternative is to use one of the canonical images. Mike From: Nick Davis <[log in to unmask]> To: [log in to unmask] Date: 17/02/2011 15:13 Subject: [SPM] Creating an "average" structural image Sent by: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> Hello SPM nerds. I would like to project my activation blobs onto some kind of average image of the brains of the subjects in my experiment. I can't see a way to create this average image in SPM8, so is there another method I could use? Thanks! Nick -- [log in to unmask] -- Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi, gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi, rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu 100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa Cyllid Prifysgol Bangor. www.bangor.ac.uk This email and any attachments may contain confidential material and is solely for the use of the intended recipient(s). If you have received this email in error, please notify the sender immediately and delete this email. If you are not the intended recipient(s), you must not use, retain or disclose any information contained in this email. Any views or opinions are solely those of the sender and do not necessarily represent those of the Bangor University. Bangor University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of the text of the email, this email is not intended to form a binding contract - a list of authorised signatories is available from the Bangor University Finance Office. www.bangor.ac.uk --=_alternative 0054298F8025783A_Content-Type: text/html; charset="ISO-8859-1" Content-Transfer-Encoding: quoted-printable Dear Nick
Assuming they are all in the same space.... you should be able to just add them all up with ImCalc. You will probably need to smooth the final sum/average to make it look nicer. If the results are in MNI space an alternative is to use one of the canonical images.

Mike

From: Nick Davis <[log in to unmask]>
To: [log in to unmask]
Date: 17/02/2011 15:13
Subject: [SPM] Creating an "average" structural image
Sent by: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>

Hello SPM nerds. I would like to project my activation blobs onto some kind of average image of the brains of the subjects in my experiment. I can't see a way to create this average image in SPM8, so is there another method I could use? Thanks! Nick -- [log in to unmask] -- Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi, gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi, rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu 100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa Cyllid Prifysgol Bangor. www.bangor.ac.ukThis email and any attachments may contain confidential material and is solely for the use of the intended recipient(s). If you have received this email in error, please notify the sender immediately and delete this email. If you are not the intended recipient(s), you must not use, retain or disclose any information contained in this email. Any views or opinions are solely those of the sender and do not necessarily represent those of the Bangor University. Bangor University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of the text of the email, this email is not intended to form a binding contract - a list of authorised signatories is available from the Bangor University Finance Office. www.bangor.ac.uk
--=_alternative 0054298F8025783A_=-- ========================================================================Date: Thu, 17 Feb 2011 15:43:36 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Creating an "average" structural image Comments: To: Nick Davis <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> Creating an average is just the same as fitting a GLM through the data, where the design matrix is simply a column of ones. Therefore, you could just use the stats options. Best regards, John On 17 February 2011 15:02, Nick Davis <[log in to unmask]> wrote: > Hello SPM nerds. > > I would like to project my activation blobs onto some kind of average image > of the brains of the subjects in my experiment. I can't see a way to create > this average image in SPM8, so is there another method I could use? > > Thanks! > Nick > > > > -- > [log in to unmask] > > -- > Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi, > gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig > gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y > neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar > unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi, > rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a > gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i > hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn > Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu > bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu > 100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn > nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract > rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa > Cyllid Prifysgol Bangor. www.bangor.ac.uk > > This email and any attachments may contain confidential material and > is solely for the use of the intended recipient(s). If you have > received this email in error, please notify the sender immediately > and delete this email. If you are not the intended recipient(s), you > must not use, retain or disclose any information contained in this > email. Any views or opinions are solely those of the sender and do > not necessarily represent those of the Bangor University. > Bangor University does not guarantee that this email or > any attachments are free from viruses or 100% secure. Unless > expressly stated in the body of the text of the email, this email is > not intended to form a binding contract - a list of authorised > signatories is available from the Bangor University Finance > Office. www.bangor.ac.uk > ========================================================================Date: Thu, 17 Feb 2011 16:56:40 +0100 Reply-To: claus lamm <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: claus lamm <[log in to unmask]> Subject: Re: Creating an "average" structural image Comments: To: Nick Davis <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> On 2/17/2011 4:02 PM, Nick Davis wrote: > Hello SPM nerds. > > I would like to project my activation blobs onto some kind of average > image of the brains of the subjects in my experiment. I can't see a > way to create this average image in SPM8, so is there another method I > could use? > > Thanks! > Nick > > > you could also use imcalc - mean(X) after loading all the individual normalized images as input ========================================================================Date: Thu, 17 Feb 2011 16:33:07 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: New Segmentation Out of Memory Error Comments: To: michel grothe <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The best possible workaround would be the following. In principle, if you have scans from other subjects that have additional information (eg structure label images etc), then you can align these scans to the population average images, using Dartel. Once you have done this, the information can be warped so that it overlays on your individuals' scans via a procedure described towards the end of the "Using Dartel" chapter of the SPM8 manual. Alternatively, you could create a copy of the MNI-space image of labels, and tweak the matrix in its header, so that it is in alignment with the population average. This is untested, but I think you'd set the matrix to M3*inv(M2)*M1, where: M1 = voxel-to-world matrix of your MNI-space labels M2 = voxel-to-world matrix of the population average space Template_6 M3 = matrix in the Template_6_2mni.mat If this works, you should be able to use one of the options in the Dartel toolbox (or the Deformations Utility) to warp the data on to your original scans. Best regards, -John On 16 February 2011 19:45, michel grothe <[log in to unmask]> wrote: > Thanks, this would be great. > > Meanwhile, I have some questions regarding a possible work-around: > > Would it be OK to use an affine transform calculated with the > Normalise-Button to approximate the affine transform from 'DARTEL-Normalise > to MNI' in order to be able to generate the mentioned composition > 'native->DARTEL->MNI'. Does the affine alignment step in 'DARTEL-Normalise > to MNI' register the DARTEL-GM template to the GM-apriori map (or the GM map > in TPM.nii,1?) or does it also use the respective WM-maps? > How much does the affine alignment in 'DARTEL-Normalise to MNI' differ from > the one in 'Normalise', when registering DARTEL-GM to GM-apriori map (or > TPM.nii,1) and setting non-linear iterations to 0? > > Thanks again for your help. > Best regards, > Michel > >> Date: Wed, 16 Feb 2011 17:16:00 +0000 >> From: [log in to unmask] >> Subject: Re: [SPM] New Segmentation Out of Memory Error >> To: [log in to unmask] >> >> > I haven´t seen such a file in my version of SPM8 (the penultimate >> > update, >> > run on win32). Is it possible that this file is just generated during >> > estimation of the affine transform and not stored permanently on disk? >> > Or >> > does it come with the latest update? >> >> This change occurred in SPM8 r3727, so should be in the current >> updates, which are to r4010. >> >> >> > It would be very helpful to have such a file, mainly for the reasons >> > Richard >> > mentioned, i.e. use it to compose the complete transform >> > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be >> > used >> > to warp Atlas-labels in MNI space to native space. >> >> There's no easy way to use the file yet, but I'll try to code >> something up for a future version of SPM8. >> >> Best regards, >> -John >> >> >> >> Date: Wed, 16 Feb 2011 15:58:16 +0000 >> >> From: [log in to unmask] >> >> Subject: Re: [SPM] New Segmentation Out of Memory Error >> >> To: [log in to unmask] >> >> >> >> This file does indeed contain an affine transform, and is generated >> >> when the affine registration between population average and MNI space >> >> is estimated. >> >> >> >> There's no script to do what you're after yet, but if the demand is >> >> high enough I could introduce the option. Can I ask how you plan to >> >> use the transforms? >> >> >> >> Best regards, >> >> -John >> >> >> >> >> >> On 16 February 2011 15:44, Richard Binney >> >> <[log in to unmask]> >> >> wrote: >> >> > ......I've just spotted that running normalise-to-mni now spits out a >> >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would >> >> > this >> >> > happen to be the affine transform I spoke of? >> >> > >> >> > R >> >> > >> >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney >> >> > <[log in to unmask]> wrote: >> >> >> >> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've >> >> >> done >> >> >> it and it makes a huge difference. Pretty obvious really. >> >> >> >> >> >> One more thing, John (or anybody else for that matter) - is there >> >> >> now a >> >> >> way in which one can easily create an inverse of the DARTEL >> >> >> normalise-to-MNI >> >> >> deformation composition (so from MNI --> study average --> >> >> >> individual >> >> >> subject space)? Is it possible (i.e., is there script) to have this >> >> >> (forward >> >> >> and/or backwards) written out for each subject or, alternatively, to >> >> >> have >> >> >> the affine template-to-TPM transform outputted such that one can >> >> >> combine it >> >> >> with a flow field, and subsequently inverse the composition using >> >> >> the >> >> >> deformations tool? >> >> >> >> >> >> All the best, >> >> >> >> >> >> Richard >> >> >> >> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner >> >> >> <[log in to unmask]> >> >> >> wrote: >> >> >>> >> >> >>> You may wish to change the voxel sizes and bounding box in order to >> >> >>> obtain slightly lower resolution versions (other than the default >> >> >>> 1.5mm isotropic with a large bounding box). See the email I sent >> >> >>> out >> >> >>> recently. >> >> >>> >> >> >>> Best regards, >> >> >>> -John >> >> >>> >> >> >>> On 15 February 2011 17:29, Richard Binney >> >> >>> <[log in to unmask]> >> >> >>> wrote: >> >> >>> > Dear John (and other knowledgeable types), >> >> >>> > >> >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to >> >> >>> > normalise >> >> >>> > a >> >> >>> > functional MRI timeseries. That worked fine - the anatomical >> >> >>> > images that >> >> >>> > I additionally warped in this way look great - but I now cannot >> >> >>> > do >> >> >>> > anything >> >> >>> > with the timeseries. >> >> >>> > >> >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to >> >> >>> > warp >> >> >>> > fMRI >> >> >>> > data can result in massive files which are problematic (and thus >> >> >>> > a >> >> >>> > work >> >> >>> > around is to apply the warp to contrast images), but I didn't >> >> >>> > expect >> >> >>> > the >> >> >>> > segment deformation field to do the same. >> >> >>> > >> >> >>> > Does this sound about right for a timeseries with 465 volumes >> >> >>> > (voxel >> >> >>> > dim 2.5 >> >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a >> >> >>> > mistake >> >> >>> > somewhere? >> >> >>> > >> >> >>> > so I'm supposing my inability to use the warped timeseries is a >> >> >>> > memory >> >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see >> >> >>> > all >> >> >>> > this >> >> >>> > being used in the task manager so doesn't seem to be a matlab >> >> >>> > version >> >> >>> > problem) but maybe this file is just way too big for the SPM >> >> >>> > platform.....???? I could crop the image, etc I suppose but maybe >> >> >>> > the >> >> >>> > biggest problem is the shear number of volumes. >> >> >>> > >> >> >>> > Thanks in advance for your thoughts >> >> >>> > >> >> >>> > Richard >> >> >>> > >> >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo >> >> >>> > <[log in to unmask]> wrote: >> >> >>> >> >> >> >>> >> Thanks John. >> >> >>> >> >> >> >>> >> It turns out the problem was due to the version of Matlab I was >> >> >>> >> running. >> >> >>> >> >> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is >> >> >>> >> 64. >> >> >>> >> >> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL >> >> >>> >> import >> >> >>> >> now >> >> >>> >> runs without any problem, without me having to alter/crop any of >> >> >>> >> the >> >> >>> >> images. >> >> >>> >> >> >> >>> >> All the best >> >> >>> >> William >> >> >>> > >> >> >>> > >> >> >> >> >> > >> >> > >> > > ========================================================================Date: Thu, 17 Feb 2011 17:55:36 +0100 Reply-To: Christian Doeller <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Christian Doeller <[log in to unmask]> Subject: PhD Student and Postdoctoral Researcher Positions in Neuroimaging, Donders Institute, Nijmegen MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001485f92356386899049c7d4470 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable PhD Student and Postdoctoral Researcher Positions in Neuroimaging (1,0 fte) *Donders Institute, Centre for Cognitive Neuroimaging* *Vacancy number: 30.03.11* *Closing date: 15 March 2011* *Job description* The newly established PI group of Dr Christian Doeller at the Donders Institute for Brain, Cognition and Behaviour seeks highly talented and dedicated postdoctoral researchers and PhD students to work on an exciting project funded by a recently awarded ERC Starting Grant. The goal of this project is to gain understanding of how the human brain maps space and forms episodic memories. Our specific aim is to infer the fine-scale properties of neural systems in humans by building on models from single-cell electrophysiology (see Doeller et al., Evidence for Grid Cells in a Human Memory Network, Nature 2010, 463, 657-661). The techniques involve combinations of functional magnetic resonance imaging (fMRI, including 7T high-field scanning), virtual reality technologies, psychophysics and advanced tools for neuroimaging data analysis. *Requirements* As a candidate you should have a Master’s degree or equivalent (for the PhD student positions) or a PhD degree (for the postdoctoral positions) in a field related to cognitive neuroscience, e.g. experimental psychology, cognitive science, biology, neuroscience or related disciplines. Excellent candidates with a background in quantitative disciplines such as mathematics, physics or engineering who are interested in neuroscience are also encouraged to apply. Selection criteria for the postdoctoral positions will consider the record of published research, familiarity with neuroimaging techniques and programming skills (Matlab). For the PhD student positions, familiarity with neuroimaging techniques and good programming skills (Matlab) are desirable. Proficiency in oral and written English is required. You are expected to work in an interdisciplinary environment, sharing technical know-how and ideas. *Organization* The Donders Institute for Brain, Cognition and Behaviour consists of the Centre for Cognition, the Centre for Cognitive Neuroimaging, and the Centre for Neuroscience. The mission of the Centre for Cognitive Neuroimaging is to conduct cutting-edge fundamental research in cognitive neuroscience. Much of the rapid progress in this field is being driven by the development of complex neuroimaging techniques for measuring activity in the human working brain - an area in which the Centre plays a leading role. The research themes cover central cognitive functions, such as perception, action, control, decision making, attention, memory, language, learning and plasticity. The Centre also aims to establish how the different brain areas coordinate their activity with very high temporal precision to enable human and animal cognition. The internationally renowned centre currently hosts more than 100 PhD students and postdoctoral researchers from more than 20 nationalities, offering a stimulating and multidisciplinary research environment. The centre is equipped with three MRI scanners (7T, 3T, 1.5T), a 275-channel MEG system, an EEG-TMS laboratory, several (MR-compatible) EEG systems, and high-performance computational facilities. English is the lingua franca at the centre. Website: http://www.ru.nl/donders *Conditions of employment* Employment: 1,0 fte PhD student: The starting salary is €2,042 per month and will increase to €2,612 per month in the fourth year. Postdoctoral researcher: Depending on experience, the gross salary will be between €3,195 and €4,374. *Additional conditions of employment* Duration of the PhD-student contracts: 4 years. Duration of the postdoctoral contracts: 3 years (extension possible). In addition to the gross monthly salary, you will receive two yearly 8% bonuses (holiday and end-of-year). *Other Information* You should submit your application in a single PDF file, including an application letter, a statement of research interests, your CV, and the names of two persons who can provide references. *Additional Information* Dr Christian Doeller, Principal Investigator Telephone: +31 24 3610983 E-mail: [log in to unmask] *Application* You can apply for the job (mention the vacancy number 30.03.11) *before 15 March 2011* by sending your application -preferably by email- to: Radboud University Nijmegen, P&O department PO Box 7005, 6503 GM NIJMEGEN, NL Telephone: +31 24 3611173 E-mail: [log in to unmask] --001485f92356386899049c7d4470 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable

PhD Student and Postdoctoral Researcher Positions in Neuroimaging (1,0 fte)

Donders Institute, Centre for Cognitive Neuroimaging
Vacancy number: 30.03.11
Closing date: 15 March 2011

Job description
The newly established PI group of Dr Christian Doeller at the Donders Institute for Brain, Cognition and Behaviour seeks highly talented and dedicated postdoctoral researchers and PhD students to work on an exciting project funded by a recently awarded ERC Starting Grant.

The goal of this project is to gain understanding of how the human brain maps space and forms episodic memories. Our specific aim is to infer the fine-scale properties of neural systems in humans by building on models from single-cell electrophysiology (see Doeller et al., Evidence for Grid Cells in a Human Memory Network, Nature 2010, 463, 657-661).

The techniques involve combinations of functional magnetic resonance imaging (fMRI, including 7T high-field scanning), virtual reality technologies, psychophysics and advanced tools for neuroimaging data analysis.

Requirements
As a candidate you should have a Master’s degree or equivalent (for the PhD student positions) or a PhD degree (for the postdoctoral positions) in a field related to cognitive neuroscience, e.g. experimental psychology, cognitive science, biology, neuroscience or related disciplines. Excellent candidates with a background in quantitative disciplines such as mathematics, physics or engineering who are interested in neuroscience are also encouraged to apply.

Selection criteria for the postdoctoral positions will consider the record of published research, familiarity with neuroimaging techniques and programming skills (Matlab). For the PhD student positions, familiarity with neuroimaging techniques and good programming skills (Matlab) are desirable.

Proficiency in oral and written English is required. You are expected to work in an interdisciplinary environment, sharing technical know-how and ideas.

Organization
The Donders Institute for Brain, Cognition and Behaviour consists of the Centre for Cognition, the Centre for Cognitive Neuroimaging, and the Centre for Neuroscience.

The mission of the Centre for Cognitive Neuroimaging is to conduct cutting-edge fundamental research in cognitive neuroscience. Much of the rapid progress in this field is being driven by the development of complex neuroimaging techniques for measuring activity in the human working brain - an area in which the Centre plays a leading role. The research themes cover central cognitive functions, such as perception, action, control, decision making, attention, memory, language, learning and plasticity. The Centre also aims to establish how the different brain areas coordinate their activity with very high temporal precision to enable human and animal cognition. The internationally renowned centre currently hosts more than 100 PhD students and postdoctoral researchers from more than 20 nationalities, offering a stimulating and multidisciplinary research environment. The centre is equipped with three MRI scanners (7T, 3T, 1.5T), a 275-channel MEG system, an EEG-TMS laboratory, several (MR-compatible) EEG systems, and high-performance computational facilities. English is the lingua franca at the centre.
Website: http://www.ru.nl/donders

Conditions of employment
Employment: 1,0 fte
PhD student: The starting salary is €2,042 per month and will increase to €2,612 per month in the fourth year.
Postdoctoral researcher: Depending on experience, the gross salary will be between €3,195 and €4,374.

Additional conditions of employment
Duration of the PhD-student contracts: 4 years.
Duration of the postdoctoral contracts: 3 years (extension possible).

In addition to the gross monthly salary, you will receive two yearly 8% bonuses (holiday and end-of-year).

Other Information
You should submit your application in a single PDF file, including an application letter, a statement of research interests, your CV, and the names of two persons who can provide references.

Additional Information
Dr Christian Doeller, Principal Investigator
Telephone: +31 24 3610983
E-mail: [log in to unmask]

Application
You can apply for the job (mention the vacancy number 30.03.11) before 15 March 2011 by sending your application -preferably by email- to:

Radboud University Nijmegen, P&O department
PO Box 7005, 6503 GM NIJMEGEN, NL
Telephone: +31 24 3611173
E-mail: [log in to unmask]

--001485f92356386899049c7d4470-- ========================================================================Date: Thu, 17 Feb 2011 16:49:21 +0000 Reply-To: Vy Dinh <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vy Dinh <[log in to unmask]> Subject: Re: VOI Batch Module Comments: To: James Bursley <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi James, I think that this issue may have been discussed on the forums before. When you specify the adjust step, you set this field to the index for your F-contrast, which means that the F-contrast needed to be specified at the first level model or that your index was wrong. Since you mentioned that there were not any errors when you did this via the GUI, the latter may be the issue. Hope that helps. Vy T.U. Dinh Research Assistant, Neurological Sciences Rush University Medical Center Phone: (312) 563-3853 Fax: (312) 563-4660 Email: [log in to unmask] ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf of James Bursley [[log in to unmask]] Sent: Wednesday, February 16, 2011 8:21 PM To: [log in to unmask] Subject: Re: [SPM] VOI Batch Module Two other pieces of information regarding this problem: 1. No errors come up when I extract a VOI using the GUI; I only encounter this error when using the batch script. 2. The error only appears when "Adjust data" is set to something other than '0', i.e., when data IS being adjusted. Otherwise, the batch runs fine. ========================================================================Date: Thu, 17 Feb 2011 11:28:46 -0800 Reply-To: Siddharth Srivastava <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Siddharth Srivastava <[log in to unmask]> Subject: help with spm_snbasis code MIME-Version: 1.0 Content-Type: multipart/alternative; boundary¼aec53f944ffb757a049c7f67ac Message-ID: <[log in to unmask]> --bcaec53f944ffb757a049c7f67ac Content-Type: text/plain; charset=ISO-8859-1 Hi everyone, I have been reading source for spm_snbasis and spm_brainwarp to get a better understanding of the underlying math, and I came across these lines in spm_snbasis (spm99 source, line no 82-85): s1 = 3*prod(k); s2 = s1 + prod(size(VG))*4; T = zeros(s2,1); T(s1+(1:4:prod(size(VG))*4)) = 1; I wanted to know why T is initialized the way it is by the last line of the source snippet? Also, what does the multiplication by 4 imply? thanks, sid. --bcaec53f944ffb757a049c7f67ac Content-Type: text/html; charset=ISO-8859-1 Hi everyone,

I have been reading source for spm_snbasis and spm_brainwarp to get a better understanding of the
underlying math, and I came across these lines in spm_snbasis (spm99 source, line no 82-85):

s1 = 3*prod(k);
s2 = s1 + prod(size(VG))*4;
T = zeros(s2,1);
T(s1+(1:4:prod(size(VG))*4)) = 1;

I wanted to know why T is initialized the way it is by the last line of the source snippet? Also, what does
the multiplication by 4 imply?

thanks,
sid.

--bcaec53f944ffb757a049c7f67ac-- ========================================================================Date: Thu, 17 Feb 2011 20:08:30 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: help with spm_snbasis code Comments: To: Siddharth Srivastava <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> There are additional parameters that model linear intensity gradients in the x, y and z directions. The parameters would essentially be coefficients for a linear combination if template1 template1.*x template1.*y template1.*z template2 template2.*x template2.*y template2.*z ... Best regards, -John On 17 February 2011 19:28, Siddharth Srivastava <[log in to unmask]> wrote: > Hi everyone, > > I have been reading source for spm_snbasis and spm_brainwarp to get a better > understanding of the > underlying math, and I came across these lines in spm_snbasis (spm99 source, > line no 82-85): > > s1 = 3*prod(k); > s2 = s1 + prod(size(VG))*4; > T = zeros(s2,1); > T(s1+(1:4:prod(size(VG))*4)) = 1; > > I wanted to know why T is initialized the way it is by the last line of the > source snippet? Also, what does > the multiplication by 4 imply? > > thanks, > sid. > > > ========================================================================Date: Thu, 17 Feb 2011 15:38:34 -0500 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jake Thompson <[log in to unmask]> Subject: How to display mutltiple spm results for one subject with multiple t-contrasts with batch editor? In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="--------MB_8CD9CF9C7F2EC49_D14_23BBC_webmail-d011.sysops.aol.com" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ----------MB_8CD9CF9C7F2EC49_D14_23BBC_webmail-d011.sysops.aol.com Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="us-ascii" How to display mutltiple spm results for one subject with multiple t-contrasts with batch editor? I am only able to display the results from the last T-contrasts, but I want to display the results for all the t-contrasts I pu tinto the batch editor. ----------MB_8CD9CF9C7F2EC49_D14_23BBC_webmail-d011.sysops.aol.com Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii"

How to display mutltiple spm results for one subject with multiple t-contrasts with batch editor? I am only able to display the results from the last T-contrasts, but I want to display the results for all the t-contrasts I pu tinto the batch editor.

----------MB_8CD9CF9C7F2EC49_D14_23BBC_webmail-d011.sysops.aol.com-- ========================================================================Date: Fri, 18 Feb 2011 00:10:35 +0100 Reply-To: Saemann Philipp <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Saemann Philipp <[log in to unmask]> Subject: sensitivity of DARTEL Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Message-ID: <[log in to unmask]> Dear SPMers, in a recent paper by Pereira et al. on Resgistration accuracy for VBM studies, NeuroImage 2010;49:2205-15 DARTEL as implemented in SPM5 is presented as very insensitive to focal GM changes - in their work, they used a small Alzheimer Disease sample to to (amongmany other analyses) check the sensitivity to detect hippocampal atrophy. However, sensitivity and regional specificity were completely regained when three preprocessing steps were performed: 1. skull stripping, 2. bias correction, and 3., "fine tuned BET". > Has anybody made similar observations on insensitivity of DARTEL? > Is the SPM Bias correction (default parameters) really so non-optimal? > Is it the order of steps (first skull stripping, then bias correction) the crucial difference? > Is DARTEL in SPM8 in combination with the "New Segment" function more sensitive? I think for VBM where DARTEL has (my perception) repeatedly been recommended as excellent/best registration tool, it would be very useful if also the preprocessing/optimizations can be performed within SPM. - It is somewhat strange that extra preprocessing steps seem necessary outside SPM. I am very interested in comments/opinions here, thanks, Philipp Max Planck Institute of Psychiatry NMR Research Group Kraepelinstr. 2-10 80804 Munich Mail: [log in to unmask] Phone: 0049-89-30622-413 ========================================================================Date: Thu, 17 Feb 2011 18:53:01 -0500 Reply-To: Lisa Pan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lisa Pan <[log in to unmask]> Subject: SVC correction MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001485f1dbbc0e0ec4049c831997 Content-Type: text/plain; charset=ISO-8859-1 Does anyone know what exactly the SVC option in SPM5 is doing? You can select and area to SVC correct and then you can also choose FWE or FDR, but you do not have to. If you just do SVC, what is it calculating? Thank you for any information. Lisa -- Become a Friend! http://FriendsofPineRidgeReservation.org --001485f1dbbc0e0ec4049c831997 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Does anyone know what exactly the SVC option in SPM5 is doing? You can select and area to SVC correct and then you can also choose FWE or FDR, but you do not have to. If you just do SVC, what is it calculating?

Thank you for any information.

Lisa

--
Become a Friend!
http://FriendsofPineRidgeReservation.org

--001485f1dbbc0e0ec4049c831997-- ========================================================================Date: Fri, 18 Feb 2011 00:09:00 +0000 Reply-To: Anup Bidesi <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Anup Bidesi <[log in to unmask]> Subject: VBM results question Content-Type: multipart/mixed; boundary="_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: multipart/alternative; boundary="_000_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_" --_000_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hello SPM'ers, I have question about VBM data results (from 10, Normals and 19, Schizophrenic subjects) upon segmenting them using VBM8 w/Dartel normalization and looking at the stats using non-parametric SnPM toolbox. For the GM/WM Segmentaion: I've used VBM8's Estimate & Write option and set all other parameters to defaults in extended options (with modulated normalized "non-linear only" selected) and later smoothed the data using a 12 mm kernel using SPM8, to obtain the the processed data as sm0wrp1/p2/p3 associated with GM/WM/CSF segmentation, To compare both groups: design/set-up, estimation/compute and Results were done using SnPM. I've downloaded SnPM for SPM8 (see Readme.txt file attached from snpm folder), however while loading up SnPM in SPM8 it is stated as version SnPM5. In the SnPM Set-up following parameters were used: 2 Groups: Two Sample T test; 1scan/subject, Subject index specified as: AAA...(for Normals)...BBB...(for SZ subjs.) ; Selected Yes, for # Perms. Use approx. test? and Specified no. of Permutions as 1000 to begin with to 10,000; Variance smoothing of 10 mm, specified Yes, for Collect supra-Threshold stats?; with "No" for Define the thresh now? , No global normalisation and grand mean scaling; specified Absolute Threshold masking of 0.15. For Analysis mask specified:Smooth modulated sample subject scan (sm0wrp1....img) with 1000 & 10,000 permutations and also tried canonical "avg_152T1_GM.hdr/.img" with 5000 permutations as Gray matter masks. Upon looking up for GM differences between 2 groups with these SnPM settings, estimate and looking up results specifying "+ve" for effects (since looking for NC>SZ) Selecting to "Write filtered statistic img"; specifying P for "Results for which img? T/P" and then specifying FDR-corrected p value threshold of 0.05 and saved the outputs attached. There GM differences between both groups did not survive corrected threshold at 0.05 threshold, but showed meaningful differences using 10,000 permutations with smoothed modulated subject GM segmented scan as analysis mask and for 5000 permutations with avg_152T1_GM .img as GM mask. Upon looking the filtered_statistic image from both the tests using xjview, the clusters seem to contain considerable WM voxels (upto 40-50%) while looking for GM differences between groups ? why do they appear and how can these WM voxels be cleaned out from the results ? Thanks for all your help! Anup Research Associate Dept. of Psychiatry, UT Southwestern Medical Center ________________________________ UT Southwestern Medical Center The future of medicine, today. --_000_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Hello SPM'ers,

I have question about VBM data results (from 10, Normals and 19, Schizophrenic subjects) upon segmenting them using VBM8 w/Dartel normalization and looking at the stats using non-parametric SnPM toolbox.

For the GM/WM Segmentaion: I've used VBM8's Estimate & Write option and set all other parameters to defaults in extended options (with modulated normalized "non-linear only" selected) and later smoothed the data using a 12 mm kernel using SPM8, to obtain the the processed data as sm0wrp1/p2/p3 associated with GM/WM/CSF segmentation, To compare both groups: design/set-up, estimation/compute and Results were done using SnPM. I've downloaded SnPM for SPM8 (see Readme.txt file attached from snpm folder), however while loading up SnPM in SPM8 it is stated as version SnPM5.

In the SnPM Set-up following parameters were used: 2 Groups: Two Sample T test; 1scan/subject, Subject index specified as: AAA...(for Normals)...BBB...(for SZ subjs.) ;
Selected Yes, for # Perms. Use approx. test? and Specified no. of Permutions as 1000 to begin with to 10,000;
Variance smoothing of 10 mm, specified Yes, for Collect supra-Threshold stats?; with "No" for Define the thresh now? ,
No global normalisation and grand mean scaling; specified Absolute Threshold masking of 0.15.
For Analysis mask specified:Smooth modulated sample subject scan (sm0wrp1....img) with 1000 & 10,000 permutations and also tried canonical "avg_152T1_GM.hdr/.img" with 5000 permutations as Gray matter masks.

Upon looking up for GM differences between 2 groups with these SnPM settings, estimate and looking up results specifying "+ve" for effects (since looking for NC>SZ)

Selecting to "Write filtered statistic img"; specifying P for "Results for which img? T/P" and then specifying FDR-corrected p value threshold of 0.05 and saved the outputs attached.
There GM differences between both groups did not survive corrected threshold at 0.05 threshold, but showed meaningful differences using 10,000 permutations with smoothed modulated subject GM segmented scan as analysis mask and for 5000 permutations with avg_152T1_GM .img as GM mask.

Upon looking the filtered_statistic image from both the tests using xjview, the clusters seem to contain considerable WM voxels (upto 40-50%) while looking for GM differences between groups ? why do they appear and how can these WM voxels be cleaned out from the results ?

Thanks for all your help!

Anup

Research Associate
Dept. of Psychiatry,
UT Southwestern Medical Center

UT Southwestern Medical Center
The future of medicine, today.
--_000_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_-- --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: application/pdf; name="nc>sz_10000p_Subj_GM_mask.pdf" Content-Description: nc>sz_10000p_Subj_GM_mask.pdf Content-Disposition: attachment; filename="nc>sz_10000p_Subj_GM_mask.pdf"; size$677; creation-date="Fri, 18 Feb 2011 00:07:18 GMT"; modification-date="Fri, 18 Feb 2011 00:07:18 GMT" Content-Transfer-Encoding: base64 JVBERi0xLjUNJeLjz9MNCjEwIDAgb2JqDTw8L0xpbmVhcml6ZWQgMS9MIDI0Njc3L08gMTIvRSAy MDA1Ni9OIDEvVCAyNDM3NS9IIFsgNDY1IDE1NV0+Pg1lbmRvYmoNICAgICAgICAgICAgICAgICAg DQoxOSAwIG9iag08PC9EZWNvZGVQYXJtczw8L0NvbHVtbnMgNC9QcmVkaWN0b3IgMTI+Pi9GaWx0 ZXIvRmxhdGVEZWNvZGUvSURbPDRENkU3RDIzQ0U3QTc2RDE1ODA3ODhDM0IwQ0QyRjRBPjw3QjhF NTBGQkQ1QzNCQTQ5QjA2MkZBRThDOUQ2N0Y2QT5dL0luZGV4WzEwIDIyXS9JbmZvIDkgMCBSL0xl bmd0aCA2My9QcmV2IDI0Mzc2L1Jvb3QgMTEgMCBSL1NpemUgMzIvVHlwZS9YUmVmL1dbMSAyIDFd Pj5zdHJlYW0NCmjeYmJkEGBgYmCKARIMfkCCsRFIMBcDCXZvIMHdAhITARImskBi8zEGJkaGmSDF DIykEf8Zj3wDCDAAkeYIAA0KZW5kc3RyZWFtDWVuZG9iag1zdGFydHhyZWYNCjANCiUlRU9GDQog ICAgICAgIA0KMzEgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0kgOTIvTCA3Ni9MZW5ndGgg NzEvUyAzOD4+c3RyZWFtDQpo3mJgYGBnYGCSZwAC73kMqIARiFkYOBqQxdihmIGhkYGPgUFfcVMX M5O/5QEuB6iO2b+gOtvAuhlWF0H4jBkAAQYAZGIJBw0KZW5kc3RyZWFtDWVuZG9iag0xMSAwIG9i ag08PC9NZXRhZGF0YSAyIDAgUi9QYWdlTGFiZWxzIDYgMCBSL1BhZ2VzIDggMCBSL1R5cGUvQ2F0 YWxvZz4+DWVuZG9iag0xMiAwIG9iag08PC9Db250ZW50cyAxNCAwIFIvQ3JvcEJveFswIDAgNjEy IDc5Ml0vTWVkaWFCb3hbMCAwIDYxMiA3OTJdL1BhcmVudCA4IDAgUi9SZXNvdXJjZXMgMjAgMCBS L1JvdGF0ZSAwL1R5cGUvUGFnZT4+DWVuZG9iag0xMyAwIG9iag08PC9GaWx0ZXIvRmxhdGVEZWNv ZGUvRmlyc3QgNzkvTGVuZ3RoIDc4Ny9OIDExL1R5cGUvT2JqU3RtPj5zdHJlYW0NCmje7JXbbtpA EEB/ZX+A7s1XKUICEhKkpInqqI2EeFjMAm6NTe0lon/fmbWXGJSkospDH/owmtm5e/eECEYYEZzw UBAhCI/hJMEREuEREYLyiSc5EQEJmA8ewrkXExERLjmUxIRHHiMSyhiX5OKCjsq8rJKtSjUe6gC7 M/IFzAgngNnv06u9uU6MMphznXCc2QTGZWHAN7YTsWwscJi1JI5p0h6qMk20mdKHyzF91HtDJxu1 0qNGTWb06X7+XafYarLBr7MdJhv4wqhp0e9P6WQ0GqpaLwj30TkDT7HQe3A0O5NAEm7XmEGj+wey VHmtwbgjnCaD9pjcEfaJCfr4a6tfPoyW2ybe70Mtjrkq0nKRFSv6LSsGRZ0dzpfZcqkrXaS6nvKY 3ta5qtc0b9QmK3Y1XVYqNVlZ0HmlnzVNVQX2ojS5rusMDZWmujB0vStWqtptcrUztFyVhf5BK5yx zOgyn7U7toPtZoMaC0nIIzpS2xudrdbN6VI3kZ5gIR3nalXDkwAYgX2j4bDcT3s8ZBBHGBiTJA7E zAY/q42mNzp/1iZLVW9Y5gs6MSrP0kGxyjVhNDF6c0O411hfrWV3w3KcXGVbU1b0qV3Il+Jwj5hy 2t19ElJrWcmq2ozWqoKXPOmJRGPKrWozuPBpspsbHI878JdN4KkWZl1P/cgDEEIipbQiwuhgOwk4 t+JiXugR3w+sRFFMQoBfyE4d+LCvy0eN4mr+JK4P9nCC8+PQt7O6EsDuGAuhv92jnWNj4I+gj80F P+a6vENda8ee1/haf/cu7A7tXqgx3tWnd+R2QI3342JO7P106uy+3fthzOaIyOrZ2TRL0SU5CJBk H0lmJJb8NZJfhTiKWobBeBdhId9A+Dx6w1N6xUfQi7eJ4l5G+v4RvfjiPOYfRu8hv0Mv+jmD32KH 2Lv4wrLotwv+Jb5uCRQviI/wdYh1Fz7YuAOIvTDQUbem8+fpLtT1CtuaVs5HNn7lxxeQ5fBP6g1k e/fzPPu500fo9rj4MHgPA86COD6BmIf/If73IQZgfwswABHdkN8NCmVuZHN0cmVhbQ1lbmRvYmoN MTQgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAxNzk2Pj5zdHJlYW0NCmjezFhL bxs3EL7v3fc5FTJqrUgOny5yaOI8GiBBUgvJwQkMRV7bSvWwtfKjKfrfO+TuaknrlTYJUDuwtLvk fN98/GbIzXXGYQSZtrnkBiyCtJhbRNBa50LBvMjew5QGXWS958f0UWZK8TDYWA1dxaqZXYMiVzSf JkgMzybZefZ2Oes6E0rmXBtg9CuQ++g1rLTGXw0nWe+3CYejGc173M96zzhw6J9nXIdJ9MGR5UaB EcaTROhPMkbUGPSH/s9d1jmevnn115uyuDmb7S3+3u9/zp72A300WIE5BM54rokKlzoPORIGPKcI LwnwM9C9OxoDr+DkI+GeVRpd07NAA5pQGl3uaYfbnkm3CjjOGoCVG4y+s/D3mJK8zqRROSPZqlDd B/OIWYo1CatxHYVjdRJxepWQohKyllLUUrJqDgN0SNOk5Y2InaOiHF1M4dVgMR/d18oxkmVn4nFy x8vBSzl2jnswLNKxHdXO3T7uoYTfBZxuChFmt+GCEGwXxy6nUM1y00JE+mvyP0cqCa39EnRULfmq 3MrxUFvfEVw5CkHAikrP8ZoBZ5spVO3gRzDQoXFgxWCLCJL/EHg0OdatpCO2CMCV7zw/ggEXvgXU DDYL8P8rOkopt1Fr1fTAJyJVaPSow+TQXq+TQSSkD+07OXr1m21mTdIWH7Cso46zGmXlRtpi32ZS uGpHC6FoEPmY5tSb1b+nlsaLqNVBA5EWo7mMaLXNSunaRuuXq2nfrWsk8zPouRF22biHs+liPigX 3jrtpimqOQIcVpulVGHT9TZ7A7eD8U1Rwk9QLgaLUbkYDctDAOjNe2esN5jeXPUuJqfT4Wn55XQy OzvljH6uGm9mVUgKXmnvWZFiiKaRq/ek1PCkyfT4yesMaU+NVKwjSLRJWflIK+mv2b0403VSQjdJ Dcc35aKY742L22L8LVRXWQoM1mpXSYiHFHGFovP1Kqm6a35/QLVAdTLUd8hruYT+Uda5nd0X44T5 NloCQ8tYw4vauX8QdZw1zOrpGJMLa2vDCEsDTNWTJTPNgGfvn+4NZ/O5HxeFsqxeh02hrK76axzq 6PdlqHUnE8lXYjYnumpKnZHM6fhEc7yACSi6JsEW9Ga6BdKXhvYbsRTMjz7p3B/8efBln2zWgclk /2P/5Ve6ia+3OZOh0Hf5nK+aCD0xaqbWLo+8Jx0taKPocstNbjosZyjpUgjE+tI/FahNB0Dm1gX+ bdoud7VqNJYxkfQNmYvKQfT8pLMnLOEwJTu0AdM3pMR9VExDdgVFqk5dQW8/le44k9JCjGihoQu/ qZmKBj3jXLEcO3t0EGlAK3juQYUIoCyAeDVrGNQ2gZHMLWFIPZ7AcK+bFIpgYE+H4DRO2ig3WedW C9KlzcHxJDXngvqiBlRoN6uvzYr6qGL5+Vb5jadI6lKwPdPQVTzE7ApZmSOixoVzGMgxH16LLcbQ LKVGMFI14OvJqWqHDVAgdbQ4fI2CkTvQqAfmsEZupiYxNYfwq6asj+59aCR6n0jR+ISz9d5gznsp QtEsRkm9wUyLIqLcvG1rRG4emEOS31JzCO0xlrXJuTOb86Ty+u+16W1n6HRhiJdafkXxNbWJ3OAm WnRLpMLwtjaFSB1ppNdrT+BuYbjCSBjBvcabGDi9XZitVSNbYZRZWkRt0SWyjLTebREviREvyVJl VKuMFi0qj0SKrbnUX4m0aaE1EQqm+vNIf6VbFBuhuJ1dS3GMuhbtIDxtznHXdGJH15Jbu1bL0egV jr5tcT88blvOJt6Qwm4mZ3cUjdzWtKJiFsv2Id3mjhVZQxiWFI1UsWVtumgi6iZVeVYdRDWg2q0z BrqQa5u6kxGGX/3YGFFfRNZiLBPTuzczZPFmJpTV6WaapCi324JvtYVPjPYIR6HqU0XotKZ2BWWn Ulc4XwctNadSZQyLW5be4Vi3dSsTS/FsXLv2q3yBVqZ7mRLxmtm0ZehozWwLGxez+uYTJ0fccdps DsvN66FHMKo6X74oRheXC1hczovycjY+O4TXs+nVYD6Y5FC/O8JgfHU5eAQ+JbTwIRyEGAM63H/Y 3+9qWnlK66i4mBf0njk7h3P6cjabwCOY9gY0/nYwHw2mwwLKyWy2uBxNL2heWxDodFi76n8lD0HA 8/ns5oreUvt3MzgeTK7GBfRhUZSLXyjtcjiYwlUxh/Lm0+diuDikI8GHzvP51a8f9g+4C18fEwB5 ICq4znExmA8v4XY2PvTvYkyjccXPTJN2kwPwb9pMQXg5K/1UVNV7URvgnX8G5ehLcQgnFEKxA4g/ PvrXB5rZbaZWjnxTzCel50g/nvbkxr+Gz6ZBKXqfPxuFqwP4dBmGPmIPmTdvRIfwLhWyOIO70eIS nr1/8QpOOLsP/z5CTYRM9Y8AAwCMefNuDQplbmRzdHJlYW0NZW5kb2JqDTE1IDAgb2JqDTw8L0Fs dGVybmF0ZS9EZXZpY2VSR0IvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAyNTk3L04gMz4+c3Ry ZWFtDQpo3pyWd1RU1xaHz713eqHNMNIZepMuMID0LiAdBFEYZgYYygDDDE1siKhARBERAUWQoIAB o6FIrIhiISioYA9IEFBiMIqoqGRG1kp8eXnv5eX3x73f2mfvc/fZe5+1LgAkTx8uLwWWAiCZJ+AH ejjTV4VH0LH9AAZ4gAGmADBZ6am+Qe7BQCQvNxd6usgJ/IveDAFI/L5l6OlPp4P/T9KsVL4AAMhf xOZsTjpLxPkiTsoUpIrtMyKmxiSKGUaJmS9KUMRyYo5b5KWffRbZUczsZB5bxOKcU9nJbDH3iHh7 hpAjYsRHxAUZXE6miG+LWDNJmMwV8VtxbDKHmQ4AiiS2CziseBGbiJjEDw50EfFyAHCkuC845gsW cLIE4kO5pKRm87lx8QK6LkuPbmptzaB7cjKTOAKBoT+Tlcjks+kuKcmpTF42AItn/iwZcW3poiJb mlpbWhqaGZl+Uaj/uvg3Je7tIr0K+NwziNb3h+2v/FLqAGDMimqz6w9bzH4AOrYCIHf/D5vmIQAk RX1rv/HFeWjieYkXCFJtjI0zMzONuByWkbigv+t/OvwNffE9I/F2v5eH7sqJZQqTBHRx3VgpSSlC Pj09lcni0A3/PMT/OPCv81gayInl8Dk8UUSoaMq4vDhRu3lsroCbwqNzef+pif8w7E9anGuRKPWf ADXKCEjdoALk5z6AohABEnlQ3PXf++aDDwXimxemOrE4958F/fuucIn4kc6N+xznEhhMZwn5GYtr 4msJ0IAAJAEVyAMVoAF0gSEwA1bAFjgCN7AC+IFgEA7WAhaIB8mADzJBLtgMCkAR2AX2gkpQA+pB I2gBJ0AHOA0ugMvgOrgJ7oAHYASMg+dgBrwB8xAEYSEyRIHkIVVICzKAzCAGZA+5QT5QIBQORUNx EA8SQrnQFqgIKoUqoVqoEfoWOgVdgK5CA9A9aBSagn6F3sMITIKpsDKsDRvDDNgJ9oaD4TVwHJwG 58D58E64Aq6Dj8Ht8AX4OnwHHoGfw7MIQIgIDVFDDBEG4oL4IRFILMJHNiCFSDlSh7QgXUgvcgsZ QaaRdygMioKiowxRtihPVAiKhUpDbUAVoypRR1HtqB7ULdQoagb1CU1GK6EN0DZoL/QqdBw6E12A Lkc3oNvQl9B30OPoNxgMhobRwVhhPDHhmATMOkwx5gCmFXMeM4AZw8xisVh5rAHWDuuHZWIF2ALs fuwx7DnsIHYc+xZHxKnizHDuuAgcD5eHK8c14c7iBnETuHm8FF4Lb4P3w7Px2fgSfD2+C38DP46f J0gTdAh2hGBCAmEzoYLQQrhEeEh4RSQS1YnWxAAil7iJWEE8TrxCHCW+I8mQ9EkupEiSkLSTdIR0 nnSP9IpMJmuTHckRZAF5J7mRfJH8mPxWgiJhJOElwZbYKFEl0S4xKPFCEi+pJekkuVYyR7Jc8qTk DclpKbyUtpSLFFNqg1SV1CmpYalZaYq0qbSfdLJ0sXST9FXpSRmsjLaMmwxbJl/msMxFmTEKQtGg uFBYlC2UesolyjgVQ9WhelETqEXUb6j91BlZGdllsqGyWbJVsmdkR2gITZvmRUuildBO0IZo75co L3FawlmyY0nLksElc3KKco5yHLlCuVa5O3Lv5enybvKJ8rvlO+QfKaAU9BUCFDIVDipcUphWpCra KrIUCxVPKN5XgpX0lQKV1ikdVupTmlVWUfZQTlXer3xReVqFpuKokqBSpnJWZUqVomqvylUtUz2n +owuS3eiJ9Er6D30GTUlNU81oVqtWr/avLqOeoh6nnqr+iMNggZDI1ajTKNbY0ZTVdNXM1ezWfO+ Fl6LoRWvtU+rV2tOW0c7THubdof2pI6cjpdOjk6zzkNdsq6Dbppune5tPYweQy9R74DeTX1Y30I/ Xr9K/4YBbGBpwDU4YDCwFL3Ueilvad3SYUOSoZNhhmGz4agRzcjHKM+ow+iFsaZxhPFu417jTyYW Jkkm9SYPTGVMV5jmmXaZ/mqmb8YyqzK7bU42dzffaN5p/nKZwTLOsoPL7lpQLHwttll0W3y0tLLk W7ZYTllpWkVbVVsNM6gMf0Yx44o12trZeqP1aet3NpY2ApsTNr/YGtom2jbZTi7XWc5ZXr98zE7d jmlXazdiT7ePtj9kP+Kg5sB0qHN44qjhyHZscJxw0nNKcDrm9MLZxJnv3OY852Ljst7lvCvi6uFa 6NrvJuMW4lbp9thd3T3Ovdl9xsPCY53HeU+0p7fnbs9hL2Uvllej18wKqxXrV/R4k7yDvCu9n/jo +/B9unxh3xW+e3wfrtRayVvZ4Qf8vPz2+D3y1/FP8/8+ABPgH1AV8DTQNDA3sDeIEhQV1BT0Jtg5 uCT4QYhuiDCkO1QyNDK0MXQuzDWsNGxklfGq9auuhyuEc8M7I7ARoRENEbOr3VbvXT0eaRFZEDm0 RmdN1pqraxXWJq09EyUZxYw6GY2ODotuiv7A9GPWMWdjvGKqY2ZYLqx9rOdsR3YZe4pjxynlTMTa xZbGTsbZxe2Jm4p3iC+Pn+a6cCu5LxM8E2oS5hL9Eo8kLiSFJbUm45Kjk0/xZHiJvJ4UlZSslIFU g9SC1JE0m7S9aTN8b35DOpS+Jr1TQBX9TPUJdYVbhaMZ9hlVGW8zQzNPZkln8bL6svWzd2RP5Ljn fL0OtY61rjtXLXdz7uh6p/W1G6ANMRu6N2pszN84vslj09HNhM2Jm3/IM8krzXu9JWxLV75y/qb8 sa0eW5sLJAr4BcPbbLfVbEdt527v32G+Y/+OT4XswmtFJkXlRR+KWcXXvjL9quKrhZ2xO/tLLEsO 7sLs4u0a2u2w+2ipdGlO6dge3z3tZfSywrLXe6P2Xi1fVl6zj7BPuG+kwqeic7/m/l37P1TGV96p cq5qrVaq3lE9d4B9YPCg48GWGuWaopr3h7iH7tZ61LbXadeVH8Yczjj8tD60vvdrxteNDQoNRQ0f j/COjBwNPNrTaNXY2KTUVNIMNwubp45FHrv5jes3nS2GLbWttNai4+C48Pizb6O/HTrhfaL7JONk y3da31W3UdoK26H27PaZjviOkc7wzoFTK051d9l2tX1v9P2R02qnq87Inik5Szibf3bhXM652fOp 56cvxF0Y647qfnBx1cXbPQE9/Ze8L1257H75Yq9T77krdldOX7W5euoa41rHdcvr7X0WfW0/WPzQ 1m/Z337D6kbnTeubXQPLB84OOgxeuOV66/Jtr9vX76y8MzAUMnR3OHJ45C777uS9pHsv72fcn3+w 6SH6YeEjqUflj5Ue1/2o92PriOXImVHX0b4nQU8ejLHGnv+U/tOH8fyn5KflE6oTjZNmk6en3Kdu Plv9bPx56vP56YKfpX+ufqH74rtfHH/pm1k1M/6S/3Lh1+JX8q+OvF72unvWf/bxm+Q383OFb+Xf Hn3HeNf7Puz9xHzmB+yHio96H7s+eX96uJC8sPCbAAMA94Tz+w0KZW5kc3RyZWFtDWVuZG9iag0x NiAwIG9iag08PC9GaWx0ZXIvRmxhdGVEZWNvZGUvTGVuZ3RoIDIwNj4+c3RyZWFtDQpo3gDAAD// AAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhI TExMUFBQVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWV mZmZnZ2doaGhpaWlqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi 5ubm6urq7u7u8vLy9vb2+vr6////AgwAy4NfRw0KZW5kc3RyZWFtDWVuZG9iag0xNyAwIG9iag08 PC9CaXRzUGVyQ29tcG9uZW50IDgvQ29sb3JTcGFjZSAyMiAwIFIvRmlsdGVyL0ZsYXRlRGVjb2Rl L0hlaWdodCAzNjQvTGVuZ3RoIDEzMTg0L1N1YnR5cGUvSW1hZ2UvVHlwZS9YT2JqZWN0L1dpZHRo IDQwMD4+c3RyZWFtDQpo3uxbh5raSrJe2xPICMVSJgwzY5/d+wp6/7e6FbqFQFnA2Meo+M6uBxS6 +++/K2fZKKOMMsooo4wyyiijjDLKKKOMMsooo4wyyiijjDLKKKOMMsooo4wyyiijjDLKKKOMMsoo o4zy2wVu8bmUWz219N3gWUGPOdTPrfczfwegPNDztThNQH3zL9iXerSnf8gfxSu+hB/XfHMaIzTM ctg7yl/B8Fk1f3Gxp/JRlzfaFS/5ii3V6ao/nB9QNQ+oOR/vzpGh/LicBPTC7c/hR92hWoEStB0J v40fzcxIkiRt34F/CDe6/JQmSXFOd53NAH5UE1z9f+D7osqD89/O7/kj+NEypNMXMiMfgihNd4eD vvPP4EfL7qjD449jSetoSngEOR531CN9+QGN1wC4tm2xODLw86s1Jn8AP6D7LY6akeP5fpQk2/1+ 300r3n3/tWwLz3GszcZYr9eGsbHt6mH9CTzpMgJ9ieeZBk1pvdmYDEkYRXEcJ/fRI9d6zGefCzwa feHf6Z9Dn7ld4gEhRBBD0mEWcOdNBS2/u5a1Wa9XS5K1aaYNtP2NHIF+l7nuZsVTWq0QEjy1XM8D 30d1Et5hGj30B7RcI3jg2Bckq80m3O0antji7N5LfwB0fPg5HjynJRLfNC2bxEGuXAQfvlR/tO+E Hnj8Po50f2dHPG48i678aNvNVfzw4riFcaenfhE/BjjXACc88MhCzSiCwDhOz2d+6bbyfTR2UX/k eDhB0DasL6YIDLjW9009J8KDbBUlG8u6+bp3jVZ1uKsHHqXj/Gv4UYyed35bBzyyGq/qbk5jpwcH AbC9u2b7CvGwPK/DsODrom5VY0ZLqRnTILBxTrmJhXisWRAQ07RvPYlO/OgWn42iQFSImOs4XNeV wE/zO6qDQTfnR5UG9D3PdV2v9m08fN+zbeWCCAwFPKziLL6IH12x74HHb2BI5UsKeGRX4nGjWbTz oymae/ZNksRhiDO0TVNcdHRnXZqu57fcX52zuik/qjQgKjwOTNmuW/k2Bgu3VBSGFJYzTTGsDIFE 8BATq/Mx0mHkV8ZITp8dpJAgG4QiBkcYyEx3Xf9Wbxk8q6rnB64r2x71slcV/XFdGj3eS/NKIKZv cbOZChNDTF6v4S2350eP/PdulyJFeNNd4jFER92SH5Vb9wyPirfleGxTykjFQN5UFR7FXXBn/dHv UDzs92iuUFRRLMTl0rAsxANu/qZbzMIzzZU2zd0wLN2DaJAfDoA/cYhdi6lPLvbVHc8LbjeJlmxH 52yFfON5HpLWda3VajadTmezhWFYFXh0ycjfjh+Vpo/v4yDnU5K5YTjh2RVoBaPyKOBx9lDXJZaI sPY5+VAwcMD3YEcPPL6WIpWPvcQjbMUja8PjFhA0+s6dOFT4hmwVPFA9z1osJi8k08XCsG2v61u7 ZrR7zapSBaLrup7PX3GIr6+z9Rr5sct/C0M03BkP0R8X/NiGoWdZJ0RwxhKIgNYZfCU7LB1DQIWB eLw+kbxMp8vNxunxzjvowMovTXPxymN8fp6uVsiPMh4ivh+d35oAWKLRSYMU8LgFBk21h104dPqm Dx4NT2x47TB+1NSCXOIBrXhAGx6lzMot91bPjeo4Bod4JIVmLxYvP358//796fUVT2f7/f1uLx70 uChyVqvp8/N3GuKT4sfxeFRwhHKjRwKFvU9yPPqOo7Kgq5VR4seVIAzOBl5MEI/jxWI+ny/XaxOV IOPx7du3Hy8vVXh0sepuxI/KZ/m+iQzmERIe67Ubwe7t7RyP6ocKHqsVTXaxWJsm6fMoqly5e/jn 3T4teMD7beombjarEh4QwRsc+bcQP/V3HqECD7y7cvQ34wd0uKaSH4vVilTIcvlawMNJ0z78qD3z B8yqrjp3M5/Ljvn+/DwzDC+GveZHm/oKCniscHJ5fA5aNeDtTrEWiV3X0HiQT24Yk6cnPJwJD/R+ L/H4MiUCdY8XPGiEr69Lywq22zetP9rWIPE8c73O8fBKCRS46YBLqYIu/OiJR5eo8i34UZfarsAD WvGANjxKL7tfkqpNtmgD6hQa4uGa5vz19RllMsHZwm73s/cY4K4T2aD+oPE9v87nhuvG+/37e8Hq CIKg/t4kcTYbqTJDPPybDr0yktSFQ5ffxJ6kPdbrDac9jDkFI2aLxcbzosPh5z//nN3/+Xk8HHbb baqoMywr2TKrhpPcXq8lWLLgYMnuHT4/PnZpqsZThUfhC1SXKkNlml75ArglP4ahG/fCIyvhcROW 9nhCCY/3VjyyNjxuMXS41Tf7iDODm83G9rw4DJHSMl4XXd+Pj18FPH79gs8CHkmSZJ16w/ryo7F0 0LIMnVQOt9vsH/glQ8n3RyM9OWPIk7Ur+JHdsJF14DN+/tzHsc8BhgAntt2GFOflaMMOjickfv38 +RPB+Hx/Jzx4PyYxyR0Y0ni773uSS06SN8VwkgY8zqer5ud5t+V2eQ8O5EcvPKCER2X+HIamE+CS X1X5j3M8oBUPaMOjLSpwT3bQUXT+zed+n4Q6jaBtle12e3iHX+d4fKAcj2+73Y4b84QfiT6kuLkq iNN0f+WE2m6msX185MdoLJ1dSdLt9k858G57ymQ96gosy8r/vV6vVqt16Zo3/DTHKX7BT/wc8bNX 9Q8x91LQJ8prCBzus4DyeAbOqku9BG13frFXnrfjXD4jAQ/xWJGs79j/0fQAy7IK7OCBXMrb21vL U4Uf6Hnt93upfyBqsEQUlCvg4cfZVaZ8vzt3Oz/H4/x+weNCkqSAxy0JUj6hz79R/ZlSqnSBx6kj rfn9Z7/9F59zhLf9fsvGVRxR91eCTMF/BAHVQkjazbY5agowJFwKrQX5F1/sAipTkgSsV1gLlBwP qMHjVMXLdnIYptVLOQCc8gMID0nN8LZV33I6hjrS8tO2O+Y4v89PZNKWReNBtmbMeJi6vNFCx2X4 pHBoic87qcvVsetaYqCjBazj7QCeTkmVdY/PIyXJEaQskOMARNT3MnTYpT9gEB7QCw8o4QH1eAwI B9FanuHRxo8ueEAbHnCGB1xvr1cVgOd4cCOzGr1DLcA2Z9D8LqtTErSw9qJC2AlLyELeonkGPMu8 A9HLhuoQYHuJ613CDiQOOeen4m9yDms88AFRVFY3MbdXqAoOcZ/4bLPZ4B/MayjBcf6ckCIHQaRf LoDEQGX6Cqe+/Ch+gyYvgoKY7IDUCXswlmEsJVS8XkvHAvR2d8m6iXIPI4q2bfzwbdtYLvMcU6iv CEiiylvQIORuBWrsFDx4l6Ky8XkDwF3M9VBGdIFHUMKj/5PP8dg14DEs73mGR9vlXfC4ELQIm/GA q1CAhmqrEA/iIh64m1OerbJNu/ADTZdKhFQlJuA/EA/0em3GY4aCS6Ne2NfdRX5wgECGh95lXj2i h1HJjzm+dD5fbjYul/a1bCvI2ELkaMS2iIfSqnDjxFSu60LCw+NqPWXhojrOEcIvW0gCTh76bbxu F8e4qSm3gHhI5eB6bQ9TIZAGnsfjs6jAVquQjbRuVPFjLS/lmkobLYuayNSFoCuVe12kY6nxNsdj EAytnxhwq2k8ck/8Ao/GJxTwaHlTBCU8iv51j1mlUMAj960VHuX6hks82BRo9vFB1TfkUYkTHur3 K5zZhlvRJCUrq2jvZsRnia5TSVix6L4sAeGBtgsVvKN12HBlkqC6QkdLlmYymUzn8zW/tPfUkB+o iniE6Fp6YbjlA8WQYWyIqJ9o5OVz5JfyK2kTWGyY9Xrh21vKS6KKSLPBduHlaVWV+8NlEu19uoYN drS+yUU1jIaS6V0BD6o+QXM+PUUYL94aclPoBo/y6fQVZTpd8Nr1dHdpf54cBEnJFPEwDAT5eDgc 9FGTIB7z+QTfOJnMVqtKPBr/JsMYn7ESl31t4Ca9W7AnLuGRlfDIeuGR/qq5uBaPnqcx3AGPRqnE YzAQzUXZkESi0ouxNtc1uWiS+7KXJtT6hbuIYqEnPKSEv9jfUuQHHoz8ZMKDi8+nq5VW6UP4Icuz cZzoAo8wlAiBjMM3zdVsNpE30nRILpJkVfw4smTiGLJhKLKig3kwP1pu5KhfIPWs+jufV205nyvD tD5M9JZCkHu/1MO22ViWGwTVTjO7NTizheCB6zOfDyJIAQ8ueYlY51k8jJXkzFXmJZYW86V+40Tj EUWtr0nZdzpYFk2Li+RmbDMvKD00mB9t1YglPKCEB1yJB3TAYyg/boRHFT+a8bhN0L1izyIQoXbI MyY+uj4bLgpTeFxUgRfkYw8JehU8ZEOfr2iS+fX8IJN3NsPVeX5+fplOV+V4d5cZsau/1njIZnJl 5SwLD6MkwmM4DNl0pAK36ZTf9zLhmhjEo9RTWKHvUKLI5VwhvofdWMZjtTIH2btlu6X0kGJOgs5J 3c645gCD5kf9K95hj4YsB+dWHAiZc6zQPYPwdD+uG+l/1K6CB1fUhVkv+1FdevJ7dG7Dkaw3LtVu FwWBREtpr3DvWhEP12V/i5RdfDhU8yMMJWLpci6EpnY1Hh02Xic8sivxyG6KR9aKR9Qfjyp23ByP chTy4k9uXhQ2Uk5C/3vF1aGCB66v0RRdADRDdmgUxPFSn69o4diFPuHTWyUmhvNbLqdccYp4LEzT 73dgnVZC7SQ5uPCcdF3JEIM4udwlaPHeOsODEz6t/Mh7DBkPBcZJfwzs/2jO9euEhNi1uK9VHJRD oRIQJSu2FF0ofj7x806RhSUXLVJMAn1g2qbBxbts3siWbdOGnXBPqODRM14ChagGsWS1kvEbts3x jT2+2WfKEjl4gicLe7kkJ0knoxq6QS7GO+N4Cy8J7rdh+ry1WrczHg3v+Pz8zN7fj28lPE5HFsi7 avEYyo+ohAcFQ/eIR9ARD5UPr+LHHfBonSW+Cm3EhYIADVaDhy7LOudJrpqjUoiHlPKjDcNDFjzQ MUSCiJvpyszQAOOMqcEnzBIdNA4oLREPSrn11h/HJAnYylJ6C/WcbeMWSMR85yPJEquPg+3yujme /YSHry+x65amMGSzuCp6SYbwoy1MI3jkmgptx+USB/4qoQWEQ4IDzfkPys3ui3iISrcpXeSpRREH hTOmND+O8s51FqQnHmpS2ygC2z7zkxCPMKRTnxUVBRxZHea6cCYtd3SW+pziYSViu1X8EDBU40Fh Vabz+YpjNLdpWBuER/YFePSe1G3xKMk98GjtshDXOscDj2B+M5kieNTim3Ht6ORpwWO73e7wCMqf I0csdyaIa6lygvITnma6qkEKDPryg/7n7S0NApdt2RwPA/1QnbYhMNjuWhYFdwO3bJ/j4fj+xesd cm/p7oVeGLQHlYE2my3vxg/2XUV/yGvxuERN+/z09PT8POGdYDXnP/gVZGTmeGiKUGeb4MHTkvQD YkyLopdJnPkgTY99J7Xfx74vynypB492hynFSnLwF2BYseZiz4pTJoQHFKxeeTJl/DMqWpLMltjJ kqkh+5xX5YW7Su7Gj054wBfgAb8XD7g7Hvs9+kVhc5jG8ygKtFyqUDLisdksNB6vwky0RppXh8s0 QnS7WCVM+aCd6HvP8OCZLXgF57pj0uKSpreeeLxtt8rY5f204GPQlqr0NSmN3GhccKAToUAH3tKl fi74aBKzdiM82J6PONzl8BEucbhVjgfPSeExmUiOsS8e220MXNDTwI/9XvzzPNWCY13PZi96J8hJ GTTzQ7oIJKqA665bPee4i7jycSOqPscDNxv/ezYYj/f3XRwHklTWg5dGU1Hf+Ssm2j5FTiBZJYVF EWjCQw4IwSPkpXL5vFhpTuV4vHBz6BkeaBNUtxk144GeatDgW++hIx4t1fxX4gFpz3j7O8RwJR6y LhoPWam74sFhvhZ+4AZht0cJuRqAzs+EXy8nJTLTb+aHBHq06Tx9eXn68ePHy8tMWC01V3jEiz+F /9Dzw/OQ9BMX7Bz64HHc7eIgwOPfzgeP5jVH8td8GCpjSBZPG9++66oCFMYjP8i5DYH4wYerwUfd QvBAqxBPKgQCJ0RY6EdqPPZ96IGMBqJlc5xf1fVIaQ9aqOgybDbzV07ecDUGebNNuTTCw2H70hA8 np9/fP/+/elpwplqn/WjkUcn53MEG3///uPHs/APn08z64HHHj1z2QRq6GQ4EPB56PDp6TvL0+sr OlVECN+XKt4cj9MKcBVvGAbssax18Ih8Dg6yPCMYMl5JoeEuHYyHf4kHDMID/n484N54pBTGYTxQ kVCmq5onh9zoU76Rai/Poz3U9FufHySbmvEw9VpMcjyWS1NH3a3cAZzPX75//w/Kt29Pk8lSvLMo 2nXH4w1Sbl2wteCIqeFK4zF7fX2SV/znPz8QD8lXRlEdHpS29NG4CnASeTAM8dDH3hPDQXhoDbjk UxySZBAe1JjEsUyvsoqDczgOh/wUT/BP9ELYMV3w5kKlUgsIP5mUEE+FdlQRD2Urg8RZSfXmePBi IR5UbxeG2+7h0j2Qpat4QdvH44qWkB1yHAMqsB/fvp3wQLPCpwuCWjwCqgHOApwEMzzHY1LCg5ZE 9BHjkV2NR/Zb8Mh+Nx5+Kx7Zl+BBDW8h5yxVb0m5UgQOFBJnBaCqIXGOUqCPxyjiwcYJ1BXmy3GX 48HmyA82RygT5ziqxAXxlkTe9XhsIeRiUTVUbsrJ0tTmDqgV66ciHovNxo0gxfU+xyOO03M1GBSK OC7wkAmRvcnGMzmUvj8sX5sklAQUm5D3fkUXoVbIYjiiYneDwNGxf/mGTfQqDSTEkpSG4PFC3guF WxYLStjJZax+TfbhX3M8cv2Rpj3UOVcYyYw4lgO45+IkscXYnc1OeIiKMk3xE3I82P+gDXbuQIRF PNDYFf2B+vzp6Sn3x3g9ZH8Oy39QSvMcj7KU8cCbynhUSise+hVNeECfcvH0NCPBQ6QBj3yoRTzK 0gsPGFhQHYYun90STSNIPO+ik1ziOGYRD9xNXOqgTjC2hp2zEgUlns5qbgQPtK/ywDTjcYoNsw1G lQxo73779g1VzGQi/iCVFPThh1/AwxV+xLHgseC6ULSv6A3olc5meeN2cImHf3EGczJFol6oN9e8 uyhuxZUwEmlfyRnJ+nQQP1QQVM7NPFrARRg5ZCAKWeNhFxoPXF0DJHnkcuDdEydZ61LJMqvQyAkP yHQggPLBEq/E8xgXyzSdINhlfep9cjxk61D4gI4elUhHPCTyhzsaPajlcqOTK2EeLxETmRgf5J4u KLN8qfGQjqp8Nqo/gvHwuH52ED/a8ci+AI/svngErXhkrXhkX4EH8gNO/UvrvM5KOoX1xsjPHKmK cy6qSfRRJmfd5RtyLHNXiiOGcgZvlD4HqRG3uH5lzktG7QYMmE/Bq354BLl9JSuLJ0+SeFwGogKX /PwJd0OxZQhiU9j5RByFSF5pzFf8yluLpLIjL7TR6Wy1B67gRxEPyQLhU3XVs/I/8pAJK/zyU9Q0 eG9c/ORrW3lTqBtaqTpnStniNT9//sxfsClU+s25X0T6uXvMblfwP2RlkSJoRgoevISqbI3TOVah mNg9zVMCEcCeh8g7ysfHp256yauIKH8i0e+ck+jP34IfHfGAm+MBHfCAe+IBrXjAV+BRww/pDTCF CuItihWFY6x8jG3nrqIcWP/7h+V//8sCbS7nESrWVMp99v3D4bDf73M7LU8vqGSt64Z9+XGAJAg8 rdhkD6EHExRyFyp/IcOxLK+gJZxcHRKUJzx+/nznblocryuWYKEUQqaTq9Er+AG+7+pYhcJDBwQ2 stkZD1WP7HnVS6M9fAIMz85fvz64EfjtePzIQLrQLDY3JamFG8nmVydJEkdRWLSkxa0R44Ivi6S9 u099+y6KfG1HqHXd70NUD/rh8nyZKZqoXnR6uKtTJjycUJD6v//7Bz6P+30ax1EQKDx0qZDgQR5O HuUYzI9sCB7Zb8CjV3l7fzyiVjw+vwIPzqW6HEc2dPfKKseD+83jHA9yy+szs1Kazr4QHGEHKST4 3xYOeJpTqi7HQypCnS3+HtHb2TaV1Sc8CoEy7hDwYa/f0YP1KbAKyfHI4I1GoTUZvc0wFjkexapy WWxDChxs9e0v6hnB8erkg6XxyDsUfUhArYCqFB/EjySgOIGKhfBAxSqV3ks0/EnB6Bqyxj7zgDva SD52uzik9D/uriSGSFKneTeGGAtRJPntE0hcaONy0YAj2QvcaUHQU5vz1ft9wtu4aBMeUIVIrbBY H4IH1z+ePTzfHyryQWbu+/thu8WdaerKko1U0+U09ryQjwkR0TqD+DEEj4ocyd3x6Fe/2xsPaMUD 2vCAG+AhRa66zEgsEtEiUthBjnEUqVyb6zY/inrxpO0zSeTfqCHCiPDw5dCT0gI+QALUW/gujmhN pTJY8Mg4HcfNMigqM99zanA8blmFqMymfHs8JtyGI6YX6pKVNiOpFf2oKiDlaNV4WDzpz7e3bRz7 prnUTQVLrjEW39HihEBaOCOu4EeWphHX/ecxTuGKmU8DOcSlrG7xrvJzCnX8uPelkQDPZZ8Ll3yA k2Lg8DBibOZNHlwKvD4Px4Lkyk6P71ff/pamYT5sfevbbrdL01TGxnpCBYdiSLbbPApgl/Cg0sHQ Xa9nHNB9la4EbeGr1A9QBxu9EJ9NXv3A/kHEIzgLj1/ikZXwqFNFN8GDH0Wr5ud49J8ZnOORneEB F3icxdSzOjzCNjyySzyGWLtq8qBrt8gW4r5mNzdt+ZpDsamxhh9bPqakqSjXJVRXJs1IecaXY0oO H1bTPFTNobjikQjcH16Ao1c9HJyNGM6NL6CHJzJNAQf3TRRt39/Vy3TdqM5kSJ7IzuvOJhPCQycZ ooRjr0CJF2mq4vqOwf3nPWdbLTj9HVfZOFw+Zov+LySp8pZVm5W16oAq4qHjcFcPrUb5f6BThAP5 LNiWVIYiWwRfHe/3Wd67XvS4yNaNg8Aq4oF6RwwOoht5SGIuiMXLhQjXtRtoNKG4jxSFL1NdVfy4 OR5w+Y9++uNsyKDwQM/o/fMz/6ISD/rtAg+qa4+hDg86XQgPuMTjui1evD0/SyErHa5liViQtKg2 eHpUGsoHsMmHqeep3khtxpJ9Q7WjOR4SWq9+/sA27ssbf/484ARwkNvtXrKfaFhyt6pyRfHc2e1O 4IEjy63rO4r8eOU6PZoNIrHb7fHJvARi0Flcb3MtP4qEgFoOlX9n/WwrFak8eSf3KtD1FTwK1Wm2 OMlSq6h7DxaGUT00gP7HFmhACqSnoyoQVRGSOUVUtvMQDqd8FB7n1tzp9aG9WklTmuRlyA9HMN7e jrwGVMIoKWzEY+hGGnZOF0G5Gx7D2VFczXykRwr65ngknfHIJWzDI7zEY9C4z7oCsl4fElOc1Nzp FYNWKjnEqaR9Q0Eqq5BayNMheRu1xMrKozkbU49ZwcU4M9iJo6dsJnQ3ud5etQ4IHnjN+b3nT3F0 BBKdQZsDdRSdy2Cteox9Ov/ECJY7voofhVsUHsW5oaYr4EHdZpxwtU7pCBUI2Oizm/rDqi0ruNFc KMLoW5Y0qImjI32kZ3iIPi/wo/iUwwFytciJ6TBN92xTazzA5fQNPX7o2KE0e+gAU+Gb++FR2mD9 +tXg4oseeJzuP1OvffAQSl7JjwEPUKFps9imznw2ChlZND62bNLEOR7OSSiZGobxfn87clTcjQrc 22yWedc2nlqCh060ogpMSnie8yOWFWdtge4jWWrykyQQOMBvMB7utXqvZl+18eMgMRBW0Xy45n1m hiS4BI8wTPRyu7qJBCRoFoZoKFcUg0Jr52/rrOCMa0nibjYL3QxLZXZSBSgllnmxQAM/KGwkdolD o4ZUqX9l07hszRB9bLvuqLk3P67Ew/cD9gnSEwgXhuqtGAKD8LgYxTkeSSMeN0GgPz84s0HspKFS PIrrmqQPWlWkneORSZLA90OIkjTdZpd+T26gXs2PouvCQRDDWEjhlbQ9yl7KB1mxDhc+PoXw2XrP KApb+E2HwVR0n1INw47/a/nx8fbGQT+6MQjc9VoKjCWeYOvWbTxrU65eulJZDZiPfinXoxqGCidP JtRQhjTVXpEDULUvuw+RIvYcTPSOR/I8b4NAtZff8E2OB/TCo7x7oMPmgP6zAvVSuAEe0DScCzxu teMGPAFfr3kd5BPGI4F7nyh65ZM/nP0WfuDAFB7ZBR6qIVkia+Ua1978oGWA9+tnBtd56OefyHGW 3HD6wiGFDXm8eW0IfOCnz7sqfh8wq4KX77D+yPEwqfOox8yhdXxw+f+/gx/ZSe1V4EFqXP368fHx 5fzIPgqatYxH03wq7KuOq3fNxKD0Z0//vHjXLgjW3FNHIcL53HCckEzC6vtbfe9b6I/sTFehPzhl A1DSe2d4VOqP8oMaxwdnu/M36Y/iXVFk6RCo5JeCajy+jB7ZWRDINFfSycB4bEql32UIB/Djplna Qfw4TbgrHuXk2f34UXhbEx7QikdHfsAttxxcd9NuV/SBdd1f1VrDV9EjK1jxprmWCMJ0OueS29vy 4xYxktvyI8tC26YSN44qUl1sw0b6Mn7k8ANI85UKKZ7hUaNyoCc/AK7d3Vfz4+yeDnh8KTfOl7gB j9KE+isDuH2KtjJo3/LNGbG3AKYUyC8WiIc7nIO35Ee+B/L+KGrHVMPLqolQEb9qHd/ghM0NCXIx hjBEFbLiNq51FR6/gxynQVi6Z27JeDgNSzBEf9zigLrxJ4QzPKAs3bPzt/DPixn0DEp4tPjn0MM/ r/DXfws/Lv6kNj3JuyEeZ6T4TcwoDlM6JVXHgGU5Ydg8nV5NWXcc93A4OGvjSDdJrUH5O/CQLZF3 unCjgFvCA2DoctzpIO772LIG+zPxyDriMXg54J7jHggf/7GPY+mltGz7D8KDBxdxwa6taypAOneb Zt8ZELjnuIddrP7gXjHwPO9PQkNGd0jTMO+rC4LwEg8YvBxw34FfAcefigeNrwWPSjJ0Ysg9zfge ZybU/JGmqpXmj8JDjtM0pRRzTDW8radV5/WA+w98KD00SfZ/Gh6nee33lcODmvm0r8edvdyOj29K 9/+JeOTzqsPj7FLoDgh81cAHw/GHSuO8SofvRf1W/UPhN4+8fAX8O/BoWjsox9Y7MeSLpt5Ws/mv hKMOkovJ5m1VAC3r8bXJg4676d9yWpWXtrovsohCM2xfvg9rGjlvH+j/LaDAgFKL1u7Wr4SldT/9 BQL/+un8VXD8BRtqXIIRjlFGGWWUUUYZZZRRRhlllFFGGWWUUUYZZZRRRhlllFFGGWWUUUYZZZRR RhlllFFGGWWUUUYZZZRRRhlllFFGGWWUUf7NsttuU5btuBYjHg8g0OeTQAgB+PhfUPi2WrJ7fB4B j47f7OI4BHAdEtfz+Du18lV3QeG3YW8tfzU28hZPqks8eL3TtBFszZRRbsuPQ5pGvu/atmWapmVZ jkMECQIfpXnvnh1hIz9uIvt9EobgOAjGBgURsW0Xl8cVtjgdMW84v0bpxY8OeECnd9TrlJEf3emR JKg5HIvOqhMe+hs6wPphPvLkGn7gYQVMDo0H/q+NnHBda7MxWDaW1ZEfBUhGfgxkhz6smvHoh3kl JKN04weZubZtimWFYtsOXuO6pmGsV6vVeo14OB348f7+fgLj9L8jP3pJHIsmJ7ERCvQ+XLJxEY81 waHw0HZvNzwKJBkXuR8/KvGAEh7QCw8oQTLyoyNeeFhZligKsawgjumHIOjLj6Z3jAvdjR8Anmej 3l6Lplgbppnqa6yC/nAhuIKD56pk5EftOuIKVOKRlfAIeuFRyZGRJM17VxYI7VrDWC2VrAzDzK+x LD7FNpbjAERX8YO+uXTeR35UnSCOg3piuVjMWRarlVG4yBPz1/ejfpg30uSxl73tU4HH6VeNB0Q3 yjgVs1nwiPmoZnKg2PYG8ZjPZyzz5XJ97o3HURQ1Z0H6ruPb2wMzpOYkzxrxCOpyiGmaJklymV3v nIMUgOM4qU5jPShM5ytRiUfNnZ3xaBLCY7t9e1A1UhV7Pfsz1x8aD8O2K/kRkq/iuZIU8byo/h2u zeK6leNAavhBEO92Jc3+kAiV96XYuwuW5Xptum5Yvg1hsCwK/6qQ78Y6eez0RE8S71nmc2CY4sS2 7VUNwOaoDITh9hEd9w6ecgUecHM8oIwHZBehlAfkR+WG5PjVmrxzo9YVt20KvzNsZBajmjFN/Szw +FfJlCBsmzU/DB9V+RxGy3Ld4BFBKB/RlXtfTnzc4OKIl67xWO2v5vPphGU6XSCTmCK7ABzTXK9W yr2XKMtqhYC55XEgFPLrZiMxysIOeTx+VM+4jEd2czyyOjweDAUo/VHpG4RhEARhPW6eNounr68v KK+v08UCLTEEz/c8wkM0ELr3EqWv4wceZHIlGnKO45+doY/GjyusGZ/DjkU8JvM5qZDDAU1gu4CH QqOOHyviEdnW8/nSMBzWVY+EQ9l86edN3woPaMIDHpEf183W911c9MViNpm8ajxWuOK7HVq6tmUZ rD+Wq5NU2ld0GT+EdBCeeO3Fdn8rCtCDDZXf+JxRX8xmotKnGo8PxsNkz2StlQdlsWy7/ETCYz6f vLw8Pz+/TCaLzcY7/fpQ59bVk70Oj6wZj4eBomy7DOWHOrJQT6hUyQJtVtNxjmhfSWG8hkRcS/Os EFs90WQl9Pr09APl+Xm6WtlhmNVafn8jHhW5nxA/QzJMEgshi3a5lBTWAnVyCJ54MDqgQnBsNnZl 9gsRnc9fvn//hvL9+8t8vimO7WH4cW5mhWEYRb35oWNTnfGoeGIlHvr3x9EfhZmGvk9Bc9f1OO7d 6zEx3suVvrjoczZ80craeF4cBBKLl4OL41NWZdtI4DgG4/EflqfpdO26VO/1KBrkwnLh0mk56GnJ PK8XP7I4DrinzV6tJqwDniaTpWX5ktDlfipyRzj0UvnEBGBTwOPHZLKy7VBd8HBKvQaPHgQZjEfW AY+HgeI0V9/PY02U6qhzx+q/oaMJV3yxePnxA1XAj5eX+Wbjpum+ALlPKUC/+v4wtJfL1xyP19el Sg8/UF9ogR4A9mazEoN1uax0EVokikirF/FAlR7H284P8Nbr6dPTN0QDb0c8TBOS5GEIAh3w6OWR dMKj6Ym1eDyWSr8VP1BNeB7i8frjx/fv3wUPO4rS7vdb1nIyYYfwxzOqH9P0omj/KASBSzwcDgtK KeKmv/7gHe6hDpg8Pz89PVHMA/EIw2S/73x/Zi6X01eSyWy23GycIEizhzF5i7OsxqOvtOPRIjV4 PAYaZ9NEe1QyrxQQN6xOu7n0DR56hrGYkswWC8qC9OQHHlmyJ+aLxQo9eYD4cHgQflxOktKr0lhe Ux/VLmI05wlzLt5JcUG7Sxg6qMZ0h4PledFu9yDxxA549OVHOx5tT6zE4zH5If6ah3LF/F3LUi1V uJqu68fx7njsfvvuHFHEIyQP/4Hiu+ffRFHUnQ1V30CxAA4g2m7f9E///Qel8X6HY8F5UxZXRAZp enik/Mf5J7q6v+YCD9jmv/wX/sFPcwdQBR6QPlT+49bfoMlrkdiuS2gcjz//+Qe//vXr18+fPz8/ 4ePj4/14PB4Ou+02ieM4v9/3bdPccM3cQndkodkdUATsMfG4iUjCA3UQdQ7A8f29iMfH8fj29rZH 2e12aUp4JEmaUXiY0idSvj2fz9hinqLFbNv+4+Jxk29a8IASHjDicU9+SF9hFMWoh4+ER/En4sd+ v2VJuaWKEMGrgwDxoJKGxQLBmHCG8XU6XXEG5VH0+b2+2ZEckBlVV38yHogFn1WIB5TxeM2rfkwT 8TiO/LhKmvDISnjEIx535kfb1UfUHYgEHWpxkgL9OwxdroVYVeHx/pjxkpvLPx/wzgpjz4VEZxQ6 4ZEiWRIuF1pzy8F8Npu8sEynlLKleMkDwFGRdbs1P6rwgBIe0AGPR/HPbyYX21/k4wDbJInCkKpL fD+IouqbuTrL1AWlcmRJHfDacaLD4VGyUTfkRwUe+NUWkATUX3XCo3S/xEgsK8dDihylzMXy/fRR 6q/uzY9qPLLeeDxQMQN02PtxTAvaizEieFcKsRxW1IIYRlWRc863kFgcvKJAJP5b2tQdx0+Stweq 322dJp7+uJScFGmt5w3OS36p8i2BSLiBcERRZaJwI4IYaKFeXhYIggSVxyPVwgG08KOERz0/uuEB PfF4rP4oaC41i/OMIdeou334Qbf4EPpUIgq+HybJ7jyWpfAw5JDaWLYtOFAsEhHU43sQJASLTsIJ XOrkcKGHSN63VU54IC3qL3sYVLJO/AB0EVi6P5dPnBM/IgrTZk38YDzkcOtE3r8UCKiDJuClFINV tQrYdnf7itBzXcKDDi4/TNO3qqtzPEzbRjyCU89g8cAa+VGHR2epwSPrgsf5sTryI8fDo7acqICH 25EffD3iAfIQXGiuVK/wPzbawmI8Kh4Jj9Qf1bD9BA+yqsIQpC0Tl6zjY8t4xHH1lZbyCK3ahz9Q NfW5xoQOeAzmh8IDuuBxGTd4mJBJ0zzDHI8o8vEfrA+6LozYY6g4dI9aLT9Q03D8ivBr2joPwA9o jF9R6AloMV1cLXSzxa2riXFRM2fRXxSvAf1BCX+hf167tGRQuxxVtOvW/0Hqd5tnKaHASzyqL+2O R9U4Snj0HOnfCkfpixiikDPbFhcexGgjVfJDOmtNUywlixcWbwwjupoKF5qOHtZjIz+6ThKVh/jl h/o+Dt93JcFHde1rw7K82tWvGUszP8b6ktM3F3hUXtMDj0H8GOuv+lKNqnWoGIHL0jcb79evfu/w PLLHaik78qPXN4LHakVZVu7744K4Q48nStVvpX8+8mMgPzrhUSPneDwmPzrsws7fXOARc6Hb7tDj iUkU5UURj8iPCEL+ZLf5cK90jgc/mXqj9oCY4H8H/n/9+Qlv+FsIAX46Pv8B8IDwokrnGo1yjgc7 H1GCeBwOb2yaaWuZr/758227pTq5IKjyaB6PH9soQo86uEh5XyNc8Kn6lDcbSfNFCaSptHlIJ1SS brd47T4Mfdd1dPVC+WHxZTnX345Hynj4lyUhV/AjyxzTNFYsaO9KTUIE3JpDNSqqJA7PMVTduhVK mjY3Fw8MQ9TtEmS0HMcLw+1D4OGX8LhOuuERdsHDf0Q8gA6sm+mPjFSIqvlkPDziH7EBYfBcJVI3 ZG8268ViNmGZzRaGcXH0IRQmF2WZgsdfH7/a8p6FxrBrXznmPQOCB1EEpDxFlehSisOhtUbNz00F zyivr9Pl8gwQ5JlU8nLJKFLs7zevWJ+TRr8hP+6Jx18PSJKQSQrRTR5FR1IYJgA2LqUUU3mncjjX YUvK5JJQxMU0N0U8Xl4m8/m68DzlV7KdZnFJ9QOE27fbGOKYKAJX8uMAvkrNAjiytTUeHpOjGo/5 vBoP01wtFnMU9mNsz4t2uwfwBxEPlFuUYh78L8Djr5cDdYHFPojl413Bj8R1xRZycjwMwxRTSuqw WWec8GDbab1con0lbYJFPJzVCtGYzWaEh2m6QZBmjxEvySgdC1Lm5nBxf9ztrl2iStvUQxxH3I4N rrvoc8QDV9471cW7uT4X4W4o5MFULN7FQj3LdY3lUuGBnollAaV6H6b+6hKPbvz4ejwepT4R8XDk IOFazTCvO0jTdLvd1dxF5bjUeaYkcpy1hBFN84QHn06OMrAUIoy8lPWqhlpRFWhK8Whiz7LWjAd9 iQC7bpg9UC8O4yFFIbiUrutDEMlC53hU8COgKEYh3Cd40Aqu1+h/qOJoFlt3bwgAuhDRkj4Pz3FU yp0PpG0QuKapUr6rFYKKtydqDI/DjyIeQQmPsgRfjccD1bej9+yIB4cnDB9Z6JCEkMAWPzV5IV5x k8xk9UH7yhA88ITR/qDC2BcPnbQUue4SbUR7mN4S438UWESE8IoDhJ5n62sQjI1leZwtgwfJR8lm d3VwAo98CqYC+GFjTGtNK2YUkxYAJhqppEBWhi6OVnhQhEyVyhlsyOaORfGRaB8kh4NXqBhCMMih IbvhgToNOuABX4AHNOFRV3Dydwp66Jalgnd4RAgkvt90B+OxPi8ldDcbOYoM6VjWesKmLDq42pSa Tdm+nU7nq5VVvB/VVQEMHImFygwxeiDlofdciN61oRARlthnZdEd8ADyG0zRK2zQ2oII7vGItILD 3saSEx6vLNPFosAwegeaySutyXEMju/HZ9WQDwNLCx7d+HEdHlk9Ho/UXqsm6/sq6LSiBaGz+2Lv X+DB4FVcw52GGTvkqr+GUhcQXeDBAavX2WxlWUF+P4cX59oNpPLfKDr3fh4JGF/7AWp7WlYTP6Qu pL7ZyWfHQiK+qJ5iz1PG7nI5n05fnzmkO5ksTFPXXGeiWkjYiTEdJ+Bm3IfqPj/tuSY8oAMecCUe UMYDCI/6zsa/XLjjxujIjyiivIb+E8+u8tMkR+sFQZJA6vvAEKKGWMxmiMcTysvL3DDciLOThoHf S2AR8ViyWx7v94/GjvM953mqAKeNHxnE8U4iKaqaxLi45u1tx4WHgXhz+33AkKDvvZrPJ8/PP1Ce n6erlS25MPRLNB6zxWKNvAlDuDysHg2bOjwqCFLCI7s9HtvHY0ep29y2ORZo1NpXvk8utAQKXdcL bBX5Kl5zOFCnwXabqByJ+i30fXr4cjlFPL5///70NFkuTTnHBA+U2QyNK/wyTFNo7vx9EFEFtTX+ h6h9VaHLIQ3lZziOcuhT6tyMt9t91TpKoPCEx2KxcRzBQxm7nIBywzDN4AHZUbHnKvCA34EHwEP2 R1UgBFJ3W/FTEIgNJmfLjA1TqedE4WJ2blUHPPu3Feuoq0aKeBjozLOrqGLsmw1qlGj79xeHduUH fkPFz6WKLDJYUdCN56IQFfOYzyWG6Eki1nUFzKDyHeyBzxDLlxfR54KHl+epON8eoK1QnQ58jP7B ik+EHyhkm+jTC4/qLFY3PCC+ePMj9UdV11ahdYS+WHZqdI25t1yi6IIHV7E9v6I5xFWdho6uS6kC 1PBDGVHFml3EQx+SHkAQRelD99c2gyVLs9uV8SAP+/n5ZTqlDc8eg7QG8EWW24yH7jGYc32Q/Cy9 U+n+URVHEz8uEIEvwAMUHgDH2qE9PD8UIgV+2Gybzgt4zM7xIPcQDa7KddxsRH/MOF+7FGsKFbi+ 7g0e0+fowQ+FSBqGos8p7YrbHFWAxGg1Hkp/SPCkDo9CvJK6OtjLAZWoh6zkAI78qJEDeRR3xEPV Z40LnXWtZkctG4qrxzHB5Wymcnw5Hkq7KDzcmidaeVEWIsGNnfFWkBj7z/tSBCDh/k+qH9R9mFMd 5KCwB5dI42pbl7HhQsZVCqvZ6Uu32+1upMUgfmSHwzYBuC0eBPHuUL/3R360gkfN0YBOOnraUh3K slyvLdVS4FpcxFa7jtKJnrC+eH8fV3QoPwqIUHEb8kDXB0kQ0AGf+jl9VjKNT6xR3iM/hgm3G1bg 4XfDg6DYw9vbx7iSN+EHSsoNoLm7zplC24MwP45q7z9B1eWtIz86SypNuVo5UxywDfbRwbgXPwgP iENU7EU8oAGHMhQjP27PkTSOJEtCbl0dFuM69ubHFZ+uMvTpD5iPukZ2uzThMPm4FH+O7P+fHTqg AQAAQBjU5f1DmsMNIlBZAAAAAAAAAODDBBgAd1NGRQ0KZW5kc3RyZWFtDWVuZG9iag0xOCAwIG9i ag08PC9CaXRzUGVyQ29tcG9uZW50IDgvQ29sb3JTcGFjZSAyMiAwIFIvRmlsdGVyL0ZsYXRlRGVj b2RlL0hlaWdodCAyOS9MZW5ndGggMTkvU3VidHlwZS9JbWFnZS9UeXBlL1hPYmplY3QvV2lkdGgg Mj4+c3RyZWFtDQpo3pK3l8eAWISwQIAAAwA6+gqnDQplbmRzdHJlYW0NZW5kb2JqDTEgMCBvYmoN PDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDkvTGVuZ3RoIDQyL04gMi9UeXBlL09ialN0bT4+ c3RyZWFtDQpo3jJTMFAwVzC0ULCx0fcrzS2OBnENFIJi7eyAIsH6LnZ2AAEGAI2FCDcNCmVuZHN0 cmVhbQ1lbmRvYmoNMiAwIG9iag08PC9MZW5ndGggMzYwOC9TdWJ0eXBlL1hNTC9UeXBlL01ldGFk YXRhPj5zdHJlYW0NCjw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5U Y3prYzlkIj8+Cjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFk b2JlIFhNUCBDb3JlIDQuMi4xLWMwNDMgNTIuMzcyNzI4LCAyMDA5LzAxLzE4LTE1OjA4OjA0ICAg ICAgICAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIv MjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgog ICAgICAgICAgICB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPgogICAg ICAgICA8eG1wOkNyZWF0ZURhdGU+MjAxMS0wMi0xN1QxNjowOTo0Mi0wNjowMDwveG1wOkNyZWF0 ZURhdGU+CiAgICAgICAgIDx4bXA6Q3JlYXRvclRvb2w+TUFUTEFCLCBUaGUgTWF0aHdvcmtzLCBJ bmMuIFZlcnNpb24gNy40LjAuMjg3IChSMjAwN2EpLiBPcGVyYXRpbmcgU3lzdGVtOiBMaW51eCAy LjYuMzQuNy02Ni5mYzEzLng4Nl82NCAjMSBTTVAgV2VkIERlYyAxNSAwNzowNDozMCBVVEMgMjAx MCB4ODZfNjQuPC94bXA6Q3JlYXRvclRvb2w+CiAgICAgICAgIDx4bXA6TW9kaWZ5RGF0ZT4yMDEx LTAyLTE3VDE2OjA5OjQyLTA2OjAwPC94bXA6TW9kaWZ5RGF0ZT4KICAgICAgPC9yZGY6RGVzY3Jp cHRpb24+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHht bG5zOnBkZj0iaHR0cDovL25zLmFkb2JlLmNvbS9wZGYvMS4zLyI+CiAgICAgICAgIDxwZGY6UHJv ZHVjZXI+QWNyb2JhdCBEaXN0aWxsZXIgOS40LjIgKFdpbmRvd3MpPC9wZGY6UHJvZHVjZXI+CiAg ICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0i IgogICAgICAgICAgICB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8i PgogICAgICAgICA8ZGM6Zm9ybWF0PmFwcGxpY2F0aW9uL3BkZjwvZGM6Zm9ybWF0PgogICAgICAg ICA8ZGM6dGl0bGU+CiAgICAgICAgICAgIDxyZGY6QWx0PgogICAgICAgICAgICAgICA8cmRmOmxp IHhtbDpsYW5nPSJ4LWRlZmF1bHQiPi9yL2QwL2FudXAvZ21fbmNfc3pfbW9kXzEwMDAwcC9zcG1f MjAxMUZlYjE3LnBzPC9yZGY6bGk+CiAgICAgICAgICAgIDwvcmRmOkFsdD4KICAgICAgICAgPC9k Yzp0aXRsZT4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24g cmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29t L3hhcC8xLjAvbW0vIj4KICAgICAgICAgPHhtcE1NOkRvY3VtZW50SUQ+dXVpZDpmYzBiYWZiZi0y ZDcxLTQxYTMtODE2Ny0zNTYzOGE3MzlhYWI8L3htcE1NOkRvY3VtZW50SUQ+CiAgICAgICAgIDx4 bXBNTTpJbnN0YW5jZUlEPnV1aWQ6YzJiZDUzZDQtODI3OC00YjQ4LWI4NTktNTMxYTUyY2M0NmRl PC94bXBNTTpJbnN0YW5jZUlEPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRG Pgo8L3g6eG1wbWV0YT4KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg IAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAog ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAg ICAgICAgICAgIAo8P3hwYWNrZXQgZW5kPSJ3Ij8+DQplbmRzdHJlYW0NZW5kb2JqDTMgMCBvYmoN PDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDQvTGVuZ3RoIDQ5L04gMS9UeXBlL09ialN0bT4+ c3RyZWFtDQpo3rJQMFCwsdF3zi/NK1Ew1PfOTCmONjQCCgbF6odUFqTqBySmpxbb2QEEGADf+gut DQplbmRzdHJlYW0NZW5kb2JqDTQgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDQv TGVuZ3RoIDI2Ny9OIDEvVHlwZS9PYmpTdG0+PnN0cmVhbQ0KaN58jlFrwjAURv/KhT3YgiY3aZfY IoKzDAaWyezmi1Bqm2lZm5QkotuvX4fv+54P3zkJICwWdG1V5Vujs8qrIEs5MoacSSYwifkMxQRx Et4pY4N8VWxWT1Mozgryyp+vxn65KbzomsCHsm48AkligoTPJRyCN44oq0NI4HVQdhTpE+y+nVd9 CptWX27AiSBRTORMCPJZs4jc5qIUMTww2OVb2KsGMlUDewSUKcZphPBerGHsRLijJKS5af7v31rT XGplg1VtzbHykLXOt12nLCRjLh9T961uzNUdwpAWre9UQC1tkFb6MtBTX+q6dD9lb5qS4biBuqEv /2zP6sgkGVy4XP4KMAB1P2BdDQplbmRzdHJlYW0NZW5kb2JqDTUgMCBvYmoNPDwvRGVjb2RlUGFy bXM8PC9Db2x1bW5zIDQvUHJlZGljdG9yIDEyPj4vRmlsdGVyL0ZsYXRlRGVjb2RlL0lEWzw0RDZF N0QyM0NFN0E3NkQxNTgwNzg4QzNCMENEMkY0QT48N0I4RTUwRkJENUMzQkE0OUIwNjJGQUU4QzlE NjdGNkE+XS9JbmZvIDkgMCBSL0xlbmd0aCA0OC9Sb290IDExIDAgUi9TaXplIDEwL1R5cGUvWFJl Zi9XWzEgMiAxXT4+c3RyZWFtDQpo3mJiAAImRr8IBiYGhjYgwZ8KYvUCCaYMoMTCkyAuAyOQYPoP JBgZAAIMAHbLBRsNCmVuZHN0cmVhbQ1lbmRvYmoNc3RhcnR4cmVmDQoxMTYNCiUlRU9GDQo --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: image/jpeg; name="nc>sz_10000p_GM_w_mask.jpg" Content-Description: nc>sz_10000p_GM_w_mask.jpg Content-Disposition: attachment; filename="nc>sz_10000p_GM_w_mask.jpg"; size9011; creation-date="Fri, 18 Feb 2011 00:07:18 GMT"; modification-date="Fri, 18 Feb 2011 00:07:18 GMT" Content-Transfer-Encoding: base64 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAPAB4ADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi uo0fQIIdaurHV44LljpV5cQGz1CKVEkjgkdGZoiwOCn3cg/dJyOGpwWdjJ4F1C9Nu/8AaMGp20Im Mvy+VJFOSoTHXdECSSewAGDuAMOiuo1u105PD0Vx/ZkGl3bSx/ZIUumlmnt2Ry0k6ljtbiEqQsQc SMyqVxt5egAooooAKKKKACiiigAooooAKKKuWemXF9b3U8MloqWyb3E13FCxGCfkV2Bc8HhQT09R QBTortINE0ma48PSJZyCKXQrrULmOSYt580BujyRghGMCjC4IXjdu+Y6EGkaF/ZNvr01hptql3bx MYr2S6NlC7TXUZVREzT7yLZWGSy/63JGYxQB53RWhrtimmeIdTsIo5447W7lhVLhlMihXKgOU+Us Mc7eM9OKz6ACiiigAooxRQAUUUuDQAlFKATRtNACUUuCK6jRvD8EOs3Vlq8cFyx0q8uIDZ6hFKiS RwSOjM0RYHBjPy5B6E5HDAHLUV2tjo+mPpen2ktgjz3+j3upNemSQSxPD9p2ogDbNh+zrncrH53w R8u1/jHwTJ4X8M6VLJpt9Fd/a5oL25njZUZ/KgdVQEbdqs0yBgTv8tmBK4CgHD0UtGOO9ACUUUAZ NABRSlSBSYoAKKUDjpS7aAG44zSgZpxGaWgCPvRV200ye+hup4XtFS2XfIJruKJiME/IrsC54PCg np6jPUwaLpE1x4edLNxHLoV1f3Eckxbz5oDdHkjBCMYFGFwQvG7d8xAOJorvLmw0ez8MQ+JxolpK 90luv2CSWf7PEXkvELLiQSZxaIfmcjLvx93by/iXTIdG8Vavpdu0jwWd7NbxtIQWKo5UEkADOB6U AZdFL0HSk7UAFFFLgkdKAEooooAKKXB9KUD1FACYHelIwtL60h+7QA2iux0jQYLP7ZFeRaHf6mPK aC3udXiFsYT5nmP5scyr5gZYgEL5w7Haeq7ev+D9H0eK606zS0u7vZfTIs8063oSCedNyFV+z7Al vuYMNzYkCkEpgA8zooooAKKKKACigDNLg4zQAlFFFABRSgUdee3pQADrSHrTs89KtWemT31vdTxS WipbJvkE93FCxGCfkV2Bc8HhQT09RkAqdRSV6LqWg6Qlib/T9Jtb23h1KG009LK/MsuoRypMYzcI HZ0cmKMlFWIndIoCnBTlvFFvY217bC0jtIZ2tw15b2Vx58EMu9wFjk3PuBjEbH52wzMMjG1QDDoo ooAKKKXBzigBKKftFNIxQAlFFFABSgDHNGDn0pwIxQAEDFIopW+7XZ6N4etNOtr641n+xpGjeBEk uL17i0XzBKcE2bs/mnysqPuhQ+7BKZAOMOPSk46YxXo994W0aygvLH7HsuRDfzRpdSub/EDzhShj P2by1EPz7/nO2bZnMefN85zQAvUcCjGKByKXHNADKcBxzShPxpwjb0oAYw4ptSlT3FM289aAG0oo 204DFAAAAKMD0paKAGN1p2B6VatdMuL+C6nhktFS2Te4mu4oWIwT8iuwLng8KCenqM+g3vgrT9Tv /wCytKt0inttSWzma1E0s0EYSVpPtKOdskwWFiBb5QlJAOseQDzPt0ApAfauk8c6Img+IktYtNut OhksrWeOC7LFwXgQvkkDJ8zeDgAZBGBjA5wGgAI9KQjHPFO64xThGW7UARgZNOwMVOtrI3O2nmzk AztNAFKg/SrDQOOoqIoQaAGU4DuRRt9KdQAmB6UEDB4paRulADKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooA+3/An/ACTzw1/2CrX/ANFLXQVz/gT/AJJ54a/7BVr/AOilroKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAt2N7caf M09rJ5cjRSQsdoOUkRo3HPqrMPx45oW9uE06awWTFrNLHNIm0cugcKc9eBI/5+wqBeQKCKANbWvE mpa/vOoCxaR5TM8sOn28Mjuc5LPGis2cknJOTz1rH2+9OooAbto28U6igBu2k280+igBu3mjaKdR QAmOlG0ZpaKAE2im4+bFPpp++KANKLWtRgksJI7jBsYmggyikCNmdmRgRh1JkfIbIIYg5HFWx4t1 gXEkpktHR0VPs0ljA9uoUkjbAUMakFnOVUHLuf4mzjYO2m0AOuJZrq5luLiaSaeVy8kkjFmdickk nkknvTNvNLRQA3bS44p20+lG00AM20bfen7TjpRtNADccUtLtNJQAUUUUAIR8tWLG9uNPmaa2k8u RopYSdoOUkRo3HPqrMPbPHNVz0NC9KANWHxDqdvph06OdBBtaNXMEZljRs7kSUrvRDlsqrAHe+R8 zZpSXtzJp8Ng0mbWGWSaNNo4dwgY56nIjT8uOpqE80lAB2pO2KWlCk9KAG4oxVhbZ2xgVL9hlx90 UAUscYoxzzVprR16iojEV6igCOilIxSUAFFFFADf460otb1GCWxkjuMNYxNBACikCNmdmRgRh1Jk fIbIIYg5HFZp4anUAbY8WawLiSUyWjo6Kn2aSxge3UKSRtgKeWpBZzlVBy7n+Ns41xPNdXMtxcSy TTyuXkkkYszsTkkk8kk85ptNIIoAOSaUDjBqSOJnOBWjbaRLMeFz+NAGUF9qXB9K6628I3MoBERI +tTv4MuVXPln86AOJK57UAcV0F34fuLfJKfrWTLbNGTkUAVqKUjBpKACmt0pfXmkbp1oA0NK1q90 kSramBo5cF4rm2juI2IztbZIrLuGWAbGQGYA4JzYfxRrL2c1vJd+b5u8NPLEjzgOSXVZiDIqsWfc oYA73yDubOOppSKAGUUVPBA0rgAUARBCakWBiehP4V1Ok+FLm9K4j4PfNd5pfw1keNWde396gDx8 Wcp52n8qRraReoP5V7+nw2hCgFef96qt58NFZCVTP/AqAPBWiI7Ypm3Br0bWfAlza7iiZx71xF7Y SWrlXXBFAFH+tNB6U4jb3phoAXPJNJ3oooA6MeNdeSaOYXUHmJvLsbOE+eWRkZpvk/fMVdxuk3H5 25yxzkalqU+q3Cz3EdqjqgQC1tIrdcZJ5WNVBPPXGenoKrdRTTQAlPSMt2p9vC00gC9Sa7rw54Kn 1AozL8pxjB60AcfBps033VP5VpweGbqUcRMf+Amvc9J+H1rZxK86bf8AgVbf9n6LaDBYA0AfPD+E 7sDPln/vk1nXOiXMGd0bfka+mB/Ycny71J9qgu/Cem6jE3kgEkdM0AfLskDRn5h+lRYx0r2TxL8P DDuaJMHnq1eWalpstjMUkXBFAGdnmm570HI4pKADPWtLStZvdIEotWgaObBkiubaO4jYjO1tkisu 4ZYBsZAZgDgnObTlPFAGyfFGsvZzW0l55vm7w08sSPOA5JdRMwMiqxZ9yhgDvfIO5s4n86kpUj3u FUck0AEcRc4A5rXsdCubtgEjJ/A10/hLwZNqLozoSpzgZr2Cw8P6VoVuHu8BgM4BzQB5Hp/w/urg AsgA91NbKfDWQjnH/fBrur7x7o2nExwKCV46ZrDl+K1ur/LED7bf/r0AcxdfDedEJVRx/smuW1Lw ld2hOYjgf7Jr1qz+J+nzMFuIiAevy10UVxofiKL9w+HPY0AfMM9rJC2GUiq9e2+L/AQVHkijwcEj 5uteP6jYSWVw8bjBU4oApUgOaCcelNyc/WgBR9+tbUvEOp6tbrBeTxsgfzHKQRxtM4BAeVlUGV+W +Zyx+ZufmOcjPOafQBNfXtxqE6z3UnmSLFHCDtAwkaLGg49FVR7455qCNC7ADmlxniuy8IeFZdVu Y2KEruHegDL0nw5c37LtjOD7GvQtH+Gckqq0iDHuprurKw0rwxZK1zjzAv3Qelc9q/xMOTFYKAB0 wlAF2H4e2UK4ZIs+9St4EsCuNkP4V5/d+MNZm+YPj8KrxeJtcDBjKf8AvkUAdrf/AA0hkjYxKvTs DXBa54GubEsVTIHoprptM+IeqWUgFyFdM8/JXc6b4l0fxPCIpfknI9Mc9KAPm24tZLdyrjBqvXs3 jbwMFDzW6eh614/dQPbysjDBBoAgzSHgYpM9jmgknrQAlFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAfb/gT/knnhr/ALBVr/6KWugrn/An/JPPDX/YKtf/AEUtdBQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEqDinYpE+7TqAGkU gHFPpQMmgBgXIqRYi3QfpVm2tTKwAHeul03w3NcgFY+MdwaAOU+zP12/pTTCy/wmvS08EzsmdgH/ AAE1nX/hG4gUny/yU0AcFtx2xSYGa1b3T3gbDLj8KzWXBoAj2/lSEU+igBmKafvCpccYqNvvigB4 6Cg4PFHNIQefSgBD1pyrk9KQDJFa+mac91Kqhc59qAKsFi8p4U1rQeH55ACsLH/gNel+HPAgkjV5 VAHqVNdtbeFbKEBdqflQB4HL4buFXmFh/wABrMuNLkhzlCPwr6ZbwvZyKQUQ/hXM634EjeN2jjXo egNAHz48ZQ1Ga67xF4dl06Vvk4yexrlJEwcUARHJpKU/SkoAD0/CkXpSnoaRelAC0UUUAPjjLtgC uh0rQJ7xgFjY5PYUnhrR31C7VQuR9K950HwzaaTYi5uVQAAHkY7UAcDpfw9mlRWdGGfYV0Mfw7jC 8qc/QVral4503TWMcS5K8cEVk/8ACzYy3+qbHsRQBRv/AIdMELLGx/4CK4bV/CNzZZIifA9q9asP iJYXLhZlxn1Ircms9O160LQCMlvxoA+XLiBo2KlSCO1VCMGvTfGvhM2LPNGmNucgD3rzaVdrdMUA R0UUUAMP3qfTG+9T6ACnxRmRwoBNMro/CeknUtSiTbkFhmgDZ8MeEZtQZGMbY47V61pXguzsY1M+ 1T6ECtCztbLwtoK3M6oGK4UdPfvXmmv/ABAvLud0tndR2waAPUz/AGRaLtMsK/UilS60aYhftEB9 sivB2v8AVLo5e4kP4mmtPqEOGS4k/AmgD3W88NafqETGJoySOMAV5v4n8BmIu0aHOTjCisbSvG+q abKvmTO6A8gmvWPD3iCx8WWPlSACbb3I69KAPmy/spLSZo5EKsPUVQPX2r1r4jeGBalp0QAggHAr yd1KsRQAwH5vSkOO1AB69aD1oAAcUZ6H+tJRQA9FLuABnNek+CPB0moukjxsQc4GK4vw7YG/1OKM DOWH86+j7dbXwj4V+2OiiQ8IOhoAd5WkeGIF8+SIygZ2nHFYOofFC1tiUhRGxwMZrzXWfEF7rN4x Mj7SSOvvUmneHLq9USFCQR3BoA6qX4s3W/KW42/U1dsPi4N4W5gCg9+a5GXwtOjkNH+hqCXwvcbf lU/kaAPY7HxFofiOPYZYkduma5Lxl4JSSF5I0/hJVlUV5sXvtHnBVpIyvpxXrvgjxPH4j05tPvTu nGApYg9v/rUAfPup2UllcyROpBUkciqFeo/E3w/9hu2nVQAWOcCvLyMGgBKKKKAFBpAMmirNhAbi 6SMDqaAO68BeFn1O7jkaNiNwxx1r1691XTfB9kIlMbXO3BHpVbw5aweGfCE2oOih1iynbnH/ANev KNV1K617VpWLs25zgZzQBuat481PUZCIXZVPTDGubuLrUrjJeaYk/wC0a7Pw54LkudrzJxzxtNdZ ceCbeOHKxKSPagDxpE1FSCXmA7cmtfTvE2saRIGEsjKDnBY16F/wjkEhEKxru78U3UPAitbNtQZx x8poAn8PeP7TWkWz1Py0cjAZvXpWV478HpPbtNCu7oVcAc1wGsaZc6Len7y7W4I4r1LwNrY8R6I9 hdtumQEqWOelAHzzeW7W87IwIIJHNVq7Xx/o/wDZ2rPhQA2T0964qgApVPNJSjrxQA6uq8G6E+ra io2ErkdveuVUFsda95+F2jxWukvqEyjEce/JoA37+/svBekrGuz7Ww6HggGvML/W9R165I3PtJPC k80niDU7nxH4ikwzMMgAZz0r0Dwx4Qito0kuI1ZuD0oA4KDwzdSpvaGQ8dxUkHhCWaUhlII9RXuc Oj2yxcRr06YrG1LS0tG81FAycYAoA8mfwnJuKojHb3Aqg9pqOjTB4/NjK+mRXvOl6NC0IkeNSzDP Sq2u+GLW6t3xEuccHFAHG+E/Haamg0zVNoZiFVmPtWL8RfCgVGuoEyDlgQByMVzmuaXPoWpCRMrt bII4716Zod2vijwgI5cNNFHgk8+tAHzlMhjcqe1R9q2fEdl9h1SSLGAPasagAp46Uyng8CgDS0Ww bUNQSFVJ+le+Wq2ngzw4LiQKJ2TMYPB6DmvOfhTo4v8AWN7AFQOpFbHxF1dtR19LCAnyovkCg8dc UAZd7qOoeJL9iodlLZABNb+l+BZZYg8ysMn0FdL4N8LxWmnR3E0YaR1BPHSu2trVSAigDvQB5lde EorfqhyOegqQ6JbPAixx5cj0r0q605JAQyAkj0rE0vSx/aM27BVCcCgDkX8Bm5hJCkHHoK43WNAv dCuvNj8xdrcEcV9Fi2RFGAMEc1zuv6NbX0To0ak89R7UAcT4Q8WR61bnS9RKiXBKsx5P51wvxD8N nT7szohCnJ4HHWl1e0m8N68kseVUHjHFd/4ot49e8FpfKAWEXJxnng0AfPLDmmmpbhPLlZcdDUR6 0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfb/gT/AJJ54a/7BVr/AOilroK5/wACf8k88Nf9 gq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQBMn3RTqan3adigAqWBN8mKiq/pke+4UUAdx4P8NPqE6fISNwr2vSvC0Fn Am5QPlHU1j/DTS0FoJSoJ4P6Vv8AiDV/sMmxWxg4xmgDVj0m1YYAGPrVW78NW86kKufxrmrXxW4f lj+ddDpviEXDgE9fegDzPxp4N8lHkSI8Y7141f2rW8zIQRivsLX7CG90l3Kqa+X/ABhZC21GQADG T296AOOPFFK33jQOTQAlRv8AfFTFfSon4cUAOB4pCfTNKOgpaAHwKXlUepr2P4c+GPtjrNInygA5 z715Jpqb72Nf9oV9O+ALRLTwuZwo3eV1/OgCn4n8S2mgRfZ4Su9QPevP38eXskhKuME/3azvF95J eazKGYnp1rLgtMIG7UAddD4/vY8b5PzWus0Dx3b3siwXLgbiBnGK8muYQE461BZyvbXkbAkcjpQB 7Z4v8Ow6jphuoV3KwJyD7V866zZNZXbxsMYNfUXguc6v4XaGX59sXGefWvA/iHYi11mQAY6UAcI3 40AUuOaUUAMbpTV6U9x8ppi9KAFp0a7pAPU02p7Rd1yg9xQB7Z8KfD4nxcSL8ozzmpviR4tYTnT7 RwFAK8D3rrfh5bC08IPcAAMA3P4ivGNYL3/iZ1YkjzCP1NAFe0sJ76QuVZ88mtE6WIVO5SOO9ene E/DECWCyOiklB2qh4o0qGCT5FC5A6CgDy6ezfJMQP4VreF/FN5oWpRrI/wC6OQQR611emaRDLDlk DEjOMVyPirSlsbpZI8ANg8duSOaTdgseweIbCDX/AA0b6ABsx84r5r1y0NpqM8RBAVyBX0f8N7ht Q8KXNtIdwC4Gf92vD/iHafZfENwAMfvDTA4uiijFADG60/tTG60/tQAAV7N8H9GFzdLMy8Bgf0rx pR8wr6Q+D1qI9DuJwOVQn/x2gDnfitr7S6gNOgcbIiVwB7VzHhbwrc6xMGEbFMnmk8SFtQ8bTxkk 5mI/WvdfBWgR6dpcZ2LuOe1AHIXHw58i03qDu29M1zJ8KytctGVOQcV73fKvlEEdBXI/ZojqROB1 OaAPMtS+HV0LJpkjYkDPWuV0S+u/DevrklMOFYEe9fSuoQzNpiraR5JbDkLuOMemDx7/AONeFfEP RzZ3a3WzY7tlhtwc1zRxMXXdCzule9tPvNXRapKpdb2t1PR/FFrFr/hQXsYDZABx9a+ZtRhMF7JG RjDEV9OeBpP7T8BSxP8AMQCefrXzr4rgEGuXCY/iP866TIwQMUhHFOpGHFADKKKUDpQB6R8KtM+2 a0rFchcH9a7T4w6rsmi02JsKiLx+dUfgjbK127kZIUH9azfiazXPjGSPORhf5UAR+BfCkusTrMyH ygw5r3XTfDltaWkcYj5AGay/AekR6do0IVBlgpPHtXbqAsZJAP8ASgDita0iNJA4Xin6ZoMMqBmT IIzya09ZZWj61PpMieWq57CgDiPGngSO5spJIIjuGD1ryLw5cTaD4qhR/kxIAc19OxyXF6buK6th EiMFQ7Thhz3PXoPzr5/+IukjSvE0FxGu1XYnIGOQ3/16ypVo1YKcevfTYucHB2Z2nxJsE1LwzDfI M749xI/3a+bZ12ykelfUko/tD4Zws3JWH+hr5j1GPZeyL6GtSCnRTivpTaACuj8F2f2zXoUxmucr u/hdEJPE0fGeKAPUfihdf2b4atLFDt3x8j8q5T4d+H21G5a5kTKcY/Otj4zM32y1i7BMfrXVfDiw jtvD0DlQGZQcigDstO01bZVCjAxzV2e1UxHA7VPbICQM5pt7OqIy0AcvZ2obV5ARxzXTraRshBXo KwLWQJqLHGc966i1ZX6HPHegDzP4h+FkvLJ5Yk+cZPX2ry3wDeyaX4nSFjt6gj8K+j9VtRcQSo6g qQR+lfO+r6bJoXjqAEbfMAcAZHByO/0NQ6kVJQb1Y1FtNm18YtMARLlV4ZC2fxrw48GvpD4qQifw nbTY58n0+lfOLDGasQylHWkpR1oAsWqeZcxr3Jr6IZhovww3j5Wlj2/oa+f9IQPqUI/2q968bZj+ HlhGvGR2/wB2gDjvh1pZv9XM7ruVSa9zitSNm1eg5rzj4T2QGlNNgZLN/MV6taxtuGCeTQA+C3dk wRjFZmtRp5QUc8iuhmcQw57kVzN/NvB570Aa2nw5s0wP4RT57ZlQAjNVtMuj5aLgkACtmYb7cEDm gDyH4iaH52myTKnzKpOa5r4T3hTULizc8MMY/HFereIrI3Om3CMNwKHPGcV434KQ2XjuaEHAEuOP 94VKnFtxT1Q7O1zn/ihY/ZPEDDGARXA4A616z8aIAmtK2Oq15QRmqEMpy8kD3pDnPNOj5dfrQB7h 8I4Ba6ZPdsOkec/jXN6HC2s+NzIw3qJyx/M11ngQeT4FuXXg+UP51j/C+HzdcuJCMlX6/nQB7PaR CK0SILjirlnAyyfMKjgjYFc8ituNFjg3lRmgChdERofWsbTD/wATCckjk1dvrklzkGse0kZbuVgT 1oA65od8OVrKuoSrHIrR02Z5Fw4496NQj+bgcUAeM/FLTg1mLlF+6y8/nUvgmb+0fA17bMQSg4H/ AAGuj8c2S3GgXG7BIwa474WsWsNRhPQj+hpKSezC1jxzWofJ1OdOmDWbW/4sjEWvXA9xWEQcdqYD aKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPPDX/AGCrX/0UtdBXP+BP+SeeGv8AsFWv /opa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKAJk6U8jApI+gp5AoAZWnpZ23INZpGMVcsm2yg0AfTnw3nVtNC+wH6VS8ZW8hu GK9MmsD4ba0IdsbMOSO/tXpt9ZR6jCHxnIzQB5DBFKJMV0+hxy+cvrxWrL4eEcuVU/lW9pWkrAFd wAfpQBpXMnl6IwfqRXzN46dX1JyvTn+de++LNWS3sWjVh+dfNviO5+0XkjZzyf50AcwwyT600deD Uh600jNAC1BJ/rBU9QycuDQA4DAooooAvaW229jP+0K+pPBDC48H7F5Plf418q2bbZlPoa+h/hZr CtZm2dhgoBjPvQB5t4oge31qUkY5qpHdhowM9K9R8d+EmuJWnhQnOOgFeWXGj3Vs5BRuPagBk0wP NRQZmuUA55pyWNxK+0I2fpXXeGfCdxPdxl42xkZ4oA9Q+HULW2iOzDCmP/GvFPibKsmsvj2r6AkW PQ/DoTgER49Oxr5n8YXf2vUpHzkZFAHI0UHqaKAEb7pqNBxUh6GmJ2oAUfWp7MgXKH3FREYqSA4k UmgD6p8AMtz4GeNT83P8xXieoIbHxWxkHHmk/qa9N+D2rK1sbSRhg7uM/Ssb4meFZYr9rqGNu5yB 70AdZoeuQJpqBW/gGeK5rxJqyXEnyngV53batfWKmF94A46mnS6s04+Y4oA7HTNZEGQW4+lYHinU VupNqnJJFYcmoSD5UGavaHo93ql9GSjkE9xmgD2T4V2rW+hXEjggbf8A2WvG/ie6v4iuCp/5aGvo S0t00DwfID8reX347V8y+Mbr7Xq9xJnOZDQBywBzTqKKAIXGGp1JIPmpaAHJ98Zr6X+D7iXw1dxg 87Mf+O18zr94V7v8FtUEcxtXYYkIGPwoA5TW0Nh4+mZxgGb+tfQfhzUIZ9KiZH5x0ryX4reHJI9T N7Ah5YnIHtWF4Z8bXOkxiCYnAJ+8TQB7xq2ookbYbnHcVxo1MLdlt38Vcne+Nmvo9qlR77jWC2sz efvL4X60Ae/afq0f2blxtxXjvxT1WK7uzCjbvmxwPasebxjPHEUjbnGOCaxLSG71/VlZlZsuOeve gD2n4aQNb+DZnbgFT/MV8++NiG8QXBHTef519MCNfD/gZkOFJHevlrxDN5+qSvnqxP60AY9B6GlH XrSN04oAjp+3p601afQB7f8AA+UC7dD3UD9az/iPD9m8b+Y33SF/lWd8JdTFprcak4DYH613Pxg0 Vptl9ChOVXBA+tAHoPhW7iuNHgZT/Co4+lbtxKEiwp7V4B4I8cPpgW2uGG3IHJNeinxpayw7vMXJ HrQBo6rOyjAPWpdKuApX5vSvP9W8XqzYjKnn1p+m+KVCrvKjv1NAHsU13FHbb3fCjqQM14J8UtQF 3qtrCpyw+YjHQkj/AOtW7rPj5Us3iQowIHUmvNrD7R4g8Swu+5gZPr3rnlRlKvGo37sU9PN9fktF p1ZoppQcUtX+R7Np6eR8MkD8HyP6GvmbVCDqEpHc19PeLHTSPAUUOQCYsc/Q18uXjbrljXQZkFMb rT6Y33qABRk13nwukEfiaPPcVwo6V0ngu7+ya7HJmgD1j40QnzbWcD5cc/nXY/D2eKXw1bAEEhBm sr4jWX9q+E47pBkiIdPwrhvAXih9KkNpM4CjgZPvQB9ExyKgBrMvWDhyDzWLF4gS4iDBxz05qpd6 yqITuH50AW7V8XjAnFdHaXCgjnjvXmsOuRtdNhxn61sw6uUUNvH50AdtNcK8hXn5eTmvn/xxfLqn xDhMTZRMImB2GT/Mk/jXZeIvF4itG+YbwCBhj6V5r4Wjm1jxYk7gkAnnr2rysPl84Y2pi6suZvSP ku39efexrKa5FGPzPSviWdngmBWHIh/wr5sb7x+tfQ3xguhBotvag/8ALI8H6ivnk9TXqmQ1ulNX rTm6Ui96ANDSG26nCT619BeLIvtXw9s2HO1M/wDjtfO1m2y6RvQ19I6ew1b4dhOpSPOPwNAGV8Jr xG0x4CfmV2/pXrVuwTmvm/wdq76Fr8kT8ISete1W2u+dGGGCCOxoA6aedZcgmud1HCnAPemyagwy RWHf6kS+CcYoA7LTmjWBT3xWot3uUrmuEsL+ZkXb0xV7+0LiPLY/WgDc1KVItHuGkPLRt+ZFeG+F gZ/iHdMnIEpP6ius8VeJfs2lTRjaGZCMA1zXwrge612a4YEljnP415eVZdLBU5KcuaUnds0qTUnp sVvjWQdUT/dryGvT/jDdifxAUUjCivMK9QzGN1p8Z+ZfrTG61IvUUAe+fD/9/wCCLiMd4x/M1z/w +uBYeLJbZjgvJx+tavwjuVuLGayYjlPX3rlr9n0XxuknIxKSc/U0AfRtrIhCk+lQQ6lO+o3O6XzL N1UwjAGzAwe2Tknv6VzWla81zZxlQD8o6E1c+2O5LbcE9awrU6spwcJWSd356bf1+hrCcYxkmr3X 3f1/W5qXjxlSaztPkjN1IGPfNUL++kSI8HGKx7LUJHusL755rcyPSYblEChOneo9Qu3laCKPA8wn c2eQBj/GufjuJ9nFJLqNxEpygYdx61zYyjUrUJU6cuVvr2LpyUJKTVxvj6WC28LyrnDcdu3Nef8A wrjxbXz9if6Gk+IWvM+mtbggMxXgGtD4dQ/ZfDN1csMDbkn/AIDWOW4FYHDqinf/ADCc+eVzx7xr j/hI7jHqOfwrnD0NbXiebz9anfORmsU9DXeQMooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8 Cf8AJPPDX/YKtf8A0UtdBXP+BP8Aknnhr/sFWv8A6KWugoAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCxHUlMj6U+gBvGcVPA21s 1D3qWOgDufC+pPaToVYjkV7VoPiJZbZVduceor590qUqw/Cu60nUCirzQB6++oJJgjHHvTJ9ZWCI 4bt61wsOrNtxuqvd6mzI2WzQBX8Wa40qOAx5x3ryTUHLyk59a67WrkvnJ4rjbnliaAM9/vGm09gM mmUAFQyffFTVDJ98UAOooooAlgOHr0rwLqrWd1GQxA47+9eaRdRXVeH5jHIhzzxQB9RW0sGq2SMw UkjvWJqvhO2mUssS59hWJ4Y1krCg3iuubUlkUcg5HNAHL2Hg6BbkllTH+7XYWOm2tim4Bfl9KpLd YPUVFdanshb5h0oAwviBrfl2bRRtgAEcH2r531SUyTsSa9Q8ZXxn3gkYya8pveXJzQBmkcmkqQjP WmEflQA09DTI6eehpkdAD+tOj4cc0lKv3qAPT/hzqhs76P5iBz3r6AvLO313Tcuis23+IZr5b8NX BhuENfRHhTVjNaRruHQfyoA5rVvh9BI7MsajJOMA1yV34G8t8KD+CmveWKSLnAyayL62id+EBoA8 x0z4fRsFZ1DHGehr0Lw94RgsNshiUYz2rWssRKBsAAq3NdFEJB4AoA5b4iaoLXRWiVsfIelfMerS GW5lY9STXs/xF1EzK6AjGCP1rxO+5lY579KAKFFKwxSUARP96lpH+/S0AKvWvQ/hzqLWesQEMR84 7158Biui8N3BgvI2HBDCgD6g1eyi13RY2ZQzbc5Iz2ryPVfBa+YxiQL+Br1DwnqAu9LiG4H5BWjd aYjtuAyDQB4HJ4avIASoJA9jTI9GvLg7QG465Fe3T6QmMBD0qra6RD5hHNAHlNr4KlklUyZ69CDX oXhfwnDZSROYl7dveuph0tI/uqa0oYlt1z0IFAHJ/EzUfsuhmBWx04FfMd+2+4YnuTXtfxP1Pz3a MEEDHSvEbnmUmgCscdKa3enn9aY3SgBi9afTV608cmgDpfCF2bTVYXBxhh396+ldShi1zwxGzLvI H1r5X0mTyrtT6EV9MeBNQGoaMlu2CRmgDyLV/C0lpdNJCOAScYPHNU42uoF2uXIHrmvZ9T0j96wZ cjPFZo8PW8vLIQaAPMEUOCz9feoy8iNtRWr02XwpbllZVPBqceG7cKPkzQB5Qum3d9KFCtg+oNei eB/Cy214kkkYJDA9K2YdFihbKpXU6TZfZYGlbgDmgDhvjBf+VYRWqnAAIwPpXztPzITXrnxT1I3W oyLuBCsen0ryOQZY0ARUxutSEYqNutADh0rQ0qQw3iODjmqA6VPanEqmgD6h0iRdb8CNExDFYwOe ewrxvWtHl0zVHkQEKXPQV6N8LdQWewks3YfOMD8qu+IdADXDrIpwScHFAHI6FrL+SEdicVc1TUS8 BCv27VEfCrI+Ydwz6ClbwvdSD5i30xQBh2cskV55juxBzjNdK2poIOG5x61VPhmWUbWDArR/wjN0 Bgb8fSgDltXmkvrgxrkgmu6+HHh/yLlJ2UcBu1Q2fhhYGDygls969C0axGnafJcMu3CnrQB5H8YN Q8/URCGJCKV6+9ePHqa7nx7eG71eZs55P864cjvQA1ulIvelbpSL3oAli4cGvoH4XXy3Wiz2chzm LABP1r5+TjFer/C3URbaiqMQAwA5+tAGb4m0uTTtceVQQMjoK7jwxqizW6gnJGOprW8YeHxcSM4U 7XA5ri4dPu9KkzErFRz0oA9KV0ZNxx0rj/Ed8sMyhTySOhqsus6gU2eU35VnXFvdXcnmSxNntQB2 ui3KyWcZJ52+tXL28WGEktx9a4W3uL6y+RYjtHSpJ768uV2bG57YoAwfE9019dCFCecj9a7/AOGu mjTrCa5YAHy85x9a56w8PPLcLLKjbiRXoNxGujeFXb7rGMjB+hoA+f8Ax5dm812VySa5IjBrX12X zr9nNZDDBoAjbrTx1FMbrUi9aAPTfhRqH2XWVQnAYVq/E3SjDrCXUa4VvmyPrXDeDro2mqxuDivc /FOlprOhxXCjJ8oZx+FAGB4Mv0ayRGbLBRxXcxneAVFeS6dDcaNdthCVzjpXaW3ilFiAfANAG9qM SC2csB0NczoTRyanMCRgE9ai1XxK00DJEucjHArnbC8uLS6ads/MSelAHrYQqoKnIqlf3KRQkso6 VgW/ipfLALLWfq2tveIVjGSegFAHE+KpDqGprGnPI4r0eCIaN8Opj0d0+nYVzGi+HHutRWWZGyea 6X4izrYeGktUIBMfIz9KAPnvUWL3cjE8k1SPQ1ZuPmlY+tVyODQBHRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFAH2/4E/5J54a/7BVr/wCilroK5/wJ/wAk88Nf9gq1/wDRS10FABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAWIz0qS q6yYxx+tO8//AGf1oAm71NGOe1U/tH+z+tPW72nOz/x6gDoLBiHH1rqrGTGMHtXn8OseSf8AUZ/4 H/8AWrTg8XGD/ly3f9tf/rUAeiRzMR1pk0xK4zXEr48IGP7N/wDI/wD9jTX8clxj+z8f9t//ALGg DU1F92ea5u570XHiUz/8um3/ALaf/WrPk1PzP+WWP+Bf/WoAVuppGGRUBus/wfrSfaTj7v60AS1C 5+cUomz/AA/rUbvls4xQBLSgZpgfjpR5oXjH60AToPmFb2lvskXn0rnFnxzt6e9WrfVvIYHyd2P9 r/61AHseg3hVF545rsLe8YgfMa8KsfHbWYA+wbv+22P/AGWtqH4smIAf2Nn/ALev/sKAPYjc7Tu3 GqF/eN5TYJxXmP8Awt4k/wDIE/8AJv8A+wqtcfFVp0KjRwvGP+PnP/slAGh4huC7sD6muDu/vGrN 94rN4xP2PZn/AKa5/pWTLf8AmH/V4/4FQAEc001H5+edv60vm57frQAMMCo46UtweKahxQBLTlqP f7UvnADgZ/GgDf0iTZIpr2XwfqBCxjcecfyrwS31LyCD5Wf+BY/pXUaT8RX0sqf7N8zb/wBN9v8A 7LQB9MQ3QeNTu5qKaX3ya8Sg+OTQrg+Ht3/b7j/2nUh+OzMD/wAU4P8AwN/+10Ae0LOVwSePSodQ vdsJIJArxs/HMng+Hf8Aye/+11VuvjS9yhQaFsB/6e8/+yUAWfGV35src+v8681ujlyfetDU/Fza i5Y2ezP/AE1z/SsSS88xs7MfjQAp6U0jFM87d2x+NG+gBr/fFKKY5yacCBzQBJWppb7ZlPvWQZMH p+tTwXpgbIj3Y/2qAPoPwDqeIkRieFHf3r00yCSPIx7V8s6J8Q5NGdWGnebjt523/wBlNdlF8fmj TafDeff7dj/2nQB7VK3XBqpCds/JFePN8et2f+Ka6/8AT/8A/a6b/wAL3I5/4Rv/AMnv/tdAHu6z qEAxzVS+uhDbO5PODXi//C/jjH/CND/wO/8AtdUr744PeQmMaAEyDz9sz/7JQBF43vDPdvye1ecT cua0dU8UNqcrObXy8/8ATTP9Kxnud5J24/GgBxpj9KA/tTWORQA1e9SKO9Rr1pwfHvQBdsztmBr2 74baoYZUjJ4Oe9eDx3Hltu25x710+h+OJNEmSRbLzdvbzdv/ALKaAPqy7gjnQNgcjrWcbRMdK8gj /aBdYRE3hoNjgH7dj/2nR/wv4/8AQsf+T/8A9roA9bMYBJ4wPalEIkPoK8gb487gR/wjOP8At/8A /tdNT48FOP8AhGs/9v3/ANroA9st7GMtk4+lO1q6Wz02QAgfITxXjCftBsgx/wAIzn/t/wD/ALXW drHxwk1WAxDQfJBGM/bN3/sgoAxPGV2bi/mYknLGuJf7xq9qWunUJWc2+zcc435/pWYZtxzjH40A OPSoW61JuqNutADx1qeLhxVcMMZpwl2kHGaAPWfhxqRtdQhBY43gdfavdbu2j1DT1l2jdjOa+TNJ 8UNpUyyLa+ZtIOPMx/SvSLL4/vaWa27eHBJgY3fbsf8AtOgD0kW5glwRkVYEW48ADPtXlMnx2ErZ /wCEZ6/9P3/2ukX45gEH/hGun/T9/wDa6APWJLRV5AHI9KgaI9F4/CvMpPjrvQD/AIRnGP8Ap+/+ 11EPjltP/It5/wC37/7XQB67YaaZpQ0gBGc81N4svEsNGeNDjgdK8oh/aD8lQB4X6f8AT/8A/a6w /EPxkk15Cn9iiAH/AKet3/sgoA5XxBMZr2Q5zkk/rWAanutSNzIXMW3P+1n+lVfMz0FACN0NNTvT mPH4U1Tg0ATDrXX+ELw22oRMCRyP51xvmAHpV6x1c2UiuIdxH+1j+lAH1rEqaxoqMcbsYz1rmZbA RuYpADjvivOdF+OMmkWohOgCYDv9s2/+yGlu/jgt05YeG9hP/T9n/wBp0Aekwabbnny1z9Ktf2RB Jk+WmAOwryqP40so/wCRez/2+/8A2urcHxy8lWB8M7s9/t//ANroA9CewgzholGOBxUAsIA2UiUV 53L8aTIxP/CPY9vtv/2uo1+NQRs/8I9/5O//AGugD2DSdNEkwdlAAPpWP8SdRFvpf2ZDjGRwfauI h/aAECbV8Mduv2//AO11yfib4mSeI3Zm0zyASSB9o3Y/8dFAHLag2+ZjVI8ilkuvNYnZjPvUYfPa gBjcNUi9KiY5anhwO1AGvpEpiuVYGvpHwjdJqehC3f5jtCjP0r5ehvfIcN5e78a7zw58WJPD6BP7 I88D/p52/wDshoA9WvtKRLh1dBjPBxVE6HC74C4/CuPvPjit3yfDe1vX7dn/ANp1VX4yH/oAZx/0 +f8A2FAHoaeHosDABPapX8NrGPnVcHn7tcFB8azFIrHw3uAOcfbf/tdWrz46m6QKPC+zA6/bs/8A tOgDp5vD8CHIA/CpLbSYEwdgJrgn+Mj7cHQP/Jz/AOwpI/jQIyM+Hs4/6ff/ALXQB7Lo2npERKVH ftXmnxU1Lz5/JVuFBHX3qr/wvzbEUTw1jIxn7d/9rrz/AF7xg+uXDStZ+Vu7ebu/oKAMSX7x+tQv 0pWm3Y4/WmM3HSgCOiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/yTzw1/2CrX/wBFLXQV z/gT/knnhr/sFWv/AKKWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4Aooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigDQ0vRb3V/NNqsCxxYDy3NzHbxqTnC75GVdxwxC5yQrED AOLB8LaytnNcyWfleVvLQSyok5CEh2WFiJGVSr7mCkDY+SNrY3NG17T5Le/hhi0bQp5WgdPtFvNd 2jKglD5jkE580mRNrbeFVwCu4h9C/wDEWiXl82sQXskaQWWpWUdjciV7qX7Q90Y334ZCALlNxaTd lH4bjcAed0UVLFA8pwBmgCLBNOEbHtXSab4Uvb4jYnBrrLH4Z3UiBnVfxoA8xELehoMTDsa9hT4Y noQv5Uyf4YSMpKAUAePFSO1JXe6n4AvbUFlQED0rkbzTZrViHXGKAKFA607aRSd+aAHAYFNb71GS KuWV7BbwXMU2mWl28ybUlnaUNAcH5k2OoJ5B+YMOBx1yAazeC9bEKSxpY3EbSiEva6lbziNirvly kh2LtjdizYUBSSQBWTqel3Wk3KwXaJl0DxyRSpLHIuSMo6EqwyCCQTgqR1BFdTp/iGx0zXNKjt50 Gn2umywCSSDegubi2YSSSoVIcLLJsJ2kmOJBh8DOL4ouLG4vbU2klrNOtuFvLizt/Igml3uQ0ce1 NoEZjU/IuWVjg53MAYgx+NKAc9DToo2dgB3rpdH8J3mpMNicHFAHNCEk9DUy2khPCN+VevaX8K5G UNMqD61uR/D2whADCMn8aAPB/scuOUb8qie3deqmvoBfA2mN8uyLP0qtd/DK3mQmFY8npigDwMoR Ta9H1z4d3dkWKIMAnpXDXVjLaOVdcUAUwOKdRRmgBD0NXtK0a61Yy/ZpbFPKxu+1X8FtnOenmuu7 p2zjjPUVRY8V0Wn6zYRXt5qkVhaaZdW9kRYQ2xmZGuGdU35d3ZXVHd1IZQGjQ9eGAK914R1y0S7a e0RHtXlWSE3MXmnyyVkZI9290Uq+XUFRsfn5TjCrrNNOlad4bkurTV9NTWJ7edJVuo7nzYVYNGYo gkZiJeMnLux/1gACFSzcooLHA60AGCalSFmPAJra0bw5dapIqxpmvTtF+FpVFkutgUYJzQB5BHpt w+NsLflVgaLdkf6l/wAq93Xw74f05dspiJHH3aCPDiH/AJZH/gNAHgz6PdKOYX/KqctnLH95GH4V 9ELb+G7gbcxc+qVXvPh/pupRFrURZ9higD53KlaSvQ/EXw/u9N3OqAqMniuFnt3gkKOOQaAIQKWg 0m6gBrda3JfCOtxNDGbRC8jeW6pcRMbdgpYrOA37ghVckSbcBHJ+42M60vYLa3uo5dMtLt5k2pLM 0oaA4PzJsdQTyD8wYcDjrn0XUvFOhXmvXGqzXdi813dTy2b29gYms45Ypl23e2NfO+eSAsP3wKxy j5g2HAPOtS0u70m4WG7RAXQOjxSpLHIuSMo6EqwyCMgnBUjqCKpiug8Wahb391YLBcwXUlvaeVPN awmG3d/NkceVGVTYoV1BARcuHOCSWajpekz6hMqRLkscUAUUiZ8AAmr1vpNzN9yJj+Fep+Hfhg8k Sz3YRUwDzXViw8NaHHiRY3cdwtAHicXhW/kAPkP/AN81MfB2oYyLd/8AvmvXJPF/h6HO23Bx6LUS +OdBJwIP/HaAPHLjw3fwA7oH4/2ayprOWE/MhH1FfQkOu+GtR+R41UtxylQ6j4D0vWIDLYeXk8jA xQB88kEdaAM967HxH4Mu9JkYlQU9RXJMhRiCMEUAJ0pD0NBIz1pCeKAL+laLe6uZTarAscWBJLc3 MdvGpOcLvkZV3HDELnJCsQMA4F0PVHg1Of7BOsel7Rel02/ZyXEYVs4wxY429eDxgHHUaL4ksm07 UrOCHQ9CkuJbaVTc2cl5bMIxMG+WQTsJD5y4IAGFbkE/NO3inwu3h/X9JtdMvrSB4rj7CpuwRI0l 3C67lMbFWEcMQOZCpEJAwz7qAPP6cqMxwATUlvbvcyBEGc16T4W+G9zqAWadVWPnk0AcBa6Tc3LA JE5J9q37XwTqU6gi3kGf9mvYV0rw74aizMsbyL/s1QuPHum2+VtbQHHGQBQB56nw71BhzE4/Cmzf DzUY1yInP4Cu0f4jvuwtn+gqaD4iDP72x4/CgDym98LX9rnfbyAe61iTWskJwykfhX0Pb+JfD2sL 5VzbqjH+8tZmvfDuz1G2a404JjBOAKAPBaco9q29b8PXOkXDJKoABPNY3SgApjdadmrVreQW0F1F LplpdvMm2OWZpQ0BwfmTY6gnkH5gw4HHXIBoSeEdciaGNrSMvI+x1W4iY27BSxE+G/cEKrkiTbgI 5ONjYztS0u70q4WG7SMF0DxvFKksci5I3I6EqwyCCQTgqR1BFei6n4p0K91i+1Ga6sXk1GW7FrJb WBheyhuIJ4yLkrGvmNulhJK+aR5cmCSfn4vxDcWssWj6fZ3SXg06yNu9xErrHIzTzS5TeqtgCUA5 Ucg9RgkAwwMmrVvYT3LARxsfoK6Pwx4Nu9anQKg25GSa9a0/wjovhu1Wa/EbOB0xnmgDybT/AARq N2ARA4H+7W/D8M7wqN6kfgK7K98dWNtmPT7QNjocCsWXxrrErfukIB6Yx/hQBnf8Kxn9T+QrOvfh xfxKSkbNgegrdHinxArbndgOwyP8K0LTx3eRsBd24kXueP8ACgDyrUPDd9ZMfMgdceq1jPE8bEMD X0VDq3h/xFH5VxCiSNx8y9zXJeK/huY0a4swhTjGKAPIAOacABVm8spbOYxyDBBqsDmgAPQ1d0jR b3Wpp0slg/0eLzpnnuY4ERNypkvIyr951HXvVEtkV1ej6xA17dy2cujeHjLbrA8M9nLd2s6b953C QTsH3LGR8uMKTlSPmAMo+F9WFnNdCGBo4t5wl3EzyKhIeSNA26SMbWy6Bl+RjnCnGPXomoeIPDt7 pJt4PskOl2tveQQadcWW693PNPJbsk+xiEUywlgZRnZIMNn5+M0rSp9TuFjiXJJoApxQSSttVST7 V0OmeEdQviu2CTB77a9L8OfDq3sLdbrU9gHoRWne+K9J0pTBp1srOBjhcUAcZZfDS4ZA02V+oFaS fDaFR8036Cm3XivW7xj5DtCvYAiqqyeJbnLLduQOvzUAWZvhqjKTFJn8BXPaj8PL+3BZI3YD0Fba X/iC1fIuZCR71sWPja6hOy/t/MXueKAPHr3R7mzYiSJlx6is8qVOCK+hpLPQvFkBWGNFuCOhXnNe ZeK/A9zo8ruigx5PSgDhgPUGn0rAocGm7qAGt1rbfwjrEa2m6K0L3aJJHEt9AZFRo/MDugfdGmz5 izgBRySKoWl7BbwXUUumWt28ybY5ZmlDQHB+ZNjqCeQfmDDgcdc9hNPoOqQWNprOvWk6q8awXkNg 0EsFtFC6+VOViJLsRboGHnhNrEEjhwDjtS0u70m4WG7SMF0DxvFKksci5IyjoSrDIIJBOCpHUEVV VCxwBW74jlW91C1gtbrTZ7WC3EdvHpyziKBS7sUzOokJ3MzEkt9/AOBgdT4Q+H0+osk9wqrFkE5o A47T9BvL5wIoXOfQV2Wm/Di7lCtMrIPdRXfS3Og+FIRCkKSXAGDheeK56+8XarfuUs1MKnpjGaAC P4fWcIAlmAPfOKk/4QfSyMC4TP4Vkva65csPMvZgW/2qkOg6vDgm8lLHn7xoAmu/hukiE28u76AV yeq+BtQsst5LlR3C11cM/iHTDuE8rIOxNblj4zikUQarbbgeC2AaAPDZ7SW3Yq6EY9agr3fWfBmm 6/ZNd6YIx7AYxXkGsaJcaVctHKvSgDJUUp6UE4NIWGKAG0UUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQB9v+BP8Aknnhr/sFWv8A6KWugrn/AAJ/yTzw1/2CrX/0UtdBQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACjvSt1po60o54x QBNa2z3EwRFJJ9K9Y8HfD9rjbNcIQvOcgYFZ3w78Kfb5luJlGxQDyPeu28TeLIdJjOn6bgFQMlSK ANyW70Lw1BjMTyKO+O1c3qHxN5ZbaJSo7rmuShsdS12bfKztk984robbwOyxhpMHjn5TQBnv8TNR L7VjJ/E1btPibfIR5kGR35NTweEraOZvNUA444qSPwhFcMdigD6UAbum+OtI1YCG8WOJm/vf/qqD xH4Ks9Us2uLIq4KkgoAa5TVPB1xakvAOndQaZoHi6/0K8W2vWeS2ZsMGPQdO9AHA6/oM+lXLo8bA AnqK5/pX0R4o0Sy8R6ML6zCHcpbjkjivA9Ss3s7ponGCKAKdKvWkooAkoEZdwFGSe1A6V0ng/Q31 jVETblfpQB0XgjwTJqMqTSowTI5IFepvd6P4TtAgMTSgYwccVn61qtr4R0tbK2A+0un8JHBriLey 1LxDd+bIWYMc5bOBQBr6n8QNQvZCtqhCdiCQK5661vWpucvz6E13emeC4Y+ZQGI68VrTeGLMQlo4 l3AdMUAeUpd64uGZ5efc1p2nijW9PkVmLsPQk13FvptvcyiHylBTg8Vem8LWs0TAovTj5aAMnSPH tnqYFtqiRox43N+XpVPxZ4Jt9QtWurLDg4wUA5rL17wbPauZbccZzwDSeF/F1xpd0un6kWe3bI+Y 9D+NAHlGp6fLYXLRyIykHuKzyfQ17P8AETwzDLbf2haqpVkLZA968ZkQq+Dxj1oAaeppVzim0qnm gBxGRXT+E/DM+s3ihUYrxk496wbK2a7ukiUdTXvWi29n4P8ADC3k6r5zphB0JPXvQBp29vpXg7T1 MhiM45wwHeuR1fxpf6rIY7YFYzwCCazS+peKb8uxcqegOcCu00fwjDbqDMisw6kigDg20zV7zLkz tn3OKii8LX80jbvMGBXtVtpFusYUIoHuKzdVsltXR41ADEA0AeXR+GNRGdiyHHpU0N3rWhyAgzLj sxNexWWnQpaqxVSSMniqOqaFbXMJ3wrk+1AHPaR4ys9bj+xamqJK3yhmrl/HPghVRruzXejZYFQM dKk1zwncWAN1aggLkjaDWl4S8RDUEbR9UyWA2oz4+n9aAPDJ4micowIIqHmu6+IPh46VqZdEAQjs K4U9aAAdafUdSKN2AO9AFzS9Ok1G8WKNCxPoK938M+HLDw1pn22+KhgAwDgDPFcv8M/D8UcZ1O7V RGq5ywqxrWs3XibVPs1puW3VigUdCM+1AF3XPG1xfSNb2I2xDgFSRWTaeHNQ1ZvMkErZPVq6vRfC EdtCktwgdjz06V11rZrEFEaBce1AHmMngOeNd0mQo68CnnwdEfLWJssRzgA16zPah4CHUYYelYmj 2Stf3AIyEYgZoA4SfwLcLGWi3E9cAVmRXGreGrn5TKADjBJxXt7Wvy42gevFYGraFbX6urIueecU AYlhreneK7U2135cdyRxnv8AnXlfjbwjLpNy0iI3l8nOPeul1rQrzQrgXdqWCrjlQa6G2urbxl4b mhlUfa4V2ndyTxQB8/FSPrSHPetLWtPfT9QkhcY2n0rMNADlp6oZGCqCSfSo1611HgzRG1fWI025 XnPFAHb/AA/8Eq0S6hertjVQ3zAY610uv+M0tl+waWq4X+Jar+J9aGnww6Hpvyso2yMvfjpTvDvh AyBbm853ZOCDQBg2eiahrk3myiRgT1bp+tdJF4AQRBnLEntiu2tNPhtU2Qoox6CtWGDfDggflQB5 gPDlpYu0UsfUcEgVatvBUN1EZMbQemQK6LXrYbomx0kANdBp9uv2cADA29KAPLNS8AzQoZINx+gr J0/WtT8OXYSTeYwfmVicEV7TdwN5WAB1rlNY8MwX0RJQeYRwcUAY99a6d400ppYQgudu5kXGc4rw 7X9Hm0m+eGRSPcivSZvt/g7VhIN/klsEDgYqx480m31vRl1ezRfuEnAz39qAPFulIM54p8qGORlI 5BxTKAJK6jwb4Zl1zUVGxvLAznFc7aQtc3McajJYgV7fo8UPg/wnHelFFzMo28YP60Aa95qVj4Qs BZ2YR7krjjqD07VyPlal4lvS0hkKk5xzgVb0LRLrxDcm6uWJQtklsnPeu/0zRY7IKkarxxnFAGDp /gaCKIG4yx9wKuXfhe3t13wIDgZxtFdrbwE4RxkVHqFsIhjHGOlAHC29jFqEix+UvycH5av3Pg+C WLHlgHH90Ve0KJBdXfy/8tCK6w2mY1bHGKAPGtZ8GTWX76338c8UzQ/Flxpj/Y79fMtjwQ5PFeqX tsJ2ZCo29DmuL1vwdHeK726hXHPAoA5jxt4Ut9RsDqWn4kUqW+QD64NeMzxNDIUYEEeor2nQNUm0 3UH0TUMtDMdo3dB271xPxB8P/wBl6m0ka4jcAjAoA4c05elNpV60AWbS0kvLlYkBYsegFe5+GNDs fC2jLf3u0Sso2qwGSetcf8NfD6XFwb65UeVGGJJFbt7dXPiTWWtoN32eJsKo6AdO1ADdV1u/8Q3B ijDLGeiITW1ovgkECW6BPQ4IGK2NM8Nx6fErlAX9cV1Flym3A9KAMVfDtpFENkS8f7IrLvLMafIv ljKSfLj3NegNZYgZ+2K5PW1UC374mWgCaw0iHyUZ1yzAZyKpal4WtLxmUIAfUAV12lwrMFGP4RUl 1aeTk45NAHi2peHrvRZ/PgL4U5BHFa+la/BrVuNN1XasgG1ZH6ntzmuzv7IXX7tlBB9RXC+I/C0l mpvLTgg5wAeKAPPPHHhaTRr5nRG8ojIOK4k5HFe6RGPxZ4YuIbgA3kCkZbqfSvF9Us2sr+aFhgqx FAFIdak27jjuajrX0HT21LVIYVGQzAHigDsfh94R+3zi8ul2wKM5YDFdprniVYozp+lYEY+UuuRn tUWqzLoWj22lWXyzuoEhTr61c0PwsTCtxcDJODgigDL0bwtPqsnn3O8Keee9d3ZeG7SzTasQP/AR V6whW1ARFAGOlbtvb+dyBjNAHMX2lRSQkbQGA44FZOj27XV44kGREStdfqMPlsQTWH4fRftN4M4P mmgDVl02B4CDEvT+6K5TWPBsN2GkiG1uvAFekpZ+Za5GOlZl0uwFQOnFAHjEbah4avt0ZcAcEHOC K19VsrHxhpEk0AQXSL8yADOetdZfaHHqCsHRcn2rg7y3uvCmsRyqxMBPzY6EZxQB5FqlhLp928Mq FSp7iqNer/EXQop7RNWtFBRwMlRXlBGDigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf 8k88Nf8AYKtf/RS10Fc/4E/5J54a/wCwVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKt6dbm6vY4gOpqpXU+BLL7Z4g hUjI5P6UAewSSx+EvAsbqNs9wm0d+xrkfCegzeIL43NxkoScnOKv/E67LXtjpkZ4jwMD6Cu+8E6Y tjpMKFcE5J4oAvWuj29hGqqgGAKttCoUnbx2q7PH+8VTyM1amtR9nXA7UAcXq9sQquowdwBrW06z K2yDHJANLqtviJMjo4resYUa2iYDnAoAz5LFPIw6A1534x8IpNayXUCYdQT1r1u6iCwkYrGmt1nh kRh8uCMUAeP/AA81p7bUJdIu2zG/yAEdCTj+tc38TtF+waqZFGFYVo+Jrc6J4yWaP5VMoPTHcVvf FC3F9ocF8BndGeR9aAPDaKU8MfrSUAPjG44r234Z2EWmaTJqsw4WPIP414xZIZLuJPVgK9y1D/iU fC6ILw0wUUAc1ZpP4t8TmSQ5jMhz2wMmvYtO0WCwso4406LXEfC+xWOxe5ZcszZGR7V6pEoa3LD8 qAM6OLBOOops2xCe9aUFuWRpMGs2SElmYjnNAGVp0a/brltvetyICVwhHFZukxbr65ycfNXSQWm1 wcZoAybm2DSeURkGvNPiD4WWGI3tumCMZwa9cvYtkwbFZOuWSXGnyo4zkdxQB554Q1Aa/wCGLrT7 g7pYRheO2P8A61eM+IbE2OqSxEYweK9G8FTHTfGstpnCSOVx+dc/8TrL7Lr8mBgHH8qAOCPJoHWi lHWgDufhzpH9o63GWXKrk11njS9fVfEsWkQH93AwQD8hT/hBaBLaa6I+6rH9Kr+F4/7T8c3ly43b XJ9f4qAPS/DXh+LTrFFCfPjJJNbUkGwg1PYjBA7VcuLfzJVCCgCn5e2HJHasXVgr26EZ3CQVsag5 jIiUEYHNYt637hAD/wAtBQBrW0iqEyOgFWZ4/Mg8xfu56VSSF3AZQTxWzawF7FlINAGRJZrcQOjK ChGK8c8caTJoWqx31t8o8zPX05r3RI9sTDoa4P4h2CXWhzHHzKCQfwoA5vxRGniPwWmogZkCHdjj kGvDZkKTOp7GvcfBLfbvAeo2r8mNSQK8Z1mPydVuE6YcigChV/Sbc3eoQRAZy4FUK6fwNbi48SWo P98UAeo+IpBoHgSztIfklnAz9K0vAegLb2SXUq5kkwRz7Vi/EY+drGm6eOiKOP8AgIr0fSI/JtIU AAVQP5UAaq22IxxToYwZAMfjWjaqJYj9KjmQWsRkbg0AVbvkbFPasXSgI7y6LEDL4GT1PFW5Lgs+ c8ms6yOJrluCfM6HsamfNb3N/McbX1OqiZZIyo61RaAea4I71DZzus6bvWtqeA70lA4aqEc9qmmw 3Nq0Uq5DDFeO2Jfwx45SA8QzPgjrkZIr3XUFBSvG/iPAINTsbtRhgev4igDnvippItr/AO0oMKwU 9a8xr3L4kxC78J2l5gZKKM/TNeHHqaABeor2r4Z2CWGiXGqOv3FbB/IV4vEMyqPevebb/iXfCaWQ cM7qp/OgDN8I2Da9r11fz5ZQ+4c+/wD9avYbWBTGEA4Fef8Aw3txHoayngvz+pr0Wwf58sAKAHLa bM5zV6JfLtyfyq2bcMA3bFZV3dqrmMEYFAGBrYdjCMZzKuAK3tNYAcnpweO9YesSCRISf+egq2Lp UwFADEDJHU1DU+ZWat17/J/8AatZ33Ny9i3AFfu5qm1qCS9XNOnF1EYmxuHSppoAkLDoferEcH4v 0WPUtLlUrmRQSDn2riPBEv2zR9S0afkqCU/KvUbxV+dSRjmvKvD3+hfEmS2HCynGPqpoA8o8S2f2 HW7mHGAHOKx67b4mWwt/E8+B1c/0riaAOs8A6d/aHiG3UjIDivRPFjtqPia20WI/u4PlIHsBWB8I LYSam8pH3cfyNb+k/wCmfEzUZDyEkbB/SgD0vRbCOztookXAAAP5Vqm1PmZWqcMgAHtW/p2JkwfS gBLSIgjcKp6yQCMDtWrcyJbpngVh30izxEq2aAMLRWxd3WOnmGu5tdktqMHORxXGaKFiubgnBUsT k/r+ua3odQCTjBGB2qYS5kpIclZ2LE9ueQBzmqUtuYVOF5NdAgWeMSDuOaxtSfaxUHiqEeSfEXTl iaPUoBiSE5Y/jVTxfAuu+B7fUVGW4BP0OK6fxmDPo95AIywCE7h24zXN+H/9N+GNzGeTHI38xXLh K8q1NuSs02n8m1+RtXpezkrbNJ/eeFsMMR70+BN8yr6nFOul23Mi+jH+dWNHj83U4E9XA/WuoxPZ VA8P/DsFflmmcLn2z/8AWrd8A6Stvpq3Tj95KMk/jWH48/c6NpVoB951OPzrvtFQQaXbIBjCCgDf S2WSEils7TZL04zRZz/OB2NbyIipuA5oArXY2WJUd64DWOBFxkectd3eSLJGyE4PpXE6oi/xFsq6 leO+QOfzNTKSirsaVzpNFYK6n1ArdvIBKmRXP6Y6xxhmwMCty1vEm+XIqhGS9oFbOCcVQvYFlRo2 TIIIrpb1VSEnAzWEbjZIwwOQa48wxTwuGnXSu18jSlD2k1E8htgdB8fpa9IbkhcdeoNcX8TNOWz1 15FXAkYn9BXb+Ox5HinSbocHzBn86yfi1bgwWNwOrrn/AMdFdhmeR16X8K9NEt7JduOIfmz+BrzS vafhnCIfCup3GOQh5/4DQBNpkba/4zu5n5iicgdvavV7GAFAgHHSvNvhqm9ry5PV5Dz+Jr1C2by2 B4oAimsnSXKDitrTImSLcwqxb7JkBIFTMVUYGBQByusz5nb8a57SGP2m6I6mU10esWxSXf1B5rnt JUPdXCjr5poA9B0lxJZlSeen6VTvbMtKcDjNWbHbbQhc8nrV8bXXNAHPfZBHH05rkvFmlpfaRcgr llUkGu31CbYSuK5+9IeCVCOGU9aLgeZaD/xOPBd1Yy/NJA7Y/Mf414vexeTdyR4xhiP1r2rwKNmu 6tZHpgtj8q8l8TQ+Trdwno7fzNAGPRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4E/5J54a/ 7BVr/wCilroK5/wJ/wAk88Nf9gq1/wDRS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeifCmASeIEJGcKf6V53Xp/wAIQP7c HH8LfyoAt+Kyb34jCI8hZAB+le1aWght4kA6CvE9Zbyviazv0Mw6/UV7vpzK0SHjGKANGG0+0EM3 anXckcCbT/CKfLqllpqRLPIyNKDsARmzjr0Bx1rLZorxpJo5hIj+/T2I7H2rNVabm6akuZdL6/cW 6c1Hnadn16GXqcvmRgg8bhWrYXnkqgbpgCsXUVWLYAc/MOK1rW3EyqfYVoQb7xrcQbgM1izxGLPv W1bMkSbCwqjexAuAG+Y9B61FSpCnFzm7JdRpNuyPDfitaiKe3uR13/4Vd1z/AEz4ZRStyyo3P4ip fi+gjsIFZSG8zvTdu/4UPnsrfzqaNanWgqlN3T6jlFxdmeCyjErj3plS3A/0iT/eNRVqSaOiJv1S 3Hq4r2Xx+/keBtKhXowBrx7w/wD8hi2/66CvXviTx4T0c4wNo/lQB1XgO2EXh+3K9WVT+grvrCIu NpXr1rhfA06voNttxwij9BXoWmyoOuOlAEs6xWkO04GaxLmdCCqjk0Xmp22q3SLEJ1j/AIZWTajn 055/MDp9KhvEjQZ3cisqVelWTdOSduxpUpTpu01Yo2LGK5uG/wBqujsL1W4foPauZ06RGuJlLZ+a uijghKDDYFamZo3dssiiQY55rCvdzoysQa6SNo5LYR7gcDFYl5Cik98etAHgsymw+JUIHygyA8fU 1H8YoQuqK+OqIf0qbxDIp+JVrt7OAf8Avo1L8ZE/fQsO6J/I0AeOnrSjlhSsOM0i/eFAHunw0Xyf CV7MOMI38xVX4bRiTVb6U8nPX/gRq38P/wDkR73/AHD/ADFVvhk4F3eAdc/1NAHr9oG3j0rogkcM BlcDgZzXO2LYcZNbV7LE1gyPIEBXGc0m0ldglcy70wSlpAeSawb4KUTHZxWx5EXkjZIGHqDWBq9w kJRQRneKE01dA1Y27e4CsFAOOnSt+wmikbyu5GcVhadJDKiuSDkCtO1igjuhNu5xxTAnv4fKB2gD IrifE0Bl0m6UjOUb+Rrvr5keHOecVxviBgmnXBJ4CN/KgDzb4ZsWsdWgPTYePwNeT+J0Ca9dj/po a9T+F7hp9UA/iQ4/75NeZeLlx4jvB/00NAGBXbfDSMN4ots/3xXEkYNdz8Mj/wAVPbf74oA7XxVm f4lRwtyEOP8Ax0V6daKVRVA4wK8x8QOB8UGJ9f8A2UV6datuRc9PY0AdLpK5z+tM1WSOQGMNyvWn aU6rGwz2qhc20b3UkgkyW96AMeYAEbTzVbT8+ZcHjPmVcvTHbRszHoCaxtE1FGvpkcjDsSKAOstz GcMwGRXQ2xjntRjnArDCQPGPmxn0rT01YoYSqOT65oAydRRhIfQV5X8UIybGCXH3T/UV6tqZbzCV PFeWfE5x/ZSA9T/iKAK3if8Af/DC0kPJBIz+JrwlvvV7rrHzfCi3/wB4/wA68MkHzUAPthmdfqK9 z1x/K+FECrxumX+Zrw20/wCPlPqK9u8Rc/C20/67L/M0AdZ8PbRBo9sJPuiNSB7812ZVHuh5XCqM HHrXG+D5MaDaFf7gziuutJVEq142HyupDHSxdWpfflXZP+uh01a0ZRUYq2i/Lc3ppkgtVDnBIwK5 HUWxOSrcE1v6tCl0kWZNpU5471h3NvGgJZifrXsnMYV/ORFEpOT5gxWrBguNw/Gua1i7DzokQyEI Y4rpdHu4L22Td94AAigDc0xkim35wDWpqefs+5fSslLWDIHmkc1rXuDZfK3bigDl7j5ifX3ryo/6 P8WbPHG91/ka9VmY56ZryrUSP+Fqadjg7xnH40Act8W0C+JHPqf6CvOq9K+Li/8AE/zjv/QV5qet AHr3wdwr3jei5/8AHTWp4LQ3HjPVpAOTK4z+ZrJ+EPS9HfYf/QTXRfDlN3iDWT3Wc/8As1efmuIl h8HUqw3S/PQ6MJCM6yjLbX8Fc9iRILW1OQAqrzUumgbWlBGxssMenasVbhzc7JRlcZBJ6HNbcEiy 2TqCACpGa8bhrCNQeKlK7krfj1fVl4n3XyFHUbyK5hby35Brm/tbISC3A61eXTY7eIr9oY5JJrE1 dorWBtj7pCMAV9ScgzTbzMt2FJ/1hPFa9u4aRWYkc1x+kzS2d9ulXKSZzXeWttbTxhg+M80AdJps qtAIwckCsjU0JlJq3pdvFFcOVmLHHSq2qkicgGgDk9fjD6TdgKAWjIPvXEeBDv8ABmqRH+GRv5iv QdZCjTbgsAB5ZrzzwGf+Kf1gD/no2P8Ax2kklohtt7ni+oDF9MP9o/zq54dGdZt/+ui/zFVtUGL+ b/fP86teHONat/8AfX+YpiPYPHo8zUtFtwPTj869M0C1WaNGkGY1QYHr/nFcD4hgW58baBE5+QjP 5Kx/pXo13ustPj8kHGOQDg18txFmdXD8uGpaOa1fZX6fcd+Fw0akeZ73sjVdLdrlIYwocDOB/dp+ o6nBp/lRSNhpDgCquj3kdxbJMEKF85U9QQcH+VZviHSxfavBcNNtSNeF969jK8M8PhlHm5r639Tl raTaas1oWLycZ3q3BrltZ1NraWCSN9r7wDj0rRurdgpInyi1xeoRzX12SuSiDj613zhGcXGSumZp tO6OyF3vRdjEBgGJz1NbOlzpENzN+dcjoGJ4RBO5SRBjnvXSjTI/KwbkgGiEIwiowVkuwNuTuzpL wia1DKcgiucuFAYncQwrft4xDpix79wC9TWPdKhyamrShVg4VFdPoxxk4vmi9Tyr4lR7G0+YHlJV /wDQhWd8UR5nhrSpO5jH/oIra+Jqr9gg9d64/wC+hWP8R/m8H6T/ANc//Za0JPFx1Fe4eBv3Xw61 Z/8AY/8AZa8Q6NXt3g0/8W11Yf7I/wDQaANf4coE0Lf0LsT/AOPGvTrO3iWyM1xkHBJycYrlfhzY QQeErSQkM7RhicAfe5/rXQaVdQXdzJE0gEsZ3FG9PUfp/nFfJVsfUzOvLBUX7Plb9671tpbp6/I7 3hvZR597Wvptf+rG5bMkVoZc/Jt3A+1YemeJItUvpo42OxTgHGK1NYV5tGuIoGw7ptB9OK43S9HG l2o8uXdNzmvq4RcYqLd7HC9zodYuU+yuc9FJrk9A1Zrqa4s5ZS0Cs21MY7569e9Q63dXQhMA5d+M A1gWFtc2F2s2Tg/e/Goq0aVZctWKkvNXHGUou8XY9WhulyCzfhV+x1KOa5a33cgcCuStbea4VXWU FTzmr9hpjx6zDcLcZx95a1JNfUoyJC3NYl67yLhgOAeRXT3qo6cnn0rnbtcZx6VzVcHRq1Y15r3o 7O7X5b/MuNSUYuK2Z5j4UXyPiXqEHZ4un4CvLPG0fl+JLof9NG/9CNer6O4X4qzMP+eAz+VeX+PR /wAVJcH/AGm/ma6SDlaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/ACTzw1/2CrX/ANFL XQVz/gT/AJJ54a/7BVr/AOilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+ AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooABXpPwmnEXiBAe4b+VebjrXVeCbz7HrkL5x1oA6z 4gxNZ+LRdLxmXOR+Fes+GdXS50uGRm5x3NcB8UbLzILS+QZD8/pVjwLO15p6Kx6EigD1c6lbl1aV Q+3O0+maovfRx72jAUM2cCqcdluAyf1pz2ShSOtcEMuoxxcsXrzy+5aW0+7rc0dWXJydDL1LUMuO p5BrX03VsQgkHOK5jVbdkkX0JArb0u0/0ZC3cV3mZupqaFgSxH401L23trma4BDNJyct/WqpsUYc H9azdQs/KhZx2Ga5cZgqOMp+zrK637a/IuFSUL8vU89+LGpm/vreIfd38DrWxcp9m+F8qHjMZ4/E Vw3iKSS/8SRwn+CQY/Su18dzf2f4KjtuhdDx+NbUaUKNNU4KyWiJbbd2eDz/AOvk/wB41XqaQ5kY +pqE9a0EamhsE1W2Po4r2P4gIbnwTpci87VXn8K8UsH2XcTejCvc5/8AibfDiIDl4lHtQAz4a6t/ xLvJlPCkAZPtXpkepKISI3CnHBGDivDfAMxNy9r0+avWLbT8oPU+9Jq6sC0Ny71a1NssMKcg5+lY d/fu0R2g5x1q2LAjtUVzbERnA7VxYDL6OBg4Ur67t/0l+Bc6jm7sx7KWaOfcCfm5NdFHqREexgRW TaQF58L1HrWwLNivT9a7iCZdRRojGZipPdXwR+RqC+1OL7G0ZfcR3JyTTH0zcOR+tYOs2htLaSUc YFceJwGHxTUq0b283+Sdn8zSFacPhZ5YHe9+IUbkk4m7/U1tfGJwTbrxnYv9azfCUTah40MrD7sn +NJ8WrwS6v5QPCKorsMzzBulIn3qcehxTU60Ae5/Dtg/g68j7lT/AErnvAl61l4huUJwCcY/4FWn 8KrkPZz2pPDK39K56Nv7N8YTR4xukx+tAHvNnexnDbhVqXUrbzEldBI0YO0Z6Zrl7CxaSJW3HkVp pp+wj5c/U1z4rDU8VSdGr8L3++5UJuD5kOn1FmLbAQGJwB2rBv7eeebcSfaulFsccLVaaEK/zcVt CEacVCKslohNtu7KemzXFvEFbcQBWxDqgEg3ZFNhti0YYDIx1pWtC38NUIurqStEQ8wY5PPA4zwP wHFcj4z1QJotwsRJYqwyD7GtebTMg5BFcN41P9m6bICPvAgflSSsrA3fUy/hT8rXxYdVI/Q1534y /wCRkvMf89DXpnw5iEGg3123GFY/pXlXiSXz9auZPWQ0wMVutdr8Nm2+JbfP98VxTda6nwRP5HiC 3Y/3xQB23jeY23j0T5PJ/oK9M0u9SSBCWA4HevMviIoj1K0vOoYZz+Arp/Dw/tGzjlRiFwP5UAei QahFDGRvHI9azJ9QIlbyyxzVSPTtoBOT+NX47dQoAWgDJ1DzrpCGYqCPWsu2snjnUqSCO4rrXtAI yWGBioLa2iaXCnmgCOG7njAEhYgVq22rRxp94qT61G9ps6r1qrLZiQYC0AXbu+idN24fnXkXxOvv OWBE+7z0+orv7rSyEzk15Z42nVr2Cz/i+nvQB0GpEf8ACq4VPXJP614dJ95vrXtnih/snw/ghPUn +teJy/eagBbX/j4Q+4/nXtmtsH+FsIz0lU/qa8StziUH3Fezs32z4asnXawPH1oA3fAeprLoMMbP hlGOtdxa3KqQxYce9eP+CbgPZi3jYiQf416Hb6fKUBeQn6UAdDf6pHKqlZCGXtmsq5up7lcLkDpm pre0RMZXn1q8ljuHTA+tAHLNaiNjuG4t1Jq1ZwSWxEkR25GcVrXNkgkwTVu3sA0QK80AVIL7Eg87 IrWbVElj2K4Ix6iqUtqF4Yc1nTaesjEqCvvQBPd3KIGO4cV5CLv7X8TbVznAlUfpXf6jatbK0juS gBrzjSCl347ini6K4OfwNAEHxaIOsqe//wBYV5m3WvQPidP52usM/d4/QVwB+8KAPVvhG22W6Hqh /wDQTWt4J1aPTfGerwuCfNlJGOnG7/Gub+GE/l38iZ+8MfoafGPs/ju7O7H7xqwxOHp4mk6NTZml Ko6cuaO/9I9ylv1uXDKu0YxjNXIr9IbcozYyPWuKslu5sEPhfWtWGzJbMpLU8Ph6eHpqlSVorZES k5O7LU97yyRFmBrONm1xJul5PYGtuGxD8ItWDYhEO4jdithHPvYRsoG0cHip7aSe1yMsyfyrTtrT Mp3HirD6aQMrgj60AJp+pRRDO8hz1yaddXYlJYsDmqNxZx4+ZeazZbKZQfLlIHvQBX8VX622h3LE 87CBzXE+AXB8Oamc9WY/yq/4r3Jo9wtxJklTgVk+Dz9m8I3zHjcW/pQB5RqnN9OR/fP86l0Ftuqw N/tr/OodQO66lYdNx/nT9I+W8jf0YfzoA9h8bai+n6tot/Gfmiwe3I5BH5Zr0ex1pb/S4WZM7kBz uzj9K8i8esbjSNPlHUYH862/C97dLpUCDB+UDNcOKy3C4uSnXhdrbf8AQ1hXqQjyxeh6XbXIicbe FHQCjUb+OU7t+GHQZrnoVvJB87gD2rSt7JDggbm967UlFWWxm227spTGa4JVSVQ9vWnw6eFjB2gZ NbcenEAM/A+tJL5QwqEcGmIw5LFopN8R2sD1FXoLwkhZyVHrWotvHcKMMNwqvNp5QYZf1oA0P7QV rURxvwB61QefceTms2a08rPkMVas+4kvoOSwIoA4/wCJl+pu7K0BHLg9fcVU+Ijj/hFNLT0T/wBl FZHi4SXHia1Ltkll/nV34gS79JsIf7qf+yigDyjGXAr2bwXIP+Fe6uh/uj/0GvHlT99ivVPBkuPD Wp2+fvJ/Q0AdD8NNZkOnvY+bhYicLx3JNehJcR24JjRUZgMkDk/X1r5/8J6hNp2vzqv3SSMV6vBq lxcJ+7TP1rGGHowm6kYJSe7sr/eW6k2rNux2w1DdalJG6jua5+4uPLkIhYs3tUMEU1yP3z8egrTt 9NJGETitiDJjtHmk8yU7mJ6ntUgslYMCoP4VuSww2UJaVgMDkmqdpd2kkpUNgnpkYzQBnWxuLPKB 2Meeg7VuaZdQq4lMuW9CafJp28bkGR6ZrPnsBnJBVh3oA17q+ZpQwb5aoX1wFt3kOMBSeaypZbyI YUiRPfrXPeINauhpk0KrhipFAGB4XvhefES7uAcqI9o/KuA8cP5mv3DDpub+Zrp/h4TFq9zO3UA8 /lXMeJU83UJn9ST+tAHLUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v8AgT/knnhr/sFWv/op a6Cuf8Cf8k88Nf8AYKtf/RS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAKo5rV0aXyr2Nh2NZa1csyVmU+lAH0Dqdr/bngW3c ctGvbntXH+Db5tPvTaSDHJOTXa/D25S/0GSzc5+QYH5isPV/DphvWeEFXU+tAHodtJ50YO8YPvVo +Wi5ZhXn9nrF5ZxhJFBx3xUk/iS5lQqijJ9qALXifUY0mjVSPvDNdHot5DNZx8jBUV5de217eyeb JzzgYrR068vdMXY6kqOBQB6k4UjKtXN+ItRFrZyAsD8prKXxLKy7RnP0rLvob3V3AfiI9aAMDQNP fWPExuCp27wc49xWh8XLoKIbYNwqdK7vwpocenxeaVxhfWvJfifffatakAPAGKAPN2+8ajPBqVhz UTfeoAng4kH1r2/4fyf2hoU1kT1QcV4hFww+tesfC29EOpLG5+VlwaAKenB9B8T4kXCNJ1PGOTXs On3JubdGQjBANcn4u8PLLdGVF5PzA596g0PW5tOjFvcDG3gHFAHo6KcZZqqahPHFbvyMY6msN/E0 G3l/0rmtb8RvcxtHbgnjHSgDodG1JJNQkQODgnvXXq6SKOcGvEdNv7uyvfOxx/EMV3Vj4qheMb2w fpQB2MgdejZFcT401VbeweMnLHAwDV+fxREIvkOT9K5C4tbjXtQEkq4iHQdKAE+HultFJPfyKR/F yPY15945ujc65M2c8ivdhbR6N4ZnIGGKf0r5112UzX8j+poAxWxzTU605gQDmiMd6APQvhze/ZtT RN2A2RWn4z017LX1vlU7Wfd0+hrkfDE/kahE2e9e2a1pseraNBNjJK/0oAueGtTS80yF48E4x1rp ojIwGcAV5RoV5L4duzBMP3BJweuK72DXbeSINvBFAHQNIkaHJBNc3f3saz/M4BPaq+oeJLaCMnfz zjivO9U16S7uy6khfpQB7JpF2hiwWypHBrSYgjMbA15h4c8TwpAsUrbXAAPFddFrduyA7x9aANSa V1B3DivJfiJfi+uI7OE7m3YIFdprXiaCK1dI23OwwOK5LSdFkv8AUTe3C5LtkD05oAntrdtF8B3C twzqeteJ6i3mXkrerGvc/iHMLLRRbJxlP614RdZMzfWgCo3WtjQZPJ1KF84w4rII+bFX7BtsyH0I oA9Z8V2p1Pw3aToMsgGcVb+G2pL9la0kIDoRgH6VZ8NhdS8OCBucKKwvsU+gaul3EMIGy465GaAP Y7eZmAAQYrRTy1GXCiuT0jXra8tUdX+bHIxV+TUoguS4FAFvU7xCNqkBe5rOhuovMUowyK5vxB4g hEbRxPlsVz+ka6yXgeZ/k57UAe2QXEUsQ3Yzio5m2ZKIpFc5ZaxBLGpV+oq9/aUapkvxj0oAZf3a xwPI4CgDPNeG3gfXPFokjBZFkxkfWu98W679tQ2Nk2WbGTjpVfwz4fFmPNcfOTkkmgDB+I8/ladB ag42qpx+deQydTXo3xEu/P1FkB4UCvOZBgmgAg++K9c8Ly/a/DE1r1yD/MV5LbjmvTPANwA3lMeo NAFPwvdHSfEfly8IzbeT717pZToUUhQwNeQeItBkjujcwLyGz1rr/CPiSOS2S2uztnTI6daAPSrd YpOWjVakuJoIYiQRwKxxfKy/e49qp3moxIh3vigBzXnmyszED05rR0i/ViyNjArzvUdY23CmJ+Cw HSui0bUYDCCX+YjmgDvWFvIucKTWfcyImR5SgeoqjFeoVyHqG+1WC3t2klkGAM9KAOY8danHZ6PI MgM4IH5V534Ftn/tFrpwcLzk/Q1e8RT3HibVSkQP2dXwO1btlpq6RoszYwwQ0AeXeNZvtGt3DZz8 xrkz94Vu6/L52ozP6saw/wCL8KAOz8DT+TqkZz1P9Ku+J2ktvED3K93zn8KwvDcxhv4m9xXc+INM +32kVxGMllyeaAO78K6hFeabDKCpOADg+1dlaeTKACn414V4N1t9GvBa3OfKY46dK9hsdRilgV4n G0jPSgDp1jgiXAwKxtWmx/q2+vNMa8HUtxWdc3ibGy3agCdLggoVPXrg10ts8TRqC3auC0u9SWWT e3RiBXRRXaBQN1AG7PDb7dxXJrHu3hCnCAYp320bOWrlPFfiW306ycKwMpwAMUAeefEPU1mvhbQs MYO7H1p+nk2fgt1PBZj/ADrCgsbrVtRM8gJDNmug8SYsdDW3HBHWgDym8OXc+5qXTv8AXKQe4qG4 Odxp9gcSCgD0nX3+0+Fbc9SpH9aseANVWT/RpWGVAwCfeqlqp1DQDEOSvSuctjPo1+Jl4w3P50Af RNmYWUBxW7b21uqh1Az71594a8SW1/aoQ4EncEV1C3wIwGoA2boKYmAYZx0rmGTyZ2BJOfWrr3YP fmsnUrpFnhy2MsKAN3SYgCXZsZ9a3gkbphiCK5qGdRGuG4xVgXoz9+gC7cw20Wcc1y+vXEMFq7lg MA+3atS81GGKFnlcBQMk15H438Ufa5GtLRsrkgnFAHMyXZ1LxRC/VVcY5rQ8azeZ5KccLj9BVTw5 psn2xJ3HAbJNQeKrndebfQmgDlY1H2k59K9C8IyqkU8fZ0I/SvPd3+k49q6/w/c7bgKD14oAq206 2HieXd90ueTXsehNHJECAMH3rxjX7SRdQaVR1Oa6zwf4pETJbXJAYEAcUAe12VtbSgEnFauxETap GK5Gz1aGWMMkgxiryagCMb+DQBHr9uLkgFjtUZ9jWZb2qXDIP7npV66nBibJ7GqGjz7nlOejGgDs rNR5KgnnHSpLi2gkXLnBrFF35YyWx6UxtTTBDS0AOu1gRDtNeZeNdSitbSVQRuYEAV03iDxJa2du 7GQEjHavFdb1K41q8Zs5TJA4x3oA3vBZ8qzuJyeWJ/pXP6v88jn61v6SpstHJIxkmubu5N8chP4U AcvRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/wCBP+SeeGv+wVa/+ilroK5/wJ/yTzw1/wBg q1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQA5KtQnDc1UXrRk+tAHtHw31M28wTOAVHf3r1DVrNZ1WdRkkDNfI1KATQB9 OPbqQcqM/SnQ6dE5+6B74r5ohtJJThVJ+grUtvDd5cAbYm/75NAH0zD4aE8ZkCnHpiqFzo6xBlZe ntXgyeC79h/qj+KmmTeDr+NSfKb/AL5NAHuMdnHG+dn5itG2tRJIo29/SvmW50e4gJDRsPwNZ7xs h+YEUAfXupzpp+lseAQp9u1fNvim5NzqEjk5rkqBQBO/Wom+8KUDimt1oAsR9RXY+Dbs2+ooQf1r hsmkoA+tG26npgkyNyisOXT0ySVHPFfM9PSJnPAzQB9Evo8Eh5XH4VZg8PZU7Ez+FfP1rotzckbI mP4GtuDwVfSLu8oj/gJoA9qfw40S7vLPzcnis9tEiVsuuPwFeVnwLfbfuc/7prPvPCd9bgkwtgDs poA9xttMhUDag9ORWzp2noXDEDj2r5ansZoCQyEY9qrEEdaAPprx1qAt9FliBxlSK+eL5t0jE1l0 o6jNAErj5SabFTWHFNBxQBt6ZL5Vwh9K+gfDF2L7REiYjIQHrXzJk+tJQB9LXumxzcsg/Ks59FTG NzL9K+ewCelWYLGWcgKjHPtQB7i3hrzGzuZ/rT/+ETDJzEV/4DivKLPwjfXKgrC2D/smtNPAN6eq 4/4CaAO/k8OBGwARjjpViDQyuC0r/TNeaT+BL5FyIyf+AmsG88P3VqTuiYf8BNAHvUOjxblyMn1Y Vv2NmtupOOntXylJC8ZwykfhUdAHsfxN1DzrlYweNvrXlE3LmqdOWgBW4cVcgOCDVE9afQB7N8Or 4bREzcEetdjqFgkxZWUHNfM7HFJ+FAHvY0bypCUZkz6cU240W9nX5LlyPQNXhKRM5wFzmtSy0G7u 2ASFjn/ZNAHpx8NXZbMm/HrSjw1IoO0turjoPAt865MePqpqwfAF5t6D/vk0Adlb6FqKMCGkjHrn /wCvWmmlTBR51xI3HQmvJ73wbfWykmIkeymsC406e3JDIwI9jQB9Bafo0aSeZjJ9SK2pSlpYyP0I U18sEMOKQE9KAOu8VXP2jU5Tn0rlpO9NHTmkPIoAkg6iux8IXfkaigzjOa4cHFLu4xQB9NNAlzap IQDuGDXP3mhKJvMiyp9hXglKAT0oA+gI7C+KER3L+3NZl1pesvJ8xkdQe3/668bt7GadgEUn6Ct6 x8H310oIhYA+qmgDuJNNuVlG8sCO1WY7HUCAYfMP0rlE+H92Ryo/75NQz+A71FJCZ+imgD0azsNY KZlnkQemT/jTp9KmmOJp3c+hOa8YvfD93aEhomGP9k1kvC8Z+YGgD6T0zSoreMHAz9Ko+L7pbbS5 FXAypr53ooA1b9/Mncn1NZrDElNAzSEYNAGvpsvlXEZ/2hXs2gEXumIhwflBFeBUUAe+3/huGUbw oBx1AqvY6VeQErDcOoHQZrwqnpGznAFAHul/HrkceY5HkHtn/Gs43Gpxwkzb1OOQc15dZ6HdXZwk TH/gJroLfwJfSKCY8Z9VNAHW6fe3MMpwxGST3q+Z9bnmXylkKHuM/wCNcU3gC8UfdH5Gsu98IX1v k+USPZTQB7BHY6k8P726dDj7oPP86oJ4fEtxunYufVua8QnsJoGIZGGPUVVIIODQB9Kw6XDBECqq MD0FeeeO7oFjGCOMDivLaKALMvQ0tq20iqtKOO1AHp3g25DYQng5FdPdeHIrsHK9ec4FeFE+1JQB 7ND4Yns5QYJWX6VrvZanbQgrcs3HTP8A9evAgCTgVYgs5ZmAVST7CgD2JNT1e2uWEm8oO7E1Vuda ubicFs/Ic9a4Ox8KXt0ARCwHuprUXwFekfcH/fJoA7238SXb2xSNcsBjrTLOTV7pizmROfevP7jw Rexrny8/RTWFd6JdWpIeJhj/AGTQB7Tf6HdvCpku5Du7A1Qt/CUSsZGBZjzkivE3ieM8imUAe+T2 CabaOwGDtNeT67cGTUJDnjJrm6UUATs588MK39FuSl3Ef9quYp2eOQKAPZZdLGoW8cgXJx2FUX8H u8qyRZU5646V5MeTRQB7pYeHb5ITi5Zdo9f/AK9V759SsgGhldmBxjJrxVUZugq/a6Rc3JGyNj/w E0Aeuv4iuyEglGCRzzU6arLYIJfX3rze38F30qg+Vj6qambwPegf6v8AQ0AegahrmoXvlCFSFbqQ TVqz0m8vlw07KcZ615Dd+GLy2BJhbHsprIltJYjhlI+ooA9jv/CM1xJseZyPSqo8Ji3ZUCk++BXj 3SigD1XxGRYWPlKMYArgZpx5LDPNZFKaAEooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8 k88Nf9gq1/8ARS10Fc/4E/5J54a/7BVr/wCilroKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA9A8EfD6XU/E0UeqW326wglthPH plylyG82XYA8kJfy1CrK7E44j25UurVHbeHNPi8Prb3tjHFqklld3LRzGZb0PCZhmMA+SsS+Th1l xJ8su0cx54zTr2403Uba/tJPLubWVJoXwDtdSCpweDggdavWuu6qmn/2ZDMnklWjVjBG0qI2dyJK V3oh3NlVYA73yPmbIBmRRNK4VRkmu28N+A7zVSrvGAnPJbFdD4J8Dx+QL/UAqxhQ3zCui1jxSkCG y0eLywP+WgxQAkPhfQ/D8Ie8kBZRnGc1VufF2l2mY7K2aTHGSv8A9es+20HUdYkEtw7lSf48muqt PAlrDa7pQpfHXFAHKN45ui+EsRj/AHf/AK9TQ+N2Df6TZ4X2X/69bz6TbaexjkiVgw+U4q9Z+FLa 8i3yRrzyBigDHg1Lw7rS7JlaN29VIrJ1z4cw3EDT2DBhjON1bWqeAljLSWxAx2waxLe81bw9chWe Qxg8qehH40AeYarotzptw0csZGDjrWYF9a+gLi30vxhp5AiWK7C5ZWxyfavG/EWhTaNetG64XscU AYtWbXTJ76C6niktFS2Te4mu4oWIwT8iuwLng8KCenqKqZpp5NAHpl/8L7mGHw/p66bqUF5Pezwa hdvbuwAWGGVvLTgMEH2gLtLGUxMVJBVV5fxxoi6F4iS1i02706CSytZ44LosXBaBC+SQMnzN4OAB kEYGMDEe9uZNOgsGkzbQyyTRx7R8ruEDHPXkRp+Xua6PQtJv/FeqxvcN5hSGOHcUx8kaCNBwOyqo 98c80AZmj6DdarOscMZIJAznFeo6N8OrWyt1uNQdU4BI3VugaX4OsRHHEkt3t4C44P41zVxc6t4i uSN7iMngDOB+VAGrca1oGjL5dpG0rj0U1lzeNr18/ZrQAdsj/wCvWxpHglPNH2rDE+xrpJfC1lFb s0Uab1GRxQB56ni/Wgcvbrtq/beNInPl39qyg9Sq/wD166Wz02DUZjGYlAj4OR3qzqHg+xmtfkiX d64oAwTpfh7xHFmF9sh7HIrhfEvw/udPLSwLuTj+KusvPCF1Y/vrZiMc4XIqbSvEs1sfsmqxtNCe u7qKAPEJrd4XKOMEU0DFev8AjDwbb3dodR03YyFSx2j8a8inhaGUo4II60ANPQ13vgn4fS6n4mij 1O2+3WEEtsJ49MuUuQ3my7AHkhL+WoVZXYnHEe3Kl1auAJ7VY0+9uNN1G2v7OTyrm1lSaF9oO11I KnByDggdaAOvg0Gygs7PS73S9l9daVfX81xK0iT28tubkCNV3BAubZQwZC3zPyPl28ZBbvO4VBkn tWrY61qxsP7LglTySrRqxgRpURs7kSUrvRDubKqwB3vkfM2fTvB/g200+yGoakEA2ggMPxoA5vw3 8PbnUAss67Y+eS1dt/Zvh7w3HmZt8q84XJqHV/E89232XS42hhHA29T+VUrLwpd3ziS6Y4J5DAmg B1z40K5TT7X5c4BYf/Xqh/wk/iCU5jhUCvQ9K8HWMMALxKTjuKivtKj0+ZTHGvlyELjHegDh4vFu sQkfaIFcd8CtS38S6PqWIr6Foyf9mu8g8NWLWiGWFGZhzxXK6x4Jhknb7OgQ/Q0AY+q+A9P1S2af TnBOM43V5brfhy70mdkkjwoOAc5r0drbVfD1wGieQKOeM4P1rdhutO8U2f2W9iWO6C/ebGGPSgDw EDDYNOrp/FvhibRLw4UmMjIIBrliSDzQBbtdNnvoLqeGS0VLZN8gmu4oWIwT8iuwLng8KCenqK7n RdG8P+ILOK7W0tLSO2aUuFmuG2qlvcSqLsnlixt1ObcD5fN4RigHnX3mrel8Xa5K8Lm7QPG+9mS3 iU3DFSpM+F/fkqzgmTdkO4Od7ZAG+KLKC3vraW0t7WO2urcTRvZyyvBKA7oWjEw8xRlCpD5O5WIO 0qAuieHbvV50SJDgnGc4rb0LSdR8Yaok14U2RpsRY4FijjXJO1EQBVGSTgAZJJ6kmvTJ7nTfCloL aziSS6K4yuPlNAGNpfgCx0y3WfUXVeMn5qnufEek6YPJ0+FpGHcrxVBrXWPEE5eR3EZORnOK39M8 DxRsrTYY+4oA5qbxVrNwcQRKoPQYpn9ueIosFwvPSvTj4YtGi2xxKrAdcVV07Rku5pPOVSsJK4I6 0AcPb+MbiMhdQtcqeCVHb860ki8PeIo9oJSU9mBHJrsdS8KafdWxxAgboDiuE1bwdLaS7rY4wcjG aAOb8S/Dua1VprUB04/irz2e1ktpCkikEdq9k03Xb7S3FvqCNLD3V6b4n8K2WtWDX+nBN20kgDkd +1AHjFB6VPd2slpO0UikEetVz0NAHQ2FlaaNqn2TWIdGuHmQMk09+89rCvzZ3fY2LlyVUDngE5U7 gy6k+j6XZ/25aXGkfZLOylu4BqF7d/6X56bvKijRGEb8+SsgVJNu9m3KrKV5fS9avtI80WpgaOXB kiubaO4jYjOG2SKy7hlgGxkBmAOCc6cXiTXNTthp8z2t4pWRBLc2EE0+HZnbEzoZMlnds7sgkkYo AwYLeS4kCIuSTXoHhz4eXF8FluV2If8AarpfCfg+z0mxGoakE+6CAw/HFS6t4iu9Rb7LpUTxQ/7H U/lQBObLw94aT94fMmA4C5NZtx4wuHJSwtwqjoSKsad4Luroia8fOTnBBNddY+E7K3jG6NSTgdKA PPf7V8RXBLqF21JF4j1q2P71FcdxivRb7QlsTF5IG2RgpGPWrzeGrOOEeZCrFh6UAefW/ifTb3EW o27IT3C8fzpuoeCdN1i3aawcEkcfNitvU/BNvMGaFQp9hXMvpmraBMJoWkCjn5QcUAefa74YvNIn cSR/KDgEHNYO3Bwa97s9RsfEVt9j1GJVuMYDtj5j0rzXxh4Tl0e6Mka5hIzkA0AchVm10ye+gup4 ZLRUtk3yCe7ihYjBPyK7AueDwoJ6eoqoTtOD1ptAHoFlo+jXqWrXekQafJ809vbrdvJJNbLbyytL dDcWVRshYlFiLo7mNWO3bz/iuxsrW6sJtPjgWG8tPOzatIbdyJZI8xeb+9C/Jg+ZzvD4+XbRd+NN bv7wXcz2IuvNaZpotNto3lZgQwkZYwXVgzBlbKtk5BrW0TSdR8Yaok96U2RpsRY4FijjXJO1EQBV GSSQAMliTySaAMPRvD13q06rDHnJ5JOK9M0rwBY6dbLcai6rxk/NW1Pcab4Tshb2kKSXRXjbj5T0 5rASx1rxNcl5HdYycjdnGPagC5ceItL04eVp8BcjuV4rNm8R61dH9wiqD0rqNP8AAaQEedtJ75Br qIPCdp9mbaiggcHFAHln9p+IYMM4X6Vag8W3KELf24Ze5Udq9B0rQo76WZZwGETFRxUWqeELSb5U jUH6UAcokPh/xFHt+5Kf4WBFcn4j+Hk1srTWgDoO26uq1DwLdQ7pbVwCOQADVfTtcvtIlFtqEbyw 9Crf/XoA8cuLaS2kKSKQRUQ617V4l8LWOuWDX2nbN+0kgDn1rx69s5bG4aORSCPUUAV24GK6bTNG lsdRltZ9J0rVbgxCUNJqyG0gjyQWkkhlVVYtsUbpAPmxtJdSOXzWppXiDUNHt7i3tPsrwXDo8sd1 ZQ3KlkDBSBKjAEB3GRjqaAOwPgW0PhbxFrVjZ6lf2gSaXSrryn2LbxXUUfmOQoDOyGbKnaUETMVw ylfP4YXmcKoyTV62vL6ae+SFkT+0V8u4SKFVVl8xZNoUDCjcinCgYxjpxXqHhHwZbWNoL/UQgG0N hh0oA5rw74AutRCyTLsjOf4ua7X+zPD/AIbjHmnfMBkKuTUWqeJJ7yT7Fo8TxxDjK9T+VP03wJeX jCa9k+8c4IJoApzeLZSSmn24VexIqn/aniGdi6BcV6Jp/gm0t8BlUn6U/VdDjsJYvIAUSMFPHrQB 55H4h1i2YCaJHUdRWhB4g0q//dX8LRse4X/69d+3hSy+zIJEUs4znFc7qHgCKUM0IAPbg1KnGTcU 9Vv5DaaV2c5qXgXT9UgafT3UkjIw1eaa34cu9InZZY8AHrnNejS6XrHhybzImcoDk7QcVtW17p/i a1+yX0SpcgY3Njk9KoR4LjnBp4GBXV+LfCk2jXTNGuYiMggGuRyRkGgC5a6ZPfQXU8MloqWyb5BN dxRMRgn5FdgXPB4UE9PUZ7Oz0fRry3ilutIgsJo4prq1tFu3klu4I7WabdcjdlMmODBUQ71lcoMY Kef10F5401u+vBeTPYi681pnmi022ieVmBDCRljBdWDMGVsq2TkGgCPxFa2iRaPf2loloNRsjcPb xM7Rxss80WE3lmwRECcseSegwBW0vR7nUrhY4Y9xJx1wK2dNsNS8X6pG9y0eETYixwrFFEuScIiA KoyScADJYk8kmvT1i0rwhYBViWS5K/KB1z6mgDB0b4e29tALjUGCgDP3qvz6voukjyrKIyOO+3iq nla14ouTgukOcgHO0D8K6bTvh5HEga4IdvoaAOVl8R6tc/8AHvEqg9OKj/tfXofmkRMGvT08IWi2 rbUUEDg4qppWgJfzTCYBliJULj0pNpK7A4WDxVyE1C147lR/9erh0vQPEUf7ptsh7HKkV2uq+AbV ogyKnPtXGaj4EvbTM9o2Mc4ANYYXF0cVT9rRd4/NfmVKLi7M4nxD4CubDdLAoePj+LmuIlheFyrg givZdP8AEF1YSfZNViaSM8Hd1H51V8U+ELXUbQ3+nbPuliAK6CTyILzzQwGKmuIJLaYxyAgioGOe lACUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v+BP+SeeGv8AsFWv/opa6Cuf8Cf8k88Nf9gq 1/8ARS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAOXvXdeAfDX9p36zypmJAT1xXE2sRmuEQDOSBXtUAXw14MQINtxOcehwT/ APWoAfrerSXcq6ZYDbBEccDknpVzQ/DYjkWW6XPtnpWj4O8PgW63U6ku4BG4V1sloI8ALgfSgCW1 sIxAAigDFWri02WgJFOsB+8VenNaOqxgWi+1AHAa1GnlW3PPnKK6HT4xuRR6AVg6ygxDz0mWul0o qzIDx0oAsXdn0GK53WtIt7i2YOnzEcV3d7GBAGxXMXSPM+OfwoA8nvdMutIuftNsSFDZFT6vaw+L 9AlnCAXcSncBxXpE+nRSQGKRAcjByK82YN4c8WQwci2uCFOenPH9aAPGL+1e0u5IXGCrEVWrvfiV o4stU+0xr8kx3AgcdBXBUAaGm2T397HAgyWYCvabGKHwhoEXlqPtkqgDvj3rjfhppCz3b3si/JD8 2SPbNdbaQv4l8TTcEwxMQMcgAcf0oAqWOlXOqT+fOcqWyxz1r0TStHht7dPLQZA9auppccMAEaAb RjgVPbAxHj8aALMNh86sB2qndt5UhUfjXU2aA2pfHPNcrfqVkY45JNAGLoRH2i99fNIrrbe2Fxb8 DOK5TRE23F3kZzKa7rRo8blPQ0AZV1bKI9u32NcH4h0BLmQvEu1u2DXpOqAxSMAveslLQStuI4+l AHllhe3GiXf2W6GbaU4YEfhmuW+IPhlbWUX9quYZADnNet+KdBW7sZCi/vEUkYHNcjpg/t3w3c6Z ON00DEjPJ7f40AeGkYOKcgycVY1G2NreyRMCCrEfrT9Ktmu76OJQTuYDj60AegfD7w0kzHUbkYij BOc10Gqahca3di2tuLaM4RQMe3NWNVP9jaBbaXD8skxBbHBxXU+G/D6W1jHKy/vHUE8UAQ+HPDsV tGryrmQ55zXUraYcLtGKbFEYW9K2bBBM6hh3oArmAQWoY965fxBcqVtQvP75f5112vBgqRIOK4fV 4HIgz2mWgDrdPlWR0U9MCpr2yCzFsdRWVYbwyn0xXXTRiWyVz1oA4vUdPintnV1zkV55qnh2a2ka 4twQAdwweleq3fPyBetVzZBosOvGPSgDzVTF4n0eayuwPtiIQpHGe9eOa5pr6bqMsDjG1iK9l8R2 zaBrtvfQjEZYbsDArnPiVpSzWttqsS/LMNxIHtQB5WvWtbQ9MfU9RihQZ3MBWSPvV6t8NtLWO2n1 KVeIhkEj2oA6VjF4V0WG1tQBduoB77R1qPw9oEmoXAubvLAtnr1o0O0bxHrtxczEmPcSO4x0r0CG 0W2wiDAFAE9rp0cESpGowBV22tNzcCn2jhgqsK244kggZ8UAYsyrbq+euDXPeHrxTd36np5xq5e3 Ek0zEZwc9KxtHgaO4uyO8poA7UW/m25ZRnFZb2m5zuHArX0ORjuiccGn6hAsbkgYFAHn+vaHBdoc J83GMGuOia78O3y7xm3c/MDzkdK9ZeDzH6ZrM17RYr3TpI2A3BTjigDyXx74dhubUarZDMbAd68q cbcqete6eHM3KXeg3PVcsgPXtXj/AIisDp2qzQlSMMcZ+tAGWg3YAHJr1P4f+GYkiOpXa4jQHknp XnmhWbX2pxQqCdzAfrXseuv/AGfptro1uMNIQzAde9AFa8ubnxFqIt4OLZDhVHHHTNd5ovh2206z G2LL8kknNVvD+hDTrSNivzsoycV09sdp2t0oAYkHyY28elWIrEuwJ+6K0rW0Rzk9KZq90LOAKuMm gDlvFN4YWtVHQTLXRW6rfWkePvbQa43WPNvFtyVI/eqea27G6mtpI9wO0YFAF6SzMZ2sKpXFokim ORAVI5rrZIluYA4xk81gXSfOVHWgDzPxF4a8uZrizUrznANV7KePXbCXSr8A3ABCHpmvSfsXnfeG R34rzzxjpraNqMGqWwIVGBYj60AeO+JtHfSNUliK4Xccc1h17D8QdNXUtAtNXjXmRckgd8V49jBx QBraDpb6nqUUKjILDNexytF4W0eG0tQBduo98Vzvw20tYLOfU5U4jG4Ej0Fbmh2r+I9eubyXJiDH HcUAaXhXwp/aL/b9Q3Md24At178138NnDCmyFAoUYqOwQW+IwMKOK1YoPMcY6GgCkIDI4AHNW7lf sto3HO2tSG1WAbiOa5zVdV8yd4Yxu6jigDM8MXbPqF4uODK1dJeWTZEgHFcfo7z2891Ii8mQnFd1 pF6L+2ZHA3LwR+FAGWYwV24rG1jw9Z6hbOJY8P2IOK6q5sXjuDIJEEX9zZz+ef6Vl3L5B9e1TFt3 urDat1PIcXfhfUiDk2zN8wPOR0qj458OwahYDVLFcqwHfvXqOraKmo2LrIuWKnBxXD+GlY3N54du RnALIG/A1QjwtlKsQeopUGTitfxPpx03WZoSpA3HGfrVbRrNr3UYoQM7mA/WgDv/AAB4ZRx/aF0M RoCeTXQ3M914m1JbCz+W1Q9uOOlSa7INI0O20q24lmYE44OOa63wloyaZZxyY/eso3ZFAFzRvCtp pESqse6XuxOa344gAOKkJ3Dd3q5aW3mfM3QUAMgtcgvjtXL+JpSstsB2lWuxv7lLO0LfgK4PWbl7 poWROkqmgDp47OW9htpIXAZCu8MTgp3wPWpJIDG+CKraTqrRSJFKuBwK6SWBZQGH3hyK5pU/ZKdW lG8nra9rtLTXoaObklGWyOXuLOK5VlkQFTXCeKvCCwf6bp4KOPmIDV6Y91DeSsYRkRZVyQRhvQ5H Ws28xITHjI71GAxbxdBVnHlvfT0fovyCpTdOXKzzKynTxHp0um34H2tVIUjjNeR+JdHk0jVJYmXA 3HHNeveLtLk0K/t9XtgQI2BYDjjOP61j/EPTE1HQrTV4l/1i7jge1dhmePjrWjpdg+oX8UKDJZgK zsc4r074Z6Oskkt/Kvyxc5I9s0AdNbx2/g/Qotqg3cigAHnnrU+geFbnWHGpakSULbgM4z3qlYxt 4o8WTSOCbWFiFHbHSvWokijtRboAFxgYoAZp+mwW0CLDGFAGK0o7feelVbN2WXyiOO1b0cQjX3NA GXeRi3gP0rn/AA3Pm8vF7ea1aetX6iUxDk4xXN6TPJbXVy5U4MhNAHbJaXIWQzujR/8ALLaTnHv7 9Kps0LlowdxU4OBwPxrV0y6W7tc8ccGq0djFpsjAGR45CWBYFivOeW79e/PHevDxiq5ZhUsDTTSe vzflq+3kjpg4VW3UevQ5LWfDNlq6MrJh8cMDivPAbzwpqf2a4O60duc88dK9h2fMSBtGeBXM+KNJ j1W1dCoDhTg45r207q5zHlXj3wzHJbjU7NcxuAc5ry4gg4PUV7j4ZLXVteeHboZePLoDyccV5Dr9 g2natNCRjDH+dMDLooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8AAn/JPPDX/YKtf/RS10Fc /wCBP+SeeGv+wVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooA2PDFt9q1u3jxkF1/mK9c8ZKDrOmaav3cAkfnXm3w/i8z xNbjGfmH8xXp/iRfP+J9pF12Rk/oaAPRdKjSG1ijAwFUCt1bVLmLtmsS2wNpLdq3NPkVmIU8g4NJ tLcLEttpxhcOSOPao9a3/ZRsGcVbvr6GwtmlnkCqo6moLe7h1C28yM7lPemBwOqJIyw7uP3o4rd0 1ZfOTaDxis/xMyRSwjj/AFi10+hhPIDgZGBtJ9KlySaQ7dTccCaLawqgdMVCWIB71NJeRwModgM1 NO+bdmByMcVQjnr6JQxAI4ry/wCI9sEgt71RhonByPqK9UeymulZ9wUc49686+IsDr4cuVdSHRQS PxBrlpY3D1qjpU5pyXT+t/O2xTjJK7Ry/wAQrYXfg7Tb3GSU5Pf7teM/xfjXumuqLr4T6fJ1Kqf5 V4aB+9x711Entfgm2Fl4C1G8AG7ZkH8K3PhxB/oLXZGWlJOfxNZ+nKYPhLdEcblUZ/CtvwFGy+Fr T3jz+poA9Ds1imj2tjcam/sgb8gjFZtmjqQc10NvIfKGfSgB0cYhh2CuW1K2lMzkqcDNWtT8VWlh qKWbNulbnAGcVfviJrTevdaAOK0qFzcXG3r5hNd1oyvHE28VyehKV1C4BHWQmuvvL1LK0Mp6AZoA kvLSeSaOW3MeRkMr5GQe+fw/WqS25An8yIRmN9owPlYbQcj25x9QasaTrEGqQFoZAxXqB2ovjJLK Il4zzXBPD0KFWWNbadtddGl5Gim3HksYF0iuHUjIINeY+H4xafEW8sAPkkTIH1ANes3unywRGUNu AHzZrylW+z/FyBjwJIR/I/4VrhMZTxUOen809GvVEyi4ux5Z48sxZ+I7hAAAWY/qad4AtBd+I4VI zhgf1FavxZh8rxMxxjOf5ml+E0XmeJk46Y/mK6iTufECLc/EO3s+qImcduBXp1iqxrEpQ7TwDjgV 5jOTP8V5dvO2In9BXpEdxcmBYhtCDuBz1rjxSxLnT9g1a/vX7eX9fgaQcEnzL0N4adFONysM1btr NbbnqaxrK6mjkAJ4rUvdQS00+W6kYBI0LEmuwzE1GDz48qAW5ritZtniaEOOsgq74f8AGses381v tIA+62OvOKv6vpzXDRkHIDg0AP0yxdgj8bSBXTKimLywOKy7X/QrQsxzhelc/Y+OIJNe+wy/JnO0 kUAav9m39vte5WK5XzVTdECGIZgN23GBjOTzwAaW8NurvDHIjSR4DqCCVyOMjtW9JJiIsOmK5+PS 47jUbi8Zcl2GPwAH9K8fGYj+zKKnFOfNK1m23s9m79tjqjyVm+b3bLovTp95wfxAtUm8OXMm354l 3A49CK53U4RqXwttpmGWjU8nnsa7rxxa7dD1CMLwYG2/lXG6IPtPwuuI+vl7h/6FXpYevGvSjVhs 1f8A4HqupzNWdjwPGJse9e6aNALD4Z3U4GGdAM/hXiDL/pxX/ar3e8/cfCpR03KP5LWwjX+HNnnR 4pFX55F3E/ia7y1hiu5ZITG8bx4yXXAOc9PyrjfAU8lloFlJ5e4eUOOldLLqkk04dYxHjt1NeXfH /X7aex+V9vv3/DqbXp+z297/AIP+R0MOlJEQxIP4VbkAMe09KybLVWk2p1P0rnfHXiq40JIktQDK xBOVzgV6hibGoab5cTNHjpWBots0l3OoI++c1q6DraeIdODSApIANwxjnFXrXS4baWSUN1bPWgDT sLQW/wAxI3VZntVuF5xmvPvFHjeXSrqOG3TcB975c967LQNXXVtGgu06sgJHvQBK+nwwIWJFYt0o fJC5Q/xDpWjqEskgIIOBjP071YIjNuRgYxXg5rnEsDWjTULq13f12Xn/AMA2hSUo3PGBGun/ABRh VDhZoxke+D/hXAfFWyFr4hYquA2f5133iVjb/ELTJsYDKBn865r40wbdTgkx95Sc/jXvGJzXw1sx deJIwRkAj+Yr0fUFW++JawEfJHGSAfpXH/B6HzPEJb0x/OuqidpPihOw5IiOcfQUAeqWwiCDzGVF A5LHAFa0WkhjncKwU1CL7EbeWMk44PbrViPXGt0CqxbHHqa48NWxFSpUVWnyxTtF3vdd/wCvToaS UFFWep00cQhXaKo31il66lmHy9qZa6hJdW7O6YOPSvOH8Xa1YeKrmOVN9nvwAE6DPrXYZnQa/a+S 8IQjbvHStzTbBZ7ZTJjpnmq8f2HVoUmJPXOM4qjruvpodoy2il5SMDjPagDtYQqxhF6CoJdNjkJf gZ61xPw/8Q6jqksseoABuSPlx6V1eoahNDlNpwenFAEd0kVuhVSM1xPjOBLvw5eAgZWIkfzrrNPl SS4d7jrxtDCsvxoizaRciMZJhYEj6V5Ms1isesEo6vrfyvt/wTRU3yc557bxDUfhgVYbjDuAzz2P +FeESJi4Kf7Ve9+Cx5/gnU4D1Tfx+Jrwy5TGqumP469YzPadLhGn/DG5kAwzoAD+Arpfh7YrF4ft 5doLSpuP5msLVR9m+F8a9Nyg/otb/gm78rw5ZKw6RDtQB3UOnCUgj5a1YLMQICcE1zEOsrahtsjH Jz8xzip7fxM0km0qSucfdqYttXkrMbtfQ6GRhswTjNYL6RDBHLLuVmOSCaj8VG8n0KT7AxWc4IIr lfD/AIi1JIhZ63EeuA+3FUI1tEgV76cOfl3muss7OGzkLIwy3auafUdKsonmhcO+clQc81zcGseI dT8S2zKhjsw3TZ1GaAPWGiEo2nGDWfPpMStvLD8aku76S1tkZV3Nj0rnbnXZLglGBVh2xQBZvGjT 5VxivL74LYfE2zlTgSxgMB3613X2hnJzn8q898Us0PjbTJjkKcDP50Acf8W7EWuvh1XAbP8AOsv4 c2X2vxHECM4IP6iuu+NUP+kWk2PvR5rK+D0HmeIS+PugH9aAOw1WFdQ+JUVqwzFDFnb26f8A169N s7UFVVeABXln2kr8Up3PI8kj9BXpdrqaRpnI6UAdBb6YxOWPHpV/YsQ2gYrnV8TeWB8wx9K0rPUv 7QhZ8YxQAmoW8d9tiMgBU5xXOa7aRWxhWMj74rL8V2OuR61HqGlyEqDhkOCMcdqnh122mgB1iMRS qfXAJ7UAdLa6dBNaxSFlDqASa3YmVoxtYEeory7WNV1a/ja10WLbGePMxnIrtfCltdWOiRQ3blpR kkk5oA3ZbRZ09DjrWPPp6QuWJB5qK68SeTKYwMYOOlUJtcSXOT19qAMLxpax3nh++BA+WJiPbvXJ Wsf9pfDBkYbmh3AZ7df8K6rXrnztHvFQHmJu3tXNeCD5/gzU4CMld5x+JoA8FkXbcFfevbvDUA03 4cXt0BhmTg/hXjN5Hs1R0/269xul+y/CdgON6A/otAGh8O9PVNEjuiP3kw3E/UmvQobF5FABriPA 1zEvhiwyeREAa7i31mOFByKANG003yT5khBIHFWXkCAkkADqTWXB4hFxciFVBB4JAqXUovtNtLBk jcMZ6UAVJNPtpTJcGRXzkisfRYIpb64SQjbuNc3ZW3iLw9LKhdJ7IsThuSB9a0ZdZJjYaXChuyfn B5we9AHdWMcFk5hSRSWOQoNaOxZRtIyDXnHhPRdWXWJNS1Sfc5+6oPAHPau6u737Fa+aBk0ALPp2 xSVIrCurXaxLVOfEvm8Fl/AVSutTjcElgc0AedMq6b8UbdkwEniAIH0P+FcD8VbAWfiIlQAGyf1r s/EVxjx7pkoyBwM/nWT8aYMXttOB95M/rQB5HRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4 E/5J54a/7BVr/wCilroK5/wJ/wAk88Nf9gq1/wDRS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAdl8NhnxTbj/aX+Yr1C7ha5 +LLbFz5cBPT6D+teYfDchfE9sT/fX/0IV6XPftY/FZnUL+8gKfMPof6VjiPa+yl7H4ul+5pS5Odc +x6GIJoWXeBk9hW7psRVdxrLtZzcOJJe+OPStqK6iUhAeazwX1j2EfrNufrbbfT8BVOXm9zY4T4i ajNcXttpVsz5PL7fr/8AWrZ0GZ7DRkjmO1gveo9TSKHU3ufJBkPO41l6leK8Odx3twFFdRBzvi3X BJqKxo2QpB4NdZ4a8QRtpsW5uQoB5rjZtCEzebJy7dea0NLtk0x0V1PlNx9KAOh8R3801oZbdmBU g5U10/hrUf7V0KKVuWA2tn1wK59JbaKMpGvmBh3rf0QQ2diwSPy1J3ED6UAOuA9u7GJ9oJ5BGRXC +O4zdaBqDkjcYTnj0rub6dJUJFcH40nSHw5fE94WrhhluFp1/rEIJT7+u+mxo6s3Hlvocu/734SR KeqAj9D/AIV4cv8Ax8Y969vjk/4tYAe4JH5NXiWP9Lx713GZ7pkp8JWUfxAfyFdL4RxB4asFxz5K 5rlpXz8LEA7AfyWus8GXCS+HrLIBHkqKAOospy0irtPWty5nS0sZJnwAi5qrZR24Xf0IqDxBsn0z y2yUJ5A78UAeZ2cFzrviZ77kjOBu7ACu51vWF03TGLHlU9apWN1bWUXlwx7K5fxGZtU8xEbKKDmg CbRPEyNqYGcB89TXdam7X+kusTZYr25rxqx0mdbpT0x0r0XRbr9wCZCCnDKRQBS8FXkul+IprOdi qynAU9Ac/wD169HvmMLJMEZsEAhRziuRWGzvNUjuPJPmq4O4euRXavLGU+YjJ7VhiaCxFKVKTtcq MuV3M6+1OL7KylHyVxjArx3WI2h+IWmT4xldv869T1BlDmvLvENwkvjvTY1PKjP865cFl0cLKU3J yk+vkttCp1OZWS0OO+MiY19G9VP86b8HU3eIs+gB/Wpfi+2/V48dk/rUfwfbZrrZ7rx+deiZnZaY BJ8Sb6UjO2LH8q9EguVU9M153o0qr8Qb1GP3o8j9K9LtbeKUgZoA0rKOOcb9oGKxvG8x/sv+z4jh 5xtOD0GRXTQwpax/LXIXUa3GpPLdS5fPAHYZoAyfDWkw6R/pEjjdjoPzq/feN7OKYxg5I9xT9SuL S109xGcnB5xXk15aXE9y02QNx6E0Aez6d4gg1e12oe3qDXHa1octtqS3sT/MrAjFZng+aXT7opIw AOPpXodzcWd1BtmHzeooA6HTrsX2ixSqckoAee+KfZTxxxFWYAgnOTWZ4ZSO3t5okYmNmyAe3FaF 9aQMhbaD3rgx+DeJjFwdpRd11RcJJb7HPeLnju7GaOPDZiYEg54xXn3hJc+DNWgP8Bfj8TXca1JH DYXDscARn+VcL4UlA8M6vJ2YuR+ZqsBg/qlL2fNzat/eFSfO72PD5Fxq5X/br3HXxs+HNnEP4lX+ leJyqRrWfVs17ZrrbvAdiccBVJ/MV2kHWeGTHb6BZIVHEKjH4V0EaW1xgBQDWDoCRz6RaMD1iU/p XT2OlgssmeAc0AWrPT0t8yEDpXK6/psOr3xmmwY0AAzXR63dTQwJDbr9/hmPYVixW00vLkBR70AZ cGo2HhyIgDA+uKmTx3ZOwU5APfIrmdfsm1PWorKJg2c55xj/ADis6TwpLHFNKjgtCf73SgDrr+ws NYPnbRlh1rovByCwR7HPygZX/P41g+GVgn01VkcBsD+VawhltrhJIHU7Txz2oA3dVdVdY87QzAEg 44zUztGtvkMu3bnOeKkmtlvoVLDDdaxbu18lDHvbb2G7ivBzfJvr81UjKztbXt/mbU6vKrPY818c DGuaZcAfdn2g+2DWD8aEDR2UnGfKH9K6HxsVbUtMhByxlB/Q1z3xfIe3skHXyuf0r2MPS9jSjTve ytdkVJc8nK1jO+DCj+1Lh+4T+tdPoKq/xC1GVuoXHP4VzPwcfbfXAPeOuq0BQvjTUQepGR+lbEHo qQ28w5AzU0GkI8gIAI+lVYLOWXGw/rW/Zwmzty8x9zQBMIFhgKqAOK5jU9PtTI7mFC56nFW2115J GOw7c/KMdqwfEOq3Vvb+asYUMcZNAGBd69Jpd41vESVABwD0zWhpM0WtAyzASEHGG5xQ+gWj2sNx cRlriUjcwPUGoL21/sG9tTYgJHOcEE0Ad1pOnQWcizQoq9jgYrcuYYpELMAQBmuZiuLyG2VmVSD6 Vo6VfvdCS2lU5x8pxQBVlAuLhY7chdmSw9fT+tU9V4tZYpOcoR+lacmlzW8zTwvtJz0PasXUkZYZ XkbJ2nk/SvAqZVXlmixqmlFdOu1mu1n3v1OpV17H2bRxPgX5bPXrfsC4A/E14jfpt8Ruv/TSvbvB 5Cw61N2dnwfxNeLX+T4jLdi+a985T2fxT8vw+skHQqn8xXVeGrO3/sGyTCj9yv8AKuX8RjzvA9nj sqH9RXTaChbSLQJ08pf5UAbJ0qMkYAINXItMQbERAD1JAplhBdGZR/DnnmtO81KHTyiPku3YCgBb tNsIUdhXF6+i/ZJm2hWCEhhxW9qviGCCIOoZ/UBay7We31mOZtu6MfKQRigDz7Qi0utBJXcqc/eP B4r1bS0QBcIBjHQVjWWiWsFxJJ5ChgTtOaS28ReTftaJGzlWwcDpQB3UsQlhAIrJudNtlbzHVRQ3 iGJUQKp/2sin6jbtqFuklucg/hQBmukCEgRHA/jxx+ded/EKNFvNNuEHIlA/Q16RcrcG2WF0ChVw Wzkt715143j8y50+DqTKD/OuDAyxklJ4pJa6JdvP+vU1qez05PmYvxkXfpljJkE+WBn8qyvgwo/t K4f0T+taXxck3aXZxjqU/wAKzfg222+uAe8f9a7zI6TRraO6+I9+0nIWPAz+FeinSY2HydK4DSIS njy+cfxJx+Qrv4vPRhtoAbLpkMVuzyKMitTQYHjtGLDAbpx2q3b232q1/wBJUBQaVtSs4omVJMBR jpQBSvgSTivP/FtpuET7erhfqa70X0N+jNCwbBwaoapo8WoRRB+Nsgbr6UAU9Ct/s9lCoXBCjPFd lZZMXNY6xLCqhR0qS1162Ny0O85TrxQBHPpyjUCJEDK/IOOlMk0e3jyz7QBzz2rYiu7a9LCMkuo4 yKy0TbeTNeISo+4cZA9a5MdiJ4bDyqwhzNdF/XTcunFSkk2ZuoW1t9imRVU5Q9q4XwIAttrkHRdz Afma7bVV+WZo/lTZwPwrjPB8RhttXlbo7v8AoTWuGqyq0o1Jx5W1s+gppRk0nc8R1RNviKVe3mV7 Z4gGz4aQRjvEv9K8V1M7vEUjDu9e26un2n4fwqvXyl/pWxJseEtIVvDdkVOP3K1ujSW2nexwB1rI 8LNNBoNmoPSJRXVWk8lwfJdfvcUAY2lKU1QonIU9a6qQlk3etVrXR4rCWSZ3A39Mmpxe2krG3jlG 8dBQBk38ZMbeh61zej2sSajdNHtL7yOO1dhJEspZDWbpuhxWNxdzKxJmk38npQBrWY2/WnaurPYO q9QCf0pIJEiRpZG2ovUmpI9QsrwFRKCOnTtQBzGm6fHeQMcfPmrv9ixx4MmDWhc240u2Z7dc7j16 1ml5JbR53lYOD90jiubFYulhYKpVdldL7y6dOVR8sdzz7x1ax22uaZJGAG8wDP51j/GRd9lZv3Ef X8q3vFsElxrOm7u0g/ka534vS/6FZp32/wBRXSQeO0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ B9v+BP8Aknnhr/sFWv8A6KWugrn/AAJ/yTzw1/2CrX/0UtdBQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHXeCt1vrdvJ0Adf5iu q8a6h9g8cR3YPAA7+1cvoA2zxOOoINavxCXzrmG5H8QAoA9i0jWYbqzjmjkXlB3q6upIkm4yDP1r yDwnNefZ0VZ22YHGa9HsbBnAZsyH35oA1J9Sku/lijyOm41FDp5aTewLMfUdK17LTfMUEptA9q0G t44IztUZA60Ac7JbqrDdx2pxsfNi6ZGK5nW9QvrvVHS3dooYwMY/iNdR4VupruyCXA+dBgk96AK8 cctm26Ndw/umr8es7l2S/IfQ1rTaeki7lG2sa7sd+VMX44oAet8qk/OpU+9eZ/E/xDGLb7BEylpB g4PuK6PVraa2iPlSsv4mvI/E6PJqMZkcu5I60AdhLNs+HFpD3Yf0NeWi03XYB475r0e/Yp4dsrfo An9K4wwst+oA9aAPQEuA/wAOJ4s8p/gP8KufDbxBA9gLKWQB4hgAn3NY9qGbw3e2w/u5x+Fef2F1 dWGosYJGRgxHBoA+n/7SAXCsMfWhtYRItrsrg/w9a8u0HVtS1BQskrAfWu60u2VyN43Me55oAknV 74/Knlr7daVrARW5UL1Hcda6WDTUjQM4BNZHidnt9IuXgGJfLIXHbPFAGRbxwyTAIVLL1xVqSwZX EseVOO3euO8Mtfw65GZS7RybgwP0r1iK2R4grKDxxQBi2mp/ZV2yRBSP4vWrcmoiXEiyg/Q0t9YJ GuSAV9xXK6jDJCGa2dkA7CgDf1DV4be2eSaRQFUnk143bawusfENLhP9Wi7Rg57VH4k1PU5o5Imu H2Y9axvA0bf2y03XYDk0Aanj3/T9XdyeFyP1qH4en7HqkUg4zgH86m1aI3Czyt1yev1qPw4nktE4 6ttoA1bvVV0v4gCaRgEZdpJPtXrmn6lC8KSxSK2RkYNeA+PVkj1VZe5A5qvpPjLVLONYlmdlAAAz QB9I/wBryF8lh9M1nX1xazMSnzSHsted6Hrd/qbqZ5WVT716JpdlE+3BGT3NAGcNPkmO+YnaOQvp WLqunw2/lzO23zDgL/WvRpbGIQFAuCR1rjvEGmPdTxKudqAAfrQA2z0eNIlAywZQytV23VrVwJ1L p/erS0iyza24kzmNduK0LmwjaPcoC0ARW+owJHtgYLn3pRqTEMrOPzrmtWjFtG0kL7HAP3TXnGt+ KtWtJWUXD9eMGgDs/iB4jt7PRp4FlVppEKgA+tc5oFz5PgiQD70oP45zXmOqare6ndBrmV2PTBOa 9C09HXw1aQjjcMmgDiLy0A1NNvQrn9a9Mu5jL4HEeeY1/oK4e7gxrca46Ic/pXWvIW8Nzx9wvb6U AdT4C1qG60aCDzF8yJNpGfSu6/tXyIiqsM/Wvlew1++0W9kNvK68kYBrfj+IurSOAZWP4igD6Anv Bdw/PKFI5zmsWa5n5jgcv71xvhjW7nWP+Pmc4AHy5r0XSra3l+QbeOSBQBg6fpbHVGmaRt6jk/XN WbfTvMN4u9vmY8V0UlpFC7GNAuRjgVBBbiN3P96gDlLGzms1/dMxUHla37GSFiGeZgw6qa0/7OgW EkKAeua57UkiQOUkCsO4OKAOm/tZowNrjH1qre38bIZHlAwPWvF9d8X6lZTNFHO5CnggiuZvPHmr 3ETRGeQBhgnNJ36DVup2OpavHqvjyBI2DRwKOQe/P+NRePD9vYkniKPbxXJeDZJJdb8+Rixwck10 viFvMimbJ5U5piKnw3Y2moFx0ZQK6iHUItP8ceZIwCTRkc+v+RXHeEJDDGkpbBOKf41uJEuUuYm2 spA3A0Ae/WF2oIkDDbjPWp7nWTK+0EBenWvnC3+JOq29ssSzPwMdRU1v8RtVefJdz7ZFAHuNxfNb zZ+XbnNYGv3lzq5igjjO3d2rn9L8QtfxrLdSbj2XNeiaFaW81mt3hWZhx7UATLb4toUYZKgCsHxf bTPHaywqx8t+cDtXWNt64pGijlG10DL6EUAczZarO0Kxs20+9b1pdPbhWMibmPHOK5fW2t9PujCG ABGVwelcLrvjK+0/KQzMwxxyOKGB7fcaq4KwyYUsuQynKn8a47xhrEVhpFyzSqWMZwM149J8TddJ CvcSMqDao44H5Vjal4mv9bmRLiaRgSAQTXNhY4hRaxDTd+nY0qOnpyHqOgT+T4Xdh96dWJ/E15jq Vuq64FXqP/rV3VleCDQYo+AQvf6VwWpTMdcR8/eFdJmer+cLjwisTHlEH6YrpvCV/FPpFuFYEogB 5rg9Ov1k0sxE8lcdfauXtfFt54d1K4iRm2BiAM9KAPpeLUktrRicbscVk3t2J18yRAW6/hXhdx8V dQkICFh+I/wq1a/E29kQI6OxxjJNRJT5lZ6dRq1nc9euL+xg0t2kChz2qHwrC40xpChUO5Iz3Fee 6Rq51y+ihuXYI553HivYoo44IY44lARVAAH0qxEIRgTxXKNcLpniktKm1XGRnoeTXZFs1zXjCGA6 Z9qbas0QJU9yKANAXsVzPlIUIPetWDVjboY1RTgHaucZrxFvGtxpzEx72A7VkXfxO1Mzboy6D0yP 8KiopODUXZ9BxaTVz3u41Jp4VkkXyyRkqTnFec6vfRah4utoVZSIRuOD9a4aX4najLAYy7jI7EU7 wnfST3c1/OxZmyoLH6U4pqKTd2Emm20rGv8AEKUXSxl+iJisz4aOLK9Lg8MvP51V8X3xmUkPlcGq /gq5KuOeg/rVCPUIZ47fxYJGIUSxnk+tehWE6BlyAR61414punhtIr6IlXjYcg0tl8VFt7VVkDM4 HXIoA9p1S71GaUR2C2xtto3BwdzN1IBz6exrIjtYppjGXKrnBBry9PiwzTBh5gGcgB8c9Kju/iXL I/7iN1Zv4q5KNPERrTlOV4PZdUaznGUEkrNfiel2HlWXiSS1tn3xyJ8wH8JBHP61tSpJgbeea4jw DeR3dxNdyz77iZOjHlea7xm2j8a6zIrXcr29hcSqu50jJA/CsXQktJtL815AZicse9bs8qJFIZMb NpzmvG7/AMRyaDfvHayNLFnOFPAoA9bspord/OTJIPU1cudZne6hSKKM27J852ncG56HOMdO1eOD 4mBbYjyWVvqKgh+Kro+CpGOnIrnxMKs6dqMrS09Pma0pxjK81dHpXiHUVtbC4eQhSEPB4rlbC4Fp 4dkk4zKrN+dcTq3jO48RXkNsrvtkYBhnqK6G8nCaWkK8BE59OlbR5uVc25k/I8t1GIrqy46kkmva tPkF14PSM8kRgfoK8VvJxJrYz0HFey+GysmiBQeCuP0qgOn8LXMcmlwqdpKLtIrsI7qO1s3njtzN KMBI16kk4/Ac8n0rxKy8Sr4f1u4tpsmJmOBnp3/rXXQ/ELTkaPMmFDAkBhkgGs63N7OXJvZ29SoW UlfY6m//ALUuds11beWmCR5bEgZ6AjHpxn9B2y7mxlSA3iOFK/dcGrukeMLHVrS7jEwaRGLorEZ2 Hp+X6ZFeby+O/wC0x/Z6SmCLruLDFePlWJxk61ShibPktraz1/C39anRXUHFTirX+49V0+7N/ZRX G3azKM/XFNtBc+bcedwu/wCTHpVPw3exXWjwiMjKKFPPsK0y/XJr3DlMPVrt7nUodMUbYyNznPXn /wCtU32Brbau/avauS8X61HbapHPDOI5IEPQj5uelUbH4hrdbVuA4Yd2NAHq0GoRxWLxuvmtGhZV J+8QM4rIEcskL3V1sUschOg+gHtxXHy+ObKFt5cH23Cquo/EO2azJDcgHaNw4rzMbgqmJr0m2uSL u13fQ6aOI9lTkor3n18i5qF0l54lhjBBEK7ziuI+J0n2p4VB4jQ1reFLiS/ae/lJZ5GIGeuBiuf8 fTKu5SfmII/WvTOY8zooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8k88Nf9gq1/8ARS10 Fc/4E/5J54a/7BVr/wCilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooA7LSQI4o2zzgGtrxYFn0iNhyVIHH41zVjc7YUGewrpZ F+3aTtHPOaAK3gzVkjkNvKwU4GM/WvYdJu43RSGGa8AisLiGUvDkMp7V2OheJby2UJMjswzzmgD3 e31MKoUgYqHUb3zrZkQ7d3U15wnjCZQCYJK0bTxZb3cbIxKsozyRQBLqEIjkiIH3iBmt3T8QNGyn BAFZVvJFqdsjjna9SvqcFvO0ZYAoPWgDsP7SQJ0GfSs+9vQVJLADHeuEn8YEzlIQWx6EVRvfFV20 BzC44oAv+JdXigiYl179fpXkEt4dS8QRN/CHFaOsXeo6rOykSKme/SqmjaXIt6rN/Cc5xQB1Oqyq beBB1AA/SsEw51RQoP3f8Ks6ndZnVM/d96pw3QbU9x7LigDrtM2iK4iP8S4rzrVE+x61Meg3muxs r7bcMoP3q5nxHbO160gB5OaAOu8M38Q2NkdRnn2r0zSp4WCurjOAeteDaSL21ZWj3Fc8iu60/Xbi 3jBEb5xzk0AewjVCseOCayGuJL+aaKb04HtXCr4ynRwZYyF6da3NI1qO51eADpJx19jQBqadZLFP K+3oTWpY6xINytjaDwaS/aOysJpun/164e78RLZwgJyzehoA727vPOUkkACuR1i/RI2VXFYo8VXh T7hYH3/+tXP61ql3OrCOJ8mgDG8R6kioyqQWbNXPBEKxW8sx6tnn8q5G8t7mScGYMST0NdZoxay0 3BGM0AX7wA2lxwOc1W0aTy4InHB4pkl0rWUu7kkHvRYBRYROp6DNAFvxpZLc6bHMASwxzXndsQsu D2Neh6ndNeaWR1A7V5+lrJJdlEBzmgDu/D1/Em1XYDrzXpmlzqFWRJOByDXkWnadcW+N6n8jXUW+ r3dvB5SrjHpQB6Ne+JpYI1HDYOD9K0QFu4Y5gPvYNeTx6xJcFklJyOxr0zw5qMV3pEZyMrkHJ9KA DVNXOkCJVAy5xzUU2uNexbQwHsK5zxfqUc2orEh4i4rmrfWbhJtysSq8daAOg1i8MSMJG+lea61d LNM5BGMmunvtSl1AHdEwIGK4vWbK7wWCsqE+lAGbp8IvtWiX+HcOlemXjLZ21suOAMc1594ehaO8 V8cA5NdjLM13MiMwwvqaAMa+uc64shxjYen4VvaTIbzzIiBtbjH4VhXlqrayqow4T/CtOx32F2p7 EUAcj4m037FqMuAQCxrKtuH/ABrs/FkX2rEoXk85rJ0fw7cXcbSBSVU+hoA1dAllVlMYOcV6XpWt /wBnxBzxJ3zXC2Fs+nDmPp7VburqSYA52rQB6PovimbWL8wMqbR3Fb1xKYLeSRRkqM4ryjwzqa6d qCMT944PNdpqviSBdNfa3zMMDmgCmnjeS4MkUgRVB24HesDVbme5LG3DBTyTXP43Slg2CT6961Yr 9o7byiN7YxQBw2tMwkKt1rnGUFwvqa7u/wBHmv3Yxx/MfY1yh02WC+2SKcg+lAHYeGNMW205p84L Z61V1G+320seeOa04JXg0xYwOtZVxp5S2eSRgcjOKAMTSbt4Y1XOOldfd6UNR0UyE5YYrkrDTJWt hIvPGa7PQ2lewaF89+tAHl9zb+TOyc8HFTwxElVUZJrotT8Pyy3rCNTkt6VoWvhae1aJpU/MGgCL RBdW0qkxlkz0xXcWHjS40orHGqbDwUNZ7RpFBtWLaxGM4rJuYcfMDkjnNAHs2nawl/YR3BKqWONt YfiXxjJo8vkwKpJ43HtXH6X4gaG2WPJwp6ZrO1W9/tW9Ukk4PegC7qGrT6jEZcB5D3FcNqdtckNJ IrD6iuwskWOUEdOhWp9Ts0urcqqDkelAHk0yYOa2/DOlpfXqM2cKau6l4XuIlSQL8jexrZ8O6YbO NpSOntQAzXZ/7OCxq24dMfhXD3dwXu0cjpmuu1W2udRvHwPlBrm9U08w3sUakElSTQB0+gXSTqAz gYwMVS8VafAJvOVvmbk1T0+1uILiPbuAPpXVS+H5dRgQsc8A8g0AeZBdr9O9adk8ysDGmT9K3Z/C FxFIWCZUd8GtnQ/D6qC0qD8RQBUsrm6QK7R7T2OMV6L4a8cHCWt7jjADenFc5fafGgUquAo6AVi3 AaynSUcZ5oA9dufFthBbySiQNg8D1rzjW/E91rUrZIEQOFUelZU1+0kGznLGrNrZqUjVlPzc5xQB z2pfa342YTtxXN3SsHwQRXro0qKWAK6qT6kVymq+HHe6ZYkzxxgGgDibWBZJlVuhNeg6fawWmmgK cD3NUdP8JSlsuuCD1INWdYs7m2tRFETj1FAHKazfNNIyDpyKj8O3htrtcnCn/Gn3OmTCJpJAVGO4 61R02Ji4IGcUAem31zb3mmGF5AeM4FecalZiGYiPO3NdXp9jc3Xy5PP1rYl8H+bCpONx68GgDzS3 LxSAkGuht4LiVlby8J61u3Hg9lcBYwcDrg10NhoQSy2sg3jpkUAYOjX1zpN0siNgLjrXplt47tZL VWlA398Zrhr3S9hYBcZ71hs8tuxjIOKAO38S+M/tsRtbQhVOdx9a8/lgnlyw5b1rSsbczOXdSfSt 6x0kFBvjxmgDzbUfOhTbIuCaxvKdmzg4r1PWfDguJQVQHA9KzU8Ju5U7AB9KAMXQLOGOVJXOJAeM 1u67frDYERuGLDB/KpLzw3LaxmSFsADPANczewXQVlkBagDmXkY3QbPOa9h8GXY/sllc46fyryCS Mx3K54r0bw3JI1lsiPp/KgBvirSBd3D3MTFmBrj/ALDeHJ+bivUrfTpJoz5vOe1MtdAVrgR7F5Pc UAefaXDPeO8EjFSg4yOtbEWlyrEVcEgV29x4INrJHdxIAGPzYU0tzoxjUlV5I6UAZOg+IZ9GATcO cZBFbd147l8k7QoJ71y93p0iMVKnPXOKpx28jyBGVjj2oAS88zUbozMSQeaoy6fIN7EkYGa6jTtN kdifL+XpjFaz+G/Ottqj5n68UAeRMt1dOQM4FSWmmzXM/lybgue1eoTeDFsYMlBuPPSqlvoKoGcA A/SgBfD3k2Nv5RkxtOeTXCeM7xrrUD/dGf511UllcQTE5OPYVxXiHLXB455oA5yiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKAPt/wACf8k88Nf9gq1/9FLXQVz/AIE/5J54a/7BVr/6KWugoAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igC3DevGAAuce9bum+LzYR7HsBKv/Xbb/Q1h6dpV/q0lwlhbSTm3t5LqYr0jiRdzOx6AAfmSAMkg VYh8PancaWdRjgjMGxpFQzxiWRFzudIi290G1ssqkDY+T8rYAN2PxvbxzF/7IJB7faf/ALGr9v8A EexhfcfDm7/t8x/7JXn9TQ2sk7BUViT6DNAHe3fxOhuSdugiMe11n/2SstPGCiSSRNLIZx/z8dP/ AB2k0nwTf3xDGFkX1IrqrfwPYWaA3lyqnHQ4oAp6N8TZdKtPJbRfO5J3fatvX/gBqhd+PZbjUJbk 6XhXOdn2jp+O2uiOl+HU+X7QmfwpreHNEvBiG5TP4UAcva+OorNyw0cnnvc//Y1sD4s2xt/Kbw0G b+99s/psqK/+H0u0vbP5g7cCuP1Hw/e2DHzYHUeuOKAOkuPiJazqQNB2E9xd/wD2FVI/HEMKts0n Bbv9o/8Asa49lKnBGDSUAa9xrrT3LS+Ttyc435/pUC6q6z+bs59M1QAzSHigDah8QPDOJfIzg5xv /wDrVduvFkN0ihtNIYd/P/8AsazT4d1dbjT7d7F0kv7f7VbCQhN0OW/eEk4VMIzbmwAo3fd5qtqW mXelXKw3aRgugeN4pUljkXJGUdCVYZBBIJwVI6gigDqdP8eW9im1tF83/t5x/wCyVpP8ULJotq+G 9pxyftuf/ZK85AJOBV+x0i6vXCxRM30FAG9d+NUuwVGl7f8Atvn/ANlq1YeO5LO8t7hNL3eSwO3z 8Z/HbVjTfh/dSgPcAxr7gVtr4R0m0GJrpc/hQBFq/wAXZ9U09rX+wvJyQd32vPT22CuYm8Z+Z5e/ TDlP+m/X/wAdrrf+Ed0SThblP0qpdeAoZ1LWs+7joAKAMuz+I8Nsyl9EEgGMj7TjP/jlXZ/inZTD 5fDIT/t9z/7JXPan4O1CyJJgdlz1UVzc1vJA2HUj6igDpbvxdb3c4lOlFSO32jP/ALLUd14t8+IJ HY+Vjv5uf6VzNFAGy/iB3h8vyMZ77/8A61TW/iiSC38n7MGGMZ34/pWCRxmr+maLe6v5ptVgWOLA ea5uY7eNSc4XfIyruOGIXOSFYgYBwAbFt4w8lGV7DzARj/XY/wDZagt/EkEF35/9nE89PP8A/saw p4JrW4lt7iKSGeJykkcilWRgcEEHkEHjFR0Aejx/FK2QAP4eDgf9PmP/AGSqd18RIZ2Jj0Ux5/6e s/8AslcMkbSHCgmtvTvDN9fMNkD4J64oAt/8JcTMZFsCM9vO/wDsa2dM+JVzpsRjXS94OT/r8f8A stTWngFkQNcybPwFXh4S0xBta5XP1FAHO3fjqS5uZZn04gyHOPO6f+O1DbeNVtxg6bvGcn9/j/2W ukl8EWs4zDcZ/KsLUPA17ACY4zIB6CgDQtvibawIVfw8ZD6i8x/7JVbU/iHb6jAYl0Pyh6/at3/s grkLvTbi0YiSN1x6iqfSgDo7TxRDaBtunEk9/P6f+O0J4rZJmkNnnPbzOn6VzlAGaAN8eJn+2i4N tnAwF8z/AOtVyTxqZJFb7Bjb287/AOxrlKuT6VfWul2epz20kdnevIltK3AlMe3ft7kAsBnpnI6g 4AOiuvG0d3EqPpZGO4uP/sa0tJ+JMGlWxhXQzID3N3j/ANkrz+igD0aX4oW8qkHw/jP/AE9//YVk 3HjlJshNLMYP/Txn/wBlrlYbWWdgEQn6Cug0/wAI31zhjC6qe5FACp4tIk3ixJ5z/rf/ALGrMvje SWMI2nnA/wCm3/2NakPggIP3suD9BUh8GQMMLPk/hQBgJ4xKOCbEkDt53X/x2r9v8QYoW3Po5f8A 7ecf+yUl34IuUUmLL/gK5y90O7tGPmQuv4UAdtH8VoIySvh/Bx/z+f8A2FYF34xgurvz/wCyihzn H2jP/stcqyspwwIpKAOtuPGwlVQmneXj/pvnP/jtR3HjIzw+WLALxjPm5/pXLUUAdLYeLBZW/kmx 8wYxnzcf+y1oWfj9bQnGlbs/9PGP/Za57UvD2p6TbrPeQRqhcRuEnjkaFyCQkqqxMT8N8rhT8rcf KcZdAHoEfxKhSUSHQ8kHP/H3/wDYVduPi1FcAZ8P4wP+fz/7CvMqUAk8CgDvp/iWkyFf7G25/wCn r/7Cs+TxwJAw/s3AP/Tf/wCxrnbXTLm6YBImOfQV0Fn4MvJQC0ZUH1FAEcfi9I+mmn/v/wD/AGNK vjBEkDjTiD3/AH/X/wAdrUXwSwHMmD9BUM/gm4AzH834CgCOLx8sbBjpWSP+njH/ALLWgvxQiUY/ sPP/AG9//YVzF74avbU/NC+PUCsaSCSI4ZSPwoA9DuvipFdWqwHQcbeh+1//AGFUk+IqJEYxpHXv 9p/+wrhaKAOv/wCE5Hzn+zeW/wCm/wD9jWRc679ovEuDbY2gjb5mc/pWPVzStKvtc1S30zTLaS5v Lh9kUSdWP8gAMkk8AAk4AoA6KLxtFHt3aVuK/wDTfH/stbNv8VVgQKNDzgY/4+v/ALCvOKKAPSz8 WYmjZToHUY/4+/8A7Cq6fE9EBA0Tr/09f/YV55TlRnOFUk0Ad5P8ShMMf2Pt/wC3nP8A7JVWfx7F cgeZpGcelx/9jXP2mh3d0Rsic/QVtQeDbxxzGR+FAAfGlsQP+JORj/p5/wDsasj4hYwBpeAvT/SP /sajfwVdAcKT+ArNuvC17Bk+Q5HqBQB0cfxS2KAdG3f9vOP/AGSlj+KUcblzoe4n/p7/APsK4Oey mgJDIwx6iq3SgD0j/hase7K6Fj/t7/8AsKp3XxHS6POj4Hp9pz/7JXB0UAdHf+KhfRsn2HYCMD97 nH/jtUNO1ZLFstbeb/20x/SsutTUvD2p6TbrPeQRqhcRuEnjkaFyCQkqqxMT8N8rhT8rcfKcAHT2 PxEgsSGXRSxHrdf/AGFaZ+LwPTQQP+3v/wCwrzGigD1AfF9QwJ0AH2+1/wD2FMb4toSSug7c/wDT 5/8AYV5lUkcEkpwqk0Ad9J8UPMfLaOMen2n/AOwqvL8QbWU5Ohf+Tf8A9hXN22g3k+CIXI+lX18K XmP9UfyoA2E+I0MSYj0TB9ftX/2FWYfiqY1w2jBv+3nH/slcxN4ZvIxnyW/AVmT6dcQH5o2H1FAH oA+LSBSP7Bzn/p7/APsKQfFpVUAaF/5N/wD2FealSvUYpKAPSJfiskqFToWAf+nv/wCwrIu/HEN0 hUaTsJ7/AGjP/stcdRQBbuLxZ5xL5W3Hbdn+ldDo3jRNJXB03zun/Lfb/wCymuf07Sr7VnuEsLaS c29vJdTbekcSLuZ2PQAD8yQBkkCrEPh7U7jSzqMcEZg2NIqGeMSyIudzpEW3ug2tllUgbHyflbAB 2y/FhFXA0L8ftf8A9hSwfFoQzCX+w9xByP8AS8f+yV5pRQB7X/wv+M2Atm8KZx/F/aH9PLrHn+Ma zE40Db/2+Z/9kryzrU0VrNKcKh/KgD0BvilG+d+hbs/9Pf8A9hUMfxKgjdnGhZJ/6e//ALCuVi0G 7kHET/lUjeHrxRzC/wCVAHXx/FgRnjRBj0+1f/YVp2fxtjtSC3hrzOf+f7H/ALTrzGbS7iH70bD6 iqbxshwwIoA9c1T44rqTEjw35Q9Pt2f/AGmKx1+KgX/mC5Hp9q/+wrzmigD0KX4mxTKQdD5Pf7X/ APYVyWp6yuoylxbeXnt5mf6CsqigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8AJPPD X/YKtf8A0UtdBXP+BP8Aknnhr/sFWv8A6KWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDpNL1rT112W7awtNLgOmXtuI7UzOr SSW0yITvd2yWdV64HB45NakOuaXP4QsrO8nsTHZ6fcWz2b2WbuSdpJpIpI5hH8satLESDIv3JBtY HD8QBkVp6NpM2p3iRRoWJ9BQA7RtCudWuVjiTOcZPpXqum+FtL8N2YudRCM/uuSatW0dj4Q0lBhW unXAXHJPX8qqafoup+LL43Fx5ixHuf6ZoAZeeKZrg/ZtKt/JU8ZGM1Ti0TVtScvcSyNnkgtXqOne DbLTIlVYwW9SorattMjThUGPpQB45/wikMTlbkENjINH/CH3JQyW+5R1HOP616b4itYVe2OAD5qg 11GmpEtuuEGCoHArlxtWrRoSqUY80lbT56/gXTUXL3tjwJZ9c0Zhukd0HUMcitSDW9L1pPs99bKk j/LkjIr13UtJsbl90kS+5xXEeIvh9azI9xZHZIMnCgYNcOXZxTxs/ZODjNK9n+j6/cXUo8i5k7o8 z8T+AjGhu7HaYzzgdhivPJYGgkKSDBFexWWoXugTmz1GNjbk7cvkgCsrxj4Whu7Y6lp4DoVydmMV 7BieYdBVm0vYLe3uo5dMtLt5k2xyztKGgOD8ybHUE8g/MGHA465qyo0UjIwIIOOaZQB1Cy6Nqr+F 7O/1H7La2unyxXcmx/kf7RcSqmQjEbg8Y3BW2784OCKq+JZ0ur+1htrrTZ7WC3EcEenLOIoFLuxT M6iQnczMSS338A4GBhBSSABya7zwT4TN/MLm5QiFRnJHWgCPwt4HuNUZJpgFhyOveu5afRPDEfkR wJLcKMEKvSmatrmB/ZekjORsLR9SemBitHQfAL3MYu74tuYZ2kUAc5Nqusay2LdmiiPGFOKU+Gbt 033Luw6k7q9Us9At7ZVCx9Oh21pT6dGtqysgxt9KmE4zvyu9tPn2G01ueMnw1GSggkZnIyQO1I2m a3pv7yCeUKvbdkV6R4esYvtt0doO1yBxXbyW1pPbhXRQoHAIxivMzPMp4Lk5KbnzX2drWt5PuaU4 Ke7seE2vipk/canb78cFgBRqXhXTPEVs1xp3lq/sMV6prHgXS9Rt5P3SiTBKsoGQa8v1PQNS8K3g mtjI8Xcj39arLM1pZhBuCs1un/WwVaXs3o7o8q1jQ7nSblo5lxgmsxa9unWx8Xac8bhEu1XG3AzX kmt6PPpN60MiMAOmRXpmRmn7tdjo+vafJb30MMWjaFPK8DJ9ot5ru0ZUEofMcgnPmkyJtbbwquAV 3EPxdFAGhrt5b6h4h1O9sxOLW4u5ZYRcOXkCM5K72JJLYIySTk9zUNjYTX06xRLksabaWsl3Osca lifQV65oOiWfhvTBe3mPOZRhWAyT1wKAKOh+Brextxd6mU2jsecVoXHia0tF8jS7UE9A+MVBI2p+ KrwIiMIB2X7ox/Wuo0jwbFbMhdC755yoNJtJXYHIeXreqne80ioecA4oTw+5YrNcurAZ5Jr2C00e OKPCxAcdMdKw/EemxqYWAwxdQeO1CaaugPOV0q/jObW4lHp8xqaPXNU019t7GZk7k4r1qz0iIW0e 2McAdhVbU9BtrqJkeJMn1AobSV2B5239ieI4zE0SpOwwAy81w/iTwXcaYzSxANEScEV6Lq/gK4tV e4tA+5eQuMflWfp+skZ07Vkwo+TdIOnbmoo1oVqaqU3dMqcHCTi90eNMpQ4YYNKOBXc+MvChsnN1 aqWhIzkDpXCEEEg9RWhJctL23toLqOXTLS7eZdscszShoDg/Mmx1BPIPzBhwOOueo8W+JdF17w5Y R2Nnd2l3FezN5Et0Jlhg8i3ijUERLkbYlUZJYeWSxYuCOLooAOtb2heG7rV5V2KAmeWNP8M+H5dX vAu1ggGScV6LdX1voFsLHT1VpyMEj+E9O3egCvb6PpHhuBWuwjzY6bcnNQT+JLi5Pk6fAIl7HinW Ph291WU3V0JMPz81drpHhuC3UAxjP+6KAOAaz1Wf5prmQD60r6ZeQqrJfSFj0G4163NpEIt2BQY2 +grF0XR0lubmQxllRyoO3gdOM1MpRirydhpN7HAi81nT8F2MqDs2Ktxa7pt+PJv7dUY8ZIyCa9Nm 0aF4iDGMfSuL1rwfHNMzQDafZRVCOS1rwRHcRG408qVP8Irz27s5bOUxyLgg16ajal4bn2yI7QHq rdKk1jSLPxFp7XVpjz1XlVHOeuDQB5MOtKSMVNd2slpO0UikEeoqCgDtPFOr6ZdW+uy2d/Hcvres JqSRJHIrWyAXGUl3KBv/AH6j5C4+VuemeLopyIXYKoyTQAscTSuFUZJrtvD/AIImugJrnCx89ave FPDEUFv/AGhfYVFXcN4FaN/qt1qsn2PT0ZYO2zgmgCZ7vRdCXyoIFlmHoveqkms6len/AEZfJTtj Fa+k+DSrxyXIJyc4IrtrXQLZY1CRj8hQB5fHbavM5JvmUgZwTSpLrVuSRMZQPXFd/r+lwxeUUwrF gDgdq17XRIfsaL5XIXkbaLgeYJ4kUfutQsx7ng0l34f0rXYDLYlFfHYY5rtta8MWs8TDZ83sBXC3 Wg3+ky/aLcPhecigDhta8PXWkzEOvy54I71i16/b31vrcBs9QVVmxgE9z04zXA+JvD8ukXZwpMZG Q2OKAOfGO9df8PfEml+HdajfU7acxzXdqXuoZ9vkxRzLIwZPLfepZI2IXa37vAOGIPH0UASTmFri VreOSOAuTGkjh2Vc8AsAATjvgZ9BUdFXtL02XUrtYY1Jz6UAGn6ZPqE6xxLkk4r0DS/BltYW63Oo smMZwa0La2svCtgCwV7phlVI5JqpHp+q+Jbgyyq6w9VHYUATya/p1oPKsLQORwGxiqjapq9yf3eI wfTFdjo3hG1gGJUDSerAVur4etkhfMYHH90UAeaGXXYVVjJuB5xkU9PEU8B23tpuHcjFdto+lwzX 86SchGIUVev/AAxayscoMY/uigDgPL0PXRtCIkx9Rg1y2ueC7izVpYMPGMciuz1bwWySM9ruzkkA cVQtNUu9McWmoxM0Xq/UUAeWSRtG5Vhgim16J4l8Mw3Nsb+wwykbiFArz10ZGKsMEUAIcY967fxf runajp14ttewXH2vUFurWOC3aKSCECXK3TFF86b94mHLSnKyHf8AMS/D0UAFAGTgUV1Hhnw2+oSi aZSsQwckcUAVNG8N3WqOCFwnqa7SLRdJ0OMNeFGfGcYzUt3qq2YFhpMYdx1dBUVl4Xu7uVZb3zPm OTu5oAbJ4kgT5LKzGOgOAKjTVNWmOUhG3r0H+FdtbeErZYk2oD68CmatpEVl5Zi+UsQp47GgDjV1 +6hOLm1DAdcYqUalo2pDZcQLGx9Vrt28NwSWaHbkkc8CsPU/BcTx7o8hvYUAcnqngxJYjNZMpBGQ K4m9sZrKVo5VwQcV6B9n1PQZNyh3izkq3Q1Ynt7PxLadAlyBkrjkGgDy2ir2qadLp10YpEK/UVRo A2PDV9b6fqs811J5cbafewg7ScvJayxoOPVmUe2eeK1NMOlaf4ae6tNX01NYuLedJVuo7nzYVYNG YogkZiJeMnLux/1gACFCzcnRQAU+KJ5nCoMknFEUTTSBFBJPpXoOh6Db6Xa/bb/CkAEBgKAMrR/B 09yqzTYVOvNdB5eiaONrKskg7Bc1FcX9/qzmCzjdIBwNmeRWhpXg0zDfcbsnsRQBnv4kiBxb2XFI fEMq4aSzGD9K7CHwlCsTHbjHsKr2WjC8mlikwViJA4oA5pNa0u7Gy4tghPHIqK58NWGpRmSydATz 0rpbvwbDKWwCPwFc7c+Hr7TZjJbNJx/doA4zVNAutOc7l+X1FZFenwanHdg2epxrG543MODXL+Iv Dj2TmaEFojzkCgDmKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/yTzw1/wBgq1/9 FLXQVz/gT/knnhr/ALBVr/6KWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD 4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigB8a7mCjqa9h8GaTb6Noj6rdIuQCBkY9K8v8P2hv NXhixnLD+dev+MN0Vtp+hwcGRgzD86AGaDpM3ijU5L67JMAOVVs4616ZplpHYtsiQKg6YFVNB0td P0qGBOCEGfrWqiHGAeaANZoy6ggZqwkKxQFj1xT7KMrbjzBzUOqM4tD5Q+Y0AcZ4lbe8JDf8tVrp tEkBRUbH3RXLanZShIjcdTIMc1s6ba3UEsUinMZwfwoA2NcX7PYSyjjGP5is7DCDczZVhwK6eubv rRrTy41yY1GAT1NeTiMunUzCni4ysoqzX32/P8PM6Y1kqDp21v8A1+Rg614ct9ZsmDRqHAO1se1e eaRK+lao2g6j80Ux2oT0BP1r11S3l57AV5v8RdLxCupxDEsGGyPYivWOY8t8e6AdK1Z3RQI3YkYF cfXtfjO0XV/A9hqQGZNnzfgteKkYJFAG14b0ttV1WGFRkFhnivVdblXRNNt9IsBtnZQHKdRWP8K9 NRYrjUZFyIRuzn2NbfhqzPiPxZfajMN0SSNt7d8f0oA2vCvhRbdI7q4UNJw3IPpXoaLsstqKARgD 2ycVBaIFITGFrVjsEuIWjYkKw6g8j3rHERnKlKNN2k07ettCoNKScthXgjZooYwA27ccdgOv+H41 X1jbGm0HHFadrYQ2YYozvIwAZ3Iy2M46YHf0rD1i2uLi4JX7grz8ny+WCoONR3lJ3f8AX5+ZpWmp StHZGDoBCXl0T08w13PkJLaZUDOK4TSrOVri4WLlg5JrutLSRLXbL1r1jEzXmeAhJGPzZxmsjULe K7ykqhlJ710mp6VDevDM5fMO7CAgBs469+3Y1izR/Mcnkdq8TD5T7HMZ4uLSi9kvRXf3/f8An0zq x9ioLfr955R4j0Ofw9epqNkf3QO5gufWqfirS7fxJ4bXVrRAH+6w6kEfSvT9VsBf6fNbuAQynFed +DEeLVNR8PT/AHWBdQfXivbOY8QdDG5U9QcU0DJxW34qsP7P1yeHGBvbH5msyxhM95HGO7D+dAHo vw+8OoyHUbpR5cYJ5Fascd14r1po4iRaxN8o7AZx2q/rX/Eh8EQWUXE9w6g/TJ/wrrfBOiDTNKhd l/eOoLHNAGpomiQaXarEsS565x1rqdI02Mq80iqST8uR061FDCrgCtCG2ZF4kIX+7x/hXBmFCpXp qENVfVXtdWf62ZcGk7lIbP7Qu41C7ERSeehOe34H8q5HxDtZoyCMCReK7Se0S3s5ktk2mRi7ck5b GM8/QVxOrWc0YiMvdxiqy7DTw2GjRm7tf5309Cq04zneJ02iyJJGqMP4Rik1K12y4A4qPRbWdJEd vuEA5ropreOUDcM12mRk3i27wNwhYjA96848UeE0u0kngVVm5PAPPFepT26InTjFYl2q5bjivIyn Kv7OjKPtHJPbpb8TWpV57aHkOhTDUIZ9C1EZmIIjL/TOP0rzPxRpD6Rq8sRXC7jjivUvGlg2i67a avbjaqupbn3FZnxM09bzSrLVY1/1q7if+A165keSVa0+1a7vI4lGSzAVVruPhxpS3urrK4+WNgT+ WaAOpEMfhbw7FsUC8mUYwORV/wAMeF5Ltxe3o3EncAwNVZYz4h8cy2w5t7UlcdOgAr1O0tlijVVX GBigCG101CI4Y0C5IXpXRahp1rZaa7oiIygkNjmqltAWYFSVYcgjtV25sI76Mx3pM0Z/hJ2gflg1 4Oa4HF4qrH2TtFLe7VnfXbfRK3zOnDzpwac+5kX0qS2EMigL5kQbGemRmsrStSiS3kso7bayuxeT IO4nv0/D2AHWtDU9PZRFDbrtgiQIoznAHAH5VhaVaytcziPkhzkV6eIwdPEwjCvrbXdrXvoZqpyT bht+h2EsayWG9RziqGnwWrSSfadoJxt3Djvn+lbNnERaBHHJHNRvp8ZyVFVjMN9ZoSo8zjfqt/67 +REJcruczrWh2WpIyhFZccHb3ryu8trrwtqytlmtnb5gOARnFe3zRCNcAVyHi3Sk1HSpl2/vApIq 8PRVClGkm3bq9wnJyfMzy7xzoEUtquqWYUxuAflFebV7L4Yb+1fD15pE4zJCxZR7cV5JqNsbW+mh I+6xH61sSVa6zwZoB1S/VnX92uSSRXKopZ1UdzXsejwL4e8Dy3uMSyfIp69TQBV1GeXUdQXSbHIg iO1tvQ9u1dvoXhiCwhViimTucVleBNF22n2+ZcySjdnPvXexFQcYoAIbBJHRG+VfUVMskWn28TTK x86RYV2joTnGfbippdLkvJbSWK6EKQtukUx7vMGRxnIx0Pr1qfU9LtriKKR4t8tvlojuI2ng+vPQ da4lHE+1qSvo+XlX5v53/BHTFUEocz73tv5dvI4vxDl5Izu6OOK6XRpTOAJMFiOeMVzWq28oMZkH WQYrotEglWRGK4XA5rrcU2pNao57u1gv7TN0VxxVG6sI5YihjBBHpXWTWySNkjmsy7gCNtHeqEeU +JfCrQg3dkNrKdxCg1mQxp4m0Se3nUG8hU4LdTXrNxAHjaNhkEYryu/hPhvxpbSAYgmcBh14PBoA 8o1KzexvpYHGCrEVUr0D4laStvfJeRj5JuevsDXn9AD4ozLIqDqTivT/AA3psOhaMdTuUBdlGwY5 Ncb4Q03+0tcgjxkbxnmvRvEMZu9WtNDg+7CMN+VAEOh6TceIL/7XdkmMNkA+nWvT9P0uG2txHFGo wOwqjo9mmn2yRKmCAK6SxKsw96AKEFp/pIwOvtVrVwIYdiDBK1tR2kaPvA5rA10SNPhRnigDl9FY rqNwW7ua7O6t1ez81euK5DTYXNzPtGTvNd5YRFrELIPwoAwI7MFdzqD9RXOeIvDsF/CzBFV+MYFe gTwRxREjpWHcKHbgUAeQWks2kagbC7Ba3lOOeg7Z5rmPGnh/+zbszxL+6cAggV6j400Xz7FrqNfn iGcg1gtCPEXgdt3NxAxUnpxnj+dAHjtFPlQxyspGMGmUAaWjabJqN6kSjIPtXol9KNNt4tLshiUj Dsv+feoPAumpaaZNqcw4QHHPfgVc8OWb6hqEt/KMhmyCfrQBv+GPDEcMazzqHkOeSK62exVYxtUD HtTbIlcLjgVvRwLPEKAKGn2+21ZmHQcVyviGRmkj9N4ru7lBb2T47CuE1QmUx5XA3igDotHC3ECo x5Cio7y1PnFAMCjRAyzqAMcCugmtUkfcRzQBy17pUM1vskjU5HcV5xrmkTaNeG5tCVXd2FeuXxVc rXOalaJexNGRng0AecazYRa/ozXsKBZowd69TXnEsZilZG6qcV6rpIOneIDp03+quPkI+o4/WuK8 Z6WdN1qUAYVmJFAHOUUVb022N3fRRAZ3MBQB1Pg/RUYm9uQojUZywrbVZfEGoLGgK2ytgDtiptWi GmaJaafFxJIAX/nXS+FtOW1tVYj5zgmgDX0nQraztFCxLnHJxVy2t9s4QDitS0jDKAatw2ISXeel AGbqKC2gwo5I7VzGjSkX85OeXNdLrc6rIEPXFc1YHE8zAfxmgDqbi0UQeavcVnrZJKTuQEH1Fb1m onsADTHtlhXIoA868SeGYZEMsKhXBHQVzdlN9pR9MvRuLcIzdvavULwqcgjIrzjxFZG2uRdxDGDn 9aAPPNc0t9MvnjYcduKy69L8U2aap4ei1JB84+VvwrzToaACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKAPt/wJ/wAk88Nf9gq1/wDRS10Fc/4E/wCSeeGv+wVa/wDopa6CgAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOy+Gtr9q8T wjGQCP5ivTNTQXvxWhhz8sUBI/L/AOvXEfCCEP4kLH+FQf1rrJpynxZZwCf3BH6UAeu2dsJQoPat aKztrdt7sufc1yserPGBtBH0FVbjVri5nESsyk9c0Ad3K6gAqQR2xXKeJfFR0G4hMlsXtnOGkH8N bFgzCxUOxLHvmqupWttew+XcwJKp7MKAMi41K08QRxy2d1E4QhiA3TFRah45tNIgS2jKXNzkIsaH PNZt5pcemybtOiCbyFIzxg9a09N0LTgyTyWqPNnJZhnmgDrdOu5LmwhllTZIw5X0q4725XZMyZPQ GqcTAbQAAMdq53xBcSQ3QkSQ8ds0Ab92luI/kK/hXE+LrYT+Hb9T/wA8Wx+VWk1ScxglW5rL12a4 uNEvAFIDRMP0oA5vSUF98L5ojyYt2P8Ax6vCJ12zsp9a978D/P4L1KBucB+PxNeF3qY1KRf9qgD2 vwhCLD4aX9x0Z04/75ro/hnYqnhmK4P35suf++jWLbp5HwokUcbos/oK2vh1qMcfhKzRuqJj9TQB 39vp7TEE5A71tRRJDHtVgSBXJS+ISqHa+0AelN0vULu6vQxc7Ce57UAdRcTmOB2UZIHArjbXx1aT zy2N9tsrkEqFkPX3zXXM4cnHI9K5PV/D2l38xnktV84Zww4NADob+y0UyXc19CI5DuHzDnNXPDni xtfvJPJgItUO0Sn+L/PFc9BpFtev5NzAGih+VAT1rrdItLaxh8q2gSJR2UUAbyuCTkjFU7qwt523 qy7vbFQ3jubNxGxDY7VycerXFpcGOSRiO3egDdurTy+O1eWXcY074r2rqNomhAP6/wCFd++ueYNr En8K85165MnxH02TsFAz+dAHEfFmzFt4lZgMB8n9a5zwhbfavENtHjPzrx+Irt/jMg/tK1kxyY65 z4axCTxVCSM4ZT+ooA9H8XRi68YaVp3BRRuI+gNekwxhVSOMfdAxivMtdmCfEmxdj8oiI/Q16ja3 sEQDk8kDHFAG5Y2zKgaTr6VcdsVymo6/KpEcL4J9K1LG7leyVpjljQBU8ReIW0UxyG3aSAn53H8I 9azZdT07xDHHLb38JVCGxnkYrb1O0gvbQxzoGRhiuKHhmw0eQtCjlZMqBu9aANibxlZ2LR2VpIt5 ckhdkZziuutbgzWqOwwx5IridJ8NabY3IuVhzMw+8xziuxDiNV4AHoKALbxrLEVY4zWHdafJG5K5 Ze1UdY1K6tbrKsdg9KSHXy8a72yCPSgDnPH1gLjwrekj50jLD8Oa5S9T+0vhTbkjLQqR+h/wrsfG N/HJ4bvsdTC1cpogE3w2nQ9BG+P1oA8HYYYivX/hnbC38PX95jBC9f8AgNeRTDEzj3r2rwcgh8A3 ZHBYH/0EUAWPh9GJbvUb1vvSStz+Jr1OxjZ05Ga8w+G8iCxlQnB3tn/vo16WNRjtrR2XqozQBsxQ rGM0Sy7ImbHQdK5zTtWuru6JLfu/StnzhK5T2oA5+PxbZyzS2d2y2k5JCiQ43e4psM9jpDPdS6hC qOd3LDnNZni3wmmoyJcREq8fOQcHFZL+FodRjt4TcSny1AbLGgDu9E8QLq8jGKMiEH5XP8X0rdDA nFYej2EOn2ixxDhAADVq4u3Fs5i4YUAWbmEkFlHNc/eJuLK69eKbZa7N55imbP4VJeXcT5IIyKAP NNEiGnfEe6thxHLFux+Fec+PLT7H4knQDALHt7mvRPO3fEtXX/niAfyrj/inGF8Qhx/Euf1oA4/S ofP1KCMDOXH869g8bL9n0TT7BON0i5A+pNeXeFI/N8QWqn/nov8AMV6j48b/AImGng/dWQf1oA9E 0C3FtpkEeMARgYq1fSpZwNcMhKg4JxwPrUOkzK9rFzxtFWbvWUsjsj+/9KzrQlODjGVm+vYunKMZ qUlddjdtcfZo32Ou9Q21xhlyOhHY1nazrUWlbGmRjGxwWAJ2+5pLLU5J7bzJepPFN1GzTULNyQGy DwatEvfQzrt7TXFjltryFkUhuCD0qT/hIbKw2WqzrcT8DZHyR+VeY2ek6hDrN7bxTvFGsbEDf6it zwPoNwLia5uSX3HAdmyetMR6pDMJoFccZqO4g8wZqBZVttqYPHasrUtYubW4G3/V0AXJ4dqnivNP iVbZsYrtRhomBz+Ir0VNSjubUOepHpXAfESdW0GaPqSOPzFAGB41gF94F0+6x8yryf8AgNeOV7bq K+d8No1b+CPP6GvEzwxoA9K+FNmGup7oj/V8j8jXS+GE+3+PtTuH5CMQKzvhggTQ76QD5iQP/Ha0 PBDhfEurZ6tKf50AejyBHcY4x6VoxLaxtbJDIzzSEArkccZP5c1Tt0TcrP06mlOpWqX6eTbRNMDj zNg3DjHXr0rjxNCtUnCVKpypPVaao1pyppPnV+3qdGzbIiSegrDGs2N60sCTRCcZG1+D+tX2uFmz HnGRXnPj2weytTeQFop05DocdxXYZHU2Np/Z1xLcTzRJGxLEkitnTtWtr+Rlt3DqpxuHT8K8Nlv9 WvJtOgnupWjkQEjd1r2HRbMWdqjgBQAMY+lAG5cw+chGawJrZopMHIq/eaobeCNokZ2d9mAPYn+l VFvpJpTFdxIjEblKnn/PXmuaeMowrKg5e8+htHD1HTdRLQpahbrNYypjIKkV5v4LTy9R1PTW+6ck D8jXpd9KqQPzxg15n4Yk/wCKxv3XoR/QV0mJ5X4htvsmtXMWMYdv5ms6Jd8qKO5FdJ47iEXia5AG AWb+ZrC05A9/Cp6Fx/OgD1u8X+zPASRAYZ2GfxNdD4btFg0G3AAyUBPFYni5dvhuJB0DJ/Ouk0Vg +lW4HQIKAOl0SytLzclzNIkgyRtIAxx6j61q20QjklWNy8athWPeseI28FoTMOCCDU+lX0JtmjiU pEhOMkk8nPUnPeuGGHrxxTqOpem1t2f+Rrem6e3vfhb/ADL17d29uFW4kVAxwN3esLV7F7po/ssQ ZVIOVxVnWbZb2HLfMvb2rzS71zV9E1O4tI7p2iEZZQxziu4yPUra4hsoIxcvHG54AJ5NaqsJIwwP B715f4TN1q0f2y8leaRjkZPA5r0SCUQRrG7c0AUL+1feWwSKzMKJMGtsXzTFw1uxjUkFsVl3sSrJ vT7prGliaVZtU5Jtbmk6U6duZWuefeM4Baa7YXScfOAT+NY3xLtfMs7K8A5Ycn/gIre8dSK72YH3 vMGPzFUfHSb/AAnaMeqnH/jtbGZ5FXV+ArMXWvxsRwhBNcpXoHwyjH2y4kI5AGPyNAG/qC/b/GHk /wAMYP8AKuxtIDEoC54xXH25x42uS3px+QrvrHaWGcYNAG4lpDHZ280E5aVwN6Eg898fTmr4OEya yJNTgswqqMseOBV1rjzbYlTyw6VzYWjVpRcak+fVtaWsu3nbuVNxdrKxVvILe5V5hh2APC81haVZ Ol5Lvt2CMx5IrN1ue/0lJLyylbC/M6E5BrIXxlqepXNvawERmRcuwGK6ST1O3aJcRxsvHYGo72Nn jOzNY2iq8R3Fy3diT1rYmuyjIscZkZ+wGamc4wi5Tdku41FydkZEGl3Oo3DQRbVYLu+ckDHA/rWD 4m0We2sZ4bmHaxUlT1DfQ132kSzpqB8yHyoyCNpOc9OaZ47Kf8I9OxAyE/8AZlr5eOfynjlSp2cG 0vPXS6f6HXPCuCtLe1zxjQE+2+GLu0b+Fyf5V5Rcp5dzInoxFeu+DVBtLoHo0jD+VeWawgj1W4Ud nb+dfVHGUKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/AAJ/yTzw1/2CrX/0UtdBXP8AgT/k nnhr/sFWv/opa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKAPTPg6QNcl/3P611kKqPig5kGVMB5/KuN+Esgj1tvdf616MLFT42 844yYSP5UAd1BDbMRyPpWTq0TQ36BVwhH3gK0YbOU421tJpsL2qvdqGKetAEFjnZGpzjbnn6VNKm 8Y7is281mNcrZxgkcZPQUWOptcMUl2rKR2PWgDN1p3tPLYRlt0ir09a17KFggJGM+tUtc1GOwhga 4QNumVRmtAT4iEmdqbc5oAvLyjY9K5rxBE7mJlUlj6fWpG1y5EmIQnlA9z1rWsry01EiJ4lEg5wa AKVtbwrZRm4wrBRxWfrdxb/2TcrGBxGf5Vd1axl+1YMoVCflXPJ+lZms20cVjcAjClDwT7Vh9Zp+ 3WHv71m/RKy19b6fMvkly8/Q47wWCnh7UWPAYyfzNeHah/yF5f8Afr3rSUFp4YuiMDcrn+deCXh3 akzHu1bkHuoG/wCGe0f88P6Cr/w6hhbw5b7+uz+pqroy/avAJj6/u8foK1fCNl5OiwrH2XHH1NAG 5q1in2F5IVyQMnFSaDOPLGOoGKv2NtJNmCQZRxg5FWYdKttIhlkkZRnpQBehIKBs9aqXkBALLzWO mszPcEW1vI8acZ7Vo/2nFLavLuClFJZSRkGso16cqjpxknJbrsW4SUVJrRmVpk/2m6uI0BzHIVPH SukgjESZJ5NcX4d123vNUvIYo1jJdmJ45q5qmoai+piOxvxHbiIBlEat8+Tnn6YqMVioYWk6tTZd jXDYd4ifJFpev9M6O6faGAOcr/SuNVTPrATbuUCt3TdQDMkN8fnCgbzjD+/tVubS4rNHuYFDM3TF XQrwr01Vpu6ZlVpypzcJboqtZ2cJyzDOOlea+I1R/H2niIfdAPH0Nd01tcyuZHYge9cnfWqP4wt3 ABZI8k/nWpBxvxkYGeyHfy6574YHHidCfb+YrZ+L0m/ULdf7qH+dYPw4fZ4kj56kfzFAHd+KkI8d WLnOCuP0Nej29ks9umCd22uP8QWKzeJLOVu3f8DXYWyywBCrHGKAMmMMuqvHLnKjjNdhayKyInQD is640Z7y5juohtJ4bAq7fCOyRFX/AFhHAHU0m1FXew0m3ZGoAJIiua5nWp47XyRI2MyBRUz6xd6c PMubOQwkffB5B+npisLU/EOlao6B3VfLdW5I6isaGKo4hN0ZKSXY0qUZ07c6tc62xiEgVz0wKuzM pU84xXJQeK7aS3a2s2PnkFVcYIU9Aat2ttdxq0z3skpkO51diQfpkkL17VyY7NaGCnGFa/vfga0M K60HNNadO4utyq8JPotZWj2Yu42Z8hR3qa/vFuilt9mKOTtG19xY/TFWXBs7SOCJGRto3DGCDiuj DY2hib+xle3k/wBUTXwlagk6itfzRh+MLeC38O3m18nymrC0MeV4AmB4zEx/nWv4jgaTRbgSknKH rWe6C08DOowP3WP0NdRzngVx/wAfL/71e1+Exu8DSqP7v/sorxOb/Xt9a9t8CfvvCsqf5+7QBB4B QmW7j3EYc/zNdhqnn2ltnkqetc94UsvIu70rw3mH+ZrsjCdQsZYH5bHGaAE0WcCIHHUDmt2Fh94H msbR7Uw2ziZOnAz+NSSXv2ZNkSu7dcjJqJ1IU4803ZeZUISm+WKuzYuwskBfPQc1zGhXAubu8jHB jlK0TeLLWOApMfLfO0qxA71jWOvWWmT3Uxdf3sxYYI6GqTUldClFxdmrM9DyI0CA/Wqk0qxhhkYI xWHBeS61epc21zJHDECoXsxPUkZweMdelaEqPbqrSr5pHVs4/SvJqZ3hKVeVCo2mvLT8Dt+oVJQj ODTv0/4fQ5nUHeG8TYPmJxWmtk3k+ZIxBIzikTTftF39pkJMajgEcfnSXd08mUTIXGOK9OlVhWgp w2fy/M5KlOVOTjLdHA2q5+IUjKchIxk/hXN/FNt2tx+yYrs9Jsv+KoupcZO0DP4CuE+JT79dx6D+ taEGP4POPENuf9tf5ivSvH4ObWXsJB/WvMPC7bNcgP8AtL/MV6/4ys/tOmoe4dTQBvaSZxpkUkZL DaKdp5Op6qYpflx97noKboMkkFnBG2Su0ClurSW11tZomKrIvO04rKvGpKnKNN2k1o+xUHFSXNsd N8kRMSHKqcAmrkEmwKuflrIv7hI1XaCGQZJHesmbxhBb4WSJhjjNVTjKMFGTu0t+4m03oO1SSKTV Lh7dSkgYQyK42nrjI9jxj610OnRx21kiqRkDFcnqeotdRQSxMVzIp4bnFTTeJYtPWNGHmFzgBTml TU1fnd/w/r1/Auo4N3grfO51UrZwxNY+rSq8PPYVJHcXN9bj5AmeeGqDVULWJUIUYDHHOTXDTzfB 1JqnCd2+ln/kb1MDXppuatbzX+ZkWE81xviTOFOMiud8eRtFpoVmyWIA/MV1tjb/ANnWIZuZJBk8 VyXi1HvJbVWJKlx/MV6RyCX/AO58AlG/55D+RrxFvvH617h4pH2fweFA6rj9K8Pb7xoA9f8Ahqf+ JBdDuWH8qXw8zw+ML9F4Jcmo/hid2mXKe4/lVuK3a18ayzIOH5/QUAdrcalLbxFXXBPArd0zSYU0 9b9pWaV03EHGB3rD1CD7ZpjOgw6rnpSaDrEv2Yw3JYpGMBTxXmZlhsTiIwjQnyq+vmvuNqU4Rbck dBHJ+9DbsVB4ktrfVdIeO5WQRnh2jxlR68//AF6zJdXgEvynHtmobrxIgURqxYnoF716FSLnBxTs 317GcJKMk2rrsIvhe2+16fdRSboIF8sAgEn3NdVLN+7MY4UcV5/Z+JJl+0CWN/KDnjHArXh8SC7C xRKzuewxXHg44mhTksXNNLZ7aeextW9nUkvYxevQ3xIMYPqKzfEN+tvcxYRQ+0fN3xzxVu284KGk TaOu4nNc/qOdV8QoI9xhjTkkcE8/415kalHG5nCrRTagmnLW1+i/P1N5U50aDjN2b6C3k08tm7nh dtcT4NBOr3svUFiM/lXZ69PssZY4hjCEVzXgyzMdu8hHLSMSa+iOA868fNu8TXB/2j/M1haV/wAh KD/fH861vGT+Zr07f7R/mayNN4v4j/tD+dAHsfikeZ4ePsVP61c8M3jHSoSRlQtN1K3NxoG0/wAS Aj86g8GSD7B9nkHTjn60Adhpaxa9qBs3kdI1jLEoec5Hr9a173T4tJVYYXZlbkliM/pXEPd3Gg6t 58LsiuMZA/x+lb6a2t/GGnl3uRjOR/SvLlh8Y8eqyqfurfD52fl38zXmhyWtqa9pcRvG8bkdOK5m fwzpmq30ksk8y3LIY9oYbcflV0yiPLLKBn3rC1LVBavEUc7w4OQa6cbRq1qXLRnyS7/o/IdCcITv ON0dB4Y0g6BbPbTurtH91hxuGTg1ee43zFs1iQam1zh2l5Kjqav2W6Z/3aGQDqe1NVnh8Op4qSTS 1fQXJ7Spy0lvsdVYBTpUrFR0Y5xXFXOo7ru5hXkrIw/U1v6lrttomktDKczuh2RL1Ynj8B/9frXK 2i5Et5Km1piXwe2STXznDtKcsRXxPK1CbdvvOnFWUVBu7X+RxfiiV5tcsYT18wH9ak8cNjwrEvff /wCymo7tTfeLYXI+WIg4o8fjZoUC+rE/pX1pwnk1eh/DVgrXOe+P5GvPK9A+GwJupU55/wAKANfU ZDbeKBIOrj+grr7fU1SIFjhsd65HxNG0N9b3AHRiprpHtVudMEseAwXPFAHe2nhezvNPt75p7jzJ I1kxuG3JGfSqBufLuBnoOMVlaH41ltbNbCZC7RjYCXA46DtVsyw3Mfmbtrd68bK6eOhUq/W3dXXL qn3+7pubVeSy5BmrRW1yHhmZhDIpDFOv8q5Gz0WKz1xJbd2eBUKgtjIPvXSzTROrkPkqOKydNmDz zKx43E5r1PZv2nPzO1rW6evqSprk5bfPqdWJkgtVVSNzCtbw8wku8sM4HFcxE8cp/wBb0rrNCi8g +Yy7Rt4zXjcR1qdPASjJ6ytbz1T/ACNMNFud10v+RZ8VXAsdJknVtjgqAynBHNeW+Itbup9OlElx M6bTw0hIro/E/iKLXLo6ZY5eBGBeYHKuRngcdM459vSuJ8WssNiIIx8zDHH1qcgwTpYZyqws3K6u tUrK3oKtO7Si+hH4SGzSwx43Ox/WvLNcOdXuD/00b+deuaNbmDTFUD7qkmvINXOdSmP+2f519AYF GiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/ AKKWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigDvfhxmLVUdepx/OvUNckawvoL0cbSFP0NeS+ALrydWiBxywH61674wK/2S3H zcdKAOu0q/MkccqsNrAGtH+x7XWtRkvLxRKVURxg4+VQAcdPUsfxrwrQPHc1kptrgHC8KSTXoXhX 4kWBne3upYoFwXEkkm0dhjkfWvIzunKeF91N2abt26nThak4T9x2bVjq9Q0u1tpUjDMu/P3mz0rl b27hj123t7eU5VvmPr0pnxA11Li2je0u1WSIH5FzlskD/GvO4NVu2u4nkH3T97nNRkDlLCKUpNtt 79NdF92o8XCUJpTVnY9n1Szh1KKETjcEYMPrVDxJO0Gj7UfaEGPqAK56DxWDCA5GV96xPEXiB72A pG2QPQ17RyncaM9ld6csnmEN3BrRimS2O+LqO9eMW+ragjqw3KP7q5roV8ZLb2+2Y4cDoSaAPTGm jluDfMzPKUACscgY7D2/qTXJ+K9XP2f7MHJln+QfjgVycnj7AbHA7YJrFsNZl1rxJDNKD5aMAOtc 1HCUaNSdWC96W7/roaTqzmkpPY7q9RoPDDoOMx4z+FeCXqhdRIHrXvniW7WDw4cDHyn+VfP8777w t710mZ7/AOBh9o8JFOox/StDw5em1vLixc42MdufTOf61l/C6YSaFMrfwkfyrK8TawNJ14XEPJ3E MBQB7JbXb/ZJhG+yYoRGwA4bHB596q/2A00a3F5d3U8jfOweYlcnrgYwPoK5HS/GVpPDHIJFVuO9 eh2l/Dc6bDJ5indGpPPqK+W4klVp8s41HFWdrNrU7MNUkouMVvvoZr3trZWRaOMLGB2A/wAa82k1 X7be3TKxRGZsDpnJNHifUbqyvJbOFzNGu35hkdQD/WuUW7aGLdLlWNejlOWYfCx9tRk5c6Tu+vXs vx1Ma1SUnyyVrHQ20cVo8kqSEFgcmtTwzffaZ3gdsNu4PrXFnUlbC7qkt725tbsPCny8c81604Rn FxkrpmUZOLutz1ia13sA7AkelbVjfR+WsMiq3l5KkjocYz+RP5157pmuNgNcnBIHU1dPiO2gcnzV +mazo0KdGHJTjZFTqSm+aTuzotR1DaJC2FUA1x2jMdS1ia9I4PyL9B/9fNYvinxfG8XkQPln4JBP HNdF4MeH+zlZSCea2IPMPilIW1rH90EfrWH4Ek8vxBAc9WH8xV74jTibXpecgEj9TWP4TYprEB/2 1/nQB734ltmjiiukXlGBzW5pV9FPbxNnIIHaotYaF9FQyMBle9cL4e8RxWd/LZSuNgPyMT70Aeta hqFzaadCdPSJ5GlCsJQcBcE9j1yBVLT5F+1G4vseeemOg69PzqvZ6nbSFPMmXYDknriq/iOZLaZJ IJFbKZ+Vgf5exFfMZnRr4vHLByk405Rvp3XT9fuO6lWpwoaR97v1Dxl4gt4rAQRgu7Eg/L0GD/jX jczF5nIBUY6V0N/qN1c3bsyZRc5JzXK3GoIZmxgZr2cvy+lgaXs6et92+py1Kjnub3h69NpMCY85 4P516bbXUv2dWU7oz69a8k0y7MsTCIBnxXW6Lqdw0QimyoB6E1risJRxUOStG6/L0CnVlTd4s7HT IIv7RW7ulDMpzGvYH1+tamp3EAbdtyx71z63iDadwAHvVbVtetoYGZpVBA6Z9qrD4alh4KnSVkOt XqVpc03cz/Et59pngsYznzGG4e2c1X8VqLTwhtHUqR+hrI8PX8WpawbqZx94BQTWz8SplXw3GE7k /wDoNbmR88y/64/Wvb/hevn6HcR+hGPyrxB/9ca9z+D3NtOD0yP5UAadjiw1+4ifgSHIrs7Axecr A1x3jVEgk+0wMBJGcnFWPD+vRXsCMHUPxkZ74oA6rWNRhglFlDC7TyIHDBcrjJB75zgHtVu2utNs rA7yARnORknmsSaeOZhIdvmAYDd8VzusahLBCUUljXiZhk7x8l7ao1FPRLt/mdixEIU1GnHXq/P/ ACOf8WONSvGmgj2qGYDIx3Nc8NOnV0LYwfer3iDU2tYI1GN7DNYMGtzG4RnIwOxNevSpRpQVOGyO Wc5Tk5S3Z6hoNy0SqEG10wPY100d3PczorqmwYyCOvrXBWt7IJYJIsFWUZxXV2lwfL8wn5iOlY1c Fh6tVVakE5IqNWcY8sXodXcvC9rhzgH0rmtQnt7WCRweAD2qSa8X7NtdwMe9cNreq/a9QisInBVj 85B966jM2fDVs1w0t0RguxOfbpXknj59+vSe2f5mvfdFgii0siNgcDt6189+NX365OevzN/M0AZ/ h3/kLQ4/vr/OvoLWbFp9GEmMjivn7w4P+JrD/vj+dfVNvaxzeHYxIQAR3oA5nQ3SeyjAPzKMV1Vv aw3MS+ZguvQ5rzBb9tB1ySBz+5kbCkn3rtbPVEKb1kGD70AWdTtQtxEpDGNs7mUHjHv2rkvFFnZK hSzUmVvvH1ro7y9Zoyd/GPWuL1PUVgkkklZRjOM1wSwPPiliJTemy6HXHFyhRdGKtffzMey0nVZ1 Zo2G0AjlqLGwuoL4G4+YIcHmubl8X3sM8ggcBG7ZNa/h7xEboyJdMNx6HPWu85D1CxeeREWOVlUd MHFdJbWsfkAPzjmuL0m5YoBnA9c1vjUNke3d29azp0qdPSEUvRWKlOU3eTuO1CGN5mO7gHpXHaji 91y3gjGQhBNXdc1tLKGRy4LHOBmo/BNr9qnN3dHDu3etCTM+IkRt/Dkaeuf5V4U/3zX0F8XAE0qF R0JP8q+fW+/QB7D8JIjKk6f3sfyrodTthZa6kjjAyVJqh8FIg1w2e5H8q7n4haQGtzNAcyBugoAL EK2FJyjdamuNFjRC8IyG681yvh3Xkmt/KlYLKnBBPNdIuqkLhZBj0zQBzWp2f2fc2MVykS366grQ D51OUyeK7TW7lZoSSRnvXmmu+KWtplWzb5k43ZqUrNspyukrbHS3g1l7ZhIE8sgbgCOtWfDXn20x Yna54HAPH4152fGeouy+Y+VzyMmu88ParFqISRCAykZANE4RnFxkrpijJxd07M9Ds47i5cPNIz8c Z6D6DoKs3FlFaxsY+Hbqaz7e+aKNcEdKiu9UBUl5AAPeiEI04qMFZLogbbd2ZWubILCTJyzDAp3h /TTHpjNjAANYD3r67rkcEXMMbYY+pz/9avU49OhttAbYRkDniqEfLHizP9tT5/vH+dZumLuvoh/t D+davi4Y1mf/AHm/maoaIu7UI/8AeH86APoBLB5PDEMu3OOP51g+H41hu5ojwQcj869S8O2MV14R VJMcjg15brUb6LrXnAHYWKtQB1c+nw6hb7W5cVzdzpctlKWiGMds1r2esW8iK6Srk+9TTagkyFW2 EEdaAOOu9UuM+WoOVPPHSqdxqzbY1ig37Tks3ejxJqUOnpJtI3OTgZrgG126DNhuPTNAHoUmozTs jQx7H43KK6qw12/m037HBHFAGwPMj3K4wc8HP+c15NoPiB47sCc5DEDJPvXrGkTQpEJBt9Rmsa2H pV7e1jezuvUuM5RTUXuaVtpAnBmucls7izHJJpl4o2lRxGvAqV9VBXG5QPTNc9r2tJHF5URDSvwA DWyVtEQVNNthd6y7oM5YKKp/E6Aw2ECEdM/yrt/AWjKQksvUN371gfGSNVRAo4BOPyoA8FPU16T8 LLcy6ltHcgfpXm7ffNeu/B6INqqf7w/lQBr+MdMMbSKRjDZWregSq1kik/KQAfyrtfH+ko0Ec6D2 bivMNPvhptw1vOdq7vlJPagDT1bRcS+fCOpzkGq7as9nCFMZz9M1tR6rA8I/eKw+tZWq3NqY/MDI MelAGVJ4lhgtppZwyOAQqAdaydJ8VwC6kSdSquT8wWuS17Uzc3rhD8oJA596yluXVgQaAPYtP8Qy 292siQiRVbK7lyD+tbuoatqfiKOKGYJDAucpDuUPkj7wJOcY4rhfCWpRXNuI5SAysOv0rv4b2CJQ FKfWueeFozqxrTinKOz7FKclHlT0HR2Menw4Vf3hrlNdTz76CMnJzk/nXQX+sQRqzPKufrWDpcMm qamJCCQ74X6V0EnRWlgy6FNNt4PANeD6yu3UJhj+I/zr6vv9OitPChjXHTnivlfxCu3U5v8AeP8A OgDHooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8AAn/JPPDX/YKtf/RS10Fc/wCBP+SeeGv+ wVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooA6nwZEW1ONh2Yfzr3fUFgubBI5ByRXhPg5ymoJ9R/Ovd/KE9oknqMUAeb ajo9vFdsyoMZ6VUvdDN7ah4I9sg4GDXVaxb7WHByTW/4Y0wTSIrDIOe1AHnmkx3sdt5TxgheORV6 5g2Qh3UA+1eran4cisY/Ojj4YZPyjrXLX+mQyxlMAEn0oA89eGZW+RuD0qSGBkZQ5yXPeulu9DCB SpPJA6VZh0JInjkZiceooAxfss6tmNVOKwrjSLvUdQ3MvyKecGvVLWwjZwqjLNxXSTeGobHSHk8v DsMklRQB4fJpkaERmIccE1u6Np9tZKJNg3ZzmrOq2ZiuCQvVq07OzV4VyOTQBS8WN5/h8jkADj8q 8NkTF2F969v8Yx/Z9J2g9QR+leKNzeigD3z4U2xGlynHBxn8qh8Z6TA12ZNmTnnmtD4WyqNPMefv Afyq74qtA+8r1z2oA86hhJUxhAAeOKdb6t4k0+6W0iu5Psw4QEKcD0zjNaWixhr0q/Y11sOmR3Lo QoJHtWdWjTqq1SKa81cuFScHeDa9DmXkvDzOgdz/ABGsTUrRpZivIAFej31kqLs2kEe1Y95psMp3 NwcVcYqKUYqyRLbk7vc4IaaykNn9a3bOOUWyOqgnpz3rSi02GRypJwK14bONVjVVGF9qYjjdajvf s4ZFwfaqFlY3Sw+dcZLH1r1f+yBdWquyfKOelYep2IiicBR7YFAHEw2UVzMFljHXtXp/hvTY7exx ED+dcDpkRmvsY4B7V6hozCK1JIGBQB8+/EKDytdl+p/mazfB8Bm1eHH95f51tfEVhJrMxHqf51T8 CADVoiexX+dAH0PrmkrceG0LZBwO/vXkL6YIpyqrznqa94u3Sbw/GCRjH9a8m1aP7LeFgON3NAHP ztq9km62fPPTrWhpt3qt7EWlABHtXUWcEUtur8EmrUFskYYKoGaAOSuVuZrOSNkCt7d65k6IWYkg 5+tenXFsoJbHB9q564eCORvu80AYGj6bNb3eQPlFb4W7fdhQoH51e05I3Hygc+lbUFvhAu3LHtig Dzu6vNb+0eTH93PXApJLW4Me64bcT1zXetaxCRgQAT14rm9e2wukS9zigBnhDSI7jVI35ADDAzXS fFHTxF4chC9s9/8AZpfCNuLa4hY8/MOtaPxUkRtBjXjv/wCg0AfMMi/6Rj3r3r4O2jSW8oA4OP5V 4VKo+24HSvob4NOscRyfT+VAGN460+8g1aUKxMZY8ZrlLe1vrX57f5cGvYfGkMdxescDOeTXIWsc UkpjOPyoA5q31XX1lAIBTp0FaF7cX8uxdoK45OK6R7eNFGFH1xSrEjIRtGaAPOtU0m5uJgz5I+tZ /wDwj82eB+tdzrcwtwsnA9ayo9VSVggYdcUAFpaXlrbIE5ar15fapDbI0AG48HiugtEUwrwDkZzT 3jQtyowaAODe6125BSUhB7AVVWwuI5t6/wCsJ6k16BdQR+WWIGBWRbxpcT5GMA4FAHa+DtKuIdBa SViSc968C8bwmPWp/dm/ma+n/D8iR6C6E88/zr5q8eYbWJyP77fzoAxvDCFtTi/3h/OvpPVob2Pw hHLbHBGP5186eEwP7Thz6j+dfV6GKTwkinB4/rQB4Hqs11qHliRMuh5IpqNrMSBLc5H4V2d3aQJI WwBz6VNBboVDDFAHLRX+sLGwn24A9Kwr201DUXLSc9cAcV6DPChOCBg1FceTDD0AypoA8tbw5MRk ryT61YtfDt1FJlOMe9aEmtm2meMnofWtrw7qIu7iR5CDxwKAG6ZPqdsDG+AR0pt3d+IH+ZAu36Cu skjjZVbaPyqQQqycCgDzm5iv7hw11liDnHauk8M3Go3OqW8EY2xhgDgVoXVvE0uw7c1u+FLeODVY iAMbhQBlfFmF10m3V+ozn/vmvn5x++xX0d8YXRrKMAjqf5V86SjFz+NAHtnwdDhpNn3u35V0es6r erq1xBc/6vcccVifBhlW6IOMZH8q7fxdbQm/LhRk+1AHld/p1wbx5rXK7uabBBrceW8wYrrtse7Y cZpzQDaaAOGvJdSnBjaUL2OBXO3Hhy5d8lc575616SLaPz2cqK5zxNqiQvGYiAVJyBx3oA5GLw1c uxwp4963NK0u/wBPbfFhR1PNVv8AhInl2RghSTzg13unTQXtmFGCdoBNAGZHcatPGVjcAgelZ1xb 605ZJpcr7YFdla26R/KByOKkmhUcmgDl9JZ9KiYquZevPNeheGr+/u9FuJLnG0k44+lc2lvG7Z4x npXe2SRw+FnxgcmgD5j8Wj/ibTn/AGm/nVHQh/xMIj/tD+daHivDanKR0LN/OqGhHF7Gf9ofzoA+ j11S6sfC8HkfdPB496wNSK6tAfNHzMOfrXW6WiXPhNQQD7Vy04EWQMAKaAOMk0S9hm/cyEL6Zp8s F9FEQZj06105RZ1BX8cVUu4AYiD/ACoA4nUdFurvDyyGQe1YMnh+cHIXIJ7GvR7iQWmnkt6HrXE2 mtNBPKG5ByRk0ARWfhuUyAEfN1611dtb39sqxtLx6VleHdUM2oO0p+8cjJ967V4w84duhoAwrqw1 GUZSfFO03RpEmE9y29vrXTG33KKjJUHy+pHWgCxa61dWt3bW9qBgsM8Vk/FSd57OJn+8ev5V0OhQ RvqcZYDIbjiub+KzruRARwT/ACoA8TcfvK9b+E8pivC69Vxj8jXkr/62vUvhY4XUgp7kfyoA9J1L xBNeXM1pOPlHHSuT1PSY7+InJDgdc4rqtetViujKoGT1rn5LgRzFCQM0Ac7Ho8kOQJG/OqV7p1zI TGkpwRyDXWNGd25e9U5mEcoIwSKAPOtQ0A28irn5m75qkNFkB3PwoOM10HiKeSW781eiH+tVr+8M 8FtHHjJUE4oAv6ZoE8CLNBIAGAJNb0dhO8YJlP1zSaHcFtPMT9eMVvQx7ohgfpQBzi+H5JrgNJKz D0zXSWJXTP3iAfIOKGVbWIsxwaaqiWPn+KgDoLXX7jUdDnEwAAJxx7189+IDu1GU/wC0f519BzRJ a+GXI4z/AI189a0d15Ifc/zoAyaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/ACTzw1/2 CrX/ANFLXQVz/gT/AJJ54a/7BVr/AOilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK7iw0HRrzUfDI8r7PBcaJc310biZ3R 5YTdHLlBuEZ8hQVQbgvAJb5jh+KLOC1vbaa0t7SK2urcTRPZSytBKA7oWjEw81RlCpD5O5WIO0qA AYdFFHWgAoq9Z6XcXbgIhOa6O08HTuoMgC5/2qAOPwfQ0mD6V6CvhCIDBkGf96opvBuQfLdT9WoA 4OiugvvDN1bZPl5HqDWJLBJCcMuKAIqKKKACiitzw1Cs9xOg0a01KUIGzfXbQW8CZwWdg8eCWKKC zgZOMMWXABh0V6BcaJ4dWVNP0+3+1Wt5p+o6hb6lK8gnVYGuvKCgFU2stsm4Mhb53wV+Xb5/QAUU UqqWOAMmgBKK1LPRLm7I2xk/jW3B4OnIBYKPxoA5Ciu0bwdLjgr+dZ914VuoQW2AgejUAc3RVm4s prdiGUjFVqACiiigAoorvLfw1aa+NOsoNMj0q8ubiM28cEz3NxJZ+W7yzTRbid6qsbKFWPzA52Kw I2gHB0V0njjRF0HxElrFpt3p0EllazpBdFi4LwIXySBk+ZvBwAMgjAxgc3QAUUVJHC8hAUZoAjor XtdBurgAiPj61or4SuiM7F/76oA5eiujm8LXaDPlg/8AAqybnTJ7diGQigClRSlSpwRikoAKKKKA Citzw1Cs9xOg0a01KUIGzfXbQW8CZwWdg8eCWKKCzgZOMMWXHUS6B4b+wTxWCx3CTW97d2cszTC9 mjiefy5I8YhEW2EFxIBJxLtHMdAHndFFFABRRUsVtJKflU0ARUVrQ6FdSjIjP51K3hu7A/1R/OgD Eoq/PpNxD96MiqTxsh+YYoAbRRRQAUUUUAFFdotro97o1tqFloUc8sd7HarZWk87SOXWXy0uS333 Zo1I+zhQR5gPlkpjH8UW9jbXtsLSO0hna3DXlvZXHnwQy73AWOTc+4GMRsfnbDMwyMbVAMOiiigA ooAz0qeO0lk+6poAgorRXSLlv4DTJNLuE6oRQBRoqSSF4z8wqOgAooooAKKK2PD02mx3hj1C1sZf M4WXUJLgQRAAkkrb/vCxIUAg4GTkHIKgGPRXpGr+E9J0yxlsra1gkvnivbgQXc8q38SwTTp8rIDb FVSDcwOWbEgUjKY83oAKKKKACilAJ6VItvI3RTQBFRVj7HN/dNRNC6dRQAyiiigAooooAKKK9Qu/ h5Fo1lpZv9JvhNF/aD3stwrpHNJDZpcJEMYxHvEse5Wy4R2VgMbQDy+itzxFbWiRaPf2lrHaDUbI 3ElvEztHGyzzRYTeWbBEQJyx5J6DAGHQAUUUUAFFKAT0pwjc9BQAyinGNh1FN6UAFFFFABRRRQAU V2nw38Ir4o15Hu7K7u9Ntri3juIrZWJcyyBQGZQSiBRI5bGMR7dyl1NWLHwFcx+BtZ1a+0jUmvEt 5JIP3LqlsIrmKOQvgYLkeeCpIKCFmKkMrKAcHRRRQAUUUUAFFKATRtNACUUEYooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8k88Nf8AYKtf/RS10Fc/4E/5J54a/wCwVa/+ ilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooA0Itb1GCWwkjuMNYRNBbgopAjZnZkYEYdWMj5DZBDEHI4qPUtUu9WuFmu3jJRA kaRRJFHGuScIiAKoySSABksT1JNU6KAHIjOwVRkmuw0DwqZgJ7rCx/7WaPC+gKyG9uhiNRuHNa9z dXGtT/Y7EbbcfgTQBLPq2n6UPJsoBJIOMrjFZz3usX7ZQuiHoAK6fTvB6WxSScbjwTzXWW+kQeWg SMcUAeWppWoO2ZLllOO+eaRrXV7Y7o5nI7da9E160hjEI24IcCtKPTYZrSMhM8UAeXQ69eW52XkB kXvVma00vXISYgqS46HqDXaX2gWtwpUx4Psa5PUvCk1kftFqSNvP3qAOE1jQp9OlbIymeCAax69M trlNSjNhfqPOAwGxjd2ridd0l9MuypHynkGgDJrU0rxBqGjW9xb2n2RoLh0eWO6soblSyBgpAlRg CA7jIx1NZdFAGwfFGrGzmtRNAscu8ZS0iV41ckvHG4XdHGdzZRCq/OwxhjnHoqW2ga5mWNBkmgCS zspb2ZY41JJOOld1pnhq0sLdbi+ZBxnBqWwtbXw5p3nTLmdh8o65NQRWGpeJLkyvxEOVA4AFAE1x 4kt7f91YWucfxcYqgdQ1m76Myj2FdjpHhG3gP71ct7muiTQbeONz5fGKAPL2GswhW85skZxSx69q FswFxEZV7/5xXe6VpsEl5OjjIViBz0q1c+HbWRG3xj86AOGjvNK1ceXPEIpCP4sdawda8LtApmts MnsOldZqXgxW3vb5B6/erItr270eb7LfDdB7jJFAHAOjRsVYYIptdl4j0NGiF7aDKMNx5rjSMHBo AK2NZ8T6lr+86iLF5HlMzzQ6fbwyO5zks8aKzZyScnk89ax6KALF7fXGoTrNdSeZIsUcIO0DCRos aDj0VVHvjnmq9FbGhaRJqV0AB8gwSc9s0AJpOhz6jKAFwvqQcV2sGl6XosIa5KM4Gcd6bfalDosA srFQZx1yM4qjZeH7/VZRNckkMeTmgCzceJ41/d2dtgDjJxVZdX1aZjsUj6D/AOtXYWPg63iRPlJP uas6rokVkkflDaWIB5oA4ZNf1CA/voi478Vdh1vTb8CO6g8tj/exXYt4ZtpbZGKZJHrWJqfgmFoy 0WQf96gDndT8MW9zEZrNlPHQc1xd3Zy2krJIpGDjpXYNBqfh6cEcw9SDzkVcube18RWJkhXEyjLD pzQB51RU93avaTmNxgioKANTSvEGoaNb3FvafZGguHR5Y7qyhuVLIGCkCVGAIDuMjHU1IfFGrGzm tRNAscu8ZS0iV41ckvHG4XdHGdzZRCq/OwxhjnHooAKciF2CqMk0gBYgDqa7Pwx4fRk+2XQxGnzd fagCnovhaa9xJKAicHLA11S2+j6OmHCO47DGaoajrskh+yacoVF+UtjrTtN8L3d8fMk79yaAJJfE 0Y+W3szj1OKi/wCEhuE5a1BU/wCfSums/BsaIzODkD1ptroYvJ5IXA2xnAxQBhRa/YXPyXFttz3O MUy70HT9SiL2rIGPOK2bzwUrbtmRj3rmbrSNQ0iXfESMHrnIoA5nU9FuNPkIZTt9cVlV6TaajBq8 RtLxQsx9sA1yGu6NJp1wSB8h5HNAGLQaKKAOgXxrryTxzC6g8xN5cmzhP2gsjRs03yfvmKu43Sbj 87c5Y5y9S1OfVbhZ7iO0R1QIBa2kVuuMk8rGqgnnrjPT0FU6KACpre2kuJAqKST7UWts91MsaDJJ r0jSdHtdCshd3n+s9M5oAytG8Gs6iW6IVeuCDXQmLRNLTawRnHGBgmsi81m71OTyrcbIjwAByas2 XhW6usM4688mgCZvENgDiKyYj8KVNc0yb5ZbUr9QKvy+HDpqqWjDbvlHPeibwY8kHmqMMRnGaAM+ fR9J1RCYGjVj6VyOseF7ixy6DcnXKg1v3ehXunN5iZGO4NTWOukj7NqABQ/LuA/nQB5w6MjFWGCK bXbeJPDqGP7ZaDMbfN1rimUq2D1oAAB3q/pmtXukeaLVoGjlwXiubaO4jYjO1tkisu4ZYBsZAZgD gnOfRQBsHxTrLWc1tJeeb5u8NPLEjzgOSXVZmBkVWLPuUMAd75B3NnHoooAKt2enz3kgWNCc+1Wd G0mXUrlUVeM88130aWHh62VSMzEdOvNAGXpng5VUPcsoHpg1rG00WyXDmMkdhWRcajfanJsU4Xsq jFWbXwzdTr5jrwOetAFz7TojfLsUfgKbLpGk38f7lowxpq+Hxc/uoUIdfvEms+50O8smLKpGPQ0A Z2q+E5YMvDhl9ga5aaB4HKupFd7Z61cWzeVcjfGODxyKl1PR7XWLQ3Nn9764oA85oqe6tntZjG4w RUFABWhaa3qNjFaxW9xsjtZZZokKKRukVUkDAj5lZUVSrZUjIxgnOfRQBc1LVLvVrhZrt4yUQJGk USRRxrknCIgCqMkkgAZLE9STVOigDJwKAAAk8Vp6fo1zfSAIhIJ64rV8O+G5L9xLIuIxnJzXXzX1 josfkQLulA/DNAGVYeC0VA1wy9M8g1o/2DpMQw0kX51lTahf6k5AYgdgoxUkWjTcm4ZlyOOaAL7+ HNMnH7t48+1YepeDZIwWhww9ganm0q7tsPHuA7EGpbXW7y0YJMQ6DqCOaAOHurKa1crIhH4VWr1G a3sNftm8sYmAxjpXBavpEum3DKw+XPBzQBl0UUUAWLC+uNM1G2v7OTy7q1lSaF9oO11IKnB4OCB1 ot764tILuGCTZHdxCGcbQd6B1kA56fMinj09M1XooAKKKkhiaaQIoyScUANVC5wBWzpvh27vyNqH HqQa6fw34PDRrc3QwuAQM10Vzrdjpkfk2aB3X24FAGDaeAjtBlZR+Bq0fAsBGA6ZqCbWNV1B8K4V T/dUCiaHU4EVxM2SM9aAKWoeBZo1LREHHoDXJ32k3Fk5EiEAd8V3dr4kv7V9k5WRR1BWtYT6Zr8X luNk2OQeOaAPHiMHFFdV4i8MS6dIzouU471yxUg0AKB3pWxikBxjikJz2oASiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKAPt/wJ/wAk88Nf9gq1/wDRS10Fc/4E/wCSeeGv+wVa/wDopa6CgAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACtTQ9NfUb9IwpI9hWXXo/gTT1h0+bUJFHyqQOO/FAEupOS8OkWo4XAfb3rtvDeiR2EC5X5jnJI rkvDdubzVJryTnLZGfrXpNo6gCgCae1yvAqxA0VlbKZztMjCNOCcsc4FXoI0lTPBpl/bIYlkwWMa nanVcnHOPUY4PufWplzW93cunyc3v7HG66GkeNv9sVv6I6lBGcfdFc/qSynyy6kZcVt6PDKsqMRh SBVEEl1at9pICnBplxar5W1hnI710DRq3JAzWReMoYgigDzLxRovkyG5hBHO7jjFZV3bjX9CkkIz cwqcgd67/VohcQuvGOa4nRwbTX/sb/6ub5SPXINAHmU0RhmaNhgqcUyt/wAXWAsdcmUDCliRWBQA V2nhHTUiV9QuBhEXIJHFcnZQm4vIowM7mAr0bV4Rpvh2zs4+JJQC2KAINPtJvEmq+c4IhRsD0xXp 2m6bFa2qxooGB6Vh+FtNS0sUwAGOCTj2rsbWMEAZGaAKi2ubgbRU19cwvbSLA+4oxifgjDDqK1Yr QK+4jmsLVES1kkjiVj5jtI2fU1L5uZW26lx5OV336fqYmkv5d9MWH8Zrq7qFZLUOvpXJWG4yzEDn cTXaWCmSyAYfgaogx4bXgsRXP+I9Civrdiq/PxjAruJoAiYArJuVGDnFAHkmnSta3MmmXWfKkOBu /hPSuT8QaY2nag6EEKcEcV3ni2y+z3SXUYAI5JH1qj4rtFv9Ag1FQN2AG9eCRQB51RRRQBJBC08y ooJJ9K9CQp4e0SNUA+0zDGO4rB8GaX9u1FWZQVUEnNb3lHWfFDRdYYWwB27CgC/4V8NtcyC7u1JZ iThhXfrbJGm1VAA9BTrG2jiiWNABitVLMyJgCgCtbmC2hEtw+1SQqnBOT+H0rE8QMXkTHTeK6i6s kFigc48txJ064zxXJapJvZP98VnFz52mtNLfqayVPkjy/Frf9DoNJVZrYIw6LUF1EfN24pNHlKzK vPIFbM9oryb8VoZHMajpUNzbFHUHI9K801GyuPDupmeFT5ZbkdiK9kuYM9OMVy3iTSkvLJwQNwBw fwoA848UWUd7aJqVsuVI5x2riiMHBr0fQo/tVle6bKMsFJUH6VwWo25tr+WI/wALEUAVaKKVV3MB 6mgDZ8O6U2pX6rg7RyTXV6vdNPKmlWPKDCts7+36UzRLcaP4dk1AgeY6gL+da/grSPPY3s43EtkZ FAGl4d8Kx28CSyrmQgHBA4rq7eALIECgfQVat7faRheKvRWObgPjj6UAVrhoY7aRUbLjIYY6GsDR iDfTk92NbOoR/ZzMGIJkkZxj0PSsGxJS4lYHjeaww8qsoXqpJ3e3bobV404ztSd0dNe2oSPeB1FY 0lktwSHXI9xXSxYurEd8DFVGtfLXpzW5ieU+JvDhtpDc2ykAY6DpVOJ013SpbaUD7TEvHqeP/rV6 de2azIUdQQR3FeX39s2ieIomXIjduewxmgDgbmBredo2BBFQ11njTTBbXvnIuEcA8CuToAKAMnAo q7pdq15fRxKM5NAHYeEtJitrZ9RuhtVVyu4dTU7m68RagAqnYOFUdBVjW3+yxWmkwcMAN+O/+c12 3hfQUs7RWZQZTyTigCPSvC8OnxLIy73HJJArqbO3g8sMxVFHUmpRbkpjHFTvp0jWK7WVcSBm3f3e c49+ayryqRpuVNXl0RrRjCVRKbsu5z/iZ0V4VTBAZelbNjCt1YLjqFrC1rY7JxnDit3QpgPkPcVp G9lfczdr6GbeWaMpjKg/hXIa/wCE1aBp4FO8AnAA5r0m8tP3+4Dg1QuLcupBUYxTEeRaVem1lawv eIm+X5v4T/k1y/inSTp1+doOxhkHFegeMtD+zg3cS4yctgVhahD/AGz4X+0nDSwghj360AeeUU51 2OV9DTaACpraBridY1BJNQ11ngvTluL0zyAbEGTmgDorZIfDmlA4BuZFyoI5qnYaXda3eNKwbBOS 3pTwJNd1kIufKDbR7CvSNM0uKytUjjQDgZwKAM7SdCg08BdgJPcgV0iWaQ2UjlQOOOKS3tvMl24q bVVNupQSMVZehPQ1E5STSSvf8C4xTTu7HP6MqNqNwuByxrTvdPRSQyAg+orJ0wiO8lb/AGzXW3MY uLQOo5xVkHnureGYboM8Q2v1yBXII11ol7hlYDoynoRXrht8KcjmuY8RaCt3btIqgSDnOKAOI8S6 XFe2Q1C2AIK5bHY1whGDivRNJky8+mzciQEAHsRmuH1W1a0vpIyMYNAFKiiigAra8O6Q+pXqrtJU YJOPesZRuYCvR9EiTRvD5u2A8yVcL696ALWq6jHpsC2FkRvAG5l7VV0jw5c6iwllV1QnnPep/Dmj Pq92bmcbkyfvdzXo8NokEarGgGPQUAZum+HYYVjRIxkdeBUniCyigECbRncueK6eyg2Wsk4XlUJX jvXN+IJDLLGSckOM1HN73LYrl93mLbabFNYRsEB49K53VPDUF0hwgVuxAFdrozrLbmJvSqt3alJm BHFWSeP3dhd6Ndq4DDByHFaEyQ+ItMYEAXMa5IHeu61XSob60dGRdxHHFecus+g6tg52BvpuXNAH CXls1rcNGwII9ar113jSxRLlLmIDZIuRiuRoAKKKKAFAJOBXfeCvDqyn7bdDbFH82WHHSuP0iza9 v0iAzk16ZrVyNK02LTLb5XdRv2/hQBV1vXHupTY2WRCpxlT97tVjRfCFzegS3Csi/wB3Fa3hPwsp jW8ukDMwBAIPHNd/Z2e1wkYGPpQBj6Z4UtobSQlBwO6j0qrpGjwXF7cIwDBWIAIrrrmZrdJYfLZP lOdwxn3HtXP6FIU1KducFzxXLhMXHExlKKas2tfI1q0ZUmlLqrmVq3g+C4LgJtIzghRXB6r4fu9L lLorsgPDDtXu+oWZZQ6YwwrBvbCOaJo5Iw2fauoyPONJ1OHVrc6dfFd5Hyue9cH4l0V9LvmBU7CS Rx712XiHRZNGvRPDxGSMEZ4NLrUceu+GvtYUedCu1/XtQB5b0NFOcFXIPam0AFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAfb/AIE/5J54a/7BVr/6KWugrn/An/JPPDX/AGCrX/0UtdBQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ACqMsB7169p0f2TwJx1bk/nXkUYzKn1Fe0JHv8JLGP8Ann/WgCTwfagaajnqwrtbWxL49K4vwldK dPSLuo/rXc2Au7mCVraN2EaFiQpOSP4RgdeelZ1KsKUeebshpN6I1reNIV2gjP1qtqOpw2BXzs7T xkDOKzdOkuxeSG6EiFOqOCCO/Q07VkS5TLDKkVaaauhEF9HBq2yS3uY9i4PX0qxBqVpZItv5wlkH GF5xXm96L3TdUnjtpZBE0ZbCk4Fbfhi2Y2cc0pLSy8ktyetMD0aORZYg4PBrOvoIS6hpkVnztUsA T9PWpYJBFEIc/NitvTvDdsVjuJiZZsA7mry8zzH6nGKhHmnK9l6bv8jehThJt1HZLsFnoGnz6XEH gUlowWOOScV4p4msl03x3aRxcDKn9K+hyI7aDbwFUYr598STC++IMLLyIwvf/ZrwcilU+vS5nduL ctet1Y6akubDtN6Jq34/ocr8SIMXUE+Pvjn8hXA16P8AEfm2tD3yf5CvOK+yPPN7whb/AGjxDbKR wGBrvfEEf2jxFaQfwD/CuQ8ArnXkb0/wNdlrhEGu2lwehyM0AdrZWpRFVR2FbtnbNGQzHA+tYdhd l/LEYLM2AoXkkntV/VjqNkkPmRyIkqhgwUgAn+Ekjrx0rOVWEZKEnq9h2e50BcLGWzwKypZbS+ST ymR5ACMZ70+GR/7M+ZiWIrg9TW7026FxZyOMtllUnnmtBHSadps9vdyPIiqhJOSa3ra7t5WMcLhi ODiuD1DVdWlitosyR+YBuOMVvaFEYHXBPP3j60AdJOodMZANZ1vpBvr7yDLhRy+3kgf49KupZHU9 SW3aQrDsLOP7w4GP1rrdN0e10+ILAiqK8PMsfWVR4XCr3ravtf8AU6KcKfJzzevRHl/xE8N2tpoU ssQIIQkZPuP8a4C3h+1eBXRhkgkj869Y+LMqw+GpsHkoQP8AvpBXmtlF5HhdoyP4Cf1oyCVR4aSq ycmpNXevYvFO/I7dP1Z45INsjD0NNqS44nf/AHj/ADqMda9w5D1DwJbCDQri6x8xBUfpVzwVZmWe 6uSOWbr+Jp/hFB/wi2wfxE1Y8GSiNZ4uhVz/ADNAHcWsMm4YU1vW67IwD1rEiu3SNtilto3HA6D1 pun6lNdXqgsdmelK6vYDcnEboY3I+b3rldb07y3jWKNmwQeKTxfd3dpNby20jLhuQO/ArV0m9lv7 JZJoufUimA7TLdI4UkkUoQO9a6lWXIxXG61qN494LaItEgbnFdJbSt/ZYbPOOtABeQOwyhrLu9Lu vsjXDpiP1z61qeHIpNWuplmkISEqSP72c/4V119p0UumPb4+UpgcdK+ezHOp4ev7KlG6jbmflvZe djop0ouN29WfOljD9l8bNGPusOR+BrjvGdsINenwOC2a75bd18Y3TPjMSrkj3XP9a43x8o/tgsO/ +Ar34SU4qUdmYyi4txlujkKtadEZ7+GMDOWAqrWt4bQPrlsD/fFUSd74oh+z6Dp1knGcZH4ZrstB tjZ2EMYXjaM/lXM+LEyLF/4VcD9K7nTHQQRnjG0fyoA2LFS+ODitUAAcCsKTVEt1wg59qvR3jNpr T9wKAKurWBm3yg8AGub02APeSxN3Y10mjaj/AGrZsZFOM45q1HpttDK0qxqGPPFAEtlbi3hCCpJo ty8Dmse11Z59aa1AIVB/j/hVrUdQNrKF7GgCpNbzzSbI48tXnvj2xYWqT4wyH8uRXqmmXC3EpI+9 tIx+VcF8RY2itnhdSDI/ynHXkf415dDMfaY6phWrcq07vv8AI6J0OWkp9zkPFlv9p8LWtyRlgAD+ teZnrXrviKMJ4PEZ/hIryNvvH616hziV2vw90/7XqwkYfKisa4qvTvhnEFt7iXvtI/lQBJZ251Px tcsRlY3z+teq2jKNqgV574ViA8TakSBncP516ZaRJgMQBQBqwQI6BiKfexs1oyoOcUy3uUZxGgq1 vG/Z7UAefatBLEyeYMHcK19ItZPMjlX7pANWte09bhgdwHSr2l2gtbUZOcCgC+Yg64IrKvUMeeOK 1lkzFvqjLNHPkHFAHKa5Yvc6c29cJIrbTnr2rz3wzAXtdVsWHO04H4V6xq8/mQqjDCxptFeb+HIx /aeoyD7rNj9KxoOrKF6qs9dF2vp+Fr+ZUrX908p1WHyNRmTGMORVKtnxMgTW7kD/AJ6GsatiRVGW A9TXo+lW507wdJc9GlAAP4155bLuuYx6sK9U1OPy/A9rGv8AcU0AW/A1kq2zXDg7mPBr0O0RZML/ ADrlfDNuU0uFVxyo/lXZ2FsUAZjQBbtbURHcRzWLrZYzNxxiulTnFZurwIYXcgZCmgDirMMZZcdd xrsdJ3Na4euV0wZ1KRe2TXcWsQSMADAoAqT2wVi4HBrMeznvXeKCPe4XcQCBx+P1roZlDKQap2t5 JpU0rEPJEyEeWGwN3GDj8MVz4udaFGUqCvNbJlRtf3tjxXWLZtN8UwPgqGfnjpzzWL49svI1LzAM B1U/pXX+MEMusWbsPneQknHXmsX4hoDDbv32D+tbxvZX3Jfkec0UUUwLulWxur+OMDOTXfeIgVks dNj42AZH1ArmfBMAm16LIyBk/pXYX0JufGSgjIXB5/CgDt9AsUtLCKOMYNdCtoX2lRms2yiO1QMj iums4isY3daAIrgfZtOK98Yri9RAkeNRjJcYycV3l/EJLVh6VwerLsZQP7woYGlpcjw3QQgg5wQR giugvLYTICBWFpiGQxZ52gYJzwM9B+Jrqwo2ikr9QMD7C80qwxIWkboM4zXDfEHQLq1thcywMhU5 JyD/ACr0+K6OmagtwY96EYcDrjPb3rP+Ik0F94Wd148yPcAcZ5GcV4+Nx+Iw+Kp01Fck2l1v59en odFGlGad+iZ4nfwG/wDByzdWiyCfxrz1hhiPevUtMiD+CbxSM8EivMbkbbhx717JzkVFFA60Adz8 O9PE+pNcOPkjXJNbOlQtr3icytyiyZ/DJpfAkHleHru4A+ZkAB/Gtb4e2wJmnI+YvxQB6TY2yrbr GBwo6Vq6Xat9oyw4Heq+n+UltLK5wUAwCcZrobeERrwACawp4iE6s6S3ja/z1RTi1FS6M53XXE10 IowC7DYPU89Kw7DSdQjuZSIOrEj51/xropLM3GsryBsPmEn0BrCOr3cesXIjcKkcpUKQCOOPrzXm 4rE4r6y8Pg1G6XM+a/otu5rCMHDmqX8jp7NvtNkyMCGXgg9RWZJZkStuzitDRX85ZJOcudzemT1x 7VYvYWChkUHnJz6V6ftJey53HW23n2MlFOVrnI+IfDVxfaXIfs5KYyDkV5ToAdb2+0yQEbwRtPqM 19GC/t7qwkQfKwBBViM9OuPTmvBTbC2+JE6qchmL9PXJ/rXk5ZmVfEV50MRFJpJ6J/Natm06KVL2 ke9jyzVrc21/LGRjBqjXS+NoBBr04Ax0rmq9w5wooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7 f8Cf8k88Nf8AYKtf/RS10Fc/4E/5J54a/wCwVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAfF/rk+or2vTvn0CEeqkV4 rAMzJ9RXuOj27N4XhkA+6aAOY0WeSK6mjj+8rY/WvU/AfiJUlk064Vldi0gbHH8IxXmNrELXxHJn 7sh44966TULMhBPEOa4swwUcZQdGTt59maU58krtXPUfEUSsI5VwCVKk469Mfzrl5RL5RQqeDxXP afeTSCNprh2SIYVWckKPQelaV1qomgCxzqGU9AetZ5Xg6mDw6o1J81m7aWsn0/r0CpJSd0ipqBS2 dNy/MxAY+1XrOIJseJfkIyKxNWuo5/JZpVDEgHJq6l4FEKpMAiAZbNeiZm/b72lMsnAArube5FvY K0hwAvNcrpAintzfXMyRwL0Z22rzxkk/WsfXPFbaiWsNJVvLOA1zyPqFB/Dn68dDXx2autmWMjQw i/h3Tl0V7XXyt6+Wh204xpwvUe/TqXfEnjPek1vp/wA7oxVywIC4PP1/CvKNKke512a4dixUYyT7 V1Go4tNPfLbpCCWY9Sa57w3bE5JB3SvX0OX5bRwULU9W9292YVa0qmmyXQyfiE+be2X0z/IV55Xo 3xHiMckSHoP8K85r0DE6vwGca0v1H9a7TxWh+yJKPvRv1rj/AAChbW4vdhXoviGyLxyxMv3xkUAQ +G9XMc9nM/3Y5UY8dgQa9ktr6z13TOBvjdcMGUjn/wDXXgvh+EhGTPKnpXRwSxhtkuAa8bNcnWPc Zxm4Sj13/wAtb9bm1OqoJpq52MCzGBoJF2yL1XP+FUJLQQK88yjKDKj3qnbatb2jBFlXHfJpJL4S vLG8oKvkqc168eblXNv/AF6/mZO19BbK6GqF4ZgMqTs46YrTs7SWPOQMZ65rnNOkSCWaTzF4JAGa 2dKvQ8qWjTDfM3Hzc4xU1aipU5VJbJN/cOEXKSiupdbWzpepxSLyq/LIAMkr3x71vy+PdGhGDcsT 7RN/hWPrsOhW1oyzSRC4VSFCHLgkZGQD/PiuGW3LqZHFfP0qNDOP9otOm1ppZX+dnf8AA3mvYu2j RH8RfFS+IdQtbS1ZxbqSG5ID5I7e2O9RXJEelSqOipisJoBNroYcrHXR3ds3/CPTTEctXuYXC08L SVGnsvv+ZjUm5yuzxK4/17/U1EOoqa6XbcP9TUaDLge9dBB6/wCECR4dT2aqFtcnSvEc8fRJTkfn /wDXra8F2xk8NkAZIJrM1+y80pOgIkRucUAdzp+pzW8chSKKUSIVIkQHrjOe5GB0zjmm6SsqXSZG Oa5XSNVlijXzccV0a67bCPJZUb61nCjCE5TitZb/ACG5NpJnSahpsd+Ymk6KcnmszUNWksbiOG2w IwQDxUaa2Jbb91KrMOo3dqztQZZnjmQghiMj0NaCOhktY9TiinGPM70g1C3eyaK3ZmAxhiMBvp3/ ADrNS8MLQxrIFCDLc11mkz6VLoZuriWBUdiXaRgADux3968jN8wqYGMKkI3Tdn/wPM6sPClOMlO9 +n/BOa0fVp9J1EzKpeJxtkTPUZ6j3H+PrXRan8QtIsdNeSWSQSBCRHsOc46elcVqF+kUskMBSQAk B0YEEevFcVrQlvJVQsTlulGKynDY2Xtm2rrWztded1/wTONSVPS2xb8PPLPFLeXDu8s7jLMckgcC uQ8dnOqn2/wr0PRrP54oF6JycVwPj+PbrDj/AGv6V6ySSsjFu+rOMrZ8Mf8AIctz/tisat3wom7W 7f8A3xTA9N8RW7XGlEKMsmGFaHh3UTe6fHg8gAH8quajbeXBC7L8jLg1xm+48P37GIFrd23YPagD 0MWrS4xzW9YxItp5DkHNcHp3iu3K5kcIcdCa0rbxDbNchhcLj60AdHIkej6eyQDDHkVQ0bV55p2S 4cEEkDim3d6LzMTEZIyjA9aztNZYZJJX6IT+dAHR+Ta2F955DNNN9xF6tjr147+tVtSt3u2D7Shz yrEZH5cVd8O6nBJqcQvJYlkKsUDNj04qbxjrumW6LsvLdp4z80SyAtggdhz6V4dTMa0MyWGaXI7e uq3+/Q6/Z03RTinzd/0+4yLS5l0mYSgK4xghj2z/APWrkfGOuQ+JNc0+2gjZfJLNKD05x0P4Gqmr +KJJAyW65z3yap6LZNBG9zNkzStxn/PvXoywNCWIjimvfXX8DBVZKDh0ZJ4qP/FPSDtuFeQN94/W vavF9oYfDI3dWOT+deLyjDn611mYyvUvhyP9Bl/H+leWjqK9g+F1t51rIpHXP9KAJ7YjTvFMjNwl wBj65Fd9bSb1GW2rXH63p7TbtoKzwMWX3qlaeMfJTyrqPY68HOaAPUbSWGFgxatQOjKZVOeK8xj8 T2syrhz+tdNpmuRNZN5Tq5A5XPagCtrd9LLdptYhAwroNHn+0WmwnJA5OK5bUQheOWNso7Dr2Nat nfLZhIVx0zIx4xQBtpeRT2ataxyTxsOqYGP++iKz5YkUlo25610OkazpR0iMpdQAKApG8Ag9wa80 17xhp9vqE4glEi7iVKtkc89RXhZXmNfEVpU6yV0r2Sfu2drPzOqvTglzQWl931Luvah9msZHcjhT j8q5bw7C0NkZHGHlYsaqPfXHiW8ClClsh3E+tdRp1qssUrrwkanFe6cp4h4p/wCQ3cf75rErf8Vp jWrj/roawKALFj/x+xf7wr2C4tjc+F4oh18sYryHTl3X0X+8K92tLbPh62kAzhBmgBvha58ywiHO 5QAfyrrlv44FAY5PsK82a8fQLkuFJt5DnPpUo8Urcn93G7+uM0AepWmoRXBCqefpVLXbghdg4yK5 LTfEESyKWyn1roNQu4r4KmQGK/IwPWgDLgt59M1OWG5j2Tq3K5BxkAjpx0NdlHKsdsryHA4/WsH+ 3jf2UMF3Cu+0bHnlsluMY59eM89qt2niK0tb2ze7dY0Z8HJ6A8AnPYE1wqtiVhZTqQtNJ6bptbW8 n23RaUedLoXZ7i4iLtNamO3BGyTeCT9R2/X8Kz7ycOnB4q74w8WaW2hTxWlzFcSHb8sThjwwPavM 5fGMZh8qFS8p4C81hlGJr4mjKdffmaWltLLo/maYiMVJcqtoN1b/AEzxDbqOViyT+f8A9asLx/za w/QfzNdLpto+4PKCZ5jk+1YnxIgEccSjso/rXqmB5XRSkYNJQB1/gAf8TkH/AGWrtbyM23iaCc/d kG3P5VyHw8XOtID0Kt/KvSNe0/d0++h3ofwoA6XT3UAMx4rYi1W33Bcn8q8tt/Fbqv2byiZR25q3 Brd1FL+/tWTPTOaAPUbmUPZM6c8VlWmjWGtabOFJN+nOSSAp52j0wce/eszR/EEc0MkT/eC8K1Rf 2lJpl5Fe2JU7z5ZVhkc9v5VwZlQr1qFsPNxmmmvO3R+TLpySfvbF/TLS5t74wXCbHjxuGQcd+1b0 11HAuWPNY768JLgyyhBIIwHIPArl73xGXdiiM47HnFdOHlVlSi6ytK2qW1xSST02Oxa/hu0deQRx 9a5DxOyw2Ewz1BA/KsseILm1JnntWSL+9ziqv9pN4inCqpFuhyW9a2JIoLY23hOeMjGYyTXkd6MX cn+8a921O3CeG7p+mUOBXhl+MXUn+8aAKtKv3h9aSnJ99frQB7H4Hg8zwsU/vAfzNaHgMCBLiFuG WUjFO+G8HneHinfbx+ZqvqMsmhaoLmGMlJDlh0waAPVdNmhgUM55xWzb38Ez7Vbn6V5EuuapLCJo 7RvLPfJq3YeIbyNvMe3baOrDPFAHdXl7Jp2pmRk3RnIYA8keo96oatp0c7DU7diRJtLZ4BGMA89O woutTtdTtojuXc68MD39KzNKvpbeO4iklP2cOQFPqfSvIxeXz+tQxmGdprSXaUe3r2/4CNoTXK4S 2Ou0iJbSy3OcZ5qaXUbflSePpXJa14j2RxQQLvkK52ITXNNrmpSymOO1JOenNeuYnY6k3lszxMQC OoOK81sIDc+Lbm5HIj4z781e1PxNdwRfZ5oGWVsADmtHw7p3lxl3B3v87nHtS5VfmtqO7tY8k8fr jXpvov8AKuPrtPH4B1yY/T+VcYRg0xCUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v+BP+See Gv8AsFWv/opa6Cuf8Cf8k88Nf9gq1/8ARS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAWLJd10g9xX014P0j7b4P2YBPPavmr S/8Aj8T6j+dfV/gS8t7Tw4nmuq5z1NAHkus2jW94JcYaKQg8V0Gn3cV1bhXwQaseJrWFtVmKEGOY kgiuP8u9064PlglO3FAG9e6eyBjE+Fx0Fctqk0mkp9pDsScjBNaE+r3oiIMXbFc74mea6sooo1Zz yzEDpxQBzV3rl7dTmQ3Eg54wa1dF1+8mYWcszMrcAk1zZgdT8ymrmmQXC3sckcbNtYE8e9AHr1jH etB9kN9ctARjymlbbx04zWzb2sVnCTgbvWuRi1aWKRSgzuHTFWJ9avXi2pCSSOuOlTGEY/CrDbb3 JdeuPMHkqcsxxW14K0X7XdKdvyRH0rl7S2nctc3IO48gH1r1HwXJa6bZbZZFEknYnk1Qjyz4s2gh uwAOASP0ryUjBr2b4vusl0CuMFif0rxlvvGgDvvhnb+brsPH8Qr2Txno/wBnsoLtV46HAryj4VBf 7ahJ/vj+VfQPiY29/wCH/s4YFwBgZoA8QRv7Ovw4/wBW5ya25Eiu4RJGQG65FUpLdbqFo2HzLxVA Q3VqSsTEj0xQA6a2WWVldzvHcMRXG6z4hvIbowW80iBOBhq7awa5iEs0yBhjgGvNL+C4lvZpnjI3 OT096AFTxDqkZz9ql5OTzXcaBey6lAl2Z5Y5ojgOkhDdPY156LWRxgIa7vwSLiOyu7ZosFsFSR7U mk1ZgnY662he7l86eZ5WOMtIxYnHuaXU7tLeHYmAfasmKe/gymw47HFC2k9zN5k2cChJJWQEen2r NMMjLzPiu/8AEGjiy8JlcDdjP61z/hyCOfXY2Y4jgIya7zxlcQzeH38t1YAAZBpgfKGoptu3+p/n UEC7pV+tW9V/4+5P94/zqvaczr9RQB9F/DHTludF8sgcg1ka5Y/ZdTuLVwMbjius+FUkcOkozEAc 81S+IsKRX8d5Hgq7HJHTpQB58qi2lMTjjqDUsUUM1wElIKmr01vHd24fqaoCA2cyy7cqO1AGfr5b R7Yz2ksiMTjhjiuXHizVyoH2hyFOetafizVHv/Lt44dihiTjvxWFFaMqDKHOaANKx8R6le36RT3M m1yAcNivQgnk6c1sLmcRZ/1RlYr1z0zjrzXlXky2lzHPGpyrA16NHqR1Oxj2QhGPUik4qW6Gm1sX QY7e2LMQTjiqMEfnTNcuOByKmh0+WVgJchfpUt6FhEdsh5YhQKYjrfB2lfarWa8cdDxkV5V8S4Nm tScfxGvoDw5FBY6B5G4BjnjNeE/FEg6zL/vGgDzQ9a6bwVF5mtwf74rmW611/gPH9uQZH8YoA+gN V0fz/CsUyD5lUGvO7q2W9tWBA3qOM17XaiKbw6sLEHKDvXjkoNrq89u3TeQPzNAHNRWiR3ipIvy7 sGuou9AsPLjaH5Q46gdPxqndWaCYSt9zOayNa8XLbqbeBRtQD86AMC88T6ppWpPBHdPIiMduTnFR yeNNWWMrvI3nOeP8KxkSS+uWlYZJbNW7u0LxLhT8oxQB23hF7jVIjfXF5OZkfCbWIKgj2+tbHiW0 t4TFJueSdwN7uSWPA6k1534d12TRpzGy5UsDn0ruzeRa/wCVJtwwAOc9aXKr81tR3drGfZ2JuZxl cIOTXRaZbfbtWgtU+6CAaTyRZWrEjDVtfD63WW9e7k/v8E/SmIr/ABIshb6GUGOMdq+erhcSn619 KfFB1fSXIII4r5uvP9a31oArqMsK9z+D9uHwD0Oa8OjGXFe8fCA4Cn3agDofFdj/AGfqYcDCucVw 2t6ZAZRPsUBsZr1Px/GssaSKckc/pXAzQfbLZUIJ5oA3NH8P6Rc+Hnl8hfNRcgqOelct4ohGg6cl 5YXDRykncoOM/hW1ceJoPCujbYSpmYc5PSvKvEniefX5QGAA3E8d6ANC38d6jJCFKlijbj/nFOtf FuoavqyW7TtDHI4D4OOM4rE021ZFkJH3lI5qg8b2dwJVzkHIoA+h9A0OyN79n8+U25UsQGIBPvzX IeKNMsBrLQ2kKqgOOB71T8HfEd7aeO2nCCJgQSTXRXNrDfamLu3y0ZO4kVKjFNtLcbk2rNlGK3TT 7BERQGYY4rs9M0wQ+GpJmHLqT0rlJlMt8kf8KmvQ7gLF4bCAg/Ic1Qj5j8YJt1q4/wB81zB6mur8 Y86vcf75rlW60AXdKGb6L/eFfRmg26z+GFBGSI1x7V866QP9Oi/3hX0b4abboBBPGwUAclqNuk9r NE4BwDjNbPw/0rTHMkU9ujsV5LD3rNu0LSPj1I/Wt/wvaQWCyXk8gUBc8n3oAn1Dw7pcd9KixBYw oOT0zXlOoeKLvR9YNssrSQxv8oznHNdb4r+JcUvn2sUaFeAGGa8iYveXRlbkk5oA6278bXEds4RS Gkbdmuq8G+Xr1jLc38vmSjhA3PbNea38DPBEQp4XFaPhLxG+gXe51DLnODQB9AaVoWiJoklw9qhf yyCGHfmvL0020OtvNHEgQZ4xXfeHPFtn4l0mWB2SJgp4B69a5e5sPsl26pkigC9osIudRBI4XpXJ /E7aH2jtiuy8OnF23riuK+I/LjPtQB5O/Wm1JJ1NRjrQB23gB9mrKfY165rciARtxyv9K8e8EA/2 kDjsa9I1qSTdEPUf0oAZoWn2Umui4ljRgeMEV6Hrel6TJFDMLZS3A4Fcb4X0kXE8cjsRjNdXrviC z0CGOOXaxx0JoA5Dx5HDpOnx3dkVhlQfw8HpXC6d4xmltQJSSyvuq18Q/EcOruv2ZhhvvKO3Fchp cZVXyCcqaAOjtPE91qeu/ZzMywvIARnGRkV7ANK0oW9szQKwb5cqK+dIi9pqAlGRhuv417ZoXjrT 4tPtrSQIzZ5JNAHYeLNL0hfD3kpbxrlOOOnSvNtNgis4mijAHJzivUNVtYNW0gTRyAgL0Bry+9ha zmlA6ZwDQBq+IZETw7Io6eWa8Hv+bmT617Nrm86C+enlmvG78YuG+tAFGnR/fFNPWpIh8w+tAHtn w2v1t9KO49E/qanubqHUb5YmUMu7v9a5zwWJDppVQcbf61r6DZNe6rGnIJagD03T20yDSEiaCNsL g8Cp9EttLmsLvFuigk/eH09azZ9J+xWwd3+QDkmn6frGnPo91AZEQ+pP0oA8n13XLrQPEX2GKZvI 8zdgHgc9qg1Xxhcw2ZMMh3M4ORWD4idr7xFLIp3KGIBH1NQ6lCz28Q29AKAPafhbHHqkDXt+wlkk 4Bfk9M/1rprNtJtdaukNsoYORlh715z8KNSgt4yLmdUMR4Q9+K7H7ZZ6tq0vkuA2/sfegCp43g0+ aUTxxIpXHQVn6Nq8RgmH8W3AGfatzxN4fddMeYkk8V53p6SpJNgH06fWgDifGs3navK3bj+Vcm3S ui8Sgm/kLetc83SgBlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfb/AIE/5J54a/7BVr/6KWug rn/An/JPPDX/AGCrX/0UtdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwBRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAF3TTi6U+4/nX0Hp3my+EkaFyGB7fWvnW3n8iQNtzj3x Xo2kfFz+y9M+xNofnD+99r2/psNAHXzyyvChlJLKeppjFJkB4Jrhrj4mCcMBo+3P/TznH/jlVrf4 hNDndpu8H/pvj/2WgD0WS1tjasdq5Ayawrn7JHbzOVBPIArnZfiR5kBjXSSpP8X2nP8A7JWQ/i0y Z3WeQTnHm/8A1qAL9taRXUsq7R8vzVf0F7WG9kEqAhjtrmh4k2TNJHabdy7SPM/+tToPEqQx7fsO 5uu7zf8A61AHqVlaWsk+CowKuzWkSyDYBj6V5nYeP2s5t7ad5g9PPx/7LWg/xRDAgaNj/t6z/wCy UAdpLtJWMAcGp7Jbm41eEJIwRCK84j+IbLO0raZuz0Hn4x/47WlYfFcWUvmf2HvP/X1j/wBkoAv/ ABOkJmRCckZ/kK8pf7xrpvEvjFvEVyZTZeRkn5fN3f0FcwTls0AeifDWTy9Xj+or03Vb+7ttS27m 8ph+FeF+H/Ep0G8S4Fr52052+Ztz+ODXYX/xdS+RQdA2Mo+99szn/wAcoA23uPK1Bh2dicVpwrEW 3yLkYzXmV345FzOkq6dsK9vPzn/x2tAfEsC2EX9j/NjG77T/AE2UAdBqupRwuyJkA9hXP3pglTIT muevPErXc/mfZtnt5mf6VIvieMQNG9huJGN3nY/9loA2ZbSKPyyVGG54rZsb+K1CEDHODXGr4pG6 MvZblQYx5uM/pTLnxMJnyln5Y9PNz/SgD2Oxa2uLXcVGcVWvGW1gJ4rz3S/iH/Z0YR9L87Axn7Rt /wDZTS6n8RP7QACaV5K8cfaN3/sooA7aznkt7OV4j+8kbrW5eTzp4TczMSx9a8vh+IqxIinSNwX/ AKeev/jtXNU+Kv8AaOmfYk0byR/e+1bv02CgDh9SOblz7n+dQWvEw+opLifz3LbcZ96ZFJ5bhsZo A+hfBdy6+GT5bEMASMVV1bUZNR0iSOYkvHkgmuC8P/E/+w7E2raP9oB/i+07P/ZDUU/xISYvt0cq Hzx9pz/7JQB1miTGe2CnkgmqXiW/W3j2KcMBXM6V4/TTJWY6UZQe32jGP/HazNZ8U/2tMXFn5IPY y7v6CgCRJjdXWTzzW1BbRuxGASFzXI2mqi1lDmDeM9N+P6Vpv4sUvuSw2cYx53/2NAGldQIYcqOe afouqtDMsJb5PTNZEniiN7URf2fhv73nf021lxal5U3mCL8N3/1qAPdYGSayEqrgBetc6sv2jxAA fuxtn+Vc/YfE9bLTmtG0YyEjAf7VjHHpsrOt/HSwXclx/ZhZnPT7RjH/AI7QB69pOp3NzqLruYRL 2z7V5l8RpfM1iTP94/yqXT/iqtgr/wDEj3s3f7Xj/wBkrlNe8RHXLx7g23k7jnb5m7H44FAGI33q 6zwQ23W4P98VyROTWpo+sHSbyO4EHm7CDt37c/pQB9Iahq09jb2pjc7WXnniuN1mQnVopsff6/Wu Yvfi0L20jgbQ9uwfe+15/wDZKoS/EhJmjZ9HyU/6ef8A7CgDtdbnFpo4fGCwP8q8jvLlp5mJJ610 eufEJNYsI7ZdJMBT+L7TuzxjpsFccbjLE7fwzQB02kWyvHuJx+Fa8ltGUVTjkZrmrPxIlrAYzY78 jGfNx/Sp08WqHDNYbgBjHnY/9loAqahH5FzxXT+DNSIukiYkjI4/GuQv9WW9l3i38sem/P8ASpNJ 1v8Asu8Sf7P5gUg7d+3OPfBoA9n8RApAQP4sdKfp17JpdhbRxEh35JH4Vw+p/FaHUURf7BMe3GT9 szn/AMcFVT8SkaWJjo5xH0H2r/7CgD0Tx1cM/h5d5yTjmvBLo5lb6/1rtdf+JX9uWIthpPkAd/tO 7/2UVw0knmMTjGfegBIvvivdPhQxW2yOuTXhSNtOcZrtvCvxFPhmMp/Zf2nOf+XjZ/7KaAPYtSv5 r24mgclgpIFZVtE0MDuyn5TXBP8AFctevcLouNxztN1n/wBkq03xgie1eFvDvzN/F9t/p5dAHM+K tUkur513Hbk8Z96xrCMy3IB5qG91D7ZcPL5WzcScbs4/Sls9QW0k3GHf7b8f0oA7m3s0VeP7vQ1n 6vZILfcMetUR4wCjC6f2x/rv/sahufFKXFuIhYbT3bzs/wDstAGXFK0MgZSQQa9l8B6g1/YOjfM2 ByfpXiTXAZs7MD0zXY+DviDH4VZy+kG83dvtPl9sf3DQB6kbdkvJZSPu54rY0/UJbrSJvMYkBTgH 6V5fe/GJLkOIvD/lbvW83f8AsgqC0+LX2Wye2GibtwI3fa8Y/DZQBzvi8/8AE3nH+2a5c9TWlq2r /wBq3bzmDy9xJxv3Y/SswnJoA09GGb+H/eFe/Wcpt/DAdTg7V6V88Wd59knSXZu2nOM4rvY/iqE0 sWLaLuwoXf8AasdPbZQB3dvF5wdsctyK5bxV4jubGBrSNmTqMg1mWXxV+yDDaL5n/b1j/wBkrm/E XiddeuDKtj9nyenm7/8A2UUAZEtzJNKWdiSetdFo9kk0OTiuWEoDZ2/rWxZ+IVtF2i0Lf9tcf0oA 6iSyR0CnGAK5TVIvs9x8vQ1f/wCEwHew7f8APb/7Gsi91QXjFjAVP+/n+lAGt4e1640u7Uxu20kZ APvXr9g76pa/adpJIrwCO4EcgbZnBz1r0fR/i5HpOmi0GgeaR/H9sx+mygDt7IyW2sKgPUc1zHxJ A4PstYifFErfNcnR8knhftPT/wAcrK8SeOP+Egx/xL/s/T/ltv8A/ZRQBykh5P1po6ilZtxPFNHB oA7zwBEH1IZHY16NrsGJ4BxyK8d8PeJ/7BuRN9j8/rx5m3+hrpb74qC9ZG/sXYVGP+PrP/slAHZn XToJ3849jXnnifxdc6vcsSzBecfNmoNW8bjVYPLOneWeOfPz/wCy1yzzb2zt/WgDUsma6uQJCW+t dXb2KRJwo5BFcTZ6gLR93k7/APgWP6Vsx+L/AC1x9hyPeb/7GgC/qFjGkJcYzya5+K/lhkBDHj3q 3d+KBdR7PsWwf9dc/wBKxXuAzlgmB6ZoA9e8M/ECZ7IWMhYk4XOR6VrasomtY37yHNeL6Zq/9nXS zeR5m05xvx/Suqk+JRlVFOk8Jj/l4/8AsaAPR9bgH/COyDjhMV4XqQxdyD0Y12l98U/ttg9r/Y2z cMbvtWf02VwdzdfaZmk2bdxzjOaAK/WprcZdR71DUkUnlsDjOKAPa/ANiJNLQ4+8v9TU9q7aTqPn DgK+f1rivDfxO/4R+2WE6P8AaNoxn7Ts/wDZDUOo/EcX5crpPlbs/wDLznH/AI6KAPS/EfxAtG0s QqWLjrgj0ryu58VXLu0cTsinsDXOXeptdSM2wqCc43Z/pVZJ9jhiuce9AHb6XbrdR+Y/LH1rVmso 3RARwB6VyFn4pFoABZFv+2uP/Zaut45DD/kG8/8AXf8A+xoALm9m0m6zbuy+uDitzwz4yNlqCyzZ OSMnPvXD3usLeEk2xVj38zP9KprdFei8/WgD6O1XxvDrFr9ntyRkdyKoaNp4lt5XOOSMV41pvipt PH/Hp5nv5uP6V1lh8XhY25iGhbye/wBrx/7JQBh+NIBDqcgHHSuQboa2te8R/wBuXbT/AGXyd2Pl 8zd/QViFs0ANooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8AAn/JPPDX/YKtf/RS10Fc/wCB P+SeeGv+wVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooA1IPDmr3FvYXCWMgg1B5VtpHIRZPKAMjZJACKDkucKMNk/K2K +paXd6TcLDdpGC6B43ilSWORckZR0JVhkEEgnBUjqCK9Al8Z+HL6PS3jt7uwniuLxVSa48+K2jez igh3ARKZIgY4wVO8lInDiTzMVyfizULe+urBYLmC6kt7Typ5rWEw27v5sjjyoyqbFCuoICLlw5wS SzAHP0UUUAFFFOVGboKAG0VOLSU/w0jW0i8lTQBDRSlSKSgAooooAK0NJ0W91uWeOyWD/R4vOmee 5jgRE3KmS8jKv3nUde9UAM/St/wtefYpb3ZrFjp0k0QiYahY/abeaPcGKkeXJhgyRkfJjg/MCAGA K58L6sLOa6EMDRxbzhLuJnkVCQ8kaBt0kY2tl0DL8jHOFOMeu4eXw5a2eo3+gajY2txdfahHBfJd Ge1gYuiwxhUaIs0RwXd2++ANhUu3D0AFFFFABRRTghNADaKf5bYppUigBKKKKACiilA5oAStx/CO sRi0zHaF7tEkjiW+gMio0fmh3QPujTZ8xZwAo5JFYpHFejWXiTS7GS1lutVsb+6WJrWyuV0zY1rA bSWALdL5eJcFrfIBmwsTgE5w4BwepaXd6TcLDdpGC6B43ilSWORckZR0JVhkEEgnBUjqCKp1ueKL ixub22NpJaTTrbhby4srfyIJpd7kNHHtTaBGY1PyLllY4OdzYdABRRRQAUUUu00AJRS7TSUAFFFF ABRRTwMUAXdM0W91fzTarAscWA81zcx28ak5wu+RlXccMQuckKxAwDiwfC2srZzXMln5Xlby0Esq JOQhIdlhYiRlUq+5gpA2Pkja2NTwrrENno+qaab3TbKe5uLedJtTsRdQbY1mVl2+VIQ5MqkHb0Df MOh1LjW/DrSpqGn3H2W1s9P1HT7fTZUkM7LO115RUgMm1VuU3FnDfI+A3y7gDz+iiigAooooAKKK OtABRRRQAUUUUAFBGKcooYcfSgDT1Hw5qelW6z3kEaJvEbhJ45GhcgnZKqsTE/DfK4U/K3HynFfV NIv9Fv2sdStntrtER3hk4ZQ6h1yOx2sODyOhweK9B1Txhop19vEDJaahcNqbX1jHY24s57RCJDiZ /KCySh2gYE+cCYnBOGO/lPGGoaXqeswT6Sk6266faQt50u8744EQjOxem0KeOSpYcEAAHPbTSbTT 6KAI8H0pcH0p9FADNpo2nNPooAZtPpRtNPooAaFpdopaKAE2ir2m6Je6sZTarAscWN81zcx28ak5 wu+RlXccMQuckKxAwDil1rqvC2sQ2ejanpzXum2c9zcW86TanYi6g2xrMrLt8qQhyZVIO3oG5HQg GUfCutLZzXMll5Xlby0EsqJOQhIdhCxEjKpV9zBSBsfJG1sY+D6V6NL4h0D7BONPnS209Le9t47C aBmvcyPMYfLm2tsiCyxb081QdsuVbed/nlADMGkxT+tHXtQAyinnOaBQAyin9qXoKAI8UuD6U+ig Bu3ijaKdRQA0rgVvt4K1oRLKiWNxG0ohL2upW04Rirvlykh2LtjdizYUBCSQBWFXTiXRr/UdG0ub UPs2iWtorSTbHBado/Ml3bUY7jJ+53hGwiR8OF5AMLUtJutKuVhu1QF0Do8UqSxyLkjKOhKsMgjI JwVI6giqe2t7xRdR3F7bR291p01pb24jgj05ZxFAu92KZnUSE7mZiST9/AOBgYdADdvNG2nUUAN2 0beadRQA3aKNtPxzSUAN2il2ilpQeaAG4HpS49qeBgUtAFnTdFutWMv2aWxTy8bvtV/BbZznp5rr u6c4zjjPUVdufB+t2kd009okb2rSrJCbiLzT5bFZGSPdvdFKvl1BUbG5+U4boN7baZPe37ybL2C0 Y6f8pI89mVN2R91kRpJFbIw6Kc8YPQQa5pU/hGys7yexMdnp9xbNaPZZupJ2kmkikjmEfyxq0sRI Mi/ccbSDhwDhdtG33px5NFADdvvRtp1FADdvvRt96dS4OKAGbaNtOooATaKMAUtOGMcigBuPakYc dKlpCOKAIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/AAJ/yTzw1/2CrX/0UtdBXP8AgT/k nnhr/sFWv/opa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigApQCxwKQDJwK6zwv4Zk1KZXkXEfPUUAZ2k+HrnUXXbG23P XFdzp/gy1tog9ywHHO7Aq/dX9hoMPkW6K82Oq44Nc9NqN/qcuA74J6LQBvtb6Fb/ACGeI49hSHTt EvF2pNFk/SsyDQZFy10WGRxwar3GiXluPNj37TyMZoAXVvBClWe2+b/dArh77TJ7KQq6MMdyK7ez 1y9sZAspZ07q1bE1rp/iSyZoVVZgOVPWgDyKlHWtTWNKk027eN1OASAcVlng980APph5NGcUlABR RRQAU5EZyABkmljjMjhVGSTivQPCvg8zqtzcgBMA/MDQBzuleF7u/IPlOFPfFdjZ+AY0QNMSPqBW 1e6zp+jR+RaIryDuuMVz02sapqL4WR1U9hQBpnwTYkYEi5/CsnUvATBGaEFsDsBT57bU4I0kFxIW IzgE0tn4kv7Ngs+ZFzyDQBw+o6Lc2EhDxtgHrissgjrXtKNpfiS3KFVjnx0bHWvPfEnhuXS52YJ8 nsDQBzFPA4oYYPNNJoAcelMpSc0lABRRRQAVJHC8rAKCT7VJaWklzKERSSfavTfDXgpEjWe6CADB JYHjigDitP8ADF5eAEROAfat+HwFOVBYMP8AgNdpd6vpWjJ5MEaySDjIxisVvE+o3LsYI8KBnGKA MG58B3KKSqucegrmr/Q7qzJ3RNgd8V6Jb+L7iN9tzDuA4PSthH0fxBEVwkcp7NigDw4qVODSV3Hi fwjJYM0sSDbgn5Qea4xk2MQRgigBijinUnSjdQA1utJRnNFABRRRQAVJHC8hwqk/SrWn6fLezBI1 J/CvS9A8DKEWS4CgerA0AeeW+h3U4BWJ/wABVs+GbwLnyn/KvV5pNF0hNmEkYD+HFVB4j0othrbA 9TigDyO60m6twS0TY9cVQZCp5Fe6NZaPrMJ8oxq5HFcT4j8FyWu6SJRjPBUHBoA8/pRjPNSzQNE5 VlwRTBQAD1obpRk+lIx7UAC9KdSL0paACiijrQAU5Y2c8Ka0NN0me/lVUUnJ9K9F0PwA0kavMoxj upoA8yWwnYf6tqa9nMnVG/Kvcx4V0q2XbIYs96jn8HafdoRA0We3FAHhJUqeRSV6Dr/gaW03Mke3 3CnBrhbm1ktpSjqQRQBBRRRQA5etRv14p2aa/WgB1HSikPpQAtFJ1NKBk0AFKAT0FX7DSp7xwFU8 +1drpngG4nUM0ePqpoA882NjpTSCOtetv8OZNnCqfoprmdX8FXNpuKp07bTQBxVFT3FrLbuVdCpH rUFABQKKOlADj0pi9BSknFIvQUAL0ooooAKKKUDJoASnAZAFWrewlnYBVP5VrReG7h1zsJ/A0Ac+ QeKaK3rnQLmEZ2H8jWRLA0TYZcGgCGlAOetLtFLQAUUUUAMf7wpc8dKa/wB8UpHAoASiiigAoopw BoAQdacOnSnpCz9Fz+FSG2kAztP5UAVaXnNPZCp5FJigBBx6ZpcUtFABSkUlIehoAgooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigD7f8Cf8k88Nf9gq1/8ARS10Fc/4E/5J54a/7BVr/wCilroKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKAM0Aamiac+oX6RgZBI/nXpup3kXh3S0sbXAuWAJOM4BrE8E2SWtnNqMowETIJ6ZzVjS LKXxLrjzS5Mfc9fwoAi0jw9datKJZc+WTknPJr0zQfCEBKxxJtKrkk81atLFLWNY1XAHtXS2DGyj E2OHIT/P5VwZpWq0cHUqUnaSWhpRjzTUbXOS8S2aW5hiK4IdQRVxtOjmsI2CAjbzTfGJ828gbHJ2 nj6mtbRQJrUwn+7WmBxDxOGhWas5K4qkOSTicHq/haG8jJRCrdiDXCzQXnh/UVfoVOQeoIr3C9t2 hJBWuZ1zQk1Kyk/dnftJBC9K6m1FXZKTbsji9ZtofEehreRL+/QZkA45x/8AWryyeMxSlT2r0fRp m0jWZLG4yEkPlNu471yni2wNjq7qFwDz0piOfooooAKKKmtozLMqjvQB1vgjw+dSvUkcfu0YE8+1 dX4j14W+NL075VT5XbHXilt5F8N+FG24W4nTC9j0FO8J+G31Bzf3Ksdx3DI680AUNG8L3epN50ox F9ea9J07wYlpp7SPCQNuQSfatXSbUfZ1Xygjc/L1rsZlD6a0fC7o8fTivnszzWeHdKrSkvZvV+a0 07r/AD37HVGio80Jr3tvQ8q0nSI7m8njkGQhIHNVNX8HQzMyouGx1zW/4fcpqVwT3c10l9YMG8wD IIzX0JyngGpaPe6HceYMhVb5WB6V0dhcweKtJezuQPtiDIIGMgV2+raXHexPHIgIOe1eVXkE/hrW Umj3BOx6cHqKV1e3UfK7X6HE6zp72F7JCwxgnFZdej+O7JLmCDUYQCskeTgd815yRg0xCUUUUAFO RdzgetNrZ8O6eb/UoowMjcM8e9AHd+BfDMawG/uxiNc5Oal8QeKXvpDaWJ2W4+Xgcmrvim/WwsIN EtCN5AZ9vXmrfhXwoFC3V2nPBAI6cUm1FXew0m3ZGHo3ha91KVWdDg4PXmunuPD/APY4QKvD/Kc+ pr0/w7pUUMjSbABtCjgetZnjqJEeyYAfNJzx9P8AGvFhmlSeP+rpJw2unre1/TyNpU4KnfW557qH g7zbRZgMMwzwa4+80e90t/NA+Ud1Ne8xWon01DjOAOgrK1DR47iJlZAM9sV7Zgee6FrsWrQnS9SA MjjbGwGK4bxl4ffSdRkwvyMxxzXS+ItAm0a6FzCG2g7s+nNXNSePxL4RjmIBuYFw+OTkA/4UAeQs abUtxGY5Sp6ioqACiiigAqxZ2zXM6RqMljgVXrvPh7ogvtRSaQYjRgScUAdf4X8O2uj6UL++G1do PXr3rO1vxbNfOYbT93B0HHJp3ivVpNR1EaZakmCA7QE7npW14e8GJ5SzzoWc+oFDdgOVstDvL/8A fMCUHJJNaVxoCTxqkCneq/NzXtNp4TtYdL43B2QccYBx9K5XRbBW1K7UgfK7KMiuHC5jRxU3Cne6 V9uhpKm4q7PIpra80ubcpKlT1BrptB8VR3CCy1MBlbChsfhXf6j4fin3q0Y5HXArzbxJ4WfT3aaA EjOSAOldxmZfjnwqLRvtVuuYjjBzXnbAqSK9n8PXy67ok2kXOGmQFoyeSQPr9a8n1qzayv5YiCMM f50AZmaM0UUAPXpS01elOoAK0tH0yTUrtYkXJPvWcq7mAr2P4aaFDb2zandgCNM8sOO1AG9oPhqx 8PaaLzUPlUKGxnrxWXrXjl5C1vpyhIgcA7azPEWu3PiTUzFDu8ncVRVJwRmtXQfBbzlXnVue2KG7 asDnYFvtRZ5C7HAJPNRR6lqGmzZSQgA9+a9C1LRF01kSIAB8KcAVYl8HxXOnqWX5iueQKUZKSuno DVtzF0fxna36C01JV+bjOODWR428FJ9lN9ZAtEVJ5PIqDXPCdxp2ZYlYqO2K1PB3iDzlbRr9sxzD apY5wcUwPFp4TFIytwQcVDXZePtDOlazNhcKzkjiuNoAKafvCnUxutAD6Q0tFAAK3fD2hTarcqqL kcd/esi1hM9wkYBJJAr6G8BaFaaB4eGrXqqCEyocDk8mgCz4d8C2ukWa3V98g68tzUer+M9Osf3F gm4gYyRmuX8SeK77X7tooiywZ4VSeaoWugXEiGR434Gc4oA208d3CPukjQof9n/69dJp/iDRvECC 2uFCu3HTBripNDaZ44ogx+XJ9qzbzRr7TX8wRuNvIK0AdF4z+HgaB7uzG+HJIO7kcV4tfWclpO0T jDCvdPBfjQq407VGDQuBGGck45xXM/FTwsmn3ZurcZibGGA4NAHkxopW+8aSgBD0NKnTpSHoaVDg UAKw5pKcTx702gBQMmug0LQZtSmUKuQTjrWdpVk19eJEgJzX0n4N8LWXh/Rl1G/CqQAyK4HPHWgD B8NfDbFuk9xHhSB1augn07R7AiJyA31rH8U/EKSV2tbDaqqSMqSK4GS8uLmVnlkbJHck0AeqHw7p WrRERkZx1BrgvFfw6ltkaRY8r2YNWbZ63fabIHhkbjtk16b4W8dWmrILPUPL+YEENyDQB8231jLZ TtG4wQaqV7d8UfBcdrH9ttAWhZSwIA6ZrxN12OVPY0ANoopM9cYoAjJy4p+AV4ph+8KfkY5oAbRS n9KQdaAHKMmtXTdIkvHAC8H3pukae9/drGqk8joPevoLwT8P44rRLu8Ty4+cbgBmgDz7SPAk0yBm jPrwa15Ph/J5fCHP1r0/VtZ0zRoDHHsGBgAVzVt41tjcFWIxnuaAPKdb8GTW2SEIwD3ri7qzktpC rjBBxX1tCmkeI7Ty2MYdhgFQK8o+IXgN9OaSSOMsmSVYKMEYoA8XoqWeIxOVOQaioAKRjxS01qAI aKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPPDX/AGCrX/0UtdBXP+BP+SeeGv8AsFWv /opa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACnxLvlVfU0yrWnpvvYx70AekXy/2Z4MsoU+Vpz8xHpj/AOvXbfDnQ/MsUKqA 7lmLn0yK5PxXCV0vS1A+VSB+leu+ALVYtIRx7j+VeRnOPng8PzU/ik7Ly8zooUfaXb2RZutKa1Ze d6nvjGKmvYAmmwfMV2SBzgZzgHita9uYji34Mj9BVK/wkEO8DYHG7PTHvXLluKljsFUWI95K6utL 6X6dfTyG06U4yjozN8S6TDLppnIAliXcrfTnH+fWsjRLkx3CDPBxXQeLB5+gSonzH5WxnsGBP6Cu a0mItOhHtWPCk5Swkk5XSlou2i/AMTum0dJqcIchgM1b03TIvsZMqAmUZx6Dt/n3p8sRZAAKqz6h eWkWdsZPvk/1rrzzD4rFU40KEbp6y1ttay+/X5EUWo3k3qjxn4o6bHpniNZ7ddm9txx65rmPHsAl jtrwD/WR5J9812XxNuHvRBJIAHMmAF965/xtBt8PWuRyqkfrXoZfTq0sNCnW+JKz1uKvKMqjlHqe XnrRQeporsMgrc8K2X23WYosZzWHXcfDOAS+IkY9FUmgDe8Q/wCm+I7XTkP7uMhCPx5/lXrXhjRW NlGgQJGgwMd68ss4TN8Qfn7zEj8Sa+htFijg0+DgfcBPHtXz+eV6l6eHpz5HLd+R14e0Yym1fojP uLRbOMFEJbOAAOSadYXL3cUiuCCh2kEYIPpWrPKkl3GE/hOTj6VlS3aW13ceawX0JOK+UxGSuOEn iKcnK0rLTdd/vv8AcdEcRz+7Ja9zjliSx1q7ijJ2q4Iz7gH+tdpGwuNM3EZIGK4VLkXeq3cwHBkw PcAAZ/Su401SNMb3H9K/Q8Gqiw9NVfisr+ttTz6lud22uULXTTdSlpEIiHfHDe1cP8S9EgGnNJFG A6FSD+denfaBFZKEUFgOR0571wni5muNOuDIeor5+hTxmMzH6y/dhC69fK35vbodEakadJw3ueXQ D+0vBN2j/M1s2Fz6ED/CvMZBtkIr1jwxCX8OauCOG6fgK8svF23Lj3r6g5CvRRRQAV6L8ObFTJPd uvyxR7s/jXnQ6ivXPBkPkeDL+cDlo8fzoAi0GBvEPiuW4mG9FOBn2Fe46Roq7VdkwgxhfWvL/hLb JPcSbwM72J/SveY2jit8YHSvm85r+2qPB8/LFK8n37L/ADOmleC51u9jNluYrJkThcnAArnvGPkz 2UMjt86SLs565IyPy/lXTApcWF0V6uDk98Y/z+deeeIpHkkt9zfdcACuDL8hxNKtSrc9ktWu3l81 o/mXKtTcWranV+HSrx+U3I2ii/s5GuTFCmSear+GdxmB7YrchkUahMHcFmOV47elfSZli6mFo+0p x5n+C835HNCKk9WcP4o0FH0+VJAGJQ44ryzwvmHWb7S5PuOrKB717frrec8gGNigjPrXi1vGY/iN Ls6GUZ/MVhk8MUsP7TFSvKWvp/W9uhrXnCVlFWt+J514htvsuqyx4xismuu+IFv5PiOYY9K5GvWO cKKKKAHRjLgV674cA0TwbdXoGJWT5T+GP615PaLuuFHvXr3iJPsvgu0hXjeik/pQAeCtJF9K99cD cztkE/WvaNH0tI4FkkTGOikVxPw8son0613H5doJGOtek3NykJjTj5jXzOdYurUqxy/Dv3pb+nb0 6vy07m9KKS52Je6h9mtm+RjxxkYBrhdDnD6pdOeC0rHH412ev7WtUZMBdmMDtXD6CpN9cDH/AC1P Nenl2WQwavzOUno2+3ZLsROpzadDs7uyHkiQDORXK69ZL9nzLD8rcE+g713FzLHHawh+MYJrN1qK K5sQylWGevBBrhzfM8Tha9OjSjpK2r7t7L+upvhYUpSvU1R4Cwfw94uiKEqjcY9iKyfiFYLb6iJV UBZBu+vNdN4/hWLWLV1I3ZGcfjVP4jwbtLsJ/WHn86+hOQ8qopTwaSgBy06mr3p1AFvTYfPvY0x1 YCva9al/sPwNBaQnY8xycen+RXlHhG3+0a9bpjqw/nXqHxG+W5srYcKqrx+dAE/gbQUmKzTIGYkY r0+yWOIIsUYIJCoO5PauS0SIW2lAoeML0/CvQdCWOKPL/wCtZcHnOK8PNKVfGVVgqb5YNXk/K+i/ D+rHZRUKdP20tXeyX9epyfieDy7q3Z2yzSLux0H0rpbK2jlskI5wozWL4tQi5tznP71a6bSiq2QL YHy162Hw9PD01SpK0Uc05ym+aRz2sWdr5JilVcsM5PT868X16zOiazFcQjau/PB6c17lrQU27kLu JI6DJryrx/bgWyvn5uv61zYXEV6larTrQ5VF6ead9fw/Q0qU4KnGUXq73/Az/iDAuo+HbDUgMvJG Cx98f/Wrx49a9vvYTc/DS3LdY0P8jXiUoxIRXeYDKY3Wn0xutADx0opq/SnUAdH4MsPt2v2ybQQZ BmvY/Hd81lpljpULbVVfmA78D/GuB+EtkbnxHbsRkCQdq6X4jvnxOEydqnH6CgC/4P8ADiXKfap1 UgZzk8ACu2tdLa7t55oTCtpHuTaF+ZyBx16dR+XSsvwmUn0iO2SYIrt+8Yfe2dwPc8D6E16CYLeH R2gtlVFCckAAscdT6njrXLUhVnWj0gtd9329PzOiEoQpPrJ/gv8AM4XwzYRT6hOsiBtrEVtazoVk tnLJLH8qIT9faqvg3P8AaN6M8CRhXU6qsVxaSQs3ykDOPY5repzqD5N7aepjDl5lzbdT5+8T6auk 6iZYMKhbKlT/ACrqdXx4g+G6zy/NNGwG4+mazPHaKsYiD7yhIzitnwlAbj4f3URGT1H5inBycU5K z7BNJSai7o+eLlPLndfQmoqv6xH5WpTL6Mf51QqiRD0NC9KG6Ui9KAHUoGaSnKMmgDvvhjpa32ux BlyOeteq/EjWZLZUsYGKRom3A/KuT+C1kTqiSleBuPSrfxJm8zW2x2z0+tAGLpWlyX7+Y2Dnk5NS 32nLa3WzGMjitfwvcwxwqDjOBUPiidRewMpAyy9KAHweGvN09ZAoJI71zdzbS6XfK0ZKkehr06xv LePSoizLkoK4PxPIs02Ux26UAehRzf8ACQ+ALhbgb3iT5SfoK+b9Yg+z6lOg4w5FfRPgQeb4UvYj ySvA/wCA14T4xtzBr10MYHmGgDnD6U0jFOPc0meKAIz94U6mt94U6gApyjmm1IikuBjvQB6b8LtI S91SMsoIBBOfrXuHirWG0uyCQYSNQAABXmnwctD9oLleij+ddN8RbxFVoQ3PGaAPNdU1q4vbpi8h Iyf51nmVg/yscmqkjgOTnPNPhkBfPtQB0/h3xDeafeRqJW2FhxXr18Y9f8JZmUM4jJBx7GvBrLi6 Vif4q918MyJc+Fti4LCPGPzoA+YvElsLbUpFAxWKeldh47tjDrMoIPauPoATvikftR3zSMaAIqKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooA+3/AAJ/yTzw1/2CrX/0UtdBXP8AgT/knnhr/sFWv/op a6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACr+jjOow/71UK0dFIGoxE+tAHs/iHTjc+H7d1A3IoYfgK0PCvi02unJEJwhGcg4 /rUl3dxL4etwTyVx+lY+i6DBO/my8KScDNZ1aNOtHlqxUl5q41JrY7QeLYBMWEw3NjJ3Cty01GLV 7Noyyk7cg5rztfD0b6lFGhHls+DzV3W7afwRcQ3EbFrSUfMOuO9OnThSioU0kl0WiBtyd2XNXWW2 ZIN7LG0gJUMdp/CtvRIVjBmkIwBkVxEvimz1CKNg5YLICDt7UyfxnGXa0tCWnkOxRt4HaqUUtkK5 3V74pjgk2mRVH+8Koz+JrW4XaZVJ9dwrK1Dwe0GixXc0m+aQjJLVWtvDdrJZOXb95jsaYGDr0q61 rMFtFyiOGPf0qv8AESERaPEox0NXLO1j0nWGDn77AA1X+JUyNZooP8JoA8bf7xptSuAXNREYoAK9 D+FaZ1skj+CvPK9E+Gcoi1JmzztoA6LV430vxVFfKOFYM2B716Fp/juD7BGjOQVUDJIrkLkrqetb TjapOaTUILOCMJEoyOpNceJy/DYmcZ1o3cdv66/M0jVnGLgtj0Ow8X2ssqgSKT3G4Vf1pEvLcXMW D8vIrgdB8Lx6xpsskT4uVyRg1QsvG7WLtpmoEiaJ9o46gcf0rrSSVkZm/o0Xm3Uy9MSGuyudVt9L tljYr93k5ry+DxVbWPnSyMVV5Mj5at6D5/jrUJZmYrZQ5AHTPemB1Mvi+zBI81R9CK5DxT4jhnsp IYDuZsdKp39ja2OryQ9UDYAqe+060lsmmhxuWgCDRLD7J4TumI5dST+VeKaiuLyTjvXt9pqCP4du YxxgEH8q8U1Ig3rketAGfRTmHem0AKv3hXtnhKHPgeZDjlP8a8Uj++K9l0GcxeFFVT95f8aAGeEd Tfw7fGQhvKfONv8An2r0yPx1Z3Mex5AB3yw5riLSC2ttLWa6GWOeKyrd7e71JUKgIzY4rjnl+Gni FiZRvNdf+Bsae1lycnQ9r0rWbW5i2xspDDnBFcx4oiEdzABwDKvSuU1qG88Gi11O2O61l+8vXA61 Hd+N4NWhhdM7kcMfl7CuwzPUdFaO0tDKxxxVO+8QWEUhLum76ivN9R8eGaKOwsCTPIdpO3p2q7qu gf2XpMdzduWncA9aANnV/Fdr5EgiJLEcAHNcNoML3His3ko++27n6irWlTWVw3lyj5s8Gppj9g12 Mxj5G6fpQBwnxIX/AIqGRvUVw5GK7n4guJNULdytcSQDQAyigjBooAvaTH5l9GPevYPFUYl0SwhG M7AP0FeUeHArarGG6GvXL6KS8mtVX7q4/CgA8L+JDo0KQTK+F4BrvLfxXY384zIoYjA3MK4DUvsM KKjAl+ho0/QI9Ut3NsSswyRg1zPB0XiFibe+la/kX7SXJydD1yW7WewePIPy8Gua8P7Vvbkn/nq1 ebWXjm+0aaSwveShKg4z04qex8epaPM7g/OxYYWukg9gv9QjUZmmVUA6Eiudu/FFhAsiKd30I5ri NKm1DxlczTOxW2iPTpx1qG4W0sb1o25AbGaLXAoa9cPq+ppcbWEaYABFWfH6+Z4eswOdsf8AhWhf W8U9hvtsFgQay/FDl/DiiX7yrigDyVhzmm1Kw+Y0xhxQALTqavenUAdp8O4N+vRORwpB/Wu6+IAN xqaFTkhV/rXKfDfZ9qZv4go/nXd6hp8lzd+fIBgd80AUdF8TJaRC3u1Zo+M5OK9F0nxNZ3W14pFy RyNwzXCjRLW+wpfDdMZrm/EelX3hp0urVv3bHnnPHWlyq/NbUd3a3Q9d8TX0Ur2bZA/ernmthL9U t0AcAbepxXzjP41vLlYtzZ8tgelX5PHOo6kI7WDhnwvT8KYj1zV/E9laDHnK7/3QwrzfxNqzay6q iELnv9a1bPwgVtVubxyWbk806fT4CvlwnNAEqgN4DeDqVjI/Q14VeJsuWFe8CB7bS5YpDhSh/lXi GrAfbnx0oAzqY3WpCOajbrQA4dKUDJxSDpTk+8KAPZ/g5bCG7E7AcHI/I074gDzdeeUdCx5q78NW RNMJX7xA/lW1qugi+ugxA5PUmgDi9C8UnSpBE7HaOQc16LZ+PLaWzbc+fl/vD0rmtT+HDyWTTRKC R6NXll495pdxJbtxtJH9KAPZvCXiiBbm+ZnwGlYjkVoa344toomCyDOD/EK+f7XVLiBm2Nyx5rrv C3hy/wDEtyGcHZkd8UATavqrapKzglgTmvRfBGI/C88TYBIPFUpvAgsIVT5Qfdq19OsfsFm6g4GO R60AfPfiy2MGsTcdWP8AOsCuz8eCP+1nC9yf51x20UAMbpSL3pWHy0i9KAHVNAheRV9TUQGat2BC 3SbvUUAe/fCe1Frb+bjBwecfSuc8euzaq8gBIyf511nge5ij0xNpxnNP1bQ4tTmclcqc8k4oA8kt tZltpPlZgOnWl1LW2upISXJ2kdTWt4k8IyWZLwKSMnvXETxyxN82QRQB2i+JXW3WMMRheOapm+e8 k5OTWJp+nXF9IoVeDXpugeCCsIkmAzjj5qAOm8AkxadOrdGX+leSfEez8vW7hwODISMV7Zp9nFY2 zgHbgV5N8R2jM7bTnJP8qAPLznqKbzj6VIeppDyKAIT94U6kYYcUtACjrVywh825Ue4qmBzmtPSW C3aZ/vUAfQfwvtPstiZQOfL/AK1gePZZZLtz82eK1vB2rxW1iiBwMqARipdcgt9QLMMZPvQB448o VjkEGo0nwxxXT6r4dcbnReOe9YkejTvLtAoAksHeWUAHvXtfgJ5EsnST7vl9K8+0Pw+sLq0vByO9 eh6fc2+m23ysBkY+tAHlPxLsx/ackmOuDXmLDDEV6t49u0nkZgRnivK5eZCaAIyMimN3+lS4xTHG RQBDRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4E/wCSeeGv+wVa/wDopa6Cuf8AAn/JPPDX /YKtf/RS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFWrFzFcxuB0NVRV2zj3zqPegD06W+e50y3QNn6VqRXU8FkgXPSsu1sj DY27sDjjv7V3OnWFte6TlPvAkUActYX102oREMxw4712Hju7Nx4PkW4QZSLKN+Fcs6LZ6qhJARXy fzqx4z8UWuo6TFp1uQW6Hj2oA8x0iWRIpFB4wSM1peDXB8YxPMNymQHB+oqSz0vZkAdQaSyU6VqU V5jhHBOfrQB7B4q1KebR4wo2AEY5rirLUboPtyx/GtjWPEVpq+lx+QQSMfLjFTeG9KiuSJJBjHOK AOT1uWZrqKQgjDA1g+MNQa4SNCei813Piq3hfUBDAOjVwXiuzaNVkxwVoA4g9TUR61M/3jUTdaAE 7103hG/+w6jnOAwrmlGTW3olu0t0oXrQB6javjz7hDlmGRWBeXczTtuJ6+tdJoEKZ8if+IY/Sk1n QBHJvQZBOc5oA0Ph9qlzaX5K7ihByDXK/FC3SPxmj2427l3Ee5Ndl4OltdLZpbhuma5fxVLHruvv dRYKg4X86AOT1YyvaxAkngV6/wCDbsaX4AU26fvZQdxH+6K87utNLQICvbk13PhHV7GPRZdNnbEi jCjHt/8AWoA4rVbuZ76R2Y7ixzzV/R7uWRXRiSpHc0+/05bjUZBGcgt0rdtdIhsNPZ5eH7DNAHI6 jdjTdPuYlblye9eZ3DF5mY969D8R2rTRyOo+UdK88mXY5B7GgCFulMp7dKZQA5etekeDtVSWzFpK w4x39685UZNdFoEUkc6yLntQB33iK4YbY4/uACufhldJVYZDZ7V1v2RNQsgW+9iqEGjiO4UuPlDe tAHa3Fyur/DWeC5XLwRFkJ69DXjWjxvtlCkjg165eXtrb+HXso/9Y6bT7DFcNp2lEb9o4IOeaAIf h7aRzeOYxcDKI4bkehFdZ8QtXlvdR2/diUAACsXQY10rXBdMcc4J/Gui8SQWuoL5sLA5xQB5/DLJ HMpTOc9q617mNNPiuJiPMVM81Ss9CJlDlcqOvNQ+IkP2fyY+gGP0oA8+8Q6gb+/aTPFY9WryJklI I5qucY96AIm60nWlbrQvWgCxZTtb3CSr1Br2PRtaiutMMnHmBPX2rxlVya6zw5NJbsAT8pNAG9eM 880jMT1rofAt/JbaqqnlDnrVFbaO4GR1Na2l28Fh+8fg0Ac58T9Pih8TrLCBtlG7AHfNcne2bpbI eeSBXceImTVtRiYcqvGTUF/o+20jJHAx3oA7Pw15Wj/DtmjC+fMOeOfuiuAuC888juxyWJrrdNvY jpi2ch6DAB+lZl3pyeYzKPlJ9aAK+hXnkyMsvKYPWuc8Z60s5+zwkbADnB962LwrbW7BMhjXn2oh 2mYt70AZ1I3SnetI3SgBq06kXpS4oA6Dwtqp03UFbOA2B1969dk1lLmxXYQSfevCIshxjtXa6JqL rEqOcigDtI53t7mN1PfPWuv1ZbPWfCLLIB5qoTx64NcLbzRzFcE1t/aTDYOhPBXpQB5V/ZbGRwAc BsdK6X4f6TE/iMGcDbGw6j3rY0vR/tSSNtyRk9aNPiGm6pLxtJP60Adl4wv13rb2+Aox0rlEma0k Vy272NWrucTfMxzWBqN2EU4544oAs+J/Eqpp/lowDkEcH2ryWeQyysxPJrW1m5eeU/WsUjFACEGo n+9U1QuPmoAUdBSg4NA6UAZoA7/wP4gaxcRM2FLDqa9Pj1zzERw49etfPtnM0MqsD0NdhY604iVd x4FAHuGneKYVj8q4ZOfWuD8caTbaiGuYFXccnKj3rnbe4nvJxtNdeLeQ2kMLA5bpQB5xYaCWul3g 7QeeK9u8L3lholjjMatgcH6Vzl3owsYUcrjfg9axdVEyIrq2OKAO81LxELiVmDKVJ61zWr+KBBaS LvGe3NcdJrMqLtY4I9q5rVdSafcM0AZus3rXt60jHOSf51mYp7klsmm0AMfOOaavSnuPlNMTpQA8 D2xUkbbZARTQMCigD0jwt4he3jWMvxz1Ndj/AMJOsYyWU/jXi9pctGRg4rT+3vIMbs0AerxeJLW+ nEUm0huOvSsLxZ4eiivYniAIkweBWD4etZri9Rx0BB6+9enXGnNqrqF5WFP8/wA6AMaysrXRNHhu JAoZ17jpV2LxhEI9iMn51n+IrZ5tLWAZ+QY/IYrzdpZbWQg54oA9L1HxQRbvtcDI9a8s8Rak17Ox LZ5PeludRaSMjcelYk7F2JoAr0Uu00EYoAif74pQM0j/AHxTloAVRip7Zykqn0NQ05Dg5oA7Kw1i SGNcORgetaP/AAk8qYG7P4muKSchQM04O0hwDzQB7h4P1CDXbSWKVVLKOv4VesdOtUnuZWQAR5x0 ri/h7DLbmSXJ2kfnxXYXMU1tDMwzmUZBoA4vVPFKpfvHEVwDjg+9UZfEkzoRvAH1NczrNtJbX8hP 8RJqj57bcE0AXdW1BrgEFifxrnmIJq1M5YGqrDNACdOaaelSYprjA/CgCtRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFAH2/4E/5J54a/wCwVa/+ilroK5/wJ/yTzw1/2CrX/wBFLXQUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQADr Wvoqhr2MH1rIooA+iItNE2ixleoAIwPauauNZu9CL+WDt7ivHKKAOv1fxJd3zsSSufQmq+lSSzTb 2JOOea5iigD121AIB6cVmaypWAgDJJNea0UAba3s9rOVDHg9M112jeMr2JRAv8XGcmvNqMGgD3fR oJtRuBPLlifmJ61kePrZYbRRjBC15Bt4pdpoAllGJDULdaXb702gB6da7XwRbia/AI7VxO33pNpo A+ibrR3hQSxA5VQRj1rm9X165gXyHTleM141tNG00Adfe6rdvNs3MFJ6Amui0f5ojnJ6c+leXbTR tNAHtk67oE9u9cNrNxJBdmaFmUgkcGuM2mjac0Ad7pniK7iVSy7jXbWNzeaxGqMhUHnA614ZtNLt NAHves6SLfSptw52nqK8T1Fdty+Kobfejb70AK3INMHBpzL3FIBmgCaIZYV6R4O0wXUY4ycDt715 ls96NvvQB9D/ANkTWqbkU7fpXMa5dXUSlUUjA614/t96NvvQB3dhfXM1+wnZiB2z713FkojZuMBl zXhe2l20Aet61+6gdx/Ea57TNRvUlKbmZR05NcJtpdvvQB7hpr3V3hdrDdV+70KTyWeVD09K8A2+ 9GygDo/EtuIL51HaufPWm7Pzo2exoAa3WlWkIwaSgC5arucCvR/D+i/aIkZVzyO1eWUUAe/JossK hihGPasTW0vEjbaGwO9eOUUAet6GrTWzO4yykGuivws9rEgI6DNeBUUAekStOdUZYicK3auosI5p 4gHVs4rw+igD3i40CR4S7IxHuK858SWAtpcAdia42igCZ+DimN0plFADl6U9etRUUAallD5kgFd3 pGimVAVUkduK8wooA9+stFlRQojIz6CjVrKaGOKMqQCw7V4DRQB9I6SEtJJ4zj7uR+VYeqwF285V +Z5M8V4VRQB9Bw6XNLbhijEeuKzL/QXZGJU9PSvD6KAO11zTfs7HIPWuXlGCRVKigCxUL8Gm0UAS U4dahooA1LSHewwOprqdN0wy7cCuCooA+gPDHhzzZlXBz36cV0OpWH2acgjHlbR+tfL1FAH1TrLp cwWsQI+6GPvWYNGN1ZzZU4Br5pooA9d1jRGilcFCMHriuO1GwMRJwa5OigC/Ku01Aar0UATP0psd R0UAWKUdarUUAacCZrWtLcu6jHU1y1FAH0L4F0NTCZmHIUHpXY6WqQXl4jY/1ecfhXyTRQB9RGyS 6jfPILNnj1NeV+JdLFteMAK8xooA6KeHZnIrPkTBNZtFAFzpSE1UooAkf74p46VBRQBYpy9aq0UA aSZNaFhB5syr/KudooA+lfD2lraaD5uMZCk8fSt/Vo43hswMYZADXyXRQB63440sQTllU8A9vevP 3Ugn1rEooA0XHrUVU6KALlMf+lVqKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wACf8k8 8Nf9gq1/9FLXQVz/AIE/5J54a/7BVr/6KWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD1KfwtoP2i8jis4D/ZMs6yRwyzb8xW 9xKI7wudpkLW4BNudnEuGGYzXG+IrW0SLR7+0tY7QajZG4e3iZ2jjYTyxYTeWbBEQJyx5J6DAAfF 2uStFI13GHjbe7LbxKbhiCpM5C/vyVZwTJuyHcHO9s0tS1O81WdZrpoyyJsRIokijjXJOERAFUZJ JAAyWJ6kmgChijGaX8aSgAwKMD0pcUmKACinBfWjbQA3FGKXBHNKBx1oATGelLtpcUtACYFdJ4Ys dNew1C/1GOxKwSwwo+pNP9mXeJCQRb/vfMPl/L/BgPu5KVzoGauadrV7pDyC2MDRzY3w3NtHcRsR na2yRWXcMsA2MgMwBwTkA7ef4efY/D/ia/Olaq7QeebEyLuFvHDdRxsZGjyjyFfPBAOFETNghlYe c7avDVb7N+zXLu+oJsunk+dpR5iyHJOTneinPXj3NU6AG7aNtOooAZtNLt5p1FADNppdtOooAbtF AX1p1FACbR6Uu3PaingEUARsvGa9Nl8LaD9smhjs4MabdSQzCKWbdGEhnfF7uOCwMALG1yMLNt5M WfNmHFbH/CXa3KYXa6jDo292W3jU3DEFSZiF/fkhnBMm7IdwfvNkAf4rsLK0ubCawigSG7tfO3Wr SG3ciSRMxeb+9C/Jg7+dwfHy7awCgrQ1HU7vVZ1mu2jLIoREiiSKONck4REAVRkk4AGSxPUk1S56 4oAZtFG0U8cnPH0pNpoAbgUBRTh17U4DnoKAGbeelGPan80c8UAMxzil2+1OAOaXmgBu2l2inDng 07A9KANnw5aWE8F68ltpt5qKvGIbXU7z7LCYiH8x9/mR5cMIgF38h2O04yupfaHpGnXHiDT5dOjj sNPuLy3TVbm7P2h5oy/lJEgKq4JEKuBG5USFiyBl28zZ6ze6OZBatA0cuPMiubaO4jYjOG2SKy7h lgGxkBmAOCczS+LtYubD7HdSWl0myRPNurGCacB3Z2xM6GQEs7tndkFiRigDDooooAKKKKACiiig AooooAKKKKACiiigD1SDwjoF+ZrmOPTYtNtLhxa3EV80puoBbXMyvdoju8ZP2ePKqsbYeQBQQNvD +KLOC1vbaa0t7SK2urcTRPZSytBKA7oWjEw81RlCpD5O5WIO0qASeL9cleF2u4w8b73ZLeJTcMVK kz4X9+SrOCZN2Q7g53tnP1LVLvVrhZrt4yUQJGkUSRRxrknCIgCqMkkgAZLE9STQBTooooAKKKKA CiiigAooooAKKKKACtzwrZ2N/qN5Bf28kwGmXs0O2XYEljt5JFZuMsAU6ZHOMkgFTh1Ys7640+dp rWTy5GikhJ2g5SRGjcc+qsw9s8c0AdZYaRpkml6daSWEbz3+j3upNemSQSxPD9p2ogDbNh+zLncr H53wR8u08U6Rplrb67FZ2Eds+iawmmpKkkjNcoRcZeXcxG/9wp+QIPmbjpjn4fEOp2+lnTo54xBs aNXMEZljRs7kSUrvRDubKqwB3vkfM2TUvEOp6tbrBeTxsgcSOUgjjaZwCA8rKoMr8t8zlj8zc/Mc gGXRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v8AgT/knnhr /sFWv/opa6Cuf8Cf8k88Nf8AYKtf/RS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUASIMilLZojHy0H6UAJmiil70AGMmlHWkx kZFPUE9smgApG6VMLeQjIU/lSNC69VNAEIBxilxS0UAFFFFADhweeKif74xT6Y3Lj6UAPooooABz RRRQAUUUDrQAvNJTsevem0AFGM0uPalx0oAAvNOoooAQ9KZF92nOcLTYvu0APJwaaTmhhSUAKBn0 +lKvTFIOhpfbP1oAO/FLj60dKWgBPx5o9KU0UAHegA0oHvSjAoAUAAUtJ04pfpQBWufvLUFTXH3h UNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4E/wCSeeGv +wVa/wDopa6Cuf8AAn/JPPDX/YKtf/RS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFcbLYWW+Yi2t VAldQotIcABiB1T0FVjbWgOPs9r/AOAkP/xFYxquSTUXr6f5mjgk7Nr8f8ju6K4T7Paf8+9r/wCA kP8A8RR9ntP+fe1/8BIf/iKftJfyP8P8xcq/mX4/5Hd0Vwn2e0/597X/AMBIf/iKq382n6fbpNLa wMrTRQgJZwE7pJFjU8r0ywz7Zo9pL+R/h/mHKv5l+P8Akei0Vwn2e0/597X/AMBIf/iKPs9p/wA+ 9r/4CQ//ABFHtJfyP8P8w5V/Mvx/yO7orhPs9p/z72v/AICQ/wDxFH2e0/597X/wEh/+Io9pL+R/ h/mHKv5l+P8Akd3RXCfZ7T/n3tf/AAEh/wDiKPs9p/z72v8A4CQ//EUe0l/I/wAP8w5V/Mvx/wAj u6K4T7Paf8+9r/4CQ/8AxFH2e0/597X/AMBIf/iKPaS/kf4f5hyr+Zfj/kd3RXCfZ7T/AJ97X/wE h/8AiKPs9p/z72v/AICQ/wDxFHtJfyP8P8w5V/Mvx/yO7orhPs9p/wA+9r/4CQ//ABFH2e0/597X /wABIf8A4ij2kv5H+H+Ycq/mX4/5Hd0Vwn2e0/597X/wEh/+Io+z2n/Pva/+AkP/AMRR7SX8j/D/ ADDlX8y/H/I7uiuE+z2n/Pva/wDgJD/8RR9ntP8An3tf/ASH/wCIo9pL+R/h/mHKv5l+P+R3dFcG be0x/wAe9r/4CQ//ABFRAQJf2axwWwJuYfmW2jVh+9ToVUH1pOq1vF/h/mHIns/z/wAj0GiiitiA ooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAJY+lK3SmocCn ZycdaAG0o+lBpyjgUAPjQuQAK6PSfD8lyVJQnPtR4Y0h768jAXOWAFfRHhPwXaWNilxdJ85UHGaA PJ7fwXIUBMXb+6aq6j4NkjQnyyP+AmverqS1ifYkaqoqaCxstQh2SICMUAfJWo6TLZuQy8D2rLK4 4xX0H458DrDC80CEpx3rwrU7RrW4ZCDQBn0UUUAFMb79PqM/foAkooooAKKKUDJoAFUt0q3BZPKe FP5VZ02wa4kHB216Do3hiWdVCQnFAHnzaZKB90/lVOS3aM8ivapPBsgTJiOcVyGu+HGtw2YyDzQB 5/0oqzd25hcgggg1WoAKTnvTTwfWjOOlACMeDmli+5TWPBpYj8tAD6ZTzjvTSMGgAA78U4dKRRzm nAdqAClCk1NDCW5xmryWTMOlAGWVI7UoXIrRls2X+GqTxlOtADAMUdKWmZNACk9qM8e9J3pKAIJu oqKpJvvVHQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v+BP +SeeGv8AsFWv/opa6Cuf8Cf8k88Nf9gq1/8ARS10FABRRRQAUUUUAFFFFABRRRQAUUUUAeRa7Db/ ANu6m2sabcX7Pct/ZjwwNIYV4BVXHEMnmbm3krwyfP8AJhMW+idprwPaSP4heSQ2V+IGKxqWPlES 42oirgMhI3Yf5X3/AD+j3Xh/WbieWQfYkEjs+37Qxxk5/wCefvVQ+EdZJzusP+/z/wDxFc9OUoQU XF6en+ZrJKUm7/n/AJHDvZXJuvEF3bxSrdS3cQRwxVngEUHmLGSQFLBWXcCPmVcsNoK1LyGOTR9Y TQtPktbGTTLiOS3W0aDzpyo2FIioOQNwLADduQZbb8vof/CI6x/esP8Av8//AMRR/wAIjrH96w/7 /P8A/EVftJfyP8P8yeVfzL8f8jhLq0NlJq0FpYs9nJbWrFMOySOZJBK7AHMrbQhdclpAApzuFUdO Nw2k3lolvIEj1ezlhSPT5bWIReZAWKRv90BlkJweuWON1ek/8IjrH96w/wC/z/8AxFH/AAiOsf3r D/v8/wD8RR7SX8j/AA/zDlX8y/H/ACM/7QfWj7QfWtD/AIRHWP71h/3+f/4ij/hEdY/vWH/f5/8A 4ij2kv5H+H+Ycq/mX4/5Gf8AaD60faD61of8IjrH96w/7/P/APEUf8IjrH96w/7/AD//ABFHtJfy P8P8w5V/Mvx/yM/7QfWj7QfWtD/hEdY/vWH/AH+f/wCIo/4RHWP71h/3+f8A+Io9pL+R/h/mHKv5 l+P+Rn/aD60faD61of8ACI6x/esP+/z/APxFH/CI6x/esP8Av8//AMRR7SX8j/D/ADDlX8y/H/Iz /tB9aPtB9a0P+ER1j+9Yf9/n/wDiKP8AhEdY/vWH/f5//iKPaS/kf4f5hyr+Zfj/AJGf9oPrR9oP rWh/wiOsf3rD/v8AP/8AEUf8IjrH96w/7/P/APEUe0l/I/w/zDlX8y/H/Iz/ALQfWj7QfWtD/hEd Y/vWH/f5/wD4ij/hEdY/vWH/AH+f/wCIo9pL+R/h/mHKv5l+P+Rn/aD60faD61of8IjrH96w/wC/ z/8AxFH/AAiOsf3rD/v8/wD8RR7SX8j/AA/zDlX8y/H/ACOS8VaXd65pRj0/VbvTr2LLQywTvGrH +64U8g+vUdR3BZ4O0m70f7BFqGqXWoXsl3AZpJp3dVO8fKgY8AevU9T2A68+ENY/vWH/AH+f/wCI p9r4Q1WK/tZpHsRHFPHK2yRySFYHjK1MnKaS5WtV26P1Gko63/M7miiitzMKKKKACiiigAooooAK KKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigB69KWmr0p1ABzUsYJYUwCrNrH5koA FAHqfwzs1e7jLKDhx29q+gHby9PQEYGK8i+FmnBZElccbh39q9U1u+htrcIDg46fhQByesX2Jtqn HTvWx4dvg6AbicAd64nU7oyXBKnjNaeh34hlXLDtQB2niZBNpz/Lu4718t+LoBHfvgY5P86+rJSl /pbYOTivmvx9YmHUZOPX+dAHntFK64JBpKAE96jP3hUtRH79AEg6UtIKWgAp8Q3PTKt2ce5xQB13 hi2UyruAxzXunh+1jgtkOwA4Hb2ry3wZpLTzIMevevdNK0oJbL9B3oArvFGQcKK4bxVZxPFIdg6G vUBZAAgg1zHiXSRLbSELghTQB82a9CElfA7mueNdr4psmhuZAR/ERXGSqVYjFAERopSMUlACHpSR n5elKx+Wmx/doAl7c0hHvSZ4pc8+tACrUsa5YColHXNXLeIkjHegDRsbcSsBxXQxWa7QMfjVDTLR sgiulhtjtGRQBi3VkAtc5ewhCRXc3Vp8pwDXKahbkMc0Ac+2aZmppU2moaACiiigCvNwwqOpJvvC o6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/wAk88Nf 9gq1/wDRS10Fc/4E/wCSeeGv+wVa/wDopa6CgAooooAKKKKACiiigAooooAKKKKACivn/wD4aa/6 lH/ypf8A2qj/AIaa/wCpR/8AKl/9qoA+gKK+f/8Ahpr/AKlH/wAqX/2qrll+0RNfWuo3EXhOMJYW 4uJQ2pnJUyxxYX9z13SKeccA/QgHulFeF6n+0RNpV0lvP4TjZ3t4LgFNTJG2WJZVHMI52uAffPXr VP8A4aa/6lH/AMqX/wBqoA+gKK+f/wDhpr/qUf8Aypf/AGqj/hpr/qUf/Kl/9qoA+gKK+f8A/hpr /qUf/Kl/9qo/4aa/6lH/AMqX/wBqoA+gKK+f/wDhpr/qUf8Aypf/AGqj/hpr/qUf/Kl/9qoA+gKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAevSlpF6U/BPJoAUHitTSlXzVJ9ay8d6vWchRvegD6J+Hs9vDaLud QRgj8q19eZ5ySh3DPavFNH8TTWKgCRtvpmuy0vxktw6xu+c4ByRQBNPFIScg5qexhl8wYBFdRBY2 t7B5wC8jrWRq19b6MvAG71zQB1um3ItrErK4Bx0Jrxr4gGKe6Z1YZ5/nVq88bswZY5G59Gri9Z1Z 7slnbJwaAOWuVAkIHrUFTTHc5NRGgBKiP36mIqJx84oAeOmKWkHSloABya1tNC71JPpWUvWr1vJ5 ZBoA9x8Cy2yRqSw3HNeqWmoxIihSD+NfMWk+IZLIDDlcehr0HQfGRndVdznPdhQB7O2oDGdwArH1 nUoDasC67iD3rltQ8TJDZ7y+OB3Fed6t42eR2VZG5z/FQBX8aGKS4dlI5Y15xcgB+K3NS1KS6csz Ek+prCnOTmgCAjNN2nNPooAib7ppsf3alcDaeKij+7QA+lAyaAM07AFACqMkc1qWWAwyKzkFXIiw wQf1oA7jRTDgbsZrpovI46V5naamYSASRit631klMhz+dAHW3DQeWeVriNaaPzDtIJ4outbcggOf zrGnuWmJOTzQBn3ON3FViParMozmq54oAYRjvTT9akH0oIFAFSX71R1LOMMKioAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPPDX/YKtf/AEUtdBXP+BP+ SeeGv+wVa/8Aopa6CgAooooAKKKKACiiigAooooAKKKKAPjQ37P8MEsnuY7BI3ZkS11FT/aBMoO2 e1ViVdcMwmYAbY0XByhB4jvLuewvGstYtB4XdwdO0xrlHaJd4KoIOWilUZ3SEDdh/wB4/mAv6TaW eiWvhnR2bRNJaVtOtnd5LCFmZmiUkklSSSTnNYlxqWlxzFRo2igD/qGW/wD8RQBi6trsupeMvE8m m6rBBqx1Bl0u9FwlvGtqJJTIkUuQkbOWR9+V3gSfMTIVkz7u/szaa6kt3BLqb6JHDe3CyArd3Qvo XJRukjCMKGYfeaN3y2d7dL/a2mf9AfRf/BZb/wDxFH9raZ/0B9F/8Flv/wDEUAefeLJ4bnWLd4JY 5UGmWCFkYMAy2kKsOO4YEEdiCKw69d/tbTP+gPov/gst/wD4ij+1tM/6A+i/+Cy3/wDiKAPIqK9d /tbTP+gPov8A4LLf/wCIo/tbTP8AoD6L/wCCy3/+IoA8ior13+1tM/6A+i/+Cy3/APiKP7W0z/oD 6L/4LLf/AOIoA8ior13+1tM/6A+i/wDgst//AIiqup6hYT6RfRxaVpCObaTDx6dArL8h5BCZB9xz QB9WUV8+2lnolr4Z0dm0TSWlbTrZ3eSwhZmZolJJJUkkk5zWJcalpccxUaNooA/6hlv/APEUAfTt FfLv9raZ/wBAfRf/AAWW/wD8RR/a2mf9AfRf/BZb/wDxFAH1FRXzANQsWhaYaHpBiRgjONKg2qxB IBOzqdrY+h9KZ/a2mf8AQH0X/wAFlv8A/EUAfUVFfLv9raZ/0B9F/wDBZb//ABFH9raZ/wBAfRf/ AAWW/wD8RQB9RUV8u/2tpn/QH0X/AMFlv/8AEUf2tpn/AEB9F/8ABZb/APxFAH1FRXy7/a2mf9Af Rf8AwWW//wARVXU9QsJ9Ivo4tK0hHNtJh49OgVl+Q8ghMg+45oA+rKKKKACiiigAooooAKKKKACi iigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAeo4qUdKjTtUlAB3qeM4PUVBUqGgC8 JdowDUlvdyxzoUPeqQb1rU0az+1XiKBySO3vQB7T4Y1WX+x139QCee9cB4+1WeW7x0TJr0/Q9GWO x8sAcRk9K4Dx1o+I/NC9cn9KAPPI7hjzmmTSZBqLG1selIxyKAK8nemYNSN1pKAEwMYqB/virFQP 98UAPHQUUoGaXBx/KgBUFWEOKrqCKmU4NAE7MQtaegyztdqFzjIrHBzXeeBNHN3dqxUEBh1HWgCX XHu1sQTnbgdq4PzmaQ7vWvevFnhwDSQVVchRnArwm9gNteOhHegBGbAqrJyam3ComoAhopcUYzQA xx8pqOP7oqV/un6VHF90cUAS9uKO9AFFAEycHirCdDVdKl3Ad6ABzzxWtZwSGDKqenNZ1vEZ51Uc 81654Y8J/a9IeUqOh/hoA8kvAyTc5wT0ojPy1u+LdKaxumwuAGPaufRqACQVWPBNWW6VXfmgBtFF OPTkUAUp/vCoqmuPvCoaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKAPt/wJ/yTzw1/wBgq1/9FLXQVz/gT/knnhr/ALBVr/6KWugoAKKKKACiiigAooooAKKKKACi iigD5h1S8eHQtJVeg0uz/wDRCV5tf6rOLtuf1rv9UBn0fTPLZCBptoM7wBkQICMk+tee3umztcsR Ja/jdxD/ANmoAh/tWf1/Wj+1Z/X9ab/Zdx/z0tP/AAMi/wDiqP7LuP8Anpaf+BkX/wAVQA7+1Z/X 9a7K7sVS3124itJPIstD066WQFysdxOLUnJ6ZYSTEA++Bxxxf9l3H/PS0/8AAyL/AOKq4W1prBLB tUBs0RkS3OpJ5aqzh2AXfgAsqsR3Kg9RQB2GpW1qjJYLpUlkv9hQ6r/aUkrnc5tlc8H5PLeT90O/ muMNjEdcL/as/r+ta2s32qaukdv9pjisUitl+yf2lG0Zkht0g8zbuA3EJ1xkA4yax/7LuP8Anpaf +BkX/wAVQA7+1Z/X9aP7Vn9f1pv9l3H/AD0tP/AyL/4qj+y7j/npaf8AgZF/8VQA7+1Z/X9as2Oo TTzSxt9020/f/pk1VP7LuP8Anpaf+BkX/wAVVizs5LWWWWWW1CCCYfLdRscmNgAAGyeSKYHpmqXj w6FpKr0Gl2f/AKISvNr/AFWcXbc/rXf6oDPo+meWyEDTbQZ3gDIgQEZJ9a89vdNna5YiS1/G7iH/ ALNSAh/tWf1/Wrmk3LajrNjZXF4lnBcXEcUlzIRthVmALnJAwAc9R06iqP8AZdx/z0tP/AyL/wCK o/su4/56Wn/gZF/8VQB6FCyX/hme1l06Tw/C3iKy0+a5uJHZUAS4DGTfgeYgbL4Kryvyp3zI76yk W+nu/D5sI9FliMkDXEqvd/vVRraVmPyyld7ZRUx5cnyY+5zly2tXlvHb3WqCeCNI0SOXUkZVVAwQ AF8AKHcAdtxx1NF42tahb2tve6oLmC0TZbRzakjrCuAMIC+FGFAwPQelAHePoGl6d/r9Qgvf7Oll vLrD7PtVr++8peGPlq/2eLa4zn7fFjOE359qunzaTpNsbA/a73Rb2/lvPPfcrwNdFAi/dGfIVWyG yMYCnLHj2i1R/M3X8bebEkEmdQjO+NNu1D8/KjYmB0G1fQULFqieXtv418qJ4I8ahGNkb7tyD5+F O98jodzepoAh/tWf1/Wj+1Z/X9ab/Zdx/wA9LT/wMi/+Ko/su4/56Wn/AIGRf/FUAO/tWf1/WrNj qE080sbfdNtP3/6ZNVT+y7j/AJ6Wn/gZF/8AFVYs7OS1llllltQggmHy3UbHJjYAABsnkimB91UU UUgCiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAcr7e1O83 2/Wo6KAJPN9v1pwnx/D+tQ0UAT/aT/d/WtnRPEyaRdLO1j5+3HHm7f8A2U1z9FAHrkPxwaFnI8Pg hk24+2dP/IdY2rfFAataGB9G2H+99qz2/wByvPKKALkt8JHJEW0em7P9KYbskfc/Wq1FAExuMn7v 60ef/s/rUNFAE3n/AOz+tRs+SDim0UASCX/Z/Wl88/3f1qKigCUTY/h/WnfaTj7v61BRQBZS72uC U3D0zXZ+HPiQvh+PC6R5zZBz9p2/+yGuDooA9e1H45HUYREfDoQbQD/puc/+Q6851TW01G5aZbTy s9vM3f0FY9FAFkXZH8H60hus/wAH61XooAn+0/7P60faeMbf1qCigCUz5H3f1pEk2jGM/jUdFAE/ 2gf3P1pPP/2f1qGigCwt1t/g/WnfbP8AY/WqtFAGrYawllMJGtTLjt5mP6V6TpPxvj0qza3Xw1vB UgH7djH/AJDryGigDtvEfj+LxBuJ0jyCSTn7Tu/9kFcqL7B/1f61TooAufbv+mf/AI9TTd5/5Z/r VWigCz9r4xs/Wg3fP3P1qtRQA+STeRxjHvTKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKAPt/wJ/wAk88Nf9gq1/wDRS10Fc/4E/wCSeeGv+wVa/wDopa6CgAoo ooAKKKKACiiigAooooAKKKKACivn/wD4aa/6lH/ypf8A2qj/AIaa/wCpR/8AKl/9qoA+gKK+f/8A hpr/AKlH/wAqX/2qpIP2llmuIon8LxwI7hWlk1FiqAn7x2wk4HXgE+gNAHvlFfP/APw01/1KP/lS /wDtVH/DTX/Uo/8AlS/+1UAfQFFeD2H7Rlxqeo21hZ+DfMurqVIYU/tQDc7EBRkxYGSR1qOf9pZY biWJPC8c6I5VZY9RYK4B+8N0IOD15APqBQB75RXz/wD8NNf9Sj/5Uv8A7VR/w01/1KP/AJUv/tVA H0BRXhZ/aImGjJqi+E43gNw1u6rqZLRMFDKX/c4AYFtvPPlv/dqn/wANNf8AUo/+VL/7VQB9AUV8 /wD/AA01/wBSj/5Uv/tVH/DTX/Uo/wDlS/8AtVAH0BRXz/8A8NNf9Sj/AOVL/wC1Uf8ADTX/AFKP /lS/+1UAfQFFfP8A/wANNf8AUo/+VL/7VR/w01/1KP8A5Uv/ALVQB9AUV8//APDTX/Uo/wDlS/8A tVH/AA01/wBSj/5Uv/tVAH0BRXz/AP8ADTX/AFKP/lS/+1Uf8NNf9Sj/AOVL/wC1UAfQFFfP/wDw 01/1KP8A5Uv/ALVR/wANNf8AUo/+VL/7VQB9AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v+BP+SeeGv8AsFWv/opa6Cuf 8Cf8k88Nf9gq1/8ARS10FABRRRQAUUUUAFFFFABRRRQAUUUUAfGhv2f4YJZPcx2CRuzIlrqKn+0C ZQds9qrEq64ZhMwA2xouDlCNjX7toraJNR1G0m0U6FaIunI6iUXZsY/Kfy8Bt4Yo5m6eWDHvP+qP u1nZ+H7XwlLruuzSQwwvM007XcqqAJWUAKrdeAAAMk4AyTXQf8Ifov8Azwuf/A2b/wCLp2QHzrq8 2kzaHFLqd7/atjZ6raSANrFvI81l+8EotrVNj2qsDFmHOV+QceWSMvT7/U4vFXhqfW/Eem3Wnx6x ay24W7jdYYg4JZFH/HtEF25jYR/w/J+7Oz6e/wCEP0X/AJ4XP/gbN/8AF0f8Ifov/PC5/wDA2b/4 ujQD5d8I64ljp3hewe/ggtZ/EEw1JGdV3WrC0BWUnnyWAfcp+VtnIO0YufDy7b+1/C0Wl6jaWUCX sS6tb3DqGupTOfLbYQTKNpjVMA+U6s5EefMb6W/4Q/Rf+eFz/wCBs3/xdH/CH6L/AM8Ln/wNm/8A i6NAPj3wX8/iVLZeZry0u7KBf7801vJFGue2XdRk8DOSQMmug8I64ljp3hewe/ggtZ/EEw1JGdV3 WrC0BWUnnyWAfcp+VtnIO0Y+ov8AhD9F/wCeFz/4Gzf/ABdH/CH6L/zwuf8AwNm/+Lo0A+XdG1xL 3UfBN/ql/Abq38QMJXkdU+z2qm1KLjgRwqTLtUYVfnwBzVjQfFUs/wDwjH9oanAPP1t7fUN5RMWA +zYhbpstvmm/dcR9fl+Xj6a/4Q/Rf+eFz/4Gzf8AxdH/AAh+i/8APC5/8DZv/i6NAPj3Rv8ARPDX iG5uP3cN5aJZW7N/y1mFxBKyqO+EQknoMqCQXUHn6+4v+EP0X/nhc/8AgbN/8XVbUPC+k2thNPDF OJI13KTdysM/Qtg/jRZAfE1FFFIAooooAKKKKACiiigAooooAKKKKAPv+iiigAooooAKKKKACiii gAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wAC f8k88Nf9gq1/9FLXQVz/AIE/5J54a/7BVr/6KWugoAKKKKACiiigAooooAKKKKACiiigDyPXrLWL nwBfXMdtYz6ZaWGoyIJLp4pI5m+0I8m0RsHxG5CruUZZ85+Ur6xAZmt4muI445ygMiRuXVWxyAxA JGe+Bn0Fc74X1C0tdBihnnSOQTTkq3BwZXI/Qg1sf2xp3/P3F+dNp3EXqKo/2xp3/P3F+dH9sad/ z9xfnRZjL1FUf7Y07/n7i/Oj+2NO/wCfuL86LMC9RVH+2NO/5+4vzo/tjTv+fuL86LMC9RVH+2NO /wCfuL86P7Y07/n7i/OizAvVR1j/AJBF1/1zNH9sad/z9xfnVTU9TsZtMuI47qNnZCAAetCTuB4x /wAMy/8AU3f+U3/7bR/wzL/1N3/lN/8AttfQFFID5/8A+GZf+pu/8pv/ANto/wCGZf8Aqbv/ACm/ /ba+gKKAPn//AIZl/wCpu/8AKb/9to/4Zl/6m7/ym/8A22voCigD5/8A+GZf+pu/8pv/ANto/wCG Zf8Aqbv/ACm//ba+gKKAPn//AIZl/wCpu/8AKb/9to/4Zl/6m7/ym/8A22voCigD5/8A+GZf+pu/ 8pv/ANto/wCGZf8Aqbv/ACm//ba+gKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wACf8k88Nf9gq1/9FLXQVz/AIE/ 5J54a/7BVr/6KWugoAKKKKACiiigAooooAKKKKACiiigAor5/wD+Gmv+pR/8qX/2qj/hpr/qUf8A ypf/AGqgD6AorwvU/wBoibSrpLefwnGzvbwXAKamSNssSyqOYRztcA++evWqf/DTX/Uo/wDlS/8A tVAH0BRXz/8A8NNf9Sj/AOVL/wC1Uf8ADTX/AFKP/lS/+1UAfQFFeF6V+0RNq+qW9hB4TjR5nwZJ dTISJerO5EJ2oqgsx7AE9qL39oiaxtdOuJfCcZS/tzcRBdTOQolkiw37nrujY8Z4I+gAPdKK+f8A /hpr/qUf/Kl/9qo/4aa/6lH/AMqX/wBqoA+gKK+f/wDhpr/qUf8Aypf/AGqj/hpr/qUf/Kl/9qoA +gKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPPDX/AGCrX/0UtdBXP+BP +SeeGv8AsFWv/opa6CgAooooAKKKKACiiigAooooAKKKKAPjSa8u30EDSNYtLTQRZKlzYTXKAtcC MCQtbnLSu0nzJIFYKGj+ZPLxHc1DUEeK/kk1GCXwjJFMNM0pbpWeFyrCACAHdHIj4LyEDdtk+eTz P3nvWnjw6mnxNqSalcXsjzMyWS3c7IgldVLLDnYDtIGQM7WxnBx0kHhfw/dW8VxbiWaCVA8ckd/M yupGQQQ+CCOc07ID5dluba+v9WsoLy08298OadawNJcJHGZY0s3dDIxCKQIpPvEcrt6kA2LjWIdL 1TxBNp2q2kkq+HNPt7a5hYEPKn2IMY9wBDrscjgOpQn5SvH09/wh+i/88Ln/AMDZv/i6P+EP0X/n hc/+Bs3/AMXRoB8u6xriX9nrVpNfwS2/9iWM8MYdcSXzG0M0vH37g7pwznL43AnAwDV9cS+07VNP kv4JLOHw/pptIA67PtSizV2AH3pgvmoW5YKGXO1cD6i/4Q/Rf+eFz/4Gzf8AxdH/AAh+i/8APC5/ 8DZv/i6NAPk3wDrdn4el1rU5pb6O6i09kt1tNQFqZfMZYmXJRiWAk81cdDDnB6g+I/iD+3PEYhiv r66tbCJLVWub/wC1I0iIqSSRtgDa5QHdjL/eON21frL/AIQ/Rf8Anhc/+Bs3/wAXR/wh+i/88Ln/ AMDZv/i6NAPh2ivuL/hD9F/54XP/AIGzf/F0f8Ifov8Azwuf/A2b/wCLo0A+HaK+4v8AhD9F/wCe Fz/4Gzf/ABdVtQ8L6Ta2E08MU4kjXcpN3Kwz9C2D+NFkB01FFFIAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKAPt/wJ/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/AKKWugoAKKKKACiiigAo oooAKKKKACiiigDyuG6s40WK71uDw5m1mCX0swi+2lrmb91ksuVj27jsIkHnja8WTv8ARNCl87w9 pkv9nf2bvtIm+w7dv2bKD93jAxt+7jA6dBWT4X1C0tdBihnnSOQTTkq3BwZXI/Qg1sf2xp3/AD9x fnTadxF6iqP9sad/z9xfnR/bGnf8/cX50WYy9RVH+2NO/wCfuL86P7Y07/n7i/OizAvUVR/tjTv+ fuL86P7Y07/n7i/OizAvUVR/tjTv+fuL86P7Y07/AJ+4vzoswL1UdY/5BF1/1zNH9sad/wA/cX51 U1PU7GbTLiOO6jZ2QgAHrQk7gbNFFFIAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/w J/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/AKKWugoAKKKKACiiigAooooAKKKKACiiigAor5// AOGmv+pR/wDKl/8AaqP+Gmv+pR/8qX/2qgD6Aor5/wD+Gmv+pR/8qX/2qrmlftETavqlvYQeE40e Z8GSXUyEiXqzuRCdqKoLMewBPagD3SivC739oiaxtdOuJfCcZS/tzcRBdTOQolkiw37nrujY8Z4I +gp/8NNf9Sj/AOVL/wC1UAfQFFfP/wDw01/1KP8A5Uv/ALVR/wANNf8AUo/+VL/7VQB9AUV8/wD/ AA01/wBSj/5Uv/tVH/DTX/Uo/wDlS/8AtVAH0BRXz/8A8NNf9Sj/AOVL/wC1Uf8ADTX/AFKP/lS/ +1UAfQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc//wAI J4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8 IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQU UAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBd D/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDB dD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDo VND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8A oVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8A CCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH /CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10 FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDw XQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8A wXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A 6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng// AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz// AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wAT R/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xN dBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A 8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/ AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/ AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P /wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc/ /wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8A E0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8 TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/ APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND /wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD /wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCe D/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAH P/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ// ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ/ /E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ /wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFT Q/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgn g/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wg ng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQ Bz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P /wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0 P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU 0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wCh U0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAI J4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8 IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQU UAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBd D/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDB dD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDo VND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8A oVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8A CCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH /CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10 FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDw XQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8A wXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A 6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng// AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz// AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wAT R/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xN dBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A 8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/ AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc//wAIJ4P/ AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P /wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc/ /wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8A E0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8 TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/ APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND /wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD /wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCe D/8AoVND/wDBdD/8TXQUUARwQQ2tvFb28UcMESBI441CqigYAAHAAHGKkoooAKKKKACiiigAoooo AKKKKACiiigD44j1uzWx8JWN3cQNYw6fO0iFBIkV0ZrryXmUAl1Quj7GDfKzYU7yGsf23Lpk9lL4 q1D+1777Wqs8N6l7IunukkdzEZVcj5w4CLuypDsPL3Av71p48Opp8TakmpXF7I8zMlkt3OyIJXVS yw52A7SBkDO1sZwcdJB4X8P3VvFcW4lmglQPHJHfzMrqRkEEPggjnNOyA+ZdS1PRbHRruKykjbUN Kt20e3lilDrIJVTzZI/m+eIsNQO4jI+0xYA48vL8A63Z+Hpda1OaW+juotPZLdbTUBamXzGWJlyU YlgJPNXHQw5weo+sv+EP0X/nhc/+Bs3/AMXR/wAIfov/ADwuf/A2b/4ujQD5N+I/iD+3PEYhivr6 6tbCJLVWub/7UjSIipJJG2ANrlAd2Mv9443bV4+vuL/hD9F/54XP/gbN/wDF0f8ACH6L/wA8Ln/w Nm/+Lo0A+HaK+4v+EP0X/nhc/wDgbN/8XR/wh+i/88Ln/wADZv8A4ujQD4dor7i/4Q/Rf+eFz/4G zf8AxdH/AAh+i/8APC5/8DZv/i6NAPh2ivuL/hD9F/54XP8A4Gzf/F1W1DwvpNrYTTwxTiSNdyk3 crDP0LYP40WQHTUUUUgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooA8rhurONFiu9bg8OZtZgl9LMIvtpa5m/dZLLlY9u47CJB542vFk7/RNCl87w9pk v9nf2bvtIm+w7dv2bKD93jAxt+7jA6dBWT4X1C0tdBihnnSOQTTkq3BwZXI/Qg1sf2xp3/P3F+dN p3EXqKo/2xp3/P3F+dH9sad/z9xfnRZjL1FUf7Y07/n7i/Oj+2NO/wCfuL86LMC9RVH+2NO/5+4v zo/tjTv+fuL86LMC9RVH+2NO/wCfuL86P7Y07/n7i/OizAvVR1j/AJBF1/1zNH9sad/z9xfnVTU9 TsZtMuI47qNnZCAAetCTuBs0UUUgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooA//9k --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: application/pdf; name="nc>sz_5000p_avg152T1_GM_mask.pdf" Content-Description: nc>sz_5000p_avg152T1_GM_mask.pdf Content-Disposition: attachment; filename="nc>sz_5000p_avg152T1_GM_mask.pdf"; size$816; creation-date="Fri, 18 Feb 2011 00:07:19 GMT"; modification-date="Fri, 18 Feb 2011 00:07:19 GMT" Content-Transfer-Encoding: base64 JVBERi0xLjUNJeLjz9MNCjEwIDAgb2JqDTw8L0xpbmVhcml6ZWQgMS9MIDI0ODE2L08gMTIvRSAy MDE4Ni9OIDEvVCAyNDUxNC9IIFsgNDY1IDE1NV0+Pg1lbmRvYmoNICAgICAgICAgICAgICAgICAg DQoxOSAwIG9iag08PC9EZWNvZGVQYXJtczw8L0NvbHVtbnMgNC9QcmVkaWN0b3IgMTI+Pi9GaWx0 ZXIvRmxhdGVEZWNvZGUvSURbPDIxMTAxRkQ5RkQ1ODQ1MjQ5Q0U3QzYyMDVDRTA3MjhCPjxFNjk5 RkNBQzcxRTBCMDQ4QkQyNzRCODhCOTZFQUEwMD5dL0luZGV4WzEwIDIyXS9JbmZvIDkgMCBSL0xl bmd0aCA2My9QcmV2IDI0NTE1L1Jvb3QgMTEgMCBSL1NpemUgMzIvVHlwZS9YUmVmL1dbMSAyIDFd Pj5zdHJlYW0NCmjeYmJkEGBgYmCKARIMfkCCsRFIMBcDCXYDIMHdAhITARKmu4DEJhcGJkaGmSDF DIykEf8Zj3wDCDAAkuAIAA0KZW5kc3RyZWFtDWVuZG9iag1zdGFydHhyZWYNCjANCiUlRU9GDQog ICAgICAgIA0KMzEgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0kgOTIvTCA3Ni9MZW5ndGgg NzEvUyAzOD4+c3RyZWFtDQpo3mJgYGBnYGCSZwACHwUGVMAIxCwMHA3IYuxQzMDQyMDHwKCvuPoU M5O/8wQuB6iOOf+gOtvAuhnWVEH4jLkAAQYAOqEIsA0KZW5kc3RyZWFtDWVuZG9iag0xMSAwIG9i ag08PC9NZXRhZGF0YSAyIDAgUi9QYWdlTGFiZWxzIDYgMCBSL1BhZ2VzIDggMCBSL1R5cGUvQ2F0 YWxvZz4+DWVuZG9iag0xMiAwIG9iag08PC9Db250ZW50cyAxNCAwIFIvQ3JvcEJveFswIDAgNjEy IDc5Ml0vTWVkaWFCb3hbMCAwIDYxMiA3OTJdL1BhcmVudCA4IDAgUi9SZXNvdXJjZXMgMjAgMCBS L1JvdGF0ZSAwL1R5cGUvUGFnZT4+DWVuZG9iag0xMyAwIG9iag08PC9GaWx0ZXIvRmxhdGVEZWNv ZGUvRmlyc3QgNzkvTGVuZ3RoIDc4Ny9OIDExL1R5cGUvT2JqU3RtPj5zdHJlYW0NCmje7JXbbtpA EEB/ZX+A7s1XKUICEhKkpInqqI2EeFjMAm6NTe0lon/fmbWXGJSkospDH/owmtm5e/eECEYYEZzw UBAhCI/hJMEREuEREYLyiSc5EQEJmA8ewrkXExERLjmUxIRHHiMSyhiX5OKCjsq8rJKtSjUe6gC7 M/IFzAgngNnv06u9uU6MMphznXCc2QTGZWHAN7YTsWwscJi1JI5p0h6qMk20mdKHyzF91HtDJxu1 0qNGTWb06X7+XafYarLBr7MdJhv4wqhp0e9P6WQ0GqpaLwj30TkDT7HQe3A0O5NAEm7XmEGj+wey VHmtwbgjnCaD9pjcEfaJCfr4a6tfPoyW2ybe70Mtjrkq0nKRFSv6LSsGRZ0dzpfZcqkrXaS6nvKY 3ta5qtc0b9QmK3Y1XVYqNVlZ0HmlnzVNVQX2ojS5rusMDZWmujB0vStWqtptcrUztFyVhf5BK5yx zOgyn7U7toPtZoMaC0nIIzpS2xudrdbN6VI3kZ5gIR3nalXDkwAYgX2j4bDcT3s8ZBBHGBiTJA7E zAY/q42mNzp/1iZLVW9Y5gs6MSrP0kGxyjVhNDF6c0O411hfrWV3w3KcXGVbU1b0qV3Il+Jwj5hy 2t19ElJrWcmq2ozWqoKXPOmJRGPKrWozuPBpspsbHI878JdN4KkWZl1P/cgDEEIipbQiwuhgOwk4 t+JiXugR3w+sRFFMQoBfyE4d+LCvy0eN4mr+JK4P9nCC8+PQt7O6EsDuGAuhv92jnWNj4I+gj80F P+a6vENda8ee1/haf/cu7A7tXqgx3tWnd+R2QI3342JO7P106uy+3fthzOaIyOrZ2TRL0SU5CJBk H0lmJJb8NZJfhTiKWobBeBdhId9A+Dx6w1N6xUfQi7eJ4l5G+v4RvfjiPOYfRu8hv0Mv+jmD32KH 2Lv4wrLotwv+Jb5uCRQviI/wdYh1Fz7YuAOIvTDQUbem8+fpLtT1CtuaVs5HNn7lxxeQ5fBP6g1k e/fzPPu500fo9rj4MHgPA86COD6BmIf/If73IQZgfwswABHdkN8NCmVuZHN0cmVhbQ1lbmRvYmoN MTQgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAxNzY5Pj5zdHJlYW0NCmjezFhN c9s2EL3z7vueOnJrUQAWX3QnhyROnKbjjFNrkoOd8SgybSvVl0XZSdPpf+8CBEXCkuikSWY6ydj8 wO57+/CwAH2TcBhBom0quQGLIC2mFhG01qlQsMiTtzClQVdJ7/CEfhWJUtwPNlZDV7EysmtQpIri KUCifzdJLpPXq6ibRCiZcm2A0T+B3GUPsNIadzecJL3fJhwOZhT3pJ/0nnPg0L9MuPZB9IsjS40C I4wjidCfJIyoMegP3Y+PSedkenz093GR317Mdpb/7PY/JM/6nj4aLMEyBM54qokKlzr1NRIGHFKG lwT4AejZRxoDR3D6jnAvSo1u6J2nAVUqjVnqaPvHjkm3TDhOKoC1B4yumf95QkXeJNKolJFsZaru vThiFmNN/GzcNNKxUESzvFJIUQoZpBRBSlbGMMAMKUxaXonYOciL0dUUjgbLxehTUI6RLA8W3izu ZDV4JceD4+4Na+hYj6pj28fdl/C7gNNDIXx0nc4LwR7i2OWUqppumoiG/pr8z5GWhNZuCjoqSL4u t8q4X1vfEVxllIKAFS29jAcGnG2nULaDH8FA+8aBJYMWEST/IfBoUgytpCNaBODKdZ4fwYAL1wIC g+0C/P8WHZWU2kZr1fTCFSKVb/SofbBvrzfRIBLSpXadHJ361TazoWiL91iGrOMkoKw9iFvs60SK rNzRfCoaRD6mmLBZfT21OF+DWkjqidQY1W2DVt2slA422jxdVfuuXSOZi6D3RthV4x7OpsvFoFg6 69SbpihjBGRYbpZS+U3X2ewY7gbj27yAn6BYDpajYjkaFvsA0Fv0LlhvML2d964m59PhefH5fDK7 OD96fPL7uWKMHVf+TMq0BFDq75iRaoimkqz3tNDwtKr25OmrBGlfbSgZMki00dJymdYk2LCDcaZD YUJXhQ3Ht8UyX+yM87t8/C1U11kK9PaqZ0qI+xRxjWLm1qykFR74/QnlJIViqPeQ31IJ/YOkczf7 lI8j5m20BPq2sYEXtXT3otF1NjAL4dgkN3ew1o+wNMCUfVkyUw14/vbZznC2WLhxjVSWhXnYlsrq ssc2Ux38sUq16XQi+VrO6lRXhoSKZEpHKIpxAkagmFUF1qC30xZItzy024ylYG70aefT3l97n3fJ Zh2YTHbf9V9+oZv4Zpsz6Rf7Qz7n6yZCR4waqrWrY+9pR8mM7Xa55SY1HZYyVHKXjimI4dYg3aI2 HQCZWuH512VnaRZUo7GMyah3yFSUDqL3p50dYQmHKdkBB8GRCndZMU7ZFZSpPHl5vV0oPcm02E4L Fd24jc2UNGgo54ql2NnhbAVawnMHKqQHZR7EqRlgUNqoesn5CobatI1ghEsthSIY2NE+OY2TtlGb DLUFQbrSpYxKyywdVhrya82216lUu/zinvyq3Mg8UEmsqp9vINmYADQq1p/zNv0Fxvq76ZXKeiRd oyINMxL9tBgP2v+5zK5kS9Uslt3dVtmlrLNLUV/TrMfC086cBeFX061V5pQXAdSKFuNLuaY8qjbp Y+cbR410pWQ7pnKK4j5nV9AHrRvdEBw1i1xhZRs3bHeFbnOFW4phToKAsiFfqyWsm9smKdVYK1k8 aX4ZhklTZgWJsoIUomkIOhzZaB1691W5ZWw31LEhqtzcXdJxm/TH7etwZQcRr0TODWt1/H9vhGhX zBxkuETxJY1QWGm30aJHfGsjFCL2oJH2IWkCqJBuZdTCZLqFQSa+rkVFwshaGGVWZlRfoovUyLaz QrveoIIuzlkVJm9IxDbvEPQBF7lesqbrRRa73tY4LqzCsQ2cbHunCsUpbRp9SqAV2wvdsEHHfUq3 9qmao6kv+fY+xTPbXDOCPkIjcfxZYmXOrNUaKNv6VLlQyr1DVdbQ2Rf0KWFEizOMjJ3R6CWN7Uqs WmNo2usnBxVPS+wL75l7vqhQWF3YCkXLbz4ncsQHzojVEbf6sHMIRpWnwhf56Op6CcvrRV5cz8YX +/BqNp0PFoNJCuGrDwbj+fXgEbiCpIYzVxl92gEdyc92d7uanEVFHeRXi5y+EGeXcEkXF7MJPIJp b0Dj7waL0WA6zKGYzGbL69H0iuJqcTHT3hvl3xP3QcDhYnY7p+/L/scZnAwm83EOfVjmxfJXKrsY DqYwzxdQ3L7/kA+X+7SLnXUOF/PHZ7t7PPOXTwiAPMZqg3RO8sFieA13szEFUG/LhBD5L0yTdpM9 QKWQBvpPqsKFoiq/ZuoEb9w7KEaf8304pRSK7UHz1zt36KfIbhVaOv44X0yoFPct7FhPbt3382zq haIP8YuRv9uD99d+5CN2n3j1GbMPb2Id8wv4OFpew/O3L47glLNP/v87CDzIU/8KMADRgOXyDQpl bmRzdHJlYW0NZW5kb2JqDTE1IDAgb2JqDTw8L0FsdGVybmF0ZS9EZXZpY2VSR0IvRmlsdGVyL0Zs YXRlRGVjb2RlL0xlbmd0aCAyNTk3L04gMz4+c3RyZWFtDQpo3pyWd1RU1xaHz713eqHNMNIZepMu MID0LiAdBFEYZgYYygDDDE1siKhARBERAUWQoIABo6FIrIhiISioYA9IEFBiMIqoqGRG1kp8eXnv 5eX3x73f2mfvc/fZe5+1LgAkTx8uLwWWAiCZJ+AHejjTV4VH0LH9AAZ4gAGmADBZ6am+Qe7BQCQv Nxd6usgJ/IveDAFI/L5l6OlPp4P/T9KsVL4AAMhfxOZsTjpLxPkiTsoUpIrtMyKmxiSKGUaJmS9K UMRyYo5b5KWffRbZUczsZB5bxOKcU9nJbDH3iHh7hpAjYsRHxAUZXE6miG+LWDNJmMwV8VtxbDKH mQ4AiiS2CziseBGbiJjEDw50EfFyAHCkuC845gsWcLIE4kO5pKRm87lx8QK6LkuPbmptzaB7cjKT OAKBoT+Tlcjks+kuKcmpTF42AItn/iwZcW3poiJbmlpbWhqaGZl+Uaj/uvg3Je7tIr0K+NwziNb3 h+2v/FLqAGDMimqz6w9bzH4AOrYCIHf/D5vmIQAkRX1rv/HFeWjieYkXCFJtjI0zMzONuByWkbig v+t/OvwNffE9I/F2v5eH7sqJZQqTBHRx3VgpSSlCPj09lcni0A3/PMT/OPCv81gayInl8Dk8UUSo aMq4vDhRu3lsroCbwqNzef+pif8w7E9anGuRKPWfADXKCEjdoALk5z6AohABEnlQ3PXf++aDDwXi mxemOrE4958F/fuucIn4kc6N+xznEhhMZwn5GYtr4msJ0IAAJAEVyAMVoAF0gSEwA1bAFjgCN7AC +IFgEA7WAhaIB8mADzJBLtgMCkAR2AX2gkpQA+pBI2gBJ0AHOA0ugMvgOrgJ7oAHYASMg+dgBrwB 8xAEYSEyRIHkIVVICzKAzCAGZA+5QT5QIBQORUNxEA8SQrnQFqgIKoUqoVqoEfoWOgVdgK5CA9A9 aBSagn6F3sMITIKpsDKsDRvDDNgJ9oaD4TVwHJwG58D58E64Aq6Dj8Ht8AX4OnwHHoGfw7MIQIgI DVFDDBEG4oL4IRFILMJHNiCFSDlSh7QgXUgvcgsZQaaRdygMioKiowxRtihPVAiKhUpDbUAVoypR R1HtqB7ULdQoagb1CU1GK6EN0DZoL/QqdBw6E12ALkc3oNvQl9B30OPoNxgMhobRwVhhPDHhmATM Okwx5gCmFXMeM4AZw8xisVh5rAHWDuuHZWIF2ALsfuwx7DnsIHYc+xZHxKnizHDuuAgcD5eHK8c1 4c7iBnETuHm8FF4Lb4P3w7Px2fgSfD2+C38DP46fJ0gTdAh2hGBCAmEzoYLQQrhEeEh4RSQS1YnW xAAil7iJWEE8TrxCHCW+I8mQ9EkupEiSkLSTdIR0nnSP9IpMJmuTHckRZAF5J7mRfJH8mPxWgiJh JOElwZbYKFEl0S4xKPFCEi+pJekkuVYyR7Jc8qTkDclpKbyUtpSLFFNqg1SV1CmpYalZaYq0qbSf dLJ0sXST9FXpSRmsjLaMmwxbJl/msMxFmTEKQtGguFBYlC2UesolyjgVQ9WhelETqEXUb6j91BlZ GdllsqGyWbJVsmdkR2gITZvmRUuildBO0IZo75coL3FawlmyY0nLksElc3KKco5yHLlCuVa5O3Lv 5enybvKJ8rvlO+QfKaAU9BUCFDIVDipcUphWpCraKrIUCxVPKN5XgpX0lQKV1ikdVupTmlVWUfZQ TlXer3xReVqFpuKokqBSpnJWZUqVomqvylUtUz2n+owuS3eiJ9Er6D30GTUlNU81oVqtWr/avLqO eoh6nnqr+iMNggZDI1ajTKNbY0ZTVdNXM1ezWfO+Fl6LoRWvtU+rV2tOW0c7THubdof2pI6cjpdO jk6zzkNdsq6Dbppune5tPYweQy9R74DeTX1Y30I/Xr9K/4YBbGBpwDU4YDCwFL3Ueilvad3SYUOS oZNhhmGz4agRzcjHKM+ow+iFsaZxhPFu417jTyYWJkkm9SYPTGVMV5jmmXaZ/mqmb8YyqzK7bU42 dzffaN5p/nKZwTLOsoPL7lpQLHwttll0W3y0tLLkW7ZYTllpWkVbVVsNM6gMf0Yx44o12trZeqP1 aet3NpY2ApsTNr/YGtom2jbZTi7XWc5ZXr98zE7djmlXazdiT7ePtj9kP+Kg5sB0qHN44qjhyHZs cJxw0nNKcDrm9MLZxJnv3OY852Ljst7lvCvi6uFa6NrvJuMW4lbp9thd3T3Ovdl9xsPCY53HeU+0 p7fnbs9hL2Uvllej18wKqxXrV/R4k7yDvCu9n/jo+/B9unxh3xW+e3wfrtRayVvZ4Qf8vPz2+D3y 1/FP8/8+ABPgH1AV8DTQNDA3sDeIEhQV1BT0Jtg5uCT4QYhuiDCkO1QyNDK0MXQuzDWsNGxklfGq 9auuhyuEc8M7I7ARoRENEbOr3VbvXT0eaRFZEDm0RmdN1pqraxXWJq09EyUZxYw6GY2ODotuiv7A 9GPWMWdjvGKqY2ZYLqx9rOdsR3YZe4pjxynlTMTaxZbGTsbZxe2Jm4p3iC+Pn+a6cCu5LxM8E2oS 5hL9Eo8kLiSFJbUm45Kjk0/xZHiJvJ4UlZSslIFUg9SC1JE0m7S9aTN8b35DOpS+Jr1TQBX9TPUJ dYVbhaMZ9hlVGW8zQzNPZkln8bL6svWzd2RP5LjnfL0OtY61rjtXLXdz7uh6p/W1G6ANMRu6N2ps zN84vslj09HNhM2Jm3/IM8krzXu9JWxLV75y/qb8sa0eW5sLJAr4BcPbbLfVbEdt527v32G+Y/+O T4XswmtFJkXlRR+KWcXXvjL9quKrhZ2xO/tLLEsO7sLs4u0a2u2w+2ipdGlO6dge3z3tZfSywrLX e6P2Xi1fVl6zj7BPuG+kwqeic7/m/l37P1TGV96pcq5qrVaq3lE9d4B9YPCg48GWGuWaopr3h7iH 7tZ61LbXadeVH8Yczjj8tD60vvdrxteNDQoNRQ0fj/COjBwNPNrTaNXY2KTUVNIMNwubp45FHrv5 jes3nS2GLbWttNai4+C48Pizb6O/HTrhfaL7JONky3da31W3UdoK26H27PaZjviOkc7wzoFTK051 d9l2tX1v9P2R02qnq87Inik5Szibf3bhXM652fOp56cvxF0Y647qfnBx1cXbPQE9/Ze8L1257H75 Yq9T77krdldOX7W5euoa41rHdcvr7X0WfW0/WPzQ1m/Z337D6kbnTeubXQPLB84OOgxeuOV66/Jt r9vX76y8MzAUMnR3OHJ45C777uS9pHsv72fcn3+w6SH6YeEjqUflj5Ue1/2o92PriOXImVHX0b4n QU8ejLHGnv+U/tOH8fyn5KflE6oTjZNmk6en3KduPlv9bPx56vP56YKfpX+ufqH74rtfHH/pm1k1 M/6S/3Lh1+JX8q+OvF72unvWf/bxm+Q383OFb+XfHn3HeNf7Puz9xHzmB+yHio96H7s+eX96uJC8 sPCbAAMA94Tz+w0KZW5kc3RyZWFtDWVuZG9iag0xNiAwIG9iag08PC9GaWx0ZXIvRmxhdGVEZWNv ZGUvTGVuZ3RoIDIwNj4+c3RyZWFtDQpo3gDAAD//AAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAg JCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVVVVWVlZXV1dYWFhZWVlaWlpbW1t cXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqqqqrq6usrKytra2urq6 vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6////AgwAy4Nf Rw0KZW5kc3RyZWFtDWVuZG9iag0xNyAwIG9iag08PC9CaXRzUGVyQ29tcG9uZW50IDgvQ29sb3JT cGFjZSAyMiAwIFIvRmlsdGVyL0ZsYXRlRGVjb2RlL0hlaWdodCAzNjQvTGVuZ3RoIDEzMzQxL1N1 YnR5cGUvSW1hZ2UvVHlwZS9YT2JqZWN0L1dpZHRoIDQwMD4+c3RyZWFtDQpo3uybh5rbRpKALU1g BpFRyAwzkr17990j4P3f6iqgQeTAMJJFFL/12hwQQPff1V0xy2aZZZZZZplllllmmWWWWWaZZZZZ ZplllllmmWWWWWaZZZZZZplllllmmWWWWWaZZZZZZpllll8ucI9PXe5118Z3V48KJoyhe2yT7/kr gPKLVufiMoD8m3/BulRve/kX+Y/yFV+iH7d8c3lH6Bnldc9ofgXXj6r/i9qaKt66udBueMhXLKlR V/3m+gFt44CO/fHhOnKtftQHAZO4/T760bWptlCCoS3hl+lHn2YkSRzHyfAK/E10Y8yfYpZk2r7w dfrRruD8zyDwATwWiOPy36q/+S30Y+CV1BdhKCMC8MMkOZxOH/kvfw/96F0d3Tx+Oy0ZfJsGD7/g 8cBzZKp+QN81vuc5lmWy2K7b/L1i8hvoB4z9iefZ+YhsFyCM4/RwyMadig9ff73LAsC1bVPX95qm 7fe6ZR1aX+t30JMxb6AucRxjT0PSdN1gJEEYRlEUP+YcudVjrvy1xqPXF/6V/jlMGVuTB4QQQTxi FPDgRdV/f9ysTFPXtN12u93tNMOIz+dOtf2FOgKTLvNtG8e0lTHpOu5ajuvi4Y7HSRBFdx/GhPMD Bq5RPLabzQbfXtchSXruOODsPur8ABh58yoPGRMqvmGYFomNuuJ5Qe+sPHLNDa+ECTx+nY6Mf+ZI HncexVj9GFrNbfrhhOGAxl3u+kX6Md25LvOgLQtPRhEEg0im3fNLlxWe55aFZx+dH8zD8v2h1/pi FYHp1/p8nsuYhEchOtrA9573sdGqEb+awKOxnX+NfpSj56P1YwSPrMOrepjTOOrGQQCuaxnGfkdC tojrkk3owx1u/qBR+H6n1ShTTOEG3IZx9ncyLCahsUmPZ4l170GM0o9x8dkoClBFHAddEHlfw3FU 4Cfs+X17MOju+tF2AuICchzH7fiJhH5oQYGHJ2NJMxo82vMKj1lpY9lP4PELNKT1ISUe2Y087jSK Yf3ojubWvknTJI4RiY1blvBA34mG60KvfnTlrO6qH20nIIBE2ywKtbX8RGChURvHaZpSFMswyLIS IMzDnLiNjHjzm2Ik5c8JjnAAHmS+lnD92MSEYgx3ecrVo2q7v5zUe143flv0x3Ho7VFDDji2E0Tg uS5SMQQJnudi8vrdT7m/fkzIf59Ox8MhaeURBNPPqHvqR+vSrfBo+UnBIzgcTqdTBlEHD7+0Ch58 fkzeFH2JKu73Yh7q6MjiKVLn8cWHSNu9w9A1DI5LbTUKfbZsVrYtPMLDofw7S5CQR0i+uu26fhTd axAD2Y7R2Qr5xnVxu8VVZO1269VqtV5v0QtBHgPnR9aWkb+ffrSaPgCWpslL7kyzckUY+jSIOo/S FXiQiNDpw+stkQvgyhd+kHaM5fG1KgLtD6vyyAZ5ZEM87oGg13cepUPFN7Zt8e6ESJDH8v39fbFY bbe6bY/SjwaRe+lH6xEIjrNfrxf4ksvltqofETq2JR4AUU0/4tg3CyL5ERJnLXG++66vicvVlD3V shzWj+UbyWK91kzTnfDMB5yBbV+iQ7Fd8jsuFttCP86UsQkUDxac6+h4LP/0GIa2GL5yhBQ87sGg r/ZwjA5dvpnCo+eOPY+9Tj/a3f8GDxjkAUM8GpmVe66tiQvVcSgzuNuRjYt2IvJ4fX15eXlbLneG 4Yw7P6568FW3iyJnv1+9veEbvjIP+fpTeHAmls9BOg+DIK7+OHZdCkFQMEvTG/pxI4Srs4FNHrud hHUppal4vOLu3MJjjFV3J/1ovZfvm8UbIg/Lkis+Pz8zxcOnMAlwrCep3pR4qIivxssvt+cbM/cI /3zkp58HhPepm7jbqBo8+NtP/GRQ49F49xYelfgDPMA/hxHXlCUpeKC7i0dIsRswD3eafnTt+dfo R3t6BfVjuy121L3jlPUjVJUjGZ8lyter+B8FD11Hf7D6kN6k5712sQFJXNco8zDNzWKBh+X7arVD 7zeOH3t6TR8I8RCLY7Mx1NPKPHrfwMbzQ/GgIrkbZ29opQ7WrsONPMZEle+hH92p7QYPGOQBQzwa D3tckmpQfN/a79nAYh6ep63Xy+WS/EHXbTq4X6Qh3fcg53xJstnv2x7OBVZ+988tMSeRRzM4d8ur t0aSxuhQ/ZsE8B3FJnddH/ddfl+qMgkgbf395ydFTYtvfv78OTCqyfrRs5NTMJETsDodHuqK8zl/ pZxHUD74qvewxAUxjLB5AdxTP66jm0zikTV4ZA0edxhNzx0aPLJBHtkQj3u8OtztmxTA5nQbjYO7 cngb8zt/LzwOKjhU4vHx0VrTP1U/eksH67sR0FORh7wPNU7QOMp7Ue1QdRyp5G/Rj+yOjawjG89w adT/HMc+x3cDHoUstDRNO2+JPI7H4+FwKJ8uqjA2pZ/Dw4aSv1lZTgE5Hn7xNhAEMpL2oFrs++jA k4ZMfvSUd4aR3bR1HjCJBzR4QJMHZHBtOgHqG29TP+o8YJAH1HhAncdQVOC+S4pfN/93SVfWLjif kzAcv5vi/vSheFwUhGNGVNQfx8cbBzTxx6EErDwvaew+rUeqbRsqK23fUz9GRSpSiCwVVcCP8LDt 2lUf+BkbE/nBnxMc+HO8xF1sLlemMoLm+1w5qjH1EuBx/saybQ9He/me3sVv3OMAYZkHvqx8H+In vl//B3Tvtswjq+iHXV8Xcg6PuusPFi6BICkyDCUeYXaTKT/tl1F04SE7Wf575tFwRA6HGg+lZGHI in21gjR36Oo30p9JuQB8MdUS6HKBhVHmAZNmBpAHOsNkYOU4Lt1huSFpmm4QVPeKSefHQEF+7YuU uwTFgLUBTnyF58mwcx7Q4MHTQFW8wgP4/PT9WNkiNydvmzcgHi7nZiQdKzxUGRn1CQ3Hptpf65Oj E0IEzxGagiSJpLyRcwuG4wRXD+oIaAABlxcGIy6n50qJLpdVsmoWKSkvSerXn06h6vOk+K4AkxlC g6bHqJw0Vy1mxFgeU/WjzgO6eVwRDqK9u8JjSD/G8IBpPOB2e72tAFx44KsJA9klpbJVbKFi77xq J8cjB5GQW4xgFI+ddPQYhpNdeYZ8QBrKm3teUR3V94qWpe/osdQCZVlezkNsvbCpH2xLSl86wsiN StsWPH4cX63X0MBRvU+AXirQQivzyIDc70uK+Sr9qH3zITxw1RnGbs2y2xluuQFpgn4gj0COPa4e Tob0A3nst1t66GajGYbLE51xfCEIovbHQxZX92rmYaLVyQsAHmKuEw+/wSNp8LhWPy4rupvHFeOq 8xihH4M8mtLPA26i0J0qiFv041LfanMD8KB+sBFr9WvMIU3jIPBME3mspFLNMErXTzo/EuQh70cV IkVIU71GXT9MU9tsVqqGz+bNuDbpg+E0xcOTDRLunJhSkteHiXle6I1tS/TQZJvd7T+X8E1VNVDf hcgDd2Xbpqnh5ASqiHndEQIJGp8S8KMCW5lTPpzIlm5ezjzokavVZr+3iAexHD4cS8LGsVXicRWG wU8EJR7FtzUe/b5vmUfPdSn6tg0eZd94in5AiUf++4JH8w27eXT6+NCs6VA88r/f4Mz2/DQKQ5/D CXbJASQcKgMgCcEB/ZCmIl3vU5DDAfUD17WJps5yuUBZrzV+6OShIQ+cfX5DfD10LVNewIbGb8Fq +rMUToiQx3pNj8RFoGmm0JigH/+czzH+CJeoFJFee4CMqraKAg4uO0XqBoiHqTqFNN5we57hs3Uv TV4m2ufnTosrDAKfj3ScnXeuAN5IIHmau8v6UQpoWBIlVDz2pomvez6S5Nsv81hIzTEqJfMY2NEh +89//v7775wHnv+2rcso8Ql08D0q2BM1eGQ38jh3XdrJY+JuDA/g0ZQhHleD6C/KhjjMS1rdyzU8 Wm273ZATpQzTjmfEnmepJk+TXdiwWqJ80Q8yroEajDebd67/WG636kifGN+VN1QudyQ8pPUPeSQJ mnOpyoK4aGSvVvTE93d8osH2bs2PbOoH+7In3PXEekRHVuaDy2xu0I9GJ0z1C3QL8paIy6TTaDUN PVqy2bfbfS+PQxC4lpWXIEvNte+nHa9CnXs4ts1m8fb2+vr6vlrtr1GQEg9UYNsWl0AKoblhkCI9 kYjv27q+RR74PF4BBvexDFYdsxuWpOlJBqdp23UuW00zrtePehCrxgMaPODuPGAEj2v1o40HTOfR 1I8hHvcJumcUhask8sn1cyQcdDk9THz+Rhy3zaafBxlO7J3tpeiENxDoUA7gDkSEvWYeVCEvXgtM G1F+aDGPPXdeu1yFQA6s78c837gP09GIhup+v10uhQdVyDGPwa5H/n2A7r8qx9ooR3ZzLY+m3cJH mXtx17k/U3UCXZqwuUJvLI+Mu9ODQFP7q2aa1SwP5AtBhKcSF+yC+zM2pulnk7ZjvhQPLcPYqwNc usscnkK6goeZh4IYP1rY76piUadAoq28+0Ts4oZ+xBJWpVZE4kFDq/C4S8z9F/HI7sojG+QRT+fR kEfwaEYhWaMLHiF6uWpUGmXtzHzP4WpdeT6dH2PiuxxIz1so0TGsmMg8gRLY55Z1NKZxM14KD8OA aRtW/vYeryQ6MErWNv3hfBb9AI450t7LHmhu0SEPikPmPLglp1U/QL0sXlps3nkf6/U86n6/xEQh gtKpKMX1W25/2jMGCYVu+J/UZz6mhgGXvFo/W5waOpKqFQWqxMwU43GzKXh4E+Ml6s5nYG9mz6tn y9WI/G0MnIz1OF1PjlTBg+1dvEzSO3nLWjEbpQ/DkPe1kDvyWMnYeEpwfFeeH/UfCY+iw300j2H9 aPKoros+HlfpB+pB2OCBRtM5nsjDj5qPfwyP5ig541V4QsCquBW7Fj0/fPXtVirDl/hw5fyYI55g lXmgPvOuCGy2ORydpJ1FKlfQo0YXHXlwQIl4UIoOpg7qI47FyirscuJxOASuZEbyGdXZDMPpXEj8 Ct1b0l0Qu5KweXVPRP1UShr2uJHvdpdZUVNyjX40q2SpZCVr5cHnFPLAF5dgBp7kWw4O2DCNB7r0 e468CgyTj6hCCzWKcRg6249rzoJM5JEPKg1DDzEXfoEsfEmr4r5fzKjYqmrNie56OQ/J7/jVhSvJ IPE58kap3a6YlaWcj7cETEoSXsUj+wIekwd1Xx7ZV/AY6rIQP/zCA+eRebDr/E5PlgacIf04nU5l HrL1mWy+0E5VbCk8a+RRq2nasnHMVSwT63fP58T3HbadLjzYXCTTV80oGV6FvSjOY96jCXyiiY11 cdPO52NRI7ankpSNmpjV+/ubOJSr1RZ5FH2Fo+V8PsQQ9oZp8vO8zAM958XilWXBK4FSlEOzkySJ Xdi7LHgrHUEyD7EWZffNdU7N0o7TFcjjPFU/jscIAHVhr3igcptcxUYWME+neoY8R9tLOkd6yvOj jRPSjlfsHRzNyQPHfAMcEL33ZoM8ZFbeuOvKvIoHNHjAVTzgC3jAr+UBD+dxPKJ6BEFfnAZUFGjL rjWZjLq+QR7UzEzBPtJMp3e7woGEJJTVEBeSc3+412l48HBl14UHjoytL9rOxXjjaCDyOE3kcU7T 3NjVNHl5atZia0omcrvZqIfQUzReWLZKKTrU+FB4fMSD2w+IkBx2TFBTZhWuIzw9ZFbQRKccI87K xL5ifGPggp7uNjm/7J+zi4vvqq1Wb/Lk5RJPP9wp/X79kDChWqrLdzb08bfcvS3Rw22Zh8qfC5tr eHx8HKLI58nT1buTOqq5lGWRJ8yFOtsXlqrJFR5SM8Q8aKrE+JACSgGieCx4THUeUXSYzAOo3i3s 9KpH8+ivaLiVh1Txj+cBH+hT38gDSjzwjgE8mMcxjgNuW+zRDwovimUn265pup5nbLeL2k7Zrx+q Ql6OoiUH0tE8w7e2uO7UKh0t5GMulxdLhT1HHNlxUn3i4RBxIL14eXxzpGrxXOZrgh+BywLtLo3P DPQTJa+s9MMXlxDNctEPDiAYysYVHuhH5jfD28k/r+SRokYX/WGcnGm5SFXCFaU95Nfq+prXA/Hg wmfoM9JUrAeXquLx8v3799fX5W6HPHx8hFilEn9Be/rt7fu3b9/wEuaNPLwwnMTjyM1zjgSk+c3x EABJG0to5PU1fwRO3naLzhC+SeS6RUIXeagDw+WTGQ9aj8tH8uALW8cEg41/GtDLC8cjCfHmfjzg Kh7w5/OAR/NIuLBKePiyxTdNrVJdqHQuWZxr26/XEu0Rs9UthbzqCsYDkvBUHibC6WYeC67k8Hlj kV2ZRrndLl9e/mJ5WSw2XALhBsEEHmdAT9BV/U5isVKdOn7DaRuy1799yx+B26ZY3rgeSwUozCPM 8qSMz/ohSedLEgePOfTJ+dj4LjwWC7Ec83gp7bJX8Ajx7SWSiU9uOuwS5lTBTKmpt1U8k3hwh1BX Wb/c+eKEcxg158GZauryZi2U4JxW4vH9/X2jCtomhEuPEKnuDFMytZyBFgWlWUQF/euvCw9UcR52 2MZDen5PJ9QPOow42C1FNZo6hl4KHuJA8XlE/jlZhbfyyJ6Thz/II/gSHlRQH0ilI0+02zgKfBUS l8iPuK9UnCC7KFojEvAp+lIaPGSjEx4r5vGieFCCiBzSPPlhcCqihQfaC+N5pBBwsWj+qtTH5QdJ IjVgO047fi94cMldmrXqRyr3wxc8nU6FfogX2coD7UEOtuvsUEKSTIny4HkekHaEXlHvjOsJZ7Z2 ikhxopSmckTB5BIN8lNVuoIOTFwPbZ6+K4plGGUeYqgTDzyr5Vdcsi2podXr6zfFA30UvLl3OEw4 zhOuiFSvSkofBFEcF8buouDx7RuaDKqrgXkU9q5XX2BB4DR5yPnBSCReoUn6htfnxHgJ8+AwTY0H 1KWFB9cy1Xm0yTCP/Bm9PGBKuXgCUOUhT+jhkb9rlUdjKNN4XFffTn2PRVRH5yIM3Pk+zqUaW47j 2DUeuF64oD/fwcSmbCQKCv0wlLErwas3VQuql2LDUKROF4uXbyS4mUh8rCVfOlU/osgqGXi4wZC5 +/Lyvl7vVVdKyP7gXrabnEf5UAS16W445iltEZT0eMtlwTE5mh4+T6/Kf0iLmM6xkHy9oxOekKhr /DzPrObeKvWo5UnvUli0cv+fP105GBQPcsKX0kywWF140K8klS2lHrn/v1rtuCH8kE2p9xEepfMD D2QJaFKymSN/uSf9/r7a7QzPk90Qp6LoU6F9olrFC1xCpOzdLWfm8tHk46H8nK6mIbwyPzjMw/8C HtljefiDPLJBHtlX8MDfqaC0yI7TpSZqa3GcsUt4mXROWlRuUkJl1/7082ceRCp4SCpCZVMu+qGO GZ23lJXyrDQ8PMLwNIkH2lelPKA4gwjENc18Che55J4bnfZS4UWHYr7m2IlEKWwUvsKRoCgHr4pK m00pnZ3PEHdJX6UfFR5FXQyfB/Iu0hhplQIPzWygrQoAm7kQUBlyvTDdeTQEHs9qx5HxFs5NnjaX 2CIrUN79BxMHpcI8EuNBX9ljHsXNVxyb0fjwzeNv7IIXwSHlulRS96VWlj0n3TeS8ueA775Ysdfy gKt4wN15wAge8EgeMMgDvoJHh37k1ZW4+USRXwopUutmazpRanzlAvzP//u///kPy3//G/ic9sQ7 sO8tLiTdnMfs4N0+Pj6KvjspB5FRMjbDcXw5yaboR4IzW1tDaO5YlkQtN0U8UOVFyoMq1hYb/07B 43//9788pEyF8IvA4parZehJ/NPb9EPs6rz1TD1BDizqW+EGy6KVFo+HQ9beP+6oaCMd6f/88+OD bObzx8dnRDlIr5rH2XP9rsfmZJoktmoNpaHIZbICuUwjkCamKfohycFygMc7HsNSB4IYL5oq7naD y80d1aqiLFeB8R/4+YljOh5TccZUAcxO8fBYJ4s5uFI/smt4tMmjeUwqb5/OIxjk8fMreMCBeBSx PPUE4UGJ0hDKPBzwezK7ebU3zvMH3jeBGP+XwInuIRH1vbLfqIQuwWvodz5bmTz55Irn01EYbHi3 g3rGhDWWQnmbdRzJGufpwrzN09gVPEpV5Z6aDbF7PcrV/g/8g1fQmAIuaxfLvFwDIzllqZ+jE5bm 6QoelNdUHR56XuOllXlww2s+sP46qzCUnk/kQelSzuYEYRhHEKKHz0Vk+7xtgQIzTpqm+BMpLssd H+bheeVsi+v7U09zuvp0kiPkwiPLPjlJZUtmDJ+leKB/DtQs+5/8t1wTpDwx6Rz+5/PzlKY4Jk8K hnClSLBYFK1QY4LFU8D5oKv04xoerf3jD+YxrX53Mg8Y5AFDPOAOPLIsxknhHUnEZIcn9wo5GB5K TBCl1H/eKhFXyBATrpSQ5qEgohovKNdFsBlGX0rxueAXHmKe4VDYyLbZM0izyUODz8+83kcSmur7 E2dCXRmqYchZgiZccDrhUffjn3/EQDcrPNA1+XE+U/QVd1Zl/WlSU8dLy2SrM5E9ghfk9fqBQOIQ ZPJYVHeaITE1yk2QC+JWup3b7pNlCQeM6dKSruCOK+3rTuHGc3+tdMpoRWkPuhq46krR5RwnG7vX 9EdRNRNItvjy09PpdDgcZBlbVt7E5lPsL07T048f3OdehK4LHp9UOhigwaypMsotu+ISkckj8/gQ dHLcvBc0vra/Fnn4vTyyBo8OuQ8PNXElHtNHBlUeWYUH1HhUgupZF49giEdW53GNtUv/QIsTVEcU 2UKqXeVyWpxOcbn2sUM/TqxqEvTx8o2U5lTaJssOmiNlolxNlrcucJFHOfwF0kt8Ocsn1cNxiWLx 0lA1vvDvvJtJs41MIW2M5/NPfphrmmpntTnu/iEay6Wy9L6rFVWQSEIYV1/uIGVJMW469a7uP2+O Nhjsva4J+tiyZUsfmNj+eBozEZEicSs1H9IBdWklQR7V8DBFmNVAp4+l7Tc/0CnCaf1bDEu8PTpG 7sWUh/h0+im/dJySp80ID6gfzn6/Lnjs97mrcbgknmJ0tHgCXLICbmvHUTcFxaOkwvVUV4t+3J8H KB7Q+dS+86PyypDzwJX++fffbFd28qCLazwyNBjx/OjgcThCfr7VeNykH5WfX/bSrLa5tgi+QiKC 6kploRa6VLnPxWxUY6Sq42JrzeAa0YIHFUF53tB7TVpm1R/+/HniCoPkcDhx8jMR61WZ2rh2wuPx ZwGPjMoidvp5qOjHgpu2aCCed8Sb8YGR6wdPgHO7fmRZ17xD84rLH3MLX1loEjWxxZREY9IwXNVV bpWFbd9dqW5aOrxaHnt52KTzozgp1E/PZzQOIDfBIzk/LBXCkeAp8ijRrD01hhB5bPh915y4yTMP qjLHhzhkfbOKXS67TUb/vgTlcTyu147ybBZveqagb8EjHs1DCfppAzziOo+r3rvSFZBN+gCo0Jte tI6r7vG8+JNTjZJBKf9deGgq+r3jIl237W0q7zTFvqp1O2RwBBVhlOoPl/uA8lJ14YHXVH9bvYtb RLzQZudQI/Vp0Gh1itmz+qn1mcEX6kchBQ/eiPO1hmZiiYfBCaVTllV4KCRFHNVtLae7fkjQ0OjU 515heUOLS60rPDwvOp1q+lG+y+kEqtstN3MPBynCka8cLhiy5IJr3x0ao4cRmOAreDQW2LR+Nah9 MYHH5feV43UKD1HJG/Vj+g2KQrpy2yd36kkQbKe6BVI2aaJL24LtKJHi2ggNn7spR8uv0bLyTFOT lgbOV1jCQ60hipc3eFb1I+LUhERW0SBIjsdTlYengDm3nnsd62pIP85ybpDlZ0pvh5x3222eThEe QZDkRyWnrXjrRbc94aMWnej0AKemyXD9amn480A8cPvfSnmOtAUXWTgpcSq5wO36gSYWiF1iB0EY Q4qWM/9FEtkex10N3iy6tppH68eNPNKUnFviISNTthDAzWdhVneoruFRe4sqj7iXx10ITNcPZULS 3mnjbrAUf4nr2C7xthKPjFoSKT8QQkQ8srrfUxioN+tH2XWhf3N1fcvhMmpL5DYfs/SS0DIPTR// wBGHEA3mhPQj/4tE6ZgHCfUdXrW8b9aPHx8fKbq8R/qh7zuatiz87f1ePA6qsw7D5ERy28OuGc9n /lQOSun6RsKX0oYqTS3iL1RDA9C5NrsEJ0H6MtAuOX/cyyxs92J6vil4wCQezdUDIxYHTB8V5Dzg Djyg73VqPD7utOKuvEO+HgJdXxfxQe4NJO1Gfxj3Wk49/Phi/eDE7Uk9q8kjD443MjvT9aO6Cm4B cJOHXv1EjrPlamUaMveiRbnHS58f+JnyrJa/XzEq4KfyvRx1fuQNsKS4E0YOg+8H9f//RfqhHtzC g47x/K8/UL5aP7IfyKOkHw0eYed4WuyrkbN3y8Cg8Z+Tzo/ar8JwjyaW8Nhs9o4TkAnS/vtB3/se 50dWPqvE3l0UPFQpd/PmHf7H0PtBZXX+uvOj+FWS2Jq25m4I4mFZXHoLd3zUlcorDzOMHbeYLZiH IW1XXa92hcXaknX5FfpxGfBYHs3kwOP0o/S0Ph4wyGOkfsA9lxzc/CPT3HEJxmqz0UyTu4va5hq+ Sj2ykhXP8asVl4dsuDr5vvpxjxjJffUjo8wzVY1wVJHqYsOwe6P9Kv0o8HNFi7QyUbmOZblD+tG3 2luHBXDr6r5ZP6q/GebxpbpReck+Ho0BTT8MHpCibQ3aD3xTV2ybE7FSN+608Ring/fUj3wNqKQA dY/v91SWd7F3oY0HTDs/rk/Y3FFBGksCJA/KOQanSe8XKId6CdeVTjBJmEsBZpgk7VNwzflxjw3q 7p8KD2jK+Oz8Pfzzcga9lQckPf45TPDPW/z1X6Ifjf/kLm2pfnXKSvGLNOPymlz9YZR7f5r6Adm1 2gwPe+/bcGTncyrVJDjg7HcRgLwboNKbChCm6Y8eABNr6h/z3tfzkEqn35FHNpLH1dMBj3vvq/HJ fyCPMJQKUTf7fQSyJKFOLVW86riuFwTR8djHYzwQeNx7X3vxpeslSQI+Q/3fikeWHeI4CgJfmRZB FB3O5/6xwxUT8cuAdCTQfkse9H4DPFqVYZSGPNKMn7BnQsd/cLsdyW/FQwU94yiXOE0/hlfimPmA L3jxq9VDdOR0OpaKSn4jIFkmL1YvsGg9DMfNx4O93JG37033/4Y8inF18ahcCuOBwFe9+PU4fk/p HVdj863Vb3XfFH7xmzevgH8Hj765g2ZsfZSGfNHQh2o2/5U4upDUBlu0VQEMzMfXJg9GrqZ/y27V nNr2vsgyhX5sX74OOxo57x/o/yVQ4IpSi6Hu1i/FMrie/gCBf/1w/igcf8CCmqdgxjHLLLPMMsss s8wyyyyzzDLLLLPMMssss8wyyyyzzDLLLLPMMssss8wyyyyzzDLLLLPMMssss8wyyyyzzDLLLLPM Msu/Wg6HNCFJ56mYeTyBwJRPDCEE4OMnKL77gHbJHvF5Bh4jvznEcQDgOiyuC0GAf2BJ236lqFz/ 1OZXcyNveaOq8+D5pp2re+sqNGWWu+tHiDBs22SxbBuBhL5/0QLoRVJXllk/bpHTKQlDcBzLMAxd 1w3DtG3H9z3HsVlQXbwRzHv2r1km6ccIHjDqGd1nyqwf49UjSXCzcizLLPNwXdy8DBbTshzPG818 1pNb9ON4TPDsRuUwi+mn8yNwHFPX9yy6adquO+kZDSKzfowU5BHcnUcrklnG6EeSBJ5Hm5USy7Js xwls29jvNZaRPD4/Py8wLv+c9WO0fJ7PaRh6aOaqc8Ki49vBs4J4aNoOZTSP0+n0o6kks5aM149O HnAjD2ggmfVjUH7+PB+POFuOacpBoTMSlyIlWXYX/SiQzIoxrB/EA51yF92OPU89HRWGYcs1UWTK +YGc0N7tm9IRPg7M+jGsH3CGY9DKI7uRR6uOzErSv3Z5gny0a/f73TaX3X5vFNcA0D6m6yaFs8Kb 9IO+qTvvs3607CDA58R2s1mjbDZbVBHLKi5KOGSCzrl/ONymHyU1ee5pH/q08ij+WvCAw10yTuVs FjxjPqpXOUQ/9IIHEtnt9pYVlC5NoyhKkol7Yp8c0ah7Zv1o+aa0ZbTy8Nt+hcbxMU3TlgzV6BwL SxiGcXsa60kxVWainUfbz6bw6BPikaanJz1GoGevYimfH+v1drfTbbtNPxIfwPO8PKHreVH3MxyL xXFa3wNv4vl+dDg0TvanJFSfBI7jovfB1u5O03WrMtfqV657SZBwjgSn+6j+eODklXACviFdgq5+ 2wtYHLN0gyB5Rsd90FNu5QF35wFNHhzWGogBP5t2MBHTLGYZXT8vbHH9qNQBL8CdbYt72wbJ7dGT //iQmwaS60WxLJu9ffLrTdMJgoZ2WBIrM3BXfEYIzS26zfZxKmVXLde4kg7ZbFZLltVqo2mm4/zE v+HBgrQ4uILOPF224zikYTi+X38PhMq6uENFlMAvPLF+dI24ziO7O4+si8eTUYDGf7T6BiFJ1M1N eOw2m+Vi8Y6yWKy2W7SMvR8/6GhRPLT9XkKTXfqhMy0y5vjn1TTis+nHDdYMSNix4PH+vtxsNNN0 k8SV3LtK7uY0hEdD23T++2ZFghpm9dba/eH6AV2rcEz+4lYe0McDnlE/bhptxGlEOj+qPJwwxK3M 4tIUnZEoHDuyrxr3IR7b7XqJWBd4h71tPxkI6P6XbNo3qCLoyePSzk/0fh4cJ276H6RG2+3q/f0N ZbHY6Lp7+etT7Vs3D/Y2Hlk/j6dB0bRd8N/iOC7HBEfn93DL2uPuL4IGEjraruu5rq1c931xiuz3 pm233NE00d5dvr29fP/+/fV1sd1abIM9jeVb5IGKnI+LU+hBqQNqfPcUqKJFmXNyIwzD5a4RRGKJ ny81jeiqt97FdS3k8fLy7a+//vr27W291vFlnq8/qjRU4VG2REfqRxxP5NF2xzYe6u/Pc36URhpy CtYVKr7vdzjkrRL5vpwWuDtt+SBBr9CkbC7gruXynS1VBOx03WW/X728/MXyslxqjkN+6LOcIDXL JQg8XOOlLgJSlPEWVxT5noezbuGcvr+/vr6+r1aabftxHAaBL/1U0jXiOJ13dN2Cx/f3922eHoYn 0o/LQFt5TFCQq3lkI3g8DYpirKGykdhh25uOM00/eMtConQGvL6+vLy8Lpdb0/TCMM+FxHEURWQ+ 1COJlTuuXl8LHhvTVFGsZ9GPknoA2IahSQ4DXQTbnjoHeWdbmYdhoBeSTrnJ9v39e8HDMLw4fhoF gRE8pujHOB4Dd2zn8VxH+p30A3ckX2zWtzc8P95Wqx3zSKbcw1mv319IXtlFd4Lg+CwKAnUejmHs JVuuaYacuhMjWlSCrWnrxUJighrnZZPjcbx+ZLq+WS4pjbJcbvZ7CyDJnsbkLY+yncdEGcFjSNp5 PAeN8jDFocsbA7mU5Ar9iDzP0nWtqIe3bW+qfuB/o0cpFV+aZrpuiD9/Dv2oDhK9hKJ7s6M+asQt +RCSNHjB43Sadg/L0ktq6lOJ8FPEr0bwmJoRGeYxQj+aPJ5RP9AlZCQUvwIIrr0pOZXSUqXr6FT6 UXTIa7FGLhKAcn8imnlk8j5RfLfyTRQilFKcZHrG0LMsQ1pATdMGCNP04++/R/6+iEjqZR7o1T9T /qP6CSG4Iv9R+tR5QAp/j/1tOw+Inir/cedvyCU0+QxyHJxJ3Kz+Gfl7tM1UQemuyDNaFurH8Ul5 3EV8n48gqmeMj/Bj/A85o4XnOJXESSp+vUYP5ol53OWbAR7dv595PESSJOIS0zhNz5/w+aNB5MeP T5Qs+6jZXcIDWSyXqpCLIpJeHJ9m/bjlm9OR5IzzfoZznQcIj/OpEDifz8fDIUadQt+FebxLRHKx oBBvkpxn/bhFenlkDR6nmcdj9SP7+fOn+BzIo/Xqz09idiA5HgGRHNLUtajeAT3zXYPH5zPGSx4h PwDn/ZAkcRSFvu+X2qsKHgiEeKCVrOvcJ7LfrVYLVaWI5wek6ZPEdxtA7qUffTygwQNG8HgW/3yE HHA+B1J8HGNpVKP8/BlFkCYJtfP4LLX2Q+EhHaHchJh3JRRV7mjv2naIJ/4zqEcLj7a1n3LBTn9E K5RynkbAOAghloAYV9cFzUgUl6RIAWPBY48qotLGJuWjnqT+apx+1Hm06scEHtlkHk9UzADD+oHO HU12FOU7Uuv5oXhUUyHgByDFcAgjosL5tPZ7aaDKy0i5LYECkfLvum5wsP30RPW7g8PE1Y1zzUkR /KfXfWFTQTwqAoZcdfDsiONDI1FolfvblVhUwkji+X6Mh8cz1cIBDOhHg0eHfTWaB0zk8Vz9UdBf ahYpGLnwxXalm6aqIkUtaJFnLHgkybHhq1vFHqXrpmXZeau765Xe70lICIsRwjPrqfnldKo98lfu 0FUVHqgW1A3ULk9DpXcriDxPSHjsIuD6dVk/WhUkZB8jt8So5QN3HO7/QEH9SNNG7OSiH/syD6Vk z8WhpT8K6vsPoeAj3ZNWAZwynCJu2mn+SkypnIdqLQDhgRsXhQXrz6jw4CYHn604yGob1qwf43lk k3hkY3hUt9VZP8o8cNup8og69CMr16YoHl7BgyvV68/g7tvcvhIezYvgmfqjepbfhQe3Tsn8ur4f sS3b/psmD2QpPJrOuVwoFXgocn5MesU/Tj9qLvUIHtDgAZN4wBge9bjB04RM+saZe4LIw7IssV9x vmox2hIJv9yIJvYVVzWIwdqlHxl3HeabYcnzaNta/3T9gP74FZ4HQEiIRxGGgrRlssLQL02rkC7U onLOt/n3vrSpl3jAU+5YQ6NUwasKjzRtvXI0jxZp8pj6pn8ojsYXMVDtDh4eaFYlKGn60VpRJZ21 haXESCjcHkFMBUBJ79YDWRR5igc8r36MHCROJ8egPnrK1AEcLtvNEximSV9S8g9Gtn74hT/f8YBn 4DHumxqP1msm8Gh/ao3HsOY+BY+rBQ8P09Q59U1tNIaB8LgycfQzxCz2/XDWjzt8o3hwlnW703Wq GzmdPn5MvOMpz1jN+nEf/RjBo1dOXa2GT8AjBqCy57voB54fVR5xTOnywxk+Zv0YI2kUBSCf6JZu qOJD1Z7q/EAe+Z1TOMIJ/3riT/UXB/w7feJxT3gCHhDUanBuOFFqPOTOMfI4nc687utL/3CII5YY RmxPfzqPhKt4WmoGrxbu3tAK/aCIVRDGgC4kCu5eNPNxnFA9YvaD/XlXsuVU9tDwOnC5iMP/VDyg xuMWi8vzbNPc73bS6UyxR7x9CIgBDykCL1HFkCqywHXtIhNlGJbj1G8JkPd2WpZbTRg+E49bZCyP cAyP7Gl5BPfSD+4ez1105sEbUV7vI1uT7E4ewmBy2zUL+49W+Ya+j7fKqxaFxx8fv0p5zRKPrrTE FcJHiCTDLS7X8lThD1XoslD417KoT3CzWS1YlsuNphnqHnEQeFzrLvchHrhi/nj3g3hQ3sG/l331 YB5/PJA4JpMUBgrXp9wvPuIRYhh5sYjwYKHejqIWlEvZDcWDmmgXi9V2uy/2Km7M0dQ5ZHJJ9ROE 29M0Qvcc8l7xW/WDSxHDMg8p+2TlmMTDlaOF/XyN9Sw8HP58f5B4oKkjFQttxVTT5OE8/ng5nVJI QmX54Mz5rcfmmG88z+LqEFd4cEeNQRUQeSW2I5ZrwYNrrnDSN9wnWOEBgJuVNONs2O5yfD/JniBe wkROIW/uFheW41FSnOzkSKfp4Xhs/eGZm/4u4jgG64SNYKTHCXng3DtOzkMqVAoa/ERx5tcrEjR5 9eJWEpRkHnuEyi1uT1HNcIIGD2jwaNOPr+fxLPWJITvVso9Qr4aqHenXD4rSl/6UOI7O5hBO34UH NaCZtphYTk4kt3el3UYaalWfYMFDVzy2u53hOO0dcn+mQCYnsBSF2Nw9E0rUL0lSTii16gdOqFfy 6oWHhBGtwm9gHpZ0VTGRvGmT259c7itwxV8RfnIv2yYedC8ydvEIinMez6MfZR5Bg0ebfDGPJ6pv p2YnZaEaOE0U8IMQYkgghUN7XojbYS0qtFUf18157PdmcTcJPnGHlMcVdfnUy+y7Lj0nAI/7a1Fc ulMEbJ6JJ4g48Uzjd3wO+wrl4PuOWKi4g3Oc1aUQStAX0mINMMoRWQCTF/WOj3GjJMjDV6Vy4lUU AcQIjYLT6fzxEXOZDyocSAZFXgbVCE2M/DB/Gv0Y5gG/mgd0vccfKVHkcR2CbCMyC4ikRz9c6WYq dxBGEULVlBso+09uX3lewJsV7mLokKN1u0RZrTaaZhY/P53iKAJhltNg9x5/m6ZP18gZsPegFUhk 0+/WjxYeQDcxpYscbVn2xiV14UQQcnBXeCCNwiHXLxoGZ7F9dxJE1NBYRpBeEMSX5z8NlgEeY/Qj u5FH1s3jmdpr88GKiaVp+XRI6KlbPzw5P6r6kRUdU+yQ2+KMo1nc4PH+RrJYrzXLUiYzmPxXOlvE N+ScRxRFhx49/ZO5SJ6VbVY0/PdkfXZfzGvfVKsbz4v6BZ7nihfuunEMkTJ2KTrCPF5fX9+Wy61h eKoPQQIndLLgYa9paCz4CdWlPFX3eWlv7uEBI3jAjTygyQOIR7m28anIBIEjWQfFw+m5Vho1ceni LsRnht64m+RoXWrhgUR6pdmKowD729vLy8vr+/ta1x128nGz2q7XSxbksWO3PKJQ5nNpR3XNuS5t 4nKClHk05gS9ak+6YAsetWs+PiJpbgvDlH+P+w65ILq+W6+Fx8vb20rTLC7qQUt5U/DYbkk70Rep h0mejU0Xj4aEQYNHdn8eh+fTjka3uWVJgRqZN23XRNwElbeKo0/NJVJk4zZm7nw8Un07ZUnyv8XI Gy/dbpdvb99RXl+Xu53JppfOPDgNst7u9/glu4H9nb/PIZILoUBF82/oQiMAPvbJSWCzOLdr+9Lv 6k54PrFJXfBYsAsiPHYq4YFLwWOtekLtaFlzLTzgV/AAeMr+qKawXeQBNEt6I98XG2yzWuV7Cxqm KFQM4fpkubZ2mat5VKnACg9VG7dX0TMHIDocnlI5svbakZCLgCrX4AEuNi668eJDcMxjudlIkAUk BcupDujgYbFRixyXcp7j+cE8XL6zpHIdzwviuCMd+Bz9gy2fED+gckD84azRWB4d3U0jeUDceYcn 1Q9qGqdaBSjyDoVZZTGP1WLBHvbb+2olVWu6qojgCrhaw3JDP945gvX+vmIenupDoMruOE6hK93x pFsYyefnZ5YpE+dwaPJ45QX+tlyuOQK4l9wTBxDRDvZ7eaxyR2OFx4+pDAdqnUqSp/Q5BvVD8ciJ wBfwgJwH9IRzn5uUmgSAsn6Ypr7brUs8aH5LPCjQiFtQ2zzaXLAr/KghGu0ptqbC8rOefc5H1Obi JCXKvnK4g3azXNL5UeaRZ2c5dlL27ct3LMcr8Vr0XdBKzouGIGs4gLN+dMiJ2mQfyEMO83m2R+pH Bh9HqskCKWrDOd9yzPyN7SsSNJPyoFYvD9e11LZme57P3el0YnTUHs760SkEBN1m9L65mFFTfWZL 6cbc7QzxIBhII11YOqJVUoTs2sPh9KRBqlv1A3kc05h2rXvyqOxRs35MlDMiYZfN43SFlLVtVGm6 KS0F3ATl1GOLpXmMY7Zr5/PiNv24/CtQatBBPSjqg7h2zQYleY1J5x07Du9ZP66TKIJbeOTRrVnu pB8oSUSTnmcJ8xJdi7pGlHT+/qIWY54668doSVBLuCvXKRpBqQo9SxJVTdWYyvnAeJR+0Lyj6ev7 FR7QzgPaj4tZPx4gURiqMLkfdejEPI+T9eOGz1i59u5PmI+6RQ6HhP2JiDKss/wOcj4fa43os8wy yyyzzDLLLLPMMssss8wyyyyz/D97cEgAAAAAIOj/a28YAAAAAL4SQIABAPgSg6QNCmVuZHN0cmVh bQ1lbmRvYmoNMTggMCBvYmoNPDwvQml0c1BlckNvbXBvbmVudCA4L0NvbG9yU3BhY2UgMjIgMCBS L0ZpbHRlci9GbGF0ZURlY29kZS9IZWlnaHQgMjkvTGVuZ3RoIDE5L1N1YnR5cGUvSW1hZ2UvVHlw ZS9YT2JqZWN0L1dpZHRoIDI+PnN0cmVhbQ0KaN6St5fHgFiEsECAAAMAOvoKpw0KZW5kc3RyZWFt DWVuZG9iag0xIDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9GaXJzdCA5L0xlbmd0aCA0Mi9O IDIvVHlwZS9PYmpTdG0+PnN0cmVhbQ0KaN4yUzBQMFcwtFCwsdH3K80tjgZxDRSCYu3sgCLB+i52 dgABBgCNhQg3DQplbmRzdHJlYW0NZW5kb2JqDTIgMCBvYmoNPDwvTGVuZ3RoIDM2MTIvU3VidHlw ZS9YTUwvVHlwZS9NZXRhZGF0YT4+c3RyZWFtDQo8P3hwYWNrZXQgYmVnaW49Iu+7vyIgaWQ9Ilc1 TTBNcENlaGlIenJlU3pOVGN6a2M5ZCI/Pgo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1l dGEvIiB4OnhtcHRrPSJBZG9iZSBYTVAgQ29yZSA0LjIuMS1jMDQzIDUyLjM3MjcyOCwgMjAwOS8w MS8xOC0xNTowODowNCAgICAgICAgIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3 LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlv biByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9l bGVtZW50cy8xLjEvIj4KICAgICAgICAgPGRjOmZvcm1hdD5hcHBsaWNhdGlvbi9wZGY8L2RjOmZv cm1hdD4KICAgICAgICAgPGRjOnRpdGxlPgogICAgICAgICAgICA8cmRmOkFsdD4KICAgICAgICAg ICAgICAgPHJkZjpsaSB4bWw6bGFuZz0ieC1kZWZhdWx0Ij4vci9kMC9hbnVwL2dtX25jX3N6X21v ZF9NQVNLXzUwMDBQL3NwbV8yMDExRmViMTcucHM8L3JkZjpsaT4KICAgICAgICAgICAgPC9yZGY6 QWx0PgogICAgICAgICA8L2RjOnRpdGxlPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgICAg PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6eG1wPSJodHRw Oi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIj4KICAgICAgICAgPHhtcDpDcmVhdGVEYXRlPjIwMTEt MDItMTdUMTY6MDk6MzQtMDY6MDA8L3htcDpDcmVhdGVEYXRlPgogICAgICAgICA8eG1wOkNyZWF0 b3JUb29sPk1BVExBQiwgVGhlIE1hdGh3b3JrcywgSW5jLiBWZXJzaW9uIDcuNC4wLjI4NyAoUjIw MDdhKS4gT3BlcmF0aW5nIFN5c3RlbTogTGludXggMi42LjM0LjctNjYuZmMxMy54ODZfNjQgIzEg U01QIFdlZCBEZWMgMTUgMDc6MDQ6MzAgVVRDIDIwMTAgeDg2XzY0LjwveG1wOkNyZWF0b3JUb29s PgogICAgICAgICA8eG1wOk1vZGlmeURhdGU+MjAxMS0wMi0xN1QxNjowOTozNC0wNjowMDwveG1w Ok1vZGlmeURhdGU+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICAgICA8cmRmOkRlc2NyaXB0 aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpwZGY9Imh0dHA6Ly9ucy5hZG9iZS5j b20vcGRmLzEuMy8iPgogICAgICAgICA8cGRmOlByb2R1Y2VyPkFjcm9iYXQgRGlzdGlsbGVyIDku NC4yIChXaW5kb3dzKTwvcGRmOlByb2R1Y2VyPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAg ICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6eG1wTU09 Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iPgogICAgICAgICA8eG1wTU06RG9jdW1l bnRJRD51dWlkOmFkYTJkYTI0LTdiZGMtNDdiMS05ZmU1LTUwZjU0MjlhZmNiMzwveG1wTU06RG9j dW1lbnRJRD4KICAgICAgICAgPHhtcE1NOkluc3RhbmNlSUQ+dXVpZDoyMDc4ODAwNC1hNmMwLTRh MmEtYmRmMy1jNzMyNjU1M2IwNGE8L3htcE1NOkluc3RhbmNlSUQ+CiAgICAgIDwvcmRmOkRlc2Ny aXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgogICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg CiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgCjw/eHBhY2tldCBlbmQ9InciPz4NCmVuZHN0 cmVhbQ1lbmRvYmoNMyAwIG9iag08PC9GaWx0ZXIvRmxhdGVEZWNvZGUvRmlyc3QgNC9MZW5ndGgg NDkvTiAxL1R5cGUvT2JqU3RtPj5zdHJlYW0NCmjeslAwULCx0XfOL80rUTDU985MKY42NAIKBsXq h1QWpOoHJKanFtvZAQQYAN/6C60NCmVuZHN0cmVhbQ1lbmRvYmoNNCAwIG9iag08PC9GaWx0ZXIv RmxhdGVEZWNvZGUvRmlyc3QgNC9MZW5ndGggMjcyL04gMS9UeXBlL09ialN0bT4+c3RyZWFtDQpo 3nzOUWvCMBiF4b/ywS5sQZMvbU20iNBZBmOWyezmjRBqm2mZTUoS0e3Xr8P73b+c88wBYbGgK6sq 3xqdV14FeRohYxgxwTjO42SCfIQ4Cu+VsUGRlevscQzlSUFR+dPV2C83hmddE/hQ1g1DIEhCkEQz AfvgLUIU1T4k8NorOxzpI2y/nVddCutWX24QEU7ihIgJ5+SzZjG5zbjkCTww2BYb2KkGclUDmwKK FJM0RngvVzA4Ee4pCWlhmv/9G2uaS61skNXWHCoPeet8ez4rC/OBGw3UXasbc3X7MKRl688qoJY2 SCt96emxk7qW7kd2ppFFtn2RU0TcUNd38u/xSR2YIL0Ll8tfAQYA/5lhng0KZW5kc3RyZWFtDWVu ZG9iag01IDAgb2JqDTw8L0RlY29kZVBhcm1zPDwvQ29sdW1ucyA0L1ByZWRpY3RvciAxMj4+L0Zp bHRlci9GbGF0ZURlY29kZS9JRFs8MjExMDFGRDlGRDU4NDUyNDlDRTdDNjIwNUNFMDcyOEI+PEU2 OTlGQ0FDNzFFMEIwNDhCRDI3NEI4OEI5NkVBQTAwPl0vSW5mbyA5IDAgUi9MZW5ndGggNDgvUm9v dCAxMSAwIFIvU2l6ZSAxMC9UeXBlL1hSZWYvV1sxIDIgMV0+PnN0cmVhbQ0KaN5iYgACJka/WwxM DIxtQIIvE8TqBRG5QImFdkAWAwMjkGD6DyQYGQACDACD3AUbDQplbmRzdHJlYW0NZW5kb2JqDXN0 YXJ0eHJlZg0KMTE2DQolJUVPRg0K --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: image/jpeg; name="nc>sz_GM_5000P_w_mask.jpg" Content-Description: nc>sz_GM_5000P_w_mask.jpg Content-Disposition: attachment; filename="nc>sz_GM_5000P_w_mask.jpg"; size9041; creation-date="Fri, 18 Feb 2011 00:08:49 GMT"; modification-date="Fri, 18 Feb 2011 00:08:49 GMT" Content-Transfer-Encoding: base64 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAPAB4ADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi uo0fQIIdaurHV44LljpV5cQGz1CKVEkjgkdGZoiwOCn3cg/dJyOGpwWdjJ4F1C9Nu/8AaMGp20Im Mvy+VJFOSoTHXdECSSewAGDuAMOiuo1u105PD0Vx/ZkGl3bSx/ZIUumlmnt2Ry0k6ljtbiEqQsQc SMyqVxt5egAooooAKKKKACiiigAooooAKKKuWemXF9b3U8MloqWyb3E13FCxGCfkV2Bc8HhQT09R QBTortINE0ma48PSJZyCKXQrrULmOSYt580BujyRghGMCjC4IXjdu+Y6EGkaF/ZNvr01hptql3bx MYr2S6NlC7TXUZVREzT7yLZWGSy/63JGYxQB53RWhrtimmeIdTsIo5447W7lhVLhlMihXKgOU+Us Mc7eM9OKz6ACiiigAooxRQAUUUuDQAlFKATRtNACUUuCK6jRvD8EOs3Vlq8cFyx0q8uIDZ6hFKiS RwSOjM0RYHBjPy5B6E5HDAHLUV2tjo+mPpen2ktgjz3+j3upNemSQSxPD9p2ogDbNh+zrncrH53w R8u1/jHwTJ4X8M6VLJpt9Fd/a5oL25njZUZ/KgdVQEbdqs0yBgTv8tmBK4CgHD0UtGOO9ACUUUAZ NABRSlSBSYoAKKUDjpS7aAG44zSgZpxGaWgCPvRV200ye+hup4XtFS2XfIJruKJiME/IrsC54PCg np6jPUwaLpE1x4edLNxHLoV1f3Eckxbz5oDdHkjBCMYFGFwQvG7d8xAOJorvLmw0ez8MQ+JxolpK 90luv2CSWf7PEXkvELLiQSZxaIfmcjLvx93by/iXTIdG8Vavpdu0jwWd7NbxtIQWKo5UEkADOB6U AZdFL0HSk7UAFFFLgkdKAEooooAKKXB9KUD1FACYHelIwtL60h+7QA2iux0jQYLP7ZFeRaHf6mPK aC3udXiFsYT5nmP5scyr5gZYgEL5w7Haeq7ev+D9H0eK606zS0u7vZfTIs8063oSCedNyFV+z7Al vuYMNzYkCkEpgA8zooooAKKKKACigDNLg4zQAlFFFABRSgUdee3pQADrSHrTs89KtWemT31vdTxS WipbJvkE93FCxGCfkV2Bc8HhQT09RkAqdRSV6LqWg6Qlib/T9Jtb23h1KG009LK/MsuoRypMYzcI HZ0cmKMlFWIndIoCnBTlvFFvY217bC0jtIZ2tw15b2Vx58EMu9wFjk3PuBjEbH52wzMMjG1QDDoo ooAKKKXBzigBKKftFNIxQAlFFFABSgDHNGDn0pwIxQAEDFIopW+7XZ6N4etNOtr641n+xpGjeBEk uL17i0XzBKcE2bs/mnysqPuhQ+7BKZAOMOPSk46YxXo994W0aygvLH7HsuRDfzRpdSub/EDzhShj P2by1EPz7/nO2bZnMefN85zQAvUcCjGKByKXHNADKcBxzShPxpwjb0oAYw4ptSlT3FM289aAG0oo 204DFAAAAKMD0paKAGN1p2B6VatdMuL+C6nhktFS2Te4mu4oWIwT8iuwLng8KCenqM+g3vgrT9Tv /wCytKt0inttSWzma1E0s0EYSVpPtKOdskwWFiBb5QlJAOseQDzPt0ApAfauk8c6Img+IktYtNut OhksrWeOC7LFwXgQvkkDJ8zeDgAZBGBjA5wGgAI9KQjHPFO64xThGW7UARgZNOwMVOtrI3O2nmzk AztNAFKg/SrDQOOoqIoQaAGU4DuRRt9KdQAmB6UEDB4paRulADKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooA+3/An/ACTzw1/2CrX/ANFLXQVz/gT/AJJ54a/7BVr/AOilroKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAt2N7caf M09rJ5cjRSQsdoOUkRo3HPqrMPx45oW9uE06awWTFrNLHNIm0cugcKc9eBI/5+wqBeQKCKANbWvE mpa/vOoCxaR5TM8sOn28Mjuc5LPGis2cknJOTz1rH2+9OooAbto28U6igBu2k280+igBu3mjaKdR QAmOlG0ZpaKAE2im4+bFPpp++KANKLWtRgksJI7jBsYmggyikCNmdmRgRh1JkfIbIIYg5HFWx4t1 gXEkpktHR0VPs0ljA9uoUkjbAUMakFnOVUHLuf4mzjYO2m0AOuJZrq5luLiaSaeVy8kkjFmdickk nkknvTNvNLRQA3bS44p20+lG00AM20bfen7TjpRtNADccUtLtNJQAUUUUAIR8tWLG9uNPmaa2k8u RopYSdoOUkRo3HPqrMPbPHNVz0NC9KANWHxDqdvph06OdBBtaNXMEZljRs7kSUrvRDlsqrAHe+R8 zZpSXtzJp8Ng0mbWGWSaNNo4dwgY56nIjT8uOpqE80lAB2pO2KWlCk9KAG4oxVhbZ2xgVL9hlx90 UAUscYoxzzVprR16iojEV6igCOilIxSUAFFFFADf460otb1GCWxkjuMNYxNBACikCNmdmRgRh1Jk fIbIIYg5HFZp4anUAbY8WawLiSUyWjo6Kn2aSxge3UKSRtgKeWpBZzlVBy7n+Ns41xPNdXMtxcSy TTyuXkkkYszsTkkk8kk85ptNIIoAOSaUDjBqSOJnOBWjbaRLMeFz+NAGUF9qXB9K6628I3MoBERI +tTv4MuVXPln86AOJK57UAcV0F34fuLfJKfrWTLbNGTkUAVqKUjBpKACmt0pfXmkbp1oA0NK1q90 kSramBo5cF4rm2juI2IztbZIrLuGWAbGQGYA4JzYfxRrL2c1vJd+b5u8NPLEjzgOSXVZiDIqsWfc oYA73yDubOOppSKAGUUVPBA0rgAUARBCakWBiehP4V1Ok+FLm9K4j4PfNd5pfw1keNWde396gDx8 Wcp52n8qRraReoP5V7+nw2hCgFef96qt58NFZCVTP/AqAPBWiI7Ypm3Br0bWfAlza7iiZx71xF7Y SWrlXXBFAFH+tNB6U4jb3phoAXPJNJ3oooA6MeNdeSaOYXUHmJvLsbOE+eWRkZpvk/fMVdxuk3H5 25yxzkalqU+q3Cz3EdqjqgQC1tIrdcZJ5WNVBPPXGenoKrdRTTQAlPSMt2p9vC00gC9Sa7rw54Kn 1AozL8pxjB60AcfBps033VP5VpweGbqUcRMf+Amvc9J+H1rZxK86bf8AgVbf9n6LaDBYA0AfPD+E 7sDPln/vk1nXOiXMGd0bfka+mB/Ycny71J9qgu/Cem6jE3kgEkdM0AfLskDRn5h+lRYx0r2TxL8P DDuaJMHnq1eWalpstjMUkXBFAGdnmm570HI4pKADPWtLStZvdIEotWgaObBkiubaO4jYjO1tkisu 4ZYBsZAZgDgnObTlPFAGyfFGsvZzW0l55vm7w08sSPOA5JdRMwMiqxZ9yhgDvfIO5s4n86kpUj3u FUck0AEcRc4A5rXsdCubtgEjJ/A10/hLwZNqLozoSpzgZr2Cw8P6VoVuHu8BgM4BzQB5Hp/w/urg AsgA91NbKfDWQjnH/fBrur7x7o2nExwKCV46ZrDl+K1ur/LED7bf/r0AcxdfDedEJVRx/smuW1Lw ld2hOYjgf7Jr1qz+J+nzMFuIiAevy10UVxofiKL9w+HPY0AfMM9rJC2GUiq9e2+L/AQVHkijwcEj 5uteP6jYSWVw8bjBU4oApUgOaCcelNyc/WgBR9+tbUvEOp6tbrBeTxsgfzHKQRxtM4BAeVlUGV+W +Zyx+ZufmOcjPOafQBNfXtxqE6z3UnmSLFHCDtAwkaLGg49FVR7455qCNC7ADmlxniuy8IeFZdVu Y2KEruHegDL0nw5c37LtjOD7GvQtH+Gckqq0iDHuprurKw0rwxZK1zjzAv3Qelc9q/xMOTFYKAB0 wlAF2H4e2UK4ZIs+9St4EsCuNkP4V5/d+MNZm+YPj8KrxeJtcDBjKf8AvkUAdrf/AA0hkjYxKvTs DXBa54GubEsVTIHoprptM+IeqWUgFyFdM8/JXc6b4l0fxPCIpfknI9Mc9KAPm24tZLdyrjBqvXs3 jbwMFDzW6eh614/dQPbysjDBBoAgzSHgYpM9jmgknrQAlFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAfb/gT/knnhr/ALBVr/6KWugrn/An/JPPDX/YKtf/AEUtdBQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEqDinYpE+7TqAGkU gHFPpQMmgBgXIqRYi3QfpVm2tTKwAHeul03w3NcgFY+MdwaAOU+zP12/pTTCy/wmvS08EzsmdgH/ AAE1nX/hG4gUny/yU0AcFtx2xSYGa1b3T3gbDLj8KzWXBoAj2/lSEU+igBmKafvCpccYqNvvigB4 6Cg4PFHNIQefSgBD1pyrk9KQDJFa+mac91Kqhc59qAKsFi8p4U1rQeH55ACsLH/gNel+HPAgkjV5 VAHqVNdtbeFbKEBdqflQB4HL4buFXmFh/wABrMuNLkhzlCPwr6ZbwvZyKQUQ/hXM634EjeN2jjXo egNAHz48ZQ1Ga67xF4dl06Vvk4yexrlJEwcUARHJpKU/SkoAD0/CkXpSnoaRelAC0UUUAPjjLtgC uh0rQJ7xgFjY5PYUnhrR31C7VQuR9K950HwzaaTYi5uVQAAHkY7UAcDpfw9mlRWdGGfYV0Mfw7jC 8qc/QVral4503TWMcS5K8cEVk/8ACzYy3+qbHsRQBRv/AIdMELLGx/4CK4bV/CNzZZIifA9q9asP iJYXLhZlxn1Ircms9O160LQCMlvxoA+XLiBo2KlSCO1VCMGvTfGvhM2LPNGmNucgD3rzaVdrdMUA R0UUUAMP3qfTG+9T6ACnxRmRwoBNMro/CeknUtSiTbkFhmgDZ8MeEZtQZGMbY47V61pXguzsY1M+ 1T6ECtCztbLwtoK3M6oGK4UdPfvXmmv/ABAvLud0tndR2waAPUz/AGRaLtMsK/UilS60aYhftEB9 sivB2v8AVLo5e4kP4mmtPqEOGS4k/AmgD3W88NafqETGJoySOMAV5v4n8BmIu0aHOTjCisbSvG+q abKvmTO6A8gmvWPD3iCx8WWPlSACbb3I69KAPmy/spLSZo5EKsPUVQPX2r1r4jeGBalp0QAggHAr yd1KsRQAwH5vSkOO1AB69aD1oAAcUZ6H+tJRQA9FLuABnNek+CPB0moukjxsQc4GK4vw7YG/1OKM DOWH86+j7dbXwj4V+2OiiQ8IOhoAd5WkeGIF8+SIygZ2nHFYOofFC1tiUhRGxwMZrzXWfEF7rN4x Mj7SSOvvUmneHLq9USFCQR3BoA6qX4s3W/KW42/U1dsPi4N4W5gCg9+a5GXwtOjkNH+hqCXwvcbf lU/kaAPY7HxFofiOPYZYkduma5Lxl4JSSF5I0/hJVlUV5sXvtHnBVpIyvpxXrvgjxPH4j05tPvTu nGApYg9v/rUAfPup2UllcyROpBUkciqFeo/E3w/9hu2nVQAWOcCvLyMGgBKKKKAFBpAMmirNhAbi 6SMDqaAO68BeFn1O7jkaNiNwxx1r1691XTfB9kIlMbXO3BHpVbw5aweGfCE2oOih1iynbnH/ANev KNV1K617VpWLs25zgZzQBuat481PUZCIXZVPTDGubuLrUrjJeaYk/wC0a7Pw54LkudrzJxzxtNdZ ceCbeOHKxKSPagDxpE1FSCXmA7cmtfTvE2saRIGEsjKDnBY16F/wjkEhEKxru78U3UPAitbNtQZx x8poAn8PeP7TWkWz1Py0cjAZvXpWV478HpPbtNCu7oVcAc1wGsaZc6Len7y7W4I4r1LwNrY8R6I9 hdtumQEqWOelAHzzeW7W87IwIIJHNVq7Xx/o/wDZ2rPhQA2T0964qgApVPNJSjrxQA6uq8G6E+ra io2ErkdveuVUFsda95+F2jxWukvqEyjEce/JoA37+/svBekrGuz7Ww6HggGvML/W9R165I3PtJPC k80niDU7nxH4ikwzMMgAZz0r0Dwx4Qito0kuI1ZuD0oA4KDwzdSpvaGQ8dxUkHhCWaUhlII9RXuc Oj2yxcRr06YrG1LS0tG81FAycYAoA8mfwnJuKojHb3Aqg9pqOjTB4/NjK+mRXvOl6NC0IkeNSzDP Sq2u+GLW6t3xEuccHFAHG+E/Haamg0zVNoZiFVmPtWL8RfCgVGuoEyDlgQByMVzmuaXPoWpCRMrt bII4716Zod2vijwgI5cNNFHgk8+tAHzlMhjcqe1R9q2fEdl9h1SSLGAPasagAp46Uyng8CgDS0Ww bUNQSFVJ+le+Wq2ngzw4LiQKJ2TMYPB6DmvOfhTo4v8AWN7AFQOpFbHxF1dtR19LCAnyovkCg8dc UAZd7qOoeJL9iodlLZABNb+l+BZZYg8ysMn0FdL4N8LxWmnR3E0YaR1BPHSu2trVSAigDvQB5lde EorfqhyOegqQ6JbPAixx5cj0r0q605JAQyAkj0rE0vSx/aM27BVCcCgDkX8Bm5hJCkHHoK43WNAv dCuvNj8xdrcEcV9Fi2RFGAMEc1zuv6NbX0To0ak89R7UAcT4Q8WR61bnS9RKiXBKsx5P51wvxD8N nT7szohCnJ4HHWl1e0m8N68kseVUHjHFd/4ot49e8FpfKAWEXJxnng0AfPLDmmmpbhPLlZcdDUR6 0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfb/gT/AJJ54a/7BVr/AOilroK5/wACf8k88Nf9 gq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQBMn3RTqan3adigAqWBN8mKiq/pke+4UUAdx4P8NPqE6fISNwr2vSvC0Fn Am5QPlHU1j/DTS0FoJSoJ4P6Vv8AiDV/sMmxWxg4xmgDVj0m1YYAGPrVW78NW86kKufxrmrXxW4f lj+ddDpviEXDgE9fegDzPxp4N8lHkSI8Y7141f2rW8zIQRivsLX7CG90l3Kqa+X/ABhZC21GQADG T296AOOPFFK33jQOTQAlRv8AfFTFfSon4cUAOB4pCfTNKOgpaAHwKXlUepr2P4c+GPtjrNInygA5 z715Jpqb72Nf9oV9O+ALRLTwuZwo3eV1/OgCn4n8S2mgRfZ4Su9QPevP38eXskhKuME/3azvF95J eazKGYnp1rLgtMIG7UAddD4/vY8b5PzWus0Dx3b3siwXLgbiBnGK8muYQE461BZyvbXkbAkcjpQB 7Z4v8Ow6jphuoV3KwJyD7V866zZNZXbxsMYNfUXguc6v4XaGX59sXGefWvA/iHYi11mQAY6UAcI3 40AUuOaUUAMbpTV6U9x8ppi9KAFp0a7pAPU02p7Rd1yg9xQB7Z8KfD4nxcSL8ozzmpviR4tYTnT7 RwFAK8D3rrfh5bC08IPcAAMA3P4ivGNYL3/iZ1YkjzCP1NAFe0sJ76QuVZ88mtE6WIVO5SOO9ene E/DECWCyOiklB2qh4o0qGCT5FC5A6CgDy6ezfJMQP4VreF/FN5oWpRrI/wC6OQQR611emaRDLDlk DEjOMVyfi3Rhp10k0YGx8EFemfSodSMZKDer2+RShJxclsj17xDYQa/4aN9AA2Y+cV8165aG01Ge IggK5Ar6P+G9w2oeFLm2kO4BcDP+7Xh/xDtPsviG4AGP3hqyTi6KKMUAMbrT+1MbrT+1AABXs3wf 0YXN0szLwGB/SvGlHzCvpD4PWoj0O4nA5VCf/HaAOd+K2vtLqA06BxsiJXAHtXMeFvCtzrEwYRsU yeaTxIW1DxtPGSTmYj9a918FaBHp2lxnYu457UAchcfDnyLTeoO7b0zXMnwrK1y0ZU5BxXvd8q+U QR0Fcj9miOpE4HU5oA8y1L4dXQsmmSNiQM9a5XRL678N6+uSUw4VgR719HauG/s+KKIbY3JDuBzw BgZ7Z5/L614j8QtG+w3S3OwozN82Rg/jXFDGwlipYVJ3ir36HQ8O1RVa+72PR/FFrFr/AIUF7GA2 QAcfWvmbUYTBeyRkYwxFfTngaT+0/AUsT/MQCefrXzr4rgEGuXCY/iP867TnMEDFIRxTqRhxQAyi ilA6UAekfCrTPtmtKxXIXB/Wu0+MOq7JotNibCoi8fnVH4I2ytdu5GSFB/Ws34ms1z4xkjzkYX+V AEfgXwpLrE6zMh8oMOa9103w5bWlpHGI+QBmsvwHpEenaNCFQZYKTx7V26gLGSQD/SgDita0iNJA 4Xin6ZoMMqBmTIIzya09ZZWj61Lpsh+zbY9vmbflDdM9qTdlcEcV408CR3NlJJBEdwweteReHLib QfFUKP8AJiQA5r6Rs5dQvJnivY5RF5ZI8yLaM5HfH1rwv4i6SNK8TQXEa7VdicgY5Df/AF658Lio 4mn7SKa9dGa1aTpS5W7+h2nxJsE1LwzDfIM749xI/wB2vm2ddspHpX1JKP7Q+GcLNyVh/oa+Y9Rj 2Xsi+hrpMinRTivpTaACuj8F2f2zXoUxmucru/hdEJPE0fGeKAPUfihdf2b4atLFDt3x8j8q5T4d +H21G5a5kTKcY/Otj4zM32y1i7BMfrXVfDiwjtvD0DlQGZQcigDstO01bZVCjAxzV2e1UxHA7VPb ICQM5pt7OqIy0AcvZ2obV5ARxzXTraRshBXoKwLWQJqLHGc966i1ZX6HPHegDzP4h+FkvLJ5Yk+c ZPX2ry3wDeyaX4nSFjt6gj8K+jtWthNbzK6ZUqRjHtXzzrOmSaD47gBwPMAdQMjjkd/oaydamqip N+81e3kUoScea2hs/GLTAES5VeGQtn8a8OPBr6Q+KkIn8J202OfJ9PpXziwxmtSRlKOtJSjrQBYt U8y5jXuTX0QzDRfhhvHytLHt/Q18/wCkIH1KEf7Ve9eNsx/DywjXjI7f7tAHHfDrSzf6uZ3Xcqk1 7nFakbNq9BzXnHwnsgNKabAyWb+Yr1a1jbcME8mgB8Fu7JgjGKzNajTygo55FdDM4hhz3Irmb+be Dz3oA1tPhzZpgfwinz2zKgBGaraZdHy0XBIAFbMw324IHNAHkPxE0PztNkmVPmVSc1zXwnvCmoXF m54YYx+OK9W8RWRudOnQgsChzxnH1rxvwUhsvHc0IOAJccf7wrOFanUlKMHdx0fkNxaSb6nP/FCx +yeIGGMAiuBwB1r1n40QBNaVsdVrygjNaCGU5eSB70hznmnR8uv1oA9w+EcAtdMnu2HSPOfxrm9D hbWfG5kYb1E5Y/ma6zwIPJ8C3LrwfKH86x/hfD5uuXEhGSr9fzoA9ntIhFaJEFxxVyzgZZPmFRwR sCueRW3GixwbyozQBQuiI0PrWNph/wCJhOSRyau31yS5yDWPaSMt3KwJ60Adc0O+HK1lXUJVjkVo 6bM8i4cce9GoR/NwOKAPGfilpwazFyi/dZefzqXwTN/aPga9tmIJQcD/AIDXReOLNLrw/cEkNwCK 4/4WsWsNRhPQj+hqYzjNXi7obTW545rUPk6nOnTBrNrf8WRiLXrge4rCIOO1UIbRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFAH2/wCBP+SeeGv+wVa/+ilroK5/wJ/yTzw1/wBgq1/9FLXQUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQBMnSnkYFJH0FPIFADK09LO25BrNIxirlk22UGgD6c+G86tpoX2A/SqXjK3kNwxXpk1gfDbWhDt jZhyR39q9NvrKPUYQ+M5GaAPIYIpRJiun0OOXzl9eK1ZfDwjlyqn8q3tK0lYAruAD9KANK5k8vRG D9SK+ZvHTq+pOV6c/wA6998WaslvYtGrD86+bfEdz9ovJGznk/zoA5hhkn1po68GpD1ppGaAFqCT /WCp6hk5cGgBwGBRRRQBe0ttt7Gf9oV9SeCGFx4P2LyfK/xr5Vs22zKfQ19D/CzWFazNs7DBQDGf egDzbxRA9vrUpIxzVSO7DRgZ6V6j478JNcStPChOcdAK8suNHurZyCjce1ADJpgeaigzNcoBzzTk sbiV9oRs/Suu8M+E7ie7jLxtjIzxQB6h8OoWttEdmGFMf+NeKfE2VZNZfHtX0BIseh+HQnAIjx6d jXzP4wu/tepSPnIyKAORooPU0UAI33TUaDipD0NMTtQAo+tT2ZAuUPuKiIxUkBxIpNAH1T4AZbnw M8an5uf5ivE9QQ2PitjIOPNJ/U16b8HtWVrY2kjDB3cZ+lY3xM8KyxX7XUMbdzkD3oA6zQ9cgTTU Ct/AM8VzXiTVkuJPlPArzu21a+sVML7wBx1NOl1Zpx8xxQB2OmayIMgtx9KwPFOordSbVOSSKw5N QkHyoM1e0PR7vVL6MlHIJ7jNAHsnwrtWt9CuJHBA2/8AsteN/E91fxFcFT/y0NfQlpbpoHg+QH5W 8vvx2r5l8Y3X2vV7iTOcyGgDlgDmnUUUAQuMNTqSQfNS0AOT74zX0v8AB9xL4au4wedmP/Ha+Z1+ 8K93+C2qCOY2rsMSEDH4UAcpraGw8fTM4wDN/WvoPw5qEM+lRMj846V5L8VvDkkepm9gQ8sTkD2r C8M+NrnSYxBMTgE/eJoA941bUUSNsNzjuK40amFuy27+KuTvfGzX0e1So99xrBbWZvP3l8L9aAPf tP1aP7Ny424rx34p6rFd3ZhRt3zY4HtWPN4xnjiKRtzjHBNYlpDd6/qysys2XHPXvQB7T8NIGt/B sztwCp/mK+ffGxDeILgjpvP86+mBGvh/wMyHCkjvXy14hm8/VJXz1Yn9aAMeg9DSjr1pG6cUAR0/ b09aatPoA9v+B8oF26HuoH61n/EeH7N438xvukL/ACrO+Eupi01uNScBsD9a7n4waK02y+hQnKrg gfWgD0HwrdxXGjwMp/hUcfSt24lCRYU9q8A8EeOH0wLbXDDbkDkmvRT40tZYd3mLkj1oA0dVnZRg HrUulXAUr83pXn+reL1ZsRlTz60/TfFKhV3lR36mgD2C5vooLYO7HGcAKMk14N8Ub8XWqWlurbmX 5jx3JH+Are1nx8qWbxIytkDua81sPtHiDxLC77mBk+veuSWHnPEqtKXuxTsvN7v7tF6v56qolTcU tX+R7Np6eR8MkD8HyP6GvmbVCDqEpHc19PeLHTSPAUUOQCYsc/Q18uXjbrljXWZEFMbrT6Y33qAB Rk13nwukEfiaPPcVwo6V0ngu7+ya7HJmgD1j40QnzbWcD5cc/nXY/D2eKXw1bAEEhBmsr4jWX9q+ E47pBkiIdPwrhvAXih9KkNpM4CjgZPvQB9ExyKgBrMvWDhyDzWLF4gS4iDBxz05qpd6yqITuH50A W7V8XjAnFdHaXCgjnjvXmsOuRtdNhxn61sw6uUUNvH50AdtNMkkpHPydc9Oa8A8c366r8Q4TG25E wiYHYZP8yT+Ndh4h8XLDauQV8zBGQxHavNvC0c2seLEncEgE89e1eRhctnTxtXGVmm5aR8l/novx 7m05rkUY/M9K+JZ2eCYFYciH/CvmxvvH619DfGC6EGi29qD/AMsjwfqK+eT1NeuYjW6U1etObpSL 3oA0NIbbqcJPrX0F4si+1fD2zYc7Uz/47XztZtsukb0NfSOnsNW+HYTqUjzj8DQBlfCa8RtMeAn5 ldv6V61bsE5r5v8AB2rvoWvyRPwhJ617Vba750YYYII7GgDpp51lyCa53UcKcA96bJqDDJFYd/qR L4JxigDstOaNYFPfFaL34SFickDsOtcPYX8zIu3pir39oXEeWx+tTNScWouz6PcatfU3NRlSDRbh pGyTGwz6kivDfCwM/wAQ7pk5AlJ/UV1vivxMbbSpo/lDMpHWuZ+FcD3WuzXDAksc5/GvMynLXgaT jOXNKTu2XUnzvQrfGsg6on+7XkNen/GG7E/iAopGFFeYV6pmMbrT4z8y/WmN1qReooA98+H/AO/8 EXEY7xj+Zrn/AIfXAsPFktsxwXk4/WtX4R3K3FjNZMRynr71y1+z6L43STkYlJOfqaAPo21kQhSf Sq1vrtxcyostqsMTKej7znt6YGM+vUVzmla81zZxlQD8o6E1bW5PVYwv0GK5a9OvKcHSnaKfvK17 r9DanOnGMlKN29vI1bx4ypNZ2nyRm6kDHvmqF/fSJEeDjFY9lqEj3WF98811GJ6TDcogUJ071Bqs 8lwbeGM4V2O8g9h2/HP6e9YUdxPs4pJdSuYlOVDDvXLjaE8Rh5UoS5W+v9fcaUpqnNSavYb4+lgt fC0qZAb5e3bmvP8A4Vx4tr5+xP8AQ0nxC19n01rfcAzFeM1ofDqH7L4Zurlhgbck/wDAayy3AxwO HVFO/wDmKc+eVzx7xrj/AISO4x6jn8K5w9DW14nm8/Wp3zkZrFPQ13kDKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooA+3/An/JPPDX/AGCrX/0UtdBXP+BP+SeeGv8AsFWv/opa6CgAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKALEdS UyPpT6AG8ZxU8DbWzUPepY6AO58L6k9pOhViORXtWg+IlltlV25x6ivn3SpSrD8K7rSdQKKvNAHr 76gkmCMce9Mn1lYIjhu3rXCw6s23G6q93qbMjZbNAFfxZrjSo4DHnHevJNQcvKTn1rrtauS+cniu NueWJoAz3+8abT2AyaZQAVDJ98VNUMn3xQA6iiigCWA4evSvAuqtZ3UZDEDjv715pF1FdV4fmMci HPPFAH1FbSwarZIzBSSO9Ymq+E7aZSyxLn2FYnhjWSsKDeK65tSWRRyDkc0AcvYeDoFuSWVMf7td hY6ba2KbgF+X0qkt1g9RUV1qeyFvmHSgDC+IGt+XZtFG2AARwfavnfVJTJOxJr1DxlfGfeCRjJry m95cnNAGaRyaSpCM9aYR+VADT0NMjp56GmR0AP606PhxzSUq/eoA9P8Ahzqhs76P5iBz3r6AvLO3 13Tcuis23+IZr5b8NXBhuENfRHhTVjNaRruHQfyoA5rVvh9BI7MsajJOMA1yV34G8t8KD+CmveWK SLnAyayL62id+EBoA8x0z4fRsFZ1DHGehr0Lw94RgsNshiUYz2rWssRKBsAAq3NdFEJB4AoA5b4i aoLXRWiVsfIelfMerSGW5lY9STXs/wARdRMyugIxgj9a8TvuZWOe/SgChRSsMUlAET/epaR/v0tA Cr1r0P4c6i1nrEBDEfOO9efAYrovDdwYLyNhwQwoA+oNXsotd0WNmUM23OSM9q8j1XwWvmMYkC/g a9Q8J6gLvS4huB+QVo3WmI7bgMg0AeByeGryAEqCQPY0yPRry4O0BuOuRXt0+kJjAQ9Kq2ukQ+YR zQB5Ta+CpZJVMmevQg16F4X8Jw2UkTmJe3b3rqYdLSP7qmtKGJbdc9CBQByfxM1H7LoZgVsdOBXz HftvuGJ7k17X8T9T892jBBAx0rxG55lJoArHHSmt3p5/WmN0oAYvWn01etPHJoA6Xwhdm01WFwcY Yd/evpXUoYtc8MRsy7yB9a+V9Jk8q7U+hFfTHgTUBqGjJbtgkZoA8i1fwtJaXTSQjgEnGDxzVONr qBdrlyB65r2fU9I/esGXIzxWaPD1vLyyEGgDzBFDgs/X3qMvIjbUVq9Nl8KW5ZWVTwanHhu3Cj5M 0AeULpt3fShQrYPqDXongfwstteJJJGCQwPStmHRYoWyqV1Ok2X2WBpW4A5oA4b4wX/lWEVqpwAC MD6V87T8yE1658U9SN1qMi7gQrHp9K8jkGWNAEVMbrUhGKjbrQA4dK0NKkMN4jg45qgOlT2pxKpo A+odIkXW/AjRMQxWMDnnsK8b1rR5dM1R5EBClz0FejfC3UFnsJLN2HzjA/KrviHQA1w6yKcEnBxQ ByOhay/khHYnFXNU1EvAQr9u1RHwqyPmHcM+gpW8L3Ug+Yt9MUAYdnLJFeeY7sQc4zXStqaCDhuc etVT4ZllG1gwK0f8IzdAYG/H0oA5bV5pL64Ma5IJruvhx4f8i5SdlHAbtUNn4YWBg8oJbPevQtGs Rp2nyXDLtwp60AeR/GDUPP1EQhiQilevvXjx6mu58e3hu9XmbOeT/OuHI70ANbpSL3pW6Ui96AJY uHBr6B+F18t1os9nIc5iwAT9a+fk4xXq/wALdRFtqKoxADADn60AZvibS5NO1x5VBAyOgruPDGqL NbqCckY6mtbxh4fFxIzhTtcDmuLh0+70qTMSsVHPSgD0pXRk3HHSuP8AEd8sMyhTySOhqsus6gU2 eU35VnXFvdXcnmSxNntQB2ui3KyWcZJ52+tXL28WGEktx9a4W3uL6y+RYjtHSpJ768uV2bG57YoA wfE9019dCFCecj9a7/4a6aNOsJrlgAfLznH1rnrDw88twssqNuJFeg3Ea6N4VdvusYyMH6GgD5/8 eXZvNdlckmuSIwa19dl86/ZzWQwwaAI2608dRTG61IvWgD034Uah9l1lUJwGFavxN0ow6wl1GuFb 5sj61w3g66Npqsbg4r3PxTpaazocVwoyfKGcfhQBgeDL9GskRmywUcV3MZ3gFRXkunQ3GjXbYQlc 46V2lt4pRYgHwDQBvajEgtnLAdDXM6E0cmpzAkYBPWotV8StNAyRLnIxwK52wvLi0umnbPzEnpQB 62EKqCpyKpX9ykUJLKOlYFv4qXywCy1n6trb3iFYxknoBQBxPiqQ6hqaxpzyOK9HgiGjfDqY9HdP p2Fcxovhx7rUVlmRsnmul+Is62HhpLVCATHyM/SgD571Fi93IxPJNUj0NWbj5pWPrVcjg0AR0UUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v8AgT/knnhr/sFWv/opa6Cuf8Cf8k88Nf8AYKtf/RS1 0FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAWIz0qSq6yYxx+tO8/wD2f1oAm71NGOe1U/tH+z+tPW72nOz/AMeoA6CwYhx9a6qx kxjB7V5/DrHkn/UZ/wCB/wD1q04PFxg/5ct3/bX/AOtQB6JHMxHWmTTErjNcSvjwgY/s3/yP/wDY 01/HJcY/s/H/AG3/APsaANTUX3Z5rm7nvRceJTP/AMum3/tp/wDWrPk1PzP+WWP+Bf8A1qAFbqaR hkVAbrP8H60n2k4+7+tAEtQufnFKJs/w/rUbvls4xQBLSgZpgfjpR5oXjH60AToPmFb2lvskXn0r nFnxzt6e9WrfVvIYHyd2P9r/AOtQB7HoN4VReeOa7C3vGIHzGvCrHx21mAPsG7/ttj/2WtqH4smI Af2Nn/t6/wDsKAPYjc7Tu3GqF/eN5TYJxXmP/C3iT/yBP/Jv/wCwqtcfFVp0KjRwvGP+PnP/ALJQ BoeIbgu7A+prg7v7xqzfeKzeMT9j2Z/6a5/pWTLf+Yf9Xj/gVAARzTTUfn552/rS+bnt+tAAwwKj jpS3B4pqHFAEtOWo9/tS+cAOBn8aAN/SJNkimvZfB+oELGNx5x/KvBLfUvIIPlZ/4Fj+ldRpPxFf Syp/s3zNv/Tfb/7LQB9MQ3QeNTu5qKaX3ya8Sg+OTQrg+Ht3/b7j/wBp1IfjszA/8U4P/A3/AO10 Ae0LOVwSePSodQvdsJIJArxs/HMng+Hf/J7/AO11VuvjS9yhQaFsB/6e8/8AslAFnxld+bK3Pr/O vNbo5cn3rQ1Pxc2ouWNnsz/01z/SsSS88xs7MfjQAp6U0jFM87d2x+NG+gBr/fFKKY5yacCBzQBJ Wppb7ZlPvWQZMHp+tTwXpgbIj3Y/2qAPoPwDqeIkRieFHf3r00yCSPIx7V8s6J8Q5NGdWGnebjt5 23/2U12UXx+aNNp8N59/t2P/AGnQB7VK3XBqpCds/JFePN8et2f+Ka6/9P8A/wDa6b/wvcjn/hG/ /J7/AO10Ae7rOoQDHNVL66ENs7k84NeL/wDC/jjH/CND/wADv/tdUr744PeQmMaAEyDz9sz/AOyU AReN7wz3b8ntXnE3LmtHVPFDanKzm18vP/TTP9Kxnud5J24/GgBxpj9KA/tTWORQA1e9SKO9Rr1p wfHvQBdsztmBr274baoYZUjJ4Oe9eDx3Hltu25x710+h+OJNEmSRbLzdvbzdv/spoA+rLuCOdA2B yOtZxtEx0ryCP9oF1hETeGg2OAft2P8A2nR/wv4/9Cx/5P8A/wBroA9bMYBJ4wPalEIkPoK8gb48 7gR/wjOP+3//AO101PjwU4/4RrP/AG/f/a6APbLexjLZOPpTtauls9NkAIHyE8V4wn7QbIMf8Izn /t//APtdZ2sfHCTVYDENB8kEYz9s3f8AsgoAxPGV2bi/mYknLGuJf7xq9qWunUJWc2+zcc435/pW YZtxzjH40AOPSoW61JuqNutADx1qeLhxVcMMZpwl2kHGaAPWfhxqRtdQhBY43gdfavdbu2j1DT1l 2jdjOa+TNJ8UNpUyyLa+ZtIOPMx/SvSLL4/vaWa27eHBJgY3fbsf+06APSRbmCXBGRVgRbjwAM+1 eUyfHYStn/hGev8A0/f/AGukX45gEH/hGun/AE/f/a6APWJLRV5AHI9KgaI9F4/CvMpPjrvQD/hG cY/6fv8A7XUQ+OW0/wDIt5/7fv8A7XQB67YaaZpQ0gBGc81N4svEsNGeNDjgdK8oh/aD8lQB4X6f 9P8A/wDa6w/EPxkk15Cn9iiAH/p63f8AsgoA5XxBMZr2Q5zkk/rWAanutSNzIXMW3P8AtZ/pVXzM 9BQAjdDTU705jx+FNU4NAEw611/hC8NtqETAkcj+dcb5gB6VesdXNlIriHcR/tY/pQB9axKmsaKj HG7GM9a5mWwEbmKQA474rznRfjjJpFqIToAmA7/bNv8A7IaW7+OC3Tlh4b2E/wDT9n/2nQB6TBpt uefLXP0q1/ZEEmT5aYA7CvKo/jSyj/kXs/8Ab7/9rq3B8cvJVgfDO7Pf7f8A/a6APQnsIM4aJRjg cVALCANlIlFedy/GkyMT/wAI9j2+2/8A2uo1+NQRs/8ACPf+Tv8A9roA9g0nTRJMHZQAD6Vj/EnU Rb6X9mQ4xkcH2riIf2gBAm1fDHbr9v8A/tdcn4m+JkniN2ZtM8gEkgfaN2P/AB0UActqDb5mNUjy KWS681idmM+9Rh89qAGNw1SL0qJjlqeHA7UAa+kSmK5Vga+kfCN0mp6ELd/mO0KM/Svl6G98hw3l 7vxrvPDnxYk8PoE/sjzwP+nnb/7IaAPVr7SkS4dXQYzwcVROhwu+AuPwrj7z44rd8nw3tb1+3Z/9 p1VX4yH/AKAGcf8AT5/9hQB6Gnh6LAwAT2qV/Daxj51XB5+7XBQfGsxSKx8N7gDnH23/AO11avPj qbpAo8L7MDr9uz/7ToA6ebw/AhyAPwqS20mBMHYCa4J/jI+3B0D/AMnP/sKSP40CMjPh7OP+n3/7 XQB7Lo2npERKVHftXmnxU1Lz5/JVuFBHX3qr/wAL82xFE8NYyMZ+3f8A2uvP9e8YPrlw0rWflbu3 m7v6CgDEl+8frUL9KVpt2OP1pjNx0oAjooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8AAn/J PPDX/YKtf/RS10Fc/wCBP+SeeGv+wVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA0NL0W91fzTarAscWA8tzcx28ak5w u+RlXccMQuckKxAwDiwfC2srZzXMln5Xlby0EsqJOQhIdlhYiRlUq+5gpA2Pkja2NzRte0+S3v4Y YtG0KeVoHT7RbzXdoyoJQ+Y5BOfNJkTa23hVcAruIfQv/EWiXl82sQXskaQWWpWUdjciV7qX7Q90 Y334ZCALlNxaTdlH4bjcAed0UVLFA8pwBmgCLBNOEbHtXSab4Uvb4jYnBrrLH4Z3UiBnVfxoA8xE LehoMTDsa9hT4YnoQv5Uyf4YSMpKAUAePFSO1JXe6n4AvbUFlQED0rkbzTZrViHXGKAKFA607aRS d+aAHAYFNb71GSKuWV7BbwXMU2mWl28ybUlnaUNAcH5k2OoJ5B+YMOBx1yAazeC9bEKSxpY3EbSi Eva6lbziNirvlykh2LtjdizYUBSSQBWTqel3Wk3KwXaJl0DxyRSpLHIuSMo6EqwyCCQTgqR1BFdT p/iGx0zXNKjt50Gn2umywCSSDegubi2YSSSoVIcLLJsJ2kmOJBh8DOL4ouLG4vbU2klrNOtuFvLi zt/Igml3uQ0ce1NoEZjU/IuWVjg53MAYgx+NKAc9DToo2dgB3rpdH8J3mpMNicHFAHNCEk9DUy2k hPCN+VevaX8K5GUNMqD61uR/D2whADCMn8aAPB/scuOUb8qie3deqmvoBfA2mN8uyLP0qtd/DK3m QmFY8npigDwMoRTa9H1z4d3dkWKIMAnpXDXVjLaOVdcUAUwOKdRRmgBD0NXtK0a61Yy/ZpbFPKxu +1X8FtnOenmuu7p2zjjPUVRY8V0Wn6zYRXt5qkVhaaZdW9kRYQ2xmZGuGdU35d3ZXVHd1IZQGjQ9 eGAK914R1y0S7ae0RHtXlWSE3MXmnyyVkZI9290Uq+XUFRsfn5TjCrrNNOlad4bkurTV9NTWJ7ed JVuo7nzYVYNGYogkZiJeMnLux/1gACFSzcooLHA60AGCalSFmPAJra0bw5dapIqxpmvTtF+FpVFk utgUYJzQB5BHptw+NsLflVgaLdkf6l/yr3dfDvh/Tl2ymIkcfdoI8OIf+WR/4DQB4M+j3SjmF/yq nLZyx/eRh+FfRC2/hu4G3MXPqlV7z4f6bqURa1EWfYYoA+dypWkr0PxF8P7vTdzqgKjJ4rhZ7d4J CjjkGgCECloNJuoAa3WtyXwjrcTQxm0QvI3luqXETG3YKWKzgN+4IVXJEm3ARyfuNjOtL2C2t7qO XTLS7eZNqSzNKGgOD8ybHUE8g/MGHA4659F1LxToV5r1xqs13YvNd3U8tm9vYGJrOOWKZdt3tjXz vnkgLD98Csco+YNhwDzrUtLu9JuFhu0QF0Do8UqSxyLkjKOhKsMgjIJwVI6giqYroPFmoW9/dWCw XMF1Jb2nlTzWsJht3fzZHHlRlU2KFdQQEXLhzgklmo6XpM+oTKkS5LHFAFFImfAAJq9b6TczfciY /hXqfh34YPJEs92EVMA811YsPDWhx4kWN3HcLQB4nF4Vv5AD5D/981MfB2oYyLd/++a9ck8X+Hoc 7bcHHotRL450EnAg/wDHaAPHLjw3fwA7oH4/2ayprOWE/MhH1FfQkOu+GtR+R41UtxylQ6j4D0vW IDLYeXk8jAxQB88kEdaAM967HxH4Mu9JkYlQU9RXJMhRiCMEUAJ0pD0NBIz1pCeKAL+laLe6uZTa rAscWBJLc3MdvGpOcLvkZV3HDELnJCsQMA4F0PVHg1Of7BOsel7Rel02/ZyXEYVs4wxY429eDxgH HUaL4ksm07UrOCHQ9CkuJbaVTc2cl5bMIxMG+WQTsJD5y4IAGFbkE/NO3inwu3h/X9JtdMvrSB4r j7CpuwRI0l3C67lMbFWEcMQOZCpEJAwz7qAPP6cqMxwATUlvbvcyBEGc16T4W+G9zqAWadVWPnk0 AcBa6Tc3LAJE5J9q37XwTqU6gi3kGf8AZr2FdK8O+GoszLG8i/7NULjx7ptvlbW0BxxkAUAeep8O 9QYcxOPwps3w81GNciJz+ArtH+I77sLZ/oKmg+Igz+9sePwoA8pvfC1/a5328gHutYk1rJCcMpH4 V9D2/iXw9rC+Vc26ox/vLWZr3w7s9RtmuNOCYwTgCgDwWnKPatvW/D1zpFwySqAATzWN0oAKY3Wn Zq1a3kFtBdRS6ZaXbzJtjlmaUNAcH5k2OoJ5B+YMOBx1yAaEnhHXImhja0jLyPsdVuImNuwUsRPh v3BCq5Ik24COTjY2M7UtLu9KuFhu0jBdA8bxSpLHIuSNyOhKsMggkE4KkdQRXoup+KdCvdYvtRmu rF5NRluxayW1gYXsobiCeMi5Kxr5jbpYSSvmkeXJgkn5+L8Q3FrLFo+n2d0l4NOsjbvcRK6xyM08 0uU3qrYAlAOVHIPUYJAMMDJq1b2E9ywEcbH6Cuj8MeDbvWp0CoNuRkmvWtP8I6L4btVmvxGzgdMZ 5oA8m0/wRqN2ARA4H+7W/D8M7wqN6kfgK7K98dWNtmPT7QNjocCsWXxrrErfukIB6Yx/hQBnf8Kx n9T+QrOvfhxfxKSkbNgegrdHinxArbndgOwyP8K0LTx3eRsBd24kXueP8KAPKtQ8N31kx8yB1x6r WM8TxsQwNfRUOreH/EUflXEKJI3HzL3Ncl4r+G5jRrizCFOMYoA8gA5pwAFWbyyls5jHIMEGqwOa AA9DV3SNFvdamnSyWD/R4vOmee5jgRE3KmS8jKv3nUde9US2RXV6PrEDXt3LZy6N4eMtusDwz2ct 3azpv3ncJBOwfcsZHy4wpOVI+YAyj4X1YWc10IYGji3nCXcTPIqEh5I0DbpIxtbLoGX5GOcKcY9e iah4g8O3ukm3g+yQ6Xa295BBp1xZbr3c808luyT7GIRTLCWBlGdkgw2fn4zStKn1O4WOJckmgCnF BJK21VJPtXQ6Z4R1C+K7YJMHvtr0vw58Orewt1utT2AehFad74r0nSlMGnWys4GOFxQBxll8NLhk DTZX6gVpJ8NoVHzTfoKbdeK9bvGPkO0K9gCKqrJ4lucst25A6/NQBZm+GqMpMUmfwFc9qPw8v7cF kjdgPQVtpf8AiC1fIuZCR71sWPja6hOy/t/MXueKAPHr3R7mzYiSJlx6is8qVOCK+hpLPQvFkBWG NFuCOhXnNeZeK/A9zo8ruigx5PSgDhgPUGn0rAocGm7qAGt1rbfwjrEa2m6K0L3aJJHEt9AZFRo/ MDugfdGmz5izgBRySKoWl7BbwXUUumWt28ybY5ZmlDQHB+ZNjqCeQfmDDgcdc9hNPoOqQWNprOvW k6q8awXkNg0EsFtFC6+VOViJLsRboGHnhNrEEjhwDjtS0u70m4WG7SMF0DxvFKksci5IyjoSrDII JBOCpHUEVVVCxwBW74jlW91C1gtbrTZ7WC3EdvHpyziKBS7sUzOokJ3MzEkt9/AOBgdT4Q+H0+os k9wqrFkE5oA47T9BvL5wIoXOfQV2Wm/Di7lCtMrIPdRXfS3Og+FIRCkKSXAGDheeK56+8XarfuUs 1MKnpjGaACP4fWcIAlmAPfOKk/4QfSyMC4TP4Vkva65csPMvZgW/2qkOg6vDgm8lLHn7xoAmu/hu kiE28u76AVyeq+BtQsst5LlR3C11cM/iHTDuE8rIOxNblj4zikUQarbbgeC2AaAPDZ7SW3Yq6EY9 agr3fWfBmm6/ZNd6YIx7AYxXkGsaJcaVctHKvSgDJUUp6UE4NIWGKAG0UUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQB9v+BP+SeeGv8AsFWv/opa6Cuf8Cf8k88Nf9gq1/8ARS10FABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAKO9K3 WmjrSjnjFAE1rbPcTBEUkn0r1jwd8P2uNs1whC85yBgVnfDvwp9vmW4mUbFAPI967bxN4sh0mM6f puAVAyVIoA3JbvQvDUGMxPIo747VzeofE3lltolKjuua5KGx1LXZt8rO2T3ziuhtvA7LGGkweOfl NAGe/wATNRL7VjJ/E1btPibfIR5kGR35NTweEraOZvNUA444qSPwhFcMdigD6UAbum+OtI1YCG8W OJm/vf8A6qg8R+CrPVLNriyKuCpIKAGuU1TwdcWpLwDp3UGmaB4uv9CvFtr1nktmbDBj0HTvQBwO v6DPpVy6PGwAJ6iuf6V9EeKNEsvEejC+swh3KW45I4rwPUrN7O6aJxgigCnSr1pKKAJKBGXcBRkn tQOldJ4P0N9Y1RE25X6UAdF4I8EyajKk0qMEyOSBXqb3ej+E7QIDE0oGMHHFZ+tara+EdLWytgPt Lp/CRwa4i3stS8Q3fmyFmDHOWzgUAa+p/EDUL2QraoQnYgkCueutb1qbnL8+hNd3pnguGPmUBiOv Fa03hizEJaOJdwHTFAHlKXeuLhmeXn3Nadp4o1vT5FZi7D0JNdxb6bb3Moh8pQU4PFXpvC1rNEwK L04+WgDJ0jx7Z6mBbaokaMeNzfl6VT8WeCbfULVrqyw4OMFAOay9e8Gz2rmW3HGc8A0nhfxdcaXd Lp+pFnt2yPmPQ/jQB5Rqeny2Fy0ciMpB7is8n0Nez/ETwzDLbf2haqpVkLZA968ZkQq+Dxj1oAae ppVzim0qnmgBxGRXT+E/DM+s3ihUYrxk496wbK2a7ukiUdTXvWi29n4P8MLeTqvnOmEHQk9e9AGn b2+leDtPUyGIzjnDAd65HV/Gl/qshjtgVjPAIJrNL6l4pvy7Fyp6A5wK7TR/CMNuoMyKzDqSKAOD bTNXvMuTO2fc4qKLwtfzSNu8wYFe1W2kW6xhQige4rN1WyW1dHjUAMQDQB5dH4Y1EZ2LIcelTQ3e taHICDMuOzE17FZadClqrFVJIyeKo6poVtcwnfCuT7UAc9pHjKz1uP7FqaokrfKGauX8c+CFVGu7 Nd6NlgVAx0qTXPCdxYA3VqCAuSNoNaXhLxENQRtH1TJYDajPj6f1oA8MniaJyjAgioea7r4g+Hjp Wpl0QBCOwrhT1oAB1p9R1Io3YA70AXNL06TUbxYo0LE+gr3fwz4csPDWmfbb4qGADAOAM8Vy/wAM /D8UcZ1O7VRGq5ywqxrWs3XibVPs1puW3VigUdCM+1AF3XPG1xfSNb2I2xDgFSRWTaeHNQ1ZvMkE rZPVq6vRfCEdtCktwgdjz06V11rZrEFEaBce1AHmMngOeNd0mQo68CnnwdEfLWJssRzgA16zPah4 CHUYYelYmj2Stf3AIyEYgZoA4SfwLcLGWi3E9cAVmRXGreGrn5TKADjBJxXt7Wvy42gevFYGraFb X6urIueecUAYlhreneK7U2135cdyRxnv+deV+NvCMuk3LSIjeXyc4966XWtCvNCuBd2pYKuOVBro ba6tvGXhuaGVR9rhXad3JPFAHz8VI+tIc960ta099P1CSFxjafSsw0AOWnqhkYKoJJ9KjXrXUeDN EbV9YjTblec8UAdv8P8AwSrRLqF6u2NVDfMBjrXS6/4zS2X7Bparhf4lqv4n1oafDDoem/KyjbIy 9+OlO8O+EDIFubzndk4INAGDZ6JqGuTebKJGBPVun610kXgBBEGcsSe2K7a00+G1TZCijHoK1YYN 8OCB+VAHmA8OWli7RSx9RwSBVq28FQ3URkxtB6ZArotethuibHSQA10Gn26/ZwAMDb0oA8s1LwDN Chkg3H6CsnT9a1Pw5dhJN5jB+ZWJwRXtN3A3lYAHWuU1jwzBfRElB5hHBxQBj31rp3jTSmlhCC52 7mRcZzivDtf0ebSb54ZFI9yK9Jm+3+DtWEg3+SWwQOBirHjzSbfW9GXV7NF+4ScDPf2oA8W6Ugzn inyoY5GUjkHFMoAkrqPBvhmXXNRUbG8sDOcVztpC1zcxxqMliBXt+jxQ+D/Ccd6UUXMyjbxg/rQB r3mpWPhCwFnZhHuSuOOoPTtXI+VqXiW9LSGQqTnHOBVvQtEuvENybq5YlC2SWyc967/TNFjsgqRq vHGcUAYOn+BoIogbjLH3Aq5d+F7e3XfAgOBnG0V2tvAThHGRUeoWwiGMcY6UAcLb2MWoSLH5S/Jw flq/c+D4JYseWAcf3RV7QokF1d/L/wAtCK6w2mY1bHGKAPGtZ8GTWX76338c8UzQ/Flxpj/Y79fM tjwQ5PFeqXtsJ2ZCo29DmuL1vwdHeK726hXHPAoA5jxt4Ut9RsDqWn4kUqW+QD64NeMzxNDIUYEE eor2nQNUm03UH0TUMtDMdo3dB271xPxB8P8A9l6m0ka4jcAjAoA4c05elNpV60AWbS0kvLlYkBYs egFe5+GNDsfC2jLf3u0Sso2qwGSetcf8NfD6XFwb65UeVGGJJFbt7dXPiTWWtoN32eJsKo6AdO1A DdV1u/8AENwYowyxnoiE1taL4JBAlugT0OCBitjTPDcenxK5QF/XFdRZcptwPSgDFXw7aRRDZEvH +yKy7yzGnyL5Yykny49zXoDWWIGftiuT1tVAt++JloAmsNIh8lGdcswGciqWpeFrS8ZlCAH1AFdd pcKzBRj+EVJdWnk5OOTQB4tqXh670Wfz4C+FOQRxWvpWvwa1bjTdV2rIBtWR+p7c5rs7+yF1+7ZQ QfUVwviPwtJZqby04IOcAHigDzzxx4Wk0a+Z0RvKIyDiuJORxXukRj8WeGLiG4AN5ApGW6n0rxfV LNrK/mhYYKsRQBSHWpNu447mo619B09tS1SGFRkMwB4oA7H4feEft84vLpdsCjOWAxXaa54lWKM6 fpWBGPlLrkZ7VFqsy6Fo9tpVl8s7qBIU6+tXND8LEwrcXAyTg4IoAy9G8LT6rJ59zvCnnnvXd2Xh u0s02rED/wABFXrCFbUBEUAY6Vu29v53IGM0AcxfaVFJCRtAYDjgVk6PbtdXjiQZERK11+ow+WxB NYfh9F+03gzg+aaANWXTYHgIMS9P7orlNY8Gw3YaSIbW68AV6Sln5lrkY6VmXS7AVA6cUAeMRtqH hq+3RlwBwQc4IrX1WysfGGkSTQBBdIvzIAM5611l9oceoKwdFyfauDvLe68KaxHKrEwE/NjoRnFA HkWqWEun3bwyoVKnuKo16v8AEXQop7RNWtFBRwMlRXlBGDigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigD7f8Cf8AJPPDX/YKtf8A0UtdBXP+BP8Aknnhr/sFWv8A6KWugoAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAq3p1ubq9 jiA6mqldT4EsvtniCFSMjk/pQB7BJLH4S8Cxuo2z3CbR37GuR8J6DN4gvjc3GShJyc4q/wDE67LX tjpkZ4jwMD6Cu+8E6YtjpMKFcE5J4oAvWuj29hGqqgGAKttCoUnbx2q7PH+9VTzVqa1H2dcDtRe4 HF6vbEKrqMHcAa1tOsytsgxyQDS6rb4iTI6OK3rGFGtomA5wKAM+SxTyMOgNed+MfCKTWsl1AmHU E9a9buogsJGKxprdZ4ZEYfLgjFAHj/w81p7bUJdIu2zG/wAgBHQk4/rXN/E7RfsGqmRRhWFaPia3 OieMlmj+VTKD0x3Fb3xQtxfaHBfAZ3RnkfWgDw2ilPDH60lAD4xuOK9t+GdhFpmkyarMOFjyD+Ne MWSGS7iT1YCvctQ/4lHwuiC8NMFFAHNWaT+LfE5kkOYzIc9sDJr2LTtFgsLKOONOi1xHwvsVjsXu WXLM2Rke1epqUFsXZgoHrxjtSbSV2CV9EZ8cWCcdRTZtiE960oLcsjSYNZskJLMxHOaYGVp0a/br ltvetyICVwhHFZukxbr65ycfNXSQWm1wcZoAybm2DSeURkGvNPiD4WWGI3tumCMZwa9cvYtkwbFZ OuWSXGnyo4zkdxQB554Q1Aa/4YutPuDulhGF47Y/+tXjPiGxNjqksRGMHivRvBUx03xrLaZwkjlc fnXP/E6y+y6/JgYBx/KgDgjyaB1opR1oA7n4c6R/aOtxllyq5NdZ40vX1XxLFpEB/dwMEA/IU/4Q WgS2muiPuqx/Sq/heP8AtPxzeXLjdtcn1/ioA9L8NeH4tOsUUJ8+Mkk1tSQbCDU9iMEDtVy4t/Ml UIKAKfl7YckdqxdWCvboRncJBWxqDmMiJQRgc1i3rfuEAP8Ay0FAGtbSKoTI6AVZnj8yDzF+7npV JIXcBlBPFbNrAXsWUg0AZElmtxA6MoKEYrxzxxpMmharHfW3yjzM9fTmvdEj2xMOhrg/iHYJdaHM cfMoJB/CgDm/FEaeI/BaaiBmQId2OOQa8NmQpM6nsa9x8Et9u8B6javyY1JArxnWY/J1W4TphyKA KFX9Jtzd6hBEBnLgVQrp/A1uLjxJag/3xQB6j4ikGgeBLO0h+SWcDP0rS8B6AtvZJdSrmSTBHPtW L8Rj52sabp46Io4/4CK9H0iPybSFAAFUD+VAGqttiMcU6GMGQDH41o2qiWI/So5kFrEZG4NAFW75 GxT2rF0lTHeXRPQyED61bkuCz5zyaz7Fts9y3U76ianpyv8A4bqNW6nUxMskZUdaotAPNcEd6hs5 3WdN3rW1PAd6SgcNViOe1TTYbm1aKVchhivHbEv4Y8cpAeIZnwR1yMkV7rqCgpXjfxHgEGp2N2ow wPX8RQBz3xU0kW1/9pQYVgp615jXuXxJiF34TtLzAyUUZ+ma8OPU0AC9RXtXwzsEsNEuNUdfuK2D +QrxeIZlUe9e823/ABLvhNLIOGd1U/nQBm+EbBte166v58sofcOff/61ew2sCmMIBwK8/wDhvbiP Q1lPBfn9TXotg/z5YAUAOW02Zzmr0S+Xbk/lVs24YBu2Kyru7VXMYIwKAMDWw7GEYzmVcAVu6a6g 7SwyAMj0rE1iQSJCT/z0FWxdKmAoAYgZI6moanzKzVuvf5P/AIA1azvubl7FuAK/dzVNrUEl6uad OLqIxNjcOlTTQBIWHQ+9WI4PxfosepaXKpXMigkHPtXEeCJftmj6lo0/JUEp+Veo3ir86kjHNeVe Hv8AQviTJbDhZTjH1U0AeUeJbP7DrdzDjADnFY9dt8TLYW/iefA6uf6VxNAHWeAdO/tDxDbqRkBx Xonix21HxNbaLEf3cHykD2ArA+EFsJNTeUj7uP5Gt/Sf9M+JmoyHkJI2D+lAHpei2EdnbRRIuAAA fyrVNqfMytU4X44HSt/TsTJg+lK6bsAlpEQRuFU9ZIBGB2rVuZEt0zwKwNVmaS1d4SGcDODnkZ5/ SlKXLFy7FRjzSUV1MbRCRdXZBGPMNdxalJrUbSCCuQR3rjdHZRczybVCtgj8h/XNbsOoBJxgjA7V lh6vtqcaq2kk/QqrD2cnB7osT255AHOapS25hU4Xk10CBZ4xIO45rG1J9rFQeK3MzyP4iaeI2TUo FKvCQS3Y81V8XwLrvge31FRluAT9Diuk8YOLnRbuDYD8r4I7YrnvD/8ApvwxuYzyY5G/mK4cvxMs RR5p/Em07eT/AMrHTiqHsZpW0aTX9etzwthhiPenwJvmVfU4p10u25kX0Y/zqxo8fm6nAnq4H613 HMeyqB4f+HYK/LNM4XPtn/61bvgHSVt9NW6cfvJRkn8aw/Hn7nRtKtAPvOpx+dd9oqCDS7ZAMYQU Ab6WyyQkUtnabJenGaLOf5wOxreREVNwHNAFa7GyxKjvXAaxwIuMjzlru7yRZI2QnB9K4rU4lIJY nKMrD67gOfzqZSUVd/1fQaVzotGYLIhz2Fb15AJUyK5/THWOMM2BgVuWt4k3y5FUIyXtArZwTiqN 3bCaN0Me4YOa6S9VUhJwM1hfatkjKMHI6VwZnip4XCzrU0nJWtfbdehpRhzzUTyC0/4kXj5LZT+4 uiBjqDkGuM+JmnLZ668irgSMT+grtvHKi18T6RcrxiRR+tZXxatwYLG4HV1z/wCOiuym5OCc1Z9f UmVuZ8ux5HXpfwr00S3sl244h+bP4GvNK9p+GcIh8K6ncY5CHn/gNWSTaZG2v+M7uZ+YonIHb2r1 exgBQIBx0rzb4apva8uT1eQ8/ia9Qtm8tgeKAIprJ0lyg4ra0yJki3MKsW+yZASBUzFVGBgUAcrr M+Z2/Gue0hj9puiOplNdHrFsUl39Qea57SVD3Vwo6+aaAPQdJcSWZUnnp+lU72zLSnA4zVmx220I XPJ61fG11zQBz32QRx9Oa5LxZpaX2kXIK5ZVJBrt9Qm2EriufvCJIJUI+8pFAHmWg/8AE48F3VjL 80kDtj8x/jXi97F5N3JHjGGI/WvavAo2a7q1kemC2PyryXxND5Ot3Cejt/M0AY9FFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAfb/gT/AJJ54a/7BVr/AOilroK5/wACf8k88Nf9gq1/9FLXQUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAV6J8KYBJ4gQkZwp/pXnden/CED+3Bx/C38qALfism9+IwiPIWQAfpXtWloIbeJAOgrxPWW8r 4ms79DMOv1Fe76cytEh4xigDRgs/tDByOR7067kjgTaf4RVpZ4rW0eVzhFUsxxk4A9BWRcQ755WL khulJWWiHruY+py+ZGCDxuFatheeSqBumAKxdRVYtgBz8w4rWtbcTKp9hTEb7xrcQbgM1izxGLPv W1bMkSbCwqjexAuAG+Y9B3NRUqQpxc5uyXUaTbsjw34rWoint7kdd/8AhV3XP9M+GUUrcsqNz+Iq X4vp5dhArKVbzOPem7d/wofPZW/nU0a1OvTVSk7xfUcouDtI8FlGJXHvTKluB/pEn+8airUk0dET fqluPVxXsvj9/I8DaVCvRgDXj3h//kMW3/XQV698SePCejnGBtH8qAOq8B2wi8P25Xqyqf0Fd5a2 q3MJhljDIw5Brh/A06voNttxwij9BXoWmyoOuOlJpSVnsNNp3RLOsVpDtOBmsS5nQgqo5NTazd28 16kLSOpUfM20hB35PTv+lU7xI0Gd3IqIVYTbUGnbR+THKEopOStco2LGK5uG/wBqujsL1W4foPau Z06RGuJlLZ+auijghKDDYFaEmjd2yyKJBjnmsK93OjKxBrpI2jkthHuBwMVh3yRRElmAx6mplOMV eTsNJvRHg0ymw+JUIHygyA8fU1H8YoQuqK+OqIf0qbxDIp+JVrt7OAf++jUvxkT99Cw7on8jVCPH T1pRywpWHGaRfvCgD3T4aL5PhK9mHGEb+Yqr8NoxJqt9KeTnr/wI1b+H/wDyI97/ALh/mKrfDJwL u8A65/qaAPX7QNvHpXRBI4YDK4HAzmudsWw4ya17+7thaCCW4SN5VwoZgC2PT1pSkoq7Y0m3ZGde mCUtIDyTWDfBSiY7OK2PIi8kbJAw9QawNXuEhKKCM7xQmmroTVjbt7gKwUA46dK37CaKRvK7kZxW Fp0kMqK5IOQK07WKCO6E27nHFMCe/h8oHaAMiuJ8TQGXSbpSM5Rv5Gu+vmR4c55xXG+IGCadcEng I38qAPNvhmxax1aA9Nh4/A15P4nQJr12P+mhr1P4XuGn1QD+JDj/AL5NeZeLlx4jvB/00NAGBXbf DSMN4ots/wB8VxJGDXc/DI/8VPbf74oA7XxVmf4lRwtyEOP/AB0V6ZYKywIGzu4rzPxA4HxQYn1/ 9lFenWrbkXPT2NctfDOrUhNTa5Xey2fqawq8sZRsnf8AA6XSVzn9aZqskcgMYbletO0p1WNhntVC 5to3upJBJkt711GRjzAAjaearafnzLg8Z8yrl6Y7aNmY9ATWNomoo19MjkYdiRQB1luYzhmAyK6G 2Mc9qMc4FYYSB4x82M+laemrFDCVRyfXNAGTqKMJD6CvK/ihGTYwS4+6f6ivVtTLeYSp4ryz4nOP 7KQHqf8AEUAVvE/7/wCGFpIeSCRn8TXhLfer3XWPm+FFv/vH+deGSD5qAH2wzOv1Fe564/lfCiBV 43TL/M14baf8fKfUV7d4i5+Ftp/12X+ZoA634eWiDR7bzOVEakD35rtGRGuVEYAAHOK4vwe+3QrV lJ5jAODXXWcqiRf5+tfN4fKsT/acsXWl7uttX8vl5bHbWrQlTUYdl+WpvTTJBaqHOCRgVyOoticl W4Jrf1aFLpIsybSpzx3rDubeNASzE/WvpDiMK/nIiiUnJ8wYrVgwXG4fjXNaxdh50SIZCEMcV0uj 3cF7bJu+8AARQBuaYyRTb84BrU1PP2fcvpWSlrBkDzSOa1r3BsvlbtxQBy9x8xPr715Uf9H+LNnj je6/yNeqzMc9M15VqJH/AAtTTscHeM4/GgDlvi2gXxI59T/QV51XpXxcX/if5x3/AKCvNT1oA9e+ DuFe8b0XP/jprV8FRm48aatIByZXGfzNZHwh6Xo77D/6Ca3vh0N3ifVh3ErH9TXDmdWdHCVKkHZp G2HjGVVKWx7OqW9raNkAKqkmpNLGEaXIKNllOO2cj9KwzdSi8SORMxnuf4a3YJFlsnUEAFSM14XD OGbg8U5Xb0/G92+rNsUnC0H6lHUbyK5hby35Brm/tbISC3A61eXTY7eIr9oY5JJrE1dorWBtj7pC MAV9WcYzTr0tLdAEn5zite3cNIrMSOa4/SZpbO+3SrlJM5rvLW2tp4wwfGeaSSWiG23udJpsqtAI wckCsjU0JlJq3pdvFFcOVmLHHSq2qkicgGmI5DxBbxyabcvs+fy2HX2ri/Ah3+DNUiP8MjfzFeg6 yFGm3BYADyzXnngM/wDFP6wB/wA9Gx/47UxhGF+VWvr8+5Upylbmd7Hi+oDF9MP9o/zq54dGdZt/ +ui/zFVtUGL+b/fP86teHONat/8AfX+YqiT2Dx6PM1LRbcD04/OvTfD9ok8KPIMxhRtB7/5xXnXi oK3jHw8rcqzIP516ZeFrLTU+z+nbj+VfLcSZjWw6hQpO3P16ndhKEai13vZdjUdbZrxII1USBdxx 6dKk1HU4NP8AKikbDSHAFVNEu457OKUKylhyGPORwfr0rO8Q6WL7V4LhptqRrwvvXsZXh3Qw0YuX NfX7+xzV1abjbbQsXk4zvVuDXMatqrWc0MkbAMWAOR2q/dW7BSRPlFri9QjmvrslclEHH1ruqU4V YOE1dPoRGTi7x3OxW5DqGRuXALN3NbWlzpENzN+dcjoGJ4RBO5SRBjnvXSjTI/KwbkgGinThSgoQ VkglJyfNJ6nSXhE1qGU5BFc5OgWQsDhulb9vGIdMWPfuAXqax7pUOTU1qNOtDkqxuvMITlB3i7Hl PxJi8s6fKDkpKvP/AAIVn/FEeZ4a0qTuYx/6CK2viaq/YIPXeuP++hWP8R/m8H6T/wBc/wD2WrhC MIqMVZIUpOTbe7PFx1Fe4eBv3Xw61Z/9j/2WvEOjV7d4NP8AxbXVh/sj/wBBqhGv8OEC6GG/ikZj /wCPGvT7a0SOzLzZMmCcZ6VyHwvt4D4PtZyAXIYHgf3iK6XTLqC8uXiaYGaM7jGewz1Gevbp618j XxtfMsXLA0pezUG9bu7tpbp62O/2ChD2iu9r+V/6sbdqRDaeY+QAu7nsKw9M8SRapfTRxsdinAOM VqawrzaNcRQNh3TaD6cVxul6ONLtR5cu6bnNfWQjyxUb3scL1Z0OsXKfZXOeik1yujazJqUtxZ3E u6JXOABg49Kr63dXQhMA5d+MA1gWFtc2F2s2Tg/e/Gsq+FoYi3tYKVtroqFScHeLseq29xGiqoIV VACqBgAegrQsdSjmuWt93IHArkrW3muFV1lBU85q/YaY8esw3C3GcfeWtyDX1OMhyQSDxg1i30jy rhgOAeRXTXqo6cnn0rnbtcZx6Vx1sDRrVoV5L347NNr5enc0jVlGLj0Z5j4UXyPiXqEHZ4un4CvL PG0fl+JLof8ATRv/AEI16vo7hfirMw/54DP5V5f49H/FSXB/2m/ma7DM5WiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKAPt/wJ/yTzw1/wBgq1/9FLXQVz/gT/knnhr/ALBVr/6KWugoAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAFe k/CacReIEB7hv5V5uOtdV4JvPseuQvnHWgDrPiDE1n4tF0vGZc5H4V6z4Z1dLnS4ZGbnHc1wHxRs vMgtL5BkPz+lWPAs7XmnorHoSKAPVTf2zTLJPh1UYQeh7/nx+VUmu4YnkkiUJvPOD2HSqkdluAyf 1pz2ShSOtefDLKEMXLGaub7vRaW0/r0NXWm4cnQy9S1DLjqeQa19N1bEIJBziuY1W3ZJF9CQK29L tP8ARkLdxXoGRupqaFgSxH40g1FIrh53kWViML0GB6frVQ2KMOD+tZuoWflQs47DNcuMwVLF0/Z1 r29WvyLhUlDY8++LOqHUL63iH3d/A61r3KfZvhfKh4zGePxFcN4ikkv/ABJHCf4JBj9K7Xx3N/Z/ gqO26F0PH41tRpQo01TgrJaIltt3Z4PP/r5P941XqaQ5kY+pqE9a0EamhsE1W2Po4r2P4gIbnwTp ci87VXn8K8UsH2XcTejCvc5/+Jt8OIgOXiUe1ADPhrq3/Eu8mU8KQBk+1eljUEa2dEkKllI3KeRn uK8O8AzE3L2vT5q9YttPyg9T70mk1ZjTs7o27rU7L7IIIY//AK1Yl/fu0R2g5x1q2LAjtUVzbERn A7VwZfltHARcaV3fdvf8Ei6lWVTcx7KWaOfcCfm5NdFHqREexgRWTaQF58L1HrWwLNivT9a9AzJk 1P5Qqz7G79D2P/1j+FQX2pQ/YXiD5fH3ick0x9M3DkfrWDrNobS2klHGBXFisuw2La9vG9ttX+SZ rTrTp/C/yPLA73vxCjc54l4z9TW18YnBNuvGdi/1rN8JRNqHjQysPuyf40nxavBLq/lA8IqiuxJJ WRm227s8wbpSJ96nHocU1OtMR7n8O2D+DryPuVP9K57wLeNZeIrhN2FJ5H41p/Cq5D2c9qTwyt/S uejb+zfGE0eMbpMfrQ1cadj3mzvYzhtwq1JqFq0scsqh2jB2+2a5ewsWkiVtx5FaaafsI+XP1Nc2 MwsMXRlQqXs7beTuVTm4S5kOn1Eln8tSoY8Adqwb+3nnm3En2rpRbHHC1WmhCv8ANxWtKlClBU4K ySsiZScndlPTZri3iCtuIArYh1QCQbsimw2xaMMBkY60rWhb+GtBFxNTDQYlmUtz0I454H4dK5Lx nqgTRbhYiSxVhkH2Na82mZByCK4bxqf7N02QEfeBA/KklZWG3d3Mv4U/K18WHVSP0Ned+Mv+RkvM f89DXpnw5iEGg3123GFY/pXlXiSXz9auZPWQ0xGK3Wu1+GzbfEtvn++K4putdT4In8jxBbsf74oA 7bxvMbbx6J8nk/0FemaXepJAhLAcDvXmXxEUR6laXnUMM5/AV0/h4f2jZxyoxC4H8qVle476WPRI NQihjI3jketZk2oMJSUJOfQ5qpHp20AnJ/Gr8duoUALRbW4jJ1DzrpCGYqCPWsu2snjnUqSCO4rr XtAIyWGBioLa2iaXCnmmBHDdzxgCQsQK1bbVo40+8VJ9aje02dV61VlsxIMBaALt3fROm7cPzryL 4nX3nLAifd56fUV391pZCZya8s8bTq17BZ/xfT3oA6DUiP8AhVcKnrkn9a8Ok+831r2zxQ/2T4fw QnqT/WvE5fvNQAtr/wAfCH3H869s1tg/wthGekqn9TXiVucSg+4r2dm+2fDVk67WB4+tAG94D1NJ dBhRmwyrjk+5rt7W5VSGLDj3rx/wTcB7MW8bESD/ABr0O30+UoC8hP0rKnQhTlKUd5O71b/MuVSU kk+h0N/qkcqqVkIZe2ayrm6nuVwuQOmamt7RExlefWryWO4dMD61qQcs1qI2O4bi3UmrVnBJbESR HbkZxWtc2SCTBNW7ewDRArzQBUgvsSDzsitZtUSWPYrgjHqKpS2oXhhzWdNp6yMSoK+9AE93cogY 7hxXkIu/tfxNtXOcCVR+ld/qNq1srSO5KAGvONIKXfjuKeLorg5/A0AQfFog6yp7/wD1hXmbda9A +J0/na6wz93j9BXAH7woA9W+EbbZboeqH/0E1o+C9SGneO9TVuQ7uMZx3rA+GE/l38iZ+8MfoafG Ps/ju7O7H7xqyr0YV6cqVTZ6FQm4SUke5y363LhlG0ematxX6Q25RmxketcVZLdzYIfC+tasNmS2 ZSWqcNhqWGpqlRVooJScndlqe95ZIizA1nGza4k3S8nsDW3DYh+EWrBsQiHcRuxW5Jz72EbKBtHB 4qe2kntcjLMn8q07a0zKdx4qw+mkDK4I+tACafqUUQzvIc9cmnXV2JSWLA5qjcWcePmXms2WymUH y5SB70AV/FV+ttodyxPOwgc1xPgFwfDmpnPVmP8AKr/ivcmj3C3EmSVOBWT4PP2bwjfMeNxb+lAH lGqc305H98/zqXQW26rA3+2v86h1A7rqVh03H+dP0j5byN/Rh/OgD17x3efZNT0S9AyYmR8Z64Jr 0qw1pb/SoSybtyDBznH6V5F49Y3GkafKOowP51t+F726XSoEGD8oGa4sVl2Gxcoyrwu47av9Gawr TguWLPS7a5ETgJwq9AKNRv45Tu34YdBmuehW8kHzuAPatK3skOCBub3rsSUVZbGbbbuylMZrglVJ VD29afDp4WMHaBk1tx6cQAz8D60kvlDCoRwaYjDksWik3xHawPUVegvCSFnJUetai28dwoww3Cq8 2nlBhl/WgDQ/tBWtRHG/AHrVB59x5OazZrTys+QxVqz7iS+g5LAigDj/AImX6m7srQEcuD19xVT4 iOP+EU0tPRP/AGUVkeLhJceJrUu2SWX+dXfiBLv0mwh/up/7KKAPKMZcCvZvBcg/4V7q6H+6P/Qa 8eVP32K9U8GS48Nanb5+8n9DQB0Pwz1mT+z3sfNIWInC8dyTXoguhEAygBiAMj0HT+dfPvhPUJtO 1+dV+6SRivV4NUuLhP3aZ+tZQoUoSc4RSb3aWr9SnOUlZs7YahutSkjdR3Nc/cXHlyEQsWb2qGCK a5H75+PQVp2+mkjCJxWpJkx2jzSeZKdzE9T2qQWSsGBUH8K3JYYbKEtKwGByTVO0u7SSUqGwT0yM ZoAzrY3FnlA7GPPQdq3NMuoVcSmXLehNPk07eNyDI9M1nz2Azkgqw70Aa91fM0oYN8tUL64C27yH GApPNZUst5EMKRInv1rnvEGtXQ0yaFVwxUigDA8L3wvPiJd3AOVEe0flXAeOH8zX7hh03N/M10/w 8Ji1e5nbqAefyrmPEqebqEz+pJ/WgDlqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPP DX/YKtf/AEUtdBXP+BP+SeeGv+wVa/8Aopa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAFUc1q6NL5V7Gw7GstauWZKzKfSgD6 B1O1/tzwLbuOWjXtz2rj/Bt82n3ptJBjknJrtfh7cpf6DJZuc/IMD8xWHq/h0w3rPCCrqfWgD0O2 k86MHeMH3q0fLRcswrz+z1i8s4wkig474qSfxJcyoVRRk+1AFrxPqMaTRqpH3hmuj0W8hms4+Rgq K8uvba9vZPNk55wMVo6deXumLsdSVHAoA9ScKRlWrm/EWoi1s5AWB+U1lL4llZdozn6Vl30N7q7g PxEetAGBoGnvrHiY3BU7d4Oce4rQ+Ll0FENsG4VOld34U0OPT4vNK4wvrXkvxPvvtWtSAHgDFAHm 7feNRng1Kw5qJvvUATwcSD617f8AD+T+0NCmsieqDivEIuGH1r1j4W3oh1JY3PysuDQBT04PoPif Ei4RpOp4xya9h0+5NzboyEYIBrk/F3h5ZboyovJ+YHPvUGh63Np0Yt7gY28A4oA9HRTjLNVTUJ44 rd+RjHU1hv4mg28v+lc1rfiN7mNo7cE8Y6UAdDo2pJJqEiBwcE9669XSRRzg14jpt/d2V752OP4h iu6sfFULxje2D9KAOxkDr0bIrifGmqrb2Dxk5Y4GAavz+KIhF8hyfpXIXFrca9qAklXEQ6DpQAnw 90topJ7+RSP4uR7GvPvHN0bnXJmznkV7sLaPRvDM5AwxT+lfOuuyma/kf1NAGK2OaanWnMCAc0Rj vQB6F8Ob37NqaJuwGyK0/GemvZa+t8qnaz7un0Ncj4Yn8jUImz3r2zWtNj1bRoJsZJX+lAFzw1qa XmmQvHgnGOtdNEZGAzgCvKNCvJfDt2YJh+4JOD1xXewa7byRBt4IoA6BpEjQ5IJrm7+9jWf5nAJ7 VX1DxJbQRk7+eccV53qmvSXd2XUkL9KAPZNIu0MWC2VI4NaTEEZjYGvMPDnieFIFilba4AB4rrot bt2QHePrQBqTSuoO4cV5L8RL8X1xHZwnc27BArtNa8TQRWrpG252GBxXJaTosl/qJvbhcl2yB6c0 AT21u2i+A7hW4Z1PWvE9RbzLyVvVjXufxDmFlootk4yn9a8IusmZvrQBUbrWxoMnk6lC+cYcVkEf Nir9g22ZD6EUAes+K7U6n4btJ0GWQDOKt/DbUl+ytaSEB0IwD9Ks+GwupeHBA3OFFYX2KfQNXS7i GEDZcdcjNAHsdvMzAAIMVop5ajLhRXJ6Rr1teWqOr/NjkYq/JqUQXJcCgC3qd4hG1SAvc1nQ3UXm KUYZFc34g8QQiNo4ny2K5/SNdZLwPM/yc9qAPbILiKWIbsZxUczbMlEUiucstYgljUq/UVe/tKNU yX4x6UAMv7tY4HkcBQBnmvDbwPrni0SRgsiyYyPrXe+Ldd+2obGybLNjJx0qv4Z8PizHmuPnJyST QBg/EefytOgtQcbVU4/OvIZOpr0b4iXfn6iyA8KBXnMgwTQAQffFeueF5ftfhia165B/mK8ltxzX pngG4AbymPUGgCn4XujpPiPy5eEZtvJ9690sp0KKQoYGvIPEWgyR3RuYF5DZ611/hHxJHJbJbXZ2 zpkdOtAHpVusUnLRqtSXE0EMRII4FY4vlZfvce1U7zUYkQ73xQA5rzzZWZiB6c1o6RfqxZGxgV53 qOsbbhTE/BYDpXRaNqMBhBL/ADEc0Ad6wt5FzhSaz7mREyPKUD1FUYr1CuQ9Q32qwW9u0ksgwBnp QBzHjrU47PR5BkBnBA/KvO/Ats/9otdODhecn6Gr3iKe48TaqUiB+zq+B2rdstNXSNFmbGGCGgDy 7xrN9o1u4bOfmNcmfvCt3X5fO1GZ/VjWH/F+FAHZ+Bp/J1SM56n+lXfE7SW3iB7le75z+FYXhuYw 38Te4rufEGmfb7SK4jGSy5PNAHd+FdQivNNhlBUnABwfauytPJlABT8a8K8G62+jXgtbnPlMcdOl ew2OoxSwK8TjaRnpQB06xwRLgYFY2rTY/wBW315pjXg6luKzrm8TY2W7UATpcEFCp69cGultniaN QW7VwWl3qSyyb26MQK6KK7QKBuoA3Z4bfbuK5NY928IU4QDFO+2jZy1cp4r8S2+nWThWBlOABigD zz4h6ms18LaFhjB3Y+tP08mz8Fup4LMf51hQWN1q2omeQEhmzXQeJMWOhrbjgjrQB5TeHLufc1Lp 3+uUg9xUNwc7jT7A4kFAHpOvv9p8K256lSP61Y8Aaqsn+jSsMqBgE+9VLVTqGgGIclelc5bGfRr8 TLxhufzoA+ibMwsoDit23trdVDqBn3rz7w14ktr+1QhwJO4IrqFvgRgNQBs3QUxMAwzjpXMMnkzs CSc+tXXuwe/NZOpXSLPDlsZYUAbukxAEuzYz61vBI3TDEEVzUM6iNcNxirAvRn79AF25htos45rl 9euIYLV3LAYB9u1al5qMMULPK4CgZJryPxv4o+1yNaWjZXJBOKAOZkuzqXiiF+qq4xzWh41m8zyU 44XH6CqnhzTZPtiTuOA2Sag8VXO682+hNAHKxqPtJz6V6F4RlVIp4+zoR+lee7v9Jx7V1/h+523A UHrxQBVtp1sPE8u77pc8mvY9CaOSIEAYPvXjGv2ki6g0qjqc11ng/wAUiJktrkgMCAOKAPa7K2tp QCTitXYiJtUjFcjZ6tDLGGSQYxV5NQBGN/BoAj1+3FyQCx2qM+xrMt7VLhkH9z0q9dTgxNk9jVDR 59zynPRjQB2Vmo8lQTzjpUlxbQSLlzg1ii78sZLY9KY2ppghpaAHXawIh2mvMvGupRWtpKoI3MCA K6bxB4ktbO3djICRjtXiut6lca1eM2cpkgcY70Ab3gs+VZ3E5PLE/wBK5/V/nkc/Wt/SVNlo5JGM k1zd3JvjkJ/CgDl6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPPDX/YKtf/AEUtdBXP +BP+SeeGv+wVa/8Aopa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKAHJVqE4bmqi9aMn1oA9o+G+pm3mCZwCo7+9eoatZrOqzqM kgZr5GpQCaAPpx7dSDlRn6U6HTonP3QPfFfNENpJKcKpP0Falt4bvLgDbE3/AHyaAPpmHw0J4zIF OPTFULnR1iDKy9PavBk8F37D/VH8VNMm8HX8ak+U3/fJoA9xjs443zs/MVo21qJJFG3v6V8y3Oj3 EBIaNh+BrPeNkPzAigD691OdNP0tjwCFPt2r5t8U3JudQkcnNclQKAJ361E33hSgcU1utAFiPqK7 Hwbdm31FCD+tcNk0lAH1o23U9MEmRuUVhy6emSSo54r5np6RM54GaAPol9HgkPK4/CrMHh7KnYmf wr5+tdFubkjZEx/A1tweCr6Rd3lEf8BNAHtT+HGiXd5Z+bk8VntokStl1x+Arys+Bb7b9zn/AHTW feeE763BJhbAHZTQB7jbaZCoG1B6citnTtPQuGIHHtXy1PYzQEhkIx7VWII60AfTXjrUBb6LLEDj KkV88XzbpGJrLpR1GaAJXHyk02KmsOKaDigDb0yXyrhD6V9A+GLsX2iJExGQgPWvmTJ9aSgD6Wvd Njm5ZB+VZz6KmMbmX6V89gE9KswWMs5AVGOfagD3FvDXmNncz/Wn/wDCJhk5iK/8BxXlFn4RvrlQ VhbB/wBk1pp4BvT1XH/ATQB38nhwI2ACMcdKsQaGVwWlf6ZrzSfwJfIuRGT/AMBNYN54furUndEw /wCAmgD3qHR4ty5GT6sK37GzW3UnHT2r5SkheM4ZSPwqOgD2P4m6h51ysYPG31ryiblzVOnLQArc OKuQHBBqietPoA9m+HV8NoiZuCPWux1CwSYsrKDmvmdjik/CgD3saN5UhKMyZ9OKbcaLezr8ly5H oGrwlImc4C5zWpZaDd3bAJCxz/smgD04+GrstmTfj1pR4akUHaW3Vx0HgW+dcmPH1U1YPgC829B/ 3yaAOyt9C1FGBDSRj1z/APXrTTSpgo864kbjoTXk974NvrZSTESPZTWBcadPbkhkYEexoA+gtP0a NJPMxk+pFbUpS0sZH6EKa+WCGHFICelAHXeKrn7Rqcpz6Vy0nemjpzSHkUASQdRXY+ELvyNRQZxn NcODil3cYoA+mmgS5tUkIB3DBrn7zQlE3mRZU+wrwSlAJ6UAfQEdhfFCI7l/bmsy60vWXk+YyOoP b/8AXXjdvYzTsAik/QVvWPg++ulBELAH1U0AdxJptyso3lgR2qzHY6gQDD5h+lconw/uyOVH/fJq GfwHeopITP0U0AejWdhrBTMs8iD0yf8AGnT6VNMcTTu59Cc14xe+H7u0JDRMMf7JrJeF4z8wNAH0 npmlRW8YOBn6VR8X3S22lyKuBlTXzvRQBq37+ZO5PqazWGJKaBmkIwaANfTZfKuIz/tCvZtAIvdM RDg/KCK8CooA99v/AA3DKN4UA46gVXsdKvICVhuHUDoM14VT0jZzgCgD3S/j1yOPMcjyD2z/AI1n G41OOEmbepxyDmvLrPQ7q7OEiY/8BNdBb+BL6RQTHjPqpoA63T725hlOGIySe9XzPrc8y+UshQ9x n/GuKbwBeKPuj8jWXe+EL63yfKJHspoA9gjsdSeH97dOhx90Hn+dUE8PiW43TsXPq3NeIT2E0DEM jDHqKqkEHBoA+lYdLhgiBVVGB6CvPPHd0CxjBHGBxXltFAFmXoaW1baRVWlHHagD07wbchsITwci unuvDkV2DlevOcCvCifakoA9mh8MT2coMErL9K13stTtoQVuWbjpn/69eBAEnAqxBZyzMAqkn2FA HsSanq9tcsJN5Qd2Jqrc61c3E4LZ+Q561wdj4UvboAiFgPdTWovgK9I+4P8Avk0Ad7b+JLt7YpGu WAx1plnJq90xZzInPvXn9x4IvY1z5efoprCu9EurUkPEwx/smgD2m/0O7eFTJdyHd2Bqhb+EolYy MCzHnJFeJvE8Z5FMoA98nsE020dgMHaa8n124MmoSHPGTXN0ooAnZz54YVv6LclLuI/7VcxTs8cg UAeyy6WNQt45AuTjsKov4Pd5Vkiypz1x0ryY8migD3Sw8O3yQnFyy7R6/wD16r3z6lZANDK7MDjG TXiqozdBV+10i5uSNkbH/gJoA9dfxFdkJBKMEjnmp01WWwQS+vvXm9v4LvpVB8rH1U1M3ge9A/1f 6GgD0DUNc1C98oQqQrdSCatWek3l8uGnZTjPWvIbvwxeWwJMLY9lNZEtpLEcMpH1FAHsd/4RmuJN jzOR6VVHhMW7KgUn3wK8e6UUAeq+IyLCx8pRjAFcDNOPJYZ5rIpTQAlFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAfb/gT/AJJ54a/7BVr/AOilroK5/wACf8k88Nf9gq1/9FLXQUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB6B4I +H0up+Joo9Utvt1hBLbCePTLlLkN5suwB5IS/lqFWV2JxxHtypdWqO28OafF4fW3vbGOLVJLK7uW jmMy3oeEzDMYB8lYl8nDrLiT5Zdo5jzxmnXtxpuo21/aSeXc2sqTQvgHa6kFTg8HBA61etdd1VNP /syGZPJKtGrGCNpURs7kSUrvRDubKqwB3vkfM2QDMiiaVwqjJNdt4b8B3mqlXeMBOeS2K6HwT4Hj 8gX+oBVjChvmFdFrHilIENlo8Xlgf8tBigBIfC+h+H4Q95ICyjOM5qrc+LtLtMx2Vs0mOMlf/r1n 22g6jrEgluHcqT/Hk11Vp4EtYbXdKFL464oA5RvHN0XwliMf7v8A9epofG7Bv9Js8L7L/wDXrefS bbT2MckSsGHynFXrPwpbXkW+SNeeQMUAY8GpeHdaXZMrRu3qpFZOufDmG4gaewYMMZxura1TwEsZ aS2IGO2DWJb3mreHrkKzyGMHlT0I/GgDzDVdFudNuGjljIwcdazAvrX0BcW+l+MNPIESxXYXLK2O T7V434i0KbRr1o3XC9jigDFqza6ZPfQXU8UloqWyb3E13FCxGCfkV2Bc8HhQT09RVTNNPJoA9Mv/ AIX3MMPh/T103UoLye9ng1C7e3dgAsMMreWnAYIPtAXaWMpiYqSCqry/jjRF0LxElrFpt3p0Ella zxwXRYuC0CF8kgZPmbwcADIIwMYGI97cyadBYNJm2hlkmjj2j5XcIGOevIjT8vc10ehaTf8AivVY 3uG8wpDHDuKY+SNBGg4HZVUe+OeaAMzR9ButVnWOGMkEgZzivUdG+HVrZW63GoOqcAkbq3QNL8HW IjjiSW728BccH8a5q4udW8RXJG9xGTwBnA/KgDVuNa0DRl8u0jaVx6Kay5vG16+fs1oAO2R/9etj SPBKeaPtWGJ9jXSS+FrKK3Zoo03qMjigDz1PF+tA5e3XbV+28aROfLv7VlB6lV/+vXS2emwajMYz EoEfByO9WdQ8H2M1r8kS7vXFAGCdL8PeI4swvtkPY5FcL4l+H9zp5aWBdycfxV1l54QurH99bMRj nC5FTaV4lmtj9k1WNpoT13dRQB4hNbvC5RxgimgYr1/xh4Nt7u0Oo6bsZCpY7R+NeRTwtDKUcEEd aAGnoa73wT8PpdT8TRR6nbfbrCCW2E8emXKXIbzZdgDyQl/LUKsrsTjiPblS6tXAE9qsafe3Gm6j bX9nJ5VzaypNC+0Ha6kFTg5BwQOtAHXwaDZQWdnpd7pey+utKvr+a4laRJ7eW3NyBGq7ggXNsoYM hb5n5Hy7eMgt3ncKgyT2rVsda1Y2H9lwSp5JVo1YwI0qI2dyJKV3oh3NlVYA73yPmbPp3g/wbaaf ZDUNSCAbQQGH40Ac34b+HtzqAWWddsfPJau2/s3w94bjzM2+VecLk1Dq/iee7b7LpcbQwjgbep/K qVl4Uu75xJdMcE8hgTQA658aFcpp9r8ucAsP/r1Q/wCEn8QSnMcKgV6HpXg6xhgBeJScdxUV9pUe nzKY418uQhcY70AcPF4t1iEj7RArjvgVqW/iXR9SxFfQtGT/ALNd5B4asWtEMsKMzDniuV1jwTDJ O32dAh+hoAx9V8B6fqls0+nOCcZxury3W/Dl3pM7JJHhQcA5zXo7W2q+HrgNE8gUc8ZwfrW7Ddad 4ps/st7Esd0F+82MMelAHgIGGwadXT+LfDE2iXhwpMZGQQDXLEkHmgC3a6bPfQXU8MloqWyb5BNd xQsRgn5FdgXPB4UE9PUV3Oi6N4f8QWcV2tpaWkds0pcLNcNtVLe4lUXZPLFjbqc24Hy+bwjFAPOv vNW9L4u1yV4XN2geN97MlvEpuGKlSZ8L+/JVnBMm7Idwc72yAN8UWUFvfW0tpb2sdtdW4mjezlle CUB3QtGJh5ijKFSHydysQdpUBdE8O3erzokSHBOM5xW3oWk6j4w1RJrwpsjTYixwLFHGuSdqIgCq MknAAyST1JNemT3Om+FLQW1nEkl0VxlcfKaAMbS/AFjplus+ouq8ZPzVPc+I9J0weTp8LSMO5Xiq DWuseIJy8juIycjOcVv6Z4HijZWmwx9xQBzU3irWbg4giVQegxTP7c8RRYLheelenHwxaNFtjiVW A64qrp2jJdzSecqlYSVwR1oA4e38Y3EZC6ha5U8EqO351pJF4e8RR7QSkp7MCOTXY6l4U0+6tjiB A3QHFcJq3g6W0l3Wxxg5GM0Ac34l+Hc1qrTWoDpx/FXns9rJbSFJFII7V7Jpuu32luLfUEaWHur0 3xP4VstasGv9OCbtpJAHI79qAPGKD0qe7tZLSdopFII9arnoaAOhsLK00bVPsmsQ6NcPMgZJp795 7WFfmzu+xsXLkqoHPAJyp3Bl1J9H0uz/ALctLjSPslnZS3cA1C9u/wDS/PTd5UUaIwjfnyVkCpJt 3s25VZSvL6XrV9pHmi1MDRy4MkVzbR3EbEZw2yRWXcMsA2MgMwBwTnTi8Sa5qdsNPme1vFKyIJbm wgmnw7M7YmdDJks7tndkEkjFAGDBbyXEgRFySa9A8OfDy4vgstyuxD/tV0vhPwfZ6TYjUNSCfdBA YfjipdW8RXeot9l0qJ4of9jqfyoAnNl4e8NJ+8PmTAcBcms248YXDkpYW4VR0JFWNO8F3V0RNePn JzggmuusfCdlbxjdGpJwOlAHnv8AaviK4JdQu2pIvEetWx/eorjuMV6LfaEtiYvJA2yMFIx61ebw 1ZxwjzIVYsPSgDz638T6be4i1G3ZCe4Xj+dN1DwTpusW7TWDgkjj5sVt6n4Jt5gzQqFPsK5l9M1b QJhNC0gUc/KDigDz7XfDF5pE7iSP5QcAg5rB24ODXvdnqNj4itvseoxKtxjAdsfMelea+MPCcuj3 RkjXMJGcgGgDkKs2umT30F1PDJaKlsm+QT3cULEYJ+RXYFzweFBPT1FVCdpwetNoA9AstH0a9S1a 70iDT5Pmnt7dbt5JJrZbeWVpbobiyqNkLEosRdHcxqx27ef8V2Nla3VhNp8cCw3lp52bVpDbuRLJ HmLzf3oX5MHzOd4fHy7aLvxprd/eC7mexF15rTNNFpttG8rMCGEjLGC6sGYMrZVsnINa2iaTqPjD VEnvSmyNNiLHAsUca5J2oiAKoySSABksSeSTQBh6N4eu9WnVYY85PJJxXpmleALHTrZbjUXVeMn5 q2p7jTfCdkLe0hSS6K8bcfKenNYCWOteJrkvI7rGTkbs4x7UAXLjxFpenDytPgLkdyvFZs3iPWro /uEVQeldRp/gNICPO2k98g11EHhO0+zNtRQQODigDyz+0/EMGGcL9KtQeLblCFv7cMvcqO1eg6Vo Ud9LMs4DCJio4qLVPCFpN8qRqD9KAOUSHw/4ij2/clP8LAiuT8R/Dya2VprQB0HbdXVah4Fuod0t q4BHIABqvp2uX2kSi21CN5YehVv/AK9AHjlxbSW0hSRSCKiHWvavEvhax1ywa+07Zv2kkAc+tePX tnLY3DRyKQR6igCu3AxXTaZo0tjqMtrPpOlarcGIShpNWQ2kEeSC0kkMqqrFtijdIB82NpLqRy+a 1NK8Qaho9vcW9p9leC4dHljurKG5UsgYKQJUYAgO4yMdTQB2B8C2h8LeItasbPUr+0CTS6VdeU+x beK6ij8xyFAZ2QzZU7SgiZiuGUr5/DC8zhVGSavW15fTT3yQsif2ivl3CRQqqsvmLJtCgYUbkU4U DGMdOK9Q8I+DLaxtBf6iEA2hsMOlAHNeHfAF1qIWSZdkZz/FzXa/2Z4f8NxjzTvmAyFXJqLVPEk9 5J9i0eJ44hxlep/Kn6b4EvLxhNeyfeOcEE0AU5vFspJTT7cKvYkVT/tTxDOxdAuK9E0/wTaW+Ayq T9Kfquhx2EsXkAKJGCnj1oA88j8Q6xbMBNEjqOorQg8QaVf/ALq/haNj3C//AF678+FbEwKsiqzs uenNc7qHgCKUM0IAPbg1EakJNxi7tb+XqNprVnOal4F0/VIGn091JIyMNXmmt+HLvSJ2WWPAB65z Xo0ul6x4cm8yJnKA5O0HFbVte6f4mtfsl9EqXIGNzY5PSrEeC45waeBgV1fi3wpNo10zRrmIjIIB rkckZBoAuWumT30F1PDJaKlsm+QTXcUTEYJ+RXYFzweFBPT1Gezs9H0a8t4pbrSILCaOKa6tbRbt 5JbuCO1mm3XI3ZTJjgwVEO9ZXKDGCnn9dBeeNNbvrwXkz2IuvNaZ5otNtonlZgQwkZYwXVgzBlbK tk5BoAj8RWtokWj39paJaDUbI3D28TO0cbLPNFhN5ZsERAnLHknoMAVtL0e51K4WOGPcScdcCtnT bDUvF+qRvctHhE2IscKxRRLknCIgCqMknAAyWJPJJr09YtK8IWAVYlkuSvygdc+poAwdG+HtvbQC 41BgoAz96r8+r6LpI8qyiMjjvt4qp5WteKLk4LpDnIBztA/Cum074eRxIGuCHb6GgDlZfEerXP8A x7xKoPTio/7X16H5pETBr09PCFotq21FBA4OKp6ZoMd7JN5+CkTFACKmc404ucnZLVjSbdkcNB4q 5Caha8dyo/8Ar1cOl6B4ij/dNtkPY5Uiu21XwBavDvRU5HpXF6j4EvbTM9o2Mc4ANc+ExtDFw9pQ ldbdV+Y5QlB2ZxPiHwFc2G6WBQ8fH8XNcRLC8LlXBBFey6f4gurCT7JqsTSRng7uo/Oqvinwha6j aG/07Z90sQBXUSeRBeeaGAxU1xBJbTGOQEEVAxz0oASiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KAPt/wACf8k88Nf9gq1/9FLXQVz/AIE/5J54a/7BVr/6KWugoAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBy967rwD4a/tO/WeVM xICeuK4m1iM1wiAZyQK9qgC+GvBiBBtuJzj0OCf/AK1AD9b1aS7lXTLAbYIjjgck9KuaH4bEciy3 S59s9K0fB3h8C3W6nUl3AI3CutktBHgBcD6UAS2thGIAEUAYq1cWmy0BIp1gP3ir05rR1WMC0X2o A4DWo08q25585RXQ6fGNyKPQCsHWUGIeeky10ulFWZAeOlAFi7s+gxXO61pFvcWzB0+Yjiu7vYwI A2K5i6R5nxz+FAHk97pl1pFz9ptiQobIqfV7WHxfoEs4QC7iU7gOK9In06KSAxSIDkYORXmzBvDn iyGDkW1wQpz054/rQB4xf2r2l3JC4wVYiq1d78StHFlqn2mNfkmO4EDjoK4KgDQ02ye/vY4EGSzA V7TYxQ+ENAi8tR9slUAd8e9cb8NNIWe7e9kX5IfmyR7ZrrbSF/EviabgmGJiBjkADj+lAFSx0q51 Sfz5zlS2WOeteiaVo8Nvbp5aDIHrV1NLjhgAjQDaMcCp7YGI8fjQBZhsPnVgO1U7tvKkKj8a6mzQ G1L455rlb9SsjHHJJoAxdCI+0Xvr5pFdbb2wuLfgZxXKaIm24u8jOZTXdaNHjcp6GgDKurZRHt2+ xrg/EOgJcyF4l2t2wa9J1QGKRgF71lCyLOd6kEdiKV1ewHldhe3GiXf2W6GbaU4YEfhmuW+IPhlb WUX9quYZADnNet+KdBW7sZCi/vEUkYHNcjpg/t3w3c6ZON00DEjPJ7f40wPDSMHFOQZOKsajbG1v ZImBBViP1p+lWzXd9HEoJ3MBx9aAPQPh94aSZjqNyMRRgnOa6DVNQuNbuxbW3FtGcIoGPbmrGqn+ xtAttLh+WSYgtjg4rqfDfh9LaxjlZf3jqCeKAIfDnh2K2jV5VzIc85rqVtMOF2jFNiiMLelbNggm dQw70AVzAILUMe9cv4guVK2oXn98v8667XgwVIkHFcPq8DkQZ7TLQB1unyrI6KemBU17ZBZi2Oor KsN4ZT6YrrpoxLZK560AcXqOnxT2zq65yK881Tw7NbSNcW4IAO4YPSvVbvn5AvWq5sg0WHXjHpQB 5qpi8T6PNZXYH2xEIUjjPevHNc019N1GWBxjaxFey+I7ZtA123voRiMsN2BgVznxK0pZrW21WJfl mG4kD2oA8rXrWtoemPqeoxQoM7mArJH3q9W+G2lrHbT6lKvEQyCR7UAdKxi8K6LDa2oAu3UA99o6 1H4e0CTULgXN3lgWz160aHaN4j124uZiTHuJHcY6V6BDaLbYRBgCgCe106OCJUjUYAq7bWm5uBT7 RwwVWFbccSQQM+KAMWZVt1fPXBrnvD14pu79T0841cvbiSaZiM4OelY2jwNHcXZHeU0AdqLfzbcs ozist7Tc53DgVr6HIx3ROODT9QgWNyQMCgDz/XtDgu0OE+bjGDXHRNd+Hb5d4zbufmB5yOlesvB5 j9M1ma9osV7p0kbAbgpxxQB5L498Ow3NqNVshmNgO9eVONuVPWvdPDmblLvQbnquWQHr2rx/xFYH TtVmhKkYY4z9aAMtBuwAOTXqfw/8MxJEdSu1xGgPJPSvPNCs2vtTihUE7mA/WvY9df8As/TbXRrc YaQhmA696AK15c3PiLURbwcWyHCqOOOma7zRfDttp1mNsWX5JJOareH9CGnWkbFfnZRk4rp7Y7Tt bpQAxIPkxt49KsRWJdgT90VpWtojnJ6UzV7oWcAVcZNAHLeKbwwtaqOgmWuit1W+tI8fe2g1xuse beLbkqR+9U81t2N1NbSR7gdowKAL0lmYztYVSuLRJFMciAqRzXWyRLcwBxjJ5rAuk+cqOtAHmfiL w15czXFmpXnOAar2U8eu2EulX4BuACEPTNek/YvO+8MjvxXnnjHTW0bUYNUtgQqMCxH1oA8d8TaO +kapLEVwu445rDr2H4g6aupaBaavGvMi5JA74rx7GDigDW0HS31PUooVGQWGa9jlaLwto8NpagC7 dR74rnfhtpawWc+pypxGNwJHoK3NDtX8R69c3kuTEGOO4oA0vCvhT+0X+36huY7twBbr35rv4bOG FNkKBQoxUdggt8RgYUcVqxQeY4x0NAFIQGRwAOat3K/ZbRuOdtakNqsA3Ec1zmq6r5k7wxjd1HFA GZ4Yu2fULxccGVq6S8smyJAOK4/R3nt57qRF5MhOK7rSL0X9syOBuXgj8KAMsxgrtxWNrHh6z1C2 cSx4fsQcV1t1ZCN96kBO4xWRcvkH17UAeQ4u/C+pEHJtmb5gecjpVHxz4dg1CwGqWK5VgO/evUdW 0VNRsXWRcsVODiuH8NKxubzw7cjOAWQN+BoA8LZSrEHqKVBk4rX8T6cdN1maEqQNxxn61W0aza91 GKEDO5gP1oA7/wAAeGUcf2hdDEaAnk10NzPdeJtSWws/ltUPbjjpUmuyDSNDttKtuJZmBOODjmut 8JaMmmWccmP3rKN2RQBc0bwraaREqrHul7sTmt+OIADipCdw3d6uWlt5nzN0FADILXIL47Vy/iaU rLbAdpVrsb+5SztC34CuC1i4e5aJkQ585TUybTVl/wADT+kNJa3OnjspbyK2lgZQ6FRJvJwU7496 lkgMb4IqtpOqtFIkUq4HArpJoFlXcMbvcVg6aoqdSlG8nrba7S09L9y3NyUYyeiOYuLKO4VllTIN cH4q8ILB/pungo4+YgNXp08yy3DIEw0XD+xIBA/I5rLvMSEx4yO9Z4DFTxNBVJwcXdqz8n5iqQ5H Y8ysp08R6dLpt+B9rVSFI4zXkfiXR5NI1SWJlwNxxzXr3i7S5NCv7fV7YECNgWA44zj+tY/xD0xN R0K01eJf9Yu44HtXaQePjrWjpdg+oX8UKDJZgKzsc4r074Z6Oskkt/Kvyxc5I9s0AdNbx2/g/Qot qg3cigAHnnrU+geFbnWHGpakSULbgM4z3qlYxt4o8WTSOCbWFiFHbHSvWokijtRboAFxgYoAZp+m wW0CLDGFAGK0o7feelVbN2WXyiOO1b0cQjX3NAGXeRi3gP0rnfD8iy3F7EfumRhWrrN+om8oc8Yx XO6XM1rd3LrllZ85xjkgZH51EkpXhJaMautUdpPZT5gMcuYQCZN/3unGOKqyiMExnBPcDnH1rT0y 6W7tc8ccGopYILS53rEQJcliqkjPvj1ya8jF05ZbhZTwMFe6dtfJaJf1ubwkqsrVDjtZ8M2Wroys mHxwwOK88BvPCmp/Zrg7rR25zzx0r2NkzI5X7uTiuX8UaTHqtq6FQHCnBxzXsQk5RUmrXOdnlXj3 wzHJbjU7NcxuAc5ry4gg4PUV7j4ZLXVteeHboZePLoDyccV5Dr9g2natNCRjDH+dUBl0UUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQB9v+BP+SeeGv8AsFWv/opa6Cuf8Cf8k88Nf9gq1/8ARS10FABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAbHhi2+1a3bx4yC6/zFeueMlB1nTNNX7uASPzrzb4fxeZ4mtxjPzD+Yr0/xIvn/E+0i67I yf0NAHoulRpDaxRgYCqBW6tqlzF2zWJbYG0lu1benTI5YIc4ODxik5JNJvVjs7XJrbTjC4ckce1R 61v+yjYM4q3fX0NhbNLPIFVR1NQW93DqFt5kZ3Ke9MRwOqJIyw7uP3o4rd01ZfOTaDxiqWv+Qt1A ksvlJ5g+bbux1xxXRaEQUY+WdoA2OcYYfnkfjWbqxU/Z639Hb77W+V7lKLa5jecCaLawqgdMVCWI B71NJeRwModgM1NO+bdmByMcVoSc9fRKGIBHFeX/ABHtgkFveqMNE4OR9RXqj2U12rPuCjnHvXnX xGgdfDlyrqQ6KCR+IrlpY3D1ajpU5pyXT+t/O2xTjJK7Ry/xCthd+DtNvcZJTk9/u14z/F+Ne6a6 ouvhPp8nUqp/lXhoH73HvXUSe1+CbYWXgLUbwAbtmQfwrc+HEH+gtdkZaUk5/E1n6cpg+Et0RxuV Rn8K2/AUbL4WtPePP6mgD0OzWKaPa2Nxqb+yBvyCMVm2aOpBzXQ28h8oZ9KAHRxiGHYK5bUraUzO SpwM1a1PxVaWGopZs26VucAZxV++ImtN691oA4rSoXNxcbevmE13WjK8cTbxXJ6EpXULgEdZCa6+ 8vUsrQynoBmgCS8spppVlgkRWUHKsvDHtz2/+vVaayZN0ssoZsYAAwMVJpOsQapAWhkDFeoHai/L yzLCvG7v6VwVcJhYVXjZr3kt7vp5XsaKpJx5EYF0iuHUjIINeY+H4xafEW8sAPkkTIH1ANes32ny W8PmhtwA+bPWvKVb7P8AFyBjwJIR/I/4VthMXTxVPnhdeT0a9SZR5XY8s8eWYs/EdwgAALMf1NO8 AWgu/EcKkZwwP6itX4sw+V4mY4xnP8zS/CaLzPEycdMfzFdJJ3PiBFufiHb2fVETOO3Ar06yQRpC Ch2NwCOgrzGcmf4ry7edsRP6CvSY7m5NssGF2DuBzXDjI4qVSk8O0kpe96f1+NjSDgk+Za9DdGnR TjcrDNW7azW256msayupo5ACeK1L3UEtNPlupGASNCxJruMxNRg8+PKgFua4rWbZ4mhDjrIKu+H/ ABrHrN/Nb7SAPutjrzir+r6c1w0ZByA4NAD9MsXYI/G0gV0yopi8sDisu1/0K0LMc4XpXP2PjiCT XvsMvyZztJFAHQf2G6uD9p3oDk+YgLH15GMflVK6ns2d4ILiGSSMkOqOCVI4OQDxXQySYiLDpiuf j0mO41G4vXUEyEfoAP6V4+NrRyyl7SlDmcpWtd9n3vbY6afLWb9o7WWmiOD+IFqk3hy5k2/PEu4H HoRXO6nCNS+FttMwy0ank89jXd+ObXbomoRBeDASo/CuM0QfafhdcR9fL3D/ANCr06FaNalGrHZq /wDXn3OdqzseB4xNj3r3TRoBYfDO6nAwzoBn8K8QZf8ATiv+1Xu95+4+FSjpuUfyWtRGv8OLItpM UigbpF3E456mu9tYYrqR4WRo5IwMlhwc56flXG+AbiSy8P2TmPeDEOOldJNqbzXCyLH5eABjrXjw jmEcwd3ei/Tt067/ACOiUqTp7e9/wf8AI6GHSkiIYkH8KtyAGPaelZNlqrSbU6n6VzvjrxVcaEkS WoBlYgnK5wK9g5zY1DTfLiZo8dKwNFtmku51BH3zmtXQdbTxDpwaQFJABuGMc4q9a6XDbSyShurZ 60AadhaC3+Ykbqsz2q3C84zXn3ijxvLpV1HDbpuA+98ue9dloGrrq2jQXadWQEj3oAkksYLaJpHZ QAMknjFY1yokDMoyn94dK0dQd5wVbIUEE9uO9WGjia2KlQVI6V4WZ5vLBV40+W8bXb+ey8zaFNSj fqeMCNdP+KMKocLNGMj3wf8ACuA+KtkLXxCxVcBs/wA677xKxt/iFpk2MBlAz+dc18aYNupwSY+8 pOfxr3TE5r4a2YuvEkYIyAR/MV6PqCrffEtYCPkjjJAP0rj/AIPQ+Z4hLemP511UTtJ8UJ2HJERz j6CgD1WBI0jj8z5UI+8eAPxrVi0kMc7hWIuowNYi3mjbcBjIHFSQ619khWOP7qAKAB0Ary8vrYyp OpHEwsk/dfdf11NqipqK5XqdPHEIV2iqN9YpeupZh8vamWuoSXVuzumDj0rzh/F2tWHiq5jlTfZ7 8ABOgz616hidBr9r5LwhCNu8dK3NNsFntlMmOmearx/YdWhSYk9c4ziqOu6+mh2jLaKXlIwOM9qA O1hCrGEXoKgl02OQl+BnrXE/D/xDqOqSyx6gAG5I+XHpXV6hqE0OU2nB6cUAR3SRW6FVIzXE+M4E u/Dl4CBlYiR/Ous0+RJZ3e4HzDBTP4//AFqyfGkSy6XdeWPmaBgcfSvLeaQ+vLBW17/K+3a3Xvoa ezfJzHn9vENR+GBVhuMO4DPPY/4V4RImLgp/tV734LHn+CdTgPVN/H4mvDLlMaq6Y/jr1DM9p0uE af8ADG5kAwzoAD+Arpfh7YpF4ft5sDdKm4k+5NYWqj7N8L416blB/Ra3/BN2IvDlkjjgRCs6znGn J01eVnZd30Q42bV9juLayjuvmidTg4O055rXgsxAgJwTXKW2o29g8skXDyYLHr06fzNW7fxM0km0 qSucfdqaEqkqadVWl1W9iqigpNQd0dDIw2YJxmsF9Ihgjll3KzHJBNR+KjeT6FJ9gYrOcEEVyvh/ xFqSRCz1uI9cB9uK2INbRIFe+nDn5d5rrLOzhs5CyMMt2rmn1HSrKJ5oXDvnJUHPNc3BrHiHU/Et syoY7MN02dRmgD1hohKNpxg1nz6TErbyw/GpLu+ktbZGVdzY9K52512S4JRgVYdsUAWbxo0+VcYr y++C2HxNs5U4EsYDAd+td19oZyc5/KvPfFLND420yY5CnAz+dAHH/FuxFrr4dVwGz/Osv4c2X2vx HECM4IP6iuu+NUP+kWk2PvR5rK+D0HmeIS+PugH9aAOw1WFdQ+JUVqwzFDFnb26f/Xr02ztQVVV4 AFeWfaSvxSnc8jySP0Fel2uppGmcjpQB0FvpjE5Y8elX9ixDaBiudXxN5YHzDH0rSs9S/tCFnxjF ACahbx322IyAFTnFc5rtpFbGFYyPvisvxXY65HrUeoaXISoOGQ4Ixx2qeHXbaaAHWIxFKp9cAntQ B0trp0E1rFIWUOoBJrdiZWjG1gR6ivLtY1XVr+NrXRYtsZ48zGciu18KW11Y6JFDduWlGSSTmgDd ltFnT0OOtY8+npC5YkHmorrxJ5MpjAxg46VQm1xJc5PX2oAwvGlrHeeH74ED5YmI9u9clax/2l8M GRhuaHcBnt1/wrqteufO0e8VAeYm7e1c14IPn+DNTgIyV3nH4mgDwWRdtwV969u8NQDTfhxe3QGG ZOD+FeM3kezVHT/br3G6X7L8J2A43oD+i0AaHw709U0SO6I/eTDcT9Sa9ChsXkUAGuI8DXMS+GLD J5EQBruLfWY4UHIoA0bTTfJPmSEEgcVZeQICSQAOpNZcHiEXFyIVUEHgkCpdSi+020sGSNwxnpQB Uk0+2lMlwZFfOSKx9FgilvrhJCNu41zdlbeIvD0sqF0nsixOG5IH1rRl1kmNhpcKG7J+cHnB70Ad 1YxwWTmFJFJY5Cg1o7FlG0jINeceE9F1ZdYk1LVJ9zn7qg8Ac9q7q7vfsVr5oGTQAs+nbFJUisK6 tdrEtU58S+bwWX8BVK61ONwSWBzQB50yrpvxRt2TASeIAgfQ/wCFcD8VbAWfiIlQAGyf1rs/EVxj x7pkoyBwM/nWT8aYMXttOB95M/rQB5HRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/wCBP+Se eGv+wVa/+ilroK5/wJ/yTzw1/wBgq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB2Xw2GfFNuP9pf5ivULuFrn4tNsGdk BPT6D+teYfDchfE9sT/fX/0IV6ZNftYfFZnAB3wFPm/A/wBKwxPtfZS9j8VtPU0pcnOufY9C+zzQ su/HzdhW5pkRVdxFZtvcfaJBJJ36DPStmK6iUhAeazwP1h4eLxSXP1t/XYVTl5nybHCfETUZri9t tKtmfJ5fb9f/AK1bOgzPYaMkcx2sF71HqaRQ6m9z5IMh53GsvUrxXhzuO9uAorrIOd8W64JNRWNG yFIPBrrPDXiCNtNi3NyFAPNcbNoQmbzZOXbrzWhpdsmmOiup8puPpQB0PiO/mmtDLbswKkHKmun8 Naj/AGroUUrcsBtbPrgVz6S20UZSNfMDDvW/oghs7FgkflqTuIH0oAJlktZGMUhCkklWGfyrh/Hi Nd6DqDkjPknt6V3N9OkqEiuD8aTpD4cvie8LV59PK8JTr/WIQtPXW7676Xt+Bq603Hlb0OXf978J IlPVAR+h/wAK8OX/AI+Me9e3xyf8WsAPcEj8mrxLH+l4969AyPdMlPhKyj+ID+QrpvCOIPDVguP+ WK5rlZXz8LEA7AfyWus8GXCS+HrLIBHkqKAOospy0irtPWty5nS0sZJnwAi5qrZR24Xf0IqDxBsn 0zy2yUJ5A78UAeZ2cFzrviZ77kjOBu7ACu51vWF03TGLHlU9apWN1bWUXlwx7K5fxGZtU8xEbKKD mgCbRPEyNqYGcB89TXdam7X+kusTZYr25rxqx0mdbpT0x0r0XRbr9wCZCCnDKRQBS8FXkul+IprO diqynAU9Ac//AF69E1GZreSGVYpJMvtbYuSBg8/TiuUWGzvNUjuPJPmq4O4euRXavLGU+YjJ7Vhi qCxFGVJu1yoS5ZXauZ97qcX2VgUfJXpxmvHNZRofiFplxjGV2/zr1PUGUOa8u8Q3CS+O9NjU8qM/ zrlwWX/VZym5uTlZdtip1OZWSOO+MiY19G9VP86b8HU3eIs+gB/Wpfi+2/V48dk/rUfwfbZrrZ7r x+deiZnZaYBJ8Sb6UjO2LH8q9EguVU9M153o0qr8Qb1GP3o8j9K9LtbeKUgZoA0rKOOcb9oGKxvG 8x/sv+z4jh5xtOD0GRXTQwpax/LXIXUa3GpPLdS5fPAHYZoAyfDWkw6R/pEjjdjoPzq/feN7OKYx g5I9xT9SuLS109xGcnB5xXk15aXE9y02QNx6E0Aez6d4gg1e12oe3qDXHa1octtqS3sT/MrAjFZn g+aXT7opIwAOPpXodzcWd1BtmHzeooA6HTrsX2ixSqckoAee+KdYXMSRlXIVgTkEjPWs3wykdvbz RIxMbNkA9uKv31nAy79gJ/lXn4/BzxKi6crSi+qujSnJK6ZgeL3ju7GWKPDExsDg5GMV574SXPgz VoD/AAF+PxNdxrUkcNhcOxwBGf5VwvhSUDwzq8nZi5H5mtMDhPqlL2blzat/f2XRCnPmd7WPD5Fx q5X/AG69x18bPhzZxD+JV/pXicqka1n1bNe2a627wHYnHAVSfzFdhB1nhkx2+gWSFRxCox+FdBGl tcYAUA1g6Akc+kWjA9YlP6V09jpYLLJngHNAFqz09LfMhA6Vyuv6bDq98ZpsGNAAM10et3U0MCQ2 6/f4Zj2FYsVtNLy5AUe9AGXBqNh4ciIAwPripk8d2TsFOQD3yK5nX7JtT1qKyiYNnOecY/zis6Tw pLHFNKjgtCf73SgDrr+wsNYPnbRlh1rovByCwR7HPygZX/P41g+GVgn01VkcBsD+VawhltrhJIHU 7Txz2oA3tUdFdEztDMATnHGankKJCSWAGPWnTWy30KlhhutYt3a+Shj3tt7DdxXhZtkzx01UjKzt bXt5eZtTq8qszzXxyANe025UfdnwD7YNYPxoQNHZScZ8of0rofGxVtS0yEHLGUH9DXPfF8h7eyQd fK5/SvWw1F0aMaTd7K12RUlzyckrGd8GFH9qXD9wn9a6fQVV/iFqMrdQuOfwrmfg4+2+uAe8ddVo ChfGmog9SMj9K3IPRUht5hyBmpoNIR5AQAR9KqwWcsuNh/Wt+zhNnbl5j7mgCYQLDAVUAcVzGp6f amR3MKFz1OKttrrySMdh25+UY7Vg+IdVure381YwoY4yaAMC716TS7xreIkqADgHpmtDSZotaBlm AkIOMNzih9AtHtYbi4jLXEpG5geoNQXtr/YN7amxASOc4IJoA7rSdOgs5FmhRV7HAxW5cwxSIWYA gDNczFcXkNsrMqkH0rR0q/e6EltKpzj5TigCtIFuLlY4CFCZLD1/zzVPVyFtJEfkspFaMulSwTGe JsEEkH0rH1JWWGV5HyQp618/WyrEyzNYynUSjp6+a7Wfr1OqFeKpezkrnEeBfls9et+wLgD8TXiN +m3xG6/9NK9u8HkLDrU3Z2fB/E14tf5PiMt2L5r6A5T2fxT8vw+skHQqn8xXVeGrO3/sGyTCj9yv 8q5fxGPO8D2eOyof1FdNoKFtItAnTyl/lQBsnSoyRgAg1ci0xBsREAPUkCmWEF0ZlH8Oeea07zUo dPKI+S7dgKAFu02whR2FcXr6L9kmbaFYISGHFb2q+IYIIg6hn9QFrLtZ7fWY5m27ox8pBGKAPPtC LS60Eldypz948HivVtLRAFwgGMdBWNZaJawXEknkKGBO05pLbxF5N+1okbOVbBwOlAHdSxCWEAis m5022VvMdVFDeIYlRAqn/ayKfqNu2oW6SW5yD+FAGc8cCfdjyP72OK86+IMaLeabcIBkSjn8DXpF wtwLVIWRUCLtJzkmvOvG0fmXOnQDk+aD/OuHBSxknN4mKir6K93bz6Gs/Z2XLuYvxkXfpljJkE+W Bn8qyvgwo/tK4f0T+taXxck3aXZxjqU/wrN+Dbbb64B7x/1ruMjpNGto7r4j37SchY8DP4V6KdJj YfJ0rgNIhKePL5x/EnH5Cu/i89GG2gBsumQxW7PIoyK1NBgeO0YsMBunHardvbfarX/SVAUGlbUr OKJlSTAUY6UAUr4Ek4rz/wAW2m4RPt6uF+prvRfQ36M0LBsHBqhqmjxahFEH42yBuvpQBT0K3+z2 UKhcEKM8V2Vlkxc1jrEsKqFHSpLXXrY3LQ7zlOvFAEc+nKNQIkQMr8g46Ux9Gt0JLbQPStiK7tr0 sIyS6jjIrLRXW/lN2mU/hPXFc2LrToUZVKcHNrot2VBJys3YzdQtrb7FMiqpyh7VwvgQBbbXIOi7 mA/M122rDKzPF8q7On4Vxng+Iw22ryt0d3/Qmqw1WVajGpOLi2tnugnFRlZO54jqibfEUq9vMr2z xANnw0gjHeJf6V4rqZ3eIpGHd69t1dPtPw/hVevlL/StyTY8JaQreG7IqcfuVrdGkttO9jgDrWR4 WaaDQbNQekSiuqtJ5Lg+S6/e4oAxtKUpqhROQp611UhLJu9arWujxWEskzuBv6ZNTi9tJWNvHKN4 6CgDJv4yY29D1rm9HtYk1G6aPaX3kcdq7CSJZSyGs3TdDisbi7mViTNJv5PSgDWsxt+tO1dWewdV 6gE/pSQSJEjSyNtRepNSR6hZXgKiUEdOnagDmNN0+O8gY4+fNXf7FjjwZMGtC5txpdsz2653Hr1r NEkktpJNJIQ+eBiuXGYynhKXtat7XS08zSlSlVlyx3PPvHVrHba5pkkYAbzAM/nWP8ZF32Vm/cR9 fyre8WwSXGs6bu7SD+Rrnfi9L/oVmnfb/UV1GZ47RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH 2/4E/wCSeeGv+wVa/wDopa6Cuf8AAn/JPPDX/YKtf/RS10FABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAdd4K3W+t28nQB1/mK6r xrqH2DxxHdg8ADv7Vy+gDbPE46gg1q/EJfOuYbkfxACgD2LSNZhurOOaOReUHerq6kiSbjIM/WvI PCc159nRVnbZgcZr0exsGcBmzIffmgDUn1KS7+WKPI6bjUUOnlpN7Asx9R0rXstN8xQSm0D2rQa3 jgjO1RkDrQBzsluqsN3HanGx82LpkYrmdb1C+u9UdLd2ihjAxj+I11HhW6mu7IJcD50GCT3oArxx y2bbo13D+6avx6zuXZL8h9DWtNp6SLuUbaxrux35UxfjigB63yqT86lT715n8T/EMYtvsETKWkGD g+4ro9WtpraI+VKy/ia8j8To8moxmRy7kjrQB2Es2z4cWkPdh/Q15aLTddgHjvmvR79inh2yt+gC f0rjDCy36gD1oA9AS4D/AA4nizyn+A/wq58NvEED2AspZAHiGACfc1j2oZvDd7bD+7nH4V5/YXV1 YaixgkZGDEcGgD6f/tIBcKwx9aG1hEi2uyuD/D1ry7QdW1LUFCySsB9a7rS7ZXI3jcx7nmgCSdXv j8qeWvt1pWsBFblQvUdx1rpYNNSNAzgE1keJ2e30i5eAYl8shcds8UAZFvHDJMAhUsvXFWpLBlcS x5U47d647wy1/DrkZlLtHJuDA/SvWIrZHiCsoPHFAGLaan9lXbJEFI/i9atyaiJcSLKD9DS31gka 5IBX3FcrqMMkIZrZ2QDsKAN/UNXht7Z5JpFAVSeTXjdtrC6x8Q0uE/1aLtGDntUfiTU9Tmjkia4f Zj1rG8DRt/bLTddgOTQBqePf9P1d3J4XI/Wofh6fseqRSDjOAfzqbVojcLPK3XJ6/Wo/DieS0Tjq 22gDVu9VXS/iAJpGARl2kk+1euafqULwpLFIrZGRg14D49WSPVVl7kDmq+k+MtUs41iWZ2UAADNA H0j/AGvIXyWH0zWdfXFrMxKfNIey153oet3+pupnlZVPvXoml2UT7cEZPc0AZw0+SY75ido5C+lY uq6fDb+XM7bfMOAv9a9GlsYhAUC4JHWuO8QaY91PEq52oAB+tADbPR40iUDLBlDK1XbdWtXAnUun 96tLSLLNrbiTOY124rQubCNo9ygLQBFb6jAke2BgufelGpMQys4/Oua1aMW0bSQvscA/dNeca34q 1a0lZRcP14waAOz+IHiO3s9GngWVWmkQqAD61zmgXPk+CJAPvSg/jnNeY6pqt7qd0GuZXY9ME5r0 LT0dfDVpCONwyaAOIvLQDU029Cuf1r0y7mMvgcR55jX+grh7uDGtxrjohz+lda8hbw3PH3C9vpQB 1PgLWobrRoIPMXzIk2kZ9K7r+1fIiKqwz9a+V7DX77Rb2Q28rryRgGt+P4i6tI4BlY/iKAPoCe8F 3D88oUjnOaxZrmfmOBy/vXG+GNbudY/4+ZzgAfLmvRdKtreX5Bt45IFAGDp+lsdUaZpG3qOT9c1Z t9O8w3i72+ZjxXRSWkULsY0C5GOBUEFuI3c/3qAOUsbOazX90zFQeVrfsZIWIZ5mDDqprT/s6BYS QoB65rntSSJA5SQKw7g4oA6f+1HjQFZBjOPvf0qpe38bIZHlAwPWvF9d8X6lZTNFHO5CnggiuZvP Hmr3ETRG4kG4YJzS1GrdTsdS1ePVfHkCRsGjgUcg9+f8ai8eH7exJPEUe3iuS8GySS6358jFjg5J rpfELeZFM2TypzTEVPhuxtNQLjoygV1EOoRaf448yRgEmjI59f8AIrjvCEhhjSUtgnFP8a3EiXKX MTbWUgbgaAPfrC7UESBhtxnrU9zrJlfaCAvTrXzhb/EnVbe2WJZn4GOoqa3+I2qvPku59sigD3G4 vmt5s/LtzmsDX7y51cxQRxnbu7Vz+l+IWv41lupNx7LmvRNCtLeazW7wrMw49qAJlt8W0KMMlQBW D4vtpnjtZYVY+W/OB2rrG29cUjRRyja6Bl9CKAOZstVnaFY2bafeugsbiSJk+dS7sAOa5XW2t9Pu jCGABGVwelcNrfjS/wBMbFvO5I5Ug8ipldxfLuB7ddao8cn2eb5TjhgflrjvGGsRWGkXLNKpYxnA zXj0vxO153Pm3Mj44GcZ/PFY2peJr/W5kS4mkYEgEE1y4KOKjC2Kab7r9Taq6Tt7O/nc9R0CfyfC 7sPvTqxP4mvMdSt1XXAq9R/9au6srwQaDFHwCF7/AErgtSmY64j5+8K7DE9X84XHhFYmPKIP0xXT eEr+KfSLcKwJRADzXB6dfrJpZiJ5K46+1cva+Lbzw7qVxEjNsDEAZ6UAfS6akttZsQFMmPlBPU1k Xd2Z08yaNd/oDnj614ZcfFXUJCAhYfiP8KtWvxNvZECOjscYyTWMo1faxlGXu9V+TLTjytNa9z16 4v7GDS3aQKHPaofCsLjTGkKFQ7kjPcV57pGrnXL6KG5dgjnnceK9iijjghjjiUBFUAAfStiCEIwJ 4rlGuF0zxSWlTarjIz0PJrsi2a5rxhDAdM+1NtWaIEqe5FAGgL2K5nykKEHvWtb6uLcGPaMDoBXi DeNbjTmJj3sB2rIu/idqZm3Rl0Hpkf4UpXs+XcatfU97udT+0R+Zyg54bqOa851e+i1DxdbQqykQ jccH61w0vxN1GWAxlnGR1GKd4TvpJ7ua/nYszZUFj9KikqiglUacuthzcXJ8mxr/ABClF0sZfoiY rM+GjiyvS4PDLz+dVfF98ZlJD5XBqv4KuSrjnoP61oSeoQzx2/iwSMQoljPJ9a9CsJ0DLkAj1rxr xTdPDaRX0RKvGw5BpbL4qLb2qrIGZwOuRQB7RqGo39w8lvZW0QiRwN7uf3o2gkAdjyfXp0rKjtYp pzGXKLnkHivLk+LDNMGBkUZyAHxz0qO7+Jcsj/uY3DN/FXFRp4mNaUqk04PZW1X+ehtOcJQSUbNf j6npdh5Vl4kktbZ98cifMB/CQRz+tbUqSYG3nmuI8A3kd3cTXcs++4mTox5Xmu8Zto/Gu0xK13K9 vYXEqrudIyQPwrF0JLSbS/NeQGYnLHvW7PKiRSGTGzac5rxu/wDEcmg37x2sjSxZzhTwKAPW7KaK 3fzkySD1Nad3rIaCLyog7OwVucbR3PTmvFR8TAtsR5LK31FV4fiq6Pgp06cisa6qum1RaUul9i6b ipJzV0el+IdRW1sLh5CFIQ8HiuVsLgWnh2STjMqs351xOreM7jxFeQ2yu+2RgGGeorobycJpaQrw ETn06VpHm5VzbkPyPLdRiK6suOpJJr2rT5BdeD0jPJEYH6CvFbycSa2M9BxXsvhsrJogUHgrj9Ko Dp/C1zHJpcKnaSi7SK69LuO0snuI7czSjASNOrEnA+g55PpXidl4lXw/rdxbTZMTMcDPTv8A1rro fiFpyNHmTChgSAw5ANZ1eZU5cm9nb1Kg0pK+qOnvf7YusT3lsEQklBCSdqnGAwPUj14+lZtzYypA bxHClfuuDSwfETR2sLj7Vd7DvYx7hn5T0HGen+FcHL47/tIf2ekpgi67iRivJyurj3VnTxS0VrO1 rt9Oz/Q6K7pSjzRVn2R6rp92b+yiuNu1mUZ+uKbaC582487hd/yY9Kp+G72K60eERkZRQp59hWmX 65Ne0cph6tdvc6lDpijbGRuc568//Wqb7A1ttXftXtXJeL9ajttUjnhnEckCHoR83PSqNj8Q1utq 3AcMO7GgD1qyvoBAIJ2BCjqxrIu4xLNc3BO2DcBGpPUDg49s8/jXFzeOLKE7iwPtuFVNQ+IVsbM7 W6A7QGHFebj8HPFyhBtKCd33duna3c6KFf2Kbj8T0LuoXSXniWGMEEQrvOK4j4nSfanhUHiNDWt4 UuJL9p7+UlnkYgZ64GK5/wAfTKu5SfmII/WvSOc8zooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig D7f8Cf8AJPPDX/YKtf8A0UtdBXP+BP8Aknnhr/sFWv8A6KWugoAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDstJAjijbPOAa2vFg WfSI2HJUgcfjXNWNzthQZ7CulkX7dpO0c85oAreDNWSOQ28rBTgYz9a9h0m7jdFIYZrwCKwuIZS8 OQyntXY6F4lvLZQkyOzDPOaAPd7fUwqhSBiodRvfOtmRDt3dTXnCeMJlAJgkrRtPFlvdxsjEqyjP JFAEuoQiOSIgfeIGa3dPxA0bKcEAVlW8kWp2yOOdr1K+pwW87RlgCg9aAOw/tJAnQZ9Kz729BUks AMd64SfxgTOUhBbHoRVG98VXbQHMLjigC/4l1eKCJiXXv1+leQS3h1LxBE38IcVo6xd6jqs7KRIq Z79KqaNpci3qs38JznFAHU6rKpt4EHUAD9KwTDnVFCg/d/wqzqd1mdUz933qnDdBtT3HsuKAOu0z aIriI/xLivOtUT7HrUx6Dea7Gyvttwyg/ermfEds7XrSAHk5oA67wzfxDY2R1GefavTNKnhYK6uM 4B614NpIvbVlaPcVzyK7rT9duLeMERvnHOTQB7CNUKx44JrIa4kv5popvTge1cKvjKdHBljIXp1r c0jWo7nV4AOknHX2NAGpp1ksU8r7ehNaljrEg3K2NoPBpL9o7Kwmm6f/AF64e78RLZwgJyzehoA7 27vPOUkkACuR1i/RI2VXFYo8VXhT7hYH3/8ArVz+tapdzqwjifJoAxvEepIqMqkFmzVzwRCsVvLM erZ5/KuRvLe5knBmDEk9DXWaMWstNwRjNAF+8ANpccDnNVtGk8uCJxweKZJdK1lLu5JB70WAUWET qegzQBb8aWS3OmxzAEsMc153bELLg9jXoep3TXmlkdQO1efpaySXZRAc5oA7vw9fxJtV2A6816Zp c6hVkSTgcg15Fp2nXFvjep/I11Fvq93bweUq4x6UAejXviaWCNRw2Dg/StEBbuGOYD72DXk8esSX BZJScjsa9M8OajFd6RGcjK5ByfSgA1TVzpAiVQMucc1FNrjXsW0MB7Cuc8X6lHNqKxIeIuK5q31m 4SbcrEqvHWgDoNYvDEjCRvpXmutXSzTOQRjJrp77UpdQB3RMCBiuL1myu8FgrKhPpQBm6fCL7Vol /h3DpXpl4y2dtbLjgDHNefeHoWjvFfHAOTXYyzNdzIjMML6mgDGvrnOuLIcY2Hp+Fb2kyG88yIgb W4x+FYV5aq2sqqMOE/wrTsd9hdqexFAHI+JtN+xajLgEAsayrbh/xrs/FkX2rEoXk85rJ0fw7cXc bSBSVU+hoA1dAllVlMYOcV6XpWt/2fEHPEnfNcLYWz6cOY+ntVu6upJgDnatAHo+i+KZtYvzAypt HcVvXEpgt5JFGSozivKPDOprp2oIxP3jg812mq+JIF019rfMwwOaAKaeN5LgyRSBFUHbgd6wNVuZ 7ksbcMFPJNc/jdKWDYJPr3rViv2jtvKI3tjFAHDa0zCQq3WucZQXC+pru7/R5r92McfzH2NcodNl gvtkinIPpQB2HhjTFttOafOC2etVdRvt9tLHnjmtOCV4NMWMDrWVcaeUtnkkYHIzigDE0m7eGNVz jpXX3elDUdFMhOWGK5Kw0yVrYSLzxmuz0NpXsGhfPfrQB5fc2/kzsnPBxU8MRJVVGSa6LU/D8st6 wjU5LelaFr4WntWiaVPzBoAi0QXVtKpMZZM9MV3Fh40uNKKxxqmw8FDWe0aRQbVi2sRjOKybmHHz A5I5zQB7Np2sJf2EdwSqljjbWH4l8YyaPL5MCqSeNx7Vx+l+IGhtljycKemaztVvf7VvVJJOD3oA u6hq0+oxGXAeQ9xXDanbXJDSSKw+orsLJFjlBHToVqfU7NLq3Kqg5HpQB5NMmDmtvwzpaX16jNnC mrupeF7iJUkC/I3sa2fDumGzjaUjp7UAM12f+zgsatuHTH4Vw93cF7tHI6ZrrtVtrnUbx8D5Qa5v VNPMN7FGpBJUk0AdPoF0k6gM4GMDFUvFWnwCbzlb5m5NU9PtbiC4j27gD6V1Uvh+XUYELHPAPINA HmQXa/TvWnZPMrAxpk/St2fwhcRSFgmVHfBrZ0Pw+qgtKg/EUAVLK5ukCu0e09jjFei+GvHBwlre 44wA3pxXOX2nxoFKrgKOgFYtwGsp0lHGeaAPXbnxbYQW8kokDYPA9a841vxPda1K2SBEDhVHpWVN ftJBs5yxqza2alI1ZT83OcUAc9qX2t+NmE7cVzd0rB8EEV66NKilgCuqk+pFcpqvhx3umWJM8cYB oA4m1gWSZVboTXoOn2sFppoCnA9zVHT/AAlKWy64IPUg1Z1izuba1EUROPUUAcprN800jIOnIqPw 7eG2u1ycKf8AGn3OmTCJpJAVGO461R02Ji4IGcUAem31zb3mmGF5AeM4FecalZiGYiPO3NdXp9jc 3Xy5PP1rYl8H+bCpONx68GgDzS3LxSAkGuht4LiVlby8J61u3Hg9lcBYwcDrg10NhoQSy2sg3jpk UAYOjX1zpN0siNgLjrXplt47tZLVWlA398Zrhr3S9hYBcZ71hs8tuxjIOKAO38S+M/tsRtbQhVOd x9a8/lgnlyw5b1rSsbczOXdSfSt6x0kFBvjxmgDzbUfOhTbIuCaxvKdmzg4r1PWfDguJQVQHA9Kz U8Ju5U7AB9KAMXQLOGOVJXOJAeM1u67frDYERuGLDB/KpLzw3LaxmSFsADPANczewXQVlkBagDmX kY3QbPOa9h8GXY/sllc46fyryCSMx3K54r0bw3JI1lsiPp/KgBvirSBd3D3MTFmBrj/sN4cn5uK9 St9OkmjPm857Uy10BWuBHsXk9xQB59pcM947wSMVKDjI61sRaXKsRVwSBXb3Hgg2skd3EgAY/NhT S3OjGNSVXkjpQBk6D4hn0YBNw5xkEVt3XjuXyTtCgnvXL3enSIxUqc9c4qnHbyPIEZWOPagBLzzN RujMxJB5qjLp8g3sSRgZrqNO02R2J8v5emMVrP4b8622qPmfrxQB5Ey3V05AzgVJaabNcz+XJuC5 7V6hN4MWxgyUG489KqW+gqgZwAD9KAF8PeTY2/lGTG055NcJ4zvGutQP90Z/nXVSWVxBMTk49hXF eIctcHjnmgDnKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/An/JPPDX/AGCrX/0UtdBXP+BP +SeeGv8AsFWv/opa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgCiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKALcN68YAC5x71u6b4vNhHsewEq/8AXbb/AENYenaVf6tJcJYW 0k5t7eS6mK9I4kXczsegAH5kgDJIFWIfD2p3GlnUY4IzBsaRUM8YlkRc7nSItvdBtbLKpA2Pk/K2 ADdj8b28cxf+yCQe32n/AOxq/b/EexhfcfDm7/t8x/7JXn9TQ2sk7BUViT6DNAHe3fxOhuSdugiM e11n/wBkrLTxgokkkTSyGcf8/HT/AMdpNJ8E398QxhZF9SK6q38D2FmgN5cqpx0OKAKejfE2XSrT yW0XzuSd32rb1/4AaoXfj2W41CW5Ol4VznZ9o6fjtrojpfh1Pl+0Jn8Ka3hzRLwYhuUz+FAHL2vj qKzcsNHJ573P/wBjWwPizbG38pvDQZv732z+myor/wCH0u0vbP5g7cCuP1Hw/e2DHzYHUeuOKAOk uPiJazqQNB2E9xd//YVUj8cQwq2zScFu/wBo/wDsa49lKnBGDSUAa9xrrT3LS+Ttyc435/pUC6q6 z+bs59M1QAzSHigDah8QPDOJfIzg5xv/APrVduvFkN0ihtNIYd/P/wDsazT4d1dbjT7d7F0kv7f7 VbCQhN0OW/eEk4VMIzbmwAo3fd5qtqWmXelXKw3aRgugeN4pUljkXJGUdCVYZBBIJwVI6gigDqdP 8eW9im1tF83/ALecf+yVpP8AFCyaLavhvaccn7bn/wBkrzkAk4FX7HSLq9cLFEzfQUAb1341S7BU aXt/7b5/9lq1YeO5LO8t7hNL3eSwO3z8Z/HbVjTfh/dSgPcAxr7gVtr4R0m0GJrpc/hQBFq/xdn1 TT2tf7C8nJB3fa89PbYK5ibxn5nl79MOU/6b9f8Ax2ut/wCEd0SThblP0qpdeAoZ1LWs+7joAKAM uz+I8Nsyl9EEgGMj7TjP/jlXZ/inZTD5fDIT/t9z/wCyVz2p+DtQsiSYHZc9VFc3NbyQNh1I+ooA 6W78XW93OJTpRUjt9oz/AOy1HdeLfPiCR2PlY7+bn+lczRQBsv4gd4fL8jGe+/8A+tU1v4okgt/J +zBhjGd+P6VgkcZq/pmi3ur+abVYFjiwHmubmO3jUnOF3yMq7jhiFzkhWIGAcAGxbeMPJRlew8wE Y/12P/Zagt/EkEF35/8AZxPPTz//ALGsKeCa1uJbe4ikhnicpJHIpVkYHBBB5BB4xUdAHo8fxStk AD+Hg4H/AE+Y/wDZKp3XxEhnYmPRTHn/AKes/wDslcMkbSHCgmtvTvDN9fMNkD4J64oAt/8ACXEz GRbAjPbzv/sa2dM+JVzpsRjXS94OT/r8f+y1NaeAWRA1zJs/AVeHhLTEG1rlc/UUAc7d+OpLm5lm fTiDIc487p/47UNt41W3GDpu8Zyf3+P/AGWukl8EWs4zDcZ/KsLUPA17ACY4zIB6CgDQtvibawIV fw8ZD6i8x/7JVbU/iHb6jAYl0Pyh6/at3/sgrkLvTbi0YiSN1x6iqfSgDo7TxRDaBtunEk9/P6f+ O0J4rZJmkNnnPbzOn6VzlAGaAN8eJn+2i4NtnAwF8z/61XJPGpkkVvsGNvbzv/sa5Srk+lX1rpdn qc9tJHZ3ryJbStwJTHt37e5ALAZ6ZyOoOADorrxtHdxKj6WRjuLj/wCxrS0n4kwaVbGFdDMgPc3e P/ZK8/ooA9Gl+KFvKpB8P4z/ANPf/wBhWTceOUmyE0sxg/8ATxn/ANlrlYbWWdgEQn6Cug0/wjfX OGMLqp7kUAKni0iTeLEnnP8Arf8A7GrMvjeSWMI2nnA/6bf/AGNakPggIP3suD9BUh8GQMMLPk/h QBgJ4xKOCbEkDt53X/x2r9v8QYoW3Po5f/t5x/7JSXfgi5RSYsv+ArnL3Q7u0Y+ZC6/hQB20fxWg jJK+H8HH/P5/9hWBd+MYLq78/wDsooc5x9oz/wCy1yrKynDAikoA6248bCVVCad5eP8ApvnP/jtR 3HjIzw+WLALxjPm5/pXLUUAdLYeLBZW/kmx8wYxnzcf+y1oWfj9bQnGlbs/9PGP/AGWue1Lw9qek 26z3kEaoXEbhJ45GhcgkJKqsTE/DfK4U/K3HynGXQB6BH8SoUlEh0PJBz/x9/wD2FXbj4tRXAGfD +MD/AJ/P/sK8ypQCTwKAO+n+JaTIV/sbbn/p6/8AsKz5PHAkDD+zcA/9N/8A7GudtdMubpgEiY59 BXQWfgy8lALRlQfUUARx+L0j6aaf+/8A/wDY0q+MESQONOIPf9/1/wDHa1F8EsBzJg/QVDP4JuAM x/N+AoAji8fLGwY6Vkj/AKeMf+y1oL8UIlGP7Dz/ANvf/wBhXMXvhq9tT80L49QKxpIJIjhlI/Cg D0O6+KkV1arAdBxt6H7X/wDYVST4iokRjGkde/2n/wCwrhaKAOv/AOE5Hzn+zeW/6b//AGNZFzrv 2i8S4NtjaCNvmZz+lY9XNK0q+1zVLfTNMtpLm8uH2RRJ1Y/yAAySTwACTgCgDoovG0Ue3dpW4r/0 3x/7LWzb/FVYECjQ84GP+Pr/AOwrziigD0s/FmJo2U6B1GP+Pv8A+wqunxPRAQNE6/8AT1/9hXnl OVGc4VSTQB3k/wAShMMf2Pt/7ec/+yVVn8exXIHmaRnHpcf/AGNc/aaHd3RGyJz9BW1B4NvHHMZH 4UAB8aWxA/4k5GP+nn/7GrI+IWMAaXgL0/0j/wCxqN/BV0BwpP4Cs268LXsGT5DkeoFAHRx/FLYo B0bd/wBvOP8A2Slj+KUcblzoe4n/AKe//sK4OeymgJDIwx6iq3SgD0j/AIWrHuyuhY/7e/8A7Cqd 18R0ujzo+B6fac/+yVwdFAHR3/ioX0bJ9h2AjA/e5x/47VDTtWSxbLW3m/8AbTH9Ky61NS8PanpN us95BGqFxG4SeORoXIJCSqrExPw3yuFPytx8pwAdPY/ESCxIZdFLEet1/wDYVpn4vA9NBA/7e/8A 7CvMaKAPUB8X1DAnQAfb7X/9hTG+LaEkroO3P/T5/wDYV5lUkcEkpwqk0Ad9J8UPMfLaOMen2n/7 Cq8vxBtZTk6F/wCTf/2Fc3baDeT4Ihcj6VfXwpeY/wBUfyoA2E+I0MSYj0TB9ftX/wBhVmH4qmNc Nowb/t5x/wCyVzE3hm8jGfJb8BWZPp1xAfmjYfUUAegD4tIFI/sHOf8Ap7/+wpB8WlVQBoX/AJN/ /YV5qVK9RikoA9Il+KySoVOhYB/6e/8A7Csi78cQ3SFRpOwnv9oz/wCy1x1FAFu4vFnnEvlbcdt2 f6V0OjeNE0lcHTfO6f8ALfb/AOymuf07Sr7VnuEsLaSc29vJdTbekcSLuZ2PQAD8yQBkkCrEPh7U 7jSzqMcEZg2NIqGeMSyIudzpEW3ug2tllUgbHyflbAB2y/FhFXA0L8ftf/2FLB8WhDMJf7D3EHI/ 0vH/ALJXmlFAHtf/AAv+M2Atm8KZx/F/aH9PLrHn+MazE40Db/2+Z/8AZK8s61NFazSnCofyoA9A b4pRvnfoW7P/AE9//YVDH8SoI3ZxoWSf+nv/AOwrlYtBu5BxE/5VI3h68Ucwv+VAHXx/FgRnjRBj 0+1f/YVp2fxtjtSC3hrzOf8An+x/7TrzGbS7iH70bD6iqbxshwwIoA9c1T44rqTEjw35Q9Pt2f8A 2mKx1+KgX/mC5Hp9q/8AsK85ooA9Cl+JsUykHQ+T3+1//YVyWp6yuoylxbeXnt5mf6CsqigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigD7f8AAn/JPPDX/YKtf/RS10Fc/wCBP+SeeGv+wVa/+ilr oKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooA6TS9a09ddlu2sLTS4Dpl7biO1Mzq0kltMiE73dslnVeuBweOTWpDrmlz+ELKzvJ7 Ex2en3Fs9m9lm7knaSaSKSOYR/LGrSxEgyL9yQbWBw/EAZFaejaTNqd4kUaFifQUAO0bQrnVrlY4 kznGT6V6rpvhbS/DdmLnUQjP7rkmrVtHY+ENJQYVrp1wFxyT1/Kqmn6Lqfiy+NxceYsR7n+maAGX nima4P2bSrfyVPGRjNU4tE1bUnL3EsjZ5ILV6jp3g2y0yJVWMFvUqK2rbTI04VBj6UAeOf8ACKQx OVuQQ2Mg0o8HXLRmS23KByOcf1r0zxFawq9scAHzVBrqNNWNLdcIMFQOBXHjq1ejR5qEOaWmn5l0 1Fv3tjwJZ9c0Zhukd0HUMcitSDW9L1pPs99bKkj/AC5IyK9ev9P066ch1jJ6EcVxHiX4fWswe4sf 3bgfdUcE15uV57Tx9V0XDlklfe+33fkbVsP7OKknf5HmXifwEY0N3Y7TGecDsMV55LA0EhSQYIr2 Ky1C90Cc2eoxsbcnbl8kAVleMfC0N3bHUtPAdCuTsxiveOY8w6CrNpewW9vdRy6ZaXbzJtjlnaUN AcH5k2OoJ5B+YMOBx1zVlRopGRgQQcc0ygDqFl0bVX8L2d/qP2W1tdPliu5Nj/I/2i4lVMhGI3B4 xuCtt35wcEVV8SzpdX9rDbXWmz2sFuI4I9OWcRQKXdimZ1EhO5mYklvv4BwMDCCkkADk13ngnwmb +YXNyhEKjOSOtAEfhbwPcaoyTTALDkde9dy0+ieGI/IjgSW4UYIVelM1bXMD+y9JGcjYWj6k9MDF aOg+AXuYxd3xbcwztIoA5ybVdY1lsW7NFEeMKcUp8M3bpvuXdh1J3V6pZ+H7e2VQsYx2O3g1pT6d Gtqysgxt9KinUhUV4NNeWo2mtzxk+GoyUEEjM5GSB2pG0zW9N/eQTyhV7bsivSPD1jF9tujtB2uQ OK7iSC1uYAsqKBjuK8/MsfVwajKFJzTvfW1vwe/6F04Rk/edjwi18VMn7jU7ffjgsAKNS8K6Z4it muNO8tX9hivVtW8CaTqUL5hCueQ6gAivLdT0DUvCt4JrYyPEOpHv60sszWlmEZOCacbXT89vyHVp ezejujyrWNDudJuWjmXGCazFr26dbHxdpzxuES7VcbcDNeSa3o8+k3rQyIwA6ZFeoZGafu12Oj69 p8lvfQwxaNoU8rwMn2i3mu7RlQSh8xyCc+aTIm1tvCq4BXcQ/F0UAaGu3lvqHiHU72zE4tbi7llh Fw5eQIzkrvYkktgjJJOT3NQ2NhNfTrFEuSxptpayXc6xxqWJ9BXrmg6JZ+G9MF7eY85lGFYDJPXA oAo6H4Gt7G3F3qZTaOx5xWhceJrS0XyNLtQT0D4xUEjan4qvAiIwgHZfujH9a6jSPBkVs6b0Mj55 +XNJtJXYHIeXreqne80ioecA4oTw+5YrNcurAZ5Jr2C00eOKPCxAcdMdKw/EemxqYWAwxdQeO1Ca augPOV0q/jObW4lHp8xqaPXNU019t7GZk7k4r1qz0iIW0e2McAdhUF/4ft70eU0S5Y91FRWqxo05 VJbRTf3FQjzyUe55y39ieI4zE0SpOwwAy81w/iTwXcaYzSxANEScEV6Lq/gG4tQ89oH3LyFA/lWf p+skZ07Vkwo+TdIOnbmlQrQr01Vpu6Y5wcJOMt0eNMpQ4YYNKOBXc+MvChsnN1aqWhIzkDpXCEEE g9RWpBctL23toLqOXTLS7eZdscszShoDg/Mmx1BPIPzBhwOOueo8W+JdF17w5YR2Nnd2l3FezN5E t0Jlhg8i3ijUERLkbYlUZJYeWSxYuCOLooAOtb2heG7rV5V2KAmeWNP8M+H5dXvAu1ggGScV6LdX 1voFsLHT1VpyMEj+E9O3egCvb6PpHhuBWuwjzY6bcnNQT+JLi5Pk6fAIl7HinWPh291WU3V0JMPz 81drpHhuC3UAxjP+6KAOAaz1Wf5prmQD60r6ZeQqrJfSFj0G4163NpEIt2BQY2+grF0XRkkuLmUx kojlQxXgdOM1M5xgrydkNJvRHAi81nT8F2MqDs2Ktxa7pt+PJv7dUY8ZIyCa9Nm0aF4iDGMfSuL1 rwfHNMzQDafZRVCOS1rwRHcRG408qVP8Irz27s5bOUxyLgg16ajal4bn2yI7QHqrdKk1jSLPxFp7 XVpjz1XlVHOeuDQB5MOtKSMVNd2slpO0UikEeoqCgDtPFOr6ZdW+uy2d/HcvresJqSRJHIrWyAXG Ul3KBv8A36j5C4+VuemeLopyIXYKoyTQAscTSuFUZJrtvD/gia6AmucLHz1q94U8MRQW/wDaF9hU Vdw3gVo3+q3WqyfY9PRlg7bOCaAJnu9F0JfKggWWYei96qSazqV6f9GXyU7YxWvpPg0q8clyCcnO CK7a10C2WNQkY/IUAeXx22rzOSb5lIGcE0qS61bkkTGUD1xXf67pUSNCI1w7OFwo5Oa17XRIRaKv lYIUZG3mlzK/LfUdna55gniRR+61CzHueDSXfh/StdgMtiUV8dhjmu21rwxazxMNnzewFcLdaDf6 TL9otw+F5yKYjhta8PXWkzEOvy54I71i16/b31vrcBs9QVVmxgE9z04zXA+JvD8ukXZwpMZGQ2OK AOfGO9df8PfEml+HdajfU7acxzXdqXuoZ9vkxRzLIwZPLfepZI2IXa37vAOGIPH0UASTmFriVreO SOAuTGkjh2Vc8AsAATjvgZ9BUdFXtL02XUrtYY1Jz6UAGn6ZPqE6xxLkk4r0DS/BltYW63OosmMZ wa0La2svCtgCwV7phlVI5JqpHp+q+Jbgyyq6w9VHYUATya/p1oPKsLQORwGxiqjapq9yf3eIwfTF djo3hG1gGJUDSerAVur4etkhfMYHH90UAeaGXXYVVjJuB5xkU9PEU8B23tpuHcjFdto+lwzX86Sc hGIUVev/AAxayscoMY/uigDgPL0PXRtCIkx9Rg1y2ueC7izVpYMPGMciuz1bwWySM9ruzkkAcVQt NUu9McWmoxM0Xq/UUAeWSRtG5Vhgim16J4l8Mw3Nsb+wwykbiFArz10ZGKsMEUAIcY967fxfruna jp14ttewXH2vUFurWOC3aKSCECXK3TFF86b94mHLSnKyHf8AMS/D0UAFAGTgUV1Hhnw2+oSiaZSs QwckcUAVNG8N3WqOCFwnqa7SLRdJ0OMNeFGfGcYzUt3qq2YFhpMYdx1dBUVl4Xu7uVZb3zPmOTu5 oAbJ4kgT5LKzGOgOAKjTVNWmOUhG3r0H+FdtbeErZYk2oD68CmatpEVl5Zi+UsQp47GgDjV1+6hO Lm1DAdcYqUalo2pDZcQLGx9Vrt28NwSWaHbkkc8CsPU/BcTx7o8hvYUAcnqngxJYjNZMpBGQK4m9 sZrKVo5VwQcV6B9n1PQZNyh3izkq3Q1Ynt7PxLadAlyBkrjkGgDy2ir2qadLp10YpEK/UVRoA2PD V9b6fqs811J5cbafewg7ScvJayxoOPVmUe2eeK1NMOlaf4ae6tNX01NYuLedJVuo7nzYVYNGYogk ZiJeMnLux/1gACFCzcnRQAU+KJ5nCoMknFEUTTSBFBJPpXoOh6Db6Xa/bb/CkAEBgKAMrR/B09yq zTYVOvNdB5eiaONrKskg7Bc1FcX9/qzmCzjdIBwNmeRWhpXg0zDfcbsnsRQBnv4kiBxb2XFIfEMq 4aSzGD9K7CHwlCsTHbjHsKr2WjC8mlikwViJA4oA5pNa0u7Gy4tghPHIqK58NWGpRmSydATz0rpb vwbDKWwCPwFc7c+Hr7TZjJbNJx/doA4zVNAutOc7l+X1FZFenwanHdg2epxrG543MODXL+IvDj2T maEFojzkCgDmKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/yTzw1/wBgq1/9FLXQ Vz/gT/knnhr/ALBVr/6KWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4Aoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigB8a7mCjqa9h8GaTb6Noj6rdIuQCBkY9K8v8P2hvNXhi xnLD+dev+MN0Vtp+hwcGRgzD86AGaDpM3ijU5L67JMAOVVs4616ZplpHYtsiQKg6YFVNB0tdP0qG BOCEGfrWqiHGAeaANZoy6ggZqwkKxQFj1xT7KMrbjzBzUOqM4tD5Q+Y0AcZ4lbe8JDf8tVrptEkB RUbH3RXM31vND5UkwBZmxg+hBFa2m2t1BLFIpzGcH8KV3e1tB9DZ1Wxb7PIbaFTMcY24BPPPP51S uIzDGFZ9wAwTjqc10Vc/qsMkc27HynmuGeX03iI4iGklp8tX+L38vx1VaXJyPY53WvDlvrNkwaNQ 4B2tj2rzzSJX0rVG0HUfmimO1CegJ+teuqW8vPYCvN/iLpeIV1OIYlgw2R7EV3mJ5b490A6Vqzui gRuxIwK4+va/Gdour+B7DUgMybPm/Ba8VIwSKANrw3pbarqsMKjILDPFeq63Kuiabb6RYDbOygOU 6isf4V6aixXGoyLkQjdnPsa2/DVmfEfiy+1GYbokkbb274/pQBteFfCi26R3VwoaThuQfSvRYI/9 F2KApC4HtVa0QKQmMLWza2oOCOlKSurAnYb5CPJHEijC5JwKraxtjTaDjitpYY487RzXOaxbXFxc Er9wVw5bgIYGgqMHfq33f/DWLnNzd2YOgEJeXRPTzDXc+QktplQM4rhNKs5WuLhYuWDkmu60tJEt dsvWu8gzJLhrZlSRj82cZ9v/ANdZOowRXmUlVWUnuK6y+tI7i2eNlyDyRnHTmucliCnHccAV4tDK nQzCWKpu0ZL4Vpr+Xn6v5nRKrF0lC2p5T4j0Ofw9epqNkf3QO5gufWqfirS7fxJ4bXVrRAH+6w6k EfSvT9VsBf6fNbuAQynFed+DEeLVNR8PT/dYF1B9eK9o5zxB0MblT1BxTQMnFbfiqw/s/XJ4cYG9 sfmazLGEz3kcY7sP50Aei/D7w6jIdRulHlxgnkVqxx3XivWmjiJFrE3yjsBnHar+tf8AEh8EQWUX E9w6g/TJ/wAK63wTog0zSoXZf3jqCxzQBqaJokGl2qxLEueucda6jSdPj2vPIqk5+XI6Dmo4YVcA VfjtNsZXOFPUetcOPw08TTVOD6636rXT77P5Fwlyu5CkUflyyr9xjleMYri/EO1mjIIwJF4rubxC LJo4hzjArhNWs5oxEZe7jFdGHoRoUo0obRViZScndnTaLIkkaow/hGKdfwtBcBozgjocVBotrOki O33CAc10U1vHKBuGa0lGM4uMldMSdjJu1t5LZ+FLMMD1rzjxR4TS8jeeBQk3J6e1epT26InTjFYl 2q5bjivIyrKFl7lao5J7Lsv8/PT0NqlbnSVjyHQphqEM+haiMzEERl/pnH6V5n4o0h9I1eWIrhdx xxXqXjSwbRddtNXtxtVXUtz7isz4maet5pVlqsa/61dxP/Aa9kxPJKtafatd3kcSjJZgKq13Hw40 pb3V1lcfLGwJ/LNAHUiGPwt4di2KBeTKMYHIq/4Y8LyXbi9vRuJO4BgaqyxnxD45lthzb2pK46dA BXqdpbLFGqquMDFAENrpqERwxoFyQvSujutMtoII0jjVXLcEDk96pQW/mcBip7Edq1Fi/eeY7bmx jNeRjsDWxOJpyUrQjvq773e297Jbq2r1NYTUIvuY+rhEi2DAOKz7S9sEsJLWzAEgJLgrglj1JPc1 a1q2mmmyn3cc1z2lWsrXM4j5Ic5FbZjltPHwUKkmktdP1HRrOlK6VzsJY1ksN6jnFUdOhtmklW5V eQCpfGBWxZxEWgRxyRzUb6fGclRXRjMN9ZoSo8zjfqt97mcJcruc1rmhWWoB4wiMuODjvXlV5bXX hbVlbLNbO3zAcAjOK9vmiEa4ArkPFulJqOlTLt/eBSRTwuHWHoxpJt26vVjnNzldnl3jnQIpbVdU swpjcA/KK82r2Xww39q+HrzSJxmSFiyj24ryTUbY2t9NCR91iP1roIKtdZ4M0A6pfqzr+7XJJIrl UUs6qO5r2PR4F8PeB5b3GJZPkU9epoAq6jPLqOoLpNjkQRHa23oe3au30LwxBYQqxRTJ3OKyvAmi 7bT7fMuZJRuzn3rvYioOMUANjs4y0avhVrQtbfyraRwoJAO3dxn6mpf7Mtr2NDOm7bnHJGM/SrN3 Hts2RPTpXJClWWInUnL3Gkku3dvzNZSp+ziorXqcRq85gvrefasmyQHY3Q10GlSm5nLHAVlHydcH 6/8A1q5nVbeUGMyDrIMV0WiQSrIjFcLgc1c8LSnWjXa96Oid3t+Qo1ZRg4LZhf2mborjiqN1YRyx FDGCCPSusmtkkbJHNZl3AEbaO9dBmeU+JfCrQg3dkNrKdxCg1mQxp4m0Se3nUG8hU4LdTXrNxAHj aNhkEYryu/hPhvxpbSAYgmcBh14PBoA8o1KzexvpYHGCrEVUr0D4laStvfJeRj5JuevsDXn9AD4o zLIqDqTivT/Demw6Fox1O5QF2UbBjk1xvhDTf7S1yCPGRvGea9G8Qxm71a00OD7sIw35UAQ6HpNx 4gv/ALXdkmMNkA+nWvT9P0uG2txHFGowOwqjo9mmn2yRKmCAK6SxKsw96AKEFp/pIwOvtVrVwIYd iDBK1tR2kaPvA5rA10SNPhRnigDl9FYrqNwW7ua7O6t1ez81euK5DTYXNzPtGTvNd5YRFrELIPwo AwI7MFdzqD9RXOeIvDsF/CzBFV+MYFegTwRxREjpWHcKHbgUAeQWks2kagbC7Ba3lOOeg7Z5rmPG nh/+zbszxL+6cAggV6j400Xz7FrqNfniGcg1gtCPEXgdt3NxAxUnpxnj+dAHjtFPlQxyspGMGmUA aWjabJqN6kSjIPtXol9KNNt4tLshiUjDsv8An3qDwLpqWmmTanMOEBxz34FXPDlm+oahLfyjIZsg n60Ab/hjwxHDGs86h5DnkiutnsVWMbVAx7U2yJXC44Fb0cCzxCgChp9vttWZh0HFcr4hkZpI/TeK 7u5QW9k+OwrhNUJlMeVwN4oA6LRwtxAqMeQoqO8tT5xQDAo0QMs6gDHAroJrVJH3Ec0Acte6VDNb 7JI1OR3Feca5pE2jXhubQlV3dhXrl8VXK1zmpWiXsTRkZ4NAHnGs2EWv6M17CgWaMHevU15xLGYp WRuqnFeq6SDp3iA6dN/qrj5CPqOP1rivGelnTdalAGFZiRQBzlFFW9Ntjd30UQGdzAUAdT4P0VGJ vbkKI1GcsK21WXxBqCxoCtsrYA7YqbVohpmiWmnxcSSAF/510vhbTltbVWI+c4JoA19J0K2s7RQs S5xycVctrfbOEA4rUtIwygGrcNiEl3npQBm6igtoMKOSO1cxo0pF/OTnlzXS63OqyBD1xXNWBxPM wH8ZoA6m4tFEHmr3FZ62SSk7kBB9RW9ZqJ7AA0x7ZYVyKAPOvEnhmGRDLCoVwR0Fc3ZTfaUfTL0b i3CM3b2r1C8KnIIyK848RWRtrkXcQxg5/WgDzzXNLfTL542HHbisuvS/FNmmqeHotSQfOPlb8K80 6GgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8AAn/JPPDX/YKtf/RS10Fc/wCBP+SeeGv+ wVa/+ilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooA7L4a2v2rxPCMZAI/mK9M1NBe/FaGHPyxQEj8v/r1xHwghD+JCx/hUH9a 6yacp8WWcAn9wR+lAHrtnbCUKD2rWis7a3be7Ln3NcrHqzxgbQR9BVW41a4uZxErMpPXNAHdyuoA KkEdsVyniXxUdBuITJbF7ZzhpB/DWxYMwsVDsSx75qrqVrbXsPl3MCSqezCgDIuNStPEEcctndRO EIYgN0xUWoeObTSIEtoylzc5CLGhzzWbeaXHpsm7Togm8hSM8YPWtPTdC04Mk8lqjzZyWYZ5oA63 TruS5sIZZU2SMOV9KuO9uV2TMmT0BqnEwG0AADHaud8QXEkN0JEkPHbNAG/dpbiP5Cv4VxPi62E/ h2/U/wDPFsflVpNUnMYJVuay9dmuLjRLwBSA0TD9KAOb0lBffC+aI8mLdj/x6vCJ12zsp9a978D/ AD+C9SgbnAfj8TXhd6mNSkX/AGqAPa/CEIsPhpf3HRnTj/vmuj+GdiqeGYrg/fmy5/76NYtunkfC iRRxuiz+gra+HWoxx+ErNG6omP1NAHf2+ntMQTkDvW1FEkMe1WBIFclL4hKodr7QB6U3S9Qu7q9D FzsJ7ntQB1FxOY4HZRkgcCuNtfHVpPPLY322yuQSoWQ9ffNdczhyccj0rk9X8PaXfzGeS1XzhnDD g0AOhv7LRTJdzX0IjkO4fMOc1c8OeLG1+8k8mAi1Q7RKf4v88Vz0GkW16/k3MAaKH5UBPWut0i0t rGHyraBIlHZRQBvK4JOSMVTurC3nberLu9sVDeO5s3EbENjtXJx6tcWlwY5JGI7d6AN26tPL47V5 ZdxjTvivauo2iaEA/r/hXfvrnmDaxJ/CvOdeuTJ8R9Nk7BQM/nQBxHxZsxbeJWYDAfJ/Wuc8IW32 rxDbR4z868fiK7f4zIP7StZMcmOuc+GsQk8VQkjOGU/qKAPR/F0YuvGGladwUUbiPoDXpMMYVUjj H3QMYrzLXZgnxJsXY/KIiP0Neo2t7BEA5PJAxxQBuWNsyoGk6+lXHbFcpqOvyqRHC+CfStSxu5Xs laY5Y0AVPEXiFtFMcht2kgJ+dx/CPWs2XU9O8Qxxy29/CVQhsZ5GK29TtIL20Mc6BkYYrih4ZsNH kLQo5WTKgbvWgDYm8ZWdi0dlaSLeXJIXZGc4rrrW4M1qjsMMeSK4nSfDWm2NyLlYczMPvMc4rsQ4 jVeAB6CgC28ayxFWOM1h3WnyRuSuWXtVHWNSurW6yrHYPSkh18vGu9sgj0oA5zx9YC48K3pI+dIy w/DmuUvU/tL4U25Iy0Kkfof8K7HxjfxyeG77HUwtXKaIBN8Np0PQRvj9aAPB2GGIr1/4Z2wt/D1/ eYwQvX/gNeRTDEzj3r2rwcgh8A3ZHBYH/wBBFAFj4fRiW71G9b70krc/ia9TsY2dORmvMPhvIgsZ UJwd7Z/76NeljUY7a0dl6qM0AbMUKxjNEsuyJmx0HSuc07Vrq7uiS37v0rZ84SuU9qAOfj8W2cs0 tndstpOSQokON3uKbDPY6Qz3UuoQqjndyw5zWZ4t8JpqMiXERKvHzkHBxWS/haHUY7eE3Ep8tQGy xoA7vRPEC6vIxijIhB+Vz/F9K3QwJxWHo9hDp9oscQ4QAA1auLtxbOYuGFAFm5hJBZRzXP3ibiyu vXim2WuzeeYpmz+FSXl3E+SCMigDzTRIhp3xHurYcRyxbsfhXnPjy0+x+JJ0AwCx7e5r0Tzt3xLV 1/54gH8q4/4pxhfEIcfxLn9aAOP0qHz9SgjAzlx/OvYPGy/Z9E0+wTjdIuQPqTXl3hSPzfEFqp/5 6L/MV6j48b/iYaeD91ZB/WgD0TQLcW2mQR4wBGBitVLYSy8D9KztJmV7WLnjaKs3espZHZH9/wCl AHQRRiFAKy9Z1qLStjTIxjY4LAE7fc0llqck9t5kvUnim6jZpqFm5IDZB4NAGddvaa4sctteQsik NwQelSf8JDZWGy1WdbifgbI+SPyrzGz0nUIdZvbeKd4o1jYgb/UVueB9BuBcTXNyS+44Ds2T1oA9 UhmE0CuOM1HcQeYM1Asq221MHjtWVqWsXNrcDb/q6ALk8O1TxXmnxKts2MV2ow0TA5/EV6KmpR3N qHPUj0rgPiJOraDNH1JHH5igDA8awC+8C6fdY+ZV5P8AwGvHK9t1FfO+G0at/BHn9DXiZ4Y0Aelf CmzDXU90R/q+R+RrpfDCfb/H2p3D8hGIFZ3wwQJod9IB8xIH/jtaHghwviXVs9WlP86APR5AjODj p6Vq28SyPEYY2UBBuJ45rPt0TcrP06mpv7bSO4W3gXPODxWE8Op1Y1XJ+70vpr3KUrRtY3mbZEST 0FYY1mxvWlgSaITjI2vwf1q+1ws2Y84yK858e2D2VqbyAtFOnIdDjuK3JOpsbT+zriW4nmiSNiWJ JFbOnatbX8jLbuHVTjcOn4V4bLf6teTadBPdStHIgJG7rXsOi2Ys7VHACgAYx9KANy5h85CM1gTW zRSYORWjdai0NtviUs2cYxVRb/zz5dzHslxkfSsnXpqoqTfvNXt5FqnJw57aFDULdZrGVMZBUivN /BaeXqOp6a33Tkgfka9LvpVSB+eMGvM/DEn/ABWN+69CP6CtSDyvxDbfZNauYsYw7fzNZ0S75UUd yK6Tx3EIvE1yAMAs38zWFpyB7+FT0Lj+dAHrd4v9meAkiAwzsM/ia6Hw3aLBoNuABkoCeKxPFy7f DcSDoGT+ddJorB9KtwOgQUAdJo0FnMkiXUrK5GBjGAMg8cdev4VqWcZTdjdsydu7rjtWXbSQ2kHm SDmrVlqy3SO2Nqr7VzUsPKnVnUc21Lo9l6FOV0lYs3t3b24VbiRUDHA3d6wtXsXumj+yxBlUg5XF WdZtlvYct8y9vavNLvXNX0TU7i0junaIRllDHOK6ST1K2uIbKCMXLxxueACeTWqrCSMMDwe9eX+E zdatH9svJXmkY5GTwOa9EglEEaxu3NAFC/tX3lsEiszCiTBra/tBpi6tbuY1JUsBVZ1skS4eVGZm XMJGRg/5x19K5PrtFqXs3zNOzS1e9vw6msqFSFnNWueaeM4Baa7YXScfOAT+NY3xLtfMs7K8A5Yc n/gIre8dSK72YH3vMGPzFUfHSb/Cdox6qcf+O11mR5FXV+ArMXWvxsRwhBNcpXoHwyjH2y4kI5AG PyNAG/qC/b/GHk/wxg/yrsbSAxKAueMVx9uceNrkt6cfkK76x2lhnGDQBsR28aQW0lvIXdlHmqT9 0/5zWmDhMmsiTU4LMKqjLHjgVda4822JU8sOlY0Kc6cOWcuZ3er9dvkVJpvRFW8gt7lXmGHYA8Lz WFpVk6Xku+3YIzHkis3W57/SUkvLKVsL8zoTkGshfGWp6lc29rARGZFy7AYrYk9Tt2iXEcbLx2Bq O9jZ4zszWNoqvEdxct3Yk9a157zyyqohkZ+gUZNTOcYRcpuyQ4xcnaK1MmDS7nUbhoItqsF3fOSB jIH9awfE2iz21jPDcw7WKkqeob6Gu+0qTbfq/lNGzKVYMKj8eFP7AmYgZCH+a185PO2sfGlTalB8 q+/S9/Lqjp+rtL3lZ6/geM6An23wxd2jfwuT/KvKLlPLuZE9GIr13waoNpdA9GkYfyryzWEEeq3C js7fzr6U5ShRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/wCBP+SeeGv+wVa/+ilroK5/wJ/y Tzw1/wBgq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQB6Z8HSBrkv+5/WushVR8UHMgypgPP5VxvwlkEett7r/WvRhYqf G3nHGTCR/KgDuoIbZiOR9KydWiaG/QKuEI+8BWjDZynG2tpNNhe1V7tQxT1oAgsc7I1Ocbc8/Spp U3jHcVm3msxrlbOMEjjJ6Cix1Nrhiku1ZSOx60AZutO9p5bCMtukVenrWvZQsEBIxn1qlrmox2EM DXCBt0yqM1oCfEQkztTbnNAF5eUbHpXNeIIncxMqksfT61I2uXIkxCE8oHueta1leWmokRPEokHO DQBStreFbKM3GFYKOKz9buLf+yblYwOIz/KrmsWUq3ALSbYmbavB/L9KzdZto4rG4BGFKHgn2rD6 zT9uqF/es36JWWvrfT5l+zlyc/Q47wWCnh7UWPAYyfzNeHah/wAheX/fr3rSUFp4YuiMDcrn+deC Xh3akzHu1bkHuoG/4Z7R/wA8P6Cr/wAOoYW8OW+/rs/qaq6Mv2rwCY+v7vH6CtXwjZeTosKx9lxx 9TQBuatYp9heSFckDJxUmgzjyxjqBir9jbSTZgkGUcYORVmHSrbSIZZJGUZ6UAXoSCgbPWql5AQC y81jrq9w0x+y2zyRqOMnAJrQXU0ms2lYqjouXTd909xWMcRSnUdKMk5Lddi3TkoqbWjMvTJ/tN1c RoDmOQqeOldJBGIkyTya4vw7rtveapeQxRrGS7MTxzVnVb+9u9REVhqQS3RF3rGAT5gYnqOewyPz 61GLxdPC0/aVNjbC4aWInyJ283/wDprp9oYA5yv9K41VM+sBNu5QK2dK1BvMW31Fw0gyBKcDfz6d sDFX5tLis0e5gUMzdMVWGxNPE01VpO6ZnXoyozcJ/wDDlVrOzhOWYZx0rzXxGqP4+08RD7oB4+hr umtrmVzI7ED3rk761R/GFu4ALJHkn863MjjfjIwM9kO/l1z3wwOPE6E+38xWz8XpN+oW6/3UP86w fhw+zxJHz1I/mKAO78VIR46sXOcFcfoa9Ht7JZ7dME7ttcf4gsVm8SWcrdu/4GuwtllgCFWOMUAZ MYZdVeOXOVHGa7C1kVkROgHFZ1xoz3lzHdRDaTw2BV29EdmiKv8ArCOAOppNqKu9hpNuyNQASRFc 1zOtTx2vkiRsZkCipG1m805fMurJxCRw+4ZHfkenXvWJqfiHStUdA7qvlurckdRWGHxdHEpyoy5k jWrQqUrc63OtsYhIFc9MCrszKVPOMVxE/idri3gttIuPKmEoDvtV8pyCOc+35VppaXy28kq6hK0k py28lh17AnA69sVw47OKGCqKFVPXsdNHAOrSVTnSv01/HQfrcqvCT6LWVo9mLuNmfIUd629SsGvL KF7VVkDEK2GORkgdAD05z6YqrMjafbraxjDgYYr616UaqlPlS6Xv0OR02oKTfy6mD4wt4Lfw7ebX yfKasLQx5XgCYHjMTH+da/iOBpNFuBKScoetZ7oLTwM6jA/dY/Q1oZngVx/x8v8A71e1+Exu8DSq P7v/ALKK8Tm/17fWvbfAn77wrKn+fu0AQeAUJlu49xGHP8zXYap59pbZ5KnrXPeFLLyLu9K8N5h/ ma7IwnULGWB+WxxmgBNFnAiBx1A5rdhYfeB5rG0e1MNs4mTpwM/jUj3ot02RK8jDk4yaipUhTXNN pLz0KhCU3aCu/I2LsLJAXz0HNcxoVwLm7vIxwY5StE3iy1jgKTHy3ztKsQO9Y1jr1lpk91MXX97M WGCOhqk1JXQpRcXZqzPQ8iNAgP1qpNKsYYZGCMVgW11Pr12J4p5IYojtRUc4c9cnGM9uDkfnWhMk toxkmPng9P4cV49TPcJTruhUumuttP8AP8D0P7NqOClGSbfT/g7fic5qDvDeJsHzE4rTWybyfMkY gkZxTIrD7VdfanJ8pc4yOPzpbu6eTKJkLjHFerSqxqwU4bP+upw1KcqcnCW6OBtVz8QpGU5CRjJ/ Cub+Kbbtbj9kxXZ6TZf8VRdS4ydoGfwFcJ8Sn367j0H9a0IMfweceIbc/wC2v8xXpXj8HNrL2Eg/ rXmHhdtmuQH/AGl/mK9f8ZWf2nTUPcOpoA3tJM40yKSMlhtFTaSw1HWdk/Gzlge+P/11DoMkkFnB G2Su0CnXNrLZ64s0TFVdecVz4qnUqUJwpO0mmky6bippy2Onk2RSGNCAo7DtVqCTYFXPy1kX9wkS LtBDIMkjvWTN4wgt8LJEwxxmlg6EqGHhSm7tLVhVkpTckP1Uo2pzSwRvG+REwkXbkbsbh6g9q6DT o47ayRVIyBiuS1XUDdwwSIcZlVsZ7VYl8Rx2IijI8xnOBhq1gpQTc5X+VtCp8s5JU42+d9TqZWzh iax9WlV4eewqWKe5v7cYQJ34aq+qoWsWUIVYDAxzk1w083wdSapwneXaz/yNqmBr07uatbzX+ZkW E81xviTOFOMiud8eRtFpoVmyWIA/MV1tjb/2dYhm5kkGTxXJeLUe8ltVYkqXH8xXpHIJf/ufAJRv +eQ/ka8Rb7x+te4eKR9n8HhQOq4/SvD2+8aAPX/hqf8AiQXQ7lh/Kl8PM8PjC/ReCXJqP4Yndply nuP5Vbit2tfGssyDh+f0FAHa3GpS28RV1wTwK39M0iBdKF40jGWVA5Jx8v0/OsHUIPtmmM6DDque lJoWsym1MF0zFIxgKeOleTm+DxWLpRp4efLrr6fJG9CcIS5pHQRyfvQ27FVvE0NtqeleVcpL5bts dohyoxnPQ4HFZ0urwCX5Tj2zUN14kQKI1YsT0C969KrBzg4xdm+vYzpyjGSclddhB4XtvtmnXMT7 7eBSgzjn0NdVLN+7MY4UcV5/Z+JJh9oEsb+UHPGOBWtF4mW62RRqzu3AA5rkwUMTRpuOKkpW2fl5 mtZ05yTpJq/T/I6BXBwp5yRWV4ju/s+pRSg4PlgYH1NaFqJRHuli299xPSsHUnTVdbRI42KxqMyH OD14x+PWvMhXhiszhVoJyjGLTfTfbWxvKjKlQcajs77f8MMvJp5bN3PC7a4nwaCdXvZeoLEZ/Kuz 16fZYyxxDGEIrmvBlmY7d5COWkYk19EcB514+bd4muD/ALR/mawtK/5CUH++P51reMn8zXp2/wBo /wAzWRpvF/Ef9ofzoA9j8UjzPDx9ip/Wrnhm8Y6VCSMqFpupW5uNA2n+JAR+dQeDJB9g+zyDpxz9 aAOx0pYtf1E2ckjpGsZYlDg5yB3HvWtfafDpCpDA7srcneRn9K4h7u50HV/PhdkDDGQAf5/SugGu JqC7pn3MehyOK8qWHxrzCNVT/dW1Xnb07679DVOHs7dTWtLiN43jcjpxXHXHhhNW1y5le5SNWh8u MDlt3qR0xjPetgyiPLLKBn3rD1HVRaSRNG537wcg12YqnWnTaoT5ZdNL/wBf1owoypxl+8V1/X9f qbvhXSX0CzNtdFGlQdVPB5NaD3G+YtmsSDU2ufnablgM5NaFkGnb92hkA6ntS9t7Cgp4qSTS1fT5 Bye0qctJeh1VgqHSpGKjkMelcTc6huu7mFeSkrD9TXQ6nrttoekmGVi07odkS9W7fgP8D1rk7Rci W9lTa0zFwDzjJJr5vhujUdetieVqEtvvOnFtJKLd2cX4olebXLGE9fMB/WpPHDY8KxL33/8AspqO 7U33i2FyPliIOKPH42aFAvqxP6V9ccJ5NXofw1YK1znvj+RrzyvQPhsCbqVOef8ACgDX1GQ23igS Dq4/oK7C11JQigkBjjGTj9a5DxNG0N9b3AHRiprpHtVudMEseAwXPFAHoNv4Ys7zT7W8aWbzTCGI Vl2knn056469AKzPtPl3A54AxisrQfGs1pZ/YJ1MjINqksFwOmOlWzLDcx+Zu2t3rxcro4+lVqrF O8enXq9u3o16ba71XTcVy7kesxQXME8LMQJIyFI7Htn2rj9O0T7Fq8UqyCRVjKkgdDXUTTROrkPk qOKydNmDzzKx43E5r1uSXPzc2nYz5lyctte51YmSC1VVI3MK1fDxEt5lgDhTjNczE8cp/wBb0rrd BhMBMjJtBXjNeNxFWhDATjJ6u1vPVX/A1wyfPdef5FzxJeLpmmPdqF3KVAyPU4rzLxV4nur7S5Ek CBNp5XPsfX2rb8UeI4tcum0yxzJAjAyTg5VyM8DjpnHOe3pXEeLWWGxEEY+Zhjj61zZHlUI0vbV6 dpN3XdLS3oOpWa0i+hH4SGzSwx43Ox/WvLNcOdXuD/00b+deuaNbmDTFUD7qkmvINXOdSmP+2f51 9McxRooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8AJPPDX/YKtf8A0UtdBXP+BP8Aknnh r/sFWv8A6KWugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4AooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigDvfhxmLVUdepx/OvUNckawvoL0cbSFP0NeS+ALrydWiBxywH61674 wK/2S3HzcdKAOu0q/MkccqsNrAGtVtPj1y5Ml2S8KDakW87cgdSOm7kjPpXg2geO5rJTbXAOF4Uk mu88O/E7T4ZGiu3WNDlg5JPPHGAD715Wc06lTDWppvVXS3a6m+GqOE7xdmdfqWmWtkUjjDDdwozk DoPyrlru7ij1u2t4ZfmVvnI6dqxvF3jmTVcCwQpEpYCRWzvGRg9BjpXJQardtdxPIPun73OazyXC 4ijRbxDd29E3ey6f1+pWIqRna2/V92ez6pZw6lFCJxuCMGH1qh4knaDR9qPtCDH1AFc9B4rBhAcj K+9YniLxA97AUjbIHoa9k5juNGeyu9OWTzCG7g1oxTJbHfF1HevGLfVtQR1YblH91c10K+Mlt7fb McOB0JNAHqU+pPcwpuKlFAbHPLe/tXG+K9XP2f7MHJln+QfjgVycnj7AbHA7YJrFsNZl1rxJDNKD 5aMAOtYwoQhUlVXxPr5dvQpzbSj0R3V6jQeGHQcZjxn8K8EvVC6iQPWvfPEt2sHhw4GPlP8AKvn+ d994W962JPf/AAMPtHhIp1GP6VoeHL02t5cWLnGxjtz6Zz/Wsv4XTCTQplb+Ej+VZXibWBpOvC4h 5O4hgKAPZrO+dLeQoFMwU7Aw4zjjNVDoT3I+1XlxPI8hMhjMzFFJ5woPQe1clo/jKynELtIqjK7s k8V3MfiDSpLVT9utwCo4MgFfK8RSr80YwckrPa+/yOzDVHCL5epXm1C2s7VikZVF77f/AK9eay6t 9uvblgdiMzAYGM5JpPEuoXllO1tETJDwQwzzxXKLdtDFulyrGu3JsqwuFgq1GTk5Lf8A4HT56k4i pUu4SVjobaOK0eSVJCCwOTWp4ZvvtM7wu2HzwfWuLOpK2F3VJb3tza3YeFPl455r2atKFWLhUV0+ 5zwnKD5ouzPV7iz81lDvypz8prc0/UQsXkNjCjivPNM1xsBrk4JA6mrp8R20Dk+av0zUUMNSw8eW lGyKqVZ1Lc7vY6LUdQ2iQthVANcdozHUtYmvSOD8i/Qf/XzWL4p8XxvF5ED5Z+CQTxzXReDHh/s5 WUgnmtzM8w+KUhbWsf3QR+tYfgSTy/EEBz1YfzFXviNOJtel5yASP1NY/hNimsQH/bX+dAHvfiW2 aOKK6ReUYHNbmlX0U9vE2cggdqi1hoX0VDIwGV71wvh7xHFZ38tlK42A/IxPvQB6zqOoXNnp0J09 InkaUKwlBwFwT2PXIFVNOkUXTXN8QZ2Ix6L16fnUWm31vdSIhlQjr1qPxIpt4VubfkICWIPTpivk M2nLEZjHATnywkl9+tvW7X9M9GjUjHDtqOvcg8Z6/bxWAgjBd2JBwOAMH/GvGpmLzOQCox0rob/U bq5u3Zkyi5yTmuVuNQQzNjAzX0OAwFLA0vZ09b7t9TinUc9ze8PXptJgTHnPB/OvT7a7kNqCPmU9 DXkWmXZliYRAM+K63RdTuGiEU2VAPQmrxmBoYyHJWjf8x0q06TvFnb6JK9rcSvIx2ysML2H+f6Ve 1O4gDbtuWPeufW8QbTuAA96ratr1tDAzNKoIHTPtW1GjCjTVOGyFVqSqzc5bsz/Et59pngsYznzG G4e2c1X8VqLTwhtHUqR+hrI8PX8WpawbqZx94BQTWz8SplXw3GE7k/8AoNamZ88y/wCuP1r2/wCF 6+fodxH6EY/KvEH/ANca9z+D3NtOD0yP5UAadjiw1+4ifgSHIrs7AxecrA1x3jVEgk+0wMBJGcnF WPD+vRXsCMHUPxkZ74oA6XW9VihuEsbZA8zYLkjIAOeODnd0PToa0be70yysDuKjGc5GWPNYM8kc 8wmLfOq7Qc9uf8a5/WNQlghKKSxrwcblEswm/rE2op6JW27+vQ75YilCnGNKOttX5nPeK2Go3jzQ R7V3NjIx1Jrnxp06uhbGD71e8Qam1rBGoxvYZrBg1uY3CM5GB2Jr2qVKNKCpw2RxTnKcnKW7PUtA vHgjCIMMuBg9DxXSreTXF2nyqI1IIyOa4G1vZBLBJFgqyjOK6u0uD5fmE/MR0rnngMNOsq8oLmX9 X9V0Zar1FHkT0OruXhe1w5wD6VzWoT29rBI4PAB7VJNeL9m2u4GPeuG1vVfteoRWETgqx+cg+9dh kbPhq2a4aW6IwXYnPt0ryTx8+/XpPbP8zXvuiwRRaWRGwOB29a+e/Gr79cnPX5m/maAM/wAO/wDI Whx/fX+dfQWs2LT6MJMZHFfP3hwf8TWH/fH86+qbe1jm8OxiQgAjvQBzOhuk9lGAfmUYrqre1huY l8zBdehzXmC37aDrkkDn9zI2FJPvXa2eqIU3rIMH3oAtalaAXMSsG8ps72Xtj37VyXim0sljMdmG Lnk88Zx/OuivL1mjJ38Y9a4vU9RWCSSSVlGM4zXBLA82KWIlN6bLodUcU40fZRVr7+Zj2Wk6rOrN Gw2gEctS2NhdQX4Nx8wQ4zn3rmpfF97DPIIHARu2TWv4e8RG6MiXTDcehz1rvOXY9RsZLiRFWOVl A/unFdHbWsfkAPzjmuL0m5YoBnA9c1vjUNke3d29azp0adJWpxS9FYqc5Td5O7HahDG8zHdwD0rj tRxe65bwRjIQgmruua2llDI5cFjnAzUfgm1+1Tm7ujh3bvWhJmfESI2/hyNPXP8AKvCn++a+gvi4 AmlQqOhJ/lXz6336APYfhJEZUnT+9j+VdDqdsLLXUkcYGSpNUPgpEGuGz3I/lXc/ELSA1uZoDmQN 0FABYhWwpOUbrU1xosaIXhGQ3XmuV8O68k1v5UrBZU4IJ5rpF1UhcLIMemaAOa1KzEG5ulcpEt+u oK0A+dTlMniu01u5WaEkkZ715prvilraZVs2+ZON2alRs277lOV4pW2/rU6W8GsvbMJAnlkDcAR1 qz4a8+2m3E7XPA4BwPxrzs+M9Rdl8x8rnkZNd54e1WLUQkiEBlIyAaJwjOLjJXTFGTi7p2Z6JaR3 F04eaRn44z0H0HQVYuLKK1jYx8O3U1n2980Ua4I6VFd6oCpLyAAe9EIRpxUYKyXRA227sytc2QWE mTlmGBTvD+mmPTGbGAAawHvX13XI4IuYY2wx9Tn/AOtXqcenQ22gNsIyBzxVCPljxZn+2p8/3j/O s3TF3X0Q/wBofzrV8XDGsz/7zfzNUNEXdqEf+8P50AfQCWDyeGIZduccfzrB8PxrDdzRHgg5H516 l4dsYrrwiqSY5HBry3Wo30XWvOAOwsVagDq59Ph1C32ty4rm7nS5bKUtEMY7ZrXs9Yt5EV0lXJ96 mm1BJkKtsII60Acdd6pcZ8tQcqeeOlU7jVm2xrFBv2nJZu9HiTUodPSTaRucnAzXANrt0GbDcema APQpNRmnZGhj2PxuUV1dhruoTaZ9jgjigDY/eR7lcYOeDn/OTXkug+IHjuwJzkMQMk+9esaRNCkQ kG31GaxrYalXt7WN7O69S4zlFNRe5pW2kCcGa5JZs7izHJJ9aZeKNpUcRrwKlfVQVxuUD0zXPa9r SRxeVEQ0r8AA1slbREFTTbYXesu6DOWCiqfxOgMNhAhHTP8AKu38BaMpCSy9Q3fvWB8ZI1VECjgE 4/KgDwU9TXpPwstzLqW0dyB+lebt981678Hog2qp/vD+VAGv4x0wxtIpGMNlat6BKrWSKT8pAB/K u18f6SjQRzoPZuK8w0++Gm3DW852ru+Uk9qANPVtFxL58I6nOQartqz2cIUxnP0zW1HqsDwj94rD 61larc2pj8wMgx6UAZUniWGC2mlnDI4BCoB1rJ0nxXALqRJ1Kq5PzBa5LXtTNzeuEPygkDn3rKW5 dWBBoA9gsdfeK5DCBZUDcB1JDfXmt/UNX1PxFHFDMscFuvVIdyh8kY3Ak5xjiuF8JalFc24jlIDK w6/Su/hvYIlAUp9awnhqNSpGrON5R2fYpSaVkOjsY9Phwq/vDXKa6nn30EZOTnJ/Ougv9YgjVmeV c/WsHS4ZNU1MSEEh3wv0rck6K0sGXQpptvB4BrwfWV26hMMfxH+dfV9/p0Vp4UMa46c8V8r+IV26 nN/vH+dAGPRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4E/5J54a/wCwVa/+ilroK5/wJ/yT zw1/2CrX/wBFLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQB1PgyItqcbDsw/nXu+oLBc2CRyDkivCfBzlNQT6j+de7+UJ7RJP UYoA821HR7eK7ZlQYz0qpe6Gb21DwR7ZBwMGuq1i32sODkmt/wAMaYJpEVhkHPagDzzSY72O28p4 wQvHIq9cwbIQ7qAfavVtT8ORWMfnRx8MMn5R1rlr/TIZYymACT6UAeevDMrfI3B6VJDAyMoc5Lnv XS3ehhApUnkgdKsw6EkTxyMxOPUUAYv2WdWzGqnFYVxpF3qOobmX5FPODXqlrYRs4VRlm4rpJvDU NjpDyeXh2GSSooA8Pk0yNCIzEOOCa3dG0+2slEmwbs5zVnVbMxXBIXq1adnZq8K5HJoApeLG8/w+ RyABx+VeGyJi7C+9e3+MY/s+k7QeoI/SvFG5vRQB758KbYjS5Tjg4z+VQ+M9Jga7MmzJzzzWh8LZ VGnmPP3gP5Vd8VWgfeV657UAedQwkqYwgAPHFUlTVLbUPJDFoWPGa6HRYw16Vfsa62HTI7l0IUEj 2oA5hpLwgeegdj/EaxdStGlmK8gAV6PfWSouzaQR7Vj3mmwync3BxSjFRXLFWQ5ScnzSd2cENNZS Gz+tbtnHKLZHVQT0571pRabDI5Uk4Fa8NnGqxqqjC+1MRxutR3v2cMi4PtVCysbpYfOuMlj616v/ AGQLq1V2T5Rz0rD1OxEUTgKPbAoA4mGyiuZgssY69q9P8N6bHb2OIgfzrgdMiM19jHAPavUNGYRW pJAwKAPn34hQeVrsv1P8zWb4PgM2rw4/vL/Otr4isJNZmI9T/OqfgQAatET2K/zoA+h9c0lbjw2h bIOB3968hfTBFOVVec9TXvF26TeH4wSMY/rXk2rR/ZbwsBxu5oAwZJdZ07EtnJyp471r2Wta/qlq yXMmV4yCoGa37OCKW3V+CTVqC2SMMFUDNZToUpyU5xTa2bSui1UnFcqbsclcrczWckbIFb271zJ0 QsxJBz9a9OuLZQS2OD7Vz1w8Ecjfd5rUgwNH02a3u8gfKK3wt2+7ChQPzq9pyRuPlA59K2oLfCBd uWPbFAHnd1ea39o8mP7ueuBSSWtwY91w24nrmu9a1iEjAgAnrxXN69thdIl7nFADPCGkR3GqRvyA GGBmuk+KOniLw5CF7Z7/AOzS+EbcW1xCx5+Yda0fipIjaDGvHf8A9BoA+YZF/wBIx71718HbRpLe UAcHH8q8KlUfbcDpX0N8GnWOI5Pp/KgDG8dafeQatKFYmMseM1ylva31r89v8uDXsPjSGO4vWOBn PJrkLWOKSUxnH5UAc1b6rr6ygEAp06CtC9uL+XYu0FccnFdI9vGijCj64pViRkI2jNAHnWqaTc3E wZ8kfWs//hH5s8D9a7nW5hbhZOB61lR6qkrBAw64oALS0vLW2QJy1Xry+1SG2RoANx4PFdBaIphX gHIzmnvGhblRg0AcG91rtyCkpCD2AqqthcRzb1/1hPUmvQLqCPyyxAwKyLeNLifIxgHAoA7XwdpV xDoLSSsSTnvXgXjeEx61P7s38zX0/wCH5Ej0F0J55/nXzV48w2sTkf32/nQBjeGELanF/vD+dfSe rQ3sfhCOW2OCMfzr508Jgf2nDn1H86+r0MUnhJFODx/WgDwPVZrrUPLEiZdDyRTUbWYkCW5yPwrs 7u0gSQtgDn0qaC3QqGGKAOWiv9YWNhPtwB6VhXtpqGouWk564A4r0GeFCcEDBqK48mGHoBlTQB5a 3hyYjJXkn1qxa+HbqKTKcY960JNbNtM8ZPQ+tbXh3URd3EjyEHjgUAN0yfU7YGN8AjpTbu78QP8A MgXb9BXWSRxsqttH5VIIVZOBQB5zcxX9w4a6yxBzjtXSeGbjUbnVLeCMbYwwBwK0Lq3iaXYdua3f ClvHBqsRAGNwoAyvizC66Tbq/UZz/wB818/OP32K+jvjC6NZRgEdT/KvnSUYufxoA9s+DocNJs+9 2/Kuj1nVb1dWuILn/V7jjisT4MMq3RBxjI/lXb+LraE35cKMn2oA8rv9OuDePNa5XdzTYINbjy3m DFddtj3bDjNOaAbTQBw15LqU4MbShexwK5248OXLvkrnPfPWvSRbR+ezlRXOeJtUSF4zEQCpOQOO 9AHIxeGrl2OFPHvW5pWl3+ntviwo6nmq3/CRPLsjBCknnBrvdOmgvbMKME7QCaAMyO41aeMrG4BA 9Kzri31pyyTS5X2wK7K1t0j+UDkcVJNCo5NAHL6Sz6VExVcy9eea9C8NX9/d6LcSXONpJxx9K5tL eN2zxjPSu9skjh8LPjA5NAHzH4tH/E2nP+0386o6EP8AiYRH/aH860PFeG1OUjoWb+dUNCOL2M/7 Q/nQB9Hrql1Y+F4PI+6eDx71gakV1aA+aPmYc/Wut0tEufCaggH2rlpwIsgYAU0AcZJol7DN+5kI X0zT5YL6KIgzHp1rpyizqCv44qpdwAxEH+VAHE6jot1d4eWQyD2rBk8Pzg5C5BPY16PcSC008lvQ 9a4m01poJ5Q3IOSMmgCKz8NymQAj5uvWurtre/tlWNpePSsrw7qhm1B2lP3jkZPvXavGHnDt0NAG FdWGoyjKT4p2m6NIkwnuW3t9a6Y2+5RUZKg+X1I60AWLXWrq1u7a3tQMFhnisn4qTvPZxM/3j1/K uh0KCN9TjLAZDccVzfxWddyICOCf5UAeJuP3let/CeUxXhdeq4x+RryV/wDW16l8LHC6kFPcj+VA HpOpeIJry5mtJx8o46Vyep6THfxE5IcDrnFdVr1qsV0ZVAyetc/JcCOYoSBmgDnY9HkhyBI351Sv dOuZCY0lOCOQa6xozu3L3qnMwjlBGCRQB51qGgG3kVc/M3fNUhosgO5+FBxmug8RTyS3fmr0Q/1q tf3hngto48ZKgnFAF/TNAngRZoJAAwBJrejsJ3jBMp+uaTQ7gtp5ifrxit6GPdEMD9KAOcXw/JNc BpJWYema6SxK6Z+8QD5BxQyraxFmODTVUSx8/wAVAHQWuv3Go6HOJgAATjj3r578QHdqMp/2j/Ov oOaJLXwy5HGf8a+etaO68kPuf50AZNFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfb/AIE/5J54 a/7BVr/6KWugrn/An/JPPDX/AGCrX/0UtdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRXcWGg6Neaj4ZHlfZ4LjRLm+ujcTO6 PLCbo5coNwjPkKCqDcF4BLfMcPxRZwWt7bTWlvaRW11biaJ7KWVoJQHdC0YmHmqMoVIfJ3KxB2lQ ADDooo60AFFXrPS7i7cBEJzXR2ng6d1BkAXP+1QBx+D6GkwfSvQV8IRAYMgz/vVFN4NyD5bqfq1A HB0V0F94ZurbJ8vI9QaxJYJIThlxQBFRRRQAUUVueGoVnuJ0GjWmpShA2b67aC3gTOCzsHjwSxRQ WcDJxhiy4AMOivQLjRPDqypp+n2/2q1vNP1HULfUpXkE6rA115QUAqm1ltk3BkLfO+Cvy7fP6ACi ilVSxwBk0AJRWpZ6Jc3ZG2Mn8a24PB05ALBR+NAHIUV2jeDpccFfzrPuvCt1CC2wED0agDm6Ks3F lNbsQykYqtQAUUUUAFFFd5b+GrTXxp1lBpkelXlzcRm3jgme5uJLPy3eWaaLcTvVVjZQqx+YHOxW BG0A4Oiuk8caIug+IktYtNu9OgksrWdILosXBeBC+SQMnzN4OABkEYGMDm6ACiipI4XkICjNAEdF a9roN1cAER8fWtFfCV0RnYv/AH1QBy9FdHN4Wu0GfLB/4FWTc6ZPbsQyEUAUqKUqVOCMUlABRRRQ AUVueGoVnuJ0GjWmpShA2b67aC3gTOCzsHjwSxRQWcDJxhiy46iXQPDf2CeKwWO4Sa3vbuzlmaYX s0cTz+XJHjEIi2wguJAJOJdo5joA87ooooAKKKlitpJT8qmgCKitaHQrqUZEZ/OpW8N3YH+qP50A YlFX59JuIfvRkVSeNkPzDFADaKKKACiiigAortFtdHvdGttQstCjnljvY7VbK0nnaRy6y+WlyW++ 7NGpH2cKCPMB8slMY/ii3sba9thaR2kM7W4a8t7K48+CGXe4Cxybn3AxiNj87YZmGRjaoBh0UUUA FFAGelTx2ksn3VNAEFFaK6Rct/AaZJpdwnVCKAKNFSSQvGfmFR0AFFFFABRRWx4em02O8MeoWtjL 5nCy6hJcCCIAEklbf94WJCgEHAycg5BUAx6K9I1fwnpOmWMtlbWsEl88V7cCC7nlW/iWCadPlZAb YqqQbmByzYkCkZTHm9ABRRRQAUUoBPSpFt5G6KaAIqKsfY5v7pqJoXTqKAGUUUUAFFFFABRRXqF3 8PItGstLN/pN8Jov7Qe9luFdI5pIbNLhIhjGI94lj3K2XCOysBjaAeX0VueIra0SLR7+0tY7QajZ G4kt4mdo42WeaLCbyzYIiBOWPJPQYAw6ACiiigAopQCelOEbnoKAGUU4xsOopvSgAooooAKKKKAC iu0+G/hFfFGvI93ZXd3pttcW8dxFbKxLmWQKAzKCUQKJHLYxiPbuUupqxY+ArmPwNrOrX2kak14l vJJB+5dUthFcxRyF8DBcjzwVJBQQsxUhlZQDg6KKKACiiigAopQCaNpoASigjFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfb/gT/knnhr/sFWv/AKKWugrn/An/ACTzw1/2CrX/ ANFLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQBoRa3qMEthJHcYawiaC3BRSBGzOzIwIw6sZHyGyCGIORxUepapd6tcLNdvGS iBI0iiSKONck4REAVRkkkADJYnqSap0UAORGdgqjJNdhoHhUzAT3WFj/ANrNHhfQFZDe3QxGo3Dm te5urjWp/sdiNtuPwJoAln1bT9KHk2UAkkHGVxis573WL9soXRD0AFdPp3g9LYpJONx4J5rrLfSI PLQJGOKAPLU0rUHbMlyynHfPNI1rq9sd0czkduteia9aQxiEbcEOBWlHpsM1pGQmeKAPLodevLc7 LyAyL3qzNaaXrkJMQVJcdD1BrtL7QLW4UqY8H2NcnqXhSayP2i1JG3n71AHCaxoU+nStkZTPBANY 9emW1ympRmwv1HnAYDYxu7VxOu6S+mXZUj5TyDQBk1qaV4g1DRre4t7T7I0Fw6PLHdWUNypZAwUg SowBAdxkY6msuigDYPijVjZzWomgWOXeMpaRK8auSXjjcLujjO5sohVfnYYwxzj0VLbQNczLGgyT QBJZ2Ut7MscakknHSu60zw1aWFutxfMg4zg1LYWtr4c07zplzOw+UdcmoIrDUvElyZX4iHKgcACg Ca48SW9v+6sLXOP4uMVQOoazd9GZR7Cux0jwjbwH96uW9zXRJoNvHG58vjFAHl7DWYQrec2SM4pY 9e1C2YC4iMq9/wDOK73StNgkvJ0cZCsQOelWrnw7ayI2+MfnQBw0d5pWrjy54hFIR/FjrWDrXhdo FM1thk9h0rrNS8GK297fIPX71ZFte3ejzfZb4boPcZIoA4B0aNirDBFNrsvEeho0QvbQZRhuPNca Rg4NABWxrPifUtf3nURYvI8pmeaHT7eGR3OclnjRWbOSTk8nnrWPRQBYvb641CdZrqTzJFijhB2g YSNFjQceiqo98c81XorY0LSJNSugAPkGCTntmgBNJ0OfUZQAuF9SDiu1g0vS9FhDXJRnAzjvTb7U odFgFlYqDOOuRnFUbLw/f6rKJrkkhjyc0AWbjxPGv7uztsAcZOKrLq+rTMdikfQf/WrsLHwdbxIn ykn3NWdV0SKySPyhtLEA80AcMmv6hAf30Rcd+Kuw63pt+BHdQeWx/vYrsW8M20tsjFMkj1rE1PwT C0ZaLIP+9QBzup+GLe5iM1mynjoOa4u7s5bSVkkUjBx0rsGg1Pw9OCOYepB5yKuXNva+IrEyQriZ Rlh05oA86oqe7tXtJzG4wRUFAGppXiDUNGt7i3tPsjQXDo8sd1ZQ3KlkDBSBKjAEB3GRjqakPijV jZzWomgWOXeMpaRK8auSXjjcLujjO5sohVfnYYwxzj0UAFORC7BVGSaQAsQB1Ndn4Y8PoyfbLoYj T5uvtQBT0XwtNe4klARODlga6pbfR9HTDhHcdhjNUNR12SQ/ZNOUKi/KWx1p2m+F7u+PmSd+5NAE kviaMfLb2Zx6nFRf8JDcJy1qCp/z6V01n4NjRGZwcgetNtdDF5PJC4G2M4GKAMKLX7C5+S4ttue5 ximXeg6fqURe1ZAx5xWzeeClbdsyMe9czdaRqGkS74iRg9c5FAHM6notxp8hDKdvrisqvSbTUYNX iNpeKFmPtgGuQ13RpNOuCQPkPI5oAxaDRRQB0C+NdeSeOYXUHmJvLk2cJ+0FkaNmm+T98xV3G6Tc fnbnLHOXqWpz6rcLPcR2iOqBALW0it1xknlY1UE89cZ6egqnRQAVNb20lxIFRSSfai1tnupljQZJ NekaTo9roVkLu8/1npnNAGVo3g1nUS3RCr1wQa6ExaJpabWCM44wME1kXms3epyeVbjZEeAAOTVm y8K3V1hnHXnk0ATN4hsAcRWTEfhSprmmTfLLalfqBV+Xw4dNVS0Ybd8o570TeDHkg81RhiM4zQBn z6PpOqITA0asfSuR1jwvcWOXQbk65UGt+70K905vMTIx3Bqax10kfZtQAKH5dwH86APOHRkYqwwR Ta7bxJ4dQx/bLQZjb5utcUylWwetAAAO9X9M1q90jzRatA0cuC8VzbR3EbEZ2tskVl3DLANjIDMA cE5z6KANg+KdZazmtpLzzfN3hp5YkecByS6rMwMiqxZ9yhgDvfIO5s49FFABVuz0+e8kCxoTn2qz o2ky6lcqirxnnmu+jSw8PWyqRmYjp15oAy9M8HKqh7llA9MGtY2mi2S4cxkjsKyLjUb7U5NinC9l UYqza+GbqdfMdeBz1oAufadEb5dij8BTZdI0m/j/AHLRhjTV8Pi5/dQoQ6/eJNZ9zod5ZMWVSMeh oAztV8JywZeHDL7A1y00DwOVdSK72z1q4tm8q5G+McHjkVLqej2usWhubP731xQB5zRU91bPazGN xgioKACtC01vUbGK1it7jZHayyzRIUUjdIqpIGBHzKyoqlWypGRjBOc+igC5qWqXerXCzXbxkogS NIokijjXJOERAFUZJJAAyWJ6kmqdFAGTgUAABJ4rT0/Rrm+kARCQT1xWr4d8NyX7iWRcRjOTmuvm vrHRY/IgXdKB+GaAMqw8FoqBrhl6Z5BrR/sHSYhhpIvzrKm1C/1JyAxA7BRipItGm5NwzLkcc0AX 38OaZOP3bx59qw9S8GyRgtDhh7A1PNpV3bYePcB2INS2ut3lowSYh0HUEc0AcPdWU1q5WRCPwqtX qM1vYa/bN5YxMBjHSuC1fSJdNuGVh8ueDmgDLooooAsWF9caZqNtf2cnl3VrKk0L7QdrqQVODwcE DrRb31xaQXcMEmyO7iEM42g70DrIBz0+ZFPHp6ZqvRQAUUVJDE00gRRkk4oAaqFzgCtnTfDt3fkb UOPUg10/hvweGjW5uhhcAgZrornW7HTI/Js0DuvtwKAMG08BHaDKyj8DVo+BYCMB0zUE2sarqD4V wqn+6oFE0OpwIriZskZ60AUtQ8CzRqWiIOPQGuTvtJuLJyJEIA74ru7XxJf2r7JysijqCtawn0zX 4vLcbJscg8c0AePEYOKK6rxF4Yl06RnRcpx3rlipBoAUDvStjFIDjHFITntQAlFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAfb/AIE/5J54a/7BVr/6KWugrn/An/JPPDX/AGCrX/0UtdBQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABWpoemvqN+kYUkewrLr0fwJp6w6fNqEij5VIHHfigCXUnJeHSLUcLgPt7123hvRI7CBcr8xzkkV yXhu3N5qk15JzlsjP1r0m0dQBQBNPa5XgVdsofLtmLDt3qzBGkqZ4NOux5do2wc47UAcRroaR42/ 2xW/ojqUEZx90Vz+pLKfLLqRlxW3o8MqyoxGFIFAEl1at9pICnBplxar5W1hnI710DRq3JAzWReM oYgigDzLxRovkyG5hBHO7jjFZV3bjX9CkkIzcwqcgd67/VohcQuvGOa4nRwbTX/sb/6ub5SPXINA HmU0RhmaNhgqcUyt/wAXWAsdcmUDCliRWBQAV2nhHTUiV9QuBhEXIJHFcnZQm4vIowM7mAr0bV4R pvh2zs4+JJQC2KAINPtJvEmq+c4IhRsD0xXp2m6bFa2qxooGB6Vh+FtNS0sUwAGOCTj2rsbWMEAZ GaAKi2ubgbRVzVcQ220dStX4rQK+4jmsjXJj5uwKTxQBz2kv5d9MWH8Zrq7qFZLUOvpXJWG4yzED ncTXaWCmSyAYfgaAMeG14LEVz/iPQor63Yqvz8YwK7iaAImAKyblRg5xQB5Jp0rWtzJpl1nypDgb v4T0rk/EGmNp2oOhBCnBHFd54tsvs90l1GACOSR9ao+K7Rb/AECDUVA3YAb14JFAHnVFFFAEkELT zKigkn0r0JCnh7RI1QD7TMMY7isHwZpf27UVZlBVQSc1veUdZ8UNF1hhbAHbsKAL/hXw21zILu7U lmJOGFd+tskabVUAD0FOsbaOKJY0AGK1UszImAKAI7CACJ5GzhRngZrn/EDF5Ex03iuvKC1smz6V xeqSb2T/AHxQB0Gkqs1sEYdFqC6iPm7cUmjylZlXnkCtme0V5N+KAOY1HSobm2KOoOR6V5pqNlce HdTM8Knyy3I7EV7JcwZ6cYrlvEmlJeWTggbgDg/hQB5x4oso720TUrZcqRzjtXFEYODXo+hR/arK 902UZYKSoP0rgtRtzbX8sR/hYigCrRRSqu5gPU0AbPh3Sm1K/VcHaOSa6vV7pp5U0qx5QYVtnf2/ SmaJbjR/DsmoEDzHUBfzrX8FaR57G9nG4lsjIoA0vDvhWO3gSWVcyEA4IHFdXbwBZAgUD6CrVvb7 SMLxV6KxzcB8cfSgCG7RLa1ZcjeVzt74rndGIN9OT3Y10GtFBJn+LZt/L/8AXXNWJKXErA8bzUxv 1KlbodNe2oSPeB1FY0lktwSHXI9xXSxYurEd8DFVGtfLXpzVEnlPibw4baQ3NspAGOg6VTidNd0q W2lA+0xLx6nj/wCtXp17ZrMhR1BBHcV5ff2zaJ4iiZciN257DGaAOBuYGt52jYEEVDXWeNNMFte+ ci4RwDwK5OgAoAycCirul2rXl9HEozk0Adh4S0mK2tn1G6G1VXK7h1NTubrxFqACqdg4VR0FWNbf 7LFaaTBwwA347/5zXbeF9BSztFZlBlPJOKAI9K8Lw6fEsjLvcckkCus0+yjlA2qBjrxThbkpjHFX 4LMJp06sdvmIVBx0yKUm0rocUm7M5fxM6LJEq4IVwOK2bGFbqwXHULWFrIQ+WBztZQPwrd0KYD5D 3FEb2V9wdr6GbeWaMpjKg/hXIa/4TVoGngU7wCcADmvSby0/f7gODVC4ty6kFRjFMR5FpV6bWVrC 94ib5fm/hP8Ak1y/inSTp1+doOxhkHFegeMtD+zg3cS4yctgVhahD/bPhf7ScNLCCGPfrQB55RTn XY5X0NNoAKmtoGuJ1jUEk1DXWeC9OW4vTPIBsQZOaAOitkh8OaUDgG5kXKgjmqdhpd1rd40rBsE5 LelPAk13WQi58oNtHsK9I0zS4rK1SONAOBnAoAztJ0KDTwF2Ak9yBXSJZpDZSOVA444pLe28yXbi rWsN5MIiUgKVxjHOfrSY0c3oyo2o3C4HLGtO909FJDICD6isnTCI7yVv9s11tzGLi0DqOcUxHnur eGYboM8Q2v1yBXII11ol7hlYDoynoRXrht8KcjmuY8RaCt3btIqgSDnOKAOI8S6XFe2Q1C2AIK5b HY1whGDivRNJky8+mzciQEAHsRmuH1W1a0vpIyMYNAFKiiigAra8O6Q+pXqrtJUYJOPesZRuYCvR 9EiTRvD5u2A8yVcL696ALWq6jHpsC2FkRvAG5l7VV0jw5c6iwllV1QnnPep/DmjPq92bmcbkyfvd zXo8NokEarGgGPQUAZum+HYYVjRIxkdeBUniCyigECbRncueK6nT7fELTbRkDjPFcz4glM0yMf74 6HIpX1sO2ly22mxTWEbBAePSud1Tw1BdIcIFbsQBXa6M6y25ib0qrd2pSZgRxTEeP3dhd6Ndq4DD ByHFaEyQ+ItMYEAXMa5IHeu61XSob60dGRdxHHFecus+g6tg52BvpuXNAHCXls1rcNGwII9ar113 jSxRLlLmIDZIuRiuRoAKKKKAFAJOBXfeCvDqyn7bdDbFH82WHHSuP0iza9v0iAzk16ZrVyNK02LT Lb5XdRv2/hQBV1vXHupTY2WRCpxlT97tVjRfCFzegS3Csi/3cVreE/CymNby6QMzAEAg8c139nZ7 XCRgY+lAGPpnhS2htJCUHA7qPSqukaPBcXtwjAMFYgAiuy1CVbGL7Kq/vGTexxxg5/qK5vQpCmpT tzgueKzhVjNyUejs/UuUJRSb66mVq3g+C4LgJtIzghRXB6r4fu9LlLorsgPDDtXu+oWZZQ6YwwrB vbCOaJo5Iw2fatCDzjSdTh1a3OnXxXeR8rnvXB+JdFfS75gVOwkkce9dl4h0WTRr0Tw8RkjBGeDS 61HHrvhr7WFHnQrtf17UAeW9DRTnBVyD2ptABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4 E/5J54a/7BVr/wCilroK5/wJ/wAk88Nf9gq1/wDRS10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAKoywHvXr2nR/ZPAnHVuT+d eRRjMqfUV7Qke/wksY/55/1oAk8H2oGmo56sK7W1sS+PSuL8JXSnT0i7qP616BpdpeX1q0lsyAKx U7ievB7A+tY18RSw8OerKyHGLk7Iv28aQrtBGfrVbUdThsCvnZ2njIGcVnae14l2zXiSx4GdkilT j8aXVkS5TLDKkVrGSkrxd0Igvo4NW2SW9zHsXB6+lWINStLJFt/OEsg4wvOK83vRe6bqk8dtLIIm jLYUnArb8MWzGzjmlJaWXkluT1pgejRyLLEHB4NUruyE2WVv1pI5Ght/KQBpMcBmwP5Gt3TfDyeS sss0js2GI3HFefjsxp4NxjKLk5Xsl5etl2NadLni5N2Cz8P6dPpcW+2QsUG5iOSe/NeJ+JbNNO8d 2scPAyv8ga+h9sdpbbRgKq4r588QSi98fxOvIXaf/Ha8DI5TeNnzNtuLb1v9pW/U6ajk6DXRNfkz l/iRBi6gnx98c/kK4GvR/iPzbWh75P8AIV5xX15wm94Qt/tHiG2UjgMDXe+II/tHiK0g/gH+Fch4 BXOvI3p/ga7LXCINdtLg9DkZoA7WytSiKqjsK3bO2aMhmOB9axdMuDPJDHGRukZVXPqeK09Zt9T0 +JGZSY26vGCQvTqccdawniaNOpGlOVpS2Xcai2ro2i4WMtngVlSy2l8knlMjyAEYz3p8Mj/2Z8zE sRXB6mt3pt0LizkcZbLKpPPNbiOk07TZ7e7keRFVCSck1vW13bysY4XDEcHFcHqGq6tLFbRZkj8w DccYre0KIwOuCefvH1oA6SdQ6YyAazodHa7vUheTCMeSOuKuJZyajqccAmaNApcle+Mf411lhotv asJOWkA+8xzXk43H8spYaknz230sr7Xu/n1NYwVlJv5HmPxE8N2tnoUssSnIQkZPuP8AGuAt4ftX gV0YZIJI/OvVfi3cR2/h90LgO6MFXPJ+ZelecWUXkeF2jI/gJ/WssgdV4aSqycmpNXfy/U1xTT5H 5fqzxyQbZGHoabUlxxO/+8f51GOte2cp6h4EthBoVxdY+YgqP0q54KszLPdXJHLN1/E0/wAIoP8A hFtg/iJqx4MlEazxdCrn+ZoA7i1hk3DCmt63XZGAetYsFzJtKxRs7AZwoycUzT9Smur1QWOzPSkp Ju19Qsbk4jdDG5Hze9crreneW8axRs2CDxSeL7u7tJreW2kZcNyB34Fauk3st/ZLJNFz6kUwHaZb pHCkkilCB3rXUqy5GK43WtRvHvBbRFokDc4rpLaVv7LDZ5x1oALyB2GUNZd1pd0bZp2iPl465rS8 PwzavcTJJKyrEV3epzn/AArrrrTYxpL2wLEeWVyTk9K8DMs5eGnyUVdxa5rro9dNd9TenST+LqfO 1jD9l8bNGPusOR+BrjvGdsINenwOC2a7m0/0jxXcTjkRgDP4VyXj5R/bBYd/8BXvmByFWtOiM9/D GBnLAVVrW8NoH1y2B/vigDvfFEP2fQdOsk4zjI/DNdloNsbOwhjC8bRn8q5nxYmRYv8Awq4H6V3O mOggjPGNo/lQBsWKl8cHFaoAA4FYUmqJbrhBz7VejvGbTWn7gUAVdWsDNvlB4ANc3psAe8libuxr pNG1H+1bNjIpxnHNWo9NtoZWlWNQx54oAlsrcW8IQVJNFuXgc1j2urPPrTWoBCoP8f8ACrWo6gbW UL2NAFSa3nlkKRx5Nee+PrBltknI+aM8/mK9U02c3EpYDjBH8q4P4jRNDbGJhzK2V4968unmKnj5 4S60Wne/U6JUGqSqHH+LLf7T4WtbkjLAAH9a8zPWvXfEUYTweIz/AAkV5G33j9a9Q5xK7X4e6f8A a9WEjD5UVjXFV6d8M4gtvcS99pH8qAJLO3Op+NrliMrG+f1r1W0ZRtUCvPfCsQHibUiQM7h/OvTL SJMBiAKANWCBHQMRT72NmtGVBzimW9yjOI0FWt437PagDz7VoJYmTzBg7hWvpFrJ5kcq/dIBq1r2 nrcMDuA6Ve0u0FrajJzgUAXzEHXBFZV6hjzxxWssmYt9UZZo58g4oA5TXbCS409hKhVJFbbnvxXn vhmAva6rYsOdpwPwr1rWJ99rGhBCopXH0rzXw5GP7T1GQfdZsfpXJgq869HnqKzu/wAG0vyNKsFC XKvL8jynVYfI1GZMYw5FUq2fEyBNbuQP+ehrGrrMxVGWA9TXo+lW507wdJc9GlAAP4155bLuuYx6 sK9U1OPy/A9rGv8AcU0AW/A1kq2zXDg7mPBr0O0RZML/ADrlfDNuU0uFVxyo/lXZ2FsUAZjQBbtb URHcRzWLrZYzNxxiulTnFZurwIYXcgZCmgDirMMZZcddxrsdJ3Na4euV0wZ1KRe2TXcWsQSMADAo AqT2wVi4HBrOfT7q9R/JgZwo+Ygcfn+PSt+ZQykGjTb5dPSS2lT5HYsJM8A4AwfyrhzGvXoYeVTD x5pLp5fgXTipSSZ4TrNq2meKoWxt3Pz9c81i+PbLyNS8wDAdVP6V2fjeNZNft2UD5pmII7jdWD8Q 0Bht377B/WuulJygpPdomSSbSPOaKKKsRd0q2N1fxxgZya77xECsljpsfGwDI+oFcz4JgE2vRZGQ Mn9K7C+hNz4yUEZC4PP4UAdvoFilpYRRxjBroVtC+0qM1m2UR2qBkcV01nEVjG7rQBFcD7NpxXvj FcpcafPfputwsjI43JuwQPXniuzv4hJasPSuF1Jnt5UaJ2RweCpwRWVZVHB+yaUvNXXz2Kjy397Y 0NLkeG6CEEHOCCMEV0F5bCZAQKxrBpLpoTK29lAwzctj0z3rpwo2iqg5Ne+rMTtfQwfsEk8ghhjL yHsK4T4haFcWlus8sJRlPUkHt7V6raXC6fqSvIg2P8pbpt56/Ssr4k+ReeHZWUo3ykggg14uLzKv h8dToOK5JNLrd363vbR9LG9KlGcW+yb+48Tv4Df+Dlm6tFkE/jXnrDDEe9epaZEH8E3ikZ4JFeY3 I23Dj3r3TnIqKKB1oA7n4d6eJ9Sa4cfJGuSa2dKhbXvE5lblFkz+GTS+BIPK8PXdwB8zIAD+Na3w 9tgTNOR8xfigD0mxtlW3WMDhR0rV0u1b7RlhwO9Q6ckRjXbvMpPORxit+3hEa8AAmpjLmvoBga03 2m8jgTqflyBnGetZtvoLQzyvbzZJOQrj8+f/AK1bsMBbVpXPQIR+Z/8ArViS6tNa67PEVHkoQMAc nIBr5jH4zGzx8sPgWk4xu9tdu69EvzR20o0/ZXqfI3rQ/aLNkYfMnymsuSzIlbdnFX9FfzfPf+F3 LAematXkHyEgc9a+kjKfslJr3rbeZyWXNbocf4j8OTXekSu0HyYznI/OvKfD+4Xl9pr9JFIx7jNf RTXlteaXNC3HylWRiASMdevTmvA7e3W3+IMsaDCb2wPbJrysqx9evOdLEK0lZ7WtfpuzWpTSipR2 2PMdWtzbX8sZGMGqNdL42gEGvTgDHSuar2jAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/A n/JPPDX/AGCrX/0UtdBXP+BP+SeeGv8AsFWv/opa6CgAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAHxf65PqK9r0759AhHqpFeKwD MyfUV7jo9uzeF4ZAPumgDmNFnkiupo4/vK2P1r1TwH4iiidtOuW2yySMyjaTnhf8DXmFrELXxHJn 7sh44966TULMhBPEOa4sfgo4yg6Mnbs/PoaU58jva56b4lMYmtsABpVdc464xj+dc3KJfKKFTweK 53TbqQrH5k7eXFnahY4XPXA7Vp3WqiaALHOoZT0B60ZfhXhMNGg3e19fVtkzlzSuVNQKWzpuX5mI DH2q9ZxBNjxL8hGRWJq11HP5LNKoYkA5NXUvAohVJgEQDLZrtJN+33tKZZOABXd21wLexUueFXmu Q02KC9s2uryZEth0LPsH1zmsvXPFr6iWsNJDGI4DXPI+oUEfTn6+xr5DNXVzHFwo4NO9O6lLor26 912+652U4qnG9Tr95oeKPFw8q5tbMkyr8p4xt46815HpDGbW5pj/AADH6V1Oo4tNPfc26QglmPUn 61z3hu2JySDulevey7LqeBpuMNW933f+RjWre0aSVktjJ+IT5t7ZfTP8hXnlejfEeIxyRIeg/wAK 85r0TE6vwGca0v1H9a7TxWh+yJKPvRv1rj/AKFtbi92Fei+IbIvHLEy/fGRQBD4b1cxz2czn5I5U Y8dgQa9jtNRsfEGmuqESIRtdWUjB4Pf8K8G8PwkIyZ5U9K6OCWMNslwDXj5plCxzjNTcZLZ7/wCX 5mtOryK1jsokl8prdiu9OuDx6/1rPktBArzzKMoMqPeqdtq1vaMEWVcd8mkkvhK8sbygq+SpzXrQ UlFKTu+rM3ZvQWyuhqheGYDKk7OOmK07O0ljzkDGeua5zTpEglmk8xeCQBmtnS74PIlq0w8yZuMn oMVNWpGlCVSWyTf3DhFykorqaMWsppusQtIxES/LIQM8Ef44rcl8e6LCMfamJ9om/wAKwdastCtY 3e4vG8/I+SOQFgcZxjtn39uRXGeR5m6UqQvYE5IH1rwY0MNm0/rMHOGiXRX+9O/yNnelo7MqfETx J/wketW8MLf6NH8qDHUkjJ6fQfhS3JEelSqOipisJoBNroYcrHXR3ds3/CPTTEctXt4bDww9KNKn sv6/ExnJyldniVx/r3+pqIdRU10u24f6mo0GXA963JPX/CBI8Op7NVC2uTpXiOePokpyPz/+vW14 LtjJ4bIAyQTWZr9l5pSdARIjc4oA9E0fXWsrR0EHmliWB3Y9Bj9Kp6Z5v2wMwAJbJwAB+AFcppGq yxRr5uOK6NddthHksqN9a5qWEo0ak6sFaU9y5VJSSi+h0moabHfmJpOinJ5rM1DVpLG4jhtsCMEA 8VGmtiW2/dSqzDqN3as7UGWZ45kIIYjI9DXSQdDJax6nFFOMeZ3q1dD7LYeWvasJLwwtDGsgUIMt zXR6Xf6FLYGS61C081ySUlmXI56YJrzsyx7wVNTUHK7toa0qane7sYej6tPpOomcIXicbZEz1Geo 9x/j61t6/wDELT7PQrhopJFujERGvl5Icjj24Ncpql9DDM8NrIkq9nU5rh9aEt5KqFict0rGtlmG xsliHdXSv0uvNNf5MFOVO8WWfDSsbaS4f70z1yXjs51U+3+Feh6NZ/PFAvROTiuB8fx7dYcf7X9K 9cyOMrZ8Mf8AIctz/tisat3wom7W7f8A3xQB6b4it2uNKIUZZMMK0PDuom90+PB5AAP5Vc1G28uC F2X5GXBrjN9x4fv2MQLW7tuwe1AHoYtWlxjmt6xiRbTyHIOa4PTvFduVzI4Q46E1pW3iG2a5DC4X H1oA6ORI9H09kgGGPIqho2rzzTslw4IJIHFNu70XmYmIyRlGB61naaywySSv0Qn86AOqg0+CC8ku +jN3qjqtqbmXcpB5qbR9VszdxHUrmCJRl181woz0HJPuaseKfEOjC2R7W/s5pFJyI5lJxj2NeVVz N08YsM6bs7e96rtbbvqbKleN09TMsrl9Jn87aJBjBUnH+elcX4v1oeI/EdlBCpWOEZdT2bPPPfoK h1fxRJIGS3XOe+TVPRbJoI3uZsmaVuM/5966ngaDxCxXL76VrkKpJR5OhJ4qP/FPSDtuFeQN94/W vavF9oYfDI3dWOT+deLyjDn611kDK9S+HI/0GX8f6V5aOor2D4XW3nWsikdc/wBKAJ7YjTvFMjNw lwBj65Fd9bSb1GW2rXH63p7TbtoKzwMWX3qlaeMfJTyrqPY68HOaAPUbSWGFgxatQOjKZVOeK8xj 8T2syrhz+tdNpmuRNZN5Tq5A5XPagCtrd9LLdptYhAwroNHn+0WmwnJA5OK5u7uHhy1vJmG4+R1Y Ag/ge/v1FaFnfLZhIVx0zIx4xUrmu7rT+v66gdDJLDHGYt3OOgrJliRSWjbnrW7o+taR/ZgK3lvu HMg3gEEnuK8517xhp8F9L5EiupO5SjZH6V5GBzOeJxEqUoctl81Z2s/N7pep0VKPLDmW3cua9qH2 axkdyOFOPyrlvDsLQ2RkcYeVixqo99ceJbwKUKWyHcT611GnWqyxSuvCRqcV7JzniHin/kN3H++a xK3/ABWmNauP+uhrAoAsWP8Ax+xf7wr2C4tjc+F4oh18sYryHTl3X0X+8K92tLbPh62kAzhBmgBv ha58ywiHO5QAfyrrlv44FAY5PsK82a8fQLkuFJt5DnPpUo8Urcn93G7+uM0AepWmoRXBCqefpVTW ZJJHW3iGXk+UD1JrkNN8QRLIpbKfWuhvr2O7KBW2yAZjdT1PWlK9ny7giidOudD1RobkKZGG9WQ5 BB//AFEfhXWRSBbYO5wMZrnv+EhudRiEd6kG23OBII/nP49s8ZxitDT9Zsvt1qL2SOIEkqGPftXn rEYmhg3VrwvOK2XW3y0NVCMp8qe5buLp0G77PJ5X/PTK4+vXP6VnXk4dODxU3ib4gaWsF1YW0c1w /llRLEoaM5XsQff0rzqXxjGYfKhUvKeAvNRleIxVeEpYiNtraW0sVWVPTkVhurf6Z4ht1HKxZJ/P /wCtWF4/5tYfoP5mul020fcHlBM8xyfasT4kQCOOJR2Uf1r1DA8ropSMGkoA6/wAP+JyD/stXa3k ZtvE0E5+7INufyrkPh4udaQHoVb+Veka9p+7p99DvQ/hQB0unuoAZjxWxFqtvuC5P5V5bb+K3Vfs 3lEyjtzVuDW7qKX9/asmemc0Aeo3MoeyZ054rPsNB0/WtNZ5N/2gPgurEFSOw7dPr1rH0fxBHNDJ E/3gvCtTU1WXS72K8s23RyMEeM9D7fX3rzs0w+Ir4e2Gm4zTuraXt0+f/DmlOUVL3kXdOs7i21F7 eYANEcEqcit2a6jgXLHmseTXlkunldVVtvzc/drl73xGXdiiM47HnFdWGlVlRi66tO2qXcmaSk+X Y7F7+G7Rl6HpXIeKHEWnygsT8pCj8DWWPEFzakzz2rJF/e5xVX+0m8RThVUi3Q5LetbW7iIoLY23 hOeMjGYyTXkd6MXcn+8a921O3CeG7p+mUOBXhl+MXUn+8aYirSr94fWkpyffX60Aex+B4PM8LFP7 wH8zWh4DAgS4hbhllIxTvhvB53h4p328fmar6jLJoWqC5hjJSQ5YdMGgD1XTZoYFDOecVs29/BM+ 1W5+leRLrmqSwiaO0byz3yat2HiG8jbzHt22jqwzxQB3txenT9VLTD90wwSO3PWq+sabbSrJeqNs hI+Yd+3NVbrU7XU7aI7l3OvDA9/Ss3TL+WGCeCWT/R0chR+Of614OPyqpLFQxuFdpq3Mr25l66+m 23pr0UqsVFwnsdbpES2lluc4zzU0uo2/Kk8fSuS1rxHsjiggXfIVzsQmuabXNSllMcdqSc9Oa945 zsNUCgsyfd9a820+A3Piy5uQPlj4z+dXtT8TXcEX2eaBllbAA5rR8O6d5cZdwd7/ADuce1AHknj9 ca9N9F/lXH12nj8A65Mfp/KuMIwaAEooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8k88N f9gq1/8ARS10Fc/4E/5J54a/7BVr/wCilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAsWS7rpB7ivprwfpH23wfswCee1fNWl /wDH4n1H86+r/Al5b2nhxPNdVznqaAPJdZtGt7wS4w0UhB4roNPu4rq3Cvgg1Y8TWsLarMUIMcxJ BFcf5d7p1wfLBKduKAN6909kDGJ8LjoK5bVJpNJT7SHYk5GCa0J9XvREQYu2K53xM811ZRRRqznl mIHTigDmrvXL26nMhuJBzxg1q6Lr95Mws5ZmZW4BJrmzA6n5lNXNMguFvY5I42bawJ496APXrGO9 aD7Ib64MBABiaVtvHQYzWzb2sVnCTgbvWuRi1aWKRSgzuHTFWJ9avXi2pCSSOuOlTGEY/CrDbb3J deuPMHkqcsxxW14K0X7XdKdvyRH0rl7S2nctc3IO48gH1r1HwXJa6bZbZZFEknYnk1Qjyz4s2ghu wAOASP0ryUjBr2b4vusl0CuMFif0rxlvvGgDvvhnb+brsPH8Qr2Txno/2eygu1XjocCvKPhUF/tq En++P5V9A+Jjb3/h/wCzhgXAGBmgDxBG/s6/Dj/VucmtuRIruESRkBuuRVKS3W6haNh8y8VQEN1a krExI9MUAOmtlllZXc7x3DEVxus+IbyG6MFvNIgTgYau2sGuYhLNMgYY4BrzS/guJb2aZ4yNzk9P egBU8Q6pGc/apeTk813GgXsupQJdmeWOaLgOkhB6e1eei1kcYCGu78Ei4jsru2aLBbBUke1JpNWY J2OutoXupfOnmeVjjLSMWJx7ml1O7S3h2JgH2rJinv4MpsOOxxQtpPczeZNnAoSSVkBHp9qzTDIy 8z4rv/EGjiy8JlcDdjP61z/hyCOfXY2Y4jgIya7zxlcQzeH38t1YAAZBpgfKGoptu3+p/nUEC7pV +tW9V/4+5P8AeP8AOq9pzOv1FAH0X8MdOW50XyyByDWRrlj9l1O4tXAxuOK6z4VSRw6SjMQBzzVL 4iwpFfx3keCrsckdOlAHnyqLaUxOOOoNSxRQzXASUgqavTW8d3bh+pqgIDZzLLtyo7UAZ+vltHtj PaSyIxOOGOK5ceLNXKgfaHIU561p+LNUe/8ALt44dihiTjvxWFFaMqDKHOaANKx8R6le36RT3Mm1 yAcNiu7/ALPtIYCQMPnqTk15j5MtpcxzxqcqwNejR6kdTsY9kIRj1IoAugx29sWYgnHFUYI/Oma5 ccDkVNDp8srAS5C/Spb0LCI7ZDyxCgUAdb4O0r7VazXjjoeMivKviXBs1qTj+I19AeHIoLHQPI3A Mc8Zrwn4okHWZf8AeNAHmh6103gqLzNbg/3xXMt1rr/AeP7cgyP4xQB9Aaro/n+FYpkHzKoNed3V st7asCBvUcZr2u1EU3h1YWIOUHevHJQbXV57dum8gfmaAOaitEjvFSRfl3YNdRd6BYeXG0Pyhx1A 6fjVO6s0Ewlb7mc1ka14uW3U28CjagH50AYF54n1TStSeCO6eREY7cnOKjk8aassZXeRvOc8f4Vj Ikl9ctKwyS2at3doXiXCn5RigDsvCyPrlq91fTySsGACk5wPpWxr+nWVjHAIVw5HPavP/DuuyaNO Y2XKlgc+ld2byLX/ACpNuGABznrQBn2dibmcZXCDk10WmW327VoLVPuggGk8kWVqxIw1bXw+t1lv Xu5P7/BP0oAr/EiyFvoZQY4x2r56uFxKfrX0p8UHV9Jcggjivm68/wBa31oArqMsK9z+D9uHwD0O a8OjGXFe8fCA4Cn3agDofFdj/Z+phwMK5xXDa3pkBlE+xQGxmvU/H8ayxpIpyRz+lcDNB9stlQgn mgDc0fw/pFz4eeXyF81FyCo56Vy3iiEaDpyXlhcNHKSdyg4z+FbVx4mg8K6NthKmZhzk9K8q8SeJ 59flAYADcTx3oA0Lfx3qMkIUqWKNuP8AnFOtfFuoavqyW7TtDHI4D4OOM4rE021ZFkJH3lI5qg8b 2dwJVzkHIoA+htA0Cw/tAxebIYWXJIYgHHTPPNcj4o0ywGstDaQqqA44HvVPwd8R3tp47acIImBB JNdFc2sN9qYu7fLRk7iRSUUndIbbasUYrdNPsERFAZhjiuz0zTBD4akmYcupPSuUmUy3yR/wqa9D uAsXhsICD8hzTEfMfjBNutXH++a5g9TXV+MedXuP981yrdaALulDN9F/vCvozQbdZ/DCgjJEa49q +ddIH+nRf7wr6N8NNt0AgnjYKAOS1G3Se1micA4Bxmtn4f6VpjmSKe3R2K8lh71m3aFpHx6kfrW/ 4XtILBZLyeQKAueT70AT6h4d0uO+lRYgsYUHJ6ZrynUPFF3o+sG2WVpIY3+UZzjmut8V/EuKXz7W KNCvADDNeRMXvLoytySc0Adbd+NriO2cIpDSNuzXV+DCmuWcl1fTF5lP7sE8jjPFeaX8DPBEQp4X FaPhLxG+gXe51DLnODQB9AaVoWiJoklw9qhfyyCGHfmvL0020OtvNHEgQZ4xXfeHPFtn4l0mWB2S Jgp4B69a5e5sPsl26pkigC9osIudRBI4XpXJ/E7aH2jtiuy8OnF23riuK+I/LjPtQB5O/Wm1JJ1N RjrQB23gB9mrKfY165rciARtxyv9K8e8EA/2kDjsa9I1qSTdEPUf0oAZoWn2Umui4ljRgeMEV6Hr el6TJFDMLZS3A4Fcb4X0kXE8cjsRjNdXrviCz0CGOOXaxx0JoA5Dx5HDpOnx3dkVhlQfw8HpXC6d 4xmltQJSSyvuq18Q/EcOruv2ZhhvvKO3FchpcZVXyCcqaAOjtPE91qeu/ZzMywvIARnGRkV7ANK0 oW9szQKwb5cqK+dIi9pqAlGRhuv417ZoXjrT4tPtrSQIzZ5JNAHYeLNL0hfD3kpbxrlOOOnSvNtN gis4mijAHJzivUNVtYNW0gTRyAgL0Bry+9hazmlA6ZwDQBq+IZETw7Io6eWa8Hv+bmT617Nrm86C +enlmvG78YuG+tAFGnR/fFNPWpIh8w+tAHtnw2v1t9KO49E/qanubqHUb5YmUMu7v9a5zwWJDppV Qcbf61r6DZNe6rGnIJagD03T20yDSEiaCNsLg8Cp9EttLmsLvFuigk/eH09azZ9J+xWwd3+QDkmn 6frGnPo91AZEQ+pP0oA8n13XLrQPEX2GKZvI8zdgHgc9qg1Xxhcw2ZMMh3M4ORWD4idr7xFLIp3K GIBH1NQ6lCz28Q29AKAPafhbHHqkDXt+wlkk4Bfk9M/1rprNtJtdaukNsoYORlh715z8KNSgt4yL mdUMR4Q9+K7H7ZZ6tq0vkuA2/sfegCp43g0+aUTxxIpXHQVn6Nq8RgmH8W3AGfatzxN4fddMeYkk 8V53p6SpJNgH06fWgDifGs3navK3bj+Vcm3Sui8Sgm/kLetc83SgBlFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAfb/AIE/5J54a/7BVr/6KWugrn/An/JPPDX/AGCrX/0UtdBQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAF3TTi6U +4/nX0Hp3my+EkaFyGB7fWvnW3n8iQNtzj3xXo2kfFz+y9M+xNofnD+99r2/psNAHXzyyvChlJLK eppjFJkB4Jrhrj4mCcMBo+3P/TznH/jlVrf4hNDndpu8H/pvj/2WgD0WS1tjasdq5Ayawrn7JHbz OVBPIArnZfiR5kBjXSSpP8X2nP8A7JWQ/i0yZ3WeQTnHm/8A1qAL9taRXUsq7R8vzVf0F7WG9kEq Ahjtrmh4k2TNJHabdy7SPM/+tToPEqQx7fsO5uu7zf8A61AHqVlaWsk+CowKuzWkSyDYBj6V5nYe P2s5t7ad5g9PPx/7LWg/xRDAgaNj/t6z/wCyUAdpLtJWMAcGp7Jbm41eEJIwRCK84j+IbLO0raZu z0Hn4x/47WlYfFcWUvmf2HvP/X1j/wBkoAv/ABOkJmRCckZ/kK8pf7xrpvEvjFvEVyZTZeRkn5fN 3f0FcwTls0AeifDWTy9Xj+or03Vb+7ttS27m8ph+FeF+H/Ep0G8S4Fr52052+Ztz+ODXYX/xdS+R QdA2Mo+99szn/wAcoA23uPK1Bh2dicVpwrEW3yLkYzXmV345FzOkq6dsK9vPzn/x2tAfEsC2EX9j /NjG77T/AE2UAdBqupRwuyJkA9hXP3pglTITmuevPErXc/mfZtnt5mf6VIvieMQNG9huJGN3nY/9 loA2ZbSKPyyVGG54rZsb+K1CEDHODXGr4pG6MvZblQYx5uM/pTLnxMJnyln5Y9PNz/SgD2Oxa2uL XcVGcVWvGW1gJ4rz3S/iH/Z0YR9L87Axn7Rt/wDZTS6n8RP7QACaV5K8cfaN3/sooA7aznkt7OV4 j+8kbrW5eTzp4TczMSx9a8vh+IqxIinSNwX/AKeev/jtXNU+Kv8AaOmfYk0byR/e+1bv02CgDh9S Oblz7n+dQWvEw+opLifz3LbcZ96ZFJ5bhsZoA+hfBdy6+GT5bEMASMVV1bUZNR0iSOYkvHkgmuC8 P/E/+w7E2raP9oB/i+07P/ZDUU/xISYvt0cqHzx9pz/7JQB1miTGe2CnkgmqXiW/W3j2KcMBXM6V 4/TTJWY6UZQe32jGP/HazNZ8U/2tMXFn5IPYy7v6CgCRJjdXWTzzW1BbRuxGASFzXI2mqi1lDmDe M9N+P6Vpv4sUvuSw2cYx53/2NAGldQIYcqOeafouqtDMsJb5PTNZEniiN7URf2fhv73nf021lxal 5U3mCL8N3/1qAPdYGSayEqrgBetc6sv2jxAAfuxtn+Vc/YfE9bLTmtG0YyEjAf7VjHHpsrOt/HSw Xclx/ZhZnPT7RjH/AI7QB69pOp3NzqLruYRL2z7V5l8RpfM1iTP94/yqXT/iqtgr/wDEj3s3f7Xj /wBkrlNe8RHXLx7g23k7jnb5m7H44FAGI33q6zwQ23W4P98VyROTWpo+sHSbyO4EHm7CDt37c/pQ B9Iahq09jb2pjc7WXnniuN1mQnVopsff6/WuYvfi0L20jgbQ9uwfe+15/wDZKoS/EhJmjZ9HyU/6 ef8A7CgDtdbnFpo4fGCwP8q8jvLlp5mJJ610eufEJNYsI7ZdJMBT+L7TuzxjpsFccbjLE7fwzQB0 2kWyvHuJx+Fa8ltGUVTjkZrmrPxIlrAYzY78jGfNx/Sp08WqHDNYbgBjHnY/9loAqahH5FzxXT+D NSIukiYkjI4/GuQv9WW9l3i38sem/P8ASpNJ1v8Asu8Sf7P5gUg7d+3OPfBoA9n8RApAQP4sdKfp 17JpdhbRxEh35JH4Vw+p/FaHUURf7BMe3GT9szn/AMcFVT8SkaWJjo5xH0H2r/7CgD0Tx1cM/h5d 5yTjmvBLo5lb6/1rtdf+JX9uWIthpPkAd/tO7/2UVw0knmMTjGfegBIvvivdPhQxW2yOuTXhSNtO cZrtvCvxFPhmMp/Zf2nOf+XjZ/7KaAPYtSv5r24mgclgpIFZVtE0MDuyn5TXBP8AFctevcLouNxz tN1n/wBkq03xgie1eFvDvzN/F9t/p5dAHM+KtUkur513Hbk8Z96xrCMy3IB5qG91D7ZcPL5WzcSc bs4/Sls9QW0k3GHf7b8f0oA7m3s0VeP7vQ1n6vZILfcMetUR4wCjC6f2x/rv/sahufFKXFuIhYbT 3bzs/wDstAGXFK0MgZSQQa9l8B6g1/YOjfM2ByfpXiTXAZs7MD0zXY+DviDH4VZy+kG83dvtPl9s f3DQB6kbdkvJZSPu54rY0/UJbrSJvMYkBTgH6V5fe/GJLkOIvD/lbvW83f8AsgqC0+LX2Wye2Gib twI3fa8Y/DZQBzvi8/8AE3nH+2a5c9TWlq2r/wBq3bzmDy9xJxv3Y/SswnJoA09GGb+H/eFe/Wcp t/DAdTg7V6V88Wd59knSXZu2nOM4rvY/iqE0sWLaLuwoXf8AasdPbZQB3dvF5wdsctyK5bxV4jub GBrSNmTqMg1mWXxV+yDDaL5n/b1j/wBkrm/EXiddeuDKtj9nyenm7/8A2UUAZEtzJNKWdiSetdFo 9kk0OTiuWEoDZ2/rWxZ+IVtF2i0Lf9tcf0oA6iSyR0CnGAK5TVIvs9x8vQ1f/wCEwHew7f8APb/7 Gsi91QXjFjAVP+/n+lAGt4e1640u7Uxu20kZAPvXr9g76pa/adpJIrwCO4EcgbZnBz1r0fR/i5Hp Omi0GgeaR/H9sx+mygDt7IyW2sKgPUc1zHxJA4PstYifFErfNcnR8knhftPT/wAcrK8SeOP+Egx/ xL/s/T/ltv8A/ZRQBykh5P1po6ilZtxPFNHBoA7zwBEH1IZHY16NrsGJ4BxyK8d8PeJ/7BuRN9j8 /rx5m3+hrpb74qC9ZG/sXYVGP+PrP/slAHZnXToJ3849jXnnifxdc6vcsSzBecfNmoNW8bjVYPLO neWeOfPz/wCy1yzzb2zt/WgDUsma6uQJCW+tdXb2KRJwo5BFcTZ6gLR93k7/APgWP6Vsx+L/AC1x 9hyPeb/7GgC/qFjGkJcYzya5+K/lhkBDHj3q3d+KBdR7PsWwf9dc/wBKxXuAzlgmB6ZoA9e8M/EC Z7IWMhYk4XOR6VrasomtY37yHNeL6Zq/9nXSzeR5m05xvx/Suqk+JRlVFOk8Jj/l4/8AsaAPR9bg H/COyDjhMV4XqQxdyD0Y12l98U/ttg9r/Y2zcMbvtWf02VwdzdfaZmk2bdxzjOaAK/WprcZdR71D UkUnlsDjOKAPa/ANiJNLQ4+8v9TU9q7aTqPnDgK+f1rivDfxO/4R+2WE6P8AaNoxn7Ts/wDZDUOo /EcX5crpPlbs/wDLznH/AI6KAPS/EfxAtG0sQqWLjrgj0ryu58VXLu0cTsinsDXOXeptdSM2wqCc 43Z/pVZJ9jhiuce9AHb6XbrdR+Y/LH1rVmso3RARwB6VyFn4pFoABZFv+2uP/Zaut45DD/kG8/8A Xf8A+xoALm9m0m6zbuy+uDitzwz4yNlqCyzZOSMnPvXD3usLeEk2xVj38zP9KprdFei8/WgD6O1X xvDrFr9ntyRkdyKoaNp4lt5XOOSMV41pviptPH/Hp5nv5uP6V1lh8XhY25iGhbye/wBrx/7JQBh+ NIBDqcgHHSuQboa2te8R/wBuXbT/AGXyd2Pl8zd/QViFs0ANooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigD7f8AAn/JPPDX/YKtf/RS10Fc/wCBP+SeeGv+wVa/+ilroKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA1IPDmr3FvYXC WMgg1B5VtpHIRZPKAMjZJACKDkucKMNk/K2K+paXd6TcLDdpGC6B43ilSWORckZR0JVhkEEgnBUj qCK9Al8Z+HL6PS3jt7uwniuLxVSa48+K2jezigh3ARKZIgY4wVO8lInDiTzMVyfizULe+urBYLmC 6kt7Typ5rWEw27v5sjjyoyqbFCuoICLlw5wSSzAHP0UUUAFFFOVGboKAG0VOLSU/w0jW0i8lTQBD RSlSKSgAooooAK0NJ0W91uWeOyWD/R4vOmee5jgRE3KmS8jKv3nUde9UAM/St/wtefYpb3ZrFjp0 k0QiYahY/abeaPcGKkeXJhgyRkfJjg/MCAGAK58L6sLOa6EMDRxbzhLuJnkVCQ8kaBt0kY2tl0DL 8jHOFOMeu4eXw5a2eo3+gajY2txdfahHBfJdGe1gYuiwxhUaIs0RwXd2++ANhUu3D0AFFFFABRRT ghNADaKf5bYppUigBKKKKACiilA5oAStx/COsRi0zHaF7tEkjiW+gMio0fmh3QPujTZ8xZwAo5JF YpHFejWXiTS7GS1lutVsb+6WJrWyuV0zY1rAbSWALdL5eJcFrfIBmwsTgE5w4BwepaXd6TcLDdpG C6B43ilSWORckZR0JVhkEEgnBUjqCKp1ueKLixub22NpJaTTrbhby4srfyIJpd7kNHHtTaBGY1Py LllY4OdzYdABRRRQAUUUu00AJRS7TSUAFFFFABRRTwMUAXdM0W91fzTarAscWA81zcx28ak5wu+R lXccMQuckKxAwDiwfC2srZzXMln5Xlby0EsqJOQhIdlhYiRlUq+5gpA2Pkja2NTwrrENno+qaab3 TbKe5uLedJtTsRdQbY1mVl2+VIQ5MqkHb0DfMOh1LjW/DrSpqGn3H2W1s9P1HT7fTZUkM7LO115R UgMm1VuU3FnDfI+A3y7gDz+iiigAooooAKKKOtABRRRQAUUUUAFBGKcooYcfSgDT1Hw5qelW6z3k EaJvEbhJ45GhcgnZKqsTE/DfK4U/K3HynFfVNIv9Fv2sdStntrtER3hk4ZQ6h1yOx2sODyOhweK9 B1Txhop19vEDJaahcNqbX1jHY24s57RCJDiZ/KCySh2gYE+cCYnBOGO/lPGGoaXqeswT6Sk6266f aQt50u8744EQjOxem0KeOSpYcEAAHPbTSbTT6KAI8H0pcH0p9FADNpo2nNPooAZtPpRtNPooAaFp dopaKAE2ir2m6Je6sZTarAscWN81zcx28ak5wu+RlXccMQuckKxAwDil1rqvC2sQ2ejanpzXum2c 9zcW86TanYi6g2xrMrLt8qQhyZVIO3oG5HQgGUfCutLZzXMll5Xlby0EsqJOQhIdhCxEjKpV9zBS BsfJG1sY+D6V6NL4h0D7BONPnS209Le9t47CaBmvcyPMYfLm2tsiCyxb081QdsuVbed/nlADMGkx T+tHXtQAyinnOaBQAyin9qXoKAI8UuD6U+igBu3ijaKdRQA0rgVvt4K1oRLKiWNxG0ohL2upW04R irvlykh2LtjdizYUBCSQBWFXTiXRr/UdG0ubUPs2iWtorSTbHBado/Ml3bUY7jJ+53hGwiR8OF5A MLUtJutKuVhu1QF0Do8UqSxyLkjKOhKsMgjIJwVI6giqe2t7xRdR3F7bR291p01pb24jgj05ZxFA u92KZnUSE7mZiST9/AOBgYdADdvNG2nUUAN20beadRQA3aKNtPxzSUAN2il2ilpQeaAG4HpS49qe BgUtAFnTdFutWMv2aWxTy8bvtV/BbZznp5rru6c4zjjPUVdufB+t2kd009okb2rSrJCbiLzT5bFZ GSPdvdFKvl1BUbG5+U4boN7baZPe37ybL2C0Y6f8pI89mVN2R91kRpJFbIw6Kc8YPQQa5pU/hGys 7yexMdnp9xbNaPZZupJ2kmkikjmEfyxq0sRIMi/ccbSDhwDhdtG33px5NFADdvvRtp1FADdvvRt9 6dS4OKAGbaNtOooATaKMAUtOGMcigBuPakYcdKlpCOKAIKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooA+3/AAJ/yTzw1/2CrX/0UtdBXP8AgT/knnhr/sFWv/opa6CgAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApQCxwKQDJ wK6zwv4Zk1KZXkXEfPUUAZ2k+HrnUXXbG23PXFdzp/gy1tog9ywHHO7Aq/dX9hoMPkW6K82Oq44N c9NqN/qcuA74J6LQBvtb6Fb/ACGeI49hSHTtEvF2pNFk/SsyDQZFy10WGRxwar3GiXluPNj37TyM ZoAXVvBClWe2+b/dArh77TJ7KQq6MMdyK7ez1y9sZAspZ07q1bE1rp/iSyZoVVZgOVPWgDyKlHWt TWNKk027eN1OASAcVlng980APph5NGcUlABRRRQAU5EZyABkmljjMjhVGSTivQPCvg8zqtzcgBMA /MDQBzuleF7u/IPlOFPfFdjZ+AY0QNMSPqBW1e6zp+jR+RaIryDuuMVz02sapqL4WR1U9hQBpnwT YkYEi5/CsnUvATBGaEFsDsBT57bU4I0kFxIWIzgE0tn4kv7Ngs+ZFzyDQBw+o6Lc2EhDxtgHriss gjrXtKNpfiS3KFVjnx0bHWvPfEnhuXS52YJ8nsDQBzFPA4oYYPNNJoAcelMpSc0lABRRRQAVJHC8 rAKCT7VJaWklzKERSSfavTfDXgpEjWe6CADBJYHjigDitP8ADF5eAEROAfat+HwFOVBYMP8AgNdp d6vpWjJ5MEaySDjIxisVvE+o3LsYI8KBnGKAMG58B3KKSqucegrmr/Q7qzJ3RNgd8V6Jb+L7iN9t zDuA4PSthH0fxBEVwkcp7NigDw4qVODSV3HifwjJYM0sSDbgn5Qea4xk2MQRgigBijinUnSjdQA1 utJRnNFABRRRQAVJHC8hwqk/SrWn6fLezBI1J/CvS9A8DKEWS4CgerA0AeeW+h3U4BWJ/wABVs+G bwLnyn/KvV5pNF0hNmEkYD+HFVB4j0othrbA9TigDyO60m6twS0TY9cVQZCp5Fe6NZaPrMJ8oxq5 HFcT4j8FyWu6SJRjPBUHBoA8/pRjPNSzQNE5VlwRTBQAD1obpRk+lIx7UAC9KdSL0paACiijrQAU 5Y2c8Ka0NN0me/lVUUnJ9K9F0PwA0kavMoxjupoA8yWwnYf6tqa9nMnVG/Kvcx4V0q2XbIYs96jn 8HafdoRA0We3FAHhJUqeRSV6Dr/gaW03Mke33CnBrhbm1ktpSjqQRQBBRRRQA5etRv14p2aa/WgB 1HSikPpQAtFJ1NKBk0AFKAT0FX7DSp7xwFU8+1drpngG4nUM0ePqpoA882NjpTSCOtetv8OZNnCq foprmdX8FXNpuKp07bTQBxVFT3FrLbuVdCpHrUFABQKKOlADj0pi9BSknFIvQUAL0ooooAKKKUDJ oASnAZAFWrewlnYBVP5VrReG7h1zsJ/A0Ac+QeKaK3rnQLmEZ2H8jWRLA0TYZcGgCGlAOetLtFLQ AUUUUAMf7wpc8dKa/wB8UpHAoASiiigAoopwBoAQdacOnSnpCz9Fz+FSG2kAztP5UAVaXnNPZCp5 FJigBBx6ZpcUtFABSkUlIehoAgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD7f8Cf8k88Nf9gq 1/8ARS10Fc/4E/5J54a/7BVr/wCilroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKAM0Aamiac+oX6RgZBI/nXpup3kXh3S0 sbXAuWAJOM4BrE8E2SWtnNqMowETIJ6ZzVjSLKXxLrjzS5Mfc9fwoAi0jw9datKJZc+WTknPJr03 QfCFsEO1CBEm5jnJqzaWKWsaxquAPaulsYNls8pHyqN31IzXNi1N0Zcjs+/l1/C5dP4kcl4ktI4P IjC4+ZauNp0c1hGwQEbeai8Vt509v7beB+NbOigTWphP92jB13XoRqvqgqQ5JuPY4PV/C0N5GSiF W7EGuFmgvPD+oq/Qqcg9QRXuF7btCSCtc1rmhLqVjIfLO4KcHb0rec4wV5uy8yYxcnaKucVrNtD4 j0NbyJf36DMgHHOP/rV5ZPGYpSp7V6Po07aTrMljPxHIfLbdx361yni2wNjq7qFwDz0qhHP0UUUA FFFTW0ZlmVR3oA63wR4fOpXqSOP3aMCefaur8R68LfGl6d8qp8rtjrxS28i+G/CjbcLcTphex6Cj wt4da93ajdKzD74BGc/nSbSV2NJt2RS0bwtd6k3nSjEX15r0mw8FCzsPNaH5Suck8jjrWrpNsBCq +UEYfw9a6xziPdKAgEZ49h6/nXi4/F1/ZLE4aS9mk2+t7fZ8r7eR0xgqcnTqR97b08zy/SdIjuby eOQZCEgc1U1fwdDMzKi4bHXNb/h9ympXBPdzXSX1gwbzAMgjNe2cp4BqWj3uh3HmDIVW+VgeldHY XMHirSXs7kD7YgyCBjIFdrrOmpdW8qvGH+U4H4V5ZeQT+GtZSaPcE7HpweoqeePNy31K5JcvP02O J1nT3sL2SFhjBOKy69H8d2SXMEGowgFZI8nA75rzkjBqiRKKKKACnIu5wPWm1s+HdPN/qUUYGRuG ePegDu/AvhmNYDf3YxGucnNS+IPFL30htLE7LcfLwOTV3xTfrYWEGiWhG8gM+3rzVvwp4UChbq7T nggEdOKTairvYaTbsjE0Xwre6nMpZCQcHGea6a40D+xyqKmN2FOR3Nen+HdNhtmeUqANoUcD61ne OVhCWTKo3PKOcdv8mvEWZ1pY1UYRTpt2v1va++2hv7Omoa35jzvUPB3m2izAYZhng1x95o97pb+a B8o7qa95itRPpqHGcAdBWVqGjx3ETKyAZ7Yr3DnPPdC12LVoTpepAGRxtjYDFcN4y8PvpOoyYX5G Y45rpfEWgTaNdC5hDbQd2fTmrmpPH4l8IxzEA3MC4fHJyAf8KAPIWNNqW4jMcpU9RUVABRRRQAVY s7ZrmdI1GSxwKr13nw90QX2opNIMRowJOKAOv8L+HbXR9KF/fDau0Hr171na34tmvnMNp+7g6Djk 07xXq0mo6iNMtSTBAdoCdz0ra8PeC08tJ50LOfUChuwHK2Wh3l/++YEoOSSa0rjQEnjVIFO9V+bm varTwnaQaX/EGKDI4wDj6VymjaejaldoQPldlGRXDhcwo4qbhTvdK+3QuVNxV2eRTW15pc25SVKn qDXTaD4qjuEFlqYDK2FDY/Cu/wBR8PxT71aMcjrgV5t4k8LPp7tNACRnJAHSu4gy/HPhUWjfardc xHGDmvO2BUkV7P4evl13RJtIucNMgLRk8kgfX615PrVm1lfyxEEYY/zoAzM0ZoooAevSlpq9KdQA VpaPpkmpXaxIuSfes5V3MBXsfw00KG3tm1O7AEaZ5YcdqAN7QfDVj4e00XmofKoUNjPXisvWvHLy FrfTlCRA4B21meItdufEmpmKHd5O4qiqTgjNa2geCnnKPOjc44xQ3bVgc5At9qLPIXY4BJ5qKPUt Q02bKSEAHvzXoepaIumsiRLgPhTgVPL4PiudPUsvzFc8gUoyUldO6Bqxi6P4ztb9Baakq/NxnHBr I8beCk+ym+sgWiKk8nkVBrnhO407MsSsVHbFang7xB5yto1+2Y5htUsc4OKYHi08JikZW4IOKhrs vH2hnStZmwuFZyRxXG0AFNP3hTqY3WgB9IaWigAFbvh7QptVuVVFyOO/vWRawme4SMAkkgV9DeAt CtNA8PDVr1VBCZUOByeTQBZ8O+BbXSLNbq++QdeW5qPV/GenWP7iwTcQMZIzXL+JPFd9r920URZY M8KpPNULXQLiRDI8b8DOcUAbaeO7hH3SRoUP+z/9euk0/wAQaN4gQW1woV246YNcVJobTPHFEGPy 5PtWbeaNfaa/mCNxt5BWgDovGfw8DQPd2Y3w5JB3cjivFr6zktJ2icYYV7p4L8aFXGnaowaFwIwz knHOK5n4qeFk0+7N1bjMTYwwHBoA8mNFK33jSUAIehpU6dKQ9DSocCgBWHNJTiePem0AKBk10Gha DNqUyhVyCcdaztKsmvrxIkBOa+k/Bvhay8P6Muo34VSAGRXA5460AYPhr4bYt0nuI8KQOrV0E+na PYEROQG+tY/in4hSSu1rYbVVSRlSRXAyXlxcys8sjZI7kmgD1Q+HdK1aIiMjOOoNcF4r+HUtsjSL HlezBqzbPW77TZA8MjcdsmvTfC3jq01ZBZ6h5fzAghuQaAPm2+sZbKdo3GCDVSvbvij4LjtY/tto C0LKWBAHTNeJuuxyp7GgBtFFJnrjFAEZOXFPwCvFMP3hT8jHNADaKU/pSDrQA5Rk1q6bpEl44AXg +9N0jT3v7tY1UnkdB719BeCfh/HFaJd3ieXHzjcAM0AefaR4EmmQM0Z9eDWvJ8P5PL4Q5+ten6tr OmaNAY49gwMACuatvGtsbgqxGM9zQB5Trfgya2yQhGAe9cXdWcltIVcYIOK+toU0jxHaeWxjDsMA qBXlHxC8BvpzSSRxlkySrBRgjFAHi9FSzxGJypyDUVABSMeKWmtQBDRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFAH2/4E/5J54a/wCwVa/+ilroK5/wJ/yTzw1/2CrX/wBFLXQUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU+Jd8q r6mmVa09N97GPegD0i+X+zPBllCnytOfmI9Mf/XrufhroSSWSMRgsWYsOvauQ8VwldL0tQPlUgfp XrvgO2S10ITk4UBieO2Aa8bPMZUw2HXsXaUnZHTh6cZc0pbJfiWb3TFtZUVWLK/TPUVauUaDTERG Vdxw24ZyO/cY+tKFlumEjuTzkDPAz6VJqB8qCEn7ocZNdVOlWhg3CvLmlZ3ffe34aGUZJVE4lDVt DtZrZZZYz5gx8wYgg1naWY4HR4bkSqWCn5SuK6HW5gulSMp6Lu4GenNchpMbNMmCcZBIr5zhiWIq wl+8fLFqy3Vuq1WnyaOrEOPKm1q7nSanCHIYDNXdPsLaSw+4SHBzu6+lLLEWQDFU5bu40+MiMKR6 NnA/WvYzvA1sZRUKXR3te19/y0sc9Gag7ni/xL01dN8StNCuwNJkfpXOePYBLHbXgH+sjyT75rsf iZM14YZnAEjSYwtYHjaDb4etcjlVI/WvRwcKkMPCFX4kkn8iKjTm2jy89aKD1NFdJAVueFbL7brM UWM5rDruPhnAJfESMeiqTQBveIf9N8R2unIf3cZCEfjz/KvW/C+hk2Ua7QsajAAHWvK7KEzfEH5+ 8xx+JNfQOnSQ6fp9sGUsWQcKB6c14Gcyq1Z08JRk4uV727HXh5KnCU2r9iCOwSK9jTA6E471PcAT 3EsB/wBXswR+dTPJFPqSTRE8JtPGB/nrWfLdpbX1wZWC8cEnHrXn4/BvD5OqVJNtSvtZ7vXv6PtY cJupVbl2OOjhFhrF1CrlgsmQSPUA/wBa7WNhcaZuIyQMVwqXQvNVu5gODJge4AA/pXcaapGmN7j+ lfT4T2nsIe1+Kyv621/E5Z25ny7ENrpUM8AllUktyBnpXn3xM0OEae0sSAMhUj8zXpaXJjtFUISw XB44FcP4vc3OnzmT+7XyeXYfMq2PWKrJpdb6aa6JPov+D1O11IRpyp306evc8ugH9peCbtH+ZrZs Ln0IH+FeYyDbIRXrHhiEv4c1cEcN0/AV5ZeLtuXHvX2hwFeiiigAr0X4c2KmSe7dflij3Z/GvOh1 FeueDIfI8GX84HLR4/nQBFoMDeIfFctxMN6KcDPsK9y0nRFCK7JhABhfWvL/AIR2qXFzJvA+8xP6 V7yjxxW5GB0r5vOK/tpvCc/JFK8n37L/ADOmleC51uylHHHFbSzcFUX5cVyfjNo5LWxO75xKAoz2 xz/IV1cJV9KljXkAEDPcV554iZnkgLHlXVRW1PLpe0w06LtTgtttWuq7u7uS5q0ubc6vw6VePym5 G0U+7sTLeGGMgcZJ9KqeGdxmB7YrdR0Ooy+u0V1ZxiqmGwrqUtHdK/a/UilFSlqcN4p8PRNZThhu LIRya8s8L5h1m+0uT7jqyge9e5+IR5i4zwBmvEreMx/EaXZ0Moz+Yri4bdeWFdStK6bdru/k/wAT oxlSMuVLdLX9DzrxDbfZdVljxjFZNdd8QLfyfEcwx6VyNfQnGFFFFADoxlwK9d8OAaJ4Nur0DErJ 8p/DH9a8ntF3XCj3r17xEn2XwXaQrxvRSf0oAPBWki+le+uBuZ2yCfrXtGj6YkVuJZE4H3VNcV8O rGKTTrXcfl2gkY6+1elXNykARTj5jwK+czfGOdeGAp3963Nbez6fdq/LTubU42XOxt9etZWzBlLH bkDHH0rhNDn36ndO3BaVicV2fiAiS2V1PG3tXD6CpN9cDH/LU816+HwNKhOVSG7/AAXRLyM3NtWO zvLMCESY6iuU8QWDeSC8ShD94kE8V3F06Lb24Y4AILfSs/XIY7jTztIPHBHevBzzNcVhcRGnT0hp r38vkd2Bp0pTXtFe54Awfw94uiKEqjcY9iKyfiFYLb6iJVUBZBu+vNdD42TbqNm5P7wEKf1qD4jw btLsJ/WHn86+mpVI1aaqR2aucdWm6U3CW6PKqKU8GkrQzHLTqavenUAW9Nh8+9jTHVgK9r1qX+w/ A0FpCdjzHJx6f5FeUeEbf7Rr1umOrD+deofEb5bmythwqqvH50AT+BtBSYrNMgZiRivVtOt0jjUQ xbv7oA5Pv7VxmhRC20xSp4wvT8K9E0d4hEoP3yo69QfSvEzOlWxtVYOD5YWvJ9Wr6JfcdVNRhT9q 972X9fM5DxTDsurdnbLGRd2OldJZW0ctkhHOFGaxfFqEXNuc5/erXTaUVWyBbA+WvVw+Hp4emqVJ WijnnOU3eRzWt29qsJikj3DPzNg/Lx/+qvG9eszomsxXEI2rvzwenNe560wNuwCbskcgZNeU+P7c C2V8/N1/WuLA4jE1K9anXjZRfu+a1/yv8/kb1YU1ShKO7vf8DP8AiDAuo+HbDUgMvJGCx98f/Wrx 49a9vvYTc/DS3LdY0P8AI14lKMSEV6ZzDKY3Wn0xutADx0opq/SnUAdH4MsPt2v2ybQQZBmvY/Hd 81lpljpULbVVfmA78D/GuB+EtkbnxHbsRkCQdq6X4jvnxOEydqnH6CgDQ8H+HEuk+1TqpAznJ4AF drbaW91DPJEI0s4gV4Xlzjj3HUH/ACaoeDXt5tI+ztKyh8glDg44r0G5Mb6Y6RhVUJgADGOK5qlO pOrF3tFa6dX29F+JtCcYQel29PT/AIP5HAeGbCKfUJ1kQNtYitnWtDsks5ZJo/lVc8DOfQVW8G5/ tG9GeBIwrqtUEU9rJCzfKRz+Fa1efkl7P4rO3r0M4cvMufbqfPvijTV0nUTLBhULZUqa6nV8eIPh us8vzTRsBuPpms3x4qKnlK27aSMmtjwlAbj4f3URGT1H5iqg5OKclZhJJSai7o+eLlPLndfQmoqv 6xH5WpTL6Mf51QqiRD0NC9KG6Ui9KAHUoGaSnKMmgDvvhjpa32uxBlyOeteq/EjWZLZUsYGKRom3 A/KuT+C1kTqiSleBuPSrfxJm8zW2x2z0+tAGLpWlyX7+Y2Dnk5NS32nLa3WzGMjitfwvcwxwqDjO BUPiidRewMpAyy9KAHweGvN09ZAoJI71zdzbS6XfK0ZKkehr06xvLePSoizLkoK4PxPIs02Ux26U AehRzf8ACQ+ALhbgb3iT5SfoK+b9Yg+z6lOg4w5FfRPgQeb4UvYjySvA/wCA14T4xtzBr10MYHmG gDnD6U0jFOPc0meKAIz94U6mt94U6gApyjmm1IikuBjvQB6b8LtIS91SMsoIBBOfrXuHirWG0uyC QYSNQAABXmnwctD9oLleij+ddN8RbxFVoQ3PGaAPNdU1q4vbpi8hIyf51nmVg/yscmqkjgOTnPNP hkBfPtQB0/h3xDeafeRqJW2FhxXr18Y9f8JZmUM4jJBx7GvBrLi6Vif4q918MyJc+Fti4LCPGPzo A+YvElsLbUpFAxWKeldh47tjDrMoIPauPoATvikftR3zSMaAIqKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooA+3/AAJ/yTzw1/2CrX/0UtdBXP8AgT/knnhr/sFWv/opa6CgAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACr+jjOow/71 UK0dFIGoxE+tAHs/iHTjc+H7d1A3IoYfgK0PDHiwwaULYzhFOQynHPapLu7iXw9bgnkrj9Kx9F0G Cd/Nl4Uk4GaipShUVpxT666jTa2O0XxbAhCrMv8A30K3LTUYtXs2jLKTtyDmvO18PRvqUUaEeWz4 PNXdbtp/BFxDcRsWtJR8w6471Yi/qslzaFLdZXWNmClQxwQTWxokKxgzSEYAyK4iXxTZ6hFGwcsF kBB29qZP4zjLtaWhLTyHYo28DtUxhGLbirXG23ud1e+KY4JNpkVR/vCqM/ia1uF2mVSfXcKytQ8H tBosV3NJvmkIyS1Vrbw3ayWTl2/eY7GqEYOvSrrWswW0XKI4Y9/Sq/xEhEWjxKMdDVyztY9J1hg5 ++wANV/iVMjWaKD/AAmgDxt/vGm1K4Bc1ERigAr0P4VpnWySP4K88r0T4ZyiLUmbPO2gDo9WR9K8 VRXyjhWDMB9a9AtPHFrLYwiThkQAEsPbt+FcjcldT1racbVJzSahBZwRhIlGR1Jrmq4SjVqxrSXv R2d2vyf5lqpJRcFseh6f4utZJVCuufTIq/rSJeW4uYsH5eRXA6D4Xj1jTZZInxcrkjBqhZeN2sXb TNQJE0T7Rx1A4/pXSQb+jRebdTL0xIa7K51W30u2WNiv3eTmvL4PFVtY+dLIxVXkyPlq3oPn+OtQ lmZitlDkAdM96AOpl8X2YJHmqPoRXIeKfEcM9lJDAdzNjpVO/sbWx1eSHqgbAFT32nWktk00ONy0 AQaJYfZPCd0xHLqSfyrxTUVxeScd69vtNQR/DtzGOMAg/lXimpEG9cj1oAz6Kcw702gBV+8K9s8J Q58DzIccp/jXikf3xXsugzmLwoqqfvL/AI0AN8Iao/hy/MjBjE+fu/X/AOtXqKeOrC4tgGkwx4IL Dj61wVpBbW2lrNdDLHPFZVu9vd6kqFQEZscV52IyvDV68cRNe8muu9tk+hqq0lDk6Htelaza3MW2 NlIYc4IrmPFEQjuYAOAZV6VymtQ3ng0Wup2x3Wsv3l64HWo7vxvBq0MLpncjhj8vYV6Jkeo6K0dp aGVjjiql74hsoJdxkUOP9oZrzbUfHhmijsLAkzyHaTt6dqu6roH9l6THc3blp3APWk0mrMDZ1fxX amCTyyWYjsc5NcNoML3His3ko++27n6irWlTWVw3lyj5s8Gppj9g12Mxj5G6fpTStogOE+JC/wDF QyN6iuHIxXc/EFxJqhbuVriSAaAGUUEYNFAF7SY/Mvox717B4qjEuiWEIxnYB+gryjw4FbVYw3Q1 65fRSXk1qq/dXH4UAL4V8StosSQTK+F4zXeweLLHUJl/eqDjgMw4rz7UvsMKKjAl+ho0/QI9Ut3N sSswyRg1ySwVGWIWKt76Vr+Rp7SXJydD1yW7WewePIPy8Gua8P7Vvbkn/nq1ebWXjm+0aaSwveSh Kg4z04qex8epaPM7g/OxYYWuszPYL/UI1GZplVAOhIrnrrxXZQRvGrlwMgANxXD6VNqHjK5mmdit tEenTjrUNwtpY3rRtyA2M1nUpQqx5akU15q44ycXdMz9enbVtTS42ssaEYBFWvH6+Z4eswOdsf8A hWhfW8U9hvtsFgQay/FDl/DiiX7yrirjFRSjFWSBtt3e55Kw5zTalYfMaYw4piBadTV706gDtPh3 Bv16JyOFIP613XxABuNTQqckKv8AWuU+G+z7UzfxBR/Ou71DT5Lm78+QDA75oAo6L4mS0iFvdqzR 8ZycV6LpPiazutrxSLkjkbhmuFGiWt9hS+G6YzXN+I9KvvDTpdWrfu2PPOeOtLlV+a2o7u1uh674 mvopXs2yB+9XPNbCX6pboA4A29TivnGfxreXKxbmz5bA9KvyeOdR1IR2sHDPhen4UxHrmr+J7K0G POV3/uhhXm/ibVm1l1VEIXPf61q2fhArarc3jks3J5p0+nwFfLhOaAJVAbwG8HUrGR+hrwq8TZcs K94ED22lyxSHClD/ACrxDVgPtz46UAZ1MbrUhHNRt1oAcOlKBk4pB0pyfeFAHs/wcthDdidgODkf kad8QB5uvPKOhY81d+GrImmEr94gfyra1XQRfXQYgcnqTQBxeheKTpUgidjtHIOa9Fs/HltLZtuf Py/3h6VzWp/Dh5LJpolBI9Gryy8e80u4kt242kj+lAHs3hLxRAtzfMz4DSsRyK0Nb8cW0UTBZBnB /iFfP9rqlxAzbG5Y8113hbw5f+JbkM4OzI74oAm1fVW1SVnBLAnNei+CMR+F54mwCQeKpTeBBYQq nyg+7Vr6dY/YLN1BwMcj1oA+e/FlsYNYm46sf51gV2fjwR/2s4XuT/OuO2igBjdKRe9Kw+WkXpQA 6poELyKvqaiAzVuwIW6Td6igD374T2otbfzcYODzj6Vznj12bVXkAJGT/Ous8D3MUemJtOM5p+ra HFqczkrlTnknFAHkltrMttJ8rMB060upa211JCS5O0jqa1vEnhGSzJeBSRk964ieOWJvmyCKAO0X xK626xhiMLxzVM3z3knJyaxNP064vpFCrwa9N0DwQVhEkwGccfNQB03gEmLTp1boy/0ryT4j2fl6 3cOBwZCRivbNPs4rG2cA7cCvJviO0Znbac5J/lQB5ec9RTecfSpD1NIeRQBCfvCnUjDDiloAUdau WEPm3Kj3FUwOc1p6SwW7TP8AeoA+g/hfafZbEygc+X/WsDx7LLJdufmzxWt4O1eK2sUQOBlQCMVL rkFvqBZhjJ96APHHlCscgg1Gk+GOK6fVfDrjc6Lxz3rEj0ad5doFAElg7yygA969r8BPIlk6Sfd8 vpXn2h+H1hdWl4OR3r0PT7m3022+VgMjH1oA8p+JdmP7Tkkx1wa8xYYYivVvHt2k8jMCM8V5XLzI TQBGRkUxu/0qXGKY4yKAIaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+3/AAJ/yTzw1/2CrX/0 UtdBXP8AgT/knnhr/sFWv/opa6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP gCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACrVi5iuY3A6Gqoq7Zx751HvQB6dLfPc6ZboGz9K 1IrqeCyQLnpWXa2Rhsbd2Bxx39q7nTrC2vdJyn3gSKAOWsL66bUIiGY4cd67Dx3dm48HyLcIMpFl G/CuWdFs9VQkgIr5P51Y8Z+KLXUdJi063ILdDx7UAeY6RLIkUig8YJGa0vBrg+MYnmG5TIDg/UVJ Z6XsyAOoNJZKdK1KK8xwjgnP1oA9g8ValPNo8YUbACMc1xVlqN0H25Y/jWxrHiK01fS4/IIJGPlx ipvDelRXJEkgxjnFAHJ63LM11FIQRhgawfGGoNcJGhPRea7nxVbwvqAhgHRq4LxXZtGqyY4K0AcQ epqI9amf7xqJutACd66bwjf/AGHUc5wGFc0oya29Et2lulC9aAPUbV8efcIcswyKwLy7madtxPX1 rpNAhTPkT/xDH6Ums6AI5N6DIJznNAGh8PtUubS/JXcUIOQa5X4oW6R+M0e3G3cu4j3JrsvB0trp bNLcN0zXL+KpY9d197qLBUHC/nQByerGV7WIEk8CvX/Bt2NL8AKbdP3soO4j/dFed3WmloEBXtya 7nwjq9jHosumztiRRhRj2/8ArUAcVqt3M99I7MdxY55q/o93LIroxJUjuaff6ctxqMgjOQW6Vu2u kQ2Gns8vD9hmgDkdRuxpun3MSty5PevM7hi8zMe9eh+I7Vpo5HUfKOleeTLscg9jQBC3SmU9ulMo AcvWvSPB2qpLZi0lYcY7+9ecqMmui0CKSOdZFz2oA77xFcMNscf3ABXPwyukqsMhs9q637ImoWQL fexVCDRxHcKXHyhvWgDtbi5XV/hrPBcrl4IiyE9ehrxrR432yhSRwa9cvL21t/Dr2Uf+sdNp9hiu G07SiN+0cEHPNAEPw9tI5vHMYuBlEcNyPQius+IWry3uo7fuxKAABWLoMa6VrgumOOcE/jXReJIL XUF82Fgc4oA8/hlkjmUpnOe1da9zGmnxXExHmKmeapWehEyhyuVHXmofESH7P5MfQDH6UAefeIdQ N/ftJniserV5EySkEc1XOMe9AETdaTrSt1oXrQBYsp2t7hJV6g17Ho2tRXWmGTjzAnr7V4yq5NdZ 4cmkt2AJ+UmgDevGeeaRmJ610PgW/kttVVTyhz1qittHcDI6mtbS7eCw/ePwaAOc+J+nxQ+J1lhA 2yjdgDvmuTvbN0tkPPJAruPETJq2oxMOVXjJqC/0fbaRkjgY70Adn4a8rR/h2zRhfPmHPHP3RXAX BeeeR3Y5LE11um3sR0xbOQ9BgA/Ssy705PMZlHyk+tAFfQrzyZGWXlMHrXOeM9aWc/Z4SNgBzg+9 bF4VtrdgmQxrz7UQ7TMW96AM6kbpTvWkbpQA1adSL0pcUAdB4W1U6bqCtnAbA6+9euyaylzYrsIJ PvXhEWQ4x2rtdE1F1iVHORQB2kc729zG6nvnrXX6stnrPhFlkA81UJ49cGuFt5o5iuCa2/tJhsHQ ngr0oA8q/stjI4AOA2OldL8P9JifxGDOBtjYdR71saXo/wBqSRtuSMnrRp8Q03VJeNpJ/WgDsvGF +u9be3wFGOlcokzWkiuW3exq1dzib5mOawNRuwinHPHFAFnxP4lVNP8ALRgHII4PtXks8hllZieT WtrNy88p+tYpGKAEINRP96pqhcfNQAo6ClBwaB0oAzQB3/gfxA1i4iZsKWHU16fHrnmIjhx69a+f bOZoZVYHoa7Cx1pxEq7jwKAPcNO8UwrH5Vwyc+tcH440m21ENcwKu45OVHvXO29xPeTjaa68W8ht IYWBy3SgDziw0EtdLvB2g88V7d4XvLDRLHGY1bA4P0rnLvRhYwo5XG/B61i6qJkRXVscUAd5qXiI XErMGUqT1rmtX8UCC0kXeM9ua46TWZUXaxwR7VzWq6k0+4ZoAzdZvWvb1pGOck/zrMxT3JLZNNoA Y+cc01elPcfKaYnSgB4HtipI22yAimgYFFAHpHhbxC9vGsZfjnqa7H/hJ1jGSyn8a8XtLloyMHFa f295BjdmgD1eLxJa304ik2kNx16VheLPD0UV7E8QBEmDwKwfD1rNcXqOOgIPX3r06405tVdQvKwp /n+dAGNZWVromjw3EgUM69x0q7F4wiEexGT86z/EVs82lrAM/IMfkMV5u0strIQc8UAel6j4oIt3 2uBketeWeItSa9nYls8nvS3OotJGRuPSsSdi7E0AV6KXaaCMUARP98UoGaR/vinLQAqjFT2zlJVP oahpyHBzQB2VhrEkMa4cjA9a0f8AhJ5UwN2fxNcUk5CgZpwdpDgHmgD3DwfqEGu2ksUqqWUdfwq9 Y6dapPcysgAjzjpXF/D2GW3MkuTtI/PiuwuYpraGZhnMoyDQBxeqeKVS/eOIrgHHB96oy+JJnQje APqa5nWbaS2v5Cf4iTVHz224JoAu6tqDXAILE/jXPMQTVqZywNVWGaAE6c009KkxTXGB+FAFaiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/AKKW ugoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4AooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAHWtfRVDXsYPrWRRQB9ERaaJtFjK9QARge1c1cazd6EX8sHb3FeOUUAdfq/iS7vn Yklc+hNV9Kklmm3sScc81zFFAHrtqAQD04rM1lSsBAGSSa81ooA21vZ7WcqGPB6ZrrtG8ZXsSiBf 4uM5NebUYNAHu+jQTajcCeXLE/MT1rI8fWyw2ijGCFryDbxS7TQBLKMSGoW60u33ptAD0612vgi3 E1+AR2ridvvSbTQB9E3WjvCgliByqgjHrXN6vr1zAvkOnK8ZrxraaNpoA6+91W7ebZuYKT0BNdFo /wA0Rzk9OfSvLtpo2mgD2ydd0Ce3euG1m4kguzNCzKQSODXGbTRtOaAO90zxFdxKpZdxrtrG5vNY jVGQqDzgda8M2ml2mgD3vWdJFvpU24c7T1FeJ6iu25fFUNvvRt96AFbkGmDg05l7ikAzQBNEMsK9 I8HaYLqMcZOB2968y2e9G33oA+h/7ImtU3Ip2/SuY1y6uolKopGB1rx/b70bfegDu7C+uZr9hOzE Dtn3ruLJRGzcYDLmvC9tLtoA9b1r91A7j+I1z2majepKU3Myjpya4TbS7fegD3DTXurvC7WG6r93 oUnks8qHp6V4Bt96NlAHR+JbcQXzqO1c+etN2fnRs9jQA1utKtIRg0lAFy1Xc4Fej+H9F+0RIyrn kdq8sooA9+TRZYVDFCMe1YmtpeJG20Ngd68cooA9b0NWmtmdxllINdFfhZ7WJAR0Ga8CooA9Ilac 6oyxE4Vu1dRYRzTxAOrZxXh9FAHvFxoEjwl2RiPcV5z4ksBbS4A7E1xtFAEz8HFMbpTKKAHL0p69 aiooA1LKHzJAK7vSNFMqAqpI7cV5hRQB79ZaLKihRGRn0FGrWU0McUZUgFh2rwGigD6R0kJaSTxn H3cj8qw9VgLt5yr8zyZ4rwqigD6Dh0uaW3DFGI9cVmX+guyMSp6eleH0UAdrrmm/Z2OQetcvKMEi qVFAFioX4NNooAkpw61DRQBqWkO9hgdTXU6bphl24FcFRQB9AeGPDnmzKuDnv04rodSsPs05BGPK 2j9a+XqKAPqnWXS5gtYgR90MfeswaMbqzmypwDXzTRQB67rGiNFK4KEYPXFcdqNgYiTg1ydFAF+V dpqA1XooAmfpTY6jooAsUo61WooA04EzWtaW5d1GOprlqKAPoXwLoamEzMOQoPSux0tUgvLxGx/q 84/CvkmigD6iNkl1G+eQWbPHqa8r8S6WLa8YAV5jRQB0U8OzORWfImCazaKALnSkJqpRQBI/3xTx 0qCigCxTl61VooA0kya0LCDzZlX+Vc7RQB9K+HtLW00HzcYyFJ4+lb+rRxvDZgYwyAGvkuigD1vx xpYgnLKp4B7e9efupBPrWJRQBouPWoqp0UAXKY/9KrUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAfb/AIE/5J54a/7BVr/6KWugrn/An/JPPDX/AGCrX/0UtdBQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHqU/hbQftF5HFZw H+yZZ1kjhlm35it7iUR3hc7TIWtwCbc7OJcMMxmuN8RWtokWj39pax2g1GyNw9vEztHGwnliwm8s 2CIgTljyT0GAA+LtclaKRruMPG292W3iU3DEFSZyF/fkqzgmTdkO4Od7Zpalqd5qs6zXTRlkTYiR RJFHGuScIiAKoySSABksT1JNAFDFGM0v40lABgUYHpS4pMUAFFOC+tG2gBuKMUuCOaUDjrQAmM9K XbS4paAEwK6TwxY6a9hqF/qMdiVglhhR9Saf7Mu8SEgi3/e+YfL+X+DAfdyUrnQM1c07Wr3SHkFs YGjmxvhubaO4jYjO1tkisu4ZYBsZAZgDgnIB28/w8+x+H/E1+dK1V2g882JkXcLeOG6jjYyNHlHk K+eCAcKImbBDKw8521eGq32b9muXd9QTZdPJ87SjzFkOScnO9FOevHuap0AN20badRQAzaaXbzTq KAGbTS7adRQA3aKAvrTqKAE2j0pdue1FPAIoAjZeM16bL4W0H7ZNDHZwY026khmEUs26MJDO+L3c cFgYAWNrkYWbbyYs+bMOK2P+Eu1uUwu11GHRt7stvGpuGIKkzEL+/JDOCZN2Q7g/ebIA/wAV2Fla XNhNYRQJDd2vnbrVpDbuRJImYvN/ehfkwd/O4Pj5dtYBQVoajqd3qs6zXbRlkUIiRRJFHGuScIiA KoyScADJYnqSapc9cUAM2ijaKeOTnj6Um00ANwKAopw69qcBz0FADNvPSjHtT+aOeKAGY5xS7fan AHNLzQA3bS7RThzwadgelAGz4ctLCeC9eS20281FXjENrqd59lhMRD+Y+/zI8uGEQC7+Q7HacZXU vtD0jTrjxBp8unRx2Gn3F5bpqtzdn7Q80ZfykiQFVcEiFXAjcqJCxZAy7eZs9ZvdHMgtWgaOXHmR XNtHcRsRnDbJFZdwywDYyAzAHBOZpfF2sXNh9jupLS6TZInm3VjBNOA7s7YmdDICWd2zuyCxIxQB h0UUUAFFFFABRRRQAUUUUAFFFFABRRRQB6pB4R0C/M1zHHpsWm2lw4tbiK+aU3UAtrmZXu0R3eMn 7PHlVWNsPIAoIG3h/FFnBa3ttNaW9pFbXVuJonspZWglAd0LRiYeaoyhUh8ncrEHaVAJPF+uSvC7 XcYeN97slvEpuGKlSZ8L+/JVnBMm7Idwc72zn6lql3q1ws128ZKIEjSKJIo41yThEQBVGSSQAMli epJoAp0UUUAFFFFABRRRQAUUUUAFFFFABW54Vs7G/wBRvIL+3kmA0y9mh2y7AksdvJIrNxlgCnTI 5xkkAqcOrFnfXGnztNayeXI0UkJO0HKSI0bjn1VmHtnjmgDrLDSNMk0vTrSSwjee/wBHvdSa9Mkg lieH7TtRAG2bD9mXO5WPzvgj5dp4p0jTLW312KzsI7Z9E1hNNSVJJGa5Qi4y8u5iN/7hT8gQfM3H THPw+IdTt9LOnRzxiDY0auYIzLGjZ3IkpXeiHc2VVgDvfI+ZsmpeIdT1a3WC8njZA4kcpBHG0zgE B5WVQZX5b5nLH5m5+Y5AMuiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigD7f8Cf8k88Nf8AYKtf/RS10Fc/4E/5J54a/wCwVa/+ilroKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooA+AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAkQZFKWzRGPloP 0oATNFFL3oAMZNKOtJjIyKeoJ7ZNABSN0qYW8hGQp/KkaF16qaAIQDjFLilooAKKKKAHDg88VE/3 xin0xuXH0oAfRRRQADmiiigAoooHWgBeaSnY9e9NoAKMZpce1LjpQABeadRRQAh6UyL7tOc4Wmxf doAeTg00nNDCkoAUDPp9KVemKQdDS+2frQAd+KXH1o6UtACfjzR6UpooAO9ABpQPelGBQAoAApaT pxS/SgCtc/eWoKmuPvCoaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKAPt/wACf8k88Nf9gq1/9FLXQVz/AIE/5J54a/7BVr/6KWugoAKKKKACiiigAooooAKKKKAC iiigAorjZbCy3zEW1qoErqFFpDgAMQOqegqsba0Bx9ntf/ASH/4isY1XJJqL19P8zRwSdm1+P+R3 dFcJ9ntP+fe1/wDASH/4ij7Paf8APva/+AkP/wART9pL+R/h/mLlX8y/H/I7uiuE+z2n/Pva/wDg JD/8RVW/m0/T7dJpbWBlaaKEBLOAndJIsanlemWGfbNHtJfyP8P8w5V/Mvx/yPRaK4T7Paf8+9r/ AOAkP/xFH2e0/wCfe1/8BIf/AIij2kv5H+H+Ycq/mX4/5Hd0Vwn2e0/597X/AMBIf/iKPs9p/wA+ 9r/4CQ//ABFHtJfyP8P8w5V/Mvx/yO7orhPs9p/z72v/AICQ/wDxFH2e0/597X/wEh/+Io9pL+R/ h/mHKv5l+P8Akd3RXCfZ7T/n3tf/AAEh/wDiKPs9p/z72v8A4CQ//EUe0l/I/wAP8w5V/Mvx/wAj u6K4T7Paf8+9r/4CQ/8AxFH2e0/597X/AMBIf/iKPaS/kf4f5hyr+Zfj/kd3RXCfZ7T/AJ97X/wE h/8AiKPs9p/z72v/AICQ/wDxFHtJfyP8P8w5V/Mvx/yO7orhPs9p/wA+9r/4CQ//ABFH2e0/597X /wABIf8A4ij2kv5H+H+Ycq/mX4/5Hd0Vwn2e0/597X/wEh/+Io+z2n/Pva/+AkP/AMRR7SX8j/D/ ADDlX8y/H/I7uiuDNvaY/wCPe1/8BIf/AIiogIEv7NY4LYE3MPzLbRqw/ep0KqD60nVa3i/w/wAw 5E9n+f8Akeg0UUVsQFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQBLH0pW6U1DgU7OTjrQA2lH0oNOUcCgB8aFyABXR6T4fkuSpKE59qPDGkPfXkYC5ywAr6I8J +C7SxsUuLpPnKg4zQB5Pb+C5CgJi7f3TVXUfBskaE+WR/wABNe9XUlrE+xI1VRU0FjZahDskQEYo A+StR0mWzchl4HtWWVxxivoPxz4HWGF5oEJTjvXhWp2jWtwyEGgDPooooAKY336fUZ+/QBJRRRQA UUUoGTQAKpbpVuCyeU8Kfyqzptg1xIODtr0HRvDEs6qEhOKAPPm0yUD7p/Kqclu0Z5Fe1SeDZAmT Ec4rkNd8ONbhsxkHmgDz/pRVm7tzC5BBBBqtQAUnPemng+tGcdKAEY8HNLF9ymseDSxH5aAH0ynn HemkYNAAB34pw6UijnNOA7UAFKFJqaGEtzjNXksmYdKAMsqR2pQuRWjLZsv8NUnjKdaAGAYo6UtM yaAFJ7UZ496TvSUAQTdRUVSTfeqOgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigD7f8Cf8k88Nf9gq1/8ARS10Fc/4E/5J54a/7BVr/wCilroKACiiigAooooAKKKK ACiiigAooooA8i12G3/t3U21jTbi/Z7lv7MeGBpDCvAKq44hk8zc28leGT5/kwmLfRO014HtJH8Q vJIbK/EDFY1LHyiJcbURVwGQkbsP8r7/AJ/R7rw/rNxPLIPsSCR2fb9oY4yc/wDPP3qofCOsk53W H/f5/wD4iuenKUIKLi9PT/M1klKTd/z/AMjh3srk3XiC7t4pVupbuII4YqzwCKDzFjJIClgrLuBH zKuWG0Fal5DHJo+sJoWnyWtjJplxHJbraNB505UbCkRUHIG4FgBu3IMtt+X0P/hEdY/vWH/f5/8A 4ij/AIRHWP71h/3+f/4ir9pL+R/h/mTyr+Zfj/kcJdWhspNWgtLFns5La1Yph2SRzJIJXYA5lbaE LrktIAFOdwqjpxuG0m8tEt5Aker2csKR6fLaxCLzICxSN/ugMshOD1yxxur0n/hEdY/vWH/f5/8A 4ij/AIRHWP71h/3+f/4ij2kv5H+H+Ycq/mX4/wCRn/aD60faD61of8IjrH96w/7/AD//ABFH/CI6 x/esP+/z/wDxFHtJfyP8P8w5V/Mvx/yM/wC0H1o+0H1rQ/4RHWP71h/3+f8A+Io/4RHWP71h/wB/ n/8AiKPaS/kf4f5hyr+Zfj/kZ/2g+tH2g+taH/CI6x/esP8Av8//AMRR/wAIjrH96w/7/P8A/EUe 0l/I/wAP8w5V/Mvx/wAjP+0H1o+0H1rQ/wCER1j+9Yf9/n/+Io/4RHWP71h/3+f/AOIo9pL+R/h/ mHKv5l+P+Rn/AGg+tH2g+taH/CI6x/esP+/z/wDxFH/CI6x/esP+/wA//wARR7SX8j/D/MOVfzL8 f8jP+0H1o+0H1rQ/4RHWP71h/wB/n/8AiKP+ER1j+9Yf9/n/APiKPaS/kf4f5hyr+Zfj/kZ/2g+t H2g+taH/AAiOsf3rD/v8/wD8RR/wiOsf3rD/AL/P/wDEUe0l/I/w/wAw5V/Mvx/yM/7QfWj7QfWt D/hEdY/vWH/f5/8A4ij/AIRHWP71h/3+f/4ij2kv5H+H+Ycq/mX4/wCRyXirS7vXNKMen6rd6dex ZaGWCd41Y/3XCnkH16jqO4LPB2k3ej/YItQ1S61C9ku4DNJNO7qp3j5UDHgD16nqewHXnwhrH96w /wC/z/8AxFPtfCGqxX9rNI9iI4p45W2SOSQrA8ZWpk5TSXK1qu3R+o0lHW/5nc0UUVuZhRRRQAUU UUAFFFFABRRRQAUUUUAfAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAPXpS01elOoAOaljBLCm AVZtY/MlAAoA9T+Gdmr3cZZQcOO3tX0A7eXp6AjAxXkXws04LIkrjjcO/tXqmt30NtbhAcHHT8KA OT1i+xNtU46d62PDt8HQDcTgDvXE6ndGS4JU8ZrT0O/EMq5YdqAO08TIJtOf5d3HevlvxdAI798D HJ/nX1ZKUv8AS2wcnFfNfj6xMOoycev86APPaKV1wSDSUAJ71GfvCpaiP36AJB0paQUtABT4huem Vbs49zigDrvDFsplXcBjmvdPD9rHBbIdgBwO3tXlvgzSWnmQY9e9e6aVpQS2X6DvQBXeKMg4UVw3 iqzieKQ7B0NeoCyABBBrmPEukiW2kIXBCmgD5s16EJK+B3Nc8a7XxTZNDcyAj+IiuMlUqxGKAIjR SkYpKAEPSkjPy9KVj8tNj+7QBL25pCPekzxS559aAFWpY1ywFRKOuauW8RJGO9AGjY24lYDiuhis 12gY/GqGmWjZBFdLDbHaMigDFurIBa5y9hCEiu5urT5TgGuU1C3IY5oA59s0zNTSptNQ0AFFFFAF ebhhUdSTfeFR0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfb /gT/AJJ54a/7BVr/AOilroK5/wACf8k88Nf9gq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUV 8/8A/DTX/Uo/+VL/AO1Uf8NNf9Sj/wCVL/7VQB9AUV8//wDDTX/Uo/8AlS/+1Vcsv2iJr611G4i8 JxhLC3FxKG1M5KmWOLC/ueu6RTzjgH6EA90orwvU/wBoibSrpLefwnGzvbwXAKamSNssSyqOYRzt cA++evWqf/DTX/Uo/wDlS/8AtVAH0BRXz/8A8NNf9Sj/AOVL/wC1Uf8ADTX/AFKP/lS/+1UAfQFF fP8A/wANNf8AUo/+VL/7VR/w01/1KP8A5Uv/ALVQB9AUV8//APDTX/Uo/wDlS/8AtVH/AA01/wBS j/5Uv/tVAH0BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH wBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAD16UtIvSn4J5NACg8VqaUq+apPrWXjvV6zkKN70 AfRPw9nt4bRdzqCMEflWvrzPOSUO4Z7V4po/iaaxUASNt9M12Wl+MluHWN3znAOSKAJp4pCTkHNT 2MMvmDAIrqILG1vYPOAXkdayNWvrfRl4A3euaAOt025FtYlZXAOOhNeNfEAxT3TOrDPP86tXnjdm DLHI3Po1cXrOrPdks7ZODQBy1yoEhA9agqaY7nJqI0AJUR+/UxFROPnFADx0xS0g6UtAAOTWtpoX epJ9Kyl61et5PLINAHuPgWW2SNSWG45r1S01GJEUKQfxr5i0nxDJZAYcrj0Neg6D4yM7qruc57sK APZ21AYzuAFY+s6lAbVgXXcQe9ctqHiZIbPeXxwO4rzvVvGzyOyrI3Of4qAK/jQxSXDspHLGvOLk APxW5qWpSXTlmYkn1NYU5yc0AQEZpu05p9FAETfdNNj+7UrgbTxUUf3aAH0oGTQBmnYAoAVRkjmt SywGGRWcgq5EWGCD+tAHcaKYcDdjNdNF5HHSvM7TUzCQCSMVvW+skpkOfzoA624aDyzytcRrTR+Y dpBPFF1rbkEBz+dY09y0xJyeaAM+5xu4qsR7VZlGc1XPFADCMd6afrUg+lBAoAqS/eqOpZxhhUVA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4E/5J54a/7BVr /wCilroK5/wJ/wAk88Nf9gq1/wDRS10FABRRRQAUUUUAFFFFABRRRQAUUUUAfGhv2f4YJZPcx2CR uzIlrqKn+0CZQds9qrEq64ZhMwA2xouDlCDxHeXc9heNZaxaDwu7g6dpjXKO0S7wVQQctFKozukI G7D/ALx/MBf0m0s9EtfDOjs2iaS0radbO7yWELMzNEpJJKkkknOaxLjUtLjmKjRtFAH/AFDLf/4i gDF1bXZdS8ZeJ5NN1WCDVjqDLpd6LhLeNbUSSmRIpchI2csj78rvAk+YmQrJn3d/Zm011JbuCXU3 0SOG9uFkBW7uhfQuSjdJGEYUMw+80bvls726X+1tM/6A+i/+Cy3/APiKP7W0z/oD6L/4LLf/AOIo A8+8WTw3OsW7wSxyoNMsELIwYBltIVYcdwwII7EEVh167/a2mf8AQH0X/wAFlv8A/EUf2tpn/QH0 X/wWW/8A8RQB5FRXrv8Aa2mf9AfRf/BZb/8AxFH9raZ/0B9F/wDBZb//ABFAHkVFeu/2tpn/AEB9 F/8ABZb/APxFH9raZ/0B9F/8Flv/APEUAeRUV67/AGtpn/QH0X/wWW//AMRVXU9QsJ9Ivo4tK0hH NtJh49OgVl+Q8ghMg+45oA+rKK+fbSz0S18M6OzaJpLStp1s7vJYQszM0SkkkqSSSc5rEuNS0uOY qNG0UAf9Qy3/APiKAPp2ivl3+1tM/wCgPov/AILLf/4ij+1tM/6A+i/+Cy3/APiKAPqKivmAahYt C0w0PSDEjBGcaVBtViCQCdnU7Wx9D6Uz+1tM/wCgPov/AILLf/4igD6ior5d/tbTP+gPov8A4LLf /wCIo/tbTP8AoD6L/wCCy3/+IoA+oqK+Xf7W0z/oD6L/AOCy3/8AiKP7W0z/AKA+i/8Agst//iKA PqKivl3+1tM/6A+i/wDgst//AIiqup6hYT6RfRxaVpCObaTDx6dArL8h5BCZB9xzQB9WUUUUAFFF FABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA9RxUo6VGnapKAD vU8ZweoqCpUNAF4S7RgGpLe7ljnQoe9Ug3rWpo1n9qvEUDkkdvegD2nwxqsv9jrv6gE8964Dx9qs 8t3jomTXp+h6Msdj5YA4jJ6VwHjrR8R+aF65P6UAeeR3DHnNMmkyDUWNrY9KRjkUAV5O9MwakbrS UAJgYxUD/fFWKgf74oAeOgopQM0uDj+VACoKsIcVXUEVMpwaAJ2Yha09Blna7ULnGRWODmu88CaO bu7VioIDDqOtAEuuPdrYgnO3A7VwfnM0h3ete9eLPDgGkgqq5CjOBXhN7Aba8dCO9ACM2BVWTk1N uFRNQBDRS4oxmgBjj5TUcf3RUr/dP0qOL7o4oAl7cUd6AKKAJk4PFWE6Gq6VLuA70ADnnitazgkM GVU9OazreIzzqo55r1zwx4T+16Q8pUdD/DQB5JeBkm5zgnpRGflrd8W6U1jdNhcAMe1c+jUAEgqs eCast0qu/NADaKKcenIoApT/AHhUVTXH3hUNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFAH2/4E/5J54a/wCwVa/+ilroK5/wJ/yTzw1/2CrX/wBFLXQUAFFFFABR RRQAUUUUAFFFFABRRRQB8w6pePDoWkqvQaXZ/wDohK82v9VnF23P613+qAz6PpnlshA020Gd4AyI EBGSfWvPb3TZ2uWIktfxu4h/7NQBD/as/r+tH9qz+v603+y7j/npaf8AgZF/8VR/Zdx/z0tP/AyL /wCKoAd/as/r+tdld2Kpb67cRWknkWWh6ddLIC5WO4nFqTk9MsJJiAffA444v+y7j/npaf8AgZF/ 8VVwtrTWCWDaoDZojIludSTy1VnDsAu/ABZVYjuVB6igDsNStrVGSwXSpLJf7Ch1X+0pJXO5zbK5 4PyeW8n7od/NcYbGI64X+1Z/X9a1tZvtU1dI7f7THFYpFbL9k/tKNozJDbpB5m3cBuITrjIBxk1j /wBl3H/PS0/8DIv/AIqgB39qz+v60f2rP6/rTf7LuP8Anpaf+BkX/wAVR/Zdx/z0tP8AwMi/+KoA d/as/r+tWbHUJp5pY2+6bafv/wBMmqp/Zdx/z0tP/AyL/wCKqxZ2clrLLLLLahBBMPluo2OTGwAA DZPJFMD0zVLx4dC0lV6DS7P/ANEJXm1/qs4u25/Wu/1QGfR9M8tkIGm2gzvAGRAgIyT6157e6bO1 yxElr+N3EP8A2akBD/as/r+tXNJuW1HWbGyuLxLOC4uI4pLmQjbCrMAXOSBgA56jp1FUf7LuP+el p/4GRf8AxVH9l3H/AD0tP/AyL/4qgD0KFkv/AAzPay6dJ4fhbxFZafNc3EjsqAJcBjJvwPMQNl8F V5X5U75kd9ZSLfT3fh82EeiyxGSBriVXu/3qo1tKzH5ZSu9soqY8uT5Mfc5y5bWry3jt7rVBPBGk aJHLqSMqqgYIAC+AFDuAO2446mi8bWtQt7W3vdUFzBaJsto5tSR1hXAGEBfCjCgYHoPSgDvH0DS9 O/1+oQXv9nSy3l1h9n2q1/feUvDHy1f7PFtcZz9vixnCb8+1XT5tJ0m2Ngftd7ot7fy3nnvuV4Gu igRfujPkKrZDZGMBTljx7Rao/mbr+NvNiSCTOoRnfGm3ah+flRsTA6DavoKFi1RPL238a+VE8EeN QjGyN925B8/Cne+R0O5vU0AQ/wBqz+v60f2rP6/rTf7LuP8Anpaf+BkX/wAVR/Zdx/z0tP8AwMi/ +KoAd/as/r+tWbHUJp5pY2+6bafv/wBMmqp/Zdx/z0tP/AyL/wCKqxZ2clrLLLLLahBBMPluo2OT GwAADZPJFMD7qooopAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQA5X29qd5vt+tR0UASeb7frThPj+H9ahooAn+0n+7+tbOieJk0i6WdrHz9uOPN2/wDsprn6 KAPXIfjg0LOR4fBDJtx9s6f+Q6xtW+KA1a0MD6NsP977Vnt/uV55RQBclvhI5Ii2j03Z/pTDdkj7 n61WooAmNxk/d/Wjz/8AZ/WoaKAJvP8A9n9ajZ8kHFNooAkEv+z+tL55/u/rUVFAEomx/D+tO+0n H3f1qCigCyl3tcEpuHpmuz8OfEhfD8eF0jzmyDn7Tt/9kNcHRQB69qPxyOowiI+HQg2gH/Tc5/8A Idec6praajctMtp5We3mbv6CseigCyLsj+D9aQ3Wf4P1qvRQBP8Aaf8AZ/Wj7Txjb+tQUUASmfI+ 7+tIkm0Yxn8ajooAn+0D+5+tJ5/+z+tQ0UAWFutv8H6077Z/sfrVWigDVsNYSymEjWplx28zH9K9 J0n43x6VZtbr4a3gqQD9uxj/AMh15DRQB23iPx/F4g3E6R5BJJz9p3f+yCuVF9g/6v8AWqdFAFz7 d/0z/wDHqabvP/LP9aq0UAWftfGNn60G75+5+tVqKAHySbyOMY96ZRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9v+BP8Aknnhr/sFWv8A6KWugrn/AAJ/yTzw 1/2CrX/0UtdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRXz/wD8NNf9Sj/5Uv8A7VR/w01/1KP/AJUv /tVAH0BRXz//AMNNf9Sj/wCVL/7VUkH7SyzXEUT+F44EdwrSyaixVAT947YScDrwCfQGgD3yivn/ AP4aa/6lH/ypf/aqP+Gmv+pR/wDKl/8AaqAPoCivB7D9oy41PUbaws/BvmXV1KkMKf2oBudiAoyY sDJI61HP+0ssNxLEnheOdEcqsseosFcA/eG6EHB68gH1AoA98or5/wD+Gmv+pR/8qX/2qj/hpr/q Uf8Aypf/AGqgD6Aorws/tETDRk1RfCcbwG4a3dV1MlomChlL/ucAMC23nny3/u1T/wCGmv8AqUf/ ACpf/aqAPoCivn//AIaa/wCpR/8AKl/9qo/4aa/6lH/ypf8A2qgD6Aor5/8A+Gmv+pR/8qX/ANqo /wCGmv8AqUf/ACpf/aqAPoCivn//AIaa/wCpR/8AKl/9qo/4aa/6lH/ypf8A2qgD6Aor5/8A+Gmv +pR/8qX/ANqo/wCGmv8AqUf/ACpf/aqAPoCivn//AIaa/wCpR/8AKl/9qo/4aa/6lH/ypf8A2qgD 6Aor5/8A+Gmv+pR/8qX/ANqo/wCGmv8AqUf/ACpf/aqAPoCiiigAooooAKKKKACiiigAooooAKKK KAPgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPt/wJ/yTzw1/2CrX /wBFLXQVz/gT/knnhr/sFWv/AKKWugoAKKKKACiiigAooooAKKKKACiiigD40N+z/DBLJ7mOwSN2 ZEtdRU/2gTKDtntVYlXXDMJmAG2NFwcoRsa/dtFbRJqOo2k2inQrRF05HUSi7NjH5T+XgNvDFHM3 TywY95/1R92s7Pw/a+Epdd12aSGGF5mmna7lVQBKygBVbrwAABknAGSa6D/hD9F/54XP/gbN/wDF 07ID511ebSZtDil1O9/tWxs9VtJAG1i3keay/eCUW1qmx7VWBizDnK/IOPLJGXp9/qcXirw1Prfi PTbrT49YtZbcLdxusMQcEsij/j2iC7cxsI/4fk/dnZ9Pf8Ifov8Azwuf/A2b/wCLo/4Q/Rf+eFz/ AOBs3/xdGgHy74R1xLHTvC9g9/BBaz+IJhqSM6rutWFoCspPPksA+5T8rbOQdoxc+Hl239r+FotL 1G0soEvYl1a3uHUNdSmc+W2wgmUbTGqYB8p1ZyI8+Y30t/wh+i/88Ln/AMDZv/i6P+EP0X/nhc/+ Bs3/AMXRoB8e+C/n8SpbLzNeWl3ZQL/fmmt5Io1z2y7qMngZySBk10HhHXEsdO8L2D38EFrP4gmG pIzqu61YWgKyk8+SwD7lPyts5B2jH1F/wh+i/wDPC5/8DZv/AIuj/hD9F/54XP8A4Gzf/F0aAfLu ja4l7qPgm/1S/gN1b+IGEryOqfZ7VTalFxwI4VJl2qMKvz4A5qxoPiqWf/hGP7Q1OAefrb2+obyi YsB9mxC3TZbfNN+64j6/L8vH01/wh+i/88Ln/wADZv8A4uj/AIQ/Rf8Anhc/+Bs3/wAXRoB8e6N/ onhrxDc3H7uG8tEsrdm/5azC4glZVHfCIST0GVBILqDz9fcX/CH6L/zwuf8AwNm/+LqtqHhfSbWw mnhinEka7lJu5WGfoWwfxosgPiaiiikAUUUUAFFFFABRRRQAUUUUAFFFFAH3/RRRQAUUUUAFFFFA BRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2 /wCBP+SeeGv+wVa/+ilroK5/wJ/yTzw1/wBgq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQB5Hr 1lrFz4AvrmO2sZ9MtLDUZEEl08UkczfaEeTaI2D4jchV3KMs+c/KV9YgMzW8TXEccc5QGRI3LqrY 5AYgEjPfAz6Cud8L6haWugxQzzpHIJpyVbg4MrkfoQa2P7Y07/n7i/Om07iL1FUf7Y07/n7i/Oj+ 2NO/5+4vzosxl6iqP9sad/z9xfnR/bGnf8/cX50WYF6iqP8AbGnf8/cX50f2xp3/AD9xfnRZgXqK o/2xp3/P3F+dH9sad/z9xfnRZgXqo6x/yCLr/rmaP7Y07/n7i/Oqmp6nYzaZcRx3UbOyEAA9aEnc Dxj/AIZl/wCpu/8AKb/9to/4Zl/6m7/ym/8A22voCikB8/8A/DMv/U3f+U3/AO20f8My/wDU3f8A lN/+219AUUAfP/8AwzL/ANTd/wCU3/7bR/wzL/1N3/lN/wDttfQFFAHz/wD8My/9Td/5Tf8A7bR/ wzL/ANTd/wCU3/7bX0BRQB8//wDDMv8A1N3/AJTf/ttH/DMv/U3f+U3/AO219AUUAfP/APwzL/1N 3/lN/wDttH/DMv8A1N3/AJTf/ttfQFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwBRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/wCBP+SeeGv+wVa/+ilroK5/wJ/y Tzw1/wBgq1/9FLXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUV8/wD/AA01/wBSj/5Uv/tVH/DTX/Uo /wDlS/8AtVAH0BRXhep/tETaVdJbz+E42d7eC4BTUyRtliWVRzCOdrgH3z161T/4aa/6lH/ypf8A 2qgD6Aor5/8A+Gmv+pR/8qX/ANqo/wCGmv8AqUf/ACpf/aqAPoCivC9K/aIm1fVLewg8Jxo8z4Mk upkJEvVnciE7UVQWY9gCe1F7+0RNY2unXEvhOMpf25uIgupnIUSyRYb9z13RseM8EfQAHulFfP8A /wANNf8AUo/+VL/7VR/w01/1KP8A5Uv/ALVQB9AUV8//APDTX/Uo/wDlS/8AtVH/AA01/wBSj/5U v/tVAH0BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwBRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH2/4E/5J54a/wCwVa/+ilro K5/wJ/yTzw1/2CrX/wBFLXQUAFFFFABRRRQAUUUUAFFFFABRRRQB8aTXl2+ggaRrFpaaCLJUubCa 5QFrgRgSFrc5aV2k+ZJArBQ0fzJ5eI7moagjxX8kmowS+EZIphpmlLdKzwuVYQAQA7o5EfBeQgbt snzyeZ+89608eHU0+JtSTUri9keZmSyW7nZEErqpZYc7AdpAyBna2M4OOkg8L+H7q3iuLcSzQSoH jkjv5mV1IyCCHwQRzmnZAfLstzbX1/q1lBeWnm3vhzTrWBpLhI4zLGlm7oZGIRSBFJ94jldvUgGx caxDpeqeIJtO1W0klXw5p9vbXMLAh5U+xBjHuAIddjkcB1KE/KV4+nv+EP0X/nhc/wDgbN/8XR/w h+i/88Ln/wADZv8A4ujQD5d1jXEv7PWrSa/glt/7EsZ4Yw64kvmNoZpePv3B3ThnOXxuBOBgGr64 l9p2qafJfwSWcPh/TTaQB12falFmrsAPvTBfNQtywUMudq4H1F/wh+i/88Ln/wADZv8A4uj/AIQ/ Rf8Anhc/+Bs3/wAXRoB8m+Adbs/D0utanNLfR3UWnslutpqAtTL5jLEy5KMSwEnmrjoYc4PUHxH8 Qf254jEMV9fXVrYRJaq1zf8A2pGkRFSSSNsAbXKA7sZf7xxu2r9Zf8Ifov8Azwuf/A2b/wCLo/4Q /Rf+eFz/AOBs3/xdGgHw7RX3F/wh+i/88Ln/AMDZv/i6P+EP0X/nhc/+Bs3/AMXRoB8O0V9xf8If ov8Azwuf/A2b/wCLqtqHhfSbWwmnhinEka7lJu5WGfoWwfxosgOmooopAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQB9v+BP+SeeGv+wVa/8Aopa6Cuf8Cf8AJPPDX/YKtf8A0UtdBQAUUUUA FFFFABRRRQAUUUUAFFFFAHlcN1ZxosV3rcHhzNrMEvpZhF9tLXM37rJZcrHt3HYRIPPG14snf6Jo UvneHtMl/s7+zd9pE32Hbt+zZQfu8YGNv3cYHToKyfC+oWlroMUM86RyCaclW4ODK5H6EGtj+2NO /wCfuL86bTuIvUVR/tjTv+fuL86P7Y07/n7i/OizGXqKo/2xp3/P3F+dH9sad/z9xfnRZgXqKo/2 xp3/AD9xfnR/bGnf8/cX50WYF6iqP9sad/z9xfnR/bGnf8/cX50WYF6qOsf8gi6/65mj+2NO/wCf uL86qanqdjNplxHHdRs7IQAD1oSdwNmiiikAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFAHwBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA H2/4E/5J54a/7BVr/wCilroK5/wJ/wAk88Nf9gq1/wDRS10FABRRRQAUUUUAFFFFABRRRQAUUUUA FFfP/wDw01/1KP8A5Uv/ALVR/wANNf8AUo/+VL/7VQB9AUV8/wD/AA01/wBSj/5Uv/tVXNK/aIm1 fVLewg8Jxo8z4MkupkJEvVnciE7UVQWY9gCe1AHulFeF3v7RE1ja6dcS+E4yl/bm4iC6mchRLJFh v3PXdGx4zwR9BT/4aa/6lH/ypf8A2qgD6Aor5/8A+Gmv+pR/8qX/ANqo/wCGmv8AqUf/ACpf/aqA PoCivn//AIaa/wCpR/8AKl/9qo/4aa/6lH/ypf8A2qgD6Aor5/8A+Gmv+pR/8qX/ANqo/wCGmv8A qUf/ACpf/aqAPoCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/ AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+ JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4 Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDg uh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQq aH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh /wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/ APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A 0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCE E8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBP B/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf /wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj /hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroK KAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/ AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+ JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4 Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDg uh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQq aH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh /wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/ APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A 0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCE E8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBP B/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf /wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj /hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroK KAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/ AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+ JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4 Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDg uh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQq aH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh /wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/ APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A 0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCE E8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBP B/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf /wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj /hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroK KAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/ AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+ JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4 Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDg uh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQq aH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh /wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/ APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A 0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCE E8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBP B/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf /wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj /hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroK KAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/ AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+ JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4 Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDg uh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQq aH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh /wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/ APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A 0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCE E8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBP B/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf /wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj /hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroK KAOf/wCEE8H/APQqaH/4Lof/AImj/hBPB/8A0Kmh/wDguh/+JroKKAI4IIbW3it7eKOGCJAkccah VRQMAADgADjFSUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8cR63ZrY+ErG7uIGsYdPnaRCgkSK6M11 5LzKAS6oXR9jBvlZsKd5DWP7bl0yeyl8Vah/a999rVWeG9S9kXT3SSO5iMquR84cBF3ZUh2Hl7gX 9608eHU0+JtSTUri9keZmSyW7nZEErqpZYc7AdpAyBna2M4OOkg8L+H7q3iuLcSzQSoHjkjv5mV1 IyCCHwQRzmnZAfMupanotjo13FZSRtqGlW7aPbyxSh1kEqp5skfzfPEWGoHcRkfaYsAceXl+Adbs /D0utanNLfR3UWnslutpqAtTL5jLEy5KMSwEnmrjoYc4PUfWX/CH6L/zwuf/AANm/wDi6P8AhD9F /wCeFz/4Gzf/ABdGgHyb8R/EH9ueIxDFfX11a2ESWqtc3/2pGkRFSSSNsAbXKA7sZf7xxu2rx9fc X/CH6L/zwuf/AANm/wDi6P8AhD9F/wCeFz/4Gzf/ABdGgHw7RX3F/wAIfov/ADwuf/A2b/4uj/hD 9F/54XP/AIGzf/F0aAfDtFfcX/CH6L/zwuf/AANm/wDi6P8AhD9F/wCeFz/4Gzf/ABdGgHw7RX3F /wAIfov/ADwuf/A2b/4uq2oeF9JtbCaeGKcSRruUm7lYZ+hbB/GiyA6aiiikAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHlcN1ZxosV3rcHhzNrMEv pZhF9tLXM37rJZcrHt3HYRIPPG14snf6JoUvneHtMl/s7+zd9pE32Hbt+zZQfu8YGNv3cYHToKyf C+oWlroMUM86RyCaclW4ODK5H6EGtj+2NO/5+4vzptO4i9RVH+2NO/5+4vzo/tjTv+fuL86LMZeo qj/bGnf8/cX50f2xp3/P3F+dFmBeoqj/AGxp3/P3F+dH9sad/wA/cX50WYF6iqP9sad/z9xfnR/b Gnf8/cX50WYF6qOsf8gi6/65mj+2NO/5+4vzqpqep2M2mXEcd1GzshAAPWhJ3A2aKKKQBRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/9k --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_ Content-Type: text/plain; name="README.txt" Content-Description: README.txt Content-Disposition: attachment; filename="README.txt"; sizeP75; creation-date="Fri, 18 Feb 2011 00:08:50 GMT"; modification-date="Fri, 18 Feb 2011 00:08:50 GMT" Content-Transfer-Encoding: base64 IF9fXyAgICAgICBfX19fICBfXyAgX18gCi8gX18pIF9fXyAoICBfIFwoICBcLyAgKSAgClxfXyBc KCBfICkgKV9fXy8gKSAgICAoICAgU3RhdGlzdGljYWwgbm9uUGFyYW1ldHJpYyBNYXBwaW5nIHRv b2xib3gKKF9fXy8oXylfKShfXykgIChfL1wvXF8pICBTblBNIC0gIGh0dHA6Ly93d3cuZmlsLmlv bi51Y2wuYWMudWsvc3BtL3NucG0KCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIFIgRSBB IEQgTSBFCgpfX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX18KClRoaXMgUkVBRE1FIGdpdmVzIGEgYnJpZWYgaW50cm9k dWN0aW9uIHRvIHRoZSBpbnN0YWxsYXRpb24gYW5kIHVzZSBvZgp0aGUgU25QTSB0b29sYm94IGZv ciBTUE0uIEZ1bGwgZGV0YWlscyBjYW4gYmUgZm91bmQgb24gdGhlIFNuUE13ZWIgc2l0ZToKICAg ICAgICAgICAgICAgICBodHRwOi8vd3d3LmZpbC5pb24udWNsLmFjLnVrL3NwbS9zbnBtCgpBIG1h bmlmZXN0IGZvciB0aGlzIHJlbGVhc2UgaXMgY29udGFpbmVkIGluIHRoZSBmaWxlIENvbnRlbnRz Lm0KVGhlIHJlbGVhc2UgaXMgZGVzY3JpYmVkIGluIHRoZSBmaWxlIHNucG0ubWFuCiAKX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgU25QTQoKICAgU3RhdGlzdGljYWwgbm9uUGFyYW1ldHJpYyBN YXBwaW5nIHJlZmVycyB0byB0aGUgZW50ZXJwcmlzZQogICBvZiBtYWtpbmcgc3RhdGlzdGljYWwg aW5mZXJlbmNlcyBvbiB2b2x1bWV0cmljIHN0YXRpc3RpYwogICBpbWFnZXMgd2l0aCBtaW5pbWFs IGFzc3VtcHRpb25zIHVzaW5nIG5vbi1wYXJhbWV0cmljIHN0YXRpc3RpY2FsCiAgIHRlY2huaXF1 ZXMuIFNuUE0gcmVmZXJzIHRvIGFuIGltcGxlbWVudGF0aW9uIG9mIFN0YXRpc3RpY2FsCiAgIG5v blBhcmFtZXRyaWMgTWFwcGluZyBieSBBbmRyZXcgSG9sbWVzIGFuZCBUaG9tYXMgTmljaG9scy4K Cl9fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fXwogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICBJbnN0YWxsYXRpb24KClRoZSBTblBNOCBzb2Z0d2FyZSBpcyBh IHN1aXRlIG9mIE1hdExhYiBmdW5jdGlvbnMgYW5kIHNjcmlwdHMsIHdoaWNoCnV0aWxpc2UgdGhl IFNQTSBBUEkgdG8gaW1wbGVtZW50IFN0YXRpc3RpY2FsIG5vblBhcmFtZXRyaWMgTWFwcGluZy4K ClNuUE04IHRoZXJlZm9yZSAqbXVzdCogYmUgaW5zdGFsbGVkIGFsb25nc2lkZSBTUE04LgoKSW5z dGFsbGF0aW9uIGlzIHJlbGF0aXZlbHkgc3RyYWlnaHRmb3J3YXJkLiBEb3dubG9hZCB0aGUgZ3pp cHBlZCB0YXIKZmlsZSBhbmQgdW56aXAgYW5kIHVudGFyIGl0OyBpdCB3aWxsIGNyZWF0ZSBhIGRp cmVjdG9yeSBuYW1lZCBzbnBtOTkuCkFkZCB0aGUgZnVsbCBwYXRobmFtZSBvZiB0aGlzIGRpcmVj dG9yeSB0byB5b3VyIE1BVExBQlBBVEgsCmVuc3VyaW5nIHRoYXQgdGhlIGZpcnN0IFNQTSBkaXN0 cmlidXRpb24gb24gdGhlIE1BVExBQlBBVEggaXMKU1BNOC4gKFlvdSBjYW4gY2hlY2sgdGhpcyBi eSB0eXBpbmcgd2hpY2ggc3BtIGluIE1hdExhYiBhbmQgZXhhbWluaW5nCnRoZSBwYXRoLCBvciB0 cnkgc3BtIHZlcikuICBOb3RlIHRoYXQgbm9uZSBvZiB0aGUgbmFtZXMgb2YgU25QTTMKcm91dGlu ZXMgY2xhc2ggd2l0aCB0aG9zZSBvZiBTUE04IHNvIHRoZSBwb3NpdGlvbmluZyBvZiBTblBNIG9u IHRoZQpNQVRMQUJQQVRIIGlzIG5vdCBpbXBvcnRhbnQKCkZvciBzdGVwIGJ5IHN0ZXAgaW5zdHJ1 Y3Rpb25zIG9uIGRvd25sb2FkaW5nIGFuZCBpbnN0YWxsaW5nIFNQTQpkaXN0cmlidXRpb25zLCBy ZWZlciB0byB0aGUgU1BNIGRpc3RyaWJ1dGlvbiBwYWdlOgoKCWh0dHA6Ly93d3cuZmlsLmlvbi51 Y2wuYWMudWsvc3BtL2Rpc3RyaWIuaHRtbAoJCgpfX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX18KICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgR2V0dGluZyBzdGFy dGVkCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgIApTblBNIGlzIGludm9rZWQgd2l0aCB0aGUgY29tbWFuZCBgc25wbWAgYXQgdGhlIE1hdExh YiBwcm9tcHQuIFNuUE04CnJlcXVpcmVzIFNQTTgsIGFuZCB3aWxsIGxhdW5jaCB0aGUgU1BNIGVu dmlyb25tZW50IGlmIG5vdCBhbHJlYWR5CnN0YXJ0ZWQuCgpXZSByZWNvbW1lbmQgeW91IHN0YXJ0 IGJ5IHJldmlld2luZyB0aGUgaGVscCBzeXN0ZW0sIGJ5IHNlbGVjdGluZyAiQWJvdXQKU1BNIiBm cm9tIHRoZSBzcGxhc2ggc2NyZWVuLiBUaGlzIGluaXRpYWxseSBkaXNwbGF5cyB0aGUgInNucG0u bWFuIgp0b3BpYywgZGV0YWlsaW5nIHRoZSB0b29sYm94LiBQcmVzcyB0aGUgIk1lbnUiIGJ1dHRv biB0byBkaXNwbGF5IGEKcmVwcmVzZW50YXRpb24gb2YgdGhlIFNQTSBtZW51IHdpbmRvdywgd2l0 aCBidXR0b25zIGxpbmtlZCB0bwphcHByb3ByaWF0ZSBoZWxwIHBhZ2VzLgoKQmVmb3JlIGF0dGVt cHRpbmcgdG8gYW5hbHl6ZSBkYXRhIHVzaW5nIFNuUE0sIHdlIHJlY29tbWVuZCB5b3Ugc3BlbmQK c29tZSB0aW1lIHJlYWRpbmcuIEl0IGlzIGVzc2VudGlhbCB0byB1bmRlcnN0YW5kIHRoZSBjb25j ZXB0cyBvZgpTdGF0aXN0aWNhbCBub25QYXJhbWV0cmljIE1hcHBpbmcgaW4gb3JkZXIgdG8gZWZm ZWN0aXZlbHkgdXNlIHRoZQpzb2Z0d2FyZSBhcyBhIHJlc2VhcmNoIHRvb2wuIFlvdSBzaG91bGQg YmVnaW4gd2l0aCB0aGUgU25QTXdlYiBwYWdlcywKd2hpY2ggZ2l2ZSBhIHRoZW9yZXRpY2FsIHBy aW1lciwgYW4gb3ZlcnZpZXcgb2YgdGhlIHRvb2xib3gsIGEgbWFudWFsLAphbmQgYSB3b3JrZWQg ZXhhbXBsZS4gWW91IGFyZSBhbHNvIHVyZ2VkIHRvIHJlYWQgdGhlIGFwcHJvcHJpYXRlIGxpdGVy YXR1cmUuCgpfX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX18KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgUmVzb3VyY2VzCgpUaGUgU25QTXdlYiBzaXRl IGlzIHRoZSBjZW50cmFsIHJlcG9zaXRvcnkgZm9yIGFsbCBTblBNIHJlc291cmNlczoKICAgICAg ICAgICAgICAgICBodHRwOi8vd3d3LmZpbC5pb24udWNsLmFjLnVrL3NwbS9zbnBtCkludHJvZHVj dG9yeSBtYXRlcmlhbCwgaW5zdGFsbGF0aW9uIGRldGFpbHMsIGRvY3VtZW50YXRpb24sIGV4YW1w bGVzCmFuZCBwYXRjaGVzIGFyZSBwdWJsaXNoZWQgb24gdGhlIHNpdGUuCgpTblBNd2ViIGlzIHBh cnQgb2YgU1BNd2ViLCB0aGUgY2VudHJhbCByZXBvc2l0b3J5IGZvciBhbGwgU1BNIHJlc291cmNl czoKICAgICAgICAgICAgICAgICBodHRwOi8vd3d3LmZpbC5pb24udWNsLmFjLnVrL3NwbQoKVGhl cmUgaXMgYW4gU1BNIGVNYWlsIGRpc2N1c3Npb24gbGlzdCwgaG9zdGVkIGF0IDxzcG1AbWFpbGJh c2UuYWMudWs+LgpUaGUgbGlzdCBpcyBtb25pdG9yZWQgYnkgdGhlIGF1dGhvcnMsIGFuZCBkaXNj dXNzZXMgdGhlb3JldGljYWwsCm1ldGhvZG9sb2dpY2FsIGFuZCBwcmFjdGljYWwgaXNzdWVzIG9m IFN0YXRpc3RpY2FsIFBhcmFtZXRyaWMgTWFwcGluZywKU1BNIGFuZCBTblBNLiBTZWUgdGhlIFNQ TXdlYiBzaXRlIGZvciBkZXRhaWxzIG9uIHN1YnNjcmliaW5nOgoKICAgICAgICAgICAgICAgICBo dHRwOi8vd3d3LmZpbC5pb24udWNsLmFjLnVrL3NwbS9oZWxwCgpQbGVhc2UgcmVwb3J0IGJ1Z3Mg dG8gdGhlIGF1dGhvcnMgYXQgPHNucG0tYXV0aG9yc0BmaWwuaW9uLnVjbC5hYy51az4uClBlY3Vs aWFyaXRpZXMgbWF5IGFjdHVhbGx5IGJlIGZlYXR1cmVzLCBhbmQgc2hvdWxkIGJlIHJhaXNlZCBv biB0aGUgU1BNCmVNYWlsIGRpc2N1c3Npb24gbGlzdCwgPHNwbUBtYWlsYmFzZS5hYy51az4uCgpf X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX18KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICBEaXNjbGFpbWVyCgpTblBNIChiZWluZyB0aGUgY29sbGVjdGlv biBvZiBmaWxlcyBnaXZlbiBpbiB0aGUgbWFuaWZlc3QgaW4gdGhlCkNvbnRlbnRzLm0gZmlsZSkg aXMgZnJlZSBidXQgY29weXJpZ2h0ZWQgc29mdHdhcmUsIChjKSAyMDA5IFRob21hcwpOaWNob2xz ICYgQW5kcmV3IEhvbG1lcy4gIApTblBNIGlzIGRpc3RyaWJ1dGVkIHVuZGVyIHRoZSB0ZXJtcyBv ZiB0aGUgR05VIEdlbmVyYWwKUHVibGljIExpY2VuY2UgYXMgcHVibGlzaGVkIGJ5IHRoZSBGcmVl IFNvZnR3YXJlIEZvdW5kYXRpb24gKGVpdGhlcgp2ZXJzaW9uIDIsIGFzIGdpdmVuIGluIGZpbGUg c3BtX0xJQ0VOQ0UubWFuLCBvciBhdCB5b3VyIG9wdGlvbiwgYW55CmxhdGVyIHZlcnNpb24pLiBG dXJ0aGVyIGRldGFpbHMgb24gImNvcHlsZWZ0IiBjYW4gYmUgZm91bmQgYXQKaHR0cDovL3d3dy5n bnUub3JnL2NvcHlsZWZ0Ly4gSW4gcGFydGljdWxhciwgU25QTSBpcyBzdXBwbGllZCBhcyBpcy4K Tm8gZm9ybWFsIHN1cHBvcnQgb3IgbWFpbnRlbmFuY2UgaXMgcHJvdmlkZWQgb3IgaW1wbGllZC4K Cl9fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fXwpTblBNIGlzIGRldmVsb3BlZCBieSAKICAgQW5kcmV3IEhvbG1lcwog ICBUaG9tYXMgTmljaG9scywgRGVwYXJ0bWVudCBvZiBTdGF0aXN0aWNzICYgV2Fyd2ljayBNYW51 ZmFjdHVyaW5nCiAgIEdyb3VwLCBVbml2ZXJpc3R5IG9mIFdhcndpY2sKCiUgJElkICQKCgoKCgoK Cgo --_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_-- ========================================================================Date: Fri, 18 Feb 2011 00:10:48 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: sensitivity of DARTEL Comments: To: Saemann Philipp <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> > in a recent paper by Pereira et al. on Resgistration accuracy for VBM > studies, NeuroImage 2010;49:2205-15 DARTEL as implemented > in SPM5 is presented as very insensitive to focal GM changes - in their > work, they used a small Alzheimer Disease sample to > to (amongmany other analyses) check the sensitivity to detect hippocampal > atrophy. > > However, sensitivity and regional specificity were completely regained when > three preprocessing steps were performed: > 1. skull stripping, 2. bias correction, and 3., "fine tuned BET". The limited set of tissue probability maps mean that SPM5 segmentation doesn't handle separating bone from CSF very well for some data, so skull stripping could be of some benefit. This is usually of less importance for SPM8 though, although there are still occasional scans that cause problems if there are strange artifacts in the background (from strange preprocessing done to the data) or if the subject is very heavily built (with lots of tissue outside the skull). The optimal amount of bias correction will depend on the data used. If the scans contain a lot of artifact, then lots of bias correction is needed. If there is no bias artifact, then the best results should result when no bias correction is done. If the data are heavily corrupted, the default amount of correction may not be great enough. In these cases, it is relatively easy to decrease the amount of regularisation used within the segmentation algorithm, rather than bias correcting the data first. > >> Has anybody made similar observations on insensitivity of DARTEL? >> Is the SPM Bias correction (default parameters) really so non-optimal? >> Is it the order of steps (first skull stripping, then bias correction) > the crucial difference? >> Is DARTEL in SPM8 in combination with the "New Segment" function more > sensitive? I'd be slightly curious about feedback too, although I no longer care that much about the accuracy of results from SPM5, as it is now six years out of date. > > I think for VBM where DARTEL has (my perception) repeatedly been > recommended as excellent/best registration tool, > it would be very useful if also the preprocessing/optimizations can be > performed within SPM. - It is somewhat > strange that extra preprocessing steps seem necessary outside SPM. All I can say is that the evaluations done for the paper by Klein et al show that Dartel and a number of other approaches achieved better alignment (as judged via manual segmentations) than the other spatial normalisation procedures available within SPM. More accurate alignment should translate into more accurate interpetation of results in terms of localising volumetric differences. This may not necessarily translate into smaller p values, as systematic mis-registration could potentially cause significant differences to occur. Best regards, -John ========================================================================Date: Thu, 17 Feb 2011 17:05:35 -0800 Reply-To: Siddharth Srivastava <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Siddharth Srivastava <[log in to unmask]> Subject: Re: help with spm_snbasis code Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary¼aec53f944f8ec7fa049c841ce9 Message-ID: [log in to unmask]> --bcaec53f944f8ec7fa049c841ce9 Content-Type: text/plain; charset=ISO-8859-1 Hi John, Thanks for the reply. regarding my other question, what does the multiplication by 4 do? I am guessing that a constant term will also be modeled (3 + 1 paramater sets for this, constant term = 1), can you please verify if I am thinking correctly about this initialization strategy? thanks again, sid. On Thu, Feb 17, 2011 at 12:08 PM, John Ashburner <[log in to unmask]>wrote: > There are additional parameters that model linear intensity gradients > in the x, y and z directions. The parameters would essentially be > coefficients for a linear combination if > > template1 template1.*x template1.*y template1.*z template2 > template2.*x template2.*y template2.*z ... > > Best regards, > -John > > On 17 February 2011 19:28, Siddharth Srivastava <[log in to unmask]> wrote: > > Hi everyone, > > > > I have been reading source for spm_snbasis and spm_brainwarp to get a > better > > understanding of the > > underlying math, and I came across these lines in spm_snbasis (spm99 > source, > > line no 82-85): > > > > s1 = 3*prod(k); > > s2 = s1 + prod(size(VG))*4; > > T = zeros(s2,1); > > T(s1+(1:4:prod(size(VG))*4)) = 1; > > > > I wanted to know why T is initialized the way it is by the last line of > the > > source snippet? Also, what does > > the multiplication by 4 imply? > > > > thanks, > > sid. > > > > > > > --bcaec53f944f8ec7fa049c841ce9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi John,
Thanks for the reply. regarding my other question, what does the
multiplication by 4 do? I am guessing that a constant term will also
be modeled (3 + 1 paramater sets for this, constant term = 1), can you
please verify if I am thinking correctly about this initialization strategy?
thanks again,
sid.

On Thu, Feb 17, 2011 at 12:08 PM, John Ashburner <[log in to unmask]> wrote:

There are additional parameters that model linear intensity gradients
in the x, y and z directions. The parameters would essentially be
coefficients for a linear combination if

template1 template1.*x template1.*y template1.*z template2
template2.*x template2.*y template2.*z ...

Best regards,
-John

On 17 February 2011 19:28, Siddharth Srivastava <[log in to unmask]> wrote:
> Hi everyone,
>
> I have been reading source for spm_snbasis and spm_brainwarp to get a better
> understanding of the
> underlying math, and I came across these lines in spm_snbasis (spm99 source,
> line no 82-85):
>
> s1 = 3*prod(k);
> s2 = s1 + prod(size(VG))*4;
> T = zeros(s2,1);
> T(s1+(1:4:prod(size(VG))*4)) = 1;
>
> I wanted to know why T is initialized the way it is by the last line of the
> source snippet? Also, what does
> the multiplication by 4 imply?
>
> thanks,
> sid.
>
>
>

--bcaec53f944f8ec7fa049c841ce9-- ========================================================================Date: Fri, 18 Feb 2011 02:45:09 +0100 Reply-To: michel grothe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: michel grothe <[log in to unmask]> Subject: Re: sensitivity of DARTEL Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_0d0961c3-b624-4bbe-adc0-9979ff4c2b23_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_0d0961c3-b624-4bbe-adc0-9979ff4c2b23_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Philipp and others, with my own data, or more exactly with the OASIS database (not skull-stripped), I´ve made similar observations, i.e. SPM8-new-segmentation (or "normal" SPM5-unified-segmentation)+DARTEL VBM failed to detect significant hippocampus atrophy for most parts of the hippocampus in AD and MCI. While the effects seemed to be consistent with the literature for most parts of the cortex (atrophy in PCC, ACC, temporoparietal areas and relative sparing of somoatosensory areas and the cerebellum in AD), effects in the MTL were restricted to the amygdala and anterior parts of the hippocampus. However, VBM procedures rely on a mixture of accurate segmentation AND normalisation to a common space and I think the error here may rather lie in inaccurate segmentaion than in the DARTEL normalisation. Repeating the analysis with "Gaser"-segmented images (VBM8) and exactly the same DARTEL-settings revealed "perfect" (i.e. expected or more consistent with the literature) results for the MTL and the rest of the cortex. A possible explanation for this observation is that registration accuracy (using tissue-priors based on a normal population) and hence segmentation accuracy of the unified approaches in spm5/8 decreases with increasing atrophy. Although the detailed tissue information in "New-segment" normally leads to convincingly improved segmentation results and the high-dimensional warping in DARTEL is capable to model large deformations, it might still be necessary to adjust the tissue priors when dealing with groups that differ strongly from the anatomy of the normal population. The tissue prior-free approach of the VBM8-toolbox produced much more "realistic" results when comparing a large cohort of healthy elderly and AD patients (the OASIS database). Would be interesting to hear about the experiences of the rest of the SPM/VBM-community. Best regards, Michel > Date: Fri, 18 Feb 2011 00:10:35 +0100 > From: [log in to unmask] > Subject: [SPM] sensitivity of DARTEL > To: [log in to unmask] > > Dear SPMers, > > in a recent paper by Pereira et al. on Resgistration accuracy for VBM > studies, NeuroImage 2010;49:2205-15 DARTEL as implemented > in SPM5 is presented as very insensitive to focal GM changes - in their > work, they used a small Alzheimer Disease sample to > to (amongmany other analyses) check the sensitivity to detect hippocampal > atrophy. > > However, sensitivity and regional specificity were completely regained when > three preprocessing steps were performed: > 1. skull stripping, 2. bias correction, and 3., "fine tuned BET". > > > Has anybody made similar observations on insensitivity of DARTEL? > > Is the SPM Bias correction (default parameters) really so non-optimal? > > Is it the order of steps (first skull stripping, then bias correction) > the crucial difference? > > Is DARTEL in SPM8 in combination with the "New Segment" function more > sensitive? > > I think for VBM where DARTEL has (my perception) repeatedly been > recommended as excellent/best registration tool, > it would be very useful if also the preprocessing/optimizations can be > performed within SPM. - It is somewhat > strange that extra preprocessing steps seem necessary outside SPM. > > I am very interested in comments/opinions here, > thanks, > Philipp > Max Planck Institute of Psychiatry > NMR Research Group > Kraepelinstr. 2-10 > 80804 Munich > Mail: [log in to unmask] > Phone: 0049-89-30622-413 --_0d0961c3-b624-4bbe-adc0-9979ff4c2b23_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Philipp and others,

with my own data, or more exactly with the OASIS database (not skull-stripped), I´ve made similar observations, i.e. SPM8-new-segmentation (or "normal" SPM5-unified-segmentation)+DARTEL VBM failed to detect significant hippocampus atrophy for most parts of the hippocampus in AD and MCI. While the effects seemed to be consistent with the literature for most parts of the cortex (atrophy in PCC, ACC, temporoparietal areas and relative sparing of somoatosensory areas and the cerebellum in AD), effects in the MTL were restricted to the amygdala and anterior parts of the hippocampus.
However, VBM procedures rely on a mixture of accurate segmentation AND normalisation to a common space and I think the error here may rather lie in inaccurate segmentaion than in the DARTEL normalisation. Repeating the analysis with "Gaser"-segmented images (VBM8) and exactly the same DARTEL-settings revealed "perfect" (i.e. expected or more consistent with the literature) results for the MTL and the rest of the cortex.
A possible explanation for this observation is that registration accuracy (using tissue-priors based on a normal population) and hence segmentation accuracy of the unified approaches in spm5/8 decreases with increasing atrophy. Although the detailed tissue information in "New-segment" normally leads to convincingly improved segmentation results and the high-dimensional warping in DARTEL is capable to model large deformations, it might still be necessary to adjust the tissue priors when dealing with groups that differ strongly from the anatomy of the normal population. The tissue prior-free approach of the VBM8-toolbox produced much more "realistic" results when comparing a large cohort of healthy elderly and AD patients (the OASIS database).

Would be interesting to hear about the experiences of the rest of the SPM/VBM-community.

Best regards,
Michel

> Date: Fri, 18 Feb 2011 00:10:35 +0100
> From: [log in to unmask]
> Subject: [SPM] sensitivity of DARTEL
> To: [log in to unmask]
>
> Dear SPMers,
>
> in a recent paper by Pereira et al. on Resgistration accuracy for VBM
> studies, NeuroImage 2010;49:2205-15 DARTEL as implemented
> in SPM5 is presented as very insensitive to focal GM changes - in their
> work, they used a small Alzheimer Disease sample to
> to (amongmany other analyses) check the sensitivity to detect hippocampal
> atrophy.
>
> However, sensitivity and regional specificity were completely regained when
> three preprocessing steps were performed:
> 1. skull stripping, 2. bias correction, and 3., "fine tuned BET".
>
> > Has anybody made similar observations on insensitivity of DARTEL?
> > Is the SPM Bias correction (default parameters) really so non-optimal?
> > Is it the order of steps (first skull stripping, then bias correction)
> the crucial difference?
> > Is DARTEL in SPM8 in combination with the "New Segment" function more
> sensitive?
>
> I think for VBM where DARTEL has (my perception) repeatedly been
> recommended as excellent/best registration tool,
> it would be very useful if also the preprocessing/optimizations can be
> performed within SPM. - It is somewhat
> strange that extra preprocessing steps seem necessary outside SPM.
>
> I am very interested in comments/opinions here,
> thanks,
> Philipp
> Max Planck Institute of Psychiatry
> NMR Research Group
> Kraepelinstr. 2-10
> 80804 Munich
> Mail: [log in to unmask]
> Phone: 0049-89-30622-413
--_0d0961c3-b624-4bbe-adc0-9979ff4c2b23_-- ========================================================================Date: Fri, 18 Feb 2011 10:59:22 +0900 Reply-To: HwangArum <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: HwangArum <[log in to unmask]> Subject: Asking "interaction analysis" in spm !! :D Comments: To: [log in to unmask], [log in to unmask], [log in to unmask] Content-Type: multipart/alternative; boundary="_d7c4f5ac-ffad-4f6f-88e1-ca978ece01e9_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_d7c4f5ac-ffad-4f6f-88e1-ca978ece01e9_ Content-Type: text/plain; charset="ks_c_5601-1987" Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bofur.jiscmail.ac.uk id p1I1xMRT025734 We conducted 2x2 within-subject design in fMRI study. Each subjects performed two different kinds of tasks A, B in two different contexts X, Y (e.g., X[A-B]-Y[A-B]). Is it allowed to make an ¡°interaction analysis¡± (e.g., [XA-XB]-[YA-YB]) with two-sample t-test in within-subject design (using spm)? For example, put [XA-XB] con image into one group and put [YA-YB] con image into another group. If yes, indepence option should be set as yes(default) or should be changed into no? Best regards, Arum Hwang --_d7c4f5ac-ffad-4f6f-88e1-ca978ece01e9_ Content-Type: text/html; charset="ks_c_5601-1987" Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bofur.jiscmail.ac.uk id p1I1xMRT025734

We conducted 2x2 within-subject design in fMRI study. Each subjects performed two different kinds of tasks A, B in two different contexts X, Y (e.g., X[A-B]-Y[A-B]).

Is it allowed to make an ¡°interaction analysis¡± (e.g., [XA-XB]-[YA-YB]) with two-sample t-test in within-subject design (using spm)?

For example, put [XA-XB] con image into one group and put [YA-YB] con image into another group. If yes, indepence option should be set as yes(default) or should be changed into no?

Best regards, Arum Hwang

--_d7c4f5ac-ffad-4f6f-88e1-ca978ece01e9_-- ========================================================================Date: Thu, 17 Feb 2011 21:25:03 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Asking "interaction analysis" in spm !! :D Comments: To: HwangArum <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> You need to use a paired t-test. On Thursday, February 17, 2011, HwangArum <[log in to unmask]> wrote: > > > > > > We conducted 2x2 within-subject design in fMRI study. Each subjects performed two different kinds of tasks A, B in two different contexts X, Y (e.g., X[A-B]-Y[A-B]). > > Is it allowed to make an “interaction analysis” (e.g., [XA-XB]-[YA-YB]) with two-sample t-test in within-subject design (using spm)? > > For example, put [XA-XB] con image into one group and put [YA-YB] con image into another group. If yes, indepence option should be set as yes(default) or should be changed into no? > > > Best regards, Arum Hwang > -- Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Fri, 18 Feb 2011 02:18:46 +0000 Reply-To: chihiro <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: chihiro <[log in to unmask]> Subject: HELP!with VBM (estimation error) Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> During my data processing, I encountered some problems. I used VBM to analyze. First, I used the DARTEL tool for VBM following the DARTEL manual and got the smwc images. In the statistical analysis, I tried to find the differences between a patient and 20 age matched healthy controls with two-sample t-test (one group was a patient, and the other group was the healthy individuals). I could do the design specification but when I did model estimation, the following error was occured. Running job #1 ------------------------------------------------------------------------ Running 'Factorial design specification' Mapping files : ...done Design configuration : ...done Saving SPM configuration : ...SPM.mat saved Design reporting : ...done Done Done 'Factorial design specification' Done ------------------------------------------------------------------------ Running job #2 ------------------------------------------------------------------------ Running 'Model estimation' SPM8: spm_spm (v3468) 10:39:13 - 18/02/2011 ======================================================================== Initialising parameters : ...done Plane 121/121, block 1/1 : ...done Temporal non-sphericity (over voxels) : ...ReML estimation ReML Iteration 1 : ...0.000000e+00 [+4.25] SPM8: spm_spm (v3468) 10:39:28 - 18/02/2011 ======================================================================== Initialising parameters : ...computingFailed 'Model estimation' Index exceeds matrix dimensions. In file "/Applications/MATLAB_R2009b.app/toolbox/matlab/sparfun/spdiags.m" (???), function "spdiags" at line 114. In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 427. In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 878. In file "/Applications/spm8/config/spm_run_fmri_est.m" (v3327), function "spm_run_fmri_est" at line 53. The following modules did not run: Failed: Model estimation I uses the MAC OS X 10.6.6, the MATLAB 7.9.0.529 (R2009b), and the SPM 8. I appreciate if you can help me! ========================================================================Date: Thu, 17 Feb 2011 21:53:22 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: HELP!with VBM (estimation error) Comments: To: chihiro <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Set the variance to equal. On Thursday, February 17, 2011, chihiro <[log in to unmask]> wrote: > During my data processing, I encountered some problems. > > I used VBM to analyze. > First, I used the DARTEL tool for VBM following the DARTEL manual and got the smwc images. > > In the statistical analysis, I tried to find the differences > between a patient and 20 age matched healthy controls with two-sample t-test (one group was a patient, and > the other group was the healthy individuals). > > I could do the design specification but when I did model estimation, the following error was occured. > > Running job #1 > ------------------------------------------------------------------------ > Running 'Factorial design specification' > Mapping files : ...done > Design configuration : ...done > Saving SPM configuration : ...SPM.mat saved > Design reporting : ...done > Done > Done 'Factorial design specification' > Done > > > > ------------------------------------------------------------------------ > Running job #2 > ------------------------------------------------------------------------ > Running 'Model estimation' > > SPM8: spm_spm (v3468) 10:39:13 - 18/02/2011 > ======================================================================== > Initialising parameters : ...done > Plane 121/121, block 1/1 : ...done > Temporal non-sphericity (over voxels) : ...ReML estimation > ReML Iteration 1 : ...0.000000e+00 [+4.25] > > SPM8: spm_spm (v3468) 10:39:28 - 18/02/2011 > ======================================================================== > Initialising parameters : ...computingFailed 'Model estimation' > Index exceeds matrix dimensions. > In file "/Applications/MATLAB_R2009b.app/toolbox/matlab/sparfun/spdiags.m" (???), function "spdiags" at line 114. > In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 427. > In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 878. > In file "/Applications/spm8/config/spm_run_fmri_est.m" (v3327), function "spm_run_fmri_est" at line 53. > > The following modules did not run: > Failed: Model estimation > > > > I uses the MAC OS X 10.6.6, > the MATLAB 7.9.0.529 (R2009b), > and the SPM 8. > > I appreciate if you can help me! > -- Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Thu, 17 Feb 2011 22:10:06 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: vbm8 for single subject MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e6d7843fdfb662049c85d9f5 Content-Type: text/plain; charset=ISO-8859-1 Hello all, Is possible use the vbm8 toolbox for compare a single subject against the template Thanks in advance --0016e6d7843fdfb662049c85d9f5 Content-Type: text/html; charset=ISO-8859-1 Hello all,

Is possible use the vbm8 toolbox for compare a single subject against the template

Thanks in advance
--0016e6d7843fdfb662049c85d9f5-- ========================================================================Date: Fri, 18 Feb 2011 03:32:16 +0000 Reply-To: Nero Evero <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nero Evero <[log in to unmask]> Subject: MATLAB Out of Memory error Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello All, We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message: Initialising parameters : ...done Output images : ...initialised Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options. Error in ==> spm_spm at 715 CY = CY + Y*Y'; Error in ==> spm_getSPM at 233 SPM = spm_spm(SPM); Error in ==> spm_results_ui at 277 [SPM,xSPM] = spm_getSPM; ??? Error while evaluating uicontrol Callback I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance. Nero ========================================================================Date: Thu, 17 Feb 2011 23:46:29 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: vbm8 for single subject Comments: To: John Fredy <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi John > Is possible use the vbm8 toolbox for compare a single subject against the template You can't get any sort of statistics out of comparing a single subject against a template because you have no variance. The best you could do is a simple subtraction but this woudn't be particularly informative I don't think. To look at a single participant you really need a sample with which to compare them—e.g., 1 patient vs. 20 age- and sex-matched controls. Jonathan -- Dr. Jonathan Peelle Department of Neurology University of Pennsylvania 3 West Gates 3400 Spruce Street Philadelphia, PA 19104 USA http://jonathanpeelle.net/ ========================================================================Date: Fri, 18 Feb 2011 08:44:01 +0100 Reply-To: Lucas Eggert <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lucas Eggert <[log in to unmask]> Subject: TIV as nuisance factor for unmodulated data in a VBM-analysis MIME-Version: 1.0 Content-Type: multipart/signed; protocol="application/pkcs7-signature"; micalg=sha1; boundary="------------ms040708000206040902000902" Message-ID: <[log in to unmask]> This is a cryptographically signed message in MIME format. --------------ms040708000206040902000902 Content-Type: multipart/alternative; boundary="------------020304020908000302080105" This is a multi-part message in MIME format. --------------020304020908000302080105 Content-Type: text/plain; charset=ISO-8859-15; format=flowed Content-Transfer-Encoding: quoted-printable Dear Dr. Gaser, Dear List, at the moment I am unsure about whether correcting for total brain volume (TIV) in a VBM analysis of unmodulated gray matter segments (density) does make sense or not. On Dr. Gaser's homepage --- I have the feeling --- there are two slightly opposite statements: statement (a) (please refer below) clearly states that TIV should not be used as a nuisacne factor with unmodulated data. On the other hand, I read statement (b) (please see below) as correcting unmodulated data for TIV gives me relative density after correcting for TIV, which seems to make more sense to me. Which procedure is the more valid one, or for which kind of question would I use which procedure respectively? Thanks for any help in advance. Best regards, Lucas Eggert ****************** STATEMENT (a): http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/calculate-raw-volumes/ "The use of raw volumes as nuisance or globals is only recommended for modulated data. These data are corrected for size changes due to spatial normalization and are thought to be in raw (un-normalized) space. In contrast, un-modulated data are yet corrected for differences in size due to spatial normalization to a reference brain and there is no need to correct for these differences again." STATEMENT (b): http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/ Interpretation If we follow the commonly used terms "volume" for modulated data and "density" (or concentration) for unmodulated data and concentrate on GM there are many possible ways to correct or not correct for different brain size: *No modulation:* *Correction * *Interpretation* nothing relative density globals "localised" relative density after correcting for total GM or TIV (multiplicative effects) AnCova "localised" relative density that can not be explained by total GM or TIV (additive effects) -- Lucas Eggert, M.Sc. Institute of Cognitive Science University of Osnabrueck Albrechtstrasse 28 D-49076 Osnabrueck Germany Phone: +49-541-969-44-28 Website: http://www.cogsci.uni-osnabrueck.de/~leggert/ --------------020304020908000302080105 Content-Type: text/html; charset=ISO-8859-15 Content-Transfer-Encoding: quoted-printable Dear Dr. Gaser,
Dear List,

at the moment I am unsure about whether correcting for total brain volume (TIV) in
a VBM analysis of unmodulated gray matter segments (density) does make sense or not.

On Dr. Gaser's homepage --- I have the feeling --- there are two slightly opposite statements:
statement (a) (please refer below) clearly states that TIV should not be used as a nuisacne factor with unmodulated data.

On the other hand, I read statement (b) (please see below) as correcting unmodulated data for TIV gives me relative
density after correcting for TIV, which seems to make more sense to me.

Which procedure is the more valid one, or for which kind of question would I use which procedure respectively?

Thanks for any help in advance.

Best regards,
Lucas Eggert

******************

STATEMENT (a):
http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/calculate-raw-volumes/

"The use of raw volumes as nuisance or globals is only recommended for modulated data.
These data are corrected for size changes due to spatial normalization and are thought
to be in raw (un-normalized) space. In contrast, un-modulated data are yet corrected
for differences in size due to spatial normalization to a reference brain and there
is no need to correct for these differences again."

STATEMENT (b):
http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/

Interpretation

If we follow the commonly used terms “volume” for modulated data and “density” (or concentration)
for unmodulated data and concentrate on GM there are many possible ways to correct or not correct for different brain size:

No modulation:

Correction	Interpretation
nothing	relative density
globals	“localised” relative density after correcting for total GM or TIV (multiplicative effects)
AnCova	“localised” relative density that can not be explained by total GM or TIV (additive effects)

-- 
Lucas Eggert, M.Sc.
Institute of Cognitive Science
University of Osnabrueck
Albrechtstrasse 28 
D-49076 Osnabrueck
Germany

Phone:	   +49-541-969-44-28
Website:   http://www.cogsci.uni-osnabrueck.de/~leggert/

--------------020304020908000302080105-- --------------ms040708000206040902000902 Content-Type: application/pkcs7-signature; name="smime.p7s" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="smime.p7s" Content-Description: S/MIME Cryptographic Signature MIAGCSqGSIb3DQEHAqCAMIACAQExCzAJBgUrDgMCGgUAMIAGCSqGSIb3DQEHAQAAoIIUEzCC BCEwggMJoAMCAQICAgDHMA0GCSqGSIb3DQEBBQUAMHExCzAJBgNVBAYTAkRFMRwwGgYDVQQK ExNEZXV0c2NoZSBUZWxla29tIEFHMR8wHQYDVQQLExZULVRlbGVTZWMgVHJ1c3QgQ2VudGVy MSMwIQYDVQQDExpEZXV0c2NoZSBUZWxla29tIFJvb3QgQ0EgMjAeFw0wNjEyMTkxMDI5MDBa Fw0xOTA2MzAyMzU5MDBaMFoxCzAJBgNVBAYTAkRFMRMwEQYDVQQKEwpERk4tVmVyZWluMRAw DgYDVQQLEwdERk4tUEtJMSQwIgYDVQQDExtERk4tVmVyZWluIFBDQSBHbG9iYWwgLSBHMDEw ggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDpm8NnhfkNrvWNVMOWUDU9YuluTO2U 1wBblSJ01CDrNI/W7MAxBAuZgeKmFNJSoCgjhIt0iQReW+DieMF4yxbLKDU5ey2QRdDtoAB6 fL9KDhsAw4bpXCsxEXsM84IkQ4wcOItqaACa7txPeKvSxhObdq3u3ibo7wGvdA/BCaL2a869 080UME/15eOkyGKbghoDJzANAmVgTe3RCSMqljVYJ9N2xnG2kB3E7f81hn1vM7PbD8URwoqD oZRdQWvY0hD1TP3KUazZve+Sg7va64sWVlZDz+HVEz2mHycwzUlU28kTNJpxdcVs6qcLmPkh nSevPqM5OUhqjK3JmfvDEvK9AgMBAAGjgdkwgdYwcAYDVR0fBGkwZzBloGOgYYZfaHR0cDov L3BraS50ZWxlc2VjLmRlL2NnaS1iaW4vc2VydmljZS9hZl9Eb3dubG9hZEFSTC5jcmw/LWNy bF9mb3JtYXQ9WF81MDkmLWlzc3Vlcj1EVF9ST09UX0NBXzIwHQYDVR0OBBYEFEm3xs/oPR9/ 6kR7Eyn38QpwPt5kMB8GA1UdIwQYMBaAFDHDeRu69VPXF+CJei0XbAqzK50zMA4GA1UdDwEB /wQEAwIBBjASBgNVHRMBAf8ECDAGAQH/AgECMA0GCSqGSIb3DQEBBQUAA4IBAQA74Vp3wEgX 3KkY7IGvWonwvSiSpspZGBJw7Cjy565/lizn8l0ZMfYTK3S9vYCyufdnyTmieTvhERHua3iR M347XyYndVNljjNj7s9zw7CSI0khUHUjoR8Y4pSFPT8z6XcgjaK95qGFKUD2P3MyWA0Ja6ba hWzAP7uNZmRWJE6uDT8yNQFb6YyC2XJZT7GGhfF0hVblw/hc843uR7NTBXDn5U2KaYMo4RMJ hp5eyOpYHgwf+aTUWgRo/Sg+iwK2WLX2oSw3VwBnqyNojWOl75lrXP1LVvarQIc01BGSbOyH xQoLBzNytG8MHVQs2FHHzL8w00Ny8TK/jM5JY6gA9/IcMIIFIjCCBAqgAwIBAgIECpckUjAN BgkqhkiG9w0BAQUFADBaMQswCQYDVQQGEwJERTETMBEGA1UEChMKREZOLVZlcmVpbjEQMA4G A1UECxMHREZOLVBLSTEkMCIGA1UEAxMbREZOLVZlcmVpbiBQQ0EgR2xvYmFsIC0gRzAxMB4X DTA3MDYxOTA5NDk1NVoXDTE5MDYxODAwMDAwMFowgZExCzAJBgNVBAYTAkRFMSAwHgYDVQQK ExdVbml2ZXJzaXRhZXQgT3NuYWJydWVjazEWMBQGA1UECxMNUmVjaGVuemVudHJ1bTEjMCEG A1UEAxMaVW5pLU9zbmFicnVlY2sgUlotQ0EgRy0wMDIxIzAhBgkqhkiG9w0BCQEWFGNhQHVu aS1vc25hYnJ1ZWNrLmRlMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEApLZxV6i2 14uWBb3c16AMckkujPKMXxl9Z4XsEIhrA47tG5YL9upeUrj+duDcrbEQvphvSXJFveVkLK1J MANHJuu/Wa32Bc8IRljYBqhaKMTGRO1q5L6jkMBDwBwenozhlGAaqG+8Cy+qcFoUaoWBRCH2 ++t5FtXyS1/1GKhWu7yQxCblFul7VXvnLKyaNlOaTalREXb9pQk2N31+rrOgwkbogxc2z30g QAJXeJ2Ra0SlReqINMmcDd4lfluXjBpFmiJa4xHhQIVJpW2vF8dbqmeKqxIoYziBh78NGaqM SC8IbDPbCM3qaDaGUWKccgb/SKZvrNTLU+jcW66yGi1YPwIDAQABo4IBtjCCAbIwEgYDVR0T AQH/BAgwBgEB/wIBATALBgNVHQ8EBAMCAQYwHQYDVR0OBBYEFKof2HcVbpnkX5DW7GMmThnq ZxRQMB8GA1UdIwQYMBaAFEm3xs/oPR9/6kR7Eyn38QpwPt5kMB8GA1UdEQQYMBaBFGNhQHVu aS1vc25hYnJ1ZWNrLmRlMIGIBgNVHR8EgYAwfjA9oDugOYY3aHR0cDovL2NkcDEucGNhLmRm bi5kZS9nbG9iYWwtcm9vdC1jYS9wdWIvY3JsL2NhY3JsLmNybDA9oDugOYY3aHR0cDovL2Nk cDIucGNhLmRmbi5kZS9nbG9iYWwtcm9vdC1jYS9wdWIvY3JsL2NhY3JsLmNybDCBogYIKwYB BQUHAQEEgZUwgZIwRwYIKwYBBQUHMAKGO2h0dHA6Ly9jZHAxLnBjYS5kZm4uZGUvZ2xvYmFs LXJvb3QtY2EvcHViL2NhY2VydC9jYWNlcnQuY3J0MEcGCCsGAQUFBzAChjtodHRwOi8vY2Rw Mi5wY2EuZGZuLmRlL2dsb2JhbC1yb290LWNhL3B1Yi9jYWNlcnQvY2FjZXJ0LmNydDANBgkq hkiG9w0BAQUFAAOCAQEAWw23L1uKK+8XtvjZ4GtyLYqHC+QHFfrt4QUVYdTiXznlTLLiytqP H/Ur13RSbhJuUV/Xgsyko7yc+wV2xpXRwGE4P7a/nCvamPM40/2B070/AGMO0xXA5GGYG5/b 7I5Al97kQH3l7vCYrYhJxrLdDFYjmWMneHerxffjLZLQrdfzAMMATGE3oLiqAz4RH6oknUzj 5lBcIlBdWhOa85u72oHX/8ohGCHKcUJ6b52VIwpYohLQC05mFvEml7wox3HZMr3HfhfZR4CT aCL1quu+m24ov7JsU8wEoXeIbQiAxz0NB4mJSyBTa+mbVqTXz/fFw0Ld7xWRhPTM72IM56/U 2zCCBWAwggRIoAMCAQICBBFfm+wwDQYJKoZIhvcNAQEFBQAwgZExCzAJBgNVBAYTAkRFMSAw HgYDVQQKExdVbml2ZXJzaXRhZXQgT3NuYWJydWVjazEWMBQGA1UECxMNUmVjaGVuemVudHJ1 bTEjMCEGA1UEAxMaVW5pLU9zbmFicnVlY2sgUlotQ0EgRy0wMDIxIzAhBgkqhkiG9w0BCQEW FGNhQHVuaS1vc25hYnJ1ZWNrLmRlMB4XDTExMDEyNjEzMTUwOVoXDTE0MDEyNTEzMTUwOVow VDELMAkGA1UEBhMCREUxIDAeBgNVBAoTF1VuaXZlcnNpdGFldCBPc25hYnJ1ZWNrMQwwCgYD VQQLEwNJS1cxFTATBgNVBAMTDEx1Y2FzIEVnZ2VydDCCASIwDQYJKoZIhvcNAQEBBQADggEP ADCCAQoCggEBAMvXUWB4xxi66T9J5HqkG9ycg9ri+Bm/F2FlVdTfqnEWcTxmRBctRms39ZuY b5Q1Uyx/bFCyoZTb6D/wUmYUEFiLo+Aess2/qPcfRYGVIJWv2zYNiWvlVQD0IG5+SbfQ+/vt D9w96d2WSiOQGAwe5b5RgM+FHZ1xDFr4qKmphHGT7g2OV2ghxXKw0Fyl1j518XB1gYFeA17c 1VjOmI9E5KyXeeOqHwOg0U5XO3YmQ2Et+cJDViFRx6qOm+c96IpakCGoZ9aMZGPlxmmFUM0i Bqm+gYCGO4S3qEY9L9hyNWMTqwNl7Y/AJxedDdXG33RgPeEre8EsSAyPzlpggVS0ONsCAwEA AaOCAfowggH2MAkGA1UdEwQCMAAwCwYDVR0PBAQDAgXgMCkGA1UdJQQiMCAGCCsGAQUFBwMC BggrBgEFBQcDBAYKKwYBBAGCNxQCAjAdBgNVHQ4EFgQUT2sRQCxHT4F6BgX351tC2QxzY68w HwYDVR0jBBgwFoAUqh/YdxVumeRfkNbsYyZOGepnFFAwNAYDVR0RBC0wK4EObGVnZ2VydEB1 b3MuZGWBGWxlZ2dlcnRAdW5pLW9zbmFicnVlY2suZGUwgY8GA1UdHwSBhzCBhDBAoD6gPIY6 aHR0cDovL2NkcDEucGNhLmRmbi5kZS91bmktb3NuYWJydWVjay1jYS9wdWIvY3JsL2NhY3Js LmNybDBAoD6gPIY6aHR0cDovL2NkcDIucGNhLmRmbi5kZS91bmktb3NuYWJydWVjay1jYS9w dWIvY3JsL2NhY3JsLmNybDCBqAYIKwYBBQUHAQEEgZswgZgwSgYIKwYBBQUHMAKGPmh0dHA6 Ly9jZHAxLnBjYS5kZm4uZGUvdW5pLW9zbmFicnVlY2stY2EvcHViL2NhY2VydC9jYWNlcnQu Y3J0MEoGCCsGAQUFBzAChj5odHRwOi8vY2RwMi5wY2EuZGZuLmRlL3VuaS1vc25hYnJ1ZWNr LWNhL3B1Yi9jYWNlcnQvY2FjZXJ0LmNydDANBgkqhkiG9w0BAQUFAAOCAQEAowJHsBfWFTNP KdWCi3AyM5OqEgHAqktwyEgaqFSMFr0HrsEia0St9YQDPQrR/QknpqtYnpIAXEqf3QTERaOU Jp217gZ8lCaUIEY7nTPefCarwpjcMfF5xBt/qFX66S6ctanypQOqf23imuRwVUwvi56rhbyo sfzCt7iYVl4FonTAd+DKR1PLl672bLHi2ngBGuhauKjXYCuiuJsEeeyJaTvATmrcwbLARWuj Mvw/QvMGQsRbhk2xrvUomI0BbO71JfkNr3N62rxsQKTnR8rEbQ8gE6FX2+OWO3cvMTfC8elz MNnNUrHI4qL0vfiNKmDY/45rMXIzgSB1EjlYJoMaTDCCBWAwggRIoAMCAQICBBFfm+wwDQYJ KoZIhvcNAQEFBQAwgZExCzAJBgNVBAYTAkRFMSAwHgYDVQQKExdVbml2ZXJzaXRhZXQgT3Nu YWJydWVjazEWMBQGA1UECxMNUmVjaGVuemVudHJ1bTEjMCEGA1UEAxMaVW5pLU9zbmFicnVl Y2sgUlotQ0EgRy0wMDIxIzAhBgkqhkiG9w0BCQEWFGNhQHVuaS1vc25hYnJ1ZWNrLmRlMB4X DTExMDEyNjEzMTUwOVoXDTE0MDEyNTEzMTUwOVowVDELMAkGA1UEBhMCREUxIDAeBgNVBAoT F1VuaXZlcnNpdGFldCBPc25hYnJ1ZWNrMQwwCgYDVQQLEwNJS1cxFTATBgNVBAMTDEx1Y2Fz IEVnZ2VydDCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBAMvXUWB4xxi66T9J5Hqk G9ycg9ri+Bm/F2FlVdTfqnEWcTxmRBctRms39ZuYb5Q1Uyx/bFCyoZTb6D/wUmYUEFiLo+Ae ss2/qPcfRYGVIJWv2zYNiWvlVQD0IG5+SbfQ+/vtD9w96d2WSiOQGAwe5b5RgM+FHZ1xDFr4 qKmphHGT7g2OV2ghxXKw0Fyl1j518XB1gYFeA17c1VjOmI9E5KyXeeOqHwOg0U5XO3YmQ2Et +cJDViFRx6qOm+c96IpakCGoZ9aMZGPlxmmFUM0iBqm+gYCGO4S3qEY9L9hyNWMTqwNl7Y/A JxedDdXG33RgPeEre8EsSAyPzlpggVS0ONsCAwEAAaOCAfowggH2MAkGA1UdEwQCMAAwCwYD VR0PBAQDAgXgMCkGA1UdJQQiMCAGCCsGAQUFBwMCBggrBgEFBQcDBAYKKwYBBAGCNxQCAjAd BgNVHQ4EFgQUT2sRQCxHT4F6BgX351tC2QxzY68wHwYDVR0jBBgwFoAUqh/YdxVumeRfkNbs YyZOGepnFFAwNAYDVR0RBC0wK4EObGVnZ2VydEB1b3MuZGWBGWxlZ2dlcnRAdW5pLW9zbmFi cnVlY2suZGUwgY8GA1UdHwSBhzCBhDBAoD6gPIY6aHR0cDovL2NkcDEucGNhLmRmbi5kZS91 bmktb3NuYWJydWVjay1jYS9wdWIvY3JsL2NhY3JsLmNybDBAoD6gPIY6aHR0cDovL2NkcDIu cGNhLmRmbi5kZS91bmktb3NuYWJydWVjay1jYS9wdWIvY3JsL2NhY3JsLmNybDCBqAYIKwYB BQUHAQEEgZswgZgwSgYIKwYBBQUHMAKGPmh0dHA6Ly9jZHAxLnBjYS5kZm4uZGUvdW5pLW9z bmFicnVlY2stY2EvcHViL2NhY2VydC9jYWNlcnQuY3J0MEoGCCsGAQUFBzAChj5odHRwOi8v Y2RwMi5wY2EuZGZuLmRlL3VuaS1vc25hYnJ1ZWNrLWNhL3B1Yi9jYWNlcnQvY2FjZXJ0LmNy dDANBgkqhkiG9w0BAQUFAAOCAQEAowJHsBfWFTNPKdWCi3AyM5OqEgHAqktwyEgaqFSMFr0H rsEia0St9YQDPQrR/QknpqtYnpIAXEqf3QTERaOUJp217gZ8lCaUIEY7nTPefCarwpjcMfF5 xBt/qFX66S6ctanypQOqf23imuRwVUwvi56rhbyosfzCt7iYVl4FonTAd+DKR1PLl672bLHi 2ngBGuhauKjXYCuiuJsEeeyJaTvATmrcwbLARWujMvw/QvMGQsRbhk2xrvUomI0BbO71JfkN r3N62rxsQKTnR8rEbQ8gE6FX2+OWO3cvMTfC8elzMNnNUrHI4qL0vfiNKmDY/45rMXIzgSB1 EjlYJoMaTDGCA+IwggPeAgEBMIGaMIGRMQswCQYDVQQGEwJERTEgMB4GA1UEChMXVW5pdmVy c2l0YWV0IE9zbmFicnVlY2sxFjAUBgNVBAsTDVJlY2hlbnplbnRydW0xIzAhBgNVBAMTGlVu aS1Pc25hYnJ1ZWNrIFJaLUNBIEctMDAyMSMwIQYJKoZIhvcNAQkBFhRjYUB1bmktb3NuYWJy dWVjay5kZQIEEV+b7DAJBgUrDgMCGgUAoIICHDAYBgkqhkiG9w0BCQMxCwYJKoZIhvcNAQcB MBwGCSqGSIb3DQEJBTEPFw0xMTAyMTgwNzQ0MDFaMCMGCSqGSIb3DQEJBDEWBBR8bxTY+Xxd eGboO2rLdRNzUhLcqTBfBgkqhkiG9w0BCQ8xUjBQMAsGCWCGSAFlAwQBAjAKBggqhkiG9w0D BzAOBggqhkiG9w0DAgICAIAwDQYIKoZIhvcNAwICAUAwBwYFKw4DAgcwDQYIKoZIhvcNAwIC ASgwgasGCSsGAQQBgjcQBDGBnTCBmjCBkTELMAkGA1UEBhMCREUxIDAeBgNVBAoTF1VuaXZl cnNpdGFldCBPc25hYnJ1ZWNrMRYwFAYDVQQLEw1SZWNoZW56ZW50cnVtMSMwIQYDVQQDExpV bmktT3NuYWJydWVjayBSWi1DQSBHLTAwMjEjMCEGCSqGSIb3DQEJARYUY2FAdW5pLW9zbmFi cnVlY2suZGUCBBFfm+wwga0GCyqGSIb3DQEJEAILMYGdoIGaMIGRMQswCQYDVQQGEwJERTEg MB4GA1UEChMXVW5pdmVyc2l0YWV0IE9zbmFicnVlY2sxFjAUBgNVBAsTDVJlY2hlbnplbnRy dW0xIzAhBgNVBAMTGlVuaS1Pc25hYnJ1ZWNrIFJaLUNBIEctMDAyMSMwIQYJKoZIhvcNAQkB FhRjYUB1bmktb3NuYWJydWVjay5kZQIEEV+b7DANBgkqhkiG9w0BAQEFAASCAQA1TGJtBTt9 j+qz0VCYIQk+M0afRosxEvrxl7fMzQMNmGEhlCb2zr4DwU1WusqOI30XNnGZtdLZAOn6DKCl 7N4boj4jDkQT9JTVliPLVWJc1N6SAkSqRG0tesbaLUJQ+K/+n+t4x5WXFnB7G5jt0O82+uuJ dc8Sfhz46TLh6cTbLP8iNeoDIy7RMVqpN9VzMH/ABewXwUc6eDIKoaI8xkQ2Na1F2Q0eJZGi pBN0KICnvTKrQnX8UlznbSKASYlDK5jaoT/i3bcoHL3JqNV4iECw7lOcKADPGSOisZ3NPXp1 CQhKgjYVUQ9pUavpy7ehgtuigdHBIIV9hg5/Plow3nFRAAAAAAAA --------------ms040708000206040902000902-- ========================================================================Date: Fri, 18 Feb 2011 12:04:06 +0100 Reply-To: Cesar Caballero <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cesar Caballero <[log in to unmask]> Subject: is possible to register to another image than first one in realignment In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-transfer-encoding: 7BIT Content-type: text/plain; CHARSET=US-ASCII Message-ID: <[log in to unmask]> Hello all, In realignment, there are two options: register to mean or register to first. Is there any way to do realignment to a different image that is not the first one in SPM 8? Thanks very much, Cesar ========================================================================Date: Fri, 18 Feb 2011 11:21:44 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: is possible to register to another image than first one in realignment Comments: To: Cesar Caballero <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> You could select the images in a different order. There is no temporal information used by the realignment. Best regards, -John On 18 February 2011 11:04, Cesar Caballero <[log in to unmask]> wrote: > Hello all, > > In realignment, there are two options: register to mean or register to first. > Is there any way to do realignment to a different image that is not the first one in SPM 8? > > Thanks very much, > Cesar > ========================================================================Date: Fri, 18 Feb 2011 12:48:50 +0100 Reply-To: Cesar Caballero <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cesar Caballero <[log in to unmask]> Subject: Re: is possible to register to another image than first one in realignment Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-transfer-encoding: 7BIT Content-type: text/plain; CHARSET=US-ASCII Message-ID: <[log in to unmask]> Hello John, Thanks very much for your answer, I was wondering about the temporal information of the realignment parameters for the analysis. What would it be the effect if I select the scans on different order? Best wishes, Cesar On Feb 18, 2011, at 12:21 PM, John Ashburner wrote: > You could select the images in a different order. There is no > temporal information used by the realignment. > > Best regards, > -John > > On 18 February 2011 11:04, Cesar Caballero > <[log in to unmask]> wrote: >> Hello all, >> >> In realignment, there are two options: register to mean or register to first. >> Is there any way to do realignment to a different image that is not the first one in SPM 8? >> >> Thanks very much, >> Cesar >> ========================================================================Date: Fri, 18 Feb 2011 11:51:34 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: is possible to register to another image than first one in realignment Comments: To: Cesar Caballero <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> All scans would be aligned to the first image you select. Best regards, -John On 18 February 2011 11:48, Cesar Caballero <[log in to unmask]> wrote: > Hello John, > > Thanks very much for your answer, > I was wondering about the temporal information of the realignment parameters for the analysis. > What would it be the effect if I select the scans on different order? > > Best wishes, > Cesar > > On Feb 18, 2011, at 12:21 PM, John Ashburner wrote: > >> You could select the images in a different order. There is no >> temporal information used by the realignment. >> >> Best regards, >> -John >> >> On 18 February 2011 11:04, Cesar Caballero >> <[log in to unmask]> wrote: >>> Hello all, >>> >>> In realignment, there are two options: register to mean or register to first. >>> Is there any way to do realignment to a different image that is not the first one in SPM 8? >>> >>> Thanks very much, >>> Cesar >>> > > ========================================================================Date: Fri, 18 Feb 2011 13:00:17 +0100 Reply-To: jmrabanal <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: jmrabanal <[log in to unmask]> Subject: CURSO DE NEUROIMAGEN AVANZADA MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Message-ID: <8244675c248d4ae58ca01f7da700c7ee@localhost> V CURSO DE NEUROIMAGEN AVANZADA EN NEUROCIENCIA COGNITIVA Y PSIQUIATRÃA. INSCRIPCIÃ“N NO PRESENCIAL: TelÃ©fono: 93 248 38 71 E-mail: â€¢[log in to unmask] INSCRIPCIÃ“N PRESENCIAL: Escola de Postgrau â€¢ Servei atenciÃ³ a lâ€™usuari â€¢ Edifici U Planta 1 Campus UAB â€¢ Bellaterra (Cerdanyola del VallÃ¨s) IMPORTE MATRICULA: PRECIO: 375 Euros LUGAR DE REALIZACIÃ“N DEL CURSO: Parc de Recerca BiomÃ¨dica de Barcelona (PRBB) â€¢ Sala Xipre 1Âª planta C/ Doctor Aiguader, 88 â€¢ 08003 BARCELONA â€¢ PLAZO DE INSCRIPCIÃ“N: Del 10 de Diciembre 2010 al 20 de Marzo 2011 El curso se impartirÃ¡ en castellano. Este curso de postgrado es una introducciÃ³n a las tÃ©cnicas y los diseÃ±os experimentales de neuroimagen que se utilizan en la investigaciÃ³n en neurociencia cognitiva y psiquiatrÃa, orientado fundamentalmente hacia la resonancia magnÃ©tica estructural y funcional. En este curso se abordan los fundamentos fÃsicos y biolÃ³gicos de dichas tÃ©cnicas, se adentra en los procedimientos de pre y postprocesado y en la descripciÃ³n de los paradigmas empleados en la investigaciÃ³n bÃ¡sica en neurociencia cognitiva y en la investigaciÃ³n clÃnica en psiquiatrÃa. Â¿a quiÃ©n va dirigido? A clÃnicos e investigadores que provienen de disciplinas como la psiquiatrÃa, la neurologÃa, la psicologÃa, la neurorradiologÃa y la medicina nuclear. El curso se estructura en cuatro mÃ³dulos: MÃ³dulo 1: â€œFundamentos de neuroimagenâ€, estÃ¡ dedicado a los conceptos fundamentales de las principales tÃ©cnicas avanzadas de neuroimagen, con Ã©nfasis en la resonancia magnÃ©tica estructural y funcional. Nuevas aplicaciones de la resonancia magnÃ©tica como la espectroscopia protÃ³nica y la imaginerÃa por tensores de difusiÃ³n reciben tambiÃ©n especial atenciÃ³n. Se exponen asimismo las bases biolÃ³gicas de la RM funcional. MÃ³dulo 2: â€œParadigmas experimentales en RM funcionalâ€, aborda los fundamentos de los diseÃ±os experimentales en RM funcional. Se estudian los principales paradigmas experimentales empleados en neurociencia cognitiva (atenciÃ³n, memoria, percepciÃ³n, emociÃ³n y mixtos), tras una exposiciÃ³n de los correspondientes circuitos anatÃ³mico-funcionales implicados. MÃ³dulo 3: â€œPost-procesado en neuroimagenâ€, estÃ¡ dedicado a los procedimientos empleados en el pre-procesamiento (realineamiento espacial, normalizaciÃ³n estereotÃ¡xica, corregistro y suavizado espacial) y post-procesamiento funcional (especÃficamente Voxel-based morphometry â€œVBMâ€). Se exponen los fundamentos de la estadÃstica inferencial y las principales tÃ©cnicas estadÃsticas empleados en post-procesado de neuroimagen (la t de Student, el anÃ¡lisis correlacional, el anÃ¡lisis de Fourier y el modelo lineal general). Se estudian asimismo anÃ¡lisis de regiones de interÃ©s. MÃ³dulo 4: â€œNeuroimagen en los trastornos psiquiÃ¡tricosâ€, estÃ¡ dedicado a la exposiciÃ³n de las contribuciones que las tÃ©cnicas de neuroimagen han aportado al conocimiento de la neurobiologÃa de los trastornos mentales, especÃficamente, en los trastornos de ansiedad, la esquizofrenia, el trastorno obsesivo compulsivo y el trastorno por dÃ©ficit de atenciÃ³n e hiperactividad. ========================================================================Date: Fri, 18 Feb 2011 07:15:33 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: Re: vbm8 for single subject Comments: To: Jonathan Peelle <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e6d58dac861487049c8d78d3 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Thanks! On Thu, Feb 17, 2011 at 11:46 PM, Jonathan Peelle <[log in to unmask]> wrote: > Hi John > > > Is possible use the vbm8 toolbox for compare a single subject against the > template > > You can't get any sort of statistics out of comparing a single subject > against a template because you have no variance. The best you could do is a > simple subtraction but this woudn't be particularly informative I don't > think. To look at a single participant you really need a sample with which > to compare them—e.g., 1 patient vs. 20 age- and sex-matched controls. > > Jonathan > > > > -- > Dr. Jonathan Peelle > Department of Neurology > University of Pennsylvania > 3 West Gates > 3400 Spruce Street > Philadelphia, PA 19104 > USA > http://jonathanpeelle.net/ > > > > --0016e6d58dac861487049c8d78d3 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Thanks!

On Thu, Feb 17, 2011 at 11:46 PM, Jonathan Peelle <[log in to unmask]> wrote:

Hi John

> Is possible use the vbm8 toolbox for compare a single subject against the template

You can't get any sort of statistics out of comparing a single subject against a template because you have no variance. The best you could do is a simple subtraction but this woudn't be particularly informative I don't think. To look at a single participant you really need a sample with which to compare them—e.g., 1 patient vs. 20 age- and sex-matched controls.

Jonathan

--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/

--0016e6d58dac861487049c8d78d3-- ========================================================================Date: Fri, 18 Feb 2011 12:15:01 +0000 Reply-To: " " <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: " " <[log in to unmask]> Subject: SPM error Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi all, I repeatedly get this error when trying to convert/filter/downsample my MEG data, although not every time... ??? Error using ==> mat2file Cant open file. Error in ==> file_array.subsasgn>subfun at 132 mat2file(sobj,dat,va{:}); Error in ==> file_array.subsasgn at 70 subfun(sobj,double(dat),args{:}); Error in ==> meeg.subsasgn at 29 this.data.y = subsasgn(this.data.y, subs, dat); Error in ==> spm_eeg_downsample at 124 Dnew(blkchan,:,1)=Dtemp(:,1:nsamples_new,1); Error in ==> spm at 961 evalin('base',CBs{v-1}) ??? Error while evaluating uicontrol Callback I've been told it's generally to do with permissions but I have got full permission/access etc to the data and the folders in which the data is in and there is plenty of room on my hard drive. The other issue is that the error is inconsistent in that sometimes when I repeat the same step having got the error, it will then work, sometimes not. Does anyone know of any other reason that I might be getting this error? Kind regards, Holly ========================================================================Date: Fri, 18 Feb 2011 12:30:02 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: SPM error Comments: To: " " <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Holly, This has to do with the backup process that runs on your PC. Disable it (from the toolbar tray) before doing SPM analysis. Vladimir On Fri, Feb 18, 2011 at 12:15 PM, <[log in to unmask]> wrote: > Hi all, > > I repeatedly get this error when trying to convert/filter/downsample my MEG data, although not every time... > > ??? Error using ==> mat2file > Cant open file. > > Error in ==> file_array.subsasgn>subfun at 132 > mat2file(sobj,dat,va{:}); > > Error in ==> file_array.subsasgn at 70 > subfun(sobj,double(dat),args{:}); > > Error in ==> meeg.subsasgn at 29 > this.data.y = subsasgn(this.data.y, subs, dat); > > Error in ==> spm_eeg_downsample at 124 > Dnew(blkchan,:,1)=Dtemp(:,1:nsamples_new,1); > > Error in ==> spm at 961 > evalin('base',CBs{v-1}) > > ??? Error while evaluating uicontrol Callback > > I've been told it's generally to do with permissions but I have got full permission/access etc to the data and the folders in which the data is in and there is plenty of room on my hard drive. > > The other issue is that the error is inconsistent in that sometimes when I repeat the same step having got the error, it will then work, sometimes not. > > Does anyone know of any other reason that I might be getting this error? > > Kind regards, > > Holly > ========================================================================Date: Fri, 18 Feb 2011 13:51:47 +0000 Reply-To: Stephen Smith <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Stephen Smith <[log in to unmask]> Subject: Announcement: The 2011 FSL & FreeSurfer Course Content-Type: multipart/alternative; boundary=Apple-Mail-198--245155489 Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> --Apple-Mail-198--245155489 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii With kind permission from Karl and John: ---------------------------- We are pleased to announce the 2011 FSL & FreeSurfer Course, to be held in Montreal, Canada. It will run immediately before the Human Brain Mapping conference, which will be in Quebec City, a short plane/train/road journey from Montreal. The intensive course covers both the theory and practice of functional and structural brain image analysis. Background concepts and the practicalities of analyses are taught in detailed lectures; these are interleaved with hands-on practical sessions where attendees learn how to carry out analysis for themselves on real data, with one computer provided for every two attendees. After completing the course, attendees should be able to analyse their own FMRI and MRI data sets. The course lasts 5 days, from June 20-24. Numbers of attendees are strictly limited and are available on a first-come-first-served basis. For full information and to register for the course, please go to: http://www.fmrib.ox.ac.uk/fslcourse/montreal2011.html Regards, course organisers. --------------------------------------------------------------------------- Stephen M. Smith, Professor of Biomedical Engineering Associate Director, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [log in to unmask] http://www.fmrib.ox.ac.uk/~steve --------------------------------------------------------------------------- --Apple-Mail-198--245155489 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=us-ascii

With kind permission from Karl and John:

----------------------------

We are pleased to announce the 2011 FSL & FreeSurfer Course, to be held in Montreal, Canada. It will run immediately before the Human Brain Mapping conference, which will be in Quebec City, a short plane/train/road journey from Montreal.

The intensive course covers both the theory and practice of functional and structural brain image analysis. Background concepts and the practicalities of analyses are taught in detailed lectures; these are interleaved with hands-on practical sessions where attendees learn how to carry out analysis for themselves on real data, with one computer provided for every two attendees. After completing the course, attendees should be able to analyse their own FMRI and MRI data sets.

The course lasts 5 days, from June 20-24.

Numbers of attendees are strictly limited and are available on a first-come-first-served basis.

For full information and to register for the course, please go to:

http://www.fmrib.ox.ac.uk/fslcourse/montreal2011.html

Regards, course organisers.

---------------------------------------------------------------------------
Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre

FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
---------------------------------------------------------------------------

--Apple-Mail-198--245155489-- ========================================================================Date: Fri, 18 Feb 2011 09:03:32 -0500 Reply-To: Fred Sanders <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Fred Sanders <[log in to unmask]> Subject: DCM and global signal MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf30433ef2b1ed73049c8efa9c Message-ID: <[log in to unmask]> --20cf30433ef2b1ed73049c8efa9c Content-Type: text/plain; charset=ISO-8859-1 I recently used DCM (in SPM8) to model the connectivity between 2 brain regions. A reviewer was concerned that DCM contains a regressor for global signal and that including this regressor could introduce artificial anti-correlations. I would greatly appreciate it if someone could help me understand this. I did not think DCM used a regressor for global signal - was this done in prior versions of DCM? Or is this referring to the constant term from the first level analysis? Also, how could that introduce anti-correlations? Any thoughts? Thanks!! --20cf30433ef2b1ed73049c8efa9c Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable I recently used DCM (in SPM8) to model the connectivity between 2 brain regions. A reviewer was concerned that DCM contains a regressor for global signal and that including this regressor could introduce artificial anti-correlations. I would greatly appreciate it if someone could help me understand this. I did not think DCM used a regressor for global signal - was this done in prior versions of DCM? Or is this referring to the constant term from the first level analysis? Also, how could that introduce anti-correlations?

Any thoughts?

Thanks!!

--20cf30433ef2b1ed73049c8efa9c-- ========================================================================Date: Fri, 18 Feb 2011 14:56:05 +0000 Reply-To: Ghazal Mohades <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ghazal Mohades <[log in to unmask]> Subject: Interpret pediatric fMRI Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear colleagues fro the mailing list I understood that there had been a few of you having the same question as mine, however I couldn't get an exact answer to it. I am using TOM template to create a group specific template based on the data from NIH study (that I suppose works the best for a group of 42 8-11 year old children). and I used that template in normalization step during preprocessing. it goes without saying that there is no meaningful dependence or relation between this template and the known template for adults (e.g. MNI template) and it makes the interpretation of the activation spots quite tricky!! since I cannot use the coordinates given by SPM and look them up in available brain atlases to find the anatomical point for activation. (as these atlases are all based on adults brains) sop how can I report about the anatomical point of activation. i am not very good at anatomy too look at the activity and guess the name of that point. even if I was that wouldn't be scientifically accepted I suppose Thanks in advance for your help regrds Ghazal Mohades PhD student VUB Brussels ========================================================================Date: Fri, 18 Feb 2011 10:01:07 -0500 Reply-To: Deepika Nandiraju <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Deepika Nandiraju <[log in to unmask]> Subject: how to save realignment output MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e646a53aabad9a049c8fc8fa Content-Type: text/plain; charset=ISO-8859-1 Hi everybody, I have a quick question regarding realignment of fMRI data. " Is there an easy way to save the realignment output plot when batching other than copying the r* text file into excel an doing it ourselves?" Thank you very much. Best Regards, Deepika. --0016e646a53aabad9a049c8fc8fa Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hi everybody,

I have a quick question regarding realignment of fMRI data.

" Is there an easy way to save the realignment output plot when batching other than copying the r* text file into excel an doing it ourselves?"

Thank you very much.

Best Regards,

Deepika.

--0016e646a53aabad9a049c8fc8fa-- ========================================================================Date: Fri, 18 Feb 2011 16:14:49 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: how to save realignment output Comments: To: Deepika Nandiraju <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Deepika, > I have a quick question regarding realignment of fMRI data. > " Is there an easy way to save the realignment output plot when batching > other than copying the r* text file into excel an doing it ourselves?" I dimly remember I already saw a code snippet on the list for this purpose, but if you cannot find it, this may help: % === Start === filt = ['^rp_*','.*\.txt$']; b = spm_select([Inf],'any','Select realignment parameters',[],pwd,filt); scaleme = [-3 3]; mydata = pwd; for i = 1:size(b,1) [p nm e v] = spm_fileparts(b(i,:)); printfig = figure; set(printfig, 'Name', ['Motion parameters: subject ' num2str(i) ], 'Visible', 'on'); loadmot = load(deblank(b(i,:))); subplot(2,1,1); plot(loadmot(:,1:3)); grid on; % ylim(scaleme); % enable to always scale between fixed values title(['Motion parameters: shifts (top, in mm) and rotations (bottom, in dg)'], 'interpreter', 'none'); subplot(2,1,2); plot(loadmot(:,4:6)*180/pi); grid on; % ylim(scaleme); title(['Data from ' p], 'interpreter', 'none'); mydate = date; motname = [mydata filesep 'motion_sub_' sprintf('%02.0f', i) '_' mydate '.png']; print(printfig, '-dpng', '-noui', '-r100', motname); close(printfig) end; % === End === Watch out for linebreaks before pasting it into the Matlab window. Cheers, Marko -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Fri, 18 Feb 2011 10:24:56 -0500 Reply-To: Xin Di <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Xin Di <[log in to unmask]> Subject: mis-registration of subject's anatomical and functional images MIME-Version: 1.0 Content-Type: multipart/mixed; boundary cf3054a15f008c72049c901f1b Message-ID: <[log in to unmask]> --20cf3054a15f008c72049c901f1b Content-Type: multipart/alternative; boundary cf3054a15f008c5d049c901f19 --20cf3054a15f008c5d049c901f19 Content-Type: text/plain; charset=ISO-8859-1 Dear Experts, I tried to coregister subject's functional images to it's own T1 image, but they did not registered very well (please see attached). I also tried to run skull strip to the anatomical image and reorient the functional images, but the results were still not good. Can any one kindly give me some suggestions on how to do next? Thank you very much! Sincerely, -- Xin Di, PhD Postdoctoral Researcher Department of Radiology University of Medicine and Dentistry of New Jersey 30 Bergen Street, ADMC 582 Newark, NJ 07101 --20cf3054a15f008c5d049c901f19 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Experts,

I tried to coregister subject's functional images to it's own T1 image, but they did not registered very well (please see attached). I also tried to run skull strip to the anatomical image and reorient the functional images, but the results were still not good. Can any one kindly give me some suggestions on how to do next? Thank you very much!

Sincerely,

--
Xin Di, PhD

Postdoctoral Researcher
Department of Radiology
University of Medicine and Dentistry of New Jersey
30 Bergen Street, ADMC 582
Newark, NJ 07101

--20cf3054a15f008c5d049c901f19-- --20cf3054a15f008c72049c901f1b Content-Type: image/jpeg; name="coregistration.jpg" Content-Disposition: attachment; filename="coregistration.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkb7wkbn0 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAOpAooDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5Pxl8Q9F8CXG lxayl2E1F3VJYYw6xBSm5n5Bx84Pygng8evWV4v8brG31Pxl8OrC8j8y1utQeGZNxG5GktwwyORk E9KAPRPGvjXTfAejQ6pqkF3NBLcLbqtqiswYqzZO5lGMIe/pXSV8uePrq+0j4c/8INrTyG/0XWIz Zyy8G7smjm8uRcuxIBBUgcICi9Qa9H1e+1fxn8Zb3wbFrupaNpOl2S3Mv9nSiOW4kwpz5mMqP36g qdwPl9MnIAPXKK+fJfGPiNPhr4/0i61i7m1Dw5ewW0GqI/lSvGZxFj5ec/u2JYsxO8gnjmn4s/4S zw78J9N8Qy+NtVurjWJbB0CSPCbdPs0pKBg53ZymTgbim45JoA+j6K+dL+TxXa+M/G3h8+N9ZaDT tHbU5ZQ4V5ZUiSTbGf8AlghkkztTHyjbz1F99evtR+Engy/1PxjrNpczXEkLW2lx777UysxjVUYM pyqjknIJYEhm2ggHsninxJZ+EfDl3rl/HPJa2uzekCgudzqgwCQOrDvVOx8Yw6hf6Nb2+jay0Gq2 SXsd6LUG3hVkZgkkgbCvhcY55Zeea8MfxFrupfCb4haHrcs8jaPd20cX2uYTzx7rrDRvKMCTaU4b Hc9sAdHoOralD8SvhnpcWoXaafN4XgeW0WZhE7eRNyyZwT8q8kdh6UAe6UV84axr3i6zvEu/Gese K/DepLqCJHPZWqS6UkJG9VKq4EjAls8yEqu0hiDjb8d+LdX1D4qXPhiKbxPDpOnW6SSw+Gow91NI UDby3BRP3qgglhlRxlsqAe6VzfiTxrpvhfWdC0u9gu5J9auPs9s0KKVRtyLl8sCBmQdAehryfVfG /i/Q/gjcNqC6lb6m2p/2XaXt3A1tdG32bxKynOXKqyZByDzuLKSYPGPhm+8M+OvhtBd+JNS1pJdT Rs37b2SUSwCRlY8hG+TCEnbtPJ3GgD2vxT4ks/CPhy71y/jnktbXZvSBQXO51QYBIHVh3qxoes2f iHQ7LV7B99rdxLKmSCVz1VsEgMDkEZ4IIrj/AI2/8kh13/t3/wDSiOvOPC/iK88H/DHXPDwlnl1K e0tbnRh5ximm+3RqCtunJbynLHKZy2funNAHr/hTx7o/jLUdZs9JE7f2VKsUkzBDHLkuA0bKx3Kd hOTjgiuor5Y0KK88I+Dvipa2Gozx3Wm3dlbJdwMYXO25dCwwcrkZ4z3xXb+INd1iH/hTXlarfR/2 h9n+27bhx9pz9mz5nPz53N1z94+tAHrGj+KdG17UdUsNMvPPutLl8m8TynXynywxlgAeUbpnpWxX zZ4Q0eaxv/ipe2+uazHPoqXQjZbsj7S2y5UPNgZdwV3A8YYk1Y13xLr0P7OXhjVItb1JNQm1N0lu 1unErruuOGfOSPlXgnsPSgD6Lrm/EnjXTfC+s6Fpd7BdyT61cfZ7ZoUUqjbkXL5YEDMg6A9DXl/x SvL6PxZqiad4q8QT3kWmNcR6TpEn2eKwRFDGS4lDYII3tt27jlFyAUzkapr994oHwa1jU2ja8n1O RZXRdocpcwpux0BIUE4wMk4AHFAHufinxJZ+EfDl3rl/HPJa2uzekCgudzqgwCQOrDvVzSdSh1nR rHVLdZFgvbeO4jWQAMFdQwBwSM4Pqa4v42/8kh13/t3/APSiOvPEm1q/8Z/Drw7YeItS0uzvfC9s ZhaykAjypSxC52hyq4D4JU4I5UUAe9399b6Zp1zf3knl2trE80z7SdqKCWOBycAHpXP+C/HFn45s 57yw0vVbS1j27Jr63EaT5LA+WwYhtpUg+mRXkGnJqmseHvihol94m1ya18PyyyWrtd5kkCpcIY5X Iy0bBF3JwMjIxk5r6TrWo+Av2f7PXdJ1K+a+1a7FtGs0qyQ2WyWYloo2UgbghDA9S2e2KAPo+ivF 7ttd+GXjnwfZHxRquuWOuyi1vINQlD/vAVTejNuKLmYNtHPyYLHPFPwdB4m8U/EvxUo8Yala6fo2 urKbIs0izIJpsRA7xsTapBUZByMg7RQB7pRXyZB4i8XwfDSLxSPGGstPBrotIYGuGZf9T5jFyxJk BKoAjfKMNwd5r0fw3fa/pvxU8Y+G7zxhdywQaObn7dfbWS3nKRMZlRvlRFMrnYCFwADnANAHtlc/ beM9G1D+3Y9Mlnv7rRNwvLW3t38wOu/5EDAB2JjYDBOT35rwzQfFWtaL4v8ACkmn674g1fS9UvWs JbvWJD5F4POVGaCEsWQKGUhy2S2RgYZSeENHmsb/AOKl7b65rMc+ipdCNluyPtLbLlQ82Bl3BXcD xhiTQB9B6Jqv9t6PBqP2C+sPO3f6Nfw+VMmGK/MuTjOMj2IrQr58i8Y+I3+GvgDSLXWLuHUPEd7P bT6o7+bKkYnMWPm5z+8UhgykbAAeeOn0i+1fwZ8ZbLwbLrupazpOqWTXMX9oyiSW3kwxz5mMsP3D AKNoHmdMjJAPXKK+XNKuvE1/8Gtc8Vz+NPEAn069WG2hjvGUEsYQ5d87mGHG1cgKQTzuNfRfhO+u NT8G6Hf3knmXV1p9vNM+0Dc7RqWOBwMknpQBz+rfFTQtO1yXRbG01XXNSt932mDR7QzmDbtB3HIH VsHGcEEHB4rc8KeLdI8Z6MNU0aaSSAP5ciyRlGjk2qxQ54JAYcgkehNeZ/s9eV/Z3iX7Z5//AAkP 9oD+0PtG/wAzbg7N+7+Lf5+f4s9e1aGreIfCvhXQfH+seDlzrsMqpqRzL8ly8rxq/wC8BU7XaRsK MHGOhFAHrFFfP+rX/iXwP4S8M+PT4s1XUptUlhfUNPuXUwSJKvnbI1IIiwEKZA75G0fKbmqQeJvF Pxw8TeG9O8Yalo1pHZRTARMzqoC25IRd67CWbJZSD1HRjQB7pRXzxrfizXfFHjnxNZC68ZQWOlSy WtnB4agEnlyA7N8zKULKTGWCnn5iAy450/Efi3xfe+DPAui3k13ouueIb0295dpG0EsSpKIwdnBB bejnaV6ED5WwAD3Ssfw3r/8AwkenSXn9karpeyUxeTqdt5EjYAO4Lk5XnGfUH0ryuGPWvDXx88Oe HW8U6zqWly2UkyxXtwWOCk/EmMCQhlJDEZA2j+EVkeDfEPj+9+DGoXugXN3qesJrDK81w/2iWK3W FHOwSE7juwNoDH5zgZ5AB73f31vpmnXN/eSeXa2sTzTPtJ2ooJY4HJwAelV9E1vTvEejwatpNx9o sZ93ly7GTdtYqeGAI5BHIrwi0uf7Q07x5pH/AAk/jJPJ0R7j+y9di23CbAr7vN3N8p+6y7U3JKOu MjDkOpeG/wBn7RtZ0vX9Zt59Q1MI0cd4yRwqhuRiMLgqGIDMMnJAPagD6jrP1vW9O8OaPPq2rXH2 exg2+ZLsZ9u5go4UEnkgcCvJ/F2l6p4X0Oxh1n4ka5PcXeqtMINMttt5el/LUQwgSfIq/OcD5MyD 5chQeIvNY13Wvhb430jW7y+3eHtQtzEs1yJZjvneNoZpBxKqkZBwPmAPQAAA9/ufFtvH/YUlnpmq 6la6ztMN1ZWpeOFG2bXmJIKKQ4PI6BuOK6CvnxzqXhq3+EVvZa/rLQarcQzXMct4xXawtR5IAwPK AJAU5xuPrW3aNrvxN8c+MLIeKNV0Ox0KU2tnBp8oT94Syb3Zdpdcwltp5+fAYY5APaKK+bNa+Ivi PWfgZZaq2pXdtqkGu/Y5ru1l8lp18p5BkRhQBhlGO+wHvXV2ja74Y+POhaFL4o1XVLfUtPkuL1bu UGNpCsxPlx/djXdEpAHIGRnBxQB7RRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXN+JPBWm+KNZ0LVL2e7jn0W4+0WywuoV23I2HypJGYx 0I6mukooA4/xv8NfD/j37NJqqTw3Vvwl1aMqSFOfkJKkFcnPI4OcEZOZPFHw90jxTqltq0tzqWn6 tbII4r/TroxSrH8/ydwAd7ZIGe2ccV1lFAHFr8LvDkPgO68H2q3dvp926STyJNmV5FKHflgQCfLX IAA64AqTX/hxo/iPwbpfhe8ub5LHTfK8mSF0EjeXGY13EqQeCc4A5rsKKAOPuvhxo934j1/XJLm+ F1rentp9yiumxI2REJQbchsIOSSOvFZ9x8IPD9x4c0PRhearD/YssktleQ3CpOhd97chdv3tpBCg jaMHrn0CigDz9fhB4fTTPEdgLzVTDr8sct0z3Cu6FJDINrMpJ5JyW3E+uea0LX4caPaeI9A1yO5v jdaJp66fbIzpseNUdAXG3JbDnkEDpxXYUUAed6b8FvCmnXDZbUrvT/tBuo9LurotaRy5GHEYA3EK NvzlsqSDmtjxR8PdI8U6pbatLc6lp+rWyCOK/wBOujFKsfz/ACdwAd7ZIGe2ccV1lFAHH2/wz8NW nga78IwW86abd4advPYyPKAuJcngNlFOANuR93GRWPa/BXw/bajo9++qa5c3WkyxvbPc3ayYSMqU iwUwI1KnAXH3256Y9IooAx/FPhuz8XeHLvQ7+SeO1utm94GAcbXVxgkEdVHasv8A4V3oJv8Awzev HJJP4etxb2rSKh81QgVDJ8uSVI3LjGGJIrrKKAOHHwq8PmDxTDLLfSx+JJRNdhpVHluHaRTHhRjD Pn5t3QZyM5z7X4K+H7bUdHv31TXLm60mWN7Z7m7WTCRlSkWCmBGpU4C4++3PTHpFFAHDp8K9Ci8R 6pq8V3qsceq+b9v09LsrbXHmIytuUDcfvuw+b5SeMDisOT4CeGZ9Lh0+fVvEE0Fu+62El4pFuDku qLs2gMSC3GSVHI5z6pRQBw+ufCvQte8R3uuS3eq2l1fWjWl0lndmNJ1KbMsMZOAE+XO07Fyp5zHF 8JtBhtfC9ut3qRTw3cPcWZMiZdmlWUiT5ORuUDjHH513lFAGP4p8N2fi7w5d6HfyTx2t1s3vAwDj a6uMEgjqo7Vj2vw40e08R6BrkdzfG60TT10+2RnTY8ao6AuNuS2HPIIHTiuwooA4+x+HGj2H/CV+ Vc3x/wCEn3/bdzp+73+Zny/l4/1rdd3QfjYsfAGhWngSPwbNHPe6QucrPKQ75lMvLJt6N6Y6V1FF AHD+G/hXoXhvWLbVhd6rqV9ZxCCzl1C7Mn2WMKy7EChRtw5GCCB2xWp4b8Fab4X1nXdUsp7uSfWr j7RcrM6lUbc7YTCggZkPUnoK6SigDzf/AIUp4b/4Q3/hF/tuq/Yf7Q/tDzPNj8zzPL8vGfLxtx2x nPetxvh5osvizWvEM73c0+sWRsbu3aQCIxFUUhcAMCRGOd3c+2OsooA83sPgr4f0+XRZE1TXJf7H u/tVqk12rIp3K+3Zs2qu5MnaFJ3HJPGLknwn0U63rOpW+pazaDWEmW9tILoCCUyo6MxUqSSDIzDJ IDHgY4rvKKAOLb4XeHJvAdr4Pulu7jT7R3kgkebEqSMXO/KgAkeY2AQR0yDVjwv8PdI8Lapc6tFc 6lqGrXKGOW/1G6MsrR/J8nYEDYuCRntnHFdZRQBwdl8JtBsfAeo+D4rvUjp9/cC4lkaRPNDAxnCn ZjH7teoPU/h2Gk6bDo2jWOl27SNBZW8dvG0hBYqihQTgAZwPQVcooA4fVvhXoWo65LrVjd6roepX G77TPo92YDPu2k7hgjquTjGSSTk81qaH4E0Dw94VufDllayHT7tGW6WSZi05ZAjsTngsoH3cAdgK 6SigDz/S/g94a0zVrS9E+q3UNhK02n2VzeM0Fk5kEmY1ABHIHUnPfJ5rcsvBWm2PjzUfGEU92dQv 7cW8sbOvlBQIxlRtzn92vUnqfw6SigDh/Enwr0LxJrFzqxu9V02+vIjBeS6fdmP7VGVVdjhgw24Q DAAB75rL+IPgqxh+GUGg6P4Yu9Rs7O48+O1tL7ypYBiR2dWcOXOWZdmGJ38DIGPTKKAPC/Ang++v vipbeKF03xBZabYW7o114gfdd305Qp86kgqAsgAIBXEI7k47CP4MeGU8FTeFWn1KSze9+3LM0y+b HLtCZUhQuNoIwVP3j3wR6JRQByek/DrQNLt9QWRLvUbvUrdrW+v9QuWluLiIgjYX42jbgfLjhVzk gGufPwR0F/DR8Py614gl08XCXEUcl0jCFlEgwg8vChvNYtgckD8fTKKAOX8Y+A9L8a/2dJfXF9aX WnSmW1urGby5IycZwSCOqqc4yCowRznLt/hJ4ct7DxJZGbUp4PEDq9151zvaNldnUo2NxIZs5ctn AznnPeUUAefwfCPR4v7B83WNcuv7Du/tVl9puUfb/qsR/c4jHkrhRjGW9eLHiT4V6F4k1i51Y3eq 6bfXkRgvJdPuzH9qjKquxwwYbcIBgAA9813FFAHF618LvDms+DrLwsq3djpdlcfaIUtZssG+fILS BiQTIx/KtC98FabfePNO8YSz3Y1CwtzbxRq6+UVIkGWG3Of3jdCOg/HpKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOX1vWdR0bUZ7aN4Jm1CJRpYmKhYrj IjIkwVPk5eI55O5nXO54UaTUp9X0/XfDNumpRyWFxcG1uVkth58zC2nfeXBCgExqdqoOR1A+WtC4 0CxvH1VrtZJxqlutpcKzbR5AVgI124IGZJDn72XPOAoEkukpcNpUlxczyzabL5ySNtBlfyniJcBQ ORIx+ULzjtxQBYvr2LT7OS6mSd40xkQQPM5yQOEQFj17Djr0qPTdTg1W3ae3ju0RXKEXVpLbtnAP CyKpI564x19DUl9YWep2clnf2kF3ayY3wzxiRGwQRlTwcEA/hUem6TpujW7W+l6faWMDOXaO1hWJ S2AMkKAM4AGfYUAYep+K7C80WSTw/rVjdTfa7S1kktJ45jAJ50i3YBIDAMxXcCMryCARVzw7c3by 6xYXd1JdnTr0W8dxKqLJIrQQy5fYFXIMpAwo4A6nJOhqmmw6tYNaTNIgLpIkkZAaORHDo4yCMqyq 2CCDjBBGRVfTdFGmhmW/u5p5rg3F1PKI91y3liMBwqBQAqx42BfuDOctuAOfXXNa03w941u765tL y80Z5WtylsYosLZxTBdm9mxuc5yxPJwQMAXLRNSbVtV0CTXb5/KtLW6ivzFbidDJJMrKAIhGVxCM ZQn5m56Y1G0Cxlh1qCdZJoNYctdxs2AQYUhKqRggFYx3zknnpivD4aCW+pCbVtSnvL+3Fs9+zRxz xxgPsCeWiqpUyOwbbnLckgAAAx9LXxJqXhVbux1qSZ7u4ee0nuxCjG0KER52Q4JfCyDKAp5g3B/L KSXNb1nUdG1Ge2jeCZtQiUaWJioWK4yIyJMFT5OXiOeTuZ1zueFG6SCCG1t4re3ijhgiQJHHGoVU UDAAA4AA4xWfcaBY3j6q12sk41S3W0uFZto8gKwEa7cEDMkhz97LnnAUAAub00/TvMvLzMdvFma6 uCqZCj5ncgBR0JOAAPQCsfTfEVh4u0y7Hh/WIFmilaGSSNo5niAkZN4UMQNwRmRmyOVYqwyp3II2 ht4onmkndECtLIFDOQPvHaAMnrwAPQCs+fQLGbSZdORZIInuDdq0bZZJzN5/mDdkZEvzYIK9iCOK AKfhO+vbu1v4b+Sd5rS78nF0sYuEBijkxL5X7ot8+R5fGwpn5t1c3p/iDxH9oksl+13OtS6Pc3b2 V9ZeTBBeRmILFDJsQSRbpWBO9+FQ7xnLdhpuijTQzLf3c081wbi6nlEe65byxGA4VAoAVY8bAv3B nOW3V7bwxDDcSTXGo6leH7PJawedOA1tC5UsqSIFck7I/ndmf5AQ2SSQDH0LVdS1C91fS7HVLuZ7 e3tp4brWtLaJ1aR5Q6mMLBuTbEMEAcs2ScbRYsJ9f1vwBoF7Y3saalc2UM9xM+1Q5aDJ48tgCWIx gYU4YhwpjfY0rRRptxcXc1/d6heXCJG9zdCMN5aFiiARoi4BdznGfmOSQABY0nTYdG0ax0u3aRoL K3jt42kILFUUKCcADOB6CgDHv9avtFv9RimgkvftCLJpESjb50uza1tuAODuUSb2/hkc42wsRqC5 TRNDhl1vVYP9HiRLm/n2wI78KXIztXc3bPfFSXumw311p1xK0gewuDcRBSMFjFJFhuOm2RjxjkD6 G5QBy95qh1zVtKstG1vyrG7tLq6+26eYZS5hkhjChnV025lbOBnKjkcg2NP1S41bwDYavNewabNd afDdT3IQbIAyK8jLvOBgFsFsgcEhgCDc1XRRqVxb3cN/d6feW6PGlzaiMt5blS6ESI64JRDnGflG CASDXuvC9tPokWjwXd3Z2dulqtqkBQm3Nu4dGUurEnKoDu3AhRwCSSAU7WPXrnw9OTf3yHzWktpT bwrfTQBMgFXVYo5GfONycJtDBXyVx21vW7yx0q2guL57mT7b5y6clsLs+RMsQ3mceR8u7Em3rIVM eUya6STw7JPZQxXOt6lNd29x9otr5lgEsDFChChYhGRtZx8yN989wpEZ8JwRwWn2PUb6yu7fzv8A TYfKaSTznEk24OjJ88gVzhRgjC7VyKAI9In1rVRomqi9tH0+4soZrhIMqsjtG5LRh494TLJwzAn5 TlNjLLTFpq51yGzsfFWq3X2aVHv2nhtPJjThvKOyBWMjr2DDarbyRlFfqLCxt9M062sLOPy7W1iS GFNxO1FACjJ5OAB1rD0rwpc6QlvDB4o1l7eF9xhljtCJfm3NvYQB2LEks27cSSc5OaANTU9d0fRP K/tbVbGw87Pl/a7hIt+MZxuIzjI6eorPe71SLx9a2Ul3A2mXGn3M0dulvtdXje3XLOWO7/WPjAXG ed3BHQVTk02GXWbbVGaTz7e3mt0UEbSsjRsxPGc5iXHPc9ewBz9hr99qXjW3iiaNdDnsrmS2IXJu jG1sPPDHnZmV1UAYIXflldcWPEsmppcQJpeqSRXsiH7HYxwxlZnB+Z52YMRAuU3FNhGSAWd41Elp 4I8N6frlrq9ho1jZ3VtFLEn2a1jjB37cscLncApAOeA7jvRceGJpNR+3weItVtLprSG1meFLY+cI y5VmDwsA2ZHzt2jnpxQBj6jrerReKJ/KuJxY22oWtk21IvsiiUQ5WbI88zHzvlMf7sbotxGJMSWG r6nJqmnXcl/I8F/rF7prWRjjEUSQ/adroQu/efsy53Mw+d8AfLt2LjwvbXOqPdm7u0gmuIrueyUp 5U08ezy5GJUuCPKi4Vgp2DIOW3Fv4XtrbVEuxd3bwQ3Et3BZMU8qGeTf5kikKHJPmy8MxUbzgDC7 QDcooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiq9vfW93Pd wwSb5LSUQzjaRscosgHPX5XU8evrms+HxVoVxPpdvFqcDXGqxLPZQZIkljKM4fZ95V2o3JAGRjrx QBsUUUUAFFZcPiHTLjVDp0c8hn3tGrmCQRSOudyJKV2O42tlVYkbHyPlbBqXiHTNJuFgvJ5FcoJH KQSSLChJAeVlUiJOG+Zyo+VuflOADUorLm8Q6Zb6oNOknkE+9Y2cQSGKN2xtR5QuxHO5cKzAnemB 8y5IfEOmXGqHTo55DPvaNXMEgikdc7kSUrsdxtbKqxI2PkfK2ADUorLg8Q6Zc28Vwk8ghkuBarJJ BIgEpH3CWUbTu+TnGJP3Z+f5auQ31vcXlzaxSb5rbaJgFOELDIUt03YwSucgMpIAZcgFiio554bW 3luLiWOGCJC8kkjBVRQMkkngADnNU9M1qy1fzRatOskWC8NzbSW8ig5w2yRVbacMA2MEqwByDgA0 KKy4fEOmXGqHTo55DPvaNXMEgikdc7kSUrsdxtbKqxI2PkfK2I7XxTo1555jvNkcMTTmaeJ4Y3iX 70sbuAskYyCXQlQGU5wwyAbFFY8PijSZbO5ujNPBHbbTOLq0lgeJWOBIyOoYR8N85G0bXJOFbFy1 1S0vLhreF5PPW3iuWjkieNljkLhCQwBBJjcbTyMcgUAXKKr2d9b6hA01rJ5kayyQk7SMPG7RuOfR lYe+OOKj1LVLTSbdZrt5AHcJGkUTyySNgnCIgLMcAkgA4Ck9ATQBcoqnDqtjPpZ1NbmNbNUZ3ll/ diMLnfv3YKFSCGDYKkEHBBqnD4o0mazubkzTwrb7fMiuLSWGb5jhMROodtzZVcKdzAqMkEUAbFFY /wDwlGk/2d9t86fb5vkeR9kl+0eZjds8jb5m7b8+Nudnzfd5qRfEOmMlk6zyML1zHBiCQkuGCshG 3KupzuVsFQkhIARyoBqUVz9/4z0jTPtJvE1WOO13mab+x7sxqFzubeItpUYJ3A4xznFdBQAUVT1L VLTSbdZrt5AHcJGkUTyySNgnCIgLMcAkgA4Ck9ATVOXxVoUEFjLNqcEX2+UQWschKySyFxGUEZ+b crsFYEZQ53YwaANiisvUvEOmaTcLBeTyK5QSOUgkkWFCSA8rKpEScN8zlR8rc/KcWNS1S00m3Wa7 eQB3CRpFE8skjYJwiICzHAJIAOApPQE0AXKKy5/EekWvh+XXbi+jh02JC0k0gK7MHaVKkbg4b5dm N275cZ4qPUPE+m6ZqJsJxfSXSxLMyWmn3FztRiwUkxIwGSjdfQ0AbFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBy/iTRvtup2ixpOYNW/4l2q+WMhrVY5pBk4Oz 5sx7hjidh97Yy3Ndgmm1jww8UUjpDqbvKyqSEX7JcLlvQbmUZPcgd63KKAM/W4tRn0eePSZfKvjt 8t/NWPHzDPzNFKBxnqjfh1Ffw3b6zbadImuXHn3RlJVvPSXCYGBlIIR1zxtP1PQbFFAHB6dbXP8A amlacbO7Wex12/v53a3dYhBL9r8tllI2OT58XyqxYZOQNrYj8ULPdf8ACQeRZX0n9veH4rKw22cp 3Tf6T8sny/uf9fHzLsHJ5+VsegUUAcncalHd+LHsdTtdSFvaXES2cUenTyQTyFUcTvKqFMKzbVUk BWQucnYY8vTLCdPFFp/ol8t9Fqt5Pd745RaJbOLjynjz+4Eh3wZMf7w7pN3WSvQKKAOf1rTbe30M 6dpmkwbruWKFEihKJERtAmYpt2+UkYZTuU/u0VWVitSeHYJrB9RsbmKRpkuPN+2upzeq6jEjHpvG DGVB4EakKiMijcooA5fxFpniDUPD3iOz+1WN1Hd6fcw2dtDaNDIHdCEDSNKVPXB+Vck54HFGl3Yu vE2qayltfJYzWllZxtNZTRO0qyzlv3bqH2gTRkvjbyeflbHUUUAcnpmpR6n4ldtStdSju7e4nhso H06cW8KoWXzvNKeWzyKCQ27hXCLyXL494mpa2+o3NhpN3FqEuhXcF1bX0bPFHdssQjiiM4EbISsg Yx/u32qXz8hr0SigDj/DVhaz6tqUsFpfSaU0Vp5T6tHOZjcRySucG5/e7VzCy/wBixX5t9XPEGmN a2un3OjRyW11avFYwi1iUiK2mlijlAQqVARQsgOMKYhnK7lbpKKAKcFgunaNFp2liO3S2txBa+Yr SLGFXamRuBYDA/iBPr3rLvLjU9O0kDV/+JjHNK0VxNpVtNC8ELRnDLGjySM28KuUII37uApNdBRQ Byek2iv4Mn0q/wBOu547tL5lttjI81u0r7Fd2K7ZXjkTPmMrklix3ByMe0sGu5td1C5m8QahZvb2 KxXFxYrBdRywzSybki8qMuIy8cn3G3HKgORsr0SigDg9Ia507T9XurqLWb+w1LU8vdPA8N2sH2SO PzPJijWQESx+WAqKwBD9BuOx4Z0mFtCtzf6fGZEuLh7Y3MI81YWuTLEW3DcHIWF2LfOXUF/nBNdJ RQBxc+vW15rMv9q2Wspa2NwVt7VNHu5VlkRuJ3ZIyrDIzGoJA4c5baI+svkvJLORbCeCC6ONkk8J lReRnKhlJ4z/ABD156VYooA4e3uvEmheCNFt9Xlnmv5vKt7m6s7GS5ms18klmZVMvmybkK+Z93c6 sVIBVrmo20E/g+wj0ezuxANTsZBHLbypLhb2JpHdZAHzwzszcnliTnNdZRQBx/jiWe60690ldOvp JpbQtp0tusrxzXJEi+XMEGwRgmMkTHy2DnIIRq0PEmsz2VrHHaRXy+Zdi1nubewlne2UxGQyIio2 /oqBsFVZ+d20oegooA8z8V6TqGo+Fbg6Fpcc+kw6ZdG2t7hri3uxcuk0ckhjeJnlcq7bQxUs0js2 4lWXQ8UQXH2ie7j03UoPEEumRpYXOnXNzJA90plKxSBAI9iOykPMArCQ5GFYDvKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK5fxDDfpeAWPiDVUvLvi106 3W1CfKAGcs8DssYyCzEnG4AAsyIS6l13TNV8KW0uqwXFvPL9lvT9jCSXEi2s7l927ailo1O0LkH+ LHFAHUUVXvr2LT7OS6mSd40xkQQPM5yQOEQFj17Djr0qPTdTg1W3ae3ju0RXKEXVpLbtnAPCyKpI 564x19DQBcorHh1iw8RWdzF4f1+xlmTaJJrSSO4MIY9cAkBiA20sCMjJDAFTX8J317d2t/DfyTvN aXfk4uljFwgMUcmJfK/dFvnyPL42FM/NuoA6CivO9P8AEHiP7RJZL9rudal0e5u3sr6y8mCC8jMQ WKGTYgki3SsCd78Kh3jOWE8TX0cWs2ltqOpXPkJYGGfUdP8As1zvuJ3iZI1aKNCcIvlsy7d7fO20 HaAeiUVxdhP4k1LSLy2tb2SK/s70wsb7yVnVDArhZfLjaIuWkBUxjCqULByrxvc1vWdR0bUZ7aN4 Jm1CJRpYmKhYrjIjIkwVPk5eI55O5nXO54UYA6iiqc13baNpYn1PUY44IEVZby7dIwTwu5jhVBJI 6ADJ4HasO81Q65q2lWWja35Vjd2l1dfbdPMMpcwyQxhQzq6bcytnAzlRyOQQDqKKw9Iv9S1zwVpe pQS2lrqF7ZQXBZ4GliRnVWYbA6kjkgfNxx1749rq+p6h4S8F5v5ILvW0hW5vIo4/MUm0knZkDKUB LRgHKkYY4AOCADtKK4M6xq8+nWZmvdSgtEuL62nvtNsRcXDyQXBihDIIpAA6LIzMIwNyjBUEKdTQ bvXdatdA1pp4I7G70+C4urdCBmR4mY7QUJCgsnG/JwDldjCYA6iiuTtNU1eZLDRGeQaxBcKuoXJi GPs8bE+fjGzE4UKFU5UyPjJhcDc1PXdH0Tyv7W1WxsPOz5f2u4SLfjGcbiM4yOnqKANCiuLv9X1O PVNRu47+RILDWLLTVshHGYpUm+zbncld+8faWxtZR8iZB+bdqazLe3PiHTdGtdRn0+Oe0ubp57ZI 2kJieFQv7xHXafOYn5c5VcEDIIB0FFcP/a2s6t4e0HUbTUvI1TUNPiuLfT7eBDHLKyBnaZnDMLdd yZKlWAJAZndFrU1GTUx4lt4rHVJJJC8bvYLDGIIrbOHkmYgvvOJAm1lBYKNpVJGoA6SisOGw1yY6 lHd6tJBHK4NpLa+U0kK+Y5wQ8W0krsGSDxhcblMslPw2mpXGoyXw12+1DRTEUhN3Fbj7Q5IPmxmK JCIwAQCSd+4sAFCs4B1FFYeteL9B8PX9lZapqdpbT3b4VZZ0Ty12O299zAhCYyoP94gd6r6jJqY8 S28VjqkkkheN3sFhjEEVtnDyTMQX3nEgTaygsFG0qkjUAdJRXP6Zd6ofGWs2F9dwTWsVpbXFqkNv 5fliSS4BDEsxZsRrk8DjhRzmvplxq1p4tOmX99PcxzWklwGniiRHZWiB+ziP5ljXzSGExLfc2lgH agDqKK4ebxBe/wDCX3sTXd9BZ2OoW9kQlrG1oRLHCV8xmxIZHeZkHlsQmI2dQud5p2t6tL4og824 nNjc6hdWS7ki+yMIhNhYcDzxMPJ+Yyfuztl2k5jyAdxRXD+B/EF7rH2Ke+u74tqWni9SG6tY0jJH l+Z9nK4cRo0gX96CXBRkYgMzdxQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAc/ceGJpNYvNStfEWq2Ul3sDpCls6qEXAVTJCzBfvNtzjc7kAFjWhLpKXDaVJcXM8s2m y+ckjbQZX8p4iXAUDkSMflC847cVoUUAV76ws9Ts5LO/tILu1kxvhnjEiNggjKng4IB/Co9N0nTd Gt2t9L0+0sYGcu0drCsSlsAZIUAZwAM+wq5RQBT1TTYdWsGtJmkQF0kSSMgNHIjh0cZBGVZVbBBB xggjIqvpuijTQzLf3c081wbi6nlEe65byxGA4VAoAVY8bAv3BnOW3alFAGHbeGIYbiSa41HUrw/Z 5LWDzpwGtoXKllSRArknZH87sz/ICGySTGPCcEkF39s1G+vbu48n/TZvKWSPyXMkO0Iip8khZxlT knDblwK6CigDP0nSU0qKf/SZ7q4uZfOuLmfbvlfaqAkIqqMIiL8qj7uTkkkx3GgWN4+qtdrJONUt 1tLhWbaPICsBGu3BAzJIc/ey55wFA1KKAI4I2ht4onmkndECtLIFDOQPvHaAMnrwAPQCs/VdFGpX Fvdw393p95bo8aXNqIy3luVLoRIjrglEOcZ+UYIBIOpRQBT0/TYdLt4bS0aRLO3t47eC2JBWJUBA IJG4kjAOSfujock5/wDwi9smg6Tpdvd3dudJSNbO7QoZYykZi3fMpQkozKcqR8xIAIBG5RQBhjw0 IbC3trHVtSsZInld7iFo2aZpHLyF0dGjyzktkINuSF2qSDqWFjb6Zp1tYWcfl2trEkMKbidqKAFG TycADrViigCnHpsMWs3OqK0nn3FvDbupI2hY2kZSOM5zK2eew6d7lFFAGHceF7a51R7s3d2kE1xF dz2SlPKmnj2eXIxKlwR5UXCsFOwZBy26S+0E3ssFymq31rfQeasd3CIS4ikYM0WHjZNuUjwdu75B 83LbtiigDm/+EPitriKXSdX1LSUjsoLERWvkupihL+WD50bnI8xhnPPGasL4dkh1u61K11vUrdLu 4S4ntEWAxOyokeMtEXAKxqDhh3xityigDP1vSU1zR59NluZ7eObaGeDbuwGBKkOrKytjaysCCpII waj03SryxuGluPEGpaghQqIrqO3VQcj5h5cSHPGOuOTx0xqUUAV7ixt7ue0mnj3yWkpmgO4jY5Ro yeOvyuw59fXFZa+HZIdbutStdb1K3S7uEuJ7RFgMTsqJHjLRFwCsag4Yd8YrcooApx6bDFrNzqit J59xbw27qSNoWNpGUjjOcytnnsOnfL0/wnBpmPI1G+PkWjWVhu8o/YYW2/LH8nzf6uPmXefkHPLZ 6CigDDuPC9tc6o92bu7SCa4iu57JSnlTTx7PLkYlS4I8qLhWCnYMg5bcW/he2ttUS7F3dvBDcS3c FkxTyoZ5N/mSKQock+bLwzFRvOAMLt3KKAMPR/C9to9xDLHd3c6WtubSyimKbbSAlcxptUFh+7jG XLt8g55bO5RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFY+oa8bTUTYWmlX2p3SRLNMloYV8pHLBCTLIgO4o/3c42nOMjOxXP3tlrFp4hn1bS baxu/tVpDbSRXd09v5flPKwYFY5N27ziMELjaOueACxF4o0e5s7a5tL6CdbnyjGglRHKyGLBKuVI 4niJB+b51ABLKDJJ4i0oEpDf2lxKtwLZoormPcr+YkbAgsOVaVMjr8wABLKDy+leAZtJ0aeyjltJ J2vdLkFzsKNJDaLaghuCQcwylVyQN/UZNYdh4Z1O81jS7NLaBI9CtILB7x7WaIzrBd2sisXkRd+5 YJSETeqHrIRIMAHoh8S6CLe4uDrem+RbpE88n2pNsSyAGMsc4AYEFSeueM1JZ6zZ3+oy2dq/m7LS C8EyENHJHMZAhVgef9Ux9MEYJ7cnbeCtSsNE0C0sJ7S1l07R3sp/IdoxLK72zyBWVcoJPJlBkA3q ZAwBNXPBnhe/8PX+oS3bQGGeJFiCXMkzgme5ncMzqCcG52BiSW2bjtLbQAblz4h02xvb23vrmO0S zt4Lia4uHWOILK8iINxPXdGRzjquM54D4i0pHPmX9okBt0uUuGuYwkiMsjAr82cbYnbJGCASCdrY 5+fw7r0+tx6/KdNfULV4Vit1ldIrhY0uU3s+1jEW+1s2wCTHlgbm3ZWnZeArxPENlqN4bGWFLtb2 WIktsk338mFyvzbHvItrHaTsLYU4FAHUf8JRo7a5p+kR30El1qFo95bbJUIkjXbgjnJ3BiVwCCEc 5+WrkepQy6zc6Wqyefb28Nw7EDaVkaRVA5znMTZ47jr25/S/Dupadrmn3ZNpJBG+qif96wZVubpZ 4yo24YgIFYEjGcgnHNi88Iabq3iq71TV9M03UIHsre3gW6gWVo2R5mcjcpABEidDztOegoAsaX4s 0rU9Ntb83EdpFdPDHAt1NGrSPLEkqIAGOHKuPlOCcEgEEEkHi/QZn1gNqdpCmkXC295JNOiojMqk EndwNzFOcfMjDtXH2fw81W28PDT3fTXndI4TMJJAYlFnbQM3AxKm6BiYHGyQbNxXbg7lx4e1hdcv NStksZNmqpqVpHJcOnm5svsrpIRGfLx98EB89CF60AdBNruj28VtLNqtjHHdRNPbu9wgEsarvZ1J PzKF+YkcAc9KjuvEug2KM93rem26I+xmmukQBtzrg5PXdHIMeqMP4TjzvxJ4Z1Oz0XVLGO2gvrrx DaTRtELWadLebz57hArKm3791sEkjRBfLEmDyo6j/hF7+10Ge1smgSefVbq+uY4bmS1+1pJLIyq0 8a+YjANESVBz5Wz7pzQBuXPiXQbK4ube71vTYJ7VA9xHLdIrQqSoBcE5UEugBP8AeHqK1K4Pw34K 1LRl02Oee0dLHU0nDI7Zkhj077GpwV4dmAYrkgAkbmxz1HhrTZtG8K6Rpdw0bT2VlDbyNGSVLIgU kZAOMj0FAGpRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQBT1bUodG0a+1S4WRoLK3kuJFjALFUUsQMkDOB6is+18Sb71bXUdKu9ILpuje+uLXEh3om1RHM zElpEHTGWAzkgGx4l02bWfCur6XbtGs97ZTW8bSEhQzoVBOATjJ9DXN658PrO7uLCLS9O0q002P/ AI+bUQCNJc3VnK2UVcNlLZ1OeuVHTJAB0i+JdBZLJ11vTSl+5SzYXSYuGDBSI+fnIYgYGeTirGma lDqtq9xAsiolxPbkOADuilaJjwTxuQke2OnSuP8AEHgvVdYOtwR3saWeq3Dlo1uJISitbQQiVigz IUMUn7knY4kG5gVFbmjafrGmeGpIcWI1N7u5uvL8x3h/e3Dy7N+1W+6+3dt4PO1sYIBYg8UaPIsP nX0FpJcXc9nbxXUqRvNJFKYmCAn5vmHGOfmHAJxUkfiLSiQk1/aW8rXBtliluY9zP5jxqAAx5Zon wOvykEAqwHJv4L1hLHVbaJ7F/wC3bSW2vWaZ1+x+ZNcyloxsPnY+1sMHy8+WDxuwp/wgV4bbxTuN ibrVNPurO1lJOY/NubuXDHbkKRPDnGeUPBwCQDrNL8Q6brN/qtlZXMcs+l3At7lVdWwxQNngk4yS vOPmRx2qP/hJLP8Asr+0fLn8n+0P7P27Ru8z7V9mz1+7v5z129s8UaTp95Y61r88wgNrfXcdzbsk hL8QRRMrKVAGDFkEE53dsc8//wAK+s/snn/2dpX9s/23/aH27yB5nl/bvPx5m3du8r5cdM8ZxzQB 0kHiLSpreKV7+0gd7Iag0UlzGWSAj/WHaxGwdN4JX0Jqvp/i/QdR0vSL9dTtIE1ZAbOOedFeRjgF AN3LqxCkDODxWH4e8HX+lXOhGb7CF0+0hSaWF5CZZEtvII8phs3Z5FwNr7FEW3bkmnb+Ddei8NDS mGm77zQodEvJBcvi3WISqJox5f70lZidh2YK43HOQAdpNruj289xBPqtjFNbY8+N7hFaLKNINwJ+ X5EZuf4VJ6A0LrujvqMenLqti19Lv8u2FwhkfYWVsLnJwUcH0Kt6GvN9e8M6nq18/h+1toG8iXU5 kvZbWZcLeQ3Bx5pQIFWSeNSqNIz4DYTYVrqPEPhe/vrqwXTWgSwsvs8lvafaZLaOF4JRIBsjUrIr 7Y0+Yfugu5QxJFAG5Z+JdB1F4Esdb026ed3SFYLpHMjIoZwuDyVUgkDoCCa1K4Oy8FalbadZW7z2 heCy0S3Yh2wWs7hpZSPl6FThfU9cda7ygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooA4e/8Ai/4E0zUbmwvNd8u6tZXhmT7JOdrqSGGQmDgg9Kr/APC7fh5/0MP/AJJXH/xu vmLx2o/4WB4kbPXVbr/0a1c/gk44oA+vf+F2/Dz/AKGH/wAkrj/43R/wu34ef9DD/wCSVx/8br5B xRigD6+/4Xb8PP8AoYf/ACSuP/jdH/C7fh5/0MP/AJJXH/xuvkCjtQB9f/8AC7fh5/0MP/klcf8A xuj/AIXb8PP+hh/8krj/AON18ggZ70YyOnSgD6+/4Xb8PP8AoYf/ACSuP/jdH/C7fh5/0MP/AJJX H/xuvkHHQEUu3jqKAPr3/hdvw8/6GH/ySuP/AI3R/wALt+Hn/Qw/+SVx/wDG6+QivAxg/SgDPagD 69/4Xb8PP+hh/wDJK4/+N0f8Lt+Hn/Qw/wDklcf/ABuvkLYQM+2eDRigD69/4Xb8PP8AoYf/ACSu P/jdH/C7fh5/0MP/AJJXH/xuvkEDP5UYxjIoA+vv+F2/Dz/oYf8AySuP/jdH/C7fh5/0MP8A5JXH /wAbr5BxjuKMfnQB9ff8Lt+Hn/Qw/wDklcf/ABuj/hdvw8/6GH/ySuP/AI3XyDj/APVSUAfX/wDw u34ef9DD/wCSVx/8bo/4Xb8PP+hh/wDJK4/+N18gUUAfX/8Awu34ef8AQw/+SVx/8bo/4Xb8PP8A oYf/ACSuP/jdfIJGKSgD6/8A+F2/Dz/oYf8AySuP/jdH/C7fh5/0MP8A5JXH/wAbr5Gjh3gcj8af JEqk8jIA6igD62/4Xb8PP+hh/wDJK4/+N0f8Lt+Hn/Qw/wDklcf/ABuvkPaMd+aQKTxjnPFAH2bo nxS8G+I9Yg0nSdZ+0X0+7y4vssybtqljyyADgE8muwr5A+Cf/JXtC/7eP/RElfX9ABRRRQAUUUUA FFFFABRRRQAUUUUAZ+t63p3hzR59W1a4+z2MG3zJdjPt3MFHCgk8kDgVx/8Awu34ef8AQw/+SVx/ 8bpfjYM/CLXB6/Z//SiOvkJlwzAY470AfXn/AAu34ef9DD/5JXH/AMbo/wCF2/Dz/oYf/JK4/wDj dfIOBjJNJQB9f/8AC7fh5/0MP/klcf8Axuj/AIXb8PP+hh/8krj/AON18gnGRRjigD6+/wCF2/Dz /oYf/JK4/wDjdH/C7fh5/wBDD/5JXH/xuvkHilAzjPT1oA+vf+F2/Dz/AKGH/wAkrj/43R/wu34e f9DD/wCSVx/8br5C2/LntRtPTBzmgD69/wCF2/Dz/oYf/JK4/wDjdH/C7fh5/wBDD/5JXH/xuvkQ xjcQDnHP1ppXHbP0/wA+9AH17/wu34ef9DD/AOSVx/8AG6P+F2/Dz/oYf/JK4/8AjdfIWw4z0H1p McZPSgD6+/4Xb8PP+hh/8krj/wCN0f8AC7fh5/0MP/klcf8AxuvkEjFBHpQB9ff8Lt+Hn/Qw/wDk lcf/ABuj/hdvw8/6GH/ySuP/AI3XyDg+lGDQB9ff8Lt+Hn/Qw/8Aklcf/G6P+F2/Dz/oYf8AySuP /jdfIFFAH1//AMLt+Hn/AEMP/klcf/G6P+F2/Dz/AKGH/wAkrj/43XyBRgigD6//AOF2/Dz/AKGH /wAkrj/43R/wu34ef9DD/wCSVx/8br5AooA+v/8Ahdvw8/6GH/ySuP8A43R/wu34ef8AQw/+SVx/ 8br5BUAsATgE9atR2okXIJ49sUAfWn/C7fh5/wBDD/5JXH/xuj/hdvw8/wChh/8AJK4/+N18jSRh SMH5fX3qNhgke/pQB9e/8Lt+Hn/Qw/8Aklcf/G66jw34p0bxdp0l/od59rtY5TCz+U8eHABIw4B6 MPzr4Yr6f/Zx/wCSeah/2FZP/RUVAHsFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfEXjo4+IPiXng 6rdf+jWrn8cA5z7Vv+O/+SheJf8AsK3X/o1q58nmgAxzR1FHb3o4wPWgBBS0c0fl0oAXHbuenHWn hcqTt5zik5wOc5zwRUgBCjfk4HA/CgBmFBG4HHHQ0MCQMZwBnpQwJKlsc/pS5Jww69B3oARlAQHA Pqc0jDoPw5px4T34B7H2oxk468c84P0oATALdDtxk4oBwAVzn3FOzty3IPTilUnbuA5wSaAG4AGd vbv60FC2Nq8fXj/PNP5OSRjHXnv/AJFJtJjzjbzxQAwr8pPr0x3/AM/0pAhxzgD3qUhW+Y55PToM f5P6UpTjCnpyDn8KAIGX+709CaMFhnsBU20qSMjJ6+3ofpTWCYz0I4weaAIgOgK80oTJOAeOop4B OecEcYx1Bp65RzjkZ4oAi8snJznn86eIHAJKninfMWJ7EcED9f1qYM+MZxzjJP8An2oAI4CQQQc5 wcdCMmnzRkovTsBzyf8AIqRXwSDgj2NEm116k9sehoAqSBF+nYZPUVGeOQAM4zx9f8/hUzqN2CT0 wM9P8/4VBvIcYAwOgoA7v4Kf8lf0IZz/AMfHb/phJX19XyF8Fcf8Ld0Ejni4/wDSeSvr2gAooooA KKKKACiiigAooooAKKKKAOA+Nf8AySPW/rb/APpRHXyCQc89/Wvr742f8kh1z/t3/wDSiOvkI4PJ zQA0jjNFGT0o7UAB5pBS9aO1AD0VS4DHGTSqFb1wO9NBxgqO/apUG4kIBkgj/P60AJtB3M4/E9/a gKWAOMDrx3qSQBJMEqQv3utMQFzgtnIx6CgBNqnlThQx4I6U1s4wvP8APtS7NuCTn+lOyAVPQg9R /n/PNADdqrnOD6HNBTBJPAzzkU8ZRsjIOMsSfpTTjcM4IxjGfegBuzPzDgZxzQqBgOce3rUjfvF6 g9Tj0pSvcHgjAzQBEFAY4zn09jQV5GQOmKeQOgOMd/X/AD/Sg8nAHygbsZ60AR7MnGMH0xSYJqTL AEKCfw4NN6jAz06c9aAGYwecigoQ2OvpUhA3c8A9QKRQQOeM8c0AIFYqTjgCgLjjOenPpTx2GPvd h+VB3ZxkZxj/AD/n0oARU5GBjjOfxrQSPaqguQDj6+9RwxK33lxjnmph98BiR+NAFKSNtzEDbn17 YNQttHBycVoS8sMd+g6//qqnMiks39c0AVyO4ORX09+zj/yTzUP+wrJ/6Kir5jP3cA8Zr6c/Zx/5 J5qH/YVk/wDRUVAHsFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfEPjsY+IPiU/wDUVuv/AEa1YHOO 3PP0roPHWP8AhYXiX/sK3Wf+/rVz4NACd6BSgdD79KSgAwOKXbgD+dLjBz79KT+L1PqKAHqu5gq4 P0qbIKNkZxj6CmqAFV+eB2/woHCgBhn69KABVO5mYjGcdcU0gZHUHo34U9gfnIxuLEf5/WkbAblm 6+vTrQAzBdhuG0t1Y1Iowu0YYkZ9P89qYehB7dDj9OaeN2xsnJU546/55oAaxIBBA4PX2/zmkCjq VAIp2AGXbz6kGlVcHLHPQEigBCSnI7dBjp+dPO4KRyCDkD0FMC4UHGWOB79Ke3IwQMkHj0/OgBA2 FUnjOO3+FBztJxwcBRgUYyOuVIGecf5/+tTgNxGMnpx2oAj2liW3YwcdKQKO5AGen1x/n8KsYw2w kZJ68Uxoc4+Xrg89evSgCLb5jZHJOP8A9X+fSnEOOrNtAC9akEY3FslQB37Uh5DKGHH3cn1/+vQA DOMNnKiplUZVlIPTHH1pPlIGSCfp17/5+tTxKCxPy5B5OPyoAVowF5zkdSDg0ybKK3AAIOTVl9rB c9N3IqBxgZJJHUnPWgCvjsx4Izz9aqv8j8E5HHuKuNzzg9O596rSA7iVOSSMEUAdx8FTn4vaFknI +0DH/bvJX17XyR8GYtvxZ0Fj6zrx0/495K+t6ACiiigAooooAKKKKACiiigAooooA4D42DPwi1wf 9e//AKUR18g/NkjnOa+v/jUM/CTWxjPNv/6UR18hHBY5AAJoAac55IoAycY5NOGB+PZqTJBHoOlA CAZ4AJ9qPqB9aAMgnrgUDnigBVPzDsfapQRtYbQBnseaiVgDnuDnB6VK3UY+YhsZFACu277w/hxn 9aT5Qm5d2fX3p25jgAHIHOQOaay45OcY44xQAmWBY5X8+lOBGSobGQMeuaFVQBxnrj2pG3K+Tweh x+dACZ2jpgq3OeaUhCpwQAOB78U5lAwefT8f/wBdIRleFB6DOKAECqSATweSB/n/ADihsj07A/Wn BAxxwSe/9aXOe3y5HU5/L8P5UAIwO8vtyG7D1pR/e2Y7c/59qUIC4JJDcZPYd6XYG5YdeeT/AJ9K AI8ELnbxjIx3GaQoVbuAAGp+3rvXPy+vvT3PzAFST/h2+lAERAI2j7x9PT/P86FyU6844PSnmOMM TuI/+tQijZtK4bGMf596AARkA444ySaeiKMZAB7Z/KhF3jGw9OQfyqXZu+/tJ7jk557UASwR5U9M U9lAG0DJI7HinKmOOemM9Ka+QWbBIHp3AoArSdN2cqeCAO1QSALEGBI4xjsKtyDJIDfOOhB5zVd0 BA3cjI6H1NAFMjHX05r6d/ZyGPh7qA/6isn/AKKhr5nVS/yD7xHX27V9Ofs8KU8B6irYyNVfp/1x hoA9booooAKKKKACiiigAooooAKKKKACiiigD4j8dY/4WD4k4GP7Vusn/tq1c+cjPHXvXR+Oxnx5 4i7D+1rv8f3z1zpxjj/P+cUANx+QNIf0p3UqD0z2o6dOooAUAcZP69KMZIwfxoIydp4xT1OTngkc nI74oAVDjjgjqQP507GTlsEA8DFNCggk9M4Ax+FOy2SA3Oc7scHPNAC4Ee4fMecgGnAc5zuJzgkU roS2f4j/AJApudm4ZJ54OevFAChQMHcAcHFOKEhcYUknp1/z0pu4EjuB36ml++RwvXH0z6ZoATbt ON2csAfcUSZXoRyOO/WnhVfduDgAY5/Wjy+COM8c5x6UAMOAQASe4AH4fhS7AANx5B5ycc1Nt+8r ccjp2H1prIVUOFyQMk4/z2oAbx9xASSeeaRkbOMjjjPepdgDDGMDrj8acgxjue+PSgBgAddo9MZ9 eP8A9VIEy5YE8DB9PenKoAA4ClgBn6cVIoXaAMD5fTj/ADxQBWRV3455HQ0YG3cOgPIA4/zg/pU7 p32kgNx+dNYMApH8+tACKrbsqMLnnNTICMgNxyOn0pigqScjHt6/nUhVlfgk4/8ArUASDewA6Y7D 0pHjHPPHt29qUABdxyAuScn9adjL8gKPT+tAFeRF2jdnjgj+lVGUHPLZA5xWg65+6Mg9MDj8aqFc huwxg4GOtAHd/BlCvxR0InvJcHn/AK95a+sq+Ufg8u34o6BnGd8+cf8AXvLX1dQAUUUUAFFFFABR RRQAUUUUAFFFFAHA/Gr/AJJJrfGebfj1/wBIjr5EkwpwPYgV9efGj/kk+s4/vW3X/r4jr5EkUKxA De/egBh5I+Ynv0pM5Ug5znrR6/TtR/CRjBFABjcx6jnpTgucZJAzj6U3nORkUE5Y578/hQAq8sDg EZ/KpCCBw33uOKYOnY555qXHsBgYI68+tAAUyjHB3E/T/P8A9elOcAHBGD3zinKDyxJPGAP8/Sk2 gFtpBGMYPpigBvGBjkA9TS7RI2CV54JobO49PT73Q08KsYOGB7jjpQA0KHDKeD0JpwQswwwJAwCR 7UpXazcc4JzTlU4UryAe3X3/AJ0AQjpjqRz+FPVAWEgwcHJ7dKkbO5eTkjHA/WlVQzHcMHGev5/y oAbsA3qBmmK46A8M2M/1/WpggIxgg9OPw9fypNny7SB0/Mf5zQA1Gz1A2g9enanAckkAZ4yev41J jaCOASeD/n60iqzMd4BYHjH86AIDFg885Ix6mnY+pPbPH5VY8s+WBggccDtzmmmPGMA9QRk0AQkN uJ5/Hn8qmVRtIAye/tUaEEHLE+oAz3qYfKgyfvcE57//AK6AHq27jtzgZ70HJ3DHOO36UgJXPpyf 1zUg5JwSW/lQBE0eQAc+pIGKrSRZDHeDjge9XcZbAGM8ZzUToCD93+RoApxKplVskdOR619M/s+f 8iNqf/YWk/8ARMNfNkcOyQkqCRjINfSX7Pf/ACI2p/8AYWk/9Ew0AetUUUUAFFFFABRRRQAUUUUA FFFFAHD+Mb3XbCeP7Dfzx+bK7Rw2zh5GjCRDPlrZTttD7snoC65Y71VOg8LT3lz4ctJr953um372 nQq5+dgMgwwnpj/lmv4/eNPxXb6ej6fqd/4lk0A2rvHFcLLbxiQuvKMZkYEYXO3oSoJBKqRoeHrg XWiQzLqkeqIXkCXiTRyiVQ7AHdGiJnAAIC8EEZbG4gHx946YDxz4hySMard4wf8Aps9c0w5ORt54 /Our8bov/Cca+x6/2rdjnp/rnrlyDnHAUL19RQBGFBP3gBjufejgAcZXvkVIYhhgueAePWkwSQTn bnqO59qAGHOflOfWgKQT2wM9afghto6kYz+VSLzkEHn06/hQAxiSQQxBPQ9utOVCVZSOwJ5xmpFQ hcHJGOhGcU5VKKSSCx9fx/xoAYuDlD1PzE+/XrTiuWKD5cHnI6Dtz+FTCPZ/Dn65zQISSSOuBwex oAiCbGAAxuBUd+KkYhePlJH3h0/z2p+3LIB6cHHQUpTDrySQM5IoAYNwb7hGOOlO3BlIJXqMY79q eifuwNxyfQUCNTjK4JGMGgBpTqOME5BJ9KCDxtXPTgipwM9fTj2p+3ceR3yARQBVKA8g4P8AKnhS Pl5wew7fSrBQA5OM5/8A1UbOMDP+NAFfGWC5fOMn/P4UrR7hjGAe/t6VOyEAk4PPftTvLCjpjJ6j tQBWI4KZGAOh/wA/Wo1TcdwzjBIA6DFWTFhxg9eoNMKe3PYUAQKpUDAA55AHfv8Azp+GRsE/Qnqe Kc4AGMkfhSKCAdxz7nvQA4AYzjr1HQ+lPUfKOeMc0qqSBg9BjjvUirkkEAHPegCNlbPGMAZqvKm6 UEYIB5IPvV4gDjnJ6VWkA34HOewoA7X4Qpt+KOgdMb58f+A8tfVlfKvwj/5Kl4f6/fn/APSeWvqq gAooooAKKKKACiiigAooooAr36XEmnXKWb7LponELbgu18HaclWA5xyVb6HpXn/9reID4y8iPUL6 aw/tDyz5CtLCE8zBQuunFRjlWHnfKQQXBBI9Irh2/s7R/EMlrbeOfJvLjUEmfRpruzjXMjqWRU8k yDcDkKCC7NksGcvQBH8Zhn4UayM4y1tz/wBvEdfI8+PMJO3dn5gPSvrn4yf8kq1fjPz23/pRFXyX OMuQB8oxjA6igCoVZWAOB3pSMdWGP5UoRGQn17+9KOowwHTJPU0AR4BIHPIwM0qkBcknBPIFPC9D 6Ej26UFQhAxkEevFACKAei8+vbNP2sCUKgYOc+vpQqAJlSDnPTofapFRSF/jx2Pp2oAaFywGCCDn 0H+f8alUAr0yG9/Wmnaq/NtOR2HNSgDHKjIPH5UAR+WxUdCOM8foTQAo3NnIxgfL2qUBtuSCDg8f SnRx4Bzg8+vI/wA8UARqoLHb7Y47U4AZdSACOMj07mnhChBJJ56j/PvT9oL9Mg8n60ANKFyx6Y/P /PSgqGI5Hrx/n3pypj5uc9DUu3o3U9MnvQBAF2kAD2pVGTg/nU/l/MWI/KgIAOeT0B65oAhZGz17 YUDvShBw54GRkVOFO4YwR0HtR5e4dD8x59R7UAQbMtvB5xke9NkHCgnd0PP6Va8naxHY8cGoxGQp 5Ye+elAEIX5xjPPPPb/Ck6OSMkk8j8BUzJhSVBPBHSmEsD0PPIz2oAQMcgjGc/zFS9G6EHHBpo+V yT271Iq7Tu685oARkO3aCc+lNZFB3bfmIyfpVgDKZAzmo2UFSCeSDQBWIG7A4zyPevo39n0Y8D6k P+oq/wD6Jhr50zhOg4I+nNfRn7P/APyJOp/9hZ//AETDQB6vRRRQAUUUUAFFFFABRRRQAUUUUAcP 4p/t2DxRp9zpVzBLdiKb7PZxacJJDb4jE295LmJCvmeSeNr52gZXfnpPD1/NqWiQ3NzLHJcb5I5t kBhCyI7Iybd74KspUkOwJUkEgiuH8cX2h2Piezkv49SkKuzS51iXT1jbywFe3aW4iic4YqwjB/1h LMp+WTuPDlnDp/h+zs7ewu9PghQpHa3c4mkiUE4BYO4Ix0G44GBxjAAPkrxsuPGGvuBk/wBq3fH/ AG2krmWUKWJxwMYI5+mPwFdd4xQnxdrrA8/2reEf9/3rm2gy7OSxPb0oAq7Tv4IJXI69PzpnkpuC hvnz0/p+tWXjGOM5PXmk8rcCUPOMcdaAIY0KsOpC56enFKoxgEAk5GKlWLbk/kfw5py4YsPl6e9A DPLKg4yW9CakK57kZzQo+bGCF9M9qeE2ksMkBcgDpQAFd7ZByemD+dP8vPQ844zSRg5zyM9vSpET 5wSee/PXigCMKpYk9T0496ekQHcjtj1qVVBUseh6GneUOMN370AReXzx24z6UixKDkcZGT3q0Iwa dszgqM80ARLFx0PFOCYUYyBUypgZ5xnpTxDwB79qAICmT+FKsWOfU1a8vsO9P8rJFAFLyRu/DHFH l4B61dMPPBx6U0RdqAKbIcj29KhaEgZAOeuPetFogeufpTGiDH0oAoFP4TnhRmotoVtoHA5rQaIZ NRfZ8McdDwaAIVHc9R2qZQMjnOOhpuwlieSewqVV45FADWU9MdKryJkg4B6c9qt4IOPwqMooPzY9 uaAOr+EyhfiroAyc758c/wDTvLX1TXyz8KlP/C1dAPG3fPj/AMB5a+pqACiiigAooooAKKKKACii igCOcTNbyrbyRxzlCI3kQuqtjglQQSM9sjPqK870fUNd0q/dPtUEmhy6q8b3a6UFiWZ59kkaZuzK N05ddzRsAzkj92Bj0C/8r+zrnz/P8nyn3/Z9/mbcHOzy/n3Y6bfmz05ry/w22hX3iiSGDS9Vub63 1A3KTJr5ZlUARiaa3kuzIjKp8tgyFiowVXd5agG78ZBn4V6uPV7b/wBKIq+T7hcFmCk8jHX2r6y+ MI3fC7VR6yWo/wDJmKvliSHlgOPagDMaInaScAjpnvStGACV4x3z/n6VbEYzjOR70w7t2V2ngkAd qAIAu4YGQByR/hS+UCOcH+Hjoeal2EKTyRgDgev+RR5fzngnoT6UAR7SFKn5QTn3PTNPiQbAWI6Y yP5VKEO4DtwCT3FORCQMDA9h+v6UAIqsP4hg56GnBT82VwCM5704LubjGCMYx2/yalWMKOCM8596 AItp5IGeOacsZxk9fu/jUiRjAye3GPSpAvvg0AQ+WWUeh708wnGQOanVOuDgHgYpwjP0FAFXyiAc cjOelSiPAHQkGrAj4xjrT/Kz2Oc0AVwmdy9aXywSR3HXFWhGB0FOEY9KAKfljkfNjNOCHnjqOaue QMHimmIgdPagCr5Y4xxUflDnH+TV4xAgAUwx7WyDx1oAo+WDnjI7e1RsgfGOavGM7h/OomgXbjHP NAFYKAnb1NPCYJyO/WpCgAz0B7E0qq2MgdqABepHT1zTWQZGOucfSpccBfyo2/KfegCkV45XqPpX 0P8AAABfBOpgf9BV/wD0RDXgJUfMoHtX0B8AhjwZqgxjGrPx/wBsYaAPVaKKKACiiigAooooAKKK KACiiigDm/FF7qttcWUejfa5LxklcW8SW0kbqCgJkSWWJyBuGDG4wSN2RgGTwqLyys/7IvdGsdNk tYo3H9lg/Y33lt3l5RdrblYlMHAdDuJbjk/G2q+Z4stbO/h022s7JJJYLq91660stKyoMoyoFchX kU7DJgHloydr9x4etL2x0SG31ARi4V5CVS6luQFLsVHmy/O5ClQScc5wAMAAHy14rjz4q10566re cf8AbeSubdD0IJ+v1rqfFQJ8T64PXVbzt/08SVzzqcdCM8UAU3jwuc5/rxQEXJw2B+tWtuAAV6Hr TdgznP5UAQMhYYx0P/1qPKIVercnk1MF468E5zTvKIODgAdD6CgCJUAAI5z2pyoc9Op5qUKCcgdf 0p23YcZHrQBCiE4OPT8akEf6+lSBcAgAnjinqg9OOlAEaRHbjPQ9af5RPf8ATr71MI/Ud+9ShO5P T2oAhEWTxxT0hG7jHNTiP9akWPNAEAj9RUixHGP1AqcRYx/hUqw+/FAFYRcg9TThHkeuKtrHThFi gCl5Z59KPKPX1q4UGCaaY8DGKAKhiJ74xTDF09KulajKA0AUzF1PftTfJODzmrmzHbigIAKAKTRC kEZC4x3q55WDx0prRjGOlAFAL8xB5/SonQ5z2FXWTBPfFRlMk89KAOl+Fq4+Kfh49i8//pPLX1FX zF8MkK/FHw6f+mlx/wCk0tfTtABRRRQAUUUUAFFFFABRRRQAVwcp8Q6nrP8Aoum2mqabbXskyNrQ twqSxsyD7PNAzshVsYDw7xhwzg4A7S/uHtNOubmKLzZIonkWPDHeQCQPkVm5/wBlWPoCeK8v8Ntc 6xrUk9sulQ6nJqBu72CLxTetIkazBSXtMABvLCLtfaAdoZFx5YANj4n3X2/4Q3V35E9v9o+xS+Tc Jskj3XER2uvZhnBHY181XEf7zPvyK+n/AIuc/DTUv+u1p/6UxV80XCHzDgDJ4oAz3QdFXJ60wrkr xnPHFTsvB69PypNuO/A5GO9AEJQrhSQCep65pBHjgdMZx6VN5ZxgHp69ulJ5eMEZI65oAb5Y2CPl dw/+tTtuEJPIAzUqrszk554OKeFJHYj3oAh2DAJ2jH4fhT0UDAxg9cYqQpjHA4NPCDBz1x+VADPK 645HrUgjz9KkCe/epFTj6daAIli55p6xfL9BU6xjpT1j7YxQBEsWVxiniLgA5qwsWakWPnNAFYRc 4P4U8RY/H2q0Ihnmn+X7UAU/LP5Uhjq6Yx+NIYz3waAKXlEccGmmHParmwdiaa0fHJ5oAoNDwQc4 ppj4wO1XShB60woOeKAKfk7h0Apph/XtV7yvxppj4weaAKJjweOlNaMhMZq6Y/lPvUbJx04oAqKn z8mve/gKMeDtVB/6Cz/+iYa8LVPmHH/1q93+BQx4T1cf9RZ//RENAHqNFFFABRRRQAUUUUAFFFFA BRRRQBxfjfUfFdnZXp0i2kjgW3kaC4sYBdztKEzGrRtjYDJhflSbKkkmLG6tzwtZaFp/hy0tfDTw PpCb/s5gnMyHLsWw5Jz8xbvx0rm9Yji8O6jrOp6j4vu9Igv7j7VDbWccMrukdvCjvseF3JXYS23I VQCcc11mifav7Hg+2/bvtHzb/t/ked9443eR+76Yxt7YzzmgD5j8Spu8R62QOuq3v/pRJWA8OWGO 1dTr8YbXtZP/AFFb3/0okrEdAc0AZ3l8k96TyyT2+tXSgOKbs4xQBUKcEcChY/mx+dWwg46UgQZo AriAg9T+VKIvxHvVrAz60bRn2oArrHjv+FSLGR9PSpQoB4PFOVc4FADRH61Iseee1PwM9RUgHHrQ A0R8/wD1qkVDTwOakHX3oAaqc+1SqmaUAdqkAwKAGhMdKCn+cVIOlLQBCVx0ppTvVjApu2gCuU6e lNMdWSnFN20AVTHxR5dWhHmpFg5oAqeVnimPFjnHatNYBnNRyRgDqKAMoxcZqDyvatCWP5uOaquo XigDo/huMfEzw9x/y2nH/ktNX0rXzX8OG/4uZ4eH/TWb/wBJpq+lKACiiigAooooAKKKKACiiigC OczLbytbxxyThCY0kcorNjgFgCQM98HHoa87ne41LxlpVv4xT7NawyvcWkLKIrYXaSRi2McwYmWQ hrgbWKlggJhTv2niXTZtZ8K6vpdu0az3tlNbxtISFDOhUE4BOMn0Nc3DfBfENvptp4t1zV7pJT59 vBFYmONUdVk8x/JUDaWQMit5gDqQvINAD/iyM/DfUB/03tP/AEpir5yuIjvOfXjivo74rDPw6vh/ 08Wf/pVFXz9cxDzD60AYzQc560wwAZI5zWi0YzTCgGTQBRMffFAjA/SrgjX6UCMcZx9cUAVRCCDk Z4604Rg88Z9asqgAx1OacE4PbvQBXEeRjAAFPEVTbcD8KUJjFADBHjBxUgiwc9/509R3qRQM0ANW PmpVTFKOvtUqgLzQA1Y81IsdOXGPapFxQAix07ZTxilx+VAEZXNMK+lT4ppHFAEGzg0wp61ZK00q CaAKzJ6Uwx89Kt7PelEO48UAVhH+lHkknir6QVIYQq0AZLxEDHrUDx4GK1JUHIFUpExmgCqsaq2c e9e3/A//AJFbV/8AsKt/6IgrxNsjjNe1/A0hvCurkf8AQVb/ANEQUAen0UUUAFFFFABRRRQAUUUU AFFFFAHD+KtM0zVZ9RtLLStVnvp4jBqNxossMLbXTaEmMrqkreWTgMHZFYMNm5Seo0S7e+0eC4lu 4LuRtwaWC3aBchiCpjdmZGXG1lY5DA5APAz5fDE39p397ZeItVsPt0qzTQwJbMm8RpHkeZCzD5Y1 70eH0TSr+78ORvPcfZolv3u52XfK9zPcM2QqqowyE8D+LGBjkA8B13/kOax/2Fb3/wBKJKw5Oh6V ta+ca5rH/YVvf/SiSsORueOlAEZHHem56cfnQzAGml+e1AAc8HgUgOD7UjEYzz+Hagn0IoAcDgnP 86cDmo+M+lOBoAepNPB5qNSD6U9eDxQBKBn/APXUi9ahB4z9KlU4oAmXGRUq8nNQK3SpVcCgCdRT wCDUQdfUU4SjOKAJR6Uu01GJAKUSrigB+O1GKb5o9aTzRQA/bk0bM96b56jqf1prXKjvQBMAoxUm 5azXvAD1/WoWv27cUAaj3AAIqnLdD+tZ73bE+9VnuD3zQBee53Hrwaqyzkt7n9Kqtc/59KiMxySR j360Adl8NH3fFPw76b5z/wCS8tfTtfLnwukLfFXw6pXHz3BH/gPLX1HQAUUUUAFFFFABRRRQAUUU UAV7/wCx/wBnXP8AaPkfYfKf7R9ox5fl4O7fnjbjOc8Yrh7O3Sy8Q6ff2drquj2XNrB/aSLLaGOV 0JSNBL5tuz+VEqK4VEwF8sOwB7TVtNh1nRr7S7hpFgvbeS3kaMgMFdSpIyCM4Poax5fClzdGBb3x RrN3BFcQ3HkSR2iq7RSLIoJSBWxuQdCKAKHxV/5J5e/9fNn/AOlUVeB3XEhr2zxxqn9tfBqLVvJ8 n7aunXPlbt2zfPC23OBnGcZwK8QuX+dqAKz9aiY053GfpURdT3oAUn8cUmeTim7wen86QsBnj60A O3cH1pS35U3OAPWjd0NAD9/OM9KcDwaj3YNLu4FAE4PrT1xnioAeKkVsH3oAsLUgHHWq6sOBmplY CgCcDAqUZqBXHepBIPWgCYGl7VEHFPEgoAkwKNtRiQGl8wCgB22jZSeauetHnKKAHCL1p6gAVWe5 UCoXvQoz0oA0C4SoZLgYNZsl8T34NVpLzPy9z3zQBekuearPcZHWqT3P5+magNwWBxz60AWjKTj+ de3/AAGJPg/Vif8AoLP/AOiYa8DE/wAwBH1r3n4BHd4N1Y466vJ/6JhoA9WooooAKKKKACiiigAo oooAKKKKAOfbUNY1GfXLbSjYwTWN3BaxNdRvIvKRSyuwVhn5JcKox8yZLYbCnhV3v7afVr1IDqbS zWE80CsiSJbXM6JhGZtvVj1P3uvAxzfi21h1PxBLHFa+H9Zv4kVFs5NAF7cQLjdiSVp0SMHLMocp kZC7j17Dw5BqVr4fs7fVotNhvIkKNHpistuigkIEDcgBdox65xxQB83eImxr+sj/AKit7/6USVgy vjOT0rZ8SHHiHWuP+Ype/wDpRJXPyuCCTxg5oADJ6enemGUZ+X9ahL4PHbrTMnnA4zxQBY8wE98n vSeb1HXsf8aqM5YEE8cdfWml2A2jOcUAXPOGTkE9qUTc9SM81RMgUnP4804OTxnnjNAF5ZgCADnj GalFwD34rMDkZxyB2pRLhuM/hQBqCfgYJ4p/2kEdeazA5GM9cc4pwcgcmgDUFyM4GacLkEjkk1le dg8kY9acH4B3UAaouRmni5HbrWT5ueKd5pA/HtQBqfaRjvTvtQHNZHn4IBzTfP8Anw2QRQBrtd9h 0qNr4DPNZRuBu5bio3kJ5yD7+1AGk1+SeDxUTXTEj5qob+OeccjmgOMDPTHNAFnz2/vGl88+tVNx 2nnoMc0wuN/p3yf1oAuecTyfxNM3biRnn3FQ5BBOTwf/ANdNLZbJGDmgCRny57568UFs56gelMEv PAOMdKZ5h+UYPXFAHb/Crn4qeHfZp+fX/R5a+pq+V/hRIP8Ahavh2MHo1xx/27yV9UUAFFFFABRR RQAUUUUAFFFFAGfrup/2J4e1PVvJ877DaS3Plbtu/YhbbnBxnGM4NY+t3viTSnsrlbnSnsfNsbad TayeZLJLcLFIynzMRqA6lQd5zkHoCegv5vs+nXM/2mC18uJ38+4GY4sAnc4yvyjqeRwOo615voek Xg1yyvPD+j+FHtYpVMupR+HzZAxHh/Ik85mdijZVgnlMCfnPQgGp8SLK30v4Uy2FnH5dravYwwpu LbUW4hCjJ5OAB1rwK5cl29a+hfix/wAk5v8A/r4tP/SmKvnG7bEjc0ARvIMkdKiMoA55NRs5Bxnv URYcjI70ATiQ8DNIZcHbyeevrVZmYEjd+dNEhYHtnk80AWjMPXt3o83nrgdeaphwSwOMim7yc4P1 /wAKALwnB4yc09ZgR3wfaqPmALls03fnOT07igDTEy56nmnCYc4OazVk4DEjH0p6yd/4e/rQBprO M57dKetwKzPNz3/KlEnv+NAGqLkE5xzT1uR0rJEp7Gn+acYBOaANX7TzilF2Bxk1k+acfrQJu+eP X0oA1TeYGc003voefesc3GAxzwOaj84nkkfjQBrNqOO5qI35J4NZxcluO/Wmb/mAzkjrxQBoG+LD qQOaYZ2ZyD3qkx3BuufWlLnOcY60AWnmOO/HtUZk6k4waiWTKsT3NI7YGB9enSgCQuCOf/1jvUIY cjOOxNML5GQPzpC+EBH60AT4xIpxX0B+z/8A8iVqvX/kLyf+iYa+eo5Bwe2M19DfAFlfwXqpTp/a 0n/omGgD1aiiigAooooAKKKKACiiigAooooA878U6XpkuqalqNxH4YkELwRzNc+GZL6cPJtRELpI C7klcKFyFZMjBBPUeEGtG8MWoshaCBHlTbaae9jGrLIysBA5LIQwIIPUgnvUcujaxbanf3WjapY2 8d9KtxPHeWDzkSCNI/lZZo8LtjTggnO45wQBc8P2S6fpPkLLJK5uLiWV3haLdK8zvIVVuQm9m28n 5dvzN94gHzP4n/5GLWuuP7Vvf/SiSudduQQAR1roPFHHiHW+P+Yref8ApRJXOS8cHrnFAETNkcD9 KiLhuRn3FO3dwcjpUb8Doevp+dADCx4wRmjdyeDjoCT1oI2sQvcnrTcfMdrDp3/z7UADMBwewx+N KvHBB6DmkYr5YfI2560ivhATuAI4B/GgBxJ4GDknsOlPGOhOCRk5NR5zznk80AjOcHHTOKAJQ53H HOO3fNPBbeOOMdvyqMMCAx6fWnfxBuce3PegCQEjnOD1yefrTiQQevWog+TgkjI604HvzQBIM4IB Gf50Fhjbzn06UwsMkH8KOCe+QcUAPMhxnbkdqYWBB3n6etN4yAeccYzTHYZ/u45FABnLdB9aUEbs jPXpnrUJwMKDx0AHJpSRkHHtQBMRkgDke/4UHtheDUHm/Moz74ppdhlWOB1JHNAEzNzyMGjJXdtA z78Zqq0mC7feJHQ0hl+ZQQeeOf8APvQBeLhUGMVA8mfmDcAYyai81QMZ5U8gnuaj+UL8x6HIOffr +lAE7ScY5Xjoe3FNEu1SST1I59KiM27CAgHk1BLNkF1C4Ld/pQB33wfYt8X9AJz/AMvH/pPJX1pX yL8GXLfGDQgW3EG4/wDSeSvrqgAooooAKKKKACiiigAooooAp6sIW0a+W4ktI4DbyCR7xA8CrtOT IpIBTHUZGRnkV5vBp+j6Tr2mLPb+GIp2e1nXyPBk8TxiWTbFmTzCIHZlIG/BBHI4r0jVtNh1nRr7 S7hpFgvbeS3kaMgMFdSpIyCM4Poaw7vS9bult01nVrGaxS7t5WjsdJlSVnSVHj+bzpMLvVdxKn5d 3K/eABS+LP8AyTfUP+u9p/6UxV833HMhNfSHxa/5JvqH/Xe0/wDSmKvm+5PzH60AUpD07+lQt6g9 s1JId2T71A2cDJAb6cUAIxBZsgcCo96KSeRgcj3pzMBk96aTkq3GfSgBFYZOOQOoHWgP13Yyefwp NytsG3JI54poBAABOQOtAEoJI3cfQU1TwSM4zgdqRWAOAepz+FC4C8MARxyKAJAdyknHPTI7fSlD ZwM8N2xTMkEAgDNOXDDI4GcD60ASjJXn60oyOhHpUW75DxuPqaeDkDk9c0ASBjgc54H40obOCOnq e9MzkFWJ45pwODjBNACknvTS+OCBTJHGDzgDrTC2MgZ7daAHSNlTn/69R45PzDFN35yRgAZpA25W UnHtjn60APEgDYz82Oad5h2kgevTrUIcb8ehz9aQSj5VwOT2oAnyueTnI6+lGcgA9e4Pb/PFV2fK 5b5Mnj9aC2Co3nj14J6UAWVO3kg4HamySgSdc4PSqxc7dm4hl6nOAajabd1UEHoc/wCfegCfedy7 jjuMfypkkwALYPIwPr6VCZAUJJOeADimFxtVt4IHOM0ATLLuVhu2jPGTjNfRv7O3/Ihan/2F5f8A 0VFXzZ5oyxYZCnocYr6S/Z058Aaj/wBhaT/0VFQB69RRRQAUUUUAFFFFABRRRQAUUUUAFRwwrAhR DIQXZ/nkZzlmLHliTjJ4HQDAGAAKkooA+UfE/wDyMetf9hW8/wDSiSuadiW4HHtXSeKf+Ri1of8A UUven/XxJXNyADoABQBXcDJUcevvUZBAC5wQOTT3PXHQd/5VGV5znn2oAY5J7Zx60pARB6DpzSNl mHPTr70Yzkrj1oAYWLcLwc4GPr/+qnZXlCCc8EdfrTSFG4Hofb+dPHYEjPGcigBuMAgEk4xn1p2Q GyewPOaaudxIPLdieBTkXGMemT70AOXkLxk560qFvlzxwOvGaCTg9ATjqaFwuGIJC5GKAHgEAD8q d6k49u1NVWCEgfn3/ClUYVcgDp+dADurEqQc+nelzjGM8dOKbnDdB6HmkY5JPHJzQAu4AkHHFV2b DfKeO3NK+CccEdcelRlwG2jn6UAKHCruPIJxyKj8zO3c3A6n3+lRkhOSWOcdf8+1I78ZIHHrn09f 89KAJS+ASCF44JHt1pgk2xqJMHg8DoP8/wBajOcYxjOCQKh8za4OSf5//XoAmMittOSCc4BHU0gl yCOpPOfWqucgL2HIFGC23jOfegCVpWYEZPI596GlJ6kn1XOKiBHUnLHvQDg5B57c80ASHcoIYj3z 1pCcqWX5Rnp6H/IpA4Azz/n0/Go2YsfoMUAegfBVy/xg0In/AKeP/SeSvr2vkD4Jf8le0L/t4/8A SeSvr+gAooooAKKKKACiiigAooooAKjmhWdAjmQAOr/JIyHKsGHKkHGRyOhGQcgkVJRQBxXxZ/5J vqH/AF3tP/SmKvm66yJT6Zr6R+LP/JN9Q/672n/pTFXzbdf6w+tAGe5544HaoioA2gDpge1SuTuw PWoSDuHUt69f89aAGcYPOM5JxTOCQT945yD3NOK7gBjr196aBhSC5BY5+nOaAFPIyQDUYOCOB/U0 4kNlieg9Kb8ofB44z0/OgB5yMHaVXqecUgbDHJGD0/KkByOWxg45oJ+UccDqcY/zxQAvBBIJAyeT zzUg+UnOQoIH0ppOASRx1xnmn4JGCxJ6j25oAXA4ODkNT87T369M1GGyuBxkZzUmDk8HA5HrQApy SRjJ9BRuJ54yaQdcYznvSMOfmGQT2oAaW3Lu7D0pjHAzyenHrzT2YbcDoRjHSoGOCMdewNACA4bG MBuTj86azEk/OQM4JPH4Uw5VRjqB+lMeYlxhhg9RQBIJGIOBhgcbgOv+cUm4bQFUD0pnygZzlc8H H0/xpDIQM4JbOAfSgB7SBeNw6k8/5+tRvIpG4nn6dPf9aiZyG3YwSaieQsNuABQBK0wbJIznoOua QzAy5BYZ6eoqDOeAPwFBHAGaALAmyGzt454ppHUA4PTk/wCfWourc05XUAZzkf5/w/KgB7YDNnIG M8V9Mfs48/D3UP8AsLSf+ioq+YpHLccfhX05+zj/AMk81D/sKyf+ioqAPYKKKKACiiigAooooAKK KKACiiigAooooA+T/FJx4j1rjP8AxNbz/wBKJK5ubOcZ5xXTeKP+Ri1rn/mKXv8A6USVzcyk5x+t AFfaMDHXnIHrUTc8qenUjpUzLg8evH501wAMgZPvQBESf4RUZjIAIbk8A57VLgffzjvSNuY4GOD3 PagCPHOc+2OtKB82Rj6Hj8v0pccBtvzYPfFAbAOMEkfLQA3BJwrDJGKen3ccdevoKYB82c4BO7FO GSwBPp9M/wD66ABHBYLkA5zj2NSd+eB1xTF3NgEjjr3p2wHJOcH3oAd3Iz0NBI7ngHjtxSAqxGGO M45+lIB2ZevUE/pQA4lgqnv0PemtnYeMj6UrYOc9aj83DYA5/lQAxuXPYtxz2pjsFXg45z0pgdsM P4h0qF3fcSwAC44oAcZAAZPmJJySR0+g/E1Htzu56nHAwKbI2SW4GTwAPwpu4AEDkfTrn/8AXQAs jsScEe+f51GcBcHjPI5/z/kUOTzk/N0PvTCc+nPpQAu7OTwM03PApKKACiiigBc8YpKKKAPQPgl/ yV7Qv+3j/wBJ5K+v6+QPgl/yV7Qv+3j/ANJ5K+v6ACiiigAooooAKKKKACiiigAooooA4r4s/wDJ N9Q/672n/pTFXzbd8SNx719JfFn/AJJxqH/Xe0/9KYq+bbsfvWH96gCgxwwwD17VCWPQdKnkXJOR k1Cc8jaCO/tQBCFI+6SMd/T/ACKYVPr04x61MVyT79PemmPcScEkjIHQfSgCMs7MTjIoOEOD/F8u DSkfPknikVcfKec/KPb/ADj9KAE4/h+Xj/I/SnHnrnk5OBQSpXg7io6Y6f5xThkNjnP06UAAzsCg AZHPpilMZGc85Oc/5+tLkYGCCe2aUNyM4AB9KABM5zkZ9MU7IHJXnp+FJkAqS30pMnPy4oAduwcA Yx/nFMcHHK55wBRgqxzkA8DNRnoM89z+VAAxGCMHcMZ71XL4AYjOOc9fxqWST5h8p5PJBqvISxI6 AjgdR7H9aAFdgGK5Ct246VEx4KkD2P8AWkzuUMRnPOfT3pH3K2SvIHHHtzQAFioOevfngHNRSOWx uzuHU/hQzgnp19ulMJzg5OR14oAc5H3x65GP60wHBHqDSEnnJpKAHFhjgYpCec9/WkooAM0UUUAF fT/7OP8AyTzUP+wrJ/6Kir5gr6f/AGcf+Seah/2FZP8A0VFQB7BRRRQAUUUUAFFFFABRRRQAUUUU AFFFFAHyp4mXPiHWuOuq3v8A6USVz0yDIOa6bxJga9rJP/QVvf8A0okrnJSO3agCm64BA496jxtG Mk4PFTuRgkkcVXOMld3agBj9COM+3pTGI37goORnPSpWZQp5x6EjvTCRyB19e3SgCPGJM46jOaUA BuT8pHrQzLkbSOmMYphZQ47ZHQUAOYd8YJOenPShflB7YPGOwpvmKwzuJJPT1/CgMrFssG4oAfkA E9+wHelDgjIzwf0qIsRyRt9B1P40hmVS3zYOBxmgCXzAAMDIA47mmmYsuHPsOenvUPmgAseB1GOv +etIZMqD8pORgHp/nmgCQk4yCWPvTSTwW59xxmomYo29cjJweM4/zihpMP8AewecGgBpUCMqpIHc 8iozk/Kc7R3x2pZpQQQuADg/Wms/3SxzjuO9ACFdmdgOc9QeuKYeBu4B5zn+dBI8v1HrUZIC46nP NAA3XGDk+tN70Z4pKACiiigAooooAKKKKAPQPgl/yV7Qv+3j/wBJ5K+v6+QPgl/yV7Qv+3j/ANJ5 K+v6ACiiigAooooAKKKKACiiigAooooA4v4sf8k5v/8Ar4tP/SmKvnG7TMjcmvo74r/8k6vv+vi0 /wDSqKvna72iQjjOaAMqQHJxjjtURAGSQVzxmrUgAY88A8VXkwRnP0yaAIWA/QfjTcYYE4weBUhI yPSmMVLYJz2GB/n0oAYufqG5680wMGO4EcjOOafvCnGOAc89qjDjIz07Z70AG4AZAxnnn60Dq4AK 5PWkL/wqc/Xt/nNIJQCc4IH8QoAkyAAAPQ5FOyVHXr+lQiQbQCckDqBxmmmZE3tggdyD/n1oAmPQ k9QOAfT/ADims42Z3+/A/P8AlULOpw2MNt49x1ppnVyAOec9P8+9AEvmFvmIJPcj9abvJUsMEEAD nvULOiApvxj7vOf5UpdSzAsp5x9TmgAZWI5PIHT1FR4242nJxzgcH0oM+VKr8oIODjtURdlOwcjo PwoAPujOB1I4pm4jPOQ3qf1oLEA5PPr601vTIFAAxyTkY9qafWgtznk8d6TP0oATNFFFABRRRQAU UUUALX09+zj/AMk81D/sKyf+ioq+YK+n/wBnH/knmof9hWT/ANFRUAewUUUUAFFFFABRRRQAUUUU AFFFFABRRRQB8q+J5Auv6yCT/wAhS9/9KJK5eWQYKn86+hNV+BnhzWNUvb+fVdcR7u4kuHjiuIwi s7FiFBjJAyT3qgf2dfCZ66rrx/7eIv8A41QB4BJNgbv0NV3k2gkjvjB719Df8M5+ET/zE9d/8CIv /jVH/DOfhHGP7T13/wACIv8A41QB86+exGChBycj2qJ7j5NwHTgc9QM//Xr6O/4Zx8If9BLXf+/8 X/xqj/hnHwh/0Etd/wC/8X/xqgD5uZ2OUHfoQe/+TSNM5CjIB68nv/nNfSX/AAzj4Q/6CWu/9/4v /jVJ/wAM4+D/APoJa5/3/h/+NUAfNjO23HIAPLA9e1HmyL8pI6gj36V9J/8ADOPg/wD6CWuf9/4f /jVL/wAM4+EP+glrv/f+L/41QB81bm8wtvI3eh5GelDO2CGbnnn1/wAK+lT+zj4QPXUtd/7/AMX/ AMapP+GcfB//AEEtc/7/AMP/AMaoA+aDM5wScE45z0pvmNtUbevp37V9Nf8ADOPhDOf7S13/AL/x f/GqP+GcfCH/AEEtd/7/AMX/AMaoA+ZN4I5BwBwP0pC/Ock8Yye9fTn/AAzj4Q/6CWu/9/4v/jVJ /wAM4+D/APoJa5/3/h/+NUAfMJOepo3Ed6+nv+GcfB//AEEtc/7/AMP/AMao/wCGcfB//QS1z/v/ AA//ABqgD5gor6f/AOGcfB//AEEtc/7/AMP/AMao/wCGcfB//QS1z/v/AA//ABqgD5gor6f/AOGc fB//AEEtc/7/AMP/AMao/wCGcfB//QS1z/v/AA//ABqgD5gor6f/AOGcfB//AEEtc/7/AMP/AMao /wCGcfB//QS1z/v/AA//ABqgD5gor6f/AOGcfB//AEEtc/7/AMP/AMapf+GcfCH/AEEtd/7/AMX/ AMaoA+X6K+n/APhnHwf/ANBLXP8Av/D/APGqP+GcfB//AEEtc/7/AMP/AMaoA8g+CX/JXtC/7eP/ AEnkr6/rzfwt8FPDfhHxHaa5YXuqyXVrv2JPLGUO5GQ5AjB6Me9ekUAFFFFABRRRQAUUUUAFFFFA BRRRQBxfxX/5Jzf/APXxaf8ApTFXzfeynzW4OQa+rvE3h618VeH7nRr2a4hguChaS3YLIpR1cYJB HVR2rzpv2efCzkltX8QEnnJuYv8A41QB4DJMwY9x2qvNM7H5QM+gPSvoM/s5+Em66prx+txF/wDG qT/hnLwieup67/3/AIv/AI1QB87SStk4bpTGnJ+ZivPHB7V9Gf8ADOXhA/8AMS13/v8Axf8Axqj/ AIZx8If9BLXP+/8AD/8AGqAPm5p3yT8uCD746U1pWZiMAgZAwR0/yK+kv+GcfB//AEEtc/7/AMP/ AMapf+GcfCH/AEEtc/7/AMP/AMaoA+aA7tkfez15zStIdzEt2Hfr619Lf8M4+EP+glrv/f8Ai/8A jVH/AAzj4Q/6CWu/9/4v/jVAHzQHGTljzn6Gl3/LlnBOBx9O1fS3/DOPhD/oJa5/3/h/+NUn/DOP g/8A6CWuf9/4f/jVAHzMpxty30phkbORwc5HNfTn/DOPg/8A6CWuf9/4f/jVH/DOPg//AKCWuf8A f+H/AONUAfMZc7j3z196aWOcjivp7/hnHwf/ANBLXP8Av/D/APGqP+GcfB//AEEtc/7/AMP/AMao A+Yckd6TODmvp/8A4Zx8H/8AQS1z/v8Aw/8Axqj/AIZx8H/9BLXP+/8AD/8AGqAPmGjPbtX09/wz j4P/AOglrn/f+H/41R/wzj4P/wCglrn/AH/h/wDjVAHzBRX0/wD8M4+D/wDoJa5/3/h/+NUf8M4+ D/8AoJa5/wB/4f8A41QB8wUV9P8A/DOPg/8A6CWuf9/4f/jVH/DOPg//AKCWuf8Af+H/AONUAfMF FfT/APwzj4P/AOglrn/f+H/41R/wzj4P/wCglrn/AH/h/wDjVAHzBRX0/wD8M4+D/wDoJa5/3/h/ +NUf8M4+D/8AoJa5/wB/4f8A41QB8wV9P/s4/wDJPNQ/7Csn/oqKj/hnHwf/ANBLXP8Av/D/APGq 7zwV4K03wHo02l6XPdzQS3DXDNdOrMGKquBtVRjCDt60AdJRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRUc0bSoFSaSEh1bcgUkgMCV+YEYIGD3wTgg4IAMu+8SWen381nLHO0kX 2PcUUEH7TO0EeOezKSfQdMnis/wL4kvPE+jyXl7HBHIv2fAhUgfvLSCdupP8UzAewHfk2L/wnBqN /FeS6jfLIJYZJgnlYnWGczwo2UOFRmIG3axB+Ysear2Gg3nhOwSz8O2sF9G/l+adRvjCV8uCKBNu yFs5WIE5xznHBwAC5P4otofEEuiJaXc96luZ1SMIN+BnaAzA4PTzCBFu+QuH+Wufs/iG0/hVLu40 67h1BdCXV52S3WSCJGSUhv8AWDILRcJuDEOvTDlOgttGmur+21XVHkjuYnMq2MV0ZreKbY0XmIzI r5MbEFeEyxO0t8xp2ngWwtdDutJN7fTQ3OlJpDSSNHvWBPNCY2oBuAmYZIP3Vzk5JAJIPGVtc3DQ w6bqTf6RcWcMjRoqz3EJk3RIWcZJWJ2DHCcYLBgVFzwpq1xr3hLSdWu7b7PcXlpHNJGMYyyg5XDN 8p6jJzgjODkVn6J4R+wXX2q8vJ5WTULu+hthLuhjeaWXbIMruDeVKVKg7MljtLfMZLSw1rw5pdho +jW1pqNnaW6wpPqOoGCXC5AXEduykBQoB4J7jPJAK8nj6yiNw0umakkED3JaciIqYbeQx3EwAk3b EbbkY3ncNqtzguPiBptrb3l1NY6klrbPdxee0KhZZrcSNJGmWyxKwuwbGzjBYNlRJ/whdnd6Y8F2 88UlxaX9vMsUwcJ9tkEs21igztYYUkDjqCapxfD+G6sL611S+u3jubi/lWCKYGOI3DzL5ibl3K4h mKlc+Xkk7S3zEA0LnxnZWN7pdvfWd3aJqbhLaW4aJCzF9ijyjJ5uTlDgIdokXftwwWnL8R9Ht4Fu Lm2vre3ltDe208yIsdxDviRGVy21d7TJgOVKjmQIME6mr+F7bVr37W13d20jJEsnkFMSGJzJAx3q 2DHIxcYwCeHDr8tc/ZeApm1LN5PJFYWVu1ppix3hmaKMSwyQkKYkVDGYEPzeaXzh2YIAQDcXxTZ3 vhqz1uwlzb3N3Bbg7BJhnuFgZflcKcMSNysy8bhvGA1PTvH1lqCWcjaZqVrFdJbyCScRERx3Dbbd 2CSMcSPlQACVKkuFGCdSfw+lzY2tpPqF9LHBLHOxd1ZppUmSYMxK8fMmNq7VCuQAAF207fwVptta 29uk92Ugt9Pt1JdclbOUyxE/L1LHDeo6Y60ASaT4us9Y1G2s4bO+i+12j31rNNEBHPbqYwJFYMfv eapCnDgD5lXK56CuP8PeFLzS/EK3k0u2xsbSWw0+H7WZsW7PGUXb5aBNgiA58x23/M52LnsKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArz/X/AIj/ AGbR7a80i2zJNp41AC7TgRvaXc8Y+VvvbrUBh0weDk5HcS28skrOt7PGp8vCIEwu1izYypPzg7Tz 0Hy7TzXJy/DTSp4ZIJL/AFJoDbi0hj3xgW8Cw3EKxodmSFW6kwWLMSFyTg5AOwnmW2t5Z3EhSNC7 CONnYgDPCqCWPsASe1cmnjc3zeHZNM02ea21PUHtJ33wuIwsUj5V1lKPyoOULgBJFOHAWtSd/ENz cS2r6Xpo0+RzG08erTJOIicbgFgG19vOA4wejd6jTwnAjWsx1G+e8h1AahJdN5W+4k8ow4cBNgUx EJhVU8A53ZJAMsfEWxNrpmpGz1KOwvrfzokaz3SyAy20asqh92A1zjAUl8EqSNvmWJ/GE51HR7S2 0i7WS41NrG+im8rfakW7TAEiTaSV2PlS42hhw+FqPVfAdvdaLo9ha3E4/syK3tI2eYoWgSe2kclk AYSbbYYK7eT24I0E8JwI1rMdRvnvIdQGoSXTeVvuJPKMOHATYFMRCYVVPAOd2SQDQ1rVk0TTDeyW 09z+9ihSGDbvd5JFjUDcyr95x1IrDk+IGmwRGS5sdShG8Ii+SsjSMJ0t5QqxsxJjlkVWGPm6pvHN WJbbXtZMFpq2mabbWa3ENw0tpqjyyBopFlQBWt1BBZFB5HBOOap6x4Dt760gjtridZI7tpSWmMfy S30N3NhkAYMPKwhBGM8nPzAAuQeMrafVLbSv7N1JNRleRJLYxoxtynks3mMrlANk8b5DEEfL9/CG vbeP9Pu3ntYrK7fVIbhbY6aktvJKXKyNjcspiUgQzEhnUjyzx8y7tDTfC9tp+qLqhu7u6vykyyzz lAZjJ5ILMEVVBC28SjaAMA5BJJrHufAgs7eOfRry7+32iRx2IlnjjWCNAyRoG8l8hI5Z1BZXJ807 iSEZACSL4leH5b+wsy88U15K0SJOqxOp894FzGzBzukjYYVWK9XCCrmqeKhpfiD+y1sru+nlSAQ2 9qkasWcXLEl5JFXG22bjAxgctuwtfQPBa6VFbyve3cdxvd7mOG4ZlmBnknSN5HHmOI2mcbgVMmTv BB2jQbwvbSeIIdamu7uW7hdWXcUC4UXKquAo4C3Tj1+VMkncWAKcPjqwuYo/s1lfTXFx5TWdsqxh 7uKRZHjlQlwqqyQyth2RhsIKglQdjRdYt9e0wX9qk6QmWWILPEY3zHI0bZU8ryh4OD6gHiufufBC WenWzaNNP/aVjaWtrZSy3Cx7BAJUDbvKkG4pPKDlGByMBT8w2PC+jvoWgxWMr75PNmnb960u0yyv KV3v8z7d+3e2C2MkAnFAGxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUVwfxZ8a6l4D8K2uqaXBaTTy3qW7LdIzKFKO2RtZTnKDv6145/wANHeMP+gbof/fib/47 QB9P0V8wf8NHeMP+gbof/fib/wCO0f8ADR3jD/oG6H/34m/+O0AfT9FfMH/DR3jD/oG6H/34m/8A jtH/AA0d4w/6Buh/9+Jv/jtAH0/RXzB/w0d4w/6Buh/9+Jv/AI7R/wANHeMP+gbof/fib/47QB9P 0V8wf8NHeMP+gbof/fib/wCO0qftGeMHkVf7O0MbiBnyJv8A47VRi5yUY7sD6eor5g/4aO8Yf9A3 Q/8AvxN/8do/4aO8Yf8AQN0P/vxN/wDHakD6for5g/4aO8Yf9A3Q/wDvxN/8do/4aO8Yf9A3Q/8A vxN/8doA+n6K+YP+GjvGH/QN0P8A78Tf/HaP+GjvGH/QN0P/AL8Tf/HaAPp+ivmD/ho7xh/0DdD/ AO/E3/x2j/ho7xh/0DdD/wC/E3/x2gD6for5g/4aO8Yf9A3Q/wDvxN/8dr2PX/GupaV8GovGEEFo 2oPZWlwY3RjFulMYYYDA4+c459OtAHeUV8wf8NHeMP8AoG6H/wB+Jv8A47R/w0d4w/6Buh/9+Jv/ AI7QB9P0V8wf8NHeMP8AoG6H/wB+Jv8A47R/w0d4w/6Buh/9+Jv/AI7QB9P0V8wf8NHeMP8AoG6H /wB+Jv8A47R/w0d4w/6Buh/9+Jv/AI7QB9P0V8wf8NHeMP8AoG6H/wB+Jv8A47R/w0d4w/6Buh/9 +Jv/AI7QB9P0V8wf8NHeMP8AoG6H/wB+Jv8A47Sj9ozxgYy39naHwQMeRN3z/wBNfaqjFydl/VtQ Pp6ivmD/AIaO8Yf9A3Q/+/E3/wAdo/4aO8Yf9A3Q/wDvxN/8dqQPp+ivmD/ho7xh/wBA3Q/+/E3/ AMdo/wCGjvGH/QN0P/vxN/8AHaAPp+ivmD/ho7xh/wBA3Q/+/E3/AMdo/wCGjvGH/QN0P/vxN/8A HaAPp+ivmD/ho7xh/wBA3Q/+/E3/AMdo/wCGjvGH/QN0P/vxN/8AHaAPp+ivmD/ho7xh/wBA3Q/+ /E3/AMdrp/h38a/Eni7x3puh39lpUdrdebveCKQONsTuMEyEdVHagD3iiivB/iJ8a/EnhHx3qWh2 FlpUlra+VseeKQud0SOckSAdWPagD3iivmD/AIaO8Yf9A3Q/+/E3/wAdo/4aO8Yf9A3Q/wDvxN/8 doA+n6K+YP8Aho7xh/0DdD/78Tf/AB2j/ho7xh/0DdD/AO/E3/x2gD6for5g/wCGjvGH/QN0P/vx N/8AHaP+GjvGH/QN0P8A78Tf/HaAPp+ivmD/AIaO8Yf9A3Q/+/E3/wAdo/4aO8Yf9A3Q/wDvxN/8 doA+n6K+YR+0Z4wMZb+ztD4IGPIm75/6a+1J/wANHeMP+gbof/fib/47VOLSTfX/AIYD6for5g/4 aO8Yf9A3Q/8AvxN/8do/4aO8Yf8AQN0P/vxN/wDHakD6for5g/4aO8Yf9A3Q/wDvxN/8do/4aO8Y f9A3Q/8AvxN/8doA+n6K+YP+GjvGH/QN0P8A78Tf/HaP+GjvGH/QN0P/AL8Tf/HaAPp+ivmD/ho7 xh/0DdD/AO/E3/x2un+Hfxr8SeLvHem6Hf2WlR2t15u94IpA42xO4wTIR1UdqAPeKK8b+LPxZ17w H4qtdL0u002aCWyS4ZrqN2YMXdcDa6jGEHb1rhP+GjvGH/QN0P8A78Tf/HaAPp+ivmD/AIaO8Yf9 A3Q/+/E3/wAdo/4aO8Yf9A3Q/wDvxN/8doA+n6K+YP8Aho7xh/0DdD/78Tf/AB2j/ho7xh/0DdD/ AO/E3/x2gD6for5g/wCGjvGH/QN0P/vxN/8AHaP+GjvGH/QN0P8A78Tf/HaAPp+ivmD/AIaO8Yf9 A3Q/+/E3/wAdo/4aO8Yf9A3Q/wDvxN/8doA+n6K+YX/aM8YIwH9naGeAf9RN3Gf+etJ/w0d4w/6B uh/9+Jv/AI7VSi4ScZboD6for5g/4aO8Yf8AQN0P/vxN/wDHaP8Aho7xh/0DdD/78Tf/AB2pA+n6 K+YP+GjvGH/QN0P/AL8Tf/HaP+GjvGH/AEDdD/78Tf8Ax2gD6for5g/4aO8Yf9A3Q/8AvxN/8do/ 4aO8Yf8AQN0P/vxN/wDHaAPp+ivmD/ho7xh/0DdD/wC/E3/x2j/ho7xh/wBA3Q/+/E3/AMdoA+n6 K4P4TeNdS8eeFbrVNUgtIZ4r17dVtUZVKhEbJ3Mxzlz39K7ygDx/9o7/AJJ5p/8A2FY//RUtfMFf T/7R3/JPNP8A+wrH/wCipa+YKACiiigAooooAKKKKACpIP8Aj4j/AN4fzqOpIP8Aj4j/AN4fzrfC /wAeHqvzFLYjooorAYUUUUAFFFFABRRRQAV9P+Mv+TXrb/sFab/6FDXzBX0/4y/5Netv+wVpv/oU NAHzBRRRQAUUUUAFFFFABRRRQAVIP+Pd/wDeX+RqOpB/x7v/ALy/yNb4f436S/8ASWJ7EdFFFYDC iiigAooooAKKKKACvQPgl/yV7Qv+3j/0nkrz+vQPgl/yV7Qv+3j/ANJ5KAPr+vkD42/8le13/t3/ APSeOvr+vkD42/8AJXtd/wC3f/0njoA8/ooooAKKKKACiiigAooooAkH/Hu/+8v8jUdSD/j3f/eX +RqOt63wU/T/ANuYluwooorAYUUUUAFFFFABXoHwS/5K9oX/AG8f+k8lef16B8Ev+SvaF/28f+k8 lAHQftHf8lD0/wD7BUf/AKNlrx+vYP2jv+Sh6f8A9gqP/wBGy14/QAUUUUAFFFFABRRRQAUUUUAS TffH+6v8hUdSTffH+6v8hUdb4r+PP1f5ijsgooorAYUUUUAFFFFABRRRQB9P/s4/8k81D/sKyf8A oqKvYK8f/Zx/5J5qH/YVk/8ARUVewUAeP/tHf8k80/8A7Csf/oqWvmCvp/8AaO/5J5p//YVj/wDR UtfMFABRRRQAUUUUAFFFFABUkH/HxH/vD+dR1JB/x8R/7w/nW+F/jw9V+YpbEdFFFYDCiiigAooo oAKKKKACvp/xl/ya9bf9grTf/Qoa+YK+n/GX/Jr1t/2CtN/9ChoA+YKKKKACiiigAooooAKKKKAC pB/x7v8A7y/yNR1IP+Pd/wDeX+RrfD/G/SX/AKSxPYjoq7ZaTf6gwW1tncnoThQfxOKuXPhTW7WE yy2R2Dusit+gOawGY1FaK6Hf+WZJYxAg7ytt/Tr+lUZYZIX2SLtbGfwoAZRRRQAUUUUAFegfBL/k r2hf9vH/AKTyV5/XoHwS/wCSvaF/28f+k8lAH1/XyB8bf+Sva7/27/8ApPHX1/XyB8bf+Sva7/27 /wDpPHQB5/RRRQAUUUUAFFFFABRRRQBIP+Pd/wDeX+RqOpVUmB8D+Jf5Gp10u9aHzlt2Mf8Ae4re t8FP0/8AbmJbsp0UEEEg9RRWAwooooAKKKKACvQPgl/yV7Qv+3j/ANJ5K8/r0D4Jf8le0L/t4/8A SeSgDoP2jv8Akoen/wDYKj/9Gy14/XsH7R3/ACUPT/8AsFR/+jZa8foAKKKKACiiigAooooAKKKA M9KAJJvvj/dX+QqOpZUZpBgfwr/IUv2WbGdhrfFfx5+r/MUdkQ0VK1vIgBK0zy3/ALprAY2ilKkd RikoAKKKKACiiigD6f8A2cf+Seah/wBhWT/0VFXsFeP/ALOP/JPNQ/7Csn/oqKvYKAPH/wBo7/kn mn/9hWP/ANFS18wV9P8A7R3/ACTzT/8AsKx/+ipa+YKACiiigAooooAKntLOa9uYreBcvK4Rc8DJ 960PDWgTeJNZj0+F/LBUu8m3O1R3x9cD8a9xsPAKWOjvaW6xllkWWLK5ORz19eKAPPZvhZdLa2jW 3mzzMymYjG0A9cfT3rZ8V6DpmjaOkUcNqk6JjKoobJzXrmkW0p09RLA0cijaQR1rzXxxptrDbXM8 qS3EqgjzRnCHBx36ZxW+F/jw9V+YpbE9j4e0PWVhlgs7M+V99FhU5H5e9chdeCLLVNa1EW6vaRQo CojUbd3JJwe3TpXrnhzQbK3je6skRUddm1Vxzx1oHh0RXN3JuVROu1Sozgc8n86wGfNGr6XLpGoS WspDY5Vh/EOx9qo17p4s8KW0mny29vpf2i7ZDi6k42nHXPJ/D2rwugAooooAKKKKACvp/wAZf8mv W3/YK03/ANChr5gr6f8AGX/Jr1t/2CtN/wDQoaAPmCiiigAooooAKKKKACtXS/Dmqawoe0tiYS23 zXO1f16/hTdE0S51y8MMAIRBukfGQo/xr2mG0itNLW3s02rDGrIvXIHbPc8UAcBa/Dq5guLKS4kW dPNHnxKMBV+uea6W30u0tfEcNnBYwx+XbySKUQDdkqAfr94VtoIwWuIY7i4eZQdoYhVOO/OBVe7s WbXbVn/4+5LWXAjYjZho8fzbt3rfD/G/SX/pLE9ilqAMGq2M8uyGMJIJNzAY4GKjstUtLvfFco6i NyY5Sh2PycYOK3k8ObtlxdwtfXSgZZx8vvhelSX2iSXkTQMwitiuNqRDK/jWAzhtaa4u/BF9eXAU oXAQ4xxvAFdLp9houmaKkpgtYYvKUyTMqru46k/561ZbTFj02LRZLOO9gAyNzYBAOeRiqE2mTazI qalCLfT4iUWyVyfNI6EkY444FAHnniyy0yfVRJoJ+0bwWmS3XcqdMEYHfmuXZWRirKVYdQRg17Kd JtbmGa10qySxVXKPMI8bsEg4x1781x/i/wAP22lWHmmaN7jeApztbHpjnNAHE0UUUAFegfBL/kr2 hf8Abx/6TyV5/XoHwS/5K9oX/bx/6TyUAfX9fIHxt/5K9rv/AG7/APpPHX1/XyB8bf8Akr2u/wDb v/6Tx0Aef0UUUAFFFFABVyy0+4vHVYomIJ+9iptF0t9UvlhHC9yRxXrunaVp1tpgt7S3CuCSZDzm gDmdL+HlvcafJcPcS+YmMrgY6VPB4fsIopFaJS2MDKiuksbmSwMsTn5Gz/Kq0UXnXZ5AXOaAMCDw zbDUogVO14nfaVGMqVH/ALNXQWssUOnvCbeIr6Ee9XpY4l1e0RZFB+yz5P8AwKKs+6tXhfBOQe9b 1vgp+n/tzEt2YUvg+31hJXiHlSDJAUAD1rhtW0G80mUiWNimSAwGR+desRTPFGY4kOT1IqS5trO6 0p4L5FcsCAx/hrAZ4fRWprekvpd4ydYzypxWXgigAooooAK9A+CX/JXtC/7eP/SeSvP69A+CX/JX tC/7eP8A0nkoA6D9o7/koen/APYKj/8ARsteP17B+0d/yUPT/wDsFR/+jZa8foAKKKKACiinIjSO qKMknAoAbV2z0q+vzi3t3k/3RXUeH/Bv2grNenapAIUr1r0DTtLgtU228MUeO/SgDznTvA9xMc3j PBjsVrXi8B2m8YupCfZBXdwWqyufNdVGepNKIY45sLIhAPUUAcbpvhaFwWwzESSJyB0Vyv8AStM+ GbKdChVt/oMVsaZcQwWkhwGb7Tcf+jnq3EitI0h+XPTFb4r+PP1f5ijsjlIfC2nbnTYS654OP8Ko z6Fb7yFiAx7V2kCCG8d2QHOaX7FH5hnkUbf7vSsBnB3PhIXFuzxxkMB2WuNv9HntJmRon4PXbXtr XK4KxxbF96zNRs7S7jP7td3rjrQB4myMhwQRTa6PXtIe0nLBMIfaudYbWI9KAEooooA+n/2cf+Se ah/2FZP/AEVFXsFeP/s4/wDJPNQ/7Csn/oqKvYKAPH/2jv8Aknmn/wDYVj/9FS18wV9P/tHf8k80 /wD7Csf/AKKlr5goAKKKKACiiigD0j4WaRHdS3N01zJBK58mLbwD0JzkH2/Kvb9I0PU7eeNnvA0Q bkHHI/KvF/h3qNlbeH5mvbuO2WG4PzMccEKQfzzXr1prk2o2cFvZSNJufY0qHkD1oA7GRorS2lub hgkUSlnbGdqjqa5Dxelh4k8G6nd6dqNvNbw2k0xCjklUY/Xt3rrkh82w+yyHcY1XcWOS/BzmvD/G Xhi40jxDq0umWk4s5NPaRzGh2IuDkHHAHy1vhf48PVfmKWx6RZeJvDy6pDoVnfi5vXDZEUR2jAJ5 PToK6C4s1YMuOB71wvww0C2i0KO/MQE1w7bpCvIUHGPpxXX+IWae3MtvK0ZhbgqeG9zWAyjqdiZb dwU34/hFfN/xA0NNG11DDCIobmLzAq/dDAkEAdux/GvoFdcFwi28s32e6Ck7Qfvev1rx34xzefrN hgKVjgxuB6knkY/AfnQB5pRRRQAUUUUAFfT/AIy/5Netv+wVpv8A6FDXzBX0/wCMv+TXrb/sFab/ AOhQ0AfMFFFFABRRRQAUUUUAew+CLaDQvCqXNxFJm6ZmklUbtvOF4xnGADz6mupt4oxZW0Ed0ZRI 2UYDBK4HB49PSo9GdI9AsZtOtjLbNAgy7ghBgdVHerVna3KXsS3VuqRIp8p4z8pDf/qFAFqy0+51 CXyIElCD5VjTAOOmSf8A69OvdL07wv4hsZtbvFtIHs7k7kYu5IeDA6H1PT0r0DwvbQWukbIivnsS XY9TycfhjFfPfizTteh8UN/bi3DXUgYqz5YMuV+6fT6Vvh/jfpL/ANJYnsd1P8Q/A9qW/wCJdqc6 j/lrjr74Lj+VNsfG/gXV7tIYLu/0+R2AHmIcE9MZ+YD9Ky/DngKHVQPtyy+X95kUheMd8g0viTwB o1o297aezi6CVCJFz2yMA/lmsBnY3/h+9SR5gmBs2rLEQQe+cdawNQ0b7eredK4CA+XtOCDjG44r oPhxr+n+X/wjsN5JcyRI0qMQ3CZGRk9Ov61t+Jra3hjW5ASN2yhIAG49vqaAPPo7aOxto3Z2WK3h 5HXoOSffiuG1Xw0PEd3ca1PI0ds6gQIuA7ADGTnPGc16Bc2Ectqtn5rASkmQk8kHlsfXNZWpXDfa 47U2E6RhSQFGeOnQdKAPEtUsfsFykWGUsgcq3OMk4we4wAfxx2qjXZeOUMdrpgaMI4MyHucDYQCe +Mn8642gAr0D4Jf8le0L/t4/9J5K8/r0D4Jf8le0L/t4/wDSeSgD6/r5A+Nv/JXtd/7d/wD0njr6 /r5A+Nv/ACV7Xf8At3/9J46APP6KKKACrFvbtIpfHAqv3rZsiEWJf4WcA0Add4WsltNMFwU+aZQR XW2U0cCbfn59qp2kX+gWsSMAFX0q9hnYIkgz9KAIpQkkhY5xnjikETkExKT61M0EluTvky3pitbw 1f28V+sM7KVlIUg/WgDE89RqVqrqdwtph07lov8ACjymcFnD4+ld9e6ZoieM9OQmPY1jcswx3DwY /TdWN4p1GzjlNrp6oVXBytb1vgp+n/tzEt2cnIvlnIyKjup/MgEYxk1ZXN0p3thhwBUU+nmJFLA4 J4OKwGZviDTI7/RNpAMi4bI9BXmYg3xsuCGT1r1vcnlNFjmvNNTSS31adfLwCen50AYjDaxB7UlP kOZG4xzTKACvQPgl/wAle0L/ALeP/SeSvP69A+CX/JXtC/7eP/SeSgDoP2jv+Sh6f/2Co/8A0bLX j9ewftHf8lD0/wD7BUf/AKNlrx+gAooooAK7jwNokct2tzdLlQeOfauQsrZ7i4QKhYZ7V63pEK21 rEuzywB1oA3J7WJZFABHoBTnhRCvOT6VXa5XfktkAcE0yfVYI4ycgv2oA0PsySRbt5VQOV9aPOs7 cAFfyFZkF1cag4RcKvrXU6VoFmyq924Ixk5oA5rTbuD7HINn/LzOenYyuas742+420+9dBocfhqH TZvtUkSuLy7AyueBcSbf0xV17DQNQQpazKGPTaoBrfFfx5+r/MUdkckshU/OfoanL7/mZyy0zV9L m02Ugb2izwxrLS6KHa3K1gMv3AjwApOCKqwph2Yg7RU5kV4wAwzUV1LiFYkc574oA5/xZIs9ixjT AUjPFeVz8zNXreq2xawkBxj3rya5j2TSY6bj/OgCGiiigD6f/Zx/5J5qH/YVk/8ARUVewV4/+zj/ AMk81D/sKyf+ioq9goA8g/aNx/wr7T9wJH9qx9Dj/llNXzHmL+4//fY/wr6b/aO/5J5p/wD2FY// AEVLXzBWkKsoKyt80n+aFYkzF/cf/vsf4UZi/uP/AN9j/Co6Kv6xPsv/AAGP+QWJMxf3H/77H+FG Yv7j/wDfY/wqOij6xPsv/AY/5BY9U8MaElx4KmuLa20+fa3zefbM7sSAc7g46ZGBt7V2nge41eSz RYr+xiaNguJbJnZl6bgRIue9ZfwZjF34cngZ8RNK6vk9CNp/liutt/Dq6Xtj0ucypdvh9jH5guDg +mM5o+sT7L/wGP8AkFjp3TXrZlmOu6QC4+7/AGZJyO/H2iuR8bHX38OXaf2jZT2wt5Hka3sZEJXY 2QSZW47dO9V9X1HWLESROy7FiZY3WPcRgjJIHJ6j+lcz4q8c6BLpn2LT55i9xYmFjLBIjSEseMHj G5VP4fWt8NiJuvDRbr7Me/oJrQ9Us7LW7e3WG11fQokcYEa6a64zycAT1WuotchheJ9U0qQsSPLX TJCWx6Dz+grk7HxO9xfpqqlGsBbrcNGokJVclMFigAbIyFyeOa1Le21jWb0X0m5fKO5Yo8Dbn39f 8Kw+sT7L/wABj/kOxx2r3upPfmOx1HTmuIif9XblT2yoy7c+3tXEfEm3ltNZskvXElw9osjqitHt yzAAhmbnjrxxjivdNU0aPQY0uYIA9zM25mbB2t1OT1JzXgPxIuWufGEodt0kUSI5A4zjdx+DCj6x Psv/AAGP+QWOWzF/cf8A77H+FGYv7j/99j/Co6KPrE+y/wDAY/5BYkzF/cf/AL7H+FGYv7j/APfY /wAKjoo+sT7L/wABj/kFiTMX9x/++x/hX054xx/wy/bZ6f2Vpv8A6FDXy/X0/wCMv+TXrb/sFab/ AOhQ1E6spWvbTyS/JBY+ZMxf3H/77H+FGYv7j/8AfY/wqOir+sT7L/wGP+QWJMxf3H/77H+FGYv7 j/8AfY/wqOij6xPsv/AY/wCQWJMxf3H/AO+x/hRmL+4//fY/wqOij6xPsv8AwGP+QWPTfAvibOnr pjalFZtb52LNAZPNUn+95i8jOMY6Ada9T0q31S+t9tnrGl3CjAMU1g+Rn+7ibP618v1raT4l1fQ2 3adeNCd27O0NzjHcGj6xPsv/AAGP+QWPqi103xPbxMIdXsU2qTsk0x/0PnGuY1K81OTxFYy3+paZ MYrW52ebp7EL88OcqZup4wcjGDwc8ed6b8fPF1jEkM0en3SKuMyREMffIOP0q43xQg1bVrK9aC1g cJJCUlR2VGdkIOBwfuHrxzz2rfD4ifO9FtL7Mf5X5CaPYoLTxE7IyX2kW+VwV/sqReOxP7+s7xD4 Y8V6naCObV9KNqTklLN1OMdeZD/OsXS5tQ1/xFE0d4WuXQqpbATy8EkcDgY9BXq3kvHZQxOQwRQr henArD6xPsv/AAGP+Q7Hz7ANb8G60dl3brfhNu9rbcrI3od3I49Ooqh4t+IPiOG5ga4khuQAHJWA qsYzxwGI5/Cu3+Ilnc3mvWOn2VoonKfJPu5bqdv0xk1Hd+GbRvDE+myR/PPGqyOeTyeGH05/Kj6x Psv/AAGP+QWOc0TxDc67a/a4bnT2cECVJbVt0I9RiX/CrOr/ANs6fcLm+tZyi/eNuwbB7ff5FeVX RufB3im5htJSfJbb82CJIzg4OPUYr0WHWItXe3ukk3KY/kYr1HTBHqDkfhR9Yn2X/gMf8gsc54tt JH8PxXt1JHIQ5ZAkRVhuK55LH27dq4LMX9x/++x/hXbfES6MT2OlqcCOPzHA6c8D+R/SuFo+sT7L /wABj/kFiTMX9x/++x/hXffBQofi7oW1WB/0jq2f+XeT2rz2vQPgl/yV7Qv+3j/0nkpSrykrNL7l /kFj6/r5C+NZQfF3XdysT/o/Rsf8u8ftX17XyB8bf+Sva7/27/8ApPHWcJuDuvyT/MZweYv7j/8A fY/wozF/cf8A77H+FR0Vr9Yn2X/gMf8AIViTMX9x/wDvsf4Vp2EimeBQD98Y3cjNZAGTV2wJSZDu 6MDR9Yn2X/gMf8gsevWcV7NbR/6TaoMcf6K38/Mrbg0zUIrcXKXunk5xhrNv/jtYfhm9S+sVt5X5 VQB7V0q3QjtvI8vzOaPrE+y/8Bj/AJBY5bXrjU45VZri0IAPKW5X/wBnNUdL1G/WYOtxaq4PyloS ec/7wrr/AOzI7xT59uNh6jNUW8JwtLK1v8vlLv256UfWJ9l/4DH/ACCws9rrF9qNpcT6laiU2s7K UgYYCtFkf6zqdw/I9e3O3015als3kDt3/ct/VzXVWfmXOo2kMYyyWlyv/j8NVLjwfILsy3H3T/Dm t6uInyQ0W38sf5n5CS3MDSbzUJbhRHcW+T3aAsP/AEKupWDU7yExvd2GQOP9Db+fmVENFisQXgiA /GmxXrQsytwW4BrD6xPsv/AY/wCQ7GfdW15bIS11Zk57WzZ/9DrzvxBdBNUdnZJGyeY12j8smvQd VvLe0hZ5ZCzGvK9YdJb2R0Hynp+dH1ifZf8AgMf8gsUWeJmJ2Pyc/fH+FJmL+4//AH2P8Kjoo+sT 7L/wGP8AkFiTMX9x/wDvsf4V33wUKH4u6FtVgf8ASOrZ/wCXeT2rz2vQPgl/yV7Qv+3j/wBJ5KUq 8pKzS+5f5BY6H9owoPiHYblY/wDEqjxg4/5aze1eRZi/uP8A99j/AAr1z9o7/koen/8AYKj/APRs teP0o1pRVkl9yf5oLEmYv7j/APfY/wAKMxf3H/77H+FR0VX1ifZf+Ax/yCxds70WkoaNWB92z/Su ph8aSJEqAocf9MCf/Zq4mnB8LgUfWJ9l/wCAx/yCx20/i+4lUDzYgPQR4/8AZqqprU07Ab1/Ff8A 69c3HIiplxSi4UZ25o+sT7L/AMBj/kFj03Q9Sv0IEU9rnPSSEt/JxXVJda3NtUXtkA3ZbV+P/Ile LWWtXFpKuw8HrxXonhjX2kdNx3OcDp05o+sT7L/wGP8AkFjWtvDV/c2jP9vt+bm4BHkP1Ezgn/Wd yCfx79a2dO8Na1aAyw31rGU6s1q5/wDatLpUN7Ja70AWBri6JfPOfPkpFvL+C7ZGmDRMcHJrfE4i arT0W7+zHv6CitEN1m+1n7B5c13YTKCRvFk4P5+b/SuKE+oXFyttE0ZLdxbt/wDFV63o8cF3J5c6 BlI7etXZrey0W4MiorN2xWH1ifZf+Ax/yHY8xk0fXdLVXmlh2kZB+zscf+PCoWS/k+f7XaZ9rduP /H69njmtNZtDBPjc42ivMPGWgXHhi5N0ik2rEnrnAo+sT7L/AMBj/kFjmdQS9+ySs9zbsoHOIGH/ ALPXmd20WZMgk7+xxXc6xrCNp5EZ4c4PFeeXChZDjPXmj6xPsv8AwGP+QWEzF/cf/vsf4UZi/uP/ AN9j/Co6KPrE+y/8Bj/kFj6g/Zyx/wAK+1DaCB/asnU5/wCWUNev14/+zj/yTzUP+wrJ/wCioq9g rFu7uM8f/aO/5J5p/wD2FY//AEVLXzBX0/8AtHf8k80//sKx/wDoqWvmCkAUUUUAFFFFAHUeB/EV xomspD9r8qyuDtlDsdins2M4zxjPoa9usvFFjBb/ANn2eo2sTNIGby7hS31wD9a+aacJHHRjnIOc 85HSgD3vxd47k0O3+3WlxH/aC3CeSCc+aMHJIB5HAB57+teHz6hdajqP2i6meR3mLncxIBY5OM9K qPI8rbpHZz6sc06D/j4j/wB4fzrfC/x4eq/MUtj0bwl8TNZl1eKy17WpG0+VzI7yvjEgUBctjp8o GOnOa9Ys/EiInmG7S0RSQoZtikjqWzXy7UslzPMipLNI6r91WckD6VgM978U+OLLTtOnvrLUrG6n uST5CTiQb+eRtz/k9a8CmmkuJnmmkaSV2LM7HJYnqSaZRQAUUUUAFFFFABX0/wCMv+TXrb/sFab/ AOhQ18wV9P8AjL/k162/7BWm/wDoUNAHzBRRRQAUUUUAFFFFABRRRQAVp6XrWoaOPNsp9hRwdrKG U55IKng52r+QrMqQf8e7/wC8v8jW+H+N+kv/AEliex7x8GvEQ1DWbJbgETeW1tuLfeKpnP48fia9 7YhEYnoASa+NfA3iZPD99xb/AL9pVkjuTOEEIUEnhuDnjvn2OQK+qvDviFfE2grd+X5JOASHDbsj qCPWsBnEaz4mi074ji3vQy2rW6uJVUsY25GcDrWvq0aLpsj2sjSpOhMTwtjIPTB7Gs/VreKTx0DG ImmjtMGR1yF9Pbv3qSSUxBLcyuFKsDztPA64FAHmd/4d0/TYL/UNTUu92+GkmQylAeip1PQgZ9vw rmLbxLYeHbrUIrW0d4zIrwRspQglVznIyBx+td1rOuxabfSWWqKz7Q0kE8qhl45wM9CP16141q2o NqupS3jqQ8mN2WznHHoO2KAIby7mvrya6uGLSysWY59f6VBRRQAV6B8Ev+SvaF/28f8ApPJXn9eg fBL/AJK9oX/bx/6TyUAfX9fIHxt/5K9rv/bv/wCk8dfX9fIHxt/5K9rv/bv/AOk8dAHn9FFFABT1 faO+aZShSeRQBp6fq81m5KyMP+BGuj0/xm8LZmy6+xOa4ipYWCnJoA9ZtviNZrCEMbe+Sf8ACobv 4i222b7KpDsmOSf8K8wL5YnAGe1NY47UAdnpnje5trwXMwU7VZBtyOGKk/8AoFbjfEuCRctFIX/3 j/hXmO4+Q2T/ABD+tMBOcit63wU/T/25iW7PQn+ITylkEZwT6msXUfF1xLL+7wqj61zKy7ZMkYp0 7RuuV6+lYDJrrVJ7uTfK7HtjJqozltxzxUdKcjigBKKKKACvQPgl/wAle0L/ALeP/SeSvP69A+CX /JXtC/7eP/SeSgDoP2jv+Sh6f/2Co/8A0bLXj9ewftHf8lD0/wD7BUf/AKNlrx+gAooooAKKspFE UJL4NQFMeuKAG54ApQCaSlXOaAHxMUkDYHWun0zxVJp8ytHBDtAAOVrlTx3pQ5C496APQofiBcQ2 0tsANrSvKOv8TF//AGatHSvEgvcb2Gc+teZu2ZAP9lf5CtXSpmjuFCtjnpmt8V/Hn6v8xR2R9AeG LwyRnyxubHSmX93MZmE2Vb0NZfgW98qSNnIPA/nWrrk8N5cFwmG4GawGSaPfG3mMrk8fd5o8Ta5L faVJFchGiYEDj2rGXzAdhlIrI8S60LTTjGwHAIGfpQB5Lqd3IlxLCAQu7isvJbg9TUt3cG5uXlPc 9KhBx0oAQ9aKKKAPp/8AZx/5J5qH/YVk/wDRUVewV4/+zj/yTzUP+wrJ/wCioq9goA8f/aO/5J5p /wD2FY//AEVLXzBX0/8AtHf8k80//sKx/wDoqWvmCgAooooAKKKKACiiigAqSD/j4j/3h/Oo6kg/ 4+I/94fzrfC/x4eq/MUtiOiiisBhRRRQAUUUUAFFFFABX0/4y/5Netv+wVpv/oUNfMFfT/jL/k16 2/7BWm/+hQ0AfMFFFFABRRRQAUUUUAFFFFABUg/493/3l/kajqQf8e7/AO8v8jW+H+N+kv8A0lie wxWKsGUkEHII7V9LfAvWl1jQ761ZwJoVQGMDAAGcH6c180V61+z/ADQ2/jjVp3by4Y9GlZmdh8oE kWST096wGeparq9jY+Kp/MnEbi3BkYj34H5V5f4u8bva3fmWM3k8bo4mTJk56kHkKfw715/e+LNX vmlaW6Znkb5piP3hHQAke3pWK7tI7O7FnY5ZmOST6mgC9q+s3uuXrXV7Lvc9ABgL9BWfRRQAUUUU AFegfBL/AJK9oX/bx/6TyV5/XoHwS/5K9oX/AG8f+k8lAH1/XyB8bf8Akr2u/wDbv/6Tx19f18gf G3/kr2u/9u//AKTx0Aef0UUUAFTCNwnHeoakEzhdoOPegCMjBxSjrz0pMk96khkEThjGrgdmoAcV QqSA3HvTdpPG01JPdea7Msaxg9l7VGszLQA8KPJcYP3lz+RqPZj3+lSCQmFzgfeX+RpnmnGMCt63 wU/T/wBuYluxCuBnacUjDA6YqRLhkUrtVsnvTZZTK+4qB7CsBjAORmnOhT6Gm7vYUFix5oASiiig Ar0D4Jf8le0L/t4/9J5K8/r0D4Jf8le0L/t4/wDSeSgDoP2jv+Sh6f8A9gqP/wBGy14/XsH7R3/J Q9P/AOwVH/6Nlrx+gAooooAKkMrgYyMfSo6KADvS/jSUUAFOAB7Emm05XKnigCbbmcZBOFX+Qrp9 O8OzyrHdfw4B+lcw8zJMGAGdqn9BW6PG+pJBDDHHbqsS7RhOv1rfFfx5+r/MUdkemeHFa0XGCxwM e1askx8w+YQvPevILfx9rtqzNDNGpPHCn/Gq954y1m+RluJw245zzkfrWAz2LUXit7Zrg3sChBnH PNeR+KNdk1O6Kg5RTgEDGaxm1S7cYklLr/dY5FV3md33HA54AHAoAc0Rji3MvXvUNSSTyS43uTjt 0FR0AFFFFAH0/wDs4/8AJPNQ/wCwrJ/6Kir2CvH/ANnH/knmof8AYVk/9FRV7BQB4/8AtHf8k80/ /sKx/wDoqWvmCvp/9o7/AJJ5p/8A2FY//RUtfMFABRRRQAUUUUAFFFFABUkH/HxH/vD+dR1JB/x8 R/7w/nW+F/jw9V+YpbEdFFFYDCiiigAooooAKKKKACvp/wAZf8mvW3/YK03/ANChr5gr6f8AGX/J r1t/2CtN/wDQoaAPmCiiigAooooAKKKKACiiigAqQf8AHu/+8v8AI1HUg/493/3l/ka3w/xv0l/6 SxPYjqe1vruy877JdT2/nxNDL5UhTzI26o2OqnAyDxUFFYDCiiigAooooAKKKKACvQPgl/yV7Qv+ 3j/0nkrz+vQPgl/yV7Qv+3j/ANJ5KAPr+vkD42/8le13/t3/APSeOvr+vkD42/8AJXtd/wC3f/0n joA8/ooooAKKKKACiiigAooooAkH/Hu/+8v8jUdSD/j3f/eX+RqOt63wU/T/ANuYluwooorAYUUU UAFFFFABXoHwS/5K9oX/AG8f+k8lef16B8Ev+SvaF/28f+k8lAHQftHf8lD0/wD7BUf/AKNlrx+v YP2jv+Sh6f8A9gqP/wBGy14/QAUUUUAFFFFABRRRQAUUUUASTffH+6v8hUdSTffH+6v8hUdb4r+P P1f5ijsgooorAYUUUUAFFFFABRRRQB9P/s4/8k81D/sKyf8AoqKvYK8f/Zx/5J5qH/YVk/8ARUVe wUAeP/tHf8k80/8A7Csf/oqWvmCvs/4j+Bf+FgeHrfSf7R+weTdrc+b5Hm5wjrtxuX+/nOe1eX/8 My/9Td/5Tf8A7bQB4BRXv/8AwzL/ANTd/wCU3/7bR/wzL/1N3/lN/wDttAHgFFe//wDDMv8A1N3/ AJTf/ttH/DMv/U3f+U3/AO20AeAUV7//AMMy/wDU3f8AlN/+20f8My/9Td/5Tf8A7bQB4BUkH/Hx H/vD+de9/wDDMv8A1N3/AJTf/ttOT9mco6sPF3IOf+Qb/wDba1oTUKsZPZNCex8/UV7/AP8ADMv/ AFN3/lN/+20f8My/9Td/5Tf/ALbWQzwCivf/APhmX/qbv/Kb/wDbaP8AhmX/AKm7/wApv/22gDwC ivf/APhmX/qbv/Kb/wDbaP8AhmX/AKm7/wApv/22gDwCivf/APhmX/qbv/Kb/wDbaP8AhmX/AKm7 /wApv/22gDwCvp/xl/ya9bf9grTf/Qoa5/8A4Zl/6m7/AMpv/wBtr1DWfAv9r/C+PwX/AGj5Wy0t rb7Z5G7PklDu2bh12dN3Ge9AHxhRXv8A/wAMy/8AU3f+U3/7bR/wzL/1N3/lN/8AttAHgFFe/wD/ AAzL/wBTd/5Tf/ttH/DMv/U3f+U3/wC20AeAUV7/AP8ADMv/AFN3/lN/+20f8My/9Td/5Tf/ALbQ B4BRXv8A/wAMy/8AU3f+U3/7bR/wzL/1N3/lN/8AttAHgFSD/j3f/eX+Rr3v/hmX/qbv/Kb/APba cP2ZyEK/8JdwSD/yDf8A7b71rRmoyu+z/FNCZ8/UV7//AMMy/wDU3f8AlN/+20f8My/9Td/5Tf8A 7bWQzwCivf8A/hmX/qbv/Kb/APbaP+GZf+pu/wDKb/8AbaAPAKK9/wD+GZf+pu/8pv8A9to/4Zl/ 6m7/AMpv/wBtoA8Aor3/AP4Zl/6m7/ym/wD22j/hmX/qbv8Aym//AG2gDwCvQPgl/wAle0L/ALeP /SeSu/8A+GZf+pu/8pv/ANtroPBPwL/4Q7xfY6//AMJH9s+y+Z+4+w+Xu3Rsn3vMOMbs9O1AHsFf IHxt/wCSva7/ANu//pPHX1/Xj/jb4F/8Jj4vvtf/AOEj+x/avL/cfYfM27Y1T73mDOduenegD5go r3//AIZl/wCpu/8AKb/9to/4Zl/6m7/ym/8A22gDwCivf/8AhmX/AKm7/wApv/22j/hmX/qbv/Kb /wDbaAPAKK9//wCGZf8Aqbv/ACm//baP+GZf+pu/8pv/ANtoA8Aor3//AIZl/wCpu/8AKb/9to/4 Zl/6m7/ym/8A22gDwQf8e7/7y/yNR19Aj9mchCv/AAl3BIP/ACDf/tvvTf8AhmX/AKm7/wApv/22 tak1KMEui/Vv9RI8Aor3/wD4Zl/6m7/ym/8A22j/AIZl/wCpu/8AKb/9trIZ4BRXv/8AwzL/ANTd /wCU3/7bR/wzL/1N3/lN/wDttAHgFFe//wDDMv8A1N3/AJTf/ttH/DMv/U3f+U3/AO20AeAV6B8E v+SvaF/28f8ApPJXf/8ADMv/AFN3/lN/+210Hgn4F/8ACHeL7HX/APhI/tn2XzP3H2Hy926Nk+95 hxjdnp2oA4D9o7/koen/APYKj/8ARsteP19X/Ef4P/8ACwPENvq39u/YPJtFtvK+yebnDu27O9f7 +MY7Vx//AAzL/wBTd/5Tf/ttAHgFFe//APDMv/U3f+U3/wC20f8ADMv/AFN3/lN/+20AeAUV7/8A 8My/9Td/5Tf/ALbR/wAMy/8AU3f+U3/7bQB4BRXv/wDwzL/1N3/lN/8AttH/AAzL/wBTd/5Tf/tt AHgFFe//APDMv/U3f+U3/wC20f8ADMv/AFN3/lN/+20AeCTffH+6v8hUdfQLfszljk+LuwH/ACDf QY/5603/AIZl/wCpu/8AKb/9trWvNTqyktm2JaI8Aor3/wD4Zl/6m7/ym/8A22j/AIZl/wCpu/8A Kb/9trIZ4BRXv/8AwzL/ANTd/wCU3/7bR/wzL/1N3/lN/wDttAHgFFe//wDDMv8A1N3/AJTf/ttH /DMv/U3f+U3/AO20AeAUV7//AMMy/wDU3f8AlN/+20f8My/9Td/5Tf8A7bQB0H7OP/JPNQ/7Csn/ AKKir2CuP+HHgX/hX/h640n+0ft/nXbXPm+R5WMoi7cbm/uZznvXYUAeV/H3VtS0bwLY3Gl6hd2M 7anGjSWszRMV8qU4JUg4yAcewr50/wCE78Yf9DXrn/gxm/8Aiq9//aO/5J5p/wD2FY//AEVLXzBQ B0H/AAnfjD/oa9c/8GM3/wAVR/wnfjD/AKGvXP8AwYzf/FVz9FAHQf8ACd+MP+hr1z/wYzf/ABVH /Cd+MP8Aoa9c/wDBjN/8VXP0UAdB/wAJ34w/6GvXP/BjN/8AFUf8J34w/wChr1z/AMGM3/xVc/RQ B0H/AAnfjD/oa9c/8GM3/wAVT4vHPi9pkU+KtcILAH/iYzf/ABVc5UkH/HxH/vD+dbYZJ1oJ91+Y nsbn/Cd+MP8Aoa9c/wDBjN/8VR/wnfjD/oa9c/8ABjN/8VXP0ViM6D/hO/GH/Q165/4MZv8A4qj/ AITvxh/0Neuf+DGb/wCKrn6KAOg/4Tvxh/0Neuf+DGb/AOKo/wCE78Yf9DXrn/gxm/8Aiq5+igDo P+E78Yf9DXrn/gxm/wDiqP8AhO/GH/Q165/4MZv/AIqufooA6D/hO/GH/Q165/4MZv8A4qvsPwXP NdeBfD1xcSyTTy6ZbPJJIxZnYxKSSTySTzmvhyvt/wACf8k88Nf9gq1/9FLQB0FFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXyx8X/FniTTPilrNnYeINVtLWPyNkMF7JGi5 gjJwoOBkkn8a+p6+QPjb/wAle13/ALd//SeOgDn/APhO/GH/AENeuf8Agxm/+Ko/4Tvxh/0Neuf+ DGb/AOKrn6KAOg/4Tvxh/wBDXrn/AIMZv/iqP+E78Yf9DXrn/gxm/wDiq5+igDoP+E78Yf8AQ165 /wCDGb/4qj/hO/GH/Q165/4MZv8A4qufooA6D/hO/GH/AENeuf8Agxm/+Ko/4Tvxh/0Neuf+DGb/ AOKrn6KAOjHjnxf5LN/wlWuZDAf8hGb3/wBqmf8ACd+MP+hr1z/wYzf/ABVYY/493/3l/kajraqk owt2/ViXU6D/AITvxh/0Neuf+DGb/wCKo/4Tvxh/0Neuf+DGb/4quforEZ0H/Cd+MP8Aoa9c/wDB jN/8VR/wnfjD/oa9c/8ABjN/8VXP0UAdB/wnfjD/AKGvXP8AwYzf/FUf8J34w/6GvXP/AAYzf/FV z9FAHQf8J34w/wChr1z/AMGM3/xVdx8IPFniTU/ilo1nf+INVu7WTz98M97JIjYgkIypODggH8K8 nr0D4Jf8le0L/t4/9J5KAPr+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooA8f/aO/5J5p/wD2FY//AEVLXzBX0/8AtHf8k80//sKx/wDoqWvmCgAooooAKKKKACiiigAq SD/j4j/3h/Oo6kg/4+I/94fzrfC/x4eq/MUtiOiiisBhRRRQAUUUUAFFFFABX2/4E/5J54a/7BVr /wCilr4gr7f8Cf8AJPPDX/YKtf8A0UtAHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFfIHxt/5K9rv/AG7/APpPHX1/XyB8bf8Akr2u/wDbv/6Tx0Aef0UUUAFFFFABRRRQ AUUUYJoAkH/Hu/8AvL/I1HUg/wCPd/8AeX+RpmCe1b1vgp+n/tzEt2JRRRWAwooooAKKKKACvQPg l/yV7Qv+3j/0nkrz+vQPgl/yV7Qv+3j/ANJ5KAPr+iiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooA8f/aO/wCSeaf/ANhWP/0VLXzBX0/+0d/yTzT/APsKx/8AoqWvmCgA ooooAKKKKACiiigAqSD/AI+I/wDeH86jqSD/AI+I/wDeH863wv8AHh6r8xS2I6KKKwGFFFFABRRR QAUUUUAFfb/gT/knnhr/ALBVr/6KWviCvt/wJ/yTzw1/2CrX/wBFLQB0FFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXyB8bf+Sva7/27/wDpPHX1/XyB8bf+Sva7/wBu/wD6 Tx0Aef0UUUAFFFFABRU8Fu0qs/YDNb/hnw7/AGrP5r8xLkMCKAMKKynk5ETlfXbW3YeGp7uBnUEe xwK72PT7CCJo1iRRjFU1b7PMY4iFXPQUAcdH4enWcQuh+Y7vy4/rU+p+H2tLfeqHP0rrp3T7dbso HEMh4+qU25lEkflsuQfUVvW+Cn6f+3MS3Z5mdPmwxVGIHXiqrKUOGGDXp8NlFGp/dqQ3XK1zniTR EhQXECEgnkAVgM5KipHiKqGqOgAooooAK9A+CX/JXtC/7eP/AEnkrz+vQPgl/wAle0L/ALeP/SeS gD6/ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPH/wBo7/knmn/9 hWP/ANFS18wV9P8A7R3/ACTzT/8AsKx/+ipa+YKACiiigAooooAKKVVZ2CqCWJwABya61/hzrsca 7olEpQuUyOn1zzQBykML3E8cMS7pJGCqPUk4Fbd14V1TSZIHv444A/zIDIGLYBP8OewPWu78C/DO WbUrSa/8xJ8hxGuDtGRzmu0+IegRRRzSCCNlgtpCp6kYibn25rfC/wAeHqvzFLY8Q1/wjqnhyOKS +EJjkxtaJ8j9QKwa+qvF/hKz8UeD8YKMANrooyhB6/59a8FtfhxrM7xrL5cJfJOedq46msBnH0V0 PiHwfqHh+3S6lHmWkkhjSYLtye3Ge+D+Vc9QAUUUUAFFFFABX2/4E/5J54a/7BVr/wCilr4gr7f8 Cf8AJPPDX/YKtf8A0UtAHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF fIHxt/5K9rv/AG7/APpPHX1/XyB8bf8Akr2u/wDbv/6Tx0Aef0UUUAFHeinxoXdQB3oA0rQMY0iH WQ7RXpekquk2UMKpyy5Y471wuiWJuNYgTnbCys1ekyo5AQICOxoAzr6XdwtZUshRsk1v31kI1U/3 u1UxojXts8kZ+ZcnAFAGKLhhdxMT/A4/VavFg4DKayzbTJeLGVO7Y3H4rXR2uhT/AGPzpMpz0Ire t8FP0/8AbmJbsjibdHgipBCLiGSNhkFSBUun2plJFSPC0M20cc45rAZ5ZqULWt3NaYztOBWWw2nF dx4tsEilF2nLDGfeuMuECP3zQBDRRRQAV6B8Ev8Akr2hf9vH/pPJXn9egfBL/kr2hf8Abx/6TyUA fX9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4/+0d/yTzT/APsK x/8AoqWvmCvqD9o1S3w+08KCT/asfA/65TV8x+RL/wA8n/75NaQo1Jq8It+iFdIjoqTyJf8Ank// AHyaPIl/55P/AN8mr+q1/wCR/cw5l3I6Kk8iX/nk/wD3yaPIl/55P/3yaPqtf+R/cw5l3Ou+GtvY zeJw9/HFKixkRxued57gewDfmK96u7KVza2tpbiRXhO2SQnIHp0r5+8L6Xc26jWYr6xhuInxFb3F wiM46MSCeBjOO5/LPtuj/EBbnT4999pdrJgL+8uUBA9eTR9Vr/yP7mHMu523hKyQF2ltHiuYVEZd hw30rlvGtnJbaHqtzOzea9vMi5PYo1dVYeNPDsNrGt14l0l5ymWK3keB7da5P4meKdA1LwvcxWGs WE87xEKsdwrE9cjAPWt8Nhqyrwbg910fcTkrHo9lBbzaMqLEFjfOVA964OfSpo78Na2MjQRZWUSA 5dT6cV0Fn458NQWkUZ13SxtGW/0yP8B976Vk+IfF+lXdsq2XiDSSD9//AE6MEf8Aj3NYfVa/8j+5 j5l3OU8e2cd1pt7Y/ZhJttHaKEJna4XK4HrmvnKvdNX1+ztZ5pW1O3vBNGYdscqPtyOvB6e9eReI NMFlrE0dsYprc4eN7dxIpB9x0Oc8f0xR9Vr/AMj+5hzLuZFFSeRL/wA8n/75NHkS/wDPJ/8Avk0f Va/8j+5hzLuR0VJ5Ev8Azyf/AL5NHkS/88n/AO+TR9Vr/wAj+5hzLuR19v8AgT/knnhr/sFWv/op a+JPIl/55P8A98mvtvwJ/wAk88Nf9gq1/wDRS1E6U6fxxa9UCaZ0FFFFZjCiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACvkD42/wDJXtd/7d//AEnjr6/r5C+Ncbv8XddKozD/AEfk DP8Ay7x1UISm7RV2Fzz2ipPIl/55P/3yaPIl/wCeT/8AfJrX6rX/AJH9zFzLuR1c09cyEkZABP6V X8iX/nk//fJrQs0KRBCpVnbBzxgUfVa/8j+5hzLuejeBNGSW0m1CUAb0GCfY11jQCHDAbh7Vg+Gd R0/TdES2n1C1B2Yx5y8frWvaaxpYDK+p2O3qN1wn+NH1Wv8AyP7mHMu5X1JMpu9Ky7G5khl2BuC3 I9Rmp9X1ixMirHfWrgg52TKf61kWt3aPeI7Xluqq2TukUcfnR9Vr/wAj+5hzLudYukwy+ILBjGP3 ltM5GPR4x/WqurXrCVoEJVAOgq7J4j0ldd06ZNRtPLitJkZhMuAS8ZA698H8jXNavqVlNfPNDeW7 LgYxKv8AjW9XDVuSHuPbs/5mJSWpraTt4BOCOKu3dh5jb92Peue07VrMTJvvLdB3JkUf1rbutX02 SBdmqWXuPtC5/nWH1Wv/ACP7mPmXcxNa0YXmnSKJNzqQenpXmNxEWjckcrmvYE1jSY4yBe22SMcz L/jXmesIh1G4ELI8TfdKHI6+tH1Wv/I/uYcy7nNd6KlaCXccRP1/umk8iX/nk/8A3yaPqtf+R/cw 5l3I69A+CX/JXtC/7eP/AEnkrg/Il/55P/3ya774KRunxd0IsjKP9I5Ix/y7yUpYetFXlBpejC6P r2iiisRhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4/+0d/yTzT/APsK x/8AoqWvmCvp/wDaO/5J5p//AGFY/wD0VLXzBQAUUUUAFSQQvcTpDEMySMFUZxknoKjrqPh9p6aj 4xs4m2ZjPnIXYgBlIIPHXpQB7ja6CkXhu302yjK2aQ4VsYy2eSR6nrmr/gGNbSxJkiChVyreue9d G1rOuiNFGEE23og4Bz2rnfBTyO88dxlJI5NrRsMcL6D0oA761tTOEmkyM4OBXJ/E2K0sfB2oNOXA kidI+n3yjY/Diu6hkVI0x025Brifitc2yeCNR89A26L93nqGPygj863wv8eHqvzFLY6pLCL7M7EP ljk89KybiBraTDsdhPX26ZrbguUe2DgEAjms/VUZrfesRLeW2QOc8cVgM8o13TrWW+kllmkkCh/K RuVLZGDWd8VNJaf4fQXUcOPslwrnA6IQV/mRXSaPpE9/4iubi7O+zjw0aEEYY9R+laPjmC2u/Bes W0o2J9mZhhgMFRuA/MCgD5UooooAKKKKACvt/wACf8k88Nf9gq1/9FLXxBX2/wCBP+SeeGv+wVa/ +iloA6CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5A+Nv8AyV7Xf+3f /wBJ46+v6+QPjb/yV7Xf+3f/ANJ46APP6KKKAADJrqdAgWbW7aPHyjaTXMx8uK7TwWPN1UnAyAMZ /GgD0kSRxSNGANnY1qwzxy2o2EbgaykkXc6yIM56gU1C0blo347jNAFPXUeV0JHJ4H4mlsbb7Kiq o+fqc1ZnkWeaMt/CKniQSPnPJ6UAZsrO+s2wIP8Ax7zdP96Opp7RXjKirgtWfxFaxKuT9lnOcf7U VPuYvJwvet63wU/T/wBuYluzBsbZ4r4AZxmusDLFbMJsKu3j3rGTak+7FV72/lkkVWY7QelYDJri 4WY7EjVVz1FedeLwsetRlGyGLZNdlLcFgNoOM9q4vxeu2+tmX+6xP50AcjMCJWPqajqa4bLkVDQA V6B8Ev8Akr2hf9vH/pPJXn9egfBL/kr2hf8Abx/6TyUAfX9FFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQB4/+0d/yTzT/APsKx/8AoqWvmCvp/wDaO/5J5p//AGFY/wD0 VLXzBQAUUUUAFeh/Bp0j8ZXDORj7C/Xv88deeVu+D7oW3ia0UnaJ28jcCQRuOBjHvgfjQB9Vw6pI i/NErLjqh5rShs7OS8S+CFZSvPv9fzryuK2uYofKlvmCk4DZw3XPrXplrMbfS/NDGRo4c4Y/ewKA L8+qQJnE64Xjr0ryH4u6wt5p4sYgkmGWaQ/3QMgfqaZ4P8Q3N94yvYJ0aeC4Z98aKdsZLd/b61q/ EbT7GPTrsJaRRubZmEgTk4BOM/UVvhf48PVfmKWxt+GfFa3tjG122yYKY2QR4wR9K62DV7W5uksg 589o87cEZA71haINMtvDdvLNBEG3E4Az368VwWgw6rY/Fe2ndZpLK5nlZZF+ZApVsAnt24NYDOv8 Uaze2l09lZxKuMZc+hFcbrWop/Yl3A6SrKIJHXOCCwUnJrv9e8M2+sXxujNLHKMgMhx/KvLviDpX 9gaDdXn2xjJIBCEZ+X3HBHr0yfwNAHiNFFFABRRRQAV9v+BP+SeeGv8AsFWv/opa+IK+3/An/JPP DX/YKtf/AEUtAHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfIHxt/5 K9rv/bv/AOk8dfX9fIHxt/5K9rv/AG7/APpPHQB5/RRRQA5M7uK63wa7HVHCDpj+tchXT+DLsQaw m7HzsAP1oA9J+0TKx3dRRbmQszMcE1McuhbAwTnpUeVi/izQBe0awGoagInOFJ5NdZPotjoyG4kJ bbyuGNc1olxHBdB2baAck1Y17W/7QlW3iP7tMgn1oAjufEyDxBZSJFjZazp93+88J/8AZa6ewFjq lqs0oI9cV5UZWk8QxRf3YX/Vk/wrpbe+ls0lh58p+vPSt63wU/T/ANuYluzS8S6PFp8yvA+UYZ65 rnTa+a3X9a2NQvUlsliD7iOc1kqVYj5sYrAZXktWh9D+NcL4zlVLm3UdShz+degSMM8tkDrXl3i2 7jutU/dtny8r196AOfY7mJpKKKACvQPgl/yV7Qv+3j/0nkrz+vQPgl/yV7Qv+3j/ANJ5KAPr+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8f/aO/wCSeaf/ANhWP/0V LXzBX0/+0d/yTzT/APsKx/8AoqWvmCgAooooAKfDNJbzxzxNtkjYOjYzgg5BplFAH0Ro+taXqemW mrC2kl81SfKJ4jYHBH5g12mk6zFqETRmHy8DGzdnIxXzR4R1+4027+xeaRbXDZ2YGA/AB56dMflX uXh22msWNzqMiLI5CxIhyAD+HWgC7HBZ6NLczQ2uwed92BcE555I5P41jeObuDVfC890zz280cZ/ dEfPjkYx6HPPtXUtOrSSSW8i+YnDqRke1YXiaaz1Hwhql35eLgW7rk/w4B6Vvhf48PVfmKWxpSWb LaR22+GBAgARpDux34/OrWjW23WvtA3Iqw42ZPPXBI9ayr2+uINP+2RRRxsoADls8E9P1rR04yWu kyz7c3TpvYsc7mx7VgMTVNcnstdCvIywEYAzx2zXjfxg1lbrW4tPhk3xKizv8+drnIxj6YP4/n6B rvjGysdNkl1eBg6odiqp+d8fd9vqa+e7u5e8vJ7qQKrzSNIwXoCTnAz2oAhooooAKKKKACvt/wAC f8k88Nf9gq1/9FLXxBX2/wCBP+SeeGv+wVa/+iloA6CiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAr5A+Nv8AyV7Xf+3f/wBJ46+v6+QPjb/yV7Xf+3f/ANJ46APP6KKKACrW n3LWl7FKOqNmqtFAHsWn3Ml9p8UySELgZ+tWHKrwpyfWvPPDGvtYv9nmb9y3AwOc5rullSVN0Jyv agCz5pTkEjirFqUcf7R75qgMvkDt1qRGZF+XrQBKtrGniaGRyMNbyfoyf41p3U0bBlUDNYvmk6rb FjkiCXr/AL0daDyRtH8qjdW9b4Kfp/7cxLdkJckGnJDvXcjDI6jNRRDfOqnuaS5JsN8xO1BzmsBm f4g1FdP0uQ52ysMAdO9eUTStNKzt94nJrc8Ua8dYvDtOY1xjiufoAKKKKACvQPgl/wAle0L/ALeP /SeSvP69A+CX/JXtC/7eP/SeSgD6/ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKAPH/2jv+Seaf8A9hWP/wBFS18wV9P/ALR3/JPNP/7Csf8A6Klr5goAKKKKACiiigAr s9O+IeowW9rbXUa3Ag4WRnIYjjG489PWuMooA+k/DV9Bc6eZ0u4Z5ZgHfy2yB1wP6fhWD4lWNdK1 Bk8+zkeFswNyr+47V4vYavqOmMTY3s9vnGRHIQD9RU0ut6nfyRR3d/cToGAxJIW7571vhf48PVfm KWx7pDqCSiOSOJXSBQI7VcDfL+PoKi1vx1p+j3MMsl9HK0cZ822gcFt3cY9frXhH9qahx/p11x0/ fN/jVVnZ2LOxZjySTmsBnQeLfFt14s1FbiaJbeCMYigVtwXOMknjJOPSueoooAKKKKACiiigAr7f 8Cf8k88Nf9gq1/8ARS18QV9v+BP+SeeGv+wVa/8AopaAOgooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAK+QPjb/yV7Xf+3f8A9J46+v6+QPjb/wAle13/ALd//SeOgDz+iiig AooooAASDkV0OieI5rGQRyHdH7k8Vz1FAHq9trdtcRAQbW3dSDVy3nVnwXH0ryS3vJrZgySMMdga v/8ACQ3mOGI4xnNAHe3GqWv9vQwq67lRkPPQsVI/ka1ZZREm5mAUeteSLfzbnuCSZdy/NnnoasXH iC+uIwhnkA+tb1vgp+n/ALcxLdnoB16yjRt0yq69PmrlPE/iiTU0FvE22NT1UnJrl3leQ5diT60y sBhRRRQAUUUUAFegfBL/AJK9oX/bx/6TyV5/XoHwS/5K9oX/AG8f+k8lAH1/RRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeP/tHf8k80/wD7Csf/AKKlr5gr6f8A2jv+ Seaf/wBhWP8A9FS18wUAFFFFABRRRQAUUUUAFSQf8fEf+8P51HUkH/HxH/vD+db4X+PD1X5ilsR0 UUVgMKKKKACiiigAooooAK+3/An/ACTzw1/2CrX/ANFLXxBX2/4E/wCSeeGv+wVa/wDopaAOgooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+QPjb/wAle13/ALd//SeOvr+v kD42/wDJXtd/7d//AEnjoA8/ooooAKKKKACiiigAooooAkH/AB7v/vL/ACNR1IP+Pd/95f5Go63r fBT9P/bmJbsKKKKwGFFFFABRRRQAV6B8Ev8Akr2hf9vH/pPJXn9egfBL/kr2hf8Abx/6TyUAfX9F FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBT1LSdN1m3W31TT7S+gV w6x3UKyqGwRkBgRnBIz7msv/AIQTwf8A9Cpof/guh/8Aia6CigDn/wDhBPB//QqaH/4Lof8A4mj/ AIQTwf8A9Cpof/guh/8Aia6CigDn/wDhBPB//QqaH/4Lof8A4mj/AIQTwf8A9Cpof/guh/8Aia6C igDn/wDhBPB//QqaH/4Lof8A4mj/AIQTwf8A9Cpof/guh/8Aia6CigDn/wDhBPB//QqaH/4Lof8A 4mj/AIQTwf8A9Cpof/guh/8Aia6CigDn/wDhBPB//QqaH/4Lof8A4mj/AIQTwf8A9Cpof/guh/8A ia6CigDn/wDhBPB//QqaH/4Lof8A4mj/AIQTwf8A9Cpof/guh/8Aia6CigDn/wDhBPB//QqaH/4L of8A4mj/AIQTwf8A9Cpof/guh/8Aia6CigDn/wDhBPB//QqaH/4Lof8A4mj/AIQTwf8A9Cpof/gu h/8Aia6CigDn/wDhBPB//QqaH/4Lof8A4mtyCCG1t4re3ijhgiQJHHGoVUUDAAA4AA4xUlFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVj33hPw3qd5JeX/h/Sru6kxvmn so5HbAAGWIycAAfhWxRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc/ /wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8A E0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND/wDBdD/8 TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/ APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNdBRQBz//AAgng/8A6FTQ/wDwXQ//ABNH/CCeD/8AoVND /wDBdD/8TXQUUAc//wAIJ4P/AOhU0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD /wDoVND/APBdD/8AE1YsfCfhvTLyO8sPD+lWl1HnZNBZRxuuQQcMBkZBI/GtiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooA//Z --20cf3054a15f008c72049c901f1b-- ========================================================================Date: Fri, 18 Feb 2011 16:39:19 +0100 Reply-To: Michel Thiebaut de Schotten <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michel Thiebaut de Schotten <[log in to unmask]> Subject: International School of Clinical Neuroanatomy on the Frontal Lobes. Comments: To: [log in to unmask] Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> We're pleased to invite you to attend to the first meeting of the INTERNATIONAL SCHOOL OF CLINICAL NEUROANATOMY to be held May 25-27, 2011 in Palermo, Sicily. The meeting is dedicated to the clinical neuroanatomy of the Frontal Lobes. Keynote speakers include Donald Stuss, Michael Petrides, Geraint Rees, Michael Kopelman, Mark Richardson and Marco Catani Topics cover comparative anatomy, cytoarchitectonic maps, connectional anatomy, executive functions, intra-operative mapping, consciousness, fronto-temporal dementia, motor neuron disease, traumatic brain injury, memory and confabulation, neuropsychological testing, virtual tractography dissections and epilepsy. A hands-on workshop on diffusion tensor imaging tractography will be run in parallel. Deadline for registration and abstract submission is 15 April 2011. For further information please visit our website http://www.isocn.eu We look forward to seeing you in Sicily! On behalf of the program committee. Michel Michel Thiebaut de Schotten, PhD NATBRAINLAB ANR-CAFORPFC/ANR-HMTC CRICM-INSERM UMRS 975 Pavillon de l'Enfance et l'Adolescence 47 Bd de l'Hôpital 75651 Paris Cedex 13, France www.natbrainlab.com skype: michel_thiebaut_de_schotten +33 613579133 ========================================================================Date: Fri, 18 Feb 2011 16:57:24 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: Interpret pediatric fMRI Comments: To: Ghazal Mohades <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Ghazal, > fro the mailing list I understood that there had been a few of you having the same question as mine, however I couldn't get an exact answer to it. That may be because there is no exact answer :) > I am using TOM template to create a group specific template based on the data from NIH study (that I suppose works the best for a group of 42 8-11 year old children). and I used that template in normalization step during preprocessing. it goes without saying that there is no meaningful dependence or relation between this template and the known template for adults (e.g. MNI template) and it makes the interpretation of the activation spots quite tricky!! since I cannot use the coordinates given by SPM and look them up in available brain atlases to find the anatomical point for activation. (as these atlases are all based on adults brains) I agree: the whole point of using a custom template is to be have more appropriate reference data for your group under study. When using such a template, you bring the images into (in SPM's case) MNI space, but they are not necessarily in alignment with the templates that were used to generate automatic lookup-helper-applications (although they may not be very far off, either). Note that this issue is by no means one of the TOM approach but will apply to each and every study using customized templates. I see that the famous Good et al paper (NI 2001) has been cited about 1500 times, and if all of them used the there-described approach to creating a custom template... you get my point. > sop how can I report about the anatomical point of activation. i am not very good at anatomy too look at the activity and guess the name of that point. even if I was that wouldn't be scientifically accepted I suppose I agree that guessing is not a good idea but I do not see why it should not be scientifically acceptable to use your eyes. Actually, I think that it may be less scientific to feed a long row of numbers into an algorithm and take the results at face value without checking at least some of them, using common sense and an anatomy textbook. The good thing is that the images may speak for themselves, so including representative slice that allow the reader to assess the localization goes a long way (you should still be able to name the structures involved, though :). I have written long paragraphs in a number of revisions, not all of them successful, trying to convey may unwillingness to include a table of activation foci, because, yes, this makes meta-analyses harder. But it is a compromise, and somehting's got to give. Cheers, Marko -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt fÃ¼r Kinder- und Jugendmedizin Leiter, Experimentelle PÃ¤diatrische Neurobildgebung UniversitÃ¤ts-Kinderklinik Abt. III (NeuropÃ¤diatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 TÃ¼bingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Fri, 18 Feb 2011 16:00:12 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: mis-registration of subject's anatomical and functional images Comments: To: Xin Di <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Are you sure these two scans are from the same subject? One brain looks much bigger than the other. Maybe the voxel sizes in the headers are wrong. Best regards, -John On 18 February 2011 15:24, Xin Di <[log in to unmask]> wrote: > Dear Experts, > > I tried to coregister subject's functional images to it's own T1 image, but > they did not registered very well (please see attached). I also tried to run > skull strip to the anatomical image and reorient the functional images, but > the results were still not good. Can any one kindly give me some suggestions > on how to do next? Thank you very much! > > Sincerely, > > -- > Xin Di, PhD > > Postdoctoral Researcher > Department of Radiology > University of Medicine and Dentistry of New Jersey > 30 Bergen Street, ADMC 582 > Newark, NJ 07101 > > ========================================================================Date: Fri, 18 Feb 2011 11:25:23 -0500 Reply-To: Xin Di <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Xin Di <[log in to unmask]> Subject: Re: mis-registration of subject's anatomical and functional images In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00221532ccd02f4158049c90f736 Content-Type: text/plain; charset=ISO-8859-1 Dear John, Thank you for your reply! I am pretty sure that they are from the same subject. The functional images might be transformed into 4D nii image by other software (AFNI or FSL). Does it cause the problem? Thank you! Sincerely, On Fri, Feb 18, 2011 at 11:00 AM, John Ashburner <[log in to unmask]>wrote: > Are you sure these two scans are from the same subject? One brain > looks much bigger than the other. Maybe the voxel sizes in the > headers are wrong. > > Best regards, > -John > > On 18 February 2011 15:24, Xin Di <[log in to unmask]> wrote: > > Dear Experts, > > > > I tried to coregister subject's functional images to it's own T1 image, > but > > they did not registered very well (please see attached). I also tried to > run > > skull strip to the anatomical image and reorient the functional images, > but > > the results were still not good. Can any one kindly give me some > suggestions > > on how to do next? Thank you very much! > > > > Sincerely, > > > > -- > > Xin Di, PhD > > > > Postdoctoral Researcher > > Department of Radiology > > University of Medicine and Dentistry of New Jersey > > 30 Bergen Street, ADMC 582 > > Newark, NJ 07101 > > > > > -- Xin Di, PhD Postdoctoral Researcher Department of Radiology University of Medicine and Dentistry of New Jersey 30 Bergen Street, ADMC 582 Newark, NJ 07101 --00221532ccd02f4158049c90f736 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear John,

Thank you for your reply! I am pretty sure that they are from the same subject. The functional images might be transformed into 4D nii image by other software (AFNI or FSL). Does it cause the problem? Thank you!

Sincerely,

On Fri, Feb 18, 2011 at 11:00 AM, John Ashburner <[log in to unmask]> wrote:

Are you sure these two scans are from the same subject? One brain
looks much bigger than the other. Maybe the voxel sizes in the
headers are wrong.

Best regards,
-John

On 18 February 2011 15:24, Xin Di <[log in to unmask]> wrote:
> Dear Experts,
>
> I tried to coregister subject's functional images to it's own T1 image, but
> they did not registered very well (please see attached). I also tried to run
> skull strip to the anatomical image and reorient the functional images, but
> the results were still not good. Can any one kindly give me some suggestions
> on how to do next? Thank you very much!
>
> Sincerely,
>
> --
> Xin Di, PhD
>
> Postdoctoral Researcher
> Department of Radiology
> University of Medicine and Dentistry of New Jersey
> 30 Bergen Street, ADMC 582
> Newark, NJ 07101
>
>

--
Xin Di, PhD

Postdoctoral Researcher
Department of Radiology
University of Medicine and Dentistry of New Jersey
30 Bergen Street, ADMC 582
Newark, NJ 07101

--00221532ccd02f4158049c90f736-- ========================================================================Date: Fri, 18 Feb 2011 18:26:09 +0100 Reply-To: Javier Navas <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Javier Navas <[log in to unmask]> Subject: Parametric or categorical design MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00235440bd494e9ac7049c91cf71 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi SPMers! I have some doubts in the analyses of fMRI data: What is the difference between the Parametric or categorical design?? and, regarding the contrast specification? And, in case you use temporal derivatives of the canonical HRF how should I´ll be the contrast for a single task? thank you all!! -- /**********************************************************\ Francisco Javier Navas Sánchez Laboratorio de Imagen Médica U.M.C.E. Hospital General Universitario Gregorio Marañón Dr Esquerdo 46, 28007 Madrid, Spain Phone : (+34) 91 426 50 67 Fax: (+34) 91 426 5108 e-mail:[log in to unmask] http://image.hggm.es --00235440bd494e9ac7049c91cf71 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi SPMers!
I have some doubts in the analyses of fMRI data:

What is the difference between the Parametric or categorical design?? and, regarding the contrast specification?

And, in case you use temporal derivatives of the canonical HRF how should I´ll be the contrast for a single task?

thank you all!!

--
/**********************************************************\
Francisco Javier Navas Sánchez
Laboratorio de Imagen Médica U.M.C.E.
Hospital General Universitario Gregorio Marañón
Dr Esquerdo 46, 28007 Madrid, Spain
Phone : (+34) 91 426 50 67   Fax: (+34) 91 426 5108
e-mail:[log in to unmask]   http://image.hggm.es
--00235440bd494e9ac7049c91cf71-- ========================================================================Date: Fri, 18 Feb 2011 12:51:27 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Parametric or categorical design Comments: To: Javier Navas <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> It could be fundamentally different or it could just be in the analysis. Categorical data has no assumption about the relationship between values or conditions; whereas parametric designs assume a relationship. However, parametric designs can also be modelled like categorical data, but not vice versa. When I've done a parametric design, I've treated it like categorical data so that I don't have to assume any apriori relationship and can define the relationship from the data. If you model something parametrically, there is one column for it. Most people just look at the canonical HRF term. If you want to incorporate the derivative terms, I'd read the earlier posts from this week or last week by Jason Steffener. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Fri, Feb 18, 2011 at 12:26 PM, Javier Navas <[log in to unmask]> wrote: > Hi SPMers! > I have some doubts in the analyses of fMRI data: > > What is the difference between the Parametric or categorical design?? and, > regarding the contrast specification? > > And, in case you use temporal derivatives of the canonical HRF how should > I´ll be the contrast for a single task? > > thank you all!! > > -- > /**********************************************************\ > Francisco Javier Navas Sánchez > Laboratorio de Imagen Médica U.M.C.E. > Hospital General Universitario Gregorio Marañón > Dr Esquerdo 46, 28007 Madrid, Spain > Phone : (+34) 91 426 50 67   Fax: (+34) 91 426 5108 > e-mail:[log in to unmask]   http://image.hggm.es > ========================================================================Date: Fri, 18 Feb 2011 17:58:01 +0000 Reply-To: Chris Filo Gorgolewski <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chris Filo Gorgolewski <[log in to unmask]> Subject: Neuroimaging Data Processing Workshop 23-24 March 2011, Edinburgh, UK Comments: To: nipy-devel <[log in to unmask]>, [log in to unmask], [log in to unmask], [log in to unmask], freesurfer <[log in to unmask]>, FSL - FMRIB's Software Library <[log in to unmask]> Comments: cc: dtc-edinburgh <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Message-ID: <[log in to unmask]> Dear all, I am delighted to invite you to a Neuroimaging Data Processing Workshop which will be held on 23-24th of March 2011 at University of Edinburgh, UK. During the workshop we will cover neuroimaging data analysis and management with reusable, scalable and reproducible processing workflows that combine different software packages (such as SPM, FSL, and FreeSurfer) using NiPyPE framework. The workshop will also feature talks and demos covering data management, encapsulation and visualization using PyXNAT and the Connectome Library and Viewer. More details can be found at the workshop website: http://nipype.blogspot.com Best regards, Chris Gorgolewski ========================================================================Date: Fri, 18 Feb 2011 18:29:02 +0000 Reply-To: Todd Hare <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Todd Hare <[log in to unmask]> Subject: Job Ad: Postdoctoral and PhD positions in decision-making SNS lab, University of Zurich Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Applications are invited for both PhD and postdoctoral positions at the Laboratory for Social and Neural Systems Research at the University of Zurich under the direction of Prof. Todd Hare. The projects will focus on the neurobiology and causal mechanisms of value computation and decision-making in different contexts using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). We are looking for candidates familiar with fMRI, TMS, neuroeconomics, and/or computational modeling approaches to decision-making. The successful candidate will join a friendly and multi-disciplinary research environment with a research-dedicated 3T scanner, TMS, TCDS, and EEG facilities and a large laboratory for conducting behavioral experiments. For details, please see www.sns.uzh.ch. The working language in the laboratory is English, but knowledge of German would be beneficial. Employment can begin immediately but is negotiable. For postdoctoral positions, initial appointment will be for 1 year with the possibility of renewal for at least two additional years. Salaries will follow Swiss National Science Foundation guidelines. Applications, including CV, names and email addresses or letter of reference from two referees, a brief statement of research experience related to the mentioned requirements and research papers should be submitted electronically to Prof. Todd Hare: [log in to unmask] The application deadline is April 1, 2011. The University of Zurich is an equal opportunity employer, and as such welcomes applications from women and minority candidates. ========================================================================Date: Fri, 18 Feb 2011 11:44:21 -0800 Reply-To: Dennis Thompson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Dennis Thompson <[log in to unmask]> Subject: Re: MATLAB Out of Memory error Comments: To: Nero Evero <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0023547c902391b159049c93bd39 Content-Type: text/plain; charset=ISO-8859-1 Window 32 bit limits you to 2 GB of memory by default . There are some tricks to boost that to 3GB as outlined by Matlab in this link http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67 Here at the IRC we do all our processing on 64 bit linux computers. Have not had a "Out of memory error" in years. On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote: > Hello All, > > We are trying to run a fixed effects analysis using SPM8 on a Windows 7 > 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of > 6600 scans (55/session). The analysis stops during the parameter estimation > and gives the following erroe message: > > Initialising parameters : ...done > Output images : ...initialised > Plane 1/46 , block 1/3 : ...estimation??? > Out of memory. Type HELP MEMORY for your options. > > Error in ==> spm_spm at 715 > CY = CY + Y*Y'; > > Error in ==> spm_getSPM at 233 > SPM = spm_spm(SPM); > > Error in ==> spm_results_ui at 277 > [SPM,xSPM] = spm_getSPM; > > ??? Error while evaluating uicontrol Callback > > I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem > to about 2GB), but I still received the error. Looking through the archives > it seems like the common solution is to change to a 64-bit OS, but I wanted > to know if there was any other possible solutions? Thanks in adavance. > > Nero > --0023547c902391b159049c93bd39 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Window 32 bit limits you to 2 GB of memory by default . There are some tricks to boost that to 3GB as outlined by Matlab in this link

http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67

Here at the IRC we do all our processing on 64 bit linux computers. Have not had a "Out of memory error" in years.

On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote:

Hello All,

We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message:

Initialising parameters : ...done
Output images : ...initialised
Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options.

Error in ==> spm_spm at 715
CY = CY + Y*Y';

Error in ==> spm_getSPM at 233
SPM = spm_spm(SPM);

Error in ==> spm_results_ui at 277
[SPM,xSPM] = spm_getSPM;

??? Error while evaluating uicontrol Callback

I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance.

Nero

--0023547c902391b159049c93bd39-- ========================================================================Date: Sat, 19 Feb 2011 13:38:33 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: acceptable high pass filtering MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3043478e322880049ca2bff3 Message-ID: <[log in to unmask]> --20cf3043478e322880049ca2bff3 Content-Type: text/plain; charset=ISO-8859-1 Dear all, Somrthing has recently been said to me that has made me think, and I want to get my head around this a bit more..... At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant? What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing some of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal? Thanks in advance for your help, Richard --20cf3043478e322880049ca2bff3 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Dear all,

Somrthing has recently been said to me that has made me think, and I want to get my head around this a bit more.....

At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?

What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing some of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal?

Thanks in advance for your help,

Richard

--20cf3043478e322880049ca2bff3-- ========================================================================Date: Sat, 19 Feb 2011 17:43:29 +0000 Reply-To: Klaas Enno Stephan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Klaas Enno Stephan <[log in to unmask]> Subject: Re: DCM and global signal Comments: To: Fred Sanders <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-588276456-1298137409=:8431" Message-ID: <[log in to unmask]> --0-588276456-1298137409=:8431 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Dear Fred, This reviewer seems to be referring to the problem of artificial anti-correlations in the context of global signal regression for resting state fMRI data (i.e., resting state data are sometimes orthogonalized to the global mean brain signal using the global signal as a confound regressor in a GLM, prior to the actual functional connectivity analyses). For details, see this paper by Murphy et al.: http://www.ncbi.nlm.nih.gov/pubmed/18976716 This is not an issue for DCM. DCM for fMRI is only informed about local signals (i.e., those from your regions of interest) and does not consider anything related to global brain signal (unless you manually construct an input that reflects global brain signal). Best wishes Klaas ________________________________ Von: Fred Sanders <[log in to unmask]> An: [log in to unmask] Gesendet: Freitag, den 18. Februar 2011, 15:03:32 Uhr Betreff: [SPM] DCM and global signal I recently used DCM (in SPM8) to model the connectivity between 2 brain regions. A reviewer was concerned that DCM contains a regressor for global signal and that including this regressor could introduce artificial anti-correlations. I would greatly appreciate it if someone could help me understand this. I did not think DCM used a regressor for global signal - was this done in prior versions of DCM? Or is this referring to the constant term from the first level analysis? Also, how could that introduce anti-correlations? Any thoughts? Thanks!! --0-588276456-1298137409=:8431 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable

Dear Fred,

This reviewer seems to be referring to the problem of artificial anti-correlations in the context of global signal regression for resting state fMRI data (i.e., resting state data are sometimes orthogonalized to the global mean brain signal using the global signal as a confound regressor in a GLM, prior to the actual functional connectivity analyses). For details, see this paper by Murphy et al.:
http://www.ncbi.nlm.nih.gov/pubmed/18976716

This is not an issue for DCM. DCM for fMRI is only informed about local signals (i.e., those from your regions of interest) and does not consider anything related to global brain signal (unless you manually construct an input that reflects global brain signal).

Best wishes
Klaas

Von: Fred Sanders <[log in to unmask]>
An: [log in to unmask]
Gesendet: Freitag, den 18. Februar 2011, 15:03:32 Uhr
Betreff: [SPM] DCM and global signal

I recently used DCM (in SPM8) to model the connectivity between 2 brain regions. A reviewer was concerned that DCM contains a regressor for global signal and that including this regressor could introduce artificial anti-correlations. I would greatly appreciate it if someone could help me understand this. I did not think DCM used a regressor for global signal - was this done in prior versions of DCM? Or is this referring to the constant term from the first level analysis? Also, how could that introduce anti-correlations?

Any thoughts?

Thanks!!

--0-588276456-1298137409=:8431-- ========================================================================Date: Sun, 20 Feb 2011 02:10:46 +0000 Reply-To: Jerome Courtemanche <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jerome Courtemanche <[log in to unmask]> Subject: SPM to Fieldtrip for ICA Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi Gurus, Here is the commands and error command. Any help greatly appreciated. D=spm_eeg_load raw=D.ftraw; D.ftraw(0); cfg=[];cfg.channel='MEG'; ic_data = ft_componentanalysis(cfg,raw); cfg.viewmode='component'; cfg.continuous='yes'; cfg.blocksize=30; cfg.channels=[1:10]; ft_databrowser(cfg,ic_data) ??? Error using ==> fetch_header at 38 trial definition is not internally consistent Error in ==> ft_databrowser at 135 hdr = fetch_header(data); ========================================================================Date: Sat, 19 Feb 2011 22:50:38 -0500 Reply-To: MP <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: MP <[log in to unmask]> Subject: SPM EEG Pre processing: Not sure if everything looks OK MIME-Version: 1.0 Content-Type: multipart/mixed; boundary cf305496b5866b64049caea6ca Message-ID: <[log in to unmask]> --20cf305496b5866b64049caea6ca Content-Type: multipart/alternative; boundary cf305496b5866b56049caea6c8 --20cf305496b5866b56049caea6c8 Content-Type: text/plain; charset=ISO-8859-1 Hi all, I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics. I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range. Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF? thanks for your help in advance - Muhammad --20cf305496b5866b56049caea6c8 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi all,

I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics.

I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range.

Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF?

thanks for your help in advance

- Muhammad

--20cf305496b5866b56049caea6c8-- --20cf305496b5866b64049caea6ca Content-Type: image/jpeg; name="Average_ERP_Cz.jpg" Content-Disposition: attachment; filename="Average_ERP_Cz.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkdeq3c20 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAGkAjADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKr3 1/Z6ZZyXl/dwWlrHjfNPII0XJAGWPAySB+NAFiisO88U6bY3tis91aR6fdWUt4NQe5VYgqPAq/Me CG88YOew6540LzVtN0+4tbe91C0tp7t9ltHNMqNM2QMICcscsBgeo9aALlFUzq2mrfpYNqFoLx3Z EtzMvmMyoHYBc5JCsrEdgwPQ0Q6tptzemyg1C0luwjOYEmVnCq5jY7Qc4DgqT2II60AXKKw9S8U6 ba6Nrt7ZXVpqE+j28stzbQ3KlkZFY7HxkoSUI5HY8cVoapqUOk2DXcyyOA6RpHGAWkkdwiIMkDLM yrkkAZySBk0AXKKx7XUNYbz7e90mCC+8ppbfybp5baXHG1pfKUo2SMgp0OV3YYLX0PWdY1HWNQsr 7S7G3hsdsck9tfvN++ZVfy9rQp0RlYt0+dQMndtAOgorm9b8UTafNcw2FhHePbvawu0lwYkE1xMk aR7gjchW3txkBo8A78gvNb17SdG1XVNU0fTUgsbKa6VbXUnlaRkXdsO6BAoIB+bJxxwaAOkorHsb nxJJeRrf6VpUFqc75INTkldeDjCmBQecfxD156VTg8RaldW8Wq2+jxzaFKgljmjuWa6eEjiQW4jw QR823fv2/wAO/wDd0AdJRWfp+p/bLy/s5YfIurOUKybsh42G6OReASpGRnGA6SKC23J0KACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsPxFbXby6Pf2l rJdnTr03ElvEyLJIrQTRYTeVXIMoJyw4B6nAO5RQBy8GkS3HibRNVm0aC0jtrS/YoCjGCaaWFlPH /LRlEpYrkAlhuYHLcnqfhrxSfAtro9laSR3D+HIrCUW0lsu6VImDx3EkisSnzYj8r+JpNzKCGHql FAHF3fhy7e4164isY/Pvdd025SQFA0lvCbQsSc5wpjmIU89cD5uTTvDl3aPpTixjidPEd/qF0ylA THIt2scjYPzErJCO5AwDjBx2lFAHldp4P10eFbzTrhNSnu7Tw5c6TbfaZrUQO7oi4gEahyhMQ+aZ lIG3gksV9A8QabNquk+RbtGJ47i3uoxISFdoZklCEgEqGKbd2DjOcHGDqUUAc++ra81ndXsPhydf JiIh0+aeET3MpIwd6yGOOMDqSSxyeBtAkj8JNeWthFp11oupWrojTTXl0bcLPO77pGCxTOVLO7tj G0cgEcCukooA8/1jTpb7/hKtMGn/AG+aXVdO1E2XyHz7UfZlb75Ccm1nG1iPu88EZLnQba48PeIL PSPAP9i313pVzbxTeVZReazJgR7opGPJwecL8vJ6V3hgha4S4aKMzojIkhUblViCwB6gEqpI77R6 VJQBx+iaZpVvrEEtt8Of7ImXdtvvs9ivlfKc8xSs/I+XgHrzxms+XQdUk8PWPhdLPVbeextBZW+s 2mpeRbqAgUTFI5RIzAKD5bIRvyu7aTJXoFFAHP6d+/8AHOvXMfzQxWlnZO3TEyGaVlx7JPEc9Pmx nIIHQVHDBDbIUgijiQuzlUUKCzMWY8dyxJJ7kk1JQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFV7++t9M065v7yTy7W1ieaZ 9pO1FBLHA5OAD0oAsVz/AIE/5J54a/7BVr/6KWtDTNZtdX837NFfR+Vjd9rsJ7bOc4x5qLu6ds44 z1FZ/gT/AJJ54a/7BVr/AOiloA6CiiigAooooAr2DpJp1tJFefbY2iQrdZU+cCBh8oApz1+UAc8D FWKjgMzW8TXEccc5QGRI3LqrY5AYgEjPfAz6CpKACiiigCOczLbytbxxyThCY0kcorNjgFgCQM98 HHoakqvf+V/Z1z5/n+T5T7/s+/zNuDnZ5fz7sdNvzZ6c1YoAKKKKACq9/wCV/Z1z5/n+T5T7/s+/ zNuDnZ5fz7sdNvzZ6c1YqOcTNbyrbyRxzlCI3kQuqtjglQQSM9sjPqKAJKKKKACiiigCOYTMgEEk aPvUkuhYbdw3DAI5K5APYkHBxg5d94s8N6ZeSWd/4g0q0uo8b4Z72ON1yARlScjIIP41oXqJJAok s/taiWMiPCnaQ6kP8xA+QgP6/LwCcCsfw9/yHPFn/YVj/wDSK1oAP+E78H/9DXof/gxh/wDiqP8A hO/B/wD0Neh/+DGH/wCKroKKAOf/AOE78H/9DXof/gxh/wDiqr3vjjwZJAok1/Q7tRLGRH9vtztI dSH+ZwPkID+vy8AnArqKKAOf/wCE78H/APQ16H/4MYf/AIqj/hO/B/8A0Neh/wDgxh/+KroKr317 Fp9nJdTJO8aYyIIHmc5IHCICx69hx16UAY//AAnfg/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDB jD/8VVjR/E+m69K8dgL5tm8M82n3ECZRtjLvkRV3BsgrnPB44NakcyyvMiiQGJ9jbo2UE7Q3ykjD DDDkZGcjqCAAc/J438JO8LL4x0aMI+5lXUIMSDaRtbJJxkg8YOVHOMgyf8J34P8A+hr0P/wYw/8A xVbD31vHqMNg0mLqaKSaNNp+ZEKBjnpwZE/P2NR3Wq2Nlf2NhcXMcd3fu6WsJ+9KUQu2B6BRyenI HUgEAy/+E78H/wDQ16H/AODGH/4qj/hO/B//AENeh/8Agxh/+KrcnnhtbeW4uJY4YIkLySSMFVFA ySSeAAOc0QTLc28U6CQJIgdRJGyMARnlWAKn2IBHegDD/wCE78H/APQ16H/4MYf/AIqo5PHnhUPC IvE/h9kL4lLapEpVdp5UZO47towccEnPGD0lFAHP/wDCd+D/APoa9D/8GMP/AMVR/wAJ34P/AOhr 0P8A8GMP/wAVXQUUAc//AMJ34P8A+hr0P/wYw/8AxVH/AAnfg/8A6GvQ/wDwYw//ABVdBRQBy7+O /BX9ow58V6V53lSbduor5e3KZ3YbZuztxn5sbtvG6rH/AAnfg/8A6GvQ/wDwYw//ABVaEWt6dL9u YXG2Gwz9ouJEZIVxndiUgI20qwbBO0ghsGqcfi7R3SZnku7cxpv8u7sZ7d5BuC/u0kQNIdzIuEBO XQdWUEAj/wCE78H/APQ16H/4MYf/AIqj/hO/B/8A0Neh/wDgxh/+KrQ0zWrLV/NFq06yRYLw3NtJ byKDnDbJFVtpwwDYwSrAHIONCgDn/wDhO/B//Q16H/4MYf8A4qoz458K/aEZfF3h8QBGDob6IsWy NpDb8AAbsjBzkcjHPSUUAc//AMJ34P8A+hr0P/wYw/8AxVH/AAnfg/8A6GvQ/wDwYw//ABVbhnhW 4S3aWMTujOkZYbmVSAxA6kAsoJ7bh61HfX1vptnJd3cnlW8eDJIVJCAkDc2OijOSx4UAkkAE0AY/ /Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FZ+p61ZaR5QumnaSXJSG2tpLiRgMZbZ GrNtGVBbGAWUE5IyAY6eOPBg1GaQa/oaSNFGGuvt9v8AvAC+EyH3fLknkY/ecEndix/wnfg//oa9 D/8ABjD/APFVuQTw3VvFcW8sc0EqB45I2DK6kZBBHBBHOakoA5//AITvwf8A9DXof/gxh/8AiqP+ E78H/wDQ16H/AODGH/4qti9vrfT4FmupPLjaWOEHaTl5HWNBx6syj2zzxUcGq2N1ql5pkFzHJeWS RvcxLyYhJu2buwJCk464wehGQCnY+LPDep3kdnYeINKu7qTOyGC9jkdsAk4UHJwAT+FaCOh1GaMX m+RYoy1rlf3YJfD4A3fNgjk4/d8AHdnH8Q/8hzwn/wBhWT/0iuq3AZvtDq0cYgCKUcOSxbJ3ArjA AG3Byc5PAxyASUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVh+L7WG88MXUFzqEmn2zPEZ7lIhJtiE ilwwIK7CoKsWBQKWLAqCK3KKAOX8Laob7UdQt7fW/wC3dNhihePUMwt+9YyCSLdCqodqpE2Mbh5n JwVxT+H0GvL4Q8NyT6lpr6f/AGZbkQJp7rLt8kbR5hmIyOMnZzg8DPHaVz/gT/knnhr/ALBVr/6K WgDYRLwRWokngaRcfaWWEgSfKQdg3HZ82DyW4BHU7gOl4YroRzwLI2fszNCSI/lAG8bhv+bJ4K8E DqNxsUUARkTfaEZZIxAEYOhQli2RtIbOAAN2Rg5yORjmPZeeRjz4PO83O7yTt8vfnbjd97Z8u7ON 3zbcfLViigCnp4P2eFopJDZm3jEKTpIJxwcmRpDuJI28MAwIbJJPEiJeCK1Ek8DSLj7SywkCT5SD sG47PmweS3AI6ncJIBMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqSgCu6XhiuhHPAsjZ+zM0JIj+UA bxuG/wCbJ4K8EDqNxkIm+0IyyRiAIwdChLFsjaQ2cAAbsjBzkcjHMlFAFOePUmsJUt7u0jvC5Mcs lszxqu/IBQSAk7eM7hk84A+WrAE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54jv0STTrmOWz+2xtE 4a1wp84EHKYchTnp8xA55OKsUAV0S8EVqJJ4GkXH2llhIEnykHYNx2fNg8luAR1O4DpeGK6Ec8Cy Nn7MzQkiP5QBvG4b/myeCvBA6jcbFFAEZE32hGWSMQBGDoUJYtkbSGzgADdkYOcjkY5p38NxJo9z FLFBes2/dB9nBWaIscxbXcAsU+TLMF3HcRj5a0Kr37pHp1zJLefYo1ictdZUeSADl8uCox1+YEcc jFAEgE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54jRLwRWokngaRcfaWWEgSfKQdg3HZ82DyW4BHU 7hYooArul4YroRzwLI2fszNCSI/lAG8bhv8AmyeCvBA6jcZCJvtCMskYgCMHQoSxbI2kNnAAG7Iw c5HIxzJRQBn3v2iOxXzNTgtJDdx4m8oBWQzLiLDMfmdSI85+82QAcLWf4e/5Dniz/sKx/wDpFa1u TGZUBgjjd96gh3KjbuG45APIXJA7kAZGcjk7TWIdE8QeJY72z1X/AEjUI5oXg0q5nR0+yW6ZDxxs v3kYde1AG3eaTe3N080PiLU7SNsYggjtii8Y4Lws3PXknrUH9g6j/wBDbrP/AH6s/wD4xUf/AAmW l/8APrrn/givf/jNH/CZaX/z665/4Ir3/wCM1sq80rWX/gK/yFYk/sHUf+ht1n/v1Z//ABij+wdR /wCht1n/AL9Wf/xio/8AhMtL/wCfXXP/AARXv/xmj/hMtL/59dc/8EV7/wDGaf1ifZf+Ax/yCxJ/ YOo/9DbrP/fqz/8AjFaOn2k9nA0c+o3N+5bcJblYwwGB8v7tFGOM9M89ayv+Ey0v/n11z/wRXv8A 8Zo/4TLS/wDn11z/AMEV7/8AGamdaU1Zpfcl+SCxz5e4TwHMLW8ns5pPErxCeAgOgfVyrYyCDwSM EEHOCCOKj1G9Njba5bTXd2LSDWIbZHn1SS2iijFjAwE1z80kaFsncvLSMoJw7Z6T/hMtL/59dc/8 EV7/APGaP+Ey0v8A59dc/wDBFe//ABmshnN+D7+51LVfD1xdmTzRZazCFkZ2ZUjvYERSXVXYhVUZ cBjjLDJNdB4gghXxJ4WuFijE76myPIFG5lWzuyoJ6kAsxA7bj61J/wAJlpf/AD665/4Ir3/4zR/w mWl/8+uuf+CK9/8AjNAB4z+bw+kR5jm1CxglQ9JI3u4kdGHdWVmUg8EEg8GuX8S63PZ+MoEtpZ1u F1Cyg2y6hKpEMkkSuyWqIY3hIdk86QgiQsoOVjFdBf8AiTQ9T065sLyx1yS1uonhmT+w74bkYEMM iLIyCelSQeLtOht4onj8QTuiBWlk0G8DOQPvHbABk9eAB6AUAcndXXiCHStRQ3E+3RPJ01pHu2Tz ke6UySTyDcVYWYgkMygbPPlIwQQklhJe6hbaPbDWJBY3WuvFG+nalLchrcWMrNH9pdVaUGRXJYZK E4VlaMbes/4TLS/+fXXP/BFe/wDxmj/hMtL/AOfXXP8AwRXv/wAZoA5eHW5x8R7K2gln/wBI1C4t 7pZtQlkkVFhmZFa22eTApMatGwYO6KG+bdIay7HWNSNur6bqupXWtHwve3N3aFmmWLUlFvkBCCFl DMQYRwmQAi7zu7z/AITLS/8An11z/wAEV7/8ZrH0/UtIsdRF7JN4rvZIomgthd6Ldt9niYqWUMLc M+fLj+aQu3yDnJbIBJ4Eupbm41IxX1pc6eqQ+Utrq02pqkuZPMJnlQclfK/dgnbgNgb8tT1PUHTx RdodRnj1dNVs4tPshdMvm2TC385hbg7ZVG66zIVYrtbkeWNvQf8ACZaX/wA+uuf+CK9/+M0f8Jlp f/Prrn/givf/AIzQBT8ExWmj+Fr6R7iSO3j1PUXklurl3CKl1KuS0jHA2rknPJyx5JJw59Y8P+MX fV01+xhWyiV7OOKRZpI0FxBL59xGpyse+GHKnbsTcXZS2I+kPjnRVuEt2i1kTujOkZ0S93MqkBiB 5WSAWUE9tw9ak/4TLS/+fXXP/BFe/wDxmgDP8J/aL3xDq+sNqcGqWtxaWsEd5axCO3Z43nLLENzE qBImW3N87OMjaUXH1HUNT/4WBPbC9ggmTULVLOOXU5kke1KwmXZZqhSZSTOPNb7p3ElRECOo/wCE y0v/AJ9dc/8ABFe//GaP+Ey0v/n11z/wRXv/AMZoA4+6uvEEOlaihuJ9uieTprSPdsnnI90pkknk G4qwsxBIZlA2efKRgghJLCS91C20e2GsSCxutdeKN9O1KW5DW4sZWaP7S6q0oMiuSwyUJwrK0Y29 Z/wmWl/8+uuf+CK9/wDjNH/CZaX/AM+uuf8Agivf/jNAHL6K0UvjXQJry9nNwkWtWVsJbx/3iwXi Ii7S2JGEYYkkFm2BmJKAix4Rniv9Ru9Ourv+1fNtHF6RevcxyNlVb7RbyqPscjbmxAvy481SP3a4 6D/hMtL/AOfXXP8AwRXv/wAZqnqviDRdY0u4065t/EAt7lPLmCaFegvGfvJnyeAy5UkYIBOCDggA seCLlbvwrok815JPqEuj2UlwJLhnYgoSHKk8Fm3/ADYy23knaMR3V/Z6D4yvtR1e7gsLG80+1ggu bmQRxtJHJcM6bjwGxKhAOCw3YztbFj/hMtL/AOfXXP8AwRXv/wAZo/4TLS/+fXXP/BFe/wDxmgDH 1e904XWk/wBozT6FoFxFeTuXuW07dcebGYyzoysrOrzvsYgnJLLuU7cO5u9al0bUdTvtR1K31DTv CVnqHkK5hVb3bcsXeMAZIZACh+Q9GVtq7e0/4TLS/wDn11z/AMEV7/8AGaP+Ey0v/n11z/wRXv8A 8ZoA5PxLeQtr1xBfaxJb3ya7piWNg1yEFxa+ZasSsJ4cCUynzFG7KFd21StdRpsENt4/1pIIo4kO mWTlUUKCzT3jMeO5Ykk9ySak/wCEy0v/AJ9dc/8ABFe//GaP+Ey0v/n11z/wRXv/AMZoAPEP/Ic8 J/8AYVk/9IrqtS2BW9nE0kj3GxSSqSLF5e+TYACSm8DIYrycKSACgHN3esQ634g8NR2Vnqv+j6hJ NM8+lXMCIn2S4TJeSNV+86jr3rrAJvtDs0kZgKKEQIQwbJ3EtnBBG3AwMYPJzwASUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFc/4E/5J54a/wCwVa/+ilrUsNVsdTe8SxuY5zZXBtbjZyElCqxT PQkBhnHQ5B5BAy/An/JPPDX/AGCrX/0UtAHQUUUUAFFFFAFewRI9Oto4rP7FGsSBbXCjyQAMJhCV GOnykjjg4qxVewdJNOtpIrz7bG0SFbrKnzgQMPlAFOevygDngYqxQAUUUUAV790j065klvPsUaxO WusqPJABy+XBUY6/MCOORirFRzmZbeVreOOScITGkjlFZscAsASBnvg49DUlABRRRQAVHOZlt5Wt 445JwhMaSOUVmxwCwBIGe+Dj0NSVXv8Ayv7OufP8/wAnyn3/AGff5m3Bzs8v592Om35s9OaALFFF FABRRRQBXvfK8hfO8/b5sePI37t29dudnO3ON2fl253fLmrFRzCZkAgkjR96kl0LDbuG4YBHJXIB 7Eg4OMGSgAooooAKjmEzIBBJGj71JLoWG3cNwwCOSuQD2JBwcYMlV71EkgUSWf2tRLGRHhTtIdSH +YgfIQH9fl4BOBQBYooooAKKKKAK9wiPPaF7Pz2SUlJMKfs52MN/JyMglPlyfn6YyRYqvcOiT2ge 88hnlISPKj7QdjHZyMnABf5cH5OuMg2KACiiigAqvcOiT2ge88hnlISPKj7QdjHZyMnABf5cH5Ou Mg2KjkMweERRxshfEpZypVdp5UYO47towccEnPGCASUUUUAFFFFAEZM32hFWOMwFGLuXIYNkbQFx ggjdk5GMDg54kqu/lf2jDnz/ADvKk27d/l7cpndj5N2duM/Njdt43VYoAKKKKACq7+V/aMOfP87y pNu3f5e3KZ3Y+TdnbjPzY3beN1WKjIm+0IyyRiAIwdChLFsjaQ2cAAbsjBzkcjHIBJRRRQAUUUUA RgTfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTniSq6Ig1GaQWeyRoow11hf3gBfCZB3fLknkY/ecEnd ixQAUUUUAFV0RBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7Fiq6Oh1GaMXm+RYoy1rlf3YJfD4 A3fNgjk4/d8AHdkAsUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVn65cajaaHez6RZfbdSWJvs0BKg PIeF3FmUbQcE8g4BxzgVoUUAcP8ADqI2kniOy/s6+tFh1CIlr14Wklc2kG5nMbsGkYjezdzJnOdw Enw+g15fCHhuSfUtNfT/AOzLciBNPdZdvkjaPMMxGRxk7OcHgZ47Suf8Cf8AJPPDX/YKtf8A0UtA GwiXgitRJPA0i4+0ssJAk+Ug7BuOz5sHktwCOp3AdLwxXQjngWRs/ZmaEkR/KAN43Df82TwV4IHU bjYooAjIm+0IyyRiAIwdChLFsjaQ2cAAbsjBzkcjHMey88jHnwed5ud3knb5e/O3G772z5d2cbvm 24+WrFFAFe2+2Nte68hN0SZhiy2yTnf+8ONy8qB8qngk9cARLwRWokngaRcfaWWEgSfKQdg3HZ82 DyW4BHU7gWHlf2dbeR5/k+Umz7Rv8zbgY3+Z8+7HXd82evNWKAK7peGK6Ec8CyNn7MzQkiP5QBvG 4b/myeCvBA6jcZCJvtCMskYgCMHQoSxbI2kNnAAG7Iwc5HIxzJRQBn36T/2Pcief++Xa3hl3+TuJ KoI23+Z5fyhlOd3zBf4auATfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTngnEzW8q28kcc5QiN5ELqr Y4JUEEjPbIz6ipKAK6JeCK1Ek8DSLj7SywkCT5SDsG47PmweS3AI6ncB0vDFdCOeBZGz9mZoSRH8 oA3jcN/zZPBXggdRuNiigCMib7QjLJGIAjB0KEsWyNpDZwABuyMHORyMc1549SawlS3u7SO8Lkxy yWzPGq78gFBICTt4zuGTzgD5auVXv0STTrmOWz+2xtE4a1wp84EHKYchTnp8xA55OKAJAJvtDs0k ZgKKEQIQwbJ3EtnBBG3AwMYPJzxGiXgitRJPA0i4+0ssJAk+Ug7BuOz5sHktwCOp3CxRQBXdLwxX QjngWRs/ZmaEkR/KAN43Df8ANk8FeCB1G4yETfaEZZIxAEYOhQli2RtIbOAAN2Rg5yORjmSigDPv YbiSxVJIoLuQXcbhfs4KqgmVgdrOPmRQDuz95dwUnCVcAm+0OzSRmAooRAhDBsncS2cEEbcDAxg8 nPEd66RwKZLz7IpljAkyo3EuoCfMCPnJCevzcEHBqxQBXRLwRWokngaRcfaWWEgSfKQdg3HZ82Dy W4BHU7gOl4YroRzwLI2fszNCSI/lAG8bhv8AmyeCvBA6jcbFFAEZE32hGWSMQBGDoUJYtkbSGzgA DdkYOcjkY5p3v2iOxXzNTgtJDdx4m8oBWQzLiLDMfmdSI85+82QAcLWhUcxmVAYI43feoIdyo27h uOQDyFyQO5AGRnIAACb7Q7NJGYCihECEMGydxLZwQRtwMDGDyc8Rol4IrUSTwNIuPtLLCQJPlIOw bjs+bB5LcAjqdwsUUAV3S8MV0I54FkbP2ZmhJEfygDeNw3/Nk8FeCB1G4yETfaEZZIxAEYOhQli2 RtIbOAAN2Rg5yORjmSigCnINSCQrFJaM5uMyyMjKFh3E4Vcnc+3auSQMkvjjYbAE32h2aSMwFFCI EIYNk7iWzggjbgYGMHk54juPK8+08zz93mny/K37d2xvv7eNuM/f+Xdt/i21YoArol4IrUSTwNIu PtLLCQJPlIOwbjs+bB5LcAjqdwHS8MV0I54FkbP2ZmhJEfygDeNw3/Nk8FeCB1G42KKAIyJvtCMs kYgCMHQoSxbI2kNnAAG7Iwc5HIxzTuEn/wBEEk/+kfayY2ihl8vb8x2uFbH+ryu5zt37W252rWhU cgmLwmKSNUD5lDIWLLtPCnI2ndtOTngEY5yAAAm+0OzSRmAooRAhDBsncS2cEEbcDAxg8nPEaJeC K1Ek8DSLj7SywkCT5SDsG47PmweS3AI6ncLFFAFd0vDFdCOeBZGz9mZoSRH8oA3jcN/zZPBXggdR uMhE32hGWSMQBGDoUJYtkbSGzgADdkYOcjkY5kooApmPUsptu7QAOxcG2Y5XzAVA/ecER7lJ5yxD YAGw2AJvtDs0kZgKKEQIQwbJ3EtnBBG3AwMYPJzxG6IdRhkNnvkWKQLdYX92CUymSd3zYB4GP3fJ B25sUAV0S8EVqJJ4GkXH2llhIEnykHYNx2fNg8luAR1O4DpeGK6Ec8CyNn7MzQkiP5QBvG4b/mye CvBA6jcbFFAEZE32hGWSMQBGDoUJYtkbSGzgADdkYOcjkY5pvDcG6hYxQPdL5m28+zjbHEZUJiwX 3bmQAZHy7o9xA+VDoVXd0Gowxm82SNFIVtcr+8AKZfBG75cgcHH7zkE7cAEgE32h2aSMwFFCIEIY Nk7iWzggjbgYGMHk54jRLwRWokngaRcfaWWEgSfKQdg3HZ82DyW4BHU7hYooArul4YroRzwLI2fs zNCSI/lAG8bhv+bJ4K8EDqNxkIm+0IyyRiAIwdChLFsjaQ2cAAbsjBzkcjHMlFAGen2g3U0Q1OB7 pfLdoPKG2OIyvg7Q27cyApuLbd0e4KPmU3AJvtDs0kZgKKEQIQwbJ3EtnBBG3AwMYPJzwAzfaHVo 4xAEUo4cli2TuBXGAANuDk5yeBjmSgCuiXgitRJPA0i4+0ssJAk+Ug7BuOz5sHktwCOp3AdLwxXQ jngWRs/ZmaEkR/KAN43Df82TwV4IHUbjYooAjIm+0IyyRiAIwdChLFsjaQ2cAAbsjBzkcjHMdv8A bBLItz5Dx9Y5I8qTlm+Uqc4wuz5t3zHcdqjANiq6eV/aM2PP87yo927f5e3L425+TdndnHzY27uN tAFiiiigAooooAKKKKACiiigAooooAKKKKACiiq99dfYrOS4FvPcMuAsMCbndiQAB2GSRySFHUkA EgAsVz/gT/knnhr/ALBVr/6KWrmla0NSuLi0msLvT7y3RJHtroxlvLcsEcGN3XBKOMZz8pyACCaf gT/knnhr/sFWv/opaAOgooooAKKKKAI4BMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqSq9giR6dbRx Wf2KNYkC2uFHkgAYTCEqMdPlJHHBxVigAooooAr36JJp1zHLZ/bY2icNa4U+cCDlMOQpz0+Ygc8n FWKr37pHp1zJLefYo1ictdZUeSADl8uCox1+YEccjFWKACiiigAqvfukenXMkt59ijWJy11lR5IA OXy4KjHX5gRxyMVYqOczLbytbxxyThCY0kcorNjgFgCQM98HHoaAJKKKKACiiigCOYzKgMEcbvvU EO5Ubdw3HIB5C5IHcgDIzkSVXvfK8hfO8/b5sePI37t29dudnO3ON2fl253fLmrFABRRRQAVXvfK 8hfO8/b5sePI37t29dudnO3ON2fl253fLmrFRzCZkAgkjR96kl0LDbuG4YBHJXIB7Eg4OMEAkooo oAKKKKAI5BMXhMUkaoHzKGQsWXaeFORtO7acnPAIxzkSVXuER57QvZ+eySkpJhT9nOxhv5ORkEp8 uT8/TGSLFABRRRQAVXuER57QvZ+eySkpJhT9nOxhv5ORkEp8uT8/TGSLFV7h0Se0D3nkM8pCR5Uf aDsY7ORk4AL/AC4PydcZBALFFFFABRRRQBXd0Gowxm82SNFIVtcr+8AKZfBG75cgcHH7zkE7cWKj Jm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPElABRRRQAVGTN9oRVjjMBRi7lyGDZG0BcYII3ZORjA 4OeJKrv5X9ow58/zvKk27d/l7cpndj5N2duM/Njdt43UAWKKKKACiiigCunlf2jNjz/O8qPdu3+X ty+Nufk3Z3Zx82Nu7jbViowJvtDs0kZgKKEQIQwbJ3EtnBBG3AwMYPJzxJQAUUUUAFRgTfaHZpIz AUUIgQhg2TuJbOCCNuBgYweTniSq6Ig1GaQWeyRoow11hf3gBfCZB3fLknkY/ecEndgAsUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAVXvrr7DZyXJt551jwWSBN77cjJC9WwMnAyxxgAnANiigDk/CkEa 6zqlxYxakNNe3t0jk1NZ/PaZWmLjNx+9KBWiIH3AWbbyXqv8PtKvIvCHhu7fxBqU0B0y3b7E8duI gDCMLlYg+Bnj5s8DJPOe0rn/AAJ/yTzw1/2CrX/0UtAGwlvKkVqhvZ3aHHmOwTNx8pX58LgZJDfK F5A7ZBHt5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAFiigCMxsbhJRNIEVGUxALtYkjDHjORggY IHzHIPGI/s8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qsUUAU9PAe3huI9SkvoJLePy5CYysnB PmgooBLggnHy8DaBzmRLeVIrVDezu0OPMdgmbj5Svz4XAySG+ULyB2yDJAZmt4muI445ygMiRuXV WxyAxAJGe+Bn0FSUAV3t5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAEhjY3CSiaQIqMpiAXaxJG GPGcjBAwQPmOQeMSUUAU54LtbCVbe5kkuQ5ljMjIm759wjJCEBMfJnaWC85Lc1YEbC4eUzSFGRVE RC7VIJyw4zk5AOSR8owBzmO/8r+zrnz/AD/J8p9/2ff5m3Bzs8v592Om35s9OasUAV0t5UitUN7O 7Q48x2CZuPlK/PhcDJIb5QvIHbII9vK8V0gvZ0abPluoTNv8oX5Mrg4ILfMG5J7YAsUUARmNjcJK JpAioymIBdrEkYY8ZyMEDBA+Y5B4xTv7f/iT3Mc97fbPnkeS3H77ZuLFE8td33fkG0b8dDv+atCo 5xM1vKtvJHHOUIjeRC6q2OCVBBIz2yM+ooABGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc5jS3lSK 1Q3s7tDjzHYJm4+Ur8+FwMkhvlC8gdsg2KKAK728rxXSC9nRps+W6hM2/wAoX5Mrg4ILfMG5J7YA kMbG4SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxiSigCnNa3bWoig1CRJftCymZ4kY+X5odo8AAYKZjB 6gEHJIybAjYXDymaQoyKoiIXapBOWHGcnIBySPlGAOcx3qJJAoks/taiWMiPCnaQ6kP8xA+QgP6/ LwCcCrFAFdLeVIrVDezu0OPMdgmbj5Svz4XAySG+ULyB2yCPbyvFdIL2dGmz5bqEzb/KF+TK4OCC 3zBuSe2ALFFAEZjY3CSiaQIqMpiAXaxJGGPGcjBAwQPmOQeMU72zMlisUhnuyLuOUfJCWUCZXHDA LtTA5xv2rkEvg1oVXvXSOBTJefZFMsYEmVG4l1AT5gR85IT1+bgg4NAEgjYXDymaQoyKoiIXapBO WHGcnIBySPlGAOcxpbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QbFFAFd7eV4rpBezo02fLdQm bf5QvyZXBwQW+YNyT2wBIY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjElFAGfcRFPskT6xPBI92W T/UhrgfM/kYKcqFB+6A+1M7s5JuCNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5ySGYPCIo42QviUs 5Uqu08qMHcd20YOOCTnjBkoArpbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QR7eV4rpBezo02f LdQmbf5QvyZXBwQW+YNyT2wBYooAjMbG4SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxivJBdhIYormRs 3G+WZmRXWPcX2qNhDD7sfODtJO7cMm5Ve48rz7TzPP3eafL8rft3bG+/t424z9/5d23+LbQBII2F w8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMaW8qRWqG9ndoceY7BM3HylfnwuBkkN8oXkDtkGxRQBXe 3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sASGNjcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4xJRQB nvb/AOlQxm9vvtH7yRZFHybPNRijYXy+m1Bkb9u7ac7mq4I2Fw8pmkKMiqIiF2qQTlhxnJyAckj5 RgDnIRN9oRlkjEARg6FCWLZG0hs4AA3ZGDnI5GOZKAK6W8qRWqG9ndoceY7BM3HylfnwuBkkN8oX kDtkEe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sAWKKAIzGxuElE0gRUZTEAu1iSMMeM5GCBgg fMcg8Yrm1u8oq6hIIw7O5MSGQ/vAyqDjaEC7kPyliCDuDDJuVXdEOowyGz3yLFIFusL+7BKZTJO7 5sA8DH7vkg7cgEgjYXDymaQoyKoiIXapBOWHGcnIBySPlGAOcxpbypFaob2d2hx5jsEzcfKV+fC4 GSQ3yheQO2QbFFAFd7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT2wBIY2NwkomkCKjKYgF2sSRhj xnIwQMED5jkHjElFAGelmRdTYM6TN5bNfbId0iCV3EHAztUEryv3ZMhi25hcEbC4eUzSFGRVERC7 VIJyw4zk5AOSR8owBzmNHQ6jNGLzfIsUZa1yv7sEvh8AbvmwRycfu+ADuzYoArpbypFaob2d2hx5 jsEzcfKV+fC4GSQ3yheQO2QR7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT2wBYooAjMbG4SUTSBF RlMQC7WJIwx4zkYIGCB8xyDxivbAR3s8LalJcShFc27mPMSs8hU4VQcHlATnIiHfcTcqMGb7Q6tH GIAilHDksWydwK4wABtwcnOTwMcgElFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFV76a4gs5JLS1 +1XHAjiMgQEkgZZj0UZySATgHAY4BALFc/4E/wCSeeGv+wVa/wDopasaTq15dajd6bqVlBa31tFF cEW1yZ42jkMir8zIh3ZifI24xt5OSBX8Cf8AJPPDX/YKtf8A0UtAHQUUUUAFFFFAFew8r+zrbyPP 8nyk2faN/mbcDG/zPn3Y67vmz15qxUcAmW3iW4kjknCASPGhRWbHJCkkgZ7ZOPU1JQAUUUUARziZ reVbeSOOcoRG8iF1VscEqCCRntkZ9RUlV79Ek065jls/tsbROGtcKfOBBymHIU56fMQOeTirFABR RRQAVXv0STTrmOWz+2xtE4a1wp84EHKYchTnp8xA55OKsVXv3SPTrmSW8+xRrE5a6yo8kAHL5cFR jr8wI45GKALFFFFABRRRQBXvXSOBTJefZFMsYEmVG4l1AT5gR85IT1+bgg4NWKjmMyoDBHG771BD uVG3cNxyAeQuSB3IAyM5ElABRRRQAVHMZlQGCON33qCHcqNu4bjkA8hckDuQBkZyJKr3vleQvnef t82PHkb927eu3OznbnG7Py7c7vlzQBYooooAKKKKAK9x5Xn2nmefu80+X5W/bu2N9/bxtxn7/wAu 7b/FtqxUcgmLwmKSNUD5lDIWLLtPCnI2ndtOTngEY5yJKACiiigAqOQTF4TFJGqB8yhkLFl2nhTk bTu2nJzwCMc5ElV7hEee0L2fnskpKSYU/ZzsYb+TkZBKfLk/P0xkgAsUUUUAFFFFAFd0Q6jDIbPf IsUgW6wv7sEplMk7vmwDwMfu+SDtzYqu7oNRhjN5skaKQra5X94AUy+CN3y5A4OP3nIJ24sUAFFF FABVd3QajDGbzZI0UhW1yv7wApl8EbvlyBwcfvOQTtxYqMmb7QirHGYCjF3LkMGyNoC4wQRuycjG Bwc8AElFFFABRRRQBGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOZKrp5X9ozY8/zvKj3bt/l7cv jbn5N2d2cfNjbu421YoAKKKKACq6eV/aM2PP87yo927f5e3L425+TdndnHzY27uNtWKjAm+0OzSR mAooRAhDBsncS2cEEbcDAxg8nPABJRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVPVbq7stLuLiw0+ TULtEzFapKkZlbsNzkBR3J9AcAnANyigDn/DCXR+1XGo6ZfW2oS7DPc3Zg/fdcJGsUsmyNMnCk8b icszOxy/h9oFnB4Q8N6ik2pGc6ZbvsfUrh4stCM/umcpjngbcDjGMCu0rn/An/JPPDX/AGCrX/0U tAGwllFHFaxh5ytrjyy07kthSvzknMnBP3s84PUA0PZRSRXUZecLdZ8wrO4K5UL8hBzHwB93HOT1 JNWKKAIzCrXCTkyb0RkAEjBcMQTlc4J+UYJGRzjGTmP7FF5Hk759vm+dnz33bt+/G7OdueNudu35 cbeKsUUAU9PtoYbeF4bWSzBt44xaFgFgVQcIEUlFI3EEr1wBkgDEiWUUcVrGHnK2uPLLTuS2FK/O ScycE/ezzg9QDRYOkmnW0kV59tjaJCt1lT5wIGHygCnPX5QBzwMVDq2o/wBl2cdx5Xm77m3t9u7b jzZkjz0PTfnHfGOOtVGLnJRjuwJnsopIrqMvOFus+YVncFcqF+Qg5j4A+7jnJ6kmpDCrXCTkyb0R kAEjBcMQTlc4J+UYJGRzjGTmSipAz7+2t49HuVlvp7KFd87Xf2khoSGMhbc5ICg87Wym0bSNvy1c EKrcPODJvdFQgyMVwpJGFzgH5jkgZPGc4GCczLbytbxxyThCY0kcorNjgFgCQM98HHoakoArpZRR xWsYecra48stO5LYUr85JzJwT97POD1AND2UUkV1GXnC3WfMKzuCuVC/IQcx8Afdxzk9STViigCM wq1wk5Mm9EZABIwXDEE5XOCflGCRkc4xk5rz2hWwljtxJJIHM0ayXUibpN+8KXGWCbuMYIC/LtK/ LVyq9/5X9nXPn+f5PlPv+z7/ADNuDnZ5fz7sdNvzZ6c0ASCFVuHnBk3uioQZGK4UkjC5wD8xyQMn jOcDEaWUUcVrGHnK2uPLLTuS2FK/OScycE/ezzg9QDViigCu9lFJFdRl5wt1nzCs7grlQvyEHMfA H3cc5PUk1IYVa4ScmTeiMgAkYLhiCcrnBPyjBIyOcYycyUUAU5oYILUI5uzGbhX+SSV33tKGHKkt s3HkfdC5BwgIqwIVW4ecGTe6KhBkYrhSSMLnAPzHJAyeM5wMEwmZAIJI0fepJdCw27huGARyVyAe xIODjBkoArpZRRxWsYecra48stO5LYUr85JzJwT97POD1AND2UUkV1GXnC3WfMKzuCuVC/IQcx8A fdxzk9STViigCMwq1wk5Mm9EZABIwXDEE5XOCflGCRkc4xk5rzWJa1EME0if6QsxZ5JGP+tEjLkO Dg8gDO0AgbSo2m5Ve9RJIFEln9rUSxkR4U7SHUh/mIHyEB/X5eATgUASCFVuHnBk3uioQZGK4Ukj C5wD8xyQMnjOcDEaWUUcVrGHnK2uPLLTuS2FK/OScycE/ezzg9QDViigCu9lFJFdRl5wt1nzCs7g rlQvyEHMfAH3cc5PUk1IYVa4ScmTeiMgAkYLhiCcrnBPyjBIyOcYycyUUAZ9xp9u/wBkje2nnCXZ nSXziWt3+Zt24tuCnJTauflfbjZkC4IVW4ecGTe6KhBkYrhSSMLnAPzHJAyeM5wMR3Dok9oHvPIZ 5SEjyo+0HYx2cjJwAX+XB+TrjINigCullFHFaxh5ytrjyy07kthSvzknMnBP3s84PUA0PZRSRXUZ ecLdZ8wrO4K5UL8hBzHwB93HOT1JNWKKAIzCrXCTkyb0RkAEjBcMQTlc4J+UYJGRzjGTmncW1un2 SB76eAPdmRE+0kNcP80nl5J3FRgtsUj5U2/cyp0KjkMweERRxshfEpZypVdp5UYO47towccEnPGC AAhVbh5wZN7oqEGRiuFJIwucA/MckDJ4znAxGllFHFaxh5ytrjyy07kthSvzknMnBP3s84PUA1Yo oArvZRSRXUZecLdZ8wrO4K5UL8hBzHwB93HOT1JNSGFWuEnJk3ojIAJGC4YgnK5wT8owSMjnGMnM lFAFM2hykCiQ2xdpnkN1IJBJ5gdVHcoTuyNwAAC7SpwLAhVbh5wZN7oqEGRiuFJIwucA/MckDJ4z nAxG/lf2jDnz/O8qTbt3+Xtymd2Pk3Z24z82N23jdVigCullFHFaxh5ytrjyy07kthSvzknMnBP3 s84PUA0PZRSRXUZecLdZ8wrO4K5UL8hBzHwB93HOT1JNWKKAIzCrXCTkyb0RkAEjBcMQTlc4J+UY JGRzjGTmm9la/aoYS99v/eTKVnn2f61HIZgdv3toCk/d3Ko2bhWhUZE32hGWSMQBGDoUJYtkbSGz gADdkYOcjkY5AAQqtw84Mm90VCDIxXCkkYXOAfmOSBk8ZzgYjSyijitYw85W1x5ZadyWwpX5yTmT gn72ecHqAasUUAV3sopIrqMvOFus+YVncFcqF+Qg5j4A+7jnJ6kmpDCrXCTkyb0RkAEjBcMQTlc4 J+UYJGRzjGTmSigCmLE5eNppDbF1lRRJIJBJ5hdsvvyUJ2gJgAAFeVO0WBCq3Dzgyb3RUIMjFcKS Rhc4B+Y5IGTxnOBiNEQajNILPZI0UYa6wv7wAvhMg7vlyTyMfvOCTuxYoArpZRRxWsYecra48stO 5LYUr85JzJwT97POD1AND2UUkV1GXnC3WfMKzuCuVC/IQcx8Afdxzk9STViigCMwq1wk5Mm9EZAB IwXDEE5XOCflGCRkc4xk5r21tDb3s5jtZA7opa6dg5ky8jbNxJfClmIBAVQ4C9wLlV0dDqM0YvN8 ixRlrXK/uwS+HwBu+bBHJx+74AO7IBYooooAKKKKACiiigAooooAKKKKACiiuHbQnk1G81q31Sx0 GwilnaeTTZmbzCpYPNJuItxJkEPvhkI2H58gFADuKKz9Jt9RtIp4NQvftqrL/o87hRK8ZVT+8Cqq 7g+8DaANoXPOTVyeRobeWVIZJ3RCyxRlQzkD7o3EDJ6ckD1IoAkrn/An/JPPDX/YKtf/AEUtWPDG p3mr6Kbq/hghulu7q3eOBiyL5U8kYwxAJ4Qc4GeuB0FfwJ/yTzw1/wBgq1/9FLQB0FFFFABRRVHW tR/sfQdR1PyvO+x20lx5e7bv2KWxnBxnHXFVGLnJRjuwLUBma3ia4jjjnKAyJG5dVbHIDEAkZ74G fQVyXxKvJ7Twygt5PKkaV5lkABZHghluoyM5H+sgQHIOQSPcdNpc9rcaXbvZSySW4QIjSs5kG35S H3/PvBBDBvmBBB5zXJfFL/kXIf8At8/9N93XZl0f9qgmv6SJn8J3FFFFcJRXv/K/s658/wA/yfKf f9n3+Ztwc7PL+fdjpt+bPTmrFRziZreVbeSOOcoRG8iF1VscEqCCRntkZ9RUlABRRRQAVHOJmt5V t5I45yhEbyIXVWxwSoIJGe2Rn1FSVXv0STTrmOWz+2xtE4a1wp84EHKYchTnp8xA55OKALFFFFAB RRRQBXvUSSBRJZ/a1EsZEeFO0h1If5iB8hAf1+XgE4FWKr3rpHApkvPsimWMCTKjcS6gJ8wI+ckJ 6/NwQcGrFABRRRQAVXvXSOBTJefZFMsYEmVG4l1AT5gR85IT1+bgg4NWKjmMyoDBHG771BDuVG3c NxyAeQuSB3IAyM5ABJRRRQAUUUUARyGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjBkqvceV59p5nn 7vNPl+Vv27tjff28bcZ+/wDLu2/xbasUAFFFFABVe48rz7TzPP3eafL8rft3bG+/t424z9/5d23+ LbVio5BMXhMUkaoHzKGQsWXaeFORtO7acnPAIxzkAElFFFABRRRQBGRN9oRlkjEARg6FCWLZG0hs 4AA3ZGDnI5GOZKruiHUYZDZ75FikC3WF/dglMpknd82AeBj93yQdubFABRRRQAVXdEOowyGz3yLF IFusL+7BKZTJO75sA8DH7vkg7c2Kru6DUYYzebJGikK2uV/eAFMvgjd8uQODj95yCduACxRRRQAU UUUAV0dDqM0YvN8ixRlrXK/uwS+HwBu+bBHJx+74AO7NiowZvtDq0cYgCKUcOSxbJ3ArjAAG3Byc 5PAxzJQAUUUUAFRgzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjmSq6eV/aM2PP87yo927f5e3L425 +TdndnHzY27uNtAFiiiigAooooAKKKKACiiigAooooAr31t9ss5LYrA8cuElSeLzEeMkb1K5Gcru HPAzkgjg+X3Xhzw7p8s4ij8Gx2v2udV83wvJcGMBt0haTzseXGXCtJgRoflOzG0esV53PYWl5Nqa 2eieINRsJnurQtby2iRKJJs3kaeZIkmJJUYMWyRg+WVGDQB6JRRRQBl6Bps2ladLbztGzve3dwCh JG2W4klUcgc7XAPvnr1rn/h94a0G28IeG9Ug0TTYtQOmW7m7S1RZSzQjcd4GcnJyc85NdpXK+GNQ i0n4TaNqU6u0NpocE8ixgFiqQKxAyQM4HrTjFyait2Bd8M32meIPD9hqNjZwJZQyyrYhYwBGkbPC rICBsygPGBgMVrVews5IrqKS0geO7z9pRowRNlQh3j+L5QF57ADpXM/DbT5dE8HRaFdMhvdMnlgu BGSVDM5lUg45BSRG/wCBYOCCBq+KdcXw5oL6rIyLDDPbrMzqWCxvMiO2F5JCsxHuBweldVegni5U aGqcrR803p96sSn7t2P8Qappnh3S5/EGpR/JZRFfMSPdJh2UbF/3mCDqBkAnpkUfF9hZv4TuNONp B9mvrqG3kQRjA8+5RXkA6eYDIXDHPz4JzXK/EfXYNc+GXi9bZf3en30dkXyfndZIC3BAxhmK987c 55rsvFn/ACB7f/sJ6f8A+lcNdFPDeyVGclaTm0/RKDX/AKUJu90Zvw30eHRfCMNovlyT209zaPci MK0qxXUwXPU4yWIGTjcfWqPj6CFdK/syGKOC1tNF1G7hjiUKFaOAQKgA4CbLh+AOoXnAIN3RL220 LS/E1x9hezsNNm85bCJUH2dBZwStGqqdgO5nOFOCxJzzmq3jT/Sf+Eh/h/s/wzc+/mfaN35bfsvv nf2xzrS5pY728tpNv/wL/gyQn8NjsHsLOSK6iktIHju8/aUaMETZUId4/i+UBeewA6VIYIWuEuGi jM6IyJIVG5VYgsAeoBKqSO+0elSUV45oZ9/pthJo9zay6TBe2rb5WsvJjKzOWMh+V8KWL85Yj5jk nvVwQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9ajv3SPTrmSW8+xRrE5a6yo8kAHL5cFRjr8wI45G KkhniuELwypIgZkLIwIDKSrDjuCCCOxBFFuoEaWFnHFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkdKH sLOSK6iktIHju8/aUaMETZUId4/i+UBeewA6VYooAjMELXCXDRRmdEZEkKjcqsQWAPUAlVJHfaPS qd/Z2Eej3KSmCytV33DTbIwsLhjIZvnBUMH/AHm5gfmGTWhUc5mW3la3jjknCExpI5RWbHALAEgZ 74OPQ0AAghW4e4WKMTuio8gUbmVSSoJ6kAsxA7bj61GlhZxxWsUdpAkdpj7MixgCHClBsH8Pykrx 2JHSrFFAFd7CzkiuopLSB47vP2lGjBE2VCHeP4vlAXnsAOlSGCFrhLhoozOiMiSFRuVWILAHqASq kjvtHpUlFAFOayhW1EMFjaOn2hZjE4Cpu80O0nCn5w2XHHLAcjO4WBBCtw9wsUYndFR5Ao3MqklQ T1IBZiB23H1qO98ryF87z9vmx48jfu3b1252c7c43Z+Xbnd8uaZpup2Wr2K3un3CXFq7Oiyx8qxV ipwe4yp5HB6jIp8rtzW0AelhZxxWsUdpAkdpj7MixgCHClBsH8Pykrx2JHSh7CzkiuopLSB47vP2 lGjBE2VCHeP4vlAXnsAOlWKKQEZgha4S4aKMzojIkhUblViCwB6gEqpI77R6VTvbDTvsKwTWn+j/ AGuOcRwRt/rvOWRXITn/AFmGYnjqW4zWhUcwmZAIJI0fepJdCw27huGARyVyAexIODjBAAQQrcPc LFGJ3RUeQKNzKpJUE9SAWYgdtx9ajSws44rWKO0gSO0x9mRYwBDhSg2D+H5SV47EjpViigCu9hZy RXUUlpA8d3n7SjRgibKhDvH8XygLz2AHSpDBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0qSigCnJp 8JSGGKC0W3Fx58sTQBgzbi+5eQFfzdr7iDyD3O4WBBCtw9wsUYndFR5Ao3MqklQT1IBZiB23H1qO 4RHntC9n57JKSkmFP2c7GG/k5GQSny5Pz9MZIsUAV0sLOOK1ijtIEjtMfZkWMAQ4UoNg/h+UleOx I6UPYWckV1FJaQPHd5+0o0YImyoQ7x/F8oC89gB0qxRQBGYIWuEuGijM6IyJIVG5VYgsAeoBKqSO +0elU7jTbB/skT6TBPGl2bhP3MZW3l+Z/OwejFifmUFtz56ZI0Kr3Dok9oHvPIZ5SEjyo+0HYx2c jJwAX+XB+TrjIIBIIIVuHuFijE7oqPIFG5lUkqCepALMQO24+tZOuXOneG/DUuovaWoh0mBpbWI7 Y1RlQoqIcYQkNsGB/FjHOKv2moRXlzfwRq4eynEEhYDBYxpJkc9MSL6cg1yXxh/5JXrP/bD/ANHx 114SgqmKp0p7ScV8m1+jJk7RbR2D2FnJFdRSWkDx3eftKNGCJsqEO8fxfKAvPYAdKkMELXCXDRRm dEZEkKjcqsQWAPUAlVJHfaPSpKK5CjPezsBdQ2pMCK3mXC2WyPbI4lSQzbSM7lcg7gfvSZOTtIuC CFbh7hYoxO6KjyBRuZVJKgnqQCzEDtuPrQTN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeJKAK6WFn HFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkdKHsLOSK6iktIHju8/aUaMETZUId4/i+UBeewA6VYooA jMELXCXDRRmdEZEkKjcqsQWAPUAlVJHfaPSq5socpbrY2hsy7TuCAMTeYJFYJtwSW3OWyCGAPJOR cqu/lf2jDnz/ADvKk27d/l7cpndj5N2duM/Njdt43UASCCFbh7hYoxO6KjyBRuZVJKgnqQCzEDtu PrUaWFnHFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkdKsUUAV3sLOSK6iktIHju8/aUaMETZUId4/i+ UBeewA6VIYIWuEuGijM6IyJIVG5VYgsAeoBKqSO+0elSUUAZ6WGnfapoBaf8852jaNvJ3ea8gdQf k8zzNzEj5s7S38NXBBCtw9wsUYndFR5Ao3MqklQT1IBZiB23H1oAm+0OzSRmAooRAhDBsncS2cEE bcDAxg8nPElAFdLCzjitYo7SBI7TH2ZFjAEOFKDYP4flJXjsSOlD2FnJFdRSWkDx3eftKNGCJsqE O8fxfKAvPYAdKsUUARmCFrhLhoozOiMiSFRuVWILAHqASqkjvtHpUdvbfZpZFiWCO1b51iji2kSM zNIxOcHcWB6A53Ek7uLFV0RBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7ABYooooAKKKKACiii gAooooAKKKKACvK7u4062u9SuU03Uo9PVNRvi1v4ivIWc21wq3REKkIpLPIyANhsDOzcdvqE5mW3 la3jjknCExpI5RWbHALAEgZ74OPQ15Xf6okWo3NxrGk/Diz8SxSuUmv9WUTRFSfIdh5O44QRkfMp IwcJnaoB6xRRWP4nvriw0USWsnlTTXdraiQKCYxNPHEzKDkbgHJGQRkDII4IBsVwdp+8+CejWnT7 fpVlp+/+59oWOHfjvt8zdjjOMZGc1uaNLe23iHUtGutRn1COC0trpJ7lI1kBleZSv7tEXaPJUj5c 5ZskjAHH6V4l0G5+HHhPS4Nb02XUB/ZCG0S6RpQyzQbhsBzkYORjjBrfC/x4eq/MUtjtNB/5DHif /sJp/wCkltXN/Fy8gfwnJoTSbbnUYppkOCcJbRm4Y+nJjROo+/nnaRV7w3rAk8a+JrVpIBZXMtnd afNu/wCPrzLbBMbZw64tyRt/uv6cc/8AFGJr/VrJrRo5Rp+law12FkXMINqgG4Zzn97Hx1wwPTmv UwdNLMKfM9opr1UOZfjbT5ESfuP+upj3dnPf+GvF3hm1j8zUdZ1ye5tRkBMCWU7WJ6MfsEo9PmTk ZO3Zn8W3usXGlWk6I0N3Z6Rfs0XCQ3H2yAyIOpyVmhOC3A2nHzZqPw5/pPi7QGTm5ubGDWp0HAVZ RfmQj/ZEt0igZJww64JqDwt4Z0zWdP0291C5+yz6Jqdngltp80WVohgOTgN5yopBBbKlRgmvSqyp RlL2y0jaV+t3p/7bH8SFfoaPiKw0+PUrSa0dLltd05rS71AFWe5jlmsbfzAygLny3JXA2gnO3kg2 /CfgrWYdG1K28TaxNfHVdMitXLEGe3z5xkjLksH2mY4Yk+mAAK3Ne0j/AISOHSoJZoIdT02+s9Te COfcq7XIYE7dxUgShThclR0wRW4l/ZyRWssd3A8d3j7M6yAibKlxsP8AF8oLcdgT0rxpZhNYWNKL 1691Z3Vn0/4FjTk965ieA/EF34o8F6frN9HDHc3Pmb1gUhBtkZRgEk9FHeujrn9Anh0+x1iG6lji XT9QunuJnYLGqyMbkHcegCTLknGCG7DJ3DPCtwlu0sYndGdIyw3MqkBiB1IBZQT23D1rjxfL7ebg rRbbS8nqvwKjtqE5mW3la3jjknCExpI5RWbHALAEgZ74OPQ1zPgTUfttnrdv5Wz7Drl9b7t2d+Zj Jnpx/rMY56Z74rQ8Qa7pWmeF9Q1S9uHawhV4pXtJDv3bvLKKyEFX3/LnI2nqRjIxPh1E1lBq+nXT RrqcM9tJeReYrOJHs7fc7YJyWcSHd/EQxyea2pU74WpKS6q3qt/uUl96E37yO1oqul/ZyRWssd3A 8d3j7M6yAibKlxsP8Xygtx2BPSh7+zjiupZLuBI7TP2l2kAEOFDnef4flIbnsQelcJRYqvf+V/Z1 z5/n+T5T7/s+/wAzbg52eX8+7HTb82enNSGeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrVee9hawl lt760jJcwRzyEPGs2/ywpAYZPmfLtyCT8vBoAuUVGJ4WuHt1ljM6IrvGGG5VYkKSOoBKsAe+0+lR pf2ckVrLHdwPHd4+zOsgImypcbD/ABfKC3HYE9KALFFV3v7OOK6lku4EjtM/aXaQAQ4UOd5/h+Uh uexB6VIZ4VuEt2ljE7ozpGWG5lUgMQOpALKCe24etABMJmQCCSNH3qSXQsNu4bhgEclcgHsSDg4w eH+EkbW3g0WXnPJDA0TRBwMoJbaGdlyAMjfM+M5OCBniusvbywksVlkEF3ALuOIfPGVWUTKg5Ygb kkA4zu3LgAtgVyfwsnhbRbm3WWMzoli7xhhuVWsLYKSOoBKsAe+0+ld9B/7JWX+F/i/82S/iR3lF V0v7OSK1lju4Hju8fZnWQETZUuNh/i+UFuOwJ6UPf2ccV1LJdwJHaZ+0u0gAhwoc7z/D8pDc9iD0 rgKLFV71EkgUSWf2tRLGRHhTtIdSH+YgfIQH9fl4BOBUhnhW4S3aWMTujOkZYbmVSAxA6kAsoJ7b h61majrVhANPU3/lLeX4s454njK+apYmNi2RyYmjIHzbmwMHkOMXJ2QGvRUYnha4e3WWMzoiu8YY blViQpI6gEqwB77T6VGl/ZyRWssd3A8d3j7M6yAibKlxsP8AF8oLcdgT0pAWKKrvf2ccV1LJdwJH aZ+0u0gAhwoc7z/D8pDc9iD0rH1nxVZWVhr0dleWsmraVZy3DWkjfMCsQcErkEp8ycjjnGc1pTpy qS5Yr+r2/NibsP0C/kvvDXhu8vb/AGXV3bQyMPkX7VI0BZlwR/vPhcH5PQEHdrjfAX2vR/AulWeo XEEj2t1NYzzS3BAG2eSJAhI+bLCNFU7eDxyAp64TwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9K0xc FDETjHZN/mEdkSUVXS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpQ9/ZxxXUsl3Akdpn7S7SAC HChzvP8AD8pDc9iD0rnGWKjkMweERRxshfEpZypVdp5UYO47towccEnPGCGeFbhLdpYxO6M6Rlhu ZVIDEDqQCygntuHrWL4i8Qado+gza1JN5yWZlMYimZY3mRXXy3ZcqMsCmH437RjftFVGLnJRjuwO F0jx819p8ur6fbPAms+LbaxXzGBZI/KhBJGCMskZGB038E4yeq+IdpFrvgy+0mO6RHnvLO0kdcOY We4h6rnrh1bHHBHrXlXhnT5dLXw7os7JJDItl4hXaT8s8t7DCvOBwITjbyNzsecLjc0fxWda1jxW Jby1Nq/iPS1shEy7ZSLhU3IckuWjt1bgkfKSABmvq8RgoQrqtQ0UGn/5NGMfwMFK6s+p6zouo/2x oOnan5Xk/bLaO48vdu2b1DYzgZxnrir1ch4d8Q6No/w80GfUNTtbaKOzs7Z2klA2SNChCt6Hawbn ovzHA5rqzPCtwlu0sYndGdIyw3MqkBiB1IBZQT23D1r5nEU+SpJJaXaXyZsndEb+V/aMOfP87ypN u3f5e3KZ3Y+TdnbjPzY3beN1WKpm9hylwt9aCzDtA5JBzN5gjVQ+7AIbchXBJYgcEYNgTwtcPbrL GZ0RXeMMNyqxIUkdQCVYA99p9KwGSUVXS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpQ9/ZxxX Usl3Akdpn7S7SACHChzvP8PykNz2IPSgCxUZE32hGWSMQBGDoUJYtkbSGzgADdkYOcjkY5DPCtwl u0sYndGdIyw3MqkBiB1IBZQT23D1qm95YG6huiIHVfMt1vd8e2NzKkZh3E53M4A2gfejwcHaCAaF FRieFrh7dZYzOiK7xhhuVWJCkjqASrAHvtPpUaX9nJFayx3cDx3ePszrICJsqXGw/wAXygtx2BPS gCxRVd7+zjiupZLuBI7TP2l2kAEOFDnef4flIbnsQelSGeFbhLdpYxO6M6RlhuZVIDEDqQCygntu HrQBGiINRmkFnskaKMNdYX94AXwmQd3y5J5GP3nBJ3YsVnpqVgbqZxq0DqvlxND50e2NzK8Y6c7m cGPBP3o8AA7s3BPC1w9ussZnRFd4ww3KrEhSR1AJVgD32n0oAkoqul/ZyRWssd3A8d3j7M6yAibK lxsP8Xygtx2BPSh7+zjiupZLuBI7TP2l2kAEOFDnef4flIbnsQelAFiq6Oh1GaMXm+RYoy1rlf3Y JfD4A3fNgjk4/d8AHdmQzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9ajt7n7TLI0TQSWq/Isscu4m RWZZFIxgbSoHUnO4EDbyAWKKKKACiiigAooooAKKKKACiiigCOaZYEDuJCC6p8kbOcswUcKCcZPJ 6AZJwATXn8eoaloc17Y2epSeR9tuZgH8IahOVMkzyEeYjhXALkBgMEAGu8vriW0s5J4bKe9kXGII Cgd8kDguyrx15I6evFcu/j147O6vG8K64LW0lMM8we0KIwIDHd5+Nqk4ZvurtbcRtbAB2FZ+taZ/ a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQCDyNCigDH0nSby11G71LUr2C6vrmKK3JtrYwRrH GZGX5WdzuzK+TuxjbwMEnjdI0xNc0n4b2NxcTR21rpUWqKkW0b5oFthHuJBO3Er5AxnPtXpVeOfD LXL2+8YafpE7I1rp3hmBrfC4ZRLDZllJ7jK5Hf5jzjAHdgYTcp1IfYi3+n5tEya2PR7r/ke9J/7B l7/6Nta5HQ2XxZfeMtJe+d5ooJ9P82RS5tjLc3eQuccbBBwDghEH8Ix11/8AufGWi3EnEUltd2iN 6ysYpFX8UglOeny4zkgHG+GmmNa6FJqM1uizamtrcm44Lzg2kO5mI5JMnmk55JZj/Fk9NKap4Vz+ 0lG3rzy/S5L1kW9K8Hf2T4stdThuc2dnocWkwxvzI22QtuYjA6Beg5JPTHO5puj2Gj/bPsEHk/bL l7uf52bfK+Nzck4zgcDj2q9RXn1cTVq/G+y+4tRSK9h5X9nW3kef5PlJs+0b/M24GN/mfPux13fN nrzVio4BMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqSsBmHoP/ACGPE/8A2E0/9JLatysOy+TxvrEa fLG1jZzMo4BkL3ClyP7xWNFz1wijsK3K3xHxr0j/AOkoSOf8aCZvDyLbyRxznULERvIhdVb7XFgl QQSM9sjPqKk0T5Nd8Sxv8sjX0cyqeCYzbQqHA/ulo3XPTKMOxqv47RJPCzRy2f22Nr6yDWuFPnA3 UWUw5CnPT5iBzycVYtf+R71b/sGWX/o26ren/usvn+cBP4v68zcooorhKCq9+iSadcxy2f22NonD WuFPnAg5TDkKc9PmIHPJxViq9+6R6dcyS3n2KNYnLXWVHkgA5fLgqMdfmBHHIxQBYooooAKKKKAK 966RwKZLz7IpljAkyo3EuoCfMCPnJCevzcEHBrj/AIZfudFa3k4lktrC7RfWJrOGNW/F4JRjr8uc YIJ7SYzKgMEcbvvUEO5Ubdw3HIB5C5IHcgDIzkeXWXiD/hF/CM+siPzJLbwrpPlqVyDIxnVMjI+X cwzg5xnFelg6cqtGpThu3FL1bIk7NM9VooorzSwrjvF95PbeJvDZjk/dxSmbyyBtZ2mt7XJ78R3U pGCOcE5xiuxrh/G//Ix6D/wH/wBOGn125ck8TFPZ3/Jkz2O4oooriKKOnataapNqEdq+/wCw3JtZ WBBUyBEYgEE9N4U5wQQR2rwrV77VLn4iRX0UyR+H/FOqiwaKGYOLyCHFszFgOFYSPjBznn+FWr0v 4a/8zf8A9jNe/wDslVPC/g2yutJ8Bah5aC10vT3uViY7t1xMsbbsMDwD5jdRhtuOBX0WEnRwFWrz K6sl98XL7+ZRsYyvJI3/AA35X9mp5nn7v7Y1Hy/K37d32i4+/t424z9/5d23+LbXSV4d4518jw54 i8FLHsv4ZZdQkBXeZI2vHm2LgjDCJopcjcNm8EKVJHuNedj8LKl++l9tyt6aNP0fN+BcJX0Ciiiv OLKOi6j/AGxoOnan5Xk/bLaO48vdu2b1DYzgZxnrisbx/aTX/hf7DHdPbQ3d5a2ty0YIkaGWdI2V WzhSd3UhgRkY5yMb4Ua7Pqvhu3tpl/dwWNu0IyPkQeZb7eAM5a2aTJ/567f4cm34in0/xB420Lw0 skkk+nzjWLkwMuYPKGI1fPQMZAeMtgDgBty+p9Wlh8c4vTkd31slr/wPUi94+pjeLdGnsvE2oarb 2XlafZeFX+ySxqBHFcW8wmjXA4G0hGAIwQpGCARWVoNmkT6tqaRzR/21faTqgjmKkqkupymP7uQM xhCRk4JP0Ho/jT/kRPEP/YMuf/RTVw2hfvbzwraPzBcaZo/mp/e8uG9mTnqMSRRtx124PBIPoYXE zqYNt/ZtfzUWn+n3kSVpFttPl1zwj4e8NQskV3DpV2jtITtWeGD7G8ZwOge4Y7hkfu+AQwI6e88W QQaToWsD91p2oZmmaZCXjh+yyz5wpPzDyxnGe+OxrC8IXkGoeKZobWTzJNKl1aG9GCPKee9DxDn7 25Y3PGcY5xkVla9/ySHwx/2DH/8ATVc1nOiqtVUqi2b/APJud/kk19/UadldHproh1GGQ2e+RYpA t1hf3YJTKZJ3fNgHgY/d8kHbmSOeKV5UjlR3ibZIqsCUbAbB9DhlOPQg96w/DmqNf6V4dlvL6Rr6 80oXLxCNQsx2xb5DheCC4AAIHzng4G2j4I1CW/vvFT3KpHN/bUoRFBG6FFSBHwTnBMLDPQsrAdCB 5Lwsoxm5fZ/HW3+ZfNsddRRWN4X1NtX0Vr03CXCPeXaRSx4KtGtxIqYI4I2KvPfrzWCptwc+iaX3 3/yHfWxs1heKr+TTdD1O6tr/AMm9g0y6ngg+Q7iig+ZtIJOw7R/d+fkHIxu15bp19e3vw18XySeW 1rNBeXaAthoBcW0dyIgAvzgGeTLkg8DjBwvTg6HtJc/RON16uwpO2h6dPPFbQSTzypFDEpeSSRgq ooGSSTwAB3rkvhX9rHw20mO+84XMPnQus+d6bJnUIQeRtAC47Yx2qPUdcXWfhdqUkrIL+Xw59tni jUhVE0L4xntlHGMkjHPbOzoHyal4jhXiKPUxsQfdXdbQO2B2y7ux9SxPUmtXTdPDTpyWvMn/AOA6 fjz3+Qr3aZleJbydPiL4HsVkxbTS3szpgculuQpz14Ej/n7CuxryAeKpfEup+Gry5sXsdQighmUE FSyS3dlG0qdSquftCAZJKcnh69fp4+hKjClGas0mn6qTd/uaCDu2RgzfaHVo4xAEUo4cli2TuBXG AANuDk5yeBjmSq6eV/aM2PP87yo927f5e3L425+TdndnHzY27uNtcl4knli+KHgdI5XRJV1BJFVi A6+UjYPqMqpx6gHtXJh6PtpuN7aSf/gMW/0KbsdrRRXI+C9QlvNS8UC6VEnbVZJEVAcGBf8ARkbq eSbaQEdcqTgAilCi505VOkbfi7A3rY66q6eV/aM2PP8AO8qPdu3+Xty+Nufk3Z3Zx82Nu7jbSz3k FtNawzSbZLqUwwjBO5wjPjjp8qMefSngTfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTnjJprcZJRRRS AKKKKACiiigAooooAKKKKAK9895HZyNYQQT3QxsjnmMSNyM5YKxHGf4T6cda87uYvO1m5tJrPw+t 3vD3Omr4wukjJkZR89sIQhEjSLkFcOZOclufSJplgQO4kILqnyRs5yzBRwoJxk8noBknABNeX6rp 0TaleWg1DUk09kv7WWJfDF7NJsu5Vkn2zL8hO5TsYKQARkP1IB6pRRWe+q2dxeXWk2WqWI1eKIub dnEjxZA2s8QYNt+ZT1GcjkZoA0K8/wDhLoEVp4M0rWruzRdZvbGJJbgkFmgUYhAwcKPLEeQME4G7 JFdB4Me4fw+4urye8mj1C+iM85Bdwl3Kq5wABwAMAADGAAOKy/h9r9nP4Q8N6ckOpCcaZbpvfTbh IsrCM/vWQJjjg7sHjGcitI1ZQi4x0vv6dvTr8kKxseJf3H9kah977JqcP7vpv87Nt17Y8/d77ccZ yDwX/wAiJ4e/7Blt/wCilqv4xvYpPh/q8gScLdaZceWGgcFcwO3zgjMfAP3sc4HUgUeDr2KP4f6R IUnK2umW/mBYHJbECN8gAzJwR93POR1BFdT1wV3/ADW+Vm/zbF9o6SiozMq3CQESb3RnBEbFcKQD lsYB+YYBOTzjODiP7bF5HnbJ9vm+TjyH3bt+zO3Gdued2Nu35s7ea4SgsESPTraOKz+xRrEgW1wo 8kADCYQlRjp8pI44OKsVT0+5hmt4UhupLwC3jkF2VBWdWBw4dQEYnaSQvTIOACMyJexSRWsgScLd Y8sNA4K5Ut84IzHwD97HOB1IFAGXa/8AI96t/wBgyy/9G3Vblc3BexR+NvEEhScra6ZZ+YFgclsN ct8gAzJwR93POR1BFdAZlW4SAiTe6M4IjYrhSActjAPzDAJyecZwcb4j416R/wDSUJHP+O3SPws0 kt59ijW+si11lR5IF1Fl8uCox1+YEccjFWLX/ke9W/7Bll/6NuqyPEV1fT+LPDZsRugt/ttxJbzR SJ5sqhbaPkRswXfOctjbsJfkAVr2v/I96t/2DLL/ANG3VdKXLh7X3i3/AOTJW/8AJb/MXU3KKrpe xSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1IFD3sUcV1IUnK2ufMCwOS2FDfIAMycEfdzzkdQRXnlF io5zMtvK1vHHJOEJjSRyis2OAWAJAz3wcehoMyrcJARJvdGcERsVwpAOWxgH5hgE5POM4OKd/e2v 9j3M06X32f54XFvBP533ihKLGPM68hlHT5gcc0AaFFRiZWuHgAk3oiuSY2C4YkDDYwT8pyAcjjOM jMaXsUkVrIEnC3WPLDQOCuVLfOCMx8A/exzgdSBQBYoqu97FHFdSFJytrnzAsDkthQ3yADMnBH3c 85HUEVIZlW4SAiTe6M4IjYrhSActjAPzDAJyecZwcAGb4l1aw0Lw/c6pqbzraWuyVvIZg7MHG1Rt IzltowTtIOG+UmvNvFGh3sXhDU9ARUmv00XQ7JRG2FeQXUqcFscE9zj8K3/GWr2GrS3Hhi+eFYLm XThHBKWhmuN180cwUEhioWIEFR0O4Egg1B4x1H7F4vit/K3/AG650S33bsbMXVzJnpz/AKvGOOue 2K9/LoSoqLivebUvKycGvzdzKep6BZXkGo2FvfWsnmW1zEs0T4I3IwBBweRwR1qeub8E3tu/gTwz 5b+aJLGGENEpdQ6xfMGK5C4KMCTgbht6kCtx72KOK6kKTlbXPmBYHJbChvkAGZOCPu55yOoIrxa9 P2dWUF0bX3Gid1csVw/jn5Na0u4biK0tpLuZv7sUV5YySN74RGOBycYAJ4o1LxPdyfEfQtChXybY X1zHOwc5m2WSSLnGBtzP0OeUU5HSsTx095qer62iaZdajYQeHXgjto7TdIt1Nc7FcIwDMAYA+4ZA Ee5eoz6WDw0qNenObSTjzfJtxX4kSd00j0XRdR/tjQdO1PyvJ+2W0dx5e7ds3qGxnAzjPXFR6/rl l4b0O71fUGcWtsoZvLXczEkAKB6kkDnA55IHNcd8LPE17qsF5oF5p6Wb+HoLaxcCXzGeRQ6OSRxj 92MAZ78nIxhfFGe71m+itvNkTRI9KluJLcmWF5ZXguZYjIhxwhtAcMAQTgg5IDhlv+3vD1NIrV2d /d3Vn3aat6hz+5dFXTfF0/hbV9VtUg3/AGjXNZ1CSMuFMsMMDFQMqSFaRCN47xMOeQPXNF07+x9B 07TPN877HbR2/mbdu/YoXOMnGcdM18+eKplns7iXEnnS+E7W4uGaNlDzTX0U0jLkYILSE/L8oJKj G0gfRpmVbhICJN7ozgiNiuFIBy2MA/MMAnJ5xnBx051CMadOcVbmun58trP7pW+Qqb1ZxXxG8P2t zpV3raWub23sblHnVQSIha3ICklhtXdLn5QSTtyMDK9rDPFcIXhlSRAzIWRgQGUlWHHcEEEdiCKz NVk0++sEtr2adLa7meya38pla63B42TBXft+8+5cfKm/dsBJ4z4d69e20V9p+vzOk9qto9wHh2/6 VezSydAAQT5sKkEAKwbtyfPjCeJwrd7um1p5S009H+aK0UvU9KqC9vINOsLi+upPLtraJppXwTtR QSTgcngHpXmL+LLq1+MNo9/Nu0e83abYRvEY5LeR4bWYll2bvmZo1w3KknO0DnrvG+pWsXg7XbaR pElltLm3jDROA7/ZnlwpxgjYCcjjIIzkYqZYCcJ0oy1VRJ6dnv8ANde3pqHNdPyMP4Y6PLoUSWE+ /wA5dFspZFkjKMjSTXchQqeQVLbfw6DpVfw5o1zH8QIdea6SGykXUrFUEiBpphfzyeWVYZIK7nGz n90c4HDdfdf8j3pP/YMvf/RtrVPwlfFvBukX8cMjLfubi5DRyK0bzuzsUTYSUEr4BOAE+fcQMnpr 4upOM6/Wpa//AJMn+VxKK0XY1vEOny6t4a1XTYGRZruzmgjaQkKGdCoJwCcZPpXK+GYIpX8BvJEj vF4cleNmUEo2LRcj0OGYZ9CR3ruBMrXDwASb0RXJMbBcMSBhsYJ+U5AORxnGRnNsbPS7WLR5LW1n jWK2+yWYaKXMUTKrbXDDKcQpy+CCAM5ODxUcTyUnTfn+MWv1X4lON3c5LwNp8WmfEr4gQQs7I89p OS5BO6VZJGHA6Zc49sda4r4gW32v4W+Fd88yx2uhpdLEj4R5A1pECw74WZ8dxn6g+ypDptne6rqs VpIlyyol3JHbyFphGhZdqgfvCA+MqCT93krgeS+NYJZPAFtYwxPK+n6VeWRKKSXW1vbSJpMDoCIi 5HO0Z5OM17eW4r22OhV84J/KDT/IznG0WvX8x/w9k1228QaZa3g32+mXM2gNcm7aQOUW4mdUjI4X 5bcbuCBCgGQWC9l4I/5GPXv+Bf8Apw1CsvxtplhFe+G7nTbG7ieXxRDc3JSGZIt29YnkcY2AlhGF Y/eySuQzEu8FahKPGVrBtTZqvhyLVpzg5WZ7mWQqvPC5uHwDk4C89crFTWLoVK8FbmS09G2/vCK5 Wkz0aaeK3QPNKkaFlQM7AAsxCqOe5JAA7kgVwfwevID4A0/S/MxfWsRmnhIIaNJZZGjJ/wB5RuHs QehBMHxf8RXWl+AbO903929zfW5R5oSrx7czKdjAYYNGuQw9QRnpU8L3droPxa8X6LBayC0i060l Ty98jIkEMahAoBZyRJ2yfl6EmuShhHLLqkn195f9uNRd/wDwN/cU5e+v6/rY7bxp/wAiJ4h/7Blz /wCimrjBp0Wj+EPGWmGWeaazsJbdZFV1jdU061UlgCUDEhcAndjdt43Vp+J9eW7/AOE50VJpHWy0 DeUEDbY5GSYtl8YyVMJAzzg46NiO9leaXxBGG2W82uWUc1vNAyPNG7WcJPzEZiby50PylX5wcA5e FjOlBU5dWn97ptfgwlZu/wDXUwNI1CK/8La3YQq4nTwvpWmqGAAknmil8sA56Ezxrk4AOc4AzXY3 GoS6HH44uo1Q3cC/2lCkgJVk+yoiE4PQvbyDGQfl7Agng7CzurDxfpnhy6tZ4bn7T5dvLJGRFNFb jT3EofGPnSzkO0FirMqtjrXVeIL2KXWbn5J/Kv4tEhKNA43RyXk25JFIyism5SHAHO08nB68VCEq itqpWfy5oR/FxZMb2LereGdP0fTrCeAPJNA2k6bHJLtLCGK8jI5AByxb5ux2rwMV2tc34vvYo9Ca QpOVtdTsPMCwOS2LmBvkAGZOCPu55yOoIo0HxnYeIdc1DTLSKc/ZYoLiOfy28uaKWNXU5wNjfNja 3JAyM4YL481Wr0PavXlbu+3wpfizTROx0AE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54858fXEtr 4p0/WvtKW0HhxrSeaQqWJgupZIZxjBycRpjGCAX6nbjukubcXU1yLGdGby4Gufsx3SESugXAG/ar EtuI2bZNwJG4jk/F8EVwnjZJokkQeHIXCuoIDKbtlPPcEAg9iAavLGo1rv0+Tai/vTaFPY7yuH8E f8jHr3/Av/ThqFdgl7FJFayBJwt1jyw0DgrlS3zgjMfAP3sc4HUgVw/gS9ijiEhScra+GdK8wLA5 LYWdvkAGZOCPu55yOoIqMP8A7rW/7d/Mb+JFj4lf8yh/2M1l/wCz12CIg1GaQWeyRoow11hf3gBf CZB3fLknkY/ecEndjhPHF3Lc+I4rZ7V2sNCs49fklGcb47hflHH3vKjuAoJwxfnG3I7e2uYbi9nE d1IXRFDWrqEMeHkXftID4YqwBJKsEBXuS8TFxw1FPz/Gz/IUd2XKKKK4CwooooAKKKKACiiigAoo ooAp6ppsOrWDWkzSIC6SJJGQGjkRw6OMgjKsqtggg4wQRkVh2fhbV7aBo5PG+uSMZZJA3lWnAZ2Y D5oWPAIHXHHAUYUdRRQAUUUUAU9M02HSrV7eBpGR7ie4JcgndLK0rDgDjc5A9sdetZfgT/knnhr/ ALBVr/6KWugrn/An/JPPDX/YKtf/AEUtAGze2cGo2FxY3UfmW1zE0MqZI3IwIIyORwT0qDRdO/sf QdO0zzfO+x20dv5m3bv2KFzjJxnHTNXqKvnlycl9N/mFuoUUUVAEcBma3ia4jjjnKAyJG5dVbHID EAkZ74GfQVJVew8r+zrbyPP8nyk2faN/mbcDG/zPn3Y67vmz15qxQBh3H7vx3p2z5fO0y683HG/Z Lb7N3rt8yTGem9sdTW5WHqn+jeKtAu/ved9o0/Z0xvQTb8+32bbj/bznjB3K3rfDD0/ViXU5vWfK /wCEqsvP8/yf7H1Df9n3+Zt32udnl/Pux02/NnpzUOrWb2nxD8Paxbxw/wClxT6XdM5YuU2NPHtH QYaJ8n/a78EWroTXfjMLaSRxT2OlSgvKhdQ1xIvlnaCCwBtn3DK9VweTiTXv+Qx4Y/7Cb/8ApJc1 005OLiv7kr/NSf8AkyWblFFFeeWFRziZreVbeSOOcoRG8iF1VscEqCCRntkZ9RUlV79Ek065jls/ tsbROGtcKfOBBymHIU56fMQOeTigCxRRRQAVm6/rll4b0O71fUGcWtsoZvLXczEkAKB6kkDnA55I HNaVef8AxjvIE+HWqWLSYuZokmRMHlEuIQxz04Mifn7GurA0FXxNOlLaUknbtfX8CZO0Wzn/ABfb xXP7QvhWOa1kuVFokgRGCkMrTMr5JHCkBiM8hSMHodnxl4dg1T4reCbnyZvMXz5Z5YsnCQbZI93U BfMfBOBnfjPTGTrzpJ8ctAvJ7z7I0V1Lp6uCoDKttFKqncCNzvcunHJBULhuT6yYImnScxIZkVkW QqNyqxBIB6gEquR3wPSvVxOKnh/q8ov/AJdNffzr/J/IhRvf1POfgjBqFn4LvLLUormCa21B0WC5 VlaJTHG+NrcqCWLY/wBrPevRp54raCSeeVIoYlLySSMFVFAySSeAAO9ea2V5Onxm1WFZMRi+ghAw PuS2DvIPxa1gPts4xls6XxXa9n8PaXpNnfPZjV9Vg0+eVFyfLkDZHY4yBkAjIyOhNZ4yg8Tj4uTS 9olJ+V1d+o4u0fQ5XxT4X1SfxvqviTw/Fc3OrC8fThDFIIxGr6cm2YSHGwqWPU8kqAQeu94E8QSe IYxq99dfZJ5tN06OWXcg8yRbq6jxyu0eYy4wBn58Kc4NafwnnlvPhzp17cyvPd3LTPPPKxaSVhIy Asx5YhVVcnsoHQCtXSLOa01PxBLDZRqZNRjEZkBjDQmKJnKkKcgO87Y6F2fJBZjWmNxV6dTCVEnK naKl1tF2a9L6rr+ijHVSXU5jwJp39keL9buPN83+3rm+uNu3b5H2a6MeOp3bvOznjG3HOci/q1l9 u+JNvG9r9othFbfaFaPenlmHUlO8dNpLBeePmA71ow6fFpnjLTIIWdkez1OclyCd0tzbyMOB0y5x 7Y61b03/AEjxdrt0nEcMVtYMD1MiB5iR/s7blB65DccAnCriXKc663cfw5uVfckvuGlpY42w0KC2 +KWmaNct9pjsfCtuofBTc8N0pVsA8fMgOMn05r06ufHh6X/hYT+JTOghGlLYLCASxbzTIWJ6AAbQ OucnpjnoK5cdX9s4O97RV/XqVFWI5DMHhEUcbIXxKWcqVXaeVGDuO7aMHHBJzxg+O/GbUJfDhney VIZtdW1HnRArIrWru5fcCCGy8AU88Kw4wtevXHlefaeZ5+7zT5flb9u7Y339vG3Gfv8Ay7tv8W2u L8f+Db3xT4h8J3MMdtLZWF4z3sc54MZKMflIIYERlcerDtkjbJq1Oji4yrfDrf5ar8UhVE3HQ5/x J4dTU/hdNqVjD/p2qStLbxrtTzBe30MqeYT/ABhViXOcDBGSADVu/wBMbW/A/wAN9Ojt0uj59hcz 27YINvHD+9ZgeCoDKD7so6sAfTZoIrhAk0SSIGVwrqCAykMp57ggEHsQDXF+Ff8AmSP+xZk/9s66 aOPnOn/hlKS+cXp8rEuCuXvGOo/8I/cafrqRedJDFdwNEW2ho/s73BwccNutUGeQAW4PGNTTdOi0 fSND0ySWeZrOOO3jkiV1V2SErlwpICkA8Odu7b/Ftrnfil/yLkP/AG+f+m+7rtJBMXhMUkaoHzKG QsWXaeFORtO7acnPAIxzkcFfTC0rdb/g3b82WviZJRRRXCUFcPpvgf7Pr/jhZU8rSvEEUWx4ZMuG ZZBN97OG3OWHBHzDHQgdxRW9HE1KMZRg/itf5NNfihNJnDfFuW7tvh3qVzbXHkiLyWBQESCQXERR lcEbcYbsSSQQRjnO8O6Ze6V4i8Kajc27xpPoFro+yT5WSQRyzuSOoK+Sq4IH+s6/KQei8deG28V+ H7vSYYkjup7ciK9kRSsRWSN/LJzuAcovQEfJk8hQZfFl5BpkmiapeSeVY2V881zMQSsafZZ1BOPV mVR6lgByQK9HC4hfVFh4ayk5+vwxt97TIkveucj45EXxD1y78BWrvbXemwG/M8qDy2mAjEa5BJ2l ZnDcAg7SMgEHsZ4IovH2nPHEiPLp968jKoBdt9ouT6nCqM+gA7VV8P6TaJ428W6zszeSXMNruIHy xrbQthTjIyX55wdq+lQeMLyfS/EHh++ik8oTSjTg+A2XmubUlMc9Yopue2OoJGac/aTp4al8Ki// AAKULv5X/ILWvJnlXhO8nm8Dazqk0nm3mt3Oo6fKqgKZ5pbVZYzgfebejoqAdZzjHRvRvEqIfGdx IbPfIsWjBbrC/uwdRbKZJ3fNgHgY/d8kHbnldF8OvB4K0KW2h8i2h8Q6VqB8zd+9ElvbozITnOZZ D7DDDtiuu8QxsfiHp9o80kaaqlosKgKVY2kstzIxGM5GI0HKj9+ThtuB6WOqQqYjmhtq/knFv7or 7kRFNLU1dd0O9vvG/hTV4FRrXTmu1uMthlEsOFYDuMrg9/mHGMkUfE8ErfEzwJOInMKNfo0gU7VZ oMgE9ASFbA74PpXa0V89TxkouN1fljKK9Jc3/wAkzVxMrxPZz6j4T1mxtY/MubmxnhiTIG52jYAZ PA5I615d8KBL4a8T61peovczPeXi2FjcSIVE62yzhmXceVURqvyk7SyCvZq8g+NeuXvhzWPC2q6c yLdwrerGzruCl0jTdjoSAxIzxkDII4r0MonOvGeXJfxL6+aV1+KRFTT3+x6ToWqJrNla6gJ9kl3Y W9y1jvVvIEgZg3ADfNyuTwfL4AO7OPcafLrkfji1jZBdzr/ZsLyEhVT7KjoDgdA9xIc4J+buAAPM fEOu6h4S0vUvDmiXD2x0hmk+zI7DyYJpbpd28EFiFuLNgNxIbacZVsenfD/UJdY0y/1iVUBv54Z9 0QPllvslurheTwrq69TgqQeQaqtgZ4SEsTB+6/h++LX3rX/MFLm0DxVqran8MLm8020ed9Ws4oba GRxG2bnbGuTyAR5oPXHHXvWb4LvbSxjs5Lu6ht4z4Z0rDTSBBwtyx5Poqsx9lJ7V5rHrWrr4r8HW 8iJ/ZbwaXp0OWJ5H2O5kIG7hstGCcYK4HJGR3nxk8I/2r4IguNNtYVk0bMiorbAlsE+dUX7vG1Dj 0Qgeh7HgqdFQwdSVo1Xfm7LSyevdW6bsnmbvJdCe51H/AISDwp4t11IvJjm8PRQNEW3FZPs8lwcH HK7bpBngkhuBxn0MGb7Q6tHGIAilHDksWydwK4wABtwcnOTwMc8dqfh6Lw/4B8aCOd5n1BdQv5Cw ACtIjfKB6ABR7nJ4zgdanlf2jNjz/O8qPdu3+Xty+Nufk3Z3Zx82Nu7jbXjY2cJxTp/Dd2+UYo1j fqWKKKK88oKKKKACiiigAooooAKKKKACiiigAoorLubzTdTuLnQo9Yjjv1QNPb2tyq3CR5UnIHzI CGA3DBAYFSDggA1K5/wJ/wAk88Nf9gq1/wDRS1H4diMGs6rDZXF3Po8SRJG9zcyXH+lBpROFeRmY gAQggHaGDAYYOKz/AIfa/Zz+EPDenJDqQnGmW6b3024SLKwjP71kCY44O7B4xnIoA7Siq6XsUkVr IEnC3WPLDQOCuVLfOCMx8A/exzgdSBQ97FHFdSFJytrnzAsDkthQ3yADMnBH3c85HUEUAWKKjMyr cJARJvdGcERsVwpAOWxgH5hgE5POM4OI/tsXkedsn2+b5OPIfdu37M7cZ2553Y27fmzt5oAkgEy2 8S3Ekck4QCR40KKzY5IUkkDPbJx6mpKp6e8KW8NpDZSWQit4yLYxBVhUggICuUyu0ghSccdiCZEv YpIrWQJOFuseWGgcFcqW+cEZj4B+9jnA6kCgDL17/kMeGP8AsJv/AOklzV7WtR/sfQdR1PyvO+x2 0lx5e7bv2KWxnBxnHXFYXjiN7jw9c6jaXM9teaBL/aMTGNlSR44ixQ9N6MjspKnGSRngrVvxVMrx 6ZpqiQ3V3qFs8IEbbSIZo5pMvjapEaOwBILbTjJFd0YRmqTe2qfyd38rMna5HIiSeKvEEctn9tjb R7QNa4U+cC93lMOQpz0+Ygc8nFWPFn/IHt/+wnp//pXDXEfD/U5JdI1HULuSSw0610rTdOivViIe MpCXdyrg4CmcNvK7NuG5UE12fiuZW0+KACTemoaa5JjYLhryMDDYwT8pyAcjjOMjOtWl7LHRhe9u T8kJO8bnQUVUttTtbu3tp7dpJI7liiEQv8rAMSHGMxkbWBD4ww2n5sCnvexRxXUhScra58wLA5LY UN8gAzJwR93POR1BFeY1bRlliq9+6R6dcyS3n2KNYnLXWVHkgA5fLgqMdfmBHHIxUhmVbhICJN7o zgiNiuFIBy2MA/MMAnJ5xnBxXnvithLPbwySSBzFHHJHIm6TfsGcIWCbv49pAX5uV5oAuUVGJla4 eACTeiK5JjYLhiQMNjBPynIByOM4yMxpexSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1IFAFivI/jT bfbbzRbVp5oonsdTkcRPt3+VCkyq3qu+NCR7djg16o97FHFdSFJytrnzAsDkthQ3yADMnBH3c85H UEVzOp6bp3iHx2bDVLaSZbLSmeAKHVSLkyRTbnXgHbGoUZB+ZyM4yvo5XXWHxHt5bRT/ABVl+LRE 1dWINEtrPVvFGt63apHc3FhqslpF5p2KhMFpHMwypO9fKYDGAckZw2R2tcF8N7LyPAUOoX813Nda ve/2jcPHHIhMski7cKqqQnyoW42EFjkxmu5EytcPABJvRFckxsFwxIGGxgn5TkA5HGcZGcse0q7p p3UfdXy/4Nxx2ucB8VvCS6v4Nv5bV0tzDO2q3TNljIYrZkAA6AkLGvYAAnk8Hp9S/wBI8XaFavxH DFc36kdTIgSEA/7O25c+uQvPBBm1eKLW/Ds1gHnhXVraS3jka2fMe+JuXUgFMDPDbecLwSBXFeDv FVx4u8Q33iAwSDSdOs2RCkbuweWO3d4UCoDIUaGTJGSfMXAxiuqjKrWw12/dpqX/AJMkl976dkS7 KXqafwe/5JXo3/bf/wBHyV2F75XkL53n7fNjx5G/du3rtzs525xuz8u3O75c1neHtPXQrFdJ8tEB nu54VgibykiadnVc4CqQsijb7NjIUmrs12WtRLAZE/0hYjvtZGP+tCN8owcHnD/dAIflRzx42qqu KqVI7Sk397KirRSM/Uv9H8XaFdPzHNFc2CgdRI4SYE/7O22ceuSvHJINB/5DHif/ALCaf+kltVD4 gXV1D4Q1KXToPNvNP+z6gFkQiMrFMsh+bgNgRMSoO7GP7y5q/DWa4PhC0vNTF1/aWoTslyJI3YrL CvkfOcEqStuCxc8uSOrBa6FT/wBiddv+5bre/Nf03Qr+9Y7Wiq73sUcV1IUnK2ufMCwOS2FDfIAM ycEfdzzkdQRUhmVbhICJN7ozgiNiuFIBy2MA/MMAnJ5xnBx5pYSCYvCYpI1QPmUMhYsu08Kcjad2 05OeARjnIkrPuLm3f7JO9jPOEuzGj/ZiWt3+aPzMEbgpyV3qD8r7vuZYXBMrXDwASb0RXJMbBcMS BhsYJ+U5AORxnGRkAkrh/Dfya1o1uvEVpbavaQr/AHYoryGONffCIoyeTjJJPNdgl7FJFayBJwt1 jyw0DgrlS3zgjMfAP3sc4HUgVweizXEXxO8SRkINJ0SB3SOON3m33flTvgKCXG6KRsDnL4GRgDvw avTqeSv+Dj+ckRLdFz4qfJ4WWZuIo/tW9z91d1lcouT2y7oo9SwHUiuwuER57QvZ+eySkpJhT9nO xhv5ORkEp8uT8/TGSKPiDSbDxFpc+g6ik5gvIiSY1YbdrKQd+NoYMVIB64PBAaprjULdPskj3M8A e7MCReSQ1w/zLt2ldxUYL7lx8qbs7Mk41K0Z0IQ6xb+52t+v4DS1bNCioxMrXDwASb0RXJMbBcMS BhsYJ+U5AORxnGRmNL2KSK1kCThbrHlhoHBXKlvnBGY+AfvY5wOpArmKLFFV3vYo4rqQpOVtc+YF gclsKG+QAZk4I+7nnI6gipDMq3CQESb3RnBEbFcKQDlsYB+YYBOTzjODgAjd0Gowxm82SNFIVtcr +8AKZfBG75cgcHH7zkE7cYnjvQp/E3g+70a2bbJdSwKXwDsQTIWbBIzhQTjPOMVrm+OUkWGQ2xdo nYxyCQSeYEXCbMlCdxL5AAAblTuFgTK1w8AEm9EVyTGwXDEgYbGCflOQDkcZxkZ0o1ZUakasN4tN eq1E1dWOV+HGoS654UHiC5VEu9WneedIgRGrJiABQSSBthU8k8k9sAW/HNnBN4XuNQlj3z6P/wAT S0ySAJoVLrkDqpwQR6E4wcEZPwo32Xw10S2uYZ4pzLcQmNoWyjiaUkPx8mNpHzY5wOpAPTaulrqW iavY3Iult2gkgnMUDl9rR5JjG07zhuNobnI5IIruxE40swk4v3Yydrdk7floSleBS8OaYsvgXQLL ULd1eCzs3aKTKMkkQRhkcEEOo4Ppg1j+KP8AkqngL/uIf+iFrtDMq3CQESb3RnBEbFcKQDlsYB+Y YBOTzjODjg/FF7F/ws3wFNsn2+bqEOPIfdu2KmduM7c87sbdvzZ280YOo6mInJ9Y1H98JBJWX3fm egUVGJla4eACTeiK5JjYLhiQMNjBPynIByOM4yMxpexSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1I FeaWWK5jxh4NsvFZ0qaeNHn068jmQSn928e9DKjLghgyL0x1A5AJzvvexRxXUhScra58wLA5LYUN 8gAzJwR93POR1BFSGZVuEgIk3ujOCI2K4UgHLYwD8wwCcnnGcHGtGtOhUVSm7NCaTVmcj4m8E2Hi vXpftJniSTTPst1LFuVmXz45YghZTGcGOXdj5huXPBU1D8Hv+SV6N/23/wDR8ldYLs5edjIbYusK Ri1kEgk8wozHuUJ24O0AAFtxU5HM/DCzutI8DWGj6haz219a+b5sckZAGZ5cYbG1umeCeCp6MCe1 YmU8vlRlLRSjZfKd/wBPwJt79zOtPBd6lhftdWyPcP4tGsWqrLzHH56AseQM+WJDjngj+Lgd5e2c Go2FxY3UfmW1zE0MqZI3IwIIyORwT0pEvYpIrWQJOFuseWGgcFcqW+cEZj4B+9jnA6kCh72KOK6k KTlbXPmBYHJbChvkAGZOCPu55yOoIrmrYurWalJ6p3Xlt/kNRSKXiezn1HwnrNjax+Zc3NjPDEmQ NztGwAyeByR1qbSr46rY22pwtiyvLWKeGJ48SLuBY7iGIPBUYA4IPJyMWzMq3CQESb3RnBEbFcKQ DlsYB+YYBOTzjODjlPh/f2V1oGlwQ20iXcWi2JlmeHYXTEiKuThiA0chHG3DgqSGpJOWHenwv81/ wEPqdfRRRXOMKKKKACiiigAooooAKKKKACiiigAqnqtg2p6XcWKX13YmdNn2i0ZVlQd9pYEA44zj IzkYOCLlFAGXomjyaNb/AGc6pd3kCoiQxzRQRrAqjACCKNABjAwc4wMYqn4E/wCSeeGv+wVa/wDo pa6Cuf8AAn/JPPDX/YKtf/RS0AdBRRRQAUUUUAV7B0k062kivPtsbRIVusqfOBAw+UAU56/KAOeB irFRwGZreJriOOOcoDIkbl1VscgMQCRnvgZ9BUlAGV4ns59R8J6zY2sfmXNzYzwxJkDc7RsAMngc kda43Ron17x5HrenXMz6LJcy3qyhGMU5Wzt4I8E8IwM04OOSYyrDKfL6PWN4V8PReFfDttosE7zw 27SmN5AA21pGcA44JAbGeM4zgdK78PilSoTXV6fKS1fytG3r90uN2Y3gOxu9N8MXYRYJbhZPIjQy FUaS3hjtWy20kKZLdyDgnaQcZyo1vGXyeDdXuF4ltLZ7uFv7ssQ8yNvfDopweDjBBHFal/5X9nXP n+f5PlPv+z7/ADNuDnZ5fz7sdNvzZ6c0zVNPi1bSbzTZ2dYbuB4JGjIDBXUqSMgjOD6Vm8Rz4lVp d0/kgtZWOD+Gdtdw6jfXNzP5ovdH0q6jy5YjdHLvJz/E0olc9clyTyTXo9Yd1/yPek/9gy9/9G2t blPHVXXqKs1bmS09Pd/QIqysFV7/AMr+zrnz/P8AJ8p9/wBn3+Ztwc7PL+fdjpt+bPTmrFZuuarF pNijtNHFPczx2lsXUODNIwRPl3KWAJ3EAg7VbHSuWMXNqMVdso0qqaXqEWraTZ6lArrDdwJPGsgA YK6hgDgkZwfWmaxrFhoGlTanqc/kWcG3zJNjNt3MFHCgnqR2qj4L/wCRE8Pf9gy2/wDRS1qqT9i6 rXVJPps7/oK+tjYnnitoJJ55UihiUvJJIwVUUDJJJ4AA71j6b/pHi7XbpOI4YrawYHqZEDzEj/Z2 3KD1yG44BJ40/wCRE8Q/9gy5/wDRTUaD/wAhjxP/ANhNP/SS2rSnC1Cc+6t9zh/mJ7mp9nMFpFb2 Igt0i2KieVlFjBGVVQRj5QQOwODggYNiq96iSQKJLP7WoljIjwp2kOpD/MQPkID+vy8AnAqxXI3f VlBXK/DjSbvQfAtjpV8m25tZbiN8AgNieTDDIBKkYIOOQQa6qitVWapSpdG0/uTX6itrcKr3qJJA oks/taiWMiPCnaQ6kP8AMQPkID+vy8AnAqxVe9dI4FMl59kUyxgSZUbiXUBPmBHzkhPX5uCDg1kM h1rTv7Y0HUdM83yftltJb+Zt3bN6lc4yM4z0zVTwxBLb6VOk0TxudQvnCupBKtdSsp57EEEHuCDW zRWvtZey9l0vf9BW1uFFFFZDK9w6JPaB7zyGeUhI8qPtB2MdnIycAF/lwfk64yDYqOQzB4RFHGyF 8SlnKlV2nlRg7ju2jBxwSc8YMlABXOabZwWvxB8QTQx7ZLqxsZpjknc4a4TPPT5UUceldHRWkKjh GUV9pW/FP9BNBUchmDwiKONkL4lLOVKrtPKjB3HdtGDjgk54wZKr3HlefaeZ5+7zT5flb9u7Y339 vG3Gfv8Ay7tv8W2sxliiiigAooooArv5X9ow58/zvKk27d/l7cpndj5N2duM/Njdt43VYqMib7Qj LJGIAjB0KEsWyNpDZwABuyMHORyMcyUAZXh6znsdNmhuY9kjX15MBkH5HuZHU8eqsD+NatFFXUm5 zc311BKwVzGq6fFefETw1PIzh7Kzvp4wpGCx8iPB46Ykb05Arp6qSW8TavbXJtZGmjgljW4DDbGr NGShGcksVUg4ONh5GQDdCr7KTl5Nfemv1E1ct0UUViMKKKKAK6Ig1GaQWeyRoow11hf3gBfCZB3f LknkY/ecEndixVdHQ6jNGLzfIsUZa1yv7sEvh8AbvmwRycfu+ADuzYoAKKKKACvP/he6LJ4msVvN /wBg1L7ElsSuUihiSGOQgDdudYuSflJQ7QOa9ArmPB2h3uiNrpuljCahqtxfR4fLKHkIAIHGNiRs DnPzkEDbk9dGcY4erFvV8tvvJa1R09FFFchQUUUUAFFFFABRRRQAUUUUAFFFFABRRWXcy6fq1xc6 Hc293IAgaZXtpkgkXKnb5u0I4OQCgY5G4EEbhQBqVz/gT/knnhr/ALBVr/6KWs/wnZ/2b4h1ey/s 2x0rFpay/YNNbdbDc8480HZH+8bZtYbOkafM2cLH8PtVvJfCHhu0fw/qUMA0y3X7a8luYiBCMNhZ S+Djj5c8jIHOADtKKrpcSvFaubKdGmx5iMUzb/KW+fDYOCAvyluSO2SB7iVIrpxZTu0OfLRSmbj5 Q3yZbAySV+YryD2wSAWKKjMjC4SIQyFGRmMoK7VIIwp5zk5JGAR8pyRxmP7RL5HmfYp93m+X5eU3 bd+3f97G3Hz9d23tu+WgAsPK/s628jz/ACfKTZ9o3+ZtwMb/ADPn3Y67vmz15qxVe2e4G2G5TfIk SF7hFCRyOchgq7mZcYzg9mGCxzgS4leK1c2U6NNjzEYpm3+Ut8+GwcEBflLckdskAFiiq73EqRXT iyndoc+WilM3Hyhvky2Bkkr8xXkHtgmQyMLhIhDIUZGYygrtUgjCnnOTkkYBHynJHGQAnEzW8q28 kcc5QiN5ELqrY4JUEEjPbIz6ipKz7+UyaPc+bo896rb4msf3JaZCxQ/fcIVK/NhmHynBGflq4JGN w8RhkCKisJSV2sSTlRznIwCcgD5hgnnABj6z+58Q+HLiPiWS5mtHb1iaCSRl/F4Ijnr8uM4JB3K5 /VpGm1DwnK8MkDvqDM0UhUshNncfKdpIyOnBI9Ca1Uv/AD7Oa6treS4jVd8BieMi6UoGBjO7GDna CxXkHtgneqvcpvy/9ukJbst15N4/ghv/ABvdwXcUc1rYaLDrbQuoIna2ml/dNnI2Oksing87TyAQ fVTIwuEiEMhRkZjKCu1SCMKec5OSRgEfKckcZ848Uwfa5/GeuraXRez0F9J8sNGPmYySOxG7kBGg kBB+65GC4KjsyqajWbfb82kTPY1viZpV74h0XTfD9ndpajU9QSGeR03Dy1jklPHXOYgRgjJAGQCa 2fB/7vwrZWnX7Bv0/f8A3/s7tDvx23eXuxzjOMnGar63do/ijw9ZhfnivvNJ3ryHtbsDjO4fcPJA B7EkNg8IXEr6ErmynRptTv8AzEYpm3/0mdvnw2DggL8pbkjtkhVZy+pRp9E7/N8y/JIEveuVdc1C W/03W7CZUECaraaawUEGSCb7N5gJz1InkXIwQMYwRmqvwi/e/DjTbt+Z7jd5r/3vLPkpx0GI4o14 67cnkknnrbW57nXtS0aaOSS4vNaW8iG6ISTC3mnOI13AFdthDFltpDOzEkFc7vwgkYfDTQohDIUZ LljKCu1SJ2wp5zk5JGAR8pyRxnsxNL2GAlT2fND7nGX5yi/uJi7yudpeukcCmS8+yKZYwJMqNxLq AnzAj5yQnr83BBwasVTmurtbUSwafI8v2hYjC8qKfL80I0mQSMBMyAdSABgE4FgSMbh4jDIEVFYS krtYknKjnORgE5AHzDBPOPBNSSiq6XErxWrmynRpseYjFM2/ylvnw2DggL8pbkjtkge4lSK6cWU7 tDny0Upm4+UN8mWwMklfmK8g9sEgFio5jMqAwRxu+9QQ7lRt3DccgHkLkgdyAMjOQGRhcJEIZCjI zGUFdqkEYU85yckjAI+U5I4zTvbj/QVkmsr7/j7jjEcB/ef65VVzsb/V9HbJ+5ncOq0AaFFRiRjc PEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xGlxK8Vq5sp0abHmIxTNv8pb58Ng4IC/KW5I7ZIALFFV3u JUiunFlO7Q58tFKZuPlDfJlsDJJX5ivIPbBMhkYXCRCGQoyMxlBXapBGFPOcnJIwCPlOSOMgEdx5 Xn2nmefu80+X5W/bu2N9/bxtxn7/AMu7b/FtqxVOSe7KQyxW0i4uNksLKjO0e4puU7wFH3ZOcnaC Nu44FgSMbh4jDIEVFYSkrtYknKjnORgE5AHzDBPOACSiq6XErxWrmynRpseYjFM2/wApb58Ng4IC /KW5I7ZIHuJUiunFlO7Q58tFKZuPlDfJlsDJJX5ivIPbBIBYqOQTF4TFJGqB8yhkLFl2nhTkbTu2 nJzwCMc5AZGFwkQhkKMjMZQV2qQRhTznJySMAj5TkjjNO4lL/ZJX0eeeRLsqn+pLW4+ZPPyX4UqT 90l9r425yAAaFFRiRjcPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xGlxK8Vq5sp0abHmIxTNv8AKW+f DYOCAvyluSO2SACxRVd7iVIrpxZTu0OfLRSmbj5Q3yZbAySV+YryD2wTIZGFwkQhkKMjMZQV2qQR hTznJySMAj5TkjjIBG6IdRhkNnvkWKQLdYX92CUymSd3zYB4GP3fJB25sVnveEXUOROkzeYq2O+H dIglRDPyc7VBDcN92TBUttUXBIxuHiMMgRUVhKSu1iScqOc5GATkAfMME84AJKKrpcSvFaubKdGm x5iMUzb/AClvnw2DggL8pbkjtkge4lSK6cWU7tDny0Upm4+UN8mWwMklfmK8g9sEgFiq7ug1GGM3 myRopCtrlf3gBTL4I3fLkDg4/ecgnbiQyMLhIhDIUZGYygrtUgjCnnOTkkYBHynJHGa5urvKMuny GMuyODKgkH7wKrAZ2lCu5z8wYAAbSxwAC5RUYkY3DxGGQIqKwlJXaxJOVHOcjAJyAPmGCecRpcSv FaubKdGmx5iMUzb/AClvnw2DggL8pbkjtkgAsUVXe4lSK6cWU7tDny0Upm4+UN8mWwMklfmK8g9s EyGRhcJEIZCjIzGUFdqkEYU85yckjAI+U5I4yAAM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5krP S4/0qaQWV99o/dxtGx+TZ5rqHXLeX03OcHft27hnatXBIxuHiMMgRUVhKSu1iScqOc5GATkAfMME 84AJKKrpcSvFaubKdGmx5iMUzb/KW+fDYOCAvyluSO2SB7iVIrpxZTu0OfLRSmbj5Q3yZbAySV+Y ryD2wSAWKrp5X9ozY8/zvKj3bt/l7cvjbn5N2d2cfNjbu421IZGFwkQhkKMjMZQV2qQRhTznJySM Aj5TkjjMdu9xLLJJKnlQ/cSJ1G8FWYFywYgqw2EDAI78nCgFiiiigAooooAKKKKACiiigAooooAK KKKACq99Y2+o2clrdR+ZC+CQGKkEEFWVhgqwIBDAgggEEEVYooAz9M0Wy0jzTarO0kuA81zcyXEj AZwu+RmbaMsQucAsxAyTnP8AAn/JPPDX/YKtf/RS10Fc/wCBP+SeeGv+wVa/+iloA6CiiigAoooo Ar2CJHp1tHFZ/Yo1iQLa4UeSABhMISox0+UkccHFWKr2DpJp1tJFefbY2iQrdZU+cCBh8oApz1+U Ac8DFWKACiiigCvfukenXMkt59ijWJy11lR5IAOXy4KjHX5gRxyMVYqOczLbytbxxyThCY0kcorN jgFgCQM98HHoakoA5H4i6PqGp+Hre70rY1/pF5HqcMDxs4naIMfLwvOTnjHUgDjORpeC/wDkRPD3 /YMtv/RS1uVh+E/+QPcf9hPUP/Suau11pSwns3tGS/FP/L8Sbe9c3K5GTT5tWsvHmnW7RrPdzvBG ZCQoZ7GBRkgE4yfQ111c3oPlf8ItqPn+f5P27Ut/2ff5m37VNnZ5fz7sdNvzZ6c1nRk6cJVI76L8 b3/8lG9XY4TwNrEvif4o/wBuT71h1DRTdw2byGRbVo5jbjaTgZwJTkAY85x3JPeeD5ZxDrNjc23k yWesXSg7w3mJI/2hW46ZWYce3boL1h4d03Tr37dDDuvP9IH2hvv7JpjMyHGMqHPGen4nNG1vINN8 aanp7yY/tD7Ncx5BJaZo5UZQRwF8uzB577ueQB3YrEU8U5+yjaKirLtyuy/8let/UiKcdzG0/TFu Lzw/qK27yTWmv6shlXJEcbvd7s44ALLGMnvgd+ZPg9/ySvRv+2//AKPkrc8IfP4at7of6u+lmv4w eojnleZAf9oLIAcZGc4J61uVli8W3GdBr7W/o5//ACf4eY4x2ZXvfK8hfO8/b5sePI37t29dudnO 3ON2fl253fLmrFRzCZkAgkjR96kl0LDbuG4YBHJXIB7Eg4OMGSvNLCiiigAqOYTMgEEkaPvUkuhY bdw3DAI5K5APYkHBxgyVXvUSSBRJZ/a1EsZEeFO0h1If5iB8hAf1+XgE4FAFiiiigAooooAr3CI8 9oXs/PZJSUkwp+znYw38nIyCU+XJ+fpjJFiq9w6JPaB7zyGeUhI8qPtB2MdnIycAF/lwfk64yDYo AKKKKACq9w6JPaB7zyGeUhI8qPtB2MdnIycAF/lwfk64yDYqOQzB4RFHGyF8SlnKlV2nlRg7ju2j BxwSc8YIBJRRRQAUUUUARkzfaEVY4zAUYu5chg2RtAXGCCN2TkYwODniSq7+V/aMOfP87ypNu3f5 e3KZ3Y+TdnbjPzY3beN1WKACiiigAqu/lf2jDnz/ADvKk27d/l7cpndj5N2duM/Njdt43VYqMib7 QjLJGIAjB0KEsWyNpDZwABuyMHORyMcgElFFFABRRRQBGBN9odmkjMBRQiBCGDZO4ls4II24GBjB 5OeJKroiDUZpBZ7JGijDXWF/eAF8JkHd8uSeRj95wSd2LFABRRRQAVXREGozSCz2SNFGGusL+8AL 4TIO75ck8jH7zgk7sWKro6HUZoxeb5FijLWuV/dgl8PgDd82COTj93wAd2QCxRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRWHrGv3elW+pSpoGpXItLcywvEEdbqTA2xoqM0oJJwSY8DBJOByAbEc8Mz zJFLG7wvslVWBKNtDYb0O1lOD2IPesPwJ/yTzw1/2CrX/wBFLWH8MxBCfEtrE13JINTSeea4spbY zSyW0BkfZIBtLSB22D7oZeApXNj4fT683hDw3HPpumpp/wDZluBOmoO0u3yRtPlmEDJ4yN/GTycc gHaUVXR7wxWpkggWRsfaVWYkR/KSdh2jf82ByF4JPUbSO94IroxwQNIufsytMQJPlBG87Ts+bI4D cAHqdoALFFRkzfaEVY4zAUYu5chg2RtAXGCCN2TkYwODniPfeeRnyIPO83G3zjt8vfjdnb97Z823 GN3y7sfNQBJAZmt4muI445ygMiRuXVWxyAxAJGe+Bn0FSVT08n7PCsUcgsxbxmF53kM54ORIsg3A gbeWJYktkAjmRHvDFamSCBZGx9pVZiRH8pJ2HaN/zYHIXgk9RtIBYoqu73giujHBA0i5+zK0xAk+ UEbztOz5sjgNwAep2iQmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8AEd/wCV/Z1z5/n+T5T7/s+/ zNuDnZ5fz7sdNvzZ6c1YqnOdSawl+zx2kd4XKxmR2eNV34DnABJ2/Ns4yfl3AfPVgGb7Q6tHGIAi lHDksWydwK4wABtwcnOTwMcgElYfhz9zNrdgvMVpqcmwn7x81EuGz9HncD2A6nJOoj3hitTJBAsj Y+0qsxIj+Uk7DtG/5sDkLwSeo2nn0uZ9L8TeI962sEElnDfQefcFEmdVZJXdyvyBQkCtjcFGxurk V0UVzQnHyv8AO6/RsTOorn/DQmbw9eLbyRxznUNREbyIXVW+1zYJUEEjPbIz6iuY8AapruoePPGi ajLNLYx3O2286RgI1WWWNRGmNpU+WwZgRynO4kkb+kKz+EtQRbCPUA+oagr2jsoEyG8lDr83yklc 4DYBOASoJI3xGHeGjOlJ3acPxi3+pKd9TqK8/wDiD4Z1LXtVsJdJuL2zubaxu5xLZt5bSzKqrBG0 mQF5llxnsZAMbiR3gM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5jR7wxWpkggWRsfaVWYkR/KSdh 2jf82ByF4JPUbThhcTPDVVVhur/irFSV1Yy/Bf8AyInh7/sGW3/opa3Kru94IroxwQNIufsytMQJ PlBG87Ts+bI4DcAHqdokJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPGdWftJyn3dwSsrEd6iSQKJ LP7WoljIjwp2kOpD/MQPkID+vy8AnAqxWfezXEdirySwWkhu40DfaAFZDMqgbmQ/M6kDbj7zbQwO Hq4DN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOcxklFV0e8MVqZIIFkbH2lVmJEfyknYdo3/Ngche CT1G0jveCK6McEDSLn7MrTECT5QRvO07PmyOA3AB6naACxVe9dI4FMl59kUyxgSZUbiXUBPmBHzk hPX5uCDg1ITN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeI9955GfIg87zcbfOO3y9+N2dv3tnzbcY 3fLux81AFiiowZvtDq0cYgCKUcOSxbJ3ArjAAG3Byc5PAxzGj3hitTJBAsjY+0qsxIj+Uk7DtG/5 sDkLwSeo2kAsUVXd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7RITN9oRVjjMBRi7lyGDZG0Bc YII3ZORjA4OeAAkMweERRxshfEpZypVdp5UYO47towccEnPGDJWfcPP/AKIZIP8ASPtZEaxTS+Xt +YbnKrj/AFeW2uNu/au7O1quAzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjkAkoquj3hitTJBAsjY +0qsxIj+Uk7DtG/5sDkLwSeo2kd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7QAWKr3HlefaeZ 5+7zT5flb9u7Y339vG3Gfv8Ay7tv8W2pCZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzxXkOpFIWij tFcXGJY2dmDQ7iMq2BtfbtbBBGQUzzvABcoqMGb7Q6tHGIAilHDksWydwK4wABtwcnOTwMcxo94Y rUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpALFFV3e8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O 0SEzfaEVY4zAUYu5chg2RtAXGCCN2TkYwODngACJvtCMskYgCMHQoSxbI2kNnAAG7Iwc5HIxzJWe /wBoN1DKdMge6XzEWfzRtjiMqZG4ru3MgD7Qu3dHtLD5WNwGb7Q6tHGIAilHDksWydwK4wABtwcn OTwMcgElFV0e8MVqZIIFkbH2lVmJEfyknYdo3/NgcheCT1G0jveCK6McEDSLn7MrTECT5QRvO07P myOA3AB6naACxVd0Q6jDIbPfIsUgW6wv7sEplMk7vmwDwMfu+SDtzITN9oRVjjMBRi7lyGDZG0Bc YII3ZORjA4OeKbzXAuoVMsCXTeZts/tA2yRCVAZclN25UIOB8u6TaSflcAGhRUYM32h1aOMQBFKO HJYtk7gVxgADbg5OcngY5jR7wxWpkggWRsfaVWYkR/KSdh2jf82ByF4JPUbSAWKKru94IroxwQNI ufsytMQJPlBG87Ts+bI4DcAHqdokJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPABGjodRmjF5vkW KMta5X92CXw+AN3zYI5OP3fAB3ZsVTEmpZfdaWgAdQhFyxyvmEMT+74Ij2sBzliVyAN5sAzfaHVo 4xAEUo4cli2TuBXGAANuDk5yeBjkAkoquj3hitTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2kd7wRX RjggaRc/ZlaYgSfKCN52nZ82RwG4APU7QAWKjBm+0OrRxiAIpRw5LFsncCuMAAbcHJzk8DHITN9o RVjjMBRi7lyGDZG0BcYII3ZORjA4OeK9sS17OZo5EuNighXkaLy98mwgkBN5GSwXkZUEkBCQC5RR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBHHBDC8zxRRo8z75WVQC7bQuW9TtVRk9gB2rD8Cf8A JPPDX/YKtf8A0UtdBXP+BP8Aknnhr/sFWv8A6KWgDoKKKKACiiigCOATLbxLcSRyThAJHjQorNjk hSSQM9snHqakqvYIkenW0cVn9ijWJAtrhR5IAGEwhKjHT5SRxwcVYoAKKKKAK9+iSadcxy2f22No nDWuFPnAg5TDkKc9PmIHPJxViq9+6R6dcyS3n2KNYnLXWVHkgA5fLgqMdfmBHHIxVigArz/4jRT3 N3aWNtc/ZpL6xmsjNsD7Umu7KJuD1+Vz6fUda9ArkfG+ny3994VS2ZI5v7aiLuxI3QorzumQM4Jh U46FlUnoCO7LpqGJjN9Lv8GTPWJBokLeH5/D8N8UtHXw4U1AyONqm2MIXLZwAvnTcg4O7nOBi14S uJJfh8NRvn/sr7YLq/Mnmo32aOaWSVX3MCvCuDlhj1HUVS8f/wDL3/2LOrf+29dpOZlt5Wt445Jw hMaSOUVmxwCwBIGe+Dj0NaYmpzYeM2tZu/3N/wCfyshRWpJRRRXmlhRRRQBHMZlQGCON33qCHcqN u4bjkA8hckDuQBkZyJKr3vleQvneft82PHkb927eu3OznbnG7Py7c7vlzVigAooooAKr3vleQvne ft82PHkb927eu3OznbnG7Py7c7vlzVio5hMyAQSRo+9SS6Fht3DcMAjkrkA9iQcHGCASUUUUAFFF FAEcgmLwmKSNUD5lDIWLLtPCnI2ndtOTngEY5yJKr3CI89oXs/PZJSUkwp+znYw38nIyCU+XJ+fp jJFigAooooAKr3CI89oXs/PZJSUkwp+znYw38nIyCU+XJ+fpjJFiq9w6JPaB7zyGeUhI8qPtB2Md nIycAF/lwfk64yCAWKKKKACiiigCu7oNRhjN5skaKQra5X94AUy+CN3y5A4OP3nIJ24sVGTN9oRV jjMBRi7lyGDZG0BcYII3ZORjA4OeJKACiiigAqMmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8SVX fyv7Rhz5/neVJt27/L25TO7Hybs7cZ+bG7bxuoAsUUUUAFFFFAFdPK/tGbHn+d5Ue7dv8vbl8bc/ Juzuzj5sbd3G2rFRgTfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTniSgAooooAKjAm+0OzSRmAooRAh DBsncS2cEEbcDAxg8nPElV0RBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7ABYooooAKKKKACii igAooooAKKKKACiiigAqvf31vpmnXN/eSeXa2sTzTPtJ2ooJY4HJwAelWK5fxXFeX2mX9pfSwabo p8ktqEUpll2iSMuskLRbFjK71ZmZlC5LKV3AAGxpms2ur+b9mivo/Kxu+12E9tnOcY81F3dO2ccZ 6is/wJ/yTzw1/wBgq1/9FLVfwtqhvtR1C3t9b/t3TYYoXj1DMLfvWMgki3QqqHaqRNjG4eZycFcU /h9Bry+EPDck+paa+n/2ZbkQJp7rLt8kbR5hmIyOMnZzg8DPAB2lFV0S8EVqJJ4GkXH2llhIEnyk HYNx2fNg8luAR1O4DpeGK6Ec8CyNn7MzQkiP5QBvG4b/AJsngrwQOo3EAsUVGRN9oRlkjEARg6FC WLZG0hs4AA3ZGDnI5GOY9l55GPPg87zc7vJO3y9+duN33tny7s43fNtx8tABYOkmnW0kV59tjaJC t1lT5wIGHygCnPX5QBzwMVYqvbfbG2vdeQm6JMwxZbZJzv8A3hxuXlQPlU8EnrgCJeCK1Ek8DSLj 7SywkCT5SDsG47PmweS3AI6ncACxRVd0vDFdCOeBZGz9mZoSRH8oA3jcN/zZPBXggdRuMhE32hGW SMQBGDoUJYtkbSGzgADdkYOcjkY5ACczLbytbxxyThCY0kcorNjgFgCQM98HHoakrPv0n/se5E8/ 98u1vDLv8ncSVQRtv8zy/lDKc7vmC/w1cAm+0OzSRmAooRAhDBsncS2cEEbcDAxg8nPABJVS70+K 8ubCeRnD2U5njCkYLGN48HjpiRvTkCnol4IrUSTwNIuPtLLCQJPlIOwbjs+bB5LcAjqdwHS8MV0I 54FkbP2ZmhJEfygDeNw3/Nk8FeCB1G4uMnF3QHH/ABM+e28NW7cxXeuQ2ky/3opY5Y5F9so7DI5G cgg812F/5X9nXPn+f5PlPv8As+/zNuDnZ5fz7sdNvzZ6c1geLtAvdevvDYt5Y0tbHVY725zHlsRq xUg7hwT8pGCfnB4CkHbnj1JrCVLe7tI7wuTHLJbM8arvyAUEgJO3jO4ZPOAPlrqrTi8PSgnqr3+b JS1ZcoqMCb7Q7NJGYCihECEMGydxLZwQRtwMDGDyc8Rol4IrUSTwNIuPtLLCQJPlIOwbjs+bB5Lc Ajqdw5CixRVd0vDFdCOeBZGz9mZoSRH8oA3jcN/zZPBXggdRuMhE32hGWSMQBGDoUJYtkbSGzgAD dkYOcjkY5ACYTMgEEkaPvUkuhYbdw3DAI5K5APYkHBxgyVn3sNxJYqkkUF3ILuNwv2cFVQTKwO1n HzIoB3Z+8u4KThKuATfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTngAkoquiXgitRJPA0i4+0ssJAk+ Ug7BuOz5sHktwCOp3AdLwxXQjngWRs/ZmaEkR/KAN43Df82TwV4IHUbiAWKr3qJJAoks/taiWMiP CnaQ6kP8xA+QgP6/LwCcCpCJvtCMskYgCMHQoSxbI2kNnAAG7Iwc5HIxzTvftEdivmanBaSG7jxN 5QCshmXEWGY/M6kR5z95sgA4WgDQoqMCb7Q7NJGYCihECEMGydxLZwQRtwMDGDyc8Rol4IrUSTwN IuPtLLCQJPlIOwbjs+bB5LcAjqdwALFFV3S8MV0I54FkbP2ZmhJEfygDeNw3/Nk8FeCB1G4yETfa EZZIxAEYOhQli2RtIbOAAN2Rg5yORjkAjuHRJ7QPeeQzykJHlR9oOxjs5GTgAv8ALg/J1xkGxVOQ akEhWKS0ZzcZlkZGULDuJwq5O59u1ckgZJfHGw2AJvtDs0kZgKKEQIQwbJ3EtnBBG3AwMYPJzwAS UVXRLwRWokngaRcfaWWEgSfKQdg3HZ82DyW4BHU7gOl4YroRzwLI2fszNCSI/lAG8bhv+bJ4K8ED qNxALFRyGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjBCJvtCMskYgCMHQoSxbI2kNnAAG7Iwc5HIx zTuEn/0QST/6R9rJjaKGXy9vzHa4Vsf6vK7nO3ftbbnatAGhRUYE32h2aSMwFFCIEIYNk7iWzggj bgYGMHk54jRLwRWokngaRcfaWWEgSfKQdg3HZ82DyW4BHU7gAWKKrul4YroRzwLI2fszNCSI/lAG 8bhv+bJ4K8EDqNxkIm+0IyyRiAIwdChLFsjaQ2cAAbsjBzkcjHIBG/lf2jDnz/O8qTbt3+Xtymd2 Pk3Z24z82N23jdViqZj1LKbbu0ADsXBtmOV8wFQP3nBEe5SecsQ2ABsNgCb7Q7NJGYCihECEMGyd xLZwQRtwMDGDyc8AElFV0S8EVqJJ4GkXH2llhIEnykHYNx2fNg8luAR1O4DpeGK6Ec8CyNn7MzQk iP5QBvG4b/myeCvBA6jcQCxUZE32hGWSMQBGDoUJYtkbSGzgADdkYOcjkY5CJvtCMskYgCMHQoSx bI2kNnAAG7Iwc5HIxzTeG4N1Cxige6XzNt59nG2OIyoTFgvu3MgAyPl3R7iB8qEA0KKjAm+0OzSR mAooRAhDBsncS2cEEbcDAxg8nPEaJeCK1Ek8DSLj7SywkCT5SDsG47PmweS3AI6ncACxRVd0vDFd COeBZGz9mZoSRH8oA3jcN/zZPBXggdRuMhE32hGWSMQBGDoUJYtkbSGzgADdkYOcjkY5AI0RBqM0 gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7Fis9PtBupohqcD3S+W7QeUNscRlfB2ht25kBTcW27o9w UfMpuATfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTngAkoquiXgitRJPA0i4+0ssJAk+Ug7BuOz5sHk twCOp3AdLwxXQjngWRs/ZmaEkR/KAN43Df8ANk8FeCB1G4gFiq6Oh1GaMXm+RYoy1rlf3YJfD4A3 fNgjk4/d8AHdmQib7QjLJGIAjB0KEsWyNpDZwABuyMHORyMcx2/2wSyLc+Q8fWOSPKk5ZvlKnOML s+bd8x3HaowCAWKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuH8J+JLTTPBuh2F5Y65HdWun 28Myf2HeHa6xqGGRFg4IPSu4ooA5/wD4TLS/+fXXP/BFe/8Axmj/AITLS/8An11z/wAEV7/8ZroK KAOf/wCEy0v/AJ9dc/8ABFe//GaP+Ey0v/n11z/wRXv/AMZroKy9a1WbTRZQ2ltHcXl9cfZ7dJZT FHuEbyEu4Vio2xvjCnnA4BJABz/hrxZFbeFdIg1S28QHUI7KFLoyaLeuxlCAPlvKO47s85Oa1P8A hMtL/wCfXXP/AARXv/xmrmi6rNqQvYbu2jt7yxuPs9wkUplj3GNJAUcqpYbZEzlRzkcgAnUoA5// AITLS/8An11z/wAEV7/8Zo/4TLS/+fXXP/BFe/8AxmugqnqU99Bbr/Z1lHd3DuFCyz+TGgwSWdsM QOMDarHJHAGWABy/iXxZFc+FdXg0u28QDUJLKZLUx6LeowlKEJhvKG07sc5GK1P+Ey0v/n11z/wR Xv8A8ZqnH4r1K4v20e30m0OtRPKJ4pL5lt1VEgclZREWY4uYeDGvO/n5QWLTxXqWrzPa6TpNpJd2 yE3i3d80SIwmmhxGyxOXG+3l5KpxsOMkhQC5/wAJlpf/AD665/4Ir3/4zR/wmWl/8+uuf+CK9/8A jNamk6lDrOjWOqW6yLBe28dxGsgAYK6hgDgkZwfU1coA5/8A4TLS/wDn11z/AMEV7/8AGax/FniS 01PwbrlhZ2OuSXV1p9xDCn9h3g3O0bBRkxYGSR1rtJ54bW3luLiWOGCJC8kkjBVRQMkkngADnNcn pvjS81ux09tN0Xy769+1uLbUbg2/lR28whbeVRyJCWT5NuB8w3fKNwBof8Jlpf8Az665/wCCK9/+ M0f8Jlpf/Prrn/givf8A4zVO08V6lq8z2uk6TaSXdshN4t3fNEiMJpocRssTlxvt5eSqcbDjJIXo NJ1KHWdGsdUt1kWC9t47iNZAAwV1DAHBIzg+poAy/wDhMtL/AOfXXP8AwRXv/wAZo/4TLS/+fXXP /BFe/wDxmugrH1bVry11G003TbKC6vrmKW4AubkwRrHGY1b5lRzuzKmBtxjdyMAEAw9f8WRT6dEm nW3iATi9tHbZot6h8pbiNpefKHHlh8juMjnOK1P+Ey0v/n11z/wRXv8A8ZqM67rU96dPs9FtPt8F uk92l3fmNFDvIieWyROXBMLn5ghAKZGSVXPXx3Nd6Rda5p+lRyaPZW6XF29xdGK4CmBLghIwjKxE ci9XXLZHAAYgGp/wmWl/8+uuf+CK9/8AjNH/AAmWl/8APrrn/givf/jNdBRQBz//AAmWl/8APrrn /givf/jNZev+LIp9OiTTrbxAJxe2jts0W9Q+UtxG0vPlDjyw+R3GRznFbmrateWuo2mm6bZQXV9c xS3AFzcmCNY4zGrfMqOd2ZUwNuMbuRgA5dp4r1LV5ntdJ0m0ku7ZCbxbu+aJEYTTQ4jZYnLjfby8 lU42HGSQoBc/4TLS/wDn11z/AMEV7/8AGaP+Ey0v/n11z/wRXv8A8ZrLXx3Nd6Rda5p+lRyaPZW6 XF29xdGK4CmBLghIwjKxEci9XXLZHAAY9pQBz/8AwmWl/wDPrrn/AIIr3/4zR/wmWl/8+uuf+CK9 /wDjNdBXLx+MfO07VLpdMnia11CGwgiuG8tpmmEPlu4wTGpM65BBZVGSu7KAAp6r4sil1HQ3tLbx AIIr1nvNui3qgxfZ5lG4eV8w8xo+OecHtkan/CZaX/z665/4Ir3/AOM1Tk8V6lb366PcaTaDWpXi EEUd8zW7K6TuC0piDKcW03AjbnZz8xKkfivUri/bR7fSbQ61E8onikvmW3VUSByVlERZji5h4Ma8 7+flBYAuf8Jlpf8Az665/wCCK9/+M0f8Jlpf/Prrn/givf8A4zWhoup/2vpgujD5Miyy28sYbcFk ikaN9rYGV3I2CQCRgkA8DQoA5/8A4TLS/wDn11z/AMEV7/8AGay9V8WRS6job2lt4gEEV6z3m3Rb 1QYvs8yjcPK+YeY0fHPOD2yNTTvEdxq1rq0lnpE4msrtbaGG4cRNKGiikWRweY1xKCQQXCj7u75K pyeK9St79dHuNJtBrUrxCCKO+ZrdldJ3BaUxBlOLabgRtzs5+YlQC5/wmWl/8+uuf+CK9/8AjNH/ AAmWl/8APrrn/givf/jNU4/FepXF+2j2+k2h1qJ5RPFJfMtuqokDkrKIizHFzDwY1538/KC25oup /wBr6YLow+TIsstvLGG3BZIpGjfa2BldyNgkAkYJAPAAM/8A4TLS/wDn11z/AMEV7/8AGaP+Ey0v /n11z/wRXv8A8ZrcnmW2t5Z3EhSNC7CONnYgDPCqCWPsASe1Zeja+mpaDJq15B/ZscUtzHMlxKp8 kQyvGxdgdo+4ScEgepAyQDn7nxJaSeMtMv1sdcNrDp93DI/9h3nyu8lsVGPKzyI3/L3FbH/CZaX/ AM+uuf8Agivf/jNZ+m+NLzW7HT203RfLvr37W4ttRuDb+VHbzCFt5VHIkJZPk24HzDd8o3SWnivU tXme10nSbSS7tkJvFu75okRhNNDiNlicuN9vLyVTjYcZJCgFz/hMtL/59dc/8EV7/wDGaP8AhMtL /wCfXXP/AARXv/xmtTSdSh1nRrHVLdZFgvbeO4jWQAMFdQwBwSM4PqasTzLbW8s7iQpGhdhHGzsQ BnhVBLH2AJPagDD/AOEy0v8A59dc/wDBFe//ABmsufxZE3irT547bxB/Z6WVyk4Gi3oXzWeAx5Xy uTtWXBxxz0zzch8WXF3o9lcQaV5d/fahcWENpc3AVUeFpt3mSIHx8sDn5Q3zEDJHzVJJrutJcW2m jRbQatMk02yS/ItzFGYwWWURFyczIMNGvIfnAUuASf8ACZaX/wA+uuf+CK9/+M0f8Jlpf/Prrn/g ivf/AIzVO08V6lq8z2uk6TaSXdshN4t3fNEiMJpocRssTlxvt5eSqcbDjJIXoNJ1KHWdGsdUt1kW C9t47iNZAAwV1DAHBIzg+poAy/8AhMtL/wCfXXP/AARXv/xmj/hMtL/59dc/8EV7/wDGa2L66+xW clwLee4ZcBYYE3O7EgADsMkjkkKOpIAJHPweKbzUdO0F9N0yCS+1bT/7QENzdmKOKMCLcN6xsS2Z kAG0AgMcjABAKcHiyJfFWoTyW3iD+z3srZIAdFvSvmq85kwvlcHa0WTjnjrjjU/4TLS/+fXXP/BF e/8Axmqdp4r1LV5ntdJ0m0ku7ZCbxbu+aJEYTTQ4jZYnLjfby8lU42HGSQtdfHc13pF1rmn6VHJo 9lbpcXb3F0YrgKYEuCEjCMrERyL1dctkcABiAan/AAmWl/8APrrn/givf/jNH/CZaX/z665/4Ir3 /wCM10FU9SnvoLdf7Oso7u4dwoWWfyY0GCSzthiBxgbVY5I4AywAMv8A4TLS/wDn11z/AMEV7/8A Gaj0TU5dV8VarPFHqSaetlaJELu0mt183fcGTasqrk7TFkgf3fSq8fivUri/bR7fSbQ61E8onikv mW3VUSByVlERZji5h4Ma87+flBYtPFepavM9rpOk2kl3bITeLd3zRIjCaaHEbLE5cb7eXkqnGw4y SFAOsori18dzXekXWuafpUcmj2VulxdvcXRiuApgS4ISMIysRHIvV1y2RwAGPaUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAVh+Ira7eXR7+0tZLs6dem4kt4mRZJFaCaLCbyq5BlBOWH APU4B3KKAMPw7bXaS6xf3drJaHUb0XEdvKyNJGqwQxYfYWXJMRIwx4I6HIG5RRQAVl6/e6nY6W0m j6ZJqF47hFjVowIwesjB3QMFHO0MCxwMqCWGpRQBw9raX9jqNjrMPh7VZHjiure6immtTd3Ekpt2 E7ESiMriErgMCuEVUCAYj0Sw1jw5qF3qD6Jd3w1FGbybSWDfbk3d1Ptk8yRVztuUHyFhlH5xtLd5 RQBl+GtNm0bwrpGl3DRtPZWUNvI0ZJUsiBSRkA4yPQVqUUUAY/ibSbzW9HNhZ3sFrvljaYz2xnSW NWDNGVDplXwFYEkFSwxzkcvo+k+ItDuLXUb2x/tKSKXVIzBYLHE+24uklR8SS7dp8tj97cPMRcEh mr0CigDg9EsNY8Oahd6g+iXd8NRRm8m0lg325N3dT7ZPMkVc7blB8hYZR+cbS3UeGtNm0bwrpGl3 DRtPZWUNvI0ZJUsiBSRkA4yPQVqUUAFcn4r0UX+s6XqE+iya1Z29vcQS2Mbx/M0jQsrMkrojoPKb hicMUIBIyvQWWpQ6lb2V3ZrJNZ3luLiK5ACrtIUqCCQ2WDZHH8Jzg4BuUAcHodhrHhq7mvG0S7vY ru3WKK2tJYA1miXFzIkTCSRFAWOeNAELAeWwHAUnPtfDWuaT4O1Xwt/Zkl3LqdlHbre280Qt4W+x Q2xL72WTAaJm+VG+UjGTlR6ZVPVNSh0mwa7mWRwHSNI4wC0kjuERBkgZZmVckgDOSQMmgC5RUcMj SoWeGSEh2Xa5UkgMQG+UkYIGR3wRkA5AkoA5fxTpZvtR0+4uNE/t3TYYpkk0/ELfvWMZjl2zMqHa qSrnO4eZwMFsZeh2GseGrua8bRLu9iu7dYora0lgDWaJcXMiRMJJEUBY540AQsB5bAcBSe8ooA8z tfDWuaT4O1Xwt/Zkl3LqdlHbre280Qt4W+xQ2xL72WTAaJm+VG+UjGTlR6ZRWPrniSz0Dy/tMc8m YpLmTyVB8m3i2+bM2SMqu9Mhcud3yqcHABsVx99omoyReIHjt9zS63Z6hboHUGaOBbRmC5OAxMDq AxAzjJAOa7CigDg7mw1i88Tw+Jxol3Elq9uv2CSWD7RKEjvELLiQx4zdofmcHCPx93cW1hrFn4nm 8TnRLuVLp7hfsEcsH2iIPHZoGbMgjxm0c/K5OHTj723sF1KGZ7qO1WS4ltLhLeeNAAUZlRs5YgEB JFY4J4yBk8VcoAx/DFjcWGimO6j8qaa7uroxlgTGJp5JVViMjcA4BwSMg4JHJ2KKKAObjj1PSD4p v7fS5L6e4vVns7ZJo0M4FtBH95jhRuRs55wpIB4Bx47PVZZbHVptB1J9Rs70XN0ZntlkulME8QSE LMyoiGYEIzLwWOXdmLd5RQBwdtYaxZ+J5vE50S7lS6e4X7BHLB9oiDx2aBmzII8ZtHPyuTh04+9t 6TwxY3Fhopjuo/Kmmu7q6MZYExiaeSVVYjI3AOAcEjIOCRydisvUtfsdLS/aZpGNhbpd3SouTFAz OPM5wCAI5CQMthDgEkAgGpXH/wDCN6pd+E59KW5gsZJdVuriTz4PtCSwNdyShCqyL8rqVDAnlWZS MnjsKKAPP9H0nxFodxa6je2P9pSRS6pGYLBY4n23F0kqPiSXbtPlsfvbh5iLgkM1SaJYax4c1C71 B9Eu74aijN5NpLBvtybu6n2yeZIq523KD5Cwyj842lu8ooAy/DWmzaN4V0jS7ho2nsrKG3kaMkqW RApIyAcZHoK1KKKAODu/DV3Lolil1pkl4lprt9fTWMUyB7iGV7oIBllQgidCyswBXcpB+6Y9M0nU tG1hNYtvD06WH+kxW+kWz26SWqSLa/w+YIlUvbzMQrnmVTjJbb6BRQBweiWGseHNQu9QfRLu+Goo zeTaSwb7cm7up9snmSKudtyg+QsMo/ONpbqPDWmzaN4V0jS7ho2nsrKG3kaMkqWRApIyAcZHoK1K KACvP08O3UXh7wlBqnh/+2Lew0oWt1pn7iTbcbIQsm2VljOwJKu4NkeZxkFiPQKKAOD0Ow1jw1dz XjaJd3sV3brFFbWksAazRLi5kSJhJIigLHPGgCFgPLYDgKTn2vhrXNJ8Har4W/syS7l1Oyjt1vbe aIW8LfYobYl97LJgNEzfKjfKRjJyo9MooAKy9fvdTsdLaTR9Mk1C8dwixq0YEYPWRg7oGCjnaGBY 4GVBLDUooA4e1tL+x1Gx1mHw9qsjxxXVvdRTTWpu7iSU27CdiJRGVxCVwGBXCKqBAMR6JYax4c1C 71B9Eu74aijN5NpLBvtybu6n2yeZIq523KD5Cwyj842lu8ooA8ztfDWuaT4O1Xwt/Zkl3LqdlHbr e280Qt4W+xQ2xL72WTAaJm+VG+UjGTlR6ZRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUc8EN1by29xFHNBKhSSORQyupGCCDwQRxipKKAP I10i5Pw+0u2s9Iu4nTwbqVrJALN42FywtwUKFQd7OshHGWwSM9a1Ne8KxQf8JP8A2fpk58jREuNP 2B3zfn7TmZeu+5+WH97zJ0+b5ufSKKAPN9R0m/m+IE8zptkbULWWznXSZJpltlWHzFS73rHDGSs4 aM8kM+AxkUHqPEfyar4Xnb5YYtVPmSHhU32txGuT2y7oo9WZR1IroKr31jb6jZyWt1H5kL4JAYqQ QQVZWGCrAgEMCCCAQQRQBwen+G11HWtPj1fS5JrNH1x3juIm8pi9/G0e9T8rBly6hsg7Qw5UEaE8 GpXXwTlt7iK7m1SXw4UkjkVmneY22CCD8xctxjrmu0ooA8317wrFB/wk/wDZ+mTnyNES40/YHfN+ ftOZl677n5Yf3vMnT5vm5uanYFfHiXSafJd3D3EDI81hIXjjwqsYb1G2QxKA7GFxlz5gwRKue8oo A5vxrCZ9Ghjawju4DcL5wltZLuONdrYZ7aMhpxu2qF/hLCT+CvO7vwrcal4a36l4c82/i0TXIYle wBaFxcL9ljQAvtwpbylVmwudhI5PtFFAHl/ivSJ73UY/sNr5FjJpUEOk40OW4ktZsy5MOHQWcgDQ fNJtGVXkeWcdZoWlLF4p8T6nNbSLPNeokMsm7Bi+y2+dmeACykMV6lADnYMdJRQB53LpCw3viZbP SJIp5/Eel3Rkis2UTRB7VmcOFw4DLOzYJ2ksTjOTHbeFYpJdPln0ycyXfiDURfOwcF7Qtdukbn/n 3ZhE2z/VsWBIJY59IooA8r1DSr37Hpcd1ZRtpdtcapF9nvNGl1GJP9KH2bbbxkMAIVcI+Nqodoxv GewnsdXX4aS6fbzXcmtDRzDHLJIEna48naGLByA+7nO8gH+I9a6SigDzO40iC8g1GLQ9Iu7LRZrj SFWCCzlsT5qXu6eRY9qOpEZiJkAHCj5vkOLF/oqwafrFkunyRaXb6wj21qumtdWnlG0izutUIMkX ms5xH0lw5+6xr0SigDy/VNOuptM8Pm40uCPTIortHtrjR59RhDmSPyHFqrb4sxrIVBz5KsYjjIFa nh3T4dD8Sz3upwSR3C+HLEXGqXcAUu0ZlExlmBZA+BCWG85Cg5IXI7yq99Y2+pWclpdx+bbyYEkZ YgOAQdrY6qcYKnhgSCCCRQBl+C4JrXwL4et7iKSGeLTLZJI5FKsjCJQQQeQQeMVuUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFAH//2Q=--20cf305496b5866b64049caea6ca Content-Type: image/jpeg; name="TF_Wavelet6_Cz.jpg" Content-Disposition: attachment; filename="TF_Wavelet6_Cz.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkdeq8xy1 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAGkAjADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiqcmrabDqkOly6 haJqEyb4rRplErrzyqZyR8rcgdj6UAXKKx9G8RWervJb+bBFfJLcr9k84NIY4bh4PM28HaSnXGAT jJqxFrujzfbvK1Wxk/s/P23bcIfs2M58zn5MbW64+6fSgDQoqnHq2mzJM8WoWjpDcfZZWWZSEm3B fLbnh9zKNp5yQO9R/wBu6P8A2P8A2v8A2rY/2Z/z+/aE8n7237+dv3uOvXigDQorLXW4ZdZ06yt/ LngvrKa8juY5AylUaEDGOGDCbOc9u+eI9Q1a8XUTpukWUF5fRxLcTi5uTBHFGxZU+YI5LMUfAC4w jZK/KGANiiuf1TXr+00M6vbabAtvBFLLeLqVxJbvbiP73CRSb8YbleDgFSwYGtCxvrgaHHf63bwa XMIjLcxG5EiW4GSd0mFHA6noOeSBkgGhRXJ2/iXXtQvRbWGgWiOLKG9dL+/eB41meUIjKIXw4WLL D+EkgFsZNi61vXob3TdNi0fTX1C6t7i4lVtSdYo1ieNcK/kEsT5qnlVxg9aAOkorDk1jUrKyhF/p 1oup3dx9ns7W2vGkSVthfLSNGmwBUkY/KeE43MQpjfW9Ys7O6a+0LN1BEbhI7Gd7hJo1I3qreWp8 4D7sZUB8qA33ygB0FFRwTw3VvFcW8sc0EqB45I2DK6kZBBHBBHOakoAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5eO0vbXXtUhfRfttrqOoQ3qXLSRi GAJFCnzgnf5itAWXahGSnzLyV6iigDi9O8OXdo+lOLGOJ08R3+oXTKUBMci3axyNg/MSskI7kDAO MHHPz+GvFN5b38U1pJsHhy/06GASWyQJPIIQqW6IoKwHZhDIxbC4YJjL+qUUAcPrvhWW8vby3t9M gfTJItGgSDCCMxwXkjypsPG1Y2HGMEHAz0qPV9A1h769v7RbuMprq30Rsmg8+SL+z1tyYxNmPO8n IfHyqxHO3PeUUAcfoGg3thqmj3EsU/lx2mpGd7iaN5BLcXMMwD7Aq7jhyQgKqRgEjBOhexXul+IZ 9ZtdOn1KO7tIbV4LZ40kiMTysG/eOqsp85gfmBBVcBgxK9BRQBx+tPr00uk21zoU99bxyi9vG0+W EIXRi0MI82VGO1wjmTjd5YG3DsqaGvxXuveAdZtotOnt7680+4hjtJ3j372RlUEq7Jycfxd+cc10 FFAHn9/ZWOt+IRr7+EP+Ek0y+0q0+xz+VbNsw87t8tw6MuVkjPA+vSjUdBtprrQZ/wDhAftGmW1p eRHS/Ksv9GkeWFlbY0gj5CSHKkn5ucZNd5DBDbIUgijiQuzlUUKCzMWY8dyxJJ7kk1JQBx8WkKlh YXek+GP7HbTdQa8/s0LbxG5zA8TFTE7IG2ycbiMmMKSoO4Fmj2Go3/ie+j1XSdNt7Sdrm2v79rne SVkMqxpJIkaoqMFCcnew2qFG7sKjmghuUCTxRyoHVwrqGAZWDKee4YAg9iAaAMvwnY3GmeDdDsLy Py7q10+3hmTcDtdY1DDI4OCD0rYoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvi7xv8A8j94j/7Clz/6 NavtGvi7xv8A8j94j/7Clz/6NavcyP8AiT9DKrsYNFFFfRmIUUUUAFFFFABRRRQAUUUUAKOlLSDp S1tHYBtFFFYgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAU7tTad2q4Aes/s9/8j9ff9guT/wBGxV9KV81/s9/8j9ff9guT/wBGxV9KV8nnX+9P 0RvT+EK+VvizBHL8VdfZ40Y74Blhn/l3ir6pr5d+Kag/FLxB/wBdIP8A0nirw67tA9fKYKeJSa6M 4X7HD/zxi/75FH2OH/njF/3yKt7B70bB71w+0fc+p+qw/lR1Hwmgji+KugMkaKd84yox/wAu8tfV NfLvwsUD4peH/wDrpP8A+k8tfUVd1B3gfLZtBQxLSXRBRRRWx5gUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUVwaNrQ03xRA9/d3s8OsW6yvCpVltzFaPOsKrllARpdoUl+eCzncQDvK+LvG/wDyP3iP /sKXP/o1q+ofCd4G8Q6vY2o1VdNitLWeFdTeZpGd3nV2HnEyqp8pV2tt5QkDDbm+XvG//I/eI/8A sKXP/o1q9zI/4k/Qyq7GDRRRX0ZiFFFFABRRRQAUuKSlPSqirpgJRRRUgKOlLSDpS1stgExSUp6U lZyVmAUUUVIBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQADmnUg60tawWgHrP7Pf/ACP19/2C5P8A0bFX0pXzP+z/ADLF8QblGEhMunSIu2NmAO+NvmIG FGFPJwM4HUgH6Yr5HOv96fojen8IV8w/E9Sfih4g/wCukH/pNFX09XzF8THX/haevxkPuMkBBCHH /HtF3xivAxX8M93IbfXEn2Zyew+1Gw+1TbB70bB715fMfd+yOl+GCkfFDw//ANdJ/wD0mlr6er5h +GMiN8U9ARRJuEs+d0bAf8e8vcjBr1LX/EerrrOtajY30kNha6Fqi2cYAKtcWzQhpyCOSJGeMKwI HlFhkSV6mF/hnwmfW+uOz6I9MorzvxFHL4d1XT9P02+1JbfUkVLkTX807EG9s4fkeR2aM7LiUZQq fmB6qpHQaJEdO8VarpMNxdyWcVlaXKLdXMlwyySPcK5DyMzYIiT5c4GCQASc9J4x0lFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFZZ0OEDVDFdXcEmo3C3LyxSBWikWONFKcYxiJSVYMDyCCp21qUUAZ+m aSmm+bK9zPeXk2BNd3O3zJAudq/Kqqqrk4VQBkscbmYn4+8b/wDI/eI/+wpc/wDo1q+0a+LvG/8A yP3iP/sKXP8A6NavcyP+JP0Mquxg0UHrRX0jMQooopAFFFFABSnpQOlLWkVoA2ijvRWYCjpS0UVu hCHpSU6m96zmuowoooqACiiigAooooAKKKKACiiigAooooAKKd2pveqcbAFFFFSAUUUUAFFFFABR RRQAUUUUAFFFFABRRSjrTSuwAdaWiitUrID1n9nv/kfr7/sFyf8Ao2KvpSvmv9nv/kfr7/sFyf8A o2KvpSvkM6/3p+iN6fwhXzV8RwT8TPEP/XaD/wBJoa+la+bviIoPxL8Q/wDXaH/0mhr57Gu1I+l4 YjzZgl5M5XafSjafSptg96Ng968bmP0j2J0Hw4BHxM8Pf9dp/wD0mmr22T4d+E3vPtEehWNvm0nt HS2t0hDpMFDElQGDbQVBBGA7+teLfDtQPiX4e/67Tf8ApNNX0jXs4J3pH5vxPHlzBryRzcng+K6J k1LV9S1C4RAttPN5KNbESJLuQRxqpO+KJvnDD5AMYLA6GlaKNNuLi7mv7vULy4RI3uboRhvLQsUQ CNEXALuc4z8xySAANSius+eCiiigAooooAKKKKACiiigAooooAKKKKACiiigAr4u8b/8j94j/wCw pc/+jWr7Rr4u8bjPj7xH/wBhS5/9GtXuZGr1J+hlV2ME9aKU0lfSS3MQooopAFFFFACjpS0Diitl sA3vRSkUg5rJp3AdRRRWwgpvenU3vUTGFFFFZgFFFFABRRRQAUUUUAFFFFABRRRQA7tTe9Opp61p PYAooorMAooooAKKKKACiiigAooooAKKKKAClHWkpRVR3AWiiitRHrP7Pf8AyP19/wBguT/0bFX0 pXzX+z3/AMj9ff8AYLk/9GxV9KV8fnX+9P0R0U/hCvnbx4pb4j+IiB/y3h/9Joa+ia+fvGqBviJ4 jJz/AMfEP/pLDXzWYu1C/mfScMu2PT8mcx5belHlt6Vd8lfU0eSvqa+f9ofo3tTS8BqV+I/h0kf8 t5v/AEmmr6Jr5+8FIF+InhwjP/HxN/6SzV9A19BlzvQv5n5zxM749vyQUUUV3nz4UUUUAFFFFABR RRQAUUUUAFFFFABRRRQAVyehWlzfnxPp2rald3RTU0XzYZntigNtbybYzGwaNAzHADE4J3FiWJ6y se68N2d3Ya/ZySTiPW932kqwym6BIDs44+VAec857cUAZ/gi0C6dc6pHc30trqUvnWaXd7NceXbA ARkGRiRvAMvQEeYFP3BXyv42/wCR+8R/9hS5/wDRrV9oV8X+Nv8AkfvEf/YUuf8A0a1e7kP8WXoZ VdjBPSkpT0pK+knuYhRRRUgFA60UDrTW4DqKKK2EIelA60HpQOtQ/iQxaKKKsQU3vTqb3qJjCiii swCiiigAooooAKKKKACiiigAoHWigdaa3AdTT1p1NPWtJ7AFFFFZAFFFFABRRRQAUUUUAFFFFABR RRQAUo6UlKOlXDcBaKKK0Ees/s9/8j9ff9guT/0bFX0pXzX+z3/yP19/2C5P/RsVfSlfH51/vT9E dFP4QrwPxc8v/CyfEMa20rIbiEmYFdqn7LDwcnd+Q7175XiHiVA3j3xKTn/j7h/9JYK+XzaXLhm/ NHu5C7YxO/RmF5b+n60eW/p+tXvKX1NHlL6mvlPaH3ftQ8HSzN8R/D0b2c0SLdT4ldkKt/o0/TDE 89eQK7rWdKm8P6xZz6Doem293I8yWlzHIWuNQm+yTOIrkkKdhddxcyOSyISASWXlPDSBfHvhojP/ AB9zf+ks9epw+ENDhcn7JJLFsZFtri4lmt41ZSpCQuxjQbSy4VRhWKjAJFfV5TLmwyfmz4TPr/XH d30Rwcmj2fhrT7DQ5fDelWF1cfY4PtVnIJnurdby1imSd/JiJ3iVMjDB8tnHAPYaHYWekeMtY07T bSCysRp9lOLa2jEcYkaS5Vn2rgbiEQE9SFX0FXI/COjokyvHd3BkTZ5l3fT3DxjcG/dvI5aM7lRs oQcoh6qpFzTNFstI802qztJLgPNc3MlxIwGcLvkZm2jLELnALMQMk59I8Y0KKKKACiiigAooooAK KKKACiiigAooooAKKKKACvi/xt/yP3iP/sKXP/o1q+0K+L/G3/I/eI/+wpc/+jWr3sh/iz9DKrsY J6UlKelJX0k9zEKKKKgBRS0gpa1jsAUp6UlKelax2YhKKKKgAooooAQ9KSlPSkrOe4woooqACiii gAooooAKKKKACiiigApRSUoqo7gLSHrS0h61c9gEooorIAooooAKKKKACiiigAooooAKKKKACndq bTu1XAApO9LSd6qQHrX7Pf8AyP19/wBguT/0bFX0pXzX+z3/AMj9ff8AYLk/9GxV9KV8jnX+9P0R vT+EK8Z11Gbxz4lIH/L5F/6SwV7NXkWrIG8a+Jic/wDH7F/6SW9fI547YR+qPZyZ2xSfkzN8t/T9 aPLf0/WrvlL6mjyl9TXxntD632pHoSMvjnw0SP8Al8l/9JZ69mryLSUC+NfDJGf+P2X/ANJLivXa +zyN3wi9WfJZy74pvyQUUUV7B5QUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV5/LNrsegeM7VLm +1LUjqEdpC9qAjw+db2wLQrkBFjMrOAWH3fmfJZ6APQK+L/G3/I/eI/+wpc/+jWr6s8Lm0iuL20S 11mzvESKSW21S/e6YRsXCOpMsqgErIMAg/LyMbSflPxt/wAj94j/AOwpc/8Ao1q97If4s/Qyq7GC elJSnpSV9JPcxCiijvUAKBiloordKwBSnpSUp6VUdmISkzS9qbWUm0MXNLTR1p3anF3QCE0lFFZt 3AKKKKQBRRRQAUUUUAFFFFABRRRQAUopKUdKqG4C0hpaQ9K0lsAlFFFYgFFFFABRRRQAUUUUAFFF FABRRRQAU4dKbSjpVw3AM0nejvRScmwPWv2ej/xX19/2C5P/AEbFX0rXzV+z1/yP1/8A9guT/wBG xV9K18lnDvin6I6KfwhXld6pbxj4nIH/AC/Rf+klvXqleVXP2w+PfEipbwGzN7HvlM5Ein7HBwE2 EEdOdw6njjn5DiD/AHJ+qPSy6XLXTHeW3pR5belXfKX1NHlL6mvgPaH0XtSjZKV8Y+GCR/y/S/8A pJcV6Ja67o99qM+nWeq2NxfQbvOtobhHkj2na25QcjBIBz0Neb2J1H/hOvDi3Frax2ov5vLkjuWd 2/0W4xlDGAOP9o46c9asfEVNvhiJ4J7GTRGtLoabDZQ7PK/4ltzhvMDMrx7A4CqqfeU7sKQ33+QJ rBK/dnzuYy5q7Z3ln4l0HULe6uLLW9NuYLRN9zJDdI6wrgnLkHCjCk5PofSrGm6tpus27XGl6haX 0CuUaS1mWVQ2AcEqSM4IOPcVxfj7/kZ/Dv8AwD/05abXQWf/ACUPWf8AsFWH/o27r2zgOgooooAK KKKACiiigAooooAKKKKACisfWLzVIpUhsRY2sLbA2oX77kV3baiJEpBkYnC8smN64LnKimZ/E2n3 CLNdaNqjsjP9iggaznZFI3PGXlkDkblG07Bl1y69wDpKyzocIGqGK6u4JNRuFuXlikCtFIscaKU4 xjESkqwYHkEFTtrUrL0XX7HxAL19PaR4rS4+zmVlwsp8tJAyH+JCsi4bo3UZBBIAaVoo024uLua/ u9QvLhEje5uhGG8tCxRAI0RcAu5zjPzHJIAA+Q/G3/I/eI/+wpc/+jWr680bX7HXn1NbBpGGnXr2 MzMu0GVFUsF7kAtjPHIOMjBPyH42/wCR+8R/9hS5/wDRrV72Q/xZ+hlV2ME9KSnU2vpJmIUd6KUV KV2AtFFFbCClPSkoppgHam0ppKxm9RgOtO7U2ihSsrAFFFFSAUUUUAFFFFABRRRQAUU6kNU42QCU UUVIBSjpSUoqo7gLSHpS0HpWj2AbRRRWIBRRRQAUUUUAFFL2pKbVgCiiikAUUUUAFKOlJRTTswDv RRRSA9a/Z6/5H6//AOwXJ/6Nir6Vr5q/Z6/5H6//AOwXJ/6Nir6Vr5TN/wDeX6I6KfwhXnix7/FX ik7Qf+JjH/6R29eh1wtqgbxP4qJz/wAhKP8A9I7avi+JnbAN+aO3CS5atx3kn+4KPJP9wVe8lfU0 eSvqa/OPaHq+1MZo9nirwsdoH/Exk/8ASO4rs7XQtHsdRn1Gz0qxt76fd51zDbokkm47m3MBk5IB OeprlbpAvifwqRn/AJCUn/pHc13Vfo/DLvgE/NnlYuXNVuZdn4a0HT7e6t7LRNNtoLtNlzHDaoiz LgjDgDDDDEYPqfWrGm6TpujW7W+l6faWMDOXaO1hWJS2AMkKAM4AGfYVcor6A5QooooAKKKKACii igAooooAKKKKAOT8dXmjtpL6frP2tbQPBeTuulT3UHlRTLIyuyIUAIjIOTwDkjHWvoFrp8niqG60 641mO2tbKeCGwvNKmgggWR4WIikkjUgAx8IWbAOFCquK0PF9nr2raNqOkaXZ6a8F9ZSW7XF1evE0 bOrLkIsLhgAQfvDPI461oabPr0twy6ppum20GwlXtdQedi2RwVaFABjPOew454ANSuTFrrSx+NBp KRwahc3qmxmuspHk2dunmA7G3BWDcYIJUqcckdZRQBx/gTTr/SpNdtLrS4NPtUu4FtUhuZJlKLaQ R4VnRSygIo3c5beD93LfL/jb/kfvEf8A2FLn/wBGtX2hXxf42/5H7xH/ANhS5/8ARrV72Q/xZehl V2MKm06m4r6WaMQpRSUoGKmK1AWiijNat2EFFJmlzmldDEPWkoNFZPcAooopAFFFFABRRRQAUUUU AFHeigdaa3AdSGlpDWstgEooorEApRSUoqo7gLR2oorVgNooorAAooooAKKKKAF/hpKXtSVUugBR RRUgFFFFABRRRQAUUUUAetfs9f8AI/X/AP2C5P8A0bFX0rXzV+z1/wAj9f8A/YLk/wDRsVfStfKZ v/vL9EdFP4QrjNPC/wDCSeKsgf8AITj/APSO2rs64qwOPEnir/sJR/8ApHbV8VxPHmwDXmjenLll c1sL6D8qML6D8qZuNG41+b+wN/bmdqAX/hJPCuAP+QnJ/wCkdzXZ1xV+c+JPCv8A2EpP/SO5rdsf FGk6jeR21vNOWlz5EslpLHDPgE/upWUJJlQWGxjlQWGQCa/SOGI8uAS82c9SXNK5sUVz9n400S+s zeQvfC18pZkml025iSVWICiNmjAdmLKFVcs2RgGtDTNastX80WrTrJFgvDc20lvIoOcNskVW2nDA NjBKsAcg4+hINCiiigAooooAKKKKACiiigAooooAx/FGs/2BoMuob4I8Sww+bcHEcPmypH5j8jKr v3EZGQuMjORn+G9e/tTUZIP+Et8OavtiL+RpkW2ReQNxPnyfLzjoOSOexsa9dXtpLL/yFY7GaJAL vTYo53tXVmLHyjGzncCi5VZOhJEeNzZem6pdpcMLK58T63O6FVg1LTUsYI8kfvGka3iOAcAhd7YY kI2OADtKKKKACvi/xt/yP3iP/sKXP/o1q+0K+L/G3/I/eI/+wpc/+jWr3ch/iy9DKrsYVIelLSHp X1EtjETvTqb3p1TAApD1paQ9ac9gEpRSUoqI7gJRRRUgFFFFABRRRQAUUUUAFFFFABSjrSUo6047 gLSHpS0h6Vq9gEooorEApR1pKUdacdwFooorYQ09aKD1orF7jCiiikAUUUUAO7U3vTu1N71pLYAo oorMAooooAKKKKACiiigD1r9nr/kfr//ALBcn/o2KvpWvmr9nr/kfr//ALBcn/o2KvpWvlM3/wB5 fojop/CFee26X/8Awm/iV47q2WxGoR+ZC1uxkZvsdvyJN4AHTjaeh5549CrhLY48T+Kv+wlH/wCk dtXymdq+Ea80KrLljc1d3vRu96i3Gjca+J+rnL7cxXj1RfGnht7q9s5bM6lN5UUVoySL/olzjc5k YNgcHCjJ546Uupa/ovjSWW20/wAS2lv9iS5Nm9nOJbuWfyJYmkjiQ7yiK7kADc7AFcKoMlm5OfE/ hX/sJSf+kdzXd19tkithEvNnVSlzRueV3eoW6QWthpHiKPXNHtntLi5kT7MyaesN7alRm3RFjTy/ OY7u0JIwFbPWaHf2er+MtY1HTbuC9sTp9lALm2kEkZkWS5Zk3LkbgHQkdQGX1FdRRXrmgUUUUAFF FFABRRRQAUUUUAFFFFAFe+uJbSzknhsp72RcYggKB3yQOC7KvHXkjp68VlweMNAmuIrV9SjtLuZw kVrfo1pPIScDbHKFdgTwCAQSCByDUniieKDQZfNSd/Nlht41guntmMkkqRpmRCGRd7LuIydueG6G n/Z+i2txL4Si0W0tdP1OynmZLRREsgBSOUMqBcErLFhgST833do3AHSVljxHpDW+oXCX0cken3H2 W5MYL7ZsL+7AAyz5dV2rkljt+9xWpXH6XfaX4ZHjC6upILDTLHUIwSF2pEgsrUKqqPwAUDngAUAd BpmtWWr+aLVp1kiwXhubaS3kUHOG2SKrbThgGxglWAOQcfH3jb/kf/Ef/YUuf/RrV9U+FtQsNc1H UNat9Rsbi6nihhe1tLqOf7NEhkaMSFCR5hMkhODt6KN20u3yt42/5H/xH/2FLn/0a1e7kX8WXoZV djDpD0paQ9K+oexiJ3p1N706pgAU2nU2iYBRRRWYBRRRQAUUUUAFFFFABRRRQAUUUUAFKOtJQOtN bgOooorYQ2iiisBhRRRQA6ijtRW6EIaSlNJWUtxhRRRUgFFFFABRRRQAUUUUAFFFFABRRRQAUUUU Aetfs9f8j9f/APYLk/8ARsVfStfNX7PX/I/X/wD2C5P/AEbFX0rXymb/AO8v0R0U/hCuCgOPFHin n/mIx/8ApHbV3tefxHHinxT/ANhGP/0jtq+czKHNQt5o5sdLlo3NHd70bveo93tRu9q+d+rni+3K s5z4o8Lc/wDMRk/9I7mu9rz+U58U+Fv+wjJ/6R3NegV9FlsOWhbzZ7WBlzUbhRRRXedgUUUUAFFF FABRRRQAUUUUAFFFFAGH4v1KHSvDF1PcLaGCR4rWQ3gBgRZpFiLyAkBkUPuK5GQCMjORh+CP7Hg1 G5g0nV/Btx5kW+SDQbFIJDtIAZysz5UbiOR1Yc9j0niGa+t9Emk04SeeHjDNFH5jxxF1ErouDudY y7KuGyQBtb7p5/wdqutX92Irs6lNbxpdLNLqFgbVsrcH7My5jj3F4SS2AQNicISdwB2lFFFABXxd 43/5H/xH/wBhS5/9GtX2jXxd43/5H7xH/wBhS5/9GtXuZH/El6GVXYws0E0lFfS8zMQpc0lHehNo B1NxinUhq5rQBKMZopRWcVdgGKSlPSkpyVmAUUUVIBRRRQAUUUUAFFFFABQKKKAHUZxSDpQa1v7t wEooorIAooooAXNGaSiq5mApOaSiik3cAooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAetfs9f 8j9f/wDYLk/9GxV9K181fs9f8j9f/wDYLk/9GxV9K18pm/8AvL9EdFP4QrzxTjxV4p5/5iMf/pHb 16HXmEltM3jvxJcC+uEiS+jDWqrH5bn7HBySVL55HRgOBx1z4teHPCx5+bPlwzd+qNfd70bveo93 tRu9q4fq58n7cgY58VeFuf8AmIyf+kdxW/L4ytrIefqmm6lptiySPFd3UabZQkbSnCK7SqfLjdsO in5SDhsKeRt7O4h8ceHJ5NTu5431CbbbyLEEjzaXBGCqBuOnLHjrk81a126bxpYapaJp+qxzLaXc WlWs+mXEAeZoJI/NkldAi7lZlRSwAD5bLMFj7qEOSFj6zKXfDJ3vqzpJPGEVqTHqWkalp9w6BraC byXa5JkSLahjkZQd8sS/OVHzg5wGI0NK1oalcXFpNYXen3luiSPbXRjLeW5YI4MbuuCUcYzn5TkA EE8f4ikl8Rarp+oabY6k1vpqK9yZrCaBgBe2c3yJIitIdlvKcIGPygdWUHoNElOo+KtV1aG3u47O WytLZGuraS3ZpI3uGcBJFVsASp82MHJAJIONj0zpKKKKACiiigAooooAKKKKACiiigDP1uPzdHnT 7LfXWdv7mwufs8zfMPuyb0x6n5hkAjnODz/hvTLyDxDJeLZa5ZWJtDE8Or6qbrMm8FWiUTygcbgx bB4Tb1fOp4w02HVvCd/aXLWi25RZJvtZCxNGjB2VmwfLBVSPMAymdw5UVyfh7wp4b1rVjqLaD4N+ y/ZNvkaa0d6k2ZCEkyYkEe0pMuVB35IJ/dgUAekUUVljxHpDW+oXCX0cken3H2W5MYL7ZsL+7AAy z5dV2rkljt+9xQBqV8XeN/8AkfvEf/YUuf8A0a1fYOma1Zav5otWnWSLBeG5tpLeRQc4bZIqttOG AbGCVYA5Bx8feN/+R+8R/wDYUuf/AEa1e5kf8SfoZVdjBooor6MxCjvRRQA6kNLTsD0ro5eZCI6U U7A9KCOKSpNahcaelJTqQ1E11GJRRRWYBRRRQAUUUUAFFFFABRRRQAo6UGkoqubSwBRRRUgFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHrX7PX/I/X//AGC5P/RsVfStfNX7 PX/I/X//AGC5P/RsVfStfKZv/vL9EdFP4QrzZzjxb4o5/wCYhF/6SW9ek15pKceLvFH/AF/xf+kl vXnQhzOx4/EEuXBN+aLW73o3e9R7vajd7Vr9XPg/bkaHPi3wvz/zEJf/AEkuK9JrzSI58XeF/wDr /l/9JLivS6xnDldj7zh+XNgk/NhRRRUnthRRRQAUUUUAFFFFABRRRQAUUUUAc/40ms7fw0899c/Z YY7u0dZyAUikFxGY2cEr+7D7S/IO0Ngg4NYfgyLTX1cvH4u0bWruFLt44dN2qUW5nWaUuPNkJAcI FIxgEg7iQRua7pniCa6F1oGswWkj+RHNFeQNNHsSXcxQBhtZlZw3B3AIAUI3Vc02DXorhm1TUtNu YNhCpa6e8DBsjks0zgjGeMdxzxyAaled3UENnZeJUgijgtLPxHpjlUUKkEMSWDMeOFREUknoApPA FeiUUAcvod/Z6v4y1jUdNu4L2xOn2UAubaQSRmRZLlmTcuRuAdCR1AZfUV8reN/+R+8R/wDYUuf/ AEa1faNfF3jf/kfvEf8A2FLn/wBGtXuZH/En6GVXYwaKKK+jMQoopcU0rgLTqTaadtNdlOEuwmJS HpTtppCOKuUJJbCuNpD1paQ9a5J7FCUUUVkAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAerfs/CY/EK5MUkaoNOkMoZCxZd8fCnI 2ndtOTngEY5yPpmvmr9nr/kfr/8A7Bcn/o2KvpWvlM3/AN5fojop/CFeY3Bx4v8AFH/X/F/6SW9e nV5beSbPGPicYz/p8X/pJb1zYKPNVseBxQ2svdu6LG73o3e9Qed/s/rR53+z+tex7FH5pzTH25z4 v8L/APX/AC/+klxXp1eW2cm/xj4YGMf6fL/6SXFdtbeJYZtZ1iznt5LO20y3jna7uiI1dWaZWYA8 qimFvmbGeSPl2s3j42PLVsfpfC7by9X7s3KK5OHxLr0eiQ6pqegWlmlykCwQC/dpVmmdI40lVoV2 ANINxBYrg4Vq1NJ1a8utRu9N1Kygtb62iiuCLa5M8bRyGRV+ZkQ7sxPkbcY28nJA5D6I2KKKKACi iigAooooAKKKKACiiigAooooAKx/FF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFbF V76G4ns5I7S6+y3HBjlMYcAgg4ZT1U4wQCDgnBU4IAOb8Ka0L/WdU0+DWpNas7e3t54r6RI/maRp lZVeJER0HlLyoOGLgkkYX5a8b/8AI/eI/wDsKXP/AKNavr3StKmtLi4v7+5jutSuUSOWWKIxRiNC xREQsxABdySWYkuecbVX5C8b/wDI/eI/+wpc/wDo1q9zI/4k/Qyq7GDRRRX0ZiFO7U2ndquADx1p w6U0dacOlelSIYtNPenU0960qbCQw9aYetPPWmHrXmVTRBRRRWIBRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB61+z1/yP1//ANgu T/0bFX0rXzV+z1/yP1//ANguT/0bFX0rXymb/wC8v0R0U/hCvHNXt4j8QfEd00k4kS8iAVZ3CEfZ IOqA7SeepGenoK9jrxvW5NnjjxMMZ/0yL/0kgrPLYqWISZ4vEV/qLt3RJ5w/vmjzh/fNUvO/2f1o 87/Z/WvpPZn537In0Szgi8f+HbhJbppJL2bcslzI6DNrOeEZio/ADHSvQr3RZr3WfELTWcc9nf6P BZqkkxjWZg1zvQsuWQYlT5gP4uMkV5/okm/xx4ZGMf6ZL/6ST17JXzWZRUa7S7H6Jw65PArmd9We bzeGpprhLjRfCn9g29v5Us9oBbRG9eO6t5k2iF2UsqQzKDIVwZRg4LEdRo0V7c+IdS1m606fT457 S2tUguXjaQmJ5mLfu3ddp85QPmzlWyAME9BRXAe6FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABXxd43/AOR+8R/9hS5/9GtX2jXxd43/AOR+8R/9hS5/9GtXuZH/ABJ+hlV2MGiijvX0iMR2KeAP Smd6eK66SVxMeAPSn4HpTBT69iklYzYmBTSKf2ph6Uq6VgRE1Mp7dKZXg1fiNUFFFFZAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF AHrX7PX/ACP1/wD9guT/ANGxV9K181fs9f8AI/X/AP2C5P8A0bFX0rXymb/7y/RHRT+EK8S8Rvt8 eeJRux/pcPf/AKdYK9trwnxW+34geJBjP+lQ/wDpLBRk6vikvJnk57HmwbXmiLzP9v8AWjzP9v8A WqHm/wCz+tHm/wCz+tfXezPifZG74cfd488NDdn/AEubv/06z17bXhPhR93xA8NjGP8ASpv/AEln r3avkc4VsU15I+2yKNsGl5sKKKK8s9kKKKKACiiigAooooAKKKKACiiigAooooAKKKr31vLd2ckE N7PZSNjE8AQumCDwHVl56cg9fXmgCxXxd43/AOR+8R/9hS5/9GtX1b4QOoT299e3Wr3eo2c9wRp7 XMcKnyUG3f8Auo1DB2Dsp5BjMZGCTXyl43/5H7xH/wBhS5/9GtXuZH/En6GVXYwaO9FHevpEYjh1 p4Bpo61Itd9CKbJY4A1MIJGGQv6io1rRi+6frXsxioxujCpNx2Kf2aX+5+oqF43XgjH41q1SuPvf iaHBT0ZMKjbsUWRvSmbT6VYPWoz1ryMRhoJnSmRYNGDTqK4uRFDaKO9FZAFFFHegAwaXBpaUVsqa bAbtPpRtNPpD0qnRjYVxmDRg06io5EMbRRRWQBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQB61+z1/wAj9f8A/YLk/wDRsVfStfNX7PX/ACP1/wD9guT/ANGxV9K18pm/+8v0 R0U/hCvAfGb7fiH4jG7H+kw9/wDp1hr36vnPx7FB/wALO8QTtBG0yzQhZCo3AfZoeAevc/nWmRpv GJLszgzaKlhmn3RU8z/b/WjzP9v9aoeb/s/rR5v+z+tfb+zPlPZHUeDH3fEPw4N2f9Jm7/8ATrNX qV54rvtPuPFL3Omxrb6PpiX1vH5v7ycZuASxGQobyBtAyQDk8kovj3gCC2T4m+H5Y7aKOV559zqg DNm2mJya9p1bw1Nq1x4iVriOGDVtHj05HALNGwNxuYrwCMTLjnnB6V8Rniaxbv2R9XlMVHDJLuzP 1PX9e0C7gsb2bTb2e+QC2mhtHgWBjcW8GXUyuZBm5DYDJ/qyM/NldjR7/Uv7ZvtI1SW0uJ7a3gul uLWBoFKytKuwozucgwk7t3O4DAxk4+p6Br2v3cF9ew6bZT2KA20MN286zsLi3nw7GJDGM2wXIV/9 YTj5cNsaPYal/bN9q+qRWlvPc28FqtvaztOoWJpW3l2RDkmYjbt42g5OcDxz0jcooooAKKKKACii igAooooAKKKKACiiigAqnq1nNqOjX1lb3clnPcW8kUdzHndCzKQHGCDkE56jp1FXKKAI4IIbW3it 7eKOGCJAkccahVRQMAADgADjFfGXjf8A5H7xH/2FLn/0a1faNfF3jf8A5H7xH/2FLn/0a1e5kf8A En6GVXYwaO9FKK+kSuzEcOtSLTFFSKBXp4aDbIY9a0Yvun61BbQo5G5c/j71vWllbsvzR5/4Ea9W c1CNmcOIrRjuZdUrj734mu1j0ixZcmDv/fb/ABqX/hHtLcAta5P/AF0b/GuZY2nF6pnCsxpQeqf4 f5nnh61Getd7J4e0sISLXn/ro3+NUZ9E05D8tvjn++3+Nc1Saqv3TrhmdKWyf4f5nG0V0M2mWatg Q45P8R/xqhJawrjCfqazjgqktmjrhiYS2MvvRVkxJ/d/Wk8pPT9amWWVk91+P+Rvzor0d6n8tfSj y19Kj6hU7r+vkHMiKlFSbF9KNi+larCTT3QcyGUh6VLtHpSFR6VUsPJIVyKipCo9KaQK55UZR3Hc jop5UYpuBXM6bRVxKKKKzAKKKKACiiigAooooAKKKKACiiigAooooAKKKUU0rgJRS4FGBVcjASil wKMCjkYHrP7PX/I/X/8A2C5P/RsVfStfNf7PQ/4r6+/7Bcn/AKNir6Ur5LOFbFP0R0U/hCvm/wCI b7fiX4hG7H76Hv8A9O0NfSFfM3xLfb8TvEAxn97B/wCk0NdHDivjkvJnJmEeajYxPM/2/wBaPM/2 /wBao+b/ALP60eb/ALP61+g+zPC9kdl8PH3fEvw8N2f303f/AKdpq+kK+Zvho+74neHxjH72f/0m mr6Zr8+4jVsc15I93L48tGwUUUV4R3BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8XeN/8A kfvEf/YUuf8A0a1faNfF3jf/AJH7xH/2FLn/ANGtXuZH/En6GVXYwaUUmKcFNfTQi77GI5alWo1U +lTJGzEADmvXwsJaaGcmXbT7y/5710Vl938Kw7O2mJX5P1HrXQ2dvLtHy9vUV04qS7nkYyS7mrD9 w/Wri/dH0qCG3lK42d/UVdS0nKjCfqK8apJX3PAqyjfczpP9Wazbnr+Nbj6ddFCBFz/vD/GqNxo9 +2SIOM/31/xrWlUgnujajVpp6yX3nO3H3vxNZUvauln0PUd2fs/c/wAa/wCNZcuhalgH7N/4+v8A jXqUq1P+ZfeexQr0v5l96MA9KStJtD1FQc2/T/bX/Gom0m9X70GP+Br/AI10SqQb0aPRVam9pL7y hRVk6fdL1i/8eH+NRtbTLnKdPcVgtTRTi9mRUU4xsvUU3Bp8r7FXCkpaSs57AIelNPSnE001yVNd ikNPSkpxpuDXJNO5Q2ilwaTBrlsxhRRRSAKKKKACiiigAooooAKKKKACiiigApR1pKUCqjuAtFFF aiCiiigD1n9nv/kfr7/sFyf+jYq+lK+a/wBnv/kfr7/sFyf+jYq+lK+Pzr/en6I6KfwhXy78U32/ FLxAN2P3kHf/AKd4q+oq+U/i3MqfFjXkJG4tAQM8/wDHvFXVwy1/aCu+jM8THmp2Ob8z/b/WjzP9 v9ao+b/s/rR5v+z+tfpnszzfZHefCx93xS8Pjdn95P3/AOneWvqKvlP4STpJ8V9BQFdwefIB5/49 5a9ctfF9w3h6+8Rf23qpnNpd39lp97pohtJECSSQx+YYFLsIwpISU52MQWUE1+Z8TOLx75XfRHpY aPLTseoUVzdmdQ0vxVaaXPq93qUF5ZXFwWu44VaJonhUBfKjQYImbO4H7q4xznpK+fOgKKKKACii igAooooAKKKKACiiigAooooAKw/GGqTaP4Tv7u1eRLsosFs6RGUpNKwjjbYASwDupIAYkA4DHg7l V76G4ns5I7S6+y3HBjlMYcAgg4ZT1U4wQCDgnBU4IAMfwxcpcfagNb1XUJF2FodTs1tZIgc4YIIY m2tgjcQQShAOQ1fJ3jf/AJH7xH/2FLn/ANGtX1zpOk3lrqN3qWpXsF1fXMUVuTbWxgjWOMyMvys7 ndmV8ndjG3gYJPyN43/5H7xH/wBhS5/9GtXuZH/En6GVXYw+1PHWmdqeOtfX0dzBj1qzb/6wVWWr Nv8A6wV7eH+Exnsblj2/z61v2fRf89qwLHt/n1rfs+i/57VxYk8DF7s3IO3+9WlF90/Ws2Dt/vVp RfdP1rxap89WH02T7hp1Nk+4ayW5gtynN/DWdL90fWtGb+Gs6X7o+tddM7qJnTdG+tULjv8A7tX5 ujfWqFx3/wB2vQpnq0TOl7VQm6N9avy9qoTdG+tehTPUpFKXtUJ71NL2qE963nsd8NhtJS0lcM9z QbRRRXKMaetFB60Vi9xje9FHeisGMbRRRXOMKKKKACiiigAooooAKKKKACiiigAp1Np1aQAKKKKs QUUUUAes/s9/8j9ff9guT/0bFX0pXzX+z3/yP19/2C5P/RsVfSlfH51/vT9EdFP4Qr5R+Lx/4uvr 3+9B/wCk8VfV1fJ/xf8A+Ssa9/vQf+k8VdPDUuXHp+TFW+E4vPvRn3pmaM1+i+2OSx3PwhP/ABdf Qf8Aen/9J5a+gtR8I32vQiy1zWY7mwjSZYvs9n5NwTJDJBud97Ix2SufljUbsEAAbT89/CD/AJKx oP8AvT/+k8tfWFfnXEsubHt+SOuj8Jh2Gj6l/bMeqavqNpdTwW8lvAtpZtbqFkaNnLbpJCxzEmME Y+bOcjG5RRXgGoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVn65cajaaHez6RZfbdSWJvs0BKgP IeF3FmUbQcE8g4BxzgUASWGq2OpveJY3Mc5srg2txs5CShVYpnoSAwzjocg8ggfHfjcD/hPvEf8A 2FLn/wBGtX0x8OojaSeI7L+zr60WHUIiWvXhaSVzaQbmcxuwaRiN7N3Mmc53AfNHjb/kfvEf/YUu f/RrV7+QK9WfoZVdjCwKfim06vsaaRzscKesjKcqcGmdqWu+m2iGWUv7qP7kuP8AgI/wqxHrmop9 24xj/YX/AArOpRWiipPVGcqNOW8V9xrr4p1lel5j/tkn+FSDxhryjAvv/IKf/E1h0Vk6NN7xX3Gb wmHe9Nfcjc/4TLX/APn/AP8AyDH/APE00+MtfIwb/wD8gx//ABNYlN7VyzpU1tFfcCwWG/59x+5G w3i7XGHN9/5CT/4moj4n1hhg3n/kJP8ACsk9KSuOSSehosJh1tBfcjRbxDqjZzddf+ma/wCFRtrW oN964z/wBf8ACqHeisfaTWzNFh6S2ivuRbOp3jdZv/HR/hUbXtw2cydf9kVBRVKvVX2n95SpwWyR IZ5W6t+gpPNf1/SmUU/rFb+d/eyrId5jetG9vWm0VPtan8z+8dkLvb1o3t60lFL2k+4WH5NApKUV vFtsQuKTApaK0sgE2j0o2j0paKXLHsAm0U3Ap/am1nOK7AIRxSUp6UlYT3GFFFFSAUUUUAFFFFAB Rk0UU7gGTRk0UUXYBk0ZNFFF2B61+z0f+K+v/wDsFyf+jYq+la+av2ev+R+v/wDsFyf+jYq+la+T zj/eX6I6KfwhXyd8YTj4sa9/vQf+k8VfWNfJnxiOPizr3+9B/wCk8Va5FLlxifkxVfhOJz70Z96Z mjNfb+3Oax3fweOfixoP+9P/AOk8tfWNfJnwdOfizoP+9P8A+k8tfWdfEZ7LmxjfkjppfCFFFFeM aBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8keMPCXiW58b6/PB4c1mWGTUrh45I7CVldTI xBBC4II7ivo+Pxj52napdLpk8TWuoQ2EEVw3ltM0wh8t3GCY1JnXIILKoyV3ZQRyeK9St79dHuNJ tBrUrxCCKO+ZrdldJ3BaUxBlOLabgRtzs5+YlezB42phJOVNJ37kyipbny3/AMIb4q/6FfXP/BbN /wDE0v8Awhvir/oV9c/8Fs3/AMTX13ouqzakL2G7to7e8sbj7PcJFKZY9xjSQFHKqWG2RM5Uc5HI AJ1K9JcRYpfZj9z/AMyPYxPjL/hDvFP/AEK+uf8Agtm/+Jo/4Q/xT/0K+uf+C2b/AOJr7FvpriCz kktLX7VccCOIyBASSBlmPRRnJIBOAcBjgHDj13WnuLnTTotodWhSGbZHfk24ikMgDNKYg4OYXGFj bkpzgsU1XE+LX2Y/c/8AMXsYnyv/AMIf4p/6FfXP/BbN/wDE0Dwf4p/6FfXP/BbN/wDE19caLqs2 pC9hu7aO3vLG4+z3CRSmWPcY0kBRyqlhtkTOVHORyACdSrXFWMX2Y/c//kg9hE+M/wDhD/FP/Qr6 5/4LZv8A4mj/AIQ/xT/0K+uf+C2b/wCJr7InkaG3llSGSd0QssUZUM5A+6NxAyenJA9SKx7DWNS/ tmPS9X060tZ57eS4ga0vGuFKxtGrht0cZU5lTGAc/NnGBlf604z+WP3P/MPYRPk7/hDvFP8A0K+u f+C2b/4mm/8ACG+Kv+hX1z/wWzf/ABNfU+meLLjX7XUpdG0rzmtdQNnE1xcCKOVBEknnlgGIjYP8 hUPuBQ8BiV1NF1WbUhew3dtHb3ljcfZ7hIpTLHuMaSAo5VSw2yJnKjnI5ABOb4lxb+zH7n/mP2MT 5CPgzxVj/kV9c/8ABbN/8TSf8IZ4r/6FbXP/AAWzf/E19pVT1XUodI0u4v51kdIUyI4gC8rdFRAS NzsxCqO5IHesXn+Jf2Y/c/8AMfskfHH/AAhfiv8A6FbXP/BbN/8AE0f8IX4r/wChW1z/AMFs3/xN fU9h4r1LWrexbSNJtHnm0y21GdLu+aFY1nDbFVlicuQY3zkL/DjOTixqviDV9NsrjU20KNNMs7f7 RdG4vQtwFVN7hI0V0YgZXmRcspHC4Yx/beI7L8f8w9kj5Q/4QvxX/wBCtrn/AILZv/iaP+EL8V/9 Ctrn/gtm/wDia+06KP7axHZfj/mHs0fFn/CF+K/+hW1z/wAFs3/xNH/CF+K/+hW1z/wWzf8AxNfU 8Pi6+/sE+I7rRo10NrJr6OS3vPMuBEIzKpeJkRVJUYwrvhiByuWFga7rUF6NPvNFtPt89u89olpf mRGCPGj+YzxIUAMyH5Q5ID4GQFY/trEdl+P+YezR8of8IX4r/wChW1z/AMFs3/xNH/CF+K/+hW1z /wAFs3/xNfXek6teXWo3em6lZQWt9bRRXBFtcmeNo5DIq/MyId2YnyNuMbeTkgbFH9tYjsvx/wAw 9mj4s/4QvxX/ANCtrn/gtm/+Jo/4QvxX/wBCtrn/AILZv/ia+v8AWtVm00WUNpbR3F5fXH2e3SWU xR7hG8hLuFYqNsb4wp5wOASRjx+K9SuL9tHt9JtDrUTyieKS+ZbdVRIHJWURFmOLmHgxrzv5+UFj +2sR2X4/5h7NHyz/AMIZ4r/6FbXP/BbN/wDE0o8GeK/+hX1z/wAFs3/xNfVtvrutanapLpei2jlH lhuGu78xIJY5XidYysTs4DRscsqcMhxksF2NJ1KHWdGsdUt1kWC9t47iNZAAwV1DAHBIzg+pq1n2 JX2Y/c/8w9kj47/4Q3xV/wBCvrn/AILZv/iaP+EN8Vf9Cvrn/gtm/wDia+yL66+xWclwLee4ZcBY YE3O7EgADsMkjkkKOpIAJHP3XjH7L8PIPFQ0yedptPW9W0hbO3MXmkNIQAqgA5YjtgAsVU1/rBif 5Y/c/wDMXsonyv8A8Ib4q/6FfXP/AAWzf/E0f8Ib4q/6FfXP/BbN/wDE19ealda1HcLFpWlWlygQ M8t3emBcknCrtjkJIxk5CjlcFuduPN4uvv7BHiO10aNtDWyW+kkuLzy7gxGMSsUiVHViFOMM6ZYE cLhif6wYn+WP3P8AzD2UT5b/AOEM8Vf9Cvrn/gtm/wDiab/whniv/oVtc/8ABbN/8TX2lWP/AG9n xbHoQspwrWktwbtxtQsjRAooPLcTKSw+UdASQwVPP8S/sx+5/wCY/ZI+RT4M8V4/5FbXP/BbN/8A E0n/AAhfiv8A6FbXP/BbN/8AE19Zxa5qkWp2EGqaRBaW+oStDbGO882ZXEbygSpsCr8kb52u+GwB kHcI4PFbX+s65pmm6bJcy6YkASRpVSOd5GkVsNzhI2jIZhkgq4CkrhoeeYh/ZX4/5h7JHyh/whfi v/oVtc/8Fs3/AMTR/wAIX4r/AOhW1z/wWzf/ABNfXek6teXWo3em6lZQWt9bRRXBFtcmeNo5DIq/ MyId2YnyNuMbeTkgak8y21vLO4kKRoXYRxs7EAZ4VQSx9gCT2pf21iOy/H/MPZo+MP8AhC/Ff/Qr a5/4LZv/AImj/hC/Ff8A0K2uf+C2b/4mvqtfFlxP4aj1GDSsX8+oPYQ6fPcBWLrcNC2WQP8AdVHk baGwqNyQN1SSa3r0Vxbac2j6b/alwk06RjUn8gQxmNWJk8jdv3SrhdmMAncOhP7axHZfj/mHs0fK H/CF+K/+hW1z/wAFs3/xNH/CF+K/+hW1z/wWzf8AxNfW8moeI3SEWugWglCZn+16j5aBtxGI2SNy 4+XdllThk4zuVNDSdSh1nRrHVLdZFgvbeO4jWQAMFdQwBwSM4PqaP7axHZfj/mHs0fHH/CF+K/8A oVtc/wDBbN/8TR/whfiv/oVtc/8ABbN/8TX13qOvfYNe0nShZTy/b5Wja4xtjhxFLIOT95j5TDaO g5YjKhqd1revWviCx09tH014Ly4dEkj1JzKkKgs0rR+RgADav3sb5EXd82aP7axHZfj/AJh7NHyh /wAIX4r/AOhW1z/wWzf/ABNH/CF+K/8AoVtc/wDBbN/8TX1PZeLr64h0y9l0aP7Bq6FtP+z3m+4k JhedFdHREQlI2HEjAMQMkZYaFhrGpf2zHper6daWs89vJcQNaXjXClY2jVw26OMqcypjAOfmzjAy f21iOy/H/MPZo+SP+EL8V/8AQra5/wCC2b/4mj/hC/Ff/Qra5/4LZv8A4mvtOij+2sR2X4/5h7NH zv8AA3RdZ0Tx3PJqmiarZRT2EkMcs9hKib96NgsVwvCN1I9OpAr6IoorzsTiJYip7Se/kWlZWCvk n4yuq/FrXgWA5g6n/p3ir62oqsJiXhqntIq4SV1Y+DvNX++v50eav99fzr7xor1P7dq/yr7zP2SP kn4NOrfFrQQGB5n6H/p3lr62oory8XiXiantJKxcY2VgooormKCiiigAooooAKKKKACiiigAoooo AKKKKACqcllO+qQ3a6ndxwIm1rJVi8qQ8/MxKF88jowHyjjrm5RQBy7aTcBPFBn037XHeahFcQQi cRvIi29upZGB+SRWjYoSV+ZVO5Rhhz7eEZTOdV/sKeS0mlgF3o91cJcXF3HElwA0rySFJG8yeMhW kIVIEIbcAi+kUUAc/wCE9Ol0+1v/APiX/wBmWk935tpp/wAg+yx+VGpXbGSi5kWR8KSPnyfmJA6C iigCnqt1d2Wl3FxYafJqF2iZitUlSMyt2G5yAo7k+gOATgHl4LrXbPS7y7g8M6lPrlw8aSTXL2oD D5uVVbggRRjOI9wJLfeLM8ldpRQBj+G4jFp0hk06+s53lLTNfvC81w+BmVjE7LzwoHG0KFCqoUVs UUUARzyNDbyypDJO6IWWKMqGcgfdG4gZPTkgepFcXBa69r1nqIu7C+0TWbu0eGO/mMMsVkjEZjgE U+/dj5vMO3cyhjgKka9xRQBwen2Xijwxaa8bfTbS/kub2EWUVhbpbxxxi3ijMmx58bFEe0R7lJKd Qrgp0nhuIxadIZNOvrOd5S0zX7wvNcPgZlYxOy88KBxtChQqqFFbFFABWH4i0fUtWfTjYajaWqWl x9oeO5s2uEmYKQgIEicKx3jr8yoRjbzuVXuL63tJ7SGeTZJdymGAbSd7hGkI46fKjHn09cUAeb6Z 4SurGz0l/EHhn+32TRLOyNrD5BS3khMpO+OaUIzBZVUOpY8SY2BsHUZPEMdlp2h6noepapZWtlCl 7cW1xbsNRmCAOGMsqP5WRkgjMhODhARJ3EMjSoWeGSEh2Xa5UkgMQG+UkYIGR3wRkA5AkoAKKKz5 dWRdWbTYbae4uI4o5pfL2gRI8hRSSzDOdsjcZ4jbuVDAHndn4N1ODQ7HTNL0iTSdYtLKS2uNbeeM faj9lkiULIjtMU81onUMqhViXAUqq1sWdlc6VezapoPgiTTUhspY305GtLc30zPGYjmORkwgWXLO QVEh2hskV3lFAHP+GEuj9quNR0y+ttQl2Ge5uzB++64SNYpZNkaZOFJ43E5Zmdj0FFFAGP4ktftW nRj+zJ9Q8uUPstbr7PcR8Ebon3JhucH50yjPyfuty+meHr3w7rCa5a6NPNHcfaS+n280bTwmVbUB neR1V2P2VmdtxO+TguMufQKx7DxJZ6jq0unxRzqw87yZXUbJ/JkEc23BJGx2CncFznK7hzQBzap4 hsNOh02PQ9Se3uri7vLuayuLdZY1muJJVgBaVdr7Xw7qTtwQhJIdO0sESPTraOKz+xRrEgW1wo8k ADCYQlRjp8pI44OKsVXv7630zTrm/vJPLtbWJ5pn2k7UUEscDk4APSgCxXH/ANiaj/wpv+wPs/8A xM/+Ef8AsXkb1/132fZt3Z2/e4znHvXWCRjcPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xJQBzfiW61 j7RBYWOj6lcWMiFrq6sZ4EkxnAiTzJUKk85ccgcL8zbkz9Rk1Gd7fTF8IakNAit491vbSWaiY7f9 QwMwCxIMAqMhzxnYCJO0ooAKx7mxuJPGWmX6x5tYdPu4ZH3D5XeS2KjHXkRv+XuK2KKAOLbT9Qv/ ABFa39t4dj0bU4nc3mpv5MiXK+Q8apmNxLKnmGJwHCZEYJ2sFFU9G0TxL4TutSuBHBrappVtBAlv EttJdzrLMxLu8zANmQu7kfMZMgZUhvQKy7LX7HULiyitmkZL6yF9aSlcLNFlckdwR5kZIYD/AFgx nDbQCn4YS6P2q41HTL621CXYZ7m7MH77rhI1ilk2Rpk4UnjcTlmZ2PQUUUAcW3h8N4dSHVfDkese TrF7eLZtJGTtknnKOquRG52yg7XYYBJ+8oU448KSLfxahceFZJ9HD3CwaDG8A8hZEtcbozIICnmQ TPtDN80iNjdkr6ZRQBwaJ4h0/SbPSItD1JrOR55pnsbi3WS3haZ2itU3SrsKxlVLISEC7Yzkh07S wRI9Oto4rP7FGsSBbXCjyQAMJhCVGOnykjjg4qxRQBj6zY3F3qvh6aCPfHaag8053AbENrPGDz1+ Z1HHr6ZosbG4bxHqmpXseNuy1sfmBAgCK7sByVZpGZW6ZEMfHygnYooA870XQrzTZdIlTwzJBqen ozanqUZtw2rEQOjKHEm9y8pST96EB27mIYAVueG31K71GS91rQr6zv3iIEs0tu8NumR+5i2Ss3PB Zyo3lcnaAiL1FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVy/ivT7e51rwndz6d9q+y6qSZBamYwhoJQr HAJVfMERLcAEKSRjI6iigDzvT/Da6jrWnx6vpck1mj647x3ETeUxe/jaPep+Vgy5dQ2QdoYcqCKd romsQeHrFtPt76LWb/wpdfbLh3dZpb4pb+V5srHPmKWlCbj8g3AYANeoUUAcX4EsDaXGpTQxxwWc iQqkFvosmlwCRTIXYRSOWLkMgZ9oBCoAWKkLXvYJpbjxFbxxSPOfEel3QjVSWMINkDJjrszFKN3T 92/9047yq/2G3/tH7eI8XRi8lnViNyZyAwHDYOcZzt3NjG5sgHD6Rob2Oo6XqEdhPHeTeINSF3OU bf8AZWN4yKSfuwlvKcLwpYq2NzZOxrmh2+teMtHGoWH2uwi0+9EiyIWhLmS22rIPut0ZgrZ5TcBl QR1FFAHm+jaJeQadpDG31WK7v/DU/wDalxE5W7luiLfZvlkP+uXdME3n5PmAwoNbHgW3Ft9vWDTY LW1PllZbbTJtNjkf5ty/ZpSSGA2kyj74ZV/5Z12FFAHm/iW1v38ZQXFppH79NQssXa2Ek032bzIh I6XO8LDHy6NCqsSN7kbZGYR+GfDNjo/i3SrlPD0dq5fWkWePT9uwm7Uw7nC/IDCHCEkAqcDrg+mU UAeN2GhaqvhXWo5LeQ6gfDl1bXsVvo0lu1zdsi4MszOftcu5ZAHRSDvc5G9Q3eeLNDt2+GuuaTZ2 HnqdPuDDBsMryS7WZW5yXkL4bccsW5yTzXUUUAed61pJvrm6Sx0+7i0+a30FIVghkgKRpfSM4XAV oyiEEgYKDBOKH8NrZXGoz2OlyQvaa7YJpwhiYLb2zG1M/kKOERt0/mFAA3zbs449EooA830TSb9P HP2i4Ty7pNQu5ZpU0mRZJbZjKIVkvS+yWMK0JEahipWMYHlsV3PFdraS6zpc2saZJqOipb3CyQCx e8UXBaExMYlVjkKswD7cDJGRuAPWUUAeV3PhzUpdG1G41Gxu7jWrTwlZrbyMWlYX6LckshBIadWK kOMsN/BG852NTsCvjxLpNPku7h7iBkeawkLxx4VWMN6jbIYlAdjC4y58wYIlXPeUUAeZ6XoVzP4x lGo28bO17etd50Z91xaP5wjjmvGfy5Ytrw4jUMRtRSo8titjw9bLb+HvhvYxWclrPbuZLi2e3aF4 ytnOkrshAIHmyKCxGCZFOTuGfRKr/Ybf+0ft5jzdCLyVdmJ2pnJCg8Lk4zjG7auc7VwAWKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKAP/2Q=--20cf305496b5866b64049caea6ca Content-Type: image/jpeg; name="RescaleTF_Log_Cz.jpg" Content-Disposition: attachment; filename="RescaleTF_Log_Cz.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkdeqfh32 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAGkAjADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiqcmrabDqkOly6 haJqEyb4rRplErrzyqZyR8rcgdj6UAXKKx9G8RWervJb+bBFfJLcr9k84NIY4bh4PM28HaSnXGAT jJqxFrujzfbvK1Wxk/s/P23bcIfs2M58zn5MbW64+6fSgDQoqnHq2mzJM8WoWjpDcfZZWWZSEm3B fLbnh9zKNp5yQO9R/wBu6P8A2P8A2v8A2rY/2Z/z+/aE8n7237+dv3uOvXigDQorLXW4ZdZ06yt/ LngvrKa8juY5AylUaEDGOGDCbOc9u+eI9Q1a8XUTpukWUF5fRxLcTi5uTBHFGxZU+YI5LMUfAC4w jZK/KGANiiuf1TXr+00M6vbabAtvBFLLeLqVxJbvbiP73CRSb8YbleDgFSwYGtCxvrgaHHf63bwa XMIjLcxG5EiW4GSd0mFHA6noOeSBkgGhRXJ2/iXXtQvRbWGgWiOLKG9dL+/eB41meUIjKIXw4WLL D+EkgFsZNi61vXob3TdNi0fTX1C6t7i4lVtSdYo1ieNcK/kEsT5qnlVxg9aAOkorDk1jUrKyhF/p 1oup3dx9ns7W2vGkSVthfLSNGmwBUkY/KeE43MQpjfW9Ys7O6a+0LN1BEbhI7Gd7hJo1I3qreWp8 4D7sZUB8qA33ygB0FFRwTw3VvFcW8sc0EqB45I2DK6kZBBHBBHOakoAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5eO0vbXXtUhfRfttrqOoQ3qXLSRi GAJFCnzgnf5itAWXahGSnzLyV6iigDi9O8OXdo+lOLGOJ08R3+oXTKUBMci3axyNg/MSskI7kDAO MHHPz+GvFN5b38U1pJsHhy/06GASWyQJPIIQqW6IoKwHZhDIxbC4YJjL+qUUAcPrvhWW8vby3t9M gfTJItGgSDCCMxwXkjypsPG1Y2HGMEHAz0qPV9A1h769v7RbuMprq30Rsmg8+SL+z1tyYxNmPO8n IfHyqxHO3PeUUAcfoGg3thqmj3EsU/lx2mpGd7iaN5BLcXMMwD7Aq7jhyQgKqRgEjBOhexXul+IZ 9ZtdOn1KO7tIbV4LZ40kiMTysG/eOqsp85gfmBBVcBgxK9BRQBx+tPr00uk21zoU99bxyi9vG0+W EIXRi0MI82VGO1wjmTjd5YG3DsqaGvxXuveAdZtotOnt7680+4hjtJ3j372RlUEq7Jycfxd+cc10 FFAHn9/ZWOt+IRr7+EP+Ek0y+0q0+xz+VbNsw87t8tw6MuVkjPA+vSjUdBtprrQZ/wDhAftGmW1p eRHS/Ksv9GkeWFlbY0gj5CSHKkn5ucZNd5DBDbIUgijiQuzlUUKCzMWY8dyxJJ7kk1JQBx8WkKlh YXek+GP7HbTdQa8/s0LbxG5zA8TFTE7IG2ycbiMmMKSoO4Fmj2Go3/ie+j1XSdNt7Sdrm2v79rne SVkMqxpJIkaoqMFCcnew2qFG7sKjmghuUCTxRyoHVwrqGAZWDKee4YAg9iAaAMvwnY3GmeDdDsLy Py7q10+3hmTcDtdY1DDI4OCD0rYoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuR1H/j9m/66N/Ouurkd R/4/Zv8Aro386+T4s/g0/V/kd+A+Nkdr/rK1F+4PoKy7X/WVqL9wfQV8BV+I6sRuKelU7v7pq4el U7v7pruyj/eYnj5p/u8iG3+5VhfvVXt/uVYX71ezmH8WZ5OB/hQIr7+H6/0rIrXvv4fr/Ssivn6P wn3GG+A1k/482/z2pI/+Qf8AhSp/x5t/ntSR/wDIP/CvSj/yLJf41+R85U/5HEf8P6owrr78n1qO L7p+tSXX35PrUcX3T9awj8J9jH4C7D1X6VoR/cFZ8PVfpWhH9wVy1DjrDqYf9cv0p9MP+uX6VmjF Fy16NTJ+h+lPtejUyfofpXbln8c8PN/4bK1v978atS/6s1Vt/vfjVqX/AFZr3sw/3uJ4GB/3VmHp X/H/AH//AF3H/oC1t/w/hWJpX/H/AH//AF3H/oC1t/w/hXpcSf74vSP5I48l/wB2+8rXH3D9KxdG /wBbd/8AXdv6VtXH3D9KxdG/1t3/ANd2/pXtZP8A8iyv8vzPKzL/AH2mdAlWH/1BqulWH/1Br8+z U+2yn4jGuP8AWmrFj95foar3H+tNWLH7y/Q15svgPrJ/wzTk+9QlEn3qErVfwUfN/wDL0dJ9wfWq k33x9KtyfcH1qpN98fSuGB69IYv3x9auH/VCqa/fH1q4f9UK1h/EiZ4v+GypL0NR2P3m+tSS9DUd j95vrX2Uf+RfV+R8Q/8AfqfzL8n3h9KrTffH0qzJ94fSq033x9K+Jifd0+g+2++ap6j1T/eFXLb7 5qnqPVP94V9Fwz/yMI/10PnOKf8AdJF+L/UfhVWrUX+o/CqteNiv48/U9vLv93iQXX+rrCueh+lb t1/q6wrnofpWuHPcwpteCf8AkLT/APXA/wDoS13VcL4J/wCQtP8A9cD/AOhLXdV+iZD/ALmvVni5 t/vL9EFfK3xZgjl+Kuvs8aMd8Aywz/y7xV9U18u/FNQfil4g/wCukH/pPFXp13aA8pgp4lJrozhf scP/ADxi/wC+RR9jh/54xf8AfIq3sHvRsHvXD7R9z6n6rD+VHUfCaCOL4q6AyRop3zjKjH/LvLX1 TXy78LFA+KXh/wD66T/+k8tfUVd1B3gfLZtBQxLSXRBRRRWx5gUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUVwaNrQ03xRA9/d3s8OsW6yvCpVltzFaPOsKrllARpdoUl+eCzncQDvK5HUf+P2b/AK6N /Om+E7wN4h1extRqq6bFaWs8K6m8zSM7vOrsPOJlVT5SrtbbyhIGG3M7Uf8Aj9m/66N/Ovk+LP4N P1f5HfgPjZDA21+ma1EbKfTismL7x+lasf3D9a+BqLVHXiO47dVedN4xnFT1FJXVg26dVSjueRi1 z02pEEMW1cZ/SrCR89f0qOOrCV146vUcpO5zYKjBRSsZ99L7dPesyr993/z6VQripL3T6uikoaGx AvmWrDOO1DReVZFc5x7U6z/49z9adP8A8erU1WmqLpX929zx50YfXlUtrsc7crmRhnrUER6r+NWZ /wDWmqkX3j9K6I/CfSQ+EvQvgA46cVoxHK49KzIvun61pRd65qqOSsiSog26UHGKlqCP74rJGMdm aNsMKfcVFcnCk+1TW33fwNQXX3D9K78pV8Sk+54Gctqk2ZumXv2ppf3e3y5Cn3s5x3rUkOUIrB0H 791/18PW833a+t4hoU6OYctNWSsfL5RUlPCPmZm2dp9nurh9+7zZN+MYx8oH9K08fLVWP/Wmrf8A Ca4c3rTq1Yzm7uyOnLKcYU3GK01K065UjNZlhZ/ZpJv3m7zJC/TGM1qz9DVSL79exltepHBzgno9 zy8fSi8VFtF6Nc45qeX5YfXNRRdRUtx/qRXxOZzbmkfY5VBbmLOcyZq5YJlhz2/nVOb74+lXrD7w +griqfAfS1dKZblfDdKEf2psv3qRO1elGlB0FofJSqSVZ6k5O9cdMVFLD8w+bt6VIlEv3h9K8mrF QnaJ7GGqSa1IBFgg7untVgfNF9DUdSJ/qz9aiMnzJmtb3oO5UnG1SetV7B8ljjvVm6+4ap6d1P1r 7vCxU8pqylvofCYmThmdKMdtTVb5sHpxUbQb8tuxgelSDpTh91vpXxVSCiro+xo1puSVyKCPaSc5 qlfruZecYNaMfRqoXn3x9a9fh2TWM5lueZxEubDtMtxf6j8KpO+04xmrsX+o/Cs+X7w+lebiP94n 6ntZYr0I+hDczZXG3tnrWROu449q07jv/u1my/eH0rSirbHu0FbY2vBqbNWm5zmA/wDoS13FcN4U mWLWtjCQmWNkXbGzAHhvmIGFGFPJwM4HUgHua/Qsg/3NerPCzV3xD9EFfMPxPUn4oeIP+ukH/pNF X09XzL8S1B+J/iD/AK6wf+k0Veji3+7OnII82MS8mchsPtRsPtU2we9Gwe9eXzH3XsjpfhgpHxQ8 P/8AXSf/ANJpa+nq+ZfhooHxP8P/APXWf/0mlr6ar1MI70z4XP48uNa8kFFFFdJ4oUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAVlnQ4QNUMV1dwSajcLcvLFIFaKRY40UpxjGIlJVgwPIIKnbWpRQBn6Z pKab5sr3M95eTYE13c7fMkC52r8qqqquThVAGSxxuZicHUf+P2b/AK6N/OuurjtS/wCPy4/3m/ma +T4s/hU/V/kd+X/GyKL7x+lasf3D9axLft/vVsw/6v8AAV8HVWqO3EqyH1FJT1606tYS9nK+55Mo +0jYrx1YSlHWq9z1/GivX9rK1rF4XDctlcpX3f8Az6VQp9x3/wB2qsf3xVwjaJ9BThaJ0Nn/AMe5 +tOn/wCPVqpWPb/PrU1z0/CsW7e6edKj+/5rmNP/AK01Ui+8fpVifv8A7tVV+8PrXbFaHswXul6L 7p+taUXesqL7x+lX4ei/WsKqOasi1UEf3xT5e1Mj++KxS0MEtDStvu/gaguvuH6VLa9Gptz90125 VLlxKfmeBnEb0mjA0H791/18PW833aqQ/fP1q9/DX1Of4r22M9ra17HzWUUeXDuFynH/AK01b/hN A61J/D+FeNjcX7SSdj0sJhvZxauVJ+hqpF9+tCSoV+9Xr4LFcuHcbbnl4vDc1dO5NF1FS3H+pFJF UF595v8APevlMdPnq2PrMrpWW5mzffH0q9YfeH0FZtaVh94fQVjV+A92srQLMv3qRO1QXn3qbH0r 6Slgr4WM+bfyPgqmLtiZR5S+lEv3h9Kgi6/jVr/lmfpXz2Oo+ynvc97AVueOxDUif6s/Wom6fiP5 1JF94/SuO9tT0Zq8bFa6+4ap6d1P1rSn6GoI+tfYYPGWy+dHl+K2tz43F4S+OhVvsWh0pw+630qp N900616V49bBf7O6vN+B6tHGWrqny/iWI+jVQvPvj61qHpVaT71ZZPifYV+e1zXN6Ht6XLewRf6j 8Kz5fvD6VpSf6g1j3Hf/AHq5py9pWlLuz2Mup8tJRI7jv/u1my/eH0q3L2qCT7hrenoezT0Nnwh/ yFpf+uB/9CWu1rjfCP8AyEZP+uLf+hCuyr9A4f8A9yXqzwMzf+0P0Cvmr4jgn4meIf8ArtB/6TQ1 9K181/EWIt8UPEEnnOFEsIMYA2n/AEaL2z+tehjf4R6HDKbx6SXRnMbT6UbT6VNsHvRsHvXjcx+k +xOg+HAI+Jnh7/rtP/6TTV7jL4ytrIefqmm6lptiySPFd3UabZQkbSnCK7SqfLjdsOin5SDhsKfD vhzCyfFDQHM8jgzT4RguB/o03TAz+tem67dN40sNUtE0/VY5ltLuLSrWfTLiAPM0EkfmySugRdys yopYAB8tlmCx+zgv4R+a8S3WPd1bRHYafrxu9RFhd6VfaZdPE00KXZhbzUQqHIMUjgbS6fexncMZ wcbFcva3Y1rxlY39pbXyWtpp91DM93ZTW2HkktygAlVS2RE/3c4wM4yM9RXWeAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABXHal/wAflx/vN/M12NcXqcn+n3C4/jYdfc18pxWv3VP1f5HoZcrz ZXt+3+9WzD/q/wABWHBJgqMdea2rdsw5x2FfCVU7o7cXpG7Hr1p1MU5NOdtpx1p1fdlZnl0k5LQc OtV7nr+NI11sJ+TOPeqlxfbsfu+v+1WXK3K6O6hSn2KVx3/3aqx/fFOmucjOz261As+1gdv613Ri 7Hrwg+U27Ht/n1qa56fhWfa3fl/wZx71LLeeZ/yzx+Nc0oS57nJKnLnvYzp+/wDu1VX7w+tTzSZ7 dRioAcEGu2K0PQgtC3F94/Sr8PRfrWdE/IOOvFX4T8o9jWFRHNVRYl7UyP74od92OMYpFO1gaxS0 MEtLGja9Gptz900to2eMdadPHuBGa6MvnGGITkeHmsJSptIoQ/fP1q9/DVeO32tndn8KtiPK9a9r NMVSnUUovT5nhZbh6sKbjJakY61J/D+FAh5+9+lP8vjrXjVsRTclZnq0qFRJ3RWkqFfvVZkiz3/S ohFhvvfpXsYXEUvZNXPLxFCp7W9iaKoLz7zf571ZRdq5zms68ueT8n614Vdqdb3T6TLaclHYo1pW H3h9BWT5v+z+taenSbnXjrilWT5T168XyEl596mx9KZqs32eJpNu7b2zimRz/IDt7etfcYXDVamX wnFaPQ/LsTXpwx04yepei6/jVr/lmfpWfbzbj93H41oL8yke1fKZzRnTqWkj6XKasJwvFkDdPxH8 6ki+8fpUMjbQOKEuNpztz+NeQ02j33FtaD5+hqCPrVlk81Sc47VGsO3+L9K9vC4mkqDi3qfOYnD1 fb81tCOb7pp1r0qG8k8qFmxnAzil02f7RbRy7du9Q2M5xXo1aFR5a6tvdvY4adWH19U762NI9KrS feqZ32gcdaa0O453fpXzOEqRpyvN2PocVSlUiuVDZP8AUGse47/71alxL5cZXGfxrHuJuvy/xetV DWTaPTwUJKKTIJe1QSfcNSO+7HGMUxhuUiuqOh6kdDb8I/8AIRk/64t/6EK7KuO8JjbqUg/6Yn/0 Ja7GvvuHv9yXqz5/M/8AeGFfO3jxS3xH8RED/lvD/wCk0NfRNfP3jVA3xE8Rk5/4+If/AElhrtzF 2oX8z0OGXbHp+TOY8tvSjy29Ku+SvqaPJX1NfP8AtD9G9qaXgNSvxH8Okj/lvN/6TTV9E18/eCkC /ETw4Rn/AI+Jv/SWavoGvoMud6F/M/OeJnfHt+SCiiiu8+fCiiigAooooAKKKKACiiigAooooAKK KKACuT0K0ub8+J9O1bUru6Kami+bDM9sUBtreTbGY2DRoGY4AYnBO4sSxPWVj3Xhuzu7DX7OSScR 63u+0lWGU3QJAdnHHyoDznnPbigDP8EWgXTrnVI7m+ltdSl86zS7vZrjy7YACMgyMSN4Bl6AjzAp +4Kz9T/5CVx/10b+ZruK4fU/+Qlcf9dG/ma+V4p/hU/V/kellvxv0KsPVfpW3bf8e4+grEh6r9K2 7b/j3H0FfDVPiR2Y74GPTrSy/eH0pE60sv3h9KnE/wAQ8zB/CUpP4/xqjN/DV6T+P8aozfw06Z7F Izpfuj61DU0v3R9ahrtjsejHYvRd6kqOLvUlYvc55blKXtUVSy9qirdbHTHYsQ9F+taEP3D9az4e i/WtCH7h+tYVTmrElFFFYHOX7PqPoKnl71BZ9R9BU8velQ/jHk47ZkQ61OvSoB1qdeldmM6Hn4Xq OXrTj0pq9aceleVP4j0o/CQvUQ+9Ur1EPvV62G+Bnl4j40S/8szWLedT9DW1/wAszWLedT9DXHH+ Iz3cv+FFCtXTPvpWVWrpn30rWt8B6GI+BjNe/wCPST6VHH/qh9Kk17/j0k+lRx/6ofSv0rKv+RPS 9X+SPxvM/wDkZ1PQs2vWtWPpWVa9a1Y+lfG8SfxmfUZB/CRUm/hqKpZv4air51bH2Edi+n3PxptO T7n402ro7M8zEfEZ+pf8e0n+6abon/IPt/8Armv8qdqX/HtJ/umm6J/yD7f/AK5r/Kvtqn/Igf8A i/Q+Sp/8jj/t39TTl6LUtRS9FqWvz9n23RFC86H6msafv/vVs3nQ/U1jT9/96uyhsejhtiCiiiuo 7Dd8K/8AIUl/64n/ANCWuvrkPCv/ACFJf+uJ/wDQlrr6++4e/wByXqz57Mv4/wAkFeEeLEZviB4k IH/L1D/6SwV7vXiHiVA3j3xKTn/j7h/9JYK6c3dsM/VHXkDtjE/JmF5b+n60eW/p+tXvKX1NHlL6 mvlfaH3XtSfwmjL8QPDZI/5epv8A0lnr3evEPDSBfHvhojP/AB9zf+ks9e319VlDvhl6s+Fz93xj fkgooor0zxQooooAKKKKACiiigAooooAKKKKACiiigAooooAK841f/kKXX/XZ/8A0KvR6841f/kK XX/XZ/8A0KvmeJv4VP1f5Hr5R/El6FSL7x+ldHZf6h/pXORfeP0ro7L/AFD/AEr4mp8UfU7cz/gy 9CS0+6341nX3+sb8a0bT7rfjWdff6xvxrfMf9+keLw//ALvEy5e1VJvvj6Vbl7VUm++PpRA+tple XtUVSy9qiroWx0x2LsP3z9KmqGH75+lTVhLc55bkU38NRVLN/DUVXHYuOxbh6r9K2LP7q/57Vjw9 V+lbFn91f89q5K+xxYnYml+8PpVKrsv3h9KpVjDY56excg7f7ta8X3T9ayIO3+7WvF90/WuescuI CX7o+tZ03RvrWjL90fWs6bo31qaRNEr0UUV0HUaVt1/GrB61Xtuv41YPWsF8Z51fcpah9xvpXOXn U/Q10eofcb6Vzl51P0NdWF2PUwWyM+tSz+/+NZdaln9/8a7K3wnfX+E6R/8Aj2T6VmXP3G+lab/8 eyfSsy5+430r3eGN4+v6n5ZxLvL0KPhf/kFx/wC83/oRrqI+lcv4X/5Bcf8AvN/6Ea6iPpU8bf8A Iwqeptw1/AXz/MzrnqfrWbWlc9T9aza+WpbH3lH4SWH+KtOH+KsyH+KtOH+KoqmdcsS0R/cb6US0 R/cb6VtL/djwYf7yZ9x9/wDE1DU1x9/8TUNZR2Pfh8JTn7/71UG+8frV+fv/AL1UG+8frXXT2O+l sJUy/wCp/A1DUy/6n8DVsuRteEP+QtL/ANcD/wChLXa1xXhD/kLS/wDXA/8AoS12tfecP/7kvVnz maf7w/RBXiWv3CD4i+IrbZMZHuoiGELlB/okHVwNoPHQn09RXtteRasgbxr4mJz/AMfsX/pJb1pn bSwjb7ovJ21ik12Zm+W/p+tHlv6frV3yl9TR5S+pr4v2h9d7UpeHLyOX4h+HbdY7hXjvJsl7eREO LWccMVCn8DzXZ6xf6vpNx45uBqckkltoUd5aLsAjtmzd7dqHIJxGhYtncR2Xaq89pKBfGvhkjP8A x+y/+klxXpN1oFjfXGpy3SySpqVkljcxFsKYlMvAxggnznBOfTGO/wBrkrTwia7s+Rzdt4lt9kZd raDRfGVjYWlzfPa3en3U0yXd7Nc5eOS3CEGVmK4Er/dxnIznAx1FY+n6CbTURf3eq32p3SRNDC92 IV8pHKlwBFGgO4on3s42jGMnOxXrHlhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXn8s2ux6B4z tUub7UtSOoR2kL2oCPD51vbAtCuQEWMys4BYfd+Z8lnoA9ArzvVoydUuun+uc/8AjxroPC5tIri9 tEtdZs7xEiklttUv3umEbFwjqTLKoBKyDAIPy8jG0nD1X/kKXP8A11b/ANCNfMcTu1Kn6v8AI9XK napL0KEUZD9RzXRWaEW7HjpisKP74roLT/j2b618RUb5o+p2Zi26TC1UgMPWs6/Hzt75rUt/vH61 mX/3z9DW2Mk5YtyZ5eRpRpKKMiU8gVXkQs2RjpU8v3h9KZVR0PqYuyKssLYByKiELEgZFW5vuD61 Ev3x9a2UnY2jJ2LEEDM5wR0q2LCUgHcn5mmW3361F+4PpXJUqST0OKrVknoZE1jLn7yce5qEWcjH AK/nWrN1b6VXj++KqNSVio1ZcoQWMpK/MnHHU1rw2UkKKWZD9Cagtuv41rN/qxWDk51FF7Hl4/E1 IQbRVNpJKcqV/E1SazkU4LL+dbEPSqsn+sNZ1HyVHFbGOExE5wuxsFpJjOV6etX0+UYNMg+5+Ap9 YfHuRWqSbGzMAuPTms6ZxyOeeavz9/8AdrNl+8PpTpI6KKIPOX0NOVw+cZ4qvUsP8VdLSSOxxSRr 23X8amZwD3qG26/jT361lRgpVLM8bGScdUQ3sZeJmGMe9c7dxMSeR3rprj/j1aueuu/41pQ0bSPT wE24pmX5LeorRsxhh7mqlW7T7y/57111HeJ6VVtxOjH722Xb2HeqFzCxUjI5q/b/APHt+FVrjrXf kGInTqWj0Z+eZ/h4STbM/RLOSws1hlKlgScqeOTmt1JAB0NZ8VW1/pXZnzeKryqVd2cmUN0aaUCO eB2UsCMZrLeMp1I644rdk/1BrGuO/wDvV8tTdm0fcYWo5RVxkRwSPWtGCQHPB5OKzY/9YKvQdv8A ep1EbVkaDKZBkfrRjy0Oe/HFPj+4KSX7o+tYe2k4+z6HlKjBVObqZsyFnOMdTUflN6ip2++frSVo noegpNIz54G+bkcHNUHgYHORzWvN1b6VQl7V005M7KU2RJZSucBk/EmrQ0ucQ53x/mf8Klt/vfiK 0v8Alh/n1qKlaSZnVrzTshnhe0kt9VlLlT+5I4PutddXPaH/AMhKT/rkf5iuhr9D4bk5YFN92eLj 5OVa77IK8rvVLeMfE5A/5fov/SS3r1SvM5kDeLvFBOf+P+L/ANJLeq4hdsE/VFZa7V7+TIPLb0o8 tvSrvlL6mjyl9TXwPtD6L2pRslK+MfDBI/5fpf8A0kuK9UrzOFAvi7wuRn/j/l/9JLivTK++4ed8 EvVnzuZO9e/kgooor3DgCiiigAooooAKKKKACiiigAooooAKKx9YvNUilSGxFjawtsDahfvuRXdt qIkSkGRicLyyY3rgucqKZn8TafcIs11o2qOyM/2KCBrOdkUjc8ZeWQORuUbTsGXXLr3AOkrLOhwg aoYrq7gk1G4W5eWKQK0UixxopTjGMRKSrBgeQQVO2tSsvRdfsfEAvX09pHitLj7OZWXCyny0kDIf 4kKyLhujdRkEEgBpWijTbi4u5r+71C8uESN7m6EYby0LFEAjRFwC7nOM/MckgADk9V/5Clz/ANdW /wDQjXWaNr9jrz6mtg0jDTr17GZmXaDKiqWC9yAWxnjkHGRgnIv4YjezExoSXP8ACPU18pxVPlo0 /V/kejl0+SbMCP74roLT/j2b61Tiij258tc59K1IlURkBQBn0r4apVXMjsxs+eDiRW/3j9azL/75 +hrZRQDwAKpXSIXbKqeD2q61dVK3MkceWQ9jGz1Obl+8PpTK13ij+X92vUdqcsEW4fuk6/3RWvtk lse77dJbGFN9wfWol++PrXTS20G0fuY+v90VD9mg/wCeMf8A3yKqOIVtio4pW2M+2+/Wov3B9KuW dtAcnyY/u/3RTnjQNgIoH0qacfrEmlpY8nHZjGk7uJkzdW+lV4/viukSCFh80SH6qKf9ltxyIIv+ +BWM6qpvlZpSx0ZQvYyLbr+Nazf6sU0wxr92NB9FFUL2SRYztdh9DXfluAljcRGMXY8LOM1hRouT i9DSh6VVk/1hqtp8sjRKWkcnHc1oBVK5IBP0p5pls8LiZQlK5llOaQrUFNReo+D7n4Cn1VkYqDtJ H0NRxyOW5dvzrKllc5QdTmQ62ZwVTl5S1P3/AN2s2X7w+laafN97n61FcRoN3yL27V5zj7KXKz2c NXUlexiVLD/FVzy4/wC4v5VJDGm4/IvT0rR1FY7pVVYntuv409+tKwCxgqADjtWXdyyBxiRh9DXo 5Rl0sZVtGVj5fOcxjho3cbmjcf8AHq1c9dd/xretyXTDncPQ81BdQxbm/dJ0P8Irjqw+rVnB6ns5 Xi4zpqSRzVW7T7y/571e8mL/AJ5J/wB8ipYYoxnEaj8Kc6qa2PXnXTjsaFv/AMe34VWuOtXlAEQA AH0qtKAewrryiooVLnxucR9onYrxVbX+lVTxnHH0qIyOG4dvzr6GrhJYttxdjwKWKWGSTVzUk/1B rGuO/wDvVpQMWXDEke9SywxED92n/fIr5HEUnhqri3c+0y7GRqQTSMSP/WCr0Hb/AHqVI03j5F6+ lW40QOPlX8qwqVD0atVMnj+4KSX7o+tKeMY4pDz15rCMb6nA52ZQb75+tJV7Yn91fyppRM/dX8q3 jG+hbxSitjLm6t9KoS9q6MRRseY0P1Wla1tyVzBF1/uCq9qqbszpo4yNr2MW3+9+IrS/5Yf59ame GJc7YkH0UVRDN523cdvpniu3C4GWMi5xdranmY/N4UJxTi3dmlof/ISk/wCuR/mK6GsbSFUXLEKA dh5x7itmvuuHI8uBS82ZVqqqy5kFeeLHv8VeKTtB/wCJjH/6R29eh1wtqgbxP4qJz/yEo/8A0jtq niZ2wDfmjXCS5atx3kn+4KPJP9wVe8lfU0eSvqa/OPaHq+1MZo9nirwsdoH/ABMZP/SO4r0OuFuk C+J/CpGf+QlJ/wCkdzWlpfim8vm0e4uNMgg03WsfYJY7syTfNE0y+bGY1CfIjZ2u+GwOQdw/R+GX fAJ+bPKxcuarc6iiiivoDlCiiigAooooAKKKKACiiigAooooA5Px1eaO2kvp+s/a1tA8F5O66VPd QeVFMsjK7IhQAiMg5PAOSMda+gWunyeKobrTrjWY7a1sp4IbC80qaCCBZHhYiKSSNSADHwhZsA4U Kq4rQ8X2evato2o6RpdnprwX1lJbtcXV68TRs6suQiwuGABB+8M8jjrWhps+vS3DLqmm6bbQbCVe 11B52LZHBVoUAGM857DjngA1K5MWutLH40GkpHBqFzeqbGa6ykeTZ26eYDsbcFYNxgglSpxyR1lF AHH+BNOv9Kk120utLg0+1S7gW1SG5kmUotpBHhWdFLKAijdzlt4P3ctZvv8Aj9l/3j/M109c5ews 1zK4IxuP86+R4udqNL1f5HZg2lJ3M+L7p+taUf3D9apJA6jBIq9GPk+tfBTaudtdprQF61Tuvvt9 DV0DBqpcIWZiMdDRdOdzLD6bmc/8P+8Kev3h9aUwMxUAjrVhbOQYbcuPrW91sd0qkYrVkcv3R9ah q21u7jAK/jSfYJf7yfmf8KT9z4jKnXpyWjJrPof90Ukn3zUkMZgB3EHjHFVZrlEkwQ3NejlWHq1a kvZxueBnOIpQScpWL0XQVKelVYp1IHBqUzrjoa4sTha3tWuU2w+JpezXvCPWXf8A+ratBp1PY1Ru UMykLgZ9a+k4fhKhiIzqKyTPAzySrUJRp6uxHpv+pT6VqL92s+ziaGNVYgkelXRIAMYNVn79ti5S p6q5OS/usNGM9HYZL0NRR/eFLPMqqSQapWOpQ3ZcxrINjlDuA6j8a3wuCxEsJKai7IyxGJorEqLl qbMXamXH8X4UsTj3pZYy4YjH418fiouNXU+twU4uK1KdSQ/fP0o8lvUU+KJt55HSsW1Y9GTVid/9 WPpWReffH1rWkOEA9qy7lC7jGOtfV8LtQq3lofFcSJzp2jqXrT7opl199voadanCinTQNJuYEYx3 rxMzVsVJs97J5JUUmZ1Sxd6d9kk9V/Onpbuucla43JWPclONty8P9WKrSVZxhAKryKa7MtklLU+c zFN3sVm71A33qncYzWfcXsUE0aMrku2BgCvu8uozraU1c+Nx040/jdjXt+lWZOi1UtpAQOtWyN4G P1r4fOISjXd0fY5RUi6Ssykv3x9atx/fFRi1cMDletTKhVgTivKk09j25yRI1NpWNN3CrhF8pxTk ri009aQyAdjUT3KKeQ1ddDDVZytGNzlrYilCN5SJ06089V+tVY7pGPAap/NU4ODWWKwtaE/ejY1w 2Jozj7shJehrNH/HxV+WQY6Gs8H99mvoMkpyjSnddDw84nGVSFn1N7Sf+Phv9w/zFa9Y2kMDcN/u H+YrZr6nIE1g7Puz1INOKaCuBs9Ot5fG3iW/d7nzotQjRVW5kWIj7Hb9Yw2xjyeSCenoMd9XFWBx 4k8Vf9hKP/0jtqy4lu8A0nbVGtOXLK5rYX0H5UYX0H5UzcaNxr819gdHtzAn0i1tPGXhu9ikvDLN qc25Zb2aSMZtLknbGzFV56YAwOBxVnRdM1ZLfwnpVxpU9svh/b595JLEYbjZayQfugrl+WkDDeq/ KDnBwpffnPiTwr/2EpP/AEjua7Wv0rhrm+oLmd9Wc9SXNK4UUUV75AUUUUAFFFFABRRRQAUUUUAF FFFAGP4o1n+wNBl1DfBHiWGHzbg4jh82VI/MfkZVd+4jIyFxkZyM/wAN69/amoyQf8Jb4c1fbEX8 jTItsi8gbifPk+XnHQckc9jY166vbSWX/kKx2M0SAXemxRzvaurMWPlGNnO4FFyqydCSI8bmy9N1 S7S4YWVz4n1ud0KrBqWmpYwR5I/eNI1vEcA4BC72wxIRscAHaUUUUAFYd1/rJf8AfP8AOtysO6/1 kv8Avn+dfHcY/wACl/i/Q6MN8RVqeP7gqCp4/uCvgpbHdLYdVaT+P8as1Wk/j/GlDcUNysv3x9av D/ViqK/fH1q8P9WK2XxIeJ+AYv3qnHSoF+9U46VpjN0cOF2ZFL0NY11/rR9a2ZehrGuv9aPrX1XC n8T5HzXE38P5lyH7oqVulRQ/dFSt0rLFfx2aYf8AgoiPWmU89aZXXS2OWoPSn0xKfXPX+M3pfCVb v7jVi6B0uP8Aru/862rv7jVi6B0uP+u7/wA6+zy3/kUVvkfNY3/f4nURVZ/gb6VWiqz/AAN9K/K8 x/iH6Hl/wIgp8X3j9KZT4vvH6VwPY9V7CS9KoS/f/Gr8vSqEv3/xr6rJOp8lnBPB0FXP+WZ+lU4O gq5/yzP0rys3/is9PKv4aIaKKK8s9snfpVeTvVh+lV5O9duB3R5WN2Kstc/qX/H7af8AXT+ldBLX P6l/x+2n/XT+lfqXC3x/Jn55nv8AX3G/afdWtGPtWdafdWtGPtXwOf8A8eR9jkn8GJJSN0paRulf NR3PoJbDG6U2nN0ptd0NjjnuRt0qpN1q23Sqk3Wvosr/AIh4OY/wxIP6VdXpVKD+lXV6Us2/iMeV /wAMZJ0ql/y0/GrsnSqX/LT8a6sr+CXoc2ZfHE29G/4+W/3D/MVt1iaN/wAfLf7h/mK26+iyb/dv mz2sP/DQVw9mceJvFXP/ADEo/wD0jtq7iuEtjjxP4q/7CUf/AKR21RnkOfCNeaLqy5Y3NXd70bve otxo3Gvivq5y+3KV4c+JvCvP/MSk/wDSO5ruK4S5OfE/hX/sJSf+kdzXd19rkUOXCJebOqlLmjcK KKK9g0CiiigAooooAKKKKACiiigAooooAr31xLaWck8NlPeyLjEEBQO+SBwXZV468kdPXisuDxho E1xFavqUdpdzOEitb9GtJ5CTgbY5QrsCeAQCCQQOQak8UTxQaDL5qTv5ssNvGsF09sxkklSNMyIQ yLvZdxGTtzw3Q0/7P0W1uJfCUWi2lrp+p2U8zJaKIlkAKRyhlQLglZYsMCSfm+7tG4A6SsseI9Ia 31C4S+jkj0+4+y3JjBfbNhf3YAGWfLqu1cksdv3uK1K4/S77S/DI8YXV1JBYaZY6hGCQu1IkFlah VVR+ACgc8ACgDoNM1qy1fzRatOskWC8NzbSW8ig5w2yRVbacMA2MEqwByDirNzPLn++f51R8LahY a5qOoa1b6jY3F1PFDC9raXUc/wBmiQyNGJChI8wmSQnB29FG7aXa9N/x8S/75/nXx3GP+70/V/kb 4f4inU8f3BUFTx/cFfBS2O+Ww6oJB85qeoJPvmpjuKO4zavoPyp1wSq8HH0pKW5+6fpXbglfERuc uYNqgzC0u4mk1C/V5pGVZwFBYkAbFOBXRISQOa5nSP8AkJaj/wBfA/8AQErpY+1fUcY04QxKUUl7 sf8A0lHzfDU5So+876iv3qq8UZOSin6irT96gfrXz+AnKPwux7GNhGW6uRkAdAB9KikYgHk1K3Wo Zehr38J701c8TE6RdiDe277x/OrEfI5qqPvVaj6CvVx6UYKx5mCbc3cmUDHQUuB6Ug6UtfOSbue/ FKxFIqnqoP1FRQ28MRPlwxpkknaoGTUz9aavWvRhUmqXKm7HDOEXUu0WYxU/8DfSoEqf+BvpXy+N +I+jwWxBTo/vim06P74rjex6T2JHA9KgZEJ+6v5VO9RN1ruwk5JaM8vFRi3qhUVRjAA/CpV9KjXt Ui9ayxLbbuaYdJLQZKAMYAFR1LL2qKuWOx6EdiccoM+lRuB6CpR9wfSo2/pW2Hb5jjxCVitIo9BV VreF5AXhjYjkEqDirclQ/wAVfW4KpOMLxbWh8vi4Rc9UTQqoxgAfhVpO1Vou1WU7V4OYtuTue1gE lFWH0wnin1GeleZR3PRq7EZJz1ppJx1px601uletTS0PLm3qMYnHWqFyxGeTV5+lUbnvX02SxXtk fPZu37Ji2pJHU1oL0rPtelaCdKxz1JV2a5K37FDiAeopvlp/cX8qdRXhKclsz2nGL3Re0kAXDYH8 B/mK2KyNK/4+G/3D/MVr19xw+74P5s3irIK4KA48UeKef+YjH/6R21d7Xmolvl8c+JUjtrdrI38Z kma4YSK32O34CbCCOnO4dTxxz2ZlDnoNHLjZctFs293vRu96j3e1G72r536ueJ7cqznPijwtz/zE ZP8A0jua7meeG1t5bi4ljhgiQvJJIwVUUDJJJ4AA5zXm0c2pv428Npc2dpFaDUZvKliumd2/0S4x uQxqFyOThjg8c9a2fGgGufDTxDJqOkXdmbeyuZI7e7kjO5lhYq5EUjqwBOQGPDKGwCFNfRZdDkoJ eZ7eCd6VzpNT13R9E8r+1tVsbDzs+X9ruEi34xnG4jOMjp6itCufs/8Akoes/wDYKsP/AEbd0eBP +SeeGv8AsFWv/opa7zrOgooooAKKKKACiiigAooooAKKKKAMPxfqUOleGLqe4W0MEjxWshvADAiz SLEXkBIDIofcVyMgEZGcjD8Ef2PBqNzBpOr+DbjzIt8kGg2KQSHaQAzlZnyo3Ecjqw57HpPEM19b 6JNJpwk88PGGaKPzHjiLqJXRcHc6xl2VcNkgDa33Tz/g7Vdav7sRXZ1Ka3jS6WaXULA2rZW4P2Zl zHHuLwklsAgbE4Qk7gDtKKKKACsG4lVJ5sg8Oen1rerm73/XT/75/nXyHF6vQpLzf5HVhUnJ3IfO X0NWoz8uPSs+r0XRq+Cmkd1RWRJmq87hGJOfwqYdaq3nU/Q0KKUrGdF8z1Gfa4/Rvyp73CT8KGHb mqFTW/3/AMRXRB+ylzx3RvXw1OpBxkQWWnTWl1dzSMhWWQOoUnIG1Rzx7Vpfa448ZDfgKVvuH6VS l7V147Mq+Yz569r2S0VtlY4MBlWHwseWknb1Lf22NzgK/PtQGEhOO3rVGP74q5D/ABVyRqSpfCdV fCUpbkjQsOciq8yFQc4rQfpVK5+6a9bKsVUqVIqR85mWHhCm3EzllVpdoBzV+KMkDpWXF/x9GtiH oK+q4iXsFFQ6pHzeRP2zk59yRYmwORTvIb1FSL2p9fA1MbVT0PtqeEptFVrZz3Wmi3cHqtXD0pla 08yruNtDOeX0b3GJGR3FDyqikEH0qVaqXHf/AHq46lSVSXvHdh6MY7DGuUU4IanR3SF+jcVTl+8P pRF94/SnyKx6Hs42NLz1kOAD+NITk1BD98/SpqI1HDSJx1MPCT1JRGR6U8DBpaKxnVlLczjTjHYi lPIFQs4U4Oall+8PpVab74+lOCOmCuTrcpgLhs9KUsDVRfvj61Zq0+R3RNSjGW4rQM3Qio/skmc5 X86uUV0QzPEQVotfcedPLqE3d3KqoYzg449Kd9oRDghuKWT75qtJ/rDWc6062szsoYWnDSJdWVWx gHmkbgVFH/B+FSyd6zpL37E4hWjoQs4B703cGHFNk70L/SvoI0YqCkeA6snPlHFCR2qtNaSSdCv4 mrg6UU8Pjq2HlzUxV8HSrxtMqwWUkfVk/A1cETBc5FOXrTz9w/SvPx2Z4ivLmna/od2Cy6hRjywv YgpjSBTgg0+oJP8AWGuRYibPTjg6TZqaQ4a4fGfuH+YrZrE0T/Xv/un+Yrbr9F4ak5YFN92ctemq c+WIV54px4q8U8/8xGP/ANI7evQ685zjxX4p/wCwhF/6R29eziIc0LHi5vLlwzfmi9u96N3vUe72 o3e1cP1c+U9uQMc+KvC3P/MRk/8ASO4rv54Ibq3lt7iKOaCVCkkcihldSMEEHggjjFee5z4r8Lf9 hCX/ANI7ivRq7sPDlhY+ryiXNhk/NmfqehaPrflf2tpVjf8Ak58v7XbpLszjONwOM4HT0FaFFFbH qBRRRQAUUUUAFFFFABRRRQAUUUUAZ+tx+bo86fZb66zt/c2Fz9nmb5h92TemPU/MMgEc5wef8N6Z eQeIZLxbLXLKxNoYnh1fVTdZk3gq0SieUDjcGLYPCber51PGGmw6t4Tv7S5a0W3KLJN9rIWJo0YO ys2D5YKqR5gGUzuHKiuT8PeFPDetasdRbQfBv2X7Jt8jTWjvUmzIQkmTEgj2lJlyoO/JBP7sCgD0 iiisseI9Ia31C4S+jkj0+4+y3JjBfbNhf3YAGWfLqu1cksdv3uKANSubvf8AXT/75/nWppmtWWr+ aLVp1kiwXhubaS3kUHOG2SKrbThgGxglWAOQcclqk8o1K5USuB5rcBj/AHjXyfFcOejT9X+R34Cm 5zdi7V6Lo1c0k0pcZkf/AL6NbOnu7D5mY9epr4WrTaR6Fei4xvcvDrVW86n6GkgdjdMCxI9M1hap cTLIcTSD6Ma6a2ClSqqDfRP7zgymf1u7jpZ2L9TW/wB/8RXDXF7dqTi6mHPaQ1WfUb5VBW9uAc9p W/xrdYCUlufSrLZzXxHpzfcP0qlL2rzxdV1EsAb+6xn/AJ7N/jUp1C9PW8uD/wBtW/xpLLZx+0KO Uzg/iR3cf3xVyH+KvP4b67+U/ap+v/PQ1fhvbvn/AEqb/v4azqYKXcyq5fL+Y71+lUrn7prn4by6 bGbmY8d3NXoZZH+87Nx3OavBy+qTUpa2PBx+UTqxcVJEcX/H0a2IegqoqII921d2DzjmopJJFUbX Yc9jXs5tnlPMLKMGrKx4OU8NVcG3zVE7vszaXtT65iS5uAwxPIOP75qGW8uhjFzMP+BmvmpYdzd7 n1VPASStc609KZXLR3lyVObiY8/3zV+2nmbGZXPTqxpewcFqxVcFJdTcWqlx3/3qerNtHJ6etZV7 LIOkjD8ayhDmkRQpNuxLL94fSiL7x+lY0k8u8/vX/wC+jToppef3r/8AfRrr9i7bno/V3y7nQw/f P0qasOOaXYD5j5/3jVvzJP77fnWEqTTOWdFp7m1RVCGRyVy7Hj1q6etc/JrY46i5COX7w+lVpvvj 6VZm+5nvg1kTyPz87dfWtKcbm9GPMW1++PrVmsXzZP77fnS+fL/z1f8A76Nauk31N5UG+p0VFc49 zcBDieT/AL7NVpLy5C8XEw5/vmoWFb6kRwcn1Olk++arSf6w1zv2u5PW4l/77NIbmc9Z5P8Avs1t HCtdTeODkup1Uf8AB+FSyd65y2uJiy5mkPA/iNbdozOp3MW47nNZuHs5XZxYvDtReo2TvQv9Khu2 Ibgkc9qmtuQM8/WvpqlJ08Iqz6nxtOop4p0l0JR0opl0SpO0447VlTTSgtiVxx/eNeHCup62PpKe WzmviNxetPP3D9K5f7Vcf895f++zUH226/5+Zv8Av4aynQc+p6VLLpR+0dRUEn+sNc1LeXIUYuZh z/fNQG7uT1uJv++zTjhJb3OuOBlvc77RP9e/+6f5ituuI8GzzS6vKsksjr5BOGYkZ3LXb1+hcOQc MCk+7PEzCm6dZxYV5s5x4t8Uc/8AMQi/9JLevSa80lOPF3ij/r/i/wDSS3r6GEOZ2Pl+IJcuCb80 Wt3vRu96j3e1G72rX6ufB+3I0OfFvhfn/mIS/wDpJcV6TXmkRz4u8L/9f8v/AKSXFel1jOHK7H3n D8ubBJ+bCiiipPbCiiigAooooAKKKKACiiigAooooA5/xpNZ2/hp5765+ywx3do6zkApFILiMxs4 JX92H2l+QdobBBwaw/BkWmvq5ePxdo2tXcKXbxw6btUotzOs0pcebISA4QKRjAJB3Egjc13TPEE1 0LrQNZgtJH8iOaK8gaaPYku5igDDazKzhuDuAQAoRuq5psGvRXDNqmpabcwbCFS1094GDZHJZpnB GM8Y7jnjkA1K87uoIbOy8SpBFHBaWfiPTHKooVIIYksGY8cKiIpJPQBSeAK9EooA5fQ7+z1fxlrG o6bdwXtidPsoBc20gkjMiyXLMm5cjcA6EjqAy+orH1X/AJClz/11b/0I16BXGX+mTTahcOrRgNIx 5J9T7V8vxQ7Uqfq/yPRy6rCnNuTsZEf3xW1pvQfjVVNHuAwO+L8z/hWjaWkluPnKnr0NfCVpxa3P RxFaE4NRZHb/APH41YGq/wCtauhVTDO0jcjOOKwtRhaVyykY969LFVqdTERlB3XKkebw9SnRjJVF bV/mcpc9T9aqS/dH1rVubCUk/MnX1P8AhVOWxlwBuT16muunONtz7mlUjZalFfvD61YpwsJQQdyf masrp8rkYZOfc/4VpKpHuaTqQ7jIei/WtCLvSQaTOQvzx8c9T/hWpDoV0f8AlpDz7n/CuOrWgupw 1q9NbsZB2/3a07ft/u02LRrhMZeLpjgn/CrkVhKnVk6Y4Jrz6lSD2Z5VWtB7Mev+p/A1Vm+4PrV9 LZyuzK5pkmmzMuA0fX1P+Fc8ZxT1ZzRqRT1Zjy/eH0qCXtWu+j3Dtw8X5n/Co30K6bGJIfzP+FdE a0FuzqhiKX8xmxfdP1rRte34UqaHcqpy8P5n/CrEdjLD95kOMdCaJSVTSGplXxNFK7kXl+4PpWRf f5/StITouFIOaqz2clx9xlH1NCwlak+apGyPOwuPw0p2U0YUn3zT4u9aDaHcs2RJD+Z/woXR7iPO Xi/An/CtVJS0R60sZQUdZIij+4KuVF9leNQCV/CpgM1o8BiZLmUHY8ypm2CUre0RZh6r9K0G61mx yBCCc8Cp21GHP3X/ACH+NYrK8ZOXu02edic0wf8Az8RPN/q/wNY1x3/3q0Wu45VwoboeoqpJbvIO CvXPNSsDiKUrTg0dOEzHCtaTRSoqz9ikH8SfmaYbZx3Wto4arL4YnbLMsJHeoivJ9w1Vl+6PrWl9 jkkUgMv4mmto9w4ADxfiT/hQ6U6XxqxtRzDCz+GaMmitQaDdH/lpD+Z/wpf7Auv+ekP/AH0f8Kj2 1Pa52fWaP8xXtvvr9BW7Y/dP0/rVCLSZ4iCzx8ehP+FaNupgBDc59KwqRdT4dTzcZiaTi/eK1597 8antegqKeMynK4/GpYPkxn9K+lxM4ywEaaeq6Hw2Hi1jZTewt51P0NY83VvpW1Ohm5XA471Sk0yZ ycNHz7n/AAr5emnDSWh9zhq9O25kVWrb/sW5/vxfmf8ACoP7Buv+ekP5n/CuiNWD6nfHE0lvIx5v uD61DW4/h+7cYEkP4sf8Kibw7dqcGSD/AL6P+FaqvTWlzeGKotfEWfBwmOuZikjVBExlDIWLLxwp yNp3bTk54BGOcjv65Hwtpc9lqckkjRlTCV+Un+8vt7V11fecPyUsEmu7Pnc0nGeIbi+iCvMbg48X +KP+v+L/ANJLevTq8kv7Oybx/wCI717OB7yO9iVLhowZFH2SDgNjIHJ/M19Ngo81Wx8bxP8A8i9+ qNHd70bveoPO/wBn9aPO/wBn9a9j2KPzPmmPtznxf4X/AOv+X/0kuK77U01iXyk0mextupkmu4Xm x0wojVk65J3FuNoG07sr5jpmnadb+PPDl3b6faw3Ut/N5k0cSq75tbgnLAZOTzXbano2rWenRWHh 0+ZBLKXvHv8AV7gTbMAbIpWWVk3Y5IxtGduGbevjYyPLVsfpvDNvqCt3f9bsp6Zr+va/dz2NlNpt lPYoRczTWjzrOwuLiDKKJUMYzbFsFn/1gGfly3SaFqf9t+HtM1byfJ+3WkVz5W7ds3oG25wM4zjO BWHb6br2nXo1Cw0rRleayhs3shfPFFbLC8pTy3EB3ArLjGxNu3jdnjc0LTP7E8PaZpPned9htIrb zdu3fsQLuxk4zjOMmuU+gNCiiigAooooAKKKKACiiigAooooAKKKKACsfxRfXGnaDLcW0nksJYUk n2g+RE0qLLLzkDYjO+WBUbcsCARWxVe+huJ7OSO0uvstxwY5TGHAIIOGU9VOMEAg4JwVOCADm/Cm tC/1nVNPg1qTWrO3t7eeK+kSP5mkaZWVXiREdB5S8qDhi4JJGFvT/wDH1L/vn+dWtK0qa0uLi/v7 mO61K5RI5ZYojFGI0LFERCzEAF3JJZiS55xtVas//H1L/vn+dfKcV/wIer/IqO40dafTB1p9fntT c7YbFO47/wC9WPc9PwrYuO/+9WPc9PwrooHp4YyLjv8A71UpfvD6VduO/wDvVSl+8PpXqUz2qWwy rUPVfpVWrUPVfpVz2Lnsadt0/Ctu3/h/GsS26fhW3b/w/jXmVzx8UWqKKK5DgHR/fFT1BH98VPWc tyJbiDqaWkHU0tEtzOOwHpVeXoasHpVeXoa68D/ERy4z+GZ7f62rsXQVSb/W1di6Cvqs0/gx9D5v Lf4svUsDpUUlSjpUUlfNYf8AiH0Ff4CjN1qNf6VJN1qNf6V93h/4CPjK/wDGY49Kgb71TnpUDfer swfxM5cVsiWKrQ6VViq0OleTmf8AEPTy/wCAcetQPU561A9cmD+I6cVsx0VXEqnFVxK4c0+JnZlv womWlPSkWlPSvmJfEfRR+EjbpUL9ambpUL9a9TCbnnYnYipV60lKvWvXlseXHcmTpUo61EnSpR1r wsT8R7OH2H0yn0yuOn1OuoKvWo5fvD6VIvWo5fvD6US+IukW9K/4+m/3D/MVsVj6V/x9N/uH+YrY r9L4X/5F69WcmJ/iBXkeqSbfGviYbiP9Ni/9JLevXK8b1uTZ448TDGf9Mi/9JIK+yyxXxFvJnzXE kb4FrzRJ5w/vmjzh/fNUvO/2f1o87/Z/WvpPZn557I0dLk3eNfDI3E/6bL/6SXFeuV43okm/xx4Z GMf6ZL/6ST17JXzeZq2Ia8kfofDcbYFLzYUUUV557wUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFczcM322f5j/rG7+9dNXMXH/H7P8A9dG/nXhZ8k6Mb9zlxLa5bDkJ9TUrE+WTmoUqZv8AVmvz rFpKR6GDbe5mXTtz8x796yZ2bnk9PWtW6/i/Gsmfv/u06J9Fh1oZ8x6VVl7Vam/hqtL2r0YHqUxI gDnIBq7bKCRwOtU4u9Xbbr+NKpsKrszc0+OMkZRTx3FX7sCNTsAXHpxVLTuo+n9KvX33D9Kyy5KW NinqfHZ9OUaEmmVLSR2+87H6mtFOQKzLKtNOgr1eIacI4iSikj53I6k5UIuTbHEYxjioJXcDhmH4 1Yaq0vT8K8nARi5q6PUxspKLsyFZZN3+sb86tRsx6sfzqkv3quR162Z04KOiR5eX1JuWrJwelOKg 9QD+FNHan18pUbT0PpoK61GeVHnPlp/3yKTaoPAA/CpKYetVCpOWjbZMqcI7IQ9KhkJ9ambpUEle hg17yOHF/CypLUAJz1qeSq46191g1+6PjMV/EJV5p+1f7o/KmL/WpK56ralodFJJx1FVQOgFGTnq aVaaetcu8nc6Noqwjs394/nULM2ep/OpHqI9a9LCxjbY8/Eyd9x8bHPU/nVpGb+8fzqpH1qylcWY xjfY7MDKVtycO2PvH86N7f3j+dIOlFfPuEb7HuKcrbjWdufmP51Xkd/7zfnU7d6rS969fL4R5tjy sbOXLuVzLJu++351PE7Hqx/Oqx+/+NWIq+hxtOCo6JHh4OpN1dWXEY+pp+5v7x/Oo0p9fGVox59j 6yjJ8u4ju4/ib86o3M0qqcSuPoxq49ULr7h+lezklGnKtFOK37HlZvVqRpStJ/eJaXEzfemkP1Y1 poxYckn61kWVa0XStOJqNOFeXLFL5GfD1apKiuaTfzNTS/8Aj5b/AHD/ADFa1ZOl/wDHy3+4f5it au3hz/cV6s+sbuFeJeI32+PPEo3Y/wBLh7/9OsFe214T4rfb8QPEgxn/AEqH/wBJYK+yydXxSXkz xc9jzYNrzRF5n+3+tHmf7f61Q83/AGf1o83/AGf1r672Z8T7I3fDj7vHnhobs/6XN3/6dZ69trwn wo+74geGxjH+lTf+ks9ezanrVlpHlC6adpJclIba2kuJGAxltkas20ZUFsYBZQTkjPyOcK2Ka8kf bZFG2DS82aFFYcni7R0SFkku7gyJv8u0sZ7h4xuK/vEjQtGdyuuHAOUcdVYDYgnhureK4t5Y5oJU DxyRsGV1IyCCOCCOc15Z7JJRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRVe+t5buzkghvZ7KRsYn gCF0wQeA6svPTkHr680AWK5a5kX7dOM/8tG/nUvhA6hPb317davd6jZz3BGntcxwqfJQbd/7qNQw dg7KeQYzGRgk1Tuv+Qhc/wDXVv515maUY1aSUuh5mZVZU1C3csowPepmP7s+9VYugqy/+rH1r82z GmoTsj1Mum5rUyruVBnn17VkzzR8/N2x0q9edD9TWNcd/wDeooQVj6zDQViGaePj5unXiq0k0bYw 36U2bo31qtXoQgrHqwpqxbjmjXOW/SrVvdwKQC/f0NZVPj/1gpyppoc6SaOtsNStEI3S47fdP+FX 7i+tp1Ijk3Z6fKRXIwdv96tW3/h/GuWC+r1VWhuu54WY5ZRxEHCbeva3+RqWjBBljitFZUAGW/Ss uP8A1Yq3Sx+Y1cXUc6iSb7f8OeJhsloYOKhTba87f5Iu7g2MHrUUsTkcDt606P8Ag/Cp2/pXFQxM 6U1ypE4nCwnFptmesEu77v6irKqYxlhipF60k/3PwNdeLzGrVtGSX4/5nJg8upQd03/XyG/aYVIB f9DSHULUHHm/+On/AArPl+8PpVNvvn61w+yU9We/TwsLbs3P7Qtf+ev/AI6f8Kab+2z/AK3/AMdP +FYlFVGjFFvBwfVm39rgZch/0NNaRGGVOfwrOj/1YqzH9wVcKjpO8TkrYClJWbf9fIe0EjnCrn8R UX2OcH/V/qK0YvvH6VIetelR4gxMPcUY/c/8zwa+Q4aT5nKX3r/IzRbyjqv6ineU/p+tWz0ph6V0 rNK1R3aX4/5nK8upU1ZN/wBfIrhGHagxtnpU1FafW53vZGf1aFrXZWaJz0X9ajMEufu/qKu0V0U8 zrQ0SX4/5mM8upT1bf8AXyKiQyA8r+tTqpHUVJRWNbG1K3xJGlLBwpfC2A6UUUHpXJuzq2Qw85qv IpOcCrFMPSvSw1R03dHn4iCmrMpGGQtwv61PFG46j9akHWpF/rXfiswqyhytL+vmcWGwNOMuZN/1 8hyKfSn7T6Uq08dK+Zr15XufRUaMbWImjY9BVO4tZnUhUz+IrSorTCZtWwslOCWne/8AmZYnLKWI i4zb17W/yMq2s7iP78ePxFaKRuByKkp9LMs6xGMnz1Ek32v/AJseX5TQwkeWm27d7f5FvTARcsT/ AHD/ADFatZunf68/7n9RWlX1nDEnLAJvuz0akVGVkFfPnjUzj4l+ISs0awfaIdyFDuJ+yw8ht2B2 7GvoOvnjx2+34j+IhjP7+H/0mhr7nI482MS8meVmyvhmvNGf5n+3+tHmf7f61Q83/Z/Wjzf9n9a+ 39mfKeyOh8Efaj8SvD5luIXh+0z7UWMhh/o02Mncc8ewr2G/1hrTS/L8RajaeG553Ihure6WRAF2 nHmTwqgc5I2FSSAxHQ7fHPAj7viP4dGMfv5v/SaavoeviM8hyYtryR9XlP8Auy9WeZ+FNW03Q9Vv rrVtQtLG2vLcfZbq7mWJb3be3zGVWYhWLLJHIdny/vVKhVZRXYeC4JrXwL4et7iKSGeLTLZJI5FK sjCJQQQeQQeMVuUV456QUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVT1azm1HRr6yt7uSznuLeSKO 5jzuhZlIDjBByCc9R06irlFAEcEENrbxW9vFHDBEgSOONQqooGAABwABxiuTuv8AkIXP/XVv512F cbck/wBo3P8A12b+ZrjxyvSuePm7sqa8yeLoKsv/AKsfWq8ParD/AOrH1r8yzR/vD18r+Ewrzofq axrjv/vVt3gHP41j3KgA/nU0HofYYZ6Iy5ujfWq1XJVGcY4NVGGGI969GD0PWg9BKfH/AKwVXd2D kA0glcHIPP0rTlbRryNo14O3+9Wrb/w/jXLpdzqww/f0FXF1C6XG2XGP9kf4VzVaEmcdbDylsdbH /qxVuuOh1W9II87gf7K/4VYXV74sB5/f+4v+FccsJO+6PPngaje6Ozj/AIPwqdv6Vxo1i/GMT9P9 hf8ACpY9a1B2Ia4zx/cX/CsFhJp3ujjq5dVktGv6+R1S9aSf7n4Gua/ta+H/AC3/APHF/wAKRtVv WGDNn/gC/wCFOeHlJ3RlRy6rDdr8f8jTl+8PpVNvvn61UN7cMcmT/wAdFMNxKTnd+grSNKSO+FCS LtFUvtEv979BSpPIXALfpVcjL9kzUj/1YqzH9wVkieQDAbj6CrCXEoQfN29BWMqbOedJm7F94/Sp D1rMjuZd4+f9BVyKRn+8c81y8ri7s86rSkOPSmHpUzgAGq0jEDg16mEXtWlE8TEv2abYtFU5J5FP DfpTVuJSeW/QV9BHKK8oc6at8/8AI8SWaUVLls/w/wAy9RVdZXI5P6U8O2Otcs8HOG7R0wxUJbJk tFRF2x1phkcDr+lKOEnLZoJYqEd0yxQelVTK+PvfpS+a+PvfpW39n1d7r+vkZfX6e1n/AF8yamHp SBiT1p4UHrRb2XxBf2mxGOtSL/WpBGmM4/WpBGgPT9a5sRjqb0szooYOfdDVp46U4IuelP2L6V4t bFQb2Z61LDTRHRRJ8rYHpTcmsvbROj6tMdT6iyaXe3rUTqJlwoSRpad/rz/uf1FaVZOlsTcsCf4D /MVrV+icLf8AIvXqzCurTsFfN/xDfb8S/EI3Y/fQ9/8Ap2hr6Qr5m+Jb7fid4gGM/vYP/SaGv0Dh xXxyXkzy8wjzUbGJ5n+3+tHmf7f61R83/Z/Wjzf9n9a/QfZnheyOy+Hj7viX4eG7P76bv/07TV9I V8zfDR93xO8PjGP3s/8A6TTV9M1+fcRq2Oa8ke7l8eWjYKKKK8I7gooooAKKKKACiiigAooooAKK KKACiiigAooooAK424/5CNz/ANdn/ma7KuQuIn/tC5O3gysevua4sfOMaL5nY8fNoSl7PlV9SWHt Vh/9WPrUESkYyKsOD5f41+Y5lJOpoz2csTUVcw7zqfoayLnofpW1dxOxOF/Wsq4tpjwE5x6ipoNa H1mHktNTJl+8PpVNvvn61pyWk5bhP1FU2s5yxPl9/UV6MJR7nqU5x7lCT75ptWpLK4Dn93+opn2O f/nn+oroU423OpTjbchX7w+tWKRbScMDs7+oqb7PL/d/UVMpLuTKUe4Q/wAVTL94fWmxQyLnK/rU ywSZB28fUVjJowlJX3JKkh++fpSeW/p+tPhifcTjt61i2rHPJqxJRTvLb0pwhkYZC/rWd0ZXRHRU v2eX+7+op32Of/nn+opcy7i549yCnR/fFTCxuWGRH/48KelhdBx+6/8AHh/jSc49xOpC26G1YX7o +lO/s+6/55f+PD/GpVsrgKB5fb+8KxlOPc55VIdyaP74q9b9v96q6Wk4YHZx9RVuKN0xuGOfWuSb T2OGrKNtyWTvVWWrT8iq0qMegr08tkotXdj53ME5J2KEvU0xOtTyQSHOF/WmLDIp5X9a+8pYij7C 3Or+qPi6lCr7W/K/uJE6VIOlMUEdacGAHWvKrJyfu6npUmorUU9KY3SlMi461G0ietVQpVP5X9xN arDugPSlHSmGRcdaUOvrXb7OfLszk9pC+5MvWpV7VAsi+tSq6+tePiac+zPUw9SHcnHSpB1qESLj rUgkXPWvBr0p32Z7VGpDuiUdafUQkXPWn+YvrXl1KU7/AAv7j0oVIW3RHL94fSmU+QhmyPSmYJpK nNLVHRGrC26CilwaTGKfJLsP2kO6L2lf8fTf7h/mK2Kx9K/4+m/3D/MVsV+k8MJrL0n3ZwV5KU7p hXy78U32/FLxAN2P3kHf/p3ir6ir5T+Lcyp8WNeQkbi0BAzz/wAe8VfoPDLX9oK76M4cTHmp2Ob8 z/b/AFo8z/b/AFqj5v8As/rR5v8As/rX6Z7M832R3nwsfd8UvD43Z/eT9/8Ap3lr6ir5T+Ek6SfF fQUBXcHnyAef+PeWvpjX9Sm03S2NmscmoXDi3so5ASrTPwpYA5KLy745CI57V+Z8TOLx75XfRHpY aPLTsalFcH4fv9Y8T2+mxSa3d2LroWn30stpFBunlnEu8t5kbgAeUCAoX7zZzxjqPDWpTaz4V0jV LhY1nvbKG4kWMEKGdAxAyScZPqa+fOg1KKKKACiiigAooooAKKKKACiiigAooooAKw/GGqTaP4Tv 7u1eRLsosFs6RGUpNKwjjbYASwDupIAYkA4DHg7lV76G4ns5I7S6+y3HBjlMYcAgg4ZT1U4wQCDg nBU4IAMfwxcpcfagNb1XUJF2FodTs1tZIgc4YIIYm2tgjcQQShAOQ1Q3H/H7P/10b+dXtJ0m8tdR u9S1K9gur65iityba2MEaxxmRl+Vnc7syvk7sY28DBJo3H/H7P8A9dG/nXhZ9/Bj6nLifsjkqR/9 WajSpH/1Zr84xfxHpYTYzpujfWqM33x9KvTdG+tUZvvj6UUz3KJTb7x+tVatN94/WqtdkTvgQzff H0qOpJvvj6VHW62OhbBRRRTGFWV+4PpVarK/cH0qZEzFqWLvUVSxd6zexlLYkqaL7p+tQ1NF90/W spbGEth9Wqq1arGRjMmi+6frUi/fH1qOL7p+tSL98fWsWc0tyzRRRWRgWF+6PpQ3Shfuj6UN0rOP xGE9mMbpUbdakbpUbda9GgcFYiPSo261IelRt1r3KB49cjPWmHpTz1ph6V7NE8mqRt0qNutSN0qN utevQPLrCUo60lKOtdMtmYLckXpUy1CvSplryMUephyQdqkHWox2qQda8GuezRHDrT6YOtPrzam5 6ENgpy9KbTl6VhU2Nobi0jUtI1ZQ+I0nsXNK/wCPpv8AcP8AMVsVj6V/x9N/uH+YrYr9C4f/ANyX qyY7BXyj8Xj/AMXX17/eg/8ASeKvq6vk/wCL/wDyVjXv96D/ANJ4q+64aly49PyZFb4Ti8+9Gfem ZozX6L7Y5LHc/CE/8XX0H/en/wDSeWvo6bwT4dkitoodP+wx20rTxJps0lmFkZdhfELLltvy5POC R0Jr5v8AhB/yVjQf96f/ANJ5a+sK/OuJZc2Pb8kddH4Tj9K8HX/h2zsotF1iBZotPgsbh7+1kuRI Ii5VkAmUpzLJwSwA2hdoXnpNJ02HRtGsdLt2kaCyt47eNpCCxVFCgnAAzgegq5RXgGoUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUVn65cajaaHez6RZfbdSWJvs0BKgPIeF3FmUbQcE8g4BxzgUASWG q2OpveJY3Mc5srg2txs5CShVYpnoSAwzjocg8ggZVwi/apjjne386yPh1EbSTxHZf2dfWiw6hES1 68LSSubSDczmN2DSMRvZu5kznO4DZuP+PmX/AH2/nXy/FEnGjCz6/oJxTeqGqoB6U6T/AFZoHWiT 7hr87qSblqz0KSS2KrRI2cjr71VeCMucr09zV2qzffP1qotnbCTK5tICc7P1NN/s+1/55f8Ajx/x qzRWnPLua+0n3Kh020Y5MP8A48f8aT+y7P8A54/+PH/GrlFP2k+7H7Wp/M/vIl0ewKg+R2/vt/jU yaJpxQE2/wD4+3+NWF+4PpViP7grKVWp/M/vMJV6v8z+8zjomnDP+j/+Pt/jTTpVkowIen+0f8a0 m71C/eu3DScnq7nm4jFV1tN/ezPOm2mf9V/48f8AGkFjbL0j/wDHjVw9ajr26VODWqR41XHYq/8A El97IRZW5H+r/wDHjThaQKMBP1NTL0pamVKF9kCxmJa/iS+9kP2WH+5+ppTEmen61LTT1pwo029Y r7iZ4vEW/iP72Qn5R8vFMMrgjB/Snv0qFute1hsHhpLWnH7kePiMfik9Ksvvf+ZYWeQ9W/Spldj1 NVkqwlefjcLQj8MEvkjswmLxEt5t/NlhZGwBmpAxPWoV/rUq9a+bxFOEXoj36M5PdjutLsU9qSn1 wzlKNrM7Ixi90M8pP7v60wwx5+7+tTUw9aulXq3+J/eyKlGl/KvuIjBHj7v6mmG3ix939TU56Uw9 K7qOKr/zv72cdXD0f5F9yK7QRf3f1NRNBH/d/WrLdaiavaw2Jrfzv72eRiMPR/lX3IqtGo6CoW46 VYeoHr6jBzlL4nc+dxcYx+FWIjI4PB/SnrNJ/e/Soj1py9q9ipQpOGsV9x5UK1RS0k/vLCzSZ+9+ lO8+T+9+lQr1p1eTUw9G/wAC+5Hpwr1bfE/vJftEv979BQbmbH3/ANBUVIelQsJh21emvuRbxVdL Sb+9jzdzgff/AEFJ9suB/wAtP0FRN0ptdkcBhWtaUf8AwFf5HJPG4lPSpL72WBeXBP8ArP0FPF1M cZf9BVVetSr2rnr4HCx2pR+5f5G9HGYl71Jfezb0KV3vXDHI8s9vcV0Fc5oH/H+//XI/zFdHXVgo RhStFWR9dls5ToXk7u7Cvk74wnHxY17/AHoP/SeKvrGvkz4xHHxZ17/eg/8ASeKvpMily4xPyZ2V fhOJz70Z96ZmjNfb+3Oax3fweOfixoP+9P8A+k8tfWNfJnwdOfizoP8AvT/+k8tfWdfEZ7Lmxjfk jppfCFFFFeMaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVxl7r1tDf3MTWWssySspaPR7t1 OCeQyxkMPcEg1bj8Y+dp2qXS6ZPE1rqENhBFcN5bTNMIfLdxgmNSZ1yCCyqMld2UEcnivUre/XR7 jSbQa1K8Qgijvma3ZXSdwWlMQZTi2m4Ebc7OfmJXgx+XUsdFRqtq3a36pgVB4ltM/wDHjrn/AII7 z/41Q/iW0KECx1z/AMEd5/8AGq6PRdVm1IXsN3bR295Y3H2e4SKUyx7jGkgKOVUsNsiZyo5yOQAT qV4r4RwTd+eX3r/5E2jXkjg/+Eitf+fDXP8AwR3n/wAaqBtegLE/2frnX/oCXn/xqu7vpriCzkkt LX7VccCOIyBASSBlmPRRnJIBOAcBjgHDj13WnuLnTTotodWhSGbZHfk24ikMgDNKYg4OYXGFjbkp zgsUa4SwS+1L71/8iaLFzXRHP/27B/0D9c/8El5/8ao/t2D/AKB+uf8AgkvP/jVdhouqzakL2G7t o7e8sbj7PcJFKZY9xjSQFHKqWG2RM5Uc5HIAJ1Kf+qeC/ml96/8AkR/XanZHnf8AbsH/AED9c/8A BJef/GqP7dg/6B+uf+CS8/8AjVegTyNDbyypDJO6IWWKMqGcgfdG4gZPTkgepFY9hrGpf2zHper6 daWs89vJcQNaXjXClY2jVw26OMqcypjAOfmzjAyf6p4L+aX3r/5EPrtTsjnl8Q2wUD7BrnT/AKAd 5/8AGqmTxLaBADY65/4I7z/41V/TPFlxr9rqUujaV5zWuoGzia4uBFHKgiSTzywDERsH+QqH3Aoe AxK6mi6rNqQvYbu2jt7yxuPs9wkUplj3GNJAUcqpYbZEzlRzkcgAmXwjgX9uX3r/AORIeJm+iObP iS0Of9B1z/wR3n/xqo28RWpziw1z/wAEd5/8arvKp6rqUOkaXcX86yOkKZEcQBeVuiogJG52YhVH ckDvW1PhjB09pS+9f5GE3z7nFnxBb5/5B+uf+CS8/wDjVM/t6D/oH65/4JLz/wCNVr2HivUtat7F tI0m0eebTLbUZ0u75oVjWcNsVWWJy5BjfOQv8OM5OLGq+INX02yuNTbQo00yzt/tF0bi9C3AVU3u EjRXRiBleZFyykcLhj1xyXDx2b/D/I5JYSEt2zBGv24H/IP1z/wSXn/xql/t+3/6B+uf+CS8/wDj Veg0UPJMO3e7/D/IawsF1Z59/b9v/wBA/XP/AASXn/xqmnXrfP8AyD9c/wDBJef/ABqteHxdff2C fEd1o0a6G1k19HJb3nmXAiEZlUvEyIqkqMYV3wxA5XLCwNd1qC9Gn3mi2n2+e3ee0S0vzIjBHjR/ MZ4kKAGZD8ockB8DICsRyXDrq/w/yE8JB9Wc42uQkcadrn/gkvP/AI1UZ1mMn/kHa5/4JLz/AONV 2ek6teXWo3em6lZQWt9bRRXBFtcmeNo5DIq/MyId2YnyNuMbeTkgbFdlPBU6ezZyzymjPdv8P8jz ddbhHXTtc/8ABJef/GqmXX7cddP1z/wSXn/xquu1rVZtNFlDaW0dxeX1x9nt0llMUe4RvIS7hWKj bG+MKecDgEkY8fivUri/bR7fSbQ61E8onikvmW3VUSByVlERZji5h4Ma87+flBbGrlVGr8Tf4f5G 1LL6VPZv+vkZy+IbYf8ALhrn/gjvP/jVSDxJaA/8eOuf+CO8/wDjVatvrutanapLpei2jlHlhuGu 78xIJY5XidYysTs4DRscsqcMhxksF2NJ1KHWdGsdUt1kWC9t47iNZAAwV1DAHBIzg+prz58M4Se8 pfev8jtguTY5P/hJLT/nx1z/AMEd5/8AGqd/wk1n/wA+Ouf+CO8/+NV119dfYrOS4FvPcMuAsMCb ndiQAB2GSRySFHUkAEjn7rxj9l+HkHioaZPO02nreraQtnbmLzSGkIAVQAcsR2wAWKqcJcI4KW8p fev/AJE3VaSKP/CTWf8Az465/wCCO8/+NU0+JLTP/Hjrn/gjvP8A41XSalda1HcLFpWlWlygQM8t 3emBcknCrtjkJIxk5CjlcFuduPN4uvv7BHiO10aNtDWyW+kkuLzy7gxGMSsUiVHViFOMM6ZYEcLh iR4RwS+3L71/8iDrSZTPiS0x/wAeOuf+CO8/+NUw+I7XH/Hhrn/gjvP/AI1Xd1j/ANvZ8Wx6ELKc K1pLcG7cbULI0QKKDy3EyksPlHQEkMF1jwtg47Sl96/yM5SctzmT4htif+PDXP8AwSXn/wAaqM6/ bn/mH65/4JLz/wCNV0UWuapFqdhBqmkQWlvqErQ2xjvPNmVxG8oEqbAq/JG+drvhsAZB3CODxW1/ rOuaZpumyXMumJAEkaVUjneRpFbDc4SNoyGYZIKuApK4bqp5FhobSl96/wAjmnh4z3OcbXIT007X P/BJef8AxqoW1iM9NN1z/wAEt3/8artdJ1a8utRu9N1Kygtb62iiuCLa5M8bRyGRV+ZkQ7sxPkbc Y28nJA1J5ltreWdxIUjQuwjjZ2IAzwqglj7AEntXoUsJCl8LZwVcnoVN2/w/yPMTqq5/5Bmuf+CW 7/8AjVKNWUY/4luuf+CW7/8AjVdSviy4n8NR6jBpWL+fUHsIdPnuArF1uGhbLIH+6qPI20NhUbkg bqkk1vXori205tH03+1LhJp0jGpP5AhjMasTJ5G7fulXC7MYBO4dD2OTascq4cwqd+aX3r/I5Uax GD/yDdc/8Et3/wDGqd/bMf8A0Ddc/wDBJef/ABquwk1DxG6Qi10C0EoTM/2vUfLQNuIxGyRuXHy7 ssqcMnGdypoaTqUOs6NY6pbrIsF7bx3EayABgrqGAOCRnB9TXO6EW7m6yPDr7T/D/I8//tmP/oG6 5/4JLz/41SHWI8f8g3XP/BLef/Gq7XUde+wa9pOlCynl+3ytG1xjbHDiKWQcn7zHymG0dByxGVDU 7rW9etfEFjp7aPprwXlw6JJHqTmVIVBZpWj8jAAG1fvY3yIu75s0lQihvJMP3f4f5HKHV0I/5Buu f+CW7/8AjVN/tZP+gbrn/glu/wD41XS2Xi6+uIdMvZdGj+wauhbT/s95vuJCYXnRXR0REJSNhxIw DEDJGWGhYaxqX9sx6Xq+nWlrPPbyXEDWl41wpWNo1cNujjKnMqYwDn5s4wM7JW0Mnw9hXrzS+9f5 HFjV0B/5Buuf+CW7/wDjVSDWYxj/AIluuf8AgkvP/jVemUVE6anuVDIcNDaUvvX+RxvhrV4ZtXEH 2PVYmkjYK0+l3MKZHPLvGFHAPU+3U12VFFOnTVOPKj1MPQjQhyR2Cvkn4yuq/FrXgWA5g6n/AKd4 q+tqK7MJiXhqntIq5tJXVj4O81f76/nR5q/31/OvvGivU/t2r/KvvM/ZI+Sfg06t8WtBAYHmfof+ neWvraiivLxeJeJqe0krFxjZWCiiiuYoKKKKACiiigAooooAKKKKACiiigAooooAKpyWU76pDdrq d3HAibWslWLypDz8zEoXzyOjAfKOOublFAHLtpNwE8UGfTftcd5qEVxBCJxG8iLb26lkYH5JFaNi hJX5lU7lGGHPt4RlM51X+wp5LSaWAXej3VwlxcXccSXADSvJIUkbzJ4yFaQhUgQhtwCL6RRQBz/h PTpdPtb/AP4l/wDZlpPd+baaf8g+yx+VGpXbGSi5kWR8KSPnyfmJA6CiigCnqt1d2Wl3FxYafJqF 2iZitUlSMyt2G5yAo7k+gOATgHl4LrXbPS7y7g8M6lPrlw8aSTXL2oDD5uVVbggRRjOI9wJLfeLM 8ldpRQBj+G4jFp0hk06+s53lLTNfvC81w+BmVjE7LzwoHG0KFCqoUVsUUUARzyNDbyypDJO6IWWK MqGcgfdG4gZPTkgepFcXBa69r1nqIu7C+0TWbu0eGO/mMMsVkjEZjgEU+/dj5vMO3cyhjgKka9xR QBwen2Xijwxaa8bfTbS/kub2EWUVhbpbxxxi3ijMmx58bFEe0R7lJKdQrgp0nhuIxadIZNOvrOd5 S0zX7wvNcPgZlYxOy88KBxtChQqqFFbFFABWH4i0fUtWfTjYajaWqWlx9oeO5s2uEmYKQgIEicKx 3jr8yoRjbzuVXuL63tJ7SGeTZJdymGAbSd7hGkI46fKjHn09cUAeb6Z4SurGz0l/EHhn+32TRLOy NrD5BS3khMpO+OaUIzBZVUOpY8SY2BsHUZPEMdlp2h6noepapZWtlCl7cW1xbsNRmCAOGMsqP5WR kgjMhODhARJ3EMjSoWeGSEh2Xa5UkgMQG+UkYIGR3wRkA5AkoAKKKz5dWRdWbTYbae4uI4o5pfL2 gRI8hRSSzDOdsjcZ4jbuVDAHndn4N1ODQ7HTNL0iTSdYtLKS2uNbeeMfaj9lkiULIjtMU81onUMq hViXAUqq1sWdlc6VezapoPgiTTUhspY305GtLc30zPGYjmORkwgWXLOQVEh2hskV3lFAHP8AhhLo /arjUdMvrbUJdhnubswfvuuEjWKWTZGmThSeNxOWZnY9BRRQBj+JLX7Vp0Y/syfUPLlD7LW6+z3E fBG6J9yYbnB+dMoz8n7rcvpnh698O6wmuWujTzR3H2kvp9vNG08JlW1AZ3kdVdj9lZnbcTvk4LjL n0Csew8SWeo6tLp8Uc6sPO8mV1GyfyZBHNtwSRsdgp3Bc5yu4c0Ac2qeIbDTodNj0PUnt7q4u7y7 msri3WWNZriSVYAWlXa+18O6k7cEISSHTtLBEj062jis/sUaxIFtcKPJAAwmEJUY6fKSOODirFV7 ++t9M065v7yTy7W1ieaZ9pO1FBLHA5OAD0oAsVx/9iaj/wAKb/sD7P8A8TP/AIR/7F5G9f8AXfZ9 m3dnb97jOce9dYJGNw8RhkCKisJSV2sSTlRznIwCcgD5hgnnElAHN+JbrWPtEFhY6PqVxYyIWurq xngSTGcCJPMlQqTzlxyBwvzNuTP1GTUZ3t9MXwhqQ0CK3j3W9tJZqJjt/wBQwMwCxIMAqMhzxnYC JO0ooAKx7mxuJPGWmX6x5tYdPu4ZH3D5XeS2KjHXkRv+XuK2KKAOLbT9Qv8AxFa39t4dj0bU4nc3 mpv5MiXK+Q8apmNxLKnmGJwHCZEYJ2sFFU9G0TxL4TutSuBHBrappVtBAlvEttJdzrLMxLu8zANm Qu7kfMZMgZUhvQKy7LX7HULiyitmkZL6yF9aSlcLNFlckdwR5kZIYD/WDGcNtAKfhhLo/arjUdMv rbUJdhnubswfvuuEjWKWTZGmThSeNxOWZnY9BRRQBxbeHw3h1IdV8OR6x5OsXt4tm0kZO2Seco6q 5EbnbKDtdhgEn7yhTjjwpIt/FqFx4Vkn0cPcLBoMbwDyFkS1xujMggKeZBM+0M3zSI2N2SvplFAH BoniHT9Js9Ii0PUms5HnmmexuLdZLeFpnaK1TdKuwrGVUshIQLtjOSHTtLBEj062jis/sUaxIFtc KPJAAwmEJUY6fKSOODirFFAGPrNjcXeq+HpoI98dpqDzTncBsQ2s8YPPX5nUcevpmixsbhvEeqal ex427LWx+YECAIruwHJVmkZlbpkQx8fKCdiigDzvRdCvNNl0iVPDMkGp6ejNqepRm3DasRA6MocS b3LylJP3oQHbuYhgBW54bfUrvUZL3WtCvrO/eIgSzS27w26ZH7mLZKzc8FnKjeVydoCIvUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABXL+K9Pt7nWvCd3Pp32r7LqpJkFqZjCGglCscAlV8wREtwAQpJGMjq KKAPO9P8NrqOtafHq+lyTWaPrjvHcRN5TF7+No96n5WDLl1DZB2hhyoIp2uiaxB4esW0+3votZv/ AApdfbLh3dZpb4pb+V5srHPmKWlCbj8g3AYANeoUUAcX4EsDaXGpTQxxwWciQqkFvosmlwCRTIXY RSOWLkMgZ9oBCoAWKkLXvYJpbjxFbxxSPOfEel3QjVSWMINkDJjrszFKN3T92/8AdOO8qv8AYbf+ 0ft4jxdGLyWdWI3JnIDAcNg5xnO3c2MbmyAcPpGhvY6jpeoR2E8d5N4g1IXc5Rt/2VjeMikn7sJb ynC8KWKtjc2Tsa5odvrXjLRxqFh9rsItPvRIsiFoS5kttqyD7rdGYK2eU3AZUEdRRQB5vo2iXkGn aQxt9Viu7/w1P/alxE5W7luiLfZvlkP+uXdME3n5PmAwoNbHgW3Ft9vWDTYLW1PllZbbTJtNjkf5 ty/ZpSSGA2kyj74ZV/5Z12FFAHm/iW1v38ZQXFppH79NQssXa2Ek032bzIhI6XO8LDHy6NCqsSN7 kbZGYR+GfDNjo/i3SrlPD0dq5fWkWePT9uwm7Uw7nC/IDCHCEkAqcDrg+mUUAeN2GhaqvhXWo5Le Q6gfDl1bXsVvo0lu1zdsi4MszOftcu5ZAHRSDvc5G9Q3eeLNDt2+GuuaTZ2HnqdPuDDBsMryS7WZ W5yXkL4bccsW5yTzXUUUAed61pJvrm6Sx0+7i0+a30FIVghkgKRpfSM4XAVoyiEEgYKDBOKH8NrZ XGoz2OlyQvaa7YJpwhiYLb2zG1M/kKOERt0/mFAA3zbs449EooA830TSb9PHP2i4Ty7pNQu5ZpU0 mRZJbZjKIVkvS+yWMK0JEahipWMYHlsV3PFdraS6zpc2saZJqOipb3CyQCxe8UXBaExMYlVjkKsw D7cDJGRuAPWUUAeV3PhzUpdG1G41Gxu7jWrTwlZrbyMWlYX6LckshBIadWKkOMsN/BG852NTsCvj xLpNPku7h7iBkeawkLxx4VWMN6jbIYlAdjC4y58wYIlXPeUUAeZ6XoVzP4xlGo28bO17etd50Z91 xaP5wjjmvGfy5Ytrw4jUMRtRSo8titjw9bLb+HvhvYxWclrPbuZLi2e3aF4ytnOkrshAIHmyKCxG CZFOTuGfRKr/AGG3/tH7eY83Qi8lXZidqZyQoPC5OM4xu2rnO1cAFiiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigD/2Q=--20cf305496b5866b64049caea6ca Content-Type: image/jpeg; name="Average_12-20Hz_Cz.jpg" Content-Disposition: attachment; filename="Average_12-20Hz_Cz.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkdeqk8e3 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAL/BaADASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqul9byajNYLJm6hijmkTaflRy4U56c mN/y9xXN+K7q0i1nS4dY1OTTtFe3uGknF89mpuA0IiUyqynJVpiE3YOCcHaCOf14TJceItWt9S1J Z9P8L2t3AzEwNJMpvGR5UCqSQQcxkBDuIZDgYAPTKK838S63PZ+MoEtpZ1uF1Cyg2y6hKpEMkkSu yWqIY3hIdk86QgiQsoOVjFWNIkvItR0u9bUr6Z77xBqVjJHNOWjW3Q3jLGqfdGGhQhsFwPl3bMKA D0Cq97fW+nwLNdSeXG0scIO0nLyOsaDj1ZlHtnnivM9Nu9asfCum3FjqOpXup6h4Sub4mZzcM9zG lv5JRCCAR5rDCjL5Bfe3NFtdS3OmXRivrS509dT0fyltdWm1NUl+2p5hM8qDkr5X7sE7cBsDflgD 0y+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASapx+INPm0ubUIvtbxQvsliWymM6Nxw0OzzA cMrYK/dIboc1T8T/ALi50HUZfls9P1Bp7qU9IYzbTx72/wBkNImT0UEscKCRT1LxpoOnaXf69A9p KgRLeC8MyRxX03zlIEm7hWJy3KJufn5ZNoBsaR4g0/XHu0sfte+0dUnW4sprcozLuA/eIuTtIOB0 DKT1GY9Q8UaTpk5guJp3mEqweXbWktw3mMjSBMRq3zbELEdQpUnAZc1/CN1o76Otlpet2OrTQZkv J7SZH3zSMzvIVVjs3uXbb0HIHArj72aCLTtHvLvV/wCyd/ivUfO1EvErLtF7GmWlVl+4iRjcDhQo GMDAB2h8W6UthcXsi6lDBA8SN52l3MbM0jhECK0YZyWIGFBxkZ61Y03X7PVbhoLeHUkdULk3Wm3F uuMgcNIignnpnPX0NcXrWoadP4Tu5Lfx1/akcWoaaZbrz7NvsQ+1x/PmOMKOhPzgj5OmM5sXLW2v eHvEGmaR42/4SO+udKuYorL7RZN8zJtDfuo0I5IGSdvzc9qAOosfFGk6jeR21vNOWlz5EslpLHDP gE/upWUJJlQWGxjlQWGQCa0LG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIrk9Q8RLq+pWNr oepaNqGbiA3GlS2rSXMQWUGSR/3g8gxqMgOmQ6hfvMq1qeHPk1XxRAvywxaqPLjHCpvtbeRsDtl3 dj6szHqTQB0FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/p3iyDUJ7H/iXX1vaaj/ AMeF7N5Xl3XyGRdoVy65jVnG9V4GDhsA9BXm/hqwaO58MxJaaqmp2mP7VS6juBaw4tpEfyBJ+4X9 6UVfI/hJC/u91dBdJqWp+Mr6wg12+061tdPtZlS0itzueSS4DEmWJz0iXpjvQB1FFYcGhajDcRSv 4s1mdEcM0UkVmFcA/dO2AHB6cEH0IrUe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sAAFiiozGx uElE0gRUZTEAu1iSMMeM5GCBggfMcg8Yj+zy+R5f22fd5vmeZhN23fu2fdxtx8nTdt77vmoAsUVG I2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMaW8qRWqG9ndoceY7BM3HylfnwuBkkN8oXkDtkEAsU VXe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sASGNjcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4wA SUVX+zy+R5f22fd5vmeZhN23fu2fdxtx8nTdt77vmqQRsLh5TNIUZFURELtUgnLDjOTkA5JHyjAH OQCSiq6W8qRWqG9ndoceY7BM3HylfnwuBkkN8oXkDtkGO7At7K/mn1KS2iKM/wBoYxqLVQgBKllx gYL5fdyTnjAABcoqMxsbhJRNIEVGUxALtYkjDHjORggYIHzHIPGI/s8vkeX9tn3eb5nmYTdt37tn 3cbcfJ03be+75qALFFRiNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5zGlvKkVqhvZ3aHHmOwTNx8p X58LgZJDfKF5A7ZBALFFV3t5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAEhjY3CSiaQIqMpiAXa xJGGPGcjBAwQPmOQeMAElFV/s8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qkEbC4eUzSFGRVER C7VIJyw4zk5AOSR8owBzkAkoqulvKkVqhvZ3aHHmOwTNx8pX58LgZJDfKF5A7ZBHt5XiukF7OjTZ 8t1CZt/lC/JlcHBBb5g3JPbAABYoqMxsbhJRNIEVGUxALtYkjDHjORggYIHzHIPGI/s8vkeX9tn3 eb5nmYTdt37tn3cbcfJ03be+75qALFFU5wI7+3d9SkhErhI7YmMLKwSQkDK7iSPmIB/5ZDGBu3SJ bypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QQCxRVd7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT 2wBIY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjABJRVf7PL5Hl/bZ93m+Z5mE3bd+7Z93G3HydN2 3vu+apBGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc5AJKKrpbypFaob2d2hx5jsEzcfKV+fC4GSQ3 yheQO2QR7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT2wAAWKKjMbG4SUTSBFRlMQC7WJIwx4zkYI GCB8xyDxiP7PL5Hl/bZ93m+Z5mE3bd+7Z93G3HydN23vu+agCxRUYjYXDymaQoyKoiIXapBOWHGc nIBySPlGAOcxpbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QQCxRVd7eV4rpBezo02fLdQmbf5Q vyZXBwQW+YNyT2wBHABJf3DpqUkwicpJbAxlYmKRkA4XcCB8wBP/AC1OcjbtALlFV/s8vkeX9tn3 eb5nmYTdt37tn3cbcfJ03be+75qkEbC4eUzSFGRVERC7VIJyw4zk5AOSR8owBzkAkoqulvKkVqhv Z3aHHmOwTNx8pX58LgZJDfKF5A7ZBHt5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAABYoqMxsbh JRNIEVGUxALtYkjDHjORggYIHzHIPGI/s8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qALFFRiN hcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5zGlvKkVqhvZ3aHHmOwTNx8pX58LgZJDfKF5A7ZBALFFV 3t5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAEhjY3CSiaQIqMpiAXaxJGGPGcjBAwQPmOQeMAEl FV/s8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qkEbC4eUzSFGRVERC7VIJyw4zk5AOSR8owBzk AkoqnaAXFlYTQalJcxBFf7QpjYXSlCAWKrjByHym3kDHGQZHt5XiukF7OjTZ8t1CZt/lC/JlcHBB b5g3JPbAABYoqMxsbhJRNIEVGUxALtYkjDHjORggYIHzHIPGI/s8vkeX9tn3eb5nmYTdt37tn3cb cfJ03be+75qALFFRiNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5zGlvKkVqhvZ3aHHmOwTNx8pX58 LgZJDfKF5A7ZBALFFV3t5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAEhjY3CSiaQIqMpiAXaxJG GPGcjBAwQPmOQeMAElFV/s8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qkEbC4eUzSFGRVERC7V IJyw4zk5AOSR8owBzkAkoqulvKkVqhvZ3aHHmOwTNx8pX58LgZJDfKF5A7ZBHt5XiukF7OjTZ8t1 CZt/lC/JlcHBBb5g3JPbAABYoqndAC/sXbUpLcF3RbYGMLdMUJwdylsqFZgEI6HOQKk+zy+R5f22 fd5vmeZhN23fu2fdxtx8nTdt77vmoAsUVGI2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMaW8qRWq G9ndoceY7BM3HylfnwuBkkN8oXkDtkEAsUVXe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sASGN jcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4wASUVX+zy+R5f22fd5vmeZhN23fu2fdxtx8nTdt77vmq QRsLh5TNIUZFURELtUgnLDjOTkA5JHyjAHOQCSqdrpsNnf313C0gN66STRkgr5ioE3jjIJVUUjOP kBABLEyJbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QR7eV4rpBezo02fLdQmbf5QvyZXBwQW+Y NyT2wAAWKKjMbG4SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxiP7PL5Hl/bZ93m+Z5mE3bd+7Z93G3Hy dN23vu+agCxVPS9Nh0mwW0haRwHeR5JCC0kjuXdzgAZZmZsAADOAAMCrAjYXDymaQoyKoiIXapBO WHGcnIBySPlGAOc04Yit5HCdYnlmt4ojLA3k5cYkUO4CAjecnjaMxDAA3AgGhRVd7eV4rpBezo02 fLdQmbf5QvyZXBwQW+YNyT2wBIY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjABJRVf7PL5Hl/bZ9 3m+Z5mE3bd+7Z93G3HydN23vu+apBGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc5AJKKrpbypFaob 2d2hx5jsEzcfKV+fC4GSQ3yheQO2QR7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT2wAAWKKjMbG4 SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxiP7PL5Hl/bZ93m+Z5mE3bd+7Z93G3HydN23vu+agCxRUYj YXDymaQoyKoiIXapBOWHGcnIBySPlGAOcxpbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QQCxRV d7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT2wBIY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjABJ RWfYRGTR7bytYnvVbZKt9+5LTIWDj7iBCpX5cqo+U5Bz81XBGwuHlM0hRkVREQu1SCcsOM5OQDkk fKMAc5AJKKrpbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QR7eV4rpBezo02fLdQmbf5QvyZXBw QW+YNyT2wAAWKKjMbG4SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxiP7PL5Hl/bZ93m+Z5mE3bd+7Z93 G3HydN23vu+agCxRUYjYXDymaQoyKoiIXapBOWHGcnIBySPlGAOcxpbypFaob2d2hx5jsEzcfKV+ fC4GSQ3yheQO2QQCxRVd7eV4rpBezo02fLdQmbf5QvyZXBwQW+YNyT2wBIY2NwkomkCKjKYgF2sS RhjxnIwQMED5jkHjABJRVf7PL5Hl/bZ93m+Z5mE3bd+7Z93G3HydN23vu+apBGwuHlM0hRkVREQu 1SCcsOM5OQDkkfKMAc5AJKKz7mIw2dmJtYnt/JliDzt5INychQj5TaN7ED5ApyQFI6VYe3leK6QX s6NNny3UJm3+UL8mVwcEFvmDck9sAAFiiozGxuElE0gRUZTEAu1iSMMeM5GCBggfMcg8Yj+zy+R5 f22fd5vmeZhN23fu2fdxtx8nTdt77vmoAsUVGI2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMaW8q RWqG9ndoceY7BM3HylfnwuBkkN8oXkDtkEAsUVXe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sA SGNjcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4wASUVX+zy+R5f22fd5vmeZhN23fu2fdxtx8nTdt77 vmqQRsLh5TNIUZFURELtUgnLDjOTkA5JHyjAHOQCSiq6W8qRWqG9ndoceY7BM3HylfnwuBkkN8oX kDtkEe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sAAFiiozGxuElE0gRUZTEAu1iSMMeM5GCBgg fMcg8YppETdTQDWJ3mXy5Wi/c7o0MrsOAmdrAGPJ/hj4IbcxANCioxGwuHlM0hRkVREQu1SCcsOM 5OQDkkfKMAc5jS3lSK1Q3s7tDjzHYJm4+Ur8+FwMkhvlC8gdsggFiiq728rxXSC9nRps+W6hM2/y hfkyuDggt8wbkntgCQxsbhJRNIEVGUxALtYkjDHjORggYIHzHIPGACSiq/2eXyPL+2z7vN8zzMJu 2792z7uNuPk6btvfd81SCNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5yASUVXS3lSK1Q3s7tDjzHY Jm4+Ur8+FwMkhvlC8gdsgj28rxXSC9nRps+W6hM2/wAoX5Mrg4ILfMG5J7YAALFFRmNjcJKJpAio ymIBdrEkYY8ZyMEDBA+Y5B4xH9nl8jy/ts+7zfM8zCbtu/ds+7jbj5Om7b33fNQBYoqMRsLh5TNI UZFURELtUgnLDjOTkA5JHyjAHOY0t5UitUN7O7Q48x2CZuPlK/PhcDJIb5QvIHbIIBYorPtojNZ3 gh1ie486WUJOvkk2xyVKJhNp2MCPnDHIIYnpVwxsbhJRNIEVGUxALtYkjDHjORggYIHzHIPGACSi q/2eXyPL+2z7vN8zzMJu2792z7uNuPk6btvfd81SCNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5yA SUVXS3lSK1Q3s7tDjzHYJm4+Ur8+FwMkhvlC8gdsgj28rxXSC9nRps+W6hM2/wAoX5Mrg4ILfMG5 J7YAALFFRmNjcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4xH9nl8jy/ts+7zfM8zCbtu/ds+7jbj5Om 7b33fNQBYoqMRsLh5TNIUZFURELtUgnLDjOTkA5JHyjAHOY0t5UitUN7O7Q48x2CZuPlK/PhcDJI b5QvIHbIIBYoqu9vK8V0gvZ0abPluoTNv8oX5Mrg4ILfMG5J7YAkMbG4SUTSBFRlMQC7WJIwx4zk YIGCB8xyDxgAkoqnMBbWo8/UpIs3C4mcxqTulG2LlcYORGONxBHO75quUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABXP2f/JQ9Z/7BVh/6Nu66Cufs/wDkoes/9gqw/wDRt3QB0FFFFABRRRQAUUUUAFFFFABR RRQAVHOZlt5Wt445JwhMaSOUVmxwCwBIGe+Dj0NSVXv/ACv7OufP8/yfKff9n3+Ztwc7PL+fdjpt +bPTmgCxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEchmDwiKONkL4lLOVKrtPKjB3HdtGDjgk54wZ Kr3HlefaeZ5+7zT5flb9u7Y339vG3Gfv/Lu2/wAW2rFABRRRQAUUUUAFFFFABRRRQAUUUUAFRxmY vMJY41QPiIq5Ysu0csMDad24YGeADnnAkqvb+V5935fn7vNHmebv27ti/c3cbcY+58u7d/FuoAsU UUUAFFFFABRRRQAUUUUAFFFFABRRRQBHAZmt4muI445ygMiRuXVWxyAxAJGe+Bn0FSVXsPK/s628 jz/J8pNn2jf5m3Axv8z592Ou75s9easUAFFFFABRRRQAUUUUAFFFFABRRRQBHIZg8IijjZC+JSzl Sq7Tyowdx3bRg44JOeMGSq9x5Xn2nmefu80+X5W/bu2N9/bxtxn7/wAu7b/FtqxQAUUUUAFFFFAB RRRQAUUUUAFFFFABUYM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5kqunlf2jNjz/ADvKj3bt/l7c vjbn5N2d2cfNjbu420AWKKKKACiiigAooooAKKKKACiiigAooooAjgMzW8TXEccc5QGRI3LqrY5A YgEjPfAz6CpKr2Hlf2dbeR5/k+Umz7Rv8zbgY3+Z8+7HXd82evNWKACiiigAooooAKKKKACiiigA ooooAjmMyoDBHG771BDuVG3cNxyAeQuSB3IAyM5ElV73yvIXzvP2+bHjyN+7dvXbnZztzjdn5dud 3y5qxQAUUUUAc/4zL/8ACPpHHPPD52oWMLvBM0T7Hu4kYB1IYZViOCOtH/CG6X/z9a5/4Pb3/wCP UeMv+QHbf9hXTf8A0thrZvHu0tXaxhhmuRjZHPMYkPPOWCsRxn+E/wBaaV3YDG/4Q3S/+frXP/B7 e/8Ax6j/AIQ3S/8An61z/wAHt7/8eqT7V4q/6A2jf+DaX/5Go+1eKv8AoDaN/wCDaX/5Grb6vPuv /Ao/5iuR/wDCG6X/AM/Wuf8Ag9vf/j1H/CG6X/z9a5/4Pb3/AOPVJ9q8Vf8AQG0b/wAG0v8A8jUf avFX/QG0b/wbS/8AyNR9Xn3X/gUf8wuR/wDCG6X/AM/Wuf8Ag9vf/j1H/CG6X/z9a5/4Pb3/AOPV PBc+JWnjE+k6SkJYCR49TkdlXPJCm3AJx2yM+orE8V+GtBvfEnhy4u9E02ee61NkuJJbVGaZRZ3B AckZYAohAP8AdHoKznTcN7fJp/kCdzU/4Q3S/wDn61z/AMHt7/8AHqP+EN0v/n61z/we3v8A8erH udVvLeXULOOw0pdI0vVdO0+2hMJJ/eNaEHbkKnl+aSpGedvC7PnsR+KroeL7bTXeCe1u7uW0RYLK cLEUjkfcbo/upG/dFWjUAqzEZPlndAzQ/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeq PwXG03w08PRJNJA76PbKssYUshMK/MNwIyOvII9QaPA0K23huSBDIUj1PUEUySM7EC8mHLMSWPuS Se9AEn/CG6X/AM/Wuf8Ag9vf/j1H/CG6X/z9a5/4Pb3/AOPVny3zx6n4m1UyQJcWEsOm2pnVnSNG jhlJVFyzyM84GxceZ5cKcEbqz4fiBPaW811qcPm2dpLc208kVlLbTSyJardqywzNmNfLEykMSSwQ jCscAHQf8Ibpf/P1rn/g9vf/AI9R/wAIbpf/AD9a5/4Pb3/49WHH4t1xNbXT57WOWe1uLWzvILTT bmVJHkSJpJVuR8kSJ5xOxwSRHyRvGLA8Say2k3V7JJptsDqdxY2u22nuZCsU0yFvJjO6RyIwNikb QryFiPkUA1P+EN0v/n61z/we3v8A8eo/4Q3S/wDn61z/AMHt7/8AHqy9P8VarrMWmQ2ENol3Ol7J I11FJGsotZ1hKhD88JkLhstvMeCCrnmst/H+rPp2nzW9n5l02iWuqS29vplxdfanmDnyUaMkQcxk Bn3539PkOQDqP+EN0v8A5+tc/wDB7e//AB6j/hDdL/5+tc/8Ht7/APHqueINSm0rSfPt1jM8lxb2 sZkBKo00yRByAQWCl923IzjGRnI4/VJtS1vU9K0bUX02eC3102d/G1mxhvV+xG5X920hCgBsYYv8 6o/G3aQDpP8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7/wDHqPEmm+H5vLu9Z0mDVJ2xBa20 0Kzs78nbEj/KrHks3HyplyFTK8HrWhanpHhLV59U0iPVtYt9MWW31x5Y2+xmK0RWKyP++VxLHLKu 1cFpFJZSzFQDvP8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7/wDHqseKNYfQtBlvok3yebDA v7ppdpllSINsT5n2792xcFsYBBOa5tfHN7p1ob7V7SRrCJ7mJpVsJbWW4aO3FyrpDMcqm1LiM5Jy 6KQQCdoBuf8ACG6X/wA/Wuf+D29/+PUf8Ibpf/P1rn/g9vf/AI9WHH4t1xNbXT57WOWe1uLWzvIL TTbmVJHkSJpJVuR8kSJ5xOxwSRHyRvGLFp4i165uId402KC91O+0u1CxO7I0JuCkz/MARiDaYxjO dwcZ2AA1P+EN0v8A5+tc/wDB7e//AB6j/hDdL/5+tc/8Ht7/APHq5/TNa1+28A+FZzN5sl1p8clx qB02e+IYohRGhifzCzBmJlJ25jOQC6gakE8x8S2z2Etoj63o8l07RsZ7cTQmFUkXGzzAVnwW4LLF EBtxQBc/4Q3S/wDn61z/AMHt7/8AHqP+EN0v/n61z/we3v8A8eqx4svrjTPBuuX9nJ5d1a6fcTQv tB2usbFTg8HBA61HbaBoui39tPZLHYXEzmN9jANftsZsSlsmVxgvuJL8Md2GcMAR/wDCG6X/AM/W uf8Ag9vf/j1H/CG6X/z9a5/4Pb3/AOPVz58aaxB4ebVpUsZPtnh+51uyiWF1+z+WkbCKQ7z5ufOU bgI/uHj5vl0LnxJrGnvf2V5HYtfL9gMLwq/lxfa7hoFVgTmTyypYsNm8HG2PrQBof8Ibpf8Az9a5 /wCD29/+PUf8Ibpf/P1rn/g9vf8A49XH6ta3l14M+I66hceXPDLM8v2JyiT7dOh25z8yqflYoD22 lnXO71CgDm/CT3K+G2RHkuni1O8gVru5d28pLyVOXbczFUHGeu0AkdR0lcv4a8r/AIRyfzvP2/23 e48jfu3f2hLtzs525xuz8u3O75c11FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVz9n/yUPWf+wVYf+jbu ugrn7P8A5KHrP/YKsP8A0bd0AdBRRRQAUUUUAFFFFABRRRQAUUUUAFRziZreVbeSOOcoRG8iF1Vs cEqCCRntkZ9RUlV79Ek065jls/tsbROGtcKfOBBymHIU56fMQOeTigCxRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFAEcgmLwmKSNUD5lDIWLLtPCnI2ndtOTngEY5yJKr3CI89oXs/PZJSUkwp+znYw38nI yCU+XJ+fpjJFigAooooAKKKKACiiigAooooAKKKKACo4xMHmMskbIXzEFQqVXaOGOTuO7ccjHBAx xkyVXt0RJ7spZ+QzygvJhR9oOxRv4OTgAJ82D8nTGCQCxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA EcAmW3iW4kjknCASPGhRWbHJCkkgZ7ZOPU1JVewRI9Oto4rP7FGsSBbXCjyQAMJhCVGOnykjjg4q xQAUUUUAFFFFABRRRQAUUUUAFFFFAEcgmLwmKSNUD5lDIWLLtPCnI2ndtOTngEY5yJKr3CI89oXs /PZJSUkwp+znYw38nIyCU+XJ+fpjJFigAooooAKKKKACiiigAooooAKKKKACowJvtDs0kZgKKEQI QwbJ3EtnBBG3AwMYPJzxJVdEQajNILPZI0UYa6wv7wAvhMg7vlyTyMfvOCTuwAWKKKKACiiigAoo ooAKKKKACiiigAooooAjgEy28S3Ekck4QCR40KKzY5IUkkDPbJx6mpKr2CJHp1tHFZ/Yo1iQLa4U eSABhMISox0+UkccHFWKACiiigAooooAKKKKACiiigAooooAjmEzIBBJGj71JLoWG3cNwwCOSuQD 2JBwcYMlV71EkgUSWf2tRLGRHhTtIdSH+YgfIQH9fl4BOBVigAooooA5/wAZf8gO2/7Cum/+lsNd BVe+sLPU7OSzv7SC7tZMb4Z4xIjYIIyp4OCAfwrH/wCEE8H/APQqaH/4Lof/AImgDoKK5/8A4QTw f/0Kmh/+C6H/AOJo/wCEE8H/APQqaH/4Lof/AImgDoKK5/8A4QTwf/0Kmh/+C6H/AOJo/wCEE8H/ APQqaH/4Lof/AImgDoKr3Fjb3c9pNPHvktJTNAdxGxyjRk8dfldhz6+uKx/+EE8H/wDQqaH/AOC6 H/4mj/hBPB//AEKmh/8Aguh/+JoA0JNE06X7Tvt8/abuK9l+dvmmi8vY3XjHkx8Dg7eQcnNdfC2j JrEeqiz/ANMileaJ/NcrE7qyuUTO1N+9i2ANzYZssARX/wCEE8H/APQqaH/4Lof/AImox4I8JG4e I+DtGCKisJTp8G1iScqOM5GATkAfMME84ANywsbfTNOtrCzj8u1tYkhhTcTtRQAoyeTgAdaLOxt9 PgaG1j8uNpZJiNxOXkdpHPPqzMfbPHFY/wDwgng//oVND/8ABdD/APE0f8IJ4P8A+hU0P/wXQ/8A xNAEkmgLLqmpGRY5NN1JI5ZotzK8dzHtUSow5BKrHggqUMKsuSxIktvC2jWdnLawWe2Ga7jvZQZX JknjMZWRmJyWzEhJJ+Yglsljmv8A8IJ4P/6FTQ//AAXQ/wDxNH/CCeD/APoVND/8F0P/AMTQBcm8 PaZcaoNRkgkM+9ZGQTyCKR1xtd4g2x3G1cMykjYmD8q4JfD2mTWH2IwSJELiS6Vop5I5Elkdnd0k Vg6kl3ztI4Yr0OKp/wDCCeD/APoVND/8F0P/AMTR/wAIJ4P/AOhU0P8A8F0P/wATQBT1PwlBDYWV po2jabJaW7zZs5rmW1XbK+913oGzEWzuhZSjZXoEANiz8FaZFo2lWF4JLl7GyhsnkSWSFbpI1wFl RWxIn3vkfcBvYfxHMn/CCeD/APoVND/8F0P/AMTR/wAIJ4P/AOhU0P8A8F0P/wATQBsX1jb6jZyW t1H5kL4JAYqQQQVZWGCrAgEMCCCAQQRVO28PaZaC28uCRntrg3SSyzySSGUxtEXd2Ys52MVG4nAw B90Yp/8ACCeD/wDoVND/APBdD/8AE0f8IJ4P/wChU0P/AMF0P/xNAFjU/DGm6vqMV/dG+W6iiMKP bahcW+1CQSMRuo5IXPrtXPQYB4X0kXkN0YZ2ki2HD3crJIyABJJELbZJBtXDuGb5FOcqMV/+EE8H /wDQqaH/AOC6H/4mj/hBPB//AEKmh/8Aguh/+JoA2L6xt9Rs5LW6j8yF8EgMVIIIKsrDBVgQCGBB BAIIIqnF4e0yGw+xCCR4jcR3TNLPJJI8sbq6O8jMXYgomNxPChegxWfD4I8JSoWfwdo0JDsu19Pg JIDEBvlBGCBkd8EZAOQJP+EE8H/9Cpof/guh/wDiaALk3h7TLjVBqMkEhn3rIyCeQRSOuNrvEG2O 42rhmUkbEwflXEkeiadF9m2W+Ps13LexfO3yzS+ZvbrznzpODwN3AGBjP/4QTwf/ANCpof8A4Lof /iaP+EE8H/8AQqaH/wCC6H/4mgCRfCOjppdlp0Ud3FBYoY7Z4r6dJY0OMoJVcPs4X5S2PlXj5VwR 6ArapNNIscNnDZf2fYQWrNEYYmwZSCm0oWKxqAM7RCCpBcgR/wDCCeD/APoVND/8F0P/AMTR/wAI J4P/AOhU0P8A8F0P/wATQB0FZem+HtM0m4aezgkVyhjQPPJIsKEglIlZiIk4X5UCj5V4+UYp/wDC CeD/APoVND/8F0P/AMTR/wAIJ4P/AOhU0P8A8F0P/wATQBInhDQ0t7u3+ySNBdW72rRvcSsscLjD RxAsRChGBtj2j5V/uri5d6Jp19LdS3FvvkuoooZXDsDtjZnjKkH5WVnZgy4YHBzkDGf/AMIJ4P8A +hU0P/wXQ/8AxNH/AAgng/8A6FTQ/wDwXQ//ABNAFiDwvpNvpOo6YIZ5LXUt/wBrE93LM8u6MRnL uxb7iqODxjitiuf/AOEE8H/9Cpof/guh/wDiaP8AhBPB/wD0Kmh/+C6H/wCJoAj8JiZtAuBBJGj/ ANsX5JdCw2/bptwwCOSuQD2JBwcYPSVnjTbCw0yGytNJg+yRSoY7WCGNEjPmBt4U4UbW+fjnjIBb AOhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/Z/8lD1n/sFWH/o27roK5+z/AOSh6z/2CrD/ANG3dAHQ UUUUAFFFFABRRRQAUUUUAFFFFABVe/dI9OuZJbz7FGsTlrrKjyQAcvlwVGOvzAjjkYqxUc5mW3la 3jjknCExpI5RWbHALAEgZ74OPQ0ASUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXuHRJ7QPeeQzykJ HlR9oOxjs5GTgAv8uD8nXGQbFRyGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjBkoAKKKKACiiigAo oooAKKKKACiiigAqvbujz3YS889klAePKn7OdinZwMjIIf5sn5+uMAWKjjMxeYSxxqgfERVyxZdo 5YYG07twwM8AHPOAASUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXsHSTTraSK8+2xtEhW6yp84EDD 5QBTnr8oA54GKsVHAZmt4muI445ygMiRuXVWxyAxAJGe+Bn0FSUAFFFFABRRRQAUUUUAFFFFABRR RQBXuHRJ7QPeeQzykJHlR9oOxjs5GTgAv8uD8nXGQbFRyGYPCIo42QviUs5Uqu08qMHcd20YOOCT njBkoAKKKKACiiigAooooAKKKKACiiigAqujodRmjF5vkWKMta5X92CXw+AN3zYI5OP3fAB3ZsVG DN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOQCSiiigAooooAKKKKACiiigAooooAKKKKAK9g6Sadb SRXn22NokK3WVPnAgYfKAKc9flAHPAxVio4DM1vE1xHHHOUBkSNy6q2OQGIBIz3wM+gqSgAooooA KKKKACiiigAooooAKKKKAK966RwKZLz7IpljAkyo3EuoCfMCPnJCevzcEHBqxUcxmVAYI43feoId yo27huOQDyFyQO5AGRnIkoAKKKKACiiigAooooAKKKKACiiigAqujodRmjF5vkWKMta5X92CXw+A N3zYI5OP3fAB3ZsVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOQCSiiigAooooAKKKKACiiigAo oooAKKKKAK9m6SQMY7z7WolkBkyp2kOwKfKAPkIKevy8knJqxUcJmZCZ440fewARyw27jtOSByVw SOxJGTjJkoAKKKKACiiigAooooAKKKKACiiigCveukcCmS8+yKZYwJMqNxLqAnzAj5yQnr83BBwa sVHMZlQGCON33qCHcqNu4bjkA8hckDuQBkZyJKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDj/C/iW81yez kbVdDl+1WiXkmlwgpd2cciK6lj5jb8b0U/JGDu3ZHCnQs/8Akoes/wDYKsP/AEbd1l6F4X1LTofD 2mS2+m2+n6C5eKe2mZnum8mSHLReWojLea0hId+RjnO4SXGhaPrfxD1T+1tKsb/ydKsfL+126S7M y3ecbgcZwOnoKAOworDg8F+FbW4iuLfw1o0M8Th45I7CJWRgcgghcgg85rYEEK3D3CxRid0VHkCj cyqSVBPUgFmIHbcfWgCSiq6WFnHFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkdKHsLOSK6iktIHju8/ aUaMETZUId4/i+UBeewA6UAWKKjMELXCXDRRmdEZEkKjcqsQWAPUAlVJHfaPSo/sFn5HkfZIPJ83 z/L8sbfM3+Zvx/e3/Nnru560AWKKjEEK3D3CxRid0VHkCjcyqSVBPUgFmIHbcfWo0sLOOK1ijtIE jtMfZkWMAQ4UoNg/h+UleOxI6UAWKKrvYWckV1FJaQPHd5+0o0YImyoQ7x/F8oC89gB0qQwQtcJc NFGZ0RkSQqNyqxBYA9QCVUkd9o9KAJKr3/lf2dc+f5/k+U+/7Pv8zbg52eX8+7HTb82enNH2Cz8j yPskHk+b5/l+WNvmb/M34/vb/mz13c9aJrb/AI+J7VYIr6WIRid4t/3dxTcAQWUFmO3I6nBGc0AW KKrpYWccVrFHaQJHaY+zIsYAhwpQbB/D8pK8diR0oews5IrqKS0geO7z9pRowRNlQh3j+L5QF57A DpQBYoqMwQtcJcNFGZ0RkSQqNyqxBYA9QCVUkd9o9Kj+wWfkeR9kg8nzfP8AL8sbfM3+Zvx/e3/N nru560AWKKjEEK3D3CxRid0VHkCjcyqSVBPUgFmIHbcfWo0sLOOK1ijtIEjtMfZkWMAQ4UoNg/h+ UleOxI6UAWKKrvYWckV1FJaQPHd5+0o0YImyoQ7x/F8oC89gB0qQwQtcJcNFGZ0RkSQqNyqxBYA9 QCVUkd9o9KAJKKr/AGCz8jyPskHk+b5/l+WNvmb/ADN+P72/5s9d3PWpBBCtw9wsUYndFR5Ao3Mq klQT1IBZiB23H1oAkoqulhZxxWsUdpAkdpj7MixgCHClBsH8Pykrx2JHSh7CzkiuopLSB47vP2lG jBE2VCHeP4vlAXnsAOlABceV59p5nn7vNPl+Vv27tjff28bcZ+/8u7b/ABbasVXmtt95bXKLAJIt yM7xbn8thyqtkbcssZPUHZjGcEH2Cz8jyPskHk+b5/l+WNvmb/M34/vb/mz13c9aALFFRiCFbh7h YoxO6KjyBRuZVJKgnqQCzEDtuPrUaWFnHFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkdKALFFV3sLOS K6iktIHju8/aUaMETZUId4/i+UBeewA6VIYIWuEuGijM6IyJIVG5VYgsAeoBKqSO+0elAElFV/sF n5HkfZIPJ83z/L8sbfM3+Zvx/e3/ADZ67uetSCCFbh7hYoxO6KjyBRuZVJKgnqQCzEDtuPrQBJRV dLCzjitYo7SBI7TH2ZFjAEOFKDYP4flJXjsSOlD2FnJFdRSWkDx3eftKNGCJsqEO8fxfKAvPYAdK ALFFRmCFrhLhoozOiMiSFRuVWILAHqASqkjvtHpUf2Cz8jyPskHk+b5/l+WNvmb/ADN+P72/5s9d 3PWgCxVe38rz7vy/P3eaPM83ft3bF+5u424x9z5d27+LdUgghW4e4WKMTuio8gUbmVSSoJ6kAsxA 7bj61Xg0+GERRmC08i1cGxjjgC/ZlEezA5IzguMqF+VtuOpIBcoqu9hZyRXUUlpA8d3n7SjRgibK hDvH8XygLz2AHSpDBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0oAkoqv9gs/I8j7JB5Pm+f5fljb5 m/zN+P72/wCbPXdz1qQQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9aAJKKrpYWccVrFHaQJHaY+zI sYAhwpQbB/D8pK8diR0oews5IrqKS0geO7z9pRowRNlQh3j+L5QF57ADpQBYoqMwQtcJcNFGZ0Rk SQqNyqxBYA9QCVUkd9o9Kj+wWfkeR9kg8nzfP8vyxt8zf5m/H97f82eu7nrQBYoqMQQrcPcLFGJ3 RUeQKNzKpJUE9SAWYgdtx9ajSws44rWKO0gSO0x9mRYwBDhSg2D+H5SV47EjpQBYoqu9hZyRXUUl pA8d3n7SjRgibKhDvH8XygLz2AHSpDBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0oAjsPK/s628jz /J8pNn2jf5m3Axv8z592Ou75s9easVTg0+FbCK3uILSQhxNII4AkbTb/ADDIEJOD5nz9SQeck81Y EEK3D3CxRid0VHkCjcyqSVBPUgFmIHbcfWgCSiq6WFnHFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkd KHsLOSK6iktIHju8/aUaMETZUId4/i+UBeewA6UAWKKjMELXCXDRRmdEZEkKjcqsQWAPUAlVJHfa PSo/sFn5HkfZIPJ83z/L8sbfM3+Zvx/e3/Nnru560AWKKjEEK3D3CxRid0VHkCjcyqSVBPUgFmIH bcfWo0sLOOK1ijtIEjtMfZkWMAQ4UoNg/h+UleOxI6UAWKKrvYWckV1FJaQPHd5+0o0YImyoQ7x/ F8oC89gB0qQwQtcJcNFGZ0RkSQqNyqxBYA9QCVUkd9o9KAJKKr/YLPyPI+yQeT5vn+X5Y2+Zv8zf j+9v+bPXdz1qQQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9aAI7jyvPtPM8/d5p8vyt+3dsb7+3jb jP3/AJd23+LbViqZ0+FBp8dvBaRQWT5jjMAPlqI2QCPBAjIDYzg/LuXHORI9hZyRXUUlpA8d3n7S jRgibKhDvH8XygLz2AHSgCxRUZgha4S4aKMzojIkhUblViCwB6gEqpI77R6VH9gs/I8j7JB5Pm+f 5fljb5m/zN+P72/5s9d3PWgCxRUYghW4e4WKMTuio8gUbmVSSoJ6kAsxA7bj61GlhZxxWsUdpAkd pj7MixgCHClBsH8Pykrx2JHSgCxRVd7CzkiuopLSB47vP2lGjBE2VCHeP4vlAXnsAOlSGCFrhLho ozOiMiSFRuVWILAHqASqkjvtHpQBJRVf7BZ+R5H2SDyfN8/y/LG3zN/mb8f3t/zZ67uetSCCFbh7 hYoxO6KjyBRuZVJKgnqQCzEDtuPrQBJRVdLCzjitYo7SBI7TH2ZFjAEOFKDYP4flJXjsSOlD2FnJ FdRSWkDx3eftKNGCJsqEO8fxfKAvPYAdKALFV08r+0Zsef53lR7t2/y9uXxtz8m7O7OPmxt3cbak MELXCXDRRmdEZEkKjcqsQWAPUAlVJHfaPSq40+HLxNBaGzLrMkAgAxN5hkaQnOCS21hwCGBbJJ4A LlFRiCFbh7hYoxO6KjyBRuZVJKgnqQCzEDtuPrUaWFnHFaxR2kCR2mPsyLGAIcKUGwfw/KSvHYkd KALFFV3sLOSK6iktIHju8/aUaMETZUId4/i+UBeewA6VIYIWuEuGijM6IyJIVG5VYgsAeoBKqSO+ 0elAElFV/sFn5HkfZIPJ83z/AC/LG3zN/mb8f3t/zZ67uetSCCFbh7hYoxO6KjyBRuZVJKgnqQCz EDtuPrQBJRVdLCzjitYo7SBI7TH2ZFjAEOFKDYP4flJXjsSOlD2FnJFdRSWkDx3eftKNGCJsqEO8 fxfKAvPYAdKALFFRmCFrhLhoozOiMiSFRuVWILAHqASqkjvtHpUf2Cz8jyPskHk+b5/l+WNvmb/M 34/vb/mz13c9aALFFRiCFbh7hYoxO6KjyBRuZVJKgnqQCzEDtuPrUaWFnHFaxR2kCR2mPsyLGAIc KUGwfw/KSvHYkdKACw8r+zrbyPP8nyk2faN/mbcDG/zPn3Y67vmz15qxVOPT4WspobyC0me6TF7t gCpcMUCMWUk5BVQMEtwAMnFWDBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0oAkoqv9gs/I8j7JB5P m+f5fljb5m/zN+P72/5s9d3PWpBBCtw9wsUYndFR5Ao3MqklQT1IBZiB23H1oAkoqulhZxxWsUdp Akdpj7MixgCHClBsH8Pykrx2JHSh7CzkiuopLSB47vP2lGjBE2VCHeP4vlAXnsAOlAFiiozBC1wl w0UZnRGRJCo3KrEFgD1AJVSR32j0qP7BZ+R5H2SDyfN8/wAvyxt8zf5m/H97f82eu7nrQBYoqMQQ rcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9ajSws44rWKO0gSO0x9mRYwBDhSg2D+H5SV47EjpQBYoqu 9hZyRXUUlpA8d3n7SjRgibKhDvH8XygLz2AHSpDBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0oAjv fK8hfO8/b5sePI37t29dudnO3ON2fl253fLmrFU5tPha1FvBBaIn2hZyrwBk3eaJGbaCPnLZYN2Y hucYNgQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9aAJKKrpYWccVrFHaQJHaY+zIsYAhwpQbB/D8p K8diR0oews5IrqKS0geO7z9pRowRNlQh3j+L5QF57ADpQBYoqMwQtcJcNFGZ0RkSQqNyqxBYA9QC VUkd9o9Kj+wWfkeR9kg8nzfP8vyxt8zf5m/H97f82eu7nrQBYoqMQQrcPcLFGJ3RUeQKNzKpJUE9 SAWYgdtx9ajSws44rWKO0gSO0x9mRYwBDhSg2D+H5SV47EjpQBYoqu9hZyRXUUlpA8d3n7SjRgib KhDvH8XygLz2AHSpDBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0oAkoqv9gs/I8j7JB5Pm+f5fljb 5m/zN+P72/5s9d3PWpBBCtw9wsUYndFR5Ao3MqklQT1IBZiB23H1oAkqunlf2jNjz/O8qPdu3+Xt y+Nufk3Z3Zx82Nu7jbQlhZxxWsUdpAkdpj7MixgCHClBsH8Pykrx2JHSo20+GR71JYLR7S8QCaEw DMrbSjGQ5w4KBFwRwF6kEAAFyiozBC1wlw0UZnRGRJCo3KrEFgD1AJVSR32j0qP7BZ+R5H2SDyfN 8/y/LG3zN/mb8f3t/wA2eu7nrQBYoqMQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9ajSws44rWKO0 gSO0x9mRYwBDhSg2D+H5SV47EjpQBYoqu9hZyRXUUlpA8d3n7SjRgibKhDvH8XygLz2AHSpDBC1w lw0UZnRGRJCo3KrEFgD1AJVSR32j0oAkoqv9gs/I8j7JB5Pm+f5fljb5m/zN+P72/wCbPXdz1qQQ QrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9aAJKKrpYWccVrFHaQJHaY+zIsYAhwpQbB/D8pK8diR0o ews5IrqKS0geO7z9pRowRNlQh3j+L5QF57ADpQBYoqMwQtcJcNFGZ0RkSQqNyqxBYA9QCVUkd9o9 Kj+wWfkeR9kg8nzfP8vyxt8zf5m/H97f82eu7nrQAWfleQ3k+ft82TPn7927e27G/nbnO3Hy7cbf lxViq9vbeXLJcSrA11J8jTRxbC0YZjGp5JO0Me+MliAM4oSws44rWKO0gSO0x9mRYwBDhSg2D+H5 SV47EjpQBYoqu9hZyRXUUlpA8d3n7SjRgibKhDvH8XygLz2AHSpDBC1wlw0UZnRGRJCo3KrEFgD1 AJVSR32j0oAkoqv9gs/I8j7JB5Pm+f5fljb5m/zN+P72/wCbPXdz1qQQQrcPcLFGJ3RUeQKNzKpJ UE9SAWYgdtx9aAJKKrpYWccVrFHaQJHaY+zIsYAhwpQbB/D8pK8diR0oews5IrqKS0geO7z9pRow RNlQh3j+L5QF57ADpQBYoqMwQtcJcNFGZ0RkSQqNyqxBYA9QCVUkd9o9Kj+wWfkeR9kg8nzfP8vy xt8zf5m/H97f82eu7nrQBYoqMQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9ajSws44rWKO0gSO0x9 mRYwBDhSg2D+H5SV47EjpQAXvleQvneft82PHkb927eu3OznbnG7Py7c7vlzViqdzp8M1vcwpBaA Xbg3QlgDrOuFVw4yNxMa7ATnHGQQMG5QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/Z/8lD1n/sFWH/o2 7roK5+z/AOSh6z/2CrD/ANG3dAHQUUUUAFFFFABRRRQAUUUUAFFFFABVe/RJNOuY5bP7bG0ThrXC nzgQcphyFOenzEDnk4qxVe/dI9OuZJbz7FGsTlrrKjyQAcvlwVGOvzAjjkYoAsUUUUAFFFFABRRR QAUUUUAFFFFABRRRQBXuER57QvZ+eySkpJhT9nOxhv5ORkEp8uT8/TGSLFV7h0Se0D3nkM8pCR5U faDsY7ORk4AL/Lg/J1xkGxQAUUUUAFFFFABRRRQAUUUUAFFFFABVe3REnuyln5DPKC8mFH2g7FG/ g5OAAnzYPydMYJsVXt3R57sJeeeySgPHlT9nOxTs4GRkEP8ANk/P1xgAAsUUUUAFFFFABRRRQAUU UUAFFFFABRRRQBXsESPTraOKz+xRrEgW1wo8kADCYQlRjp8pI44OKsVXsHSTTraSK8+2xtEhW6yp 84EDD5QBTnr8oA54GKsUAFFFFABRRRQAUUUUAFFFFABRRRQBXuER57QvZ+eySkpJhT9nOxhv5ORk Ep8uT8/TGSLFV7h0Se0D3nkM8pCR5UfaDsY7ORk4AL/Lg/J1xkGxQAUUUUAFFFFABRRRQAUUUUAF FFFABVdEQajNILPZI0UYa6wv7wAvhMg7vlyTyMfvOCTuxYqujodRmjF5vkWKMta5X92CXw+AN3zY I5OP3fAB3ZALFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAV7BEj062jis/sUaxIFtcKPJAAwmEJUY6 fKSOODirFV7B0k062kivPtsbRIVusqfOBAw+UAU56/KAOeBirFABRRRQAUUUUAFFFFABRRRQAUUU UAV71EkgUSWf2tRLGRHhTtIdSH+YgfIQH9fl4BOBViq966RwKZLz7IpljAkyo3EuoCfMCPnJCevz cEHBqxQAUUUUAFFFFABRRRQAUUUUAFFFFABVdEQajNILPZI0UYa6wv7wAvhMg7vlyTyMfvOCTuxY qujodRmjF5vkWKMta5X92CXw+AN3zYI5OP3fAB3ZALFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAV7 NEjgYR2f2RTLITHhRuJdiX+UkfOSX9fm5AORViq9m6SQMY7z7WolkBkyp2kOwKfKAPkIKevy8knJ qxQAUUUUAFFFFABRRRQAUUUUAFFFFAFe9RJIFEln9rUSxkR4U7SHUh/mIHyEB/X5eATgVYqveukc CmS8+yKZYwJMqNxLqAnzAj5yQnr83BBwasUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXP2f8AyUPWf+wV Yf8Ao27roK5+z/5KHrP/AGCrD/0bd0AdBRRRQAUUUUAFFFFABRRRQAUUUUAFRzmZbeVreOOScITG kjlFZscAsASBnvg49DUlV7/yv7OufP8AP8nyn3/Z9/mbcHOzy/n3Y6bfmz05oAsUUUUAFFFFABRR RQAUUUUAFFFFABRRRQBHIZg8IijjZC+JSzlSq7Tyowdx3bRg44JOeMGSq9x5Xn2nmefu80+X5W/b u2N9/bxtxn7/AMu7b/FtqxQAUUUUAFFFFABRRRQAUUUUAFFFFABUcZmLzCWONUD4iKuWLLtHLDA2 nduGBngA55wJKr2/lefd+X5+7zR5nm79u7Yv3N3G3GPufLu3fxbqALFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUARwGZreJriOOOcoDIkbl1VscgMQCRnvgZ9BUlV7Dyv7OtvI8/yfKTZ9o3+ZtwMb/M+f djru+bPXmrFABRRRQAUUUUAFFFFABRRRQAUUUUARyGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjBk qvceV59p5nn7vNPl+Vv27tjff28bcZ+/8u7b/FtqxQAUUUUAFFFFABRRRQAUUUUAFFFFABUYM32h 1aOMQBFKOHJYtk7gVxgADbg5OcngY5kqunlf2jNjz/O8qPdu3+Xty+Nufk3Z3Zx82Nu7jbQBYooo oAKKKKACiiigAooooAKKKKACiiigCOAzNbxNcRxxzlAZEjcuqtjkBiASM98DPoKkqvYeV/Z1t5Hn +T5SbPtG/wAzbgY3+Z8+7HXd82evNWKACiiigAooooAKKKKACiiigAooooAjmMyoDBHG771BDuVG 3cNxyAeQuSB3IAyM5ElV73yvIXzvP2+bHjyN+7dvXbnZztzjdn5dud3y5qxQAUUUUAFFFFABRRRQ AUUUUAFFFFABUYM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5kqunlf2jNjz/ADvKj3bt/l7cvjbn 5N2d2cfNjbu420AWKKKKACiiigAooooAKKKKACiiigAooooAjhMzITPHGj72ACOWG3cdpyQOSuCR 2JIycZMlV7PyvIbyfP2+bJnz9+7dvbdjfztznbj5duNvy4qxQAUUUUAFFFFABRRRQAUUUUAFFFFA EcxmVAYI43feoIdyo27huOQDyFyQO5AGRnIkqve+V5C+d5+3zY8eRv3bt67c7Oducbs/Ltzu+XNW KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigArn7P/koes/8AYKsP/Rt3XQVz9n/yUPWf+wVYf+jbugDoKKKK ACiiigAooooAKKKKACiiigAqOcTNbyrbyRxzlCI3kQuqtjglQQSM9sjPqKkqvfokmnXMctn9tjaJ w1rhT5wIOUw5CnPT5iBzycUAWKKKKACiiigAooooAKKKKACiiigAooqOeeK2gknnlSKGJS8kkjBV RQMkkngADvQlfRAZur313Fd2VhprQfbbne+J4yyLEoAZzhh91nj4GS2dvygl0qyXutaM8U2rSafd 6czYuLm3he3NouDh2UvJvUnAJyuwfMcrkqmkWrSyf2re6e8l3d3jyRvIig2sKq6QkByGQGPqoywe d8hQzAbs8EVzBJBPEksMqlJI5FDK6kYIIPBBHaupyhTfs2rrr/wH0t06O2ujsTvqSUVjeHp5Y4Jt Ju5Xku9OYQmSRiXmhIzFKSeWJXhmwAZEkA4FbNYVIcknEadwoooqBhRRRQAUUUUAFFFFABUcYmDz GWSNkL5iCoVKrtHDHJ3HduORjggY4yZKr26Ik92Us/IZ5QXkwo+0HYo38HJwAE+bB+TpjBIBYooo oAKKKKACiiigAooooAKKKKACiiigCOATLbxLcSRyThAJHjQorNjkhSSQM9snHqakqvYIkenW0cVn 9ijWJAtrhR5IAGEwhKjHT5SRxwcVYoAKKKKACiiigAooooAKKKKACiiigCOQTF4TFJGqB8yhkLFl 2nhTkbTu2nJzwCMc5ElV7hEee0L2fnskpKSYU/ZzsYb+TkZBKfLk/P0xkixQAUUUUAFFFFABRRRQ AUUUUAFFFFABUYE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54kquiINRmkFnskaKMNdYX94AXwmQd 3y5J5GP3nBJ3YALFFFFABRRRQAUUUUAFQXl7aadavdX11DbW0eN808gRFycDJPA5IH40Xt5Bp1hc X11J5dtbRNNK+CdqKCScDk8A9KzdN0xriddW1a3RtR3ObdXw32OMkhUTGQrFCN5BO5iRuKhANYQV uee35/11fQTZpWd7aajapdWN1Dc20mdk0EgdGwcHBHB5BH4VPWHrH/Ek87Xrf5Yl2vqMX8MkQwGm 9njQZyASyrsIJ2FNylOCSUo7P+rf8Hr+CEFFFFZjI4BMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqS q9giR6dbRxWf2KNYkC2uFHkgAYTCEqMdPlJHHBxVigAooooAKKKKACiiigAooooAKKKKAI5hMyAQ SRo+9SS6Fht3DcMAjkrkA9iQcHGDJVe9RJIFEln9rUSxkR4U7SHUh/mIHyEB/X5eATgVYoAKKKKA CiiigAooooAKKKKACiiigAqMCb7Q7NJGYCihECEMGydxLZwQRtwMDGDyc8SVXREGozSCz2SNFGGu sL+8AL4TIO75ck8jH7zgk7sAFiiiigAooooAKKKKACiiigAooooAKKKKAI4RMqETyRu+9iCiFRt3 HaMEnkLgE9yCcDOBJVezRI4GEdn9kUyyEx4UbiXYl/lJHzkl/X5uQDkVYoAKKKKACiiigAooooAK KKKACiiigCOYTMgEEkaPvUkuhYbdw3DAI5K5APYkHBxgyVXvUSSBRJZ/a1EsZEeFO0h1If5iB8hA f1+XgE4FWKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigDk7bX769+I39nQtGNFSyuVB2/NNcxSQB2Dcgool2 cYO9ZQw+UVHca7o+ifEPVP7W1WxsPO0qx8v7XcJFvxLd5xuIzjI6eoqxYeBdN0rxDZ6rYz3yLbxX KGCW+uJld5nRmf55COqsSMfMzBjyoNWLP/koes/9gqw/9G3dAB/wnfg//oa9D/8ABjD/APFVuGeF bhLdpYxO6M6RlhuZVIDEDqQCygntuHrUlFAFf7fZ+R5/2uDyfN8jzPMG3zN/l7M/3t/y467uOtSC eFrh7dZYzOiK7xhhuVWJCkjqASrAHvtPpUlFAFdL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCel D39nHFdSyXcCR2mftLtIAIcKHO8/w/KQ3PYg9KsUUARmeFbhLdpYxO6M6RlhuZVIDEDqQCygntuH rUf2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/lx13cdasUUARieFrh7dZYzOiK7xhhuVWJCkjqASrAHv tPpUaX9nJFayx3cDx3ePszrICJsqXGw/xfKC3HYE9KsUUAV3v7OOK6lku4EjtM/aXaQAQ4UOd5/h +UhuexB6VHqF3bQ280U2ox2Lm3kl80uitGigbpRvBGF3KSSCBkZ61cqOczLbytbxxyThCY0kcorN jgFgCQM98HHoaAI/t9n5Hn/a4PJ83yPM8wbfM3+Xsz/e3/Ljru461IJ4WuHt1ljM6IrvGGG5VYkK SOoBKsAe+0+lSUUAV0v7OSK1lju4Hju8fZnWQETZUuNh/i+UFuOwJ6UPf2ccV1LJdwJHaZ+0u0gA hwoc7z/D8pDc9iD0qxRQBGZ4VuEt2ljE7ozpGWG5lUgMQOpALKCe24etR/b7PyPP+1weT5vkeZ5g 2+Zv8vZn+9v+XHXdx1qxRQBGJ4WuHt1ljM6IrvGGG5VYkKSOoBKsAe+0+lRpf2ckVrLHdwPHd4+z OsgImypcbD/F8oLcdgT0qxRQBXe/s44rqWS7gSO0z9pdpABDhQ53n+H5SG57EHpUhnhW4S3aWMTu jOkZYbmVSAxA6kAsoJ7bh61JRQBX+32fkef9rg8nzfI8zzBt8zf5ezP97f8ALjru461j6zPDf+It J0ESxlw39pXMJYZ8mIjy+nIPnGJh0BEbjPBB6CsPTv8ATPFWs3b/APLn5WnxIedvyLM7qf4d/mxq QOvkqSTwBvQ0cp9l/wABfc3cTNA3dtcDT54dRjEVw+6Dy3QrdAxswUEg7hty/wApB+TOcZBke/s4 4rqWS7gSO0z9pdpABDhQ53n+H5SG57EHpUkhmDwiKONkL4lLOVKrtPKjB3HdtGDjgk54wZKwGc/f Tw6d420pvNjV9VgltGiDDfI0QMsb4P8AAgMwJHeVAQeCux9vs/I8/wC1weT5vkeZ5g2+Zv8AL2Z/ vb/lx13cday/GH7vwre3fX7Bs1DZ/f8As7rNsz23eXtzzjOcHGK3K3qe9ThP1X3f8B2+QluRieFr h7dZYzOiK7xhhuVWJCkjqASrAHvtPpUaX9nJFayx3cDx3ePszrICJsqXGw/xfKC3HYE9KsUVgMrv f2ccV1LJdwJHaZ+0u0gAhwoc7z/D8pDc9iD0qQzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9akooA r/b7PyPP+1weT5vkeZ5g2+Zv8vZn+9v+XHXdx1qQTwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9Kko oArpf2ckVrLHdwPHd4+zOsgImypcbD/F8oLcdgT0oe/s44rqWS7gSO0z9pdpABDhQ53n+H5SG57E HpViigCMzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9ap2+pWD/a5U1aCeNLsW7/voytvL8qeTkdGL EfKxLbnx0wBoVHGZi8wljjVA+Iirliy7RywwNp3bhgZ4AOecAABPC1w9ussZnRFd4ww3KrEhSR1A JVgD32n0qNL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelWKKAK739nHFdSyXcCR2mftLtIAIcK HO8/w/KQ3PYg9KkM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPWpKKAK/2+z8jz/tcHk+b5HmeYNvm b/L2Z/vb/lx13cdakE8LXD26yxmdEV3jDDcqsSFJHUAlWAPfafSpKKAK6X9nJFayx3cDx3ePszrI CJsqXGw/xfKC3HYE9KHv7OOK6lku4EjtM/aXaQAQ4UOd5/h+UhuexB6VYooAjM8K3CW7Sxid0Z0j LDcyqQGIHUgFlBPbcPWo/t9n5Hn/AGuDyfN8jzPMG3zN/l7M/wB7f8uOu7jrViigCMTwtcPbrLGZ 0RXeMMNyqxIUkdQCVYA99p9KjS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpViigDPs9SsP7JN wNWgure2iUz3jTR4x5Yfe5XCjKMr8ADDAgAEVcM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPWiAzN bxNcRxxzlAZEjcuqtjkBiASM98DPoKkoAr/b7PyPP+1weT5vkeZ5g2+Zv8vZn+9v+XHXdx1qQTwt cPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KkooArpf2ckVrLHdwPHd4+zOsgImypcbD/ABfKC3HYE9KH v7OOK6lku4EjtM/aXaQAQ4UOd5/h+UhuexB6VYooAjM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPW o/t9n5Hn/a4PJ83yPM8wbfM3+Xsz/e3/AC467uOtWKKAIxPC1w9ussZnRFd4ww3KrEhSR1AJVgD3 2n0qNL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelWKKAK739nHFdSyXcCR2mftLtIAIcKHO8/w /KQ3PYg9KkM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPWpKKAM+41KwT7JK+rQQRvdm3T99GFuJfm TycnqwYH5VIbcmOmQbgnha4e3WWMzoiu8YYblViQpI6gEqwB77T6USGYPCIo42QviUs5Uqu08qMH cd20YOOCTnjBkoArpf2ckVrLHdwPHd4+zOsgImypcbD/ABfKC3HYE9KHv7OOK6lku4EjtM/aXaQA Q4UOd5/h+UhuexB6VYooAjM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPWo/t9n5Hn/a4PJ83yPM8w bfM3+Xsz/e3/AC467uOtWKKAIxPC1w9ussZnRFd4ww3KrEhSR1AJVgD32n0qNL+zkitZY7uB47vH 2Z1kBE2VLjYf4vlBbjsCelWKKAK739nHFdSyXcCR2mftLtIAIcKHO8/w/KQ3PYg9KkM8K3CW7Sxi d0Z0jLDcyqQGIHUgFlBPbcPWpKKAK/2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/AJcdd3HWpBPC1w9u ssZnRFd4ww3KrEhSR1AJVgD32n0qSigCul/ZyRWssd3A8d3j7M6yAibKlxsP8Xygtx2BPSq/9pWE d5qBfVoP9CiQ3UDTRgWgwzb3/iXcvPzHGEBAHJOhUYM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5 AAzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9aj+32fkef9rg8nzfI8zzBt8zf5ezP97f8uOu7jrVi igCMTwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KjS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47Anp ViigCu9/ZxxXUsl3Akdpn7S7SACHChzvP8PykNz2IPSpDPCtwlu0sYndGdIyw3MqkBiB1IBZQT23 D1qSigDm9Sv7PVNXs9PF3AbK2lS5vJPMG0yCYxwQ54+Yzox4OQ0Gwj566ATwtcPbrLGZ0RXeMMNy qxIUkdQCVYA99p9Kx/DX+mx3euHkapKJbcnkrbKoWIA9drAGXaQNpmYEZyTuVvX91qmvs/n1/HT0 SEu5XS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpWH4ev7PTrbVdIlu4Ei0CXyizyBfKtjGssZ bP3VVG2bmJ3eUWJyTjpKw7z/AEDxdp10OI9RiewlA5LSIGmiJz0VVFyOOSXXIOMqUfejKHdX+a1/ K/3g+5sGeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrUf2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/lx1 3cdasUVgMp6fd201vDFDqMd84t45fNDozSIwO2U7ABhtrEEAA4OOlSJf2ckVrLHdwPHd4+zOsgIm ypcbD/F8oLcdgT0qSAzNbxNcRxxzlAZEjcuqtjkBiASM98DPoKkoArvf2ccV1LJdwJHaZ+0u0gAh woc7z/D8pDc9iD0qQzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9akooAr/b7PyPP+1weT5vkeZ5g2 +Zv8vZn+9v8Alx13cdakE8LXD26yxmdEV3jDDcqsSFJHUAlWAPfafSpKKAK6X9nJFayx3cDx3ePs zrICJsqXGw/xfKC3HYE9KHv7OOK6lku4EjtM/aXaQAQ4UOd5/h+UhuexB6VYooAjM8K3CW7Sxid0 Z0jLDcyqQGIHUgFlBPbcPWo/t9n5Hn/a4PJ83yPM8wbfM3+Xsz/e3/Ljru461YooAjE8LXD26yxm dEV3jDDcqsSFJHUAlWAPfafSo0v7OSK1lju4Hju8fZnWQETZUuNh/i+UFuOwJ6VYooAz9Q1Kwt7O 4muNWgsobSWMXE7TRqIjlG2OWyF3BlHODhxggkGrhnhW4S3aWMTujOkZYbmVSAxA6kAsoJ7bh60T GZUBgjjd96gh3KjbuG45APIXJA7kAZGciSgCv9vs/I8/7XB5Pm+R5nmDb5m/y9mf72/5cdd3HWpB PC1w9ussZnRFd4ww3KrEhSR1AJVgD32n0qSigCul/ZyRWssd3A8d3j7M6yAibKlxsP8AF8oLcdgT 0oe/s44rqWS7gSO0z9pdpABDhQ53n+H5SG57EHpViigCMzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9t w9aj+32fkef9rg8nzfI8zzBt8zf5ezP97f8ALjru461YooAjE8LXD26yxmdEV3jDDcqsSFJHUAlW APfafSo0v7OSK1lju4Hju8fZnWQETZUuNh/i+UFuOwJ6VYooArvf2ccV1LJdwJHaZ+0u0gAhwoc7 z/D8pDc9iD0qQzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9akooAr/b7PyPP+1weT5vkeZ5g2+Zv8 vZn+9v8Alx13cdaEdDqM0YvN8ixRlrXK/uwS+HwBu+bBHJx+74AO7NiowZvtDq0cYgCKUcOSxbJ3 ArjAAG3Byc5PAxyARpf2ckVrLHdwPHd4+zOsgImypcbD/F8oLcdgT0oe/s44rqWS7gSO0z9pdpAB DhQ53n+H5SG57EHpViigCMzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9aj+32fkef9rg8nzfI8zzB t8zf5ezP97f8uOu7jrViigCMTwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KjS/s5IrWWO7geO7x9m dZARNlS42H+L5QW47AnpViigCu9/ZxxXUsl3Akdpn7S7SACHChzvP8PykNz2IPSpDPCtwlu0sYnd GdIyw3MqkBiB1IBZQT23D1qSigCv9vs/I8/7XB5Pm+R5nmDb5m/y9mf72/5cdd3HWpBPC1w9ussZ nRFd4ww3KrEhSR1AJVgD32n0qSigCul/ZyRWssd3A8d3j7M6yAibKlxsP8Xygtx2BPSh7+zjiupZ LuBI7TP2l2kAEOFDnef4flIbnsQelWKKAKdpd2zEQLqMd1K7zMuXQthJNrqAoHEbMEPGRwGJPJk+ 32fkef8Aa4PJ83yPM8wbfM3+Xsz/AHt/y467uOtSQmZkJnjjR97ABHLDbuO05IHJXBI7EkZOMmSg CMTwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KjS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpVi igCu9/ZxxXUsl3Akdpn7S7SACHChzvP8PykNz2IPSpDPCtwlu0sYndGdIyw3MqkBiB1IBZQT23D1 qSigCv8Ab7PyPP8AtcHk+b5HmeYNvmb/AC9mf72/5cdd3HWpBPC1w9ussZnRFd4ww3KrEhSR1AJV gD32n0qSigCul/ZyRWssd3A8d3j7M6yAibKlxsP8Xygtx2BPSh7+zjiupZLuBI7TP2l2kAEOFDne f4flIbnsQelWKKAIzPCtwlu0sYndGdIyw3MqkBiB1IBZQT23D1qP7fZ+R5/2uDyfN8jzPMG3zN/l 7M/3t/y467uOtWKKAKd3d2ykwNqMdrKjws2HQNh5NqKQwPEjKUHGTyFIPIuVHMZlQGCON33qCHcq Nu4bjkA8hckDuQBkZyJKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn7P/AJKHrP8A2CrD/wBG3ddBXP2f /JQ9Z/7BVh/6Nu6AOgrD1Avrd/Lo0E00Ftb+XJeXMErRuSTuWFHUgqxCgucghHUAfvNy6OqahFpO k3mpTq7Q2kDzyLGAWKopYgZIGcD1qDQ9Pl0/TQLpke/nYz3kiEkNM3LBSRkqvCLnkIqjtW9P3Iur 16evf5fm0J66FGfwdpEcEjaPZW2j3+0+Td2MIiZGx8u4Jt8xQcEo2VbAyK0tK1H+0rV3eLyLmGVo LiDdny5FODzgEqRhlJAJVlbAzV6sO5/4lHiSG8Hy2ep7ba47Kk4/1Uh6AbhmMsckkQKKpTlWTjN3 e6/Vf1123FaxuUUUVzFBRRRQAUUUUAFV7/yv7OufP8/yfKff9n3+Ztwc7PL+fdjpt+bPTmrFRziZ reVbeSOOcoRG8iF1VscEqCCRntkZ9RQBJRRRQAUUUUAFFFFABRRRQAUUUUAFYfg/954Vsrvp9v36 hs/ufaHabZnvt8zbnjOM4GcU/wAVzyw+GL5LeV4rq5UWdvKjFTHNMwijbI5ADupJHIAJAJ4rVggi toI4IIkihiUJHHGoVUUDAAA4AA7VvtQ9X+S/+2F1I7jyvPtPM8/d5p8vyt+3dsb7+3jbjP3/AJd2 3+LbVio5BMXhMUkaoHzKGQsWXaeFORtO7acnPAIxzkSVgMKw/B/7vwrZWnX7Bv0/f/f+zu0O/Hbd 5e7HOM4ycZrcrD0n/R/EviG1fmSaWC/UjoI3iWEA/wC1utnPpgrzyQN6etKce1n92n6/gJ7m5RRR WAwooooAKKKKACiiigAqvb+V5935fn7vNHmebv27ti/c3cbcY+58u7d/FuqxUcYmDzGWSNkL5iCo VKrtHDHJ3HduORjggY4yQCSiiigAooooAKKKKACiiigAooooAKKKKAK9h5X9nW3kef5PlJs+0b/M 24GN/mfPux13fNnrzVio4BMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqSgAooooAKKKKACiiigAooo oAKKKKAK9x5Xn2nmefu80+X5W/bu2N9/bxtxn7/y7tv8W2rFRyCYvCYpI1QPmUMhYsu08Kcjad20 5OeARjnIkoAKKKKACiiigAooooAKKKKACiiigAqunlf2jNjz/O8qPdu3+Xty+Nufk3Z3Zx82Nu7j bViowJvtDs0kZgKKEQIQwbJ3EtnBBG3AwMYPJzwASUUUUAFFFFABWV4kvJ7LQLp7STy7yXZbWshA ISaVhFGxBz8od1J4PAPB6Vq1h6x/pev6Fp45Allv5Vb7jxxLtAI7sJZoXGeBszkEDO2HSdRN7LX7 tf0E9jVsrODTrC3sbWPy7a2iWGJMk7UUAAZPJ4A61PRRWTbbuxhWH4w/d+Fb276/YNmobP7/ANnd Ztme27y9uecZzg4xW5RV0p+znGfZpiaugorD8H/u/CtladfsG/T9/wDf+zu0O/Hbd5e7HOM4ycZr coqw9nOUOzsCd1cr2Hlf2dbeR5/k+Umz7Rv8zbgY3+Z8+7HXd82evNWKjgEy28S3Ekck4QCR40KK zY5IUkkDPbJx6mpKzGFFFFABRRRQAUUUUAFFFFABRRRQBXvfK8hfO8/b5sePI37t29dudnO3ON2f l253fLmrFRzCZkAgkjR96kl0LDbuG4YBHJXIB7Eg4OMGSgAooooAKKKKACiiigAooooAKKKKACq6 eV/aM2PP87yo927f5e3L425+TdndnHzY27uNtWKjAm+0OzSRmAooRAhDBsncS2cEEbcDAxg8nPAB JRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFez8ryG8nz9vmyZ8/fu3b23Y387c524+Xbjb8uKsVHCJ lQieSN33sQUQqNu47Rgk8hcAnuQTgZwJKACiiigAooooAKKKKACiiigAooooAr3vleQvneft82PH kb927eu3OznbnG7Py7c7vlzVio5hMyAQSRo+9SS6Fht3DcMAjkrkA9iQcHGDJQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFc/Z/wDJQ9Z/7BVh/wCjbuugrn7P/koes/8AYKsP/Rt3QBJrn+l6rommD51kuTdz x9MxQruDZ9p2t+Acn0K7q3Kw7D/TPF2r3XWOzihsED9UkwZpCvorLLAD3Jj5HyqTuVvW91Rh2X56 /k0vkJBVHWNO/tXSprRZfJlba8M23d5UqMHjfbkbtrqrYPBxg8Gr1FZRk4SUo7oZR0XUf7Y0HTtT 8ryftltHceXu3bN6hsZwM4z1xV6sPQf9Dv8AV9Ibjybk3cOeWaK4JkLEjj/XeeoHBAQZHIJ3KuvF RqNR23Xo9V+AlsFFFFZDCiiigAqvfokmnXMctn9tjaJw1rhT5wIOUw5CnPT5iBzycVYqvfukenXM kt59ijWJy11lR5IAOXy4KjHX5gRxyMUAWKKKKACiiigAooooAKKKKACiiigDD8Q/vbzQLR+YLjU1 81P73lwyzJz1GJIo2467cHgkHcrDl/f+O7Ty/m+yaZP5/bZ50sXl/XPkS9Om3nGRncrerpCEfL82 /wBLfKwl1K9wiPPaF7Pz2SUlJMKfs52MN/JyMglPlyfn6YyRYqvcOiT2ge88hnlISPKj7QdjHZyM nABf5cH5OuMg2KwGFYdx/ovjeykHyR31jLDKx6SSROjRICf4gsly2ByRuJzt43Kw/Ef7mbRL9uYr TU494H3j5qPbrj6POhPsD1OAd8PrPl7pr8NPxt8xM3KKKKwGFFFFABRRRQAVR1HUfsflwQRfaL6f Igtw23djGWY4O1FyNzYOMgAFmVTannitoJJ55UihiUvJJIwVUUDJJJ4AA71leHoJZIJtWu4nju9R YTGORSHhhAxFEQeVIXllyQJHkI4Na04qznLZfi/63+7S9xPsM/tnUrD95relw29p/FdWl358cA7t LuSNlXkcqGAGSxUDNaluiJPdlLPyGeUF5MKPtB2KN/BycABPmwfk6YwTYrm/Dbppuo6h4dF550Vm Y2sUJU+Tb+Wg8nIAJZDgnOSFliJYlqpqNSLcVZrprt87/wDDdramx0lFFFYDCiiigAooooAKKKKA CiiigAooooAr2CJHp1tHFZ/Yo1iQLa4UeSABhMISox0+UkccHFWKr2DpJp1tJFefbY2iQrdZU+cC Bh8oApz1+UAc8DFWKACiiigAooooAKKKKACiiigAooooAr3CI89oXs/PZJSUkwp+znYw38nIyCU+ XJ+fpjJFiq9w6JPaB7zyGeUhI8qPtB2MdnIycAF/lwfk64yDYoAKKKKACiiigAooooAKKKKACiii gAquiINRmkFnskaKMNdYX94AXwmQd3y5J5GP3nBJ3YsVXR0OozRi83yLFGWtcr+7BL4fAG75sEcn H7vgA7sgFiiiigAooooAKw9B/wBMv9X1dufOuTaQ54ZYrcmMqQOP9d57A8khxk8ADR1TUItJ0m81 KdXaG0geeRYwCxVFLEDJAzgetQeH9Pl0rw9p9lcMj3UUCi4kQkiSbGZHyQCSzlmJPJJJPJrePu0Z PvZfq/0F1NKiiisBhRRRQBh6P/omv67p54Bliv4lX7iRyrtIA7MZYZnOODvzkknG5WHL+48d2nl/ L9r0yfz++/yZYvL+mPPl6dd3OcDG5W9fVxn3S/DR/imJFewRI9Oto4rP7FGsSBbXCjyQAMJhCVGO nykjjg4qxVewdJNOtpIrz7bG0SFbrKnzgQMPlAFOevygDngYqxWAwooooAKKKKACiiigAooooAKK KKAK96iSQKJLP7WoljIjwp2kOpD/ADED5CA/r8vAJwKsVXvXSOBTJefZFMsYEmVG4l1AT5gR85IT 1+bgg4NWKACiiigAooooAKKKKACiiigAooooAKroiDUZpBZ7JGijDXWF/eAF8JkHd8uSeRj95wSd 2LFV0dDqM0YvN8ixRlrXK/uwS+HwBu+bBHJx+74AO7IBYooooAKKKKACiiigAooooAKKKKACiiig CvZokcDCOz+yKZZCY8KNxLsS/wApI+ckv6/NyAcirFV7N0kgYx3n2tRLIDJlTtIdgU+UAfIQU9fl 5JOTVigAooooAKKKKACiiigAooooAKKKKAK96iSQKJLP7WoljIjwp2kOpD/MQPkID+vy8AnAqxXP an4i8vctpZahcQx3CRPd2r2yR+b5gBhBmddxJwh2g8sVBDg7ehq505Qtzdf6+XzFcKKKKgYUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAVz9n/AMlD1n/sFWH/AKNu66CuG1+eWHXPE6W8rxXVzo+n2dvKjFTHNNPdRRtk cgB3UkjkAEgE8VdOHtJqC6uwPQ3fCX77QI9QPJ1KWS/DN98xysWiDn+8sRjTuBsABIArcqOCCK2g jggiSKGJQkccahVRQMAADgADtUlOrPnqSkurElZBRRRWYzDuv9C8ZWFx9yLULaS0kbr5ksZ8yFfb CG6OeB2JztFblYfiv9xpUWpj5W0y5iuzJ18uINtnbHf9w03GCeeBuxW5W9X3qcJ+q+7/AIDS+Qlu FFFFYDCiiigAqOczLbytbxxyThCY0kcorNjgFgCQM98HHoakqvf+V/Z1z5/n+T5T7/s+/wAzbg52 eX8+7HTb82enNAFiiiigAooooAKKKKACiiigAooooAw9L/0nxVr9393yfs+n7OudiGbfn3+07cf7 Gc84G5WH4W/e2eoXb8z3Gp3fmv8A3vLmaFOOgxHFGvHXbk8kk7lb4n+I49rL7lb9BLYjkMweERRx shfEpZypVdp5UYO47towccEnPGDJVe48rz7TzPP3eafL8rft3bG+/t424z9/5d23+LbVisBhWH4x +TwjqV0P9ZYxfb4wehkgImQH/ZLRgHGDjOCOtblRzwRXMEkE8SSwyqUkjkUMrqRggg8EEdq0oz9n UjN9GmJ6okorG8Izy3PgvQp55Xlml0+3eSSRizOxjUkknkknvWzSqQ5JuD6OwJ3QUUUVAwooooAw 9f8A9OutN0QfNHeStLdp620Qyw54KtIYY2U53LIwx1I3Kw9O/wBM8Vazdv8A8uflafEh52/Iszup /h3+bGpA6+SpJPAG5W9b3VGHZX+b1/Ky+Ql3Cuf8RmaztH10xxo2kObhSjktNbbB5yMMDkjcVXO0 vHExPUDoKr2/lefd+X5+7zR5nm79u7Yv3N3G3GPufLu3fxbqilPkmpf1bqvmDVyxRWH4a/0KO70M 8DS5RFbg8FrZlDREDrtUExbiTuMLEnOQNyirDkk4/wBW6fgCdwooorMYUUUUAFFFFABRRRQAUUUU ARwGZreJriOOOcoDIkbl1VscgMQCRnvgZ9BUlV7Dyv7OtvI8/wAnyk2faN/mbcDG/wAz592Ou75s 9easUAFFFFABRRRQAUUUUAFFFFABRRRQBHIZg8IijjZC+JSzlSq7Tyowdx3bRg44JOeMGSq9x5Xn 2nmefu80+X5W/bu2N9/bxtxn7/y7tv8AFtqxQAUUUUAFFFFABRRRQAUUUUAFFFFABUYM32h1aOMQ BFKOHJYtk7gVxgADbg5OcngY5kqunlf2jNjz/O8qPdu3+Xty+Nufk3Z3Zx82Nu7jbQBYooooAKKK KAMPxX+/0qLTB8zancxWhj6eZEW3Trnt+4WbnIPHB3YrcrD1L/SPF2hWr8RwxXN+pHUyIEhAP+zt uXPrkLzwQdyt6mlOEfV/jb9EJbsKKKKwGFFFFAGH4j/czaJftzFaanHvA+8fNR7dcfR50J9gepwD uVh+Mfk8I6ldD/WWMX2+MHoZICJkB/2S0YBxg4zgjrW5W89aMH6r5aP9WJbkcBma3ia4jjjnKAyJ G5dVbHIDEAkZ74GfQVJVew8r+zrbyPP8nyk2faN/mbcDG/zPn3Y67vmz15qxWAwooooAKKKKACii igAooooAKKKKAI5jMqAwRxu+9QQ7lRt3DccgHkLkgdyAMjORJVe98ryF87z9vmx48jfu3b1252c7 c43Z+Xbnd8uasUAFFFFABRRRQAUUUUAFFFFABRRRQAVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4 GOZKrp5X9ozY8/zvKj3bt/l7cvjbn5N2d2cfNjbu420AWKKKKACiiigAooooAKKKKACiiigAoooo AjhMzITPHGj72ACOWG3cdpyQOSuCR2JIycZMlV7PyvIbyfP2+bJnz9+7dvbdjfztznbj5duNvy4q xQAUUVU1DUItOgWSRXkkkby4YIgDJM5BIVQSBnAJySAACSQASHGLk7IC3RWH/Zet3n7661+aykPS DToYfLQdcFpY3Z2HI3DYCAPkXnJu1nR/mmk/tewXl5PLCXca9ztRds3XoqoQq8CRjg7ewT0jNN9t f1Vvx9BXNyio4J4rmCOeCVJYZVDxyRsGV1IyCCOCCO9SVg1bRjCiiigArG8QzyyQQ6TaSvHd6ixh EkbEPDCBmWUEcqQvCtggSPGDwa2aw9O/4mXiTUNRbmKyzp9qRyp+48zhh1y4SMjnaYDyCWA3oKzc 39nX59Px/C4n2NTyjaWkUFjbwKkeyNIs+WiRggHbgHoucDGCQBkA5Fiq975XkL53n7fNjx5G/du3 rtzs525xuz8u3O75c1YrBu+rGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHHxaHpdt4qsBoNhBBcWUrPqt5G mHkRoXAill+9LIzvHKQxP3A7EEpux/EV7PH8UI7GDTLvUVltbG4ngtWiDFIXvHU/O64Al8ls5A+X aT8wVus0vwlpWjXCzWTakpV3fZJqlzLGWcksSjyFSSWJyQeTnrzVGDT4JPivqGpFpPtEGh2sCqCN pWSediTxnOYh37n8NqElGXM+if320/ETL0Gu6jNcRRP4T1mBHcK0sktmVQE/eO2cnA68An0BrUS4 leK1c2U6NNjzEYpm3+Ut8+GwcEBflLckdskWKKxGV3uJUiunFlO7Q58tFKZuPlDfJlsDJJX5ivIP bBMhkYXCRCGQoyMxlBXapBGFPOcnJIwCPlOSOMyUUAUL2NNR0m4tbrT55IbktbSwb1DNGzFC2Q3C 7SX4O7b23fLVXwvqF1qPh/TpbxJGuTZxG4mIQAz4KyoVByrqykMCoAJwOQQNmsPw1+4/tfT/AL32 TU5v3nTf52Lnp2x5+33254zgbx96jJdmn8tn+Nv6QupqJcSvFaubKdGmx5iMUzb/AClvnw2DggL8 pbkjtkge4lSK6cWU7tDny0Upm4+UN8mWwMklfmK8g9sE2KKwGRmRhcJEIZCjIzGUFdqkEYU85yck jAI+U5I4zH9ol8jzPsU+7zfL8vKbtu/bv+9jbj5+u7b23fLViigCMSMbh4jDIEVFYSkrtYknKjnO RgE5AHzDBPOK8k93LZQmC2kguJ0x++VHFqxQkGRVcbgGAUhGOSRg4ywuVXv0STTrmOWz+2xtE4a1 wp84EHKYchTnp8xA55OKAB7iVIrpxZTu0OfLRSmbj5Q3yZbAySV+YryD2wTIZGFwkQhkKMjMZQV2 qQRhTznJySMAj5TkjjMlFAFf7RL5HmfYp93m+X5eU3bd+3f97G3Hz9d23tu+WpBIxuHiMMgRUVhK Su1iScqOc5GATkAfMME84kooArpcSvFaubKdGmx5iMUzb/KW+fDYOCAvyluSO2SB7iVIrpxZTu0O fLRSmbj5Q3yZbAySV+YryD2wTYooAjMjC4SIQyFGRmMoK7VIIwp5zk5JGAR8pyRxmP7RL5HmfYp9 3m+X5eU3bd+3f97G3Hz9d23tu+WrFFAEYkY3DxGGQIqKwlJXaxJOVHOcjAJyAPmGCecRpcSvFaub KdGmx5iMUzb/AClvnw2DggL8pbkjtkixWN4unltvBeuzwSvFNFp9w8ckbFWRhGxBBHIIPerpw9pN QXV2B6FTwfcSjwHps/2KeSUWMUxAKbruRolkZwS2NzOzAlypLbieDuPQGRhcJEIZCjIzGUFdqkEY U85yckjAI+U5I4yQQRW0EcEESRQxKEjjjUKqKBgAAcAAdqkp1Z89SU11bYkrIpyT3ZSGWK2kXFxs lhZUZ2j3FNyneAo+7Jzk7QRt3HAsCRjcPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xHcIjz2hez89kl JSTCn7OdjDfycjIJT5cn5+mMkWKzGV0uJXitXNlOjTY8xGKZt/lLfPhsHBAX5S3JHbJA9xKkV04s p3aHPlopTNx8ob5MtgZJK/MV5B7YJsUUAc/4WkaO1ubQwybl1C/aVsqBETcu6KwJ3AskiuvGCvOR lc7H2iXyPM+xT7vN8vy8pu279u/72NuPn67tvbd8tZejfufEPiO3k4lkuYbtF9YmgjjVvxeCUY6/ LnGCCdyt8T/Fcu9n96v+olsRiRjcPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xGlxK8Vq5sp0abHmIx TNv8pb58Ng4IC/KW5I7ZIsUVgMrvcSpFdOLKd2hz5aKUzcfKG+TLYGSSvzFeQe2CS4u0tSXnXy7Z InlluXdVjiC4+8ScjIJOcYAU5I4zYrD8YfvPCt7adPt+zT9/9z7Q6w78d9vmbscZxjIzmtKMPaVI wfVpCeiIvCjXUPhGxnuNOuory5c3FxbPsV45JpS8hwW4QF2YAncFABBbit0SMbh4jDIEVFYSkrtY knKjnORgE5AHzDBPOJKKVSfPNzfV3BKyK6XErxWrmynRpseYjFM2/wApb58Ng4IC/KW5I7ZIjE93 GNQd7aSURPm2jjVFaVRGpwCXwSW3DLbPTGBua5Ve3REnuyln5DPKC8mFH2g7FG/g5OAAnzYPydMY JgZj6hI2m+MNOvPJkFpfwNZXVwCuxJVYNbh8nKgl51BA5aRQeStbH2iXyPM+xT7vN8vy8pu279u/ 72NuPn67tvbd8tVdf0+XU9Du7W2ZEuyoktXkJCxzoQ8THAPCuqnGCDjkEcVPpeoRatpNnqUCusN3 Ak8ayABgrqGAOCRnB9a3n71OM+q0f6fqvRCW5OJGNw8RhkCKisJSV2sSTlRznIwCcgD5hgnnEaXE rxWrmynRpseYjFM2/wApb58Ng4IC/KW5I7ZIsUVgMrvcSpFdOLKd2hz5aKUzcfKG+TLYGSSvzFeQ e2CZDIwuEiEMhRkZjKCu1SCMKec5OSRgEfKckcZkooAr/aJfI8z7FPu83y/Lym7bv27/AL2NuPn6 7tvbd8tSCRjcPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xJRQBXS4leK1c2U6NNjzEYpm3+Ut8+GwcE BflLckdskD3EqRXTiyndoc+WilM3Hyhvky2Bkkr8xXkHtgmxRQBGZGFwkQhkKMjMZQV2qQRhTznJ ySMAj5TkjjMf2iXyPM+xT7vN8vy8pu279u/72NuPn67tvbd8tWKKAK9s9wNsNym+RIkL3CKEjkc5 DBV3My4xnB7MMFjnAlxK8Vq5sp0abHmIxTNv8pb58Ng4IC/KW5I7ZILBEj062jis/sUaxIFtcKPJ AAwmEJUY6fKSOODirFAFd7iVIrpxZTu0OfLRSmbj5Q3yZbAySV+YryD2wTIZGFwkQhkKMjMZQV2q QRhTznJySMAj5TkjjMlFAFf7RL5HmfYp93m+X5eU3bd+3f8Aextx8/Xdt7bvlqQSMbh4jDIEVFYS krtYknKjnORgE5AHzDBPOJKKAK6XErxWrmynRpseYjFM2/ylvnw2DggL8pbkjtkge4lSK6cWU7tD ny0Upm4+UN8mWwMklfmK8g9sE2KKAIzIwuEiEMhRkZjKCu1SCMKec5OSRgEfKckcZj+0S+R5n2Kf d5vl+XlN23ft3/extx8/Xdt7bvlqxRQBGJGNw8RhkCKisJSV2sSTlRznIwCcgD5hgnnEaXErxWrm ynRpseYjFM2/ylvnw2DggL8pbkjtkixRQBTnnuwJXhtpCLdyTGVQtdL5eQIzvAU7iBl8fdYYAIar BkYXCRCGQoyMxlBXapBGFPOcnJIwCPlOSOMx3CI89oXs/PZJSUkwp+znYw38nIyCU+XJ+fpjJFig Cv8AaJfI8z7FPu83y/Lym7bv27/vY24+fru29t3y1IJGNw8RhkCKisJSV2sSTlRznIwCcgD5hgnn ElFAFdLiV4rVzZTo02PMRimbf5S3z4bBwQF+UtyR2yQPcSpFdOLKd2hz5aKUzcfKG+TLYGSSvzFe Qe2CbFFAEZkYXCRCGQoyMxlBXapBGFPOcnJIwCPlOSOMx/aJfI8z7FPu83y/Lym7bv27/vY24+fr u29t3y1YooAjEjG4eIwyBFRWEpK7WJJyo5zkYBOQB8wwTziNLiV4rVzZTo02PMRimbf5S3z4bBwQ F+UtyR2yRYooArvcSpFdOLKd2hz5aKUzcfKG+TLYGSSvzFeQe2CZDIwuEiEMhRkZjKCu1SCMKec5 OSRgEfKckcZkooAr/aJfI8z7FPu83y/Lym7bv27/AL2NuPn67tvbd8tCPcHUZkZMWoijMbbRy5L7 hndk4ATjaMZ6tkhbFV0RBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7AAJcSvFaubKdGmx5iMUz b/KW+fDYOCAvyluSO2SB7iVIrpxZTu0OfLRSmbj5Q3yZbAySV+YryD2wTYooAjMjC4SIQyFGRmMo K7VIIwp5zk5JGAR8pyRxmP7RL5HmfYp93m+X5eU3bd+3f97G3Hz9d23tu+WrFFAGHYfvvGWtXEfM UdtaWjt6SqZZGX8EniOenzYzkEDUS4leK1c2U6NNjzEYpm3+Ut8+GwcEBflLckdskZfhj99Dql+3 Et3qdzvA+6PKf7OuPqkCE+5PQYA3K3xP8S3ay+aST/IS2K73EqRXTiyndoc+WilM3Hyhvky2Bkkr 8xXkHtgmQyMLhIhDIUZGYygrtUgjCnnOTkkYBHynJHGZKKwGV/tEvkeZ9in3eb5fl5Tdt37d/wB7 G3Hz9d23tu+WpBIxuHiMMgRUVhKSu1iScqOc5GATkAfMME84kooAoXUaanpa2l5p85gv4jDcws6g woyHcHKt/wAB+Qtywxxkij4b1S8vvB1hqFzbTz3TWMMx2+WpunaFXJQbgoyzFfm28g9BgndrD8K/ Jpt5C3Esep3u9D95d1zI65HbKOjD1DA9CK3jrQl5Nfk7/p+AupqQvcJ9nhuE82QxEy3EShI942jG 0sWG7JIHzYCnJzjJ9ol8jzPsU+7zfL8vKbtu/bv+9jbj5+u7b23fLRYIkenW0cVn9ijWJAtrhR5I AGEwhKjHT5SRxwcVYrAZGJGNw8RhkCKisJSV2sSTlRznIwCcgD5hgnnEaXErxWrmynRpseYjFM2/ ylvnw2DggL8pbkjtkixRQBXe4lSK6cWU7tDny0Upm4+UN8mWwMklfmK8g9sEyGRhcJEIZCjIzGUF dqkEYU85yckjAI+U5I4zJRQBX+0S+R5n2Kfd5vl+XlN23ft3/extx8/Xdt7bvlqQSMbh4jDIEVFY SkrtYknKjnORgE5AHzDBPOJKKAK6XErxWrmynRpseYjFM2/ylvnw2DggL8pbkjtkge4lSK6cWU7t Dny0Upm4+UN8mWwMklfmK8g9sE2KKAIzIwuEiEMhRkZjKCu1SCMKec5OSRgEfKckcZj+0S+R5n2K fd5vl+XlN23ft3/extx8/Xdt7bvlqxRQBXle4eVooU8rb5b+dIodHBY70ADBg20dSMDep+bBWhLi V4rVzZTo02PMRimbf5S3z4bBwQF+UtyR2yQXqJJAoks/taiWMiPCnaQ6kP8AMQPkID+vy8AnAqxQ BXe4lSK6cWU7tDny0Upm4+UN8mWwMklfmK8g9sEyGRhcJEIZCjIzGUFdqkEYU85yckjAI+U5I4zJ RQBX+0S+R5n2Kfd5vl+XlN23ft3/AHsbcfP13be275akEjG4eIwyBFRWEpK7WJJyo5zkYBOQB8ww TziSigCulxK8Vq5sp0abHmIxTNv8pb58Ng4IC/KW5I7ZIHuJUiunFlO7Q58tFKZuPlDfJlsDJJX5 ivIPbBNiigCMyMLhIhDIUZGYygrtUgjCnnOTkkYBHynJHGY/tEvkeZ9in3eb5fl5Tdt37d/3sbcf P13be275asUUARiRjcPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xGlxK8Vq5sp0abHmIxTNv8pb58Ng 4IC/KW5I7ZIsUUAV3uJUiunFlO7Q58tFKZuPlDfJlsDJJX5ivIPbBJvuF1HYU3WrxZVlUDy3B5DE tk7gwwAvGxsnlRViq6Ig1GaQWeyRoow11hf3gBfCZB3fLknkY/ecEndgAPtEvkeZ9in3eb5fl5Td t37d/wB7G3Hz9d23tu+WpBIxuHiMMgRUVhKSu1iScqOc5GATkAfMME84kooArpcSvFaubKdGmx5i MUzb/KW+fDYOCAvyluSO2SB7iVIrpxZTu0OfLRSmbj5Q3yZbAySV+YryD2wTYooAjMjC4SIQyFGR mMoK7VIIwp5zk5JGAR8pyRxmP7RL5HmfYp93m+X5eU3bd+3f97G3Hz9d23tu+WrFFAEYkY3DxGGQ IqKwlJXaxJOVHOcjAJyAPmGCecRpcSvFaubKdGmx5iMUzb/KW+fDYOCAvyluSO2SLFFAFd7iVIrp xZTu0OfLRSmbj5Q3yZbAySV+YryD2wTIZGFwkQhkKMjMZQV2qQRhTznJySMAj5TkjjMlFAFf7RL5 HmfYp93m+X5eU3bd+3f97G3Hz9d23tu+WpBIxuHiMMgRUVhKSu1iScqOc5GATkAfMME84kooAp20 92be2a4tpPMncl1VUX7OpDMof52BIAVCULZY5AC9JHuJUiunFlO7Q58tFKZuPlDfJlsDJJX5ivIP bBJZokcDCOz+yKZZCY8KNxLsS/ykj5yS/r83IByKsUARmRhcJEIZCjIzGUFdqkEYU85yckjAI+U5 I4zz+n3Eur6ldatJZTvb2d01jYwEpkMkjRTXI+bA5LLg/MEjbb/rCp0te1CXTNHlnt1Rrp2jgtxI CU86V1jjL4Odu913Y5xnGTU+l6fFpOk2emwM7Q2kCQRtIQWKooUE4AGcD0rePuUnLq9F+v6L0bF1 JxIxuHiMMgRUVhKSu1iScqOc5GATkAfMME84jS4leK1c2U6NNjzEYpm3+Ut8+GwcEBflLckdskWK KwGc3dXEvh6+muorKc6ZNKUeBChLXLlPLeIFgFWR3ZG3EfvNrYALyHoDIwuEiEMhRkZjKCu1SCMK ec5OSRgEfKckcZJ4IrmCSCeJJYZVKSRyKGV1IwQQeCCO1ZXh6eWOCbSbuV5LvTmEJkkYl5oSMxSk nliV4ZsAGRJAOBW8v3kObqt/Tv8Ap9wtmaP2iXyPM+xT7vN8vy8pu279u/72NuPn67tvbd8tSCRj cPEYZAiorCUldrEk5Uc5yMAnIA+YYJ5xJRWAyhdaoljpa6heW08ECxGa53bWNqioXYuFY5xt2/Ju 5IxkZIo+Ho7zT/DZ+1afOL4b7maBXjPmTS/vpFjO7G0SSMg3Efd5JGGK+K/3+lRaYPmbU7mK0MfT zIi26dc9v3Czc5B44O7Fblbv3aKXd/lt+b+4XUr3D3Bljit027vnaZ1DIoDLlCNwbcylsEAgYyey tYqveokkCiSz+1qJYyI8KdpDqQ/zED5CA/r8vAJwKsVgMKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACufs/+ Sh6z/wBgqw/9G3ddBXP2f/JQ9Z/7BVh/6Nu6AOgooooAKKKKACsNP9G8dzb+ft+mJ5WO32eV9+76 /aY8Yz0bOMDO5WHq3+j+JfD10nMk0s9gwPQRvE0xI/2t1sg9MFuOQRvQ1co90/wV/wA0Jm5RRRWA wooooAKr37pHp1zJLefYo1ictdZUeSADl8uCox1+YEccjFWKjnMy28rW8cck4QmNJHKKzY4BYAkD PfBx6GgCSiiigAooooAqalqEWmWLXUyu4DJGiRgFpHdgiKMkDLMyjJIAzyQMms77F4km/fvrFlbS jmO3iszJEM9VkLOGkx2ZDFzyQQdoLb/ib+JJrw/NZ6Zutrfurzn/AFsg6g7RiMMMEEzqa3K6G/Yp JJX63Sfy1/H7ugtzGg1prSeOy1tEtJ3YRQ3O4LBduTgCPLEqxyP3bc53BS4UtWzUc8EVzBJBPEks MqlJI5FDK6kYIIPBBHasf7Jqujf8g5v7RsRybS6nY3Cdzsmcnf04R8csf3iqAoVoVNtH+H/A+enm tg2NysPxd8/h5rduYru5tbSZf70Us8cci+2UdhkcjOQQea1bO8gv7VLm2k3xPnBIKkEHBUg8qwII IIBBBBAIrK8S/v8A+yNP+79r1OH9512eTm56d8+Rt9t2ecYLw6ca8b9Hd/LVg9jcooornGV7h0Se 0D3nkM8pCR5UfaDsY7ORk4AL/Lg/J1xkGxUchmDwiKONkL4lLOVKrtPKjB3HdtGDjgk54wZKACii igDDi/ceO7vzPl+16ZB5Hff5MsvmfTHnxdeu7jODjcrDv/3PjLRbiTiKS2u7RG9ZWMUir+KQSnPT 5cZyQDuVvX1UJd1+V1+SEgooorAYVh6p/pPirQLT7vk/aNQ39c7EEOzHv9p3Z/2MY5yNysO1/f8A jfU5P9ZHbWNtCjdRFIzytIgP8LFfIZh1I8sn+Gt6GnNLsn+On6iZuUUUVgMKr27o892EvPPZJQHj yp+znYp2cDIyCH+bJ+frjAFio4zMXmEscaoHxEVcsWXaOWGBtO7cMDPABzzgAElYeg/6Hf6vpDce Tcm7hzyzRXBMhYkcf67z1A4ICDI5BO5WHqf+geJNK1EcRXG/T7g/dX5vnid274dDGoPe4ODk4bej 7ylT7r8Vr+V18xPublFFFYDCiiigAooooAKKKKACiiigCvYOkmnW0kV59tjaJCt1lT5wIGHygCnP X5QBzwMVYqOAzNbxNcRxxzlAZEjcuqtjkBiASM98DPoKkoAKKKKACiiigAooooAKKKKACiiigCvc OiT2ge88hnlISPKj7QdjHZyMnABf5cH5OuMg2KjkMweERRxshfEpZypVdp5UYO47towccEnPGDJQ AUUUUAFFFFABRRRQAUUUUAFFFFABVdHQ6jNGLzfIsUZa1yv7sEvh8AbvmwRycfu+ADuzYqMGb7Q6 tHGIAilHDksWydwK4wABtwcnOTwMcgElFFFABUc88VtBJPPKkUMSl5JJGCqigZJJPAAHepKw/GPz +EdStR/rL6L7BGT0Ek5EKE/7IaQE4ycZwD0rSjD2lSMH1aQnoh/hKCW38IaOlxE8d0bOJ7hZFIcz MoaQvnncXLFieSSSea2aKKVSfPNzfV3BKyCiiioGFFFFABWHo37nxD4jt5OJZLmG7RfWJoI41b8X glGOvy5xggncrDT/AEbx3Nv5+36YnlY7fZ5X37vr9pjxjPRs4wM70dYzXdfk0/yQmalg6SadbSRX n22NokK3WVPnAgYfKAKc9flAHPAxVio4DM1vE1xHHHOUBkSNy6q2OQGIBIz3wM+gqSsBhRRRQAUU UUAFFFFABRRRQAUUUUAV710jgUyXn2RTLGBJlRuJdQE+YEfOSE9fm4IODVio5jMqAwRxu+9QQ7lR t3DccgHkLkgdyAMjORJQAUUUUAFFFFABRRRQAUUUUAFFFFABVdHQ6jNGLzfIsUZa1yv7sEvh8Abv mwRycfu+ADuzYqMGb7Q6tHGIAilHDksWydwK4wABtwcnOTwMcgElFFFABRRRQAUUUUAFFFFABRRR QAUUUUAV7N0kgYx3n2tRLIDJlTtIdgU+UAfIQU9fl5JOTVio4TMyEzxxo+9gAjlht3HackDkrgkd iSMnGTJQBh6j/pnirRrRP+XPzdQlcc7fkaFEYfw7/NkYE9fJYAHkjcrD0f8A0vX9d1A8gSxWETL9 x44l3Eg92Es0yHHA2YwCDncrevo4w7Jfjr+thIKKKKwGFYeq/wDEu17TtW6QSf8AEvuscf6xl8l2 xy22T5AMcfaGbIAbO5VTU9Pi1XTZ7KZnRJVwJIyA8bdVdCQcMpAZT2IB7VrRmozXNs9H6P8ArTzE y3RWboGoS6nodpdXKol2VMd0kYIWOdCUlUZJ4V1YZyQccEjmtKonBwk4vdDWph3X+m+MrC3+/Fp9 tJdyL08uWQ+XC3vlBdDHI7kZ2mtysPR/9L1/XdQPIEsVhEy/ceOJdxIPdhLNMhxwNmMAg53K1r6O MOyX46v8WxIr3rpHApkvPsimWMCTKjcS6gJ8wI+ckJ6/NwQcGrFRzGZUBgjjd96gh3KjbuG45API XJA7kAZGciSsBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVz9n/AMlD1n/sFWH/AKNu66Cufs/+Sh6z/wBg qw/9G3dAHQUUUUAFFFFABWH4u+Tw81w3EVpc2t3M392KKeOSRvfCIxwOTjABPFblVNU0+LVtJvNN nZ1hu4HgkaMgMFdSpIyCM4PpWtCahVjJ7JoT2LdFZvh7UJdW8NaVqU6os13ZwzyLGCFDOgYgZJOM n1rSqJwcJOL3Q1qFFFFSAVXv/K/s658/z/J8p9/2ff5m3Bzs8v592Om35s9OasVHOJmt5Vt5I45y hEbyIXVWxwSoIJGe2Rn1FAElFFFABVHWNR/srSprtYvOlXakMO7b5srsEjTdg7dzsq5PAzk8Cr1Y d1/pvjKwt/vxafbSXci9PLlkPlwt75QXQxyO5GdprWjFSleWy1fy6fPb5iZe0fTv7K0qG0aXzpV3 PNNt2+bK7F5H25O3c7M2BwM4HAq9RRUSk5ycpbsYUUUVIGGv/Es8XNGOLbV4jKSeALmIKuAT1Z4s fKMYFuxAOWINe/5DHhj/ALCb/wDpJc0eKP8AR7Wx1MddPvoZiW+4sbEwyu/oqxSyNnIA2gngEEvf n8b6PG/zRrY3kyqeQJA9uocD+8Fkdc9cOw7mu6Hvcs/7sl90X+liWblFFFcJRXuPK8+08zz93mny /K37d2xvv7eNuM/f+Xdt/i21YqOQTF4TFJGqB8yhkLFl2nhTkbTu2nJzwCMc5ElABRRRQBh6/wDJ qXhyZuIo9TO9z91d1tOi5PbLuij1LAdSK3Kw/FvGhJIeI4b6ymkY9EjS5iZ3J7KqgsSeAASelblb 1NaUJeq+7X9RLcKKKKwGFYfhr9//AGvqH3ftepzfu+uzycW3XvnyN3tuxzjJ3Kw/Bvz+DdIuG5lu 7ZLuZv70so8yRvbLuxwOBnAAHFbw0oya7pfLV/mkLqblFFFYDCq9v5Xn3fl+fu80eZ5u/bu2L9zd xtxj7ny7t38W6rFRxiYPMZZI2QvmIKhUqu0cMcncd245GOCBjjJAJKo6xp39q6VNaLL5MrbXhm27 vKlRg8b7cjdtdVbB4OMHg1eoqoycJKUd0BR0bUf7W0a0vzF5Mk0QaSAtuMMnR4ycD5lbKnIBBByB V6sOP/iS6+tuP+PHVpZHjH/PG5ChmRQP4XVZJOgwyuSSZABuVdaKUrx2eq/ry2EgooorIYUUUUAF FFFABRRRQBXsPK/s628jz/J8pNn2jf5m3Axv8z592Ou75s9easVHAJlt4luJI5JwgEjxoUVmxyQp JIGe2Tj1NSUAFFFFABRRRQAUUUUAFFFFABRRRQBXuPK8+08zz93mny/K37d2xvv7eNuM/f8Al3bf 4ttWKjkExeExSRqgfMoZCxZdp4U5G07tpyc8AjHORJQAUUUUAFFFFABRRRQAUUUUAFFFFABVdPK/ tGbHn+d5Ue7dv8vbl8bc/Juzuzj5sbd3G2rFRgTfaHZpIzAUUIgQhg2TuJbOCCNuBgYweTngAkoo ooAKw/Ef76bRLBuIrvU495H3h5SPcLj6vAgPsT0OCNysO6/f+N9Mj/1kdtY3Mzr1EUjPEsbkfwsV 89VPUjzAP4q3w+k79k3+Dt+InsblFFFYDCiiigAooooAKw9S/wBH8XaFdPzHNFc2CgdRI4SYE/7O 22ceuSvHJI3Kw/Ef7mbRL9uYrTU494H3j5qPbrj6POhPsD1OAd8N/Et3TXzaaQnsalh5X9nW3kef 5PlJs+0b/M24GN/mfPux13fNnrzVio4BMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqSsBhRRRQAUUU UAFFFFABRRRQAUUUUAV73yvIXzvP2+bHjyN+7dvXbnZztzjdn5dud3y5qxUcwmZAIJI0fepJdCw2 7huGARyVyAexIODjBkoAKKKjnnitoJJ55UihiUvJJIwVUUDJJJ4AA70JX0QElFYf27xBc/6TY6ZZ C1P+qjvbiSGWRT/GwEbeX04QgkhwT5ZUqdHT9Qi1GBpI1eOSNvLmglAEkLgAlWAJGcEHIJBBBBII J1nRlFXf57eorluiiishhRRRQAUUUUAFV08r+0Zsef53lR7t2/y9uXxtz8m7O7OPmxt3cbasVGBN 9odmkjMBRQiBCGDZO4ls4II24GBjB5OeACSiiigAooooAKKKKACiiigAooooAKKKKAK9n5XkN5Pn 7fNkz5+/du3tuxv525ztx8u3G35cVYqOETKhE8kbvvYgohUbdx2jBJ5C4BPcgnAzgY/jD954VvbT p9v2afv/ALn2h1h3477fM3Y4zjGRnNaUoe0nGHdpCbsg8HfP4R026P8ArL6L7fIB0Ek5Mzgf7IaQ gZycYyT1rcoooqz9pOU+7uCVlYKKKKzGFFFFAGHpP+ieJNb05eYm8nUEA4WPzd6MgX3eB5Ce5lPG ck7lYetf6FrOjat/yzWVrCdjyFjn27SAOdxmjgXuAHbI7qeLv33hu505eZdTxp6AcsPN+RnC/wAW xC8hHoh5AyR1yh7WcH/NZeltH/n8/mTe1w8H/vPCtld9Pt+/UNn9z7Q7TbM99vmbc8ZxnAzityii sKs/aTlPu7jSsrFe98ryF87z9vmx48jfu3b1252c7c43Z+Xbnd8uasVHMJmQCCSNH3qSXQsNu4bh gEclcgHsSDg4wZKzGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXP2f/ACUPWf8AsFWH/o27rQtdd0e+1GfT rPVbG4voN3nW0NwjyR7TtbcoORgkA56Gs+z/AOSh6z/2CrD/ANG3dAHQUUUUAFFFFABRRRQBh+FP 3ekT2p+U2t9dQiHp5MYncxJj+FfKMZUdNpXHGK3Kw9J/0fxL4htX5kmlgv1I6CN4lhAP+1utnPpg rzyQNyt8T/Fcu9n96v8AqJbBRRRWAwqvfokmnXMctn9tjaJw1rhT5wIOUw5CnPT5iBzycVYqvfuk enXMkt59ijWJy11lR5IAOXy4KjHX5gRxyMUAWKKKKACsPQf9Mv8AV9Xbnzrk2kOeGWK3JjKkDj/X eewPJIcZPAA0dU1CLSdJvNSnV2htIHnkWMAsVRSxAyQM4HrUHh/T5dK8PafZXDI91FAouJEJIkmx mR8kAks5ZiTySSTya3j7tGT72X6v9BdTSooorAYUUUUAZXiezn1HwnrNjax+Zc3NjPDEmQNztGwA yeByR1qil5BqPizQb61k8y2udHu5onwRuRpLQg4PI4I610dcP4D/AHn9nb/m8nwzpnlZ52b/ADt+ 303eXHnHXYuegruw/wDAnJ/Z/wDblb/L8SXudxRRRXCUV7hEee0L2fnskpKSYU/ZzsYb+TkZBKfL k/P0xkixVe4dEntA955DPKQkeVH2g7GOzkZOAC/y4PydcZBsUAFFFFAGH40/5ETxD/2DLn/0U1bl RzwRXMEkE8SSwyqUkjkUMrqRggg8EEdqyvCM8tz4L0KeeV5ZpdPt3kkkYszsY1JJJ5JJ71vvQ9H+ a/8AtRdTZooorAZh+Mvn8G6vbrzLd2z2kK/3pZR5ca+2XdRk8DOSQOa3Kw/E/wC+h0uwXiW71O22 E/dHlP8AaGz9UgcD3I6DJG5W89KMV5t/LRfoxdQooorAYVXt0RJ7spZ+QzygvJhR9oOxRv4OTgAJ 82D8nTGCbFV7d0ee7CXnnskoDx5U/ZzsU7OBkZBD/Nk/P1xgAAsUUUUAZuuafLqGmkWrIl/Awns5 HJAWZeVDEDIVuUbHJRmHep9L1CLVtJs9SgV1hu4EnjWQAMFdQwBwSM4PrVusPSP+JfrOp6S/yxyS m/s17GOTHmgE8swm3s3UKJo+RkAbx9+k49Y6/Lr+j+8XU3KKKKwGFFFFABRRRQAVjT6+/nyR2Gj6 hqUcTGN57VoFjDg4ZQZJELEHgkAgHIzkMAarPLqE8uhWUrwzPAHurlGKtbQuWUFD1MjFXCnou0se iq+rBBFbQRwQRJFDEoSOONQqooGAABwAB2rdKNNJzV2+nl3du/T79rC3KmiXNpd6JZTWNv8AZrYx KqWxQIbfHBiKrwrIQVK9ipHar9c3C6abqdhqEN59r0/VYo4JrrKlXmC/uZyUAT94uULD7x8hVAFd JUVIKNnHZ/1b5f8ABBBRRRWYwooooAKKKKACiiigAooooAr3CI89oXs/PZJSUkwp+znYw38nIyCU +XJ+fpjJFiq9w6JPaB7zyGeUhI8qPtB2MdnIycAF/lwfk64yDYoAKKKKACiiigAooooAKKKKACii igAquiINRmkFnskaKMNdYX94AXwmQd3y5J5GP3nBJ3YsVXR0OozRi83yLFGWtcr+7BL4fAG75sEc nH7vgA7sgFiiiigArD03/SPF2u3ScRwxW1gwPUyIHmJH+ztuUHrkNxwCdysPw1+//tfUPu/a9Tm/ d9dnk4tuvfPkbvbdjnGTvS0pzb7JfO6f5Jie6NyiiisBhRRRQAUUUUAFYfi/5PDVxdH/AFdjLDfy AdTHBKkzgf7RWMgZwM4yR1rcrK8T2c+o+E9ZsbWPzLm5sZ4YkyBudo2AGTwOSOtbYZpVoN91+Yns XbBEj062jis/sUaxIFtcKPJAAwmEJUY6fKSOODirFVNLuIrvSLK5gupLuGaBJEuJFCtKpUEOQAoB I5wAOvQdKt1k007MYUUUUgCiiigAooooAKKKKACiiigCveokkCiSz+1qJYyI8KdpDqQ/zED5CA/r 8vAJwKsVXvXSOBTJefZFMsYEmVG4l1AT5gR85IT1+bgg4NWKACsPUv8Aiaa9Z6SOYLbZqF574Y+Q nY8yIz5B48jaRh63Kw/Cf+kaN/azcyatK1/uPUxvjyQR0DLCIlOOMqeTkk70vdi6nbRer6/JJ/Ow n2NysPUP+JRrNrqUfyWl5KtrfKOhkfasMpHdtwWLgZIkUscRjG5Uc8EVzBJBPEksMqlJI5FDK6kY IIPBBHaopT5Ja7PR+n9arzsDRJRWNoM8qPf6VcSvJLYT7YnlYl5IHAeNjnkgZaLcSSxhYk5yBs0q kOSXL/XkCdwoooqBhRRRQAVXREGozSCz2SNFGGusL+8AL4TIO75ck8jH7zgk7sWKro6HUZoxeb5F ijLWuV/dgl8PgDd82COTj93wAd2QCxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFezRI4GEdn9kUyy Ex4UbiXYl/lJHzkl/X5uQDkVl6//AKRqOhWH345r7zp4h1McUbyK5xyFWYQc9MlQfvYOpZukkDGO 8+1qJZAZMqdpDsCnygD5CCnr8vJJyay3/wBJ8dw7OPsGmP5ue/2iVNm36fZpM5x1XGcnG+H0k5dk /wAtPxt8xM3KKKKwGFFFFABRRRQBU1PT4tV02eymZ0SVcCSMgPG3VXQkHDKQGU9iAe1Y9nFq2p6r ps2rWH2YadE7SEOnlzXTKEEkO1mbYFM4w+04kXgkfL0dFbQrShFxS7/K6s/vQmgooorEZXvUSSBR JZ/a1EsZEeFO0h1If5iB8hAf1+XgE4FWKr3rpHApkvPsimWMCTKjcS6gJ8wI+ckJ6/NwQcGrFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFeb3aW+qaiZrnw5Y60t7qFxY2Umr3hfZLEZd48owskEe2CQBo8s+2PeMksu54X 02xjurlrTTo9FurC4NteWenTZtJWaJJAdu1VY7ZIyX2K+U25Kj5gDrKKKKAPP9F/5Fj4W/8AbD/0 2z1oXE2sRfEPVP7JsbG6zpVj5n2u8e32/vbvGNsT579cYwOueOgtdC0ex1GfUbPSrG3vp93nXMNu iSSbjubcwGTkgE56ms+z/wCSh6z/ANgqw/8ARt3QBJBdeKmuIluNG0aOAuBI8erSuyrnkhTbAE47 ZGfUVqb7zyM+RB53m42+cdvl78bs7fvbPm24xu+Xdj5qsUUARgzfaHVo4xAEUo4cli2TuBXGAANu Dk5yeBjmNHvDFamSCBZGx9pVZiRH8pJ2HaN/zYHIXgk9RtNiigCu73giujHBA0i5+zK0xAk+UEbz tOz5sjgNwAep2iQmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8SUUAc273lt40hKQQGa/sXEqmY7R Hb3CBGU7fvFLmRipBG4KoYAFj0AM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5x9Z/c+IfDlxHxLJ czWjt6xNBJIy/i8ERz1+XGcEg7lb1tYwfdfk2vyQl1K6PeGK1MkECyNj7SqzEiP5STsO0b/mwOQv BJ6jaR3vBFdGOCBpFz9mVpiBJ8oI3nadnzZHAbgA9TtFiisBkZM32hFWOMwFGLuXIYNkbQFxggjd k5GMDg54oapqF1pmg3uotBaeZaq8pWS4dY/KUk7iyxswOwbsBDz8oJ+9WnXN+JvK1C5tdJk89raM G/v1t9+8RRg+WFMf7xZDLsZNuC3kvgnGDpSgpys9t36L+tPMTC30FdZIu/E2lWV1NJEjpDcBZhaE 53RKpBUY+XMgJMhzwqqigsnvND1DT9OkggSwvcJDGsxYWsojkd4UO0FkwgKAhQAJBlQI4z0lQXln Bf2r21zHvifGQCVIIOQwI5VgQCCCCCAQQRWn1hy92fw9u3ovL8dr6hbsYXid7y7ih0iKCB5Ly5Gx DMcPBGolfzRtO1HZRA33gBKpOSdh6Amb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8Z1jpM8V+L7UL 77bcxRNb27iIRbImKltwBwzsUTcwwvyjaqfNnVqaso8sYRd7fm/XXay+QLuV9955GfIg87zcbfOO 3y9+N2dv3tnzbcY3fLux81SAzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjmSisRldHvDFamSCBZGx 9pVZiRH8pJ2HaN/zYHIXgk9RtI73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2ixRQBGTN9oRVj jMBRi7lyGDZG0BcYII3ZORjA4OeOH8HGZNb1iO3jjYRKkEcbuUWOFNQvkCrgHAWMYVcY+ULlRyO8 rkfBuny2mreJnuWQzJqDwJ5ZO3yWZrpCcjO7N2wPbgAdCT20JL6vVj1938yXujqgZvtDq0cYgCKU cOSxbJ3ArjAAG3Byc5PAxzGj3hitTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2mxRXEUU55NSUS/Z7 S0kIciMSXLJuXy8gnEZwfM+XHOF+bJPyVYJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPEdx5Xn2n mefu80+X5W/bu2N9/bxtxn7/AMu7b/FtqxQBX33nkZ8iDzvNxt847fL343Z2/e2fNtxjd8u7HzVI DN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOZKKAK6PeGK1MkECyNj7SqzEiP5STsO0b/mwOQvBJ6j acPwm95H4Ve2iggb+z5Z7CyDTEedHA7RIZDt+RjswdoYfxAc7R0lYfhj9zDqlg3MtpqdzvI+6fNf 7QuPok6A+4PUYJ3hrRkl3T+Wq/VC6mwTN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeI9955GfIg87 zcbfOO3y9+N2dv3tnzbcY3fLux81WKKwGc/qBmuPG2jWckca2sMFxfxSK53tMgWEqy4wE2XBPByT joF+bYR7wxWpkggWRsfaVWYkR/KSdh2jf82ByF4JPUbTlxfv/Hd35nzfZNMg8jts86WXzPrnyIuv TbxjJzuVvX0UI9l+d3+TEiu73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2iQmb7QirHGYCjF3L kMGyNoC4wQRuycjGBwc8SUVgMr77zyM+RB53m42+cdvl78bs7fvbPm24xu+Xdj5qIXvGvLlZoIEt V2/Z5EmLPJx825SoCYPTDNnrx0qxVe38rz7vy/P3eaPM83ft3bF+5u424x9z5d27+LdQAI94YrUy QQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpHe8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O0WKKAIyZ vtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzxh62ZrM2WuyRxxyWU7QzBHLKbSWQKzMxA2hQIpmOOPKK 7tpLV0FQXtnBqNhcWN1H5ltcxNDKmSNyMCCMjkcE9K0ozUJpvbr6dfwE1dDwZvtDq0cYgCKUcOSx bJ3ArjAAG3Byc5PAxzGj3hitTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2ml4bvJ73QLV7uTzLyLfb XUgAAeaJjFIwAx8pdGI4HBHA6Vq0qkHCbg+mg07ld3vBFdGOCBpFz9mVpiBJ8oI3nadnzZHAbgA9 TtEhM32hFWOMwFGLuXIYNkbQFxggjdk5GMDg54koqAK++88jPkQed5uNvnHb5e/G7O372z5tuMbv l3Y+aiW4NsZ5rkwQ2UUQkM7y4xjcX3AgBVACnOTnJyBjJsVh+Kf9JsLbSP8AoK3K2j5+6YsNJMpP UboY5VBHOWHI6jSlDnmovb9Ov4CbsiLwzHqJ05dTvLOO3u9Wn+2XduZGH2ZTEERQCgJYLHEGB2/M XIPAWtl3vBFdGOCBpFz9mVpiBJ8oI3nadnzZHAbgA9TtFiilUnzycgSsZl7ZT6tpv2K9ijhSe3Pn SQTkvbzfKVMRKDJU5YOdpBRTt5+WDQNT1HU/Dlpd3NtapqJcxXUMcrCON0kMcoBKk5Xa3HIJGAxH z1pWHlf2dbeR5/k+Umz7Rv8AM24GN/mfPux13fNnrzWXo/8Aomv67p54Bliv4lX7iRyrtIA7MZYZ nOODvzkknGkPepSj21/R/fdfcHU2AZvtDq0cYgCKUcOSxbJ3ArjAAG3Byc5PAxzGj3hitTJBAsjY +0qsxIj+Uk7DtG/5sDkLwSeo2mxRWAyu73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2iQmb7Qi rHGYCjF3LkMGyNoC4wQRuycjGBwc8SUUAV9955GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81SAzf aHVo4xAEUo4cli2TuBXGAANuDk5yeBjmSigCuj3hitTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2kd 7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7RYooAjJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHP Ee+88jPkQed5uNvnHb5e/G7O372z5tuMbvl3Y+arFFAFd3vBeKqQQG1ON0hmIccPnC7cHBEePmGd zHjaAwj3hitTJBAsjY+0qsxIj+Uk7DtG/wCbA5C8EnqNpLjyvPtPM8/d5p8vyt+3dsb7+3jbjP3/ AJd23+LbVigCu73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2iQmb7QirHGYCjF3LkMGyNoC4wQ RuycjGBwc8SUUAV9955GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81SAzfaHVo4xAEUo4cli2TuBX GAANuDk5yeBjknnitoJJ55UihiUvJJIwVUUDJJJ4AA71j/8ACTJF+9vdK1OysT928niXy8dQzKrF 4lxkkyKgUD5ip4rSFKc/hX9fqK9jUR7wxWpkggWRsfaVWYkR/KSdh2jf82ByF4JPUbSO94IroxwQ NIufsytMQJPlBG87Ts+bI4DcAHqdosUVmMjJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPEe+88jP kQed5uNvnHb5e/G7O372z5tuMbvl3Y+arFFAEYM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5jR7w xWpkggWRsfaVWYkR/KSdh2jf82ByF4JPUbTYooAru94IroxwQNIufsytMQJPlBG87Ts+bI4DcAHq doN95/aOzyIPsPlZ87zj5nmZ+7s242453bs54296sVXTyv7Rmx5/neVHu3b/AC9uXxtz8m7O7OPm xt3cbaADfeeRnyIPO83G3zjt8vfjdnb97Z823GN3y7sfNUgM32h1aOMQBFKOHJYtk7gVxgADbg5O cngY5kooAro94YrUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpw/CD3jeCLK5WCBrm4tlu03TECeWV BK7Odp8vdK78KGAGCB/CLfi2eW38Iaw9vK8d0bOVLdo2IczMpWMJjncXKhQOSSAOa1YIIraCOCCJ IoYlCRxxqFVFAwAAOAAO1b7UPV/kv/thdQJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPEe+88jPk Qed5uNvnHb5e/G7O372z5tuMbvl3Y+arFFYDIwZvtDq0cYgCKUcOSxbJ3ArjAAG3Byc5PAxzGj3h itTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2mxRQBXd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU 7RITN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeJKKAK++88jPkQed5uNvnHb5e/G7O372z5tuMbvl 3Y+apAZvtDq0cYgCKUcOSxbJ3ArjAAG3Byc5PAxzJRQBz/hAzJ4M8OxRxxkx2kUFyHcq0LJHtZcY OXEi7Spxj5ucjB2He8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O0YfgTyv+EWXyPP8AJ+3Xuz7R v8zb9qlxv8z592Ou75s9ea6St8V/Hn6v8xR2RGTN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeI995 5GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81WKKwGRgzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjm NHvDFamSCBZGx9pVZiRH8pJ2HaN/zYHIXgk9RtNiigCu73giujHBA0i5+zK0xAk+UEbztOz5sjgN wAep2iQmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8SUUAV9955GfIg87zcbfOO3y9+N2dv3tnzbc Y3fLux81SAzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjmSigCuj3hitTJBAsjY+0qsxIj+Uk7DtG/ 5sDkLwSeo2kd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7RYooAr3D3iyxi2ggkjP+saSYoV+Z RwApz8pc9RyqjoxZTfeeRnyIPO83G3zjt8vfjdnb97Z823GN3y7sfNRe+V5C+d5+3zY8eRv3bt67 c7Oducbs/Ltzu+XNWKAOf8WmafRLzTPLj26miWELK5Mm6YlJG2Y5EaEycHkK+doXdWwj3hitTJBA sjY+0qsxIj+Uk7DtG/5sDkLwSeo2nLv/APTPF2kWvWOzimv3KdUkwIYw3orLLOR3Jj4PysDuVvU0 pwj6v79P0/ES3K7veCK6McEDSLn7MrTECT5QRvO07PmyOA3AB6naJCZvtCKscZgKMXcuQwbI2gLj BBG7JyMYHBzxJRWAzm9Xe80/UtM1dIIFaS6Gn3g847DbySEROSFy0gfYFGCFM0gyATIOgBm+0OrR xiAIpRw5LFsncCuMAAbcHJzk8DHMGp6fFqumz2UzOiSrgSRkB426q6Eg4ZSAynsQD2qDQNQl1PQ7 S6uVRLsqY7pIwQsc6EpKoyTwrqwzkg44JHNby9+kpdtP1X6/gLqWke8MVqZIIFkbH2lVmJEfyknY do3/ADYHIXgk9RtI73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2ixRWAyMmb7QirHGYCjF3LkM GyNoC4wQRuycjGBwc8R77zyM+RB53m42+cdvl78bs7fvbPm24xu+Xdj5qsUUARgzfaHVo4xAEUo4 cli2TuBXGAANuDk5yeBjmvFJqReAS2loqMiGYpcsxRirbgo8sbgGCAE7chmOBtAa5VdPK/tGbHn+ d5Ue7dv8vbl8bc/Juzuzj5sbd3G2gAd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7RITN9oRVj jMBRi7lyGDZG0BcYII3ZORjA4OeJKKAK++88jPkQed5uNvnHb5e/G7O372z5tuMbvl3Y+apAZvtD q0cYgCKUcOSxbJ3ArjAAG3Byc5PAxzJRQBXR7wxWpkggWRsfaVWYkR/KSdh2jf8ANgcheCT1G0jv eCK6McEDSLn7MrTECT5QRvO07PmyOA3AB6naLFFAEZM32hFWOMwFGLuXIYNkbQFxggjdk5GMDg54 j33nkZ8iDzvNxt847fL343Z2/e2fNtxjd8u7HzVYooAjBm+0OrRxiAIpRw5LFsncCuMAAbcHJzk8 DHMaPeGK1MkECyNj7SqzEiP5STsO0b/mwOQvBJ6jabFFAFd3vBFdGOCBpFz9mVpiBJ8oI3nadnzZ HAbgA9TtEhM32hFWOMwFGLuXIYNkbQFxggjdk5GMDg54kooAr77zyM+RB53m42+cdvl78bs7fvbP m24xu+Xdj5qx9HM1x4u8SXMkcapC1tYxMrnLKkXnHcuODm4PIbkYGBty2xZ+V5DeT5+3zZM+fv3b t7bsb+duc7cfLtxt+XFZfhT95pE90fmN1fXUwm6+dGZ3ET5/iXyhGFPTaFxxit6elKcu9l+N/wBB Pc1Ee8MVqZIIFkbH2lVmJEfyknYdo3/NgcheCT1G0jveCK6McEDSLn7MrTECT5QRvO07PmyOA3AB 6naLFFYDIyZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzxHvvPIz5EHnebjb5x2+Xvxuzt+9s+bbjG 75d2PmqxRQBGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOY0e8MVqZIIFkbH2lVmJEfyknYdo3/N gcheCT1G02KKAK7veCK6McEDSLn7MrTECT5QRvO07PmyOA3AB6naJCZvtCKscZgKMXcuQwbI2gLj BBG7JyMYHBzxJRQBX33nkZ8iDzvNxt847fL343Z2/e2fNtxjd8u7HzVIDN9odWjjEARSjhyWLZO4 FcYAA24OTnJ4GOZKKAKbyakLe0ZLS0ad3QXSNcsFiUj5yjeWS5B6AhM9ytXKr3vleQvneft82PHk b927eu3OznbnG7Py7c7vlzVigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPL9Qsbmz1y3tII/GSt/aF1e2Mdq2lsgd/MMk i78uI8TMMydPMQHDFRXUeCdrWurTN/av2uTUG+1jVfs4mEixRoPlg+VV2IhHAJBDcqyk7Gp6LZav 5RulnWSLISa2uZLeRQcZXfGyttOFJXOCVUkZAxJpul2mk27Q2iSAO5eR5ZXlkkbAGXdyWY4AAJJw FA6ACgC5RRRQAVz9n/yUPWf+wVYf+jbuugrn7P8A5KHrP/YKsP8A0bd0AdBRRRQAUUUUAFFFFAGH 4t40JJDxHDfWU0jHokaXMTO5PZVUFiTwACT0rcrK8T2c+o+E9ZsbWPzLm5sZ4YkyBudo2AGTwOSO tXrK8g1Gwt761k8y2uYlmifBG5GAIODyOCOtbvWhHyb/ABS/yf3C6k9FFFYDCuf0oTXt74g1GCSM Syz/AGOznkQsoSFduCgKkhZ2uO4J9cbTWrqmoRaTpN5qU6u0NpA88ixgFiqKWIGSBnA9aoadpjaf 4Oh069tUv5ks9l1CgVhdyFf3n38Bi7FiS2MliW6mt4e7SlLvZfq/0+8XU2qKKKwGFFFFABRRRQAU UUUAFVLTT4rO5v542cvezieQMRgMI0jwOOmI19eSat0U1JpNLqAUUUUgI5BMXhMUkaoHzKGQsWXa eFORtO7acnPAIxzkSVXuER57QvZ+eySkpJhT9nOxhv5ORkEp8uT8/TGSLFABRRRQAVh6J8mu+JY3 +WRr6OZVPBMZtoVDgf3S0brnplGHY1uVh2v/ACPerf8AYMsv/Rt1W9H4Knp/7dET6G5RRUF7eQad YXF9dSeXbW0TTSvgnaigknA5PAPSsUm3ZDMrw9/pN1rOpnkXF88MRb76xwAQlD6L5qTMADj95ngs RW5WV4Zs57Dwvpdtdx7LxLaP7UCQxMxUGRiR95i5Yk5OSScnNata4hp1ZW2Wi9FovwEtgooorEYV HGJg8xlkjZC+YgqFSq7Rwxydx3bjkY4IGOMmSq9uiJPdlLPyGeUF5MKPtB2KN/BycABPmwfk6YwS AWKKKKACiiigDD07/Q/FWs2j/wDL55WoROeN3yLC6KP4tnlRsSOnnKCBwTuVh61/oWs6Nq3/ACzW VrCdjyFjn27SAOdxmjgXuAHbI7ruVvW95Rn3X5afkk/mJBRRRWAwrDb/AE3xusb/AOr02xEyqeQ0 k7socD+FkWB1zySJmHHO7crD8Nfv/wC19Q+79r1Ob9312eTi2698+Ru9t2OcZO9L3Yzn5WXq/wDg X/4ewn2NyiiisBkcAmW3iW4kjknCASPGhRWbHJCkkgZ7ZOPU1j6p/o3irQLv73nfaNP2dMb0E2/P t9m24/2854wdSwRI9Oto4rP7FGsSBbXCjyQAMJhCVGOnykjjg4rL8Yfu/Ct7d9fsGzUNn9/7O6zb M9t3l7c84znBxit8NrVUe+n36fqJ7G5RRRWAwooooAKKKKACiiigAooooAjkExeExSRqgfMoZCxZ dp4U5G07tpyc8AjHORJVe4RHntC9n57JKSkmFP2c7GG/k5GQSny5Pz9MZIsUAFFFRzzxW0Ek88qR QxKXkkkYKqKBkkk8AAd6Er6IDHuf+Jv4khsx81npm25uO6vOf9VGeoO0ZkKnBBMDCtieCK5gkgni SWGVSkkcihldSMEEHggjtWV4XglTQ4rq5ieK7v2a9nSVSJI2kO4RtnkmNSseTjiMcAAAbNb13yz5 I/Z0/wA394kY2lTy2mpXOi3MryeUqzWUkrFmkgPBUseXZHBBPJ2vEWJZiTs1h+J/9CsF12PiXSd1 y+OskGP30fvlAWVcgb0jJ4FblFVcyVRdd/Vb/o/mC7BRRRWAwooooAKjAm+0OzSRmAooRAhDBsnc S2cEEbcDAxg8nPElV0RBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7ABYooooAw/FP72z0+0Tme 41O08pP73lzLM/PQYjikbnrtwOSAdysPVv8ASPEvh61TiSGWe/YnoY0iaEgf7W65Q+mA3PAB3K3q aU4R9X+Nv0QluwooorAYUUUUAFFFFABRRRQBz/hgTRXWv280kbNFqCg+UhSMM1tA7lUJO0M7u+Mn liSSSSegrm9BRLTxFqNpbWf2G2awsrr7JhR5UjCWIj5SVGEgiXCkqNnHUk9JW+I+P5R/9JQlsFFF FYDCiiigAooooAKKKKACiiigCOYTMgEEkaPvUkuhYbdw3DAI5K5APYkHBxgyVXvUSSBRJZ/a1EsZ EeFO0h1If5iB8hAf1+XgE4FWKAMPS/8ASfFWv3f3fJ+z6fs652IZt+ff7Ttx/sZzzgblYfhD5/DV vdD/AFd9LNfxg9RHPK8yA/7QWQA4yM5wT1rcrfE6VXHtp92n6CjsFFFFYDCsPTP9A8Sarpx4iuNm oW4+6vzfJKiL3w6CRiO9wMjJy25WHrn+iarompj5FjuTaTydcRTLtC4951t+QMj1C7q3oatw7r8V qvvtb5ifc3KKKKwGFFFFABUYE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54kquiINRmkFnskaKMNd YX94AXwmQd3y5J5GP3nBJ3YALFFFFABRRRQAVz8GtavfQR6hp2kW1xpcyiWF2vjHPLGRkMsZj2gs OVDOvBG7YcgSeIv9P+zeH1/5iW77T7WqY87/AL63JFwQw83cPumtyuhKNOClJXb732+TW7/LzFuQ Wd5Bf2qXNtJvifOCQVIIOCpB5VgQQQQCCCCARU9Ya/8AEs8XNGOLbV4jKSeALmIKuAT1Z4sfKMYF uxAOWI3KzqwUWnHZ6r+vJ3X4gmFFFFZjCiiigCpPdrpum3N7qM8Yht1kmkkSJgFiXLD5csSQuMkd SCQBnAq+GLOfTvCejWN1H5dzbWMEMqZB2usagjI4PIPSqHiVE/4QrVLOKz+yNfGSyjQhQPNuJTEs h2kjazyByfvYYkjdkV0lbvSgvN/kv+CxdQooorAYUUUUAFFFFABRRRQAUUUUARzCZkAgkjR96kl0 LDbuG4YBHJXIB7Eg4OMGSq96iSQKJLP7WoljIjwp2kOpD/MQPkID+vy8AnAqxQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF AHB+NrnTZb3S7i7uPDF3p6pdRNZ63qKwxPMHjG9QY3DPHtkQ8Ar5hGeSK2PBUmmy6NM2l2nh+2g+ 0MGTQrlZ4C21eWZY0AfGMjHQLzzxYn0vWobiWTSdcjjilcu0Go2puljJOf3ZWSNwCSSQzOBwF2AY qSx0zVPtkd3q2s/aWiz5dvZwfZoM4I3MpZ3ZsFhgvs+6dm5Q1AGxRRRQAVz9n/yUPWf+wVYf+jbu ugrn7P8A5KHrP/YKsP8A0bd0AdBRRRQAUUUUAFFFFABWH4O+Twjptqf9ZYxfYJCOhkgJhcj/AGS0 ZIzg4xkDpW5WH4a/cf2vp/3vsmpzfvOm/wA7Fz07Y8/b77c8ZwN4a0ZJd0/lqvzaF1NyiiisBmH4 q/fadaWC/NJfX1vD5R6SxiQSTIe20wxy5B4IyOcgHUv3SPTrmSW8+xRrE5a6yo8kAHL5cFRjr8wI 45GKy73/AErxlpVufnitLae7ZR/yylJSOJmx0yj3AAPBwxwSuRsTmZbeVreOOScITGkjlFZscAsA SBnvg49DW9TSnCPq/vdvySEtySiiisBhRRRQAUUUUAFFFFABRRRQAUUUUAV7h0Se0D3nkM8pCR5U faDsY7ORk4AL/Lg/J1xkGxUchmDwiKONkL4lLOVKrtPKjB3HdtGDjgk54wZKACiiigArDl/ceO7T y/l+16ZP5/ff5MsXl/THny9Ou7nOBjcrD1L/AEfxdoV0/Mc0VzYKB1EjhJgT/s7bZx65K8ckjfD/ ABNeT/Jv8xM3Kw/F/wA/hq4tT/q76WGwkI6iOeVIXI/2gshIzkZxkHpW5XPpPF4j1jT7uylSbS9P Z5hdRsCs05QxhUYZDqqSSbiON2wAkq4Dw6tNVOkdfu1S+ewPsdBRRRXOMKKKKACq9u6PPdhLzz2S UB48qfs52KdnAyMgh/myfn64wBYqOMzF5hLHGqB8RFXLFl2jlhgbTu3DAzwAc84ABJRRRQAUUUUA ZXiWznvvDd/DZx775YjNaDIH+kJ88R544kVTzxxzxmr1leQajYW99ayeZbXMSzRPgjcjAEHB5HBH Wp6w/Cn7jSpdMPytplzLaCPr5cQbdAue/wC4aHnJPPJ3Zrde9Ra7P89/yX3i6m5RRRWAyjrWo/2P oOo6n5XnfY7aS48vdt37FLYzg4zjrijRdO/sfQdO0zzfO+x20dv5m3bv2KFzjJxnHTNUfEv7/wDs jT/u/a9Th/eddnk5uenfPkbfbdnnGDuVvL3aKXd3+S0X4839WF1CiiisBlewdJNOtpIrz7bG0SFb rKnzgQMPlAFOevygDngYqSeCK5gkgniSWGVSkkcihldSMEEHggjtRAZmt4muI445ygMiRuXVWxyA xAJGe+Bn0FSUJ21QGN4Tnln8KaYLmV5buGBbe6aRizefH+7lBY/eIdWGeQcZBIOa2aw/Dn7mbW7B eYrTU5NhP3j5qJcNn6PO4HsB1OSdyt8Sv3smtnr8nqvwEtgooorAYUUUUAFFFFABRRWNqtxdXOpW 2j2Fy9tK6tPdXESqzQQjhQNwZQzvgDcpBVJccqKunBzdkJuxo3Dok9oHvPIZ5SEjyo+0HYx2cjJw AX+XB+TrjINiubdJ/Deq6dHbvNNpN7KYLg3l5JK1vKVJjdXfcdrEeWVLAbmj2gEtu6SnUhy2ad0w TCsPxb++0CTTxydSljsCq/fMcrBZSg/vLEZH7gbCSCAa3Kw9S/0jxdoVq/EcMVzfqR1MiBIQD/s7 blz65C88EG8NpVUu139yv+gPY3KKKKwGFYfhX/RtMk0Y8nR5RYBv70aojRHPdvKePdwBu3YGMVuV hr/oXjdo0/1epWJmZRwFkgdVLkfxM6zoueCBCo54270vehKHz+a/4FxPublFFFYDCiiigAqujodR mjF5vkWKMta5X92CXw+AN3zYI5OP3fAB3ZsVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOQCSii igDD/wCPnx3/AHf7P0z6+Z9ol/Tb9l987+2OdysPRv33iHxHcScyx3MNojekSwRyKv4PPKc9fmxn AAG5W+I0aj0SX4q/5tiQUUUVgMKKKKACiiigAooooA5uwdIvFVtJFefb49S0dCt1lTvFu4w+UAVt /wBqz8oAG3gENx0lc/cGaPxVoF1eRxxNLZ3NqRG5dRO3ky7AcAkbYJTuIA+XnBIFdBW9bVQl1a/J tfkkJdQooorAYUUUUAFFFFABRRRQAUUUUAV710jgUyXn2RTLGBJlRuJdQE+YEfOSE9fm4IODVLxN eT2HhfVLm0k2XiW0n2UgBiZipEagH7zFyoAwckgYOa0ZjMqAwRxu+9QQ7lRt3DccgHkLkgdyAMjO Rj+Jf3/9kaf937XqcP7zrs8nNz0758jb7bs84wdsOk6sb7LV+i1f4Cexq2VnBp1hb2NrH5dtbRLD EmSdqKAAMnk8AdanoorJtt3YwooopAFZXiWznvvDd/DZx775YjNaDIH+kJ88R544kVTzxxzxmtWi rpzcJqa6ag9SCyvINRsLe+tZPMtrmJZonwRuRgCDg8jgjrU9YfhP91ohsfu/YLme0SI/eiiSVhCp 7/6nyiCeSCGyc5O5TrQUKkorZMS1QUUUVmMKro6HUZoxeb5FijLWuV/dgl8PgDd82COTj93wAd2b FRgzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjkAkooooAKKKjnnitoJJ55UihiUvJJIwVUUDJJJ4A A70JX0QGPpn+n+JNV1E8xW+zT7c/eX5fnldG7ZdxGwHe3GTkYXcrG8KQSw+GLF7iJ4rq5U3lxE6l THNMxlkXB5ADuwAPIAAJJ5rZrfEP944rZafdp+O4lsYfi79z4budRXiXTMaghHDHyvnZA38O9A8Z Po54IyDuUVh+Ev3OgR6eeDpsslgFb74jiYrEXH95ohG/YHeCAARR8VH/AAv8/wDK34h1NyiiisBh RRRQBzesOl1BpVjHefaVu9YUG4yreWYHe4KYUAHBtzF6jqSSDnpK5++M114m0G0njjieF7q/BRy4 ZY18hV5AwWW5DH0KlfmzuroK3q6U4Jdm/ndr8khLdhRRRWAwooooAKKKKACiiigAooooAr3rpHAp kvPsimWMCTKjcS6gJ8wI+ckJ6/NwQcGrFRzGZUBgjjd96gh3KjbuG45APIXJA7kAZGciSgAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKAOf8T3Wo232U6TLfG+O/y7aGxWeGbpnzWYoEwMlczR5P9/G0mnan4gWexi1fRoI47n93 51rOzvG+wvmWPbtiU7SOJJMMyqCwO6svXrPX31mDUbK78TrAUmiaxsP7P2x4ZAr/AL44IYIWGd7D d1j5U7HheG8isrl786yZ5bguf7Wkt2cDYijYLc7FT5enBzuJ65IBuUUUUAFc/Z/8lD1n/sFWH/o2 7qS28Sia/tra40nUrGO8cpaXF0sarOwRnACBzIhKI7YdFxtIOGwDHZ/8lD1n/sFWH/o27oA6Ciii gAooooAKKKKACsPTf9H8Xa7apzHNFbX7E9RI4eEgf7O22Q+uS3PIA3Kw7/8A0PxdpF10jvIprBwn V5MCaMt6qqxTgdwZOB8zEb0NeaPdP8Nf0EzcooorAZh6X/pPirX7v7vk/Z9P2dc7EM2/Pv8AaduP 9jOecDUv/K/s658/z/J8p9/2ff5m3Bzs8v592Om35s9Oay/Cv77Tru/b5pL6+uJvNHSWMSGOFx22 mGOLBHBGDzkk7E4ma3lW3kjjnKERvIhdVbHBKggkZ7ZGfUVvif4jj2svuVv0EtiSiiisBhRRRQAU UUUAFFFFABRRRQAUUUUAV7jyvPtPM8/d5p8vyt+3dsb7+3jbjP3/AJd23+LbVio5BMXhMUkaoHzK GQsWXaeFORtO7acnPAIxzkSUAFFFFABWH4j/AHM2iX7cxWmpx7wPvHzUe3XH0edCfYHqcA7lVNS0 +LU7FrWZnQFkkR4yA0bowdGGQRlWVTggg45BGRWtGajUTlt19Ho/wE9jHFlaeKr+e41C1hutMs5Z LW2t5oxJHLIpAklZW/iV1eNeAQA5yRIMdHUFlZwadYW9jax+XbW0SwxJknaigADJ5PAHWp6KtTnd l8K2/ru+oJBRRRWQwooooAKr2/lefd+X5+7zR5nm79u7Yv3N3G3GPufLu3fxbqsVHGJg8xlkjZC+ YgqFSq7Rwxydx3bjkY4IGOMkAkooooAKKKKACsOy/wBF8Zarbj5Iru2gu1U/8tZQXjlZc9cIluCB wMqcAtk7lYerf6P4l8PXScyTSz2DA9BG8TTEj/a3WyD0wW45BG9DXmj3T/DX9BM3KKKKwGYcv+me N7ePrHp1i0zq/TzJn2xuo/vBYZ1J4IEmBnc2NysPRv33iHxHcScyx3MNojekSwRyKv4PPKc9fmxn AAG5W9fRxj2S/FX/ADYkFFFFYDK9h5X9nW3kef5PlJs+0b/M24GN/mfPux13fNnrzVio4BMtvEtx JHJOEAkeNCis2OSFJJAz2ycepqSgDDt/9F8b3sZ+SO+sYpolHSSSJ3WVyB/EFktlyeSNoGdvG5WH qn+jeKtAu/ved9o0/Z0xvQTb8+32bbj/AG854wdyt6+qhLuvyuvyQkFFFFYDCiiigAooooAKw/DP +mWs+tv80mpymWJ/+nYEiALnlVMeJNp6NI5wMkB/iueWHwxfJbyvFdXKizt5UYqY5pmEUbZHIAd1 JI5ABIBPFasEEVtBHBBEkUMShI441CqigYAAHAAHat17tFv+Z2+S1/VfcLqUNb0+11W0jsrprpPN ciKS2LgxybGIckAgFcblL8BwhHzBaXw/qEuq+HtPvbhUS6lgU3EaAgRzYxImCSQVcMpB5BBB5FXp BMXhMUkaoHzKGQsWXaeFORtO7acnPAIxzkY/h7/RrrWdMPAt755og332jnAmLn1XzXmUEDH7vHJU miPvUZLtZ/o/0DqblYem/wCkeLtduk4jhitrBgepkQPMSP8AZ23KD1yG44BO5WH4X/0i1vtTPXUL 6aYFfuNGpEMTp6q0UUbZyQdxI4IAKelObfkvne/6MHujcooorAYVh+Jf3H9kah977JqcP7vpv87N t17Y8/d77ccZyNyqOtad/bGg6jpnm+T9stpLfzNu7ZvUrnGRnGema1oSUaictuvo9H+AnsXqKo6L qP8AbGg6dqfleT9sto7jy927ZvUNjOBnGeuKvVEouEnGW6GFFFFSAVXTyv7Rmx5/neVHu3b/AC9u Xxtz8m7O7OPmxt3cbasVGBN9odmkjMBRQiBCGDZO4ls4II24GBjB5OeACSiio554raCSeeVIoYlL ySSMFVFAySSeAAO9CV9EBj+Ffn028mbmWTU73e5+8225kRcnvhERR6BQOgFblY3hGCW28F6FBPE8 U0Wn26SRyKVZGEaggg8gg9q2a3xLvWnbuxR2CiiisBhRRRQAUUUUAFFFFAHN3/lfaPB3kef5P247 PtG/zNv2K4xv8z592Ou75s9ea6Suf8RiaLRNPkmkja8i1CwBmiQoAzXEcblQSSoZXdcZPysQSQTn oK3qa0oS9V9zv+oluFFFFYDCiiigAooooAKKKKACiiigCve+V5C+d5+3zY8eRv3bt67c7Oducbs/ Ltzu+XNZd/8AvvGWi28nMUdtd3aL6SqYo1b8EnlGOnzZxkAjYmEzIBBJGj71JLoWG3cNwwCOSuQD 2JBwcYOPYfvvGWtXEfMUdtaWjt6SqZZGX8EniOenzYzkEDehpzS7J/j7v63EzcooorAYUUUUAFFF FAGHpv8Ao/i7XbVOY5ora/YnqJHDwkD/AGdtsh9clueQBuVh3X7jxvpkn+rjubG5hduglkV4mjQn +Jgvnso6geYR/FW5W9fXll3S/DT9BIKKKKwGFV08r+0Zsef53lR7t2/y9uXxtz8m7O7OPmxt3cba sVGBN9odmkjMBRQiBCGDZO4ls4II24GBjB5OeACSiiigArD8X/P4auLU/wCrvpYbCQjqI55Uhcj/ AGgshIzkZxkHpW5WHq3+keJfD1qnEkMs9+xPQxpE0JA/2t1yh9MBueADvhtKql21+7X9BPY3KKKK wGFYel/6N4q1+0+9532fUN/TG9DDsx7fZt2f9vGOMncrD1L/AEfxdoV0/Mc0VzYKB1EjhJgT/s7b Zx65K8ckjehrzR7p/h736WEzcooorAYUUUUAc3ZeVc+NG8nz1XT7GTPn7y0jXFw27G/kKptTtxlS rjbhQM9JXP6KJp/E3iC7nkjLwvDYAIhUFUVp1bknnbdBT6mMtxu2r0Fb4jRqPRJfir/m2JBRRRWA wooooAKKKKACiiigAooooAr3vleQvneft82PHkb927eu3OznbnG7Py7c7vlzVio5hMyAQSRo+9SS 6Fht3DcMAjkrkA9iQcHGDJQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHH+O4dsVtePrVjpcflXFms17deQIJJlAFxG3ea NVkCr8pIkf51Gc3PBqaatlqD6Pe6bPp8l6xgg02VXgtVCIuxdvALY81gAMNKev3mp+MroaRqNhra 6vBYyW1pdRskumTXu6JjE7ybInVlVPKXLn5RvAOCRnc0O5ubi3uVvL+0u7iC4aGQ21o9t5ZAHysj u5zzuByAyspAwQSAalU7yPUnuLVrK7tIYFfNyk1s0rSLkcIwkUIcZ5IbqOOOblFAHH6frEPiLxHb yXNnqtslpLIbCGfSrmIF9joZpJGjCjKM4RN3Rstl2VYy4066v/iHqn2bWb7TdmlWO77IkDeZmW7x nzY36Y7Y6nOeMdhXP2f/ACUPWf8AsFWH/o27oAkg0LUYbiKV/FmszojhmikiswrgH7p2wA4PTgg+ hFaj28rxXSC9nRps+W6hM2/yhfkyuDggt8wbkntgCxRQBGY2NwkomkCKjKYgF2sSRhjxnIwQMED5 jkHjEf2eXyPL+2z7vN8zzMJu2792z7uNuPk6btvfd81WKKAIxGwuHlM0hRkVREQu1SCcsOM5OQDk kfKMAc5jS3lSK1Q3s7tDjzHYJm4+Ur8+FwMkhvlC8gdsg2KKAK728rxXSC9nRps+W6hM2/yhfkyu Dggt8wbkntgDH8VRtHHpmoRzSLJa6hbLGgClcyzRwsxyM58uSVRzj94TjIUr0FYfjL5PBur3C8S2 ls93C392WIeZG3vh0U4PBxggjit8L/GgvNClsan2eXyPL+2z7vN8zzMJu2792z7uNuPk6btvfd81 QaleJpFjqGrXEk721tamVoEVTgRhmYrwCWI45bHyjGOSb9Yfi/5/DVxan/V30sNhIR1Ec8qQuR/t BZCRnIzjIPSpoQU6sYvZtA9ibQ9LuNM8PaLp8txtlsLaKKYQ4KSlYthGWXO3PzDG05UdsgzalZmf SdUhcz3S3MTgQKkLEAx7diBxsOSCf3mRljk7eBoVXv3SPTrmSW8+xRrE5a6yo8kAHL5cFRjr8wI4 5GKic3OTk92NaEhjY3CSiaQIqMpiAXaxJGGPGcjBAwQPmOQeMR/Z5fI8v7bPu83zPMwm7bv3bPu4 24+Tpu2993zVYoqQIxGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc5jS3lSK1Q3s7tDjzHYJm4+Ur8 +FwMkhvlC8gdsg2KKAK728rxXSC9nRps+W6hM2/yhfkyuDggt8wbkntgCQxsbhJRNIEVGUxALtYk jDHjORggYIHzHIPGJKKAK/2eXyPL+2z7vN8zzMJu2792z7uNuPk6btvfd81SCNhcPKZpCjIqiIhd qkE5YcZycgHJI+UYA5zJRQBXS3lSK1Q3s7tDjzHYJm4+Ur8+FwMkhvlC8gdsgj28rxXSC9nRps+W 6hM2/wAoX5Mrg4ILfMG5J7YAsUUARmNjcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4xH9nl8jy/ts+7 zfM8zCbtu/ds+7jbj5Om7b33fNViigCnPADf28zxSXADjywVjK2rBJMygnDZYNsOCeowACxqRLeV IrVDezu0OPMdgmbj5Svz4XAySG+ULyB2yCXDok9oHvPIZ5SEjyo+0HYx2cjJwAX+XB+TrjINigCu 9vK8V0gvZ0abPluoTNv8oX5Mrg4ILfMG5J7YAkMbG4SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxiSig Cv8AZ5fI8v7bPu83zPMwm7bv3bPu424+Tpu2993zVII2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDn MlFAFdLeVIrVDezu0OPMdgmbj5Svz4XAySG+ULyB2yCPbyvFdIL2dGmz5bqEzb/KF+TK4OCC3zBu Se2ALFFAEZjY3CSiaQIqMpiAXaxJGGPGcjBAwQPmOQeMR/Z5fI8v7bPu83zPMwm7bv3bPu424+Tp u2993zVYooAjEbC4eUzSFGRVERC7VIJyw4zk5AOSR8owBzmNLeVIrVDezu0OPMdgmbj5Svz4XAyS G+ULyB2yDYooArvbyvFdIL2dGmz5bqEzb/KF+TK4OCC3zBuSe2AI4IAL+4mSKS3Bc+YAsYW6YpHi UkZbKhdgyR0OQQFNXKr27o892EvPPZJQHjyp+znYp2cDIyCH+bJ+frjAAAfZ5fI8v7bPu83zPMwm 7bv3bPu424+Tpu2993zVII2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMlFAFdLeVIrVDezu0OPMd gmbj5Svz4XAySG+ULyB2yCPbyvFdIL2dGmz5bqEzb/KF+TK4OCC3zBuSe2ALFFAEZjY3CSiaQIqM piAXaxJGGPGcjBAwQPmOQeMYfimNrfwtc3Ek0ky2U8eoSFwu4xQzrOyLgAZCIVXPXAyerV0FQXtn BqNhcWN1H5ltcxNDKmSNyMCCMjkcE9K0ozUKkZvo0xPVDxGwuHlM0hRkVREQu1SCcsOM5OQDkkfK MAc5jS3lSK1Q3s7tDjzHYJm4+Ur8+FwMkhvlC8gdsg0vDN5Pf+F9LubuTfePbR/aiQFImCgSKQPu sHDAjAwQRgYo8T3k+neE9ZvrWTy7m2sZ5onwDtdY2IODweQOtP2Mva+y63t+NgvpcoeE7eWbwq9w l7Oi6pLPf27AJuto7h2kQDKkbgHBO7eNxYZK4FdAY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjDL Kzg06wt7G1j8u2tolhiTJO1FAAGTyeAOtT0q01OpKa6tsErIr/Z5fI8v7bPu83zPMwm7bv3bPu42 4+Tpu2993zVII2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMlFZjM/TbMwaTpcKGe1W2iQGBkhUkC PbscINgwSD+7wMqMHbwbD28rxXSC9nRps+W6hM2/yhfkyuDggt8wbkntgAsHSTTraSK8+2xtEhW6 yp84EDD5QBTnr8oA54GKsUAc/wCKo2jj0zUI5pFktdQtljQBSuZZo4WY5Gc+XJKo5x+8JxkKV2Ps 8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qzvFsEtx4Q1hLeJ5LoWcr26xqS4mVS0ZTHO4OFKkc ggEc1qwTxXMEc8EqSwyqHjkjYMrqRkEEcEEd63lrRi/N/p/wfxF1ARsLh5TNIUZFURELtUgnLDjO TkA5JHyjAHOY0t5UitUN7O7Q48x2CZuPlK/PhcDJIb5QvIHbINiisBld7eV4rpBezo02fLdQmbf5 QvyZXBwQW+YNyT2wBIY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjElFAFf7PL5Hl/bZ93m+Z5mE3 bd+7Z93G3HydN23vu+apBGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc5kooA5/UY2l1fw7pTzSTmN nvZ3mC4mSFAgLBQFL+bNC4AAAKEjBVRWw9vK8V0gvZ0abPluoTNv8oX5Mrg4ILfMG5J7YAy7f/Sv G97IfnjsbGKGJh0jkld2lQkfxFY7ZsHkDaRjdzuVvW0UYdl+ev5NCRTuoBJf2MzRSTCJ32gLGViY of3pLfMCBuQbD/y1OQRyuP8AZ5bbxp5f22dm1Cx8zzMIGjW3uN2z7uCrC62dAwVOpY7huXDok9oH vPIZ5SEjyo+0HYx2cjJwAX+XB+TrjIOXrP7nxD4cuI+JZLma0dvWJoJJGX8XgiOevy4zgkEw+rce jT/BX/NIGTa/eXGkaFrGrQSb3trGSWKCRQYw8au2eAGOeAfmxhRjByTPYaWmmaZpun21xOsFhGkS g7SZUVNgDnb9G+XacqO2QaXi3nQkjPMc19ZQyKejxvcxK6Ed1ZSVIPBBIPWtyh6UFbq3+CVvzYdS u9vK8V0gvZ0abPluoTNv8oX5Mrg4ILfMG5J7YAkMbG4SUTSBFRlMQC7WJIwx4zkYIGCB8xyDxiSi sBlf7PL5Hl/bZ93m+Z5mE3bd+7Z93G3HydN23vu+apBGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc 5kooA5vwrby21pc6dJezyT6dqdx58hCf6T5pM67sKP4Z0J2hfnUgfL13Ht5XiukF7OjTZ8t1CZt/ lC/JlcHBBb5g3JPbAGXa/uPG+px/6uO5sbaZF6CWRXlWRwP4mC+QrHqB5YP8Nblb4nWpzd7P5tXf 4iWxGY2NwkomkCKjKYgF2sSRhjxnIwQMED5jkHjEf2eXyPL+2z7vN8zzMJu2792z7uNuPk6btvfd 81WKKwGRiNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5zThsyl5GSZ/OiiiEt6yQg3gAkARyBn5Sxf gKMsMHBZa0Kro6HUZoxeb5FijLWuV/dgl8PgDd82COTj93wAd2QAe3leK6QXs6NNny3UJm3+UL8m VwcEFvmDck9sAY/jWNn8I6ztmkBk064t0twFxNLIu1AONxfdhVAPJfGCcY6CsPxT+9s9PtE5nuNT tPKT+95cyzPz0GI4pG567cDkgHfC/wAaD80KWxqfZ5fI8v7bPu83zPMwm7bv3bPu424+Tpu2993z VII2Fw8pmkKMiqIiF2qQTlhxnJyAckj5RgDnMlFYDK6W8qRWqG9ndoceY7BM3HylfnwuBkkN8oXk DtkEe3leK6QXs6NNny3UJm3+UL8mVwcEFvmDck9sAWKKAIzGxuElE0gRUZTEAu1iSMMeM5GCBggf Mcg8Yj+zy+R5f22fd5vmeZhN23fu2fdxtx8nTdt77vmqxRQBGI2Fw8pmkKMiqIiF2qQTlhxnJyAc kj5RgDnMaW8qRWqG9ndoceY7BM3HylfnwuBkkN8oXkDtkGxRQBXe3leK6QXs6NNny3UJm3+UL8mV wcEFvmDck9sASGNjcJKJpAioymIBdrEkYY8ZyMEDBA+Y5B4xJRQBy+twCy8AyTLFJCmnpHqCWrrG pjWCRZxb/u/kUAIIwRuwAD82MnpBGwuHlM0hRkVREQu1SCcsOM5OQDkkfKMAc5wtfRr74d6pHZzv qTT6TKsM0YV2uS0R2sNgAJbIPygA54FbsE8VzBHPBKksMqh45I2DK6kZBBHBBHet5fwI+r/KIupG lvKkVqhvZ3aHHmOwTNx8pX58LgZJDfKF5A7ZBHt5XiukF7OjTZ8t1CZt/lC/JlcHBBb5g3JPbAFi isBkZjY3CSiaQIqMpiAXaxJGGPGcjBAwQPmOQeMR/Z5fI8v7bPu83zPMwm7bv3bPu424+Tpu2993 zVYooAjEbC4eUzSFGRVERC7VIJyw4zk5AOSR8owBzmNLeVIrVDezu0OPMdgmbj5Svz4XAySG+ULy B2yDYooArvbyvFdIL2dGmz5bqEzb/KF+TK4OCC3zBuSe2AJDGxuElE0gRUZTEAu1iSMMeM5GCBgg fMcg8YkooAr/AGeXyPL+2z7vN8zzMJu2792z7uNuPk6btvfd81SCNhcPKZpCjIqiIhdqkE5YcZyc gHJI+UYA5zJRQBn3NmTZ2ccxnvvIliLhkhJmIIAdwQFG1sS/JtIKDaP4Tn6DG15b65dpNJbvfahc KrRhS0JiAttwLAgk+Rv5GBuwQcZOxeukcCmS8+yKZYwJMqNxLqAnzAj5yQnr83BBway/B3z+EdNu j/rL6L7fIB0Ek5Mzgf7IaQgZycYyT1reGlGTXdL5av8ANIXU2DGxuElE0gRUZTEAu1iSMMeM5GCB ggfMcg8Yj+zy+R5f22fd5vmeZhN23fu2fdxtx8nTdt77vmqxRWAyMRsLh5TNIUZFURELtUgnLDjO TkA5JHyjAHOY0t5UitUN7O7Q48x2CZuPlK/PhcDJIb5QvIHbINiigCu9vK8V0gvZ0abPluoTNv8A KF+TK4OCC3zBuSe2AJDGxuElE0gRUZTEAu1iSMMeM5GCBggfMcg8YkooA5vxHbywjRLtr2cpaaxH I7gJvKy74VQDbt2gzop/i2AnJbBPQCNhcPKZpCjIqiIhdqkE5YcZycgHJI+UYA5zj+Mfk8I6ldD/ AFljF9vjB6GSAiZAf9ktGAcYOM4I61uVvPWjB+q+Wj/ViW5XS3lSK1Q3s7tDjzHYJm4+Ur8+FwMk hvlC8gdsg0dbuZdM0XUbhLid55cJaopRSsrhY40QlCBmQjlwwBY5+UYGtWHef6f4u061HMenRPfy kcFZHDQxA56qym5PHIKLkjOGmjFSleWy1fy6fPb5gyp/whlvtt7vz0/tyJAz6n9nTzJpgVbex++F O0p5auq+W7IMDbts6JMdUtPtZjntrt9ouLjELMGSaTfa7gvzLGwdM7RlWyrFiWG9XN2DpY+P9Ysf tm7+0LWDUEtiVJVlzDIwAG4KVWD73BO7b0bGvPKtGSm7tar9V+vlb1Fax0AjYXDymaQoyKoiIXap BOWHGcnIBySPlGAOcxpbypFaob2d2hx5jsEzcfKV+fC4GSQ3yheQO2QbFFcpRXe3leK6QXs6NNny 3UJm3+UL8mVwcEFvmDck9sAY8cbXHxDuJXmk2WWlRLDEAu3M8snmMeMk/wCjxgc4HPHNdBWH4e/e 3mv3acwXGpt5T/3vLhihfjqMSRSLz125HBBO9LSE5eX5tfpf5XEzU+zy+R5f22fd5vmeZhN23fu2 fdxtx8nTdt77vmqQRsLh5TNIUZFURELtUgnLDjOTkA5JHyjAHOZKKwGV0t5UitUN7O7Q48x2CZuP lK/PhcDJIb5QvIHbIOH4qt5Y7S2vI72dZ49Tt/IYBP3HnEWrbQV5wszuN2758Zyo210lZXiezn1H wnrNjax+Zc3NjPDEmQNztGwAyeByR1rbDtKrG+19fR7/AICexomNjcJKJpAioymIBdrEkYY8ZyME DBA+Y5B4xH9nl8jy/ts+7zfM8zCbtu/ds+7jbj5Om7b33fNS2V5BqNhb31rJ5ltcxLNE+CNyMAQc HkcEdanrJpp2YyMRsLh5TNIUZFURELtUgnLDjOTkA5JHyjAHOY0t5UitUN7O7Q48x2CZuPlK/Phc DJIb5QvIHbINiikBzfh2zR4fEM9mfscV/qc5jaFFDxuirBI+CCu4yxSOMg5yCRkkV0BjY3CSiaQI qMpiAXaxJGGPGcjBAwQPmOQeMYfgmeG88LQX1vLHLDez3N3GUYNtWWeSQI2OA67trDnDAjJxXQVv if40l20+S0X5CjsV/s8vkeX9tn3eb5nmYTdt37tn3cbcfJ03be+75qkEbC4eUzSFGRVERC7VIJyw 4zk5AOSR8owBzmSisBldLeVIrVDezu0OPMdgmbj5Svz4XAySG+ULyB2yCPbyvFdIL2dGmz5bqEzb /KF+TK4OCC3zBuSe2ALFFAEZjY3CSiaQIqMpiAXaxJGGPGcjBAwQPmOQeMR/Z5fI8v7bPu83zPMw m7bv3bPu424+Tpu2993zVYooAjEbC4eUzSFGRVERC7VIJyw4zk5AOSR8owBzmNLeVIrVDezu0OPM dgmbj5Svz4XAySG+ULyB2yDYooArvbyvFdIL2dGmz5bqEzb/AChfkyuDggt8wbkntgCQxsbhJRNI EVGUxALtYkjDHjORggYIHzHIPGJKKAM+9szJYrFIZ7si7jlHyQllAmVxwwC7UwOcb9q5BL4NaFV7 10jgUyXn2RTLGBJlRuJdQE+YEfOSE9fm4IODVigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOD8aXOh6jqi6Het4gt7uSy uYmuNO02WYNbv5Qljz5Thg2YyWQEqVwWUnDbnhM272t/LAuqvJJd5nutTtTbyXT+VGA4QomFChU4 RcmM8E5Y1/FMOoz6jp6xWGq3umCKY3EemXq2sgmzH5RLebExXb53AYjJBI4BGh4bh8jTpF+warZZ lJ8vU737VIeByG82XC+24cgnHOSAbFFFFABXP2f/ACUPWf8AsFWH/o27roK5+z/5KHrP/YKsP/Rt 3QB0FFFFABRRRQAUUUUAFQXtnBqNhcWN1H5ltcxNDKmSNyMCCMjkcE9KnopptO6AyvDF5PqPhPRr 66k8y5ubGCaV8AbnaNSTgcDknpUGv/PqXhyFuYpNTO9D91tttO65HfDojD0Kg9QKPCPyeHlt14it Lm6tIV/uxRTyRxr74RFGTycZJJ5ol/f+O7Ty/m+yaZP5/bZ50sXl/XPkS9Om3nGRnsso4ipbRLm+ W9vxtYnojcqOczLbytbxxyThCY0kcorNjgFgCQM98HHoakqvf+V/Z1z5/n+T5T7/ALPv8zbg52eX 8+7HTb82enNcRRYooooAKKKKACiiigAooooAKKKKACiiigCOQzB4RFHGyF8SlnKlV2nlRg7ju2jB xwSc8YMlV7jyvPtPM8/d5p8vyt+3dsb7+3jbjP3/AJd23+LbVigAooooAKKKKACiiigAooooAKKK KACo4zMXmEscaoHxEVcsWXaOWGBtO7cMDPABzzgSVXt/K8+78vz93mjzPN37d2xfubuNuMfc+Xdu /i3UAWKKKKACiiigAooooAw/DX7j+19P+99k1Ob9503+di56dseft99ueM4B4p/e2en2icz3Gp2n lJ/e8uZZn56DEcUjc9duByQCWH7nxlrVvHxFJbWl26+srGWNm/FIIhjp8ucZJJNU/wBJ8VaBafd8 n7RqG/rnYgh2Y9/tO7P+xjHOR3f8xCn1tzfPl5vzJ6G5RRRXCUFFFFAEcBma3ia4jjjnKAyJG5dV bHIDEAkZ74GfQVJVew8r+zrbyPP8nyk2faN/mbcDG/zPn3Y67vmz15qxQAVh+Dfk8G6RbtxLaWyW ky/3ZYh5ci++HRhkcHGQSOa3Kw/C37qz1C0fie31O781P7vmTNMnPQ5jljbjpuweQQN460ZLzX6/ 5oXU3KKKKwGFFFFABRRRQBh+HP302t368RXepybAfvDykS3bP1eByPYjocgblYfg75/COm3R/wBZ fRfb5AOgknJmcD/ZDSEDOTjGSetblb4n+NJdtPktF+Qo7EchmDwiKONkL4lLOVKrtPKjB3HdtGDj gk54wcfxV8mm2cy8Sx6nZbHH3l3XMaNg9so7qfUMR0JrUuPK8+08zz93mny/K37d2xvv7eNuM/f+ Xdt/i21l+M+PBGuSDiSGxmmjYdUkRCyOD2ZWAYEcggEdKML/AB4eq/MJbBr/AM+peHIW5ik1M70P 3W2207rkd8OiMPQqD1ArcrD1T/SfFWgWn3fJ+0ahv652IIdmPf7Tuz/sYxzkblFX4Kfp/wC3MFuw ooorAYUUUUAYeqf6N4q0C7+9532jT9nTG9BNvz7fZtuP9vOeMHcrD8U/urPT7tOJ7fU7Tyn/ALvm TLC/HQ5jlkXnpuyOQCNyt6mtOEvVfjf9UJbsKKKKwGFRgzfaHVo4xAEUo4cli2TuBXGAANuDk5ye BjmSq6eV/aM2PP8AO8qPdu3+Xty+Nufk3Z3Zx82Nu7jbQBYrD1b/AEjxL4etU4khlnv2J6GNImhI H+1uuUPpgNzwAdysP/j58d/3f7P0z6+Z9ol/Tb9l987+2Od8Po3Lok/xVvzaEzcooorAYUUUUAFF FFABRRRQAUUUUARwGZreJriOOOcoDIkbl1VscgMQCRnvgZ9BWP4L/wCRE8Pf9gy2/wDRS1qWHlf2 dbeR5/k+Umz7Rv8AM24GN/mfPux13fNnrzWX4Q+Tw1b2o/1djLNYRk9THBK8KE/7RWME4wM5wB0r eP8AAl6r8pC6m5RRRWAwooooAKKKKACiiigAooooAwvGlxd2ngjXLixO25jsZmR/NMZTCHLKwBO4 DJHqQBkZyNmCCK2gjggiSKGJQkccahVRQMAADgADtWP4q+fTbOFeZZNTstiD7zbbmN2wO+ER2PoF J6A1uVvLShH1f5L/AIIuoUUUVgMKKKKACiiigCpqmnxatpN5ps7OsN3A8EjRkBgrqVJGQRnB9Kg8 PahLq3hrStSnVFmu7OGeRYwQoZ0DEDJJxk+taVYfhD5PDVvaj/V2Ms1hGT1McErwoT/tFYwTjAzn AHSt1rQd+jX4p3/JC6m5WHof+l6rrepn51kuRaQSdMxQrtK49p2uOSMn1K7a3Kw/B3z+EdNuj/rL 6L7fIB0Ek5Mzgf7IaQgZycYyT1ohpSnL0X5v9ED3Nyuf18zW2p6TqJjjW3truKJ5Ec+bIs+6Ex4w BsEj27n5udpOMou7oKxdd0/+1dP1aytWkS/lswIZJTIIo5PnMTqcFQ6uNxK/MMIT0Slh5KNRc22z 9Ho/wB7G1RVTS9Qi1bSbPUoFdYbuBJ41kADBXUMAcEjOD61brKUXFuL3QwrD8H/vPCtld9Pt+/UN n9z7Q7TbM99vmbc8ZxnAzirXiHUJdJ8NarqUCo01pZzTxrICVLIhYA4IOMj1qfS9Pi0nSbPTYGdo bSBII2kILFUUKCcADOB6VstKDv1a/BO/5oXUt0UUVgMKKKKAMPwd8nhHTbU/6yxi+wSEdDJATC5H +yWjJGcHGMgdK3Kw/Dn7mbW7BeYrTU5NhP3j5qJcNn6PO4HsB1OSdyt8T/Gk++vyeq/MUdgqOeeK 2gknnlSKGJS8kkjBVRQMkkngADvUlYfjT/kRPEP/AGDLn/0U1RRh7SpGD6tIHohPBcV3B4I0OC+t /s9zFYwxvFkkrhABuyAQ2AMjHByMnGTu1Xs/K8hvJ8/b5smfP37t29t2N/O3OduPl242/LirFKpP nm5vq7glZBRRRUDCiiigAooooAKKKKACiiigCOYzKgMEcbvvUEO5Ubdw3HIB5C5IHcgDIzkSVXvf K8hfO8/b5sePI37t29dudnO3ON2fl253fLmrFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcfql/8ADi4lNnq934UkktZZ cw3cluTFIzZk+VvusWyW7k9ea2PDf/CN/wBnSf8ACL/2V9h807/7M8vy/MwM58vjdjb74xWH4g1a KXVLaazk1KG8095Yx53h29u7clsKXUIqguACFcMRteQch8jU8IqP7Ou5vOnkknu3llD6fNYxq5C5 8uGX5lU/eJydztIc5JAAOgooooAK5+z/AOSh6z/2CrD/ANG3ddBXP2f/ACUPWf8AsFWH/o27oA6C iiigAooooAKKKKACiiigDD0D5NS8RwrxFHqY2IPurutoHbA7Zd3Y+pYnqTRpf+k+Ktfu/u+T9n0/ Z1zsQzb8+/2nbj/YznnAIv3Hju78z5ftemQeR33+TLL5n0x58XXru4zg4PCv77Tru/b5pL6+uJvN HSWMSGOFx22mGOLBHBGDzkk91TSMp9Gor56P/wBtd/MlG5Uc4ma3lW3kjjnKERvIhdVbHBKggkZ7 ZGfUVJVe/RJNOuY5bP7bG0ThrXCnzgQcphyFOenzEDnk4rhKLFFFFABRRRQAUUUUAFFFFABRRRQA UUUUARyCYvCYpI1QPmUMhYsu08Kcjad205OeARjnIkqvcIjz2hez89klJSTCn7OdjDfycjIJT5cn 5+mMkWKACiiigAooooAKKKKACiiigAooooAKjjEweYyyRshfMQVCpVdo4Y5O47txyMcEDHGTJVe3 REnuyln5DPKC8mFH2g7FG/g5OAAnzYPydMYJALFFFFABRRRQAUUUUAYepf6P4u0K6fmOaK5sFA6i RwkwJ/2dts49cleOSQRfv/Hd35nzfZNMg8jts86WXzPrnyIuvTbxjJyeKv3OnWl+vyyWN9bzeaek UZkEcznttEMkuSeAMnjAIPDn76bW79eIrvU5NgP3h5SJbtn6vA5HsR0OQO5fwOfsuX581/xTf4k9 bG5RRRXCUFFFFAEcAmW3iW4kjknCASPGhRWbHJCkkgZ7ZOPU1JVewRI9Oto4rP7FGsSBbXCjyQAM JhCVGOnykjjg4qxQAVh6T/o/iXxDavzJNLBfqR0EbxLCAf8Aa3Wzn0wV55IG5WGn+jeO5t/P2/TE 8rHb7PK+/d9ftMeMZ6NnGBnejrGa7r8mn+SEzcooorAYUUUUAFY3i2eW38Iaw9vK8d0bOVLdo2Ic zMpWMJjncXKhQOSSAOa2aw/FXz6bZwrzLJqdlsQfebbcxu2B3wiOx9ApPQGt8Mr1oX7r8xPY2III raCOCCJIoYlCRxxqFVFAwAAOAAO1SUUVg3fVjI5BMXhMUkaoHzKGQsWXaeFORtO7acnPAIxzkVda 07+2NB1HTPN8n7ZbSW/mbd2zepXOMjOM9M1NcIjz2hez89klJSTCn7OdjDfycjIJT5cn5+mMkWKq MnCSlHdAcrp+o/2xr3hjU/K8n7ZodzceXu3bN7WjYzgZxnriuqrh/Af7z+zt/wA3k+GdM8rPOzf5 2/b6bvLjzjrsXPQV3FdWOiqdX2a+z+rb/UmOquFFFFcZQUUUUAZXiezn1HwnrNjax+Zc3NjPDEmQ NztGwAyeByR1q9ZXkGo2FvfWsnmW1zEs0T4I3IwBBweRwR1qesPwf+78K2Vp1+wb9P3/AN/7O7Q7 8dt3l7sc4zjJxmt96D8n+a/4CF1NyiiisBhUYE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54kquiI NRmkFnskaKMNdYX94AXwmQd3y5J5GP3nBJ3YALFYel/6T4q1+7+75P2fT9nXOxDNvz7/AGnbj/Yz nnA3Kw/DH76HVL9uJbvU7neB90eU/wBnXH1SBCfcnoMAb09Kc2/JfO9/0YnujcooorAYUUUUAFFF FABRRRQAUUUUARwCZbeJbiSOScIBI8aFFZsckKSSBntk49TWP4V+TTbyFuJY9Tvd6H7y7rmR1yO2 UdGHqGB6EVqWCJHp1tHFZ/Yo1iQLa4UeSABhMISox0+UkccHFZeg/wDIY8T/APYTT/0ktq3p60px 7Wf42/UT3NyiiisBhRRRQAUUUUAFFFFABRRRQBz/AIhE0us+GYrWSMTJqLTuGQviFbeVXbAIIHzq u7oGdcg5APQVzepIl140sh9j+2LZ2LGaPCnyzLcQGJ/nIBwbeR+MkeXwMlQekret8MF5fq3+Ql1C iiisBhRRRQAUUUUAFYfhz9zNrdgvMVpqcmwn7x81EuGz9HncD2A6nJO5WHpf+jeKtftPved9n1Df 0xvQw7Me32bdn/bxjjJ3pa05p9k/ndL9WJ7of4tnlt/CGsPbyvHdGzlS3aNiHMzKVjCY53FyoUDk kgDmtWCCK2gjggiSKGJQkccahVRQMAADgADtWP4p/e2en2icz3Gp2nlJ/e8uZZn56DEcUjc9duBy QDuUS0oxXm/0/wCD+IdQqMCb7Q7NJGYCihECEMGydxLZwQRtwMDGDyc8SVXREGozSCz2SNFGGusL +8AL4TIO75ck8jH7zgk7sYDMvwp+40qXTD8raZcy2gj6+XEG3QLnv+4aHnJPPJ3ZrcrDt/8ARfG9 7GfkjvrGKaJR0kkid1lcgfxBZLZcnkjaBnbxuVvidanN/Nr9+/43QlsYfi75/DzW7cxXdza2ky/3 opZ445F9so7DI5Gcgg81uVh+If3t5oFo/MFxqa+an97y4ZZk56jEkUbcdduDwSDuUT0pQj6v8l+j BbhRRRWAwooooAw4v9D8b3EfSPUbFZkVOnmQvtkdh/eKzQKDySI8HG1c7lYerf6P4l8PXScyTSz2 DA9BG8TTEj/a3WyD0wW45BG5W9bVQl3X5Nr8kJBWH4s/5A9v/wBhPT//AErhrcrD8Q/vbzQLR+YL jU181P73lwyzJz1GJIo2467cHgkEw2lWMu2v3a/oEtjYhEyoRPJG772IKIVG3cdowSeQuAT3IJwM 4ElV7NEjgYR2f2RTLITHhRuJdiX+UkfOSX9fm5AORVisBhRRRQAUUUUAFFFFABRRRQAUUUUARzCZ kAgkjR96kl0LDbuG4YBHJXIB7Eg4OMGSq96iSQKJLP7WoljIjwp2kOpD/MQPkID+vy8AnAqxQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFAHH+PrDXb7TiujpfS/wCiXCJHYXgtpFumC+RKzb0zGuJNy7jksp2tjK7Hh9dR8q+m v4p7eOe7aW1trmRZJYIyq5VmVmHMnmMAGYBWUDAG1eb1HQNXn1KO0n8LeGNb01Hu547i/YRMjSyq 4XYIXCnDMCQG37NzFTw3SeG9N/svTpIP7E0rSN0pfyNMfdG3AG4ny4/m4x0PAHPYAGxRRRQAVz9n /wAlD1n/ALBVh/6Nu6kHiWF/Go8NRW8jutk91LcgjYjK0Y8r/f2yKxBxgMh538R2f/JQ9Z/7BVh/ 6Nu6AOgooooAKKKKACiiigAooooA5zxQmpWs1nq+j2v2q+hiuLURMMriVAykgEH/AF0UCk5wFZyc Abl2NL0+LSdJs9NgZ2htIEgjaQgsVRQoJwAM4HpVuitpVnKnGn2/r8Lv7xW1uFV790j065klvPsU axOWusqPJABy+XBUY6/MCOORirFRzmZbeVreOOScITGkjlFZscAsASBnvg49DWIySiiigAooooAK KKKACiiigAooooAKKKKAK9w6JPaB7zyGeUhI8qPtB2MdnIycAF/lwfk64yDYqOQzB4RFHGyF8Sln KlV2nlRg7ju2jBxwSc8YMlABRRRQAUUUUAFFFFABRRRQAUUUUAFV7d0ee7CXnnskoDx5U/ZzsU7O BkZBD/Nk/P1xgCxUcZmLzCWONUD4iKuWLLtHLDA2nduGBngA55wACSiiigAooooAKKKKAI54IrmC SCeJJYZVKSRyKGV1IwQQeCCO1MsrODTrC3sbWPy7a2iWGJMk7UUAAZPJ4A61PRT5nblvoAUUUUgC iiigCvYOkmnW0kV59tjaJCt1lT5wIGHygCnPX5QBzwMVYqOAzNbxNcRxxzlAZEjcuqtjkBiASM98 DPoKkoAKw9S/0fxdoV0/Mc0VzYKB1EjhJgT/ALO22ceuSvHJI3Kw/Ef7mbRL9uYrTU494H3j5qPb rj6POhPsD1OAd8N/Et3TXzaaQnsblFFFYDCiiigArD1n994h8OW8fMsdzNduvpEsEkbN+DzxDHX5 s4wCRuVhy/v/AB3aeX832TTJ/P7bPOli8v658iXp0284yM74f4m/J/k1+YmblFFFYDK9w6JPaB7z yGeUhI8qPtB2MdnIycAF/lwfk64yDYqOQzB4RFHGyF8SlnKlV2nlRg7ju2jBxwSc8YMlAHD+Bfm1 rW/L5gt82abfuxeXeXu2IdhtjaLC9lZOMEV3Fcx4P0eXRrnxNFJvZLjWpbuN2jKBlljifj1AJZc9 yp6dB09dmPnGeIlKOzt+SJhogooorjKCiiigArD8OfuZtbsF5itNTk2E/ePmolw2fo87gewHU5J3 Kw7D9z4y1q3j4iktrS7dfWVjLGzfikEQx0+XOMkk70tac0+yfzul+rE90blFFFYDCq6Oh1GaMXm+ RYoy1rlf3YJfD4A3fNgjk4/d8AHdmxUYM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5AJKw/B3z+E dNuj/rL6L7fIB0Ek5Mzgf7IaQgZycYyT1q14h1CXSfDWq6lAqNNaWc08ayAlSyIWAOCDjI9an0vT 4tJ0mz02BnaG0gSCNpCCxVFCgnAAzgelbrSg79WvwTv+aF1LdFFFYDCiiigAooooAKKKKACiiigC vYOkmnW0kV59tjaJCt1lT5wIGHygCnPX5QBzwMVl6b/o/i7XbVOY5ora/YnqJHDwkD/Z22yH1yW5 5AGxAZmt4muI445ygMiRuXVWxyAxAJGe+Bn0FY9v+78d6jv+XztMtfKzxv2S3G/b67fMjzjpvXPU VvR+Cp6fqhPoblFFFYDCiiigAooooAKKKKACiiigDm0dG8Waxc/bPs0EcdhZNNlV/fCSSTyvnB+8 s8Q4HPmYUhhx0lc/ohme5168tY45I7jVQIy7lAVjjhglPQnKtFKACBuKjkAhq6Ct8T8dvJfkhLYK KKKwGFFFFABRRRQAVhxfuPHd35ny/a9Mg8jvv8mWXzPpjz4uvXdxnBxuVh3X/I96T/2DL3/0ba1v Q1co90/w1/NCYaz++8Q+HLePmWO5mu3X0iWCSNm/B54hjr82cYBI3Kw5f3/ju08v5vsmmT+f22ed LF5f1z5EvTpt5xkZ3KK3wwXl+rf5AuoVXR0OozRi83yLFGWtcr+7BL4fAG75sEcnH7vgA7s2KjBm +0OrRxiAIpRw5LFsncCuMAAbcHJzk8DHOAzH1T/RvFWgXf3vO+0afs6Y3oJt+fb7Ntx/t5zxg7lY fin91Z6fdpxPb6naeU/93zJlhfjocxyyLz03ZHIBG5W9TWnCXqvxv+qEt2Yd/wDvvGWi28nMUdtd 3aL6SqYo1b8EnlGOnzZxkAjcrDi/f+O7vzPm+yaZB5HbZ50svmfXPkRdem3jGTncor6KEey/O7/J ggooorAYUUUUAYfif9zDpd+vMtpqdtsB+6fNf7O2fok7ke4HUZB3Kw/GPyeEdSuh/rLGL7fGD0Mk BEyA/wCyWjAOMHGcEda3K3nrRi/Nr5aP9WLqFYeqf6T4q0C0+75P2jUN/XOxBDsx7/ad2f8AYxjn I3Kw5f3/AI7tPL+b7Jpk/n9tnnSxeX9c+RL06becZGTD/E35P8mvzBmpZukkDGO8+1qJZAZMqdpD sCnygD5CCnr8vJJyasVHCZmQmeONH3sAEcsNu47TkgclcEjsSRk4yZKwGFFFFABRRRQAUUUUAFFF FABRRRQBXvXSOBTJefZFMsYEmVG4l1AT5gR85IT1+bgg4NWKjmMyoDBHG771BDuVG3cNxyAeQuSB 3IAyM5ElABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABVO8sp7m4tZYdTu7RIX3SRQrEVnGR8r70YgcEfKVPJ56YuU UAcPpHhjxBpvjKzv7rULG8tTFfPczx2TRSF5pIWVCTMxOBGqqcHakIU9VIsXGjWur/EPVPtMt9H5 WlWO37Jfz22cy3ec+U67unfOOcdTXYVz9n/yUPWf+wVYf+jbugCSDwnp1tcRTpc6yXjcOok1q8dS Qc8q0pDD2IIPetgQqtw84Mm90VCDIxXCkkYXOAfmOSBk8ZzgYkooArpZRRxWsYecra48stO5LYUr 85JzJwT97POD1AND2UUkV1GXnC3WfMKzuCuVC/IQcx8Afdxzk9STViigCMwq1wk5Mm9EZABIwXDE E5XOCflGCRkc4xk5j+xReR5O+fb5vnZ89927fvxuznbnjbnbt+XG3irFFAEYhVbh5wZN7oqEGRiu FJIwucA/MckDJ4znAxGllFHFaxh5ytrjyy07kthSvzknMnBP3s84PUA1YooArvZRSRXUZecLdZ8w rO4K5UL8hBzHwB93HOT1JNSGFWuEnJk3ojIAJGC4YgnK5wT8owSMjnGMnMlFAFf7FF5Hk759vm+d nz33bt+/G7OdueNudu35cbeKjvYYFt72eY3ex7cpKIJJS2xQx/dqhyH+Y8oNx+XrhcXKjnEzW8q2 8kcc5QiN5ELqrY4JUEEjPbIz6igCNLKKOK1jDzlbXHllp3JbClfnJOZOCfvZ5weoBoeyikiuoy84 W6z5hWdwVyoX5CDmPgD7uOcnqSasUUARmFWuEnJk3ojIAJGC4YgnK5wT8owSMjnGMnMf2KLyPJ3z 7fN87Pnvu3b9+N2c7c8bc7dvy428VYooAjEKrcPODJvdFQgyMVwpJGFzgH5jkgZPGc4GI0soo4rW MPOVtceWWnclsKV+ck5k4J+9nnB6gGrFFAFd7KKSK6jLzhbrPmFZ3BXKhfkIOY+APu45yepJqQwq 1wk5Mm9EZABIwXDEE5XOCflGCRkc4xk5kooAr/YovI8nfPt83zs+e+7dv343Zztzxtzt2/LjbxUg hVbh5wZN7oqEGRiuFJIwucA/MckDJ4znAxJRQBXSyijitYw85W1x5ZadyWwpX5yTmTgn72ecHqAa HsopIrqMvOFus+YVncFcqF+Qg5j4A+7jnJ6kmrFFAFO6hga/sZ5Td+ajukQiklEeWQk+YqnYRhTg uMA4xgkZk+xReR5O+fb5vnZ89927fvxuznbnjbnbt+XG3ipJBMXhMUkaoHzKGQsWXaeFORtO7acn PAIxzkSUARiFVuHnBk3uioQZGK4UkjC5wD8xyQMnjOcDEaWUUcVrGHnK2uPLLTuS2FK/OScycE/e zzg9QDViigCu9lFJFdRl5wt1nzCs7grlQvyEHMfAH3cc5PUk1IYVa4ScmTeiMgAkYLhiCcrnBPyj BIyOcYycyUUAV/sUXkeTvn2+b52fPfdu378bs52542527flxt4qQQqtw84Mm90VCDIxXCkkYXOAf mOSBk8ZzgYkooArpZRRxWsYecra48stO5LYUr85JzJwT97POD1AND2UUkV1GXnC3WfMKzuCuVC/I Qcx8Afdxzk9STViigCMwq1wk5Mm9EZABIwXDEE5XOCflGCRkc4xk5j+xReR5O+fb5vnZ89927fvx uznbnjbnbt+XG3irFFAEYhVbh5wZN7oqEGRiuFJIwucA/MckDJ4znAxTs7K1jYxxPfE2sq8zTzkM wiCjlz+8XaRnqu/JPzgmtCo4xMHmMskbIXzEFQqVXaOGOTuO7ccjHBAxxkgEb2UUkV1GXnC3WfMK zuCuVC/IQcx8Afdxzk9STUhhVrhJyZN6IyACRguGIJyucE/KMEjI5xjJzJRQBX+xReR5O+fb5vnZ 89927fvxuznbnjbnbt+XG3ipBCq3Dzgyb3RUIMjFcKSRhc4B+Y5IGTxnOBiSigCullFHFaxh5ytr jyy07kthSvzknMnBP3s84PUA0PZRSRXUZecLdZ8wrO4K5UL8hBzHwB93HOT1JNWKKAIzCrXCTkyb 0RkAEjBcMQTlc4J+UYJGRzjGTmP7FF5Hk759vm+dnz33bt+/G7OdueNudu35cbeKsUUARiFVuHnB k3uioQZGK4UkjC5wD8xyQMnjOcDEaWUUcVrGHnK2uPLLTuS2FK/OScycE/ezzg9QDViigCu9lFJF dRl5wt1nzCs7grlQvyEHMfAH3cc5PUk1IYVa4ScmTeiMgAkYLhiCcrnBPyjBIyOcYycyUUAZ9hZW v9j20MD332f5JkNxPP533g4DtIfM68FWPT5SMcVcEKrcPODJvdFQgyMVwpJGFzgH5jkgZPGc4GCA TLbxLcSRyThAJHjQorNjkhSSQM9snHqakoArpZRRxWsYecra48stO5LYUr85JzJwT97POD1ANYfj GyiPhm9kd5xALm2u7plncGOKKWJpGQg5TEcZbCYOQSBuPPSVjeLoJbnwXrsEETyzS6fcJHHGpZnY xsAAByST2rfDO1eDfdfmJ7GqYVa4ScmTeiMgAkYLhiCcrnBPyjBIyOcYycx/YovI8nfPt83zs+e+ 7dv343Zztzxtzt2/LjbxUkE8VzBHPBKksMqh45I2DK6kZBBHBBHepKwatoxkYhVbh5wZN7oqEGRi uFJIwucA/MckDJ4znAxGllFHFaxh5ytrjyy07kthSvzknMnBP3s84PUA1YooArvZRSRXUZecLdZ8 wrO4K5UL8hBzHwB93HOT1JNY9pCsvj/VbtTIwi061tiVkbYH3zOysAdpcK0Z5GQHGMB+egrD0H/k MeJ/+wmn/pJbVvR+Cp6f+3RE+hqfYovI8nfPt83zs+e+7dv343Zztzxtzt2/LjbxUghVbh5wZN7o qEGRiuFJIwucA/MckDJ4znAxJRWAymYYLYafBm7PlvshIklfJEbD942TuG3PMhILbf4ttSPZRSRX UZecLdZ8wrO4K5UL8hBzHwB93HOT1JNSSCYvCYpI1QPmUMhYsu08Kcjad205OeARjnIkoAjMKtcJ OTJvRGQASMFwxBOVzgn5RgkZHOMZOY/sUXkeTvn2+b52fPfdu378bs52542527flxt4qxRQBGIVW 4ecGTe6KhBkYrhSSMLnAPzHJAyeM5wMRpZRRxWsYecra48stO5LYUr85JzJwT97POD1ANWKKAK72 UUkV1GXnC3WfMKzuCuVC/IQcx8Afdxzk9STUhhVrhJyZN6IyACRguGIJyucE/KMEjI5xjJzJRQBX +xReR5O+fb5vnZ89927fvxuznbnjbnbt+XG3isuX9x47tPL+X7Xpk/n99/kyxeX9MefL067uc4GN ysPWf3PiHw5cR8SyXM1o7esTQSSMv4vBEc9flxnBIO+H+Jryf5N/mJmollFHFaxh5ytrjyy07kth SvzknMnBP3s84PUA0PZRSRXUZecLdZ8wrO4K5UL8hBzHwB93HOT1JNWKKwGRmFWuEnJk3ojIAJGC 4YgnK5wT8owSMjnGMnNNLK1+1TQh77f+7mYtPPs/1ruArE7fvbgVB+7tVhs2itCowJvtDs0kZgKK EQIQwbJ3EtnBBG3AwMYPJzwAYfi2FTo0wJkK3s9pYzoZG2tDJcLG6hc4UlZXBYYbpz8q42Esoo4r WMPOVtceWWnclsKV+ck5k4J+9nnB6gGsvxD+9vNAtH5guNTXzU/veXDLMnPUYkijbjrtweCQdyt5 6UoR9X+S/RiW5Xeyikiuoy84W6z5hWdwVyoX5CDmPgD7uOcnqSakMKtcJOTJvRGQASMFwxBOVzgn 5RgkZHOMZOZKKwGV/sUXkeTvn2+b52fPfdu378bs52542527flxt4qQQqtw84Mm90VCDIxXCkkYX OAfmOSBk8ZzgYkooArpZRRxWsYecra48stO5LYUr85JzJwT97POD1AND2UUkV1GXnC3WfMKzuCuV C/IQcx8Afdxzk9STViigCMwq1wk5Mm9EZABIwXDEE5XOCflGCRkc4xk5j+xReR5O+fb5vnZ89927 fvxuznbnjbnbt+XG3irFFAEYhVbh5wZN7oqEGRiuFJIwucA/MckDJ4znAxGllFHFaxh5ytrjyy07 kthSvzknMnBP3s84PUA1YooAz7OytZNJMcT3wguol5mnnEyqYwo5c+ZG2AM9G3ZJ+Yk1Tuv+R70n /sGXv/o21rYgEy28S3Ekck4QCR40KKzY5IUkkDPbJx6msfXf3Or+HrofIFvmhlm6YjeCQBGP91pR CMHgsE74rfD/AB/KX/pLE9jU+xReR5O+fb5vnZ89927fvxuznbnjbnbt+XG3ipBCq3Dzgyb3RUIM jFcKSRhc4B+Y5IGTxnOBiSisBldLKKOK1jDzlbXHllp3JbClfnJOZOCfvZ5weoBoeyikiuoy84W6 z5hWdwVyoX5CDmPgD7uOcnqSasUUARmFWuEnJk3ojIAJGC4YgnK5wT8owSMjnGMnMf2KLyPJ3z7f N87Pnvu3b9+N2c7c8bc7dvy428VYooAjEKrcPODJvdFQgyMVwpJGFzgH5jkgZPGc4GI0soo4rWMP OVtceWWnclsKV+ck5k4J+9nnB6gGrFFAFd7KKSK6jLzhbrPmFZ3BXKhfkIOY+APu45yepJqQwq1w k5Mm9EZABIwXDEE5XOCflGCRkc4xk5kqC9vINOsLi+upPLtraJppXwTtRQSTgcngHpTSbdkBheHf KfwvFcSeeYLu/lu4Gg37mSW7aSFjs+YKQ6FgeApIcAbhXQCFVuHnBk3uioQZGK4UkjC5wD8xyQMn jOcDGV4esb2w8H6LYv5cF3bWltFMrr5gUqqh1+VhzgMAckA4OCBg7Na4lp1ptd3+YlsV0soo4rWM POVtceWWnclsKV+ck5k4J+9nnB6gGh7KKSK6jLzhbrPmFZ3BXKhfkIOY+APu45yepJqxRWIyMwq1 wk5Mm9EZABIwXDEE5XOCflGCRkc4xk5j+xReR5O+fb5vnZ89927fvxuznbnjbnbt+XG3irFFAEYh Vbh5wZN7oqEGRiuFJIwucA/MckDJ4znAxGllFHFaxh5ytrjyy07kthSvzknMnBP3s84PUA1YooAr vZRSRXUZecLdZ8wrO4K5UL8hBzHwB93HOT1JNY+vwq3iHwrOTJvTUJUAEjBcNaTk5XOCflGCRkc4 xk56CsPxV8mm2cy8Sx6nZbHH3l3XMaNg9so7qfUMR0JrfDa1VHvdferfqJ7Feyson8YawqPO0K21 nucTuSJRNcTFC2c8CRDszjY6jG0gV0AhVbh5wZN7oqEGRiuFJIwucA/MckDJ4znAxj+Hv3t5r92n MFxqbeU/97y4YoX46jEkUi89duRwQTuUYn47eS/JAtiullFHFaxh5ytrjyy07kthSvzknMnBP3s8 4PUA1X+xWsl5qEe++El1EnnHz5wiqQyjyjnbG3Bz5eG+6TyVNaFRgTfaHZpIzAUUIgQhg2TuJbOC CNuBgYweTnjAZh+NYV/4RHWbvMgmttOuHhIkYBHC71cDON6silWxlSOCMnOx9ii8jyd8+3zfOz57 7t2/fjdnO3PG3O3b8uNvFSTwRXMEkE8SSwyqUkjkUMrqRggg8EEdqyvCM8tz4L0KeeV5ZpdPt3kk kYszsY1JJJ5JJ71vvQ9H+a/+1F1GaT/pHiXxDdPxJDLBYKB0MaRLMCf9rdcuPTAXjgk6iWUUcVrG HnK2uPLLTuS2FK/OScycE/ezzg9QDWX4a/f/ANr6h937Xqc37vrs8nFt1758jd7bsc4ydyjE/wAS 3ZJfNJJ/iC2K72UUkV1GXnC3WfMKzuCuVC/IQcx8Afdxzk9STUhhVrhJyZN6IyACRguGIJyucE/K MEjI5xjJzJRWAyv9ii8jyd8+3zfOz577t2/fjdnO3PG3O3b8uNvFSCFVuHnBk3uioQZGK4UkjC5w D8xyQMnjOcDElFAGLruh/wBpeFbvSbaSRZGs5YIGluJCNzRNGDI2SXHzZO7dzhuSAan097XWtEW+ hN0lvqsCT4ad1dFeNQApDfuzjH3CMHJHJJOnWH4M48EaHGeJIbGGGRT1SRECuhHZlYFSDyCCD0rd a0Hfo1+Kd/yQupsGFWuEnJk3ojIAJGC4YgnK5wT8owSMjnGMnPP2tlF/wmmrQ759v2Gymz577t32 i6fG7OdueNudu35cbeK6SsPQf+Qx4n/7Caf+kltRR+Cp6f8At0QfQ0LGGBTLPCbv53dGFxJKeVkc narngbmbBAwV24yoXEiWUUcVrGHnK2uPLLTuS2FK/OScycE/ezzg9QDUkImVCJ5I3fexBRCo27jt GCTyFwCe5BOBnAkrAZXeyikiuoy84W6z5hWdwVyoX5CDmPgD7uOcnqSakMKtcJOTJvRGQASMFwxB OVzgn5RgkZHOMZOZKKAK/wBii8jyd8+3zfOz577t2/fjdnO3PG3O3b8uNvFSCFVuHnBk3uioQZGK 4UkjC5wD8xyQMnjOcDElFAFdLKKOK1jDzlbXHllp3JbClfnJOZOCfvZ5weoBoeyikiuoy84W6z5h WdwVyoX5CDmPgD7uOcnqSasUUARmFWuEnJk3ojIAJGC4YgnK5wT8owSMjnGMnMf2KLyPJ3z7fN87 Pnvu3b9+N2c7c8bc7dvy428VYooAjEKrcPODJvdFQgyMVwpJGFzgH5jkgZPGc4GI0soo4rWMPOVt ceWWnclsKV+ck5k4J+9nnB6gGrFFAGfqFlayWdxHO98I7qWPebaecOrEoo2mM7o14XO3C/eLcFjW hUcwmZAIJI0fepJdCw27huGARyVyAexIODjBkoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACufs/wDkoes/ 9gqw/wDRt3XQVz9n/wAlD1n/ALBVh/6Nu6AOgooooAKKKKACiiigAooooAKKKKACq9+iSadcxy2f 22NonDWuFPnAg5TDkKc9PmIHPJxViq9+6R6dcyS3n2KNYnLXWVHkgA5fLgqMdfmBHHIxQBYooooA KKKKACiiigAooooAKKKKACiiigCvcIjz2hez89klJSTCn7OdjDfycjIJT5cn5+mMkWKr3Dok9oHv PIZ5SEjyo+0HYx2cjJwAX+XB+TrjINigAooooAKKKKACiiigAooooAKKKKACq9uiJPdlLPyGeUF5 MKPtB2KN/BycABPmwfk6YwTYqvbujz3YS889klAePKn7OdinZwMjIIf5sn5+uMAAFiiiigAooooA KKKKACiiigAooooAKKKKAK9giR6dbRxWf2KNYkC2uFHkgAYTCEqMdPlJHHBxViq9g6SadbSRXn22 NokK3WVPnAgYfKAKc9flAHPAxVigAooooAw/Bf8AyInh7/sGW3/opa3Kw/CXGhPGOI4b69hjUdEj S5lVEA7KqgKAOAAAOlblb4r+PP1f5ijsgooorAYVh+E/+QPcf9hPUP8A0rmrcrD8G/P4N0i4bmW7 tku5m/vSyjzJG9su7HA4GcAAcVvH+BL1X5SF1NyiiisBle4RHntC9n57JKSkmFP2c7GG/k5GQSny 5Pz9MZIsVXuHRJ7QPeeQzykJHlR9oOxjs5GTgAv8uD8nXGQbFABRRRQAUUUUAFFFFABWH4p/dWen 3acT2+p2nlP/AHfMmWF+OhzHLIvPTdkcgEblYfjL5PBur3C8S2ls93C392WIeZG3vh0U4PBxggji t8L/ABoLzQpbG5RRRWAwquiINRmkFnskaKMNdYX94AXwmQd3y5J5GP3nBJ3YsVXR0OozRi83yLFG Wtcr+7BL4fAG75sEcnH7vgA7sgGXf/vvGWi28nMUdtd3aL6SqYo1b8EnlGOnzZxkAjcrDT/SfHc2 /j7BpieVjv8AaJX37vp9mjxjHVs5yMblb1tFCPZfm2/yYkFFFFYDCiiigAooooAKKKKACiiigCvY IkenW0cVn9ijWJAtrhR5IAGEwhKjHT5SRxwcVl+LP+QPb/8AYT0//wBK4a1LB0k062kivPtsbRIV usqfOBAw+UAU56/KAOeBisvxp/yIniH/ALBlz/6Kat8L/Hh6r8xS2NyiiisBhRRRQAUUUUAFFFFA BWH4z58Ea5GOZJrGaGNR1eR0KogHdmYhQBySQB1rcrD8VfPptnCvMsmp2WxB95ttzG7YHfCI7H0C k9Aa3wv8eHqvzFLY1L1EkgUSWf2tRLGRHhTtIdSH+YgfIQH9fl4BOBViq966RwKZLz7IpljAkyo3 EuoCfMCPnJCevzcEHBqxWAwooooAKKKKACiiigAqjrOnf2to13YCXyZJoisc4XcYZOqSAZHzK2GG CCCBgir1FVGThJSjugM3QdPl0zR4oLhka6dpJ7gxklPOldpJAmRnbvdtuecYzk1pUUUTm5ycnuwW gVXREGozSCz2SNFGGusL+8AL4TIO75ck8jH7zgk7sWKro6HUZoxeb5FijLWuV/dgl8PgDd82COTj 93wAd2ZAsVh+EuNCeMcRw317DGo6JGlzKqIB2VVAUAcAAAdK3Kw/D37q81+0TiC31NvKT+75kMUz 89TmSWRuem7A4AA3hrSnH0f5r9UJ7h4R+fw8twvMV3c3V3C396KWeSSNvbKOpweRnBAPFblYfgv/ AJETw9/2DLb/ANFLW5Riv48/V/mEdkFFFFYDCiiigArD8J/8ge4/7Ceof+lc1blYegfJqXiOFeIo 9TGxB91d1tA7YHbLu7H1LE9Sa3p60px7Wf6fqJ7m5WH4W/e2eoXb8z3Gp3fmv/e8uZoU46DEcUa8 dduTySTuVh+EPn8NW90P9XfSzX8YPURzyvMgP+0FkAOMjOcE9aI6UZPzX6/5IOpqWaJHAwjs/sim WQmPCjcS7Ev8pI+ckv6/NyAcirFV7N0kgYx3n2tRLIDJlTtIdgU+UAfIQU9fl5JOTVisBhRRRQAU UUUAFFFFABRRRQAUUUUAV71EkgUSWf2tRLGRHhTtIdSH+YgfIQH9fl4BOBViq966RwKZLz7IpljA kyo3EuoCfMCPnJCevzcEHBqxQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/Z/8lD1n/sFWH/o27roK5+z /wCSh6z/ANgqw/8ARt3QB0FFFFABRRRQAUUUUAFFFFABRRRQAVHOZlt5Wt445JwhMaSOUVmxwCwB IGe+Dj0NSVXv/K/s658/z/J8p9/2ff5m3Bzs8v592Om35s9OaALFFFFABRRRQAUUUUAFFFFABRRR QAUUUUARyGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjBkqvceV59p5nn7vNPl+Vv27tjff28bcZ+/ 8u7b/FtqxQAUUUUAFFFFABRRRQAUUUUAFFFFABUcZmLzCWONUD4iKuWLLtHLDA2nduGBngA55wJK r2/lefd+X5+7zR5nm79u7Yv3N3G3GPufLu3fxbqALFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARwG ZreJriOOOcoDIkbl1VscgMQCRnvgZ9BUlV7Dyv7OtvI8/wAnyk2faN/mbcDG/wAz592Ou75s9eas UAFFFFAGH4c/cza3YLzFaanJsJ+8fNRLhs/R53A9gOpyTuVh6N+58Q+I7eTiWS5hu0X1iaCONW/F 4JRjr8ucYIJ3K3xP8S/dJ/NpNiWwUUUVgMo61qP9j6DqOp+V532O2kuPL3bd+xS2M4OM464o0XTv 7H0HTtM83zvsdtHb+Zt279ihc4ycZx0zVHxl8/g3V7deZbu2e0hX+9LKPLjX2y7qMngZySBzW5W7 0oK3Vv8ABK35sXUKKKKwGRyGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjBkqvceV59p5nn7vNPl+V v27tjff28bcZ+/8ALu2/xbasUAFFFFABRRRQAUUUUAFVNU0+LVtJvNNnZ1hu4HgkaMgMFdSpIyCM 4PpVuinGTi1JboDN8PahLq3hrStSnVFmu7OGeRYwQoZ0DEDJJxk+taVYfg75PCOm2p/1ljF9gkI6 GSAmFyP9ktGSM4OMZA6VuVriIqNacVsm/wAxLYKjBm+0OrRxiAIpRw5LFsncCuMAAbcHJzk8DHMl V08r+0Zsef53lR7t2/y9uXxtz8m7O7OPmxt3cbaxGZek/wCkeJfEN0/EkMsFgoHQxpEswJ/2t1y4 9MBeOCTuVh+GP30OqX7cS3ep3O8D7o8p/s64+qQIT7k9BgDcrfE/xLdrL5pJP8hLYKKKKwGFFFFA BRRRQAUUUUAFFFFAEcBma3ia4jjjnKAyJG5dVbHIDEAkZ74GfQVneJ7OfUfCes2NrH5lzc2M8MSZ A3O0bADJ4HJHWrth5X9nW3kef5PlJs+0b/M24GN/mfPux13fNnrzVirpzcJqa6ag9SCyvINRsLe+ tZPMtrmJZonwRuRgCDg8jgjrU9Yfgv8A5ETw9/2DLb/0UtblOtBQqSgujaEndBRRRWYwooooAKo6 jrWlaP5f9p6nZWXm58v7TOse/GM43EZxkfmKtTzxW0Ek88qRQxKXkkkYKqKBkkk8AAd6ytBglnST WryJ4ry/Vf3bqUMMCljDGy9mAcls5O9mGdoUDWnCNnOey/F/1/WomaVne2mo2qXVjdQ3NtJnZNBI HRsHBwRweQR+FZWvf8hjwx/2E3/9JLmiX/iUeJbdo/8Aj21mVkmTqRcrFlHHopihZW5PKx4Ay5Jd f8j3pP8A2DL3/wBG2tbU4KM7rZxk19z/AFE3obExmVAYI43feoIdyo27huOQDyFyQO5AGRnIkqve +V5C+d5+3zY8eRv3bt67c7Oducbs/Ltzu+XNWK5CgooooAKKKKACiiigAooooAKKKKACowZvtDq0 cYgCKUcOSxbJ3ArjAAG3Byc5PAxzJVdPK/tGbHn+d5Ue7dv8vbl8bc/Juzuzj5sbd3G2gCxXHane T6MnjhrWTbcrYrqsU2AdjmBoguDkHBtQ2T134xxk9jXD+Kv+Z3/7FmP/ANvK7cCk5tPy/wDSoky2 OxsrODTrC3sbWPy7a2iWGJMk7UUAAZPJ4A61PRRXG227soKKKKQBRRRQAVh6X/o3irX7T73nfZ9Q 39Mb0MOzHt9m3Z/28Y4ydysO1/5HvVv+wZZf+jbqt6PwVPT/ANuiJ9DYnnitoJJ55UihiUvJJIwV UUDJJJ4AA71leEYJbbwXoUE8TxTRafbpJHIpVkYRqCCDyCD2pnjT/kRPEP8A2DLn/wBFNW5RtQ9X +S/+2DqRwmZkJnjjR97ABHLDbuO05IHJXBI7EkZOMmSq9n5XkN5Pn7fNkz5+/du3tuxv525ztx8u 3G35cVYrAYUUUUAFFFFABRRRQAUUUUAFFFFAEcxmVAYI43feoIdyo27huOQDyFyQO5AGRnIkqve+ V5C+d5+3zY8eRv3bt67c7Oducbs/Ltzu+XNWKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn7P/koes/8A YKsP/Rt3WH4O8QtrI0q9k8RXc0t/brLLaS2Krab2j3tFbzCNcujZG0ySEKkgYZBZblxruj6J8Q9U /tbVbGw87SrHy/tdwkW/Et3nG4jOMjp6igDsKK5//hO/B/8A0Neh/wDgxh/+KrcM8K3CW7Sxid0Z 0jLDcyqQGIHUgFlBPbcPWgCSiq/2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/lx13cdakE8LXD26yxmd EV3jDDcqsSFJHUAlWAPfafSgCSiq6X9nJFayx3cDx3ePszrICJsqXGw/xfKC3HYE9KHv7OOK6lku 4EjtM/aXaQAQ4UOd5/h+UhuexB6UAWKKjM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPWo/t9n5Hn/ AGuDyfN8jzPMG3zN/l7M/wB7f8uOu7jrQBYoqMTwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KjS/s 5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpQBYqOcTNbyrbyRxzlCI3kQuqtjglQQSM9sjPqKje/s 44rqWS7gSO0z9pdpABDhQ53n+H5SG57EHpUeoSWzW81nNDHdvNbyN9hJQtcIAAyhXIBHzKpyQPmG SM0AXKKr/b7PyPP+1weT5vkeZ5g2+Zv8vZn+9v8Alx13cdakE8LXD26yxmdEV3jDDcqsSFJHUAlW APfafSgCSiq6X9nJFayx3cDx3ePszrICJsqXGw/xfKC3HYE9KHv7OOK6lku4EjtM/aXaQAQ4UOd5 /h+UhuexB6UAWKKjM8K3CW7Sxid0Z0jLDcyqQGIHUgFlBPbcPWo/t9n5Hn/a4PJ83yPM8wbfM3+X sz/e3/Ljru460AWKKjE8LXD26yxmdEV3jDDcqsSFJHUAlWAPfafSo0v7OSK1lju4Hju8fZnWQETZ UuNh/i+UFuOwJ6UAWKKrvf2ccV1LJdwJHaZ+0u0gAhwoc7z/AA/KQ3PYg9KkM8K3CW7Sxid0Z0jL DcyqQGIHUgFlBPbcPWgCSiq/2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/lx13cdakE8LXD26yxmdEV3 jDDcqsSFJHUAlWAPfafSgAkExeExSRqgfMoZCxZdp4U5G07tpyc8AjHORJWfLeWE7aVMBBdLcy5s 51eNgCYnYOhJ5ygYfJk4Y8bdxFh7+zjiupZLuBI7TP2l2kAEOFDnef4flIbnsQelAFiiozPCtwlu 0sYndGdIyw3MqkBiB1IBZQT23D1qP7fZ+R5/2uDyfN8jzPMG3zN/l7M/3t/y467uOtAFiioxPC1w 9ussZnRFd4ww3KrEhSR1AJVgD32n0qNL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelAFiiq739 nHFdSyXcCR2mftLtIAIcKHO8/wAPykNz2IPSpDPCtwlu0sYndGdIyw3MqkBiB1IBZQT23D1oAkoq v9vs/I8/7XB5Pm+R5nmDb5m/y9mf72/5cdd3HWpBPC1w9ussZnRFd4ww3KrEhSR1AJVgD32n0oAk oqul/ZyRWssd3A8d3j7M6yAibKlxsP8AF8oLcdgT0oe/s44rqWS7gSO0z9pdpABDhQ53n+H5SG57 EHpQBYqOMTB5jLJGyF8xBUKlV2jhjk7ju3HIxwQMcZIZ4VuEt2ljE7ozpGWG5lUgMQOpALKCe24e tU7e8sE+13CCCCN7sRPcb4wtxL8sXUHlgwEWGw25NuMYyAaFFRieFrh7dZYzOiK7xhhuVWJCkjqA SrAHvtPpUaX9nJFayx3cDx3ePszrICJsqXGw/wAXygtx2BPSgCxRVd7+zjiupZLuBI7TP2l2kAEO FDnef4flIbnsQelSGeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrQBJRVf7fZ+R5/2uDyfN8jzPMG3 zN/l7M/3t/y467uOtSCeFrh7dZYzOiK7xhhuVWJCkjqASrAHvtPpQBJRVdL+zkitZY7uB47vH2Z1 kBE2VLjYf4vlBbjsCelD39nHFdSyXcCR2mftLtIAIcKHO8/w/KQ3PYg9KALFFRmeFbhLdpYxO6M6 RlhuZVIDEDqQCygntuHrUf2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/AJcdd3HWgCxRUYnha4e3WWMz oiu8YYblViQpI6gEqwB77T6VGl/ZyRWssd3A8d3j7M6yAibKlxsP8Xygtx2BPSgCSATLbxLcSRyT hAJHjQorNjkhSSQM9snHqakrPs7ywttJJAgsLexiUTwM8aiyAjDbH2kqm1Cp4OMYIOMGrhnhW4S3 aWMTujOkZYbmVSAxA6kAsoJ7bh60ASUVX+32fkef9rg8nzfI8zzBt8zf5ezP97f8uOu7jrUgnha4 e3WWMzoiu8YYblViQpI6gEqwB77T6UAY9r/yPerf9gyy/wDRt1W5XNtf2a+NtKlt7uAx6vpk211k BFz5TRvFsPfCzTt8vUEk5CjG49/ZxxXUsl3Akdpn7S7SACHChzvP8PykNz2IPSt6+rjLul+Gn5oS LFFRmeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrUf2+z8jz/tcHk+b5HmeYNvmb/L2Z/vb/lx13cd awGZfiz/AJA9v/2E9P8A/SuGtyuf8QTwz6roemGWNvN1BHuIAw3bVimmiJHUDzYFIPfYRyMithL+ zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelbz0pQj6v8l+jEtyxRVd7+zjiupZLuBI7TP2l2kAEO FDnef4flIbnsQelSGeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrWAwkExeExSRqgfMoZCxZdp4U5G 07tpyc8AjHORJWfcXlg/2S4cQTxpdmJLjfGVt5fmi6k8MWJiwuW3PtxjOLgnha4e3WWMzoiu8YYb lViQpI6gEqwB77T6UASUVXS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpQ9/ZxxXUsl3Akdpn7 S7SACHChzvP8PykNz2IPSgCxRUZnhW4S3aWMTujOkZYbmVSAxA6kAsoJ7bh61H9vs/I8/wC1weT5 vkeZ5g2+Zv8AL2Z/vb/lx13cdaALFFRieFrh7dZYzOiK7xhhuVWJCkjqASrAHvtPpUaX9nJFayx3 cDx3ePszrICJsqXGw/xfKC3HYE9KALFFV3v7OOK6lku4EjtM/aXaQAQ4UOd5/h+UhuexB6VIZ4Vu Et2ljE7ozpGWG5lUgMQOpALKCe24etAGP4V+TTbyFuJY9Tvd6H7y7rmR1yO2UdGHqGB6EVuVzeg3 9n/aXief7XB5P9sJB5nmDb5n2e2j2Z/vb/lx13cda6ATwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9 K3xP8Vy72f3q/wColsSVGBN9odmkjMBRQiBCGDZO4ls4II24GBjB5OeI0v7OSK1lju4Hju8fZnWQ ETZUuNh/i+UFuOwJ6VQvdUsNOXWNSa33nT7UNdTQ+WzsEVpPK+9u3KrbgGwMSgg8nGMYuTUVuxkX g/8AeeFbK76fb9+obP7n2h2m2Z77fM254zjOBnFblZOgW40fQtH0W5ngN7b2McZRH+/5aojsoOCV BK84/iGcZq99vs/I8/7XB5Pm+R5nmDb5m/y9mf72/wCXHXdx1rXESUq05LZt/mJbFiioxPC1w9us sZnRFd4ww3KrEhSR1AJVgD32n0qNL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelYjLFFV3v7OO K6lku4EjtM/aXaQAQ4UOd5/h+UhuexB6VIZ4VuEt2ljE7ozpGWG5lUgMQOpALKCe24etAElFV/t9 n5Hn/a4PJ83yPM8wbfM3+Xsz/e3/AC467uOtSCeFrh7dZYzOiK7xhhuVWJCkjqASrAHvtPpQBJRV dL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelD39nHFdSyXcCR2mftLtIAIcKHO8/w/KQ3PYg9K ALFFRmeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrUf2+z8jz/ALXB5Pm+R5nmDb5m/wAvZn+9v+XH Xdx1oAkgEy28S3Ekck4QCR40KKzY5IUkkDPbJx6mpKp6fJbLbw2cMMdo8NvG32EFA1uhBCqVQkAf KyjBI+U4JxUiX9nJFayx3cDx3ePszrICJsqXGw/xfKC3HYE9KAMvwh8nhq3tR/q7GWawjJ6mOCV4 UJ/2isYJxgZzgDpW5XN+Fr+zj0LUpZLuBI7TU9Q+0u0gAhxcyud5/h+UhuexB6V0BnhW4S3aWMTu jOkZYbmVSAxA6kAsoJ7bh61viv48/V/mKOyJKKr/AG+z8jz/ALXB5Pm+R5nmDb5m/wAvZn+9v+XH Xdx1qQTwtcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KwGSUVXS/s5IrWWO7geO7x9mdZARNlS42H+L5 QW47AnpQ9/ZxxXUsl3Akdpn7S7SACHChzvP8PykNz2IPSgDL8U/6TYW2kf8AQVuVtHz90xYaSZSe o3QxyqCOcsOR1G5XPmeGf4hpbtLGk9lpTOkZYbplnlAYgdQENuoJ5z5o6Y52Pt9n5Hn/AGuDyfN8 jzPMG3zN/l7M/wB7f8uOu7jrW9X3YRh8/v8A+BYSKXiSznvdAuktI/MvItlzaxkgB5omEsaknHyl 0UHkcE8jrVHSLyDWPFF1qVrJ9os/7MtPs8uDhDI0sjqM/dZk+zsw4OPLJHSt8TwtcPbrLGZ0RXeM MNyqxIUkdQCVYA99p9KoaZHo2n2duumtaxW162+3McgKzkpuGw5+YCNPlA4CIAMKoAcKyjRlB79P na/5L8QtrcvzCZkAgkjR96kl0LDbuG4YBHJXIB7Eg4OMGSs/ULywFncPcCC4hs5YzcBnjxblSkm9 9xAXYCsnPIABAJxm4Z4VuEt2ljE7ozpGWG5lUgMQOpALKCe24etc4ySiq/2+z8jz/tcHk+b5HmeY Nvmb/L2Z/vb/AJcdd3HWpBPC1w9ussZnRFd4ww3KrEhSR1AJVgD32n0oAkoqul/ZyRWssd3A8d3j 7M6yAibKlxsP8Xygtx2BPSh7+zjiupZLuBI7TP2l2kAEOFDnef4flIbnsQelAFiiozPCtwlu0sYn dGdIyw3MqkBiB1IBZQT23D1qP7fZ+R5/2uDyfN8jzPMG3zN/l7M/3t/y467uOtAFiioxPC1w9uss ZnRFd4ww3KrEhSR1AJVgD32n0qNL+zkitZY7uB47vH2Z1kBE2VLjYf4vlBbjsCelAFiiq739nHFd SyXcCR2mftLtIAIcKHO8/wAPykNz2IPSpDPCtwlu0sYndGdIyw3MqkBiB1IBZQT23D1oAkqMCb7Q 7NJGYCihECEMGydxLZwQRtwMDGDyc8R/b7PyPP8AtcHk+b5HmeYNvmb/AC9mf72/5cdd3HWo45LY 6zcxJDGLtbeFpJQU3MhaTYpwd+AQ5GQB8xwSd2AC5XD+NP3fiPR9ny+d5Pm4437NQstm712+ZJjP Te2OprsEv7OSK1lju4Hju8fZnWQETZUuNh/i+UFuOwJ6Vyvi1bOfWdHuJNQgijt7k29yzMMQbPKv SXOfl+W0AwccShugw3ZgJKOIUpbK/wCCbJnsdlRUZnhW4S3aWMTujOkZYbmVSAxA6kAsoJ7bh61H 9vs/I8/7XB5Pm+R5nmDb5m/y9mf72/5cdd3HWuMosUVGJ4WuHt1ljM6IrvGGG5VYkKSOoBKsAe+0 +lRpf2ckVrLHdwPHd4+zOsgImypcbD/F8oLcdgT0oAsUVXe/s44rqWS7gSO0z9pdpABDhQ53n+H5 SG57EHpUhnhW4S3aWMTujOkZYbmVSAxA6kAsoJ7bh60ASVh3X/I96T/2DL3/ANG2tan2+z8jz/tc Hk+b5HmeYNvmb/L2Z/vb/lx13cdax9Tnhg8ZaG6yxvO6TWbwBhuRZUMyyEdcZs2UDHOSc/Lg74f4 36S/9JYnsSeLP+QPb/8AYT0//wBK4a3K5/Wp4b+Tw2kUsc9je6gjlo2DLKqQyzxkMO2+KNsg84xy CQdh7+zjiupZLuBI7TP2l2kAEOFDnef4flIbnsQelE9KUI+r/JfowW5JCJlQieSN33sQUQqNu47R gk8hcAnuQTgZwJKp2kltCRarDHZyyPNKtuSgZwJPnlAUnIZnDE9f3g3YJxUn2+z8jz/tcHk+b5Hm eYNvmb/L2Z/vb/lx13cdawGWKKjE8LXD26yxmdEV3jDDcqsSFJHUAlWAPfafSo0v7OSK1lju4Hju 8fZnWQETZUuNh/i+UFuOwJ6UAWKKrvf2ccV1LJdwJHaZ+0u0gAhwoc7z/D8pDc9iD0qQzwrcJbtL GJ3RnSMsNzKpAYgdSAWUE9tw9aAJKKr/AG+z8jz/ALXB5Pm+R5nmDb5m/wAvZn+9v+XHXdx1qQTw tcPbrLGZ0RXeMMNyqxIUkdQCVYA99p9KAJKKrpf2ckVrLHdwPHd4+zOsgImypcbD/F8oLcdgT0oe /s44rqWS7gSO0z9pdpABDhQ53n+H5SG57EHpQBYoqMzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9a j+32fkef9rg8nzfI8zzBt8zf5ezP97f8uOu7jrQBJMJmQCCSNH3qSXQsNu4bhgEclcgHsSDg4wZK p3cltMTatDHeSxvDK1uChZAZPklIYjAVkLA9f3Z25IxVygAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqneaTpuoX FrcXun2lzPaPvtpJoVdoWyDlCRlTlQcj0HpVyigDl9L8LXli2j29xqcE+m6Lj7BFHaGOb5YmhXzZ DIwf5HbO1Ey2DwBtNiz/AOSh6z/2CrD/ANG3ddBXP2f/ACUPWf8AsFWH/o27oA6CiiigAooooAKK KKACiiigAooooAKr37pHp1zJLefYo1ictdZUeSADl8uCox1+YEccjFWKjnMy28rW8cck4QmNJHKK zY4BYAkDPfBx6GgCSiiigAooooAKKKKACiiigAooooAKKKKAK9w6JPaB7zyGeUhI8qPtB2MdnIyc AF/lwfk64yDYqOQzB4RFHGyF8SlnKlV2nlRg7ju2jBxwSc8YMlABRRRQAUUUUAFFFFABRRRQAUUU UAFV7d0ee7CXnnskoDx5U/ZzsU7OBkZBD/Nk/P1xgCxUcZmLzCWONUD4iKuWLLtHLDA2nduGBngA 55wACSiiigAooooAKKKKACiiigAooooAKKKKAK9g6SadbSRXn22NokK3WVPnAgYfKAKc9flAHPAx Vio4DM1vE1xHHHOUBkSNy6q2OQGIBIz3wM+gqSgAooooAw735PG+jyP8sbWN5CrHgGQvbsEB/vFY 3bHXCMexq1qGriznW2t7K51C7K+Y1vamMMiZIDMXZVAJBABOThsAhWxV17/kMeGP+wm//pJc0eGv 9LhvNXk+aW+uZNj9VMEbskOw/wBwookHJBMrsOGrtcV7OM5K6S/Fyl/kyetjVs7yC/tUubaTfE+c EgqQQcFSDyrAggggEEEEAip6w7P/AIlnii409f8Aj21CJ7+FR/yzkVkWYY4AVi8bjGSWaUk8ityu arBRfu7PVf15bDRh6z++8Q+HLePmWO5mu3X0iWCSNm/B54hjr82cYBI3Kw7r/ke9J/7Bl7/6Nta3 Kut8FP0/9uYLdhRRRWAyvcOiT2ge88hnlISPKj7QdjHZyMnABf5cH5OuMg2KjkMweERRxshfEpZy pVdp5UYO47towccEnPGDJQAUUUUAFFFFABRRRQAUUUUAYdl8njfWI0+WNrGzmZRwDIXuFLkf3isa LnrhFHYVuVhv/o3juHZz9v0x/Nz2+zyps2/X7TJnOei4xg53K3r6uMu6X4K35oSCub8XOkmhaxYr ebri8tY7JLYlcQtcO0MchAG4KzPyTkYjO0ZBz0lc/wCJzNLHZWQjj/f6hZG3w53O0cwmlBGMKFih Zgd3PIwDjcYX+NB+aCWx0FFFFYDCiiigAooooAKKKKACiiigAooooAr2DpJp1tJFefbY2iQrdZU+ cCBh8oApz1+UAc8DFWKjgMzW8TXEccc5QGRI3LqrY5AYgEjPfAz6CpKAMPQPk1LxHCvEUepjYg+6 u62gdsDtl3dj6liepNblYeifJrviWN/lka+jmVTwTGbaFQ4H90tG656ZRh2Nblb4j4/lH/0lCWwU UUVgMKKKgvbyDTrC4vrqTy7a2iaaV8E7UUEk4HJ4B6U0m3ZAZXhr9/8A2vqH3ftepzfu+uzycW3X vnyN3tuxzjJ3KyvDNnPYeF9LtruPZeJbR/agSGJmKgyMSPvMXLEnJySTk5rVrXENOrK2y0XotF+A lsFFFFYjK966RwKZLz7IpljAkyo3EuoCfMCPnJCevzcEHBqxUcxmVAYI43feoIdyo27huOQDyFyQ O5AGRnIkoAKKKKACiiigAooooAKKKKACiiigAqujodRmjF5vkWKMta5X92CXw+AN3zYI5OP3fAB3 ZsVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOQCSo5IIpXieSJHeJt8bMoJRsFcj0OGYZ9CR3qS ihOwBRRRQAUUUUAFFFFABWHr3/IY8Mf9hN//AEkua3Kw/Fn/ACB7f/sJ6f8A+lcNb4bWqo99Pv0/ UT2DUv8ASPF2hWr8RwxXN+pHUyIEhAP+ztuXPrkLzwQdysP/AI+fHf8Ad/s/TPr5n2iX9Nv2X3zv 7Y53KK2ihHql+bb/ACaBdSvZukkDGO8+1qJZAZMqdpDsCnygD5CCnr8vJJyasVHCZmQmeONH3sAE csNu47TkgclcEjsSRk4yZKwGFFFFABRRRQAUUUUAFFFFABRRRQBXvXSOBTJefZFMsYEmVG4l1AT5 gR85IT1+bgg4NWKjmMyoDBHG771BDuVG3cNxyAeQuSB3IAyM5ElABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAVz9n/yUPWf+wVYf+jbuugrn7P/AJKHrP8A2CrD/wBG3dAHQUUUUAFFFFABRRRQAUUUUAFFFFAB Ve/8r+zrnz/P8nyn3/Z9/mbcHOzy/n3Y6bfmz05qxUc4ma3lW3kjjnKERvIhdVbHBKggkZ7ZGfUU ASUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXuPK8+08zz93mny/K37d2xvv7eNuM/f+Xdt/i21YqO QTF4TFJGqB8yhkLFl2nhTkbTu2nJzwCMc5ElABRRRQAUUUUAFFFFABRRRQAUUUUAFV7fyvPu/L8/ d5o8zzd+3dsX7m7jbjH3Pl3bv4t1WKjjEweYyyRshfMQVCpVdo4Y5O47txyMcEDHGSASUUUUAFFF FABRRRQAUUUUAFFFFABRRRQBXsPK/s628jz/ACfKTZ9o3+ZtwMb/ADPn3Y67vmz15qxUcAmW3iW4 kjknCASPGhRWbHJCkkgZ7ZOPU1JQAUUUUAY3ifT9Q1DR8aQ1smqQTxT2slyWCKyuCwJUbgGTehx1 DEdCavaXp8Wk6TZ6bAztDaQJBG0hBYqihQTgAZwPSrdFaOrJ01T6Xv8A1/XVitrcw/FX+jaZHrI5 Ojym/K/3o1R1lGO7eU8m3kDdtycZrcorD8H/ALvwrZWnX7Bv0/f/AH/s7tDvx23eXuxzjOMnGat+ 9Rv/ACu3yd3+Fn94dQT/AEnx3Nv4+waYnlY7/aJX37vp9mjxjHVs5yMblYdr/wAj3q3/AGDLL/0b dVuUYjSSXZL8Un+bBBRRRWAyvceV59p5nn7vNPl+Vv27tjff28bcZ+/8u7b/ABbasVHIJi8JikjV A+ZQyFiy7TwpyNp3bTk54BGOciSgAooooAKKKKACiiigAooooAw735PG+jyP8sbWN5CrHgGQvbsE B/vFY3bHXCMexrcrD17/AJDHhj/sJv8A+klzW5W9b4Kfp/7cxLdhXN635X/CU+Hsef5324bt2/y9 v2W7xtz8m7O7OPmxt3cba6SufuRN/wALD0xmkjMB0q7CIEIYN5ttuJbOCCNuBgYweTngw/xv0l/6 Swex0FFFFYDCiiigAooooAKKKKACiiigAooooAr2Hlf2dbeR5/k+Umz7Rv8AM24GN/mfPux13fNn rzVio4BMtvEtxJHJOEAkeNCis2OSFJJAz2ycepqSgDDtf+R71b/sGWX/AKNuqvajq9npfli5MzSS 52RW9vJPIQMZbZGrNtGQCcYBYDPIzRT/AEbx3Nv5+36YnlY7fZ5X37vr9pjxjPRs4wMnh/8A0251 PWJP9bPcyWiL/wA84reR41X3y4lkzjP7zbkhQa7KkIt+0l8No/N2t59n06WJXY1bO8gv7VLm2k3x PnBIKkEHBUg8qwIIIIBBBBAIqesO2/4lHiSazPy2ep7rm37Kk4/1sY6AbhiQKMkkTsa3K56sFF+7 s9V/XlsNBWH4w/eeFb206fb9mn7/AO59odYd+O+3zN2OM4xkZzW5WH4h/e3mgWj8wXGpr5qf3vLh lmTnqMSRRtx124PBIN4bSrGXbX7tf0CWxuUUUVgMKKKKAK975XkL53n7fNjx5G/du3rtzs525xuz 8u3O75c1YqOYTMgEEkaPvUkuhYbdw3DAI5K5APYkHBxgyUAFFFFABRRRQAUUUUAFFFFABRRRQAVX Tyv7Rmx5/neVHu3b/L25fG3Pybs7s4+bG3dxtqxUYE32h2aSMwFFCIEIYNk7iWzggjbgYGMHk54A JKKKKACiiigAooooAKKKKACsPxj8nhHUrof6yxi+3xg9DJARMgP+yWjAOMHGcEda3Kqapp8WraTe abOzrDdwPBI0ZAYK6lSRkEZwfStaE1CrGT2TQnsZ2m/6R4u126TiOGK2sGB6mRA8xI/2dtyg9chu OATuVznglNSl0E6nrNr9k1XU5ftVzbgbVjO1Y0ABJK/u40JBJIJPToOjq8UrVXG97WX3JJ/kEdiv Z+V5DeT5+3zZM+fv3bt7bsb+duc7cfLtxt+XFWKjhEyoRPJG772IKIVG3cdowSeQuAT3IJwM4Elc 4wooooAKKKKACiiigAooooAKKKKAK975XkL53n7fNjx5G/du3rtzs525xuz8u3O75c1YqOYTMgEE kaPvUkuhYbdw3DAI5K5APYkHBxgyUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXP2f/JQ9Z/7BVh/6Nu66 Cufs/wDkoes/9gqw/wDRt3QB0FFFFABRRRQAUUUUAFFFFABRRRQAVXv0STTrmOWz+2xtE4a1wp84 EHKYchTnp8xA55OKsVXv3SPTrmSW8+xRrE5a6yo8kAHL5cFRjr8wI45GKALFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAV7hEee0L2fnskpKSYU/ZzsYb+TkZBKfLk/P0xkixVe4dEntA955DPKQkeVH2g 7GOzkZOAC/y4PydcZBsUAFFFFABRRRQAUUUUAFFFFABRRRQAVXt0RJ7spZ+QzygvJhR9oOxRv4OT gAJ82D8nTGCbFV7d0ee7CXnnskoDx5U/ZzsU7OBkZBD/ADZPz9cYAALFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAV7BEj062jis/sUaxIFtcKPJAAwmEJUY6fKSOODirFV7B0k062kivPtsbRIVusqfOB Aw+UAU56/KAOeBirFABRRRQAUUUUAFYehfudX8Q2p+QLfLNFD0xG8EZLqP7rSiY5HBYP3zW5WHb/ ALvx3qO/5fO0y18rPG/ZLcb9vrt8yPOOm9c9RW9H4Jry/VCfQNG/feIfEdxJzLHcw2iN6RLBHIq/ g88pz1+bGcAAblYeg/8AIY8T/wDYTT/0ktq3KMR8a9I/+koEFFFFYDK9wiPPaF7Pz2SUlJMKfs52 MN/JyMglPlyfn6YyRYqvcOiT2ge88hnlISPKj7QdjHZyMnABf5cH5OuMg2KACiiigAooooAKKKKA CiiigDD8T/uYdLv15ltNTttgP3T5r/Z2z9Encj3A6jIO5WH4s/5A9v8A9hPT/wD0rhrcreetGL82 vlo/1YuoVz9tBC3xD1S4MUZnTSrNFkKjcqmW5JAPXBKqSPYeldBXN6C6SeLfFZa88y4jubeLyCVJ hi+zoykcbgrM8vBO3IYgAliSj8FT0/VA+h0lFFFYDCiiigAooooAKKKKACiiigAooooAr2CJHp1t HFZ/Yo1iQLa4UeSABhMISox0+UkccHFWKr2DpJp1tJFefbY2iQrdZU+cCBh8oApz1+UAc8DFWKAO V8T6j/Yms2+rvF5kdro+oMqltokkXyJBGGwfmKxOe5wrHHBrc0XTv7H0HTtM83zvsdtHb+Zt279i hc4ycZx0zUeraHZa2+nNeq7CwvEvYlVtoMiBgue5ALZx6gZyMg6VdNStF0YU47rf73b8395KWtzD 8Wf6Po39rLxJpMq3+4dRGmfOAHQs0JlUZ4yw5GARuVHPBFcwSQTxJLDKpSSORQyupGCCDwQR2rK8 KTyzeGLFLiV5bq2U2dxK7FjJNCxikbJ5ILoxBPJBBIB4qX71G/8AK/z/AMrP7x9TZrDl/f8Aju08 v5vsmmT+f22edLF5f1z5EvTpt5xkZ3Kw7D994y1q4j5ijtrS0dvSVTLIy/gk8Rz0+bGcggFDRTl2 X52X5MGblFFFYDCiiigCveokkCiSz+1qJYyI8KdpDqQ/zED5CA/r8vAJwKsVXvXSOBTJefZFMsYE mVG4l1AT5gR85IT1+bgg4NWKACiiigAooooAKKKKACiiigAooooAKroiDUZpBZ7JGijDXWF/eAF8 JkHd8uSeRj95wSd2LFV0dDqM0YvN8ixRlrXK/uwS+HwBu+bBHJx+74AO7IBYooooAKKKKACiiigA ooooAKKKKACiiigCvZokcDCOz+yKZZCY8KNxLsS/ykj5yS/r83IByKsVXs3SSBjHefa1EsgMmVO0 h2BT5QB8hBT1+Xkk5NWKACiiigAooooAKKKKACiiigAooooAr3qJJAoks/taiWMiPCnaQ6kP8xA+ QgP6/LwCcCrFV710jgUyXn2RTLGBJlRuJdQE+YEfOSE9fm4IODVigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo qneatpun3Frb3uoWltPdvsto5plRpmyBhATljlgMD1HrQBcrn7P/AJKHrP8A2CrD/wBG3dcv4au7 xrnwzdyHVUk1HH2m9ur0y2uoZtpJD5EPmN5W5lEi5jiwqFflJ2HYuNZtdI+IeqfaYr6TzdKsdv2S wnucYlu858pG29e+M846GgDsKKw4PFmnXNxFAltrIeRwimTRbxFBJxyzRAKPckAd61EvYpIrWQJO FuseWGgcFcqW+cEZj4B+9jnA6kCgCxRVd72KOK6kKTlbXPmBYHJbChvkAGZOCPu55yOoIqQzKtwk BEm90ZwRGxXCkA5bGAfmGATk84zg4AJKKr/bYvI87ZPt83yceQ+7dv2Z24ztzzuxt2/NnbzUgmVr h4AJN6IrkmNguGJAw2ME/KcgHI4zjIyASUVXS9ikitZAk4W6x5YaBwVypb5wRmPgH72OcDqQKHvY o4rqQpOVtc+YFgclsKG+QAZk4I+7nnI6gigCxRUZmVbhICJN7ozgiNiuFIBy2MA/MMAnJ5xnBxH9 ti8jztk+3zfJx5D7t2/ZnbjO3PO7G3b82dvNAFio5zMtvK1vHHJOEJjSRyis2OAWAJAz3wcehoEy tcPABJvRFckxsFwxIGGxgn5TkA5HGcZGad5e2smkiSVL4QXUTcQwTiZVMZY8IPMjbAOOjbsAfMQK ANCiq73sUcV1IUnK2ufMCwOS2FDfIAMycEfdzzkdQRUhmVbhICJN7ozgiNiuFIBy2MA/MMAnJ5xn BwASUVX+2xeR52yfb5vk48h927fsztxnbnndjbt+bO3mpBMrXDwASb0RXJMbBcMSBhsYJ+U5AORx nGRkAkoqul7FJFayBJwt1jyw0DgrlS3zgjMfAP3sc4HUgUPexRxXUhScra58wLA5LYUN8gAzJwR9 3POR1BFAFiiozMq3CQESb3RnBEbFcKQDlsYB+YYBOTzjODiP7bF5HnbJ9vm+TjyH3bt+zO3Gdued 2Nu35s7eaALFFRiZWuHgAk3oiuSY2C4YkDDYwT8pyAcjjOMjMaXsUkVrIEnC3WPLDQOCuVLfOCMx 8A/exzgdSBQBYoqu97FHFdSFJytrnzAsDkthQ3yADMnBH3c85HUEVIZlW4SAiTe6M4IjYrhSActj APzDAJyecZwcABIZg8IijjZC+JSzlSq7Tyowdx3bRg44JOeMGSs+4vbX/RJpEvv+PswxiKCf7/zJ l1Uf6vqdzjZ91s/dNXBMrXDwASb0RXJMbBcMSBhsYJ+U5AORxnGRkAkoqul7FJFayBJwt1jyw0Dg rlS3zgjMfAP3sc4HUgUPexRxXUhScra58wLA5LYUN8gAzJwR93POR1BFAFiiozMq3CQESb3RnBEb FcKQDlsYB+YYBOTzjODiP7bF5HnbJ9vm+TjyH3bt+zO3Gdued2Nu35s7eaALFFRiZWuHgAk3oiuS Y2C4YkDDYwT8pyAcjjOMjMaXsUkVrIEnC3WPLDQOCuVLfOCMx8A/exzgdSBQBYoqu97FHFdSFJyt rnzAsDkthQ3yADMnBH3c85HUEVIZlW4SAiTe6M4IjYrhSActjAPzDAJyecZwcAElFV/tsXkedsn2 +b5OPIfdu37M7cZ2553Y27fmzt5qQTK1w8AEm9EVyTGwXDEgYbGCflOQDkcZxkZAJKjjMxeYSxxq gfERVyxZdo5YYG07twwM8AHPOBGl7FJFayBJwt1jyw0DgrlS3zgjMfAP3sc4HUgVGJoLYahPi7Pl vvmBjlfJEan92uDuG3HEYILbv4t1AFyiozMq3CQESb3RnBEbFcKQDlsYB+YYBOTzjODiP7bF5Hnb J9vm+TjyH3bt+zO3Gdued2Nu35s7eaALFFRiZWuHgAk3oiuSY2C4YkDDYwT8pyAcjjOMjMaXsUkV rIEnC3WPLDQOCuVLfOCMx8A/exzgdSBQBYoqu97FHFdSFJytrnzAsDkthQ3yADMnBH3c85HUEVIZ lW4SAiTe6M4IjYrhSActjAPzDAJyecZwcAElFV/tsXkedsn2+b5OPIfdu37M7cZ2553Y27fmzt5q QTK1w8AEm9EVyTGwXDEgYbGCflOQDkcZxkZAJKKrpexSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1I FD3sUcV1IUnK2ufMCwOS2FDfIAMycEfdzzkdQRQBYoqMzKtwkBEm90ZwRGxXCkA5bGAfmGATk84z g4j+2xeR52yfb5vk48h927fsztxnbnndjbt+bO3mgCSAzNbxNcRxxzlAZEjcuqtjkBiASM98DPoK kqnp80DW8MFuLvYlvG6m5jlDbGBC7mkGS/yncCdw43YyMyJexSRWsgScLdY8sNA4K5Ut84IzHwD9 7HOB1IFAFiiq73sUcV1IUnK2ufMCwOS2FDfIAMycEfdzzkdQRUhmVbhICJN7ozgiNiuFIBy2MA/M MAnJ5xnBwASUVX+2xeR52yfb5vk48h927fsztxnbnndjbt+bO3mpBMrXDwASb0RXJMbBcMSBhsYJ +U5AORxnGRkAkrDvfk8b6PI/yxtY3kKseAZC9uwQH+8VjdsdcIx7GtRL2KSK1kCThbrHlhoHBXKl vnBGY+AfvY5wOpArD8Q3sUeqaFIUnK2upyeYFgclsWNw3yADMnBH3c85HUEVvh/jfpL/ANJYnsWP DH76HVL9uJbvU7neB90eU/2dcfVIEJ9yegwBuVz/AIUmVdPlgIk3vqGpOCI2K4W8kBy2MA/MMAnJ 5xnBxsfbYvI87ZPt83yceQ+7dv2Z24ztzzuxt2/NnbzRif40l2dvktEEdixRUYmVrh4AJN6IrkmN guGJAw2ME/KcgHI4zjIzGl7FJFayBJwt1jyw0DgrlS3zgjMfAP3sc4HUgVgMkkMweERRxshfEpZy pVdp5UYO47towccEnPGDJWfeXtrGwklS+JtZW4hgnIZhEWPCD94u0nHVd+APnAFXDMq3CQESb3Rn BEbFcKQDlsYB+YYBOTzjODgAkoqv9ti8jztk+3zfJx5D7t2/ZnbjO3PO7G3b82dvNSCZWuHgAk3o iuSY2C4YkDDYwT8pyAcjjOMjIBJRVdL2KSK1kCThbrHlhoHBXKlvnBGY+AfvY5wOpAoe9ijiupCk 5W1z5gWByWwob5ABmTgj7uecjqCKALFFRmZVuEgIk3ujOCI2K4UgHLYwD8wwCcnnGcHEf22LyPO2 T7fN8nHkPu3b9mduM7c87sbdvzZ280AWKKjEytcPABJvRFckxsFwxIGGxgn5TkA5HGcZGY0vYpIr WQJOFuseWGgcFcqW+cEZj4B+9jnA6kCgDL8af8iJ4h/7Blz/AOimrcrC8Vb73wd4jtraGeWcWM8I jWFsu5hJATj587gPlzzkdQQNKz1K1v4bSe1aSSG7g+0QyCJwrJhSCSRhSQ4wDgnnA4ON3rQj5N/i l/k/uF1Ldc/4VM0sep3Uscf7/ULktKHO52jmkhAK4woWKGEAg885APLas+p2ttYyXs7SRW8TlJHk hddmH2FiCMhAed/3dvzZ281k+D4Ps+gaak8V1HfnTrd7pZlkAMjBmYnPy+YXLlyPmJILfw0R0oyf mv1/4H4B1Ohoqul7FJFayBJwt1jyw0DgrlS3zgjMfAP3sc4HUgUPexRxXUhScra58wLA5LYUN8gA zJwR93POR1BFYDLFFRmZVuEgIk3ujOCI2K4UgHLYwD8wwCcnnGcHEf22LyPO2T7fN8nHkPu3b9md uM7c87sbdvzZ280AWKKjEytcPABJvRFckxsFwxIGGxgn5TkA5HGcZGY0vYpIrWQJOFuseWGgcFcq W+cEZj4B+9jnA6kCgCxRVd72KOK6kKTlbXPmBYHJbChvkAGZOCPu55yOoIqQzKtwkBEm90ZwRGxX CkA5bGAfmGATk84zg4AJKKr/AG2LyPO2T7fN8nHkPu3b9mduM7c87sbdvzZ281IJla4eACTeiK5J jYLhiQMNjBPynIByOM4yMgElFV0vYpIrWQJOFuseWGgcFcqW+cEZj4B+9jnA6kCh72KOK6kKTlbX PmBYHJbChvkAGZOCPu55yOoIoAkgMzW8TXEccc5QGRI3LqrY5AYgEjPfAz6CpKp2U0C29lBCLvY9 uHiM8cpbYoUfvGcZD/MOHO4/N1w2JPtsXkedsn2+b5OPIfdu37M7cZ2553Y27fmzt5oAsUVGJla4 eACTeiK5JjYLhiQMNjBPynIByOM4yMxpexSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1IFAFisPRP9 G1vX7Fvk/wBJS7hiH3RFJEoLDHA3TRzkjrnLEfMCdR72KOK6kKTlbXPmBYHJbChvkAGZOCPu55yO oIrL/wCPbx3/AHv7Q0z6eX9nl/Xd9q9sbO+eN6OsZx7r8mn+SYmblYfhz99Nrd+vEV3qcmwH7w8p Et2z9Xgcj2I6HIGp9ti8jztk+3zfJx5D7t2/ZnbjO3PO7G3b82dvNY/g6ZZdCjdRIBdtLqKZjYAR XM0kqAtjaXCsAygnB9iCSGlGbXkvlq/0QPc6Ciq6XsUkVrIEnC3WPLDQOCuVLfOCMx8A/exzgdSB Q97FHFdSFJytrnzAsDkthQ3yADMnBH3c85HUEVgMsUVGZlW4SAiTe6M4IjYrhSActjAPzDAJyecZ wcR/bYvI87ZPt83yceQ+7dv2Z24ztzzuxt2/NnbzQBJMZlQGCON33qCHcqNu4bjkA8hckDuQBkZy JKp3c0DEwSi7+R4XzDHKOWkwvzIORuX5hnAX7+FPMiXsUkVrIEnC3WPLDQOCuVLfOCMx8A/exzgd SBQBYoqu97FHFdSFJytrnzAsDkthQ3yADMnBH3c85HUEVIZlW4SAiTe6M4IjYrhSActjAPzDAJye cZwcAElFV/tsXkedsn2+b5OPIfdu37M7cZ2553Y27fmzt5qQTK1w8AEm9EVyTGwXDEgYbGCflOQD kcZxkZAJKKrpexSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1IFD3sUcV1IUnK2ufMCwOS2FDfIAMyc EfdzzkdQRQBYoqMzKtwkBEm90ZwRGxXCkA5bGAfmGATk84zg4j+2xeR52yfb5vk48h927fsztxnb nndjbt+bO3mgCxRUYmVrh4AJN6IrkmNguGJAw2ME/KcgHI4zjIzGl7FJFayBJwt1jyw0DgrlS3zg jMfAP3sc4HUgUAWKjBm+0OrRxiAIpRw5LFsncCuMAAbcHJzk8DHMb3sUcV1IUnK2ufMCwOS2FDfI AMycEfdzzkdQRUYmgXWXgAu/tD26uSY5TBsViBhseWHyxyAdxGM5AGAC5RVf7bF5HnbJ9vm+TjyH 3bt+zO3Gdued2Nu35s7eakEytcPABJvRFckxsFwxIGGxgn5TkA5HGcZGQCSiq6XsUkVrIEnC3WPL DQOCuVLfOCMx8A/exzgdSBQ97FHFdSFJytrnzAsDkthQ3yADMnBH3c85HUEUAWKKjMyrcJARJvdG cERsVwpAOWxgH5hgE5POM4OI/tsXkedsn2+b5OPIfdu37M7cZ2553Y27fmzt5oAsUVGJla4eACTe iK5JjYLhiQMNjBPynIByOM4yMxpexSRWsgScLdY8sNA4K5Ut84IzHwD97HOB1IFAFiiq73sUcV1I UnK2ufMCwOS2FDfIAMycEfdzzkdQRUhmVbhICJN7ozgiNiuFIBy2MA/MMAnJ5xnBwASUVX+2xeR5 2yfb5vk48h927fsztxnbnndjbt+bO3mpBMrXDwASb0RXJMbBcMSBhsYJ+U5AORxnGRkAITMyEzxx o+9gAjlht3HackDkrgkdiSMnGTJWfp97ayWdvJAl8I7qWTYLmCcOrEux3CQbo14bG7C/dC8FRVh7 2KOK6kKTlbXPmBYHJbChvkAGZOCPu55yOoIoAsUVGZlW4SAiTe6M4IjYrhSActjAPzDAJyecZwcR /bYvI87ZPt83yceQ+7dv2Z24ztzzuxt2/NnbzQBYoqMTK1w8AEm9EVyTGwXDEgYbGCflOQDkcZxk ZjS9ikitZAk4W6x5YaBwVypb5wRmPgH72OcDqQKALFFV3vYo4rqQpOVtc+YFgclsKG+QAZk4I+7n nI6gipDMq3CQESb3RnBEbFcKQDlsYB+YYBOTzjODgAkoqv8AbYvI87ZPt83yceQ+7dv2Z24ztzzu xt2/NnbzUgmVrh4AJN6IrkmNguGJAw2ME/KcgHI4zjIyASUVXS9ikitZAk4W6x5YaBwVypb5wRmP gH72OcDqQKHvYo4rqQpOVtc+YFgclsKG+QAZk4I+7nnI6gigCSYzKgMEcbvvUEO5Ubdw3HIB5C5I HcgDIzkSVTvpoFMUEwu/ndHU28cp5WRANzIOBuZcgnBXdnKhsXKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gDn9O8JwafPY/wDExvri007/AI8LKbyvLtfkMa7SqB2xGzIN7NwcnLYILP8A5KHrP/YKsP8A0bd1 0Fc/Z/8AJQ9Z/wCwVYf+jbugDoKKKKACiiigAooooAKKKKACiiigAqOcTNbyrbyRxzlCI3kQuqtj glQQSM9sjPqKkqvfokmnXMctn9tjaJw1rhT5wIOUw5CnPT5iBzycUAWKKKKACiiigAooooAKKKKA CiiigAooooAjkExeExSRqgfMoZCxZdp4U5G07tpyc8AjHORJVe4RHntC9n57JKSkmFP2c7GG/k5G QSny5Pz9MZIsUAFFFFABRRRQAUUUUAFFFFABRRRQAVHGJg8xlkjZC+YgqFSq7Rwxydx3bjkY4IGO MmSq9uiJPdlLPyGeUF5MKPtB2KN/BycABPmwfk6YwSAWKKKKACiiigAooooAKKKKACiiigAooooA jgEy28S3Ekck4QCR40KKzY5IUkkDPbJx6mpKr2CJHp1tHFZ/Yo1iQLa4UeSABhMISox0+UkccHFW KACiiigAooooAKw/FP7qz0+7Tie31O08p/7vmTLC/HQ5jlkXnpuyOQCNysPxZ/yB7f8A7Cen/wDp XDW+F/jQXmhS2Dwn/wAge4/7Ceof+lc1blYfgv8A5ETw9/2DLb/0UtblGK/jz9X+YR2QUUUVgMjk ExeExSRqgfMoZCxZdp4U5G07tpyc8AjHORJVe4RHntC9n57JKSkmFP2c7GG/k5GQSny5Pz9MZIsU AFFFFABRRRQAUUUUAFFFFABWH4L/AORE8Pf9gy2/9FLW5XP+GJ4rXw3O80qQ2treXyBnYKkMMdzK qjnhVVFAA6AADoK3gr0ZJd4/lIXUk8XfvvDdzpy8y6njT0A5Yeb8jOF/i2IXkI9EPIGSNgCb7Q7N JGYCihECEMGydxLZwQRtwMDGDyc8ZWmQS6m8Gs6hE8bld9pZyKQbVWHVwf8AlsQSGP8ACCUX+Jn0 URBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7BVfLFUu17+rt/l9/kC7liiiisBhRRRQAUUUUAF FFFABRRRQAUUUUARwCZbeJbiSOScIBI8aFFZsckKSSBntk49TUlV7BEj062jis/sUaxIFtcKPJAA wmEJUY6fKSOODirFABRRRQAVh6z+58Q+HLiPiWS5mtHb1iaCSRl/F4Ijnr8uM4JB3Kw/FXyabZzL xLHqdlscfeXdcxo2D2yjup9QxHQmt8NrVUe9196t+onsaOqahFpOk3mpTq7Q2kDzyLGAWKopYgZI GcD1qDw9p8uk+GtK02dkaa0s4YJGjJKlkQKSMgHGR6VV8Z8+CNcjHMk1jNDGo6vI6FUQDuzMQoA5 JIA61uUPSgrdW/wSt+bDqFFFFYDCiiigCOYTMgEEkaPvUkuhYbdw3DAI5K5APYkHBxgyVXvUSSBR JZ/a1EsZEeFO0h1If5iB8hAf1+XgE4FWKACiiigAooooAKKKKACiiigAooooAKjAm+0OzSRmAooR AhDBsncS2cEEbcDAxg8nPElV0RBqM0gs9kjRRhrrC/vAC+EyDu+XJPIx+84JO7ABYooooAKKKKAC iiigAooooAKKKKACiiigCOETKhE8kbvvYgohUbdx2jBJ5C4BPcgnAzgSVXs0SOBhHZ/ZFMshMeFG 4l2Jf5SR85Jf1+bkA5FWKACiiigAooooAKKKKACiiigAooooAjmEzIBBJGj71JLoWG3cNwwCOSuQ D2JBwcYMlV71EkgUSWf2tRLGRHhTtIdSH+YgfIQH9fl4BOBVigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA K5+z/wCSh6z/ANgqw/8ARt3XQVz9n/yUPWf+wVYf+jbugDoKKKKACiiigAooooAKKKKACiiigAqv fukenXMkt59ijWJy11lR5IAOXy4KjHX5gRxyMVYqOczLbytbxxyThCY0kcorNjgFgCQM98HHoaAJ KKKKACiiigAooooAKKKKACiiigAooooAr3Dok9oHvPIZ5SEjyo+0HYx2cjJwAX+XB+TrjINio5DM HhEUcbIXxKWcqVXaeVGDuO7aMHHBJzxgyUAFFFFABRRRQAUUUUAFFFFABRRRQAVXt3R57sJeeeyS gPHlT9nOxTs4GRkEP82T8/XGALFRxmYvMJY41QPiIq5Ysu0csMDad24YGeADnnAAJKKKKACiiigA ooooAKKKKACiiigAooooAr2DpJp1tJFefbY2iQrdZU+cCBh8oApz1+UAc8DFWKjgMzW8TXEccc5Q GRI3LqrY5AYgEjPfAz6CpKACiiigAooooAKw/GPyeEdSuh/rLGL7fGD0MkBEyA/7JaMA4wcZwR1r crN8Q6fLq3hrVdNgZFmu7OaCNpCQoZ0KgnAJxk+lbYeSjWhJ7Jr8xPYq+C/+RE8Pf9gy2/8ARS1u UUVNWftJyn3dwSsrBRRRWYyvcOiT2ge88hnlISPKj7QdjHZyMnABf5cH5OuMg2KjkMweERRxshfE pZypVdp5UYO47towccEnPGDJQAUUUUAFFFFABRRRQAUUUUAFcPdf6Rfazpg66h4htoSG+40a2lvN Kj+qtFFIuMEHcAeCSO4rGPh6J/F58QSTu7rZpbR25A2KytIfM/3sSMoPGAzjndx1YWrGm5Sl209b pr8iZK5s1XR0OozRi83yLFGWtcr+7BL4fAG75sEcnH7vgA7s2KjBm+0OrRxiAIpRw5LFsncCuMAA bcHJzk8DHPKUSUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXsHSTTraSK8+2xtEhW6yp84EDD5QBTn r8oA54GKsVHAZmt4muI445ygMiRuXVWxyAxAJGe+Bn0FSUAFFFFABWH4z48Ea5IOJIbGaaNh1SRE LI4PZlYBgRyCAR0rcqC9s4NRsLixuo/MtrmJoZUyRuRgQRkcjgnpWlGahUjN9GmJ6oyvFn/IHt/+ wnp//pXDW5XHQ3k+q+H/AAO17J5p1GW3mujgL5jrbPcKeMYxLGjcY6Y6EiuxravB0oRpy3u3+n/t rEnfUKKKK5SgooooAr3rpHApkvPsimWMCTKjcS6gJ8wI+ckJ6/NwQcGrFRzGZUBgjjd96gh3Kjbu G45APIXJA7kAZGciSgAooooAKKKKACiiigAooooAKKKKACq6Oh1GaMXm+RYoy1rlf3YJfD4A3fNg jk4/d8AHdmxUYM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5AJKKKKACiiigAooooAKKKKACiiigA ooooAr2bpJAxjvPtaiWQGTKnaQ7Ap8oA+Qgp6/LyScmrFRwmZkJnjjR97ABHLDbuO05IHJXBI7Ek ZOMmSgAooooAKKKKACiiigAooooAKKKKAK966RwKZLz7IpljAkyo3EuoCfMCPnJCevzcEHBqxUcx mVAYI43feoIdyo27huOQDyFyQO5AGRnIkoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACufs/wDkoes/9gqw /wDRt3XQVz9n/wAlD1n/ALBVh/6Nu6AOgooooAKKKKACiiigAooooAKKKKACq9/5X9nXPn+f5PlP v+z7/M24Odnl/Pux02/NnpzVio5xM1vKtvJHHOUIjeRC6q2OCVBBIz2yM+ooAkooooAKKKKACiii gAooooAKKKKACiiigCvceV59p5nn7vNPl+Vv27tjff28bcZ+/wDLu2/xbasVHIJi8JikjVA+ZQyF iy7TwpyNp3bTk54BGOciSgAooooAKKKKACiiigAooooAKKKKACq9v5Xn3fl+fu80eZ5u/bu2L9zd xtxj7ny7t38W6rFRxiYPMZZI2QvmIKhUqu0cMcncd245GOCBjjJAJKKKKACiiigAooooAKKKKACi iigAooooAr2Hlf2dbeR5/k+Umz7Rv8zbgY3+Z8+7HXd82evNWKjgEy28S3Ekck4QCR40KKzY5IUk kDPbJx6mpKACiiigAooooAKKKKACiiigAooooAr3HlefaeZ5+7zT5flb9u7Y339vG3Gfv/Lu2/xb asVHIJi8JikjVA+ZQyFiy7TwpyNp3bTk54BGOciSgAooooAKKKKACiiigAooooAKKKKACq6eV/aM 2PP87yo927f5e3L425+TdndnHzY27uNtWKjAm+0OzSRmAooRAhDBsncS2cEEbcDAxg8nPABJRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFAFew8r+zrbyPP8nyk2faN/mbcDG/zPn3Y67vmz15qxUcAmW3iW 4kjknCASPGhRWbHJCkkgZ7ZOPU1JQAUUUUAFFFFAHD6T/wAg7wRYL80+n3MttMF5/wCPe3nt5JB3 2eZtAY4++mcE4ruK4fw7/wAjHY/9x3/04R13Fd2P/iL5/wDpUiYbBRRRXCUFFFFAFe98ryF87z9v mx48jfu3b1252c7c43Z+Xbnd8uasVHMJmQCCSNH3qSXQsNu4bhgEclcgHsSDg4wZKACiiigAoooo AKKKKACiiigAooooAKrp5X9ozY8/zvKj3bt/l7cvjbn5N2d2cfNjbu421YqMCb7Q7NJGYCihECEM GydxLZwQRtwMDGDyc8AElFFFABRRRQAUUUUAFFFFABRRRQAUUUUAV7PyvIbyfP2+bJnz9+7dvbdj fztznbj5duNvy4qxUcImVCJ5I3fexBRCo27jtGCTyFwCe5BOBnAkoAKKKKACiiigAooooAKKKKAC iiigCve+V5C+d5+3zY8eRv3bt67c7Oducbs/Ltzu+XNWKjmEzIBBJGj71JLoWG3cNwwCOSuQD2JB wcYMlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABUZnhW4S3aWMTujOkZYbmVSAxA6kAsoJ7bh61X1K9nsbdZbfTL vUHLhTFatErAYPzHzHQY4x1zyOOuMOaxt4Pidp93HH/pFzpV6JZCxYkLJaBVGeijJIUYGWY4yxJA Ni113R77UZ9Os9Vsbi+g3edbQ3CPJHtO1tyg5GCQDnoaz7P/AJKHrP8A2CrD/wBG3dc/ov8AyLHw t/7Yf+m2etC4m1iL4h6p/ZNjY3WdKsfM+13j2+397d4xtifPfrjGB1zwAdhRWHBdeKmuIluNG0aO AuBI8erSuyrnkhTbAE47ZGfUVqb7zyM+RB53m42+cdvl78bs7fvbPm24xu+Xdj5qALFFRgzfaHVo 4xAEUo4cli2TuBXGAANuDk5yeBjmNHvDFamSCBZGx9pVZiRH8pJ2HaN/zYHIXgk9RtIBYoqu73gi ujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2iQmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8AElFV99 55GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81SAzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjkAkoq uj3hitTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2kd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7 QAWKr36JJp1zHLZ/bY2icNa4U+cCDlMOQpz0+Ygc8nFSEzfaEVY4zAUYu5chg2RtAXGCCN2TkYwO DninfzXEej3MsssFky790/2gBYYgxzLudCAwT58MpXcNpOPmoA0KKjBm+0OrRxiAIpRw5LFsncCu MAAbcHJzk8DHMaPeGK1MkECyNj7SqzEiP5STsO0b/mwOQvBJ6jaQCxRVd3vBFdGOCBpFz9mVpiBJ 8oI3nadnzZHAbgA9TtEhM32hFWOMwFGLuXIYNkbQFxggjdk5GMDg54AJKKr77zyM+RB53m42+cdv l78bs7fvbPm24xu+Xdj5qkBm+0OrRxiAIpRw5LFsncCuMAAbcHJzk8DHIBJRVdHvDFamSCBZGx9p VZiRH8pJ2HaN/wA2ByF4JPUbSO94IroxwQNIufsytMQJPlBG87Ts+bI4DcAHqdoALFFRkzfaEVY4 zAUYu5chg2RtAXGCCN2TkYwODniPfeeRnyIPO83G3zjt8vfjdnb97Z823GN3y7sfNQBYoqMGb7Q6 tHGIAilHDksWydwK4wABtwcnOTwMcxo94YrUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpAC4RHntC 9n57JKSkmFP2c7GG/k5GQSny5Pz9MZIsVn3k1wjACWCKbzWFnC1wFF4fKJCOShK8hm+TccRhskbl q4TN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeACSiq++88jPkQed5uNvnHb5e/G7O372z5tuMbvl3 Y+apAZvtDq0cYgCKUcOSxbJ3ArjAAG3Byc5PAxyASUVXR7wxWpkggWRsfaVWYkR/KSdh2jf82ByF 4JPUbSO94IroxwQNIufsytMQJPlBG87Ts+bI4DcAHqdoALFFRkzfaEVY4zAUYu5chg2RtAXGCCN2 TkYwODniPfeeRnyIPO83G3zjt8vfjdnb97Z823GN3y7sfNQBYoqMGb7Q6tHGIAilHDksWydwK4wA BtwcnOTwMcxo94YrUyQQLI2PtKrMSI/lJOw7Rv8AmwOQvBJ6jaQCxRVd3vBFdGOCBpFz9mVpiBJ8 oI3nadnzZHAbgA9TtEhM32hFWOMwFGLuXIYNkbQFxggjdk5GMDg54AJKr26Ik92Us/IZ5QXkwo+0 HYo38HJwAE+bB+TpjBJvvPIz5EHnebjb5x2+Xvxuzt+9s+bbjG75d2PmqO1lZ7++QXEc0SOgwJFL QuUBMZUKNo27HBZmJ8w9AFyAXKKro94YrUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpHe8EV0Y4IG kXP2ZWmIEnygjedp2fNkcBuAD1O0AFiioyZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzxHvvPIz5E Hnebjb5x2+Xvxuzt+9s+bbjG75d2PmoAsUVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOY0e8MV qZIIFkbH2lVmJEfyknYdo3/NgcheCT1G0gFiiq7veCK6McEDSLn7MrTECT5QRvO07PmyOA3AB6na JCZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzwASUVX33nkZ8iDzvNxt847fL343Z2/e2fNtxjd8u7 HzVIDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOQCSiq6PeGK1MkECyNj7SqzEiP5STsO0b/mwOQv BJ6jaR3vBFdGOCBpFz9mVpiBJ8oI3nadnzZHAbgA9TtAAWCJHp1tHFZ/Yo1iQLa4UeSABhMISox0 +UkccHFWKp2UrSW9k0NxHe2j24b7aZFLSnC7WARQjBgWJIwBxgEHiTfeeRnyIPO83G3zjt8vfjdn b97Z823GN3y7sfNQBYoqMGb7Q6tHGIAilHDksWydwK4wABtwcnOTwMcxo94YrUyQQLI2PtKrMSI/ lJOw7Rv+bA5C8EnqNpALFFV3e8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O0SEzfaEVY4zAUYu5 chg2RtAXGCCN2TkYwODngAkoqvvvPIz5EHnebjb5x2+Xvxuzt+9s+bbjG75d2PmqQGb7Q6tHGIAi lHDksWydwK4wABtwcnOTwMcgElFV0e8MVqZIIFkbH2lVmJEfyknYdo3/ADYHIXgk9RtI73giujHB A0i5+zK0xAk+UEbztOz5sjgNwAep2gAsUVGTN9oRVjjMBRi7lyGDZG0BcYII3ZORjA4OeI9955Gf Ig87zcbfOO3y9+N2dv3tnzbcY3fLux81ABcIjz2hez89klJSTCn7OdjDfycjIJT5cn5+mMkWKpzy st/bpJcR28TOBCBIu65fZIWjKsvQKA4Ktk7TnAU7pEe8MVqZIIFkbH2lVmJEfyknYdo3/NgcheCT 1G0gFiiq7veCK6McEDSLn7MrTECT5QRvO07PmyOA3AB6naJCZvtCKscZgKMXcuQwbI2gLjBBG7Jy MYHBzwASUVX33nkZ8iDzvNxt847fL343Z2/e2fNtxjd8u7HzVIDN9odWjjEARSjhyWLZO4FcYAA2 4OTnJ4GOQCSiq6PeGK1MkECyNj7SqzEiP5STsO0b/mwOQvBJ6jaR3vBFdGOCBpFz9mVpiBJ8oI3n adnzZHAbgA9TtABYoqMmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8R77zyM+RB53m42+cdvl78bs 7fvbPm24xu+Xdj5qALFFRgzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjmNHvDFamSCBZGx9pVZiRH 8pJ2HaN/zYHIXgk9RtIBYquiINRmkFnskaKMNdYX94AXwmQd3y5J5GP3nBJ3YHe8EV0Y4IGkXP2Z WmIEnygjedp2fNkcBuAD1O0RiVv7ZeJLiN0FurSQGRd0J3EIwQLkh8OCS2P3QAHLGgC5RVffeeRn yIPO83G3zjt8vfjdnb97Z823GN3y7sfNUgM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5AJKKro94 YrUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpHe8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O0AFi ioyZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzxHvvPIz5EHnebjb5x2+Xvxuzt+9s+bbjG75d2Pmo AsUVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOY0e8MVqZIIFkbH2lVmJEfyknYdo3/ADYHIXgk 9RtIBYoqu73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2iQmb7QirHGYCjF3LkMGyNoC4wQRuyc jGBwc8AElFV9955GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81SAzfaHVo4xAEUo4cli2TuBXGAAN uDk5yeBjkAjsESPTraOKz+xRrEgW1wo8kADCYQlRjp8pI44OKsVn6bNcS6Tpcolgu/NiQz3C3AcM DHnejKgWTLbegQEEkAYCmw73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAep2gAsUVGTN9oRVjjMBR i7lyGDZG0BcYII3ZORjA4OeI9955GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81AFiiowZvtDq0cY gCKUcOSxbJ3ArjAAG3Byc5PAxzGj3hitTJBAsjY+0qsxIj+Uk7DtG/5sDkLwSeo2kA4/QefiDqFh D80Wk/a95P3l+1tb3C59cuLgDHQIM8kFu4rlNM0XU9N8aeLtWWGCSLUorRrPdMVDPHGyFXIBK845 AbhgRk5A6cmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8deNnGc48ruuWP3tXl/5M2TFaElFV9955 GfIg87zcbfOO3y9+N2dv3tnzbcY3fLux81SAzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjnkKJKKr o94YrUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpHe8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O0 ABeokkCiSz+1qJYyI8KdpDqQ/wAxA+QgP6/LwCcCrFU76VozFuuI7W3LoGnMiq28yIEjAZSCHyVJ yDyAvJysm+88jPkQed5uNvnHb5e/G7O372z5tuMbvl3Y+agCxRUYM32h1aOMQBFKOHJYtk7gVxgA Dbg5OcngY5jR7wxWpkggWRsfaVWYkR/KSdh2jf8ANgcheCT1G0gFiiq7veCK6McEDSLn7MrTECT5 QRvO07PmyOA3AB6naJCZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzwASUVX33nkZ8iDzvNxt847fL 343Z2/e2fNtxjd8u7HzVIDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOQCSiq6PeGK1MkECyNj7Sq zEiP5STsO0b/AJsDkLwSeo2kd7wRXRjggaRc/ZlaYgSfKCN52nZ82RwG4APU7QAWKKjJm+0Iqxxm Aoxdy5DBsjaAuMEEbsnIxgcHPEe+88jPkQed5uNvnHb5e/G7O372z5tuMbvl3Y+agCxVdEQajNIL PZI0UYa6wv7wAvhMg7vlyTyMfvOCTuxIDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOacM1w15Gjy wG4MURurRbgMLYESfOnyBn3ONvzbQQhIAIIYA0KKru94IroxwQNIufsytMQJPlBG87Ts+bI4DcAH qdokJm+0IqxxmAoxdy5DBsjaAuMEEbsnIxgcHPABJRVffeeRnyIPO83G3zjt8vfjdnb97Z823GN3 y7sfNUgM32h1aOMQBFKOHJYtk7gVxgADbg5OcngY5AJKKro94YrUyQQLI2PtKrMSI/lJOw7Rv+bA 5C8EnqNpHe8EV0Y4IGkXP2ZWmIEnygjedp2fNkcBuAD1O0AFiioyZvtCKscZgKMXcuQwbI2gLjBB G7JyMYHBzxHvvPIz5EHnebjb5x2+Xvxuzt+9s+bbjG75d2PmoAsUVGDN9odWjjEARSjhyWLZO4Fc YAA24OTnJ4GOY0e8MVqZIIFkbH2lVmJEfyknYdo3/NgcheCT1G0gFiiq7veCK6McEDSLn7MrTECT 5QRvO07PmyOA3AB6naJCZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzwAR2aJHAwjs/simWQmPCjcS 7Ev8pI+ckv6/NyAcirFZ9nNcSWLPHLBdyC7kQt9oBVUEzKRuVB8yKCNuPvLtLE5ergM32h1aOMQB FKOHJYtk7gVxgADbg5OcngY5AJKKro94YrUyQQLI2PtKrMSI/lJOw7Rv+bA5C8EnqNpHe8EV0Y4I GkXP2ZWmIEnygjedp2fNkcBuAD1O0AFiioyZvtCKscZgKMXcuQwbI2gLjBBG7JyMYHBzxHvvPIz5 EHnebjb5x2+Xvxuzt+9s+bbjG75d2PmoAsUVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOY0e8M VqZIIFkbH2lVmJEfyknYdo3/ADYHIXgk9RtIBYoqu73giujHBA0i5+zK0xAk+UEbztOz5sjgNwAe p2iQmb7QirHGYCjF3LkMGyNoC4wQRuycjGBwc8AElFV9955GfIg87zcbfOO3y9+N2dv3tnzbcY3f Lux81SAzfaHVo4xAEUo4cli2TuBXGAANuDk5yeBjkAjvUSSBRJZ/a1EsZEeFO0h1If5iB8hAf1+X gE4FWKz7ma4Szs2uJYLNnliFwy3AwhJHyIWT59z7Y+QpIckENgVoUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFR mCFrhLhoozOiMiSFRuVWILAHqASqkjvtHpUlFAGfa6Fo9jqM+o2elWNvfT7vOuYbdEkk3Hc25gMn JAJz1NZ9n/yUPWf+wVYf+jbuugrn7P8A5KHrP/YKsP8A0bd0AdBRRRQAUUUUAFFFFABRRRQAUUUU AFRzmZbeVreOOScITGkjlFZscAsASBnvg49DUlV7/wAr+zrnz/P8nyn3/Z9/mbcHOzy/n3Y6bfmz 05oAsUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBHIZg8IijjZC+JSzlSq7Tyowdx3bRg44JOeMGSq9 x5Xn2nmefu80+X5W/bu2N9/bxtxn7/y7tv8AFtqxQAUUUUAFFFFABRRRQAUUUUAFFFFABUcZmLzC WONUD4iKuWLLtHLDA2nduGBngA55wJKr2/lefd+X5+7zR5nm79u7Yv3N3G3GPufLu3fxbqALFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUARwGZreJriOOOcoDIkbl1VscgMQCRnvgZ9BUlV7Dyv7OtvI8/ yfKTZ9o3+ZtwMb/M+fdjru+bPXmrFABRRRQAUUUUAFFFFABRRRQAUUUUARyGYPCIo42QviUs5Uqu 08qMHcd20YOOCTnjBkqvceV59p5nn7vNPl+Vv27tjff28bcZ+/8ALu2/xbasUAFFFFABRRRQAUUU UAFFFFABRRRQAVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOZKrp5X9ozY8/wA7yo927f5e3L42 5+TdndnHzY27uNtAFiiiigAooooAKKKKACiiigAooooAKKKKAI4DM1vE1xHHHOUBkSNy6q2OQGIB Iz3wM+gqSq9h5X9nW3kef5PlJs+0b/M24GN/mfPux13fNnrzVigAooooAKKKKACiiigAooooAKKK KAI5jMqAwRxu+9QQ7lRt3DccgHkLkgdyAMjORJVe98ryF87z9vmx48jfu3b1252c7c43Z+Xbnd8u asUAFFFFABRRRQAUUUUAFFFFABRRRQAVGDN9odWjjEARSjhyWLZO4FcYAA24OTnJ4GOZKrp5X9oz Y8/zvKj3bt/l7cvjbn5N2d2cfNjbu420AWKKKKACiiigAooooAKKKKACiiigAooooAjhMzITPHGj 72ACOWG3cdpyQOSuCR2JIycZMlV7PyvIbyfP2+bJnz9+7dvbdjfztznbj5duNvy4qxQAUUUUAFFF FABRRRQAUUUUAFFFFAEcxmVAYI43feoIdyo27huOQDyFyQO5AGRnIkqve+V5C+d5+3zY8eRv3bt6 7c7Oducbs/Ltzu+XNWKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn7P8A5KHrP/YKsP8A0bd10Fc/Z/8A JQ9Z/wCwVYf+jbugDoKKKKACiiigAooooAKKKKACiiigAqOcTNbyrbyRxzlCI3kQuqtjglQQSM9s jPqKkqvfokmnXMctn9tjaJw1rhT5wIOUw5CnPT5iBzycUAWKKKKACiiigAooooAKKKKACiiigAoo ooAjkExeExSRqgfMoZCxZdp4U5G07tpyc8AjHORJVe4RHntC9n57JKSkmFP2c7GG/k5GQSny5Pz9 MZIsUAFFFFABRRRQAUUUUAFFFFABRRRQAVHGJg8xlkjZC+YgqFSq7Rwxydx3bjkY4IGOMmSq9uiJ PdlLPyGeUF5MKPtB2KN/BycABPmwfk6YwSAWKKKKACiiigAooooAKKKKACiiigAooooAjgEy28S3 Ekck4QCR40KKzY5IUkkDPbJx6mpKr2CJHp1tHFZ/Yo1iQLa4UeSABhMISox0+UkccHFWKACiiigA ooooAKKKKACiiigAooooAjkExeExSRqgfMoZCxZdp4U5G07tpyc8AjHORJVe4RHntC9n57JKSkmF P2c7GG/k5GQSny5Pz9MZIsUAFFFFABRRRQAUUUUAFFFFABRRRQAVGBN9odmkjMBRQiBCGDZO4ls4 II24GBjB5OeJKroiDUZpBZ7JGijDXWF/eAF8JkHd8uSeRj95wSd2ACxRRRQAUUUUAFFFFABRRRQA UUUUAFFFFAEcAmW3iW4kjknCASPGhRWbHJCkkgZ7ZOPU1JVewRI9Oto4rP7FGsSBbXCjyQAMJhCV GOnykjjg4qxQAUUUUAFFFFABRRRQAUUUUAFFFFAEcwmZAIJI0fepJdCw27huGARyVyAexIODjBkq veokkCiSz+1qJYyI8KdpDqQ/zED5CA/r8vAJwKsUAFFFFABRRRQAUUUUAFFFFABRRRQAVGBN9odm kjMBRQiBCGDZO4ls4II24GBjB5OeJKroiDUZpBZ7JGijDXWF/eAF8JkHd8uSeRj95wSd2ACxRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFAEcImVCJ5I3fexBRCo27jtGCTyFwCe5BOBnAkqvZokcDCOz+yK ZZCY8KNxLsS/ykj5yS/r83IByKsUAFFFFABRRRQAUUUUAFFFFABRRRQBHMJmQCCSNH3qSXQsNu4b hgEclcgHsSDg4wZKr3qJJAoks/taiWMiPCnaQ6kP8xA+QgP6/LwCcCrFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAVz9n/AMlD1n/sFWH/AKNu66Cufs/+Sh6z/wBgqw/9G3dAHQUUUUAFFFFABRRRQAUUUUAF FFFABVe/dI9OuZJbz7FGsTlrrKjyQAcvlwVGOvzAjjkYqxVe/ufsenXN1ugXyYnkzcS+VGMAn53w dq8ctg4HODQBYooooAKKKKACiiigAooooAKKKKACiiigCvcOiT2ge88hnlISPKj7QdjHZyMnABf5 cH5OuMg2Kr3Fz5E9pHugHnymPEsuxjhGb5Bg72+X7vHy7jn5cGxQAUUUUAFFFFABRRRQAUUUUAFF FFABVe3dHnuwl557JKA8eVP2c7FOzgZGQQ/zZPz9cYAsVXt7nz57uPdAfIlEeIpd7DKK3zjA2N83 3efl2nPzYABYooooAKKKKACiiigAooooAKKKKACiiigCvYOkmnW0kV59tjaJCt1lT5wIGHygCnPX 5QBzwMVYqvYXP2zTra63QN50SSZt5fNjOQD8j4G5eeGwMjnAqxQAUUUUAFFFFABRRRQAUUUUAFFF FAFe4dEntA955DPKQkeVH2g7GOzkZOAC/wAuD8nXGQbFV7i58ie0j3QDz5THiWXYxwjN8gwd7fL9 3j5dxz8uDYoAKKKKACiiigAooooAKKKKACiiigAqujodRmjF5vkWKMta5X92CXw+AN3zYI5OP3fA B3ZsVXS53ajNa7oP3cUcmFlzINxcfMmPlX5OGyckMMDbyAWKKKKACiiigAooooAKKKKACiiigAoo ooAr2DpJp1tJFefbY2iQrdZU+cCBh8oApz1+UAc8DFWKr2Fz9s062ut0DedEkmbeXzYzkA/I+BuX nhsDI5wKsUAFFFFABRRRQAUUUUAFFFFABRRRQBXvXSOBTJefZFMsYEmVG4l1AT5gR85IT1+bgg4N WKr3tz9lgWTdAuZY48zy+Wvzuq8HBy3Pyr/E2BkZyLFABRRRQAUUUUAFFFFABRRRQAUUUUAFV0dD qM0YvN8ixRlrXK/uwS+HwBu+bBHJx+74AO7Niq6XO7UZrXdB+7ijkwsuZBuLj5kx8q/Jw2Tkhhgb eQCxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFezdJIGMd59rUSyAyZU7SHYFPlAHyEFPX5eSTk1Yq vZ3P2qBpN0DYlkjzBL5i/I7LycDDcfMv8LZGTjJsUAFFFFABRRRQAUUUUAFFFFABRRRQBXvXSOBT JefZFMsYEmVG4l1AT5gR85IT1+bgg4NWKr3tz9lgWTdAuZY48zy+Wvzuq8HBy3Pyr/E2BkZyLFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABVM6rYrrKaObmP+0Ht2uhbjlvKVgpc+g3MAM9ecZwcGpSalFbq2l2lpcz 7wGS6uWgULg8hljck5xxjueeOce4ghh+I2lvFFGjzaZfvKyqAXbzLJct6naqjJ7ADtQBcsfFGk6j eR21vNOWlz5EslpLHDPgE/upWUJJlQWGxjlQWGQCar2f/JQ9Z/7BVh/6Nu6y7PxLoPizxPYrZ63p rx6dcSPaxR3SNPdzCOSNiEzkRKryc4y5+YYRQZNS90bWP+Ehn1bSdUsbb7RaQ20kV3YPP/q3lYMC syYz5pGCD0FAHQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP/Y/GH/Qd0P8A 8E03/wAlUfY/GH/Qd0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Yf9B3Q/8AwTTf/JVA HQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP/Y/GH/Qd0P8A8E03/wAlUfY/ GH/Qd0P/AME03/yVQB0Fc/47/wCSeeJf+wVdf+imo+x+MP8AoO6H/wCCab/5Kqnq2g+KtZ0a+0u4 1/RlgvbeS3kaPR5QwV1KkjNyRnB9DQB1lFc/9j8Yf9B3Q/8AwTTf/JVH2Pxh/wBB3Q//AATTf/JV AHQUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8 Yf8AQd0P/wAE03/yVQB0FFc/9j8Yf9B3Q/8AwTTf/JVH2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/ 0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE 03/yVQAeIf8AkOeE/wDsKyf+kV1XQVyd7oPiq+utOuJdf0YPYXBuIgujy4LGKSLDf6T02yMeMcgf Q3PsfjD/AKDuh/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf +x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoKK5/7H4w/6Duh/wDgmm/+SqPsfjD/AKDu h/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A 4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoK5/w9/wAhzxZ/2FY//SK1o+x+MP8AoO6H/wCCab/5KqnZ aD4qsbrUbiLX9GL39wLiUNo8uAwijiwv+k9NsannPJP0AB1lFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Y f9B3Q/8AwTTf/JVAHQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP/Y/GH/Qd 0P8A8E03/wAlUfY/GH/Qd0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Yf9B3Q/8AwTTf /JVAHQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP/Y/GH/Qd0P8A8E03/wAl UfY/GH/Qd0P/AME03/yVQAeBP+SeeGv+wVa/+ilroK5PSdB8VaNo1jpdvr+jNBZW8dvG0mjyliqK FBOLkDOB6Crn2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/ 8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE03/yVQB0FFc/9j8Yf9B3Q/8AwTTf/JVH 2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/8lUAdBRXP/Y/ GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE03/yVQAeIf8AkOeE/wDsKyf+kV1XQVyd7oPiq+utOuJd f0YPYXBuIgujy4LGKSLDf6T02yMeMcgfQ3PsfjD/AKDuh/8Agmm/+SqAOgorn/sfjD/oO6H/AOCa b/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoK K5/7H4w/6Duh/wDgmm/+SqPsfjD/AKDuh/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8A oO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoK5+z/AOSh6z/2 CrD/ANG3dH2Pxh/0HdD/APBNN/8AJVU49B8VRazc6ouv6N59xbw27qdHl2hY2kZSP9JznMrZ57Dp 3AOsorn/ALH4w/6Duh/+Cab/AOSqPsfjD/oO6H/4Jpv/AJKoA6Ciuf8AsfjD/oO6H/4Jpv8A5Ko+ x+MP+g7of/gmm/8AkqgDoKK5/wCx+MP+g7of/gmm/wDkqj7H4w/6Duh/+Cab/wCSqAOgorn/ALH4 w/6Duh/+Cab/AOSqPsfjD/oO6H/4Jpv/AJKoA6Ciuf8AsfjD/oO6H/4Jpv8A5Ko+x+MP+g7of/gm m/8AkqgDoKK5/wCx+MP+g7of/gmm/wDkqj7H4w/6Duh/+Cab/wCSqADwJ/yTzw1/2CrX/wBFLXQV yek6D4q0bRrHS7fX9GaCyt47eNpNHlLFUUKCcXIGcD0FXPsfjD/oO6H/AOCab/5KoA6Ciuf+x+MP +g7of/gmm/8Akqj7H4w/6Duh/wDgmm/+SqAOgorn/sfjD/oO6H/4Jpv/AJKo+x+MP+g7of8A4Jpv /kqgDoKK5/7H4w/6Duh/+Cab/wCSqPsfjD/oO6H/AOCab/5KoA6Ciuf+x+MP+g7of/gmm/8Akqj7 H4w/6Duh/wDgmm/+SqAOgorn/sfjD/oO6H/4Jpv/AJKo+x+MP+g7of8A4Jpv/kqgA8Zf8gO2/wCw rpv/AKWw10Fcnqeg+KtVtUt59f0ZUS4guAU0eUHdFKsqjm5PG5AD7Z6daufY/GH/AEHdD/8ABNN/ 8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE03/yVQB0FFc/9j8Yf9B3Q/8AwTTf/JVH 2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/8lUAdBRXP/Y/ GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE03/yVQB0FFc/9j8Yf9B3Q/8AwTTf/JVH2Pxh/wBB3Q// AATTf/JVAHQVz9n/AMlD1n/sFWH/AKNu6PsfjD/oO6H/AOCab/5KqnHoPiqLWbnVF1/RvPuLeG3d To8u0LG0jKR/pOc5lbPPYdO4B1lFc/8AY/GH/Qd0P/wTTf8AyVR9j8Yf9B3Q/wDwTTf/ACVQB0FF c/8AY/GH/Qd0P/wTTf8AyVR9j8Yf9B3Q/wDwTTf/ACVQB0FFc/8AY/GH/Qd0P/wTTf8AyVR9j8Yf 9B3Q/wDwTTf/ACVQB0FFc/8AY/GH/Qd0P/wTTf8AyVR9j8Yf9B3Q/wDwTTf/ACVQB0FFc/8AY/GH /Qd0P/wTTf8AyVR9j8Yf9B3Q/wDwTTf/ACVQB0FFc/8AY/GH/Qd0P/wTTf8AyVR9j8Yf9B3Q/wDw TTf/ACVQAeDf+QHc/wDYV1L/ANLZq6CuT0zQfFWlWr28Gv6MyPcT3BL6PKTullaVhxcjjc5A9sde tXPsfjD/AKDuh/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf +x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoKK5/7H4w/6Duh/wDgmm/+SqPsfjD/AKDu h/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A 4Jpv/kqj7H4w/wCg7of/AIJpv/kqgA8Zf8gO2/7Cum/+lsNdBXJ6noPirVbVLefX9GVEuILgFNHl B3RSrKo5uTxuQA+2enWusoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACub1uI6j4q0rSZri7js5bK7uXW1uZLdmkje3VCXjZWwBK/y5wcgkEgY6SsvVdFGpXFvdw393 p95bo8aXNqIy3luVLoRIjrglEOcZ+UYIBIIBX8JTzT6EwmlklMF7eWyNIxZvLiuZY0BY8sQqKNxy TjJJJJrcqnpemw6TYLaQtI4DvI8khBaSR3Lu5wAMszM2AABnAAGBVygAooooAKKKKACiiigAoooo AKw9f0/T5Ea/1S/1KG3hQKsdreTQDcWxwISryOxKqFO7kAKAWO7crD1fw7JquqWl+mt6lZPaowij t1gZFZuC+JIn+faduewLAY3NkA4vdrEnhHxJq+r3t9Hr+iWi7PJvHjjjmSwhmO6KNhFJ+9dydysC DjlQBWhun/s3/hIPtt9/aH/CQfYv+PyXyfJ/tH7Nt8jd5X+q+XO3Ofmzu+aty58HxXYuY5NX1IW9 8gXUYF8nbeny1iZnPl7lLIiqfLKAYyoU5NSf8InB9qz/AGjff2f9r+2/2b+68nzvN87du2eb/rf3 mN+M8Y2/LQB0FFFFABRRRQAUUUUAFFFFABRRRQB5nr/iPV11nWtRsb6SGwtdC1RbOMAFWuLZoQ05 BHJEjPGFYEDyiwyJKseIo5fDuq6fp+m32pLb6kipcia/mnYg3tnD8jyOzRnZcSjKFT8wPVVI3JPh 34Te8+0R6FY2+bSe0dLa3SEOkwUMSVAYNtBUEEYDv61JJ4PiuiZNS1fUtQuEQLbTzeSjWxEiS7kE caqTviib5ww+QDGCwIAaJEdO8VarpMNxdyWcVlaXKLdXMlwyySPcK5DyMzYIiT5c4GCQASc9JWXp WijTbi4u5r+71C8uESN7m6EYby0LFEAjRFwC7nOM/MckgADUoAKKKKACiiigAooooAKKKKAOf8aX z6f4aeZJJ41a7tIZTbqxkMUlxHHIE2fNuKMwG35gT8vOK5sSKdR0/QlbxBp4u71Bd2V7qLSSiI29 06Ok6Su6hngAKiQf6r7oDnf3GqabDq1g1pM0iAukiSRkBo5EcOjjIIyrKrYIIOMEEZFY58HxSObq bV9Sl1QOjRak3kiWIIsiqqqIxERiaYfMhP7w88LtAOfs45bzxnc+GZr7Ujpdq9y8SrfzLLlYrBl3 TBxKwBuZjhmI+Yf3Vx1HhKeafQmE0skpgvby2RpGLN5cVzLGgLHliFRRuOScZJJJNVx4PijcXUOr 6lFqhd2l1JfJMsodY1ZWUxmIDEMI+VAf3Y55bdsaXpsOk2C2kLSOA7yPJIQWkkdy7ucADLMzNgAA ZwABgUAXKKKKACiiigAooooAKKKKACuL18zXXiqa0a11nUYIbKCRLbSb82jQM7zBnkPmxBw4RQoy 23y24Xdlu0rH1DQTd6ib+01W+0y6eJYZntBC3mohYoCJY3A2l3+7jO45zgYAODgvL3UfBOv+IrjW LufUtKskktLm1uZYYGYafBOHMI2o4Mkjth05BCkYAUam6f8As3/hIPtt9/aH/CQfYv8Aj8l8nyf7 R+zbfI3eV/qvlztzn5s7vmrYbwLYJayWFpe31npU8SQ3OnwtG0dwixLDhmdGkGYkRDtdeFyMMSxs f8InB9qz/aN9/Z/2v7b/AGb+68nzvN87du2eb/rf3mN+M8Y2/LQB0FFFFABRRRQAUUUUAFFFFABV e+huLizkitbr7LM+AJhGHKDI3FQeN2M4JBAOCQwG02KKAPP9GafW9O8E2N9e3zQ3Xh9r2dobyWGS WZBagM0iMHPEsmRnBJyQSARH4djl8RarqGn6lfak1vpqMlsIb+aBgBe3kPzvG6tIdlvEMuWPyk9W YnpB4Tgg07Sbax1G+sptLtBZQXcPlNIYcICrB0ZDkxRknaDleCASDHH4PitSJNN1fUtPuHQrczw+ S7XJMjy7nEkbKDvllb5Ao+cjGAoABxdhqF/q/gDW/E13qN8dV03T45rZ4bqSGNXGnwXGWhQiN/3s jk7lOQdp+UAD1iuXbwLYJayWFpe31npU8SQ3OnwtG0dwixLDhmdGkGYkRDtdeFyMMSx6igAooooA KKKKACiiigAooooA5vXWvovFPhh47+SOzlvXhe1jXaJD9luHJdurAFE2qMAEEncdu2vrGnmz1Kzn sr/UpdZub2Nlia8kMf2cSp5/7kERBFhJXcVzkpyZGUnoL3TYb66064laQPYXBuIgpGCxikiw3HTb Ix4xyB9Dlw+GJoNcudVj8RaruuZVeWEpbMhRfuxAmHeIwCcAMPvMc7mLEA4e/wBQv9I8AaJ4mtNR vhqupafJNcvNdSTRs50+e4ysLkxp+9jQjaowBtHykg9ha2g0XxlY2Fpc3z2t3p91NMl3ezXOXjkt whBlZiuBK/3cZyM5wMC+BbB7WOwu72+vNKgieG20+Zo1jt0aJocKyIshxE7oNztw2TlgGGhp+gm0 1EX93qt9qd0kTQwvdiFfKRypcARRoDuKJ97ONoxjJyAbFFFFABRRRQAUUUUAFFFFABXN6U18njrX YLu/kuIvsVpNDFt2xwBpbpcKvPO1E3MSSxHYbVXpKz30azl1G+vJ0877daR2c8MoDRtGhlIG0jnP nMDnIIxx1yAefjXNU1C91i8+3zpYaj/Y5sYkfb5VpPeSQFlIwVaWMeZu4dfMC5BQGrl5HLZ+M7bw zDfakNLuntnlVr+ZpctFfs22YuZVBNtCcKwHyn+82dwfD7w3DdXdzZadBYTXEUMYazgji8lopTKj oAv3t+wnOVby0BBAwZD4Pikc3U2r6lLqgdGi1JvJEsQRZFVVURiIjE0w+ZCf3h54XaASeGC8Vzr2 n+fPLb2GoLDb+fM0rqjW0EpBdyWb55HPzE4zgcAAdBWfpOkppUU/+kz3Vxcy+dcXM+3fK+1UBIRV UYREX5VH3cnJJJ0KACiiigAooooAKKKKACq99DcXFnJFa3X2WZ8ATCMOUGRuKg8bsZwSCAcEhgNp sUUAeZwajc3mjeCLW6Gs3/23QjdSRWFy8U886rbYdpg6YAWSUne4Ukjq20VoaRHB4j1e4spL7WTZ 2NlCYozfywSxSme6jlSR4XUyFTAqbmZ/uZDMWZm3B4Tgg07Sbax1G+sptLtBZQXcPlNIYcICrB0Z DkxRknaDleCASDHH4PitSJNN1fUtPuHQrczw+S7XJMjy7nEkbKDvllb5Ao+cjGAoABxdhqF/q/gD W/E13qN8dV03T45rZ4bqSGNXGnwXGWhQiN/3sjk7lOQdp+UAD1iuXbwLYJayWFpe31npU8SQ3Onw tG0dwixLDhmdGkGYkRDtdeFyMMSx6igAooooAKKKKACiiigAooooA5vxi19FZ6fPaX8lrEmp2SzJ EuGmD3UKbS3ZNrPkAZbjkDcrc34lu7xbnxNdxnVXk07P2a9tb0xWun4to5B58PmL5u1mMjYjlyrh fmI2DvNT02HVbVLedpFRLiC4BQgHdFKsqjkHjcgB9s9OtZeo+E4NQnvv+JjfW9pqP/H/AGUPleXd fII23FkLrmNVQ7GXgZGGySAc/un/ALN/4SD7bff2h/wkH2L/AI/JfJ8n+0fs23yN3lf6r5c7c5+b O75qNBaeOy8Eav8Abb6S71vZ9v8AOvJZI5N9nLM22JmKR/vEUjYq4AwMKSK6D/hE4PtWf7Rvv7P+ 1/bf7N/deT53m+du3bPN/wBb+8xvxnjG35aNO8JwafPY/wDExvri007/AI8LKbyvLtfkMa7SqB2x GzIN7NwcnLYIAOgooooAKKKKACiiigAooooAK878Vrc+GrS4udE1TUptUgsrq7vnuLt51WIW8xSQ xMTFGTOse0Iq52uFGxXA9Erl7bwWsUV5b3Wvarf2d/5v2y2uRbgXHmKVbc6RLJwMAYYbQqqMKAKA MPxFHL4d1XT9P02+1JbfUkVLkTX807EG9s4fkeR2aM7LiUZQqfmB6qpBeRy2fjO28Mw32pDS7p7Z 5Va/maXLRX7NtmLmVQTbQnCsB8p/vNnoJPB8V0TJqWr6lqFwiBbaebyUa2IkSXcgjjVSd8UTfOGH yAYwWBD4Pikc3U2r6lLqgdGi1JvJEsQRZFVVURiIjE0w+ZCf3h54XaASeGC8Vzr2n+fPLb2GoLDb +fM0rqjW0EpBdyWb55HPzE4zgcAAdBWfpOkppUU/+kz3Vxcy+dcXM+3fK+1UBIRVUYREX5VH3cnJ JJ0KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKAMfxXq1xoPhLVtWtLb7RcWdpJNHGcYyqk5bLL8o6nBzg HGTgVTvfFVtpc1zLew6khit7F5LVYkkMRuJniXATLM+4YYAsMKuzJJzqa7pn9t+HtT0nzvJ+3Wkt t5u3ds3oV3YyM4znGRWXeeF5r+7e7uL+Pz5U00SeXblVLWtw05IBckBy23GTtxnLUAEXjK2e48ib TdStnjuI7W7MsabbSWQqIkdlchy/mJjyy+3eN+yrFv4otrnVEtBaXaQTXEtpBesE8qaePf5kagMX BHlS8soU7DgnK7o7jwx9o/tL/TNv23VbTUv9VnZ5H2f5OvO77P14xv6HHNPTPA9tpniV9VjXTcG4 nuVkGmoLsvKWLB7gkkoDI2AqqcBAWIDbgDU1O+uP7c0nSbSTymuPMup5NoJ8iHYGVc5+ZnliHI+5 5mCrbTWPB48sItOtZDb6rcxjSrfVJ7poY/3VtIHzLKVIXcPLJZEBJz8isAdupqkE0PiXRtWjikmi RJrGZUUkxrMY2EvGSQHhRCAOBIWJAQ1lw+BfK8PXuk/2jn7T4fg0TzfI+75STL5uN3OfOztzxt6n PAB0Gtaxb6Dphv7pJ3hEsURWCIyPmSRY1wo5blxwMn0BPFZcXjK2e48ibTdStnjuI7W7MsabbSWQ qIkdlchy/mJjyy+3eN+ytTWdM/texjtvO8rZd21zu27s+TMku3GR12Yz2znnpWfceGPtH9pf6Zt+ 26raal/qs7PI+z/J153fZ+vGN/Q45ALFn4gS+1FreHT742olkgS/2KYXljLK64DF1wyONzKqkrgE 7l3WNT1OXT/KW30u+1GaTJ8u0CDaoxklpGRByRxu3HJIBAYjLh8LNH4sOtvc2mQ7PvhsliupQVKi OaZWxJEoPC7AcxxksSpLSeJ/C6eJPsollg8uHerQ3VqtzGQ2MuqP8omXbhHYMFDOCrBqAOX1jx1q X9tQzaPZX1zpaxadNEYltxHdJdzGPL+Y4kHAATGzDht/yYrrLjxRbW2qPaG0u3ghuIrSe9UJ5UM8 mzy42BYOSfNi5VSo3jJGG246eBJoNJhtLfVY1ngstLtY5ZLUsu6ymaUOVDgkOTjbkYx1NWLjwPbT +LH1sLprGa4iuZHn01JbpXjVFURTMcImI142EglyGUkFQDY0fXIdbN4be1u447W4ktmlmjCq8kcj o4TnLAFM7sY+YDO4Mq4+i+KJ9Tt9Plv7S7sJrjWLuxjiURESCIT4D4Z8ALEQSpBMicfIedzRtM/s ixktvO83fd3Nzu27cedM8u3GT034z3xnjpWXZ+F5rZ7My38ciWesXWpRBbcqSs6z/u2O85Ia4Y7h jIUDaOtAFey8e22o29lLaaLrMj39uLmxiMCK1ygC7yNzgIE3qCZCgbI2F8jNi58a6bDbx3dvBd3l n9ij1Ge5hRQttauGKyuHZWIISQ7UDN8hyuSAZNH8Mf2V/YH+meb/AGRpTab/AKrb5u7yPn6nb/qO nP3uvHOHL8NLaS30yJ5NNuXtdMt9NllvtLS4bZEGw8O5sROd7E7hIOE4O07gDsNU1KHSbBruZZHA dI0jjALSSO4REGSBlmZVySAM5JAyax5PGVtDDD5mm6kLyS9+wGxWNHlSfyTMqsVcpgoFO4MVG8bi oDFdTWtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5GXbeF5kvLa/u7+OW8XUzqNwYrcx xyN9la2CopdigClCcs2SD0BAABIfFkEkFp9j06+vbu487/QofKWSPyXEc24u6p8khVDhjknK7lya NQ8XWen5b7HfTQxWi313IsQT7HbtuxJIsjK/SOT5VVnGwgqCQDTvPBrXGkpYifTblBe3V2YtT0xb qAmaZ5Qdm9SHTeVDBsEFsryNtfU/h7bahcWsry2l28dlFYyz6tYJezlIyxDxuxAWU72LMyuCQp28 EEALrxRqYh8TXZtJLWDw9eqzbhGRdWywpJKvDMd+12dPuDJiBP8ArFrtK4/XNDvPsHiS0hk+0L4k lSFESAg2u+BIJZGfcQyqkfmAEJkjZkl1rsKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooA//9k--20cf305496b5866b64049caea6ca-- ========================================================================Date: Sun, 20 Feb 2011 08:49:56 -0500 Reply-To: Steven Liu <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Steven Liu <[log in to unmask]> Subject: Conjunction analysis bug in SPM8 ? MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear Experts: In SPM software, using the minimum of two T maps as their conjunction without doing this for posive and negative activations separately! If within the same regions, the voxels are activated from map X, but deactivated from map Y, they are NOT conjunctive, right ? But using SPM, these voxels may be considered as conjunctive if the minimum (negative values in this case because they are always smaller than positives no matter how big or how small their absolute values ) are significant! Why we do not do positive and negative voxels separately so that we just compare voxels in the same direction (positive or negative) ?? Thank you! Steven ========================================================================Date: Sun, 20 Feb 2011 14:26:49 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: medial temporal lobe roi mask MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e6d7e01e8b7aa3049cbbba86 Content-Type: text/plain; charset=ISO-8859-1 Hello all, I am using a memory encoding paradigm and I use the wfu pickatlas for roi definition but the mask seems that only covers gray matter (I use the broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that appears active when no use roi. Exists any mask that cover all the medial temporal lobe region including white matter? Thanks in advance John Ochoa Universidad de Antioquia --0016e6d7e01e8b7aa3049cbbba86 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello all,

I am using a memory encoding paradigm and I use the wfu pickatlas for roi definition but the mask seems that only covers gray matter (I use the broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that appears active when no use roi. Exists any mask that cover all the medial temporal lobe region including white matter?

Thanks in advance

John Ochoa
Universidad de Antioquia
--0016e6d7e01e8b7aa3049cbbba86-- ========================================================================Date: Sun, 20 Feb 2011 14:20:37 -0800 Reply-To: Vladimir Bogdanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Bogdanov <[log in to unmask]> Subject: Check Reg Figures SPM8 - size and arrangement MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="0-1068215501-1298240437=:67343" Message-ID: <[log in to unmask]> --0-1068215501-1298240437=:67343 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPM experts, By default the size of the images in Check Reg figure does not allow to see well anatomical details (see screen1 in attachment). This can be changed manually (screen2), but those changes take time. Is there a simple way do it fast? Is it possible to change SPM settings somewhere to make the figure in the same way (different from default) every time we call Check Reg? Thank you in advance for your help, Vladimir Volodymyr Bogdanov, PhD Headache Research Unit GIGA - Neurosciences University of Liege CHU T4 +1, Sart Tilman Av de l'Hôpital 1 4000 Liege, Belgium --0-1068215501-1298240437=:67343 Content-Type: image/jpeg; name="screen1.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="screen1.jpg" /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMD AwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/ 2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAHCAyADASIAAhEBAxEB/8QA HwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1 dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDC8Y+Mv+Ed 1DQNPhgmurrxFq8GlL5DL5lqr5LzlTn5URWPQgkDPGa9C+C3w6f4wXt7Zw30 Vvd2tk80bKnmRXE4ljhWLO4YVpJB8/IA5wRXv0X/AATma3JaLxxLGZEZHKaW VDIR8yn9/wAgjqD6U+y/4J4T2ccv2bx3LDHdRGKURaYVE0ZIbacT8rkA46ZA 9K45VG6/tFK0OVJLX4ru8v0S8jvUqSwiouF6nM3zX+zpaKX4t3PKtV/Zqaxt dZ+z67BeGxhhl09xbfu9VZrM3kyod2QI4RnODuLIPl3Via78Iv7I0lrz+0BJ 5fh6213Z9nwD51zFD5X3v4fMzu74xgZzXvkf7COsQ3Fo8fxL1NWsQfszJZOD bjYseEP2gFf3aInH8KKOgFTaT+xHrei3az2HxP1a0uYIBZJJBbSJLHCPmEO4 XGQoODt6dDitFVXc5eR9jxH/AIUDLbeGtPvm1JGluLO6ur6FYf8AkHslgb+C MnfkmWEZ3YAUhh82055z4geCW8EeNp9Gguv7TVRC8FwkJQXKSxpIhChmIyHG Bn8ulfStp+xBr1lqE97B8TNWhvZpFle4W1kWRmVWRWZhcbiQjMoPUBmA4Jqg f2BLz+3hqJ+IFyNUaX7Qtx/Z7Cd5Qd3mb/P3b8/NuznPNNVo33D2btsefeML zwB+yV+zT4t8beMfBsPxEfwvqMkF7HHf3Vi8Hkaf9pnii8qVUco6SIC2NxHV RXzt8Lf+C0nwE+LdlaX+lfst+KV0W58Qaf4ZfU59euVtbe9vvM+zo+L0vgiG QkqrbQozyyhvpb9tP9j/AMRn9hPxL4C8NzS+J9a8R3t08Mn9nOViefT5oFMi r5p8sPty2D97GDxn4V8IfsDftEaN4Y8J6M/gPwrYW8XiHTtZ8WXNhp95EuqL Yz77ZLO1isIY7Xy1LMw3N5kuOUQsjY151VCPsI8zcknd2Sj9p/dokurV9Ls6 MLToynJYiXLFRbVlduX2V8923sk7XbR946J8TfA3iSwF5Yfs6pe2hmW2E8Hi DV5Y/NYqFj3B8byXUBc5+Yeoq5L408GW10IJv2chFM1ytkEbXdYDG4Zd6wgF 8+YVIIX7xBBArnfAn/CbeE9P0e1uPh94k1CPRHhmtWfS2jlhdL37S+yRbfzA jrlNjMwDMX9FHTeHviZ8RNIj0yP/AIV5qwi0u6ikghTTrryoIUjijEcYdXIf aki+YzOcTvx0roaOUgn+JPw9sNKtr2f4BWdtZ3hIgnk8RassUxBwQrGTBwQc 4zV5/Fnga3sLm7l/Z9ihtbKQQ3E7a5q4jgchTtdjJhT8y8H+8PUVzev2Xi/x HoVjYSeBvE0UdibUx7rOdtwiiZHD/uwZNzNld5byl3ImFY11OpX3iiwv9ctr bwbr+oadq95f3wkfTponcXaxFY2/db1EbRjeqMFkIxkqMsBoUI/ip8L5D/yR XSB/3Mup/wDx2rq+Pfht9ijuT8E9LFvK7RpL/wAJFqmxmUAsoPm4yAykjtuH rXQ+FNf1+8103954Y1/SpLScXAkFrdPdXMAmnk+yKVhwY9skSBWZFHlKc4wF p6V4s8X2k2mufB3idRpmwQpD9phRFC2isgAT7jC2k+U5XM2SCVyy+X4hYzI/ H3wwf/mjWkD/ALmPU/8A47V3TvE/w21S6jgt/gxpk08rBUjTX9UdnJ6AAS8n 2qzH4g8ULpyQx+C9die3hSK1k8iXdaMttJB5kf7sbWJZGOMf6sDrhhpWWra9 rWqW/n+FtX0tE1JLt7iOymZhbpI8wg2rH8zGR2bdwCxXIUDIfogMyx1z4cal N5Vv8GtMmk2s+yPXtUZsAZJwJuwBJ9hViS++HNqIjN8G9OgE8YliMmu6ovmI ejLmbkH16cVFpo8T6d4ltNTh8Oa2stlaSW0KPaMVi3JIq4AjHyqXztOScY3c 8auq6t4l1XTUgi8M6nYt5IgkMdtKVePEo8vayn5R5o2jJ27Mg5wQW/q4FGPU PhnJ/wA0l0gf9x7U/wD4/ViOX4Zvj/i0+kD/ALjup/8Ax+t0+Jdeub1Z5fDG tuUuEn5EhbCzSyqmTGeAJQv0UfQcnB4J1oEZ0fVB9bST/CnFJ7ifka0UPw0b kfCrSB/3HNTP/terMem/DWT/AJpZpA/7jep//H6zYPB2sADOlakP+3Z/8KvW /hHVV66ZqA/7dn/wquRC1LcWhfDWTH/Fr9IH/ca1P/5IqxD4X+GzdPhjpA/7 jOpH/wBuKgt/C2pqBnTr4f8Abu/+FXbfw1qK9bG8H1gb/CjkQajo/Bnw3kP/ ACTTSP8Awcal/wDJFWI/AXw3f/mm2kD/ALi2pf8AyRSwaFfA82V3/wB+W/wq 7Bo14OtpdD/tk1HIg1Llt8DvANzAkifDzQcSKHA/tfU84IyP+W9ct8Qm+Bnw e8MXGt+NNL+HXgvR4NYOhLe694rvNOt57v7LDdCNJJrpVYmKYELncfLk4wuT X8X+Kfirb/EPR38O2kFp4Ts1hTUrS88KpfX2obZW81re7GsW6QB4tqp5lrIU YFyHB8sHjnw3f+MNY8Ea82heJYJ/CvxBvvFq2EdvayyzwyeGv7GRHc3KpHue 5lkBUyHFttZV3hhh793c10M/w145+AvxAt/D9z4S0bwH4207xHrr+HYb/wAO +KbrUrS2vEspb1klkiuyqkQwklRlh5keQA2R1kfhP4RnXRpf/CN+Bl1M6n/Y 32Q+JL/z/tv2QXv2XZ9p3ef9k/0jy8bvJ/eY2fNXmKfDLXtP+NzeJLXw14mn g1r4iDxtepPFZw/YIo/CY0ZYFIun81nlt4mDYQD7Rg4CFzxTfsTI3/BR7/hp M6X4y/4ST/jw/soeH9BFp/Zn9l/ZfL87zftH237T+9+2+Zv+z/6Jt8rmm+aw JIo/tufFiL4K/s6fEf4gfDnRtH8O32neAtI1XTFvdPi1hLaa58RWNtJKYr4X EZka3kaPoQoYlcH5j+bvw6/4KtftY/EP4XeMPFaTeCtOsfCuh/2/FFd/D7w5 HNq1uLqCB3gB0zlFEzPvOFYQuq7mVgv6bfta/steMfjd+xf8S/B/hrTP7Q8Y n4d+G9Pi0pp4YDI6+IreeUmWV1jXYls55b5u3YH8zNJ/4JHftuaD8JpPCFpo N3FZzOkct43iDTpL2SySMImml2vyv2FeW8gKFLMd2RgD2Iqq8vSwXs/a8+vt OT4fc/mV/wCa1upjQ9h9cvjef2XL9i976220Wtr36bHYeF/2/wD9rjxR8FT8 Rbe+8AR+Cl1a40H+1LrwX4QtImv4LFr97YCW0VjIbdCyKBmRykab5HVD62ni j/goJH4evtUbw14VmstO1ZtDlNv4N8G3EjXqanNpbwJHHbF5GW7gmRvLDAKn mEiIiQ8d8Bv2JP2x/wBnH4NeNvB3hf4UWFgPH1vPY6rqg8XQpcTWk0DW8ls9 vHqa2VxH5Mk6qbm2leI3MjxNHJtde8tvhP8At8WGr/E2/s/BsGmXnxP1SfVL iW38Q2DnQBPLqks9tpwkvnW3hkbWL8kYZw0u9XWTLn9Eqzyr2j9ksPy6Wvy6 7X2+ev4HysI4vl972l/mYnxH+Ov7bXwa8Gax4h8T2XgbTtJ0BrpdQl/4RPwZ M1v9m1NNKnPlx27OwW+kSHKqQc7hlMtWr4d+NP7Y3i7WNM03RZ/h5rOp39lp 95PaQ+DfC0EmmG/0+91S0hnaeyjQSSadYzXXyM6IhRHdZiYhf+NXwX/bW+Pv gvxXomvfC7w/bw+MNRvbu8kstbs0MNtd6murTWMUTX7QCI36Rzec0bXWI0iN wYVEVdLo3ws/au8JeI59Z0L4HaBDrdza+GZhf6j4mtLiXTdX0LSptMttTtFg vIIwWinkdoLlLmEux3KyfIMPa5d7LWOH579OW3TfX1+5Gjhifae66nL87nj+ mftxftX31mbmPQ4prYW320zJ8JdFMS24htpzNuGnY8sQ3tnJu+7suoGziVCe ph/am/apt/hz4h8R39voGmr4avYLK6069+GGjQ38hkGp+ZLHEdN5igbSL9Jm JHlvCykEq+z1bwl4a/axGjRaN4s+CHhzxH4fuLW2t9bsovEdnZP4nkgGmRRT 3kou3dZI4NLthH9m8hRKgkdZQ0sc25401L9rrxp4pk1Sf4F+BPOOuS6us8ut R/a9nnavLawGWLUYghtm1q7MVxAsNwrrC/mb41IKmIyy9o06HrePf9f8vRTG lirXcqn49v6/rU8Sj/ai/a4RY/M8H3aebdNYru+D+krunW6SzMQP9nffF06Q FevmuseN5C0/w5+2N+0hqstpLeWmk6RpE6LcS6td/CvSzZ2dp5dtM93IYtNd /JSC7tZmZFY+XcRMA3mIG9wu9f8A21LvxdJri/DHwbY6k7Owkhv7VlXfraa0 w2vfsCDcRiMg5/dMy/eO8c54p+HH7WHjL4ZeKfBNx8NPD9h4Q8UWekaYNMst WtQNFs9OMZjgs5Jb2R080xQmV5TK7mFMtgYpxrZY/ihQ36cu2mvr18/ImUMX f3XU/E5e/wD2gP2otL+M9t4BfTPD0viK/wBbn8P2AX4faAlrqN3DN5Mghney WN0VsZk3bVUgsQOaqQftfftBTeGr3WreDwrqmjaWqPqF/pngXw9qFrpoeeSB PtMkFo6wF5In2LIVLrtdQyujN3HxK+D37TPjD46Wfj3w78MdE8E65o2vXuta ZfWOo6ZLfqJ5VaKC5mabbdLBGqxLvjAKl9wO411N/pf7Ud5P4TFv8J/B2n2f gq70e50qzXVEu4rdNM+3/Zod9zqEspTGozjPmb02ReW0WwVLq5XyxajQu1r8 Pz/Db5+QOGM5mrzt03OF+Hvx1/aT8fePdJ0BNH0vTrjV7yCzW4v/AIZ6VDb2 xma0USSuNPOyMC+s2LYOFuoT/wAtE3bmu/Fj9orwr4e1rUL2w0YnQL17e8hX 4WWWYrdDMhvTKdLEC2/mQSxgtKHLRt8mFYj06Lxr+07DcDVo/hH4Yj8VPqgv pNVm10XJWBYdGi+zIsl8zHzBosPnSyvLI4kbY0R3M2P9j/aWv/h9ceE9T+F/ g/UvDeoxQ22o2E2oRouowxXd5eJG8kV8kigXF60mYnRj5EKklfMWTn9vgGk+ Sgu6938P6RXJiVpep+J5Frf7Zfxl8JtENXttB0wzY2C7+HujQb8xxyjG6yGc xTQuMfwyo3RgTFa/t6fEl8brrwec/wDUkaH/APIldl8bP2d/j38eT4eXVvAu n2cPhnTxptksOsxXMpj3s+HuLq8mnZRnCx+Z5UYzsRNz7uPtf+CffxdUc+Es Y/6ill/8er1KM8icE6qpJ+XKcdSOZX9xzt8zQtf25/iLL9648Hn/ALknRP8A 5ErQtf22/iA4G6Xwec/9SVoo/wDbSqNr+wV8WIvveFMf9xOz/wDjtX7f9hj4 px4z4W6f9RK0/wDjtNy4e/6df+Skcuaf3/xNC3/bN8dyYy/g/wD8IzRv/kWt C3/bB8byHlvCB/7k3Rv/AJFqjb/sR/E+Pr4ZHH/URtP/AI7V6D9jP4kxnnw3 /wCVC1/+O1HNw/f/AJdf+Si5c0/v/iaNr+1p4zl+8PCH/hHaP/8AItaFr+1N 4ukIynhH/wAJDSP/AJGrPs/2QPiKnXw7j/t/tf8A45WjbfsneP4iM6B/5PW3 /wAcqXLIf+nX/kocuaf3/wATRtv2mfFEuMw+ET/3KOk//I1Xrf8AaO8SyYzB 4R5/6lLSf/kaqFt+y947ixnQj/4G2/8A8cq/b/s0eNo8Z0Ucf9Plv/8AF1De Q/8ATv8A8lC2af3/AMTRtP2gfEDgZtvCRz/1KelD/wBt60bX45a3L1tPCR/7 lXS//kes21/Z38YxAZ0fp/09wf8AxdaFn8BvFiDnSsf9vUP/AMXUt5F/07/8 lJ5c1/v/AImna/GfV3+9ZeEz/wBytpn/AMj1o23xa1N8ZsPCXP8A1K+mf/GK zLf4J+J4+umf+TEP/wAVWhbfCPxFHjOn9P8ApvF/8VUOWR/9O/8AyUds1/v/ AImjb/Ey+kxnTvCfP/Usab/8YrStviDdSfe0zwmf+5Z07/4xWZb/AAs16LG6 xAx/03j/APiq0Lb4e6xHjdZ4/wC2qH+tZ82Sf9O/wFy5r/f/ABNS18ZTSddL 8Jn/ALlrTv8A4xWja+JvMxnSPCZ/7lzT/wD4zWXZ+C9TXra4x/00T/GtG28M 3sWN0OP+Br/jUt5J/wBO/wAA5c1/v/iel+INM8MaFr17Zf8ACN2Eps53g3/2 dpa79rFc4+xHHTpmuA+H3jyHxh4r+I1nF4BsNXi8I+IIdNtksrXQ7WRLZ9L0 +6Mkz3UKRs3n3bqChX5WjGwkM58o+Kdn8fviJ8YLfxRYyWPhfTYrhZ7jw7p3 jGzl07UcTtK4mkuPDkt0PMVhGxhni+VQV2OS5j0HRfjd4c1XxxcWOgeDNOHj bWItUmNp4xfzIUSxsrXyCZtHlVlLWSybgqnLBegO786/e33j/wCSH2yVK32v /Jj6CgutLbx54e0a78FWWkvrGl6lqzCa30S6dY7TA2AwWzx5dj94O20KQVyf l8z8J/t0fAvx3+0DdfCrSoftXxCsLi8t73RP+EVjjubAWsUMsk8+/SwsNq6X MHlXMhWC4MqrDJI3FUfDOt/F+y8QaPqepeFPBd7c+HNC1HSbQv4vlL37Xk0b ZmdNLjSMRp5pBWNtxVFIG5pB5T4U/ZJ1PwR+0Fd/FXS/g/4Kt/iFqNzeXF7r bfFXWpLi/F1HDHJBOGsSs1qq28Hk2zhoLcxK0Mcbc1pPn5Y25b21+DuyIKHM 783/AJMV/hr8SL74veGbTxNqUFha3uuWGm380FhbLbWkDzaZaTMsUS8Im+Rj jnqSSSST0SfeFWf2dP2cbzQfAq6DqWo28Wo+GINO0i5+zxmWGSSDSbFGdCSp 2lgcZAOAOB0rvx+z9g/8hb/yV/8As6yrVaaqSS7m1GnNwVzzqpF6CvQv+FAf 9Rb/AMlf/s6cPgHgf8hb/wAlf/s6y9tDua+zkeep1p1egj4C4P8AyFv/ACV/ +zpf+FD/APUV/wDJb/7Oj20O4ezkcCvQU5Otd8PgVgf8hT/yW/8As6VfgZtP /IU/8lv/ALOj20O4ezkcFUi9BXdf8KP/AOop/wCS3/2VOHwSwP8AkJ/+S3/2 VHtodw9nI4VOtOruR8E8H/kJ/wDkt/8AZUv/AApY/wDQTH/gN/8AZUe2h3D2 cjiV6CnR967b/hTP/US/8l//ALKlHwbx/wAxL/yX/wDsqPbQ7h7ORxVSV2X/ AApz/qIn/vx/9lTv+FQf9RE/9+P/ALKj20O4ezkccnSnL1Fdgvwi2/8AMQ/8 gf8A2VL/AMKkz/zEP/IH/wBlR7aHcPZyORp6dK6z/hU3/UQP/fn/AOypy/Cn b/y/5/7Yf/ZUe2h3D2cjk16in11X/Cq8/wDL/wD+Qf8A7Knf8Kv/AOn7/wAg /wD2VHtodw9nI5ZOlOXqK6cfDDH/AC/f+Qf/ALKnD4ZYP/H9/wCQf/sqPbQ7 h7ORzNPTpXS/8K2/6fT/AN+f/r0q/DgqP+P3P/bH/wCyo9tDuHs5HNp94U+u iHw6IP8Ax+/+Qf8A7Kl/4V6f+f3/AMg//Xo9tDuHs5HPp90Vb0KTydbs3Cxs UmRgHQOp57qwII9iMGtceACB/wAfn/kL/wCvUOofDqe60+eK21i4064ljZI7 u3gjaW2YjAkQSB0LKcEb0ZcgZUjII60O4ezkd0niBnUn+ztAGPXRrP8A+NV8 q/teft/+LPg74C8rwj4Ni1DV/CXw90T4heMNcPgfRNX06x066srt5P8AR5dV 0yYzNJY3Mn7nzFVU2hCXXbq+Dv8Agn1pvw5+JGq+MfDni688P+LNd87+09a0 7wh4YttR1HzpVml864TSxJJvlVXbcTuZQTkgGs74j/8ABMvwx8ZbzSrrxlrg 8YXui6bb6NZ3eu+CvC2pXNvZQLtht1ln0tnCKCcDPVmJyWJOCklvI0cfI734 L/EbVfEv7QGoT6k+n3Yk+Hnh27aN/DR0m0+0TXOqedOum3AZ7SR9ibklzMio kbsSlcr8I/28Lv8AaR8DeJZLPwronhWTQr7SbBpDa2kr6wlzeeVJqOnEW6ef o06BhZaiNhutlwRFCYAW6XT/ANmHXNP8Q6jrS/Fjxy2u6nb2dlLqJsdH81ba 2a5eOBU+xeUFMl1IzMULkhAGABBs3H7PHiHVJLZdU+LPjfVrOC6guns7jT9G jiuDDKkqq5iskkClkXO11OM8inzre4lHyNa3+FWqWvx/8ZeKlubRbLxD4X0f RrVVkcTxT2dzq00jv8uAhW+h2kEnKuCBgFsj9nf4N+KfhZ4s8b3niPxf4g8S WWs6gH0W1v8AVory30y08yacpDEtnA0GJLqSLbJLckxW1viRQojT3r4X23ib 4mfEHVILjxj4k07RdNskupGt79RM0s7bIkRXhb92ognZmLbi0qAYC1g6J8V/ Eeo2OpGTXLxX0zUL7TWke+miika2upbfzS2WMasYt2MNtDY+fGThOfs8NLEV PdhG979lrf00v38jaXvVI0U03vp52VvXY8z+JWneOdc8U3elaVo/huXwhrWk SW95qzeK77TNWs7gxzKFght7RjtyYv3y3cUibnKrmNd+NpPww8Y33xu8FeO9 RtvD1pPZ6VqekaxpcGq3FzDZC9NjOZ7adrdDcFJtPjj2PHAGSd33KYhHJ0Xx b8b/ALRHgr9tHQIbK7sR8DpdLX7bfz30y6hd3M+4L8rYMU8U0caCBPMBineR 2zxbepf8J/r29j/ber88/wDH7J/8VUYfFUK1GNbD1FNSV9Ol+j7PyM+eTcot NWdtetuq8jwr47fCHxD45+Nng/WNO8P+DPFPhdNC1nwv4n07xBqMtsr2Ooz6 Y8jxxLaXCXWI7KVTBKYlfzQDIBmuA+Hv7Mfxh+HsF5pes+Nb/wCJunWt3a2u kahrfjKbSLmCyeC7huZmjsdNSb7REtygVZry4894kmEtm8aAe/fDv9p3WvHv jXWdLj12/MGnK5t5o7qUi72SlXKtvKkKrQZ6EmTOMEVi/G347/ELwd4mgOme ItagsUghYh3d4JZTMysHb7wGNmQrA88cmt4Kz5F/XX9Qcna5seE318W2pxa9 aaVbiDUZU02Wy1CS8a8s8AxyzB4IvJmJLgxL5qqFUiRtxVbkUU11erDE6KZH CJ5jqign1Y8Ae54FV/CfxXuvi9osl/ereLc2dw1m4uLg3GAFDrtc8lSrg8gY JIx3My/8hBP98f0r6Ph+nB1JupFStFuzV9VY8/HSdlZ2u0bf/CvdX/566R/4 N7T/AOOUf8K91f8A566R/wCDe0/+OVZ1X4laD4K8Lo+s3sGnIEktI2vbm3it pJJMtuBkThwEHQl2C8OgQAxv+118OZNZW5a88HPbJcmdLNtZtfIRTAImXaBg ksqPk5xsAAGSTgs9v/y4p/8AgL/zK+pP+eX3kX/CvdX/AOeukf8Ag3tP/jlH /CvdX/566R/4N7T/AOOVmL+0p4JWymMvivwvcX0nkqjvf2RjiWJVVOG3McgY IG0EKMlwSBdv/wBqT4d6kt5INU8E2M9xCIl+y6lbqMlMM3zsyr8wBARFbn/W DB3P+3H/AM+Kf/gL/wAw+pf35feTf8K91f8A566R/wCDe0/+OUf8K91f/nrp H/g3tP8A45VdP2qvAUtxby3GqeBpHi8rzPK1C1iWXZIrFdoO0BgvPG7JPzbP 3dI/7VHgSCVprPXPCNpcrK80DjU7Ii1Y3CTLtAUE7QpT5ieG42jKk/t3/pxT /wDAX/mH1L+/L7yz/wAK91f/AJ66R/4N7T/45R/wr3V/+eukf+De0/8AjlS6 X8bPCHj2w1Cw0S+8P3uoSHdHDZSW87W1qrAg7kO8sGKqWKjcHbcWygR+l+H7 /XBJ9hsbu88rG/yIWk2ZzjOAcZwfypPPtbfV6f8A4D/wRrA/35feV/8AhXur /wDPXSP/AAb2n/xyj/hXur/89dI/8G9p/wDHK1P+Ff69/wBATV//AADk/wAK P+Ff69/0BNX/APAOT/Cl/b7/AOgen/4C/wDMPqS/5+P7zL/4V7q//PXSP/Bv af8Axyj/AIV7q/8Az10j/wAG9p/8crU/4V/r3/QE1f8A8A5P8KP+Ff69/wBA TV//AADk/wAKP7ff/QPT/wDAX/mH1Jf8/H95l/8ACvdX/wCeukf+De0/+OUf 8K91f/nrpH/g3tP/AI5Wp/wr/Xv+gJq//gHJ/hVTVfD1/oSxG+sruz88Ep58 LR7sYzjI7ZH5ij+33/0D0/8AwF/5gsD/ANPJfeVv+Fe6v/z10j/wb2n/AMco /wCFe6v/AM9dI/8ABvaf/HKs6X4fv9d3/YbK8vRFjf5ELSbM5xnAOM4q3/wr /Xv+gJq//gHJ/hR/bz/6B6f/AIC/8w+o/wDTyX3mX/wr3V/+eukf+De0/wDj lH/CvdX/AOeukf8Ag3tP/jlan/Cv9e/6Amr/APgHJ/hR/wAK/wBe/wCgJq// AIByf4Uf2+/+gen/AOAv/MPqS/5+P7zL/wCFe6v/AM9dI/8ABvaf/HKP+Fe6 v/z10j/wb2n/AMcrU/4V/r3/AEBNX/8AAOT/AAo/4V/r3/QE1f8A8A5P8KX9 vP8A6B6f/gL/AMw+pL/n4/vMv/hXur/89dI/8G1p/wDHK8I8efHO6vfE/wAA 7rwzqgfw58SPEzwXLfZhnUbBvDesX8IxIm+PM1tbScbX+TacAsp981XR7vRL gQ3lrc2khAYLNE0ZYZ6gEDI9/rXxp4W/5EH9iP8A7Ctr/wCoPrlepg8XDGYa u50YRcY3VlZ/qc9Wk6U4Wk3d9T6t+HB/4uX49Hc+FdLP/lSuK8x07xX8Rz+z 94jvoLH7V43t9b8SRaTBq1rJbQSRQapfppglWFA5gaBLT94ilpIyHG8tuPtn 7Mng5/Ff7SfisXVjHc6EPCVjBdynWrmBkma+uXhQWaL5MwYRSkzvIskHlhI1 dbmYp9C/8KH8Kf8AQJ/8mpv/AIuvlpU25XR6cKqirNHyD8APGHiT4gfCHR9a 8Wabpuka3qgmuXsrFb5Y7SBpnNvGwvbe2uRIIPK8zzLeI+Zv+RRgVyupftIx XGoy2thY+NIH0LWP7M1k3Hw91xxcCT7TFEbSTyESWMTrC7XMPnwrECWKLKky fdH/AAofwp/0CT/4FTf/ABdH/Ch/Cn/QJP8A4FTf/F0vYO9xxrJW0Pin4F+K vE2q+N/iBpviaDUo47DU7WfQXlsGjt202TTrRMLOECSSC9h1AujOZkDoWVY5 IS3KftV/E34t/DaK/l+HnguXxc0a6fdWIkuohBezy6hb2U+nvGqeZFEtvK93 9pLERtGxfMSMjfoD/wAKH8Kf9Ak/+BU3/wAXR/wofwp/0CT/AOBU3/xdDoSu mJVUj85PA/7R/wAZ/Gt74KvYvhdo8fh7W2i/tmY3WoWs+lhtRnt5AsOoWdnd MY7dYZj5lvHkb9oZSjN6l+0d4n8Q+Evh3Z3XhiG/n1STxHoNrKtnZG8lNlNr FnFe5jCP8n2R5yz4/dqGfcu3cPsn/hQ/hT/oEn/wKm/+Lo/4UP4U/wCgSf8A wKm/+Lo9g7r1uEayTuz4M+I/xU+Ivgaz0vRbPS9H8QeKNa8O31xa3seiatBp y6tb2sLxxzeVHcxW1vJL9oJ8+6STHlRxLcyBjXF/B74+/GHxlYaHrPi3wtD4 f09PiENPvLfRNH1OYS6BdaGZbR3F5bRXjMmo3VpHNMttAImgl8xY4opmH6T/ APCh/Cn/AECT/wCBU3/xdH/Ch/Cn/QJP/gVN/wDF01RfZB7VHzVqk8trYTSQ bjMqHaFGSSeOleGeJP2rfF3hLx7HZ3nw78RN4e0K/wBRi8V6hZ6Jqd9Lp9sZ GXSJrGOC3c6o1ymxpksxKbPeRN5ew1+hH/Ch/Cn/AECT/wCBU3/xdH/Ch/Cn /QJP/gVN/wDF1KoSH7Zdj82f2B/id8XvEb3mj/E3wzeaNcebrGsSvNb3kjae 82tXSw2BupiIZo44kcwtamZWgaEOLZUgN76r8eviyngOK00qKHxwdS8SW89t pkvhzwzPqskN0pRU3z+RJaWpJcYe9eKHG4swVHZftH/hQ/hT/oEn/wACpv8A 4uj/AIUP4U/6BJ/8Cpv/AIum6LbvoHtlr/XY/KX9pb47ftDj4bfFDTNO8JaU jtHqegaG3hjT9eudckkMF1cQ30cjW8FshW2+yp+6ndfPeUxyvPHHp83pfiz9 pLxh8Nfj/qUeq+G/Feo+C7ezsUuJtL8K6hqC6fIrai13Lbi1gllumP2jQYtq 5BD3MiIv2W8WL9D/APhQ/hT/AKBJ/wDAqb/4uj/hQ/hT/oEn/wACpv8A4uj2 MrWEqqR+dn7E/wC1P44/aPgii8UeEToGpaK2uWPidY9Pu4LLTr621U2lnbWl zKPJvwYYrozS2zyIkkShvKMioe6+Oev+PdJ8Saba+D9Jv9Vg1GyuLmWcXNpb WunT2Y86GBzJG8hF87i3dlDeXGhZPKf5z9s/8KH8Kf8AQJP/AIFTf/F0f8KH 8Kf9Ak/+BU3/AMXQ6Dbug9qj8vrb9qH47RzfFn7B8Mb/AFmLw9N4Wn8GWeqa VNY3OrjVpY3v7aW5XEBWxEzRGRE/ciHdOZCHZnfDb9pj4heI/ib8RrbR/CPj GTTvFvifTR4IvPEfhjWrG1jgXT9Oi1YzRXMMRsre32T3EYmaEXkrzRwl5A2f 0/8A+FD+FP8AoEn/AMCpv/i6P+FD+FP+gSf/AAKm/wDi6aotdv6t/l+LD2y7 Hxf+z98Rde+Jun6jqGrafd6XaAwtZ219oNxpF2onj+2IHSaV95jt7q1t5Cu3 /SrW84UYijq/F34n+JPDHjq10Lw1pTazqmq2EtzDHd2t/aafaGKOcqX1CKyu LbdLMLeNopGSRIy8iLKwET/bX/Ch/Cn/AECT/wCBU3/xdH/Ch/Cn/QJP/gVN /wDF1PsJXuCrJHwMdC+My+IdEaTW7OPTb5TceIYkuoJ5dFeO4e6it9Ob+zUF 5FKH+xStOIpBbxQyR7LjzJJNP9pD4z+L/gzaeGb7QPDE3ibT5/E62fiARWVx czabowsbme4vI0hVnZ42hTbGEdpmYQoPMljI+5v+FD+FP+gSf/Aqb/4uj/hQ /hT/AKBJ/wDAqb/4un7GXkP2y7H5RaH+338YPEl/odrZfDbU3l8RfFgaRaSv 4G1uK2g8E/aZLb+0rmRsJa3wkjffFcNG6oBI1sqMGP0H8CfHXxB8XePfH1n4 x0Tw3o+j6BqZsdDk07+0ftF/F5s8izSm5tooSPsr2I/0WSdPNFyDIpHlp9tf 8KH8Kf8AQJP/AIFTf/F0f8KH8Kf9Ak/+BU3/AMXQ6Dtb+un+X4sXtVZI+Frz xd8RrbTvjO+nWSXGqaPq4i8FQ6paPDZXEf8AYmnyqGeNC8sBvpLpXdQ7A+Yg IMYVfnz9lT9sb4wa3+0h8S/Cni3wnMmrazrHhzXvDlpb6Nrz6bYaReRpHe77 y8toFtlhtLOR1S4htTLerdoqO7eWP1s/4UP4U/6BJ/8AAqb/AOLo/wCFD+FP +gSf/Aqb/wCLpxotdun6DdZPofOVFfRv/Ch/Cn/QJP8A4FTf/F0f8KH8Kf8A QJP/AIFTf/F1H1eQvao+cqK+jf8AhQ/hT/oEn/wKm/8Ai6P+FD+FP+gSf/Aq b/4uj6vIPao+cqK+jf8AhQ/hT/oEn/wKm/8Ai6P+FD+FP+gSf/Aqb/4uj6vI Pao+cq8x+M9n8Zl+InhW7+Huq/DIeEv7StI/E2neIdKvv7R+xea5up7O7guP L87yvLWOGS227tzNNjCV9t/8KH8Kf9Ak/wDgVN/8XR/wofwp/wBAk/8AgVN/ 8XQsPIPao+SvAPy+K/G5JHOtRn/ym2NdPvFWZ55/h58ZfiTpmiXl/pthFrtu 6ww3UirltJ08k/e5JJ6n+gq//wAJ/r3/AEG9Y/8AA2T/AOKrKSSdmWm7GPvF G8Vsf8J/r3/Qb1j/AMDZP/iqP+E/17/oN6x/4Gyf/FVOg9TH3ijeK2P+E/17 /oN6x/4Gyf8AxVH/AAn+vf8AQb1j/wADZP8A4qjQNTH3ijeK2P8AhP8AXv8A oN6x/wCBsn/xVH/Cf69/0G9Y/wDA2T/4qjQNTH3ijeK2P+E/17/oN6x/4Gyf /FUf8J/r3/Qb1j/wNk/+Ko0DUx94o3itj/hP9e/6Desf+Bsn/wAVR/wn+vf9 BvWP/A2T/wCKo0DUx94o3itj/hP9e/6Desf+Bsn/AMVR/wAJ/r3/AEG9Y/8A A2T/AOKo0DUx94o3itj/AIT/AF7/AKDesf8AgbJ/8VR/wn+vf9BvWP8AwNk/ +Ko0DUx94o3itj/hP9e/6Desf+Bsn/xVH/Cf69/0G9Y/8DZP/iqNA1MfeKN4 rY/4T/Xv+g3rH/gbJ/8AFUf8J/r3/Qb1j/wNk/8AiqNA1MfeKN4rY/4T/Xv+ g3rH/gbJ/wDFUf8ACf69/wBBvWP/AANk/wDiqNA1MfeKN4rY/wCE/wBe/wCg 3rH/AIGyf/FUf8J/r3/Qb1j/AMDZP/iqNA1MfeKN4rY/4T/Xv+g3rH/gbJ/8 VR/wn+vf9BvWP/A2T/4qjQNTH3ijeK2P+E/17/oN6x/4Gyf/ABVH/Cf69/0G 9Y/8DZP/AIqjQNTH3ijeK2P+E/17/oN6x/4Gyf8AxVH/AAn+vf8AQb1j/wAD ZP8A4qjQNTH3ijeK2P8AhP8AXv8AoN6x/wCBsn/xVH/Cf69/0G9Y/wDA2T/4 qjQNTH3ijeK2P+E/17/oN6x/4Gyf/FUf8J/r3/Qb1j/wNk/+Ko0DU8t1TWdQ 1S+NwvhP4jWEzRrE7ab4hg0/zVUsVDiC/QPgs2N2cbjjqaht5XtvCd1oi+Av Gj6dercCcS6pZyTzGdnaVmma9MhZmkdt27dluteYftz+Nvir4DsfEOs+AJPE +oW/hrwlPNbaP4f0aO6vb/Vrtpba0kZbizlS8hhkCzvHb3EEtusDNJHdJcRR pzvxT/aE/aL+Hnhb4ivpngrQ/E1/8NIUW2ktfDV7FH8QnuoEkjewi+2O9stg Zh56lro3ZidImtn3CPWblVp+znrHs9u223UUYKMueO/c9q0+3m0/V7a+PhD4 kX09o7SQ/wBo+JIr9InZWQuqT6g6htrsu4DOHYZ5Nb//AAsDVs/8iN4p/wDA jTf/AJLryXwNrvx6T47+IdF1fUPDE3hi21K2NvqT+Ep4omt4dM0l7uC3Avg2 Lu5ur1oZXMwtmsp0c3AeNYmfFb4pfFLWPjF4Vt/h69tpnh9dHu11ttf8Hajc WzXs8mjrZspQxS5ijvbiUqG8phb3MMr27I9xa8uGwdHDx5KEFFb2St08uvT1 NZznN3m7s9G0Kb/hGrpbiz+HPiWK4jg+yrMbnT3kWHduEQZrskID0UHCgAAA ACtDU/Fl9rVi9td+APEtxbyY3RyTaaVbBBH/AC99iAat6v4o8V33wH1HV/Af hrQde8d6jplr/YNh4svZtA0+2ubm4tomur4EidYrWKWS5ltl/futu8SYkZSO P/aG8Q/FzwNpnwmh8N6R4L1/xjeaha23jG107ULq28MSP/YV5LdmO8e2u7mC 1F+kRidoy7HyYnZfMauv2cuXnZjF3dkdn4OtYbTRZfJ0G48OiS4LtbTmEtId oHmfuZJFwQAOWz8nTGCbS/8AIQT/AHx/Ssn4beLNY8U6LeLr+nJpmsaZcpa3 cEKXDWyyG2hmYQTzQw/aolaUqsyIFO0qQsiSImsv/IQT/fH9K+j4d+Kr/gl+ h5+PVnFf3kc78cV1N18NrotxZWmsPqNytjPd2r3VvBOdLvhG8kSSRtIgfaSg kQsAQHXO4eC6/wDt2/GH4aaRq1hqv7PfjTxfrOiWXnw6hoVu1rp/iCaGZWvY oYiZ3tpI7F45o1kd4rqeK5toJ5mSGW4+nPGGo6FoaafqOu31hpyWF15lpNd3 S26iZopI8AsyhiY3lGDnjJxwCM//AIXd4L/6G3wx/wCDWD/4uvmE9D0/6/I+ O/hb/wAFNvjfN4a8EW3ij9nnVpvEuv2OuX+owwi/0qeCLSts0rQWL2tw7JJB cWkMDSTIZ7yR4iIk8uR/pX4k/EnxzaXnifUPBsfhnxDF4IhvvtPhlYHbUteu 1063ubOyW889ItPleSZSTJBcBopYmwmSa6//AIXb4Lz/AMjb4Y/8GsH/AMXR /wALu8F/9Db4Y/8ABrB/8XVyaeqVv6/QG12OP+AfxQ+Jfjb4keKdP8eeDdA8 L6Xp2l6ZeaTNpF9eajFeSTyXy3KNc3FraZePyIP3Sw5QSqzOfNVU9hg+0r4S 8Sz2VpaXmowaX5llFdSmGJpvPhCBpVSRo1YnaWCMQrH5W6Hjv+F3eC/+ht8M f+DWD/4uqPiD4jfDjxbZLbatrngnVLZJBKsV3fWsyK4BAYBmIzgkZ9zS3d0h MteEtS1K6+IepRakI7eddB0yaa0t7p7i1tp3lvRKInZELDKqN5RCwRcquMD0 Sx/5EPUv+v8AtP8A0Xc1518N77wHBe3Nr4Rl8JpdToJZ49Ie3DuiHAZhFyQC 5wTwC/vWz49+Kdr8P/BzWbXXh+C+1K8imhXV9WXTonjijmEhV9jlmBlj4C4w SSRwDL1dkJaIPHHimLwL4J1jXJ4WuINFsZ76WNZ4YDIsUbOVEk7xwoSFPzSy Ig6sygEjzvw5+0bq6aJ4eXX/AAe8evavrMmiXlno2pCS30147m4hefzNSj0+ WeBUtzITFAzurboY502yNa/4aC/6iHwv/wDC0/8AuWk/4aB4/wCP/wCF/wD4 Wn/3LTUH1KurHLfCH9vHTfjXp/gW+0zwX400q08c6HrOuQ2+uW0en6jZJp19 ZWflyW7OeZ/tgljYPt2BSSN/y6/hz9sOz8W/CvSPGWneDPF0+i6zpkusRl5d NgktrQQSzQSzeZdqircIkXl4clPtMfniALKY9L/hoHH/AC//AAv/APC0/wDu WlX9oIAg/b/hhx6eNcf+2tU4q234gmranceA/Gdl8RfBWk69pzpJYazaR3tu yTxTq0cihlIkid4nBB4ZHZT1BIIJztK/5LRr/wD2BNM/9H6hXJ2nx3jsLdIY Lz4WQwxKFSNPGQVUA7AC1wK2Phbr3/CZeNdc1gX/AIXuhJY2VmItH1f+0fL8 uS6fdI3lpt3ebgDBzsPNS42vbYlPod3RVfUNJ8Vals/4RrQbfWwn/HwZbqaH yc4248uCXOfm67cbeM9q3/CLfFL/AKETT/8AwZ3f/wAhVPK2O6PL/iR+1h/w qnxn4w/tbQV/4Q7wZot1qV5qlretc3zzW0dpPNH9nSIwQoILyNl+0XUVzIY5 DHbPEonNTWv20Ro4uIh8N/G9zqNtPYWrafHqfh5Lkz3Vs87Q4k1NVEkJTy3U sDIzAwfaIw0i+t/8Ir8UT/zImn/+DO7/APkKj/hFPijn/kRNO/8ABnd//IVU oq2v5hzK9zyz9kj9r6P9qOHxBb3XhHXfBOt+HbmVJ9P1K4tbkyW4v7+xjmWS 3kkTmXTrpHQkFXhfaZIzHNJ7JWcPCvxRHTwJp/8A4M7v/wCQqP8AhFvil/0I mn/+DO7/APkKhx10EnoY/gD/AJGvxx/2G4v/AE3WNfMvhb/kQf2I/wDsK2v/ AKg+uV9UeEfBmveEtQ12fxFp8Wl32tagt8ltHJJIEiFtBADueOMnLQMfu456 mvlfwt/yIP7Ef/YVtf8A1B9cr6TI/wDdsT/hR5+M+On6n3F+x/r72v7Rni7T RaSyR3fhnTbprkOoSExXd8oQgncS3nEggYHlnJBK5+na+Xf2Qf8Ak57xT/2K th/6WXlfUVeRHqbyCiiirEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHxh8WvFF9on7R 3xKitfDetavG2r2jGa0ltFjU/wBk6eNhE08bZ4B4XHzDnOQMn/hYGrf9CN4p /wDAjTf/AJLrsPHX/JwXxN/7Ddr/AOmjTqgrz6jXMzqjsjlv+Fgat/0I3in/ AMCNN/8Akuj/AIWBq3/QjeKf/AjTf/kuupoqLrsUct/wsDVv+hG8U/8AgRpv /wAl0f8ACwNW/wChG8U/+BGm/wDyXXU0UXXYDlv+Fgat/wBCN4p/8CNN/wDk uj/hYGrf9CN4p/8AAjTf/kuupoouuwHLf8LA1b/oRvFP/gRpv/yXR/wsDVv+ hG8U/wDgRpv/AMl11NFF12A5b/hYGrf9CN4p/wDAjTf/AJLo/wCFgat/0I3i n/wI03/5LrqaKLrsBy3/AAsDVv8AoRvFP/gRpv8A8l0f8LA1b/oRvFP/AIEa b/8AJddTRRddgOW/4WBq3/QjeKf/AAI03/5Lo/4WBq3/AEI3in/wI03/AOS6 6mii67Act/wsDVv+hG8U/wDgRpv/AMl0f8LA1b/oRvFP/gRpv/yXXU0UXXYD lv8AhYGrf9CN4p/8CNN/+S6P+Fgat/0I3in/AMCNN/8AkuupoouuwHLf8LA1 b/oRvFP/AIEab/8AJdH/AAsDVv8AoRvFP/gRpv8A8l11NFF12A5b/hYGrf8A QjeKf/AjTf8A5Lo/4WBq3/QjeKf/AAI03/5LrqaKLrsBy3/CwNW/6EbxT/4E ab/8l0f8LA1b/oRvFP8A4Eab/wDJddTRRddgOW/4WBq3/QjeKf8AwI03/wCS 6P8AhYGrf9CN4p/8CNN/+S66mii67Act/wALA1b/AKEbxT/4Eab/APJdH/Cw NW/6EbxT/wCBGm//ACXXU0UXXYDlv+Fgat/0I3in/wACNN/+S6P+Fgat/wBC N4p/8CNN/wDkuupoouuwHLf8LA1b/oRvFP8A4Eab/wDJdH/CwNW/6EbxT/4E ab/8l11NFF12AqeCPGOn3/7PXgjxprUMEdx4o0e1untre+uGuLi7eEySRW1r DZzzS4VJHCoXZURyeEZq1rPxL4SvNA07VP7a8JQWWrT21ravceLxbmSe4hSe CDbJYqwmeGRHERAcq6nbg15B8L7C98SfAX4MS2sd2W8K6Np+o2skMsajzjYy wsrB85QxXMgOMHkEEEVmeK/2dLjxxeadPqtx4ju3sfE1l4sk3XNkBe31raW9 rG0gEeNpS3BZU2gmR8YG0LenUhXPb9K8Y+CdcsrW5svE3gO7gvpYYLeSDx1F Is8k0kscMaEWeGaR4JlUDlmicDJUgQS/EDwKl1pcSeJPBtwdav7bTLNrbxgJ 0kuLmB7i3jLJYlUM0UbNHuIEmVCks6BvLH+Ds/2Zoo7S+QStrPnbpLWZbhNV na4uY5EkVo3USFSuVyBGqsWUurweFfgbN4P1PwxeWkOsGfwlp+nadYmS7tyD HZW1/bRFwAMlo9RuN2MAkIQFwciaCzPUPEXxR8G+ENZ8SWOqzwWEnhKGSXU5 Jb++FvGUsxfPFHN/ZnlzzC1PnGGFnk2Bm2/KcbOj6z4W8RXthb6drXgy/n1V mWyjt/Gscr3bLCk5EYWzJciGWOT5c/JIrfdINeK+PvgZN8SItdj1GLVxF4hv Lm/uUiurdQs0+kf2QzISCRi23bQcjexJBGAKsn7O6XPgKLw2bPUjpKaDZeG5 YmuLZ/tFtaS+ZC5ypCzAtIfMj2NmXcMOkTIXQWZ7Jql9p2rWFpe6Td6Vf6de IZIbnTtUGo28uHdG2yiONThlKkBTgqeSeBjr/wAhBP8AfH9Kk0afUJPD9vHq TSvcW4EQeXyt8ihR8xEYCgk54AA9ABVa8vodMka4uJYoLeD95LLK4RIlGCWZ jwABk5PpX03DnxVf8Ev0POx32fVHM/tF+KL3wRoWka1pmj3HiHVNJuL28s9L t3KS6nNHpOoOluhwcNIyhQcHluleb/Ej9v3QvBXw+vfE+gaz4C8eaTDpmtan Zva62bB9ZGm6ctzLHpvyTx6gBLvWaWFwlsNyt5kkMi16prHxN8C61qOlXUvj Dw6r6PdNdwhdVt9rs0EsJDZY5G2Zjxg5A54wb/8Awu7wX/0N3hn/AMGtv/8A FV8yl3PTujy7w7+2NqGpSSW+peH/AAtoN/aeK4vCt3bX3ixI5BMYpTIkUbW4 ledmh8+CF44xcWNxbXIkQu8EXnXhz/gpjq3io6Bf23hLw/cWd7HpeoX2j6Nr 39teIxa3nhPUNcEf2SOKMW87XFrHbQLK7NcgSOEiDRk/Ruo/FfwDqxgN14l8 I3P2aVZ4fN1G2fypFOVdctwwOCCORirP/C7vBf8A0N3hn/wa2/8A8VQuugXR Q+DHxaf4p+CdN1ueDQra21Gwiuo5tN1qPVbO5ZmkBe3nRFWa2ZUWSKX5XdJB vihYFB6BAk0ng7xLcWFrY399b6aZLKO5n8qB5xPCFVpVRzGCTtLqjFQzfK3Q 8d/wu7wX/wBDd4Z/8Gtv/wDFVT1z4p+CPEFgbaXxtpUETEM32PxILNnwcgM0 Mqsy552k4yFOMqKGtbr9RX0J/CGo6jc/EXU4NRENtcLoWmTzWdvctPbWs7y3 okEbMqFhlAu8opYIuVGMDN+J+r6joPxD0a70rTn1a8g0bUHFpHt825jF1p3m Rxl3RBIybghd1TcRuIXJDPC3i/4d+Dru7uLPxdo8lxfJHHNNeeJTeSMqFyqh pZmIAMjnAx941oT/ABU8BXGtW+ov4p8MG8tYZLeKT+14RtjkaNnXAfHJij5x kbeCMnI0wjtZnzp4L/b0+NPiXwt4SjH7O2tf25rGh2Wo38uqXF9o1lazTXdr p7xuBZXLwSLcyXNw0DlmjsIY5yzO7wRZXx4/4KK/GHwj8R/C2j+Fv2evGt9o lxr32HXtZfSL+9jhgt7uWC8SKKOKNxuhNpcW92d8MqzyKFLwSon1V/wu7wX/ ANDb4Y/8GsH/AMXR/wALu8F/9Db4Y/8ABrB/8XVXV72K0PIfAX7ZPi3xLL4h i8TfCXXvh5a+H/Cs2sTeINau1GhRanbBPt1i0xRJPJh86Ipd+WIrpYrp4d0c KyS+x/DuDxPHDqz+J9R0TUJJtRnfTV07SZdPNpZ5xFDN5lxP50owS0y+UrZG IkxzF/wu7wX/ANDb4Y/8GsH/AMXR/wALu8F/9Db4Y/8ABrB/8XS+Qv6/D+md T4l/tK2+E+s3Oh6ZpOqa1He2cdtDqN61jA6sJ94adIZnQbRu4jbJRQcD5hzn gm8mufEvi+KSaWSO11eOKFXcsIkNhZvtUdhuZjgd2J6k1i+JfG3wx8ZeR/a+ reBNU+zbvJ+13drP5W7Gdu5jjOBnHXAqXw98Rvhz4SsWttK1zwTpkDv5jR2l 7awIzYA3FVYDOABn0A9KXSxPU7eiuE8R/HjRbUwjSNf8CX2c+d9s8Sx2mzpt 27Y5d2ec5xjA654zP+Ggv+oh8L//AAtP/uWlyMrmRwHxO/4KQeGf2d/GU+if EbTb/TLl9TdIJPDtnqGvxWWlMxit9Sv3jtEW2EtyFtvLiM+2aWNN7YlMXP2H /BXLwZN4b8Q6leeAfirYnQPEVz4NFlHptlqV7qevwQyyyaVbQ2V3O0k/7sIH IWEtIuJcBivrx/aA/wCoh8LwD1/4rT/7l61U0T4xWPhuye209/hLY28k8100 dv4uWJHmmkaWaQhbUAvJK7uzdWZ2JJJJNJaaofMrnG/sz/8ABSvwh+1n8Y7D wt4T0LxMllqfh/UPEtrq2pRQ20V5ZW2oRaek8UQkaUJLObpQsyxSr9lJaILI jH6MrzAftA4/5f8A4X/+Fp/9y0v/AA0F/wBRD4X/APhaf/ctEo9hXR0vgD/k a/HH/Ybi/wDTdY18y+Fv+RB/Yj/7Ctr/AOoPrle8fD74oaBptxrt5qvinwTb 3Gtait2sVpr0VwkSrbW8ABdghJJhJ+7xnr1rwfwt/wAiD+xH/wBhW1/9QfXK +jyP/dsT/hR52N+On6n2x+yNcL/w1J4ntzC5aXwnYuJg4CxgXt2Cu3GSTuBB yMbTwc5H1PXzF+x+If8Aho/xaWb/AEj/AIRnTQgx1X7Xfbufrt/P619O15Ee p0SCiiirJCiiigD84v28/wDg58+Af/BPD9q/xX8HfGvhL4vap4m8HmzF7c6J penTWMv2mzgvI/Laa+ikOI7hA2YxhgwGRgnyOP8A4PSP2XJmAXwB8f2JwBjQ 9I6noP8AkJ1+QH/B0KrN/wAF1vjltwSW0AfX/in9M4/z6V8/fsy/BfydPk8S araQOY0M9kbhCYbZFxuuXBHPzMqRg8Fzz0wQD+jG3/4Oy/gVdQWcifCn9oDF 9EJ4lOmaGHEZGQ7p/au6MEcguBkcjjmqNx/wd3fs+2UM0lx8MPj7bJbkhzNp mhxjj03aqN3/AAHPSv59vFfxTXTr+/VohcRNvE9zdZJupVVQHfBC8nHyDKp9 35todu38CfspeJf2h/hZqHi5NQmtZrmI39tbTwEiQljln7Df2ODwQcA9QD9y rX/g8A/Z6vomeD4WftEXATlvK0LSJNowDnjUzxg9e/Poabo3/B4L+zn4iLiz +G/x6laN/LZTpmiIVbBOCG1UEcKf8kV+YP8AwTB+EWl674J1HTdZ08PrFjc3 EV+Z0/eSSLvIOTztChABwMdsVyf7Vf7L8Xgjx5oOpaZZ6faatrF/Npb5jAjm MsD4ldVAyVK7unQ+9deCoRrVeSW1m/uTf6Ck7I/WO6/4PHP2arIuJfh/8dVe NthQafoZbPsBquSPccVf0n/g72/Z511ytp8M/j5KRyP+JZoagjuQTqoBA9R6 GvzC8F/8EvvDmj/DHU9U1mKPV72/QyNcSxhnXIbJTGNi5OR+HJwK+dvhx/wT 28S6xpi31pr7I4upLaOH7IzLtR5EUBgec7ScYwMgHNcgz9zj/wAHb3wEUHPw r/aCGOoOlaICMng/8hXoeoPQjnpVbx9/wd7fs9/DA2n9ufC/9oOyjv0320w0 bRpobgYBOySPVGRsblyAeM81+Afxo8Pax8E/F40TWWlk1G0Cx2ctsSk8kWcq SpAHylSCCCDt5BGQeo1mw0n4g6LJ4e1RftemXaM9pcRAFrKVW+aSHecq64Yt Fkb1DHjYSoB+2Lf8HqH7LSDnwD8f+f8AqB6R/wDLOvr79rr/AILU/Cz9i/8A YG+GH7RfinQfH994J+LH9lf2RZaXY2kuq2/9o6dLqEHnxyXMcS7YYWV9kr4c gDcMsP47vi18LNQ+EniqTSNQ8qRo4xJDPb5aG5jOSJEb+JffseK/dP8A4L6Y P/BrT+xZnJH/ABQ/T/sUb6gD6Ab/AIPUf2Wk6+Afj+AemdD0n/5ZUrf8Hp37 Latj/hAP2gMnoP7D0nn/AMqdfzFWVrLfXqJtd3dwNuMscnjA79a+pvhX+xzp Xwx0fTvEHxFjGq6zdiFtO8KwSiJgrN1vGBEke3BygAbuWGMEA/oD+GP/AAdj fA/4x2lzc+G/hD+0TqNnYsFurs6NosFtbMQSFeWTVFRSccBmBPaqGo/8Hd/7 PWlalcWk3w1+PHn2ozKI7DQZFQd/mXVivHPfsfQ1+J83ju6vLH+yNN002emQ SyhdD0mMw2Wn5kJzcSD5iXChiZDyAM7u/nHirXvBnhPxQj393Z6pqsrB0tbE htOt3wSMkkmQgkHOOpJAHFAH78r/AMHdX7Pz2Mdz/wAKw+PywS7vLd9M0NfM x1251XLfh6j1p+mf8HcPwD1lFa2+Ff7QUqt0xpOiDPOO+q1/OX8Ufi5qWp61 CwMMcRBMISPBkHG/aAOMknGMHoan+HWva74l15FN/JozXR2ReZKIkVjkKFBz x06f/WoA/o2n/wCDsj4E2piEnwr+PqmV/LTOnaFktjOMf2rnpViX/g6z+CsU Ujf8Kg/aGcxjcUTStDeT8EXVSx/AV/Ofbap4w8IeNbixF+2p3Nq6gKilxNyM YH0ODjniu2tfiF4o8MapZ6nqy3lvZ6gVM9peK7NEmNpeNjgrzg4zkcZJBxXV jqEaOJqUYbRk19zsTB3imz93dX/4O/8A9nrw/cpBf/Cv9ouyuJOVin0DSIpD xn7ramD0rZ8Ff8HYPwM+It5DbaJ8K/j3qN1c2z3cNvHZaAJpkVmUqqHVgzSb lIEQHmHK4U7lz+I+txQeO/DrXbww+ILNsnyrlS09t90ZyDvjKhR8yHuRyM14 74hvNV/Z/wDGEOueHZ9Q/sS4dZkeQb5tMux8ytFIuG3KQNuCpdSVYkMxrlKP 3ln/AOD0j9l22lKP8P8A4/q6sVZTomkAqQcEH/iZ8c01/wDg9P8A2WYyM+Av j8M8j/iSaR/8s6/CH9rr4ZaR8WPB9x8VfCGjro5F1HbeK9PhXbbR3M7Myalb qABFZzurJtKqscgVBnzAqfNaW6h3bAkWL1z83p/+qgD+v/8A4Jg/8HCnwX/4 Kx/H3V/hz8OfDHxP0bW9F8PzeI55/EenWNtatbxXFtbsqtBeTOZC91GQCgGA 3zAgA/d1fzB/8GVsZi/4KoeP1PBHwq1EH/wb6PX9PlABRRRQAUUUUAfOg8FW niL40/FCefG5PEFtGM33kcDRtNP3fJf165/DudP/AIVZp3ov/g3/APuWuS8R +EfGfiD48fE6bw9pT31kmu2qPILmGLEn9j6cSMO4PQqc4xzT/wDhWnxQ/wCh ek/8D7b/AOOVxThJy0RvFxtqzqv+FWad6L/4N/8A7lo/4VZp3ov/AIN//uWu V/4Vp8UP+hek/wDA+2/+OUf8K0+KH/QvSf8Agfbf/HKnkn2K5o9zqv8AhVmn ei/+Df8A+5aP+FWad6L/AODf/wC5a5X/AIVp8UP+hek/8D7b/wCOUf8ACtPi h/0L0n/gfbf/AByjkn2Dmj3Oq/4VZp3ov/g3/wDuWj/hVmnei/8Ag3/+5a5X /hWnxQ/6F6T/AMD7b/45R/wrT4of9C9J/wCB9t/8co5J9g5o9zqv+FWad6L/ AODf/wC5aP8AhVmnei/+Df8A+5a5X/hWnxQ/6F6T/wAD7b/45R/wrT4of9C9 J/4H23/xyjkn2Dmj3Oq/4VZp3ov/AIN//uWj/hVmnei/+Df/AO5a5X/hWnxQ /wChek/8D7b/AOOUf8K0+KH/AEL0n/gfbf8Axyjkn2Dmj3Oq/wCFWad6L/4N /wD7lo/4VZp3ov8A4N//ALlrlf8AhWnxQ/6F6T/wPtv/AI5R/wAK0+KH/QvS f+B9t/8AHKOSfYOaPc6r/hVmnei/+Df/AO5aP+FWad6L/wCDf/7lrlf+FafF D/oXpP8AwPtv/jlH/CtPih/0L0n/AIH23/xyjkn2Dmj3Oq/4VZp3ov8A4N// ALlo/wCFWad6L/4N/wD7lrlf+FafFD/oXpP/AAPtv/jlH/CtPih/0L0n/gfb f/HKOSfYOaPc6r/hVmnei/8Ag3/+5aP+FWad6L/4N/8A7lrlf+FafFD/AKF6 T/wPtv8A45R/wrT4of8AQvSf+B9t/wDHKOSfYOaPc6r/AIVZp3ov/g3/APuW j/hVmnei/wDg3/8AuWuV/wCFafFD/oXpP/A+2/8AjlH/AArT4of9C9J/4H23 /wAco5J9g5o9zqv+FWad6L/4N/8A7lo/4VZp3ov/AIN//uWuV/4Vp8UP+hek /wDA+2/+OUf8K0+KH/QvSf8Agfbf/HKOSfYOaPc6r/hVmnei/wDg3/8AuWj/ AIVZp3ov/g3/APuWuV/4Vp8UP+hek/8AA+2/+OUf8K0+KH/QvSf+B9t/8co5 J9g5o9zqv+FWad6L/wCDf/7lo/4VZp3ov/g3/wDuWuV/4Vp8UP8AoXpP/A+2 /wDjlH/CtPih/wBC9J/4H23/AMco5J9g5o9zqv8AhVmnei/+Df8A+5aw9Sj8 E6R40t/Dd14h0G28RXfleRpcviaJL2bzY7qSIJCbfe29LG+ZcD5ls7gjIifb R/4Vp8UP+hek/wDA+2/+OV49p/8AwS20jRf2iIvixp3wV8MaV8RY7hrw67p7 2tpcyzvHeRSzSeVKokklW/ufNZgTKTE0hcwQmMUJdUHNHoz1Lxv8TPhH8NPi Vo/gzxH8Q/Afh7xh4h8j+ytC1Pxra2ep6n58phh8i2kgEsvmSq0a7FO51ZRk jFXvjB4j+G/7PfhqDWfH3jHwl4H0e6ulsob7xB4tt9MtpZ2R3WJZJoFUuUjk YKDkhGOMA0H4ZfFDjb4dkBHc39sT/wCjK4r9oH9inXv2pvhvdeD/AB94FfxD 4Yvt32rTZNZSGC6zFJGPMEU679nmeYm7PlzRwzJtlhikR8j7C5l3PUfEnhXw 54P06O71W/sNNtZbm3so5bvXkgje4uJkgt4QzWwBklmkjiRervIqgEkCuZ+J GteGPB3wS8VeNdOktvElj4Xt795I9O8Qo/m3Fm8kU9vvFqQrxzwyRODkxvG4 I3KVrC+NP7Lfj/48fCvXPButaJqEeheJrVtO1WK21GzR72ykIW4tSzMSqTw+ ZC7JtkVJWMbxyBJF4Tx7+zd4p/Zv/YU8S+FRpWoQeEfC3hK/0+wN1f207afZ Jbypa2oMZDPHCnlQIzh5GSNTI8jl5GFB9UHMtrnB3XxU8c/DD9kDwcfAPh7w /wCJte1LwC8NlZa5dvY2Ml2unwtbhrpQDDIWDbAXiEh+TzYN3nxeveGviDL4 u+JevWcdpaW1hZ6dpl0Vs7tr60t7yaOUXNvBctFEZokaJSGZFcmRiQoKxpyf 7PGtix/Zl8I6nqninRfDemwW2kaFbm50rzvOnlsdO8pTI13Cu+Wa9jijQKSz FQMlgK6L4nfFXwn8E/DkeseM/i54L8I6TNdiwjvda0yGwt5LgqzCESS6mqmQ qjsFBzhGOODXj/2XNYx4lVNG72/7dUbXva32rWvdvU6/rEfZKHLrtf5t/wDA 32R1mueLfhJeeCl0nWviTpnh/V/NYTOuu29tcRyrNkxv86lVG3ZgbG2Z3MWO +vPfi38U7b4Z/A5/GWu2keo/2Nod/qrWiWcVy13dJpF3JDDDBMSkszziNYo2 J3OyL3rqPBOvad8TPDNtrfhv4haD4h0W93/Z9Q0zQEu7W42OyPskj1Iq211Z Tg8FSOoNax0K7x/yNVtycYPhY8/+VGpyPLa+BrSqYis6l9ult/N9/wAB43Eq tT9lCPKrt9+3kn07nxV/wS6/av8AjR8cfi34msfiz4I8L6PpupaHFf8AhXWf C2nQWFnf21tdzLI+o2iXUsljeyreW3lQzQxO0dtMGBaEk/XnwSubCx0GS41W Iz6Xb6/qkl5ELdrnzIRqdwXXylVmkyoI2BSWzgAk4rZ/sK7H/M02/wD4Sp/+ WNVdG8EPoFm1va+JbeKJ55bhh/wi7tmSWRpZDzqJ6u7HHQZ4wK96dbmqKp6d exwxp2hyHGfAnxneeMtI183UentHpurfZLa7sdJ/sy3v4/ssEpkWL7PA3Dyv GWZMkxdSAMda0YlvVVs7WcA49OK0JbOWytgsuoJqTZ/1i6f9i28fd2edNn/e 3DrjAxk0F/5CCf74/pX0nD1R+1q1FvySf5HmYim4U4wk76+nV/podFp3wov9 Wt5JILe4KpJFECy7FcyFQuGK7erx8E5w4IBGSI734az2COXVmaJ545UR1LxN CR5i4/iKhgx25G0k5+VsXvDWnWl5HqM16lzJDY2omEcMqxM5M0ceNxVsD5ye nYU/7boP/QN1f/wZR/8AxivH/trG/wDPx/ed/wBUo3+Exh4Ify0ZiIzK6Iiy zxxO28Aq21iGCEEfORt96fY/D261K0jnhgnkhllaBHDLhpAoby/98gjC9WPA yeK2W1XRmtlh/s/WmijYuqDU4yAzYBIHkdThfyFM+26D/wBA3V//AAZR/wDx ij+2sb/z8f3h9Uo/yoyV+H1w+nxXQhnMNxFJNCcrmVEba5UdSVIJIHIUbsY5 qa/+F15psW+WLEeSvmLPG0efKEoG4HaSUYEc/N2yRitAXugszAabq/y5z/xM o+3/AGwoF7oOf+Qdqy46/wDEyj/+MUf21jf+fr+8PqlL+UxrLwPJqMttHFHM Xu5EigVmVDKXLKpUHGVyrDcOARgkcVZb4XXkesNYPEyXUbSK6PPEnl+Wm9ix YgBdvIY8EdCa0ReaCc/8S3V+Bn/kJR//ABik+26D/wBA3V//AAZR/wDxihZ3 jetR/eH1Sl/KjFXwLcFZSba6UwCQyBgFZTHjzFwQMsuQWA5AySMAmpL/AMBn TL37NPNElwFLlPPQgL5ayKwcfIQytxtJJIxjkZ12u9BXH/Et1jB5B/tGPn/y BTWvNBI/5Bur/wDgyj/+MULOsb/z9f3h9Uo/ymZF8N7y4MQhtbqbz4/Mh8va 32gDbkR4++V3DcFyVw2QNpw228ASzx3bMs0LWgfckg2s5QqJFUY5ZA25h/Co JPSuU/Z+8fXHxU+CPgfxRqsEMd34i0LT9UvIrQmONZJrdJXWPcWKqC5AyWIH XNeg/bdB/wCgbq//AIMo/wD4xR/beN/5+P7w+qUf5UYEXhPztPW6USmFn8rO 5flbGcMOoyMkZHzYbHQ4i/sKH+9J+Y/wrpPtug/9A3V//BlH/wDGKPtug/8A QN1f/wAGUf8A8Ypf21jv+frGsJR/lOb/ALCh/vSfmP8ACj+wof70n5j/AArp GvtAGMabq7ZOONRj9/8AphSrd6C2f+JbrGRzgajGf/aFH9tY7/n6w+q0f5Tm v7Ch/vSfmP8ACj+wof70n5j/AArpPtug/wDQN1f/AMGUf/xij7boP/QN1f8A 8GUf/wAYo/trHf8AP1h9Uo/ynN/2FD/ek/Mf4Uf2FD/ek/Mf4V0n23Qf+gbq /wD4Mo//AIxR9t0H/oG6v/4Mo/8A4xR/bWO/5+sPqtH+U5v+xIgD80nPHb/C vmLXPHF38TZP2QfEt/Hbw33iHxMup3MdurLCks3gvXpHCBixCgsQASTgDknm vpzQfFNv4ws7i8tbSaygjvrqzWKW4E7fuLiSDcWCJ94x7sY43Ac4yfkrwt/y IP7Ef/YVtf8A1B9cr3Mqx2IxGGxKrycrR6nFiaUIVKbgran27+yD/wAnPeKf +xVsP/Sy8r6ir5O/ZL8Q6fa/tga/pcuqWMOqXngy2u7fTnZRc3UMV/Oss6De G8uJpoVchGANxHlkJUP9Y18/DqdkgoooqyQooooA/k5/4OFvhi/xR/4ODPjT ZFZhaQtoM106AnZEvh3TWfAAJLEAgAA5JFeN/Ga+Sx1Ox+H/AIY8/UdRuHSX xA9rGzLBJESv2aMKxBghIVEcglgZnDFWQj7R/wCCucaeFv8AgvL+0J4nmCp/ Zg0a6trhlzHbT2nhzSJ4vM4Pyvcm1TAwSGbtyPkr9g3T7TX/ABH4j1Rb4aV4 q1CcFJTHvjtmbayoFJwQGYe2APXJAPXfAv7KFh4C+GutanFYW15pet6CFmVo yfLlRZVIjjblQdxJHfbnrzXtP7AHhaC5+EXhqaOZJIRpdsrDeP3itF7gDrn8 M98V43rmq/FOP4pTxa1r2kTtpkAEVv8AZGRNUjfdwx3fKRt4x355Brsv2Jjr ll8TNfit7Sey8NIpZYp1XdbzFyWCEElkLEkZ6AnoMAAH1P4Y+FumfDfxBqms acsdu9+ofYRwCFO4YHHYfga+FP8AgoD8RNX8TLZXFtfSwXsWrqtmA20wP5My pg469O5619N/tTftB6v8EfAL3GmaXdaxeTSeUkUIXgnaM/MQOjHv2r4b+Psf i7xjeeH72U20V9c3ovLexKZSRkDS7mPU/cYDoMOeK9HKv94+Uv8A0lkz2Psf 9g/Wta1/wLdaR4nvl1vZaRmeQqVVXZdrAcDgY6HvzXpXjDwzpfwl8HGz8OWR +03cxRVfMhLnLE5PTqce9fLn7E37ZviO617/AIRfW/DQt1RxE00WOJCcZ68j n3FfXvjC6u9T0SX+z44n1KFSbfe2F3HGCT07en6V5xR8R/GL4Iv8Sv2mPDKX uljUDFG014pwVhXd8pzzgnewAz1B44OPHP2pfgJ4i/Zf8SX+tO9vbaFqGoCQ pFMQyRMGYOP7rbuuRnnoTivrTwV4vvdL+KeoWd7E02tyKZtTnRQIoUDDaik5 6jnIHQZ44FeAfHC+v/2oPEcweSGLQLK+dpbclvPuHRyN4I4wpBGAMEMfagDy n4z+DV+NfwWt9Xjj02C8g+0PYGKQsyNEFaS3C5ZgsivG6g8B94U9c/qH/wAF 8Yyn/Brb+xavOUPgf26eEb6vzn+Cmny6LonxM8ApeWywaX9n8XaUsyM8148C NFJbh1BKDy5ZSOm54o8kZzX6/wD/AAV18AadL/wQD/ZItdQhbVLXw1L4QnWy Uf8AISaPw1eRqhIHyrlhIemVjI4zmgD8Sfgb4R039m7wXpniC9tBcfEnWo1u dJilUMuiwsR5UhUsAZZAS4JDbAEIKncV7zWbTQfg54Y/4STxXqE1/q2sQJIs TZEySMpJh2kDaCW5yeAW4B+UbHwl09ZrzVPHetC2vtSuLiSOziclxvUlzKwy NqowRhx82Op+YH5n+M/jq1+JGsNqFyWld5NsHOwKu8YI7H06dDmgCT4g/F7V viDNc6RYxx22lGMyQ2sCCKOIBFZjhT8xIwcnOevBNcHDq76fDJHFEmNyjzAu XQAjgHHHof8AOdfUPB9/pGk23iFLm3kS64Kt8hGMKePTGOf8nI0zw/Hr93aQ 6d5kt5qExiaR2UIpUZYgdeAQfTpigDSg1mSa7spbiVIpbUkpLIT8qk55XPbk 9Me1Xf8AhH5bvSbXVBeRGPATEcg/cnpz+OfQVNL8MJ7O6ktbEC5vIogtzcOf 3Nsc+nc8Z9OlP0T4La03hYXXmWtvZzqsgBkLO7ZxH8oGOS3qPvUAddqt5qmj anYa1aNPLIiR7rhF3DcvTn6U/wAWeMde+LOkwC4Es96p37IQQyLnPI9iP/Hq sSfCzxV4DiENnq0N1G0RaaF4xtjfHGD/ALvP1zXWeDWuvA3gvTdTe3xNdSwz SMzcMz9Uxz0B7+hr0M2/36t/jl+bJp/AjA+HOo3+k3VhIl1cW7QJkMzFDznD A5+o/E16Hd29j4+024hu1jdbtHF5AcKs0ef9YOuCCQeOhGR0rA1GSWx1ezJ0 0tCsBhAZwQxyx4I7g5/MV2ngq51GySe5htY1s/MO5GPzPnG5c/l0rzyjx3wf rmr/ALKni64kitrfU9Pvo5La7stQhWaz1qwfAMciqRnlEYgndG6RsuCQT5d+ 0L8OrH4e+PXuNAaS68G66p1HQZ55g8jWzOcRSMAB50LAxyADG6MsuVZSfpLx j4P0bxzaXukpdpC9tdcGVCxs5VOMgDBZCrMhwehzgkLXluiQWWradqnw88R2 8enrLdTR2txLGWfR79RHG0oZT0byY1lBDbkUlf3gWgD74/4MtEC/8FUfH5VS qN8KtRx6H/ib6N0r+nmv5kf+DM/Rrrw3/wAFZviRpt6jRXen/DDU7aaMkN5c iaxo6suQSDgjHU1/TdQAUUUUAFFFFAHAfBv/AJKL8WP+xqg/9MelV39eLeD/ AIfat4n+LPxUuNP8b+J/DUKeJbeNrbToNOkikb+xdLPmE3NrM+4ggcMFwo4B yT1P/Cm/En/RWfH/AP4B6J/8r6SA9Aorz/8A4U34k/6Kz4//APAPRP8A5X0f 8Kb8Sf8ARWfH/wD4B6J/8r6YHoFFef8A/Cm/En/RWfH/AP4B6J/8r6P+FN+J P+is+P8A/wAA9E/+V9AHoFFef/8ACm/En/RWfH//AIB6J/8AK+j/AIU34k/6 Kz4//wDAPRP/AJX0AegUV5//AMKb8Sf9FZ8f/wDgHon/AMr6P+FN+JP+is+P /wDwD0T/AOV9AHoFFef/APCm/En/AEVnx/8A+Aeif/K+j/hTfiT/AKKz4/8A /APRP/lfQB6BRXn/APwpvxJ/0Vnx/wD+Aeif/K+j/hTfiT/orPj/AP8AAPRP /lfQB6BRXn//AApvxJ/0Vnx//wCAeif/ACvo/wCFN+JP+is+P/8AwD0T/wCV 9AHoFFef/wDCm/En/RWfH/8A4B6J/wDK+j/hTfiT/orPj/8A8A9E/wDlfQB6 BRXn/wDwpvxJ/wBFZ8f/APgHon/yvo/4U34k/wCis+P/APwD0T/5X0AegUV5 /wD8Kb8Sf9FZ8f8A/gHon/yvo/4U34k/6Kz4/wD/AAD0T/5X0AegUV5//wAK b8Sf9FZ8f/8AgHon/wAr6P8AhTfiT/orPj//AMA9E/8AlfQB6BRXn/8Awpvx J/0Vnx//AOAeif8Ayvo/4U34k/6Kz4//APAPRP8A5X0AegUV5/8A8Kb8Sf8A RWfH/wD4B6J/8r6P+FN+JP8AorPj/wD8A9E/+V9AHoFFef8A/Cm/En/RWfH/ AP4B6J/8r6P+FN+JP+is+P8A/wAA9E/+V9AHoFFef/8ACm/En/RWfH//AIB6 J/8AK+j/AIU34k/6Kz4//wDAPRP/AJX0Ad9IcAehNfDfidvjq37Dvx9/4Xd2 8OY8LeT/AGf/AMgzy59n9pfZv+Y/0+3/AGb/AIlf/Hr9i/5ea+pv+FN+JP8A orPj/wD8A9E/+V9eN/t9eB9e8H/smeOJ7n4g+LddtbjRb+CayvrbS0hmX7HO wy0FnHICGVSNrjpzkZBmew1ufMOh/CvXvjD+xF4Q0fQPFfxA8GXAuoZpdS8J aCdWuXT/AIRvTohE+bedI1LypIGKhyYfkZGG9e9+IHwlsvij4di0vV9J+MVv bQ37agr6Hp3iXQbguUdApnsRDKyYckoWKMQCVyqkfFnjFp/Auo+GfHbeGPCn ibSPC1p4Gk1mwvLWyl1bVrf+y9QY2Fql38k4mjW4BgAMjSi3aPDJuH3v+zX4 18SeLfBPwdvfD3gvT/h1FF8T7+DxfpXhTQZrLThbp4Z1QMbuKBijr9qFoglL vEZUttrOVTPBSqxqTqUrNctvnddP69D38dlM8NgMNjeeMo11PRbxcG4tSXmn devkfMH7NX/BLbwz8EP2ar/wD4g0XxR4+1DUbeex/t3VPhVczS2do8v2iK0h huLadDbxXQF0sM3mxG4aSQod2B9UXF1cXF/o90dA+ISSaNex38ajwdqhjlkj B2b1a2IYK5WQA9HjRs5Wun8Z/Gj4t+LvCHh1f7N1PQfENx4i8LyatoeneEtT L6VC+peF31GH+1SxtbmGKG91ISOkarLE8irtbTrwv6V+zt8bvHfxR+LXiCw8 Q+EZvD/hWDwh4a13Rr250+6s7y6u78agb61uUkBihntvs9sGto5Zmi81WeT9 6qJ2yo31bPDdVvVnjdx4inmupJF8LePIkckiNfB+rFY+ScDNuTjnHJJ46mm/ 2/cf9C14+/8ACP1X/wCR6unwP46b4h6hL4rk+I8eg/ES813T/Eb6Lrers1ms XiWysvDv2FLeY/2Yj6VPO9xPYrBuVJLieQNEZV0Phd4f+IWofHLw74shbxwN Cvtbg8P241S/1GJYvCR8K/bI7i5025cRJqP9uAI9zNbi8AJhZhH8lQsPFq9/ 6/r8Buq07GOt497ab3sdXsDuwI9R024sJW46hJkRivbcBjIIzkHFNf8AkIJ/ vj+lej/H6OeHXtNS5knmnSwVWkmwHkxLMAxAAAz14AHPFecL/wAhBP8AfH9K +gyCPLOrH+5L9DhxruovzR2Hhn/kD6//ANeCf+lVvWPVLxP8MrX4p+FtQs72 /wBesLeyRLtv7J1m90qWf96kYRpbWaJymZN20sVJRSVyFI4T/hlXwx/0FPiR /wCHD8Qf/JtfKaW1PUF+NXgrxT4n8X+EZvD9rpDWVpeKdWv7nVTbXFnarc2s 5SK2ayuYbnzDbhW3NbyIFBimjZt6+L/s8/s8fG3RvgN4MsfEPiS6svFKaZff adTvfGN9qlx4UvJ9NihjkMDRtDrRN2sty0N1IIrU3Bit3ljijJ9n/wCGVfDH /QU+JH/hw/EH/wAm0f8ADKvhj/oKfEj/AMOH4g/+TacWkrIG29zzb4k/sz/E PxBdeCtX8O65D4R17w74PuvDvmDWbvxFLZXF1qGjTsRPeCN7yPydPuEeaYrM Q6kIzHA7T49fAPWfiN48Gu6PefZXh8AeI/CyPFrU+l3kd1fyabJbyQ3McUpt sfYpN06Kzo3ksEk24Gr/AMMq+GP+gp8SP/Dh+IP/AJNo/wCGVfDH/QU+JH/h w/EH/wAm0N369/x/4cd3e55v4X/ZM8VWPw41/Try70iHVfEPjXwl4pZP7Zvt VjtotMi0BbmE3Nynn3En/EpuNksgDTGSNpCjM+2cfAf4o/8ACBfZxfQEto32 N/C5+IGr+Ubwan5wu18QfZ/7QXdbEqU8kgECIfuxvr0H/hlXwx/0FPiR/wCH D8Qf/JtH/DKvhj/oKfEj/wAOH4g/+TafN5/1awuli/8As9fDa++FPgK+0rUJ 7S4uLnxHr2sK9u7MvlX2sXl7EDuVSHEVwgYYwGDAFgAx7k9K83/4ZV8Mf9BT 4kf+HD8Qf/JtNf8AZU8MnH/E1+JIGe3xD8Qf/JtLRvcLsd+xv/yaJ8K/+xP0 j/0iir0iuA+AQ0rxx+y34KXT9OutB0PV/CtgLWxttRm8/TbaWzj2wpdKUm3I hCiUFXyu4EGq3/DKvhj/AKCnxI/8OH4g/wDk2lZdQPSKK83/AOGVfDH/AEFP iR/4cPxB/wDJtH/DKvhj/oKfEj/w4fiD/wCTaLLuF2VPjR8EvGnjbxcNY8K/ EnU/CW3TprOPTjpVhfWKTvbXccV8PMg+0CaKaeF9ouBE6QGNo/nZq89vf2aP iYU8KPZa/fQJolvcTX2nz/ETVpvt0lzpV9ZtYNdC3WR4YJl0lobt0Nxn7ZO4 Nwd03pv/AAyr4Y/6CnxI/wDDh+IP/k2j/hlXwx/0FPiR/wCHD8Qf/JtVfzHd 3ucz8Ofgp8UNG+LWi63f+O1h8H29lbw3XhISSaqsDjT0hdBqMyrPc7blBIJp UWSQM+9QWr26vN/+GVfDH/QU+JH/AIcPxB/8m0f8Mq+GP+gp8SP/AA4fiD/5 NpOzJWh6RRXm/wDwyr4Y/wCgp8SP/Dh+IP8A5No/4ZV8Mf8AQU+JH/hw/EH/ AMm0rLuO7Og+EX/Ip3f/AGG9W/8ATlc18yeFv+RB/Yj/AOwra/8AqD65X0v8 FtK07QvAC2OlW97b2dlqOoQAXeoz6hPK63s6ySPPOzSOXcM/zMcbsZIAr5o8 Lf8AIg/sR/8AYVtf/UH1yvpsi/3fFf4Uedjfjp+p9s/sjSH/AIaj8Tx+VcMr +FLEmRYXMS4vLv5WfG1WOchSQWCsQCFbH1NXzn+xr/yWHx3/ANgTRf8A0o1W voyvIj1N2FFFFWIKKKKAP5kv+DhjxQvhb9u39qu2ALXHiTXfDNsjbeIIbbw/ otxKQfVpPso28EhT12nHzT+zr+yV4onvIvGdhfrNfOEuJLW6uIogvygqnmu4 JbGM4yORxxXr3/ByPqgk/wCCsHxz0+SeZ3Ou6KLOHJZIGl8L6QJZAgHVhHEp JySFAHIrzJ/EOm6z4dtLq9k1bTHS3TbBJBIgjyFJJKg85PccY713U6OFcU51 Gn/hv+PMTeXRH2n4A+Cf/C3fBVndeN9Jm0bU9PHlR3FtfK+5VI+YsMg+nXni vTr3RrbwdpcUWnLALUD5ZFKZfjqcDknPXvk18H+FPjzb+CfhdrumafqGrX4m hLSyozSra/L6AZB74r079hvUtcg+H11aazrVjq7TSR+WIXb9wXLnlmJOOOOB +NX7DB/8/X/4B/8AbB73Y9K+N/7S/hL4bvYw+J9Pe40WZwt9fbfNjsSxwrMg UkjdtzjOB14rxz4+Wesa98c/hz4p8K6Jpnifw1cXTrYXWmTJKl432V2EZjDE 8pHMx46RGvZvHdvK2lbTHH5ewjG3dnPJAPp7V83eL9a1fwP4v8OP4VS206ST XVlfEBA3x2V1GWHQY8uSbI6k45HJPfldHCLEfxXtL7P91/3iZuVtj2bxX8f/ AAR8ItU0i1n8P6daeNtddY5bKx/10Sk8vIwBCcgHGQTg17hoHiKLVxFc2/7s TIpGDjaeOp9j/Kvnv4cStfSRzyJEbiWQtO/lZadmbJZvfnp2zXqPiuKWfwDr UKzR2T3tlMILjAUoxUkEntjk/hXB7DB/8/X/AOAf/bFe92PRNX8H6BrUVyXX Tbe5vomt5Lhdm6TqPvf/AF6+HviX+yzefC6/kTSfEFtfXKXrXQsYbqO3ZIg+ 4krnLZXHAz1rzLxP+0Tqfw18jwxOmq6sYbkxzagZDsuElIAwF4zjrkgfpXJa vaeI7vW5PEesNqmmW2nqYrNbaORBPnhTIuSozsBILc9umaPYYP8A5+v/AMA/ +2D3uw/VruXwV+1/4Ykt/tVuviGNLO9Rhsd45WktZIn4xhkkwOMbiTySK/Yv /guBPJ4Q/wCDen9mO2kcmON/CltcyRlQ21fDd4crxjlkUEd1Yjoa/DzVrjWN P+Jmlarr8cVtcwXsN3C0zBdsMckKo46glTuwp6l85wGz+6n/AAcpeHVtf+CH XwM0S0H7t/E3hmwjD8Kq/wBkXiru4xjp1GMkD0rGpSw6klCo2nv7trfjqNc3 Y/Gr45/EBvBfwoTRZVX7RJCbVEK5ki24acqMH5TMzqG9EHXaa+cV0mwsdPuY 7ppY5ogPJIO4MwBGMY4Gcf49a7n9pO2v/FfxDmklK28dri3MLfKYgpGWUD5d zYLHGc7jnmotF8EReLfh/YsRAJotWhR2C4d0dkU9Oo5z+HtitfYYT/n6/wDw H/7YXvdjkvBfh4+IZ7QatfvBpUkojhidlJKZzI209ABntn0PWuntle41WO7s rnSvDVheMY7WebBkZOjPGhwdxVVzx1IxnHOzc/Cr+2/GuiaNoVs0+q+Jnj07 T4hnO5nILHjIXaVOT93JJOK+6/D37Kfwr/4Jm+AB4o8VGDxV4paONFu7yFJF M68bLVGBZByOSc4UdKPYYT/n6/8AwH/7YPe7HzR8Iv2YvHHizww1h4H8Jalr zar+8u9d1eM2ls4bcpkQPt3AYJAXPUfNzz2N9+w58aPDVrbWEvhDQNc01FSV Z9PuogyeXKGXKO4JyoHrznt19A0n9vHxr+0TrosfC1tFollLIYYljD7hHuAH AIAPXpXfeMP2gZ/hvpv/AAj/AIaEPiDXbIGO71S8ZhGJBwQrDlucjjA4p+ww f/P1/wDgH/2we92PkzUvDvxG8H3F/qnizwB4o0Tw/cNDFHK1o8qoyo+8naCy ggnk8fKMe/R+OvgR4r8R/Dg+KToOo2/hTS4RcSJcRtER0XcEbDnAYEcdM1cf /gqF8UvgD42u4dWSw8QWT4W506/3kIrYP7o7jjIDdu54Pf7J+JX7Tfg74tfs L634xTUNKWDXfDrg2sU6SSW13NDs+zFRht6SPtOQOmeld+a0MJ9drfvX8Uvs +b/vEU3LkSsfnRqNtJbXFhJb3Ump6fGwkRBP8uSuTtIOf/rjmu88KarHrNsL 2b7XawadMf8AQo5SVLL/ABNjklifoQRj28K+E3xEso9dudNu7hoLO6nZ7SVi D5bkfd5x8rbeMdGPQ5zXsXh8INTisLk26Qk+fdytksSn3RkA8n5evoD2rg9h g/8An6//AAD/AO2L97sX/BvgZddtPEk1xbuviq/uvOisXKo9vGHz8pONybWI 3A4OOa8i/aC8Iy2fi+S+SH7LexWcDzNHGgcMvmR5U9TtWOEkc/ePOMCvpPw3 ptr4w1fTNPg1iaXUopJLqK5yrzwxRDHllsYKtuwc4yM8Z6eV/tZeCtRsPE0N 9dRxsBBMzfZ2LiXfEDKSmcqUCh8c7lV8HINHsMH/AM/X/wCA/wD2we92Pvz/ AINSPDdgn/BTfVfEGn20NsuqfCHU7W6ZHLG6uIdW0MvMwPILLKnbDFWYE5OP 6Ia/AH/g0a09L79prxPfhJE+zeC7+xjjDZjgUalp52hTyoIRSM5yOfYfv9XF VjBStTd13tb8Lsa8wooorMYUUUUAcB8G/wDkovxY/wCxqg/9MelV39cB8G/+ Si/Fj/saoP8A0x6VXf0kAUUUUwCiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8C/wCCmX/JnHjP/sF3 3/pDc177XgX/AAUy/wCTOPGf/YLvv/SG5qZ7DW58f/s3ftceMvgz8JbDw/4Z 07TLrzpfCCJK+gXWtTw/btDuvNk+zw3MLMkf9mQMSpyiNO2JDtUfRPh7/gqj p3xE+FHgfxL4H+Hfivx5c+L/ABWnhV9H0q90+C8V28Pza6tzbSXVxDbTQtbJ Cy+bPAxjmJx5iiF/j/4RxfCvxX4V0Pwr468N/EPWdU1lvBZspdIvtJs7B7td IvRYW++6uYn8+TOoZBGxsW6q3mNtb6q+Gf7Fvwq8J/DDw/4P8Xab8W/DuqeK vHf9p6Pcar4rksNevtbh8OvbtcRXmgXSrbxtplldIYxJFFhJUEYDRqb9rTlS UIfEnr+FjlhgcbTxU8TiL+xmlydtLqTXz8+mppfEL/gsF8N7G7uNN8BQj4m+ I47XwzqFro2l67pdrf6jba3qOm2sJjtri5S4idItUs5wbmKCBxcRoJw3m+Vt eFfEPwG/4Klatq+m+LfhX4Y8ZXPw6uHNnD410DStYMdtPcXFqLu3G+4+y+bP pt1E9vP5F5E9ky3FvCQgPZfFj9mn4Sab4dupfEVreaLomsjR/DkdrZa7qGnW NpMt7axaYbS3tpkjs7pboWaR3NuscymOAeYFjTbu/An9kLwF+zZ4o8Q6v4N0 rUNNu/EsFpZ3S3OtX+oQW1raNcNa2dpDcTSR2VnAbq48q2tVihjEpCooAAV4 20/rY6b6HF/D/wDaj0e2vvCHgXwD4NsLS0eTWtK02wWZdMsNMsPD2vWWhXqx pHC4QRrcNJBCqhXECRlog+9JLT9t54byHR7rwdfXfixPEh8H3ek6RfxThtWX wsfETw209wLdJEMY8hJJvIDOwZxEucc54tt/gKfFPxZmu08dnVEhRdafTV8T cEzxmYeHjb/L55vFg+1DQ/3v2wQ/af8ASNlbHhW6+CmmfHzS77TV8UN4o0/w zFc2TSQ67NpCRrajYSsgNg2ufYSAQwOqmzJBBtycia5Vf+v6QnvoRfHTVb7W 73RbnVLS1sNSk0yP7XbWt011BBKHlDqkrJG0ihgQHMaFhg7V6Dzpf+Qgn++P 6V3HxW8a6V8RV0TW9Fh1W303UNPWWKLU9LutLvI/3s25Zba6jjuIXDZyksas PSuFmiu5ZyLG2+2XhOIINxUTSY+VSQrEZOBkKTzwD0r28hjetVUf5Jfoc2Nf uxfmjsvDP/IH1/8A68E/9KresesPUPDvxR1PRb7T5PATRW+oxrFObfWL63lK rIkgAkjsw6/Mi/dIyAQcgkHm/wDhRPj3/oSfEH/haa5/8Zrwf7LxX8n5Hd9Z p9zB+M/7OR+IvxZ0LxzbW9hPrnhZbODSpr+aR1tEN/FLfSW4RFntpzaLJDug nWO7W48q7imihjAq2/hH43f8J94RuofGfhpfCVppyR67pl5pwm1W9ufs2oKZ Uu44oolXz309in2ZciFyCmDHP1H/AAonx7/0JPiD/wALTXP/AIzR/wAKJ8e/ 9CT4g/8AC01z/wCM1SyzFJW5Py/zG8TTdtf60OJ8QfDrxP4O8far4jtrHT73 X/EvieHT9FutN0+AXdnptzYaZb38l3c/Y2C+UbCa7BlO2ZrGwtzJzHCOv+Jn wju9U8bQeKvDVp4b0XxhDLpmnPrj2sTXt3oyalb3N9ZtK0DOEa3W6WOPJXzJ t+Y2AdZ/+FE+Pf8AoSfEH/haa5/8Zo/4UT49/wChJ8Qf+Fprn/xmj+zMVp7m 39d/6uw+s073v2/A818afAH4vfEz9mXXvCOueLPDV54i13wS3h/ULq4hMttf 3k2ky29xLH5MMDWu++dJC5WdTFGypBEXGzqtU8H/AByg8RTSWfjnwCNKmu9Y eASeGZjNaW8tuP7LQr9qInkt7gM0rboxMjYAiIzXQf8ACifHv/Qk+IP/AAtN c/8AjNH/AAonx7/0JPiD/wALTXP/AIzTeW4p/Y/L/MPrVNu9yp+zj8LPE3w3 ufG954s1LRdW1fxbrNpq8tzpkEkEMrpoumWMreU5Yxbp7KZlQPJtRk+YnIHp Z6V5/wD8KJ8e/wDQk+IP/C01z/4zSf8ACifHwYEeCvEAwc/8jprnP/kGpeWY pu/J+X+Yliafcg/Y3/5NE+Ff/Yn6R/6RRV6RXHeGvhX8QPBvgOx8M6V8OEsd G0ywj0y0hj1i/D20EcYiRVk+yeYCEUDdu3cZznmqH/CifHv/AEJPiD/wtNc/ +M0v7LxP8n5f5h9Zp9z0Cmyx+dG0ecF1IGenSuB/4UT49/6EnxB/4Wmuf/Ga P+FE+Pf+hJ8Qf+Fprn/xmj+ysT/I/wAP8yo4ummnc8w+Ev7FviL9lnw5oehf C3xHYW2kNqw1DxHBdaHomki+iaSxR1hTTNLgjEghgmIlkVmZWeFsGWG6seg+ Mvw++N+t6tqx8B+MfDmi2NzpKQWP2yCN5be+/s/WI2nYtayZBu5tEk5LDZZz fLyUm6//AIUT49/6EnxB/wCFprn/AMZo/wCFE+Pf+hJ8Qf8Ahaa5/wDGav8A s7Ffyfl/mSsTSslfY43x98LfjhqvxLuNX0Hx34T0+ygjvLfTIrrTrie3t45r vRpUF1apIi3UiQ2uqxiQSwsBdxqpTMjj1P4U2PiLS/hX4ZtPGN7Y6n4vtdKt YtdvbKPy7a8vlhQXEsS7V2o0odlG1cKRwOlc9/wonx7/ANCT4g/8LTXP/jNH /CifHv8A0JPiD/wtNc/+M0nlmKatyfl/n5h9Zp3vc9Aorz//AIUT49/6EnxB /wCFprn/AMZo/wCFE+Pf+hJ8Qf8Ahaa5/wDGan+ysT/I/wAP8w+s0+5sfCL/ AJFO7/7Derf+nK5r5k8Lf8iD+xH/ANhW1/8AUH1yvpvw38P/AIjeEtIWysfh 2iW6SSSgSavfTOzySNI7M72ZdizuxJJPWvnSXw//AMIlpv7G+k/btO1P+y/E EVp9ssJvOtLvy/BWvJ5sMmBvjbG5WwMqQcc17+UYSrSw2JdRWvFHFiqkZ1Ka i+p9x/sa/wDJYfHf/YE0X/0o1WvoyvnP9jX/AJLD47/7Ami/+lGq19GV4Eep 1MKKKKsQUUUUAfyrf8HGtr9k/wCCxPxs1co0/wDZniLw4PIxkS7vDWmHnn/Y I6j7wpfgh8dvBN74fsbLxl4IuLaW8AZZg4Dw5A+8eAcse+OoGM9et/4L46Ot z/wVf/aVnnhllii1Tw3dYCArst9A0Z58kjjEbnOTgcH3rN+FfgXS/iZ4AvNG 0q2gfXNyyotyyqCmACqk8dAO9AFf9oz4V3elfDC41f4T6Jouu6JrELPfXP7x 7q3lOFCYUFQQuByeo785779kf4TvoHgXRhHpWoWMl0Yw8csZLBI1OSxP3hk9 SBnNei/spfAmP9mjwpqd74ou9P0Sy1R0WOCeRPs4bHYEYyxOOvOBXmvxv/a5 8WPr2r6RG1xolnZXksKnSowJnt0kKoylgRhwocYxw69yaAPevEvw3Gr2N5Yg EyRnC4jwig4wD6Ef09q+Uv2ifDWp+EfGPheNLG5xLr4hRPLJ3lrS4UkY9Ax/ Cuw/Zj/4KEW918RYvAPifWdVk1O8Rf7FuNS09UeYYYlJJEUKwHRWPUAgkkiu 0/a5+O6fDn4l/CddasfOkfxOXXybcyAIbOe1zuXjIN1GwA6hScfLivRyr/eP lL/0lkz2JPhT8KrsaYlxNaTQq5EmZIiCSew74GP1rsfH3w1uNW8NRx+Xcuko YTRRQEsI2G0579GGMVV+PP7Tmjfs8+CY9a16/wD7JSUGG0TyGZbiTazBRgHA woJz2Brw3Tf2zvFVxrieJY/GWrXcU6t5dq9ii2kKZAyq+XyMZOTycdTjFecU V/ht+zHJpvxF1nQfEPhyW78P3MvnR6lMjCSFGAyfmGAF69ePbFa3xg+Hfgjx /oP/AAiem+M7O01RIyA93bxpDMVwSpdSDkkgZxj+vqelfEnUf2oPg2sNx4js NJ8Q6iZYbIOwie8C/KrBSOhyAcDGQeK+dta/YX8V+F7S+1vx/i9ttPR2ibR7 jzrrLHbnCk44AycDpmgD5c/bJ+C2qeEvin4VS/mHlXNxFZr5MhkUqZM4BI5y SOM9FyOtftd/wc3a9A//AAQz+Cs2oNJbTX/iHw+0bQqQIp/7Dv5Rx2XKkd8f XFfi7418OaprPxwstL06/wBd1DTrNV1NItSB8u2EUiMVUkZDFRjcSSd55r9c f+DnfXZPEf8Awb8fs3ap1N94h8MXTFe2/wAN6kf/AGb9aAPxL+J3iZvEmsLr Tbna7iS6UeaNqGTBKt74468Zx0rUZLe31Pw5faYWt9Me4LXMEQJUSlG8vcvO NpZvyHfGfN/Al1Jr2jyQT6lElpAyubeJQ11MX4KRnGeoH4segOT3PxB0/W/A tw8eoaLrFldFRPDIyKFvoAw3NIFLIwGQDIOQTyFyMgHp3wH+MVr+y5+0Fpfj 2/0G88XaPa+fgQT5uNLaRSu6JGOw4zwDg8tyOCPoD9rP/goh8Gv2pfAkuiS+ HvE11qEX7/TxqSxWohkCYUgpI54yAR0wD6V8u6TqeoNZ2mo6fd6O5eNJFt9U VyXHdC6DawyWw3UeuOr/AI0Nqfib4bS3Op6HpYjs/wB+Lmz1SNtny7QEBbli cZABzigD2j9i2/sdGl1uexsobS50LTrnVDLLcBV2xxlg6hiSSOOPryADXmfw 1/ay03/hXHmanBeS6/ZM7XT4X/SZPMY57Zz9O9ea/D/wd44+Kng+6GjBDo1l aPBdXs1xHEkKOuVViesu1CdnLfeONoyH/C34j6f8KfCPiO90i1bVvEcFxbQx XU9qLmxt4C7GVsHHzHCgHbnPAPzZoA2db0O+/am+IlzcCyh0eXT7OMpHP+5k vTvC7ULAKW+fue/4jzvxxfP4OgufDuntrempaTYubW5lZBJIdp5jGAB8wbpk 4Ffqf8G/H/hL9rb4DWtxLoVtHdyMbe+sJoPKEDYwXQYztYNkMOMqR1GK+Q/i b4YXw7+1Po+i39lbXb+HJ53e5n2zTXNj5O6BmY53okhILdRsJz1I9DNv9+rf 45fmyKfwI8xu/wBnXTSYZLuSN9NsrGZob+NDtudrQCGXKEjduuipxnJgPqSO i+EnjOTU9I0+W5ie2u5ETzSV2mdeAZMN1Un06MCPr6rf/CeX4bfCLxFp1iZY 0a8iWBjLvys0bFfKJyV3PtHTGyOP+IMazbrwNNo3w6s1sYVVtOt3mjnkVQsS bCwQseD1KnHAU9chceeWdd4O8DLJ4t+2q08FyLdf9P09lAlDclXQgg5Izkc1 0lz4Ut9T0e6n8VTwNcaSZbeV1h8lLvz4gkJGexVyCfUnGe3m3ws8UwaPbXFx e6hd2trK0awWsK58ydzjKnBO1hgjnBAz9U/ag+MEPhbwrdaLLJ/xMdZ0iazf awDcSt5MpGeMKzHOBgjtQB9pf8GdU1xF+0v49tGlaWGDwtdr8xZfmF1pjfdw V/jbuCM8jnNf0JV/PZ/wZ06nb3/7VXxRZCDLNoV66naQxjF3pijOewPTj1/D +hOgAooooAKKKKAPPvg4dvxE+LGf+hqg/wDTHpVd60o/z3r5g0D9mPUvE37T v7RXjLStd0i31PxHc+GdJ0+DVNFOoWel3mkWS31veSR+ennqZdQTMaGGQfZg VmVmVo/UND+AVxovxE8O6nazeFYdJ0XTYrSa2Tw8q3bTW8csds1pcLKPssMa XN4hhKSgpOFRoR5omLLowZ6DoHia38S2L3NtHexpHcT2pW7s5rRy8MzwuQkq KxQtGxSQDZIhSRGZHVjfEhIz+lfIF1/wTs+JHin9mDxH8MfEnxN+Geq2vimX xpPd6hD8NrqKa1k8QR3rBrZZNYlERt7jUbwsSXM1u6QfumDzyd98OP2TvFng X9sKHx1e+MtK1rwdp+jeI7LTNOl0uePV7ObWNT07UZhPetcSJcQRy2kyQIkN v5ELRRnzyvmAXYHbofQlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F/wUy/5M48Z/9gu+/wDSG5r3 2vAv+CmX/JnHjP8A7Bd9/wCkNzUz2Gtz4S+G3/BOXxT+1b8KdM8Z+ENS03Q/ Fei2/hiDQdYl1J7eTRprbSTNLO0P2aZJ1ElzYuIiULmBx5kYHz/YXwA/4J/6 v8NfA/w70nWfEWlXsvw28cXXiq0YWJuP7QgfRr3TIYpCTGqTKbzzi6IEzFtV FDAp8DWiW7eA7P7VYPqdqk/w98+AaBFravG2i60pD20rohRt2wvvVk3blO4L XsHws/ad+Knxm/ZZ8C65qnjL4saZZ6H8T4NP1LxL8OfDMviK9/smTwU10Gjg XTroX9j/AGpdpCtxLZzBgIpCwkVZliOCpUo/WI7z3+Wn6mtTifGY6Ucnq/w8 LH3Hd/8ALxuTVtktL6dz6pk/4J7eZoeh6Z/bnhyRNB1TQtRXWp/DTS+JNRGm XPhufF3qBuszyXH9gFJpDGNytYfL/oObijpPw08WfsbfFK88YWHhL4g/HDUP GXhHw/4Z1KHwwNG0yHTZ9HF7uvPJ1LVrWCCO7OoZW2soxHE1vIzZaXcfHPjX 8SP2m/Gf9s6brlp4o8Aw2Wj+DNXvbjwJHc6leaXIuq6TPrD2UX9jTQXMqxnV ontl1C+aSG1gH2HE7NN6/wD8E7PjT8VviX8QfiXpXxHsPGMVvpIsry2n13w7 JpcVnfT3epx3Wm2Uv2O2iubW3gtbCQGKS/VGvHUanfp5c1bu9t/60/r5GPQ6 f4e/sly6dfeEfF2lXV/4W1qwk1u/k0zW7SG+ltE1/X7HXNStJfs9wYvPQ28l tHJFNJHG0gk/fqm13wfsWahZXEPiC38VWC+PIvEZ8anUJNGkbSn1s+Fm8PGQ 2YuRJ9k2ET+QLkPkFPO53jzs+P8Ax4PiXqEHiuL4lS6B8P7rXtS8SHRNF1df tSyeJrG98OLZPbxA6mq6VDOtxDYmfapkt7hN8vlPWj8W+M/DvjO28MXsfxVk 8GWV5/wkE7xaXrt1er4LXwe1r5LX0cbTS6t/bfzfZY5m1NjiURlfnqV8K17/ AJ/0/wAQd+Z/1/X/AAfM7346eFk8Gatpmnpd3+oNHZ+bLd3svm3F1LJPNJJI 5ACjc7MQiKsaAhUVEVVHna/8hBP98f0rc1PxrafEnw1o3iCytvFllba1Hc3i WviXSNR0nUrTzL26YwyW2oAXEewkqAQI9oXyQsPlqMNf+Qgn++P6V7WRfxK3 +CX6HLi/hh6r9TvtG8RaJp/2MT6emoRxx7bmSW3IcszneibZVXARm2uwZtwH Qbdo2reGZba5T+x51kW5eWzkjnYbo/nKRyqWPAJQEoQSCfmBU7uZor5PmZ6n Kbr6nodxod6n2CWK/kSL7MyljHA4kZpOS+SCpAXI44ByQXbCoopNlBRRRSAv 6b4juNJtzFFHYupbdmeyhnb83QnHHTNWP+E2vP8AnjpH/gqtf/jdcd4i8e2H he9W2uYdYllZBJ/oekXd4gBJAy8UbKDwflJyOOORVD/hcGk/8+nij/wmtR/+ MVWvQWh6B/wm15/zx0j/AMFVr/8AG64Hw5+2p4U8SeMNS0AappOl6zpd9/Z8 lnrPh1dImuZNly6tbLdQRm6iZLO8ZJoPMidbSdldhG5Df+FwaT/z6eKP/Ca1 H/4xXlPxo+AHwU/aF1eDUPGHgPWdZvrRxPa3DaBq8MtlP5kMn2iF441aKcm2 gVpoysjRxLGWMY2U0n1uFlY9F8R/8FD/AIX+FpfCi3Pj74fT/wDCc6tb6JoR sreyvRqV3PFFLEi+TG+FZLi2bzGwgF1ASw82Pd3Hw++P2k/FzwfaeIfCeseD /E+gagX+y6lpVvYXtnc7HaN9ksSsjbXR1OCcFSDyCB8zv+x/8A5fF3h7XH8C +LW1Lwpr7eJdIb7J4hEWnXpWzRTFF/q1iSPT7KOOAL5MUduqRxohKn13wX4r 8J/Dnwpp+haFo3iLS9G0m3S0srKDw1qSw2kKKFSKNfIwiKoCqq4CgAAAVVtN L3B20PVP+E2vP+eOkf8Agqtf/jdH/CbXn/PHSP8AwVWv/wAbrz//AIXBpP8A z6eKP/Ca1H/4xR/wuDSf+fTxR/4TWo//ABiotINC98NPEF34t+HWgatfyLLe 6ppttd3DLGsatJJErsQiAKoJJ4UADoABW3XOfB6wuNL+Efha2uoJLeeDR7SO SORSrxuIEDKwPIIPBB6GujpPca2CiiikAUUUUAB6V8eeFv8AkQf2I/8AsK2v /qD65X2GelfHnhb/AJEH9iP/ALCtr/6g+uV9PkP+64r/AAo87HfHT9T7z/Y1 /wCSw+O/+wJov/pRqtfRlfOf7Gv/ACWHx3/2BNF/9KNVr6MryY9TZhRRRViC iiigD+bz/guRa2epf8FRvi/a3mya01HxNFp91CZCheObwh4a5yMYO1ZMHP8A ePpXyd+z5qEPifUm0/W/E8Hhi80a7GlNKBva+ZF+VkXv5gGRzgZHPevpX/gu 14k0zwv/AMFX/jdfajJEI7PxLpXnArukWCbwpotvI6DqWXzAwC5Py5HTNfmt +0v4O1bxf8W/7Q0nTrmRbqGJ4mijYGV8YPHUP0BU8jnODmgD7a+O+qSeN9a8 MeHV1q41Dwtb26iW5MZ8zIfcSfmyM88egAB5Nem/F39oTwt4Eh0rU4dL0m+h sbeCC7vrxnEjx7FQdCACMZb2X8K+FbD4efHXTfh1p93BoesPZ2bpJdCzkS4k eNdpXfFGSykKOcjjBJAFe4/sz/CXw1+0d4U1zxh4/wDHGq+CV0+eOw0O0htU uY0vGjxK0sZyW2HO0DacKzErtwQDH+Of7Vp1L45+BPFNomit4QTUrW6uGt9x mVIZlJUxlQU2lcgfxkE9OK+7PG2rJ4t8Q/CG8tmilhbxLJcwkICGU6NqL7j9 doIPtX5t/EXw5pGqavcWGsapJrtpaXN5BaajBa/Z3vUVdu9VA+ULtSYD5h8r jJyTXF33xV8c+CdT06zs/GWurb6KTc6dBLcSeZYMqYxGFZtw8t2TKluDtwM7 a9DK3bEfKX/pLJnsfZP/AAWI+J1v9r8BaXbWMN/r8N79shimPlw+V9zL9zub A78K3QcHT+FP7YFtqWn2vhy40Dw0I9PhcXMRPnYZcqVSZfvBGKg4BB3deDXy UdG1T4qeLT4k8WeJNW1TVHEZjM8TBUj2syxnDcIRuJWQAbRyMkmtjT9T01dW e08T/wCn2emLtso7O7+xqzO6llMoG4jazDGVJOATzz55R9JeM/jVp3hX4oaZ 40utOtoLe0tUks7RZWMUTxk8cdcmNiMnvzWZ+0X+0JpMelS+OV1u60zVbixm u5NOEbNCkoTCxDgZ3NtI6YyMk8184fEzwLqX7RvxR1vw34FfWJvCHhyOCaKa 5v7fy7PzI0eQTS7U8wIxkVd58zAOckNngP2h/gD45+H1nouh/wBo2/iPw3Op a1utNxP55UAsJNrM24ZPDHaOowOgB69+zp8QdV+I2nz+KdfuI21bXQ2kWEYt 0t4YbWN4gzHBA3vLLGMHBwmeea/az/grP+ypf/tX/wDBA/4B+BvDtjNcap9i 8N3OnBtoa3aDQLhgWBZeqgocZwXzjAJH4p/Bv4eF/BHhzwlqVzHo+qaG8GrX L3emSM0qszTJbBRn97KGjRWH3dqk4BxX75/8FA/iBqngr9gL9kXxN4evZtNt tP8AEugXd3LChEZsW8M6msiPnO2NlcKcnjIznoQD+VnRPD8mj6+8KRzJqFtO EJ37VjlDnaBgHnIB4PQ9eMH66+N/ij/hE9L8KWviG4tbrTzp8WpxC4uPLMC3 EXzpJsDFyBGmcDnIOOK8B8YeHYvir8bddv8ATRJBof8AalxdA4ChY3lJSMEb edigZxu5PXrXbeP47HX/AAyqvougaX/YNuSJbe3jTeokjXLbyWc/cHXsc9TQ Bz158QfAfg+HUJNImuttw6vBaWsUxiPXc26TYQAOmPT6CuM1L46TateW1lZ2 0C2ok8uITKPlUjBO0Y55PJJPuOtd54I+Elr4z8TSWttBb6gzSrlzblIIYy2w ZkDHGCrcHkquFryTxvq9pqHiS8aztpLe3hGY42bDHkLg4AycdSf/AK1AH0z+ y/qn7P8A4b+Huvt8SfFHjr/hJ50kltLHQrZVha6KstuJHfhYFYh5SuXIACgE 5rn/AIK6h4d+H/7QVjqNleLq3g/xdqNtbXto0JT7RbQ6tZ3JByxxkW4ccqfk YZHf54soobm2eZo5JHRACuGRyPm4zgjHb1/SvWPA37PHivTfhrdeKriw1Cx0 D7QtuLryiqxylSwX5vlLbc5U4LKT2waAP27l0yPUNPgtb1Veexl+VooQGjX+ 8CORjBPcV8A/8FJPhPrng74reG/H3hm5gZ7e7Xw3K8kYJEjvKULhjgqys6n0 Hc5zWT+yx/wVR1LwH4f07w/4ztpdV82Q2lrfJclnU5UqJWIcvGQ52tnIAOSV UY+n/wBsT4dah49/Zm8VzPaxNPLpy6glugLGCeArONpBzzsxnjqfpXoZs/8A bq1v55fmyafwI81+HOq/bvBK6Vrlnouo6jZSCcS2ySxqTlioILn5gHYg5AwQ Olc18T559RsJ7i5WO80aytfMjjiUxGFcFVU4++owR0GR71z/AMGPiBqXif4d aTq+my3Oo23IVFC+ZAwbGD3PIJGeTk9a4Xxr8bD4t8bal4YtFCadDbS32r6h NG8X9mWsS5kbaQPmyECnPLTAKDkCvPKOD8WavqPw0+E0C2ZWTV9VuhaWLFdw SMNvLEN14B5PQ4ri/EvhvWPGfizTYv7QGr6vq0iwSPL8ojiUnOcDGAS2eO5x ziuo12fU/GOtwX8OlR3F9ZwrDo+kbgq2Foc4eY/dWUgDOSOhFdj+xt4F+zfH LTNc1KX+1kg1DawRvlunDcHZknYvOBjkgHJzgAH6c/8ABsD8PtJ+H/8AwUL+ JOl6Eh+xeHfA8ui3Mrrte7vY7+xaeXHoWYIPaIdya/duv59/+DS34nzfET9v 34ryiNlgn8JXmob5JRI08k+q2rltwA4+n/1h/QRQAUUUUAFFFFAHzj8KPjFr fhn9rX9pix1bRtSPgXwnBoXiCzv7DQr29ur67l0xlvraLyVf7VJHDZ2LLbW8 bTg3IyH86JRpftNftNeMvhv8TfDXhTwD4E1vxnrmqC6vLiN9Dv49LFsmkarN bo2rCP7DayyajaWNuRLIzol0GMeHRx3nwb/5KL8WP+xqg/8ATHpVegUN3BaH yD+05+0nc+KP2eNM8V6PF8Y5BY/Evwwltb6F8NPF2k6vHaW+o2EuqC5slgN1 c2b2iX5LtCLZ1dIQJZgpf3r44fHC6+GXgbxbeaF4X8QeK9f8M6RFqsemwade JBeLM8qIFnSCUymMwyPNDaR3N2kQUpazSTW8U3olFK2ll/Wi/wAhnyd8AP21 PiT498O+Gx8QfAGq+AtV8W6hNbabJY+C/EOtWZMevXtuYrkG1t5bCP8As2Gx lS8v0to5W1AyCJVt5Ia938f/ABv03wP4b13UpdP8WXMPhjUbewvksfCuqahc SNMIGD2sNvbvLdxgXMe6W2WWOMrMHZTBME7rFFNq5J8xfsy/tS6zq0ng5fGP gj4laP4j+JPhy01rXH/4RvXJdK0DW4tJsZrvTys6OlhGY5k8ra5hknt7yNmF 2rCWp+07+0D8Uv2ePAfhu/8ADXhm98ReLPHcl49zo03hrW9afRZpFh+zYk01 biyjNqu2Fra4nsLa9kZ5v7Qswkzv9UYopt3dxs+fvjp+0N488Fa3qWn+FtEu dYv7PXtCthF/wh2pXVsum3+q6dZT3Au4ZfJlkt4W1KaRF2vEiwySIkSb7lf2 Xfix47/aM+G/ha/8VaJq/h6aTVdYvL+Y6Bf+G/Ps7PVbi30qNrS7lW8ge6gS 3umVg6lIpEkVUuEU+/hQOw9aWktrA9QooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACvAv+CmX/JnHjP/ALBd9/6Q3Ne+14F/ wUy/5M48Z/8AYLvv/SG5qZ7DW5+dXgL9trxT+zHfeC9KTxn4i8KeBNUXwjFq 91b2mlpp2lx3WlypcXMlzdWlw8cwW3tpFQ/u2itLkfIx319z/Bv9sSbxP4c+ EF54d1nxL400j4l+OrjQLu88TWmnQ31var4f1HUlNv8AYVhg2iSyhO9hISkk wGcxlfmL4E678IZPgIdD8d+EfFXijUNaTwlYXNrp2rx2EOqxT6Jc/Y7WXzL2 2jlTMOoq0LkiU3MK7ZTgJ9Zn4u/sv/s//C7w/wCJb/xB8OPDnhDVvFc8lhq+ q3cQsrHXprC5eYyXMxK2dy1qtzG3mvGf3jRfelCPz0aFeLlUm7wlbl8rb/ee pjM0yuvh6GDw1PlxFJS9q/5lKV4P5K6fojU8Y/ttPJ8L/DPi3wx4S1u78P8A irWvDVnYavfiCKzubTVtR0W3WURCb7VG5i1WbYssKYk0+cOFVoDP23wk/ah8 N/Gz4l654X0aHVWvdA0DRfEk11LbqdPurXVftZtjbXKM0V0ALOQtJCzxDcqi RmDqnJ/Gvxt+zn8Ab/V9c8dTfCjw5qOkSaZqGrXV/BZpeacLy+0+ysb24+Uy QxNdadpqLcyYRTpluxdRaq0deb4U+D/jveXc3wb+Lf8Awry78HS/8Id4gm+G 1t4ZvJVewyIdJvjeafe+SbLzptlsvlGL7VJlfmGOp2f9en9fM8roczoP7Vfx A8dfErVdA0u48JaSPFJ8Q2/hB9R8P3Mg8PvoOuW2iXcl+y3y/wBoC6kuVuII 4VszGieUzyFvNWfSP2qvHeofHSHQGHhk6dLrP/CvhENGuFeTXh4a/wCEhOqi 6+1lf7NMYNsLXyDL5mJPtOP3dd0fFHw1tvD/AIb0PX7218calBdpbwXV1okd 9fajf2Wr2VrLevFaWyxJJFqrWck0sUUcVvMolIiWHcjdP8cfBuX4U6fplvpO jjw5NbLpdp4eHhmVZVH9itdLp403yPNSYaVn/RDEJBEfL8vnZST93+v68hPd nmOu+Ntf8e6Jpt14qtILPxHYG90fUPIsbmwgvZLLUby0F5Fb3GZYYblYVuI0 LyhY50CzXCbZ5OcnsotSnNtPJcQw3B8uSSAkTRqeCyEMpDAE4wwOe4613XxY 8S+HPFepR3PhY2jaZFcX9pM1tbmBPt0GpXkF8CCoJcXcdxubHzNuYFt248Qv /IQT/fH9K9zIJWq1Wv5JfocuMV4w9UN/4Ub4U/6Gbx//AOB15/8AJ1H/AAo3 wp/0M3j/AP8AA68/+Tq2qK8D+06v8sf/AACP+R6H1ePd/e/8yNP2V9CaNGbx T4thMiLIEl1+SNwrKGUlW1EEZUg8jPNO/wCGVtA/6G3xR/4Ubf8Ayyra8bf8 hiD/AK8LL/0lirHp/wBpVP5Y/wDgEP8A5EX1aPd/e/8AMb/wytoH/Q2+KP8A wo2/+WVH/DK2gf8AQ2+KP/Cjb/5ZU6il/aVT+WP/AIBD/wCRD6tHu/vf+Y3/ AIZW0D/obfFH/hRt/wDLKj/hlbQP+ht8Uf8AhRt/8sq80/ad+IPjL4eaT4Xn 8IWtvMl1rEg1yeXQL3WzZ6dBp19eOUt7R0l82SW2gt0Yb/nuVAilZljaGf41 eNdZtNVfQvh08k2jXmoWMsOsasbE3rwQlreW2aKC4SSCaYxIXcxmJTKxVmj8 p6WY1Wr8sf8AwCH/AMiNYVd397/zPUf+GVtA/wCht8Uf+FG3/wAsqP8AhlbQ P+ht8Uf+FG3/AMsq8R8IftJ/EPxRoguLn4S3OhTr4Ms/EE8F/f3LNFqdyjk6 Upis23mEwzCaVAzoHtisEhmKpseJPHvxPg1i1lsdA8PQwWPh5b25sGNxdR67 q0kVyRYW9+BG1nFA8EZa4nsnEy3cYVImRlLlmFWO8Y/+AQ/+R8hfVo7Xf3vv buerf8MraB/0Nvij/wAKNv8A5ZUf8MraB/0Nvij/AMKNv/llXjWi/tIeNvEX hv4eywfCvVLTWfEUmmv4rsdRubi2i8H21zaSzzOJxasL2WKSLyPKiVSXkRpG gQlhg3X7T/xXg+GfhHXLj4O39vrV94lvdM1/w3Z3D6hNaWUEOqbLqC62QoUk e1snVpIwrLciMDe6NQ8wqpNuMdP7kP8AIPq0drv73/mfQf8AwytoH/Q2+KP/ AAo2/wDllR/wytoH/Q2+KP8Awo2/+WVfH+nft/8Axlj+EXhrU7n4AeMm8TPZ 6NqetJFplzsa1kvYbPUIra1ILrqAl+0SRW0svlpZmO5muFbda1wvhT/gpb+0 nf8A7Qmq6Zqn7L3iSx8H2WnTSyQy2d4l1Z3VtaCa7jj1CFJ7S+DyQ3MVqI44 hcNLa/vItzVSxta7XLHT+7Dp8gWFXd/e/wDM++v+GVtA/wCht8Uf+FG3/wAs qP8AhlbQP+ht8Uf+FG3/AMsq81/Zm+Pmp/Hmx8WNq/hS48H3nhnW00wWNxdN PcNFLp1lqELTAxRiKcRX0aSwqZFjljkVZZQAx9MqHmNVbxj/AOAQ/wAg+rR7 v73/AJjf+GVtA/6G3xR/4Ubf/LKj/hlbQP8AobfFH/hRt/8ALKnUVP8AaVT+ WP8A4BD/AORD6tHu/vf+ZV1P9mrwxo1k1xdeMvE0MKlV3HxGx3MzBVUAalks zEKqjkkgDJIFZ/8Awo3wp/0M3j//AMDrz/5Oqj8X/wDkU7T/ALDek/8Apytq 6in/AGlVtflj/wCAQ/yD6vHu/vf+Zjx/Azwruz/wk3j/AAvJ/wBOu/8A5Or5 kl1Cw1jT/wBji80nTjo2lXfiCKay077Q1z9ghbwVrxSHzG+aTYpC725bGT1r 6zPSvjzwt/yIP7Ef/YVtf/UH1yveyjFTq4XE8yStFbRS/JI4sVSUKlO19+7Z 95/sa/8AJYfHf/YE0X/0o1WvoyvnP9jX/ksPjv8A7Ami/wDpRqtfRleDHqdT CiiirEFFFFAH5A/8FR/+DrEf8E2P26vHXwWPwF/4TT/hC/7P/wCJz/wmv9nf bPtWn217/qPsEuzb9o2f6xs7M8ZwPAo/+D5MPz/wy6AOT/yUf/7118Af8HRK b/8Aguv8ch76AOuP+Ze0yvg3R9Hn1zUobW3AaSYhFy20DPAyf89KAP3/APD3 /B7nN4ov1tbP9ll5Z3BIUfEXOccn/mF+mTk+hr2/xt/wdZ23ws0aI+J/glYa fr08Bk/sSHx35t3ZyZAEdzu09FiLckAF3XGJEjb5a/FPwlb6f+xH4E8PW+m2 kVz8avEFwtw7Sq06+GISsLWpCkKgvGLyy4Il8nFsyskwkVOA8IfBbxXrfjXW Dr2l380Omqt7JD9rVbiffklkk3cgBXJIfPADE9wD9qLz/g8wtYJXEf7Ou9Yg Hff8QEiYISeQrafk8DnHGSBk1XtP+DzRtQPlW37MGqTXSjLxjxuAo6kAN/Z+ CcAnnHCt1xX5N/Db9jG6+LPxV0YeHvD2oR6NdxBb+K63BrWQgkMxbOAScdT+ eRVf9qnR/DHwU8RWfh3wFFbDxFpLl57y3iU3MkgMYKsMfMHLkBSDuAA6HB7M BRjVrck9rS/CLf5ky2P1lk/4PQDbSulz+zfYWbCISos/xLRWlBzgKBppyTjP pgg5rS8K/wDB5baa3rtvZ3/7O40yKeVENy3j0PEFO3JGdOXcwBPyj069SPzl 8P8AwF1z9rr9nWx1l/Do/wCE68PEQXBMCKl/Gdw3FTyB13LgdVOOMVxf7QX7 Ovhbw7p97pfh60ht9c8PWf2jUpYZlW1tZRghHUDBPOOhxliCMLnjKP12+MH/ AAdm3/w18Pxa7pv7Olv4j8N3W42+oW/j9kVgC3yuBpjBJAEJZQzAA7gzLlh4 3N/wfIiGUr/wy4Pl4OfiRjB9P+QXX5F/Bz9oHxH8B/EDXF7bXN5oetQiPUNI vomks7+EHpg7SUwuMhgyHaQwIQiL9rz4B6RYrF458DyLdeDNbkZoytzDcXNh nH7q4SM742Vg6guieYF3AclVAP12f/g+WCH/AJNcGf8AspA/+Vdft7+yd8cf +Gn/ANlj4afEo6X/AGJ/wsPwrpfib+zftP2n+z/ttpFc+R5uxPM2ebt37F3b c7RnFfwls2+T5ckjqO3fnnJr+3b/AIJQqF/4Jbfs1gdB8K/C4/8AKRa0Ae+7 B6D8qNg9B+VLRQAmweg/KjYPQflS0UAJsHoPyo2D0H5UtFACbB6D8qNg9B+V LRQAmweg/KjYPQflS0UAJsHoPyo2D0H5UtFACBQOgApaKKACiiigAooooA4D 4N/8lF+LH/Y1Qf8Apj0qu/rgPg3/AMlF+LH/AGNUH/pj0qu/pIAooopgFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAV4F/wUy/5M48Z/wDYLvv/AEhua99rwL/gpl/yZx4z/wCwXff+ kNzUz2Gtz4H+G/wy0fx58OrNbr4qfCLwTqSx+C9RjsPFd9aNNLBaaPeJI5tb lWUo5v1EcmD89vN90oCfcf2Of2GvE1v8BNDn8DfFz4UyeIfCHxCi8UWF5p9t c+NNEswnhGLw++nTMLy0nmlEcjzeeZUZi0bNH85Wvl+x8BfFa00nwz8QPhzY 65qsngweC76DRLPT7wr4kuodJvG+zrd20TmHEcphk8wrBs1AeawGwH7g+FWh /GX4w6F8FtY8dadq7+IfAnxJvb7WZTBaWZWxHhvVrNZo1ZYxLDJdXcUYYRrI RKWKRhTs56WLnU5qEo6QtbzueliuHsPhKFHNaVVSniFJSWvuezbSTuktU76C v/wSfTw7eSt4X+IOs2dvp+leHLfQotan1bVobK+0W50qaC4uLT+0orCWKQaN ZLIsFrbTsTOftI8wBO5/Yt/Yl1n9lDx94v1TU/HEPi6w1nS9M0HRbVNJms5N KsbC71S4jEzyXc6SSMdTZdtrFaWkSwIkFrbxhY15nxt4J+NHxA8HeHdP1mPx PPr9t4i8L6lrIs5dGt/DrLaaj4Xur97UhxfiICHVWjSYlnK6gjhlbTwbOkft dXv7OPxWvLv9ofx98P8A4WeDvEfhDw/N4Xs/E/iDSdNmTWUF7/blt9oM6m9k iL6duljiig2yRiNCfMdutt9X/Wn9fI8tbaGxpn7E2veE/iBrev8Ahzxromm3 NiusyeDEufDk12NHl1rVYNX1U6iftq/2gkl7bp5KwCyMMLPGXlcrKs9j+xLq 9n8Y7fxR/wAJjpggivv+EseBNBkF1L4pOhHQWvvNN0YxYGxOfsQh3+d8/wBq 2/uq848Jfstf8Jh4h/4S+bw74Q+JnhL4rHWX8XXGnLp9xH450688RWE3h24u 5nKpf2thojXKhXeTEAeKJZPMVHqaz+yp8U7DXrbx/wCAvC3hjRPHcmpwWWmj WNQtNN1DQvCbeFltv7CfUIbXUNsdvrX+mCBI7i3adFfDBmcCvypf1v8A0wfx HZ+Nfh3P8JfDfhPw1cNoBk0TQ7ezC6FpB0jSoEjMipFaWZlm+zW6KFWOHzXC KoAYgVxl/fR6WXuZRKY7f94wjjaVyBydqKCzHHQAEnsK6fWtI8SaB4P8L2Pi zRNG8O63Y6b9mksNL1+4161gjSedYtt7cW9vNMxiCMzSRKQzMMvjcefktDJI zbvvHP0r1cmxFKjWqe3lZOLV7d7GGKpylFci1Tv+Zlf8Lp0T/nj4j/8ACd1D /wCMUf8AC6dE/wCePiP/AMJ3UP8A4xWp9hPqKPsJ9RU/2VlX/QQ//Af+AV9a xH8iOY8afG281/XvP06+lsbJLe3gjhuPA+rXMq+XCkZJkVowclSwG0YDY5xk 5X/C09Z/6DMX/hv9Y/8Ajtd59hPqKPsJ9RVf2blf/P8Af/gIvrOI/kX3nB/8 LT1n/oMxf+G/1j/47R/wtPWf+gzF/wCG/wBY/wDjtd59hPqKPsWASWAAGaP7 Nyv/AJ/v/wABD61iP5F95wf/AAtPWf8AoMxf+G/1j/47Sf8AC0tZ/wCgzF/4 b/WP/jtdYde0yPTI76TUrCOymcpFcNcIIpCM5AbOCflPAOeD6VZ863G7NzAN kqwtmQfK7bdqn0J3rgd9w9RR/ZuV/wDP9/8AgIfWcR/IvvOL/wCFp6z/ANBm L/w3+sf/AB2j/haes/8AQZi/8N/rH/x2u2ia3uLua3iuYJJ7bb50auC8O4ZX cOoyORnrT5Yo4JIkkmijed/LjVmwZGwTgepwCfoD6Uf2blf/AD/f/gI/rWI/ kX3nDf8AC09Z/wCgzF/4b/WP/jtH/C09Z/6DMX/hv9Y/+O13n2E+oobTyAuD nJweDxzij+zcr/5/v/wEPrWI/kX3nB/8LT1n/oMxf+G/1j/47R/wtPWf+gzF /wCG/wBY/wDjtd59hPqKgeS3jYg3UAKSLC+XAKOxUKh9Cdy4HU7h6ij+zcr/ AOf7/wDARfWsR/IvvOJHxS1gdNZi/wDDf6x/8dpf+Fp6z/0GYv8Aw3+sf/Ha 6vUNf0vSGC3eqafakuY/31wiYYKrEcnrhlP0YeorRNgR3H50f2blf/P9/wDg IfWsR/IvvOD/AOFp6z/0GYv/AA3+sf8Ax2j/AIWnrP8A0GYv/Df6x/8AHa7z 7CfUUfYT6ij+zcr/AOf7/wDAQ+tYj+Rfeec6t40vPEkdrb6jrEzWcN9a3siW /gPVopHMFxHOFDM7BcmMAnaeCa7L/hdOif8APHxH/wCE7qH/AMYrU+wn1FH2 E+opPLMrf/MQ/wDwH/gAsTiP5F95lj4z6I3/ACy8Rjn/AKF3UP8A4xXzhpmk Xfh/wt+xbYaha3Fjf2OtwW1zbXEbRzW8qeCNdV43VgCrKwIKkZBHNfUf2E+o rkPiP8HP+FheOPh1rJ1BbQeAfEM2veSbfzft3maTqOneVu3Dy8fb/M3YbPlb cfNuXsorAYWhVhRquTmraqxnN1qs4uUbWPd/2Nf+Sw+O/wDsCaL/AOlGq19G V85/sa/8lh8d/wDYE0X/ANKNVr6Mr5uPU7GFFFFWIKKKKAP5Bf8Ag6KG7/gu l8csAZJ0Dt1/4p7TK8O/YzvD8CtGvvi1JFZS6j4dvraz8PRXDuI/7SlDPHcN 5eDsgSMy7TlSwjDKysUb6D/4OYdFm1//AIL1fGu1jjlkLyaBlY9ucf8ACPaZ 0zgZ44/+vXmf7SfhPT/hlb+DfhdpsU88Ojwfb9XtnCv5mszvHHebGySyotrH AowFDJIRlTQBX+C/hf8A4XJ8QdX8U+KZdW1aG5vBdyz2a4E85YTSM4LhgclG B5AXbnlWB+kNP8SeF/E3iQX0fh/V7SHRbZrWO+F4GDqybQJY9oGDsGSpHQdC MVwnhH4E+KbaPSvEPwW1S2tJ45g2o6RPcxx+aykLuCNzvwCGI6kHIHU/oD8L /Bs/iD4XRW/jDw9ottfXkWLxI4omDkk8uU4LHrx60AfOPwX/AGhbD9mf9l3W NVuJ5yl3qV0lgksbPh2l2xgd8HcPw44r5h8ZeJ9Y+LGsaFeato9ro2sSar5z ajb7WSQoHYLsLEc7QTgDnn0r9FfGHwD8K654UtdGuNMgj06zDeTDG+xY+/Az 14P5Zr4J/bN0fU5/j1ZeGJl/sPR47opp11FIYXQ7QI/mHQEkZbHfjHNejlX+ 8fKX/pLJnsfYn7HOneNPDXh6fTtZudI1HR7qAtp9zbQCOSNM8qxyS3Bzz0Pp nnl/EP7Ii6p4L1awaHUt6ah9uuPJmRjqr7nkjQbs7MkkZ54PfAIr/sa+BfHH wtuJodbvnudFubVTC3n+esjEcMG/3c5zzn6V9LeIbq+h8PXbabC0t/HbnyYm BILYJAx9cYx615xR+ZP7Rn7LPiLWL3Txqtra+F7Kz/5BtqD9olc92chs9cdc AkmvOv2Xr63XxPrHwn1t1/sDxvdHR3lZ/KhtL5HeK1u97JJhY5HCtwGaKV1G 04Ye1/H/AMT/ABCv/Gl4viu0vIXWQeVbRLFAyqP7xZg7DBUfKDz9K8d+OHhP RvB9z4f1SeztLS0yzXUMMX+lysdpyoBIOBubccnIH3s0AfN/xT+H118LvHN3 peopNFdWsjRywnh42DFWQ4GMhlYHHvX9pn/BKBt//BLT9monqfhX4XP/AJSL Wv5Zf27rWX9q/wCBPg74wR+dqPiCS1Nj4kvnWJJb6eEyItwIoxhEeCGLDEb5 J4b9ySFO3+pr/gk/x/wSz/Zqz1/4VV4X/wDTRa0Ae/0UUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQBwHwb/5KL8WP+xqg/8ATHpVd/XAfBv/ AJKL8WP+xqg/9MelV39JAFFFFMAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvAv+CmX/JnHjP/ALBd 9/6Q3Ne+14F/wUy/5M48Z/8AYLvv/SG5qZ7DW58N/CD9pvxt8IPhXYaR4e8S zaDp883gqJjBNpVpKgu9D1AXDLNqETwlj9gtWCPjd5OxGQyMx+hvAf8AwUw8 f/Gv4Z+ALzwP4E8LS+LNf8eJ4Pv9K8S6xdaRbSxN4Tm8QiXzktZprObZ5AMT 205UiSIkbhOnzF8G/i34J0oeFfAfiX4c+EPFt94rHg2Gzu9W8VXlhIbqTSrh LT/RoLSciKF/ORrhTw+oxCRQqhx9X+APhH8BNJ+Dng/wN4r+F/wl8QeGviN4 4Npp9pb6pH450m41WHQ5p0vrq51CJGNy1rp8kO/bJIS0RLHzXKuOJpTpqlBr mi9fnaxn/Y2Ow9d4/EJ+yrRXs73s+VtSaW2+9u2vQo+PP+Crer+J9KT/AIVl 4Burq41GLwfcaTc+LLDWdL068fWtS0iGS1mvEsJLOJ0tdXgKtDc3MqyrNvti kH731v8AYz/bcH7Wev8AifT5fDi6A+k2dlr2mPFey3Salo19cX9vZ3LNJbwA Su+m3RP2c3No0Zhkt7y6jk3h/wAaPEPwBk1zV5fEmmfDXxRrviltO8Gazbmy sNR1HULe5vdPhgtbpCDJJaxy61p8rJJlES+hkx+9Td2Xwd+HHwr8A+NPFF34 A0P4faL4h8W+R4h8RT+H7Gztr3WvtElwYL27aFQ83mSLdlJZM72E2CTvp6dv 60/r5lX0PN9H/a28S+MviJ4b8P2Wm6XoR1rUPEySXWp28syRwaH4qsNIZAok j/eXltczPE5OEkMTbJVBR6dv+2l4n8N61pWjar4dTxBqDeN4fA09zotjLDFq 0reEhrZvbWN5JBFBJckQDzJWSJCWkmwjOKutfET4SeLfF/xYsI/gjF4r1Pxl A9pqLRaNoU0nxcGlXCaZdW/724Vphp9zNHaOdTFvGhkzEzxbpBB4n/ak+EPw Y+IWp+L5fhc2na14R8HS6LJ4lg0/Rbe7tfs2nHXT4RSVrlLhJlskN55ZVbBQ vNwJBtArcv6/122B/Ea/xnl1WaXQTrtzp97rSaVFHf3FjavaWtxOryiR4oXk laJGYEqjSSFQQC7Y3HhnnRCQXTIOMbh6CtvWvjJa/tB+EPC3jSw0a80HT/EW lLeWlrc3+m37tC00/lzCfTrq6tJElXEitFO4KyLnDZUZvhrRIvEGvPbzNIiL bXE4KEA7o4HkXqDxlAD7Gu/L8JDEVJ+0fKopy012sZ16rik4q93Yq/aY/wC+ n/fQo+0x/wB9P++hU/8AYcX9+X8x/hWR4c8SeHPGOr3un6Rr2marfaaoa7tr O+inmtR589vmRFJKfv7W6i+YD57aZfvRuBV8r/5+S/8AAf8Aghy4n+VfeaH2 mP8Avp/30KPtMf8AfT/voVINJgMnliVjLt3BAwyRnGRx6/zpy6LEerSgjqMj /Ci+V/8AP2X/AID/AMELYn+VfeQ/aY/76f8AfQqK7WC+t3ilEMsUqlHjcgq4 IxgjuD6VzPxS8T3HgfxT4CsrVIZYfE+vSaXdGYEtHGumX92GTBGG8y1jGTkb Swxkgju7rwtBb6PpNwsk2+/tmnkGRgMJ5Y+OOmIwfqTXW8HgFhvrXtJct7fD 1tfuZe1r+09lyq9r7nKx6XfX19Fd3mpyWsschb7NaSo8BjIj+RiyAvzGW34V gJWUHAzUWneDbLR7+Rooraa3mvZNQZriVXYXDknzApTgphFTDLwzk5IBbbgO mXmoS2sGoQTTw/fjSdGdeXHKjkcxyD6o3oatSaNbwoGeWRA2MZIGc9O3f9a5 P+Ez/n5L/wAB/wCCa2xP8q+85bXtP17UIJha32lxF2V4t8jokJjmZ4+g3OJF 8tJBuXbhiN2cVZ1jQDM1rPZzubqwiEMKveFBIvmwuwkk2uxyIVBPO4FgeuRq G50geIbfSDqlquq3UM1xDZm4T7RLFC0SyyLH94ojTwhiBhTLHnG5c3P7Ei3E bpOPcf4UXyzf2kv/AAH/AIIcuJ/lX3nOm31SOBWeSzuZ51E08ZvJYY0nAhAW JlDFYvklJBB3FgDwxovNKu4NYN/YS28Unnu7wPdEQ36vFHGS/wAjFHQxjbtB 4U/89GxyPxG+K2oeDvgF488WWsFk+o+GLLW57SOZXMDtZPciLeAwYg+Su7DD OTjHaHw5oXxI8Y6Ta6hpHxM+Fuq2F7BHc29zaeD7meGeGRQ8ciOmrlWRlIZW BwQcjPGevE4LAUIwlUqStNXXu9PvMqdSvNtRitNNzqrPw1qdto9kn9tSG+gB hmlSYss8e5AJAJd4WXZGpPBGWlHBfzA9vBUF3o9lHdNBdXumXYu4mM+z7Y8a tHC074LFtnl72x95eAV+WuefwB8WI3CHx78O/MOTt/4Qe8zjjn/kLdOR+dPb 4dfFkHA8e/Do44P/ABQ95x/5Vq5f+Ez/AJ+y/wDAf+Ca2xP8q+86KPw80Ud2 qJp8MkiN5c0EghuLiRmPmtJIifL5ipDuZF3A7sfdQ1sWEaafbtGbh5cyySZl kDMNzs23Pou7aB2CgVyXh34TfFbWtVEEvxB+H0EYilmd08B3cjBY42kOFOrq CTtx1HWs3wB4j1LVvEOmSwePvBvj3w1rWmXt1b3uieH2sE823ubaH5Zv7Ru0 lXMsqsNqkNGOTgitKNLLas404VZXbS+Hv8yJyxEU5OK08z0b7TH/AH0/76FH 2mP++n/fQrz/AMTSeL/EXj2fSvDev+FtBhsbK3uZTquhzak9w00lwuE8u8tw ioICTkNnd2xzyN/431/SvCF14huvjj8Dbbw9Yi3a51SXw/IlnbCdIXgLynWN iiRbi3ZMkbhPERneudMVhcvw9V0alSV1/d/4IU54icFOMVZ+Z7d9pj/vp/30 KPtMf99P++hXnv8Awrv4tf8AQ+fDv/wh7z/5bUf8K7+LX/Q+fDv/AMIe8/8A ltXNfK/+fsv/AAH/AIJdsT/KvvPQvtMf99P++hVPUfFOmaPe6da3eo2Frdav cG0sIZrhI5L6YRSTGKJScu4ihlkKrkhInbopI891VfEvg7UdK0/xN8ZvhfoO qa9P9n0u1n8FSpJqT5RdkKya4jSPudF2opOXTuwFcF471i/8SeKf2Zb/AFSe 0utRb4peJLCea1tWtIJvsWmeMLBHWFpJWj3R2qOVMsmGZsMRirVHA1Iy9hUb aTeqtt8yXKtFrnSs33Ps79jX/ksPjv8A7Ami/wDpRqtfRlfOf7Gv/JYfHf8A 2BNF/wDSjVa+jK8iPU6GFFFFWIKKKKAP5kf+CvHgc6l/wcNfHXxQ1paajH4L Oh6yLSYZWeW38NadNDE/YCWVI4x7npXxj8NfFDXP7UMGpHT7XxBFPqc1vYef Mpm8u1h5JJBALLJG+B1Yk4yK/QL/AILV65b/AAx/4KOftReJc2q3Goa/4e0y VJI8S3SweF9JvY0jl3BUG+Jg4ZW3CRQuwgk/In7E3wS8G/FDwzqen61qcQ1S /SKWG5eAi402XAaEAg7wVO4buM9sEUAfU3wz/Yu8H/FIy+I9S0zWvCWq3Uiy OIbmIiYAY3gquWHXAYgjAHYV7x4L8DxfDfQGsobi7u4VAbzJnUuueoyOAM4/ PrXlX7M/7OOsfC8LL4k8RTTWti4SB4X2GdeQCwBwT90H1OScE4r3DxWbBgkM MzKXBypO6RRjngfzA785HUA4T4p/GnTfh9cRvLD9r+XzJIVzlFLY79+O/qa8 x/aM+BnhP9pDV/hpq3mvJba5rLQeZGNhCR6deXKggg8h7ZM8dMj0NRftJ+H4 Lq4WaNmKojLIMEY75IIzzz0GOecV8QeAf2sdc8F/F3XLGyvBcWfh++vdQsUD MxjljsbyFuAT1MzHgf416OVf7x8pf+ksmex+hF98QdK+B9vZeGdOkmvH06Py iGztjUdOgHPPH49K9W8Nazb6jHaiCURwzxpt+XBO5e2D1z/Pivz1/Y1+IOqf G9NRvdXla51U6rNHMJFJZV3K/A7j58dyNuK+6/h/J/Z1nApVibQIqIw2DGBz t6jA9QPpXnFHnP7SP7Ddl8bfGsmq32vaokMiiP7LFEvK46b85wcdDjk18k/t D/ss2Pw9FzqraX4d8J2mjzjUoIbcwz3d2YvnCSeWoUq2G3A4yM8HpX6JaZ47 ufFPim/0W+0+OKxgQMtyshIQMOg5wfoK+YPi/wDBnwR8HfiNd6l4h1fV9Z1z UfN+zR3EjRW9oGwWZRyu7pyCCe+QSKAPnT9j2HS774P+N/BXifzprUxy3EUV oux5LuK0E9k46/LKiX8e4fMBdyED5uf6kP8AgmdpTaD/AME4f2frF1KNZfDb w5AVPVSul2y4/Sv5a/2YtRsU/bmRYHgfw/Lq2maiPMAWJYLa4t4pgQ2GKiI3 fTqoJHUE/wBVf7B9pNYfsPfBqC4z9oh8DaIkueu4afAD+ooA9XooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigDgPg3/yUX4sf9jVB/6Y9Krv 64D4N/8AJRfix/2NUH/pj0qu/pIAooopgFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F/wUy/5M48 Z/8AYLvv/SG5r32vAv8Agpl/yZx4z/7Bd9/6Q3NTPYa3PhHwl+yB8PPj/wDs 9f2p4u+KHg74c3y23hS0t9R1KZbbUNEEWjSuk1rOLuAxzTG4kaMuGTdpzEpL tPl/dHwO/YJ0b4P+EfCGmr4l1jUI/Aviq68U6NIqxpsabS7rTBbylxI0qJBe SsHZvMZ1Qs7YYN8E/DT4e+OPEHw90/V/CXhjxjrX2afwG5l0KX7LIFh0XVVm HnNDKjKv2mASxlGylwqtgSA13Xws+DHxf+JP7LXgZfF/gn41eLbfwb8T4dTW w0nX38HeJJtIbwU1q1zZyXOo2U1nB/at5L/o32qN0iMkRQqrRFLC0acPbR+K e/y0Qnn2PxVX+zK7fsaCvC/Rzu5JfNL0Z9pJ+xDoVp4H0HwpB4l8VweDvCt5 o15pOhobH7NYf2TcaTcWUSyG289kSTSsndKzMNQuwWwLYW/Ny/s4+Mf2aviJ ceLfhL4d8N/EDxD4q8O6N4b8TXnjfxnLoU90mjrcLZ3jzWuj3kl5eTLezLNJ MygCCAIANwHg3xq/ZZ/aM+J39s6f40vNR8baZFo3gy8uYfDN3LoTaveaZquk 3uoJYXX9tQJb3jva6m8cg0+yyLq1Vr3EKpF7B/wTt8I/GbwV8QfiVb/E+w8T WmkXH2O4sbnWNei1WLU9Ua71M39xZYvbmSGzNv8A2UqJ5enwlkleLTbHe8I1 e12/60/r5B0PQ/Cn7J+nRW/hfWpVuPCPivS3vr24TRNQW+t7abU9WtNZ1a1S a4t1M1vPd2vlmRoYpDFI+wQuylLOlfsf+HdIFleDVNduPENlqw8RHXZGtvtt zqw0I6Gb90WEW5lazPKCERbwCIwPlrxk/B34h2nxMvrzXPA+ueONC8A3Ovar bQPe6X5fjWfUPEthrelHT1luhtm0qGz8ndfC0AmWLyXePMq5+pfsveNtW8Qn wjH4S1jS/h7JcP4hWCzu9Mi03TtKbwc2gL4Vhj852+0JeN9pXNt9hEfPnGT9 0ZV+Ra2/4f8AXf8A4In8T/r+v68z0v8AaJ0U6H4rgjNzNdNNE9w0kqopHmTz OEARVXaoYKDjcQoLFmLMeD8Oa5F4d1m4upllkC2N2iRxgF5Xa2kREXJAyzMo BJABPJAyR0nxKvdX1aDR77XLHXNJ1DULR7t9N1lrJ7/S1kuJ3S1layd7djEr CMGN5PlRd0kjbpG8p+Kerajo/h4tpEtnBqNzd2tnBLd2z3METT3EUO940eNn C+ZnaHXOMZFe1kSg6lb2nw8kr97aXObF35Y8u90W/wDhat7/ANCZ4o/7+6f/ APJVeKat+z2NT+F/jXwrHc/GfT7XxjqV/qUN3Z3egRXnhw3t5LeXEdhJgiPM 00pWWUSzR7wUkVkjZPRYfh/8V5xlPH3w6ZTn5l8D3hHHv/a3qCPwpsXgP4r3 KsYvH3w4k2na23wTeEK3of8AibVzqlkq1U6n3L/I358ZbZHLwfDIWGopcWGn fEXSEXxHP4ja205NAtreUzQvHLayKpy8MjyPM7sTcNK2fOCqqL0XwJSX4HfC TRPC1t4S8TTppUJDyLPa+W8ru0kpjE9/NKsZkd9iPLIUXau9tuTZPw7+LX/Q +fDv/wAIe8/+W1MuvCPxH0G0nvtZ+JPw40zSbKGS4vLx/A9yEtYkUs0jl9YR QoAJLFgAATTcMlenPP7l/kJyxl72iRfEK61j4jePfhzLB4X1mws/D3iGbUr6 5u57PZFEdJ1G2XCxzu7Ey3EQwFPBJ6A16R4t+I50vTNBtbTQ9Y1g21iyTyWj WyJC5uZ32HzpoyTtZTkAjDDnOQPOvhl4tvdd8e2Nsvjrwj4+8Oaz4X1PWrW+ 0LQ20+NZrPUdLtNolGoXiTIRfTAhdhV4RyfmWvV7rwtBb6PpNwsk2+/tmnkG RgMJ5Y+OOmIwfqTXquGV/wBmW5p+z599L3t+RyJ4j6xey5rfKx4longiz0fW 9SuH8L+Nb/TrjU5dZ07SrpdENroF5OZmup7VldZ988txPK7SyyFWlYRmNDsr J0P4YahpESede/Fy/uE0kaabiSPwvbyvIl09zBdlreKImWLeYljz9naPPmQy Ozu3vX9hxf35fzH+FeCeL/8Agoz8EPAOtavp+seLtTsLnQmvhfeZ4d1MxW62 dwbWeUyC2KGH7UDbJMCY5ZwYY2eUFK8mNPJb2U6n3L/I7HLGdUh3xC+BfhT4 g+O7nxMfh3r+ka/qM6Tanf2NpoPn62qwi28m9aV5PtUJtGntvLl3KsdzIVCy JDJFD+x38NtV/ZZ8B61pd7YeKfFF/rmsNqUl0LfT7OK2hS1trK0tY0a+lkZI LOztYfMmlklkMZd3ZmNe1+Eta0Px7pst7oer2OsWUNxNZvcWN3FcxpPDI0U0 RZMgPHIjIy5yrKQQCCK1P7Di/vy/mP8ACjkyVac8/uX+QOWMbvZHlHiT4Zan 8TP2efGfhZRBo2p+L7XWrW3OoSZitGvZLnyjM0PmfKFlUtsDkDOASMU/4Of8 E8fhl+z/AOGNI0Hwh4ktNE8PWscg1nS4tPje18VyvYW9i1xeh7BmWUpaxSO1 q0Alk3NIH3sD1/grWJPElvNJOqJ5epXllhMjKQ3csKnknkqgz7k9OldL/YcX 9+X8x/hXs5nHLPZ0PbymlyLltbbz0OPDPE3n7NLfX1OUvP2X9APiJtT0/wCI baPcSatbajKLTTbcia3hiEY092k05nNs3zsTu89DK4jmjUhR0mifDGw8OzWc dl4+jt9MsJ0aDTY7IJax2y2gt1swBZBxCHAnGG8zzON/lfuqy/FXi/wv4EvL KDXPEOlaPLqVzDZWi317Fbfap5nWOKKPeRvd5GVFVcksygDJAPLXX7Sfw60y 00V7/wATJpU/iK/XTdMsdQgms9Qv5mvIbMGO0ljW4aMzXNv+9EZj2TxS7vKd XPkezyR6c1T7l6djrcsbfVK/9f8AAPQbDS9S+HmkXF5pvjLTvGOqppUWmw2u uTSabbTSbZY5buSa101mSWRZdzKsRjLQxqiQhnY8fpWk+LtV8bw6r4hTwDZW thpl1awxaNrmoahcXEks1owys+m2yKqrA5J8wnkACuj8KajovjzwzYa1oerW es6RqkK3NnfWNzHcW13EwBWSORMq6sDkMpII71R8a3reHL/QIYQHXVtQazlL 8lFFrcTZXGOd0SjnPBP1HTgo5MsRT9lKfNdWvbe+nQxrPFunJytaxQf4CeF/ iT40GseK4/C17Fpr2F5pEOqWL3UthfWrXZS8i/cuscii6/dyKwdTu6cE4Hxc /wCCf/wu+OHgm90rxFrGlXt9cW19aWuuPpqS6rpEV58ky2k0liwh/cbLcMF3 eTFGpJKhq9E8Rtp/hP4XSeJL46i0Fm929wlnayXkxihiik/dwRI8ssh3sAka s7HaFUk4PmHgL9r/AOGHxQ1ybTfD/iDUdVvLJ5YtRSHRb4jRJY7dLh4b5jBt spQrlPLuTG5mingAM0EsadGaRyp4uo60pqfW1rdPIWGeK9jFQS5f+D/me5f2 fpn/AEMekf8Afq7/APjNH9n6Z/0Mekf9+rv/AOM15t4Y+K3hTxfHpv2TUb2G fVvKFva39jPYXYaUTlEeCeNJY5MW1xlHVXXym3AVzngj9r/4NfEfw5pWraN8 UPBV5Z63dW9hZA61bxTS3c6K8NoYnYSJcsrriBlEuWAKg8V53ssk/mqfh/kb 82N7ROu8e23imTxbC2l6X8KNcsNKlW50y91XxBqdpdxSGFo3kEa6RMIm2yzR grKSUY5wHK14v8SvCl94F8V/sv6Vq1xpFxqyfE3xFqF3/Zc089pC17pnjG/W JJJoYZH2x3UaljEoJBwMV6hL+0H8NU8f6N4UTx34auPEviC6ubPT9Lg1SCa6 uZbf7SLgCNCWAiazukdmACyQPGSHG2vIfi34xuvEX7VXwx0udLdYPCvxfm0y 0aNSHkjfwBqd2TJkkFvMupBkADaFGMgk9GHhlihUeFcnLle+1jGo8RePtbWv 0PtX9jX/AJLD47/7Ami/+lGq19GV85/sa/8AJYfHf/YE0X/0o1WvoyvAj1Ot hRRRViCiiigD+Yn/AIOP/E9rZft//G6yBgluo/EtvNIoXdJbbvCfhnyHIHID tFKikjkh8d64j9mr4XeB/BHh7QtQv9dtvCWsWioZfs8aySXcZjTeJCB8vzhj uPTAwoHBm/4OXiyf8FWvi5F5UflX3iGxWeTO1pVj8K6AwQnpwXJGT/EfWvEv gL8EPDXjGHRtR8Q+PdVQ28EUtza3twzwuCAxVUPBBIxht2ccnFdtOWE5Vzxl fya/+RJ94+1fiz+13P4Y8Hvc+FNNtfGGi2sISadJjJLAQQMug+Y5yCTt7Z7c bv7Of7Rcnx48BjV7iOytpVQ74bfcDAcEMp3YO5SCMfp2r5b8QarofhP+1Lvw N4gilhtVIlspbYCGZQMZ4wDkbug6gZzjFd/+w5rF5rWuX8q6bLZaZqNus7Ll vJ37iQQM4wevHXJq+fA/yz/8CX/yIe95HvvxFv8AS9T0CSC4i86DGSGUgnOO Bxgc5z16mvlfxX4G8K/Dj4k6RqGl6Db27SXk8dy6RR5keXTr7YjEJliX+vQe 1dn+1B+2b4R+GPjOXw0DLe67BEXaBASIiR/GwBC4wepzgcjHNR/Cn4Xal8Xv DPwx17VYTYSeJ/FhugGTAlhh0/UZITgkEKyQ5xxxJ7V6GVywX1jSM9pdV/K/ 7pE+a3Q7L4L6Xo3h94ri00rTLQXxN2XjiUM0rHl+Bknpkdc5r1bVvEEPh7w/ f3cj+VaWltJJJJjaIlUZzxgce3p7V8t/GX9oJP2UvjPq+j6tocktksq3dmft CxA27jJkQt12MNpUHPceleyX3i2D4/fACe98LPCy6nakq6vv8sE4ORxk4GPr x1rz+fA/yz/8CX/yJfveR4x8L/jt428f+MdXvYfFkuleEYLhvsE0pdTeKp+f YgOCAeAeMj8K6j4yftrfDrxdpdlp+p6bc6tqumyq0I1CH7PI7A8unLMc9QQM c+uK+XviT8Pr9dXnsb7xtd6NY6Quzy0jaIZIGSdrg4+XHp14r0nw9puu/E7w /preEvCmmXtvFFiTW7yykm3xjjCb2AyeTk9MHrRz4H+Wf/gS/wDkQ97yPK/i Z410oftb+HNb8NzzNZkPDMJoiNkhjaMI4U46SA/h2zX9cP7IUUtv+yf8MYp5 4rmePwnpSSSxpsSRhZxAsFJOATzjJxX8ifx08Car8N/2g/C/9pebB/at/Fva K3CFCvlqQAvDYDIRx+nI/rs/ZAhW2/ZM+F8SvFKsfhHSVDxHKOBZxcr7HtXP iHQaXsU16tP8khq/U9FooornGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQBwHwb/wCSi/Fj/saoP/THpVd/XAfBv/kovxY/7GqD/wBMelV39JAF FFFMAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACvAv+CmX/ACZx4z/7Bd9/6Q3Ne+14F/wUy/5M48Z/ 9gu+/wDSG5qZ7DW5+Zun/GvxT8D5fDmuRSePR8O9FsfB9z4tm0bX9Wt7XTdO fS7v7VI1pZTxku8UQk+0DlDYQo29X2H7e+BX7VcviLwr8GLzwtFrHh/TvGHx LvNB8QWHiDxJd65c+XF4Z1S8aBp74PPbsk9rbt5QEbBomVtvmSA+BfBL49+C /hr8E7DT/Evwz8CeLroXPg7yb/xJcLBBHNdaLci1LOLO4ZPJeylVGI2g38hJ jAdm+uNN/wCCgvwd8DfC7w74g0HTfEFzovivxOfDjWnhfwld6ld2mpNpc2ps ZLKzhe4mBtoQ3m28UykSRyZMW6VcKWFqwcqspe7K1l2tv956WLzzAYmhQy6h SUa1BS52k/fU23BtttNpXWlrK2nUxvGv7emrar4N8O3mjW/hfQr7WPEXhe0m 0m71xJ/EenW9/qXhZLmC707yQIZUg12RZcTP5Ik0+QbvtgEPo/7O37X9p+0N 8V9e8O2GivbWOk+EPDXi+z1gXiyQ6zb6yNQMZhiKrPHHGLEjdcJFI7O37pVV Wkb8Y/29Ph58G/C9xq88niLxHZW40Z/M8N6Beaul1Hqt/bWNs1tJBG0d24e7 gkeC3aScRSxuIm82ISZ16vws/wCCgs89tZ+JPiOkngyU5Gg+J/Evgk3cNwSI rxDaTWZ1Cxm+zyfZ7xfOtpPLm8iRsSV09Nv60PM6annLfGP4gah8RdQstb8b a/4I0j4gXmvaXZ3Ulppaw+C5dO8S2Oh6YunmazO+fVYrwTYvmulMxj8lFjzE 1Nv2qPGegeKF8Yf8JXNqngCK6Ph+K5mXTU8PXmlr4QPiAeK3nEUcgd7pGtSR dpZCInEayDza9n8J/tK+D/D0Phrwn4cg8T+JLiV73R7KM3JvbuSLSdWtNEv5 57m8n8yb7NPco8skkjzSpHKy+bJhXoeKv2/fBXgXwFf61rVrq2k3Okzy6fqG mXclnDcaffxeHm8QS2U0zXAtUkSxQlpDP5Af/lrty9C+Hb+v60/4IP4tf6/r 9PU86upfELeCvDMPi3WY9f8AFFjp5stWvY723vlkuorm4jlXz7e0s4pCjqUJ W0t/mQgxIwYVynibwy/i37JaLd2liI9Ssrp5rnf5arBcxTsMIrMSVjIGF6kZ wMkeq/HfxXB4y8QwXcERhSEXFiUM8ExLW95c27NmGR1AZoiQpYOudrrHIrov FeGtEi8Qa89vM0iIttcTgoQDujgeReoPGUAPsa9rIuT2tb2mkeSV7dtLnLi7 8sOXe6OF+Kv7JOgfFTV728f4lTaO15pUemBLTSbO5EJS8F0twDd6ZOzSZBTY 7NCFbcIhKFlCa1+x74S1Hxq+t2XjeDRprnVrDWbyKz0i2Zb+e0WWPMsk2nST MZIXjhaQSCWOO2iEMkJMpk7j+w4v78v5j/Cq2qx6foVhNdXt4lpa28bSyyzS rGkaKpZmZjgABQSSeAATWDWRpfHUt8v8jWU8bvLl/r/hxPhD8FvCXwP+H9h4 Y8O61odjo2mBxb20NlLBFFvdpHKpFbIg3O7McAZJJ7mrXjx9Q0nR45fC994M 1jUDKFkh1XUNQ02FIsHLiSOwuCzhgoClACCx3DGDFHZWUupS2a3StdwJHLJC JFMkaOWCMV6gMUcAnglWA6Gpf7Di/vy/mP8ACko5FLXnn+H+RMZ4t/DynBeB vBGvab48tdT1S1+HehaPoXhG98OaZpnhzVb69Yme+0mZFVJdNtI4oo4tPcYD H7ygKAK0/Gnx41XRZbDTLL4aeOddh0y18n7fY3GkJbXLNLJKdgnvopcL5oU7 41+ZWxlcMa/xS8T3HgfxT4CsrVIZYfE+vSaXdGYEtHGumX92GTBGG8y1jGTk bSwxkgjjfiB4z8QaDP4l1Sbx/wDDjwJ4W8OXUVpNdeI9HlmVS8Nu+97g39vG gL3CRqpX72PmJbFeyoZX/Ztry9nz/O9vTY5r4n6xpbmt8joP+GhvEH/RH/iR /wCBmg//ACyrxjX/ANnf4aeJ77Ubq/8A2WfEtzd6q2sS3dy8uhG5ml1Zgb+Y y/2pv86RVCLKCJIo8xxskZKV7AngD4sSkhfH3w6ba21seB7z5T7/APE1/H6U 7/hXfxa/6Hz4d/8AhD3n/wAtq8lRyNa80zq5sbtZHP8Awr8UD4KeG30fwz8E vifp2mPN54tm1fR7iON/LRGZBJqrbC5TzH248yWSWZ90ssjv0v8Aw0N4g/6I /wDEj/wM0H/5ZVF/wrv4tf8AQ+fDv/wh7z/5bVPqPhLxh4bvPDun6z8X/hvp +t+KYp5tM07/AIQSYz3yws4l8pX1xGkKKm5tqnaCCaOXI39qYXxq6If8M9Q1 LTfh/dajeeHdWtNRN7qOojRnltGvCHu55o4g6zG38x0ZMfvtgLgFhziL/hob xB/0R/4kf+Bmg/8Ayyrb+GurXfinwL4c1DUWt2vNT020ubo20RiiMkkSM+xW Ziq5Y4BZiBjknk9T/YcX9+X8x/hXr5pHK+Sj9Ycrcq5bdvPzObDPE3nyW319 TxrxX4zbxt4g0bVdT+CHxHub/wAP3TXthL/aWip5ErW1xas21dUAbMN1OuGB Hz5xkKR5n8YP2cPh9+0CdOPjj9nf4reLDozQPp7ar4rsrprAxQeRiIvrRMay IEMyrgXDxxyTea6Kw+ovtukHxFNpI1O1/tO3iinltPtMf2iOOVpVicx/eCu0 MwUkYYxOB91sO1qfSvDlmLjUNRt7GA3ENoJLidIlM00qQwxAtgb5JJI0VerM 6qMkgV5KWSaWlM6m8bu7f1Y8/wBJ+N+taLbPFD8Ifii6vNLOTPqmiztukkaR gGk1RiFDOQqA7UXaihVVVEV38RNe+InjDwvDN8OfGPh6z0/UZLu5vtSutJaC JPsVzEBtt72WQkvIgGEI5ySBzXqK6LEygh5efccfpWF41vW8OX+gQwgOurag 1nKX5KKLW4myuMc7olHOeCfqOjBxyb6xT9nKfNdWv3voZVXi/ZvmtaxX8d/F 298MeH7TQrb4f+MPFKs813Ld6XcaZHBGJBGgiP2m8hk3jySThNuHXDE7gvjH hnwX4e8Ef8Jk+h/AL4m6Jd/EC5kvPEF5p2u6XaXmpTvE8TSNPHq6yq2JJHXa w2yu0q4kYvX0pB4bgm8JWl8XlEs13PAwBG0KiQsO3X94c/QVB/YcX9+X8x/h W+aLKPrdT27mp9bbdCcK8V7KPs7WPGNO8Y3Gla7FqcXwe+Lr3kLXbKZ/EOlz xk3NwtxIWjfVijFWULEWB8iMtHF5cbMh8ol/ZW+Hq+HfA+j2v7PfxSstI+H8 Gr2emWUev6U8b2mq29xBf20rSaszvHKLlnzuDqyJtZV3K31wLKzaVo1uQ0iP sKiRchtobH12kNj0IPQ0yxjsNSubuG3vI55bCUQXKRTI7W8hRJAjgcq2yRGw cHa6noQT50Z5FfSczudDM4x53DSyd7O1uny7Hzj4B+F3hD4W+N18SeH/ANnf 4iaZrUT6e8U6azpTLH9g0yfS7QCNtXKAR2VzNFgKA2/c2XVWGJ4ltdXuf2l/ htrereH9S8NL4m+Ms9/ZWl/Nay3BhT4fajal2NtNLGMyW0uBvJwASBmvrL+w 4v78v5j/AAr5m+LfjG68RftVfDHS50t1g8K/F+bTLRo1IeSN/AGp3ZMmSQW8 y6kGQANoUYyCT10f7N5Kv1VycuV77W0OSo8TePtbWufav7Gv/JYfHf8A2BNF /wDSjVa+jK+c/wBjX/ksPjv/ALAmi/8ApRqtfRlfOx6nUwoooqxBRRRQB/Lb /wAHJXhm48Uf8FJfj9cwyZi0DxHo0tzG2cLHP4W0UCTr8uGgC++7GRjBwPgd 8LPCXxd+FemPfRxWEstjC0J8kTFd0Y46jGMcc85r0H/gu5fw6h/wVa/am0d7 aaUXDaLcSMHIRvK8NaTMiDbhixMLDhgPmAIIJr5m/ZM+LWpDwU2m2d1FbXej t5Et5csGigixwwXq3CnAJweOe9AHouqfsAx+GfiBZ3U/jjwtB4ZWUXIsb6JT KRjJARvrnIavrmz06w0D4RXcXgk2L+VZP9jktJAV8zBG48n+Id84xj1r5C1v w9o97Fb6nrXjDxHfT6o53QRwKrEDHKnfkIc8HGePSvsX4EeCtJ0P4ZWtpZ29 7b2U9uMfapCZSCepz/SgD89v2M/2C/EX7S/x41XWvG26LR9D1Vn1g3Jbzb+f czNGhBGQSF3MCMBgBnIr7k+Pf7R3w4+D/jv4bWN7r+lWX9h69JcXUcCSOLSH +yr+3G7y0bAEk8S4HI3DjAOLXiL4qeC/2dtLk0bS7W6JvppJ55rRVbM0jfMW LN94n8htxjFfFH7UNlH4s+KWlWs1kBM+psyy+QY0KFQ46fRT+H1r0cq/3j5S /wDSWTPY+wv2ivgL8Ov+CgHw/i+w6np76pApm0/Uo4W823dlxgqdpKE4JBx2 9Aa87/4J8/skePf2U9d8T6f4oa2k0e/mjazltbhZYLlgXDuF4dSQVBBUe2a8 Y1j9r7VPgZqen6HoJsiJIhFJNMUihglABOH2sS/JIBHRQeAQa+nP2Qf2qtT+ N8p0XWoTNqGnwrNLcfdJYnrjA4Oc559a84o5f9sb9qHTvgddMmjaFYaxqpcR zOttC7xZ6ZLDtwTzxkV4V4b/AGj/AIi+LNRlu9Whb7Mp3ixhkRILSM8YDYUO SM5wAAema9x/bI8J+Cf+EsSTVdI1bzp1/e31hInYkAMh/A9RXzn8R9BHw+0G bU9J8TR67o2pRN+7u1w1nHsIz8mNvJPUvnHUUAP+IPiSz+I37RnhOC5FvKuh ganOslt5tu6iSMusgGQCYopMdslSeK/qV/4J430uqfsB/A66nkklmufh9oEs jyffdm06Akt7knmv5HvgZqkmi6N4m14/bZWv7D+yrWW6mCzvNMChfPAwFEoI BJG8YBOSP62P+Cat/wD2t/wTn+AN1gD7T8OPDsuB0G7TLc/1oA9rooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigDgPg3/wAlF+LH/Y1Qf+mP Sq7+uA+Df/JRfix/2NUH/pj0qu/pIAooopgFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F/wUy/5M 48Z/9gu+/wDSG5r32vAv+CmX/JnHjP8A7Bd9/wCkNzUz2Gtz4T+CHh/wX4t8 N6Zp2pfEfxt4P8VXUfg+8srfw34V1TUbiRbbRL4Ii3FpGylp47i8JiBDqlk7 EFX49+/Zw/YM8L+GPgn4e0PTviz468G+IIfHqa5oGp2fhS38KXKajD4WGkPY 2Om6xZzpJajTobl1Ty5WUI5EhMLMPlrw/wDsffEz41eC9E8bfDmC9vPEHhJP Bt1olq81rJpdxe2umTT+bewy3MLLHE8lqBMgdwlxMqI5L7fs/wCEXwC+JQ0v 4QWXxA1+zvfFHgXx9qHiNv7Q1+R7zWtNTQdQ05ZLcAyPnz9Qgd4mdlRC+53J AfnpYmrNyozj7sbW877/AHM9XF5NgMPh6GYYarzVqykqkesHB2j1+1F3Wnfc 19d/4JVfBvwrr9/r+khfAV9e6boOg2Go6dYaTFfaXJpt1YNpkkV9cWkl1NIJ bDTY1gupp7dzbxDyCzMX7n9lP9hLw3+yJ448Y63oOveJ9XfxhFbWpttU+xeV plvb3WoXaRxtb20Us8jTandPLc3klxdTsweWeR8s3E+I/wBkr4i+LPCnh7St X1W01rUtH1zw3q1/r9z4t1EjWDpt94YurmX+yvI+yW8k50u/2JG22J1R1IOo 3nlGkePPFH7I/wAVLzX/AIqat438SaT4u8I+H9NsNI8IeF/EPjC00XV7AXv9 qyRx2VhPLBBObqzKS3crzzmKQEhYUVetvz/r+vyPJs7Caz8MvhH4V8a/FbUo vjenhTVfBttJcaikOt6JCfhGNWuY9Uu7jbJbs0X9oXMEd239qfaEbyyIljhL xtNf+Afgr4d+L7avq3xh08XemeFz49utGu/EOlW6eWunnSZPGUrrGl2p+wbr U3HnLYhfmESygSCx8Pf2dfEKan4K8ZaLqK6xYLceINROm+IYrzSpXtte8T6f rSvJDNCZYbmytYZkjhmiRvNCRsYAXZNLwH+yp4o07xtoniXUdXsrG5XxtH40 v9JsrqW4s7Uf8Ih/YT6bBIyRl4EuMTozRoCFBKI3FSmlHf5f194W97+v6/r1 OPv/ABb4c8deDPDGqeFPiFafFfRrjTdieLLe/sb5dakjnnjllaWxSO1Lh1ZW EMaIrIVCrjApeHNci8O6zcXUyyyBbG7RI4wC8rtbSIiLkgZZmUAkgAnkgZI6 L4geBJ/hpoXhbRLy6h1DU7PR4zqd/FbR239qXzySyXd4yRqqB553lmfaoy8r HHNcjLaCR92SK9PKsTRpVKirt8souOnnYwxFOUox5N00zE/4Wre/9CZ4o/7+ 6f8A/JVct8XLHS/jh4Rl0XxH8PNe1CzdZDF5y6XM1rK0bRiePzLhgsqK7FXA ypOfavQvsI/vGj7CP7xpywGSyXLKU2vl/kTUniJx5Zxi0eD2nwPsdP8Ailpf iWHQ/HxXTrRLSS0kbRyLpYbmOe0V5RMJCIDFEu4kyOsEIeQgSCX1/wD4Wre/ 9CZ4o/7+6f8A/JVbP2Ef3jR9hH941NLLskprlpyml8jHDwq0b+yhFX/ruec/ EK61j4jePfhzLB4X1mws/D3iGbUr65u57PZFEdJ1G2XCxzu7Ey3EQwFPBJ6A 1U+Mf7EHh79qjwd4h0Lxn43vNJ8NeI7qSS90bTjs/tBRb2iQmeU2ckiPBcWo nj8iVAx2iUSIWir1H7CP7xo+wj+8a9ONTKlhfqvNPl5ua+l7lf7T7T2qSvax g3X7NOlv8Y4fGVr8U9X0+eLTDph0y3jX+z51a8t7mSaRGsGeSVlgMAZnIjin n8oRSSNIe30Hw3ZaPp5huPGlpqkhmmkE9zbyrIqPIzpFiK2RdsasI1O3cVRS zO252xvsI/vGj7CP7xrkeGyZ7yn+H+Rr7bFt3sjpv7P0z/oY9I/79Xf/AMZr k/iD4j8aNrEFnoFp8M7/AErTYRBDdaj4m1WyubjLtKxaFNHmWPDOyjEj5Chu NxURpe2bpeMblEXT3K3LP8iwYRXJJPGNrKSegz14OKFh4y0fUfC0utpeFNLh 3bp5YnjBx/dDAFuTgYBycgcgil9Vyb+af4f5B7bFvohnhDR7nwF4D0SxuGtd RvNG0q1gm/s6RminligRXWFpliYjepUF1TPUgCn/APC1b3/oTPFH/f3T/wD5 KqrffE7w3pmmW91caqkEd3B9phV4nWSSPcFDBCu7BJ445GSOASE134oeG/Dn F1q9uG2RyBYw0pZXDFCNgOchc+wK5+8uevGTyrExhGcprkVltsZUfrNNtpLV 3PHfjT+zXpHxs+PsfxIudH+Iej+JIPDUvhiCaxg8NyyWkLx3qCe3uLlZrm3m X7fKcwTIkmyNZUlQFGf4m/Z10/xp4ytdb1ix+KOqXVrqGlamkdw/h9ohPptz Dc2rqBhomEq3ZZoijuup3SMxjW3S39ztde0q+ur2GHUIJJNPnW2uBvA8uRiA F5PJJOOP4gR1BA0PsI/vGuNYXKNuef4G3t8X2X9fM4T4QXKfBT4SeFvBmk+E PGMuleEdItdFs5LmfTGmeG3hWGMuVuFUttQZIUDPQAcVc13XtQ8beIvDYHhz WdMg0zUJLuee7ltCgX7HcxAARTuxJeVP4fWuv+wj+8aPsI/vGtaNLKadWNZS ndNPp0dyJzxUouLS1JNb8dnw94I0u1t9J1PWJze3U0i2bQL5CmO3VSxmkjHz FXwFzjac44zhf8LVvf8AoTPFH/f3T/8A5KrZ+wj+8aPsI/vGrxkcpxNaVecp py9BUZYmnBQSVkeKfEv4R2HxO8TJqs+ifEnTbiLVLfXoo7K60fyk1KCEQxXu 2WR8yKkcACk+X+4X5Pnm8y1+y98NdM/ZM+Hlz4b8OeF/H17Z3moPqMsuo3Wl yTGR444yAUmQbdsS8Y9ea9h+wj+8aPsI/vGuCOWZHGXOnO/yPXnxBnE8N9Tn NOnp7vTS1vuMb/hat7/0Jnij/v7p/wD8lV88eNtK1OP9qP4c6vqGlXekw+JP jJNfWMdzLC8kkKfD3UbYsfKd1H723lGCc4AOORX1B9hH941znjb4Rad498Ve C9YvJ72K58C6zJrlgsLqqSzvp97p5WUFSSnlX0zAKVO9UOcAq3VCOW0ITdCU nJprW1tTynLETaU0rJntX7Gv/JYfHf8A2BNF/wDSjVa+jK+cv2Nm2/GLx176 LooH/gRqtfRteFHqdTCiiirEFFFFAH8q/wDwXr+In/CFf8F//jfC9w8cVzP4 fYozHyiR4e0wgsBz94KcjPAIxyK+Udd0KL4JfFuz1OaOaTwpeNJtS3lwZLYt iMMVPJjO0FTk425xuwfYP+DoC6e0/wCC7vxwkQZKNoBGemf+Ef0zFeJ/CPxN ZfG34ajwrqEsKajYyCewuJnSNI2MYjKOBz5bDAOOm1GPCNuAPf7D4saHosa3 mk+HiLqeAS29/qF15wUMMrsUjGOhxt78elfTn7LPiHxH46+C03/CU30y3V8G ihaaRVZid33VP1BGPy7V+f8A8IviPB8JPFL+H/Hq30P2EiKxMZX5YwD8m5l4 yCu1sEMCO9fXfiPxppmr+D/AkXhgz6fqUl2ktvBeN55TJGWlCYPTcRwOccDN AHnvjP4XfEu4+IuveDkureSylJuYb9pWMkcLhhtWPbhmAXruBAK1Y1fwlr8N x4CtvFmmskaX0kURunEYuf8ARbg5x95QSoboOtfTfhL4++GoTp+neIb611Hx C9wbPdbW/l8jkLgsD03dO3NeN/tXa5da5458JWut6U8VvB4h3wywh1iaMW85 ALnk8gE+wPPFejlX+8fKX/pLJnsdr4D/AGbvAPxk8MQWV74fsxFa3IuGkjlF wu8HO1snPPX6dutdT4U8HaF+zZd6prfiG8trOa9DQ2lrAAzrGCcKFHU4x2FZ mleK9S8OeMdFttPaSx8PNaK8KW5UpdStgMHfb7djx6VyHi671j4l/EO+udQ0 q90aJYjHLdzttitXQYUqWABBx0Pc4ycjHnFGx8evhLpvxZ0K38baX4kk0iKe 3IWC7gZ1bAOCcbsZx2Wvhr4+x6fqXiqx8N2V9DqOpeazak9kAqnPCqynADSE ggEbunTdXqv7Rf7YmoeBNFbwfbXtprGo2ofZdIFMEQcZUOVJ+blSFAyc5yBi vFbTTh8JPDI1bULnytXu4ZXclgJrXeGDuyE5aWQbwiZxg5bttAM741eLLfwN 4WNhZNbTR2qzwRNHl4rieRB5kgBJJMcZSLLEA4BAyu2v65/+CT5Lf8Es/wBm onkn4VeF/wD00WtfxTfELx3L461SOUq0VraxmK3ichjGuSTlsDcSSST69MDA H9rP/BJ45/4JZ/s1E9T8KvC//potaAPf6KKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooA4D4N/wDJRfix/wBjVB/6Y9Krv64D4N/8lF+LH/Y1 Qf8Apj0qu/pIAooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F/wAFMv8Akzjxn/2C77/0hua9 9rwL/gpl/wAmceM/+wXff+kNzUz2Gtz5Z/Zq07UdZ/Zje00jVDoerXui2NvY 6j9mW5/s+dtEshHP5TYWTYxDbScNtxnmtv4X+HdY8G3Pw20jxDetqniDSvBl xZane+c84u7qM6YksnmOA77nDNuYZOcnBJrpfhD+zD8KPEHwk8JXd/4F8BeO ZJNC09Yta1q20i4uL2FbSJI3VLyCSWBCqhhCT8hZsjcWJ6L/AIZH+DX/AERj 4P8A/gq8Nf8AyJXLobXJN4o3io/+GR/g1/0Rj4P/APgq8Nf/ACJR/wAMj/Br /ojHwf8A/BV4a/8AkSo5I9x8zJN4o3io/wDhkf4Nf9EY+D//AIKvDX/yJR/w yP8ABr/ojHwf/wDBV4a/+RKORdw5meR/tA/Ge7+H3jC10+38c+EfDCSWazm1 1Twlf6vNIxeQeYJYLuFFUhQNhUsCpOcEAcIf2ndTAz/wtn4bYH/VN9Y/+WVf GH/BzRongzQYvh3Ya/a6hpXgqy1K0gltfDUVr5trBHBqojitlOyBQMKo2gIF +6uAFPzl+yBrXw6k/aR8P+HPBjabNrPgrwBe6XrF1pKK2m3ok1SG7i8mdWPn tH57xvIwy2F+ZwuQVKqpzpU0m+dvbZJLd/OyXdvyZtRoOrTq1XJJU0nq9ZNu 1o92km30S9Ufq+v7S+pt0+LXw0/8NvrH/wAsqev7R+rt0+LHw1P/AHTjV/8A 5ZV55qHh3wBo15JcyRWpgEMV02nQ34uZYx9psUeNJorgpNuj+1lVIikUbmZV GwrrQ6T8MGtorhkjt5/sljcSQ/bpXSMedbGfcwJLylJpVKKPlEDHYCRXRyrz OW514/aI1punxW+Gv/huNX/+WVSp8fddk6fFX4a/+G51b/5Z1438UdO0XSta tINGSCNVtibpIbn7SqymaUr84eRCfJ8nO1iAcg85rFt+tVGCauJux9BL8c/E LdPin8Nf/Ddat/8ALOnr8a/EjdPij8Nf/Dd6r/8ALOvB7XtV+3/pT9mu4c3k e2L8Y/E7dPih8Nf/AA3eq/8AyzqVfiv4qbp8T/hp/wCG81X/AOWdeOQdRV6D oKPZruHN5HrA+J3i5unxM+Gp/wC6e6p/8tKevxG8Yv0+Jfw1/wDDe6p/8tK8 zt+lXrXtR7Ndw5vI9CTx740k6fEn4a/+G+1P/wCWlSL4z8bt0+JHw1/8N/qf /wAtK4e2q/b9aPZruHN5HVr4r8dv0+I3w1P/AHIGpf8Ay0qRfEfj5unxE+Gp /wC5A1L/AOWtc9b/ANKv2/Sj2a7hzeRqJqXxBIIHxF+G2OengLUv/lrUi3Xx Ffp8Q/hucjH/ACIeo/8Ay1qrb9Kv2/WnyeYuYYr/ABIfp8QvhueMf8iHqP8A 8tasWOnfFDU7uK3t/Hnw7nuJ2EcccfgLUWaRjwFA/tXnJ4x71Yte1a2gDdqt sOP9avX60nDzBSD/AIU38d/+g14W6Y/5Jpqv/wAtKQfB347NnGt+Fj2/5Jpq v/y0q34M+Peh+PPiHq3hixsfGkGo6L532ifUfB+r6bp0nlSiJ/Jvbi2jtbj5 mBXyZX3rl03ICw+bviz/AGSvx80YePD+0CfCX/CofBX9g/8ACC/8JidOF75e o/b/ADP7B/d+d5X9n5875tvl7eM1ips15ex7vH4J+MM/iqfQk8XeBpNbtbSO +m09fh7qRuo7Z3dEmMX9q7hGzxyKrYwWjcZypq3e/Cn446dayT3Gv+E4YYoy 7yP8NtVVEUDJJJ1TAAHPPavJv2CX8Seb4UXxeNZPitfgt4J/to6x5o1D7bnU /P8AtPm/vPO83fv3/Nu3buc1J8Nx8YF8AeIP+FtY51bQj4d8oWQzYf2gmz7e YOutcD7YLf8A4l//AB7fZP8AlvT5ncVj7H/YF+HviGw0rxB4w8QeI9F1uTxD s0iG307QJdKWy/s67v4XZjJeXJl8x5CQcptVRkEnj6NrwL9hnXLibwXNp7Op toNT1p0UXcBwf7XuefKVfNX7x5ZiPbkV77WtN6GcgooorQkKKKKAP5BP+Doq PP8AwXU+OjZHy/2Bx3/5F/Ta+E/Dniy68MapHdWkhhljbdkcZ+v61/TR/wAF R/8Ag1LP/BSj9uzxz8a/+F8jwZ/wmn2D/iTf8IT/AGj9j+y6fbWX+v8At8W/ d9n3/wCrXG/HOMnwAf8ABjKwOR+1CP8Aw2//AN9KAPyc8HfHHwR8YvC0OleO FFrqNnD9ntdRRWDRqQCc4OGG7edrDAz8pHCi3aXXxP8Agdbz6no+tvq2l2QW Fb+zmGo2KR7tqgHBeEHOBv2Htg7eP1bj/wCDGtogdv7UOD2P/CuOn/lU+ldH 4J/4Mw/EPw81BbnSf2sZLVwuwqPhySjKexB1SgD80fhH+374c03VV1fxfZwa j4iieGFZobRpVjhUYL7g33928kttP3flwATzn7Qn7Uz/ABH1Y3XhuZ5tDhuD PAZhIiO7RvGw+ZjIpCu4BOBznDDp+u0n/BoVr+oQuuo/tJaFqckjMWkm+FpR juOSP3erKD36jPNWov8Ag0ENzpkMF58f7J5IpC4e18ANbqRsZVBX+026E54x 39cjswNaNKtzz2tL8YtL8RSV0fjd4W/bGTwP4Yjsnt7gXkVzHOjJcecq7eMM uVKgc4xkYPUYqfWv2ofiZ8dIZNP0u81VNKuTLtuS5jTkFnXznPlrghhyzE8A c4r9g7L/AINALnRUQaf8evDUDRkMskvw1nlkDAnnJ1gDuO3aue8cf8Ga/ir4 jW8cOsftbS3dvDny4v8AhWqoidegXUx6muMZ+Kt9468L/Bi3b98viPxWxMxu 3HnLbOWDIylwCJASxLkbwcY28k+O+NvHWpeN9Qe41G6ecszMqlshMkE4+p/P GetfvGf+DGs+WAP2oACOc/8ACuP/AL6Z701v+DGgkY/4agA4x/yTj/76UAfg G+VYYyPwx+lf28/8Enf+UWX7NP8A2Srwv/6aLWvyCm/4ManmfJ/aiHOBj/hX HHHT/mKV+3n7J3wL/wCGX/2WPhp8NP7U/tz/AIV34U0vwx/aX2b7N/aH2Kzi tvP8re/l7/K3bN7bd2NxxkgHoFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAeARfB3xD8SvH3xdXRfip48+HrS+KdOCNoFnos5h8rRLIuV+ 36fdAmbz4vM3Zx9jg8vyszed0a/s3eMB4lW+Px9+LRtV0r+zjp39neGBbPP9 j+z/AG8t/ZHnfaPN/wBK2iQQedx5PkfuK3fg3/yUX4sf9jVB/wCmPSq7+kgP EZ/2VPHc3hGy04ftLfGqO9tbya6l1VdK8Ifa7yORIlS3kU6GYRHEY3ZCkSyE zyb3dRGsejqn7N/jHUNb8U3UXx++LVlb+IVdbCzh07wwYfDRa4jlDWZfR2kc qiNAPtb3A8uVyQ0oSVPXaKYHkOm/s2+MrHV/ClzJ8f8A4uXkXh7Z/aFrNp3h cReJ9ty8x+2FNHV03I6wH7I1v+7iQjEu6VqEP7K3jqLwrqOnn9pP41SXd7dw XMOptpXhEXVgkazBoIlGhiExyGRGYyRvIDbxbHQGQSe20UAeRr+zf4w/4SVL 8/H34tG1XSv7OOnf2d4YFs8/2P7P9vLf2R532jzf9K2iQQedx5PkfuKzZ/2V PHc3hGy04ftLfGqO9tbya6l1VdK8Ifa7yORIlS3kU6GYRHEY3ZCkSyEzyb3d RGsft1FAHkWqfs3+MdQ1vxTdRfH74tWVv4hV1sLOHTvDBh8NFriOUNZl9HaR yqI0A+1vcDy5XJDShJUTTf2bfGVjq/hS5k+P/wAXLyLw9s/tC1m07wuIvE+2 5eY/bCmjq6bkdYD9ka3/AHcSEYl3St69RQI8Sh/ZW8dReFdR08/tJ/GqS7vb uC5h1NtK8Ii6sEjWYNBEo0MQmOQyIzGSN5AbeLY6AyCTRX9m/wAYf8JKl+fj 78WjarpX9nHTv7O8MC2ef7H9n+3lv7I877R5v+lbRIIPO48nyP3FeuUUDPEZ /wBlTx3N4RstOH7S3xqjvbW8mupdVXSvCH2u8jkSJUt5FOhmERxGN2QpEshM 8m93URrHo6p+zf4x1DW/FN1F8fvi1ZW/iFXWws4dO8MGHw0WuI5Q1mX0dpHK ojQD7W9wPLlckNKElT12igDyHTf2bfGVjq/hS5k+P/xcvIvD2z+0LWbTvC4i 8T7bl5j9sKaOrpuR1gP2Rrf93EhGJd0rUIf2VvHUXhXUdPP7Sfxqku727guY dTbSvCIurBI1mDQRKNDEJjkMiMxkjeQG3i2OgMgk9tooA8w8L/AbxV4f+Jel 67dfGr4l65pen2kVtceHb2w8PJpupyJaiFriZ4NLju1leQfaGEVxHGJSQqLF iEen0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXgX/BTL/kzjxn/ ANgu+/8ASG5r32vAv+CmX/JnHjP/ALBd9/6Q3NTPYa3Pn7wD8QNWtP2Vfgdo dld6jBpD6VYHXVsL9rG5azaxuNpSZHSWPbc/ZXJidXKow5UsrUbj4+/FyyvN Lh0bUdXhsYdU0iwaLU5tPnmXSUjsWu7mST5me/aRruJvmMZSNyqiTy3k2f2B /wBkLwP+0n8BrTVvF8Xiq8vNLt9L0y0+w+LtX0uGC3GiadNtEVrcxR58yaVi 23cS/JOBXt3/AA63+Df/AEDvHf8A4cXxH/8AJ1YKi3qac9tD5j8UfH74+/YN Th0zxBdNJG0gsJjFpQeQQxa0kO4NHtHnsmiSSfKMPPKE8lNyR3fjJ+0p8cdP 0TxdB4Onvbm80yYN4bvJJ9M2apHcxw71nidFwbVmuTEwaPdsgEizjez/AEW3 /BLj4OsjBNP8dBvX/hYniP1/6/68++K//BLzw1b+Pfhp/wAI1YePLjQ5fErr 4t8r4g60vk6X/Y+o7STLqO8g6j/Z+PIzIM85j8yn7EFUueafGT41/F7W9B8c aNo+va99jmt9VbRL+ym063vpHks7BLO3MhVRHEs82onzFCTAWsR83ODLqeK/ iz8Ur3xr4huNP8SwQeHLnxlol9bWDJKbx7CEaX9rWG5F2qQwEw3W+AwEyZm6 +cK9E+HP/BHvwV4V10S654q+IfinT00XTrBYJ/GGtWsr3sH2kXV+8kN6oLXX m2+YlVY4jbHYMSEDoPEn/BKT4Y6jqeiSabL44022sr1p9Rgbx54hm/tOA28y CDcb/MWJXhl3rk/uNvR2q1h1e3MvxGppuzPAdE/Z/wBM+JPw78G3nxAsLrV/ Fdt4fsrXULi51KZ5nnWPMpd0kxIxlaQl8ksSTk0//hjz4c/9C7/5P3X/AMcr 1bWfClr4E1CTQ7ITCz0Ymwg864kuJPLiJjXdJIzO7YUZZ2ZmPLEkk1Vq1FLQ ifxOzPNV/ZA+HS9PDxH/AG/3X/xynr+yR8PV6eHz/wCB9z/8cr0einYm550v 7KPgJOmhEf8Ab9c//HKlX9lzwIvTQsf9vtx/8cr0CiiyC5wa/syeB06aIR/2 +3H/AMXT1/Zt8Fp00Yj/ALfJ/wD4uu5oosgucUn7O/g5Omj/APk3P/8AF1Iv wA8Ir00g/wDgXP8A/F12NFFkFzkV+BPhRPu6Vj/t6mP/ALPT1+CXhhemmEf9 vM3/AMVXV0UWQXOYj+DnhuLppx/8CJf/AIqpB8JvD6/d08D/ALby/wDxVdHR RZBcwF+F2hL0scf9tpP/AIqnr8N9FXpZn/v8/wD8VW5RRZBcyF8B6Qn3bPH/ AG1f/Gnr4L0xeltj/to/+NalFFkFzOXwnp69IP8AyI3+NRar4E0fXtKubC+s IL2wvYmguLaceZDcRsNrI6tkMpUkEHgg81rUUWQXPNP+GL/hB/0S34e/+E/a /wDxFH/DF/wg/wCiW/D7/wAJ+1/+Ir0uiiyC553D+yH8KoNIm09fhv4HWxuZ kuJrddEthFLIgdUcrswWVZJACQSBI2MZOTSf2RfhVoOqW19Y/DfwPY3tlMlx b3FtoltFNBIh3K6OEyrKwBBHIIr0SiiyC53f7B3jr+0LbUNB3g/Yr7Wptuem dWmP/s9fSNfOv7CXwE8NaBpF78QLWHVl8Ua5qGtWN3LJrV7LaNEurTgBLN5T bRNiCP544lY4bJO9930VUUlZDk7sKKKK0JCiiigDyD4of8FCPgJ8D/HV94X8 a/G/4QeD/E2meX9s0jW/GWnaff2nmRrLH5kEsyum6N0cbgMq6kcEGsD/AIev /stf9HK/AD/w4Wkf/JFfzAf8HR3/ACnX+OX/AHAP/Uf02vlv9kn9ly//AGk/ HK26+fFo9rIi3MkKgzTuxwkEQPG9j3PCjJPoQD+yI/8ABWD9loH/AJOU+AH/ AIcPSP8A5IpP+HsX7LRPH7SnwBJ/7KFpH/yRX4aadJ8Jf+CVPhDRU8X6QfF/ xLu7IXeheC7Ro10+yAX5Z9SuSCFJ5YpGrOSgAVdwI8m8SeJvi1/wUa8aT+Jf EWrySaQs26306FvJ0nS1OB5dras7MqqAQDIzNweBnAAP6Jf+HsH7LWP+TlPg D/4cLSP/AJIpD/wVh/ZaJH/GSnwB9/8Ai4Wkcf8AkxX89Xi79k278HeIfCmj tq0k+landBbyNECOGyCce1dX8b/2ex4I+K3w/h0m6a3sNR1h4Wjc7haP9huW DIP90SEg9wvIrowuH9tU9ne2jf3Jv9BN2R+9f/D2D9lr/o5X4Af+HC0j/wCS KD/wVg/ZaGf+MlPgDx/1ULSP/kivxy8G/seaGLG6OoSvq7XMTFTdKrFHK5O3 AGM/jivIfiN/wT7j1/w6dS8N6pLb6lZfOi3IA/eDjKyIFdGyOG+bHoa5xn71 /wDD2H9lnHH7SvwAP/dQtI/+SKB/wVf/AGWiP+TlPgCP+6haR/8AJFfzyfDX /gpH4v8A2Xftfwy+NvhJfif4OtZDFDPdXfm63oqk5Z7W4fMcybXJ8t1Vidvz Adc39sb9hLwn+0R8B4fiF8Odbttb8KXs7HTtVkt/s9/p0jcLb30JG5VLApuT IBXOWFAH9F3/AA9f/Za/6OV+AH/hwtI/+SK77x/+1j8LfhT8K9F8deKPiT4A 8N+CfEnkf2R4g1XxDaWel6r58LTw/Z7mSRYpfMhVpE2MdyKWGQCa/hY8beCd T+HniW50jV7ZrS/tDtkjJDD6gjgj3FfvT/wX0/5VZf2K/wDuRv8A1Eb6gD9g P+Hr/wCy1/0cr8AP/DhaR/8AJFJ/w9g/Za/6OV+AP/hwtI/+SK/iEihaeRUR SzsQqqBkkmvuz/gnF/wSq8QfG3x5Z3eoJpwlhiF5It7MiWGkQdTc3Ttx8oyR GpzxyRwCAf1H/wDD2H9lkHH/AA0r8AM/9lC0j/5IpB/wVi/ZZJx/w0r8AM/9 lD0j/wCSK/C3X/23vhD+w1f3fgz4SeFbX40fEOyBguvEuuw/Y/Dej3ABVkt4 Aqz3AXLc7owW243d/lz4s+MPjh+1prFzdfEL4i6nDoN5MZVtLe6kEJB/gjt0 JwoHADnIGM85FAH9O3/D1/8AZaz/AMnKfAH/AMOFpH/yRR/w9g/ZaH/NyvwA /wDDhaR/8kV/M54C+FXh/wCE+hwf2foZvbmVcpf30SPJN7/w4GQCBjPXmrl1 8Wr+DxClh9gRIy+2QyEh5OOgAOF79zQB/SsP+Cr/AOy3jn9pT4Aj6/ELSP8A 5IpT/wAFYP2WR/zcr8AP/DhaR/8AJFfzeWHxhl0hhb3k77Y1zGzR7n/E5wfr itDQP2hJdPhaW5tra6hZidwLKVz0HvXRi8P7CvOje/K2vudhRd0mf0Zj/grB +y0T/wAnK/AD/wAOFpH/AMkUf8PYf2Wsf8nKfAH/AMOFpH/yRX87rXHhz476 e9reW1vbXTjCK6hlPPZux47r3615xYah44/YB8cnXNEEmreEBkXuktOVRo3B DFRnAI7ZBFc4z+mkf8FYf2Wj/wA3KfAH/wAOFpH/AMkUv/D1/wDZa/6OV+AH /hwtI/8Akiv5qP2jfCPw8/bm+FLa/wCCHukltcT3UctoIp9NfqQezLyR8p7Z r86fFnhi68GeI7zS71Nl1YytFIAcjIPUHuCOfxoA/uc+C37bnwY/aT8U3Ghf Dr4u/DDx9rdraNfz6f4c8VWOq3UNurojTNFBK7rGHkjUsRgF1GcsK9Pr+YH/ AIMqf+Upvj7/ALJVqP8A6d9Hr+n6gAooooAKKKKAOA+Df/JRfix/2NUH/pj0 qu/rwCH9lP4X/tB/ED4vJ4++G/gLxulz4p04TLr/AIftNSE32bRLI227zo2z 5X2y78vP3PtM23HmPnoh+wF8Ch4mTWh8F/hMNYj0oaEl+PB+n/aU077F9g+x iTyd32f7H/o/lZ2eT+7xs+WkmB67RXiM/wDwTR/ZyufCVl4fk+APwUk0LTby fULTTW8D6WbS2uZkiSadIvI2LJIlvArOAGYQxgkhFxoan+wB8Cdb1rxTqV58 FfhLdal45V18R3c3hDT5JvECvcR3Ti8cxZuA1xFFMRIWzJGjn5lBDQHr1FeP 6Z/wT9+A+i6t4Wv7T4KfCS1vvApX/hG7iHwfp0cvh7bcvdL9jYQ5t8XEkkw8 oriSR3+8xJowf8E1f2dLbwrqGhR/AP4Kpomr3cF/fWC+CNMFre3ECzLDNLEI dryRrcThHYFlE0gBG9sgHt1FeRD9gL4FDxMmtD4L/CYaxHpQ0JL8eD9P+0pp 32L7B9jEnk7vs/2P/R/Kzs8n93jZ8tZ0/wDwTR/ZyufCVl4fk+APwUk0LTby fULTTW8D6WbS2uZkiSadIvI2LJIlvArOAGYQxgkhFwrge3UV5Dqf7AHwJ1vW vFOpXnwV+Et1qXjlXXxHdzeENPkm8QK9xHdOLxzFm4DXEUUxEhbMkaOfmUEN 0z/gn78B9F1bwtf2nwU+ElrfeBSv/CN3EPg/To5fD225e6X7Gwhzb4uJJJh5 RXEkjv8AeYksR7BRXiMH/BNX9nS28K6hoUfwD+CqaJq93Bf31gvgjTBa3txA sywzSxCHa8ka3E4R2BZRNIARvbOiP2AvgUPEya0Pgv8ACYaxHpQ0JL8eD9P+ 0pp32L7B9jEnk7vs/wBj/wBH8rOzyf3eNny0rjPXaK8Rn/4Jo/s5XPhKy8Py fAH4KSaFpt5PqFppreB9LNpbXMyRJNOkXkbFkkS3gVnADMIYwSQi40NT/YA+ BOt614p1K8+CvwlutS8cq6+I7ubwhp8k3iBXuI7pxeOYs3Aa4iimIkLZkjRz 8yghoD16ivH9M/4J+/AfRdW8LX9p8FPhJa33gUr/AMI3cQ+D9Ojl8Pbbl7pf sbCHNvi4kkmHlFcSSO/3mJNGD/gmr+zpbeFdQ0KP4B/BVNE1e7gv76wXwRpg tb24gWZYZpYhDteSNbicI7AsomkAI3tkA9uorzDwv+xT8HPA/wAS9M8aaL8J /hnpHjDRLOLT9N12y8L2Nvqen20VqLOOCG4SISxxpbAQKisFWIBANvFen0AF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXgX/BTL/kzjxn/2C77/ANIb mvfa8C/4KZf8mceM/wDsF33/AKQ3NTPYa3OW/wCCSH/JsT9f+PjTen/Yv6TX 1NXwn+xb+1L4U/ZH/Ymt/EvjK8g07R7vV9K077Vc6lY6bbW8j+G9NkUy3F7P BBGp8sqC0gLOyKASwFe/+FP2/fhz46+Cl7450fWLTVLDTbObU7yxsNRsr6+t 7GGfy5b3ZbzyLJbhMTCSJ3DxMpTeXVWnnUYc0tkTOSinJ7I7ebXfiAPErRr4 a8IPoouiouf+EluPtRt9+PM8j7Bt8zZz5fm4z8u/HzV4r4h+NXxr0j4c6lq1 jH4d1y5eK31rTobb4ea5bXws9Qt76Kx05rOe5QPeQXyWD3Ej3MG23kuPOtrD EU0n0np2r22q2VvcQTRvFcxrLGwcMHVhkEEEgjHcGrMUyzD5WDfQg1SaEpp2 szP8LQ6pZeFdOi1y807UNaitYl1C6srJ7K1uZwgEskUDyytFGz5KxtLIVBAL uRuPjXiPxV8ZdN+Jfwy8PabL4UksJtC1DVPGesyeFL+a3u7m0u9JjW1tAL8C xa6iur94xPJdMnkKdsyxSbk8QfBjwV8E/iTrfxLuLz4sTarpkGo6+1nL8TNc vNMuEWEtOYtIm1E2IiHnbUQwLHE+wqI8RtXr2o+MI9OjjBhdrm4vEsYLfzY1 eZzySu5hkLGGkYDLbI3IUkYKjVi3/XoR7aCfLc+a/iUSfHusE5Gb2c4Of+ej Vh1u/E7B+IOtY6G+n/8ARjVhUU/hVjee4UUUVZIUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUVYhstw+c7c9B3NAFeirkmnqVO1mz781UZSjEHgigBKKKKAC iiigD3T9ir/k362/7Dmuf+ni8r1ivKP2Kv8Ak362/wCw5rn/AKeLyvV6iGw2 FFFFWIKKKKAP5KP+DjH4b3PxW/4OE/jHo1ur4uJPD4ldV3bE/wCEf0zJ+vYf WvY/Ednpf/BK74J6Jc6FZ2V18VPFVtGmg2skQcaCWAV7tohnfKig/e6bk4yR n3f9sb4NWXiX/gv/APtF+M9RjjfTvCtz4furncgZm8rw1prooz6s/I9q+ENW +NOpftA/tj6v8Qr1TPpXhnU1soInYnzhHP5rggnHBZfxEZ/h4APRPgJ+z3de IfjDoni/4opNrGv+LZpb1mvnDmR/LdhuGTzwCF7DHFfYPgT4SWnw58dX9xpK INJ1hhK8Kj5YXwBwOwPJrnrm6sPj78M7J9Hu7e01W3MM9i+zmKWJ1kCnvglN pHcE+tdd8MvF2oahDPaanYtY31mAJVDbkJz1U9xQBR8RfCg+JviLYahcTPHZ aVIZYoxgiRyRyfYYH614/wDtKSatB8bvANxc3RGnx+IWSKEJ0Y2N1tbPsNwx 717z491u+0/w3dy6dD596BtjXpyep+gr5R/aXsvEF/f+CLDVdVlg1CXXBcI8 bEtbAWd0ePqOPxr0cq/3j5S/9JZM9j7L8EXcctnCVljkZUUkK2cH39Kx/Hd9 N4W0i7Swt2mnuSdiherE+n1Neb/s1aNf+Fta2X0891/aNuCkxbKSAdO/WvYr 3UpF0yZk5kRCUwcAmvOKPm74yfC7wi3wsu5fHKWI1zV5JJ0EzATLIybUVcc7 gAg4/u18qfCfxr43/wCCfnxXe9/s2W+8E+JYGh1rR71g1prVlgO4UZws8YYu jD5s5wD0r6g8ZWNt8R/HFydVfyktXI+f5nDdOPTirfiL4R6V8afgJq3hbUpH ZrNALG/ZAzq+SUIJycjHWgDwf/gpB+wn4Z+JvgzS/Gvw5v11rQtSt2n0e/iI dpMBibSQ5B3bgMbgDzxX19/wX8tpLP8A4Nbv2L4ZUaOWFvA6OjDDIR4SvgQR 2Oa+Uv8Aglvr0Vs/in4NeMvNkgs72NYXV8rY+cQgmAbj5HbPY46V+pn/AAWk /ZnuPit/wSH/AGZPBlxbQve6Lr/h3zLaQARNLD4e1CIo2eigt/47QB+KH/BN n/gn5a+LbI+O/GUi6L4f0VBfapq94q/ZNCtipZGfJ+aWQZKKMtgKdvzDPofx 0/bE1D9oTw9N4E8FwxeA/hJYysZ1tyUvPFLrkLNdOcOwKbT5WAF/iHzCub/a K+Pt1+0F4ds/Bvhe8urH4W+CLh0URu8f/CU38smZL6dAQrEtGoiDAmNMYOSa 6nwv+x5Fq/w383UJ2g1q8jD2kSjEUCBQQmPc5zj1oA5L4DfD3QfGHiuDS9Ln W3it033D7MPIwB+Vc8duoz161654ZuNE8B6je6RfaKdLvp9yWU9xmQXRPQo2 CM8jvW74bsl8H+GtNXStDs52sk8qQpsicSfdODj16n0qPxjK+ox6dDqptHvf 7QR4FhUnaoJJ5PoMZxQB5B8QfFOqz6XZ6XskNz4e1N2vCgyRETlCf9nBrtfi T8PNF8ceH7W/g1CP+1n2tb+TID5pIAAwO3/163PFemW2oeKL17G0SOeW3WG9 uiAqhT0XH8TY5+h965bwH8OLf4YfENL/AEeEXFpcw/ZwZZPuSH5i5GORw3Sg DgPHfw91TR/ASS6nOEvlLwQI42tIMdu5rz/TdPv9J8XR2Fw0kohIRgzZRWIH 5da+hdZ8FN8Sdbg1O/1i4guAzCzSPhNi4IIXp1rGsfA1l4OSbS1la/vr4vNN NKoLMTkgk/gK9DNv9+rf45fmyKfwI5M6ZfQWwjlSKwnzvDqwwig8E89z/Wus 0H4wWk9l/YHiO4t9Qt3+RjJy4U9+5wPQ/h2rT8F+B9Qa0Oqag1vfXrNHCi4x HEgJweepOfzxXpdt4D0WS9i1CXSNM+3RjAmNsm8DGCN2M9688s+TtTudT/YC +Nlr4s8PD+0PC+ubxPBIBJbTKSSFYdD1BwfSuE/4KKfBC0sdXsPGOgf6Ro+q 2cdzHKmGVreTBiJPcrloyR3j5x3+h/j/AODtJj0fUfClzKpsdRQTwAgE2jZO No7cenpXmP7PssXxM+Buv/DHWFLan4fMz2TOdxML5EsQJz0aLIHvQB9Of8GV P/KU3x9/2SrUf/Tvo9f0/V/Mr/wZueF5/Bf/AAV0+JemXAPm2fwv1KPP94DW NHwfyr+mqgAooooAKKKKAPP/AIN/8lF+LH/Y1Qf+mPSq9ArgPg3/AMlF+LH/ AGNUH/pj0qu/pIAooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F/wUy/5M48Z/wDYLvv/AEhu a99rwL/gpl/yZx4z/wCwXff+kNzUz2Gtzzb/AIJpfDHR/ih+yraQaumo/wDE s1LTb20msdSudPuLab/hGtNi3rLbyJIMxyyKRuwQ5yK+odF+FGieHfh3ceFr OK+i0i6W5EoOo3Mly5uZJJJn+0tIZvMZ5Xbfv3AtkEYGPn7/AIJIf8mxP1/4 +NN6f9i/pNfU1JRTjZikrmX4W8L6d4M0Cx0nSrSCx03TIEtbS2iQLFbRINqI g6KqqAABwABjitNVC9AB9K4yb4Za2PERvx8Q/GH2b7Sbgab9n0oWpTfu8jd9 i84R4+XPmb8fx7vmrwr4z/s23nh7R/gZ8O9J1X4vXfhN/GU0HiC/tfHHiCbV 5rI6Brkhe+1WO4+1pD9uNrtLTJGJBboNo8tA16ChCKSS0SPoLxB8I9B8UQeI 4r+ze5j8WWhsNU3XMoa4tzE0Xkhg2UQK8hCoQA0kjgB3Zjonw7a22tQ6gZrl XhSVBG1w3lO0hjy5QnBcCMBW6qGcDG458U/Zr/Zu+IPwu+Jq6z4u8caj4qt/ +EXsrSVX1q+khuNblmuZdYvBZyMYYopmXT/s8a5W1SOeOFII5JFl639or9n3 /hePir4d3jXWp21t4T1ubULwWWu3ulySQSWF1BhDbOm6QSyQnLEER+cobEjo +tHDUnUSk7Kz1Xzflu/zClh6Up2lp5nknxMAHxB1rH/P9P8A+jGrCrQ8VaKn hzX7uwSS5lFlK8G+4uZLmZwrFcvLIS8jccu7FmOSSSc1n1HKotxjsjSokpNJ 3CiiiggKKKKACiiigAooooAKKKKACiiigAoopxj2x5PBPQetAEtjCJJdx5C/ zqKbJlbd97PNT6dIFdlPBbkVZkgSX7yg0AQWNyWOxj9DUN7j7S2Pb+VW47WO MghRkU8KATgAZ9utAGZRV+a0SVegU+oqi6GNyp6igBKKKKAPdf2Kv+Tfrb/s Oa5/6eLyvV68o/Yq/wCTfrb/ALDmuf8Ap4vK9XqIbDYUUUVYgooooA/DP/gq L47tfhX+0j+2HfZmh1LXPE2kxQzLkL5dp4Q0J3X3yblP0r4d/Zo/Z3l174Ia PcRzNpuq37x3zzGIMsjnD5YEjJJxznsPSvd/+C9XjHXNP/ay/arsI7TTpNDh 13TZnne8kS5ill8K+HlfZF5RRxtij6yJ3z0GfOPh94i8W+Jfhr4aXT9E8MHT /ssWAPEE0ZlGxR84+xnb9ATz371208BVnFSTjr3nBP7nK5POv6R6D8Lfgp4o i8V2yTWVpFBDIsjarbfuy4HfaGODxjknrX0VqUYtbdsE8jJOOa8DX9o/xJ8N LI6Yvg/wbALWESO48V3DB/Vh/wAS/wBeta3gD4t/E3x/plzenwt4Ia2PzxLJ 4luomUfhYvnj6Vf9mVu8P/BkP/khc68/uZH8b/2w/DPwE+ImjaHrbXMM2p2w u2uFXMcEZdo1yAOeUbPIwB7155+0Hbv8WPG/w+1TRDpmq29/rbKJ7W6ChkFj dPyOcfu0kPUZ2471V/an/Z+8XftEwWkmp+H/AAlp9zZptimt/Es8jqpYEqd2 n9D6V89a58Ptd/Zi8RaeW0d76PUNQEUAs9cZkUNaXUMiLm3VlkZZywfaQvlc ghsj0Mry2ssRvDaX/LyH8r/vEzmrdfuZ9j+LP2sfCn7OPhK2sdZv7SK7QbI7 KwJmmxj+Mgnv1Ne0aTrUOu6faXVo++2u4UniYgglWUMP0I4r86vCP7CXirxt qGnavJa6KplmW7uLa/1yR5JU3hvLZktGAOAQSC3XpX2jB4g+JOkaZti8I+BQ LWHCj/hKbvACr6f2cPQcZrz/AOzK3eH/AIMh/wDJF868/uZ2ur/CbRdb1ttY msIZb8x46cMRyOPWvNPjNq1w1mmnRfaJb4HdDaW6+WIjx1xnI55zXMWP7W3x BsftttL4Z8JTXdrMVfOt3ZCDGQoAsjngg/ez7Vp3/wAdvFXifw3PKPDPgXT9 Tkix9oHiW7EqYOcY/s4jnAH3vxo/syt3h/4Mh/8AJC515/cz5c1BLv4Rft3a FeajayafbeNrUaZMGI8uWVnVE7ckE55r9af+Dhb4yazpv/BED4TXguZ0vPHG paDZalcIMTbJtHu55iMdCxi2n2Y1+Ov7X/xP1fWfiH8M73UbDTIbjRPEUEqS 29/LcR3DrJGwR2aCMovAyyiQj0PQ/rp/wXnW7uP+CKX7NrTw2Ud4mu+G5J4h csYEceHtQLKJDHuZcggEoM9cDpWVTBVISUW469pxf32enzsNSR+Sn7LHgu+j 8Taalta2Mun+HGENxbuQree65kkUdyCNoP8AdRB2zXt0t/feJ/jE9xHEF0fQ LKWGO4Ziqi6dlzxkZxGB+dea/CKw8Rab8LIp7XSPD95ZSgyecurSxXtsx6lj 9mYE55wAfxre8P6l4o1fwqbSz0zSZrae4VvtVxrsu+4CkMwH+hqcELg5UdTW v9mVu8P/AAZD/wCSDnXn9zOktp77VPDl4txINPjlmc3FxGcPOM8lefkyBjPP XjB5rlfDniSHTdRuBaW1ra2kszR2+pylpbm/XPJyxyccgEk8AGmeL/DPjD4m vb6VbaTpEImYW0MNvrcoNwx4HJtRgD1IAB5PArqfiD4dvP2ZbS18Q+O9F8E6 hbW6iHTdLh8QXQcPjB4FgwY4HXcqj1NH9mVu8P8AwZD/AOSFzrz+5mS2h+Jv HGrf2foelS3lpJl5p0kDGTJxlzjAHTof6V1X/DP/AI60+GzkXTdMb7O27yUu xHuwhUAnnH3j27CuT0f9vLxf8YL+LQPAfg/QfD6yDbj+0JJGUn5dwZYkC9uq k8Vf174j+NPg7cmDV7bTPFevMvzRweJJgtiSOGZWs0HOf4ZD0o/syt3h/wCD If8AyQc68/uZ0/gr4B+OprSFNQ0HSrQwgqJV1BZeT2GFH8+9cR4gt5vCWqXm j6ht0+W0BjumBBbd0Zg3Q5GDnFVv2Y/2pPiVN8bbjSb+HRLy21GFmhtb/VZL e2iYYxslSKX5jnoU59RWJ+3v4/1qXxW8txp3h/T7pY4VnXTtYlvMsARzutov 4doPXoPoO/NctqvG1tYfHL7cO7/vE05rkW/3M6b4WabpdxoUMZt0vo7l5mZ7 3Mzkq/H3s8Y9Pb1rvbLw9psM8MdpY2MGolS0bCIfuY+jPn2H55r5N+Gfxs1T Trq11AQ2rGw3xCOW8kRGDdSVETH079hXvHhL4jeNfFdwunQ+DdJtz4lheO11 KTXCscYKnHSBmBABOCozXB/ZlbvD/wAGQ/8Akiudef3M9hufhZpvjLTbvR5b NJlkiSY5/wBZNxySfXNfBPxQ0nU/2ZP2nrHW7d8XFgsUlxHtDLdKSVkJPI+4 VHQ/ePevs7wX8PvikL1r5o/Dss9g7RW7S+I51YoCfvbbEhx7jZ9K80/bD/Z/ 8Y6h4BXxLqmgeF4Z7V3e7lttZluZ3R2C7SrW0YCgYGNx4Hej+zK3eH/gyH/y Qc68/uZ9i/8ABuD4DsrD/grr4o8SaWA1hq3wm1HDqOMNq+kOnPrnzB9Fr96K /Ar/AINKNf1HVP2rPHOkXsNs3/CH+E7uwNyZiZpfNvNNKIFClSiiFjuLg5cf LySP31rjq0pU5ckrX8mmvvTaKTuFFFFZjCiiigDgPg3/AMlF+LH/AGNUH/pj 0qu/rgPg3/yUX4sf9jVB/wCmPSq7+kgCiiimAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXgX/AAUy /wCTOPGf/YLvv/SG5r32vAv+CmX/ACZx4z/7Bd9/6Q3NTPYa3Pm/9nH9onXP 2aP2F9J1rQdPt9TutQ8Q6Vp0kEn2MN5beFrKXchvL/T7YMGhX/W3UY27gody iN758N/2svHHxC/ZU1Xxsvg8WOr6XYX2px3N7c6dFo9+LS5kVrYPBqNx5UrR Rspm8yS3SRWcSSxhfMyv+CSH/JsT9f8Aj403p/2L+k19TEZFQ4uUGk7OxNSN 00nYxfBvi6Pxn4T0rV4Le8ih1W0iu1W5t2t5og6BgskbfMjjOChGVIIPIrVi n80H5HXH97g0BxGuOy+2BXmdz+238GrL4T2Hj6b4tfDKLwLq072tj4jfxTYr pN5KnnF44roy+U7qLeclVYkeRJn7jYpJ9SIRkklJ3F+LvxquPAnhzxzdadPZ 3114S0W81ORXt28i0kjtPOiglYSDfK2DIUTBWJkLbN8bSdNf+KLiW8tNNtZo DqN7dkISqMYrWPa00pQyKxADLEGXdtkmiLKVyKj8LfHDwT438Vy6Ho3i7wrq 2uwtepLp1nqlvcXcZspooLwNEjFwYJpoY5Rj928qK2CwBp/Fn47+BfgVqWhD xh4k0Lw5deIZpLLTW1C5jt/PZU8yQbmICoAq5ZiF3NGudzoCqWHqzmoxu/LX 1/Izjh6s52i9+n9eX3HhXxN5+IOtH/p+n/8ARjVhVp+MdcsvE3ii/wBR067t r/T7+4kuLW5t5VlhuYndmSRHUlWVlIIIJBBBrMqoRcVyy3R1TVnZhRRRVEhR RRQAUUUUAFFFFABRRRQAUUUUAWLS08z5mHHYetS3tt5wDL1A6etNtrgrb+vl nkeoqwjiRQVOQaAMwEg9wRU66hIox8p9yKlu7TzPmX73cetU6ALsEzzxOeAR 0wO9Vzdyg/eIx7VNp0g2Fe4OaW6s/NO5cBu4PegAtbwyttbqeh9ahv8AH2g/ SnRwG2YPJgAdB3JqCRzI5Y9TQAlFFFAHuv7FX/Jv1t/2HNc/9PF5Xq9eUfsV f8m/W3/Yc1z/ANPF5Xq9RDYbCiiirEFFFFAH84X/AAXy0iXxB+2F+0np8PEm peOfDtnnoMSeGNBBqt8O/A3hn4a/Di20zULO7t7yyt0ibY7IXwuAfTqK2P8A gt9HLfft3ftKxwYV7Dxn4bvWY9FA8N6EM/pXYeGdXPjHw7b6TrRsrR7pESzu Y03yg/3T7f8A1qAOZ8Q6L4c8L/Ci81jTtFn1rzY23q482ROnHT34ArZ/Zl8J X+ieEybqZphfySSIpORHHvdlUewBCj6CvRfh18F/+FdW+oGS8F6l5yy7cKq+ mDXBeJ/2gniGpxaXZxiaxlktogSymQoxUgbegJU/gaAOl8U2Yi3K+0ZB+gr5 m/aevINB8S+BZLj5428Uwxhhzy9vcIP/AB5h+VeufDz9oi3+LV3d6Dqdumke KrALIbYfN58ZXdvRu+Bw3PX61R+NyWGueI/hnFPCkscXigylGjDAPHpt+6nB 7h1U57EA16OVf7x8pf8ApLJnsdF8OITHp8bMevzDJr0BLMz6dK2GbcmCME5H Q9K5uGS3u9Sh8pUCpguwGBgY6/57VwXhP9qy68Va1Lc6bpv/ABTSsUtb1pGR rzaSpJQdBkN9QBXnFBotsfAXxfvNIuYHvLPVn+0RTNEW252jGQOmOmfQ1a8W fB3wH4e8R3N54lujNPe4EduGeIKoz6cdDz68V6D8PfiNZ/Em5uohCkd3agOh 6gqT1GeeuPzFY3iPwP8AEf8A0p7caVrUDOxihuxvEY7bQaAPkD/gp3r3ht/B /gyx0W1s4ZE1yBoUjHzBQwyScdD0r9Pf+C+sVs//AARU/Z4jnmSFW1vw+sbO QAW/4R3UcDmvze/ba+Ceo6j4Y8LzeI3W11uXW7KG1t4CRCnmTKvK+p459K+7 /wDg5Y1gaF/wQj/Zpu9ocReJ/DJwe/8AxTmpUAfnt4PvrrxzY29/ovh3VtDk khBlvElVLZiq8koT82cdhWtdWmo658E7drcMdXiXzYxAQhLK53becZIyMUzw pe6bp3wk0HxLp02qW639uipHbSt5TS4C/MnAIJGfxpkYu9AdJtVs5tCeb5jP ay5ti3q8YwOaAPW/2K/jz4MNteW3iW7sfDPiaKRbeEapIId64wWV3AUZOe/8 68i/4KO/sz+KPGPxBn8WaJqieJ9OnYFbOG7ic2I2KMIu/LAkfwg1tvol5rtq yXUVpd286DbOi/6we4rz7QbCP4OxxW2291Zr3cF8sfcAYjbj26e2KAOh/ZC8 G3XwA+DHi3xlq+nPBq8Ftm3hkjy8aZ+8QMntn8K5rSv2nPDNnY3FzfrFcTXW TLI4LPKSSck4zzk1p+HfH158UNL1rw7Gt7bWFhP5s8bsRn5cbGGecEyHHvWr 8OYvhv8ADr4m+H9LmtIL2e4XMty8SPFBIwBRSpHc7gD7UAeGSzX3xH16/wBW 0WOPSbG0P7tmkW3UsBnhnIGfYHNcFc3N34l1yW1e8mvLi8uBEssrE78tgEk+ 3vX6S+LfBtlBZyyaPb29tu+aVIYQiSjHIIAwcjivmn4RfDbw1qvjXxXDZWa/ aYpm+yLPCrGAhEZSuRxhiwH0r0M2/wB+rf45fmyKfwI82tPgJe+GvBcGtLdX KXwMqy2v2csu5Zo41RSOCSGLfTnoK9x+AnxHn0uytdC1u3kgV5spJPlDbyEZ CE/wnPT1967CLToL3wxJe6lcBgksLuUJxcSbZIsf+O4I/wCmY9sec2/g288Q +Jda1eeeZ41H2a0sSpW3cBkiUFTwP3jrz6ZPavPLPoy+t9T0Pw9PeafqcjSI MxiQhlz7+vWmeP8AQLhfhlr73+pHVbW4ieOVHTasLleR78kflXg3hv4x6hpO m6h4fSaa5lQFUNwzF4cEjOSfmAx1pP2jf2lJ10yTRdOlUDX7ZRLAyHdFjgyk +4OB/wBcz7UAfUX/AAaamK0/b/8AiTEilXvfA9xcMD3ZdQsEJz3/AIa/oFr+ fb/g08aCP/gol8SIIB8sHgCdeeoP9o2Oc/pX9BNABRRRQAUUUUAcB8G/+Si/ Fj/saoP/AEx6VXf1wHwb/wCSi/Fj/saoP/THpVd/SQBRRRTAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igArwL/gpl/yZx4z/wCwXff+kNzXvteBf8FMv+TOPGf/AGC77/0huamew1uc t/wSQ/5Nifr/AMfGm9P+xf0mvqavln/gkh/ybE/X/j403p/2L+k19TUQ2B7n FSfs8+AZfEra1/whHhH+3Ddfbv7R/sW2+1C437/O8zZu8zf827Oc85zzXN/F 34Fal4r+IHwb1HRZdNg0v4e+K5NYvoJ28ny7M6Bq2mqlsiRFS/m30JKsUURi TDAhVbpJdG+IB8SvKniXwedGF1vW0bwzc/afI358vz/t+zzNvy+Z5WM/Nsx8 teKa/wCGPjrF8OtRn0bxT8QbrWLyK31i1s9TsvDFvd2VxfW19bS6T9qiSSBL fTppLG8LNbzyf6MyLc6iszQRvR6CvY9K+CX7IHhb9n/xJDqXh6bV1+z+FtF8 IQW9zOs0UVlpX20WpyV3tIRfSh2ZiG2IQFO4t2Xjb4b2njrXPC+oXNxdQz+E tUfVrQRFNssrWdzaFZAVOV8u6kOBg7gpzgEG/wCGNHuPDXhfTrC81bUNcurK 1it5tTvkgS61GRUCmeUQRxwiRyNzCKNEDE7UVcKPG/Efw6+Kz/E34ZWGl+Of FFr4S8PaFqM3ia+S10X7R4k1KG80k2Ud2GtDsS4txqnmmyjtwNx2tCwixrGp Pnunr/ne/wDXmOEnF8yOF+Idutr431WIc+XdzJn+9iRhmsatv4kFj491ktnd 9tnzkY/5aNWJWSk5e8+pVRtybYUUUUyAooooAKKKKACiiigAooqxaWnmfMw4 7D1oAr0VoywJN95efXvVeXTiOUOfY0ARWsmyUZ6NwackrWc5XqM8j1qN42iO GBWpLj97Eknf7rfWgC6jiRQVOQagu7TzPmX73cetV7e4Nu3HKnqKvo4kUFTk GgDNVijAjII/SpRfyAY+X645qa7jiQ7mHzHsD1qozF2yaAFklaU5Yk02iigA ooooA91/Yq/5N+tv+w5rn/p4vK9Xryj9ir/k362/7Dmuf+ni8r1eohsNhRRR ViCiiigD+fr/AIK2eBZPEn/BRH9oyaQ+fYXXibTrOeAKO3hTw8ys3OerDH+6 1eBfst/tH2XjG0n8KvqA0bxFpM5gmtXmLTyBBgtGW5Ax1AP8q+2f27PhXH4s /bW/ao1b7Tp9vLaeL9JBaZHd41j8JeHmztGN6HzMHady4yeoB/Gf9t3wnr3g n9or/hJtFhFhLdp5yy2MrsBKHZWAO1TnbtPTofXIAB+rX/Ce/wDCM/Di4tLm /mttRuIJBZyyS+ZISASCT2J7VwN54u02TwFp+vSaXZ3uqs/+nPKzKG5O5yFI BYgZJPU896/Jzx38SviRqcdjLr+ta3b27YVQ87rxwMn869B0n9p/xfrngzTd Fu7t9K8NyqLZtXjhmaRwDjBYnBPGCQO1AH6B6f4u0f4nftAaO+jWkEVtpFo7 /aIiVcFlBKq2OQccgei10nxg0m2uPFnw0SaNZF/4SWR8NwCy6VqDA8f7QB/C viL9mb4z3fwR+JNi9zqdzrnh6ymkdplBdBDMFDOMf7g4P5V9d+PPjJ4W8XeK vh1Lp/iDTJ1XXZriQLLzFH/ZWoDLDsMsoPoSK9HKv94+Uv8A0lkz2Op+Mdne az8KfFFtp9z9hnbS7kKyjIYeS3ynpgHpkdK8n/Zj/aK0PxX8M7PQr2O3e7s4 QrRvlHR1BwM4z6c5zg1k/tgftb2GmeFpfDfhe7ttVvNWje3uZoGZhbxspUqp Xqxyfp+lfHXxL8SeKdcNz4mtfttjHb7BcT2cJjiQqgjGMnknbyfXNecUfoxD 8RrjSPClnqGlW0WkSHUpIbk2+SbhEC7QS2SQct9a6fxbqZ+IFxb6ro2tS6fq 9qoM9oJyFCgAZ6j/ABr82PDP7fPiPQvhDFpEq2El5bIwgu5Wd5GIOBuTcMN/ tAngfdOcjxXXP2gvG2u65e3z65qUdxfyZkSGZ1VvYDPAoA/SD4q/EW6+IPxs tdF1BY73+woI9RMxbePtG8eTuA6bCN3v7V9if8HLtpHd/wDBvh+zhuLjy9b8 Lsm5tzE/8I7qA5Pc4J5r83P+Cenwr8Y3nhLVrm7srq51fXJ4oYvtkjG4lUny wkcYDSNISeFIA6ElRzX6k/8ABx38Obmb/ghj+z1oLD/SdN8QeHIZONqho/D+ oocgE4Ge2T9TQB+J/wCyR8dfEuieDbnRLU2N7YWjC4C3pz5HHO38v0r3PRf2 n9etrFZNd03SLrS5E6pOqzhf723JyMewrw/4M/AuzXQp5Q9/OJypZ1UCIrzk euPzrtdS+HelQCOAaHBNDJkP5Tfvm44K5Bz+JFAHpE/7avwu8A6NcbLyWK6k HmGzto2X5/w4GT3Ar5/8e/tyWvjnxMtrZ+F7T+zrm8DO088jyTgnAAAC7Ac8 gc89RWd+0P8AB7w94TsrW5lmmtJyhb7L8jSYPQHrjn1zXz3czs9y0seQS24Z OCP/AK9AH3j4m8F/ETxn4at82dj4P8Lz2H2ix06xIt7aWMBeHJcvI7BgS0jM T07cVP2J/gnp/wAVfindxa9b/aLTRLJ7v7KXKrJIJI1AbBB28k4GM4HbIr5C 8I/GHX9H1rTpp9Sv763snUC2muXePYP4cE4xX2n+yZ8Rovht8c9N1u6iki0r xRbtZ3C8A23mYdHI4yNyg+4BoA+4H8P2MWmg2C+QkYOEDZUcdga+G/i58Yde /Zs/aM8RWUEEd1pV3ci/itZBsB81VJZGHIO4Edx7V9qN4p0+xYKuoWsqXJ2w ESqfNfphRnJOa+cf25fAaG48P+KGhi+0QzpaybwMEZYpu9Rluea9DNv9+rf4 5fmyKfwI7f4aeLIfir8LIruWzvfDs17KzyW4uG3uASoYOApAPzY47j2pba30 2xtpdItUk0y4CHbMVDtGm4Z25GN2cEFi3TPUAjkvA3xw0FNGd9Qv7eyumAWR pnAjG3jCnkY6H2zWT8Y/2kfDUfhS6gsr201XWJ4tlpDaMZJC5GAdy5AHc5PT pmvPLM/4l/DHTfD0V/4hi1DUdOktTuZvtvmLctk/O5YcnP0FfNep+LdX8aaz ca8llPe3+sMy6ZEAzERBisZA6AAZbJAGWY8ZNa/xO+J+r/FfwbFoiMZLWF1W +urcs6LtGPJjz9+TI5xkAd+9ex+GdEj+GPwjhlNrHHqV9HkRrgtDGfuxr04C 8nB5LHjGBQB+g3/Bq18Er3wh+0l421+Zjc3B8INbatLtAFvNNeWzwRLjhvlt 7gk9egPQV+5Vflh/wbgaEPBng/UbeUCW88QQ3moySk/Mscf9nKkZ74BlkI+r c81+p9ABRRRQAUUUUAcB8G/+Si/Fj/saoP8A0x6VXf1wHwb/AOSi/Fj/ALGq D/0x6VXf0kAUUUUwCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAK8C/4KZf8mceM/wDsF33/AKQ3Ne+1 4F/wUy/5M48Z/wDYLvv/AEhuamew1uct/wAEkP8Ak2J+v/HxpvT/ALF/Sa+p q+Wf+CSH/JsT9f8Aj403p/2L+k19TUQ2B7nnfiz9o3TPDA1KK38P+Ptb1KxE qRWVl4WvlF9Mm7EUd1NFHaDewCrLJOkPIZpFTLjn/Df7YKePPhZrnifw38Pv HniGfSNan0K30a1Olw3+rz2032e8aBp7yO3RLe4ju4nNxNEWaylMQlR7d5/X 3TzsgkspyCMcGsGw+EfhbTWsPs/hzQoDpeo3GsWRj0+JPsl7cC4E91Hhfkml F3dB5Fwz/aZsk+Y2a0t5i0PAfgL8OPg18Qf2mz4/8Jfs2eHNOu9TsI/FelfG CHw94ehGvtqECSNJFMk39qrPLHcyhnmt4w22YMxDJ5npPxC/aiX4TfCDSfF+ t+DfFqWUsH2/X4Y207zvBtikTS3V5fE3nlPHahcSLZyXMrnJhjmUEjvLn4b+ Hr7V9Lv59C0aa+0SVZtOuXs42msJFgnt1eFiMxsIbm5iBUghLiVR8rsCkvgj Q9a1aHU59I06e/trW40+C6ms1M8NtO0T3EKsw3LFK1vAzoPlcwxkg7Vw7oXm fNvxLy3j3WWIKk30/wAp6j941YVb3xObPxA1occX0/T/AK6NWDWcL8upctwo ooqyQooooAKKKKACiip7eyMoy3yjt6mgCFGCtkjd7VPFfHJDk4buOMVKbCPH 8Q981VngMD4JyD0NAFhrp7dsOA4PRh3qaK5SUcNz6GqcE4C7H5Q/+O02eAwt 6g9D60AaJAIIIBBqN7ZfKdQMbv51Tiu3i4ByPQ1Yi1BX+8Np/OgCmRg1Pay/ Zo2Y9+g9abeIBNlSCrc8VFQA6SQysSetNoooAKKKKACiiigD3X9ir/k362/7 Dmuf+ni8r1evKP2Kv+Tfrb/sOa5/6eLyvV6iGw2FFFFWIKKKKAPyA/4KTfGH 9iv4YftufE/SPib+1h8X/hZ471W/sdT8R+GdD8NxX1jbXD6Np0ETpK+g3bES WVtZMVFw6hi3Ckso+UvEk/8AwS38X3bz6l+2n8e7yVzuLP4UXr7Y8M8fhXxd /wAHR3/Kdf45f9wD/wBR/Ta+Gvh54Ev/AImeMrDQ9MiaW81CURoApbGe+Bzx QB+0V/8ABr/glP8AFkQaNL+15+0DqplOyKCPwwwYknsV8NAjp6167qv/AASS /wCCfHhz4OWN1qv7S3xx0TwnaxiW1bVLexsCynkECbQklfOc9CTnNfBOh/Bn 4ef8Ew/CHhfUfEdjZeOfivrca39ro9xgWWk5wFe/H+s2jJfyowu4LguQSK43 4p2Xjn9qnxJpmo+MNS1rVYtRfy7UXAaKNIQQFWG3DGG2iVAoEcIACgDI60Af cEX7L3/BLiyiktYf2uvjyIvuHydHRkfHoy+HcMPcE5rQ0f8AZB/4JnSX9sbL 9q/9oxrjEjQBdEJPETl8D/hHf+ee/wDDNfNyf8E6n8K+FNIl0aC31I6hGgVj Yq7W7E4JEjB3GDnoRUf7RPwU0D4a3PhPR9Ftb6HWpNZhsb7UBM484yW06uFJ JOeTyRkYrswFPnrct2tJbeUW/wDhyZbH0Vcfspf8EwdE1PzZv2tP2hLe53b8 y6Hg5/Hw7Wr4b/ZF/wCCZvxHg/sGz/a8+ObQXzbvKutPt7OKUls/fl8PovUn jdXjWhfsoaP488UaLpV1pMd1piQiWeSXdIXORncTnPGa8h8efs06drL6vd+C dPg8OR6VPNF5N0/nW90Y2ZD+7IZcHaew61xlH2R8Yv8AgjP/AME2vg7FHqXi j9oT9oa2trmFbiK5tNPW9glQjIZWt9BdDwO1eTD9m7/gkDbXMb/8NbfHpJbc /Lnw9LkHpz/xTnP4189/AT9s/wAQfAq0u/BfiPQNO8aeCZwRP4e1YuG098kS TaZOMPDuUt+75iOc7QcVyv7d/wDwT38M/wDCqrX4v/CS+m1bwrrDF5rLYDc6 Y+ATDOqZVXzvwVOG2NwDwQD9BPA3xT/4JqfDWdJdE/bl/aDsJIiWjZfCiOUJ 7jd4YOPwr9V/jf8AsG/DT/gql/wT4+E3haX4gfECfwJDZaP4m0HxLpX2Oz1X XIBprxW89wlxZGNPOhuvNdFt4SHIAWMApX8XZBU4IwRX9vv/AASd/wCUWX7N P/ZKvC//AKaLWgD5P8P/APBrp8IPC+nra2Pxe+PMNug2qhn8OPgf8C0c1cn/ AODZP4VXON3xi+PAwMDa/hpcD8NGr9I6KAPyp8X/APBol+z/AOPLtp9X+Kf7 QV7K5yWbVdEX/wBB0oVzx/4Mvv2YD/zUL4//APg60f8A+VlfrvRQB+R1v/wZ lfsyWr7k+IXx8BHrq+in+el12F3/AMGo3wRvYbZH+LPx+xZoI4il/oCFFGMD I0kHjAr9QaKAPzL0T/g1u+EWgavbX9r8aP2iUubUgwl9U0KVUIOfutpJXqO4 ra8ef8G1Pw1+JugtpmufGr4/X1kziTy/P8NxkMOhDJowYfnX6NUVU5ynJzm7 t7sEflin/BpL8BktXgHxV/aA8qT7wOpaESfx/srNVY/+DQv9nyGQunxQ/aBV yCu8arom4AjHB/srI69q/ViipA/Lu2/4NPfgXZw20cPxS+O0Mdod0SR3Ph5V BxjJA0jDHHdsmt3Uf+DYr4U6qEFx8ZPj7IEACj7R4cAUAYGANH9K/SeigD5d /Yc/4JX+H/2EPHlxrmh/Ev4o+LoptIk0ePTfEb6O1nbo8sEhlT7Hp9tL5g8h VG6Qrh2ypO0r9RUUUAFFFFABRRRQBwHwb/5KL8WP+xqg/wDTHpVd/XAfBv8A 5KL8WP8AsaoP/THpVd/SQBRRRTAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArwL/gpl/yZx4z/AOwX ff8ApDc177XgX/BTL/kzjxn/ANgu+/8ASG5qZ7DW5y3/AASQ/wCTYn6/8fGm 9P8AsX9Jr6mr5Z/4JIf8mxP1/wCPjTen/Yv6TX1NRDYHucTN8C9Fm8SNqv2/ xit2bn7XsHizVRaiTfuwLf7R5Pl7v+WezZj5du3ivGdf/wCCavhvXvh5qGjQ 2fw/0a81iKC8vbnTPCRjtZdZltr6x1jVfsUtzJbPcXtjqE8AaZJZBlTcS3qp FHH7PLrPj4+JzF/wjnhH+xDdFPtf/CSXH2sW+/Hm+R9g2eZs58vzcZ434+au L134hfFS7+FT6v4D8J6ZruqXV3GtpZeNtbm8OTGwEJX7W/2fTbpo55ZFVxav ErKkuZDBIDbI0tWLsib4cftG+AdI8eWPwp8OeGfH+hvoO7RrGCP4X6/pnhyy itImVY4b9rBNOW3VItsTJN5bgIsZbcgOb4o/YK8E+Kfij8Mdcm0PwodJ+Euh X+keHNFfQIHttHlmu9JuLW5tAfktXtP7KVYvLQFfNBVk2Ybr/hz4t8U+IfE6 2us6TcaVJZWkb6x5cxn0yPUJYYmNnYTPbQTXcEQ3M106RqWlVAGcSxWtrStH 8WzxeHNR1vWjp+o2hmj1TSdIMNxpd/5si+W7STW4uMxIny+W0QJlk3iTCFaV 7p/11F0dulzwr4kqU8fayM5xezD/AMiNWJW78Tv+Sha1/wBf0/8A6NasKs4J KNkXN3d2FFFFWSFFFFABRRUkRWP5mG4g8L/jQBLa2eV3sM9wD3qOS7l3nJK4 7VchnWdcr+I9KZdWonGR98frQAlrd+d8rYDfzovxmD3BFV7VG+0qMEFTzT76 fzHCg8L19zQBXqWCcBdj8of/AB2oqKAHzQGFvVT0PrTSpUAkYB6VYt5/s9uS ecn5R3qCSQysSetADaKKKACiiigAooooAKKKKAPdf2Kv+Tfrb/sOa5/6eLyv V68o/Yq/5N+tv+w5rn/p4vK9XqIbDYUUUVYgooooA/kE/wCDotDL/wAF2vji qglm/sAADuf+Ef0yrf7CHwbi/Yr+Bmu/GnxTpqN4iltHh8LRTKP3VwVjxOd3 9zzU2/KQ0jx84Vseu/8ABZv9nqT9of8A4OWfi1p8iuNMs5/D0946g8Ivh7Tm xnGP4TXDfty+OtS+OXx08E/Cnw+oW1jtQw4yLa2RpViz6FB5z8/xOvpQAn7E H7PWo/tW+M9b+JXi5Dq1/dXxltVvJCBcOM5JJ4wCQAOgIr6M+L/w5u9X1jRd NvdIbS7SCXCHGxFYAY2sOMZz0Nc3+zt4g1D9m/wnb+GZ9LivYtLUB5EkI7E5 Knknmvq/wN4q0/4r+D7e5ktVkiQq4WQ5GfagCD4Z6OPhz8MbSG4ZitpC0hJb oOTXyP8AtV/EVvGmteEiscNnHH4mDpP5YCqPsl0NxPGcdc+1fafibTE1rQbm xZjFHcxmLIP3QfSvjX9rjwdH4U1rwboctog0yXWmiMhb57jfY3SnPt83T3r0 cq/3j5S/9JZM9j3f9mS/vri0aC4vdPvrQRhonhYeap7gr1APrnFeWfHP4GTe GPEZjSK6NjeXZkjWEErLubdg4PXLc1d+CXwg1HwZ410ufTYpF0+UpIWTJiVN w3AnGM4zX1JAonRFfDBfmXHOD1zXnFHyn8Yv2I9P+K3gm0jvLG1tL61sFlt5 IxtngchumMHoV49q+Y/2NPGV5+zV+03qngnxklxqPhrxJAdO1KwVwkd/G7YV k3cLId3yMOVm8r1r9E/iJFZ+A7i41u/vZ2nux5MaEgLGoGQo/Kvzz/bA+EM/ jfUdV8YWSXl9/Z0ZeVEX5khXBLDA+8Cqt9UoA8R/4Ki/sQr+zv46tvEfhudd W8G+IIxLaXkNnJboSOCSj5MZBGGUk7T3Oa/rH/4JO/8AKLL9mn/slXhf/wBN FrX8+f7NN9Z/t5/sj3ug+I2tl1o+ckFxuAY3SoQqKM8GXIAHqBX9D3/BNHw4 /g//AIJyfADSJDufS/hv4dtGPqY9Mt0J/SgD22iiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKAOA+Df8AyUX4sf8AY1Qf+mPSq7+uA+Df/JRf ix/2NUH/AKY9Krv6SAKKKKYBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeBf8ABTL/AJM48Z/9gu+/ 9Ibmvfa8C/4KZf8AJnHjP/sF33/pDc1M9hrc5b/gkh/ybE/X/j403p/2L+k1 9TV8s/8ABJD/AJNifr/x8ab0/wCxf0mvqaiGwPc4t/2hvASeJ20QeN/CLa4L r7D/AGb/AGzbfa/tG/Z5Plb9/mb/AJduM54xmsf4m/tdfDv4FeFbjWviF4u8 M/DzTba9h0yS48Sa5Y6fEt7LbC5W08x5vL8/yiX8vduKqXAKYY+jyfNn529u wrE8HfDfRvAvhSLRNLtGt9NhDDy2mkleV3dpJJZJHJeSaSR3kkldjJI7s7sz EktW6klTwl8X9D8dx6RcaNe2eqab4gszqWk6hZX1tdWuq2QSFxdwNHIxkgbz 4wHAxkjoHjL1NA+Ntj4yi8O3uhafqGueHvESTldcs5LY2Vm0boiJIHlWZjKW fYYopFHkvvMeU3a+k+AdK0fxZq2t29tIura20P2u6eZ5JJUhTZFECxOyFN0j LEuEDzTSBd8sjNqXUMWoMFYhvJkDYB+64wRn6ZBppoO58t/E45+IOtEd76f/ ANGNWFW78TBj4ga0cEZvp/8A0Y1YVRTvyq5ctwoooqyQooqZEWGIO43M33Vo AS3tzOSMEDHB7A0ySMxOVYYIp7XshP3sfQUSXJmTDqCR0I4IoAZFKYXDD/8A XV+CcTpkfiPSs6nRSmFww/8A10AXp4yyNtOGPf1rPIKkgjBFaME4nTI/EelM urUTjI4cfrQBRoqR4DEvzEBuw61HQAE5ooooAKKKKACiiigAooooAKKKKAPd f2Kv+Tfrb/sOa5/6eLyvV68o/Yq/5N+tv+w5rn/p4vK9XqIbDYUUUVYgoooo A/Cj9vHwVbaT/wAFPv2sPGDpEL261rw/otvKeXVB4X0h5QPTiZOa+Av2KtMX xB+0H438e6po9/qjfbpLa0NvGfk2E5AGejZHT3r7s/4KueJn8Mfts/tNMkmz zPEltK3y5wY/B3hwqQe3LmviH9hrxVc+FPhPY65rI1a3027dIozaFGW5mZic tkFlLFlGeF4oA+5Pht4w0zx+WE/hZ7aVSFZrmzXdwO5Iz+tdwNb0zTJ47BPK ikwRFEiADHXgDpXIfCHx9deL7MiXSpbO3VA8crSK+8Y/ix3rZg0bTJPEMmpW 4Z7lSVLliwH0H4UAc9+0X8e7f4DeFrbWby1mvLPzkS5EK5aCNmIMmM9B/SvH Pj54T8L/ALS+q/DbUNF1yZodY8SKZJ7W5+aMJp17OMDB2NmHA7V6F+0Tp8Pi TTfs1wglt7iCSKRG6EZ/+ua/MDw/461P4afEm8/sW7liFjJeiAZBVSbe4i3Y PGQsjc16OVf7x8pf+ksmex+pekfELwb8C7jR/Cv9rPNf6jOlvBCZPOnd3YLu kI+6uSMkgAV6tazZ8psjay7hg+v86/M/9gqRvFut69rmoStd6r5yRCWRtxCn kkD61+iPg25lh8OWMcgUmOFRgNn3rzijoNU0jT9ft/KvrW2vI87lWVQ2Dzzz 9TXknxd1jwj/AGHe6NaT2enyLuE6JBiNxtIIZsdOvevUrjUTHYymJFaQDgHj J7D868U13wf4x+ItjqS65Fp+laUu8uLWNZpp4+cqpwMEgY5J69aAPk79iWzb 4XfG/wAYeE7G4jFuUi1GzkhbrKpwSjD0GK/qI/ZYa2f9mP4ctZxxxWjeF9MM KRjCqn2SLaAPQDFfy72UFt8NP29NCgtNLudF0y80ieygjndZWeQkbTkE9z68 elf0/fsexmL9kn4WqeSvhHSVP4WUVAHo1FFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAcB8G/wDkovxY/wCxqg/9MelV39cB8G/+Si/Fj/sa oP8A0x6VXf0kAUUUUwCiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAK8C/wCCmX/JnHjP/sF33/pDc177 XgX/AAUy/wCTOPGf/YLvv/SG5qZ7DW5y3/BJD/k2J+v/AB8ab0/7F/Sa+pq+ Wf8Agkh/ybE/X/j403p/2L+k19TUQ2B7nEt8M9Yk8Ufbv+E/8XNafafPOmNb aX9kaPdn7Pu+x+d5ePlz5m/H8eea4zXf2e/F/jb4UNpsfj3U/hlr93dx3ks/ gmz0t4rWBITFHpsY1CwuUkgRPLzOYklkkj3KIIiLZezk+KOtReJGsv8AhXvj B7QXX2f+0RcaUbby9+PP2/bPO8vHzY8vzMfwbuK8Z1/9u7xJ4a+HmoeIZfCX gC9s4YrfV7K50zx+L+1vNGv7a+/si6/dWRuTcXd9awWohht5oz9sV7e4u2il iVp6k+Z6ToXgDxd4LbUL4TWPii60bSvsulG6a3tL/wAS3n2aISX2o3ENqkUM sskUcQS3gEUaK0hEm+OG2nn+Dt7ptn4Y0nTP7F/s+xMtzrWq3FnCupalK0yX EiokcSwRm7ud09xMqjBQiOIPKs9v3Hhe81O68L6bLrdnYafrUtrE+oWtjePe WltcFAZI4p3iiaWNW3BZGijLAAlEJ2jx3xF+0l458NfE74Y+Do/BXhe91fxb oeoa/wCJJZPFc0VvoUVhd6TbXSWjLp5a+b/iZs0XmJah/s4DGLzMo9W0v66i SujhfiWAvj7WQMAC+nwAOn7xhisOtz4lOH8f6yRjm9mPXOP3jVh1FP4VY1ne +oUUUVZADrVy+t2k2lRnHGKp1dtbsSKFY4YcfWgCutnIx+4R9eKlTTv7zfgK tUx7lI+rD+dADBp8YH8X1zUFzZmEbgcr39qkbUgG+VSR7nFSxTLdRH8iKAKM Uphfcp5q5NdiKIEfeYZAqkDgg4B+tDuXYk8k0ADuZGJJyTSUUUAFFFFABRRR QAUUUUAFFFFABRRRQB7r+xV/yb9bf9hzXP8A08Xler15R+xV/wAm/W3/AGHN c/8ATxeV6vUQ2GwoooqxBRRRQB/O/wD8Fs742n7X37X/AO8ZPL1TStmDgqW8 K6CCR+Qql+zj4C0DQP2adE0K70O+1RLq3hYwQw7mjCRowbIIwdzHpVT/AILx ztbftV/tfBNwdtV0dQ4HP/Ir6Ec/yrnPgH4rvfEXw68JXM3id9LWOyicRQq5 WT92o554Ix1rup0cK4pzqNP/AA3/APbiby6I9rPxE0DwB4al02Sz13TYPKba bmAq2MHgHParHwi8cweJ9GnaxglhsIIwsEjjmUep9/8AGsDxVrE+uQC/0+4b VbSKAQ38Mi4DYGN6g9D1P4Vz/wCzd4pjv/EWs2cMMvlMS3nFdqlgxBQ+4OR+ FV7DCf8AP1/+A/8A2we92O9+KHh1/Fnh9xFPHDKgZcyPtBz06A+lfF+qfsba WPilawPOjy6ldTaZJHE2FSSfS9QlVunXzIFr6U+Mf7SHh/4f+Ip9Cnlnm1S2 h+0TIifLCrfd3H3x+hr57+Pl94vvvEfw8uPDehX91qsWtLqkpUYSe5SF5oYc 56iKO4Jz2zg9c+hllHCLEL969pfZ/uv+8TNytsdL+x3+zLeeBfFWqTWs0Uuh 3ToqEt+9DK2HUjGegOK+wrQpp8hhiJAjXaOOBx1r5m0b476h8M/EsqXujyQJ fW6XrWzkJIj/APLRQOgIbP6V9Azao3iz4Xyaro6tM+oWrNajhSTyCufXII/C uD2GE/5+v/wH/wC2K97sea+Kfjbrcfjq+0aO6tLJfMK27rGzyudxBJGOg459 xXpvw38SrHpd9NqOrpqMthCDcLChKRjn2GTwfyrwfRdZvrDW59KvLWexu1jW W4eFhLLliwKmQd+P6110fxSHhfTJ9MtV04adLGVl81cSuCOSx9ef1o9hg/8A n6//AAD/AO2D3ux4B+37e6bbfHH4N+IdJnDxz+KbaBz0fYZoyyken+Nf0vfs jjH7KfwyA5A8J6V/6RxV/LF+25JpLfFL4SR6NbwQlfEdu7tEgCyHzY+nAyc+ tf1OfshgH9lD4YkdG8J6Uf8AyTirnxFOjFL2U3L1Vv1Y1fqeiUUUVzDCiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooA4D4N/8lF+LH/Y1Qf8Apj0q u/rgPg3/AMlF+LH/AGNUH/pj0qu/pIAooopgFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F/wUy/5 M48Z/wDYLvv/AEhua99rwL/gpl/yZx4z/wCwXff+kNzUz2Gtzzb/AIJpfE7S Phf+ytaTau+of8TPUdNs7SKx0651Ce4m/wCEb02UqsVvHJIcRxSMTtwAhyRX 0m3x78LJ8LZvGkuoT2vhu1kliubq5sbiBrQxXBtpfOidBJEI5VYOzqoQIzMQ qlh8jfsWfss+FP2uv2JIPDPjKzt9R0a01bStR+yXWm2OpWtxInhvTY1Etvew TwSKPNLANGSrqjAgqDX0b8OP2H/h58JvhHe+ENC0HSLCC/0y60eW+h0bT4bn 7LcMzGHZHbrB5KblVIfK8pUijXYVUColzcl47k1L2fLuetwTLcRq4zhuRkYp +0egrG8HeD7TwZ4V0vSbNriW10i1js7drmZp5jGihV3SMSztgDLMSx6kkkmt aK3SBSFAUVSb6kQc2lzI8VuvAmr/AA3+Jur+Nda+OHxWu/C2mPe6jceF7zR9 CGiRQCFpDEkkOkrfvHCrhkxdNIxiAZpMOp9ZuvEVrY2LTSRzsFuI7UpHA0j+ Y7qijABOMsMt91RliQoJGB44+Cmn/EKy8T2+pX2pyL4n0ubRnKumdPtZYvLk S3BUqm5iZGYhmZggYskcaJryeDUuPEdjfy3BlWxaaZIHhjZfPdFjWYNt3Kyx +ag2kAid854xlF1L2aMnKrzPTS582/E0g/EHWsf8/wBP/wCjWrCrd+JuB8QN ZA7X0/8A6NasKtad+VX3OuW4UUUVZIUUUUAAGaKVWKMCCQRVmAR3f3htfvjj PvQBVqxbN5EDueM8L7mlkWG3OMM7ehPFQSzNM2T+A9KAG0UUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAe6/sVf8m/W3/Yc1z/08Xler15R+xV/yb9bf9hz XP8A08Xler1ENhsKKKKsQUUUUAfztf8ABfPR/N+Pv7VE4jbfceJbNA+OGEXh PwwxGfbzM/jXm/7Ntr4Rv/hRoemxWzNPJaQ+aYoTIwfYN2cjk9O9fVH/AAU0 8K2fxt/aP/bQ8HPKBquleINH1axhEas9wk3hLQ0mUZIII+zxHgHPOegr4e/Y I+N8ninwDJ4Mstul+ItFkW2vbmddqiJAVEgbI4JDD/gOfTIB9M6d8Ab7TtRs rrRdYvtPtmXdJGUGHyDwVbJHuFK1c+LttB+z38FPFniHS4AdSgs2neTJVWkw AGA5C4JzgUnhz4iR+BIBpVu1/wCLdVdg0siPhFY/wgkc8V1X7QXwym+M/wAG NW0HzDZSarEiFyu4x8gnjvwPpQB8d/sD/Au/+M+ual8QvFN1c3ls+oNcOrni /uQMksf7sZ28Yx82BjbXrv7SXx2tvB/xC+HtrZ6c99LZa7JKuZBFHITp15Dt U4PefOfYetejeHz4d/Zl+G2g+F1eb7Pa24tIpEhH7+QAeZI3QAszFvxr5b/b L8IR6jeeEbqbULu4F5rvkxNlYTEHglIOTngFV6dgfXI9HKv94+Uv/SWTPY+o Ne8AaP8AtAfD6OO/tpoZinmIyMRLZSbT86OMZI/I45BrwT9g248Q/BX9pnWv hzrN3e/YbiGWWCCct5Sup374j0wwbPB7+tekeA/jKnw/t9F0qzMdzGloplUh 3eVuAeRwPqa9Z0DQLHxjq2heJr3STp+q2qSNDv2mSMP8pUkckEAH8a84ok8b /CGz8Q6kLl76406Nh+/ji2otwe5Ldc++a5DWfhl8Pvh3qiXs9pFHeznH2nyd 7OOMlmA57dTWh8ePF4u4rrQnK2lxF5c8EtxNsS5BVsgEA4wWA546+nPm/gfW byeFrfWru0uLRAwYW90JVIweCrc547UAeHft4aYviD9qz4R2emvFLa2uuQX3 lxqBsiSeMs+B2AzzX9Mn7HUnm/si/CtwchvCGkn/AMkoa/l4+AbXf7QX7bGu 61oXn65Y6XAul6SFO+F5JSASG6KmSCW7cntX9Sv7MWjWnhz9m34fadYXX26x sPDWm29tc/8APxGlrGqv/wACAB/GgDuaKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooA4D4N/8lF+LH/Y1Qf+mPSq7+uA+Df/ACUX4sf9jVB/ 6Y9Krv6SAKKKKYBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFeBf8FMv+TOPGf/AGC77/0hua99rwL/ AIKZf8mceM/+wXff+kNzUz2GtzmP+CRv/JsUv/XfTf8A1H9Jr6kr87/2NPip 4p+Gf7NOpT+FoH1O/h0ywu7bSzNFCuoTpoGnmOESSKViLsFXeRgZBOQK7b4Z ftM/EXxtL8MtW8QXt1pGr6p4Pn1DU9MS3NtFBeMNOMivAw6xtJIqiTcyZYdS SclVS0/rYvkbPqabRPiB/wAJG0qeJfCP9jfad4tB4auDdfZ92fL883+zzNvH meVtzzsx8teF/Ge0+Inw30b4FfD8fGbx1HrHjLxlPoer+L303w8ur6pF/wAI /reobI4TYNZR7J7W3xtti2yDaS5Z3fb/AOF8eKv+gp/5LQ//ABFH/C9/FX/Q U/8AJaH/AOIqPrEddBqm0XP2bNG+OFt8ThP8SNWluNH/AOEYsrqW2t0077Cu t3c1zJf20TRxLcm3sVgtIrYu2XjvJjM91IEeHq/2jfh34s8e+KPh0fDPibxN 4csLDXJpNcOkNZASWpsLoI8i3MUgk23HkKEAI/fM5UskckXEf8L48Vf9BT/y Wh/+Io/4Xx4q/wCgp/5LQ/8AxFdFLMFTnzxiuvTTVW/UulGUJ86sch4o0+40 nXbq2vLu51C8gldJ7q4WNZrlwx3SOI1WMMxyx2Kq5JwAOBn1d1/UZtW1OW5u H8yadmlkfABZmYknjA6+lUqrn5ryta5lUd5NhRRRQQFFFFABQpKsCDgiiigB 0khlcscZNNoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPdf2Kv+Tf rb/sOa5/6eLyvV68i/Ysyf2fbbnI/tvXPr/yF72vXaiGw2FFFFWIKKKKAP5v P+CiX7UT/Av/AIOVfjPot5Kn9g+Ibnw/HeQvnbKjeHNORx6fdP6V84ftNfCt v+CfX/BQqNLv7Tb+CvFLG7tbvOyK7sJ2ZUlyM5WNlQnPPBrF/wCDmPxFP4T/ AOC/Xxl1K2d0ms5vD0qlTg8aBpnGfcV7p4p1m3/4K7fsA6LZaFLBd/FP4Qwm bTbWUjztasHSMS2wPG54yGbaQeCccmgD174d/FHwp4M8NxXOliK/uLpVkSXd 5hIODxgHrXumkaodZ0KCeSNonuYQ5iP3lzzg1+Xf7Efxog+HPiqPwZ46uW0Z 9BumVba7BjVmBYmFicYwRxmv0A+AE13qtvquvalL5VxqzDZEZMhVyduOcD5Q PTvQBP8AEzTbW60i/W9tDdi2UvHhCzgnuuAT2HSvkX9o/wAPa1jwjbyabcbp da3W1pcXEZMkv2S5McfJyu4gfewPU5r7NuvHek6t4wfSoLpDqdsm8qvORz+v Br5+/bE0eWx13wxd2zaVYSR629158MSNdSeXpt9Kd2R90hCPYkHrXo5V/vHy l/6SyZ7HR/AH4NpbWVu4kii11lE93YTjc6HrtUt2+hPWvfkaKxt4rm9khs1R EjAdgqhsAYH41x/woGpax4X0y9GqtNIiJubygCVIB29P1qH4s+Ibfxjqn/CL whzLBJH9rkIKpFnDfe45IPavOKN/4p6Lo174fN1f28dyURvLm8vfsHrx17V8 Y/tr/Fbw98H/AITXl3Ya1bT67qUElnptpbBoXjdwFaQr1/dglsnjIxnJAr6F +N37R3hf4AfDTUrO5vhay6WxhhWRvnnPl7sRhuSCcivz9/Ze+D+s/t6/tWT+ I/EUT2vgvTblNQ13ULgkW9hYRksYwRgbiEIAHLNgUAe9/sY3EH7D/wCwfq3x E1KFLPXvEzzwacLsbXihVG82Ze5YDCqe5xjNf0V/8E1PE8vjf/gnN8AdauF2 z6x8N/Dt7IM5w0mmW7kZ78tX8of/AAV7/bSs/jv8TYfD3hdDpnhnRoxHDYQN tgtkxxGqjgDkkj3r+qn/AIJO/wDKLL9mn/slXhf/ANNFrQB7/RRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHAfBv/kovxY/7GqD/wBMelV3 9cB8G/8AkovxY/7GqD/0x6VXf0kAUUUUwCiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8C/4KZf8mce M/8AsF33/pDc177XgX/BTL/kzjxn/wBgu+/9IbmpnsNbngfwn/ZdGgfDXw/b 3nxF8ZeCdbj0mwg1jStNfT2ht7yG0ht5AftGkXLBgIlQ7ZWQlCV61sS/swWM +swahJ8bPiQ95awyW8UhbRvkSRo2cY/sDHJij5Iz8vGMnPNft0+IPinZ+PYN N+Hek23kahr7f2nrDTRtLp0Syu21InwCZGWNNxDAK8mQpCtXm/7T/ij4x/8A C0fAMng681TSdAtbrQ5vEelWGlC7uXS81WK3njdpLOaC6iWISCT7NeWctnGJ blzcq0UY8/23732VtbLXprfS/fTVdLo7nhWsOsQ2rNtWvrpbW3bXR9bM93/4 Z5i/6Ln8Sv8Ayi//ADP1V1X4I6foVqk998ffiBZQPNFbrJPLokamWWRYokBb QANzyOiKOrMygZJAr5I1L9qX9o74efs4eBrhvCdz4w8fQ+EH1PxIkvgC9hS7 v5tJ1i5tIYzBdfu54bvTIILqLyQJG1G28owGZIx3PiK/+Jsz+O7Tx5qdhqOj ab8QfA1v4ea18Pvpscsf2nQ3u5opGmk8yB7uScKjF3iZJFaWQFBHtZ67GLjY +iP+GeYv+i5/Er/yi/8AzP0f8M8xf9Fz+JX/AJRf/mfrborLnY+Uotp/9kRR Wf2651T7LGIvttwI/OvNvHmv5ccabmxk7Y41yThFHyhtZfiLx9YaPq8tpNBr bywABjb6Le3MfIzw8cTIevZjg5BwQRVL/haGmf8APt4l/wDCc1H/AOMV2Qku WxhKLudDRXPf8LQ0z/n28S/+E5qP/wAYo/4Whpn/AD7eJf8AwnNR/wDjFXzx J5WdDRXPf8LQ0z/n28S/+E5qP/xij/haGmf8+3iX/wAJzUf/AIxRzxDlZ0NF c9/wtDTP+fbxL/4Tmo//ABij/haGmf8APt4l/wDCc1H/AOMUc8Q5WdDRXPf8 LQ0z/n28S/8AhOaj/wDGKP8AhaGmf8+3iX/wnNR/+MUc8Q5WdDRXPf8AC0NM /wCfbxL/AOE5qP8A8Yo/4Whpn/Pt4l/8JzUf/jFHPEOVnQ0Vz3/C0NM/59vE v/hOaj/8Yo/4Whpn/Pt4l/8ACc1H/wCMUc8Q5WdDRXPf8LQ0z/n28S/+E5qP /wAYo/4Whpn/AD7eJf8AwnNR/wDjFHPEOVnQ0Vz3/C0NM/59vEv/AITmo/8A xij/AIWhpn/Pt4l/8JzUf/jFHPEOVnQ0Vz3/AAtDTP8An28S/wDhOaj/APGK P+FoaZ/z7eJf/Cc1H/4xRzxDlZ0NFc9/wtDTP+fbxL/4Tmo//GKP+FoaZ/z7 eJf/AAnNR/8AjFHPEOVnQ0Vz3/C0NM/59vEv/hOaj/8AGKP+FoaZ/wA+3iX/ AMJzUf8A4xRzxDlZ7x+xD4yhv/hs2iq1uZLPVdblO15DKAdXuuo8sIB8/Zye nHXHutfMH7Ae43l87wXdubiTVLlEubd4JAkmpu6Eo4DDKspwQOCK+n6ii9Cp rUKKKK1ICiiigD+QL/g6O/5Tr/HL/uAf+o/ptfMX7G37Y/iX9j/4pafr2iXc 6RW86SvGr4KkH7y++CQQeCCQeDX6t/8ABer/AIIM/tYftpf8FYPir8TPhn8K D4l8E+Jf7I/s3Uf+El0ez+0eRo9jbS/uri7jlXbNDIvzIM7cjIIJ+P8A/iF1 /br/AOiGN/4WXh//AOTqAPY/iV8MPh7/AMFYfBV38QfAt/p3gv4qy2Ze/wBI dlFn4iuFHOwLlop2CtxhgW28gdPF/hb/AMFEPiT+zBfzeEvG2i3upfYCbQC7 DW97CVJALMwPmcEckAkfxc10fgH/AINwf+Cg/wAMNZi1DQvg9eaddROJAYvG ugBSR6j7dzX2DpX/AASm/a/+O3h210r4y/s2Sau0KBDqS+L/AA8bhCOrLIl5 vzwOCDwAM96APMv2U/2gfCXii0/4S671exTxLrV40ZtLqUI1lGGYBTk5Ixz9 WrP/AG1/iD4Uuk8Pa4mvCU31/cRHyZY91or6fdwhii8ld7pkk9M8HNdP4w/4 Ncf2gNa1GSbwzo9/oFlK+9bTU9Q0yZrcckKJI75iwGfQdawP+IX/APa10a6t Cmn6feLLIYXaHVLBWtIzHITJh7gZUkLHgEnMoPQEjuy6pGFfmk7K0v8A0lky 2Nj9mz9qfT797ex1bxnZPBpNshs41nW3WfGP9ce+OmABxXD/ALYn/BRrw34W E0HhAQat4muG8ue6gkC2rHbwWwSzEcYwO3WvRYv+DXH47T23m6rpet3k5T5o 7a60khz6bm1BfzxXS6D/AMG/Hxj+EkC3Whfs63/iDV4VGy4v/FmiAFgOvlte FRz/ALVcJR8N/Br9m/4l/wDBRDxPL4k8Z61Jovh6wCLNqd+rJbwxgn5IVxtL bSe+44OSK6j9r39ujwv+zF8Gbj4OfBu2jtLOQrDqmp7h9p1Zo+Vln2sRgMzl IxwCcsWKjHvH7R3/AASa/wCCkHxntLfSdN+Dd9o2g2QZIrWLxn4dRACNvyqt 6Avy4GeScda+cZf+DXr9u2eRnf4HO7sclm8Z+HySfr9uoA+BtQ1CfVbt7i5l knmkOXd23Mx+tf27/wDBJ3/lFl+zT/2Srwv/AOmi1r+X/wD4hdf26/8Aohjf +Fl4f/8Ak6v6n/8Agnp8L9d+B/7AvwP8FeKbD+y/E3g/4f6Domr2fnRz/ZLy 206CGeLzI2aN9siMu5GZTjIJHNAHsFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAeI+Fvilb+Bfi18U7Wc2O6XxJbygzSzIx/4kuljokLjH HqD7evS/8NE2X97SP/Am6/8AkWvE/HX/ACcF8Tf+w3a/+mjTqgrinVknZG8a aauz3P8A4aJs/wC9pH/gTdf/ACLR/wANE2f97SP/AAJuv/kWvDKKn20h+yie 5/8ADRNn/e0j/wACbr/5Fo/4aJs/72kf+BN1/wDIteGUUe2kHsonuf8Aw0TZ /wB7SP8AwJuv/kWj/homz/vaR/4E3X/yLXhlFHtpB7KJ7n/w0TZ/3tI/8Cbr /wCRaP8Ahomz/vaR/wCBN1/8i14ZRR7aQeyie5/8NE2f97SP/Am6/wDkWj/h omz/AL2kf+BN1/8AIteGUUe2kHsonuf/AA0TZ/3tI/8AAm6/+RaP+GibP+9p H/gTdf8AyLXhlFHtpB7KJ7n/AMNE2f8Ae0j/AMCbr/5Fo/4aJs/72kf+BN1/ 8i14ZRR7aQeyie5/8NE2f97SP/Am6/8AkWj/AIaJs/72kf8AgTdf/IteGUUe 2kHsonuf/DRNn/e0j/wJuv8A5Fo/4aJs/wC9pH/gTdf/ACLXhlFHtpB7KJ7n /wANE2f97SP/AAJuv/kWj/homz/vaR/4E3X/AMi14ZRR7aQeyie5/wDDRNn/ AHtI/wDAm6/+RaP+GibP+9pH/gTdf/IteGUUe2kHsonuf/DRNn/e0j/wJuv/ AJFo/wCGibP+9pH/AIE3X/yLXhlFHtpB7KJ7n/w0TZ/3tI/8Cbr/AORaP+Gi bP8AvaR/4E3X/wAi14ZRR7aQeyie5/8ADRNn/e0j/wACbr/5Fo/4aJs/72kf +BN1/wDIteGUUe2kHsonuf8Aw0TZ/wB7SP8AwJuv/kWnf8NC2nrpOPX7Rdc/ +S1eFVT1y/l0ywM8VvJdCKWIypGCZFh8xPNZAASXWPeVAUkkAYOcU1Vk+oez R9A/8NC2f97Sf/Ai6/8AkavPP2qdcg/aC+Avibwpb3uiWF7q2nXNtazzT3Ri SWSCSJS+LUnb+85xz9eh8d8UweKtB0LXbWODxGt5a2c/2K8+wwySxvBBuWSa Exghp5yIhhNpVVdQA6sen0bT307S1hll1K7ltZ5raS8u4Y0F2VlYq8ZjREZT E0TcKCpbDAMDVOcrak8qvocr8dta+GXwp8XWT+N/F3j+11rxesmoxx6euq3M UjFYpZdq28+2NV+0IMbUXnCjA4vaB4P+GPiy9u7bSvGvirVLiwCG6itNduZn tt+7ZvC35K52tjPXa2Ohqh8WvCHiG6/absvENxYaZf8Ag6z8N2Vl5IkJv4pv s4leaNAn3T+6TbvySn3SOvivxt/Yhtf2j/h1eWEC6noNrr/iWPV9SNybPN5b 29nOtvHc2V3Y3Ntdwi6eN/s88ShSBOH3wxo3z1bH4yjnEcC1GVKcOdSipe7r a0pW5Oa/2U7218zulGjVw0alCDi4txm5Sj7z3i4RWvLbRt317bH0d/wpfwZ/ 0HPiB/4Mrz/5OrH8d/CD4c6T4XuNW1e/8d6jY+Hmj1swPeXUwL2ci3UbBHvt rFZIUYBuCQM8V8baH/wTv+M3gD4b6/b+FfEPhfQPFPiS+ttWknsPF3iG00/S by18LWdhai1tkYQ/Z11WKZmgnhlieyS2jaNvKijg7HUP2KfE/h7xL/wsHxX4 n1HWr7w94c+IEEpXxRqkyMmp6pHd6ZGtu7LC0MFhG8UkbLsEkVthZfJiki+g 6pX/AKtf/gHJbRv+tz6u8SeMtH8Gwwyavq2naUlw/lxNeXUcCytjO1S5AJx2 FJoXjTRvE5I03V9M1Bhni1u45icf7pNeLf8ABQL4R+Kvi/8ADfTLLwnpk+p3 9tcySukUlvG23Zt2h5pI0UsCRkt+XUcp/wAE/vg/4l+F/iu/XxH4G1rwyv8A ZbpDeXc1hLFLL50P7sG3uZWBKh2ywC4Q85IB+qwmR5XUyeWOq4tRrq9qdtXb bXzPCxGZ42GPjhaeHcqbted9r/5H0f8A8LB0FfiLp3hF9Y0+PxNq8MtxZ6c8 6rNOkalmIzwMqrsoJBYRyEAhGIk8I+NtI8e6bJeaPfw39vDO9tKYzhoZUOGj dSMqw44IB5B6EV8D/wDBQ7wL468BftPX/izQ9N1GaHWbSC50nUbS2YW0F3HF HA0VxOsM0lvIojDrIqjeshTsa+gv+CcXwz8R+B/hHHd+I77Wbm5urOC2K36P GbicTXV3c3YR1VlMk99JHyMsltExZs5r88oZtCpL6tKElVU5Jqzt7NRup81k vedlypt7vo7fd5jkFXDqGLp1IyoTpxle65vat2lTtdv3Vq20vx1+isfWsaz+ I3h7UfF8/h6217R7nX7USmfTIr2J7yHyktZJN0IO9diXtmzZHyrdwE4EqFtm sWw+G3hzTPGN14ig0DRodfvDKZtSjs40u5fNS2jk3Shd7b47KzRsn5ltIAf9 WmPTT7niFnxr8SPCHwp8L/2r4s1jU9Lt2m8mM22lte7zsZ+Ajbs7Uc/dx8vX nFHg34sfDz4gXOnRaR4k1m8OraZHq9ow0Qok1rIoZH3NIAGKnOwkOADlRg48 q/bttdaPwJlvdFh806Vc/abwpB588NuYJozLFEQRI6NIjlCDuVHGCcA/HX/B PrRNf+LPx0uZIfE7a1ZaRc217GY2kFtYQC7W5uOBDHGk0kiJEkaAAQyzjpnH zNbiCrh8wrYOrSbXLB0mlL3pOcVNN/ClGLctWtluj6inw7DEZVSzDD1FdTkq qcorlioycWk9W3JRXW931P1C+x6D/wBBLWf/AAWxf/H6Pseg/wDQS1n/AMFs X/x+vla6/Yi1UfEi8vLDU103R/G3iS+1jxve2upS2niXVLMJONOsINWs4be8 S2hmMEojeZniQm2Sb7LGIZK3if8AYy8V+KfEHh6EeK/EOk6LHqTvr8Oj+OdY 06PULKLSp4bGO3ggKfZHjupozIttLCkyQCWYTSMiw/UJKx8trex9ZfY9B/6C Ws/+C2L/AOP0fY9B/wCglrP/AILYv/j9fNml/sx+Mrywl1DWfF3iC/8AE+oa td6ldXFn4x1KwtbcRS3R0j7Pbxg2qxxQtBDPbm28i5OZZ1uXiAl8k8U/8Ezv EkHg/wCD2h+DpPCvgWx8H6toes+LZdB1S70m48Q3KW9zaarcI1rDG8dzJbyG NblXSW5W5kWVoxBEWaS5rMV9Ln3d9j0H/oJaz/4LYv8A4/R9j0H/AKCWs/8A gti/+P1802n7JPiTWvH1o2t+LPFg8JNHe6jqGn6f461eCa61k3MS21zFKjR3 EFr9lWbfYrcmzV5lC25Mfnv6V+zf8KG+B3wB8I+EnmuribQtMhtp5LjVbrVH MwXMm24uWMzx7y2wNgKm1VVFVVEq1r/11/y/EHvb+un9fI9M+x6D/wBBLWf/ AAWxf/H6Pseg/wDQS1n/AMFsX/x+vkfTv2F/Efhn4ieJLjQtT03wtoXjjXTq niBvDt5JpF/dhZdcuY5vNihDvdm5vNKMsjSbZYrQwujRoy3Pv9pqvir+2kS7 0Xw/DppnZGni1qWScRA3W1hEbVVLlUsiV3gA3E43HyENw9LJj62NDwF420nx n4z8caXI2p2kHhDWIdJhnW3SV77fp9neNIyGRRHg3ewAM+fL3ZGdo6Nm8NLf i1/tjU/tJjMoi+wQ7ygIBbb5+doJAzjGSPUV5D8GhcN8QPjULRokuT4uh8pp FLIG/wCEe0fBYAg4zjOCK80/Z3+APxW8LftBeLvG3jDxNa6idY1KK1SEynYd LhS5WK3jAiURqsk5mwBknguxLvWeIqezgpRV25JW8nq5drJed76JanVgcNGv KaqTUFGLld9XolFLdtvysldt6H1f9j0H/oJaz/4LYv8A4/R9j0H/AKCWs/8A gti/+P18n+Nv2NPGM3xh+IXjjQ/EGiDUtY16LxB4csNTE0thYXkGhW2m291J sAMb7xfJKqBhLDLAdytFivO/EH/BJ4eLfgRdeGfEmlfCvxf4i0Hwj4h8M+F/ EV5orrLZi7MU+lKonFzNaxWMkl1DHtuJ2SJY3U75ZEXVKNrs5JXTsj70+x6D /wBBLWf/AAWxf/H6Pseg/wDQS1n/AMFsX/x+vlj9o39kjxx8dPjPqet2fifw 1Y+FdT8IX3gm40e90s3l0LW6trlp7iGaQtbxma6ew82GS2nV49MhPmDe8Ldj 8Gf2bIvhf+yhP8N77RfD3iSythqtrBpV8Y203U7WW7uJLaO4RbZYolkjkjEk MNt5EJLxwxGKONSmkkPrY92+x6D/ANBLWf8AwWxf/H6Pseg/9BLWf/BbF/8A H6+R/in/AME79K8U/s1eHvhvpWjeDBp3w+ceItBhvNMs5dH1bXGS/E0V9pct nLDFYSSXjSMLVkkTzmEPkeUhf0PXfg34j8W/Hzwp40ni0DSpfDepzpNc215J LeXukvZ6jAbAt5CHy3nfTLx42Yp5sRHzG1hlkdkI91ax0FT/AMhLWP8AwWx/ /H6Pseg/9BLWf/BbF/8AH6+Q9E/ZbuPFPxps/Gtt4W0HT9X8L6na2Zk1m1kg 1QBdS1W51K5sLyNW8iK8XULa4/dF0uESWznWAs7Q+ofAz4O6n+z54Ng0XQPD /g+x06+vZNQudK027fTdK8OiSa3T7LYW6W7L5SwfaJWbEPn3SvIY4vtbmBK1 gaaOh1f9o3xJ4D/aH1zwz4Ij8I2tp4b8D/8ACY634i8Y+IP7AsrCx+0yxy7i trdqqRi3EjSuyKF3ZwFyaXhX/gpjrXjvxHpmkaH8Zv2Nda1bW7yPTtNsrD42 21zcahdSOqRwQxppRaSV2dVVFBYllAGSK8f+L/h7XfiH4s/aU0iPQLjU/FOu /sr6naJouivLfy3d3K+oJ9ltiIlkmZpGCIREGclfkBO2vzk/Y+/ZR8e+F/jz +zjpenfspfHPwtA3xc8M+LdXvrvwZq1tY+CobPVVi8u4uZrRftIaAyzyTsyp GjxZZirpHdWpKnGHJFycpJO3Ra3k+llbvrokXhqMKsp+0moqMW031elopb3d +1kk22j96N37VH/QF+EH/haXf/yjo3ftUf8AQF+EH/ha3f8A8o68L/bq/wCC TnjH9pX4tfFHxr4a1TwxJrHj/wAPa14PhHiqSC9XSNOufCs1hbvp9wumfb9O /wCJnKTNaLdzWrRS3VwENxP5cfivjr/g3Q8dQaP8RYPBvxS8O+H5/F/gDVvC 8X9k6bcaNpqDUNT1bU5dFtLESXCaXo5uLjSt3lyXEhS2vECKLliOtJaHLf8A r7j7e3ftUf8AQF+EH/ha3f8A8o6DL+1Ru/5A3wh/8LS7/wDlHXC/su/8E+PG PwH/AGsvH/xQutR8NSah8RLbX4b+a3uZ5p7Vm1C2bRRGWhWS4UWUb+eL2a5F rKqRaf8AZbV5om+dvgZ/wSf8eeLb/wDZ8+LGkrp/gvxj8KvBPgzwm+na7C9p eWF/ptzrMPiSaaRLQXU+8XzNALa8is9RkJluftNsYhKrfp+v5W/FEcz/AK+X 5n2Hu/ao/wCgL8IP/C0u/wD5R0nn/tT7sDRvhD/4Wl3/APKOvin9o/8AZY/a j+EHwSHhe7+JnxL8V6p4n0OeHQbXwn4g8U3V/B4xk0vQ4Ev59XtrZBa6aNQG uSfZr6e3sEjuIZNgCCG07vxv/wAEp/2hNT1X4ravpfxNtv8AhKviRaTQjXn8 f+JtPvdMlgufGdxpZtWtHj+zQRHVvDyGzHmWirBfHyZpFjkntQW9/wCv6/rs 7s+nN37VH/QF+EH/AIWl3/8AKOjd+1R/0BfhB/4Wt3/8o68TT9gL9pi98b3F xd/Ge6/sHxJrV9J4itIfGOsRTTaXJ4psr63gsXVFOnyx6OdUti9oYm3SwRB9 iRzW/vH7PGk/tCfCb4K+DvDHinTfh/471zRNMtLPU/EN140vYbjUZFS0E0jL /ZTb3XdeAOzgzG2gdhEbqRbZcqsg5ncTU/it47+D3w+utc+LOv8AhvweLQXN 1LLpN1HqWn2tlBEjvcT3NzBZ+UBmQtlCiqgYvyQvm/hv/gq58G/GWt6fpmj/ ALR3w+1XUdWuo7GwtbPU9Fnmvrl2VUgiRbos8jMygIoJJYADmtf/AILM6Bfe Lf2HfiRpGm6Xf61qeq+BfEdnaafYxtJdX0smnsiQRIoLNI7MFVVUkkjAJr8E /wBnb4C+JND+InwJ0XSP2avjB4aW4+J+geKdUu7jwtqUFp4MitNR8oQz3Etq DdboDLNJMzKsaPFlmKukfDiq06fKoJybklp0Vm3J9LK3q9lqduDoQquXtJKK jFu76tWSilvd39Erts/or/4XZe/9DPrH/gktf/jtH/C673/oZ9Y/8Elr/wDH a+J/jz+wlF8YfiZrPiGTT/C+tHxTq+iRamniWGLUdmhWUtrPc2VqxthNBHcS QnfaPNLasUMgSOS5mZeP1L/gmvr3g6b4h3Pg3W9LSG/1E6p4T0fUb27trDSJ o9Ch060dJLPypbERFryDZa422zWojeJoEUVGo3u/60MuRan6Ej42XpYD/hJ9 XBPT/iR2vP8A5EpP+F2Xv/Qz6x/4JLX/AOO18aeHv2b7v9n34cfDHwzpUize GPC3jZrm+trPTIYvtNncQX0VrvtLC1jtxs1G6s7lzFBDFD5Jmc5heV+H+An/ AAT68afs1+E/7K8LeJLayGk6tZX2jNBqMmn20YePT4tSludPsraDT5/Miju0 iiMG1Whim3rc3c88T53rqHIrKyP0E/4XXfFT/wAVPrHAzj+w7X/45Sf8Lrvf +hn1j/wSWv8A8dr82/Dv7LvjX46+Btb1LTdS8aR6PbfEHQ/E/gyy8Y+IfEdr LFBZSaHPfpcQXZF1JE9xaal5EV3CoiYRywoiTJKncv8AsefE3wzrXibVfD/j rVYbrS/D8Nh4FivfG2t31rZ3p/tI3VxfRXTTpciRrm2KG5F20PlBEO2GPL5p Wvf+tP8AO3yYuVdv61/y/E+7B8a74/8AMz6vx/1BLX1x/wA9KT/hdl7/ANDP rH/gktf/AI7Xx3bfAnxF8Itbe9Ou6zqvw3s4rqbV9KvtS1fxPqclhbwyvbWd ukhmnupZp7m5nmYs0rpFa2ipNEq7ej/Zt8H/ABB+HHwp0DTdds7HVNZXT9Mj 1a+vvGF5fzXNxFYabb3EoEluUjJaO8by4iEkeGOViJLydoZdSXcOVdj26+/b TOgfELxdpeseKLnRtE8IeHbHxFd6zeWNnFBHDPJqCymXc4WKOJLDeZGfBEhy FCZbnPDf/BVz4NeMtb0/TNH/AGjvh9quo6tdJY2FrZ6nos817cuyqkESLdFn kZmUBFBJLAAc18z/ALV2ja1418AftJ6YNDlv/EWsfBC2tBpOkSSXklzcyR+I 0+zW5EavMzOwVP3QZsj5ATivzC/Z2+AviTQ/iJ8CdF0j9mr4weGluPifoHin VLu48LalBaeDIrTUfKEM9xLag3W6AyzSTMyrGjxZZirpHnXrzgo8qcm5JadF ZtyfSyt69Fqb4XDwqynzyUVGLd31aslFLe7+5K7bP6K/+F2Xv/Qz6x/4JLX/ AOO0f8Lrvf8AoZ9Y/wDBJa//AB2vif48/sJRfGH4maz4hk0/wvrR8U6vokWp p4lhi1HZoVlLaz3NlasbYTQR3EkJ32jzS2rFDIEjkuZmXj9S/wCCa+veDpvi Hc+Ddb0tIb/UTqnhPR9Rvbu2sNImj0KHTrR0ks/KlsREWvINlrjbbNaiN4mg RRpGo3u/60MeRan6Ef8AC673/oZ9Y/8ABJa//HaP+F13v/Qz6x/4JLX/AOO1 88fswfAmD9mz4RWvg6ze1fT9LurlrPyLK1sgIpJWkG6G0hgtUclmYiCCNMty GbdI/oVKVSSdkwUEei/8Lrvf+hn1j/wSWv8A8do/4XXe/wDQz6x/4JLX/wCO 151RS9rPuHIj0X/hdd7/ANDPrH/gktf/AI7R/wALrvf+hn1j/wAElr/8drzq ij2s+4ciN/RP2qNX1r44614PTVdSSDSdF03VFvTp1t5k8l5cX0Ih8rcAoUWW d/mHPm42jbk9nf8AxL1rS42efX9ajVW2Mf7FtGCt/dOJTg+x9RXzR4LlEH7Y 3jWQ9E8I+HG/K+12u4vvHV7rHiTxVdSWV3sutQiFrGqHEuwRQ53HAChQzk9P lxXh5pnGIw2KoUacHKNR2bs3bVK/lv1OmhhYVKcpN2sepf8AC7L3/oZ9Y/8A BJa//HaP+F13v/Qz6x/4JLX/AOO18q/Fb9nfxB8SPi9qerT69rS+Fr/RtI0m PS9P8W6rorKY728lv5lNm6COZkksCkqnzH+zPCWijldj5H8IP2afiZ4x8MxT 6z4p8awwal4m8RJ4vtNQ8ZatZ3usW8GpaoulGxmhWP8As+Jdtk2bGO1S5gkB ZSkaxS+8qku5zOCR+g3/AAuu9/6GfWP/AASWv/x2j/hdt7/0M+rj/uCWv/xy vjH9n39j3xJ4C+JWgeLvG3jDW/FPiew/4SL7feL4q1YWVwbzUEezEemtL9kj iWzUq0QTZHIkZQOUWVYfjT+yr48+K/xO13Vo/FN/Y2mp6pp9vY/YfFup6W2n 6DHcaJPeWqQ2oSNLmc2+sA3Kt5+24gj87yjsganK9m/6v/TFyLt/X9aH1PfW +h6n4i1TVZ9T1h7/AFmdLm8k/s2IebIsMcKnAnwMRxRrgAD5c9SSWrZ6AzY/ tLWAcZ502Pj/AMj18vfs2fse+JfhT+0P438VeIPE19qHh+5ubG68MWNv4g1B njlTSodNuptTVii38rxWtoEa6a5KGAyKUkeRn6n4k/s1al4n+J+peI9M1bXb KTUhbORb+LtS077NPZwzm1dIkL25jEsgElu8LQTiUvMkxiWN82+u+hVt/L8T 3j7HoP8A0EtY/wDBdF/8fpfseg/9BLWf/BbF/wDH68A/Yk/Zv1n9lr4PaL4X 1LV5r+Kw0HSrOW3fVr3VI01CC0WK7lgkumLQ20jCNY7aNUijEBdVUyuq6HwE +CmofAPw5c2uieG/BWmr4k1Fta1PTNIcabpmizSC0ieC0WO1BuAIo5pWmlEb zTjO2KOYJbNpXaA9A8V+ObDRPi94f8KWS3l2db0fUtWe6nRIPs/2SawiEYRW ffv+2k53Lt8rGG3ZXYx9a8svZb24/ah+Hkmo29raahJ4J8QG5htrhriGGX7Z oW9UkZEZ1BzhiikjB2jpXqdS2NBj60Y+tFFK47Bj60Y+tFFFwsGPrRj60UUX CwY+tGPrRRRcLBj60Y+tFFFwsGPrRj60UUXCwY+tGPrRRRcLBj60Y+tFFFws GPrRj60VyH7Qfw1m+M3wC8ceELeW3gn8VaBf6PHJOqtFG1xbSQhmDI6lQX5B RxjqrdKExWOvx9aj8aWWp6Lomm3GlQWOoXN/EZ2iu7p7RI0EksZw6xSknMYO CoGGPPHPy5p//BNDRfh/qnhu60Cw8MXN74Pk0KPStal0nTdJ1ixtrfW7zUNQ hgksLGKC2jmiuzGyQwoLhPOikMbTG4X6+8S/8gfw9/14P/6VXFVoJnAjVPGv fw/4YHGedfnx/wCkVU73xf4usGnD+HdBY26JI4j1e7lOGcoMBbEknIPAyQBk jHNYvwM/Z8uvg78RfiBr0+vf2svjvURqbW/k3CGzkEk4UbpLiUSYt2to97KJ B5GxXW0jsrKx3vGEV3onjWz1h9Lutf0N7f7DfWNtOyXMIJb/AEiEAgF13Dgg g4wQPvD5vivNsTl2AeKwkOZpq+jdovd2jrZbuyelzlxladKl7SCu/v8AyF07 xP4t1a28630Tww8e4qc69cKVI6gg2QIPsQDU/wDanjT/AKAPhf8A8KCf/wCQ qw/hR8SdX+JHj3xBeXmj22lWbrGqW1pD5Ftauny7Ah5LsDlj3K844Fanx7+G 0Hxf+Eur+G7nQ/CfiGHVhFE1p4msFv8ASlImRhNNbMpWfyiolWIlN7xKvmRZ 8xVwrn6zjLYY5RtdyWzV+WTV0nqk7X12vrsPA4j6xSVTbf8AAsf2p40/6APh f/woJ/8A5CpP7T8a7cHQvCx/7j8/t/05V5hp/wCyNJ4I+L9j4p0OOC7N1pOn 6Vr0txrNxp+qaq9le2Btr24vLWISX032SC48xb0zBykdvG0ENzetNyfw5/Yn 8UeEF8Ai61Xw1enwj4dktrgtGz/b/E8/9n+f4oO6Pm9O3VMs2Xk+1NulX7TP t+mSj3/rX/Jff626f6/L+vl6HtHib4j654Lhhk1i18CaUlw/lxNeeKZIFkbG cAvaAE47VL4W+I+qeKb6KO2i8EXcLuBK9j4le5dEz8xVRajccZIG4fUdR5z/ AMFAvhH4q+L/AMN9MsvCemT6nfW1zJK6RSQRttKbdoeaSNFLAkZLf4jlP+Cf /wAH/Evww8WX6+I/A+teGl/sx0hvLuawkill86H92Db3MrAlQ7ZKhcIRnOAf rsJkmV1MnljquLUa6vanbV2218zwMRmWNhj44anh26bted9r/wCR9GeJ/hv4 d8W61NfaroGi6neybUe4u7GKaVlRVRAWZSSFVVUc8BQBwKz/APhSfgz/AKFH wx/4K4P/AImiivlWe6g/4Un4M/6FHwx/4K4P/iaST4I+Cyhz4R8Lng/8wqD0 P+zRRQB1lFFFc5oFFFFA2FFFFAjC8R/DzQPF2qLcatoej6ncRwLEkt3ZRzOq AsdoLAkDJJx7mqP/AApPwZ/0KPhj/wAFcH/xNFFbx+FEB/wpPwZ/0KPhj/wV wf8AxNH/AApPwZ/0KPhj/wAFcH/xNFFMQf8ACk/Bn/Qo+GP/AAVwf/E0f8KT 8Gf9Cj4Y/wDBXB/8TRRQAf8ACk/Bn/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDB XB/8TRRQDD/hSfgz/oUfDH/grg/+Jo/4Un4M/wChR8Mf+CuD/wCJoooAcPgr 4NiimiXwl4ZWJrp7goNLgCmRo4kZ8bfvFI41J6kRqOijDf8AhSfgz/oUfDH/ AIK4P/iaKKGAf8KT8Gf9Cj4Y/wDBXB/8TR/wpPwZ/wBCj4Y/8FcH/wATRRQJ h/wpPwZ/0KPhj/wVwf8AxNH/AApPwZ/0KPhj/wAFcH/xNFFAw/4Un4M/6FHw x/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miigA/4Un4M/6FHwx/4K4P8A4mj/ AIUn4M/6FHwx/wCCuD/4miigDU8K/D3QPBd5dT6Poej6TPeJHHcSWdnHA86o XKK5QAsFMjkA9N7Y6mtvA9BRRVS3AMD0FGB6CiioQBgegowPQUUUMEGB6CjA 9BRRTAMD0FGB6CiigGY3iXwlpXjSC2i1jTNP1aKzLvAl5bJOsDPgOVDg7SwR M467Fz0FZP8AwpPwZ/0KPhj/AMFcH/xNFFN7C7B/wpPwZ/0KPhj/AMFcH/xN H/Ck/Bn/AEKPhj/wVwf/ABNFFIYf8KT8Gf8AQo+GP/BXB/8AE0f8KT8Gf9Cj 4Y/8FcH/AMTRRTYmH/Ck/Bn/AEKPhj/wVwf/ABNH/Ck/Bn/Qo+GP/BXB/wDE 0UUhsP8AhSfgz/oUfDH/AIK4P/iaP+FJ+DP+hR8Mf+CuD/4miigGMHwH8DJd G4XwZ4UE8saxvINIt97qrOVUnZkgFmIHbcfU0/8A4Un4M/6FHwx/4K4P/iaK KBPcP+FJ+DP+hR8Mf+CuD/4mj/hSfgz/AKFHwx/4K4P/AImiigYf8KT8Gf8A Qo+GP/BXB/8AE0f8KT8Gf9Cj4Y/8FcH/AMTRRQAf8KT8Gf8AQo+GP/BXB/8A E0f8KT8Gf9Cj4Y/8FcH/AMTRRQAf8KT8Gf8AQo+GP/BXB/8AE0f8KT8Gf9Cj 4Y/8FcH/AMTRRQA23+C3g6GS4VPCfhlFupI/OA0uACby1l8vd8vOzzZduenm PjG45d/wpPwZ/wBCj4Y/8FcH/wATRRQCD/hSfgz/AKFHwx/4K4P/AImj/hSf gz/oUfDH/grg/wDiaKKAD/hSfgz/AKFHwx/4K4P/AImj/hSfgz/oUfDH/grg /wDiaKKAYf8ACk/Bn/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRQAf8A Ck/Bn/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRSBlvw/wDDDw14U1pN Q0vw9oem34ja3Fza2EUMwicqzR71UHaWjQkZwSinsK6Ciis6m5cfhCiiioGF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVO8z3MG2RmkWEbIwxy EXG7A9Bkk49SfWiiqiTIj2D0H5UbB6D8qKKvoSGweg/KjYPQflRRUU9kCDYP QflRsHoPyooq+gBsHoPypsqgIeB0P8jRRSGz/9k --0-1068215501-1298240437=:67343 Content-Type: image/jpeg; name="screen2.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="screen2.jpg" /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMD AwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/ 2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAHCAyADASIAAhEBAxEB/8QA HwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1 dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDC8Y+Mv+Ed 1DQNPhgmurrxFq8GlL5DL5lqr5LzlTn5URWPQgkDPGa9C+C3w6f4wXt7Zw30 Vvd2tk80bKnmRXE4ljhWLO4YVpJB8/IA5wRXv0X/AATma3JaLxxLGZEZHKaW VDIR8yn9/wAgjqD6U+y/4J4T2ccv2bx3LDHdRGKURaYVE0ZIbacT8rkA46ZA 9K45VG6/tFK0OVJLX4ru8v0S8jvUqSwiouF6nM3zX+zpaKX4t3PKtV/Zqaxt dZ+z67BeGxhhl09xbfu9VZrM3kyod2QI4RnODuLIPl3Via78Iv7I0lrz+0BJ 5fh6213Z9nwD51zFD5X3v4fMzu74xgZzXvkf7COsQ3Fo8fxL1NWsQfszJZOD bjYseEP2gFf3aInH8KKOgFTaT+xHrei3az2HxP1a0uYIBZJJBbSJLHCPmEO4 XGQoODt6dDitFVXc5eR9jxH/AIUDLbeGtPvm1JGluLO6ur6FYf8AkHslgb+C MnfkmWEZ3YAUhh82055z4geCW8EeNp9Gguv7TVRC8FwkJQXKSxpIhChmIyHG Bn8ulfStp+xBr1lqE97B8TNWhvZpFle4W1kWRmVWRWZhcbiQjMoPUBmA4Jqg f2BLz+3hqJ+IFyNUaX7Qtx/Z7Cd5Qd3mb/P3b8/NuznPNNVo33D2btseF/tL /GXwR/wTx/ZcvfHHib4d2XxWvYPFcfh2aFtau9IFm/8AZz3MoRoWIk8uSJ48 so3HkEAAH5r8P/8ABeX4beI/Atz4otf2Nc+G7LVLXRrnUW+IOoLDDdXKTPDG eNxysEpJUELtAJBZQ3vP/BY/9jXxkf8AgnnYeEvBmm678QtYn8eRaq8em6JL dSwo2m3kRZ4o/NPlh9gLHjMgB6jP57eIfgt+0frXwOs/BsH7PPi3TTqV7b3n ia9tvB97DDqJtmb7ItrawWcSWqxhizYLeZJz8iFo296jhqssJRnhKCqSlO0m 21aN1d/Eul0kk7tq+iZzU5Ufb1I4qo4RUG42V25fZW3V7u+iTtq0fbHw7/4K b2HxX8JHxB4b/YU13X9AXU49FOp6b4p128s/t0jxJFa+bHCU892nhVY87mM0 YAJdc7ur/t+/8I3fpaan+wb4o027k1eDw8sFz4g1+KVtTniWaCwCtCD9pkid JEh/1jIysFIIJ8M/Zd+I3xz+BHhjwDo+rfs0/EnxhZfDm607UdEa78Lm1vNL ubbxGusTC3vI9M+1C2niDwG3mmmRZZXnGcRwx+2fB/8A4KGftN/DnTPBNm37 MniRrHwRrdpdadYQ+HNWFnpumW9pY2iWdmtwk8kdx5FrdR/bJ5LhwupXICj5 SPvK2R4OMpqnh4NJu37zdX0+1+n/AAfmYY6u4pyqNO38vX7iPUv+Clnhrw54 M0jxJqn7G13pfhvX5JIdN1W68Wa1FY6g8bMrrDM0YSQqysGCk4IINdNf/ts2 Wi+GNY1y/wD2KNbsNG8PXCWmq6hc+INdjtdMmbyisU8rRhYnImhIVyCRNHx8 wz4V8WtV+MXxl+Fvh7wpc/s//E+0s/DcuhPaNNpd7OJFsLKe3uEuP9FV7oTS TCSL7Q8n2KMSQW+yKV1PtXjP4x/Erw34r+Jmk6L8FPiD4p8K+PvEHinxKt1J 4evLC5kXXYbB4rSdjZfaI1tZLUfaIbeZIbthsZ5IUDSZyybApK2Hjdt6c72V ra829m/muxTxte+lR20+z6+XSy+8yLX/AIKm/C+4x/xjTpA/7nnU/wDCt+P/ AIKHeAP7Bh1WT9mGzi0m5uJLSG9bxjqot5p41RpIlfG0uiyxMygkqJUJxuGf UPgd+07498QfEs+K/EXwr8feB7nQNVXVBeRadq1xrmsaYt/qd4PD8Lw6d5b2 my6s4Fhnlt4QbOJi2zbHBy/gb9rj4xeHdR8H3D/A74qIPBX2VNNgsrvVbGC3 gjh0GKa1RI4MfZ5RpFyfJffHu1IsyyNDukxeWYHmcVhVp/08/wCD/XYSxeIs m6r/APAf+Ac9a/8ABQ74YXTD/jHXSF/7nPU/8a3vCv7Z/gHxrrdppml/s3Wm o6jfyrDbWtp4r1Waa4kY4VERSWZieAACavp+0r8Um8MQ6fa/Avx1YS6ZptvZ 6NcpBeB9Ami0a6037TaYth5UjPNbzErg/wCiIuc7JI+s0D49+N/iF450gar8 JfFPgi1j8ZW2vXWr2ek3skqaRbXc+orphjitsySveTSTeb8iNM8e5YkXcrll WASf+zJf9xL/AIJ3foiPruJ6VW/+3TlfDP7WfgHxdqCWmmfs6Wd/dMkkvk23 ijVZpNkaNI7bVJO1UVmY9FVSTgA1s6r+0L4I8Npp76p+zpBpa6raR39mbrxH q0P2u3fOyaPcw3Rtg4ccHBweDWB4Q8ffFLwr8bNA8b2Xw38bJeeG9CutH0+2 m0xvKsjLBdRRtGEtEHlRNchhG4djsKmTDAr2vjz4+/E7x58PoNFs/hd4i8My jTY9InmsrG5aKezUX6/ZPKkib/R1F5H5SFiYjbhgxYo0aq5RgVJclCLVlf3+ uvn6CjjsS071Hf8Aw+nl6mXaftLfDO6/5obpC/8Ac06mf/Z60bT46/DO6A/4 sppC/wDczan/APHK9FP7WPjbXPEkepan8I/GF1NHq8Gph5JpzKUt9Svr6C33 tan93Gt4IQMcLECAoO1fn7TPg74ujA3eFfEi/XTJh/7LVYfJ8um2qtFQt/fv f8TOtmGMjbkqN/8Abtv0PULT4q/DS4wf+FM6QM/9THqZ/wDataNp45+GtyR/ xZ/SFz/1MOp//Hq8+0/4V+KIwufDevj66fN/8TW7pnw28RoRnQNaH1sZR/7L W7yHKP5V/wCBP/MweZ4/u/u/4B3Fnrfw2uf+aS6Qv/ce1I/+1q0baP4bXGMf CvSF/wC45qZ/9r1y+m+AtdixnRdWGfW0kH9K3tO8HauuM6VqQ+tq4/pWTyPK f5V/4E/8yP7Ux/d/d/wDdtfDfw3uSP8Ai2OkLn/qM6kf/bitS0+Hvw3uiP8A i2+kL/3FtSP/ALcVm6f4b1GMrusL0fWBv8K6HTNHu0I3Wtyv1iasnkuV/wAq /wDAn/mH9q4/u/u/4BbtvgT8PriJXX4faEAyg4OrannkZ/5+K5X4hH4FfB3w xca3400z4e+C9Hg1g6Et7r/im8063mu/ssN0I0kmulViYpgQudx8uTjC5MPj HxH8VY/iPpL+HLa0tPCdmIU1K0vPC8d7fagFlbzWt7saxbpCHi2qnmWshRgX YMD5YTxt4V1HxjrHgnXm0PxHbT+FfiDe+LVsI4bSWWeGTw1/YyI7m5VI8vcy yAqZDi22sq7ww/PZwrKckqWnTR9z7SnOk4pupr6ooeGPG3wB+IUHh+48JaP4 G8a6f4j12Tw7Df8Ah3xPdalaW14llNetHLJFeEKRDCTtGWBkjyAG3Dqo/CXw gOujS/8AhHPBK6mdS/sb7IfEV/5/237J9t+y7PtO7zvsn+keXjd5P7zGz5q8 0i+Fmv6Z8b38S2vhvxHcQa18Q/8AhNb1JksofsEUfhMaMsCkXT+azy28TBiE A+0YOAhc8S37Dqv/AMFHv+Gk/wCzfF3/AAkmfsH9lDQdA+yf2Z/Zf2Xy/O83 7R9t+0/vftvmb/s/+ibfK5qpU6vJFqnrr0fkKM6fO06mmnVHonhzwR4Q+Kf7 RfhexsdBGkeGtb8Lyav9jiuZyzSi3vpVZneR3z+6iBCvt+XjqSflfxd/wVZ/ Z/8AA/xWuPA+oeDp18V2xRW063vdUuZGZ4llVVMVuysSjKcKT1x14r7Z/Z1+ Fl1bfGTQLq/mNjd+E/AsdvPaGPf5kj2upRsCwOBtwT3z7da/Hfxd4Z+I/gL9 p/xv4u0L4UaZ4ki2wah4a1GO9s7aTUL9bSzt1jvmkuUl+xw7J5FRVDGSNQCo k81ODMZzpxquhG8lfljtd9Ffou76I7suhTq1KUa8uWDa5pb2XV26vol3PuL4 WfttfCv4y+BbDxL4a+H39o6JqnmG2uP7du4fM8uRon+SSJXGHRhyvbPTBrqd U+OnhLQ757W9+FE9ndRY3wz69cxyJkAjIMeRkEH6EV8q/sAaPqfwY/ZMuPDf ifRI9B1y/tLuI6faSRzQWLSax9sVQyyMNgiTAwzEZUE9SPqq0+KHw6aysrw6 Pa+ZYWmnW0unjSIibuRLmJ7iYv03siOu0kgqyfNl3VKu0r/qKVlJpO5Av7Q3 glh/yTM/+FFcf/EU5P2g/BOP+Saf+XFP/wDEVStfi34M0q30+0tfDsF3bQny 7uW80i2ElxF9iihEnDlw4mV5cLIpbOPMXcSMv4nePPDHinR4RpWi2lrqUdyH 8230z+z42iBlLIyi4m3ljIvTbtCYywC4P63JOjH7QXgoDP8AwrXGP+phn/8A iKf/AMNB+DAcH4a4PofEFwD/AOgVyfw98e6LoviDUpNT0yO30rUoI4pbKzil mRlSSOTYrNcJIhLRK2/zTgjGxgRt68ftNW1t4htLix06WOzgsEAS4/fSfaY7 K5t41yXw8Qaf5iQGYDPbbTfl+YLzET9oDwamVPw2wR2/4SGc4/8AHKtj41+F f7OW8/4VhOLVpfJEx1y5EfmbdxTdsxuwQcdcHPSluP2iPDuqWAzYa1Zm205b S3t4tkiJiHUIUi8xm3BUS8TEhVifL5QdTj+D/iTpGj+GLCyvLm/Qf2Rf6VND BbCVoZpp1kW5Csyq4ZCqEbt37gZ420rsZqf8L78G5I/4Vz0/6j8//wARU9j8 a/Cl+XW3+Gc07Ro0riPXLhiiKpZmOE4AAJJ6ACq8vxf8I6n4uk1Gfw68kVxf lp4pbeF9ytefaWuWkPzGTZmLyMFNv8Z5WmJ8RfCPh95obO3uZUXTXto54LGK GS9aXTo7dvMYnMW2USMdvmBjIx+bALFwLK/Hjwd1/wCFdf8Alfn/APiKePjr 4PI/5J5/5Xp//iKqat8SPA10+yy8OmO2WDyEzYxhxiK/UMcyuxO6e1JbdkmE njagptv8T/CkN/NKfD8MsMwmu41NhAn2acvC8NvgffhTyWUudrOs75QYAJf+ riRqXPxn8LadN5Nx8N5baYKrmOTW7hGCsoZTgp0KkEHuDSD44eEAAf8AhXvX /qOzf/E1x/ivX7DU/Cml2ayTXWo2drbRNN5abCA1y7IWYeZlFmhjAGFPlN94 KhrrNa+MXhy/+13dvpEzandwy7ftdvHcW9rKTB5SKju0ZWNEkQOI0ITYu04J pgTD44eESP8Aknw/8Hs//wATVxvit4ci84N8NLpTbwrcSA6xcDy4227XPycK d6YJ4O9fUVAvxE8BLbPLDpQSS2e3kSKbSoS98UmmkeNjuZVDK0aM4A6fLGFU IsGjfFbwrAlvJqWgHUbiCG3j3vbRH/VxWEZ5LcgG3uMA8ESAHh2AV32GiwPj b4R7fD/H/ccm/wDiaePjX4SI/wCRA/8AK5N/8TXLeLPEWhXvhKzs7C3aXULZ lT7YbBLN3iUScSBJXEjksvzYU4TBLdRzAIAFUlclnqY+NPhPH/Ig/wDlbm/+ Jpy/GjwmOR4C/wDK3N/8TXl4xinggAU+Vdwuz1AfGbwoR/yIY/8AB1N/8TTv +FxeFv8AoRj/AODqb/4mvMQcAcVLRyruF2elj4v+Fh/zIx/8HU3/AMTTx8XP CxI/4of/AMrM3/xNea59jUgODRyruF2ekj4ueFyf+RI/8rEv/wATT1+LXhgc DwTj/uMS/wDxNebhsHpUmcGjlXcLs9GHxX8ME/8AIlf+ViX/AOJp4+K/hkn/ AJEv/wAq8v8A8TXnQPNSZwRRyruF2eiL8VPDQP8AyJnX/qLy/wDxNOHxS8NE /wDIm/8AlWl/+JrzwOMipA4yKOVdwuz0MfFHw0T/AMiaf/BtL/8AE05fif4b HH/CHf8AlWl/+Jrz9WyRjmpUBY9KOVdwuzvh8TfDmR/xR/8A5VZf/iaePiX4 dJ/5FAf+DWX/AOJrggpzUqoQwo5V3C7O7X4leHen/CI/+VSX/wCJp6fEjw7u /wCRS/8AKpL/APE1wqqQwqRR81HKu4XZ3I+I3h4n/kUv/KpL/wDE09PiL4f6 f8In0/6icn/xNcOg+btUiDmjlXcLs7ZfiF4fJ/5FT/ypyf8AxNKvj7QGP/Ir H/wZyf4Vxife61Igweoo5V3C7OyTx5oPQeGMf9xKT/Cnr460In/kWT/4MZP8 K45Bz1qRBzxzRyruF2dgvjnQmP8AyLP/AJUZP8KcnjfQ+n/CNf8AlRk/wrkU HNSIOaOVdwuzrV8a6GT/AMi3/wCVCT/Cnr4z0Rj/AMi5/wCVCT/CuTQYNSJ1 o5V3C7OrXxlop4/4R3/yfk/wp6eL9GOf+Kex/wBv8n+FcqnWpI+9HKu4XZ1C +LdGb/mX/wDyef8Awp6+LNHb/mAf+Tz/AOFcxH3qSPvRyruF2dMninR8f8gD H/b8/wDhT18T6OR/yAf/ACdf/CuajYYqSM8Ucq7hdnSL4n0hh/yAv/J1/wDC np4l0nH/ACA//Jx/8K5yNsipEPB4NHKu4XZ0I8R6Sw/5AgH/AG+P/hT18Q6U w/5An/k4/wDhXPx9KkRsDpRyruF2b6a/peP+QKB/29v/AIU9dd0s8/2N/wCT b/4VgxnINSR9KOVdwuzbXWdNYf8AIJP/AIFN/hTk1fTcf8go/wDgU/8AhWMj YFSIeO/5Ucq7hdmwuq6b1/sr/wAmXqQappxH/IL/APJlqx0YbakRhto5V3C7 NZdS0/H/ACDP/Jhq6PwZ4dsdciW+STSbCW2mwsV49xKGxgglVidGXPGD1wQR jrxSsNvWuS+MP7ReqfBnQYLXw14afxPr0s6yy291b61BYx2zK4L/AGqw0y/H mh0UeUyqdr7iw+UPE00vd3HHfU+gPEPjDS/CNlf3Wra38M9Ks9L0y41m9urz TVt7ezsrdolmuJJXsgiIhmj3FiMBieisR5lH+3l8H9d8JeK9V8KfE74GeP7n whoV14hvdK8L3Njqeova2ybpGWJIO5KoCxVN0iAsuc14r+0J8arH9oP9nHx5 4Y/s/wAS6P4j8c/CzXfC+B4O8RT6dZanfCxVI/P/ALNWV4VMcx8zyFJWPJRW YIfLfi58WvGfxx13W9a8ReCv7D+y/DHxX4S0yz0LT/E+r3GpXuqtprxbjcaF ZRwov9nspYu2TKv3QCazXNpcppH6B6fotvNrg18XXhOC8vbCG3e3mgma3ijV nkX/AEf7MYFl3SMGlVd7AKpZlRAPPv2g/jdP4VTTtM0g+FLia58RWOjajNa6 LEpgSZDK6Iz28ZDmPb86g4EhwQ4+XwP9sDS/Cv7Rr6DYXGs+OPD03hFrpJDZ fDrWr+LWmPlK2nXkscIS80Ocxf6XZIwW8EVsRPEIgX7NhZ/HLxRHPat4g0uy u/HVpeQS3ekz6dPL9m0gyY8q7iRzGzwFC20ZG/awYBg02nqDStZHotv8KtUt fj/4y8VLc2i2XiHwvo+jWqrI4nins7nVppHf5cBCt9DtIJOVcEDALZH7O/wb 8U/CzxZ43vPEfi/xB4kstZ1APotrf6tFeW+mWnmTTlIYls4GgxJdSRbZJbkm K2t8SKFEae9fC+28TfEz4g6pBceMfEmnaLptkl1I1vfqJmlnbZEiK8Lfu1EE 7MxbcWlQDAWsHRPiv4j1Gx1Iya5eK+mahfaa0j300UUjW11Lb+aWyxjVjFux htobHz4yeWc/Z4aWIqe7CN737LW/ppfv5HRL3qkaKab3087K3rseZ/ErTvHO ueKbvStK0fw3L4Q1rSJLe81ZvFd9pmrWdwY5lCwQ29ox25MX75buKRNzlVzG u/G0n4YeMb743eCvHeo23h60ns9K1PSNY0uDVbi5hshemxnM9tO1uhuCk2nx x7HjgDJO77lMQjk6L4t+N/2iPBX7aOgQ2V3Yj4HS6Wv22/nvpl1C7uZ9wX5W wYp4po40ECeYDFO8jtni29S/4T/Xt7H+29X55/4/ZP8A4qow+KoVqMa2HqKa kr6dL9H2fkZ88m5Raas7a9bdV5HhXx2+EPiHxz8bPB+sad4f8GeKfC6aFrPh fxPp3iDUZbZXsdRn0x5HjiW0uEusR2UqmCUxK/mgGQDNcB8Pf2Y/jD8PYLzS 9Z8a3/xN061u7W10jUNb8ZTaRcwWTwXcNzM0djpqTfaIluUCrNeXHnvEkwls 3jQD374d/tO61498a6zpceu35g05XNvNHdSkXeyUq5Vt5UhVaDPQkyZxgisX 42/Hf4heDvE0B0zxFrUFikELEO7vBLKZmVg7feAxsyFYHnjk1vBWfIv66/qD k7XNjwm+vi21OLXrTSrcQajKmmy2WoSXjXlngGOWYPBF5MxJcGJfNVQqkSNu Krciimur1YYnRTI4RPMdUUE+rHgD3PAqv4T+K918XtFkv71bxbmzuGs3FxcG 4wAoddrnkqVcHkDBJGO5mX/kIJ/vj+lfR8P04OpUdSKlaLdmr6qx5+Ok7Kzt do2/+Fe6v/z10j/wb2n/AMco/wCFe6v/AM9dI/8ABvaf/HKs6r8StB8FeF0f Wb2DTkCSWkbXtzbxW0kkmW3AyJw4CDoS7BeHQIAY3/a6+HMmsrcteeDntkuT Olm2s2vkIpgETLtAwSWVHyc42AADJJwWe3/5cU//AAF/5lPBP+eX3kX/AAr3 V/8AnrpH/g3tP/jlH/CvdX/566R/4N7T/wCOVmL+0p4JWymMvivwvcX0nkqj vf2RjiWJVVOG3McgYIG0EKMlwSBdv/2pPh3qS3kg1TwTYz3EIiX7LqVuoyUw zfOzKvzAEBEVuf8AWDB3P+3P+nFP/wABf+YfUv78vvJv+Fe6v/z10j/wb2n/ AMco/wCFe6v/AM9dI/8ABvaf/HKrp+1V4CluLeW41TwNI8XleZ5WoWsSy7JF YrtB2gMF543ZJ+bZ+7pH/ao8CQStNZ654RtLlZXmgcanZEWrG4SZdoCgnaFK fMTw3G0ZUn9u/wDTin/4C/8AMPqX9+X3ln/hXur/APPXSP8Awb2n/wAco/4V 7q//AD10j/wb2n/xypdL+NnhDx7YahYaJfeH73UJDujhspLedra1VgQdyHeW DFVLFRuDtuLZQI/S/D9/rgk+w2N3eeVjf5ELSbM5xnAOM4P5Unn2tvq9P/wH /gjWB/vy+8r/APCvdX/566R/4N7T/wCOUf8ACvdX/wCeukf+De0/+OVqf8K/ 17/oCav/AOAcn+FH/Cv9e/6Amr/+Acn+FT/bz/6B6f8A4C/8w+pL/n4/vMv/ AIV7q/8Az10j/wAG9p/8co/4V7q//PXSP/Bvaf8AxytT/hX+vf8AQE1f/wAA 5P8ACj/hX+vf9ATV/wDwDk/wo/t5/wDQPT/8Bf8AmH1Jf8/H95l/8K91f/nr pH/g3tP/AI5R/wAK91f/AJ66R/4N7T/45Wp/wr/Xv+gJq/8A4Byf4VU1Xw9f 6EsRvrK7s/PBKefC0e7GM4yO2R+Yo/t5/wDQPT/8Bf8AmCwP/TyX3lb/AIV7 q/8Az10j/wAG9p/8co/4V7q//PXSP/Bvaf8AxyrOl+H7/Xd/2GyvL0RY3+RC 0mzOcZwDjOKt/wDCv9e/6Amr/wDgHJ/hT/t5/wDQPT/8Bf8AmH1H/p5L7zL/ AOFe6v8A89dI/wDBvaf/AByj/hXur/8APXSP/Bvaf/HK1P8AhX+vf9ATV/8A wDk/wo/4V/r3/QE1f/wDk/wpf28/+gen/wCAv/MPqS/5+P7zL/4V7q//AD10 j/wb2n/xyj/hXur/APPXSP8Awb2n/wAcrU/4V/r3/QE1f/wDk/wo/wCFf69/ 0BNX/wDAOT/Cj+3n/wBA9P8A8Bf+YfUl/wA/H95l/wDCvdX/AOeukf8Ag2tP /jleEePPjndXvif4B3XhnVA/hz4keJnguW+zDOo2DeG9Yv4RiRN8eZra2k42 v8m04BZT75quj3eiXAhvLW5tJCAwWaJoywz1AIGR7/WvjTwt/wAiD+xH/wBh W1/9QfXK9TB4uGMw1dzowi4xurKz/U56tJ0pwtJu76n1b8KP+S8+JP8AsVov /QdWrzj9tz9p7/hkv9l3x74+Wz+2XXhfTkaxjMfmQy3lxKtvbeau+M+SJpIz Jhw2zdjLYB6/wTrVj4f/AGmLltRluLcat4dGlaU0Wu6jb/adSa31m5S3ewgQ 2t1GbS0v5PNu3XyHt4xErPPlN34p+H/hr4u8Hazpviq1uvEmgR2zXGrWtz4V XVLRYrfy5i9wgd0AjzHIN44yrDsa+TqW9ol1b0XfyPXpRfK3bRfl/WhzfwH+ Pmg/tG+Cv+Eh8OW/iOHSndUhl1jQbzSGulaKOUSwrdRRtLERIAJUBjYq21jj NUbD9oXTxqnjiLU7abT7Xwd4s0/wqsqM1w19NfWumSwNtVR5YMuqRxHkqBGX ZgCQvd/D74c/Djwfp9/deE4rbSbXxJeya7fTaVodrbx6tdThWkvJGjmAllkA UtK2WbAyTxXm978MP2Yfil+0n4j0y+0fwJr/AMWLbSA+tG78J6fcaybG4ge0 KzSu5kaNrdhE8ZY4imiDqEmTdMablJqK2Wvltq/Lz8yXOK1el9il+yZ+0437 R8Pju3l082l/4E8ba74Su2SPZDMbK7xA6Zd2bNtLbly2394JMIF212/j74g3 Pg7WtItYdEvdTg1RwjXEEkY8l/tFtHs2sQWxDLcTnbnEdpNwTjNb4ffs1fAr wdrNs3hXwh4b0O88HXc4sm03wbY2kmjXFzawrM0DI6+W0tv5CO0eNyKqk/Lg d5f+EfCes3djcXst7eS6VMbq0kl0eF3s5TE8JkjJmJRzHLKm5cErI69GIMyS urF8yvex4Xo/7buha1rWrWn/AAinxA0230S8NjcajrGjjR9PmdLqaGV7ee7k iS6jit7ee+d7cyBbaMnJlIhPrWg61Jq/hyz1CSyvNMmurVLiSyuwgubQsoYx SbGZQ65wdrMMg4JHJg1L9nv4U6t/aAvPD+lXQ1e4e8vxN4Ws3N7PJZtZNLLm X53a0d7cs2SYmZCdhIOprNr4D8ZPq/hPUdTXV3m09G1XRbnTba4LWVz50S+f A05zDN5NwgLDa/lSqM7WAGtNCYSSaueGa1+334U8PaXp91PoniyWG+k8QK8s FrA0VlHo1/Hp9zPO5mCxQNcyxgTPhI0fzZ2giSR06r4QftQaJ8efDsWr+GtI 8Xf2RNbvcx32raHcaPHKAlu6eWl2sc0qyLcZSaKOSE+RL84OwP1dh+zH8H9J 0HRdLtPCuh2uneGjnSLSLwlZJBpR+0x3ebeMSbYj9phhm+QD95DG/wB5QRe0 74D/AAt0fQvEemWmg6dbad4wnubvXrSPwxaLb63NdKFupbpBKBO8ygLI0m4u B82afujT7+Rf3buBkEdeMV5T8P8A9p9/GnwW8M+Lx4Q1pk8Q+GdP8R/Z7S8s pFiN3ZzXItkeaaFmZGhWPeyIha5iOQizND7D4e8IeEfCHh6w0jSHu9K0rS7e OzsrKz0aCC2tII1CRxRxrMFRFUBQoGAAAOBWVbfBb4a2d3o1xFpFrFP4d05t I0qRPDlqr6ZZOqK9rARLmKFljjBjXCkIuQdoqbLX+v66Anpqc/p3xWivPGse iz6ZrNrNdG6MLtZmS3QQTrETJcRloYzKHSSJHdZHXf8AIGjkRLvj3xlc+BbG 2vk0i+1TTUeZ9Ulskee40+2jtp5vOjto0ea6dpIooVhhVpGM4IB2kGzpn7PH wl0S+8MXNj4a0qxn8EwS2vh2S38LWcb6BFKgjljs2WUG3V4wEZY9oZRg5HFa PxC+FXgD4ueELvw94rsk8UaBqGz7Vpmr+H7a9s7nY6yJvikmZG2uqsMjgqCO QDRJJ7Amrnx3oH/BW6HXPBPhHxDB8PtUu9E1XW9Q03X9ctp7pNH0e2sxfyvf Wlzc2kH9qR/ZNOubjbaoWQRNG4SYxRS+u/AP9ra8+MWo+GbPUfB9z4Wv/EVp 4puLi0uNRjuZ9MfRdYt9MaF/LUxs8hn3ko5VGjKAygiSva7f4beBrT7H5VuI /wCz76fVLXboFuv2a7n87z7hMTfLLJ9puN7jDN58uSd7ZWx+G/gTSb6O7s7X 7JeQC7Ec8Og26SJ9rnW4u8MJgR58yJLJjHmOoZssM1V4jTV1dHPeG/iFF4ht 4ZH0+/sGuJLlI0naFmZYZni3/upHAVwm9QTuCuocI4ZF5rSf2mtE1P4rad4K fSfGFt4g1K2a9RBoVxdWdvbGS8jimuLy3WS1t1m+xSGNZpUdg6DYGLKuxpv7 IHwY0zWtR1AeHYru71XVotfupL7Ror0yajFNLNFeATXDBZ42lKpIoDJGkMak RwxKnTah8I/h7rHibQtbvrCK+1zwsLhdG1Gfw9bSXekC4QJP9mlMxeHzUAV9 hG9eDmj3SXtZDdQvpLUxxwxJLNPuVAz7FyFLDJwcDjrg49D0rwCL9vEa38eP hx4E07wxr5uPE2ptBq2rjSLqbw6YP7Al1RDYaqVjt7lnk8tV25ZkguWMaYRj 7x8U/gT8L/jpoNvpXjjQ7DxnpVpcrew2Wu+GLTUbeGdVdFlVJpWUOFd1DAZA dhnk1m/En9lj4M/GS5nn8XeEdA8VT3YgFxJrHhCxvmm8jzfI3GWRifL86bZn O3zpMY3NmVYd1oeY/Cn/AIKA+BPjFP8ADNNJh8RRf8LaGuHw+bqxCB/7JnEV x5wDsYtw3SR7hyqENscrG3R65+05ZWv7G958ZdL0y7utNTwW/jO0026kS2uJ oRZG7SGRk8xY3KgKSN4BJI3Dr0ni79kP4IfEDVte1HXvBHhrWtT8VQxW2t3l 74OsJ59ZiiaF4o7l2kLTIjW8DKshYAwxkAFFx0tl8K/ANh8OY/CCW8j+FYtO GkDSZdFglszZCLyvszRtOVaLy/lKkEFeDwabtbT+t/8AgAmtDyjxN+1xpvgD 9oVfhzrmheJm1TU4bW80S60bR77WLW8tJZEgeS5eGArZtFcFxJ5hMSxNbyGX Mpij3vDXx+sPF2t29lbWGr2sl9og1+GLUbNtOnji3RqY5IZykyygyruTyyYs DzfLMsIk6DSv2Y/g7onhLQfD9r4S0K20DwpqS6xoemw+ErGO00W9V3dbq2iE myGcPLIwkQK4LsQQSadpf7Nfwh0T4e23hGz8MaRa+FbJJ47fRovCtkmnwLOs yTBIBII1EiXFwrgL86zyg5Dtk90EzD1D40S2Mk6r4T8Rz+SLk7kuNOVWWE3g 3Za6AUObVNu7BH2yDcE23Hkb3j3xRd+EPh9rOt6dpVzrt9penT31rpkLCOXU ZEiZkgUtwGdgFG7gEjNZ6fsh/BCKysraPwT4ajttOvrXU7WBPB1gsVvd2sAt 7W4RBJgSwwhY43xujRQqkACu+NjoLcHUtYI/7Bsf/wAfpSStoCeqPEtD/aV1 eW+8Qwaj4A1mB9LfUJ9Ljsr61upNYtIHt47WZWd444mvGlm8pZZAoFs5Z1A4 6jxH8V5NDg17Z4f1i4fQrdLhlWS0H24s048mLdOuJCIUYNMY48XMWXyswi6z w78KPh74Qt4YtJsU0yO2sLXS4ltfD9tCI7O18z7NbALMAIYfNk8uMfKnmPtA 3Gk1b4SfDrX/ABZba9fabDe63Z28tnDqM/h61ku4oJdpkhWUy71jcopZQcNt Gc4qvd0C+h5R4u/bS8H/AA/+L2j+B9bg1/T9e125itrVfsPnwfvryWzhdpYm dFWSSMEZO5VlTeqtuVex+M3xR/4VD4Rs9V+w/wBofbNc0fRPL87yvL/tDU7W w83O1v8AV/ad+3HzbNuVzuHVeF/hj4B8D+HbDSNFtP7H0nSoo4LGysdBtre2 s442DIkcazBUVTyAoAB6Crms+FfCniOyS21GW8v7eO4hu1iudHhlRZoZVmhk AacjfHKiSK3VXRWGCAamy0/rQSep8t/t0f8ABSOx/Yw0y9kbwn421ifSbjSb m8vrPw5cXmkw2Fxe+Vc7rsFLeG4WKKRY0llXMtxa4Dqz7fffCXj+x8Z614js LVLqO58Laiml3olj2qZXs7a7BQ5IZfLuoxkfxBh2pvxf/Zl+Efx90a9sfGeg W3iKO/sJtMlku9At2uFt5mjeSNJvO8yMF4YXyjKQ8MbghkUjb+H/AMLPAHwl 8IWfh/wpZJ4X0DTg4tdM0nw/bWVnbb3aR9kUcyou53dzgDLOx6mnpy26/wDD Db10/pD6K1vsWhf9BLWP/BbF/wDH6PsWhf8AQS1j/wAFsX/x+osFzJorW+xa F/0EtY/8FsX/AMfo+xaF/wBBLWP/AAWxf/H6LBcyaK1vsWhf9BLWP/BbF/8A H6PsWhf9BLWP/BbF/wDH6LBcyaK1vsWhf9BLWP8AwWxf/H6PsWhf9BLWP/Bb F/8AH6LBcyaK1vsWhf8AQS1j/wAFsX/x+j7FoX/QS1j/AMFsX/x+iwXMmitb 7FoX/QS1j/wWxf8Ax+j7FoX/AEEtY/8ABbF/8fosFzJorW+xaF/0EtY/8FsX /wAfo+xaF/0EtY/8FsX/AMfosFzJorW+xaF/0EtY/wDBbF/8fo+xaF/0EtY/ 8FsX/wAfosFzJorW+xaF/wBBLWP/AAWxf/H6PsWhf9BLWP8AwWxf/H6LBcya K1vsWhf9BLWP/BbF/wDH6PsWhf8AQS1j/wAFsX/x+iwXMmitb7FoX/QS1j/w Wxf/AB+j7FoX/QS1j/wWxf8Ax+iwXMmitb7FoX/QS1j/AMFsX/x+j7FoX/QS 1j/wWxf/AB+iwXMmitb7FoX/AEEtY/8ABbF/8fo+xaF/0EtY/wDBbF/8fosF zJorW+xaF/0EtY/8FsX/AMfo+xaF/wBBLWP/AAWxf/H6LBcyaK1vsWhf9BLW P/BbF/8AH6PsWhf9BLWP/BbF/wDH6LBcyaK1hY6Ef+Ynq/46bF/8foaw0Jeu qar15/4l0fA9f9fTsFzJorXXTtDY4Gp6v/4LI/8A4/TVstCbpqWsY/7Bkf8A 8fosF0chofjP+2fF+t6NJpOtWEujC3kS6ubXFnqcMyFllt5lLI210ljeNisq NEGaMRywSS+VeL/2iNf+FPw/+AtrpnhiXxtqvxN1zS/DdyZdWWyfToJNNub2 71BnkVvOMMFnNIYsq0h4VtxAP0KbDQwOdS1gH0/s2LP/AKPqj4Y8IeGvCPhf TNKtdU1xoNLtIrSN5NOiZ3WNQgLETAZwBnA69OwqhXPKv2q/2j/+GXfh5ZeI /wDhGda8ZifUksf7D0BftOv6jvilfGnWeP8ATJk8vzZIt8ey2iuptx8jy3tQ eLD460j4Va4X0ecazcx3/m6PqH9padJ5uk3b5trrZH58B3ZSXYm9drbVzgei 2y3d3AksfhH4gGORQyn7Po4yD0/5idc74x1ObS/GngaaC38QaLfwa9MrJerb wyqDpl5hke2uJlIIYj7wPHTmmoNLUXMmYmqazqGqXxuF8J/EawmaNYnbTfEM Gn+aqliocQX6B8FmxuzjccdTUNvK9t4TutEXwF40fTr1bgTiXVLOSeYzs7Ss 0zXpkLM0jtu3bst1rzD9ufxt8VfAdj4h1nwBJ4n1C38NeEp5rbR/D+jR3V7f 6tdtLbWkjLcWcqXkMMgWd47e4glt1gZpI7pLiKNOd+Kf7Qn7Rfw88LfEV9M8 FaH4mv8A4aQottJa+Gr2KP4hPdQJJG9hF9sd7ZbAzDz1LXRuzE6RNbPuEbm5 Vafs56x7Pbttt1HGCjLnjv3PatPt5tP1e2vj4Q+JF9PaO0kP9o+JIr9InZWQ uqT6g6htrsu4DOHYZ5Nb/wDwsDVs/wDIjeKf/AjTf/kuvJfA2u/HpPjv4h0X V9Q8MTeGLbUrY2+pP4Sniia3h0zSXu4LcC+DYu7m6vWhlczC2aynRzcB41iZ 8Vvil8UtY+MXhW3+Hr22meH10e7XW21/wdqNxbNezyaOtmylDFLmKO9uJSob ymFvcwyvbsj3Fry4bB0cPHkoQUVvZK3Ty69PU1nOc3ebuz0bQpv+EauluLP4 c+JYriOD7KsxudPeRYd24RBmuyQgPRQcKAAAAAK0NT8WX2tWL2134A8S3FvJ jdHJNppVsEEf8vfYgGrer+KPFd98B9R1fwH4a0HXvHeo6Za/2DYeLL2bQNPt rm5uLaJrq+BInWK1ilkuZbZf37rbvEmJGUjj/wBobxD8XPA2mfCaHw3pHgvX /GN5qFrbeMbXTtQurbwxI/8AYV5LdmO8e2u7mC1F+kRidoy7HyYnZfMauv2c uXnZjF3dkdn4OtYbTRZfJ0G48OiS4LtbTmEtIdoHmfuZJFwQAOWz8nTGCbS/ 8hBP98f0rJ+G3izWPFOi3i6/pyaZrGmXKWt3BClw1sshtoZmEE80MP2qJWlK rMiBTtKkLIkiJrL/AMhBP98f0r6Ph34qv+CX6Hn49WcV/eRzvxxXU3Xw2ui3 Flaaw+o3K2M93avdW8E50u+EbyRJJG0iB9pKCRCwBAdc7h4Lr/7dvxh+Gmka tYar+z3408X6zoll58OoaFbta6f4gmhmVr2KGImd7aSOxeOaNZHeK6niubaC eZkhluPpzxhqOhaGmn6jrt9YaclhdeZaTXd0tuomaKSPALMoYmN5Rg54yccA jP8A+F3eC/8AobfDH/g1g/8Ai6+YT0PT/r8j47+Fv/BTb43zeGvBFt4o/Z51 abxLr9jrl/qMMIv9Kngi0rbNK0Fi9rcOySQXFpDA0kyGe8keIiJPLkf6V+JP xJ8c2l54n1DwbH4Z8QxeCIb77T4ZWB21LXrtdOt7mzslvPPSLT5XkmUkyQXA aKWJsJkmuv8A+F2+C8/8jb4Y/wDBrB/8XR/wu7wX/wBDb4Y/8GsH/wAXVyae qVv6/QG12OP+AfxQ+Jfjb4keKdP8eeDdA8L6Xp2l6ZeaTNpF9eajFeSTyXy3 KNc3FraZePyIP3Sw5QSqzOfNVU9hg+0r4S8Sz2VpaXmowaX5llFdSmGJpvPh CBpVSRo1YnaWCMQrH5W6Hjv+F3eC/wDobfDH/g1g/wDi6o+IPiN8OPFtkttq 2ueCdUtkkEqxXd9azIrgEBgGYjOCRn3NLd3SEy14S1LUrr4h6lFqQjt510HT JprS3unuLW2neW9EoidkQsMqo3lELBFyq4wPRLH/AJEPUv8Ar/tP/RdzXnXw 3vvAcF7c2vhGXwml1Oglnj0h7cO6IcBmEXJALnBPAL+9bPj34p2vw/8ABzWb XXh+C+1K8imhXV9WXTonjijmEhV9jlmBlj4C4wSSRwDL1dkJaIPHHimLwL4J 1jXJ4WuINFsZ76WNZ4YDIsUbOVEk7xwoSFPzSyIg6sygEjzvw5+0bq6aJ4eX X/B7x69q+syaJeWejakJLfTXjubiF5/M1KPT5Z4FS3MhMUDO6tuhjnTbI1r/ AIaC/wCoh8L/APwtP/uWk/4aB4/4/wD4X/8Ahaf/AHLTUH1KurHLfCH9vHTf jXp/gW+0zwX400q08c6HrOuQ2+uW0en6jZJp19ZWflyW7OeZ/tgljYPt2BSS N/y6/hz9sOz8W/CvSPGWneDPF0+i6zpkusRl5dNgktrQQSzQSzeZdqircIkX l4clPtMfniALKY9L/hoHH/L/APC//wALT/7lpV/aCAIP2/4YcenjXH/trVOK tt+IJq2p3HgPxnZfEXwVpOvac6SWGs2kd7bsk8U6tHIoZSJIneJwQeGR2U9Q SCCc7Sv+S0a//wBgTTP/AEfqFcnafHeOwt0hgvPhZDDEoVI08ZBVQDsALXAr Y+Fuvf8ACZeNdc1gX/he6EljZWYi0fV/7R8vy5Lp90jeWm3d5uAMHOw81Lja 9tiU+h3dFV9Q0nxVqWz/AIRrQbfWwn/HwZbqaHyc4248uCXOfm67cbeM9q3/ AAi3xS/6ETT/APwZ3f8A8hVPK2O6PL/iR+1h/wAKp8Z+MP7W0Ff+EO8GaLda leapa3rXN881tHaTzR/Z0iMEKCC8jZftF1FcyGOQx2zxKJzU1r9tEaOLiIfD fxvc6jbT2Fq2nx6n4eS5M91bPO0OJNTVRJCU8t1LAyMwMH2iMNIvrf8Awivx RP8AzImn/wDgzu//AJCo/wCEU+KOf+RE07/wZ3f/AMhVSira/mHMr3PLP2SP 2vo/2o4fEFvdeEdd8E634duZUn0/Uri1uTJbi/v7GOZZLeSROZdOukdCQVeF 9pkjMc0nslZw8K/FEdPAmn/+DO7/APkKj/hFvil/0Imn/wDgzu//AJCocddB J6GP4A/5Gvxx/wBhuL/03WNfMvhb/kQf2I/+wra/+oPrlfVHhHwZr3hLUNdn 8RafFpd9rWoLfJbRySSBIhbQQA7njjJy0DH7uOepr5X8Lf8AIg/sR/8AYVtf /UH1yvpMj/3bE/4UefjPjp+p9QeBLPS9Y+Pt9BqXh651SfTtHh1fTdRS6EUW i3KWev2pkdN6mXzILy4iC7XUF9xAIVh3vgn4x2PweF8tt4G1rVtQ1R0mutQ0 xLCMzhUCIkrTXEUjFcNjgqA3B6iuQ+Ex2/HrxJwP+RVi684+XVa+of8AhQ3h T/oFH/wJm/8Ai68WGlTmsnbudU23Dku0n2/I+Tbn+2j4GnudMN5o+tC9fUoE VoXuYf8AS/OCj5/JZyvG0ybCThmCkmuR8UfCrwz8Q/2rfD/xN074WS+D/Eln DdR6nqtzFppN8JYnXzD5M0khuWLKpnOW8tPL3bWIP2+PgR4UIz/ZRx/18zf/ ABdL/wAKG8Kf9Ao/+BM3/wAXXkYrJnWx/wDaKqyjK1mou0Wrt2ate13rrrZX N4YiEaKouKdtU3utLHyX8P8A/ka/HH/Ybj/9N1lXO/A34feLvAUtra67dafc afZ6etnCLfUZp8OvlqpCPEgUbVI4JIz05yPtK3/Z48HWs08keixxyXTiSZ1n lDTMFVAzENydqquT2UDoBUn/AAobwp/0Cj/4Ezf/ABdemqM1sZe0V7s+TfHX xt1z4MeIPCd/o3wy8Z/EY2eqyanNHoF5pNv9mCWV3aKkp1C8tv8AWfbS6mMS Y+zsGC7lJ5LwV448WfHb9sjx98Rdd+Gnib4aaVf+DPDfhyxttd1LS7u5vZ7O +1+5ndRYXVwqoF1K3A3lSSWwDgmvt/8A4UN4U/6BR/8AAmb/AOLo/wCFDeFP +gUf/Amb/wCLp+zny8oueN72PnOivoz/AIUN4U/6BR/8CZv/AIuj/hQ3hT/o FH/wJm/+LqPq8ivao+c6K+jP+FDeFP8AoFH/AMCZv/i6P+FDeFP+gUf/AAJm /wDi6Pq8g9qj5zor6M/4UN4U/wCgUf8AwJm/+Lo/4UN4U/6BR/8AAmb/AOLo +ryD2qPnOivoz/hQ3hT/AKBR/wDAmb/4uj/hQ3hT/oFH/wACZv8A4uj6vIPa o+c6K+jP+FDeFP8AoFH/AMCZv/i6P+FDeFP+gUf/AAJm/wDi6Pq8g9qj5zor 6M/4UN4U/wCgUf8AwJm/+Lo/4UN4U/6BR/8AAmb/AOLo+ryD2qPnOivoz/hQ 3hT/AKBR/wDAmb/4uj/hQ3hT/oFH/wACZv8A4uj6vIPao+c6K7z41fGX9mz9 mzxTb6F8RviR8MPAGt3dot/Bp/iPxnbaVdTW7O6LMsU9wjmMvHIoYDBMbDOV Nch/w3R+xdjP/DQHwBx/2UzTv/kqj6vIPaopUVd/4bp/Yu/6OB+AH/hzNO/+ SqP+G6f2Lv8Ao4H4Af8AhzNO/wDkqj6vIPaopUV0ug/tSfsneKbVp9M+MfwZ 1GFDhpLXx/ZTKPqVuSKhk/a1/ZGhmaN/jX8E1dTgqfiFYgg/T7TR9XkHtUYF FbN3+2N+x/p94tvP8cfgbDO3SOT4jWCufwN1mrKftXfslSPtX4z/AAWZsZwP iDYk4/8AAmj6vIPao52itqH9sT9j+4JEfxx+Bz44+X4i2B/9uqdH+17+yHLM kafG34INI5wqj4iWJLfQfaeaPq8g9qjDorpm/ak/ZOjZVb4x/BlWcZUHx/ZZ Yeo/0mnL+05+yk9o06/GH4OGFCVaQePrMopHYn7TjNH1aW4e1Ry9FdE37Vv7 JSjn4z/BYY9fiDY//JNRzftc/si2+zf8a/gknmEhN3xDsRvI6gf6TzR9XkHt UYNFbx/a3/ZFCs3/AAuv4JbUOGP/AAsOxwv1/wBJoX9rj9kVrdpR8bPgkYk+ 8/8AwsOx2r9T9po+ryD2qMGiuhg/aw/ZJu1UxfGj4KyhuhT4g2LZ/K5p/iP9 qb9k7whFG+rfGP4M6WswzG158QLKAOPYtcjNH1eQe1RzdFXf+G6f2Lh/zcD8 Af8Aw5mnf/JVH/DdP7F3/RwPwA/8OZp3/wAlUfV5B7VFKirp/bo/YuH/ADcB 8Aef+qmad/8AJVen/BS9+C37Snha4134deI/CHj/AES0u2sJtQ8N+I01W1hu FRHaFpYJXRZAkkbFCchZFOMMKPq8g9qjyOivDf8AiIT/AOCdIx/xd48/9Sr4 m/8AkOj/AIiFP+CdGf8Akr3/AJa3ib/5Do+ryD2qPcqK8N/4iFP+CdJ/5q9/ 5avib/5Do/4iE/8AgnT/ANFe/wDLW8Tf/IdH1eQe1R7lRXhp/wCDhP8A4J0g DPxeIz/1Kvib/wCQ6cn/AAcHf8E65D8vxcY9v+RV8Tf/ACHR9XkHtUe4UVy3 gL/gqr+xh8S9M+2aR42uZLUnCyXHh7xBaK/uvnWybh7jIql4j/4K9/sPeEhK dQ+JEFuIGKuf7H1xwCP923NH1eQe1R21FeYaf/wW4/YK1VFaD4mOyNkhz4a8 Rqpx7m1Aqe1/4LTfsI312ILf4lG4kPaLw74hfH1xanH40fV5B7VHpFFcbL/w Vv8A2I4T8/xDVSBnB0TXQfy+z1CP+Cv/AOw7nH/CxlB9DoevA/l9no+ryD2q O4orgl/4LEfsNtGGHxGyD3Gg6/x9f9GpG/4LHfsMoUB+I4HmZ250HXxnH/bt TeFmvi0D2yO+orgv+Hxf7DWzcPiKSOnHh/xAe+O1tVHUv+C137CGkrmf4j3C /wC74Y8SOf8Ax20NL6vIPao9Lorxw/8ABez/AIJ8CVkPxWKspIYN4Y8Sjbj1 zacVYP8AwXZ/4J++SZB8VlZAcZXw54jP8rWj6vIPao+jvgb4s07wb4subvU7 j7NA9m0Sv5byfMXQ4woJ6KeenFL8cPF2neNPFdvc6dcfaY4rNY3by2TDCR+M MAe/p/WvnnSv+C4P7A+t2pmtPih5yKxQ48OeIgVI5IINrkfiK6jQf+CrP7Fv ijwPfeI9N8eG+0jTk8y4lg0TXXkRcZLCIW/mMAOpVTjnPSrVKfLyk88b3PpH 4tfF/wAPeJPAOoWFlfme7m8vYht5UziVCeWUDgAnrXiH7K9u3w1/bO+KnirW P9D8PeIvBfhTSNNuv9b9ourK+8RS3UexMuuxL61OWAVvNwpJVwvi7f8ABwf/ AME60kKH4uEMvBH/AAivib/5Dpo/4OE/+CdLHA+LxJPb/hFfE3/yHVcs+bm0 FzRtY6D4X+C9a8EfCb4g295B4gtb3VPGPjfW7FNBewfVZLa/8Q6re2ctp9rJ tFuHt7iCSNbrEau6iYKFcDx79hr9gbw3+z18RdC8T+FfDWv+CdJ8L+Gb3wnH p/izwv4WtPFWrfaJdLlFzdal4el+z3lvEunKsQuY2u/OuL2SSd1kjVfQ1/4O Df8AgnW+MfF3Of8AqVvE3/yHSH/g4R/4J1oTj4vEbev/ABS3ib/5Dpeznrtq Pnjoej/Ar/gn/wDCTx/8VfjR4s+Mfwb+GPjDUPFfiyxvfD+p+I/DFhrd3Lpc fhjQ7Taskkckkca3lveqI2KnIZwuJAzX/wBpTUJtM+IHha5gsLrUpYtaJFtb NGssn/EsuwcGR0Tgc8sOAcZOAfP/APiJU/YG/wCi86x/4IfFX/yJXoXi34// AA6/a1+HvwU+KHwt1258T+DfFes6kljqs8d7E10La2v7dx5d2qzIFmjmX5lX OMjIIJdSL5degotXL3gjxjp9/wDs9eCPGmtQwR3HijR7W6e2t764a4uLt4TJ JFbWsNnPNLhUkcKhdlRHJ4RmrWs/EvhK80DTtU/trwlBZatPbWtq9x4vFuZJ 7iFJ4INslirCZ4ZEcREByrqduDXkHwvsL3xJ8BfgxLax3Zbwro2n6jayQyxq PONjLCysHzlDFcyA4weQQQRWZ4r/AGdLjxxeadPqtx4ju3sfE1l4sk3XNkBe 31raW9rG0gEeNpS3BZU2gmR8YG0LzadTVXPb9K8Y+CdcsrW5svE3gO7gvpYY LeSDx1FIs8k0kscMaEWeGaR4JlUDlmicDJUgQS/EDwKl1pcSeJPBtwdav7bT LNrbxgJ0kuLmB7i3jLJYlUM0UbNHuIEmVCks6BvLH+Ds/wBmaKO0vkEraz52 6S1mW4TVZ2uLmORJFaN1EhUrlcgRqrFlLq8HhX4GzeD9T8MXlpDrBn8Jafp2 nWJku7cgx2Vtf20RcADJaPUbjdjAJCEBcHImgsz1DxF8UfBvhDWfEljqs8Fh J4Shkl1OSW/vhbxlLMXzxRzf2Z5c8wtT5xhhZ5NgZtvynGzo+s+FvEV7YW+n a14Mv59VZlso7fxrHK92ywpORGFsyXIhljk+XPySK33SDXivj74GTfEiLXY9 Ri1cReIby5v7lIrq3ULNPpH9kMyEgkYtt20HI3sSQRgCrJ+zulz4Ci8Nmz1I 6Smg2XhuWJri2f7RbWkvmQucqQswLSHzI9jZl3DDpEyF0Fmeyapfadq1haXu k3elX+nXiGSG507VBqNvLh3RtsojjU4ZSpAU4KnkngY6/wDIQT/fH9Kk0afU JPD9vHqTSvcW4EQeXyt8ihR8xEYCgk54AA9ABVa8vodMka4uJYoLeD95LLK4 RIlGCWZjwABk5PpX03DnxVf8Ev0POx32fVHM/tF+KL3wRoWka1pmj3HiHVNJ uL28s9Lt3KS6nNHpOoOluhwcNIyhQcHluleb/Ej9v3QvBXw+vfE+gaz4C8ea TDpmtanZva62bB9ZGm6ctzLHpvyTx6gBLvWaWFwlsNyt5kkMi16prHxN8C61 qOlXUvjDw6r6PdNdwhdVt9rs0EsJDZY5G2Zjxg5A54wb/wDwu7wX/wBDd4Z/ 8Gtv/wDFV8yl3PTujy7w7+2NqGpSSW+peH/C2g39p4ri8K3dtfeLEjkExilM iRRtbiV52aHz4IXjjFxY3FtciRC7wRedeHP+CmOreKjoF/beEvD9xZ3sel6h faPo2vf214jFreeE9Q1wR/ZI4oxbztcWsdtAsrs1yBI4SINGT9G6j8V/AOrG A3XiXwjc/ZpVnh83UbZ/KkU5V1y3DA4II5GKs/8AC7vBf/Q3eGf/AAa2/wD8 VQuugXRQ+DHxaf4p+CdN1ueDQra21Gwiuo5tN1qPVbO5ZmkBe3nRFWa2ZUWS KX5XdJBvihYFB6BAk0ng7xLcWFrY399b6aZLKO5n8qB5xPCFVpVRzGCTtLqj FQzfK3Q8d/wu7wX/ANDd4Z/8Gtv/APFVT1z4p+CPEFgbaXxtpUETEM32PxIL NnwcgM0Mqsy552k4yFOMqKGtbr9RX0J/CGo6jc/EXU4NRENtcLoWmTzWdvct PbWs7y3okEbMqFhlAu8opYIuVGMDN+J+r6joPxD0a70rTn1a8g0bUHFpHt82 5jF1p3mRxl3RBIybghd1TcRuIXJDPC3i/wCHfg67u7iz8XaPJcXyRxzTXniU 3kjKhcqoaWZiADI5wMfeNaE/xU8BXGtW+ov4p8MG8tYZLeKT+14RtjkaNnXA fHJij5xkbeCMnI0wjtZnzp4L/b0+NPiXwt4SjH7O2tf25rGh2Wo38uqXF9o1 lazTXdrp7xuBZXLwSLcyXNw0DlmjsIY5yzO7wRZXx4/4KK/GHwj8R/C2j+Fv 2evGt9olxr32HXtZfSL+9jhgt7uWC8SKKOKNxuhNpcW92d8MqzyKFLwSon1V /wALu8F/9Db4Y/8ABrB/8XR/wu7wX/0Nvhj/AMGsH/xdVdXvYrQ8h8Bftk+L fEsviGLxN8Jde+Hlr4f8KzaxN4g1q7UaFFqdsE+3WLTFEk8mHzoil35Yiuli unh3RwrJL7H8O4PE8cOrP4n1HRNQkm1Gd9NXTtJl082lnnEUM3mXE/nSjBLT L5StkYiTHMX/AAu7wX/0Nvhj/wAGsH/xdH/C7vBf/Q2+GP8Awawf/F0vkL+v w/pnU+Jf7StvhPrNzoemaTqmtR3tnHbQ6jetYwOrCfeGnSGZ0G0buI2yUUHA +Yc54JvJrnxL4vikmlkjtdXjihV3LCJDYWb7VHYbmY4HdiepNYviXxt8MfGX kf2vq3gTVPs27yftd3az+VuxnbuY4zgZx1wKl8PfEb4c+ErFrbStc8E6ZA7+ Y0dpe2sCM2ANxVWAzgAZ9APSl0sT1O3orhPEfx40W1MI0jX/AAJfZz532zxL HabOm3btjl3Z5znGMDrnjM/4aC/6iHwv/wDC0/8AuWlyMrmRwHxO/wCCkHhn 9nfxlPonxG02/wBMuX1N0gk8O2eoa/FZaUzGK31K/eO0RbYS3IW28uIz7ZpY 03tiUxc/Yf8ABXLwZN4b8Q6leeAfirYnQPEVz4NFlHptlqV7qevwQyyyaVbQ 2V3O0k/7sIHIWEtIuJcBivrx/aA/6iHwvAPX/itP/uXrVTRPjFY+G7J7bT3+ EtjbyTzXTR2/i5YkeaaRpZpCFtQC8kru7N1ZnYkkkk0lpqh8yucb+zP/AMFK /CH7WfxjsPC3hPQvEyWWp+H9Q8S2uralFDbRXllbahFp6TxRCRpQks5ulCzL FKv2UlogsiMfoyvMB+0Dj/l/+F//AIWn/wBy0v8Aw0F/1EPhf/4Wn/3LRKPY V0dL4A/5Gvxx/wBhuL/03WNfMvhb/kQf2I/+wra/+oPrle8fD74oaBptxrt5 qvinwTb3Gtait2sVpr0VwkSrbW8ABdghJJhJ+7xnr1rwfwt/yIP7Ef8A2FbX /wBQfXK+jyP/AHbE/wCFHnY346fqfVHwt1VLP9obXbY2k80l34Vj2TpOES22 pqpO5NpL7ugwy4689K+1x0FfHfwZis3+MXjJpnAu08K232ZdpO/P9rbuQMDj 1r7EXoK8aHxM6ZbI/kC/4Ojf+U6/xz/7gH/qPaZXwEGAUjnJr79/4OjTj/gu x8c/+4B/6j2mV8Bb8ptAHHOa1ICQBW4xilxhcEAe5zSxRNJkDp3OK674SfC+ 8+JXim0s7W3uLiW4kWKGG3jaSS4duAqjnn6+/oaAM/wX4DvPFN6git3eHgtI BwmSAPckkjA6k8da+sPD/wCzz4d+AfhCK/8AiBNd/b5Io7iHw1ZTC3vLgZzv u5sM1urD/lki+Y6kksgxnS8Qv4b/AGDYrC10mS28T/EW0gdb6aYrcWOgXJyA luvAeeMZVpWJCtuVApUu3zl49+Lup+LtbmvdUnnvry9TcwRi3lMSSTlucngn JLE5JPNAHqPxL/ak1G/sLrw9oUFlo+mpAHjtNMiVYsZyFZgS5wBjLuevNeYa n4wH2R47y6gWRRsjJd52OcDKkN09ieOcV6H8N/2TovGvh+1vNXvr6ya7iEjQ wOkShM/KWyrFs/hWpr/7CEXh1Be6VdXc4gHmp5rI8UhHYkKuM9PxoA898OfB zXdb8Pvfi2m8rgRTtEMt1wApYtgnk1kXeo+INAuY9HfVLEGGQRxyTq6GMDoM 46Z74NfdP7K/wz074i6PtukminjjRJbdlA8twCGUAjge/esz9qD9nzwp4X0V 4bi2XeFYs8ihtqgElgwFAHy1cfC/VdVvo4dV1bfM0QmRbAEtchiQAueuCrEk 9M1Dr3wDltrWR3sNfku1XaFkmRtxOW5OPzrpv2cfFMCm7vJALs2EaWsDSEk7 AzH264H4V9GeC4dM+O1h5+lrHaazYqxltImOy4QDkgZyD04zXoZj8UP8Ef8A 0lEw2Pg19IOmat5N5Jc6Q8Z/crcxkqSDwPUc1uWnjLU9I0dYpVEzRy/Ndhzc LJGPuqU64B6E84wOMV9jXHwC0jxhp7y6lpqvNCm1oHQSeUwHJ3N8y89ga8s8 YfsSWEcUt3YSalYSuGKtDJnH/AWGeuOh9a88ozvB3x98P/E3S4tM8b2LzfdW 21aBxFdWoBHKSc7Mf3X3L6+tYX7Rn7JU+gaaviPRJU1Tw9dMBa6laxhYlY8+ TcLn9zKOh5KNxgkcnz7xFoV3oviN7O/t44m06Ty5poAY45wvQOO+QO+a7T4L /tTeJfgDqtxYQJp2paDdoRd6dfIbmyvYnOWTaW5XBHG4lSDg0AfPfiHS5NM1 GW3ZQjRk7gQQQwOCORnr7fXFZ7EHI5OOhzivsz41/s7eDvi58H7Hx74Eu5jZ jbFq+n3EiPPpMrEhTuAy1s3ARiCybSjFiu4/IPiTQLjwxq01ndqVlhYoRnB4 OOR2oAofdbr06EUEl27kmnL8obgHPAz1pCvXORj270Aff/8AwXxfYf2LMdT+ yr4G/wDb6vgIHg52gn1HrX37/wAF88Bf2LOoYfsreBsf+T+a+A5LdhMFyCW7 5/nQAixnAJUnd0wetemfBH9nyb4jl9V1CV9N8PWjYnndOXJztRB/ETj+VT/s +/B6DxfcPqmuyyW2iQKVeQdXcjCqOMHrn8K90zY+ILSxluo5tO8J2Evl2tnA AtxcbSOQvdjj7xGBnPPQgGbpOpT6tYpp2lg6F4ZtQViJJjLspADtx1P9Kpap OkfhS4h02KWzDyq4vCo827YE4UKT8oOSS7ZOAMZrnfEnxLW3u7xpHCLDIGt4 F2uLckDIA9hkZPJHtWHdePJdVthNJM8geLYwcnbIxx82Ocnnt6UAamra3YeC r2W5tYzLcXLBuT5kjDvlsZP06dDXNa94zn1qKUXsh063xlokYFyOMAAdOa9e +Df7NMvxDilv7+O4hkuVCpGrEHCjhsfwjHbr71x/7R37K118KlN+BczwMxMi 8l4eOCfUfjQBf+D37Pt18VtMjePzLLSipKDGGkbrliep613mqfsCyRlbqy1i 6tHRf3Q6Hd65z06/5yK779jTxDb678MrBLTYXt08uZSB97BOf8+lfX+q+DIb nwTZWqwKk2wyMSoJK9Sw/EYoA/MO4XU/g74si0jxPMz2k3myRXZdipAHyqPT 06nP86HxL8Tf2BY3lkdQfZbzTpBHCc5Jkb5vpx075r6d/bE+CUuu+FJo4hEt zbTRupYnLKWwcenUVwv7MHwBtviDfXOsahYx31xNdNGnmDcoxu3Hn3Br0KH+ 51fWH/txP2kfN+ieGvGvxI01Z7eO6l0+J9pBXlTjjk4zWZJrGqeENYls9SE6 xqcOrjAViCNy+nfoa/T6L4H2nhTQsR20SRKQFjHVeeR1r5F/a6+Bo8U/ELR4 9Pijhm1NjC5K8kA9T9K88o8b03x+ZIY4nDahasCryHPngLnbk5zgV2ovdN16 W4uLAm1uXVPLEA2g5PIIzgkY5ya1tc/YZl8N2VvPZX0wkYfKZ1ADepH61Xvf 2VfEWm+HBd2xiN1bgvt2n51HJ/2TnHpQBzc18NKvZxfI1tBbOEaWMF0jZgSB tBJXOPpXe+BvFceq6GNJ8QR/274cuFIhkmG4wj1BznPvx/WvKpPFCukFtfQi KFiWd4/uEjIA+boc9iCPSuh8DatE3h1GsMTaiLiSYWbNiJ12lQVx/H/FyMHi gDhv2gv2eJ/hxqyajpTDUPDmokm1uQeYzn/VuOzD8c15dCirI3mAkAE4B219 f+APH6R6bLEIY9S0K6ylxbTAFraVG43A8K2QDg445AwefFf2kPgkvgy7XXtE Wabw5qbfuSE/492x8yMR6EHB4z+GaAPKJBk7V5A/HNf09/8ABlXj/h1l4+wC MfFXUf8A00aPX8wpQsAVycYyR64/+tX9PX/BlaCP+CWvj/cMH/hauo/+mjR6 AP5gggIHzAE/pSxx7wTuUH0PemowVgSMjuM9afKhDqQMZGRQA2MEsMdalMJC g5+YnIH86taXotxrF5FaWsDTz3EgSNEGXlJ6ADvyRX1f8Gv2QtE+DOjw+JPi XHHPdNse20dX2LDlc5nJI55Hyjdn0oA8T+DX7H/jn41wG70nRZRp0TqJbq5b yYUUgncTy2OD0Br6U8AfAv4a/su6ZLqfiS+t/EuvQsI1jdMWaNyPkjBLtyRy WUcdKr+OP2k9X1+yjt9BjfTdNdfIiw+yCNQw4WNeMAexriI9ZsfCF9c3mpzS atqibUDSRjeGyMlSfu5GOgzyaAPQtZ+MfiP4g3csUG/StOaTZEGt9snlcH5V BAGOR1+ua8b8VWc8HijyBDc6rcyTKzPu8yRcHH+6n4L3613en6Hrvjby2QHT 7QAYlwPMkB5J3HnsBmrUes6H4GvmgsWtzO0ZMsseXYEDklvf+goA5+D4d3q6 NJLrN+La3Ub3jVuWGeMnBPX061t2/iiLQ9PjXTLaSaGUjHlJgAcdPTFc5qHx HstVikSSB2875AzyZK/h079hV/Q/Hf8AadvFBYwJD5XCLtHzMMHJoA0bazvZ y7tcTbi5Y9Sef5dqmvdHkinzMGaUZO53XK5A6DrXKaz8StRttywRp5ofDEjo c444qXwz4nm1y6uI7gxrMwXLlgcnucf/AF6AOjXw+l4hme4CiEAyhCQOfQd+ lVJV0/TnLG4uJxGWY7UJCduDVTVtH1G5g2WFyxwSSFGM4IOMfhVj4XeIYbLW JLfULdSeSoI+8SAGB/HJ+lejmPxQ/wAEP/SUTHY0tMttC1DThMb3U4JX4ZjC VVD0zj/GtKLwJf28UclvcW2rR7t6SFgdy88DPNW/Ed9bz6VK9tbRqFTDjZwp /P6+vSvK4vifP4cX7DBcMkMhwUbkAnjI7jGP0rzijpfEek2V9fzpf6eI2UAu JoN2Qepz74zXNaz8GbfVrJ59AmSzv7RS0cMn+qmODlck5BIIHORn06jsNN0T xTe6Wly86zRqp2qybty4zzn61Fo9/ZzusF/L9jk34JUkY5OD79qAPM7e8+y3 oAWXTNWtMi8gbKmRwOpHT5gCK7v4E/HufwHqMviHSWkS2Qo2saW8n7uNGIV5 ogedh43KOnXGK6nx38K9O+KVkLo/6NrcVoYba7hbi4XB2CT1IIIB96+af7S1 f4f+KTJJC9rqdq6xmMj5Bj5WVh90g+/HNAHvn7bf7HAu7UfE/wAC6c7+ENaj lubq1g/e/wBkzKY96NjOFJlBU8559K+SZFwgZdibR0B57V9lfsy/tgL8KZ7S 2dGv/Ams3skeraNIDMIYmRUeNx94RgPlG9Q3J28eTfttfsvR/AHxnpmpaHKb /wAFeKrNL/Sb08gK2T5Eh7SpjBH0PQ0AeGrkwgfOybu3GKXy1lB3N8ycH0P+ TSuWgYhWAVsggeh9qAyxRjcAdw5+Xp6UAV6/ph/4JKeNYPhv/wAEGf2VvENz ZTajBodx401B7WK4Fu9x5V1r0gQSFHCZKgZ2N9K/mgkADnHQ9K/pA/4J3/8A KuR+zt/17ePP/Ruv1E/hKjue83XxU8c/DD9kDwcfAPh7w/4m17UvALw2Vlrl 29jYyXa6fC1uGulAMMhYNsBeISH5PNg3efF694a+IMvi74l69Zx2lpbWFnp2 mXRWzu2vrS3vJo5Rc28Fy0URmiRolIZkVyZGJCgrGnJ/s8a2LH9mXwjqeqeK dF8N6bBbaRoVubnSvO86eWx07ylMjXcK75Zr2OKNApLMVAyWArovid8VfCfw T8OR6x4z+LngvwjpM12LCO91rTIbC3kuCrMIRJLqaqZCqOwUHOEY44NfOf2X NYx4lVNG72/7dUbXva32rWvdvU9H6xH2Shy67X+bf/A32R1mueLfhJeeCl0n WviTpnh/V/NYTOuu29tcRyrNkxv86lVG3ZgbG2Z3MWO+vPfi38U7b4Z/A5/G Wu2keo/2Nod/qrWiWcVy13dJpF3JDDDBMSkszziNYo2J3OyL3rqPBOvad8TP DNtrfhv4haD4h0W93/Z9Q0zQEu7W42OyPskj1Iq211ZTg8FSOoNax0K7x/yN VtycYPhY8/8AlRqcjy2vga0qmIrOpfbpbfzff8B43EqtT9lCPKrt9+3kn07n xV/wS6/av+NHxx+Lfiax+LPgjwvo+m6locV/4V1nwtp0FhZ39tbXcyyPqNol 1LJY3sq3lt5UM0MTtHbTBgWhJP158ErmwsdBkuNViM+l2+v6pJeRC3a58yEa ncF18pVZpMqCNgUls4AJOK2f7Cux/wAzTb/+Eqf/AJY1V0bwQ+gWbW9r4lt4 onnluGH/AAi7tmSWRpZDzqJ6u7HHQZ4wK96dbmqKp6dexwxp2hyHGfAnxnee MtI183UentHpurfZLa7sdJ/sy3v4/ssEpkWL7PA3DyvGWZMkxdSAMda0YlvV Vs7WcA49OK0JbOWytgsuoJqTZ/1i6f8AYtvH3dnnTZ/3tw64wMZNBf8AkIJ/ vj+lfScPVH7WrUW/JJ/keZiKbhTjCTvr6dX+mh0WnfCi/wBWt5JILe4KpJFE Cy7FcyFQuGK7erx8E5w4IBGSI734az2COXVmaJ545UR1LxNCR5i4/iKhgx25 G0k5+VsXvDWnWl5HqM16lzJDY2omEcMqxM5M0ceNxVsD5yenYU/7boP/AEDd X/8ABlH/APGK8f8AtrG/8/H953/VKN/hMYeCH8tGYiMyuiIss8cTtvAKttYh ghBHzkbfen2Pw9utStI54YJ5IZZWgRwy4aQKG8v/AHyCML1Y8DJ4rZbVdGa2 WH+z9aaKNi6oNTjIDNgEgeR1OF/IUz7boP8A0DdX/wDBlH/8Yo/trG/8/H94 fVKP8qMlfh9cPp8V0IZzDcRSTQnK5lRG2uVHUlSCSByFG7GOamv/AIXXmmxb 5YsR5K+Ys8bR58oSgbgdpJRgRz83bJGK0Be6CzMBpur/AC5z/wATKPt/2woF 7oOf+Qdqy46/8TKP/wCMUf21jf8An6/vD6pS/lMay8DyajLbRxRzF7uRIoFZ lQylyyqVBxlcqw3DgEYJHFWW+F15HrDWDxMl1G0iujzxJ5flpvYsWIAXbyGP BHQmtEXmgnP/ABLdX4Gf+QlH/wDGKT7boP8A0DdX/wDBlH/8YoWd43rUf3h9 UpfyoxV8C3BWUm2ulMAkMgYBWUx48xcEDLLkFgOQMkjAJqS/8BnTL37NPNEl wFLlPPQgL5ayKwcfIQytxtJJIxjkZ12u9BXH/Et1jB5B/tGPn/yBTWvNBI/5 Bur/APgyj/8AjFCzrG/8/X94fVKP8pmRfDe8uDEIbW6m8+PzIfL2t9oA25Ee Pvldw3BclcNkDacNtvAEs8d2zLNC1oH3JINrOUKiRVGOWQNuYfwqCT0rlP2f vH1x8VPgj4H8UarBDHd+ItC0/VLyK0JjjWSa3SV1j3FiqguQMliB1zXoP23Q f+gbq/8A4Mo//jFH9t43/n4/vD6pR/lRgReE/O09bpRKYWfys7l+VsZww6jI yRkfNhsdDiL+wof70n5j/Cuk+26D/wBA3V//AAZR/wDxij7boP8A0DdX/wDB lH/8Ypf21jv+frGsJR/lOb/sKH+9J+Y/wo/sKH+9J+Y/wrpGvtAGMabq7ZOO NRj9/wDphSrd6C2f+JbrGRzgajGf/aFH9tY7/n6w+q0f5Tmv7Ch/vSfmP8KP 7Ch/vSfmP8K6T7boP/QN1f8A8GUf/wAYo+26D/0DdX/8GUf/AMYo/trHf8/W H1Sj/Kc3/YUP96T8x/hR/YUP96T8x/hXSfbdB/6Bur/+DKP/AOMUfbdB/wCg bq//AIMo/wD4xR/bWO/5+sPqtH+U5v8AsSIA/NJzx2/wr5i1zxxd/E2T9kHx Lfx28N94h8TLqdzHbqywpLN4L16RwgYsQoLEAEk4A5J5r6c0HxTb+MLO4vLW 0msoI766s1iluBO37i4kg3FgifeMe7GONwHOMn5K8Lf8iD+xH/2FbX/1B9cr 3Mqx2IxGGxKrycrR6nFiaUIVKbgran1d8J/+S9eJP+xVi/8AQdVr7RXoK+Hf hn4gsLb9qDVtKk1zT7TVLzwcbu30uRFe51GGL+0UlljzKrKkTTQh2EcgzNGD s3An7ir52G7O2WyP5Av+Do7/AJTr/HP/ALgH/qPaZXwCo3EDpX3/AP8AB0Zk /wDBdb45egOg4/8ACf0yvgLaZHGep9q1INbwv4Vn8QaikMQyjtt3DHA9vc9v XmvtnQ9Os/2FfgzDqhSF/iT4wsQdHt8fv9DtJBtMrDos82MpkZWPY2P3leW/ sJfDb7Fp3ifx3qVrG1h4HgS7RJ1Bjurls+XBz95iBnH+ya5Lxt4v1n4q+MNW 8Wa1dSXusa5cyPAd5KqM4LrnooxtQdFVQBwBQBn6BpcWs/EGKbxDqFvGjzlL mZioiVjtPl+4AIA7YAA6V7j4/wDg5pXjP4aC80yGzmns490bQR4aF/UnPIbF ch8Av2erT4k6FHfSot3BNI5t0ydrorcuQf4mIJ57Yr69+AP7O9p4Z06eG6tk hs54yDAMqu0/e7+/SgDx/wCGeiWfxS+FqRQXM1leW9qjeWBtZWVQHB/3Wzio fgJdaza+J5NGuNSgvZLa6FvFG5GZFKn7w+hP1r6B8Dfs5ad4G8V3+oaXdFob hXYIIsEMR0PavO/hL8MI4Pii1+wid0uRJK4csQQGHJxycAemM0AfRfgX4bab 8NLS9vNPRnku49xJAKg+3Q5+tfHP7ZPi7xX4vvtUsLOyL7g0QDjYxQdSvbpX s37VH7ZcHwVsjZ2JWS9ULncAdgPcDnJrx2b9rhfGXhlV1dLF53QsJJgFkXI6 cD0oA+efg/bX1nbR6ZDAkcl7BI1wG4eNkkC/rk17B+zhp+tfCL4nLq10WaC4 m2PjktGCOOO3euH8CeH5/ij4/vNS0i7mt5bN3SJoF4cE87lPuh4HNfR/7P1l eeJZzpetW6boZOZRDtCFTgEN/tCvQzH4of4If+komO3zPpPxK2kf8I1Nqa2k atdRrIDET864z+NeA/ETVl0rSLjUr9PLSQGGzikYAnJ5Y+wGK+mPCfg+BNCS wmLtbwQ7SGGAO4Bz6Zrxn9pX4C6n4+msY7NY0sIzjZwoQEjJJ6npn8K88o+R Nb8Hx6/ZX09ysXnX4O8sMnd614FF4L1Ky1e70e7tp51jUhPLXPknPysPwwcV 9o/E6PQ/As7aRpCLe6qVAmuGG8A45wO31rzrWVjsNLn1BbeOVjmSV1G3fx24 7UAeU/s0fH7VP2dviBbxl4pNHuM22oWs8ZmguY3OJEZT1DA+oI9jzXpH7ZH7 Mumh/wDhI/CU0154e123a+0qV2DOi7gHtpmGR5sL7om7EqCM7s15h8a9FstQ +z3lrGgMqlygYLu7nPfHfNewfsKfHbSFkuvht4wuZT4X8QXQtjPI426Yrxso uFLdPLIjcgf88R60AfGl3bmCVgRjB59j6VHHnJwN2OTxmvWv2oPgbqPwk+IW p6Vf2klpqGm3c1new7T8k0chRxnpgsp2j+7tNeXtDHbw4Zi3mDcGQ8j2oA++ P+C+Cma5/YsPyLn9lbwOfm+6P+P+viP4a/D+4+IXiWGwtow5Z/nfogXGSc9g ACfoDX2//wAF47CTUJ/2KkjQuT+yr4GGB1PF/wD4GvLf2U/gxK+jWVskRN/4 okaASqdqWlqqNLLMxHIAUODzjC0Abvw48FW8HhNb+8kEXhTR5PscUcQ2yapc sWLGIHAIUliSzAbUxycA8J4+8VS/ED4h6hHodvFaCOIJsMpe304DgBeM5bA/ hzknPGK2P2s/jSlp4it9E0Cxih03S4VsLa1VuAseA7OFOCzuhlLZ6zY7VQ/Z j+HesL4gs9RutPMumrO1zdN1aZyc4A7qDz+PvQBH8KfgLeeJPGf9j61CLJ5o GuTezYOT/dAwRn1//Xmx8Xf2Y4vg/wDFzwwrS/brLUp4/MBYttx94c49a+4o vhDpGt6fZ6sUS0vIkI8pjt3buvP45/CvBv20k/4R3x34MuZ42hs49UQSsy5V EK4yD2G7FAHu/wAFvD1q0CyJZxKioB9zkcY4rX+Ofwhs/FfhC8YxbnwGlDjG 5CMEZ5wa0PgZqdhPpLAsh3/MSDwRjg5P0r05bO31OylVQrRn5AN2cCgD44/Z N+DH/Cutfvra1TfbNLJIoJ4Xg8frX1/r/jfT/CXhsXFyqsVTaiLk8YGRz3z/ ADqDSvA+k+G9SeW3ihMlwOSMliw68fjXlv7UmoyW0EUlux2792QRjjggj0oA 8F/bI/aRtLXTZJIRcRyXjqIUVQzYDBiTz7VxH7L/AO1PZeBdffQZbK9a5kun eJokVsq3z85YY5Y9qx/2sPs7eGNOed0DNMoJ2AEfKxIqT9m3wJpyaheauyrL cNK0MEp5XYBjj616ND/c6vrD/wBuJ+0j7y8IeMYPiRoCXMMQj2DayyJtIH/6 /wCteUePv2Z/EOtfELR7mGOGWzhkeZJA3QN26fpXcfs+xxyaTNgyRsp4xyK9 TtxIJIpQ7M6K7Lkk7j6e1ecUeY+LvhBNqmj2UTiFGiXHGcr+HpXN+MdEttG8 MQ6fEY5QpO9imWPGOv8An6V694z1Oa4t412xfMQBIMgPgZ7/AI1514u8OIdL nkP7olCFBOdzA+tAH58fGL4GXsnxiew0uXyI7rzGZJd3lqAC2cAc803xr8EL 34LWGnXbxxtewM22RSPImDDBDKR+XavfvDPg6X4lfHm+1IeZFp2mwfZI5M8T yYO4/TFewfEL9n+0+K3hf+zpwjPax8SuCQ2Mnbj25/OgD4Z0rxvb3uvW9xpj i0u44yt7bXbkxzHacbwoLfKeQwGflx0NekaXrGkaxourxTWCTeHLp2t7+zkY iS0JHyOuCQc5yGB52/hXDftIfBBvhx4jhu7TyonOYDvfYGwOn19Ovvipf2bv iZp1z4whXV/9Hsb1hZaksi7yYifmGOrMhAdR6rjIDGgDxj4y/Bi++E3iAxz/ AL3T7wedZ3ikbJkPIxznOCAc459etf0h/wDBlaSf+CWnj/PJ/wCFq6j/AOmj R6/Eb41fCeTVIdR8GziCbVNKuBPpxL7klhYg5Un+BkIYEdeK/cH/AIMwNOk0 r/gmF8Q4JFKsnxW1EYPp/ZGj4oA/l8h2rJ8wBB457e9bvw68C6l8RfFlhpGm QPPc3UgVVRSeCeSfYDqay9OsH1K+it4YzJLPhUXH3mPAA9yf519p/DbwRafs jfDFpZ083xdrEAM7LGJGtQwyIlwx7EEn14oAu+GfCXgn9j/QxexTLrfjOCAr JMwZo4ncciMYGCMYyeeT7V5zrmtaz8dtZaW72TWksvmRIsTcg4wpY9TjqPeq vhXwlrfxU15729uGQ3nmS4LMVIH48cA5J5HNewaXoGjfC/wqURlFwkQMkgRU VM4+VepwcDknJNAHJ6b4BubbTUs4QI8KcjI3Qg9hj5fX6VFJpHh34TwRPLs1 HVABkbwSuF4ySOnt3rO8S/GW58W6jd6Zprm1trdjbuc/MRjv+uK4XxD4YvNU SGRrlTKsJjXc3XGSPxoA2Ln4lXPj298pLv7LbI+0IjBVVc88fh3rDvLO3shq LLKkgCiFSwB478/lXKZl0tHKt87nIIOMdu1TQTk6TOkr7sksCRnJzjtQA+18 WxWGmSBLcPjKhgMbSOM5611eneJrfw54bgitQzyzkyMduSGxxz9frXCvZtJF 5Yj+UAA8cDgYI7V0fg65s4n8u7mRSnIDn5l9P8+9AG5aQo/h83kzRq0qGRTv xx9PX2rH8O65HBq8krMWEbbUIOMn+H/GqHi3xJY32p+RBet5eQgjU/e9uK7/ AMJ/DPTIPColuYj5j55+7g+5x+tAFTwp8X7jQrmTcplhlJbDduDkYH1FReK/ FSP4hS9gSSP7QcggECNtpxgfX+tZXjnQLPRLpDazEiUbiQcnIIqWx0869pCe agjRQCGY4O7tivRzH4of4If+komO3zOkvPiGx8MNvYRyxxnzUJOf8fWvL7qO 4u76O4SF2YcOF5xzxg1seJCQsds5/eom0nJw3PX8v5V1vgzSLBJoreB0mldM 4OOoHvXnFG/o/wASr6KCwjht5AGXawDZ5xjB/wA965nxdY3fi74jwafYxhbs oZZiMhVG7jn9D9K7PTpF0fXFD2yRLGd4bOM9c1a+FbW194l1/XwE85HMUeF4 wqjj8zQBjT2niT4W6QjXKy3lrEC4ljPzQrnoR9W/nVD4leAh8ffDEOoaS0a6 5aq3IAVbxAPmB/2sdK92t7q38SadDDdR7WkUYGMiTvk9e4rgNY8HJ8PNZL6a rLau+XQZwnuBQB8yeD9L1O3VzYXBgGNs8bOQWK7gdy+o5yPpX0l8A/HWi/Hz wHP8FPF16sEOsZu/Dup3fTTLxY2CRl/vbXDBTnuAT0rD+J3wdHiTUpNa8Ozw QaxHB++0zgJqDhchkOcbmUtnPcCvF9QY3aJeQm4sZ7edWlDLslt5ox1455YL x/sg+tAHG/ErwHqfwx8d6loOrWz2epaXM0Mscn3gQePrxgj2NY0RR5QeWZsD t1yPbnvX1r4j8GT/ALe/guy1CNoIfidoEX2S7SaQJ/a9qiFYW7ZlDBE5yTur 5Sv4LjSbt4JoPKmhJWRJBgg9OQfcUAU7iMjBwQPcc1/R7/wTv/5VyP2dv+vb x5/6N1+v5v1GVJOcDjjtX9IH/BO//lXI/Z2/69vHn/o3X6iexUdz3jQ/hXr3 xh/Yi8IaPoHiv4geDLgXUM0upeEtBOrXLp/wjenRCJ8286RqXlSQMVDkw/Iy MN6978QPhLZfFHw7Fper6T8Yre2hv21BX0PTvEug3Bco6BTPYiGVkw5JQsUY gErlVI+LPGLT+BdR8M+O28MeFPE2keFrTwNJrNheWtlLq2rW/wDZeoMbC1S7 +ScTRrcAwAGRpRbtHhk3D73/AGa/GviTxb4J+Dt74e8F6f8ADqKL4n38Hi/S vCmgzWWnC3TwzqgY3cUDFHX7ULRBKXeIypbbWcqmeGlVjUnUpWa5bfO66f16 HvY7KZ4bAYbG88ZRrqei3i4NxakvNO69fI+YP2av+CW3hn4Ifs1X/gHxBovi jx9qGo289j/buqfCq5mls7R5ftEVpDDcW06G3iugLpYZvNiNw0khQ7sD6ouL q4uL/R7o6B8Qkk0a9jv41Hg7VDHLJGDs3q1sQwVysgB6PGjZytdP4z+NHxb8 XeEPDq/2bqeg+IbjxF4Xk1bQ9O8JamX0qF9S8LvqMP8AapY2tzDFDe6kJHSN VlieRV2tp14X9K/Z2+N3jv4o/FrxBYeIfCM3h/wrB4Q8Na7o17c6fdWd5dXd +NQN9a3KSAxQz232e2DW0cszRearPJ+9VE7ZUb6tnhuq3qzxu48RTzXUki+F vHkSOSRGvg/VisfJOBm3Jxzjkk8dTTf7fuP+ha8ff+Efqv8A8j1dPgfx03xD 1CXxXJ8R49B+Il5run+I30XW9XZrNYvEtlZeHfsKW8x/sxH0qed7iexWDcqS XE8gaIyrofC7w/8AELUPjl4d8WQt44GhX2tweH7capf6jEsXhI+Fftkdxc6b cuIk1H+3AEe5mtxeAEwswj+SoWHi1e/9f1+A3VadjHW8e9tN72Or2B3YEeo6 bcWErcdQkyIxXtuAxkEZyDimv/IQT/fH9K9H+P0c8OvaalzJPNOlgqtJNgPJ iWYBiAABnrwAOeK84X/kIJ/vj+lfQZBHlnVj/cl+hw413UX5o7Dwz/yB9f8A +vBP/Sq3rHql4n+GVr8U/C2oWd7f69YW9kiXbf2TrN7pUs/71IwjS2s0TlMy btpYqSikrkKRwn/DKvhj/oKfEj/w4fiD/wCTa+U0tqeoL8avBXinxP4v8Ize H7XSGsrS8U6tf3Oqm2uLO1W5tZykVs1lcw3PmG3Ctua3kQKDFNGzb18X/Z5/ Z4+NujfAbwZY+IfEl1ZeKU0y++06ne+Mb7VLjwpeT6bFDHIYGjaHWibtZblo bqQRWpuDFbvLHFGT7P8A8Mq+GP8AoKfEj/w4fiD/AOTaP+GVfDH/AEFPiR/4 cPxB/wDJtOLSVkDbe55t8Sf2Z/iH4guvBWr+Hdch8I694d8H3Xh3zBrN34il sri61DRp2InvBG95H5On3CPNMVmIdSEZjgdp8evgHrPxG8eDXdHvPsrw+APE fhZHi1qfS7yO6v5NNkt5IbmOKU22PsUm6dFZ0byWCSbcDV/4ZV8Mf9BT4kf+ HD8Qf/JtH/DKvhj/AKCnxI/8OH4g/wDk2hu/Xv8Aj/w47u9zzfwv+yZ4qsfh xr+nXl3pEOq+IfGvhLxSyf2zfarHbRaZFoC3MJublPPuJP8AiU3GyWQBpjJG 0hRmfbOPgP8AFH/hAvs4voCW0b7G/hc/EDV/KN4NT84Xa+IPs/8AaC7rYlSn kkAgRD92N9eg/wDDKvhj/oKfEj/w4fiD/wCTaP8AhlXwx/0FPiR/4cPxB/8A JtPm8/6tYXSxf/Z6+G198KfAV9pWoT2lxcXPiPXtYV7d2ZfKvtYvL2IHcqkO IrhAwxgMGALABj3J6V5v/wAMq+GP+gp8SP8Aw4fiD/5Npr/sqeGTj/ia/EkD Pb4h+IP/AJNpaN7hdjv2N/8Ak0T4V/8AYn6R/wCkUVekVwHwCGleOP2W/BS6 fp11oOh6v4VsBa2NtqM3n6bbS2ce2FLpSk25EIUSgq+V3Ag1W/4ZV8Mf9BT4 kf8Ahw/EH/ybSsuoHpFFeb/8Mq+GP+gp8SP/AA4fiD/5No/4ZV8Mf9BT4kf+ HD8Qf/JtFl3C7Knxo+CXjTxt4uGseFfiTqfhLbp01nHpx0qwvrFJ3truOK+H mQfaBNFNPC+0XAidIDG0fzs1ee3v7NHxMKeFHstfvoE0S3uJr7T5/iJq0326 S50q+s2sGuhbrI8MEy6S0N26G4z9sncG4O6b03/hlXwx/wBBT4kf+HD8Qf8A ybR/wyr4Y/6CnxI/8OH4g/8Ak2qv5ju73OZ+HPwU+KGjfFrRdbv/AB2sPg+3 sreG68JCSTVVgcaekLoNRmVZ7nbcoJBNKiySBn3qC1e3V5v/AMMq+GP+gp8S P/Dh+IP/AJNo/wCGVfDH/QU+JH/hw/EH/wAm0nZkrQ9Iorzf/hlXwx/0FPiR /wCHD8Qf/JtH/DKvhj/oKfEj/wAOH4g/+TaVl3HdnQfCL/kU7v8A7Derf+nK 5r5k8Lf8iD+xH/2FbX/1B9cr6X+C2ladoXgBbHSre9t7Oy1HUIALvUZ9Qnld b2dZJHnnZpHLuGf5mON2MkAV80eFv+RB/Yj/AOwra/8AqD65X02Rf7viv8KP Oxvx0/U+nPAK6lJ+0hepZZS0Hh5H1Bm0me6jaH7NrQVPtCMsdq5laJg8u4Os ckapucPH9xjkV8u/s6/8jJ8V/wDsVdM/9GavX1EvQV48N2dD6H8g3/B0ajf8 PzvjmxJ250AAdif+Ef02vhHw7pD6lqUKqFYlgqruxubOMDPfmvu//g6H+f8A 4Lq/HJMNhm0Dof8AqXtN/wATXin7A3wVtfHnj661TWV+z6P4Qtm1u8edMKwG PKjPbJO3APqfStCTsf2qtR/4Z3/Zl8DfCm0Kx6pqUTeKPER8pQVlmIFurHrl EXd3/wBZXkNp8MvEmu+AzdafHFKZl+f51jlMQ6Ii5x0IyeCc9OK2/wBqXxZL 8SvjZd3M+59SuignDkmKAFVdUGOoCsq+22vQvgR4ou/Bmn22l61pqfZpXKRy E/fjb7pYHkYGKAJP2TPjbpXgPRrO0uLeS3+wJ5csMqlG6nPXvX2x8Jvjr4a+ IUAt45QDL3LZZePUfrXzF8UP2Hk+KFuNW8M3UkhcD5I9oBPoScnjHY1Z/Zy/ Yv8AiL4F11DcSyR23ngZZmIjGeQM9zQB90votk+jIkUiO0i7EcDPXrz9P515 7pnwrj8JtcqrRGeSQsrIeT7Hgc/411+jW9zpGiLDImOkbSYAL465oklDQliz SKB6dPxoA+G/2o/2U9d8beOrm/dr2aC5UkFdsmMnbzkHB69OenpWRp/7Pq+A dJOoatGITHHtVnAEjkH7ozmvunVIhcbclQFG3AXBxnPB6+tfM/7cmjSaHpUW oSGR7TzGcp5gUpnsPw70AfLdt8UtQ+GvjfUJNO05zL5mT5UvKHAYdOOS7cf7 NfSv7Nn7YT+KL0afrNkIDdABmMQWVTjPzYHI68+9eBfs26j4e1zVxpupxTWy 3UYQMsoO+Tex3E9TkOv5fSvsbwN+yJo3hy/TU45WlVSJEjI55xzXo5j8UP8A BD/0lEx2+Z7Z4W1D7TYwzxzSLE6KVYcMwI4BA9qn1e9jv5v3o5VCM7O2eayv DNqtnDDAJCIkOBjGMfj/AErWuYklmkRBIxYHb8vIBPevOKPmf9oDxB4M+Fc9 zdDT7CXVJXOTIeFb6djXzH4z8bat8Vo3jktcaczbQtsvlqw7DI6DPvX0Z8ef 2RrrxL45u9RvhLNaR/vkXDOoz6gYrzlfE2l+CNRXT49PISBvLy8Zxjucd+KA PG/EH7N/ifxZqMF1NcQ6RHaIRDEIyxxjoSCOo+vFeWeK9J1H4d+PFidVDwTq /wAi5X7wzwRkg+lfdPjCK88YeB0axYWwTEgC7QOnB5GfXvn3r5W+KWn32g/E 2LVddtbS7j0mZbgLACEuUyCNw64BBJ46A9qAPof9o7wbF+1x+zl4a+I8CSya 8baLw74ngY/vRqMEQjhuTnABuI0XLH+InvXwFrOmTaZeSRzRlJYzzj6D9O/4 ivvb9gL4iaB4k1LWfC99dPp3hHxmo0PVZZJGkFjNM2ba+6hVMVz5eW/ukivn D9sz4N33w6+JWpi7txaXVveyWeoICNkN2jlZVXn7u7JHH3cUAfVn/BarTX1P Vv2LkjBL/wDDKngbbtUs2cahwB9AazfhfqFj8Ov2efFus3sU4tBaReFtPAY7 0ke3ke4kHcZRSvryRXqX/BTDToZvFX7IFzND50dl+yf4EdSR91m/tFAfyJr5 6/aP1hfBn7N3hXwW8T/23rtsPEE4wdwaeZ40xn/pmrfnQB4l8HPh/qfxd8X3 Oq3lyIpYtz7mThWfORwPTFe+t8Zbr9nTT7O3k0ySe1twUE0aGQOOuWByMk55 rif2S9cisYNS0+9UwSQKhmHBZWIBwPx4/Cvqq/8AB2j/ABa8PyG2t4mm8sBA 0YAkxkA5FAHz74w/a68UfGCwtZfC2l3fm7vndlKxqoB4ArrvEXj62+NPg+PS PEMLQ6n5ADQyjBBAHKnvg88VD4Z8HH4VeLJdOu1RLfazRzsAgPqDx1rq9c+F Nn8StNnMSq1zan5WhPz59j34/nQBzv7KPxhutE1q+8M39y0sul5WOZjzLD/D ntkdK+ydE8RL9jgmL8yrkbe+Bgf/AKu9fKf7O/7Jl9oWuXd9eLNJ525ZJZF+ baOg/PFfTenaf9ns47VgyGIAJjOQBxQBd1TxqlhbGedzEAR8zn5T6E187fHX 462Wq6m8WYBaQkoQGyWI64Fej/HDwlqviDwmINOLiUSAsAT03D/9ePevlLw7 +x/d/wDCai5uZL2W8lZ2xITsXPfHtjjNAHlP7UnjDUvFtpA0drLFpVnNwx+8 xIwMj/GvTf2UviTomn+CtMsrhVa4jj8yWLfhjk5GR2+XFXvjT8N7PTDDpu5Z UXmQkZJPB/pXmlr8FLvTY/7Y0wyxtCzwkkHypPLcxjnsflr0aH+51fWH/txP 2kfoX8HvjN4d16zS3gdLF48kDHXjk16BbaylwzMki8EncpyT7Y/z1r89/grp fiDXNaYRvIs6g5jB46c5r7e8GQXFvoEQnGbgRKGHbkYJrzijodV1RBjzFDeW v3T2JxmvnX4yfHKTVtVfRdODfaX3KCBgRjlSx9q96urX7ZE6uhkKYXA6gdq8 I/aB+FEngX7XrmkWjzS3MZUYONn/ANbmgDnNG8TWvw00i2tLYxPNNkkk/MTx knvXosfi6Pw34Miu5rry5nhL56/MQcr9K+cPgT8MtV8f+Nr2/wBX+0qyp5YQ sWRPcZ+tdb+1T4sXw7pK6aJJI90YhAXtx1zmgD59/ac+Kkvxu1qazs/LSLSp 8B2wrXDEfMRnsDmvPvF2laj8P7nStWkTekjxyTBOrMMZGMD7w7j3r3bw74Is L7R4pGjt1kK8jjAz1J781z37Qnhic+GrKyQohY5jlP3Y2QbsZ9CKAPUfjLer 4m8A+A/G9lAFiurJtClTIV3lt/miIwPu+Q4QZxxbGv23/wCDTDTI9M/4J+/E Ywptguvihf3MXOSVfSdJbr9Sce2K/BX9mjV9T8cfst+JfDsUUM13pd1Z6jbN INzKpZocp6ARkAkf36/fz/g1Ihjj/wCCbniEx7v3nju8Ziwxk/2dpoH5KFH4 UAfzyfsnfAi18GeBrP4l+IHj/drINMtZPvMxACS8HoCJD+ANQeI9R1Hxn43c SSPNdXHzRwEb3QOcnoclmzz6DArpdD1yWT4R+Gbq/WNobfR7S3tYXzhytuqq cDGQCGfOeSuOc1ofAjT7a2sdT8YagENxYF1tjMo3bznbwQRwPm+uKAO4fw1o 3we8DJp11HjXruNftR2Y+zBjlohlQRgKM9f4q8J+LPxWgmgXTYpAyy7l27jv BHQ+mM5H41V8bfHC68YeKWV5/wB3dShtzAYHXjP+c1zXxM8Ki4VL61O9o1wy j5g3P0oAl+GFmIv7RuixkHnhC6HONuRyaZqfiMTXt1AZpVbzEaFkO5VPAx9M VzXhPxTJoQy7mRXO9weDk5BrRstc0PUb2RruWQeYOdi5A5xk/TOaAM9JG1TU DBD5jy4CLleGPOc/lXe6D8B9Q1WBDJK0GWBYscqP4uB+f5VzNnPa2fiu0u7A xy2sbmSQkklcLwOa6rUPjDq3iu/jt9JKQxJx5r9xjGfzoA37H4d6T4SspW1G 9hlYLuIBHGB0ryUeArnx54zvG0ddkAYkS9ABn9fpXpWgfB278SzJc6nNcXJJ G9dxVR7Hn6V32m6Dp/hSyAQwxRxnaAnBbpkZoA+dLn4fXnw38V2t5qsK3Fnu EryoNyqBwc/iRX0B4P8AiX4Z+JFisFrJHH5Y2tGxwzHHPB61oaroWneIysCe XJbvEPMjcDDEg/4Vz3jX4O2MGgMLO1FnKqN5bxDaVbnGce9AHEfHa+0/W9ft dI0dXnuTGF3RngHIPJ+gNZg+HXinRUWKNg8eMspJwOK9B+AfwGOm2EWpTsZp 2JeWVudvykfyNega1pkVrYyzxIrRY2lieDwcj9K9HMfih/gh/wCkomO3zPF/ D/gmPXbSW6mkdijYdc/MMccVRu/At9oWqmWCeUeUSUYDkc5wSK9Dju9M8PaI gkmt1mdfMBLAbmJ7Dv1xVPUdQfxRp7yWkaRJM2ZML82cYyK84o4TR/Eeu+MF kjUgNCTEJCMHnPP6V634Sj0/4a+CpIr2aMSSbnOTnnB4PuTivPovAE5kEGm3 bxlzh+e57/zqz4A8JWuu6jc2fiG8k+02jghWY7WGe3NAHY+CP2hNNj02Q3Uj B4pW8hdn3l7Yx04xW1N8aYNZuWs/7NlZbgZDyKckHNWNP+H3hnTordYBEGjH BIHX1/WujkWyW0WTyIgW7nqAP8aAPLDcP/bFxGk0haF8xE8NHn0PtWN8WPB1 v4y8Lza1bmSLXLYqdTiXhbuBV5nwP40wNxA5HPrm14j8V/aviPd+XZZtZZUU HGFI6HH+ec1s6qv/AAjN9HcQKWilb92GGQ6Hgg+2ODQB4j4B8Uz/AA98QC5t Lu4spOGguImwAVKHn1IOD+Fdr+0f4Eg+MfgST4j6NbpFrFnti8SWUCcr0QXW OgDEDdjoW96xPix4WtPCWri4jgJ0HU4jNbh8skD/AMceMcFfbsetaX7OnxTi 8LeKhDeIb23uIn8xXP7q+gw2YGwepGMe/p1oA+e5kaBymCpBIIPXr3r+j3/g ngCP+Dcn9ncEY/0bx3/6N1+v5+Pjz8NIPh38S7qxspjcaVcQpe2M5GWlhdQR +IbcvplTX9A3/BPD/lXJ/Z3/AOvbx3/6N1+onsVHc9+/Zu/a48ZfBn4S2Hh/ wzp2mXXnS+EESV9Autanh+3aHdebJ9nhuYWZI/7MgYlTlEadsSHao+ifD3/B VHTviJ8KPA/iXwP8O/Ffjy58X+K08Kvo+lXunwXiu3h+bXVubaS6uIbaaFrZ IWXzZ4GMcxOPMUQv8f8Awji+FfivwrofhXx14b+Ies6prLeCzZS6RfaTZ2D3 a6Reiwt991cxP58mdQyCNjYt1VvMba31V8M/2LfhV4T+GHh/wf4u034t+HdU 8VeO/wC09HuNV8VyWGvX2tw+HXt2uIrzQLpVt420yyukMYkiiwkqCMBo1Ont acqShD4k9fwsckMDjaeKnicRf2M0uTtpdSa+fn01NL4hf8FgvhvY3dxpvgKE fE3xHHa+GdQtdG0vXdLtb/UbbW9R021hMdtcXKXETpFqlnODcxQQOLiNBOG8 3ytrwr4h+A3/AAVK1bV9N8W/Cvwx4yufh1cObOHxroGlawY7ae4uLUXduN9x 9l82fTbqJ7efyLyJ7JluLeEhAey+LH7NPwk03w7dS+IrW80XRNZGj+HI7Wy1 3UNOsbSZb21i0w2lvbTJHZ3S3Qs0jubdY5lMcA8wLGm3d+BP7IXgL9mzxR4h 1fwbpWoabd+JYLSzuludav8AUILa1tGuGtbO0huJpI7KzgN1ceVbWqxQxiUh UUAAK8baf1sdN9Di/h/+1Ho9tfeEPAvgHwbYWlo8mtaVptgsy6ZYaZYeHtes tCvVjSOFwgjW4aSCFVCuIEjLRB96SWn7bzw3kOj3Xg6+u/FieJD4Pu9J0i/i nDasvhY+Inhtp7gW6SIYx5CSTeQGdgziJc45zxbb/AU+KfizNdp47OqJCi60 +mr4m4JnjMw8PG3+XzzeLB9qGh/vftgh+0/6RsrY8K3XwU0z4+aXfaavihvF Gn+GYrmyaSHXZtISNbUbCVkBsG1z7CQCGB1U2ZIINuTkTXKr/wBf0hPfQi+O mq32t3ui3OqWlrYalJpkf2u2tbprqCCUPKHVJWSNpFDAgOY0LDB2r0HnS/8A IQT/AHx/Su4+K3jXSviKuia3osOq2+m6hp6yxRanpd1pd5H+9m3LLbXUcdxC 4bOUljVh6Vws0V3LORY232y8JxBBuKiaTHyqSFYjJwMhSeeAele3kMb1qqj/ ACS/Q5sa/di/NHZeGf8AkD6//wBeCf8ApVb1j1h6h4d+KOp6LfafJ4CaK31G NYpzb6xfW8pVZEkAEkdmHX5kX7pGQCDkEg83/wAKJ8e/9CT4g/8AC01z/wCM 14P9l4r+T8ju+s0+5g/Gf9nI/EX4s6F45trewn1zwstnBpU1/NI62iG/ilvp LcIiz205tFkh3QTrHdrceVdxTRQxgVbfwj8bv+E+8I3UPjPw0vhK005I9d0y 804Tare3P2bUFMqXccUUSr576exT7MuRC5BTBjn6j/hRPj3/AKEnxB/4Wmuf /GaP+FE+Pf8AoSfEH/haa5/8ZqllmKStyfl/mN4mm7a/1ocT4g+HXifwd4+1 XxHbWOn3uv8AiXxPDp+i3Wm6fALuz025sNMt7+S7ufsbBfKNhNdgynbM1jYW 5k5jhHX/ABM+Ed3qnjaDxV4atPDei+MIZdM059ce1ia9u9GTUre5vrNpWgZw jW63Sxx5K+ZNvzGwDrP/AMKJ8e/9CT4g/wDC01z/AOM0f8KJ8e/9CT4g/wDC 01z/AOM0f2ZitPc2/rv/AFdh9Zp3vft+B5r40+APxe+Jn7MuveEdc8WeGrzx FrvglvD+oXVxCZba/vJtJlt7iWPyYYGtd986SFys6mKNlSCIuNnVap4P+OUH iKaSz8c+ARpU13rDwCTwzMZrS3ltx/ZaFftRE8lvcBmlbdGJkbAERGa6D/hR Pj3/AKEnxB/4Wmuf/GaP+FE+Pf8AoSfEH/haa5/8ZpvLcU/sfl/mH1qm3e5U /Zx+Fnib4b3Pje88Walourav4t1m01eW50yCSCGV00XTLGVvKcsYt09lMyoH k2oyfMTkD0s9K8//AOFE+Pf+hJ8Qf+Fprn/xmk/4UT4+DAjwV4gGDn/kdNc5 /wDINS8sxTd+T8v8xLE0+5B+xv8A8mifCv8A7E/SP/SKKvSK47w18K/iB4N8 B2PhnSvhwljo2mWEemWkMesX4e2gjjESKsn2TzAQigbt27jOc81Q/wCFE+Pf +hJ8Qf8Ahaa5/wDGaX9l4n+T8v8AMPrNPuegU2WPzo2jzgupAz06VwP/AAon x7/0JPiD/wALTXP/AIzR/wAKJ8e/9CT4g/8AC01z/wCM0f2Vif5H+H+ZUcXT TTueYfCX9i3xF+yz4c0PQvhb4jsLbSG1Yah4jgutD0TSRfRNJYo6wppmlwRi QQwTESyKzMrPC2DLDdWPQfGX4ffG/W9W1Y+A/GPhzRbG50lILH7ZBG8tvff2 frEbTsWtZMg3c2iSclhss5vl5KTdf/wonx7/ANCT4g/8LTXP/jNH/CifHv8A 0JPiD/wtNc/+M1f9nYr+T8v8yViaVkr7HG+Pvhb8cNV+Jdxq+g+O/Cen2UEd 5b6ZFdadcT29vHNd6NKgurVJEW6kSG11WMSCWFgLuNVKZkcep/Cmx8RaX8K/ DNp4xvbHU/F9rpVrFrt7ZR+XbXl8sKC4liXau1GlDso2rhSOB0rnv+FE+Pf+ hJ8Qf+Fprn/xmj/hRPj3/oSfEH/haa5/8ZpPLMU1bk/L/PzD6zTve56BRXn/ APwonx7/ANCT4g/8LTXP/jNH/CifHv8A0JPiD/wtNc/+M1P9lYn+R/h/mH1m n3Nj4Rf8ind/9hvVv/Tlc18yeFv+RB/Yj/7Ctr/6g+uV9N+G/h/8RvCWkLZW Pw7RLdJJJQJNXvpnZ5JGkdmd7MuxZ3YkknrXzpL4f/4RLTf2N9J+3adqf9l+ IIrT7ZYTedaXfl+CteTzYZMDfG2NytgZUg45r38owlWlhsS6iteKOLFVIzqU 1F9T7N/Z1/5GT4r/APYq6Z/6M1evqJegr5d/Z1/5GT4r/wDYq6Z/6M1evqJe gr5+G7Otn8jH/By7pv8AaP8AwXs+N6FHcMNCyF46eHtMPXoOw/Gsf4d+Hovh V+zT4e0S4Biv/G15Lq2pNJgMmn2qAohI5IZ5PX/lnXrv/Bej4bS+Pv8Ag4M+ LVkqJ5WoXnh2ByScbDoWmKOg4xIUP4V4n+1F4jR7/wAUT27rbQabDD4Y0leo SIEyTbenDBW5NaEnzct1c6t4+l1kNNNcT325ECny28yQsqjGf4UPHJ6V9s6V 8Gp/ip8NYrm3WGPVJHYCPcBJGAF4wOvP418S63dvoGhaNFYlZbi7dr+4lwQL XDBIuORkbWyT13j3r64+CEvj290mCXRtaM0hReEtlHnKR02jIxyeRxQB6/8A s/8AgTx14NjjgubdWgQEBQcjvyBjNe9abePY2gku12FULZbkZHf6fhWD8JdU 8RHwtbLrji3vRxu8tRkfTGM10Or3KNYgM3nxIfLCuq/mT/hQBK+t2mq6cxVF EikEEjAJ59P6+tZuolo7IvHkBVy2eoP09apXARoJYUIEJfKqn3iPqef1rnfi F40l0fT5Wid97J90Lu7KPw/nQBq6b4x0/V9ZFjLdRx3LcRM/ylm7j8MVB8Uf gxp3xe0Uwaksfkk7NwHAGPrzXzb4p1O9Go/ajIVMbbgxY7lPbHcVY8Dft6Xv w+8QLoXikvcwSj9xLwD6HPHOP8KAL/hH9g/w/pHxo1OziVHh0qwstQjATJHn S3asoI/691719EXUFp4W0iBbl0is4wApkbBOOmc/yrxPxj+1fp3w9+Ier6zH ceZBf6DpyxJtBMgWfUDx7fvB+dcdqXxx1b4vWcF1csscc4LrDjGR/DXo5j8U P8EP/SUTHb5n0nofiq21mSAWTRyEyB3H8LoDzjHIzxzXR6fcpKzJyZCFAJGQ McH+lfOvwo1W80rX7eRGeNFXa+SQAPpjnjt9a9wsb9pQs28HJz0I4+vQV5xR 1OtWsUtipCDDfKWYA5Ug/lg4P4V8a/F3w3Z+FvFlw1jb3mo3il2ZVTpz1B6g V9g6Vqq6kIts8uEJzyCG57+v+etZPiHRoZt1wNOtZbhS8aTeUGC/jQB8S3Wl ePvEsCyJAum2mOHlhMgQDtncM/SvHfjt8P4NC1C01XVdWa9lgkjSeFZdpkja REZVA5BwzfTrX1r4++DHj/xNqtxcapr8en6JG7MilkQD8h6enrXjfxO8A+Dr KNra7uYNVPlyq8jofNYsOCijkEEZB65FAHmX7P8A4WttJ+I2u6VMsgtdTtbq zMQOAm1TIGyOcjbkd8ivZ/2w9Mf46/B/S/G8ryf2jrVq2nasqqCE1KyXY7em +eOMP9WOa+f9F+IzeEvGGneIJGa3jhuknvIShWeSaMESxyA/MAyA/Wvqvwzb Cb4XfEHRBIbm20iO18SafiMHaYyEuDxzlog2Rg9c0Aeu/t7+FP8AhKvHP7JG iO0aS3P7L3gBGDdcJHrDnHb7yr3r4f8A24vE6X37Rt9BZTLDFonkaXGFPzxC LeM5PozMcetfff8AwVCvLL4TeNPgDfNIyXWh/sz+AbO0uGOQgM2rxvz0LEAD 8Se1fmD4vuE8d/GOKYySTjVb9WdnBD3EmxDIzZHdix/GgDf8O+BPGtvrV1r2 kIhiuRtCzMcyrhVBOOOvNfQHwD+Ok/hl7f8Atu3ks5mUAxu/ygeoOPc13Xwz 1/QPA3hiC61q2WZHURJCqg4xj+v8q0j8Uvhd4hvFS78OoYySNycHrjFAHe6t 8MtG+PekW99ZXEKySAMzMP8AOOM1H8Mf2Sr7wZ4iedtU8yCNsqmcjdj27V23 ww8R+DE0ONNDCpGAMQcZXI6mpPFX7Rfh/wAD3Bt9TG91cBhx8v8AhQBu3Yk0 0QxRL5UmzgoeWGfaqEWri5vwZGWNg2Cc8LwfmPftTYPG2l+KbD7Zpz74MAYH MgzyKw9S1ERGRogFfeVI7txQBsat4r0rTTGLm6toGZdzF3VB+tQ30en2Vob2 0t4rmOZQfMjIIx9a8V+Lvwy1LxldyTJv8ibHl5PKgV5R8M9c+LP7Mvin7JPb P4i8MyStiE3G5o0HTaT2x29qAKX7SHxK0KX4oQQApA0ryK0YIG3ao5IH417B +yn4s8MDwJDpt4kN1PcX94ArIDwbuTB/LFWrr4UfDX9sGxbWLnR3stRtXAmy BG6tjBUkVT8C+EvDfwa+H0uoQWn2i4GsalaW+PmysN/cRJ/46g5FejQ/3Or6 w/8AbiftI9Zbwh4W8EtNqVva21i0hKu+7A5PbPvV3wXr2n6tazmwuorgRkAO pHIx0/OvkP4s+Gvib8e/EaouNK8PW7M8caSlWc5OM/jivbvgn4JvPAWgrbuf LEKCMoO/fP55rzij12yvl83ETmNid2D1weufxArV0iCDWImhvIFmgJ2bSMgA +1chYXaupOAMjJG7oOcGoLz9ozQvAt9Bpt0qNMW28Eb8+v6d6AOptvhZpOjX DtZ28UDSHdlR/nFfNX7ZPwD1LxfqSpZWklwk7iRCgyY/X9a+iJPi7oc8K3UM zGScByhbHHqM8VxnjX9rrwxprSuql7uyJVsxgAnH1oA+LfiD8Bx4K0BYE1S7 h1JSJivmMWAJwwIPGe9UPGEKeJvg8n2mYStCFMhJJZuCpPsSD+leyeJv2kdD 8Y+IHgv4dKeSfJRwRuIGDyeteb/FXSdFs7J7+NlXzQsKxI+RKzNtTH580AZn /BNTx/YeHPj/AKbHqZMWn6jp1xbz9AFG0SKDnjIZEr+hT/g170oeH/2K/ilp ygBdP+K+q26gdAFsNM6e3f8AGv5hfhPr58E/EGG9c7DdGVNgBJRSud+OMfdA /Gv6qP8Ag3W0hdD/AGLvFcACMf8AhNZnMoAzcbtL0whyRwSQRz6AUAfzGeGd bbVPh9p2oXUohhs9Nt7K2jccDZGiZAHdmDc/7Vdp8aNcPg74aaTo+6QZT7Tc gMf9Y3OD6njFeb/DXTIfFXj3wJowZTaNbWlzcxo46IhZmPYdMn05z3NWvj/8 QDH41FrMY3t5gzyRu3JUseAfbAxigDzWbTtQlujdvHLHED5iEDOecjHviui8 J+PNjNBeF3iuQV2v1/zk1R0n4hSy38KzxrLEZAqsOVRegx7DmtTx74eiglso 7SDEt58uc5yeO2eO9AD9G+Gun67rRlmu/JtZJAyoD/rPb9AK6XxL+zbYx2Ek tjM4lVSYyGyQQCcVBp3wQnntIJDqEylAdyqDwfT8zW03iub4f+F7nR552vNX mJ8lOWYBhxk+woA818E+CtU8azSwooisRL5TsCBuwPmIrsLa/sfA3iLyYLcv EIUjMoU7S6nkA49OK2vBGjanpXh210byo7ZGgDTSx8eUDknv1OO9Xrzx7pek xx6TotgNZveMiKMMFYZ4J+v4UAWD8QdT8Sr9n0uERRfcFw/Hl+4z1Ix1rQub vS/DWiRyarfhhEC0gL8Mc+vc9elbvgv9kf4o/FmSzlitIPDdlc8O0oYybTk7 tnHP1xXuHg//AIJk+DtI0lT4pvrrXL0KPOkuZcLkDkBRwfpjH160AfM2hftC 6JFdsttp07QOQqSFQBtAPYcivTvCnxL0TxXp7JNalYJlH8PJyPf3r6Q0n4Z/ CP4R6amn2mn6WGjXf8yhmZvx5P41dvPCngTxRdQfZ9CsWHl8yRxBct6ZH50A fPNp4k0Twx4Ymt7eUL5iszBlHHGR/h+dcH4Q8Q23jfwzrcKSJujvXWMJz8ox +XBP519U+P8A9jbwZ48jaQK1lmPYDFIUxnHJNcRD/wAE1LPwRpMknhPWZmmd i8iSkSLO/f8Al2/WvRzH4of4If8ApKJjt8z5h8M/COP+0phqU7SfZpA0XmE8 xkcbfXoaq+KNXtdG8RxQwZWCN/Lfb0P9OmPyr6X8FfsM+IvEFwsuvXK2QVtp SIlt455ySOvHavVW/Y+8K6D8NtV0tbFJ7i8gcLcvt3iUrw2cdjg15xR8MaEr eHGmmU7kkPmBucqMGtLwNpumeLZWv51QSyuQ3qMHp6e9c9qfi+LRjLpM4Jun ZoUJ4C44/wAKseEI/wCw7hLdP3mZMsOw9SP896APSbbwHp8sJT7TIm7HSQj6 dKL+1fQdCEElzJcyF/Li6knPRf5VlanOtlZLcO7bkG4Ad+R+davhe50+61C5 1HWvMie0+eGBh/rOMh8etAE2oeHLKKxs9N8uJbgwKpIXo3qPyrI1XwnI3hww STKzId8JyBt4+7/P9K6X7bpfjuEa5Yy+TdRRMhib5G3jdjj/AD1rBs7Gaext JLgsXkPC5+77fj1/GgDkZJ1v/hx4g0qWO3edY3nhM6hz/qnUhMj75yv5V4tb 2SRWCtE4tfLUXMKMcsxwC2D9A3HqK951nRBpuqELgKVIk29146fWvFLi8t7X UpdLuFUT2N06K5xkxkkg/wCexNAHY+IfAMPxY+BVjc2se/VvDvmFm/5a/Z9j Hae21WXP/bQV+2f/AAT3iMH/AAbofs8ocErbeOwSD1/e6/X4q/APUobK9LiR 2s7k/YbqMMSDgor7h2DIFz9M1+4P7G3hl/Bv/BAf4H6W4INi3xAiU5zuUT+I NrfiMH8aiew47nb/AA2/4Jy+Kf2rfhTpnjPwhqWm6H4r0W38MQaDrEupPbya NNbaSZpZ2h+zTJOokubFxEShcwOPMjA+f7C+AH/BP/V/hr4H+Hek6z4i0q9l +G3ji68VWjCxNx/aED6Ne6ZDFISY1SZTeecXRAmYtqooYFPga0S3bwHZ/arB 9TtUn+HvnwDQItbV420XWlIe2ldEKNu2F96sm7cp3Ba9g+Fn7TvxU+M37LPg XXNU8ZfFjTLPQ/ifBp+peJfhz4Zl8RXv9kyeCmug0cC6ddC/sf7Uu0hW4ls5 gwEUhYSKsyzHBUqUfrEd57/LT9TWpxPjMdKOT1f4eFj7ju/+XjcmrbJaX07n 1TJ/wT28zQ9D0z+3PDkiaDqmhaiutT+Gml8SaiNMufDc+LvUDdZnkuP7AKTS GMblaw+X/Qc3FHSfhp4s/Y2+KV54wsPCXxB+OGoeMvCPh/wzqUPhgaNpkOmz 6OL3deeTqWrWsEEd2dQyttZRiOJreRmy0u4+OfGv4kftN+M/7Z03XLTxR4Bh stH8Gave3HgSO51K80uRdV0mfWHsov7GmguZVjOrRPbLqF80kNrAPsOJ2ab1 /wD4J2fGn4rfEv4g/EvSviPYeMYrfSRZXltPrvh2TS4rO+nu9TjutNspfsdt Fc2tvBa2EgMUl+qNeOo1O/Ty5q3d7b/1p/XyMeh0/wAPf2S5dOvvCPi7Srq/ 8La1YSa3fyaZrdpDfS2ia/r9jrmpWkv2e4MXnobeS2jkimkjjaQSfv1Ta74P 2LNQsriHxBb+KrBfHkXiM+NTqEmjSNpT62fCzeHjIbMXIk+ybCJ/IFyHyCnn c7x52fH/AI8HxL1CDxXF8SpdA+H91r2peJDomi6uv2pZPE1je+HFsnt4gdTV dKhnW4hsTPtUyW9wm+XynrR+LfGfh3xnbeGL2P4qyeDLK8/4SCd4tL126vV8 Fr4Pa18lr6ONppdW/tv5vssczamxxKIyvz1K+Fa9/wA/6f4g78z/AK/r/g+Z 3vx08LJ4M1bTNPS7v9QaOz82W7vZfNuLqWSeaSSRyAFG52YhEVY0BCoqIqqP O1/5CCf74/pW5qfjW0+JPhrRvEFlbeLLK21qO5vEtfEukajpOpWnmXt0xhkt tQAuI9hJUAgR7QvkhYfLUYa/8hBP98f0r2si/iVv8Ev0OXF/DD1X6nfaN4i0 TT/sYn09NQjjj23MktuQ5ZnO9E2yquAjNtdgzbgOg27RtW8My21yn9jzrIty 8tnJHOw3R/OUjlUseASgJQgkE/MCp3czRXyfMz1OU3X1PQ7jQ71PsEsV/IkX 2ZlLGOBxIzScl8kFSAuRxwDkgu2FRRSbKCiiikBf03xHcaTbmKKOxdS27M9l DO35uhOOOmasf8Jtef8APHSP/BVa/wDxuuO8RePbDwverbXMOsSysgk/0PSL u8QAkgZeKNlB4Pyk5HHHIqh/wuDSf+fTxR/4TWo//GKrXoLQ9A/4Ta8/546R /wCCq1/+N1wPhz9tTwp4k8YaloA1TSdL1nS77+z5LPWfDq6RNcybLl1a2W6g jN1EyWd4yTQeZE62k7K7CNyG/wDC4NJ/59PFH/hNaj/8Yryn40fAD4KftC6v BqHjDwHrOs31o4ntbhtA1eGWyn8yGT7RC8catFOTbQK00ZWRo4ljLGMbKaT6 3Cysei+I/wDgof8AC/wtL4UW58ffD6f/AITnVrfRNCNlb2V6NSu54opYkXyY 3wrJcWzeY2EAuoCWHmx7u4+H3x+0n4ueD7TxD4T1jwf4n0DUC/2XUtKt7C9s 7nY7RvsliVkba6OpwTgqQeQQPmd/2P8A4By+LvD2uP4F8WtqXhTX28S6Q32T xCItOvStmimKL/VrEken2UccAXyYo7dUjjRCVPrvgvxX4T+HPhTT9C0LRvEW l6NpNulpZWUHhrUlhtIUUKkUa+RhEVQFVVwFAAAAqraaXuDtoeqf8Jtef88d I/8ABVa//G6P+E2vP+eOkf8Agqtf/jdef/8AC4NJ/wCfTxR/4TWo/wDxij/h cGk/8+nij/wmtR/+MVFpBoXvhp4gu/Fvw60DVr+RZb3VNNtru4ZY1jVpJIld iEQBVBJPCgAdAAK265z4PWFxpfwj8LW11BJbzwaPaRyRyKVeNxAgZWB5BB4I PQ10dJ7jWwUUUUgCiiigAPSvjzwt/wAiD+xH/wBhW1/9QfXK+wz0r488Lf8A Ig/sR/8AYVtf/UH1yvp8h/3XFf4Uedjvjp+p9u/s6/8AIyfFf/sVdM/9GavX 1EvQV8u/s6/8jJ8V/wDsVdM/9GavX1EvQV5EN2bs/nM/4K56Qnhj/gt78bvF +pSQw6bpw0cQmRgN9wPD+nmKNe7O0gTAHOcV+bf7Qmqzw+HvDVjqDvFNe3E2 qy7lzyQyZJ+jk/hX6Y/8F7bIQft9/tCai5jP2G20V7BdwJXUJNIs0RyM8FVV SD7A1+Vvxd1r/hJ/jBqFqJrq6W0uF00RSNu3RxHy2259wTWhJ7B+zd8CNNvd FuMGCe41CNsvgshVedoz67uR0rq/A2h6/wDA/wAcx2qQTx2YkDW7Es3lgnlM 9NvcYziuK+EPh7xB4H0kRSXnlQ3Um6CI4ym4g7ck8EAf0r65+Cia1rmgJ/bc VvemNw8BwrOE7Hn8OBQB6h4O1tL7SLae7aFfMVWBcrxxzj6/0q091Y6lBJ9k uLclHOYwcflzXgv7UHxQ1fwTbC2tLd8Y4Krgbe4/DPY1zHwS8ei8jaY3nn3u csnmbQoOOCD3/GgD6L1G/htAdoUFx25yAOOtczr9u2uW84a1LxumUdex69uv QVHr2pi7s7YBxHI5Hyjtx/nvU9vLcTKnnWN0mOPNhUFPrn/GgDxDx7ocljJL GVYBec445/GvjD9sDxL/AGZ8RNMtoDJCba3aRtpwG3yNz69q/RT4z+EE1TRZ p7KN5rjGcDClvc44HWvzz+OPwF8X+MfigtzJYSLDcBII5CuAm3IwSeM5P60A Zfx38aXf/CMeFmWeRvtOkxBsHO7az4Gf+BV9Afsr6yviP4ZaJJ8sskcBgcnG UZXPyge9cj8Uf2P9Q1yy0rTCWN/pOjW0ioh6mV7gdf8AtlXoP7GP7P3iDwh4 Wgh1GB4k+0NcPA7gBATjAGfb9a9HMfih/gh/6SiY9T6F+G/htVjWSSElSS20 kjn65r0K1vvs5KFlzt+TBwRnsRz/APqrP0rSYLOyijQRwqq8nGQvsapzxrcX 8cjyS+Yh2KVQBGH9cV5xRR8X/HLTfhRcyPeW52ryXQ7QR3rN8F/t/wDgvxne ixjmhVpHKjbKpJ+vSuY/aCKSpJb/AGRL64lX5A2Bsx3+teX/AAC/Zz1K98bv qF7pf2ZJH3EFFVEGeSMDuaAPfv2kfDGu/E/w1bjw8zLDPnHlk5C4xnLfWvDP hz+xLqnh68l1HU7mS6ublzJK8zAuMKQAM8IvPvnnpX2D4WFjbWX2F5bWIwLs jRZATJjHYfWvL/jh46bwiZoZZFRnjLKCwXABGfrQB8P/ALWvge20m50+8UBb i5uyH2r1whJOfqK9s/Yv10eKvEPhmCOWO5bxV4avNEusMRvkEDK2evUg14D+ 0d4t1LxdrYlS3mbTkHl20j5UCQsAz89BgkfhXd/sI+Kz4f8AHfgx7UC2/sTx ZDDlmAVhcKwYk54y1AH1b/wWfu5Zdb+AVg9rZzR6h+zb4CDXVzKypZypPq7q 20KdxYeYvPTd0Pb85tN1FIPjDHM8sjS297J5jMAkOVBjHz9QBgHO3nHbmv0m /wCC4enTXvxq/Z+0cIfs0/7PfgoSbTwHjl1squR7bjx6V+aPgrRl8c+O59OL FR58wcLncUEpP67v0rspYmnCKjKjGT7tzv8AhJL8CXF9z6jsJLzxdYLGYdER bdekmqSrk56/8e59atWfwtutUhdLZfDKHb5hzrM2eucAfZf0qLwx8Ar3V7Qm 1F4BuYhg5GcH9eKx/GfwU8WeGZxLYX13aKV3SLOu9T79PWtPrtH/AJ8Q++f/ AMmLlfd/h/kej+EPEGsfDOzdEsPDku0gH/ieSggdMYFtXiv7UR1/xxqxfDxC 7YB4LO7NzhccfejQ4z37Vq/C74d+KfHyTS3skkPlOQvkKVEi46knp/8AXrN8 b2y/Cz4nWECyTXL6on2dEMmfLIOc/TNH12j/AM+IffP/AOTDlfd/h/keyfso xePfDXhMwtFpGpwFVw15qcls4wMc7YH9vT9a9RvNZ8Xan8w8O+GflOMjxDPt Jx6Gz/WuQ+BWt3o8PzTSxkJGQgOMAHv/AEr0/wAMsby2kyfLCMHPofSj67R/ 58Q++f8A8mHK+7/D/I4+/wDEXjWxjxJovhtFbAZRrs/Pf/n0rjNU1fxVZsxk 0nQjC7HJOrykD8fsw/lXtGr6TFPbITuPZu65rz3xfoLToSVUJH8/QnOBxR9d o/8APiH3z/8Akw5X3f4f5HkPiv4k+MvAltfz6bYabZw3LKZ0guXnIYn7wzEo 579azPg14n8V32hwyXEFrqMK3EzQw3l+8YjkM8jSEKIm5Ls2efX6V6BrttFB YSFol3hV27uR94E1i/DvTxc+Fo2BEbrfXxJzzn7VN0/CvQoYyj9Tq/uI7w6z /vf3yXF8y1f4f5HVWXinxZI5uBpOhAnhV/teUBR/4DV1ug+IfGdzpyFdG8Ms GOctrs449D/ohrP8N6UfsYVzgyAHA7fSu/8AC9mbSxlmdS3GEB6kcc4rz/rt H/nxD75//Jl8r7v8P8jNtNZ8Y3EZRPDvhAPgY/4qGcHj2+x+9fLP7RPgzWdM +MEHiW+t7S0EUpDQwalJLA+fcwr79q+stbmayukJm8oqu9lxjOT7V88ftg+M IQ1zDdgNbiPCnbjJYYHv3o+u0f8AnxD75/8AyYuV93+H+Rxt78f/AC1h+zDS ZDAog+TUJXwoHXmEDFcL8WfHVxJab0FnZzXzeSTHI0pkB4PBVOueufwqv4N/ ZX1nxLYW8wU2pMIYOkY+cHPXGO1dV4B+AU3iPx00uuhYpbA+XGrKQrE8BgM0 fXaP/PiH3z/+TDlfd/h/kclYfBDfbRXF6sIYruUm6aMnPXpG36HFZ3xDgh8P aPYwTzW6YlRYGE5kwQ2RkFV9q+gfiF8E/sUBtZbwoEIw27BGPfrXh/jT4Vy6 Tot1fKq3RgyyF/myB/ECf88UfXaP/PiH3z/+TDlfd/h/kcFIkVpeNceWzXNx JJhwgxEgwFU8478Hr7Cv6nf+DcG91HUf+Cess2pW9nBO3iBlRoLprjzo107T 1R3LRx7WKqBtG4AAHcc4H8ofjPxiYXvoIS5W9uvNVyQPLG0MFzjtkdK/q6/4 NrLp7/8A4Ji6NcSEeZNqsuRnJG22tkGfqFB/Gsa2IpzjyxpRj5rm/WTX4DSa 6n8sHwEvPsfxDsrkytHJDprYcn5lUQDp+HHtz6Cun03wrpfxeup72/XbPDIV TDkFUB6CuH0Kz/4R3xXoFxMWNtPYWzNwAFDxDn3wa1m8L6hpt3eSW+orbhHa NAXwZB6/1xWqzCaVuSP/AIDH/IXIu50niH4KadZ2D2+l34SVW3xIG3A88Hmu d0hNa1jxrbQKVuZdIU7o3+6cAg8Dr/8AXrlbbWNZ0vxRG7GaV0YEq3RjnGf6 1658G76y1rxHql8rKm5Y+/zDg0/7Rn/JD/wGP+Qci7s6LWfGMcHhm/YR/Zrm 2jcyJ/ccLkcdP/1VyXwa0Zb1bnxHrDPdXbE+W7thdgHPTA9ao/EvxJYfY9Rg 8+M3Op4G1DkkBgDx16Zpmm6/JL4DsdA04tJqN7IluARguzEAYHpkj9aP7Rn/ ACQ/8Bj/AJByLuz0j4C/BnxX+1h4zmFrdHT9AiuRBfTRgYbKs2wHnG0KBket fbXw7/Zb+Hv7N2jtcWWmwNdwovnX00zSM/U5+YkD8MV1v7LXwBtPgJ8GNK0O Pb/aIi+0X8nVmmkGWPY+3PpXG/tgeG9Sb4T6tBopdpfIKqUXlucn+ZHrR/aM /wCSH/gMf8g5F3Z4z8bv+ClQ8C389n4eljk+zqU37VZSc9B9MmvnjX/22fiF 8RNWaFdYMYklOBFEi4Bz0IXPevDNa8M39jr91Hcxzm4jkO4Ebjn8a9I/Z++H 8l94itWuImVQ4AOMk5IGMUf2lP8Akh/4DH/IORd2fTfwa+HytpNvq/iW9kvr 1mEiNLIQEXHQjPIP0rrfGvjrVoEaz0K7WwtzhEdUUtx6ZFc94omsl8SQ6IHE E2nxx4XBy4YZx+AH61bSNob0GVG8hANrY6A9en50f2jP+SH/AIDH/IORd2cF 4x8bfEOzs1f+07+aKMHgSYXAbnAA44OeK820/wDbF+JfgG9lvdJ8R3LJC2ZL a4iikH5Fa+h5hZS2zRl0kQ8A44U4FfLnx58Pr4Z8Yz3WnhZbGRgZEUD92eM8 Dsa9DMcxmpw9yHwQ+zH+VeRMYeZ9vfsG/tz3X7TDXOmeII7O21y3ZI4lQCP7 YCsjE4A6gITx619H6veQWljPcXqqllaRtLISeBhSe9fi5oHjfVfh/wCLtP8A EHh2a50/VNNmEkTxnkEZGMj2Y/nXunxL/wCCmfjLxd8NDpmq20Vs93GVd40I acgfpmvP/tGf8kP/AAGP+RXIu7PEPjr4qhv/AIlahNavsUXEjJyDgbyfxwK3 vhn49hvYCsshe8twCjA4LAdsd68mjF/4p1BpbeJrhwvKj73c5x3x1/Cn6Guq 6BrDXSQybrKcRSJ3ViCcf+O4/wD10f2jP+SH/gMf8g5F3Z9Q+DLfXPE88U7R I9nE3yRyYzJ15z6cCu/i1HV9Qvle40m2+SMlSxHOB0ryzwj8abyz8P2z6fYm RJFG8KQdjdCo/wA969Q8N+O5b2VILuKa0ublNyB/u46cHoeMce1H9pT/AJIf +Ax/yDkXdla5udF8TXDwXtodD1VmMYAcgMQchhg4b8ao6Xd3s/iYafqhVrNU P2OeKMKrnGMkjvx61reLYYEngtdQi3294PLinC4MZ46HsclfzFYOk6yvh66f RtYLCJebe5PPmKOhJ/vZyKf9pT/kh/4BH/IORd2Q+KdIvdPEk15ELyPzHWJ0 xkKOxwK8G+IFnBB8YJ4REqi/tY3XIIz2OPpg/lX0DrutLq8dpZ2M5kjcssnQ +Zk7cV5z8dvh7LceIpbuK2dDo9rHO78gSrnDdPz/AApf2jP+SH/gMf8AIORd 2YXwSgax8QaxY7FmVmiUspBZQ4ILEHgr8gyRyMf7Rr99/gPtb/ghn8DpFOTc WfjKeQkYLSPHrTuT7lmYnHGTxxX4O/Au2tNR8fXgMtskZhhuUErmMnDkDY/q Q33TxnPpX75/DO2jtP8Agij8E44gRElj4vCAnOB5GsYFc2KxUqsOVxivRJfk XBWkc34C/ba8U/sx33gvSk8Z+IvCngTVF8IxavdW9ppaadpcd1pcqXFzJc3V pcPHMFt7aRUP7torS5HyMd9fc/wb/bEm8T+HPhBeeHdZ8S+NNI+Jfjq40C7v PE1pp0N9b2q+H9R1JTb/AGFYYNoksoTvYSEpJMBnMZX5i+BOu/CGT4CHQ/Hf hHxV4o1DWk8JWFza6dq8dhDqsU+iXP2O1l8y9to5UzDqKtC5IlNzCu2U4CfW Z+Lv7L/7P/wu8P8AiW/8QfDjw54Q1bxXPJYavqt3ELKx16awuXmMlzMStnct arcxt5rxn940X3pQj+dRoV4uVSbvCVuXytv956uMzTK6+HoYPDU+XEUlL2r/ AJlKV4P5K6fojU8Y/ttPJ8L/AAz4t8MeEtbu/D/irWvDVnYavfiCKzubTVtR 0W3WURCb7VG5i1WbYssKYk0+cOFVoDP23wk/ah8N/Gz4l654X0aHVWvdA0DR fEk11LbqdPurXVftZtjbXKM0V0ALOQtJCzxDcqiRmDqnJ/Gvxt+zn8Ab/V9c 8dTfCjw5qOkSaZqGrXV/BZpeacLy+0+ysb24+UyQxNdadpqLcyYRTpluxdRa q0deb4U+D/jveXc3wb+Lf/CvLvwdL/wh3iCb4bW3hm8lV7DIh0m+N5p975Js vOm2Wy+UYvtUmV+YY6nZ/wBen9fM8roczoP7VfxA8dfErVdA0u48JaSPFJ8Q 2/hB9R8P3Mg8PvoOuW2iXcl+y3y/2gLqS5W4gjhWzMaJ5TPIW81Z9I/aq8d6 h8dIdAYeGTp0us/8K+EQ0a4V5NeHhr/hITqouvtZX+zTGDbC18gy+ZiT7Tj9 3XdHxR8Nbbw/4b0PX7218calBdpbwXV1okd9fajf2Wr2VrLevFaWyxJJFqrW ck0sUUcVvMolIiWHcjdP8cfBuX4U6fplvpOjjw5NbLpdp4eHhmVZVH9itdLp 403yPNSYaVn/AEQxCQRHy/L52Uk/d/r+vIT3Z5jrvjbX/HuiabdeKrSCz8R2 BvdH1DyLG5sIL2Sy1G8tBeRW9xmWGG5WFbiNC8oWOdAs1wm2eTnJ7KLUpzbT yXEMNwfLkkgJE0angshDKQwBOMMDnuOtd18WPEvhzxXqUdz4WNo2mRXF/aTN bW5gT7dBqV5BfAgqCXF3Hcbmx8zbmBbduPEL/wAhBP8AfH9K9zIJWq1Wv5Jf ocuMV4w9UN/4Ub4U/wChm8f/APgdef8AydR/wo3wp/0M3j//AMDrz/5Oraor wP7Tq/yx/wDAI/5HofV49397/wAyNP2V9CaNGbxT4thMiLIEl1+SNwrKGUlW 1EEZUg8jPNO/4ZW0D/obfFH/AIUbf/LKtrxt/wAhiD/rwsv/AElirHp/2lU/ lj/4BD/5EX1aPd/e/wDMb/wytoH/AENvij/wo2/+WVH/AAytoH/Q2+KP/Cjb /wCWVOopf2lU/lj/AOAQ/wDkQ+rR7v73/mN/4ZW0D/obfFH/AIUbf/LKj/hl bQP+ht8Uf+FG3/yyrzT9p34g+Mvh5pPhefwha28yXWsSDXJ5dAvdbNnp0GnX 145S3tHSXzZJbaC3Rhv+e5UCKVmWNoZ/jV411m01V9C+HTyTaNeahYyw6xqx sTevBCWt5bZooLhJIJpjEhdzGYlMrFWaPynpZjVavyx/8Ah/8iNYVd397/zP Uf8AhlbQP+ht8Uf+FG3/AMsqP+GVtA/6G3xR/wCFG3/yyrxHwh+0n8Q/FGiC 4ufhLc6FOvgyz8QTwX9/cs0Wp3KOTpSmKzbeYTDMJpUDOge2KwSGYqmx4k8e /E+DWLWWx0Dw9DBY+HlvbmwY3F1HrurSRXJFhb34EbWcUDwRlrieycTLdxhU iZGUuWYVY7xj/wCAQ/8AkfIX1aO1397727nq3/DK2gf9Db4o/wDCjb/5ZUf8 MraB/wBDb4o/8KNv/llXjWi/tIeNvEXhv4eywfCvVLTWfEUmmv4rsdRubi2i 8H21zaSzzOJxasL2WKSLyPKiVSXkRpGgQlhg3X7T/wAV4Phn4R1y4+Dt/b61 feJb3TNf8N2dw+oTWllBDqmy6gutkKFJHtbJ1aSMKy3IjA3ujUPMKqTbjHT+ 5D/IPq0drv73/mfQf/DK2gf9Db4o/wDCjb/5ZUf8MraB/wBDb4o/8KNv/llX x/p37f8A8ZY/hF4a1O5+AHjJvEz2ejanrSRaZc7GtZL2Gz1CK2tSC66gJftE kVtLL5aWZjuZrhW3WtcL4U/4KW/tJ3/7Qmq6Zqn7L3iSx8H2WnTSyQy2d4l1 Z3VtaCa7jj1CFJ7S+DyQ3MVqI44hcNLa/vItzVSxta7XLHT+7Dp8gWFXd/e/ 8z76/wCGVtA/6G3xR/4Ubf8Ayyo/4ZW0D/obfFH/AIUbf/LKvNf2Zvj5qfx5 sfFjav4UuPB954Z1tNMFjcXTT3DRS6dZahC0wMUYinEV9GksKmRY5Y5FWWUA MfTKh5jVW8Y/+AQ/yD6tHu/vf+Y3/hlbQP8AobfFH/hRt/8ALKj/AIZW0D/o bfFH/hRt/wDLKnUVP9pVP5Y/+AQ/+RD6tHu/vf8AmVdT/Zq8MaNZNcXXjLxN DCpVdx8RsdzMwVVAGpZLMxCqo5JIAySBWf8A8KN8Kf8AQzeP/wDwOvP/AJOq j8X/APkU7T/sN6T/AOnK2rqKf9pVbX5Y/wDgEP8AIPq8e7+9/wCZjx/Azwru z/wk3j/C8n/Trv8A+Tq+ZJdQsNY0/wDY4vNJ046NpV34gimstO+0Nc/YIW8F a8Uh8xvmk2KQu9uWxk9a+sz0r488Lf8AIg/sR/8AYVtf/UH1yveyjFTq4XE8 yStFbRS/JI4sVSUKlO19+7Z9u/s6/wDIyfFf/sVdM/8ARmr19RL0FfLv7Ov/ ACMnxX/7FXTP/Rmr19RL0FeBDdnWz+cb/g4R1+10P9u74zOkiG4k17QVmjYH Lqnh6zkUgDkj3PHGOuK/LH4Wyy+MvjRDcXckofzFmd8Dgv8AMx9s55r9Hf8A g5Ht7yH/AIKbfFuaW8trbSY7bT5m3xtGWlGiWQ8tW3/NIRyMAYHXPJr86Pgb rtr4b8XzzPcwae1yEhL3a+cgKqx4AZMMcev5V6UMHCUVJ1Yq/TX/ACM+Z9j7 rg+Euj674O0fVLnUI7WVGFw6FcgjPHQ56e1avjL4/wAfhPw6YdERLy7hQJnb 93HtxXh3/DSuoQ21vajUfC96YVAjhTTJpZMADACJclm49B/jUdl+0zqGh3MZ vbTw7pcrkgSX/hq8twc5xzLMo/Kq+oQ/5/Q/8m/+RFzPszd8Y/tuwy6UbPxf axI8jYUzRYVeMcHBHGa5XwL44Xxhr8N14dhXypHDI645APpjJ696seOUtPil ozyT3nhby5hhxDo0io31JuT1+leNfDbxXrPwk8fQ6TpUayxG4KQzshZAD1GA RgY9T+NH1GH/AD+h/wCTf/IhzPsz9D9H3yabZi7gXgLvzhcHvzmuga5ihid4 xHEicqqnOPWvMPDh8aeIvDVnMdd8NqjqrKh0KZivHr9sH8qfdR+M0tW/4nvh twoPXQZx+v2zpR9Rh/z+h/5N/wDIhzPszpte8WW9tASVwTx1GCK8+8Y6zBf6 XJcyRRLEsLNnZyMdCeOTmsvxMPFISRJdX8OnjIKaRMD+Gbk+tcF8QNc17TvD kol1bRihhIymnSIfXGTO2Pyo+ow/5/Q/8m/+RDmfZnRaR8SrPxt8Trry2jEc Gh2duFHXdHJcMQc9/wB5XfW/iW5t7tFU5hyAcjBHGc49+K+SPhL4lv18e3Mk N7ZQTXLKC8tu0obAc7QBIp/Wvd9Kn8QzMC2raIN/8TaTMQPT/l5FehmOBg5Q /fR+CP8AN/Kv7pMZ6bHvfh/VjcWvG0Ltx90dfrVp4XupVdlkZQwOA2A3bivL /C9t4tCssHiDw8W24wdBnK5+v2sV09jZ+NordpP7c8LY5J/4p+4OD/4GivP+ ow/5/Q/8m/8AkSuZ9meJ/tf+FPEL64NQ0vX/AOz1h4MW1XJP0PIHvXk3w3+L fxD8bXqadaHekLeUWlMyF+eNuGKnPptA966b9obx5q1x8TktNW1nTVdpige3 sXgSQdgVMzHn0zWbpHx31jwvr8FhpY0O1VQA13JZi3Qg5x80k+3r7jrR9Rh/ z+h/5N/8iHM+zPfvgb8NNR8J3K6z4kv2R7nLCNrjBZiecA8gdK9S8X+CfCPx JSGXUlVygKhjOPm6f4frXxX45+JnjbU/EHlxnSNZGdrNZ3Cygc9MxTOP/HhV jSfHPjCxtwsrQWpPIQwuwB9j5g/LFH1GH/P6H/k3/wAiHM+zOl/bW+FMGl+F r230VFSKOAzR87vk2uDjByee/bFeL/sd+ILvRfENt9hSGXUheWt/aW0rH/TZ I5FBi45yVZj0421B44+Pmua5qh0uYmQTt5U0yBhhehHLHjkn8fwql+zZeyv8 W/ArabKbS9e68qJ5lMioxfC71DJvzgdHWj6jD/n9D/yb/wCRDmfZn6Kf8Fm9 XGmftU/s4S3ix+Yv7PPhB2j+8u7frakj6Fv1r8w18UT/AA/+KWuT2EAe5sby Yxx9plRm3A+2Rn8K/TH/AILsC8tv2pv2e7qWWC5uI/2f/CUMzw27KkrNNrRL KhZmVcoeCzYyMk9a/Pa+8GJc/Ha/RvKSSZ3nC9QEYIOR05YtXA1Zln01+zx+ 1U+m6FBNqtrFbGePzTESAASMfh0/Wvb9C+Nfgv43WKW96xs5AwXKyAKwz0z/ APWr5s0j4WNrNhLcmBIkAVYyOmB7VzOt+HrrwvfmTTLgQXUWNyhSUzmkB7v+ 1T4O8V+B9PhPgm3t5tO3AM6sOcnjn8fWvk3QPHmtS/F9x4osJWulVVjIwVtg cZA6+lfZf7IHx6udWSTw/rLiVZQWCuu+PIGehz1xXqGsfsVeHvEWtTamZreG QjzMeUAST246daAPLfAEdk3gIXEaSIjyAYLYLHrXp/gnTE1HR7hcrHJID90D HHTmug1T4X6F4Y0qCO6eNIoEwY0cZznANcrqf7VHw18J+IIdPbZBDG3lSTxq XRMjGcjk0AbNhYJd6e9lMds6NuQkZB9f5VieIPB81vbMJYQ6uMY2bs+9dl4a 8TeHfFzySeHNRhvYVOC8ZLYyMjIIyOPWuguLe1IKboXeFQrqc7sE/e+tAHxJ +0n4gt/hh4Hub8q6mCVI0JyM5cDp9M1B+zDdP4p+GWlXrK8gujNKRznLzSEn 6/NXv/7U/wAHtA+KHhJrG5C+V5yEgNs5ByBkn1/nVP8AZK+GmmeEfhNHbxiI eXqOo2q/xfJFezxqAR7KOa9Gh/udX1h/7cT9pGj4f8HySW8IaIoqZGO5HYiu l/slY1igLGMy4wc9B2FdqJrKKRPtASNFyfLXqR05qrqPxB8C+D0N7rN3FZQK xwXBO7PtzXnFHmfxN0Oa3tYp/wDXPEcY9QemcfjXkPx/+HcfiHwqL4W3mXCw lJF9B3B4r33w5+1R8NfGniGSytZLdlZC0PnkL5qgnJ9unGa7yLwr4V1eGe2h jtJftkW7AkBHYevpQB8Sfs5+PtWj0+LT5NPO62Uxv83DADjAx24r6IPwN8Ny 6tBrOpXiWImUN5e8DJHPNbmuaT4S+CCPewWFsN0pQsRuyCexr55+Mvx6t/iB 5qaVO1rbK7fJkDIBGeeuOaAPSviN8PfDPxJuXXS9Xghkt8KQZMbse/f8a8g+ IPwIuvDWm3SXASazWExZQ7lyw6/XB/WvDNb8V69p/jhv7El+025j/eDzMDd7 EZrs5v2qdW03w59j1m2njRBuEcoZyx6Zz6dO1AHzB8UvBv8Awjvj68sI0S4V Bvg/4GFbp0xtFf1W/wDBtVG0H/BNuGJsYh8QTooB4AFpZ9PxzX8vetzWvxt8 fQajbpNDNI/2Uwkbh8sUh3DHbao/Kv6h/wDg2zI/4dy4wFK+JbsEen+j2uP0 xQB/Kv4/8S6XceF/DX2G4VtQtdOsopYyc4Iiww6e4qm+nazqV6z2xyZmU7d2 dvBrjbeEFogvliRkQgEDc3A6V6z8PYUfwn/aksd4PK/d5KZ3qD06e1AC+HPh T4m1LLMIJQqEBWJb5u3P49/WtPw14P1b4aatJFqMKR295hQ6cANycHt07+1O 0zxn4x8W3CDTtOlsrRwf3jphTg4J/lWlr/xC8R+EdIgTxBp8GoW0jbEdOc9e M9BkUAdAPhJ4e14x3yxwx3afvRuHzMeuD69OtUvEfht5TZ6jpSiDVNKmS4VV Xjej7lP1B6GpdJfXoNOa+i0yOS2Vd9tE0hMsfHTPQ544rX0Lx1o/ixPMWUab ewMq3Fu5wynvweeB+HFAH0Z8MP8AgqlaRaNbW3jDRZoL9MRSTRgjd/tenJ+n Wuu8R/8ABQLwP4lsWtLeK4IkLIWZAM5BwPxr5+0WPwwsMYvLm2vJJBkBUyWH PTP+eKZ8RvBXhvUPC8t3oscaNEhnH98EEDgUAS/FPw34e8Uag97b2MKzMAzF RgNkjk/rWh8PvAYk13T1skRJPtCL8w+XINeW+HfGs/hPUotO1F0KT8xB2GJV I5Gf8Otej6T8QbPw5p7XDO8FvBJHlyPuHOA2f89aAOn/AGvvBL+DviEniexS PHlBbuJTkKYyF4/76NVfDvxQtdd0aKXCsyphgPTGf1rOn8dXHihr6O9Zr+G+ lZ43ZSUKED/D9BV34PeAra2W7v8AVVEOj6fEzyTuQFQd8n+tAHM6z4lvbyN1 sLeUjJ27VJz0/wA81xfhb4Ea7488R65LeM0MS2hIjmHE3zAY9sdevavsj4Ce FfC3i3RLm80WeC8iW52qRhy2OOOvHX8q6vxh8MoJrOUW1vDbsDjdgA8gcfyr 0cx+KH+CH/pKJjt8z89fHHww8QfDTRnS50RpxZQ58yIZRsd+PXivD/EviCPx E3n3ALhYiqoOi5PAHv1r9CdduLrwzczafqSJdWy5j/ejOR0wfbOK+RPjv8DN G8N/F7TdN0t3ittaxKIlG77Kd2CMenU15xRX+CHgB08EQ6/pn72/02YyyQle blQeF/75LD8a6weA4rfx34n86FI7OWK21KBiMLhgwxyOMZP5ewqzbeHNZ+Df jDT7XQnjksdUjjnZCMqHC44PbPGPWvSviBZWetfDqbW5Ld7MXsC21zaFeY5I mPy/juX8x6UAfLXgXxzP4K114BIj2rTbXAGflGRuHvjB/AV7hN8Tpm8K6dC3 k3M0tzE1lMBnd84x9Dg1554N+EtpP4d0nXApnNxckuuCcLvRWGMYyoJq9dT2 3gHxjPoV8rtpsJL+anBtZs8EH0x2oA+qde8MWmueEHtpNmZ4sqV6xMSOR7gg fpXnY0ufXfCr2mo2QnmUNGzj7+3n5h+Q/M1B4U+Kv2DTY4fNF/aMq4uUOdy5 6+1bGmeMLU6krBZXWUFi6qeT/n+dAHN6L8PoPBXjK1uWZmsbuJfKVv8AlnMH U5PoMHp9a7j4g6DY2XgDU9Rnnhklv4ThOhAAAKmpte06w8d6Gxs5VDw5eN0b 7r7TtJ+h6ivKPj18Rl1j4Y6d9lmAupJxa3MRyDG2TvIHpkcfUUAeefs/Ti38 USxF5f3uiyBxhXRR5kYyw7DHVhyOcHmv35+FRLf8EUvg0WKkmDxoTt9fL1r8 z79+vev57Ph1fy2nxsiFoVMbQJEoUlSrGWPhfU8ZweDX9CfwpjEf/BEv4JgH Kmy8YMDjHBh1kj8Oaiew4/EM+G/wy0fx58OrNbr4qfCLwTqSx+C9RjsPFd9a NNLBaaPeJI5tblWUo5v1EcmD89vN90oCfcf2Of2GvE1v8BNDn8DfFz4UyeIf CHxCi8UWF5p9tc+NNEswnhGLw++nTMLy0nmlEcjzeeZUZi0bNH85Wvl+x8Bf Fa00nwz8QPhzY65qsngweC76DRLPT7wr4kuodJvG+zrd20TmHEcphk8wrBs1 AeawGwH7g+FWh/GX4w6F8FtY8dadq7+IfAnxJvb7WZTBaWZWxHhvVrNZo1ZY xLDJdXcUYYRrIRKWKRhTswpYudTmoSjpC1vO56WK4ew+EoUc1pVVKeIUlJa+ 57NtJO6S1TvoK/8AwSfTw7eSt4X+IOs2dvp+leHLfQotan1bVobK+0W50qaC 4uLT+0orCWKQaNZLIsFrbTsTOftI8wBO5/Yt/Yl1n9lDx94v1TU/HEPi6w1n S9M0HRbVNJms5NKsbC71S4jEzyXc6SSMdTZdtrFaWkSwIkFrbxhY15nxt4J+ NHxA8HeHdP1mPxPPr9t4i8L6lrIs5dGt/DrLaaj4Xur97UhxfiICHVWjSYln K6gjhlbTwbOkftdXv7OPxWvLv9ofx98P/hZ4O8R+EPD83hez8T+INJ02ZNZQ Xv8Ablt9oM6m9kiL6duljiig2yRiNCfMdutt9X/Wn9fI8tbaGxpn7E2veE/i Brev+HPGuiabc2K6zJ4MS58OTXY0eXWtVg1fVTqJ+2r/AGgkl7bp5KwCyMML PGXlcrKs9j+xLq9n8Y7fxR/wmOmCCK+/4Sx4E0GQXUvik6EdBa+803RjFgbE 5+xCHf53z/atv7qvOPCX7LX/AAmHiH/hL5vDvhD4meEvisdZfxdcacun3Efj nTrzxFYTeHbi7mcql/a2GiNcqFd5MQB4olk8xUeprP7KnxTsNetvH/gLwt4Y 0Tx3JqcFlpo1jULTTdQ0Lwm3hZbb+wn1CG11DbHb61/pggSO4t2nRXwwZnAr 8qX9b/0wfxHZ+Nfh3P8ACXw34T8NXDaAZNE0O3swuhaQdI0qBIzIqRWlmZZv s1uihVjh81wiqAGIFcZf30ell7mUSmO3/eMI42lcgcnaigsxx0ABJ7Cun1rS PEmgeD/C9j4s0TRvDut2Om/ZpLDS9fuNetYI0nnWLbe3FvbzTMYgjM0kSkMz DL43Hn5LQySM277xz9K9XJsRSo1qnt5WTi1e3exhiqcpRXItU7/mZX/C6dE/ 54+I/wDwndQ/+MUf8Lp0T/nj4j/8J3UP/jFan2E+oo+wn1FT/ZWVf9BD/wDA f+AV9axH8iOY8afG281/XvP06+lsbJLe3gjhuPA+rXMq+XCkZJkVowclSwG0 YDY5xk5X/C09Z/6DMX/hv9Y/+O13n2E+oo+wn1FV/ZuV/wDP9/8AgIvrOI/k X3nB/wDC09Z/6DMX/hv9Y/8AjtH/AAtPWf8AoMxf+G/1j/47XefYT6ij7FgE lgABmj+zcr/5/v8A8BD61iP5F95wf/C09Z/6DMX/AIb/AFj/AOO0n/C0tZ/6 DMX/AIb/AFj/AOO11h17TI9MjvpNSsI7KZykVw1wgikIzkBs4J+U8A54PpVn zrcbs3MA2SrC2ZB8rtt2qfQneuB33D1FH9m5X/z/AH/4CH1nEfyL7zi/+Fp6 z/0GYv8Aw3+sf/HaP+Fp6z/0GYv/AA3+sf8Ax2u2ia3uLua3iuYJJ7bb50au C8O4ZXcOoyORnrT5Yo4JIkkmijed/LjVmwZGwTgepwCfoD6Uf2blf/P9/wDg I/rWI/kX3nDf8LT1n/oMxf8Ahv8AWP8A47R/wtPWf+gzF/4b/WP/AI7XefYT 6ihtPIC4OcnB4PHOKP7Nyv8A5/v/AMBD61iP5F95wf8AwtPWf+gzF/4b/WP/ AI7R/wALT1n/AKDMX/hv9Y/+O13n2E+oqB5LeNiDdQApIsL5cAo7FQqH0J3L gdTuHqKP7Nyv/n+//ARfWsR/IvvOJHxS1gdNZi/8N/rH/wAdpf8Ahaes/wDQ Zi/8N/rH/wAdrq9Q1/S9IYLd6pp9qS5j/fXCJhgqsRyeuGU/Rh6itE2BHcfn R/ZuV/8AP9/+Ah9axH8i+84P/haes/8AQZi/8N/rH/x2j/haes/9BmL/AMN/ rH/x2u8+wn1FH2E+oo/s3K/+f7/8BD61iP5F955zq3jS88SR2tvqOsTNZw31 reyJb+A9WikcwXEc4UMzsFyYwCdp4Jrsv+F06J/zx8R/+E7qH/xitT7CfUUf YT6ik8syt/8AMQ//AAH/AIALE4j+RfeZY+M+iN/yy8Rjn/oXdQ/+MV84aZpF 34f8LfsW2GoWtxY39jrcFtc21xG0c1vKngjXVeN1YAqysCCpGQRzX1H9hPqK 5D4j/Bz/AIWF44+HWsnUFtB4B8Qza95Jt/N+3eZpOo6d5W7cPLx9v8zdhs+V tx825eyisBhaFWFGq5OatqrGc3Wqzi5RtY9h/Z1/5GT4r/8AYq6Z/wCjNXr6 iXoK+Xf2df8AkZPiv/2Kumf+jNXr6iXoK+ahuztZ/Mv/AMHQt35f7cPxOdAw 26lptvLt4QhtD07G7HV/mOM9sV+Xuk+BZfiF4knit4/s0UFzKxIAORkgdj2/ yOK/UT/g5h0ubxH+3f8AGqKBTLLpmpeHrpgR9xG0jT0OPUZIJHtX54/s2+JY l8fXdnKvz38hJOSqk7T+fI/8erQk7D4S/BDwvp1uxv5fNvmbDPLIRjHOABwO 3Wu5174IeFdXtjHHqs0N1t4Ccqe+cnI//XXoWm/AzR/HMcVyl1DFOU3PuAUn t1PfgVmeMP2S4LXTmkiha4VfmEgJJRh3DAg0AeY614Qt/BHhiZIrPT7shCIb iLzIpT/vETKh/wC+O/WvKL648SvdIum3P2g7QwRFUyI55wME/wA66T4nXOqe GQNMjvL0MNwYuu8kDoo+vrz1r1/9i74C65JeLqep2ytbyqGVJEIMR45yTzQB 7/8AswarPq3w8skuFf7TGoOHjwSQOnsc11euWk1xIf3f2ZC2SOxBPate5ls/ Afhee9eSC0t7SEySsXyAAOv+fWvz++N/7ZetfErxZdSaTf3VvoSEx7jJ5fng H+E9hxjjOaAPpX4galFp15smuP3u5isfJLDJ4A9cCvKviFpcviP4b6rrs93C ttaN9mtrROpk4+Zj3PA47YrxPw9aeIPi14gg0XRhcRzasFadlJYomedzdq/Q f4IfsdaT4d8GaTZajuvF01AUjJwhfGWdgOSTz1OBxQB+dXw40bUL/wAVwJIJ Ule43BsfcDYUE/iGr6t0yym8ORrDfk3Eexf3kZIYH/aHUflXutt8EPD198aP EdimnxJDBoOlyqE+Uo73GpBmH4Iv5V4b+3X+x/qWsaYNX8P3D3N7YRYkgVjv nQdMDoWAH1r0Mx+OH+CH/pKJjsei/Dia2aVXEyMpYAlXBz19+leh3emy6tpE 0UGI5ih2EHgHHHfmvyg1XxZqsLW9z9p+zahYS7UeSNhJGR/CTnI9Oa+2P2BP 2pLj4oafLpGqYGoadtVm/gkU/wAQHbmvPKMT4k/suPd60NY16Vmljk8xWU/K mOcY9fevCPj147tvCt8dGs4IGtmQh7nG7Z6dgAa/RT4i+BNO8c6a0F8GEcuA cnkj078V5j4h+BHgDwjpoTUYDcRSHb5J78c44oA+JfhR4+1vTvDK7NJfUlBL QPbuFJB7EEEHFer+FNL8d/EhIzJpT6baklFjA82Z1Pckfd6fzr1q++Mngv4T aaU8P6dbwx2ykFnI4A9M9/avIdR/bs8R+I7+SGx0++WEYjEhzGvf2wOlAHC/ tD/Aa5+HGiTXo8+0u5iftSSvu81SMHB7EE+tcb+zk09l8XPB7Sym1tYryGZp WOVjz5ihjjsCvP416f4m8VX3xfjeO/mZSATLvbexOPqOK5f9j/wbZ+LvjXFb 3HmzWNy06swkCh4lWSUpnPdVP5UAfoP/AMF7NFmbxv8ACLWInGdL+BvgKI3E DZUGWbxEOD/dOw/pXw14n0mLTPjBot9GY3Gu6d5bSlgu9kPCDPfCqT9a/QD/ AIKoaV/wn0fwr0C3McL6n+zR8OruBMZZfKvtW+6M9lkI+je9fn38RLK51r4V 6FrttC02o+G5VuZIAm51jICzICD24P8AwAUAfSfwghbxD4WNpFE/7kkM6qCG 6Ed/85rI1n9nXxG2szSW1tI0Nw3AZODyTnFcF4F+Kb6HYQz2TCSG6RWiIbcA CvUfnXoXhn9rfxa80VpaWl1LGr4+SI8/kOaAPXf2cf2YbzwtcjUdUjWG5wV2 424BGMY/GvVvGOo2uk+HNRktblJr63jdkt0k5ZgpK5x7jpj0rxj4t/tfX3gv wdpu7bZajcxB5WY7TFyMZz68dq8J+B/jrxX4y+N0N4st2+jTzIpIyRLkfM5J 4/D/ACACVPiP4p8R6QJ9U1GYXExaaSNgcLlyAAM9AMVjv4zsdPUPLplvqLFD I0irlSccD659a+gfiP8ABPwvY6nLdXHiKysxIxxC21m5PTb1qDQfg14NTTBd JrlhfQqpYxqFDDOT0ycf/roA8I+D37cPh74SeO547iwv4re4UreGOFnVG2kx njPcYr7P8M+JbD4t6BbatoF8t0t4gcEtgyjHGc9K/Pj9qHRvAvhPXPtMpiN4 HIjto5fmdSRncM52j+f419Bf8ExPEoX4KbJLwTwQ3DxKBgmFf7h7nk/lQByH /BTD4x3fw+0ux8LWzXNrcaiEu2eMjlQTjHTuP0rs/wDgnB47k8V/BhrJpJJr jSZCVkY4wJWMmW9yzNn617N8XvgF4L+NBhk1bSoL5rZfLVpgUOM54wenWqH7 J/gDw98M/hveR6dpkdrMdZ1K1m2jaWSDULiKMep2xoq/hXo0P9zq+sP/AG4n 7SNL4lfF3w78HPD73niTU4InkdVUltoIJ4Az/nivl7xD+0T4b+KfiG8uJLxL q0kbakEcnMSgYA4/Onf8FU4ZNVOh+dqtmbB3kCxM4VnYdzjH5GuS/ZK+EHgX Xb2G8F3b3RfYl0RKGETbcEkA+oB59a84o3E8U6PouoltMgtrKVo1K8AFfb69 a6PRfizqdvqVhcR35SVZf3io2d5LD5cemD+leta18DfhUJrcQa7ZQ34XPyuj OOOe4/WqUPwL8OeAIbrX7LU49RNqnnJCxXAPTOMnPJoA9kvfA+jePtCS3vbq 389kVvKMmFVhg8j25/OvnH4rf8E19V8S660mm6httnYuIo5miUDoQcEdcD8K yvhZquqeLPiZdT3utiytZDI4LMBtwOh596+hPjP8Q7zR/A6yeH9VXULqFVWQ W7B2Ve545oA8V8M/seH4KeGhJd6dDfLEwQxqd3zHgknHNeN/GPx7aByf7AWK LTQSY1j4bb0PPvXo1r+0b4tt9buLfUp7ua2jJx5wJyT6g15X8Z/2pvDiQXmn y6c93rTq0bMIsQ7sHjj3IBPagDzv9m3TY7L4j3Et1FG8OliO/ZUHy5ngIA44 yPMIOPQ1/Sb/AMGxmpDVv+Cbk8wIwPFNzERknaUsrJWHP+0DX4DfAbwI2n+D In1LTxpd/rD/AGiS3eMiWNAi7I8EZwMMen8VfvB/watxT2//AATd1+KUyPHF 4+1IQOw++htrI8diNxYfhQB/JzHK9vdRygHEarnI46CvoX9mT4qW9tpMml3k cTCFzJEuQH55xzwR14zXn/xN+E0nhHQ/D2ppbyNa6xotnd+ay/eZ4fmAPb5l YD2HvXA385s5oRGXt5AASVJBGR/XrQB9SeK/2hYJidJ05LeG8lAjXcoBX1PH 0rC/4XBpzWq6N4hiSYqMB0GN56hh3Brwfwmkl1q8PmSOMtuXnnI7fp+le6fC XwDZXmpR6rfssoRjhWbPbv8Ahj8qAOh8EeNLbWj5Wm60B5O1I7aUc8ZIHzD/ AD7VqTab4Z8SXTJ4g0B4rgsGae3wN4/x96sJoHh/Ub2ZEhjgnlwFZHAZTjhh 69uaZouqT6FqhtdThWReQZRgqSOp/GgDNHwq8DrdKtrqGp2SwvuBl3ZGTkd/ atyz+Ciz2U02meMilkRu2yMQ+OcgZ9frVnXr3TJi7wyWzwhMOSgyOOntXDeM PjlpHgLTI1aBJPJ+RbaGQgSMASOnAA6/jQBxfxwi1TRNY0ywt5ZdRktI2aNl GTtypA+oweK6zwb4S8V/HeKTR7S5jk0m2hj/ALQv0Xy4rNMZ/eOSFLDH3Rzx XPXP7TEXiqeG4h8P29ndIwWHYN5dmxn6dhXrP7K37Gfxn/aE1a6tdCW5SHxZ MqDSopWQ3TAEJ8o4PXofWgDnviV8cNF+AlyfDthcTazJYIVEpIHnHPBPUj6c 1zV3+2Pr/ij4Ta5oNppinzY28+4RiBHbuQvTrkFgM16t8ff+CeF58N5tRttS aNr3RTJb6jN56TSRzJtzkjqcnbx0KmvBPgb4xX4HeMPEH2jTrfUy2mTxPaXY zFcB4sqSuQc5dHUjqQKAO5/Yq/aP1b4OXVrFYxXl7bNdBrqMEuCcMDgAdwfx xX6K23xDXx74btL7T9qm4iG+BuJNx7/lXJfsi/Bbw18P/g34INvottNNe2Ed 9dTOPMZpXyxy3PIJxx0r2Dxv4W0y8gtporL7IY8Zlj+TAI4Jx26V6OY/FD/B D/0lEx2PLvHvwvtvE/hqWSeJobt4DjB5J68+9fCvijwHqz/H+zeCaSbUNPDK 0bnBZY8kMM/30DEe+K/RLW9Ju/LjSWRSM7UPQOA2MH6Y/wA5r4J/bX1LUfhL +0bb6lZJ9n8yFZIn2nDle3HavOKPTIPCsXxC8DyThRJeQQ7JiIiJI9uHTj+8 CSDjHAFaXiqzuLX4LyX95DGbmKQsvVUmZYvvnjk8n8RXEfA/9rXwvqnim1Oq 6dd6de3cuy68iPdbysVI3kDp+FfSPizwD4f8V+Fre3ttRgGnLvdVYbgjMp6j 156HHY9qAPnX4W+Cx4f8Ew2U8YuZtPuIpFKR7Q4m8tyCOnBYgepB+tV/Ffws g1VL5bm3TGq3r7nfgJFkc5+g/X2r13xX4s0r4YXIgh0eS8Zihkn2bwdoQKwA 7Yxx7VwXibXW+MJu5bXUBYszsktuVCqybQXG0/dIGcfSgDxfSriPwN4mfSy0 bWMFwU5bIdAcgZ/TivUNI1ezHhye50wxrLIMrEzZ2sSR/L+deH+JPC13a+IJ rW3k84RuUidh17A/yrpPC+nTeGtD+1NdsfKhEsSY27nxyp9wcZ+lAHfaV4pt vhVbapcy3OyS8k8wR8bQAmSwHbkV4b/wk0ereJpdQkCkXjySBWJKL82dwHQd q5myutW8bG7l1G5kkMs7KAAdqdRx+GKheNtTW00eIHzRIqJjhnB4579z+VAF n4b6qbb4y+dh1hSWNGYAsgXzVyD6jjtzX9FHwY1Aap/wQ++B869HsvGPfPSL WR/SvxB8XfAnTf2b/gzp2o37rc6/4qmZYFCZ8hAjEHnn7xXnNfth+znbm0/4 IQfAWNiSyad4wByMf8staqJ7Dj8R0vwg/ab8bfCD4V2GkeHvEs2g6fPN4KiY wTaVaSoLvQ9QFwyzahE8JY/YLVgj43eTsRkMjMfobwH/AMFMPH/xr+GfgC88 D+BPC0vizX/HieD7/SvEusXWkW0sTeE5vEIl85LWaazm2eQDE9tOVIkiJG4T p8xfBv4t+CdKHhXwH4l+HPhDxbfeKx4Nhs7vVvFV5YSG6k0q4S0/0aC0nIih fzka4U8PqMQkUKocfV/gD4R/ATSfg54P8DeK/hf8JfEHhr4jeODaafaW+qR+ OdJuNVh0OadL66udQiRjcta6fJDv2ySEtESx81ytRxNKdNUoNc0Xr87WM/7G x2HrvH4hP2VaK9ne9nytqTS233t216FHx5/wVb1fxPpSf8Ky8A3V1cajF4Pu NJufFlhrOl6dePrWpaRDJazXiWElnE6WurwFWhubmVZVm32xSD9763+xn+24 P2s9f8T6fL4cXQH0mzste0x4r2W6TUtGvri/t7O5ZpLeACV3026J+zm5tGjM MlveXUcm8P8AjR4h+AMmuavL4k0z4a+KNd8Utp3gzWbc2VhqOo6hb3N7p8MF rdIQZJLWOXWtPlZJMoiX0MmP3qbuy+Dvw4+FfgHxp4ou/AGh/D7RfEPi3yPE PiKfw/Y2dte619okuDBe3bQqHm8yRbspLJnewmwSd9PTt/Wn9fMq+h5vo/7W 3iXxl8RPDfh+y03S9COtah4mSS61O3lmSODQ/FVhpDIFEkf7y8trmZ4nJwkh ibZKoKPTt/20vE/hvWtK0bVfDqeINQbxvD4GnudFsZYYtWlbwkNbN7axvJII oJLkiAeZKyRIS0k2EZxV1r4ifCTxb4v+LFhH8EYvFep+MoHtNRaLRtCmk+Lg 0q4TTLq3/e3CtMNPuZo7RzqYt40MmYmeLdIIPE/7Unwh+DHxC1PxfL8Lm07W vCPg6XRZPEsGn6Lb3dr9m0466fCKStcpcJMtkhvPLKrYKF5uBINoFbl/X+u2 wP4jX+M8uqzS6CddudPvdaTSoo7+4sbV7S1uJ1eUSPFC8krRIzAlUaSQqCAX bG48M86ISC6ZBxjcPQVt618ZLX9oPwh4W8aWGjXmg6f4i0pby0tbm/02/doW mn8uYT6ddXVpIkq4kVop3BWRc4bKjN8NaJF4g157eZpERba4nBQgHdHA8i9Q eMoAfY135fhIYipP2j5VFOWmu1jOvVcUnFXu7FX7TH/fT/voUfaY/wC+n/fQ qf8AsOL+/L+Y/wAKyPDniTw54x1e90/SNe0zVb7TVDXdtZ30U81qPPnt8yIp JT9/a3UXzAfPbTL96NwKvlf/AD8l/wCA/wDBDlxP8q+80PtMf99P++hR9pj/ AL6f99CpBpMBk8sSsZdu4IGGSM4yOPX+dOXRYj1aUEdRkf4UXyv/AJ+y/wDA f+CFsT/KvvIftMf99P8AvoVFdrBfW7xSiGWKVSjxuQVcEYwR3B9K5n4peJ7j wP4p8BWVqkMsPifXpNLujMCWjjXTL+7DJgjDeZaxjJyNpYYyQR3d34XgttG0 m4V5i9/bNNIMrgMJ5Y/l46YjHHPJNdbweAWG+te0ly3t8PW1+5l7Wt7T2XKr 77nKx6XfX19Fd3mpyWsschb7NaSo8BjIj+RiyAvzGW34VgJWUHAzUWneDbLR 7+Rooraa3mvZNQZriVXYXDknzApTgphFTDLwzk5IBbbtn0y91CW1g1CGa4h+ /EkytInzOvKjkcxyD6o3oas/2NCuN0jpk7RuIGSenauT/hM/5+S/8B/4JrbE /wAq+85fXtP17UIJha32lxF2V4t8jokJjmZ4+g3OJF8tJBuXbhiN2cVZ1jQD M1rPZzubqwiEMKveFBIvmwuwkk2uxyIVBPO4FgeuRpSXekxa7baU2pWq6reQ S3UFkbmMXEsMLRJNIsf3iiNPCGIGFMsecblzefRIYzgySZ/D/Cj/AITN/aS/ 8B/4IWxP8q+8502+qRwKzyWdzPOomnjN5LDGk4EICxMoYrF8kpIIO4sAeGNF 5pV3BrBv7CW3ik893eB7oiG/V4o4yX+RijoYxt2g8Kf+ejY5H4jfFbUPB3wC 8eeLLWCyfUfDFlrc9pHMrmB2snuRFvAYMQfJXdhhnJxjtD4c0L4keMdJtdQ0 j4mfC3VbC9gjube5tPB9zPDPDIoeORHTVyrIykMrA4IORnjPXicFgKEYSqVJ Wmrr3en3mVOpXm2oxWmm51Vn4a1O20eyT+2pDfQAwzSpMWWePcgEgEu8LLsj UngjLSjgv5ge3gqC70eyjumgur3TLsXcTGfZ9seNWjhad8Fi2zy97Y+8vAK/ LXPP4A+LEbhD49+HfmHJ2/8ACD3mccc/8hbpyPzp7fDr4sg4Hj34dHHB/wCK HvOP/KtXL/wmf8/Zf+A/8E1tif5V950Ufh5oo7tUTT4ZJEby5oJBDcXEjMfN aSRE+XzFSHcyLuB3Y+6hrYsI00+3aM3Dy5lkkzLIGYbnZtufRd20DsFArkvD vwm+K2taqIJfiD8PoIxFLM7p4Du5GCxxtIcKdXUEnbjqOtZvgDxHqWreIdMl g8feDfHvhrWtMvbq3vdE8PtYJ5tvc20Pyzf2jdpKuZZVYbVIaMcnBFaUaWW1 Zxpwqyu2l8Pf5kTliIpycVp5no32mP8Avp/30KPtMf8AfT/voV5/4mk8X+Iv Hs+leG9f8LaDDY2VvcynVdDm1J7hppLhcJ5d5bhFQQEnIbO7tjnkb/xvr+le ELrxDdfHH4G23h6xFu1zqkvh+RLO2E6QvAXlOsbFEi3FuyZI3CeIjO9c6YrC 5fh6ro1Kkrr+7/wQpzxE4KcYqz8z277TH/fT/voUfaY/76f99CvPf+Fd/Fr/ AKHz4d/+EPef/Laj/hXfxa/6Hz4d/wDhD3n/AMtq5r5X/wA/Zf8AgP8AwS7Y n+VfeehfaY/76f8AfQqnqPinTNHvdOtbvUbC1utXuDaWEM1wkcl9MIpJjFEp OXcRQyyFVyQkTt0Ukee6qviXwdqOlaf4m+M3wv0HVNen+z6Xaz+CpUk1J8ou yFZNcRpH3Oi7UUnLp3YCuC8d6xf+JPFP7Mt/qk9pdai3xS8SWE81rataQTfY tM8YWCOsLSStHujtUcqZZMMzYYjFWqOBqRl7Co20m9Vbb5kuVaLXOlZvufXH 7Ov/ACMnxX/7FXTP/Rmr19RL0FfLv7Ov/IyfFf8A7FXTP/Rmr19RL0FePDdn Sz+a7/g4Fzrn/BTP9oXSYDi6lsNIljOM4KaNYMeO5xtr8om1eXwx4ms9Qjl/ e74yinhUGVbH6iv1V/4LU3Dn/guP8YVWQ7ln0iPBcFQkvhvTsnb6DyWJr8s/ ilaRw+JpVjuIrzEzBZYo2RTtc8YIGeCv5H0rQk+hNL+I2r6JHHiBjG/3WWTd x1zgV7h8I/i+99APMTcrnaqyMcKK+dfA/imDVvDFlLP8zyIqhW42lQAR1z2F dVpet6ldYg0iykk3cFwSATigD6a1X4bfD/VJYb3UksYrhyHLM+NpP1r0HwZe 6A2mGHSp7Eww/KPIwVPXsD/OvjCz+GvjXxA7ebHdCM5BIiOV9MEn9a+hf2ef hhqngrT/APTASOCA3X8TQBZ/bI0yfVvgZrttaysrfZGbKjkgHn+dfmL4G02f UdegtLO3S6upJFiiidS3zY5wOh55/Cv1V/aN8MXeu/CzV7eyRpby5tNqLGCu 4nqK8s/Y7/YSg+HWlp4g16JJNaeYSqr4ItR8wA9z82c+woA7H9i79mq1+C/h JLvUMT6/fxh7y4KjKDsg+gx+Oa9K+I37RPhj4OJE+salIuVIWGKMNIe5PX09 wKn8WNNZaQLdZAmRglQcj6Gvhn9qq+g12S+hEc5nBMYmcZI9OeuKAPofQ/26 fh+3xi1rVbm81GyttS0jTrO3d7Y5d4pr5nyBnjbPHz7n0r2DR/id4V8cRGbS 9Uhu4ZBnDHBA+h5r8pfhXod/rfia2a8jmYW8r8SqcoMKFxnkjhjn619hfDj4 jaT4CuLSwntw9y4Ad1IGwdK9HMfih/gh/wCkomO3zNv9rP8AYC0r40W1xrmg 28Vjr2z97sAEV3jnLKP4vcV5B+xX+zv40+E3xpvo9X0Wa0tpLdlEpIKNggjm vp6L9qXSNC1qHTgkk6SgL5gYYA9B644NeoWep2HiexS8tpI2ZkADd8Ee1ecU ZuvatFo2hRzzbQwTJDDG/A/pXyD+0l8UIfGd1JHp7NBJBhEcucDqN1fYPi7w yuqaL5E2ZISu2Qk8f4V8y/Gv9jK6162mfRrgxscujqvKE/z6UAeJ6d4SsDaR yale/aWC8hlJUnuSKwpvHGivdtZ6VHJcupwWjA2bvY+mBWL8QPCXjD4epPpm qQrqFp/qzPCh85R05X6VY+H2iaXa2sYiR7dmI3mVCvP4D+ZFAFPX/DU3gzTb 7V5Lli1zhwobgHnOR+X512n7FtivgrRvFHiVjHFHaaRcqhZSJYppQsUbD/ZK m5GR6VzvxquLG20hNNlmeO6ZDKiowk3nOAfYVv8AgLT5fCP7PurLOS76zdQ6 faZ+V22MzuoGOcTSSjgYoA/QH9sFoT+0t+zStwUFrcfsmeDIncrvKf6TeuCP xTH418H+B9Uy08U8bRpqML+Xhjm4AlKuMZwDgt+Qr7Z/b91QaT+1R+y8rCOS GT9lXwehX+EkT35Bz6cH86+OfBM9jr/hfW4XtHe+8Na7JO0gGGihkO11x3UE Kfbn1NAHnXw+8baD+z9471vQvE80n9nWkrNa+VGWwrHcAqjr1r3f4W/t3+DL u1lttKtBA1ofLQTIA1wvrkdPpXyT+2HcQy/FQwWyBvItokb++eDgnH+yBXlm ia0+gaxbXkSgPayBlBHBx1BoA/QD496la/HK2UwxK+7YHiVwu3ByM1b+AHi6 3+HNpFCoyIo0jbJyyFODgfQfpXzp4T+M7ajYAh7OK7kG943m2bWz0znp/hVf 4W/HeWHxnepq13ClndOUjROuV459unegD6T+K3xU0/W/Edxd3Erx2ZbjcuDu 9q8z0T9piLSNNu1itbu62SblWH5go3EAMffFbfwo/Z08VftefEPUNF8PTW1o dJtUuZZ9RcwwRxNkKQ2053EEDA5612Hxa/4JdfFr9nvwVbXds/hvXrOXe99J p7B3VGPDCNjk9CO34UAeC/tIWmifEfUk1Wytb2C6uY1e6a6hKxscL8iYzwD6 8/Sr/wCw5+0pF8DPHZ0+/VP7J1FlRg5P7ll4DfiOD9K1NS0q9u/CU+mXc0TS tIGsBsVJAylQ3BPdc5GfSuJ8f/BW11uGDVNNZbW6vbePMFxIIV8/bh1Uk+uR QB+lNp+0p4E0LR4r3UNfsktp13MokwcknAGKwfg18bPC+o+AL+S01a2Yxa3q 1zjcMyxy6jcyxnJ9UdTnHevzFkj1/wACMbHVtOuJkX7sbAsDz1B6Gp7K21TU LJ0sba4tw4TbKZRHhSi5TccdDn6HPrXoUP8Ac6vrD/24n7SO8/bT+J1z+0P8 Wpbezd/sdjK6WkStjgfeJyOar/BbwtpnwpskvEh1u88QsvlGOBDJBzzgkccV i6B8CNTbw9dtPMl5c37KYpI7kfu1HLZPXrx+HSux8T6vJ8OvsmmMrs9vCpa4 Em9OgA3HOCc+np0rzyjuPh/8RrK9v7q31ETtOH85Y5mClVIxw3UfSvV/D/jy 01y5e2MzRWrhrdjvwCMcfiK8c8O/CvRdd8MtrfifULu3kn2lGthvuJATycng DJI6dK6zxF4Y0fw58Fv+Eo8GeIRd6npM0VvfWmqSId8ErBVuE2jkqxAYdhk9 qAIri5tPCfiedZ7vyreXOHEgYtj/AOv61d+FPxzh8KeKLuW2uY72xJ+zhJXD OcAE4H+elfJHxT8f6r44uLhL3WkufKkO77Ovl/Nu6AdwKq/CW7vvhvJ/bc+k 3uoWvl7oTGD5ZPVQxx0Oe350AfX3jL9pzRPB2rXOsaxpO6BwFiwRzvJ5zz0B xXyr4KupvjH+0E11NJst9S1Oa5GAGCJI7vj34/CuN+KfxY1P4ma39o1DEcS8 RwoNsSDsAPX3612/7GWqWuifFu0vLy3mkt7OJ5SR0GVIHXt8xoA+i/HeuayN Hu/E7zraxC/bSrBzIA84jWN5XI7cyqvHFfvp/wAGx08Z/wCCfevW9vHJHZ2n jS4S38xsswbTdNkc/jI8lfzz/tLaBdeKYfBfhrQx9suzBJfz6fCQ5gkkRWkV scEgqAee1f0e/wDBvL4aTwd+xHrulLsLWHi2aKQqQct/Z2nk5xxnkUAfhzc/ s3w/Gb/gmVba7ss5p/D3hKxu4vLyJMQ2kBJY+gZ2Uj61+ZVzNNqF40xUMWYA 5Xp26H2x+VfqF/wSj8Uah43/AGdPFHhjU3aW0uPAmsLDGwJSSKOFNgU/89F2 ZAOB8melfmnPp6pqgeUHyvvABtrMPb8aANfwvpT+G5bPUHAWY4MW6XlfQ4A4 79/TivUfHHxf0LRdUFhpFhIkL2yST3EAKmWRl+Y5Ocg5rk/Cfhiw8QKt1qdx cRbYy+I1DN8o4A46Y/ya6WHw3BrXh/7RHZrFZHcImbAkdV9fXJJPGKAOQ1D4 5Rs8Qg04rLGMfOW+fGOwPHfoe9Sp+0pqD2dxCbCGTzlJblgYn6bh3/Wmz2S6 pqcIXSI45yVjjaKTDQ4bqR3NYfivwnLpuoXCyrcx+ex+RgApzg9Ov/6qAMm8 8e69qrTSR3lzFC2coGOwcdBnt+Nc3PcSTEmRizEkEHrn1ru/BOj2tzeNAipL lV4LDAYqdw6eoOPrXOePYra08U3UVqvlRKxGBg4I46jtQBRttSextI9hcSI2 9TnhSCCDj6jvX0h4E/4KrfEzwL4WWx0m8tNN1FE8qPUraLy7hAV2krjgNjPz Dn+dfMsqqUHLM3Qe31pN3lx52gkjr6UAev8A/DXPjWy8J3Ol/wBt3EqXEkkk rO3mSTF2DMS2M5zms/UVj8R2Wk61DcPcTSQCxvGLHKDG1WI64UY79q82lk+0 2uQDuz82Onbuea6f4a6pLp97JZygyWlyCGG/t0yvv0oA/TX/AIJofExPFn7M 1vpjX5uNX8MXEsTRyt85hJ3jAPbAfivpl7tfEujTJFGCjrscbxlcdOg96/Gv 4b/EXXvgb43t9T8P3ssUqSLcRuuPKu1XcvzKfYsCPc1+iHwA/bf8M/GOyhS6 lTw74iiRhc2U0oWORgB88Z6FTn+VejmPxQ/wQ/8ASUTHb5nqviu8a1sVWRD5 6spTHOeV/wD1180f8FKfhCfHPwZg8QaaolvNAlzKUB3+U5AOfoQP++q+l38V 6drchFs6TfZ8FXbuSAeuO39K4fx5oUl/4M8Q2FyoIvbeVCW+7naCG/RT+Fec UfI/7PFrpdj4NiMul+ddsFBmWLcxZQc/iM9q9i8DC8vLKW1eUpFfMylSwyrD 7jY7VxnwZkgm8IQPb/u2gJWWMYGHXgj1PQdq6Vhc6RcvdlWMcSt5eDyxIbtx z0PtQBvaJ4iN3JcaXqUUQlgLxyB8qehwy+xwOnTFeZfE7wjDZb9U0a6SK4Ct Iih/+PiPG0g5PLZxg9+tXfH3jBJNWtru5l8u5t0ViR95lAUnPqQdwz7CuG1n 4w2Piq4s/D+nq91eajdFISFyyqSpOT+BoAi0vQptW0xkVVaTYC4cDcjrlgAe vXGayfi9rNt4a0WaZhugvlM6xq3+rlxzx7irmva1J4U165muZo4WtnLeWTta bA6ivJdZ1t/H3iGd7yVk09CXVcgKueeTQA/RvEtrpXg62Nyifap5SSQo+YnJ XOa6H9nvwfa+KfihHcvFmUt5SqBlQc8lfyx+Neealp03jXWUWMPa6bakRwDB Vp+xZfU//Wr6c/Yu+F0uneM31S6xFFahCqFT8pJyOP8APSgDZ/bBsk8eftG6 N4ftzGtj4X02Iuhxje8gyOfYY454+tfsX8PbRLH/AIIsfBWKNgyrY+LuQc5z b6wT+tfhN4v8ZS63+0H431I3YnjjuQiHfhwEz8ue9fuT8E5Wn/4IcfAx2JZn 0/xe2T15h1monsOPxF/wl+yB8PPj/wDs9f2p4u+KHg74c3y23hS0t9R1KZbb UNEEWjSuk1rOLuAxzTG4kaMuGTdpzEpLtPl/dHwO/YJ0b4P+EfCGmr4l1jUI /Aviq68U6NIqxpsabS7rTBbylxI0qJBeSsHZvMZ1Qs7YYN8E/DT4e+OPEHw9 0/V/CXhjxjrX2afwG5l0KX7LIFh0XVVmHnNDKjKv2mASxlGylwqtgSA13Xws +DHxf+JP7LXgZfF/gn41eLbfwb8T4dTWw0nX38HeJJtIbwU1q1zZyXOo2U1n B/at5L/o32qN0iMkRQqrREWFo04e2j8U9/lohPPsfiqv9mV2/Y0FeF+jndyS +aXoz7ST9iHQrTwPoPhSDxL4rg8HeFbzRrzSdDQ2P2aw/sm40m4solkNt57I kmlZO6VmYahdgtgWwt+bl/Zx8Y/s1fES48W/CXw74b+IHiHxV4d0bw34mvPG /jOXQp7pNHW4WzvHmtdHvJLy8mW9mWaSZlAEEAQAbgPBvjV+yz+0Z8Tv7Z0/ xpeaj420yLRvBl5cw+GbuXQm1e80zVdJvdQSwuv7agS3vHe11N45Bp9lkXVq rXuIVSL2D/gnb4R+M3gr4g/Eq3+J9h4mtNIuPsdxY3Osa9FqsWp6o13qZv7i yxe3MkNmbf8AspUTy9PhLJK8Wm2O94Rq9rt/1p/XyDoeh+FP2T9Oit/C+tSr ceEfFelvfXtwmiagt9b202p6taazq1qk1xbqZree7tfLMjQxSGKR9ghdlKWd K/Y/8O6QLK8Gqa7ceIbLVh4iOuyNbfbbnVhoR0M37osItzK1meUEIi3gERgf LXjJ+DvxDtPiZfXmueB9c8caF4Bude1W2ge90vy/Gs+oeJbDW9KOnrLdDbNp UNn5O6+FoBMsXku8eZVz9S/Ze8bat4hPhGPwlrGl/D2S4fxCsFnd6ZFpunaU 3g5tAXwrDH5zt9oS8b7Subb7CI+fOMn7oyr8i1t/w/67/wDBE/if9f1/Xmel /tE6KdD8VwRm5mummie4aSVUUjzJ5nCAIqrtUMFBxuIUFizFmPB+HNci8O6z cXUyyyBbG7RI4wC8rtbSIiLkgZZmUAkgAnkgZI6T4lXur6tBo99rljrmk6hq Fo92+m6y1k9/payXE7paytZO9uxiVhGDG8nyou6SRt0jeU/FPVtR0fw8W0iW zg1G5u7Wzglu7Z7mCJp7iKHe8aPGzhfMztDrnGMivayJQdSt7T4eSV+9tLnN i78seXe6Lf8AwtW9/wChM8Uf9/dP/wDkqvFNW/Z7Gp/C/wAa+FY7n4z6fa+M dSv9Shu7O70CK88OG9vJby4jsJMER5mmlKyyiWaPeCkiskbJ6LD8P/ivOMp4 ++HTKc/Mvge8I49/7W9QR+FNi8B/Fe5VjF4++HEm07W2+CbwhW9D/wATaudU slWqnU+5f5G/PjLbI5eD4ZCw1FLiw074i6Qi+I5/EbW2nJoFtbymaF45bWRV OXhkeR5ndibhpWz5wVVRei+BKS/A74SaJ4WtvCXiadNKhIeRZ7Xy3ld2klMY nv5pVjMjvsR5ZCi7V3ttybJ+Hfxa/wCh8+Hf/hD3n/y2pl14R+I+g2k99rPx J+HGmaTZQyXF5eP4HuQlrEilmkcvrCKFABJYsAACabhkr055/cv8hOWMve0S L4hXWsfEbx78OZYPC+s2Fn4e8QzalfXN3PZ7IojpOo2y4WOd3YmW4iGAp4JP QGvR/F/xFOm6XoVpaaHrGsG3sHSaS0a2RIZDczPsPmzRknayn5QRhhznIHnf wy8W3uu+PbG2Xx14R8feHNZ8L6nrVrfaFobafGs1nqOl2m0SjULxJkIvpgQu wq8I5PzLXq914Wgt9H0m4WSbff2zTyDIwGE8sfHHTEYP1Jr1XTyv+zOXmn7P n30ve35WORPEfWNlzWPE9C8GWui63qU7+GPGl/ptzqkmtadpVymhm10C8mM7 3U1q6us++eW5nkdpZZCrSsIzGh2Vj6J8MNR0tUa4vvi5qFwmkjTPtEkXhe2m aRLt7mC7LW8URMsW8xLHn7O0efMhkdndve/7Di/vy/mP8K8E8X/8FGfgh4B1 rV9P1jxdqdhc6E18L7zPDupmK3WzuDazymQWxQw/agbZJgTHLODDGzygpXlR p5K3ZTqfcv8AI63LGW1S/rcPiL8CPCnxD8bz+JT8PPEGj+INQmSXU9QsLXQv tGtqsP2byb1pXk+1Qm0ae28uXcqx3MhULIkMkUf7HXw41X9ljwHrOl3un+KP FN/rmsnU5rkW+n2UUEKWttZWlpGhvpZCkFnZWsPmSyySyGNnd2ZjXtfhLWtD 8e6bLe6Hq9jrFlDcTWb3FjdxXMaTwyNFNEWTIDxyIyMucqykEAgitT+w4v78 v5j/AAo5MlWnPP7l/kDljHrZHlHiT4Zan8TP2efGfhZRBo2p+L7XWrW3OoSZ itGvZLnyjM0PmfKFlUtsDkDOASMU/wCDn/BPH4Zfs/8AhjSNB8IeJLTRPD1r HINZ0uLT43tfFcr2FvYtcXoewZllKWsUjtatAJZNzSB97A9f4K1iTxJbzSTq ieXqV5ZYTIykN3LCp5J5KoM+5PTpXS/2HF/fl/Mf4V7GZxyz2dD28ppcita2 3nocmGeJvP2aW+vqcpefsv6AfETanp/xDbR7iTVrbUZRaabbkTW8MQjGnu0m nM5tm+did3noZXEc0akKOk0T4Y2Hh2azjsvH0dvplhOjQabHZBLWO2W0Futm ALIOIQ4E4w3meZxv8r91WX4q8X+F/Al5ZQa54h0rR5dSuYbK0W+vYrb7VPM6 xxRR7yN7vIyoqrklmUAZIB5a6/aT+HWmWmivf+Jk0qfxFfrpumWOoQTWeoX8 zXkNmDHaSxrcNGZrm3/eiMx7J4pd3lOrnyPZ5I9Oap9y9Ox1uWNvqlf+v+Ae g2Gl6l8PNIuLzTfGWneMdVTSotNhtdcmk022mk2yxy3ck1rprMksiy7mVYjG WhjVEhDOx4/StJ8Xar43h1XxCngGytbDTLq1hi0bXNQ1C4uJJZrRhlZ9NtkV VWByT5hPIAFdH4U1HRfHnhmw1rQ9Ws9Z0jVIVubO+sbmO4truJgCskciZV1Y HIZSQR3qj41vW8OX+gQwgOurag1nKX5KKLW4myuMc7olHOeCfqOnBRyZYin7 KU+a6te299OhjWeLdOTla1ig/wABPC/xJ8aDWPFcfha9i017C80iHVLF7qWw vrVrspeRfuXWORRdfu5FYOp3dOCcD4uf8E//AIXfHDwTe6V4i1jSr2+uLa+t LXXH01JdV0iK8+SZbSaSxYQ/uNluGC7vJijUklQ1eieI20/wn8LpPEl8dRaC ze7e4SztZLyYxQxRSfu4IkeWWQ72ASNWdjtCqScHzDwF+1/8MPihrk2m+H/E Go6reWTyxaikOi3xGiSx26XDw3zGDbZShXKeXcmNzNFPAAZoJY06M0jlTxdR 1pTU+trW6eQsM8V7GKgly/8AB/zPcv7P0z/oY9I/79Xf/wAZo/s/TP8AoY9I /wC/V3/8Zrzbwx8VvCni+PTfsmo3sM+reULe1v7Gewuw0onKI8E8aSxyYtrj KOquvlNuArnPBH7X/wAGviP4c0rVtG+KHgq8s9burewsgdat4ppbudFeG0MT sJEuWV1xAyiXLAFQeK872WSfzVPw/wAjfmxvaJ13j228UyeLYW0vS/hRrlhp Uq3OmXuq+INTtLuKQwtG8gjXSJhE22WaMFZSSjHOA5WvF/iV4UvvAviv9l/S tWuNIuNWT4m+ItQu/wCy5p57SFr3TPGN+sSSTQwyPtjuo1LGJQSDgYr1CX9o P4ap4/0bwonjvw1ceJfEF1c2en6XBqkE11cy2/2kXAEaEsBE1ndI7MAFkgeM kONteQ/FvxjdeIv2qvhjpc6W6weFfi/Nplo0akPJG/gDU7smTJILeZdSDIAG 0KMZBJ6MPDLFCo8K5OXK99rGNR4i8fa2tfofX/7Ov/IyfFf/ALFXTP8A0Zq9 fUS9BXy7+zr/AMjJ8V/+xV0z/wBGavX1EvQV8/DdnYz+Wj/gvh4gOlf8F4fj fEZI40mTQ1BzhlddA01xk54G1WH/AAKvif8AaS8K7tYF/A8kMOpWy3touRtc 4xIB7HP6V9N/8HIGqTaX/wAF8PjdLDkSRv4feM54DLoGmMM+3GDXjXjTRo/i B8F1u0TZcaMTcWhl+VZYm++ucHqeOeK0JOe/ZR0TTvEGnzfbZbieWKYOULH9 wgB5C++a9rtviHdpejTvCdottDC2x5SoDt65J6V8l6NcR+HPGtjNDqk8NtfS eRcvbymMxqxHO4cdDuPpivpPQvDGj2VlGv8AwkTxR7dqiRmeRge5Pf8AGgD6 N+FnxCuNNtLazuryC7vHGW2SAhDnpkf/AF69c0q9Opyh5TGMfMitxk18caL4 90HwC5i0i7k1PU2GCzJgLjuAa6H4QfEPxNcePUudU1OSO0MoVYmXLFSecnp+ VAH114hv0aGEvAoBTDNnHQc5rxf4sftY2/hSa507SVjmaNCjyZzsYHp7969R 1m2Hirw08UJHmGMorZ5UnHOa+K/jP+z3cHxu0rXZgKHkbyc8/wCelAHWeIv2 kda1yK2c3aR3G7G0fLv9sVg/GXQJde8L/wBrNs33UDK2zjY/UHjp2rzDxj8M JfEd/HZ2N9JHc2pMiyxuSYyPf8uDV7wN8TbzRtDvNI1aW5u2hOx2c5UMM/Nj jk0AZvg3XRPrskj7Y5/s8AAAz83zFv1/Guug8Ha943vhdWmyNIgS5lRiWzz0 BGBVH4S6np8Gt6re/ZgU8iFgvlhmB3zA8duMflXdeEf2otJTXBZSRy2+G27H jKlv09PSvRzH4of4If8ApKJjt8zybxF8FvEevayb231u6gm01gyopZIwQenX v05Br6u/ZG8d3MumHTbueR7mKPoZMnjGc5A9q3vB2jaJ4x8Oz3NtZ75pRgoA fmOMfSrvwe+C02l+Kp9VaDZAoMYXpuB7/hXnFHovirUzp+hs6SmQpFuIA68f rXyB49+OHiTw74nkniu7wW8khUovHGewr7C8eeG73VtDmt9Pby2MWIwp4LfX FfG3xK/Z8+IE2rSTtb26RIc7yrO2MnGD/wDqoA6jwv8AGfw/8ULJovEdnBeu q7DOibJom9cDp+dct420Lw/4fnea1hVrfBcszYVFHUn6DJ/CvLPE3gfU/AWr NdpqDw3rgnBG6JyOuRXm/jP4yeIfGELaLJNb2sZxFdGFfvcgkA9umMd+RQAz Xtal+IPxAvdUhQvb2yCHT4tu7zGBwi4yBlmwPYmvUPiDfN4ZtNH8P3VyNRGg QyXtzdhArSOVLdATzu9+a5z4R6fD4Vvp9XukT7BokTXCoysvmy7CEGSACVzu IGTuUVy/xQ8XGDwdLdS7kvdZfc6FsYRW4Bwem/b9RmgD9gf2kv8Agmj8e/2p dN/ZV+IXw1+GWpeOPCNr+zV4M8Pz31lrmj2jxXsSXU7xmO9vIHI8u4hbcoKn fjOQQPnTTf8Agg7+3DpXxKutVh+A2oR6fqLPHewp4w8N77qJhhsg6hjcevX8 a/oQ/wCCUXP/AAS2/Zr5z/xavwv/AOmi1r36gD+Ub4hf8G2f7YvjjxTPfL8C tZhjOEjLeLPDRJQDADKNSAH5mtXQP+Da79qDTEge8/Z31/UJYFwIm8V+GY43 PfJGp5wfpX9UlFAH8zkH/BBL9on+zUh/4ZN1W0IADLD4z8Msh/PUQa8u8d/8 GyP7Ut/qzzaF8CvFkUEwZzBdeKvDC+S5z91l1QkjPrX9WlFAH8xv7M//AARX /bt/Z6gvHj+BWrT3N8iRyBPGHhvyiqZ2jB1EHAJP516Rcf8ABOv/AIKF3k08 s3wa8RTNPEYij+MfDTIBzjrqOeP6V/RVRQB/Ltqf/BBr9tjVtIkU/AnVRfyX L3Auf+Eq8NFk3dQM6l068YrOv/8Ag3w/bH1C+jmuv2e9S1F1iQSed4v8NoDI OrADUTtBr+pqigD+U28/4N4v25LqRFX4G6jBbKSRAPGPh2RASeCM6iMVc8P/ APBu9+2Rpl0s91+z7qd/JE4eMP4y8OBF6dR/aPPI/U1/VNRWsa0o05Uls7fh f/MVtbn8wlv/AMEJP2y38P3ETfs+alZ6hcTFy8Pivw3JFGuQTtB1NTk4HHQc 1N4g/wCCE/7YupeGoNNT9nvV3SCZZC8ni3wyrSkAjccakcHk8cjmv6daKyGf y96h/wAEOP257zT1tf8AhQEzQxAbFk8WeHGGR3I/tL09K4rxT/wbu/tz61HK kHwKvIjdR4mC+M/DsSJnsqjUCDj1OPpX9XNFAH8xnwe/4N3/ANoPwd4Yg/t/ 9mnxTq2sLnzBF4o8Krbk9N246oGJx6iu+sP+CVf7ZuhvJa2P7LcsGmBTEif8 Jp4b3hDxk/6fjOPSv6NaKAP5afiX/wAG4n7UXxF8Sz6j/wAM+eJtNNy5keOH xV4XYZP11MfyFW/hf/wb0ftYfDuDWpf+GfNeuLy4hSHTz/wlPhgRqNwLNJjU 8525xjoa/qLooA/mE8Gf8EFv2xvA1xc3lt8DvFIv7mVmedfF3hmORkJzguNT 7HjGOQeoxX7P/wDBBn9mr4mfstfsm+KtA+KnhLUPBuvX3jGfULSyvdRsL+WW 0OnafCsxks554xukhmGGcN8uSACCftyigD+c34Pf8Eh/2ufgj+zTceFtG/Z0 8Rr4nu/D0mitff8ACWeGVgiecKLiRSNU3cqZFHyjIbnAJFfMcf8AwbVftsXO vxzXXwGvTYx5XyU8Y+HCSvOOuoe/6fjX9Z1FAH8uPhf/AIN+f2u/DcEZX9nH U5JA2CjeMvDRjYYAOf8AiY5J49u1WL3/AIITftq3F3Jt/Zyn8o4VCfGXhzci 5/7CGM4r+oaigD+XRP8AggR+19pNs0mnfsxSrqEh3mabxp4cZQ/Tdj+0DnjJ xxzXDav/AMG4P7cWs6zHLcfBPUbi3icsI38W+HFzk55A1H9M85r+sSigD+Ub Sv8Ag22/bI0q3nkt/gVq8F3MSfk8VeGSmecYzqWR1Pr2rh9W/wCDYH9ufUb2 WZPgdITI5b5vGPh3v1z/AKfX9eNFAH8g8X/Brx+3UjAH4GErnqPGfh4MPf8A 4/qkb/g15/bnO4r8C2DEY58ZeHsH/wAn+K/r0ooA/ka8P/8ABr5+2vDMHvPg lfR4bOIfFfhyTK9vvakuMV6n4I/4Nvv2ntA8M3iX3wH8XXOptE8VsIvEnhdY EDjDAn+1sjqegNf1JUUAfyk6n/wbn/tpqzDT/gDcwRkrhZPF3hwspBHzBv7R 4JHtg4FZF5/wbe/t1HWDc2/wNuUAVFXPjHw6GXGO41D26V/Wbiita1aVRpy6 JL5JWQkrH8+/wF/4JjftZeBPhpZaf4l/Zy8W3Wt28ao8tn4r8LSQtjgHL6qp PGOwrpfEv/BOv9qLXLQxL+zf41jJUrk+JfCnp/2F6/eOishn8xXhD/ghX+2X 4L+It9qFr8Btf/sm4u3uEtT4s8M7iGB4I/tPAOT2NekXv/BIr9rXUZlln/Z3 8VM6EFF/4SvwvtU+uf7U/TFf0W0UAfzR+Of+CHX7WnjeG4ab9n3xPFcPGY4m XxV4XwM85P8AxNPUmuK+AX/Bvr+2B8IPGOra7efADWtSuYrRotHiHi3w0FSZ hgvIx1LKjGfuhvwr+pGigD+UrUv+Dd/9uTxFqMuoal8C7+6u5G3BP+Ex8OeU n+yP+JhnH4Vc8N/8G537YsNy0+qfATV5WRWEdvD4s8N+UxPTcTqYOM+1f1U0 UAfy123/AAQA/bNXUFvpf2e783ltIDaovi7w0IokB3AY/tHrkDn616x4A/4I 7/td+CtLkVv2e/Es1w8BXI8U+F8bz1Of7UHH4V/R5RQB/Jbd/wDBtl+3Pe6l qFw/wNvN93M8gY+M/DxJ3En5v+Jh79q/XrS/gZ4w/Zq/4I9fB7wP4+0C58L+ LvD9j4si1DTJ7y2u5LYva6vLHmW2llhYNG6ONsjYDgHDAgfqxXyH/wAFfP8A kj2k/wDXvrv/AKZL2onsOO58Gaf8a/FPwPl8Oa5FJ49Hw70Wx8H3Pi2bRtf1 a3tdN059Lu/tUjWllPGS7xRCT7QOUNhCjb1fYft74FftVy+IvCvwYvPC0Wse H9O8YfEu80HxBYeIPEl3rlz5cXhnVLxoGnvg89uyT2tu3lARsGiZW2+ZID4F 8Evj34L+GvwTsNP8S/DPwJ4uuhc+DvJv/ElwsEEc11otyLUs4s7hk8l7KVUY jaDfyEmMB2b6403/AIKC/B3wN8LvDviDQdN8QXOi+K/E58ONaeF/CV3qV3aa k2lzamxksrOF7iYG2hDebbxTKRJHJkxbpVxpYWrByqyl7srWXa2/3npYvPMB iaFDLqFJRrUFLnaT99TbcG2202ldaWsradTG8a/t6atqvg3w7eaNb+F9CvtY 8ReF7SbSbvXEn8R6db3+peFkuYLvTvJAhlSDXZFlxM/kiTT5Bu+2AQ+j/s7f tf2n7Q3xX17w7YaK9tY6T4Q8NeL7PWBeLJDrNvrI1AxmGIqs8ccYsSN1wkUj s7fulVVaRvxj/b0+Hnwb8L3GrzyeIvEdlbjRn8zw3oF5q6XUeq39tY2zW0kE bR3bh7uCR4LdpJxFLG4ibzYhJnXq/Cz/AIKCzz21n4k+I6SeDJTkaD4n8S+C Tdw3BIivENpNZnULGb7PJ9nvF862k8ubyJGxJXT02/rQ8zpqect8Y/iBqHxF 1Cy1vxtr/gjSPiBea9pdndSWmlrD4Ll07xLY6Hpi6eZrM759VivBNi+a6UzG PyUWPMTU2/ao8Z6B4oXxh/wlc2qeAIro+H4rmZdNTw9eaWvhA+IB4recRRyB 3uka1JF2lkIicRrIPNr2fwn+0r4P8PQ+GvCfhyDxP4kuJXvdHsozcm9u5ItJ 1a00S/nnubyfzJvs09yjyySSPNKkcrL5smFeh4q/b98FeBfAV/rWtWuraTc6 TPLp+oaZdyWcNxp9/F4ebxBLZTTNcC1SRLFCWkM/kB/+Wu3L0L4dv6/rT/gg /i1/r+v09Tzq6l8Qt4K8Mw+LdZj1/wAUWOnmy1a9jvbe+WS6iubiOVfPt7Sz ikKOpQlbS3+ZCDEjBhXKeJvDL+Lfslot3aWIj1Kyunmud/lqsFzFOwwisxJW MgYXqRnAyR6r8d/FcHjLxDBdwRGFIRcWJQzwTEtb3lzbs2YZHUBmiJClg652 usciui8V4a0SLxBrz28zSIi21xOChAO6OB5F6g8ZQA+xr2si5Pa1vaaR5JXt 20ucuLvyw5d7o4X4q/sk6B8VNXvbx/iVNo7XmlR6YEtNJs7kQlLwXS3AN3pk 7NJkFNjs0IVtwiEoWUJrX7HvhLUfGr63ZeN4NGmudWsNZvIrPSLZlv57RZY8 yyTadJMxkheOFpBIJY47aIQyQkymTuP7Di/vy/mP8KrarHp+hWE11e3iWlrb xtLLLNKsaRoqlmZmOAAFBJJ4ABNYNZGl8dS3y/yNZTxu8uX+v+HE+EPwW8Jf A/4f2Hhjw7rWh2OjaYHFvbQ2UsEUW92kcqkVsiDc7sxwBkknuatePH1DSdHj l8L33gzWNQMoWSHVdQ1DTYUiwcuJI7C4LOGCgKUAILHcMYMUdlZS6lLZrdK1 3AkcskIkUyRo5YIxXqAxRwCeCVYDoal/sOL+/L+Y/wAKSjkUteef4f5Exni3 8PKcF4G8Ea9pvjy11PVLX4d6Fo+heEb3w5pmmeHNVvr1iZ77SZkVUl020jii ji09xgMfvKAoArT8afHjVdFlsNMsvhp4512HTLXyft9jcaQltcs0skp2Ce+i lwvmhTvjX5lbGVwxr/FLxPceB/FPgKytUhlh8T69Jpd0ZgS0ca6Zf3YZMEYb zLWMZORtLDGSCON+IHjPxBoM/iXVJvH/AMOPAnhbw5dRWk114j0eWZVLw277 3uDf28aAvcJGqlfvY+YlsV7Khlf9m2vL2fP8729NjmvifrGlua3yOg/4aG8Q f9Ef+JH/AIGaD/8ALKvGNf8A2d/hp4nvtRur/wDZZ8S3N3qraxLd3Ly6Ebma XVmBv5jL/am/zpFUIsoIkijzHGyRkpXsCeAPixKSF8ffDptrbWx4HvPlPv8A 8TX8fpTv+Fd/Fr/ofPh3/wCEPef/AC2ryVHI1rzTOrmxu1kc/wDCvxQPgp4b fR/DPwS+J+naY83ni2bV9HuI438tEZkEmqtsLlPMfbjzJZJZn3SyyO/S/wDD Q3iD/oj/AMSP/AzQf/llUX/Cu/i1/wBD58O//CHvP/ltU+o+EvGHhu88O6fr Pxf+G+n634pinm0zTv8AhBJjPfLCziXylfXEaQoqbm2qdoIJo5cjf2phfGro h/wz1DUtN+H91qN54d1a01E3uo6iNGeW0a8Ie7nmjiDrMbfzHRkx++2AuAWH OIv+GhvEH/RH/iR/4GaD/wDLKtv4a6td+KfAvhzUNRa3a81PTbS5ujbRGKIy SRIz7FZmKrljgFmIGOSeT1P9hxf35fzH+Fevmkcr5KP1hytyrlt28/M5sM8T efJbfX1PGvFfjNvG3iDRtV1P4IfEe5v/AA/dNe2Ev9paKnkStbXFqzbV1QBs w3U64YEfPnGQpHmfxg/Zw+H37QJ04+OP2d/it4sOjNA+ntqviuyumsDFB5GI i+tExrIgQzKuBcPHHJN5rorD6i+26QfEU2kjU7X+07eKKeW0+0x/aI45WlWJ zH94K7QzBSRhjE4H3Ww7Wp9K8OWYuNQ1G3sYDcQ2gkuJ0iUzTSpDDEC2Bvkk kjRV6szqoySBXkpZJpaUzqbxu7t/Vjz/AEn4361ots8UPwh+KLq80s5M+qaL O26SRpGAaTVGIUM5CoDtRdqKFVVURXfxE174ieMPC8M3w58Y+HrPT9Rku7m+ 1K60loIk+xXMQG23vZZCS8iAYQjnJIHNeorosTKCHl59xx+lYXjW9bw5f6BD CA66tqDWcpfkootbibK4xzuiUc54J+o6MHHJvrFP2cp811a/e+hlVeL9m+a1 rFfx38Xb3wx4ftNCtvh/4w8UqzzXct3pdxpkcEYkEaCI/abyGTePJJOE24dc MTuC+MeGfBfh7wR/wmT6H8Avibol38QLmS88QXmna7pdpealO8TxNI08errK rYkkddrDbK7SriRi9fSkHhuCbwlaXxeUSzXc8DAEbQqJCw7df3hz9BUH9hxf 35fzH+Fb5oso+t1Pbuan1tt0JwrxXso+ztY8Y07xjcaVrsWpxfB74uveQtds pn8Q6XPGTc3C3EhaN9WKMVZQsRYHyIy0cXlxsyHyiX9lb4er4d8D6Pa/s9/F Ky0j4fwavZ6ZZR6/pTxvaarb3EF/bStJqzO8couWfO4OrIm1lXcrfXAsrNpW jW5DSI+wqJFyG2hsfXaQ2PQg9DTLGOw1K5u4be8jnlsJRBcpFMjtbyFEkCOB yrbJEbBwdrqehBPnRnkV9JzO50MzjHncNLJ3s7W6fLsfOPgH4XeEPhb43XxJ 4f8A2d/iJpmtRPp7xTprOlMsf2DTJ9LtAI21coBHZXM0WAoDb9zZdVYYniW1 1e5/aX+G2t6t4f1Lw0vib4yz39laX81rLcGFPh9qNqXY200sYzJbS4G8nABI Ga+sv7Di/vy/mP8ACvmb4t+MbrxF+1V8MdLnS3WDwr8X5tMtGjUh5I38Aand kyZJBbzLqQZAA2hRjIJPXR/s3kq/VXJy5XvtbQ5KjxN4+1ta59f/ALOv/Iyf Ff8A7FXTP/Rmr19RL0FfLv7Ov/IyfFf/ALFXTP8A0Zq9fUS9BXzkN2dbP5Dv +DnTU30v/gu98cnV2QH+wM45/wCZf02vC/2evjTD5K6ffSpcwykJ9nn4hmRj ho2z68kHjn0r2j/g6N/5Tr/HP/uAf+o9plfDXhDxB/ZWoxq3KbuDnB61oSe3 /Hn4MnwVdyXNkDc6FqUhlspnU/M5bJjYdVI75xXS/svr4c8cabJp2u3U8mpW 8g3I2dxGQN3TGPpXS/DDxfYfGn4W3XhzWDGWuUIS6Y7RBMB8srd8N398ivEP FvhrW/gp463SxmDVtPfcD/DcKOVPOMgjHI6g0AfVviPw/wCDvhxas9rIZ5h3 ZdoQ+tc/8C9Yv/iR8QkMMVyNPFwFhfP+u55PHYVxvw01jRvjAxGo38Er2wDP alSNgx1255+ua+n/AIAXfg/weXt7adzdwKAC8XlpEnfaT9KAPb9B1S30y8+z SXUW9kBMWdhOOP8AOK5j4xfs+f8ACyIJJrZLczyxY3CUAHp7j0NeF/FTx5ce IPiZajw/cSSu053AgnKDq3HTtjPWvefA+s3/AIf0GGPXL20huGXkOQh5HPp6 0AcA3wQ0vwH4RkhmjhtryRMPPHHvwe3Kg9q4ax8K+HriWS3sQbqQLukneEjB /EDnNfR011p2rQSxre2k8YB4icdP8TXAXninSLKyvV0y2iuLxMpL50nze+D3 /WgDw3wjZaXpXxL197i4ja1SC1cBkbj95cAgADPv+Ndc/wAMdF+KmoRzaT+9 ktHB/eRGMAd+SBkfWuF0/Q9U13433pbT02m3hMkYl4ZS8oVvfOGr6D167n+H Pw1T7PaNYykZLL820d69HMfih/gh/wCkomO3zO0+G/gX/hGbSNAiRB9pOBwW 74OeK6HxR4lbw/YSQxh3kBwuBgNzXE/CDx7eX3gCS+uRLeyRjcoUgYB69fb2 rmLT4navr3jtUjsryO25DqV45474H/6684o67U/ireazZ3ENvNai8jUhEDqW fg9ff6187+KPHviTW9VlF5dzKLdmCxu4II9M9sf1rb+LvgHVPBHjCTWdOeRr eZ8gAlcH055BB71418bPj1Y6Zor73T+2p/ljt4smSVumWx2659aAKnxh+KFr 4WiaGUxz396jJDBkO2fQ9duTnvzXjfgnw1LqF7CUjW7llbzpHbCKznJLEk4C gg9+cHHaoPC1lqXiPXd0jm81HU5WLsTuAPHyAdgB1PtXo+pvY+ANOGiwsLu+ cebLOmPLMhI6kjnGMAEnAVfWgCLxnJaWGjW2iwTK9nZES3s7KMkkZZj6knJw Oe3WvAPiZ4v/AOEo1t3TcIIzsiRmz5ajgKfcDjI6nmul+L3jdBD/AGXbvkqN 8rqfvOSSfc4Ofxz2xXmrPvkJJ+8ck4oA/t6/4JP/APKLP9mrHT/hVXhf/wBN FrXv9eAf8Enf+UWX7NP/AGSrwv8A+mi1r3+gAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAK+Q/8Agr5/yR7Sf+vfXf8A0yXt fXlfIf8AwV8/5I9pP/Xvrv8A6ZL2onsVHc+Uvgh4f8F+LfDemadqXxH8beD/ ABVdR+D7yyt/DfhXVNRuJFttEvgiLcWkbKWnjuLwmIEOqWTsQVfj379nD9gz wv4Y+Cfh7Q9O+LPjrwb4gh8eprmganZ+FLfwpcpqMPhYaQ9jY6brFnOklqNO huXVPLlZQjkSEwsw+WvD/wCx98TPjV4L0Txt8OYL288QeEk8G3WiWrzWsml3 F7a6ZNP5t7DLcwsscTyWoEyB3CXEyojkvt+z/hF8AviUNL+EFl8QNfs73xR4 F8fah4jb+0Nfke81rTU0HUNOWS3AMj58/UIHeJnZUQvudyQHwpYmrNyozj7s bW877/cz1MXk2Aw+HoZhhqvNWrKSqR6wcHaPX7UXdad9zX13/glV8G/Cuv3+ v6SF8BX17pug6DYajp1hpMV9pcmm3Vg2mSRX1xaSXU0glsNNjWC6mnt3NvEP ILMxfuf2U/2EvDf7Injjxjreg694n1d/GEVtam21T7F5WmW9vdahdpHG1vbR SzyNNqd08tzeSXF1OzB5Z5HyzcT4j/ZK+Ivizwp4e0rV9VtNa1LR9c8N6tf6 /c+LdRI1g6bfeGLq5l/sryPslvJOdLv9iRttidUdSDqN55RpHjzxR+yP8VLz X/ipq3jfxJpPi7wj4f02w0jwh4X8Q+MLTRdXsBe/2rJHHZWE8sEE5urMpLdy vPOYpASFhRV62/P+v6/I8mzsJrPwy+EfhXxr8VtSi+N6eFNV8G20lxqKQ63o kJ+EY1a5j1S7uNsluzRf2hcwR3bf2p9oRvLIiWOEvG01/wCAfgr4d+L7avq3 xh08XemeFz49utGu/EOlW6eWunnSZPGUrrGl2p+wbrU3HnLYhfmESygSCx8P f2dfEKan4K8ZaLqK6xYLceINROm+IYrzSpXtte8T6frSvJDNCZYbmytYZkjh miRvNCRsYAXZNLwH+yp4o07xtoniXUdXsrG5XxtH40v9JsrqW4s7Uf8ACIf2 E+mwSMkZeBLjE6M0aAhQSiNxUppR3+X9feFve/r+v69Tj7/xb4c8deDPDGqe FPiFafFfRrjTdieLLe/sb5dakjnnjllaWxSO1Lh1ZWEMaIrIVCrjApeHNci8 O6zcXUyyyBbG7RI4wC8rtbSIiLkgZZmUAkgAnkgZI6L4geBJ/hpoXhbRLy6h 1DU7PR4zqd/FbR239qXzySyXd4yRqqB553lmfaoy8rHHNcjLaCR92SK9PKsT RpVKirt8souOnnYwxFOUox5N00zE/wCFq3v/AEJnij/v7p//AMlVy3xcsdL+ OHhGXRfEfw817ULN1kMXnLpczWsrRtGJ4/MuGCyorsVcDKk59q9C+wj+8aPs I/vGnLAZLJcspTa+X+RNSeInHlnGLR4PafA+x0/4paX4lh0Px8V060S0ktJG 0ci6WG5jntFeUTCQiAxRLuJMjrBCHkIEgl9f/wCFq3v/AEJnij/v7p//AMlV s/YR/eNH2Ef3jU0suySmuWnKaXyMcPCrRv7KEVf+u55z8QrrWPiN49+HMsHh fWbCz8PeIZtSvrm7ns9kUR0nUbZcLHO7sTLcRDAU8EnoDVT4x/sQeHv2qPB3 iHQvGfje80nw14jupJL3RtOOz+0FFvaJCZ5TZySI8FxaiePyJUDHaJRIhaKv UfsI/vGj7CP7xr041MqWF+q80+Xm5r6XuV/tPtPapK9rGDdfs06W/wAY4fGV r8U9X0+eLTDph0y3jX+z51a8t7mSaRGsGeSVlgMAZnIjinn8oRSSNIe30Hw3 ZaPp5huPGlpqkhmmkE9zbyrIqPIzpFiK2RdsasI1O3cVRSzO252xvsI/vGj7 CP7xrkeGyZ7yn+H+Rr7bFt3sjpv7P0z/AKGPSP8Av1d//Ga5P4g+I/GjaxBZ 6BafDO/0rTYRBDdaj4m1WyubjLtKxaFNHmWPDOyjEj5ChuNxURpe2bpeMblE XT3K3LP8iwYRXJJPGNrKSegz14OKFh4y0fUfC0utpeFNLh3bp5YnjBx/dDAF uTgYBycgcgil9Vyb+af4f5B7bFvohnhDR7nwF4D0SxuGtdRvNG0q1gm/s6Rm inligRXWFpliYjepUF1TPUgCn/8AC1b3/oTPFH/f3T//AJKqrffE7w3pmmW9 1caqkEd3B9phV4nWSSPcFDBCu7BJ445GSOASE134oeG/DnF1q9uG2RyBYw0p ZXDFCNgOchc+wK5+8uevGTyrExhGcprkVltsZUfrNNtpLV3PHfjT+zXpHxs+ PsfxIudH+Iej+JIPDUvhiCaxg8NyyWkLx3qCe3uLlZrm3mX7fKcwTIkmyNZU lQFGf4m/Z10/xp4ytdb1ix+KOqXVrqGlamkdw/h9ohPptzDc2rqBhomEq3ZZ oijuup3SMxjW3S39ztde0q+ur2GHUIJJNPnW2uBvA8uRiAF5PJJOOP4gR1BA 0PsI/vGuNYXKNuef4G3t8X2X9fM4T4QXKfBT4SeFvBmk+EPGMuleEdItdFs5 LmfTGmeG3hWGMuVuFUttQZIUDPQAcVc13XtQ8beIvDYHhzWdMg0zUJLuee7l tCgX7HcxAARTuxJeVP4fWuv+wj+8aPsI/vGtaNLKadWNZSndNPp0dyJzxUou LS1JNb8dnw94I0u1t9J1PWJze3U0i2bQL5CmO3VSxmkjHzFXwFzjac44zhf8 LVvf+hM8Uf8Af3T/AP5KrZ+wj+8aPsI/vGrxkcpxNaVecppy9BUZYmnBQSVk eKfEv4R2HxO8TJqs+ifEnTbiLVLfXoo7K60fyk1KCEQxXu2WR8yKkcACk+X+ 4X5Pnm8y1+y98NdM/ZM+Hlz4b8OeF/H17Z3moPqMsuo3WlyTGR444yAUmQbd sS8Y9ea9h+wj+8aPsI/vGuCOWZHGXOnO/wAj158QZxPDfU5zTp6e700tb7jG /wCFq3v/AEJnij/v7p//AMlV88eNtK1OP9qP4c6vqGlXekw+JPjJNfWMdzLC 8kkKfD3UbYsfKd1H723lGCc4AOORX1B9hH941znjb4Rad498VeC9YvJ72K58 C6zJrlgsLqqSzvp97p5WUFSSnlX0zAKVO9UOcAq3VCOW0ITdCUnJprW1tTyn LETaU0rJnqn7Ov8AyMnxX/7FXTP/AEZq9fUS9BXy3+zw4TxJ8Vs/9Crpn4Dz NXr6kHArwYbs62fyBf8AB0b/AMp1vjkf+wD/AOo9plfAKsVzjPIxX3//AMHR mD/wXX+OeT0/sD/1H9Mr4AJ544FaEncfC/4qXHgfVYHildVQYOMgMD1U+1fT cd7ovx/+G0wnkhk1a2X/AEQzYMofr5OQPmB7MeCMEnOa+LQxByCBiux+HHxR ufAurwzRSMrIoG7OQR/dI7jvQB2nxP8Ahpqvwx8bJPBHdabLEquxxxEWAbGe 456fQc9a9G+A/wC0BoOn+G5rHUZo9O1eVnWWS5Y7Z8k42Mc59McYrofC/wAY fDnxg0S20vXDaPuXCi4k2zQ8YyrnG70AYjAArzn4ifsyXsLXk+hN/bGmQMCy rBm5t1Jz8yHkjnqMgjvQB9TeBviR4V8DaVb3ccNrfX02CriXOTnqB+NdH8Vp tL8b6ZJfC9a2nu7bDIsmGUgcYr4F8M+JLvwBrVtJawzr5JxJaXIPlNnghME7 a9Osf2qbfXdb0+K7gOnW8DhJI5SQpAOTtYfKR75HWgD7O/Z3+HVno/h6KS4n mkM0e1nkb5nPfOfal+LXwq0CK2mv9NvpLS7cMWeGcKWPp34+teb6z+1ppnhq 30zyNQik0+VdkscbBgwIzuPtXlHxx+Pvh+5spIvD+vX5v7hgwtLdhM2CSTyB hfxIoAsL46v/AAt8R9Ru4tVE7QLDEGcBQ6hpeOD1Bz04Ne+eA/GWt+MfD8j3 09rqFhImW/e52fnXw/deLtXsNQspCLDUIr1C7RFxmFASVUOD97JfP1Fd18L/ ANp1vhZcst/pV/BpUgyJVnWVUY9cKAD/ADr0cx+KH+CH/pKJjt8z69+EnxFH gy01Ob7O80UL7liUjbx0/lXTt+0d4d/s5FbEN1OC4RQDyR618ceJf2z/AAzp TSyaNdXV6tyP3tvGhXaT2ycD+leNeJvjtca5e39zBClpNcMZIQZWZkxnG3gc n2rzij2r9pz9oq98NeKJmtdSklaUnZaYX90c5yzc4BznHU+1fPeieHfEXxW8 S3c1tJcy3t2PMubiVwiwR55csOI4wM8gfgeldP4G+CuteKimseJ3l0XTbmQN 517xNdg9Nkf3mOOhIAGav+PvjdpPguG40LQ7NGg8otc+Y25p3A4Mjd8AD5Rw OBQBt6Rd6N8BNLcfbTqF7c2rRzz/ADNGQSCTEu0MTkYycD27jx7xp8YrjUIZ reyxHCcqXAAdsnOc/wD6u9cz4s8cXXi26R7kqyIoVVAxgDoKx3kzJuBJPqRj 9KACeRpHLM28vyaZSnkZ70Fice1AH9vn/BJ3/lFl+zT/ANkq8L/+mi1r3+vA P+CTxz/wSz/Zq/7JV4X/APTRa17/AEAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABXyH/wAFfP8Akj2k/wDXvrv/AKZL2vry vkP/AIK+f8ke0n/r313/ANMl7UT2KjueMfs1adqOs/sxvaaRqh0PVr3RbG3s dR+zLc/2fO2iWQjn8psLJsYhtpOG24zzW38L/DuseDbn4baR4hvW1TxBpXgy 4stTvfOecXd1GdMSWTzHAd9zhm3MMnOTgk10vwh/Zh+FHiD4SeEru/8AAvgL xzJJoWnrFrWtW2kXFxewraRJG6peQSSwIVUMISfkLNkbixPRf8Mj/Br/AKIx 8H//AAVeGv8A5Erm0Nbkm8UbxUf/AAyP8Gv+iMfB/wD8FXhr/wCRKP8Ahkf4 Nf8ARGPg/wD+Crw1/wDIlRyR7j5mSbxRvFR/8Mj/AAa/6Ix8H/8AwVeGv/kS j/hkf4Nf9EY+D/8A4KvDX/yJRyLuHMzyP9oH4z3fw+8YWun2/jnwj4YSSzWc 2uqeEr/V5pGLyDzBLBdwoqkKBsKlgVJzggDhD+07qYGf+Fs/DbA/6pvrH/yy r4w/4OaNE8GaDF8O7DX7XUNK8FWWpWkEtr4aitfNtYI4NVEcVsp2QKBhVG0B Av3VwAp+cv2QNa+HUn7SPh/w54MbTZtZ8FeAL3S9YutJRW029EmqQ3cXkzqx 89o/PeN5GGWwvzOFyCpVVOdKmk3zt7bJJbv52S7t+TNqNB1adWq5JKmk9XrJ t2tHu0k2+iXqj9X1/aX1Nunxa+Gn/ht9Y/8AllT1/aP1dunxY+Gp/wC6cav/ APLKvPNQ8O+ANGvJLmSK1MAhium06G/FzLGPtNijxpNFcFJt0f2sqpEUijcz Ko2FdaHSfhg1tFcMkdvP9ksbiSH7dK6RjzrYz7mBJeUpNKpRR8ogY7ASK6OV eZy3OvH7RGtN0+K3w1/8Nxq//wAsqlT4+67J0+Kvw1/8Nzq3/wAs68b+KOna LpWtWkGjJBGq2xN0kNz9pVZTNKV+cPIhPk+TnaxAOQec1i2/WqjBNXE3Y+gl +OfiFunxT+Gv/hutW/8AlnT1+NfiRunxR+Gv/hu9V/8AlnXg9r2q/b/0p+zX cObyPbF+MfidunxQ+Gv/AIbvVf8A5Z1KvxX8VN0+J/w0/wDDear/APLOvHIO oq9B0FHs13Dm8j1gfE7xc3T4mfDU/wDdPdU/+WlPX4jeMX6fEv4a/wDhvdU/ +WleZ2/Sr1r2o9mu4c3kehJ498aSdPiT8Nf/AA32p/8Ay0qRfGfjdunxI+Gv /hv9T/8AlpXD21X7frR7Ndw5vI6tfFfjt+nxG+Gp/wC5A1L/AOWlSL4j8fN0 +Inw1P8A3IGpf/LWuet/6Vft+lHs13Dm8jUTUviCQQPiL8Nsc9PAWpf/AC1q Rbr4iv0+Ifw3ORj/AJEPUf8A5a1Vt+lX7frT5PMXMMV/iQ/T4hfDc8Y/5EPU f/lrVix074oandxW9v48+Hc9xOwjjjj8BaizSMeAoH9q85PGPerFr2rW0Abt Vthx/rV6/Wk4eYKQf8Kb+O//AEGvC3TH/JNNV/8AlpSD4O/HZs41vwse3/JN NV/+WlW/Bnx70Px58Q9W8MWNj40g1HRfO+0T6j4P1fTdOk8qURP5N7cW0drc fMwK+TK+9cum5AWHzd8Wf7JX4+aMPHh/aBPhL/hUPgr+wf8AhBf+ExOnC98v Uft/mf2D+787yv7Pz53zbfL28ZrFTZry9j3ePwT8YZ/FU+hJ4u8DSa3a2kd9 Np6/D3UjdR2zu6JMYv7V3CNnjkVWxgtG4zlTVu9+FPxx061knuNf8JwwxRl3 kf4baqqIoGSSTqmAAOee1eTfsEv4k83wovi8ayfFa/BbwT/bR1jzRqH23Op+ f9p839553m79+/5t27dzmpPhuPjAvgDxB/wtrHOraEfDvlCyGbD+0E2fbzB1 1rgfbBb/APEv/wCPb7J/y3p8zuKx9YfsjfD3xDYfCvx54w8QeJNF1uTxFp8u kQ2+neH5dKWz/s6fUYXZjJeXJl8x5CVPyYVRkEnj6kHQV8vfs2+J7uL4N6hp wlH2RbzxEQovrUf8v1++fJ2ed1/2vf7vFfUPWtKT3M5I/kC/4Ojf+U6/xz/7 gH/qPaZXwATk1+33/Ber/grsv7L/APwVi+K3gY/swfsg/EQ6H/ZH/FQ+Nvh1 /a2u6h5uj2M/+kXP2hPM2eb5afKNscca87cn5A/4f5L/ANGVfsBf+Gi/+661 JPgCl6D3zX39/wAP8l/6Mq/YC/8ADRf/AHXR/wAP8l/6Mq/YC/8ADRf/AHXQ B8KaXrC2enPGH8p9+/Kg7n9q6/4dfHjW/A94ssd7KFTKhXfOMjrjt74wT0Jx X3B8Pv8Ags9q3xJ1WK2079iD9geQSHBcfCDIUev/AB919lfA7xH4h+JvhZfE HiH9mL/gnx4J8N7Q76pefAx54IwRkZxqCnJwe3agD8n7/wDaI8JfE/TvL8Xa Ik2oKwUX9ltgmZe25gcHHcnNc/cfD/wJqd5JFpPi69tbZyAi3aqiyH0HYjqP TkV+n/7QX7fnwx+DKBNF+D/7Anjudyw26R+zyoi+UkZLPrAPODj5e49a8Qn/ AOCxlqXPl/sifsDeSv8AFP8ABjYw/AXzfzoA+MNP/Z4tby+Ji1Zp2lGFSCGO Qce6tgdu2K2dN+A2oCUw2eopZRsNsqfZlyBgn0Pf8ye1fXVn/wAFgJtVQrYf sdfsGXrKM7Y/gxxn/wADv6V1Hhj/AIKweJLG3ee2/ZJ/YR0kxjP7v4QvGQM9 Plvv84oA+Hbv9n+9uo2s/wC0rh2ihS4Hl2m5yrsy4IBwMGM9/wCL2xVGf9mT WJ4ojcXmpW8Owsr3Fo6ooHfOMYAFfpQP+Cn3jCfw1Dqf/DOv7CZlu28jyD8J 5yxC5IGft3T5mx9T71wPiz/grD4nOTc/sgfsKamq8En4QPJ/6FfV246UnKPM re7H8lr8yY7HwJZ/Czwp4ciJ1nxLvZwSUtzGGJyOoyT69quWfxf8DfD6Fj4b 0KB7+MZivbsb5YmH8S7skZ9sGvs6f/gsD9nkVbz9j79gm07AzfBjgf8Ak+au Wn/BWZboRmP9kP8AYGuI3P8Ay7fBfzGX8DfqP1riKPzx+JHxu1n4hXskt7qE g8xhlIydsgx/ETyR9ciuFnmMqgllJ6cf/q4r9r/2bv20fBnxn102Pin4GfsC fDVnyLebV/2fvMhnbsu5NWyCW4HymvQvj7P4v+DWgjWdP/ZX/wCCf/ijw9Ku +DU4PgW0FvOO2CdRbrjj6UAfgXwM5OeKa2Ooziv0l+JP/BZjVvhdqr2+pfsR fsDogOFkPwgKh+cf8/dcx/w/yX/oyr9gL/w0X/3XQB8AgZHUCggdq+/v+H+S /wDRlX7AX/hov/uuj/h/kv8A0ZV+wF/4aL/7roA/p+/4JO/8osv2af8AslXh f/00Wte/1+R+jf8ABwBqn7KX/BAj4DftI6z8IPCGr3XjPxBN4Kj8J+Fr1/DO jaLb202rQ2/2VWjuykaQaVEnldMyEqVVQlfOP/Ec5/1a7/5kj/710Afv9RX4 A/8AEc5/1a7/AOZI/wDvXSj/AIPm8n/k13/zJH/3roA/f2ivxO+BX/B3r4i+ Ol6Dafssmx01Dia+l+IhaOEZwSQNL7V9H/CP/gvh4l+NniCGw0D4BTXvmEKZ 4/F+Y1J7f8eQP6UAfpFRXznqv7f0fwt+G934q+KGg6V8OtItcgS3mtvMJDzt AItl5YjA+tfDXxG/4OwPDGj+Iry28IfB7UvGGmWsmxdV/wCEljs7WX0IP2aR uee3agD9caK/IfwV/wAHTWo+Mr/yl/Z8jt4h1lPjoso/8pwrXm/4OjdOsNR+ zXnwgt7c92/4TEsB/wCSIoA/WGivy80z/g5Y0zWbYy2vwsguFU4by/FhOPzs hVqX/g4zH9my3kXweQwRkYMvi4oWyM5AFka0hSlJSlFaRV36XS/NoVz9OaK/ KnU/+DmO7soI5IfgWt4kkhjHleNOSR14NhWBqP8AwdN3WkxuLr4BxWc6jKxX PjkoXz0wRp5rMZ+utFfjmf8Ag7Nm00vLqH7OOqiyRgDc2XjJLhBnud9lH719 J/si/wDBf74a/te6XHFpdrpei+JZSFTQ77WmNw7EdAUtyOuBn3oA++aK+O/2 hv8Agpn48+A2krqMfwRbX9OkXzEng8UGLepAIYA2Z4II796+YfFX/Bzdq+iW 9yln+z8l7qNsMmyk8cGGVuccY09h6/lQB+sVFfhF8Q/+D07V/hjrMtlqv7KU tvJG20FviPgN/wCUuue/4jnP+rXf/Mkf/eugD9/qK/AH/iOc/wCrXf8AzJH/ AN66+vv+CKv/AAcgf8PhP2p9e+Gn/Cmv+Fd/2F4VuPE/9pf8Jb/a/n+VeWdt 5HlfYoNufte7fvOPLxtO7IAP1Aor8Af+I5z/AKtd/wDMkf8A3ro/4jnP+rXf /Mkf/eugD9/qK/AH/iOc/wCrXf8AzJH/AN66P+I5z/q13/zJH/3roA/f6ivw u+HH/B5d4j+Ks6jRf2S57qMyCNpF+I2QpPr/AMSuvddB/wCDmbU9WjgST4Cw LezHb9lg8cGV1Poc6etAH6uUV8W/BP8A4KffE3422cNxafs+XFpDLyTJ4rLF VwTniy9BXd+Pf+CnXgf4H6ELr4kal4W8DXW0lrS91iV5Fbn5ci2A6igD6Xor 8kvj9/wd1/B34QeI7nTtD8Haj438gHZc6frMUVvMQexePdz/ALvavH7X/g9G Gvaqtto37M95qHmHCF/HqxM3/ARp7fzoA/c2ivx68Nf8HTHjLxFCkrfstSWk T/xSfEA8flpldpoP/ByN4i1aQCX9n61th3z47ZiB/wCC0UAfqjRX5mN/wcTS xsqn4NB3PXy/Fu4D87IVNF/wcOyyLlvg4EA6lvFnT/ySrSpSnBpTVrpP5PVB c/S2ivzWj/4OGDcMqxfCKKRmOAB4sP8A8hVNdf8ABwHqNrCHb4K8MMgnxbgH /wAkqzA/SOivzC1X/g4u1TTgdvwMSXjI/wCKzK5/8kDXm3xA/wCDqjxD4Ijd ov2al1EJ1Efj8oQP/BaaAP2Gor8SfCP/AAeZ6JqetR2Ot/AO70KR5VjZv+E1 WdUzwT/x4r0r6x8Mf8F69I8f+CF1nw94EtNU3BT9n/4SQo/PuLQ0AfoLRX5Z +M/+DknVPB11LbyfAXfcRg4RvGZQsf8AwANfOfxP/wCD0rUfhX4hk0+//ZbJ 2/dk/wCFjbQ/4f2XQB+7FFfgD/xHOf8AVrv/AJkj/wC9dH/Ec5/1a7/5kj/7 10Afv9XyH/wV8/5I9pP/AF767/6ZL2vy9/4jnP8Aq13/AMyR/wDeuvsLxr+3 L/w8m/4JafDD41/8It/whf8Awmtt4s/4k39pf2j9j+y2mrWX+v8AKi37vs+/ /VrjfjnG4xPYqO533gH4gatafsq/A7Q7K71GDSH0qwOurYX7WNy1m1jcbSky Okse25+yuTE6uVRhypZWo3Hx9+LlleaXDo2o6vDYw6ppFg0WpzafPMukpHYt d3MknzM9+0jXcTfMYykblVEnlvJs/sD/ALIXgf8AaT+A1pq3i+LxVeXml2+l 6ZafYfF2r6XDBbjRNOm2iK1uYo8+ZNKxbbuJfknAr27/AIdb/Bv/AKB3jv8A 8OL4j/8Ak6sVRb1L57aHzH4o+P3x9+wanDpniC6aSNpBYTGLSg8ghi1pIdwa PaPPZNEkk+UYeeUJ5Kbkju/GT9pT446foni6DwdPe3N5pkwbw3eST6Zs1SO5 jh3rPE6Lg2rNcmJg0e7ZAJFnG9n+i2/4JcfB1kYJp/joN6/8LE8R+v8A1/15 98V/+CXnhq38e/DT/hGrDx5caHL4ldfFvlfEHWl8nS/7H1HaSZdR3kHUf7Px 5GZBnnMfmU/YgqlzzT4yfGv4va3oPjjRtH17Xvsc1vqraJf2U2nW99I8lnYJ Z25kKqI4lnm1E+YoSYC1iPm5wZdTxX8Wfile+NfENxp/iWCDw5c+MtEvrawZ JTePYQjS/taw3Iu1SGAmG63wGAmTM3XzhXonw5/4I9+CvCuuiXXPFXxD8U6e mi6dYLBP4w1q1le9g+0i6v3khvVBa6823zEqrHEbY7BiQgdB4k/4JSfDHUdT 0STTZfHGm21letPqMDePPEM39pwG3mQQbjf5ixK8Mu9cn9xt6O1WsOr25l+I 1NN2Z4Don7P+mfEn4d+Dbz4gWF1q/iu28P2VrqFxc6lM8zzrHmUu6SYkYytI S+SWJJyaf/wx58Of+hd/8n7r/wCOV6trPhS18CahJodkJhZ6MTYQedcSXEnl xExrukkZndsKMs7MzHliSSaq1ailoRP4nZnmq/sgfDpenh4j/t/uv/jlPX9k j4er08Pn/wAD7n/45Xo9FOxNzzpf2UfASdNCI/7frn/45Uq/sueBF6aFj/t9 uP8A45XoFFFkFzg1/Zk8Dp00Qj/t9uP/AIunr+zb4LTpoxH/AG+T/wDxddzR RZBc4pP2d/BydNH/APJuf/4upF+AHhFemkH/AMC5/wD4uuxoosgucivwJ8KJ 93Ssf9vUx/8AZ6evwS8ML00wj/t5m/8Aiq6uiiyC5zEfwc8NxdNOP/gRL/8A FVIPhN4fX7ungf8AbeX/AOKro6KLILmAvwu0Jeljj/ttJ/8AFU9fhvoq9LM/ 9/n/APiq3KKLILmQvgPSE+7Z4/7av/jT18F6YvS2x/20f/GtSiiyC5nL4T09 ekH/AJEb/GotV8CaPr2lXNhfWEF7YXsTQXFtOPMhuI2G1kdWyGUqSCDwQea1 qKLILnmn/DF/wg/6Jb8Pf/Cftf8A4ij/AIYv+EH/AES34ff+E/a//EV6XRRZ Bc87h/ZD+FUGkTaevw38DrY3MyXE1uuiWwilkQOqOV2YLKskgBIJAkbGMnJp P7Ivwq0HVLa+sfhv4Hsb2ymS4t7i20S2imgkQ7ldHCZVlYAgjkEV6JRRZBc0 v2a/ibnwZqXhzzB/x9+I/kz/ANPV9JX10vQV8mfs3fsteDD8P9Q+JhttdPi4 XfiP97/wkepfYf8Aj7vrb/jx8/7J/quP9V975/v/ADV9Z1nSja/mVN3P5Av+ Do7/AJTr/HP/ALgH/qPaZXwBX3//AMHR3/Kdf45/9wD/ANR7TK+AK1IFRS7h R1JwK+gv2Tv+Cf8A4y/aR8S2cVnpVzNBMwIRcAsMjkkkBQPfFZP7GH7MeofH n4k6dElvJJbtMoUBeGbcMEnsvPXtX6xfHL4xaH/wSx+AEnh7w69u/wAQdTj8 m/uYmw2nMQY3hiKkhkIIOSOtAHnnhXwD8J/+CbRD+PZ7LUvEFsmyLRLBvtEs c38O6VFeJgGCk4bBBrxr9oj9uLVP2nfFE134rlk0zw+Ay6fo2jZtSM/c8zyf kOGBzkdDXzeg8S/tDfEWa8u5rrUdRnkJlkJ3CPoScgV9mfs1/sYaHqWlSxXt ulxqJiJEssY3Odp5I7GgD5Tu/hjrvxEA8mwa1sEO/eAGZwe2V5HGOtei/Cn9 jyQyw3U8TJabsyNLJuJ/D647V9ReB/Cum/Bi+ksNQs99hMBHv2Ftu35SSM8c AV7V4B+Gfhn4h2Hk2EoWM85VRxk9OtAHiXw7/Zc8MaXaRz6dZi4lIAbAByR6 8eua9A1n9mezsvCbyXlraQecvCDYHxx3Fe6/D/4KaX8N7dirmbkkb1Axz7Gv DfjV4w1rxB4kkWOYw2kDFQN/UDjuKAPmrw98Ibbwz8a57G6uXksbULcW6Bif mYnI/Aete/a58NdFg8KpeG3tXh2/wwgngDOeK8d8I6hA/wAbNSOpv5sxt4vL YkHByfyr64+GmhaJ438LmyhkU5HzKSMg8e9ehmPxw/wQ/wDSUTHb5nzron7H nh/4galNqN3p7RW2PvsQi9fTHvXk/wAdP2AJtJkuLzw1HM+4YVPPAWPHfkgH 8K+9fino8XgzwRb2touImZlfAxkYH9a4/Utb0rSfDm28ePcV+7genXrXnlH5 o6jeeIPB9hJo3iGyK27HzBOkS+YjYyCGGTjntXuf7FX/AAUw8Y/suWMuhaog 8Y+BblSk1rNEtzcwLjaNrSEkYBJ4Xqc16r8RfD/hn4n2E0EGnfaJ0QqjyQFS vy+ua+L/AIi/DrVvg94zmktldYxuAiOQCndQR1GPzoA+5fGf7Ofwh/4KPeEb nUvhff2ttrk6mZ9G1BzFck4JO1pFRBgAcZySa/MP9q79iHxb+zh4qurfUNKu II4XIkQlSYj6DB+b8K9F+GnxQ1H4U+P4vFfhuZ7GWOZWubdDs2cg5A6kgDP5 V+mWm2mhf8FUv2eZ55TbJ46s7VpoJpmCtf26qzzO7MeH6AKBzQB+CvSivXv2 sv2bNU+Bfjm7Se3mjtnlPDIQYj6MOx+teQ0Afr9+2R/ypt/snf8AZVb7/wBK vFdfkDX6/ftkf8qbf7J3/ZVb7/0q8V1+QlvbPdyrHGpeRzhVAySaAEiiaaQI iszMcBQMk19Nfse/sX3XjK+t9d8TWU8FgpDWtq6Mkt23VSOOmeOnepf2S/2T jc6lDrWvW7yrHLsgtghYu+eDgc/eHX3r9bP2WP2fPD/wf+HM3xV+KDW2laJp MDPYWMjLuYlCyqEYhj8ykceooA4j9kv9gW++Jrrc65aL4F8F2j5nmvUFog5w 2X+UHt16V6j+0l/wVo+Gf7CuinwV8BbLSvGXieGII+vQrFd2NvLtGdwdQTtY EHBr4c/4KJ/8Fb/E37aoHhbwQbrw58PjGGntrfehvGZcSIY3BJ5APB5rkf2Z v2Hdc+JnhkXk7fYtLLMyW6MP9I553A8jPWgCb46/tE/EL9sHxFFeeNtcvden VVWHTtLaW3t0C4GZIwSpPAOcd66P4M/so634nu1S9tpLGzbnyvJaMAe+OCa7 z4NfA2T4JfFi0ttTtlFncRM6tnIVuPw6mvszQtEtGsFmVFYqB+FAHyb8QP2P 5PDPg2WXSmnikjDNgOx549Kyv2H/AIIWvi2a81DV4hdz+c8Ch+QCj4zg5r6n +JdzHa+GrgycgIWxXJfsmeFl0bQ7iZoTDuu5ZQCCM5cnP60AcB+0x8AV8MaK upaHC0N5AVV41X5WQ/eO0cdO/auV+EXwpk+Lumwy3DzRabGMBA7Izdwc17h+ 0f8AFLTdG8OXZmmiyYGjz1IZlOBXEfsd6+NR8BQONgyijr14r0MF/Br/AOBf +lwIluv66M7P4YfszaN4avllhjkdVxxO/mDI6kAjjNZv7S37Oema3c2he3Rd p3b0TaMFfUCvRtH1aS11dE52SH16d66b4paENb8JiTAMka5H5V55Z8ba5+xY viKwzpVwyyRLgLIzvGTgnlehr5n+NXwU8V/CLXheS2Oq2V3G26PUtND20akH gnZzzj9K/S74bw/2UJUm4GT268Vxf7Smpafd+GLi1eNJXvG2Rrj+I5A6e5oA +YP2Mf8Ags18Tv2SdRTQ/EI/4TrwZ9y6tbmHz7tY85KrJMSVI4Gce1faPiKD 4E/8FEvAY8S/DvxLoegeMwp83QLi/VbsOAADjCg5JOAM18W6/wDsK3XxD8Oz alaN9h1QzSSI8ePnTPyDJ9jXyv4m8EeIfgt4vimtHm0XxLpsxngniOBJyQp3 EYyeTwO9AH11+1r+wxqK2E2k+KNGmgi2kWmqCM+S2M7fnGd2WPr61+a3xo+A eu/BfxHLaX1rM9nuPkXixsIZlzjINfrz/wAEyv8AgpRp3x28LyfBn40yLJO8 Hk6ZqdxliNqEKNxwoy7fpXM/ttfsdx/D++vPDWsQJqOgXG77BeJ+8SPHC4K/ LjcaAPxzr9fv+DKn/lKb4+/7JVqP/p30evzb/ap/Zuu/gh4teS3jaXRbs74J V5C5528egr9JP+DKn/lKb4+/7JVqP/p30egD8gaKKn0zTLjWb6O2tYpJ55jt REGSxoAjtraS7nWKJGkkc4VVGSTX0l+zz+wRd+NrK01fxJcSWdpOytFZRRGS eYE9CAwK88Hg4zXf/sy/sO2/hhdM1nXEbUdYn/1VhEvmqpfHlsdvIwfav06+ BPwk+HX7I3w+b4ofG3U0QwQl7HSNqSzF2X5IxHlX2b1AJxxmgDnv2Pv+CRGv fErwLa3qtpng7wzblFEl4io4iIJ3NKxj6HjJ9a3/AI1/te/swf8ABL25TSdM sNS+MXj6y+aSGOYWdmjY4ImC3Ab5gw6Cvlf9sj/gtX8W/wBr9bnQ/AdxN8P/ AIcLbvp502wncC9hZgwZ0kBORjAANfN/wq/Z0bXbg6jeLI0M0hee6uFEc6sT yyqeDkmgD6b+P/8AwW/+Pv7U1vNY6Ba6B4D0B4zDHZ2UKvcxxkg/61FjJOEA Hyjv6mvkLxL8Jvid8b9Yaa51jWtQSR+ZNRvZTuGeu12OPzr37TdH8P8Agu3U Jaw3V3Fytyww528KOD2/rUVx8U9S1u5eK0LiRc7zyAmRQB574D/4J+aTYwxT a/qr3NwuDNBHH5Y6HID7z+eK9h8KeFPDHwuQpaaXbhYVyrMiu4xjncRzXNyX OqKRJNcyNIc5yelX/Cirrc32aeQsCc4PfNAGzP8AFnVPEd55dpp6xW4IRQvf B9APak1D4k6npdwqzxNGHHy/Nz/Kt8+ItG+G9gEEaEgFiNuck4Hb61xvjnxb F4suoZ9vkqg+XHKgZ9aAPR/Bel6x4v0L7SjrAeMbst19+Kh1LSfEF/ZSQw3E MzqCMIvfp1BNa/wm8RPffDxI7PIk2DaffqKx/wBnTxNcjx/qNlqcrNOjll3H nBYgV6GY/HD/AAQ/9JRMdvmeZ6p8M/HD3kskc8ypn7quwzg13Xwmi8eWoeGW zeVIlLbppDkgD3FfQXiDW9O0i2RmkgjAJYncBxx61zfjP9pzQ/DuiNFaOLi4 mibaEXco4x1B4/8ArV55R5O3x/1JNcudMbR45bm3kEZ/e5UnHT7lWJ/E0fjE m31PRJbeRhyyJkfoorM+Dsf/AAlus3+sSxoGa7WRMtjOV612/if4v2XhJWiu 4k37cKE+bP19KAPCPij+y54c8Us9w4eGYKcOiBSvPBxnnFeV6T8QvFv7HutL LY3Uur6CzcqxaIkdPVscdK+h/EfiGTU5TcxJDHG4yNr5yPp+NYOv/D7T/it4 QnQxgyKB8p7cE96APSvhN+2R4C/aH8HwwXaCw1N4xBteQb1Zsk8kAt9fevLf 2qv2WLL4g+FWvNjG3kZljvI4gPLwO474+tfHfxU+G+rfBvxm01kJIBbMZVdF xtx36Y7195f8E4f2mrP9o3wnd/DnxLNG9zqsQtoJZzgWjbhmQE4GSB3oA/MH 4lfD+7+G/ieXTbrLMnzI+3bvU9Diufr7l/ba/ZmOsandwRgfb9JeVmkBHzxq GCc987envXw7cW72szRyKVdDgg9qAGV/SF/wTv8A+Vcj9nb/AK9vHn/o3X6/ m9r+kL/gnf8A8q5H7O3/AF7ePP8A0br9RPYqO5+in/BJD/k2J+v/AB8ab0/7 F/Sa+pq+E/2Lf2pfCn7I/wCxNb+JfGV5Bp2j3er6Vp32q51Kx022t5H8N6bI pluL2eCCNT5ZUFpAWdkUAlgK9/8ACn7fvw58dfBS98c6PrFpqlhptnNqd5Y2 Go2V9fW9jDP5ct7st55FktwmJhJE7h4mUpvLqrLnUYc0tkROSinJ7I7ebXfi APErRr4a8IPoouiouf8AhJbj7UbffjzPI+wbfM2c+X5uM/Lvx81eK+IfjV8a 9I+HOpatYx+HdcuXit9a06G2+HmuW18LPULe+isdOaznuUD3kF8lg9xI9zBt t5LjzrawxFNJ9J6dq9tqtlb3EE0bxXMayxsHDB1YZBBBIIx3BqzFMsw+Vg30 INUmhKadrMz/AAtDqll4V06LXLzTtQ1qK1iXULqysnsrW5nCASyRQPLK0UbP krG0shUEAu5G4+NeI/FXxl034l/DLw9psvhSSwm0LUNU8Z6zJ4Uv5re7ubS7 0mNbW0AvwLFrqK6v3jE8l0yeQp2zLFJuTxB8GPBXwT+JOt/Eu4vPixNqumQa jr7WcvxM1y80y4RYS05i0ibUTYiIedtRDAscT7CojxG1evaj4wj06OMGF2ub i8Sxgt/NjV5nPJK7mGQsYaRgMtsjchSRgqNWLf8AXoR7aCfLc+a/iUSfHusE 5Gb2c4Of+ejVh1u/E7B+IOtY6G+n/wDRjVhUU/hVjee4UUUVZIUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeufs2/wDJoeo/9ffi P/0531e4V4f+zb/yaHqP/X34j/8ATnfV7hWcP8ipH8gX/B0d/wAp1/jn/wBw D/1HtMr4k+Fvw2v/AIoeLrXSrGMu88ihjjgc19uf8HRSGT/gu18clHJY6AB/ 4T+mVo/8Enf2WoEt/wDhK9ct3itY1M6yN0EgXKr+OK0JPqP9kD4T6F+wJ8II db1XSRfeI7+xeSxQIBBHFtZWlLEhg6nBAAI45r8+v2nvjBf/ALSPxyvr/wC2 XOovPOVimkIJmViMufpX1Z/wUK/aRntdNuLCNvs97q532sXVYbbJWRQOgHPa vlj9mrwNFd+Lor1yHt4T5cO/J85Tj5ge/egD7C/YT+BPhvwd4CspJ5IH1K7R ZJ5ipyWwR1I/pX2H4E+Fdvp0pvoTE2Y2AZAcjIr5m8K+FE/sqCG0MkckQ4RW IAxXp/wj+JfiHwhqUVrd2txPbMdu/AwO3c0AS/Gbw1FdIYbiBCsxYEkcYyTi q37Hnh+78I+JJbLDyW3mGTfjgAt0+gr2rW/Bun/E/Sg2BG2Sc5+6SM9vrWr4 Q8AWng7T/IthljnL96AMP4z+On8G+EpZxkvKCqkfw45r5U1rVtd8bajJJ5og tQSTgneee/GMV9RfH7wRP4z8MLawBt0eW+XPPFeR6l4Uh+HfhV/t4X7RcKY4 16HtzzQB4D4V8IWEPxS1Br64SaUW8ZUsM7Tk/Svob4D+FV0LxNFdWerQvAxX fCSRnv6f5xXz1qvhm08a/EeeRJpUNuiPmOQru5IwcV2elX2oeAZUuITI6xgA LuOeK9HMfih/gh/6SiY7fM+t/iD4Y/4TLwlPBFhpwh2ehY4rw/Vfh7/aV00e oRfNF8uGHBxwK9f+CXjV/GfhGOeVdsg4IxXTXvhu01SUNLGoOeoGK84o8e8C fBmERTSRxIuAQBjPY14l8dPgOPGvjoWrWpzCrx78ZA59a+3rLQYNOiKxAqM8 1zvjrU9H8P2c9zJBD54y2So6/lQB+W/x7/4J7a74ctLjWdGmiVFQtLE24M68 lj93HT3qv/wT2/aQufgv44XTZ5J7OaOZRbXGRthAx+468Bz7V9b/ABX+LF34 3e7sbG1Uq8bRqwjXAyP/ANVfB3xl8Aa18JPEdxDdRpbJezLf+cIwpaWMjYB7 Ejp04oA+/wD9tj4D+H/2z/CbapoVj5Hih7d5tb08p89xLgkzRkFtyKuMs20+ 3evxk+N/we1D4OeM7jTryP8Ac7z5Mqg7XA+oFfqj+wZ+01P4302O5mkWLW9F ZbTUoiBm4VxmTA7fLxzXK/8ABYb9k6y8T6XH4x0CAfZ9diN7awRjm0QMfkY4 AJOM8cUAdJ+2R/ypt/snf9lVvv8A0q8V1+fH7F3wHX4heKodUvbcz20Mg8iM qSJmBwR+tfon+1to0uo/8Gev7JViAUmf4s30ZBHIP2rxXml/4JCfsm2XiDXt KutQiU+HtAWO9uJHGApb5XznjrigD2X9nL9mzS/hF4DuPHPi21URaXKYrWCR MjDEndjg8MpP418cf8FHP24td/bO+KK+ELe4uf8AhFdDlEENtHloncOGQD2y TyPWvpX/AIK+ftinwf4AHh7RroRhysMcKMdsqyHLt+BY+3FfJP7CHw3stV8U 6XPq0EaLAhZVJ3C5bIIkb3GMccUAavwf/Zg1Hw9oQ1TU9NjjNnIz29uASkbj gsO/INfcv7Kd1Zz+B4CqIqq7AgfwtwD/ACrprbSNE1fRjZziMtKSoIH6V5B4 X8TRfAz4wXHhq4ZodJvQslpIRhDI5LNnsOPWgD2D4x/C238W2EU1oNskLqxI 4PBz3+laXw8vZodPNpcKfMVcMfQ4q/4a8TQ6tDuRlkR+AQc7ueT/AErTfTre a5MqqAwOSQM5oAwfFHhBdek8qVcQnrjoa0LXw/FpPh9reFVXI+XjFX5rslsI cDHpXKfEP4q2PgqLFxIrsy9M+1AHkPx/+D9n4m065F5GJWMRKnZkqQvGDWf+ x94ZTSPDkOnK5U2yhSX4JwP/AK9dH4h+NWneMrB0RQhQfex1rxjw18ZdQsPG Os2drb4tRMUSYZyBtH+fwr0MF/Br/wCBf+lwIluv66M+yvD2g28d4kjyJMFw du4Eiuk15VudEeLaMBSB6V8y/BTxlqieM4J57qSS2nYKY2Ofw9a+k4blpIRz 8rAV55Z5x4lun0czBAy9ePwrzvTfAd98R/FcUk6v9kjcMwbgHGCDzX0NqOhW Opxh5reOVwcEt1rA8RXVt4Z06RLeFIwRjIoAxp59K+HfhYWlw0AZEAJJCgAY 96+O/wBqLwvpXx48SSJp8UbyjCxThMtC2MFhzjPWvdvinbDxkJIJmaQPkbCB ivNr7wNN8NtQhuIIALEnLqAcgYyaAPib4k/B3UvCniZLN5Xg1Cyk8ywuwQuQ rBskngHcBX6Pf8E9f2nNK/bN+Cl18J/iXJbyeKtCQQ2N5JJvkk2IXzliFHzM O3auJ+J37N9j8c/heL+FTFepF5iNgFlbkjr9K+CIfFWv/sr/ALRNtfSedBeW dz5F1J0Mysyhmz349PSgD7k/bc/YkTQorvw9fCO90e/3S6bcAq4jLcjBBwML itr/AIND/hfd/Cb/AILCfEfS7pGCr8LNSMTH+Jf7Y0evSPgN8QYP20fgJp+m PN585iCWszkbkuCAG56ceteo/wDBCL4fSeBP+Cv+prcw+TfS/CrXoZs5y4j1 jw+FPP8AvGgD+b+3t3upljjUs7nAA7mvtr9iD9kC5sbjTtRk006n4k1Ntlhb hCwhY8q5IGVBGeSK8q/ZC/ZuTxPqNj4k1OAz29sWuIrV1yLgDbt46nqTx6V+ qGn+P/Df/BMT9nI+OdcEGo/FLX4jD4f08fObXaFkhZip3oChIyRjigDpb7T/ AIV/8E0fhg3i/wAb3VrrnxFigzBp6bLgx/aFzCPkbcArAZ44r8+viP8AELxp /wAFCvi7ceM/HV7PbaOspFpZeZvSzVsERR7gDtyOtYcOi+KP2rfirceMPF13 dalf30kksk1wfkSFWLLGrYGdoJAHXiu61TXrPRJV03TEd7a2BVcKQ3HIyKAN nRfhxonhmSJWsrINGcoQ2Qn1OeKm8XeOoL21eHTZPtbQrhizbQAOAOK8n1Hx DqOv6tNES1rDI5Dpzg/nXa6N8Oh4bsSftA828TaIyRjsc/WgDoU+Hl5qWli6 EcRLqWOW5WsbTpjot80Qh8l1b5sA/N05rvx4lh8J+GY1mnVnVCTjnHSuYkvr fxRp73SIUnX1GOMZ70APCtqDbRj95x16DFS2Gi/8I5raylspLgccD86XwTp5 12PejYwwGOODWl4vDaf5MbKSynOVGSaAOb+JWm3V7Kk6xkKQACvbkc/rW74T +FEPi3w1GstzJGJMFtuDuIzzWpDPDrGi7WjcERkZKHHT/wCtVHwMuuWKSG1C PbFspufb+mKAPSfBmmReA9MtrKAmRY+NxGM15p8Udf8A+FZfFGDVm3JHfBY2 K9sAn+tN1r42atomvLDLZK8aNgmMsw/l9KfqHhg/GyRLrUf9GsrflVx1OMHI PavRzH4of4If+komO3zNeazuPi4EnTUWjgkUKVBU8cE5/Ksz4j+E9K+GXh5r awhFxqUo5YL82Oh6fhVxPhDP4VtFfQdQljBXIjOFGf8A9RNVfDWgX9j4xivt bla5kwY2BO5YyxHQivOKO0/Z+0W0ufh1ah40jvNoVweGD+4NcDdeB77xF8Vr jT9UuG+zAKY8kHdkkY/AV6ndeDGTUornTr6SyA+aSNMASH3zXN/EzUI/Cuv6 ZdQ7bu/Mg8wjk4C8Zx+NAGT42+BF1pWmmawvJWVFJ8vKqFIwf8/SuT8MX994 XuLaSbJeYfvD035ODn8K9I1H41Wl9prRGK7SbYVIaEqM/jXC+IZ31bTIthk3 YHJXgfnQBs/EX4O6X8WdCnuPs8bmeEqeh5J7818XXlprP7LHxaj1G03xQRTb 8AkCReSF/CvrD4e/EifwhqqWU8jGyab95n+EAYrF/bG+EEPxE8F/2hYqHurZ DLGAAM5AoA9N1tbT9pP4Zaf4j0lIoZzD5N9FEeNqRgHPfk59K/OX9qv4Zf8A CD+PHu7eForHUi0sY2kbOcV7h+xD+03d/B/WLzw/fyNDZXrC1bBPy7pMP7dK 9E/bl+Cln4v0q7bTts8M6m606ZsAiJAcj2yaAPz4r+kL/gnf/wAq5H7O3/Xt 48/9G6/X84N1avZ3DxSKUdDtYEYINf0ff8E7/wDlXI/Z2/69vHn/AKN1+ons VHc+9P8Agml8MdH+KH7KtpBq6aj/AMSzUtNvbSax1K50+4tpv+Ea02LestvI kgzHLIpG7BDnIr6h0X4UaJ4d+Hdx4Ws4r6LSLpbkSg6jcyXLm5kkkmf7S0hm 8xnldt+/cC2QRgY+fv8Agkh/ybE/X/j403p/2L+k19TUKKcbMmSuZfhbwvp3 gzQLHSdKtILHTdMgS1tLaJAsVtEg2oiDoqqoAAHAAGOK01UL0AH0rjJvhlrY 8RG/HxD8YfZvtJuBpv2fShalN+7yN32LzhHj5c+Zvx/Hu+avCvjP+zbeeHtH +Bnw70nVfi9d+E38ZTQeIL+18ceIJtXmsjoGuSF77VY7j7WkP242u0tMkYkF ug2jy0DXoKEIpJLRI+gvEHwj0HxRB4jiv7N7mPxZaGw1Tdcyhri3MTReSGDZ RAryEKhADSSOAHdmOifDtrba1DqBmuVeFJUEbXDeU7SGPLlCcFwIwFbqoZwM bjnxT9mv9m74g/C74mrrPi7xxqPiq3/4ReytJVfWr6SG41uWa5l1i8FnIxhi imZdP+zxrlbVI544UgjkkWXrf2iv2ff+F4+Kvh3eNdanbW3hPW5tQvBZa7e6 XJJBJYXUGENs6bpBLJCcsQRH5yhsSOj60cNSdRKTsrPVfN+W7/MKWHpSnaWn meSfEwAfEHWsf8/0/wD6MasKtDxVoqeHNfu7BJLmUWUrwb7i5kuZnCsVy8sh LyNxy7sWY5JJJzWfUcqi3GOyNKiSk0ncKKKKCAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooA9c/Zt/wCTQ9R/6+/Ef/pzvq9wrw/9 m3/k0PUf+vvxH/6c76vcKzh/kVI/lB/4L9fCOf4y/wDBxX8YNJhB2yS+Hixx n/mAaZgV9MnwZZ/s6/BvT/DVsqtM0IuNTyNpgkjJwo6nGPp9K6j9t79n6Pxz /wAHBHx41+YOEsk0a4WTbwjR+HtNIGfqteY/tQ/EZ18PeJ9evJDI+pJKu5j1 doyo/WtCT8+/jJqGr/tI/tXX2mac5aCK4eK3bO5Viypb2r63+HXwMh+CHhix W+hN8kSBB5cR3DJx75ry3/gmD8GkuNY1HxJqLoblJhHEzsD8rpzX6Dya/wCC 9JiW21N7eWRBjkAg/rQB5JofhhNTgjvdNluoC4zskjb/ABHoe1d58PNB1fU9 bghmhCIh4YrkMO/Nd14c8U+Db8Rx2gtlRRwvT+tdbpd/pvkqLVoR3CqwIP60 AaOnWSabb+XGNowM4NWRMUQbWYHvXI6l4yOm3YikkQ7jjazAV0Gn3jXNh5ih VOwEDNAFmeQTRNuZTnI59a8b/aUtYZbGHdFK7k4yoPy8fSvOdc/b/t/A/wAd JvDGq25S1+XbIyt8uScnsMcCvdrHxB4f+KWmRyW8lvMJFBHzAnJH196APjnQ tdtdK+Ik8SQ3AljRSwdCMjJ4zivonwt8PdO+JHh+G4WKS33HY+Uzkjr1xXQP 8CvDs+rm9ntYt4wQ2zpWzf8Aizw98M9MdpLi2toEycBhx+tejmPxQ/wQ/wDS UTHb5m14B8K23gfR0soWJVSTkjqSea6DqK4L4bfFqz+JcUs9lta1ViiuP4mB wa7gyLaqEPJAzx3rzii7HceZEys2GIIBrxT45eFtYEzzoDc2pzhVJH0z1r1a +8Q22nFRK5Utzz2px1HTdZs9jFXBwSDzQB8veHvhxePKdUvR9ntYVP7lYtzv 3HPB7eleY/tV/DC4+O/gyd9O0/ymtCJkaT5SSMtjoOuK+5L8aXcQeU1okyg9 Nh7Vz/jDwlp1/wCGZYrO1SAAbmXaRnrQB+PH7LXxA1D4HftKo9+CGuJDZ3UJ yqmWQhQcdDjNfpX4m0KL4keBdX8M6i/lQzK08U23zChRT+7UZ4Dex/A18C/t 6/Cx/hf8a7DxNADFFLcC5kVByGEi4/lX2p8E/HA+I/wu8N68HLC/RJ3QnIcb jkevQfrQB0/7SHw5W5/4Nnf2bdIjjLDR/jZraqpHIEepeLEH86734QW8X7NH 7E8SJD5V34xMtnIWGMLhZFrsviN4Y/4WN/wRH+DNvbQqiTfHfxRLHEBwinXP FYAA9ga8k/4KXfES4+G/geezhnP2Hw5oltfQbW4ErRqrj60Afmp+1H43u/2k /wBovSNIgQsmkw4kAPDAhJCfyNe8+BPAbeFo7Ga0LxLar+4jA+6meV9hwPzr yX9gnwCPid451zxFMhMsc8cMbleSPKUH/wBBr6j11Ljwb4p+a2meCIMuEQnb 09BQB6P8I/ibpupRr/acbwzK5J3Ic88cdM8V3/xC+FegfGzR0ljjjklRcRyH G5DgD1rx3wpq2jeI0Vrm2MciHjzoyjfqa9c+E0Zjvf8AQ3lMLYyNuFIzQBzf gLw5r/wz1yK0NzPfWcp4aT70PT5VAHQ9Sa9sjmZbYDj5xnjORThpscTb5ERg o44qvdTBE6A5PTtQApkImIJUAcjnmvDPij8JNW8aeMiJ4xJb7gV5yMH8K9ov Z0hXzmdY1J5LMAF/GoU8RR21s0kp3RjoyHfn/GgDwDxf8KIPh3oZMgDSSgtj GR3Fcv8ADPw9Z6neSRELB9qfkgDGeg616p8f/ENhreiGZC8fkptO9NgPfvXl HwQW18Q68Wa4iNvA4bKsD2BAxXoYL+DX/wAC/wDS4ES3X9dGei2Pwm1XwvqM N5YKJomIdcN7Z6D6177p90zabbhwRKyKGH9045rn/BniuyfSlgthITAgXcYy FOOOvfpWxZX32+72+YpIwcZ5FeeWXvPEOFbJI6kdK4b4wW1x9kE1sHZSCW7d 66+6lWKQ5YnJ4460T2Ueo2skMnIcEAUAeEaRd28eqM91/rEbGG4NaXirVLLX rExJGzkDGAuc/Wtvxj8E5NR15Li2YrGTllPUVf0PwFB4eX9+vmv1AKZx+VAH N/C7UZ9PEkP2cpaLlcnI7V8e/wDBWb4IRX2lQ+KbKHEtuwikKfxbn6/pX3R4 kXy9LlWNktx1HzgV88/tNfZvFHwv1SwvZI5XKFUBYHc2Dj8jQB4n/wAEjPjX faRp1/oMEzx3Ph1nvoiD8255NuB6V+yf/BPrRLS8/wCCr/hXxTYpGtv4g+Dv iOB2Q53zW+qeFxJ+OZa/AL9k7xBP8Hv2y7fS9wjttbuxDMc8GMs7Cv3L/wCC Oni+fUf+Cgfg/QmVVttG+GXjG4hw2SftGseFSSR2/wBWKAPy+/Z907Sf2bP2 bPAnxG8Y28TW1vodnqWkaay5Oq7ouFGcY3FZznP8NeXTWHif9t34n33jvxbq OywhVUh82Qs0MCMVjjjG3BZVIGCQOOteZSeOfEf7SUXgXwhbyyS2Gk6LpWn2 UPJXdBZKrcA4++0nbvX0R49u7T4XeA7fQNIj2JDEJJm6t5jKN43dcZ7UAY/i zxrbeHdMPhvw/DJ5SbcLGAFJPBY85zVfwld2vgzL6tb+ZdXJBJUZIB47kVL8 Jza6jAD5Ye6O8lmGTjrXLfEHVJb/AMWC3ZsLEdnH1FAHeeI/hzbeKLUaxpYE e2MuUAwWJPHrVWy8MS+JLdZBdMs1r/CegIGPSnNLqnhfwSscG2SCWHLORwgB x1zWl4FjtbPw4bgtvnmLZVXPPINAGHHpa6le+Tcg/IQue3+eK2U05dMtWtrZ C5YYJxx3FGp6gulzMyoGkK5VAAS3Xv8AjSaTr1/qMaqbU2K92lQHH5UAWvCo ufC8whS3E7O2WKjocY5rqIre41S6Rp7by8HO4njFY2j+KLfS7mO1t/8ASJZD jdkHaffNSanf3/iTWVsbNxGJAA7YyV460AbyeJtPuz9ktRFJIpwwXtn2+gP5 VeWeO2aO2VZAm3BKjj+dSaLoGneCNMG/YHwWZmO4k9+fqajk8X6FqYAkvIoW yCuTgtigCnrmh2el6dcSPCJJ5F3FiuGY0+SRNO8GzcBcqW2jjrg1VuvF2nDX pQ99ARC+FVgSDx6VX8R+M9LvYkhtJFvLknCxxHqf8/yr0cx+KH+CH/pKJjt8 yVfFGr2tgqDTwWA+Xn1xVTX/ABBeW3h43d1A32tplcIp6Y7CtqG8kvIkjkTy pdg+Vhgjjis26vorbUpBdqCyA7dw4H1FecUUNE1bxB4+vRcJeNpEMT7XhBy4 B56YI/WtRvCT6f4ptbp7yW9kyAxcYxxnjFV4ZLDxDZSyKzRSkYfynKdfpVeD wtDC4a3upxITyZJWb9KANXxde2UOIzsWU5AJOe4rPuTYNbW8E19hwvKryW55 61lSaEmoX4/eSOVbDHdnODXTarpFlo2mw3oQtKVIVSfegDzvx14Wt7W6ZbaX ctwpJDjDD8s10fgDxXF4g0htJvAfNiBHI4IJwPftVw+GG1PQJ7t4wJ2TcFx9 2qOv+C7q1uYL6xhbfEi7tnfjn9c0AfIP7Snw2f4VfEqS4SIG1uHEkLqOrZ3H +lfUv7OOt6b+0z8BE0w3rQeKdDiXYkoAR4lVnkAIyefp3rmf2gPBCfE/wol0 0YW4sA8hyMHKofT6V4p+xh8Z9Q+Enxgt2lAhhvyVcuoKhGwrfpQB5x+1d8I5 /Bfjue+SMR29+WlMfOYzzxj6Cv3m/wCCd/8Ayrkfs7f9e3jz/wBG6/X5rftw fCjT/Hc9zcaaN9vqjPNblBkhdjLj8zX6X/sAWbad/wAG7H7Ptu4IeGHx6hB9 RN4gFRPYqO59T/s4/tE65+zR+wvpOtaDp9vqd1qHiHStOkgk+xhvLbwtZS7k N5f6fbBg0K/626jG3cFDuURvfPhv+1l44+IX7Kmq+Nl8Hix1fS7C+1OO5vbn TotHvxaXMitbB4NRuPKlaKNlM3mSW6SKziSWML5mV/wSQ/5Nifr/AMfGm9P+ xf0mvqYjIqXFyg0nZ2IqRumk7GL4N8XR+M/CelavBb3kUOq2kV2q3Nu1vNEH QMFkjb5kcZwUIypBB5FasU/mg/I64/vcGgOI1x2X2wK8zuf22/g1ZfCew8fT fFr4ZReBdWne1sfEb+KbFdJvJU84vHFdGXyndRbzkqrEjyJM/cbFJPqRCMkk pO4vxd+NVx4E8OeObrTp7O+uvCWi3mpyK9u3kWkkdp50UErCQb5WwZCiYKxM hbZvjaTpr/xRcS3lpptrNAdRvbshCVRjFax7WmlKGRWIAZYgy7tsk0RZSuRU fhb44eCfG/iuXQ9G8XeFdW12Fr1JdOs9Ut7i7jNlNFBeBokYuDBNNDHKMfu3 lRWwWANP4s/HfwL8CtS0IeMPEmheHLrxDNJZaa2oXMdv57KnmSDcxAVAFXLM Qu5o1zudAVSw9Wc1GN35a+v5GccPVnO0Xv0/ry+48K+JvPxB1o/9P0//AKMa sKtPxjrll4m8UX+o6dd21/p9/cSXFrc28qyw3MTuzJIjqSrKykEEEggg1mVU IuK5Zbo6pqzswoooqiQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooA9c/Zt/5ND1H/AK+/Ef8A6c76vcK8P/Zt/wCTQ9R/6+/Ef/pz vq9wrOH+RUj8fP277CDwp+3/APHvVZE2zapPo6xzdwP7Es48D8RX5o/8FCvF baH8HbfQ1kKXeoXtuqNn5sbyD/Ov1A/4KyXCx/tF/ElVCI5vtJ3ED5n/AOJf bd/pX43f8FS/FIX4o+CbOGTd5ce513fxCZcVoSemfsl+FrTwh4aFlqGtX1pc 25WNxHIqpIxHUgqTn8a+iNO/Z60v4ilZ01e6uGjGAWZSRn/gNedfs+eNvDE/ g60GsC3W62KZQcKztzznFe5+HPjNpWiqkOkWLSwsRlsq2R0/lQA3QP2WrLw9 H53253UDBwRkcf7tVNb8W2Hw1Z4rW5kklHyZZhjPPsK9g0nxLb634e+0tCVU jLLkcZzXnXj5fC15NIkgCXDH+93+mKAOS8Cajq3xM8Y2xkmbyUcO+0ZG05/x r3zVLmSw0GWOFyJY4gOO+K8/+CHh+3sLua4tx+7KLz1GP8mu+kkS+R+4YkHn rQB8cftTfs6ad8Xdan1YmaDV1UIjqRtbaMDggn9a+ZvC/wC0340/Z28UTafH eCaK2faEkVuAD7MPSvv74kaMNM1CUgHDAn86/Oz9sFbWL4zXa220r5Me4gYy 2DmgD6C8e/8ABSzWT8MrCW0iia+nkaOU/N2Uf7X9a86+G/ivxb+1BrjnVNRe GwByyQqR3wfvE14p4hglt/B1kWGI5JTj8hX0P+wLJBJ4dulwRKGYE+o3V6OY /FD/AAQ/9JRMdvmfU37P+mJ8L9Ms9M035YFkLkNg5LYyeMV9BWt+dQjVyDna v8q8b+HOmRm8WeUAxqA2P73tXrWkXayRDghTxz2rziivrugQ6rcIJWYhe3+R Ulv4Zt9PQeUrE98kc1W8X65FpFt5rElRgnHHSsrwx8VoPEV55UAACtg5wTQA z4gan4k07y10Kyg+bl3aNmKn8GArz/WPGnjrR43e8ktVj2nI8pxxjp96t/8A aI1/XLHTFGlI8iODjYvIPrnIrzDwT8OvGPj+NW1i5ZbfG5jsYbepx1+v50Af Ln7Yuu3nxeurtL+0Nnb6RG6M3aRvvA9TXoP/AATV8ejxL+z59kkaMDQClsB/ dypb8K6X9ss6D4S+GF9o1skc96ymN5FwWdiCMjIz35rxf/glIW0/R/GOizON 0eox4Uk54ioA/X39lbw5/wAJj/wRz+B3mHEUHxd8T3UrHqqjXfEoz+bCvzN/ 4Kr/ABJ1Kw+HesWt5P8AaZtanuNPjY8fIjZUD6Cv04/Z2ml8O/8ABA3w7dwu sdzpXjbxbLETn76+JtdH9TX5C/8ABVfW2i8KeD5ZiSZdWmll4PeLP5c0Aem/ 8E7fg43hHwGWKFWvpXlJIH8LMv8AQV9IavrfhrSJSl6UnkHB2EAj8xXnv7JV /DP8K7TY33km5HB/1sg612PhHwbZXut+ZMkjtvH3mJBoA1/Dvwl8IfE+dpkh vEeADnzEPt/cr07w34S07wjYrBaI+FXG5iMn2zj+lYeq+ONE+HFuLWJU85yT hcZ6Z9K5Xx18ZYtIhEgmVfMUEDdx0oA9VvkVrZtu4HrzWDrFwwcbRwOvoK8o +H/xW1fxP4thSGUPb4IYFc56Z7/WvVtbdYYSzEKCOp+tAHz3/wAFDPFHibQf g3bT+Grqe0KTSNeGLljHs49utfJXwg/4KKeOfhnKINWmi8RaenDpdqzTRqOc IVZQD9Qa/Qnx54Rh8a6DJbTqgjIPyyDKt7Yr5W13/gnPoz+I5LyC52IZC7RF mJYkkkDtjmgDo/h7+3B8OP2gZxpOvabDpcsp2bbuQFZs8ADHr0rsPGXij4Uf s0aSsog0q0knUyxWyMVefHUjtweK+fZPgF4Y+Bni0a1rkwgt7a2dreAuVaWX AKYJGPvfzqr4g8Q+G/2p/BkP9p6rbaP4k02aK2t7e4J3TIx3OQFHuBz3r0MF /Br/AOBf+lwIluv66M6L4rf8FQ3srN7DwTodvabWKm4u2aUD/aTY64J988Vo /wDBPD46+Pvih8Yr9tZ1e71CwdEaWOXmNQXPC46Y46k1z2kf8E94PG2hr5F/ FZXBGUnlLNG4x94Adq+mv2Vv2Zrf4CeGRAl3bXlwWJeaNGHfOOea88s9sluM yMNqnB4yOlXrWLzJM8hRzWUOa4f42+Or7wrBBDbuI436vjnrigD1kKCDz+GO tZWv6G11aSvbgrMRgeg/CvI/BHxev4bIvLcLI7A4z0Az/wDXrXj/AGhbmz1B IZI1kjcgOVA4HrQBjeL/AAP4g8Ra/NbzyTwW6kgygY4HPHWuP8Z/BXQfsL2t 5KLmRgeWxuH6V9AWus2/xA8N5jdYWlXOQcEHB9K+cfifZzeA/FbGS4MttGxW XJJ+Yng8/jQB8M/tq+C4fgH8eNI8RaUZfkkRkyQQrBWPYD1r9kv+CG982sf8 FHfC9+xy138HNfdh2B/tPwwT/OvyA/4Kg63b65Z6G9s4LxzEtt7jy6/WT/g3 w1pdd/bp8Gyqchfg94hT8tT8LigD8qf2CvA8Nh4UsfEc+5LjTLVbm2J6E7Is cd/vN+VanxP8QrqmqurMGeSRiec4ya2/gWq6H+yl4buVQI0+jxZb+8cD/wCJ rkZLI6vK1yYZC6k5O7j8q7Fl+Kauqcv/AAF/5C5o9zqPh7qGmeHLTzPMHnGN 1KluoI7VD4R8GxfEbxJPesGEKvvPYggZHNVbHQbSaEO86Q4HzFieBjmut+Ht /Y6DHN9lcy7v4geOlP8As7Ff8+pf+Av/ACDmXc37ie3XT5NJCLJLgRDOPlNY FmP7Bv8A7DDESfvbiM4JrR0C+N3e3ExdS3mA5I6cVqW1pbgvIkkcs7cBgM4o /s7Ff8+pf+Av/IOZdyomkQ25aVyrztzgjI9qxL/QvM1MT6peR21gvO1SyH1H IqC1vtW1jxBNZ6XY3eq3sZwqQgfKTnGQT7H8q99/Zc/ZC1GW7k8Q+N45BOfm W0cFFXI5+U5XqKX9nYr/AJ9S/wDAX/kHMu55RoFhZ6pbKmj2kl8JOPlYM+fY mvX/AIN/sv6g7C/v1Omwzja1vOp87b1yGGeua734i/H3wB8JpZoWi0z7bCCW ghiSN9wPT7uO9eB/E7/goXqniiVrPw/ZNZW+cEyhZD+n0FH9nYv/AJ9S/wDA X/kHMu59PWPwt8PeEI13RKyqpZvtDeZzjnqOlUdT1zwPGwilOjKen+oUEfjj ivkPwVb+O/izdrLHPfFZHwxR2246k4Degr2TRP2eoPBGjjUPEWrFIo+XZ5HA HXr1o/s7Ff8APqX/AIC/8g5l3PUdNg8GX/EcWluOgPkqSfrxXX6f4Q0CaJJb fTNLfH3WFsmf5V8RfGv9rDRNElmsNBuobgLGVSaJxgnPB7HpWJ+xf+0x4jvf j5pGkm6a4stVuVimEmXwPmPGTx+FehmOX4pzh+7l8Efsv+VeREJR7n2/8Vfh bYeLvDM/lW0UE0ETMGijClsYIHA/2cV8SfEVrvT7143VlcnDZ5xzX6ET3qR2 kpZgqbCG3LxjGK+Efj7DZ6v8TtcRJAtukjlMHHbtivP/ALOxX/PqX/gL/wAi +Zdzh9H8cDw4klq6OmSNrE8n3otvH9/rMnkW7CAM2BuXP8vpXB2mvw3WqnTL 6URRM4QSE42L7nqa6a1tZPD1x9luAC6gSRMBgsp6fpR/Z2K/59S/8Bf+Qcy7 nqngaRtP0z9+pMjEsSDw39a14tRNw/nzrugh+ZBxgj1rj9B8Xm509M2k67OM kjt/Ote210eJEe2RGWRD5YUdee9P+zsV/wA+pf8AgL/yDmXc7vQ7W31LRpbi Aq0NzhlXP3farfgS9svEWkTNbskrRSOjAHOCpxXjOv8AgHW9E8Q6bZWt/IsN 8PnILfLg/pXq3hX4QWfhHSSNMvZ1uyN7s8rOm447fXNH9nYr/n1L/wABf+Qc y7nl3xqshoWuNHGmIbgFXUDI5HevlH4geGn03W9UuLQfv7C9VrcIMEwrgufp X3Lp3wa1DxPe3Vz4hcMXHlw7EKY5ODjntivmn48/DOXwL8b4LRw4tZoJYBkE hi5ABP8A9el/Z2K/59S/8Bf+Qcy7nr/wM8X2/wAc/gPBNGAb7SUFtCvB3gq7 c+vzY/Ov08+Anh0eFP8Agh18F7ALt8mPxwxX0Lf245/VjX4o/s6eN3/Z7+Nm o6Ddu5tGuUMaHODh4+x/Gv3X0nTBpP8AwR++EUQGA9r4vnA9BJb6xJ/7NXPi cLWpR5qkGl5poqDTejPrP/gkh/ybE/X/AI+NN6f9i/pNfU1fLP8AwSQ/5Nif r/x8ab0/7F/Sa+pqxhsD3OKk/Z58Ay+JW1r/AIQjwj/bhuvt39o/2Lbfahcb 9/neZs3eZv8Am3ZznnOea5v4u/ArUvFfxA+Deo6LLpsGl/D3xXJrF9BO3k+X ZnQNW01UtkSIqX82+hJViiiMSYYEKrdJLo3xAPiV5U8S+Dzowut62jeGbn7T 5G/Pl+f9v2eZt+XzPKxn5tmPlrxTX/DHx1i+HWoz6N4p+IN1rF5Fb6xa2ep2 Xhi3u7K4vra+tpdJ+1RJJAlvp00ljeFmt55P9GZFudRWZoI3o9BXselfBL9k Dwt+z/4kh1Lw9Nq6/Z/C2i+EILe5nWaKKy0r7aLU5K72kIvpQ7MxDbEICncW 7Lxt8N7Tx1rnhfULm4uoZ/CWqPq1oIim2WVrO5tCsgKnK+XdSHAwdwU5wCDf 8MaPceGvC+nWF5q2oa5dWVrFbzanfJAl1qMioFM8ogjjhEjkbmEUaIGJ2oq4 UeN+I/h18Vn+JvwysNL8c+KLXwl4e0LUZvE18lrov2jxJqUN5pJso7sNaHYl xbjVPNNlHbgbjtaFhFjWNSfPdPX/ADvf+vMcJOL5kcL8Q7dbXxvqsQ58u7mT P97EjDNY1bfxILHx7rJbO77bPnIx/wAtGrErJScvefUqo25NsKKKKZAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeufs2/8AJoeo /wDX34j/APTnfV7hXh/7Nv8AyaHqP/X34j/9Od9XuFZw/wAipH5Cf8FebJrX 9r/xREHxHqYsrhlPAJjs7df6V+MP7TFq3xL/AG2LHRlhE62NwSQckoqyIxH6 1+1n/BaNkX9sWwdJGVk00CUEYBYxxYx+GK/GfSdXB/4KY69KYzMIJbhMAEk/ KnatCT79T9m2w8b/AAtjjtLOzt7hNpjPlhPU5z+VaXwU+Fl74SjtrXUrW1m8 hNhdiGLc9/esMfFnWW02DTtJSQeUOhBXIH4e9T/8Jv40sogHaY8cFmI/pQB3 vxl1a68N6EYrG2zxjEYOB19K8d8IWdtrWsi41SWaInnDKcA+hz61r+If2jdW 8MaQ8Os28uQPvIGfGAeK43w34y1D4oI0sFnbqjOAp3kFh9KAPpfwv9lGhSPa IkRKD7mMdB3pbbMW4SNyw4K9qz/AGmNovhARTAA7BknofkX/AAqzNKixlVO1 hyCvUUAcl8X9Jknsy0KhmUZJC89P1r89Pi/+znr3iHx5c38iFYbiQrljyME+ tfpLrV1iDDYlY92PJ/KvIvjbPaWnhfU7yaFFSCHfx1B4oA8L/aB/Y7YfDHw2 NNVBcSYOFAXJ8sZzWf8AsW/CHXPCOr39ncQeWwzuyTjG88g16Z4a/aKj+Ise haadubK4KtlyDtxgdevSvaPCCQ2qJKkS+c38fTI6/h1r0cx+KH+CH/pKJjt8 zpvAmhpa6cJGiUFVxz3PAroYDJHOoU/uz2ziq+m3Be3BOAQe1WYrtZ7pehOe cDg15xRkfEbV4dMsQJDHukQqofoc15NodvH4Q1H+0tRv1gtychY5A2fbGa9J +NHhZPEWjopYoyc7uPWvl74t6VLFcw/appLiyWVS6gbsc9gKAPo3Uf2iPDtv YkI/2hoxwHQHdXNWPxy1TxDe+daWnk2ETBXCgjIz6YrjfBfhjwhc6PHqMsBi 2gbQ0OB+p9q9Y+EniDSfEiSQWulwxWyDLOVZcjGaAOe+N3hnQviH4Jlmaxti 5gJZWiBO7nHWvg39ha8k8MftweJvD6O6xObiRk+6CVjUcj8a/Q7xnqGhX0d3 a2ZCSxfNgLgNgV+dfwpv08Of8FRNfkiClZTdLxwCSiCgD9gtA8JaL43/AOCB Xgyz17RNH1y3b4l+LHii1C0iuo7eX/hIvEQEqBwQrgFhuHIDMM8mvxu/4KZe GtH8F6nplhoOk6Xorq+4tp9rHbNICnQlAM/jX7MeB7g23/BB3wAVPB+J/isc 9x/wkPiOvyB/bi8J3PxF/bB0HRIRuDi3OMEgbo2rro4/E0Y8lKpKK7JtL8BO MXujqv2OPDGna34c26p4D0HWyJHJuJtMgm2DJ4JdSc17zdfDnwVYQ+Y3w28I AkZ2Nodpz9Pkq5+zLoUXwf8AD0lte2yqZpN4JUD1r1C7+L2lzyiJrGG4QsAd iIpXn6Vr/a2O/wCf0/8AwJ/5i5I9kfPl78KNF8fa19k0z4c+GNLyoBmfRrYx p+CpVHxP+y7p+iajFNqHhnwubaHaWNvpUMavxyOFr7C8MXOhX+kPfWdmYZly NrYPQ+mK8J+NHxOfxN4hbQxEUEnyq5AGM5/wo/tbHf8AP6f/AIE/8w5I9kZ/ wL+GPwv1zVjBJ4H8H30oJyJtHtpAOOhBSvW9S/Zl+GjyzGP4ceBFER28aBaj H/kOuH/Zn+EP9iapd3TStJkFjjPUL/8AWr3PQ7J7vRppWRyZWBB654o/tbHf 8/p/+BP/ADDkj2R5qf2ZvhutmT/wr3wQDnvoVrn/ANArn9a/Zw+H0dpIy+A/ BaBBnP8AYlqCT/3xXr15GUhYEEd6wNbthNEQ5+VuBR/a2O/5/T/8Cf8AmHJH sjwHxt+z94MfSZhb+DPDaSJGSph0yBDnHB4XpmuG+DP7OXhn7bc3epeG9Kkn WTaUuLOOSE5H8KFcAV7p46uf+Ec0+6lfDJFCz+uAFJNed/s9/Eqx+JWl3U1p hmjmVH6ZUkdK78HmuN9jX/fT+Bfaf88PMiUI3Wi/pHeeFf2ePh9eRAv4F8GO YwDzotsc9ufkrr9L/Zm+Groxk+Hfgkk8AHQrQgH/AL91Y8GweXAGXGeFrsNN ++DnowOK4P7Wx3/P6f8A4E/8y+SPZHJWf7L3w1e4Ofh34FPGSDoNrjr/ANc6 4r4+/s+fDbQ9HWWPwL4LszKvDQaLbR9/9lK93srUmWQ5GWBxXK+PfDUfijw9 e2lwys9s2xTjtjNH9rY7/n9P/wACf+YckeyPl/wT+zF4S8UyvNa6D4clhEhy G06J8c8jle1dJr/7HXh3T7TzLfw14XSQ9TJpULKfbG2lvbk/CPxI8qsRaMxL ITkdSc17xoMMPxR8IwskoVmTJOcEZA9KP7Wx3/P6f/gT/wAw5I9kfJKeENE8 OeJZdLf4ZeHNYeMkCaDRrQLx9V96v237M9p8X9Ua2s/AWiaBbRtl2XSoEZsc /KyKMV9UaF8OdC8CXbXWoXNs8znndncT+Iqv4u+LOnWFuYdNFsmSQWCglufW j+1sd/z+n/4E/wDMOSPZH58f8FBf2WbT4OfCq1vtLtY7KSF2MxgQI2NvcjBx npmvv/8A4NedIsbb9tHQr2306xtLy8+FviZbmaG3SKW5Cat4a2eYyjLY3NjO cbj618gf8FQPiGNU+DX2aRw09yzxAA+oBr7G/wCDXYEftc+GA33v+FY+Ks/T +1vDFZ1cwxVWDhUqyafRybX3XGoxWyPzn8G+LFtP2VPCtqrq2NHgUgdRlSay vDus3Vlag+UHiY8gEEsPzzXFfDvxrFrXwd0fSomXzrbT44yM9cL/APXrY8Pz 6jo5CkrKoPA39K5/az/mf3hZHokmsaTq+njzPNtn6P14x9BXW6TBpumeBpJr N423R9Sh3ZwfUV5yktt9nV5V+91HYe/NekeFNMs9c8CtFBHG5CYJHc4PFHtq n8z+8LIw/CRuLnQCSjK0wGJN4wR613fw28G33xK1qLw94c/fXUmDc3TENHbK 2cMcnnkYIXJFZuieDriy8EC2iRJW8rCxnA2//rr0X/gnn8RNG+EHiTVNM1uC 7tr29O2KcW7OhzJkBm6AADrR7ap/M/vCyPpr4O/ADQfgt4UW2jt45J1JnnuJ cO28jLEMRnHXA7V47+1P+0//AGNDNpmkOVjj/dvKqn5j7d8V9M/23pXiG3cJ qOnSRzxlWRrlOQRjBGfSvCPjZ+yjoGrwPNDqVlbvg4iaZFx19TR7ap/M/vCy PhPxLqd54p1JpiJpmck7pG3FvxNd78C/glJ4n1eAmLiRgDk8Ct29+B40XXFg S606VY2I+W4Unp7HFeyfCq0t/h/4dvdRmMf+jQ7htIOcEDtR7ap/M/vCyN+T xjofwX0pNOtlinvkjGY0jyVY4GScY6k9+1eZeOfDmrfGW5+2as9wkajakEE/ lKQeuQDgnPrXj1/+0mj+KLqe4i3Si5dQTnhQxA/Su90H9p/S4LEeY5Tcc8Dn +dHtqn8z+8LI4zxb+zzo+hKwk0u9ICk5+0KSfrXnOhNB8JPiBY67pKSWtzpl x50e9hKCRnqB9a9J+Kf7TNnq0bixLu5jIztIOc8cVxvhLwMvxFlS7urq4tvt blMOoVUIPXn1r0Mxqz54e8/gh1/uomKX4nrXiD/go5KdFkto4MXc0JUtsXBJ GMjjjvXmXwt+Ium+ItXu9S1u6jjkjVsqQQGbbnpg+1eY/F7RrDwp4iezs5jd eUoPmcYJ/DjisnQkgmsnEyieeaQFRjPtXn+2qfzP7yrI0viXq0HiLxvfXVjh baSUtGqKc9O2K67wJ40i1TR00fVvNtrlCTazSKflJ9TjJAA7mtLw78F7Xw3P oOsz2qSWE6/aLpCmFiwSMe/rXd/Ej4RaV4mvdYlKGBLXS4Z7fanQnHI9eD0o 9tU/mf3hZC+FfEkdhqC2VxAFwFCvxtIIOD+PH5126aQNLvIr60SNpgMOpAw3 PJ9OleUaRbX+m6TBDq8IdI3xFeg7mI42qeMDGFH/AAI16NoHiYXGh4kYLdxL 8pU5R/cGj21T+Z/eFkbp8V6Xq2vWct0wtpYG2kSRnIz+BrttP1PSryxZbTUI DIeRuyoJ/wCBYryrwvpc3iC6E17aQzIXwGJzkY/xrqPGXw3s9W8KytZQpZ3S KfLkjGGDfj+NHtqn8z+8LI9Vv72wvLeBftdnuijUnDqOcc182ftv+AZtTtrP WbJUl+xTxsHTBCrvBP8AKvcdA+BOnXnh60Mro9wiqzs2Nx4Fct8dtNtPDHw4 1C3Y7IRCwQEDH3W/rR7ap/M/vCyPhz4v3h8Q/FTRNVjEZE1tukdF2gnzVJz3 PT3r98tE10eI/wDgjh8F7sFSH0zxSnyjA+Wz1Zf/AGWvwFmR5bbTSSxjWAjJ HK/MO1fuz8EbkXf/AAQ5+BzjGDZeMRx7Ra0P6VlVnKStJ3KitT7V/wCCSH/J sT9f+PjTen/Yv6TX1NXyz/wSQ/5Nifr/AMfGm9P+xf0mvqalDYHued+LP2jd M8MDUorfw/4+1vUrESpFZWXha+UX0ybsRR3U0UdoN7AKssk6Q8hmkVMuOf8A Df7YKePPhZrnifw38PvHniGfSNan0K30a1Olw3+rz2032e8aBp7yO3RLe4ju 4nNxNEWaylMQlR7d5/X3TzsgkspyCMcGsGw+EfhbTWsPs/hzQoDpeo3GsWRj 0+JPsl7cC4E91HhfkmlF3dB5Fwz/AGmbJPmNmtLeYtDwH4C/Dj4NfEH9ps+P /CX7NnhzTrvU7CPxXpXxgh8PeHoRr7ahAkjSRTJN/aqzyx3MoZ5reMNtmDMQ yeZ6T8Qv2ol+E3wg0nxfrfg3xallLB9v1+GNtO87wbYpE0t1eXxN55Tx2oXE i2clzK5yYY5lBI7y5+G/h6+1fS7+fQtGmvtElWbTrl7ONprCRYJ7dXhYjMbC G5uYgVIIS4lUfK7ApL4I0PWtWh1OfSNOnv7a1uNPguprNTPDbTtE9xCrMNyx StbwM6D5XMMZIO1cO6F5nzb8S8t491liCpN9P8p6j941YVb3xObPxA1occX0 /T/ro1YNZwvy6ly3CiiirJCiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigD1z9m3/k0PUf+vvxH/wCnO+r3CvD/ANm3/k0PUf8Ar78R /wDpzvq9wrOH+RUj8if+C1MwH7Ut43yq1vZRnOP+neI1+Jngbx0nh7/goTr+ qS7UjeWdGfqMlU55r9nv+C0up+R+1d4oRiDssIWXnoBZwGvw7+FlxFrP7Z1/ LIA6zyyMATnnC16UMwqwiopR0/uQf4uN2Z8i/pn6R2vxs1CytLVtI09byJ8Z mSNWwM9ea7PT9Yv/AIo6PLa3MTw3EQLh1baOBk9D9K4fwhot/Bo0T2MSvlQx QJnFHiTxt4k8OQiRNNlwjAMY4gpxkc5qv7Trdof+C4f/ACIuRf02UPEupNHY y6dqZI52KxGM9sZ/Gsb4bXMfgzxBBZIzRQMRsDHkgEd63tbe3+J+hRNcFYbv hmTIVgQcjpTfC/hzTtfU2F/Oq3sTfuGDbScc9e/NP+063aH/AILh/wDIhyL+ mz6H0q7i1DSS0TB0wPX0BrNlRrBmcnLk4GQar/DTw7eaHYyJczCQMgVOOBwP Wsjx38XNB8HTG3vr+1F65+SDzhvORxx24FL+063aH/guH/yIci/psra5rTAt uCjrXz1+178R4bDwxPAsqBinzKBwRgdq9OufHS+O7149PLQxJzJI3IC9K8S+ Kf7Lviz4/wDiFbOxEmk6PCci6u42bz2xg4IOcd/pR/adbtD/AMFw/wDkQ5F/ TZ85eFfiAdP+KFrcRSBN5Vc/3cA1+gXwq8Xx+IvD9o6yKflUMSM5OBz/ACr5 vuP+CVmuRReYvijTPtCjtDJkmvaPhh4V13wX4POm6xZzLc2ikQzrGVW45AAH vgd69DMcyrKUNIfBH/l3D+Vf3SYwX9M+gPD6PNCe4YY9a17TTfJlDYz+PSvC vCn7TP8AwhurJaayoFizcz5CeV65zknpXu3g7xRpnjbRzd6Zd295bsPvRyBy v1xXn/2nW7Q/8Fw/+RK5F/TZ4j+018Vl0+7ttOimU3FxjEanBwTjPX3rho0u NGto2ngM9xIeA3I+pBr3Txn8G9Mj1f8AtmeB7maCIqg+9jj3HtXgXxW8b6hN rggs9PeyRTxLKgIIzR/adbtD/wAFw/8AkQ5F/TZvWujx3JSfVHiSJeQgG0Gu r034yaRpenvZ2uyNcYB3AHpjr+NePy+OGuoYbW5SWeXaSrRA7Tj6Vds7hDbL /wAS66aYkLsUYJo/tOt2h/4Lh/8AIhyL+mzO+MWlWet3Yv47+ZJIUZB5MzIO eecdelfHfwbu2tv273ZzvdJpVLFiSfu5yfevuTWfhLqnijRnnmVbK3CbgkkR D55xyK+JPhHoctr/AMFAtUsXdZpLeeYlkXKtgIaP7Trdof8AguH/AMiHIv6b P268FqZP+CDngmRQSq/EzxW5AGAB/wAJF4iGa/LL43XcOmf8FBPCtxMQsM5t o2cnAXEbZr9SvBk7Qf8ABvn4XnON0XxA8WP9P+Kk8QV+Xv7TPhGC/wD2ifAe p3eUgv7xIQ4O35liJPPauCTu22WfaHhHQtI8eeHJYjcRi4jYmMjgkc5/WvP/ AB54Vt/CGqMJHIkXqu7GOa0vhr4JhRIZIdQMKxvnDSMdw3dq9rs9F8PeNxFa XcXmyoMb8jL989M0gPF/hb8R2sdZjtxIjqzBSOvUmu78c/s96f4wv7e9iAik wrls89M9vrXcWfwF0DSdQjuLaAh4zkfN/wDWrc1iIaXp2CQiKBz6CgDh9A+G sfhDTjb2rs0szZYhs8d/0NbUV5FpQhsBG+Shy3UDmvHLv9oC88Qa/eW4V4Ib TbHju25A2cj6muC+JlrdeJJomtr++W+U7oWSdwEXOSrAHnPHNAH0trtgslsx BEbkcVy90kV2/lyZXBxnOK8K+HH7Qtx8NfF1pofiS5LWF422OeRuEJ5JJOSa 921LLPFdwlXhmRXDDupGc/rQBj+LfhRZeJfD11ZSF91zG0YKuRgEEf1ryj9l z9kuL4N3OuwtdG5t7q6WSAIzDYoTGDnrz3r28eIk+wyFsgqDnJ7Vm+GvFNre 3FwYnDiNsMQeAcV6GC/g1/8AAv8A0uBEt1/XRlyLQI9GjG1mA6itTQ5luAxb KgNjHrWRqmpvdwOsKtIZMqpByAaxfiv8XrD4KeBEvrtXluJspDGCAZHAzgZr zyz0+2vVtGVQoZs5XBpbzTxd3KzGErHKCJAQOSa+QdF8Ra58TvEB8R3OoTWm l3OZILZWZDGD0BwcHoa9N0T4lXWlWI3XZmRhuA3E7aAOy+If7O9l4wv1usv8 pBI3Hn+neti01bSfgp4aj+2OqKOilsEAD6e1VPg78UP+Em1CSzndA4OEY/x5 PT8BV/4wfCFviJCrI3CnlQDxgUAcj4p1/wAKfGRxJHqTWMnq07bT+AFcjqfw 30Twez3C60l5u9JX4/A1sTfsbXM6eZDPPHJgEKruM/lWTZ/sha3beIBNcG98 iLkkyNsHI680AfJH/BRnRD4i8KC6063uJrbS900s+7MZXbjp7Gvv3/g2Gj8n 9snw2uAB/wAKu8UMPx1bwzXgP7a3hWy8IfADxTaSJHLJDp5yQvJOR0zX0/8A 8G6fh4+Gf28vDdueA/wl8RzAY6BtV8MmgD8E/hR4lbTNSsDuIUBVIPTAAH9K 9+WwvhaR3kIUo/JO76V8r+HZms5IHUqnAwfwr6w8E38mv/C9FDskkeSSBweB 3oA6XT1i1XTY4bwKpYjv0/zmuu8H6TeeBGCpI0ml3RGxgP8AVk8DpxivFNK8 SteaqtubortfGGIHOcV9G/C3xPb23h+PTdZtIzA6jEi5baPw4oA6jRvD19HA JbS4W4T72GcDPoMitqKSXytuoWceD1KEt0rDbwRfaY5l0DVi9r98W+9Quey8 ZOOtP0z4laxpOoGDVbGEIgGSrEk5/D2/WgDSuZ7S2tnNotxayDLIUi/i7frX j/xh+LHiHS7sw3V/Pd56+bx5fIG2vbtP+IlhrNuzW0TlwCP9WQN3NeEftCeG L26MuqX0bQWc8g2lfmbk8cdqAMfRvFTXNstx5zRz5yRmu/tf2l00v4YalZzw C4utQia3gkfduiIIPHbpmvCr+AaLp0UkN07rKhfaRjB9Mdqhih1DxBp7bI5G gtAZWODtXNADmsxq2pNIrMzyNkADOSTx+tej/D39mDV/Gt1D57Ja2svXcwEn /fJFY/wd07TtG1+K9114UtInRghOWyAc5Xr1xXR+M/2jtS/4WlNfaPfPDo9t OBFCrAZTIPA6+tAHqkX/AAT+077KjPqF3I+3PMa/l/n1rpY/hTZeANBS31XT rfVbKHJMkgLOB9Frvvgt8bbT4n+EoZ1kiEgCrIpbkMcmtnxDZjVbaRWwyvkE Mc4Br0cx+KH+CH/pKJjt8z4w/aO/Z2jtLl9V0C2Q6fJEH8tOxyM8DJHX9Kh/ Z9/ZvsvFPhoalJNOl0yGSMNGPlwM8Z6gGu68afEy/wDhxq2o6Tc20bWkkbmN nzn5gwwPxK12HwEu5rzwfpLPp9nCYbfyxIXO7BJ4Ga84oyPA+s2Gv+Hbvwzq SyGWEC0E7octxkk54/Wts6HPqdjo26JSbi5e0l56woPlP0q9ceH7LTvFN1ZX Uez+1pd1vIqbhEuMdeg/+vW21zNoXgb7TPFJcXRlkitwoLBCCMNkdMigDhfG Emi+F/C93ot2895NGJZU8mLzUBPIGR0I2jPpXmfwpt9T1bw+0l7BtsEw0Egy WRcZwoxjGa7Dx3quoQSx6dZ6Vaacbt1882rs7MrsuSQe+N2R9a6/w1pkXhDw rfWju8tjayrZW425IDjGcD3NAHK6P40utBtTGFE6Z+U7ju6eg6Vuv8Ukk0KQ IT5jDBB6j8K89+JmiS/DDWYY0u2UzqZHIwCSDjirGh6YPFFik1ldhJVyXGVB ccDn8aAPYPE/xNuNGs9PmtpSXkRRIo/u4FeU/tc/F1vE3g200/Z5NxcyI+zn O0NhutT2muTafze5ZLf58tzwP/1V47418bR/FX4m3l3hHsrMtHaNnkrgH8Oa AOM8YIdCe3CnbFEhGf73Oc1+4fwDt/sv/BDD4GJ0zZeMn/76j1pv61+HPxNl NxqUMR43A7R0CjOCPav3F+AUjy/8EL/gY0n3/sPjEHnPSLWgKiew47n23/wS Q/5Nifr/AMfGm9P+xf0mvqavln/gkh/ybE/X/j403p/2L+k19TU4bA9ziZvg Xos3iRtV+3+MVuzc/a9g8WaqLUSb92Bb/aPJ8vd/yz2bMfLt28V4zr//AATV 8N698PNQ0aGz+H+jXmsRQXl7c6Z4SMdrLrMttfWOsar9iluZLZ7i9sdQngDT JLIMqbiW9VIo4/Z5dZ8fHxOYv+Ec8I/2Ibop9r/4SS4+1i33483yPsGzzNnP l+bjPG/HzVxeu/EL4qXfwqfV/AfhPTNd1S6u41tLLxtrc3hyY2AhK/a3+z6b dNHPLIquLV4lZUlzIYJAbZGlqxdkTfDj9o3wDpHjyx+FPhzwz4/0N9B3aNYw R/C/X9M8OWUVpEyrHDftYJpy26pFtiZJvLcBFjLbkBzfFH7BXgnxT8Ufhjrk 2h+FDpPwl0K/0jw5or6BA9to8s13pNxa3NoD8lq9p/ZSrF5aAr5oKsmzDdf8 OfFvinxD4nW11nSbjSpLK0jfWPLmM+mR6hLDExs7CZ7aCa7giG5munSNS0qo AziWK1taVo/i2eLw5qOt60dP1G0M0eqaTpBhuNLv/NkXy3aSa3FxmJE+Xy2i BMsm8SYQrSvdP+uoujt0ueFfElSnj7WRnOL2Yf8AkRqxK3fid/yULWv+v6f/ ANGtWFWcElGyLm7u7CiiirJCiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigD1z9m3/k0PUf+vvxH/6c76vcK8P/AGbf+TQ9R/6+/Ef/ AKc76vcKzh/kVI/Hb/gtN4Paf9qPxhqe1irWMEZOOMmyhAGa/Fj9lPwhFrX7 cF/Zzqu22E5b1yFUiv3m/wCCvER8Q+KfiO9oiSTaPqGl2kyg/NiWygcn8ARX 4feDpl+Df7fupG5X7Ja6nLM1vL138Iox9TWhJ+l3w98PxabZJgou8ZwWAwK6 TVvBdhrVg6yNHuwRkkY5/GvPvCxa6hjvbvUBDbY+bef6Vtv450JZ47CDUSZi cLhev+c0AcP4w/ZivZtUE2lX72UK9Vj2jzeR1zXQeBfgEumzw3d+BJcIfvHB z/nFep6Zan+z4SzZHA69efar7wr5KABQP8DQB518evidF8FvhNq2uhNzWMJk jTJ+ZhjjivzQj8f6p498eXGvXDTXt9JIWhXlmdcnCHA6AHj6V9v/APBTzxVb +Hfg0lnLwdUeSBQAcdATz+FfOP7Cfwfi+Ifj+1v4g72ejkXE5ONkozt2+pOa APq/4GfCZfD3gWxn1FfNvJ184E9Y9wBA/DNbnxC+LGl/DO3h+3O2ZMqoVN2M DvjpXaXunSDSWSKQ2+1AFZeqjtXyj+1Xr0Nn4Z1N1Ek1zbRttldcNuz7ccUA d3pP7X+h6h4ma0NvJ5YxzFGzsc+1es2d5Z+I9PDW7LIs0fKnhlHuK/PT9k74 safb+K5bbVH83UbqUpAzKTu+YkKD9K+9/h/e6DCluWaK0vZlH7sZ+bjrn1Ne jmPxQ/wQ/wDSUTHb5nif7Z/wJubnQYNV0cNDHakyXUCDAUBfvL3JJ7V5J+wd 8cNX+GHx00/w9cXMr6br04gMbthYiSWYjHfjvX33rnh+21nTJAVS4hK4dDnD D39q+C/2mvgvqfwG+J1r4g060WfS2mMsMwIDwSYJbjsBnGe9ecUfotdRR6la b1AYScHkfSvM/Gn7N9p4p1CWSeY4kbO0gEY7/wBa6v4H60/iH4ZWNxPkzSO5 bv0lcD+VX/iLeXtj4bml04F7lDnAOPl70AcR4W/Zx0TwuVWKOMkZyNoJHate T4SaLbX4kkW2Dg52uQuf19q8xsP2h7zwrrSW2sLLGWJzIwJUj6il+Jnjm38Y wR3el6rIm35iTwRznj60AN/aQ0jWY7O6TT7gxQrE5hCOMbcHvX56/slaFc6j /wAFBdTW6cSTJDdGRg24Z2Jxmvr/AONH7UmleEPhpdQ67qYS6+yyR25wXLOV YDHbrXzB/wAE0tBuvEvj3xF42vQ/nW8jRO7MCSXjH49qAP2U+DdsG/4N8NG3 wtMYfGni5tiqSSR4m10dB9a/MX4v6AvjjwPcG4V5bzwzI9/CGXG7d8oA9ePT mv1C/Zp8ZQaJ/wAEG/A97MkcsN9488WIUk4DBvEevNj9K+JfjH4Ft9K8ULJa MTFMBM0O0BSG5x15oA4z9lXxFBf6fKw1De1tNiSAsp2856deR/OvZNX+J2q3 Oo+XprtaRLwShxnn3r87/jVr+vfsg/Fe11PT5WWy1SMSzwrwjFQiduc4X9a+ q/2af2nNK+PvheO4U29rdrhZ0LYZSeeMnnigD6k+GHxDuoo/s+pXbzXTtgfN u78dPap/jz8UrrwJBaPbpvE33ySRwVzXHeHY9D8KRNfzX8MtxF9wM6jGD7Gu J+MPxv07xz4eaC4vbSOSBmKETKSRjHTNAHZr4H034raK+pWwSzniISfZj58c AnPoAKo+FPBfh+XV/wCz47+Se7iGDlFC8dec1hfAzxktpYXVq9xmCdHJ+bqc EVwni/xWnwr1yXVIpxy2wc4J3f4UAd7+0l4D0HQ/B8sN0kMqOpL5wdo496v/ ALP+l6hpvwstob6TzXMrtFhg2IuNo49ABXgHj/4l6n8Yop5rfZdWFsga4JfH y9M4+tfUngF7eXwZpv2WNY41gQfKOrbRk0AaV7p8N/YywS4EUkbIcDpkVynw 7+E+lfDT7YLAlvtbiVgRjkDFdhO7C1cLg8E4PFULcstvykak9cdq9DBfwa/+ Bf8ApcCJbr+ujJIH2S5HAz9eK+PPix44bxR+09GdVBhjtxGsUT52/LuGRn1r 6+Te0hBUY7YPWviv9u+3nvfjxHcaNbNPfW8ELv5QLEfKcZxnvmvPLPrj4R2m l+JPAqwhIYyFwB68dazdQ+HNlo9wEuJo4opW4xggjpXyh8Ff2k9X8K2ws79J IJLaRYnEhKvz1OPQcV7f4r+Io8baVZPBco8gK5xIOm7rQB6vNFp/wo8IPrFh 5dxNcAqpIxyOeoruvhF8R/8AhNfDv2ifZDcKSMKx5H414rdF7z4K28Ukm9o5 XfOcnoKq/DDxi3g3Tri7nlIiiBwG4U4PegDr/jJ4u13w34znltrmRbbzPlG7 CkfWp9K+Nl1q2iMlzI4k2EHJ4zz3qnYfGPwz8RFkg1N7RBz8wcHB/OuI+M/i vwr4P8I6ibDU7bcltIUO9A33SQBzQB55+034wTx34t03wtbus/265K6iFbcE idDgnt1HfFfav/BBctN/wVCtJFkLWqfCXxBbwqeCPL1bw4pOO2ePyr8zf2d4 brxn8TPFOto5kF2rWcD57xynGD9P51+mH/BBZIk/4KjQ7ZpHlHwp8RLJGVGx Mav4cAIPcnnP0FAH87MGnzRQW5VWG6NGGeDyoI/nXtXw++LI0/4fHQmMv9oz E7CQQRnGP5V5VHfSHTbaZAjutvEo46bYwv8ASuh+EVg+oeNrKef5k3jd+tAH 2j8Kv+CZ3iz4n/DGPXLTR9bhuCrXSz/Y3CPsG4LvK45zWD4P8Q3fhjULvw3r 0HkahpriGdJMrkgc8HHrX6NfFT/gqV4J+Cf7BNn4d0SfTZPEKQ20bRrtMqo8 YViRjPGPWvyp1L496Rq+s3OsXWm311qeoyb5plmUKxwO1AHsena63h2F20vU jApAIiLAAkdMVd0rxDqms3ks2otK3mKF4G4Y7GvnXxN8doDP5llZXVqwB+SZ w/P0rgvFn7Q3i22maNJfLjdQASvUfXNAH2D8OPGjeB9VuPtt3AkAmZwZ5AgU Hdnr7EVi/tAftLeEdWsUs5Nbs7gnBMUFwkoQg98HiviLXPG3iHVw8tzdXOyQ 84cgfTrXPTStOxLMWJOck80AfYvwX1/4eatfG+8QajPNFbTjy7SGBZo7mPHI Yg5HPpXoPiPxfZ6p4Gub/RdAs9DtJpHgt7a23broKf7rckkc4FfPH7Cvj7wb 4P8AGFm/iplji+3Id8zDy40CnOQQeMmvuD4n/tY/s/WgtLgOmqXNp88MVjci OMNjHK7cHj6UAfFOuXVze6lI90somJxiRSGA7D8K7L9nn4N3vxn8dxabbs8K KrSSOv3lVeT+lSfEW6tPiG9xrOnQG2indwkfGdoYYJx7Gvcf+CaiQ3Hii8Eo ja4jVghAAbBTmgD3v4QfszaN8O9LEVpPO9yjhjLJEEdjjHQV2Op+GbuytXKK 0yAfeHX+VbPntaIWVSSO2OauW+pYgZX7jpXo5j8UP8EP/SUTHb5nyJ+2h4RN zpX9oRI/nJGckLkja8fP8682+B37T9pptjbaTr032UW4CxXROeB2JPAHPNfY fxX8FW/i3w/fWzhWMlvJgD1xkY/EV+ZHj7wxJ4U8V3mnuSXtpfKCkc9K84o+ 8NI+InhfxuYCPEXh8zgbET+0It79+m7NdZ4stL1NPistOkufLChlliXMeSOu elfKX7MH7Nx1EQ+I9QdoxbzLLEm4r8pB/OvqTRrySHTDGjuyRZHJzkDgUAcv pnwyn0e2+0PP9q1OaQmSedgrBcjAz7AsK1r7RY9A0yZIo3v5Ln5gIYzLGHGc ElemKv6j4fbU7Vbi3lOc8gknOCMisW11SXSL+eK5kGJ33K3TysD7p/H0oA84 +M/gjzfCVxe6q7XOpfZy6KefsxyPlB/XmvD/AAhqOreHNeVoWmUOQGAyPl9+ K91+Ovi9bq6j0+GRZTJEUlcAjBz3/CuIsNEhm8O3F+xEf2cHBI4OOKAPMPjz 8eZr+ZdJ0iVmnWP/AEl4zkbWXGCRnnrxXDeGbz/hBfDsd5cy48zGAzAHHeq/ h+5tbnxTq087KclkA/vEMeK7vwf+zdqPxSnivtT3W+khg0MJBV5k67wRxjg0 AZHw+8E678ffEUk+nWlw9mXA+07GMcJLKMFsEDrnr2Nfun8N9PXSf+CK/wAF 7VFKrb2fjCPBGDkQ6yD+ua/NL4Q+BYfEOp2vgnwiv2ZkxHxyzksEBJHOcyCv 2J/af8IQeAv+Cefw00i3h+zpZ6RrysmMYc6RqDOfxYsfxqJ7Djue1/8ABJD/ AJNifr/x8ab0/wCxf0mvqavln/gkh/ybE/X/AI+NN6f9i/pNfU1OGwPc4t/2 hvASeJ20QeN/CLa4Lr7D/Zv9s232v7Rv2eT5W/f5m/5duM54xmsf4m/tdfDv 4FeFbjWviF4u8M/DzTba9h0yS48Sa5Y6fEt7LbC5W08x5vL8/wAol/L3biql wCmGPo8nzZ+dvbsKxPB3w30bwL4Ui0TS7RrfTYQw8tppJXld3aSSWSRyXkmk kd5JJXYySO7O7MxJLVupJU8JfF/Q/HcekXGjXtnqmm+ILM6lpOoWV9bXVrqt kEhcXcDRyMZIG8+MBwMZI6B4y9TQPjbY+MovDt7oWn6hrnh7xEk5XXLOS2Nl ZtG6IiSB5VmYyln2GKKRR5L7zHlN2vpPgHStH8WatrdvbSLq2ttD9runmeSS VIU2RRAsTshTdIyxLhA800gXfLIzal1DFqDBWIbyZA2AfuuMEZ+mQaaaDufL fxOOfiDrRHe+n/8ARjVhVu/EwY+IGtHBGb6f/wBGNWFUU78quXLcKKKKskKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPXP2bf+TQ 9R/6+/Ef/pzvq9wrw/8AZt/5ND1H/r78R/8Apzvq9wrOH+RUj8o/2hfiza3H /BVj42eCbvy5bS9s7JWikAObg6HZmLGenLj8a/GX/gpX8Lb/AOEfxctL5Y5E u/CVylpftnpKZA+PyHSvsD/gpp+1H/wpv/g4Y+M1m4AFvLoLqTJjIOg6YTxj 3NXv+CoP7M8Px3+A0/xd0ydJNJ1lo7rV4wu6SO7YnywBnOCF9PwrQk8v+DHx P1T9oz4X6bc2B+zySKpmyuQvPI4PXFez+HfBWhfD+GK61W7eWePkQ+adykfX Nfnb+xF8d9S+FfxEi8PNJGunauC7q5AMLgBVAP4+1foToPw/XxLH9rv7/wDd Zy3OTz260AaHiT9oC51ycJpAACOFCbQWHP8A9evZPAV3e6r4UtnvUWOcqC+R jmvN9EHhPwPOjpHJcTR/xHAz/OvRvBfxH0zxc3lWeEk/uDp0oA+df+CoHgS4 8a/DfSUtI2ln+1SmNB1cla6X9j74Kw/Bn4WWOn7St7ckzSsw5AfDbT3OOa9t 8a+ErXxLaw/aoElFqxdcgEKcYrmtOvFS9KbSPJP09qAOc8e/Hm00LUn0+KJp XiPz4I5/zivJPH/iTSPHdncxvYeU8ynduKkfXp71e+K/wq+zeLJr9bto3Y+Z liSv5Z96871KeeyuTG01vNEBguhGT7YB/rQB4v8ACvwdpGjfFOzuY3VZYL9z IpPy7MtgAYr6f0rxHDqfjOFBKFEKIQFYjjGBXkHwR+GuneIfFmo3V+zoLU+d Fs+XksevPPBr2nwt8KovGusNPG0kOwBAVPPHAPbNejmPxQ/wQ/8ASUTHb5nq l58ZhoSWltAhl3AByMHIx71a+JXgO2+InhqfTbiGNxewgruXd5ZYAnHp6Vx2 mfBm/tPEtpEbhpYwwLFoscEHvmvYF0wR3USk58tETPXkDFecUO8DaSvhPwj9 lgQsLZJGGe53MwH615B4p+LGq6pBfWlxMltJG+1AFwcehx7171ZW222KHBVw ece+K82+JX7Oun+MtdN/9tktJeoRRlScgk43CgDwy81G9uLJvtkUMkqKcN5Y HT61nacq6hboXR0jI3EA4x7V6n4g+B9r4ej3SajPc5GQqW5wfxDGuD8d+H4P DXhfUdQllNqlrA8iKxwWIUkelAHxv/wUj8X2aajpuhWbq80wO7HJDBwAv619 C/8ABOT4NSR/D/wvo9tA732viK7vF6sI1bbIx+i5NfE/hHSLr9o39ocyTTSy xNc/bIy4JG1CrFRk4GcV+t/gPSrX9lD4JWvjK/Xy9T8Q2Zk0u1eMRtawMCjB u+dwz90UAfRM3gSHTf8AgjB4W0TSo2u7TSvjD4xggUZffFH4l8SIp9eQB+df Nnxx8D6hpcEevDT0mfTrdHu4WJEkcSrgNGvV2z29K+0f+CZ9vcfF/wD4Ir/D 7U08uZrjxp4r1WRi/BD+JNcJIPfmQV5X+1Tpc3iDw5YaxpDRWesaVcOLhA4V pY1G1Qc8nqTxQB+S3/BTHxHpHjL4b2t1a2l0l7avHHueBk2Bnzg56E9a+O/h X8YNY+FOrCfTpprdud4H8eRjP6frX6N/tP8AgS18f6JPpVykSSzlZmwwOXUf L/Svku6/ZLSLVIhfTRxRocEowbPIoA57Xv21fFniHSxHBf3cW0liFbqOnpXD WXxa8R6rqvmz6jdsUcyEZ9/pX0Ton7MnhLT7CbyJ45XZcfOqjv0GDXhfxh8G H4daxLLacQ3X7s4AA4yaAPZ/h/8At+3Xg3RPJu7NWdGIDgtyGJP9alb4i+Kv 2oL6U2t6LGyjQkKJAMgcjIb8a4H9mn9n6L4l2l/qOsuRbWpKJEcHcdp5/Ait G9ttQ+G3iqSHw9I8EW4xssZ4cf8A6qAOg0jXPE/wde609dZmew1RBHIEdeRn J6fSvur9i3472/jfwFDZTSL9tticF25I6A/z6V8FPYXviu8hS6R41ZgN+OQP qRjNdN4Q8ZXXwp8QxXWl3sitGQHRsKr4zx60Afp44+1hm+XDf7QFVY7QxzEN gjHUHIr4g0L9vfxDZTxmdsorBSN5OQTyelbnjj9t3VtMi82xuWmW4G8AuQF7 Yr0MF/Br/wCBf+lwIluv66M+j/jR8WLL4OeFbrUbgxs8ILRLu5Y9ceucV8GX v7S2uN8R7zxBA2yS7AiKbiAUUkj371X+Kvx113443KJfTFYozkBnyBxjv7Vg 3vw9kh0Q3AmDbcmQBgSF9vU155ZzfxU+I+u+LPF8uoecBcXDH5RJ0zjvj2qH Rvjp4j+EWu6Zc3901zbuvEHmbgnIGMDmuttPhZYS+HRdSySyzPEWyyAFTXlf jH4Vaxq5kvIJGuIrc7wGIGFHJ6fSgD6s8K/8FAtM1bwpFZPFIt4qARxOjBCx A79u9eM/Fn9vXV9V8PXej2yeQZHcHYWIUE8HmvBZvEE+g6h5TwIZrdu5PUcf 41o6J8PbrxtC9yFIaUnGOQOf5UAW7X9orxHp8Fxtv50Mx3Eq+MnGPSsif4qa 54wv4Y7rUJ5UZ1QoxBHJFReKvhjqXh4gTQO0ZbClefavRv2YP2fIPGPiaG41 JvLt7aUN5bgbXxg859xQB9Sfsa2tt4M+B0NwTJPePJJMIIU8ySQsQRhRzzmv 0X/4IM6Y1l/wU2s5hCscV18LPErq2MMx/tfw2SCO2M18x/slfDO00bW47PRd M086lrdvHp+n3Pm7RpkyjInU5wCcYO7iv0Q/4J0/CjR/hL/wUx8FWmmXIu57 n4U+LZbyUBcNJ/a3hXPKnB+YtQB/MxoWiDUfDFgIoWTfbRl3PIB2jn9K3vCv gyCwlDSF93BUoxXFel+EP2e9Z1L4P+Hr20tjIbrSbWeMKnLb4lb8etV5/gB4 z8J2Zu7rTZzHGuceXgj9aAOg0nVbU6IsEqfacr/y2O8jA461jXsVvZz7/s8T pIcgKoG3n6VXGpXmiRZlt8Y4PygYzU0Wvz3zBBApYjqwGM0AaUPh2y8RWSO0 cCMw+VQoyc/SsX4ofCyK50OLy4CCG4cDIxiur8MvdC4j8+FXiDAnYu0AVb+J mhTanorTxOYYFTnOcHFAHyl4tu30+Q2oAAQY6c9a5wHBrpvHul7NYlYYIOcM CfWuc+zOf4TQBGzMoJQ4btUiXkm8PufPb5qeLCVgCEJBqa10C8uzhIH+vagD 174IfGdoLBNNuwGEjbPmxyGBH6V9C/sy+PV+CXxognO5bK6JiDFvkIbC5rxf 9kv9mWb4lahFJKsiJHKAWyR0DE9PpXq3xV8Jjw/rsulhgpsiRbzDICgc4J6k k0Afo1Dfw61bJd2kqSW8gyCOc/jUa3CtIQ2MRnI96+Nv2b/2k5vh3pKWOo3M soDr5kbuSy4B4BOa958M/tO+G/Gmt22nWc8f264YLsMmTk/hXoZi7zh/gj/6 SiY7fM7bU7gS3zAAkDj8DXxD+238OB4V+K6Xypshv3MjEDvuA9K+6vse5VLx 4J579a+cv+ChHhN9R8HWt5Gu54CmcDtvJNeeUcl8APHkVtpa6bcvsUusaMWx xgnNe0Wd7H9nCQup5zn2IFfJPwk+IytqFpY/Zy8jSBWkGD6mvpnTtIhS0ilk vURmQDCsQAcc5oA6K31E2alVYc8kZx+Nc78RvGtjpWgSyTz2kBVSqs68liOB 0654r5/+PHxq1rw14gmhsL2IKEGDtJ4zj1riPD2rXOsWlxrviPUHjtlB8uN3 YCTIPI6jqMUAaVz42LeIZZLgsTdSb3O7heK5z45ftBxaNocmlaS4ImXD87jk 47/hXJ+ItR1b4k6pIujWskdh/q/P4OQecgjmrvwZ/Zmvvib8QbPTYC92zy7Z XOWHU8GgDrP2W/gdD4ghfWddjkZpXD28CSbGVg2Q57EH0r3Hx/4pXQ1VLNAr RRNGI0AAAOcnHtXrt/8ABbTPg94TstMsIJbieOIGX58lDtGTyOmfSvnz4ga9 t8aTRM8ZDq8YXHOSMD+dAH1R/wAEgvhrHqvxfi1+/YPiWAr3Df6RA2f/AB2v 0q/bG16LxN+xD4PvITmKSPxSi85+5p+pp/7LX58/8E29bf4e+HLYOY/tUflB 02/MCGBr7i+Nl59v/wCCcPgCbYU81PFzYPXm01aonsOPxH0F/wAEkP8Ak2J+ v/HxpvT/ALF/Sa+pq+Wf+CSH/JsT9f8Aj403p/2L+k19TU4bA9ziW+GesSeK Pt3/AAn/AIua0+0+edMa20v7I0e7P2fd9j87y8fLnzN+P4881xmu/s9+L/G3 wobTY/Hup/DLX7u7jvJZ/BNnpbxWsCQmKPTYxqFhcpJAieXmcxJLJJHuUQRE Wy9nJ8Udai8SNZf8K98YPaC6+z/2iLjSjbeXvx5+37Z53l4+bHl+Zj+DdxXj Ov8A7d3iTw18PNQ8Qy+EvAF7ZwxW+r2Vzpnj8X9reaNf219/ZF1+6sjcm4u7 61gtRDDbzRn7Yr29xdtFLErT1J8z0nQvAHi7wW2oXwmsfFF1o2lfZdKN01va X/iW8+zRCS+1G4htUihllkijiCW8AijRWkIk3xw208/wdvdNs/DGk6Z/Yv8A Z9iZbnWtVuLOFdS1KVpkuJFRI4lgjN3c7p7iZVGChEcQeVZ7fuPC95qd14X0 2XW7Ow0/WpbWJ9QtbG8e8tLa4KAyRxTvFE0satuCyNFGWABKITtHjviL9pLx z4a+J3wx8HR+CvC97q/i3Q9Q1/xJLJ4rmit9CisLvSba6S0ZdPLXzf8AEzZo vMS1D/ZwGMXmZR6tpf11EldHC/EsBfH2sgYAF9PgAdP3jDFYdbnxKcP4/wBZ IxzezHrnH7xqw6in8KsazvfUKKKKsgKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKAPXP2bf+TQ9R/6+/Ef/pzvq9wrw/8AZt/5ND1H /r78R/8Apzvq9wrOH+RUj+R7/g5H8TN4R/4OCfjLfqSPIl8Pk49P7A0w/wBK /QD/AIJJ/EPTv2nvgLrXw38QrBf2niGxkv7VJiSscsUDKmDnqGbPevzk/wCD oxiv/Bdj45EZBH9gcj/sX9Mqh/wSb/bY1P4PeOdOSK6RLjT3VF8wn54cguOD 3HFaEnG/tk/AHVP2XP2lda0Kbelzpt6XhkxjOwqcZ+tfSP7Ln7Xt58WdATR5 o5/7ZgG2dSrMGYDPJ/Edq+p/+Cwn7JGl/tjfBGy+OXgpJjqSxrJf2tmVZXd3 ZixyMgBV9a/Kf9nz49al8APisuriCORg/lXcMgIyGwC/BHQUAfprofg6/wBQ 0OSe8xEAmQueOhxj8q6j9m/w5La6/LeyyLFaJlA7EAEleMVzvw0+I1n+0F4S sLrTL1EtLnY/7o5IH5fWtT4h+ME8O3tv4U05vJTHzsDy5U9f1oA+gmlS9icB 43ixyUbd/n/69cN4ltE03VSXOEmxyOgrO+Fficafbx2BleYnap3NyvAH9K7D xBplprdqY59oZhgA8Z4oA8F+Kmg65rt4dLhRZrSfGyffkpnntxx71wXjb4En S5IbKymmhuWAaRo1G0k9eenWvfdc8GarosxOn33lK3QTAsB9MYrJbT5bWUy3 E4vLv0C4XPuP/r0AfPnwh+DWvQeJL6xub2SBpIwwdWUkgtkD8q9s8M2ep/Dl hKZG1FI8ZGdx49hXJeLvE+r+C/ihYl47Fv7QZY1TY4K4BP8AervPEF1f6R4K n1vTyYp0Ukxf8snI9R1/WvRzH4of4If+komOx6t4R8QReINJFzJaeSyAYLA5 /Wr0MIlDzyEoinJ9+a4L4Ral4p1+2jfWRYvbygBBDE64HXncx7VsfFLxonh+ 1j022IFzd/uxgHOcV5xR2Fjq0F85iilDuB3PJrwj4xeO9c0jxndwRXEiW0ZI VVboevpXo/g37P4d09r/AFCYQO0gUl/TOP6VzPxe0my1bUkvoissMyM7Hrk5 oA8duP2jNU8NLKt7K0kaElW3EgdeTXyJ+2r+27fePlk8O6VMQjNvuJw7ZUKf uemCCa3P29fj9F4f1FvDPh5Y/tkuWncZPkAHDAjPXBzXz38Bfg/d/Hb4l6fo OnpcTrd3UYuLlMNuyyjnj39KAPq//gi9+yTc/F34jwaheWqDRrLdftdNx5Sx KH8vkY+YA9816P8A8Fov2xLe51zUoNNkSC10gPY6bAr4UQueSO45r3/4uapp H/BMj9mOx8IeF5Psev6laiXVZHcZZl3KRgBSCVI9a/E/9qn9oK++Nnju6klY NbRSnZ7/AK0Af1Q/8GyFlB4n/wCCFPwR+3QQXSzP4gkdJYw6lj4h1PnB47mv tO//AGd/AGqzNJdeB/CFzI33ml0e3cn6kpXxj/wa4/8AKCj4Gf8Acf8A/Uh1 Ovv+gDyy7/YZ+CmoTGW5+EHwvuJW6vL4VsWY/iYqp3X/AAT4+Al8MTfBH4RT f9dPB+nt/OKvX6KAPGk/4Jz/ALPkf3fgV8HF+ngzTR/7Rqvqf/BNH9nLWkC3 vwB+Ct4o6CbwRpkgH5w17bRQB45p/wDwTt/Z/wBIhMdp8DPg9axngrD4N05A fwENN/4dzfs+GQv/AMKK+Dhc9WPgzTcn8fJr2WigDyCT/gnz8BZUCt8EvhGy joD4P08j/wBE1Uk/4Jt/s7TNl/gL8GHPXLeCdNP/ALRr2qigDxX/AIdt/s7b cf8AChPgvj0/4QnTf/jNKf8AgnB+zy0ew/Aj4NFB0U+C9NI/Lya9poo1A8V/ 4dsfs6/9ED+C/wD4ROmf/Gamj/4J1/s/QxbE+BnweRf7o8GacAfw8mvZKKAP H2/4J6/AN4th+CHwiKYxt/4Q/TsY9MeTUcP/AATq/Z+tlIi+BnweiDDBCeDN OAP/AJBr2SigDwi4/wCCW37Mt5O0s37O3wKmlflnfwFpTMfqTBVzTv8Agmx+ zto8ZS0+AvwYtUPVYfBOmoP0hr2uigDxi4/4Jw/s83igTfAj4NSgHID+C9NY A/jDUll/wTs/Z+00f6N8DPg9b/8AXPwZpyfyhr2OigDzbRP2N/hF4acNp3ws +HNgV5BtvDdnER/3zGK3/DXwM8GeCvEcOsaN4S8MaTq1tbT2UN7Z6VBBcQwT vE80SyIoYJI9vAzqDhjBGSCUXHVUUAJsHoPyo2D0H5UtFACbB6D8qNg9B+VL RQAmweg/KjYPQflS0UAJsHoPyo2D0H5UtFACbB6D8qNg9B+VLRQAmweg/KjY PQflS0UAJsX0H5UbB6D8qWigBNg9B+VGweg/KlooATYPQflRsHoPypaKAE2D 0H5UbB6D8qWigBNg9B+VGweg/KlooATYPQflRsHoPypaKAE2D0H5V8h/8Fex j4PaT/1767/6ZL2vr2vkP/gr5/yR7Sf+vfXf/TJe1E9io7lX/gml8TtI+F/7 K1pNq76h/wATPUdNs7SKx0651Ce4m/4RvTZSqxW8ckhxHFIxO3ACHJFfSbfH vwsnwtm8aS6hPa+G7WSWK5urmxuIGtDFcG2l86J0EkQjlVg7OqhAjMxCqWHy N+xZ+yz4U/a6/Ykg8M+MrO31HRrTVtK1H7JdabY6la3EieG9NjUS297BPBIo 80sA0ZKuqMCCoNfRvw4/Yf8Ah58JvhHe+ENC0HSLCC/0y60eW+h0bT4bn7Lc MzGHZHbrB5KblVIfK8pUijXYVUCplzcl47kVL2fLuetwTLcRq4zhuRkYp+0e grG8HeD7TwZ4V0vSbNriW10i1js7drmZp5jGihV3SMSztgDLMSx6kkkmtaK3 SBSFAUVSb6kQc2lzI8VuvAmr/Df4m6v411r44fFa78LaY97qNx4XvNH0IaJF AIWkMSSQ6St+8cKuGTF00jGIBmkw6n1m68RWtjYtNJHOwW4jtSkcDSP5juqK MAE4ywy33VGWJCgkYHjj4Kaf8QrLxPb6lfanIvifS5tGcq6Z0+1li8uRLcFS qbmJkZiGZmCBiyRxomvJ4NS48R2N/LcGVbFppkgeGNl890WNZg23crLH5qDa QCJ3znjGUXUvZoycqvM9NLnzb8TSD8Qdax/z/T/+jWrCrd+JuB8QNZA7X0// AKNasKtad+VX3OuW4UUUVZIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAeufs2/8AJoeo/wDX34j/APTnfV7hXh/7Nv8AyaHqP/X3 4j/9Od9XuFZw/wAipH8gX/B0d/ynX+Of/cA/9R7TK+GfAXjGfwJ4nt9Rtywa E8gHGRnkV9zf8HR3/Kdf45/9wD/1HtMr4ArQk/bf/gk7/wAFFrN/DH/COa4B qegayn2a7tzt/cs67ARuyBgE/lXlv/BYX/gmA/ws1Z/iJ4JtJ5/COqN5kcyA tGis2ACeByAe1fmZ8GvjdrnwV8Tw3+kXbxAMPMj4KuM89Qecd6/W39gf/grx a+OfB0fhLxl9h1zw5doIrjT76FCIcgr8rY38Ak9RyaAPhH9mX9q3Wf2ede/0 fz7rTJPmms9wMkZAxwScAAelfX/wn/aF034+apLrUEmyePKrCzAuuRnBIrs/ 27/+CT3gv4k+C7nx38GmdbWdTcNp4mbdbgknYAzu54XPPrX5k2XiDxd+z14w dbaS7069ibEiNHxx2IYY/SgD9cvh949XTLl5LpPLdVHzEDmmp4uvviF4lmkg uHS1tOeGIHXHY18LfCL/AIKFzaxcraeJEEKS4Bniixg8ZzgAe9fV3w2+Jemf EiyTR/C+oW8csyhriXcofD/XOTkfrQB9A/DXx5D4/wBKnhjcSTWxZCc9CDis Dxp8P7SLX4rl7qWBo2DMplbHT0re+FfgnTfhfo5t7bOWBkndmLEluSeSe9Qe OPE+nXGnR6hFbpeR7zGzM7LgqPY0AeX/ABy0u18R+NPDM7lxaWtwC8qnHATB 6c+leveEPDos9CkhuL22utOkQeWApyueeSetfPv7VvjCw1htJsNP1CK3Er5u IUcZwU9c5HPpirfwg+Lc8dtBod5cGaW4Pk27twW9BxxwB1OTXo5j8UP8EP8A 0lEx2+Z9T+FZ7NtDd7SQSJESEAP3SDXnvhO/tPE3jCeXUWH2qOUqI2PIG449 R0rr/CeoWPhCxt7AyReZMQ7/AD5+9zXhP7Vvxl8KfB7UE1yC6j+2A58iGTcZ CB0Kg+/pXnFHqfxf1C01jQ/sayG0MZDqxbAIHPQe9fHH7R//AAUA074YaDfe GbORr7WbbNspjkHyH1IPbkH8K8K/an/4KF+K/iKkdno839mW7REOUQLK7f8A AlJH4GvMv2d/2WfGf7VPjG2tbS1vLn7XIFedg3GSOSxH9aAObit9Y+MXjlmE 0l9qeqy5nZOArEgbQCeMiv1r/YC/Y98OfsHfBiH4l+NPLGrXlk32HT5OJCzK VDHIKnawFS/Cj9h/4Uf8E4/Adtr3itbTxB44ji3i1kuGkhtpcHJOyTB2sAfm Ujrnivhb/gox/wAFVPE3xW8U3dhaahG8ilo8QxRpDbKeoRVUKc+uMigDD/4K efts3vxX8canG90Zby/lZp0QkCD1Ueg9hxXw8SWOTyTVjVdWudbv5Lq7mee4 lO53c5LGq9AH9fv/AAa4/wDKCj4Gf9x//wBSHU6+/wCvgD/g1x/5QUfAz/uP /wDqQ6nX3/QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFfIf/BXz/kj2k/9e+u/+mS9r68r5D/4K+f8ke0n/r313/0yXtRP YqO5sf8ABI3/AJNil/676b/6j+k19SV+d/7GnxU8U/DP9mnUp/C0D6nfw6ZY XdtpZmihXUJ00DTzHCJJFKxF2CrvIwMgnIFdt8Mv2mfiL42l+GWreIL260jV 9U8Hz6hqemJbm2igvGGnGRXgYdY2kkVRJuZMsOpJOaqpaf1sVyNn1NNonxA/ 4SNpU8S+Ef7G+07xaDw1cG6+z7s+X55v9nmbePM8rbnnZj5a8L+M9p8RPhvo 3wK+H4+M3jqPWPGXjKfQ9X8Xvpvh5dX1SL/hH9b1DZHCbBrKPZPa2+NtsW2Q bSXLO77f/C+PFX/QU/8AJaH/AOIo/wCF7+Kv+gp/5LQ//EVH1iOug1TaLn7N mjfHC2+Jwn+JGrS3Gj/8IxZXUttbpp32Fdbu5rmS/tomjiW5NvYrBaRWxdsv HeTGZ7qQI8PV/tG/DvxZ498UfDo+GfE3ibw5YWGuTSa4dIayAktTYXQR5FuY pBJtuPIUIAR++ZypZI5IuI/4Xx4q/wCgp/5LQ/8AxFH/AAvjxV/0FP8AyWh/ +IropZgqc+eMV16aaq36l0oyhPnVjkPFGn3Gk67dW15d3OoXkErpPdXCxrNc uGO6RxGqxhmOWOxVXJOABwM+ruv6jNq2py3Nw/mTTs0sj4ALMzEk8YHX0qlV c/NeVrXMqjvJsKKKKCAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooA9c/Zt/5ND1H/AK+/Ef8A6c76vcK8M/ZqYj9j/Uj1AvPEmOP+ onf17nWVN3Kkj+QL/g6O/wCU6/xz/wC4B/6j2mV8AV/Q7/wWX/4Ngfj7/wAF FP8AgpN8SPjH4J8XfCDS/DHjD+zPsVtrmq6jBfx/ZtLs7OTzEisZUGZLdyu2 RsqVJwSQPmD/AIgqf2p/+h+/Z/8A/B5q/wD8rK1JPyBrT8K+Lb7wZq0d7YTN DNGcgjvX61/8QVP7U/8A0P37P/8A4PNX/wDlZR/xBU/tT/8AQ/fs/wD/AIPN X/8AlZQB4J+wx/wVx1L4V31rY6nfXFsJf3cq5LJOp4YOfQjivrv42fCP4G/8 FAvAkmsaPJB4a8aFcutvCoiuJDluXdvYDpXAD/gyq/anH/M/fAD/AMHmr/8A ysrs/h5/waVftp/DGZTpXxX+B0caEYibxHrRRfoP7NwKAPgL40/sUeLvg9cM HtH1KJdxYxL5igAkfwjHOB+lch8PfibqXwi11Lm0vNS0XUYm+7HFtDY6ZJHF ftH4P/4IGftvaANuoeNfgHrMOADFP4r1ny24A5VtJYHpWj4i/wCDd749+PLf b4h8G/ss38zDDXEHibULaVvfcNBJzQB+X9h/wU58bW+jG2dra5YrsM0t0Vci otJ/bn8X+INCGhNf2mmwXkrM9wl0C0e45zg8V+hmq/8ABqf8U75z5HhP4A2y tyyj4g6qcn/wQiqy/wDBqF8VgBnwx8Amwef+LgasM/8AlBoA/NuTXdBuLk3+ o+NNTl1AnLy+VGxHp0NctrPx9u/Dmu29zpXi7X52s5N8BaADyz7Y9q/WbRf+ DWf4k6U4MngT9n65A7N8Q9U/r4fNbsX/AAbSfFOzANv8Pf2cEZTkGTx5qcg/ L/hH67MbVhUlFwd7RivmkkyYrQ/IbUv25PiLqlwjr4m8Q5UAAqmScD/drmrS 08f/ABv8QRzMdUvbgyfLcTRkbT652/Wv2ri/4N5v2gdOUrY+Df2UIFP/AD11 +/mYfidCFPu/+CDX7XsNl5OkXP7NWh+hsvEmqRY/750Za4yj82vgN/wTduvF erwal451SOGwtn+YySxl1UHOQDgnpX1h4n/bI+Gf7CvgdtF+HsFqLlIWiuNS mUwzu2CNwAJBOD/Sux+Iv/Btt+294+tTGPiN8B7TjGT4m1mTj/wVivG/FP8A wZz/ALX/AI1n36p8T/gXenOcS+IdZZR+B02gD4G/a1/4KFa58bNevzY391It 0533chIklB7n3r5lnne5laSRizucsSeSa/Xv/iCp/an/AOh+/Z//APB5q/8A 8rKP+IKn9qf/AKH79n//AMHmr/8AysoA/IGiv1+/4gqf2p/+h+/Z/wD/AAea v/8AKyj/AIgqf2p/+h+/Z/8A/B5q/wD8rKAP1/8A+DXH/lBR8DP+4/8A+pDq dff9fL//AARl/Yr8Vf8ABO//AIJsfDf4OeNb/wAP6p4m8H/2n9tutEnmnsJf tOqXl4nlvLFE5xHcIG3RrhgwGRgn6goAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACvkP/gr5/yR7Sf+vfXf/TJe19eV8h/8 FfP+SPaT/wBe+u/+mS9qJ7FR3OC+E/7Lo0D4a+H7e8+IvjLwTrcek2EGsaVp r6e0NveQ2kNvID9o0i5YMBEqHbKyEoSvWtiX9mCxn1mDUJPjZ8SHvLWGS3ik LaN8iSNGzjH9gY5MUfJGfl4xk55r9unxB8U7Px7Bpvw70m28jUNfb+09YaaN pdOiWV22pE+ATIyxpuIYBXkyFIVq83/af8UfGP8A4Wj4Bk8HXmqaToFrdaHN 4j0qw0oXdy6XmqxW88btJZzQXUSxCQSfZryzls4xLcublWijHB7b977K2tlr 01vpfvpqul0drwrWHWIbVm2rX10trbtro+tme7/8M8xf9Fz+JX/lF/8Amfqr qvwR0/QrVJ774+/ECygeaK3WSeXRI1MssixRIC2gAbnkdEUdWZlAySBXyRqX 7Uv7R3w8/Zw8DXDeE7nxh4+h8IPqfiRJfAF7Cl3fzaTrFzaQxmC6/dzw3emQ QXUXkgSNqNt5RgMyRjufEV/8TZn8d2njzU7DUdG034g+Brfw81r4ffTY5Y/t OhvdzRSNNJ5kD3ck4VGLvEySK0sgKCPaz12MXGx9Ef8ADPMX/Rc/iV/5Rf8A 5n6P+GeYv+i5/Er/AMov/wAz9bdFZc7HylFtP/siKKz+3XOqfZYxF9tuBH51 5t481/LjjTc2MnbHGuScIo+UNrL8RePrDR9XltJoNbeWAAMbfRb25j5GeHji ZD17McHIOCCKpf8AC0NM/wCfbxL/AOE5qP8A8YrshJctjCUXc6Giue/4Whpn /Pt4l/8ACc1H/wCMUf8AC0NM/wCfbxL/AOE5qP8A8Yq+eJPKzoaK57/haGmf 8+3iX/wnNR/+MUf8LQ0z/n28S/8AhOaj/wDGKOeIcrOhornv+FoaZ/z7eJf/ AAnNR/8AjFH/AAtDTP8An28S/wDhOaj/APGKOeIcrOhornv+FoaZ/wA+3iX/ AMJzUf8A4xR/wtDTP+fbxL/4Tmo//GKOeIcrOhornv8AhaGmf8+3iX/wnNR/ +MUf8LQ0z/n28S/+E5qP/wAYo54hys6Giue/4Whpn/Pt4l/8JzUf/jFH/C0N M/59vEv/AITmo/8AxijniHKzoaK57/haGmf8+3iX/wAJzUf/AIxR/wALQ0z/ AJ9vEv8A4Tmo/wDxijniHKzoaK57/haGmf8APt4l/wDCc1H/AOMUf8LQ0z/n 28S/+E5qP/xijniHKzoaK57/AIWhpn/Pt4l/8JzUf/jFH/C0NM/59vEv/hOa j/8AGKOeIcrOhornv+FoaZ/z7eJf/Cc1H/4xR/wtDTP+fbxL/wCE5qP/AMYo 54hys6Giue/4Whpn/Pt4l/8ACc1H/wCMUf8AC0NM/wCfbxL/AOE5qP8A8Yo5 4hys9Z/Zt+IMD/APUtAV7Myi88R8GWTzf+P++f7vl7P/AB/p78VP/wAL48V/ 9BX/AMlof/iK8+/ZpL/2NdO8F3bm4k165VLi3eCQRyS3roSjgMuVZTyBwRWj vFcTlJPQ6FFdTsP+F8+K/wDoK/8AktD/APEUf8L58V/9BX/yWh/+Irj94o3i p55j5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnY f8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9 BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+ Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r /wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5L Q/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcf vFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w 5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L5 8V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/y Wh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4 Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCg r/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8A xFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8 Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnY f8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9 BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+ Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r /wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5L Q/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcf vFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w 5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L5 8V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/y Wh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4 Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCg r/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8A xFcfvFG8Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8 Uc8w5YnYf8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnY f8L58V/9BX/yWh/+Io/4Xz4r/wCgr/5LQ/8AxFcfvFG8Uc8w5YnYf8L58V/9 BX/yWh/+IryP9rifXP2gPD2ieHbvVbOK51SXUtPtLi7j8uCCSfR79AXMUbNt ztyQrEeldarIFd5JBHHEjSu2x3IVVLHCoC7nAOFRSzHAUMxAPF+LdTuNR8c+ EkktpbSCDW5PLikVS/FhqCMzuqlWcsmDtlZBtHlo0ey8vri5Pdky5VsL8dta +GXwp8XWT+N/F3j+11rxesmoxx6euq3MUjFYpZdq28+2NV+0IMbUXnCjA4va B4P+GPiy9u7bSvGvirVLiwCG6itNduZntt+7ZvC35K52tjPXa2Ohqh8WvCHi G6/absvENxYaZf8Ag6z8N2Vl5IkJv4pvs4leaNAn3T+6TbvySn3SOvivxt/Y htf2j/h1eWEC6noNrr/iWPV9SNybPN5b29nOtvHc2V3Y3Ntdwi6eN/s88ShS BOH3wxo3gVsfjKOcRwLUZUpw51KKl7utrSlbk5r/AGU7218zslGjVw0alCDi 4txm5Sj7z3i4RWvLbRt317bH0d/wpfwZ/wBBz4gf+DK8/wDk6sfx38IPhzpP he41bV7/AMd6jY+Hmj1swPeXUwL2ci3UbBHvtrFZIUYBuCQM8V8baH/wTv8A jN4A+G+v2/hXxD4X0DxT4kvrbVpJ7Dxd4htNP0m8tfC1nYWotbZGEP2ddVim ZoJ4ZYnskto2jbyoo4Ox1D9inxP4e8S/8LB8V+J9R1q+8PeHPiBBKV8UapMj JqeqR3emRrbuywtDBYRvFJGy7BJFbYWXyYpIvoOqV/6tf/gHJbRv+tz6u8Se MtH8Gwwyavq2naUlw/lxNeXUcCytjO1S5AJx2FJoXjTRvE5I03V9M1Bhni1u 45icf7pNeLf8FAvhH4q+L/w30yy8J6ZPqd/bXMkrpFJbxtt2bdoeaSNFLAkZ Lfl1HKf8E/vg/wCJfhf4rv18R+Bta8Mr/ZbpDeXc1hLFLL50P7sG3uZWBKh2 ywC4Q85IB+qwmR5XUyeWOq4tRrq9qdtXbbXzPCxGZ42GPjhaeHcqbted9r/5 H0f/AMLB0FfiLp3hF9Y0+PxNq8MtxZ6c86rNOkalmIzwMqrsoJBYRyEAhGIk 8I+NtI8e6bJeaPfw39vDO9tKYzhoZUOGjdSMqw44IB5B6EV8D/8ABQ7wL468 BftPX/izQ9N1GaHWbSC50nUbS2YW0F3HFHA0VxOsM0lvIojDrIqjeshTsa+g v+CcXwz8R+B/hHHd+I77Wbm5urOC2K36PGbicTXV3c3YR1VlMk99JHyMsltE xZs5r88oZtCpL6tKElVU5Jqzt7NRup81kvedlypt7vo7fd5jkFXDqGLp1Iyo Tpxle65vat2lTtdv3Vq20vx1+isfWsaz+I3h7UfF8/h6217R7nX7USmfTIr2 J7yHyktZJN0IO9diXtmzZHyrdwE4EqFtmsWw+G3hzTPGN14ig0DRodfvDKZt Sjs40u5fNS2jk3Shd7b47KzRsn5ltIAf9WmPTT7niFnxr8SPCHwp8L/2r4s1 jU9Lt2m8mM22lte7zsZ+Ajbs7Uc/dx8vXnFHg34sfDz4gXOnRaR4k1m8OraZ Hq9ow0Qok1rIoZH3NIAGKnOwkOADlRg48q/bttdaPwJlvdFh806Vc/abwpB5 88NuYJozLFEQRI6NIjlCDuVHGCcA/HX/AAT60TX/AIs/HS5kh8TtrVlpFzbX sZjaQW1hALtbm44EMcaTSSIkSRoABDLOOmcfM1uIKuHzCtg6tJtcsHSaUvek 5xU038KUYty1a2W6PqKfDsMRlVLMMPUV1OSqpyiuWKjJxaT1bclFdb3fU/UL 7HoP/QS1n/wWxf8Ax+j7HoP/AEEtZ/8ABbF/8fr5Wuv2ItVHxIvLyw1NdN0f xt4kvtY8b3trqUtp4l1SzCTjTrCDVrOG3vEtoZjBKI3mZ4kJtkm+yxiGSt4n /Yy8V+KfEHh6EeK/EOk6LHqTvr8Oj+OdY06PULKLSp4bGO3ggKfZHjupozIt tLCkyQCWYTSMiw/UJKx8trex9ZfY9B/6CWs/+C2L/wCP0fY9B/6CWs/+C2L/ AOP182aX+zH4yvLCXUNZ8XeIL/xPqGrXepXVxZ+MdSsLW3EUt0dI+z28YNqs cULQQz25tvIuTmWdbl4gJfJPFP8AwTO8SQeD/g9ofg6Twr4FsfB+raHrPi2X QdUu9JuPENylvc2mq3CNawxvHcyW8hjW5V0luVuZFlaMQRFmkuazFfS593fY 9B/6CWs/+C2L/wCP0fY9B/6CWs/+C2L/AOP1802n7JPiTWvH1o2t+LPFg8JN He6jqGn6f461eCa61k3MS21zFKjR3EFr9lWbfYrcmzV5lC25Mfnv6V+zf8KG +B3wB8I+EnmuribQtMhtp5LjVbrVHMwXMm24uWMzx7y2wNgKm1VVFVVEq1r/ ANdf8vxB72/rp/XyPTPseg/9BLWf/BbF/wDH6Pseg/8AQS1n/wAFsX/x+vkf Tv2F/Efhn4ieJLjQtT03wtoXjjXTqniBvDt5JpF/dhZdcuY5vNihDvdm5vNK MsjSbZYrQwujRoy3Pv8Aaar4q/tpEu9F8Pw6aZ2Rp4talknEQN1tYRG1VS5V LIld4ANxONx8hDcPSyY+tjQ8BeNtJ8Z+M/HGlyNqdpB4Q1iHSYZ1t0le+36f Z3jSMhkUR4N3sADPny92RnaOjZvDS34tf7Y1P7SYzKIvsEO8oCAW2+fnaCQM 4xkj1FeQ/BoXDfED41C0aJLk+LofKaRSyBv+Ee0fBYAg4zjOCK80/Z3+APxW 8LftBeLvG3jDxNa6idY1KK1SEynYdLhS5WK3jAiURqsk5mwBknguxLvWeIqe zgpRV25JW8nq5drJed76JanVgcNGvKaqTUFGLld9XolFLdtvysldt6H1f9j0 H/oJaz/4LYv/AI/R9j0H/oJaz/4LYv8A4/Xyf42/Y08YzfGH4heOND8QaINS 1jXovEHhyw1MTS2FheQaFbabb3UmwAxvvF8kqoGEsMsB3K0WK878Qf8ABJ4e LfgRdeGfEmlfCvxf4i0Hwj4h8M+F/EV5orrLZi7MU+lKonFzNaxWMkl1DHtu J2SJY3U75ZEXVKNrs5JXTsj70+x6D/0EtZ/8FsX/AMfo+x6D/wBBLWf/AAWx f/H6+WP2jf2SPHHx0+M+p63Z+J/DVj4V1PwhfeCbjR73SzeXQtbq2uWnuIZp C1vGZrp7DzYZLadXj0yE+YN7wt2PwZ/Zsi+F/wCyhP8ADe+0Xw94ksrYaraw aVfGNtN1O1lu7iS2juEW2WKJZI5IxJDDbeRCS8cMRijjUppJD62Pdvseg/8A QS1n/wAFsX/x+j7HoP8A0EtZ/wDBbF/8fr5H+Kf/AATv0rxT+zV4e+G+laN4 MGnfD5x4i0GG80yzl0fVtcZL8TRX2ly2csMVhJJeNIwtWSRPOYQ+R5SF/Q9d +DfiPxb8fPCnjSeLQNKl8N6nOk1zbXkkt5e6S9nqMBsC3kIfLed9MvHjZinm xEfMbWGWR2Qj3VrHQVP/ACEtY/8ABbH/APH6Pseg/wDQS1n/AMFsX/x+vkPR P2W7jxT8abPxrbeFtB0/V/C+p2tmZNZtZINUAXUtVudSubC8jVvIivF1C2uP 3RdLhEls51gLO0PqHwM+Dup/s+eDYNF0Dw/4PsdOvr2TULnStNu303SvDokm t0+y2Fuluy+UsH2iVmxD590ryGOL7W5gStYGmhPjV4xTxr8VPEfw3g8O/D/W vC+h+DI/G/iDUviDrQ0fSbW0S6mRjIn2S7j2ReQJjJIyKoGeNmT454V0z4Ie O/EWmaPoaf8ABO7WtW1u8j07TbKw+KOn3FzqF1IypHbwxppBaSVmdVVFBYll AGSK2Pi/4e134h+LP2lNIj0C41PxTrv7K+p2aaLory30t3dyvqCfZbYiJZJm aRgiERBnJX5ATtr85P2Pv2UfHvhf48/s46Xp37KXxz8LQN8XPDPi3V7678Ga tbWPgqGz1VYvLuLma0X7SGgMs8k7MqRo8WWYq6R1Wk4RhywcnKSTt0Wt5PpZ W+d0kXhqUaspqpNQUYuSb6vS0Ut7u/aySd2j9b/+HYGvf9G+/ss/+Dh//lDR /wAOwNe/6N9/ZZ/8HD//AChqX9ur/gk34x/aV+LXxR8a+GtU8MS6z4/8Paz4 PhHiqSC9XSNOufCs1hbvp9wumfb9O/4mcpM1ot3NatFLdXAQ3E/lx+K+Ov8A g3Q8cwaR8RYfBvxS8O+H7jxh4A1fwvH/AGTptxo2moNQ1PVtTl0W0sRJcJpe jm4uNK3eXJcSFLa8QIouWI6lCOlzmUn3/rQ9l/4dga9/0b7+yz/4OH/+UNB/ 4Jga9k/8Y+/ss/8Ag4k/+UNd/wDsu/8ABPjxj8B/2svH/wAULrUfDUmofES2 1+G/mt7meae1ZtQtm0URloVkuFFlG/ni9muRayqkWn/ZbV5om+dvgZ/wSf8A Hfi2/wD2fPixpK6f4L8Y/CrwT4M8Jvp2uwvaXlhf6bc6zD4kmmkS0F1PvF8z QC2vIrPUZCZbn7TbGISr2ce3b9fyt+JPPL+vl+f6HpX/AA7A17/o339ln/wc P/8AKGk/4dh66WwP2ff2Wv8AwcSf/KGvIv2j/wBlj9qP4QfBIeF7v4mfEvxX qnifQ54dBtfCfiDxTdX8HjGTS9DgS/n1e2tkFrpo1Aa5J9mvp7ewSO4hk2AI IbTuvG//AASn/aE1TVfitq+l/E22/wCEq+JFpNCNefx/4m0+90yWC58Z3Glm 1a0eP7NBEdW8PIbMeZaKsF8fJmkWOSe1Shv/AF/X9ehzyOk/4dga9/0b7+yz /wCDh/8A5Q0f8OwNe/6N9/ZZ/wDBw/8A8oaup+wF+0xe+N7i4u/jPdf2D4k1 q+k8RWkPjHWIpptLk8U2V9bwWLqinT5Y9HOqWxe0MTbpYIg+xI5rf3j9nfSf 2hPhN8FfB3hjxTpvw/8AHet6JplnZ6n4huvGl7DcajIqWomkZf7Kbe67rwB2 cGY20DsIjdSLbL2UbB7SV9DwbU/2Efhh8Hfh9da58Wfhr8E/CAtBc3Usuk6Z aalp9rZQRI73E9zc21n5QXMhbKFFVAxfkhfNvDesfsI+Mtb0/TNH8S/s76rq OrXSWFha2dt4dnnvrl2VUgiRbgs8jMygKoJJYY619If8FmdAvfFv7DvxI0jT dLv9a1PVfA3iOztdPsY2kur+WTT2RIIkUFmkdmCqqqSSRgE1+Cf7O3wF8SaH 8RPgToukfs1fGDw0tx8T9A8U6pd3HhbUoLTwZFaaj5QhnuJbUG63QGWaSZmV Y0eLLMVdI+HEzdPlUI8zcktOis25PpZW+ey1O3B0Y1XL2k1FRi3d9WrJRSWt 3ftZJNtn7Z/8MT/AP/onvg//AMIfTf8A4uj/AIYn+Af/AET3wf8A+EPpv/xd eP8Ax5/YSi+MPxM1nxDJp/hfWj4p1fRItTTxLDFqOzQrKW1nubK1Y2wmgjuJ ITvtHmltWKGQJHJczMvH6l/wTX17wdN8Q7nwbrelpDf6idU8J6PqN7d21hpE 0ehQ6daOkln5UtiIi15BstcbbZrURvE0CKLi09/62Mmtz6Q/4Yn+Af8A0T3w f/4Q+m//ABdH/DE/wD/6J74P/wDCH03/AOLqh+zB8CYP2bPhFa+DrN7V9P0u 6uWs/IsrWyAiklaQbobSGC1RyWZiIII0y3IZt0j+hUpOzsgSOM/4Yn+Af/RP fB//AIQ+m/8AxdH/AAxP8A/+ie+D/wDwh9N/+Lrs6KnmHynGf8MT/AP/AKJ7 4P8A/CH03/4uj/hif4B/9E98H/8AhD6b/wDF12dFHMHKeMX3wN/Z20D4heLt L1n4feAdG0Twh4dsfEV3rN54V0yKCOGeTUFlMu4hYo4ksN5kZ8ESHIUJluc8 N6x+wj4y1vT9M0fxL+zvquo6tdJYWFrZ23h2ee+uXZVSCJFuCzyMzKAqgklh jrWV+2ToF74t8M/tPaTpmmX+talqvwWgs7TT7GNpLq+lkTxGiQRIoLNI7MFV VBJJGATX5cfs7fAXxJofxE+BOi6R+zV8YPDS3HxP0DxTql3ceFtSgtPBkVpq PlCGe4ltQbrdAZZpJmZVjR4ssxV0jmvUcFHkjzNtLTorNuT6WVvnstTXC0Y1 JTVSaioxbu+rVkoq2t3ftZJNtn7Z/wDDE/wD/wCie+D/APwh9N/+Lo/4Yn+A f/RPfB//AIQ+m/8AxdeF+LP2JD4a+P3xU+M814r6jrkivbQ6dpMc+oJpcWkW NvMkdxbWZ1Zbp5LW4VIre4kjAljkSFp8U34F/BT4paj4A8FzReI/Ello1l4q 1rVUj1/W9SOq3OiTr4hi023uBcBrhnSO80WXZdMJUMB8xVlgVW1jqrmPLrY9 2/4Yn+Af/RPfB/8A4Q+m/wDxdH/DE/wD/wCie+D/APwh9N/+Lry7Sf2U/Gej rprt4y1u4ubPxNFqXk3vi/WLi2OnWuoutrDtSWKV5jpLRxyLNJJbTXMIknhu nd7hvTv2jfhEvx++EGr+EJbq9srfXTbwXElrcxwuIFuI3lUiWGeGZDGrq1vN C8NwpaGUeXI5Cbtt/WwJa2H/APDE/wAA/wDonvg//wAIfTf/AIuj/hif4B/9 E98H/wDhD6b/APF183+K/wDgl7b+M9B8RTR2fhHwf4ivfFuj3Wlah4Th/sqe x8O2UulY0/zkh8xfLSxlljhUiD7SIZdqEL5fSfD/APYU1zwv+0XonjHU7/w1 dxeGne9tpre3xNc3t9LfSarceVKkhszMZbcqLKaEyGSZblrmKC2jWlbq/wCt BNe7c9s/4Yn+ARUEfD3weQf+pH07/wCKo/4Yn+Af/RPfB/8A4Q+m/wDxdfOn jv8AYs8R/GnXtL1v+07ez8a/DrxxrGo21/f6ejI0d5rNne2sqXMlmZ5Vt9KW 3iC2k0Ucj24s5pHhSZV9r+Dngrxt8KrG90lYLPVtDbUEnsG1Xxhf6pfafbPH YCeJri5t5JpyJ21SRPMlPCW0YMaSFbWU1y3G1aVkQaJ+zH8Ddb+OOteD0+Ff gpYNJ0XTdVW9PhHTvMnkvLi+hEPlcBQost2/zDu83G0bcns7/wDYH+B+lxs8 /wANfCMaq2xj/wAIRprBW/unD8H2PqK5b4W3d6/7VHii51S3tbPUT4L8MyXd va3DXMEMv2zXC6JKyRtIobIDFELAAlVzgeh33jq91jxJ4qupLK72XWoRC1jV DiXYIoc7jgBQoZyeny4rws0zWvhcVQoU4c0Zuzdm7arW60W/U6cPh41Kcpt2 scz/AMMT/AP/AKJ74P8A/CH03/4uj/hif4B/9E98H/8AhD6b/wDF1xvxW/Z3 8QfEj4vanq0+va0vha/0bSNJj0vT/Fuq6KymO9vJb+ZTZugjmZJLApKp8x/s zwloo5XY+R/CD9mn4meMfDMU+s+KfGsMGpeJvESeL7TUPGWrWd7rFvBqWqLp RsZoVj/s+Jdtk2bGO1S5gkBZSkaxS+6mc7jY+jv+GJ/gH/0T3wf/AOEPpv8A 8XSf8MT/AAC/6J74PH/cj6b/APF15V+z7+x74k8BfErQPF3jbxhrfinxPYf8 JF9vvF8VasLK4N5qCPZiPTWl+yRxLZqVaIJsjkSMoHKLKsPxp/ZV8efFf4na 7q0fim/sbTU9U0+3sfsPi3U9LbT9BjuNEnvLVIbUJGlzObfWAblW8/bcQR+d 5R2QNWva/wDV/wCmK39f19x65/wxP8A/+ie+D/8Awh9N/wDi6Rf2KPgEzY/4 V74PBxnnwPp3H/j1eUfs2fse+JfhT+0P438VeIPE19qHh+5ubG68MWNv4g1B njlTSodNuptTVii38rxWtoEa6a5KGAyKUkeRn6n4k/s1al4n+J+peI9M1bXb KTUhbORb+LtS077NPZwzm1dIkL25jEsgElu8LQTiUvMkxiWN05WV12/pBbfy Ou/4Yn+Af/RPfB//AIRGm/8AxdL/AMMT/AP/AKJ74P8A/CH03/4uuK/Yk/Zv 1n9lr4PaL4X1LV5r+Kw0HSrOW3fVr3VI01CC0WK7lgkumLQ20jCNY7aNUijE BdVUyuq6HwE+CmofAPw5c2uieG/BWmr4k1Fta1PTNIcabpmizSC0ieC0WO1B uAIo5pWmlEbzTjO2KOYJbPq0Fir4r/Zs+CWifF7w/wCFLL4U+A7s63o+pas9 1P4W0+D7P9kmsIhGEVX37/tpOdy7fKxht2V2P+GM/hD/ANEr+HH/AITNl/8A GqrXst7cftQ/DyTUbe1tNQk8E+IDcw21w1xDDL9s0LeqSMiM6g5wxRSRg7R0 r1OobYJHmv8Awxn8If8Aolfw4/8ACZsv/jVH/DGfwh/6JX8OP/CZsv8A41Xp VFLmY7I81/4Yz+EP/RK/hx/4TNl/8ao/4Yz+EP8A0Sv4cf8AhM2X/wAar0qi jmYWR5r/AMMZ/CH/AKJX8OP/AAmbL/41R/wxn8If+iV/Dj/wmbL/AONV6VRR zMLI81/4Yz+EP/RK/hx/4TNl/wDGqP8AhjP4Q/8ARK/hx/4TNl/8ar0qijmY WR5r/wAMZ/CH/olfw4/8Jmy/+NUf8MZ/CH/olfw4/wDCZsv/AI1XpVFHMwsj zX/hjP4Q/wDRK/hx/wCEzZf/ABqj/hjP4Q/9Er+HH/hM2X/xqvSqKOZhZHmv /DGfwh/6JX8OP/CZsv8A41R/wxn8If8Aolfw4/8ACZsv/jVelUUczCyPNf8A hjP4Q/8ARK/hx/4TNl/8ao/4Yz+EP/RK/hx/4TNl/wDGq9Koo5mFkea/8MZ/ CH/olfw4/wDCZsv/AI1R/wAMZ/CH/olfw4/8Jmy/+NV6VXIftB/DWb4zfALx x4Qt5beCfxVoF/o8ck6q0UbXFtJCGYMjqVBfkFHGOqt0o5mFjE/4Yz+EP/RK /hx/4TNl/wDGqj8afsT/AA00XRNNuNK+Enwp1C5v4jO0V3odraJGgkljOHW2 lJOYwcFQMMeeOfJtP/4JoaL8P9U8N3WgWHhi5vfB8mhR6VrUuk6bpOsWNtb6 3eahqEMElhYxQW0c0V2Y2SGFBcJ50UhjaY3C/X3iX/kD+Hv+vB//AEquKu/m Sz53H7I/hbv8C/ggOM8wQ4/9N1U739mLwfYNOH+BPwZY26JI4jsFlOGcoMBd MJJyDwMkAZIxzXYfAz9ny6+DvxF+IGvT69/ay+O9RGptb+TcIbOQSThRukuJ RJi3a2j3sokHkbFdbSOysrHe8YRXeieNbPWH0u61/Q3t/sN9Y207Jcwglv8A SIQCAXXcOCCDjBA+8PmuK82xWXYB4rCR5mmr6N2i93aOtlu7J6XOXG1Z0qXP Ba/f+R5tp37LXg7Vrbzrf4IfBB49xU5t4lKkdQQdOBB9iAan/wCGR/Cv/RDf gf8A9+Yf/ldXX/Cj4lav8SPHviC8vdHttKs3SNEtrSHyLa1dPl2BDyXYHLHu V5xwK0/j38NoPi/8JdX8N3Oh+E/EMOrCKJrTxNYLf6UpEyMJprZlKz+UVEqx EpveJV8yLPmK+FeIVnGWwx0YtXcls1flk43SeqTtfV6dSsDiFiKSqPTf8Dz3 /hkfwr/0Q34H/wDfmH/5XUf8MkeFP+iGfA/Pr5MP/wArqh0/9kaTwR8X7HxT occF0brSdP0rX5bjWbjT9U1V7K9sDbXtxeWsQkvpvskFx5i3pmDlI7eNoIbm 9abz7Q/2AfGcSfC6OXxdo8Vp4AsYbzU7ZbC1vYPFPiZZNPmfXLoXVrKwleaO /dpImiui0odLqJ5pSv0qavo/6/r8/W3R0/r+v+G9DtPE37Pvw98Fwwyax8If 2fdKS4fy4mvHtYFkbGcAvp4BOO1a3wp+F/g7SPGNjqfhf4e/BPTb+2chdQ0K 4gN5bIwKOYzHZKclGYYDLkEgkAmqP/BQL4R+Kvi/8N9MsvCemT6nfW1zJK6R SQRttKbdoeaSNFLAkZLf4jlP+Cf/AMH/ABL8MPFl+viPwPrXhpf7MdIby7ms JIpZfOh/dg29zKwJUO2SoXCEZzgH67CZLldTJ5Y6rjFGsr2p21dttb9TwMRm WMhj44anh26bted9rn0Z4n+G/h3xbrU19qugaLqd7JtR7i7sYppWVFVEBZlJ IVVVRzwFAHArP/4Un4M/6FHwx/4K4P8A4miivlGe8g/4Un4M/wChR8Mf+CuD /wCJpJPgj4LKHPhHwueD/wAwqD0P+zRRQB1lFFFc5oFFFFA2FFFFAjC8R/Dz QPF2qLcatoej6ncRwLEkt3ZRzOqAsdoLAkDJJx7mqP8AwpPwZ/0KPhj/AMFc H/xNFFbx+FEB/wAKT8Gf9Cj4Y/8ABXB/8TR/wpPwZ/0KPhj/AMFcH/xNFFMQ f8KT8Gf9Cj4Y/wDBXB/8TR/wpPwZ/wBCj4Y/8FcH/wATRRQAf8KT8Gf9Cj4Y /wDBXB/8TR/wpPwZ/wBCj4Y/8FcH/wATRRQDD/hSfgz/AKFHwx/4K4P/AImj /hSfgz/oUfDH/grg/wDiaKKAHD4K+DYopol8JeGVia6e4KDS4ApkaOJGfG37 xSONSepEajoow3/hSfgz/oUfDH/grg/+JoooYB/wpPwZ/wBCj4Y/8FcH/wAT R/wpPwZ/0KPhj/wVwf8AxNFFAmH/AApPwZ/0KPhj/wAFcH/xNH/Ck/Bn/Qo+ GP8AwVwf/E0UUDD/AIUn4M/6FHwx/wCCuD/4mj/hSfgz/oUfDH/grg/+Jooo AP8AhSfgz/oUfDH/AIK4P/iaP+FJ+DP+hR8Mf+CuD/4miigDU8K/D3QPBd5d T6Poej6TPeJHHcSWdnHA86oXKK5QAsFMjkA9N7Y6mtvA9BRRVS3AMD0FGB6C iioQBgegowPQUUUMEGB6CjA9BRRTAMD0FGB6CiigGY3iXwlpXjSC2i1jTNP1 aKzLvAl5bJOsDPgOVDg7SwRM467Fz0FZP/Ck/Bn/AEKPhj/wVwf/ABNFFN7C 7B/wpPwZ/wBCj4Y/8FcH/wATR/wpPwZ/0KPhj/wVwf8AxNFFIYf8KT8Gf9Cj 4Y/8FcH/AMTR/wAKT8Gf9Cj4Y/8ABXB/8TRRQAf8KT8Gf9Cj4Y/8FcH/AMTR /wAKT8Gf9Cj4Y/8ABXB/8TRRQAf8KT8Gf9Cj4Y/8FcH/AMTR/wAKT8Gf9Cj4 Y/8ABXB/8TRRQAwfAfwMl0bhfBnhQTyxrG8g0i33uqs5VSdmSAWYgdtx9TT/ APhSfgz/AKFHwx/4K4P/AImiimwD/hSfgz/oUfDH/grg/wDiaP8AhSfgz/oU fDH/AIK4P/iaKKQg/wCFJ+DP+hR8Mf8Agrg/+Jo/4Un4M/6FHwx/4K4P/iaK KbGH/Ck/Bn/Qo+GP/BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUhB/wAKT8Gf 9Cj4Y/8ABXB/8TR/wpPwZ/0KPhj/AMFcH/xNFFAxtv8ABbwdDJcKnhPwyi3U kfnAaXABN5ay+Xu+XnZ5su3PTzHxjccu/wCFJ+DP+hR8Mf8Agrg/+JoooYB/ wpPwZ/0KPhj/AMFcH/xNH/Ck/Bn/AEKPhj/wVwf/ABNFFAB/wpPwZ/0KPhj/ AMFcH/xNH/Ck/Bn/AEKPhj/wVwf/ABNFFAMP+FJ+DP8AoUfDH/grg/8AiaP+ FJ+DP+hR8Mf+CuD/AOJoooAP+FJ+DP8AoUfDH/grg/8AiaP+FJ+DP+hR8Mf+ CuD/AOJoopAy34f+GHhrwprSahpfh7Q9NvxG1uLm1sIoZhE5Vmj3qoO0tGhI zglFPYV0FFFZ1Ny4/CFFFFQMKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAqd5nuYNsjNIsI2RhjkIuN2B6DJJx6k+tFFVEmRHsHoPyo2D0H5UUV fQkNg9B+VGweg/Kiiop7IEGweg/KjYPQflRRVoaDYPQflTZVAQ8Dof5GiikD P//Z --0-1068215501-1298240437=:67343-- ========================================================================Date: Sun, 20 Feb 2011 22:27:16 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: SPM EEG Pre processing: Not sure if everything looks OK Comments: To: MP <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (iPad Mail 8C148) Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <-101706040748529818@unknownmsgid> Dear Muhammad, The way one would usually do TF analysis is to apply TF to an epoched file, then average the result and then rescale. If you apply TF to an ERP which is quite noisy you'll get basically single trial TF which is noisy as well. Filtering will not help since you will either remove your frequency range of interest or not affect it. Best, Vladimir On 20 Feb 2011, at 03:51, MP <[log in to unmask]> wrote: > Hi all, > > I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics. > > I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range. > > Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF? > > thanks for your help in advance > > - Muhammad > > > > ========================================================================Date: Sun, 20 Feb 2011 15:53:22 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: medial temporal lobe roi mask Comments: To: John Fredy <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf30549f63930a26049cbf7571 Message-ID: <[log in to unmask]> --20cf30549f63930a26049cbf7571 Content-Type: text/plain; charset=UTF-8 try AAL, they have Hipp and Parahipp. Talarach deamon may have them too.or you can take your BA mask and dilate it. Cheers, Michael On Sun, Feb 20, 2011 at 11:26 AM, John Fredy <[log in to unmask]> wrote: > Hello all, > > I am using a memory encoding paradigm and I use the wfu pickatlas for roi > definition but the mask seems that only covers gray matter (I use the > broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that > appears active when no use roi. Exists any mask that cover all the medial > temporal lobe region including white matter? > > Thanks in advance > > John Ochoa > Universidad de Antioquia > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf30549f63930a26049cbf7571 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable try AAL, they have Hipp and Parahipp. Talarach deamon may have them too.or you can take your BA mask and dilate it.

Cheers,

Michael

On Sun, Feb 20, 2011 at 11:26 AM, John Fredy <[log in to unmask]> wrote:

Hello all,

I am using a memory encoding paradigm and I use the wfu pickatlas for roi definition but the mask seems that only covers gray matter (I use the broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that appears active when no use roi. Exists any mask that cover all the medial temporal lobe region including white matter?

Thanks in advance

John Ochoa
Universidad de Antioquia

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--20cf30549f63930a26049cbf7571-- ========================================================================Date: Mon, 21 Feb 2011 00:22:19 +0000 Reply-To: Anna McCarrey <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Anna McCarrey <[log in to unmask]> Subject: Reporting stats Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi all, I have a 2x2 flexible factorial design in fMRI and wish to report main effects and the interaction. When I run the F contrast for Main effect of Group and click whole brain analysis, many voxel clusters appear. So I must follow up with t-tests to understand in which the direction the groups differ. Do I write this as: Group1 had significantly more voxel activation in the (e.g.) amygdala region than Group2 (t(64) = etc. and then report the t and p values? If there are many voxel clusters, what's the best way to set a minimum threshold? Lastly, when I run the Group x Condition interaction, no suprathreshold clusters appear - with no F statistic, how do I formally report this? Many thanks in advance. Anna ========================================================================Date: Mon, 21 Feb 2011 03:35:30 +0000 Reply-To: G Elliott Wimmer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: G Elliott Wimmer <[log in to unmask]> Subject: Re: medial temporal lobe roi mask Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi, Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to. Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs. Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out. Best Regards, G Elliott Wimmer Dept. of Psychology Columbia University ========================================================================Date: Mon, 21 Feb 2011 11:02:28 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: SPM to Fieldtrip for ICA Comments: To: Jerome Courtemanche <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Jerome, Since you are getting the error not in SPM but in Fieldtrip on data that has already been processed by a Fieldtrip function I suggest that you ask this question on the Fieldtrip mailing list. Just note that you don't need the D.ftraw(0) line. You could try raw=D.ftraw(0) , but that probably wouldn't help in this case. Vladimir On Sun, Feb 20, 2011 at 2:10 AM, Jerome Courtemanche <[log in to unmask]> wrote: > Hi Gurus, > > Here is the commands and error command. Any help greatly appreciated. > > D=spm_eeg_load > raw=D.ftraw; > D.ftraw(0); > cfg=[];cfg.channel='MEG'; > ic_data = ft_componentanalysis(cfg,raw); > cfg.viewmode='component'; > cfg.continuous='yes'; > cfg.blocksize=30; > cfg.channels=[1:10]; > ft_databrowser(cfg,ic_data) > > ??? Error using ==> fetch_header at 38 > trial definition is not internally consistent > > Error in ==> ft_databrowser at 135 > hdr = fetch_header(data); > ========================================================================Date: Mon, 21 Feb 2011 11:29:52 +0000 Reply-To: Poornima Kumar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Poornima Kumar <[log in to unmask]> Subject: Realignment parameters Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi I have searched through the archives for info on acceptable threshold for movement in our analysis, but with no luck. Can any one suggest if we use the realignment parameters as covariates in 1st level, what is the maximum movement (translation, rotation....) that is removed by these steps? For example, if a participant has moved say 8mm and 12deg in one block, can we include the participant in the analysis, assuming that using motion regressors in 1st level has taken care of this? Any advice on this will be very helpful! Thanks Poornima ========================================================================Date: Mon, 21 Feb 2011 07:50:09 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy Subject: Re: medial temporal lobe roi mask Comments: To: G Elliott Wimmer <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --001636c598d9d2363d049cca4dfb Content-Type: text/plain; charset=ISO-8859-1 Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for epilepsy patients, so, I will have time to explore more and more in working with roi analysis. Best wishes from Antioquia On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]>wrote: > Hi, > > Regarding the AAL template (which is most likely what you have already seen > in the WFU pickatlas), I too have noticed that the AAL hippocampus > definition tries to only cover grey matter. This exclusion leads to gaps and > holes in the mask that I think you are referring to. > > Given the anatomical variability between subjects in the hippocampus, it > appears to me that the AAL mask attempts to be far too precise; moreover, it > is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' > function to correct these problems with the AAL mask and used the resulting > modified mask to define ROIs. > > Alternatively, I've seen probabilistic anatomical masks that I think were > used in FSL, and that may be worth checking out. > > Best Regards, > > G Elliott Wimmer > > Dept. of Psychology > Columbia University > --001636c598d9d2363d049cca4dfb Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for

epilepsy patients, so, I will have time to explore more and more in working with roi analysis.

Best wishes from Antioquia

On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]> wrote:

Hi,

Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.

Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.

Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.

Best Regards,

G Elliott Wimmer

Dept. of Psychology
Columbia University

--001636c598d9d2363d049cca4dfb-- ========================================================================Date: Mon, 21 Feb 2011 13:16:01 +0000 Reply-To: Joseph Whittaker <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Joseph Whittaker <[log in to unmask]> Subject: SPM functions for BMS MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e65a09c0517a27049ccaaab5 Content-Type: text/plain; charset=ISO-8859-1 Dear DCMers I am doing BMS with families of models, which is easy using the DCM GUI in SPM8, but I like to write a script to speed up the process. This is because I will be doing it multiple times and would like to save the results into one file. Is there anyone who has done anything similar who can tell me what the relevant functions are and how they relate to one another? Thank you. Regards Joe -- Joseph Whittaker, M.Sc Ph.D Student G.803 Neuroscience and Psychiatry Unit Stopford Building University of Manchester Oxford Road Manchester, UK --0016e65a09c0517a27049ccaaab5 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable
Dear DCMers

I am doing BMS with families of models, which is easy using the DCM GUI in SPM8, but I like to write a script to speed up the process. This is because I will be doing it multiple times and would like to save the results into one file.

Is there anyone who has done anything similar who can tell me what the relevant functions are and how they relate to one another? Thank you.

Regards

Joe

--
Joseph Whittaker, M.Sc
Ph.D Student
G.803 Neuroscience and Psychiatry Unit
Stopford Building
University of Manchester
Oxford Road
Manchester, UK
--0016e65a09c0517a27049ccaaab5-- ========================================================================Date: Mon, 21 Feb 2011 14:16:56 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: Realignment parameters Comments: To: Poornima Kumar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Poornima, > I have searched through the archives for info on acceptable threshold for movement in our analysis, but with no luck. This is because no-one really knows for sure. There is a rule of thumb that "motion exceeding one voxel size" may be bad, but then again, cannot see that from the realignment parameters as you will have to combine translations and rotations. See the toolbox by Paul Mazaika for an approach that does that for you. There also likely is no fixed number as I guess it would depend on your data, paradigm, sequence... > Can any one suggest if we use the realignment parameters as covariates in 1st level, what is the maximum movement (translation, rotation....) that is removed by these steps? This question I believe cannot be answered, as you do not remove motion, but the additional variance associated with it. See Friston et al., 1996, or Johnstone et al., 2006, for some hints. > For example, if a participant has moved say 8mm and 12deg in one block, can we include the participant in the analysis, assuming that using motion regressors in 1st level has taken care of this? I am used to working with children, but 8 mm and 12 degrees is massive motion even in my book. I would not expect the realignment parameters to "take care" of this. Cheers, Marko -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Mon, 21 Feb 2011 16:07:40 +0000 Reply-To: "Veena A. Nair" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Veena A. Nair" <[log in to unmask]> Subject: PostDoctoral Job Announcement - University of Wisconsin-Madison Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello All, posting this on behalf of the neuroimaging lab at UW-Madison: "postdoctoral traineeships in the Computation and Informatics in Biology and Medicine (CIBM) Training Program, funded by the NLM Grant 5T15LM007359" see complete announcement below:- ------------------------------------------------------------------------------------------------------------------ PostDoctoral Job Announcement University of Wisconsin-Madison University of Wisconsin-Madison HERI & WIMR Radiology are currently recruiting a post-doctoral fellow or research associate interested in characterizing brain abnormalities affecting cognitive, affective & sensory processes, in psychiatric, neurological (Stroke, Epilepsy) and neurosurgical populations(Tumor and Vascular lesions). The individual will have access to a state-of the-art 3T MRI, 19 HD-EEG machines as part of World's largest Sleep Center, TMS, neurophysiology bioinstrumentation at HERI as well as state-of the-art MRI and PET systems in Radiology, WIMR, and at the Waismann Center Keck Lab. The fellow will work as part of a multidisciplinary team of Psychologists, Neuroscientists, Neurologists, Psychiatrists, Neuroradiologists, Engineers & Medical Physicists integrating findings from a broad spectrum of approaches including: - resting state functional MRI - task-based functional MRI - DTI, MRS, Perfusion imaging, HYPR-VIPR MRA - Cortical Thinning, VBM - Brain-Computer Interface - Multimodal approach: Awake EEG/ERP and Sleep EEG, PET, TMS Minimum qualifications for a successful candidate include - Must be a US Citizen or Permanent Resident to apply. - completed MD and/or PhD in Cognitive neuroscience/psychology, Statistics, Computer Science, Engineering, or Medical Physics or other equivalent background - significant prior neuroimaging experience with functional MRI/EEG/TMS, strong skills in usage of one or more common functional neuroimaging (FSL, SPM, AFNI), programming experience in Matlab, C/C++ or similar platform are all plusses, but not required. postdoctoral traineeships in the Computation and Informatics in Biology and Medicine (CIBM) Training Program, funded by the NLM Grant 5T15LM007359. The postdoctoral traineeship is for a one- to -two-year period. One is to begin by June, 2011, and the other to begin Summer 2011 or later. (The last year is contingent on renewal of the NLM Grant supporting the CIBM Training Program.) These traineeships are designed for cross-disciplinary training - the candidate will have a secondary mentor in addition to the primary mentor (one from computational / informatics areas, and the other from biological areas). How to apply: Please email CV and 3 references to [log in to unmask] Vivek Prabhakaran M.D., Ph.D. (Stanford Neurosciences, 2001) Assistant Professor, Director of Functional Neuroimaging in Radiology Board-Certified Neurologist, Radiologist, & Neuroradiologist (Johns Hopkins,2008) University of Wisconsin Health Hospital and Clinics Department of Radiology 600 Highland Avenue Madison, WI 53792 Office: 608-265-5269 Office: 608-232-3343 Feel free to contact with any questions prior to applying for the position. For more information about HERI & WIMR Radiology, please visit us at http://healthemotions.org// or http://ntp.neuroscience.wisc.edu/faculty/prabhakaran.html http://www.radiology.wisc.edu/research/neuroimagingLab/index.php ========================================================================Date: Mon, 21 Feb 2011 16:27:39 +0000 Reply-To: Molly Crockett <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Molly Crockett <[log in to unmask]> Subject: ArtRepair and Realign-Unwarp Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hello, I'm looking into using the ArtRepair toolbox to deal with motion-related artifacts in a couple of subjects. My study is a repeated-measures pharmacological design, and I don't want to have to exclude subjects that only moved in one of the sessions. I see from the ArtRepair documentation that detecting and repairing bad volumes should take place after realignment. On my non-moving subjects, I've used the Realign & Unwarp procedure, and I'd like to keep things consistent across subjects. Is it reasonable to use ArtRepair on images that have been realigned & unwarped? Or is ArtRepair only valid for use on images that have been realigned without unwarping? Thanks, Molly ========================================================================Date: Mon, 21 Feb 2011 11:39:14 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Realignment parameters Comments: To: Marko Wilke <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Additionally, You could exclude TR-to-TR motion that is excessive, by adding a column of a single 1 in the design matrix for each bad point, but again there is no standard for a bad point. We've used .75mm translation and 1 degree rotation. In summary, if you have 5 bad points, then you will have 5 columns added to your design matrix. As a side note to the above procedure, the question arises as to how many bad points are allowable in a single session. We have excluded runs that have greater than 10-15% bad points. Finally, neither motion correction nor motion parameters can correct for within TR motion, so data inspection is also necessary to make sure there is no motion artifacts. We usually check the ResMS, spmT, and beta images. One can also inspect the raw EPI data for artifacts as well. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Mon, Feb 21, 2011 at 8:16 AM, Marko Wilke <[log in to unmask]> wrote: > Hi Poornima, > >> I have searched through the archives for info on acceptable threshold for >> movement in our analysis, but with no luck. > > This is because no-one really knows for sure. There is a rule of thumb that > "motion exceeding one voxel size" may be bad, but then again, cannot see > that from the realignment parameters as you will have to combine > translations and rotations. See the toolbox by Paul Mazaika for an approach > that does that for you. There also likely is no fixed number as I guess it > would depend on your data, paradigm, sequence... > >> Can any one suggest if we use the realignment parameters as covariates in >> 1st level, what is the maximum movement (translation, rotation....) that is >> removed by these steps? > > This question I believe cannot be answered, as you do not remove motion, but > the additional variance associated with it. See Friston et al., 1996, or > Johnstone et al., 2006, for some hints. > >> For example, if a participant has moved say 8mm and 12deg in one block, >> can we include the participant in the analysis, assuming that using motion >> regressors in 1st level has taken care of this? > > I am used to working with children, but 8 mm and 12 degrees is massive > motion even in my book. I would not expect the realignment parameters to > "take care" of this. > > Cheers, > Marko > -- > ____________________________________________________ > PD Dr. med. Marko Wilke > Facharzt für Kinder- und Jugendmedizin > Leiter, Experimentelle Pädiatrische Neurobildgebung > Universitäts-Kinderklinik > Abt. III (Neuropädiatrie) > > > Marko Wilke, MD, PhD > Pediatrician > Head, Experimental Pediatric Neuroimaging > University Children's Hospital > Dept. III (Pediatric Neurology) > > > Hoppe-Seyler-Str. 1 > D - 72076 Tübingen, Germany > Tel. +49 7071 29-83416 > Fax +49 7071 29-5473 > [log in to unmask] > > http://www.medizin.uni-tuebingen.de/kinder/epn > ____________________________________________________ > ========================================================================Date: Mon, 21 Feb 2011 11:47:22 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Reporting stats Comments: To: Anna McCarrey <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> (1) The main effect of group is inflated in your model. To test the main effect of group properly, you need to average your conditions and then compute a model using the averages. If you only have two groups, I'd skip the F-test and just do the T-test. (2) If you have more than 2 groups, you need to do post-hoc t-tests. I would say that the had greater activation rather than more voxel as more voxel seems to imply extent, which isn't being tested. (3) For the interaction, make sure you have subject in your model and in the design matrix. If you still get no significant results, you can say that the interaction was not statistically significant or no voxels passed the significance threshold. (4) There are a number of ways to determine thresholds. Personally, I like using AlphaSim in AFNI if possible. For exploratory analysis, you can lower the voxel extent criteria a bit. In general, most studies use p<0.001. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Sun, Feb 20, 2011 at 7:22 PM, Anna McCarrey <[log in to unmask]> wrote: > Hi all, > > I have a 2x2 flexible factorial design in fMRI and wish to report main effects and the interaction. > > When I run the F contrast for Main effect of Group and click whole brain analysis, many voxel clusters appear. So I must follow up with t-tests to understand in which the direction the groups differ. Do I write this as: Group1 had significantly more voxel activation in the (e.g.) amygdala region than Group2 (t(64) = etc. and then report the t and p values? > If there are many voxel clusters, what's the best way to set a minimum threshold? > > Lastly, when I run the Group x Condition interaction, no suprathreshold clusters appear - with no F statistic, how do I formally report this? > > Many thanks in advance. > > Anna > ========================================================================Date: Mon, 21 Feb 2011 18:22:01 +0100 Reply-To: Alexander Hammers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Hammers <[log in to unmask]> Subject: Re: Anatomical Regions Comments: To: Carlton CHU <[log in to unmask]> Comments: cc: "Prof. Dr. Robert Turner" <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: multipart/alternative; boundary=Apple-Mail-7-26658180 Message-ID: <[log in to unmask]> --Apple-Mail-7-26658180 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 Dear Carlton, Thanks and sorry about the delay in getting back to you. I don't think there is a right or wrong in the choice of probabilistic or maximum probability resources - as long of course as you avoid population inferences based on single brains or hemispheres ;-). Re: numbers: For healthy controls, there is probably not that much to be gained after averaging of say 15-20 or so datasets in MNI space. The boundaries of the Jülich maps at n=10 can be a bit rough; visually the maxprobmaps didn't change that much after about n=15 (Figure 1 in Hammers, Allom et al. 2003). This will depend a bit on the region, too - compare the more or less invariant basal ganglia (Ahsan et al. 2007) with a cortical region, e.g. the inferior frontal gyrus (Hammers, Chen et al. 2007; Amunts K et al. 1999). After that many datasets, there is also increasingly less (but still some) gain from multi-atlas propagation and label fusion in individual space (Fig. 5 in Heckemann RA et al. 2006; see also Aljabar P et al. 2009). However, large atlas sets may be more important with very large age ranges and/or pathology; well-done intermediate propagations steps (Wolz R et al., LEAP, NI 2010) are another option for anatomically segmenting individual MRIs. I need to stop expanding to Simon Warfield's STAPLE, Louis Collins' work, etc., because you wanted a pragmatic answer and not a review on atlasing! Pragmatically I would check out the regions you are interested in (are they available?), and compare the protocols and distributions for those. One major difference between the Loni and our maps is the number of people drawing and checking the regions; one would expect systematic differences but I haven't checked. Depending on your research question cytoarchitectonic fields may be more important (in which case the Jülich maps are for you), or macroscopic landmarks (in which case LONI's or our maps may be more appropriate). Bob Turner sent me a mail off-list after the recent discussion and correctly pointed out that in vivo cytoarchitectonic mapping is possible in principle, too (e.g. Geyer S et al. Frontiers in Human Neuroscience, in press) - the examples for the moment are mainly V1 / Brodmann 17 and some other primary areas at 7T, but SNR keeps increasing. Then there's of course also folding, connectivity, etc. etc. (Fischl, Behrens,...). Happy mapping, and I hope the brief comments above are useful. WBW, Alexander ----------------------------- Alexander Hammers, MD PhD Chair in Functional Neuroimaging Neurodis Foundation http://www.fondation-neurodis.org/ Postal Address: CERMEP – Imagerie du Vivant Hôpital Neurologique Pierre Wertheimer 59 Boulevard Pinel, 69003 Lyon, France Telephone +33-(0)4-72 68 86 34 Fax +33-(0)4-72 68 86 10 Email [log in to unmask];[log in to unmask] --------------------------------- Other affiliations: Visiting Reader; Honorary Consultant Neurologist Division of Neuroscience and Mental Health, Faculty of Medicine Imperial College London, UK --------------------------------- Honorary Reader in Neurology; Honorary Consultant Neurologist Department of Clinical and Experimental Epilepsy National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK On 6 Feb 2011, at 04:51, Carlton CHU wrote: > Dear Alexander: > > In your opinion, how does LPBA40 (http://www.loni.ucla.edu/~shattuck/resources/lpba40/) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps. > > I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions? > > Best, > Carlton > > On Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers <[log in to unmask]> wrote: > Dear Steffie, > > > Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al. > > They are great but don't exist for all areas. > > The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007. > > One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached). > > There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009). > > The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-). > > Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise. > > Hope this helps, > > ATB, A > PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially). > > > On 4 Feb 2011, at 19:38, Michael T Rubens wrote: > >> WFU pickatlas >> >> http://fmri.wfubmc.edu/software/PickAtlas >> >> cheers, >> Michael >> >> On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote: >> Hi Everyone – >> >> >> I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas? >> >> >> Thanks so much, >> >> Steffie >> >> >> >> >> >> -- >> Research Associate >> Gazzaley Lab >> Department of Neurology >> University of California, San Francisco > > > > --Apple-Mail-7-26658180 Content-Type: multipart/related; type="text/html"; boundary=Apple-Mail-8-26658180 --Apple-Mail-8-26658180 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 Dear Carlton,

Thanks and sorry about the delay in getting back to you.

I don't think there is a right or wrong in the choice of probabilistic or maximum probability resources - as long of course as you avoid population inferences based on single brains or hemispheres ;-).

Re: numbers: For healthy controls, there is probably not that much to be gained after averaging of say 15-20 or so datasets in MNI space. The boundaries of the Jülich maps at n=10 can be a bit rough; visually the maxprobmaps didn't change that much after about n=15 (Figure 1 in Hammers, Allom et al. 2003). This will depend a bit on the region, too - compare the more or less invariant basal ganglia (Ahsan et al. 2007) with a cortical region, e.g. the inferior frontal gyrus (Hammers, Chen et al. 2007; Amunts K et al. 1999). After that many datasets, there is also increasingly less (but still some) gain from multi-atlas propagation and label fusion in individual space (Fig. 5 in Heckemann RA et al. 2006; see also Aljabar P et al. 2009). However, large atlas sets may be more important with very large age ranges and/or pathology; well-done intermediate propagations steps (Wolz R et al., LEAP, NI 2010) are another option for anatomically segmenting individual MRIs. I need to stop expanding to Simon Warfield's STAPLE, Louis Collins' work, etc., because you wanted a pragmatic answer and not a review on atlasing!

Pragmatically I would check out the regions you are interested in (are they available?), and compare the protocols and distributions for those. One major difference between the Loni and our maps is the number of people drawing and checking the regions; one would expect systematic differences but I haven't checked. Depending on your research question cytoarchitectonic fields may be more important (in which case the Jülich maps are for you), or macroscopic landmarks (in which case LONI's or our maps may be more appropriate).

Bob Turner sent me a mail off-list after the recent discussion and correctly pointed out that in vivo cytoarchitectonic mapping is possible in principle, too (e.g. Geyer S et al. Frontiers in Human Neuroscience, in press) - the examples for the moment are mainly V1 / Brodmann 17 and some other primary areas at 7T, but SNR keeps increasing.

Then there's of course also folding, connectivity, etc. etc. (Fischl, Behrens,...).

Happy mapping, and I hope the brief comments above are useful.

WBW,

Alexander

-----------------------------

Alexander Hammers, MD PhD

Chair in Functional Neuroimaging
Neurodis Foundation
http://www.fondation-neurodis.org/
Postal Address:
CERMEP – Imagerie du Vivant
Hôpital Neurologique Pierre Wertheimer
59 Boulevard Pinel, 69003 Lyon, France

Telephone +33-(0)4-72 68 86 34
Fax +33-(0)4-72 68 86 10
Email [log in to unmask];[log in to unmask]
---------------------------------
Other affiliations:

Visiting Reader; Honorary Consultant Neurologist
Division of Neuroscience and Mental Health, Faculty of Medicine
Imperial College London, UK
---------------------------------
Honorary Reader in Neurology; Honorary Consultant Neurologist
Department of Clinical and Experimental Epilepsy
National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK

On 6 Feb 2011, at 04:51, Carlton CHU wrote:

Dear Alexander:

In your opinion, how does LPBA40 (http://www.loni.ucla.edu/~shattuck/resources/lpba40/) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps.

I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions?

Best,

Carlton

On Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers <[log in to unmask]> wrote:

Dear Steffie,

Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al.

They are great but don't exist for all areas.

The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007.

One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached).

There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009).

The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-).

Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise.

Hope this helps,

ATB, A
PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially).

On 4 Feb 2011, at 19:38, Michael T Rubens wrote:

WFU pickatlas

http://fmri.wfubmc.edu/software/PickAtlas

cheers,
Michael

On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote:

Hi Everyone –

I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas?

Thanks so much,
Steffie

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--Apple-Mail-8-26658180 Content-Transfer-Encoding: base64 Content-Disposition: inline; filename=image.png Content-Type: image/png; name="image.png" Content-Id: <3335947574_1343002> iVBORw0KGgoAAAANSUhEUgAAALAAAAA6CAIAAACf/kS3AAAgAElEQVR4Ae2ceZxVxbH4+2x3mXtn hhmG2cFhF8GYKLKoEX9KDD81RiCaRY1LNL9sRhOXmBhjjJo8RNREn8a4JmqIAU2UKCQmGhcERRZZ BEEWYdgZmJm7nv19+zZcBh0igx/+ePymxUOfPtXV1VXVVdXVfdHCMBTdpZsDezig76l0/93NAcmB boXo1oN9ONCtEPuwo/ulWyG6dWAfDhxahbCFcAoxqye8vMgI4YpAhME+FBzQixvupLsn+3p+iwh5 5njuv6RzwuW74wvhMS5Pl2p3+VgOaId2lxHaIvSEowsjLjQiFtvVXV4MEf9YyjoCIEtb5ErcuFSF iJ0JI0lNM3xHGJGOYHvreSFiKF9OBGW+KYzAdXTNEDp/9sJ01zrjwKFlkBdGhZ+wY7pt+oL/dMsS UUNEO6PkP7UZvigJ457li0igeVq5VC6RN/ZrInw5giXcMscUaWySjmaYBhR0l4/jwKFVCE1khWVH 83rUMzJRlrcubCvX9WXqaCG+RpNOwNVN08ll9FBE9u97NE9IyxRtdcOg3E9rXlmEJo/W7vIxHDi0 CqHrJakgKiJWxhJWKMycsKMiHhBadK1E9KxvCMM0867t4zTiCTRED639YfEsW9Ah08PXskKLpU0A TRF2zU/tD/nh3X5oFUILvFIvyOs4iTDieU48Gwo7PIgxfR8l8jRhWAlfmFukxxDe/oNT04t6phYm 7BIR+gY14Wh2er/6c3iLuGuzOwjhdGEAR2dtZmOuPWOlVvH0xoqpy5dviWpul2MIT4vSx/QCyzHe 3NEy4p6Xbc8zJfLOCzpnPbCOmNP0S42MqH1izXPbIsm82zl0d2sHDuyXpx1gDr7qC0eYQVqPPrhm +5iq5C0je/cpTQs/2VWMpogGRIREpo6e08o3JPsaETMr3BIix86KJtqTkbwVusIIw4S18cJ+ZY4Q +9mRdIbg/9+2Q2shrCDiibKol3MjsaFlkaElernwXYOV2k4oILyUR5gYsKWk7Cj8JRtEEMjwEQ1w A9eVqQtH93TfbUP8ughzlojkTdFa4hl5iQTn4Ung0GUfKjMVLlBJ1zd1Ox6ElubbUmvYr4r2TGAD ImPL0M+ylQ194eZFaNPCX8LP5an5gXRGQQYAYefe4QkNniTDc3N8OAg7EwTSvfEsbvJ9f/eWp/BF FNu9QuTLaxGAjqooJHveDtXfhzgPIby3t6aO/3eLaK+o19p2mJH8RXVEAjcvdSct3+7aO+Ju4vKB fW8b7bumtmiDOeaVJef0PXLOknVbIvqwMu++0waPrsDI2BvWRi6Zu2GOERUtO8b0a3hj844dFw8o EWJhJn3JjPTytp1GaZkb5F46s+qzFfFfLWz72eKUIP8QMcbU2P8+pd/IlzK/GVk6vIcwRPrW1/L3 Nme2Ou1l0YarBkV/fkxC09x/bzc/9/y8EXVNi1pE1m8/tiQxfXxdXz0jXO3uNbmrBiSERQgkTFNu Yf1QNz7ZOkK0ui5RcNKsqXjI8wzD0NRLQXX2AMgFotRFtRwqRSjiZbBDV/ww2MH6C7NjZm348cJN qL0dOE9vDMxH197/dhDa4fT1zoCn1l87vy0fpt/Y7IiHtn3t5VUbwnCrHx737PIvz94U2rtCNxww fctXZje/F4ZrXW/s06vFg2v9cFvoZUfOWj9hxrowCFNueM2irSf+cxPAOf4PvOTjK9t96uk21yt7 ePnrm1q9MPz1B17vJ5pf2GiH+XDG+63lf3znd6vonXllQ6A/sOXiV1dn7XDFtvyQGYuum7MxDBw3 DH80bwMdqfBHPjybDgdRUAIKHYsVjF/hdTcybANWoSMAkKpFNe6GO8R/fTJVL6rVfip6XuvpaYZn RNyErsXZGERCa+rC1vMH+eceJ+xIZmJjcO3AxL9WtxDLtLmWFYgbj2loFLvK9fSEAf1bNqbzkR7/ 2uR/0N5++wkNg0K3KdS//el+ESuqiTJPj4/uGfvJSRWhJpK6GGYlYtmcpyxxaKTbSpJ6oIlomWG0 a7FcEME8PDF/6zcG1YytJ0XhjetvnHtkr2mrNpDtEppjlKbv+Ey/SMQb3Mv7al3ff6aipEww9PF4 KYvU9RjkExWlB6DADChLsHnz5smTp9x1192zZ8+hvWgAWlpa3n777ZdeeimXy9GIAvCV7p9o+APu fGgVwiF/7AjH1EMErgdhxMSBt8ZS4xvqe9qaLhJOGG1qKl+QskORSJgZt2RXU0Vc+BWhSLZqoRmN xDxnVV6M6lXdOyd8TfNNf1SlKHFSvoiatrhreHR1c9k5M3ZVTltzxdLWwIoxjpk3CRjoG4jAIzCx RdSu6GFEbWHNawvPbCA+KeF7kNUuq+v1ry0R05WRtevZkaQwbJIk5tZIe09T5tEsT+g5QEVMh1oR EKDouIwD5m4HQESLUyg2sPS3bNly5513/vrXv54zZw5SR0uAQfAffPDBWWeddfHFF8+fP59XpT1F dSliOESVQ6sQuFykZ/lkAvRoaJAPSEUCx8+145AD4scgEgSmkxKW5ZCIJBmdMmOsBMONkXbeommx 2nbTyKUyKZEr7Cd8Q/NXtIlWspUi7cTEwGfTN77bfuKR4T9P6/H88OpYoLVGcyLW7uiup7cbwo2F cZTSrmhtzzCcXZIQbWK7JQKfU5WScGc21MO8Y6WF0S6CnqUEnHokFNESrdLbtTUbJ/FRiFgLnh7i pWxoOiieKbmCT614BOw4Do2YAaUNRUuQz3MSI02CaZqA0a4cxyHSgA+hlYvj0JVoGBH5iGYJO26n CNGD8oihHxlN/H5Z68T+pSWoRd58ba05qqKnxQ5R9NRjqcKJVFzG+RVbUtvak6LxrP7h1fP91YFo ClK+Ht+g+SSxObxascNek965/Zz6SktkI+IHK7N+LlIi4qYXN01b8/PCifsRhO9qbY5ZZsRyZadU hk+vTZ1Uo0f9qBGJ/nbtu+OGlhpB0sd0mauFmcCix9xE0tkVjbglhKXIngyrhq8LdV2qA3GFDnAX ixK20oniE4OBvIuxpFILlKBPnz7XXXcdIzQ0NPAEvqNp6eLIXQbv8ty6NIJGVBQnM5TGPleXVnLa yZbzymGNI2c1nz9r4/eOa/hbc9vdK9qeOS1pRe2U4bpa3jbj0UBwEpU1I74W071Ynx4lpzRtP3vm zslHV7V64orX1opY0g/MqpgbOPH/WrLz5L7ajHW5362yhRv926aNE6obAi+q69X/3byrTstNOKI+ tGxbC9yYceMga/S/ORPPXzTYe3x5MGtD2T9ObTQCP94e4/QrDJMB56G6yOtlWTMm/NDDXsQMVwuw 5tJyYR2Ic1DIvbb/gPiBUNVCRwOoK0fAk0BStYBFGQPAevfuffXVV9Oi1EgpinpVynRAQx4sUOfm D1ILRkvu2LFXHZEzDfWqyJV5gkKhXX2ivdiFA0k0wAyT4yo3VFalM2GUFFNTT+fZ8Y1x3b1p/o5N 21MvntbjC3XJvIhWRGNnV8nNf8CRmC8GR40hNVqbyWpOP37agO/0yT08f8l9H2y5+IS+F8Sb23VR nwgf/3yv97e6t853zNZtb57Z9+uDKx5Yks9xamGKrx3p/nXpNi2eIIawIptwDKYmRvaOvXxmXXt2 w42LUy1u6/QvNA7vwxr0/J7ivGQkzcE8CRJXDEkYo5KlfoQIVTdyrsX5CShd/Jbpa75upPPQJ29m FFIaIucFKbyNzIUUSnH66hWGUKiz0HETVPaqBV7T8/XC4S3cQ958Ur14KjUqdqdFZSnUk1e6KMzU i5Wif1Hi4FPXihrvI08fnUCyqt22bVWBFCrMSpFS/Kra1asiiDowduiEeS/0wp1h2EYTvTPgckI3 x1Rwnm2BfIbsAt02ckXU3TwvhZF9tqUAsYeUMBnG9cN04ZNDk+/IXoENmXzK0u5JAHJNobPLlX+T 3Qp3wLKAofnghn5awQAvh5C7STfMZ8PA9X2GlcNl2Vi6rmSzEwBG318sbvFCX24Qc8wIDHJXCz6P nFnopH3HB1qicsBQZAsVxQd4JdHsKdQpasM5e/bsxvre9bUNkyfdUeyoANRT9VV1JQKFRrWAvyPb aVRfi/xXYF197tXHD+kRiFBlxqA9Eonw5ro+Ok67ZVnKdjE2AHylXYVCkKh0nAowBksoKhdHhSvK 1BlniU5MRwSguXYsyJeJgO8hJ6JaGaeRMT8Xmk6UxKOPccpF/IyIsWMIYr6I+3jzIEFIYGMCNMFR J73CiG1kSoQbD4lISRrZEWE7FqnxCAlH0xM9Q65PiLK8CNhC6vQOrDAko2U5XLjCGjlsVjzNJNtk 4DMCuhE3mHmO0qysKdrpqzkxImJ5rcLKaxg7PSS0ELYfagS2RkIYwHhwUV71yDBlxQcqHdc6HKAF 1sEx6gQKVGhRBgPuUadd8ZOKYjtg1NEexWSegFHUV7ApcVChAKm+fmhc2rtU9hdDSEVhYLBDUEED 2ApIz8mora2tb7zxxrJly/ja2Nh4zDHHDB48OBaL8VXNgXkWdIh9BXdaOKXMW8TLmk7dD/2IZixa 9u7i995dv2m94ftjR48ZOvjIkooyvhph3DcCy897Jt47jpVOGaLECrjyxMigx0ob8TgmvEALu0S0 IgEzNDcg1HPZykBEiOX3s5ZRkg7CpK+zV7SiWS0SFzA01Aydy3Tr17UuWbhg3YY1377mCsZNcN7m uNy+8ULT4zoX93oCwkovFw3ao+LGN5vdaE3fhHdCeVu/ml4ldIhg4ZIoIBY/ovu2Zhkax/GmH/iK D5KKwpJ955133nrrLbYS8G306NFHHXVUeXn5Xv7sUQUlYwRMR9SCioKhDvNpVFKgsmXLtqVLly5e vBiY4447DpzgoJ0VqJhP/ROVgnp18oBENaWGht51dQ1nnHEWr2272n9w5Q8b6hrrauoxd7XVdfyp rqoZMWLUzJl/t21sL4ZLYqMu/yrUqeWkvXbSTv7ZZ2d85phjezf0qe9Z36+2qb6qqqGhorynNf68 L86Z+5ZKB2KB6SddFqbfk3a9of6I+tq6O6b8quAcMPJ56TIwmfIjBh2/gNPIv/n6W/WVvavrG+65 fTImPfQw49n1a9+t711dW1v929/+FpQznv/b6BOOr62qqK+o7FNbD434o9bQx19I5+WnwmAXB/UQ INOGufCG+d64WVvEY5vFE1vEQ6s/9dy6V3aELQXq5Lg4QE+SIL2YJJqqfK55f+111/wIRuER+AO7 +EOFiV95xVXvLl0OzOuvzqaxplftnXfcJXlVKBiYuXPn1tbW8+e112bTBj8dB8JleWrqn4cOGQbP 6VhV2QuEJ4w6cck7S9VX9fyQH+n46UDq+3UZSsvQ6xEjRqAcaOXrr7+BPj7++ON8Qm0hHQuh/EVz c/Oll176k5/8hE8YP4+MZMTE28gcDuedrD2hrVn23te/eN63vn7Z5m07bMenr+fahhlzgkRJj6ZX X1149hfO/f6Pr2hr2YYL0YgADM82PdMgiSGCvKWFSVsvcUUsTUaCjayLOzLk8YKM0bDs8cBha+/H TCMWWr7DWjN9w9JEvK5pSEXP3pqI/WXqc/+c8a9vXnb56g3r/NJIu1m4qOOLmCfKfWHnSWuyPBO+ 02NFi3XXisz1y5o5m0uK7cPL7NpEPpLb3kN4y1qSY6Zuu37uph3c0NHy8ozDMPMBTsOTN4iZsCam 3HHnySef/Nhjj8E3VjY8lJMthF88p06deuqpp/7ugQej0SirnK8Mi6gApkKjqtAFJqOUNCrbPG3a 01ddddXOnTtVr3g8jv1YvXr1l7/8ZQwGYKrQUVXAuaetC393rhDQBKEoJphQAuq+F06ccC5Z1ZKS kgsvvPAf//jHtm3bNm5q/utf//rNb36TsdGPadOm3XzzzcyLhAodIUzShBUMxftL3vvqOee9OWeu GWWf3XDfPbevWrvkg+3r1m5YPeell793/iVHlFckQvuJx6ede9HXW9JbPYPY29TwFUJjo2ImbM9u 1fN5y9WSuPl8wHm4iLioSkbkOQvNuLYXMfJJry3YldczWomNgNAXXbhO6Ez8P+OIS95dueCWm3+h OZqZMj53zNhJP5ryl6l/k6wyvNBwSoVDPuhnC9qrZqwc9velV7+x6TfzZX4yr1k/HdG47ktNq84f 9o3jK8u1TUap+eCy9slv7mglGS+PTEVAOktKU6rm1T+8ZsqUKcSAiPbcc8/905/+1LxxA3+2bd8K 0+BVjx49cCu33nrr3Xff/aEYQomwKEiUCYVRYsVT3HjjjQi7oqLi9ttv37ixecWKFeeddx6iQSj3 3HMPykFRgpOkFJRMVbr2ZPhOC6hp5/HGG3NxGbU1jfV1fYYMPmrRgncwd/C/aB4xksuWLR8yZGhN TV2vXjVz5rypEBYQ+I7npjLpAQMHV9fU1dbX3XHnFAwypl5tYjCFrV6ewH37js3nj5/QWNfUo1fD 9y79LktN4lcHSU5YWVvdp7H3pLum4ETwPnxKKQtPf6J49h6+3DjM/fe8QVVN1Q29bp/8y7Rsko6A vyf91501jUdgaXvX9B3Sf+CCN2cTmtK1sLPgmAJ348xtD8yp681Hl4nHlopHdlY8vEL/3SbovHoB mwmISYcpj73Q/Hx44p/Xise2G/cvu3lhypY+Ai+j9iE+xh8XgF8YefyoxYuW7GYRc4Fkxis4lOb1 G8ee+jn8bJ9G/GADboVeyLIoBXYf9fWNVVXVnHHQiLklnH/xxX/BXqYg/Qt48CN5uf8aMXwkSGpq avbwnG90YbCDLJ1biKJOsXsig0/WlgsELMe7fn3n0KOPkuEdl1x55z9ZF0OOGnQX0iKw140H7v8t JkHaPU0eFpMC/P53vp/PZLUgvPfue6++6oeGTkRoytyO3PeLciMaE2ZVz9onnnl65KjhSUuf/rdn Zv19JpsPTy8kj7mpTaznsWvAWoQxOTYRnZBBLP0JuRICEokngUgZjulHdC2WACyiyTsx7A1IN7k4 KXYn6XumPjlsxAhN46wrg7vR8D1u/JHVbSc/tctrJfo3Tq6umnZ8uOorg+3Ly7BSpUFbyChaVCT5 3zxWD188p35k+VbDrL95/uZ1MiNhOkSUuljbvH7y5Mks4tLS0hdmPX/0McMc12GOkkukGtjGyKMQ 0dC7fvoz0z716aNtrmIUIvfiskaGkl72EVxXtyIFC8sZh4/FnT9/HjEjJ2wnnHSi50u2W1GObMRV P/wBLSeccMKmTZvQA/pinPApChXYulr2qxBKwZieKryyoTjttNMgC3VWAW3HUU855ZSBAwdC0KxZ s1AIeilSOMKhhTmPGjVq4pcm8ImikH/0+cgjj+CSAMCigkqy4KAcoRr6o8/Tzxj32eHHS8psGdtA ixXG5+XED99wHDOtW9t+dkrvWeNqxg+tjJfwmUz27oIK7q7x+w7DmHTaUNPfHljRyfO2G6RT+RZ6 v5l0u+LVQw89VFlZCeUEWEy8yAoqNOII2GjgVsCzB/2H/2bigNEKE+iFpBOJhAraCBqUR2aFEH4Q QEyb9tQzzzxTW1urEKoohI5yTXa97FchoAOkIIQyNAC3N3bsWAjtOIyigK+AQQfqQp3GxYuX0MIe jJ9LIWMleEJO6WhBWTglAvlHS1l56Ve+8hUwMG02bBLJvnlSWj5JOWXsqWxnlWHLkqKAxkDcOvv9 Ng/Z5+/+/IBr+pfE/cAI27nlhb8ojrVXL7neoxtj4uLEvmVCM//8AWgkVGhnZkyfCbXDhg0bPXoU LcyOp2JXkWk0EioSOnzqU58aP3687NlZAQBWqy/0hSGsN0RA/ZZbbnnhBRbY3kANb4PGKHlhHuhF Hanx7Az3x7R13kfNn+kxAdQc1O3t7b169VIDQx+UFUXFqyfzSEJlI2jPZrM86WVaBh4RFnBgM2Lk 8Rwhy1/UkVXaTwEJgRKjwA5CMF6LfPmYeRzY5379+smlLu9nihKNzEXwyjYxc4NjaFu+37/misao hWAJZ0mI4OoKEi1o7W7p7lWLMDtxYE3c81O2trZN/kRx4btruMMLwFe/+lUmCH8ovCr6YYUSFePS qJI0Z5999v6ohqV0VBymLzQMGjQIE0vHHTt2fOtb32JxPvDAg+vWrQcDgSfADEedZal6gWF/yP9z e+eJKSgojCSRKguByWL/WRQPs1JDKuGZhsRzxBFHkIFRxPGVpeg6HsEwGDjjJ6RUaJkhMVanZHmO y5QAYImAio4oE0N0CnwQjRyU4sVNdq3czQg4swr+sLbZNau50XnTSeUi34YlTES5P5kwuW5J9nIP ewrH3nsH9K1wMNkBju+Fti6n9UuG8+Yt8SMuRzdS5wrmQU0WRlFBWsyLOp+YGnUajz322L0Y960h VI4OmDvNSrFQhQcffJC9PWkudOv9999n0/GLX/wC/fvud7/bt+8RgIFTiYNeaqx9sR7QW+cWQmGE dPBCFkXpHaN+SAGLigwpEE2hL9qgwCzuRmezEAoGpUBF+E6pQ+0YAki6sLdW4xa1sNMuXWq0dCtt s1mVqfNCqGe91mpj+cdX13EHizQ2iVCXVY1E2Vrs0YZ9hijEEnQuMyzUgi6uvG4TsVMpR2uFbITN MqCieinBMCleqSsAeEVLVVXVPpg7vDBxVoVaCVRgCGujoqL8L395mgs1JDkymYzKQ/z+978/88wz Fy1apNYhHRmCgeiihu6A9YCqnU5a5kGhQ0kX0aKSahgai1iBUaJibLyYogC61eR3Nwqd8Aorx7r5 4he/CEKUTCIhldRZ8V25LMAciVl1dXUMrfSjM9iDadOMaEmUZRfkOPIm4xvq67YS6mdPqi+NEjBY Ii3MSiqml9ZJUQXECx2HkWIuEI5km2V3jZ8g1/GLkVCwq7X0kqBwLsYyoJfioeIYjFLWTumE4tva tWs7Iu9YBzgalaJl+orlBbZwZUY/55xzvvSlCZs3byXx9eSTT5KESKVSF1xwAQmhAQMGwDFGpCMV RNAR5wHWO1cIKFb6BUFK3SCoqA1qMGCKo5LAgVWEwawPRK560YjdHTp0KDcE2Wtce9010ESCQYeV neuD5CzORK4uMkoyLSMLoxzgZD4WLEMSq7AZhldkFjmqkr8aiuY28ZMewge/pBwe2m3CKC8RWHX2 t3JZS0vPIUgH7J6WnLWyhS2EIzJHVUiy65oahF0q9Pzrr78+ctQIaFZSL3Tnco1MO4KggEtGJLBo 1apVHVDuU1W85VlQCGkq4IPaXKgn8dyPf/wj0lxf+9rXFixY4Lo2yoETAUwhKgprH7wH8LLPCugI r0gHL2NQZ4YQpwCKoxYrTJRPummQhmL1EyLIxUT6RPijThiZd3LZfOaFmS9IPSgkMBSbisPxypzl qyaemvbnyezJpkyRGAoclATIbvrrs+coEvZ8kjkSGSTyp5B35sgNOhUvoBYwzrXBCRkqja7FyObo jhZEuDnpZbI6cYJt+VUZPyacMq7VMo+wtJT0hk6zLn+3we+M0QXOxyA9E6Yz3PHhPrAvpq1Pe27p uU11Gr8q9gWnORU9Y2Qb/vTUNKYCzSwMRb9a4kqtoQ1Fpx3yHn74YSZOC3OnImdfmLLspQUwM5PL qr4FPu9WSMUBLlsBRtITJKgaHZcsWVIUENiKdcC6VDpXCKhXxIEX7KooRneKHWAgi/pBhVcaqaj0 Kq9cKKWv4gsV1UXhp67WxMKF71x77bX33nsv64wWNRajY2Z4Ll22uBDWFNeZUTAmGBVuNHEY4Uyf Ph0TxRAUqKWLIolXvJXUOTfE78swh3mb5KbEifUVrpZbtLatPcLZiGlo3L3z0wCQcw+jXKAqEQQ0 /LynDYklRDLhoTbiey9uzgX5wMh/b2AF6gU2UJ0xdizTIUH0yKMPITblcAtkSFkq7ilKIAyr+fLL L1OhBZo78pYWlIaZlpWVKQ5Qb2rqy3HXxInnKp1hefKJWKu+vp4TVPDwqjhGd8hg4qpRYTjwZ+cK ATqEBBaQgp2ReFWqvT/UCpKJUZSYgYQ40lnnn38+3VeuXPntb38bdQaSVwUDMK9qMeFxbrjhBp6E LFTozoiAAXzSSSdhv9va2jgZgi7Q7iajwAhV/+Mf/7h+/XpFJATTUbWDX2kDjXFdpjfldSd5Bs59 S3HeoCoRpOZnWuZudgy597FShpXkl1qMormm3657EW54x3M4F7Q4yNjhFfObp7WWZoQ5riY1ut7k X0KxdXm7+6ofXYEI+f0XCq2SKAxNgX5FCU8YC2Gs5ksuuQRWKBECo6iFeOrAEDBSITiAP0wWjaGR Opt/kDEzZSFAiAkh+lZgii3UAaY7pTjugVc6Vwj6KxIVHYwE9uJJ/0exA6wgC2RIM8iraoQdP/3p T1ELxDxjxgw2TsyKT0W6qSCwefPmEXXiDpn8N77xjeHDhwMDy8DD6JddfqkadNKkSZyzw0elQ5JO jJmuT5/2NIkvIMFGo2Kl/Fq4t6gqUFJIhudZ0MQxMn0uxOnVkVqRIvl8w/w2W4trXAtnZ2jaJrf2 SWNqZa2s/lC0xeOeKVa16OPf/uDe5RYb1Gor9+zpQ9JSOm7UNsmo925suO2Xt8ABjATTJH2kVEF5 BOpKYBg/VgjKnUwmIQ/aitGlMv5AsirgCYWvigMkHsDMmTNHS0xRqQVfH3roEbXGxowZoyDlNAtF sQKYLpXOg0pIBztooZiiRvoPFoKxAWbmas6KBaoR+thM/uEPf5g4cSJHtzNnzoQjZ53xBU4CWU/c tdm+fTsR8ksvvcRAIGFjfeutkq10Z0Rkz+ic/990002/+tWvwEAkNWHChDPOOAO0lqHv2rXr+eef nz79GeDJlCAM7MSrr74KFxiawFDhgWv5HFfsZEzAuiIlxWm1YYnBCXHBUX3ueD9YsMP77qv5e08o j2ttwi31YrYrfC7iJ0rsR1e2L0iVv7Vm+5KcneegO5I5Lt76wGePxY9XcIOKe9r86xci5wiL+J/h SAxs3LjxsssuO/LII/GYXGOJx6NsFEnGYAIlChAAAAVTSURBVOEIdJgR5LGBvPGGn3FzQJkTmMZk FRsLlEu7AgfUqrjooouee+45ADAtpB9YYNTh5C9/+UvMCTD8lAN4JSzFPYWQxi6VzhVCoYAsRbp6 7Vj/6BiQAllQyScqBcrkvHilkWMOfo5y+eWXv/LKK+l0moPyp556Chg1cyag6tdffz3cpBOqRS/w KAZRv+yyS3G4t9xyGzyiO9l7zyWSlDt+YIAllwe7b7vttkcffXT58uUkv/v376/oBDmaCjYiIwwI /oZbcRyOy0gxdL95XNWLG9cvyUeeXNay3nZ+89m+gzAhQdzQU65R2uol567ZOXOnXuZF8mFORLOX 9u95w6h+/WRyluu8CEHnskZKxEsLDeecczYJgyuuuBKdWLNmjTyz1kwrIm/LoSvwEBOIKhMMciL1 9lvz8S/vvfce18+OPvpo6JT6WwADHokqDkDliSeO/s53vnP//fdj/Likr75is7EiTG3SpNvZcypW KySqrqbfpafx85///KMdoAm+K2nt2tnKBqmpqen000+vrun1UWBaYDjbotbWNgIFfk0wbty42toa GuE/1g9xghDqWdkkVZjSiuUr6KVmyygcD+IvuHrzudPHKu8IPGLmE2AwUVks/Mi4cf8XNiHsXNYG BpcC/p49e1555VWslWQyUV1djRgaGhsHDRrIMipg07Zu3drenmps6P25M89orCzXNQs94rYFO3Zc fqVp/L9B5c+927Itkl61Pfvf7+Y/2JXbks/mncr+5cHzm/3tuWxbKje8p3/+sNgjo/pdfESyQidN bXGFJ9R0Iyu8aIr/5bYSTdJ1eHXBBRfyZPW37NjFNDnx5yPC4wjq0ksvve+++1ghtCQTpdBG48BB A2hhmkwZ/qxcuapfv37jxn2+srKC6cNGnDasQ2nwreR1AIOfqMWnP/1pDOeECePxyHBMLVq+ggpK GKKrRS6v/9BHSoJ/uw0QKZo9z046YO0hQwHJzwVxKpnKRiVR1U+2Cu2tN+cxB3jEWX5T3yMKfdhu Se9IHg8VKppQ4JkandB9Jgy9YFy+fBXuht/jcnI4dNgwx3YjBYmAR6Y6zN1JXAJ2g5Si8oCB4Jps nF/8myb/xFGC7aKmcz4dCdpzejQtoje8tPOJDU6O5ELhYjwD+RdW376g9frP1PLb0x6oppvxrYjh WtI0FWwrRqGEfy/RjwsjF9oJjWs7hZvJShgYkVwuLz2CJhMJ7JWwDUU5KT5wuMOJDyfjkn0dEkpy mjJ+lBlCtEFJmpADVqxfj6Y1ww10rrGxnikrRhcrB+cspAik7fyPCqGADv+nawv5D6OJhbvEk0s3 /n1j61onbgfR9EUNz76z4LyjjuUKGLcn+OeMyHx4Fkntg1l8/yvY2K0QUkxcyDd8j9wkq9LTdDYY LP1cVvQoEe+3tw8r5dcCwjEEG1BpgKTNPGxLt0JI0SJoLjTJLSRFuji5C6HVIVuFU+DcVl75IeiX PwfZ6xcl9OFWuhWiIFGSTsQE3PLDAMgGhC/P30h4owBct9f5ISC5GD90I/L34YexUhRCo8NNy7s8 H8SOlH35yxt56M1FS3n6QZPJfX+ZmcZ6GJyERQrWoVshuszg/20dCCBCg1/ykZPk4r+6HUzqmjxF wYkU3AkHJwT+aExhcoet3+i2EEp58QicZGEhCCzxF/JaM6cZAdu9wiF44MrNKt5EXnw+bHcYkhWH 9eSUrA/kaXgut3zIALF/4MxbbsXlDxC40kFv9IBL8VRwJDI5LBsO29IdVB62oj24iXVbiIPj22Hb q1shDlvRHtzEuhXi4Ph22PbqVojDVrQHN7H/AWFrGkuL8nFSAAAAAElFTkSuQmCC --Apple-Mail-8-26658180-- --Apple-Mail-7-26658180-- ========================================================================Date: Mon, 21 Feb 2011 18:26:07 +0100 Reply-To: Alexander Hammers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Hammers <[log in to unmask]> Subject: Re: medial temporal lobe roi mask Comments: To: John Fredy <[log in to unmask]>, G Elliott Wimmer <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: multipart/alternative; boundary=Apple-Mail-10-26904456 Message-ID: <[log in to unmask]> --Apple-Mail-10-26904456 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 Dear John, Elliott, Thanks. Just a comment on two of the points made - 1. I don't think the AAL mask is trying to be "too precise", rather the contrary: I've just measured the AAL hippocampi and they weigh in at a hefty ~7,500mm^3. Ok, they're in Colin Holmes / MNI space which is about 35% (Ashburner et al. 1997) to 46% (Hammers, Allom et al. 2003) bigger than average, but still they're big - as partly explained by the protocol (Tzourio-Mazoyer et al. 2002). For comparison, our protocol (Niemann K et al. 2000) yields volumes of about 2100mm^3 in native space and 3300mm^3 in MNI space (Hammers, Allom et al. 2003); they are the volumes that went into the maxprobmap in MNI space recently discussed here. There are reviews/overviews of protocols and differences (e.g. Konrad C et al. 2009). If a "GM only" mask bothers you, you can always get rid of the "holes" with simple morphological operations - e.g. by smoothing say with a 4mm kernel and then thresholding back. Obviously if you loose many voxels, your activation may not be in the hippocampus at all - which might also be interesting. 2. "moreover, it's not left-right symmetric": Well, why would it be? The physiological R>L asymmetry is well known (e.g. Pedraza O et al. 2004), certainly depends a bit on protocols, but should probably be around 5-12%; personally I use its detection as a rough indicator of the quality/plausability of a delineation protocol. In the case of AAL, it's present but a bit small at 1.5% (remember this is an atlas based on a _single_subject_). Ignoring the physiological asymmetry is in my view dangerous, especially when clinical decisions are at stake, as for example in epilepsy surgery (see Hammers et al. NI 2007 for a discussion with example). The only instance when hippocampi would be expected to be symmetrical is when only _one_hemisphere_ is available and then mirrored, as in the Talairach atlas (and derivatives). But of course that carries no information about physiological asymmetry. Hope this helps, Best wishes, Alexander ----------------------------- Alexander Hammers, MD PhD Chair in Functional Neuroimaging Neurodis Foundation http://www.fondation-neurodis.org/ Postal Address: CERMEP – Imagerie du Vivant Hôpital Neurologique Pierre Wertheimer 59 Boulevard Pinel, 69003 Lyon, France Telephone +33-(0)4-72 68 86 34 Fax +33-(0)4-72 68 86 10 Email [log in to unmask];[log in to unmask] --------------------------------- Other affiliations: Visiting Reader; Honorary Consultant Neurologist Division of Neuroscience and Mental Health, Faculty of Medicine Imperial College London, UK --------------------------------- Honorary Reader in Neurology; Honorary Consultant Neurologist Department of Clinical and Experimental Epilepsy National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK On 21 Feb 2011, at 13:50, John Fredy wrote: > Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for > epilepsy patients, so, I will have time to explore more and more in working with roi analysis. > > Best wishes from Antioquia > > On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]> wrote: > Hi, > > Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to. > > Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs. > > Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out. > > Best Regards, > > G Elliott Wimmer > > Dept. of Psychology > Columbia University > --Apple-Mail-10-26904456 Content-Type: multipart/related; type="text/html"; boundary=Apple-Mail-11-26904457 --Apple-Mail-11-26904457 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 Dear John, Elliott,

Thanks. Just a comment on two of the points made -

1. I don't think the AAL mask is trying to be "too precise", rather the contrary: I've just measured the AAL hippocampi and they weigh in at a hefty ~7,500mm^3. Ok, they're in Colin Holmes / MNI space which is about 35% (Ashburner et al. 1997) to 46% (Hammers, Allom et al. 2003) bigger than average, but still they're big - as partly explained by the protocol (Tzourio-Mazoyer et al. 2002). For comparison, our protocol (Niemann K et al. 2000) yields volumes of about 2100mm^3 in native space and 3300mm^3 in MNI space (Hammers, Allom et al. 2003); they are the volumes that went into the maxprobmap in MNI space recently discussed here. There are reviews/overviews of protocols and differences (e.g. Konrad C et al. 2009).

If a "GM only" mask bothers you, you can always get rid of the "holes" with simple morphological operations - e.g. by smoothing say with a 4mm kernel and then thresholding back. Obviously if you loose many voxels, your activation may not be in the hippocampus at all - which might also be interesting.

2. "moreover, it's not left-right symmetric": Well, why would it be? The physiological R>L asymmetry is well known (e.g. Pedraza O et al. 2004), certainly depends a bit on protocols, but should probably be around 5-12%; personally I use its detection as a rough indicator of the quality/plausability of a delineation protocol. In the case of AAL, it's present but a bit small at 1.5% (remember this is an atlas based on a _single_subject_). Ignoring the physiological asymmetry is in my view dangerous, especially when clinical decisions are at stake, as for example in epilepsy surgery (see Hammers et al. NI 2007 for a discussion with example).

The only instance when hippocampi would be expected to be symmetrical is when only _one_hemisphere_ is available and then mirrored, as in the Talairach atlas (and derivatives). But of course that carries no information about physiological asymmetry.

Hope this helps,

Best wishes,

Alexander

-----------------------------

Alexander Hammers, MD PhD

On 21 Feb 2011, at 13:50, John Fredy wrote:

Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for

epilepsy patients, so, I will have time to explore more and more in working with roi analysis.

Best wishes from Antioquia

On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]> wrote:

Hi,

Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.

Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.

Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.

Best Regards,

G Elliott Wimmer

Dept. of Psychology
Columbia University

--Apple-Mail-11-26904457 Content-Transfer-Encoding: base64 Content-Disposition: inline; filename=image.png Content-Type: image/png; name="image.png" Content-Id: <3335947574_1343002> iVBORw0KGgoAAAANSUhEUgAAALAAAAA6CAIAAACf/kS3AAAgAElEQVR4Ae2ceZxVxbH4+2x3mXtn hhmG2cFhF8GYKLKoEX9KDD81RiCaRY1LNL9sRhOXmBhjjJo8RNREn8a4JmqIAU2UKCQmGhcERRZZ BEEWYdgZmJm7nv19+zZcBh0igx/+ePymxUOfPtXV1VXVVdXVfdHCMBTdpZsDezig76l0/93NAcmB boXo1oN9ONCtEPuwo/ulWyG6dWAfDhxahbCFcAoxqye8vMgI4YpAhME+FBzQixvupLsn+3p+iwh5 5njuv6RzwuW74wvhMS5Pl2p3+VgOaId2lxHaIvSEowsjLjQiFtvVXV4MEf9YyjoCIEtb5ErcuFSF iJ0JI0lNM3xHGJGOYHvreSFiKF9OBGW+KYzAdXTNEDp/9sJ01zrjwKFlkBdGhZ+wY7pt+oL/dMsS UUNEO6PkP7UZvigJ457li0igeVq5VC6RN/ZrInw5giXcMscUaWySjmaYBhR0l4/jwKFVCE1khWVH 83rUMzJRlrcubCvX9WXqaCG+RpNOwNVN08ll9FBE9u97NE9IyxRtdcOg3E9rXlmEJo/W7vIxHDi0 CqHrJakgKiJWxhJWKMycsKMiHhBadK1E9KxvCMM0867t4zTiCTRED639YfEsW9Ah08PXskKLpU0A TRF2zU/tD/nh3X5oFUILvFIvyOs4iTDieU48Gwo7PIgxfR8l8jRhWAlfmFukxxDe/oNT04t6phYm 7BIR+gY14Wh2er/6c3iLuGuzOwjhdGEAR2dtZmOuPWOlVvH0xoqpy5dviWpul2MIT4vSx/QCyzHe 3NEy4p6Xbc8zJfLOCzpnPbCOmNP0S42MqH1izXPbIsm82zl0d2sHDuyXpx1gDr7qC0eYQVqPPrhm +5iq5C0je/cpTQs/2VWMpogGRIREpo6e08o3JPsaETMr3BIix86KJtqTkbwVusIIw4S18cJ+ZY4Q +9mRdIbg/9+2Q2shrCDiibKol3MjsaFlkaElernwXYOV2k4oILyUR5gYsKWk7Cj8JRtEEMjwEQ1w A9eVqQtH93TfbUP8ughzlojkTdFa4hl5iQTn4Ung0GUfKjMVLlBJ1zd1Ox6ElubbUmvYr4r2TGAD ImPL0M+ylQ194eZFaNPCX8LP5an5gXRGQQYAYefe4QkNniTDc3N8OAg7EwTSvfEsbvJ9f/eWp/BF FNu9QuTLaxGAjqooJHveDtXfhzgPIby3t6aO/3eLaK+o19p2mJH8RXVEAjcvdSct3+7aO+Ju4vKB fW8b7bumtmiDOeaVJef0PXLOknVbIvqwMu++0waPrsDI2BvWRi6Zu2GOERUtO8b0a3hj844dFw8o EWJhJn3JjPTytp1GaZkb5F46s+qzFfFfLWz72eKUIP8QMcbU2P8+pd/IlzK/GVk6vIcwRPrW1/L3 Nme2Ou1l0YarBkV/fkxC09x/bzc/9/y8EXVNi1pE1m8/tiQxfXxdXz0jXO3uNbmrBiSERQgkTFNu Yf1QNz7ZOkK0ui5RcNKsqXjI8wzD0NRLQXX2AMgFotRFtRwqRSjiZbBDV/ww2MH6C7NjZm348cJN qL0dOE9vDMxH197/dhDa4fT1zoCn1l87vy0fpt/Y7IiHtn3t5VUbwnCrHx737PIvz94U2rtCNxww fctXZje/F4ZrXW/s06vFg2v9cFvoZUfOWj9hxrowCFNueM2irSf+cxPAOf4PvOTjK9t96uk21yt7 ePnrm1q9MPz1B17vJ5pf2GiH+XDG+63lf3znd6vonXllQ6A/sOXiV1dn7XDFtvyQGYuum7MxDBw3 DH80bwMdqfBHPjybDgdRUAIKHYsVjF/hdTcybANWoSMAkKpFNe6GO8R/fTJVL6rVfip6XuvpaYZn RNyErsXZGERCa+rC1vMH+eceJ+xIZmJjcO3AxL9WtxDLtLmWFYgbj2loFLvK9fSEAf1bNqbzkR7/ 2uR/0N5++wkNg0K3KdS//el+ESuqiTJPj4/uGfvJSRWhJpK6GGYlYtmcpyxxaKTbSpJ6oIlomWG0 a7FcEME8PDF/6zcG1YytJ0XhjetvnHtkr2mrNpDtEppjlKbv+Ey/SMQb3Mv7al3ff6aipEww9PF4 KYvU9RjkExWlB6DADChLsHnz5smTp9x1192zZ8+hvWgAWlpa3n777ZdeeimXy9GIAvCV7p9o+APu fGgVwiF/7AjH1EMErgdhxMSBt8ZS4xvqe9qaLhJOGG1qKl+QskORSJgZt2RXU0Vc+BWhSLZqoRmN xDxnVV6M6lXdOyd8TfNNf1SlKHFSvoiatrhreHR1c9k5M3ZVTltzxdLWwIoxjpk3CRjoG4jAIzCx RdSu6GFEbWHNawvPbCA+KeF7kNUuq+v1ry0R05WRtevZkaQwbJIk5tZIe09T5tEsT+g5QEVMh1oR EKDouIwD5m4HQESLUyg2sPS3bNly5513/vrXv54zZw5SR0uAQfAffPDBWWeddfHFF8+fP59XpT1F dSliOESVQ6sQuFykZ/lkAvRoaJAPSEUCx8+145AD4scgEgSmkxKW5ZCIJBmdMmOsBMONkXbeommx 2nbTyKUyKZEr7Cd8Q/NXtIlWspUi7cTEwGfTN77bfuKR4T9P6/H88OpYoLVGcyLW7uiup7cbwo2F cZTSrmhtzzCcXZIQbWK7JQKfU5WScGc21MO8Y6WF0S6CnqUEnHokFNESrdLbtTUbJ/FRiFgLnh7i pWxoOiieKbmCT614BOw4Do2YAaUNRUuQz3MSI02CaZqA0a4cxyHSgA+hlYvj0JVoGBH5iGYJO26n CNGD8oihHxlN/H5Z68T+pSWoRd58ba05qqKnxQ5R9NRjqcKJVFzG+RVbUtvak6LxrP7h1fP91YFo ClK+Ht+g+SSxObxascNek965/Zz6SktkI+IHK7N+LlIi4qYXN01b8/PCifsRhO9qbY5ZZsRyZadU hk+vTZ1Uo0f9qBGJ/nbtu+OGlhpB0sd0mauFmcCix9xE0tkVjbglhKXIngyrhq8LdV2qA3GFDnAX ixK20oniE4OBvIuxpFILlKBPnz7XXXcdIzQ0NPAEvqNp6eLIXQbv8ty6NIJGVBQnM5TGPleXVnLa yZbzymGNI2c1nz9r4/eOa/hbc9vdK9qeOS1pRe2U4bpa3jbj0UBwEpU1I74W071Ynx4lpzRtP3vm zslHV7V64orX1opY0g/MqpgbOPH/WrLz5L7ajHW5362yhRv926aNE6obAi+q69X/3byrTstNOKI+ tGxbC9yYceMga/S/ORPPXzTYe3x5MGtD2T9ObTQCP94e4/QrDJMB56G6yOtlWTMm/NDDXsQMVwuw 5tJyYR2Ic1DIvbb/gPiBUNVCRwOoK0fAk0BStYBFGQPAevfuffXVV9Oi1EgpinpVynRAQx4sUOfm D1ILRkvu2LFXHZEzDfWqyJV5gkKhXX2ivdiFA0k0wAyT4yo3VFalM2GUFFNTT+fZ8Y1x3b1p/o5N 21MvntbjC3XJvIhWRGNnV8nNf8CRmC8GR40hNVqbyWpOP37agO/0yT08f8l9H2y5+IS+F8Sb23VR nwgf/3yv97e6t853zNZtb57Z9+uDKx5Yks9xamGKrx3p/nXpNi2eIIawIptwDKYmRvaOvXxmXXt2 w42LUy1u6/QvNA7vwxr0/J7ivGQkzcE8CRJXDEkYo5KlfoQIVTdyrsX5CShd/Jbpa75upPPQJ29m FFIaIucFKbyNzIUUSnH66hWGUKiz0HETVPaqBV7T8/XC4S3cQ958Ur14KjUqdqdFZSnUk1e6KMzU i5Wif1Hi4FPXihrvI08fnUCyqt22bVWBFCrMSpFS/Kra1asiiDowduiEeS/0wp1h2EYTvTPgckI3 x1Rwnm2BfIbsAt02ckXU3TwvhZF9tqUAsYeUMBnG9cN04ZNDk+/IXoENmXzK0u5JAHJNobPLlX+T 3Qp3wLKAofnghn5awQAvh5C7STfMZ8PA9X2GlcNl2Vi6rmSzEwBG318sbvFCX24Qc8wIDHJXCz6P nFnopH3HB1qicsBQZAsVxQd4JdHsKdQpasM5e/bsxvre9bUNkyfdUeyoANRT9VV1JQKFRrWAvyPb aVRfi/xXYF197tXHD+kRiFBlxqA9Eonw5ro+Ok67ZVnKdjE2AHylXYVCkKh0nAowBksoKhdHhSvK 1BlniU5MRwSguXYsyJeJgO8hJ6JaGaeRMT8Xmk6UxKOPccpF/IyIsWMIYr6I+3jzIEFIYGMCNMFR J73CiG1kSoQbD4lISRrZEWE7FqnxCAlH0xM9Q65PiLK8CNhC6vQOrDAko2U5XLjCGjlsVjzNJNtk 4DMCuhE3mHmO0qysKdrpqzkxImJ5rcLKaxg7PSS0ELYfagS2RkIYwHhwUV71yDBlxQcqHdc6HKAF 1sEx6gQKVGhRBgPuUadd8ZOKYjtg1NEexWSegFHUV7ApcVChAKm+fmhc2rtU9hdDSEVhYLBDUEED 2ApIz8mora2tb7zxxrJly/ja2Nh4zDHHDB48OBaL8VXNgXkWdIh9BXdaOKXMW8TLmk7dD/2IZixa 9u7i995dv2m94ftjR48ZOvjIkooyvhph3DcCy897Jt47jpVOGaLECrjyxMigx0ob8TgmvEALu0S0 IgEzNDcg1HPZykBEiOX3s5ZRkg7CpK+zV7SiWS0SFzA01Aydy3Tr17UuWbhg3YY1377mCsZNcN7m uNy+8ULT4zoX93oCwkovFw3ao+LGN5vdaE3fhHdCeVu/ml4ldIhg4ZIoIBY/ovu2Zhkax/GmH/iK D5KKwpJ955133nrrLbYS8G306NFHHXVUeXn5Xv7sUQUlYwRMR9SCioKhDvNpVFKgsmXLtqVLly5e vBiY4447DpzgoJ0VqJhP/ROVgnp18oBENaWGht51dQ1nnHEWr2272n9w5Q8b6hrrauoxd7XVdfyp rqoZMWLUzJl/t21sL4ZLYqMu/yrUqeWkvXbSTv7ZZ2d85phjezf0qe9Z36+2qb6qqqGhorynNf68 L86Z+5ZKB2KB6SddFqbfk3a9of6I+tq6O6b8quAcMPJ56TIwmfIjBh2/gNPIv/n6W/WVvavrG+65 fTImPfQw49n1a9+t711dW1v929/+FpQznv/b6BOOr62qqK+o7FNbD434o9bQx19I5+WnwmAXB/UQ INOGufCG+d64WVvEY5vFE1vEQ6s/9dy6V3aELQXq5Lg4QE+SIL2YJJqqfK55f+111/wIRuER+AO7 +EOFiV95xVXvLl0OzOuvzqaxplftnXfcJXlVKBiYuXPn1tbW8+e112bTBj8dB8JleWrqn4cOGQbP 6VhV2QuEJ4w6cck7S9VX9fyQH+n46UDq+3UZSsvQ6xEjRqAcaOXrr7+BPj7++ON8Qm0hHQuh/EVz c/Oll176k5/8hE8YP4+MZMTE28gcDuedrD2hrVn23te/eN63vn7Z5m07bMenr+fahhlzgkRJj6ZX X1149hfO/f6Pr2hr2YYL0YgADM82PdMgiSGCvKWFSVsvcUUsTUaCjayLOzLk8YKM0bDs8cBha+/H TCMWWr7DWjN9w9JEvK5pSEXP3pqI/WXqc/+c8a9vXnb56g3r/NJIu1m4qOOLmCfKfWHnSWuyPBO+ 02NFi3XXisz1y5o5m0uK7cPL7NpEPpLb3kN4y1qSY6Zuu37uph3c0NHy8ozDMPMBTsOTN4iZsCam 3HHnySef/Nhjj8E3VjY8lJMthF88p06deuqpp/7ugQej0SirnK8Mi6gApkKjqtAFJqOUNCrbPG3a 01ddddXOnTtVr3g8jv1YvXr1l7/8ZQwGYKrQUVXAuaetC393rhDQBKEoJphQAuq+F06ccC5Z1ZKS kgsvvPAf//jHtm3bNm5q/utf//rNb36TsdGPadOm3XzzzcyLhAodIUzShBUMxftL3vvqOee9OWeu GWWf3XDfPbevWrvkg+3r1m5YPeell793/iVHlFckQvuJx6ede9HXW9JbPYPY29TwFUJjo2ImbM9u 1fN5y9WSuPl8wHm4iLioSkbkOQvNuLYXMfJJry3YldczWomNgNAXXbhO6Ez8P+OIS95dueCWm3+h OZqZMj53zNhJP5ryl6l/k6wyvNBwSoVDPuhnC9qrZqwc9velV7+x6TfzZX4yr1k/HdG47ktNq84f 9o3jK8u1TUap+eCy9slv7mglGS+PTEVAOktKU6rm1T+8ZsqUKcSAiPbcc8/905/+1LxxA3+2bd8K 0+BVjx49cCu33nrr3Xff/aEYQomwKEiUCYVRYsVT3HjjjQi7oqLi9ttv37ixecWKFeeddx6iQSj3 3HMPykFRgpOkFJRMVbr2ZPhOC6hp5/HGG3NxGbU1jfV1fYYMPmrRgncwd/C/aB4xksuWLR8yZGhN TV2vXjVz5rypEBYQ+I7npjLpAQMHV9fU1dbX3XHnFAwypl5tYjCFrV6ewH37js3nj5/QWNfUo1fD 9y79LktN4lcHSU5YWVvdp7H3pLum4ETwPnxKKQtPf6J49h6+3DjM/fe8QVVN1Q29bp/8y7Rsko6A vyf91501jUdgaXvX9B3Sf+CCN2cTmtK1sLPgmAJ348xtD8yp681Hl4nHlopHdlY8vEL/3SbovHoB mwmISYcpj73Q/Hx44p/Xise2G/cvu3lhypY+Ai+j9iE+xh8XgF8YefyoxYuW7GYRc4Fkxis4lOb1 G8ee+jn8bJ9G/GADboVeyLIoBXYf9fWNVVXVnHHQiLklnH/xxX/BXqYg/Qt48CN5uf8aMXwkSGpq avbwnG90YbCDLJ1biKJOsXsig0/WlgsELMe7fn3n0KOPkuEdl1x55z9ZF0OOGnQX0iKw140H7v8t JkHaPU0eFpMC/P53vp/PZLUgvPfue6++6oeGTkRoytyO3PeLciMaE2ZVz9onnnl65KjhSUuf/rdn Zv19JpsPTy8kj7mpTaznsWvAWoQxOTYRnZBBLP0JuRICEokngUgZjulHdC2WACyiyTsx7A1IN7k4 KXYn6XumPjlsxAhN46wrg7vR8D1u/JHVbSc/tctrJfo3Tq6umnZ8uOorg+3Ly7BSpUFbyChaVCT5 3zxWD188p35k+VbDrL95/uZ1MiNhOkSUuljbvH7y5Mks4tLS0hdmPX/0McMc12GOkkukGtjGyKMQ 0dC7fvoz0z716aNtrmIUIvfiskaGkl72EVxXtyIFC8sZh4/FnT9/HjEjJ2wnnHSi50u2W1GObMRV P/wBLSeccMKmTZvQA/pinPApChXYulr2qxBKwZieKryyoTjttNMgC3VWAW3HUU855ZSBAwdC0KxZ s1AIeilSOMKhhTmPGjVq4pcm8ImikH/0+cgjj+CSAMCigkqy4KAcoRr6o8/Tzxj32eHHS8psGdtA ixXG5+XED99wHDOtW9t+dkrvWeNqxg+tjJfwmUz27oIK7q7x+w7DmHTaUNPfHljRyfO2G6RT+RZ6 v5l0u+LVQw89VFlZCeUEWEy8yAoqNOII2GjgVsCzB/2H/2bigNEKE+iFpBOJhAraCBqUR2aFEH4Q QEyb9tQzzzxTW1urEKoohI5yTXa97FchoAOkIIQyNAC3N3bsWAjtOIyigK+AQQfqQp3GxYuX0MIe jJ9LIWMleEJO6WhBWTglAvlHS1l56Ve+8hUwMG02bBLJvnlSWj5JOWXsqWxnlWHLkqKAxkDcOvv9 Ng/Z5+/+/IBr+pfE/cAI27nlhb8ojrVXL7neoxtj4uLEvmVCM//8AWgkVGhnZkyfCbXDhg0bPXoU LcyOp2JXkWk0EioSOnzqU58aP3687NlZAQBWqy/0hSGsN0RA/ZZbbnnhBRbY3kANb4PGKHlhHuhF Hanx7Az3x7R13kfNn+kxAdQc1O3t7b169VIDQx+UFUXFqyfzSEJlI2jPZrM86WVaBh4RFnBgM2Lk 8Rwhy1/UkVXaTwEJgRKjwA5CMF6LfPmYeRzY5379+smlLu9nihKNzEXwyjYxc4NjaFu+37/misao hWAJZ0mI4OoKEi1o7W7p7lWLMDtxYE3c81O2trZN/kRx4btruMMLwFe/+lUmCH8ovCr6YYUSFePS qJI0Z5999v6ohqV0VBymLzQMGjQIE0vHHTt2fOtb32JxPvDAg+vWrQcDgSfADEedZal6gWF/yP9z e+eJKSgojCSRKguByWL/WRQPs1JDKuGZhsRzxBFHkIFRxPGVpeg6HsEwGDjjJ6RUaJkhMVanZHmO y5QAYImAio4oE0N0CnwQjRyU4sVNdq3czQg4swr+sLbZNau50XnTSeUi34YlTES5P5kwuW5J9nIP ewrH3nsH9K1wMNkBju+Fti6n9UuG8+Yt8SMuRzdS5wrmQU0WRlFBWsyLOp+YGnUajz322L0Y960h VI4OmDvNSrFQhQcffJC9PWkudOv9999n0/GLX/wC/fvud7/bt+8RgIFTiYNeaqx9sR7QW+cWQmGE dPBCFkXpHaN+SAGLigwpEE2hL9qgwCzuRmezEAoGpUBF+E6pQ+0YAki6sLdW4xa1sNMuXWq0dCtt s1mVqfNCqGe91mpj+cdX13EHizQ2iVCXVY1E2Vrs0YZ9hijEEnQuMyzUgi6uvG4TsVMpR2uFbITN MqCieinBMCleqSsAeEVLVVXVPpg7vDBxVoVaCVRgCGujoqL8L395mgs1JDkymYzKQ/z+978/88wz Fy1apNYhHRmCgeiihu6A9YCqnU5a5kGhQ0kX0aKSahgai1iBUaJibLyYogC61eR3Nwqd8Aorx7r5 4he/CEKUTCIhldRZ8V25LMAciVl1dXUMrfSjM9iDadOMaEmUZRfkOPIm4xvq67YS6mdPqi+NEjBY Ii3MSiqml9ZJUQXECx2HkWIuEI5km2V3jZ8g1/GLkVCwq7X0kqBwLsYyoJfioeIYjFLWTumE4tva tWs7Iu9YBzgalaJl+orlBbZwZUY/55xzvvSlCZs3byXx9eSTT5KESKVSF1xwAQmhAQMGwDFGpCMV RNAR5wHWO1cIKFb6BUFK3SCoqA1qMGCKo5LAgVWEwawPRK560YjdHTp0KDcE2Wtce9010ESCQYeV neuD5CzORK4uMkoyLSMLoxzgZD4WLEMSq7AZhldkFjmqkr8aiuY28ZMewge/pBwe2m3CKC8RWHX2 t3JZS0vPIUgH7J6WnLWyhS2EIzJHVUiy65oahF0q9Pzrr78+ctQIaFZSL3Tnco1MO4KggEtGJLBo 1apVHVDuU1W85VlQCGkq4IPaXKgn8dyPf/wj0lxf+9rXFixY4Lo2yoETAUwhKgprH7wH8LLPCugI r0gHL2NQZ4YQpwCKoxYrTJRPummQhmL1EyLIxUT6RPijThiZd3LZfOaFmS9IPSgkMBSbisPxypzl qyaemvbnyezJpkyRGAoclATIbvrrs+coEvZ8kjkSGSTyp5B35sgNOhUvoBYwzrXBCRkqja7FyObo jhZEuDnpZbI6cYJt+VUZPyacMq7VMo+wtJT0hk6zLn+3we+M0QXOxyA9E6Yz3PHhPrAvpq1Pe27p uU11Gr8q9gWnORU9Y2Qb/vTUNKYCzSwMRb9a4kqtoQ1Fpx3yHn74YSZOC3OnImdfmLLspQUwM5PL qr4FPu9WSMUBLlsBRtITJKgaHZcsWVIUENiKdcC6VDpXCKhXxIEX7KooRneKHWAgi/pBhVcaqaj0 Kq9cKKWv4gsV1UXhp67WxMKF71x77bX33nsv64wWNRajY2Z4Ll22uBDWFNeZUTAmGBVuNHEY4Uyf Ph0TxRAUqKWLIolXvJXUOTfE78swh3mb5KbEifUVrpZbtLatPcLZiGlo3L3z0wCQcw+jXKAqEQQ0 /LynDYklRDLhoTbiey9uzgX5wMh/b2AF6gU2UJ0xdizTIUH0yKMPITblcAtkSFkq7ilKIAyr+fLL L1OhBZo78pYWlIaZlpWVKQ5Qb2rqy3HXxInnKp1hefKJWKu+vp4TVPDwqjhGd8hg4qpRYTjwZ+cK ATqEBBaQgp2ReFWqvT/UCpKJUZSYgYQ40lnnn38+3VeuXPntb38bdQaSVwUDMK9qMeFxbrjhBp6E LFTozoiAAXzSSSdhv9va2jgZgi7Q7iajwAhV/+Mf/7h+/XpFJATTUbWDX2kDjXFdpjfldSd5Bs59 S3HeoCoRpOZnWuZudgy597FShpXkl1qMormm3657EW54x3M4F7Q4yNjhFfObp7WWZoQ5riY1ut7k X0KxdXm7+6ofXYEI+f0XCq2SKAxNgX5FCU8YC2Gs5ksuuQRWKBECo6iFeOrAEDBSITiAP0wWjaGR Opt/kDEzZSFAiAkh+lZgii3UAaY7pTjugVc6Vwj6KxIVHYwE9uJJ/0exA6wgC2RIM8iraoQdP/3p T1ELxDxjxgw2TsyKT0W6qSCwefPmEXXiDpn8N77xjeHDhwMDy8DD6JddfqkadNKkSZyzw0elQ5JO jJmuT5/2NIkvIMFGo2Kl/Fq4t6gqUFJIhudZ0MQxMn0uxOnVkVqRIvl8w/w2W4trXAtnZ2jaJrf2 SWNqZa2s/lC0xeOeKVa16OPf/uDe5RYb1Gor9+zpQ9JSOm7UNsmo925suO2Xt8ABjATTJH2kVEF5 BOpKYBg/VgjKnUwmIQ/aitGlMv5AsirgCYWvigMkHsDMmTNHS0xRqQVfH3roEbXGxowZoyDlNAtF sQKYLpXOg0pIBztooZiiRvoPFoKxAWbmas6KBaoR+thM/uEPf5g4cSJHtzNnzoQjZ53xBU4CWU/c tdm+fTsR8ksvvcRAIGFjfeutkq10Z0Rkz+ic/990002/+tWvwEAkNWHChDPOOAO0lqHv2rXr+eef nz79GeDJlCAM7MSrr74KFxiawFDhgWv5HFfsZEzAuiIlxWm1YYnBCXHBUX3ueD9YsMP77qv5e08o j2ttwi31YrYrfC7iJ0rsR1e2L0iVv7Vm+5KcneegO5I5Lt76wGePxY9XcIOKe9r86xci5wiL+J/h SAxs3LjxsssuO/LII/GYXGOJx6NsFEnGYAIlChAAAAVTSURBVOEIdJgR5LGBvPGGn3FzQJkTmMZk FRsLlEu7AgfUqrjooouee+45ADAtpB9YYNTh5C9/+UvMCTD8lAN4JSzFPYWQxi6VzhVCoYAsRbp6 7Vj/6BiQAllQyScqBcrkvHilkWMOfo5y+eWXv/LKK+l0moPyp556Chg1cyag6tdffz3cpBOqRS/w KAZRv+yyS3G4t9xyGzyiO9l7zyWSlDt+YIAllwe7b7vttkcffXT58uUkv/v376/oBDmaCjYiIwwI /oZbcRyOy0gxdL95XNWLG9cvyUeeXNay3nZ+89m+gzAhQdzQU65R2uol567ZOXOnXuZF8mFORLOX 9u95w6h+/WRyluu8CEHnskZKxEsLDeecczYJgyuuuBKdWLNmjTyz1kwrIm/LoSvwEBOIKhMMciL1 9lvz8S/vvfce18+OPvpo6JT6WwADHokqDkDliSeO/s53vnP//fdj/Likr75is7EiTG3SpNvZcypW KySqrqbfpafx85///KMdoAm+K2nt2tnKBqmpqen000+vrun1UWBaYDjbotbWNgIFfk0wbty42toa GuE/1g9xghDqWdkkVZjSiuUr6KVmyygcD+IvuHrzudPHKu8IPGLmE2AwUVks/Mi4cf8XNiHsXNYG BpcC/p49e1555VWslWQyUV1djRgaGhsHDRrIMipg07Zu3drenmps6P25M89orCzXNQs94rYFO3Zc fqVp/L9B5c+927Itkl61Pfvf7+Y/2JXbks/mncr+5cHzm/3tuWxbKje8p3/+sNgjo/pdfESyQidN bXGFJ9R0Iyu8aIr/5bYSTdJ1eHXBBRfyZPW37NjFNDnx5yPC4wjq0ksvve+++1ghtCQTpdBG48BB A2hhmkwZ/qxcuapfv37jxn2+srKC6cNGnDasQ2nwreR1AIOfqMWnP/1pDOeECePxyHBMLVq+ggpK GKKrRS6v/9BHSoJ/uw0QKZo9z046YO0hQwHJzwVxKpnKRiVR1U+2Cu2tN+cxB3jEWX5T3yMKfdhu Se9IHg8VKppQ4JkandB9Jgy9YFy+fBXuht/jcnI4dNgwx3YjBYmAR6Y6zN1JXAJ2g5Si8oCB4Jps nF/8myb/xFGC7aKmcz4dCdpzejQtoje8tPOJDU6O5ELhYjwD+RdW376g9frP1PLb0x6oppvxrYjh WtI0FWwrRqGEfy/RjwsjF9oJjWs7hZvJShgYkVwuLz2CJhMJ7JWwDUU5KT5wuMOJDyfjkn0dEkpy mjJ+lBlCtEFJmpADVqxfj6Y1ww10rrGxnikrRhcrB+cspAik7fyPCqGADv+nawv5D6OJhbvEk0s3 /n1j61onbgfR9EUNz76z4LyjjuUKGLcn+OeMyHx4Fkntg1l8/yvY2K0QUkxcyDd8j9wkq9LTdDYY LP1cVvQoEe+3tw8r5dcCwjEEG1BpgKTNPGxLt0JI0SJoLjTJLSRFuji5C6HVIVuFU+DcVl75IeiX PwfZ6xcl9OFWuhWiIFGSTsQE3PLDAMgGhC/P30h4owBct9f5ISC5GD90I/L34YexUhRCo8NNy7s8 H8SOlH35yxt56M1FS3n6QZPJfX+ZmcZ6GJyERQrWoVshuszg/20dCCBCg1/ykZPk4r+6HUzqmjxF wYkU3AkHJwT+aExhcoet3+i2EEp58QicZGEhCCzxF/JaM6cZAdu9wiF44MrNKt5EXnw+bHcYkhWH 9eSUrA/kaXgut3zIALF/4MxbbsXlDxC40kFv9IBL8VRwJDI5LBsO29IdVB62oj24iXVbiIPj22Hb q1shDlvRHtzEuhXi4Ph22PbqVojDVrQHN7H/AWFrGkuL8nFSAAAAAElFTkSuQmCC --Apple-Mail-11-26904457-- --Apple-Mail-10-26904456-- ========================================================================Date: Mon, 21 Feb 2011 19:28:23 +0000 Reply-To: G Elliott Wimmer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: G Elliott Wimmer <[log in to unmask]> Subject: Re: medial temporal lobe roi mask Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi, That is very useful information! Regarding hippocampal lateralization, I think an asymmetry may not be known broadly enough in the imaging community (after checking, I now see it in a recent dataset, at approx. 2.5%). This may particularly be an issue to consider when researchers are using atlas-based ROIs, as sometimes they want (and assume) equal numbers of voxels in left/right ROIs. (Also, I should have qualified my answer by noting that these aspects of the AAL may be concerns if you are considering an fMRI study of a population.) However, I would quibble about the AAL hippocampus definition, as it does seem, to me at least, to be more *precisely* defined than the rest of the atlas, with respect to excluding white matter. (e.g., compared to frontal regions - unless the atlas brain has a huge amount of grey matter!) If this is the case - and it may not be - there is probably a good reason for it, but nevertheless it may be something to think about when using such this ROI. ========================================================================Date: Mon, 21 Feb 2011 21:29:36 +0100 Reply-To: Andrea Cherubini <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Andrea Cherubini <[log in to unmask]> Subject: Research Assistant and Postdoctoral Fellowship Positions Content-Type: multipart/alternative; boundary=Apple-Mail-2-37912977 Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> --Apple-Mail-2-37912977 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Postdoctoral/Research Assistant positions in medical neuroimaging with a particular focus on magnetic resonance imaging (MRI) are available in the Neuroimaging Laboratory, Magna Grecia University/CNR, Catanzaro, Italy. One position for a period of five years, four positions for a period of one year, with possible extension to a second year. Research in the host laboratory focuses on the development and application of quantitative MRI methods, including advanced image processing and mathematical modeling. Current projects in the laboratory include the development of methods for acquisition, processing and analysis of images from multiple modalities with applications toward the elucidation of structure, function and physiology at the tissue level, with particular focus on the brain. The laboratory is staffed by physicians, psychologists, physicists and others focused on translational use of MRI. Anticipated activities entail the further development, implementation, validation and maintenance of imaging techniques as well as large-scale image data processing and analysis. Candidates are expected to actively participate in translational studies applying the above methodology to patients affected by neurodegenerative disorders. Candidates are expected to have an advanced degree in any field related to neuroscience (physics, mathematics, informatics, engineering, neurology, radiology, or psychology). Candidates with documented experience in medical research are preferred. Experience in scientific programming related to medical imaging is a plus. Fluency in English language is essential. Applicants are requested to submit their curriculum vitae along with two letters of recommendation as attachments to email. Andrea Cherubini Neuroimaging Laboratory National Research Council University Magna Grecia Germaneto (Catanzaro) Italy Tel: +39 0984 9801768 Fax: +39 0984 9801791 For enquiries please e-mail: [log in to unmask] --Apple-Mail-2-37912977 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=us-ascii Postdoctoral/Research Assistant positions in medical neuroimaging with a particular focus on magnetic resonance imaging (MRI) are available in the Neuroimaging Laboratory, Magna Grecia University/CNR, Catanzaro, Italy. One position for a period of five years, four positions for a period of one year, with possible extension to a second year.

Research in the host laboratory focuses on the development and application of quantitative MRI methods, including advanced image processing and mathematical modeling. Current projects in the laboratory include the development of methods for acquisition, processing and analysis of images from multiple modalities with applications toward the elucidation of structure, function and physiology at the tissue level, with particular focus on the brain. The laboratory is staffed by physicians, psychologists, physicists and others focused on translational use of MRI. Anticipated activities entail the further development, implementation, validation and maintenance of imaging techniques as well as large-scale image data processing and analysis. Candidates are expected to actively participate in translational studies applying the above methodology to patients affected by neurodegenerative disorders.

Candidates are expected to have an advanced degree in any field related to neuroscience (physics, mathematics, informatics, engineering, neurology, radiology, or psychology). Candidates with documented experience in medical research are preferred. Experience in scientific programming related to medical imaging is a plus. Fluency in English language is essential.

Applicants are requested to submit their curriculum vitae along with two letters of recommendation as attachments to email.

Andrea Cherubini
Neuroimaging Laboratory
National Research Council
University Magna Grecia
Germaneto (Catanzaro)
Italy

Tel: +39 0984 9801768
Fax: +39 0984 9801791

For enquiries please e-mail: [log in to unmask] --Apple-Mail-2-37912977-- ========================================================================Date: Mon, 21 Feb 2011 16:40:59 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: TIV as nuisance factor for unmodulated data in a VBM-analysis Comments: To: Lucas Eggert <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Lucas > at the moment I am unsure about whether correcting for total brain volume (TIV) in > a VBM analysis of unmodulated gray matter segments (density) does make sense or not. > > On Dr. Gaser's homepage --- I have the feeling --- there are two slightly opposite statements: > statement (a) (please refer below) clearly states that TIV should not be used as a nuisacne factor with unmodulated data. > > On the other hand, I read statement (b) (please see below) as correcting unmodulated data for TIV gives me relative > density after correcting for TIV, which seems to make more sense to me. > > Which procedure is the more valid one, or for which kind of question would I use which procedure respectively? One important issue that you bring up is the use of modulated gray matter (GM) images and what an analysis using these images would tell you. I think the prevailing view on this is as follows: if normalization were perfect, and there was no modulation, then all GM images would be perfectly aligned. Because we haven't modulated to account for changes in size, the images will be identical. Thus, any differences we see in an unmodulated analysis are likely due to errors in registration. So the first issue, i think, is to make sure that unmodulated images are answering the question you want to answer. I think that the most common view of TIV and unmodulated analyses is that unmodulated GM images represent a property of the tissue, which (unlike its volume) should not depend on head size. Thus, the advice you came across not to include TIV in an unmodulated analysis. [One thing I wonder about the above interpretation of unmodulated analyses, though, is the application to patients with various types of neurodegenerative disease. If there are small pockets of damage (e.g. spongiform encephalopathies), then I would think that an unmodulated analysis might pick this up. Which is to say, I think there may be things other than registration differences that may lead to differences in unmodulated analyses. However, I would be very interested to hear from someone who has had some experience with this.] So, as to your specific question, if you don't have a reason to think that what you are looking at (e.g. tissue density) would be impacted by head size, then I wouldn't think there is a good reason to include TIV. (Although you may want to do some exploratory analyses, preferably in another group of subjects, and include it—then you could see empirically what variance in density, if any, was accounted for by TIV, which might be informative for you, and others.) :) Best regards, Jonathan -- Dr. Jonathan Peelle Department of Neurology University of Pennsylvania 3 West Gates 3400 Spruce Street Philadelphia, PA 19104 USA http://jonathanpeelle.net/ ========================================================================Date: Mon, 21 Feb 2011 18:06:31 -0600 Reply-To: Erik Kastman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Erik Kastman <[log in to unmask]> Subject: Research Specialist Position - University of Wisconsin, Madison Comments: To: [log in to unmask] Content-Type: multipart/alternative; boundary=Apple-Mail-4-50927819 Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> --Apple-Mail-4-50927819 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii The Wisconsin Alzheimer's Disease Research Center NeuroImaging group is seeking a talented Masters or Bachelors level research specialist to join our functional and structural brain imaging team. We focus on neuroimaging research studies in early Alzheimer's disease and other dementias, with a focus on memory and other co-occuring cognitive changes. We are looking for someone to assist with image processing, multi-modal data analysis, documentation, scripting and management of a wide range of modalities, including fMRI, DTI, ASL, FDG PET and PiB PET data. Familiarity and skill with image analysis and imaging software is a plus. Further details and contact information can be found here: http://www.ohr.wisc.edu/pvl/pv_066683.html If interested, please send resume and cover letter referring to Position Vacancy Listing #66683 to Amy Hawley at [log in to unmask] Erik Kastman brainmap.wisc.edu --Apple-Mail-4-50927819 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=us-ascii

The Wisconsin Alzheimer's Disease Research Center NeuroImaging group is seeking a talented Masters or Bachelors level research specialist to join our functional and structural brain imaging team. We focus on neuroimaging research studies in early Alzheimer's disease and other dementias, with a focus on memory and other co-occuring cognitive changes.

We are looking for someone to assist with image processing, multi-modal data analysis, documentation, scripting and management of a wide range of modalities, including fMRI, DTI, ASL, FDG PET and PiB PET data. Familiarity and skill with image analysis and imaging software is a plus.

Further details and contact information can be found here: http://www.ohr.wisc.edu/pvl/pv_066683.html

If interested, please send resume and cover letter referring to Position Vacancy Listing #66683 to Amy Hawley at [log in to unmask]

Erik Kastman

brainmap.wisc.edu

--Apple-Mail-4-50927819-- ========================================================================Date: Tue, 22 Feb 2011 11:46:58 +0000 Reply-To: tony han <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: tony han <[log in to unmask]> Subject: bandpass filter in REST toolbox Content-Type: multipart/alternative; boundary="_79ec0d2c-25d8-4464-aa26-c8a8eb8dcd08_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_79ec0d2c-25d8-4464-aa26-c8a8eb8dcd08_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear REST developers, Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows: >> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img'); Ideal rectangular filter: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1" Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage Meet error while reading data. Please restart MATLAB, this problem may be solved. Error in ==> rest_readfile at 61 [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex); Error in ==> rest_to4d at 41 [theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename); Error in ==> rest_bandpass at 39 [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir); What's the problem? And how can I solve it? Thanks. Tony --_79ec0d2c-25d8-4464-aa26-c8a8eb8dcd08_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear REST developers,

    Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows:

>> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img');

Ideal rectangular filter:    "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"
    Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage
Meet error while reading data. Please restart MATLAB, this problem may be solved.

Error in ==> rest_readfile at 61
            [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);

Error in ==> rest_to4d at 41
        [theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);

Error in ==> rest_bandpass at 39
    [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);

What's the problem? And how can I solve it? Thanks.

Tony
--_79ec0d2c-25d8-4464-aa26-c8a8eb8dcd08_-- ========================================================================Date: Tue, 22 Feb 2011 11:48:38 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: acceptable high pass filtering MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e6d447daaea4f7049cdd8f85 Content-Type: text/plain; charset=ISO-8859-1 Dear all, Something has recently been said to me that has made me think, and I want to get my head around this a bit more..... At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant? What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal? Thanks in advance for your help, Richard --0016e6d447daaea4f7049cdd8f85 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear all,

Something has recently been said to me that has made me think, and I want to get my head around this a bit more.....

At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?

What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal?

Thanks in advance for your help,

Richard --0016e6d447daaea4f7049cdd8f85-- ========================================================================Date: Tue, 22 Feb 2011 19:54:22 +0800 Reply-To: YAN Chao-Gan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: YAN Chao-Gan <[log in to unmask]> Subject: Re: bandpass filter in REST toolbox Comments: To: tony han <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf30549fcd20a463049cdda4f6 Message-ID: <[log in to unmask]> --20cf30549fcd20a463049cdda4f6 Content-Type: text/plain; charset=ISO-8859-1 Dear Tony, What's you operation system? Usually, run "rest_Fix_Read_Write_Error" right after you started MATLAB, then this problem would be fixed. Further questions about REST are also welcomed to the REST forum: www.restfmri.net Best wishes! YAN Chao-Gan On Tue, Feb 22, 2011 at 7:46 PM, tony han <[log in to unmask]> wrote: > Dear REST developers, > > Sorry for the simple question but when I try to bandpass my dataset, > the error occurs as follows: > > >> rest_bandpass('C:\Program > Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', > 'C:\Program Files\MATLAB71\work\Masks\mask001.img'); > > Ideal rectangular filter: "C:\Program > Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1" > Read 3D EPI functional images: "C:\Program > Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> > rest_ReadNiftiImage > Meet error while reading data. Please restart MATLAB, this problem may be > solved. > > Error in ==> rest_readfile at 61 > [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex); > > Error in ==> rest_to4d at 41 > [theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename); > > Error in ==> rest_bandpass at 39 > [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir); > > > What's the problem? And how can I solve it? Thanks. > > Tony > -- YAN Chao-Gan, Ph.D. Candidate State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing, China, 100875 Phone: +86-10-58801023 [log in to unmask] http://www.restfmri.net/forum/yanchaogan --20cf30549fcd20a463049cdda4f6 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Tony,
          What's you operation system?
          Usually, run "rest_Fix_Read_Write_Error" right after you started MATLAB, then this problem would be fixed.
          Further questions about REST are also welcomed to the REST forum: www.restfmri.net
          Best wishes!
                                                      YAN Chao-Gan

On Tue, Feb 22, 2011 at 7:46 PM, tony han <[log in to unmask]> wrote:

Dear REST developers,

    Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows:

>> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img');

Ideal rectangular filter:    "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"
    Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage
Meet error while reading data. Please restart MATLAB, this problem may be solved.

Error in ==> rest_readfile at 61
            [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);

Error in ==> rest_to4d at 41
        [theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);

Error in ==> rest_bandpass at 39
    [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);

What's the problem? And how can I solve it? Thanks.

Tony

--
YAN Chao-Gan, Ph.D. Candidate
State Key Laboratory of Cognitive Neuroscience and Learning
Beijing Normal University
Beijing, China, 100875
Phone: +86-10-58801023
[log in to unmask]
http://www.restfmri.net/forum/yanchaogan
--20cf30549fcd20a463049cdda4f6-- ========================================================================Date: Tue, 22 Feb 2011 14:39:31 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Check Reg Figures SPM8 - size and arrangement Comments: To: Vladimir Bogdanov <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> There are no default settings that could be changed to make SPM do what you would like it to do. However, the beauty of having SPM in MATLAB is that it is relatively easy to tweak the code to make it do exactly what you want. Best regards, -John On 20 February 2011 22:20, Vladimir Bogdanov <[log in to unmask]> wrote: > Dear SPM experts, > > By default the size of the images in Check Reg figure does not allow to see well anatomical details (see screen1 in attachment). This can be changed manually (screen2), but those changes take time. > > Is there a simple way do it fast? Is it possible to change SPM settings somewhere to make the figure in the same way (different from default) every time we call Check Reg? > > Thank you in advance for your help, > Vladimir > > Volodymyr Bogdanov, PhD > > Headache Research Unit > GIGA - Neurosciences > University of Liege > CHU T4 +1, Sart Tilman > Av de l'Hôpital 1 > 4000 Liege, Belgium > > > ========================================================================Date: Tue, 22 Feb 2011 06:58:14 -0800 Reply-To: Vladimir Bogdanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Bogdanov <[log in to unmask]> Subject: Re: Check Reg Figures SPM8 - size and arrangement Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear John, Thank you for your response! Where is the part of the code responsible for this located? The SPM script is huge and I am only a beginner. :) Sincerely yours, Vladimir --- On Tue, 2/22/11, John Ashburner <[log in to unmask]> wrote: > From: John Ashburner <[log in to unmask]> > Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement > To: [log in to unmask] > Date: Tuesday, February 22, 2011, 3:39 PM > There are no default settings that > could be changed to make SPM do > what you would like it to do. However, the beauty of > having SPM in > MATLAB is that it is relatively easy to tweak the code to > make it do > exactly what you want. > > Best regards, > -John > > On 20 February 2011 22:20, Vladimir Bogdanov <[log in to unmask]> > wrote: > > Dear SPM experts, > > > > By default the size of the images in Check Reg figure > does not allow to see well anatomical details (see screen1 > in attachment). This can be changed manually (screen2), but > those changes take time. > > > > Is there a simple way do it fast? Is it possible to > change SPM settings somewhere to make the figure in the same > way (different from default) every time we call Check Reg? > > > > Thank you in advance for your help, > > Vladimir > > > > Volodymyr Bogdanov, PhD > > > > Headache Research Unit > > GIGA - Neurosciences > > University of Liege > > CHU T4 +1, Sart Tilman > > Av de l'Hôpital 1 > > 4000 Liege, Belgium > > > > > > > ========================================================================Date: Tue, 22 Feb 2011 15:08:49 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Check Reg Figures SPM8 - size and arrangement Comments: To: Vladimir Bogdanov <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> When you run Check Reg, Display or any of the other routines calling spm_orthviews.m, there is a data structure saved as a global variable. So if you call spm_check_registration and type the following, you should get access to various pieces of information that could be useful.... global st st.vols{1} Some of the fields you see refer to the positions of the axes on the screen. You'd need to modify these. Best regards, -John On 22 February 2011 14:58, Vladimir Bogdanov <[log in to unmask]> wrote: > Dear John, > > Thank you for your response! Where is the part of the code responsible for this located? The SPM script is huge and I am only a beginner. :) > > Sincerely yours, > Vladimir > > > --- On Tue, 2/22/11, John Ashburner <[log in to unmask]> wrote: > >> From: John Ashburner <[log in to unmask]> >> Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement >> To: [log in to unmask] >> Date: Tuesday, February 22, 2011, 3:39 PM >> There are no default settings that >> could be changed to make SPM do >> what you would like it to do. However, the beauty of >> having SPM in >> MATLAB is that it is relatively easy to tweak the code to >> make it do >> exactly what you want. >> >> Best regards, >> -John >> >> On 20 February 2011 22:20, Vladimir Bogdanov <[log in to unmask]> >> wrote: >> > Dear SPM experts, >> > >> > By default the size of the images in Check Reg figure >> does not allow to see well anatomical details (see screen1 >> in attachment). This can be changed manually (screen2), but >> those changes take time. >> > >> > Is there a simple way do it fast? Is it possible to >> change SPM settings somewhere to make the figure in the same >> way (different from default) every time we call Check Reg? >> > >> > Thank you in advance for your help, >> > Vladimir >> > >> > Volodymyr Bogdanov, PhD >> > >> > Headache Research Unit >> > GIGA - Neurosciences >> > University of Liege >> > CHU T4 +1, Sart Tilman >> > Av de l'Hôpital 1 >> > 4000 Liege, Belgium >> > >> > >> > >> > > > > ========================================================================Date: Tue, 22 Feb 2011 16:16:14 +0000 Reply-To: Jonas Persson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonas Persson <[log in to unmask]> Subject: Regarding scanner sequences Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello, I have a couple of questions regarding settings of scanner sequences and how it affects subsequent data processing in SPM. 1. If we scan, say, coronally or saggitally during our T1-sequence and acquire our functional data with an axial EPI-sequence, will this create a problem later in SPM for example with the normalisation process? I.e. is it recommended to always acquire all the data in the same direction? 2. A follow-up question: If i scan during the same paradigm with the same EPI-sequence, but collecting the slices in different directions (i.e. coronally vs. axially) should I then expect any differences in the data acquired? (other than the slice gaps obviously appearing at different places) 3. We use an EPI-sequence at our lab with a TR=3, which collects all slices as fast as possible and then rests during the reminder of the TR. I guess this should be good for minimizing the time differences between slices, but could this create a problem for an event related paradigm? So far we have only used traditional block designs. Thank you for any reply! Best regards, Jonas ========================================================================Date: Tue, 22 Feb 2011 16:39:32 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Regarding scanner sequences Comments: To: Jonas Persson <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> > 1. If we scan, say, coronally or saggitally during our T1-sequence and acquire our functional data with an axial EPI-sequence, will this create a problem later in SPM for example with the normalisation process? I.e. is it recommended to always acquire all the data in the same direction? This should be fine, providing the orientation information in the image headers is correct. If you used the DICOM conversion routines in SPM, then this should be the case. > > 2. A follow-up question: If i scan during the same paradigm with the same EPI-sequence, but collecting the slices in different directions (i.e. coronally vs. axially) should I then expect any differences in the data acquired? (other than the slice gaps obviously appearing at different places) The distortion and dropout in the data is likely to differ, depending on whether the phase-encoding direction is the same or not. There may be other effects, but the easiest way to discover them would be to try it. > > 3. We use an EPI-sequence at our lab with a TR=3, which collects all slices as fast as possible and then rests during the reminder of the TR. I guess this should be good for minimizing the time differences between slices, but could this create a problem for an event related paradigm? So far we have only used traditional block designs. I can't really comment on this as it's not my area. Best regards, -John ========================================================================Date: Tue, 22 Feb 2011 09:24:50 -0800 Reply-To: Manish Dalwani <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Manish Dalwani <[log in to unmask]> Subject: SPM8 batchscripts? MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-151767339-1298395490=:7828" Message-ID: <[log in to unmask]> --0-151767339-1298395490=:7828 Content-Type: text/plain; charset=us-ascii Hello SPM'ers Is there a model preprocessing SPM8 batchscript? ...that you can modify as per your preprocessing plans! If so, where can I find it? Regards,Manish DalwaniSr. PRADept. of PsychiatryUniversity of Colorado --0-151767339-1298395490=:7828 Content-Type: text/html; charset=us-ascii

Hello SPM'ers

Is there a model preprocessing SPM8 batchscript? ...that you can modify as per your preprocessing plans! If so, where can I find it?

Regards,

Manish Dalwani

Sr. PRA

Dept. of Psychiatry

University of Colorado

--0-151767339-1298395490=:7828-- ========================================================================Date: Tue, 22 Feb 2011 13:56:23 -0600 Reply-To: Pamela LaMontagne <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pamela LaMontagne <[log in to unmask]> Subject: modelling assumed & unassumed hrf together MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e6dab4dfff0f36049ce45f78 Content-Type: text/plain; charset=ISO-8859-1 I have a mixed-design study with blocks representing a task-set (2 tasks) and events representing different trial-types (2 trial types/task) within the block. Based on Visscher 2003 I am interested in modeling the assumed hrf function with the blocks and an unassumed hrf function for the trials. I know that the unassumed response can be modeled using FIR. However, how do I model these in the same model specification? The current in-house program I am using allows for conditions to be specified with an assumed or unassumed response within the same design matrix but I would prefer to switch over to SPM8. How is this implemented in SPM? Is there a toolbox that can do this? Thanks, Pamela LaMontagne Post Doctoral Fellow Washington University St. Louis --0016e6dab4dfff0f36049ce45f78 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable
I have a mixed-design study with blocks representing a task-set (2 tasks) and events representing different trial-types (2 trial types/task) within the block. Based on Visscher 2003 I am interested in modeling the assumed hrf function with the blocks and an unassumed hrf function for the trials. I know that the unassumed response can be modeled using FIR. However, how do I model these in the same model specification? The current in-house program I am using allows for conditions to be specified with an assumed or unassumed response within the same design matrix but I would prefer to switch over to SPM8.

How is this implemented in SPM? Is there a toolbox that can do this?

Thanks,

Pamela LaMontagne
Post Doctoral Fellow
Washington University St. Louis
--0016e6dab4dfff0f36049ce45f78-- ========================================================================Date: Wed, 23 Feb 2011 04:16:00 +0800 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Kuo Bo-Cheng <[log in to unmask]> Subject: Re: SPM EEG Pre processing: Not sure if everything looks OK Comments: To: Vladimir Litvak <[log in to unmask]> In-Reply-To: <-101706040748529818@unknownmsgid> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; DelSp="Yes"; format="flowed" Content-Disposition: inline Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Vladmir, Can we rescale single trial TF before average? Many thanks! Bo-Cheng å¼•è¿° Vladimir Litvak <[log in to unmask]>: > Dear Muhammad, > > The way one would usually do TF analysis is to apply TF to an epoched > file, then average the result and then rescale. If you apply TF to an > ERP which is quite noisy you'll get basically single trial TF which is > noisy as well. > > Filtering will not help since you will either remove your frequency > range of interest or not affect it. > > Best, > > Vladimir > > > On 20 Feb 2011, at 03:51, MP <[log in to unmask]> wrote: > >> Hi all, >> >> I am doing the TF analysis on EEG data and I see some worrisome >> "spots" in my rescaled TF, which translate as sharp "dips" in the >> waveform of averaged TF over 12-20Hz when I convert TF into image. >> I know that these spots could be from the noise (since it is a >> single subject data), but I am worried that they will influence the >> statistics. >> >> I have attached the ERP waveform (-200 to 2000ms), its TF >> representation (4-20Hz, -198-2000ms), rescaled TF (using Log >> rescaling, baseline=-148 to 0), and the waveform when TF is >> averaged over 12-20Hz range. >> >> Is it possible that since these dips are random they will not >> influence the results in the scalp-space considerably? Is there >> anything I can do to minimize these dips, like lowpass filter the >> ERP before doing TF? >> >> thanks for your help in advance >> >> - Muhammad >> >> >> >> > ========================================================================Date: Tue, 22 Feb 2011 21:05:13 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: SPM EEG Pre processing: Not sure if everything looks OK Comments: To: "[log in to unmask]" <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (iPad Mail 8C148) Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Message-ID: <-3545688754431432339@unknownmsgid> Dear Bo-Cheng, Technically, yes. The question is whether this is something that makes sense. Average of rescaled data and rescaled average are usually not the same thing. Vladimir On 22 Feb 2011, at 20:16, "[log in to unmask]" <[log in to unmask]> wrote: > Dear Vladmir, > > Can we rescale single trial TF before average? > Many thanks! > > Bo-Cheng > > > å¼•è¿° Vladimir Litvak <[log in to unmask]>: > >> Dear Muhammad, >> >> The way one would usually do TF analysis is to apply TF to an epoched >> file, then average the result and then rescale. If you apply TF to an >> ERP which is quite noisy you'll get basically single trial TF which is >> noisy as well. >> >> Filtering will not help since you will either remove your frequency >> range of interest or not affect it. >> >> Best, >> >> Vladimir >> >> >> On 20 Feb 2011, at 03:51, MP <[log in to unmask]> wrote: >> >>> Hi all, >>> >>> I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics. >>> >>> I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range. >>> >>> Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF? >>> >>> thanks for your help in advance >>> >>> - Muhammad >>> >>> >>> >>> >> > > ========================================================================Date: Tue, 22 Feb 2011 15:21:45 -0800 Reply-To: J S Lee <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: J S Lee <[log in to unmask]> Subject: Can't find driving effects for PPI analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_2B18_01CBD2A4.37747680" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------=_NextPart_000_2B18_01CBD2A4.37747680 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 7bit Dear list, I conducted a PPI analysis in an experiment with 6 conditions. To replicate a previous study's PPI analysis, I was interested in connectivity differences between 5 of the conditions compared to the control (6th) condition, so extracted my VOI (using an all effects of interest contrast), then created a PPI model with a [1 1 1 1 1 -1] weighting for the psychological context regressor. I get a reasonable replication of the same PPI effects from the previous study, so the results are sensible. However, in that previous study, there were not enough trials of each of the 5 conditions to realistically analyze them separately, which is why I collapsed across them. In this study, there are many more trials, so I was hoping to look at which of the 5 conditions were driving the original PPI results. I was given hope when the initial PPI replicated in this new study. However, when I create separate PPI models for each condition versus control (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1] for model 2, etc.), NONE of these analyses show the same pattern as the 1 1 1 1 1 -1 model does. Mostly there are no significant (or anywhere near significant) results, and those random speckles that do show up at low threshold are not in the same places. Is it theoretically possible that 5 conditions vs 1 other can produce a PPI, but that none of those conditions singly vs the 1 other can do that? Or must there be an error? I have checked the microtime onset files to make the context is specified correctly, and made sure everything matches up in terms of specifying the conditions. Everything about the models looks fine to me. I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the previous study (in which the 5 vs 1 were equal in terms of number of trials), and I understand that when creating the context variable one does NOT sum the vector to zero the way one would in defining a contrast for a regional activation analysis. Many thanks in advance for any thoughts, Jamie Lee

_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage – POP3 – Calendar – SMS – Translator – Much More! ------=_NextPart_000_2B18_01CBD2A4.37747680 Content-Type: text/html Content-Transfer-Encoding: 7bit Dear list,

I conducted a PPI analysis in an experiment with 6 conditions. To replicate a previous study's PPI analysis, I was interested in connectivity differences between 5 of the conditions compared to the control (6th) condition, so extracted my VOI (using an all effects of interest contrast), then created a PPI model with a [1 1 1 1 1 -1] weighting for the psychological context regressor. I get a reasonable replication of the same PPI effects from the previous study, so the results are sensible.

However, in that previous study, there were not enough trials of each of the 5 conditions to realistically analyze them separately, which is why I collapsed across them. In this study, there are many more trials, so I was hoping to look at which of the 5 conditions were driving the original PPI results. I was given hope when the initial PPI replicated in this new study. However, when I create separate PPI models for each condition versus control (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1] for model 2, etc.), NONE of these analyses show the same pattern as the 1 1 1 1 1 -1 model does. Mostly there are no significant (or anywhere near significant) results, and those random speckles that do show up at low threshold are not in the same places.

Is it theoretically possible that 5 conditions vs 1 other can produce a PPI, but that none of those conditions singly vs the 1 other can do that? Or must there be an error? I have checked the microtime onset files to make the context is specified correctly, and made sure everything matches up in terms of specifying the conditions. Everything about the models looks fine to me. I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the previous study (in which the 5 vs 1 were equal in terms of number of trials), and I understand that when creating the context variable one does NOT sum the vector to zero the way one would in defining a contrast for a regional activation analysis.

Many thanks in advance for any thoughts,
Jamie Lee

_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage – POP3 – Calendar – SMS – Translator – Much More! ------=_NextPart_000_2B18_01CBD2A4.37747680-- ========================================================================Date: Tue, 22 Feb 2011 18:52:25 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Can't find driving effects for PPI analysis Comments: To: J S Lee <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> Are you using SPM8? The issue of summing was fixed in one of the later releases of SPM5, so if you have an older version, that could explain some of the issue. You could check to make sure that negative aspects of the SPM.xX.X for the PPI term are the same for all subjects. You could plot them. Are you using the same adjustment for all models? Are you using exactly the same voxels for all models? Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote: > Dear list, > > I conducted a PPI analysis in an experiment with 6 conditions. To replicate > a previous study's PPI analysis, I was interested in connectivity > differences between 5 of the conditions compared to the control (6th) > condition, so extracted my VOI (using an all effects of interest contrast), > then created a PPI model with a [1 1 1 1 1 -1] weighting for the > psychological context regressor. I get a reasonable replication of the same > PPI effects from the previous study, so the results are sensible. > > However, in that previous study, there were not enough trials of each of the > 5 conditions to realistically analyze them separately, which is why I > collapsed across them. In this study, there are many more trials, so I was > hoping to look at which of the 5 conditions were driving the original PPI > results. I was given hope when the initial PPI replicated in this new study. > However, when I create separate PPI models for each condition versus control > (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1] > for model 2, etc.), NONE of these analyses show the same pattern as the 1 1 > 1 1 1 -1 model does. Mostly there are no significant (or anywhere near > significant) results, and those random speckles that do show up at low > threshold are not in the same places. > > Is it theoretically possible that 5 conditions vs 1 other can produce a PPI, > but that none of those conditions singly vs the 1 other can do that? Or must > there be an error? I have checked the microtime onset files to make the > context is specified correctly, and made sure everything matches up in terms > of specifying the conditions. Everything about the models looks fine to me. > I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the > previous study (in which the 5 vs 1 were equal in terms of number of > trials), and I understand that when creating the context variable one does > NOT sum the vector to zero the way one would in defining a contrast for a > regional activation analysis. > > Many thanks in advance for any thoughts, > Jamie Lee > > _______________________________________________________________ > Get the Free email that has everyone talking at http://www.mail2world.com > Unlimited Email Storage – POP3 – Calendar – SMS – Translator – Much More! ========================================================================Date: Wed, 23 Feb 2011 01:29:50 +0000 Reply-To: Dashjamts Jargal <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Dashjamts Jargal <[log in to unmask]> Subject: DARTEL modulation versus no modulation: accuracy? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all, I am trying to examine the diagnositc performance in DARTEL-based VBM. My unmodulated data show larger relevant difference areas and have higher diagnostic accuracy than the modulated. What could explain such result? Why the unmodulated demonstrated much more affected areas than the modulated? Is the following correct according to the definition of how modulation works: "perfectly registered unmodulated data should show no difference between the groups"?Does it mean that unmodulated data have some compound of registration failures? What areas in the brain are more prone to DARTEL-registration failures? Thank you in advance for your consideration. ========================================================================Date: Wed, 23 Feb 2011 08:15:40 +0100 Reply-To: Lucas Eggert <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lucas Eggert <[log in to unmask]> Subject: Re: TIV as nuisance factor for unmodulated data in a VBM-analysis Comments: To: Jonathan Peelle <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/signed; protocol="application/pkcs7-signature"; micalg=sha1; boundary="------------ms040108020106000704050504" Message-ID: <[log in to unmask]> This is a cryptographically signed message in MIME format. --------------ms040108020106000704050504 Content-Type: text/plain; charset=windows-1252; format=flowed Content-Transfer-Encoding: quoted-printable Hi Jonathan, thanks very much for your response. I guess for now, I will rather go for unmodulated images without correcting for globals; and I will definitely do some tests to see what difference the correction for TIV would make. All the best, Lucas > Hi Lucas > >> at the moment I am unsure about whether correcting for total brain volume (TIV) in >> a VBM analysis of unmodulated gray matter segments (density) does make sense or not. >> >> On Dr. Gaser's homepage --- I have the feeling --- there are two slightly opposite statements: >> statement (a) (please refer below) clearly states that TIV should not be used as a nuisacne factor with unmodulated data. >> >> On the other hand, I read statement (b) (please see below) as correcting unmodulated data for TIV gives me relative >> density after correcting for TIV, which seems to make more sense to me. >> >> Which procedure is the more valid one, or for which kind of question would I use which procedure respectively? > > One important issue that you bring up is the use of modulated gray matter (GM) images and what an analysis using these images would tell you. I think the prevailing view on this is as follows: if normalization were perfect, and there was no modulation, then all GM images would be perfectly aligned. Because we haven't modulated to account for changes in size, the images will be identical. Thus, any differences we see in an unmodulated analysis are likely due to errors in registration. So the first issue, i think, is to make sure that unmodulated images are answering the question you want to answer. > > I think that the most common view of TIV and unmodulated analyses is that unmodulated GM images represent a property of the tissue, which (unlike its volume) should not depend on head size. Thus, the advice you came across not to include TIV in an unmodulated analysis. > > [One thing I wonder about the above interpretation of unmodulated analyses, though, is the application to patients with various types of neurodegenerative disease. If there are small pockets of damage (e.g. spongiform encephalopathies), then I would think that an unmodulated analysis might pick this up. Which is to say, I think there may be things other than registration differences that may lead to differences in unmodulated analyses. However, I would be very interested to hear from someone who has had some experience with this.] > > So, as to your specific question, if you don't have a reason to think that what you are looking at (e.g. tissue density) would be impacted by head size, then I wouldn't think there is a good reason to include TIV. (Although you may want to do some exploratory analyses, preferably in another group of subjects, and include it—then you could see empirically what variance in density, if any, was accounted for by TIV, which might be informative for you, and others.) :) > > Best regards, > Jonathan > > > > -- Lucas Eggert, M.Sc. Institute of Cognitive Science University of Osnabrueck Albrechtstrasse 28 D-49076 Osnabrueck Germany Phone: +49-541-969-44-28 Website: http://www.cogsci.uni-osnabrueck.de/~leggert/ --------------ms040108020106000704050504 Content-Type: application/pkcs7-signature; name="smime.p7s" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="smime.p7s" Content-Description: S/MIME Cryptographic Signature MIAGCSqGSIb3DQEHAqCAMIACAQExCzAJBgUrDgMCGgUAMIAGCSqGSIb3DQEHAQAAoIIUEzCC BCEwggMJoAMCAQICAgDHMA0GCSqGSIb3DQEBBQUAMHExCzAJBgNVBAYTAkRFMRwwGgYDVQQK ExNEZXV0c2NoZSBUZWxla29tIEFHMR8wHQYDVQQLExZULVRlbGVTZWMgVHJ1c3QgQ2VudGVy MSMwIQYDVQQDExpEZXV0c2NoZSBUZWxla29tIFJvb3QgQ0EgMjAeFw0wNjEyMTkxMDI5MDBa Fw0xOTA2MzAyMzU5MDBaMFoxCzAJBgNVBAYTAkRFMRMwEQYDVQQKEwpERk4tVmVyZWluMRAw DgYDVQQLEwdERk4tUEtJMSQwIgYDVQQDExtERk4tVmVyZWluIFBDQSBHbG9iYWwgLSBHMDEw ggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDpm8NnhfkNrvWNVMOWUDU9YuluTO2U 1wBblSJ01CDrNI/W7MAxBAuZgeKmFNJSoCgjhIt0iQReW+DieMF4yxbLKDU5ey2QRdDtoAB6 fL9KDhsAw4bpXCsxEXsM84IkQ4wcOItqaACa7txPeKvSxhObdq3u3ibo7wGvdA/BCaL2a869 080UME/15eOkyGKbghoDJzANAmVgTe3RCSMqljVYJ9N2xnG2kB3E7f81hn1vM7PbD8URwoqD oZRdQWvY0hD1TP3KUazZve+Sg7va64sWVlZDz+HVEz2mHycwzUlU28kTNJpxdcVs6qcLmPkh nSevPqM5OUhqjK3JmfvDEvK9AgMBAAGjgdkwgdYwcAYDVR0fBGkwZzBloGOgYYZfaHR0cDov L3BraS50ZWxlc2VjLmRlL2NnaS1iaW4vc2VydmljZS9hZl9Eb3dubG9hZEFSTC5jcmw/LWNy bF9mb3JtYXQ9WF81MDkmLWlzc3Vlcj1EVF9ST09UX0NBXzIwHQYDVR0OBBYEFEm3xs/oPR9/ 6kR7Eyn38QpwPt5kMB8GA1UdIwQYMBaAFDHDeRu69VPXF+CJei0XbAqzK50zMA4GA1UdDwEB /wQEAwIBBjASBgNVHRMBAf8ECDAGAQH/AgECMA0GCSqGSIb3DQEBBQUAA4IBAQA74Vp3wEgX 3KkY7IGvWonwvSiSpspZGBJw7Cjy565/lizn8l0ZMfYTK3S9vYCyufdnyTmieTvhERHua3iR M347XyYndVNljjNj7s9zw7CSI0khUHUjoR8Y4pSFPT8z6XcgjaK95qGFKUD2P3MyWA0Ja6ba hWzAP7uNZmRWJE6uDT8yNQFb6YyC2XJZT7GGhfF0hVblw/hc843uR7NTBXDn5U2KaYMo4RMJ hp5eyOpYHgwf+aTUWgRo/Sg+iwK2WLX2oSw3VwBnqyNojWOl75lrXP1LVvarQIc01BGSbOyH xQoLBzNytG8MHVQs2FHHzL8w00Ny8TK/jM5JY6gA9/IcMIIFIjCCBAqgAwIBAgIECpckUjAN BgkqhkiG9w0BAQUFADBaMQswCQYDVQQGEwJERTETMBEGA1UEChMKREZOLVZlcmVpbjEQMA4G A1UECxMHREZOLVBLSTEkMCIGA1UEAxMbREZOLVZlcmVpbiBQQ0EgR2xvYmFsIC0gRzAxMB4X DTA3MDYxOTA5NDk1NVoXDTE5MDYxODAwMDAwMFowgZExCzAJBgNVBAYTAkRFMSAwHgYDVQQK ExdVbml2ZXJzaXRhZXQgT3NuYWJydWVjazEWMBQGA1UECxMNUmVjaGVuemVudHJ1bTEjMCEG A1UEAxMaVW5pLU9zbmFicnVlY2sgUlotQ0EgRy0wMDIxIzAhBgkqhkiG9w0BCQEWFGNhQHVu aS1vc25hYnJ1ZWNrLmRlMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEApLZxV6i2 14uWBb3c16AMckkujPKMXxl9Z4XsEIhrA47tG5YL9upeUrj+duDcrbEQvphvSXJFveVkLK1J MANHJuu/Wa32Bc8IRljYBqhaKMTGRO1q5L6jkMBDwBwenozhlGAaqG+8Cy+qcFoUaoWBRCH2 ++t5FtXyS1/1GKhWu7yQxCblFul7VXvnLKyaNlOaTalREXb9pQk2N31+rrOgwkbogxc2z30g QAJXeJ2Ra0SlReqINMmcDd4lfluXjBpFmiJa4xHhQIVJpW2vF8dbqmeKqxIoYziBh78NGaqM SC8IbDPbCM3qaDaGUWKccgb/SKZvrNTLU+jcW66yGi1YPwIDAQABo4IBtjCCAbIwEgYDVR0T AQH/BAgwBgEB/wIBATALBgNVHQ8EBAMCAQYwHQYDVR0OBBYEFKof2HcVbpnkX5DW7GMmThnq ZxRQMB8GA1UdIwQYMBaAFEm3xs/oPR9/6kR7Eyn38QpwPt5kMB8GA1UdEQQYMBaBFGNhQHVu aS1vc25hYnJ1ZWNrLmRlMIGIBgNVHR8EgYAwfjA9oDugOYY3aHR0cDovL2NkcDEucGNhLmRm bi5kZS9nbG9iYWwtcm9vdC1jYS9wdWIvY3JsL2NhY3JsLmNybDA9oDugOYY3aHR0cDovL2Nk cDIucGNhLmRmbi5kZS9nbG9iYWwtcm9vdC1jYS9wdWIvY3JsL2NhY3JsLmNybDCBogYIKwYB BQUHAQEEgZUwgZIwRwYIKwYBBQUHMAKGO2h0dHA6Ly9jZHAxLnBjYS5kZm4uZGUvZ2xvYmFs LXJvb3QtY2EvcHViL2NhY2VydC9jYWNlcnQuY3J0MEcGCCsGAQUFBzAChjtodHRwOi8vY2Rw Mi5wY2EuZGZuLmRlL2dsb2JhbC1yb290LWNhL3B1Yi9jYWNlcnQvY2FjZXJ0LmNydDANBgkq hkiG9w0BAQUFAAOCAQEAWw23L1uKK+8XtvjZ4GtyLYqHC+QHFfrt4QUVYdTiXznlTLLiytqP H/Ur13RSbhJuUV/Xgsyko7yc+wV2xpXRwGE4P7a/nCvamPM40/2B070/AGMO0xXA5GGYG5/b 7I5Al97kQH3l7vCYrYhJxrLdDFYjmWMneHerxffjLZLQrdfzAMMATGE3oLiqAz4RH6oknUzj 5lBcIlBdWhOa85u72oHX/8ohGCHKcUJ6b52VIwpYohLQC05mFvEml7wox3HZMr3HfhfZR4CT aCL1quu+m24ov7JsU8wEoXeIbQiAxz0NB4mJSyBTa+mbVqTXz/fFw0Ld7xWRhPTM72IM56/U 2zCCBWAwggRIoAMCAQICBBFfm+wwDQYJKoZIhvcNAQEFBQAwgZExCzAJBgNVBAYTAkRFMSAw HgYDVQQKExdVbml2ZXJzaXRhZXQgT3NuYWJydWVjazEWMBQGA1UECxMNUmVjaGVuemVudHJ1 bTEjMCEGA1UEAxMaVW5pLU9zbmFicnVlY2sgUlotQ0EgRy0wMDIxIzAhBgkqhkiG9w0BCQEW FGNhQHVuaS1vc25hYnJ1ZWNrLmRlMB4XDTExMDEyNjEzMTUwOVoXDTE0MDEyNTEzMTUwOVow VDELMAkGA1UEBhMCREUxIDAeBgNVBAoTF1VuaXZlcnNpdGFldCBPc25hYnJ1ZWNrMQwwCgYD VQQLEwNJS1cxFTATBgNVBAMTDEx1Y2FzIEVnZ2VydDCCASIwDQYJKoZIhvcNAQEBBQADggEP ADCCAQoCggEBAMvXUWB4xxi66T9J5HqkG9ycg9ri+Bm/F2FlVdTfqnEWcTxmRBctRms39ZuY b5Q1Uyx/bFCyoZTb6D/wUmYUEFiLo+Aess2/qPcfRYGVIJWv2zYNiWvlVQD0IG5+SbfQ+/vt D9w96d2WSiOQGAwe5b5RgM+FHZ1xDFr4qKmphHGT7g2OV2ghxXKw0Fyl1j518XB1gYFeA17c 1VjOmI9E5KyXeeOqHwOg0U5XO3YmQ2Et+cJDViFRx6qOm+c96IpakCGoZ9aMZGPlxmmFUM0i Bqm+gYCGO4S3qEY9L9hyNWMTqwNl7Y/AJxedDdXG33RgPeEre8EsSAyPzlpggVS0ONsCAwEA AaOCAfowggH2MAkGA1UdEwQCMAAwCwYDVR0PBAQDAgXgMCkGA1UdJQQiMCAGCCsGAQUFBwMC BggrBgEFBQcDBAYKKwYBBAGCNxQCAjAdBgNVHQ4EFgQUT2sRQCxHT4F6BgX351tC2QxzY68w HwYDVR0jBBgwFoAUqh/YdxVumeRfkNbsYyZOGepnFFAwNAYDVR0RBC0wK4EObGVnZ2VydEB1 b3MuZGWBGWxlZ2dlcnRAdW5pLW9zbmFicnVlY2suZGUwgY8GA1UdHwSBhzCBhDBAoD6gPIY6 aHR0cDovL2NkcDEucGNhLmRmbi5kZS91bmktb3NuYWJydWVjay1jYS9wdWIvY3JsL2NhY3Js LmNybDBAoD6gPIY6aHR0cDovL2NkcDIucGNhLmRmbi5kZS91bmktb3NuYWJydWVjay1jYS9w dWIvY3JsL2NhY3JsLmNybDCBqAYIKwYBBQUHAQEEgZswgZgwSgYIKwYBBQUHMAKGPmh0dHA6 Ly9jZHAxLnBjYS5kZm4uZGUvdW5pLW9zbmFicnVlY2stY2EvcHViL2NhY2VydC9jYWNlcnQu Y3J0MEoGCCsGAQUFBzAChj5odHRwOi8vY2RwMi5wY2EuZGZuLmRlL3VuaS1vc25hYnJ1ZWNr LWNhL3B1Yi9jYWNlcnQvY2FjZXJ0LmNydDANBgkqhkiG9w0BAQUFAAOCAQEAowJHsBfWFTNP KdWCi3AyM5OqEgHAqktwyEgaqFSMFr0HrsEia0St9YQDPQrR/QknpqtYnpIAXEqf3QTERaOU Jp217gZ8lCaUIEY7nTPefCarwpjcMfF5xBt/qFX66S6ctanypQOqf23imuRwVUwvi56rhbyo sfzCt7iYVl4FonTAd+DKR1PLl672bLHi2ngBGuhauKjXYCuiuJsEeeyJaTvATmrcwbLARWuj Mvw/QvMGQsRbhk2xrvUomI0BbO71JfkNr3N62rxsQKTnR8rEbQ8gE6FX2+OWO3cvMTfC8elz MNnNUrHI4qL0vfiNKmDY/45rMXIzgSB1EjlYJoMaTDCCBWAwggRIoAMCAQICBBFfm+wwDQYJ KoZIhvcNAQEFBQAwgZExCzAJBgNVBAYTAkRFMSAwHgYDVQQKExdVbml2ZXJzaXRhZXQgT3Nu YWJydWVjazEWMBQGA1UECxMNUmVjaGVuemVudHJ1bTEjMCEGA1UEAxMaVW5pLU9zbmFicnVl Y2sgUlotQ0EgRy0wMDIxIzAhBgkqhkiG9w0BCQEWFGNhQHVuaS1vc25hYnJ1ZWNrLmRlMB4X DTExMDEyNjEzMTUwOVoXDTE0MDEyNTEzMTUwOVowVDELMAkGA1UEBhMCREUxIDAeBgNVBAoT F1VuaXZlcnNpdGFldCBPc25hYnJ1ZWNrMQwwCgYDVQQLEwNJS1cxFTATBgNVBAMTDEx1Y2Fz IEVnZ2VydDCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBAMvXUWB4xxi66T9J5Hqk G9ycg9ri+Bm/F2FlVdTfqnEWcTxmRBctRms39ZuYb5Q1Uyx/bFCyoZTb6D/wUmYUEFiLo+Ae ss2/qPcfRYGVIJWv2zYNiWvlVQD0IG5+SbfQ+/vtD9w96d2WSiOQGAwe5b5RgM+FHZ1xDFr4 qKmphHGT7g2OV2ghxXKw0Fyl1j518XB1gYFeA17c1VjOmI9E5KyXeeOqHwOg0U5XO3YmQ2Et +cJDViFRx6qOm+c96IpakCGoZ9aMZGPlxmmFUM0iBqm+gYCGO4S3qEY9L9hyNWMTqwNl7Y/A JxedDdXG33RgPeEre8EsSAyPzlpggVS0ONsCAwEAAaOCAfowggH2MAkGA1UdEwQCMAAwCwYD VR0PBAQDAgXgMCkGA1UdJQQiMCAGCCsGAQUFBwMCBggrBgEFBQcDBAYKKwYBBAGCNxQCAjAd BgNVHQ4EFgQUT2sRQCxHT4F6BgX351tC2QxzY68wHwYDVR0jBBgwFoAUqh/YdxVumeRfkNbs YyZOGepnFFAwNAYDVR0RBC0wK4EObGVnZ2VydEB1b3MuZGWBGWxlZ2dlcnRAdW5pLW9zbmFi cnVlY2suZGUwgY8GA1UdHwSBhzCBhDBAoD6gPIY6aHR0cDovL2NkcDEucGNhLmRmbi5kZS91 bmktb3NuYWJydWVjay1jYS9wdWIvY3JsL2NhY3JsLmNybDBAoD6gPIY6aHR0cDovL2NkcDIu cGNhLmRmbi5kZS91bmktb3NuYWJydWVjay1jYS9wdWIvY3JsL2NhY3JsLmNybDCBqAYIKwYB BQUHAQEEgZswgZgwSgYIKwYBBQUHMAKGPmh0dHA6Ly9jZHAxLnBjYS5kZm4uZGUvdW5pLW9z bmFicnVlY2stY2EvcHViL2NhY2VydC9jYWNlcnQuY3J0MEoGCCsGAQUFBzAChj5odHRwOi8v Y2RwMi5wY2EuZGZuLmRlL3VuaS1vc25hYnJ1ZWNrLWNhL3B1Yi9jYWNlcnQvY2FjZXJ0LmNy dDANBgkqhkiG9w0BAQUFAAOCAQEAowJHsBfWFTNPKdWCi3AyM5OqEgHAqktwyEgaqFSMFr0H rsEia0St9YQDPQrR/QknpqtYnpIAXEqf3QTERaOUJp217gZ8lCaUIEY7nTPefCarwpjcMfF5 xBt/qFX66S6ctanypQOqf23imuRwVUwvi56rhbyosfzCt7iYVl4FonTAd+DKR1PLl672bLHi 2ngBGuhauKjXYCuiuJsEeeyJaTvATmrcwbLARWujMvw/QvMGQsRbhk2xrvUomI0BbO71JfkN r3N62rxsQKTnR8rEbQ8gE6FX2+OWO3cvMTfC8elzMNnNUrHI4qL0vfiNKmDY/45rMXIzgSB1 EjlYJoMaTDGCA+IwggPeAgEBMIGaMIGRMQswCQYDVQQGEwJERTEgMB4GA1UEChMXVW5pdmVy c2l0YWV0IE9zbmFicnVlY2sxFjAUBgNVBAsTDVJlY2hlbnplbnRydW0xIzAhBgNVBAMTGlVu aS1Pc25hYnJ1ZWNrIFJaLUNBIEctMDAyMSMwIQYJKoZIhvcNAQkBFhRjYUB1bmktb3NuYWJy dWVjay5kZQIEEV+b7DAJBgUrDgMCGgUAoIICHDAYBgkqhkiG9w0BCQMxCwYJKoZIhvcNAQcB MBwGCSqGSIb3DQEJBTEPFw0xMTAyMjMwNzE1NDBaMCMGCSqGSIb3DQEJBDEWBBTqoaLl859b fqYYRihAKC9Uf+oYFTBfBgkqhkiG9w0BCQ8xUjBQMAsGCWCGSAFlAwQBAjAKBggqhkiG9w0D BzAOBggqhkiG9w0DAgICAIAwDQYIKoZIhvcNAwICAUAwBwYFKw4DAgcwDQYIKoZIhvcNAwIC ASgwgasGCSsGAQQBgjcQBDGBnTCBmjCBkTELMAkGA1UEBhMCREUxIDAeBgNVBAoTF1VuaXZl cnNpdGFldCBPc25hYnJ1ZWNrMRYwFAYDVQQLEw1SZWNoZW56ZW50cnVtMSMwIQYDVQQDExpV bmktT3NuYWJydWVjayBSWi1DQSBHLTAwMjEjMCEGCSqGSIb3DQEJARYUY2FAdW5pLW9zbmFi cnVlY2suZGUCBBFfm+wwga0GCyqGSIb3DQEJEAILMYGdoIGaMIGRMQswCQYDVQQGEwJERTEg MB4GA1UEChMXVW5pdmVyc2l0YWV0IE9zbmFicnVlY2sxFjAUBgNVBAsTDVJlY2hlbnplbnRy dW0xIzAhBgNVBAMTGlVuaS1Pc25hYnJ1ZWNrIFJaLUNBIEctMDAyMSMwIQYJKoZIhvcNAQkB FhRjYUB1bmktb3NuYWJydWVjay5kZQIEEV+b7DANBgkqhkiG9w0BAQEFAASCAQAi8YGHotR0 GTEtLa3wITqfKF+E/YLLXBXu+5yWzJay3Fn6MGw+a5bcmU2I0+0xHdAMaTjcNH/YJyJ+ibHF QCyQ5ybXU8Zy9+cPoDWtmIK+MDbezmX7407lpTONdiADgStiui7w4KuilFifN1pVHDsM1/D9 i+2+ZOg9X1Bd2CGqwaJIPBXSPU8nf4xyUjdvO7KjObKlKX7Rs9cyU350kwprxDN89iVP4UM2 JJ40ZFPaASk12jmDnFvcY5pbzolqG9zbtFfpV3JKXrGktmzCU5l2+RJNNbmVMGc6aX2hNg09 ndJ89xT0zwQTkyCed0zAKcc/1Qnd7HwG5afaZ/bs8KpZAAAAAAAA --------------ms040108020106000704050504-- ========================================================================Date: Wed, 23 Feb 2011 10:10:09 +0100 Reply-To: Marine Lazouret <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marine Lazouret <[log in to unmask]> Subject: Batch issue MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba3fccf3b65375049cef7647 Message-ID: <[log in to unmask]> --90e6ba3fccf3b65375049cef7647 Content-Type: text/plain; charset=ISO-8859-1 Hello, I made a batch for spatial pre processing and it stops every time during segmentation with this message: Running 'Segment' Failed 'Segment' Error using ==> spm_vol>subfunc at 111 File "/Applications/MATLAB_R2008a/spm8/tpm/grey.nii" does not exist. In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc" at line 111. In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc1" at line 84. In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc2" at line 70. In file "/Applications/spm8/spm_vol.m" (v2237), function "spm_vol" at line 54. In file "/Applications/spm8/spm_preproc.m" (v1143), function "spm_preproc" at line 66. In file "/Applications/spm8/config/spm_run_preproc.m" (v2312), function "spm_run_preproc" at line 19. No executable modules, but still unresolved dependencies or incomplete module inputs. The following modules did not run: Failed: Segment Skipped: Normalise: Write Skipped: Smooth If i treat my images step by step, nothing goes wrong and I can get to the end of it. Does any one have an idea where this problem could be coming from? -- *Marine* --90e6ba3fccf3b65375049cef7647 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello,

I made a batch for spatial pre processing and it stops every time during segmentation with this message:

Running 'Segment'

Failed 'Segment'

Error using ==> spm_vol>subfunc at 111

File "/Applications/MATLAB_R2008a/spm8/tpm/grey.nii" does not exist.

In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc" at line 111.

In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc1" at line 84.

In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc2" at line 70.

In file "/Applications/spm8/spm_vol.m" (v2237), function "spm_vol" at line 54.

In file "/Applications/spm8/spm_preproc.m" (v1143), function "spm_preproc" at line 66.

In file "/Applications/spm8/config/spm_run_preproc.m" (v2312), function "spm_run_preproc" at line 19.

No executable modules, but still unresolved dependencies or incomplete module inputs.

The following modules did not run:

Failed: Segment

Skipped: Normalise: Write

Skipped: Smooth

If i treat my images step by step, nothing goes wrong and I can get to the end of it. Does any one have an idea where this problem could be coming from?

--
Marine

--90e6ba3fccf3b65375049cef7647-- ========================================================================Date: Wed, 23 Feb 2011 03:03:28 -0800 Reply-To: Vladimir Bogdanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Bogdanov <[log in to unmask]> Subject: Re: Check Reg Figures SPM8 - size and arrangement Comments: To: Guillaume Flandin <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="0-1398234352-1298459008=:63850" Message-ID: <[log in to unmask]> --0-1398234352-1298459008=:63850 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Thank you, Guillaume! This was very helpful. But I faced other difficulty. The position parameters are in relative units and if we change the proportions of the figure, the images are stretched. I suppose, I need to get the real size of the figure and to incorporate this in the script to keep correct proportions of the images, but I do not know, how to do it. Thank you in advance for your help and sorry for my primitive questions. Sincerely yours, Vladimir --- On Tue, 2/22/11, Guillaume Flandin <[log in to unmask]> wrote: > From: Guillaume Flandin <[log in to unmask]> > Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement > To: "Vladimir Bogdanov" <[log in to unmask]> > Date: Tuesday, February 22, 2011, 4:49 PM > Dear Vladimir, > > the following code should get you started (to be executed > when images > are displayed with checkreg): > > global st > h = 1; > for i=1:nnz(~cellfun('isempty',st.vols)) > p1 = get(st.vols{i}.ax{1}.ax,'Position'); > p2 = get(st.vols{i}.ax{2}.ax,'Position'); > p3 = get(st.vols{i}.ax{3}.ax,'Position'); > l = p1(3) + p2(3) + p3(3); sc = > (1-0.01)/l; > p1 = p1*sc; p2 = p2 * sc; p3 = p3 * sc; > set(st.vols{i}.ax{1}.ax,'Position',... > [0 h-p1(4) p1(3) p1(4)]); > set(st.vols{i}.ax{2}.ax,'Position',... > [p1(3)+0.005 h-p2(4) p2(3) > p2(4)]); > set(st.vols{i}.ax{3}.ax,'Position',.... > [p1(3)+p2(3)+0.01 h-p3(4) p3(3) > p3(4)]); > h = h - p1(4)-0.005; > end > > Best, > Guillaume. > > On 22/02/11 14:58, Vladimir Bogdanov wrote: > > Dear John, > > > > Thank you for your response! Where is the part of the > code responsible for this located? The SPM script is huge > and I am only a beginner. :) > > > > Sincerely yours, > > Vladimir --0-1398234352-1298459008=:63850 Content-Type: image/jpeg; name="scr1.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="scr1.jpg" /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMD AwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/ 2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAHCAyADASIAAhEBAxEB/8QA HwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1 dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDC8Y+Mv+Ed 1DQNPhgmurrxFq8GlL5DL5lqr5LzlTn5URWPQgkDPGa9C+C3w6f4wXt7Zw30 Vvd2tk80bKnmRXE4ljhWLO4YVpJB8/IA5wRXv0X/AATma3JaLxxLGZEZHKaW VDIR8yn9/wAgjqD6U+y/4J4T2ccv2bx3LDHdRGKURaYVE0ZIbacT8rkA46ZA 9K45VG6/tFK0OVJLX4ru8v0S8jvUqSwiouF6nM3zX+zpaKX4t3PKtV/Zqaxt dZ+z67BeGxhhl09xbfu9VZrM3kyod2QI4RnODuLIPl3Via78Iv7I0lrz+0BJ 5fh6213Z9nwD51zFD5X3v4fMzu74xgZzXvkf7COsQ3Fo8fxL1NWsQfszJZOD bjYseEP2gFf3aInH8KKOgFTaT+xHrei3az2HxP1a0uYIBZJJBbSJLHCPmEO4 XGQoODt6dDitFVXc5eR9jxH/AIUDLbeGtPvm1JGluLO6ur6FYf8AkHslgb+C MnfkmWEZ3YAUhh82055z4geCW8EeNp9Gguv7TVRC8FwkJQXKSxpIhChmIyHG Bn8ulfStp+xBr1lqE97B8TNWhvZpFle4W1kWRmVWRWZhcbiQjMoPUBmA4Jqg f2BLz+3hqJ+IFyNUaX7Qtx/Z7Cd5Qd3mb/P3b8/NuznPNNVo33D2btseLS/B yO08Vahpd3qxs4rBPs7372E0lqL4CMPalowxG15NhfHYHbhhVrw/+z/qd++o R6jL/ZNzaXMlqscsW9JWSC8lZg+QNgezZNw3Dlj/AAc+26N+xDrfh1caf8St WsDvd821rJF8z4DH5bgckKuT1O0egplr+wzq1pp72ifEbUI7aSD7K8S2ThHh yzeUR5+Nm5mbb0yx7ml7ZdwVN9jxzwb8EofGPhq0e3vJk1e50641dBIY1t/J juBbJFlioDtIJCWLYACjBJJEvhv4CL4n1q/gj1O5tLe21iLRobi608wrczST tGVVWcMHWIeYUOCOVO35Wb2GP9iHVIdJTT4/H1zFaxCVREmllf3cpUvGWEwJ jLIp2ElQcnGWObep/skeJNQtBBd/FXXJ4JJEJSaORo3dSCvBuMZDKpHuBjkU 3VV9H/X/AAwKD7Hi037PptPCkF+dWDm80976BEtQ2PLsI7x1kO8bSd+0YBzj dgZxUuj/AABTUdFWA6h5evSvpirbyKUitnvGl2xyEKzFtixPwAF3kHJFet3X 7EOraguoif4h6hKuqbHvQ9k7fa9h+TzMznftPIznFOX9jDWLAajN/wALK1KE amMX7/ZXX7WMHPmnz/n6n72ep9TS9qu4ezfY+dvF+gW+jW+j3Fm9w1rq2npd qs5DSRuHeGQFgACPMhcrxkKy55zWQn3RX03q/wCwrda19nNz4zMgtIEtoQNI CrHGoOAAJQB3JOMklickkmr/AMO+ShwfFo4/6hX/ANuqlWj3E6bPnBPvCn19 GR/8E/1eRlHi9WKEBsaWCVPof33uPzp//Dv7/qbR/wCCv/7dTVaAezkfOi9B Tk619FL+wAMY/wCEtxj/AKhn/wBtpy/sAYwT4s4I6/2Zxn0/13+c0e2h3D2c j51qRegr6FP7BChgp8XKHbO1TpnLYGTj996U8fsEYH/I1/8AlM/+20e2h3F7 OR88p1p1fQ0f7BHJ/wCKrJOOg0vP/taq2pfsUWOj3mn29143tLa41Wc2tnFN YrG93MI5JTHGDNl3EcUr4XJ2xs3RSQe2h3H7KR4MvQU5OtfQX/DB/wD1NX/l M/8AttKv7CO3/mav/KZ/9to9tDuHs5Hz9Ui9BXv3/DCf/U0/+U3/AO207/hh f/qaf/Kb/wDbaPbQ7h7OR4DH3p1e+j9hnb/zNP8A5Tf/ALbS/wDDDR/6Gkf+ C3/7bR7aHcPZyPBaenSvev8AhiA/9DQf/Bd/9toX9iAr/wAzP/5Tf/ttHtod w9nI8HXqKfXu3/DER/6Gf/ym/wD22nf8MSf9TMf/AAXf/baPbQ7h7OR4UnSn L1Fe6L+xPt/5mb/ynf8A22l/4Ypz/wAzL/5Tv/ttHtodw9nI8Np6dK9x/wCG LP8AqZT/AOC//wC20q/sXbR/yMv/AJT/AP7bR7aHcPZyPD16in17d/wxhz/y Mn/lP/8AttO/4Yz/AOpkP/gB/wDbaPbQ7h7OR4knSnJ94V7Yv7G20Y/4SP8A 8p//ANspR+xxg/8AIx/+SH/2yj20O4ezkeK09Puivaf+GOv+pjP/AIAf/bKV f2Ptox/wkWf+3D/7ZR7aHcPZyPGE606vZh+x/g5/4SL/AMkP/tlL/wAMgn/o Yv8AyQ/+2Ue2h3D2cjxxegpyda9jH7IhAx/wkX/kh/8AbKVf2Rip/wCRh/8A JD/7ZR7aHcPZyPHakXoK9f8A+GST/wBDD/5If/bKcP2TMD/kYD/4A/8A2yj2 0O4ezkeQJ1p1evp+yeEP/Ifz/wBuX/2ynf8ADKQ/6Dv/AJJf/bKPbQ7h7OR5 EvQU5Oteuj9lYD/mO/8Akl/9spR+ywB/zHf/ACS/+2Ue2h3D2cjyOpK9Z/4Z ZH/Qc/8AJL/7ZTv+GXf+o4P/AAC/+2Ue2h3D2cjyaPvT16ivV1/Ze2/8xwf+ AX/2yl/4Zg/6jY/8Av8A7ZR7aHcPZyPKqenSvVP+GZP+o2P/AAD/APtlKv7M 23/mN/8Akn/9nR7aHcPZyPLF6in16iP2aMf8xof+Af8A9nTv+GbP+oyP/AP/ AOzo9tDuHs5Hl6dKcvUV6ev7Nu0f8hr/AMk//s6Ufs4Y/wCYz/5J/wD2dHto dw9nI8yp6dK9M/4Z0P8A0Gf/ACU/+zpV/Z0CjH9sf+Sn/wBnR7aHcPZyPNF6 in16SP2dgD/yGP8AyV/+zp3/AAz1/wBRcf8AgL/9nR7aHcPZyPN0+6Kcn3hX o6/s9hR/yF//ACV/+zpR+z9g/wDIW/8AJX/7Oj20O4ezkedVIvQV6F/woD/q Lf8Akr/9nTh8A8D/AJC3/kr/APZ0e2h3D2cjz1OtOr0EfAXB/wCQt/5K/wD2 dL/wof8A6iv/AJLf/Z0e2h3D2cjgV6CnJ1rvh8CsD/kKf+S3/wBnSr8DNp/5 Cn/kt/8AZ0e2h3D2cjgqkXoK7r/hR/P/ACE//Jb/AOypw+CWB/yE/wDyW/8A sqPbQ7h7ORwqdadXcj4J4P8AyE//ACW/+ypf+FLH/oJj/wABv/sqPbQ7h7OR xK9BTo+9dt/wpn/qJf8Akv8A/ZUo+DeP+Yl/5L//AGVHtodw9nI4qpK7L/hT n/URP/fj/wCyp3/CoP8AqIn/AL8f/ZUe2h3D2cjjk6U5eorsF+EW3/mIf+QP /sqX/hUmf+Yh/wCQP/sqPbQ7h7ORyNPTpXWf8Km/6iB/78//AGVOX4U7f+X/ AD/2w/8AsqPbQ7h7ORya9RT66r/hVef+X/8A8g//AGVO/wCFX/8AT9/5B/8A sqPbQ7h7ORyydKcvUV04+GGP+X7/AMg//ZU4fDLB/wCP7/yD/wDZUe2h3D2c jmaenSul/wCFbf8AT6f+/P8A9elX4cFR/wAfuf8Atj/9lR7aHcPZyObT7wp9 dEPh0Qf+P3/yD/8AZUv/AAr0/wDP7/5B/wDr0e2h3D2cjn0+6Kt6FJ5Ot2bh Y2KTIwDoHU891YEEexGDWuPABA/4/P8AyF/9eodQ+HU91p88VtrFxp1xLGyR 3dvBG0tsxGBIgkDoWU4I3oy5AypGQR1odw9nI7pPEDOpP9naAMeujWf/AMar 5V/a8/b/APFnwd8BeV4R8Gxahq/hL4e6J8QvGGuHwPomr6dY6ddWV28n+jy6 rpkxmaSxuZP3PmKqptCEuu3V8Hf8E+tN+HPxI1Xxj4c8XXnh/wAWa7539p61 p3hDwxbajqPnSrNL51wmliSTfKqu24ncygnJANZ3xH/4Jl+GPjLeaVdeMtcH jC90XTbfRrO713wV4W1K5t7KBdsNuss+ls4RQTgZ6sxOSxJwUkt5Gjj5He/B f4jar4l/aA1CfUn0+7Enw88O3bRv4aOk2n2ia51Tzp1024DPaSPsTckuZkVE jdiUrlfhH+3hd/tI+BvEsln4V0TwrJoV9pNg0htbSV9YS5vPKk1HTiLdPP0a dAwstRGw3Wy4IihMALdLp/7MOuaf4h1HWl+LHjltd1O3s7KXUTY6P5q21s1y 8cCp9i8oKZLqRmYoXJCAMACDZuP2ePEOqSWy6p8WfG+rWcF1BdPZ3Gn6NHFc GGVJVVzFZJIFLIudrqcZ5FPnW9xKPka1v8KtUtfj/wCMvFS3Notl4h8L6Po1 qqyOJ4p7O51aaR3+XAQrfQ7SCTlXBAwC2R+zv8G/FPws8WeN7zxH4v8AEHiS y1nUA+i2t/q0V5b6ZaeZNOUhiWzgaDEl1JFtkluSYra3xIoURp718L7bxN8T PiDqkFx4x8Sadoum2SXUjW9+omaWdtkSIrwt+7UQTszFtxaVAMBawdE+K/iP UbHUjJrl4r6ZqF9prSPfTRRSNbXUtv5pbLGNWMW7GG2hsfPjJwnP2eGliKnu wje9+y1v6aX7+RtL3qkaKab3087K3rseZ/ErTvHOueKbvStK0fw3L4Q1rSJL e81ZvFd9pmrWdwY5lCwQ29ox25MX75buKRNzlVzGu/G0n4YeMb743eCvHeo2 3h60ns9K1PSNY0uDVbi5hshemxnM9tO1uhuCk2nxx7HjgDJO77lMQjk6L4t+ N/2iPBX7aOgQ2V3Yj4HS6Wv22/nvpl1C7uZ9wX5WwYp4po40ECeYDFO8jtni 29S/4T/Xt7H+29X55/4/ZP8A4qow+KoVqMa2HqKakr6dL9H2fkZ88m5Raas7 a9bdV5HhXx2+EPiHxz8bPB+sad4f8GeKfC6aFrPhfxPp3iDUZbZXsdRn0x5H jiW0uEusR2UqmCUxK/mgGQDNcB8Pf2Y/jD8PYLzS9Z8a3/xN061u7W10jUNb 8ZTaRcwWTwXcNzM0djpqTfaIluUCrNeXHnvEkwls3jQD374d/tO61498a6zp ceu35g05XNvNHdSkXeyUq5Vt5UhVaDPQkyZxgisX42/Hf4heDvE0B0zxFrUF ikELEO7vBLKZmVg7feAxsyFYHnjk1vBWfIv66/qDk7XOY+IUvxN0+3tb2y8J WXiK50jXbj7Fa2HjCazW9082k/8ApN0WhiQTHc0aWpE8XnNbsZYwDPb+j+J7 bU18B3sOlQ2N9rS2Li0hvbp7a3uLgJ8iSSrHI0aF8BnEblQSQjY2m98Nfj7r nxE0Ka7Ooa1Yy21y1tJG2oSSLuCqysrZGVKup5AOcjtk8A/7TnxIs/2jtbtZ /EOlR+EbVJLaHS18QXl3qlysUoZb+LFhDDCv2czNPDJczHdAgiOcmbH26deO FWs5JyS0vaPKm99bOS27otyaj7Z7JpfN/wDDMxLHwR8SVTRdXTT/AAb4e1bU PE66h4l0rRdelSyayNsbZ2kuZNPMl/PhY5QBFZEskUfnBYWefkPgL+zd8Tvg r+z5deHLi60LxJqegalpg8OWb+KtQ0+xlsNN0/TbSLzJktnktfNlsp7mS2ji mizcvGzzBnZvoj4p/HHxd4J8MfadLfxDrd3NJ5KwwXM8jREqxDlUySoIAI4+ 8OaPhr8YfGGvWmpx6trkhvdMvms2bT7+doGxHGx5Zs7gzspyBgqR2rZSaTaF zO9u6t+Fjx74m/B/x145+H8nhq3j8PGHw/faDreh3N14ivJ5dXuNOu7a9e1u y9uzwRtJaJGLnzLqQiVpWQsmx6HwU+Dvxe8EWVtD4w8QR+PLxb67s59Q1HxS 0J/s5o7Xy7mOztdMt4TMZbX/AI95vNeFZpdt9KJJEf1Xxd+0z428OeJtQhso dZ1qw08rbvFZahNJfJM0CyozIM4ifdsyASCrHPAFd+PiBryn/kNauc8c3kn/ AMVRzNahzN/1/X9eR5H+1J4A1/4mfCP7B4Wh0e516w1vRdbtYNVvZLKzuTYa taXzRSTRwzPHvS3ZQwifBYfKRXk2rfsx/F7SPH+o634V8XHw9o/iqxvr/UvC Oma7bWWmeH9ZlsJVMtpNLotzLcJNfSG4eQrbbZfMneC7aRoRn+L/APgr9q/w w+M3jfRZb+98Uafo/ibT9Etra31g2l/p4kns7K53QyhXlRJZmkBK4JkQCTy5 opF+jvin+0j4t8IaB4eudAnk1u98S6lBp1nb6h4m/seKRpo3aMedKCu9mVUV DgszgA7iFPRHB1PZwqxs41G0ndWurX1emmn9XFV9rSlKlXhKEo9JRafk7NXa e6a3RwZ8K/E3w/Yahq1nb+Gdc1vVLKymOlaj4ju7Wy069iZBLDFcpZurwuJJ 3ExtI5S8KKwMckaWef4e+F/j3Tte8OxTWNodMjllg1K6/wCFh6zcS2lvA1n9 mmjikgxdTzCzTzUlkRYt848y5+0XHndx8Ff2pvEXxi1S6Emr67pF74cvdT8O axpgv5LmMalZvZGc+e0URZIxOio0Y8uQyzEGRI45ZaN7+2BqaftWWfwv/wCE tv4Nd1gwz2sc2palGlvbsoy58vS57d8urhVkv7dpGKxjy9yO3TgMsxOMqypU FdxTk+nux3evTrfqtr3RjXrxhHml10+/X+vmja+MnhG58cfDy8tLCOC41S0m t9T06Ce8ks4Li8tJ0u7aOWWNHdIWmhjEhVHJQsNrZ2nhfhH+ylb/AAltZ9I0 t73TPDNjeWK6Tb22vXhke2t5Xvi87fK3myXtzciRN7xywJCr5Vnjr2TwJ8Z9 e8beBNF1g6pq1sdVsYLzyvt8r+V5kYfbuyM43YzxnHQV5l+0l+3yf2ePEjaZ qmv6vFdW2gnxIy/2gQl5bebcQpbgkho55Lq3igUqs21bvzWTZC7K8qyfFY7E PDYWN56u3p+pyZnm+HwFFV8S7Ren3r+vIzta+HvjSzvddTTLXTby2int9V0q W88Z6nBLeXUKxKLSbEMhtbYi2i3PG0olMs5kgbzJTNW8F/ALx7oPxu0vxI/x J1WbwpbpHFL4UlzeQbRYCAgXMuZpGFwqS75CWb58klia9u8O/FvWvEujR3se qeJLcSsw8u8eW2njKsVIePedjZB+XcSOhJxXP+MP2oNQ8BOkmrXfiy1sG1+y 8PG++1B4FlvBCltNhZTIYnubiG1yE3LK+5lWFWmXz5RlCfI1qtP0O+M1Uipr Z/8AD/1/wEYHxU+HV7448d/DTVLSazjg8GeJJtZvVndleWF9H1KxCx4Ugt5t 5ESGKjarnOQFb5k/a5/Y5+Nfib44eG/HfgXxdqurWfhjxppXiGz8M3vi7+zd MERUWl/GEjsDNEvlKjMxuZw0d1fhLbe4Sf6g0P8Ab28N+I9L8TX1n461qSw8 Ga1qHh/XLp476K30m8sbdri7WaR0CJFHGhJmJ8okqocsyqaWp/8ABQ3w6nwg 8R+OtG8Zaz4t8MeG9IuNZl1HQZ5r2yvYoFuzIkF2CLaSQGxnUqJflbyg+3zo i8QbjZr1/I1blszY+EWga74b8B28Xie/S+126ubq/uvKna4hszcXMtwtpFKy I0sVusot0kaOMukCsUQnaOl3iuU+Iv7aL/DHUfFsF/feLpU8DaJYeI9YmguQ Y7bT7q4vIWnG6VWYW6WFzNKqguY0AiWaQiKuPuP+CtfwosPh14R8WXvxjj0r QPHYuToNzqVzdWJ1P7NdxWc/lpMqv8k0yAgqDs3Sf6tHdU43f9f10Js0j1ve KN4rDH7XCx/GW48CT+INftNcjsrG8gE9zJHHfG7GotHDDl97Sqml3buNoAVQ QThgtT4fftxeHPil4R0fXtD+Ik13pHiCSeHTLl72eFb5ob2OwfyxJtLD7VLF EGAw5lj2lg6krlB3R0+8UbxVLXPEem+I/F8t14/8W+N9N8FeF/CeteJNSk07 xNq2ktCLSSyLTO1lNHNIqRPOQnzA7shSdtdv46/Z48G+AvhPP4s1O5+L+jWm m2kWp6tHq3xj8TQrpFou2S8eWSHUZk328HmuQpKO0YUSAN5g1hQ5lzXM5VGn Y5XeKN4qT4wad8FPgN8QfCfhzxV4j+OGm3HjOz1C6025X4meLJ7d5LSeyt/s p8vUGf7RPLfwRwRhD50hESkyyQxyUdA8VfsxeLvBOoeKNF+MHjvXvCujhzqm u6T8X/Fd/pOjlGtBILy7hv2gtSgvbd3EzoUj82RsRwTNHf1V9xe1fYtbxRvF T23gz4Zr4Wn1Ce++NUtxaeILDQbqys/it4sNzp7ahd2cdm1zHcX0EkLm21Cy uZonUPCsjr+8ZBv6Hw58GvhX4rvLeC11b9oKF7mx/tFTe+NvHVkiQ+XaSfO0 10gjfbewgRuQ5ZLhdu62uFiX1bzF7Zt2SOX3ijeKyvjv44/Zu/Ze+HWmeLvi N8Q/jH4K8O63bQ3OnT6r8QvG8FxdmURFYBbG688XO2ZW+zlPOCpOxjCwTGPp 9S8N/AfStY8Y6bN8QPisdT+H91ptjr9jH8UfGEt3p8+oiI2ERhW+Lu9wZolj CBi7tsGWDKH9VfcPbdkZu8UbxW9o3wh+GWu/Eo6BbeJPijMl1afaNMmj+L3i 3fqhieIXbxK16Ekt4ftmnjz4pJFMlw8ZCNCwMOo/DP4ZeG9O1CbWn/aQ0qfS 0kkubZPHHjTUpFRReMhVrK+njkaRLGRkijdpf31qjIstzDG6+rdbh7Yx94o3 irM+m/AqC61C2fxZ8dlvdM8TXXhCa0bxx47F02qW2mSarJBHD9q3yg2MTTxy Rq0cylBEzs6Bp/F2jfs+/DrV/D+neJ/id8RPCmpeLNQl0rQ7LX/i94q0m61m 6juI7ZoraK41BHnYyyxhfLDB1dWXchDU1hX3H7V9j8jP+C8v7Tv7QHw6/bR8 G+D/AIO/ETx/4bjv/CEF4+maR4ovNLslxc6k81wyRTRpu2RKSx+YhABk7RXy D8WP2y/2sfgzq/g2PVv2gfjFNa+MPDo1uCWHxnq8aCQXDRNGC1xkgAI/KqcS rxwSf0B/4K+f8EgvGP8AwUr+Ofhbxl8P9f0vR/DGl+HrWytB4pvNVtNbJjuL uaN5hcW0twJPLuE3faCJ/MD+YAwOfnPxB/wbk/tB+MviZq/i/W/iB8ONY1/X XMl5cXGp3iq5yuAsaWCpGihVVEQBERVVVCgCvsMDiIQxuEm68I0IR/eRau27 WSty9Hdt3d9NDzZwi8LiIunKVWTXs2tFHW7b11urJLS2u+gtwf2xYvDnxbey +PHxt1nVPgl4ubwx4omt/H00GiwATSWyzR3NzqEVxI8lzHsSJbT5ldSXDERm 1pPhX9v+/wDFVzoo+InxlGo2LyR3KSfFQokJjl1aF8yNfhAFk0PVASWA/wBF znEkRf3r4U/8E9v2wfhd4k8b6k/jb4HeIl+JOvJ4m8TWGsR3FxYazqC3DzmW aBbFUZSZZQI8eWhkEsapPDbzQ7+rfshftw6te6lKvxT+DlgmqahqGoSJa2C+ YhvZdSlmi8+TTGneFW1fUfLjkkZYvtT7ApwR9N/bWCV4qdF7auL7K+0e9/lY 8X6jXf2ZrXv0v5vtb5ny78VNb/ba/Z78IaD4n8e/FX4xeHfB/iO+gsbHxCfi Jd6hplwZrdbmOaOW0uZvOhMDbxJEHDBXVdzIyjG/aM/aV/aW/Zf+M2o+CdY/ aH+JesXunW1ld/bdK8b6w1pcxXdnDdxMhmaKT/VTpkMikNuGOAT7nrP/AAR8 /aQ8TeGfDmk6p4s+Cupw+FGgOn3N1YrPqHlwLshgmvH003FzbxxgRpBPJJEk aRoECRoosfHf/gkP+0f+0x48XxN408YfCDUNbWzhsDcWVudL8yGFdkQdLXTo kdkQKgZlLBERM7UUDtoZ3k6lH206Vtb2j6cv2fW+v3nPPA4yz5Iy6dfv6ny3 a/8ABQ34/NjPxy+MJz6+M9RP/tatO0/4KB/Ht+vxv+Lx+vjHUf8A49XtNt/w QS+MCdfEvw14/wCohe//ACJV+2/4IR/F2Lr4j+HB+moXv/yLXY874d/mh/4D /wAAx+o5l2l9/wDwTxu1/b++O74z8a/i2f8AucNQ/wDj1aNr+3v8dGxn40fF k59fF2of/Ha9gg/4IbfFmLGfEXw7OPS/vP8A5Fq5B/wRG+K0WM+IPh7x6X15 /wDItZ/23w9/ND/wH/gB9RzK+0vv/wCCeTWv7d3xwbGfjL8Vj/3Nuof/AB2t O0/bp+Nr4z8Yfimfr4sv/wD47Xqlv/wRa+KUXXX/AAAfpfXf/wAjVdt/+CNf xPh6674DP0vbv/5GqXnXD/8AND/wH/gB9RzLtL7/APgnmVr+3B8aWxn4vfFA /wDc1X//AMdrStf21/jM2M/Fz4nH/uab7/47XpVt/wAEfviUnXXfAvH/AE+X X/yNV6D/AIJHfEiLGdb8EHHpeXX/AMj1P9tcP/zQ/wDAf+AL6hmXaX3/APBP OrT9tH4xE8/Fn4ltn18T33/x2tG1/bL+L7Yz8VfiSc+via9/+OV6BB/wSh+I kRGda8Ff+Bdz/wDI9XIP+CWXxBixnWPBpx/093P/AMYqHnOQX+KH/gP/AABf UMy7S+//AIJw9p+2F8W3xn4pfEY/9zLe/wDxytOz/a7+LBxu+J/xDbPr4jvP /jldpb/8ExfHsPXV/CH4XVx/8Yq5B/wTY8dRYzqvhM4/6ebj/wCMVDzjIf5o f+A/8APqGZdpff8A8E5G2/a1+KjEZ+JfxAP18RXn/wAcrStf2rPii3X4k+Pj /wBzDd//AByupg/4J1eNoyM6p4W/8Cbj/wCM1ft/+CfHjOI86p4XP0uZ/wD4 zUf2xkX80P8AwH/gB9QzLtL7/wDgnNWn7UXxOcDPxG8eH66/d/8AxytWz/ae +JTEZ+IXjg/XXbr/AOLretv2DfF8IGdS8N/+BE//AMaq7b/sP+LITzqHh049 J5v/AI1Uf2vkd/ih/wCA/wDABYDM+0vv/wCCY1t+0t8RiBnx/wCNj9dcuj/7 PWja/tG/ENsZ8e+NDn11u5/+LrVh/Yx8URDm/wBA/wC/8v8A8bq5B+yH4lix m90M49Jpf/jdS82yT+aH/gP/AABfUMy7S+//AIJRsv2h/iAfveOfGDfXWbn/ AOLrUtfj/wCPGxnxt4uOfXWLg/8As9T2/wCyr4hg+9eaN+Esv/xutCD9mXXo iM3mkHHpLJ/8RUPNsk/mh/4D/wAATy/Muil9/wDwSK0+O/jh8Z8Z+Kz/ANxe 4/8Ai60rP44eNT97xh4ob66rP/8AF0W37O2tw9bnSz9JJP8A4irsHwK1eLrc 6af+2j//ABFQ81yX+aH/AID/AMAP7PzLtL7/APgk1r8afGTEZ8WeJT/3FJ// AIqtO0+MPi58Z8VeIz/3Epv/AIqqcHwb1SE8z2H/AH2//wATV22+F+oRdZrM /R2/+JqP7Vyb+aH/AID/AMAX9n5n2l9//BNK0+LPit8Z8T+IT/3EZv8A4quh 8JfFXxD/AG1AbjWtYvIvm3Qy6jcBX+U9Skitx14I6fhXM2/gK9h6yWxx6M3+ FP1jwj4hh0iX+wdR0bT9WwPInv7SW9t4+Ru3RJJCzfLuAxIuCQeQNp48dmWV zoTjRlHmadtLa201sb4XA5hGtB1Yy5bq+vT7zvPiN8dtV8JfD/XdWtojJcaX p9xdxrLqmolHaOJnAYC5BxkDOD0qt8SP2gvEHhLUviDJY2Oky6b4DubiF1vP FWsRXl4sVtHcnYkaPHkrIFG6RcspztHNfNPgv9jz4hfD/wAMeItK0/4i6TdW /ie0NpdNra+JPEDwrskXMP8AaGu3AhOJGzsADYXcG2rjo/GvwJ+Jnj/xHrep 6j4w8AiTxFO8+oQ2ug61bW9wzIEOYk1oJgoqqcjkKM5r4SM6vWovvPr+Wn0g /uPR/Av7SXit/FvxkaaPWfEkfhbWYbfR9HtdTmimKf2LYXRtoXe5iUvLPNKQ 1xMFBkA3og4yvhN/wUK0/wDak+HPhjxb8PLnU7jwh4q1yTTNP1uW7v4f7Thh gmeWaCGW4MiKtxBJbn7REjEwylUKNFK/GJ+zz49OkeMLe68R/DXVh4/vZLjX 7bUvB1zc6ddwtYWliLcW5vx8jRWzeYJHkD+bjaoB3avhr4MeN4PiBZ6vq+t/ DuS1GqnWdRj0bwncadd6rciyNkkks7XswZliES7mRmKQRpkADHR7V+1i3UVv dvr5K5h7JezklB31tp9xo/sc/J+yL8KweP8Aij9I/wDSKGvR94ri/wBjDxxr dr+x78J4otY1SKKLwbo6oiXciqgFlCAAAeAB27V6Z/wn+vf9BvWP/A2T/wCK rxXa56iuY+8UbxWx/wAJ/r3/AEG9Y/8AA2T/AOKo/wCE/wBe/wCg3rH/AIGy f/FUtB6mPvFG8Vsf8J/r3/Qb1j/wNk/+Ko/4T/Xv+g3rH/gbJ/8AFUaBqY+8 UbxWx/wn+vf9BvWP/A2T/wCKo/4T/Xv+g3rH/gbJ/wDFUaBqY+8UbxWx/wAJ /r3/AEG9Y/8AA2T/AOKo/wCE/wBe/wCg3rH/AIGyf/FUaBqY+8UbxWx/wn+v f9BvWP8AwNk/+Ko/4T/Xv+g3rH/gbJ/8VRoGpj7xRvFbH/Cf69/0G9Y/8DZP /iqP+E/17/oN6x/4Gyf/ABVGgamPvFG8Vsf8J/r3/Qb1j/wNk/8AiqP+E/17 /oN6x/4Gyf8AxVGgamPvFG8Vsf8ACf69/wBBvWP/AANk/wDiqP8AhP8AXv8A oN6x/wCBsn/xVGgamPvFG8Vsf8J/r3/Qb1j/AMDZP/iqP+E/17/oN6x/4Gyf /FUaBqY+8UbxWx/wn+vf9BvWP/A2T/4qj/hP9e/6Desf+Bsn/wAVRoGpj7xR vFbH/Cf69/0G9Y/8DZP/AIqj/hP9e/6Desf+Bsn/AMVRoGpj7xRvFbH/AAn+ vf8AQb1j/wADZP8A4qj/AIT/AF7/AKDesf8AgbJ/8VRoGp5bqms6hql8bhfC fxGsJmjWJ203xDBp/mqpYqHEF+gfBZsbs43HHU1DbyvbeE7rRF8BeNH069W4 E4l1SzknmM7O0rNM16ZCzNI7bt27Lda8w/bn8bfFXwHY+IdZ8ASeJ9Qt/DXh Kea20fw/o0d1e3+rXbS21pIy3FnKl5DDIFneO3uIJbdYGaSO6S4ijTnfin+0 J+0X8PPC3xFfTPBWh+Jr/wCGkKLbSWvhq9ij+IT3UCSRvYRfbHe2WwMw89S1 0bsxOkTWz7hHrNyq0/Zz1j2e3bbbqKMFGXPHfue1afbzafq9tfHwh8SL6e0d pIf7R8SRX6ROyshdUn1B1DbXZdwGcOwzya3/APhYGrZ/5EbxT/4Eab/8l15L 4G1349J8d/EOi6vqHhibwxbalbG31J/CU8UTW8OmaS93BbgXwbF3c3V60Mrm YWzWU6ObgPGsTPit8UvilrHxi8K2/wAPXttM8Pro92uttr/g7Ubi2a9nk0db NlKGKXMUd7cSlQ3lMLe5hle3ZHuLXlw2Do4ePJQgoreyVunl16eprOc5u83d no2hTf8ACNXS3Fn8OfEsVxHB9lWY3OnvIsO7cIgzXZIQHooOFAAAAAFaGp+L L7WrF7a78AeJbi3kxujkm00q2CCP+XvsQDVvV/FHiu++A+o6v4D8NaDr3jvU dMtf7BsPFl7NoGn21zc3FtE11fAkTrFaxSyXMtsv791t3iTEjKRx/wC0N4h+ LngbTPhND4b0jwXr/jG81C1tvGNrp2oXVt4Ykf8AsK8luzHePbXdzBai/SIx O0Zdj5MTsvmNXX7OXLzsxi7uyN/RNeuPDdvJDYfDzxHaRSuZXSObTQGYgKT/ AMffXCgfhXHaZ8HvDPh2S1OjfCTxBoEdkjJBbaTe2Wn2kSsWLgQQXqRgNvfc AvzbmznJqprvxw+Jmg6RrNzB4Rt/EOs6bb20X/CN2FhewhrpWtXu3i1adI7e 4g8q8URYt4yzW02SWWaODC8b/Gb41a18Efh14k0Hwa+geLG8QXx8SeGbmzF9 9osrWw1V1thKJEEJuZ7e0WG53bUa4hLLKu6NyE5wd4O3zCVNSfLJbfoezf8A CwNW/wChG8U/+BGm/wDyXUGn+Lb3R7Vbez+H3iO0tkJKQwy6YiJkknAF1xkk k+5NebePfHvxn8N/ADxXDp2i23iT4laXqkOn6NdaNpSWNnr8TpbXEl1Fa3t4 UtxFHLcQkT3eGksyykGVIRyWvfF34/eKf2hfCsXh6ysNB8G+MrXXreJNU8GX VyvhVrO5tRaX2pSfa4WnN5Ek3kW6G0aL7bl/tBt2rP8Ar8LlWPeP+Ey1H+0D d/8ACBeJxcmLyWkFxpwLoCSAR9rw2CTjOcbmxjccy/8ACwNW/wChG8U/+BGm /wDyXXL/AAg8U+M9b/Yq8M63rkd//wALAuvBNrfagkunfZ7oaq1gkkqta7B5 cnnlv3WwbWyuOMV1fxKuvFOh/tN+GrNf+Ef/AOEM1TS/FN3JFoQmvYLMW93o UOki7vJeTdyxSanP5aLDGBM8IW4+yfapdo0ZPmt0v+BN7NI8l1r9l/wF4j8e a54mvvgz4huNc8Q3Fnd3d0dTtlYS2tzBdRNEBfhYf39vDI4iCCVox5gfnPde M45fHOn6fb3Pgv4gWH9k3C3VlNpGvQaTcWsixvECstrfxyAbJHG3djnpwMcn 4C1/4vf8JPaDXZ9CuLG8ihDw2uk3UDWJtbOaLUHEsibX83UntvIU7d9ussi7 wCRU+JPjz41eHLrw14c01/D11q19JJZ6n4jTwPeS6PA7297dx3MdumpNIiRr YfZpIpJcPLqVo6TZU27xC8UowdkrtdvN/hqb161SvP2leTk3ZXbu9NEteiW3 kdP8LvB9l8HJ9Wl0H4feOY59dvZNRv573XINRmu7mQIJJnkuL+Ri77E3NnLb RnJFa2p3d7qOoTXKeFfibYvcENKlh4mjsopGChdxjh1BE3bVUZxkhRk8V5x4 q8TfF/wR8H/itZz6vH4l8W6V4Z1a68N3mjeD7qB7jUI7cXMZUOZICA95b29v bKJ2dbKRnnnkM0cG94o+KPxGiubDTNJttNg123d7XUG1Hwxqb6bfMJLe38+C a3Z0hQteQXSo8ku6OKaEvCYrm4ttI16qlzqWve5z+xg4baLy/rudroPia98N aHZabZeAvFMNnp8CW1vH9p05vLjRQqrk3ZJwAOpJqDxFep4ui2at8L9U1QKj JEbyPSZ2ttzxSFoWe5LRP5kED70KsGgiYEFFIg+AvxD8R/EqLVL7WLT7Fp6M gsYptDvdKutsry3MRdbrDMRZT6ekgVBsuo7xDgr5cfoVRCvUpz54NqXdPXUU 6UKkeWauvNHKQ+O9Ut4VRPAvihVQYAE+mgD/AMm64Dx38H9D+JHii+1bVfh3 8Q/P1K3SG6hs/E62NrPIm3y7swQaikP22MxwmO82faYjBAUlUxRlfaqKy5up olbRHiXwi+Cfhj4H+Ctc8PaD8K/Fz6V4m1C51XWI9V1m31d9VurmJIriSeS7 v5XlMqIA4diG5JBJJOvofg+y0X4ZyeD5fh/461vQJ7eW1uIte1yDWpryKQuX jnnvL+WaZT5jjEjsAp2D5QAPVqKfOx6niMfwW0RPG2l+IW8BfE+bVdK0WHw+ Xn8YNNDqVnCtwI472F9TMV8ym6nYSXSSybpWbduwRF4G+AXhP4a/DPw94N0T 4U+L7Hwt4Y+1LZ6YNbhe3ljuvNNxDcq2oH7VC7TyEw3HmR5KnblFI9zopcwH gGqfs3eGNZ0+ziuPAPxWa7sLU2cOqL46uE1YRmZ5l3Xw1UXTOhlnSN2lLRxX VzChWKeWN+k8HeAtK+HWg6FpPh74Z+KtD0jw3qd3q9jYWN/Yw20c9ybkygoL zBiL3czLEf3aHy9qr5abfW6KfOKxzfgX4mav4O+KVvrknw68RanYDRb/AEqe 1mvdODSG5mtJB1uWBTbbuCD/AHlABGcdd4K/aETwHY63bWnwv+JF9B4h1G41 S8TWPGaawBJOAskcX2u9l8i3wPltotkEeW2RruOatFXGs0rImUE3dnGx2fhS /ttR/t74UfFPxZd63aW9nqlzrHjpZhfRRmNpohbrerbW8F28SNeW1tDFbXhU CeKVQFHR+GfiLo/gz4fa74W0r4U/FHT9F8QPO0kUHj90l01JlRDBp8wv/N06 BFjURQWTwxQY/dJHk5v0U/rErWJ9kjF/4TDSIvHfhzWrT4X/ABO0e08I2MFh o+gaL4yg0fQrVIlKDzLK0u4orsGJYY9l0Jo0S2jEaR5kL0NU1u1udZ8O3Vh4 N+NmjQ+HdQudTS0tvHkEsN/JPPBI8dz59zI8tsI4ngS1Zvs8Udw3lxJJFbSQ dTRR9YluNUkeffFzwN8Pfjx8M08G+Lvg18W9d8Lrcy3T6dcfFC8ENy0kqy+X Nt1MGaGOREaKGQtFBsXykTaMdFe6t4Nv/jRcfECT4F+Mh4nvtTi1i/lj8WRx 2mq3UNtb29tJd2i3otrr7MlrA1uJ4nW2mVpoRHNI8jb9FP6zP+v68g9kjE8X +LND8S/DTSvBunfCTx94I8L6TdPcJY+DPE1n4ad1dnkeFbixuYriCNp2Wdhb yxF3iUOzRtJG9/wx8SdD8HTaI2nfBzx5CugWM+nwwyeL0mgvEncyTS3kb3pS 9uXkaRzc3QlnLzTt5m6aQtcopfWJB7JHK6aPBulfErw94uj+DXxLm13wzGgt Jbn4gS3MNxMli+ni9uoJNQaG8vjZyNbm9uUluWjCKZSEQL1Xjr4zab8RNc0D U9Q+EPj2DUvDN6t7YXem+LotMnyHDtBK9teRtPayOqNJazF7eUxRGSN/LTaU U/rUw9miPxBqk3iB2vo7OWwa8uFuXtHkVntVZtxjZlJViudpwSDjgmvkX9l7 wv8AtGeB/wBrrx7ceK7HT4/hz4z8d6pqUc6WNjc3YsYbJLWxklnTUIjbRPDB ZpGqWl1MzQSGYQBhI/6Sfs6eFNM1vwTdTX2n2N5Mt6yB5rdHYKI4zgEjpknj 3rvv+Fe6B/0A9I/8A4/8KuMJay7idRJctulj4J/abv8Axj4fuvh/rXhLw74m 8WQ6L4ke41zSdDv7O0uLyxfStRgAP2u5toZEW6ltHKNJnKBgCUGPEfFFz8eP E/7VHw38TR/DnxHZaN4X03xTDrzxX2hr/aNlfQQ3Wm6fArXzyfa4pbS0hncm OL7RG5SV7dvMr9ZP+Fe6B/0A9I/8A4/8KP8AhXugf9APSP8AwDj/AMKI0XHV f1pYft9nY+PrqC++IHwvkiKat4Q1LXNMx8zwtf6NNLDjkxvJCZombqjum5OG ZcE+OeMfDvxs8Q3fgE+HvEmoeFrDWLiebxPFPoem6veaSk9wl4sIla7jjjNv GkliJIo7sN56v5R2+an6Sf8ACvdA/wCgHpH/AIBx/wCFH/CvdA/6Aekf+Acf +FHsXe9yfaq1rHwz8AJPGmo2GpX3jO21jS7i4aFrfTb+ewuDbebH9rlVZLRF BEMl0bAbyxZdMSTLGVpJOK+Neu/GG++IXiy28C6DrVlp2l+A9ZbSr+5n0p7L W/ELpZnTFhV5mnTyz9sVzPHDEWIyXUIw/Rv/AIV7oH/QD0j/AMA4/wDCj/hX ugf9APSP/AOP/Cl7B3v/AFt/TGqvkfl94TT9pCX4nW+mavfNB4K1P7daNqkW i6W2q2fkxTxwXcp+2+Ta+c32aRFjt9R8yRJHkSwjkFrD794a8Qalf+M/EWn3 mg6xYWWnyQPZ6pcTWr2mrLJHlhAsczzr5TDa/nxRZZgU8xcsPsP/AIV7oH/Q D0j/AMA4/wDCj/hXugf9APSP/AOP/Cm6LYOr5HwJ4y0/xvpvxd8aa5psPiG9 0NfD+gNpVjpt7Z+Ze3dvf6nLfW8MV04gjea3e0iaSTygyuoEqNGHiztbtviV Z6Dr8ViPFl/qlloRvdKuL2LRbi2u7028KxwiFJbZ3uY5reSRlklgtnN5IBME 2C2/Qz/hXugf9APSP/AOP/Cj/hXugf8AQD0j/wAA4/8ACiNFrqHtvL+v6f4L sfJHg3UL7WfCWmXupWV1pmo3dtHPcWVy0Jms5GQM0T+S8ke5Sdp2SSLlTh3H zH5N0vT/ANpDSPgR4F1nw/p2v6H408O+H7bw14w0jxM1j4kufE00UEQTU7CN dYhtA6zyXReSa7t5ZkOZFlMNvGf1p/4V7oH/AEA9I/8AAOP/AAo/4V7oH/QD 0j/wDj/woVFpt/1/Wv8AVhe10sfn94Yv/iZqHiiK91LS7+Oy0rxDqGnyw+Vp 9pLqunySstpdQqt1dBoIkkRnDvb3EnkGQRxbfslxjL8QPiT/AGRPrDeBvi5Z yQPbXcegrceFZri5U3NtC9pGxuWQMFtppnaS4A8m/k2ymYRR236Nf8K90D/o B6R/4Bx/4Uf8K90D/oB6R/4Bx/4U/ZMbrJ9P6tb+vM+SvBkF5ZeF9Ni1G4mu 7+K3jW5lmMfmzSBQGZ/LVY9xOSSiKhOdqqMAeL/C34T+PfCXiuyuZPEHj2/S WExX51m+s57Y/wBm276fAUjRzsOotN/aLMAcG3Cy+WxEdfo7/wAK90D/AKAe kf8AgHH/AIUf8K90D/oB6R/4Bx/4VKoNS5rhGta2mx8G39h4u8Nal4e08y+L PF1pPZGwvroHSIkin8uA/bLsbYGxmCUYtVIDXkn7koIjBxHjHTvj/qHhjTpP BV7aabe2huob2LxlDYz3F88MVvHDLCdPHlLFcPDPKdwDj7ScpEAsMf6U/wDC vdA/6Aekf+Acf+FH/CvdA/6Aekf+Acf+FV7Fi9ppb+un+X59z5I8Y+HW8XeE NU0hb/U9LOqWc1mL3TpxBeWhkQp5sL4OyVd25WwcMAccV8Z+MvhN+0z8Yz8a tVt9S1X4c6f4l8PK/hzRor3/AInZvYBqIt4IZU1a4stMmOLFLiSESR3MbpKh s7gyCD9g/wDhXugf9APSP/AOP/Cj/hXugf8AQD0j/wAA4/8ACpVBp7j9sflp 4f8AgV8afCPiDxKt54q8ReJbSe68Frpc7a0ixx2Nh4huBf8Ay/uiJptJS2mv CVxPNPPHGXjWOFPUPGXxI8SeGfjDfaPZaB461yG90yK5s5LGxs/7KtZYxdv5 BmkKMLicRsPmdoUMVuHa3adPtH33/wAK90D/AKAekf8AgHH/AIUf8K90D/oB 6R/4Bx/4VXs5d/wD2ytax+c/x11b4ySfDca/8Pra5sNbunszL4c1XTNN1O5s IGhnEwhRb+1ge6WaWDeZNR8kR2zGPzGOyb2nVY55dFlWFyLjZkMh2/Nj68cn 19Oa+r/+Fe6B/wBAPSP/AADj/wAKP+Fe6B/0A9I/8A4/8KToNh7b3uY/Mkad 8d9O/a/8WR7Y7n4P+IdTFxZbrWC8ns44tEtY3Bmk1CJrS3kvFbYkVpcs0sc7 OIEkWWT1n9nvUvEes/D6K78TWXivS7+cxgWXiM6WdRgCQxxM7nTma3HmvG82 1XYgyn/Vrthj+3f+Fe6B/wBAPSP/AADj/wAKP+Fe6B/0A9I/8A4/8Kbotx5Q dVO2n9af5Hy3RX1J/wAK90D/AKAekf8AgHH/AIUf8K90D/oB6R/4Bx/4VH1Z 9w9r5Hy3RX1J/wAK90D/AKAekf8AgHH/AIUf8K90D/oB6R/4Bx/4UfVn3D2v kfLdFfUn/CvdA/6Aekf+Acf+FH/CvdA/6Aekf+Acf+FH1Z9w9r5Hy3WLouia zp3i/XLq713+0dF1D7PJp9hLZxpLpMioUmVZkx5kL7Y5FWRDIkjT/vXjeKKD 6+/4V7oH/QD0j/wDj/wrmPjJ4T0nQvhvqd1Z6TpdvcRiNVkWzj3LukVTjjg4 J56jqOaPYNa3D2qfQ+Fv2RvHGqWf7KXwxhTwb4kuki8JaUizRT6eI5gLOIB1 DXKtg4yNyg+oHSvQ/wDhYGrf9CN4p/8AAjTf/kusD9jf/k0T4V/9ifpH/pFF XpFYNrsaI5b/AIWBq3/QjeKf/AjTf/kuj/hYGrf9CN4p/wDAjTf/AJLrqaKV 12Gct/wsDVv+hG8U/wDgRpv/AMl0f8LA1b/oRvFP/gRpv/yXXU0UXXYDlv8A hYGrf9CN4p/8CNN/+S6P+Fgat/0I3in/AMCNN/8AkuupoouuwHLf8LA1b/oR vFP/AIEab/8AJdH/AAsDVv8AoRvFP/gRpv8A8l11NFF12A5b/hYGrf8AQjeK f/AjTf8A5Lo/4WBq3/QjeKf/AAI03/5LrqaKLrsBy3/CwNW/6EbxT/4Eab/8 l0f8LA1b/oRvFP8A4Eab/wDJddTRRddgOW/4WBq3/QjeKf8AwI03/wCS6P8A hYGrf9CN4p/8CNN/+S66mii67Act/wALA1b/AKEbxT/4Eab/APJdH/CwNW/6 EbxT/wCBGm//ACXXU0UXXYDlv+Fgat/0I3in/wACNN/+S6P+Fgat/wBCN4p/ 8CNN/wDkuupoouuwHLf8LA1b/oRvFP8A4Eab/wDJdH/CwNW/6EbxT/4Eab/8 l11NFF12A5b/AIWBq3/QjeKf/AjTf/kuj/hYGrf9CN4p/wDAjTf/AJLrqaKL rsBy3/CwNW/6EbxT/wCBGm//ACXR/wALA1b/AKEbxT/4Eab/APJddTRRddgK ngjxjp9/+z14I8aa1DBHceKNHtbp7a3vrhri4u3hMkkVtaw2c80uFSRwqF2V EcnhGataz8S+ErzQNO1T+2vCUFlq09ta2r3Hi8W5knuIUngg2yWKsJnhkRxE QHKup24NeQfC+wvfEnwF+DEtrHdlvCujafqNrJDLGo842MsLKwfOUMVzIDjB 5BBBFZniv9nS48cXmnT6rceI7t7HxNZeLJN1zZAXt9a2lvaxtIBHjaUtwWVN oJkfGBtC3p1IVz2/SvGPgnXLK1ubLxN4Du4L6WGC3kg8dRSLPJNJLHDGhFnh mkeCZVA5ZonAyVIEEvxA8CpdaXEniTwbcHWr+20yza28YCdJLi5ge4t4yyWJ VDNFGzR7iBJlQpLOgbyx/g7P9maKO0vkEraz526S1mW4TVZ2uLmORJFaN1Eh UrlcgRqrFlLq8HhX4GzeD9T8MXlpDrBn8Jafp2nWJku7cgx2Vtf20RcADJaP UbjdjAJCEBcHImgsz1DxF8UfBvhDWfEljqs8FhJ4Shkl1OSW/vhbxlLMXzxR zf2Z5c8wtT5xhhZ5NgZtvynGzo+s+FvEV7YW+na14Mv59VZlso7fxrHK92yw pORGFsyXIhljk+XPySK33SDXivj74GTfEiLXY9Ri1cReIby5v7lIrq3ULNPp H9kMyEgkYtt20HI3sSQRgCrJ+zulz4Ci8Nmz1I6Smg2XhuWJri2f7RbWkvmQ ucqQswLSHzI9jZl3DDpEyF0FmeyWXxD8Cajqd1aQ+JPBjvZ6SNdlk/4TECAW BmlgNyJfsPlsiSwyI5Vj5Z27tu9c62mXnhrW9dttMstY8G3mo3ql7e2g8aJJ NOohjnyiCy3EeVLFJwPuSo3RgT49rHwx1LX9Se8uhq0lzLFo8crie2TzP7Mv WvbZtoUKCZXYOAACrYAU4I5DxT+zLqD/AAs8T+H9FOr2d94t12PXbnU2v4Uu 7W4JgWeWB0X5C0cUnygAHzpEBRG+UTQan0b4r1Twr4C8OWmsa9rngvRNHv45 Zba/v/GsdtazpFazXkjJI9kFZVtbeediDxFDI5wqMwm+JMnh34M+Cb7xL4w1 Xwl4U8O6bs+16prHjFbCytt7rGnmTS2Som6R0QbiMs6gckV4T+0P+zT4a/av +HWn+EviH4C0/wATeGtKme4s9Onn8q3tpWs7izDqsci7WSC5mCEYMblJE2yR xus+tfAawuPhDpHgTRPC9p4O8K6Frel69Yab4eitNPtba4sNVg1WMJEi+WqS XMAMgVQWDvghjuCuh6nsHwf8V+C/2hfDM+t+APEvgTxzo1tdNZTX/h/xzFqd rFcKqO0LSwWbqJAkkbFScgSKcYIq94Hl8O/E7+1/+Ea1Xwj4i/4R/UptG1T+ zPGS3Z0y+h2+daT+XZHyp49y7omw67hkDNeWeAPCWq/DnxR421eyt7+S78e6 3Hr2oiW5hMcc6adZacqxAYITybCEkMWO9nOcEKtD4GfClv2d9E16y8N6O8H/ AAk/iPVPFWqXDtbLPfX+oXclzNJIyBd+3zFhQtllhghQs2wGhvsCR7CJ/Dp8 QDSf7T8J/wBqnUxov2P/AITFfP8At/2P7f8AZNn2Ld5/2P8A0nysb/J/eY2f NXG+OPjNoXw+1i/s7/QNSY6dDcXEksOsiSNo4ColdT9kBO0svGMnPAODXm3/ AAzu4/apHxm266PHY0z/AIR77T9stvJOi43/ANl+Vt2+R9r/ANM8z/j5875f P+z/AOj16zpPws8PfEfwdokvivULrTNU07WZtUeCJS4nxczFIpSqsHidGQso IJ4BIIIoug1MH/hoPw6upNZvoGoRXYmNusUmuxI0sgRHKoGthvIV1J25xn64 Za/tHeF7+VVg0a7nV4lmDp4ghaN1bzcFX+zbW/1E33SSPLYnAFdH4e/Zn+Hf hvRreyh8S6vMsMlvI8s6tJLcGC9+2R72MXZyyZXBKEA5KqVksf2afhrZT2rn Wr+YW/h+Pw0ySRsVuLRFcYcCIfMxZSWGOY1AwC4Z80RcrOcsP2hPDGqsBa6R PcZQyDy/EcDkqFViwxb9Aroc+jr6iiP9oXwzLDFIujTsk8L3ETDxJBiWNPvu p+z4Kr3YcD1rT0L9m7wpbQazbXXi/VxDNq0moabdxSzSX0KyaYtkd8rxFt6M 0xQgnAERJPIrTH7O/gGM3xi8TapEb+RJXIh3FWS7ubxMFoT92a5Jwc5WKNTk b95eIJMxrL4waXqVpFcW/hXVZ7edBJFImuRMsikZBBFtggjnI9al/wCFp2P/ AEKOs/8Ag6j/APkavQNH8LeDNFtGhj1VGVppZubN0wZJGkIwsYHVzyRk9SSS Sbf9meEP+gnH/wCA0v8A8TUuXYqx5p/wtOx/6FHWf/B1H/8AI1H/AAtOx/6F HWf/AAdR/wDyNXpf9meEP+gnH/4DS/8AxNH9meEP+gnH/wCA0v8A8TS5gsea f8LTsf8AoUdZ/wDB1H/8jU7WPivo2h+G4tRuPDWsIrzvCynV04I8oIBi2yzM 0u0KBkkADJOK9J/szwh/0E4//AaX/wCJql4q8I+C/F/hqLTptWkt0hu471JI rdw6SRyxSoRlCPvxLkEEEZFNMTR5VD+0f4Ymt4pG0LULbzkmlVLnXI7eTZES JWKPahgE2tuyPlxzSy/tGeFrcOX0mVFjUsxbxLbgKBJ5ZJ/0fj958n+9x1rq vG/7PHgD4g2esQaj4m1Vk1qCO2m8uHYUjiluZYgpEPGxrkgHqRDGG3fPvn1P 4BfD3VvFEury69frczXcd4VSJhGGR9PcADyicE6dCDzn95Lzyu2rxFys5Jf2 gvDTwLKNFuSjSxwAjxHAQ0kiho0BFvyzKQQByQQRnNMj/aM8LSWT3A0qXyIz GHk/4SS3Kp5mPLyfs+BuByM9R0roLj9mb4fz6YtovivW4YR4ci8NOsakLLBG kiLKw8nBlxK/JG0Z4UHmrVh+z14A0qbRpbbxLqcUuh2VrZW7+TuJW3t7uBHY GEguVvJGPGNyJgAAhi8Q5WclD+0j4an02e8Xw7q5tbbTo9VeT+1xhbWRXZZM fZM4Ijfjr8vTpUV7+094T063lluNGvoEt3Mcu/XolMWJzblmBtshRMNm7G3P fHNdRe/sr/CrU9B+w3d816U0KDQLe5ubMSz2cUKyqs0TGH5Jj5pJYDGUT5Rg 5dbfsz/Duz1S7uovEuso19ftqM0QDCFpjexXisYxFtJR4tiuwL+XIyFiAm1p xDlZz13+0P4YsHnWfR54mtWKTK/iOBTC2xpNrf6N8p2Izc9lJ7HF+1+MOlXg HleF9UlyiyDZrsTZRs7WH+jfdODg9Dg1vQ/s/fDyLxPpOqnXb+SbRby8vbaN 4mMe+7ed5QwEQJGZvlycr5aYPL7pPhL8JfCfw8t9b87WY55NV1W4vIlEMrra 2xciGBD5a4RVGQuMIXZQWxuaW10GkzC/4WnY/wDQo6z/AODqP/5Go/4WnY/9 CjrP/g6j/wDkavS/7M8If9BOP/wGl/8AiaP7M8If9BOP/wABpf8A4mp5h2PN P+Fp2P8A0KOs/wDg6j/+RqP+Fp2P/Qo6z/4Oo/8A5Gr0v+zPCH/QTj/8Bpf/ AImj+zPCH/QTj/8AAaX/AOJo5gscH4O8d6V4n8d6Ho0/hrWrNdbuxZC4/taK QQkqzbiptxkYQ9+4rrotE0PTfB41fVrgWltDZm8ubia/FvFGPOmjwB5Dk/6s d85YACrMGiaDJ4z8LTaZeJc3FrrUEhQROhCkMhOWUDqw4rm/iX8Jte+L/wAP 9J0zRrK4uo0s47h57e6hhlsp47+Wa2mXzSFLJLErgEMuUAYEEg6RV+hDdj2n 9mG+g1HwHeT2qzpbTX7yRLPDJDKqmOMgOkiI6tjGVdFYHIKqeB6PXnf7Nltd WXgzUIL6MxXkWoyJPGSp2SCOMMuQSDg55HB/WvRK7YbGEtwoooqhBRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABXMfGW0W8+GerKwGFiEnLbfusreh9OmOemRnI6euS+ONtc3f wt1WOzjMsoWN2AYLiNZUaRuSOiBjjqcYGTxUy2Gtz5k/Yg+HVlqX7FfwgnkC l5vBWjSMf7U2HJsICRj7McfTJx6nrXqH/CrNO9F/8G//ANy14T+xb8P/AIj3 37HfwnmsdCeayl8G6PJbyfbbdd8ZsYSpwZARkYOCAa9M/wCFafFD/oXpP/A+ 2/8AjlcXJPsdHNHudV/wqzTvRf8Awb//AHLR/wAKs070X/wb/wD3LXK/8K0+ KH/QvSf+B9t/8co/4Vp8UP8AoXpP/A+2/wDjlHJPsHNHudV/wqzTvRf/AAb/ AP3LR/wqzTvRf/Bv/wDctcr/AMK0+KH/AEL0n/gfbf8Axyj/AIVp8UP+hek/ 8D7b/wCOUck+wc0e51X/AAqzTvRf/Bv/APctH/CrNO9F/wDBv/8Actcr/wAK 0+KH/QvSf+B9t/8AHKP+FafFD/oXpP8AwPtv/jlHJPsHNHudV/wqzTvRf/Bv /wDctH/CrNO9F/8ABv8A/ctcr/wrT4of9C9J/wCB9t/8co/4Vp8UP+hek/8A A+2/+OUck+wc0e51X/CrNO9F/wDBv/8ActH/AAqzTvRf/Bv/APctcr/wrT4o f9C9J/4H23/xyj/hWnxQ/wChek/8D7b/AOOUck+wc0e51X/CrNO9F/8ABv8A /ctH/CrNO9F/8G//ANy1yv8AwrT4of8AQvSf+B9t/wDHKP8AhWnxQ/6F6T/w Ptv/AI5RyT7BzR7nVf8ACrNO9F/8G/8A9y0f8Ks070X/AMG//wBy1yv/AArT 4of9C9J/4H23/wAco/4Vp8UP+hek/wDA+2/+OUck+wc0e51X/CrNO9F/8G// ANy0f8Ks070X/wAG/wD9y1yv/CtPih/0L0n/AIH23/xyj/hWnxQ/6F6T/wAD 7b/45RyT7BzR7nVf8Ks070X/AMG//wBy0f8ACrNO9F/8G/8A9y1yv/CtPih/ 0L0n/gfbf/HKP+FafFD/AKF6T/wPtv8A45RyT7BzR7nVf8Ks070X/wAG/wD9 y1h6lH4J0jxpb+G7rxDoNt4iu/K8jS5fE0SXs3mx3UkQSE2+9t6WN8y4HzLZ 3BGRE+2j/wAK0+KH/QvSf+B9t/8AHK8e0/8A4JbaRov7REXxY074K+GNK+Is dw14dd097W0uZZ3jvIpZpPKlUSSSrf3PmswJlJiaQuYITGKEuqDmj0Z6l43+ Jnwj+GnxK0fwZ4j+IfgPw94w8Q+R/ZWhan41tbPU9T8+Uww+RbSQCWXzJVaN dinc6soyRir3xg8R/Df9nvw1BrPj7xj4S8D6PdXS2UN94g8W2+mW0s7I7rEs k0CqXKRyMFByQjHGAaD8Mvihxt8OyAjub+2J/wDRlcV+0D+xTr37U3w3uvB/ j7wK/iHwxfbvtWmyaykMF1mKSMeYIp137PM8xN2fLmjhmTbLDFIj5H2FzLue o+JPCvhzwfp0d3qt/Yabay3NvZRy3evJBG9xcTJBbwhmtgDJLNJHEi9XeRVA JIFcz8SNa8MeDvgl4q8a6dJbeJLHwvb37yR6d4hR/NuLN5Ip7feLUhXjnhki cHJjeNwRuUrWF8af2W/H/wAePhXrng3WtE1CPQvE1q2narFbajZo97ZSELcW pZmJVJ4fMhdk2yKkrGN45Aki8J49/Zu8U/s3/sKeJfCo0rUIPCPhbwlf6fYG 6v7adtPskt5UtbUGMhnjhTyoEZw8jJGpkeRy8jCg+qDmW1zg7r4qeOfhh+yB 4OPgHw94f8Ta9qXgF4bKy1y7exsZLtdPha3DXSgGGQsG2AvEJD8nmwbvPi9e 8NfEGXxd8S9es47S0trCz07TLorZ3bX1pb3k0coubeC5aKIzRI0SkMyK5MjE hQVjTk/2eNbFj+zL4R1PVPFOi+G9NgttI0K3NzpXnedPLY6d5SmRruFd8s17 HFGgUlmKgZLAV0XxO+KvhP4J+HI9Y8Z/FzwX4R0ma7FhHe61pkNhbyXBVmEI kl1NVMhVHYKDnCMccGvH/suaxjxKqaN3t/26o2ve1vtWte7ep1/WI+yUOXXa /wA2/wDgb7I6zXPFvwkvPBS6TrXxJ0zw/q/msJnXXbe2uI5VmyY3+dSqjbsw NjbM7mLHfXnvxb+Kdt8M/gc/jLXbSPUf7G0O/wBVa0SziuWu7pNIu5IYYYJi UlmecRrFGxO52Re9dR4J17TviZ4Zttb8N/ELQfEOi3u/7PqGmaAl3a3Gx2R9 kkepFW2urKcHgqR1BrWOhXeP+RqtuTjB8LHn/wAqNTkeW18DWlUxFZ1L7dLb +b7/AIDxuJVan7KEeVXb79vJPp3Pir/gl1+1f8aPjj8W/E1j8WfBHhfR9N1L Q4r/AMK6z4W06Cws7+2truZZH1G0S6lksb2Vby28qGaGJ2jtpgwLQkn68+CV zYWOgyXGqxGfS7fX9UkvIhbtc+ZCNTuC6+Uqs0mVBGwKS2cAEnFbP9hXY/5m m3/8JU//ACxqro3gh9As2t7XxLbxRPPLcMP+EXdsySyNLIedRPV3Y46DPGBX vTrc1RVPTr2OGNO0OQ+cvhx+0T4kuv2q7HQZ7PRL3QZxNpsM2j6e+my3++CK 4a+ktVtocW9u8a24knWNi91L5TTBJFj+k9Vn1CK900WdrZXFrJcMuoSTXbwS WsHkyEPEqxuJXMoiQozRgI7uHJQRySf2Fd/9DTb/APhKn/5Y0f2Fd/8AQ02/ /hKn/wCWNZ1qjqS5n/SOXAYWrQU1Uqc922tLWT6Fmiq39hXf/Q02/wD4Sp/+ WNZ3iLUYPCEcU+reN9N0uwZLqae+u/DYhtbKK3s7i8llmkbUgEQRW0hLHgHG cDJGajc77m1RXB/CH45eB/2gl1A+AvjN4E8b/wBkeX9u/sDT7fUvsXmb/L83 ydUbZu2Pt3YzsbGcGuz/ALCu/wDoabf/AMJU/wDyxocQTLNFVv7Cu/8Aoabf /wAJU/8Ayxo/sK7/AOhpt/8AwlT/APLGlYLlmiq39hXf/Q02/wD4Sp/+WNUP FUsvhDwtqer3PidHttKtZbyZYvChMjJGhdtoOogE4HqKFG4XNiik1jwhf6Jr F1ZS+K7RprOZ4XKeFmKkqxUkZ1AcZFV/7Cu/+hpt/wDwlT/8saLAmWaKrf2F d/8AQ02//hKn/wCWNc34Q+I/hz4g+J9d0TQPin4T1zWvC8y22s6fYaRFc3Wk Stv2x3EaamWhc+XJhXAPyN6GiwXOtorhPi38RP8AhUF74XbUfFWkW+la3qNx aX+oXuh/ZIdIt4dMvr97lma/KlQLPadzIFWQuW+Ta2v8PfF2j/Fzwja+IPCf xJ8OeKNBvi4ttS0jQ472zuNjsj7JYtSZG2urKcHgqR1FPle4cx0lFZehNH4o 8P2erab430vUNM1G3ju7S6tvDYmguoZFDJLG66iVZGUhgwOCDkVV8Pa7p/i4 QnSfiFoWqLcWMGpxG00BZvNtZ93k3C7dSOYpNj7HHytsbBODRysLm9RWTrcs fhqKN9Q8b6VYpNLFbxtceHBEJJJJFijQFtRGWaR0QAclmAGSQKdrjx+GfD93 q2o+N9LsNL0+3e7ury58NiK3toUUs8ryNqIVUVQWLEgAAknFHKwualFVv7Cu /wDoabf/AMJU/wDyxo/sK7/6Gm3/APCVP/yxpWC5Zoqt/YV3/wBDTb/+Eqf/ AJY0q6BeMD/xVNvx1/4pU/8AyxosFyxRXlPwF/aP8OfHLTNAsofiT4Ot/HOq eHbHxFfeE7awiu9T0uG5t4JgXi/tFZfLH2iMCRokDB0OBuAr03+wrv8A6Gm3 /wDCVP8A8sabi1uFyzRVX+xrncFPiu2DHkKfCrZI6Z/5CHSsvQNf03xXqN5Z 6V8RNB1O70/P2qC00BZpLbE89ud6rqRK4ntrmLn/AJaW8y/eRgDlC5vUVW/s K7/6Gm3/APCVP/yxo/sK7/6Gm3/8JU//ACxpWC50HgD/AJH3Q/8AsIQf+jFr zv8Ab4g8bz/sPXo8F6XNrcKrat4h06G2a4lvdM87UQyKigsyic2zyAcGJJQ2 ULg+i/C7wteT+NtPuD4gtr2OwureeSD+wGtTKDPHGAJPtkuCGkU8ochSOM5p fCuveIdX/aE8CeDNL8Tan4Z0rV/BGs63eS6fbWktxNPZ6rZQwruuYZlVNt/O SFUEnbzxg92Aq+xrwq2Ts72fU58RHng47XOw/wCCdF8uqfsn+FblJkuEuLC0 lWVCSsgaztyGGVU4PB5VTz0HQe51xHwMsZdN0vXoJ7ufUJoNauInup1jWW4K hAXcRqqBmIydqquTwAOK7et5T55Ofd3M0rJIKKKKkYUUUUAfIH/Bar/gq0P+ CPn7LOgfEs+Av+Fif254qt/DH9m/23/ZPkebaXlz5/m/Z592Psm3ZsGfMzuG 3B/MH/iOcH/Rrx/8OR/9669//wCD1b/lFl4B/wCyq6d/6aNYr+YGgD9//wDi OcH/AEa8f/Dkf/euj/iOcH/Rrx/8OR/966/AWxsZtSu44LeKSeaVgiRxqWZy egAHJNfXv7Hn/BJTxb+0DcW95q8F3Y6c0QuJlGIIrKM/x3dy48q2UdSGy/bA OcAH6hWv/B8dLfShIP2WJpnPRU+IpYn8BpdbkH/B6Nr0lu0jfsk30agZQN8Q iDKf7qj+yuTjJ+imvmKH4W/sx/sUaAdKt4tb+M/ja1UyT2Pg6aG10S2bt5+q zK4kxxnyFkXGQWU8V5l4k+Mmsavqvh6+tPgt4Be2TVZZ7SwGtzTy3f8Aod0v 2aWQOAQFYyDaoJeJR0OK68DQjVrck9rSf3RbX4oUnZH2xqf/AAe6ahoozd/s n3tsM4zJ8Qio/P8AsqqH/Ec4P+jXj/4cj/711816d8UPgn4+s0i+JPwg8Z+B 3aL59Q8M6lBrkMZ/2rSdIZNoOMmOSQ4DYB4rgPi1/wAEk/AnxjlvNQ+Dviyx 8a7kNwLbQtw1C2Ugt+/sJlWZcfxbQMH8K5Bn2n/xHOD/AKNeP/hyP/vXR/xH OD/o14/+HI/+9dfiL8df2YPFfwC1iaHV9OuWs4m2i7SB1jU9NrggGNs8bWAN edUAf0f/ALJ3/B5QP2oP2pvhp8NP+Gcjof8AwsTxVpfhj+0v+E/+0/2f9tu4 rbz/ACv7NTzNnm7tm9d23G4ZyO//AOCo3/B1qP8Agmv+3X45+Cn/AAoY+NP+ EL+wf8Tn/hNv7O+2fatPtr3/AFH2CXZt+0bP9Y2dmeM4H4Af8Enf+Upv7NP/ AGVXwv8A+ne1r6A/4Ojv+U6/xz/7gH/qPaZQB+gH/Ec4P+jXj/4cj/710f8A Ec4P+jXj/wCHI/8AvXX4AU+CB7qZY4keSRyFVFGWYnsBQB+/n/Ec4P8Ao14/ +HI/+9dH/Ec4P+jXj/4cj/711+PP7PX/AATv+If7QF1Ctpo+qWizOFSJbCS4 unGRyIVwQPd9o96+uPAP/BIr4cfBuNtT+Lnj/wAJeBba0QySW/iXV4ZtRuBj pFp9qWnJyCBhZME96APtWx/4PiLjU5RHbfsrXFw5/hj+IhY/ppddBZ/8Hmni XUwhtv2Rb+4ZxkqnxDO5DngEHS+uMH6MK+KIvjB+zv4AtZbL4d/B34q/Edk2 hdRuHg8O6c0mOdqJBPLJHnJyxjbGPlB6M8AfFDxdY674kn0n4PfCSOK41aOe UapqF1cvY/6DagW6NHKuQVCysxGd8jjgAAdeGoxnTqylvGKa9eaK/JsluzR9 u6l/weWeLtIi33P7HWsxJ6nx+xH6aVXNXX/B8dLYyFJ/2WJoXHVX+IpUj89L r5s179ozxCunQQ63+z94T1GKJyWn8O69PbzbM52gSBx+JB9aoamv7Mf7UGlz aFrmn+MPhJ4wnXENp4rhgu9OncDhY7+JYijEAY3qoycZPAPIUfTv/Ec4P+jX j/4cj/710f8AEc4P+jXj/wCHI/8AvXX5M/tj/wDBO3Wv2fbs6josV1qegvH5 odXW4+Xn545EAWSPA7cjuK+ZjxQB+/8A/wARzg/6NeP/AIcj/wC9dff/APwQ y/4LnD/gtH/wtHHwuPw2/wCFa/2T/wAzJ/bH9o/bvtv/AE6weXs+x/7W7zP4 dvP8gNfv9/wYx/8AN0X/AHKn/uaoA/f6iiigAooooAKyPiD/AMiDrn/YPn/9 FtWvXPfFaxl1D4c6ykN5cWLravIZIVQsyqNzR/OrDa6qUYgbgrnaythgmNHA f8E9f+TBvgf/ANiBoP8A6brevYK+Zv2C/hPr+o/sOfBm5h+J3jewhuPAuhyJ awWmjtFbqdPgIjQvYs5VQcDczNgDJJyT6z/wpvxJ/wBFZ8f/APgHon/yvpiP QKK8/wD+FN+JP+is+P8A/wAA9E/+V9H/AApvxJ/0Vnx//wCAeif/ACvoA9Ao rz//AIU34k/6Kz4//wDAPRP/AJX0f8Kb8Sf9FZ8f/wDgHon/AMr6APQKK8// AOFN+JP+is+P/wDwD0T/AOV9H/Cm/En/AEVnx/8A+Aeif/K+gD0CivP/APhT fiT/AKKz4/8A/APRP/lfR/wpvxJ/0Vnx/wD+Aeif/K+gD0CivP8A/hTfiT/o rPj/AP8AAPRP/lfR/wAKb8Sf9FZ8f/8AgHon/wAr6APQKK8//wCFN+JP+is+ P/8AwD0T/wCV9H/Cm/En/RWfH/8A4B6J/wDK+gD0CivP/wDhTfiT/orPj/8A 8A9E/wDlfR/wpvxJ/wBFZ8f/APgHon/yvoA9Aorz/wD4U34k/wCis+P/APwD 0T/5X0f8Kb8Sf9FZ8f8A/gHon/yvoA9Aorz/AP4U34k/6Kz4/wD/AAD0T/5X 0f8ACm/En/RWfH//AIB6J/8AK+gD0CivP/8AhTfiT/orPj//AMA9E/8AlfR/ wpvxJ/0Vnx//AOAeif8AyvoA9Aorz/8A4U34k/6Kz4//APAPRP8A5X0f8Kb8 Sf8ARWfH/wD4B6J/8r6AO+kOAPQmvhvxO3x1b9h34+/8Lu7eHMeFvJ/s/wD5 Bnlz7P7S+zf8x/p9v+zf8Sv/AI9fsX/LzXqfx61fxV8GtRd5Pi54ntdLtdPN /dXl/aaKkdsil97u5slVEVU3EngAEk46eI/tRfE3xldfCbxjoGreNNe1qw1H wdrV5LDcWumxRMYIY1VW8q0STrOGyrrzGAdwJFZSmvhLUHuefaH8K9e+MP7E XhDR9A8V/EDwZcC6hml1LwloJ1a5dP8AhG9OiET5t50jUvKkgYqHJh+RkYb1 734gfCWy+KPh2LS9X0n4xW9tDftqCvoeneJdBuC5R0CmexEMrJhyShYoxAJX KqR518OPhTafFT4L/CeLV/iRpXw70LTJlv7uXUIrZo9QMeh6J5cKtOQofzGS XvkQEFSCwP1Jof7TNzpHjDw1o3g/UtC1Xwn4j+J0vhO2nSJtQistOTwlcau0 ds0Ei8i8tigyXWNHaMKAqBJ9jeL3tZP5ttNLrpZffpsa8yUEk0229OqstG+m t356a2PiX9mr/glt4Z+CH7NV/wCAfEGi+KPH2oajbz2P9u6p8KrmaWztHl+0 RWkMNxbTobeK6Aulhm82I3DSSFDuwPqi4uri4v8AR7o6B8Qkk0a9jv41Hg7V DHLJGDs3q1sQwVysgB6PGjZytdF8Svjl8ZfEGgeCLaPwxe6JqOsaz4cu9btd O0rUGuNDjGqeFjf2oulSS2ubcQ3uriWd3t2McLKkMi291MmF8DPjb8afCnwL 8D6Lr1rqunXFl8JfDeoXWr6v4F1vxDqV1rsuka69/Bci3mWUzQy2GnSOjDzX eVrfPn31sY9HQctW+vkSpubuWLjxFPNdSSL4W8eRI5JEa+D9WKx8k4GbcnHO OSTx1NN/t+4/6Frx9/4R+q//ACPXU/HLxj8UfiF8GLbR4/D2ueH21fxVa6Jq Q0q6votYi0keKtMsWuIri2QeWlzpEt5dPOk0M1qrLsy0Us0Xpnxb8b/ELwv8 XPBVloFppl74Y8Q6wNN1N5NFubibSYE0jWLprp5knVFR7q30yFd8YC75F3O8 8ZizWGTVxRquWx4V/b9x/wBC14+/8I/Vf/kej+37j/oWvH3/AIR+q/8AyPX2 Lpkz3NhDLInlySIGZcY2kjpirFH1aKFGs2rnxl/b9x/0LXj7/wAI/Vf/AJHq Kya/1nx74cmh8N+Nlh0yTUbq4ebwvqMCon9j6hGMF4VDMzyIqquWZnAAJNfa VV78N9il2bt+xtuOucfzoWHSY/aN6H5s6V8Etc8J2Okabp9l48uLKbxLrer6 27+ENathPZX+o3WqLboiRlWkS5ktoi8gZWt/tigIZwA2D9m2GDxTpt/aeDLj S7PStXOvWlvZ/DDWba7tL2R7Np7hbiEp+/mEeoJNIEAmS8RJFdEnS7+0fgxq PijUfE8NlrH9rxW3g2JtFeSZ7g/2zKyiVLmUy20SzMLX7EfOgkaIXFzfQkb7 fiX4hW2u/DHw/wDELxT4V0fX/FXiSeGXU7PRp9Zujb6jc29rGkFpbrM7w2iS mNVbyo0UuzyMCzOzXGglqn5/1+YnWco3f9f1Y8M/t+4/6Frx9/4R+q//ACPR /b9x/wBC14+/8I/Vf/kevqrwJrura1PrC6nBaRw2l3FHYSxRXET3UDWsEjSS Ryovlv5zzLtRpVComXDl406Os/q8QVZnxl/b9x/0LXj7/wAI/Vf/AJHrA+Kl xqHiH4Y+I9Os/Cvj6a81DS7q2gj/AOEQ1Rd8jwuqjJtwBkkcmvuyimsPEHVZ 8l/EXXZk+IOuqnh/xtOq6hcAS2/hXU5opP3rfMkiQFXU9mUkEcgkYNY39v3H /QtePv8Awj9V/wDkevs2vD/25/g7qPxq8B+GrGw8WeJfCkGla8uq340R9ein 1i1htLky2Zm0a8tLqHep3I5kePzo4VMMzMkbL6vEPatHkX9v3H/QtePv/CP1 X/5HryPxx+y9onxK8C+KfCniLwz8QPEXhPxletfappGteFtf1SBnklLyiE3K yNbLtEXlJbmOO3kTzYlRiAPsT4M/A2w8K/tAeOvFdne/EIG42WBs9b8U6/qF gbiUJeXE9rbXd7LYiHEtvFGbW3i8loLqMOVYonjv7JPwo+Inxl/Zg8Fad8ab v4kWGqX/AIH8PwNHpniXU9Glt8CNrk3N5by2+onV5JLeKW6aYRpGk4toTLi/ muajQV7p9v6/AarOzf8AWx8tfHr9kfUvHNnc6TYeHvFVkvjvUtUj1e+0v4d6 1ClmLjRdfs4r2eFi0c0yx3lhA8iiN5TbruYRiGG39G8dfAS7+IvxbufE2s6T r+rW0UunS6TpepfDDU7230eWyl8yG8iMiErfg3GoqtzF5eFmtRsJtmM/3L+z hosvh34Wx6U0+sTxaPqWpaday6pLqVxeSW0N9PFAZJtReS7nbykTM8jssxzJ ETC8ZPzd8MfgN488S2Pwz1Dxb4++J9xP4C8a+L2vbW+bXbW78W2x8TSR6bPc SaXc2dqEWJbZ0iuLaa0e1llEcEUCs6Doq6TYlVaTseLfsj/saeGf2RPDdla6 N4P8U3Op2Wk22jJqafDm+tLiG2jtYI5YImjttywTXUUt68TMwNzd3Ehyz7qk n/ZPstN0K4sNO0LxhqUF7qcmpzQ+KvAOsa5BbkJI9pBbIVjMFva3hjnhiBZY gJVjETOksX1J4eiu/EnizRtW1jV/jLpPifUdNe4sxHY38Vho0GpjU7pYLi2j STTZbuzit1g3XCStHJBaA4N4qz6Pw++BPiDw5OfC2leLvHkHhK2XUNU0jUr/ AFe8vtc8PST20dpbQNc6g11/aa+Y+p3u2+MvkO1khhCRRqCVO122Htna1tP+ G/yR8afEn9iXSPitYa7bav4PuZV8Uazpeqa5N/wqfU3ufEKWOrrexwX8jqxu l+yxxWq7xtT96yrsdYI+f+MH/BO7TfjdonjGz1vRNZlXxTo+qWiwn4XavLZR apcu6WmtyW0u+OTUbW28qBbhBFM6iQeYsZhht/0G/Yxg8Vn9k34KS+LrjxGf Ev8AwhOkp4gg1YP9tN9/Z8Jne6Mi+d5/m7w/mHO4ncN3NegeJ/D2oa3Nqdna 65rGjf2jpssFveWcdu8ulSsNonhE0UkbSrkMBMkiZUZQruU3Kjyddr/1+A5V W9+n/AR8s/2/cf8AQtePv/CP1X/5Ho/t+4/6Frx9/wCEfqv/AMj1s+JtJ+Ju uf8ABFKOK11b4iaP8XovhBb3Bns1dvE8msR6SkjwHzY5JvtU06NE5Ci4DSOY 2jmCyL6t4c+Cslj+2Z4h8arc+OYbOXw7bW4im8Y6jdaDqFzNKVl2aTLI1pay 28djbFZbdUMn9oXG8FvmMSw8VJq4Oq0eIf2/cf8AQtePv/CP1X/5Ho/t24OP +Ka8ejnv4P1X/wCR6+zaKX1eIe1Z+R/wl/ZI8S3f7J8Phe4ufit4NvvFh0PV dWl03wRq1vq2jSWWh6ZZrb284j2pKLrTYZGlKOpiMsOwM4nXvdQ/ZC8JWj2o 8P8Aw0bQLJNKPhy606H4S3Zs73R5b2C6ubBglskiwvsugIlkFuXvHeaC4KhT 7Cnwd+LXiD9vL4i+L0PiHSvB3h/xZp8lhrFx8QfEOwaZDomly3dnZ+FUgbSr +K4kN5B9qaUSxTXE0iKZrWMN6He/A/WPifoV14b+IF98So31sx+MbWbRfG2q aZNpF626G60dLzSFs/8AQ7ZZIPJSaVmuTJNJ5ebbcHCknaz7fkv0/L5hKq72 Z8meC/2S7Twt8KtU8P3/AIQuNe1PxFrOreI9RvLr4Uan/Z9zqd3eSXdvcy2h UvL9mc26JmfzNttHiSPapXgdL/4JceHrD4j+MvERsfG/2nxZBfXMflfDW9jl 07Wp9U1G/t9fST7OSdTs0vxb29wArxpE2Gw4WP8AQL9rPw34k8F/sC/E/RfA c3jKXxDpng/U7Pw7La6hfX+uPMmnSJbNHcM73c1zvCESF2leT5ixYknzX4pf slvpPxG+HvgXw74j/aFn8A6t4ujuPE6n4geI5Ws400HXXDprJuTqKQyXKacs kP2v7KskNsFjSSaYzVKj0bG60m/66Ox4n+yF+yxbfsg6Mmm6Lo3jAac/h3R9 Lu7Wx+Hep6fDfalZrcJc6vIiQkNc3aS26yMQXxaRhpHAXb7P/b9x/wBC14+/ 8I/Vf/kevXvDv7E/hTS/F+g3qa38YH/4Q6OH+zBcfFXxTNBOVuZrn/S431Ax 337yQqftYlzGqRHMSIg5r4weAPEd58f4vGOlw/FK8v8AQ/E+j6ctva+KrnTN Mg0eZIhcTwacjS6bqUfmySpP9tgivIozdSRTKYNP3TOinK7e4lVb2Ivgdqk1 74qlR9H8T2AAtj5mo6De2ER/0+14DzxIpbvtByQCcYBqj8L/APk+D4Zf9kw8 T/8Ap70auv8Ah18ZPEPjZdEsdYj1G/GowWWoSX6+BNT8N21tKiaHI0Mi30zO kkkl7PIkeGKhJrd2WexnMvIfC/8A5Pg+GX/ZMPE//p70apjFRnyoTd43Pefh H08Tf9h+7/8AZa66uR+EfTxN/wBh+7/9lrrq2jsQwoooqhBRRRQB+QP/AAer f8osvAP/AGVXTv8A00axX8wtpaS39zHDDG800rBERFLM5PQADqa/p6/4PVv+ UWXgH/squnf+mjWK/Df/AIJJ/srQftDfGu51HU3W30Xw5H9pubp03LbooLO2 P72AAP8AfoA9f/YP/Yl8I/AzwXN8U/i/f32kaNYREGSCJWlu52w0djYqwHm3 LAMXcZVFBzjk16rrvxz+KP7Z3h3TPD0ejv8ADz4JS3n2W002whMUc0IdvJe8 csXuZiFG6U7Y97HahGCMjwn4utf+Ci37UzpfTLpvw88GrcR6Bo8DZtgElRfM XpkspHOPuhOSWOPpb4Tz6fosmpfD+/WINps5MCldnn2zHzIpU9Qudhx3X3GQ Dyrxj+xp4f8Ah38NWtlnuTfM0eJBJgXClwBGQOoIOMCrPx7+EvhvQPiT8MIr Qpp8s9+ba5trZ1QyIbK7O8r1yCMbv+mh74r6P13RrB4raW7CyDTh5imToNoy Cfp1/CvmL9qf4k6Be+OfBPiDTo7q9ltPEAjka3hLO6G0ugRGf4h04HpXo5V/ vHyl/wCksmex7tefs9+GB4BvLW30mGRFdJzGy53YPOMjI6ivAf2nv2QD4JWw 8WfDrWtT8O+Lxcxm1FvOUYs/ylUK4ZcbsnkjaDkHFfSvwb+KKfEbQLmcWTWa xMIxufO8EcHGOPoazfiZBZ+EL6PXbuVr17Yn7LAw2pGxH3s5PbPavOKPlXQP 2i/+GnfGbfC39ofS9G8OeLtThNnY+MrxRbQXMmwFIdUXJRlk4VZ0MYDMu7HJ Hw1+3F+xXq/7K/jqWL7BqMWlk4kWddxs5Dn5d4ADxMMMknRgcdQa+8Pix8CJ P2tbjVn1K2+xa5eR+dprgFreSNDzGehJYA59N3HSsz4L6lJ+1n8E/EXwc8a6 dczeOPAUM1hp8skvm3NxphVmS3ZiBvaNt4jfJJGzjBoA+Jv+CTv/AClN/Zp/ 7Kr4X/8ATva19Af8HR3/ACnX+Of/AHAP/Ue0yvKf2Bvhhc/Bz/gsx+z74euZ POaw+LXhcRy7dvnRnVrRkfHbII/WvV/+DowZ/wCC7Hxz/wC4B/6j2mUAfBOj aLd+IdThsrG2nvLu4YJFDChd5D6ADmv0O/Y1/YE8FfATwRa/Fb40areaFotm gkEUCxy6nqsjDAtdMtmI8+TJ2tNny0BZiQBmuK/Ym+CHhf8AZv8Ahbe/Fv4l RXV5GwW10rRLfalxrN2+HSzEhP7iPYrSTT7SERdoDM4B1/Gfi3xd+0J4q1Px 9q+l/b4bEeTa2MXyWPh+AnEdtChGcIuAeO2T3wAereOP29/HPxN8O6j4S+He kp8HPAFzvSCz07dc67exHgtd3nymVjknbCiAZ2krjceB8B/ssWeha3Zave21 5qOp38g8q/15yJJpeoIixnr/AHj2716h8K/hm3gPRtTu/Euo6bJPd2Ss80R2 PZR7exPbnr603XPCmu6rpctjHqMOoTaUkd9o9y42NJknCsecn5SM+lAHB+IN Y1q50S8u9LuraSKyma3lWCMgw7eoUZPGc9q5rw98SL3RLjWb+OcXEUt7GJU3 kE/6LCAxXqPu4zXa+Gr3Ufg5dXcVnpiavp9/On2ieSUKLKZyQ0fQ+ZzycYxm sTX/AAzp9nrHjCw06zn1i8uNQhiZ7eLfFbRG0t2aQYPJLs42j07V6GC/g1/8 C/8AS4ES3X9dGM8N/tFX9ou2yuftEk+5/JYb9uT1z/IV1N1e+GfjZpR0nxLE ljqF0CUmcARkjpyeh6c9PYda800n4TmwkuNYuln06wtyYlt5Yys0sQ+UuR1U 5BOPaux8MfDH/hK7MSWLwRLKrNZPclt5UHGRxkc8556155ZnW3xj8Q/spzx+ CPF1pF4m+GusN5Vtfz7jcaQz5UOsmSpReCVIyRnB7V81ft4fszwfBTxpBqej Ml3oOtjz4rm2+e2YMAysjDswJ4PQqa+wPE/wx1W8+G0+i+Lo7C90y+3QCW3Z nbT3bASTJAyuTz0xivHPBfhaX4k/D7xV8I/EU7vqXhaKS80m4272uLbIMgUH ksmFkA5yAw70AfEdfv8Af8GMf/N0X/cqf+5qvwK1fSp9D1S4s7lDHcWzmORf Qg4r99f+DGP/AJui/wC5U/8Ac1QB+/1FFFABRRRQAVkfEH/kQdc/7B8//otq 16yPiD/yIOuf9g+f/wBFtSY0ebf8E9f+TBvgf/2IGg/+m63r2CvH/wDgnr/y YN8D/wDsQNB/9N1vXsFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFAHzF+3h4bfxtrdtpE3hnw14r0i8sE+3WOtzbbaTZMz xgoYJlkwwDfMBgopGe3zr8ZPCmrWHwh8TSz6N4e0HRtB8CaxpdnaaZeNMsav BEY0VPIiVERbfaAOmQAABX1f+01x4+s/fTk69T+8krwT9pXn9m7x/wD9i3qH 1H+jS9K5p6M2jsjwW3/4KJfED9hz9nv4aWHgnwbpXiGDxYYTeXWpAtFGIdF0 YhYhHOj78MVO9QuZ4SrNtdK9p8J/8FKdV+LPwN0XxbqfhLT9Z8Y6R441DQdA 07QIbR59Vuk08XCzWZ1GSOO2mXTrq7WQtcAMkVwFc+asVSfsqfsReEP2zf2R /DUXiW41jT7vw9LaNZ3mmyQiVEm0LSRLEVniliKsY4mzs3gxjDAFw3p6f8Ek vB2nfCuHwfp/i3xvbaY3iGfxLdzrfLZ39xcSWcVpsjurJbaa2QJCjE27xyOQ yM7RSSxSenUqYd4Hkgv3v4bvrfs/wOaHMq3vfDr+S/I5TSv+CkV1q/wN8GfE CK41tdG8dSaJHp8Emn2q3UJ1aa1itjKmdq7TdxFwGYgB8BiADL8Nf29/GHxF /Zo8P/ES307U7q48QaJY6vBoWnLZzXTvc20Uwt0lmEERIMoXfI0a8ZJUZxv+ B/8AgkF4E8BeGdL0i21bxBeWOkQaLBaQXuqX89tbHSfs5sXitzc+RAytawO/ kxxiZkzIHyQe68C/sFaL8NfA+j+HNE1H7Do3h+0t7LT7byZJBbwwRpHEpZ5m Z9qIoyxLHHJOTXmShLXlv5HZz07f15/8D7jif2Uv21Nf/aX0/RNajsvEGiaL rNzay6f/AGtb2Cvq9hOsckN5GLeSUpFKj8LKY5l2tviTjP1p1rxL4OfsVaT8 ENL8NaZod8tvpPhW3s7KytfIdyltaqqRRb3lZjhFC7mJJ6kk17bWsE1cxm1f QKKKK0ICoruXyLd3wD5alsfQVLUc0SzoVflTwR+FAK19TxFv22vDXhXTbXU/ GGq+EfBmgaZpbT+K9X1jW30+x8P3pvDYw26zXcEEc8Mtzb38azloiTbwkRsL lCu8P2lYvD3hfxxr3jHw7qngTw94Ju7vdqWqXdpPFqOmW0SSPqkYtZZjHAQZ CqSBJsRZaNCwFdzoXgLS/DeoX93ZwSpcandre3DPcSygyrbxWwKq7EIPKhjG 1QFJBbG5mYya14H0nxJp93aajp9rf2l+jx3MFwgkhuEcAMjocqysBgqQQQSD 1NUmhL4bPc87/Y1/bN8GftyfCq68WeC7mKW203UpdI1C3TVNO1Q2F0iRy+Ub nTrm6s5SYZoJf3FxIFEwV9kiyRp63XI/Bv4JeH/gL4ROi+HItUNvLcPd3F1q ur3msalfzMFXzLm9vJZbm4cIkcatLI5SOKKNSEjRV66pBX6hRRRQMKKKKAPP PCfxtm8Q/tK+Nfh6+iSWkXhHw9ouvR6m92r/ANpf2jPqkJRYlHyLGdN++zbn MrDYoRWk4f4Y/t36F46/Zk8OfEy50fXYLfVPD2ja7qOk6Rpeoa7qNj/acEE0 K29taWr3F7EpmdDPDEV3QSg4MUwi9fsPhxoumfEXVvFkFn5ev63p1npN7ded IfOtbSW7lt49hbYux726O5VDN5uGJCqF4rUP2PvA178K9E8F28PifRPD/h7R Lbw7p8eheLNW0a4t7K3a3aCMXNpcxzl0+zRqsrSGQI86b9txOsgmuv8AW9xt qxufBv4qt8ZfhhF4ltNL1LTVvZrtbO01XTdQ0i6eOK4liiaa3vraC5hZ1jVi rwDG87DKmyR+P1X9pzWE8SzW+keC7jWtLXxfF4Shv7e9Zo7k7bOS5vNyQtDF b2yvqcchnmjJudMW1QPNdRoO+0v4SaVo3wtPg6CfXxo509tNM767fSaoY2Qo 0h1BpjeGcgk/aPO84N82/d81cN8Kf2HPAHwV8M+G9H8P/wDCcxaZ4Suzd6Vb 3vjzXdRW3zBHALdvtN5IZLRUijKWsha3R1DrGr/NS6kvYveGvjF4s8V6vHNa eEdHg8OzXBiivrnxAftFxD5mpJHcJFFbyI0Uog02RD5wJi1CUkK1sEnTQv2k R4l+I2reHrHRJbmTT7+/htZo7pWj1i2sbaz+1TWzhfK3x316tk0UskZElvcn d+6Zat6j+yl4KvrjS3gtNa0ZdE0J/DdhBoviDUdJt7WxMTQqqw2s8cYkijd1 in2+dBvYxPGSTTf2b/2TfB37KHhl9H8GnxWmmGCC1it9a8W6v4gWzhgUrFDb /wBoXM5t4lVsBItq8DIO0YUl/KJXMz9m79ozV/2gfh98N/FB8HtoujeO/Bun eJ5rl9VjuI7S6vIFnWwhARZJvKXf5k0iQL89vsEheUQdjb+NtZfxj4hspfDF 1BpulQxvp919tgafXJPLDymKLdtjiUukStNIjtIk+Y0jWKWa18OvhboXwn8A +GvC/h+xFjoXhDToNK0i286SX7JbQQrDFHvdmd9saqu5yWOMkk81t/Y4zdCX bmQKU3Z5wTkj86upZ35fP/gFN7/11/yPLPgL+0w3xh/Z58JfETU9ETwnoviH wbpnjC9vLzVIG0/T47u0e5lgWY7XY2wEZkklhhjKTIysSsiJofA345aj8UNN sz4g8Ia14J1TUraG7hsL2Cd2jBsbG4mjllMKwpNFNePBsDsX+yyspOyVIsTx l+wZ8OPHPwn0HwNdW/i+y8KeGLbSLPSrDSPGut6QLKPSt5sNr2t3G++NnDFy 2+RobdpC7W8JTsfAXwK0H4a6+2paZN4lku30y10ljqPiTUtTR4bdQiSFLmeR DcMqqJLkjz5tqmWSQqMJ25m1t0FJdjtKKKKBhRRRQAUUUUAFFFFAGP4440SH /r/sv/SqKvnD4X/8nwfDL/smHif/ANPejV9H+Of+QJD/ANf9l/6VRV8s6f4T bx5+1L4M0NNU1XQ21n4Q+L7FdR0yYQ32nmXV9HQTwSEMElTduVipAYA4PSs4 pOqlJ2RS2PpD4R9PE3/Yfu//AGWuurzn9mrwnaeAPBF9odhaaZYWOjai9hb2 unWgtLO2jijjRY4YQSIolVQFQEhVAGTivRq1aim1F3S69yWFFFFIAooooA/I H/g9W/5RZeAf+yq6d/6aNYr8p/2Pnb9nj/gmB4o8SRCP7f4tYQxYk2yFEDMQ PUEyoP8AGv1a/wCD0xFf/glx4AVyyg/FTT+gzn/iUaxX5mftR3tn8O/2B/hB 4ejWD7Gj2l5cBGG93nutsqt9Ehi6/wAuvVQwdSrHmi4/OUV+DaZLkk7CfDD4 Iat8GdP0BtLtruW10+zG+4tColib5eWQnLZ+YkDOSx4r6z+FdvcfETwxBfa7 avK1tIHtbmSIwzSDvweRgjHPUHGK5HwxqVv/AMJIbm5uJk0yzhE0aW8gUykD IGc966fS/wBpOz8YeJLHStHSKeafa04ZwwgU9iR344/+tW39mVu8P/BkP/kh c68/uZu/Efx3oXgbRTdeINRsNO0+eRbLfdzCJJHkBAQE8ZIDfgCegNeBftS6 WE8cfDr+x7O8FlLqrJGIbZpUYmzuiWBQHOVOfXapOMAkekftT/B2z+PHw2vd CumeEGRZ4ZsZMUqhgHHPbcR7gkV8o6j4A1j9lXVPDltBqPiXW7O41RTbizCM tpI9leQyNGrN/rtsoZRwuEbJ5r0Mry2ssRvDaX/LyH8r/vEzmrdfuZ9q+F/E vhvwjqOm6c13Bp2reIQI4re5lWGW9aMEsUizknB5x6444rT+IPgKP4k6ELQz PbzQtvglAztYccjI4r5f/Zv/AGLrnw/8XtK8ZazrlxqtxZqZxHOC7+eRgEtk 8AE8djjn1+jviN8Tn+Gmm2F0sBkguLkRTMELMgPTCjnrgV5/9mVu8P8AwZD/ AOSL515/czBvPCn/AAre1W4nvHuL+yiyJ3by4YNwIzjv1r5O8YeNr3wJ+3n8 P/GOnMsE+t7dJkkVWVTMHJilyRyc7cfSvr6L416H4xvpLLW7DyLRGEtu8qsD JjkHacZ/DNfMP7bPxDs5bjwnPkXP9h+LdOna4hi2xQxi4Q4J6Akds559MUf2 ZW7w/wDBkP8A5IXOvP7mRfGbwNa+HP8Agvp+zFqdnZRafb6/8SvCl/Bbo28Q Qz6hpt3Gm7A3BRclQcD7vboNr/gst+zi37Rn/ByV8ZLKZoItPsZfDktxJcfL bp/xINNOZG6BFCs7Z6qhFTfEq4Ovf8FU/wBjO+eTzJrH4m+HNNlPBIZNZtmR foIWiH4V7N/wWDmvfCf/AAUD/bOvtItXk17xnN4U8EWE6sEEL32h6c0uXbCr utrW6jySMGQdskZ/UanP7O8b2v8AHC3381r+V7j5tL/oz4U1vxwfjd8X31Gx sr1tDsgdL0CzhzK9hZg/PcbRwZCuWPHDP/sivo7SdQsvAfiDRvCWi28j2UVp JcXWULGVdhADk9GZuecZrxL9lTw3bzX6zReVPc2VpuSIP5b4JBZoj0LDgHNe zaZdPqun38MNzeSST3KxzXM0YidNhG+Ic5JCgrnGOetaf2ZW7w/8GQ/+SDnX n9zKza59o8LWt3aW9uPsqyyG5uU/49FBb92M4LFcbc8Djr2qjBr0OqaRPcSz Xt5FeQr5szQGOSWPH+rjjHzAHJ5961YvhfrHxc8XadaaWSthaOJXgJ2xFFP3 pD2UkDHXPoaq+Lvij8Nv2XvFN5a6sJ/GXjV5PNuorTa1jYSABViO49RtGRgk c5ANH9mVu8P/AAZD/wCSFzrz+5mXot5qfjCyuJrLQtUvNOVNkMNtp7yRqQT1 O3g8jjHH5034f6de6n428WR2uka8JBqtvu2aXcYwtjb/ACOdmEHORkgkNnGD k+h6P8bvEmveDovE+v31t4W0Cb91YWNnGGur4/3Y92B6dSAM9RXnnwc/a013 Tf2mtRtLiKG08N+IdWRr0XMqvJbyGzto1ZnB4O2FfbJIzkV34PLa3sa+sPgX /LyH88P7xMpq6/yfYh8UWskt9dB9w1O3VoylxmKO15JZWXGQ3y+mfpXUeFbG 41CG1m+3RrNFGFYpbBlbcAwwc9Of51xf/BQfx5pWjfEFJdFuIZ5ZxEt+iEHE mDkH8FQ/XPvWF8HPjPJNDJDFHPqF3qgAt7YbUKOo2lQxIVRt5+Yjn61wf2ZW 7w/8GQ/+SL515/cz3eTwm/irRbvT7q6d7VyqSyxgIXywBRQOgAIyfevk79q3 wFrH7N/xY0DxPpvnTS6XdiSByMNKiqXKEk8kAY99wr6z8JWWt+NNPt209ZNC gcjTrmxukUyQMpYvLlSeQGUjnBI54qT9pD9nXVviv8M9QSaeC81TRoPN0uRm 2mWRTuDEY67lXjoefej+zK3eH/gyH/yQudef3M/OL9v74a6fo/i/TvFOiyLL o/iOJZYmU55I3AeuQMjnH3a/Xn/gxj/5ui/7lT/3NV+V3jLwtL4z/Zw8TeH5 IC134Onj1ezmwWK2rqzFPYgFhj1Ir9VP+DGyNEb9qEIxYY8KdRj/AKDVZVsF UpR5pOPylF/gm2NSTP37ooorkKCiiigArI+IP/Ig65/2D5//AEW1a9ZHxB/5 EHXP+wfP/wCi2pMaPNv+Cev/ACYN8D/+xA0H/wBN1vXsFeP/APBPX/kwb4H/ APYgaD/6brevYKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKAPB/wBprjx9Z++nJ16n95JXgn7SvP7N3j//ALFvUPqP9Gl6 V73+01x4+s/fTk69T+8krwT9pXn9m7x//wBi3qH1H+jS9K5qm5tDZHq3/BJn /k1e2+unf+mTTK+na+Yv+CTP/Jq9t9dO/wDTJplfTtbU/hMnuFFFFWIKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAMUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAY/jn/kCQ/8AX/Zf+lUVfNXw+kMP7avw2dSFdfhb 4pZTsMhB/trRudo5b6Dk19K+Of8AkCQ/9f8AZf8ApVFXzh8L/wDk+D4Zf9kw 8T/+nvRqxfxlLY9g/Zk1O51rwLfXt5Mbi8vNRknnl/syfTPMdo4yzfZZy01v kk/upCXT7rEsCa9HrkfhH08Tf9h+7/8AZa66tY7EsKKKKYBRRRQB+Rv/AAea W327/gml8M4CcCb4u6Wh/HStXFfm7+2R8MYPEw+DfheVltbK8Ni8gKkg7rNJ SCCOcsT+Oa/TH/g8Ltzd/wDBPj4RRKMmT4zaOuPX/iW6tXwn/wAFC/CK6R4i +HOoA+fFpmkaRqqBHKFYfJ2O2fUDaPqGoA6fWtE0rwgLOy1jwbawWwYLIyqq qcfxKBwR+FWLr4Yarr/xd0zUY7OCz8P6Ui3FnLacNOjRsFR+nGGU49eeMDOv 4O1TRtUlg03WLb+1LQpugSVXllhJwfmfPC9eM4roPi1rc4uLTQvDdwIL6JVn e3T92XtwjfKj8AEHb0OcAjoTQBc8RaYyacZAFlULtkKEMFPfNfOP7TujX1r4 g+H9xbaZLfCfxTbwoqjc3zW9yCw9lXLk9tma6q7j8YeBdbsdb/tc6lpGq3kd pe6fc3guVYO6x5hO47WUktgH5jhfSr3xb+1x+LPh1HDGswTxK4jGQvH9mX/P pwMn8PevRyr/AHj5S/8ASWTPY7bwVZtYbYHVxKgGQR0/Gp/iz4An8Y+FFisZ VW9s3WeApIAwdex+vofQ15/8ePi/c/C/w1DJazwQa1rd1Hp1nLKm+G2JO5pG HQ7QOAepI7Zq5oel+LfD0NnBba1c6gbsq0c11e7vOYgE/KzYJ9gK84o2rj4e 23iPwRp8/iTRYNSv4Fy2lq0ZFy4JGdrY3Yz9a8B/bt8TWOgfBa20Oz8LR6Ct xqNukduEij8o+cpLbc5P4A+9fSPjSTwp418HNrmo213etYN9nkktHeOSJs7W K9DjPevG/jDofgy08GQW2nXF5e/2/ewxyyajK8jxqJFZlXcTtARWPHT2oA57 xRH5v/BRD9jy6VT5c3xt0RAxGMlNS05SM9+MfnXpP/BxvrmmeE/2r/jAuoTz W0erePfDV1M0Slm22vhSNQB2yTcADNcP4liM37Zv7EdzIhSWb44aYdrDDKv9 qaWBn3+9x7Vo/wDB0raN4h/a58e2NlmS4PiqwWVVHKsPDemFB+IY4+tAHzj+ zX4jtfGHguCOGHRrXTkZZLe71DdFNE5AU+WyjGRyQNwr0iXSZZvDWqaDNquN Ta9d7Sa6chpV3goQ3VgRkZXPBr55/wCCf37QEPh3w3rnhPUryCxu5IWl0+W5 wYvMHGxs8Y579q+iPDsdlq+lC207xFYapdyZlutPlkR4JSeWCJ1RAem3gUAe nfCD9qLT/hT8PptK8a6JqGm3Mkki3GraasclttOQjt84kXAIydpwcmvmm5/Z Q07V/irbajoniaHU/Dl7eCUG6ikS6cF8mM5BBY+pIr2KXwCl34RuFnspFRo2 3Wkk3mRcdBnP3Tj8M15P4h8RXlvql/a23iHS9K0/TGWc7poCLJM7ljVV/eSS D+6gLcUAdl8e/jpd+HvjJLo2naS2raZoEKWuni1AeNPkTzAMcbi4YEjqFX0r zO++GXjH4u/ErXb6XRbrQ44Z1Z4/kWTzjbW+IwSQMlDG+NwPzd+cW9S/afg0 J7mx8MW73F/drHGmoPCvmM4PzBUYH73TmvpL4Mx6r4zsfG8viDTF0+9m1qEX UKukqxONLsMDcpI6AH2zjqOPQwX8Gv8A4F/6XAiW6/roz4d8PfDHV/HPjG60 lLe7WOK+8m5uJASLY/OF3k8DJBHJ7V6vD8BLnwLqdlPo108YEJup1ebO1ERR uBx18xZeWwuBjJzXdeAr2yn+JHjfw7qd7Zf2hcXxAtimySQKF2OCBhs7Vbj5 hzmu9+J/hO71zwpbaOtrMTeRhbsKhdipdmwxzyoUnA5ySDjIyPPLLPwb+KVj 46hS3vbxIdRYBo5IJdqXigY3KQecdCOoru9Btt17NPcatfSywmYwI0uYioA+ QjvxzXgPxQ8FTeENJiTRLC4+06YYpHht4fkhjGfnBHSXqcKdxG3I4rgvih8b r218N2OjQvdNqWpXLS/6QhilsyBtaQlsfMchRn+8xHIoAq/BPRbTxfrXxZtL e3jeO48PXDxbVCqfLuoWGBjpt3D86+/v+DHeyOm6n+1Vbk5MEvhaMnpnadbH 9K+GP2HrW+8ReN/Geq6cpk0TT/C98k8gUbWCxE4JPQbygz6sBnOK+6/+DIS8 XUdf/awuE5Se48LyKfUFtbP9aAP3yooooAKKKKACsj4g/wDIg65/2D5//RbV r1kfEH/kQdc/7B8//otqTGjzb/gnr/yYN8D/APsQNB/9N1vXsFeP/wDBPX/k wb4H/wDYgaD/AOm63r2CmIKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigDwf9prjx9Z++nJ16n95JXgn7SvP7N3j/AP7FvUPq P9Gl6V73+01x4+s/fTk69T+8krwT9pXn9m7x/wD9i3qH1H+jS9K5qm5tDZHq 3/BJn/k1e2+unf8Apk0yvp2vmL/gkz/yavbfXTv/AEyaZX07W1P4TJ7hRRRV iCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigDH8c/8gSH/AK/7L/0qir5w+F//ACfB8Mv+ yYeJ/wD096NX0f45/wCQJD/1/wBl/wClUVfJ139i/wCGjvCw1H+3Bp5+DnjH 7SdF+2f2l5X9raRv+y/Y/wDSvP258v7P++37fL+fbWL+MuKurH038I+nib/s P3f/ALLXXV5V+yDr1z4q+Ew1S9S8ivNSuPtc8d5BPb3EbyQwsyyRzwwSo4JO 5ZIYnByGjjbKD1WtVpoRe4UUUUwCiiigD8of+Dv29i039gP4P3E24xQfGfR5 HA6kDTNXOK+WP2rPBOrfEXQ7aS/0ySTw9p1ulpaSQRmV3t3gjduhy5XePlwW GSR1Jr7K/wCDqPwZb+P/ANkP4H6VeOyWdz8adI89hjhBpWrk9fpXy9+1r8Y7 ex8NeF/Cug+F4p0NlCt3cxyM63TIi7ZW2ngqT7DBxQB+W/wT/wCCi3if4Aal f+HPF9g3iW10iV7OyE8PlSxGNiuGOAz84Pz5Pr3rs/B//BVa58SeP31XxLpc llZWayxQSRBpJArdI2A4xhse2RR8Tfhtp7eMLe6OgWl9rN7K7yq+47l4AbGc DIA/KseL9gq/8N+ObHxdqWkyW/hnxBp9zd6fDLny2nVljYAEcqNzEZz932oA 6/wt+1m+r/EfRJNWsp7TwxLewXml5mRkC+eglkZRnICh2A7ED0r6v+KN2mo+ JfhvNbMJoZPEMrI45V86TqHTtXwr4V+Aq6fY2kEl4tv/AKTJmC6BMVuDxlVI zkk9uPxqLWPEPjr4Q65o9vcz6pb2VhNK+nSDzGt3BgljzGeQGCuwA7b/AHxX o5V/vHyl/wCksmex9Lfty/EXQLDQ9I8LahcTLcXN8l9O1qA01lGivh19GLMo 9cZrw74R/t7Dw74s0nTfFX2ySx0+YPBeofNcui8ZVOSDzkgcVx1n4U1jxXo9 9rOqFJ5rkiIG7lbziCOXBJwO3Xtmq/xX+AuoeL9Rubm3jtrJdI04fZJbRQiz sq9sDnjjPv1rzij0rx5/wVF074W668Hhizk1RZZDJP542wyRuu4KAQe55z79 6qfBb9o27/ay+Lza5c6RaaTa+HbN0toLfO2SV+ufQAdSeAASSBk14l4Z/Y8v 9a8B22o60NQ0+81S2+12QlhKrNGRlWBYcgqBivoj9mTxDf8A7KHgiwvLHw9Z XSIy3NwkiMzXGSGZZAOHXAAZSMELg8UAe1+J9Us7X9sr9ibRooHt3sfjJoTF HwzCVtS0z7QwYfeDSAtnJ+93JJPV/wDBYGex+JX/AAVd/a/8IawrMNIh8N65 o8wO021yvh2wzhuMZCZ5PJA96pfEXWNG+Nf7Y/7FvjKysrvTdRtPi3oCXkbM Nk8k+t2Ll+AOQMLx/crT/wCC0vwc8Sap/wAFXv2h5/DGnatda34zvvD1uht4 Swezh8O2UcgGBzmR8H2z6UAflh8J/h9q0njyDUDb2skSsZmjuGXy5TjptPXO fSvXW8L6rp9oji50qwu5G8xXSCQeS3X/AFgPyjsOg4r6H8F/sMeIvAEsep3O mQaszhY54Id6SW6kZ+UMMHpg/Wn+Kvh5YZeHT9FlubyCUwtG7O2x84wyg8YP b2oA+Nvj34g+JVhYLBq/i7Up9EmhysK3xVHXGCMA5bPqc1578EvjJa/CXxJL eahodp4jt5YmT7LdTSJErdm+UjOPevrD44fsR+IPincvqV9qbxPHFjyhCQqY HCqPTivDdM/Yn1G78Z2ujXGpWFpPfbmjjkuE89lBI3FOoHH1oA7rwD8W9L+I t1oWvRafY6fLodzHHe2lunyxQIy7CSfv8ZO489uwr76+GOpwnxj8QzbTxXNl c67C8UikMjg6Vp5BBHsa+L9N/Zci/Z/8B6hLG1xqU188UcpCZaEDOSAOoOec jt2qP4Y/tHeIPBl3JEdUu4NOl1SKW5gCqNyiCKMZbGfuxqOCAQvSvQwX8Gv/ AIF/6XAiW6/rozvf2y/g5ZH486LrUxmg0rxEu27aJtrCSIAce7Lt/BSe1e2+ HZ7aTwhY2iTSNaRJsj2ylyiAfL83rxzz39qzP2tPDMnxF+C1tqWkt/pOnsmo W0iHrGYyG/8AHTmvjG++JeqQadHHZ6rqVr5Cs0vk3DIDz6LgdcfnXnln0x8T Pj1YfDrxLNo9/Ot1HYWhvfMMZJhwTgMccdBx34HcV4j8OfCcnx78TeIdf1l5 rSy3rI7IdkkUIDFI0/ulhkluo2+pFaHwS+BsHjyzs7rWLq4vL7UpgdPt4nDo 07H5ZZsgmQpgkLnaMepNeveMPhzb/ArwE2mKZbqe5Vnu3JKl3J2g5wB1J6UA a/gqez+B/wDwTv8Aif4k062j0yC/QeH7JUGQkJBuZJBnJJLxRBick8c19Nf8 GNTrJN+1KyrsVm8KEL/dH/E64r5u+Oegz6p+wj4l8G2sTRhYJLm3QjAefYev 4ZH0r6O/4MY/+bov+5U/9zVAH7/UUUUAFFFFABWR8Qf+RB1z/sHz/wDotq16 yPiD/wAiDrn/AGD5/wD0W1JjR5t/wT1/5MG+B/8A2IGg/wDput69grx//gnr /wAmDfA//sQNB/8ATdb17BTEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQB4P+01x4+s/fTk69T+8krwT9pXn9m7x/8A9i3q H1H+jS9K97/aa48fWfvpydep/eSV4J+0rz+zd4//AOxb1D6j/Rpelc1Tc2hs j1b/AIJM/wDJq9t9dO/9MmmV9O18xf8ABJn/AJNXtvrp3/pk0yvp2tqfwmT3 CiiirEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGP45/5AkP/X/Zf+lUVfOHwv8A+T4P hl/2TDxP/wCnvRq+j/HP/IEh/wCv+y/9Koq+cPhf/wAnwfDL/smHif8A9Pej Vi/4hS+E95+EfTxN/wBh+7/9lrrq5H4R9PE3/Yfu/wD2WuurSOwmFFFFUIKK KKAPiX/gul+zR8Qf2mvgF8LrT4deAr34j6l4T+JFj4i1DR7XV7HS5XsotO1K FnE15LHHxJPCu0MWO/IBAYj8+vEH7E37XOrWqwQfsma9HGkSxL5nj/ww5G0Y DZN7kkgDP0r6R/4O6v2hvH/7NX/BNzwRrvw58ceL/AOt3fxKsbCfUPDms3Ol XU1u2l6q7QtLA6OYy8cbFScExqcZUV/Ol/w9l/am/wCjlfj/AP8AhwtX/wDk igD9R5v+CUv7Y010Zm/Zd1aSRNywtJ498NsYlLbuP9O6jnH1NeyfGf8AZQ/b D+KHwS+F/h21/ZJ1Wz1XwAZ457h/HvhloNSikxkEC+3Ajavt1PpX4qf8PZf2 pv8Ao5X4/wD/AIcLV/8A5IrW8Hf8FLf2vPiDrsOmaL+0L+0RqV9cHCQweP8A V2Y/+TFAH6seK/8Agnd+2b4r165vJv2Vr5YpXDR248c+GhHGNoHT7d6gf/rr ifiD/wAEdv2s/HNtoscv7L/iMppt79pmik+IXhzy3Qxyq6xqL7EbEuhzj/lm K8V+G+i/texeG01n4mftgfGf4eaftR5I7/4kapFcbWJxtQzlmOATgLzVPxd+ 2frXg2+0+zsP2zP2q/Fk32lk1B4PGerwJFCIZSHiZphubzhEDxjaXPpXXgVU dX93vaW/+F3/AAuTK1tT6Q/4dPftV6HdqNG/ZGvre1RgVW58c+GJpOOMlje9 SM56Csbxv/wSQ/bL8S20cdp+zHqdmZEZLg/8J14axhmydgF6AMDGO30rxyD9 orxJ8QtEhPh39uv9pTRtWdRm01vxvq9tGz5A2iUy7TjP6e9ed/tI+Jv26v2f tCg1tf2iPj7rmgXRzFeWXxF1WZGGCc7kuCD/AJ4rkKPuX4w/8E5P2t/iHH4a 0zT/ANlTxBY6D4W0yHT7eOTx/wCGTNN5cQTc+L3bkkE8Y4wK5Twv/wAErP2x dDmdZv2W9UubVvlER8eeGsbOmP8Aj94OPTj0Ar8t7j/gq7+1TazNHJ+0l+0A kiHDK3xC1cFT6f8AHxUf/D2X9qb/AKOV+P8A/wCHC1f/AOSKAP2S+Gv/AATO /al8QfHn4Azax+znrPhHRPh/8SdB8QajqUnjbw/dwWljb6nazzyGKO9eVisU THbGpJwAFJ6/Uv7c3wY/aHuv22fibrHgb9nHWfiB4X1xdOXTdetvGGg6cLgp Y2iyERXV0kyBZYmT5lGShIyCCfw4/wCCZf8AwU5/aS8ff8FIv2fdB1z9oL43 azomt/Erw5Yahp9/461S5tb63l1S2jkhliecrJG6MysjAqwJBBBr+wugD8SP EP7PP7Y+qwSpb/sm65bblbY3/CfeGSVJ7/8AH9Xzjf8A/BKT9ujU9WmvZ/2f NceZpWkQDx34bROTn5gL7k5r+kWigD+aPxD/AMEkP2+daXEfwE1e3AzhU8ee HVUH8L7mvJvGH/Bv/wDt++I/HFvrsHwPvLe9tNvlSf8ACc+Hw6EHOci+Hcmv 6taKAP5vof8AglP+274i8DDTPE37M97e3LKqyy2vjvw7EsmOp/4/sg/SuFh/ 4IP/ALXcvhm++1fs267JrNzcLs8vx74bFmIVijQFlN9uMmUZsggfORiv6e6K 1p1pQjKMdpKz9Lp/mkJo/nP+G/8AwS+/bc8K/C2fw1qf7Nmr6kqxPb2s48ee GwYoiMBTm+7V5b4F/wCCC37YGl3V7/bf7NWq3sF0u1Ut/HfhtSAGDAEte9Mg dPSv6gqKyGfz6/BT/gmF+058KbDULqb9kTxBqGveUYNJmPxB8M/Z9NTHLhft uS5PU9MAYA5zyvxO/wCCWX7a3xE1SCVv2atZSFMbw/j3w2WIBzgf6dgV/RpR QB/PH8U/+CbP7aPizwjeadpH7L2qWklyrbDcePPDbIjeWUBwt9nGTk19O/8A BqV/wS5+Ov8AwTYPx6/4XV4G/wCEM/4TT/hH/wCxv+J1p+o/bPsv9qef/wAe k8uzZ9oh+/tzv4zg4/X+igAooooAKKKKACsj4g/8iDrn/YPn/wDRbVr1kfEH /kQdc/7B8/8A6Lakxo82/wCCev8AyYN8D/8AsQNB/wDTdb17BXj/APwT1/5M G+B//YgaD/6brevYKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKAPB/2muPH1n76cnXqf3kleCftK8/s3eP/wDsW9Q+o/0a XpXvf7TXHj6z99OTr1P7ySvBP2lef2bvH/8A2LeofUf6NL0rmqbm0Nkerf8A BJn/AJNXtvrp3/pk0yvp2vmL/gkz/wAmr23107/0yaZX07W1P4TJ7hRRRViC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigDH8c/wDIEh/6/wCy/wDSqKvnD4X/APJ8Hwy/ 7Jh4n/8AT3o1fR/jn/kCQ/8AX/Zf+lUVfOHwv/5Pg+GX/ZMPE/8A6e9GrF/x Cl8J7z8I+nib/sP3f/stddXI/CPp4m/7D93/AOy111aR2EwoooqhBRRRQB+Q P/B6t/yiy8A/9lV07/00axX8wNf0/f8AB6t/yiy8A/8AZVdO/wDTRrFfzKeD /CV9468S2ek6dCZ72/lWGJADyTQB6V+xt+yJrf7YPxRXQdMmi0+xtozc6hqE +BDZwqRudmJwAAepr6+8NfGTSPgT4U1HwJ8C7E2ms6fNLBqfjaNSb2+ROCbT g/ZY8qQZgTLIHIUxKxBZ8avA9v8AsvfBjQfgh4L8iLxd4nvI38Sapbn95dAB l+zFwAViVydw581ozniCOvqb4R/skp8NPg9Dp/hqz0hro6eq3LmILcyy7PmY sR82Tnv7YwBQB8pfCz9g7WPiL4JvvGV7d3OuahfytLcQXMszSzEk4bcrhst/ tM3XJrZ+JP7N6/A6w8HWdtJPb+I9T1FLa5jWJBbWi3FvcIVC4wWx156dc9a+ 4P2YfhmPhh4JluJI3t2n4kibK7Spbcdv1r54/a9+IU/jPW/Dt5LG8en2fiTK yxRjzAEs7osehyQuTk56V6OVf7x8pf8ApLJnschr37D/AISv9P8AEWs6jY31 pa+H7JZzPaN5Ul255PK4yRjp09q81s5vHP7E99JrvhTUHtdO1lfLuEeITJqF vwxtr+3B2TQttBPyhgwUhgRX3T8ELrT/AIp/DzWNFn1aLVYLhAm14dkkSkH7 3qAcf5Ncr4c/Zul0bxTrs+sW8AsY7V0juLo+bG+ThVQNn1z+FecUfIPxu+BP gP8A4KFeHNZ8V+BNPh8FfEfT7Vb678Lws11b30SgCWW1lO1mQAFvLYM6AH5n Ayfz+1vRLrw5q1xY30D213auY5Y3GCjCv0r/AGpfgfP+yh8UdD+Ingm8Om2q 3kDGC1nNvsLkI3lEf6sPlgccAncK4/8A4KPfAHw/+0f8K7L44+ArS2s7iW2D eIbG1jVIjLks06Rr/qlbPMfSOQSIv7sIFAPnn/gk7/ylN/Zp/wCyq+F//Tva 1/b7X8QX/BJ3/lKb+zT/ANlV8L/+ne1r+32gAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACsj4g/wDIg65/2D5//RbVr1kfEH/kQdc/7B8//otq TGjzb/gnr/yYN8D/APsQNB/9N1vXsFeP/wDBPX/kwb4H/wDYgaD/AOm63r2C mIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig Dwf9prjx9Z++nJ16n95JXgn7SvP7N3j/AP7FvUPqP9Gl6V73+01x4+s/fTk6 9T+8krwT9pXn9m7x/wD9i3qH1H+jS9K5qm5tDZHq3/BJn/k1e2+unf8Apk0y vp2vmL/gkz/yavbfXTv/AEyaZX07W1P4TJ7hRRRViCiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigDH8c/8gSH/AK/7L/0qir5u+GjGL9tr4aOFaQr8L/FBCKRlv+J3o3Ay QPzr6R8c/wDIEh/6/wCy/wDSqKvmz4bwJc/tufDOORVdJPhf4oVlYZBH9t6N nI+n86xf8QpbHuHwD1ObWtD1q7uNPu9KnudYnmks7pomntGZUJicxO8ZdSdp KOykg7WYYJ7uuE+Aug2XhTQ9a0zTLO007TNN1ee1tLO1hWGC1hjVESONFAVU VQAFAwAABwK7utY7EsKKKKYBRRRQB+QP/B6t/wAosvAP/ZVdO/8ATRrFfin/ AMEhvg7Z6h4i8SfEHVI4jF4WSKPTvNGVNzJu+bHcKFyfpX7Wf8Hq3P8AwSz8 A/8AZVdO/wDTRrFflh+zbp8PwT/4Jky6uYSk+oC+vZSMbnKokW3n+67MR/vf SgCn+x58HdT/AGrPiR4w+I+ovfD7RdMPD6u4iVI0YqpJB+8qhVweCSxOSSa+ 3fg34V8e+FfEVqmrXST6VGmH82SNn4HAGwfrgV4X+yJ4Z8O23wv0zT45tR0a xtYwqXX9qJGJ2XI3BASTuIJwQvXOK+pxFdWngyNNFulmdkTyZ5pBLuX+9kdf 5UAdDf3KtE5lxtYbSCR0NfI/7c9hbeEPF3gN7a3uhaNrKAxxx5jUvbXSsVAG ScEk9TgcV7p8ZEutQ+E2pae1xIs9/bNBLcR/LJEWRl3LjoQSCPcCviTR/wBt zXPBXjbRfDvjqFdcfwNq1xqMN6AqTTqmnXkKRMMYOWmU7uo/KvRyr/ePlL/0 lkz2PtH4H/Cuz8H3j6xbC5gXUIABbzLhoskEknueBXpl9p9rrViYbuBLhAwb Y3Qn3Pfmvk39kz9qTxd8c/Et14j1Vra20Rn+x2WlwL+6X5l3Ss33y4wFHIGC 3HSvqu2vPMJ2MoBJPPOP1rzijwH9oj9n/WfjCby01jTrez0GMOgljdSUjAO0 qASfp9a+ef2N0b4d/Ez4i/CjxLdmbSNTJcSsoImtZkWN2CAfeC7XIA5Zfc19 jfG/4hf2NNFp9vrV9Z3bpu+zWdok73HUj7wPb0Br4r+IV/qfgz9s/wADarqY tftOvGXTvKjUJMIGDFfOUHAkPt2AwBQB4X+yX8E7n9n3/guN8CPDFxD5CWfx d8MtAudwEZ1i2wAe+CCuf9mv7N6/lE8fRvdf8F2f2TNRkG6S/wDG/g4vJsC7 2XVbRT0755J7kmv6u6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKyPiD/AMiDrn/YPn/9FtWvWR8Qf+RB1z/sHz/+i2pMaPNv+Cev/Jg3wP8A +xA0H/03W9ewV4//AME9f+TBvgf/ANiBoP8A6brevYKYgooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPB/2muPH1n76cnXqf 3kleCftK8/s3eP8A/sW9Q+o/0aXpXvf7TXHj6z99OTr1P7ySvBP2lef2bvH/ AP2LeofUf6NL0rmqbm0Nkerf8Emf+TV7b66d/wCmTTK+na+Yv+CTP/Jq9t9d O/8ATJplfTtbU/hMnuFFFFWIKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMfxz/yBIf8A r/sv/SqKvmv4eW/2r9tT4bx4jPmfC3xSmJF3oc63ow5Hce3evpTxz/yBIf8A r/sv/SqKvmr4fSGH9tX4bOpCuvwt8Usp2GQg/wBtaNztHLfQcmsX8Za2PaP2 aPDX/CG+Br7SPK0mE6VqElmY9LsfsFinlxxpiC33v5MQxhY97bFAXc2Mn0Wv OP2ZNTuda8CX17eTG4u7zUZJ55f7Mn0zzHaOMs32WctNb5J/1UhLp91iWBNe j1qiHuFFFFMAooooA/IH/g9V5/4JZeAff4q6d/6aNYr8wvi14an8P/8ABNn4 b6SrhX8S6XbSSZHIN1eyxMMeywJ/9fNfqN/webWrX/8AwTK+HFugDNc/FrTI gCAeW0rVx3r8w/24Nal0j9kD4URxfuf7PsNGCngjPnyzkj6ebj8K66FKhKN6 s3F/4b/qha9EfRdn8JfBnh660G8N+LPUNOshDLEtsZbeRW2thgABuA4z7muz m+I2jDX9OtLPVY3hKhIrSGIrubpk57Ae1cP4A+KNtaak1xdeJ7m4imiEa2sd ogjwRwVbdk/XFZXiu4ufD3ijSLtbF7+aS6At5oFUfa1wSNx4CkDJPXJGOpFb ewwf/P1/+A//AGwve7HsXjmyfXNHvLeBVMiruUdjj3NfBHxM/Y+1nWfjQ91N Olu+uvc26oZRIwnaxvpY+gwEP2bGOTyea+u/jp8ftE+EYtLfUGaXUtXU+RZw gedtAy8jf3UXnJPpjrgH54+Ms/xE8UeI/h9qvhnw7cRzT60L+FpriOEXEkcE 8kMJDsD/AKhLpmJ4xIFzniu/LKOEWIX717S+z/df94mblbY6z/gnz8Htc8D6 Jd2eqwtFb290ZracMGSdSAdw9M8V9IePviMfAXhH+0Psz3pDhBHG4VuQTxnj tXiPhP47T/BrxJJpHiLSm0zSDIPJnnfy/JhI4bJGGVW+U88delegftDRakug Wd7YW8Go6bChmKLISbiQj5FUKpDZHuBz71wewwf/AD9f/gP/ANsV73Y2fh14 /u/iB4ihvG07R4bSVikMzXCyS8D5lA/vfTIrwf8Ab18HaNb+P/APiawa3M1j 4u063uJI/vDzLgLICe/DE12Oj+PLbwDLaXU7waZfMPKtorzYgjB42oinrz+t eM/tpaqmsN4RuLWynS4v/FumtLJsdInb7QuAN3v9PrR7DB/8/X/4B/8AbB73 YsfECzf/AIe6/sWXLIilviPoKFlbcWA1+3AzwMcL0/piv6n6/l58bBrr/gpZ +xhcyABl+KuhRRqfvLH/AG/H144JcMR7EeuB/UNXJXjSjK1KTkvNW/VjV+oU UUViMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsj4g/8iDrn/YPn/8A RbVr1kfEH/kQdc/7B8//AKLakxo82/4J6/8AJg3wP/7EDQf/AE3W9ewV4/8A 8E9f+TBvgf8A9iBoP/put69gpiCiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooA8H/AGmuPH1n76cnXqf3kleCftK8/s3eP/8A sW9Q+o/0aXpXvf7TXHj6z99OTr1P7ySvBP2lef2bvH//AGLeofUf6NL0rmqb m0Nkerf8Emf+TV7b66d/6ZNMr6dr5i/4JM/8mr23107/ANMmmV9O1tT+Eye4 UUUVYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAx/HP/ACBIf+v+y/8ASqKvm34bz/Zv 22vhpIVdkT4X+KGO1Sx41vRjwByT7CvpLxz/AMgSH/r/ALL/ANKoq+bfhvCL n9tv4ZozMFb4X+KFO1irc63o3QjkH6Vi/jKWx7h8A9bh8S6BrOo26XUdvf6v PcxLdWstrOquqMBJDKqyROARlHVWU5DAEEV3VcL8A9Eh8NaDrWnW73ckFhrE 9vG91dS3U7KgRQZJpWaSR8Dl3ZmY5JJJJruq1WxIUUUUwCiiigD8jf8Ag801 IaN/wTQ+Gl4wLC0+LulzEDqdulauf6V+Y37Wraf42/4JxfC7XrAQS21pfJb3 QC5ZfKaBhu/2Skg5Poa/S3/g9W/5RZeAf+yq6d/6aNYr8nv+CY3iKD9qv9k3 x58G7wrf+JYYUvNFhkG15dgmLRK3bKn1ydgHOBQB9aeF7Ox1bTbWTxHbaRpu mzoFhjeUtK5PQBVU55yOeK9L8H/CLStAg3wiS4RWEkSzMXSLJyCobpjOBjoK +Of2CfjdqvxW8P3Nle2y634v8OGO1aSYIBHG2dj7eAT8hDcdVOccV9n/AAy0 G501pn1XWn1HUbgAzQeYClueThQOB17UAfK3gv4G3Hx8/bY8beJtYkmm0Lwv qENtGGJCSsiqUgUH+AEbmHT5unz5rX/bF+Ps3w98e+Bo7R7a3h03WHkZ1j85 4n+yXMByoOCu2c8D+mK+iPF+lS+HdLv7XQEt7G9u3a5VtoxLKzAsxzwSV4z7 Cvkv9q7WXvpvCj3MEcN+mvbrl1CBJEFtPu3OvHK5yDzjNejlX+8fKX/pLJns fQPxT+DGmftM/CcW15BaRaj5PmW0xX57Zjz0IyFbaNwHBA9hU/7GmiatpXwK fw14mtpY7zw5dz6UrOSVkhQ4Ty2OCU29OnH6cn8IoNd8aeJTcXlzPoYvRGoQ HEjKpGPmHGDgcCvdr/SLjVPDN5bWtw9jPcA+TMvDIQevH0x9DXnFHBXV54P0 CW/sbXT7NtTs5CrJdrgStnrv59+Tivmv9sb4g2Xiz4l/Cbw5HFJZasniC1vX skcMFigm8xpP904wD6+mK9R8Z+EdQ8P+Onv9QsEm1knczwTSjduxk/8APMDH J/GvFPg1bJ+1X+2n4l8aX8X/ABSXw3smtZ543CgQxE+cwccbnYlEI65QigDq PivcS2H/AAVi/Ys0yYSJInjrwbd/Mchln1SC5BHPpOv5V/UNX8g/gj9qhv2r f+C8P7P+tRPC2nwfFnwvDbCJQsaKuq2cYRAP4FVFUfQnvX9fFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZHxB/5EHXP+wfP/wCi2rXrI+IP /Ig65/2D5/8A0W1JjR5t/wAE9f8Akwb4H/8AYgaD/wCm63r2CvH/APgnr/yY N8D/APsQNB/9N1vXsFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFAHg/wC01x4+s/fTk69T+8krwT9pXn9m7x//ANi3qH1H +jS9K97/AGmuPH1n76cnXqf3kleCftK8/s3eP/8AsW9Q+o/0aXpXNU3NobI9 W/4JM/8AJq9t9dO/9MmmV9O18xf8Emf+TV7b66d/6ZNMr6dran8Jk9woooqx BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQBj+Of8AkCQ/9f8AZf8ApVFXzX8PQ0n7anw3 RV3M/wALvFAA3lOf7b0bjcOR9R0r6U8c/wDIEh/6/wCy/wDSqKvmr4eTfZv2 1vhtLmMeV8LfFL/vH2JxrWjdWwcD3xWL+MtbHs37M2l3GieB76zu4jBd2moP DPF/aU+piORY4wy/ap1Wa4wQR5sih5PvMAxIr0evOv2aPEh8ZeBr7V/M0mb+ 1dQkvBJpV/8Ab7GTzI43zBcbE86Ln5JNi71w20ZwPRa1jsQ9wooopgFFFFAH 5A/8Hq3/ACiy8A/9lV07/wBNGsV/PD+wz+0ZffsxftGeH/E1pdvZrb3KGRw2 AuGBBP8AL6E1/Q9/werf8osvAP8A2VXTv/TRrFfzA0Afp3+3L4AH7IP7UNl8 Xvhsjp8KfiSoms5rQ+ZBA77ZLmwc8hJEfkKeQB7mvo74A+PxqdhoerWUhvdQ 8VsDbMWMixWwG5mJHAJHGex478/CH7BX/BQTR4PA9/8ACD4wQvrPw51+NUUm QiSxuFOIriMk7VmQMwDY5UlT2I7vxL4a8d/8E3vFsXi3wdcr43+FGqkNaXa/ vUVGyyrLt/1M4yA2MKxOcE4IAP0f8YRWdw6RTzxQyzExxq0oVpcg5A5znk9K +Xf2sfh7pvhzXvB5ttHaa0Os+ZNG9upSdRY3ruGlblyVQkqTjCn2rlfg7+27 oP7SvxCttV1PUf8AhHIdBjS4t7C4l2NdXHzDAboVH1/i9q3P23fi34d1GLw5 fw+JII4kvLi1eKGYOkDNp95FvK5JILyqu4DgE+xHo5V/vHyl/wCksmex9P8A w908W1nCps7hbWVQ8QmkEn2Ur2UnsfrxVSx+Ik8vj24KajZx+HbVVgZnKhGl LbRtf1/GvHvgp+2d4ev/AA1fz6rrnhqyh08LF9niYRIVYEmU9yxx0HAzXzJ8 cv289b8Y63N4H+EVhLe2dw5toLgW5mkQdFeJRwGHZyD1+lecUdr/AMFIf2o7 nw74puPA/grUtUOuasyw3S2chlaNGUEKq87XbsRhh94etcL+0zrg/wCCcn7F Nv8ACe2uoofHvjy3XU/EsafLcWDyLhLVweVMaBWI6hmweRWl4f8ADukf8E4t Kf4ofETUF8SfGSRXksNGuil1Fpssg2CaZXzvmCsGUZwjAEjC7a+Bvjr8btc/ aD+I994m1+dp769cscuz7QWLHknJJJJJPUkmgD0//gk7/wApTf2af+yq+F// AE72tf2+1/EF/wAEnf8AlKb+zT/2VXwv/wCne1r+32gAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACsj4g/8iDrn/YPn/8ARbVr1kfEH/kQdc/7 B8//AKLakxo82/4J6/8AJg3wP/7EDQf/AE3W9ewV4/8A8E9f+TBvgf8A9iBo P/put69gpiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooA8H/AGmuPH1n76cnXqf3kleCftK8/s3eP/8AsW9Q+o/0aXpXvf7T XHj6z99OTr1P7ySvBP2lef2bvH//AGLeofUf6NL0rmqbm0Nkerf8Emf+TV7b 66d/6ZNMr6dr5i/4JM/8mr23107/ANMmmV9O1tT+Eye4UUUVYgooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAx/HP/ACBIf+v+y/8ASqKvm34bzpa/ttfDSSRlSNPhf4oZ 2Y4Cga3oxJPtX0l45/5AkP8A1/2X/pVFXzb8NojN+238NFV2jLfC/wAUAOoG 5T/bejcjORx7g1i/jKW2p7f8AvEFj4s0HWtU0u8tdS0zUdYnubS7tZVmguon WNkkjdSVZGUghgSCCCCQa7uuF+AmmzaNoWtWlxf3eqz2usTwyXt0sSz3bKsY MsgiRIw7EbiERVyThVGAO6rVbEhRRRTAKKKKAPyB/wCD1b/lFl4B/wCyq6d/ 6aNYr+YGv7bf+Cn/APwTB8Bf8FY/gHpHw6+ImseL9E0TRfEEPiOCfw5dW1vd PcRW1zbqjNPBMhjKXUhICA5C/MACD8H/APEFR+y1/wBD/wDH/wD8Hekf/Kyg D+YKvYv2a/23fGv7NN20Wm3r6hos8ZhuNLum3280Zz8hUgjbz90gj271/Q3/ AMQVH7LX/Q//AB//APB3pH/yso/4gqP2Wv8Aof8A4/8A/g70j/5WUAfhJq/x 9+DHxZmfUNS8M674E127BFzLoF4BaTE5G4wshA4P8JUe1ZsOtfDLSpYpNP8A GfjOSBHP2oLGsTwwFGXKFf4i7Ip4+67V+9v/ABBUfstf9D/8f/8Awd6R/wDK ypbf/gy4/ZdtIrhE8f8Ax8xcJ5b51rSc43K3H/Et9VFdeCrxo1eeW1mvvTX6 ikro/CK++Kn7P+ksk8mkeOvE9wrglbvWSsbgDuBHn8m4pb//AIKXXngPQrnS /hl4ctfA1vcRmB57OV455o+PldwfMYZAOC+MgcV+63/EFR+y1/0P/wAfv/B3 pH/yspf+IKj9lr/of/j/AP8Ag70j/wCVlcgz+ZXxd4w1Px3rs2pave3F/ezn 55ZW3H2A9APQcVmV/T7/AMQVH7LX/Q//AB//APB3pH/yso/4gqP2Wv8Aof8A 4/8A/g70j/5WUAfgD/wSd/5Sm/s0/wDZVfC//p3ta/t9r8r/ANnr/g0T/Zv/ AGa/j94H+I2heN/jdd634A8QWHiPT4L/AFfS5LWa4s7mO4iWVU09GMZeNQwV lJBOGU81+qFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZHxB /wCRB1z/ALB8/wD6Latesj4g/wDIg65/2D5//RbUmNHm3/BPX/kwb4H/APYg aD/6brevYK8f/wCCev8AyYN8D/8AsQNB/wDTdb17BTEFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4P+01x4+s/fTk69T+8 krwT9pXn9m7x/wD9i3qH1H+jS9K97/aa48fWfvpydep/eSV4J+0rz+zd4/8A +xb1D6j/AEaXpXNU3NobI9W/4JM/8mr23107/wBMmmV9O18xf8Emf+TV7b66 d/6ZNMr6dran8Jk9woooqxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBj+Of+QJD/wBf 9l/6VRV80/D/AD/w2t8Ntnm+b/wq3xTs8vbvz/bWjdN3y59M8V9LeOf+QJD/ ANf9l/6VRV83fDRjF+218NHCtIV+F/ighFIy3/E70bgZIH51i/jKWx7H+zJ9 s/4QO9/tH+1f7Q/tCT7T/an2b7d5vlx7/P8Asv8Ao/nZzv8AI/dbs7PlxXo1 cJ8A9Tm1rQ9au7jT7vSp7nWJ5pLO6aJp7RmVCYnMTvGXUnaSjspIO1mGCe7r WOwnuFFFFMQUUUUAeQftq/t6fCf/AIJ3/Cyw8a/GLxWPB/hnVNVj0S1vf7Mv NQ828khmmSLZaxSuMx28zbioUbMZyQD8w/8AEUV+wp/0XNf/AAjfEH/yDXz9 /wAHq3/KLLwD/wBlV07/ANNGsV/MDQB/X/8A8RRX7Cn/AEXNf/CN8Qf/ACDR /wARRX7Cn/Rc1/8ACN8Qf/INfyAAZoIxQB/X/wD8RRX7Cn/Rc1/8I3xB/wDI NKv/AAdB/sMTK5T44FhGNzkeDPEB2jIGT/oPqQPxr+P8DNaOiDFjqnP/AC6j /wBHR104TDqtU5G7aN/cm/0FJ2R/Xd/xFFfsKf8ARcx/4RviD/5Bo/4iiv2F P+i5r/4RviD/AOQa/kB20lcwz+v/AP4iiv2FP+i5r/4RviD/AOQaP+Ior9hT /oua/wDhG+IP/kGv5ASpFJQB/Y98KP8Ag45/Yy+OHxT8NeCvC3xlGqeJvGGq 2uiaRZ/8InrkP2u8uZkhgi8ySzWNN0jqu52VRnJIGTW/+1D/AMF6f2T/ANjD 46678NPiX8Vh4a8beGvs/wDaOmnw1rF59n8+3iuYv3tvaSRNuhmjb5XON2Dg ggfyg/8ABJ3/AJSm/s0/9lV8L/8Ap3ta+gP+Do7/AJTr/HP/ALgH/qPaZQB/ QB/xFFfsKf8ARc1/8I3xB/8AINH/ABFFfsKf9FzX/wAI3xB/8g1/IBRQB/X/ AP8AEUV+wp/0XNf/AAjfEH/yDR/xFFfsKf8ARc1/8I3xB/8AINfyAYpdhoA/ r+/4iiv2FP8Aoua/+Eb4g/8AkGlf/g6C/YYhRC/xw2rINyE+DPEHzDJGR/oP qCPwr+QDaa0NY/5Bek/9ezf+jpK6aFBVIVJN/Cr/APk0V+om7NH9d3/EUV+w p/0XNf8AwjfEH/yDR/xFFfsKf9FzX/wjfEH/AMg1/IBj3FGK5hn9f/8AxFFf sKf9FzX/AMI3xB/8g0f8RRX7Cn/Rc1/8I3xB/wDINfyAUUAf1/8A/EUV+wp/ 0XNf/CN8Qf8AyDXv37DP/BUb4Ff8FJz4p/4Ur45/4TT/AIQv7J/bP/Em1DTv sf2rz/I/4+4It+77PN9zdjZzjIz/ABBV+/3/AAYx/wDN0X/cqf8AuaoA/f6i iigAooooAKyPiD/yIOuf9g+f/wBFtWvWR8Qf+RB1z/sHz/8AotqTGjzb/gnr /wAmDfA//sQNB/8ATdb17BXj/wDwT1/5MG+B/wD2IGg/+m63r2CmIKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDwf8Aaa48 fWfvpydep/eSV4J+0rz+zd4//wCxb1D6j/Rpele9/tNcePrP305OvU/vJK8E /aV5/Zu8f/8AYt6h9R/o0vSuapubQ2R6t/wSZ/5NXtvrp3/pk0yvp2vmL/gk z/yavbfXTv8A0yaZX07W1P4TJ7hRRRViCiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDH8 c/8AIEh/6/7L/wBKoq+a/h1FHP8AtsfDRZQrRn4X+KA4YZUg63o2c9ulfSnj n/kCQ/8AX/Zf+lUVfNvw1iE37bfwzQjKt8MPFAP/AIO9GrF/GXHY9i/ZkOht 4FvG8MHSj4aN+/8AZH9liP7D9j8uPyPs/l/J5Pl7dmz5du3HGK9IrjPg8hSL xGp25Gu3QO0YGfkrs6uDbV2KduZ8uwUUUVZIUUUUAfkZ/wAHnFqL3/gmZ8N4 mAIf4s6aCCM5/wCJRrFfzeweC4rqVY0t0Lscfdzmv6Rv+Dy3P/DtT4aY6/8A C2tN/wDTRrFfit/wTr/Zhf8AaI+KwW4SX+y9O2y3MpGFVfc9B0oA7P8AYY/4 JS3nxkf+19a0+O10qKPe0sxAB56V9Pav+w94F8LFdH8PaFa3l8Bt3hd2SMfh ivonwtPL40nt/CXhG3MGk2qgSSRD7wHGSRxjnpXvnwI+AGmeGZtrxfaLth88 smCzHFAH502//BJK++IGpRy3WmRW9u5yELKOPoDXGftBf8EmIvhf468CWEWn RkeKNWOm43jDkWtxPjr/ANMc/hX7beHvhcjjLxDbGe44r52/bw0WOL4zfAVE XI/4TiRT/wCCbVD/AEr0cq/3j5S/9JZM9j4S17/gii+lxRy22nW4fGdoINV9 O/YStvhxMsXiLQUFoD5bOUV1/MV+w9v4Ei1XTFkKqSo5ArjfiD8KbDUtFuIb m2SRZM8MM56+1ecUfkZ8ev8Agkr4W+J/gi51fwXFGbxU3PaBwHGOCQD/ACr8 3viz8ALv4T+KJ9N1KyMEsJIIdf1r9+viX8HtQ+D1tJrnh2SZ4IjmSIDdsB4P A7c18Z/8FDvgFpv7Q/wtvvG+j2a2+s6NEw1C3hX0y2/HXB/pQB8Df8E19Ki0 3/gqf+y+YwAZPir4azgemr2n+NfSH/BzJpEdz/wW5+NshRCzDQuo540HTf8A Cvnv/gnjAbX/AIKr/svxEYKfFbw1/wCnezr6X/4OVIlf/gth8ZhjLH+wxj/u A6dQB+f1r4W/tCZYo7VXZsAAD+lfQn7N/wDwTd8UfGeaG5XSpIdOcAtO4AXG M9zXrf8AwTl/Yoj+KviJNZ1yKaDS7dd+Su0McjjkfWvvvVvGtj4csovDPhqO G1sbQCIyQj5iBxwfXjrQB8Z3n/BO3w78M9EEb2y3moRJkoq7jn3PQVxt3+yH quv3Li10qCytycA+UufzNfeaaHHdxEGJmJGSX6moz4bg8kEK2QSOOBQB8Fy/ 8E+GvZVN2glc+rDA7+tN8J/sAReIdX8QWiWq7dIv0s/vAEZtbefHX/ptmv0B g8CrcooMTJtHBwMNXB+ALSTTvGfxHMaj914khHT/AKhGm/416GC/g1/8C/8A S4ES3X9dGfFfjf8AYBm8Mg7LIMB3L/8A164UeAbj4YX/AJwsVR4DuDMgYcfW v0o1C1/4SqUQTRAgD5uK5fxj+ypYeOdMlRoWV37qP/rV55Z43+zjr3wu/aU0 A+E/F2laZZaxefubW7Eezc5GF6YHU96+YP28v2C9V/ZW8aL5unkaRelntbhc OjKCO/0Ndv8AtFfs8az+zv4vi1Cxiu47eJhKr4PykHg5/Cvr79lrxzoX/BSn 4CXnwv8AG1zbr4msrfOh3TMFkeTB+Qls5JwP0oA/IVdGgB5jjIx/dFfuh/wZ L262us/tRKgAHl+Ejx/3G6/GX40fCjUvgv8AELUtA1S2mtrmxnaPbKhUnBx3 r9nv+DKAY1/9qP8A65+Ev/c3QB+8dFFFABRRRQAVkfEH/kQdc/7B8/8A6Lat esj4g/8AIg65/wBg+f8A9FtSY0ebf8E9f+TBvgf/ANiBoP8A6brevYK8f/4J 6/8AJg3wP/7EDQf/AE3W9ewUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAeD/tNcePrP305OvU/vJK8E/aV5/Zu8f/APYt 6h9R/o0vSve/2muPH1n76cnXqf3kleCftK8/s3eP/wDsW9Q+o/0aXpXNU3No bI9W/wCCTP8AyavbfXTv/TJplfTtfMX/AASZ/wCTV7b66d/6ZNMr6dran8Jk 9woooqxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBj+Of+QJD/1/2X/pVFXzB4asbnVP 2uPAdrZ391pd3c/CfxZFBeWyRvPaSHWdHCyxiVHjLqfmAdGXIGVYZB+n/HP/ ACBIf+v+y/8ASqKvnD4X/wDJ8Hwy/wCyYeJ//T3o1c81eVjSEuXU9a/ZZubi 9+HU89zcardzz3jSST6nbpb3srmKMs88aRxrHKT99FjQK2QFXGB6bXIfCTp4 m/7D93/7LXX1rTVopMdeanUlOKsm727eQUUUVZkFFFFAH5H/APB5eM/8E0vh sPX4s6d/6aNYr87/ANhWzX9n/wDYX1HxKqf8TLxderZQNjDFUHOD/wACr9Ev +DyOPzf+CbPw0Ud/i1pvt/zCdYFfCGv6eug/slfs/aJuUJdXD3kqg9cvH1/A V10MBia0eelTlJd0m1+AnKK3Z+g37IHwgfwN8ObdmXff3qCWZivPIzj+Ve6e CPBlxNr8DoCEQ5bjiq/wgvLCysoS7KVCDOB93gcV6jpHjjRzrsOnW+DPOOgX 2qnl2KW9OX/gL/yDmj3NY6WYNIlaBcusZIAXOTivkP8AbI8IXtx8YPgE1yCj XHj+ZVHf/kA6u3/stfTvxS1vWbfwDqH/AAjwVtVtT5saOCA4GDt/GvmP9oX4 n33jD4kfAN/EFq+lX+n+O5XZHUFXP/COa2Rg+7KB+Ir0Mqy/FOvdU5bS6P8A lfkROcUtz6c+H/h2+sormK5Q7RgqT+NU/iV4Yku9O3QL0PzjFc18Mvil4y8Z /EhWlsDYeG7CFizSffnY9MAenWu/1/xja6DpAvLgSGOR9oO3j9a855dik+V0 5X/wsvmj3PGfFHg4yaFNFLFlJFKsD3FfAvxF8Px/Cv8AagPg2UE6L48sntCp 4UNIWTp7Gv0s8ceN9K1jTXWJl5HtX5r/APBSbW00T4/fDDVImxJZ6jESy9QB LmrjleMltSl/4C/8hc0e5+bHwC+HTfCv/gt/+z7ojKV+xfF/w9GOMcDWbSvo L/gvt4Jfx1/wXp+L9mgJ3voWeM/8wHTqyfjX4VTQP+Dhr9nuaHZ5d98UfDF2 CD136tZnjvXu3/BVbwuuo/8ABef4137gFYDoPsQf7C0//Co/s/Fc/svZS5rX tyu9u9rbBzx3uaGjXkHwd+FVnounAJM8Y8wrwScdPrXXfCPwQ6W0V7dgtNcD ftPUZ5rz7T4f+Ep8Y6fAxLbT5zZ4wRwBX0t8P9AQwxg7QAMAfhWn9k43/nzP /wABf+Qc8e5X0zwNLe8EMqAk5I6VoP8ADxLOHylAJkPPHSu4sNKLxiKMEr/O vRvA37Ot9r1j/aWpzRaVYD+KUHew6+lTPLMXFXlSkv8At1/5DUo9zxGDwX8q /JkAY6YrzT4VeAoL/wCI/wAVllcYi8VQAehH9h6Uf/Zq+3LH4ReD79ZIba6v LuRF5kjHyivBvgb8CtO8T/Gn41WdprttE9r4zt0iSQ/NKp8PaK2eOD8zFfwr uweX4pUa96cvgXR/zw8iJSjda/1Y4bUfhzDpqSSRIMt1IFP8N6KjSBOOcjpz XoXxx8Dat8BozNqRjl01sYmXJAzwOorgvAvjjTvEd8Xt5lYE5yCBjtXFHKsY 1dUpf+Av/Irnj3OT/aQ+Adr8TvBd1ZGOMyvCwU7MkHBr84fhbo2sfs7/ALSl vawTS2t7b3IMDjK/MpyPzr9itN0yPUZxI21t5wMkZFfD/wDwVX+AUnh3xfof jHSokV433SFOCSCpH8iKf9k43/nzP/wF/wCQc8e5wH/BcD4Q6d498DeF/jHp FusEmtstvqESr/q5gpJyffHpX07/AMGTTmTW/wBqInr5fhL/ANzdcX8dfC1v 8c/2DfGVhalJIo7CPXbRW6o6EK36Ma7X/gyhhNvr37UiH+BfCi4+h1usq2Ax NKPPVpyiu7TS/Eaknsz95aKKK5BhRRRQAVkfEH/kQdc/7B8//otq16yPiD/y IOuf9g+f/wBFtSY0ebf8E9f+TBvgf/2IGg/+m63r2CvH/wDgnr/yYN8D/wDs QNB/9N1vXsFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFAHg/7TXHj6z99OTr1P7ySvBP2lef2bvH//AGLeofUf6NL0r6J/ aH12bRvHdusMdm4ksEz59pFPj95L03qcfh175wK+YP2vfE97rHg/X7aR4Yrc +BPEUrRW9vHbpI6pahWZUUBiAzAE9AzYxk55Zv3rG0U+VM9s/wCCTP8Ayavb fXTv/TJplfTtfMX/AASZ/wCTV7b66d/6ZNMr6dren8Jk9woooqxBRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQBj+Of+QJD/1/2X/pVFXzh8L/APk+D4Zf9kw8T/8Ap70a vo/xz/yBIf8Ar/sv/SqKvmv4eBn/AG1PhuiLuZ/hd4oA+cpz/bejcbhyPqOl Yv4ylse/fCPp4m/7D93/AOy111ecfszaXcaJ4GvrO7iMF3aag8M8X9pT6mI5 FjjDL9qnVZrjBBHmyKHk+8wDEivR61jsSwooopgFFFFAH5K/8HkC7v8AgnB8 Lx6/F3Sx/wCUnWK+GPiiVk0v9nvTs7YG0+NVA7Mdmf1FfdX/AAeL5/4d0fCz HUfF7S//AE1axXwlqsUPirRPgZdO21LOKNC2cbT8tAH6k+BfAdxcRQrHcEgo B97+lbfwas7jRfjTdRTM8yW5LFnP3eDxWJ8N7y5X7OYplLAcjNemXl5ovhK+ bV3KJd3EYV/m5ZiOeKAOylnYa3cSIcJcJuAr50/bT8GWnij4yfs/C6ITPj+U 7lJG7Z4e1qUA+uWRR+J9a7e9+PITX0W4VLfT1wqz7xjJ7GvOv2rPiDptv8Vv 2e5biZCo8fTOp3cDPh7WkVvpukSvRyr/AHj5S/8ASWTPY+k7cnT9JMSBQSuC AMfSuT+PhnPwl8qBQ0sLBm57Y9ayPHXxij8NRhYpY57thuht94DSkUmlfFG3 8c6d9k1aOOJ51AdM8ZrzijjdJ+HUmo+F7G7+1SosqbmVnxiviL/grhoNr4dv PBVzbyl7g6nFFkHnHmA194fEmS6ixb2hSKwjQBdp4xivgr/gpzbR63rfgi1L iW4XU45Nm7JIDdaAPk/47Si8/wCC6v7IdyQPMn8deEy59SNVshX0N/wVQVYf +C0nx4lOAy/2F+P/ABI9Pr5m+J+qjUP+C6H7JkQIP2X4g+FU4/7C1lX0Z/wV 11dNN/4LJ/HjLBX2aLj/AMEVhQBmfCK7OoeL1mXbxCFGee4r6u+GpNxbBSgy oA96+Df2QPivaax4weyvLiJJQhCb2A5yK+8vhNexrIjtINpA+Ycj8aAPcv2Y dR8PwfEVbfXmgXySrQrOuUZs9a3P2oJde8afEuxstOvo4tClKrsgl2qBu7gH 0NedX/h7TtVsvM3bJsYDq3I9CCK8X134van8KvGRt5r+4vbVmBh3fMQc9M0A fauuf2V8OrXSdCtJ4bczwGW4f+OY/wC97f1r4P8AGPwQ1K2+L3xc17wZrl9B qFh45t4I4o7kgTIfD+jTEkZ/vSEfhivW7T44DxxLb3/iOUW0NrDsRpG2EKBV X9inxZ4T8e+KPjHfbFFxL4xgkgy5yIxoGjpn3+dH/OvQwX8Gv/gX/pcCJbr+ ujM79oP9qI/Ff9n238Hazbtb6/HFFHKzHJZ1xkg+5FfHi6P4v+CGvJfeRdya RIQxYNnA/Cvrn40fDvS/FHxc0+KOFo7qN2dpBkLKg5GR/nrXomqeAtG1TwPJ ZXtpFLEsW3aw54rzyzzn9nr4p2Xj7w1BcxSB2RgHB6g8VZ/a98L2Xjr4VSQ3 EUcjqCwLDkcV41rnhPUf2cfFA1bSw/8AY1zLmWIcrHz2/D+VaXxk/aOstf8A hfK8cwEgQ4AOPm9KAOE/ZHvjqPhHXfDlyRJBdaTfWQDcgqUyP5V6L/wZu6X/ AGL8Vf2ubXAUQz+F1wO2H1wV5P8As43C+H9btw5USfYLq5nXOMAoR/Wvb/8A g0YAHx9/bD242m68MEf9/NdoA/baiiigAooooAKyPiD/AMiDrn/YPn/9FtWv WR8Qf+RB1z/sHz/+i2pMaPNv+Cev/Jg3wP8A+xA0H/03W9ewV4//AME9f+TB vgf/ANiBoP8A6brevYKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKAPCf2nf+R+tP8ArwT/ANGSV8u/tUf8i7r/AP2IPiT/ ANAs6+ov2nf+R+tP+vBP/RklfLv7VH/Iu6//ANiD4k/9As65J/xP67HRH4Ue 9/8ABJn/AJNXtvrp3/pk0yvp2vmL/gkz/wAmr23107/0yaZX07XRT+Ewe4UU UVYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAx/HP/IEh/6/7L/0qir5q+Hk32b9tb4b S5jHlfC3xS/7x9ica1o3VsHA98V9K+Of+QJD/wBf9l/6VRV82/DedLX9tr4a SSMqRp8L/FDOzHAUDW9GJJ9qxfxlrY9l/Zo8SHxl4GvtX8zSZv7V1CS8EmlX /wBvsZPMjjfMFxsTzoufkk2LvXDbRnA9FrhPgF4gsfFmg61qml3lrqWmajrE 9zaXdrKs0F1E6xskkbqSrIykEMCQQQQSDXd1qtiAooopgFFFFAH5N/8AB4in mf8ABOr4Wr6/F3TP/TRrFfA/h3RLjxL+yT4f1a2b/SfDyrIuO2Oetfe3/B4p cC0/4J0fC6Q9E+Lmmn/ykaxXw3+xn4kt/E37Kd3bPJGjmPZgntg9qAPrT9lP 9ray8b+GrC6aeMTtEokXONp4z3r03xN8bo729leSWN44juAB6ivxy8P/ABA1 r4SeIrmOxlZoxIQAH4Iruk/bV1S3spLe53+bIMZ3570Afefx3/ap0+5soNG0 10M1xPGJmLEFF3DpTfjt46s9e8V/AqSWeORLTxZK/XkD+xNUwf8AvrbXxT4T +LGma7Yy3N85F3IPlJPQ54r0j4sSTXurfCsQXZdDrTOMHhT/AGXfnP5Z/OvR yr/ePlL/ANJZM9j6R+NHx8Twj8Y9G1GWdZYo0dZVJPAbbz9a9A0X4/aX4mWO ewnj27d2e/SviH46eL7HRPGQt76cXLvb7k+b7pzXlg/aum8CX5itgxXlVAfA Pp2rzij9SNd+Occ3hgNPdRKyrkjODivivxj4tP7TH7T0KW7htO8NwbsqchnB J/qK+cvFf7U+veJrSZFdhG6lcBzkCvXf+CeKJYWWr315cIk98zM24jcFCjig DwjW7sXn/Bd/9mcjBEPxR8NQgj0XV7IV7j/wWk8Viw/4LnfG+xY7VnGhKO3P 9g6ea+b/AAxrseu/8Fyf2bnidXWP4s+HVBBzx/bFnXr/APwXntpn/wCC73xm aI4Jl0Dn0xoGnUAeBarp2sfDPx59rsSyFW3qwz0zX3n+zL+0bcSfDmG4vjE1 zFEC6nIzx/OvAtK8Lx+K9NidguVUKCVyWrb0CAeGcw7sR42kdBQB754t/wCC jKeC9PnEOmfabhFJCnJBr5P17/go1rHxS+NdjDqUVpptn9oRcqp+T5uvJrvN ce1lsZ5FWN5dhAB9a+Mfj54B1C48YTXMSMuOflGB9aAP0o+KvxK0rUvDNjBp etJeSyRbnCuDk8ZHFeu/8E25bJtN+JU0aKJR4mgHJ7f2LpWT/wB9bq/L39kf SdS1HXliuLpggC9c4Gc19o/swfGs/s8fETx7ZXDfaLG51yEM2cc/2Zp5yB9K 9DBfwa/+Bf8ApcCJbr+ujPrb48+IJ/D9/a6taxo8sDbPz4rb8G+MJfFXhSKW 52RPIuWVe1eY+NPjRo/jHwlNMkqklQ6/Nn3rgfD37T9j4f0uRHkG2EY+905r zyz0n9p+6tbn4f3EO4K2044618WX1yPEGuJYGQCwsCZZsHG89hVz9qD9ty21 tvsVkckqUPzjgnivLPC/jdE0GWV5l8y4yzNuxQB618GvFVxPZ+MtelAEcdg9 rBg8Akj+gr6d/wCDPy6F98Y/2uJg27zZfCzZ+r65Xwx8RPi/aeCP2e7y0tSi TXKliQeuQR/Wvsf/AIMr9V/tnxf+1XPnO4+FB+ut0Afu/RRRQAUUUUAFZHxB /wCRB1z/ALB8/wD6Latesj4g/wDIg65/2D5//RbUmNHm3/BPX/kwb4H/APYg aD/6brevYK8f/wCCev8AyYN8D/8AsQNB/wDTdb17BTEFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4T+07/AMj9af8AXgn/ AKMkr5d/ao/5F3X/APsQfEn/AKBZ19RftO/8j9af9eCf+jJK+Xf2qP8AkXdf /wCxB8Sf+gWdck/4n9djoj8KPe/+CTP/ACavbfXTv/TJplfTtfMX/BJn/k1e 2+unf+mTTK+na6KfwmD3CiiirEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGP45/5AkP /X/Zf+lUVfNvw2iM37bfw0VXaMt8L/FADqBuU/23o3Izkce4NfSXjn/kCQ/9 f9l/6VRV80/D/P8Aw2t8Ntnm+b/wq3xTs8vbvz/bWjdN3y59M8Vi/jKWx7p8 BNNm0bQtatLi/u9VntdYnhkvbpYlnu2VYwZZBEiRh2I3EIirknCqMAd1XnP7 Mn2z/hA73+0f7V/tD+0JPtP9qfZvt3m+XHv8/wCy/wCj+dnO/wAj91uzs+XF ejVqtiWFFFFMAooooA/If/g9FujZf8EwPh5KOPL+K+nN/wCUjWK/CX4G/t2w fDTwDNpD3k8PmADASQ/yFf01/wDBwD/wUu8e/wDBLD9kLwj49+Hek+ENX1vX /HFp4Zmi8R2lxdWsdvLY39wzqsE8D+YHtYwCXI2luCSCPyY/4i/f2pP+hO/Z /wD/AAndX/8AlpQB+dk37Yulzs7NcMzOcljC5P8AKoh+1R4fmQPJOfMU5A+z v0/Kv0Wl/wCDwL9qOFMnwd8APb/indX/APlpTYf+DwL9qe4kCJ4J+ALMTjA8 O6v/APLSgD84Na/av0+fDW13KhUggLG6jj8K6Rv+CgepX8Wmyfb7hv7AkN1F w+VzE8HGe+JjX6XeE/8Ag6K/bJ8ZBGs/h98BGRzjcfDmr4/9OldDff8AByP+ 2bpk9hHceCP2fY31CYwQj/hHNYOWEby/9BT+7G35V2YCU41rwV3aX/pLv9y1 Jlsfkj4r/bTk8beIFvb+/uGZV2qSrkgZ+lOl/ak0K6jDSXT+YuMYhkPT8K/W rWv+Dkf9tXR4PMPgH9n+VOT8vh3V+f8AyqV554j/AODtv9rfwrM0d74A+AkL KcfN4d1cf+5SuMo/M6P9qvTIkZPOkOeh8t/8K7L4bft92fgeynRb25QuCFCx vzkfSvuU/wDB4z+1IDg+CfgAD/2L+r//AC0pP+Ixr9qT/oSf2f8A/wAJ/V// AJaUAfBv7AHxB/4WP/wV5/ZuvwXKt8V/DJG731i1NfXf/BwN8XvD3w9/4Lg/ G0and+ROh0LIEEjkZ0HTiOVUjoa93/ZA/wCDsr9o/wCPX7Z3wd+HGueDPgjB onxE8a6P4b1Cew0XVIbq3t7u+gt5XiZ9RdRIElYqWVgCBlSOK/odKgnoKAP5 BPDn/BQjwFpWnCFtXmUAYwLGfn/xyqOv/wDBQfwZeZ8jVrj2/wBEmH/slf2E 7B6D8qNg9B+VAH8aq/t6+HY5ww1G4YA/dNtLj/0Go9Y/bn8Ia1lriR5HK7eb V+n/AHzX9l2weg/KjYPQflQB/FGn7YWkaHqby6Xdz2yscgRwuuP0pdQ/bduX W4lmvbgtqk4vdxDEsVjSDn04hFf2t7B6D8qNi+g/KuijX9nCcbfErenvKX6W E1dpn8X+hf8ABRWTT9K+zSajeBduCAr4rmPEP7cM94ZxBf3pEvP8Qr+2fYPQ flRsHoPyrnGfwsj4/NqGsi5urmdgDn5txzzn3rpIv2poo4PKF5OEHQbXx/Kv 7gtg9B+VGweg/KgD+GH4g/tMXPizTntVnuXhYbQpZgq/hX7S/wDBjZMbif8A akc5+Y+FD/6eq/fnYPQflSgAdABQAUUUUAFFFFABWR8Qf+RB1z/sHz/+i2rX rI+IP/Ig65/2D5//AEW1JjR5t/wT1/5MG+B//YgaD/6brevYK8f/AOCev/Jg 3wP/AOxA0H/03W9ewUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAeE/tO/8j9af9eCf+jJK+Xf2qP+Rd1//sQfEn/oFnX1 F+07/wAj9af9eCf+jJK+Xf2qP+Rd1/8A7EHxJ/6BZ1yT/if12OiPwo97/wCC TP8AyavbfXTv/TJplfTtfMX/AASZ/wCTV7b66d/6ZNMr6drop/CYPcKKKKsQ UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAY/jn/kCQ/8AX/Zf+lUVfN3w0YxfttfDRwrS Ffhf4oIRSMt/xO9G4GSB+dfSPjn/AJAkP/X/AGX/AKVRV82fDeBLn9tz4Zxy KrpJ8L/FCsrDII/tvRs5H0/nWL/iFLY9w+Aepza1oetXdxp93pU9zrE80lnd NE09ozKhMTmJ3jLqTtJR2UkHazDBPd1wnwF0Gy8KaHrWmaZZ2mnaZpurz2tp Z2sKwwWsMaoiRxooCqiqAAoGAAAOBXd1rHYlhRRRTAKKKKAPyR/4PLDj/gmr 8NcZ/wCStab0/wCwRrFfzo1/Rb/weWc/8E1fhp/2VrTf/TRrFfzpqpZgAMk8 AUAT6Ro1zr+oxW1rC89xK21ETkkmvtz9k7/gnvZ6WlhqPiyB59SulEsVkCSQ CM8gd/rWf+wZ+y7/AMIx4b/4T7XdOmnuLhhbaTaOn+vkPO/BGcAD9a/Xr9h/ 9icDw4nijxJG82salGJUiYACBGGQqjHAAoA8H+F/7C+p6tpkB0zQrTSrJ8BW YB5WGevXHrXBftafsl6n8Nfiv8KtPmimum1rxDJChXYoz/Z19x+hP4Gv19+G Hwrgt42aSERwWilsYx05r5P/AOCgmmw337QvwDljQ7B46mjXjgj+xNVP/ste jlX+8fKX/pLJnseF6z/wT11vTNLUxSsxK52Sokg6fSvnL9ov9iTT72xnt/Ee iJayITtvIc7QfUgdM+1ftt4Q8H2fjLwdKxT/AEm2HGMV4/8AE/8AZ3sfHdhc wX9tgMCC2zmvOKP5jf2pP2T9V+B2uSSrA0ukzHdBcLypB7fnXjT2zITnHFft T+2N+yhDoeuX/gzWrdv7B1Q7bK7cYNrI2CnzHjaW4/GvyX+P/wAGtQ+BvxJv 9C1G3lhMLkxl1x5i5OCPagDc/wCCbIP/AA9N/ZeJ4z8VPDJ/8q9nX9rdfxUf 8E5Bj/gql+y9/wBlU8M/+ne0r+1egAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACsj4g/8AIg65/wBg+f8A9FtWvWR8Qf8AkQdc/wCwfP8A+i2p MaPNv+Cev/Jg3wP/AOxA0H/03W9ewV4//wAE9f8Akwb4H/8AYgaD/wCm63r2 CmIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gDwn9p3/AJH60/68E/8ARklfLv7VH/Iu6/8A9iD4k/8AQLOvqL9p3/kfrT/r wT/0ZJXy7+1R/wAi7r//AGIPiT/0Czrkn/E/rsdEfhR73/wSZ/5NXtvrp3/p k0yvp2vmL/gkz/yavbfXTv8A0yaZX07XRT+Ewe4UUUVYgooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAx/HP8AyBIf+v8Asv8A0qir5r+Hlv8Aav21PhvHiM+Z8LfFKYkX ehzrejDkdx7d6+lPHP8AyBIf+v8Asv8A0qir5q+H0hh/bV+GzqQrr8LfFLKd hkIP9taNztHLfQcmsX8Za2PaP2aPDX/CG+Br7SPK0mE6VqElmY9LsfsFinlx xpiC33v5MQxhY97bFAXc2Mn0WvOP2ZNTuda8CX17eTG4u7zUZJ55f7Mn0zzH aOMs32WctNb5J/1UhLp91iWBNej1qiHuFFFFMAooooA/JH/g8r/5Rq/DT/sr Wm/+mjWK/Cj9jf8AZ9uv2jfjZpuhW4PlGVWmbbkBc8/1r91/+Dyv/lGr8NP+ ytab/wCmjWK/OX/gj54Vg+HHwV8dfEC5UfaCi2tq2ORw2cH8RQB9d/sveDNM +PP7Vv8Awi+lRgeFfhzaC2Urysku5dzHtk4x+Ffqp8NdDitWtbVI1WK3QRqB 2AHFfn7/AMEV/hgPAXwg1TxHfRmS98W373JduCUzwM85FfpF4Qijt7NJk2gE DrQB2v8AYMVrodxHEAGljPIHfBr4Q/b58Nz6N8aP2fGkQYf4hyr6ZzoOs8V9 qeNfF8vh7wZd3EXzTQxeYq5+97V8fftOfETR/jL8Tv2epp5Vhey+IdwbiNj9 zZ4Y16Tn/gSivRyr/ePlL/0lkz2Pq/4MeE20OC4LsrpOFAwOO/rS/EfwzBBp 8piUHJye2etc/wDB/wCNVt401CZLQZsrZdqSBshzXX+IbsT53HI5Oc9a84o+ Hf8AgoB8DU+KHwc1iVIcX2nRedEyjLYUhvzGP0r8jf26vg9H+0x+zRH8R7FV /tzwgr6bqyKu5pBFyHJ9wRX71/FqW3ltLq1MYkEytGw9c1+UHg/4eW/gP48/ E/4b3akab4skmdEZeFE0e0ED2I/SgD8sv+Ccy7f+CqX7LwPX/hanhnI9P+Jv aV/atX8av7K3gOX4Z/8ABaf9nrRJuHsPjB4eixjBwNZtK/sqoAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigArI+IP8AyIOuf9g+f/0W1a9ZHxB/ 5EHXP+wfP/6Lakxo82/4J6/8mDfA/wD7EDQf/Tdb17BXj/8AwT1/5MG+B/8A 2IGg/wDput69gpiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooA8J/ad/5H60/wCvBP8A0ZJXy7+1R/yLuv8A/Yg+JP8A0Czr 6i/ad/5H60/68E/9GSV8u/tUf8i7r/8A2IPiT/0Czrkn/E/rsdEfhR73/wAE mf8Ak1e2+unf+mTTK+na+Yv+CTP/ACavbfXTv/TJplfTtdFP4TB7hRRRViCi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigDH8c/8gSH/r/sv/SqKvm34bz/AGb9tr4aSFXZ E+F/ihjtUseNb0Y8Ack+wr6S8c/8gSH/AK/7L/0qir5t+G8Iuf22/hmjMwVv hf4oU7WKtzrejdCOQfpWL+MpbHuHwD1uHxLoGs6jbpdR29/q89zEt1ay2s6q 6owEkMqrJE4BGUdVZTkMAQRXdVwvwD0SHw1oOtadbvdyQWGsT28b3V1LdTsq BFBkmlZpJHwOXdmZjkkkkmu6rVbEhRRRTAKKKKAPyR/4PK8/8O1fhpj/AKK1 pv8A6aNYr8+fg/FL8Pv+Cc/hmzj/AHd14s1BlXacFxkDn16mv0H/AODyJd// AATb+GaknLfFrTF6+uk6xXw3440yTRPgR+z1pMSDaLf7YVz9471wa66GGjUj zOpGPk73/BMlyt0P1l/ZC+H8HhT4P+HNCeFIjYWcYYqNvzYGTXvVlr0en39t pkYbH94ng/5xXz/8NfifP9hiwFU+Wo6eldFpfjqaTx3YySzqYi+CCeVP8+tU 8HH/AJ+R/H/IObyPcPGcgv8ASbuIfNhCDx2xX5Q/H258SXX7R/hdLP7RBb/8 Jbei1XeQN48Pa1yP+A7q/Snxv4wfwroGpXt3NFDbiMhGOQGJHAFfGPxktf7e +L3weu7C3Ei3fji4iLgf6wt4b10sP++a78qwq+sXdSO0ur/lfkTOWmx79/wT 0S+HwjjfUVeO5gdlfeck+le7eMfFX9naPDccsd23GeteG/s7/EmyjnuNESSO 1uZh8sbAgsw6496634x6tPpfhu1t5pFjnaTfz/AMda8/6or29pH73/kVzeRr eLIrW7sDehFaVgDg81+YP/BQNn+Hn7dvgvXYx9nt9YhigbaMZYSN/Svu25+I FxYaYAJUfI6nnPvX59f8FUfEF1q3xK8BX7qFSzu49rjA/wCWn/16tYKL3qx/ H/IXM+zPifxj4aHhr/g4Y+AqBFRbj4teGpgAMfe1ezP9a/rMr+WT9ofTI9P/ AODgz9mZwpD3nxD8Jz5B7tqtlya/qbrmr0lTdlJS9L/qkUncKKKKxGFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWR8Qf+RB1z/sHz/+i2rXrI+IP/Ig 65/2D5//AEW1JjR5t/wT1/5MG+B//YgaD/6brevYK8f/AOCev/Jg3wP/AOxA 0H/03W9ewUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAeE/tO/8j9af9eCf+jJK+Xf2qP+Rd1//sQfEn/oFnX1F+07/wAj 9af9eCf+jJK+Xf2qP+Rd1/8A7EHxJ/6BZ1yT/if12OiPwo97/wCCTP8Ayavb fXTv/TJplfTtfnv+yD8VfFXw0/Zhnn8LQNqd/Bo9hd22lmaKEahOmh2RjhEk ilYi7BV3kYGQTkCuu+GX7TPxF8bS/DLVvEF7daRq+qeD59Q1PTEtzbRQXjDT jIrwMOsbSSKok3MmWHUkm41UtDNwbPtqivnL/he/ir/oKf8AktD/APEUf8L3 8Vf9BT/yWh/+IpfWY9h+yZ9G0V85f8L38Vf9BT/yWh/+Io/4Xv4q/wCgp/5L Q/8AxFH1mPYPZM+jaK+cv+F7+Kv+gp/5LQ//ABFH/C9/FX/QU/8AJaH/AOIo +sx7B7Jn0bRXzl/wvfxV/wBBT/yWh/8AiKP+F7+Kv+gp/wCS0P8A8RR9Zj2D 2TPo2ivnL/he/ir/AKCn/ktD/wDEUf8AC9/FX/QU/wDJaH/4ij6zHsHsmfRt FfOX/C9/FX/QU/8AJaH/AOIo/wCF7+Kv+gp/5LQ//EUfWY9g9kz6Nor5y/4X v4q/6Cn/AJLQ/wDxFH/C9/FX/QU/8lof/iKPrMeweyZ9G0V85f8AC9/FX/QU /wDJaH/4ij/he/ir/oKf+S0P/wARR9Zj2D2TPo2ivnL/AIXv4q/6Cn/ktD/8 RR/wvfxV/wBBT/yWh/8AiKPrMeweyZ9G0V85f8L38Vf9BT/yWh/+Io/4Xv4q /wCgp/5LQ/8AxFH1mPYPZM+jaK+cv+F7+Kv+gp/5LQ//ABFZXiv42+Kb59At 21m4iiuvEuiW8vkKkLvG+qWquhZADtZSysM4KsQeDTWITdrCdNo+oqKiK5zy aCvXrV+08ieUloqIr160FevWj2nkHKS0VEV69aCvXrR7TyDlJaKiK9etBXr1 o9p5ByktFRFevWgr160e08g5SWioivXrXgn/AAUE+OXj34CfCm01b4daOPEn iN7y2gj0nYv+nq95axSLvYERYhkmbzW+SPG9wVUih1UHKe0eOf8AkCQ/9f8A Zf8ApVFXzX8PQ0n7anw3RV3M/wALvFAA3lOf7b0bjcOR9R0qvo3xc8R+Lf2w fB+kX+sXk2lv4Y1S7kswRFbyzx6x4dSOV0TCs6LLKFLAlRK4GNzZn+Hk32b9 tb4bS5jHlfC3xS/7x9ica1o3VsHA98VCleVyuWyPZv2ZtLuNE8D31ndxGC7t NQeGeL+0p9TEcixxhl+1Tqs1xggjzZFDyfeYBiRXo9edfs0eJD4y8DX2r+Zp M39q6hJeCTSr/wC32MnmRxvmC42J50XPySbF3rhtozgei1vHYze4UUUUwCii igD8k/8Ag8lfyv8Agm38Mn/ufFvTG/8AKTrFfE3irUotV+Gn7NOoFlNtdac1 uWzwGWRcj619p/8AB5px/wAEzvht7fFrTf8A006xX5w/BbxXcfFf/gmn4a1O D95f/CfX2WbHJENwFK/hmM/nQB+uXgTTbrai25JXaMcZr1DwB8HJ9a1qC5uE eNEbLDHBHWvH/wBmv4pWnjrwDpOrWTK8V7bo4Oc4bHI/A5r6m+EHidtTQQtw duVPpQB84/tjS+LPib8dNC8CaNFLHolmYnupIwSZGbBO49MAVY+OOgaN8F/i F+zfps7o7SfEG4MpYjJ3eGddi/8AQnWvo/VI9F8KeLJ9UmgSS9ugAX7gYAr5 f/b1lPiX47/s8Twoqxn4gSBTn/qB6s3P5V6OVf7x8pf+ksmew39rb9nrW/DO rWfi/wAGvc+daS+c6xruwcg5wOveve9F0U/HT4T6PqGoIsGpTWsckwA27X2/ MMduatzfFKGO2SyNss8aja5PPaum028hs/Cu+3Ty4/LJ2jtmvOKPDfFHwxtd I8xZbl5CmflUgV8C/wDBUa1jvfGvw/0iziLy3moxKqjliDIB/Ov0K+IN4Lqe WQkISefrX51fFnxBD8cf+ChOlmJxJ4f+GNr/AGnfyjlFNuWmcZ/ACgD5L/aL 1uHU/wDg4m/ZxtYnV/7K+JfhOzbBzgpqdiOfev6oK/jx+DnxWb40/wDBdf4E eImJxqPxl8PyqCc4U6zaY5+lf2HUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAVkfEH/kQdc/7B8//AKLatesj4g/8iDrn/YPn/wDRbUmNHm3/ AAT1/wCTBvgf/wBiBoP/AKbrevYK8f8A+Cev/Jg3wP8A+xA0H/03W9ewUxBR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeEf tOsB4+tP+vBP/RklfLv7U5z4d1//ALEHxJ/6BZ16Z/wVG8O2Gjs/i+1+C3gf 4w+ItL0qJXsdX02znv3s0lnZltnntZyzIzFhCoUuGfbufaj/AD5458BaZoHw t1LVP+FZeAvhl4l1P4b+IzqNh4d0+yjNuCLRlhkuLa3hExUYzwVViwUsMO3L JLnv/WxvFvlPUfg7+y7c+HvhtodrqnxL8V+DtXg0uwt7/SdKWwEdpcQ2VvBI khudLu2aUNEQSkojwFAXILvvS/sw2M2swag/xs+JDXdrDJbxSFtG+RJGjZxj +wMcmKPkjPy8Yyc81+3T4g+Kdn49g034d6TbeRqGvt/aesNNG0unRLK7bUif AJkZY03EMAryZCkK1eb/ALT/AIo+Mf8AwtHwDJ4OvNU0nQLW60ObxHpVhpQu 7l0vNVit543aSzmguoliEgk+zXlnLZxiW5c3KtFGOdV/3vsrdFr01vpfvpqu l0dDwrWHWIbVm2rX10trbtro+tme7/8ADPMX/RcviV+ei/8AzP1W1X4JafoV qs998fPiDZwPNFbrJPLocamWWRYokBbw+BueR0RR1ZmUDJIFfI+pftS/tHfD z9nDwNcN4TufGHj6Hwg+p+JEl8AXsKXd/NpOsXNpDGYLr93PDd6ZBBdReSBI 2o23lGAzJGO58RX/AMTZn8d2njzU7DUdG034g+Brfw81r4ffTY5Y/tOhvdzR SNNJ5kD3ck4VGLvEySK0sgKCPez8v60MXGx9Ef8ADPMX/RcviV+ei/8AzP0f 8M8xf9Fy+JX56L/8z9bdFZ+18g5EYn/DPMX/AEXL4lfnov8A8z9H/DPMX/Rc viV+ei//ADP1t0Ue18g5EYn/AAzzF/0XL4lfnov/AMz9H/DPMX/RcviV+ei/ /M/W3RR7XyDkRif8M8xf9Fy+JX56L/8AM/R/wzzF/wBFy+JX56L/APM/W3RR 7XyDkRif8M8xf9Fy+JX56L/8z9H/AAzzF/0XL4lfnov/AMz9bdFHtfIORGJ/ wzzF/wBFy+JX56L/APM/R/wzzF/0XL4lfnov/wAz9bdFHtfIORGJ/wAM8xf9 Fy+JX56L/wDM/R/wzzF/0XL4lfnov/zP1t0Ue18g5EYn/DPMX/RcviV+ei// ADP0f8M8xf8ARcviV+ei/wDzP1t0Ue18g5EYn/DPMX/RcviV+ei//M/R/wAM 8xf9Fy+JX56L/wDM/W3RR7XyDkRif8M8xf8ARcviV+ei/wDzP1No/wAALCw8 SaPf3nxg8faxDo+pWmqCyu5NLS3untriOdEkMOhRybC8ahtjqxGQGGc1q0Ue 1fYORHuP/DQtl/f0n/wIuv8A5Fo/4aFsv7+k/wDgRdf/ACLXh1FL2rDkR7j/ AMNC2X9/Sf8AwIuv/kWj/hoWy/v6T/4EXX/yLXh1FHtWHIj3H/hoWy/v6T/4 EXX/AMi0f8NC2X9/Sf8AwIuv/kWvDqKPasORHuP/AA0LZf39J/8AAi6/+RaP +GhbL+/pP/gRdf8AyLXh1FHtWHIj3H/hoWy/v6T/AOBF1/8AItH/AA0LZf39 J/8AAi6/+Ra8Ooo9qw5Ee4/8NC2X9/Sf/Ai6/wDkWvLPjxpafHLX7O8X4l+J vCVvZQGFLHRXsmgdixLSsbvR7iTeRtXAcIAi4UEsWwaKPaMORCfCH4J6J8OP i3Z+Lr34j+KfFV/Z2EmlQrrEloYLWCa8srqYpHaaValpGawhALuQBnjnNdV4 ITQtD+InhvxnLer/AGxofh2/8PJayyyRwiO7voLmV2C27lmzaW+whwAGkBVi QV5aij2jvcORH0J8CtRTWNH1y7TYVudZnlBQkqdwQ8EgHHPcA+wruK85/Zh/ 5EG8/wCwg/8A6Ljr0au6Hwo55bhRRRVCCiiigD8i/wDg87/5Rk/Dj/srOm/+ mnWK/LL/AIIdfGPSovHPi34X+IpI49F+I2nrZCSQgrFOu7YwB64Le3Wv1N/4 PO/+UZPw4/7Kzpv/AKadYr+c/wACeNb74f8Aimz1bTbiW1u7KVZY5I3KspBz 1FAH7U/8E6fizffAL40ap8KfFJa0+zzSJbGU7fLkVgCgz6jkV+qHwrv49LVJ mfgrkflX45WXxF0X/gqh8IdM8WeDWTR/jP4UgU6vbo4ifVI0GPPjZeTIDjrj IPtXsn7Hf/BYCDwatr4H+Klrfadq2nH7ENS25R9ny5lyQQ3HPvQB+ofjnR18 SbLm2IZTxjHSvmj9rjT30r4sfs/LKxZR8Q5MY46+H9ZFe0fB74zaT460GG90 rULW/srhdyMkgbcPzr5u/wCCn37RHhv4WeNfgfqGpXQjTS/HD3k4XBZIzouq w7uv9+VR+NejlX+8fKX/AKSyZ7H1RongkXsY2DG7vj1rodX1aCx0/wCwIclF AJ9xXMeD/jBpOveF4r6xvrdrOeIOJhIADkZ/CvlL9tT/AIKteCf2aftFjp8k niLxKxaOO2tWVkjbB5ds8DivOKLv/BR79qW0/Z0+EuoNbyRyeIdTH2ext9+G yxAL49ACT+FfA3xV8Ut+xR/wTP1/WNWJHjr44+dDZq/yypp5G1pT3O99/wCA rtvg34RvvjzrV1+0d+0LePZ/DLRnNyLNpCGvCuVjtreNsAgsFDHPIz1r89/+ Cn37euoft6/tGXXiBYTpnhnSoxp+haVH8sOn2qE7FVB8o65OOpNAHCf8E6WZ /wDgqp+y8zHJb4qeGiT/ANxi0r+1Wv4qv+Cc/wDylR/Zd6Y/4Wp4a/8ATvZ1 /arQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO/FnUF034c6 vKdnzWzRAsSBl/k7A8/NwMYJxkgcjoq5D49f8kn1X/tj/wCjo6mT0GtzxD9h H45Wuh/sPfBq1dtK3W/gbRIjuuLjcSunwDkLbkA8diR7nrXqv/DRNn/e0j/w Juv/AJFr5E/Y3/5NE+Ff/Yn6R/6RRV6RXE60jf2aPc/+GibP+9pH/gTdf/It H/DRNn/e0j/wJuv/AJFrwyij20g9lE9z/wCGibP+9pH/AIE3X/yLR/w0TZ/3 tI/8Cbr/AORa8Moo9tIPZRPc/wDhomz/AL2kf+BN1/8AItH/AA0TZ/3tI/8A Am6/+Ra8Moo9tIPZRPc/+GibP+9pH/gTdf8AyLR/w0TZ/wB7SP8AwJuv/kWv DKKPbSD2UT3P/homz/vaR/4E3X/yLR/w0TZ/3tI/8Cbr/wCRa8Moo9tIPZRP c/8Ahomz/vaR/wCBN1/8i0f8NE2f97SP/Am6/wDkWvDKKPbSD2UT3P8A4aJs /wC9pH/gTdf/ACLR/wANE2f97SP/AAJuv/kWvDKKPbSD2UT3P/homz/vaR/4 E3X/AMi0f8NE2f8Ae0j/AMCbr/5Frwyij20g9lE9z/4aJs/72kf+BN1/8i0f 8NE2f97SP/Am6/8AkWvDKKPbSD2UT3P/AIaJs/72kf8AgTdf/ItH/DRNn/e0 j/wJuv8A5Frwyij20g9lE9z/AOGibP8AvaR/4E3X/wAi07/hoW09dJx6/aLr n/yWrwqqeuX8umWBnit5LoRSxGVIwTIsPmJ5rIACS6x7yoCkkgDBzimqsn1D 2aOm+OvheD43eLrfVo/iP4m8IC3s0szZ6I9m1vKVeR/Nb7XpFxJvO/Bw4XCL hQck+ZeOv2VrXWfBnieKP4o+Itf1nVfDmoaHp/8Ab8lotlaNdCPMjCz0iCQn MSDliAC3yk0/xTB4q0HQtdtY4PEa3lrZz/Yrz7DDJLG8EG5ZJoTGCGnnIiGE 2lVV1ADqx6fRtPfTtLWGWXUruW1nmtpLy7hjQXZWVirxmNERlMTRNwoKlsMA wNNye4kuhyvx21r4ZfCnxdZP438XeP7XWvF6yajHHp66rcxSMVill2rbz7Y1 X7QgxtRecKMDi9oPg/4Y+K7q8t9K8beKtUuNOCG6itNduZ3tt+7bvC35K52t jPXafQ1Q+LXhDxDdftN2XiG4sNMv/B1n4bsrLyRITfxTfZxK80aBPun90m3f klPukdfFvjX+w/a/tJ/Dy8063XVNBt/EPiRNW1A3H2Qm8gt7OdbeO5sbuxub a7hF08bfZ54l2nbOH3wxo3gVcdjKWcRwMlGVKcOdSipe7bTllK3JzN/ZTvbU 7JRpVMNGpQg4uLcZuUl7z3i4RWvLbRt317H0YPgv4MOP+J38QeTj/kI3v/yb WR46+EPw50rwzPqur3/jzULLw+0etfZ5Ly6mDSWciXMZCPfbGZZIkYBvlJAz xXxtoX/BO/4zeAPhtr9v4W8QeGPD/inxNe22rvPY+LfENrp+lXlr4Ws7G2Fr bRsIfs66rFMzQzwyxPZJbRtG3lRRwdfqH7Fnifw/4l/4WD4q8TX+tX/h/wAN /EG3lP8AwlOqSoV1PVIrrTIlt3ZYWihsEeGSNlCCSO32rJ5MUkfvLdK/9Wv/ AMA5baX/AK3PrDxJ4y0fwbDDJq+radpSXD+XE15dRwLK2M7VLkAnHYUmheNN G8TkjTdX0zUGGeLW7jmJx/uk14t/wUC+Efir4v8Aw30yy8J6ZPqd/bXMkrpF Jbxtt2bdoeaSNFLAkZLfl1HKf8E/vg/4l+F/iu/XxH4G1rwyv9lukN5dzWEs UsvnQ/uwbe5lYEqHbLALhDzkgH6vCZHldTJ5Y6ri1Gur2p21dttfM8LEZnjY Y+OFp4dypu1532v/AJH0f/wsHQV+IuneEX1jT4/E2rwy3Fnpzzqs06RqWYjP AyquygkFhHIQCEYiTwj420jx7psl5o9/Df28M720pjOGhlQ4aN1IyrDjggHk HoRXwP8A8FDvAvjrwF+09f8AizQ9N1GaHWbSC50nUbS2YW0F3HFHA0VxOsM0 lvIojDrIqjeshTsa+gv+CcXwz8R+B/hHHd+I77Wbm5urOC2K36PGbicTXV3c 3YR1VlMk99JHyMsltExZs5r88oZtCpL6tKElVU5Jqzt7NRup81kvedlypt7v o7fd5jkFXDqGLp1IyoTpxle65vat2lTtdv3Vq20vx1+isfWsaz+I3h7UfF8/ h6217R7nX7USmfTIr2J7yHyktZJN0IO9diXtmzZHyrdwE4EqFtmsWw+G3hzT PGN14ig0DRodfvDKZtSjs40u5fNS2jk3Shd7b47KzRsn5ltIAf8AVpj00+54 hZ8a/Ejwh8KfC/8AavizWNT0u3abyYzbaW17vOxn4CNuztRz93Hy9ecUeDfi x8PPiBc6dFpHiTWbw6tpker2jDRCiTWsihkfc0gAYqc7CQ4AOVGDjyr9u211 o/AmW90WHzTpVz9pvCkHnzw25gmjMsURBEjo0iOUIO5UcYJwD8df8E+tE1/4 s/HS5kh8TtrVlpFzbXsZjaQW1hALtbm44EMcaTSSIkSRoABDLOOmcfM1uIKu HzCtg6tJtcsHSaUvek5xU038KUYty1a2W6PqafDsMRldLMMPUV1OSqpyiuWK jJxaT1bclFdb3fU/UE2eg8Y1HWSScf8AINj/APj9L9j0H/oJaz/4LYv/AI/X xZ8Wv+Cc2ueKrP4wHw1qOm6C3j3QtQg06xs9ZvdP0y41S/k1lb291CzgAt7m WW3vNOTzpo5pAbT5dpijZuqv/wBkXxtF4i1mO28Y663hi0iii8L2UnjnW0ub OKZ7ganHdXIczXL3EZgEM88kr2JmZrZYzboZ/qND5V6f1/X9NH1SLLQCwH9p awCen/Etj5/8j0fY9B/6CWs/+C2L/wCP1846p8OvH2jeA/hnDr+sf8JDqeke LGuPElxbJc3MOspLBf21lMLeOMtFEt5cabdNCP3dmIWbz5FtvPfgvg9+xL8S fhFbaHpemeK/A+j+CPD/AIkt9aGgnRJLmS7iZbCS42zWz2VtayRzR6hHEsNk IpFmWedHupJZKaSvZ/1sLW1z7LFloBfaNT1cn/sHRf8Ax+j7HoP/AEEtZ/8A BbF/8frwD4R/DG8+HvjzSNOudNMup2eoeK9cn1uGAvZwWGqazNdR2JlcKTNI XtpGRAwT7C4ZgGiMvQ/tJ/AYfHPw/psNtcNp9/Z38KTTxXD27XGlzSxx6pYu 6As0dxZ+au3tKlvKrRyQxSxp20A9d+x6D/0EtY/8FsX/AMfpfseg/wDQS1n/ AMFsX/x+vkuf9ifxPqf7QGi+LZdevLWx0PTtTuYILfxlrbumrXd5plwABJIc 6eEsGQ2Rc2pKowtl3sF+gfDl94nur2BdX0jQrG3khLSPaavLdOknlwkKFa2j BXzGuV3Eg7Yom25lZITSyY7Gj8MfEmn/ABJ8f+NdDjGpwDwprMOkW8qwRyPq BfTrS9aQoZFEWDdbAu58+XuyN20dXqHhrStLCGe91lFfIVv7OiIJHUZE/Ud6 8j/Zp1EaR8a/izdNysHjW3c/+CDR66XRPGN7rGnyNc2V2k1/q9xMqMhCQxyP I5dmOMAHYBxzu46V4WNzStQzGlhIU7xnu7PTfrstjppYeMqMqjeqOu+x6B31 LWB/3DYv/j9H2PQf+glrP/gti/8Aj9fM3xQ/Zi8afEb4teIdak12R9P1S9tb Kxjh8X6zpI0zQw+jyXUMUNg8AS8d4dXdbkP5n7y2jeR4GaGLgPht+xB8VfBu qeHtem8YSXHig3Wk6j4luW8c6/Pb6hcRf2FFfbLNnFk0c0EOuqEe1yDJYFDA FIi96Nna5ytNbf1/X6n2wbLQQMjUtYYeo02PH/o//OaPseg/9BLWf/BbF/8A H68b+MH7Pg+J3jMaxHqeu6e7aUNFlNl4i1DTcW73tvPP5S28iLHM0cRUTptl HChwvTyyw/Y++I3iHwTZWHi/xWmuXmkeM9B8WWrPr1+8ANs+jXN+silVEqNd 2+ryQW7q0MXn2uxYREggIJPR6BLS9tdP6R9b/Y9B/wCglrP/AILYv/j9H2PQ f+glrP8A4LYv/j9ZFFRddh2Nf7HoP/QS1n/wWxf/AB+j7HoP/QS1n/wWxf8A x+siii67BYy9O8daRffGnXfCjNqcVpomh6drBvzbIXma7nvovK8rzMAILLdv 3nd5mNo25PS+KIdI8GabcXmpp45tbOzRpZ528KzGOGNRlnZg5AUDknoBXhmu a1H4a+PnxW1KV1ijsPh/otw0jMFVAlzr7Ek+nFfM37SXxs8L+JfjF4p8VRfE L4THQoNQ1a70jQ0vNUna9a9SaN7qUT2hSK72tbSKsbiMSxRguqRrjhx2MdC3 Kl08vn5njZnmFXDtKmr/ANbn6B+F9Z8KeM/Den6vpusazc6dqltHd2sv9lon mxSIHRtrTBhlWBwQCM8gVf8Aseg/9BLWf/BbF/8AH6+Jfhj+yTqvxm8N+AvG 39sWcOi6l8NfB+nHS5WkeK+kspbm/wB8qgYR455bGSGUFyPLuUZQsm6tn4C/ sVfEL4SfsYWfhSTxhdP8ULHS1tINQtfEuoR6Pay2MdwmjxwQY8qGCJRZrOiW wW5EL/aEudzb+6m1KHM99D14tu3mv0R9g/Y9B/6CWs/+C2L/AOP0Gx0EKD/a WsEHp/xLYv8A4/XyZ4p/Y/8AiJp/iazHhnx9rEfhU3UWk3Om6p4u1ue7s9Fe 5jku3t7wzPJJfSJ9oUS3AkliW4hS2uLP7N5k9H9nX9gDW/hb+1hqfxJ1/wAQ 32qb57/+z4YvFOryiO3fT9Ds7SK4hkkEN1JEmn3SyTzB5Lgi1mfMiqILSX9f L+vl6A7/ANf1/X3n2D9j0H/oJaz/AOC2L/4/SfY9Bz/yEtY5/wCobF/8fr5C 0X/gn3q3gLUvEuq+FPFM2iat8QPGs+s+JfsUVjpIutKkudQke0FzZWUN7NK8 d2qrNNcNNbSASQzLsZJui1D9lfxjrPjiGebxDrNrpU0N7f6kIvGuqzf2jq32 iJLRltZVaGCy+yfa/NtYz5Je7UCN2t4blSNna+n/AA39Ib6n1z4X+ICeDdPa 103X9YtoJJDKy/2PbvliAM5aUnoB+VaCfG++fP8AxU2sDHrolr/8drxT4t6d 4l1H4O+JbDwfqVrpHi+70a7t9EvrhVaGyvmgcW8zBo5AVSXYxzG4IByrdDzX ww+H/if4K+C5NB0bTNP1Sxt7q7ns5dX8Y6hf3kgllu5YxNcXME0zEn7KCWkk 2iaYDcIIxM1N23J5Vo7HtOt/tWatpfxq8OeEk1fUpYdc0fU9WlvG062R7b7J NYxCNYgSH3m9JJLrt8ro275eivfj9Npqxm48W6jbrNIsSGTR7RA7scKozLyx PAHUmvnfxMSf2u/A+eD/AMId4iz/AOBmh15j8bf2efi18VP2pdA1mDxVY6b4 Q8NWl1c6VbICq2uoSxTQR3DxlDvkjikOCzsP3h2onzFoxGIlToyqrVpaLW7b drLp5tuySOrAYSFfEwo1JKEXe8ntFJX16vskk23Y+3P+F13v/Qz6x/4JLX/4 7R/wuu9/6GfWP/BJa/8Ax2vkP9p79k/Vf2jPiH4T1Q6pa6XY6T4a1zRdSsvM kli1Qak+mrLayIFUSW729vdqWJVkka3kCMUKjziP/gnh4i8da/4pPxFj+GHj jw94r1fSdUj0e60eV4dJl0y7SGKfFwZo57m50g/Z5pEjtwv2aKPZIskkg0hU k1duxzTgltqfU37Z37Pnw2/4KE/DHTvBvxgTWfF3hzSdWj1y0tBbnT/JvI4Z oUl32t1E5xHcTDaWKnfkjIBHzZ/w4D/Yv/6J3rP/AINdU/8AllS+Iv2H/G+n /s56B4G8A6z4J8GHwx4z1nxbp8Umly3mklG1O/vdJsvssbQBEga5tJ9ykpDN p8SiKeMmu6/ZX/ZPl/Z0+LXxO11JbCez+Id8usO8KwQSrdte6jNKjxQW0MbK Iri2xcSGa5lYyLJIUhgAFUl3/rQHFIo/Bv8A4I8/svfs++MIdf8ABvhjxJoO rQDCTwapqTHGQcFW1EqRx0INdZ8Uv+Ccn7Pnxo8QSap4j8M3l/fy48yZIZbd pD6t5V4oY98nmuUb9iXQI9F+K9hZeGPCugR/Gx7nS/EkuiRw2MsGmNa3UMcy 4tWiurtpZ5ZmM8eQb2RGkmSCNXwtV/Y88UXH7KjfC3TbD4e6Zb6RZ6nH4d1C wiTTF06+aS/bTdUFtbWkcUF7A/2Cd3thGv2me6liSLyIllFUl37A4JHqXw4/ YG+CfwhjUeG7bxXpMYOQkV9elD/wFr4jqfSqPxf/AOCcPwE+Pl5bXHi/S/Em uT2bb4WnvL1fKbay5AW/A6M3X1ri/wBpv9muH9pT4opo154d0G9iZIdTGoeI 9EXWNDuoodP1a1t7eS2J2yy2t7fxXPlTND5iTHypGaJ/L6f4Z/ATU/AXj6bx kuieGtN8QahpGmaPqMVpqc0suteWLZbi81C/a3WXUJ7eNHjtTNCGCpKTJGLx 1t6hXnF3jK39W/4ApU12GeKv2Xfg98MrjwR4XstN8WPpvizV5NHcDVr6P7Ei ade3nmAC8YyEm0Ee3K/60tk7drUrX/glv+y/Y+JGvv8AhHr2bUonDubi5urh txYY3I98wOSR1B64rV+Il7rd949+Dza9p+labdjxe5WKw1GS+jOfDOqNJl3h hI2zGZF+X5kRHOwuY09jubbTvEXw3TRtT1HU7m0QXUF3ocSyxW1yZbmeVWmZ iIp4SkyEovOUwxbmOmpSa77dfPfXt9/Tcm0U9fyPOPj7+wr8FP2n/CtjoXjX T9f1PRNNRY7bT7d7jT7WJR0Ajt72Nfxx1rxs/wDBA39jQY/4t5rHI/6Cep// ACyrvP2lf2ObD9pr4u/DrWNZlsV0XwKt5c/ZpdK07UXuLp5rF4VEd9aXEaR7 LeYNLD5VwuUEcgDPXiY/4Jv+P/Evxe+Evi3WvFSjWvBq6nqWtauvifVLm6ut XvNJsYjPAn7pVtPt9tK5sEaK1MIRREqyNCkQqSe7/r+vzLlBLp/V0v69D0X4 c/8ABEf9kj4SfFPwv418PeB9Z0/xN4M1W21vRr3+0NRl+x3lvMk0MvlvqDRv tkjRtrqynGCCCRX2t/wuu9/6GfWP/BJa/wDx2vmP4N/BDW/BWsiLVtU1ebRt G1CW90S2fxbqWqzRLLY2ySR3M9wVkulW5fUWRLhplVXt3URlI44N741/CG1+ ND6LpeuaJ4f8UeDmupG17RdZQTWdzGInaGUwNG8dy8dwkOIpdqDe0obzIYlI 6ku4KC2Pfv8Ahdt7nH/CT6vkdR/Ylrkf+RaP+F2Xv/Qz6x/4JLX/AOO18b/G v9nvxR8R7bwGtjaRaRJZeINKuNVsLPW3utJsbaz1DTdXMxMsccjbH0r7JAkU YCf2gzlFUybOe8H/ALEXjHQdd1AXfiWaXw7rGreILm/sdO8V63pMy2uoeIn1 WDyGs54ooJY47mfzJBE08jRJELpLeV0QVSVtxci7H3V/wuy9/wChn1cfXRLX /wCO0n/C7L3/AKGfWP8AwSWv/wAdr4c8L/sa/EK+0K0h174geIH1O4v/ABE+ q6gPFeqXMlvFqAvktZLWFWt7QT20Z03aslq9vG0UzQQ28mZZKv7H/wCyx42/ Z+1zxP43XSfDkWv/ABRu9V1vxBo11dW1s+mTz315eWNv9ss7IyXRjW4WGZrp 7nyyzG2dYkMU753e1w5Va9v60Pqj9ob9szxZ8LJfA1h4YmHiPXPHfim28MWs GqLDpNpbvPDPIJnmjiuXwDCBtERJ35zxg+f+J/8AgpfrfgrxPq2iaz8Zf2Nt J1nQbmWz1OwvfjZbQXWnTxOY5Ypon0oNG6SAoysAVYEEA8Vznx2mvZ/it+zu b+3tbacfF/TQqW9w06GMQ34jYsyIQzRhGZcEKzFQzgB2/Kj9qT9lbx1oP7Wn 7UfiC3/ZP+OXiTUNf8aeKdP06bTfB2rXMXiuDU9R1D/iZRXotGjgt4bcptWM t5zTQsoYF3jVatOnh3VhFzlpZLd3aXorbtuysa4ShCtiI0ak1CLveT2SSb23 d9kkm7s/dXwl4z/aT8feFtN1zQ4fgjrWiazaxX1hqFh49uLm1vreVA8U0Uqa IVkjdGVldSVYEEEg1oib9qk/8wX4Qfh41u//AJR181eD/wDglhq37WH7Pv7B Vz4uht/D+k/BT4aQW3iLRtZ0SyudTW/l0rR0igNnqVjdQIySWcyyl0jniOPL ZWyR5V+zl+wZ+0t8O/Fek/C+w+JHjO11PwN4U0rTtQ1G21fxJp3gy2isP+EH a0srMzQRWkkssVh4mR5LGOU7LtluWRpfITrhHmdnoccna1tT7q3ftUf9AX4Q f+Frd/8Ayjo3ftUf9AX4Qf8Aha3f/wAo68g8I/sH/tFX9x4lPir4rXLWPiHV 9NdrHTvHGuI1tpX/AAlmu6jqFtBcxrDJbTHSb7S7eKaEK5/s/wCz7oItkte7 +Pf2ZfEHie6/ZxaHXJNXPwe8Srq+tX+t3fm6jrES+G9X0rzC8UKJLcvcX8Mj nZChHmkBSFjLUV17/wBMVSXLtrp/SMfd+1R/0BfhB/4Wt3/8o6N37VH/AEBf hB/4Wt3/APKOvpCijlHc+b937VH/AEBfhB/4Wt3/APKOjd+1R/0BfhB/4Wt3 /wDKOvpCijlC58/6JrPx20GK9vvHS+B9F0a1hVlm0LWpNWupJWljQKYptOtV C7Gdt3mE5RV24YstfX/2kLjwtc6emoaz42tYdUvrbTbe7bwU32J57meO3t1N x/qlEkssaKzMFJcc816z8ef+SU6r/wBsf/RyV+Wv7bGo2tt8a/DN/wCJbrwj pCWHiTS/EEOnX3xTaK1nt7G5sV+0yaO+mopkmtrO4jXNwyh7q43PII02+jlO BpYqtKnUm1Zaba/ecWPxU6FNThG/6H3t4e/aMXxboyahpfjO+1CykLqJoNIt HXcjtG6kiXhldWVh1VlYHBBANf8Aic3ijSZbG+8Q6vPaz43p/Y1sucEEciUH qAfwr88Pib8K9Q/b10X4han8NfFdtZPYfE/Utd0vWbHXLvTyJW8HvY2kkVxa Zk+W8u7ZpFztaNJVZX/1beuL+y14kt/EV3Z23iXxEfDeq28llqUknjXWDevD FdWJs/Iy7PazJaC/hlngnjkncxSymR5MweXjaboVpUb7Ox6GGaqUo1Nro9+8 GeA/CHw+8I6XoOkXWs2mk6LaQ2Flb/YVkEEESLHGm5rgscKoGWJJxySa0/se g/8AQS1n/wAFsX/x+vCv2d/hF8QPA3wv8VaP4t8Ytf6xrFwLnTdTjuZr99Me XS7NLplM6r8o1Nb+aOIfu0iljRFjRRFHz2ifs5+KvAvjPQNV8KaZ4L8G6bNp c03ifRtIv54I9T1y7vtKkub+aZbdftskdvZXSJPNEs0pm2uYxK7Jxv8Ar+vw Noq9j6WNjoO0H+0tZwSR/wAg2P8A+P0fY9B/6CWs/wDgti/+P1+bH7AX/BL3 41/sc6Y1m/ivwvFbf2u10bfSfEmsJbXVo+mXkMkMlqyC1Di8XSXSZIBMscVw DMy7IW9t8d/sl/F7xD4I1j+zviFeWfiWW98QSaXcL4o1OK2t4bm81e90tHRR tbyJpNDVgUbEFpcW4LwO8U9yjFPfsKLbV2j66+x6D/0EtZ/8FsX/AMfoNjoI 66lrIPodNjGP/I9fLnxo/Yy1v4zfAXxP4Kv9ZkdPF+uaTqeoNf6pJrNuot9Z iubySCC/t544RLaQRlLIrJZrIfLEQTzJZ9n4Vfs3eIPgjr3h86LcW13p+i26 aElvJrNzYWcumA53/wBnW8Q02O6DNvDW9rDgQLEHSKUrHKS/r0Gz2H4xeNtJ +GHgyDU7VtT1Se41jStJWCW3S2T/AE3ULezMhcSOf3YnL42/Ns25XO4aWPrX kn7QNxrV18I9NbXNP0vT7g+MPCZRLHUHvUbOtaWZMs8MRG2UyKPlO5ERztLG NPW6lsaDH1ox9aKKVx2DH1ox9aKKLhYMfWjH1ooouFgx9aMfWiuQ/aD+Gs3x m+AXjjwhby28E/irQL/R45J1Voo2uLaSEMwZHUqC/IKOMdVbpQmKx1+PrUfj Sy1PRdE0240qCx1C5v4jO0V3dPaJGgkljOHWKUk5jBwVAwx545+XNP8A+CaG i/D/AFTw3daBYeGLm98HyaFHpWtS6Tpuk6xY21vrd5qGoQwSWFjFBbRzRXZj ZIYUFwnnRSGNpjcL9feJf+QP4e/68H/9Kriq0EzgRqnjXv4f8MDjPOvz4/8A SKqd74v8XWDTh/DugsbdEkcR6vdynDOUGAtiSTkHgZIAyRjmsX4Gfs+XXwd+ IvxA16fXv7WXx3qI1NrfybhDZyCScKN0lxKJMW7W0e9lEg8jYrraR2VlY73j CK70TxrZ6w+l3Wv6G9v9hvrG2nZLmEEt/pEIBALruHBBBxggfeHzfFebYnLs A8VhIczTV9G7Re7tHWy3dk9LnLjK06VL2kFd/f8AkLp3ifxbq1t51vonhh49 xU5164UqR1BBsgQfYgGp/wC1PGn/AEAfC/8A4UE//wAhVh/Cj4k6v8SPHviC 8vNHttKs3WNUtrSHyLa1dPl2BDyXYHLHuV5xwKv/ALSXw3v/AIyfs7ePvCGl zWtvqPinw5qOkWsl0zLBHLcW0kKM5UMwQM4J2qxwOAelHCufxzjLYY5RtdyW zV+WTV0nqk7XSeutmPA4j6xRVT1/Au/2p40/6APhf/woJ/8A5CpP7T8a7cHQ vCx/7j8/t/05VzGv/B/WLv43+I/E8Atrm01vTfC+nwQjV7rTJkbTtWv7q5dp IEYsghu0Kw5KXBjeCXZHIzHx2T/gnPaHUvD1zZ+GvA+ix6D4lbxXFaWdlpUl obueSweZWgfSNnyJFNFHcQC3vClpblrjfcyvB9KkrnTfQ928TfEfXPBcMMms WvgTSkuH8uJrzxTJAsjYzgF7QAnHapfC3xH1TxTfRR20Xgi7hdwJXsfEr3Lo mfmKqLUbjjJA3D6jqPOf+CgXwj8VfF/4b6ZZeE9Mn1O+trmSV0ikgjbaU27Q 80kaKWBIyW/xHKf8E/8A4P8AiX4YeLL9fEfgfWvDS/2Y6Q3l3NYSRSy+dD+7 Bt7mVgSodslQuEIznAP12EyTK6mTyx1XFqNdXtTtq7ba+Z4GIzLGQx8cNToN 03a877X/AMj6M8T/AA38O+LdamvtV0DRdTvZNqPcXdjFNKyoqogLMpJCqqqO eAoA4FZ//Ck/Bn/Qo+GP/BXB/wDE0UV8se4H/Ck/Bn/Qo+GP/BXB/wDE0knw R8FlDnwj4XPB/wCYVB6H/ZoooYHWUUUVzGoUUUUDYUUUUCMLxH8PNA8Xaotx q2h6PqdxHAsSS3dlHM6oCx2gsCQMknHuao/8KT8Gf9Cj4Y/8FcH/AMTRRW8f hRAf8KT8Gf8AQo+GP/BXB/8AE0f8KT8Gf9Cj4Y/8FcH/AMTRRTEH/Ck/Bn/Q o+GP/BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUAH/Ck/Bn/Qo+GP/BXB/wDE 0f8ACk/Bn/Qo+GP/AAVwf/E0UUCYf8KT8Gf9Cj4Y/wDBXB/8TR/wpPwZ/wBC j4Y/8FcH/wATRRQMcPgr4NiimiXwl4ZWJrp7goNLgCmRo4kZ8bfvFI41J6kR qOijDf8AhSfgz/oUfDH/AIK4P/iaKKGAf8KT8Gf9Cj4Y/wDBXB/8TR/wpPwZ /wBCj4Y/8FcH/wATRRQJh/wpPwZ/0KPhj/wVwf8AxNH/AApPwZ/0KPhj/wAF cH/xNFFAB/wpPwZ/0KPhj/wVwf8AxNH/AApPwZ/0KPhj/wAFcH/xNFFAw/4U n4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miigBtv8FvB0MlwqeE/D KLdSR+cBpcAE3lrL5e75ednmy7c9PMfGNxy7/hSfgz/oUfDH/grg/wDiaKKm W5nLcP8AhSfgz/oUfDH/AIK4P/iaP+FJ+DP+hR8Mf+CuD/4miiqRaD/hSfgz /oUfDH/grg/+Jo/4Un4M/wChR8Mf+CuD/wCJoooYw/4Un4M/6FHwx/4K4P8A 4mj/AIUn4M/6FHwx/wCCuD/4miigSD/hSfgz/oUfDH/grg/+Jo/4Un4M/wCh R8Mf+CuD/wCJoooYwh+C3g22uJJI/CXhmOSS3e3dl0uAM0ZeNyhO3lS8cbY6 ZjQ9VGD/AIUn4M/6FHwx/wCCuD/4miigA/4Un4M/6FHwx/4K4P8A4mj/AIUn 4M/6FHwx/wCCuD/4miigA/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCC uD/4miigA/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miigA/4U n4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miigBk/wI8D3BjeTwb4U d7d/MiZtJtyY2KspZTs4O1mGR2YjvT/+FJ+DP+hR8Mf+CuD/AOJoooEH/Ck/ Bn/Qo+GP/BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUB1D/hSfgz/AKFHwx/4 K4P/AImj/hSfgz/oUfDH/grg/wDiaKKBh/wpPwZ/0KPhj/wVwf8AxNH/AApP wZ/0KPhj/wAFcH/xNFFIA/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCC uD/4miimBNpnwj8J6Nq9pf2fhjw9aX1lIZLe4h06GOWBmVo2ZGC5UlHdSQeV dh0JrqcD0FFFPoIMD0FGB6CiikMMD0FGB6CiigAwPQUYHoKKKADA9BRgegoo oAi1CJbrRr61lVZLa8gaC4iYZSeM4yjr0ZTgcHjiuV/4Un4M/wChR8Mf+CuD /wCJoopw3JYf8KT8Gf8AQo+GP/BXB/8AE0f8KT8Gf9Cj4Y/8FcH/AMTRRR1G g/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miikNh/wpPwZ/wBC j4Y/8FcH/wATR/wpPwZ/0KPhj/wVwf8AxNFFAMP+FJ+DP+hR8Mf+CuD/AOJo /wCFJ+DP+hR8Mf8Agrg/+JoooF0HRfBTwbFLFMvhLwws1rLHcwuNLgDQyxuH jkU7chldVYEcgqCORXU0UVFbcuIUUUVkUFFFFABRRRQAUUUUAFTvM9zBtkZp FhGyMMchFxuwPQZJOPUn1ooqokyI9g9B+VGweg/Kiir6EhsHoPyo2D0H5UUV EPhBBsHoPyo2D0H5UUVXQaDYPQflTZVAQ8Dof5GiigGf/9k --0-1398234352-1298459008=:63850 Content-Type: image/jpeg; name="scr2.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="scr2.jpg" /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMD AwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/ 2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAHCAyADASIAAhEBAxEB/8QA HwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1 dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwBf2Ivjr+0l +3v4Tv8AxH4P+E/7KOkeGNNv5NMuNW8Q2b2ECXCRRytGE+1NKcJLGchCvzdc g48Jsv8Agr/8X9V1vXbCw+BnwQ1efwzbz3mqHS/CVzfx2NvAQJrh3humUQoS N0udgyPm5r0D/gmd+1V8ZP8Agnd+z7rfgq4/ZF+J/j261LXZdctL25sdRs7e zeS2t4SrW32GVZgDbqwJYEZ4wea8C/Y7+Hfxb/Zs8b+O7zXP2cfjN4o0Xx/4 R1Lwje2en6Xe6Vc20V60RaaKd7G4UMgjIAaIglsnpg/qFLK8B9YxHtKdL2aS 9n7+snre/vaLZLbufMSxVf2dNRlLmfxaaJeWh6n4C/4Kq/F34j+B9b8S6Z8H P2dW0Dw7PDa6je3WlrZJBNNb3lzDGPPvk3vJHYXWxUyWeNYwDJJGj5+tf8Fi /iP4W8Q3+kav8G/gppeq6VcyWd7ZXfhG6t7mznjYpJFJG9yGSRXBUqwBBBBx XbeFvjl8TPA/wTtPB2ifsk/GKy+xaNdeHIrqZbuRDYm08UW1qzRJpif6Sv8A wk80k0oYJO9sCsUHmtt7Hwl+1/8AFTwZ8T9X8T2H7InxUebUfGN74sjtr2K7 miQ6hqekanfxShdNTzpPP0iIW0xwbdXXetw0UbB/Ucu9q/3FPl6fvFf/ANKe /p/wUsRifZp88ub/AA/8A8Xh/wCC0HjSQf8AJK/gUM8f8izOP/bmunX/AIKk fFSHQl1SX4G/CaLTHsU1NLx/Bl4tu1o9w1qtx5hn2+UbhGhD52mRSmdwxXlX jnwV+0d8RPGPjrWNS+E/xC1Sfx7OrXlxrvgaTWtQtooywgigvLq1ee38uMrH mBoyVjjU/Kiget+HNV+IEviDwH4k8RfsyfGPUvGHgrRLHwyL/TbvWNGt5rCy tzb27xJBbCeC78oRxvILhoXTzf3CvIJE7J5XkyjFqnTv19+9tP8AEuul/nbX TFYrG8zXNK3T3fP07a/gaupf8FGfizpV5LDN8Efg9K1tpdhrM7Wnhea7jt7S +S3e1lkeK5ZUEn2q3UBiCJJVjID/AC1Q1v8A4Ko+OvCniG80nV/hF8HdM1TT biS0vLO68LXENxaTRsVeKSNrgMjqwIKsAQQQRmpPhhL8RfCPw08N6LrfwI+M etm20GbwlrcNrpNzp7XOnw61BrlhLBcvbT7ZluxNDIrwMn2eONVIZt0Xteif t1fHSDW4tUn/AGdviCNX1B76TV9Qi0TbcReffrqUUenrJpkkMUcN75kiG/i1 CVBPKIpYWkd24amBy2LahQpyWv27ej+L+rrU0jiMW1rOS2+z9/Q8Qtv+CsHi iaIv/wAKy+CmB6eHZsn/AMmK79P2y/i/J461bw1b/Af4a6nrOhalJo99b6b4 SuNQ8i8RZ3MIaCd1YlLW5dSpIZIJHUlVJHQfEz9qf40/FjSPE+l6t8Cvize6 FrPhu10XT9GvrEXOnabeQ2NxZnU/K/stTHOROJALJrMBk2t5kZCCv8NPi58R PhZLZ6tbfAv4s6h4mFr4fuZJv7PuLSyTUdH07UdJQSxm1le7tLmyuozOm+3k ZzMocKQ5ieCy7kbVCnfoue99uvMrW1v8vQI4jFXV5yt/h/4D/q5wUP8AwVF8 RP8Ae+GnwaA3bR/xTsozx/13ro9K/b58a6v4E1PxNB8LPg6+i6NeWun3tyND YeRPcpO8CbTcbm3LbTnIBA2YJBKg9/4O+Pvji/8ADUWga/8AAb4o6Xpeoadb 2OtXOjaXObyby4tFtR9hLwKllD5GjwtJEwnEx3RZiRkeHf8AHn7UHi7xv4hv Lo/s2fEryr7WLi7m/dzQR3FqZtflgX7ObGWNZv8AifSNMJGngmMO14WSSRWi eFy6/LHDw/8AA1/8kvMca2Ktd1Jf+A+Xp/Wx4vB/wUj16Q/8k3+EJ+Xdx4fk wef+u9aWm/8ABQnXb69jt1+HXweV5XEYMmiNGiknHzM04VR7kgV7Rfftt/Fm 68UnUoP2f/G0cYupL2OyuILya2jmfxDDrLSbRar8/wC68ncMNnbJnKhK5Lxb 8Y/idr/wf8T+C9P+CXjPTNP1PS9N0XRryWxuZtQ0e1t47WK63zLbR/aJLqOx s43OI1VIAgQjo4YPLm/eoQX/AHEX+f8ASIlXxS2qS/8AAf8AgFa9/aT+JGlX OqRXHwT8BZ0S9vNOv3i8KzSxWtxaI0lzGzpKV3RRozvz8qAsfl5rAtv29tVn Yg/Dz4UDb1/4kTHH/kavT4Pj7498O6BJJo3we+IT+KZ70+IBq15p0qwrqt1o sWm6hPLaiB/PEkgubhSJIx5ky7kZVZHk1D49fEHxZZ6vZan8GviKlnr0kupX c9i9zb6jZahLdWNyxsbhrR/s1kr6fb+XbFJGTnMzEKVlYLL+ZJ0YW/xr5/a+ 7b9QeJxVrqcv/Af+Aec2n7cuo3H/ADT/AOFQH/YDfH/o2r9r+2pfS9fAPwtJ HXGhNkf+RK9bt/2pfHp8Qpqj/BDxn9sn1hNcvR/pPlS3H27RbuRY1+y5jjLa PtUMXZRc5LMY/n0/A37XnxK8MWGj20vwi8ZGPRodPTdBaKJr9reC0hlE8k9h M4jk+xQMFhMTo24l5CEKZ/VcBa/1eH/gxf5jdfFf8/Jf+A/8A8lsf2sdXvtK kvk+HHw5NjBNHby3A8POYopJA7Roz+ZtDMsUpAJyRG2Ohx0Xh74z+K/FVno0 umfCrwDqn9vz3FrYRWGgNczXMsCo8yCNJC+5UkRiCAdrA9Oa6q2+PPjfUbmw uL/4W/EMX+l3NlfWt7bOwuEuba71WcSMbixmVwV1V1wy8GFWyQSob4V+J/in Q/Eeq6yPhd4rtZk1aS90HT7axnSDS4Z9PuLG4jEhT5MqbF8Igj/0QKiQoVVV 9TwFpfuoeXv/AHfa7/h+KeJxWlpy/wDAf+B/TMDTfi54r1NEeH4SeEplk09t WRofC0zB7NSQ10CG/wBSCCDJ90EHmp9P+MGu32n2t2vwv8GPbX0c8ttMvhyQ x3CQKWndDuwyxqCXI4UA5xiu5X9p/wCJV5rOm6vcfDHVxrNr9jtrieKwnihk sINSnvmso4/KPlROXtovvMQlrg7t5AxtF8Wa7ptj4Z00fDDxgNG8OW2vWIhV 5VuJbfU45YsCU2xCvEkn3ijBiudq5xULB4GzvSprt7976P8AvLy/EbxOJurT l/4D6eXqZui/FDXde0u3vbT4XeELqzur1dMgng8OSSRT3bAMtujBiGlIIIQf MewrRuvG2saZ4efVbz4b+CbOyX7ORJPonlGRZ1laF1VnDMjiGTDqCvy4zyM9 H4P+LXiHwTDo2m2Hwx8UN4c0GaxvbSyvDJLMl7bai92brzVtlG94pZbc4QfI UPOwKa0ninXdVu5YZ/A/imPRIPDkeg20dxbPeXOIJxdWxmfy4kkAmjSIkIu2 D5cMVyx9TwHO70ocv+LW3/gW9vxEsTi+VPnlf/D1+7Y5eD40xTk7vBngEAZH /IIx/wCzV2niKz1vwn4bg1jUPAPw8GlXTRpDdWtvbXUUrOZwoBilbvbzA+hQ g4JGe71D9ofUU8UW13Y/DnxY9p9qj1SeC4LrMt3Hql5fLtlEJDQkXbAqUDA4 G47C0ln/AIaR1PUtM8PW9/8ACvWbpvDiaUbRzczoFmsWlKyYEPO5ZmXacgdf mOMczoYRtONCFuvvq/8A6UrF+2xKunUle38vX7meR23xItZRz4N8EL2P/EpX rj/eq7B43sZcZ8H+Chn00pf8a6r4lfELxN8VPB0OlXPhPXYZIjaE3EvmzFzB 9tycGMfe+1+vHl993HE2vgTXFxnRtVH1tJP8K9ChgMqlFurCEXf+a/6nHVxe PUl7OUmv8P8AwDYg8UafMefCXg38NLX/ABq5Bq+my4/4pTwgM+mlp/jWZa+D NYQjOk6n/wCAr/4VpWfhLVQBnTNQGPW2f/Cr/s3J+0f/AAL/AIJm8bmXeX3f 8A0ILnSZcZ8KeEhn00xKuQW+jS4z4W8LDP8A1DUqra+GNSXGdPvh/wBsH/wr QtdCv1xmxvB9YW/wqf7OyftH/wAC/wCCL69mXeX3f8Ant9G0Obr4Y8Mj6adH XVeDfhR4W1/Rr6+vdI0SzhspoYQIdEhnZzIsrZ5dMACI+vWsK00a8TH+i3P/ AH7b/Ck8cfEHxH4D+Gmrad4d8JeIdc1vViJLO5tLazntdNljilWN7iG4vbR5 VLzKdkUgLBHBeMlSfNzbBZfDCylhVHn0trfqul+x25fi8bPERjXcuXrpbp6d yz8XND8EfDHwraalFo9jqD3OtaVpJibQbeAKt7qNtZtLuErZMYnMm3A37Nu5 c7hj6B42+EXiL4oS+EINC8axatFcT2pnu/g9qlrpZeEOWI1CWNbModh2SCbZ ISoRmLrnyvVvFvxj8Z/CyHS/FPw813Xdeg8T6Rq8dzpOl6XodotpaajZ3bxG KXXLx2l2wTYbzFUl0UquGc6eg/F747wfFF7vVfBd7e+CvtNwyaVaeFNOtdUW Bt/kIb5vE0kRdCY97i1AkCsAke4FfirYi3wr7kfVXo3+J/ez1uaP4SaH4I8G 67r+o+F/DNv4+mgttDj1PSrK2kvri4eRbe1TfcKHuZBExESFiSCF3YzXjX7S /wAUvh5rfwR1uT4cXPhnxLpuv/D/AMU67YeJdGS28qCXTkghH2eaCSVXbzbh 8urqY2tyMEk7an7SXwy/4af/AGU/B/wi8ZfDP4u3nhvTbTSm1+DQ73QoINc+ zTi7NhJJNd+csPm+WJGg8mUtAdkuw5fgte+FOs/Dn9mSTw5D4b+INj4Y+GXw d8U+G7XVPF0+jvd3CPBZfZISNOkKMIoLRk3GNCVRCzO7Mx3lGXtJJpcuvbto YRnHkTTfNp37q45OlOT7wr2xf2Ntox/wkf8A5T//ALZSj9jjB/5GP/yQ/wDt lcftodzu9nI8Vp6fdFe0/wDDHX/Uxn/wA/8AtlKv7H20Y/4SLP8A24f/AGyl 7aHcPZyPGE606vZh+x/g5/4SL/yQ/wDtlL/wyCf+hi/8kP8A7ZR7aHcPZyPH F6CnJ1r2MfsiEDH/AAkX/kh/9spV/ZGKn/kYf/JD/wC2Ue2h3D2cjx2pF6Cv X/8Ahkk/9DD/AOSH/wBspw/ZMwP+RgP/AIA//bKPbQ7h7OR5AnWnV6+n7J4Q /wDIfz/25f8A2ynf8MpD/oO/+SX/ANso9tDuHs5HkS9BTk6166P2VgP+Y7/5 Jf8A2ylH7LAH/Md/8kv/ALZR7aHcPZyPI6kr1n/hlkf9Bz/yS/8AtlO/4Zd/ 6jg/8Av/ALZR7aHcPZyPJo+9PXqK9XX9l7b/AMxwf+AX/wBspf8AhmD/AKjY /wDAL/7ZR7aHcPZyPKqenSvVP+GZP+o2P/AP/wC2Uq/szbf+Y3/5J/8A2dHt odw9nI8sXqKfXqI/Zox/zGh/4B//AGdO/wCGbP8AqMj/AMA//s6PbQ7h7OR5 enSnL1Fenr+zbtH/ACGv/JP/AOzpR+zhj/mM/wDkn/8AZ0e2h3D2cjzKnp0r 0z/hnQ/9Bn/yU/8As6Vf2dAox/bH/kp/9nR7aHcPZyPNF6in16SP2dgD/wAh j/yV/wDs6d/wz1/1Fx/4C/8A2dHtodw9nI83T7opyfeFejr+z2FH/IX/APJX /wCzpR+z9g/8hb/yV/8As6PbQ7h7OR51Ui9BXoX/AAoD/qLf+Sv/ANnTh8A8 D/kLf+Sv/wBnR7aHcPZyPPU606vQR8BcH/kLf+Sv/wBnS/8ACh/+or/5Lf8A 2dHtodw9nI4Fegpyda74fArA/wCQp/5Lf/Z0q/Azaf8AkKf+S3/2dHtodw9n I4KpF6Cu6/4Ufz/yE/8AyW/+ypw+CWB/yE//ACW/+yo9tDuHs5HCp1p1dyPg ng/8hP8A8lv/ALKl/wCFLH/oJj/wG/8AsqPbQ7h7ORxK9BTo+9dt/wAKZ/6i X/kv/wDZUo+DeP8AmJf+S/8A9lR7aHcPZyOKqSuy/wCFOf8AURP/AH4/+yp3 /CoP+oif+/H/ANlR7aHcPZyOOTpTl6iuwX4Rbf8AmIf+QP8A7Kl/4VJn/mIf +QP/ALKj20O4ezkcjT06V1n/AAqb/qIH/vz/APZU5fhTt/5f8/8AbD/7Kj20 O4ezkcmvUU+uq/4VXn/l/wD/ACD/APZU7/hV/wD0/f8AkH/7Kj20O4ezkcsn SnL1FdOPhhj/AJfv/IP/ANlTh8MsH/j+/wDIP/2VHtodw9nI5mnp0rpf+Fbf 9Pp/78//AF6VfhwVH/H7n/tj/wDZUe2h3D2cjm0+8KfXRD4dEH/j9/8AIP8A 9lS/8K9P/P7/AOQf/r0e2h3D2cjn0+6Kt6FJ5Ot2bhY2KTIwDoHU891YEEex GDWuPABA/wCPz/yF/wDXqHUPh1PdafPFbaxcadcSxskd3bwRtLbMRgSIJA6F lOCN6MuQMqRkEdaHcPZyO6TxAzqT/Z2gDHro1n/8ar5V/a8/b/8AFnwd8BeV 4R8Gxahq/hL4e6J8QvGGuHwPomr6dY6ddWV28n+jy6rpkxmaSxuZP3PmKqpt CEuu3V8Hf8E+tN+HPxI1Xxj4c8XXnh/xZrvnf2nrWneEPDFtqOo+dKs0vnXC aWJJN8qq7bidzKCckA1nfEf/AIJl+GPjLeaVdeMtcHjC90XTbfRrO713wV4W 1K5t7KBdsNuss+ls4RQTgZ6sxOSxJwUkt5Gjj5He/Bf4jar4l/aA1CfUn0+7 Enw88O3bRv4aOk2n2ia51Tzp1024DPaSPsTckuZkVEjdiUrlfhH+3hd/tI+B vEsln4V0TwrJoV9pNg0htbSV9YS5vPKk1HTiLdPP0adAwstRGw3Wy4IihMAL dLp/7MOuaf4h1HWl+LHjltd1O3s7KXUTY6P5q21s1y8cCp9i8oKZLqRmYoXJ CAMACDZuP2ePEOqSWy6p8WfG+rWcF1BdPZ3Gn6NHFcGGVJVVzFZJIFLIudrq cZ5FPnW9xKPkaVt8Vr+4+PnjTwy0Fn/Z3hzwvo+twMAwnea7utVikVySRsC2 EJUBQQWfJIIAzP2dP2h9V+N3irxtpd/4Sk8PR+C78aYbx9c0++bUpvMmOfIt pXltgbYWc4W5SJyL3AQqm9/YdI1zTPEvi99B0rwx4i1HVRbveLDFrNmhaBDE rS/vFUhN0yqCQCWSTAIUmpY/EPhiSzlufsWqLbwPJHLI+pRoIWjYrIr7oAUZ GVlZWwVZWBwQQMYyjKm5q1k2r+fmb1Iyi4xkrPf1TtY8s+JX7Sfhn4b+Jr3w vN4m8FQ+OZ9Gl1XR/Dmqa7HZXmqKsc77wmJJvJ/cPuljhk2rHIdrbCtYmj/t M2mv/HDwZpOm6p4c1HwZ420rVLrT9btrxZotRu4TYva29rOreXI0ltJqE2wB 2ZbV2UhYnJ6rxN+2L8J/CP7UWmfBy/bW4vHmpWr3BshdBks2EYljgncW+Ipp Yd8qI3JSPJ2+ZCJO28RfELwd4TkgTULbVrdrkMYgb4MXC4yfltzj7w61r7KU YxlJaSV12ad9V3XYxVaErqPR2fk+xTvrlrGKIrhi0qId3TBYA/oT1rwz4L/t vTfHKwsrvS/BE+mxtrj6Fq1tqPiLTXvNBkiFq0r3MVpLcGIxtO8LQSFLlJ2t lkhijlkng9vj+NHgS88F63r1naa5f2Hh8T/bPKvFVwYY97qoeBcnGMc4JOM9 a29Y8UeFvD9l9pvbPUra3DpG0kmqRhVLsEXJ8jgZYc9BWSj9ktyuro82/Zh+ K1x8b/2evB3inUDYR63qulQPrNrZkiLTdRVAt5aFCzNG8FyJonjdi8bRsjfM pryfxL/wUS8PfDn9oGHwZ4ivrC20vTE1BvFGuuLWw0vwkTcMNPXUZprwTWLS xrGkZmtxFetdwyW8oRWQ/Umm674d1bToLqDTdYeK5jWWMnUEUlWAIyDACOo6 gU/WNS0bwt4lj0XWtC1zSNYng+1RWk+rW7vLFlhvDRROvVW43bvlJxjmtFSl K8orRfh/wxDqRTs+p51D8SNX1P4BJ4vs/Cmq/wBvT+HxrMHhi6dbW+Nybbzl sJGYYjl3kREsMK2c8Cvnr9tT/goDf/BT4D6s/gGTw74+8QRaZrOl3GvaHr2l W1v4b16xto5ZUktrq5d5JI4Wubr7IgnmUWgVkZXMifV/jz4s+CPhrp/2jV7b WLdGjeSMC+VjJtKggEW+Acuo+Ygc/WpfBHxO8E/EbTJLzRYdUvbaKQRO4v1T a2xXxhrcH7rLz/UGocftdCubp1OB/ZK8c6p8S/2Vfhl4k1y4+2654g8J6Vqd /cmJYjcXE9nFJLIUQKi7nZjhQAM8ADArmvBf7UN9qelaadVTwpa3Gs+Kdf8A DljFPqbWU162n65PZRQ28TJJ59w1lBPOyh1y8GAqxu7wexeJPip4C8H6xp2n 6pNNYXurOI7SGfWIkediyqAoMHJLOigdywHWtHWvE/hbw9pU97e2ep21rbLv llk1OMKi+v8AqPXAAHJJAFbVsPUhapODipbXT19O9iYVoSbUdbb67f8ADng9 t+2FpijwxcajdeExb+JNbj8MRyad4ntpraHUjblntt84geWdLuKW1+zwJJOf kYxKRKkPT/tSfG2L9nf4QnxXcXej6fY2muaLZ313qsnlWlnZ3erWlpczu5ZQ nlwTyuHY7VKgsCAQfTtA8YeEfFWlR3umwahe20oBDxapGwGQDg/uMhsEcHBG RSeFvFvhvxfZNPaaTriot7PY/vdSiT54rh7d2/1OAu5CQc5284zxWXLzNRS6 /wBIrmt7zPlbUf8AgoDdaF8RLzwxdaRpF1a6ra3mteEfGZ17TdM8OeIdNWye /SaMzXMlzMIE2Qzy2sNyqgfaWWCORIh6Uv7Veg6r4bvDpGr+DNV1uXSrjXtE sovEiGLWNPWVo4b4SLE0gtWIh3zJFIiGQhTKNrSe0Qax4euJrmNdO1QvaztB IF1WJtrL64g7ghh6qynvXJeNvij8MT8JNf1fxdbfYPBkH2vTdWfXNStlspY1 ne0kjlDwlGSRgUCn74dVxlttNxs+RrXYFNW5ugvgLx5pfxK8NW+s6JqOm6zo 96okstQ0+7S7tb2IjKyRyJlWUg9VJHoTXjnxL/bq0PwfbeJNS8Nan4Y+IA8B q8finRNI1Szju9CK30UEkl1eT3MdpZmKI3T+RdNE0pt5NrqsUpHrHwR+PnwZ +LS6ta/DzUdL16LS5hdahHoet2syW0t28kxeTy4SA8r+a5J+Yv5mfmzW/wCJ viv4E8HeKPDuiakL+11jxZcS2uk2bakhmvXigkuJdoFucKkcTEscKCUXO51B apScuWMde2uxPtYqPM9j5Y+H37emv+L/ANo7TNF8Q6FYeCdO0TwhqWqeKbNt dtdRtNPuEm0aRZJb2BGjj+z2t4+9PMVg0xeSNbc213Nl2v8AwVj0Lw38IPB+ oeJLTR4fFN7e6Jp/ieyOrQ2EXh+C6W2i1DV5UdpJILCyv5prGRpQAt3bvA7q 4JH2wt9oLMQdM1YDGQf7Tj5/DyKxbnVvAnjq6vdHn0mfUptGuraee1ubyKUW 06NHc28m1rf7yusciOPuvHkEMnE6J6r+r6/nYtS2OZ+DnxMg+MHgKDxDZ25h 0+/ubtbGUTLNFqFrHcyxQXsUi/LJBcxRxzxsuQY5kIJBzXifxO/bd1z4Z+DP E2sv4fvJ4/DdvqsEcl3oc9lb6he6NC93eqshmZkgvLdJVtZNjiNraRmacMkZ +nNV8b+DtCvoLW9g1S3ubqCa5hi+2mRnihMYlfCWzbUQyxBnbCgyoCQXXN6L UvD88KSJpurssqLIhGpR4dWAKsD5HIIIII4OR61U6E4pVGrJ7b+n+f8ASJUv snxB8Zv+CoVvonwH1O7ktrbwFrmufB638eaDq97qMD2MOq39veNZ6UhnRRPe M9uzRR7G85YZvkAjIb0CP/gojoVzY/2hDZafbaZZWsDaxcX2rbP7GvliuLvU dJmEEUwTULDTrK8uJrdyjKy28Z2+eJE+mJfEfhtNdh0uPSvEFzqVxE88Vpb3 PnzyohAdlRLYllQsm4gEL5iZxuXL7fWvD09zcwHSdegubNxFcQT3ghlgcorg Mj24YEqysMjkMD3pNKScorS/y9P67BGvCUrRd2vM5/SL2+uPDVrcanZQWOpS WyS3VrFObiO2m2bnjWTapdVbIDbV3AZwM4rwD4q/t4T/AAc/at0f4f6l4al1 DS/Emk6FJY6pHfWOnRWd/f3t9bNBLJe3UQlZ0thJDb2yyXEnk3G1HA+T6k+2 6CT/AMgzVsd/+JnHx/5AriPAfxjl174i3/hfxT8EfjT8N9Rs9Ph1OG51+XT7 rS7+N5XiMceoac93aLcIyAtbyypMUdXWNk3MqjG95W0KU1Gyf/DnO/Av442n xkuNZgs77wtqP/CPXD6dfPo2tjUms7yOedHtrlREggnESwSNEWZkaZ0I2okk voVcJ8Uf2qfC/wAOPiZovgvQ/AHjTx74k1uK7uWs9D8SaDZjTxBPpkBSd9Rn tI1lll1iwWOJWaSQzqQuCCfRx4x8HNZ6hdJ5s9npNzdWd7cQa5byw2s1rNJB cxu6xFQ0MsMqSDPyNG4OCpAUknqtgalHSSZTorm/CH7SngTxjr8tlDpHiG2g E8lvBfzarafZL11m8oCJlRid7Z25UBhgDllBi1z4la5eeK9Xs/DHgTSdU0/R 7iO0e51TxxJp8zyNBFMcRx6TOu0CZRkyZJDcDHK5H1EpJnU0Vx//AAmfj8/8 018Hf+HLuf8A5RVZ03xN47vpykvgDwNZKF3eZN8Sb1lPI4+Tw+xzz6Y4PNHJ 5oOY6eiuZ1bx1r3hC/0GTWvCXg/+zdV1/SdDlbS/Ht7dXcH2/ULexWVIptCg jk8trgOVaVMqp5ziu++3aB/0DNWz0A/tKPk/9+KHC3UFK543rPxG+KWlftIa H4et/hnoeq/DPWPPF14vtvFire6L5dqJE+0adLbIW824Pkp9nnlwoMj+X9w9 h+1Re+OrD4DW7/DS30a58cvYRxaSmsW5n05ZH1CRWe5VZ4H8lULs7Ru0iKrM kVw6rBJh6J+2B8NviDokkvhCDU9c1KLWNI0SWwur6TTZrSfUr+KxhadJrMSx ok7zRSERsY5rK8t2Antpokd8Cv2o7D9qW78L23hHwp4n1Gw8Q2P2rRNUvdF8 W6Vo1zatG92ko1G68MRWnlyIWZHaUCTeioWLKDpGnJ7IlzSOc1z4kfEnwz+x tpHiO78M2U3xbGiadPqHhm0gkuba51aTyPP02N4pHECPK0kC3bySQ2ysLiUy QwyBud+I3i/xH8Qv+Ccnj3V/FmkHQfEt74O11tQ0n7LJD/ZMywXStabnJ+0e SQIvtceIbry/tESrFMij6K8eeG/E3w7uLdL7wdosi3SsY3g8ayMp2kAg50oE EZHbHPXrjxj9pzx1beJf2YPj1ot3oUukav4f8CNfK8Osi/t54r621VAMm1hY Mp0+TI6fvFOeCClTd7DTvsfMn/BZb9r745fs26z4Ss/g7repJqOqrDBJYPqt 1a2UUOzUJ5p9sU8QVgsAJOckZGCSteBfBT/goZ+05rfxj8OaF4v8eaumleJf BL+KLa40zVdUgPnrex2xizLctuTa6ycopxImeh3fVf8AwUw/4JnWn/BSqDw6 LDW9Y0bwxpi215p8t/dXlrrhMf2nyvtAvbeS4Enl3Pz/AGgCfzFPmYYNnyb4 V/8ABDrX/hh8e/EHxJk8cyeI/FHia2ktb2XVdQBh2vJE/wC7jhsoljC+SiIi YREAVVAAAKnN7WnyySir8ye72sl2tq3q+isaUXS9jU9pFucuXka0S35m+99E trau56nL8QfjNFY+IWi+I/xAup/C+otYX7R+Ip1tkw5jDK7zq7FnGAoj5yOc 8U+38Z/Ht76S2PjXx2s8TMrK3iuQAbWuFJyZ8AA2s/Ocfu/QqT2OgfsyfFPQ r/VJ21rwZejWrwX99Fc+Y8VzMHL7mQQgEZZ/lxtG7cAGVGW3d/s+/F27af8A 4qPwlEk8s0zBEO4GVp2YbzblyoNzNhSxC+YcYrb2i7o5+R9jgPEPxM+N3g3T LK/1bx147s9Ov3WOC8PiS5mgkLIHDBo5W3LsOdy5BwwGSCKh8Z/HX4qeBfEk +mXPxO8ZXUsCxv5tv4hvTHIskayKRuZW6OOoHINdXc/sa/EG/sLO2uNV8I3C WG3yXdC82FGFV5Dbl3RRgBHJUAAYwABL4r/Y7+IHjjVhfanqvhSa6ESQ74g1 uCqDC5WOBQSBxnGcADoABSqR6tC5X2OAj/af+Jh/5qL47/8AB/d//HKsx/tQ fEo/81C8c9f+g9df/HK6hP2E/Fy/8xLw3/4ET/8AxqpV/Ya8WL/zEfDv/f8A m/8AjVV7SHcXLI5mL9p34ksefiD43/8AB7df/F1Yi/aZ+JB/5qB43/8AB7df /F10afsReK0/5iHh7/v/ADf/ABqpU/Yp8VJ/y/8Ah/8A7/zf/GqPaQ7hyyOe i/aV+Ix/5n/xt/4Pbr/4up4/2k/iMf8AmfvGn/g7uv8A4ut9f2MPFKf8v2gf 9/5v/jVSr+xx4nX/AJf9A/7/AMv/AMbo9pDuHLIw4/2j/iIT/wAj740/8Hdz /wDF1PH+0X8Qj/zPnjP/AMHdz/8AF1uJ+x/4mU/8f2hf9/pf/jdSp+yN4kX/ AJfdD/7/AEv/AMbo9pDuHLIxY/2iPiCf+Z78Zf8Ag6uf/i6sxftE+PyefHHj D/wc3P8A8XWqn7J3iNf+XzRf+/0v/wAbqVP2VfESdbzRv+/0v/xuj2kO4csj Mi/aE8fN/wAzv4v/APBzc/8AxdWI/wBoHx4f+Z28Xf8Ag4uf/i60of2XNfA/ 4/dG4/6ayf8Axupk/Zh15f8Al80f/v7J/wDG6PaQ7hyyM6P4/eOz/wAzr4t/ 8HFx/wDF1Yj+PXjon/kdPFn/AIN7j/4uryfs0a8vW70j/v7J/wDEVKn7OGuI ebrSv+/sn/xFHtIdw5ZFOP47+OT/AMzn4r/8G9x/8XU0Xxz8bnr4y8Vnn/oL 3H/xdXE/Z31tf+XrSz/21k/+IqVP2ftZTrc6YP8AtpJ/8RR7SHcOWRWj+OPj ZsZ8Y+Kf/BrP/wDF1Yh+N3jTv4v8UH/uKz//ABdTJ8BtXTH+k6b/AN/H/wDi KmT4Hasv/Lxp3/fx/wD4ij2kO4cshkfxt8ZHr4t8T/8Ag1n/APiqmT41eMT/ AMzZ4m/8Gk//AMVTk+Cuqr/y8af/AN9v/wDE1Ivwc1RP+W9gf+Bv/wDE0e0h 3DlkOT4zeMGP/I1+Jf8AwZz/APxVTx/GPxef+Zq8Sf8Agzn/APiqYnwk1JDz NYj/AIG3/wATUqfCzUF/5b2X/fbf/E0e0h3DlkTR/GDxaevinxH/AODKb/4q p4vi54tOP+Ko8Q/+DGb/AOKqBPhlfr1mtP8Avpv/AImpU+Hd8vWW1/76b/4m j2kO4csizH8WPFZ6+J/EP/gxm/8Aiq2fBvxV8RP4ht/tWua1fQ4bdDNqVyFf 5T3SRW468EdPwrCTwFer/wAtLb/vpv8ACotX8G+I7fTJW0HUNF0/VgB5E9/a S3lunI3bokkhZvlyBiRcEg8gbSpVINaMajK56F8RvjlqnhL4f65q1tE8lxpe n3F3GkuramyO0cTOAQLoHGQM4NV/iR8fte8I6l8QZLGw0qbTfAlzcQut54s1 mK8vFito7klEjV48lZAo3SLllOdo5r5n8Ffse/EX4f8AhjxHpNh8RNIu7fxP afZLptbXxJr7wrtkXMP9oa5cCE4kbOwDdhdwbauOk8a/Av4nfEDxHrWp6j4v 8ACTxDM89/BbaFrdtb3DMgQ5iTWQmCiqpyOQozmsFLuy3HyPRvAf7SHiqTxf 8ZDNHrfiSLwrrENvo+j2uqzxTFP7Gsbk20LvcxKXlnmlIa4mCgyAb0QcZfwo /wCChOn/ALUXw58LeLvh5c6rP4Q8U68+madrct7qEP8AacMMMzSzwQzXBkRV uIJLci4iRiYZSqFGilfjIP2e/Hy6X4xt7nxD8M9WX4gXr3GvWupeDrm60+7g Nha2Itxbm/HyNFbt5gkeQP5uNqgENq+Hvg145j8fWeravrXw5a1Grf2zqSaL 4UuNOu9VuRZGzSSSdr2UMyxCJdzIzFII0yABhqSvuHL5Gn+xz8n7IvwrB4/4 o/SP/SKGvR94ri/2MPHGt2v7Hvwnii1jVIoovBujqiJdyKqAWUIAAB4AHbtX pn/Cf69/0G9Y/wDA2T/4qsHa5auY+8UbxWx/wn+vf9BvWP8AwNk/+Ko/4T/X v+g3rH/gbJ/8VS0HqY+8UbxWx/wn+vf9BvWP/A2T/wCKo/4T/Xv+g3rH/gbJ /wDFUaBqY+8UbxWx/wAJ/r3/AEG9Y/8AA2T/AOKo/wCE/wBe/wCg3rH/AIGy f/FUaBqY+8UbxWx/wn+vf9BvWP8AwNk/+Ko/4T/Xv+g3rH/gbJ/8VRoGpj7x RvFbH/Cf69/0G9Y/8DZP/iqP+E/17/oN6x/4Gyf/ABVGgamPvFG8Vsf8J/r3 /Qb1j/wNk/8AiqP+E/17/oN6x/4Gyf8AxVGgamPvFG8Vsf8ACf69/wBBvWP/ AANk/wDiqP8AhP8AXv8AoN6x/wCBsn/xVGgamPvFG8Vsf8J/r3/Qb1j/AMDZ P/iqP+E/17/oN6x/4Gyf/FUaBqY+8UbxWx/wn+vf9BvWP/A2T/4qj/hP9e/6 Desf+Bsn/wAVRoGpj7xRvFbH/Cf69/0G9Y/8DZP/AIqj/hP9e/6Desf+Bsn/ AMVRoGpj7xRvFbH/AAn+vf8AQb1j/wADZP8A4qj/AIT/AF7/AKDesf8AgbJ/ 8VRoGpj7xRvFbH/Cf69/0G9Y/wDA2T/4qj/hP9e/6Desf+Bsn/xVGgamd4W+ IniL4Wa/qd3oejaPrSavbW8Ev2zVZLFoDC0zAjZbzbw3ndyuNvfNc1oVjr2k +FdTmT+zLfxJf3+papbqtw8ltBPcXc9xEpkMYYqplQFtnYnaatS/tAX2meK9 bttXtPDegaLo8cLx6pqFgipqDSBPkhSO0cuyljuCkkBScYDYNU+OepjwdBqc Om6XbSvq8GlywXWj24ePdqCWkuV2AqwyxAYAg43KCCtdOLoSrYN0Kq9ySs+7 umu/a5hRlGNfni/eX/A8jM8S22qfELx/pOt6n4Q8HaLe6fAlk+r2+pTalqBt IzK6W6tJbR7AZJGLSbixVpVORIxHPftCfCvXPidHpS6PdWdoLQy+e01zJCzB jHwpRG/unOenHWtD9pj9pzX/AIK+FrnU1h8H6JoNvpdxdXPijVtHju7HS7xb mzhtLWW0t4xcyLdefOPOiyIDb5ZX3AVh/DH9ulr/APYov/jBriaLqlhoOhXe t6j/AGN4ahthMtrE8txFBFcZJaNkkhBaTbI8RYFVYBfCyvDUcvqxy3D87bjz Ju8o2b5Uua7SenwXTS1tZnqVsPUqUPrUkkm7aX8/K343+Rn6P8LdT+FP7NHj zT9Unsbi5urXULvdbSvIiq1oF+ZnVSWyhJ47iui/ae+EerfHP4YDwxpmpW+l QalfW/8AaU0sQlLWiP5kiKhU7mYqoxuTAJO442N4l8Xv+Co/jb4f/t6QfCm0 sPh8dLl1vSNMBm0BWvniu0tWlYSBxGHHnvt+TACjINfWHjv4sN4Q8Faxqt/f 6BoVlpdhPe3GpT+HYb2LT440LNO8EarJKsYG4xowZgpUEEg16mX5k3iZOkmp U5cvvR0b20vpJdmroyxmVzjSSnqpRvo3e1lLsuj6abrdMxvh3ompeGvA2kaf q93a3+p2NssNxPbRNFDKwGPkUksF7AFicdSep6PxD4/8T6r8OdU8Nzyw63Hq Y3rf6lc7bq1kG0psWOLaQjIrgnB3E8jg15R+xR+2BqX7SnwxutZudT8Ea/d2 d/NZ3NzofhV9PsYmSR1WJPtamSXdCIZ9+Ewt2iMivG2fbvG/i9vCWtauj/2N BY6ZNMGmn020VY442YbnYxgAADJPFdrrVaNR66vXb57HmxhTqwi1tbT8jyf4 x/Cm5+JP9mzWmoRWc+lmRljli3JclihClgQU5QfNhsAn5TVT4D/C3WvhodW/ te9tboXxiaIQzySBCu8MTuUAEgqOOoX6VufHT9oG+8M/s1+NfGHhu48N3k2j eHtR1KwuYtMs7i3aaC3lZTkIQwDpgjPYg16d4R0vwp41/wCEp+yfE/xfbHwX 4kXwlq66l4W0rTWg1NxbGKBBcaShmEovLUxSRb45hPGY2fcKxhGUo8qZvJpO 9jx3xT+z9ofiPxVc6wou4LvU7m0n1EG8uXhu1tpYZYgIfNEKPvt4R5gQtsDr /GTXR+O/CMPjvwjf6TO2xL2Exh8E+W3VWwCM4YA4zzivaPDHwDs/GOnSXel/ EnxBfW0V1cWTyRaToDqs0EzwTRn/AIl33kljkRh2ZCO1YfxZiHwaltrNr6zv ILXTzdXWo6lptgkj/vJSXkMcEcahVAHCqMKCcnJO+JrV5xiqs21Hb8F+SS9E jKlCnFtwja+/9erZ5j4W8G2nh25ur5LS2ttU1VY31Jrct5U0yg5ZVPAyzMc4 BOctk81k/Dj7S3gS/Nibc3i63qskDSEKFcalcEEOVbY4OMOUcI21tj42nrfA Xxxh+JHhe11bSn0O4t7mNGZV06yd7Z2RXMUgVSFkUMMrniug8W+LZPCe+SVt GhtIbOG7mln020RIg8CSuxJjACjJ5PQDk1yLe9zfyPDPgN4J+Iml+IbnU/HF x4dtyqXEENro1yZ4riOSWN4fMzbQFWgCyoD84cTk4j2kSeZ/te/sh65+27+y nP4I0Hxqvgsy+LtRu72Z9P8At9vqVst5fI9rJFvQdZUlVzkxyQROoDqrr9R+ EvjPH420yS70ubw9eQRzyW7Nb2djOoKnAO6NWUZUqwBOQrDIB4EP7Geq2f7T vw70OO58feK7fxzH4O8PeKPEFlH4Q02zsoRq1m08L2s0+l+XcQl4rlMwyy7G hZHYOpFaw5pS509TOSSjytaHx3+w1/wSX/4YM/a+8a+PPDPxG1nUfBHjLRvs Uvhi8tUh2XvnxSi6YweXbsEAuUjVLePy0uWVeNxfoP20P+CW2h/tnfF3SfEO p+LfEWlacImh1jT4JBK9wqRsIWtXlDra4cqZECMj8sqxyNI8v3B8FPAeh/tB /Dy38U+F/iJ43uNHubm7slN/4T0rS7qKe1upbS4iltrrSYp4njnglQrIinKZ 6EE3PiX4Tk+EGm6bbxaidblvZZ5HudR0rTvNVQIgsYENvGm0HceV3Zc5JAAH XQxmJwtT6xRnyyta68zCph6NaHsakbrseEfDz4HaV8MvgVpnw90+51KTRNL0 caLFNcSI120Qj8vezBAu/HogHtXNfCz9lmD4WfE6bW7XW7+4sVtzDDbswWRy ww6ylQFZQQGUADnaSPkBb0/wL+0DafEhb46QbGcabN9mufN8Opb+VKOsZ82B fnX+JRyuRnGRnpfFfjU+FPD+n38/9lRx3Nt5shbSrZsuZ5IlRFWLcxYqoCjL MzYGSQK4ZVJyblJ6vc6IwSSSW2x594s+Dvh3xh4ktfEF9pOjXHiDShbDStRm 0mCe601Y7gTSbJnBYeYVRAVCvEDI0bo77hr+HrnWL3TC2upa/wBqCaVXe3uD OLiNZGSKZj5USpJJEscjxogWN3dQ0gUSO7Svj1PqfjufRlsdIgjh3RLNNpcK NNMvzGNR5G37m5wGcOVBcIYyshwfjV+2BpnwVtJI79vDx1jyEu7bTrqwtrNt Rt/MxcNbyPD5btDErySZZY4V2PcSW8L+eumKx9R0owrT92NrLtb+vzM6k6dJ OpPQ5P4nfA7XvHnx10nxCusyW/h3RrW3C6daXj2V1c3KTTSmTzxG/kgfuQCi lzl9rRMqse/1zSJviA/jB9YSSwTxZKQ66bqlxFcW8Rs4bZilzGIpY5P3bMJI yroSpVgwzWx8B/i/cfGNrUXGnaZpsh1Z9LvIEsbS4+zOsoUx+csPlSSxghJf JaWOKdJoRJIYWNeXftIftpap8K/ENx4f0W38Ox63Y+HrzxPNJf8Ah+CaCW2t rS9uTFGqGMlyLGXLbvkGz5H3/LhlOXQm5QwkVeo+Zvq7d7vSx5+HwmEw0pYy mrOerd3rf+tixafsz6XD+y7e/Cm717xhrekah4fudAvNZ1rVG1XWrtLiJ45r iW4uVkEkreY7AMhiXIVY1jVYwfs1Dxz8P/h/o3gbXfBPh7w94c8P6Jpul2d9 afEy/wDEk0n9nWqWdugsZNJsbS23w7nke2EYZ1XMRzlOq/Z4/abk/aC+C2ge MLTTbDTotZgZmtptLtWaCRJGikUN5Y3LvRtr4G5cHAzgdp/wmt7/AM8dI/8A BVa//G60qQlSnKnLRrR/LoespupTir+7e6V9LtWvb0Pln9or9nb4j6v+1V4P +Jvgq18E66dC0u70a4sdU1m58MXcNvLPYSKYdSs7O5vRIVj1BS0Utuq+fACs kYuYrr0v9nL4b6/4M+FGrWPjCDSrLXvEnibxL4jvLfRdTnuraxGra3f6ikMV 00VvK7Rx3aoZBFGdykgDivW/+E1vf+eOkf8Agqtf/jdH/Ca3v/PHSP8AwVWv /wAbrGCjFcserv8AM6sTiq2IcXWd+WKivKK2W3r97PnWy/Zw8Q6d40hljk0p tMt9bXUEkk1S6kuDAL4XOGRoiHl2/KWZySf4jmvQNB+2Jq3jttOgtbq7GuwP HHcztbxOBp9jnLqjkcZIwp5wOM5HpX/Ca3v/ADx0j/wVWv8A8bo/4Ta9/wCe Wkf+Cq1/+N1bnfc5VG2xxnh3TL7w3othp8NjY/ZbCGK2Vvt7FgiCJenlAEgC b0B2J03t5drxfrNpo2jOLxNReG7zAfsFtcTzjIOdvkKZEOM4cYwccgkV1X/C bX3/ADy0j/wVWv8A8bo/4Ta+/wCeWkf+Cq1/+N0rxvcep4jdXNnLongbw1oc Hj3WrmLx14YvZbrVdL1Ce5mEevadLPLJPJCqJGkURIUbURUJ5Yu7erkZ+uOP Y1r/APCbX3/PLSP/AAVWv/xuk/4TW9/546R/4KrX/wCN0SkmCTR5XrHwc0nw j4c8P6f4P8N6LotpZ+MPC2qXVvplpBZRLbafqel7pCqBQ3k2NkiKAC3l28ca g7UWur/4J+eOvil+zr8E/hN8K/G3h74QReGfh/4U0/wzeeIdE8c6vf39x9h0 9LdJ47CbQYI/3skSZRroeWrsQ0hUK3U/8Jre/wDPHSP/AAVWv/xuj/hNb3/n jpH/AIKrX/43WkK3KrEyhfcd+2b46074r2VholhpHi7WtPMTSXF7omojSnRj IjCNZhcwXCsDEC2zClXCksCyj5u/aCsdWu/gf+0l4g1HR73Q7DUvh5Z2Fkt5 PbPLcS21v4jefCRSuQqi7g5bGS+B0r6P/wCE1vf+eOkf+Cq1/wDjdeY/tr+K LnUP2OPivDLFpwR/B+rcxafBEwxZynhlQMOnY0KonK4KLR2niDVJvEDtfR2c tg15cLcvaPIrPaqzbjGzKSrFc7TgkHHBNfIv7L3hf9ozwP8AtdePbjxXY6fH 8OfGfjvVNSjnSxsbm7FjDZJa2Mks6ahEbaJ4YLNI1S0upmaCQzCAMJH+Rf8A gvr/AMFb/j/+wP8AtieG/B3wp8bWPhvw7qXg211m4tZ/DGkaoz3Ul7fQs4kv LWaRQUgiGwMFG3IGSxPw/wD8RJv7Zn/RV9I/8IDw1/8AK+tY05O8r7idRJct ulj+gb9pu/8AGPh+6+H+teEvDvibxZDoviR7jXNJ0O/s7S4vLF9K1GAA/a7m 2hkRbqW0co0mcoGAJQY8R8UXPx48T/tUfDfxNH8OfEdlo3hfTfFMOvPFfaGv 9o2V9BDdabp8CtfPJ9riltLSGdyY4vtEblJXt28yvxn/AOIk39sz/oq+kf8A hAeGv/lfU+n/APBx3+2pq12kFr8UNNuJ5DhY4/h94bZmPsBp9EaDjs/6tYft 9nY/o0uoL74gfC+SIpq3hDUtc0zHzPC1/o00sOOTG8kJmiZuqO6bk4ZlwT45 4x8O/GzxDd+AT4e8Sah4WsNYuJ5vE8U+h6bq95pKT3CXiwiVruOOM28aSWIk ijuw3nq/lHb5qfl38B/2/f8Ago/8b7Nb4fEXRNE0raHkur/wF4ZiSJD/ABEt YBcfjXaeNP8Agpp+0Z8K9NUax+1r8PdR1Vbm3t5rHTvBXhu4kRZJ0jlkATTv +WaM8hGf4AOc4rXD4KdWtGlBq8mku127fqQ6yUdj9TvgBJ401Gw1K+8Z22sa XcXDQtb6bfz2FwbbzY/tcqrJaIoIhkujYDeWLLpiSZYytJJxXxr134w33xC8 WW3gXQdastO0vwHrLaVf3M+lPZa34hdLM6YsKvM06eWftiuZ44YixGS6hGH5 36H/AMFJP2jPiVosjeGf2tvAa6qXdIrDUPBPhm3mkIbAwG04DkcjnpXmnxl/ 4KNf8FHPhDpD6m3j7T9T0peVubf4e+G2Rh6gjTyp9sHmsfYO97/1b+mUqvkf pr4TT9pCX4nW+mavfNB4K1P7daNqkWi6W2q2fkxTxwXcp+2+Ta+c32aRFjt9 R8yRJHkSwjkFrD794a8Qalf+M/EWn3mg6xYWWnyQPZ6pcTWr2mrLJHlhAscz zr5TDa/nxRZZgU8xcsP5y77/AIOPv209MunguPijpkE0ZwyP8PvDasp+h0+o v+Ik39sz/oq+kf8AhAeGv/lfTdBvqJ1V2P6AfGWn+N9N+LvjTXNNh8Q3uhr4 f0BtKsdNvbPzL27t7/U5b63hiunEEbzW72kTSSeUGV1AlRow8Wdrdt8SrPQd fisR4sv9UstCN7pVxexaLcW13em3hWOEQpLbO9zHNbySMsksFs5vJAJgmwW3 4H/8RJv7Zn/RV9I/8IDw1/8AK+vYPGv/AAX8/aw0n9gb4aeNrf4k6RH4m8Qf EDxbol/ef8IN4ePn2djp3hma1i8s2JjXZJqF425VDN52GLBUCioNdR+28v6/ p/gux+8vg3UL7WfCWmXupWV1pmo3dtHPcWVy0Jms5GQM0T+S8ke5Sdp2SSLl Th3HzH5N0vT/ANpDSPgR4F1nw/p2v6H408O+H7bw14w0jxM1j4kufE00UEQT U7CNdYhtA6zyXReSa7t5ZkOZFlMNvGfxs/4iTf2zP+ir6R/4QHhr/wCV9H/E Sb+2Z/0VfSP/AAgPDX/yvoVBpt3/AK/pi9rpax+/fhi/+JmoeKIr3UtLv47L SvEOoafLD5Wn2kuq6fJKy2l1Cq3V0GgiSRGcO9vcSeQZBHFt+yXGMvxA+JP9 kT6w3gb4uWckD213HoK3HhWa4uVNzbQvaRsblkDBbaaZ2kuAPJv5NspmEUdt +Dv/ABEm/tmf9FX0j/wgPDX/AMr66XwJ/wAF7f28fiXPs0b4gWd4o6yD4e+G hGP+BHT8U/YvuN1k+n9Wt/Xmf0ReDILyy8L6bFqNxNd38VvGtzLMY/NmkCgM z+Wqx7icklEVCc7VUYA8X+Fvwn8e+EvFdlcyeIPHt+ksJivzrN9Zz2x/s23f T4CkaOdh1Fpv7RZgDg24WXy2Ijr8lNJ/4Kh/8FDdRgSSX4j6MgYbii+APDjn HpldOqnr3/BVv/goZodu0r/EbRwgzw3w/wDDiH8M6dWn1Gah7e6s219yT/US rpNaban7RX9h4u8Nal4e08y+LPF1pPZGwvroHSIkin8uA/bLsbYGxmCUYtVI DXkn7koIjBxHjHTvj/qHhjTpPBV7aabe2huob2LxlDYz3F88MVvHDLCdPHlL FcPDPKdwDj7ScpEAsMf4meL/APgv1+3Z4DkK6t8QrSzGcb3+HvhsofxGn4rn /wDiJN/bM/6KvpH/AIQHhr/5X1kqDD2mlv66f5fn3P6RfGPh1vF3hDVNIW/1 PSzqlnNZi906cQXloZEKebC+DslXduVsHDAHHFfGfjL4TftM/GM/GrVbfUtV +HOn+JfDyv4c0aK9/wCJ2b2AaiLeCGVNWuLLTJjixS4khEkdzG6SobO4Mgg/ Ib/iJN/bM/6KvpH/AIQHhr/5X0f8RJv7Zn/RV9I/8IDw1/8AK+ksO11H7Y/b Hw/8CvjT4R8QeJVvPFXiLxLaT3XgtdLnbWkWOOxsPENwL/5f3RE02kpbTXhK 4nmnnjjLxrHCnqHjL4keJPDPxhvtHstA8da5De6ZFc2cljY2f9lWssYu38gz SFGFxOI2HzO0KGK3DtbtOn2j8XfBX/Bfv9q/Vv2BviX42uPiTpEnifw/4/8A CWh2F5/wg/h4eRZ32neJprqLyxY+W2+TT7NtzKWXycKVDuG8g/4iTf2zP+ir 6R/4QHhr/wCV9V7KXdfcHtla1j95Pjrq3xkk+G41/wCH1tc2Gt3T2Zl8Oarp mm6nc2EDQziYQot/awPdLNLBvMmo+SI7ZjH5jHZN7Tqsc8uiyrC5FxsyGQ7f mx9eOT6+nNfzZf8AESb+2Z/0VfSP/CA8Nf8Ayvo/4iTf2zP+ir6R/wCEB4a/ +V9J0Gw9r73MfuMNO+O+nftf+LI9sdz8H/EOpi4st1rBeT2ccWiWsbgzSahE 1pbyXitsSK0uWaWOdnECSLLJ6z+z3qXiPWfh9Fd+JrLxXpd/OYwLLxGdLOow BIY4mdzpzNbjzXjebarsQZT/AKtdsMf88H/ESb+2Z/0VfSP/AAgPDX/yvq9o 3/BxR+254jnEVh8SrO9kLBQsPw68OOcntxp1DoNx5dAdVO2n9af5H9JVFfz7 eGP+Cxf/AAUK8UIjR+PdMtlcZBn8BeGVJ/D+zyf0rvNB/wCCjH/BRDXgnl/E rS4dyliT8OPD7KMDk5GnVccvnKnKqnorL77/AOQvbK9j9zaK/DfxJ/wUL/4K JeHLWOV/ip4eYS8oG+H3h6Pd+enV5h44/wCC1P8AwUF+H8csl946spIIj80t v8P/AA1MoHr8un5HHtWX1Z9x+18j+heiv5qH/wCDkr9s1GKn4raSCOCD4A8N ZH/lPpP+Ik39sz/oq+kf+EB4a/8AlfR9VfcPa+R/SxWLouiazp3i/XLq713+ 0dF1D7PJp9hLZxpLpMioUmVZkx5kL7Y5FWRDIkjT/vXjeKKD+cH/AIiTf2zP +ir6R/4QHhr/AOV9fTP/AAR3/wCC4H7Tf7Wv/BRj4e/D3x/8QNL1zwj4iXVF v7JPBuhWTTCLS7uePE1vZRyoVkiRso4+7g5BIJ9Xa1uHtU+h+qf7I3jjVLP9 lL4Ywp4N8SXSReEtKRZop9PEcwFnEA6hrlWwcZG5QfUDpXof/CwNW/6EbxT/ AOBGm/8AyXWB+xv/AMmifCv/ALE/SP8A0iir0iudtdjRHLf8LA1b/oRvFP8A 4Eab/wDJdH/CwNW/6EbxT/4Eab/8l11NFK67DOW/4WBq3/QjeKf/AAI03/5L o/4WBq3/AEI3in/wI03/AOS66mii67Act/wsDVv+hG8U/wDgRpv/AMl0f8LA 1b/oRvFP/gRpv/yXXU0UXXYDlv8AhYGrf9CN4p/8CNN/+S6P+Fgat/0I3in/ AMCNN/8AkuupoouuwHLf8LA1b/oRvFP/AIEab/8AJdH/AAsDVv8AoRvFP/gR pv8A8l11NFF12A5b/hYGrf8AQjeKf/AjTf8A5Lo/4WBq3/QjeKf/AAI03/5L rqaKLrsBy3/CwNW/6EbxT/4Eab/8l0f8LA1b/oRvFP8A4Eab/wDJddTRRddg OW/4WBq3/QjeKf8AwI03/wCS6P8AhYGrf9CN4p/8CNN/+S66mii67Act/wAL A1b/AKEbxT/4Eab/APJdH/CwNW/6EbxT/wCBGm//ACXXU0UXXYDlv+Fgat/0 I3in/wACNN/+S6P+Fgat/wBCN4p/8CNN/wDkuupoouuwHLf8LA1b/oRvFP8A 4Eab/wDJdH/CwNW/6EbxT/4Eab/8l11NFF12A5b/AIWBq3/QjeKf/AjTf/ku j/hYGrf9CN4p/wDAjTf/AJLrqaKLrsBwmp/FDw9400W32DxDd2E5S5QHwlcX kE3B8uQCW0kX7rthlxkOeSCKpeIfE9hrfhy202xt/FcssGoWmoyyTeHNTd5F guormZ2ItySxWNznpk84GSOg+CH/ACRbwh/2BLL/ANEJXO/sUftJQ/Gr4r/G Lw1L4p8EeIdS+HGu32jhNAYRyW9q+lJMguYjcTMJUme6tmkyivJZy4RCrouz qSa9nd2X9fqZqmleaWrNbX/Glv480O80bT/+E1srjU4XtmktPB1xeStE6lZY /JubKeF1eMujK8TDazdDgil4O+G9p4c+Ew8GanoPxF8W6OdOXSZl1vwPeTfa 7RbZLVYJUjso4njEEaRkFPmUfMWJYnr/ANjHxJ8VNW+PFhH458G/D7w5pK20 7Q3OheM7zWbiSfYQsbQzaXaKqFS5LiQkFVGw7iy/Z1aUk3Hf8vz3M5xip89t bH5Rf8O2PAFr8c28e2OkfGjTb6XxGviqSwg8N372Ul99oW6kYiWxeXbJOGdl EmBvIXYoVV97120n1jWbHVYNL+Kuk6xpCTjT7ux8Naui2ryps81rdrdredkw GQXEUiqwyF65+4q+LPjL/wAFNNJ8U/syfHfU/DfiTTvA+sfDP4eWevvqT6nY z3Xh7Xbz+0Y49FvIJUkghv4bmxihkt3LuXulTYrbC3p0Mtr46fMveacbt95N JN9W727sVGs8O3Kj7rldO2l7qz+9XT8jlvg18P2+DXhv+yYLP4o6tapJI1uL zwNc262SSXEtw0UUdpp8Eax+dcTOF2fLv2jCKqr33inxlJrfifUbyHwz8QBF dXUsyBvB2qhgGckZ/wBH969J1v8Aa98L+MJdV0bR/GPhvRwdKfWBrJ8QWKXH 9hNpwnHiPT43WeK4s4p5YofNmVYN8c5JcRrHN59+0J/wVK0n9mv9ne88a6ho +n+KbmN7m0s7fRPENpHbTXlrZCW6tbi6vfsscN1HdLPB9jjE14yxNIls5juI oNKeQYus0ox1drLRb7dl/l1M41I04qMVoef/ABz8N6l8XPgh4y8K2Oh+MbO+ 8TaHe6Tbz3nhHWFt4JJ7eSJXkKWrOEDMCdqscZwCeD6JZfDPwdHffFOe+8V/ HbVv+FoeIbPxWI7vwDNs8L6lZpbJZ3FgE0dcmD7Dp5Vbv7SpNjEXD75vN7v4 cftGWGv/APBQH4lfDZfHuj6o2j+ENA1m18NC6sjd6TPLPqS3jBI1FwVMQ0uR hKXCC4hZdqzKGo/tDft46b8BP2nPBHwvlsvDmpax8QJLBNPi/wCEss7fUbVJ bwwTzTaYc38kQjG6GS0guY3kiuBctYQQtdHjq4OeHmoS3kk/lJJr+u60Lc29 Ta+DPj7wb8Evh5aeHtOt/iXfLFNcXt3fXngbV/tOp3lzPJc3d3KIrGOISz3M 00zLFHHGGkIREQKg5D45/E638aeL4LnTdB8fXMEdmkTP/wAIXrCYbe5x81sO xH516D+yt+0ZH8dNP8W2F/e6DH4u8G+KdZ0bVdHsp8XWlW0OqXkOmyXMDO0k TXNjFb3Cs+1ZVl8yMeWy49ZrGdPmWoRfK9D4tstSbTbSK3t/CnjqC3gRY4oo /BmqKkaqAAoAtsAAAAAcCtLX/GsmtahHNF4X+IKotrbw/N4O1UEtHCkbdLfp lTj29K+wKKj6vEr2rPi201E6fE0cHhTxzAjO8pWPwZqqqXZi7HAt+rMzMT3J JNWP2N/hppn7OF3oOv6p4q+MOo63D8PPDvgfU9Ci8C3j+H7f+yYCI5LV/wCy I70/vZ71t0s5DC8fKDZCsX2VRVQp8uzE5tnzr+y3d+HP2YPh9qHh4eIvjX44 j1DXdR183niHwFdrcwTX9y93cxp9i0q2j8s3Ms8oDIWUzMobYqIjvj98XLHx udJ/szRPH119l87zP+KK1hNu7Zj71qM/dPSvoiiqlDmVmyVKzufD/hzR9N8G +d/Y/gLxXpX2nb532TwLqUHmlc4LbbUZxk4z0ya3PEfidvEGiWNn/wAI18Rk FvZPaTFfCWrxMd0sr5RlgDLxIMMCCCOOxr7EorP2C7l+0Z8TeH0h8L2kltYe EPHVpaM29beHwfqywxHABCJ9n2opxkhQAWJYjJJNDxT4YsPGmr6Ve6n4T+I9 y+jTfabaD/hG9cS0aXKMkktusIhmaN40eMyoxidQ6bW+avuiiplhISVpakTa muWaTR8Z/DnXrzwVq1jcXulfE3WBbak1401x4Mv1mWNrhpVgUR2kabIlIiQk biiKZHd90jcX8Yvg14V+P9haW3i74e+NdZSwZmtnbwnrMMsIcASIJI4VfY4U B0ztcKNwOBX6AUVrTg6cuenJprqtCpSUlaS0PhD4a+Hrr4baRd6etn8T9VsX vHm0+3ufA1zbxaJalVWKwtltdPhH2aFVwnmiSXBO6R8DHRf2/cf9C14+/wDC P1X/AOR6+zaKh0E3dsaqNHxl/b9x/wBC14+/8I/Vf/kej+37j/oWvH3/AIR+ q/8AyPX2bRS+rxH7Vnxl/b9x/wBC14+/8I/Vf/kej+37j/oWvH3/AIR+q/8A yPX2bRR9XiHtWfGX9v3H/QtePv8Awj9V/wDkej+37j/oWvH3/hH6r/8AI9fZ tFH1eIe1Z8Zf2/cf9C14+/8ACP1X/wCR6P7fuP8AoWvH3/hH6r/8j19m0UfV 4h7Vnxl/b9x/0LXj7/wj9V/+R6P7fuP+ha8ff+Efqv8A8j19m0UfV4h7VnxV qvjiDQLCa71LS/FelWkEck0lxf8AhvUbSFFjjaRiXkgVc7UYgZycYAJIFecf tUePrPXv2WfivZ28GswXUfgvWJSt7pF3ZDaLOUHmaNQTkjgHPtwa+wP21PDY 8ZfA690czfZxqrPZ+bs3+V5lvMm7GRnGc4zzXxx+1h4QvrT9nX4t6pc6p9rj l8D6zD9n8nZgm0kbO7cf7uOnf2rhxPtKdWEacbpvV32/zOPEYjFKrTjRp80W /ed/hXe3U/F7/g64vbfUv+Ch3gye1WeO2m+HNi8SzwyQyqh1PVCodJESRWwR lXRWByCqkYH5j1+nn/B2Db3Vp/wUZ8IRX0ZhvIfh5ZRzoSG8uQanqgZcgkHB zyOtfmp4T8L3vjTxHZ6Xp8Mlxd3sqxRogyck4zXqRWiOiW52v7Mn7MPir9qz 4oWPhbwpptzqF7eSBCIY95A6nA7nAJ9AASSACa+2LLVPAf7EuhyeHPhxoWg+ LfiLZNJb6p4kv7aLU7WzuEOHisoiSkxU5DTPuiBA2g/eqLx75n/BPX9nPTvh l4JM8fxP+IKmDxHqcSmO7soGIX+z4WI3I0jMBIy442xg/u5fM9d+E37IWjfA P4SM2p6O2qaxqFkDNeyqXhlYrkhCc4AJOM8+tUI8F0XwR8Wfj7oOpeIfFHiz xTruiBmM0B1LEVuOpCIdyADnhYgOwqfWP2VrX4ZfBKx8aX5s4rh7u3mtdNu4 PMkni+1RxlnLndtYHkY2nkbRzj7g/Y4+H17o3hydL2YX2nTxKqxTxqVUE/d6 YOM159+374i0HXPDWp2kVgjXdteadAZhyuxb2EFAvRR1HvXoZT/v1H/HH80R U+BngR/YdsPF1xoNm0s2n6nq6PLNJZxskMKIhYjYSQTkYyR26VlNeePv2aLv 7T4U8YeI9Z8O6cSpNu8j2MRB4861cPbzKv8AErJyM19ufAG0l16NYr/SbcMt s4i1CKXzFUsMMvOPU44B/OvI/Fvwtv8Aw/4qt/DMl3MmmS3BDwKhD3AYk7c+ hz2rzyzxq++Gfw7/AOCmekf2Ytn4d8B/GdA7232VUtNN8W/Ln/RiSFhmBA/c Nw275GHQ/n98ZPg7rnwL8eXvh3xBZy2eoWMjRukiFDlWKnIPIIIIIPIIIr9C /wBsT9iO8+HenS+I/ClnJaW8MwnudPjzlWjGRcQH+GRNzHH8QJqx4t+Hlt/w VK/ZSTVrme1X4weE7aQT3LrtfX4IIg29mON08cQ3nHMsJHWS3PmAH5hV7/8A Eb/lFl8G/wDsqvjz/wBNHgyvDPEOgXfhbW7rTr6IwXllI0M0Z/hYHBFe5/Eb /lFl8G/+yq+PP/TR4MoA8ArS8JeEdS8deILbStJs57/ULt9kUMK7mY/0AGSS eABUXh3w9d+K9ctdOsIWuLy8kEUMa9XYngV+mv7GX7KvgP8AY8+B0/xN+K6z tpLsIIbW3wmoeM7vgpp1kDyLdTgyy42kcnI2ggHN/sNf8Ef4tc8OSeOvHd9p Gl+FNCT7RrHiHV3CaFpC5wFG7/j5lyQoAG0swA3cA+9y/tjfCf4IWf8AYnwM +G+keKNQU7U8X+PYF+xgD/lrb6YpUKvUgzEAgj93Xmvjn4gfFv8A4KFvbeKP ENrD4b+GnhmZYdF8MaXIYrDSIDtXfBEB+9m2tlrqT5mz8m1cV73Y/sxeEfh1 8OrowWUN1bX1uSZJgJHPGc7iMk/1oA8Hk+O3xX+MOqya/qXxf8QaNKjmGD+w NBh0fTUA/hjW3RYiR6ZB/lWz4N+OPx38F6tPZWnxJT4gSFhILHWfDen6qAm1 sIyzRM3Ybtki449jXqFloGieCP2Q0tNXMFnE8DlPNHlnqSOvc8fWvW/gvoWm xeF9KkgtIU2Rgrgcjjn8Tzz6mvQn/uMf8cv/AEmJP2j5o179qz4efEfS5tN+ K/wYttE1AyrB/bngd2gTAPzyT6Xc73ZlwSxhkYZ4AxivM/2lP+CQuheOPh+3 j74ZatpPijwlcjfDrvh354YSckJeWhAktpPVSquP7vFfbXi74NaD4s0/WNO1 3T7O9s/MLx+dGGAB5yOMgg9xggjrXxDqXw/+KP7Fnj7U/iL8J9a1Cw01LqRL iMt9pivLcMcx3ULApcxEcHcCwHPvXnlH57fFH4Sa38IPEJ07WbbynI3RSp80 Nwv95G6EfrXM1+rvinwv4E/4KcfAXU9Y0vR4fD/jjQ1Nx4k8N2pyYCT/AMhG wDHLRE/eToBxX5k/F74W6j8HvHFzouogF4gJIZVGFuIm+7Iuexx+BBHagD1j 4c/8osvjJ/2VXwH/AOmjxnXgFe//AA5/5RZfGT/sqvgP/wBNHjOvAKACvQPg d+zh4i+POovHpMcMNnC6xzXc5IRGboigAs74/hUE+uBzXXfsb/sZ67+1D470 +1gsrqWwubkW8ccPEt/J/wA84yeAB1Z+iiv0T1D9orw3/wAE5LQ+CvgxYeEP G3xbjg8i/wDEDQLe6P4HI6xWMRBS7u8nLzPujjZBkNn5QDg/hh/wRU0L4S+C dP8AGHxa1jwz8P8Aw/cIJLfUPGl99ne+4629gjeZLwDxh+2V542tG/aa/Zw+ B0k+h/Cv4f8AiL46+Jo33Jfpb/2B4dix0H2cJ5sierMsJ54avG9S+Hmv/tRe MpvEvjrUPFXxA8S6pP8A8TDU7q982V2H/LMyuSiIOBsjyAAMMPu16bafDCP4 V6Pd6bFJY6Iba1+3Pb2SlldQSMs7cyNxjJHYUAbV3+1b+0J4sgEen/8ACpfh ZYSPva00HwrZTXVpH6C4limmzjuZc59Kc2o/EfWp4Luf9oD4oyvCGCx2t/8A YkhY43AxqxGCQMZ7V5Z4x8P3eoeD7LWLO5ubyzb97LDGvMZz95gowfqay7rx vLo1kmo2kqSrtZJlZQXjI4G769q9Ch/udX1h/wC3E/aR7q+v/HIWBj0f41+I NTt45nl+z6xaW+oRlidxyWVh17Hion/bs8VfDsnT/jL8IvAvj3RceU+t+H9N TR9WiH9/EG2KQ9yNoJ9RXgnhv4uXDSBNOkmhupFLPhyiAH37mvTfDfxZs7qy h03xTbia3uBs89ApYdP++h9efevPKMf9oP8AYi+H37UPw7PjX4YXlvMhyW8q IQ3lo/XZPGMCT/e+8O+etfnV4u8JX/gbxFdaVqcD215ZyGORGHoeo9Qexr73 8b+HfEn7KXiVvH/wz1LztMmO+/sSvm2t7HnlXQ9//HhXIftjfC7Qv2g/gjo/ xN8JOs01xDIbm3ChZrOaPmS3deu3YQ0bdyki9qAPiWvtn/g3WtFvf+CxHwkR sYWLXZOW2/d0HUW9D6dMc9MjOR8TEYr7U/4N4LW6vP8AgsL8IktIzLKF1t2A YLiNdD1BpG5I6IGOOpxgZPFJ7Ma3P6YP2IPh1Zal+xX8IJ5ApebwVo0jH+1N hybCAkY+zHH0ycep616h/wAKs070X/wb/wD3LXhP7Fvw/wDiPffsd/Ceax0J 5rKXwbo8lvJ9tt13xmxhKnBkBGRg4IBr0z/hWnxQ/wChek/8D7b/AOOVw8k+ x0c0e51X/CrNO9F/8G//ANy0f8Ks070X/wAG/wD9y1yv/CtPih/0L0n/AIH2 3/xyj/hWnxQ/6F6T/wAD7b/45RyT7BzR7nVf8Ks070X/AMG//wBy0f8ACrNO 9F/8G/8A9y1yv/CtPih/0L0n/gfbf/HKP+FafFD/AKF6T/wPtv8A45RyT7Bz R7nVf8Ks070X/wAG/wD9y0f8Ks070X/wb/8A3LXK/wDCtPih/wBC9J/4H23/ AMco/wCFafFD/oXpP/A+2/8AjlHJPsHNHudV/wAKs070X/wb/wD3LR/wqzTv Rf8Awb//AHLXK/8ACtPih/0L0n/gfbf/AByj/hWnxQ/6F6T/AMD7b/45RyT7 BzR7nVf8Ks070X/wb/8A3LR/wqzTvRf/AAb/AP3LXK/8K0+KH/QvSf8Agfbf /HKP+FafFD/oXpP/AAPtv/jlHJPsHNHudV/wqzTvRf8Awb//AHLR/wAKs070 X/wb/wD3LXK/8K0+KH/QvSf+B9t/8co/4Vp8UP8AoXpP/A+2/wDjlHJPsHNH udV/wqzTvRf/AAb/AP3LR/wqzTvRf/Bv/wDctcr/AMK0+KH/AEL0n/gfbf8A xyj/AIVp8UP+hek/8D7b/wCOUck+wc0e51X/AAqzTvRf/Bv/APctH/CrNO9F /wDBv/8Actcr/wAK0+KH/QvSf+B9t/8AHKP+FafFD/oXpP8AwPtv/jlHJPsH NHudV/wqzTvRf/Bv/wDctH/CrNO9F/8ABv8A/ctcr/wrT4of9C9J/wCB9t/8 co/4Vp8UP+hek/8AA+2/+OUck+wc0e51X/CrNO9F/wDBv/8ActYepR+CdI8a W/hu68Q6DbeIrvyvI0uXxNEl7N5sd1JEEhNvvbeljfMuB8y2dwRkRPto/wDC tPih/wBC9J/4H23/AMcrx7T/APgltpGi/tERfFjTvgr4Y0r4ix3DXh13T3tb S5lneO8ilmk8qVRJJKt/c+azAmUmJpC5ghMYoS6oOaPRnqXjf4mfCP4afErR /BniP4h+A/D3jDxD5H9laFqfjW1s9T1Pz5TDD5FtJAJZfMlVo12KdzqyjJGK vfGDxH8N/wBnvw1BrPj7xj4S8D6PdXS2UN94g8W2+mW0s7I7rEsk0CqXKRyM FByQjHGAaD8Mvihxt8OyAjub+2J/9GVxX7QP7FOvftTfDe68H+PvAr+IfDF9 u+1abJrKQwXWYpIx5ginXfs8zzE3Z8uaOGZNssMUiPkfYXMu56j4k8K+HPB+ nR3eq39hptrLc29lHLd68kEb3FxMkFvCGa2AMks0kcSL1d5FUAkgVzPxI1rw x4O+CXirxrp0lt4ksfC9vfvJHp3iFH824s3kint94tSFeOeGSJwcmN43BG5S tYXxp/Zb8f8Ax4+FeueDda0TUI9C8TWradqsVtqNmj3tlIQtxalmYlUnh8yF 2TbIqSsY3jkCSLwnj39m7xT+zf8AsKeJfCo0rUIPCPhbwlf6fYG6v7adtPsk t5UtbUGMhnjhTyoEZw8jJGpkeRy8jCg+qDmW1zofgicfBbwh/wBgSy/9EJXQ /B74kaX4z8V3FpZDU0uV8OHWGjvNLurJkt7u2uxDuE0abZc28oeE4liwvmIm 5c898EP+SLeEP+wJZf8AohKi/ZL+ANz8Cta8TtcePfGfjl/EYvdRmk8Qx6YJ YJjaz72V7Ozt2cFfLQCUyCOOCGOPy0QLUP4ncv7JufsYftZ/Cv48fHix0jwN 8TPh94z1a1tp7yay0LxFZ6jcRQKhRpWjhkZggaRFLEYBdR1Ir7Nr5y+A3/JV 9K/7bf8Aol6+ja6sP8LMKvxAa4L4O/E/wp8VHmvtL/sy18SvYQPrGlm7s7jV dKQXF3AsF39mllT93dQahD8sjx+bb3KozFXrva8u+CP7MqfBzxtqviW78Z+L vGviDWtFsNCu77XE0+NpobO81O7hfZZWtvGJM6pNGSFCmOGH5d4kkk6E7X1M 3Y0Jv2r/AIWW/wAcU+GD/EjwAnxLf7nhJvENmNcYGD7RxZeZ55Hkfvfuf6v5 vu81qfGX4u6b8EfCdlrGqWl/eW19rmkaAiWaI0iz6lqVtp0Lneyjy0luo2c5 3BFcqrthG6oRAtnJIrzT9q39nA/tR/Dmw8Pf8Jt4v8CJYa3p+vC+8OR6c91N NY3CXdsjC+tLqPy1uYoJSFjDMYVUsUZ0eVfS/wDSuC3O+t/E2m3XiW70eK/s ZNXsLaG8urJJ1NzbwTNKkMrxg7lSRoJlViMMYZAM7GxoYrD0TwUui+KdY1dt S1m9n1dbeMw3N2zWtnHCpCrBAMRxlmeR3cL5jlwGcpFCke5QhBRRRTGFFFFA BRRRQAUUUUAFFFNRw4yORQA6iiigAooooAKKKKACiiigAooooAKKKKACiiig AooooA84/af/AORAtP8Ar/T/ANFy18hftkY/4ZD+KuRlf+EP1fPpj7FNmvr3 9p//AJEC0/6/0/8ARctfIX7Y4/4xF+Kbc5Xwfq+P/AKauWcrVIv0N4q8Gfgn /wAHHt8mqftN/Bi5SZLhLn4OaJKJUJKSBrvUCGXKqcEc8qp5+6Ogx/8Agkd8 FbbwjoOtfF7Voo86cGtNHeZfkjnHLTHPaNQzH3CD+Ln1b/g7e06e1/4KeeHL OW7uNRuIvA1tC11Osay3LDVtUXe4jVUDMRk7VVck4UDiqHjXTJvg1/wTL8Pe HtPBiu73SREI8AGe61C78qQDuWMWwf8AAa7Jz55OXc50rKxwf7O2jeJP2iPi bqXxekiSWw0rUHFjFcEs7oFKAeuVVhz3ZnNfoJ8DfjQ/ie+TSL3SpbZ3iG4Y 3IR6nt6141+yTo2pfBj4Y6Po2k+GrW/McCJcOtyrM/TLFevPU/SvpuLX7bw/ oa315aw2LsoLoirnJHQY681IzZstLs9GgeGxt4baEszlEGFZj3xXyf8AtreC 7f4d+AXkQJNBqGq2E9zcSkFwBfQHaB2GByfavon4j+NrvRvhfq2r6VbifUYL KSextpRjz5QhKIfqwAr5f+Jvx78EftbfCSz0vUZzonil9Y0qxvNMnfy7iISa naxyhQfvhVZj7Y56GvQyn/fqP+OP5oip8DOw/Z/+FeoeFvGOjX+nPKNKZcSe XKSsWFPysPXnFfTT6dBfSK7RRGeIHy5CgLIfrXhHgn44fD74b6xpfgzw7qkW tX08hWVbaf7SbYBcNJM44UfKBz3Ir3G3u0cIwcSKVyGB3ZzzXnlnkvx9mtfB vhqfTReXV1e6gTPuK7sEA9PbmvgfSm1z9iP49eF7+X7VaaDr94ltJdb9irC7 ZLHH3WjdvNU+hcdDX6leIPsI097m704X5t1yqrD5krD0UdTzXxV/wUf03Sfi j8NbqOSC/sdSilFxZ2t1BtLOoxtXHQANn/gNAHzV/wAFkvglY6d490bx/otr DaW2v24g1K3gTCW92hKsfYPgOPQSAdq8h+I3/KLL4N/9lV8ef+mjwZX2R8eZ bP48/wDBMaTW75zcalHp1tqAdFyPPVRFMSew3oxr44+I3/KLL4N/9lV8ef8A po8GUAex/wDBFr9nPTPH3xK1rxn4jx/YHhOxe8uF6GcIyBYlPYyStGvHbNev fCrxTa/8FAv2z9Q8U+KJRJ4dsHnTRLE/8evkxyAKET0JO73CqOgNZPwx00fs /f8ABJXUdRtnWDXvF0y20SJIBNIsimFDjqB5kwb6xg1u/Cn4C6t8LtN0AWFp cm1060DfabV08yNvl5K5ycjcxxnljQB9O+EGt/hH8R7/AMPTxhNL10/brAlc RuSu2WEdsjaGHrvPpXqM2mWY0pLcRK0HVFP3cf4V5t8ILu++KmgzR6zDJfWM W0w3U0BgkR1xwAwDe+a7jxP4gsPCGgz3l9cxWdhp0fmSzzSBUiQd2J/CgDzr 4x/E3Q9YW50RreW7kt42V2RAUj45HvVL9mb433usf2NpU2mSWcDQbW87iQNj gj1B/qKk8e32geL/AIVXmqeFAmopfguLnTUN0JMH5lGzJznt69cUvwdTStD+ HsWqeIy2nNp5F2s12psxbKCPvs+MDIyc9PavRn/uMf8AHL/0mJP2j2DxXo1v 4lsnSaWSNRnzAnBIHbNfO3xf1rUvFbJoGhxquiaeP31qg5njX+At2zXv+ieI bPxTpFrqOn3UF7ZX8QmimgcPFKh6EEcEYrgrn4HS6Xr17ex37w6TLmQxouX5 PI+lecUfJP7Q/wAJ9U/ZtvNK+Mnw8jNi2nyx3E0UZO62YH5h/utyGFN/4KEf CfTf2wf2brL4u6BplpaPPb/aWW2AHlXIQvLGV/hWRQWX3javoD43XNpaeE5v Dix7tO1CF43gc+dM5YEbiBnH9K8v/wCCcVsdS/Zp+LXw8uozeXGlpqL28Jbm FrWAXsMuD28pLuMDvvNAHwr8Of8AlFl8ZP8AsqvgP/00eM64v9lT4CXf7Qnx bsNEhjka3DCS4IHG0EfKT0GeeewBPavUIPDn9gf8E9/j5pUCcWvxk8E28Sju BpnjQLj8MV6l8O/h4/7Kf7IcVzDeLYePPiHKbO1gH+uFqF/0u4PdBGDFCgOC xlnIzsOAD0T45fHqy+G/gt/hP8KbgRWBRbXxP4htBt/tB162Vsw5EKngkffO e1c54H/Z98RjV9M0WRY/D1nd2rXYmXl2AABU/wC182eela37K/wu0W7Rbm7t Lr7Jo6CS3eSNhBdydXlLYwcN0GfevTrb4inxJpiajMJI7aTVhFYyiFv3aA4P bLBxlQRxkigCW10LRD8PdI8OTX4lurbFvbXNrlZI2XOH46dO/XmuQ1rwFqni bRIbvUdSS4uNPuZ7K7kkbYZ7dX2gZ6ds103ivXJNAvnmhS00exupo4TMsPmT yuQcYQdjjHIyME1navqdlomkosq3k1tbO0xQxtK80zElmk2g7fmJ4agDnvC+ pa/4E+y6RGttF4ev7hoYbuU/vUQ88j0/+tTfiZo0d/bTaPoOkS3VnNOv9o3S qCzHYp2oDjtg10dtY634j0J7htD1i9jdg0OzTpXjQEjoQpyAO4rW0PTdT1a8 xbaL4jjdZ3Z1OjXKJJlQoyxTHTjr2FehQ/3Or6w/9uJ+0jwyw+Fci3Nxrl3b mwkRi1vZsPmaJBghvcgH9K63wh4Ej8ThJLfy7K3u932XzYvnKAcYU8gd66TU rN7y5uGkUtqiQtD5Mh8tbYsCGVgeQQRznnj3rc8G2d1quk2Mz3kMc4hV2EcA dQGAOAT1FeeUc94a+FPiHwLa3NzJqNtqVsBzaLGcTREHK8/xY6V4t4Qu7X9m /wDaBuNBkfd4A+I8IUtn5bGQsNkoB4zG5GfY4r67s9Ou51lt/tQuDIhDuqBF hUg/m3WvnL9tn9mOfQvBT3mnLc3GnW1u13DIfmMDKADz6ODx7gUAfH37W/wa l+Dfxf1C08sR2d1K8kIX7qMG+dR7Z5H+yy19L/8ABtn/AMpmfhR/2D/E3/qN 6pXPfHCG3/aQ/ZOt9cGP+Eq8GoLXV4yRv3xBgko9VeFSpxnmJPUZ2v8Ag3Fs 5b7/AILIfCZIbu4snW18QyGSFULMqeHtSZo/nVhtcAoxA3BXO0q2GCY0f1ef 8E9f+TBvgf8A9iBoP/put69gr5m/YL+E+v6j+w58GbmH4neN7CG48C6HIlrB aaO0Vup0+AiNC9izlVBwNzM2AMknJPrP/Cm/En/RWfH/AP4B6J/8r6Yj0Civ P/8AhTfiT/orPj//AMA9E/8AlfR/wpvxJ/0Vnx//AOAeif8AyvoA9Aorz/8A 4U34k/6Kz4//APAPRP8A5X0f8Kb8Sf8ARWfH/wD4B6J/8r6APQKK8/8A+FN+ JP8AorPj/wD8A9E/+V9H/Cm/En/RWfH/AP4B6J/8r6APQKK8/wD+FN+JP+is +P8A/wAA9E/+V9H/AApvxJ/0Vnx//wCAeif/ACvoA9Aorz//AIU34k/6Kz4/ /wDAPRP/AJX0f8Kb8Sf9FZ8f/wDgHon/AMr6APQKK8//AOFN+JP+is+P/wDw D0T/AOV9H/Cm/En/AEVnx/8A+Aeif/K+gD0CivP/APhTfiT/AKKz4/8A/APR P/lfR/wpvxJ/0Vnx/wD+Aeif/K+gD0CivP8A/hTfiT/orPj/AP8AAPRP/lfR /wAKb8Sf9FZ8f/8AgHon/wAr6APQKK8//wCFN+JP+is+P/8AwD0T/wCV9H/C m/En/RWfH/8A4B6J/wDK+gD0CivP/wDhTfiT/orPj/8A8A9E/wDlfR/wpvxJ /wBFZ8f/APgHon/yvoA9Aorz/wD4U34k/wCis+P/APwD0T/5X0f8Kb8Sf9FZ 8f8A/gHon/yvoA76Q4A9Ca+G/E7fHVv2Hfj7/wALu7eHMeFvJ/s//kGeXPs/ tL7N/wAx/p9v+zf8Sv8A49fsX/LzXqfx61fxV8GtRd5Pi54ntdLtdPN/dXl/ aaKkdsil97u5slVEVU3EngAEk46eI/tRfE3xldfCbxjoGreNNe1qw1HwdrV5 LDcWumxRMYIY1VW8q0STrOGyrrzGAdwJFZSmvhLUHubvwQ/5It4Q/wCwJZf+ iEqh+y/p+uw/GTxcbvQfF+heHdJ8Px+HtIOva+motqv2SLU997Gi3NwwEsc1 vme4dbmbYBLGpiUtf+CH/JFvCH/YEsv/AEQldT8JfEF7rXinWo7vT47GG0t7 yOzYXPmyXMP2SUebIm0CImQSbVDPlAjEqzNGnJ9pnR9kzP2Mfgx4i+Hvx3sL 3WPiz8QfHlrLbzwLp+u2ehw28LlCwmU2OnW0u8BSoDSFMSNlSdpX7Or4x/Yx +MviP4ifHaxs9X+EvxB8CW0NtPOt/rt5oc1tM4jKiFRY6jcyhyGLAtGExG2W B2hvs6umhszCruFfOUMuveEPiJ498XeGvgTremazPrUCa9epH4fXVfiLp8MF xbW7WU66ivzQuLeQf2i0BW3eRETzDtT6Nor0cNinR5kknzaO9+9+jX9arUyk r2+88J8My/FHwz+1dpmk6pb+N/EXw6l8G2NsusodBjsIdYSa9e8uL9d0V6Zn ijsEQWkJtt1xKfLQDdHd/br1LxnYfB7R/wDhBPh/qvxG1lfF2gXc+m2I0cvB ZWmqW17dzY1S4ggDiC2kSJkYypPJA6eXsM0ftNfjJ/wdueBX8dXnwBVdRstN hsYtflmmuormRFWS60O3z+4ikYBTMHOQMqjBdzlEftwdBZjjKdF2p36paaK9 7X621OfEVvYU3Ute3yP1Gs/ht4s+GnifxV4yk8f/ABP+IdpNaXdxYeBJbfw7 DawyFhLHBZyrZ2s3mAL5MZu74x4kJkckCReQ/YY8K+MPAH7Gv7P3hjxD4W8W +Ddc8JaJY+H9e0n7Ro9wlv8AY9Llti1y8U86tavLFG8ZtJDPve28xUj+0Iv8 q/in4VWGgfsz+EvF6zXp1jW/FWu6FdxNKjW0cNjaaPNEUAXdvL6hOGJdgQse AuGLVfAvwt0Pxb4C1TWNQ+JXgvwtqOnmX7Poep2usS3+p7Ig6+S9rYzWy72J jXzp48Mp3bVwx+u/1AUYOTxGmi+Ds/8AF5njvPnfl9nr6/8AAP6nPBvj3413 vxDku9T8E/E+z8La0NCjtbSafwo19oE51jWm1KS5MV0ytaiyXSUbynnm8mSP ygbkXDLP8BPHHxyOr/BrQPHvg3xagbwRYXvjTxJbz6BJZnxCbOVLqzu0S6Wa JElQODYW0ySy3EGJYIYJlm/lC8IfD/UvG2leIb6y8hbTwtp6apqMssuwQwPd 21mGA5Zz511CMKCcEnopI9Y+Mn/BP3xl8B9I8XX3iPxB8NrOPwjcXtp9mPi2 yXU9YmtdXfSpVtNPZxeuPNinlDvAiGGCQ7twCEnwFCElCeJSbenu/h8Xn+QL PnNXVL8f+Af1KfDHwn40tbTxpHa6p4X03xJNrmtPe31z4SuTHfTyywvo07Os tqLtLbSvstrMULmR4RGLiNraRW5Hxp4o+PuoR+MNM0LRL3TPEmhyXd5omuXF npV/4S120fUYZLe2S2F/DqTX8enK0WJns7Zrl5yZvL8h1/mE1b9mhfCP7Ynj H4bTXx1qy+Hupa6uozpMNLl1Sz0aO6uLnymZJ1gmlgs5BGGWVVkdA24ZJ0/h R+w9qfxO1L4eSR+LvCraF8QvFWieFI7vTLg6jd6bNqJdXNzaqFe2aBopU8u6 aA3JjZ7b7RArzpm+BIKKbxOjSt7j6/Py2LWeSbsqet+/b5H9FNt4d+MXiab9 l7xJ44+G/iXxj418N+Otb1fxJqkOn+HdFl8JaddWerWEUTQDWbkeUFv7MhbW 5u5HgsWeUm42xPqeB/CXiHwtB+1Xb6h+z14p1Tw/458ZDUtP0ZJfDUkXjuzu NK03S7x0hk1EQgO9lcTyrfGAyQzplXlaSFf5UA5ZeDk5xjnrXoesfs0eI9D8 Ey61PcaMBb6WmuTWYvP9KisJf7PWC6AxtdJZNRiiURszq8FyHWPyWNay4BSs 3iFr/cfk/wCbyIjn72VPbz/4B/Ur4j8VfEfwDqPwq0P4dfBzxTofgbSbJb7W dMsn8NW1vptimi6mkOgxRfbgEuo79dLUGD/RQuMXRjEoFeCz+KXiz4EfEePx vY6mNMk1jRbi30/X9L07WLq/8Mrpmiya5YSWmlpOtxcTOuuQeWqOXmlxCTCY CP5kdK+AXhu9+CfizVJdeWHWPDfj/RvDI1zz5joMthe22qyNdNb/AGT7aNja ekmceZ5cjKbcSDFUvFv7NOofBzWkh8UXGi6pDL4ZuNe26D4ktJ4cmaeyt/Ku 1EtvfbLpI5JUsnnAiWZGkgkhuDbQ+BYJ2eIV/wDBvopfzdmv1Gs8k7JU/wAf O3Y/rnv/AARbeFfGfiTxvaWmrarrWo6Ja6c9hayQqbhLKS8miSHzGRRK7Xki 5kkCcR5KYZj4r8N3+MWmaB8JdJTw3rWgxW2k6XZeKLf7PokdjZPbSbZzBJBc sYRIiNmOO1nR4mgjjNlJ5kqfyuar8O/7M+A3hrxh5eRrniDVtG8z7ZuLfY7f TJseT5I8sgX/APrPOffux5cXlbpmfHn4f23wl+N3jDwtYXd5f2Xh3WrzTLa4 u7N7O5uI4Z3jR5YHAeGQqoLRsAyMSrAEED0KPBEl7sqyk33g9oq1l73mvM1h xO4x1pX23flZdD+qHTLn9oSLX2Osi+u7azvYormPRdL0uxivLdNP1RpJLR7j ULiSRpLo6co89bYDbGdiq05HonwhuPHzaX4Nt/FkesyavJoVm+vzxtYi0gv0 tcXKSBMNullkDKbfdGDb9Y14l/lW8RfsFfEbwppvhJ72DQF1HxjearZQaXFr lrNeaU+m21rc3Y1II5TTmijulMkd00UkAhmMyQqu48J4i8G2Hhvwhqkpu4dZ uE19tMsNSsNQEdvLDbxuZ3azliW7VZTJaNDJMsIxHOnlu4fyCXCkKytGrDXt Tt1t/Nb/AIb7958USknzUEvw6X7dj+tP4cD4g+Fv2ePCunXlvqsXjHSbW30H UJdRMWpjUpvKSBtTDJdKWhWYi4PmSJM8KSgxrKyY7HxPoFh8K9U8X/EgW2ua tqR8OQW1zZWNu13NdQ6eb24jWCGNDLJO7Xcq7V3FiIwq5zu/k48Tfs5aNpfx 0g8LWt/rcVlL8PLPxd5xtzez/bJPCcWtNHtiQbIDcsY95GIYTvkciNnPmHw+ +H2r/FPxjZ6DoVst3qd6W2rJMlvFCqK0kkssshWOGGONHkkmkZY4443d2VEZ hFXgqVRSf1hRum37ttG7/wA3Rr+tDKrxM3/y73bvZ77Psf1ffGHxB8fJfjv8 OLPwxo2rWnhvVY9Mm8avZro15o+lGO6MlzDa39zcQ36s0e+OUnS7pbiL7OsL abK01ynU/ATVfHnw78C/EGw8T+FPF+v6n4f1/XdV0ecanp9x/wAJVZ3WpXt9 ZWti0t2rRtDbS29psvPs0aPGFRjColr+Vf4hfsbeKfhb4k8SWGs6l4XS38Ka Vp2p6hqdpqYvtOLahpq6hZ2kE8CutzPKjbFEO9CUll3/AGaKW4TmfhX4F0dP i94Bi+Jf9v8AhjwB4h1Gzl1LU4rV1uG0drvyLm6tcxv5m1Y7gKVRxvhZcMVK 1xQ4CjOLcMQn10j31094wlnso2Uqf49vkf1w/Fy1+Keo+PGt/B17pemaWuh3 N5Y3d3BHNbf2qkM8MVnfqW8420klza3KtbBXU6bMjybZ0VvIP2rdM8d/Fv8A Zz0LVoPhD8QvE/iFPiT4a8SWfhO+ufDC6p4WttL1SzuJ5km+3JaYlhsbhk23 U02/UdrGOIslv/Nj8OP2NdU+KvwK0Txjo+s6MLvxH4sufB9jpF7qNjZTTXaf 2T5DJ59xHLMjnUmDvFC6weQjSlUl3plfBT9la/8Aj3q2madoXi/wNFqmp+Uy 2OoX81nchHuZrdvKSSEG7lUpC4trPz7mYXcSwQzSJcRwS+BqcLuWJtyv+R9G n/MOOdy0tTv8/wDgH9O3hm7+NfiX4x/FHT/FOieN5fhhZaRq0WgRuuh6XqOr zzTRtEkFzZX80021FmjtppE0preJoxOt7O5ubb3u6sLh/iVodx9n1GQQ6Zex z3cdxssULSWuI5IfMG6ZypaN9jbFinXcnmYf+Rj4KfsyH4yr8I7Zb4aQPif4 /n8EnUTL9p+y7P7J/efZfLTGz+0g2ftDebnbsh8vfLyPw1+HR8deC/iFqgXf /wAIZoMWsFvtnkCPdqun2Odnkv53/H7jZuhxnzPMby/Kl1XAP2frG39zzt/M S88b1dP8f+Af2e9KK/jK8T/BC88Ga/qukaprWg2mr6Hf6npt/aCSeY201jEH YeZHE0Uiyyb4Y2jdwHjJkMcbRyPrfFD4N6H8E4/iT4V8Q6xNN8SvA/jGDw5a W+ns76dfWsJ1KLUrkO8IbKXENiI9xjJWeQlGwdkrgKL0jiL/APbj7pd/P7tR vPWld0/x+fY/sfor8P8A/g1h8V6R8HNY/aSdZ7i90m41vwfpOnzXUtrYT3Au 7zUra3Z1mmVFk/fIzQrI8hIaOITSFEf9wK+KzTA/U8VPDqXNy21tbdJ7a9z2 cNX9rTVS1r/52CiiiuA3POP2n/8AkQLT/r/T/wBFy18hftkf8mh/FX/sT9X/ APSKavr39p//AJEC0/6/0/8ARctfIX7ZH/JofxV/7E/V/wD0imrjq/GvkdEP gPxr/wCDsO3S7/4K++FYZAGSXwnaoQRnrrOqjpXLftx2F5NoPwu8K2x8i9vv EGmGADnYIbYbz07F9x+n1r0P/g6L0FtV/wCCy/gh2Aa3Xw1Zl1Rg0pA1rVCd sY+dj6AA57VgftSfFvw9qv7ZXwunaPW7XT9Nl1F2W50G9t5SxtIUQJDJCsjn O/IRSV4zgEE+xRy3FzgpwpSafVRdvyOZyj3PT/hp4d8SfDHxHZeGYYtP0qze 0heLUY7IqLvcAu08EKQQCRk5z7GvoAeINs9tpdyEvLxIlad1jwinHX8TXlFv +1JpfiOW6NlrvijzImYrbW/gfUpkRR6f6OSMDua5Jf2prq68aWS3cniOTS3c KHtvC2pK9wR/sG3BI9hk1p/ZON/58z/8Bf8AkLnj3PdviLGG8MXMZ24VPfj3 4r8vf2j9NuF/ajmltbae3F1qFqtvII2HnSARBip7nf6d/rX6Gaz+0F4fuEuY 5tM8ayRSRkFT4O1dQRj3tq+dPjR8T/APhzVINYj0bVIGs9SsLoz3OhXkLCYa ja3Dxh5olAYwxXBwSM7cDPSu/KsqxqxtF+xl8cfsvuvIipOPK9TzL/gnI8U3 jbxBEyMZ3igKPtywXc5I9eSQfwr9IvDsnkaTCgZz5cYHzDkjFfJfwP1D4d+E fiZeauuneIodUkDRWxtfDOqOt1Zl98MgRbc5ymzBI5AFe065+0f4esNGvJJb HxlEjoVUv4O1ZAScgDJtwB+dcH9k43/nzP8A8Bf+RfPHudr8TYT4w8Nf2fDr J0WadsLKjKGIHGMNwRz+leZ/F34FSab8Lrx9DvLW5utQgaC4vrtWmZlbA2IA QFBJOSCcHt2ry/wd8TtJh1S7ur7SPEWqlDvtYpPD+rSSo7dST5OFUegPNej+ NP2t9NHhrTbTTNH8SajA6eVfwQeFdSRYcgZKl4VHy/j0o/snG/8APmf/AIC/ 8g549z5g+AGnjXP+CdHirw5NuE2gf2npcuUPDRzvNyOx/eivmbxPpSal/wAE x/gvG9xDDn4teOUCuGzJnSfBvAwp/XHX619K/s9+PdIuPhr8ZbSaz1lV1bxN q1zaLFo1zdCHzUiwsjRIyxPwcq5BXuORXgfiLQGT9hT4GWDBQf8AhdHjhMAh sD+zPBnHfn1HY8VMsHXw7VTEUny+aaX36BdPRM+lv2pvFEmgfBj4IeGLzwdr Okafp+s6YXkuZrFhqgYySbF8q4c5dlG3zNg+U7ivGfZ9A+I+uS+J40Twh44s rKBA7Cyn0tJWAUsfma8A7f56Vwv7ZnwcuPH/AIk+FfhW2ZVWLUo53Ez4LrDa NKBkdycDH8uterz+H/DFnBa2Us3ifT3Y+VJslJ2HGMEs27H41X1vD/8APlff L/MXK+5r3v7Z0gWCDT/h/wCLpbi4fy4o5bnS1LYJB4W8JBwD1H41W+LXirVv iJ4G1XRbz4Z+NUi1W1aGUNd6RgAgdxfdiKzfGnw3sdO8d6BYeHdIeG4bbcf2 wQAkkeQzgsMlmbk4b0JzkV7JfWEh0xJHAyVPPOSOlH1vD/8APlffL/MOV9z8 9PBfwt8f/svawdas08Z2GmpcZuYrZbGYTxc4DRLdOGPTJ28DJpnxB0T4j/tg z3erWMXi280SSdH0+xuPsdtaxDClgEadAxUsyiTaSwXkgkgfXHxct1XQb2Zo 5J4o4ZHeOMEsw2nOPw4ql+z7dR6r4G0i+h85LW8tIJYRKMMFZFIz3J969CWK ofUY/uV8b6y/lj5k8r5tzS+EWuat8K/htomgW3w08byw6ParbqxvNIJkwCSx /wBO6kkk8d+lQ2v7b80Vo4n+H/iBp45jE8a3+mqE7Y5us5/AV61pcEs9t5iK ZCiZC8gtjJxXlfhPwy+meP8AU9DvbBriO8d57a88nA2t0DYxn/CvP+t4f/ny vvl/mVyvuZHi34uJb2Eur6b8OPEUM1yfNuHu7zSZCq452gXxI/75rxX9mX43 v4e/ax+Il/pvhzXLmPVLWwmu7W3lsfNhhxPFOxL3CxMHjlZRtc+jY7+33nwj 8G+DtelTX59avb0y+b9milmFifRdhbYR+FeLaXe6VrH7bXjpdPtkhtv+EOt7 Z0YEAubkgjHfhlGfaj63h/8Anyvvl/mHK+54to/hgr8CfjFBd2cwd/jV8P5p rElDMpXRvGDFSc+Xk4JGGx6kVsftd+Orzx1+0VHB/wAI/q2laX4J0mDwpYQX H2ZpIJrOJPtwMkchikY3dy7krIx2vHnngegeAvCv/CW+L/i3pCYja/8Ajd4F tkJ7MdB8aqv64ryj4t+ObPWf20fGOr316mp2F54r169hsDJuba+oL2OAC6p+ Oz6VMcTQTd6K++X+Y7Puen+H9b1L4feBbfT18Oa5pEEsWWLTWVxa3Ct3Be6U qzA/dDHr0robj4hXzvaynwd4mW1tIVEEZksVy5yM7PtGQccAc9TWfqWk3tn4 UF7o8F5Z6O0sTS2OqMs0a5ccxEMWTHuce1aXjnXp/B/iO21R7XUNR0m4Qx3C WbK/kNkAS4PopPAPPOcVX1vD/wDPlffL/MXK+5meEvBnjz4geINR1SPwd4h1 Sa3XybYGWwjtrAt98bmuQGfaVHUHB/hDVJpX7RHw6+Aaf2PD4X8UeKPEtltQ vqN1p0sCyDjhoJ5IwQfQMw7tmvdvit4t8N/GH9nTU/Dfw98X6T4cv57cRJHc O1tLsJyyMMZBcZBbkksc9a+Rf2cv2StdsPjTaw69BZfYdPmEkkkc3mrNjkbc dRxzmj63h/8Anyvvl/mHK+575cftQePLbSl1zxD4X1PQ9CmP7hdPjsJrmUeg WW63n6rGfwrK+E37fPiXxN8cIvDs/g6/u9Kv0kNpGDBBqO5Yy+SZJUhI+U8b hwOCeh4fxv8AtS+HtV+I2oTavY3ky6ZI1nbQGMOtsikrhBnAJ4JI615rrEup fETx3f6r4WsJ9E07Tis3nTbo28zbncAMgMSPvcEAcHPFehRxVD6nV/creHWX 97zJ5XzLU7/9ub4kzaR40a6g8O6xokmqKn2qG8ms2ZiVOf8Aj3nkxkKp5x1P rWD8Jv2iZrBUafSdRvIZoES2igNsrLtGAFUygtnjgDPoK8WuL7Vvib4rSxlv bjUdQ1O+SJbi5ZiGYkqCzHJHBH616PoHwW8Q/D5tOmieW6N1EjmIQYkt3KZb GTj5W+Xrk+navP8AreH/AOfK++X+ZXK+5734Z+Leu61o2/w34J8TfarCRjfJ fRWke92A4w9yrA++Djjg103iDTPGvi/wTD4Y1XwR4rvLVkOZY5tKjcoFzt/4 /GUfMF5Jz14qP4RfEGHxYkduXbTNYihRGfIxcbR1wR838xXoM134gm1IWcer 28EUZiKTPCXd3ZnG04IG3A4x3P5n1vD/APPlffL/ADDlfc/Pbw7pd38M/ijq fhrVNE1KKz15ZvDtxYFot7XITdanO/y92ww7iW2kKcE8iu7/AODejwgfCX/B bf4c2UtxCs9lbeKIjAQ/mLjw5qgwfl25A68446muv/azsLRf2tvDLH/XPf2U 1ztJ5ljkMRbaD18uYAf7tbP/AASn8KHwj/wca+DIMKFkt/E7jGef+Kb1Qf0q Z4mg4tKik/WX+Y1F33P6Uf8Agnr/AMmDfA//ALEDQf8A03W9ewV4/wD8E9f+ TBvgf/2IGg/+m63r2CuEoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigD5i/bw8Nv421u20ibwz4a8V6ReWCfbrHW5tttJsmZ 4wUMEyyYYBvmAwUUjPb51+MnhTVrD4Q+JpZ9G8PaDo2g+BNY0uztNMvGmWNX giMaKnkRKiItvtAHTIAAAr6v/aa48fWfvpydep/eSV4J+0rz+zd4/wD+xb1D 6j/Rpelc09GbR2Rf+CH/ACRbwh/2BLL/ANEJXR/Cf4feG9A+IniPxFpWh6HY 674msZhrOp2lnFFd6sbeymjh+0SqA8vloSi7ydgyBgVznwQ/5It4Q/7All/6 ISrPwD+H3ibw98XPGOueIPFNrq9trFhNa6Tpdhp8thZ6RaQxX0iFkkuJxLdv 9p2zTp5SyCCHESbOef7TNfsnp3wG/wCSr6V/22/9EvX0bXxj+xj+yZ8K/gP8 eLDV/Avwz+H3gvV7m2ns5r3QvDtnp1xLAyF2iaSGNWKFo0JUnGUU44FfZ1dW H+FmFXcKKKK3Mwr5t/b0/wCCafhf9v3XPBupa541+JXgjU/BEGp21hd+D9Vh 064li1BIY7lJJHglba0cATClQVkkVtwbFfSVcH8efhZefFfQdPtLCXw7b3Fr deYZNa0UavamCSN7e5j8gyRHdJbTTxq4cbTICyyxh4ZNaFapRqKpTdmiKlOM 1yy2PzV8Kf8ABEX9m/41SXPwOsfj3+1FqekeD5bq6tvDl3qhi8PLNbXBhvJN PM2mrY3MlvcXLRTm0aQwSzskux3IO3/xCQ/s5/8AQ7/G3/wbaZ/8r6+9Phd8 LPib4a+O3ivX/FHj7wr4j8I6vH5GjaLaeGLywvdFjRwYEa5fU54JvlMxldbS KSZ3Ql1jhihXV+If7J/wt+LvxM0Xxp4r+G/gHxP4x8N+T/ZOu6t4es73U9L8 mUzRfZ7iWNpYtkrGRdjDaxLDBOa9aPEuZxXu1mr77f5HE8twz3gj4N+H/wDw a2fBT4QeMLPxD4U+Kv7Q/hjxBp/mfZNU0jxLYWN5a743jfZNFYq67o3dTg8q xB4JrofEf/BuF8OPFWj+LNOvvjf+0o+keOtWk1zxDpkfiewh07Wb55RM1xPa pYiF38xUcEpwUTGNox9oftEfCvX/AIo+HNIfwr4jsvDXiPw7qX9pWE+padLq mlXBa3ntZIryziubZ7mIw3MrKonj2TpBIdwj8t/nr9nL/gltf/Bnx78GfEF9 44m1kfDXw9p9lfaZd3muS251G20AaL9qsIE1OHTbZGiDlvO0yeVvOn/eJvXy 4fEWZSk5uq76dvXt0f4spZbhkrKP5ng+tf8ABuz8BfFWjaH8Sr/4xfHvx9Ye HdJtr/Rb+bVtP8VRS6dAnn20VtAdOnF1blOY7eNJEkD7VRw+06vxP/4Nz/hB N8MtOi8aftAftITeDvh7bG6sE1zxvZNpnhqCCLmSIS2flW0cUSfeXaFVOoAr 6u8Ffsla14D/AGPdG+GV3rmm+KH8ASaVJ4UeCCXQBNFo09rc6XbX8u+7LM0l lElzcRRKrq8hS3j4WuMuf+CZsnirx/8AGW/8QeK9GTQfip4d1vw1aaboOiXd pJocGqurXMytd6hdwR3DsiyTGwt7GO7uC09zFPIITC3xDmN1+9em2i/yGstw v8p4V4l/4NkPhL411/xDq2tfGP8AaR1bVPFkC2uuXl74rsri41mFXikWO5ke yLTIrwQMFkLANBEQAUXEfhH/AINjvhH8PvEOg6x4e+MX7SGh6v4Yge10a/0/ xVZWtxpMMjTNJHbyJZBoUZricsEIyZpCcl2z99eEdE8c2PhvwzFrniLwnqOr 2lw7eIbmw8O3FlbapCYpgiWkL3srWjiRrdi8ktyCsUqhVMqvF5zr/wCyTrVp qGt694X8YWmn+M5dbXUtG1LV9LuNRtre0CXe3Tr+CK8t3v7aKbU9TmgTzoRC 01sAGFsN7/1jzK3L7Z2+X+XYlZbhr/B+Z8ceFv8AghB8HfhL4ztfhDoP7QX7 V3hy6163m8aw6Lp/iQWum3hsrqzR73elgLVrmKeSxPL+cv7lwMKGFv4pf8G5 3wd1PxS3jHxr8Wv2j/Get6iieH3vtZ1Cz8S3bpef6CoIk02Z1VBcZE+ALYAz b4hF5ifUvxN/Yr1/xD8UfC974c134a6V4C8M/D7WPAS+EdW8Byala3MWoC13 bjHqFvCLVDp9iv2QQbTEtxHvXzY2gX9m/wDY5179m34V29hPr9r438UP4vi8 RXV615qmlwTq8MNhMZpLm61G9v3isld401C5ud0sdvGr20UNt9lUM/zC9/av XyXf06qxTy/DW0j+fr+Z8s3v/Bt58IfEPg2L4ZXfxk/ajufCHhtItVtdAuNf iOgWjzSXOHt0ew+ymbcLgusWZE84M4UToXpaJ/wbT/AT4vWGpeMNO+Mfx31q 0+J9qNQ1DU7bxJpzReJLe5ljvQ8jLY4mjeVYpwGypdEfqoI+4PCvwJ8X+Hv2 y/GvxIufFvhu88K+KvDWl+H7bw/H4cmg1CxNhLdzRzPfm9aOXdJqF7uQWkfy m3AYGJ2m2vgv8LfEHwk+DPwz8Kx61oMreEdMs9M1yWPRBBFqkcFi0JFnFDJH FZZuBDIoCSRpEjxLGu5ZIxcR5kk/3z/Drv08kDy3DfyfmfG03/Buz4Hl8P2+ lv8AtB/tVSaVa6PJ4fhtH8c25t4dMkjjjksVT7HtW2eOGFWhA2MsSAqQoA87 +Lf/AAbn/s5aIviJ/G/xU+Ocx1WK58a67r+rXWnXLItiG8+4uNUfTGMJIvXd o2mUz7Gk2S/Zi0X0f4H/AOCbnxD8EeNvHesJ8cru+l+ImoaJHq13d6bqdxqR 0jT77ULma2gnfVmSxubpL4xGWwitba32u9tZQM6eV7V4P+Efif4d6PpVjo0n hCzvLbwVHoLX9vbXdto2m31oFWyNpoKzGKO0JmuTIsd3FKEgtYTJKAkluR4h zFaqq/uXr27g8uw38v5+n5XPgDT/APgjn8Av2nIJPiBq/wC0T+1Vql/YTf8A CDTXnjbVjYaxpr36rbrpksGqaYl1ALhNSRVjdFEi3yMoYSgt0+h/8GufwW8K R6KNL+K37Q+nDw1qTazpAtPEtjD/AGXfMIQ13BtsR5U5+z2+ZVw58iLn5Fx9 ceFf2cPiG3wDtvC/jbx54U8XeI4fFema8+tW3hSazjuLe21O0v5Y3t7u9vcX DmGdY5YpI0t/Mg8iKL7Oldt8cfhf4q+LPgnxdoOl+MYvC1vrmkxWmmXVlYzr fafdB5TO8txHdRyNBLGYI9tsbW5iCztFdxyyRSW5/rFmKjaNV+mnb07th/Z2 Ge8T431v/g3f8EeJo7uPUv2hf2rNQj1CeS6ukufHNvKtzNJZtYPI4a0O52s3 a2LHJMLGM/ISted/Gb/g2J/Zw07w9qPi7xb8RfjNb6b4Z0aP7Xcx3elIttZW Nosany4dNyxWCBckKXcgsxd2JP0z8Ef+Cd/jX4Q+HPBWgXPxO0DxH4a8H6hf a4+naj4Yv7s6nql34i/tc3c89zq008xt4HmhthcPMY7l472R55IIkX6E8I+E tV1jw14ks/G8lhrUOvajfIumlYrixt9MZjDBbYMMbOJLdFklWUSETTzoJHiW PF0uIsfCSftXbysnb7uwf2dhm1zQuup+eC/8G0PwEj8LyfDdfjR8exoZvI9d fwx/wlWn/ZDdSRyRJeG0+xbPNaOCZFl27isLqDhCB3Fr/wAG/Hg2y1rXtVg/ aP8A2r4tS8X28Vprd3H49t1n1yKGNooormQWm6ZEjYoquSFUkDAJFfVvjP8A ZgPjb48av4un8RanZWOreEl8MtZ6fPdWlxBIsl2wu1ljuFiZgl5IFEkDtGyq 8bodwbnfir+yBrPxb8P2MN7rngyx1eee5k1vUbLwxcwNqKSpDCESNb/aUMFv CkkV59rgnMEO+EpEsddazrETspYl673V9fu8l+B1RyrL9OmnmfI/iH/g3Z+D Wm/Db/hDNW+Ov7TFl4F8FRrrsem3niu2h0DRyWuH+0xB7IW0cinz3cp86CXc 20SgtvXH/Bv54C03VtckP7QX7WjXnxOIsPEFxD4xjlPiJI7eUKuoSLZHzIhC JIwbg7PnEYOZFVvrbxr+zRqvxE8d/Ei41PxJYDwz8QvCMXhM2FrpLx31hHGt 1iYXLTtG7Fr65OPIUY8kfwMZLviv4PeMvF+qfDS/n8U+F4rvwTrEmral5fhy cR6sWtp7TZCpvSbYCC6n5Zpv3nlt91WR4edYlxX+0b76dbJ/y/zb/Jk/2Xgb Lv8APt/nofHD/wDBt/8ADQRqn/C+P2nBHDoB8JBf+EztMR6ORzpY/wBD4sz3 t/8AVn+7XDeNv+DY79nLxN8W/s/iD4o/HrVfGHimG812Se+1ixurnUFhlgFz cSTNp53P5l1CSXbexkJGcMR9+aF8BNb0GwsYjrXhnUZrG9sYTeah4flubu70 mzLtBbzytd7pbxZJHkF0TsDuzC3BYk9B8GPg5afCH4X+CdDkNpqupeC/D8Gg QaobUQzPEkUKSbRlmjSRreJmQMRlEyTtBoef4ymnKGIbfkkuj1u13tddRTyr ApWtf70eDf8ABOT/AIJdfC39iG58UeL/AAT4o8WfECX4nz2WvSav4kv7TUme VI7sx3dvNDBGd0qahPukJYssnBALZ+sa4PWvhNfeLfAPhbRdS1XTjLpN/p2p anLFpEMsV9LaSpcqIIrgyrbg3MUTK37ySJFPlukoSePvK8HEYipXqOrVd2+v ySKp04wioxWgUUUViaHnH7T/APyIFp/1/p/6Llr5C/bI/wCTQ/ir/wBifq// AKRTV9e/tP8A/IgWn/X+n/ouWvkL9sgD/hkP4qHv/wAIfq//AKRS1yTSdRJ+ RvH4HY/KD/g5AtY7j/guh4ELru8nwpbShR/ERrOq8V5n+2tYX8/7b/wjjghR rmSe8mRZTlcNBExBx6Ia0f8Ag5lktvgN/wAFXPhxBp9tpumaf4a8C6fZxWum 2gtLO2hi1TVIxHDCCRFEqqAqAkKoAzxVb9uu5M2ofA74uCYQaFqigvdI4Jty 8aWcr4HTY9urHPadPWuyUUm1F3XcwPeYdW8XeHgEtVhuYgN58pApAHIGcfTr VNPAU3xR1Wxu9PjvtMjWfzbuJv3YBzkshPbP6Gtf4S6rFbaiF0i2vNYAUG5v bi6At4sj5uh5x7//AFq9Z8L+JNI8TR3EWl3EFybVtsixKQqn05FID5Z/ax/a U1SL4saV8OvCYNzqLyRpdyIcGR2ICwZ7Z4ye1WPjd+y/psXwe0aLxNqlzezp r+nNcuJvKjmkudQtreZ/osDMq/3cE9zXRfCL9nK50b9pbxz8QfEKW9tbPeuu nI5GOgzMc9Bjgc9a4/8A4KCS3t74Pa6t5IltLDUrRo7iSfdEAZ48AquTgNgn jOAfpXoZT/v1H/HH80RU+Bm78U/2dR4I8EWuveE7m9uT4fsRBFC0plM1urZU K46snIB/ufL2FdV+zR8XbD9r34XXukarZPb6to7LHeQlyBKf4JB3x/Wrvwa+ INn4d8D6PBrSLDJqVqJGWEEwWqhcEEHkcg9O2Ku/Af4C2Pw7+M2teLfD16n9 geJbIFrUA/JKH3blz0U/nXnlmBF8I9a8M6nqVjpMF1PJK5YNL8tuoYcAdyOm aVfhR4g0rRbi51Ka2siibnNrGW3AnAwTyDzXb/Fn46t4OurWXSoJNVtmaRJk t4GkZWUj24xz9e1eUfHP40H4W/CvXvGbXdxPZW8WJbCeU4d2I2KAcEHcVH40 AeIfsSQq3wa+LmoZd0g8S6rINw5Y7IArH3G6vEIrIap+zD8CIAAUk+OvjsH0 x/Zvg2vov9n/AEh/ht/wTZ8T+KNWuIYLvxAwu5GDDMs91KZunb9wIyfSvn34 SMniD9mL9nZ5JVSO7+N3jpy7DjDaZ4NP9aAPtb9tqGbwl+0X4AkeQ6clnqMc fmMOAJdNmGT6A7cfUivVNP0iw+KFpHZ69dSw3MaErexbY0lB6D3PPWuT/bs8 PJ8XdWkuNHNwNUhtFsEnkgPkrcwIGidSfRlG4cgqxGRmvnP9l7/goP4V+Irp YeN5ovCutaQRC7yndbzsMgtGpxtII5Vm4znOM0Afb3jbyfhR8PIhYRi7kswk Ue87mKs6hj+G4k/4V47rvx68WaBq41KGyOp6AoAu4UiAlVe7p9ByfpxXKX// AAUH8CfEb4u6Ppmk67a3lzp8rxxqqOyXMRTLZJAXPXoeaw/iR+2PZ2XiK/8A C9hrWiO+oZIM0ixLaAZyvAwDjOM98UAe/wBp4itbjTPtluPOs72L7QhIzhSO 9QaXNbXmiWMFjAqW+xPLKgKqoPT22/0qTwdDbweDNPe1ANsbRGjY8hsqD29f 61HqJiurWWOfENn5TLIwbb5SEYY57cd+2K9Gf+4x/wAcv/SYk/aOO0348eIf GGv3epaHBCvhHT52topWQO95IrYMy9hHngZ5OPevTfhv4xl+IVhexzxmzuI/ kin27Scjt7j2r4n+DP7VD/s8wromtT6bH4f1Ii506eWdZXa3ZmCFwvCkbeQc HnpxXuMfxjiu/C/hvX4dZshbRytMkzOI4vLblWY5AzivOKO88RfC/wAQXs0m n6b42spZEb95Fc7WlXJ56968WtPh9ZeGv2n/ABHcRFp5DodhaXEzHIuZnu7h sj3AiHHoRUfiP9uz4P6ne3WqXfiT7FrVvIVb7KskiTkcdAnt1BNR/steJ7r4 uX8OuXlzDHFrGoTXlpJIAm63hXKr82MMVUKo6lpCBgtuAA/4OaUJf2ofifbO Co/4aJ8Chsj7oXSfG2f5V+fV/wCM18J/tb6hqtyubNtcuGcNyArTsTX2z8TP irB8B/Fnxp8YI4Nvpvx3+H91Izuf3Mcuj+MtzZxyVWQnpzt5Ar5h/wCChPwU t/g18U5LazaO7s9adtYsLjo5gl+bawHHVs+xLDnFAH1vrg/tdbGPwxDDFHqd utx9p8wtb4/uMvf6CoWsbnRrlYL7bpN0TtSSLc9rcezA8rj+pr5W+BPxX1Sy +HdpZjUNfM6TbIorWDzwidSQCQfp8w/rXpGoftV+JfA9vHFBpWra1EVI2axZ rC+fUSRyOoH+9igD1T4kfDeC7az1C8Gbiy3mIQthJpCvyocc84wPrXOaH8UN f1jxHCthpxgWNGivJYpMLboR1Yn7px6nNfPHjz/go5498URSRW2m6HosUbAf JA8kynnuzEE574HStX4F/Gfw74z+F+p2/j/4h3+hXcd+ZotPt7GSVb7Kr87u oxnquD02jg0Ae2N8TPAnw7kSZbWx1vVRfhboMMrHGDywPQn+tfTESaZ8QfCd vqWiwpCt1EskTKgQkehGPw5r42+DngDw7e/tG+HrZDcXHh7Up0kthd7dzjBK iQDg8/Q/yr79Fvbrp7Wn2eO3a3+RfLG38OO1ejQ/3Or6w/8AbiftI+UvhP4E 07XdY8bwW+kx6ZcSXhitZRj/AEeVEBwvcEPu49HruvGcKaZ4KivyjtezpIIn bblSxz0PRifrg5rxr9ou38UfCP8Aafnl8MXDovisJcQxycxPJ911IPHDDOew avoPShL4g8N6c2qJYz6pbRrG8sce2AyhfmZAT03Zwa84o8fbwJL8PfBEd2JZ jNbI1zdSvKfM3k9EY9Dk5x3xW7p/7Q8tv4R1XU9SvPLtBZqjXMRG+3Kb23Ad 5Du4HqBXS+LNSslmgt9ctYY5MmO2yh8k+jc8E9c5/CvmH40Gw1v4jDwj4Xgk g0uBIbjUBEpdp58HyYhjqWJzyMHahzg5oAbqnj++8c/EfRfEN6XutRvtZtVj ycssayLtU+ue59a+nv2DGjP/AAchfDULGsUh0XxC8qqfus3hnUzivK/2QP2a H1f9pHwpb+I7iK81Oe7jb7DAQ1vp1lHhp5N2DvlSFXbgYGD1613X/BOfxpB4 +/4OZPDGoWxQQeR4mhjRPuoieG9VVQPwApMaP6OP+Cev/Jg3wP8A+xA0H/03 W9ewV4//AME9f+TBvgf/ANiBoP8A6brevYKYgooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKAPB/2muPH1n76cnXqf3kleCftK 8/s3eP8A/sW9Q+o/0aXpXvf7TXHj6z99OTr1P7ySvBP2lef2bvH/AP2LeofU f6NL0rmqbm0NkX/gh/yRbwh/2BLL/wBEJTv2Z/jCvxL8WeJNPl03xNp+oeH0 1O3uxqehX9hbY2XSRfZri4toYrtSkZJe2eZFyP3jq8cjt+CH/JFvCH/YEsv/ AEQldD8GvHLeLPEt3bto2q6XLbaE1zci7e2f7NPPZ3DNZMYZpB58KhDJjMeJ 4ijyAnbz/aZo/hKv7GHhz4qaP8d7GTxz4y+H3iLSWtp0httC8GXmjXEc+wlX aabVLtWQKHBQRgksp3gKVb7Nr4x/Yx/aZ8O/GX472Gl6PpnxBtLq3tp7pn13 wHrmg2xQIUIWe+tIYmfLgiNWLkBiFIViPs6uqhszGr8QUUUVuZhXB/Hf4gal 8OdAsbzTluG8+5NrILfw9da7KrSxvHA32e2dZdi3LwGRwGVYxIXMSbp4u8rz z9pL403vwH8HaDrNnolprkWpeK9C8O3cc2oNZtaw6nqltp32iMiKQSvE90j+ UdgdVYeYpxldUgRxXg+S3k/bx1xtJHxaV/8AhHpP+EmGonVz4RF3nTvsIsft mLPzvJ+1bjpfyk+f9q/feVXafELw98U9R+JeiXXhTxh4A0TwdB5P9raXq3g6 71TU73EpM3kXsep28dvui2qu+1m2OC53g+WPRK89+In7S/hz4X/EvRvCepab 4/udU17yfs0+k+Bdb1fTIvNlMS+ff2lpLaW2GBLefKmxMO21CGo2SX9dRW3F /aE+LuofBPQNH1qDQtW1rRTqQt9cl0rSrzWL7TLVrefy7iKxs4pbm6JuxaxM kSEok7ysQkTkfPH7On7a/wAb/i349+DGm698NfDeheH/AB94e0/xFqusLBr8 8pSfQBdTwosWnSadYOmpMYlF7qYkaKBgImaWJz9ca/4htvDVpDNdC5aOe5ht VEFtJcNvlkWNMrGrELuYbnI2ou5mKqpYcSv7QEN18dvBPg+10yWS08Z+FtX8 TJfztJbT2gsbnSYRA9tJGHDP/aZLbyjRm32lCWOwSumn1/yf9fIdzyrwX8YP F/iv/gnpo2peKYtV0/xjpljpOkfEa616C68JHT9r2kfiG+huD9lCrBbvfTxX do/klolaF3AFc34r/ab+NvjvXfjp4S8JeGpobvwf4V19/CevR+EdX086nrAI GmwhtVghsmaMl4w1pJqEF55ZuHewTy7af1b4iftJeJ7L4S+IvGfgbwr4Z8ba b4a1u+s54JfEs2mXFzZ6f5sOoNEGsnQ30d9b3FvHbu6QSCNZDdxhti+qaHr8 mrarq1tLYXVn/ZlysMckrIUvkaGOTzo9rEhAztHhwrbon427WZ7+8F7GJ4T+ M+keL/DXhnVbSz8VxW3iyd7axS98L6nYXMDrFNKTdwTwJLZJtgcB7pYlZmiQ EvLEr+ca5+0b418E6hrer634Ovx4S8Pa4ujahHpmh6lqmqxQBLuVdStYbWKW XUYZll0iLy7eHNvI9+ZHcW7BfTPH3xf034deLPBGj3sN5Lc+Ptak0LT2gVCk M6ade6gWlLMCqeTYTKCoY72QYwSy7ev+Irbw1aQz3QuSk9zDaqILaS4ffLIs a5WNWYLuYbnI2ou5mKqrMEK1j5c+LHxZ8VfD39rHwSk03j2TU3+EviH+1r7S vA3iTVvB6auJdNnsZpLO1MluJ9trqhS3Fz9qKusAmLTwGTV+Cf7RXi347/By z8R/EPQIPh5YXXjuxsI4pbvWfDckFuDbfYxDJfW1ne3zXGqmG2MVxaaekyXE kIiuY0R7/v8ATv2gfFer/H7R9Bg8DaengbXLZ5rXWrnxEttrkgS1jnkuv7He EOtlHJNDaPI8y3CXMqK1qIiJzV+In7SXiey+EviLxn4G8K+GfGum+GtbvrOe CXxLNplxc2en+bDqDRBrJ0N9HfW9xbx2zukMgjWQ3cYbYtRdmv663/4AN3Vv 62I/BnxpuNb/AG7viD4Ll0Pxlb6bpfhHRLi11afQdah0a7uhcag13DDdyr/Z jSRxXGnsTbkTSGV0dpfsuy32P2afiNezfs2/CC48TyeKNQ8Q+KtB06O4ur3Q JYLt7ttONzLLfwwiSOwZvKk3iRxEkzLCJGd4w/oGha/Jq2q6tay2F1Z/2Zcr DHJKyFL5Ghjk86PaxIQM7R4cK26J+Nu1myfH3xf034deLPBGj30N5Lc+Ptak 0LT2hVCkM6ade6gWlLMCqGGwmUFQx3sgxgllm1tPT9f8x3ufMvgj/goP8UtY 8b+PbXWvgh4n0yy0/UND0LwlawaLrk82oX97fajb3Lajdtp6Wttb20UFrLPc WhvLOFHZoru882FW9q+GPjV/GHjfwdd6zqMaarqfgn7fp8Bkv9CudUZ5bY6l NLoFyd9usDHTgjTtNPAb2WFjDuY3Ppev+IbbwzaQz3QuSk9zDaqILaS4bfLI sa5WNWIXcw3ORtRdzMVVWYcDqPxm8TQ/FzwZZ2XhTTr3wB4wgMQ1ttWmtdX0 69+z3NyEm0ya1UC38u3VDJ9o85ZplRrYKrSBrayX9WFL+vvPF/hLr13oP7PO r3Hg26+ONpoT/EnQLbQm8Y2Gq3utvp0t7o0V4mzWSNSWyeR75ZJbvLwxtcSx BoY4FPufxz+Nt38LvBPi270Pwvr/AIr8QeGNJi1SPTrfT7tbe9E0ksaBbiOC XzTGYZHmhtEubtIwpS1mkmt4puZ+In7SXiey+EviLxn4G8K+GfGuneGtbvrO eCXxLNplxc2en+bDqDRBrJ0N9HfW9xbx27ukEgjWQ3cYbYvqejeIZNV1PVra WwurP+y7lYY5JWQpfI0McnnR7WJCBnaPDhW3RPxtKsyd3H+uyQbP+u58v/BP 9uT4j/Ebw94ItfEnw41/wn4n16/vrvW5U8GeIbnSNG0uLxEdNs40kltLeWW7 u7doZB5scHkQme+mijhhW3l9wvNd/wCGkvhR470vw5qWqeHGeTU/DFn4ggeN mju4g9tLdW3lS7wbe6E0RDmKTzbWTAC7JG7fw14it/FXhyw1S1jvIrbUbaO6 iS8s5rO5RHUMBJBMqSxOARujkVXU5DKCCK84+F/7bHwv+M2pfY/DXi2z1O8F 9FpMkC288clvqTxXEzaZKHQGPUYYrWeS4snxc2qKGniiV0LXGo4yUo7rX8dB xbTUl0Mfxl48+IelftL6vo2g6PLq2gWvg6HVrJbkzWltcagkuoI9sLj7LJEW kJstweeN41VXRJAZBWX8V/2i/GOg+G7HVfCemXmtR67NcwafZ3XgrWoL2AIk KCSVRHgMs4m2x3X2KOdJUxcwrC0svsk3xDsIfh3J4oNvrZ02LTzqZhXR7ttR MQj8woLIRfajNtGBAIvNLYUJu+Wsv4K/HXRfj94audW0Kx8Y2Fra3RtHTxJ4 S1Xw1dM4RXJSDUba3lePDjEioUJDKGyrAdkcXTuuamnZff6/13+XWsTDTmgn bT+v6/4HnHxL+I3iufx58XNC8J6d4ji13TvAcM/h2+fSr0WNxqqi/byopZ1N g7qZbE/J8zmRldnEG2HX8c/GQtrnwmm0/TPGF1ZeIddkW7f/AIR3V4msrf7F dRLJdRpGvkr9pe2Xbdrsw5kCjyvNj7iH4lRX3xkuvCFp9gnn0jRYtY1XdcOl zZrczyRWWyPyikkcptNQ3N5qtGbZBscS7k8y+EX/AAUO+GPjP4f/AAjufFnj XwB4B8b/ABg8N6Rr+k+ENT8U2qalO2oRIYoLaOUxS3OZWaFHSIeYyEBQflEr E02kuTb9Vbt5XXz7k+2jZe7/AFb0+Zt6F8ZNb1PTLK41Cy1TSJ7m+sdHubEe FtSuzp+oEs12omCKJrTbsRLwIturKzmWQNsS98I4fEnxF0P4eeOfEF3eaDfT eEWXW/DogmtYPt12tjMXaKR90b25gnjVXDOBcONwwd0Pwd+PeteOfjh488D+ IPDulaLd+Fbax1jTrnTtZk1GPU9MvbnULe2kmEltAbe63abOZIF86NA0e2eT LbU+Ifx71v4e/tF+BvCs3hzSLnwp45uJNHg1hNZkGo2uqLY3+oeU9kbbyjbG 20+T9+LrzPMdV8jbmSpniFZqELN/lZ3X4/0xTrJ/DGxdTxfrWm/CXwTJpY13 W9U1ifSrZ7m70bddSW7tG1zcXcJe1W2f7MszMW2+VIQFgmfZbSekV5n8Gf2g p/i14Ug1X/hHp7SOTxdr3haXyrtJktF0zUtQsUu5GYRnbM1ivyIrsj3KL8yq 0tdNqXjG/n8R6npuj2FteTaZpxuXluriW2ga6fP2e38xYXXkI7SkZeJWhPlu JRjkk0tWc2x01Fecfs9/tBxfG74AfCzxtcaTeaTN8T9DsNWisbZJtQi06S5s PtpiluEiVVjVQyCaVYkd9ijDyIh9HqmrOzGecftP/wDIgWn/AF/p/wCi5a+R P2wzj9kb4q8jJ8HauBn1+xTfrX13+0//AMiBaf8AX+n/AKLlr5C/bI/5ND+K v/Yn6v8A+kU1cVX40bw+A/EL/g681K51r/goj4Nvryb7ReXnw7spp5f7Nm0z zHbU9TLN9knLTW+SSfKlYun3WJYE079iOX/hvT/gmr4p+FhMc3iz4e2dzrWh wZy12kZWS4twDnkosUwA5Y2uO5rS/wCDvr/lKbpH/Ykw/wDp31avjv8A4Jk/ tlXf7GP7TWh+I4yGs1uozMjNhGGSpVuPusjOhPbdmuuOxiz68/4J4fEeT4j/ AA3m8I6hrw06bw5ceU9oybWa22gxlv7xB3J2I2cnnJ+3/hbq3hxIpNM0GNcW SBXkCj95jjcT2J9K+Av25/2f5P2b/izZ/HX4an+1vhl42u21GIRAoujSyszS abcbScKMAqxHOVOOtfRP7L3xXtfiZoXh9PDt6sYvwl3qkyOFeA4z5BXtjoRn 86Yj3b4kaJa6zp1xY3IP2S9Ro5Ap2lQRyc9q+Qf2m3kvPhbe2NtLc3WmnVLK JLg2zrsIvIgMOAVYZwOSK+w/Gvi7TNNubS3ubqOOa9mWG3P+2TgfhmvE/wBs /wAILqHw/tRHHaRtNqel20ZklfYpbU7YYECkK/3sk8HGenWvQyn/AH6j/jj+ aIqfAzB+BHwyvfE8en6tqKXOpRoAYrO5cR3FjHt5ygAJJ4+8MgenOfp/R9OS zCCJFihVVjiToOnpXA/BAv4l8OaRrW7Tjerbx/vEtSrlHjBwSTz+eKl8cayn jrxfb6bZTXFrDpE26e6i+UBgMlR/KvPLM74x/D/S9M1Fbi1uns7q/fLW41AW 0bN1yMjHrnkfrXxH+2brF/8AFn4q+EvhDp+pTaxeX2orNqFranewDMBDAGH3 nKlyAereV6g17d/wUG/bX0r4bfDiystLhh1LxHexyRQLOFL2zDA85xz04wv8 RIryX9kfwPB+w78FdV+P/j1Zk8XarbyReFLa5P8ApEbuu6TUm3A5PKrHkfeb IwUBoAwf+CxnxosvhDpOi/BPw3NG39kWyya1NbvmCW8ORceURjKI6mBeM7IB Xnv7PHg6+8cfsj/s4WWnqzXC/GLxxPkfwquleCyTXyx8Z/irf/Gn4j6l4i1J 3a4v5CwDNnYueB/n1r79/wCCZPhLUfFX7NPwQ/sqIy3ln8SviBMOMhVGkeDM 5/KgD9Ev20vh1ofgvwDpkd1NoTX+o7bxruKV43jYwtwCpys6kqQVxuG5GBVi D+PvxR/ZGtr7x7eanBq1vY6XqV5LMvmM7SAs2WXkcgMSM5J9ea/Tf43+Dx4q 8ULrd9FG1/bRAhMkqZMDnPtjp0xmvlzxh8LU8WazFJKscdjppd54Xbh23AEg jk54PPoaAPjK6+A95o3xcsfDlqw+y62x8i9CvtKorM2Bxk8dD6Cut8MeD73S tLu9Kls7SWHS7k7NTAVJOuDvz1H05r9I9X/ZO8G+Gf2C9B+JEdpBJrtr4qOn 3E6r+9sopLZkMfsoLD6k15P4m+A+meFdSW1sNYE51CCK7cCJHijduTgnp25z QB518GP26734ReH4vDeq2MOsQWQC28js0UyxnsQc59jVb9oj9te7+KOkyeG9 Jgh0iwvSEkuRKWkuI852DA4B6kDJwO3Ndd46/Z2i+IOnXdzPYNqWpWaBIdTh kEDRMRnDgYDDj3rmPhp+yvrvhRbXxNqcFtGt5kW91cMJCoI5Kg5AUk9cdxXo z/3GP+OX/pMSftHgXiT4fWV74S1q9l1OPzdNCvZhYVKTsSvzEk8D5j09K8q1 XUNW+xReF2iuXyQyRGQvGSejIOgz/s1933PwJtofDtxBItjfRkvqE0SwBFDR qTn6Hb0GO1b37NH7Ivh342+IvGnijWLUw2/hXSESC6jcxAXBP3cg4LY4wc15 xR+enhL4B6pqfjSz0zUVfTYrhgWldM/L7Dufav1Y/Yo+B3hDxp4ds9AsvEdp Jq6xwWNpbzSqrIplXdI27CxhQ20BBvcs+47QoHgQ8AAalb6hdWvnRWzvDFLL wXUnHXv9a734afBRE+IOl+I7Gb7GYyIZ4xKV2g8g9f8Ae/CgDwz9pf4aeJPE fwf/AGh/Ds1o66zrHxy8DWtmj5wytpnjJIyeMgDHJ9iaxv2mb2Dx58QrPR5I hJL4StDpS+cQVmkSU7vwX3zgg19b+IPhmfEPjHxlpeqLNZQap8U/B8YmJKuM eHvHMgkBB6gsG9Mivd/DP7Eng34Nj7VFGZNTvIkkvL+Z/Ma68xVkIw2Qo3bs gdd2TzQB8H/Dr4I3OpeG7Z7S8024AAX5ZFRIvUZHPFU/EfhA/Dae4lvrh7t5 FYpYWzmZ5nA4zlflX6V9P/tQaB4O+FulfbdOsxYXuoyZd7VCsbjuWQHaT74z XjWn+N9Dhkmu73TZhJtxGztgkc8gds/560AfA3xii1nxL4iupjpVzb2sczEL FAwRCefTrXG6Z4M1LUroJa2N1M5OMJGT+GMZr7/+KHxe8LaxYPbTaMXgJ4B5 DnA5ODnB6fhXj/wU/aj0TQPixf2V/oWk2Gl39wI7V7Sxih+yqCcZKjJz1znP vQAnwo8O694O+HOm6hqU1xa39hchrZZAUkhVTwRkcr7DpX1r4L/bU07xrq+h aXJpt/HqmrOLZ53CJBnoW4yS2e2B161w/wAc/CEHjnwfa3mnRpdNAWZJEfaC hXLZ7MMdjXi+o/DDxF4W0Sx160huY7WKRJ7G8JJilPXYrf3gVOR/hXo0P9zq +sP/AG4n7SPoz9vHw7Hb+D9C10xmVdMu2gnI6tFKhyOn95V7V5Za/tgah4a8 OWnn6bHeqQqoWmCOAB1IAIP1/lXvngrx5YftH/BqSW6tFeRo2iu7eRc7Zgvp +RFfFXhL4dap401y60+xjluLm1DqkYGdrA4rzijpfEXxQ8T/ALSvim2tNPsI TPauTBZRSERxess0hwMAdFGCfTvXV/s5fBeXQdV1LxJfNdvDE7Ok138sl/cO CDOV7ZUttHZfXJNd7+yv+yr4n8QzW3hfTYoYLxla6vpioVLWP+JmbGS3YZOe eK9N/aE1Ox0XSVtLAulrFKwbH3H2Lt35PzZznJz2HrQBn/sYxNovxl8ReJLq VJ3/ALMvrS18sg+XNNDNDjsDhWQ9+D9K8W/4IimUf8HCPhNZyxkjPitDuPTH hzVOK9t+AfhkaF8LrPUpEdPMN0zsSQQyhWDEdOd0a/hXm3/BJZbT/iIs8ESW ghCzWXiOSTy8YMh8M6oWzjvmkxo/pZ/4J6/8mDfA/wD7EDQf/Tdb17BXj/8A wT1/5MG+B/8A2IGg/wDput69gpiCiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooA8H/aa48fWfvpydep/eSV4J+0rz+zd4/wD+ xb1D6j/Rpele9/tNcePrP305OvU/vJK8E/aV5/Zu8f8A/Yt6h9R/o0vSuapu bQ2Rf+CH/JFvCH/YEsv/AEQldn8NfD9hovifV57S0gt7jVbe7u72REAa7lFg 0Qkcj7zeXFGmT/DGo6AVxnwQ/wCSLeEP+wJZf+iEqj+zN8N/EWgfHXx94i1+ x0LT49V0yXTNMTT9Tmvpbm0tzqtylxcb4IVhkf7eR5KCUJs/10mRt5/tM1+z /XY9l+A3/JV9K/7bf+iXr6Nr4x/Yx/Zn8OfBv47WOp6RqXxBvLm4tp7Vk13x 5rmvWwQxlyVgvruaJXygxIqhwCwDAMwP2dXVh9mYVfiCiiitzMK8/wD2jP2Z /Cn7VXgmz8O+Ml8RSaVY6nbaxCujeJdS0GYXVs/mW8hmsJ4JW8uULKqs5USR xyY3xoy+gVwXx68M+IvE2gWCeGobe5vobrbNDca9eaLE9vLG9vMxntVaTfHH M8salGDSxRgNC224iT3QHVWPirSrrxLd6FBqdjNrWmWlve3Vgtyr3VtbzvMk E0ked6xyPb3Co7DDtBKASUbGkVBOSATXifgP4b/EHRv26/Hvi/UdM8Hj4f6/ 4S0TQdOvYNeuJdakuNPn1CdnmsjZLDGkjapMoK3TlRaRttPnFYer+If7NXh7 4m/EzRfFmo6n4+tdU0DyTawaT451vSdNk8qUyr59haXcVpc5YkN58T71wjbk AUHYDpPiN8PdO+KXg+90LVWvlsL9PLlayvZrG6j9HiuIWSaCVThllidZEYBl ZWAI4HU/2fPhw/7TPgvxVcX+sQ/EPw74evLDQLNfGWpRI+lq8C3jHTRdC3uk Ek1l5s0sMjb/ALGXbckBXr/jNp2u6n4EnHhqC2u9dt5Yruyt7rU59MtrmWGR ZUimuIFeSKJ2QI7COUbWbdFMuYn8rm+EHxFtf2wPhd4jt9M8IXfgXwr4J1Xw 3q95eeKL2TWlub6bTp8wwPZOtzHGdLhTzJrpJJPtUjsA0WJmv6+7+kFvyOi8 R/s3eAP2h/AeraPe6lf674PvfEV3ezabo2uS6ZYLcKZLa/sJ/wCzngN3A90L x7m2vGnV55pxICFRI/TdF8MW+hahqlzC9082r3S3dx51w8qhlhjhAjViRGm2 JfkQBSxdsbnZj8hfFP8A4J1eIPg78OdO8Kfs3LpPgCx1W9up9ensddj8JQCO e3tIJQbPTtJkW7MptxKZIptPvbfaYrS+tIpNsfs/gnwh8U/D3xC8SnV4fD/i DSrWPVbnw5rUni2+t7m8e7uVmgsbvSksxawxW0YjtkulnnlKwGQRo1zMtLpo Fj1nVvC+na9qOmXd9p9jeXWiXLXmnzTwLJJYztDJAZYmIzG5immjLLglJXXO GIPGftOeIPAPhn4L6tc/E7xVpfgvwa4SC91e/wDEH9gR2jO4SMpfCSJreXeV 8uSORJFfaUYOFI6X4a+GLrwT8O9B0a9vjqd5pOnW9lPeFrhvtckcSo0mbiae c7iCczTyyc/NJI2WPhvxT+GnxeGg6tceD/DvgDVfElnrIfw3feI/GN8j6dbS rdme/iEunXyWd+i31zaxKkcsbQupdvKVbJVtsC21O48L/speBbP4y2nxO0q7 8ZPrklhBaxNH461qXRrq1jt3ihDad9rNjKoSV3Vmhb95I02fNYyGb4i/sj+E vi14S1Lw54hbXLvwzqWsyay+j2Oqz6NaFpYitxayrYNAbu0nke4nmgvDOk0l 1KXDKI0j6Hwl8PoW+Bem+FL3SLPwpB/YUWkz6X4Z1OeC20dfs4ia3sbuJLaZ Y4hlIpo0gcBFYLG2Arfgp8C9G+AXhi50nQ77xhf2t1ctdu/iPxbqviW6Vyio QtxqNzcTJHhFxGrhASzBcsxNPfQS2Leuan4c+CuheJfFOu61a6Ho4Y6rq+pa vqfk2WnxxwRxFzJMwjt4VSJSQCqZLufmdmJ4b1Xwj8btA8NeK9IuvDni3Skb +1tA1izkhv7cNJDJD9ptZ0LLloZpo/MjPKSuucMQfMvi98P/AIiane+L7rw5 4Z8C+JpUS31Hw1b+K/FGoRae+prPasv2i1W2uIoI7ZrKC5hmhBkM29VS3LSX Evb/ALNXhXXPBfwa0mw8T6N4f0LxJvuLjVLfRdRGoWk91LcSzS3RnFlYiSW4 d2nlK2kI82aQBSAGK9Q6mj4u0Tw78cfDmv8AhifUTdw2sw0/VU0vVHtb3TZ2 hjnVfOt3We1uBFNBMjoySoJIpEKko1Zs/wCzT4Tuvip4d8ZTQ63c614SsvsO kRz+INQm0+yHlzRGdbFpzam7MVxNGbsxG4McrIZCh21Q+Cv7JXgj9n74i+N/ FPhfRNK0rV/iHdC/16a00ixs31C4E9zN50kkEEckr/6Sy/vXcfJvx5stxLN5 X8V/2d/jZ45/an1bVPDvjV/BPw/m065NnMviqfU3j1STSpLKK+XSGsIljMMk kbiF9SnsX+zmU2K3c32qE9B2ueofEX9kfwl8WvCWpeHPELa5deGdS1mTWX0e x1WfRrQtLEVuLWVbBoDd2s8j3E80F4Z0mkupS4ZRGkfZS/DywuLTxBBJJqLR +JnZr3F/Mrrm3jtyIWVg0ACRqQIiuHLOPnZmPm/g7wb8R/Efg2IeINJ8M+Et auvE9lfX0em+N9Z8RWy6fafZ2HkyvDYSJLL9mWN4Aotz5kksoud8sEvpXxF0 SfxL4G1XTrWae2uL61kgjlhmlgkjZlIDLJE6SIR/eR1YdQwOCB9gW5i3fxE+ HvwM1Pwb4Dn13wb4QvNdibTvCfh17220+XUI7WNA0FjbFlMiwxtGCkKnYrLw ARXEaf8A8E+Phnp+havZCHxxdXWtTW802tXnj7X7vX7Y24nEK2uqy3rX1oiL dXShLeeNdt3crjbPKHg8YeAfiZ418K+BdHl8OfD4Q3OvWuv+LZb/AMT32qDQ nttTt9RSLTlkslN9l4nRJJXsxbERMkbqghHo3xp+CWj/AB68KQ6Lrd74tsLO G6W7WTw74q1Tw5dl1VlANzp1xBMyYc5jLlCQpKkqpDv1/roJdjofDXh6Dwp4 esdLtpL2W2062jtYnvLya8uHRFCgyTzM8srkAbpJGZ2OSzEkmsT4m/Gbwf8A BKx0u58Y+K/DXhK21vUYtH06XWdTgsEv76UMYrWEysokncI5WNcs2xsA4NX/ AA54Uh8F+CrHRLCXUp7bTLJLO3kvtQuL+7dUQIpluZ3eaaQgDdJK7uxyzMSS T4h8QPhV8Ute/Zg1/wAIWOg+A9T1XxX52gXieIvFup39hbaS9o1mL5hLaSy3 E7RpFJJYFo42aSYG8Zy08oG1j1rxd8H9P8ZazqV3PeapYrreiS6DqQ0uf+z7 i9hZt0Un2yELeQyW++58loJ4/LN5O2C+x0ofswfDLwX8HPgD4V8PfDi5nvPA Vhp0R8Pyvr1zrkbWTjfD5N3cTTSSQBGUR/vGRY9iphFUDo/iP4C0/wCKfw91 zwzrEMFzpXiHT59MvIZrWC6ilhmjaN1aGdJIZFKsQUkR0YHDKwJBx/gL8BfD P7NPwtsfBng7S7DRPDelS3MllY2dlb2cFqs08k5jVII0TCmQjeQZHxukeSRn kadrjMvw/wDsreDvC3/CctYx+JIrv4jeYNav28T6nLqHluZ2WG1umuDPYwxN czvDFaPDHbvM7RLGzEm/q37PnhfX/jLpfj6+h1a78RaJaGz08S61etp9nlZk NxHYed9kW68u4ni+1eV5/lSvH5nlkrXzp8H/AIBftJeAbLV9YvNe0jU7+y1K xbRfC2q/ES+1SyvNNghv4xYTar/Y8EkCI93bziZrG7vbhrAJd3tykqC29/8A APgLxDpniLwvdX5tLCw0Twv/AGdPZLr2pavLLeyvA0m6ed41uEhFvtW4uIXu JfPcg2w81Liuuv8AWgPy/rUqfAz9kLwX+zn4bg0jwqfF0WnQa5deIxHqni7V tad725WUTyNJe3MzskjTSytGWMZmkabb5p311mo/C3RdQ8PeI9MWC5sIvFvn Nqtzp17Pp99cPJEsJlF1A6TxyrEkaJKjq8axRhGUIm3o6KTV9wPNvhD+yb4D +B3g7wToHh/Sr5tM+G8E1t4aTVNYvdYk0dJVMZ8uW8mlkysRaGNixMULvDGU idkPpNFFMDzj9p//AJEC0/6/0/8ARctfIX7ZH/JofxV/7E/V/wD0imr69/af /wCRAtP+v9P/AEXLXyF+2Qu79kX4qEHBHg/V/wAvscua5KivUXyOmkrxsfit /wAHfX/KU3SP+xJh/wDTvq1flgCVORwRX6W/8HSuu3Xij9vDwBql6l7Fean8 M9Oup47yCa3uI3k1HU3ZZI54YJUcE/MskMTg5DRxtlB+aVdaVtDmTvqfaP8A wT4/4KaRfBjQ9Q+H3xG0+PxP8PvE0Isr62ueVCfwtnqrqfuuOnfiu7+I/wAC vFn7LutN8TvgtqcniP4f3581ViYSSWUZ5WG4jHPyggbx1xX5516l8Bf2xfHf 7OsrLoGqubJxtezuCZIHHptz09ulAH2t8If+ChGnfFHxvban45t/7HbRoDJb RRAvDLOOm7uPyr1L9qX9qfwF498FWstnrUer6npuqaeFtrcn/SBFdQSyNGPU qGUf44r4b1D9sDwF8V5ln8ZeAUsdQfcJrzQ5Ps7yk9Secf8AjppJPiP8FZFz pNl47S6R0lVZtQRkWJGVpQP3QO7ylfac8NtPOOfQyn/fqP8Ajj+aJqfCz7C/ Z+/bi8N+CJtPsNc8SXtvp+kWbNZLcQMgVxgYf+9wflHQYrj/ANp3/gohc/E/ W20f4W2N5LqNyDG16kR3zPxyiDv71833vx7+C1jZZt/B+vancIvym+vGcZ9w GWlb/gorJ4LsvJ8D+DNB8NSbcC5SMmYHsc5J/WvPKPoH4W/s0aL8CLL/AIWr +0LdvcX+ftmn+H7iXMt/IP4p88qmcfL1YHHFfL37cf7cWv8A7Yfj57q6kNvo lpiKys0XZHHEnEaheioq8Kvb615p8WPjj4n+Nutm/wDEeq3OoTE7gruSin2B J/WuSoAK/oN/4Ncf+CcPwd/b8/4JgatdfFXwvf8AiC48GfFPXE0eW08Q6no7 2i3WkaB9oG6yuIS4f7PD9/djZxjJz/PlX9P3/BlT/wAosvH3/ZVdR/8ATRo9 AH0/J/wbk/sezff+GPiB/r8RPEx/9yFVT/wbXfsYndn4T6sd3X/ivvEnP/lQ r7pooA+Hz/wbj/seto76efhjr5sJG3tbf8LD8TeSzdiU/tDBPvioP+IbP9jP j/i1GscLtH/FwPEvT0/5CHSvueigD4ZT/g26/Y2jjKJ8K9aVTxgfEDxLj8v7 QpW/4Nuv2Nnt0ib4V600Ua7VQ/EHxLtUegH9ocCvuWinzO3LfQD4XX/g2x/Y yVWH/Cp9Xwwwf+K/8S8j0P8AxMKks/8Ag29/Y407T57S3+F2uQWtywaWGP4g +JVjlI7so1DBP1r7kopAfCz/APBtf+xhJEEb4TasUHIU+P8AxIR+X9oVJD/w bc/sa22PL+FetR46bfiD4lH/ALkK+5aKAPjWL/g3/wD2TofAmqeGx8NdWbSN Z1Sx1m7STxx4gkmku7KC9gtpFma+MqbItRvF2qwVvOO4MVQrnzf8G7P7Ilx9 /wCHHiZ+3zfEfxOf/cjX21RQB8M33/Btx+xrqaBbn4V61cKpyBL8QPErgfnq FV5f+DaH9iuf/WfCLUX/AN7x54kP/uQr7uooA+DH/wCDZL9iOT73wavGz6+O fEZ/9v6q/wDELx+wuGyPgg4Pr/wmniHP/pfX37RQB8Jp/wAG0/7F0cCxL8JN UWJfuoPHviQKPXj+0KVv+Da79jGXTo7N/hPqzWkJzHAfH3iTy0J6kL/aGB+F fddFPmdrJ6AfCtl/wbV/sX6bGyW/wl1W3Rzllj8feJFDH1IGoVHY/wDBs/8A sU6ZcGW2+EGo28rZy8XjzxIjHPXkX9fd1FID4f0//g3I/Y90mSV7X4Y6/bNO MSGL4h+JkMg9DjUOfxqtdf8ABtf+xheptm+E2rSr6P4/8SMP11CvumigD4ZX /g23/Y0W28kfCrWRCM/ux8QPEu3nrx/aGK0vhN/wQd/ZT/Y78ft8UPh18LZN A8d+HrC/On6q/ijWb5oDPZzW0uY7i7kifdDNIvzocbsjBAI+1KyPiD/yIOuf 9g+f/wBFtSY0ebf8E9f+TBvgf/2IGg/+m63r2CvH/wDgnr/yYN8D/wDsQNB/ 9N1vXsFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFAHg/7TXHj6z99OTr1P7ySvBP2lef2bvH//AGLeofUf6NL0r3v9prjx 9Z++nJ16n95JXgn7SvP7N3j/AP7FvUPqP9Gl6VzVNzaGyL/wQ/5It4Q/7All /wCiEqn+yl8a9R+L3xI8dW76OLLRvDX9oaVa3y3NncpqNxCdQhnKvbXVwFCC KFTHN5Vwj+Z5kUeVUY/w6+LeheCfCfwr8M6nPfwav4v0mKDSgmmXUttcSwWP 2iSJrlYzBFJ5MckipK6M6xvsDbWx6V8NfiFo+tfE/V/DlreGXWNJ0F9TuoPJ kURW9zFfRQvvKhW3SWdyuASR5eSAGUnnt739dzX7Jn/sY+JPipq3x4sI/HPg 34feHNJW2naG50LxneazcST7CFjaGbS7RVQqXJcSEgqo2HcWX7Or5y+A3/JV 9K/7bf8Aol6+ja6sP8LMKvxBRRRW5mFfPf7ef7eunfsN6b4emvrLw7qtz4rF 1aabY3fiyy0i+uLxBEYjHbT/AL67t8uVm+wJdXqM9usNldtKRH9CUUdQPj3x 9/wU11ubxr8Y/A3hT4c3EHjX4ZeDPEfiO2j1fxBpFzc6nc2Dxx6d5ei2l6+q taXhcyCaaC2G3yQhf7QjD2/VfjbZ/EzQfh1qXw91ZfFHhjxxr6Qt4m8L3trq NlaWkEF1dvK0uyaF4JpLJbFipVlN58kiSBSPU8VQ8Q+GNO8WWMdrqlhZajbQ 3NveJDdQJNGs8EyTwShWBAeOWOORG6o6KwIKg0raDPj/AMK/8FXrf4qfCvxn 4o0Oz8F2+l/DrW/DOm6/rFr4stPEejR295qyW2rXqXdi5t47S2shJcJLcyw3 KIDLd2NpEIvtH1XpnxN8Oa7eaNa2HiDQ7288SaZJrGjww38Uj6rZRmHfdQAM TLApubYNIgKr9oiyfnXPC3v7Vsk2t/FDTtC+HfjrxRe/C/UrDSJYrCbSIP8A hILu6tLa8aGyN1fQgGC3vLd5GuTAp34iMrKQOq8JfGuw+InwC0z4ieGdP1Px Bpeu+H4vEWk2VuIoL3UoZbYXEMSCd440lkVlUCWREDN8zKASC11p/V1/TC2t rHlcX7etmdAsL+80rSPDkHima80zwxceIfEEWn2l9rEC20I0W6k2O0GovqEl 7arbwJcsV0y4kXcdsZ4b4m/8FDdF+A/7Vnxy0bUviJ4L1S38AeDPC3iM+EdW 8U6Pok2jvcXt/DqCpPN5e2V4DpbIl5MsRmu7RTNbR3QkH1B8Pfin4b+LGkyX vhnXdH161gMImk069iulgaa2hu4lcxswVnt7i3mUE/NHPG4yrqTZs/HWiaj4 gn0m31fS7jVrQus9lHdRtcwmNYHcNGDuBVbm3Y5HAuIieHXJJpLUUWjyj9n3 9oe8/aq8FfDnxjplrqWg6Hr82qak5sb2x1CxuYIJJrS3tLicqTI0yyLdLLYF 7cvZMI72e3aJ7zh7j9u7RvEvgz9rNvCXjrwnd+IvgXcXiD7TqcOp2uitDoNt ch57eyi+0x2yXovI5EbzpjLa3aq42rBD6j4X+MnhT43/AAi+EvxGj8OyXx8X jTtb8KWeoJZJqunyX9k5aWPzJtizw2FxeNMIJHfyEulTzc7XZpX7V/hDxx8V fGngaC01XUJPB8WkjULtLEXWnXR1K9vLBYoihZpPJuLGeO4YoI4SrBmzHKI6 e7VvL8UC0O08XfFLQ/BDGDUr5E1M6Xd6zFpUCNd6nd2lp5X2mWC0hDz3HltP AhEKOd88SgFpEDfKHw4/4LV+AfiZ8Oda1mG20HQNR/4SG80Hw7p2r+N9Glj1 hLTQ7XWLvUpr2wuLuzgsbaCeR7iaOa48qOEHa88iWp+0JHWFC5ICqMkk9BXB eOv2k/C3g3wHZ+JILxPEemXuuaJoKSaLPBdbZtWvLK1tXY+YFEf+n28zHduM LbkVyUVpS11/rX+kCHy+Oo/Cmu+OvEfiTWLjw94R8O2MDNLq89hBpFvbwwSX NxqiTqfMSNhN5UpuZFC/2fuWJEYzT+L/ALKX7Xt98Z/2Vf2U/E8HjLw14kvP ihFZ2HiK/jCTtqt6vh7ULq8jg+yr5ENzHe2TNIrCONFguIwBJsjPtPiXxLpf hH4ha/c6P4Rk8ReMF8MreX7aS2nR6ldwW8s50+wkaeeFszSy6h9mMhFurJdl 5YS2Xj+FPxp0/wCM2i6HeyeHfEfh7U7wX7jT9a09VudLnsrn7FdRSSxNLbrI JGKqY5nWZNzxNJGC9Un28vwuK/RnP/tMftpeDv2Z/hlrniW+mGvr4c1a20PU bLTdS0+3fT7yeOKaOG6ub25t7OxJhnhkU3lxAJPPgSMvLcQRy8N+zt/wUu8L ftI6p8N9I0p/Dtn4i8YeHNG8Q63ps3iGFpfDjappdzqFppygLuur6RLWeQQq qf6LbT3DtGBDHcfRmt6/Y+G7WO41G9s7CCaeK1SS4lWJHmlkWKKMFiAXeR1R V6szKBkkCuXHx20O5+K3hTwlaefqM/jLw7qHibTtQtWil0+S0s5tOif94HyW kOpwNGUVlZUkJZcKHUbddf8Ahtf8/kBDrHia/wDiz8OtP1LwHqYszcaxaH7V fWUkK3FlBqMYvVRJYtxWa2iuFilC7XEkciPsZZa4W1/auubD4v8AxS0W/sEf SPhzf2EU13DNaotpa3drYSCe433AlRIjPeSySNCsXkwHa5kidWtfE39qTwL+ yR+zl4l8Svo09n4O+HMVxp2maboKWUh1SPT7QvNb6bBFMFAtxBcxNFJ5JiNh cFlSGMSH0nUPHYXSvEs2naRq+s33hzejafBCkE+pSi3S4SK3e4eKBy6yIgdp ViDllZ1KPt6aGIhTi1UgpL7u3X0VvK9zelVjHSUb/wBL9EeWa7+2XbeH/H/h PQRB4V1J/G0sD6VNp3iWGd57SW7eJbvyNouZI3twkkb28U8e/wA5ZJIYYvtT VNT/AGu7WKP4oSaLreiajJ4A8aaRpGoie9huIdL0+c6al5OwiEbQRwiW+LNM 77JLWcs2xPKj9D+CP7QGi/Hf4b+AvFGlWWsWdp8Q/DUHinTYb20xJb2ssVtK I53jLwxzAXUX7vzDuxIULqjEdbo/iPTPEF9qdtY31jeXOjXIstRigmSR7Gcx RziKVQSUcxTQyBWwdkqNjDAnZYihGSbpbefZry7afM09vSTT5Px87/loeV+P f2o9F8KfGfw9oNpqkOt3Wr+D9W8Q2mk6dfWrXGrmFrR7dYYWHmSyTRm68krK qEQz5Em3dFU+I37ZWj+Cfhlqfiex1LwRr1pHY6pqOjLa+JEJ8Qw6fYm4nEBE bBpEmR4nVPMCIvmFt2Yh7f5a7s7Rkd8UeWuMbVwPaohWoLl5qd7b67/h6f1s o1aSteF/mcm1zq/h7xt4h1vVdWsYvA8Wi2j2kDAK9ncRPePeTyNt/wBW0TWg GXIHlP8AKuctcs/ibodxpnhu6mv103/hLyiaPb6nG+n3d9K1vJc+QtvOEmE4 gilkaFkEiLDIWUbGxp+J/DGm+NfDeoaNrGn2Wq6Rq1tJZ3tjeQLPbXkEiFJI pI3BV0ZWKsrAggkEEVeIB7VzOacryX3adP6v317nPJ3Od1D4s+F9J0vxPfXf iLQ7Wy8Fhz4guJb6JItCC26XTfamLYgxbyxzHzNuI5Ff7pBroUkEiqQchhkH 1pcD060Uny20ROoUUUVIwooooA84/af/AORAtP8Ar/T/ANFy18hftkf8mh/F X/sT9X/9Ipq+vf2n/wDkQLT/AK/0/wDRctfIX7ZH/JofxV/7E/V//SKauOr8 a+R0Q+A+BP8Ag4F8PfsbeI/2/Yk/aEk/aYXxunhxXsv+FdnQ/wCyv7O/tK/8 vzPtw837R5xn3Y+TZ5WOd1fEn/Crv+CYf/Pf9vL/AL68J/4V6t/wdUH/AI2w WQwOfAkH1H/E41WvznrrjsYM+wH+Gf8AwTCjXJm/bzx/veE/8Kj/AOFef8Ew P+e37en5+E/8K+QRbm7kVQpfd91QDmvof9mj9gfXfilHBqeo2/2LTJMOGdgp YHnj0FMR6FYfB7/gmXqRAh/4b3fJwP8AkU/8K27D9nr/AIJvWB89bf8Ab2UM DAC//CKYPmAx46dTuwPevcvhT+wfoOjWyJY6RNqcwwCUi+XPruPFQ/tQfs6T fDbwTYTXuiR6bHPrekxqXKkkNqFsCOPYn867cshzYylG9ryjtvutiZ/Czwy7 /Zu/4JuWMZaW2/b6UD/sVP8A4msW8+F3/BMewbEv/DfCn3PhMf0r69sf2PNQ 1LRFll8MJNbSjcrKygsDz0P4fpXkfxp/YL0PWbeRJNNk0i6PQyR4XP1HFcRR 4o3gf/gl8hwZP29gfr4T/wAKT/hCf+CX3/PX9vb8/Cf+FeG/tHfsm658FNR8 ya2d7GQny5lwV/T61468LRk7lIxQB9pTeDv+CXkELu0v7e21AWPPhLt+FfoT /wAE2f8AgtV+xh/wR7/YGsR4B0H9p7XfBPxD8f655f8AwkFhodzqsGo2WnaH 9pz5N1BEtuYbuz8vG5y6z7toCZ/BzUgVsJwR/wAs2/ka9G+Iqlv+CWXwbwCf +Lq+PP8A00eDKAP3+/4jVv2WP+hB/aA/8Eekf/LOj/iNW/ZY/wChB/aA/wDB HpH/AMs6/mD8l/7rflTo7WWVsJHIxPYKSaAP6ev+I1b9lj/oQf2gP/BHpH/y zo/4jVv2WP8AoQf2gP8AwR6R/wDLOv5k7Xwbq99/qNK1Gb/rnbO38hUlx4B1 20TdLourRL6vaSKP1FAH9NUf/B6f+y1PIqDwD8f8scD/AIkekd/+4nTT/wAH qv7LKkj/AIQH4/8AH/UD0j/5Z1/Mna+H76G5iZ7K7QBxktCwHX6VGfDuoSSH bY3hycjELH+ldLoxWHVVbuTX3JP9RX1sf04f8Rq37LH/AEIP7QH/AII9I/8A lnR/xGrfssf9CD+0B/4I9I/+WdfzL2vgTW74fuNH1Wb/AHLSRv5Cku/Aut2A zPo+qwj/AKaWki/zFcwz+mn/AIjVv2WP+hB/aA/8Eekf/LOj/iNW/ZY/6EH9 oD/wR6R/8s6/mFktJYWw8cin0KkGm+S/91vyoA/qr0T/AIO6/wBm3XvgF4m+ I0Pgn43rofhXxBpPhy7gfR9LF3Jcalb6ncQNGo1AqY1TSrgOS4ILxYVgWK8h /wARq37LH/Qg/tAf+CPSP/lnX4A/DhSf+CWfxkABJ/4Wr4D/APTR4zrwL7NJ /wA83/75NAH9Pf8AxGrfssf9CD+0B/4I9I/+WdH/ABGrfssf9CD+0B/4I9I/ +WdfzBmBwcbG/KnpYzSH5YZWz6KTQB/Tz/xGrfssf9CD+0B/4I9I/wDlnR/x Grfssf8AQg/tAf8Agj0j/wCWdfzHw+GNSuQPL0+9kz02wMc/pUh8GawP+YVq X/gK/wDhQB/TZ/xGrfssf9CD+0B/4I9I/wDlnTpf+D0/9lqAgN4B+P8AyAf+ QHpHcZ/6CdfzFTaFfW7Yezu0Po0LA/yqW70a8lZGW0uWAjUEiJiOn0rqp0Yy oTqPdOP43/yJb1sf03f8Rq37LH/Qg/tAf+CPSP8A5Z0f8Rq37LH/AEIP7QH/ AII9I/8AlnX8xf8AYl5/z6XP/fpv8KntfCGrXzhYdL1GZm6BLZ2J/IVylH9N v/Eat+yx/wBCD+0B/wCCPSP/AJZ0f8Rq37LH/Qg/tAf+CPSP/lnX80Vx8JfF VpB5svhnxDFFjO99OmVceuStYs2m3Fu5WS3mRh1DIQaAP6d/+I1b9lj/AKEH 9oD/AMEekf8Ayzr0f9kz/g6B+AX/AAUR+Odh8HfBPhH4v6X4n8YWGp/YrnXN K06Cwi+zafc3cnmPDfSyDMdu4G2NssVBwCSP5Pvskv8Azyk/75Nfc3/BtvA8 X/BZf4UlkdR/Z/ibkqR/zLWqUmNH9WX/AAT1/wCTBvgf/wBiBoP/AKbrevYK 8f8A+Cev/Jg3wP8A+xA0H/03W9ewUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAeD/ALTXHj6z99OTr1P7ySvBP2lef2bv H/8A2LeofUf6NL0r3v8Aaa48fWfvpydep/eSV4J+0rz+zd4//wCxb1D6j/Rp elc1Tc2hsjkLD4EN8XfBnwU1seLvE/h0+AIrXWLez0uOxa31KZrE25W5+0W0 0mzyJ7mPELxHFw5zvWN09H+AHwQt/h38V/GHiWTXNd1/VvFNjcQtJqP2ZV06 yhivprexgW3hiBhie8uCrS+ZM3mYeVwq7fFNb0zxVaXn7N2q+GfDvjjXbHTd sfiOXSdegtNOttOm0qSD/S7We8hW5IupbWZSkM0iJazbCrMEl9A/YqsPFegf Fn4xWWveHvG+laFNrl9qPhq88Qa7Bqsd3ZSaUkcgtit5cTQQ/a4LqZIZFiVI 7qIKikPHHj1evf8ANGt9PuPefgN/yVfSv+23/ol6+ja+Mf2Mfgx4i+Hvx3sL 3WPiz8QfHlrLbzwLp+u2ehw28LlCwmU2OnW0u8BSoDSFMSNlSdpX7Orow/ws wq/EFFFFbmYUUV89/t5av8adM07w7F8F7HxFe6pqQurLUHtLTRJrGyicRbJ5 JL+8gltrhGB8m4ig1GKNTcGbT7s+QgOtgPoSivj3x/4a/aj+IXjP4yeFpdYg 0nw5eeC/Eln4J1LSdEtdLthqF26DSnk1MatPqH2q1iyCU0y1i3PM3mSGOBm9 i8aalrfxM8KfDbWNS8L6h4a0xfFFrdeI/DWu6dbavqKRhZ47IYsWu4Ukh1Nt Nu/PjlKQx27SPIgR8K+lx2KKfsq+Ik8TfGKez+JF/wCGrT4na7pviHTbrQdG tF1nw3c2+n2NjcATXou7S5jmj0+HAazQxiSbl2ZHj73wz8GdP+H3wJ074f8A ha91Hw3peiaDH4e0m7tmjnu9LhitxbwyRm4SWN5I1VSDMkill+ZWBIPzD4UH 7S3jD4V+MrnxBpnjaC78P634Zu/Den3LaDo2ta7p+n6ul3qML/YNQubV7u7s o/IeWS7tLW4Z/LFlp8SyS3H1Xpni3UdTvNGgl8Ma7p66rpkt7c3M0tmyaNMv k4s7gJOzNO/muVMCyw/6NLukXMXml2o2X9WWgN63ueSW3/BNL4TeI11KT4ie GND+M19qmow6s15488P6VqsltdJpWnaZJJAotEjhM0Wl20kgjQAyl9oVAkae hap8Gr288QeJr+18beKtJOv6e1paQWq2MkOhXLKynULUT20p+0YMY2zGW3/c qfIy8pl81jv/AI3WOg2Lara6vfz+JJ7zR9Ri8PR6RHd+FZytraW2q2r3cwhb TgYL2/aOVbq7DX9vH5MixPEOE+JeifE3wd+1V8cvE3gf4X+NLa41vwZ4WtNI 8TaTF4auYfEeqabfX8ssT2t3qlvNJEYdRhifz2tXaG0u1iuIZBayuSd0KK0X 9eR678KP2PrP4NfAz4XeC7HxDqfiWb4Q3FqfD+seJreCe7gt4oZbJoytktnE zjTbi4tY5Ch2l0lkWd1bfpWn7MsFj8bfGXjiDxX4mW/8ZWmh6dLYlLL7Fptn pdxc3C29uBbiTbPJeXfnNNJK+JyI2i2pt5L4KWfjXV/DXwl1D4uaatz4s+36 rPJBP4fiu7nR9RlS7a1nWeyea102KPTjeWxVpLg/6VDbnULmTMl7yWqQ/Ebx Z4A/a3068+Gfi/QP7alvX8F3Vlf6Jp174ojOg21gjW9zaTTzRXRurOVo576I SRxT2YxiIwQ073vfz/FAtdz6nu7f7RbPHnG9Sv0yK8S+LH7Fc/xb+Ddp4Quf it8StIe08R2XiOPWLBNHkvwbK7W7srP/AEmwmiNvBNFbspaMzv8AZ182aXfL 5np3i7xJrumu1lovh6TV76bS7u6t7ue8is9MS7i8oQ2txIDJcR+eZGIkit5l RYJS+G8pJfk/4d/E39rS7+HOs2/i7wHrz67r/iG8e01LSLLw5pcnhzRIdEtZ USGyl1m+ia/uNRW5gtzNdXEKSS+fcbbeNbd0m76f1Zguh9M+I/gzJqPjjWdd 0nWG8Oap4j8NDw/f6raWkc2qo0Ekr2FxA0/mWqfZmvL9tktrKsj3EZb5IjHI nwj+B/8AwqTQ9FsF8UeJfECaWl89zPq728s+rXd5ci5lu5jHFGqOHMoSOBYo USdkWIKsYTzr9oPwbZ+I9B+LEeofC+b4tanqOm6O0fh7TrCPSZPEWnxyyG30 q61G7njs7yNLsahLNC0wRba8Mcls4n/0vO+DHwy1Pwr8L/gBYaX4A8S+AbbQ vEl7c6roC3tnZwaDE+m6wrCS3tru+gNg1xND9nsoLtxbia0wwW2MVNC3Pcvi Z4Jm+IPg670q21rVfDl1cAGDVNMMX2zT5Ryk0QmSSIurAELLHJG2MOjqWU8L ffsqm8/aR8GfEhfH3je3m8F+H7zw9HoUcelnS9Uhu3hkuZrktZm686SS1s3J iuI1BtUAUK8wlg/aZ8ffFXTfhlrj/C/wf9o8S6bq9vZW8upQ2d8l7aPHE8t5 a2h1GzWcI8hhaO5vbF18qeVRMI4Yrrhv2dfG3x71jU/h1pfxE8I+IdIi0fw5 oyeK9Zim0N4te1ybS7h79mSK5d4LG1uY40YQRiWa5uoDHttIJ2mUW/69At1O 2+Jn7HenfFH9nn4ifDB9bvfDHhrx9PqX+keHreKPUrGDUnM2op5l4LqJ5Li4 nviZBCgSO6CIivEJT6BeeFNYs9J8TNpetx/2zqys+mT6nYi6tNKlFukUYMMT QPNCJEMrIZVdjJIokRSgTntM0tv2mPgxoN34k0q/8NG+vtP8RQ2AeRLu3W2v or6zS4SeCN4pWWGHz4WjDRM8sYdiolPHWukfFDRvjB8UtTghlm0GK9sLvw9a zeXI2o25tbBb2G3c3qrG/wDo10ESaCJVmuBJ5zI8ijpo4eFWLTml6/JLX569 krnRSpRmtZWa7+q/rySOr/Zw/Z9m+APwV+GPg+XxDPrCfDnwlaeGHdLNLaDV ZYLe2gF4YyZHjbFu21FlIUXEgYyEKy9j4N8Hx+E31V11DV9Rm1a/k1CZ7+8e cQlwqrFCh+SGFERFWONVBKs7bpZJJH8l126+KifEDwlFo9r4qGianJBd6yNQ j0fbpcLXjzNbefHP5iSpCfJk2W12sqJCqTQyGW7qlqenfEHVk+KBs/C2uaNK njPSNa0eWC5061m8Q6fbf2ct1Ejw3G5pJUsrlV+1mPMc8EbMqhki2+p80lzV I6+a6tL9b+hawybs5r+nb/gn0AZApA706vGvHt9471L4x+HLjw94RubaFvB+ q251bUl0+a20nU7g2klrFOVn+1GNTayCZbcNG5kgOX2b4qfxH134lap8MNTn 8M6B460/xJq9jqk2l293PobDQ7uKxMdpFcHzmVo5rlRKmxpyGfErRxfu1mOD vb34q/mtPUUcNe3vJX8z3GiuF8UaJYfC/VPGHxJFpreqak3hyC3ubCwt2upb iHTze3EaW8EaGSSd2u5V2ruLkRhVznds2fiLWrfS/DI1Hw87ajqpSLVU0y9j ubPRH+zySu7SzeRJNAJUEKtHCZGaaNjEieY0fK4q9oO/4dNf66/Oxzystjoa K53UfGeo2WleJ7iPwprt3NoJcWNrFNZLL4jxbxzD7IXnVF3O7QD7S0H7yJyc RFJW6FCWQEjBI5B7UnFpXZNxaKKKkYUUUUAecftP/wDIgWn/AF/p/wCi5a+Q v2yP+TQ/ir/2J+r/APpFNX17+0//AMiBaf8AX+n/AKLlr5C/bI/5ND+Kv/Yn 6v8A+kU1cdX418joh8B+P3/B1T/ylhsR6+BIf/TxqtfnQBk1+i//AAdU/wDK WGy/7ESH/wBPGq18R/s6/BPU/jx8T9M0LTrWW5a6mVW2ISFGeSa6o7GDPXv2 F/2T4viLcyeKvEY8jw1ppBLOBi4bso/Sv1U/ZD/ZNufjFYLq91b/AGLQoQBY 2wXaCg6Ej3FeWfCv9ny3174h6F8LvDiLNpnhmOM6i8IyklyR82cf3R+tfrR8 GPhfaeEfClhpFpFHHBbQJGFUY6AD+dUI434Q/s+adp8dvZQ2kW1MZOwdq8I/ 4K0fDvT4vg/o8a20TG28Y+G4y2MZD61ZIf5/rX6BeFfA66Fb3EwUqShKj8K+ JP8Agp9YmT4K2ssm848ceFT6dfEOnj+VehlP+/Uf8cfzRFT4Gex/Dv4QWPib wbHHFBD5kESjaF54ArzH4wfsz6Z4siktWtkVwCuduQxr374CwS22swwYYxSR c5HHQ4rqfF/wzRr2W4CDaRuzjvXnln4t/tbfs0ReA7qbQ/EFqLjQtV3LDO43 G0boP+A8/hX5VftW/s733wE+IE9pLG32Cdi9vL2kXORX9L37Yv7P9r8VvAF7 aNEj3cKtJDx8x/2a/J/9ob9mcfHj4PeIPDEkBk8WeDGaSzUria5thnGB1JXp QB+UWtRAaZctySY259OK9R1O1F5/wTQ+C8Z7/FPx9/6Z/Bted+N9FuPD39o2 V1G8VxbB0dHXBBHtXpMx2/8ABNj4LEAn/i6fj7p/2B/BtAHm1j4TN/cpDHEd 7+gzmvtv9g3/AIJcXnxdaLV9Xg+x6cg3tI44I/yTXAf8E3v2Y5Pj78XUa6iY 6VpsZnuZmU7FAI69q/VHT9ZbVfsnhDwZblbGICN5IkxnHGTgdP50AeR+JP2Y fB3gW1GlaJYw3N4wCeZs3lj9MVzg/wCCauo/EW4Se5tEgifs3avuL4Dfs72f hvVlkvV+13z8l5BnHsK960b4ao0nlJCCp6DbwKAPy0+If/BIWz0n4f3F0lvG 7Qx7/fP5UzwR/wAEi4Ne8B2l40MSPLErjbnuK/T743+GY9I+HeqRBPmFsxwO 3FRfA3wpFqPw50mMgEm1Q9P9kV6M/wDcY/45f+kxJ+0flzL/AME0tS+H0nmw 2n2iFTnCsd361v6D+y54F+IenPoviCzNtdD93vc4KMPav0+1r4cxxybWiBAO Tx1rwr4z/s4WPibV5ri0VbW7BJDKMAn3rzij8eP26/8Agl7dfCESavpcLXWn S5dJIxkFa+Kb3wvFp128UkbKynBAPav3k1/U30GW78J+MbZpdKuP3QeZeI/R gT2r8sP+CjH7NcnwK+Lcn2aMtpWox/abWVR8jqWPQ9KAPOPhFaq3/BOj4zRD hV+LXggD8NH8aV5TJpgB4CsMd8V638GwH/4J5fGbcQR/wtvwTz0z/wASfxrX MfDbwNdeP/E1vp9lA8skz4wqknHSgDI8EfCXUPHmqQ21lZyTySNgBATivtL9 nz/glPdLpSap4nV7SMgOqPheMc5r6e/Y6/ZO8O/s9eBLfXNdt4G1PYHjSUDK HHXB716LqfjWfx9qBZyyWcZxHCo4+poA+SvHP7NOneF4TDo9m1yyfKXMe1c4 7etcO37Hut+JWEl2ZbdB92MAIMV9zT+HoriEOYxtA6kcikm8NQmRQgL7gMA0 AfEdv+wXbfaNrw5Y9S7A1sD/AIJ9w3GEaBNpGRyc19lp4K88+YsRG3qNoqHV opNNKOiABABjFejQ/wBzq+sP/biftI+D/HP7C7eHuI7dNuepFea6l8N7zwFc ki3aPyzkFQcAjv8ApX6QapCPE8ghkizhcNgVzHiv9miy8X2Mq+SFZ88+lecU eC/sl/tV6GZ4/DvjKxtbuxuP3XnSLlkzxkk1z3/BR3/gm83w5sl8ZeFYVvfD t6PMDQjcEHXtXPftOfsm6r8LLx7+xtbjyQcllU19E/8ABNX9sbTPHOiTfC/4 iTR3Gg6whto5Lhgfs7EYB56c0Aflg+nCJip3BlOCCOntX2l/wb0wLH/wV9+F jDjNl4k4/wC5b1SuD/4KVfsWah+yJ8cby2WB5ND1B/PsbpFJiljbkFT0Ixiu /wD+Dezn/gr18K/+vLxJ/wCo3qlJjR/T5/wT1/5MG+B//YgaD/6brevYK8f/ AOCev/Jg3wP/AOxA0H/03W9ewUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAeD/tNcePrP305OvU/vJK8E/aV5/Zu8f/8A Yt6h9R/o0vSve/2muPH1n76cnXqf3kleCftK8/s3eP8A/sW9Q+o/0aXpXNU3 NobI5Pw78VdT8AaX8C9FTRbG+0XxxAmkT376k8Nzp08ekT30ZW38llljdLKd WYyxshKYWQE7fS/2fPiNqXjzxVq8Vxpulw2mk6TLbXF7YahJd27aqLa6W/s4 meCLzY7ZliTzx96R5o2SKSB1ry7RfCPwn8a6T8G7PxvYfD3VfG1rpUV94Qg1 uCzm1OOSGGCWabT1mBlDR7IndoeV8tGOMKa7f9kr4Z/B/wCGviHVh8LdB+Gm hS67oX9r3h8KWVjatqNnLa3n2O6k+zKPMhcrc+VI2VbbLtJw1YdTV/Cew/Ab /kq+lf8Abb/0S9fRtfOXwG/5KvpX/bb/ANEvX0bXRh/hMavxBRRRXQZBRRSF toPoBQAtFZ3ibxRpvgzw5qGs6xf2ek6RpNvJeXt9ezpb21nBGpeSaSRyFSNF DMzMQAASTik8Q+JbbwxYRXN0l60ct3b2ai2tJrqTfNMkKEpErMEDOpeQjZGg Z3ZERmCuB5P8N/jx4j1b4w/GS21hNIufDXgvxdpHhPRLSws/s9+huNL0y8nu bq4muvKmXzNTULHHFEyJbsB57yKo0l+Oeq3n7Snw98KR2EFlo3i3wXrviO7j uU3X9pc2V3okMMQeOVodoXUbgPt8wMyRlHCg75PEfwA+Gfhu98fav4gs7J7P 4s3VjaeJrXXNTludH1mdoYtMtoTZXEjWqPOhgtmWKNTc4hR/MIQDXvP2afhx qPxV0Tx3cfD/AMEz+N/DFkNO0fxDJodq2q6TaBZE+z29yU82KILNMNiMFxM4 x8xy1bR/1t/nqDtfQ85+Jfx38aah+zz41+IHgrW/BWj23g3WtYZofFehXCwy 2GjtcWl5bSTx3yeS8t5ZzyR3+10jt5E3WkjKWPtOh6ve3urazDd21rDDZXKJ ZyQzmVriEwRtukBUeW4kMq7AW+VEbcC5Vef8bfs6eA/iixHizwvovi+KPW4f EdrB4ggGrwaZqMMCwRXNpFc70tXRE48gJhnlfG+WRmf4L8e+Ep/jF408JaSr weK9LjsNc1+I6ZPbrOt5HJb2tx57RrHcbk0+SLMbuU+zBG27QKl3tYXXQ8a1 f9r7xfr37P8A8DPivoMHh3SvD3j3/hGJdf8AD+oWU99eGPXr3TLOJba+SeFI WtWv2kZpLWYTiMKBBktXuXxgHie48BXMPgvV9C0PxJcSRRWV/rOjy6vY25Mq 7zJaxXNq8oKB1G2dNrFWO4KVbO+IvgfwLrPxN8Caz4i8J6RrXivS725tfC+r TaB/aF3oMsts8lw0VyInNikkVuUaUvGjsIo9xd40bF+J/gb4S+KPh38SoPHf hLwnc+D3ul1LxqPEXh2MaXqkltaWswvbk3EXlXiwwQ2o8/8AeLGbQR7g1uVS pWeiVt/0Hdf1/Xc8x+G/7Xnjbx8/7OfifZ4Wg8KfHHSbKO70QadcHUdM1G40 C/1r7RHffaPLa3VbIQeQ1qHJcyefx5R739iP43eJ/jN8LdRh8eWLaV8Q/COr SaP4msTpEOlraXLQQ3kKrDBqWpxYNnd2j7kvZgS5z5bbok7tPgh4Lg+Jlt42 Xwl4YTxlZ6SNCt9eGlQDU4dPDmQWS3OzzVt95L+UGCbiTjNO+EnwZ8HfADwX B4a8CeE/DXgrw7au8sOlaDpkGnWUTuxZ2WGFVQFmJJIHJOacnG7aB62sec6/ +15B8HfG154d8baT4ovbzydY1mxvPDvhi41JbvTbO70uJVjsrOW7vZZV/tm2 iZlhG9rS7mMcEXlhur8M/tGaZ410bRdR0nRPFr2euQyTxLqekS6De26R3kNq 5mstRFveR4MpkGYfmjiJXJkhWXOsvCHwt+DHjLxXrmkeBNN0rxBo1pdeJtXv 9D8FyyXt4dQbfdvFJbW5e8u7htLhM0MBluHMFpvQlrfc7SfH3wu+PumeA/FU i6Rq7XWrzp4VfWtLe3vbPVbeK7SeOKC6jWe2voYoL9JEZEmjEdwjBcOKXSzE +rX9f0jkvA3x78c/Dr4WfFLxl8Ubnwfr2jeBLCbVQPBelX0Mls9tBNPe6ahu pGTUGtwkca3kRhWaVpo2t7Z4HU3viL8a/HXw4+MnwS8JS2OhXtn4x1STRvEu t/ZZLeG5uBoerXwGnwfaHeIibTVZ/OaQLHPGitK5eSHsPgr+yr8Lv2aBqx+H Pw38BeAP7fMZ1M+G/D9ppR1Ex7/LM3kRp5mzzJNu/O3zHx945vn4E+DZL3wf fXvh7TNZ1f4fW8lv4c1fWI/7T1XR1khWCV4r248y4EksSqssnmb5R99mJNK4 7o3PBGrXuueD9Nu9StrWz1K4tY3u4LadriCGYqN6xyMqGRA2QrlV3AA7RnFc P8V/ipr48P8AiOy8KR6Lous6UWjXXPE0kcmiaciC0kmuZooJ1uHVILiV1jYw CRrVkea3SWKZuo1HV/DvwN8DaXA622iaFZy2Oh2EFvbsIYGmmis7WBEjU7VM kkSDgKoOSQoJHLeL/gh8IvjRrHijTvEfhH4f+Lr/AFa2g0nxFb6ppdpqEt1b 27peQ2tysqsXiie4jnSN8qjTrIAC4Y6eyk486Ttrr6W/z/EcYSaul/X9WMjw 78YvFuk/Dv4Y2vih9Ij8Z+PtdGlpd2Og3kOnyQxxXeoNLJZ3EyXNi8+nWMgE cryta3M6I32hYyXd4X+Inj7Rfiz468O63qHgHxDHpjaVq+kx6db3Gk3djpl9 fXcDQ3Ubz3XnTRRWjNHcJ5KXMm+PyYAhc9f4R+Bngr4efDfSvB3hXw/pXg3w ro9zFe6dpPhpP7EtbV0uRdfJHa+WBG82TIg+SUPIsgdZHU5GifCv4VfDs+Jp LDwp4Q8Kx6v4mttS1uddFh0yLWdbeaCeG7kkMaLd3LTyQbZsuxmG3d5iEBRi 3KyX9af1/wAOChJ6JHpFxMLeBnOcKpbHfivCfjL8aviNq/hvQL74eJ4P8OyX ksQNv4stJtVn1yeaC3lt7C2XTbgiBXEtx518zTLarZvL9muIGMye03OsW7av DpzLcNLeW8s6lbeQw7EaNWDShdiMTIu1WYM4DlQQjlfM/G/7DfwM+KGjWVj4 m+EPwq8SWFhfXepWsGq+FrC9jhu7uQSXdwgliYCaeRVeWQfNIwBYsalRYcjN nWfi9cQfGDX/AA9YWd5qJ8I+F4NevLCGyX7Rqsl7PdR2cdpcPOkSuP7NvFkj kUAm4tm82NVcNzf7JXxw8YfGPwPZ6h4p0/w1BdjXPFWjXzaTNOIoJNL1650+ 2WJJAWkV4IGaSVmT94q4jAl2x+gw6noetfF6azjvbxvEvh3SElntFuLhLeO0 vpmEcjxAiCVzJp8gViGkiCyBSizMHg+G/wAAvBHwc07S7Lwh4P8ADHhSw0SC 6t9OtNH0uCxt7GO5mWe5WKOJVVBLLGkjhQN7qGOSM03Fx0krf8Hb700KSa0t 2OE+Enxn8Z+JPEfxg0zVl8ManN4C+IFvoOmtaW82mq+lT6dpF/mXfLceZdQx 6jKu5PLSZoUASEOSvpd78QbOx+JWmeF3UfbtV0y71SNvtdupSO3ltomBhaQT tlrpPnjieNdpEjxs8SyYfg/9m/wf4GfxO9nptxcP4y8Sr4v1c6nqFzqZuNTQ WyxzJ9pkk8pYhZ2oiii2xRCCPy0XaK7GDRbS31afUEtrdb+6hjt5rkRKJpY4 2do0ZgMlVaWQgE4Bkcj7xzK2X9dP8wfkWqKKKYBRRRQB5x+0/wD8iBaf9f6f +i5a+Qv2x+f2RviovHPg/V//AEilr69/af8A+RAtP+v9P/RctfIX7ZBI/ZD+ KuCQf+EP1fGO3+hTVx1fjXyOiHwH45/8HRWozax/wVE0W7uLC70qe6+HlrNJ ZXTRNPaM2raoTE5id4yyk7SUdlJB2swwTuf8EnvhrafBn9nTxJ8V9QhX7Y0M kOn7h/FyNw/z3rG/4OgvDtj4b/4Kl+H9H0yzs9N06w+H9pZWdpbQrDb2sKav qiJHGigBUVQAFAwAABwK9J1Yr4A/YM+HfhS3+SbWY/nRf4y8jE/pXWtjBn2H /wAET/hpdap8KtT8eaqhfU/EuoSyoz/eKbutfpH4G082rrI/G3mvmD9jz4cN 8Ifgd4W0qCJgkNpGz4H8RGa+o/D2obNMhZh87ADB69BTEd3daxDaaU80pCxo nJJ7V8df8FVtHg1T9mqxexRZZJfG/hEJg9S3iTTVA/EkV9KePLwyeE7u2BOJ YGThunFfnv8AtP8A7STaB4ai8H6jKZ/sXjnwfcqWOcrH4n0uQj8lNehlP+/U f8cfzRFT4GfoD8LY7fSdMsknCLclAijv0rrvEF0ssRQ8gjGM18kfs1fH+4+N 3ju7vklK2+mSlEQHgcGvpi91YyaeshK5HBxxjNeeWefePfDiPcM+Bgg5z6V+ V37e9ncfs8ft1eFPEdlHt0rxEWt7pQPlORgg/pX6q/Ee7lntAYFLbgQSO3Sv z7/4K7/DeS/+C1p4lIP2rw/eJOD1OCcH6UAfkX/wWd+A9t8LfjvPrGlRBdL8 UWAv4wowoLbgwH5V4zdJ5n/BNT4Lj1+Kfj3/ANNHgyvs3/gqhbR/Ej9jXwl4 kG2SbS0e03/7BBYD9a+OLeH7R/wTk+CCf3vit49H/lI8GUAfdn7Iejr8Af2E xryII9T8X3qWMTdCUOScH8K/Qn9jP4Tnwl8O7a9uIg2oX6iRyRkgEcCvijxT pcelfBX9n3wwAogur6G4nUcZz6/mK/VL4PaFbxafbRttVURQBntigCtoPh24 k1i3McbBt/IxnivbtG0UQWyEKA+BknscVU0jTNK0u7iYSJ5j8DJ71o6nrLaJ peoSwR+bPDGzRJ13EdqAPKPjLpt7rmnauoiZYUhdScY7U34Q6Te6Ro+irsZo DBGuce1UtL/aXtPix4a1DR5bM2GqSrJblJFxlsEDmk8SftM2vwd8LWelraNf 6pEI7dYo1zhuBzXfJ/7FFf33/wCkxJ+0et6roizwSDYQ+Pl9a8k1bw5K2qS7 42JRzgAYx717Hputya7plhLPF5FzPCrSJ/cYjkGszUptKuZZstEZVJUkYyTX AUfGX7Y/weXxn4Ku5oo9l7Zxl4mC4JwK/On9sTwsPjt+w22uuu/VvBGovYSs RlvK4Kk/ma/X74z2sEljdIu1kdGBHbpX5e+FdMXxB8Pf2h/CnymNVa9hXGdr KecUAfnB8Hl2/wDBPD4zKOT/AMLb8ED/AMo/jSvon/gl/wDB+B/EMniDUIx5 NrEXUke4rwb4J6f5/wCwp8ZbcDP/ABeHwSuOvTSPGtfa/wCy5o6eD/hJBGu1 GmQFz3wBk/rQB614z8Y3fxC8TxWNvu8sNgKvRVHc16T4Q8HjTNLWNRmXCkmv P/2f9AN5PJqMgy8z5UnsM9q+ifCfhwCMswAHegDnrLwJLqEZyoVT17Zq2fAg t5oolUEx8k49676O1LgEDCjso6npXZ+BfgFrPjK1+1CEWtszH95LgZ+lAHj8 XhEvnCE/QZrPu/hot4+HIOOce3b+tfSV3+zQ0cDBNVgabpgc4Ncfffs4eI5b hzbIlxEmQSp64/ya9Cg19Uqrzh/7cT9pHhF38N4dKMkioN3Wp/D+jKuFIGCM DP5V2fjWzvPBNybXV7Rrc8gM44P41haFd2t7qbeUyEZyAO1eeUYPxZ+EVr4w 8IXFtPDHJvQjkc9K/Kz4u/C2++Afxp8jbJbxiffG3K5XNfs62mi9mQFgyDA4 /Kvk/wD4Kpfswx674Bg8S2UGLqy4lKjll/yKAIPjH4dj/bd/4JqajDqSLN4v +HNobm3mIzJcWwUnr3wK+Rv+DfSJrT/gsT8L7d1wY7PxKOf+xb1Svr//AIJ6 eIh4j+FVlFJgnULWfw/qcR/iEiFVJH4/pXzx/wAEhPAT/DP/AIL8eEtGZSot B4pRQRjgeHtVpMaP6Rf+Cev/ACYN8D/+xA0H/wBN1vXsFeP/APBPX/kwb4H/ APYgaD/6brevYKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKAPB/wBprjx9Z++nJ16n95JXgn7SvP7N3j//ALFvUPqP9Gl6 V73+01x4+s/fTk69T+8krwT9pXn9m7x//wBi3qH1H+jS9K5qm5tDZHDD4Q+J viP4a+Aus6F4k0HRLPwI9vrd7a6hoMuoy6rv0yWyMUcqXcAt8QXdz8zJL85i bG1GST1L4D/D/wARaH8X/GPiDxF4g0nWP7Z0+az0y10/Rm09dOsYIr6SJJXe eZ7if/SWDyAxxnYpWGMly3iet/ErW/h3d/s3xabd+Nm0bXtun69p+k+F5dV0 42j6VJ5Vxd3ENpK9oY737EFczQoUlmLh0jd4/b/gN4tl1T4r+NtIuZ9eubnS beWUm50Oey06COawlMcVncvCkd2AqBpWSWYpLI6sYwUiTGzvoa/ZLP7GP7Jn wr+A/wAeLDV/Avwz+H3gvV7m2ns5r3QvDtnp1xLAyF2iaSGNWKFo0JUnGUU4 4FfZ1fOXwG/5KvpX/bb/ANEvX0bXRh/hMKvxBRRRW5mFfPf7eP7FOpftlab4 dsLDxF4d8MW2ni6t9SubvRL2/vryzuBEJbSNoNQtYWt5BGDNaX8N9ZXDR25m tZVhCt9CUUdbgfJms/8ABLi38S/Fj4p6vqXjTVdQ0D4leFNf8MxQXd7rV7qe grrUsUt59ma41SXTIoN0abIYNMhYCGDdK+x/M9i1T4T+Jtf0H4dX/iTUtD8Q +NPA2vJq73GlwT+H9KvDLBdafO32d3vZAEsr6d0iaVvMniiBkiViy+o0UraW C58f/DP/AIJi614I+HXjawuPHHhddd8QanoGqaHLpfhe+h0rw8dE1H+0bC1W 2u9Uu7j7AtxhfsNvd21pDCXW2htXlmlk+n9L0rxGl5o32/VtDubOHTZIdYig 0mWF7+9PkbJ4GNy4t4AFud0LrMzebFiZfKbzfnX4d/GNPDPx6/ajvI/EOr61 rPhLxhoEd3oSXF9r82j6GdA0q4zaaPFMWiaeSTVCrW0PmTyo3yXLQrFW7ffF 7XIv25/hBol9bePns9S+H2uPq01h4W1hPDn9ozXOkSWZuZhG9pbz+VZ6nsS4 mMsIcxls3Kea7XS8/wDK/wCWg3dP8SdP2IL3S9BsbWz8TaRqM11NeWHij/hI NDm1W18WaPcrbWxjurf7XGsuopp9hZWi305mUqtwzW7C4ZFy/jX+xN46+K3x X+KGtweOfAMOhePPDGieH9N0zUfBmoXF1ocul3k93Bem9ttYtpXnWW8vHjkg FtLDILOSORXt2afE/aI1ptZ/Z28Zyaw/xPj+IEHjTUrfS7L4aatrV/fafqMc UyaKJ47KWSG1hl04afdSwXwTTTLdLJcIDLk/R/g29lu/EXipPtmp3cdtqUaI l3bmKK0Js7djFbtsXzYstvL7nxLJKm4eXsRNvlv/AF0EtNEedfs9/sz6p+zr 4G+HeiRX9p4lutAl1SLWdQN1daZDJHfSTXs1yloXuftdybtbdPNvJ5LopLcz SXk00k/2rAh/ZF8cv4f/AGkbLUfG/gzUYfjhNdXWjxp4MaNfDskmkw6Ui3S3 F5cw6giwWlmWUwwq7rOSuyVY4tX4dfEP4jeEPhf8G9N8cRzyeN9e0HT7XxN5 WgSamF1kRWz3fnS2Li3tIiI75fOZfs4kaHD58uGfzb9lvxXqP/CqvjRqHw3T 44a54r0+wY6Np/xYs9fsk1rU0guTFeR/2ta2sdut5cDD21hJ9lgijtmWK0aZ 4hUrpt/1uNXTsv66n034s8Na7rzG003xCug6ZNpd3aSy29gs+pwXb+UttdQT TM8C+SonLRzW0wkd4SSqxuk3yh8N/wDgmB4+8AfDrWvDcvxd0HW9O8WeIbzx H4it9X8O6zq8etu2h2um2llNLf67c3klik9rHcXFvJcv9qjH2XdDAXV+/kn8 N3f7SPwel8Gar8W9aVtCR5NSstU1rUvCF/4eNhfeRPd3UzvplxcvcG2YTK76 k+YGZmty5ru/F1743v8A9nzwjceC7rUr/wAQz6l4ae7utRtoLW8udLOp2X9p yTwzRxpFK2nm6Z0EaOrErGqyBABaO/y/Elb2Ogm8I+JdP1nxw2iT6Xp82u2M Fzpep6lc3erRwap5L27b9PLxCKzjWGzkEVvdRiZ5bkkQyFppuV8L/s8+JNJ0 X4ZprXirSde1bwj4o1DxLrV8NAjtk1Bru21RDDZxs8r2apLqCbWEskpggaOS WTzZWbmfCX9oyf8ABRXWm0gfEYaJF4avE8VjVDrH/COtfl9IOknThd/6CX+z jU/NOmcB8/af3vl1m/tW+J7vxzrPwW1bwfD8V4PEWra3pd/pKWNjr+nWEemv qWnPqy6zbhY7SE/2WbnZFqyJIr71gUXAIoSu15/8MH9fp/Xkd3+0r8AfGXx+ +GWu+HbH4gDwobzVba60+fTbbULN0so44vMtLuey1C2vHZphNKstndWRH7iO RZoknS64b9nX9hnxZ8EtU+G8WqeP/Dvivw98LfDmjeHNE0+bwrNDLbLaaXc2 V3fLKb91+3XTywYnaNvItY7i3RSbuac9B+yT8TdQ8b/HP9oSwvbf4gw2mmeN 7c6O/iDQdV0+xeyGi6bbONOlu4Uhmg+3Wl+x+ysyFnM3IuEkkyfiF8X/ABr4 J/aRl0PwHoereIl1rXLe31iHW9G16DS7Fm8O6tcRSwaqyS2kNrJc2GkwSNaw yx28k0/mI9xdoAo3tp1Sf4f8Edtz134deDdWu/hdo1n49ksdf8QK0WoajlIp 7SC9EwuVS3Igh3RW8uxYJHiWXbBE7ky7nPGSfssXqfEj4j+JLfxTc29x4zu7 DUdOhQ3iw6VPaw2iYmiW6ENxHK1lDv2RwyvE0sTSsjLtgvfi58T/AAv8NoNS 8Z+GPDPhfW5tTeyWz8KSan47iMMkOy2mYLaWFwhF3JH5oWF0SFGZpI1Z5ben 4QeCT9vDXTpA+LKufD0n/CTDUTq58Ii7zp32EWP2wCz87yftW7+y/lJ877V+ 98qtaOIq0n+7drr8rP8ARGkK06d3F2v/AJr/ACJdf/ZHvfEHj/wnrJ1bwxp0 WjywX2rx6foE8DaxdpePeu237YbbyjcO8iLcQXMkDyzSxyrM/mrPefsx+IdX X4i/bvFWiufF/ibTvFWleToUkf8AZNzYmy8iOfN032mMjT7UOE8hmPnEMm9R H5R8UfE/jjVP2ZfgzqFhqPiS38QX/jnw6PFeneEH1HXni1VvENo+tWU15C85 t9KtVTV4ZUkYQoIoYPMjjTyJfp74Y38l/oN28l3qd8qanfxxy39t9nm2LdzK EC7EzEmNkb4PmRpG+59+9uhY+vbmvtpsujT7d/6savF1bp3/AC73/T9Di/HX wC8R/EL4naLrN34s0+z02z8Laj4cv4LDSpre+na+8gzXNvdC6/0dle1t2jBj kKYk+ZiytHl/Ef8AZi1/4n/DTVNCvtb8FW974lsdUt9bvrXwnIhuJrmwaxt7 mBWvGMUkUOxXMjSmVF2AxLjb1usfEPVdO+OVh4cSK5awv7YXcbJ4fup4Qkay i4El+jmCB90lmY45kVnCTCPzcu1v4R8MdcfQv2TfH114bk+J9v4IXxNbRxXf xM1bWtJ1PTPDrrpyaxex3eqSxatbpBE2qTxyXMiyLJETEfJEAM08dWjZJ2st NF39PP8AqxKxVSNkn+B9JeNdAv7Tw54g1LwvY6IfGt1pLW1lc3wMENzNEkrW sdxKiPIIFllc8K5QSyFVJJBks9B1zStM8NWkOvpqH9mlI9Yu9TsEku9ZiW3k QspgMEME7T+TKzrE0e1ZEWJN6vH4X4pv7XT/ANqn4g3GjXvxv0xoPBt7J4jv BbavdeHrW7CWBsJNLhvEeylu1hFydmlxujP5oulaUxA/SFsZGtkMiqshUFgD kA9xXKpNe/3/AOCv6+RzttvX+tjB1HSvFMml+JltNa0KC9uww8PSy6RLLFpf +joqm7QXKm7xcCSQ+W1vmN1j4ZTM2nLBf/21aul1aLp6QSrcQG2YzSzEx+W6 yb8IqgShlKMWLoQy7CH8P+J3xEsvGPwt8f2XjiP4jDw9odzBaXZ8G+H/ABNp usO5vPNhNjPpTveXcZhezEkliSqlLgS7QZYILHw71vxLZxfAWy8UPf2sd34Y llvrjW9XFlrF14ijsbbyrOeGCRYrp3tX1meWMJJGklksqhTEjAcnLR9Lfl/w P6uJx1t/X9f8Me8UV8vfsn614vXx/wDESLxLrPxBvGb4qPbWUupaFfWWn38C eGbEXKWkd1GfI0lL6O9aGSJ/LeSJAJ5mmZpjT/HFof2zv2ldN03xj4ikXRvA Hhe+v7LT9Vm1iXw1qMp14M1rp7mZLa4ktYLGTyY4QJiInaOQyZZW2LSufUNF cn4q8S6xY+MPB+m2VlcyRanczyavKlh58Frax2shy05miELG5e2VcLO7guBC F8y4t+soJTuecftP/wDIgWn/AF/p/wCi5a+Q/wBsZgP2QfiuCM7vB2rj/wAk pq+vP2n/APkQLT/r/T/0XLXyJ+2GcfsjfFXkZPg7VwM+v2Kb9a4qvxo6IfAf j5/wcY+C/wCwv+CrvgLQPK0mE6f8P9OsPL0qw+wWK7NW1OPEFvvfyYhjCR72 2KAu5sZPr+qeHY/Fn7Unwj8ESFRaabp8Esqfwg7Q2COnc151/wAF/ZpvG3/B Xv4ZjUZTLJqfgbTZrqaPT59Ilkd9V1LcwglY3FtknPlO5eP7rEspNdFH8EdK l/b9g0uO/wDF32axgtwH/wCEq1Q3KgwBiBP9o80DngB+PavapUcK4JzqNPty /wD2xzty6I/Zf4YLFo9tCsjoY4VCqoPGABXRWfjaO58axQiYtHuwEH8NfHXg 39mLw5PbJ5mt/FUH/Y+J/iRM/gL+tTRf2YfB8fi23Yax8WTlwsm34n+Jldv+ BC/3Vp7DB/8AP1/+Af8A2wve7H23q2sJfLdRswUBT/KvzJ/bI+Dv2rx8+uz3 DmK78beF4oxnhVbxBpqfyY19JfGX9lzwD8L/AAbc6vd+Ivi7Es0RECH4teKT vYj0Oo818z/GX9lHRPEnwrstXt9Q+JDI/i/wrZK1z8QNfuVb7R4i02EnbJeM NwEhKtwyMFdSrKrDvyqhhFjaP71/HH7Pmv7xFRy5XofSn7BnwwT4Z+ItYzI7 RXzK6KTwpXINfTd14hC6fe4ORGpNfA198AvDHwa8fxwavrHxUhsfOOXj+Jvi WHKscdVvx617lrn7GXgkeA31bT9c+Lt3HeoJImX4seKWBB7n/iY4I+ua4PYY P/n6/wDwD/7Yv3ux6h4f8dRS200Tu8h3EYI6fnXhP7cPhq18b/ArxTZ3EiPG 1lJIFPOGAJH6iuM/4ZX8JWsEh/tX4rI3JyPib4kG4/8AgdXn3xk/Zb0W68G6 r9m1f4mMUtn/ANf8QtfnVvlPVXvSD9CMUewwf/P1/wDgH/2we92Pz1+Mu7xZ /wAEvddMn7xtG1KSJTgHaAMfyAr5L8PKZf8Agnv8Cox0f4s+O8/+CjwbX1Jr Xwr02y/YG+KuoNca+bjSr3bDG2uXxtW+Rfv25m8lz7uhNfMnhm7N1+wd8BpX WME/Fnx3kIgQcaR4N7AYqJOnRtUw1V83py2+d2Fm9Gj9FPicrXfxL+C2mREq 0FpbFMk4BG01+jvgq11iG3iUF8FQOtfm/wCKkm1H43/Be6gG7yYoQcdCPLr9 N/h94taFLYvHlcD+lDzTGvetP/wJ/wCYckeyJfC+o3zfFuxsNQd2ReUXeSDx 1Ne6ausSa3GyKrRMvPHU1wFv4K8/4h2evKUWHyjuzxg4re1n4gadp0kMDXcR ui+VXPJqHj8S96kvvY+WPYxvib8O7HV9BuzY2VtaX7RuYp44wrq+Dgkim/B/ 4a2mh+GbAataW19qltCnn3MqB3kkAGTk89a2NY1f7XotyYpERxGSCT904607 S9XTT9HhEjiRgg3N/fOOT9K9B5pjPqi/ey+J/afZeZHJHm2N3TFiuNclLRr5 KpgcYwT0rwHWJNQ1L4harZ2UjotvI2ELEZ5OMV67pXxJ0m4mltUuY/tgbLoG 5XngVzep+HIPD3ifUdfd94myUAH4156x+KW1SX3svlj2PM9d8L6vqmnzrcSl cKR1IxX50eBbY+GP2lvi9o7sHS40m4R8E/MSrnn16D8q/SLxV8QrnU0mSPEc eDjtX5t2SSW37T/xU1CdslrV1z7eW2a0WaY1aKtP/wACf+YuSPZHwL+z9F53 7J3xigcllT40eDUAPYDSfG3+FfYvgmVbHwfbxHAb7P0HfOK+Pf2dSsn7Lfxk OflPxr8HEH/uEeN6+m7bxbHo1vo6SOMTwgHNKOZ4yN3GrJX395/5hyR7I+qv gOkdrodsm04AB55/T/PSvoDQ4kjsY5FJWM4JzmvAfgoVbQ7WQHeJVBU+n+eK +jvB2mLquiLGwwSoGe3Tiq/tbHf8/p/+BP8AzDkj2R9CfsxfBfRde8KnxDqE KXoQM8UH3lXHt3rhfF/xH8Q/FH4rHw7psMuj6dC/l7IsxgDJ5OK43TvHnjD4 H6TN/YtwZLVCWMLHcAOtVfhp+1mmpeI/7Vv1itrsPiVcAHiolmWLl8VWT/7e Y+WPZHvmsaBD4etX0Sw3G8EX767kO4liOx9q+QvjB8Qvi1+y98TYdTOoSap4 fupgXiGcBc4OBXsF38T7zxf44v8AUdLvSkEyhiHPAA71uap8O7z4i6PbXuoX 1tqVqsbCOM445OcfjXZSx+KeFqN1Jbx6vzJcY8y0OJ/a0+MXhj40fs4pe6e0 S63LCrHauJI+MnNfn94Z+Mut/C/xIYdRedoXbasjEkAfWvpD4+fCa60jx1ar pTn7JeSbHhU4KYPOR0rsdd/ZG0Dx34GjiubdFuTEP3mPmBxXLHNMalZVp/8A gT/zHyR7IT4MePbfxtoEE8UwfeOcN0rsPiz4OsvHnwv1CyuUWRXgPBGcnH+N fKvhS11T9mPx9JZTM8+jO+1ZDkhK+j5/iTZ6l4MneGdG3R8c+1P+1sd/z+n/ AOBP/MOSPZHxf+zxbv8ABb40ajpClkt5p47qJP4VKyAcD6Vd/Zx8Gx+G/wDg 5B8D3VvGI4tTsfENyMDAy3hrUzmqN1qQ1f4zanqEfAtI8bh0DFxXc/AdRef8 F4fg3dEgu+h68Dj1/wCEa1OonmeMlFxlWk09/ef+Y1CN9EftV/wT1/5MG+B/ /YgaD/6brevYK8f/AOCev/Jg3wP/AOxA0H/03W9ewVxDCiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8H/aa48fWfvpydep/ eSV4J+0rz+zd4/8A+xb1D6j/AEaXpXvf7TXHj6z99OTr1P7ySvBP2lef2bvH /wD2LeofUf6NL0rmqbm0Nkc94I+MUPgPQPg14cudE1q4Xx1YR6fa6nB9mNnZ XMOmSXginDTLMDJDbTlWjidQY8Oybl3ewfDrxNYal4yv9Njurc6lZ6Tc3M9n 5q/aIIZILpIpXjB3KjtBMFYgBjE+PunHjmh/APw38ZPAXwk1rXE159R8E2dp qWjmw8QahpsUFw1skZeSK2mjjn/dl48TK6+XLKmNssit6R8D/g5oHw5+Ifin WtNi1STWPFFvdzajdahq15qMjqtvdPHDEbmWTyLdGmmKQRbIYzK+xF3GsHa5 p9kX9jH9pnw78ZfjvYaXo+mfEG0ure2numfXfAeuaDbFAhQhZ760hiZ8uCI1 YuQGIUhWI+zq+cvgN/yVfSv+23/ol6+ja6cPszGr8QUUUVuZhXn/AMcfjzY/ s9WC694kjW28E2WmalqOsatFHd3U+liztjd7jbwW8m6EwQ3ZaRpEIkjgjRJX nUL2Gr+KtM8P6hpdpf6jYWN3rly1lp0NxcJHJfzrDLOYoVYgySCGGaQquSEi kbG1WI4/46/sz+Ev2kdMFj4ui168046bqWkzWNl4i1HTLS9tb+1a1uY7iG1n ijnzE7BGlVmhY74jG/zUebQaX1Oh+J3xJ074R+Br7xDqttr13YacEMsWi6He 63fPvdUHl2llFLcS8sCfLjbaoZjhVJEfws+KGm/GLwLZ+ItItfENnYX5kEcO uaDfaFfLsdkPmWl7DDcR5KkjfGu5SrLlWBPQpGEjCjJAGOTn9T1p2OMUrAef /Bz482HxP1G40G7iGm+NtE0y11HXNKjiu2gsBPc31ojQ3E9vB9oha4029Ecg RS8caSbFSWMsrftWfC9Pjkvwwb4j+Ax8SmXcPCR1+0/twjyPtGfsXmefjyP3 v3PufN93mm/B/wDZm8JfAvV7rUNBi1+XUr7TbbSLm91jxHqOt3Vxa291e3UM by3s8zsUm1G7IYndtkVM7I41T0HpT0voD8jF8X+OLHwNawy3yai6z+bsFnp9 xev+7hkmbKwo7D5I2AyPmYogy7orc74b+NH/AAkH7RnivwENORIfDXhrRfEK 6kLrd9r/ALRutVgMPlbBtEY0wNv3tv8API2rsy+t8SvhXpPxZ0m1stY/tEQW l1HextYajcafOJI2DDE1u6ShDja6BtskbPG4aN3RsPw/+zB4S8MftD698U7R fEY8Y+JtNt9H1CSXxNqc2nSWluSYI0097hrOLyy0jK0cKsGnuGBzPKXT2EcN 42/bYTw18Jfh94u0zw9H4lsviNruj2unz2F5MtlDo+qavaWNrqUk8tum2QxX 9pL9kKhy7yIrMkUlwvc+Pv2nPBHwR8Cf8JH8SvE/hr4Y6T/ak2kpdeKdbs9N t5ZleURhZZJRGTNHEZkTdv2H5lVgyrX139lfwp4v8AeHPDGtf2rf6L4R1211 /R7a2v5NIis5LO7NzYW5jsDbxy2tqwhWOCVXQi2hMglkTzD3Phrw3b+F7CS2 tnupEluZ7t2uLh53Mk0zzP8AM5JChnYKgO1FCqoVVUB6Wt/XT/gjdjy3WP2p poPjJ8M7HStO8NeIfh18UlFtpPijTfEJnuHu206+1OJltltzBJZSWli7Lcpd li8qAQlD5tX/ANsL4961+zN8EdW8caT4c0nxLaeF7a41fW7a81mTTJI9Mtbe W4uZLYpbTia6CxgRwSeSjljunjxzvax8AfDHiH41aV4/vodVvPEeh2rWunrL rF62nWRKzIbiOw837Gt15dxPF9q8nz/KlePzPLJWs34k/sreDvi94Fj8NeI4 /Eup6Guqz6vLat4o1SNb153naW2uWS4DXNi32iRDYzF7Ty9kfk+XHGimiYHH fE79rDxR8NPi34p8NP4I0vUYLfwfq3irwtcWer3t3day2mCwW5tbu0g06Sa3 ZptRgWL7IL6SVVkIiD7In7T4GftB2Pxj+E3g3xH9nkiufFJeznt7C0v7iDTL +FZvtdtM09rbzQCGa2nhLXcFswlRY2RJXWImofsteD9U8eeKvE80fiQ694v0 uTRbu8TxPqaPp9rJHHHKmnBbgLpjSeTAzyWIgeSSCKRmMiK4n8AfszeCvhnp nhi00vR3Mfg17yfSJL6+uNQnt7i8Z2urtpbiSSSW7lMs2+5lZpm+0XAL4ml3 jtawPyO+ooooAQqD2HFLRRQAUUUUAIFAJ4HPWloooAKKKKAECgDAAApaKKAC jFFFABRRRQB5x+0//wAiBaf9f6f+i5a+Q/2xV3fsi/FQZAz4P1cDPc/Ypq+v P2n/APkQLT/r/T/0XLXyF+2Px+yH8VemD4O1cHj/AKcpa46vxo6IfAfkp/wc X+MoH/4LKeAdegF1DbXPguwvI1urWS1nRG1jVGAkhlVZI3wRuR1VlOQwBBFe saVqcVp/wUl0+VziDXNHtbiFj/Fm3QdevY14H/wdGWUXg7/gqh4as7V7t4dK 8A2sML3V3LdXDKmr6oAZJpWaSV8Dl3ZmY5JJJJrvPHHiGaD4Rfs7/GuzJkja zOj6lIDkLLbzEYY9sxkflXWtjnP1M8CeETqZUrIyA479M17N8N/grbNdQ3l0 PNeM7lIJGfrXhPwY+I0Hizwtp+o2UokgvIkkRlPABGcV9JfB/XXvYGikJPHF MDwv9qv4Raz8dPjzoeirNOug6cUkkjXIU8jJNU/2+pLH4HfsyaHa2FsAIPHP g6TA7+V4j02Xv/uV9H+Kdfs/C+pG5ESNPL1Yjk18u/8ABT/UG8Y/s/6fMroE PjXwopX0zr+nj+tehlP+/Uf8cfzRFT4GetfHj4B2P7RHw6nIgEd6yB45BwQ3 Wof2OPB2seD/AIdT+GvEPmyx2DlIjIc4Q9AD6Vt23xAn8PWSWVqQ5UAEn6V2 PgjXP7YsnkZNjsecfSvPLOI+I3gDRtAuCywbmfJAznmvAv2jdYt/Dnwx8QXS iOGK3spXJ6D7px/Svf8A4o6kbnUJUkHyJyDnpjpXwJ/wVc+NaeB/hF/wjdhM Tq/ieQQrGp+YR5+Zv6UAfB/xe1r/AIRT/gl543u3bD+JPEMkKZ/jVIkPH518 meE4vN/4J+/AoZxj4r+PWH4aP4Nr6c/4K+wH4Bfs3/C74YnauoyaKfEGpxBu VluGcqCPXYq/pXzR4KCn/gn78DyxAA+Kfj4/+UfwZQB9/wB94kfw54Y+G/ir 76aW8KO5HAG0g1+iPwZ+K1r4i06zuY3jaGdFYH681+f3hjSo/Gv7FLq212t4 RJE+M4IHQVzX7Lf7ac/w+05NK1Kd8Wn7tWc8nHSgD9ZvGfxfKW4sbacRrgAl T0rgvHHjCDSvD9xqdzdRgwIWSQkZB7c18qr+2jY66HmF0nCZ+91rzrxr8eNY +JMhsrO6aSzz8ybuCKAPuLwB8btS8dfBe5kluMXU8TJHIvYdM/lTPHXxx1Pw D8FLZ4rgS31nbKhkbvjjP5V8vfCb423OgfDWWAllWHKcDpUfxL+Odxrnwujh 2kPNiPPYnpn+VehP/cY/45f+kxJ+0fVXgLxbb6z4ZtdViuIjPcRiSVx1Ld67 fQviwt7p/wBiu3Eykd+Sa/N/wX8ddZ+H062092UtVORGTx+FekP+2Tp2iwxT m7TzBGMfMK88o+lfjD8T7Pwxo97cExwxQRsxJ46Cvz/0jXZ9d8LfELxc+FTU BcpG5H8IjwD+oqr+1N+2hP470j+y7CY4vDskZT2JrovF+mxeDv2IJIkQI0ts TIcc7mxn+QoA/Pb4A3v2D9jb4yTEjKfGXwYSf+4R42r1Dx/4guZfAulX0DH9 yFwR24NeOfDWYw/sI/G+Reo+Mngoj/wVeNa9f+ADx+Pfh/NplxhmjX5SecY/ /XQB71+xl+160qW2i6g43KQqkgV95eEPinZ6VZRP9qjVXAyGYAV+W3gv4Wnw d4liuoWI2NuGOxr6D1DxnfeItCt4UmdGiUcqcHPFAH2r49/aL8OeH/DNzNqG tWsACYI3jJr5P8N/tB6F8SPi5JbaTLLdQ7sFY2xuOTzXz38a/CWoan4YuZ7j UJ84OBuIya+aPhJ8fdT+AHxH+12ZZzG/VuhoA/WD4nfG7U/AlkLKwDWnnqI2 I64PbJr6Q/Zi1HV7X4Tae1zqLywzDzFBOduSehr8ufAf7Umo/Hi+Ml4Cx4OB 0Br9C/2SfipDq3wytNPnnRbq0BXaepGTivRof7nV9Yf+3E/aRvfFmS10b4hW mpTuxjbhg3TJ712eleKINU0QyWkgZJFGNtef/HiyXxD4dk5/1R3Bu4qh8LfG Ftpfh6G38wAxoAQTXnFFz4xeCbPWPBV79oiV3Kl8kcqa+Orz4oX/AIK0G6h+ 0yOzDy4YyeSa+nfjn8XrbQvDlwWmUbwRjNfD934xi8XeM/MXm1tnJ29QT1/p QB0Oiai+jappems+/UNevInuOOdhdcivRv2UNcGr/wDBdT4VoG3fZ9O8RR/T HhvUwK8s+GaDxF8Zjq1wVEGmKZl5GAVGR+taH/BN/wAcp4w/4Ln/AA2CuGCW viUZHf8A4pzVKTGj+gT/AIJ6/wDJg3wP/wCxA0H/ANN1vXsFeP8A/BPX/kwb 4H/9iBoP/put69gpiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooA8H/aa48fWfvpydep/eSV4J+0rz+zd4//AOxb1D6j/Rpe le9/tNcePrP305OvU/vJK8E/aV5/Zu8f/wDYt6h9R/o0vSuapubQ2R5Vrfwf 8ReN739m7xX4b8N+CtTk8DBbvU9S1bUpLPUbayuNKkspILTy7OfzAxuFmaNp IVZrKFSTv8yL2f8AZjs/HGm+KPE1p4r03w1babCNUk0m+03WHvLjVIpUupN9 xAbK3W1ZEMYCLJcE7iGlYp5knjmrftKx/A29/Zt0O+8UeBtG0z4ikaJNp+rs IdRu2GlSTQTWkpuEUAXEdvbshik3vewqGRiok92+BGta9qnjDW7bV57K4j0X S20+WaHSZrFbu/8A7OkmuZ4TJLIGtmE0CooLGN4p0aSQj5cXv9/5mjXu6+Qf sY/sz+HPg38drHU9I1L4g3lzcW09qya7481zXrYIYy5KwX13NEr5QYkVQ4BY BgGYH7Or5y+A3/JV9K/7bf8Aol6+ja6MPszGr8QV8tfB34BfHD4eeK9afxJ4 om+I1le6nolrb3Wr+NlshFp1rNcy3F1FZWGiW6Q3LGSMG3ee4FygWOW6VIh5 n1LRXpYfGToxlCKT5rbrVW2s91+vXZGTVz5J+FH7Mfxs0HxR8DLfxdrXhrxR pfw+ez1fWdXuvEN5cakl4nhi40W4tII3s/8ASo5LqZr03lxOkrGeVDEMKx+t qKKeMxtTEyUqiSsraK3Vv9RRilsFFFFcZQYooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA84/af/5EC0/6/wBP /RctfIX7Y5C/sjfFQnkDwdq5Ixn/AJcpa+vf2n/+RAtP+v8AT/0XLXyH+2Oh f9kH4rEfw+DtXP8A5JTVxVfjRvD4D8Wv+DoXTrjQv+CknhO0u4jBd2nw3soZ 4v7Sn1Py5F1TUwy/ap1Wa4wQR5sih5PvMAxIrtP+CSfjay/a4/ZF8X/APU5o Ydas2Ot+G5XYHM65LRAdtwyODXFf8HRPiL/hM/8AgpJ4T1jzdImGrfDeyvfM 0q/+32L+ZqmpvmC42J50Rz8kmxd64baucD4u/Zj/AGg9a/Zo+L2keK9EuHgu 9LmWUANgOAclT9a7FsYH7H/8Ez/2lX8GeKJfhj4pke0vLKUx2TTttbcDho+e 45/Cv1E+GOsR6RD5hI6D8a/ID4x+HNM/bt+Glp8dvhLN5Xia2RJ/EWl2rYub SdRn7Sijk8g5H+Ne0fsQf8FgtG1bSrLwn8RHOi69ZqtqL2UbYrsqMbiexOOh pgfpn4vsI/F1sk1ucleCMV8y/wDBQjR20f8AZ8tCzbkHjTwkSPX/AIqLTa9u +GPxIs/EGnx3en3lve20oDB43DAj868J/wCCr3xO0nwn+zLFc30scPk+LfDV yQWAO2LXbCVzj2VCa9DKf9+o/wCOP5oip8DPoHw/4Xj1T5o1YbxnnkiumjuY fBVgsAy0j9cVwfwO+NGjfETwFp2raNewX1rdwqwkRwccc1xv7Uv7W/g39nbw zcap4k1m3iaIEpArgyyn+6F6155ZsftDfFHR/hf4N1DxDrNzHa6fZQs7s7Y3 egHua/NL4BadL+3/APtVa38TfFm7S/ht8PrdtSu3myUS3iJZIgTgb5CBWJ8R fjn8RP8Agr/8bLHwd4Q026tvCtpJ5nlZKxmPIzPO/RVUL39a47/grF+2V4T/ AGUf2eof2ZvhFqEV98wm8Ya3ARnUrkD/AFYYdEXoBQB8L/8ABTj9qeb9sL9p 7xf4ux5NjKzwWMW7KwwRgqij2CgVzn9p/wBkf8E3vgnP/d+Knj0f+UfwbXlu qln065PVvLY/U4NffH/BOf8A4IufFb/grr/wS48LP8Mdf+H+ht8Ovir4sOon xNfXlt5/23SPC3leT9ntp92Pskm7dtxuTG7JwAdh+zN+0vp1j+zXJp9yFYiP vKOuO4rw7XtRsdQ1i4uYZY4kdyxw3NfWnh7/AINL/wBsbw1pptbf4gfs8NEe Pn1rWSf004U1v+DSv9sVj/yP37Ow/wC41rP/AMrqAPkGXxjdWKZtboqo4P7z NdJ4c+O1x4ChEhmEjlcn5+a+n7f/AINNv2xreNl/4Tz9nNg/XdrGs/8AyuqK 7/4NKv2xLxSH8e/s68+ms6z/APK6gDkfg5+194fm+HzQai8KXDSFmzIM4NVv jh+174dtvhmLLSzG1x56spEgzXWW/wDwaH/tfwjaPiD+z6Fzzt1vWMn/AMp1 Mm/4NDP2v7jG74g/s+MAeM63rH/yurtlVi8JGnfXmb+Vor9CftXPm7W/j7L4 xsGdX8uQDHL+1c0nj+eRlW5uWbJIxuwBX2JZf8GlP7YdgPk8efs6nr97WdZ/ +V1F5/waV/tiXrhm8efs6qR026zrP/yuriKPkTR/E1lb+JLSaeQssbhsFsji voD9pH9qmwuv2UW0y3jiEkh25384wPau4X/g0k/bDVlI8ffs78eus6wc/wDl Op3iL/g0r/bH8T6QtlcfEL9nkQL0CazrA/npxoA/PX4KZ1f9gf4yMMHzvi/4 Jfnp/wAgjxqa9t/Zh05dHs0VpABIAzHOM9DX2N8NP+DTb9pjwD+yT468EDxp 8DZPEXiTxt4b8S2c39r6qbOO307T/EFtcJI39nhxIzatblAqkEJLllIUM3Qf +DX/APbU8OKFtvG37NAAGAG1bW/6WFAHl93PYpCSNmcZ3YxWNP8AEW30QkBs 9+eK9/uP+DbT9uK4jCHxx+zIoHpquuf/ACDWRqH/AAbAftr6kxL+O/2bASc/ Lq+t/wDyBQB4J4h8X2XjnT5LWSVI1KkE7u9eFeNf2fbWe9mu4riKQq3GRjIz X3IP+DWX9tJX3L49/ZwU/wCzrGtD+Wn0tx/wa0/tqXK4b4gfs5YxjjWNa/8A lfQB8nfs5eN9C+Ft6GvVRyrYIzxXs2t/tiad4M8RQ6hotysSygblWUYHHtXZ Tf8ABpb+2LO7s3j79nbMnX/ic6zx/wCU6qr/APBot+2BtKj4g/s+sOD82tax 1/8ABdXbSqxWGqQb1bjb5Xv+ZP2kzdsf+ChFjr/heSOeVDI68/vM5/SvMrr9 vKDQ5blBKRtYgYf3ruLH/g0r/bH09NsfxC/Z6wPXWtZP/uOqrff8Giv7YeoO Wk+If7Ppz/1G9Y/+V1cRR8ufGj9uq88Y3jWyTP5TnA+c5qn4L+Nlto9jJ5sm JG59yD719Mn/AIM4f2tGuPMbx/8As/Mw6Z1vWP8A5W1or/waD/teqRj4gfs9 8f8AUb1n/wCV1AHzhY/tQW3hjSb6USbZLkFEK9PfmtT/AIIV+Pj4z/4LgfDO XjD2nic5/wC5b1WvdNQ/4M+P2u9Ri2P8Q/2f1UDA263rHH/lNr2z/glf/wAG xf7QH/BPf9u3wp8ZfHHi/wCEWreGvCNhrZvbbRNV1Ge/l+06Pe2cflpNYxIc SXCE7pFwoYjJABTGj9lP+Cev/Jg3wP8A+xA0H/03W9ewV4//AME9f+TBvgf/ ANiBoP8A6brevYKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKAPB/2muPH1n76cnXqf3kleCftK8/s3eP8A/sW9Q+o/0aXp Xvf7TXHj6z99OTr1P7ySvBP2lef2bvH/AP2LeofUf6NL0rmqbm0NkX/gh/yR bwh/2BLL/wBEJXf+Cf8AkMzf9eF7/wCkstcB8EP+SLeEP+wJZf8AohK7/wAE /wDIZm/68L3/ANJZa5n8Rr0Nj4Df8lX0r/tt/wCiXr6Nr5y+A3/JV9K/7bf+ iXr6Nrqw/wALMavxBRRRXQZBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHnH7T/wDyIFp/1/p/ 6Llr5D/bFXf+yJ8VRnGPB2rn/wAkpq+vP2n/APkQLT/r/T/0XLXyH+2M239k T4qdCP8AhD9XyD3/ANCmrjq/Gjoh8B+Mv/B1Fr1j4q/4Kf6Dqml3trqWmal8 Pba6tLy1lWaC6ifVtUZJI3UlXRlIIYEgggjINfnBX6w/8HK/7Ffxv+Mv/BQr Q9V+H3wt+K/xX0vTfBcGk3eu6N4UutTjluo9T1FnE0lnb+Qs5V45GRVQASrh FUqK+Co/+CZf7TTRqW/Zs/aBViOR/wAK61jg/wDgPXWtjnKH7Iv7aPjT9jf4 jW+veFtRmiUEC5tWOYblO6svQivu8/Fr4Af8FAhFqlndJ8NPH18FkvrG+jVd Nurg8syOpJTLZ7Yr4g/4dk/tM/8ARtv7QX/hudY/+R6fB/wTS/adtZQ8X7OP 7QkbryGX4dawCP8AyXpgfpH8Ofg58Z/2f/LfwP4vmn05SGRLa7F3asPbuAay P2rPBnxY+PHgW0Hj3WNQe2k1vSLPyoYMRkz6lbQZPqQZMj3Ar418Cfs8fttf DfaNI+C37S9rGvRF8A61t/L7PXa6jo/7d/iHTo7K/wDgv+0fLaxTwXiK/wAP dZI863lSeE/8e/USxoR7iu7K5RjjKMpOyUo/miZ/Cz6w+H3wL+N3wM0VtE8I +INTi09iQvmRbSo79elYHjL9kJru/wD+Eh+NXxO03SrGM+ZP9pvTc3bDqVji HGcepr5s17TP29/Elu0Vx8IP2mfLcYIX4f6yM/lb15X4t/Yd/a18dXRm1f4B /tJahKf4p/h/rT/zgrhKPqj9pP8A4K6+DPgN8HLz4Y/s16XfaNa6mhi1vxJe Kq3up8YAUqTtTluAR1r839Z1m68QanNeXk8lxc3DF5JHbLMScmvXV/4JrftL gAf8M2ftCH/unOr/APyPTl/4JrftLHr+zb+0GP8AunOsf/I9AHiOpf8AIOuD zxGx4+lf0af8GVP/ACiy8ff9lV1H/wBNGj1+G97/AMEz/wBpWaymRf2b/wBo Iu6FQP8AhXOsdwf+nev33/4NFP2e/H37Nf8AwTd8b6D8RfBHi/wDrl18Sr6/ h0/xHo1zpV3NbtpelIsyxTojmMvHIocDBMbDOVNAH6n0UUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWR8Qf+RB1z/sHz/wDo tq16yPiD/wAiDrn/AGD5/wD0W1JjR5t/wT1/5MG+B/8A2IGg/wDput69grx/ /gnr/wAmDfA//sQNB/8ATdb17BTEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQB4P+01x4+s/fTk69T+8krwT9pXn9m7x/8A 9i3qH1H+jS9K97/aa48fWfvpydep/eSV4J+0rz+zd4//AOxb1D6j/Rpelc1T c2hsi/8ABD/ki3hD/sCWX/ohK7/wT/yGZv8Arwvf/SWWuA+CH/JFvCH/AGBL L/0Qld/4J/5DM3/Xhe/+kstcz+I16Gx8Bv8Akq+lf9tv/RL19G185fAb/kq+ lf8Abb/0S9fRtdWH+FmNX4goooroMgooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDzj9p//AJEC 0/6/0/8ARctfIf7Yr7P2Rfir93/kT9X4Pf8A0KXj/PpX15+0/wD8iBaf9f6f +i5a+Qv2x+f2RviovHPg/V//AEilrjq/GjeHwH19+zJ9sHgO9/tH+1f7QGoS faf7U+zfbvN8uPf5/wBl/wBH87Od/kfut2dny4r0auE+Aepza1oetXdxp93p U9zrE80lndNE09ozKhMTmJ3jLqTtJR2UkHazDBPd11x2MHuFFFFMAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigArI+IP8AyIOuf9g+f/0W1a9ZHxB/5EHXP+wfP/6Lakxo82/4 J6/8mDfA/wD7EDQf/Tdb17BXj/8AwT1/5MG+B/8A2IGg/wDput69gpiCiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8H/aa4 8fWfvpydep/eSV4J+0rz+zd4/wD+xb1D6j/Rpele9/tNcePrP305OvU/vJK8 E/aV5/Zu8f8A/Yt6h9R/o0vSuapubQ2R0fgXw2fBfgzSNHM4uhpVlDZ+b5ez zPLjCbtuTjOM4yceprd0zVJNJumliEZZ4ZYCGGRtkjaM/iA2R9KrUVy3NrGl 4T8UXHg7xDb6lapC89tu2rICUO5Spzgg9Ce/pXaf8NO6/wD8+mkf9+pP/i68 5oq41JR2JcU9z0b/AIad1/8A59NI/wC/Un/xdH/DTuv/APPppH/fqT/4uvOa Kft5hyR7Ho3/AA07r/8Az6aR/wB+pP8A4uj/AIad1/8A59NI/wC/Un/xdec0 Ue3mHJHsejf8NO6//wA+mkf9+pP/AIuj/hp3X/8An00j/v1J/wDF15zRR7eY ckex6N/w07r/APz6aR/36k/+Lo/4ad1//n00j/v1J/8AF15zRR7eYckex6N/ w07r/wDz6aR/36k/+Lo/4ad1/wD59NI/79Sf/F15zRR7eYckex3mtftaazoW myXU9jZSRRYLLb2FxcyckDhI2ZzyRnAOBkngE1hf8N9XX/QJ1H/wldV/+N1j afp0+rXiW9tDLPPJnakaF2bAJOAOTwCfwrxH4beCf2iF1jVtH8VaB4zm0zWl uLLR/EFjp+lQXeh+Qs6C8vIC8kY+0KLcwrCt3uk82SaO0jkS0trjUm+ovZxP of8A4b6uv+gTqP8A4Suq/wDxuj/hvq6/6BOo/wDhK6r/APG6+S/hHL+1BdfE P4j6T4k+Hvj2607w5451m80y/bTtMt7bWPDwsSdO0yxYSxyPdNPLbyedMqxB UmjkuVkRoh9QeHPCPiXWvDun3t14S8RaPc3ltHPNY3dsHuLJ2UFoZDC0kZdD lT5bumQdrMMEt1JJJ3FyI1P+G+rr/oE6j/4Suq//ABuj/hvq6/6BOo/+Erqv /wAbqv8A8K/17/oCav8A+Acn+FH/AAr/AF7/AKAmr/8AgHJ/hUe2mHLEsf8A DfV1/wBAnUf/AAldV/8AjdH/AA31df8AQJ1H/wAJXVf/AI3Vf/hX+vf9ATV/ /AOT/Cj/AIV/r3/QE1f/AMA5P8KPbTDliWP+G+rr/oE6j/4Suq//ABuj/hvq 6/6BOo/+Erqv/wAbqv8A8K/17/oCav8A+Acn+FH/AAr/AF7/AKAmr/8AgHJ/ hR7aYcsSx/w31df9AnUf/CV1X/43R/w31df9AnUf/CV1X/43Vf8A4V/r3/QE 1f8A8A5P8KP+Ff69/wBATV//AADk/wAKPbTDliX5v26L+30+3un0i/WG6LiL PhjVA7bcAtt2bguTgNjBKsASVYCD/hvq6/6BOo/+Erqv/wAbrhf2wvB3j+5+ C2jL4J8N+Ktb8SaReaLfzaXpMkdpfXNpFr6T3cIaaSGMeZaR3C7ZJUWRWKk4 ap/AXgHxxqky6vqWieNdOj1hhcNompWlqX0VWt4B9n3WysGZJEkJPnTDdPLh 2jEQjvnla9/60Dkja52f/DfV1/0CdR/8JXVf/jdH/DfV1/0CdR/8JXVf/jde bftE/Bf4ieJfC+kDwvpviOG8tPEeh3VxHaLJaPJZx6vZyXu59yho/sa3QaI5 8xSVAY4U+g/8K/17/oCav/4Byf4VPtZWvf8ArQHCJY/4b6uv+gTqP/hK6r/8 bo/4b6uv+gTqP/hK6r/8briPjF8LPHF1pel32kaL4puodDvjqd/pGmwGC98Q RRW8xis4ZZJIUjZrr7M53ypG6xPHIfLkcHE8JeDviP4h8aLNe+DfiLpGmTW9 vqYt9Tg06S2tWlW3DWWYGefz7d4Jmcl3hP2qQJLMvlrA41JvqHJGx6l/w31d f9AnUf8AwldV/wDjdH/DfV1/0CdR/wDCV1X/AON1w3xn+GvjCDSdK1jSfDPx B1a/8P3/ANrj0rRPJt/7U8yCa1KXK3MsEctvGLg3BTzUbfbRMu5lCN2Efw/1 /wAtc6Jq2QADi0kP67aSqztcOSJYk/b9mjnSH7BO07QrcGFPDmpPLEjPIil0 Clky0T4DAEhcjjBpf+G+rr/oE6j/AOErqv8A8brgI/CmtP8AGPxTHb6Dq9/d WOi6TK9pbq0UxPn6gyjJGATjhXIV/usVUlh1em+AvEsmrWF2uk67p1zJG13N HdaV9ts7eZ3lzFLGCrMAtwjFIm2KbQoj4KO1KpLuLlQviX9qyT4tQW2kS6Zq 6ZmEqmPw3qUWCFYZZ3j2qgDEljgKBkkAGvK/2xlJ/ZE+KuDj/ij9X5Hb/Qpq 9f8Ahn4N8SaLJfS63peqRzLBqMk93LAUt5TJBM+6MY/dRAvsWNuUChAXAEjf On7ZPxQiv/gP8SND08xSRnwlrS3NwcMrMun3f7qP5hlg0b5b5uY3RVYpO1vm +aUi48qXkfdP7MraG/gW8bwwdK/4Ro37/wBkf2X5f2H7H5cfkeR5fyeT5e3Z s+XbtxxivR64z4PIUj8RqduRrt0DtGBn5O1dnXZBtq7OeduZ8uwUUUVZIUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAVkfEH/kQdc/7B8//otq16yPiD/yIOuf9g+f/wBFtSY0 ebf8E9f+TBvgf/2IGg/+m63r2CvH/wDgnr/yYN8D/wDsQNB/9N1vXsFMQUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHg/7T XHj6z99OTr1P7ySvBP2lef2bvH//AGLeofUf6NL0r3v9prjx9Z++nJ16n95J Xgn7SvP7N3j/AP7FvUPqP9Gl6VzVNzaGyHfsQfs6eIv2lvgvH4l134zfEy0v pPsgMdhZeH44v3unWd03D6Y54e4cDn7qr1OSfYv+HeNz/wBFv+MH/gP4b/8A lTVH/gkz/wAmr23107/0yaZX07WsIxavYzcnc+cP+HeNz/0W/wCMH/gP4b/+ VNH/AA7xuf8Aot/xg/8AAfw3/wDKmvo+iq5I9hcz7nzh/wAO8bn/AKLf8YP/ AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H0Uckewcz7nzh/wAO8bn/AKLf 8YP/AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H0Uckewcz7nzh/wAO8bn/ AKLf8YP/AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H0Uckewcz7nzh/wAO 8bn/AKLf8YP/AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H0Uckewcz7nzh /wAO8bn/AKLf8YP/AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H0Uckewcz 7nzh/wAO8bn/AKLf8YP/AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H0Uck ewczPnD/AId43P8A0W/4wf8AgP4b/wDlTR/w7xuf+i3/ABg/8B/Df/ypr6Po o5I9g5mfOH/DvG5/6Lf8YP8AwH8N/wDypo/4d43P/Rb/AIwf+A/hv/5U19H0 Uckewcz7nzh/w7xuf+i3/GD/AMB/Df8A8qaP+HeNz/0W/wCMH/gP4b/+VNfR 9FHJHsHM+584f8O8bn/ot/xg/wDAfw3/APKmj/h3jc/9Fv8AjB/4D+G//lTX 0fRRyR7BzPufOH/DvG5/6Lf8YP8AwH8N/wDypo/4d43P/Rb/AIwf+A/hv/5U 19H0Uckewcz7nzh/w7xuf+i3/GD/AMB/Df8A8qaP+HeNz/0W/wCMH/gP4b/+ VNfR9FHJHsHMz5w/4d43P/Rb/jB/4D+G/wD5U0f8O8bn/ot/xg/8B/Df/wAq a+j6KOSPYOZ9z5w/4d43P/Rb/jB/4D+G/wD5U0f8O8bn/ot/xg/8B/Df/wAq a+j6KOSPYOZnzh/w7xuf+i3/ABg/8B/Df/ypo/4d43P/AEW/4wf+A/hv/wCV NfR9FHJHsHMz5w/4d43P/Rb/AIwf+A/hv/5U0f8ADvG5/wCi3/GD/wAB/Df/ AMqa+j6KOSPYOZnzF4j/AGBbzRtPjmi+N3xcZmubeAh7bw4RiSZIyf8AkE9Q GOPevALf9ip/iF8WNE+HF78S/HS+H/Gvw88QX2oi203w/b3DeRd6bp4gR00w FIngnwwHzfuYdrKEAr9BPHP/ACBIf+v+y/8ASqKvnD4X/wDJ8Hwy/wCyYeJ/ /T3o1Yyj73Ki4ytqetfss3Nxe/Duee5uNVu557xpJJtTt0t72VzFGWeeNI41 jlJ++ixoFbICLjA9NrkPhJ08Tf8AYfu//Za6+taatFJjrzU6kpxVk3e3byCi iirMgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigArI+IP8AyIOuf9g+f/0W1a9ZHxB/5EHXP+wf P/6Lakxo82/4J6/8mDfA/wD7EDQf/Tdb17BXj/8AwT1/5MG+B/8A2IGg/wDp ut69gpiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooA8H/aa48fWfvpydep/eSV4J+0rz+zd4/wD+xb1D6j/RpelfRP7Q+uza N47t1hjs3ElgmfPtIp8fvJem9Tj8OvfOBXzB+174nvdY8H6/bSPDFbnwJ4il aK3t47dJHVLUKzKigMQGYAnoGbGMnPLN+9Y2inypntn/AASZ/wCTV7b66d/6 ZNMr6dr5i/4JM/8AJq9t9dO/9MmmV9O1vT+Eye4UUUVYgooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAx/HP8AyBIf+v8Asv8A0qir5w+F/wDyfB8Mv+yYeJ//AE96NX0f 45/5AkP/AF/2X/pVFXzX8PAz/tqfDdEXcz/C7xQB85Tn+29G43DkfUdKxfxl LY9++EfTxN/2H7v/ANlrrq84/Zm0u40TwNfWd3EYLu01B4Z4v7Sn1MRyLHGG X7VOqzXGCCPNkUPJ95gGJFej1rHYlhRRRTAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKyPiD /wAiDrn/AGD5/wD0W1a9ZHxB/wCRB1z/ALB8/wD6Lakxo82/4J6/8mDfA/8A 7EDQf/Tdb17BXj//AAT1/wCTBvgf/wBiBoP/AKbrevYKYgooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPCf2nf8AkfrT/rwT /wBGSV8u/tUf8i7r/wD2IPiT/wBAs6+ov2nf+R+tP+vBP/RklfLv7VH/ACLu v/8AYg+JP/QLOuSf8T+ux0R+FHvf/BJn/k1e2+unf+mTTK+na+Yv+CTP/Jq9 t9dO/wDTJplfTtdFP4TB7hRRRViCiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDH8c/wDI Eh/6/wCy/wDSqKvmr4eTfZv21vhtLmMeV8LfFL/vH2JxrWjdWwcD3xX0r45/ 5AkP/X/Zf+lUVfNvw3nS1/ba+GkkjKkafC/xQzsxwFA1vRiSfasX8Za2PZf2 aPEh8ZeBr7V/M0mb+1dQkvBJpV/9vsZPMjjfMFxsTzoufkk2LvXDbRnA9Frh PgF4gsfFmg61qml3lrqWmajrE9zaXdrKs0F1E6xskkbqSrIykEMCQQQQSDXd 1qtiAooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFZHxB/5EHXP+wfP/6Latesj4g/8iDr n/YPn/8ARbUmNHm3/BPX/kwb4H/9iBoP/put69grx/8A4J6/8mDfA/8A7EDQ f/Tdb17BTEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQB4T+07/yP1p/14J/6Mkr5d/ao/5F3X/+xB8Sf+gWdfUX7Tv/ACP1 p/14J/6Mkr5d/ao/5F3X/wDsQfEn/oFnXJP+J/XY6I/Cj3v/AIJM/wDJq9t9 dO/9MmmV9O18xf8ABJn/AJNXtvrp3/pk0yvp2uin8Jg9woooqxBRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQBj+Of+QJD/wBf9l/6VRV82/DaIzftt/DRVdoy3wv8UAOo G5T/AG3o3Izkce4NfSXjn/kCQ/8AX/Zf+lUVfNPw/wA/8NrfDbZ5vm/8Kt8U 7PL278/21o3Td8ufTPFYv4ylse6fATTZtG0LWrS4v7vVZ7XWJ4ZL26WJZ7tl WMGWQRIkYdiNxCIq5JwqjAHdV5z+zJ9s/wCEDvf7R/tX+0P7Qk+0/wBqfZvt 3m+XHv8AP+y/6P52c7/I/dbs7PlxXo1arYlhRRRTAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKyPiD/yIOuf9g+f/wBFtWvWR8Qf+RB1z/sHz/8AotqTGjzb/gnr/wAmDfA/ /sQNB/8ATdb17BXj/wDwT1/5MG+B/wD2IGg/+m63r2CmIKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDwn9p3/kfrT/rwT/0 ZJXy7+1R/wAi7r//AGIPiT/0Czr6i/ad/wCR+tP+vBP/AEZJXy7+1R/yLuv/ APYg+JP/AECzrkn/ABP67HRH4Ue9/wDBJn/k1e2+unf+mTTK+na+Yv8Agkz/ AMmr23107/0yaZX07XRT+Ewe4UUUVYgooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAx/HP /IEh/wCv+y/9Koq+bvhoxi/ba+GjhWkK/C/xQQikZb/id6NwMkD86+kfHP8A yBIf+v8Asv8A0qir5s+G8CXP7bnwzjkVXST4X+KFZWGQR/bejZyPp/OsX/EK Wx7h8A9Tm1rQ9au7jT7vSp7nWJ5pLO6aJp7RmVCYnMTvGXUnaSjspIO1mGCe 7rhPgLoNl4U0PWtM0yztNO0zTdXntbSztYVhgtYY1REjjRQFVFUABQMAAAcC u7rWOxLCiiimAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVkfEH/kQdc/7B8//otq16yPiD/y IOuf9g+f/wBFtSY0ebf8E9f+TBvgf/2IGg/+m63r2CvH/wDgnr/yYN8D/wDs QNB/9N1vXsFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFAHhP7Tv/I/Wn/Xgn/oySvl39qj/kXdf/7EHxJ/6BZ19RftO/8A I/Wn/Xgn/oySvl39qj/kXdf/AOxB8Sf+gWdck/4n9djoj8KPe/8Agkz/AMmr 23107/0yaZX07XzF/wAEmf8Ak1e2+unf+mTTK+na6KfwmD3CiiirEFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFAGP45/5AkP/AF/2X/pVFXzX8PLf7V+2p8N48RnzPhb4 pTEi70Odb0YcjuPbvX0p45/5AkP/AF/2X/pVFXzV8PpDD+2r8NnUhXX4W+KW U7DIQf7a0bnaOW+g5NYv4y1se0fs0eGv+EN8DX2keVpMJ0rUJLMx6XY/YLFP LjjTEFvvfyYhjCx722KAu5sZPotecfsyanc614Evr28mNxd3moyTzy/2ZPpn mO0cZZvss5aa3yT/AKqQl0+6xLAmvR61RD3CiiimAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AVkfEH/kQdc/7B8//otq16yPiD/yIOuf9g+f/wBFtSY0ebf8E9f+TBvgf/2I Gg/+m63r2CvH/wDgnr/yYN8D/wDsQNB/9N1vXsFMQUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHhP7Tv/I/Wn/Xgn/oySvl 39qj/kXdf/7EHxJ/6BZ19RftO/8AI/Wn/Xgn/oySvl39qj/kXdf/AOxB8Sf+ gWdck/4n9djoj8KPe/8Agkz/AMmr23107/0yaZX07XzF/wAEmf8Ak1e2+unf +mTTK+na6KfwmD3CiiirEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGP45/5AkP/AF/2 X/pVFXzb8N5/s37bXw0kKuyJ8L/FDHapY8a3ox4A5J9hX0l45/5AkP8A1/2X /pVFXzb8N4Rc/tt/DNGZgrfC/wAUKdrFW51vRuhHIP0rF/GUtj3D4B63D4l0 DWdRt0uo7e/1ee5iW6tZbWdVdUYCSGVVkicAjKOqspyGAIIruq4X4B6JD4a0 HWtOt3u5ILDWJ7eN7q6lup2VAigyTSs0kj4HLuzMxySSSTXdVqtiQooopgFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFZHxB/5EHXP+wfP/6Latesj4g/8iDrn/YPn/8ARbUm NHm3/BPX/kwb4H/9iBoP/put69grx/8A4J6/8mDfA/8A7EDQf/Tdb17BTEFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4T+ 07/yP1p/14J/6Mkr5d/ao/5F3X/+xB8Sf+gWdfUX7Tv/ACP1p/14J/6Mkr5d /ao/5F3X/wDsQfEn/oFnXJP+J/XY6I/Cj3v/AIJM/wDJq9t9dO/9MmmV9O1+ e/7IPxV8VfDT9mGefwtA2p38Gj2F3baWZooRqE6aHZGOESSKViLsFXeRgZBO QK674ZftM/EXxtL8MtW8QXt1pGr6p4Pn1DU9MS3NtFBeMNOMivAw6xtJIqiT cyZYdSSbjVS0M3Bs+2qK+cv+F7+Kv+gp/wCS0P8A8RR/wvfxV/0FP/JaH/4i l9Zj2H7Jn0bRXzl/wvfxV/0FP/JaH/4ij/he/ir/AKCn/ktD/wDEUfWY9g9k z6Nor5y/4Xv4q/6Cn/ktD/8AEUf8L38Vf9BT/wAlof8A4ij6zHsHsmfRtFfO X/C9/FX/AEFP/JaH/wCIo/4Xv4q/6Cn/AJLQ/wDxFH1mPYPZM+jaK+cv+F7+ Kv8AoKf+S0P/AMRR/wAL38Vf9BT/AMlof/iKPrMeweyZ9G0V85f8L38Vf9BT /wAlof8A4ij/AIXv4q/6Cn/ktD/8RR9Zj2D2TPo2ivnL/he/ir/oKf8AktD/ APEUf8L38Vf9BT/yWh/+Io+sx7B7Jn0bRXzl/wAL38Vf9BT/AMlof/iKP+F7 +Kv+gp/5LQ//ABFH1mPYPZM+jaK+cv8Ahe/ir/oKf+S0P/xFH/C9/FX/AEFP /JaH/wCIo+sx7B7Jn0bRXzl/wvfxV/0FP/JaH/4ij/he/ir/AKCn/ktD/wDE UfWY9g9kz6Nor5y/4Xv4q/6Cn/ktD/8AEVleK/jb4pvn0C3bWbiKK68S6Jby +QqQu8b6paq6FkAO1lLKwzgqxB4NNYhN2sJ02j6ioqIrnPJoK9etX7TyJ5SW ioivXrQV69aPaeQcpLRURXr1oK9etHtPIOUloqIr160FevWj2nkHKS0VEV69 aCvXrR7TyDlJaKiK9eteCf8ABQT45ePfgJ8KbTVvh1o48SeI3vLaCPSdi/6e r3lrFIu9gRFiGSZvNb5I8b3BVSKHVQcp7R45/wCQJD/1/wBl/wClUVfNfw9D SftqfDdFXcz/AAu8UADeU5/tvRuNw5H1HSq+jfFzxH4t/bB8H6Rf6xeTaW/h jVLuSzBEVvLPHrHh1I5XRMKzossoUsCVErgY3Nmf4eTfZv21vhtLmMeV8LfF L/vH2JxrWjdWwcD3xUKV5XK5bI9m/Zm0u40TwPfWd3EYLu01B4Z4v7Sn1MRy LHGGX7VOqzXGCCPNkUPJ95gGJFej151+zR4kPjLwNfav5mkzf2rqEl4JNKv/ ALfYyeZHG+YLjYnnRc/JJsXeuG2jOB6LW8djN7hRRRTAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKyPiD/yIOuf9g+f/ANFtWvWR8Qf+RB1z/sHz/wDotqTGjzb/AIJ6/wDJ g3wP/wCxA0H/ANN1vXsFeP8A/BPX/kwb4H/9iBoP/put69gpiCiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8I/adYDx9af9 eCf+jJK+Xf2pznw7r/8A2IPiT/0Czr0z/gqN4dsNHZ/F9r8FvA/xh8RaXpUS vY6vptnPfvZpLOzLbPPazlmRmLCFQpcM+3c+1H+fPHPgLTNA+Fupap/wrLwF 8MvEup/DfxGdRsPDun2UZtwRaMsMlxbW8ImKjGeCqsWClhh25ZJc9/62N4t8 p6j8Hf2Xbnw98NtDtdU+Jfivwdq8Gl2Fvf6TpS2AjtLiGyt4JEkNzpd2zSho iCUlEeAoC5Bd96X9mGxm1mDUH+NnxIa7tYZLeKQto3yJI0bOMf2BjkxR8kZ+ XjGTnmv26fEHxTs/HsGm/DvSbbyNQ19v7T1hpo2l06JZXbakT4BMjLGm4hgF eTIUhWrzf9p/xR8Y/wDhaPgGTwdeappOgWt1oc3iPSrDShd3LpearFbzxu0l nNBdRLEJBJ9mvLOWzjEty5uVaKMc6r/vfZW6LXprfS/fTVdLo6HhWsOsQ2rN tWvrpbW3bXR9bM93/wCGeYv+i5fEr89F/wDmfqtqvwS0/QrVZ774+fEGzgea K3WSeXQ41MssixRIC3h8Dc8joijqzMoGSQK+R9S/al/aO+Hn7OHga4bwnc+M PH0PhB9T8SJL4AvYUu7+bSdYubSGMwXX7ueG70yCC6i8kCRtRtvKMBmSMdz4 iv8A4mzP47tPHmp2Go6NpvxB8DW/h5rXw++mxyx/adDe7mikaaTzIHu5JwqM XeJkkVpZAUEe9n5f1oYuNj6I/wCGeYv+i5fEr89F/wDmfo/4Z5i/6Ll8Svz0 X/5n626Kz9r5ByIxP+GeYv8AouXxK/PRf/mfo/4Z5i/6Ll8Svz0X/wCZ+tui j2vkHIjE/wCGeYv+i5fEr89F/wDmfo/4Z5i/6Ll8Svz0X/5n626KPa+QciMT /hnmL/ouXxK/PRf/AJn6P+GeYv8AouXxK/PRf/mfrboo9r5ByIxP+GeYv+i5 fEr89F/+Z+j/AIZ5i/6Ll8Svz0X/AOZ+tuij2vkHIjE/4Z5i/wCi5fEr89F/ +Z+j/hnmL/ouXxK/PRf/AJn626KPa+QciMT/AIZ5i/6Ll8Svz0X/AOZ+j/hn mL/ouXxK/PRf/mfrboo9r5ByIxP+GeYv+i5fEr89F/8Amfo/4Z5i/wCi5fEr 89F/+Z+tuij2vkHIjE/4Z5i/6Ll8Svz0X/5n6P8AhnmL/ouXxK/PRf8A5n62 6KPa+QciMT/hnmL/AKLl8Svz0X/5n6m0f4AWFh4k0e/vPjB4+1iHR9StNUFl dyaWlvdPbXEc6JIYdCjk2F41DbHViMgMM5rVoo9q+wciPcf+GhbL+/pP/gRd f/ItH/DQtl/f0n/wIuv/AJFrw6il7VhyI9x/4aFsv7+k/wDgRdf/ACLR/wAN C2X9/Sf/AAIuv/kWvDqKPasORHuP/DQtl/f0n/wIuv8A5Fo/4aFsv7+k/wDg Rdf/ACLXh1FHtWHIj3H/AIaFsv7+k/8AgRdf/ItH/DQtl/f0n/wIuv8A5Frw 6ij2rDkR7j/w0LZf39J/8CLr/wCRaP8AhoWy/v6T/wCBF1/8i14dRR7VhyI9 x/4aFsv7+k/+BF1/8i15Z8eNLT45a/Z3i/EvxN4St7KAwpY6K9k0DsWJaVjd 6PcSbyNq4DhAEXCgli2DRR7RhyIT4Q/BPRPhx8W7Pxde/EfxT4qv7Owk0qFd YktDBawTXlldTFI7TSrUtIzWEIBdyAM8c5rqvBCaFofxE8N+M5b1f7Y0Pw7f +HktZZZI4RHd30FzK7BbdyzZtLfYQ4ADSAqxIK8tRR7R3uHIj6E+BWoprGj6 5dpsK3OszygoSVO4IeCQDjnuAfYV3Fec/sw/8iDef9hB/wD0XHXo1d0PhRzy 3CiiiqEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFc78WdQXTfhzq8p2fNbNECxIGX+TsDz83 AxgnGSByOirkPj1/ySfVf+2P/o6Opk9Brc8Q/YR+OVrof7D3watXbSt1v4G0 SI7ri43Erp8A5C25APHYke5616r/AMNE2f8Ae0j/AMCbr/5Fr5E/Y3/5NE+F f/Yn6R/6RRV6RXE60jf2aPc/+GibP+9pH/gTdf8AyLR/w0TZ/wB7SP8AwJuv /kWvDKKPbSD2UT3P/homz/vaR/4E3X/yLR/w0TZ/3tI/8Cbr/wCRa8Moo9tI PZRPc/8Ahomz/vaR/wCBN1/8i0f8NE2f97SP/Am6/wDkWvDKKPbSD2UT3P8A 4aJs/wC9pH/gTdf/ACLR/wANE2f97SP/AAJuv/kWvDKKPbSD2UT3P/homz/v aR/4E3X/AMi0f8NE2f8Ae0j/AMCbr/5Frwyij20g9lE9z/4aJs/72kf+BN1/ 8i0f8NE2f97SP/Am6/8AkWvDKKPbSD2UT3P/AIaJs/72kf8AgTdf/ItH/DRN n/e0j/wJuv8A5Frwyij20g9lE9z/AOGibP8AvaR/4E3X/wAi0f8ADRNn/e0j /wACbr/5Frwyij20g9lE9z/4aJs/72kf+BN1/wDItH/DRNn/AHtI/wDAm6/+ Ra8Moo9tIPZRPc/+GibP+9pH/gTdf/ItH/DRNn/e0j/wJuv/AJFrwyij20g9 lE9z/wCGibP+9pH/AIE3X/yLTv8AhoW09dJx6/aLrn/yWrwqqeuX8umWBnit 5LoRSxGVIwTIsPmJ5rIACS6x7yoCkkgDBzimqsn1D2aOm+OvheD43eLrfVo/ iP4m8IC3s0szZ6I9m1vKVeR/Nb7XpFxJvO/Bw4XCLhQck+ZeOv2VrXWfBnie KP4o+Itf1nVfDmoaHp/9vyWi2Vo10I8yMLPSIJCcxIOWIALfKTT/ABTB4q0H QtdtY4PEa3lrZz/Yrz7DDJLG8EG5ZJoTGCGnnIiGE2lVV1ADqx6fRtPfTtLW GWXUruW1nmtpLy7hjQXZWVirxmNERlMTRNwoKlsMAwNNye4kuhyvx21r4ZfC nxdZP438XeP7XWvF6yajHHp66rcxSMVill2rbz7Y1X7QgxtRecKMDi9oPg/4 Y+K7q8t9K8beKtUuNOCG6itNduZ3tt+7bvC35K52tjPXafQ1Q+LXhDxDdftN 2XiG4sNMv/B1n4bsrLyRITfxTfZxK80aBPun90m3fklPukdfFvjX+w/a/tJ/ Dy8063XVNBt/EPiRNW1A3H2Qm8gt7OdbeO5sbuxuba7hF08bfZ54l2nbOH3w xo3gVcdjKWcRwMlGVKcOdSipe7bTllK3JzN/ZTvbU7JRpVMNGpQg4uLcZuUl 7z3i4RWvLbRt317H0YPgv4MOP+J38QeTj/kI3v8A8m1keOvhD8OdK8Mz6rq9 /wCPNQsvD7R619nkvLqYNJZyJcxkI99sZlkiRgG+UkDPFfG2hf8ABO/4zeAP htr9v4W8QeGPD/inxNe22rvPY+LfENrp+lXlr4Ws7G2FrbRsIfs66rFMzQzw yxPZJbRtG3lRRwdfqH7Fnifw/wCJf+Fg+KvE1/rV/wCH/DfxBt5T/wAJTqkq FdT1SK60yJbd2WFoobBHhkjZQgkjt9qyeTFJH7y3Sv8A1a//AADltpf+tz6w 8SeMtH8Gwwyavq2naUlw/lxNeXUcCytjO1S5AJx2FJoXjTRvE5I03V9M1Bhn i1u45icf7pNeLf8ABQL4R+Kvi/8ADfTLLwnpk+p39tcySukUlvG23Zt2h5pI 0UsCRkt+XUcp/wAE/vg/4l+F/iu/XxH4G1rwyv8AZbpDeXc1hLFLL50P7sG3 uZWBKh2ywC4Q85IB+rwmR5XUyeWOq4tRrq9qdtXbbXzPCxGZ42GPjhaeHcqb ted9r/5H0f8A8LB0FfiLp3hF9Y0+PxNq8MtxZ6c86rNOkalmIzwMqrsoJBYR yEAhGIk8I+NtI8e6bJeaPfw39vDO9tKYzhoZUOGjdSMqw44IB5B6EV8D/wDB Q7wL468BftPX/izQ9N1GaHWbSC50nUbS2YW0F3HFHA0VxOsM0lvIojDrIqje shTsa+gv+CcXwz8R+B/hHHd+I77Wbm5urOC2K36PGbicTXV3c3YR1VlMk99J HyMsltExZs5r88oZtCpL6tKElVU5Jqzt7NRup81kvedlypt7vo7fd5jkFXDq GLp1IyoTpxle65vat2lTtdv3Vq20vx1+isfWsaz+I3h7UfF8/h6217R7nX7U SmfTIr2J7yHyktZJN0IO9diXtmzZHyrdwE4EqFtmsWw+G3hzTPGN14ig0DRo dfvDKZtSjs40u5fNS2jk3Shd7b47KzRsn5ltIAf9WmPTT7niFnxr8SPCHwp8 L/2r4s1jU9Lt2m8mM22lte7zsZ+Ajbs7Uc/dx8vXnFHg34sfDz4gXOnRaR4k 1m8OraZHq9ow0Qok1rIoZH3NIAGKnOwkOADlRg48q/bttdaPwJlvdFh806Vc /abwpB588NuYJozLFEQRI6NIjlCDuVHGCcA/HX/BPrRNf+LPx0uZIfE7a1Za Rc217GY2kFtYQC7W5uOBDHGk0kiJEkaAAQyzjpnHzNbiCrh8wrYOrSbXLB0m lL3pOcVNN/ClGLctWtluj6mnw7DEZXSzDD1FdTkqqcorlioycWk9W3JRXW93 1P1BNnoPGNR1kknH/INj/wDj9L9j0H/oJaz/AOC2L/4/XxZ8Wv8AgnNrniqz +MB8Najpugt490LUINOsbPWb3T9MuNUv5NZW9vdQs4ALe5llt7zTk86aOaQG 0+XaYo2bqr/9kXxtF4i1mO28Y663hi0iii8L2UnjnW0ubOKZ7ganHdXIczXL 3EZgEM88kr2JmZrZYzboZ/qND5V6f1/X9NH1SLLQCwH9pawCen/Etj5/8j0f Y9B/6CWs/wDgti/+P1846p8OvH2jeA/hnDr+sf8ACQ6npHixrjxJcWyXNzDr KSwX9tZTC3jjLRRLeXGm3TQj93ZiFm8+Rbbz34L4PfsS/En4RW2h6XpnivwP o/gjw/4kt9aGgnRJLmS7iZbCS42zWz2VtayRzR6hHEsNkIpFmWedHupJZKaS vZ/1sLW1z7LFloBfaNT1cn/sHRf/AB+j7HoP/QS1n/wWxf8Ax+vAPhH8Mbz4 e+PNI06500y6nZ6h4r1yfW4YC9nBYaprM11HYmVwpM0he2kZEDBPsLhmAaIy 9D+0n8Bh8c/D+mw21w2n39nfwpNPFcPbtcaXNLHHqli7oCzR3Fn5q7e0qW8q tHJDFLGnbQD137HoP/QS1j/wWxf/AB+l+x6D/wBBLWf/AAWxf/H6+S5/2J/E +p/tAaL4tl168tbHQ9O1O5ggt/GWtu6atd3mmXAAEkhzp4SwZDZFzakqjC2X ewX6B8OX3ie6vYF1fSNCsbeSEtI9pq8t06SeXCQoVraMFfMa5XcSDtiibbmV khNLJjsaPwx8Saf8SfH/AI10OManAPCmsw6RbyrBHI+oF9OtL1pChkURYN1s C7nz5e7I3bR1eoeGtK0sIZ73WUV8hW/s6IgkdRkT9R3ryP8AZp1EaR8a/izd NysHjW3c/wDgg0eul0Txje6xp8jXNldpNf6vcTKjIQkMcjyOXZjjAB2Acc7u OleFjc0rUMxpYSFO8Z7uz0367LY6aWHjKjKo3qjrvsegd9S1gf8AcNi/+P0f Y9B/6CWs/wDgti/+P18zfFD9mLxp8Rvi14h1qTXZH0/VL21srGOHxfrOkjTN DD6PJdQxQ2DwBLx3h1d1uQ/mfvLaN5HgZoYuA+G37EHxV8G6p4e16bxhJceK DdaTqPiW5bxzr89vqFxF/YUV9ss2cWTRzQQ66oR7XIMlgUMAUiL3o2drnK01 t/X9fqfbBstBAyNS1hh6jTY8f+j/APOaPseg/wDQS1n/AMFsX/x+vG/jB+z4 Pid4zGsR6nrunu2lDRZTZeItQ03Fu97bzz+UtvIixzNHEVE6bZRwocL08ssP 2PviN4h8E2Vh4v8AFaa5eaR4z0HxZas+vX7wA2z6Nc36yKVUSo13b6vJBbur Qxefa7FhESCAgk9HoEtL210/pH1v9j0H/oJaz/4LYv8A4/R9j0H/AKCWs/8A gti/+P1kUVF12HY1/seg/wDQS1n/AMFsX/x+j7HoP/QS1n/wWxf/AB+siii6 7BYy9O8daRffGnXfCjNqcVpomh6drBvzbIXma7nvovK8rzMAILLdv3nd5mNo 25PS+KIdI8GabcXmpp45tbOzRpZ528KzGOGNRlnZg5AUDknoBXhmua1H4a+P nxW1KV1ijsPh/otw0jMFVAlzr7Ek+nFfM37SXxs8L+JfjF4p8VRfEL4THQoN Q1a70jQ0vNUna9a9SaN7qUT2hSK72tbSKsbiMSxRguqRrjhx2MdC3Kl08vn5 njZnmFXDtKmr/wBbn6B+F9Z8KeM/Den6vpusazc6dqltHd2sv9lonmxSIHRt rTBhlWBwQCM8gVf+x6D/ANBLWf8AwWxf/H6+Jfhj+yTqvxm8N+AvG39sWcOi 6l8NfB+nHS5WkeK+kspbm/3yqBhHjnlsZIZQXI8u5RlCybq2fgL+xV8QvhJ+ xhZ+FJPGF0/xQsdLW0g1C18S6hHo9rLYx3CaPHBBjyoYIlFms6JbBbkQv9oS 53Nv7qbUocz30PXi27ea/RH2D9j0H/oJaz/4LYv/AI/QbHQQoP8AaWsEHp/x LYv/AI/XyZ4p/Y/+Imn+JrMeGfH2sR+FTdRaTc6bqni7W57uz0V7mOS7e3vD M8kl9In2hRLcCSWJbiFLa4s/s3mT0f2df2ANb+Fv7WGp/EnX/EN9qm+e/wD7 Phi8U6vKI7d9P0OztIriGSQQ3UkSafdLJPMHkuCLWZ8yKogtJf18v6+XoDv/ AF/X9fefYP2PQf8AoJaz/wCC2L/4/SfY9Bz/AMhLWOf+obF/8fr5C0X/AIJ9 6t4C1LxLqvhTxTNomrfEDxrPrPiX7FFY6SLrSpLnUJHtBc2VlDezSvHdqqzT XDTW0gEkMy7GSbotQ/ZX8Y6z44hnm8Q6za6VNDe3+pCLxrqs39o6t9oiS0Zb WVWhgsvsn2vzbWM+SXu1AjdreG5UjZ2vp/w39Ib6n1z4X+ICeDdPa103X9Yt oJJDKy/2PbvliAM5aUnoB+VaCfG++fP/ABU2sDHrolr/APHa8U+LeneJdR+D viWw8H6la6R4vu9Gu7fRL64VWhsr5oHFvMwaOQFUl2McxuCAcq3Q818MPh/4 n+CvguTQdG0zT9Usbe6u57OXV/GOoX95IJZbuWMTXFzBNMxJ+yglpJNommA3 CCMTNTdtyeVaOx7Trf7VmraX8avDnhJNX1KWHXNH1PVpbxtOtke2+yTWMQjW IEh95vSSS67fK6Nu+Xor34/TaasZuPFuo26zSLEhk0e0QO7HCqMy8sTwB1Jr 538TEn9rvwPng/8ACHeIs/8AgZodeY/G39nn4tfFT9qXQNZg8VWOm+EPDVpd XOlWyAqtrqEsU0Edw8ZQ75I4pDgs7D94dqJ8xaMRiJU6Mqq1aWi1u23ay6eb bskjqwGEhXxMKNSShF3vJ7RSV9er7JJNt2Ptz/hdd7/0M+sf+CS1/wDjtH/C 673/AKGfWP8AwSWv/wAdr5D/AGnv2T9V/aM+IfhPVDqlrpdjpPhrXNF1Ky8y SWLVBqT6astrIgVRJbvb292pYlWSRreQIxQqPOI/+CeHiLx1r/ik/EWP4YeO PD3ivV9J1SPR7rR5Xh0mXTLtIYp8XBmjnubnSD9nmkSO3C/Zoo9kiySSDSFS TV27HNOCW2p+gf8Awuu9/wChn1j/AMElr/8AHaP+F13v/Qz6x/4JLX/47Xwh 4i/Yf8b6f+znoHgbwDrPgnwYfDHjPWfFunxSaXLeaSUbU7+90my+yxtAESBr m0n3KSkM2nxKIp4ya7r9lf8AZPl/Z0+LXxO11JbCez+Id8usO8KwQSrdte6j NKjxQW0MbKIri2xcSGa5lYyLJIUhgAPaPv8A1oJwXY+tf+F13v8A0M+sf+CS 1/8AjtH/AAuu9/6GfWP/AASWv/x2vj1v2JdAj0X4r2Fl4Y8K6BH8bHudL8SS 6JHDYywaY1rdQxzLi1aK6u2lnlmYzx5BvZEaSZII1fC1X9jzxRcfsqN8LdNs Ph7plvpFnqcfh3ULCJNMXTr5pL9tN1QW1taRxQXsD/YJ3e2Ea/aZ7qWJIvIi WUVSXfsNwSPuA/Gy9C5/4SfV8Hof7DtRn/yJSf8AC7L3/oZ9Y/8ABJa//Ha+ MP2m/wBmuH9pT4opo154d0G9iZIdTGoeI9EXWNDuoodP1a1t7eS2J2yy2t7f xXPlTND5iTHypGaJ/L6f4Z/ATU/AXj6bxkuieGtN8QahpGmaPqMVpqc0sute WLZbi81C/a3WXUJ7eNHjtTNCGCpKTJGLx1txVJdwcF0R7H+0N+2Z4s+Fkvga w8MTDxHrnjvxTbeGLWDVFh0m0t3nhnkEzzRxXL4BhA2iIk7854wdm78cftIW Gqiwnj+B8V8zKot38fXCyksQqgKdEzkkgDjkkV88fF+91u++J/7O7a9p+lab dj4taKVisNRkvoznT7lpMu8MJG2YzIvy/MiI52FzGn2Lr/wem8caubPUxqV9 4f8At06ahowLRWl6s11LNum3MI7iExypuRRnKEMW5jrop8zhe13ddbfPXt95 jKylZ6L0/wAjlN37VB/5gvwg/wDC1u//AJR037R+1PkgaN8IeOf+Rzu+f/KH XK/8FJf+CZo/4KEfGH4K3Wo6jpNh4O+H02sz6xFPpWm6rdTm6tYo7cQWupWN 5ZSYeMljLEGVcFDuwR8u/tM/8EM/jH+0Ne+PNQ1Lx9o+r6x4wGoM17qPizWd yubbx/bWMUYEbfZbNIfEmho9nAfI2W9+pSXObqkroTZ9nbv2qP8AoC/CD/wt Lv8A+UdG79qj/oC/CD/wtbv/AOUdeK/F/wD4J6ftG6l8QNWn8C/GvV9E0CXU 7OGwivfGesXEseiWs/hqWC2KSCULd4s/EAluy8ktyLuJJ3kSdha/cvhXwxD4 O8LadpFpNfzWul2kdnDJfXs19dOkaBFaW4nd5ppMAFpJXZ3OWZiSTT5fduie bU8DMv7VAGf7G+EP/ha3f/yjpfM/aoJI/sb4Qcf9Trd//KOvPf2f/wDgnP4i +B3gz4e/utJb4h2WrxXvj/x1ofim70fU/Hwt74vFPqS/YpTqPnwFpHt7iQC3 aSS3hn8pmlPnvxa/4Jd/En4nfCvXvBOmT+HdL0zSfAHjv4WeHpNRuDDDHpms ato93ozQrAkreRZafaPZuJQkrS2aEKyS+aBLW39f0/62BS7n0Ju/ao/6Avwh /wDC1u//AJR0bv2qP+gL8IP/AAtbv/5R14j4J/YA/aV0z4papL4h+MN1q3hC XUNT8U2D6b451vTtbj1iSe1utOhuDKlzYvYW2L2ya2htYLKaGdJ5LB5Y444v Xv2T/hP8eP2Yv2dPDHhnVE8N/FHxhBpFvJ4g17xF8SNWZ7/VEtZIXEXm6dcG O3zb2Iyuzebi6maLzVY3Qoq1x3Z0PhzV/jjoQvb7x8fBOhaJa2+5J9A1eTWb qSYyIioYZtPtFCYZiX8wkFQAh3Er5jr/APwVa+DfhXxBqWk6p+0d8PtO1XRp 5LbUbK61PRYbiwljcpJHNG10GjdHBVlYAhuCM19KfGR7h/gzfG8iihujHbma OKUypG/mx7lVyqlgD0JVc9cDpX8y/wAbfgb4h8L/ALQH7Rmtr+zN8Xtf1XWv E3iPTLJ9P8M6lOniiHUr2+B1GK8Fq0cEENuUCpGW81pYWAYb3j48ZVnRpynT Tk1ayXW7S9Fbdt6WOzAUYV60adSShF3vJ7JJN7bu+ySTd2f0O+Gv2kY/Gfh2 w1fR/G13quk6rbR3lle2elWU9veQSKHjljkWUq6MpDKykggggmrv/C7L3/oZ 9Y/8Elr/APHa/OL4efsBXnx2/Z8/ZSuPEkceit8LvhnBa3Gl6ppFpPeR6o9t oxiikhvbSf7OE+x3CySReTdRMVEci7nrTi+EHxN8e6t8brTwb4j8YaT/AGr4 bPh+zvtc1TW7C1tfEgi1qKe90+K5UvDbgz6O6vZJ9m+VjC8rQtmlOT6mfItD 9Cv+F13v/Qz6x/4JLX/47R/wuu9/6GfWP/BJa/8Ax2vmPwv8DPEFn+0PfeLb 7xJrTaCIkmsdNTXrx7c3UhvIrsS2z5hMJgXSjGq8RSwTugjM07XPRfCr4c33 gfxx8SdTu5LZ4PGPiOHWLNYmYvFCmkadZFZAQAG8yzkICkjayHOSVVe0lffp +qVv1E4Lt1/r/I96/wCF13v/AEM+sf8Agktf/jtH/C673/oZ9Y/8Elr/APHa 86oqfaz7j5Eei/8AC673/oZ9Y/8ABJa//HaP+F13v/Qz6x/4JLX/AOO151RR 7WfcORG58Q/2r9V8F+KPBGmwavqV7/wl+uPpEkkmnW0P2JF0+9vDKAC3mH/R AmwlB+8zu+XDbGv/ALSFx4WuNPTUNZ8bWsOqX1tpttdt4Kb7E89zPHb26m4/ 1SiSWWNFZmCkuOea8E+OH/JT/g1/2OFx/wCmDWK8J/bY1G1tvjX4Zv8AxLde EdISw8SaX4gh06++KbRWs9vY3Niv2mTR301FMk1tZ3Ea5uGUPdXG55BGm33M kwdPGTcKs2tNLW177nmZliJ4eKlCN/0Pvbw9+0Yvi3Rk1DS/Gd9qFlIXUTQa RaOu5HaN1JEvDK6srDqrKwOCCAa/8Tm8UaTLY33iHV57WfG9P7GtlzggjkSg 9QD+Ffnh8TfhXqH7eui/ELU/hr4rtrJ7D4n6lrul6zY65d6eRK3g97G0kiuL TMny3l3bNIudrRpKrK/+rb1xf2WvElv4iu7O28S+Ij4b1W3kstSkk8a6wb14 YrqxNn5GXZ7WZLQX8Ms8E8ck7mKWUyPJmDzsbTdCtKjfZ2O7DNVKUam10e/e DPAfhD4feEdL0HSLrWbTSdFtIbCyt/sKyCCCJFjjTc1wWOFUDLEk45JNaf2P Qf8AoJaz/wCC2L/4/XhX7O/wi+IHgb4X+KtH8W+MWv8AWNYuBc6bqcdzNfvp jy6XZpdMpnVflGprfzRxD92kUsaIsaKIo+e0T9nPxV4F8Z6BqvhTTPBfg3TZ tLmm8T6NpF/PBHqeuXd9pUlzfzTLbr9tkjt7K6RJ5olmlM21zGJXZON/1/X4 G0Vex9LGx0HaD/aWs4JI/wCQbH/8fo+x6D/0EtZ/8FsX/wAfr82P2Av+CXvx r/Y50xrN/FfheK2/tdro2+k+JNYS2urR9MvIZIZLVkFqHF4ukukyQCZY4rgG Zl2Qt7b47/ZL+L3iHwRrH9nfEK8s/Est74gk0u4XxRqcVtbw3N5q97paOija 3kTSaGrAo2ILS4twXgd4p7lGKe/YUW2rtH119j0H/oJaz/4LYv8A4/QbHQR1 1LWQfQ6bGMf+R6+XPjR+xlrfxm+AvifwVf6zI6eL9c0nU9Qa/wBUk1m3UW+s xXN5JBBf288cIltIIylkVks1kPliIJ5ks+z8Kv2bvEHwR17w+dFuLa70/Rbd NCS3k1m5sLOXTAc7/wCzreIabHdBm3hre1hwIFiDpFKVjlJf16DZ7D8YvG2k /DDwZBqdq2p6pPcaxpWkrBLbpbJ/puoW9mZC4kc/uxOXxt+bZtyudw0sfWvJ P2gbjWrr4R6a2uafpen3B8YeEyiWOoPeo2da0syZZ4YiNspkUfKdyIjnaWMa et1LY0GPrRj60UUrjsGPrRj60UUXCwY+tGPrRRRcLBj60Y+tFch+0H8NZvjN 8AvHHhC3lt4J/FWgX+jxyTqrRRtcW0kIZgyOpUF+QUcY6q3ShMVjr8fWo/Gl lqei6JptxpUFjqFzfxGdoru6e0SNBJLGcOsUpJzGDgqBhjzxz8uaf/wTQ0X4 f6p4butAsPDFze+D5NCj0rWpdJ03SdYsba31u81DUIYJLCxigto5orsxskMK C4TzopDG0xuF+vvEv/IH8Pf9eD/+lVxVaCZwI1Txr38P+GBxnnX58f8ApFVO 98X+LrBpw/h3QWNuiSOI9Xu5ThnKDAWxJJyDwMkAZIxzWL8DP2fLr4O/EX4g a9Pr39rL471Eam1v5Nwhs5BJOFG6S4lEmLdraPeyiQeRsV1tI7Kysd7xhFd6 J41s9YfS7rX9De3+w31jbTslzCCW/wBIhAIBddw4IIOMED7w+b4rzbE5dgHi sJDmaavo3aL3do62W7snpc5cZWnSpe0grv7/AMhdO8T+LdWtvOt9E8MPHuKn OvXClSOoINkCD7EA1P8A2p40/wCgD4X/APCgn/8AkKsP4UfEnV/iR498QXl5 o9tpVm6xqltaQ+RbWrp8uwIeS7A5Y9yvOOBV/wDaS+G9/wDGT9nbx94Q0ua1 t9R8U+HNR0i1kumZYI5bi2khRnKhmCBnBO1WOBwD0o4Vz+OcZbDHKNruS2av yyauk9Una6T11sx4HEfWKKqev4F3+1PGn/QB8L/+FBP/APIVJ/afjXbg6F4W P/cfn9v+nKuY1/4P6xd/G/xH4ngFtc2mt6b4X0+CEavdaZMjadq1/dXLtJAj FkEN2hWHJS4MbwS7I5GY+Oyf8E57Q6l4eubPw14H0WPQfEreK4rSzstKktDd zyWDzK0D6Rs+RIpoo7iAW94UtLctcb7mV4PpUlc6b6Hu3ib4j654Lhhk1i18 CaUlw/lxNeeKZIFkbGcAvaAE47VL4W+I+qeKb6KO2i8EXcLuBK9j4le5dEz8 xVRajccZIG4fUdR5z/wUC+Efir4v/DfTLLwnpk+p31tcySukUkEbbSm3aHmk jRSwJGS3+I5T/gn/APB/xL8MPFl+viPwPrXhpf7MdIby7msJIpZfOh/dg29z KwJUO2SoXCEZzgH67CZJldTJ5Y6ri1Gur2p21dttfM8DEZljIY+OGp0G6bte d9r/AOR9GeJ/hv4d8W61NfaroGi6neybUe4u7GKaVlRVRAWZSSFVVUc8BQBw Kz/+FJ+DP+hR8Mf+CuD/AOJoor5Y9wP+FJ+DP+hR8Mf+CuD/AOJpJPgj4LKH PhHwueD/AMwqD0P+zRRQwOsooormNQooooGwooooEYXiP4eaB4u1RbjVtD0f U7iOBYklu7KOZ1QFjtBYEgZJOPc1R/4Un4M/6FHwx/4K4P8A4miit4/CiA/4 Un4M/wChR8Mf+CuD/wCJo/4Un4M/6FHwx/4K4P8A4miimIP+FJ+DP+hR8Mf+ CuD/AOJo/wCFJ+DP+hR8Mf8Agrg/+JoooAP+FJ+DP+hR8Mf+CuD/AOJo/wCF J+DP+hR8Mf8Agrg/+JoooEw/4Un4M/6FHwx/4K4P/iaP+FJ+DP8AoUfDH/gr g/8AiaKKBjh8FfBsUU0S+EvDKxNdPcFBpcAUyNHEjPjb94pHGpPUiNR0UYb/ AMKT8Gf9Cj4Y/wDBXB/8TRRQwD/hSfgz/oUfDH/grg/+Jo/4Un4M/wChR8Mf +CuD/wCJoooEw/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miig A/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miigYf8ACk/Bn/Qo +GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRQA23+C3g6GS4VPCfhlFupI/OA 0uACby1l8vd8vOzzZduenmPjG45d/wAKT8Gf9Cj4Y/8ABXB/8TRRUy3M5bh/ wpPwZ/0KPhj/AMFcH/xNH/Ck/Bn/AEKPhj/wVwf/ABNFFUi0H/Ck/Bn/AEKP hj/wVwf/ABNH/Ck/Bn/Qo+GP/BXB/wDE0UUMYf8ACk/Bn/Qo+GP/AAVwf/E0 f8KT8Gf9Cj4Y/wDBXB/8TRRQJB/wpPwZ/wBCj4Y/8FcH/wATR/wpPwZ/0KPh j/wVwf8AxNFFDGEPwW8G21xJJH4S8MxySW727sulwBmjLxuUJ28qXjjbHTMa Hqowf8KT8Gf9Cj4Y/wDBXB/8TRRQAf8ACk/Bn/Qo+GP/AAVwf/E0f8KT8Gf9 Cj4Y/wDBXB/8TRRQAf8ACk/Bn/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8 TRRQAf8ACk/Bn/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRQAf8ACk/B n/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRQBNpnwj8J6Nq9pf2fhjw9 aX1lIZLe4h06GOWBmVo2ZGC5UlHdSQeVdh0JrqcD0FFFAuoYHoKMD0FFFABg egowPQUUUAGB6CjA9BRRQMMD0FGB6CiigEVNa0u21zSbmwvbeC8sbtAs9vPG JIpgrq6hlOQwDKrDI4Kg9QK5v/hSfgz/AKFHwx/4K4P/AImiihbAw/4Un4M/ 6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miigA/4Un4M/6FHwx/4K4P8A 4mj/AIUn4M/6FHwx/wCCuD/4miigA/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6 FHwx/wCCuD/4miigA/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4 miigB8PwU8GwOk6eEvDKTQuRHIulwBkDxvE4B25G6N3Q+quwPBIpn/Ck/Bn/ AEKPhj/wVwf/ABNFFVHclh/wpPwZ/wBCj4Y/8FcH/wATR/wpPwZ/0KPhj/wV wf8AxNFFLqNB/wAKT8Gf9Cj4Y/8ABXB/8TR/wpPwZ/0KPhj/AMFcH/xNFFIb D/hSfgz/AKFHwx/4K4P/AImj/hSfgz/oUfDH/grg/wDiaKKAYf8ACk/Bn/Qo +GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRQLoOi+Cng2KWKZfCXhhZrWWO5 hcaXAGhljcPHIp25DK6qwI5BUEciupooqK25cQooorIoKKKKACiiigAooooA KneZ7mDbIzSLCNkYY5CLjdgegyScepPrRRVRJkR7B6D8qNg9B+VFFX0JDYPQ flRsHoPyooqIfCCDYPQflRsHoPyooqug0Gweg/KmyqAh4HQ/yNFFAM//2Q= --0-1398234352-1298459008=:63850 Content-Type: image/jpeg; name="scr3.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="scr3.jpg" /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMD AwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/ 2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAHCAyADASIAAhEBAxEB/8QA HwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1 dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD6Z+CVl4t+ O+iz6lpnw++CVjpNrcNaS6hqlrDZxJKqK5XDTbzhWU5VSOeteeaV+0hNrkM7 WXw0+G9ylmsjzGPw6zCKNG2tISH+4D/H90gggkEE+h/szfFvxZ+z34Dv9Fuf g7r/AImluL5r+1uLlLiKK2kMaIQ1v5DpKP3akFiCO2DzXl3wo8BeLPh1Lfpd +A/F2pWuo6XLpciRWc1tIqvtG8MYnGRt6Fe9RJ7q6tpbV3v1v27Ipejubukf G+81nSr29i+HPwrW1sGVJpZNIjiVWZJXVfmlGSRDJgDqQFGSygxXX7Qstjez 21z8OfhrBc27mOWKTQNrxspwVYF8gg8Y+tbVpqvifTfCsemWvwy8WxGK3ks0 keGVgIfKv448qLcfOPtzFmBAcx8Km440tO8YeKNL1y5vYvhh4nYzanJqKpLD MwHmz288ytiAbm32y7GwCgPIcqCIur+XqVrY5KP9o1CxH/Cv/hkD3H9hj/4u rx+N1ylp55+Gnw+EBjEwk/4R1ghjLlA+d2NpcFc9N3HXisfUfCXj/WdQ1W4m 8LeIZ31Vh5r3ejPczIo4UJK8ZdCB8uUK8ADoBXR2mmeI3utLvLv4feLptR02 2isjLB9qtkaGJdiFQse9JNoUFg5UjPyAnId1b/ghrcbc/F+7tZHV/ht8PG8u CK5cx6D5irHKEMbEq5AB8xBz3bB54plz8bTp95JbXHw8+HdvcQsUkik0HY6M DhlIL5BBBGD3FWvD+meJNN0W0tLrwV4tuwlodPuUjspYC8K3SXUTLIY3+cSb 1YFcbAoByfl6a38eeNVvhcP4E8QefMZWuZUsSHXfL5yiHNuVVVlyw85ZmG5t rKWJpXX9MSTOPT46wuR/xQfw3APQ/wBiYyP++q1x8QrxtUuLJfhr4EmubWc2 0iQaB5u2QByVyjkE4jkIxnIQkZANaOueIPGPiG0v7e48GeKpba5s4raC3lti 8MEiRPGZ9v2cYchs/ujH0wdy8UuhXPiPQGW5XwX4pnvzHaMX+yyRxCa3hmtx uXy2MkbxSKWGUJJYZxzRdf0xpGCvxnt/+hE+HYGcY/sZef8Ax+r9t8TRLpc9 6PAPw9NtbSxwyv8A2MuUZw5UY35OQj+3y1uaZqWvy2S2t14L8TW8EkUcNy1t aS+Y+BbR/usoBGuy2UlTu3HK5UEFbuq+Idb1W6d/+EA8RAPctI2IpArx7rtl BQwsu7N0xbduRtuCpViCXX9MFc5NfjDalsN4G+Hg6n/kDDP1+9U8HxWtppAg 8C/D4Fm286OoAP1LcV1b+NPFTX7Tp4H1tQZGkWN4ZWRWa8W5LYEY5woTP4+1 Z+oXfia68O6hpsPg7W4IrmCG2tpTbSNLbRosavlhGN5kEUYJwoAUDGKE11/M LMrSeLJ4DOZPhz4JX7NJJDIw0LcEeMEupIOMqBk+g56VWT4n2bOVPgjwDkf9 Qdfw/iroV1XXra1Jt/CHiD7c0puzPJauE897ZYZmZNh3gne4+YDLDIIBBdc6 t4h1CG4im8JeIViu2aaRoUlSaOVpIpD5TmM7IgYUwmCRz8x4wXV/+CGpgx/E eydsDwT4Dz6DRx/j/nFSr8Q7Esf+KK8CZGM40hep/wCBV08fifX0vBP/AMIf rYka5FzJ+7kILebbSMF/d8KTbnGckb+Sccz6X4s8R2ENuh8Ja1/o6wrlbc5l KRxodxaFjg+Up+UqQc8njBddfzFY5lfGto8LSf8ACD+CjGrBGcaMNqkgkAnP UhW4/wBk1esdYTUEga38CeC5xdF1iEWjh2cpjcNoJOQCD06GtWPU9ekaF5vD Gv8AnW7xSJKiMHDpJO2TuiYHInIwRwVB9qbYyazbXlzcnw1q8b+e0lrFHbSB IEeFonUHHGR5R4GP3eAFHAE1/THYz4NWjnwU8BeEmBhNx8uhg5iHV+v3Rg89 KdHrVrLCkn/CD+ECkisyN/Yww4QZcjnnaAc+mK208Q+JGuYLh/DeoG4jEcbs LaQI0KzPKYgu3hTlF78J3zxXt49Tihsof+Eb1kW9nHdQhQr7mWcMvUxkAqD6 ckdulF/6uFina39vdwrJH4G8JPG8ogVl0YENIeQgI6nHYc10fwzkifxfZyJ4 W8OaXgNi5tdOWCZQ0TlSrZzhgrDIBBGR3qtYX2q6eltBF4a1U2Vo0UkUciOz rIkpffu2DkqzJ06EdcYrV+HralH43tGk0rULO0W0NoHuI2O1Vyylm2qM5woO BgYHvSbWtvzGtD8Wfg3/AMFfP2wfjn8ZtM8LaV47Frp99qlrpUuuXegaYljZ yXBQKHIsjl/mJ2Lliqs2Aqsw0/hN/wAFSv20fjBD4tXSvFzNeeAtLvdZ8RQN pGhodNtLS4S3nky9uvmbXkQbU3Md3AIBIyfh/wD8Ei/2zfhTrOq3Xh/wg9pF qEL/AGe3m8QabLDpd00ccf2+3QXyol2qJtWUqSoY45wR1X7P3/BNb9sn4AWX jtYfhvZa/d/EbwzeeFdZu9T17TXme3upoZpZkK3qnz90CfPJvBy5ZWJyPu8C qP8Aatd1vYfVuX3L+zvzc27trbl0116nkYjl/s+moe09vze9bmtyW21+1e9+ hqXP7fn7cD3umxaVr/ivxFFq1nZXltPp3w9hMc32mKwkWNPM05C7K2p2EZZA 0bvd25jeRJ4XkTQf2/f27/Elk1zp4+IWoWym3UzW3w7tZYw1xdSWcC7lsSMy XUUsCD+KWJ0GWUqPoDwtYftseH/gzYeG3+Bfhb+3vDGk6bpPhXxHb+IdPjud BSxm8PSR5he8eC4V/wDhHrdnEibvNlLI8cYaB86Xwp+3NZ674W1DRfgv4F8M yeD9bsdd06PT9YtnRZbW51q4ETG41OVmikOvXyuN27aIgrIVYt7ka+WP3fZ4 fR7vl1XfT+vLv5XJit+ap+Pb/P8AA+f9e/4Kfftl+FdCg1XVdd8T6dplzbrd w3dz4JsYYJYmS1dZFdrMKUKX1kwYHGLuA/8ALVMz/C//AIKe/tefGOHxHJ4d 8b/2lH4R0WbxDq7f2NosP2OxheOOSb95Au/DTRjam5ju4BAOPXP2gPgP+2h+ 0Z8KNa8Haz8IdKi0vX9YtdZupZ/HNxrlyk1vF5SeS2razeCBivDSxKkzL+7M nkkxnl/C37HP7bOk69451bWvCF9421j4g+FJ/B2p6h4l8XWepXaWMskMhMcj 324SKYECltygEjaeMdVOpksqLc4UFLp8Pl/wevRGLjjudJOpb59/8ihc/t2/ tqPeabHpeueKPEEOq2dnd20+n+AIjHMbmKxkWJfM09GdlbUrGMsoKM93AY3k SaF5GaF+3b+3F4ksjc2A8fX1urW6GW3+H1tKmZ7p7OAblsiMyXUUsCD+KWJ0 GWUqPe/DNl+2ZoPwesvDr/A/wuNc8N6Vp2leF/EMGv2EdzoSWU3h+SPMLXjw 3Cv/AMI/bs4kTd5shZGSMNC+dJ4c/bZs9c8L32i/BzwR4abwjrdlrmnxafq1 s6LLa3Gs3CxP5+pSs0Uja5eq43biqxBWQqxbjjXyzb2eH0e75dV307/0u+zj i7X5qn4/1v8AgeKeBP8AgoP+2D8RPE2o6Lp/i8xavpWgy+J57LUdK0TTZ306 O1F4Z4kuIYzNm1IuFSIO7xZkVWQFhyMf/BY39o9uvxFH/gg0v/5Gr23SP2Yv 2qrT9qDXvirqPwO8OeJNZ17SLnRZLLXPGVxqNvBDcWP2CZlnk1k3ju1s0sYM ty4QTNsCFYvL8Wl/4I7ftGy3Mjr8NY4Edyyxpr+mlYwTnaM3ROB0GST6k16G HnkDl+9jRWi/k31v122sc81mFvdc9/PbS3z3HR/8Fh/2jW6/ETP/AHANL/8A kap0/wCCwH7RTdfiH2/6AOmf/I1QR/8ABHj9o1Dz8Ov/ACv6Z/8AJNTp/wAE f/2ikxn4eY4/6D2mf/JNdTfDP/Tn/wAlMrZn/f8AxJ0/4K9ftEMefiH/AOUH TP8A5HqaP/grn+0M2M/EL/yhaZ/8j1DH/wAEhv2iAf8Aknv/AJXtM/8Akipo /wDgkZ+0MuM/D0/+D3Tf/kipvw1/05/8lD/hT/v/AIliL/grd+0Jjn4gZ/7g Wm//ACPViL/grT+0E3Xx/wD+UPTf/keqyf8ABJP9oRRz8Pv/ACu6b/8AJFWo /wDgkr+0Ep58Af8Alc03/wCSKV+Gv+nP/kof8Kf9/wDEmj/4KxftAN18ff8A lD03/wCR6mj/AOCrnx+b/mfe3/QE07/5HqGP/gk78f16+Af/ACuad/8AJFTJ /wAEo/j6vXwF/wCVvTv/AJIpX4b/AOnP/kof8Kf9/wDEmj/4KrfHxhz49/8A KJp3/wAj1Mn/AAVQ+PTf8z5/5RNO/wDjFQx/8EqPj4o58B/+VvTv/kip0/4J X/HlevgTv/0GtO/+P0r8N/8ATn/yUP8AhU/v/iWIv+Cpvx3br46/8ounf/GK mj/4KjfHVuvjkf8Agl0//wCMVXj/AOCWvx4Q8+BD/wCDrTv/AI/ViL/glx8d V6+Bx/4OtP8A/j9S3w3/ANOv/JQ/4U/7/wCJNH/wVA+ObDnxx/5RtP8A/jFT x/8ABTn44sefG/8A5RtP/wDjFQR/8Ev/AI5qOfA//lZ0/wD+P1Yj/wCCY3xx Xr4I/wDKzp//AMfpX4c7UfuiH/Cn/f8AxJo/+Cmfxvbr43/8o9h/8YqxD/wU u+Np6+Nc/wDcHsP/AIxVeP8A4JmfG9evgn/ysWH/AMfqeP8A4Jp/G1Ovgk/+ Dew/+P0N8Of9Ov8AyUF/an9/8SxF/wAFKfjU3Xxp/wCUiw/+MVPH/wAFIvjQ 3Xxn2/6BFj/8ZqCH/gmx8as/8iZjH/UXsP8A49U8f/BN340L18Gjp/0F7H/4 9Ut8Of8ATr/yUSWaf3/xJ0/4KN/GZj/yOX/lJsf/AIzU8f8AwUU+MjYz4x/8 pNj/APGagj/4Jx/GZTz4N/8AKtY//Hqnj/4J1fGRf+ZO6f8AUWsf/j1K/Dn/ AE6/8lF/wqf3/wASZP8Agod8YiR/xWP/AJSbH/4zU8f/AAUN+MDYz4v/APKV Zf8AxmoE/wCCeHxiXGfBx/8ABrY//Hqlj/4J7fF8Y/4pH/yq2X/x6lfh3/p1 /wCSh/wq/wB/8SxH/wAFBvi83XxcP/BVZf8Axmp4/wDgoB8XH6+Lf/KXZf8A xmq0f/BP34ur18I/+VSy/wDj1Wov+Cf3xcXr4S/8qll/8epf8Y7/ANOv/JQ/ 4Vf7/wCJLH+3z8Wm6+LP/KXZf/Ganj/by+LDdfFf/lMs/wD41UMf7AvxaQ8+ E/8AyqWX/wAeqeP9g34rr18K4/7idn/8dqW+Hv8Ap1/5KC/tT+/+JPF+3b8V iefFWf8AuGWf/wAaqxF+3T8VG6+Kf/KbZ/8Axqq6fsJ/FVTz4W/8qVn/APHa mj/YZ+Ka9fCx/wDBlaf/AB2lfh7/AKdf+Sh/wq/3/wASxH+3F8UWHPif/wAp tp/8aqaP9tz4nt18T/8AlOtP/jVQQ/sPfFEDnwvj/uJWn/x2p0/Yi+KC9fDH b/oJWn/x2k3w9/06/wDJQ/4Vf7/4k8f7avxNY8+Jv/Kdaf8AxqpY/wBs/wCJ h6+Jv/Kda/8Axqoo/wBin4mqefDWP+4jaf8Ax2p0/Yu+Ja/8y3/5ULX/AOO1 N+Hv+nX/AJKH/Cr/AH/xJ4v2zPiS3XxH/wCU+1/+NVMn7Y3xHbr4iHT/AKB9 r/8AG6gj/Y2+JKdfDf8A5ULX/wCOVNH+x38Rx/zLnb/n/tf/AI5Rfh//AKdf +Sh/wq/3/wASxH+198RW6+If/JC2/wDjdTx/tcfENuviH/yQtv8A43UEX7IH xF/6F3/yftf/AI5ViP8AZG+Iajnw/j/t+tv/AI5U/wDCB/06/wDJRf8ACr/f /Emi/ax+ILdfEH/kjbf/ABurEP7Vnj49dez/ANuNt/8AG6gj/ZN+IC9dA/8A J62/+OVNH+yn4+XroP8A5PW3/wAcpXyD/p3/AOSj/wCFX+/+JYT9qfx43XXe 3/Plb/8Axup4/wBqHx0/XXP/ACSt/wD43VeP9lrx4vXQf/J23/8AjlTx/swe Ok66Ef8AwMt//jlF8g/6d/8Akof8Kv8Af/EnT9pvxwc/8Tv/AMk7f/4ipV/a W8bN/wAxv/yTt/8A4ioo/wBmTxxznRMf9vlv/wDHKnT9mfxsM/8AElH/AIGQ f/F1LeQ/9O//ACUX/Cr/AH/xJI/2j/Grdda/8k4P/iKsx/tGeMn66wP/AAEg /wDiKgj/AGbvGi9dG/8AJuD/AOLqaL9nXxkvXRv/ACbg/wDi6P8AhB/6d/8A kol/av8Af/EsRftC+MG66v8A+SkH/wARU8fx+8XMedW/8lYf/iKrx/s9+MF6 6Of/AAKh/wDi6nj+Afi1euk/+TUP/wAXUv8AsLp7P/yULZr/AH/xJ4/jv4sb rq3/AJKw/wDxFTx/HLxS3XVf/JaH/wCIqGH4EeKz/wAwrHH/AD9Q/wDxdTx/ AvxSnXS//JmH/wCLpf8ACF/07/8AJRWzX+/+JNF8bPE/fU8/9u0P/wATViP4 0eJX66l/5Lxf/E1BH8EvE69dM7/8/EX/AMVU0fwZ8Sr10z/yYi/+KqW8i/6d /wDkoJZr/f8AxJ0+MPiNsZ1Ht/z7xf8AxNTRfFvxC3XUP/IEX/xNQJ8IPES4 /wCJd2/5+Iv/AIqp4vhL4hXGdP8A/I8f/wAVSvkf/Tv/AMlD/hV/v/iTx/FX X266gev/ADwj/wDiami+J+ut1v8A/wAgR/8AxNQxfCrX/wDnwIx/02j/APiq nj+F2uqebEf9/wCP/wCKpXyP/p3/AOShbNenP+JYT4l622M3vb/njH/8TU6f ETWW63n/AJBj/wDiarx/DTWx/wAuX/kaP/4qp0+HmsLjNn3/AOeqf40r5J/0 7/8AJQtm39/8SeLx7qzdbv8A8hJ/hUyeN9Ubrdf+Qk/wqCLwHqy9bQ/9/U/x qePwTqi4za9v+eqf40r5H/07/wDJQtm39/8AEsR+MdSbrc/+Q0/wqeHxXqB6 3Gf+2a/4VDH4M1If8u3f/non+NTx+EtQUDNvj/ga/wCNK+SdPZ/+ShbNv7/4 k8Xia9bGZv8Axxf8K6DwXqMF5HcDUlvp9hXyzbTxwYznO7dE+f0x71z8fhq9 TrD/AOPr/jXL/Gjw98RrzwhLpfgYwaZe6htMurLrMFjeadsdGHkrNp9/BJvU OjeZF8qn5fmIZPLzf+zfqsvqvs+fS3w90d2WrH/WI/Wefl1v8XY9bl1LRU8R aNp32TXcavNcRFxqUI8rybG5u84+zc5+z7OoxvzzjB8H+EP7f/h/4t3Pwxmk +EPxr8MaB8Xtv/CNa7q2qeHWs7rfplzqcW+O2uZriPzLW1lI3QjDbVbaTR4A g+N/g6w8OR6lovhfxde6DNdyy6nq3jMR3moefa3duA4tNEggXYLoY2QjKwgH LMXrxv4IfsgfFb4YyfDRI9Zn8XeHPhBIY/Dejal44037DavDp11pKrJJa+GY biRooLidMNN98AtuK18alVtry/8Akh9R+7/vf+TH0P4B/ao8IaF+y34C8f8A xM1E+G4/FGmWVxf6mbtLPR7Ge4tzMPMlkhdLWIsPLR55QrSPFGGMksaNL8Sf ixZeI9AgTQbfxBp0Etvomo+ZqLPb3UkV5qCJ5T27wQywMEjYOkqhv3hRlUqQ fMk/Z98XXnwH8G/DnxJ8Nvhz498PeFNM0+2lg1bxbcx2WqTwWaxN9os/7Plh niWQs8aTGRdyQzbUljQx9L4b+HHiyRoLfxFpmlaJaTTeGNAsPs/iS68Q3DrF qqgyTz3MEMsj4mTLO0juQxZsnJ3n/Fk/d5dduX5bamMPgj8XNpvzd9d9Drrb 4rX9x8fPGnhloLP+zvDnhfR9bgYBhO813darFIrkkjYFsISoCggs+SQQBmfs 6ftD6r8bvFXjbS7/AMJSeHo/Bd+NMN4+uaffNqU3mTHPkW0ry2wNsLOcLcpE 5F7gIVTe/sOka5pniXxe+g6V4Y8Rajqot3vFhi1mzQtAhiVpf3iqQm6ZVBIB LJJgEKTUsfiHwxJZy3P2LVFt4HkjlkfUo0ELRsVkV90AKMjKysrYKsrA4IIH jxlGVNzVrK6b8/M9epGUXGMlZ7+qdrHlnxK/aT8M/DfxNe+F5vE3gqHxzPo0 uq6P4c1TXY7K81RVjnfeExJN5P7h90scMm1Y5DtbYVrE0f8AaZtNf+OHgzSd N1Tw5qPgzxtpWqXWn63bXizRajdwmxe1t7WdW8uRpLaTUJtgDsy2rspCxOT1 Xib9sX4T+Ef2otM+Dl+2txePNStXuDZC6DJZsIxLHBO4t8RTSw75URuSkeTt 8yESdt4i+IXg7wnJAmoW2rW7XIYxA3wYuFxk/LbnH3h1rX2UoxjKS0krrs07 6ruuxiq0JXUejs/J9infXLWMURXDFpUQ7umCwB/QnrXhnwX/AG3pvjlYWV3p fgifTY21x9C1a21HxFpr3mgyRC1aV7mK0luDEY2neFoJClyk7WyyQxRyyTwe 3x/GjwJeeC9b16ztNcv7Dw+J/tnlXiq4MMe91UPAuTjGOcEnGetbeseKPC3h +y+03tnqVtbh0jaSTVIwql2CLk+RwMsOegrJR+yW5XV1/X9foebfsw/Fa4+N /wCz14O8U6gbCPW9V0qB9ZtbMkRabqKoFvLQoWZo3guRNE8bsXjaNkb5lNeT +Jf+CiXh74c/tAw+DPEV9YW2l6YmoN4o11xa2Gl+Eibhhp66jNNeCaxaWNY0 jM1uIr1ruGS3lCKyH6k03XfDuradBdQabrDxXMayxk6gikqwBGQYAR1HUCn6 xqWjeFvEsei61oWuaRrE8H2qK0n1a3d5YssN4aKJ16q3G7d8pOMc1qqUpXlF aL8P+GIdSKdn1POofiRq+p/AJPF9n4U1X+3p/D41mDwxdOtrfG5Nt5y2EjMM Ry7yIiWGFbOeBXz1+2p/wUBv/gp8B9WfwDJ4d8feIItM1nS7jXtD17Sra38N 69Y20csqSW11cu8kkcLXN19kQTzKLQKyMrmRPq/x58WfBHw10/7Rq9trFujR vJGBfKxk2lQQCLfAOXUfMQOfrUvgj4neCfiNpkl5osOqXttFIIncX6ptbYr4 w1uD91l5/qDWbj9roVzdOpwP7JXjnVPiX+yr8MvEmuXH23XPEHhPStTv7kxL Ebi4ns4pJZCiBUXc7McKABngAYFc14L/AGob7U9K006qnhS1uNZ8U6/4csYp 9TaymvW0/XJ7KKG3iZJPPuGsoJ52UOuXgwFWN3eD2LxJ8VPAXg/WNO0/VJpr C91ZxHaQz6xEjzsWVQFBg5JZ0UDuWA61o614n8LeHtKnvb2z1O2tbZd8ssmp xhUX1/1HrgADkkgCtq2HqQtUnBxUtrp6+nexMK0JtqOtt9dv+HPB7b9sLTFH hi41G68Ji38Sa3H4Yjk07xPbTW0OpG3LPbb5xA8s6XcUtr9ngSSc/IxiUiVI en/ak+NsX7O/whPiu4u9H0+xtNc0WzvrvVZPKtLOzu9WtLS5ndyyhPLgnlcO x2qVBYEAg+naB4w8I+KtKjvdNg1C9tpQCHi1SNgMgHB/cZDYI4OCMik8LeLf Dfi+yae00nXFRb2ex/e6lEnzxXD27t/qcBdyEg5zt5xnisuXmail1/pFc1ve Z8raj/wUButC+Il54YutI0i6tdVtbzWvCPjM69pumeHPEOmrZPfpNGZrmS5m ECbIZ5bWG5VQPtLLBHIkQ9KX9qvQdV8N3h0jV/Bmq63LpVxr2iWUXiRDFrGn rK0cN8JFiaQWrEQ75kikRDIQplG1pPaINY8PXE1zGunaoXtZ2gkC6rE21l9c QdwQw9VZT3rkvG3xR+GJ+Emv6v4utvsHgyD7XpurPrmpWy2UsazvaSRyh4Sj JIwKBT98Oq4y22qcbPka12BTVuboL4C8eaX8SvDVvrOiajpus6PeqJLLUNPu 0u7W9iIyskciZVlIPVSR6E1458S/26tD8H23iTUvDWp+GPiAPAavH4p0TSNU s47vQit9FBJJdXk9zHaWZiiN0/kXTRNKbeTa6rFKR6x8Efj58Gfi0urWvw81 HS9ei0uYXWoR6HrdrMltLdvJMXk8uEgPK/muSfmL+Zn5s1v+Jviv4E8HeKPD uiakL+11jxZcS2uk2bakhmvXigkuJdoFucKkcTEscKCUXO51BFSk5csVr21J 9rFR5nsfLHw+/b01/wAX/tHaZoviHQrDwTp2ieENS1TxTZtrtrqNpp9wk2jS LJLewI0cf2e1vH3p5isGmLyRrbm2u5su1/4Kx6F4b+EHg/UPElpo8Pim9vdE 0/xPZHVobCLw/BdLbRahq8qO0kkFhZX801jI0oAW7t3gd1cEj7YW+0FmIOma sBjIP9px8/h5FYtzq3gTx1dXujz6TPqU2jXVtPPa3N5FKLadGjubeTa1v95X WORHH3XjyCGTidE9V/V9fzsWpbHM/Bz4mQfGDwFB4hs7cw6ff3N2tjKJlmi1 C1juZYoL2KRflkguYo4542XIMcyEEg5rxP4nftu658M/BnibWX8P3k8fhu31 WCOS70Oeyt9QvdGhe7vVWQzMyQXlukq2smxxG1tIzNOGSM/Tmq+N/B2hX0Fr ewapb3N1BNcwxfbTIzxQmMSvhLZtqIZYgzthQZUBILrm9FqXh+eFJE03V2WV FkQjUo8OrAFWB8jkEEEEcHI9acqE4pVGrJ7b27f5/wBIlSXwnxB8Zv8AgqFb 6J8B9Tu5La28Ba5rnwet/Hmg6ve6jA9jDqt/b3jWelIZ0UT3jPbs0UexvOWG b5AIyG9h+E/7amhfFvxjpumWlotrHfwQ2k0j3iTSWGuNBc3M+hTJGGWO+tbe ymkuY3dWi3QgqfNBHu8viPw2muw6XHpXiC51K4ieeK0t7nz55UQgOyolsSyo WTcQCF8xM43Llk+raNeC6t7fTtYs9Qs5FimjvLja1uxVXw8bQowJRlI5HDA8 jqNKScktL/0v67eolWhJuMXd+ux5lBr3ic/HjxnYtFeP4Zs/C+j3Wkk2mIWv 5LrVluwsoUF38qKy3JuOwGNsL5hLZf7Ofjn4i+L/ABT41tfG2jeH9K0zQr4W OjTWP9o/atQTzJpBNMbm2ihP+ivYr/osk6ectzmRSPKT2vwtqF/458c3Gh6V 4Q8Nu9tbG8e5urzUIreOImNEDOodRI7mYKmSdtu7HGQKr2HjzTtQtrh4/DVg ZbS4ntJ4F+1ySpPBK8UsYCTHcyyRsvyZDEfKWBBMKcfZOo/hV03+f/D7WNqk WpRg1rv6p2PK/iV8aX8LeJb3wvY2/iRPEl/pEt5pN2vg3VNU0iCVY52BnuYI 1ttwaL/UPcxSPlFUqZUJxtI+KXiDXfjj4Mu7a18Sp4B8TaTqaSG68P3VtJFf sbCWy+0xyxC4tAII9SGZRFHvZUf948KHe8d/tp6X4D/bU0b4LXHwy1WW/wBT 09ryfW0trwabauyPJAgkMuJo3SG4DTRkpHIiRnLeb5Psk2oWcOg218dD0gi4 uJYBHuuht2LG2c+dznzPT+H34uK9yFRWakrprW6d9dDFVVJuK6Oz9Ujy74s3 /iXTo9H/AOEbsbrUP7Qv003UGhureBtKtZgd+oL5sbiR4CqkRfdYSNkNgCvn b4Q/taftF/EP4YR3F78GdE0zxhbDTPtOn3E+qWMey6trmZ5i1zaR24KlbYGG C5unhMrJO0Uq7K+vJPi34bh1q105tP8AD5v72draK3Sa5eQyLG8jKQJjtwkb HJwOAM5IFN074n2WtfHTQ/h7Y+EbS613XtC1PxFA6yypbx21hcafbzB2a5BD l9St9oCkELJkrgBpjG3u9ynLS5h/D3W9S8QfD7RNS1rTzo2r32nwXV/p/mCT 7DO8atJDu6NtcsuR1xmvFfh58Y/iT4V/ZK8KeNPFunz6vrk+maNNqemjwtqU WqwtNDp4vDJbWcNxO1wkrXz7I7SNBuiRxCsUkzfUWo6pBpWo39pP4c0iO40y 7+w3SebcnypvIguNmRNg/uriFsjj58ZyCBB/wk1l/wBC/pH/AH8uv/j1Gib+ Q4y9239bf0z4+1j41ftFa54z+J/hy18K+G9Og07wr4jbwtqWn2eoPcy6xbyQ HS/NkvYIrEeZBeQnZG9zG8ttc5lURvGv054X8RWvivw/Zanax3yW2oQJcxR3 tnPZXKJIoZRLBMiSxOAeUkRXU5BUEYrpf+Emsv8AoX9I/wC/l1/8eo/4Say/ 6F/SP+/l1/8AHqTaasK7Pn/44/Hn4gfDq7uNJ8K+A7nxZr2o2lvL4dR4p47P ULhbqYX0N1drGbewWG1WCSJrl0Fw8hjj3urLXmnhD9of4o+K/wBsTVNQ0vwn 4jT4Y69B4f0nSDqvh7V7SWSMS6lJfXwt7hIUtJEZJIZVujCzRLZzRmc/Z7W7 +y/+Emsv+hf0j/v5df8Ax6j/AISay/6F/SP+/l1/8epqS3G2+xw3xT8Wf8IJ 4FvtZI1Jo9O8uaRLDSrrVbiVPMUMiW9rHJO5YErmONyoJbadteY3Nz8Z/HWj 3mo2yaN4QmttNt5tItotTa7j1S+kW1uHivUuNKjlt7eN4pbYSRkyvb3UztFB OsXk/Q//AAk1l/0L+kf9/Lr/AOPUf8JNZf8AQv6R/wB/Lr/49UxdhuTeljzr 4WW/iiy8N6XH4ouPtWuTzyXGoCJklgtXkBl+zQyLDFvggZvJjkaNZHSJDIWc sT8w+BP+CgvxOvNe+FFv4q+Gmt6Os2kX+p/E2C08F65ezaKvlWw042Bjjb7Q 8stzEZoIVuZbYO6y+WYJZK+4/wDhJrL/AKF/SP8Av5df/HqP+Emsv+hf0j/v 5df/AB6q5lr/AF/W4X8j5B+CP7SPx88ffC34MavrvgLw3oOq+Nr+8tvFtjqW m6zYT6GkWoDynihWG4KeZYQ3RBu5IImlkt8SYYRP6t+0v8WtU+Dd38P9UtrP xNfaBN4ke38SRaH4eutdufsJ0rUTGTBawTThPti2WXjTg7QTtZgfaP8AhJrL /oX9I/7+XX/x6j/hJrL/AKF/SP8Av5df/HqG03oHM73sfJPhz9oL4t/D7xVr Hhnxx4clOj+Fo1vrPxRp+i6xrupeJNOXULeGN3tNP09bRbuWBpfNiinEqSAy rZC2HPfeL/2i7jwL4Lml1Y31hqGgS6Q2s3beD9Te1uo7qWJTFZRqSLi4lJaF Ybea4khlliVklbCS+8f8JNZf9C/pH/fy6/8Aj1H/AAk1l/0L+kf9/Lr/AOPU cyFd9v61/wA/wRyHgvxZb+NfBOn61ZXMV7aavaLd2lxCpEdxE6745VDc7WQq Rn1rwXxx+0j8QfEXhc6/8LPD+paoJ7u00me18deGtZ0K1tJpIrlPOggj09tR cLcSWTTSSQ/ZvK3hZoyszxfVP/CTWX/Qv6R/38uv/j1H/CTWX/Qv6R/38uv/ AI9RdXuJttWt3PiaP4v/ABw1m4+Neopo2sWmoW3haz0fwo1pol6tjeawsutW 6XFrDd9PMuTZvNJNEEijEQYz2qx38ut40/4KD+IPCfxB+H9tN4E8V2XhjXNc 1Gw164fwZrlxfQWz2d5d6ObO3W2EtxNcQWwluFijlNlteK4WJ8FfsP8A4Say /wChf0j/AL+XX/x6j/hJrL/oX9I/7+XX/wAeo5kVzPscN4Kk8QeI/hNpT67G PDfirUNHhOox2Lx3A0y9eEeaImcPG4jlZtpYOp2jIYdfmj48ftHfH3wJ8CdV 8VeHvhxrj+LDZQa5p+hSWsetRRS/bY7CfSporRVlK/Z5o79HSUyGRZlLiKIx H7Q/4Say/wChf0j/AL+XX/x6j/hJrL/oX9I/7+XX/wAepXXNcSb0PjL9qb9o r4taXqfjPwt4f+H3iS51Gw8UeG18EX2ladqKweJljn0u81OK7uoo2hsrYJJL C088iRSI80Y3NBMD2g/aO8b+K9Re60Xw3qthoeqXnlaFcaj4L1NbiaALbWbT XkMr281lt1G/R9ssQLWel3sq5WRJo/pn/hJrL/oX9I/7+XX/AMeo/wCEmsv+ hf0j/v5df/Hqakv6+X+X4sG2c9rEssOmztDkyhDtAXcSfpXzvH8efizpf7Xf irwXeeD3n8AXupj+wfEMtvfo8EMeh2s88MX2exkt9gu2b99d3UG95ZYoTK8J jj+qf+Emsv8AoX9I/wC/l1/8eo/4Say/6F/SP+/l1/8AHqUWk7/10KjK1tDy /wDZ++I2ofFX4fw61fG6ZLnYImufDN/4cmLLFGsxNnekzopnEzIWC/u2QDeF 86XuK2P+Emsv+hf0j/v5df8Ax6j/AISay/6F/SP+/l1/8epOzehnG6VmY9Fb H/CTWX/Qv6R/38uv/j1H/CTWX/Qv6R/38uv/AI9SsVdmPRWx/wAJNZf9C/pH /fy6/wDj1H/CTWX/AEL+kf8Afy6/+PUWC7Meitj/AISay/6F/SP+/l1/8eo/ 4Say/wChf0j/AL+XX/x6iwXZj0Vsf8JNZf8AQv6R/wB/Lr/49R/wk1l/0L+k f9/Lr/49RYLsx6K2P+Emsv8AoX9I/wC/l1/8eo/4Say/6F/SP+/l1/8AHqLB dmPRWx/wk1l/0L+kf9/Lr/49R/wk1l/0L+kf9/Lr/wCPUWC7Meitj/hJrL/o X9I/7+XX/wAeo/4Say/6F/SP+/l1/wDHqLBdmPRWx/wk1l/0L+kf9/Lr/wCP Uf8ACTWX/Qv6R/38uv8A49RYLsx6K2P+Emsv+hf0j/v5df8Ax6j/AISay/6F /SP+/l1/8eosF2Y9FbH/AAk1l/0L+kf9/Lr/AOPUf8JNZf8AQv6R/wB/Lr/4 9RYLsx6K2P8AhJrL/oX9I/7+XX/x6j/hJrL/AKF/SP8Av5df/HqLBdmPRWx/ wk1l/wBC/pH/AH8uv/j1H/CTWX/Qv6R/38uv/j1FguzHorY/4Say/wChf0j/ AL+XX/x6j/hJrL/oX9I/7+XX/wAeosF2Y9FbH/CTWX/Qv6R/38uv/j1H/CTW X/Qv6R/38uv/AI9RYLsx6K2P+Emsv+hf0j/v5df/AB6j/hJrL/oX9I/7+XX/ AMeosF2Y9FbH/CTWX/Qv6R/38uv/AI9R/wAJNZf9C/pH/fy6/wDj1FguzHor Y/4Say/6F/SP+/l1/wDHqP8AhJrL/oX9I/7+XX/x6iwXZj0Vsf8ACTWX/Qv6 R/38uv8A49R/wk1l/wBC/pH/AH8uv/j1FguzHorpNBuF8TatDY2XhnSZrmfO xfOuFzgEnkzAdAat+KdKufBQt/7U8KaRa/at3lf6TO+7bjP3ZzjqOvrVKF1c XN0OQorux4N1NtDOpf8ACHaR9iFv9q8z7XLny9u7OPtGenPTP48Vyej+PdK1 /wCKmm+CrTQtIm8TavpV7rdpZ7rtfNs7Oa0huZd5lCDZJf2i7SwZvNyoIVyp yMOY5Z28RwfEdh/xJZfCU2ncZ8yPULO+SX/gUc0Esb/9Mnha3/5bif8A0fyT 4yaN8SPEn7K+tab8JdbPhnx1f+LGt7XWBZWt7/ZVs3iMLfXPkXREM3l2RuX8 skM+zCfOVr3+z+JWiX/jHxToEWjaO2r+CtSi0nWoC12PsNzJZWt8iFjLtfNt eWz5Qsv7zbncrBfNvBf7b3wz13x3ZeF9M8N6rbz6vr+p+H7K8l8G+IrPRb/V bM3kl9bw6jJEtlLKjWV8WCTHc0EuNxBppP7hXLn7Qh8Yf8Ke1n/hABnxniH+ x/M8j7L9o8+Pb9r83/ly6/afK/0j7P532f8A0jyqyfhIfGB+Efgc+P8A/kc/ +Eo0r+2PLEH2b7R/bkG77L5X/Ll0+zeb/pH2fyftH7/zav8Aj79pTTvA3xBm 8NTRz32tR6fDrT6d4e+Evi7xW+n2NxcXUFs882mzSxoZHs7lVD7C3kOQoFS/ Hbxdqnhf4MWviFY9KWUXGi63YH+wNQ0e7gIvYZBHcW17cSSo2du6ORI5EZWV hnIFxptWYua53fhb4ieIvhZr+p3eh6No+tJq9tbwS/bNVksWgMLTMCNlvNvD ed3K42981zWhWOvaT4V1OZP7Mt/El/f6lqluq3DyW0E9xdz3ESmQxhiqmVAW 2didpq1L+0BfaZ4r1u21e08N6BoujxwvHqmoWCKmoNIE+SFI7Ry7KWO4KSQF JxgNg1T456mPB0Gpw6bpdtK+rwaXLBdaPbh492oJaS5XYCrDLEBgCDjcoIK1 eLoSrYN0Kq9ySs+7umu/a5nRlGNfni/eX/A8jM8S22qfELx/pOt6n4Q8HaLe 6fAlk+r2+pTalqBtIzK6W6tJbR7AZJGLSbixVpVORIxHPftCfCvXPidHpS6P dWdoLQy+e01zJCzBjHwpRG/unOenHWtD9pj9pzX/AIK+FrnU1h8H6JoNvpdx dXPijVtHju7HS7xbmzhtLWW0t4xcyLdefOPOiyIDb5ZX3AVh/DH9ulr/APYo v/jBriaLqlhoOhXet6j/AGN4ahthMtrE8txFBFcZJaNkkhBaTbI8RYFVYBfC yvDUcvqxy3D87bjzJu8o2b5Uua7SenwXTS1tZnqVsPUqUPrUkkm7aX8/K343 +Rn6P8LdT+FP7NHjzT9Unsbi5urXULvdbSvIiq1oF+ZnVSWyhJ47iui/ae+E erfHP4YDwxpmpW+lQalfW/8AaU0sQlLWiP5kiKhU7mYqoxuTAJO442N4l8Xv +Co/jb4f/t6QfCm0sPh8dLl1vSNMBm0BWvniu0tWlYSBxGHHnvt+TACjINfW Hjv4sN4Q8Faxqt/f6BoVlpdhPe3GpT+HYb2LT440LNO8EarJKsYG4xowZgpU EEg16mX5k3iZOkmpU5cvvR0b20vpJdmroyxmVzjSSnqpRvo3e1lLsuj6abrd Mxvh3ompeGvA2kafq93a3+p2NssNxPbRNFDKwGPkUksF7AFicdSep6PxD4/8 T6r8OdU8Nzyw63HqY3rf6lc7bq1kG0psWOLaQjIrgnB3E8jg15R+xR+2BqX7 SnwxutZudT8Ea/d2d/NZ3NzofhV9PsYmSR1WJPtamSXdCIZ9+Ewt2iMivG2f bvG/i9vCWtauj/2NBY6ZNMGmn020VY442YbnYxgAADJPFdrrVaNR66vXb57H mxhTqwi1tbT8jyf4x/Cm5+JP9mzWmoRWc+lmRljli3JclihClgQU5QfNhsAn 5TVT4D/C3WvhodW/te9tboXxiaIQzySBCu8MTuUAEgqOOoX6VufHT9oG+8M/ s1+NfGHhu48N3k2jeHtR1KwuYtMs7i3aaC3lZTkIQwDpgjPYg16d4R0vwp41 /wCEp+yfE/xfbHwX4kXwlq66l4W0rTWg1NxbGKBBcaShmEovLUxSRb45hPGY 2fcKxhGUo8qZvJpO9jx3xT+z9ofiPxVc6wou4LvU7m0n1EG8uXhu1tpYZYgI fNEKPvt4R5gQtsDr/GTXR+O/CMPjvwjf6TO2xL2Exh8E+W3VWwCM4YA4zziv aPDHwDs/GOnSXel/EnxBfW0V1cWTyRaToDqs0EzwTRn/AIl33kljkRh2ZCO1 YfxZiHwaltrNr6zvILXTzdXWo6lptgkj/vJSXkMcEcahVAHCqMKCcnJO+JrV 5xiqs21Hb8F+SS9EjKlCnFtwja+/9erZ5j4W8G2nh25ur5LS2ttU1VY31Jrc t5U0yg5ZVPAyzMc4BOctk81k/Dj7S3gS/Nibc3i63qskDSEKFcalcEEOVbY4 OMOUcI21tj42nrfAXxxh+JHhe11bSn0O4t7mNGZV06yd7Z2RXMUgVSFkUMMr niug8W+LZPCe+SVtGhtIbOG7mln020RIg8CSuxJjACjJ5PQDk1yLe9zfyPDP gN4J+Iml+IbnU/HFx4dtyqXEENro1yZ4riOSWN4fMzbQFWgCyoD84cTk4j2k SeZ/te/sh65+27+ynP4I0Hxqvgsy+LtRu72Z9P8At9vqVst5fI9rJFvQdZUl VzkxyQROoDqrr9R+EvjPH420yS70ubw9eQRzyW7Nb2djOoKnAO6NWUZUqwBO QrDIB4EP7Geq2f7Tvw70OO58feK7fxzH4O8PeKPEFlH4Q02zsoRq1m08L2s0 +l+XcQl4rlMwyy7GhZHYOpFaw5pS509TOSSjytaHx3+w1/wSX/4YM/a+8a+P PDPxG1nUfBHjLRvsUvhi8tUh2XvnxSi6YweXbsEAuUjVLePy0uWVeNxfoP20 P+CW2h/tnfF3SfEOp+LfEWlacImh1jT4JBK9wqRsIWtXlDra4cqZECMj8sqx yNI8v3B8FPAeh/tB/Dy38U+F/iJ43uNHubm7slN/4T0rS7qKe1upbS4iltrr SYp4njnglQrIinKZ6EE3PiX4Tk+EGm6bbxaidblvZZ5HudR0rTvNVQIgsYEN vGm0HceV3Zc5JAAHXQxmJwtT6xRnyyta68zCph6NaHsakbrseEfDz4HaV8Mv gVpnw90+51KTRNL0caLFNcSI120Qj8vezBAu/HogHtXNfCz9lmD4WfE6bW7X W7+4sVtzDDbswWRyww6ylQFZQQGUADnaSPkBb0/wL+0DafEhb46QbGcabN9m ufN8Opb+VKOsZ82BfnX+JRyuRnGRnpfFfjU+FPD+n38/9lRx3Nt5shbSrZsu Z5IlRFWLcxYqoCjLMzYGSQK4ZVJyblJ6vc6IwSSSW2x594s+Dvh3xh4ktfEF 9pOjXHiDShbDStRm0mCe601Y7gTSbJnBYeYVRAVCvEDI0bo77hr+HrnWL3TC 2upa/wBqCaVXe3uDOLiNZGSKZj5USpJJEscjxogWN3dQ0gUSO7Svj1Pqfjuf RlsdIgjh3RLNNpcKNNMvzGNR5G37m5wGcOVBcIYyshwfjV+2BpnwVtJI79vD x1jyEu7bTrqwtrNtRt/MxcNbyPD5btDErySZZY4V2PcSW8L+eumKx9R0owrT 92NrLtb+vzM6k6dJOpPQ5P4nfA7XvHnx10nxCusyW/h3RrW3C6daXj2V1c3K TTSmTzxG/kgfuQCilzl9rRMqsfS7ee91bxN4g1e+gtraXWr5LlYYbhpxGq2t vBy7IhJJhY9O4q58B/i/cfGNrUXGnaZpsh1Z9LvIEsbS4+zOsoUx+csPlSSx ghJfJaWOKdJoRJIYWNcf40/adFt8eNA+HX2fTo7zV7Y31xKdLhi3Q7LpkWJ0 UHfutXLnPChRg+ZlebLcFTtP6rHWd5yfV2V+r6LocGFweFoTliqSs6ju3d6t nVaZ4k8VeCtbvLzw1qehWg1K3gguk1LS5bwnymlZShjuIdv+ubIIboOnfE0n w3qen+DNTtG1aAa1qU9/eG/gtGjignuriW43JEZCdqPKcAyEkKPm71/K9/w9 i/an/wCjlv2gP/Dh6v8A/JFH/D2L9qf/AKOW/aA/8OHq/wD8kV2Yii69B4aq 7was1bdWt6nfTkoVPaxXvH9Ut+nirxZq1g+v6v4aksLEoPI0nQpLCWaOPLRQ NK1zKfISTbIIwB80ac4BB7C9uYz4L0+EOhljvrpmUHkAx2+Dj0OD+R9K/kq/ 4exftT/9HLftAf8Ahw9X/wDkij/h7F+1P/0ct+0B/wCHD1f/AOSK8/L8lw+C pexwytHfdvt3b7G1XFSqPml/kf1FWHwQsLP4hxeI31bWbi4guZruK2kMAtke RJUPCxByAsrAZY9BknFZnxN+HXxCPx18J/EL4aeN/DHg/XfDmg6v4dnTXfCs uvW97bahcaZcMVWO9tTG6PpkeDucESNwMZr+Yj/h7F+1P/0ct+0B/wCHD1f/ AOSKP+HsX7U//Ry37QH/AIcPV/8A5Irv9i73uZ+06WP6lPhnD8Sk1LX7v4je LfBfim41e6jvYW0DwrcaF5UwgjgkaUS392JN0UFqqhPLC+UxIcuSOs3iv5UN A/4Ka/tdeKrkQ6b+0P8AtFXshOMQ+P8AWG/lcV7V8LvFv/BRL4vbH0j4v/tH fZ35+0T/ABH1OKID13G5xSdBt3bBVbbI/pF3ijeK/AG60b9sjwOhbxn+2N8U vDzDrG3xXvmdMcnI+2egP+TXUeHPih4t0WJD4k/4KJfGBn6yRWfjrxNcyJx0 /cLIh/77pfVvMftfI/dbeKN4r8Nbr9uHQ/CCMsn7YH7ZfiiQcA6frusBG/Gb VYT/AOOVzviP/gp3eLqmjw+HfjH+2lqSfaz/AGgt949v7R5oPJlCrFjUZcP5 3knJx8iuO+DvhsvdafIpW0b+5X/QTrW6H72bxRvFfgnYf8FRtQfVpV1P4r/t i2FqoAjNv8RtSuHznncDqUYGPrXSR/t/eH/FFrmL9qn9tPQZH6G48R6lNGoP qYtYc/kv4Vh9W8x+18j9yN4o3ivwY1f9ofxR4iu3i8Oft7fHJCDwuo+OvEti 4H94+YCgx6eYfrVO48P/ALX3i2wkuvA/7ZfxU8TQqRgRfFi98w56DZ9sD8/7 tH1bzD2vkfvnvFG8V/N78RvFf/BRD4ZSS/2h8Yv2jjDEpcSx/ETVSrqDjIP2 ivGvE/8AwUh/bB8GXZg1T9oP9oyykHUSePtYA/P7Rij6t5h7XyP6p94o3iv5 QP8Ah7F+1P8A9HLftAf+HD1f/wCSKT/h7F+1P/0ct+0B/wCHD1f/AOSKPq3m HtfI/rA3ijeK/k//AOHsX7U//Ry37QH/AIcPV/8A5Io/4exftT/9HLftAf8A hw9X/wDkij6t5h7XyP6wN4o3iv5P/wDh7F+1P/0ct+0B/wCHD1f/AOSK/b// AIImftY/FP4s/wDBt7+1z488U/Erx/4k8beGv+Ey/sjX9V8Q3l5qmleR4Xsp 4Ps91JI0sXlys8ibGG12LDBOaPq3mHtfI/QHeKN4r+T/AP4exftT/wDRy37Q H/hw9X/+SKP+HsX7U/8A0ct+0B/4cPV//kij6t5h7XyP6wN4o3iv5P8A/h7F +1P/ANHLftAf+HD1f/5Io/4exftT/wDRy37QH/hw9X/+SKPq3mHtfI/rA3ij eK/k/wD+HsX7U/8A0ct+0B/4cPV//kij/h7F+1P/ANHLftAf+HD1f/5Io+re Ye18j+sDeKN4r+UGP/gq9+1RK4Vf2lf2gWY9APiFq5J/8mK6zwv+3T+2x4zU Npvx0/aTuEbo58favGp/FrgCj6t5h7XyP6kt4o3iv5svBPxQ/b38a3SRR/Hv 4/Wxfpv+JGrSt+Udwxr3Lwh+zf8A8FEfFVlFcp+0D+0j5EnPmJ4y1kRkezSX SCj6t5h7XyP3b3ijeK/Cu7+EX7a+jXptb39qD4/R3Y5aJvifdxEZ6dbw4pLr 4dftq2ni3TvDq/tKfHeS71qyudSgf/hbVyx8u2e3jfDC6woLXcfBOTgEcKa3 w+XurJwUtlJ/+Apv9CXWt0P3V3ijeK/Dyb9mX9vmXK2/7RP7Q079hb/E28uC 34Jec1xnjP4Xf8FA/CaMz/Hv9peNUGS0/jfVgg/Fbs1h9W8yva+R++28UbxX 823iX4l/t5+HHkDftBfHmVU7j4jazHnr/fmFcNr37Yf7cfhpGa7+OX7RqqvU x/EXVJeP+A3Jo+reYe18j+oLeKN4r+UrUf8AgqL+1no8hS7/AGjP2h7ZhwRL 4/1hP5z1U/4exftT/wDRy37QH/hw9X/+SKPq3mHtfI/rA3ijeK/k/wD+HsX7 U/8A0ct+0B/4cPV//kij/h7F+1P/ANHLftAf+HD1f/5Io+reYe18j+sDeKN4 r+T/AP4exftT/wDRy37QH/hw9X/+SKP+HsX7U/8A0ct+0B/4cPV//kij6t5h 7XyP6wN4o3iv5P8A/h7F+1P/ANHLftAf+HD1f/5Ir9Tv2rv23fjR4c/4NQv2 ZviPp/xd+J1j8QvEHxKvdP1PxRb+Kb6LWdRt1uPEoWGa7WUTSRgW8ACMxAEM fHyLg+reYe1P1y3ijeK/k/8A+HsX7U//AEct+0B/4cPV/wD5Io/4exftT/8A Ry37QH/hw9X/APkij6t5h7XyP6wN4o3iv5P/APh7F+1P/wBHLftAf+HD1f8A +SKP+HsX7U//AEct+0B/4cPV/wD5Io+reYe18j+sDeKN4r+T/wD4exftT/8A Ry37QH/hw9X/APkij/h7F+1P/wBHLftAf+HD1f8A+SKPq3mHtfI/rA3ijeK/ k/8A+HsX7U//AEct+0B/4cPV/wD5Io/4exftT/8ARy37QH/hw9X/APkij6t5 h7XyP6wN4o3iv5SrD/gqN+1nqv8Ax6/tF/tE3P8A1y8fay/8p663Qv20P24f EkCS2nxw/aTaOQZVpPiBq0QP/fdyKPq3mHtfI/qI3ijeK/mZ0v4//t66tdLF H8cv2gV3HG4/E+/YZ/4Ddk/pXpvgHQP+Chfj0f6L8ffj8xz92Hx7r07f+OyY /Wj6t5h7XyP6Gd4o3ivwp0n9nL/goPPkXX7Qf7ScTdt3jfVIFHrzLeL/ACrU tf2eP25l2rN+0p8dQ5OT5nxcuIWXAPGDdnH41vh8vdWTipdG/wDwFN/oS61u h+4W8UbxX4gTfB39r/SdVW01L9qn44WkhAJ/4vVL3HA5uBz+NbEH7P8A+1k7 wo/7WH7QKtctsiK/F+Vg5xnjF1zWH1bzK9r5H7T7xRvFfin4r/Zl/bT0CIOn 7Tn7Sbxkbt6/EuedSPX5b4H9K5ib4Dft13qn7B+0v+0nIR3PjHVpQR/2zvD6 e9H1bzD2vkfunvFG8V/PV490b9vrwGXE/wC0d8e2IBKiXxzr8Bb6bpMfrXlX iP8AaK/by8MoXm+OP7Qs0agndB8StSk6e32rd+lH1bzD2vkf1A+BPFo8E+Kb XU/I+1fZt37vzPL3bkZfvYOPvZ6dq1vil8Vh8S/sH+gmy+xeZ/y383fv2/7I xjb+tfyQ63/wU8/a38NXHlah+0T+0TZy/wB2bx9rCH9Z6of8PYv2p/8Ao5b9 oD/w4er/APyRVqi0uW4vaK97H9gh/aA3eAP7COk/8w/7CZ/tfX935e/bs/HG fx715T4Q0EeFP2pvDfxL837R/wAI94V1rwz/AGdt2ef/AGjd6Rc+f5uTt8v+ ytuzYd3n53Lsw38qP/D2L9qf/o5b9oD/AMOHq/8A8kUf8PYv2p/+jlv2gP8A w4er/wDyRT9lK6d9hc6tax/VFpPgsaZ8Yvin4sN0X/4WX4it9e+yiPB07ytG 0zS/J8zP7zd/Z3m7tqY87Zg7dzeU/Bn9inQvAnjnTvEWqeE/gjoeo+Hbo32j r8MPA114FtTcPbXNq81/bR6lcQahIkF1PHA8yZtxc3Ww/v2x/Nn/AMPYv2p/ +jlv2gP/AA4er/8AyRR/w9i/an/6OW/aA/8ADh6v/wDJFL2Utddw512P65vg D8P4vhh8bvFPxBl1bSp38YeFtD8PHTXvIYms/wCzrzWbrzjIZCWMv9r7dhRd n2fOW34Tzv8A4KN3ol+EGu6gstnLFK1tIPIuY5wpF9E207TkdRgnHt3x/K7/ AMPYv2p/+jlv2gP/AA4er/8AyRX3T/wb9/thfFz9rv8AbM1jwf8AFb4q/Ez4 leFZ9DspW0jxP4qv9VsvMPiPRYjIIp5WUP5csqbgAwWRwDgmqcHa1xJ6n7O6 n8UPD3jTRbfYPEN3YTlLlAfCVxeQTcHy5AJbSRfuu2GXGQ55IIql4h8T2Gt+ HLbTbG38VyywahaajLJN4c1N3kWC6iuZnYi3JLFY3OemTzgZI6D4If8AJFvC H/YEsv8A0Qlc7+xR+0lD8aviv8YvDUvinwR4h1L4ca7faOE0BhHJb2r6UkyC 5iNxMwlSZ7q2aTKK8lnLhEKui87qSa9nd2X9fqaqmleaWrNbX/Glv480O80b T/8AhNbK41OF7ZpLTwdcXkrROpWWPybmynhdXjLoyvEw2s3Q4IpeDvhvaeHP hMPBmp6D8RfFujnTl0mZdb8D3k32u0W2S1WCVI7KOJ4xBGkZBT5lHzFiWJ6/ 9jHxJ8VNW+PFhH458G/D7w5pK207Q3OheM7zWbiSfYQsbQzaXaKqFS5LiQkF VGw7iy/Z1aUk3Hf8vz3M5xip89tbH5Rf8O2PAFr8c28e2OkfGjTb6XxGviqS wg8N372Ul99oW6kYiWxeXbJOGdlEmBvIXYoVV97120n1jWbHVYNL+Kuk6xpC TjT7ux8Naui2ryps81rdrdredkwGQXEUiqwyF65+4q+LPjL/AMFNNJ8U/syf HfU/DfiTTvA+sfDP4eWevvqT6nYz3Xh7Xbz+0Y49FvIJUkghv4bmxihkt3Lu XulTYrbC3p0Mtr46fMveacbt95NJN9W727sVGs8O3Kj7rldO2l7qz+9XT8jl vg18P2+DXhv+yYLP4o6tapJI1uLzwNc262SSXEtw0UUdpp8Eax+dcTOF2fLv 2jCKqr33inxlJrfifUbyHwz8QBFdXUsyBvB2qhgGckZ/0f3r0nW/2vfC/jCX VdG0fxj4b0cHSn1gayfEFilx/YTacJx4j0+N1niuLOKeWKHzZlWDfHOSXEax zefftCf8FStJ/Zr/AGd7zxrqGj6f4puY3ubSzt9E8Q2kdtNeWtkJbq1uLq9+ yxw3Ud0s8H2OMTXjLE0iWzmO4ig0p5Bi6zSjHV2stFvt2X+XUzjUjTioxWh5 /wDHPw3qXxc+CHjLwrY6H4xs77xNod7pNvPeeEdYW3gknt5IleQpas4QMwJ2 qxxnAJ4Poll8M/B0d98U577xX8dtW/4Wh4hs/FYju/AM2zwvqVmlslncWATR 1yYPsOnlVu/tKk2MRcPvm83u/hx+0ZYa/wD8FAfiV8Nl8e6PqjaP4Q0DWbXw 0LqyN3pM8s+pLeMEjUXBUxDS5GEpcILiFl2rMoaj+0N+3jpvwE/ac8EfC+Wy 8OalrHxAksE0+L/hLLO31G1SW8ME802mHN/JEIxuhktILmN5IrgXLWEELXR4 6uDnh5qEt5JP5SSa/rutC3NvU2vgz4+8G/BL4eWnh7Trf4l3yxTXF7d3154G 1f7Tqd5czyXN3dyiKxjiEs9zNNMyxRxxhpCERECoOQ+OfxOt/Gni+C503QfH 1zBHZpEz/wDCF6wmG3ucfNbDsR+deg/srftGR/HTT/Fthf3ugx+LvBvinWdG 1XR7KfF1pVtDql5DpslzAztJE1zYxW9wrPtWVZfMjHlsuPWaxnT5lqEXyvQ+ LbLUm020it7fwp46gt4EWOKKPwZqipGqgAKALbAAAAAHArS1/wAaya1qEc0X hf4gqi2tvD83g7VQS0cKRt0t+mVOPb0r7AoqPq8Svas+LbTUTp8TRweFPHMC M7ylY/BmqqpdmLscC36szMxPckk1Y/Y3+Gmmfs4Xeg6/qnir4w6jrcPw88O+ B9T0KLwLeP4ft/7JgIjktX/siO9P72e9bdLOQwvHyg2QrF9lUVUKfLsxObZ8 6/st3fhz9mD4fah4eHiL41+OI9Q13UdfN54h8BXa3ME1/cvd3MafYtKto/LN zLPKAyFlMzKG2KiI74/fFyx8bnSf7M0Tx9dfZfO8z/iitYTbu2Y+9ajP3T0r 6IoqpQ5lZslSs7nw/wCHNH03wb539j+AvFelfadvnfZPAupQeaVzgtttRnGT jPTJrc8R+J28QaJY2f8AwjXxGQW9k9pMV8JavEx3SyvlGWAMvEgwwIII47Gv sSis/YLuX7RnxN4fSHwvaSW1h4Q8dWlozb1t4fB+rLDEcAEIn2fainGSFABY liMkk0PFPhiw8aavpV7qfhP4j3L6NN9ptoP+Eb1xLRpcoySS26wiGZo3jR4z KjGJ1Dptb5q+6KKmWEhJWlqRNqa5ZpNHxn8OdevPBWrWNxe6V8TdYFtqTXjT XHgy/WZY2uGlWBRHaRpsiUiJCRuKIpkd33SNm3fhzTPFnjDQ9V1Dwl4lXVNB kmk07UNQ8MahZiwMsTRSjz5IUVFdDgqzAMQnVlWvt6uP+PH/ACSnVv8Atj/6 Ojq1S5Pei2i+e+jR/BnRRRWxmFKiGQ4AJP0ro/hj8Kdb+LfiWDTNFsLm8mmY KTFGWCZ9cV+iv7O3/BObwX+zL4UPiv4l3e7VYYxPDYSyLF5XQhpm4MaEHhsg 5FAHyD+zR/wTq+Iv7TcjPo+nG3tVjMjTTKQFHbOcAZ6DJ5r6Pb9hb4IfsgFf +FqeNrfX9ejBL6LoEA1G7Dj+FizRwx9u78HIz0qH9r/9vLWPija23hTwtdWu i+EtMk8y2stKT7PbSSAAM6xgklztBL/xYzTv2Kv2KrX4iXFr4i8XFrqOTDxW Ezkls/xSZ5Lgjjt7UAdB4a/bKsYrCTQvg98KtF8K2xwi61d6NBrGrv8ATcFt 4Mg8lVJBAIIrD+Jmi/GfUNNjv9c8S+LIrOfCytNq1zKrEk4Jh3iFf+AjHsa+ /PCnwQ0rSbWGDTJ7jTI4l2xxxwoI0HsABgVi/Fn9mfXvE+mj7NqMVyocMAMq SM5I9OaAPhD4SfsN6X45lN1q+qwl5mB8xrgzSOMjJ+VkH6V7ld/8Ezbvw7pq y6OLDWYdgYJJZwuW4zgs6sRXvnwz/Zqitrc/b9LawZCOygH6V7Vp+n2+jW0V vAAIogB0z2oA+IPAH/BOtfC/ma140soo7VOYrPTyQ/B/uqqjp2PpXGftE/Db SPB8Gg3XhzQtS0mcalIkc0kzb3As7lhxk4Py54HYjvX1T8Zfilr99rx07RY7 yNVO0+ShBHHfHb0rwP8AaNfWdQPheK7nWS5fU5cKzEup/s+768kjjPWvRyr/ AHj5S/8ASWTPYPhN+zhpeo6OJr+KTUbuaMOzTytKWLYO3B46kjpVzQ/2HLXx 544AvPDGm2umIGxsi2h+hGcADNQ+C/GFlYaWkV1YeIbQx/KdkjEMRj5gWJ47 ivePgV8YdGjRLR9S1eQzEbEvFB2HoACAMV5xR83/ABo/Zw+Hnhfxonh+08JF LuQO4uoLg2yoQcc4ry3Xv2TNQ8ETf2no+vaJY3Kg+VDeH7Rx6fOhO70Ociv0 c8efB3SfiGhmdBDckY8wAENn1rxq9/Y11OLxGkltb6W0CNkykDA5/n/hQB8a 2vxI+MngazWa017x7bG2G5fseqTXenPjubeUmNk/2doX2ru/C/8AwUH0n4ja K+j/ABd+Fngjxatum19R0i3/ALF1rbnHMa/uJGwP7vUkkmvtO+8FaH4T8PfY b69nmvZI/mWFd3IPQcfzrifiT+yn4L+LvhKYX+lRTOik77mJUkHbKsAGB96A Pk3Wf2I/gN+2LNN/wqrxPL4Y1zGW0nxFapp0+48YSSNnikGem7ZwMnk18m/t JfsE+P8A9mnVZYtY0mdrZfmWRVJynZugBBHII617p+0z+zSnwRv7e68P6neX RSctDCG3XFscE7lYDPsM9q9r/Zm/4KFxad4RXwf45ttI1Xw2F/dxapE7WEcr Y3lJN2+2m4+aRWBPSgD8tJIzE5VgVYHBBGCKSv1k+Pv/AATO+Fvx80W41jwX cT6frWoBHhs5CqksQT8kgARlJI2gguwxyTzX5yftDfsteK/2cPF19pevaXe2 pspTExlhZGQ+jgj5T7GgDzav3+/4IF/8qsv7an/c8/8AqI2NfgDX7/f8EC/+ VWX9tT/uef8A1EbGgD8AaKKUKWOACSaAEp8ELXEqois7scBVGST9K9x+AP7C Hir413UY8i7tBIU2Qx2zSzuGYAHaPug9ietfeXw+/wCCdXgz9j7wva63458R aZ4RZo90gniWfUZDz8mXIWJmxjbjPQigD8+/hZ+w/wCOPidBDOLWHSLacgIb 3cs0mTj5YwCxOeO3NfbX7O//AAQfu73w+Nf8SrBBp0SB5L7xHejSdOjGMkhR l34GQNw6EYNd3bf8FIbnw7bTeHP2c/hobbVJjsl8Yayh1DUp16GSKIoEiyp6 5O0qDXkvjf8AZt+IHxZ8b2PiD4xeMfEerXt5Mpe61Gb7UyFmHCFjshyR0VT6 5FAHsGj/AAw/ZT+DGsfYtb8crr1xayBpIfAnheK5iOB91Z5Sh3HIwS4HvXR6 j/wUJ+F3gC3XTfg/+z/Z69LHwNW8e3rXUpPqLS3yAM5wPO6elX/Dn/BOz4bS 6PwbuW8dRi7uLt5Zd3qCTgfQCu4+DnwkuvhRb/2TPaxajZJIxS5RAuQTwCB6 AdSeTQB5FfftX/tG/FqwaPRdTs/BWmsCpTwp4bstDaIf3RLGpnbHHPm5PXgk 15V438VeP9U8S2ui6r4x+LXiDVb1wgj1XxXfrC5IPAAnPoetfeuu+HVjs99l CFbrtC8+/SvL7n4C3/jL4iWOsXssFpBpsokhWSPe7MARknNAHiGjfsVatPpg vtQ8NaHqDOu93fW7qWc8ZOd8Zyfq34159ffDHw83xbs9Ek8NT2n+hXayRi4m yXElrtKFXHQM444+bnOOPvbxTql54e0N7awtY7lgoXG7APHP0r5U8cazrn/D UHhq5vrOGzgj0y9SMwuXJUzWwbdwMclB+Nejln8Z/wCCp/6RIie33fmVtD/Y /wBU0W4Evh+18RaZDIuPNbWZGmBI4+UsQPfkfSuph/Zr+MWgaKt9pvxF8U2A hG3yrfxRqls+fU7JdoPAx8tfRfhLVYX0eDfcW6swGAZFB/WuysVSSzILK4kB DgHg+lecWfI/g/XP2lvCpWSHx/da1bp832fxBZ2HiPcPRmuoNz+vLevrWXrn 7WHjzwpr5f4m/BT4feKtII2yy6bpf/COXO0ZyRJAsqAnjnbjrx2r6q/4RWHR L954twUNu2jgD/OKwfHnjOT7K8VlplxfzDhQiE5P1xigD5Y1747fsi/FewVN W0D4l/DzV2YqY5rC017TI/QmQusmPbyc9OtZqf8ABLT4aftNadLL8O/E/wAO PFchG9oNMvW0nU4lPT9067GbJxjHXPWvVdZ/ZN8P/EW4u9V8b21jYm5QKIGU RiEDowxghiOpzXzL8b/2K/CN5rotvBWuf2vczN5clqP9K8kYOCHU70yR6nGO lAHmfxu/4JCeKfho0/2ea9tjHIVC6patGnGPl8xAynrweh/OvmX4nfA3xL8I 73ytZ06WKM/cuIxvgkGcZDjj86+5/hZ8dP2jP2IbhJbPUNe1LwvB+4n0jW4T qGmzRZJ2nfnYcOwDdV3H0r2nwz+0r8H/ANr/AFqKDXtMtfgj4p1hCXZp0vtD 1RzwQoYKUycIAcnqQaAPyBor9HP2vf8AgkDLpv2jV9GitrSCPrqOkL9p0q6Y 5wRtJ8vPQDPYmvg/4o/BPxF8INS+z61p88EbE+XcBCYZRnGVboaAOTr9fv2y P+VNv9k//sqt9/6U+K6/IGv1+/bI/wCVNv8AZP8A+yq33/pT4roA/IGiiigA ortvh3+z94o+JbxPY6bNHZSEA3k6lIBk4A3HgkkHA719o/Aj/gkHFoOhx+KP iLqFhoehZDpqfiGcaTph9Ngchp84OACMkEYoA+D/AAj4C1jx3qC2ukafdX8z HGIkJA+p6CvfPgx/wTE8ffFfWre02LHLOQPs1pE93cr9VUbR/wB9V9n6b+0F 8B/2dNW/s3Q/Deq/HXULXP2dLaT+xfD8DDuEVJHmQHDZOO43DrWH8V/+Ci3x y+MOlz6dYeKNJ+EPgqRTGNF8ERrpjLGf4JLhczycEg7n5Bwc0AT+DP8Ag3t1 Hwppran40ubLw/Ywkk3firWItJtsA8/u0Du2B6NU8nwO/ZP+COoCw174gad4 gmgP72DwR4WGquuOoE1wVBORj7w5+tfON34I8OXcpvr/AFu9127C7Va6kaec gdBukLD6DbXt3w6/Ztm1rwyl0k1jDbMN8UCx/aWUcdFZhGD9EoA6yP8AbK/Z R+HQNr4V+Dfxb8a3icIdR1K30aFz2ykH2jGaS0/4KG+J2dn8C/sx/CfQbVj8 lz4na61icD/eaSJMnkf6vvWTo/h/TPB0/wBn8RX3ie0tBwC8Qgt8Z7Mq5H4E Vo+KdJ8NvpUdx4StbDVZYz+9YyGZsHA5ZsnPNAEfiL9sH9pLx7F5A8T/AAl8 AWn/ADy8OeFbC3uIh3xKLfzAf+2n41yEGv8AxD1LUpJ/EX7QHxa1SQks6wa3 cW0a+3+uIH5Y9q1pPijY6r4Lls10yFfEEA2G3RAWU4OeMfSsr4Oa9DqM01h4 jhtorlhwl2PKOMdgRz+FAGXe3unXOol9U+IPxK1yXkbbrxDcToQeufX863rX TvAU6xPP4ejvygGZGvrlpm923TYJ+nFT+O/gJpl3CLiw1/TNPkzlYtobcewB 3Z//AF1zMvgvxZprx+VYnVLFR8zqjDePYV6OWfxn/gqf+kSInt935nUaf4b+ Gt3MTB4Y00sepmgy4/Esf50/UNI+H9pKqPoMKsp48mEgJ75DDFcd48+HF1o9 gL6ZbTTn2Ai2eXDE454PP6V55DBrttaS3uDFBGSQWHBwf1rziz3qLwX4E1sh Yr7VdPkXBxDqdzFj8A2KdH8PbG3lZdF+JnjnTbnkKbfxDeRkD8HHSvBNG8U6 jq92I7xphB0G0FFrsNP0S8tyGTTr1kIz5guAFA9emaAPT4v+F9+B3W68MfG7 x7NDFzHFqGrS38DD0KSl1I+tT3H7eXxh8PFE+Jfg/wAE+PdPtvlNxBpsdneE H+L92gQngnkZ9xXn8XiTV9MWI6fezQ+Ucb2l2Jj0B75rq7P9o/U9Lgjh12LT b63IwZHxvXn+8MH8Tn+lAGkvxn/Z2/aWjktdRsLzwbrszYaDVIIp7R2PoHJK emA3fNcj8V/+CVfhrxjocuo+FbvSJooV+abS5xgYwDmMZHX0NWvGejfDb41R ul0LSwnfJRyVcA+pYYzz6ivIPFn7JHif4YSjxD8O9akuUtwGdbQ5zj1AJHX1 FAHg3xg/ZK8U/CW5naWGO+tIST5kBJZV/wBpSAfyry5lKMQQQRwQeor9DPhl +3XpvjnRk8F/FXQotK1KJ9lnriKIJbSQ8b3Xb842jgEgZNec/tW/sSfarc69 oAglS5+aG4tQHt73jIXI4D4OTz3oA+OKKsarpNzol9JbXcMkE8TFWR1KkY9j VegAr9Iv+DXP/lInqX/YAsf/AFKNBr83a/Rb/g2WvJdO/bw8R3ED+XNb+GLW RGwDhh4l0Ijg8dQOtJ7DW5/QF8ETj4LeEP8AsCWX/ohK6H4PfEjS/Gfiu4tL Iamlyvhw6w0d5pd1ZMlvd212IdwmjTbLm3lDwnEsWF8xE3Lnnvgh/wAkW8If 9gSy/wDRCVF+yX8Abn4Fa14na48e+M/HL+Ixe6jNJ4hj0wSwTG1n3sr2dnbs 4K+WgEpkEccEMcflogWvPfxO51fZNz9jD9rP4V/Hj48WOkeBviZ8PvGerWtt PeTWWheIrPUbiKBUKNK0cMjMEDSIpYjALqOpFfZtfOXwG/5KvpX/AG2/9EvX 0bXVh/hZhV+IDXBfB34n+FPio819pf8AZlr4lewgfWNLN3Z3Gq6UguLuBYLv 7NLKn7u6g1CH5ZHj823uVRmKvXe15d8Ef2ZU+DnjbVfEt34z8XeNfEGtaLYa Fd32uJp8bTQ2d5qd3C+yytbeMSZ1SaMkKFMcMPy7xJJJ0J2vqZuxoTftX/Cy 3+OKfDB/iR4AT4lv9zwk3iGzGuMDB9o4svM88jyP3v3P9X833ea1PjL8XdN+ CPhOy1jVLS/vLa+1zSNARLNEaRZ9S1K206FzvZR5aS3UbOc7giuVV2wjdUIg WzkkV5p+1b+zgf2o/hzYeHv+E28X+BEsNb0/XhfeHI9Oe6mmsbhLu2RhfWl1 H5a3MUEpCxhmMKqWKM6PKvpf+lcFud9b+JtNuvEt3o8V/YyavYW0N5dWSTqb m3gmaVIZXjB3KkjQTKrEYYwyAZ2NjQxWHongpdF8U6xq7alrN7Pq628Zhubt mtbOOFSFWCAYjjLM8ju4XzHLgM5SKFI9yhCCiiimMKKKKACiiigAooooAKKK ajhxkcigB1FFFABXIfHj/klOrf8AbH/0dHXX1yHx4/5JTq3/AGx/9HR1MtmO O6P4Mq6X4VfDS++KvjC10mwjLyzOAwHBArnIY/NlVc43EDNfe3/BH/4P22of Ee31GYxrNZslzDM0eAxJxt57D1qhH01+zX+zHon7HPgPTpLe0STxncqJTOv3 LVGUMsik/ebqCCMV8p/tdfGXWv2jviYvgjw3eOlg0v8Apd0TsWdmyGWTjG0M K+s/2qvjDL4Z8BeJ/EqbRqGmW7JAGfIkKNtAH4V8kfsOMPEbz6tJoYvZ7++m e6uZbI3aKhkJC4HoSaAPoX9lr9h3wj4F8KrJry6ReahdIBLcy3bPvXqFCngY +levWH7LXhb7du0PXrDTIXPEVvM0JPp91hmuX8PeKfAMjm3vrfw/eTxjJt4d JDSJ7kZyv/1q9H8Gah4KtrqP7L4Ums0kPyulkAuT6gUAW9K/Zl1HS5I3s/E8 sSAhuGaXd+LZP616z4T0y48O6aIJ9QlvpAAC8gx+lR20Ntb2qmJVtoyOybQv 4Vzmo+F5dav/ADo9buFjDZaKLKg+xIP86AO0knEZ+YjnoB1rL8Y+N9L8D6RL eavewadYxLvlmuG2RqvqSaiSf7JYxqGBIB5LZNeffHSJPFuky2V9FDe6ddJ5 Utu4LIw75BoA7WPV9O8f+H5JdMurXU7adPkkgn3Rt7blNfJn7VXwq8RadrHh ltI0Ez3Vzq8sdtHDL5vmuLG7bHJznarHp/DXjHx78Pap+y5d2+reDfEeraAl 9MyG2t7l404G48A4I9sYFVvAH7fPjbUvEfho60660NC1Fr2IyfLK7NZXUGC3 U/LMx/CvRyr/AHj5S/8ASWTPY+tfhh8LfEV9o1umseF7eEFRv+03K+YM4yf3 ZJ/OvS9C+AvhzR70Xcdq8ZjYEfvSIwcj8fzr8/PHP/BSn4k6zqMy2d5BpMas yhYY8tjp1PevUvgHZa18TdH0/wATeKfE+ua7d3IWeK3mu3WG3ORjgHBOQD0r zij7ztJEkiAChv8AaCggCrNrdqJCF2smMEdP0rz34aeKdSvYJftAaWM4yxUD PHrjmuujuCyDKkevHH50Aa/mhX3Kqhv7zcmuJ8f+BPEnie5Y2evWttakf6to zn36CuliukxzKQPQGodb1t9K02WeCCW6kjXKop+Z/pQB4/q/7ItxrUzS6vqc V9Dj7iRnLewyABmvmH9rf9hGwi8M6hd+H7TVNOvRGSyvJI6XOOi4yR78V9Ja 74k1vxrrksS+EvEYYHImlvmSE/TBz/47WF41+HGvvpAbUNYuvDMRY7VtriS6 mf2A/wDrUAfFn7I/7UWs/A7xfZeC/FNyV0jzX8t/vzxzOwEeGPIAP5V9t/FP 4Wab+1L4On8O65aQXGp28TfYr2RCC6AElX2/NI5JGGYHHrXwh+2D4WtvCMU1 7pUWqXF5b3EMnn3Vo8ErkMSWwwzjIzn8q+u/2RPiXd+OPgf4R1JZ3fUPs8cl 1IHPmK24nr16UAfmH+1n+zbqH7NnxKuNJuoZoraR2MCyffQAgYYdjX7V/wDB Av8A5VZf21P+55/9RGxr8+P+CzPhu41TxPDr4SSZbhiXlYF2fLdd1foP/wAE C/8AlVl/bU/7nn/1EbGgD8Aa+lv+Cfn7I+ofHj4hafJFa/aHlnCW6Ntwrf3j u4Ir510DR5PEGs21lEQJLlwi1+0v/BOz4Rr+zZ+z2/ix7ixsNXkhI0oyRZeS dSpPb0PagD0S++I/hf8A4J8+B5dH8O/2a+rmzeTUNQudspkzHuMMZ/hZGHY1 +cXi/wAY+Lv23vidL4i1me7g0GJi1ksjfvZUOGDMSctgg/N2rb/4KG+PL/x3 8YtK8IRXAikW7ivbh1OxZDccN+HJr6d+EfwT0v4WfDizsZ47aQW0QTIh3lhz 0FAHGfsm+NpPg/Pc2q6dBqVsZR506KrzBguBk5y/1r6Q1P4teEvH+iyW+qi7 tzKu0l7GRShI6hivvXn/AIb+F3hnW9QKWPh3UmuJfmMtuRAfr94V6HoX7L1n bTxzz6rrcCsMtay3Ctj2yDz/APXoAyvBngDxB4X0dI/DXiCz1nS0A8lL5SZY l5+XeCpOeeWBNek+C9Q1p7Y/2rZLaY/iEgcN7jGeKs6fbaH4IsPKie2tkAyx d8M2PXNS2niGx15N1jdwXKrwRG2dtAFme/baxG7aB7Vg+K7zV20vGjLbm7bI /etgL6YqxLdeTJuIDLnJAPNfFHxr/aC+OXwt8fXE32eDUtKMreULayM0Spkk AgDcpxjJIxmgD6OvPBvjy7lkub7XpbSJFLMkO3G0c47189/GO8k8VfF3Q7Sb U9Sg2adekXERVJf9dZnGcH+6O3atDRv+ChPiCXRll8R+ANfERGHlsZGXPGMh WximjW/CfxH+KvhnWruw8XaNDf6HqF3DFeNHHJcsJrLyxEAxBBHmjHGSF9K9 DLP40v8ABU/9IkRU2+a/M77wF8KNR8WaYrWur6nqZtyMpdMVOev3wBmvffht BqGiaALbVojbPBhU3TrJuGOvBNfLFz+3Va+B7mfTdF8G+Obi7sCYniuGWFI2 HPzKrkg49a4aw/bm+K/xQ+I9vpWi6VBppaUedbvbFmVQRncz4IwCa88s/QCH UZYxxyrDPTBqn4hu7qXSphY4NwV4Utgk1l+HNTvrjw7ZnUfI+2mL9/5BIQNk 9BjitE6hDY2pmmuIoI0GWd3ChR7mgD59+JH9qS6i8WsQzwRucE7TLnr6ZA7V V0b4NaZq2mxiwtNWS4Vi6zQKYSrHvnGK+ibHxToviQ7Ibu0uyvByMj9RUeqv pN+Ehi1exs5FOCqTKp+lAHnfh3RLy0s2t/EenwPpwjKM9xIhdhyBkAkkke1f L37Xf7F3g3x9Z32peDZDY6mpMpt432wysBndt7MMcYIHtX174o+HOsagSumj S3Rz/rZJSWIx16HPevMtf+FtvYak6anrP+lAlSlvAyJnpjJUZ7UAfMf7AH7a Wt/D3xE/w18ZypIsCmK3W9dhHtVQPKfBG5mJwCcmvqn9oz9jbw98cPgfqPiH w9pwW3th5eraczAfYZ2Viph3fMY1UZJGRmviv/goL8BoPhjqGneOdOuGP2a5 jaQ7QrO5kBByOv3a+3P2Cvjjp3iH4baR4j8QWv2xfEOjTWc0W0PmedWjRiPY 0Afjh8dPgvP8I9dWPeJrCZnSGXIOSGbj8gD+NfqF+2R/ypt/sn/9lVvv/Snx XXzT/wAFOvgh/wAIbYyWYVVOjPLMcqA332Tt/u19O/ta2Emqf8GdX7JVvFjz JvivfKMnA/4+fFdAH5GeFfCOo+N9bh07SrWS8vbg7Y40wCx/HivsT9m7/gm+ lt4VPjbxjLDZ6DpO6a/vbuVFsYFUjcFX79w6kjdHFlh1xXtP7BH7E3hzwT8M db8ceLZo7XQvDGnpqepSJskmuYiwBWNCRuYbgcZrx/8AaK/aY8R/tV+K7HTV ujZ+D9Pumi0bRkGy0tyoKfaGhU7Q8iAbjjJNAHpmj/tx6F8MvEMUPwm8DweJ 9biiMEGseIoHmW2AAHmxW6MijBJ2s4IURoT1NcR8QNM+If7WHjT+1fGPiDXf HXiPcTKrXKva2OcFo48YhjU4B2xgD6GsTwv4RljnS00wx6bG3yz3lzuTj+IK 6gkKSW9sY9K+hvhR4Ds/hpYRXdmuozF1/fSWFytzBI3Tdt4bp3xQBy/wx/ZF g0vTHuvF7ixt4xvNlaShFYAdXZcliPc960p/2bPA/j6KPUPDM0yrbOGMbsxh cdf4xnnHrXTeL/tfxhuBYRzavpOlDmZ3tpYZJSOflLAcEcVciNl4J0608M2i 3N1uhKRsjZCKMglmzweelADvB+jaZpMc+lwaJpVrqKDzD+4VkAY8ZKjn86fe eKbzwLcrHcS+GNNtZWJWTJQZ5JwCev8AntWZoPh1/BltLPdalKkIXJjR8IvP XJ5rnNW+JVtJr8aaf4fvr2+mPlLcXUHyH0yzElV98d6AOwsviBY63ay28lw+ uRycEQafIYm56bvun8TVT4ZaPH4EsLprmzS0SaV2VHKthS2R0PHHbtXNW+na 5Zzy+Vc6hqWqXP8AqtKsGfbk8hRjofc8cV3/AIT+Anj3XIlmv9ETTnYAj7dd xu65HT5Wcj9D9KAOX17SdP17XUvrDRbESLlmvpcxgE+gXBbOOvTisjxF4Y8P +LtVSPWWm1ORUP7uBHWFPxX39TXoviv9nWXTow/iLxrpeiRHllN8R+QYKD+V Z9toPwh8HRxQyeML/U5l4H2ESYbn1RSD270AebaZ8M7X4e+I21bTVtls/NEa 2twWITdgDk+ldt4t8X6sbaG1t7qK1uLhDsaGJdkZz33ZrtbX4eeCPGUQay03 xrewg7hKSY09jmQgGsrx14Y+Gnhm5D65rfiDTmiQthr5CAM85VSc/SvQyv8A jP8AwT/9IkRU2+a/M838QfCDS9d1W3m1O+8QXV84DNN5pIQ49xjHWqniT4N6 JfXsK3mp61eJDhhbGQMo7ZIVBivZPhNe/CD4navHY6J4w1G91DOxLe4upIWk PQBVdQG6dBmus+Iv7MMLaTPe+HJZo9QjTDRzSb1mHYDjivPLPnqz8Nnf9lTT IodLChN7FTuHcEA5/OuZ01IZNQksn8TLbwsxUW4CNJjJ4HBPGf1HpV7xf8QY fAjSWurgzFXZDnLJuB5BB9PpWT4R+KmnXfiVbvy9NtY0RhthjAkI4AHT2/Wg DurXwZY+HrG3+xW80w4DSS2EsrHnuFXb+ldPHaaFqcIS80pLhAMbZdKkIP4F KxfBfxVtda8RTRFrq3VjmI3IKK4x/CT/APWruzqosZI4VkE11LzHFvHTpnnt 60AUPDvhPw7bWksdhodnbQTAlwNOaFScHsygVz/iX4dDS5Y5/CsNybsf8sbU D7O2BwGzwPzrutaSLw/piXusF7gM6p5VurSIpY44AGT+VJcfE3RobRLezlni voWLxWr2clvJMT2VHUE+vAoA+Vf2nvg+PGdkH1nRm0fWEB8qZNpRzj+Irn37 1558EP2i9Y/Zy1Kbw/4ijbUvDd8fLnhuXLiFM5LIQeM4FfoRfT+G/iF4ZltN Usr+xmkjwwu4NgfOOFJr5a/ac/ZWbxXZ/wBoeHtOEthYEsoldUWRhwQQTkig DnP2gf2QdL+M/hS18WaBJJe6dckLFeRFRKjfLuikU/wrnAOOSe9fFnxJ+Gmo /DHXWsr+M7ST5Mw+5Mo7ivo34V/FfXf2cb2W5027mudDuy0FzaZLNG4JJ3Kf 4d+MH2r0H4t+AtG/a3+GVx4i0WKyhZSC6KNskEhBIRAP4eBmgD4Pr9Ef+DaL /k+PxT/2Klt/6kmhV+fniXw7deE9cudOvU8u6tXKSKORn2r9A/8Ag2i/5Pj8 U/8AYqW3/qSaFUy2Gtz+gf4If8kW8If9gSy/9EJVD9l/T9dh+Mni43eg+L9C 8O6T4fj8PaQde19NRbVfskWp772NFubhgJY5rfM9w63M2wCWNTEpa/8ABD/k i3hD/sCWX/ohK6n4S+IL3WvFOtR3enx2MNpb3kdmwufNkuYfsko82RNoERMg k2qGfKBGJVmaNOH7TOr7JmfsY/BjxF8PfjvYXusfFn4g+PLWW3ngXT9ds9Dh t4XKFhMpsdOtpd4ClQGkKYkbKk7Sv2dXxj+xj8ZfEfxE+O1jZ6v8JfiD4Eto baedb/XbzQ5raZxGVEKix1G5lDkMWBaMJiNssDtDfZ1dNDZmFXcK+coZde8I fETx74u8NfAnW9M1mfWoE169SPw+uq/EXT4YLi2t2sp11FfmhcW8g/tFoCtu 8iInmHan0bRXo4bFOjzJJPm0d7979Gv61WplJXt954T4Zl+KPhn9q7TNJ1S3 8b+Ivh1L4NsbZdZQ6DHYQ6wk1695cX67or0zPFHYIgtITbbriU+WgG6O7+3X qXjOw+D2j/8ACCfD/VfiNrK+LtAu59NsRo5eCytNUtr27mxqlxBAHEFtIkTI xlSeSB08vYZo/aa8H/bZ8ZL4RTwv/oN3qEt416kcVvLBGxMdv5/WaSMfMIto wTywJ2oHdccXifaNT5UrW2Vr26/5lU4a2Ojs/ht4s+GnifxV4yk8f/E/4h2k 1pd3Fh4Elt/DsNrDIWEscFnKtnazeYAvkxm7vjHiQmRyQJF5D9hjwr4w8Afs a/s/eGPEPhbxb4N1zwlolj4f17SftGj3CW/2PS5bYtcvFPOrWryxRvGbSQz7 3tvMVI/tCL4b4a+Juoa1+0d4r8JvHZjR9G8M6JrVq6xuLlpr261aKUOxbaUC WMBUBQQWkyWyoSfxt8Udc8K+PNK0jT/hx4z8TadqPlCfXNNutIjsNM3yFG85 bm+huW2KBI3kwyZUjbubKDiVfpbe34X/AMzV0dNz1Xwb49+Nd78Q5LvU/BPx Ps/C2tDQo7W0mn8KNfaBOdY1ptSkuTFdMrWosl0lG8p55vJkj8oG5Fwyz/AT xx8cjq/wa0Dx74N8WoG8EWF7408SW8+gSWZ8QmzlS6s7tEulmiRJUDg2FtMk stxBiWCGCZZvPfFPjqx8H6noVndi4a58SXz6bYJFEX8ydbW4uirHog8q2lO5 iBkAZywB8t+FP7ePhL40az4VsPD+gfEW8l8VWtpei5HhS8bTNKhutKXU4mut QVDZoTFJBHtSZ282eNdu0lwLEaN2/qzB0r6n2H8MfCfjS1tPGkdrqnhfTfEk 2ua097fXPhK5Md9PLLC+jTs6y2ou0ttK+y2sxQuZHhEYuI2tpFbkfGnij4+6 hH4w0zQtEvdM8SaHJd3mia5cWelX/hLXbR9Rhkt7ZLYX8OpNfx6crRYmeztm uXnJm8vyHX5/0z9ot/Fv7JvhP4hW9suj3njzTtFaxhMR1KLTrrVntoLfzVV4 Wmhimu4zJtaNmRGI2nGMv4m/trad8O7Px/C3hXxUuteAfDOseJpLbUrYafaa lFYKjKLe5YstysyyRvvtlnFuHVbkQTOkDjqtPltt+iGqT3PVbbw78YvE037L 3iTxx8N/EvjHxr4b8da3q/iTVIdP8O6LL4S066s9WsIomgGs3I8oLf2ZC2tz dyPBYs8pNxtifU8D+EvEPhaD9qu31D9nrxTqnh/xz4yGpafoyS+GpIvHdnca Vpul3jpDJqIhAd7K4nlW+MBkhnTKvK0kKqoLDoQeuD/n6fnXD6R+0DomteMo tGhttZJuNQOkQXZtP9GlvU+3NPbZzuRoo7CSRmkVUZJ7co7mUCm8Rq9P60/y Eqdup7B4j8VfEfwDqPwq0P4dfBzxTofgbSbJb7WdMsn8NW1vptimi6mkOgxR fbgEuo79dLUGD/RQuMXRjEoFeCz+KXiz4EfEePxvY6mNMk1jRbi30/X9L07W Lq/8Mrpmiya5YSWmlpOtxcTOuuQeWqOXmlxCTCYCPBdT+OHiKx+L3hiwi0OS XSfEPgTWPEjaIIYRriX1ncaYiWy3H2r7Id637xkbtnmRqwn2EmrfhD9pCz+M GiNN4Xi1nSXXxJBoZk17w5dQyYWKK8uPNtGaK4sd9sZI4pL1YP3jQyLHNHLb i4art68v9Xt+a+4PY6LyPtu/8EW3hXxn4k8b2lpq2q61qOiWunPYWskKm4Sy kvJokh8xkUSu15IuZJAnEeSmGY+K/Dd/jFpmgfCXSU8N61oMVtpOl2Xii3+z 6JHY2T20m2cwSQXLGESIjZjjtZ0eJoI4zZSeZKnmGjfEb+1vjj4j8KBwP7E0 TS9WCfZNuDdz6jEG87zTvH+hf6vyk2bc75PM2xT/AAS8cXPxR+DfhLxLd21p ZXXiHRrTU5re1vEvLeB5YUkZI50JSZAzECRCVcAMCQQa6aeZyjF80FLbfWys 0l/XkbwlKK1s/X0seh6Zc/tCRa+x1kX13bWd7FFcx6Lpel2MV5bpp+qNJJaP cahcSSNJdHTlHnrbAbYzsVWnI9E+ENx4+bS/Btv4sj1mTV5NCs31+eNrEWkF +lri5SQJht0ssgZTb7owbfrGvEvyB4X/AG4vh/4v1PxTHZXOu/YPCVtpt1Pq U2i3UFpqaahcXNtaGwLoGv0lktiI5LVZY5jNEIXlLYHbeHvGl94j8WadD9km 0m3bRF1C/wBOvtPZrmKaZ08hFu4pWtWaIR3KzRxGYkyQPvjQp529XN5TTvSi r9lb+tDWdZyTvFfd5pHvHw4HxB8Lfs8eFdOvLfVYvGOk2tvoOoS6iYtTGpTe UkDamGS6UtCsxFwfMkSZ4UlBjWVkxtfGH4a2Oj23ivxYk10+p61pWl6RcKzL 5QitLu4ljZQBkMWvJdxJIwqYAwSfjfwv+0LrWo/BaXxLdWejS3sXj278KiIT iygFrH4pk0dJN0rndMtuqybAczSjYiqZFQevHgjPeuevmUqielua97db2dvT YzqTlLXa+/3n8ZNlp88t1EoicbnAB2n161+tX/BIn4Wpov7KfjfXrgMl9Bpe +3mD9D5q4/T3r8tPg9on/CRePbOBm4TMoBGQSvNfr3+wLFOn7LF7o9gsxur4 SQXACHBjDLjkHjp6V3KeC6xn96/+RPP948C/4Kaa2tv8MtAs47hoW1W8limO 7H/LMHJ+pr6N/Y0+G+h/Cn4W2DW9k03+jJcqgmXEkjBSSSccE18jf8FVFb+w /D1qxZTBeOBkcAiPmvef2ZBd6/8ACHTlu9EuLj9wqBZrllR1CrjGDmnz4H+W f/gS/wDkQ97yPdtR8G6V421pr8eEdOkujxui1VEk7jn1rasNcu/hwDCPD1wo l52Pqcbt+AxXnemT2vhEBU8IQROveO5mbn8ZK0dY+LFnrNukOtaHNBaxD93L EjAwjPTOT35o58D/ACz/APAl/wDIh73kaPxF+LqXMsbT6HqoePqiy4UYPfjF bnwh+I83iy4x9ltbaCJSqxo53jjPPWvO5pY/EukPHomrLfROMxxXURUnrgZH P45/OrvwQ8C+L9M1rzrg29hZrkPA0LMD/uyE5/8AHe9HPgf5Z/8AgS/+RD3v I9l1PU5EkYbWOPbFY3ijS7XWdLJkuTbyLyG2kgn9K5H49ftK+Evge0S67dyG 5mRpEtbZQ8jqoOeCQOoxyepFfPL/ALfuq/EXxFHbaFpCQWEr7Y0aAz3dyf7q qDtB79T9KOfA/wAs/wDwJf8AyIe95He/Gb9m7QvHEgudV1AT28A3KrQY2HGC c5/pXkOifAf4c/CXWPCmp6pqN0I9U12e3uUuWCrDAtld7WwF4BlaEAn1r3Dw p8KfHXju9F94nlsLTT1VZYtNViGus/wysF+QYOflBINebftCfsQax8QfiF4X n1jxnbxf2/qTaZHBBpf7qySOyu7hWGZBvOINhOFzuz2wfQyuWC+saRntLqv5 X/dInzW6HF+N/wBhfw5D4uurVbzVLeO5UXdrdgrJDKJDlQBjtkc17h+zP+zH efDjw0lpdavBfR5UxGKIqAvIwfU1seCP2aPEHhXweui33ie38S21uG+yzS2n 2Oa2YjAO4NJvA4wpC49a5ebwD8S/g5aXjx376tYo+YpbMDftx1kiZev+6SMV 5/Pgf5Z/+BL/AORL97yPo7TtGt9DsVjt1bccZ5rRt777NaySOXjSMZ3njAxX yt8P/wDgonp3h3UE07xnb6k2DtN5DZJweACy7l2+/FfRdtNpvx1+HkVzoGqu bC9UPHcW4BbHbgg4+ho58D/LP/wJf/Ih73kcB49+M+u2+sTx6RPZTRqcIHt2 l3/kwqHR/if4o8QlTeT3WnquN8q20caD1+VmJxVbxX8APEfhnR5v7L1WeZ0j J+03HlDkdCVSIZ/CsfRrC/jwLkQ6hdoBu8uzkYZ6fxNT58D/ACz/APAl/wDI h73kereDfFf9q6hHarq2q6oOMvDaiJAe+STXTfEfx7aeBtJUXNpcXEbDG5eo 4+nWvI5p/EflCK00TU1jwNvkOsKtx6Fs1YtdK8WXqrH9lnGedl3d8A/98mlz 4H+Wf/gS/wDkQ97yOB/aJvfB3jr4W644uZFtTbyOI542V1lCMeu3GM14H/wS d8QvP4H8U6a7F47e8UQueNqiInivZP2op9V0n4W619qm0qBRCyMke5myVYYy Rj9PxrwH/gkxpEz+HvE95EyDy7hYtrHqzRcUc+B/ln/4Ev8A5EPe8j0v/gpr 4Im8Q/BvRCvmMr6e0+doz8rsexP6gda+tv8AggZ5lp/wa2/ton5o5EPjhlPQ r/xSVjg188/tn2UWi/C/SLW6Zpru70qWRgSCseCwwuAOOe9fRf8AwQgUJ/wa 7/ttqOQr+OgP/CSsayrTw9v3Kkn5tfokCv1PxC/Z1t7/AMdfFvSLKe6uLi3a cGSOSQsjj0IPUe1frhqP7O3g/R/AWiadeeCvBBvlRbg3CaJbiaYOgIDt5eWH 1OM1+YH/AATw8LHxR8fbGEqxDOgGDjJyeM1+y/7UVmng+fw4zROv2mzt4Fyu 1QViGc8Vr/a2N/5/T/8AAn/mHJHsj8kPF3gLU/jH8f8AUYvDGlyWtlpcyLLF Z2hFuFWQqcogCjOOc/jX3X8L/wBmHw9rHhG0W/0nwH9qMYLifR4I5Aee/NeI /s3z6l8Iv2jPF+mzQ2qm6jW5Ecs23AkmLAg9DnPSvsSx1G11gxNquhWKpjH2 uCcgnJ6lQDk/Sj+1sd/z+n/4E/8AMOSPZHCJ+zR4V027WKHw/wDC2d2Uj9/Z 2xP5Mg5/GnT/ALE/h+G3fUNQ0/wTp0OCNsWj20kXI4IOCK9FHwCtPF073Oi6 zZpzwZreWMr+fWux+H3wg1Pw5p8kGq6jbajbtwYVQlDxjPJyMUf2tjv+f0// AAJ/5hyR7I+NfF3wk8IaLc3b6XpXh3xPLB922ttFhQFv7haNSFz1+avdPhF8 B/A+oeEo5dT+GXg+KaVR5fnaJbO7tgfLkpn1r0PxT8PvCHw9gudUl0tdkI85 /KiJZsccAdevrWPafHbT0iSe301SAP3STTspQdjgRsAce9H9rY7/AJ/T/wDA n/mHJHsiG8/Zc+HkLbh4B8GcjOP7Cthj/wAcrD1b9mr4ftuK+BfCCEdAui24 /wDZKbq37WGo6TqMjXvg+e906LLPd2d1uGPQI8aMxH0x6E1teF/iz4d+LcUo 0a+zfRIJJbKaNopowTj5g2OM9xR/a2O/5/T/APAn/mHJHsjz7XP2evBUED7P BPhQAD7y6Rbgg8+icfga+e/jH8DPBem/FbQ7NYdN0u21K0uJJYUAjDSCa3VA OeDhnxgdjX2rHZ2LMY7uNvN/iB4BP0Ga8b+Onwf8I/EL48+Eor+MIkOlX9yG SQxlpIrmw8vn/trIPxNd+W5rjXWf76fwT+0/5JeZE4Rtsun5lX4e/s2eB9Pt Bb/8IjoF3g/624s47h2GO5ZT+leiaL+zf8P25bwJ4Pkwe+i2x/8AZK6Sx0vS dPtgIVw2OAmQ361LZ6hb6JbT319MtrYwKWaSQ7RwM5/Tp1+tcH9rY7/n9P8A 8Cf+ZfJHsipF+zb8OZgpHw88E8/9QO1I/wDQKj8Q/sv/AA+n0aa3g8AeCop5 RtiZdDtkJb03BK5+f9rm1uJAvhPQdS8U848yJTax+pI80Lkfd6evsa6HSv2g pLqxC6toqWkhOdjXBYx/iIefwNH9rY7/AJ/T/wDAn/mHJHsjwC6+BHhTw540 nTWvCujadasBtKadBJFnJ5O5AQMdzx0rvbD9j3wDr+km80zRtBu3UZEa6bbk N+GMGvRZPHXhX4gXSWWpaerGQ4DBS6njucA4/Cu0tvBp8PaO48N21pHMy5j8 0YT26Gj+1sd/z+n/AOBP/MOSPZHzrD+yjE+qBYvB3gi1s8EGS/0K13DH4Yx1 7+nFXbz9jTRNUj2XEXwxs4hw5tdFt9+O+OCM16X4m0TxzrUTpf6X4ZvQTlVa TyT6dTJn0ritQ+FnjYSA2fgbSTyMyJq7Mq89e4NH9rY7/n9P/wACf+YckeyP Av2wv2T/AAZ4O+EV9e6fe2dpc25UrcQ20duWOGO0bcHnFYv/AAT+8D6D46+B V1c6xoOh6neQFES5vdNhnlB2E/eZSTz6mvV/2j/2d/8AhMfAV5D4nuRp9xb2 zzrDZsCFZVYjOd3ce1cf+xF4Pk8IfAGHzpiINTeKWJ14LqAR07dKP7Wx3/P6 f/gT/wAw5I9kc3+2H8J9D8J/AO4v7PSNH0+/kVgs1rp8Ns4+VTxtGRyTX0h8 UNJn8Zf8Gov7HNo807m7+Ll8kzFiWkT7T4q4JJ5HA6+leef8FHvDMek/sp2M 65LXMjruIJ3fuUPsO9e1Wul/2h/waq/slKGYPD8TtRkQgdxe+Jx/U1nPMcXN pzqydndXk9H3WocseiPmD9sj4ReHPhD+xx8OktfCujW3ijVdauo7qeG1jF5c Q+UGjDOE3kenzfgK+eI7T+3fHN+39g2OkR2umW+yzEQYO44LhW/ibqa+gP8A gtR4kvvDt/4LsrW5kjmsrC1uAgOQha2HP1r4D0T4m6jZeJBd3l5dSMGG4s5B ZeeK0/tbHf8AP6f/AIE/8w5I9kfbvw4+F3glbeOcav4Xa5ZAWt9SsIZVDEnc MMcjBrpdS8GaBJE+PBng3WEUcyaRFEHA/wCuZj259t1c9+ypf+EfHEECR3zr qTxB9u5cHlsj51PoP1r2HxP4sj8CTGw+2zm5nQ+VBLp4mWQ9MZjAx6ckdaP7 Wx3/AD+n/wCBP/MOSPZHnPwf+HHhLxdd6vJceG7Nvsk4jSK706CMoCueQowe aXxz4f8AD/gXVI0bwT4WubS5O4zNp8cSwDOOvllffrWr4ej12yu7q6bw1qlo LuTzXNneQMshxjPlucjgdMmtfVPEHnQbHuzalj/qdTsCy/mhA/Wj+1sd/wA/ p/8AgT/zDkj2RQ+Gnw3+GnxD8T22nwL4JjnmcKwktoE8sc4O1lUH6DPvX0d4 H/YS+GujILiXw14f16XruawhMQ78IFI9snqK+ZZPBVtrNyrf2Roeoyg482ym EZH4E1p3fgvVtP0+STSb/VtFniXKNHeShUPrtGQaP7Wx3/P6f/gT/wAw5I9k O/bM02/+GtzLZeF/gP4WgsQu3+1Y/DdvdscjsBEQuOOc18b6z9vvNRkN7pUd rKTkxxaatvj/AICqj+VfW1t8TPGHhKyW41bxjq2rwElNsPXjruz+HNUtR+MG l6qhuJ4rkzDGXlt1LGj+1sd/z+n/AOBP/MOSPZHln7NP7Mt18YNbiNxYtbWI OXea0ADcA8Zxkdea998Z2fwl+DrPoVn4Z8Gavq9sfIuLnUbSGQQv6bSrEtgg 56Vh/C79pu/u76e10/TGndcxQXLWzFAhHJOOhz+HFcn44+FOi+PPHsl54k1q DT7y6YySStIELHjkgDrR/a2O/wCf0/8AwJ/5hyR7I05fh/4E8Ty+fqE3hG3D 9EtNNtrRV9vkwTXK/EH4beAdDsZntdV8PKEQlUWCOR259Qw5qzqPhL4P+H/F UOm3l/r+owIdk91E6eQjZGM4yfxGa9q8MfsyfBjxnpccumW8F+hGPME5L5Pb nHP4V6GW5rjXWf76fwT+0/5JeZE4Rtsun5nyB4dSytvElveaX4eTWTby7ljl sxLaS9R8wbcpHsRXfeNf2ibGx0VbZfhP8PtNnlHl/aDotuHU45IUxjH196+p V/Z90Hw7pJh0awiTZkp5gz6ehB/Wvnz9pj4J+HtMsZ72UahZ6qUOyFMyxzHr xu5Hf+I15/8Aa2O/5/T/APAn/mXyR7I8C8V6xba9DHImm6ZaTO5Oy1t0j/8A HVA4pvh34baz4g1iG2ttHne5kXeqTxLCrKMHPz4HQg/Svon4V/sqeEvFXhuK R5Lx79lzG53JhuOCu0cfifrXf6D4duUIs9Rs7Jby12rBLDlJiqnvncMbQuRk E0f2tjv+f0//AAJ/5hyR7I+XbjwRqHh4XI1TwzbRx2UqwyzxwRsqk9B0wa9F +E2n+C7jUIINY8NaZc2UsZ3XPlIZUboMqg+X8RXvfir4fafqWmWt5qLAJOnm TRRQH9/Jk7WPBxj1xn2rzH4i6bf6vqtjo/h630+KSMFpZxG0kkCKQSrOwjGS O2059R0o/tbHf8/p/wDgT/zDkj2R6B8PPgn8OrUztpuj+GNYhkbKx3thFcMg x0DEE469RXVWnwU8IrfGT/hWng2TIxgWMDxj8DF/SvKj4G1HwxBZ3FlczX19 dKJovsq/Zi6A/M53HaCMdDye1aU37QmsfDrUBBeXkR3gfubuBlduvAdMj8xR /a2O/wCf0/8AwJ/5hyR7I9N1LwL8P9PktoLr4V+GHluX8tRa6NaHnHc7V2j3 NTaf8LvCN3MYJPhB4TEB6yjSrN9g9SQuc1xVt+0NNrupadcLZpFDFLvnckyE rj+EAevrVjx5+1JafDJo7ywvbG3ExxJHdpIxn4yVjUYy3fkij+1sd/z+n/4E /wDMOSPZHj37VvwC8PfDjxFFrGkafpsVnqGIJbAWwMdvgbg6gcKS2OmK8q/Z 48P6VH8cZlubHS9S07VpSlrBfQpNDCrhVGA+RkHkV1fxQ+Mmp/HDVJZnQafZ LhisTENIc9cHOOcH1rjPhRNZ2Hxe0+2TzpbazmDqu0l8qymj+1sd/wA/p/8A gT/zDkj2RS/4KOfBZ/hJ4wmbRkm0+C0kMXlWilI5QTneduBx617B/wAG2WtX upftveJ47i7uJkHha2ba8hYZ/wCEk0LnBNdL/wAFPdI03Wfhbp+vGOaGW/tW kAlwpbLEHA59K5D/AINov+T4/FP/AGKlt/6kmhVlWzLFzg4TqyafRybX5jjC N9j+gf4If8kW8If9gSy/9EJXR/Cf4feG9A+IniPxFpWh6HY674msZhrOp2ln FFd6sbeymjh+0SqA8vloSi7ydgyBgVznwQ/5It4Q/wCwJZf+iEqz8A/h94m8 PfFzxjrniDxTa6vbaxYTWuk6XYafLYWekWkMV9IhZJLicS3b/ads06eUsggh xEmznw/tM7Psnp3wG/5KvpX/AG2/9EvX0bXxj+xj+yZ8K/gP8eLDV/Avwz+H 3gvV7m2ns5r3QvDtnp1xLAyF2iaSGNWKFo0JUnGUU44FfZ1dWH+FmFXcKKKK 3Mwrgvjd+z14W/aD02wsfFen2+r2Wn3C3cVrcwRTwmVJI5Y3ZJEZS0ckUbqc ZVlUjkAjva4P48/Cy8+K+g6faWEvh23uLW68wya1oo1e1MEkb29zH5BkiO6S 2mnjVw42mQFlljDwyJq+jGnZ6Hgnwu+D37Ofjb9qzxQ3hiwRPitFb3FtqevL 4XayudZhtZobW6ih1R7REv0tZlt4JlgmlFvIsccmxwq17h/wzFoP/P3q3/f2 P/4is/4XfCz4m+Gvjv4r1/xR4+8K+I/COrx+Ro2i2nhi8sL3Ro0cGBGuX1Oe Cb5TMZXW0ikmd0JdY4YoV1fiH+yf8Lfi78TNF8aeK/hv4B8T+MfDfk/2Trur eHrO91PS/JlM0X2e4ljaWLZKxkXYw2sSwwTmo9nCy0Dnkc/8Q/2H/APxY8I3 Xh/xVpqeJtAvyhutN1W2tr2zuDG6yJvikiZG2uisMg4ZVPUVh6L/AME1/hH4 e1bwrqVr4Y09dV8D6Yui+H9TeytpL/R7JYzEIILlojLGnlsykBuQ7ZzuOe7/ AGiPhXr/AMUfDmkP4V8R2XhrxH4d1L+0rCfUtOm1TSrgtbz2skV5ZxXNs9zE YbmVlUTx7J0gkO4R+W/z1+zl/wAEtr/4M+Pfg14gvvHE2sj4a+HtPsr7TLu8 1yW3Oo22gDRftVhAmpw6bbI0Qct52mTyt50/7xN6+WlTjZ2X9Wf/AA3zG5vu d9pn7Jfwg0D4F6xqXhpJrfwZ4js7nX7m58GxgPqy3MXmveW506LzpriZCrLL b7pnYoUJYrUqfsG/BX4Jv4v8ZxaL4b8Hvq8M1/4p1yOysdPN5Eu+Wae9uBEu 9QDI7NKxAy7E8k1Y8Ffsla14D/Y90f4ZXeuab4ofwBJpUnhR4IJdAE0OjT2t zpdtfy77sszSWUSXNxFEquryFLePha4y5/4JmyeKvH/xlv8AxB4r0ZNB+Knh 3W/DVppug6Jd2kmhwaq6tczK13qF3BHcOyLJMbC3sY7u4LT3MU8ghMLdKCen 9f1sPnfc9E8NfsCfDTwdoWgaXpGgWWk6X4Tna50Szs7G0gg0eVkljaS2RYgs LmOedCYwCVmkHR2BTxb+wL8MvHvhzWdG13QbLXdI8SzR3Gr2N/Y2lzbarKix KklxG8RWV1WCAAuCQIYwPujHfeEdF8c2PhvwzFrniLwnqOr2lw7eIbmw8O3F lbapCYpgiWkL3srWjiRrdi8ktyCsUqhVMqvF5zr/AOyTrVpqGt694X8YWmn+ M5dbXUtG1LV9LuNRtre0CXe3Tr+CK8t3v7aKbU9TmgTzoRC01sAGFsN46Ub3 sSpyOO8f/sY/s++OP2k9K0PxR4V0vW/iNfeFr64sLrUvDENzK+jROlpdwpev amMRg6gqPb+aGK3bHYUdjW9/ww18Jvgj8NLHQtF0W/8ACnhuLWLF7bTvCdi1 mkd3JfQ+U3lWEQYRGYoZiQIvK80z/uRKaj+Jv7Fev+Ivij4XvfDmu/DXS/AX hn4e6x4CXwjq3gOTUrW5i1AWu7cY9Qt4RaodPsV+yCDaYluI96+bG0C/s3/s c69+zb8K7ewn1+18b+KH8XxeIrq9a81TS4J1eGGwmM0lzdaje37xWSu8aahc 3O6WO3jV7aKG2+y1GlD+vX/LUbm7b/1b+kWtG/YX+EulfHXW/E1j4YSz8fXG nwHU/EcWgxQ3l/bSFkSE6gLceft+yKGhWVmjVIC6qrxFrnw7/ZI+Fvin4d+C L/w2LbUPDWlWdtf+E7i2W2mt7OBrVoYJrRvLOwG1meNWTGY5WXO1iDr+FfgR 4v8AD37ZfjX4kXXi3w1eeFfFXhrS/D9t4fj8OTQahYmwlu5o5nvzetHLuk1C 93ILSP5TbgMDE7TbXwX+FviD4S/Bn4Z+FY9a0GVvCOmWema5LHoggi1SOCxa Ei0ihkjiss3AhkUBJI0iR4ljXcskcKnC2q7fr+Q3OXc87i/4JffBK31qbU4v AnhePUbnVY9emvF0SwE8upRySyxXzP5GWuEknmdZid6tK5Byxy3W/wBnX4T/ AAJk07z7jVfCuk+E/C1/NELZZLXQNH0m2NoZzKI4xZRGMLCYlkxII1uPJGwX GOH8D/8ABNz4h+CPG3jvWE+OV3fS/ETUNEj1a7u9N1O41I6Rp99qFzNbQTvq zJY3N0l8YjLYRWttb7Xe2soGdPK9q8H/AAj8T/DrR9KsdGk8IWd5beCo9Be/ t7a7ttG02+tAq2RtNBWYxR2hM1yZFju4pQkFrCZJVCSW79lC39dv6QOcu/8A V0eI/AT9nv8AZkk+DdyfBWjWngnwnB4psLe+0m48KnwnJa66Z7M2QuLC4tLe aK5eWTT2iMsQZxJbMuVZCfVPiJ+zF4W8CaZq/iqxtnGvS21tZzXjRxedcwRz OYopJAgdo42uJ2VScKZnIA3NmHwr+zh8Q2+Adt4X8bePPCni7xHD4r0zXn1q 28KTWcdxb22p2l/LG9vd3t7i4cwzrHLFJGlv5kPkRRfZ0rtvjfaasvhbxDNL fac+hPa2SWlpHZOl3DcC5czySTmUo8bo1uEjESNGYpWMkgkVYlKEVG9v6shK b/r1P4VvCniW48Ja7BfWxxJCw/EdxX6h/wDBI79s3REuYNL1xofs021LtDIP MjXPLDPJyf0r8q66H4a/Ei/+GHiaLVNPKieMjqTyB24IrUk/T7/grT+yvrFv 4agnhV76zt5JNUs7nGVvRKOIYyCcsoIyBz9Kwf2Nfi9ZePPBcNlN4qfR3sV8 mWzLJ50IXC7jv+bBI9q9P/Yb/wCCgHhj9rz4fWfgP4j3S2djEdsOoRIzz6K5 wGnXeGByBjoetfP/APwUE/4J3+Lf2HvHsPivS4ftPhrUWF1p2t2oWSFmYFwz DucMMgjGT0oA+uvCniEWs5TT18Xa6y8b4bjyoz+StXpWi+KUurNV1vSY9Hhj XI/tC6V5G+uVGa+Jv2Xf24bPxf4Zi0vxB4kbw9rNuCpCx+VFOo+6Q3ILYGSK +jPD0Hg2/wBl3q/iGfVhINxVSG59+MfpQB69oPjTwzd3n2Wxv9OlkHRYipIH HoBW/dWa+XIUUsQpIwMZwK878EfErwna6wltpWksj52pIkJL46Zya9RtLoOE 4CMwPHp+FAH53/Eb9jjxv8df2l/FV1FYvpumPffLeXCb4udnTPUYJPH92vq7 4J/ADwz+zr4bihSSzl1BxifUJ1SKSbodoPA2g9O/rXqerWQEZNuoVG6qFx+V cH438G2Hjqy+x6ik4jiJYGJir8/zoAr/ABI8Zz6fABpUpuFk4K2sqBun97n+ VfGH7V516w8WaHeq00cT3jFVvbtpF3+RLnJJ4G3f0xX0Tc+H7qy1Wa30OPVr iKADJmgAjTBx1fH868c/ao0vVNaufDKXsVvGh1GRDIsqKykWdwem7A4BP1AH evRyr/ePlL/0lkz2PU/2aNQvdQ8JpJ5MGkGMbpRFeygOuRkjnnPXGPzr1vSv jJojXv2Kaa6k2ZHnSKMDp3AH6184+AvAfiu90W38y91TQbMAbGZYZxKOO4Vs Ajn+lek+AvgZbatqAkt5bDW7hf8AXSSXLCVj/ukhV49AK84o1PjH8AfAH7R/ nQJqMVrq8WR9pspAJM9TvHRu3UZx3qx+xV+z5r37OUWtaZfaxaapp15cLLA6 xFGCqmOBuIH0ya9N8HeD7fw/ZsiaZHaXI4BOGJHsfStVbiy8PMiXl1BDNIMK rygDr70AS+IPFulaCi/bru3tXdcosr7Sy56gZrh/iN8ZbrwvIsmnWcGoWhUN 5iq77sjOAV4rudY8L6d4ts9t7BFcxldquPvAexHSvKvHvw10HwdZtJDqeuW5 LE7fMfb+GFJoApx/tGy+J7ZrZ9N1XSJUGfNXMQbPoWTp/jVe11/XLeN9QtvE N1LHH8zQ3Lqwb2GK4nX9b0zQtLnuW8VR6ZFECXmurtx5Y+jgCvlT9qP9t4+L NPPhjwTq95qzXjNBcXscRjyMf8sSQCGBHUcYNAFz9rr9u7/hN3vvBuhaVFNf XkyQG8MwmjLFirLsA65PXPFe8f8ABPL9nK98LeF9J8P2MedYu/LvdYQLxCIz 85Y5wMJzg1xX7Bn/AATE1PULCy+IniuJdH0P98tzqmoOvlno2FhbMhlIztbB Ga6r9sr/AIKW+Ev2afDN14P+G4kELnabl+L7VAMjzJiNuzg4IwMgUAc//wAF W/jBo3hz4hatYabqlle6fosU+n27RuFQ7xkAHv1r6u/4IHOZP+DWj9tViclj 45J/8JGxr8K/ix8ZNZ+MXiOXUdVmx5jErDGSI4wTnAFfun/wQL/5VZf21P8A uef/AFEbGgD8sv8Agk54fOv/AB9soPMjiLyIFZn2lTuI44NfsJ+2B4n0X4u+ E7DQ5bu4h17TreNFFuhInwgVQ5AJUcdR+Vfkx/wSd/Z/8QeJvilpXie13Q6f ZXIfeVyCQSDn86/b7x78PPBHg/QoNf1vX/Cd1qc9nEn2WZxbXC7VH3ZA64PP cGgD81vHnwTn8XzSSJth1ixG5btYw0sxUYSOQkD5AfTmvOPC3/BQk/AvVJ/C /wAQNJvbLUrBzA9zpEYdJDjlgHZcDntX158V/EXw/wDF/iu6uNOm0RgIxGCk 8qXiuMgkzQOrSDPRXyo9K+Lv2nP2adL+LHiyJ47y9gvYdw865aSTzhnJZizc HtgYHtQB9FfDL9qjwD48sobq18WRQFwHKahctEyE9mVWPNeian+3r8Pvhvog ivvE+mXkqjYfInx5eeOMkk/iBX5f6t+yFbaJq0sV34jitgp2kJASwPpkmna7 +yXbSaZ5mkaoNSuUTcQYipOPoaAPua+/bM8O+LPHCyaJ4uW8jnlyLeS4JGey kZ2nivUfFPxb0K98MRXVpDoVneBf3jy28bDoOQSMcn2r8itI8GeIJvE9t4eT TZo7i4cQgESbZD7gH+VemeM/2VtR8C6JDqTag1xcoAzWptSUBx0ILHjmgD7h j/bs8P8AhuKXTLltHvtUJIiliiiSME9B25ArT+At3H41+KN9fXr6eL6CCO4g htiGKqxOCWRhuyP4TwPSvi/RPCWseJPCptL3wvp8bRLv+0QRGJgp4G7IK1q+ ANG8UeC5k1nRBqa3Fm5KyQ2bsi47EjCnH9aAP0r+zhboSsmeMFiMGuC8YWlq 37Q3hdZba1mUeH9WYb4lfkXOmYPTqOfzNfOmhf8ABTHVvDkUdnrehWF1ex/K 7+a9uzjGMlSG569+ami/b9tte+J+lavceHBCmnaZf2irHMXEhmls3DZ28Y8g j/gVejln8Z/4Kn/pEiKm3zX5n1hPdMxARYwMY+4M4/pXlH7Zmmw6p8JN8mqX NhPFPEkcAf8Adzktx8hwCc8ZzxXkfjr/AIKQ38FtJb2Ph2wsndcJNPcF9vbO NoyfxrxHxFquv/Fm9m1zXL3UJY5iWE0ls5i57L/COnavOLPYfhn+0T4i+BVr p8GuaQ8NsAAk3mp5WD3I7D5Gr1PT/wBunwd4utZjLJpNrdhfvzqhOfYGvjXR /h63iS9eIXRW3UnLxWvmswGOeuB19P51x/xT+FtppGv/AGHRbt7x50XzN8Cl xkEnIGcc0Afpd8L/AIlDxHoGoSuNHBeP/Rri3iRSDnggqOuKp+DPjRf+Ctfu hc6gsyRnewu7olNpPoTxX5f3SeJfgzarvh1O0E33Z4LiSJTnnoMVkjRNe+MN +JrfT7iZ5MBpUix5n1IGD/OgD9SvFP7avwr8b3T2eqa1HZ3kbEGW3mG1COD1 b2HftXO3X7T/AMPPBeZLH4mLKg6RmRifpnJ/Q1+bmofsy+LLC0MzabOVXqoQ 5FXfhj+zhc+M9XRL27awgTmVhbmRkP8AdIyMH8aAPsS/+PmoftP/ABTk0rSb 0XPheyctcXkIZZJ5FwyqSScqwODwM+le2fDfwxc+Iddg0mwj0u1e1UyeXKoW G0jT5mjQAqDkdMnOfWvJP2avCvgT4OaVHbXuragbFkMd1CZltGmlxhZPMCEh VHVBy3ZlPNfS/wAI/Bnws8ZaKtnpPjHS7nUJ5o1WSRli8hf4gGkZpnXnOWf6 5oA4z/gpRaWmtfsnqttcSIdPLzSJNjao8tF+Xvn8uKs/HD4hN8If+DTf9kHW w08kNh8Xb9pkgfaZ0+1eKzt7ZBOOD6Vz3/BVb4Da14a+DkGm+F9V0/XLd5JR LLazB1kUDAAwT1wD+NO/bT0C8vf+DQT9lHToLaWa+f4tX0QhRcuz/afFeQB6 9aAPPvj34Ai/4KHfDTTvGnhzVLG8vNLso1khkOfKEcYQA4yOpxxzx0r4K+M3 hOz0HVpbBbD7HqVvlZEVw6ORwWBIB5x0r64/Zw0G/wD2Jvgtp13qbJfX+qyt KttazDcuQrhTnPI78V5dqHgy5+M/jPUNTj0rU55Lptxt7W1eb+InB2AetAHz p4Y8Q3/hTUI7iynurSeJhteOVlxz6Cvp7wR+0H4hvvB8dzqHigrLGAqJLYiS Rs/7ROetT+G/2ddS0e5WbUdGfRYk53zac4Yc8ZJA9vzr1fwz8HY9dsVa01iw vlQYJVFGPbA/rQB51of7Rfiwx7k168hRvuountMQPQgrj8jWzL+2Rq+kRCPU rC01eNepaxlgd/foVH5V3t98DZI7MMdTs4zjG1ZjmuXuvhwPCkbNHr0kak5a KJWnLew3EgUAZh/b10LSYTdP4Ev3kh+Ym28tQP8AgRUH9DXNa7/wVSW4k8vS vA6oxwAbu/3nPuAg/nXTeILu5h01hYeG9X1hmyPMniCp/wB8Bea+evHvgfx1 q9zcpbeF9SggYliYrNlGCfXA4oAu+K/2+PGGsy3CQadoGlicYcRWnmOR9WJH 6CrXwL/aX1O416eO/vPCunzTIf8AS9T0+G4XucIHjcKfoBnNeN618ONe05z9 s029tjk7jKm3P1zWfb+Dby6bEdvLISf4OR9KAPtDxbrlrfAXM/xu0K4Dld9t ZSywRKM4+VERU47DArlfF/w6sr+zOo6Frkuu238cs0W0t/utk7hjucV89aR8 CPF2qFPsug6rMjcApbkj88c17D8CfgT490HURDeQ6jpulvmRw8bFDgccZHPW gD1P9jf4V6D8S/ipDZ66BeQxQyOLaX5ULAAgkg889iMGvuPQfBWieA4mtrTQ tKtIQwwsVnEn8gM18Da9APCHilL7TNQ2XNnIGkuIGxsYEHDr/ATjp6V7r4P/ AOCiBfT4IdW0X+1LlRta7jkVTJ7ghcfjXo5Z/Gf+Cp/6RIipt81+Z9Oz29jd RfuVEGOy5P8A9avKv2kfhC3xR8A3VnalRqEKs1u7DJDnAHPXpXBW37fWmHXI rRdBvITKQvnTXaBEz9FJNe46FeQ65pUF9HMkiToHUIcryBxn8a84s/PvR/jN 48+GGtyaUL+e4lgcxrBeDzgcZHBb5hwOMGvoH4SfEbx9rN1BceI9D8NR6ds3 NI7yrOf7uBuI5x/KoP21vhRJbanbeJ9Mt3Z8hbkQqWLKq9wM9z6V4x4T+O1z 4LvEGnWzWecqyysu0nPPYdye/egD7L1G6tPE2mB3gnEeNvl+Z8ijuMDBx+NZ EOgR6EEnsdIsGREIQo7KWHYEAjcfck14Vcfth32maerzWNneMewuDGSPbANY 13+3leQ2Msi+HYFAPLNeYVTjudgoA938SeINLvoI21VbvTlj48mGUwIDnpuQ AsPqa5vXPC/hXxrZljb2csCcGfDeePoxYtx9a+evFHxg8X/Hm0Ty7Wf7G7DE Gm2z+U/PUytxj8au6L8EPGFv4eee3urTQUYkJsn+26hMR1EagqAe+QPWgDV+ M3jfwv8ABa8hXw8t3c6s5wsS3MrLMdudpBY4+orzjTPCXiP4gXn9oX7zanqV wxMdqg3tCv8ACMHCjjjJ9Otdn8Of2Pta1nxMXisdUsHdt093eZNzJkk5BAAj 55GBnk17pp3h3T/hVoA060aJWhJaSaQlSzn7xJ+tAHznrHwM8Y3lyGv9T0fw 3YoigJFIZJDxxkIoHp3r6j/4J+fsR2nxU8S2y2Uzmz04G71LXrtBIv7sbzEq 9U8xQRjJ9a8tsvB93471sy218kw3uJkCcBBnadwAJ5xX21+z74ysvhL+zvqf hfS28u81Rld5UkAJwhUqQcnkHrQB8X/8FufipomqX0Phbw55dppukAx2ZXGD GGyRu6nOetYn/BtF/wAnx+Kf+xUtv/Uk0Kvnv/gpNaX2kfGoWtw0iRbXZFyd hw3XFfQn/BtF/wAnx+Kf+xUtv/Uk0KplsNbn9A/wQ/5It4Q/7All/wCiEp37 M/xhX4l+LPEmny6b4m0/UPD6anb3Y1PQr+wtsbLpIvs1xcW0MV2pSMkvbPMi 5H7x1eOR2/BD/ki3hD/sCWX/AKISuh+DXjlvFniW7t20bVdLlttCa5uRdvbP 9mnns7hmsmMM0g8+FQhkxmPE8RR5ATt4PtM6X8JV/Yw8OfFTR/jvYyeOfGXw +8RaS1tOkNtoXgy80a4jn2Eq7TTapdqyBQ4KCMEllO8BSrfZtfGP7GP7TPh3 4y/Hew0vR9M+INpdW9tPdM+u+A9c0G2KBChCz31pDEz5cERqxcgMQpCsR9nV 1UNmY1fiCiiitzMK4P47/EDUvhzoFjeactw3n3JtZBb+HrrXZVaWN44G+z2z rLsW5eAyOAyrGJC5iTdPF3leeftJfGm9+A/g7QdZs9EtNci1LxXoXh27jm1B rNrWHU9UttO+0RkRSCV4nukfyjsDqrDzFOMrqkCOK8HyW8n7eOuNpI+LSv8A 8I9J/wAJMNROrnwiLvOnfYRY/bMWfneT9q3HS/lJ8/7V++8qu0+IXh74p6j8 S9EuvCnjDwBong6Dyf7W0vVvB13qmp3uJSZvIvY9Tt47fdFtVd9rNscFzvB8 seiV578RP2l/Dnwv+JejeE9S03x/c6pr3k/Zp9J8C63q+mRebKYl8+/tLSW0 tsMCW8+VNiYdtqENRskv66ituL+0J8XdQ+CegaPrUGhatrWinUhb65LpWlXm sX2mWrW8/l3EVjZxS3N0Tdi1iZIkJRJ3lYhInI+eP2dP21/jf8W/HvwY03Xv hr4b0Lw/4+8Paf4i1XWFg1+eUpPoAup4UWLTpNOsHTUmMSi91MSNFAwETNLE 5+uNf8Q23hq0hmuhctHPcw2qiC2kuG3yyLGmVjViF3MNzkbUXczFVUsOJX9o CG6+O3gnwfa6ZLJaeM/C2r+Jkv52ktp7QWNzpMIge2kjDhn/ALTJbeUaM2+0 oSx2CV00+v8Ak/6+Q7nlXgv4weL/ABX/AME9NG1LxTFqun+MdMsdJ0j4jXWv QXXhI6fte0j8Q30NwfsoVYLd76eK7tH8ktErQu4Arm/Ff7Tfxt8d678dPCXh Lw1NDd+D/Cuvv4T16Pwjq+nnU9YBA02ENqsENkzRkvGGtJNQgvPLNw72CeXb T+rfET9pLxPZfCXxF4z8DeFfDPjbTfDWt31nPBL4lm0y4ubPT/Nh1Bog1k6G +jvre4t47d3SCQRrIbuMNsX1TQ9fk1bVdWtpbC6s/wCzLlYY5JWQpfI0Mcnn R7WJCBnaPDhW3RPxt2sz394L2MTwn8Z9I8X+GvDOq2ln4ritvFk721il74X1 OwuYHWKaUm7gngSWyTbA4D3SxKzNEgJeWJX841z9o3xr4J1DW9X1vwdfjwl4 e1xdG1CPTND1LVNVigCXcq6law2sUsuowzLLpEXl28ObeR78yO4t2C+mePvi /pvw68WeCNHvYbyW58fa1JoWntAqFIZ00691AtKWYFU8mwmUFQx3sgxgll29 f8RW3hq0hnuhclJ7mG1UQW0lw++WRY1ysaswXcw3ORtRdzMVVWYIVrHy58WP iz4q+Hv7WPglJpvHsmpv8JfEP9rX2leBvEmreD01cS6bPYzSWdqZLcT7bXVC luLn7UVdYBMWngMmr8E/2ivFvx3+Dln4j+IegQfDywuvHdjYRxS3es+G5ILc G2+xiGS+trO9vmuNVMNsYri009JkuJIRFcxoj3/f6d+0D4r1f4/aPoMHgbT0 8Da5bPNa61c+IlttckCWsc8l1/Y7wh1so5JobR5HmW4S5lRWtREROavxE/aS 8T2Xwl8ReM/A3hXwz4103w1rd9ZzwS+JZtMuLmz0/wA2HUGiDWTob6O+t7i3 jtndIZBGshu4w2xai7Nf11v/AMAG7q39bEfgz403Gt/t3fEHwXLofjK303S/ COiXFrq0+g61Do13dC41BruGG7lX+zGkjiuNPYm3ImkMro7S/Zdlvsfs0/Ea 9m/Zt+EFx4nk8Uah4h8VaDp0dxdXugSwXb3bacbmWW/hhEkdgzeVJvEjiJJm WESM7xh/QNC1+TVtV1a1lsLqz/sy5WGOSVkKXyNDHJ50e1iQgZ2jw4Vt0T8b drNk+Pvi/pvw68WeCNHvobyW58fa1JoWntCqFIZ00691AtKWYFUMNhMoKhjv ZBjBLLNraen6/wCY73PmXwR/wUH+KWseN/HtrrXwQ8T6ZZafqGh6F4StYNF1 yebUL+9vtRt7ltRu209LW2t7aKC1lnuLQ3lnCjs0V3eebCre1fDHxq/jDxv4 Ou9Z1GNNV1PwT9v0+AyX+hXOqM8tsdSml0C5O+3WBjpwRp2mngN7LCxh3Mbn 0vX/ABDbeGbSGe6FyUnuYbVRBbSXDb5ZFjXKxqxC7mG5yNqLuZiqqzDgdR+M 3iaH4ueDLOy8Kade+APGEBiGttq01rq+nXv2e5uQk2mTWqgW/l26oZPtHnLN MqNbBVaQNbWS/qwpf1954v8ACXXrvQf2edXuPBt18cbTQn+JOgW2hN4xsNVv dbfTpb3RorxNmskaktk8j3yyS3eXhja4liDQxwKfdPjf4ptrrwr4h0VYdR+2 WNrZXksj2E6WjRzXLogjuSghkkBgffEjtJGDEzqqyxF+Y+In7SXiey+EviLx n4G8K+GfGuneGtbvrOeCXxLNplxc2en+bDqDRBrJ0N9HfW9xbx27ukEgjWQ3 cYbYvTfFzX5NW+H3iu1lsLqy/sy5t4I5JWQpfI32eQTR7WJCBnaPDhW3RPxt 2s0yu4f12S/4I1vb+tz+EaiiiqA6H4dfEzVPhlr8V/ptxLEUYF4w5CSgdiK/ Ub9iX/gsR4Z+Inw5f4bfFjT5df8ADd+nkmK6ZDPbliNzROx64AA6cV+TFAOK AP1a+Mf/AAR3s/jdeXHiH4AeK9B8R6XMRNJoeou9tqVmWPzEKybSoJ4KschT Xztb+IfiZ+xF4wk8MeMtH1jSYIZNk1lcKV24wMRSH5SBn15NfOfwp/as8dfB mQHQ9cuYkUEKkrF1XPpzkfnX1b8MP+C5vjObw4mgfEXRNG8ZaPtCbbu28/av PZm3DqejUAe4fCn9vfwRqGnQaZpV3JoeuXSgCfWFWHHYgSBirHkdDz2r6Y+A cdtZ251LUNVW91GZTuaN2ZHyOcA/h0r4QttZ/ZL/AGnJGnvbnV/hhrlySRJY zRyWKk9jbzAMD2+WQ9+tZN5+xf4k8BzLe/CX4weG/EtiqF4kg1MabOFHZo5i F3H0UmgD9PNe+IGm6NAJbicqpXI3Yz3/AMK4DR/2jdA8aeLzpdtb3bSLj9+k eUGT6jjjFfnF4m1/9oS0sfs+sad4s1WNVI82Cx+2xEYOcPE3pmu88G/t8+M/ hZ4YFlJ8PktZYo9pn/s+8tJWPAJwyPz6nODz0oA/RXWtIW80adZxG0LR8NLH 5kYHup618z/GPT9FvPEnhOy/tazty2uTCTyLFbTygdOvuc4GecD/AIFXl1t/ wVpv7a1t7V/A84gJ/wBJ+0zSO0uRyB+7yOfUVxnxI/b61L4h65pEulaHp/hw aXeNdRm4juLtWY208ByiwqSNszcAk5x2BI9HKv8AePlL/wBJZM9j61/Zz8SX eveG5FttT1OZLa6kiIjnhBZVbaOGYHnHpXsqiyt7uHdq32O8x80T3Eayt/vA E/pX5caT8f8AxDbrO1rolvdmd2Z3s9K1GHOTknIQ557V3Xwo/bm8ffDyF4R8 L9Q1XP3JEsL1ZcAdS7REk/hXnFH6P+MvGEvgfw8br7DfalKxAURBSRnuckce teUa7cL4+vIbnVrmys7MMHMYdmZTnp8o4r5R1L/goR8ctfWYaB8Lr6wMh2+b c2F1Nwf9pigBx7VwHivRfjr8eNUNxrN9p/he2kBVxcX0VkqA+q7g3HNAH3v4 o/aJ8K/BAI7T2FvpUSmSW5lv1T5QcZCZyfyr56/aT/4Kx+EUs2i8LSXeqTEE CRIzHag8feZsFh/ug14d4e/YO+G2irFc+PPjvoNrJkNcRabbT3ksa9CCSgQt 9Hx71r3/AMaP2Vv2bbkP4Y0i98dahbhf9I1icTrMwHJWKFVVef70hODgjNAH m/hf4e/GL/gol8RobLTbW61VZpv3MbFobK2zkA8/KOMAk9a+rvhP+zf8Kv8A gnPY3GvfE270Hxv4zt4g0GnWwS6s9OcHIMrH5W4ypVdxB6ivlf45f8FlPHfj /TDpHhmx03wtoKArHZ20PlxgYwTsU4JOO5Pbr1r5R8Z/ETWviDqT3Wsajc3s rnPzudo+g6UAfYP7Xf8AwV38R/GG/wBQstHu9Rt7EhIYFWZo4IUTICogOMAY A46CvjDXfEF74m1KS8v7ma6uZTlnkYsTVOigAr9/v+CBf/KrN+2n6H/hOf8A 1EbGvwBr+n3/AIMqef8Aglj4+/7KrqP/AKaNHoA/M7/gmv4p8I/Dj4SJeXfx T+HHhme3j8yCyv8AxLYwzeZkZyjyB8HPp2r6Q+JX7XPwr8d6asFz8VPhpM0k YR5IdfskdSByQTJntX9D+weg/KjYPQflQB/MaPiZ8FtBT/Q/iB4BuHDFt83i S1YhuuR+9x19q4Dxj8dvAFzqsv8AxWvgeZZ8lpodZtgyntyHNf1Y7B6D8qNg 9B+VAH8jSan8Nbm/m1G8+InhKaTdlIzq1szNx3+evVv2avHPwLuPiPpn/CU+ PfB+mWDTI2RrdskSfMPv5fA/wr+o/YPQflRsHoPyoA/nh/4KO+JP2dLbXdL1 74e/FD4Ta2un2cjyCx8Vaa8yyb+AqLLvYkdhk189+BPit8EPihqixeK/Hvh/ R7c4EyLqdupmHTCuchSeuSR0r+qLYPQflRsHoPyoA/k8Txb8JtI1C7tdM+I2 mi381xEz+JLVRs3fLkM208Y96sW/xG+GsB+X4oaFHklWWPXLZc491Ycfjiv6 u9g9B+VGweg/KgD+UtPE/wAMfEdlJDqXi34T3MTbj/pev24mcgcZYSHr9a8a +IPhD4fWHi2y/sjxl4Ks7G7t52kFnrkcsFo++IJGziQ7dwLkeyMe3H9jOweg /KjYPQflXRhq/sZudr6SX/gUXH9RNXP5EPCPwh+HHhyw+33PxT+FKyOQyouv 21xIfw8zcPxrqJfH3w8itEs5Pif4diKjbsh1uBosdxkSEYr+sfYPQflRsHoP yrnGfydaZ42+GGhoslr8R/Diy9x/atiq/iQQT0rN8S+M/h3odvDfQeN/BFze 3shSQx6zbSSIvXJw5Ir+tfYPQflRsHoPyoA/kB8U+L/B/wAa7q00BPFng22g uX2PPea1aWcSDHUvK6qv4mvqD4qeHv2Vvhh8JdIstK+IPw5vPERgUTnSvEtn NtfyxktJE5HUHj1r+l7YPQflRsHoPyoA/ka0jx74ON1JDF438HRWfzKDPrtp krk46vk0/S/iJ4B8FzziPxd4JnF04dgurW8gY9MkhyBX9cWweg/KjYPQflQB /J6nxK+GHiax8rUPGHgaCNhggapaZb35bNXPh9N8IfBetpdWHxO8ExW7sG2P rtspT2+/kV/VtsHoPyo2D0H5UAfzRfGb4z/DXW/hXNBpfxN+G15dBSVifxVY bz8q8AGXr+fSvoOSw8FRf8G1v7NVx4t1nw1b6Tp3xI1K8tLy81CFLRrj7d4j 2hJGYIzbXkwATnB9DX7tbB6D8qNoxjAxQB/OF8PvDX7KGp2txd6l8V/h/Iyx B0tbzxTYxxO+eQVLg4/LtXZ2nxk/Zs8M2rLpnxG+FunIE8sRWniizRRj+IYl zk/Wv6Ctg9B+VGweg/KgD+fJP2rvgVo8Bhg+KXgedHGG83xNaSZ6+sh9a86+ M/7T/wAELXSpJtO8RfDfVb3GFeDWrHzE68grJnI61/SlsHoPyo2D0H5UAfyU xftUeFZJZY4de8Py5OQbnWrWOP8APcD+tO0/9pXwPZ3zXN5qngqV8cldajkA Psolr+tTYPQflRsHoPyoA/k6vP21fAtuQ0d74WkYdv7RQ5/8frF1n9uvw1cb lSfwyE24AGoISfx3Yr+t/YPQflRsHoPyoA/jp1n9sPw9Zzm9tI/DX2yE7lZp oZxn2BJGfwrxfxJ+2J4l1PxO14NYjMSk7IVZREvUdB7V/b9sHoPyo2D0H5UA fxtfCz/gpBFJNb2nia1sRbqADNDIQc9iVJI7mvd/Dv7Vfw28Q2ytD4v8P2ok HzJdX8NsV/77YV/VdsHoPyo2D0H5UAfyAfFfUvAet+JLq903xn4DjlXczums wq030KybSa5Pwlpng3xdaEQeNvCmmX0jbSt3q0Nsm/rkuzhce5r+yvYPQflR sHoPyrow1f2M3NK+kl/4FFx/UTVz+M/xp4S0PQLFWn8beA3mJIUWPieyvg3v +7kJXPua7n4Eftyw/CS0i0fUdX8O6jpQfiVdRiMkQPU8P7DtX9e+weg/KjYP QflXOM/mVt/2sPhj4i0uKWbx54HQOoYxTazbKynHIIZ818o/tR+L/B174ga7 0jxT4Xu4mkbIs9Ut5MAqnOFY+hr+xjYPQflRsHoPyoA/ic8NanoeualHHL4j 0O2jONzT38EYP4swArudR+GXhe81S3mf4ieAI7dATHGfEVlIUXPcCTG761/Z VsHoPyo2D0H5UAfyS/DzXPCd7fWujWnxH8DgoObvVPE1na20QB65aRVzznA6 4r3Xw54Z+DHgedda8RfH34X67fRMSllp/iWzaLA6E4ly2R2Nf0y7B6D8qNg9 B+VAH8yXjH9rT4Y39rJ9k8beDUiiUpEsWv20OSOMsqSAsPqK8F8Z/tA+D9TW WFfFfhqR7jIDLqcLBef727j8a/rn2D0H5UbB6D8qAP5V/gL8RvhlZ+FbiLUv iB4Gs5IhvUTa9aK0m5h8uDJzjrXYp+058MNB0B2Tx54INxj9ysOt2zYXHIID 8Gv6d9g9B+VGweg/KgD+Lz/goT8Z/CvxE8P2sOl6xp2p6jK6yf6JMJlVQxzk rlVPsSD7V6z/AMG0X/J8fin/ALFS2/8AUk0Kv66tg9B+VeIft9qB8GNIwAP+ Kq0b/wBLYqmWw1ueF/BD/ki3hD/sCWX/AKISuz+Gvh+w0XxPq89paQW9xqtv d3d7IiANdyiwaISOR95vLijTJ/hjUdAK4z4If8kW8If9gSy/9EJVH9mb4b+I tA+Ovj7xFr9joWnx6rpkumaYmn6nNfS3NpbnVblLi43wQrDI/wBvI8lBKE2f 66TI28H2mdX2f67HsvwG/wCSr6V/22/9EvX0bXxj+xj+zP4c+Dfx2sdT0jUv iDeXNxbT2rJrvjzXNetghjLkrBfXc0SvlBiRVDgFgGAZgfs6urD7Mwq/EFFF FbmYV5/+0Z+zP4U/aq8E2fh3xkviKTSrHU7bWIV0bxLqWgzC6tn8y3kM1hPB K3lyhZVVnKiSOOTG+NGX0CuC+PXhnxF4m0CwTw1Db3N9DdbZobjXrzRYnt5Y 3t5mM9qrSb445nljUowaWKMBoW23ESe6A6qx8VaVdeJbvQoNTsZta0y0t726 sFuVe6tred5kgmkjzvWOR7e4VHYYdoJQCSjY0ioJyQCa8T8B/Df4g6N+3X49 8X6jpng8fD/X/CWiaDp17Br1xLrUlxp8+oTs81kbJYY0kbVJlBW6cqLSNtp8 4rD1fxD/AGavD3xN+Jmi+LNR1Px9a6poHkm1g0nxzrek6bJ5UplXz7C0u4rS 5yxIbz4n3rhG3IAoOwHSfEb4e6d8UvB97oWqtfLYX6eXK1lezWN1H6PFcQsk 0EqnDLLE6yIwDKysARwOp/s+fDh/2mfBfiq4v9Yh+Ifh3w9eWGgWa+MtSiR9 LV4FvGOmi6FvdIJJrLzZpYZG3/Yy7bkgK9f8ZtO13U/Ak48NQW13rtvLFd2V vdanPpltcywyLKkU1xAryRROyBHYRyjazboplzE/lc3wg+Itr+2B8LvEdvpn hC78C+FfBOq+G9XvLzxReya0tzfTadPmGB7J1uY4zpcKeZNdJJJ9qkdgGixM 1/X3f0gt+R0XiP8AZu8AftD+A9W0e91K/wBd8H3viK7vZtN0bXJdMsFuFMlt f2E/9nPAbuB7oXj3NteNOrzzTiQEKiR+m6L4Yt9C1DVLmF7p5tXulu7jzrh5 VDLDHCBGrEiNNsS/IgCli7Y3OzH5C+Kf/BOrxB8Hfhzp3hT9m5dJ8AWOq3t1 Pr09jrsfhKARz29pBKDZ6dpMi3ZlNuJTJFNp97b7TFaX1pFJtj9n8E+EPin4 e+IXiU6vD4f8QaVax6rc+HNak8W31vc3j3dys0Fjd6UlmLWGK2jEdsl0s88p WAyCNGuZlpdNAses6t4X07XtR0y7vtPsby60S5a80+aeBZJLGdoZIDLExGY3 MU00ZZcEpK65wxB4z9pzxB4B8M/BfVrn4neKtL8F+DXCQXur3/iD+wI7RncJ GUvhJE1vLvK+XJHIkivtKMHCkdL8NfDF14J+Heg6Ne3x1O80nTreynvC1w32 uSOJUaTNxNPOdxBOZp5ZOfmkkbLHw34p/DT4vDQdWuPB/h3wBqviSz1kP4bv vEfjG+R9OtpVuzPfxCXTr5LO/Rb65tYlSOWNoXUu3lKtkq22BbanceF/2UvA tn8ZbT4naVd+Mn1ySwgtYmj8da1Lo11ax27xQhtO+1mxlUJK7qzQt+8kabPm sZDN8Rf2R/CXxa8Jal4c8Qtrl34Z1LWZNZfR7HVZ9GtC0sRW4tZVsGgN3aTy PcTzQXhnSaS6lLhlEaR9D4S+H0LfAvTfCl7pFn4Ug/sKLSZ9L8M6nPBbaOv2 cRNb2N3EltMscQykU0aQOAisFjbAVvwU+BejfALwxc6Tod94wv7W6uWu3fxH 4t1XxLdK5RUIW41G5uJkjwi4jVwgJZguWYmnvoJbFvXNT8OfBXQvEvinXdat dD0cMdV1fUtX1PybLT444I4i5kmYR28KpEpIBVMl3PzOzE8N6r4R+N2geGvF ekXXhzxbpSN/a2gaxZyQ39uGkhkh+02s6Fly0M00fmRnlJXXOGIPmXxe+H/x E1O98X3Xhzwz4F8TSolvqPhq38V+KNQi099TWe1ZftFqttcRQR2zWUFzDNCD IZt6qluWkuJe3/Zq8K654L+DWk2HifRvD+heJN9xcapb6LqI1C0nupbiWaW6 M4srESS3Du08pW0hHmzSAKQAxXqHU0fF2ieHfjj4c1/wxPqJu4bWYafqqaXq j2t7ps7QxzqvnW7rPa3AimgmR0ZJUEkUiFSUas2f9mnwndfFTw74ymh1u51r wlZfYdIjn8QahNp9kPLmiM62LTm1N2YriaM3ZiNwY5WQyFDtqh8Ff2SvBH7P 3xF8b+KfC+iaVpWr/EO6F/r01ppFjZvqFwJ7mbzpJIII5JX/ANJZf3ruPk34 82W4lm8r+K/7O/xs8c/tT6tqnh3xq/gn4fzadcmzmXxVPqbx6pJpUllFfLpD WESxmGSSNxC+pT2L/ZzKbFbub7VCeg7XPUPiL+yP4S+LXhLUvDniFtcuvDOp azJrL6PY6rPo1oWliK3FrKtg0Bu7WeR7ieaC8M6TSXUpcMojSPQ+Lvhm30H4 c+KbmF7p5dYuYLu4864eVVcfZ4QI1YkRptiU7EAXcXbG52J5nwd4N+I/iPwb EPEGk+GfCWtXXieyvr6PTfG+s+IrZdPtPs7DyZXhsJEll+zLG8AUW58ySWUX O+WCXt/j1/ySnVP+2P8A6OSlPSLBbn8GdFfVPw+/4Kg/tN63aXTXX7SHx9kM bKF/4uLrCkZBz0ua6D/h5P8AtI/9HFfH7/w4+tf/ACTVAfG1FfZP/Dyf9pH/ AKOK+P3/AIcfWv8A5Jo/4eT/ALSP/RxXx+/8OPrX/wAk0AfG1FfYkv8AwUu/ aQQ4H7RXx/Zs9P8AhY2tf/JNRv8A8FKv2lhkn9ov4+oB/wBVH1n/AOSaAPj+ r+keKdS0B91lf3dof+mUrJ/I19bWv/BRv9p28dVj/aI+P7Fug/4WNrPP/kzX YeFf2qv2vvFzL9k+PP7RDq2Of+Fh6yB+tzQB8laD+1R8Q/DEIjsfFmq26DgA OrfzBrorH9vn4r2akP4rmuQf+e9tC/8A7JX2z4d8UftlayoL/H39oSMHHX4j 6x/8k12mk6X+1xcKGuf2iv2gYx1P/FyNYGP/ACZoA/Pyb/goR8ULhlMmsWLl ehbToT/7LSS/tyfEHxDF5l5q1iJdKBurMrYQJtlP7on7vzfu5JBg+ue1fouu m/tMxKDN+0x+0EuR/wBFL1f/AOSqw/FHjX9onw5q2hWp/aW/aBc6tetaHPxJ 1n5cW082R/pP/TKu7Lub23ub2l/6SyZ7HwDB/wAFAvipaoVi8QxxgnJ2WUK/ yWmT/t//ABauInQ+LrtFfskMa4/8dr9E4tY/aHnbaP2mv2gFPqfibrH/AMk1 JdWn7TcqE237Tfx+b0z8TNWJ/wDSquEo/Mu7/a8+JN9u83xdqjBuo+Tn/wAd rntY+NHirX8/a9e1GUHqPM2g/liv061iL9rq2Vmt/wBoz4/yAdAPiTq5P/pT XA+K/iV+2X4cZz/wv39oJgnYfEjWP5faaAPzivNRnv5C8800znqZHLH9ahr7 h8Qftnftb+HZXW5+P37REZTk/wDFw9ZO3/yYrlrv/gpN+0/ZSbH/AGjP2gAf f4i6wP8A25oA+R6K+sG/4Kc/tMqxB/aP+P4I/wCqi6x/8kUf8POv2mf+jj/j /wD+HF1j/wCSKAPk+ivrD/h51+0z/wBHH/H/AP8ADi6x/wDJFH/Dzr9pn/o4 /wCP/wD4cXWP/kigD5Pr+n3/AIMqT/xqz8fjH/NVdQ/9NGj1+EXiX/gqT+09 p+nB4f2kfj8rFwCf+FiaweMH1uK/Z7/giX+1j8U/iz/wbdftc+O/FPxK8f8A iXxv4ZPjH+yPEGq+Ibu81TSvI8L2U8PkXMkjSxeXMzSJsYbXYsMEk0Aft9RX 8Qn/AA9l/am/6OV+P/8A4cLV/wD5Io/4ey/tTf8ARyvx/wD/AA4Wr/8AyRQB /b3RX8Qn/D2X9qb/AKOV+P8A/wCHC1f/AOSKP+Hsv7U3/Ryvx/8A/Dhav/8A JFAH9vdFfxCf8PZf2pv+jlfj/wD+HC1f/wCSKP8Ah7L+1N/0cr8f/wDw4Wr/ APyRQB/b3RX8Qn/D2X9qb/o5X4//APhwtX/+SKP+Hsv7U3/Ryvx//wDDhav/ APJFAH9vdFfxCf8AD2X9qb/o5X4//wDhwtX/APkij/h7L+1N/wBHK/H/AP8A Dhav/wDJFAH9vdFfxCf8PZf2pv8Ao5X4/wD/AIcLV/8A5Iq7B/wVa/ajfQLq Y/tJ/H0yRzRIrf8ACwtX4BWQkf8AHx7D8q2oUJVZOMezf3Jv9BN2P7bqK/iE /wCHsv7U3/Ryvx//APDhav8A/JFH/D2X9qb/AKOV+P8A/wCHC1f/AOSKxGf2 90V/EJ/w9l/am/6OV+P/AP4cLV//AJIo/wCHsv7U3/Ryvx//APDhav8A/JFA H9vdFfxCf8PZf2pv+jlfj/8A+HC1f/5Io/4ey/tTf9HK/H//AMOFq/8A8kUA f290V/EJ/wAPZf2pv+jlfj//AOHC1f8A+SKP+Hsv7U3/AEcr8f8A/wAOFq// AMkUAf290V/EJ/w9l/am/wCjlfj/AP8AhwtX/wDkij/h7L+1N/0cr8f/APw4 Wr//ACRQB/b3RX8Qn/D2X9qb/o5X4/8A/hwtX/8Akiv1O/av/bf+NPh3/g1C /Zl+I+n/ABd+J9h8Qtf+Jd5Yan4nt/FN9FrOo263HiULDNdrKJpIwIIAEZiA IY+PkXAB/RbRX8Qn/D2X9qb/AKOV+P8A/wCHC1f/AOSKP+Hsv7U3/Ryvx/8A /Dhav/8AJFAH9vdFfxCf8PZf2pv+jlfj/wD+HC1f/wCSKP8Ah7L+1N/0cr8f /wDw4Wr/APyRQB/b3RX8Qn/D2X9qb/o5X4//APhwtX/+SKP+Hsv7U3/Ryvx/ /wDDhav/APJFAH9vdFfxCf8AD2X9qb/o5X4//wDhwtX/APkij/h7L+1N/wBH K/H/AP8ADhav/wDJFAH9vdFfxCf8PZf2pv8Ao5X4/wD/AIcLV/8A5Io/4ey/ tTf9HK/H/wD8OFq//wAkUAf290V/EJ/w9l/am/6OV+P/AP4cLV//AJIo/wCH sv7U3/Ryvx//APDhav8A/JFAH9vdFfxCf8PZf2pv+jlfj/8A+HC1f/5IqZP+ Cr/7UrWkrf8ADSnx+3KVAP8AwsLV++f+nitqFF1ZOMezf3Jv9BN2P7daK/iE /wCHsv7U3/Ryvx//APDhav8A/JFH/D2X9qb/AKOV+P8A/wCHC1f/AOSKxGf2 90V/EJ/w9l/am/6OV+P/AP4cLV//AJIo/wCHsv7U3/Ryvx//APDhav8A/JFA H9vdFfxCf8PZf2pv+jlfj/8A+HC1f/5Io/4ey/tTf9HK/H//AMOFq/8A8kUA f290V/EJ/wAPZf2pv+jlfj//AOHC1f8A+SKP+Hsv7U3/AEcr8f8A/wAOFq// AMkUAf290V/EJ/w9l/am/wCjlfj/AP8AhwtX/wDkij/h7L+1N/0cr8f/APw4 Wr//ACRQB/b3RX8Qn/D2X9qb/o5X4/8A/hwtX/8Akij/AIey/tTf9HK/H/8A 8OFq/wD8kUAf2914h+35/wAkX0j/ALGrRv8A0tir+PP/AIey/tTf9HK/H/8A 8OFq/wD8kV9w/wDBA/8Abe+NH7S37XfiDQ/iL8Xfif4+0Wz8P2l/Bp/iPxTf araw3K+IdFjWZYp5XQSBJJFDAZAkYZwTUy2Gtz9tvgh/yRbwh/2BLL/0QlU/ 2UvjXqPxe+JHjq3fRxZaN4a/tDSrW+W5s7lNRuITqEM5V7a6uAoQRQqY5vKu EfzPMijyqjH+HXxb0LwT4T+FfhnU57+DV/F+kxQaUE0y6ltriWCx+0SRNcrG YIpPJjkkVJXRnWN9gba2PSvhr8QtH1r4n6v4ctbwy6xpOgvqd1B5MiiK3uYr 6KF95UK26SzuVwCSPLyQAyk8Fve/rudX2TP/AGMfEnxU1b48WEfjnwb8PvDm krbTtDc6F4zvNZuJJ9hCxtDNpdoqoVLkuJCQVUbDuLL9nV85fAb/AJKvpX/b b/0S9fRtdWH+FmFX4gooorczCvnv9vP9vXTv2G9N8PTX1l4d1W58Vi6tNNsb vxZZaRfXF4giMRjtp/313b5crN9gS6vUZ7dYbK7aUiP6Eoo6gfHvj7/gprrc 3jX4x+BvCnw5uIPGvwy8GeI/EdtHq/iDSLm51O5sHjj07y9FtL19Va0vC5kE 00FsNvkhC/2hGHt+q/G2z+Jmg/DrUvh7qy+KPDHjjX0hbxN4XvbXUbK0tIIL q7eVpdk0LwTSWS2LFSrKbz5JEkCkep4qh4h8Mad4ssY7XVLCy1G2hube8SG6 gSaNZ4JknglCsCA8cscciN1R0VgQVBpW0GfH/hX/AIKvW/xU+FfjPxRodn4L t9L+HWt+GdN1/WLXxZaeI9Gjt7zVkttWvUu7Fzbx2ltZCS4SW5lhuUQGW7sb SIRfaPqvTPib4c1280a1sPEGh3t54k0yTWNHhhv4pH1WyjMO+6gAYmWBTc2w aRAVX7RFk/OueFvf2rZJtb+KGnaF8O/HXii9+F+pWGkSxWE2kQf8JBd3VpbX jQ2Rur6EAwW95bvI1yYFO/ERlZSB1XhL412HxE+AWmfETwzp+p+INL13w/F4 i0mytxFBe6lDLbC4hiQTvHGksisqgSyIgZvmZQCQWutP6uv6YW1tY8ri/b1s zoFhf3mlaR4cg8UzXmmeGLjxD4gi0+0vtYgW2hGi3Umx2g1F9QkvbVbeBLli umXEi7jtjPDfE3/gobovwH/as+OWjal8RPBeqW/gDwZ4W8Rnwjq3inR9Em0d 7i9v4dQVJ5vL2yvAdLZEvJliM13aKZraO6Eg+oPh78U/DfxY0mS98M67o+vW sBhE0mnXsV0sDTW0N3ErmNmCs9vcW8ygn5o543GVdSbNn460TUfEE+k2+r6X cataF1nso7qNrmExrA7howdwKrc27HI4FxETw65JNJaii0eUfs+/tD3n7VXg r4c+MdMtdS0HQ9fm1TUnNje2OoWNzBBJNaW9pcTlSZGmWRbpZbAvbl7JhHez 27RPecPcft3aN4l8GftZt4S8deE7vxF8C7i8QfadTh1O10VodBtrkPPb2UX2 mO2S9F5HIjedMZbW7VXG1YIfUfC/xk8KfG/4RfCX4jR+HZL4+Lxp2t+FLPUE sk1XT5L+yctLH5k2xZ4bC4vGmEEjv5CXSp5udrs0r9q/wh44+KvjTwNBaarq Eng+LSRqF2liLrTro6le3lgsURQs0nk3FjPHcMUEcJVgzZjlEdPdq3l+KBaH aeLvilofghjBqV8iamdLu9Zi0qBGu9Tu7S08r7TLBaQh57jy2ngQiFHO+eJQ C0iBvlD4cf8ABavwD8TPhzrWsw22g6BqP/CQ3mg+HdO1fxvo0sesJaaHa6xd 6lNe2Fxd2cFjbQTyPcTRzXHlRwg7XnkS1P2hI6woXJAVRkknoK4Lx1+0n4W8 G+A7PxJBeJ4j0y91zRNBSTRZ4LrbNq15ZWtq7HzAoj/0+3mY7txhbciuSitK Wuv9a/0gQ+Xx1H4U13x14j8SaxceHvCPh2xgZpdXnsINIt7eGCS5uNUSdT5i RsJvKlNzIoX+z9yxIjGafxf9lL9r2++M/wCyr+yn4ng8ZeGvEl58UIrOw8RX 8YSdtVvV8PahdXkcH2VfIhuY72yZpFYRxosFxGAJNkZ9p8S+JdL8I/ELX7nR /CMniLxgvhlby/bSW06PUruC3lnOn2EjTzwtmaWXUPsxkIt1ZLsvLCWy8fwp +NOn/GbRdDvZPDviPw9qd4L9xp+taeq3Olz2Vz9iuopJYmlt1kEjFVMczrMm 54mkjBeqT7eX4XFfozn/ANpj9tLwd+zP8Mtc8S30w19fDmrW2h6jZabqWn27 6feTxxTRw3Vze3NvZ2JMM8Mim8uIBJ58CRl5biCOXhv2dv8Agpd4W/aR1T4b 6RpT+HbPxF4w8OaN4h1vTZvEMLS+HG1TS7nULTTlAXddX0iWs8ghVU/0W2nu HaMCGO4+jNb1+x8N2sdxqN7Z2EE08VqklxKsSPNLIsUUYLEAu8jqir1ZmUDJ IFcuPjtodz8VvCnhK08/UZ/GXh3UPE2nahatFLp8lpZzadE/7wPktIdTgaMo rKypISy4UOo266/8Nr/n8gIdY8TX/wAWfh1p+peA9TFmbjWLQ/ar6ykhW4so NRjF6qJLFuKzW0VwsUoXa4kjkR9jLLXmfiP47aj458d/F3wXLZwGy8C3dhGt xE8GfLuLbT7iIOvnmcSGSS6wxgWJlRQrl43B3Pib+1J4F/ZI/Zy8S+JX0aez 8HfDmK407TNN0FLKQ6pHp9oXmt9NgimCgW4guYmik8kxGwuCypDGJD1fxd8S W2ufDnxVbQJcpNpNzBaXHm27xK7n7PMDGzACRNkqjehKhg653IwG0a0Y0pRc U9HZ9m7a/crW6XNqdRRTjJX/AE2/yP4efhWpNneHGRvXnHTg11dcn8K/+PW8 /wB5P5GusrEyCgrnHWprGwl1GYRxKWY+nNfSH7KP/BPnxJ8b9TgnntbizsFY GSWSI4Az6k0AeB+Ffh9q3jK68qws5p2xn5VzX0V8Gf8Agmrrviy0g1DXpItM sn+bbIxDkfgOK+vdA8B+GP2fIYtB8OWMGs6oqj7ROijagxkjjPcEfjXr3wT/ AGRfFHx81mK41h7m3s3bdsEREYHHA5GRQB84fCn9kDwN4a1CKz0zR5NcvxwX CmRcj3JxX1B8O/2P9dnhj8jQ7HT0YDbvQsQPyr7g+BP7HegfCiwhjgto5JFA G4oBk17JbfDWK6iAiiMYHovNAHw/4T/YVvb63U3uoSRcA7YvkH6AV2Gmf8E4 9J1F4/NE9xg5yZpCP0r7C0z4UOJMeWxUdMqRXd6X4Qh8O6dvWMBgM9KAPhjU v+Ccnhizsm86ziV8dX3Nmviz9sP9l9vBHxl+HFlYvbRxX/iKWFQsecEaXfv6 eimv1q+ImoTahdPGEVV5PWvib9ufw4p+M/wVZygEnjCdCdvPGg6sf6V6OVf7 x8pf+ksmexd+E37B2h6v4Zglv4reeaRBk+VgfyrqNS/4JueHni8wabD3wQCD j8K9g8B2j6do1ts5GxcED2Fez/DTXF1IrBLHuIGMn6V5xR8H65/wTq0gOxhi MGO6TSDFef8AjP8AYZ1DTN6WVw0sa8AM3mD9RX6ieNvhomrxs8SKp68LXA3f wdnlkYeW3HXKnBoA/I74gfsnX1i0327QbC+VQSzqnlsR+Q5r5x+JX7Engnxq 8sSRf2JqTZASXcu4+3b1r94/EXwytyjJLbI/HTHv6V4F8ef2J9I+IVhNJDGk NzgkAR8k/wCc0AfgD8b/ANgHxF8N0lureFb2zj+YNFlyB+VeA6n4al0a4aK4 jdHBwQe1fs98VPgf4j+BV9cf6PJfacpJktpYzhl/2T3JrxX4s/sR+Hv2mPDc +o+HZIrTWYlPmWewebkcHABB65oA/L5bJeeSfQUxrRt3A49zXqHxh/Zr8SfC PWbqC+0+6RbdsZMLAYJOOfwrzogg4IwaAOc8bxeTpYBOfnB/Q1+6P/BAv/lV l/bU/wC55/8AURsa/DHx9/yDh/vj+Rr9zv8AggX/AMqsv7an/c8/+ojY0Afg 1oXhQ6xY+duK/MV4NXh8PSRxIT7Z/wDrVrfDqQJ4dI+UEyN179K6bTtOl1WU RRwhi2MKBmgDgh8PWZsB3P0xV/SPgzeazMEhEhLcDjPP5V9c/s7fsIa/8WrV J1sbuOHcvzGPC43DJ/Wvvb4Hf8E/vBHwgsILjX7iNruMZ8tnTOcemKAPym+G f/BOzxZ8R5I1tra5Ik77TgfpXuvhb/ghj4m1S1SW9umswRkiRtoH/jtfqPY/ FPTPDEa6f4U0WHIGxX8osT74FWW8OeNfH8+Z4Hs7du7IVwfpQB+Ylz/wRV03 QlAvtdmkfGSIpM4/SsHVf+CUnh7TSVF9qMrDP8ZGf0r9gPDP7HdvrJjk1K9u pnYjKqQB9DxXpGifse+HtGgRktDK3X58H+lAH4J6h/wTGgkcrZwak59TKTj/ AMdo07/gkjr+sMfJgulX3Y5H/jtf0Jab+z1pSR7UsIwQeyD/AArotI+BFrbg EWoXoeUx+FAH861z/wAEdfEUNuWxNuXr85/X5a4bUP8Agnxq+g+I4/D0/mLL fq10uGySsJVD29ZxX9NeofDDT4rd1a2iPr8vtXxT8e/h7p9v+3R4Lt47ZPLl 8P6zIw2dSLzSQD+pr0Ms/jP/AAVP/SJEVNvmvzPya0z/AIJEeINVtVljW6wR 6/8A2NR33/BJTXtPZhLBcgKeu4j/ANlr+ijwV8NLBtJhVbZBkdAo4/StO++B VndLn7GrK3P3R/KvPLP5wLb/AIJftAyi7TUI/X5+P/Qa39H/AOCWeiX5xLLq CHjkXA/wr+ge/wDgBpvlOHsoycY5jGK5nUv2VvD+qq3naeiDHVFAP4cUAfiR pP8AwRk0fxGpW11S4ik/6aXI/wDiaq+Kf+CE3ieztWm0uZL9ADgJOWP5ba/X fxv+xfp+nyGfTpru2c9AGGP5VyB+FnjLwOzyWTyXigY+XJIoA/Ejx9/wTJ8a eCVkMtlPtjYjn/8AZryPX/2f9U8NXDR3ccsZX1//AFV/QFqXxcvdPjNprmjw TwkbH82HDfmeO9eb/Ej9mb4ZftC2LtDs0nUpAT5blVGe2OPoKAPwrf4dtGxB dwfqBSD4fMejuc+hFfdP7Vv/AATv1j4W3Es9pa3NzZrkrIiblYe2BXylr3h2 bRLh45I2R0PzKQRj86APMPEPh1tDCEkkOSBmv14/aq0n+2f+DOf9lGLcV2/F K/fj/r68VD+tfk/8R+Le2GD94nP4Cv11+PsXm/8ABnx+yqM4/wCLnagf/Jvx TQB+Q0fgNCDl5s446Co18ERkjMsnuOK7L7MFXlxknAAGc1YtdHa8dUjWRnP8 I5oA4T/hCF/57N/n8KevgPeOHc/5+le9/Dn9mXWfH19BDDZXeJRkHyzjHSvs b4A/8ElE1qGK611pbKIYJ38MR9CKAPzM034P3eqsqwrK5b0H/wBauz8LfsZ+ JvFZH2awvCD6gc/pX7LeFf2SfhN8F7VN8K6hdIMZkZSfyArYPirTLLMGi6HZ RRg4DMoH6UAfkp4b/wCCX/i3WQWmt5YQDggOCw/DFb03/BLG+0uIveS3UQAy QTjH6V+pV5NcarE2944hjBWD5R/WsO++GFlqO52RpXbqW5zQB+W+qfsF6Pos Z868u3cHBCyZ/p1rHuP2PdHjyIoNbmPsRiv1Ll+B2kSyZa2RsnocYFWT8GNO s8BLO36dcDJoA/KX/hjFZWxHp+qAHoWfH9KZP+wtqMjfu7aaMdt03/1q/WI/ DOyDriBQfZRUrfC+zm4VFB74H/1qAPyWX9gXV5z8pkHsDn+lXLb/AIJ/6uIW DGXacE/Nx/Kv1bt/hNDbnJtQB69RWrB4JsreIq1sC3GPl4Fehln8Z/4J/wDp EiJ7fd+Z+SDf8E/NTP3Flcn/AGzx+lV7r9gLWLQZeKTHqHP+FfrZJo9lYTEt artPAIGKR7HSLmJzJbRhcc8V55Z+QWo/sXXOmn955wz7n/CqT/spC3fEvnge oJ4/Sv1k1v4c6Tq8rBLdB34xXPap+zrY3yMDHkHsFx/SgD8wbX9mbSkfE73X H92Tr+ldH4X/AGbvAPnourf2wFY/ejnA/pX3nrf7GVtqULtFG6dwAOT+leY+ Of2GdUjV3gSYdxgcmgDgvh5/wTu+C/xJEaW/ibVbGdxys82Ap7dq7LW/+CEu n6xp7XHhjxEt8F6hpyTn/vn1rynxX8H/ABV8O53ZIbrEfGQjVb+HH7VXjT4Q atFJb3kyANuKybue/TNAHm/xw/4Jp+KvgxPIt1a3LImTuUE4/HFeJX3wsOnS skpuEZeoOOP0r9g/2e/+CoulfEO0i0fx/pmn3dvJiNptmGA9eSewrsvjl/wT D+HH7WPhSbxB8O9TgbUHQubRJkLg+gGMnk0AfiGPAKZ/1kw/FaRvAUan/WTH 6Y/wr6F/aK/Y28Xfs++JJbPUtKvo41fajtCwUjPHOK8iljaGVkdSrL1BGCKA Oab4fRj/AJbSH6Ef4V98/wDBuVoCaF+3J4kCl23+FLbO4j/oZND9K+Ka+5/+ Dek/8ZyeIRn/AJlS2/8AUk0OplsVHc/aOw+BDfF3wZ8FNbHi7xP4dPgCK11i 3s9LjsWt9SmaxNuVuftFtNJs8ie5jxC8RxcOc71jdPR/gB8ELf4d/Ffxh4lk 1zXdf1bxTY3ELSaj9mVdOsoYr6a3sYFt4YgYYnvLgq0vmTN5mHlcKu3xTW9M 8VWl5+zdqvhnw74412x03bH4jl0nXoLTTrbTptKkg/0u1nvIVuSLqW1mUpDN IiWs2wqzBJfQP2KrDxXoHxZ+MVlr3h7xvpWhTa5faj4avPEGuwarHd2UmlJH ILYreXE0EP2uC6mSGRYlSO6iCopDxx8XV69/zR030+495+A3/JV9K/7bf+iX r6Nr4x/Yx+DHiL4e/HewvdY+LPxB8eWstvPAun67Z6HDbwuULCZTY6dbS7wF KgNIUxI2VJ2lfs6ujD/CzCr8QUUUVuZhRRXz3+3lq/xp0zTvDsXwXsfEV7qm pC6stQe0tNEmsbKJxFsnkkv7yCW2uEYHybiKDUYo1NwZtPuz5CA62A+hKK+P fH/hr9qP4heM/jJ4Wl1iDSfDl54L8SWfgnUtJ0S10u2GoXboNKeTUxq0+ofa rWLIJTTLWLc8zeZIY4Gb2LxpqWt/Ezwp8NtY1LwvqHhrTF8UWt14j8Na7p1t q+opGFnjshixa7hSSHU20278+OUpDHbtI8iBHwr6XHYop+yr4iTxN8Yp7P4k X/hq0+J2u6b4h0260HRrRdZ8N3Nvp9jY3AE16Lu0uY5o9PhwGs0MYkm5dmR4 +98M/BnT/h98CdO+H/ha91Hw3peiaDH4e0m7tmjnu9LhitxbwyRm4SWN5I1V SDMkill+ZWBIPzD4UH7S3jD4V+MrnxBpnjaC78P634Zu/Den3LaDo2ta7p+n 6ul3qML/AGDULm1e7u7KPyHlku7S1uGfyxZafEsktx9V6Z4t1HU7zRoJfDGu 6euq6ZLe3NzNLZsmjTL5OLO4CTszTv5rlTAssP8Ao0u6RcxeaXajZf1ZaA3r e55Jbf8ABNL4TeI11KT4ieGND+M19qmow6s15488P6VqsltdJpWnaZJJAotE jhM0Wl20kgjQAyl9oVAkaehap8Gr288QeJr+18beKtJOv6e1paQWq2MkOhXL KynULUT20p+0YMY2zGW3/cqfIy8pl81jv/jdY6DYtqtrq9/P4knvNH1GLw9H pEd34VnK2tpbaravdzCFtOBgvb9o5VursNf28fkyLE8Q4T4l6J8TfB37VXxy 8TeB/hf40trjW/Bnha00jxNpMXhq5h8R6ppt9fyyxPa3eqW80kRh1GGJ/Pa1 dobS7WK4hkFrK5J3QorRf15Hrvwo/Y+s/g18DPhd4LsfEOp+JZvhDcWp8P6x 4mt4J7uC3ihlsmjK2S2cTONNuLi1jkKHaXSWRZ3Vt+lafsywWPxt8ZeOIPFf iZb/AMZWmh6dLYlLL7Fptnpdxc3C29uBbiTbPJeXfnNNJK+JyI2i2pt5L4KW fjXV/DXwl1D4uaatz4s+36rPJBP4fiu7nR9RlS7a1nWeyea102KPTjeWxVpL g/6VDbnULmTMl7yWqQ/EbxZ4A/a3068+Gfi/QP7alvX8F3Vlf6Jp174ojOg2 1gjW9zaTTzRXRurOVo576ISRxT2YxiIwQ073vfz/ABQLXc+p7u3+0Wzx5xvU r9MivEvix+xXP8W/g3aeELn4rfErSHtPEdl4jj1iwTR5L8Gyu1u7Kz/0mwmi NvBNFbspaMzv9nXzZpd8vmeneLvEmu6a7WWi+HpNXvptLu7q3u57yKz0xLuL yhDa3EgMlxH55kYiSK3mVFglL4bykl+T/h38Tf2tLv4c6zb+LvAevPruv+Ib x7TUtIsvDmlyeHNEh0S1lRIbKXWb6Jr+41FbmC3M11cQpJL59xtt41t3Sbvp /VmC6H0z4j+DMmo+ONZ13SdYbw5qniPw0PD9/qtpaRzaqjQSSvYXEDT+Zap9 ma8v22S2sqyPcRlvkiMcifCP4H/8Kk0PRbBfFHiXxAmlpfPcz6u9vLPq13eX IuZbuYxxRqjhzKEjgWKFEnZFiCrGE86/aD8G2fiPQfixHqHwvm+LWp6jpujt H4e06wj0mTxFp8csht9KutRu547O8jS7GoSzQtMEW2vDHJbOJ/8AS874MfDL U/Cvwv8AgBYaX4A8S+AbbQvEl7c6roC3tnZwaDE+m6wrCS3tru+gNg1xND9n soLtxbia0wwW2MVNC3PcviZ4Jm+IPg670q21rVfDl1cAGDVNMMX2zT5Ryk0Q mSSIurAELLHJG2MOjqWU8Lffsqm8/aR8GfEhfH3je3m8F+H7zw9HoUcelnS9 Uhu3hkuZrktZm686SS1s3JiuI1BtUAUK8wlg/aZ8ffFXTfhlrj/C/wAH/aPE um6vb2VvLqUNnfJe2jxxPLeWtodRs1nCPIYWjub2xdfKnlUTCOGK64b9nXxt 8e9Y1P4daX8RPCPiHSItH8OaMnivWYptDeLXtcm0u4e/ZkiuXeCxtbmONGEE YlmubqAx7bSCdplFv+vQLdTtviZ+x3p3xR/Z5+InwwfW73wx4a8fT6l/pHh6 3ij1Kxg1JzNqKeZeC6ieS4uJ74mQQoEjugiIrxCU9D8WPD0ui/DrxTcS6jd3 /wDaNxbzRpMsYWzRfIj8pNiqSpZGkO8s2+V8ELtVW6ZpbftMfBjQbvxJpV/4 aN9faf4ihsA8iXduttfRX1mlwk8EbxSssMPnwtGGiZ5Yw7FRKfPvEXh/xtZ+ Ovi/d6w1xF4PkurCXw+JisgnV7awjm8tlum2Ik1vMTG9shDTM6SuJHUbQpQl Sk3KzSbt32St3ev3K5tTpxkm3KzX/A2/rofxffCv/j1vP95P5Guz03TptVvE ggRpJJDgAVxnwtYLa3mSBl06n2NfVv7AvwTj+J/xUshOiNCsikgjOeSMViZH vf8AwT3/AOCcj+OprfXfEUT22mQ4kZmx0z2496+iv2pP2ltN+BvhtPB/ge3j VpR5TyKuHJxzyMdxXpvxi8cWHwF+EUWn2WyzC24B2KBu+UeleA/sn/BG4/ac +MyX90plt4pg7eaM5GSOnNAH0l/wTr/Yyu/F/hw+IfEMU4luwzjcRk7uf61+ hXgLwLa+FNMjtrKDYkYAGAOa5H4e+DH8AeGbWwtIsJFGq4QdOAK7zw5qU6OA 0c3XnI/xoA6bRtHlkZAynGcjmu58P2qWo+ccj8+9YejTq8O5wANvsD3rQg1V ZZQoHORQB2dvLAVGwKB2JFRavqFrBbss8iqD0zWNJqn2aMYY469a4/4n6lJr GlGJTIjgEAqcHGKALuueD7HUpWlSUNk7uK+KP+Ch/hE6f8W/gc1qk0rS+NLl FUD73/FPawcD8j+VdX8RP2h9W+A96Xu72W5ti+dpJOB1/lXGfFD9rTQ/ih8R PgPeSwqx03x1dTTBlU7l/wCEY15P/QmFejlX+8fKX/pLJnsfUPwq8KRXPhu2 F1bMp2L94DjgV6d4X8O6foO3ZIN3YV8W+K/+Chdtb60thpieUgfYDwoGTjsa 9e+EHi7UvFcMOo3F5LKJMMFLHaOa84o+pI5IngBODx6c1m6mYZ4mBUD1x/nm sDSvFLzwIHPygYPOM1PqN/mHeOw4xQBha/oQlZ2Q9R+NcPr/AIVl8w5ye/Xr 1rvbjWI33Dcd59xXN+JbubbmNGI64AzQB438XfghpvxD0Ce2votzlSFIA46V +Vf7WWheIP2O/iwurafGy2Zkz8o+VgATggH3Ffr1rkt/cTNtVkB4GK+bf23/ ANmuP4tfD28MqQGeONipdckcYoA+YtPtvAv7YXw8SHVLaGLU7+HBkVMEsq4H Uepr83P26P2C9W/Z38V3M9tBJLpsjEpJkHjOO1fR/gnxrd/Br4gjS5ZGiW2m YBk4X72Bx+FfU/xW0TTf2lPgDcQ3kUU15BAWR2QFiQGPUCgD8DfiCpTTVBXB EgGfwNfuZ/wQL/5VZf21P+55/wDURsa/Gr9rPwGfAXiG7s9iqI7gAYPtX7K/ 8EC/+VWX9tT/ALnn/wBRGxoA/Dv4eb30MKh6yNwa+5v+Ccf7G7fGfxdBc3US taxsrOWyBjOK+Kvglp51FIIxkl5mXgc1+3v7Dfha2+Cv7Lc+q+WqXNxC2wkb T1U9/qaAHfFT44eHv2bbWHw9ocUcc6psdlUHJK+/uKp/Bfwz4j+PWpR3dxIw tpTud24zXynq2sTfFD42XEly+6AXaY7gZlwa/V/9m3wRZeHPAWnw2yIqiMcj GD1oAs/DL4DaH4IsoytlE9xjlySxJ/Gu3fQlkxti2r6bcV23hnRbR1G6RR2w TxXXWfhLSJUzJKg+jUAeQ2Ok3FnKBFGME4yvavS/BejyX1oomTBJ9eK3bS30 LSpAjTwBh0BYZNb8X2ae0823ZWQd16UAZ9p4UiiIIjyQOuMUniBV0ewLeXgY PQZrW07U4k4f5iM4B715R8X/ANpq/wDhnq/lT+FrvUNPJGJYwSAPoFoAj1TU bi/nkEcEhVjnkV8m/HfwrPdft6/D/MYUv4X16QDnoL3Rh/WvrPR/2tfDeu2U f+hSWUj8FXTG39K8Q+K9qPF37e3w41Cylhe2/wCEL8STffGAqX+gg/8AoQ/K vQyv+M/8E/8A0iRFTb5r8z2LRo73SrOJRA2FHOBxXoXhS7bVbQAoykdeOMYr kLr9pTw/4Os2injW6eHIKRgM38qzfAH7YMvjzxcLHSvCN8trnBmcMqY4/wBn HTNeeWenT+Ho7hzuVQPX/wCtWLrvhj7NCxQBsDiuwu9XjmtxlAjkcrjpUAdX hLkhUxklugoA8T1zS3mlZfLI57j/AOtVFfDmB0bOORg17Bf6nodzcFWlgLg8 4YCql4mkGM7JUye2QaAPn7x78JdK8TWEkV3YxuzA/MBjnFfK37QP7Nd/4Rjk 1DR5SIowWCjOVwc1+gHiO2tXRijDrjgDpXlfj+xgvLC4ikCsCrDt6YoA+Dvh F+0NY+I9Rl8HeM4kuo3UxRtIBlDjA54PU18v/wDBR/8AYhX4e30mtaQiNp1w d6MmTwTxXc/tq2A8BfHhbqxyjGfc23gH5lr6OufL/aH/AGOmEwjkvLKAfNjc fusaAPwi+Klm9nNbhgBhmGBX63ftAuU/4M9v2VCCQf8AhZ1/0OP+XvxTX5i/ tdeEj4T1+3hIx+8kHTA4Zh0/Cv0++O1qbv8A4M//ANlFAM5+J9/x/wBvfimg D8wfDugXGu6hGiAsz9O9fWX7KP7Hf/CR30FxfQh0Yg4Oflriv2ZfhWuo6hFP KhChhjcvBr7g8GSReFdGit4AokIGSDjtQB3ngnwj4V+EWnRtHbQPcxII/kGf XufrVLxj8fbufMFpIYUbgKoGa4LxP4gfPlo5eaRsZGTj/Oa6j4W/D4uy3Fx+ 8c4PK4x/n+lAC+HdE1bxXc+fOzMHIOWJr0LQvAq24/eklsds4ro/D+gF0VFA 29hjFdVovhIOgO3lSOSKAOGi8OBJdqxq6n0PIq7b+E5pHHlxAAdu1ek2ehJA Q5wSD0xkVfjs8qfLjUH1C9KAPLJPhvd3UgJUIB9eKsN4Aa3hxN8xzXp9vpcs pEcStNJnOI1Of0rotD+A/iLxAu+PSrtkIzmSMgGgDwq28BGO6BC/IfU5rQXw QFxtQH6Gvoq1/ZU1zywbo2loCP48cfrVgfs0iIhZNe0+NvTj/GgD51Hg0+Xt wTntiqVx4AmI+VFGe2MCvpN/2bZmQmHWLWQ9js/+vWDffs9a3ExSO5tpcjg7 +tehljSrO/8ALP8A9IkRPb7vzPnq5+FM16Tymw9+tUrv4K7YyAQM/U/zr6FX 9mzxbK/7q38xe21utUdX+Bni7Q4mZ9IuZVA5IUnFeeWeBW/wq/swE8Fh1zzm hfDRSXGFA6Y9Pwr0nX9SOhTGO/spbUjqzggfnXP3viXRrrOJIFf18wHH4YoA yNN8PK+0FB1/CtWLwXDdgKsSY/iPWk0vVbOeQKJYmHRSDnFdZoiRqMgpx39e KAPNvG37PekeKLBluLSJi2QDgivmf48fsCafqVnJJYWyo+3jAIGa+7bi0N0c Jt2t3XFPtPCcV3uDLv8AcrQB+IfxG+C2tfCfWJVeKSMKeCMgHrz0r0f9ln9q Xxb8LvFEEmn6nNA8TDCcYb2r9Lf2iP2PtK+KXhqZWiVLlkIVwvSvzL+On7N+ s/BbxdLLHBOUhclG8sgHrxQB+mPgT4o/D79uTwPFpXjrSo4tct0VxcFQglC4 9Dzwpr4C/wCClP8AwS5uPgXq9xrOgJHc6NcMXjkjLHAzgDp7V6z+yT40sPiD 4dWCaaOz1SyXqeGI4GOfbNfXnhO/T4k/D+68FeIEW5WSIrDK/wAzZCnBH4mg D+fLUYW0m6eKYNvU4xX3J/wbyssv7cfiFhnI8KW3/qSaHXkv/BRf9mW5+Bfx b1CBYpDAZG2N5ZUFcjH616v/AMG7zn/huTxCPXwpbHP/AHMmh1Mtio7n7V+H firqfgDS/gXoqaLY32i+OIE0ie/fUnhudOnj0ie+jK2/ksssbpZTqzGWNkJT CyAnb6X+z58RtS8eeKtXiuNN0uG00nSZba4vbDUJLu3bVRbXS39nEzwRebHb MsSeePvSPNGyRSQOteXaL4R+E/jXSfg3Z+N7D4e6r42tdKivvCEGtwWc2pxy QwwSzTaeswMoaPZE7tDyvloxxhTXb/slfDP4P/DXxDqw+Fug/DTQpdd0L+17 w+FLKxtW1Gzltbz7HdSfZlHmQuVufKkbKttl2k4auHqdL+E9h+A3/JV9K/7b f+iXr6Nr5y+A3/JV9K/7bf8Aol6+ja6MP8JjV+IKKKK6DIKKKQttB9AKAFor O8TeKNN8GeHNQ1nWL+z0nSNJt5Ly9vr2dLe2s4I1LyTSSOQqRooZmZiAACSc UniHxLbeGLCK5ukvWjlu7ezUW1pNdSb5pkhQlIlZggZ1LyEbI0DO7IiMwVwP J/hv8ePEerfGH4yW2sJpFz4a8F+LtI8J6JaWFn9nv0NxpemXk9zdXE115Uy+ ZqahY44omRLdgPPeRVGkvxz1W8/aU+HvhSOwgstG8W+C9d8R3cdym6/tLmyu 9EhhiDxytDtC6jcB9vmBmSMo4UHfJ4j+AHwz8N3vj7V/EFnZPZ/Fm6sbTxNa 65qctzo+sztDFpltCbK4ka1R50MFsyxRqbnEKP5hCAa95+zT8ONR+KuieO7j 4f8Agmfxv4Yshp2j+IZNDtW1XSbQLIn2e3uSnmxRBZphsRguJnGPmOWraP8A rb/PUHa+h5z8S/jv401D9nnxr8QPBWt+CtHtvButawzQ+K9CuFhlsNHa4tLy 2knjvk8l5byznkjv9rpHbyJutJGUsfadD1e9vdW1mG7trWGGyuUSzkhnMrXE JgjbdICo8txIZV2At8qI24Fyq8/42/Z08B/FFiPFnhfRfF8Uetw+I7WDxBAN Xg0zUYYFgiubSK53pauiJx5ATDPK+N8sjM/wX498JT/GLxp4S0lXg8V6XHYa 5r8R0ye3WdbyOS3tbjz2jWO43Jp8kWY3cp9mCNt2gVLvawuuh41q/wC194v1 79n/AOBnxX0GDw7pXh7x7/wjEuv+H9Qsp768MevXumWcS218k8KQtatftIzS WswnEYUCDJavcvjAPE9x4CuYfBer6FofiS4kiisr/WdHl1extyZV3mS1iubV 5QUDqNs6bWKsdwUq2d8RfA/gXWfib4E1nxF4T0jWvFel3tza+F9Wm0D+0LvQ ZZbZ5LhorkRObFJIrco0peNHYRR7i7xo2L8T/A3wl8UfDv4lQeO/CXhO58Hv dLqXjUeIvDsY0vVJLa0tZhe3JuIvKvFhghtR5/7xYzaCPcGtyqVKz0Stv+g7 r+v67nmPw3/a88bePn/Zz8T7PC0HhT446TZR3eiDTrg6jpmo3GgX+tfaI777 R5bW6rZCDyGtQ5LmTz+PKPe/sR/G7xP8ZvhbqMPjyxbSviH4R1aTR/E1idIh 0tbS5aCG8hVYYNS1OLBs7u0fcl7MCXOfLbdEndp8EPBcHxMtvGy+EvDCeMrP SRoVvrw0qAanDp4cyCyW52eatvvJfygwTcScZp3wk+DPg74AeC4PDXgTwn4a 8FeHbV3lh0rQdMg06yid2LOywwqqAsxJJA5JzTk43bQPW1jznX/2vIPg742v PDvjbSfFF7eeTrGs2N54d8MXGpLd6bZ3elxKsdlZy3d7LKv9s20TMsI3taXc xjgi8sN1fhn9ozTPGujaLqOk6J4tez1yGSeJdT0iXQb23SO8htXM1lqIt7yP BlMgzD80cRK5MkKy51l4Q+FvwY8ZeK9c0jwJpuleINGtLrxNq9/ofguWS9vD qDb7t4pLa3L3l3cNpcJmhgMtw5gtN6Etb7naT4++F3x90zwH4qkXSNXa61ed PCr61pb297Z6rbxXaTxxQXUaz219DFBfpIjIk0YjuEYLhxS6WYn1a/r+kcl4 G+Pfjn4dfCz4peMvijc+D9e0bwJYTaqB4L0q+hktntoJp73TUN1Iyag1uEjj W8iMKzStNG1vbPA6m98RfjX46+HHxk+CXhKWx0K9s/GOqSaN4l1v7LJbw3Nw ND1a+A0+D7Q7xETaarP5zSBY540VpXLyQ9h8Ff2Vfhd+zQNWPw5+G/gLwB/b 5jOpnw34ftNKOomPf5Zm8iNPM2eZJt352+Y+PvHN8/AnwbJe+D7698PaZrOr /D63kt/Dmr6xH/aeq6OskKwSvFe3HmXAkliVVlk8zfKPvsxJpXHdG54I1a91 zwfpt3qVta2epXFrG93BbTtcQQzFRvWORlQyIGyFcqu4AHaM4rg/jB8RI9Y8 O+INFGm30X2OUQtdtLbtAzIbOUAKsplXcLggbo15hcnAaJpes1HV/DvwN8Da XA622iaFZy2Oh2EFvbsIYGmmis7WBEjU7VMkkSDgKoOSQoJHD/FTQfBZHi3V tNtfDjeNWt7bTNVvYoojqfkxSx3EdrLIB5nlp9rWVY2O1ftW7A83JuVKTg5J O2v6f5/iOMJWTS/r+rH8THwji+0TyrzlpEAx78V+q3/BHv4UYsptanXasC79 x4xh/wD69fl58FtTn1jWFS8uJrhPOQYkkLDn61+uf/BOv9nn4f8AjD9nbUdS 1fwX4R1m/igZhNfaRb3Ei/Mo4LqT37Gu1U8F/wA/J/8AgC/+WEe95ff/AMA5 v9tj4my+NPiZDokLEQq4R9pOAMEZ/SvvD/gnp8F9M8DfD22vI5oluLhAxyQD zg1+W2qfCrwzrfxfurRPDuhQwiYokMenxKg+ZuwXFfoL8Dv2QvhcfCFkdQ+G Xw9vJTEpaSfw5ZSuflHUmMk/jT9ngf8An5P/AMAX/wAsD3vL7/8AgH6BeHrm LyVD3CYx0VuK6+w8WWtjb8FX44b04r4j0j9jz4JmMb/hF8LGx/e8J2B/9pVs xfsYfBBowU+D3wpdsdP+ER0/n/yDR7PA/wDPyf8A4Av/AJYHveX3/wDAPqTx P8TI1uAq3Ow5+7kV1Xw18TpqEBdhnaM5PPvXxPqH7GHwZkxt+C/wuVR3Xwnp 4x+UVdh8Jv2F/gxqF0Em+Bvwku4fWTwfpzkfnCTR7PA/8/J/+AL/AOWB73l9 /wDwD6+vvFoN2VJVdvGARVbV7uC6sXd5AAe5rwnxJ/wT2/Z08P232m9+DXwd s48E4bwfpw4AyePJ9Aa+bPjBo37NOnao2leHPgv8IbmdWKtJH4M088/XyfUU eywP/Pyf/gC/+WB73l9//APRP2u/hHJ8VNYVI551tkPzAHA6c8Y+leL3vwMs fCHxC+DcM00nl3XjO5hcs3QDw5rbenqB+ddF4C/Yl+HPxN8s23wX+GUUch5I 8J2Axn/tlWn8aP8Agmd8K/D3xD+Blinwx+HtsuveNbmxvVi8O2SC6iXw7rVw I5AI8MgkgjcBsjdGhxlRXoZXTwX1jSpPaX2F/K/+nhM+a2y+/wD4Bi6n+yda 6p4ieaG5lQrNv4YfMN2fSvrf9m7SI/Dfhi10+Z2LQoFXcf8APrXhXxE/4JZ/ DDTZVl074S/DYKRyo8MWJBwM/wDPPHNeP+I/2ePhj8L74f2l8E/hiyxn5ll8 IaewYZ/6415/ssD/AM/J/wDgC/8AlhXveX3/APAP0vvtbSzTAIGMDkVLaeIz JpzsWzgZ/nXxD8BPh9+yv8XJo9OvPg18HrHUxxhvBunqGP8A34x6CvZvEP8A wTj+BK6OTpnwO+DsshGVf/hDdNP6+TR7PA/8/J/+AL/5YHveX3/8A6vxV46W 31ZvnMXJ6HFXND+KETQY3kj1POa+Z9e/YB+E+n30mfgt8LQwOfk8J6ftH4CK qA/Ym+D0ZKv8GPheSPTwjYf/ABqj2eB/5+T/APAF/wDLA97y+/8A4B9Uav4r tr+EjzEj49RXAeLpbTUbeWCS5VkdcHLA/wA68Mu/2IvhDKx2/Bn4ZKPbwnYj /wBpViat+wn8LLtCB8IvhrDx1XwxYqR/5Do9ngf+fk//AABf/LA97y+//gHx 5/wUU+Fen+E/E2pX9hIS6vC478ksSK3P2CvHzeM/D72EzDebRwy55zsPFUP2 3f2RvAngjRr660/wf4U0vY0Qxa6XbwlSd3ZUGK8y/YD+CPhDxt8RZYdT8IeF 9WgCudt5pEFwOFGMb0IFHs8D/wA/J/8AgC/+WB73l9//AAD5J/4Kz+DD4V+J d/lGTddHhhgjmv00/wCCBEjRf8Gtf7aLoxV0PjhlIOCCPCVjyK/Pb/gsp4T0 P4YfEye28K6Zpnh+KG4K7NNto7RRzjpGFGK/Q7/ggzfTaj/wa2/tpyXEss8g HjhdzuWOP+ERseMn61lVjh4rmozk35xS/FSf5DV+p+Mv7NEUnifXrFb1mvFe 5KsJTvBHHXNfrh4x/Z/+HPh79mWzuY/AHgx7+WAFrs6LambOxTkvsyfz71+S f7JNyLXWLRjgYuup+lfsJquqr4m/Zss4kcvshwRnj7qitf7Wx3/P6f8A4E/8 xckeyPkL4BfCjw3488X3EUug6JII7oDy3sIipXzcYxtxj6V+nPwt/YQ+GN54 atWk+Fvwzun2ZLSeGrJmb65ir8/P2YPA72HxOv2Wf7OWlRgGHX9771+q3wzv 7qHRbdRL92Md+tH9rY7/AJ/T/wDAn/mHJHsjH039gH4VtgN8IPhUx6c+E7An /wBE1J4k/YB+Fej6Y03/AAqH4TpnrnwjYHH0Pk16xouuX8Tqyndg5xmu70+I eKNNMF6oQMOvYUf2tjv+f0//AAJ/5hyR7I+SvB37HPwavr5Uuvg78MpmaQL8 vhSw29PTyq+ivB//AAT6/Z6GgB7j4GfCF5JOjP4N04t2PUw1r6d8MNK8NX3n fbECq28ZwO2PWt66+JelWUSIlykgiOOHGT2o/tbHf8/p/wDgT/zDkj2R57rH /BPn4D298pT4H/CARH/qTdO+v/PGrrf8E/8A4AGFQ3wL+DxbHbwZpy/+0a1f EH7QNrCywLYXMo6B1A/OrOhfGGx1IBWvIlZv4HYBhR/a2O/5/T/8Cf8AmHJH sjjdU/4J3fAaSJtnwR+EqcdU8JWAP/oqvmX4y/8ABNf4c3v7afgPSNJ8C/Dz TdKvPCmv6hcWaeG7MQStb3uiopZBHtZwt0wVjyNzAY3HP3PB4phnB/e8Ef3s 187/ABg8c6Laft+fDb7RqqQCPwZ4ljcLJwHa/wBAKqfrtb/vmu/Lc1xrrP8A fT+Cf2n/ACS8yJwjbZdPzOu8L/8ABOf4D2sKeb8FvhTPjgtJ4VsGz+cVdCn/ AAT8/Z9teB8DfhA59/BunN/OGu5TxXBHCrJKhRhldrZz+lZWs/Fm10uL57iB Wx03qSa4P7Wx3/P6f/gT/wAy+SPZHES/8E/PgNdX22P4H/CAAtjA8HaaAOP+ uNbF1/wT3/Z9XSJEX4GfB/7QB95fBunZB+ohosv2lra2ufLTS7qX+Ey4AU++ a6LTvjLYanIC5EAcAkM2KP7Wx3/P6f8A4E/8w5I9kfLHxQ/Y0+D2k688cPwc +GNshOMr4UsAB1/6ZVo6F/wTx+FOt6cs0Xwq+FcjMM4PhSxGPyir6G1vwxov ja9E3nRsV5Kgg5qDW9SbwtYiCwtlZU4BXnsPaj+1sd/z+n/4E/8AMOSPZHz7 N/wTv+GLPtb4SfC4Y9PClgP5RVR1n/gnd8KILVmb4YfDNGK9U8M2QI/8hV63 qnizWZiQfNiUE9+lc9q+s3bW8nnXDg44O7NH9rY7/n9P/wACf+YckeyPy/8A +CjH7N3g34OeIBJpnhjw1YROeEtdNhiX72OioK2f2MvhD4B8W/Da6OreBPB+ o3DWzETXOi20rq2w8hmTOc1o/wDBTmx/4STXrWNbkzStIvyhs/xitL9mrTx4 S+HMuUClLZhz2+U0f2tjv+f0/wDwJ/5hyR7I/Nr/AIKKaDbfDvxdaR6JBHpM TzSqY7RRCpAd8cLj0r9EPizu1f8A4NG/2U2uWaYyfE3UN5dslv8ATPFA5Jr8 7P8Agpdr6a74usmTnE0pznP8b1+ivxDYL/waM/soFuR/ws3UP/SzxRWc8xxU 2nOrJ2d1eT0fdahyx6I+bf2fvhd4f1XTRJdaJo12VH/LzZxyE9PUV6zZfC7w NBcDf4Q8LtwBg6Vb/wDxNebfAXVo4LGIFhjjjPA6VpeL/ig2iawADhC3UNxW n9rY7/n9P/wJ/wCYckeyO30X4J+DdQ1ld3hDwyY8k4/syDH5ba9m8Hfs4fDq SFPP+H/gqU9y2h2x/mleIfDD4v6dqWsQpNPGhcDJ3CvqLwJe29xZxvbSwzA4 6ODij+1sd/z+n/4E/wDMOSPZF3w9+yz8LLx0jf4beAmK8c+HrPn/AMcrsLP9 jn4SuAP+FYfDvGM5PhyzJ/H93U/hXKTqScc9ufSvTfD1zDdIqM8Te5ODR/a2 O/5/T/8AAn/mHJHsjy4/smfB/T0Jn+Fvw5OOcHw1Zfz8uvY/gL/wTm+AnxCj Se9+G/wydCceWvh2yB645/d1J/wjdnqqbJCnPByARUUPgptEdjp+p3Fo3X91 IVH6Gj+1sd/z+n/4E/8AMOSPZHtGr/8ABN39njwZohlsfgR8J9SmVc5l8LWM ueB13RV83fFX9nr4cWuqSW9j+z78H7JEbgr4H0snH18irnjHx5468J2zNaeK L6VF6K07Y/nXn0f7Vus29w39szSSurEb2cn+lH9rY7/n9P8A8Cf+YckeyOr+ G/8AwTz8BfEm9U3nwo+GFnEx52+FLGP9FhGK9e1j/gnV+z38NNKia6+EHwwv 7tgP9d4asivp3jryfwR+2mumTK1vLLMV6qGJre8Z/tIyfFSBfPgeFBgZ3HJ5 zR/a2O/5/T/8Cf8AmHJHsjpZf2GPgj4gsD5Pwa+EFmsi8FfCOnFh+Pk15f46 /wCCS/g/WZ5pdH8IfDSAMpMca+FLAAHj0h967DTPGVhpWkCafxAbRVBJXzuf 51seAvjF4Z8TukbeKLqFywTcJyvJHXOeK7svzXHOq7Vp/DP7T/kl5kzhC2y6 Hxp44/Y0f4Ka6d3w7+FGtQxHJjk8IafISPxgr134DTfA6O1SDxv+z98KoZRw ZP8AhBtMK/n5VfT4+HXhy6zcDVGvC3O6Uh/fkk1x3xJ+HmjmykDwwTREffjU Aj34rh/tbHf8/p/+BP8AzK5I9keP/tA/Cb9mzxZ4ek/4Rv4UfDTT7plOGt/D FjCVPb7sYr408W/steHp7uU6f4S8KAB2wqaRb/h0Svojxl8Hre+8Tqmk6hI6 u3zIykbc54616b8OPgfDpKQvdBZMKOqfWj+1sd/z+n/4E/8AMOSPZHwIPhNp 3gm7/wBM+HnhO8iU8ltCtm/mldf4DvPhdc3Sxaj8NvA0WCFIfw7Z5/Ly6/RI /CPQNTgKz6fZupXHzRLnFeXfFj9j3whqlo8kVpBbznJ3RRrnP6Uf2tjv+f0/ /An/AJhyR7I4PwD8Evgt4ss4Xj+Gnw5kYjgN4cssn/yHXdWX7Ifwdkw3/CrP hwf+5asj/wC068VuP2evE3w8vGudFu5TDEdwjIIwPpW14e/aJ1nwjcLBrtrO pY53PnBz9aP7Wx3/AD+n/wCBP/MOSPZHt9n+xr8F5IwG+E3w0APU/wDCMWX/ AMaryj9of9gz4V3Glu2meA/BunSKCQbTR7aHt/soK7rwz+03pOoRrm4SNyBg GQc/hWF8WvjLYXWkyPFdoMLnhhg0f2tjv+f0/wDwJ/5hyR7I+DPGv7N+hfDv xGXGgaHNC5KeW9nHIo4PYqR6V7H8J/h58OtXtNNuLv4feBpjlQ6yaBZkPk9w Y8HiuX8ca5L4/wDEhSBdwQkluoPWun09Y/C+nWFskg84lcqhwc5x0o/tbHf8 /p/+BP8AzDkj2RR/4Kj/ALK3gm1+F9jr3hPwn4b8Ou0eZBpumw2gYlj2jUDp Xif/AAb9JcWX7d3iW3eaYxjwvbEozkqT/wAJJoeDg19Yfthaml7+zZbW1zgy iBTgn3btXzL/AMELLZbf/goH4m2qAP8AhFrf/wBSTQqyrZli5wcJ1ZNPo5Nr 8yowjfY/XsfCHxN8R/DXwF1nQvEmg6JZ+BHt9bvbXUNBl1GXVd+mS2RijlS7 gFviC7ufmZJfnMTY2oySepfAf4f+ItD+L/jHxB4i8QaTrH9s6fNZ6Za6foza eunWMEV9JEkrvPM9xP8A6SweQGOM7FKwxkuW8T1v4la38O7v9m+LTbvxs2ja 9t0/XtP0nwvLqunG0fSpPKuLu4htJXtDHe/YgrmaFCksxcOkbvH7f8BvFsuq fFfxtpFzPr1zc6Tbyyk3Ohz2WnQRzWEpjis7l4UjuwFQNKySzFJZHVjGCkSe LZ30Ov7JZ/Yx/ZM+FfwH+PFhq/gX4Z/D7wXq9zbT2c17oXh2z064lgZC7RNJ DGrFC0aEqTjKKccCvs6vnL4Df8lX0r/tt/6Jevo2ujD/AAmFX4gooorczCvn v9vH9inUv2ytN8O2Fh4i8O+GLbTxdW+pXN3ol7f315Z3AiEtpG0GoWsLW8gj BmtL+G+srho7czWsqwhW+hKKOtwPkzWf+CXFv4l+LHxT1fUvGmq6hoHxK8Ka /wCGYoLu91q91PQV1qWKW8+zNcapLpkUG6NNkMGmQsBDBulfY/mexap8J/E2 v6D8Or/xJqWh+IfGngbXk1d7jS4J/D+lXhlgutPnb7O73sgCWV9O6RNK3mTx RAyRKxZfUaKVtLBc+P8A4Z/8Exda8EfDrxtYXHjjwuuu+INT0DVNDl0vwvfQ 6V4eOiaj/aNharbXeqXdx9gW4wv2G3u7a0hhLrbQ2ryzSyfT+l6V4jS80b7f q2h3NnDpskOsRQaTLC9/enyNk8DG5cW8AC3O6F1mZvNixMvlN5vzr8O/jGnh n49ftR3kfiHV9a1nwl4w0CO70JLi+1+bR9DOgaVcZtNHimLRNPJJqhVraHzJ 5Ub5LloVirdvvi9rkX7c/wAINEvrbx89nqXw+1x9WmsPC2sJ4c/tGa50iSzN zMI3tLefyrPU9iXExlhDmMtm5TzXa6Xn/lf8tBu6f4k6fsQXul6DY2tn4m0j UZrqa8sPFH/CQaHNqtr4s0e5W2tjHdW/2uNZdRTT7CytFvpzMpVbhmt2FwyL l/Gv9ibx18Vviv8AFDW4PHPgGHQvHnhjRPD+m6ZqPgzULi60OXS7ye7gvTe2 2sW0rzrLeXjxyQC2lhkFnJHIr27NPiftEa02s/s7eM5NYf4nx/ECDxpqVvpd l8NNW1q/vtP1GOKZNFE8dlLJDawy6cNPupYL4JpplulkuEBlyfo/wbey3fiL xUn2zU7uO21KNES7tzFFaE2duxit22L5sWW3l9z4lklTcPL2Im3y3/roJaaI 86/Z7/Zn1T9nXwN8O9Eiv7TxLdaBLqkWs6gbq60yGSO+kmvZrlLQvc/a7k3a 26ebeTyXRSW5mkvJppJ/tWBD+yL45fw/+0jZaj438GajD8cJrq60eNPBjRr4 dkk0mHSkW6W4vLmHUEWC0syymGFXdZyV2SrHFq/Dr4h/Ebwh8L/g3pvjiOeT xvr2g6fa+JvK0CTUwusiK2e786WxcW9pERHfL5zL9nEjQ4fPlwz+bfst+K9R /wCFVfGjUPhunxw1zxXp9gx0bT/ixZ6/ZJrWppBcmK8j/ta1tY7dby4GHtrC T7LBFHbMsVo0zxCpXTb/AK3Grp2X9dT6b8WeGtd15jaab4hXQdMm0u7tJZbe wWfU4Lt/KW2uoJpmeBfJUTlo5raYSO8JJVY3Sb5Q+G//AATA8feAPh1rXhuX 4u6DreneLPEN54j8RW+r+HdZ1ePW3bQ7XTbSymlv9dubySxSe1juLi3kuX+1 Rj7LuhgLq/fyT+G7v9pH4PS+DNV+LetK2hI8mpWWqa1qXhC/8PGwvvInu7qZ 30y4uXuDbMJld9SfMDMzW5c13fi698b3/wCz54RuPBd1qV/4hn1Lw093dajb QWt5c6WdTsv7TknhmjjSKVtPN0zoI0dWJWNVkCAC0d/l+JK3sdBN4R8S6frP jhtEn0vT5tdsYLnS9T1K5u9Wjg1TyXt236eXiEVnGsNnIIre6jEzy3JIhkLT Tcr4X/Z58SaTovwzTWvFWk69q3hHxRqHiXWr4aBHbJqDXdtqiGGzjZ5Xs1SX UE2sJZJTBA0cksnmys3M+Ev7Rk/4KK602kD4jDRIvDV4nisaodY/4R1r8vpB 0k6cLv8A0Ev9nGp+adM4D5+0/vfLrN/at8T3fjnWfgtq3g+H4rweItW1vS7/ AElLGx1/TrCPTX1LTn1ZdZtwsdpCf7LNzsi1ZEkV96wKLgEUJXa8/wDhg/r9 P68ju/2lfgD4y+P3wy13w7Y/EAeFDearbXWnz6bbahZullHHF5lpdz2WoW14 7NMJpVls7qyI/cRyLNEk6XXDfs6/sM+LPglqnw3i1Tx/4d8V+Hvhb4c0bw5o mnzeFZoZbZbTS7myu75ZTfuv266eWDE7Rt5FrHcW6KTdzTnoP2SfibqHjf45 /tCWF7b/ABBhtNM8b250d/EGg6rp9i9kNF022cadLdwpDNB9utL9j9lZkLOZ uRcJJJk/EL4v+NfBP7SMuh+A9D1bxEuta5b2+sQ63o2vQaXYs3h3VriKWDVW SW0htZLmw0mCRrWGWO3kmn8xHuLtAFG9tOqT/D/gjtueu/Drwbq138LtGs/H sljr/iBWi1DUcpFPaQXomFyqW5EEO6K3l2LBI8Sy7YIncmXc5858Z/AR/BHi H4peNJdYlvY/GdxYXFvYq1zFHppihtLWTKG4a3kZxbRsJFgjkALKzuu0Lcvf i58T/C/w2g1Lxn4Y8M+F9bm1N7JbPwpJqfjuIwyQ7LaZgtpYXCEXckfmhYXR IUZmkjVnlt+F+I0lu/7TnjE6SPi0j/8ACPL/AMJMNQOr/wDCIi68/S/sQsft mLPzvJ+1bjpfyk+f9q/feVVxrThGUIuykmn6b/oi4VZRTS66fij+Nr4U3v2O 5kfkFJEYYJBOK/XT/gil8ZrbxTpV74PupohJfII4t7jI3P1wfpX4/wDgGTyk uGxnDDv7GveP2U/j9e/Av4kafq9pO0PkShiMnnGfSoIPtT9prwjefAb9pctO hW3afzQzcDDbiMV+iX7LHjiz8ffD6xkgdWIQK2OewzXlHi/wT4a/4Kg/szW+ veHp4D4z0m3y9up2mfaixqvI9z3/AK18xfs/fHXxR+yL46k0XWYZI7SCYxyp IDlQGPQfhQB+tGgaDaSA7gmfqOa6/RvCulyAB9uSccda8E+B37QmjfFvw/Fd 6dfRs23cyDIKnj1r2DQLx5lU7j+HQnFAHqnhH4f6NIRgo/PRhnFegaT4csdJ jAhgjj46qgGa8m8F3cqTrh2HPNesaTf+dZJllLAUAeL/ALTXwl1X4rXv2a3e ZIEDLgMQDkEdPxrkfgx/wTv0jwrfC81JIZZt27a0YIzn1zX0P4kv7iBAYwuc HtXnuo+LNUjuDtkKgdh/+ugDtI/CNj4I0xYrWC2iWMYAUAV8Y/8ABQr4x6x4 e+OXwOW1lCrD4xnmj2twjHQtViP/AI7K1fST+L7mYgXC+YvcnrXzD+3hpVtq Xxk+BcyxHLeMp1PoR/YWqt/SvRyr/ePlL/0lkz2Prr4d+K7jXtAtWuZN8jRq WLnPUCr/AI0+D/h3x3p7rf29nLuByWRc/UVw2l3d3b6JaxW8KxhYl655+UU4 2uoXxy8rnI5AOK84o4HW/wDgnzo9p4lXUtHvo7RlbcFRB69ODX0J8M9Du/DP hyK1u5TP5a4BJLZ4rh9Esr5LtER5iO4LcV6Lo3mwWyiZuAP4jyaALWoafYT2 paa2hdtvJK5B571wPis20BbybeDaueAorqPE2ohoiiMT8pGQeK871wvHcfe5 zj60AYms6wQWGxFHPQYrifEesJbQSSTOEjVSTg1teOvFNt4T06S7v7iG1gQb neQ4AxX58ftr/wDBQ/8A4SKefw14KkWZXzFLdBW+Xjt05BH60Aef/t+/H2z8 UeIr/SbWYSndEu1XyMgkHjFd5+wb8K1+E3wZ1XxzrCGFBbt5G9dpbdG3OT7i sD9kD/gmrr3xQ1Oz8Z+MY5tN8No0l1cXU7gZ2EPjGc4IzivOv+CtP/BRPRLa 1Pw4+H0yJoGlD7OJEDAygMSD2/vGgD4N/wCCk3xeb4r/ABJ1O9WQvFJdkj5t 3Ffq1/wQL/5VZf21P+55/wDURsa/Dr4mX76haGWUks8gJ568Gv3F/wCCBf8A yqy/tqf9zz/6iNjQB+OP7MUPl2dtLg8XLd+vFfrL+zWx8a/DWKwk2nC8Ak+g r8kP2eNfTS9EjU43CdjjP0r9PP2FvHsGoafBFIyovA+9j0oAz/i38Pta+Efj AX9pGRHIwclCOcfNyDXvn7Mn7dFlremxWGp3oju4gFKlcdPwxUvxtsLfxFpo BMTkIcMwyR8uB3r4Z+JOg3vgnxJLNYTuiq5OY225/KgD9f8Awh8erGZUkW8j kVuevWu4uf2gLe0079zcqSf9rmvxX8NftR+KvC3lotzOwXpl2zXX6f8AtweJ kixK0xx1/eEYoA/VO++Nn9r5BugB/dbj+lZcnxh0qyO2aYDHevzRsv297yz4 k8zP++TVXUf217vXZiokmw3GS5oA/SbxF+0z4T8P2pJ1FHlAztGeteGX/wC0 g3i34iwSWk8ltao4ywbG7r+lfKWjfFWy8TTg3srnJ6eYc10Eni/R4rm3Nhds kpYZ+agD9JPB3xkD6PAftyv8uMlvb3FeF/FfVtM1f9s7wbdXM27/AIpnXGZt xGGF5o23/wBmrhfCN9eJ4dimj1AncuRlj6fWvM/HV7q95+0T4bf7U+8aFqu1 g5GF+06bnv8AT8q9HLP4z/wVP/SJEVNvmvzPvq6+LBtPDwjS62qifKc9h/n1 r5s8e/He60fx6lxLfvPaqxJTfweRWFFf6nN4Zk8y/aMoh5Lk9q8I1rx9YHxF Pb390ZXjYjO7jpXnFn3d4S/aw8K6np8InvkhmIwQRwDXQv8AGjRb1d1peJKC Mkgnivza1Hx7p1llraRjj7vzdazZP2q5vDEi+XI/y+jmgD9RLT4ujTYA0d0q KOcZya3tN+PAu4My3CAAZya/K6P9v298oIgctjGd5NUrv9uXX71P3TOo6f60 jNAH6h+IfjJCEc/a4dv1HTFeHfHT9r3SvBWj3BN6rTAFUUdSSK+ENb/a88Ta hbOgmufmGSVkPFcENd1b4j68n2m5lIZwWLuWGMg+tAHufhyy1n9p/wCJQvpF eS2Viy54AHB/GvYPiNoH/CrvAV4mdqpGY8555UjNH7KmiWvhbR0ijlgMjp8z kDI4wcelUv22fEdvoXg+VBcox2njd160Afkx+2nc/bNZs5D1aaTnOf4j/jX6 YfGzUv7K/wCDQT9lGXp/xc7UBn0/0vxTX5e/tXasuq3VkwKnEshwPqa/TT9o xDJ/wZ6/sohcZ/4Wjfn/AMm/FNAHyN8DviTHviikl+9gcivQ/HHgqfxJpv2i BS5IyOQPSvmf4cwzwywGPJZWyD2r6q+FWpSjTolnk3cfcxn+dAHjb22teHb4 sgmiaMkDBz/9avUvhJ+0z4q8MTRQSTNImcYKjpXpDeB7TWR5hhhRuvMY5qE/ CCKOTzFihGOchBxQB9BfAr9pJ9ftY/tj7JT2454r1ub9oKz0e1SSYgj1UjNf G+j6W+gyjawXb/d4zXQw+IHnQLLI2PQsTigD6cl/bZ0LS3/ey3Cj3U8fpVLU /wDgpH4Y0SPJW5uSB0VWr5tmjsb0N5hQ54FUL/RdOit2/cROeoyoOaAO8+MX /BWzS4opEs9Fk+Xo0gP+FfPGv/8ABSm58U6mQ1rHEmemMYrG+Mnhpb6GTyrK IAZAKoK+c/FXw/v0v3MKPkngAYxQB+hv7NP7QVt4xZDLqtjZl8Z8wA8Y9cV7 t4q+J1npOlAQ+I7Vj32Y5NfjjosPiPw9cjybi6gGcfLKw/l0r0fwJr/ijULq KOTUbllyD80rHv8AWgD7p8WfETUNY1DK35lhJ4KnjFe6fs0aZouqafbm4cG4 LqDkn0PvXyL8IfDOo6tBEst1gkfxE/rXsWiWWr/D8pNb3AfysPhWwDjOK9HL P4z/AMFT/wBIkRU2+a/M++dGsLTTNOjWAsy4xjccYp2rXkc9oyERlSuOef1r 5X8B/tuxaciWuqwgEHbvL9e3pXo2k/tOaJ4kgVopwN4zjcOK84sp+PV/4RDV Wv7eBTnnK9aTw3+0RNNcJE1o7g4BIAHtTvFPjvStZsXDSrnnALA15Lqnj+x0 C9EiOg2MeQR7UAfU+h+MzqlqGCiPI6HtS3+Llh5ki/j3r5z8P/tMWlsvM4IA 5+fmrep/tb6ZYW7F5cA+rmgD27VJ7OG2P3Bwc8dq8E/aFsLO/tPkMbNt7da4 D4g/t4aTaW0qxSEMc4/e49a8C8fftp/23IyxlmLAY/ed8UAdzq+qQ+GJGleX 7hJwCf5Vz8+u6r8RbxYhI0NopweQCRXl1n8SZvFt9vnlyGP3d2c/jXYWXjGH TrQJFJtJHODQB0WveLbL4cW6w2Y8y4fG5yOnf/GrfwOttQ+JnjJLy6RzDESx yQAOhx+ledpqNrrurFriUSFQf4s8c16p4f8Ai1pnw98L+TbqqzOnJ3YzjIoA m/bk+IqReGWsYJAIoUKjP1NeXf8ABCqUS/t/+JWyCT4Wtzx/2Mmh14v+1p+0 HJr95NEJcrzn9515r0r/AIN79e/tr9vLxK2enhW34zn/AJmTQ6mWxUdz9sfB Hxih8B6B8GvDlzomtXC+OrCPT7XU4Psxs7K5h0yS8EU4aZZgZIbacq0cTqDH h2Tcu72D4deJrDUvGV/psd1bnUrPSbm5ns/NX7RBDJBdJFK8YO5UdoJgrEAM Ynx9048c0P4B+G/jJ4C+Emta4mvPqPgmztNS0c2HiDUNNiguGtkjLyRW00cc /wC7Lx4mV18uWVMbZZFb0j4H/BzQPhz8Q/FOtabFqkmseKLe7m1G61DVrzUZ HVbe6eOGI3MsnkW6NNMUgi2QxmV9iLuNcLtc6Psi/sY/tM+HfjL8d7DS9H0z 4g2l1b2090z674D1zQbYoEKELPfWkMTPlwRGrFyAxCkKxH2dXzl8Bv8Akq+l f9tv/RL19G104fZmNX4gooorczCvP/jj8ebH9nqwXXvEka23gmy0zUtR1jVo o7u6n0sWdsbvcbeC3k3QmCG7LSNIhEkcEaJK86hew1fxVpnh/UNLtL/UbCxu 9cuWstOhuLhI5L+dYZZzFCrEGSQQwzSFVyQkUjY2qxHH/HX9mfwl+0jpgsfF 0WvXmnHTdS0maxsvEWo6ZaXtrf2rWtzHcQ2s8Uc+YnYI0qs0LHfEY3+ajzaD S+p0PxO+JOnfCPwNfeIdVtteu7DTghli0XQ73W7597qg8u0sopbiXlgT5cbb VDMcKpIj+FnxQ034xeBbPxFpFr4hs7C/Mgjh1zQb7Qr5djsh8y0vYYbiPJUk b413KVZcqwJ6FIwkYUZIAxyc/qetOxxilYDz/wCDnx5sPifqNxoN3ENN8baJ plrqOuaVHFdtBYCe5vrRGhuJ7eD7RC1xpt6I5Ail440k2KksZZW/as+F6fHJ fhg3xH8Bj4lMu4eEjr9p/bhHkfaM/YvM8/Hkfvfufc+b7vNN+D/7M3hL4F6v dahoMWvy6lfabbaRc3useI9R1u6uLW3ur26hjeW9nmdik2o3ZDE7tsipnZHG qeg9Kel9AfkYvi/xxY+BrWGW+TUXWfzdgs9PuL1/3cMkzZWFHYfJGwGR8zFE GXdFbnfDfxo/4SD9ozxX4CGnIkPhrw1oviFdSF1u+1/2jdarAYfK2DaIxpgb fvbf55G1dmX1viV8K9J+LOk2tlrH9oiC0uo72NrDUbjT5xJGwYYmt3SUIcbX QNtkjZ43DRu6Nh+H/wBmDwl4Y/aH174p2i+Ix4x8Tabb6PqEkvibU5tOktLc kwRpp73DWcXllpGVo4VYNPcMDmeUunsI4bxt+2wnhr4S/D7xdpnh6PxLZfEb XdHtdPnsLyZbKHR9U1e0sbXUpJ5bdNshiv7SX7IVDl3kRWZIpLhe58fftOeC Pgj4E/4SP4leJ/DXwx0n+1JtJS68U63Z6bbyzK8ojCyySiMmaOIzIm7fsPzK rBlWvrv7K/hTxf4A8OeGNa/tW/0Xwjrtrr+j21tfyaRFZyWd2bmwtzHYG3jl tbVhCscEquhFtCZBLInmHufDXhu38L2EltbPdSJLcz3btcXDzuZJpnmf5nJI UM7BUB2ooVVCqqgPS1v66f8ABG7HlusftTTQfGT4Z2Olad4a8Q/Dr4pKLbSf FGm+ITPcPdtp19qcTLbLbmCSyktLF2W5S7LF5UAhKHzav/thfHvWv2Zvgjq3 jjSfDmk+JbTwvbXGr63bXmsyaZJHplrby3FzJbFLacTXQWMCOCTyUcsd08eO d7WPgD4Y8Q/GrSvH99Dqt54j0O1a109ZdYvW06yJWZDcR2Hm/Y1uvLuJ4vtX k+f5Urx+Z5ZK1m/En9lbwd8XvAsfhrxHH4l1PQ11WfV5bVvFGqRrevO87S21 yyXAa5sW+0SIbGYvaeXsj8ny440U0TA474nftYeKPhp8W/FPhp/BGl6jBb+D 9W8VeFriz1e9u7rWW0wWC3Nrd2kGnSTW7NNqMCxfZBfSSqshEQfZE/afAz9o Ox+Mfwm8G+I/s8kVz4pL2c9vYWl/cQaZfwrN9rtpmntbeaAQzW08Ja7gtmEq LGyJK6xE1D9lrwfqnjzxV4nmj8SHXvF+lyaLd3ieJ9TR9PtZI445U04LcBdM aTyYGeSxEDySQRSMxkRXE/gD9mbwV8M9M8MWml6O5j8GveT6RJfX1xqE9vcX jO11dtLcSSSS3cplm33MrNM32i4BfE0u8drWB+R31FFFACFQew4rkfjx/wAk p1b/ALY/+jo66+uQ+PH/ACSnVv8Atj/6OjqZbMcd0fwq+Bz+4uB/tL/Wuht7 gwMCM4HP0rnfA3+ouP8AeX+tb1UI+k/2HP8AgoT4o/ZF8c2t7YX8rWauDJCQ pDAEnGD9a/Ue18UfBv8A4KkeCobvTL228MeP1iAPmEJHO+Ao3AE99x6V+EoO D3Fb3gv4ma78PNWjvdG1O806eNgwa3laM5/A0Afp54x+F/xZ/Yf8VyC185LW YlY5YVMttOpzgg464Oa96/Zs/wCCv9ppCW+mePrG6tphhDdQqDGfc8gjmvh3 9nL/AILreOvhzo39k+NdMsvHelquEj1IiVlx0ILBu4H5V7bF+2H+yx+18qjW dN/4VVrNz9+W3dWtdx7lcKB2oA/V74PftU+DPidYw3Gia7YziQbghkUMPrzX qVt8RHKgxTlh6qM1+LEX7Ag1B/7S+EPxk0TXkb544tN1MW1yPTKCTrx/KtXR NX/as/Z7mXff+Kb+zi+6by2kuYj/AMDGaAP2p07x69wmJnD+mcVfaz07VW3j hj1r8l/B3/BWr4q+EYfI1vwXZarMmMtHM0DE+4KmvUfDf/BbO4hjUan8Ndag OMMYLrev/oFAH6FX2gQZJIQqOnbNfKf7esCWPxP+CDAooTxpcng8j/intY/w rzPVf+CzSanasmm+BNf89/urLIQP/Qa+Tv2yP2o/ix+0D4p8FSWWk3WkNBrU r6dib5vOOnXqnGBx+6Mv616OVf7x8pf+ksmex+wHgK5j1fRLZg5baijn8K7W wtLWFQJMZA+8DjHFfjD8Cv26Pjx8E7d7PWdKvdXgBGNznKjOTzjjgV77pn/B afVNLtsX3w81SSfGOJ8ZPJ/u15xR+lM2q2emp+7iy57k5rn9c8U3LSMyybEH Xp+tfnD4n/4LZeL9WiMeifDt4pT90z3bN+OAleTeNv2mv2qP2k2+z6HpWtaX azEgDTrGT5Qf9vFAH6k+J/jbonhG2kl1TV7O1SNS7mSUDAHc18r/ALQ//BYP 4afDrz7PSZbrxBqi/Kq2qK6Bvdi3HftXxxb/APBNT4v+PC2o/EH4hQ+H1kwZ jrus7DEh6sUZxx0/GtOw+GH7JP7IaLd+N/ifa/EnVYTuk0zRZEhjdvQuGcnu KAMTxh8ePjP+3z4w/sjQrW8j026fy0s7WMcg5A3Njjp69q9++GH7CHw2/YR8 NQ+OPj5rllcXsK/aIdCtpvMlkYYb5uV6q35ivmn45/8ABwrp/gbw3N4d+Bfw 60XwDZqvli/twpu34+8ZAoOcgHr61+enxv8A2qfHf7Q+vTah4q8Satqs0zFi J7p5FGc8AE9OaAPuf9v3/gu1r3xettV8J+A1fw94R/dw21vEiKdqblyTyeRj NfnBrmvXXiPUJLq8laaaQlmZupqmeT3NFAGF4/I/sZeRnzBgfga/cv8A4IF/ 8qsv7an/AHPP/qI2Nfht4+UHRAcciQY9utf0kf8ABlTz/wAEsfH3/ZVdR/8A TRo9AH8z/hbxSNFiCs2Arlu/oPSvfPgn+2rD8OGjEs21UxjKSN/IV/bFsHoP yo2D0H5UAfx3at/wVAstTttv23aQuMeTN6Y9K891j9tjTtYuC8lzuGc8wyHP 6V/ansHoPyo2D0H5UAfxRSfthaOfuvGvv5Mp/pUUn7Wui3LgvMv18iTj9K/t i2D0H5UbB6D8qAP4nG/af8MXCZe4Af8A695f8KLL9qXw9ZMSlwCQeP8AR5PX 6V/bHsHoPyo2D0H5UAfxNXX7WukyZ23RU+ohkwf0rMj/AGsILO/WaK7LFTkZ jk4/Sv7eNg9B+VGweg/KgD+MPR/+Cmep6PaJAl2piQYGYpMikm/4KLajd69b 619pXzbCCWzDeXIMCdo3P/ogV/Z7sHoPyo2D0H5V0Yav7GbmlfSS/wDAouP6 iaufxi6l/wAFNdWv7R4ftuFcYJ8uSvObr9qtNTv3uJ7tw7nJISSv7gNg9B+V Gweg/KucZ/Ejp37V+nwlfMvWbA5DRSH+lWpv2otAulzLeDLZBzBL/wDE1/bJ sHoPyo2D0H5UAfxKN+0z4ftwxhvCSR08iXH/AKDUKftVaXCx2ynHp5Tkfyr+ 3HYPQflRsHoPyoA/iUk/ax0qRD++CkdP3Ln/ANlp+m/teWOnzq8d2UI9YXP9 K/to2D0H5UbB6D8qAP43vCP/AAUg0/w3boDfsGXsIJcH/wAdrk/jp+3vH8TI XRLh5Qen7uRe/uK/tN2D0H5UbB6D8qAP4IPGnio+JrhG5IUlvzr9gP2g9Vs9 H/4M+v2UZr2TyoP+FoX652lsn7X4p4wOfWv6X9g9B+VG0YxgYoA/iN8GfHTw joQUzagAR0xbTZH5LXp3hr9tPwLpO3fqbAj/AKc5yfz2V/ZJsHoPyo2D0H5U AfyH6f8A8FEvAFt01opkAH/Qbnp/3xU1x/wUY+HxIK627Hvmxuf/AI3X9dWw eg/KjYPQflQB/IHe/wDBQ/wPcZ26w4P/AF5XH/xFY13+374SdyI9WcDHB+yz /wDxFf2K7B6D8qNg9B+VAH8bcn7ePhlj/wAhVwf+vaf/AOIpsf7e3huFx/xM WkHobWb/AOJr+ybYPQflRsHoPyoA/jjP/BQbwxJEEa6Ugf8ATpL/APE1n3H7 bfgqdy7yqxPb7LN/8RX9lmweg/KjYPQflQB/GBrH7XvgbUUJ3JvPTFrKMf8A jtc2/wC1ToFhdbrW6ZBn+GGQf+y1/bHsHoPyo2D0H5UAfxZaf+3mukL+41KQ Y6DyZB+uK2IP+CmGpRxEfaldV+XJjkyc1/ZrsHoPyo2D0H5V0Yav7GbmlfSS /wDAouP6iaufxV67+3q+syrK1zhx2EUlS6N/wUDn0iUYuRt9oXGP0r+0/YPQ flRsHoPyrnGfxs2f/BTmSOHY9+wB/wCmMv8AhWdrP/BRSLUEYfbHO70ikB/l X9m+weg/KjYPQflQB/FFcft0yxE+RfzEZ7xsP6Vj6/8AtrX+qRFVvZRnJOEa v7ddg9B+VGweg/KgD+FnUf2g7nUZmaS7kO4+jUul/HGG3lVpp2IHP3Gz/Kv7 pdg9B+VGweg/KgD+H20/akgsQDFcFCOwjfn9Kst+1uJ1O67bI6Dy35/Sv7ed g9B+VGweg/KgD+IaD9rSOx5iuW3nusbDH6Vm6v8AtcX1/Ix+1SuOQoO4YBr+ 4vYPQflRsHoPyoA/gt8V/EmfxLctJKzksT3r7s/4Nqrhrj9ubxSTn/kVLbqc /wDMyaFX9c2weg/KvEP2+1A+DGkYAH/FVaN/6WxVMthrc+J9b+D/AIi8b3v7 N3ivw34b8FanJ4GC3ep6lq2pSWeo21lcaVJZSQWnl2c/mBjcLM0bSQqzWUKk nf5kXs/7Mdn4403xR4mtPFem+GrbTYRqkmk32m6w95capFKl1JvuIDZW62rI hjARZLgncQ0rFPMk8c1b9pWP4G3v7Nuh33ijwNo2mfEUjRJtP1dhDqN2w0qS aCa0lNwigC4jt7dkMUm972FQyMVEnu3wI1rXtU8Ya3bavPZXEei6W2nyzQ6T NYrd3/8AZ0k1zPCZJZA1swmgVFBYxvFOjSSEfLxPf7/zOlr3dfIP2Mf2Z/Dn wb+O1jqekal8Qby5uLae1ZNd8ea5r1sEMZclYL67miV8oMSKocAsAwDMD9nV 85fAb/kq+lf9tv8A0S9fRtdGH2ZjV+IK+Wvg78Avjh8PPFetP4k8UTfEayvd T0S1t7rV/Gy2Qi061muZbi6isrDRLdIbljJGDbvPcC5QLHLdKkQ8z6lor0sP jJ0YyhFJ81t1qrbWe6/XrsjJq58k/Cj9mP42aD4o+Blv4u1rw14o0v4fPZ6v rOr3XiG8uNSS8TwxcaLcWkEb2f8ApUcl1M16by4nSVjPKhiGFY/W1FFPGY2p iZKVRJWVtFbq3+ooxS2CiiiuMoMUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV yHx4/wCSU6t/2x/9HR119ch8eP8AklOrf9sf/R0dTLZjjuj+EnwzrUOlRzCX d85BGBn1rbtPEdpeuVWTaQM/P8tf3k7B6D8qNg9B+VUI/g+/tG3/AOe8P/fY o/tG3/57w/8AfYr+8HYPQflRsHoPyoA/g+/tG3/57w/99igajbg58+H/AL7F f3g7B6D8qNg9B+VAH8Kvhr4naj4OuVl0zWprN05BjuNuPyNe2fDL/gqt8Zfh OI10vx7etDH0iuJ/OTH0Ymv7Ptg9B+VGweg/KgD+RTTv+C8HxMeJV1jTvBGt uOsl1psZdvxBFa9l/wAFybi4AN/8OPh5K5GCYrd4/wCUlf1qbB6D8qNg9B+V AH8n1p/wXC0iIZf4ZeDSf9lpgP8A0ZVXxJ/wWk0rxPrnhi/j+Hfha1HhXUn1 Rkjkl23IazubTY3z9B9q3dRygr+svYPQflRsHoPyrow1f2NT2lr6Nfemv1E1 dH8qF3/wXi05/wDmmPgo59XlP6eZWbe/8F1QhP2b4ZfDtD/Dvjdsf+RK/rB2 D0H5UbB6D8q5xn8lt5/wXx8dWiMNH8MfDjR8/daGwBYfm5rgviD/AMFrfjl4 9tnh/wCE5TSIHBUx6cqwAA+mDxX9h+weg/KjYPQflQB/Dx4z/aR8VfEO9lm1 fxZf6g03+sM18XL/AIE1zEurxzsS91GxPUmUf41/ddsHoPyo2D0H5UAfwnf2 hb/894P+/goOoW4/5bwf9/BX92Oweg/KjYPQflQB/CaNStz/AMt4f++xT47y CT/l4tx9ZF/xr+6/YPQflRsHoPyoA/g18c3MM+jBVkjdxIMAMCe9f0m/8GVP /KLLx9/2VXUf/TRo9fr7sHoPypQAOnFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4h+35/wAkX0j/ ALGrRv8A0tir2+vEP2/P+SL6R/2NWjf+lsVTIa3PCvgh/wAkW8If9gSy/wDR CV3/AIJ/5DM3/Xhe/wDpLLXAfBD/AJIt4Q/7All/6ISu/wDBP/IZm/68L3/0 llrz38R1dDY+A3/JV9K/7bf+iXr6Nr5y+A3/ACVfSv8Att/6Jevo2urD/CzG r8QUUUV0GQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXIfHj/k lOrf9sf/AEdHXX1yHx4/5JTq3/bH/wBHR1MtmOO6OvooopoQUUUUwCiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA K8Q/b8/5IvpH/Y1aN/6WxV7fXiH7fn/JF9I/7GrRv/S2KpkNbnhXwQ/5It4Q /wCwJZf+iErv/BP/ACGZv+vC9/8ASWWuA+CH/JFvCH/YEsv/AEQld/4J/wCQ zN/14Xv/AKSy157+I6uhsfAb/kq+lf8Abb/0S9fRtfOXwG/5KvpX/bb/ANEv X0bXVh/hZjV+IKKKK6DIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igArkPjx/wAkp1b/ALY/+jo66+uQ+PH/ACSnVv8Atj/6OjqZbMcd0dfRRRTQ gooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAV4h+35/yRfSP+xq0b/0tir2+vEP2/P+SL6R/wBjVo3/AKWx VMhrc8e8C+Gz4L8GaRo5nF0NKsobPzfL2eZ5cYTdtycZxnGTj1NbumapJpN0 0sQjLPDLAQwyNskbRn8QGyPpVaivOuddjS8J+KLjwd4ht9StUhee23bVkBKH cpU5wQehPf0rtP8Ahp3X/wDn00j/AL9Sf/F15zRVxqSjsS4p7no3/DTuv/8A PppH/fqT/wCLo/4ad1//AJ9NI/79Sf8Axdec0U/bzDkj2PRv+Gndf/59NI/7 9Sf/ABdH/DTuv/8APppH/fqT/wCLrzmij28w5I9j0b/hp3X/APn00j/v1J/8 XR/w07r/APz6aR/36k/+Lrzmij28w5I9j0b/AIad1/8A59NI/wC/Un/xdH/D Tuv/APPppH/fqT/4uvOaKPbzDkj2PRv+Gndf/wCfTSP+/Un/AMXR/wANO6// AM+mkf8AfqT/AOLrzmij28w5I9jvNa/a01nQtNkup7GykiiwWW3sLi5k5IHC RsznkjOAcDJPAJrC/wCG+rr/AKBOo/8AhK6r/wDG6xtP06fVrxLe2hlnnkzt SNC7NgEnAHJ4BP4V4j8NvBP7RC6xq2j+KtA8ZzaZrS3Flo/iCx0/SoLvQ/IW dBeXkBeSMfaFFuYVhW73SebJNHaRyJaW1xqTfUXs4n0P/wAN9XX/AECdR/8A CV1X/wCN0f8ADfV1/wBAnUf/AAldV/8AjdfJfwjl/aguviH8R9J8SfD3x7da d4c8c6zeaZftp2mW9trHh4WJOnaZYsJY5Humnlt5POmVYgqTRyXKyI0Q+oPD nhHxLrXh3T7268JeItHuby2jnmsbu2D3Fk7KC0MhhaSMuhyp8t3TIO1mGCW6 kkk7i5Ean/DfV1/0CdR/8JXVf/jdH/DfV1/0CdR/8JXVf/jdV/8AhX+vf9AT V/8AwDk/wo/4V/r3/QE1f/wDk/wqPbTDliWP+G+rr/oE6j/4Suq//G6P+G+r r/oE6j/4Suq//G6r/wDCv9e/6Amr/wDgHJ/hR/wr/Xv+gJq//gHJ/hR7aYcs Sx/w31df9AnUf/CV1X/43VfUv2vJ/ihaNoT6feW632P3kmgahaoNhD4MkqBF ztxyeeg5Io/4V/r3/QE1f/wDk/wqG/8AB+r6VZvcXOl6jbwR43SS2zoi545J GOuB+NDqyejDlifVdFFFdqOcKKKKYBRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeIft+f8kX0j/satG/8AS2Kv b68Q/b8/5IvpH/Y1aN/6WxVMhrc8I+Af7M/iT4mfs3p4zu/in8YtX1g6cL1N I0W28Lwy38n2VJhbwG509IxI7NsUyzIgJXc6jc1aX7U/7Jut/A74Nap4j0j4 0/FKe/sIJpY0u7Pw88JKQSyDIXS1JGUHccZ+tdH8EvhsPi7/AME/NB8PPc/E Czg1C60H7RN4L17+w9XSBJ9Okl23YmhkitzGji48mRZ2tvtCw5laNT0H7W/w 0074P/sQ6p4d0m58Q3en6faXvlS65r19rl+2+3upG8y8vZprmX5nIHmSNtUK q4VVUZNLlvb+rlqT5j8xP+Gm/i3/ANFQ8Qf+CnR//kKj/hpv4t/9FQ8Qf+Cn R/8A5Crj6K15I9ieZnYf8NN/Fv8A6Kh4g/8ABTo//wAhUf8ADTfxb/6Kh4g/ 8FOj/wDyFXH0UckewczOw/4ab+Lf/RUPEH/gp0f/AOQqP+Gm/i3/ANFQ8Qf+ CnR//kKuPoo5I9g5mdh/w038W/8AoqHiD/wU6P8A/IVH/DTfxb/6Kh4g/wDB To//AMhVx9FHJHsHMzsP+Gm/i3/0VDxB/wCCnR//AJCo/wCGm/i3/wBFQ8Qf +CnR/wD5Crj6KOSPYOZnYf8ADTfxb/6Kh4g/8FOj/wDyFR/w038W/wDoqHiD /wAFOj//ACFXH0UckewczOw/4ab+Lf8A0VDxB/4KdH/+QqP+Gm/i3/0VDxB/ 4KdH/wDkKuPoo5I9hczOw/4ab+Lf/RUPEH/gp0f/AOQqP+Gm/i3/ANFQ8Qf+ CnR//kKuPoo5I9g5mdh/w038W/8AoqHiD/wU6P8A/IVfdv7Gn7Lmu/tE/s3+ HfGOtfGf4n2mpax9p86Gws/D0dunlXU0K7Q+lswysYJyx5J6V+cVfr5/wS4/ 5MW8D/8Ab/8A+nC5o5I9g5mUP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP /Afw3/8AKmvo+ijkj2Dmfc+cP+HeNz/0W/4wf+A/hv8A+VNU9e/Y0m+FGlye IG+KnxH8SLp+P+JdqsOiLaXO8iP5zbadDL8u7cNsi/MozkZU/Tdch8eP+SU6 t/2x/wDR0dKUI22Gm7nX0UUVSJCiiimAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXiH7fn/JF9I/7GrRv/S2K vb68Q/b8/wCSL6R/2NWjf+lsVTIa3OO/Zx8Z+DPAX/BPVNQ+IPirT/BHg5tK t7PU9bvdfOgR2CXFrbW6kX6yxNbSM8qqkiSI6uybGD7TW/8Atu+LtL8f/sZ6 hrmhalYazoms6VJf6fqFjcJc2t/by2EzxTRSoSkkboysrqSrBgQTmtH/AIJ5 tj9lnw2P+nS2/wDSS3pf+Chf/JrfiH/r0uv/AEjuKyfwf13Kj8R+OVFFFbkB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV+vn/BLj/kxbwP/ANv/AP6c LmvyDr9fP+CXH/Ji3gf/ALf/AP04XNAH0BRRRQAVyHx4/wCSU6t/2x/9HR11 9ch8eP8AklOrf9sf/R0dTLZjjujr6KKKaEFFFFMAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvEP2/P+SL6R/2 NWjf+lsVe314h+35/wAkX0j/ALGrRv8A0tiqZDW5xv7NvgLw98Uf2DdE8PeL PAth8SfDur/2Tb32gX1jZXtpcIfsP7+WG8ZYXjtzi5Ycvi3JjSSUJG3Qftue GrfwX+xjf6PaSahNaaTpUtnBJf389/dyJHYzIpluJ3eaaQgAtJK7O5yzMzEk 4H7OPjPwZ4C/4J6pqHxB8Vaf4I8HNpVvZ6nrd7r50COwS4tba3Ui/WWJraRn lVUkSRHV2TYwfaa3/wBt3xdpfj/9jPUNc0LUrDWdE1nSpL/T9QsbhLm1v7eW wmeKaKVCUkjdGVldSVYMCCc1m/g/ruVH4j8hKKKK2ICiiigAooooAKKKKACi iigAooooAKKKKACiiigAr9fP+CXH/Ji3gf8A7f8A/wBOFzX5B1+vn/BLj/kx bwP/ANv/AP6cLmgD6AooooAK5D48f8kp1b/tj/6Ojrr65D48f8kp1b/tj/6O jqZbMcd0dfRRRTQgooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAV4h+35/yRfSP+xq0b/0tir2+vEP2/P8A ki+kf9jVo3/pbFUyGtxP+CeRx+yz4b4/5dLb/wBJLej/AIKEnP7LXiLgj/RL rr/153FcB8ENS8daT/wT70G4+G2jf8JB4vju9A+zae2s2+jw3EBn04XXn3U9 rdLHbrbGdpfLgedolkWDbO0Trf8A2krfxjb/ALCOsHx+mnw+K5xq1zdwWGrD VbW0jk+3SQQRXIsbEyRxQNFGpe2VwEAd53DTyZv4P67lR+I/KOiiitiAoooo AKKKKACiiigAooooAKKKKACiiigAooooAK/Xz/glx/yYt4H/AO3/AP8AThc1 +Qdfr5/wS4/5MW8D/wDb/wD+nC5oA+gKKKKACuQ+PH/JKdW/7Y/+jo66+uQ+ PH/JKdW/7Y/+jo6mWzHHdHX0UUU0IKKKKYBRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeIft+f8kX0j/satG/9 LYq9vrxD9vz/AJIvpH/Y1aN/6WxVMhrcb/wT04/ZY8NnJx9ktuP+3S3o/wCC hL7v2XfEY9LS5/8ASS4qh+w/d6rZfsYadLoVlp+pa5FpUb6daX969la3VwLG ExRyzpFM0UbPtDSLFIUBJCORtK/tv+LNL8efsZ3+uaHqVhrOi6zpUt9p+oWN wlza31vLYzPHNFKhKyRujKyspIYEEEg1m/g/ruVH4j8gqKKK2ICiiigAoooo AKKKKACiiigAooooAKKKKACiiigAr9fP+CXH/Ji3gf8A7f8A/wBOFzX5B1+v n/BLj/kxbwP/ANv/AP6cLmgD6AooooAK5D48f8kp1b/tj/6Ojrr65D48f8kp 1b/tj/6OjqZbMcd0dfRRRTQgooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4h+35/yRfSP+xq0b/0tir2+ vEP2/P8Aki+kf9jVo3/pbFUyGtzjf2bPhvbfF39gzRfD15oun6/Z6iNJE9vd 6nPphtkT7DI15b3MCPNDeWwQ3Ns8XluLiCErNbtiePR/aw+F2hfA79gePwV4 XsP7K8M+D/D/APYmkWXnST/ZLO206WGCLzJGZ32xoo3OzMcZJJyaz/2c9f8A DPhz/gnms3jPRL/xL4UudKt7DVtJs/DV34lk1G3uLW2geE6fawTzXMbiTEir E4EZdmAQMR0H7b3ie38a/sZahrFnFqEFpqulS3kEd/Yz2F0iSWMzqJbedEmg kAI3RyorocqyqwIGb+D+u5UfiPyDooorYgKKKKACiiigAooooAKKKKACiiig AooooAKKKKACv18/4Jcf8mLeB/8At/8A/Thc1+Qdfr5/wS4/5MW8D/8Ab/8A +nC5oA+gKKKKACuQ+PH/ACSnVv8Atj/6Ojrr65D48f8AJKdW/wC2P/o6Opls xx3R19FFFNCCiiimAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABXiH7fn/JF9I/7GrRv/S2Kvb68Q/b8/5IvpH/ AGNWjf8ApbFUyGtzO/Yg8VaV4D/Yu0/Xdd1LT9G0TRdJjvtQ1C/uUtrWxt4r GF5ZpZXIWONEVmZ2ICgEkgCtD/goUc/st+Ien/Hpdf8ApJcV4L4W0jS/i3+w 3a/DTxNpx1Lwf4u0C1g1a1h1O+02a7geCEPAZ7OeGUROqbHQOBIjuj7kdlPo Pi+7ufix4Au/BMsdwbHXrq7Ys19cXF15t5JM0ipLcyyFYw1w4jj/ANXCgSNF WJFQYOouWxooO9z8rKK+gfEH7N/w5vPHHw3svCvi8eJ9M8WeJDo+oXWm6zZ6 jHBE/h681iBo5YFKB3jjs5ASWBhuAwHzqw9G8V/sBfD7wZ408P8Ah2/1rxTB rfirSb3XNKtvOhf7VZ2b2kdxLvWAqmx7+0G1mDN5uVDbX291KlzpO6V/X/I5 5Tt5nxxRX2P/AMO9PBX/AEFfFHXj/SIOfX/ljXkP7SHw++Fn7Mvjjwz4f1W3 +J+s3/irTNW1S0/sqTTmSJNPW33pIZjFh5pbu3hi2hl3yZkaONWkHKq0Xojd 02jxaivobSPhv8AdJ/ZF0343+LPEXjXw94C1Tw7Z+JhdX+oWlrLY2tzCksaS IttNvnbzY0WOIsWkYIm9mXOr8J/2cvhT8cNE+GvibQB8QbXw34/8KN4pjhv9 SsxfRxyJZPBGxS2MaMEujvH7wblADYGSKqhcjPmSivt//h3/APC3+98QP/B3 Z/8AyFR/w7/+Fv8Ae+IH/g7s/wD5Cpe3gHs5HxBRX2//AMO//hb/AHviB/4O 7P8A+QqP+Hf/AMLf73xA/wDB3Z//ACFR7eAezkfEFFfb/wDw7/8Ahb/e+IH/ AIO7P/5Co/4d/wDwt/vfED/wd2f/AMhUe3gHs5HxBRX2/wD8O/8A4W/3viB/ 4O7P/wCQqP8Ah3/8Lf73xA/8Hdn/APIVHt4B7OR8QUV9v/8ADv8A+Fv974gf +Duz/wDkKj/h3/8AC3+98QP/AAd2f/yFR7eAezkfEFfr5/wS4/5MW8D/APb/ AP8Apwua+Yv+Hf8A8Lf73xA/8Hdn/wDIVfR3wE8f6X+zt8J9K8HaLpWoXema P53ky32opJcP5szzNuZIVU/NIQMKOAOvWj28A9nI+j6K8h/4aqP/AEAR/wCB v/2uj/hqo/8AQBH/AIG//a6PbwD2cj16uQ+PH/JKdW/7Y/8Ao6OuQ/4aqP8A 0AR/4G//AGusbx78fj438KXWl/2T9l+07P3v2rft2urdNgznbjr3pOtFqw1T le57vRRRWyMwooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAV4h+35/yRfSP+xq0b/0tir2+vEP2/P8Aki+k f9jVo3/pbFUyGtzifg7+z1qNl8LvD9ouraJcnTrCCykkja4EcjxRqjFd0QJX KnkgVq+Jf2W5PGfh6/0jWG8NarpWq20lne2V5G89tdwSKySRSRtGVdHVirKw IYEg5BrW+GN+1npbyCKJY3ghQT+WgkciW5JTdu3sF3AgbQqlyQWL4T5s+EP/ AAUM+IumP4BtfHvh7Q/ETfFDwk3jLRb/AMNWk2mRWSK2l+bYzwSz3WRCmoGR r15oVlMYiS3EhTzOVRi9zdOTdkdB46/4J2an8OE8BXHwf8OfCTRV8IeKX8QX OjiWXw9ZXwfRr7TC2+1sZv3qrcW+MxHKW4XIAXHRwfCL43+JvGOi33iHwR8G GXTY305NSj8fale32lWM8kDXMdsr6ImVb7PAxiEkayNbxbmG0MvO6D/wUW8R 3nhPwp4o1PwLoGj+FvFw0/ULVpvFbjUxpmqT21rpbiB7JIXvPtF1El3bJcFL VZITHPdtMkZx9P8A+Cout6T8TNO8KeJfB3hOz1iSzX+0rfSfFN7fPp17Ppmq 6rZxK0+l20U0EtlpM+6VJPNimIQ25j2zv2wryhpu133Od01Kz7n0IfgDqhAx qGj8D+/L19f9XXnvx9/4JoeCP2prLSrb4jeFvAPjOLRLlLqwOq2ZuJLN1mhm IjcxblSRoIhJGDsmRdkiuhKnz/xR/wAFZINF1PSNNtfClnNqWqeJ9f8ADs7X XiBLez0WPTpfEUMF7eyLC8kMFy/h+Vi3lMsUf2ltzm22y91P+0x8a734l/Dv TdH+DfhbVfC/iAz23i/xAvxDiRPB1zbTPHcqkAs2kvo32H7OyeXI7ZW5isdp I5fYpa/qbOT2YePv2U/F1l4Ti8HeFfB3we1bwE2kDSZNK1TWbnTbQW2xoTZi zj0y4iNuIdqBchSCV2ADJ5LwF+yJ8XPhZY+EdM8O+Evg9pugeCdAbw5pen/8 J/q03k2oFqsQ8yTRy7bEtVXLli27JORz7h8Y/ifoPg6Hw/ceI9Rm0zTpdTaL 7TArvJDJ9jumRlVAScMoPQj14zS/Av406l8U/AKarqmiX3h+5W5mtPKu2gD3 IicoZgkMsvlhmDDY7blZWU5ADNp7G0OfoZ+19/k6nnP/AAqX46f9Cx8Hv/C8 1L/5S0f8Kk+Oh/5lj4Pf+F5qX/ylrC+PX7dVx+zJ4N+DKPDdavJ4ke1m8Qyv pt/eiz0VEghv9SkuYEeK1W2kvLW4kluysRhjuF3B2Qji/gR/wVH+Jfxs8CeH fEyfCLwfaaRquo20F9DbePbu+1G3tbjw9aeII5LeBdIAuLj7Hcsn2dnizPGq iQpJ5q5KEWm10NOZo9R/4VL8dP8AoWPg9/4Xmpf/AClpR8JPjof+ZY+D3/he al/8pa8p8ef8FZfEPg3UfsbeFPAJvfCs80/jDy/GrzWJsF0ebWFk0q6Nmgup FsUWWUTx26xvJBHuMdwt2nc3P/BQnxL43+OWu+A/h54J8N+Ib7QZrGN9Q1jx a+m2F0JZtdhufLe3srtt9vLojxFGVSZXmQ7PIDSv2cQUmzcPwk+Oi/8AMsfB 7/wvNS/+UtH/AAqb46f9Cx8Hv/C81L/5S1Qt/wDgor/b+k+Hdd0jwrnwrruu Wukte6rq8djdRxzz6fYlltwkhFxDqd/9iltZ3gkSSzufvMI0ljj/AOCgutfE PVPiZ4f+Gvgix8Z+N/h14S8P+Iho134gbSI9Tu9UF7KdOFzJbOkbpb2sbpKQ Y5HuURzAEeQCpxauv6/qwOTW5p/8Kl+On/QsfB7/AMLzUv8A5S0f8Kl+On/Q sfB7/wALzUv/AJS167Dqep+Kvh2lrrLw6HrOpaaIr5dHv2uF064kixJ9nuHi jZxG5bZI0SFgqsY1ztHL/DX4z6rpnigeDPGcluviQI8unX8MflW3iO3TGZYx 0SdQV82EH5Sdy5Q8LkiHMziv+FS/HT/oWPg9/wCF5qX/AMpaP+FS/HT/AKFj 4Pf+F5qX/wApa9a+I+p69f8AhO5/4RrVbfS9ZgAntpLiETW8zJ83kyjBYRvj axjw4ByDkYPA/sgftp2/7WHhDX7p/D+ueD9a8Ka1PoeqaNrEElvdW8iKjpKE lRJDDLG6Ojsi7gTxwaOSIczMT/hUnx0/6Fj4Pf8Aheal/wDKWk/4VJ8dD/zL Hwe/8LzUv/lLXX6n8LtH8f8AjTxBqGoar41im+2RxLHp/jHV9Oto1FrBwsNv cpGuSSThRkkk8muM+B2sXHgf9uH4neDrXW/Ed74a0/wL4T1i0tNW1u71X7Ld 3WoeJIbiWN7qSR13x2dqCobb+5U4BJJfs4hzMl/4VJ8dP+hY+Dv/AIXmpf8A ylo/4VJ8dP8AoWPg9/4Xmpf/AClrY8D2kXxF8AWfi28+I3iF9TurX7Q1/Yam LKy0tmALItmQbYrGwK4uo5ZFwQzE5rd+DXxZ8QfEn4CtrN7b6bb6y39oR2L3 U8lpZajHBczx2d6zR7niguooobjChmVJ8AHGKqFKLaQnNpXOJ/4VN8dP+hY+ D3/hean/APKWpLX4e/FbQp1uvEuhfDaz0SL/AI+JtL8W319dpkYXZDJpkCP8 5UHMq4Uk/MQFOd42+Od34b0zwVp13430lvEc2u6XYaha6XezXAvw2qRR7vNM cXLwMpkAjRdxkCqFxXqXxh+KFlpFlp2j3DzfavEcrQWeyJnjZokM77mA2r8k bYyQSemcGumrl04WSi22m/ktzGGKi767afefQm8UbxXyJRXF9Z8jf2XmfXe8 UbxXyJRR9Z8g9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8Ub xXyJRR9Z8g9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxX yJRR9Z8g9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJ RR9Z8g9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR 9Z8g9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z 8g9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z8g 9l5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z8g9l 5n13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z8g9l5n 13vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z8g9l5n13 vFG8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z8g9l5n13vF G8V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8UbxXyJRR9Z8g9l5n13vFG8 V8iUUfWfIPZeZ9d7xRvFfIlFH1nyD2XmfXe8V4v+3PYPq3wq0C1jKK9x4v0S JSx4UtfxDJxk45ryysXxdfW8eqeGrVriFbmbxJosqRFxvdF1azDMFzkgFkBI 4G9fWmq/M7WD2dtbnoOl+H77T4WCXOjQu6qjkapbBnClyuSH52mR8Z6bm9at tYX7EE3mjZwBn+1bfn/x+vE/2k9Y8TaH8IrubwjDdSatNfafaSy2kC3F1YWM 19BDfXcEbKyyTW9o9xPGrJIC8SAxSg+W3CanL8Vfg5rNtHH4ysPF/g/StA1T UL691/wtt1u5uoGUwo95Zy2tmqs042qtiC0do4yzMZUxUlb+u1zTk/r8D6m/ s6//AOfvRv8AwaW//wAXR/Z1/wD8/ejf+DS3/wDi68W+AvxA1vxj4VFr4otJ bfxLpU9zb3ssOi3mm2V6sV9d2kdxAs+7CzLa+cIxLKUSaJt7JJFJJL8B9X8Q 674GvrjxRHcxamniTXbaFbi2Fs5sYtXu4rJgoVcobRICr4+dSrksW3Eeja7E pXsz2T+z7/8A5/NG/wDBrb//ABdA0+/H/L5o3/g1t/8A4uuOoLFVYjPCk+tR KaSuy4wbdjrbvQrjUIDHcTaFOhBG2TUrZhggqeC/cEj6EjvWX4A+GUHwu8IW Wg6F/Ymn6Vp6ssEC6vC4TcxdjuaQsSWYkkknmvlD4da18c/DPg3RLXVNfh1i 6voIppL7XPCD3t4Td2cAjNx9hewitja3pvHeBYJPMtoYVaeCaXeOq+EHxt8Z eK/inHpviC40Gw0/TXuvDV7A+gXWm3Oq61bqLj7VZyzXLJ9jms281IAkroUl /wBIkETZ2V/hX5mas0pM+ltL8O3Gi6XbWVrc6PHbWkSwxJ/a0DbEUYAyXyeB 3qcaffjpeaN/4Nbf/wCLrzTwzqGoXfiDxHDdxSR2tpfRpYM0RRZYjawOzKT9 7960oz0ypHatqs3IrlOj1bwR/b2o6Td3kmjTT6Hdm+sm/tiFfJmMEsBbAkwf 3c8q4bI+bOMgEXTpt8zAm80fjt/atvz/AOP1x9FHMHKavhb4UW3gy20qPT30 xDo1nJY20s+vpczmORkeRpJJJWeWR2jVmlkLSO2WZiWYncGn346Xmjf+DW3/ APi68J8YeNNcs/EA0iB59On1XxXZWGmXLaa08D2iW0V9eKzYIUSwwXsCzHhZ ZIwDuxWl8B9X8Q674GvrjxRHcxamniTXbaFbi2Fs5sYtXu4rJgoVcobRICr4 +dSrksW3F30/r+uora/1/XQ9l+wX/wDz+aN/4Nbf/wCLqKbRbq5lieWfQpHt 33xltTti0TYK5U7+DtYjI7EjvXJVmfGi/wBa0r4P+Kbrw3Al14itdMM2lwOS FmultXMSEiOQgF9oJEbkZ+63QrmKULux6L9gv/8An80b/wAGtv8A/F0n9nXw ct9r0XceCf7Ut8n/AMfr5D+HXxv+Mv8AwvzRdF8T+ErS10jULx7TVFsNNur+ z03zIdWnhuINXbyBLEn9m28MkcthFtfUbci4cusZ6K6+PvjLQPjvr1prPw78 Zad4J0+2+zaTfwJYX0Ov3clzZQwlBBcPcQPJLcSri4jihjihaaSVF3iJvRpd xJaN9j1bx1+xl8Nfif4qu9d8S/Dz4SeItbv9hudQ1Ox0u8urjYixpvlkBdtq IqjJOFVR0Favwt/Zr8IfAy51GbwR4T+G/g6TWFiW/bQ49O09r0RGTyhIYdpc J5sm0Nnb5j4xuNfJuuftF/Hs/B3xZrkfge88I+ItBtr3UbXw9rfhk+JJNYcW sDQ2VrNpF9sUC4mCebLveVfMYRRiKTZ7P+zl8SvG/wASZPGzeN/CLeDJNG15 LDSbNmMxuLQ6ZYXDT+eCY583M9ygeIBQIghG+NyXd2v5X39Atr+B6tqnwJ8O 694rg16/8P8AgG+121OYdSuDYSXcR/2ZSS6/ga1vEvgeLxpoc+l6xD4X1bTL pQs1neXtpPBMAwYBkdipwVBGR1ANeR/Gs65qx0LQNG1nXvCo8RXb28uv6Np8 N7d6a0cLzqoFxBPbQpIInQzXETIOI1xLNEy4v7OfiTx/4m8R+NH8V3umXXhm z1CCHwwV8LXeiahcWklnb3XnXLT3DiSQfaPIfZBBtltpcqhPlRLnsr9h8mqX c9K0v9kvwBomrWt/ZeBPhXaX9hMlzbXMNrpiS28qMGR0YDKsrAEEHIIBrr/E GnaidEuWWTTbxo0LrBFqtoJJiOQql5VUE4wNzAepFfOP7D/iv4meMPhhPf8A xV0i40XxJqbwaoLP7Sk9tpkdxbRObGPFnauhhk8zdG/2ooZAPtk5DLF7QTgV VSrL7WtvMmEF9nQz/tHiH/oT9R/8Hmif/J1H2jxD/wBCfqP/AIPNE/8Ak6vk 74D/ABr/AGhL/wAAeGZ/EOk6iPFV3olvq+q2epeHGe1m+z2emA2VtJEtsLe+ v5r6V5JLomOyks7iJbaaGJrl/on4H+IPFXiTS9Tu/E81jLFc3S3WkC30O50m W2spYo5I4biOeeVpLiMsySOBECwx5UZU0nFJXK62Oo+0eIf+hP1H/wAHmif/ ACdR9o8Q/wDQn6j/AODzRP8A5Oqr8N9R1DVvh/ot1q0ckOqXFlDLdo6eWySs gLgp1Ug5GMDFbVS2lpYFqrmd9p8Rf9CfqP8A4PNE/wDk6l+0eIf+hP1H/wAH mif/ACdT/EiXMnh+8WzkeG8aIrbyKwUpIeFO4xyBeSOSjADnBFcFofiDxZ4k ne2lj13T7O8tbQWupLYwW53K0InYwyB2jkdpJRtkTaqwAqTksGrMNjuvtHiL /oTtR/8AB5on/wAnUfaPEP8A0J+o/wDg80T/AOTqzvEM+raV8K9RltnurjW4 NJma3kMKtPNcLExU7AoUuWA4C4J4x2rB0nxB4g1XXtYi1CbV9Itbe6N5aPb6 ejtJbrbWzGA5jkX70kgIXLu4cIQEYUadgOv+0eIf+hP1H/weaJ/8nUn2nxD/ ANCfqP8A4PNE/wDk6vONAbx3dw20J1fWDeXWn2V6xu7K3hSG6eG8aW3Yi3+W PzEtgwwZFDDDAsK9L+JyatL4Ku00SVodSeSyWKRZFj2g4D/M0UoA2FuTG307 gaSBXGfaPEP/AEJ+o/8Ag80T/wCTqT7T4hz/AMifqP8A4PNE/wDk6vPbzxd4 ln8OWuq211qMk5gktPsi2cOy8nXTpJTII9hlSUXCGMxlsBoypXNeh+GLS5sN K8q9u7y+ljldftFzGiSSLvO3IjRF6f3VHGD702khJ3D7R4h/6E/Uf/B5on/y dSfafEP/AEJ+o/8Ag80T/wCTq4uLxD4qsfFENulpqmpaemqSLdyi3jj8iCXy hApZlj3xqJJJHaNC6NEI2LAGR9v4Y6j4kuNd1u31xX+yW+l2P2WVo1Bnk86Z JZGZQF3uI43KDhA6DAJOSytewza+0eIf+hP1H/weaJ/8nUfaPEP/AEJ+o/8A g80T/wCTq+fvif8AtS+P/BHxS+KuqAeFpfh98Ndc+y6lEdGuzfWOnwaRp2sX 1/JdLcFJH8m6nt4bZLdXaV4XLlI5gdDw/wDEz41eKf7aupbDQfCFoLi20zTr bUfCd3qd1bSXMVpdR3k/kX6LJDALlrCWOLK+fbS3P2hIg0EaSTV0v6/r9Qem /wDX9f1ue4/aPEP/AEJ+o/8Ag80T/wCTqPtHiH/oT9R/8Hmif/J1fLTftefG fTvgP421hfA0mp+NdLTU4PDmmDwXrFnFrAt31hra+bLyFEnisoMWjFJQ4QGT /iYWm3c8L/EDxf4Y8RfHiTUbvXfD3hDT9Oi1HQ/EWuLOY9Id7zVob6SP7Uix /Z7U2yXSowkzFMhEptHs4YHy+Q7H0T9o8Q/9CfqP/g80T/5OpPtHiL/oTtR/ 8Hmif/J1fPX9t/Gvw9qk17a69NdQ+LvE2saXHFr3hg39r4OtbO4v5LW5htbF LW4mgntLRIybi6YvLd2zxuADDcfSfhPVL3XPAml3upabJo2o3elwzXWnvKsz WMrOrPCXX5WKMSu5eDtyOKnS17CW9il9o8Q/9CfqP/g80T/5Oo+0eIf+hP1H /wAHmif/ACdVP4neJPCvg34a+IdW8dX97pXgnS9NuLvxBe2c91BcWenRxs91 LHJaEXKOkIkYNARKCAU+YLX50fBD/gqH+z5+098SdO8AeA/Efgv4efDnxw+n 2Gj/AA0TwpfaJqbXP/CQXLtpVysYn0UWlzb3dxPK1rLBNdb7K0l85I5IpaUb xukaUoKUrSlZb/8ADd35aH6R/aPEP/Qn6j/4PNE/+TqQXPiE/wDMn6h/4PNE /wDk6tGr2r/x/wDXzN/7LUXXYixg/aPEP/Qn6j/4PNE/+TqPtHiH/oT9R/8A B5on/wAnVoUUXXYLMzhc+IT/AMyfqH/g80T/AOTqX7R4h/6E/Uf/AAeaJ/8A J1b2r/x/9fM3/stUaLrsFmZwufEJ/wCZP1D/AMHmif8AydS/aPEP/Qn6j/4P NE/+Tq3tX/j/AOvmb/2WqNF12CzM43PiED/kT9R/8Hmif/J1L9o8Rf8AQnal /wCDzRP/AJOrQPSii67BZmf9o8Rf9CdqX/g80T/5Oo+0eIv+hO1L/wAHmif/ ACdWhRRddgszP+0eIv8AoTtS/wDB5on/AMnUfaPEX/Qnal/4PNE/+Tq0KKLr sFmZ/wBo8Rf9CdqX/g80T/5Oo+0eIv8AoTtS/wDB5on/AMnVoUUXXYLMz/tH iL/oTtS/8Hmif/J1H2jxF/0J2pf+DzRP/k6tCii67BZmf9o8Rf8AQnal/wCD zRP/AJOo+0eIv+hO1L/weaJ/8nVoUUXXYLMz/tHiL/oTtS/8Hmif/J1H2jxF /wBCdqX/AIPNE/8Ak6tCii67BZmf9o8Rf9CdqX/g80T/AOTqPtHiL/oTtS/8 Hmif/J1aFFF12CzM/wC0eIv+hO1L/wAHmif/ACdR9o8Rf9CdqX/g80T/AOTq 0KKLrsFmZ/2jxF/0J2pf+DzRP/k6j7R4i/6E7Uv/AAeaJ/8AJ1aFFF12CzM/ 7R4i/wChO1L/AMHmif8AydR9o8Rf9CdqX/g80T/5OrQoouuwWZn/AGjxF/0J 2pf+DzRP/k6j7R4i/wChO1L/AMHmif8AydWhRRddgszP+0eIv+hO1L/weaJ/ 8nUfaPEX/Qnal/4PNE/+Tq0KKLrsFmZ/2jxF/wBCdqX/AIPNE/8Ak6j7R4i/ 6E7Uv/B5on/ydWhRRddgszP+0eIv+hO1L/weaJ/8nUfaPEX/AEJ2pf8Ag80T /wCTq0KKLrsFmZ/2jxF/0J2pf+DzRP8A5Oo+0eIv+hO1L/weaJ/8nVoUUXXY LMz/ALR4i/6E7Uv/AAeaJ/8AJ1H2jxF/0J2pf+DzRP8A5OrQoouuwWZn/aPE X/Qnal/4PNE/+TqPtHiL/oTtS/8AB5on/wAnVoUUXXYLMz/tHiL/AKE7Uv8A weaJ/wDJ1H2jxF/0J2pf+DzRP/k6tCii67BZmf8AaPEX/Qnal/4PNE/+TqPt HiL/AKE7Uv8AweaJ/wDJ1aFFF12CzM/7R4i/6E7Uv/B5on/ydR9o8Rf9CdqX /g80T/5OrQoouuwWZn/aPEX/AEJ2pf8Ag80T/wCTqPtHiL/oTtS/8Hmif/J1 aFFF12CzM/7T4jAOPB2o8jvrmicf+T1cf4d+Fvi6b4h6VqWo2M+6XWNLnvbi 41TSo4LeG31C1uGYLHqEjbUWObamxyAy7cO08k/oFFVGdtkJxuZXxb+L/wAP /gLqWi2PizxDrdlf6/b/AGm1htPD01+rARwu43Qk42+fGMsFyTxnFaVh4g8A ePo9S06y8Sx639mjVL61gtba68pZAwUSoLg4DbW+8MHa3oa474teEPEN1+03 ZeIbiw0y/wDB1n4bsrLyRITfxTfZxK80aBPun90m3fklPukdfFvjX+w9a/tJ /Dy806BdU0G38Q+JE1bUDcfZCbyC3s51t47mxu7G5truEXTxt9nniXads4ff DGjeFVxuMpZxHAyjGVKcOdSipe7Z25ZStyczf2U721OuSpVMNGpQg4uLcZuU l7zesXCK15baNvr2PrQWuhHH/Ew1jk4/5Bcf/wAfqh4p1jw14R8L6jq1zfa4 9vplrLdypHpcRkdY1LMFBuACcDjJH1FfA2hf8E7/AIzeAPhtr9v4W8QeGPD/ AIp8TXttq7z2Pi3xDa6fpV5a+FrOxtha20bCH7OuqxTM0M8MsT2SW0bRt5UU cHX6h+xZ4n8P+Jf+Fg+KvE1/rV/4f8N/EG3lP/CU6pKhXU9UiutMiW3dlhaK GwR4ZI2UIJI7fasnkxSR++krpX/q1/8AgHHZ2v8A1ufWHiTxpo/g2GGTWNV0 3So7h/Lia8uY4FlbGcKXIBOOwo0Lxro3ickabq+l6gwzxbXUcxOP90mvFf8A goF8I/FXxf8Ahvpll4T0yfU7+2uZJXSKS3jbbs27Q80kaKWBIyW/LqOU/wCC f3wf8S/C/wAV36+I/A2teGV/st0hvLuawlill86H92Db3MrAlQ7ZYBcIeckA /WYTI8rqZPLHVcWo1lzWp21dttfM8LEZnjYY+OGp4dypu1532v5eR9If8LD0 JfiLp3hF9Y02PxNq8EtxZ6c86rNOkalmIycDKq7KCQWEchAIRiH+EfHGk+Pd NkvNHv4L+3hne2lMfDQyocNG6nlWHHBAPI7EV8D/APBQ7wL468BftPX/AIs0 PTdRmh1m0gudJ1G0tmFtBdxxRwNFcTrDNJbyKIw6yKo3rIU7GvoL/gnF8M/E fgf4Rx3fiO+1m5ubqzgtit+jxm4nE11d3N2EdVZTJPfSR8jLJbRMWbOa/PMP m8Jy+rSg1W55Jqzt7NRup81ktXZKKbe76O33eY5BVw6hi6dSM6E6cZXuub2r dpU7Xb91at2X46/RefpWLZ/Ejw9qPi+48PW2vaLc6/aCUz6ZFexPeQ+UlrJL uhB3rsS9smbI+VbuAnAlQts1i2Hw28OaZ4xuvEUGgaNDr94ZTNqUdnGl3L5q W0cm6ULvbfHZWaNk/MtpAD/q0x6al3PDsWfG3xK8IfCnwx/avizV9R0u3aby YzbaU17vOxn4CNuztRz93Hy9ecUvg34tfD34gXOnRaR4j1e8OraZHq9ow0Rk Sa1kUMj7mkADEHOwkOADlRg48p/bttdaPwJlvdFh806Vc/abwpB588NuYJoz LFEQRI6NIjlCDuVHGCcA/HX/AAT60TX/AIs/HS5kh8TtrVlpFzbXsZjaQW1h ALtbm44EMcaTSSIkSRoABDLOOmcfMVuIKuHzCtg6tJtcsHSaUnzSc4qab+FK MW5atbdUfU0+HIYjKqWYYeorqclVTlFcsVGTi0n7zbkorre76n6hG00Lj/iY 6ucnH/IMj/8Aj9H2TQf+gjq//gti/wDj9fFnxa/4Jza54qs/jAfDWo6boLeP dC1CDTrGz1m90/TLjVL+TWVvb3ULOAC3uZZbe805POmjmkBtPl2mKNm6q/8A 2RfG0XiLWY7bxjrreGLSKKLwvZSeOdbS5s4pnuBqcd1chzNcvcRmAQzzySvY mZmtljNuhn+oPlWrf16/180fVItNBLAf2lq4J6f8SyPn/wAj05o9FcENqmss CVPOmxfwjA/5b9hXzfqnw68faN4D+GcOv6x/wkOp6R4sa48SXFslzcw6yksF /bWUwt44y0US3lxpt00I/d2YhZvPkW289+C+D37EvxJ+EVtoel6Z4r8D6P4I 8P8AiS31oaCdEkuZLuJlsJLjbNbPZW1rJHNHqEcSw2QikWZZ50e6klkprezY W0ufZYtNBL7RqWrE/wDYNi/+P0fZNB/6COr/APgti/8Aj9eAfCP4Y3nw98ea Rp1zppl1Oz1DxXrk+twwF7OCw1TWZrqOxMrhSZpC9tIyIGCfYXDMA0Rl6H9p P4DD45+H9NhtrhtPv7O/hSaeK4e3a40uaWOPVLF3QFmjuLPzV29pUt5VaOSG KWNX2Cx699l0H/oJav8A+C2P/wCP0fZNB/6COr/+C2L/AOP18lz/ALE/ifU/ 2gNF8Wy69eWtjoenancwQW/jLW3dNWu7zTLgACSQ508JYMhsi5tSVRhbLvYL 9A+HL7xPdXsC6vpGhWNvJCWke01eW6dJPLhIUK1tGCvmNcruJB2xRNtzKyQn RMdjR+GPiaw+JPj/AMa6HGuowDwrrMOkW8q28cj6gX06zvWkKGRRFg3WwLuf Pl7sjdtHWah4c0rSghnvdYRXyFP9mxEEjqMifqO9eRfs06iNI+NfxZum5WDx rbuf/BBo9dLonjG91jT5GubK7Sa/1e4mVGQhIY5HkcuzHGADsA453cdK8LHZ rWo5jSwkKd4z3dnpv12Wx00qClRlUb1R132TQe+pauP+4ZH/APH6Psmg/wDQ R1f/AMFsX/x+vmb4ofsxeNPiN8WvEOtSa7I+n6pe2tlYxw+L9Z0kaZoYfR5L qGKGweAJeO8OrutyH8z95bRvI8DNDFwHw2/Yg+Kvg3VPD2vTeMJLjxQbrSdR 8S3LeOdfnt9QuIv7CivtlmziyaOaCHXVCPa5BksChgCkRe9HW1zlatsfbDWm ggZGpasR6jTY8H/yP/nNH2TQf+gjq/8A4LYv/j9eN/GD9nwfE7xmNYj1PXdP dtKGiymy8RahpuLd723nn8pbeRFjmaOIqJ02yjhQ4Xp5ZYfsffEbxD4JsrDx f4rTXLzSPGeg+LLVn16/eAG2fRrm/WRSqiVGu7fV5ILd1aGLz7XYsIiQQENd 9AmrXtrpf/gH1v8AZNB/6COr/wDgti/+P0fZNB/6COr/APgti/8Aj9ZFFRzD say2Wgr/AMxLWP8AwWxf/H6X7JoP/QR1f/wWxf8Ax+siijmCxl6X460m7+NG ueFC+px2mh6Fp2rm/NsheZrue+i8ryvM4CCy3b95LeZjaNuT03iiPSfBmnXF 5qcfje1s7NGlnnbwpMY4Y1GWdmDkBQOSegFeF65rUfhr4+fFbUpXWKOw+H+i 3DSMwVUCXOvsST6cV8zftJfGzwt4l+MXinxVF8QvhMdCh1DVrvSNDS81Sdr1 r1Jo3upRPaFIrva1tIqxuIxLFGC6pGuOHHY10LWS6f8AD+Z42Z5hVw7Spxv/ AFufoH4X1vwp4z8N6fq+m6vq9xp2qW0d3ay/2UiebFIgdG2tMGGVYHBAPPIF aTR6I6YbU9YP3f8AmGxfwjA/5b+lfEXwx/ZJ1X4zeG/AXjb+2LOHRdS+Gvg/ TjpcrSPFfSWUtzf75VAwjxzy2MkMoLkeXcoyhZN1bPwF/Yq+IXwk/Yws/Ckn jC6f4oWOlraQaha+JdQj0e1lsY7hNHjggx5UMESizWdEtgtyIX+0Jc7m399O XNDmb10PXi21HzX6I+wfsmg/9BHV/wDwWxf/AB+lay0LYpGp6xjsRpsX/wAf r5L8U/sf/ETT/E1mPDPj7WI/CpuotJudN1Txdrc93Z6K9zHJdvb3hmeSS+kT 7QoluBJLEtxCltcWf2bzJ6P7Ov7AGt/C39rDU/iTr/iG+1TfPf8A9nwxeKdX lEdu+n6HZ2kVxDJIIbqSJNPulknmDyXBFrM+ZFUQUkn1/r+vyBn2D9k0H/oI 6v8A+C2L/wCP0qwaGmQNS1cBxtI/s2L5uQcf6/2/SvkDRf8Agn3q3gLUvEuq +FPFM2iat8QPGs+s+JfsUVjpIutKkudQke0FzZWUN7NK8d2qrNNcNNbSASQz LsZJui1D9lfxjrPjiGebxDrNrpU0N7f6kIvGuqzf2jq32iJLRltZVaGCy+yf a/NtYz5Je7UCN2t4blSNna7/AKt/SG+p9Fz+BPBVzHrSskpXxJKZtXB0O3xq jmFIC048796TDFFHls/JGi9FAGglvoL5/wCJjrAx2Omxf/H64L4t6d4l1H4O +JbDwfqVrpHi+70a7t9EvrhVaGyvmgcW8zBo5AVSXYxzG4IByrdDzXww+H/i f4K+C5NB0bTNP1Sxt7q7ns5dX8Y6hf3kgllu5YxNcXME0zEn7KCWkk2iaYDc IIxMl1Dsd3rfjbSdK+NPhzwlG2oyw65o+p6tLeNbIj232SaxiEaxeYQ+83pJ JddvldG3fL0Gpnwva24S81fUIoblhABLp8KrKzcKnM/JJOAO9eT+JiT+134H zwf+EO8RZ/8AAzQ68x+Nv7PPxa+Kn7UugazB4qsdN8IeGrS6udKtkBVbXUJY poI7h4yh3yRxSHBZ2H7w7UT5i2eJqezoSqpXaWkerbdrLp6t2SR1YDDxr4mF GpNQi73k9opK+vV9kkm2z6l0Xw14V8N6Naadp09/Y2FhCtvbW1vpEMUNtEg2 pGiLMAqqoAAHAA4q4U0Qx7f7T1jG3Z/yDYum7d/z39a+bv2nv2T9V/aM+Ifh PVDqlrpdjpPhrXNF1Ky8ySWLVBqT6astrIgVRJbvb292pYlWSRreQIxQqPOI /wDgnh4i8da/4pPxFj+GHjjw94r1fSdUj0e60eV4dJl0y7SGKfFwZo57m50g /Z5pEjtwv2aKPZIskkg1i+ZXehyzXLtqfav2TQf+gjq//gti/wDj9H2TQf8A oI6v/wCC2L/4/Xx94i/Yf8b6f+znoHgbwDrPgnwYfDHjPWfFunxSaXLeaSUb U7+90my+yxtAESBrm0n3KSkM2nxKIp4ya7r9lf8AZPl/Z0+LXxO11JbCez+I d8usO8KwQSrdte6jNKjxQW0MbKIri2xcSGa5lYyLJIUhgANO4NH0P9k0H/oI 6v8A+C2L/wCP0+RNFmGH1PV2yxf/AJBkXU9T/r/YV80N+xLoEei/FewsvDHh XQI/jY9zpfiSXRI4bGWDTGtbqGOZcWrRXV20s8szGePIN7IjSTJBGr4Wq/se eKLj9lRvhbpth8PdMt9Is9Tj8O6hYRJpi6dfNJftpuqC2trSOKC9gf7BO72w jX7TPdSxJF5ESyr/AIANWPrI2ughc/2jq2D0/wCJZGM/+R6T7JoP/QR1f/wW xf8Ax+vl79pv9muH9pT4opo154d0G9iZIdTGoeI9EXWNDuoodP1a1t7eS2J2 yy2t7fxXPlTND5iTHypGaJ/L6f4Z/ATU/AXj6bxkuieGtN8QahpGmaPqMVpq c0suteWLZbi81C/a3WXUJ7eNHjtTNCGCpKTJGLx1txA0er/ED4gaR4Q8U+Ct M87VbxfF2tzaZJK1nHGbPbp15eGUKJW8wk2ax7Mr/rS2fl2tutceGFmeM61f +bHjen2CEsuSAMjz88kgfjXiHxEvdbvvHvwebXtP0rTbseL3KxWGoyX0Zz4Z 1RpMu8MJG2YzIvy/MiI52FzGnsdzbad4i+G6aNqeo6nc2iC6gu9DiWWK2uTL czyq0zMRFPCUmQlF5ymGLcx1aWm19u3ffXtv36E3s9zVkXRJxhtT1hssX/5B kXU9T/r/AGFNNnoQx/xMdX5H/QMj/wDj9fPv7Sv7HNh+018Xfh1rGsy2K6L4 FW8ufs0uladqL3F081i8KiO+tLiNI9lvMGlh8q4XKCOQBnrxMf8ABN/x/wCJ fi98JfFuteKlGteDV1PUta1dfE+qXN1daveaTYxGeBP3Srafb7aVzYI0VqYQ iiJVkaFIjrvp/X9feVJW/rzX9fI+7HstBdcf2lrA+mmxf/H6X7HoP/QS1n/w Wxf/AB+vEfg38ENb8FayItW1TV5tG0bUJb3RLZ/FuparNEstjbJJHcz3BWS6 Vbl9RZEuGmVVe3dRGUjjg3vjX8IbX40Poul65onh/wAUeDmupG17RdZQTWdz GInaGUwNG8dy8dwkOIpdqDe0obzIYlI7Al0PUPsegdP7S1jI6j+zosj/AMj0 fY9B/wCglrP/AILYv/j9fMvxr/Z78UfEe28BrY2kWkSWXiDSrjVbCz1t7rSb G2s9Q03VzMTLHHI2x9K+yQJFGAn9oM5RVMmznvB/7EXjHQdd1AXfiWaXw7rG reILm/sdO8V63pMy2uoeIn1WDyGs54ooJY47mfzJBE08jRJELpLeV0Q0A+ux ZaD/ANBLWB/3Dov/AI/R9j0H/oJaz/4LYv8A4/XyV4X/AGNfiFfaFaQ698QP ED6ncX/iJ9V1AeK9UuZLeLUBfJayWsKtb2gntozpu1ZLV7eNopmght5MyyVf 2P8A9ljxt+z9rnifxuuk+HItf+KN3qut+INGurq2tn0yee+vLyxt/tlnZGS6 Ma3CwzNdPc+WWY2zrEhindle1w6XPev2hfin/wAKsl8Daf4YsP8AhI9c8d+K bbwxaQapcf2Ta27zwzyCZ5o1uXwDCBtERJ35zxg+f+J/2t9P8FeJ9W0TWfih +xvpOs6Dcy2ep2F78dkt7rTp4nMcsU0T6YGjdJAUZWAKsCCAeKt/Haa9n+K3 7O5v7e1tpx8X9NCpb3DToYxDfiNizIhDNGEZlwQrMVDOAHb8qP2pP2VvHWg/ taftR+ILf9k/45eJNQ1/xp4p0/TptN8HatcxeK4NT1HUP+JlFei0aOC3htym 1Yy3nNNCyhgXeNVpOnh3VhDnlpZLd3aXorbtuysa4SlGtiY0ak1CLveT2Vk3 tu77JJN3Z+03hL4d/Gfx94X03XND0T4Ja1ous2sV9p+oWHxGvrm1vreVA8U0 UqaMVkjdGVldSQwIIJBrRX4GfH0/8yh8If8AwvtQ/wDlLXjPg/8A4JX6t+1h +z9+wVc+Lobfw/pPwU+GkFt4i0bWdDsrnVFv5dK0dIoDZ6lY3UCMklnMspdI 54jjy2VskeVfs5fsGftLfDvxXpPwvsPiR4ztdT8DeFNK07UNRttX8Sad4Mto rD/hB2tLKzM0EVpJLLFYeJkeSxjlOy7ZblkaXyE7IUYSdmrHHKpJWs7n15/w or4+/wDQo/CH/wAL7UP/AJS0f8KK+Pv/AEKPwh/8L7UP/lLXHeEf2D/2ir+4 8SnxV8VrhrHxDq+mu1jp3jjXEa20r/hLNd1HULaC5jWGS2mOk32l28U0IVz/ AGf9n3QRbJa9L/Y8/ZS+LfwC/aK8Ya34x+JV5498Ga9pFpb6fHrWuaje6vpl xbRwWwVow0OlGOSO3Nw8sFhbzma7kV2mCCWRKjDqgnNrZ3MQ/Az4+DH/ABSP wi5/6n7UP/lLQvwM+Pp/5lD4Q/8Ahfah/wDKWrH/AAUB/wCCbT/t2ftPfAvx Lqd9o0PhH4XLrc2p211plhqdzdz3QsDbCKC/sru2KA2sm9yqSoGUxOGJI+PP 2cv2DP2lvh14r0r4X2HxI8ZWup+BvCmladqGo22r+JNO8GW0Vh/wg7WllZGa CK0kllisPEyPJYxynZdstyyNL5COFKEnZqwpVJJJpn15/wAKK+Pv/Qo/CH/w vtQ/+UtH/Civj7/0KPwh/wDC+1D/AOUtcd4R/YP/AGir+48SnxV8VrhrHxDq +mu1jp3jjXEa20r/AISzXdR1C2guY1hktpjpN9pdvFNCFc/2f9n3QRbJa938 e/sy+IPE91+zi0OuSaufg94lXV9av9bu/N1HWIl8N6vpXmF4oUSW5e4v4ZHO yFCPNICkLGRUYdV1/phUqSjtrp/SOI0P4QfErQI72+8daR4I0bRbWFWWbQvE dzq108rSxoFMU1haqF2M7bvMJyirtwxZYNf1nw54WuNPTUJPG9rDql9badbX beE7j7E89zPHb26m43eUokmljRWZgpLjnmvob48/8kp1T/tj/wCjkr8tv22N Rtbb41+Gb/xLdeEdISw8SaX4gh06++KbRWs9vY3Niv2mTR301FMk1tZ3Ea5u GUPdXG55BGm30spy2hiq0qdSVrLS1tfvOPH4yrQpqcFf9D7H8Par4S8W6NHq Gl69f6hZSF1E0FjC6bkdo3XIn4ZXVlYdVZWBwQQLv2PQf+glrP8A4LYv/j9f CXxN+Feoft66L8QtT+Gviu2snsPifqWu6XrNjrl3p5EreD3sbSSK4tMyfLeX ds0i52tGkqsr/wCrb1xf2WvElv4iu7O28S+Ij4b1W3kstSkk8a6wb14YrqxN n5GXZ7WZLQX8Ms8E8ck7mKWUyPJmDysbQVCvKj/K7HfhqntaUandH0j9j0H/ AKCWs/8Agti/+P0fY9B/6CWs/wDgti/+P14V+zv8IviB4G+F/irR/FvjFr/W NYuBc6bqcdzNfvpjy6XZpdMpnVflGprfzRxD92kUsaIsaKIo+e0T9nPxV4F8 Z6BqvhTTPBfg3TZtLmm8T6NpF/PBHqeuXd9pUlzfzTLbr9tkjt7K6RJ5olml M21zGJXZOZpL+vL+kbR1tc+ljY6DtB/tLWcEkf8AINj/APj9H2PQf+glrP8A 4LYv/j9fmx+wF/wS9+Nf7HOmNZv4r8LxW39rtdG30nxJrCW11aPpl5DJDJas gtQ4vF0l0mSATLHFcAzMuyFvbfHf7Jfxe8Q+CNY/s74hXln4llvfEEml3C+K NTitreG5vNXvdLR0UbW8iaTQ1YFGxBaXFuC8DvFPcoxT37Exbau0fXX2PQf+ glrP/gti/wDj9BsdBHXUtZB9DpsYx/5Hr5c+NH7GWt/Gb4C+J/BV/rMjp4v1 zSdT1Br/AFSTWbdRb6zFc3kkEF/bzxwiW0gjKWRWSzWQ+WIgnmSz7Pwq/Zu8 QfBHXvD50W4trvT9Ft00JLeTWbmws5dMBzv/ALOt4hpsd0GbeGt7WHAgWIOk UpWOUl/XoNnsPxi8baT8MPBkGp2ranqk9xrGlaSsEtulsn+m6hb2ZkLiRz+7 E5fG35tm3K53DSx9a8k/aBuNauvhHpra5p+l6fcHxh4TKJY6g96jZ1rSzJln hiI2ymRR8p3IiOdpYxp63UtjQY+tGPrRRSuOwY+tGPrRRRcLBj60Y+tFFFws GPrRj60VyH7Qfw1m+M3wC8ceELeW3gn8VaBf6PHJOqtFG1xbSQhmDI6lQX5B RxjqrdKExWOvx9aj8aWWp6Lomm3GlQWOoXN/EZ2iu7p7RI0EksZw6xSknMYO CoGGPPHPy5p//BNDRfh/qnhu60Cw8MXN74Pk0KPStal0nTdJ1ixtrfW7zUNQ hgksLGKC2jmiuzGyQwoLhPOikMbTG4X6+8S/8gfw9/14P/6VXFVoJnAjVPGv fw/4YHGedfnx/wCkVU73xf4usGnD+HdBY26JI4j1e7lOGcoMBbEknIPAyQBk jHNYvwM/Z8uvg78RfiBr0+vf2svjvURqbW/k3CGzkEk4UbpLiUSYt2to97KJ B5GxXW0jsrKx3vGEV3onjWz1h9Lutf0N7f7DfWNtOyXMIJb/AEiEAgF13Dgg g4wQPvD5vivNsTl2AeKwkOZpq+jdovd2jrZbuyelzlxladKl7SCu/v8AyF07 xP4t1a28630Tww8e4qc69cKVI6gg2QIPsQDU/wDanjT/AKAPhf8A8KCf/wCQ qw/hR8SdX+JHj3xBeXmj22lWbrGqW1pD5Ftauny7Ah5LsDlj3K844FX/ANpL 4b3/AMZP2dvH3hDS5rW31HxT4c1HSLWS6ZlgjluLaSFGcqGYIGcE7VY4HAPS jhXP45xlsMco2u5LZq/LJq6T1SdrpPXWzHgcR9Yoqp6/gXf7U8af9AHwv/4U E/8A8hUf2p40/wCgD4X/APCgn/8AkKuX1/4P6xd/G/xH4ngFtc2mt6b4X0+C EavdaZMjadq1/dXLtJAjFkEN2hWHJS4MbwS7I5GY+OH/AIJ5XL+JPDv9m6T4 E8L6Ho+u3Hi9rLTra3YHWZH09oisS2MVsFhRbyJbqOKPUQlvalbyM3N2K+li k3Y6b6XPePE3xJ1zwXDDJrFt4F0qO4fy4mvPFMkCyNjO1S9oATjtUnhb4kap 4p1CGO2j8E3cDOBK9j4me6kRMjJVRagMcZIG4A+o615z/wAFAvhH4q+L/wAN 9MsvCemT6nfW1zJK6RSQRttKbdoeaSNFLAkZLf4jlP8Agn/8H/Evww8WX6+I /A+teGl/sx0hvLuawkill86H92Db3MrAlQ7ZKhcIRnOAfrsJkmVVMnljquLU a6vanbV2218zwcRmeNhj44anQbpu1532ufRnif4b+HfFutTX2q6Boup3sm1H uLuximlZUVUQFmUkhVVVHPAUAcCs/wD4Un4M/wChR8Mf+CuD/wCJoor5Y9sP +FJ+DP8AoUfDH/grg/8AiaST4I+Cyhz4R8Lng/8AMKg9D/s0UUMDrKKKK5jU KKKKBsKKKKBGF4j+HmgeLtUW41bQ9H1O4jgWJJbuyjmdUBY7QWBIGSTj3NUf +FJ+DP8AoUfDH/grg/8AiaKK3j8KID/hSfgz/oUfDH/grg/+Jo/4Un4M/wCh R8Mf+CuD/wCJoopiD/hSfgz/AKFHwx/4K4P/AImj/hSfgz/oUfDH/grg/wDi aKKAD/hSfgz/AKFHwx/4K4P/AImj/hSfgz/oUfDH/grg/wDiaKKBMP8AhSfg z/oUfDH/AIK4P/iaP+FJ+DP+hR8Mf+CuD/4miigY4fBXwbFFNEvhLwysTXT3 BQaXAFMjRxIz42/eKRxqT1IjUdFGG/8ACk/Bn/Qo+GP/AAVwf/E0UUMA/wCF J+DP+hR8Mf8Agrg/+Jo/4Un4M/6FHwx/4K4P/iaKKBMP+FJ+DP8AoUfDH/gr g/8AiaP+FJ+DP+hR8Mf+CuD/AOJoooAP+FJ+DP8AoUfDH/grg/8AiaP+FJ+D P+hR8Mf+CuD/AOJoooGH/Ck/Bn/Qo+GP/BXB/wDE0f8ACk/Bn/Qo+GP/AAVw f/E0UUANt/gt4OhkuFTwn4ZRbqSPzgNLgAm8tZfL3fLzs82Xbnp5j4xuOXf8 KT8Gf9Cj4Y/8FcH/AMTRRUy3M5bh/wAKT8Gf9Cj4Y/8ABXB/8TR/wpPwZ/0K Phj/AMFcH/xNFFUi0H/Ck/Bn/Qo+GP8AwVwf/E0f8KT8Gf8AQo+GP/BXB/8A E0UUMYf8KT8Gf9Cj4Y/8FcH/AMTR/wAKT8Gf9Cj4Y/8ABXB/8TRRQJB/wpPw Z/0KPhj/AMFcH/xNH/Ck/Bn/AEKPhj/wVwf/ABNFFDGEPwW8G21xJJH4S8Mx ySW727sulwBmjLxuUJ28qXjjbHTMaHqowf8ACk/Bn/Qo+GP/AAVwf/E0UUAH /Ck/Bn/Qo+GP/BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUAH/Ck/Bn/Qo+GP /BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUAH/Ck/Bn/Qo+GP/BXB/wDE0f8A Ck/Bn/Qo+GP/AAVwf/E0UUAH/Ck/Bn/Qo+GP/BXB/wDE0f8ACk/Bn/Qo+GP/ AAVwf/E0UUAMn+BHge4MbyeDfCjvbv5kTNpNuTGxVlLKdnB2swyOzEd6f/wp PwZ/0KPhj/wVwf8AxNFFAg/4Un4M/wChR8Mf+CuD/wCJo/4Un4M/6FHwx/4K 4P8A4miigOof8KT8Gf8AQo+GP/BXB/8AE0f8KT8Gf9Cj4Y/8FcH/AMTRRQMP +FJ+DP8AoUfDH/grg/8AiaP+FJ+DP+hR8Mf+CuD/AOJoopAH/Ck/Bn/Qo+GP /BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUwJtM+EfhPRtXtL+z8MeHrS+spD Jb3EOnQxywMytGzIwXKko7qSDyrsOhNdTgegoop9BBgegowPQUUUhhgegowP QUUUAGB6CjA9BRRQCDA9BRgegoooAi1CJbrRr61lVZLa8gaC4iYZSeM4yjr0 ZTgcHjiuV/4Un4M/6FHwx/4K4P8A4miinDclh/wpPwZ/0KPhj/wVwf8AxNH/ AApPwZ/0KPhj/wAFcH/xNFFHUaD/AIUn4M/6FHwx/wCCuD/4mj/hSfgz/oUf DH/grg/+JoopDYf8KT8Gf9Cj4Y/8FcH/AMTR/wAKT8Gf9Cj4Y/8ABXB/8TRR QDD/AIUn4M/6FHwx/wCCuD/4mj/hSfgz/oUfDH/grg/+JoooF0HRfBTwbFLF MvhLwws1rLHcwuNLgDQyxuHjkU7chldVYEcgqCORXU0UVFbcuIUUUVkUFFFF ABRRRQAUUUUAFTvM9zBtkZpFhGyMMchFxuwPQZJOPUn1ooqokyI9g9B+VGwe g/Kiir6EhsHoPyo2D0H5UUVEPhBBsHoPyo2D0H5UUVa6AGweg/KmyqAh4HQ/ yNFFIbP/2Q= --0-1398234352-1298459008=:63850-- ========================================================================Date: Wed, 23 Feb 2011 11:56:05 -0000 Reply-To: Lorelei Howard <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lorelei Howard <[log in to unmask]> Subject: contrasting/masking parametric variables MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Hi everyone, I am running a study which has two types of blocks (1 experimental and 1 control). For each of these blocks I have second-by-second measurements of the same parametric variable. I was wondering whether it would be valid to run a standard t-contrast [1 -1] to identify regions associated with the parametric variable that are more active in the experimental blocks than in the control blocks. Is it OK to do this with parametric regressors? Or, alternatively, could I use some sort of masking procedure to remove brain regions associated with the parametric variable in the control blocks when I examine the effects of the same parametric variable in the experimental blocks? Many Thanks, Lorelei -- Lorelei Howard Ph.D. Student Institute of Behavioural Neuroscience Cognitive, Perceptual and Brain Sciences Department University College London 26 Bedford Way WC1H 0AP +44 (0)20 7679 8553 ========================================================================Date: Wed, 23 Feb 2011 03:57:39 -0800 Reply-To: Vladimir Bogdanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Bogdanov <[log in to unmask]> Subject: Re: Check Reg Figures SPM8 - size and arrangement: solved! Comments: To: Guillaume Flandin <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="0-2071618827-1298462259=:64948" Message-ID: <[log in to unmask]> --0-2071618827-1298462259=:64948 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear Guillaume and SPM community, The problem is solved pretty easily. I had to set new figure position parameters and calculate the value of ratio new/old figure proportions (1.7 in my case) which can included in the script, what gives the necessary result (attached) . set(1, 'Position', [600 40 1000 800]) global st h = 1; for i=1:nnz(~cellfun('isempty',st.vols)) p1 = get(st.vols{i}.ax{1}.ax,'Position'); p2 = get(st.vols{i}.ax{2}.ax,'Position'); p3 = get(st.vols{i}.ax{3}.ax,'Position'); l = p1(3) + p2(3) + p3(3); sc = (1-0.01)/l; p1 = p1*sc; p2 = p2 * sc; p3 = p3 * sc; p1(4)=p1(4)*1.7; p1(2)= p1(2)-p1(4); p2(4)=p2(4)*1.7; p2(2)= p2(2)-p1(4); p3(4)=p3(4)*1.7; p3(2)= p3(2)-p1(4); set(st.vols{i}.ax{1}.ax,'Position',... [0 h-p1(4) p1(3) p1(4)]); set(st.vols{i}.ax{2}.ax,'Position',... [p1(3)+0.005 h-p2(4) p2(3) p2(4)]); set(st.vols{i}.ax{3}.ax,'Position',.... [p1(3)+p2(3)+0.01 h-p3(4) p3(3) p3(4)]); h = h - p1(4)-0.005; end Thanks a lot for your advices! Vladimir --- On Tue, 2/22/11, Guillaume Flandin <[log in to unmask]> wrote: > From: Guillaume Flandin <[log in to unmask]> > Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement > To: "Vladimir Bogdanov" <[log in to unmask]> > Date: Tuesday, February 22, 2011, 4:49 PM > Dear Vladimir, > > the following code should get you started (to be executed > when images > are displayed with checkreg): > > global st > h = 1; > for i=1:nnz(~cellfun('isempty',st.vols)) > p1 = get(st.vols{i}.ax{1}.ax,'Position'); > p2 = get(st.vols{i}.ax{2}.ax,'Position'); > p3 = get(st.vols{i}.ax{3}.ax,'Position'); > l = p1(3) + p2(3) + p3(3); sc = > (1-0.01)/l; > p1 = p1*sc; p2 = p2 * sc; p3 = p3 * sc; > set(st.vols{i}.ax{1}.ax,'Position',... > [0 h-p1(4) p1(3) p1(4)]); > set(st.vols{i}.ax{2}.ax,'Position',... > [p1(3)+0.005 h-p2(4) p2(3) > p2(4)]); > set(st.vols{i}.ax{3}.ax,'Position',.... > [p1(3)+p2(3)+0.01 h-p3(4) p3(3) > p3(4)]); > h = h - p1(4)-0.005; > end > > Best, > Guillaume. > > On 22/02/11 14:58, Vladimir Bogdanov wrote: > > Dear John, > > > > Thank you for your response! Where is the part of the > code responsible for this located? The SPM script is huge > and I am only a beginner. :) > > > > Sincerely yours, > > Vladimir > > > > > > --- On Tue, 2/22/11, John Ashburner <[log in to unmask]> > wrote: > > > >> From: John Ashburner <[log in to unmask]> > >> Subject: Re: [SPM] Check Reg Figures SPM8 - size > and arrangement > >> To: [log in to unmask] > >> Date: Tuesday, February 22, 2011, 3:39 PM > >> There are no default settings that > >> could be changed to make SPM do > >> what you would like it to do. However, the > beauty of > >> having SPM in > >> MATLAB is that it is relatively easy to tweak the > code to > >> make it do > >> exactly what you want. > >> > >> Best regards, > >> -John > >> > >> On 20 February 2011 22:20, Vladimir Bogdanov > <[log in to unmask]> > >> wrote: > >>> Dear SPM experts, > >>> > >>> By default the size of the images in Check Reg > figure > >> does not allow to see well anatomical details (see > screen1 > >> in attachment). This can be changed manually > (screen2), but > >> those changes take time. > >>> > >>> Is there a simple way do it fast? Is it > possible to > >> change SPM settings somewhere to make the figure > in the same > >> way (different from default) every time we call > Check Reg? > >>> > >>> Thank you in advance for your help, > >>> Vladimir > >>> > >>> Volodymyr Bogdanov, PhD > >>> > >>> Headache Research Unit > >>> GIGA - Neurosciences > >>> University of Liege > >>> CHU T4 +1, Sart Tilman > >>> Av de l'Hôpital 1 > >>> 4000 Liege, Belgium > >>> > >>> > >>> > >> > > > > > > > > > > > > > -- > Guillaume Flandin, PhD > Wellcome Trust Centre for Neuroimaging > University College London > 12 Queen Square > London WC1N 3BG > --0-2071618827-1298462259=:64948 Content-Type: image/jpeg; name="scr4.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="scr4.jpg" /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMD AwYEBAMFBwYHBwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/ 2wBDAQICAgMDAwYDAwYMCAcIDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCAHCAyADASIAAhEBAxEB/8QA HwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1 dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEB AQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD6Z+Clj4r+ O+kT6npXw9+CdhpFrcNaS3+qW0NnCkoRXK4aXecKynKqRzXnmlftIT67FcPZ /DT4b3S2iySTGPw6zCJEba0hIf7gP8f3SCCCQQT6H+zN8W/Fn7PfgO/0W5+D uv8AiaW4vmv7W4uUuIoraQxohDW/kOko/dqQWII7YPNeXfCjwF4s+HUt+l34 D8Xala6jpculyJFZzW0iq+0bwxicZG3oV71m7Jva2ltXfzv07W+ZS7Wd/kbu kfG+81nSr29i+HPwrW1sGVJpZNIjiVWZJXVfmlGSRDJgDqQFGSygxXX7Qstj ez21z8OfhrBc27mOWKTQNrxspwVYF8gg8Y+tbVpqvifTfCsemWvwy8WxGK3k s0keGVgIfKv448qLcfOPtzFmBAcx8Km440tO8YeKNL1y5vYvhh4nYzanJqKp LDMwHmz288ytiAbm32y7GwCgPIcqCJur+XqVrY5KP9o1CxH/AAr/AOGQPcf2 GP8A4urx+N1ylp55+Gnw+EBjEwk/4R1ghjLlA+d2NpcFc9N3HXisfUfCXj/W dQ1W4m8LeIZ31Vh5r3ejPczIo4UJK8ZdCB8uUK8ADoBXR2mmeI3utLvLv4fe LptR022isjLB9qtkaGJdiFQse9JNoUFg5UjPyAnId1b/AIIa3G3Pxfu7WR1f 4bfDxvLgiuXMeg+YqxyhDGxKuQAfMQc92weeKZc/G06feSW1x8PPh3b3ELFJ IpNB2OjA4ZSC+QQQRg9xVrw/pniTTdFtLS68FeLbsJaHT7lI7KWAvCt0l1Ey yGN/nEm9WBXGwKAcn5emt/HnjVb4XD+BPEHnzGVrmVLEh13y+cohzblVVZcs POWZhubayliaV1/TEkzj0+OsLkf8UH8NwD0P9iYyP++q1x8QrxtUuLJfhr4E mubWc20iQaB5u2QByVyjkE4jkIxnIQkZANaOueIPGPiG0v7e48GeKpba5s4r aC3lti8MEiRPGZ9v2cYchs/ujH0wdy8UuhXPiPQGW5XwX4pnvzHaMX+yyRxC a3hmtxuXy2MkbxSKWGUJJYZxzRdf0xpGCvxnt/8AoRPh2BnGP7GXn/x+r9t8 TRLpc96PAPw9NtbSxwyv/Yy5RnDlRjfk5CP7fLW5pmpa/LZLa3XgvxNbwSRR w3LW1pL5j4FtH+6ygEa7LZSVO7ccrlQQVu6r4h1vVbp3/wCEA8RAPctI2IpA rx7rtlBQwsu7N0xbduRtuCpViCXX9MFc5NfjDalsN4G+Hg6n/kDDP1+9U8Hx WtppAg8C/D4Fm286OoAP1LcV1b+NPFTX7Tp4H1tQZGkWN4ZWRWa8W5LYEY5w oTP4+1Z+oXfia68O6hpsPg7W4IrmCG2tpTbSNLbRosavlhGN5kEUYJwoAUDG KE11/MLMrSeLJ4DOZPhz4JX7NJJDIw0LcEeMEupIOMqBk+g56VWT4n2bOVPg jwDkf9Qdfw/iroV1XXra1Jt/CHiD7c0puzPJauE897ZYZmZNh3gne4+YDLDI IBBdc6t4h1CG4im8JeIViu2aaRoUlSaOVpIpD5TmM7IgYUwmCRz8x4wXV/8A ghqYMfxHsnbA8E+A8+g0cf4/5xUq/EOxLH/iivAmRjONIXqf+BV08fifX0vB P/wh+tiRrkXMn7uQgt5ttIwX93wpNucZyRv5JxzPpfizxHYQ26HwlrX+jrCu VtzmUpHGh3FoWOD5Sn5SpBzyeMF11/MVjmV8a2jwtJ/wg/goxqwRnGjDapIJ AJz1IVuP9k1esdYTUEga38CeC5xdF1iEWjh2cpjcNoJOQCD06GtWPU9ekaF5 vDGv+dbvFIkqIwcOkk7ZO6JgcicjBHBUH2ptjJrNteXNyfDWrxv57SWsUdtI EgR4WidQccZHlHgY/d4AUcATX9MdjPg1aOfBTwF4SYGE3Hy6GDmIdX6/dGDz 0p0etWssKSf8IP4QKSKzI39jDDhBlyOedoBz6YrbTxD4ka5guH8N6gbiMRxu wtpAjQrM8piC7eFOUXvwnfPFe3j1OKGyh/4RvWRb2cd1CFCvuZZwy9TGQCoP pyR26UX/AKuFina39vdwrJH4G8JPG8ogVl0YENIeQgI6nHYc1K93awWjTyeC vB8cS7DltJQbg4JUgE5IIU8jjitWwvtV09LaCLw1qpsrRopIo5EdnWRJS+/d sHJVmTp0I64xUZh1SaZlbQ9WW3FmtqivbtKx2MHTd8qg/MAvAGE4+pdX/wCC FmZia3pYJB8IeDSeP+YUn+Nad3b2mn2aXE3g3wYIJCAHTT4nBJ3f3WPdWH/A TWzNrmoLqCSw+HtX2b1nZHiYESCeWUYO05UiT0B4HPHLNUkvfEOmQ2lzo+r2 qwJEoeK1aYuUDgcHbgEOe56enQuv6YWMa0n028lKR+EPB7OEZyBpidFBY9T2 AJpftWjjr4T8IjHX/iWJWvatqFrbw266RqcsEKSRM5gaN5VZXVeMMFK+Y/c9 R6Ves9W1G0u5J10K+WWaYSSMBJ8+C+Afl9GH1K5xzTur/wDBBJmAraOxx/wi nhP/AMFiVLGmjkf8ir4UGf8AqGJU1/pOpahdCU6bfIRHHGQYWOdqBc9O+KI/ D2oL/wAuF71/54N/hVLlJsxEtdFbH/FLeFv/AAWpUqWGit/zK/hf/wAFsdOT Qr9etjdjn/ni3+FWIdDvT/y53Qwe8Tf4UWiFmRppWin/AJljwxz/ANQ2Ouo8 D/C7wv4m0fUL6+0jQrGGxmhhAg0OK4ZzIsrZOXTAAiPr1rDTRbxetrc/9+m/ wqLxh8QPEfw++Gusaf4f8I+I9c1vVyslndWlpaXFrpssUUyo9xDcXto8ql5l OyKQFgjgvGSpMztb3dxxTvqXvi3oHgn4Y+FbTUotH0/UHuda0rSTE3h+CEKt 7qNtZtLuEzZMazmQLgb9m3cudwx9B8a/CPxF8T5fCFvofjOLVorie1M938HN UtNLLwhyxGoSotmUOw7JBNskJUIzF1z5Xq3jL4xeNPhZFpfin4d6/ruvQeJ9 I1eO50nSdM0O0W0tNRs7t4jFLrl47S7YJsN5iqS6KVXDOdPQvjH8doPii93q vgq/vfBX2m4ZNKtfCWn2uqLA2/yEN83iWSIuhMe9xagSBWASPcCuN5WNLK56 5Inwm0XwP4O17X9S8LeGrfx7PDa6HFqekWdtJfXE8kqW9qm+5UPcyCJiIkLE 4IXdjNeF/tg/tNfs/wDgv4DW91ZfE34a6bp3xJ0PW7Tw74ht9Lu7lDewJHCk lvNpcF4myOaZg8nmI8bRAKjksY1/aP8AhuP2n/2UvB/wh8ZfDH4wXfhzTbTS 28QW+iXehwQa59nn+2Gwkkmu/OWHzfLEjQeTKWhPly7Duf8APr/gur8NdE+D v7Kf7Lvg7XT8QI/DHhW21PSZF1b+zZfEMOnwT6ZFHGTabbRnSBFVCMkqEMhZ y5Pp5XhYYjEezqXtaT0fZX8+3Y5MXVlSpc8d9PzPoxf22/2d14Px78Gc/wDU v+Jf/lXT0/bZ/Z5Lf8l68G/+E94l/wDlXX5FfGeT4T6TpPw10HwNN9q8Q2CX Gp6jdWwWa1W1vbdJYLaW43FmuYVUGSMApG88qZV1eNftH9mz9n34J/Fb4gfE 7wpd+I9J0nRP+FT+HNc0qGy1DSzPqfiJdH065u7ZdQvYLmSzQXsl2100LxJb xxzGYpBbuE+jyHK8DmGBeNqRqwSlJW02i173wJ2kmmtNjlzipXwOKWGU4TvG Lutk5bx3avF6Pp8j6oX9tb9ntunx48Gn/uX/ABJ/8q6lT9tH9n0j/ku/g0/9 y/4k/wDlZXhx/wCCV/wC8DfEePQvGnxVbw1PpF1p1h4gtr3xpolrd2VxcxeF ZH8uN03SRKNR8QZmRXjj/s6IO4KP5vj/AMd/2fP2ddN/Zb1Xxf8AD7xR42uP Ftjff2e2kX/iTQb2G1li1CWCRipNrfXKT2htbiOS1s5YYy7I00hEhh9ulwvl VRxUZ1dbLaNtfl+P+aPKea4pJ3UdPU+1Y/2y/wBn8nj47eDv/BB4j/8AlZUq /tjfAFunx08Hf+CHxH/8rK+Cf2+vhb4M8B2nwk1PwZb+AdPfX/BkMniWw8L+ LY9eis9bS5uPPEg+2XUsBNu9mcMwRjv2crIF8Ch616FDgPAVYe0jUn16x6O3 byOeefYiLtyx6d+qT7n67r+2H8AyBj45+Dj/ANwHxH/8rKkj/a++ApPHxy8H n/uA+Iv/AJWV+R8Hb8KtRdq2/wCIfYH/AJ+T++P/AMiR/rDX/lX4/wCZ+taf tc/AeTp8cPB5/wC4F4i/+VtSp+1n8CmUY+N/g/8A8EfiH/5W1+TNv1q5B2/C p/4h9gf+fk/vj/8AIh/rDX/lj+P+Z+ryftXfAzP/ACW3wgf+4H4h/wDlbUi/ tVfA5hx8bPCB/wC4H4h/+VtflTB2/Crdv0o/1AwP/Pyf3x/+RD/WGv8Ayr8f 8z9UY/2pPgg44+NfhD/wSeIP/lbUkf7TvwSPP/C6vCH/AIJPEH/yur8trfpV uHpUvgHBf8/J/fH/AORD/WGv/Kvx/wAz9Qh+0z8FG6fGjwj/AOCXxB/8rqlX 9pb4LN0+M3hH/wAEuv8A/wArq/MOHrVuDt+FJ8A4L/n5P74//IiXEVf+Vfj/ AJn6axftIfBls4+MvhL/AME2v/8AyuqVf2ivg2enxk8Jf+CbXv8A5XV+Z8PS rkPSp/1DwX/Pyf8A5L/8iP8A1hr/AMq/H/M/Slf2hvg63T4xeEv/AAT69/8A K+pE/aB+D56fGDwmf+4Prv8A8r6/N2HrVyHrSfAeC/5+T/8AJf8A5EP9Ya/8 q/H/ADP0bj+Pfwic8fF/wnx/1CNd/wDlfUq/Hf4Rt0+L3hP/AMFGuf8Ayvr8 64Ogq3b9an/UTBf8/J/+S/8AyIPiGv8Ayr8f8z9DI/jp8JiOPi54TP8A3Cdc /wDkCpF+N3woJ4+LfhT/AMFOt/8AyBX59wdvwq5B2/Cj/UXB/wDPyf3x/wDk Rf6x4j+Vfj/mffyfGj4VP0+LXhT/AMFWt/8AyBUifGP4WdB8WfCh/wC4Vrf/ AMgV8Dw9KtQdvwpPgbB/8/J/+S//ACIv9Yq/8q/H/M+8k+L3wuYjHxX8K/8A gr1r/wCQKlX4t/DBunxW8K/+CvWv/kGvhODqKtw9KX+o2D/5+T/8l/8AkQ/1 ir/yr8f8z7lT4sfDIjj4p+Fj/wBwvWv/AJBqVPif8NScj4p+FT/3DNZ/+Qa+ IYelXIO9S+CMGv8Al5P/AMl/+RB8R4j+Vfj/AJn2uPiV8N26fFHwt/4LdZ/+ QakX4j/DkgY+KHhc/wDcN1j/AOQa+L4etW4O34Uv9SMH/wA/Jf8Akv8A8iH+ sVf+Vfj/AJn2SnxD+HeePid4XP8A3DtY/wDkKph46+HzdPib4X/8F2r/APyF Xx1D0q5B3qf9SsJ/z8l/5L/8iH+sVf8AlX4/5n16vjjwAeB8S/DH/gv1f/5C qRPGXgLP/JSvDB/7h+r/APyFXyVD1q3b9aX+pWE/5+S/8l/+RF/rFX/lX4/5 n1cPF3gRunxI8M/+C/Vv/kOpl8UeBiBj4j+GD/24at/8h18rQdvwq5B2/Cl/ qZhP55fh/kP/AFir/wAq/H/M+oU8R+COo+I3hn/wB1X/AOQ6kXXvBTdPiL4Z P/bjqv8A8h18zQdvwq5D1FJ8GYT+eX4f5C/1ir/yr8f8z6RXW/Bh4HxD8NH/ ALcdV/8AkOpo9V8Hc/8AFwvDX/gFqn/yJXzlB3qzF/Sk+DMIvty/D/IP9Yq/ 8q/H/M+h11Dwg3T4geGv/ALVP/kSpVvPCTdPH/hr/wAA9T/+RK+f4elW4O9L /U3C/wA8vw/yB8R4hfZj+P8Ame8x3HhQ8Dx74bP/AG56n/8AIlTL/wAIuTx4 88Nn/t01L/5ErwyDvVyDvU/6n4X+eX4f5C/1kxH8sfx/zPbBF4Zbp468Of8A gJqX/wAi0+O38NkceOPDh/7ddR/+Ra8ct+lWoO34Uv8AU/C/zy/D/IP9ZMR/ LH8f8z11LLw8x48b+HD/ANu2o/8AyLUw0vQG6eNfDv8A4Daj/wDIteTxf0q3 B3qf9UcL/PL8P8h/6yYj+WP4/wCZ6imkaF/0Onh0/wDbvqH/AMjVIuh6ISP+ Kz8Pf+A+of8AyNXm0PWrUHb8KT4Rwv8APL8P8hf6yYj+WP4/5noa+HtGbp4x 8Pf+A9//API1TJ4Y0nH/ACOHh7/vxf8A/wAjVwUHb8KuQ9RS/wBUsN/PL8P8 hf6yYj+WP4/5naJ4U0onjxf4e/7833/yNUo8IaY3Txb4f/7833/yPXIw9KuQ 9KX+qWF/nl+H+Qf6yYj+WP4/5nTJ4L04j/kbPD//AH5vv/ketPwx8OorvxDZ raa/ol9OsodYY7O8nL7fmI2G2IYYHIIxjOa5GDvXX/CQ/wDFXsDx/oF9/wCk k1c2L4Zw1KjOqpSfKm+nRehth8/r1KsabjHVpder9T0dvBl+o/48tG/8JCX/ AOQq4XxH4U8c/EH4PeGL74d+I/hhbaje2Fhcz61qHw3uvEGmaqhtAZZbdLcW nyyykSJKJCmwkCP5gU4/wZ8e9C8efEPVvDFlZeM7fUtFMv2ifUfB+r6dp0nl SrE3kXtxbR2txlmBXypX8xcum5AWrkf2atR8LWH7HvwfGq2Xia/1ZvCGmbxY NriwRQfYYPLBFi6xFixlzuy+AueNtfCRnTt8Mvv/AOAfXONTuvu/4J6z4G+D PjLTvi/e6truq+GtT83wrpWnSWtn4Uvba1W+imvWubtLF7aRYEm8yIKTI8m2 EozsIwzN8YWPi/SvBJm8Qx/DrwXHLDpMc98NPuHiivJ7oRXVokkmlQ/Id0Uc Ew2yTST48uBlUvkeI7eaDRvj7D4f/tfz49ehGmDRHs3vy39knyvspvP9FMuQ uw3H7ncB5nybq+OPgd/w0ef2edZP7SY/4qVvFngX+yv7P/sv+x/s/wBu0rzv L+zf6R9t+0+f9q83/R9+z7J+6zXSoUvaRhZ62697eRjzVOSUrrS/Tt8z6ntv itf3Hx88aeGWgs/7O8OeF9H1uBgGE7zXd1qsUiuSSNgWwhKgKCCz5JBAGZ+z p+0Pqvxu8VeNtLv/AAlJ4ej8F340w3j65p982pTeZMc+RbSvLbA2ws5wtykT kXuAhVN7+w6RrmmeJfF76DpXhjxFqOqi3e8WGLWbNC0CGJWl/eKpCbplUEgE skmAQpNSx+IfDElnLc/YtUW3geSOWR9SjQQtGxWRX3QAoyMrKytgqysDggge XGUZU3NWsrpvz8z06kZRcYyVnv6p2seWfEr9pPwz8N/E174Xm8TeCofHM+jS 6ro/hzVNdjsrzVFWOd94TEk3k/uH3SxwybVjkO1thWsTR/2mbTX/AI4eDNJ0 3VPDmo+DPG2lapdafrdteLNFqN3CbF7W3tZ1by5GktpNQm2AOzLauykLE5PV eJv2xfhP4R/ai0z4OX7a3F481K1e4NkLoMlmwjEscE7i3xFNLDvlRG5KR5O3 zIRJ23iL4heDvCckCahbatbtchjEDfBi4XGT8tucfeHWtfZSjGMpLSSuuzTv qu67GKrQldR6Oz8n2Kd9ctYxRFcMWlRDu6YLAH9CeteGfBf9t6b45WFld6X4 In02NtcfQtWttR8Raa95oMkQtWle5itJbgxGNp3haCQpcpO1sskMUcsk8Ht8 fxo8CXngvW9es7TXL+w8Pif7Z5V4quDDHvdVDwLk4xjnBJxnrW5q3iTwvoVg 9zeWmo2sEZUNJLqsSIuSFGSYMDkgfjWXL0Zblfb+v6/Q81/Zh+K1x8b/ANnr wd4p1A2Eet6rpUD6za2ZIi03UVQLeWhQszRvBciaJ43YvG0bI3zKa8n8S/8A BRLw98Of2gYfBniK+sLbS9MTUG8Ua64tbDS/CRNww09dRmmvBNYtLGsaRma3 EV613DJbyhFZD9TafrPh7UrGC4j0vWtlxGsihr9EYAjIBVoAQeRwQDXOeEPj X4I8ZXTW0NhrdrejO21udShWWQAE7lxEVYcHocjGSAKq125IV+hz0PxI1fU/ gEni+z8Kar/b0/h8azB4YunW1vjcm285bCRmGI5d5ERLDCtnPAr56/bU/wCC gN/8FPgPqz+AZPDvj7xBFpms6Xca9oevaVbW/hvXrG2jllSS2url3kkjha5u vsiCeZRaBWRlcyJ9XfEz4weAfg94N1HX/EhvdI0nSrf7Tc3FxqsSRohlihHJ gxkyzxIBnkuPeqPwh/aE+Gfx60q9vvCF1da9Z6fLFBPLbaku1Hkt4blB81uM 5injORkAllOGVlGalByaT26Be2j3OV/ZK8c6p8S/2Vfhl4k1y4+2654g8J6V qd/cmJYjcXE9nFJLIUQKi7nZjhQAM8ADArmvBf7UN9qelaadVTwpa3Gs+Kdf 8OWMU+ptZTXrafrk9lFDbxMknn3DWUE87KHXLwYCrG7vB7F4k+KngLwfrGna fqk01he6s4jtIZ9YiR52LKoCgwcks6KB3LAda0da8T+FvD2lT3t7Z6nbWtsu +WWTU4wqL6/6j1wABySQBXTWw9SFqk4OKltdPX072JhWhNtR1tvrt/w54Pbf thaYo8MXGo3XhMW/iTW4/DEcmneJ7aa2h1I25Z7bfOIHlnS7iltfs8CSTn5G MSkSpD0/7Unxti/Z3+EJ8V3F3o+n2Nprmi2d9d6rJ5VpZ2d3q1paXM7uWUJ5 cE8rh2O1SoLAgEH07QPGHhHxVpUd7psGoXttKAQ8WqRsBkA4P7jIbBHBwRkU nhbxb4b8X2TT2mk64qLez2P73Uok+eK4e3dv9TgLuQkHOdvOM8Vly8zUUuv9 Irmt7zPlbUf+CgN1oXxEvPDF1pGkXVrqtrea14R8ZnXtN0zw54h01bJ79Joz NcyXMwgTZDPLaw3KqB9pZYI5EiHpS/tV6Dqvhu8Okav4M1XW5dKuNe0Syi8S IYtY09ZWjhvhIsTSC1YiHfMkUiIZCFMo2tJ7RBrHh64muY107VC9rO0EgXVY m2svriDuCGHqrKe9cl42+KPwxPwk1/V/F1t9g8GQfa9N1Z9c1K2WyljWd7SS OUPCUZJGBQKfvh1XGW21TjZ8jWuwKatzdBfAXjzS/iV4at9Z0TUdN1nR71RJ Zahp92l3a3sRGVkjkTKspB6qSPQmvHPiX+3Vofg+28Sal4a1Pwx8QB4DV4/F OiaRqlnHd6EVvooJJLq8nuY7SzMURun8i6aJpTbybXVYpSPWPgj8fPgz8Wl1 a1+Hmo6Xr0WlzC61CPQ9btZktpbt5Ji8nlwkB5X81yT8xfzM/Nmt/wATfFfw J4O8UeHdE1IX9rrHiy4ltdJs21JDNevFBJcS7QLc4VI4mJY4UEoudzqCKlJy 5YrXtqT7WKjzPY+WPh9+3pr/AIv/AGjtM0XxDoVh4J07RPCGpap4ps21211G 00+4SbRpFklvYEaOP7Pa3j708xWDTF5I1tzbXc2Xa/8ABWPQvDfwg8H6h4kt NHh8U3t7omn+J7I6tDYReH4LpbaLUNXlR2kkgsLK/mmsZGlAC3du8Durgkfb C32gsxB0zVgMZB/tOPn8PIrFudW8CeOrq90efSZ9Sm0a6tp57W5vIpRbTo0d zbybWt/vK6xyI4+68eQQycTonqv6vr+di1LY5n4OfEyD4weAoPENnbmHT7+5 u1sZRMs0WoWsdzLFBexSL8skFzFHHPGy5BjmQgkHNeJ/E79t3XPhn4M8Tay/ h+8nj8N2+qwRyXehz2VvqF7o0L3d6qyGZmSC8t0lW1k2OI2tpGZpwyRn6c1X xv4O0K+gtb2DVLe5uoJrmGL7aZGeKExiV8JbNtRDLEGdsKDKgJBdc3otS8Pz wpImm6uyyosiEalHh1YAqwPkcggggjg5HrTlQnFKo1ZPbe3b/P8ApEqS+E+I PjN/wVCt9E+A+p3cltbeAtc1z4PW/jzQdXvdRgexh1W/t7xrPSkM6KJ7xnt2 aKPY3nLDN8gEZDegR/8ABRHQrmx/tCGy0+20yytYG1i4vtW2f2NfLFcXeo6T MIIpgmoWGnWV5cTW7lGVlt4zt88SJ9MS+I/Daa7DpceleILnUriJ54rS3ufP nlRCA7KiWxLKhZNxAIXzEzjcuX2+teHp7m5gOk69Bc2biK4gnvBDLA5RXAZH twwJVlYZHIYHvQ0pJyS0vv8Ap/XYI14Sdou9vM5/SL2+uPDVrcanZQWOpSWy S3VrFObiO2m2bnjWTapdVbIDbV3AZwM4rwD4q/t4T/Bz9q3R/h/qXhqXUNL8 SaToUljqkd9Y6dFZ39/e31s0Esl7dRCVnS2EkNvbLJcSeTcbUcD5PqT7boJP /IM1bHf/AImcfH/kCuI8B/GOXXviLf8AhfxT8EfjT8N9Rs9Ph1OG51+XT7rS 7+N5XiMceoac93aLcIyAtbyypMUdXWNk3MqjHeVtClNRsn/w5zvwL+ONp8ZL jWYLO+8Laj/wj1w+nXz6NrY1JrO8jnnR7a5URIIJxEsEjRFmZGmdCNqJJL6F XCfFH9qnwv8ADj4maL4L0PwB408e+JNbiu7lrPQ/Emg2Y08QT6ZAUnfUZ7SN ZZZdYsFjiVmkkM6kLggn0ceMfBzWeoXSebPZ6Tc3Vne3EGuW8sNrNazSQXMb usRUNDLDKkgz8jRuDgqQFJJ6rYGpR0kmU6K5vwh+0p4E8Y6/LZQ6R4htoBPJ bwX82q2n2S9dZvKAiZUYne2duVAYYA5ZQYtc+JWuXnivV7Pwx4E0nVNP0e4j tHudU8cSafM8jQRTHEcekzrtAmUZMmSQ3AxyuR9RKSZ1NFcf/wAJn4/P/NNf B3/hy7n/AOUVWdN8TeO76cpL4A8DWShd3mTfEm9ZTyOPk8Psc8+mODzRyeaD mOnormdW8da94Qv9Bk1rwl4P/s3Vdf0nQ5W0vx7e3V3B9v1C3sVlSKbQoI5P La4DlWlTKqec4rvvt2gf9AzVs9AP7Sj5P/fihwt1BSueN6z8RvilpX7SGh+H rf4Z6Hqvwz1jzxdeL7bxYq3ui+XaiRPtGnS2yFvNuD5KfZ55cKDI/l/cPYft UXvjqw+A1u/w0t9GufHL2EcWkprFuZ9OWR9QkVnuVWeB/JVC7O0btIiqzJFc OqwSYeiftgfDb4g6JJL4Qg1PXNSi1jSNElsLq+k02a0n1K/isYWnSazEsaJO 80UhEbGOayvLdgJ7aaJHfAr9qOw/alu/C9t4R8KeJ9RsPENj9q0TVL3RfFul aNc2rRvdpKNRuvDEVp5ciFmR2lAk3oqFiyg6RpyeyJc0jnNc+JHxJ8M/sbaR 4ju/DNlN8WxomnT6h4ZtIJLm2udWk8jz9NjeKRxAjytJAt28kkNsrC4lMkMM gbi/Ef7Lng79vv4NeDtc+PPgRm8QaOL5zpRuNQ07/hH5ZZgs9pujmX7V5XkR Rfal/c3Pk/aIFSKZFH05488N+Jvh3cW6X3g7RZFulYxvB41kZTtIBBzpQIIy O2OevXHn3xN+N/gb4XaXaJ8QvGvw8+GjayJFtYfEPjOxsRdeXt8zyJbv7N52 0PGW2R/J5iA9QWulOrRlzUpOMl1T1BqE1aorrzPlTRP+CXX7HfiD4y+JfAlt 4CgbXvCdhp1/qS/8JTqRWD7c86wQkC8LrNiBWKMo+W4gILeZxueMf+CP37J/ w+0eG/1b4efZLSe/s9Mjf+3tYk3XF3cxWtumFuSRvnmjTJ+Vd2WIUEjG8V/C 79j7xl+1Ze/GS9/aD+CU3jO+1PS9S3XPxA8IX9taiwiSNYYI7oS+QJRFCzzR lblWiHlTQhnVvW/jn+0V+z78dPhzL4em/aY+C/h8m+sNSg1LSviX4aN3Z3Fn eQ3kDoLiWWE4lgjyJI3UjII5ruebZhZWrT8/efz6mKwuGvrCP3I8n+JP/BMX 9kL4R694SsNb+H0do/jPXU8OWMjeJtRSOO7e1uLmJJC14MeYLZo0ChmaSSJQ vzEjsLb/AIIv/syXFuk0fw2zFIoYH/hIdVHB6f8ALzXGeIvB/wCzf428S+Gd U8Qftj+B9bl8PeJrLxlPFN8SfBUEet6zZgRwXlz5EUbgrbJDbGOBoozHCjbP NLyv7f8ADr9r34H+BvBdjpN9+1D8F/FVzZKytqmq/EjwxHeXQLsy+YLWSCH5 VIQbIlyEBbcxZiPNsw5dK07/AOKX+YvquF09xeei8v8Agnjel/8ABNn9jTU9 CGpxaBoUWnlFfz5/F2owpg2Qv85e6HS0InPpHljwpxpeHP8Aglt+yR4t1I2e k+GtB1S8Cl/s9l4wv7iUKIreYkql2SP3V5aSf7l1A3SRCdb4o6/+yj8aPBlp ofij46/s8a/ZWetnXIxqfjbwhqKhvOZhD5VwZIfLWBzaK3l+akAULIHAkrqv h9+0b+z98PfEWpajH+038H9UfUVdVh1D4p+HpIbINeXV0RCFnUqMXKQ8k/ub O1XrGWY/tbMP+f0//An/AJieEw3SEfuXzOP1X/gkx+y74f1PTrK98D2ljeax M1tYQ3HiXUo3vZVjeUxRA3OXcRRyPtXJ2xu2MKSKXhf/AIJo/sjeNYY5dF8P +H9XilgW5R7LxhfTq8TTPAsgK3ZBUzRyRhuhdGXqCB13jX45fs/+OfjV4O8Z XX7Unwjhi8Eyy3VloUPxL8LDTZrqS1urVrmUs5uS/k3cqhROsfCnYSCT5L4c +C37HfhzUPBt6n7Q/wAFLzUPA11p0+mXF7488GXW2OztrW3EZjkVolllFlbt Jeoi3/7qNEukijjiQWa5h1rT/wDAn/mV9Uwt/gX3I9B8P/8ABKH9mLxVpVtf aT4JtNSsL2CK6trq18SalLBcQyrujlR1uSroy8hlJBBB71Q8Uf8ABNf9lTwJ rcOnaz4UtNKuJtJvtc3XOv6pHBFZWTW63U8kpuPLjWP7VATvYcPkZCsV9EtP 2sPgZZePk1lP2ovg3/Z66Ymn/wBhn4keF/7PMqyFlvC2/wC0+dtJTAn8rbz5 e/56reKP2kP2dPFXxD0/xLcftD/BKDUNL0XUdChWL4n6AiCG9ls5ZXINyT5i tYw7CDgBnyDkbT+1cx39rO3+J/LqSsJhusI/cjhf+Hcn7KP9otZQ+H9DuL9Z DCLSHxZevcPIrXSlAn2rO4NZXgI65tLgf8snxv2H/BJ/9nW9t1lt/AiTRvgq ya9qTKQeQci47gg/Qj1q7o37QnwO06fSpLr9rT4V6ubK0e3vRdfEnwoo1qY4 2XU4iKbJU+baIPKi+Y7o22ptt/A/9pH9nz4GfCvR/Cdp+0l8C9UtNEh+zW89 x8QfCtmwiDHZGIbJ7e2jRFIRVihQBUGQW3MR5pmFtK0//An/AJj+qYbT3I/c jgtK/YR/ZS1XU9MtIPCm1tZvtS0uylm1PWIYJ7zT7lra6tRK8oTzhLHLsj3b 5UhmkjDxxSMu1ff8E3P2atLSB5fCdjHDczRW8creI75Y2klKCJNxucbnaSJV A5YyoB94Vyfibw5+yr8QPA3ifwx4p/aX+D3izw7rut3XiTTdN1f4g+Dri28L ahcXN1cyT2alR5v768lPl3puoyuEZWQura/gXVf2ZvAFhd29h+038FoA8Wmn T3s/HXgvS20e5srBLFLuH7GkIaV4Y0DrMJIdi+QkaWxMBP7Ux9ta0/8AwJ/5 g8JhekI/ci7r/wCwF+yz4QknTVdI0HSpLWA3UyXviq8t3hh2XD+YyvcghNln dtuOBttZmziNsbWj/wDBMj9nnXbV59P8H217BHNLbtJBr9/IqyxSNHLGSLgg MkiujL1VlZTggiqFz8ZvgzcS6ww/bO+HYGqfbDBEfiJ4MMWmGbHkGIbAW+yn e0XmtJuMh87zwqBO38FftjfArwl4YtNPuP2lvglr11bpifUdQ+JfhwXV9J/F LIIZ44gxPaONEHRVVQAB5pmFrqtP/wACf+YfVMLfSEfuR5JZfss/skXfjh/D cOk2j6wkbytAdW1VVUJqZ0pgXMuwN9vBgC5yWHAI5rorT9hr9l+8e3S203w/ cvd3c1jAsXim6dp7iE3IlhQC5y0iGzvNyDlfsk+QPLfG5dfH/wDZ2vtd+2T/ ALR/wQkgGuDW1sv+Fj+GVtwRb7BbcS7zD9pzfDLGQXR3b9mIqn8T/tM/AbXN J0+2sP2oPg54el06ZJFurD4l+GXmlQPueFxPJLGUkGVbCBgDlWRgGCWaY+2t af8A4E/8x/VMLf4F9y/r+rHOWX7Ef7MV5HI6aZoAEJImA8U3JMBEk8RV8XPy sJLW5jIPR7eZTzG2Oitf+CanwLurdJYfBiSRSKHR11q/ZWUjIIInwQR6VkX8 n7OP7SurWfhHVfjl8I/iQ+sa5bSaB4eb4i6BO8F2yG3jtbSO2mWaUyvK2EkM rs8u1SFCIvfWP7RPwK/Z7sIPAVx8avgpoE/giNdAk0zUviNo0N7pz2gEDQTJ JdCRZYzGUZX+YMpB5pPNMw6Vp/8AgT/zJWFw+l4R+5HjL/AH9lS1S0kl0eW3 tL7wpJ43gvJp9Zis30aPyvNuvPZhEPLE0JeMsJEWVGZApBrC8LaF+xn4w8OL qukPZahZN4TvfHDGG71hpYtHs5zb3F08e7egWYOgRlEjtHIFVvLfbq2Ph/8A ZBuP2lJviv4h+O3wC8ZeMDczXNpNrXjzwlJDprGS1e3eFIjGBPai0SOG6bdd eW5SSaVUhEUGneEv2RNE8BSaBZftDfCC1jufBd74Hu7pfiv4fa4vLa6sdLsH uH3TlFuFt9ItFXy1SLPmMYyXNNZpjra1Z/8AgT8/+AV9Uwt/gj9yPSrz9hD4 B6XrcOmXHh/T7fUbi3lvIbWTX7tJ5oYniSSVUNxuKK80KlgMAzICcsMyXP7C vwF07WLbTpNC06LULyFrmC1bX7sTTQq8cbSKhn3MiyTQoWAwGljHVhmj8Sv2 mPhD4x8aaHqWj/tX/Afw3Y2JK6pYReO/DtyutxGSH9y7yXeY08sT4MYD+Y8L 79kckM2R408e/s2/FH4Jan4H8X/tM/BfxfaapAIZb7VfiD4SlnylwLi3YwqV tJPIkSLaksDxuIlEyTAvvf8AaeO39rP/AMCf+Yng8J/JH7l5f8H8PM7SD/gn v8F7i0WeHwrHLA6CRZF1e9KMpGQwIm5BGOnrWLB+yL8ApvGt54cTQ4hrdhp1 pq0tq2pX6EW11NNDBIGMoVt8tvMm1SWBVcgb0zt/Dr9qz9nn4ZfDfQfDGn/t Bfs//YPD+mwaZAy/ELw1a70ijWMN5VvNFBHkLnbFEka5wqKoCjyz4s6Z+yV8 ZPj54b+JWrfH/wCBJ8UeFo9NtrGWTx94Svoo4LO5uLkRqt2ZvKaWScb54THO ogj8qWHMnmSszx19as7f4mEcJhdLwj9y8v8AgnofhT9jv4D+MXlTSdJ0zUZr dd00Vtr9zNJbgSzQHeqzkria3njOejwSr1RgNtf2BfhInTwmf/Bpef8Ax2sX 4Y/tO/A34etqhu/2qPg/4nN/dSTWw1P4k+F0TSoGYstpALeSLMS54aXzJW/i kbAA6v8A4bp+An/RwP7P/wD4czQv/kqk8yx62qz/APAn/mSsJhusI/cigP2D PhQvTwpj/uJ3n/x2nr+wn8Kl6eFv/Klef/HauxftyfAaZiqfH/4BOwBOF+JW hngDJPF10ABNJ/w3T8BP+jgfgB/4czQv/kqj+0sxtf2s/vf+Y/qmF/kj9yKo /YZ+Fq9PDB/8GV3/APHakX9iL4Xr08Lj/wAGN3/8dqb/AIbp+An/AEcD+z// AOHM0L/5Ko/4bp+An/RwP7P/AP4czQv/AJKpf2lj/wDn7L73/mP6phf5I/ch i/sU/DJOnhnH/cRu/wD47Th+xh8NR/zLf/lQuv8A47S/8N0/AT/o4H9n/wD8 OZoX/wAlUf8ADdPwE/6OB/Z//wDDmaF/8lUv7SzD/n7L73/mH1TC/wAkfuQo /Y2+G69PDn/lQuv/AI5Tx+x58OR08O/+T91/8cqP/hun4Cf9HA/s/wD/AIcz Qv8A5Ko/4bp+An/RwP7P/wD4czQv/kqj+0cf/wA/Zfe/8w+qYX+SP3IsL+yN 8PE+74ex/wBv9z/8cp4/ZN+H69NA/wDJ65/+OVU/4bp+An/RwP7P/wD4czQv /kqj/hun4Cf9HA/s/wD/AIczQv8A5Ko/tHH/APP2X3v/ADD6phf5I/ci6v7K ngFemg/+Ttz/APHKev7LXgRemh/+Ttx/8cq/4e/aS+Fvi7wJ4j8U6T8W/g/q nhnwf9m/t7V7Tx5pE9hon2mQxW32qdbgxwebICkfmFd7AhcnisD/AIbp+An/ AEcD+z//AOHM0L/5Kpf2jj/+fsvvf+YfVML/ACR+5Gmn7MXgePpon/k5cf8A xdPH7NXglf8AmCD/AMC5/wD4usn/AIbp+An/AEcD+z//AOHM0L/5Ko/4bp+A n/RwP7P/AP4czQv/AJKo/tHHf8/Jfe/8w+qYX+SP3I2B+zh4LXpo3/k3P/8A F09f2d/By9NH/wDJuf8A+LrE/wCG6fgJ/wBHA/s//wDhzNC/+SqP+G6fgJ/0 cD+z/wD+HM0L/wCSqP7Rx3/PyX3v/MPqmF/kj9yN0fs/eEV6aTj/ALepv/i6 kT4DeFI+mlf+TM3/AMXXPf8ADdPwE/6OB/Z//wDDmaF/8lUf8N0/AT/o4H9n /wD8OZoX/wAlUf2hjv8An5L73/mL6phf5I/cjpB8D/Cy9NL/APJmb/4qnj4K +GF6aZ/5MS//ABVcx/w3T8BP+jgf2f8A/wAOZoX/AMlUf8N0/AT/AKOB/Z// APDmaF/8lUf2hjv+fkvvf+YfU8L/ACR+5HUj4N+G16adj/t4l/8AiqcPhF4e Xpp//keX/wCKrlP+G6fgJ/0cD+z/AP8AhzNC/wDkqj/hun4Cf9HA/s//APhz NC/+SqX1/Hf8/Jfe/wDMf1TC/wAkfuR16fCnQE/5cD/3/k/+Kp6/DHQ16WP/ AJGk/wDiq40/t0fAUY/4yA+AIz0/4uXofP8A5NUf8N0/AT/o4H9n/wD8OZoX /wAlU3jsct6kvvZP1PC/yR+5HaL8ONFTpZY/7bSf/FU8fD/SF6Wn/kV/8a4j /hun4Cf9HA/s/wD/AIczQv8A5Ko/4bp+An/RwP7P/wD4czQv/kql9fx3/PyX 3sf1PC/yR+5HdL4G0tTxa/8AkR/8akXwhpq9LX/yI3+NcD/w3T8BP+jgf2f/ APw5mhf/ACVR/wAN0/AT/o4H9n//AMOZoX/yVR9fxv8Az8l97D6nhf5I/cj0 AeFrBRxBj/gbf41S8UfDDw9440KfStb0bT9Y0u62+dZ30IuLebawZdyPlWwy qwyOCAe1cZ/w3T8BP+jgf2f/APw5mhf/ACVR/wAN0/AT/o4H9n//AMOZoX/y VSeNxr0dSX3v/MFhMKtVCP3Id/wxT8Hv+iWfD3/wn7X/AOIo/wCGKfg9/wBE s+Hv/hP2v/xFN/4bp+An/RwP7P8A/wCHM0L/AOSqP+G6fgJ/0cD+z/8A+HM0 L/5KrP6xif5n97NPY0f5V+Bal/Y/+FE2lQ2L/DbwM9jbzSXENs2iWxhhkkCL I6rswGZY4wxHJEaA/dGOM+G/g/8AZl8SfFq70DwfZ/BHUfHXhOZ7i603Rxps uraPJbzLG7yRRZlhaKYopZgpRyASCRXU/wDDdPwE/wCjgf2f/wDw5mhf/JVY f/DTn7ML/ED/AIS0/GL9mb/hLDp/9kHW/wDhP/D39ofYvN877L5/2nzPI835 /L3bd3zYzR9YxPWUvvF7Gj/KvuPcvC3xE8RfCzX9Tu9D0bR9aTV7a3gl+2ar JYtAYWmYEbLebeG87uVxt75rmtCsde0nwrqcyf2Zb+JL+/1LVLdVuHktoJ7i 7nuIlMhjDFVMqAts7E7TVqX9oC+0zxXrdtq9p4b0DRdHjhePVNQsEVNQaQJ8 kKR2jl2UsdwUkgKTjAbBqnxz1MeDoNTh03S7aV9Xg0uWC60e3Dx7tQS0lyuw FWGWIDAEHG5QQVqcXQlWwboVV7klZ93dNd+1x0ZRjX54v3l/wPIzPEttqnxC 8f6Trep+EPB2i3unwJZPq9vqU2pagbSMyulurSW0ewGSRi0m4sVaVTkSMRz3 7Qnwr1z4nR6Uuj3VnaC0MvntNcyQswYx8KURv7pznpx1rQ/aY/ac1/4K+Frn U1h8H6JoNvpdxdXPijVtHju7HS7xbmzhtLWW0t4xcyLdefOPOiyIDb5ZX3AV h/DH9ulr/wDYov8A4wa4mi6pYaDoV3reo/2N4ahthMtrE8txFBFcZJaNkkhB aTbI8RYFVYBfCyvDUcvqxy3D87bjzJu8o2b5Uua7SenwXTS1tZnqVsPUqUPr Ukkm7aX8/K343+Rn6P8AC3U/hT+zR480/VJ7G4ubq11C73W0ryIqtaBfmZ1U lsoSeO4rt/jV8PLv4oeD00m1vFslkuo5LiRmODEucjaB8/O3CkryAc8YPz78 Xv8AgqP42+H/AO3pB8KbSw+Hx0uXW9I0wGbQFa+eK7S1aVhIHEYcee+35MAK Mg19YeO/iw3hDwVrGq39/oGhWWl2E97calP4dhvYtPjjQs07wRqskqxgbjGj BmClQQSDXdhsZGvVnCKacJcuqaTe2j6rzV0Kvl9SjCMnZqUebTtZS7Lo+l1e 63TKPhfTZ9E0GytrmRLie2hSN5FDBXKjGRuJbpjqSfUk81xOofARIdbs7jTL tbeC2ZJGjmLyOXV92Q2eOAo6cEZqt+xR+2BqX7SnwxutZudT8Ea/d2d/NZ3N zofhV9PsYmSR1WJPtamSXdCIZ9+Ewt2iMivG2fbvG/i9vCWtauj/ANjQWOmT TBpp9NtFWOONmG52MYAAAyTxXZKMqUnF6M86nONSKlHY888WaN4gTxd4a8Qe G9TsbK98P3izXNreW7yQ6raiSKWS2Do6mB3aGMCYrKEBbMUhIA5z4IeBPGPh nxD4h1PxhrEer3WrpaqjjUJ7xwYvNBLNKinlWjUYzwg9BW/8dP2gb7wz+zX4 18YeG7jw3eTaN4e1HUrC5i0yzuLdpoLeVlOQhDAOmCM9iDXp3hHS/CnjX/hK fsnxP8X2x8F+JF8JauupeFtK01oNTcWxigQXGkoZhKLy1MUkW+OYTxmNn3Cj DUlBVPZpXnbm76WX6JDqNuUXLpseO+Kf2ftD8R+KrnWFF3Bd6nc2k+og3ly8 N2ttLDLEBD5ohR99vCPMCFtgdf4ya6Px34Rh8d+Eb/SZ22JewmMPgny26q2A RnDAHGecV7R4Y+Adn4x06S70v4k+IL62iuriyeSLSdAdVmgmeCaM/wDEu+8k sciMOzIR2rD+LMQ+DUttZtfWd5Ba6ebq61HUtNsEkf8AeSkvIY4I41CqAOFU YUE5OSejE1q84xVWbajt+C/JJeiRnRhTi24Rtff+vVs8x8LeDbTw7c3V8lpb W2qaqsb6k1uW8qaZQcsqngZZmOcAnOWyeayfhx9pbwJfmxNubxdb1WSBpCFC uNSuCCHKtscHGHKOEba2x8bT1vgL44w/Ejwva6tpT6HcW9zGjMq6dZO9s7Ir mKQKpCyKGGVzxXQeLfFsnhPfJK2jQ2kNnDdzSz6baIkQeBJXYkxgBRk8noBy a5Fve5v5HhnwG8E/ETS/ENzqfji48O25VLiCG10a5M8VxHJLG8PmZtoCrQBZ UB+cOJycR7SJPM/2vf2Q9c/bd/ZTn8EaD41XwWZfF2o3d7M+n/b7fUrZby+R 7WSLeg6ypKrnJjkgidQHVXX6j8JfGePxtpkl3pc3h68gjnkt2a3s7GdQVOAd 0asoypVgCchWGQDwIf2M9Vs/2nfh3ocdz4+8V2/jmPwd4e8UeILKPwhptnZQ jVrNp4XtZp9L8u4hLxXKZhll2NCyOwdSK1hzSlzp6mcklHla0Pjv9hr/AIJL /wDDBn7X3jXx54Z+I2s6j4I8ZaN9il8MXlqkOy98+KUXTGDy7dggFykapbx+ WlyyrxuL9B+2h/wS20P9s74u6T4h1Pxb4i0rThE0OsafBIJXuFSNhC1q8odb XDlTIgRkfllWORpHl+4Pgp4D0P8AaD+Hlv4p8L/ETxvcaPc3N3ZKb/wnpWl3 UU9rdS2lxFLbXWkxTxPHPBKhWRFOUz0IJufEvwnJ8INN023i1E63LeyzyPc6 jpWneaqgRBYwIbeNNoO48ruy5ySAAOuhjMThan1ijPlla115mFTD0a0PY1I3 XY8I+HnwO0r4ZfArTPh7p9zqUmiaXo40WKa4kRrtohH5e9mCBd+PRAPaua+F n7LMHws+J02t2ut39xYrbmGG3Zgsjlhh1lKgKyggMoAHO0kfIC3p/gX9oG0+ JC3x0g2M402b7Nc+b4dS38qUdYz5sC/Ov8SjlcjOMjO38RvihbfDLwZaa3qU mmW9pJbCWU/2NbzO8jXLwRxRRpEXkkkYIqRoGeSSQKoLMq1wzqyblOb31Z0K MYxXRL8kcR4s+Dvh3xh4ktfEF9pOjXHiDShbDStRm0mCe601Y7gTSbJnBYeY VRAVCvEDI0bo77hr+HrnWL3TC2upa/2oJpVd7e4M4uI1kZIpmPlRKkkkSxyP GiBY3d1DSBRI/M+Hv2wX8RfHa/8ACK6XpNlZWRks0vrvS1glu9QjxI1tGjWv lf6nfKqvOs7xp5yQNbNHcva+NX7YGmfBW0kjv28PHWPIS7ttOurC2s21G38z Fw1vI8Plu0MSvJJlljhXY9xJbwv56qrmrq0lCpO8YaLyt2/rqzmjiqHLKqmr LdnJ/E74Ha948+Ouk+IV1mS38O6Na24XTrS8eyurm5SaaUyeeI38kD9yAUUu cvtaJlVj3+uaRN8QH8YPrCSWCeLJSHXTdUuIri3iNnDbMUuYxFLHJ+7ZhJGV dCVKsGGa2PgP8X7j4xtai407TNNkOrPpd5AljaXH2Z1lCmPzlh8qSWMEJL5L SxxTpNCJJDCxry79pD9tLVPhX4huPD+i2/h2PW7Hw9eeJ5pL/wAPwTQS21ta XtyYo1QxkuRYy5bd8g2fI+/5ZynLoTcoYSKvUfM31du93pY5MPhMJhpSxlNW c9W7vW/9bFi0/Zn0uH9l29+FN3r3jDW9I1Dw/c6BeazrWqNqutXaXETxzXEt xcrIJJW8x2AZDEuQqxrGqxg/ZqHjn4f/AA/0bwNrvgnw94e8OeH9E03S7O+t PiZf+JJpP7OtUs7dBYyaTY2ltvh3PI9sIwzquYjnKdV+zx+03J+0F8FtA8YW mm2GnRazAzNbTaXas0EiSNFIobyxuXejbXwNy4OBnA7T/hNb3/njpH/gqtf/ AI3WlSEqU5U5aNaP5dD1lN1KcVf3b3Svpdq17eh8s/tFfs7fEfV/2qvB/wAT fBVr4J106Fpd3o1xY6prNz4Yu4beWewkUw6lZ2dzeiQrHqClopbdV8+AFZIx cxXXpf7OXw31/wAGfCjVrHxhBpVlr3iTxN4l8R3lvoupz3VtYjVtbv8AUUhi umit5XaOO7VDIIozuUkAcV63/wAJre/88dI/8FVr/wDG6P8AhNb3/njpH/gq tf8A43WMFGK5Y9Xf5nVicVWxDi6zvyxUV5RWy29fvZ862X7OHiHTvGkMscml Nplvra6gkkmqXUlwYBfC5wyNEQ8u35SzOST/ABHNegaD9sTVvHbadBa3V2Nd geOO5na3icDT7HOXVHI4yRhTzgcZyPSv+E1vf+eOkf8Agqtf/jdH/CbXv/PL SP8AwVWv/wAbq3O+5yqNtjjPDumX3hvRbDT4bGx+y2EMVsrfb2LBEES9PKAJ AE3oDsTpvby7Xi/WbTRtGcXiai8N3mA/YLa4nnGQc7fIUyIcZw4xg45BIrqv +E2vv+eWkf8Agqtf/jdH/CbX3/PLSP8AwVWv/wAbpXje49TxG6ubOXRPA3hr Q4PHutXMXjrwxey3Wq6XqE9zMI9e06WeWSeSFUSNIoiQo2oioTyxd29XIz9c cexrX/4Ta+/55aR/4KrX/wCN0n/Ca3v/ADx0j/wVWv8A8bolJMEmjyvWPg5p PhHw54f0/wAH+G9F0W0s/GHhbVLq30y0gsolttP1PS90hVAobybGyRFABby7 eONQdqLXV/8ABPzx18Uv2dfgn8JvhX428PfCCLwz8P8Awpp/hm88Q6J451e/ v7j7Dp6W6Tx2E2gwR/vZIkyjXQ8tXYhpCoVup/4TW9/546R/4KrX/wCN0f8A Ca3v/PHSP/BVa/8AxutIVuVWJlC+479s3x1p3xXsrDRLDSPF2taeYmkuL3RN RGlOjGRGEazC5guFYGIFtmFKuFJYFlH4ff8AB21qM2rab+zrcz2V1p0ko8SZ trkxmWHB0ocmNmTnGeGPUdDxX7f/APCa3v8Azx0j/wAFVr/8br8T/wDg8Bv3 1K6/Z9mkESs3/CRjEUSxIMDSRwqgKOnYVdKSlO5Mo2ifizRRRXUYhRRSgZoA TNWNP0u41Wfy7aJ5n9EGTXuv7Kn7B/if9orxtp9hBbzpHdk7VSLe0mFLEDkd ACfwr7zj+EPwg/4JnWmlt4muE8X+Kp1y+gWSrFcWjKTlJy3GTkfdNAHwZ8HP +Cffi34qpbuwewFzjYGhLE8/WvsT4c/8EBr7S7UXnjO/k8P6dEgY3moWv7hy eeNrE9OaofGj/gtL4l8TXp0T4c+E7P4ZWFuQsMmn3btOTwSzBgQGJJrwHxVF 43+KN0dS8Sw6nrLzSb3vL5RtycnPH1oA+s9V/Yf/AGavhOrQaj8aPANzd2w3 tDDBKHY/3eRjPFefX+l/s6jVrWwtvEOkSwSXKQPcyf6qNd+C/HYA5/CvnyH4 Lx6jITDOyqrEGW0UGFeO5bn/APWK6jUvgjaRWmn2sPiq7ea5jw0DRggEnGP/ ANdehS/3Gp/jh+UyPtr5/ofUy/8ABPb9nn4waWB4d+MHgSa8kUH7MkExlORj H5mvFvjD/wAEV9V8P6o8WlNdz2kr7Ib6G2/cOAPvDJzjtXlHi34Har4Vso0u dOt79ZWxH8pDn39OlWvhV8ZfiN+zbqzSaDrmu+DVkIBWNlVGXOcdCfevPLPJ fi7+xj4o+FVvNO8Ul3FB98rEV749a8iubWSzmaOVGjdeCrDBFfr9o/8AwVQ/ 4Wx4Njtfil4ItNb0fTx9muvENncsbqDJyHIkwg7Z4rn/AI5f8E1PCX7Vvwwn 8afDPWItV0yHJuHgtw0lozchXI4/hbp/dNAH5O0V3vxu+AOr/BbX5rW6WS4t Y2wtwE2qa4KgAooooA+gP+CTv/KU39mn/sqvhf8A9O9rSf8ABWL/AJSm/tLf 9lV8Uf8Ap3uqP+CTv/KU39mn/sqvhf8A9O9rR/wVi/5Sm/tLf9lV8Uf+ne6o A8AooooAKKKUDJoASivW/gd+yLrvxiQXMkn9j6acN9qmiLKQc88HpX3L+xp/ wSzfxQ8SaPor6/JIwQ6jLb5jdgSMLg+vH1FAH56fD/4AeJviOUaw0+cwMcGQ rwoHU4r07Rv+Cfuu3qFrnUoIjn7qREnH51+zXij9hf4Z/sb+GP7e+M3xK0/S 5IEDJoMkOye6JwAiEgjPOeT0FeDfEL/grn8FfCFtLovw2+EFxrczEqbu8vIw o/ukbOaAPzmuf2IL3wpCLk31xKZHW0/1IAzMwh9c9ZPzqB/2BtUMTsNQaPHQ yQcD9a+7PiD/AMFeV8Q/Dq00q5+A2i2c1trWk6mt0byQyulnqFvdmHrgLN5P lE9Qsh711/h//gul4B+IWqGx1L9nCCCNUKzHT73MigAnOGOOo5r0Kn+40/8A HP8AKmSvifyPy98T/se69op/0W4i1BuuyOMhq838SeCdU8I3DR6hZzWzKcfM PXpX7Z+BPj9+zT+1Bfiy1PVf+FQTzgAvq7xyId3XGwE8EfrXS+N/+CSM/jLw c+u+CNWs/Hvhe4QmK7gtj5coGQSDx3DD8K88o/BKivub48/8E37fT9QuY5Y5 fDd8nZoQUc54zz3r5d+Lf7NfiH4TmOW4ge6spc7biNCF4x6/WgDzuilZSrEE YI6ikoAKKKKAPv8A/wCCd/8Aygo/4KK/901/9SC4r4Ar7/8A+Cd//KCj/gor /wB01/8AUguK+AKACiiigAooooAKKls7OS/uFiiRnkc4AAzmvXfAv7F3ibxh bRXE8sOlWsvImuI2K4z14oA8dpyxFiMck8YFfZ/wr/4Jwac/ii1k+13filAc GCOALBKSO/OcV99/Af8A4I9eK7zwCNQ0zR4fCukQMSfItnYr3aQknHQZ/CgD 8afCH7PPi7xqQ1no14YSAfNKfLXoGk/sIa9dxg3OoQWzk/6vyWLV+qfxEsv2 b/2Y5Y08W/E3T/HWs26KLrTbVRHcQSgfPGdwxuB3KfcVxL/8FcP2fvhxqElr 4X+C+o+IZVBAa8u4VQDPUbTnGcfnQB+ftr+wDqN2kaNcahIqKfmjt1wecnqf euh0b/gmZJqdujyaxfW5fHDWynr+Nfe1p/wXZ/s2GW30f9m3w7IlwhRGN9Lj BGAxwev+NQ6D/wAFrPHlnpbx3/wI0qe3VT5QW8l/0f0K89uOvpXfmX8Vf4Kf /pESYbff+Z8L6z/wS7bS7QyHxQ0Styvm2uM9fQ+1crq//BObWoU/0DWrW9fs ogbmv0ZX/gvFbWHhBdM8UfAO3vZAAXvDfEEsM89fQmub8Mf8FSvgX44vGTxX 8Pb/AErz24eK5iIjHY8/U1wFH5eeOv2X/GPw/t5J77SLoQR9ZNmB1xXn8sTQ yFWBVlOCD2r97Ph3+z78Ff2nYGPw1+K+n2mrTruTShH5kp4yw4GM4DflXzf+ 3j/wTWttJvvI1NYjLalxJqVvCQV6Z8zcevTpQB+UNFej/HT9m7WPgxrUyOr3 mnA5iuVQqGBAPI/HH4V5xQAUUUUAFFFFAH9gup/FDw9400W32DxDd2E5S5QH wlcXkE3B8uQCW0kX7rthlxkOeSCKpeIfE9hrfhy202xt/FcssGoWmoyyTeHN Td5FguormZ2ItySxWNznpk84GSOg+CH/ACRbwh/2BLL/ANEJXO/sUftJQ/Gr 4r/GLw1L4p8EeIdS+HGu32jhNAYRyW9q+lJMguYjcTMJUme6tmkyivJZy4RC rovE6kmvZ3dl/X6nQqaV5pas1tf8aW/jzQ7zRtP/AOE1srjU4XtmktPB1xeS tE6lZY/JubKeF1eMujK8TDazdDgil4O+G9p4c+Ew8GanoPxF8W6OdOXSZl1v wPeTfa7RbZLVYJUjso4njEEaRkFPmUfMWJYnr/2MfEnxU1b48WEfjnwb8PvD mkrbTtDc6F4zvNZuJJ9hCxtDNpdoqoVLkuJCQVUbDuLL9nVpSTcd/wAvz3M5 xip89tbH5Rf8O2PAFr8c28e2OkfGjTb6XxGviqSwg8N372Ul99oW6kYiWxeX bJOGdlEmBvIXYoVV97120n1jWbHVYNL+Kuk6xpCTjT7ux8Naui2ryps81rdr dredkwGQXEUiqwyF65+4WbaK+JdC/wCCz2iav4A8U+Ko/Bttr/h/wTf2Ok6z qPhbx94e1LTbeaaO+ea6/tKa7t9OisFa2gSM3V1b3rm7j86xtd9uLjWsnVlz 1Hd73ffTV93puPD1JUHKVH3ea6dtL3Vn96un5HPfBr4ft8GvDf8AZMFn8UdW tUkka3F54GubdbJJLiW4aKKO00+CNY/OuJnC7Pl37RhFVV77xT4yk1vxPqN5 D4Z+IAiurqWZA3g7VQwDOSM/6P71237Pf7arfFj4q+HND8Rr4U8Ky+Ofh1oH jHw7pia7b6hcapeXX9oPqdvaXUUhg1GG0ihsGMtqpULcrIzbJo8c3+07+2JJ +z7+zxJ438VeLrf4X/2r8WtK8OQJ4outPtIoNNj8SW1hdLA80SBobrTLW8v9 0nmSxxXUzpKqRRtHMqN2k/T8bEU2opRgtEea/HPw3qXxc+CHjLwrY6H4xs77 xNod7pNvPeeEdYW3gknt5IleQpas4QMwJ2qxxnAJ4Poll8M/B0d98U577xX8 dtW/4Wh4hs/FYju/AM2zwvqVmlslncWATR1yYPsOnlVu/tKk2MRcPvm836Z0 fx5pniDxTqmjWc0897osdvLdkWsogTzwzRosxXynk2ruaNGLorxMwUSxlqvj LTPEN74j8KyaNqFtZ6daam8uvQyIGe+szZ3KJFGSjYYXTWrkgodsbfMc7Wuj SSbSdt9/vt+GnmNT5n2PN/gz4+8G/BL4eWnh7Trf4l3yxTXF7d3154G1f7Tq d5czyXN3dyiKxjiEs9zNNMyxRxxhpCERECoOQ+OfxOt/Gni+C503QfH1zBHZ pEz/APCF6wmG3ucfNbDsR+da3w1/bMXU9P8AFGoeJVsNJ0Pw74x1Lwtda1Jd 2cOnad9nubxY5bl/tTPEpiitIyJkjZprmMorRyKV2dC/avkvfj3pnw7vtO8P x65dW6XN8lj4givW09DZGd1kgCLdI/m4CtLBFA0Lxv54mkS1PbWyqvrFq9tX Z9v8jp+qVYN2Wx4xZak2m2kVvb+FPHUFvAixxRR+DNUVI1UABQBbYAAAAA4F aWv+NZNa1COaLwv8QVRbW3h+bwdqoJaOFI26W/TKnHt6V23hD9s3TPEPgbw5 4isPEfh6+8N3Xj7VvDmt6vcahBcW+m20R1I2j+dD5cUQlaGwWLzASyXcI3Sv Isr9B4g/as0Pwr8ePGvhqXV7a7n8L6Ho+oXdlHqNqDpyXF3cx3NzLGQskUdv C1tPO7yMqwvEwRCczZvJ6qfLbX/J2/P/ADG6NVO1tf8Ag2PF7TUTp8TRweFP HMCM7ylY/BmqqpdmLscC36szMxPckk1Y/Y3+Gmmfs4Xeg6/qnir4w6jrcPw8 8O+B9T0KLwLeP4ft/wCyYCI5LV/7IjvT+9nvW3SzkMLx8oNkKxeyeKP2tLHw 3Fo5jn8Jarcavcae0FtZeIFlmuLC+1m30221CJPLzJAUuBIWA2B1ESu4bzR1 cnibUPhXa+OfEHjDU1uPD8epJPo0VlZSXNza2f2S1i+z+VDEZZpnvBcFEQSO /nRquThFl4GdFOU3b9dv8769DOrTnFe/p/SPMP2W7vw5+zB8PtQ8PDxF8a/H Eeoa7qOvm88Q+ArtbmCa/uXu7mNPsWlW0flm5lnlAZCymZlDbFREd8fvi5Y+ NzpP9maJ4+uvsvneZ/xRWsJt3bMfetRn7p6V9EA5FfCPxW/aj8YeCP2PfEOu 6h8aNA8Fa34g+KPibRdL1zxbrOi+F9P0iz0/Wr+zg0y2vp9OuYInNvYBwZ7K /nl/0lf3YZbi155Q5lZswi+qL3hzR9N8G+d/Y/gLxXpX2nb532TwLqUHmlc4 LbbUZxk4z0ya1/GGur4w8L2+lyeG/iXFGNPksZ3g8LazazDfJKxaOWOEOhCy DDqVZSMgggGtTxf+0PLpX7WXj7w7H8ZZbfw9pngPVNY8U27yaKG+E08CaV9i umc2pa1Nxb3V/dY1Mzxv9l3xosMciH1P9hD4l6r8XP2atL1vVtePixpNU1i0 sNfKW6nxDptvqt5b6fqObdI7d/tNnFbz+ZAiQyedvjVUZQIlhY8t31G6l1Zo +dvAPhHSvhXpEuneHfA/jXRtMkmM6WNr4O1aO0tnKqp8mEQbIVO3cUiVVLtI 5BeR2Z/inwxYeNNX0q91Pwn8R7l9Gm+020H/AAjeuJaNLlGSSW3WEQzNG8aP GZUYxOodNrfNW/47/akuoPjt+0B4c8X/ABv0H4WeGfBWh+E7n+0dOv8AR0k8 CSX2o6tHuuLnUbeWFZ7y3t9OYx3MTKi3UaRAsRcS/Qn7KvjrxD8SP2Wfhv4k 8YWraf4q1/wtpmpa3btbG2NtezWkUlxGYm+aMrKzDYeVxg9Kz+pU3CzWit2I cIcqpuKt2sfPHw51688FatY3F7pXxN1gW2pNeNNceDL9Zlja4aVYFEdpGmyJ SIkJG4oimR3fdI3F/GL4NeFfj/YWlt4u+HvjXWUsGZrZ28J6zDLCHAEiCSOF X2OFAdM7XCjcDgUeJf8AgoZa+P8A/gnv4o+IXhX486B/wk+jeOPF2meGZtFv tEu4/FV7aX+qvonhwxvDKs/2uzjsdkdtsvJ4jG8cu6Qu/qHjr9oR9H/a6+IX huL4yGDw7pPgXVdW8VW/naNj4Tz266V9huzI1sWtvtFvd391jUzcRv8AZC8a rFHIh6aVKUJKVOTT7rToaSfMrSSaOA+Gvh66+G2kXenrZ/E/VbF7x5tPt7nw Nc28WiWpVVisLZbXT4R9mhVcJ5oklwTukfAx0X9v3H/QtePv/CP1X/5Hr2v9 hD4l6r8XP2atL1vVtePixpNU1i0sNfKW6nxDptvqt3b6fqGbZI7d/tNnFbz+ ZAiQyedvjVUZQPE4f2loJZfjxbax+0dbaD4M8K6hYR3HjGPUPDYb4fX8+rah bT6O1zJaNZR+XFb6evk30Mt1Gb5g8rs8RTKVBNtt6gqrQv8Ab9x/0LXj7/wj 9V/+R6P7fuP+ha8ff+Efqv8A8j1L8L/2nfFviT4qfsz2mq+N3t/G3xA8K2N9 42+HBgsIjY2s2iahdy60bYw/2jbbdTgtrPzGn+zAyeUY/OYNXon7JXxL8S6n 8HvDy+JfGa+INX/4WD4r8LyXmsWlrDf69bWGrazbW8aLapbwLOkFlDIzJDho 7eb5AW3rUsKouzH7WVrnm39v3H/QtePv/CP1X/5Ho/t+4/6Frx9/4R+q/wDy PX2aORRUfV4h7Vnxl/b9x/0LXj7/AMI/Vf8A5Ho/t+4/6Frx9/4R+q//ACPX 2bRR9XiHtWfGX9v3H/QtePv/AAj9V/8Akej+37j/AKFrx9/4R+q//I9fZtFH 1eIe1Z8Zf2/cf9C14+/8I/Vf/keuN+If7VfgT4Ra1DpvizWLrwxqNxAt1Haa tpV5ZTvCzMokCSxKxQsjgMBglWHY19/18Sf8FR/gX8H9f8Vf8J78UrG2Sz8P aDFFPqM1/dW6wW63EzKu2GRd7F5SFUKXdnVVySopSoxSuxqo2cj4S/bG+Gvj y6u4NG8UQanLY2kt/cC3tLh/IgiXc8jkR/KoGBk9SVAySAfyk/4O29Tj1ex/ Z4uI47yFXPiUbLq1ltZVIbSgQ0ciq69O4GRgjgg1+lPgf9ln4b+DNcaHwDpt jYQ+OvCOpRDVoNQub9ZIHNmI3i3TNG0bCYPkfe2rhgCc/m1/wdxxyxQfs9ie Xz5SfEpZ9uM5OlnpnsMD8Kypu1ZKK0s9TKpOrzqPL7tnd9npZfPU/GOiiiu4 Q6KMzSBVBLMcAetfb/8AwTU/4Jg63+0lqUWrTpZ29pArzyzXZcQWsaZLMSqs d20ZAxXmf/BOj9jDVf2ofi1axxR2jWsSmT/Sd3kghl+aQhSQoz2B5I4r6q/b 7/a9i+CmgWfwf+GN7eaPpui24S/v7PbbvqUzHzJfMKsThTI8a9Mqq5wcgAGv +0J+3t4N+EenX3gL4QC7trm5QWepeJnRIJGjH340COwIbayjKjIftXzHN4Ju PEmmTXMEl1Bpu7zHublAs9xxjcqrlSccdRXpn7KP7FMuqXNl4k8XJALK4Hmp aPl2lyOGfIHBBBHJ5FfTnxH8IeG/DHw4mtLbTrX7MIiCqwqNg6YFAHgX7Lv7 Kw8WKrXEcmnW8mQiqmfOzn5j9TzXbfEP9jjUND1GKw0uBLi4uJQrs4K4XPPa us/Z88Ba5o09oy30s1uJcxqWPCl9wHTtnFfaEVtAkaPLBbNLEuS5UE9O3FAH yzD4e039njwBpWg3Fv52qshkcxr/AKsHgZJI5yvpXkXwY8Pp4k+Leu6n9mWP bfGJd33l3Dn8K+pviz4j8Oal4guN1uJ9QVQplMalQcnvnP6V8/8AhLV10f4j a3Jb2U0kX2sSu0aDapAGAee9ehS/3Gp/jh+UyPtr5/odB4c8AXvif4vxxvEj WUEZ289xx6VQ+Ourw3PxA1DwdaaWsrwop+0EZADfjn9K+g/gr440TxjdSfZb GO3v4Y/nIUZZc465Jryj4+fDO50n4saprVr5SvdouwHIzgcDpXnlnx78Yf2U 9Y+FMa+I7R7OVbfBlhgZsvk4/u9fxrlfgH+0B4q/Z/8AiwfEngDVptA1Mqkd 7bB9i3a5PyEgMSTlx2+9X1D4c8Kal4z8coNYufP08Mc2gYvG5xx1A4FVv2if 2JrHx2ofw/Hb6Pf2sYkEkS7A+QDgkAnqPTvQB6XonjD4df8ABUHw7LpepOPC XxcOVvLO7UfZr2QnCjzFZ3zgZPyDt1r8sv2r/wBlfV/2dvFdxb3lpJbwwzeR LGwO+CTBO1h24FejeJ38S/Az4iGN559P8S6VJv8AtVtMyC844G7g9D3Ar9Ar /wAKaV/wU/8A2ZLa3g06zPjyxhW/E8sIM2vWoDK/IBzc+ZJFgEBSisS4IAIB +K1Fei/tK/AnUPgT8QJtNu40ELs3ltHkgFWKlTkDBBGMV51QB7//AMEnf+Up v7NP/ZVfC/8A6d7Wj/grF/ylN/aW/wCyq+KP/TvdUf8ABJ3/AJSm/s0/9lV8 L/8Ap3taP+CsX/KU39pb/sqvij/073VAHgFFFXNB0G68S6pDZWUTTXE7bUUd 6AI9M0ufWb6K2tonmnmYIiKMliTgCvt39j//AIJ9z23jLTGv4YNd8STndb6b EGZISVOGckAjAJPAPK11P7BP7Dk901nZQaXaar4x1Ihywj3f2ehPQEjI+Uqe nr+P2b+0R+2p8Kv+CZfg268LeB7WDxh8Yp7fyvt6xKbbS2fG5zIWDhlHmYAj I3YHTOADttG/ZP8AAH7E+hWnjL9orxNZaX58Yn0vSI18y5n2/fSJXCKTz3Yc d68R/a0/4LSeK/iNCPCnwesb/wCGngC3iFtBeyssN/fr/G7Rq0qDczSAYb7p XpzXyLe+L/id+1FNc+KPE+reIPF88Z86S6v5zLFbFuohUnbg47qK+lv2aP2R dO8WeALHUZ3W7u7jeztIgO3DsuOmARtoA+eND8BJcaudb1i3vbmGMF5J2jUv cHnG7kD7xru/g5p2ufFDxJer4StLGKzsSoYXcrxooOcD5A3NfWniD4X6TpXg +4smsrMp5JXiIHt9K4X9hzwTFoOv+KljjjEAvk2lR/Ds+lAHkHxe8Raj4Xjt dK1S2jsdTTU9Ok2wsSsoW9gfchODjpnIHPatzU/Csvi94FsfDtxPYHKyzBAQ xxjI55+bGfau1/b18KacbG01IxxLcQavpsKuq5IRrqHcM/56V7x4A0qwXRoC ttBsAAA2gDFehU/3Gn/jn+VMhfG/kfI3ir9kHX7bw3DfqbK/t/LI+wSM4V1/ unC9c89a8tvPhH4t+BrPqmg3Ot+FLp2L77CNPs+D94FmO4fke9fpdrnh1L7w zcRJEhIGYxgYxwK5zQPB9j4i0po760gm2EqRJErg8kd/pXnlnhnwB/4LJWFj 4asfCPxw8Grrvhi43QzeI9Jb7VdxfL8mVmeJMBlQH2Zq9H8Qfsd+Dv2o/Bl3 4x+APijTvEOlInmXumHck1sT9xZgqsqk4/hZvrXgX7aX7HGjv4auNT0aK206 6hkRTFGhCyb5FQcYx1bmvlnTNd+Kn7G3je2ufDfiDWPCV20wUfY7lo7a+IHG VIwcZ6laAJ/2p/2B5H1XVrjRrdNN1yzeR7jTnBAlcZLCPAOfbdjrXxxqel3G jX0ttdRPDPCxV0YYKkV+2P7N37XPw8/4KReH/wDhCfEthbeFPjD5S29tfvGv l6lcKn3t4YsWeQr/AAck18oft0/sF6hLfXNk2mWOk+K9PYsZAhRL6IZ7hck8 Z6DqKAPz1oq74i8P3fhXWbjT76FoLu1fZIjdQapUAff/APwTv/5QUf8ABRX/ ALpr/wCpBcV8AV9//wDBO/8A5QUf8FFf+6a/+pBcV8AUAFFFKqlzgck0AIBm vT/gV+zBrfxhvknaM2OiRsPPvJcgDjgLgHk13n7Ln7G8vjb+z9a15AbG+bFn Yopaa8OcY2kAYPbntX6seE/2Y/BH7BXgqy+JnxzmsdM0S2jFxaeFrGAS3WpK flCxpII42w3HzSCgDwv9hr/glZ4g+Im608FeHssoR7nVrtSjouGyV2BuMBj0 6Yr6E8XWP7Mv/BPbU5bb4p+Lm8WeOdO5n0vQ7aO4uYpOu1vOeEA7T2NfMf7Z /wDwWK8d/tQy2ei/Ce0n+DPga03wtHp832WXVMlVQyGMucgKcANj94e1fOp+ Gj6xZm81/UZLq+vP9df3ztNdynuyls5/EjpQB9efHb/g4ma+8PXWkfCT4ZWn hVzhINXklMN5b4PUKkjrkjPevlbxjrXx7/bm1E6x418W31za4EayapM20IOV VdsZyBnviuy+GXwb8P2CRHQtBj1efP8Ax9TxKEjI6hsE4z9K77TvA+ra1p87 xXEFiYCcw27Hav4Y9qAPJPA37I3hTwTdwzarfLql8PvQ+WCpfvzwcE5r0HTP D3g/wJfrNFYabY3jH5duQ69/6CqVr4evPEl7exW+os91EGR/Ygkemc1wOtaT d+G9eQahMTIW2lixJHvzQB9C2fxojhK2eo3crtjMUhwQy44GSe1TTeOrZoDK ZmaJRneMYArxXy57/T2a0maVY1yxbqOM8VmSfFKSzsFglUeX9wgnnivQzP8A jL/BT/8ASIkQ2+/8z6B0LxLpHi2JlWeGQMMbZurflmqfi74K6Br9sTJHbAsv JRf8a8LtvHUFvarKs0kbdVSM4IPPFaek/E2/a8Dl7pSFwAGyR+FeeWcv8Uv2 PZdDlOqaDPFb3VuwdGiBBBz1Jx716F+y1/wUZ8Sfs+Tw6B8SGuNZ0yYqLaYu JFCDqrZ28frXc+GPi/pWtRQafqsZ3Sjb5xVd3Tvz0rG+PH7MWmfETRFubP7J IrKxjliThskc9M4+lAHsXxW+DXh74/6A/iXwkIjp+pRiQwMuIQ2MNtIyScg/ jmvzI/ar/ZXvPhRqU+p2ixtphbDhA2Y2Jwcgj1Nem+Dvjp8Q/wBibxs0K6le Xmgwy5Nm07GMqcHhSMd2796+n7zStG/a/wDhPq3iDRFs5ZoIRLPYFMu4LoGw ACPlBLdf4aAPyhor0P8AaH+EDfCzxWrRMrafqJaS2IHRQR17flXnlABRRRQB /Yx8ETj4LeEP+wJZf+iErofg98SNL8Z+K7i0shqaXK+HDrDR3ml3VkyW93bX Yh3CaNNsubeUPCcSxYXzETcuee+CH/JFvCH/AGBLL/0QlRfsl/AG5+BWteJ2 uPHvjPxy/iMXuozSeIY9MEsExtZ97K9nZ27OCvloBKZBHHBDHH5aIFrzn8Tu df2Tc/Yw/az+Ffx4+PFjpHgb4mfD7xnq1rbT3k1loXiKz1G4igVCjStHDIzB A0iKWIwC6jqRX2bXzl8Bv+Sr6V/22/8ARL19G11Yf4WYVfiCvO/jL+0r4d+D Xwt8aeKJGl8QDwFoTeJNU0rSJ7aTUlsQsziZY5ZY1AZbe4KFnUP5MgUkqRXo lfMcX/BNHT08G/E3wyvxG8dW3h/4ieFdO8EW0NvFpiz+F9Fsnvtmn2btaMjR GG/lh33Ec1wFyTOz7HT1MFTw0m3iZWs1Zd1dc23ZamMr9D3rxr8SdP8ABenk uUvdUnEq6dpMVzbxXmr3CW8twLW3E0kcbTNFDKwVnUbY2ZmVFZhkeF/iN4At fDviXxTpur+FbXTtyar4i1SC4t44o3FhbSC4vJlO0MLEWrb5DxAITnYENYWi fsxzaBquqXtv4/8AGKXGuaYsGpv9l0gfb9TW2htU1uTFl/x/LDbwoEXFp+7U /Zs81w37Sv8AwTT8J/tZ/CeHwh4z8T+L9UsLa6upYZriHS7ySGCaNo1gWO4s pYA0CMFhvBF9ujAbF1++nMu9KjgW1GpUaWl2k366afL8eymTl9n+trHqHgbx 3ob/AB78d+DrDw9caVrdhaaX4l1TUPs9tHBrYvUuLSGbfG5lklRdKaFjMikJ FCFLKBjd134r+F/C2rzWGp+I9B06+tjYCW3utQhhliN/ctaWIZWYEfablHhh yP3sqMibmBFcv4Z/Z1bw3+0v4j+Jf/CaeLb6bxLotloUug3EenjSrWG0eWSB 4ilqt1vElzdsS9wwJunBXakIjw/jX+xdpHx2/aE8AfEPVfEmv2t18OJPP0nT 7Ow0kRpKZA0rreyWT6lAJgsUc0dteQxTxRCOVHjeRX4cQqSnH2T0sr772V9/ P5FrzPR/h78RdF+KmgXOpaFdLfWNvqV/pMsnlPHi6sryayuo8OoJ2XFvKm7G 1tmVLKQx3ti7s4GfXFcJ8GPgTb/BG/8AEw03X/EF9o3iHVrrWoNHvfsrWmiX F3cTXV39mdIUnZZrmeWZhcSzbS+2MxxgIO8rFFXEEagY2rjr0o8tck7Rk9eK Wii4rieWv90flVLxD4Y07xbYx2uqWFlqVtDc294kN1Ak0azwTJPBKFYEB45o 45EbqjorAgqDV6igArn/AAH8K/DPwuGs/wDCN+HtB8Pt4j1ObW9WOmafFZnV L+YKJryfy1HmzyBE3yvlm2rknAroKKADFFFFABiiiigAxRiiigAoxRRQAhUH tXO/Df4S+Fvg54NsPDnhDw3oHhTw9pZkNlpej6fDY2VmZJHkkMcMSqiF3d3b AGWdieSSejooFYKKKKBhRRRQAUUUUAFfOf7X9lrd14+04aRf6VYhbAGX7ZYS XXmEyPjbsmj24565zkdMc/RleE/tO/8AI/Wn/Xgn/oySs6vwsun8R4Bp9hqd r8XtJbWtUsLy4/se/Fuljpj20YTz7LzC7PcSEnPlhQF6F8kYGfyX/wCDvHp+ z3/3Mn/uKr9etU/5LToH/YE1P/0fp9fkL/wd49P2e/8AuZP/AHFVhS+JPyNJ /Cz8YK6r4MfDi6+KXxH0rR7WISm7uY42DZwQWx2BrlQCxwOSa/RL/glF+zJ/ Zmmp451GwjMVjH9s8yWNSccbcd8gkV1mB9BxeDNA/YY/ZpFuwhtdZu0W4u57 UbHtoFDK0BPyneW8tsdMLnPGK+EvD3he+/aK/aIQXLMv2yb7XMZDuIjQrsB6 /eQA/j17165/wU4+NGoeINS0Xw5DPPcXt/P9tuV3Hc5VXTyyOm078/gK2/gL +yhrXgfwJpev3E0tlqckZkl8tgS4ZiVBPb5do+lAH3V8LfDem6doNtYzWsF1 AiBQAgO1QBj06YFS/Ef9nC08Z6VINPlSHzFOYivy9vTNeG+EvjN4m0OJGuQI 7WIgM0cu8n0yMfSvor4G/E5vHenSFs7YlB3Ec80AY/wU+GF54bj8nUrIRi2f YrnowBPI/Suy+IutjQPC95crnONgxwBkGtyaZnyu7cAevrVHWdPg1WyMFwkc kbcsr8hvwoA+TrTS7/xjql1cbp9sLbTk8Edd36/pXA6Z4oufh349uYWs7m+h uJzLviAbbjAwckf1r6I+OnhO50WyjGkxRWFhsLS+UcZPPPr2FeL/AA7sIfGG v3dlJIskwkMYkPUt2BNehS/3Gp/jh+UyPtr5/oev/AX476DqWqmMWK6fPMu1 nVVBbnoefWvYfF3gu18d6S0DhElIOyRgMj2r558D/A3WNJ8XQt9lIhUiTeuN pAOeor6Zs1KwJh2GOO1eeWeN/Df9ma68K+PBqOoXVpJZW7FtisxB491Fda3i XRdZ8RTWsNvEzBdhfaBg8j1r0CW0TULJ42QHI2+1cv4rh0L4eaYbl7aFHckF wpyT9fxoA+Ov+CgHwP0TUdB1i7QWsN9YxGSKZUw54AxmvDP2JP2gLj4TeK7O 0/tDUNKGpxg2t3G/FldAgLGMnhSoc9DyBx3r6i+O2t6f8RXuITEJUuCVOV6j FfFv7UXwU1z4SWEmpf2ettpl3JsSaOTmOTBIYd1OFYfjQB9h/t+/ACL9sf4e /wDCU6XpEENzY2yW9+YrddzSqpKSL67xlmJwc9M1+QXizw3P4Q8R3mmXO3z7 KVoXx0ypI/pX64f8E3f2gb7xP8G4b6/uGuJIpn0u9jlJZRCQEDNng/JmvkP/ AIKs/sxr8OPipf65pVqqaTeTPNbyRqAJLZiWjkOOfmUg/jQB5z/wSd/5Sm/s 0/8AZVfC/wD6d7Wj/grF/wApTf2lv+yq+KP/AE73VH/BJ3/lKb+zT/2VXwv/ AOne1o/4Kxf8pTf2lv8Asqvij/073VAHgUMLXEqog3O5CqPUmvvL/gmP+w9q fxD11oFjsZNS1Py9skgLfYV+Y5ztPNfJv7Mfw8PxG+LenWjRmWCJzI6juwB2 f+PgV+zvgGK1/YN/ZVl8S6jHFY634qtxdySsN7WsCjG9AMgfN6AUAcR+1H+2 f4e/YH+G3/CvfA1jH/wnl8pEutRRqk8eSQ+GznADj+H+Ee1fJ/wf/Zi8Q+Jo F8VeJbSa/gjkaeea7cyT3pYkHcTxtDMHBznjpXJ+F/EEn7RXxuvvEuvsLqLU dRVftTjBjUMI1QIMDDKoycZ79a/T+x8A6LqXgA6bbqoR4fKGzPTbjHX0FAGF +zz4N0Cb4W2Nvb2llFFNAu390oHXqcda1Phz4Jufhprs9kqgac0jPCEJ2KGO 49R3ZjXn3wh8Un4Y+J77wpqzrZpbyiPSlc5M8WPXuc+pr3nSL2LU7RZBhu47 0Ac58TfDVxrVlLHbfK04CZ7c9f0qp8LPhlH8NPDVwICjXc4BkdRgyMO5Ndne Tgrt7g8k1w/xM+MWmfDkRRXEoNzOG2R4OTj6UAfMP7ZnhfXL1rW+n1WRrOPV rFXtWY7WLXMaqenYnNfVXwq3Xvh+FgRhlwOenFfO37THxKg8T/D1btbYpINU 01wpQ5YrfQHv6gVN4M+OHiGbSbOJbd9Om+XaCAFbGPXPUV6FT/caf+Of5UyF 8b+R9hWabbMqc4ZcdOlc/ql+nh2GVlBwxyMdqi+HfiW41rwxDPcN+9KgucdT WvfWVpqtuFmjWY9/mI/lXnlnhw0zUPir48tkkiY6Vbu0j+YflbhsZHscEfSs j9rzwl4RuPAjw3VtZzXflstvviG9nyPuntxXpPxK8Uw+D9IuINPiitmYAFkH +PNfOfiTwpqPxM16TUonnvZLH5oFL4jBP+z0P5dqAPkL4jfCPxL8F/Elrq+m i50a9W5S60vUo5GjliYOGTJHQ7gPXpX35+yl8eF/4KQ/D6X4deMLUaX8UfD9 t/aFtqkygm/iQbVTzAS/IHTZ3pG+GNh8ffhZcaNqVrGmoxW5idB8rxyBSMqQ RyODXw1451Lxz+w78ftP1xLiezljlW1N2pU+ZblsFccjIUemfegDof8AgoZ+ w1rEHiK5nNpa2fiHR4dt3EgwLtdxIfIGSSSRyAPlr4QurZ7K5khkUrJExRwe xBwRX7rTwTftxfC7T/F2k+TfajZWQcMQsb3cGSVLLgA/M784zx9K/JP9uP4H v8KfitdTxWxgtdSmklIz919+GGM+uaAPpH/gnf8A8oKP+Civ/dNf/UguK+AK +/8A/gnf/wAoKP8Agor/AN01/wDUguK+AKAAAk4HJNfUf7Ev7IN3421+z1DV NNXUJb6RY9MsCu4ztnDOc4ACkpnnv0rz39kf4AXHxa8f2U91p8t5pEMu1lDF fPkwSqAgg8+x7V+mGtfGnw9/wTP+FJdLHS9Z+M2vwhNNsny8ei2IUgSnH7sO yyqRuUkmI56HIB1HiP4q/DX/AIJZ+HYY/EWl2fjH4w3Ee+Dw/FArWmjsRuQu zFGRSuRlUY5xwa+IPE/iH4gftQ6+fEvxA8Uapq4kLPH9quGljQHr5SEbcZ9h zXPa1rGt/Gfxxd+LPGl/LrPiHVnE0jsFVrpumNqBVRcDjYBXofjP4JeNvDfh 86heRCGHGGWNkxF7YAAAoAz7d9DsdPhtdMaOTULh1hDtGA3ULnAyB17DNe6a H8HoPCvgKC7n0+LW70JuZsbiOegzjisLwB+yxoFt4attU1SaZrmTa4k8xlEb YzwA39O1erL4t0nwJ4Pga9v0FsqYSSRch/8AOaAMTwB8afDWpaWbaCCPTrlR gwmJUbvxwT/kVw1v43k+H3inVLS9lcfbkaaAKcggqQOuO9a/xE+HOmahfWWu 6ZH5DPMqyeWxUSbs8kZx27UnxU+CS+NJNPv7OZ0u7SFVIDcMOCck8UAebfA7 U5p/iTfwTzzQS3Fwz4PIIZycfWvV/ip8I9I1zw6s1yyxXEZ3CTZlm4Ixn9fw rhfHHgi51yezHh6JU1q0I8+VDhY2A754PIPam/CyTXtS8eXOm+K7+SeDT0Z2 5VQGBUYO0DnDGgDntD+Ges2Xij7JFdEW1whI2MSCOnPHpjpXKfFf4f3eleJ4 7JWjJ8zaChPX8q+i/G/j+y0WH7LolrFeahLEfKbb8vpwT3+teOweGfEizX2o 6lo9xc3zlmVg2QpJJzxgcZ9K9DM/4y/wU/8A0iJENvv/ADPJ54pdD8QiOTcf Kfn+6DXomkQantiNrBFJBIuWY5yvr2rR8C/CKS4WW98QwlZrnBVCSMf98n2r S/te08CtNogZ5Lqb54FUZyrZ/lXnlmRd6VeatPHJb4tY4jxISVDnoQMZrp/A fxQu/BNzJDc3DSx8YRnJimGfun0/DFanh74eLMLSLUJnjSXlFBwd2MnIGKte IP2W7LxJeLPBqd3aspztX5gSfcmgCD4o/DXQ/wBofwxM9hHbpqAXlGQbo8Z5 yM8dx7EV8w/s2fFXXP2Svjhb2+oTX0WmzO9rcrDJuDRyK6cK2Afvd6+sr34T 3Hw8igv9CmuGMKATxn5gx9cNnsD+deQftgfDDTvH/wAP5PEekxAalZFHfyzt PVQ2QT2yTQBpft0fAOw8deGoNW0cwvZ6orXWmLtxJH0GzAGB36HvX5+39lJp 15LBKpWSFyjA9QQa+9f2HPiJJ8ZPAmpeDNem8zULCKN9IL/f3HgjPHGMnnIr 5l/bB+E03gj4lXt7DbmKyuGCsA2RHIAFP5kE/jQB4/RRRQB/Yx8EP+SLeEP+ wJZf+iEqh+y/p+uw/GTxcbvQfF+heHdJ8Px+HtIOva+motqv2SLU997Gi3Nw wEsc1vme4dbmbYBLGpiUtf8Agh/yRbwh/wBgSy/9EJXU/CXxBe614p1qO70+ OxhtLe8js2Fz5slzD9klHmyJtAiJkEm1Qz5QIxKszRp5/wBpnX9kzP2Mfgx4 i+Hvx3sL3WPiz8QfHlrLbzwLp+u2ehw28LlCwmU2OnW0u8BSoDSFMSNlSdpX 7Or4x/Yx+MviP4ifHaxs9X+EvxB8CW0NtPOt/rt5oc1tM4jKiFRY6jcyhyGL AtGExG2WB2hvs6umhszCruIRx0zXgX7HWm+PNC8TalYap4T17wP8OLPw1psP hrQdUTQ430W7XU9bFzaoulyyIsUdl/Y6xLvZBCsQ3GYXNe/V5za+PNc8M/FG 7tvEGo6RcaH4k1OLSPCthYaJcR3lrNHZTXNybu6NxJFIrLBKyMIbcIEEZMru ue2jRlUUuXor/wBfn6IybsiSX4N+I5Pjovi1fiv4+TQF6+CltNEOhN+48rmQ 6edQ+/8AvuLwfPx/q/3dcR/wUQ0zxVrnwN0O18IeAfEHxE1WDxx4Y1iXTtIu tMtpobbTdbs9TnlLX91bRH93ZMiKrljLLFwE3yJ2Pgj9oaPxv8Vz4QTwt4m0 7UbbwxYeJ9Qmu2s/I01bye4ghtJDHcOz3G6zuSTEskO2HIlO5A1/9oP4qf8A CnfAlhq3mGL7X4k0LQ8/Yvtmf7Q1ezsMbPOhxn7TjzN58rPmeXNs8mSalGdK UYzVno/yaHF6l/w1J4gu/ib4imvbe5tfDsdrY22mpLdwSCecedJczJEkXmRq RLBHmSd9zQNthhC+bc9VXnngn4zeIfFnxU1nw9ffCnx74Z0nSxObbxPqd3ok mlav5cioogS21Ca9XzFJkXzraLCoQ+x8Icz9lb44a38aP2Xvg54z1bSFuNX+ IfhrTNX1VtKVIbHS5bnTVupJNk0xkEHmkRKqGaQGWPcCokkXFPr6fj/wwWse rUV5ppH7S1nrfxM1vwnF4c8SLq+g2ml39wHNmI5LbUNR1GxilRhcchf7Nmnd ThxE8QVWlLQpF8HP2q9C+OfhvwBqOhabrjnx/wCHLLxXHbSRwibQ9OurczQS 322RkiLt+5VEZ2kkEpjDxwTyRVqB6hRXlXgn9pOy1jRfEOs3kWvT6Xa6lrcF jBY+FL+4uYINIuxp10kn2fzxPNJdxTywKgjkmgdQkLmGZzD4t/bG8MeDfh9e +LptM8W33hbTdVutEub/AEzRZtQlS9t9Uj0poEsoA97Oz3bSInkwSAi3kbIV ojIrjsz1uivn9f2udU8ca38ENS8PaLq+geGviP4y1fwxqWn+MPDF7pGt+Ta6 ZrFzBdQwzPFJbLJJpauonhZnguUJWJ+BreAPi1408cz/AB50j7X4XtdX8B+J 30Lw3e/2RPJbRRy6HpeoW73kH2oNcNHLqDK/lywCRIxjyid1Gt7f1/WoWZ7V RXm9/wDGdvhVL8PfD3i6Uat4o8Y340E3mk6f9ksZr6PS77UZZhDJcSyQwNHp 1xtTzJnVnjUsw3SDmtW/a8ttX8BeM5tO0zWNB13w14j0fwUV1SwhvktNV1aD SZLWV4re6Amt4W1m1EwWaMnybgIxUJI9NO9ha6eZ7bRXNadqXiFvifrNrcWV onhWHTLKXTrsMPPnvWluxdRMNxIVI0tGU7BzK/zNjC8D4V/autJvBHw91DUN K164HjvTNJu7XUYdNW3sJJb1o0KSMZpI7RkMiN5c02ZPNSO3a5lylbRw1SXw a7beav8A8Oaxoyl8Ov8AVz2OivFdF/be0DxTf6VDpfhrx5cxa7PBa6fc3OhT aZBeSy2l9dBI2vBD5m2KwfcyBkBngwxDMU674afHSz+Kvhrw/q+maXqSWHiP TbLVIGuHgR4I7u3eeMSIJCwYBArbQw3Ou0sA5XWpl+IppucLWNJ4OtH4o2O8 orzfwH+0FB4o+CHhTxrfaVe2cHijRYdY+zWcUuovbF7T7V5OIoy7tsDKpCDe yqoG90Vug0/WNfT4kaxFf2dna+EoNJsbiwvDIPOkvGluxdxP8/CRxpaMDtA/ ev8AM2CFyqYadO/Ppa/Xs7OxEqE4/F/XQ6iivIfHv7ZXhvwJ4h8IWo0rxLrm k+PW01dA8R6RaR3fh+++23KQLv1ASfZ7baJYJF+0PF9qFxHHZ/a7gmAdJ+z9 8Zf+F5+BtQ1v+zf7L+xeI9d8P+Sbjzt/9maveab5u7auPN+yeZtx8nmbctt3 Hn8zE7qivNvix+1D4d+Dur31nqdtrdy+j6XJruqtY2TXA03Tkt72ZrtlB3yo GsmhKQLLIJbi2BQLKGrD/aO+PHiP4b+EbbUND062sJLTx74X8N3f9sWy3UWp 2Op6np1nPJbGC5DRSIL9grTDIktnzC8bxyO1q7IFqey0V45H+1nFr3xO8ReA 9O8J+J7Dxnoel6lqcUfiG0/szS75baWGOFoLs71u4pxcQyGWyW5Fqrql2La4 eO3k9Hk1m8TxXpkEdvbNpV9ZzzTTvMy3EMytD5aiPaQUZGl3MWBQog2tvJU6 XB6G5RRRQAUUUUAFeE/tO/8AI/Wn/Xgn/oySvdq8J/ad/wCR+tP+vBP/AEZJ WdX4GaU/iPEtU/5LToH/AGBNT/8AR+n1+Qv/AAd49P2e/wDuZP8A3FV+vWqf 8lp0D/sCan/6P0+vyF/4O8en7Pf/AHMn/uKrCl8S9DSpsz8e/h34Vl8Z+MLL T4QS00i8D0yK/Z7RdOj+Cv7Lvhbw5b26Wr2WnR6leSKctIHt1YhvUDB4r8xv +Ce3gJfGfxksm8pZntrhG2n+6GBNfpp+1n4ih0vw34p1NpBFa2OkvCAFA8sK hQDHtnFdZznwz8N9Cg/aW/bB1TVL2eSXSIJHlty3Aj5jAAHXoTX6TfD2Lwvo /hO303ULmKcKqgBgflxwORz0r4Q/YM8K+FtK8PQ32vXDJeazCLqIF3Q8YXIK kcV9ieGv2evC/jWH7XY3WpAoRkmeUAfQF6APXdP+GPg/WIQ1vYwSJ7SyYP8A 49XR+H/DOn+GLUwWVultG3OFPU/ic1h+DfAsHhW3jEMsssgHcn069azviH4g 1q2mig0yNi7ZBk6/TrxQB3DMsBAwSW981zHi34qaH4P1i2stWv4rK5vnKW6S Z+cjkgYFXtCupYtGtpLly8pA3knqc18v/t4+D7/4q+G7ebTpHTVNFuzcwuj7 GIwcjsOMUAe5fGPwLe+NrGCSxlZXRMGMMAr9eua+ePhZ+zX49074jalLMgst KvbzzmYyJuOOAV4z098V51+zb/wUT1f4aMPD/jaT7Zb2cgUSNGpkjGBwdqgn oeuTzX0L8R/27fB3w/8AD1teC8FxcX0AuLeERsd+RwMj8Pzr0KX+41P8cPym R9tfP9D3TRrMaZaRxGQuYY1R2PUn1/StSzn3rkHMYGcjt/nmvgjwl+0741/a l+IUDW9w2k+H9Ndbg+SE/fbWA2nK7ufrX254L8Xx+JdCZvL8t1O5iM4rzyzo 7WRhGTvPzc8dqqa/4U0/xfYG1v4zIhII+dl/kRSLdNEo2Krd8ljzVmKZ5F3M EC9jmgDk4v2fPDMDq62fmeU27DO3+NeA/tq/C658afDHWLOfSlMf3rcKf4sE ZBHfBNe1/F+61a5kMVhrCadCFGQQoJ49SD/OvB/ib448SeHdLW3sb1fEN68m FtpAg3DB5yF9sfjQB83/APBPPxJ/wrzx3rngO5Z2n1BnvokPOwRpjAr6L/bQ +D7fF39lHUb9LQXeo2FlLDcSHA8mGJAI/bsBnr718g3+rX3wZ/a10S8vohaX +poVZHAyqySAFRj6198eMtdmHwW+IWnQyItvPo0qurKOvOcHGRQB+WH/AASj ha3/AOCqX7NaOCrL8VvC4I9D/a9rTP8AgrF/ylN/aW/7Kr4o/wDTvdVqf8E0 EEX/AAV2/Z3VRtC/FrwwAPT/AIm9rWZ/wVhGf+Cp37SwHJPxV8Uf+ne6oA9m /wCCOnwFX4gfGHSJ54EKedJNOWfGY442k5/75zXvv/Bar446nr95a+EtAnD2 N/cTafZwRqCHt9oYAZGR8wNa/wDwSG8O2Pwt+GuqeKr63Y/YdOkjJ2lthuFN upPPrKPxryD9oK0PxL/ao8G6bCz3H9hxu9xk9GLkc+vFAHOfDv4f2ngvwDpN si7JYyvlknDM7MWbP0Ykc19dfBv44a1oGmRR6zas/lAKGVN4YYPUj2Fczq37 O9l4z0y0t7dpLSWFQyvGehHPc461etvgj488KSxRWcUmrWpON8wRSo9cAcke 9AHrF7onhv8AaA08zmFVvbb7xRmiZSR2IIPauq+Hfhy+8K6P9nu52mVCRE5I zjnA4HYYHvVD4K/Da+8J6dNcalGUubkBTHwNvU9BXXXv7kkE/Kn6d6AKt3Iy MCWypYHrzXFeLfgtaeMvED308rSqWLbD/DnsKtfFf4naf8IvhxqviTUmP2PS 1TcFBYlnlWNeB6s4/OvKvhD+3n4P+KkrWSaqNK1WXaqQzxsodjnhNwOTxQBx n7X0EXhaGLTrWERW1rqWmY45b/SoHPXPc133wo8K23j23ltzC0ckSB0nHUHP TGce1ch+2D4N8S+JtBsLqyhs7uKXVNNi82SQIXZ723jj4Axje0Yz6EmvT/hv bar4A0NJLs2mmQWylrmVCsm0Y6cg8Z4r0Kn+40/8c/ypkL438jv/AId+Gr7w VpdzbXs6T72BiXcM9OnAroUlaDpznGeeK8Ev/wBvfwHb+NbLSotUbUJbqUxt JBCzRxfjXuEeoLfxwTROvkyorD6EAj9DXnlnm3x68EX93byahabpY12713dR wD/OuL8BXd3LG9tYaexkPynIxz9a+iIoBeRNFIA8R6ggGqM/ha20ezmksLRB O3OFH3j2oA808J+HNR8E6jPq9+yW8RUybBtO7jPpmvnH/go74VtPjR8HmudO jjlv9PuhOGXKkKAdx9OlfSnj6/h0y3Z9QuJGnkUnySOB144rxbxb4c1HxRZ3 0dvpObOaJkKsxG7OR0z70Acf/wAEe/jnNpPguFJ7l7i48LaqwNue9p5MWAQB hhvLdfWsf/gsl8F4tPOsXIt0XzN2p2jg4Pl48xhj0GcV5z+xbO/wX/aw8SeF r5DZLrJjt4IWOfm3E8e/HrX1x/wVm+HUV5+zj4P1eNZJ5L7wheC4ZnJIkJkV R+goA+VP+Cd//KCj/gor/wB01/8AUguK+GfAXge/+Inie10nTYjLdXThVGen vX3R/wAE84zF/wAELP8Agoup6qfhsD/4UNxXEf8ABMT9mW/+J+sRzwWDXD6j P5MciN+8iAAO5Rn6/rQB718NvBFx+xf+zdB8RpoLa0n0y2jh0KCVFkOq3Lja s5jIIKqwZeQR04r5+8ETar8TfFeoeMfFt5NeTXsjrEZcN57OzYjQAAAI2BgY +8K7v9uf486h8ffjLZ+B9PnI8PeAFfQdMt1jEQZlIYmTgFmEgflifyrG8D+E Lzx5rVnpGgbETSAJmiYhR5pwXOT15j96APXfgH+zfcS6nb+I/EEEcG3ElvbY XYuCcZA56V6h/wAJ5pHjrWr7w/JGZhGWjlBU4GAD19eafa65eeE/hutzq6pB NZQ7p9mCCc8dP6Vw/wACPCWvaZp+rXWuQLbXepTCWN+GYDbjpjFAHVaBpsdz JfaJegy29vtkXa5GA24gZUg8AVgalP4N8B69cQ3Uqi4ADeVNLJKNp6AKzEfp R4D1XVVfUIPLR180j7UzAsfmYYHY4x096qeLk0fwrp95d6lKs984LiaVATIe OAOn4DFAGld6zfeLrmzt7fTzaadHIJZJXGx+DkAD3/pT/Ftxea40enWM7wRI qmaVQMgd159R+Vc1ZeMdXu7WGLV5Y9MklX5oogrN157VJ4l1y+0nTlGkJalJ B80k0m1nPQ+uTQBojW7PwpNb2NjGzuW2luXPQ8kk+1VNb0PT7rVodQmV47ue 6ETOHZd2VJ24Bx2H5VJ4O+BPjvxFax3qaX9ma5bzBJLIArA88ZyO/wCVdHf/ ALOnjwXEEz2+nXAtn83yzcqo38jPAHYmgDKg0qCTxULkIGa0tzFGAOACMn+l WrbWfM0qWS5jEZRnIXH3l7Gpbb4K/FFdce4j0PRmRk2oTfYXGMc8fzrfl+Av jOXw28l5aaes0alzHFcBgMDOBx/nFehmf8Zf4Kf/AKREiG33/mebeILe+8RX UKQR+RawtudjgOw5GMenPb0rPvNW0TTfF8cVyYBdCNPL3KWbpyB+NaV9rEra rJbFntmCkOrKODkcVnabFoUfju1WNBNfNExYsWY9R6kivPLOyntrZtWt7z7B fSMo6qjYHyntnHf0robHXolCj7HfkucAeT1/XiqPhrVZrlZfNCM8bHHAAxnp W6upx2iIJgonnwIl28u3oKAG3l3tsHQwYnmQiKMjLNnPUV4L44+HPiXwjoF3 Nf2sf9nXZKyqr7toc9cduSK+kPCugvDLNeXgZ76VGIz0iGMAAdOmPxrT1Dwi nj7wXdfuEmVojHt7g4wD+fNAH5fT6je/AT4rWep2cstraLP5kZU/ejA/PrX0 h+0j8N7T4z/C2x8Tw2sX9meIrQTEqxHlTxAwgDJz8zRlj7mvPv2zPgPceD49 HiuN0cId3kkBBKIB/jXTfsT/ABWhuvCGo/DzxE5mgjZ57d3+ZkbBeNVxwAd4 oA+CL6zk0+8lglUpLCxRlPYg4qKvTv2pvh1/wg/xDnkhUi1uTjP/AE06tXmN AH9jHwQ/5It4Q/7All/6ISuj+E/w+8N6B8RPEfiLStD0Ox13xNYzDWdTtLOK K71Y29lNHD9olUB5fLQlF3k7BkDArnPgh/yRbwh/2BLL/wBEJVn4B/D7xN4e +LnjHXPEHim11e21iwmtdJ0uw0+Wws9ItIYr6RCySXE4lu3+07Zp08pZBBDi JNnPnfaZ1/ZPTvgN/wAlX0r/ALbf+iXr6Nr4x/Yx/ZM+FfwH+PFhq/gX4Z/D 7wXq9zbT2c17oXh2z064lgZC7RNJDGrFC0aEqTjKKccCvs6urD/CzCruFeay /sc/CSaz8YWz/C74dPb/ABCnW68Uxt4bsyniWZZWmWS+Hl4uXErvIGl3EOzN nJJr0qiuuFWpD4JNej7bGTVzjtS/Z/8AA2s/FTSvHd74M8J3fjjQrU2OmeIp tIt31XTrcrKphhuShljjKzTAqrAYmk4+Y5Z8bv2evAP7Snha20P4i+B/B/j7 RrS6W/t9P8R6Pb6paw3Co6LMsc6OquEkkUMBkB2GcE57SuD+PPwsvPivoOn2 lhL4dt7i1uvMMmtaKNXtTBJG9vcx+QZIjuktpp41cONpkBZZYw8MkynKTXM7 27glbYta3rHg/wCKOveKPhhqdvp+tyxaFbXGu6HfacZ7OfTNQa8tokkEiGGV JTZXaNFliFT51CuhY8Afs+eBPhR4U0DQfC3gnwj4b0PwpdS3ui6dpej29naa PPKsyyzW0UaBYZHW5uFZkALCeUH77Z5LwZ8A/F3hn9snxl8Rrnxd4dvfCnib wzpXh220BPDk8WpWR0+W7mjmfUDetHNuk1C93KLVPla3AYGN2m3fiH+yf8Lf i78TNF8aeK/hv4B8T+MPDfk/2RrureHrO91PSvJlM0X2e4ljaWLZKxkXYw2u SwwTmoDyKGkfsV/Bvw1Z6Rb6d8JvhpYwaBc213paW/hixiXTZ7ae5ubaWELE BG8U97eSxsmCkl3O64aVybej/sl/Crw7458MeKLD4afD+x8SeCdLTQ/Durwe HrOO+0GwRJI0s7SdY/Mt7cJNKoijZUCyuMYY5f8AtEfCvX/ij4c0h/CviOy8 NeI/Dupf2lYT6lp02qaVcFree1kivLOK5tnuYjDcysqiePZOkEh3CPy3+ev2 cf8Agltf/Bnx98GfEN944m1kfDXw9p9lfaZd3muS251G20AaL9qsIU1OHTbZ GiDlvO0yeVvOn/eJvXy1d2f9f1/wRn0zpPiXw5ovgnVPG+l6hea3oWt248R/ atOnudaiu4fssQR7GKIylkkhiRkitUxK7s6o0krM+N4t+B/wm8Laz4y+IWue Dvh3p+o65ostl4s8S3+k2cM+oaWkSiWK+u3QGS2WKJQyysUCxDIwvHFeCv2S ta8B/se6N8MrvXNN8UP4Ak0qTwo8EEugCaLRp7W50u2v5d92WZpLKJLm4iiV XV5ClvHwtcZc/wDBMyTxV4++Mt94g8V6Mmg/FTw7rfhm003QdEu7STQ4NVdW uZla71C7gjuHZFkmNhb2Md3cFp7mKeQQmFu17dA0PXIP2Ifgvb6X4Nso/hF8 LorP4b3Ml54SgXwrYCLwvPJOtxJNYL5WLV2mRJGaHaS6qxOQDTLr9h74K3tl 4ztZvhD8Lp7X4kXUd74tifwrYNH4onSdrhJr5TFi6dZ2aVWmDEOxYHcSa6jw lonjmx8N+GYtc8ReE9R1e0uHbxDc2Hh24srbVITFMES0he9la0cSNbsXkluQ VilUKplV4vOdf/ZJ1q01DW9e8L+MLTT/ABnJra6lo2pavpdxqNtb2gS726df wRXlu9/bRTanqc0CedCIWmtgAwthvGJDdd+Bv7P9x8bfDvgjUfhj4Am8XR+C rm20SOfwTE8cHhy2K2M1lBdG38mO3jXUhEbRZBiO8bEex2J6+++FXgD4Q/Df UNF0zw1B4W0PxC9no9xB4S02axnLSRW+l2zKdPVZofKgS2iFwpX7LDboxeKO DcnnfxN/Yr1/xF8UfC974c134a6X4C8M/D3WPAS+EdW8Byala3MWoC13bjHq FvCLVDp9iv2QQbTEtxHvXzY2gX9m/wDY5179m34V29hPr9r448UP4vi8RXV6 15qmlwTq8MNhMZpLm61G9v3isld401C5ud0sdvGr20UNt9lqOu/9a/5aje2n 9aHtusy6T4xk1fwxqNhJf2zaejX0F3pkj2F1b3Bmj8rzHTyZiRDIHiVmZFeM uqrLGX5D4Z/BX4S61pPhLxb4R8H+AZLSCwS58N6tpukWoEFpPvnR7WREykb+ e8nyEA+cx/iOc7wr8CPF/h79svxp8Sbrxb4avPCvinw1pfh+28Px+HJoNQsT YS3c0cz35vWjl3Sahe7kFpH8ptwGBidptr4L/C3xB8JPgz8M/CsetaDK3hHT LPTNclj0QQRapHBYtCRZxQyRxWWbgQyKAkkaRI8SxruWSOoVpxi1GTW36/18 y1OUVaLLtz+zt8Pbm+1W6k8DeDZZ9dlafU5W0e2L6hI8U0LPMdn7xjFcXCEt klZ5V6OwL7Pw14Q8L+PNA0210C3ttUsdJuP7Ikt9Hf7PpdnEbaKWCOdY/Ktt 263Cw70aVYmKK6wSFPnHwP8A8E3PiH4I8beO9YT45Xd9L8RNQ0SPVru703U7 jUjpGn32oXM1tBO+rMljc3SXxiMthFa21vtd7aygZ08r3j4VfCrVPhjeeGrb 7P4dezsvClvoepS6U1zpen209myC2TT9HLTQW1u6zXe4rOJEWG1jY3CqjQUq 1RrWT/pA6s7Nc39X/wAil4c1r4Vah8O5NS02Lw/p/hp/GElhMfsAsIJ9fg1n 7AdyMib5/wC04FRJMHzJFjZGYMjH0Hx14PsPiF4K1fw/qsdxLpeuWM2n3aW9 zLbSvDLGyOEliZZI2KscOjKynBBBANeEfCb9kPxz4L/Z6n8GeJfHHg7xLqk/ xBHjZdQsPBz6fbxxS+Ik1y5tvs9xeXn7zzWuUhnWRWhDwMo8yHzH9L+OPwv8 VfFjwT4u0HS/GMfha31zSYrTTLqzsZ1vtPug8pneW4juo5GgljMEe22NrcxA TtFdxyyRSW+c6kpL3nff8kJzbtdlnxJ+zd8OfGXxZ0Xx7q/gPwVq3jnw5CLf SfEV5otrPq2lxAuRHBdOhliXMshwjAfvH4+Y5oePvhj8L/h14K+JPiLXfCXh C10bxPp01146uW0KGX/hILSK2dJPt6rGWu1FvuTbIHymVAIOK8S+CP8AwTv8 a/CHw54K0C5+J2geI/DXg/UL7XH07UfDF/dnU9Uu/EX9rm7nnudWmnmNvA80 NsLh5jHcvHeyPPJBEi/QnhHwlquseGvEln43ksNah17Ub5F00rFcWNvpjMYY LbBhjZxJboskqyiQiaedBI8Sx4I8qkubVdemn+bJja6vtoXZ5fDM/wAQrZpP 7FfxTYWLQW7MsZv7a0uW3siEjekUrWQJAIDm0zyYuOK1z9i34K+J/hDpngDU vhN8LdR8B6PevqOm+G7nwvYS6RZ3TGUtPFatGYUlJmmJdVDHzZOfmbLfGf7L 58bfHjV/F0/iLU7Kx1bwkvhlrLT57q0uIJFku2F2ssdwsTMEvJAokgdo2VXj dDuDc78Vv2QdZ+Lfh+xhvdc8GWOrzz3Emt6lZeGLmBtRSVIYQiRrf7Shgt4U kivPtcE5gh3wlIljrtjRwzcf3ltNdNmdSp0Hb37dzobP4B/Bj4Ya58QtSg+H /gHwxP4m09r3xpq6eHLfT49atpTOZWvrsRqlznEzSCR2KiTc4AkUt3upQ6TD 4r0bUZ7LztWlWfTLG7jtGneCORVmlUyKpEUT/ZYySxVGdIlyWKA+eeNf2aNU +Injv4kXGp+JLBfDHxC8IxeEzYWukvHfWEca3WJhctO0bsWvrk48hRjyR/Ax kveK/g94y8X6r8NL+fxT4Xiu/BOsSatqRj8OTiPVi1tPabIVN6TbAQXU/LNN +88tvuqyPPsqFl7+vz7X7d7on2dL+b+renfQ9Na8jAP7yMbTg/MOD6fWsu98 eaVp/jXT/Ds1yU1jVLC51K1t/Kc+bb27wJM+4DaNrXMAwSCd/AIDY4LQvgHr eg2NjEdZ8M6jPY31jD9s1Dw/Lc3d3pNmXaC3nla73S3iySPILonYHdmFuCxJ 6D4M/By0+EXwu8E6FIbPVdS8F+H4NAg1Q2ohleJIoUk2jLNGkjW8TMgYjKJk naDUTp0IptS5vTTo9deztdClCktpX/A6rw/4jsPFmg2Oq6Xd22oabqdvHd2l 1byiSG5hkUOkiMOGVlIII4IINXq4PWvhNe+LfAPhbRtS1XTjLpN/p2papLFo 8MsV9LaSpcqIIrgyrbg3MUTK37ySJFPlukoSePvK5jnCvCf2nf8AkfrT/rwT /wBGSV7tXhP7Tv8AyP1p/wBeCf8AoySs6vwM1p/EeJap/wAlp0D/ALAmp/8A o/T6/IX/AIO8en7Pf/cyf+4qv161T/ktOgf9gTU//R+n1+Qv/B3j0/Z7/wC5 k/8AcVWFL4l6GlTZnxh/wSE0ZJvipDeGNGSNpY3x97ouCR34z9K+pv29PEQs vg78RYizRm4e6ihGOg809PwryP8A4IJ+Ax4r8S+I7lt2bK2u5EPYFbfdkflX Tf8ABQeZ7jwL4ctDK+de1ERSZPLbgpP866znOU/Ye8K65o/w0sdaOmC/sI7E IivHucKWByBjPbt619LfDf4sa/42iCaTB9js4yyIogAYsrEEEFc/ezXV/sp/ CvSPh98KltLueNNOt7cWod5VXnO7g/QGtvQfhF4S8KarNdaT4ijhjml8145L iMgEHtz0oA7j4ealq82iF9ZQQzIcFigQ4HtgVoRa/aaxdukJilII5VgRWd4n 1jTtT8N3EKavaRhgAZFnQkYI96xPBOraTp17BZWt6lzMCN7bgd3HsTQB1lzI YoFUKxYZ4Ix3ryD4qWa/bpTsKKw5BPfH8q9Y1O9/esCQoBAznrXm/wAYfDV3 cwSPbxTSrIjY8sFskKaAPzO/aNEUPxt19YAqqlwQNvTOKzfiJLP9n0wzyFgt uNuWOFHH5CvcPiB+yLqvjDxFBLDY3Vpc6pcEzmRm3dOwYVs/tOfsXTWI0b+z 2nbbYFGUkfPNwFXp3NehS/3Gp/jh+UyPtr5/oaP7Bht3+GxRBGJzPJ5g43bd w696+zvhfZm20YMzhTIThN3v6V8W/sLeENb0i61DT77TLq2SDpI0LqJCXxgE gCvt7w/o66LpMMasMt6nv/k155Z1CSMoyzBx/sjpUovzcqVDZxjPFYPiLW30 TQJ7lIzIsahmHrXla/tJ3V1c3NrZWLedGRlpUcKTzgZ6dqAJvif8KvFHi/xw bqz1KaGxlkDMgCEMuOnI4/A1Z1T4Nr8M9FN9ZWMuoao7BE8ze6R5BOQfYjv6 1c8F+P8AxjrZxPpkcSSHAkWNtgHru6V6lcwxwaDsvJ41aQfeZgBn8fxoA/Iv 9uW08Qx/H3wvrmuWkVkYZ4VzGT5ZUTjHOAM+o7V94Ryprvwz1WWMgjW9LZlC 8hgybh+GD1r5/wD+Cs3w0/sj4dx6suZJrG6h2MMbWVnJ7deles/BW9l1X9nX wNcQqsrz+G7UyAPnJ+xqTgDrzQB8JfsGaHJ4f/4LP/AK2dQu34veGcYOeP7X tK5//gqZYSXP/BVn9o0eXIVk+LPiYZC54OsXNehfso6e1n/wWu/Z5kYnFx8W PDDjjt/bFrXDf8FLbVtQ/wCCvH7QsIYgN8XfEn5f2zc1tQdJP96m15NL80xO /Q/UX4F+Hbf4T/sTRQPb4l8VulvvZOQsMkU/HH+yfrXyr8AfI8Wftu/EcXRX dpkiJDkj5QWBzzX1NeeLnuP2a9FsmhWL+ylluBngn90B09O9fBf7N3xDvYP2 t/F+qWdrLeDVXTz1iiaQgbs4AHeurnwP8s//AAJf/Ii97yPtzW/EF4NT+y2n lRxqB83Rug7V6z4D8PQeBrBbvUdYTdcqAI5ZVxzjoCeeteKrq0d0yXk9neWz bcsJIWXBwB3+lL4t+Id/8Qbm3t9Ksbp7uLb5ayxOsZAxkk4wOAaOfA/yz/8A Al/8iHveR794j+Imn+H7VpZpo2JyR8wO6oNB8YW/jfRzc2ysuCQRgjOD/wDW r5m8cat4g0i+ibxHbRWOm8nzo2JCgddxPAFe+fB69tX+H1rJakNHjG8c5JJI o58D/LP/AMCX/wAiHveRlfHnwND8TfhjrWgSpI8WoxoGVAd5KyK4x+Kivz51 f9izxp4e8XSpaWd4tlFIfLuUDBwBjk4HBr9Krq3MKyKcg9SSTXJ65ai3kZjk kjOWHHSjnwP8s/8AwJf/ACIe95HyH4L8VeMvhl8KZIvEmopHpmn+IdLaGO6b M86x30M5dSxyyqUOSOBij9oy2+KXxO8U/wBnzPdLpdxEtxbvZI8UU6sNy4de GOMZHat39sD4VWes2CasPt7XBvbSAxROfJw88aZ2jv0A+td/8AvhLZeCII3F 9f39xPEoBvLhpPLTGQApJC+n6V6FSWC+pU/dn8c/tLtT/ukLm53t0PDf2YP2 PfE+o/FpJ9f0fWLGz0mRZRM1tIsdznIIDMuG9cg1+h2nQCCCCII22JFQDB6A Yrn/AAvGIFBLMQTg55A/wroY3BkIxnGCcDtXn8+B/ln/AOBL/wCRL97yM3Xf i3Y+Gdci07bvnfG4EHgY4xWlqfxR0fRYozdTlTKONvIFeM/tHQLa+IIZYZkt r1mQRByAMnr168E/nUK+BdZ17TV824c+UOSsIYGjnwP8s/8AwJf/ACIe95Hs F/4G8P8AxQkg1NJ5JdmHCo4IJ64Pp0rhvEetzaXqD6dLp0VnEgOHKHJ/EiuD Pxt1X4SXsOlPYXtw86bo/s1o9wSqnBJ29Kp6l428S/GrWhFY6TqVuUUSSTT2 MkXyjjABHPU0c+B/ln/4Ev8A5EPe8j5b+Npj8P8A/BS7wbLEyqkyQzSsMct5 lwO3GeBX2N+1BrU/j79kzWYLhkZNJlitLeMcs0bqxPHpn0r4i/bN8Ean8NP2 pvCGrzmSe4njQ73BwXDzEjHbjtX1t43u7vUvAgsjAn2K50ZrqUjkiRYsj+Zo 58D/ACz/APAl/wDIh73kfPn7BFgYv+CKP/BRqF0aMNdfDhACMcf8JHcCvcP+ Cd+v6b+zB+xZ44+ICSRQ6zpGjCTSI7hV8ua4WRPlAJBzhicDngV5H+wvZF/+ CQH/AAUbtwDmTUfhqoHXr4kuOlaHxit7fwZ/wTg8NWkl1LENR8XXtncRhQGM YsrVh05xmohLCXfNGVumq/HQPePnfw5f3PjLx7da1Goa7uXnvLpguQkzsXA/ 8er3v4F+ArLw5KbnVPt1jd3R3RXfzLCxOTgngdx3rxH4d2lvq3inXNO0NJTa 3F1HDCWJLDMQzk/UH86+kfBniyTwjokWi+KdMnt4cZiumgbYeOcuQAP4avnw P8s//Al/8iHveR1PiK18TardRW0UGn3elZy07SAFx9OhrY8Z6o91Zx2Vmw+0 zyKkgVc7EzyfY/WuQ1KedrrTLDR9Sjm0TUJTGWjkV3C4yQGB9RWtrev2PgzU 4kunMNvtwJZD8ufdjxT58D/LP/wJf/Ih73kSaxff8Ilomy1TzbiXiNMZLN3P HOK840fw7qXifxvJfeJXWC2sSs0KBsIWPBBB68V6bpGg3HinUo5baF7y4mAE KRjcuDxu47cjmvd/hx8AtL8EaUNT12OOXUSmbnzHV4UOegBGOPWjnwP8s/8A wJf/ACIe95HhHw//AGYNY+MfjGDWtQW80XSbZGRNyYafdyMIw9RjivRE0b4a /s87mvtZsbi8A3GGe7jlkJ9oicivnz9qn/go/fazPe+HPB8djDo6Exi8UHzX XIORg4H4V84aXrWreM9diM01zeTStgs7Mx5PfOaXPgf5Z/8AgS/+RD3vI+69 b/biOozx2PhXSlvxK3lxZhdcdhgDNdb4b8c+MZNLfU/EK2Xh+xjActKoU/jv xjv+Vcb8G/hXo/wa+F0fiPVQEvfsyzQRyEZlk2ghVU8k5PbtXE/ELVfFf7Qy qdVgNhYw8wR2rtFu7fMO/wB4/nRz4H+Wf/gS/wDkQ97yND4sf8FEx8Ndb+y6 R/ZuvRkAu6vuA555QkZr6H+AnxbtPjl8M7DXrZNguf3cyAfKkgA3qM9QCcZr 88viZ8J4vD+qD7TYXEEGOJ4yXLZJ7V63+yr+1pp3wS+H8mhX7RC3tneaF8nz mLHoy8Y4ArvzKWC9srxn8FPqv5I/3SIc1unX8zqP22NAj+Gesy6lZgK14Tcg HAC4yMcdjXz94A+KKvqUk7LH9pJO3IJ5x/Ktr9pX9qU/HSW7REhS2iDeQVzn b7/ia8IsdRksL1ZVkMZRjxkgNz61wc+B/ln/AOBL/wCRL97yPrn4O+L7oanN b6vsjefLRlegzk4PboK9H8N+JNH8QeLmjnvYEuNNkCRReaoJbk8jOa+UfCPj ubWrmA291DG+fnB5wvOSOevWvZvAPwP0b4i6XPqOm398NZ0+QEFbo7JWI7jt x/Knz4H+Wf8A4Ev/AJEPe8j07XPHfiDw3r1/cWWmxXuiCRI1nG5mOUXIUDrz uHtitv4eal4p8G2/2y5sw2kXO9myh3IGHB5HbI6+9WtFGmv4XtNPvWFrLb7Q yynazFT1weSP8a9DsfEul6pYGzWW2eIgDAZTx3/lS58D/LP/AMCX/wAiHveR 84/tC/Cdfjd4Tvr26NxDb2oZLfy4+JFbnJJr4i1i+uvA3ie08SWkciNZzi0v 02nahD7YyQOhMaqea/WO2udCitptKiaBlyBgsDz9K+Cf2ofhnZeEPiV4r0to bltN1lBqFsQuQGS3UcYHTfuo58D/ACz/APAl/wDIh73kcd/wUF8CWOqfDfTd a0YG8t98U3nRkOpZ0+cZHYZ6Z4r43+xy/wDPKQY/2TX354Zs4fHn7Gx0m6R3 n01pJi5XnLptUZP8q+B9XsZdH1Ge1kZt8LlGByOlJzwXSMvvX/yIe8f2HfBD /ki3hD/sCWX/AKISnfsz/GFfiX4s8SafLpvibT9Q8PpqdvdjU9Cv7C2xsuki +zXFxbQxXalIyS9s8yLkfvHV45Hb8EP+SLeEP+wJZf8AohK6H4NeOW8WeJbu 3bRtV0uW20Jrm5F29s/2aeezuGayYwzSDz4VCGTGY8TxFHkBO35/7TOx/CVf 2MPDnxU0f472Mnjnxl8PvEWktbTpDbaF4MvNGuI59hKu002qXasgUOCgjBJZ TvAUq32bXxj+xj+0z4d+Mvx3sNL0fTPiDaXVvbT3TPrvgPXNBtigQoQs99aQ xM+XBEasXIDEKQrEfZ1dVDZmNX4gooorczCuD+O/xA1L4c6BY3mnLcN59ybW QW/h6612VWljeOBvs9s6y7FuXgMjgMqxiQuYk3Txd5WJ4v8AG9j4HtoZr9NQ cT+bsFnp1xev+7hkmbKwo7D5I2AyPmYogy7orHUD5+/Zu1vTNT/bP+IUFre/ Hv7faxXK3p8VWOvxeGdUkN1Gc6f9qs49LiitEMdvA1jKsl2j3Ms0c/lJdv6x 8QvD3xT1H4l6JdeFPGHgDRPB0Hk/2tpereDrvVNTvsSkzeRex6pbx2+6Laq7 7WbY4LnzAfLCaR8UPFZ/aIufCup+FdGg8J3ulSaloXiCx15rq5ujCbRJoruz e3jFsxe6/dGGa5V0hdnMLFUZ/wARP2l/Dnwv+JejeE9S03x/dapr3k/Zp9J8 C63q+mRebKYl+0X9paS2lthgS3nypsTDttQhqXRWJXUX9oT4u6h8E9A0fWoN C1bWtFOpC31yXStKvNYvtMtWt5/LuIrGzilubom7FrEyRISiTvKxCROR88fs 6ftr/G/4t+PfgxpuvfDXw3oXh/x94e0/xFqusLBr88pSfQBdTwosWnSadYOm pMYlF7qYkaKBgImaWJz9Y+JvG2k+EbzSbbUr6C1uNeumsdOhc/vL6dbea5aK NRy7iG3mkwBnbEx7V5N4N/4KP/Av4heP/B/hHRfij4T1Dxd48gt7rRfD6XeN XuYZ9MOqwzPZkCeCNrIeaHmRFyVTO9lQro7/ANaP+vkX00OU8F/GDxf4r/4J 6aNqXimLVdP8Y6ZY6TpHxGuteguvCR0/a9pH4hvobg/ZQqwW7308V3aP5JaJ WhdwBXN+K/2m/jb471346eEvCXhqaG78H+Fdffwnr0fhHV9POp6wCBpsIbVY IbJmjJeMNaSahBeeWbh3sE8u2n9s079qXSPFH7LXhr4oaTCILTxxpmk3mgWe syNZ+dc6qYI9OtbiSFJ/JMk91bxO6LKE3lsOBzjeNP8AgoD8MvCOm/EhodYu 9b1j4TaJqev+J9D0+ykOo6TBYqWZZ0kCLbyXCgvaidoxeRBpoDLCjyrUvid/ P+vkCv26neeE/jPpHi/w14Z1W0s/FcVt4sne2sUvfC+p2FzA6xTSk3cE8CS2 SbYHAe6WJWZokBLyxK/nGuftG+NfBOoa3q+t+Dr8eEvD2uLo2oR6Zoepapqs UAS7lXUrWG1ill1GGZZdIi8u3hzbyPfmR3FuwX3YMGGe1cXZftCeDtS8Yx6D BrtrJqc8y2sEYV/Lurjdfq1vFJjy5Z4zpl+ZIkYyRC2ZpFQFSUSjwH4sfFnx V8Pf2sfBKTTePZNTf4S+If7WvtK8DeJNW8Hpq4l02exmks7UyW4n22uqFLcX P2oq6wCYtPAZNX4J/tFeLfjv8HLPxH8Q9Ag+HlhdeO7Gwjilu9Z8NyQW4Nt9 jEMl9bWd7fNcaqYbYxXFpp6TJcSQiK5jRHv+71j9rSPSfi74b0k6Bfy+F/Ef gLVvHEepLb3p1iD+z59NSS0bSPsv2gyNHqSMEB88SRNEYNxFXfC/7Xfg74ve EtN1f4ceIvCnjOz1fxDD4f0+9TVTBpusuFW4vDp90sUkd88Fmt1Ji33oZbOe F5IWinaGo+n9X/z0G/h/rsZHgz403Gt/t3fEHwXLofjK303S/COiXFrq0+g6 1Do13dC41BruGG7lX+zGkjiuNPYm3ImkMro7S/Zdlvsfs0/Ea9m/Zt+EFx4n k8Uah4h8VaDp0dxdXugSwXb3bacbmWW/hhEkdgzeVJvEjiJJmWESM7xh+n0n 4mxal8Y/EPg6P7E11oGiabrLqs032jbeT6hCu9TCIlTNg21kmkcnzA8cQWNp rXwi+IP/AAtn4U+GvFH9lapoR8RaVbamdM1OHyb3TTNEsht50/gmjLbHXsyk VK628v1/P9Bs+XvBH/BQf4pax438e2utfBDxPpllp+oaHoXhK1g0XXJ5tQv7 2+1G3uW1G7bT0tba3tooLWWe4tDeWcKOzRXd55sKt6XceN/+Es0ezvNc8Q61 o+o6j8NLrUbGfQ7fUdO164LiE6ldw+GZ45pEmtmOnGFZY7u4ikvHgKIXIuup 8Nfto/Cfxj4m8eaPpHxB8J6pffC5Ym8X/ZdRjlh8Ml5LqPy7yZSY4JFeyuQ8 bsHi8vLqgZSdzw38XYfGfjbTtP0tLN7O40BNdvo7yaax1nTVuZFWwEunSwiW NJhHfhmnaJ45LMxiNyZPJa2sl/Vv6Ynt/Xdf8MfPXwN8XyQfslLqPg+f46aQ l18QtFgi0/xppuuX+u6XaS6tpsU1qW1yKK9ktZLd5HnnZ7hLY3N2IZZkto1H u/xz+Nt38LvBPi270Pwvr/ivxB4Y0mLVI9Ot9Pu1t70TSSxoFuI4JfNMZhke aG0S5u0jClLWaSa3im57wZ+0V4tt/h/qV942+H8eleIdG8Uad4cu7Dw1rDa/ Zul5JYqt9FcPb2spghS+DzmW3iMYtrggMiq7+peMPF2leAPCmp67rupWGjaJ otrLfahqF9cJb2thbxIXlmlkchUjRFZmZiAoBJIApP4f67L/AIcF/X3ny38E /wBuT4j/ABG8PeCLXxJ8ONf8J+J9ev7671uVPBniG50jRtLi8RHTbONJJbS3 llu7u3aGQebHB5EJnvpoo4YVt5fcLzXf+GkvhR470vw5qWqeHGeTU/DFn4gg eNmju4g9tLdW3lS7wbe6E0RDmKTzbWTAC7JGg8Cftf8Aw2+KXgDw/wCKvDHi vT/Enh/xbq02iaHe6Ukl5HrN1Ddy2kotvLVjNGkkEztKgMawwyTlxAjSjq/i t4uvvAHww8Ra7pmi3PiPUtG0y5vrTSbYsJtTliiZ0t02q53SMoUYVjkjAPSt qXN7SPLvfT1vpvpp56DhfmXLvoeZ+MvHnxD0r9pfV9G0HR5dW0C18HQ6tZLc ma0trjUEl1BHthcfZZIi0hNluDzxvGqq6JIDIKy/iv8AtF+MdB8N2Oq+E9Mv Naj12a5g0+zuvBWtQXsARIUEkqiPAZZxNtjuvsUc6Spi5hWFpZe88S/tNeFf BfxYufB2tahaaRqVrosevu95eQQIbNvtfmSoGcOywizcykKQgkiJOCxWz45/ aR8JfDKYJr99eaS7zPaQC40+4UXtwscUq29udmLmeRZl8uGEvJKyyqis0Mqp 6UXK8L0b6feu/X+vVW9Bc3u3p30+/wDr+uhwfxL+I3iufx58XNC8J6d4ji13 TvAcM/h2+fSr0WNxqqi/byopZ1Ng7qZbE/J8zmRldnEG2HX8c/GQtrnwmm0/ TPGF1ZeIddkW7f8A4R3V4msrf7FdRLJdRpGvkr9pe2Xbdrsw5kCjyvNj6Lx9 8e9P+GsvjiS/haa18C+GYfE93HalmvJIHN7kBHVYuRZuExMSW3hljARpNrxX 8QIvDeueFLF1gVvFGpPp0fnvIjhls7m6wgRGDPi2bh2jXaGO8sEjfLm92N6f 4r+VeXo18zK+i9z+renzOJ0L4ya3qemWVxqFlqmkT3N9Y6Pc2I8LaldnT9QJ ZrtRMEUTWm3YiXgRbdWVnMsgbYl74Rw+JPiLofw88c+ILu80G+m8Isut+HRB NawfbrtbGYu0Uj7o3tzBPGquGcC4cbhg7um074saNrUEM1hLd6hbXcFpdWtz a2ks9veQ3TOsMkUqKY5FOwlirHYhR32o6sc/w38Yl8ca54Rk0LTLrU/C3i7w 9ca/DrgWSKGAKbM28Lo0YKvPHdO6hyrAW7jaedsTc7Plp8u/5O618vn21HNy 6Q5f+G/r7u5np4v1rTfhL4Jk0sa7reqaxPpVs9zd6NuupLd2ja5uLuEvarbP 9mWZmLbfKkICwTPstpPSK52w+IEUvgjStavNN1ewbUjaxPYi0a8u7Ka4kSIR yrbeaB5ckgEkgJijVXdnEas46IdK4OrOFBXhP7Tv/I/Wn/Xgn/oySvdq8J/a d/5H60/68E/9GSVnV+BmtP4jxLVP+S06B/2BNT/9H6fX5H/8HcelyX4/Z98s wjYfEYPmTJH/ANAr+8Rmv1w1T/ktOgf9gTU//R+n1+Qv/B3j0/Z7/wC5k/8A cVUYVwVSLqJtW6Oz++z/ACLqX5XY8c/4IWeN9P8AhP4d8Tzanaa/O1xBeKr6 Vod7qqjMG0Za1ikCj1JIxXM/tt/E2x1pPh0sUWpQx2etrLOLvTbmzyu0cL5q LvPHRcn2r0v/AII56dN4Y+AviO4TJcSbCu7nbJD1ryX/AIKTMEj8FRkjbBqi n3OAv9K9f2uC/wCfc/8AwNf/ACs5ve7/ANfefUdv+0x4H0zwii30viWKIurq v/CPagiOcNjBMOPX9a6DSfip8NvFeneda2vjZ58fdh8L6rIDxycrbkfr3rzz 4U6h4f8A+EI06a5ja+WVAdrPu2nJ+vOP516x4X+JuleHYNmm2kEbSYDZfcQO 4xR7XBf8+5/+Br/5WHvd/wCvvPNtf+IHh7ULOePSpPG285LRnwtqbFcHn/lh T/gX8XtI8OeJJ45bTxZdBGA81fDWpysh/wB1YCRxXeeKfB/2YPrGkSF1dSXj ByOfb6mvOvAfinWpPiWq2Fmii3mC3ucjj1UfxUe1wX/Puf8A4Gv/AJWHvd/6 +89e1P8AaA0NbgsbHxqVJBCt4N1gc/8AgLWdr/7SmlRwBF0/xf8AKQct4Q1V QB3HNuK7vxBrFvomhtfXckcMcMW5mkIAB29P0r5X1v8AbYu/FQvmsrSL+zYm 8mB9jeZLJkgjHpnuPWj2uC/59z/8DX/ysPe7/wBfeb/i39obSofHFhqgt/EP 2LRSLu6d/DmoIET5l+bMIwDxycD3rmtE/aj0343+ANQimGrvq2jaiksK2Oj3 l0JoUZnLMY42C4AHDEdKs6x4O8a+Pfgu+m6VYQ/214sDPdG4YxiC1ZQEUMRw 29HJB6Bge9cx8Bv2RPiT8CfHA1O4gsG0m4ie21BVuw7iBwFkdEHVwmcD1x61 306uC+pVP3cvih9tdp/9OyHzc617/p5ntnww+PPhRbVFjtfEz3KjEnkeFNTk I5HUpbnvXd3H7QehuExp/jYbfu/8UZrHP/ktXzHDqPiH4W/EDUbe7DwLtE1p JgmKVSSyqegyFxxX0p+zf8YT8TdENpezWY1S2T98iuFLHOOn5Vwe1wX/AD7n /wCBr/5WX73f+vvM34i/tH6FZ+F5rVLLxgtxdKAqy+FNVizz2LW4rxn/AISz RrvUluL0+K7JGxmNPDOo7n9OfIr6J+NfgO68TLDdQXclrFbrliEDHGff614h pz6vD42vIo4/ttnCkY8yQFArfMCMdD0o9rgv+fc//A1/8rD3u/8AX3m1YfHO O7S3s7SLxiuk23Pn/wDCL6oxI7k7bc11nxd/aC0nxP4Tji0S28ZXdzE4O3/h E9XUYAbnJtgPT86x9a8WQw6bFDqEsOnlCQvlS7A3semaybfxHZWKq8Nzezh/ mAjZm/kfaj2uC/59z/8AA1/8rD3u/wDX3nzX+3d8YtY8W/Ak6RqWmXljKbqJ hLe6dd2QChiFG+eNFyfTNesfsx/FjTNL/Z98FWtzb+IZDa6NDC72+hX88fFq q/JJFCyuMjqpIxg5wc15N/wUv8VT678LY1eN0tzPDguhBOGPc9K9s/ZRieH9 nzwBaMNq3WmQndjOF+yofpzj9aPa4L/n3P8A8DX/AMrD3u/9fefOn7PVgtx/ wWH/AGY7uBJ44R8UPDW4XMLW0hP9s2vRJArH8BXm/wC3xYf2l/wWZ/aAj4/5 K74lbB9tZua9u8ArG/8AwWG/ZYlQcH4p+Gh+esWteU/tg2J1H/gtr+0BEP4v iz4n/wDTxc1y15Um17GLS82n+SiNX6n6Q/H/AE8aL8PzbRwvHF/YXmgFMZY2 5J7frXxZ/wAE5vhrcXt9rfiwQyTuLuMlVBIRdvXpnrX3h+2LYDSvDFvvJ3T6 CkQH/bscV8gf8ExvHC+CF8U208BnS2vovl6ZXZ06HvWIz6iuPilYW+lqJLK3 LKMMHjxk1t/DjXNK1+1llt7GCDUEUtHGrZ34B6Dr0zS6j4csfiT4fN/oliys xxIkp75PTj0Fcp8PPh/r9n4+tSLVreFWJLliARtOR0x/+ugDh/jBeav8VNeu 9Bv7N7CzkjK+YsLBnBIBAzwCK9r+Ffg5fDPhfTNNjLOBFvfLDI2k4zj2xXY6 v4X09btL25ggEgyyk4UL9eK53Ufizo/hxpmQzM6oSWSAunHHBHXp+dAGjrNm UBYkZJOTXIa3GkpyzDGOR6f5zU/gP9obw38ULqWwtJpWu0z8sqeXuxngA9wA fyqXUrD7NqSpIqmF8nlsnHTr1oA+av2xvER8HfDWScIromp2MmTwMJcJJ/7J XpPwtnTXbGxuFCjzYEfA91B/rVz9qzwTYeJPhRDZzQq0c+t6REcAZKyalbRt z/uuw/Gu28O+ELLR/DlvbRoY47eNUQjqMKByfwr0Kn+40/8AHP8AKmQvjfyL 2hsPL5Y5LdB3+tdLploJVDYYBxgc8+lc14ZgElxOMZ8rHGTyfrWZrn7Seg+E vEjaM9xL9tQgSJHF5hUkBhwD6MK88sj+OHgdfF3hK4fyZjeW7BuUwwAIwfUd K5n9nbxdfXF/c6ZewS7YWWNZDGwDjnkHpXqNr8TdK11lIkkaSb92Wkt/LVj3 ySavW3h6y02I3trHE5PzMFGN59iBQByGteEvDPgHUH1PUpZmmfMiLJjHXP5Z qvJ8ddGNqyacLSN2PUAbsVjeLPj9dC6vIdW0FJtOikKjYcvtBP8AsHmuB1X4 0eHru/S10vQbyO8lP/LTJUg++wUAfMX/AAUe8f2/i/49+ELSzljd7SNZpCpz tYtMCM59AK+nNQzN4AndCrC38PygqWxz9nz0r5u/bL+D+oP8SfCniOSWEtfX RtjbLAFMSKHbG7PznLHnAr6f8NaEs/w98RPJmUx+Grt1QHG0i1OD+BFAHh3/ AAT8j+1f8Epf+ChisCBJrHwwUj6+Jpq0v27NFh0/9lT4VaCjrH/aOuXGqFnI B2vbrHnPp+6P5VU/4J3QNJ/wS0/4KCIcBpNb+Fw9Bz4nmrmP+CqN/NpPwa+D uyRw40VZRg4A/e3A/Hp1oA8n+GWs2PgnV9cisLqGa6tr1JoAZVbzNqYPI6jk 9K+ifDvxO8YfEPSIUj8J2cqY8vzJlcKBjqCRXwb8CdUW8+Jloly8jLO7K4z1 OK/Rnwn8PrrQ/D9vNpGtLBFLGjFZbfzADjOMlx60AcLf/D+88HfFTw7LJJJE 01yGltxJuiT5STtrtvjR4euPE3g25SziE86MrpGR9/B6Vj/EfxlYz65Z6X4i Vo723IeC9tz94kdSo6DHv3rUt21WOwS4stRs9WhQf6kIIix/3tzY/KgCD9ln 9qT/AIV78SRa+M7A6VYW8JS2lMDKykjDZzgEcLyfevsSH4jeCfix4dmSz8U6 HPFcptdE1KHzl57qGJHbqK+MpdfsdcuPK1HTVgul4UMBJ/49tFcX8V/Bd9bz wXvh5pbW5ZzkplEPHHAIBoArftIf8E9dR8D6ldX/AIVu7fWNNyWCC7R5eo6K vJqj+y1+zdqo8Tx3+u21xa28EoRUcFSx47EDIyK9A8DXVzZ6b5epakzzHgxt Icr69TWtoXxZj8FeK0tZXSWOaMzIS4wCOgx9cUAUv2wPjvN4M+IsHh+SEjT9 PtoXgbaSrOVwcnoRgVxVj+1xaaJChbySDweM1d+K3g2f45ePTeXZHnXs+6MK OEUnIH4A103gv9jbw9p/iWytNUv1mnMRfyVIBJBA/vH27d6APB/i98brv4u6 7Ba2FuTGVCkQKdxO7rjtVbX/AIRN4c8O/ap7+1HBeRJbpROxxkrs+9kGvt+6 /Zb8LadNHNbWapNAoKsAMNj1rkPi14BtfEmj6hpV5a20YmjZYbpUVWRiOpHX 0716GZ/xl/gp/wDpESIbff8AmfBUN4tvFImGJZNmcYK8jmvSfgv8DbbxVqVh PqaXU2m3iyNmBssrIRtHA4ya6z4L/syWet+OtY0/VJxcQ2v+j/uuATlSGJBP QZ4r1rwZ4Uh8J6Tc+GjE1vFZXK3MMwHmF+4BPBGcDua88s8W8RfBFfClpaXW jJqU+oy300a20kZUqg9R1xtzg967T4UeL9Q+GUt1i0miMzA3sEisDA49MjOM eteueL/BZfVZNRu2ZLGO3ify4o90hk9iDnqRnjpmvKtb1rWvG3xKa30yztoL bToR5pcgvOCepGBhvY5PFAH0T4R1Sx+IegRT+ZBL5ig5jxlT0xmrPjjwcn9j vHbyzQMXj+dDg43rn8xkV4Lo2u3vgHWrpdNlBa3dDPbytlVLKG4b6H+73r0u 5+PlnrWgblilW6MsS7G4GfMUE9OwyelAHfaD8JtKudRMsNzetf2fJQy5BP07 18y/t8X+3X7ScMh+yJ9mccZ+cjr3r3vxn8YG+H11p97a2ovDeEpPiXYFUdyc HFfIf7QPjIfGz4jX1xGDbWluBuUneHcIOc8dwfyoA6n9l6yi8QfB7xfpZVjJ bxJMpJ5xvbtXwf8AHDSjpPxM1NSciaQyjPUA8Y/Sv0U/4J6aXBr+ieKInZVM 9jIDuIz8pOK+EP2xNNXSPjffQKchIx2x/E9AH9Y3wQ/5It4Q/wCwJZf+iErs /hr4fsNF8T6vPaWkFvcarb3d3eyIgDXcosGiEjkfeby4o0yf4Y1HQCuM+CH/ ACRbwh/2BLL/ANEJVH9mb4b+ItA+Ovj7xFr9joWnx6rpkumaYmn6nNfS3Npb nVblLi43wQrDI/28jyUEoTZ/rpMjb532mdf2f67HsvwG/wCSr6V/22/9EvX0 bXxj+xj+zP4c+Dfx2sdT0jUviDeXNxbT2rJrvjzXNetghjLkrBfXc0SvlBiR VDgFgGAZgfs6urD7Mwq/EFFFFbmYVwPx803wVqeh6LbeONcg0Gzu9bs7bTZG 16TRZbvUHlAt7WKeOWKRnmb935KN+/V3iZXSR0bvq8W/bs+HXj74p/B7R9K+ HWleENW1q08XaBrs8fiPXrnRrVbfTNUttTYLLBZXbGSR7SOEAxgKJmfcTGI5 Dqgsdnov7PnhnQvjVqfxCii1i48U6pZf2cZrzW768tbG3Ig3xWdpLM1vZrIb a3aUW0cfnPCjSb2Aau12A9QCRSRksoLBQ2OQDkCvP/iH+zV4e+JvxL0XxZqO p+PrXVNA8k2sGk+Odb0nTZPKlMq+fYWl3FaXOWJDefE+9cI25AFC7IXmavxi +C+g/HfwmujeII9UFvFcJd29zper3mj6jYzKCvmW95ZyxXNuxRnjZopFLxyy xtlJHVvP/hd+yr8MvgR4p+Hmi6FqOs6VrXhHw4un6NpMXi29tYtVsLC2ttPa 5udMinS1vnhils4muJreRoy9sAyEQgegfGbTtd1PwJOPDUFtd67byxXdlb3W pz6ZbXMsMiypFNcQK8kUTsgR2Eco2s26KZcxP5Y3wi+I1l+2B8LfEltpnhG6 8DeFPBOreG9XvLvxTeya0lzfTadPmGF7J1uUjOlwp5k10kkn2qRyA0IEzS/r 5P8A4Yb/AEH6no/wU/Z4+Dl54GPjrQvA+jT6/DoenDVvFMN23h/W5Ioruwsr BNTknit54UFvcWtksflxqkTRwbMA9LZfsb/DuXxj4t1u9sda8R3vjCxvdJ1S LxB4m1PXLSOzvGR7uztrW7uJYLOCYpFvhtkiRhFECu2NAOS+Jvwu+I+rfs8e N/Aei6boviy98S61rNhc6l451NreyOkao1xc+bDDbx3T3Udot3HZLazm081b N/3kKeUz+xeDdAk0jxJ4oun07T9PXVNRjnWW2fdJqO20t4vtE42LtlzH5WMv +7giO4Z2Ib6/10FcpeFvgvpHgzw14a0qzvfFk1r4SuHubF77xRqd/czs0c0R F3PPcPLeoFnchLp5UVlicKHhiZMzxZ+y74M8Z+Hte0uez1axg8R6r/bl1PpG uX+k3sF75McLT2tzazRz2bPHHtf7M8e8STbt3nS77XxT+DbfEvx38Nta/tP7 F/wr3xHN4g8n7P5n2/zNI1HTfJ3bh5eP7Q8zdhv9Vt2/NuW58ZtO1zUvAlwP DUFtd67byxXdlb3Wpz6ZbXMsMiypFNcwK8kUTsgR2Eco2s26KZcxOt2M8u+I Xww+CXxJ/a30u31TxhcW3xh0nwnd6dYaDpnxJ1LStQg0W6yk8q6Za3sY2syo ftPk7xJb27CQPbwmNbXS/g7+yp4IfQ7f4k2vgcXvjSxtr3Udd8aLqWq6prbR 2txHp1xd6tLcTTXFxYxwRCJ3M4tWTyTHtidK/hL4S/EPR/2ldHmufA/wv/4V 1pkSXrawPEtxceI5NXOmCyfUJYZdLLTzpAi2Mcragjm2eVpTKWjhiPib8Lvi Pq37PPjfwFounaL4svfEutazYXOpeOdTa3sjpGqNcXPmww28d091HaLdx2S2 s5tPNWzf95CnlM7Td1/XUTen9djv/Dn7NPhHQP2hvEXxRsm8R/8ACZ+JdNg0 bUpZPE+pT6e1tBkwxrp73DWURjLSMrRwqwaeds5nlL6Xgj4GeH/hv4B8C+Gd HTVLDRvhzbW9notvDqtzGqwwWb2ccdwFkAukELn5J/MTzFjk2+ZHG66Pg3QJ NI8R+KLp9O0/T11TUY51ltn3SajttLeL7RONi7Zcx+VjL/u4IjuGdiZPxT+D bfEvx38Nta/tP7H/AMK98Rza/wCT9n837f5mkajpvk7tw8vH9oeZuw3+q27f m3Kn29A33OB07/gm58ItI1L7RbaL4jiC6lpGqR2y+L9Z+yW7aTdT3mmW0Vv9 r8mKxtZ7iV47JEFqv7sCLEcYXp9A1fwR4A+Lvhn4eWnxEhg8S6R4Wf7J4Ovv EMN7q2paf5sUcepTfaTJqM5iNtLGJzLtczT+b5rhGj6P4zadrmp+BJx4agtr vXbaWK7sre61OfTLa5lhkWVIprmBXkiidkCOwjlG1m3RTLmJ+G1H4b+ONT+K /wAMIH0jwY/gzwQv9q3er3utXeoa4b/+z72x+zwQyWwG0JdKxvpLppXXzY2g zJ5oFsOT/r5/8A5zUvAXwB+Avw6bwvrPi7SNH0268eaTLcT+JvHU15qV/wCJ oTZahYW097f3MlzPdmO1snjgkkZzbxxKq+UFFeqePvgd4Z+LlprtrrsWpahb 67a2tldQprF3DHam1mkuLee2WOVRZ3kcsgkW6t/LuA0Vu3mZt4DH5f8AE34X fEfVv2efG/gLRdO0XxZe+Jda1mwudS8c6m1vZHSNUa4ufNhht47p7qO0W7js ltZzaeatm/7yFPKZ/VdO0PUNGm8aXmn6Rotvf6nc/aLL/SHjTVZVsoIkmumW ItG+6MQkqJCIoImGSfLU6f15CW9jg/Bv/BP34ZeAdc0nU9Ks/F1tf6JDcwWc 7eNtbmeMXWrx6zdMd92297i+hjeZ3y06L5MheEmI+j/Df4YWHwwt9ZFm811d a/q91rV/eXEcIuLuaeQlQ7Rom8QwiG3jLAuIbaFWZiu42Phr4XuvBXw60HRr 2+/tO90nTbeynvN1w32qSOJUaTdcTTzncQTmaeWTn5pJGyx+Wfg/8Av2kvAN lq+sXmv6RqeoWepWL6L4W1X4iX2q2V5psEN/GLGfVf7HgkgRHu7ecTNY3d7c NYBLu9uUlQWzb1GtVqfTN78HfD+p+P77xPcWc8+ranpC6FeB7ydrO7s1kkkW OS1L+Q5VppcOyFwJXXdtYisLW/2W/CfiTwrp+iag/iq+0zTzNmGfxVqkgv1m IMkd2TcbryMgbQlwZFCZQAKSp2/D/gZ9S+DVp4c1hb6wluNGTTr5bLxFfXFx bM0ISQRamxiu3dSTtuSY5iQH+R+kPwU+BejfAHwzc6Tod94wv7W6uWu3k8R+ LNV8S3SuUVCFuNRubiZI8IuI1cICWYLlmJ2WIqxeknptqaRr1FtJmRqXwi8B SfF/WLrUJDceKPHehSWN5pd5rtxLDqemW5jjkCWDymHyomukVmSIBGvGyQ07 b4dG+DXgX4peGvh9r2m6rrHiDSfD2zW/DGqWvi7ULqG7WdNyXJnW5IvEeKRg rStIpimdR8kjBp/EWi+LNB+LXiTXNEjufEC614ThttL0y/v1sdE0/UbKe6dR LMPNnie9+3RK0kVrKETTSWy3lRv5h+yn+yh8TPAXwJ+DOj+JPiZrngvUPhp4 d0zw/qnhrwfPpWq+HfEAsAIhLJcaho4vx9ohRBIkMkPlg7UbcvnOLE1f53p5 vzX5afPsHtp/zP8ApHefDLSPhf8AESbWdO8GeLI9ck8HeJi2qW2i+M7m5bQt Sgyp0+ZIrk/Z4o+QbBgsK94a3fCPjH4e/CvxDoXwi0vxH4c03xDpugxT6X4X fV431YaXCPISdYJHM7wqY9nmkEbkILE5rh/h38LPiJ4b+L/xY8f3+leDW1fW NFsvDvhXSYfEN09vqEGnXOrXNrcXt0bJWs3nbUwkkUUN0IBCWWS43BF0Pin8 J/GPxC/ae+HGqrb+GrXwP4EvpPEj3zapO+q3moNpmqaZ9iFn9mESweXqKzfa ftJfdEY/Iw3miZ1pzVpyb9RTqSl8TudVo3hDwR498F+H9PgnsfE+jeE9SWOx aTUW1FFv9Nle3xK7O3nXFtcQvu80s8dxAHOJYgy9PrvjPSPCyStqeq6dpwhs 59Qk+1XKQ7LaDb5053EYjj3puf7q71yRkV4T+yP8GPiH4H+HWnaf8QfDHw9t tYtviD4h8UxS6J4hudXj0+11O61K+EkUk9hat9oV9Qe0ZQgVoS8m8F/JHq02 g+IdFHjLXbKPR9Z8Q6hD5eh6fc3k9hZiOCJvs9vPOqzbN87zyPPHbllSdU2S +SpbKV7aGb3sjo/CfizS/HfhjTdc0PUrDWdF1m1ivrDULGdLi1vreVA8U0Uq ErJG6MrKykhgQQSDXjf7Tv8AyP1p/wBeCf8AoySqv7JHwP8AiF8Lf2dPgR4Q 8Vjwppt98MdGj0zWxoutahfwagbWwawtvJby7MOsqt9odbmKRInjVEjkcR3U Nr9p3/kfrT/rwT/0ZJUVklFpGlP4jxLVP+S06B/2BNT/APR+n1+Qv/B3j0/Z 7/7mT/3FV+vWqf8AJadA/wCwJqf/AKP0+vyF/wCDvHp+z3/3Mn/uKrCl8S9D Wpszjf8AgkjpkXiDwpPbtLIiSKrkIufuxDqM15Z/wVM0JIPHHh2MMPI/4SGS 2PHQK2D9Krf8EWPjK/hnXoIJyGVb3yGy2DsYKM/gK9I/4K/fD86f4xuZ7RvO tdP1ufWg23GIJpCydz2IrrOc9U+D/gvRtB8DWcKSoEMQCyMoO45OO/8AnFb4 +Bq+JVeSCW3KNnDRyBWHHsa8r/Zk8G2PxB8IW2rXupzJazwCdQp+VBnGAN1e 0+Ffif4d8EX8VhpmmXN5I2QJjN8p9eNpx16ZoA5/wl8PvFela46rfRT2UY2p CYNhOeOX3HPHtXpvhL4ZQaFqJ1CYf6ZKvzopwg5612VsfNSOTy9hbB9MZH0q zc24VRtYEMODigD8/v8Agpf8drvWvH7eB7DfHBp6xm6ZXwZXZUkXGOwVwD1r of2DP2fLzUr0+I9ZQtplrA0drC8ZXdKSAxIJ6YHXFcV8b/hXqPx4/b98QaZZ RvGfMtnlYq20Ilrb7sHjnFffOmaTFoOiR2cGBBawiJCFxkBQM49eKAMfxP4l 07wHpqS3c0cUQGVAYZP0HeuS8LftD+HfGviZdOtHnMhbasjptQc9z2FedftF a/Be22o2ypLcXcScSMuQDnoF5/n3rxT9kLx9q1t4pbRX01GWWRjFNIMZAOAB leck9c16FL/can+OH5TI+2vn+h9kfFn4QWPxV8HyafcufLYiSKSP7yOOhB64 9eea/PX4r+GNe/Z1+MN3LbagwvtOlBWU5US8ZDYz79Mmv0m8H6nYWTR2W7ZP IgLo0m4BjXEftOfsuaX8f/DL27y/ZNRgB+z3KpnDYwN3I3ADPGa88s9M+HPi CH4lfDzS9aVdiapB5u3OQoyfoD0rlPiB8Ir7xNqkUljdQ21uPvosWCfU53D3 7VF+xr4d1PwV8BtF0rWNqXGn26RbRk7OvGe/Wuz+IniKbw74emu7dVkljZQo PAOaAODt/wBmzTxb/wCmTJckHdmWIMAfbJrV0D4VaPoN0qWn2ZgFPAZR+lcr pn7RI1NXsdRgQyy/K2yXYQO2OK5y+0OO2uRqek3V35lucvHLnGPTOfXHOKAP mb/grXdXC+F7W2Hlm389AqqnGfMOK+gP2WLF4P2fvCSkIJLbwpayMSu7YfsS 54+tfH/7c/xWf4z/AB18O+GLCOQw2rwm73A/OxlB6Y7DNff3wW8ENZ/Bb4kF WCxeH/DCpExGA23Ee0HPUD0oA+H/AIJ3Zn/4KxfsmFjlpPif4ZfI7/8AE5th XJftJWAuv+C4f7QEh/5ZfFrxNjn/AKjNzn9KtfszawdT/wCCv/7K0R/5dfib 4ZT72ck61amrvxo06TUv+C5H7QUUYBZ/ix4oHLY/5jFxQB+m/wC354L/AOEW 0XwtezTq8MttCsnGNyyQhcDnsW/SviL9nS+X4V/Gvxvp9zaSSWmoairWcgXI 2BQORj175r79/bN022+KXwf0+zkdRqGjxRNDl9qy4CqQT2KqCe+SO1fnF+0j 4n1z4TR6J4h0yGKWPRGeO6Tr5sI+YEtg87j1x2oA+w9P1f8AshoL434trRsN 5UafN+Iz6g16x4P+Jtj41Q/ZhNhVxlx1FfHHwC/aT8NfHTRrR4rl49TEf+kW zgDYwz09RgDtX0Z4ZubjwTprardyW32WKP5IIyA7dufTrnoelAGt8c4E8S+H 7nQIb0W17qMbJEOgPPTdkYrxY+GdUsgNMeKc+WojLRhtvPcEdPXNW/jB8Q4P FGu6bqdkdkuSZEZ8kHOa2PiZ4xvdZ8I2EtlMsD+UPMAbksDj+lAHmnxA/Zku rDSU1bSrhtLvdNf7UjlS/mP0IbleDk/nXonwn8f3nxD8BpLqsSJrdgQl1sP7 tpWBzt4HGMV598Sv2nn1fwRFpVskslyzCF2wSc5AJwB0GCfwr074N6QumfDD SVMiTSSW4LOE2b+oyeTzQBzv7Rj3Vn8PLJlQzsmvaOQmcAt/adqQM4PfArtb RLs6YjS4TcuWQ/MVz/8AXrE/aAlNv4E09hxnxDoZ4OQSNVtMcfhXRXlyzAAO CvchTz7fyr0Kn+40/wDHP8qZC+N/I85/aa+LB+EHw9S0sstqHiTfBFNnBgIA O/Hf06j61l/BP9nlLj4e22pSXhvtSnDySzuMO5LMcZJJGBgdegrnf2+/Dqar 8PdN1NZWin0ySTYv3hL8o46/41zP7MP7W66f4bgsJYpIjbnypCxJySSRyR3B H5155Z65/wAIzqt4PJhWZlJxtYHH/wBevX/hjqI8L6XZaXqNyhuGAESdwOmC c+/WvI9V+JN7f+MtLltptscsih1U8AY/rxUnxK1ubTfiUl4qZkmc7RkcUAew /En4k6R8PpI0vdLW6F0N3YADof4TWXo+qeC/ELtfWtiYbqNC4BYbQcdOlYFx 480n4g2MOlau3lzyhYVZAGEZPHPTjn1rlPGvhSH4X6Zc3kN/DIVidwBgAYBY cZ+lAHmv7SvmeO/jZ4KgQBYrLUDezovO2JlZBwPdTXpvhGz/ALW8CeOZ1HlQ jw7qSxEScsRauF/UdK+ePgZ4svfiZqfiHXrw7pLu+bTI5UGAsCJHKCB2O52r 6d+HGhyp8MvGS6jMWW+0a8S02Q42k27qvIPzAnqeMUAfN/8AwTxIg/4Jh/t/ +ZgBdc+FhY9gP+EomrlP+CuNubrwp8IrRPmA8MRKu3sftF3+tb37IRPhn/gk h/wUbuJPnFrffDWQgDqE8SXB4/Km/Gzwk/7Vf7K/g3xlpkjMbOza2hiK5dCp Z+eeB+89KAPzu0i7uPC3iWO4RdssDkqSDwa/Q39lP422nxd+EVzpDbre7tVw HeTliwYZHTHK18P/ABaW3e/PlwPBNESkqHoGHBxwK0/gFf3Z8UeVbXTWQkUc gk5xkZ6j1oA++/hn8ONP0y1MGpul5fE7mklbPBPA5Jq3q/wiXS7x7/RpzBcj 5gh5j/LIFfPLaRq8/iVtOh1u3leLDNLNceQACOvLHp9a6nTfGGv/AAmtHZNc 0rWLNfmeJrpTIR7Hcx/SgD2bQ9QvNUvPsur6bEkgGY5om37xjn+EbT0796s+ I9Ijg09ntWlWaH5grLlW7V4tc/8ABR/Q/DES/btFuGuskBUuDtP4+X/SvN/i T/wVD1DVfPh0Xw9HaRkfI0lz5uPr+7HegD0X4paAHlbWbiCdoLzMvlpMY2OT jrjIP4VF4a+AEviDTB4l1C7HhjSIGCKl1N59xJ3JAYocH1ANfKes/tLeJfEW tx3d7cCV0bd5akqh/DoOTX1P4I/Zb8XfH7wHB4hudYtvKVCqRSXPmABQOASw /ICgCx4l/acsfBGp2kegWUl2unkKZ55dhfA2/dKn09e9cZrHxw1rxf8AE221 62gkOohy1vFCSdgP8GAPmycHOPwq38Tv2eI/AvhW2vYNRS5uzEr3dsP+WZ25 box78dK9A/4J0eELbU/idqd2+2eS30uQpGyg+WfNh+Ye/UdP4qAPov4NeMNb 8UeDbe41uweyuG42hi+5QBg9Bg+1W/ij4aTWvB+pbWMcwt3ZSP4jjOPbpXoG h6mtoDA3zjnAIrP8U6VHcxSAkD7QrDgdMj/69ehmf8Zf4Kf/AKREiG33/mfH P7MlvFb+LdauPt2y4Y4ksyNzHlcsGJ55AHTvXueqQPd6rDLGCYbiLy5Iz97d jAO7tx2r49/aP8O3vwr+Nt9LbySRmaZnhdMrxx7+tekfsrftAeMtb8RCyaK0 n06NWaSUxfNkAcbsHnFeeWe4/GrWI9Ns4YHurpkhVFWGK3O53YBRlweOSO3a uV+F/wAPpdF1mW/uYmM8l4Li6jWYuV+XG3IHzevQV6fqc9vfjz44syMBt3MG Unrj2rn5fHMul6gllcWyxQ3b+W0q8FQOdxbH4f1oA8u+Mun2OkaBLahrifVY 5GciAFjtZi6ksCTwCBz6V594B8fWd/ewWdzNM1zK23e0e0KQDkHnnpX0ddQW lhBI9oIIZ5CS7OBI8meM547YrzT43afpPgb4bXepLDarKpEgJVVPmb1PX6mg Dzr9oj4uXfhO1t9BsJWlGooy3BGSRGOhHoc157Jew6H4YlSZvKklGRk56jI/ nXL6RqNz4um1TxLePsS6l82OMZYqCM4X/Cuk+HvwQ1j4v+IY9c1CUaZpETqF jlG55VUAY2kr1weeetAH0R/wTZ8DTGLVbu4MpP8AZt5Jt2EdFJBr4J/bA1Qa v8dtWkwQVIVhnock/wBa/X74K+Bz8PP2S/EvxOEjQWusK2h2tqyYaLySQW3d TuH+yPxr8Y/j7dDUPi5rV2rblvJzMp9j/wDqoA/rn+CH/JFvCH/YEsv/AEQl U/2UvjXqPxe+JHjq3fRxZaN4a/tDSrW+W5s7lNRuITqEM5V7a6uAoQRQqY5v KuEfzPMijyqjH+HXxb0LwT4T+FfhnU57+DV/F+kxQaUE0y6ltriWCx+0SRNc rGYIpPJjkkVJXRnWN9gba2PSvhr8QtH1r4n6v4ctbwy6xpOgvqd1B5MiiK3u Yr6KF95UK26SzuVwCSPLyQAyk+db3v67nX9kz/2MfEnxU1b48WEfjnwb8PvD mkrbTtDc6F4zvNZuJJ9hCxtDNpdoqoVLkuJCQVUbDuLL9nV8ZfsYftZ/Cv48 fHix0jwN8TPh94z1a1tp7yay0LxFZ6jcRQKhRpWjhkZggaRFLEYBdR1Ir7Nr qobGFXcKKKK3MwrhPjt8Yrb4KeHbPU72fw9Z2l3dfYjca1qh020SeSN1tYzK IpAGmujbwKCBlpxt3ybIpO7rF8XeLtC8FR293ruq6VpEZMphlvrlLdT5cMk0 mC5A+WGKWRvRI3Y4Ckg66geO/Dr9qrS/Ff8AwUT+JHwpXxt4furnw14H8O6z F4XS8tDqOnXU91q322V41/f58htILK5Kos1uwC+fuk7r4h+JfilpnxL0W18K eDfAOteD5/J/tfU9W8Y3emanZZlIl+z2UWmXEVxtiwy77mHexKnYB5h1tF+L +na38bPEPgSK2v01Xw3omma9cXLogtZYb+fUIIkQhy5kVtNmLhkCgSRbWYlg nHePP2yfC/w+8IeEdYvrXVbhPHOvWejaNb2D2l5LeQXepwadBqgMU7R/YGa7 tJfN37lju4FZBNIsJS1aBpnafF74l2fwe8C3HiLU7nS7DSdMkik1K91K8Nna adaeYv2i5ll2MESKLfIS4VPk+d413SL4xqH7Ymnw/ty/CH4dXfjXwnpd/wCO Ph9ruuzeGF1S1lnv7uO50n7FLBvVLiRPJGrmMqqCVIp3ZMwnyvTfiR+058Of hD4FTxN488Y+GvAXh86pLpMd/wCK9Qi0SBryKSVPKV7oxgsxglZMEh0Xeu5C Gra8M/Grwf41t9Ll0XxV4b1ePXrW3vtMay1OCddRguIppoJoSjHzI5Yred0d ch1gkYEhGINN/wCtv6YHyX4w/bo+I/7Bfwosj8b7nwP4w8V+K9X1GLQY473T vB11MGW3ntYY7F7+8lvYIGuJLaZrH7Rfq0dukNlqJk+1Se7+DP2zfCvjD4ie JfDkmseE9F1bwZHqlz4g0XUvEEMXiDSLW0uVhhv5dPUMyWVxHuuEmkdB5Mlq 4DecRHv+Hv2ldG8UfCLWfGFpYan9n0nWdV8PRWFxJaW11qV/Y6rcaUIIGlmW Ddc3dvsg8yVN3nRbvLYlVp+Jv2tvC3hj463Hw+e31q/1iy8N6h4mvLiws/td taRWT2AltWCEyteFNRtZVgSNmMbqx2+ZEJDfTr/wAO1+Gfia68a/DvQdZvrL +zL3VdOt72ezC3C/ZJJI1do8XEME42liv76CGTj5442yo+bf2r/2y9A8L/C/ X5dW+M3hv4Mw+Gtci0nUtR0zVtKn1lrp/tTW2mINXhFlZXNxELG5D3McqfZ5 Lg/u4wl9XtHxl/ae0L4H+OPAGgano/jbU7j4iaq+k2Vzofhm91az051iZ/Nv JreN0toiQqBnP8ZfAihnli1/hl8VdL8f+KPHOiadZ39rP8P9cj0G/adEWO4n k06x1IPDtdiY/K1CJSXCNvSQbSoVmGr3EtEReA/Eeq63+z5o+r6Vq/hn4g63 d+H4byy1W1uPsOjeJLhrZWjuI5Yhc+TbTuQwdBPtSTKiTABf8E9c8f6/4YuZ fiL4Z8H+FdYW5ZLe18N+JrnX7WS32IRI809hYsshcuCgiYAKp3ksVWh4s+Od 94S+Ovhfwc/w/wDG99pfikSIni20/s99E06dILmfyLlTdi9RiltgOtq0W6eF fM3MQvZeHPFGm+MLKS70q/stStYrmezea1nWaNJoJXgniLKSA8c0ckbr1V0Z TgggNvW4k+h4X+0t+1Vo/gfQfHulat8SvCPwlTwrZx6hqXiGe+huNR0PTpJb ZEuxZ3MPkq07G9toJH8+IXEMWIrljLbx9l+xn8Qk+Kn7NXhjxBD48sfibBqM czQ+I7aXTpftyC4kVUmfTneye5hAEM72pELTQysiRqQi9xpvjLQbzxBdaTa6 rpc2rW7yfaLKK6ja5iZEgd98YO4FUubZjkcCeI9HXPKfD79pPSPib8Efh345 0vT9T+zfE200y90fTLmS0g1Ax3sKXGGV5hGzwWxlnlSKR28u2mMYlIVWSat9 w7XZlfACy+LFp8UviTJ8QNY0nVPC17qi3Pg+C00CPTn0qzBlhNtJMl9cNcNi CKcu8URzdFgRv+yWfnHxW/4KS2Xwz/al1b4UWvhuz8WeIdI0251x7TQPEtlq GrvaQ6TJei3OlRk30V9JNGqRiaGOyaKeBhffaJo7J/SfhV+1SnxHt9Rm1H4f /EzwRHazWH2T/hINHRG1W2vpmhtrqNbeWZolLKTLDciG5tlKtcQwggnXm/ax +Ftv8cU+GD/EnwAnxLkxs8JN4htBrjZg+0ZFl5nnn9x+9+5/q/m+7zRYd9Wc 14P/AGxPDfxM8HJrvhHxH8PPG2m3niex8NWFx4a8SPrUFxLJ9nN0rNaW8pSe BHuJPLwyeVAskstvG0jwem+PdfuPCvgnVdTs7WK+vLC0lngt5ZXiSd1UkIzI kjqCQASsbsOyseC7xR450XwPaRT61q+l6PDL5nlyX11Hbo/lwvNJguQDthil kb0SN2PCkjF0z4saZ4k+NHiLwCLS+GpeHNE0zXbqaRENpNBfz6hBEiEOXMit psxcFAoDxYZiWCDsLqeLfGP9pvR9Um+EOuaB8adF8Py+L/EUVtoGg6TeabrN j8TNOl1G0hlliDwG7mENpKZfNsZY0t2mMkzTwRgn2r41a3480DwpDP8ADvw1 4S8U641yqS2niLxHcaDaJAVYtIs8FjeuzhggCGEAhmO8FQG4yf8Aa7VvEvhu 1sPhz8Rtb0XxDJCW8R6Xa2N3o9hb3N5Ja2N08qXRaeG52CYG0SdreCWKW6W1 Vga9R8N+JbfxVYPc2q3KRxXM9owuIHgkDwzPC/yuASpZGKuPlddrKSrAktp8 /wDL+vmLZkPhrUdWufBVjc6/Y6fpuuNZpJqFnYXz3trbT7AZI4p3iheVFbIV 2hjZhglFJ2j5q/aP/a2065/ZWvPF9t8XtB+Fup6XqRs9MuNIv7DWIdf1qGzk L+H5Ib2182eb7Wk0T2lqsV8/2UBJIZGkiT6SvfG+iReIxokur6XHq8gQLYPd Rrcv5iTvHiMnd8y21yw45FvKRwjY5Dwx+0honiL4P6v4usdO1JbXR9Z1Tw7D p88lpa3Oo31hqlxpQt4DLMsG64u7fZB5ksYbzot3lszKppuPyOu+JE2tW/w8 1yTw35H/AAkSafO2l+faC7i+1CNvK3Qme3Eq79uU8+HcOPNjzvHM/sv6f4/0 X4GaFY/FDV7LxB4606OS01TVbTS49Mh1RopXjS5EEc8yJ5sapJkMm7fu8m3z 5Eed4m/a38L+GPjpcfD5oNav9XsvDeoeJryews/tdtaRWT2AltWEZMrXhTUb WVYEjZjG6sdvmRCT0LVfFemaJe6XbXuoWNrc61cG006KadY3v5hDJOYogTl3 EMMshVcnZE7dFJBbS/cD5P8Ag/8A8FefCHxIstZ1TU4/COi+E/DOp2Oi614r tfHGm6t4c0y4lhvpZbqXUbdmtobItaQwwGeSK9klvbdZ7KyEsDze+eAvjePH Ov8AhbT4W8O3x1zwv/wkl1caNqM+pWkKO8C27Q3C2ywy284e4aOWSSF5BbEx wyqJmg9FwKMUxvyCiiigArwn9p3/AJH60/68E/8ARkle7V4T+07/AMj9af8A Xgn/AKMkrOr8DNKfxHiWqf8AJadA/wCwJqf/AKP0+vyF/wCDvHp+z3/3Mn/u Kr9etU/5LToH/YE1P/0fp9fkL/wd49P2e/8AuZP/AHFVhS+JehpU2Z+XX7H3 xWm+GfxUtWUZguDhhuxgnAz0r9h/20/DUP7Rn7Cfh/xJpqw/ao9Cjs9UxGMi 3htUVGz3JYYOe9fhJYX8um3STQyPHJGwYMrFT+Yr9gP+CRn7SsXxk8AxfDnW pnu7TxZZ/wBjeVMRJHHJtAVjknHzAV1nOfMP7Cfxk1TTdUbwJftHFA0W6KV2 5QKVGzBGc/MT17dK+7vD/izw98OtEjaOD7ZfMu4n5VAJ7gYPtX59ft0fAnX/ ANj79pXV9Lv/ANzqGm3rSJPaFkjkZQB8hGM9e1fYv7OFx4c+K3gLRdYjmZ1l hxI0x3SbkO1we4+ZWoA73w5J4q+JeuRziU2sCSAlRnBA9xjFe8aTpjW9vEkz h2XALYxmuKm+K+heDrSGCyhKICA5AXLHFdf4c8QxeJdLju7dXWOXj5xz09KA MDT/AAdZ+F/FWsatHBGL3UpEZ22jICoEHP0UVwfxL+Md/wCFPE6WS24+ysm7 fjqSPp/WvV/EsYkUOrYA4YVwHjtNPufD0k9/BG+07A5UbuAeMnmgDg/E+rR+ J9Hnu3S3LY6qFyenP1rwv4Fa9B4c+JOpXM8uw2l4zRbxtCgHOMn3rtdU1aHZ O2ki/gVTsG6N3iPfgDjHP6V5h4V0w6/43k0u9sWmm1aQvPM0G2OMZ25JI+U4 5x7V6FL/AHGp/jh+UyPtr5/oetWvjSXWfG8rWNtJdADcZQ52qTnocYJr234Q aTq8Nnd319OzRzL8isCdpB6cmuT8A/Be88KW1smmtYG2jO50jIy3Q9B3969f 0bfdeHoYxCUcErjGK88s1NGQtEwQYAYdR7eleJ+PNbvPC+sX1hrIuLi1v2BR oyVCgbsev94cV7rDbeQoiXYWAy3fJrN8YaVpt3Z7r+3tZmVSYzLEr7T68/hQ B8o3HhTw1caytxG+oCVH3bsE5PTnnpWz49+Ldh8K/hbquqSXC3UFpFuYYCsR 6Drk+1djq3hCfUZ5xYtoSQLwAkaI4/rXxH/wUF+Iuk6J4VsPBunXE9zfTXQn uzG/ytCqSKyN77mQ4PpQBzn7Hvhq7+Lvxx1LxvcBJIbe5e0hjccu0pzGR9OO 1ff/AO1b8V4/2aP2K7vwfvjn1jU7aa81SfGwxCZFcxAc52sCOo+grz3/AIJF /AXT/hb8EtS+I3jD+zrrQ9MmljS1eISPcXLx7oHAYYOxgBnqM8V8Sf8ABSz9 sq++LvxK1nTre5vF+03MjXe6QhQrMT5YAPY8emKAMD/gnJ4zk8d/8Fd/2cr9 xtR/iv4Y8tf7q/2va8V6T8ffEX/COf8ABcL4+zA4LfF7xKmfrrVwK8Y/4JO/ 8pTf2af+yq+F/wD072tfQ/xX+D138Sf+C0v7RdxFEHgs/i54mZjnGCNYuT/S gD9GPiD8Jr/VfiBd3tys1zYx6fE6+VIcRuyEDIGR1Ir5k/ad8Hjw98O9b0TW 7OZ5tbtibeS2XzVkOVzwB8h/2Tziv068Y/DSH4Y/CeHxHFqUMlw1tCs9qkpK yJ8gwydDjJPOcEA9q+Df2k9fsdRvdU1WK+L2t5cGe2tgdxtn2gZC5wOPQUAf jvYeI9Q+HHjW6uNPkntpLS4dFYFlwN3Genp0Neja/wDts+NvEfh1rEX0gH8Z AOccd89q9Q+Jn7Mdn4n1W+1S2ltba0u5fM2+Xg7uAeQMckE/jWSf2etAi0K4 W0aH7UUARgmQxyPSgDyv4XfHPX/DfiaDUbq+vLlLdwfJLM2/H1OK94vP297/ AMV2Q0fRbOa1u2XBe4KlORz/ADr5v1P4darpPjyPQ0doZbucqjZbaxx1xXp/ xJ+BNv4G0XTLi1aWPUZEQvIHYZcnnmgDe8MeJfGHwe8cLeXNzayNdRurbl3j 542GRn/er7i/Yn+L9v8AEH4ZQ6e+WvtFjSKQthRJnPIHpmvhpdK1zxD4eifW ZIpxAyqbiRsuoBAxk8n09s1raL4+vfhFr8N3oeoX1jk5lCysEuMdA3IB/GgD 7+/aBgdvAtidpA/4SHRCPx1W04/WuiuIDb2zSvhQnLZPQetfDWs/tteIdf02 wtb+6kmhjv7G6baeAYLyGZSMn/pmKd8Wv2x/FHiGGaytb6W2tbgbf3blW2nv uB7j+dehU/3Gn/jn+VMhfG/kdd+2F+0rp2qeNNJsNKjkkTw/dPLcBgGjnbbt AGeD17185fGz4mXvijxPc6tp8cNgtwI8QoqoAVRVzgcc7c/jU+hWltrU11Lc 3BaYgbCehJPr3rqfh74G8O3Wj3Vxq9s17dFJthZgQuNwHBzjjFeeWcj8Fv2v 9X+HXjm2bXWfUbIrtK24BdTtOOp7HH5V658V/wDgoHZwWFnqul2dzcOkjecZ VUbeM/dJya+T7zwjdafNPq1o0ciWzN8obc+Cdox9M1zkurXfiSYRLn963KBT zx0NAHY+M/2rvE3iXxreaxDez27XDsyxqSqpn2B9qxtR+NXiXxkyWs+rXzec 20DznwSTj196rX/wj1K0sDdGD92BuOAePevUf2YvgxpcniP7brkUNxFboska bfMAbcCMqRQB9N/s12qeD/gdomlmOWW81FNrSxoWSFyF++38PAHJ9q+kv+Fn X9j8OpdO1KNETQdGnSAxIBtTy2c7iPvZ65rL/Z61Lwb9htrDUZbOz0Ca6bUL qG3iWKa7Z1WNodw58sBFYKflBLHGTXs/7Q/7PFvr/wCzB8Q/EGgHT7eBdOnt 7SOJwXVHtnx936Dn3oA+C/2WdQGqf8Ea/wDgpROGLCS5+HRGe3/FR3NVP+CP fxLe6+B3iTw9fZminvWt4SxG2NPLhOPrkn86d+x74cvPDP8AwRR/4KN2d6pF wsnw46A/NnxFcY681w37JGnah+zf+zBb+ILxU3+I7+S7t0CZcJ5UShSD0bKE /iKAPEPjh4MmvPjD4miiKNnUZVRAcDGTznpWl8J/gVevqLzS3VtF5a/Ku4g9 +hrrLhY/Ek+o3o2pqWoTM6BuxY55r1X4LfCGLUrUGee3e6PIQv7eh/GgDhtM +C1jLLNdai9zcTSDO5ZDt/Wo7/wR4b1m+aw8uW2bqsuNqEj/AGgK+hE/Z+v5 LlkW4tFgK9C2Tmua8QfBlbISNf3VobaLrGo+9QB4P4p/Zkjv9BnSxmidAd5a RixGM4wa8M8R+DW8FKwlnjlduCq84r6m+LPxCFvoIsPD9rLBGoYSuINhY8Dq Pxr5W8Y2WpapqckskVxIQxOSjYFAHMPJ9njwqsccZ7D/ADmuh0T4u+IdL0v7 HbavqEFupzsWdwvPHABrG/suVWwYJF74KkD8qmh8M3N2QUt2bHYIeeaAPdv2 ffi9FqOhXGlajNNLeTqV86Q7zKWOAMnmvev2J/F0Pwu/aBFvfyBLbVYGtVfH y5LxtnJ7fIa+WPgd8JdRl8baZcTQTR23moWcIw2DvXvvjnTV0DXrSWIxtHDJ utpBw6rz8p79yfTigD9BdYh8m5WeNkaGUZRlOQwPNVru8VbUNzhx2r5e8E/t xnSPDq2t5YXl+lqoQAYLKAuOp7cV6V+z38bm+Nr3U7p5FvGMxw4AKLnA3Y64 GPyr0Mz/AIy/wU//AEiJENvv/M85/wCCg3w/TUvDWla0m1GjuBDI3XarJIxz +IFZn7PGuW9r4YOlQNHbPv8AMLnH7wEDPPr2r2j9qXwr/wAJV8FtXt40VzEP MQEdMAj+tfFmkeOJ9JuI7WBWRoMpvjOxgc88jmvPLPtj7XEmnxpG24Jhtx5x +NQ67PZatakXcbsCOAjAY7da+VNf/aA8UeGdMQ298pUcYfL/ANa5HxV+0f4s 8SwxW41OS0hHBMchh/76IPIoA9K8R/HG38NfFi7sLQTzxRMIwQd6glVJ/Ik1 5T+0B8XE+KWsQ6DYl3QSLLeTH/VbF+YBR2O5ce+af4A+HPiD4tSmHwufIbJ+ 16hOSrSZ5YKfvYxkfhW74Y+DsGpeJYNB0ixEMaSf6XdSJhpmGSRvxlhuXpnv QB137OHgKy1KL+0GtmazsmUW6yklZGIzuIIxjHGK1vHmuvr3iJbS1YLKkyIu 3kYG3qB0r0Y+F4vAfh210yDyopRHh9uFQN/ntXK/Cn4f/bviXZXzNBIgaQ3J H94P8v6CgD7D/wCCnfiOy+EX7Fvh3w9aFoV8lbq4RCMb3t8lsf71fgf4n1g6 3rEs2cpnahxj5QeK/Z7/AIKr+LG+IXwPkuYonU2lvHC2SPurEVyMdRxX4o0A f1tWHwIb4u+DPgprY8XeJ/Dp8ARWusW9npcdi1vqUzWJtytz9otppNnkT3Me IXiOLhznesbp6P8AAD4IW/w7+K/jDxLJrmu6/q3imxuIWk1H7Mq6dZQxX01v YwLbwxAwxPeXBVpfMmbzMPK4Vdvimt6Z4qtLz9m7VfDPh3xxrtjpu2PxHLpO vQWmnW2nTaVJB/pdrPeQrckXUtrMpSGaREtZthVmCS+gfsVWHivQPiz8YrLX vD3jfStCm1y+1Hw1eeINdg1WO7spNKSOQWxW8uJoIftcF1MkMixKkd1EFRSH jj4Or17/AJo676fce8/Ab/kq+lf9tv8A0S9fRtfGP7GPwY8RfD3472F7rHxZ +IPjy1lt54F0/XbPQ4beFyhYTKbHTraXeApUBpCmJGypO0r9nV0Yf4WYVfiC iiitzMK5f4n/AA9uPiHpdrbWviLW/DEtrcx3H2zSfI+0MqsN8RE8csZSRN8b ZQsFkLRtHIqSJ1FcF8er/wASad4f0+Tw1aeIr+5e6+zz2+iyaelz5c0bwibN 8PJ2QPIlww3KxFuQomP+jzGt9AMrwl+zJ/wiP7U3if4ox+N/GN23ijQrDw/J 4Znj03+xbG3snmkt2hKWi3m9ZLm8YmS5cE3cgIKpCIsnV/2ONP1/4KeB/AZ1 2/8AD+hfD/XNK1HTYfD1tDAs9jpN9HdaZps5u1unMSC3shLJG8csr25ZWiWR oq4v9m74Vw+Hv2z/AIh68PgPrfgGW9iuYh44nu9MnXxm73SSyy3MkOpzXkwL bRZxXVov2O3glRJIlnFrHzPxS+BXi3x1+zJ8G9Og0LUvEXijw9468O6h4ot9 Hs7XQdNfVLTxDaXus69LFdfZZJITJa6jJH5Kt57X6zLFLmOaMW8de35/oC6n 1X4Y8OP4fsJ4p7651KWa7nujNOqKyiWZ3WMbFUbI1ZY1yN22NdxZsscrUfhr NqHxLtPEC+INatbW3t3gm0eHyDY37cGOaXfG0okiPmbfKkjVvNbzFk2ReXzO ufCrXPiF8NZNL0Xxn8QPhTcrr17eLfaYdMv9RmiNzcHYTqNtfQi3lLiVFCK8 cfkoPKCtHXP+P9B8WWNu/hW7h+JHxC0CfwvJb397ZXuj6dcarLFa3EUsEkim ykhvL3z42jktWihjktQS1mnE6T3Brv8A1qR/Df8AYjj+GX7Pus+B28b+I/HV xeeIpvGOnan4wtLKY6dq7aiNWjlaLTIbBZYU1MfajESpYu0YkWPYqdR41/Zq j8X/ABvj8eReLfE+kanaeE9S8J2NnZpY/YrD7fPazT36B7dpWu91lahfMlaE LCP3OWYt4l8FfhPceGP2IvE+mWXwXT4JWl3r8eoa94Hl0jT9S0650cTWbala 2WnaXcahC63Gmx3FsI4kie4ummm+zwtcc7nxe+C+v6j+3E3i9fDXjLXtAT4X eItGm1Sz1Sys1imu7rR2ttKsQtxFcxzn7DdzG4ZFUPcRD7RhEjha3X9dP12C Nz6H1nwDoniN7N9R0fS9Ql0/Z9le6tUma32TQzqULAlSJre3kBHR4Im6opHn Hw6/ZSuvhtrvxc1Oz+J3xAub34taidWaW5g0c/8ACLXX2SOzjk08JYKD5dvB aIq3guVP2SNmDs8xld8ff2UrP9oDx/8ADXxHfavqdje/DPVRrNglpqep2cU1 wTFHIZI7W8gjlBtvtUQEySgfaSCGha4t7nkf2dbnxn4J+Jf7S+s6n8KfGNja ar4tTxD4cBvdFaTxfDDoWm6bttQl+fKkeXS2KC9NsAlxb7mQ+asR39H+a/r5 CW2h7hd+Cob3x1pmvteagtxpdhdaelss2LWZbiS2kaSSPGGkU2yhG/hEko/j o8BeD08BeFrbTE1HV9WaFnkkvNTu3urq5kkkaSR2duAC7thECxxrtSNEjREX z/xx+zb4V8bftYeBPiJefD7wzd+JPCmnXxt/GTW8K6zpr7Ps8NgJdvmvbSRX +ouU3eWrorY3MDXZ/CA6/L4Ghm8TQz2urXd1dXRtp7uC6ls4JLmWS3geSCGK MtHA0SEKH2lNpmuCDcSq4jK1T4NX154g8TX9r428V6Sdf09rS0gtVsZIdBuW VlOoWontpT9owYxtmMtv+5U+Rl5TLzHwo/Y+s/g18DPhf4LsfEWp+JZfhDc2 reH9Y8TW8E93BbxQy2TRlbJbOIuNNuLi1jkKHaXSWRZ3Vt/N/tJ6V4j8YaB8 QNF1L4ZeMfix4ZFpHdWvhyLUdGs9N8TlpLULpuZ5reXbG9tJJOlyxt5YLuaM m63fZYqXwv8Ahtc+FP2c/g7pq+Bn8C+HtG8TxXHiHwdf6PBfSWqtJdfZVtbe yuNRhtIYtWl068hEVy0dlbQKDJEkBVXr+RV9WvX+vmdf8Ff2TdT+D1j4kiuf i18Q/GsvijV7bWLq516z0JZ0kjkVpUD2WnWxkWaJI7dvO8wxwQxJCYQi17GI hnOSR6V8j/sh/B+PwNpPjY2X7PfjH4NyajrujtDCNV0wy69HDd/LqN1PZard G6u875724uBFNcxSxwMbww7395l+DfiOT46L4tX4r+Pk0BevgpbTRDobfuPK 5kOnnUPv/vuLwfPx/q/3dPsI2vif8Pbj4iaXa29r4i1vwxNa3Mdx9s0ryPtD KrDfCRPHLGUkTfG2ULBZCyNHIqSJyvhL9mT/AIRH9qbxP8UY/G/jG7bxRodh 4fk8Mzx6b/Ytjb2TzSW7QlLRbzeslzeMTJcuCbuQEFUhEWr8er/xJp3h/T5P DVp4jv7l7r7PPb6LJp6XPlzRvCJs3w8nZA8iXDDcrEW5CiY/6PN5B+zd8K4f D37Z/wAQ9eHwH1vwDLexXMQ8cT3emTr4zd7pJZZbmSHU5ryYFtos4rq0X7Hb wSokkSzi1jm/fzG2dFpf7EdzpXiH4a3Nh8SvFfhvQ/hVGLHRfC+h6dpX9iPZ xO8cCSi/tby888WBjs5LiG6idk84xCDz5FPb+Pv2YPA/xt8Ct4c+JPhjw38T 9IGqS6ulr4p0W01K3hmZ5TGVikjMYMUcphRtu/YPmZmZmPzRB8FJL74h/BG5 1r4B6p448TaF5B8SfEPTbXT9Lt9A1iLUJmv7ixt7u+sru2jutQa4u7q5tLWQ X1oY4yl2kyeT9Ea14Y8f3vw1ltvh9rGieB9ZOu3s5l8Y6JceJontWubgnbFB qNq6CRmSSL9/iKErGYlICxt7A9zX0f4GWXhbxNoU2haheeHfDfh/TE0q18L6 ZBbQaMYY1VLb915ReL7OoZY1geJNr4dZNkXl8J8OP2Io/hl+z7rXgdvG/iPx 1cXniKbxjp2p+MLSymOnau2ojVo5Wi02GwWWFNTH2oxEqWLtGJFj2KnS6dP4 8sPiVoOnam2p6xavpkMmq6rpVtZWWhrdQxSpOhtp5JL2MXDzpJGscs4T7IFe RMH7V4t8F/CnjZv2IvGemeJfh5rHgSabx3rmt6pofiC00/xA+teHr7xLPq19 Altpk18s8lxpl1cWvk4MjT7gqkFJHLsX/APbPGv7NUfi/wCN8fjyLxb4n0jU 7TwnqXhOxs7NLH7FYfb57Wae/QPbtK13usrUL5krQhYR+5yzFux1XwRFqniP QtQN/q1vHoHnPDZW120FpcvJH5QadEwZgiGQJG5MYaTeUMkcTx/Pvxd+C+v6 j+3E3i9fDXjLXtAT4XeItGm1Sz1Sys1imu7rR2ttJsQtxFcxzn7DdzG4ZFUP cRD7RhEjh9z8U/8ACRS+NPB8GnRz/wBmx3E9zrl0t1BDCYltpI44GjeKSWRn nlikVY2g2i3ZmmwBb3B9lf11YP8Ar8DrKKB0ooGFFFFABXhP7Tv/ACP1p/14 J/6Mkr3avCf2nf8AkfrT/rwT/wBGSVnV+BmlP4jxLVP+S06B/wBgTU//AEfp 9fkL/wAHePT9nv8A7mT/ANxVfr1qn/JadA/7Amp/+j9Pr8hf+DvHp+z3/wBz J/7iqwpfEvQ0qbM/GCvoX/gnt8fn+Dfxn0ktJNGGvYZI2RsbSsgY/wAq+eqs aVqUuj6nb3cDFZraRZUIOCCDkV1nOfun/wAFg/hxYftnfsW+Gfiz4btLeXXN JmWPUgsYFwQ8c7ln2gk8qg59a/M39m/476n8G/Ecdi95dxaJcSiOS2VmzA5O MAZAHJJP1r66/wCCQ3/BRzStKv7vw94xjm1TwvqdmU1PTplFxHK29MPsbIPA I6Z5NcV/wUq/4Jtaj8L9evfiX4GW11H4e+I2W8iitQ3mWxbAKlQMAqwce2KA Pb/hbLb/ABG8UW6wmRrZAZHkmbI2gE9/pXuHhX4ii98a2ej6XGhsIm2OeDj0 6dPxr86f2Tv234fh14bXQ/EEVy7XLGK3votu6IHAAY9QAMD8a+6f2ThpemR3 moJcQ3kl+EdnRhIrMM8g5oA9rvVSYMhUFTnOR3rkNZ8K2t8xtr6FZ7Ytu2kZ BP41c8bfEe18O6a1xIcFgdvTA6+/tXCfA74xD4tzXsbq2IWJQtHjBDY4oArf EPwu2lm0TTr7T9H02FNph8rDscnpjHt+VecfCnxXoWm/EHWLK7F3dxyzsXaS EnsBlSe3XpXsXxXHh7TmtbjW7SSZ1Tag8sMFAz6j3NeVw6jpsnx40a206C0k hubWRghVQOoAz7816FL/AHGp/jh+UyPtr5/oeweDIdM8I6wl7aPqDWl0pRYz IzICcfw9BXotq8UyGdCVU/3ug96w9NW0XT4EnitLWVekYdB+IxXO/G/xXqPh 3RrW102NlkumKmVeFjGM5NeeWczL4012f4oxrp1xJNbyb2dWc7Rx0xmvTNcu I7vwx517EFOw7lOM9OcV5r4MNv4UuriWa4hfUpVPlHqoPTOa8X+Pv7ell8Hd FvNK1e9n1HVpHZ4I7YeYFBzjJzwOR1oA4v8AbV/aJ0f4JaVPHos1yut32RbJ FJgxtgEFwD0PNfNn7Dn7MGu/tlftI2mlveC+mfN3f3t9K0qqAVUlmOSR8y9f auB8Rza9+1H8X57uCOWXUtWkVQGVmWBQMBVxnGOenrX6Z+Hvhjo3/BJr4Bau bp7H/hYHiG2+y/abJ1k+x22Qz7jwd/mRx984NAHJ/wDBR79oTw7+zP8ADiz+ HfhhXsLDTLVZJIoGURy3PR34x/EOMjI/OvyE8Sa7N4n8QXuo3Du899O87l2L ElmJPP416V+1n8frr44/Ei5ujcXMtpGdoErltzZJJ6+9eUUAe/8A/BJ3/lKb +zT/ANlV8L/+ne1r9APC1zb+Hv8AgqJ+09cXEJka5+LvilkwuSFGr3ann8K/ P/8A4JO/8pTf2af+yq+F/wD072tfp38PPBXhzU/2/wD9pzU9a8QaDprQ/FTx giR3eoRQOM6zfc7WYHPHHHcYoA9o/at8Z6pq/hJm0zWdRiUQoiwQTvHkHavO 04PWvmHTvh7c6Z4cup9TkV5p4xs82XzWDZyc55r3fxL8TPBK28ItvFPhl1iA R92oQlmwMdS3rzXmfxG+IPhHVbXMeveHY0GfnS/g3N78Nn86APn74rWbaDob WKI7rI4dGH3cE5OPzNY/w/8AhFcXBhnR3dpZABGrnkmu6PiDwxrjra32v6RI sEhKB76M4XcSeS1b/gD4neE9A8dWdzHq+hx2tnKp8oXcQWUE7cEZxxnPPpQB f+OH7BEXgDw98PvG2oQxNc3sjSOrEsSAD6jA7V578WPhNceLJjFa2KzvbKLh IwmRsUBjjjFfoN/wUH+MvgH4hfsOeDL/AMPeJvCcl7pMUk91EuqW4nTK4A27 g34AE18yfsw/tJ+B9P8ADF9Lqer6HJrkVtLDH508REiMrZJJ4yMgcntxQB4N caLaT6HbWl7ossWHEoBiBV+h5wMdqsa/8NtD+LmixaemlWemMg2iQRpEGz6A AYrvLf4teH9UTEus6KbKPAJN9DnPTAXdn9MU6+8R+EvE97DZw6z4etYg5Es4 vYVaIdsYIP5UAfK/xT+BGtfBfUIo7sWtzp8zxNAVfccb1459TxWPpXhK98Wa tHb3LW9raKdxCsFLD0yPXFfTn7R+h+Ff+EUaRvF2m615F9p8cJbUxMyRtPCJ MAsSAAWJwOxPQVs+B/gJ8NNJ8INrV54u8M3IIXZCb1N+4kcYJ7EivQqf7jT/ AMc/ypkL438jyfUfAfh6z8G2tjDpqR3Kr8k8e3O714GTVjSfBOnXvgN7SGN/ tg3KZSDGxBLZyep9Pwr07/hIvBGjMLZ73wmZ4D8khkgfOO6kZx+dWZfFnhLx HfwwDVfC8CrtO6K4gizjucEZrzyzxrQfgxpGheH721MEBcwkNLtBJJHPOM1z v7LP7D2pfEi78T63p0di9poU6RiOVCzNkZyvHWu08TePtB09L9ItY0qR5pnj Tbcx8/N9cAY5r7A/Yk8QeDv2eP2T/EWqa74h8Lw3/iARz2aLqVtJNkKRkhWL KfrigD5BvvDEaQahpN9aW8c8KtGFEQAyAR6VX+FPwmhsdLkAMMdw0jFn7lSe Bnr+Fdrq2uaJ42+Imq3x8QaH9kaeRgZL6LfNnkYJaqP/AAk/h3RNSFuuvaPF KBuLJdxmLpkD5TjPvQByXxK+HuqaNcx3Gl3BWO2Hz+XuX16Y619Ufs+fEXxL efCC702fUL421xpjxygzuUbMRGSCewz1rhPC3jfwXdWa2t/r3h4i4OC5uYiF PqPSvTvhZ4w8FDTNQ0y18RaLEZoZE8x9YhWFRtYbdhYZzntQB518FfC3/CQf 8E4v2+9MhMYk1O/+FtoGI43P4mlXJ/Fq5W6/Ze8W/FJLPwvbiE2WkPvtGdW8 lARt5HSvW/2atNtdB/YT/bQlfUNLuIW8S/Cl5ZoLqJ444x4rJBZlYgDGeTxw a+jPgD8Z/h4fAN6k/ivwja3CWwg3NfQI5YEHruz360AfOXwy/YP0r4baZIdS 0HStW1CDAkkeJJA7Ecldyk1r+Mf2KdEuZbe70aJdEvVUljC3locgYBVQM4Of zr3bWvj34DsIw3/CW+Gbjyz/AA6pD/8AFVx2uftH+EJoJzF4l8OFo1Ygf2lD znp/F7UAfOvjXS774d60ulXs7XE6AfvoFYKwPNc5rXw/u/FWoQwFoXjn++ZZ RxjsetYHx2/aDNx8Q7todb0iR2cBGW9hYBccYYNXN2vxGXQ5xLceJLKU7t7J /aiMB+G+gD1mD4HWdrbJBJb6c8RBJBCtn17Vh+LfgfosdnJBDp+mxMeSViQZ PHXArzvWv2iYYFxb6/Zq3QBbpfl/WuN1/wCP1xNJKy+JEkcncQL3gfT5vagB 3jv4HeCvBHh281fxBJcTywOCkFq+0EH17da6T9mDwN4G8Z+Ebm7s9O3yLclF +0bGZe44Ir55+OnxXl8R6Q1vPqMMyzYZgsuQcduDXFfDb4v6l8OdXglstYur SHzAzpFcMqt0zkA88UAfpDZ+BbHTrd4ltYIwFONkQXH6V86/GPwff6T4l1FJ Zmeyv38yECU74MDGBknA68CvWPhF+1D4d+ImgWXnarp9vdyIvmCaZUy2Bk5O PfrWB8cl8L+I11W+TX9Ka6tAWWOO+T5/pzg9aAPGtOuNQtN1uyCR3Q/OvGVA 6flXd/si/FZvBPxUtrGWaRLW9kWFlz8pye4zW74H+EulfGvwqup6D4g0HSLi ybyZRd6tBCZMcngupIPT8a838f6TpnhjxZcfZNT0xL6wmbM0V7EUkkRiCVKt gAkflivQzP8AjL/BT/8ASIkQ2+/8z9B9es4tW0S4gba8cyY6gg18Pftc/De1 +F3j2BLJfLS7g+0MU4+bdXtn7LP7RmmeJ/CZtdZ1/T0urNQubi+QbvcEnnrX m/7d/ijQ9Z1OwvrTWdIuvKh8oiK8jdh8/PRq88s8Gkvp9eukidmbvjHOAMmp PBumWHiTx3Fa3sEtxYwSeUscYOJjj+IdCPrXRfBOx0zxV4pVG1DSY2kUorTX caAZUgnLMO2a9k0b4Z+EfhheXVxNr/h2abO0vBewnDDnK4b+VAGhoGrxeB9J jt9Mj+wSS4ijhtwI8K3Bztx1z17ZNfQHhv4TweGNAtLqWxtZbxF81YUjC9Ry ScYPBJ59Kn/Y5+D3w01vQJvGHjTxL4TYyM40+yl1W3MiKpZNzLuOPmXPIB5q H4mfH/w5cavdQ6d4i0KKOzQhTHfwqCDleMNycUAeOfGXV5J7jUJlBikTJ2g4 OeOmKufAfQLl/CMN4kgje7laR2bkja7Lj8hXAeP/AIjaPc3klumt6bI9wxCk 3iE9v4t3H1zXsvwX8U+GbD4LuJ/EOgJdrcoiA6hFvAYtk/e6AmgDS+PennXv hTeaZeneZ43yGbkgoccn61+PnxF8PReFPG+p6dDnyrOYxrk5OB71+rvx4+On hp9Mk2a/o8wtoGDeXdo5O1DkjByfp1r8qvixr1t4p+I2sajZyedbXlw0kb7S u4cc4IB/SgD+rvw78VdT8AaX8C9FTRbG+0XxxAmkT376k8Nzp08ekT30ZW38 llljdLKdWYyxshKYWQE7fS/2fPiNqXjzxVq8Vxpulw2mk6TLbXF7YahJd27a qLa6W/s4meCLzY7ZliTzx96R5o2SKSB1ry7RfCPwn8a6T8G7PxvYfD3VfG1r pUV94Qg1uCzm1OOSGGCWabT1mBlDR7IndoeV8tGOMKa7f9kr4Z/B/wCGviHV h8LdB+GmhS67oX9r3h8KWVjatqNnLa3n2O6k+zKPMhcrc+VI2VbbLtJw1ef1 Ot/CdB+xj8ZfEfxD+O1jZ6v8JfiD4Etobaedb/XbzQ5raZxGVEKix1G5lDkM WBaMJiNssDtDfZ1fOXwG/wCSr6V/22/9EvX0bXTh/hZhV+IKKKK3MwrnPH/j 5fAkNm39mXuqSXrTJHFazW0bloraa4x+/ljU7hDsGCcM6ltqB3To6wfHvw48 PfE/Rk07xNomk6/psVxDdiz1G0jurfzoZUmhkMcgKl45UjkRsZV0VhhlBB1A 4rT/ABr4u0r9qgeH9S1nwbfeEPEmhXer6JYQ6fPZa5prWkmnxTebI9xJHeRS PeM25IbYwYhQiYyb18+8b/tdeK7X9nj4W+O/Del2GoWHxE17w5NNeajYvZx6 bo2sa1YWsMDQC5eQ6gtrqMY3BjCZLaeRgg8u3f1ub4efDz4Q+JfF3xLk0Pwb 4X1jVLNZ/E/ip7K2sbq7tbWLCve3mFZ4oYk4MrlUROwFV/Hvwo8CyeHNIi8U +GofFVlpXiaDWdLTVdPm8Qy6bqs14xgu4BIs0kBhluW2SLtS0izgwwxfKota X6f5/wBIO5D43+MHiXwJ4DfU7T4c+I/H2qpq09h/ZPhS/wBNE6wLJL5dy0mp XVlDgxpFvQSMySSlQJFUvWh4b+L0+u32mWl14V1/RL2+tbO4ura9uNPeTSXu Irh/JnEFzJl42t/LYwmSMtKhR5EWV49hpfD3wn0Ib5NK0DTbnUAA0kkdtDLe 3t3hRk4BmuLqcAD70kswAyz8t1D4c+H9U8cWPia70TSbrxFpNtNaWOqS2sb3 ljBMU8+OKUjdGknlRb1UgP5abs7VwaMVjx7Qvjv408GfCjxpb+Mtd8F614y8 PeLNP8G2mq+HNBuI7FbrUxpiWc11psl9JLEsU2qRGWMXpLwRiRXQyBEveN/2 gfG2jfta2/gfTvD+iroN14I17XLKbUrhoZ9U1HT59GVNskZdYbMjU3jZniaU yROwQIimfuvB/wCzn4E+GvwkufAnhDwto3gfwlcxTxf2b4WgGhRW/nZ82SE2 flNBKSxYSxFXVvmDBhmsOfRPhV8Vf2hvFul3nhjQ9U+I2heGbXTNbvL7w4Rd SaFqMly0VoLyWECe0lltbndDFI6B4mDqrdWntf8ArT/PUaK3x/8Ain8SPBvj z4a2/gbwr4e1/wAOazq2zxbdalc6hDc6dYMYoEe1W2s542l865jc+c8YCQOC BGZrq01vgn8WtV+InxK+L+kahDp8dp4C8XQaDpjW0bpJLbvoWkagWmLOwaTz 7+cAqEXYsY2lgzt2et+LrPw/q2j2U63pm1y7aytfIspp0WRYJZyZXjVlgTZC 4EkpRC5jjDF5I1by9fgv8Dvhponxe1aT4feA/DGla8tw3xG1CfwrBp1r4kiN u1zcTX0rQql/B5V3MZJWMsYZ7hGbesqgX+f6f18xLY0/G2s/ECw/aa8F2Oj+ IvAP/CE6la3U+s6DeaLcnX5I4IpA15a3q3gh8tLifTY2hazY7ZZG83LKq9p8 OfiBZfEzw5LqdgFFvFqF7p+Vure6DPa3c1q7B7eSROXhY7SwkTO2RI5FeNdK TRrN9Ugv2trZr62hkt4Lgxr5sUTlGeNWxkKxijJA4JjTOdoo0XSLPw5pNrp+ n2ltY2FjClvbW1vGscNvEg2oiIuAqqoAAAwAABSA5fWPHep+I59f0LQIRpWt 28V1bWOrajbxX+nW90sEEkTy20V1HcSR7rgExkwlhbyDfGHhkk4HwJ+0H4gu /h74Ctda1Dwzd+MPFPjO/wDCEuo6Lps02jyvps2oyXTmFrnzLVpbXSrlArTT fZrmVFY3Cxtv7DUvhr8PPGvj7xfp+o+ENF1bWNZ8Pwad4huNQ8P+bFq2lTvd qlhLcyRGK5hBFyWtd7+WJ9zoouEMmX8N9X+Fur/An4ZQeEJNO8PeBvFcOn3n gmz0lJdAjuIkh/tO2hggjELohgt3ke2KqGiSVJEKF0Iu3oFtfv8A+AYvwS+I fxVv7PxNN4tuPhvr32HU7K2tz4fttQ06CwZpQuoWXm3BlF+1ojIEu4hCtxMJ YXt7R4nrsJvjR4hj+OaeEl+FHj99Bbr40F3og0Nf3Hm8xHUBqP3/ANzxZn5+ f9X+8ql8Mf2NPhD8DLXUYfBHwt+HfgyHWbq0vb9NB8OWemi/ntJTNayzCGNf MeGUl42bJRiWGDzXpbzBCScYAp6Azn/H/j5fAkNm39mXuqSXrTJHFazW0blo raa4x+/ljU7hDsGCcM6ltqB3Tj9P8a+LtK/aoHh/UtZ8G33hDxJoV3q+iWEO nz2Wuaa1pJp8U3myPcSR3kUj3jNuSG2MGIUImMm9d7442Hgu+8IWyePNO0nV dDfVtOhht9SsBe27X0l7AlkTEVYbxdtAUcjEbhXyuzcK/hLwL8OdF+MPirxF oeh+DbPx9rXk23iXVbGxto9WvvIhiMUd3MiiWTy4ZbfashO1JIsYVlzapTa5 0nbXp5fpdf0y+STjdI8t8NfHL4mfE6b4UeIvDOp/D5fCHxAtLbXE0zUtA1BN c1DTrqRp2G6K4eLT2stPktt00q3EV3dyCLFj5kJf0jxv+0HafBzwG+ueLNL8 RSourT6asPhTw5qviqYqJJfJkaCwtZZ1DQxqzsY/Ljkby97HaWpax+x38JPF 3j/RfFuvfD3wX4n8X+Hrk3mj6/r2mQ6vq+lObuS8T7Nd3IkngSO4ld4kjdUh yFjCKqqOt0nWfDfgjT7+CH7LoVla6qYJmmhNlBNe3kqzEozhVlaae6GXQtum kdcmQMoSi3pFByNuyRR0D4y2ninUNNis9J10W2p2llepPeWwsZLeO6iuJI/P tLho7uF18gK8ckCurzKCvyTeV5z4X/ag1jV/2atZ8VX62FnrcnjzVvAulyW2 myXVrbSjxXc6Bp1xPB9oR5EUi2kuNs0e4CYp5YKqvp+qeEfC+p/FHSNXvNCs r3xTaWF3HpurSaZ5s1hbO0AuIo7ooRCJT5GYw6mUQ5wwiJXC+H37Lfwz+D/w y1fwf4G8G+GPh74a8QGVr2z8IWcfh4SyyRrC86tZeUyT7FRRMhEi7EKsCowr eQuV9jlPG/7QPjfRv2tbfwPp3h/RV0G68Ea9rllNqVw0M+qajp8+jKm2SMus NmRqbxszxNKZInYIERTP6j4k+I9j4Y1vwtYTqDP4tvnsbQNdW8DKyWk90W2S yI8vyW7DZAskg3BigjSSROc1j4efDT4g/GbWl1Dwz4X1Tx3B4Yi0zU7y50eO S+bQ7ya422bXLJua1kltpyYA5XchLL8wJ7ybRLO41aDUHtbdr+1hkt4bkxqZ oYpCjSIr4yFZooiQDgmNCfujDlGUUlJWf+eq/CwSTTs1b+tC1RRRUiCiiigA rwn9p3/kfrT/AK8E/wDRkle7V4T+07/yP1p/14J/6MkrOr8DNKfxHiWqf8lp 0D/sCan/AOj9Pr8hf+DvHp+z3/3Mn/uKr9etU/5LToH/AGBNT/8AR+n1+Qv/ AAd49P2e/wDuZP8A3FVhS+JehpU2Z+MFFFFdZzm98PPiLqfwz8Qx6lpc7QTp weAQy5yRj8B+Vfqz/wAE+f8AgrL4e8Q/DCLwF48ZNU0O5DrPpkqbWQkt8yPj j7xbAOcmvyIqfTNTuNHvo7m1leCeI7kdTgg0Afpt+2//AMErodH8J33jf4Py wa54VJSS4soJwbi2UuvJSQq7AMw+6pxgnGBmvmn4IftieN/2aNV/s+Wa5fS7 d1T7FNCN0aqDkIT061v/ALIX/BVrxR8F9et49Wv5PsjgxTsIg0ciMpUqygZI IP619I+OtB+BP7cOhR6hpyWHhfxEGZ5LyISiKbd32KCRgjvQBx+s/wDBRfTv jVpFnp0Il0y5nkSKUXC5HLYJyBjHOa+tvhdBpXgDTrK10e6s7m6m5aRHDALw WPWvzi+Jf7AHjvwC8t1oljZ+JdGifdFNbTws7Lnn5TIHB4PG2sWw+NnxF+Gz wLbR+J7C4jDR/NEWVVxjA4oA/U/4o22l+M45buS+F1BZjyZLdGKB2HzY5wP4 hXzF4/8AFehj4qWN/wCF9WtbWfS1aOWPzANsoYEA7sbhxg4/OvlOw/a08fi1 uLeTUPETR3Mpkn8uPZ5mQAT93rgCs3+2YtavY7jT/CfiCS/lJaSXf88shx83 IHfnFehS/wBxqf44flMj7a+f6H6Lfs/fER/iJrM1x4k1W2vNSs49y28Uqoqg HCtxwc/Wu++JP7RPhjwpoly3iXVbCJcECIHe6fQJk9Pavy78O2HxU0nUpbnw /o3iexlmTaWyjMR6ZNalr+y38ZfjPNHqGpW7qzklZb66t43B6d3HavPLOk/a G/4KCarr0V7aeDJ7m0s0mCxXZUCUjg8BgGGfcV5z8Fv2d/H37WfjVpLKyv8A U53Kie6l2r8pJzuYkYHDdfQ1758KP2N/h18LtWt9S+IuoQarBAd09jbzuHft w6gr1967X4of8FM/Av7Mvh6XRfhhp0nh2O+BFyYn826ugvC5kxwAGfg4+9+Q B6d8J/hL8Lv+CcXw9jutcj0zVPiKq7z+8aWGwbJKknlGbHXaxr88/wBtn9vL XP2kvEd1GupT3FrNKXmnZArXBwR6Agc/pXAfHX9q3xT8b9dvJry/mFjcN8sB Cg4xg5IHevL6ADrRRRQB7/8A8Enf+Upv7NP/AGVXwv8A+ne1r+wzxb/wTH/Z t8feKtT13Xf2ffgjrWt61dy3+oahf+BdLubq/uJXLyzSyvAXkkd2ZmZiSxJJ JJr+PP8A4JO/8pTf2af+yq+F/wD072tf2+0AfP3/AA6a/ZZ/6Nq+AH/hvdI/ +R6P+HTX7LP/AEbV8AP/AA3ukf8AyPX0DRQB8/f8Omv2Wf8Ao2r4Af8AhvdI /wDkej/h01+yz/0bV8AP/De6R/8AI9fQNFAHz9/w6a/ZZ/6Nq+AH/hvdI/8A kej/AIdNfss/9G1fAD/w3ukf/I9fQNFAHz9/w6a/ZZ/6Nq+AH/hvdI/+R6P+ HTX7LP8A0bV8AP8Aw3ukf/I9fQNFAHz/AB/8EoP2W4CSn7NvwDUkFSV+H2kg 4PBH+o7ik/4dNfss/wDRtXwA/wDDe6R/8j19A0UXewHz9/w6a/ZZ/wCjavgB /wCG90j/AOR6P+HTX7LP/RtXwA/8N7pH/wAj19A0UAfP3/Dpr9ln/o2r4Af+ G90j/wCR6P8Ah01+yz/0bV8AP/De6R/8j19A0UAfP3/Dpr9ln/o2r4Af+G90 j/5Ho/4dNfss/wDRtXwA/wDDe6R/8j19A0UAfP3/AA6a/ZZ/6Nq+AH/hvdI/ +R6P+HTX7LP/AEbV8AP/AA3ukf8AyPX0DRQB494d/wCCeXwC8IeA/EfhbSfg h8IdL8MeMPs39vaRZ+DtOgsNb+zSGW2+1QLCEn8qQl08wNsYkrgmuf8A+HTX 7LP/AEbV8AP/AA3ukf8AyPX0DRQB8/f8Omv2Wf8Ao2r4Af8AhvdI/wDkej/h 01+yz/0bV8AP/De6R/8AI9fQNFAHz9/w6a/ZZ/6Nq+AH/hvdI/8Akej/AIdN fss/9G1fAD/w3ukf/I9fQNFAHz9/w6a/ZZ/6Nq+AH/hvdI/+R6P+HTX7LP8A 0bV8AP8Aw3ukf/I9fQNFAHz9/wAOmv2Wf+javgB/4b3SP/kej/h01+yz/wBG 1fAD/wAN7pH/AMj19A0UAfP3/Dpr9ln/AKNq+AH/AIb3SP8A5Ho/4dNfss/9 G1fAD/w3ukf/ACPX0DRQB8/H/gk5+yyx/wCTa/gDx/1T7Sf/AJHo/wCHTX7L P/RtXwA/8N7pH/yPX0DRR6gfP3/Dpr9ln/o2r4Af+G90j/5Ho/4dNfss/wDR tXwA/wDDe6R/8j19A0UAfP3/AA6a/ZZ/6Nq+AH/hvdI/+R6P+HTX7LP/AEbV 8AP/AA3ukf8AyPX0DRQB8/f8Omv2Wf8Ao2r4Af8AhvdI/wDkej/h01+yz/0b V8AP/De6R/8AI9fQNFAHz9/w6a/ZZ/6Nq+AH/hvdI/8Akej/AIdNfss/9G1f AD/w3ukf/I9fQNFAHz9/w6a/ZZ/6Nq+AH/hvdI/+R6Q/8Emf2WTj/jGv4A8f 9U90n/4xX0FRSYH5pj4Q+JviP4a+Aus6F4k0HRLPwI9vrd7a6hoMuoy6rv0y WyMUcqXcAt8QXdz8zJL85ibG1GST1L4D/D/xFofxf8Y+IPEXiDSdY/tnT5rP TLXT9GbT106xgivpIkld55nuJ/8ASWDyAxxnYpWGMly3iet/ErW/h3d/s3xa bd+Nm0bXtun69p+k+F5dV042j6VJ5Vxd3ENpK9oY737EFczQoUlmLh0jd4/b /gN4tl1T4r+NtIuZ9eubnSbeWUm50Oey06COawlMcVncvCkd2AqBpWSWYpLI 6sYwUiThs76HX9ks/sY/smfCv4D/AB4sNX8C/DP4feC9XubaezmvdC8O2enX EsDIXaJpIY1YoWjQlScZRTjgV9nV85fAb/kq+lf9tv8A0S9fRtdGH+Ewq/EF FFFbmYV89/t4/sU6l+2Vpvh2wsPEXh3wxbaeLq31K5u9Evb++vLO4EQltI2g 1C1ha3kEYM1pfw31lcNHbma1lWEK30JRR1uB8maz/wAEt7bxL8WPinq+peNN V1DQPiV4U1/wzFBd3utXup6CutSxS3n2ZrnVJdMig3Rpshg0yFgIYN0r7H8z 2LVfhR4m1/Qfh1qHiTUtE8Q+NPA2vJq73GlwT+H9KvDLBdafO32d3vZAEsr6 d0iaVvMniiBkiViy+o0UraWC58f/AAz/AOCYuteCPh143sLjxx4XXXfEGpaB qmhy6X4XvodK8PHRNR/tGwtVtrvVLu4+wLcYX7Db3dtaQwl1tobV5ZpZPp/S 9J8RpeaN9v1bQ7mzh02SHV4oNJlhe+vT5GyeBjcuLeABbndC6zM3mxYmXym8 35YuvGXhnWviB+1pZ+KPEvxJ1RdD8XeHrO90XwnrviG61nRNLk0TR5rVrKw0 iU3lok13NfM8lpGhnCT+azpC2z2b4WReIIf2DPDkfxOPiX/hKU8A2y+LG01r ltYN6NPQXnkHT/8ASDc+Z5uz7J+9348r5ttPeN35fiv8irPmSuc0n7EF5peg 2NrZ+JtI1Ga6mvLDxR/wkGhzara+LNHuVtrYx3Vv9rjWXUU0+wsrRb6czLtW 4ZrdhcMi5fxr/Ym8dfFb4r/FDW4PHPgGHQvHnhjRPD+m6ZqPgzULi60OXS7y e7gvTe22sW0rzrLeXjxyQC2lhkFnJHIr27NOz4afHz4++NdH1c6B8PPBfiCP TdSs7W3u/Fl3rXgBr2zk0HR7w3MdvLp2oSyMb271CJkfyjAbZYX3ypKw9NuP jVe6B8QPFtrqdtrEukeFrCbVnFj4R1G6eW18qIxi3uIfNS8ufMivAbWCM3BD wfulGx7kk2l36kRdkmYn7Pf7M+qfs6+Bvh3okV/aeJbrQJdUi1nUDdXWmQyR 30k17NcpaF7n7Xcm7W3TzbyeS6KS3M0l5NNJP9qwIf2RfHLeHv2kbHUfG/gz UYfjhNdXWjxp4MaNfDskmkw6Ui3S3F5cw6giwWlmWUwwq7rOSuyVY4sL9mj4 h+I9c/Ye/Zqn8Rr4m0+W4TQtK8cXni+bUfD2uWt5DZtGvmfamguZprjW4bK3 KyB1u0unGJY5stL4V1jxjZftlfGFNV1r4gNo0Nj4NTTni0O+i0SG5fVdVMtr Y+bG8Ewazk01L24hZ8F3ZmgESJBTT5mvl+KK1Wp7/wCLfDWu68xtNN8QroOm TaXd2kstvYLPqcF3J5QtrqCaZngXyVE5aOa2mEjvCSVWN0m+UPhv/wAEwPH3 gD4da14bl+Lug63p3izxDeeI/EVvq/h3WdXj1t20O1020sppb/Xbm8ksUntY 7i4t5Ll/tUY+y7oYC6v9l3kphs5HXG9UJH1xXyt+0l+0X4itf2YdJ10WXxHl 1FfH3hi0nTwt4C19dQ8i01ewl1fda2yXFw1m0NtqH74E288MiRI9wJEadRvf TyX3sS6Hu83hDxLY6z43bRLjS9Pm12xgudM1PUrm71WODVPIe3bfp5eIR2ca w2cgit7qMTPLckiGQtNN558Cv2TPEnwi+CXwE8Iav4r8O+IJ/gzNHDcajbeG vsX9q2kGk3um2yxRSz3L2s4W4gaSWObMgimUBI52jGt8UdejuPil4rsvFEmr WnhZfh9Lc6ZHoOqajHrupK00y6yYLKxk+1SS28a6QIZreL7TFJfMkLh5ipof siXd3F4E8IW7XfxMWzlg12SztPF9vctfyaeupr9hkvJrpDdJMls0SxJdOty8 UshnV5o3KCbf9driu1sa37S3wC8ZfH/4Z674dsfiAPChu9VtrrT59NttQs3S yjji8y0u57LULa8ctMJpVls7qyI/cRyLNEk6XXDfs6/sM+LPglqnw3i1Tx/4 d8V+Hvhb4c0bw3omnzeFZoZbZbTS7myu75ZTfuv266eWDE7Rt5FrHcW6KTdz Tn234z+N7r4ceBJ9bt4rqdNOmhnu47XSZ9WuXtVkU3AhtYCJppTCHCLEHfcV 2xTNiJ/Hb34va3F+3P8ACHRL628fPZ6l8PtcfVprDwtrCeHP7RmudIkszczC N7S3n8qz1PYlxMZYQ5jLZuU81Rv06/omP+vxPXPh14N1a7+F2jWfj2Sx1/xA rRahqOUintIL0TC5VICIId0VvLtWCR4ll2wRO5Mu5zxkn7LF6nxI+I/iW38U 3Nvc+M7uw1HToUN4sOlT2sNomJoluhDcRytZQ79kcMrxPLE0rIy7fJP2v/EX jq+/YO+PVzoWpeIdM8f213r1rLZ+DJNQ13W4JBDJDokFlFayTS2E89sdGuZR FGAiXNxLshaU3Ce+a1q+kyeFPiU/iXxBqmmeGLVZTqF9eXUmhx6Ja/2dC07Q XqeS8USIWlNysu6KV5gJEMW1NaWJqUYylTdtNfwf5pGtOrOm/de/+f8AwDkN f/ZHvfEHj/wnrJ1bwxp0WjywX2rx6foM8DaxdpePeu+37YbbyjcO8iLcQXMk DyzSxyrM/mrPd/sx+IdXX4i/bvFWiufF/ibTvFWleToMkf8AZNzYmy8iOfN0 32mMjT7UOE8hmPnEMm9RHifsA+MtS8Zfsnfs66lqOv8AiDWL7W/hZpuoX810 XvI9Tley05mu7i7dWY3Jd227pQZRNOxEhj3J6/8ADnxJq3iXUPE76hZ3VpY2 esvZ6Sbiw+yPPbxwwq8gzPI0qG5FyFlaODcoXbGyBLifoeOrxla+q8l0afbu v0LeLq3vf8F6/ocd46+AXiP4g/E7RdZu/Fmn2em2fhbUfDl/BYaVNbX07X3k Ga5t7oXX+jsr2tu0YMchTEnzMWVo8v4j/sxa98T/AIaapoV9rfgq3vfEtjql trd9a+E5EM81zYNY29zArXjGKSKHYrmRpTKibAYlxt9toqYY6tC3K9ttF/l5 ijiakbcr28jmvGugX9p4c8Qal4XsdDPjW60lrayub4GCG5miSVrWO4lRHkEC yyueFcoJZCqkkgyWeg65pWmeG7SHX01D+zSkesXep2CSXesxLbyIWBgMEMEz T+VKzrE0e1ZEWJN6vH0NFcqk07mD1Oc1DSvFL6X4mW01rQ4L27DDw9LLpEss Wl/6Oir9rQXKm7xcCSQ+W1vmN1j4ZTM3RICEAJBOOTjGaWim5Nqz/IVgoooq RhXhP7Tv/I/Wn/Xgn/oySvdq8J/ad/5H60/68E/9GSVnV+BmlP4jxLVP+S06 B/2BNT/9H6fX5C/8HePT9nv/ALmT/wBxVfr1qn/JadA/7Amp/wDo/T6+Bf8A g4L/AOCXHx2/4KTL8I/+FK+BT40/4Qs6z/bI/tnT9O+xfavsHkf8fc8W/f8A Z5vubsbOcZGcKXxL0NKmzP54KK+/v+IXT9uv/ohh/wDCy8P/APydR/xC6ft1 /wDRDD/4WXh//wCTq6znPgGivv7/AIhdP26/+iGH/wALLw//APJ1H/ELp+3X /wBEMP8A4WXh/wD+TqAPgGtvwh8RdZ8BzF9Kv5rTcQSFwQSPY19x/wDELp+3 X/0Qw/8AhZeH/wD5Oo/4hdP26/8Aohh/8LLw/wD/ACdQB4N8Kf8Ago148+HV qIG1GaVF6GNUVm5J54r0qD/gqBb60wfVdN+0PjrPEHOTjPQf5zXX/wDELp+3 X/0Qw/8AhZeH/wD5Oo/4hdP26/8Aohh/8LLw/wD/ACdQBxtv/wAFBPCETlpP D9g7E7jm0PJ/KktP+CjGi6Fdb7bRlt5oidrRxcA5z0xXZ/8AELp+3X/0Qw/+ Fl4f/wDk6pbv/g16/bpmuJHX4GnDHI/4rHw//wDJ1dtOpFYScG9XKL+5T/zR NveT/rocnef8FbdXt4JksZb2HeONqqP6V514t/4KVeNfEkbI1zOyNkhZgjAf kK9s/wCIXT9uv/ohh/8ACy8P/wDydR/xC6ft1/8ARDD/AOFl4f8A/k6uIo+P fFf7SXi7xcHS41N1iY8KiKMD8q4a4uZLuZpJXaSRuSzHJNfff/ELp+3X/wBE MP8A4WXh/wD+TqP+IXT9uv8A6IYf/Cy8P/8AydQB8A0V9/f8Qun7df8A0Qw/ +Fl4f/8Ak6j/AIhdP26/+iGH/wALLw//APJ1AHwDRX39/wAQun7df/RDD/4W Xh//AOTqP+IXT9uv/ohh/wDCy8P/APydQB4B/wAEnf8AlKb+zT/2VXwv/wCn e1r+32v5Yf8Agnr/AMG4v7ZvwO/b5+B/jbxT8Gzpfhnwf4/0HW9XvP8AhLND n+yWdtqME08uyO8Z32xox2orMcYAJ4r+p1WJJ447H1oAWiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKCcUmB+dfgj4xQ+A9A+DXhy50TW rhfHVhHp9rqcH2Y2dlcw6ZJeCKcNMswMkNtOVaOJ1Bjw7JuXd7B8OvE1hqXj K/02O6tzqVnpNzcz2fmr9oghkgukileMHcqO0EwViAGMT4+6ceOaH8A/Dfxk 8BfCTWtcTXn1HwTZ2mpaObDxBqGmxQXDWyRl5IraaOOf92XjxMrr5csqY2yy K3pHwP8Ag5oHw5+IfinWtNi1STWPFFvdzajdahq15qMjqtvdPHDEbmWTyLdG mmKQRbIYzK+xF3GuB2udX2Rf2Mf2mfDvxl+O9hpej6Z8QbS6t7ae6Z9d8B65 oNsUCFCFnvrSGJny4IjVi5AYhSFYj7Or5y+A3/JV9K/7bf8Aol6+ja6cPszG r8QUUUVuZhXnnx2/al8B/szx6Y/jnXk0BdbMsWmGW1nl/tS5TZtsbby0bz7+ Xf8AuLKPdc3GyXyYpPKk2+h1558ff2XfCX7TVlYWnjAeJbrTbESo+n6f4o1T SbHUopQokhvbe0uIor6FlUKYrpZYyrOu3DuGXUDhvGv/AAU0+CXhO88e6ZZe PdA8VeKvhr4e1vxN4g8N6Dew32rafbaQyJepLEHAglEkiIiTtGXJJXKo7L6l 8R/iXD4Fu/DFiv2CTVfF2tRaRptvd3Elulywilurna6RSfvI7K1u5kVgqyNC sZePfvGDcfso+Cb3xN4n1K4s9Yu4PGOmXOkavo1xr+oTeH7qC5O64I0ppjYR yysWMkyQLK5kl3OfMfdoaX8B9J0DwP4c0LT77XkTwpfRX+m6hqWoya7qkTK7 eYpu9RNzM/nQyT27uzGUQ3Eio8Z2sq6D0OL03/goB8MvFHhm71Xw7rF14nh0 zXNE8OalHp9lKJNI1DVdSi06KzuvNEYgu4Jpka6s3IurVCplhUyRLJ7Q8oRC xBwBk8ZP5V8/+Bf2XvgfqNz8R/h/pur33ifX3GlnxbBf/ELVdc8SaNsaS70v dc3F7LfafskMlza+XJFskLzRbXJevUrHRfDfhzxx4Z03+27wa9Y6JdwaZp1z 4iuZpr6xjeyS4uJIJJj9seJzaKbqZZJIjckeYv2lxI+ll/WgnYzrH9qLwNq8 F+9hrZ1N7Gxm1NYbCyuLufULWK0tLuSezijjZ72MRX9n89ssgL3CRgmQlBz2 ufteab8Pfi58RdG8W21joHhTwH4f0HxI3iNbyW5je21O51C2eS6jWAC0ht30 9nknMjxJC5lkaFI3IyfiFofwJ+Hek6Dp+veLNC8HW3h3xeLDTwfG82iyJrF6 w1IaQzpcxvKk4ljm/sx2aGSIQDyDGkYXX8U/sS+AfHXxQ8YeMbi5+IFr4j8a 6ba6NrFzpPxC8QaYv2W1kWW3ihitr2OO0KPvIa3WNj9oucki5n8we2gK3U2/ CH7SOh/Fc+DLnwVf6Lr2l+LmvbuKea8ks5rrTLUNHJqNjE0JN7AbmSyVZVKQ SQ3sc8czq8KzQ6z+0RFpPhn4u6la6VceIZ/hTNLDLpWiia51HUHj0m01IW6x vDGv2mRbtVRInmRg0R8wO0kUWbp2lfDf9mu9+GvgG08bWfgq5vNSvrjQtC1D XoZtR8ZStHO92jPfNLd3knnXYu5ZI5PPaZUaSRkeRJOe+JX7N/wV+BHwv+NO teMtcufC3hL4uiT/AITzU/E/jq//ALMb7VH9iLK17dNBYlo5IrdfIEQ2pbxg bYolWnbp/X9IFvqe+iZGhDghkYZGOcj+teTeC/27PhD8Tfhv4g8X+FPHugeL vDHhnUxot5qWgStqtvLfGC3mW0tmtw/2ud1uoFSK38x3lkESgy5QX/Ffif4W fEn4nWvg7WfE/hnV/Ees+F7uRPCU2vpINa0W7aNJriTS/N8u6gYwbFneFwga ZEdRLKr+ZfBv9jX9n74x+BtM8V+B9e1zxtod1f6hqdl4i0z4q65rC3t7Ppv9 iXF0L5dQkaW4jtIjbRymRpLcq5iaKQs1Jb6/1r/l+IKx6r4w+LPiNJPGMHgz w94f8Zaj4bFjY29mviRbSRdTnIeWC+zC4s4IbWeyujIvnzvFO5S2ZlhW4ofD n9oXVPEvgf4c6jrHhJ7S88eajNp8h0TUk1vS9PRLa8uIr8XqKgksbhbRPJmM cbt9ttg0cbsyLN4w+E/hP9p7wj4t0y98S6tr3hLxHAvh7ULHRdeewS1msrq4 jukS8sWivEmaTMFxGbgri28vYhacSXvD37M/hjw1o/gexjfxHqMfw81SfWNG n1nxDf6zex3E1teWrtNdXk0txOPJv7lFEsjBQyBcBECmgkb3xV+LPhX4G+Bb 3xR428S+H/CHhnTDH9s1fW9Qh0+xtPMkWKPzJpWVE3SOiDJGWdQOSBWL4M/a V8FfEXTfA95oesjVIfiPpaa34e8i1nZ76we3FwLtl2boYNjxAyTBEWSeCIkS TRo3Jx+HfhB+13Y+KrPSfFlv4xk0fxKJ9Tn8O+N7przwtq8Fuls0UE9pdCbS 5PJRo5ILd4Q6z3IkRvtM/mY3wh/Z3+B/wd1nwXr3hLV2spfEen6VpfhRl8d6 hc2utWOmaVeLY2tpFJdtFcwR2VzeT+Wisjkm5cPJGsyqPmM9f+JXi++8EeH7 a80/RbrX7ifVNPsHtoCweKK4vIbeW4O1WO2GOVpm4xtibLKMsObsf2n/AAnd /E/xL4Rm1C2stX8LXVvaXcNxdwLIzXEdm9uyx7/MKStexRIxUBpVdBzt3b3w c+Gum/CD4aaP4d0qV7m006DD3cqQrPqU7sZJ7ubyUSNp55XeaVlRQ8krtgbq wPHngHwD8OND8f8AirxJfReH9I1+KPUfEupahr09nY2f2aBI0uw7zLHZvHHF EfPi8pgYY2LbkVh1UJ0Emq0X5Neq+WivbzeptRlStaovu+Xy2uXL39o3wlpP inT9Dvb66stb1ac29jY3FhcRXF+63D27+QjIGmWNkLyNGGWOFkmcrC6SNDq/ x/0/w4PET3UDzxaD4l03wyyWRaSYTXwsBE0gkWNVw1/GSEaQeWA27eWiTAtf Cvwr8ceGPB3jaPxSut+GIhp0Ohaj/wAJndXWi6pI1yi2Mh/0k217O91JF5cs gkkeUxYZnCYqfD7wb8HfijdfEy18NeJLLxTcaj4gEHi+Kx8ZXGoyaVrNsy4j IW4Y6fdwmOIBYvKkj8iEAL5UYXWP1RSV+b8O68+1/nb1LUsPe+tv+D/kd94s +LGneC/GFnp2qXOn6daTaJqGu3FxdTvEbeCze1Ejn5DEI1FyC7PIpXC7VcF2 jZ4v+Nug+A/D+sarq7anZafoCTS387aZcMsEcNr9qklBVDvjEPO9NylwYwfM BSqupfs5+Fta8a6Pr97DrF7qOiaVLoluLjW72W2ezlUCWKa3aUwz+ZhC7So7 OYoixJjQrxvxn8JfB/4Y+CtD0T4h+LxoOka5Ld+HLBPEfjy8tTr82oRtG9mz z3Qa9lZCwjRzI8YA8rYVBEw+q+7zc3nt57a+n9bkfq+nNc9K0rxhf3/xP1jQ n0W5g0zTdLsb+31Ys3k3ss8t2kluo2gboRbxM2GJxcplV4LdHXL/ABDi0/xd pF94LbxDLoWseJdHvVtXsbmFNUhhCpDLd2ySq4Jha5g+dkdFeWIMDuAOf4G0 7wV4q8NeGR4RvdOfQPCNxLa6VF4d1Mw6dA1qs2nyWpjtnEUkcB82I28gZI5Y VOxZIUKczcZPay8ten6v+tDnk1ul/X/BO4orzzw5deBfi3pPj6z0HxYuuw3u q3GieIm0nxXPLLo2oJbRW01nHJDPv064RBGxigMLpI5lwssjO3oKIEUAdFHH NKSjbRk3HUUUVIwrwn9p3/kfrT/rwT/0ZJXu1eE/tO/8j9af9eCf+jJKzq/A zSn8R4lqn/JadA/7Amp/+j9Pr8l/+DsNyniX9m4jqJvEWP8AylV+tGqf8lp0 D/sCan/6P0+vyX/4Ow2KeJf2bSuc+d4ix/5SqwpfEvQ0qbM/K48UUEljk8mi us5wIyKrXLqgKqoB4ycVZPORnBpkcRC/MQ5J70AUypBxjmrFnpU964CIefau n+Hvwxv/AB9rSW9rHueQjBIIxwa+u/gF+wlMqiXUoLORiFwG+bHX1FAHyh4T +CWpeISPLgDbum7Iz19q9E8PfseatdHLpbKMc5BJ/lX314Y/Z70DwrbJHLbW wdeAkcYJP6V1Fr4LtZH2WWkBivOTEOf0oA+ALb9i28iXLrbMQMgBT1/KnH9j 6aY8wxE4wOD/AIV93eJfh9f2UBZrKKBTyDgDHNYmj/D+91uTNqlq7hscgZJr 0KX+5VP8UPymR9tfP9D4g1T9ivUEibyktvoQc/yrh/E/7K+r6SXIgjwOcrn0 r9OR8NNVsoh9p023lA4JEYJH6Vkaz4R06RmS70kqe5EQA/PFeeWfkjrvw/u9 FLmWBlHuDn+VYF3ZtbORg4Hr1Ffp98Rf2UtJ8Y6bP9mgsA7DgGMKSfyr5I/a C/ZL1DwTIZYYoigLcJkjt7f5xQB85UVoavo8thcNGyBWQ+mM1nsNpIIwR1oA KKKAcUAek/8ABLjj/grH+ziBwB8WfDH/AKeLWqv/AAVi/wCUpv7S3/ZVfFH/ AKd7qrX/AAS4/wCUsn7OX/ZWfDH/AKeLWqv/AAVi/wCUpv7S3/ZVfFH/AKd7 qgDwCiiigAooooAKKKKACiiigDR8L/8AISf/AK9rj/0S9Z1aPhf/AJCT/wDX tcf+iXrOrvqf7lT/AMc/ypk/afy/UKKKK4CgooooAKKKKACiiigD7/8A+Cd/ /KCj/gor/wB01/8AUguK+AK+/wD/AIJ3/wDKCj/gor/3TX/1ILivgCgAoooo AKKKKACiiigAooooAKKKKAJrgYtrf/dP/oRqGprn/j2t/wDdP/oRqGu7MP4q /wAMP/SIkx2CiiiuEoKKKKACiiigAooooAKKKKAP6tNb+D/iLxve/s3eK/Df hvwVqcngYLd6nqWralJZ6jbWVxpUllJBaeXZz+YGNwszRtJCrNZQqSd/mRez /sx2fjjTfFHia08V6b4attNhGqSaTfabrD3lxqkUqXUm+4gNlbrasiGMBFku CdxDSsU8yTxzVv2lY/gbe/s26HfeKPA2jaZ8RSNEm0/V2EOo3bDSpJoJrSU3 CKALiO3t2QxSb3vYVDIxUSe7fAjWte1Txhrdtq89lcR6LpbafLNDpM1it3f/ ANnSTXM8JklkDWzCaBUUFjG8U6NJIR8vA9/v/M6mvd18j0H4Df8AJV9K/wC2 3/ol6+ja+Mf2MfEnxU1b48WEfjnwb8PvDmkrbTtDc6F4zvNZuJJ9hCxtDNpd oqoVLkuJCQVUbDuLL9nV0YfZmNX4gooorczCiivnv9vL9vXTv2GtN8PTX1l4 d1W58VC6tNNsbvxZZaRfT3iCIxGO2n/fXdvlys32BLq9Rnt1hsrtpSIzrYD6 Eor488ff8FNdbm8a/GPwN4U+HNxB41+GXgzxH4jto9X8QaRc3Op3Ng8ceneX otpevqrWl5vMgmmgtht8kIX+0Iw9w1X422fxM0H4dal8PdWXxR4Y8ca+kLeJ vC97a6jZWlpBBdXbytLsmheCaSyWxYqVZTefJIkgUhX0uOxwfhvwJ4+0T4if tCnT/C00Umt+MdK8U+F9QvdcSw0zxCI9D0m2a3aa0klu7dUuNMkSYy2xUxzI VS5UyRVfb4RfEay/bA+FviS20zwjdeBvCngnVfDer3l34pvZNaS5vptOnzDA 9k63KRnS4U8ya6SST7VI5AaECbzDwr/wVet/ip8K/GfijQ7PwXBpfw61vwzp uv6xa+LLTxHo0dveasltq16l3YubeO0trLzLhJbmWG5RAZbuxtIhF9o+q9M+ JvhzXbzRrWw8QaHe3niTTJNY0eGG/ikfVbKMw77qABiZYFNzbBpEBVftEWT8 65d7JeX+X+QO7d/kePfE34XfEfVv2efG/gPRdN0XxZe+Jda1mwudS8c6m1vZ HSNUa4ufNhht47p7qO0W7jsltZzaeatm/wC8hTymf2Dwf4ek0vxF4ouW03T9 PXVNQjnWW2fdJqO20t4vtE42Ltl/d+UBl/3cER3DOxfHIv29bQ6BYX95pWke HIPFM15pnhi48Q+IItPtL7WIFtoRot1JsdoNRfUJL21W3gS5YrplxIu47Yzw 3xN/4KG6L8B/2rPjlo2pfETwXqlv4A8GeFvEZ8I6t4p0fRJtHe4vb+HUFSeb y9srwHS2RLyZYjNd2ima2juhIBrSzFFN6o6bVf2RPGOgfAL4G/CfRbrw7qnh rwCvhmLXvEN9eTWV6BoF5pl5A1rYJBLHMbp7Bo3El1D5AkDAzkbK9d+I1/8A EH/hFPEEnhrw74Q1PW7G/tzoFrqPiW50+11S22wGV7ueOxna0dWa5VUjiuQ4 iiJdDMyw8d+z7+0Pe/tVeCvhz4x0y11LQdD16bVNSc2N7Y6hY3MEEk1pb2lx OVJkaZZFullsC9uXsmEd7PbtE95w9x+3do3iTwZ+1m3hLx14Tu/EXwLuLxB9 p1OHU7XRWh0G2uA89vZRfaY7ZLwXkciN50xltbtVcbVghck3dPzf5Id7/wBf Im+G/wCyJ408D3f7O/hSWfwxeeCvgdpFjJc60L6dNT1fULfQb/RTbrYeQYor dkvRcef9qLhozF5BB80dv+w38HfGnwU+GWr6b40uVWW81h7zStKHjHU/GI0G zNvbx/Zv7W1KOO8u908dxcZlQeX9p8pcpEpPovi74paH4IYwalfImpnS7vWY tKgRrvU7u0tPK+0yQWkIee48tp4FIhRzvniUAtIgb5Q+HH/BavwD8TPhzrWs w22g6BqP/CQ3mgeHdO1fxvo0sesR2mh2usXeozXthcXdnBY20E8jXE0c1x5U cIO155EtSczk2+v/AARO7sei6t8Avix45+OGoar/AMJZc/DTw/cwa9Zifw14 ofWL2Rpp9AOnXy2epae9hBL5GnX8ckPlSrD9rdopJHuZpE7A/CXxp4dTwZYJ 4n1jx4unyyQ63rniDWBpGoPA1wl0s4g0u1hs7mVHhhgETQQqYXk3SnMsdzpe MPEE1vZ/EzU9Z8X6x4A8OaVo6QxaxcS6VFZaNElrLPNrdvLLHKq7fP2P9uBi VtNDfZxGzSXHnPwT/aE1PW/hr8Fp7bx/pnjmx8a+MtX0D/hILrTxFN4p0+3t tbltZ7c2sa2xlZLC3mNwixW08UczwqolhjIuiDe/n/w4z4SfAf4mav8ACn4m 6D4n0P4dfCbVvEGhDw14V1bwBrUmoyaBYR29zBZQRo2mae0KWXm+bEvmTbpb i5KmCPy4hsfEz4HeIfix8fPgR8QrPQLDSofCd9Pq3iCDW9YYalosMmi6jZx2 VrbQJPaPK0+pZuJEuI1IsosNOFi8vd/aY/bS8Hfsz/DLXPEt9ONfXw5q1toe o2Wm6lp9u+n3k8cU0cN1c3tzb2diTDPDIpvLiASefAkZeW4gjl4b9nf/AIKX eFv2kdU+G+kaU/h2z8ReMPDmjeIdb02bxDC0vhxtU0u51C005QF3XV9IlrPI IVVP9Ftp7h2jAhjuFG71X9aC2PePhP4fk8KfDTw/pkmnafo0lhp0FudOsH32 en7YwvkQtsTMUeNiHYuVUfKOleW/F74ffETU73xfdeHPDPgXxNMqW+o+Grfx X4n1CKwk1NZ7Vl+0Wq2txFBHbNZQXMM0IMhm3qqW5aS4l7jWPE1/8Wvh1p+p eA9TFmbjV7Q/ar6ykhW4soNRjF6qJLFuKzW0VwsUoXa4kjkR9jLLXC2v7V9z YfF/4paLf2CPpHw5v7CKa7hmtUS0tbu1sJBPcb7gSokRnvJZJGhWLyYDtcyR OrdVDCzrJyg16dd0vxbsjelh5VF7r21/Jfjck8FfDHxn4f8Ahf8ADKbUtPh0 TX/DniWTU/Emi+FdThvrTXjeC9t5557iS302OQGa/XVJytrGTLbuI4pH2bl0 /wCGXjXxH8TvG+v+JvCfw6ig1G50PTNFjs9Tnvrmew07U7q6jv7qaS0iEU6C 582K0RJVimib/SWEu6OLXf2y7bw/4/8ACegiDwrqT+NpYH0qbTvEsM7z2kt2 8S3fkbRcyRvbhJI3t4p49/nLJJDDF9qapqf7XVrFH8UJNF1vRNRk8AeNNI0j URPew3EOlafP/ZqXk7CIRtBHCJb4s0zvsktZyzbE8qPSOXV3Jab/AKtL82aL B1bqNv6vb82e83qs9rIqE7ihC445r5e+J/wW+LupeCtKs9I8BfCXxRe3T3Wj 64fFHjjUJJJtCKWsRtIp7jSb9guoJaQG8gZdq7GCyzzOLtPRPH37Uei+FPjP 4e0G01SHW7rV/B+reIbTSdOvrVrjVzC1o9usMLDzJZJozdeSVlVCIZ8iTbui qfEb9srR/BPwz1PxPY6l4I160jsdU1LRltfEiE+IYdPsTcTiAhGDSJMjxOqe YERfMLbsxCYYCvKzjHfb73/wSY4SpJKy/r+kdXrHw+1LVfjLr8lzf+JZvDni 3wtBpey0uxaW2hTW090ZJI5EmW4jubuPUEAkhT5BpgLSIxiDcH+xf+z34j+B /gnQdP8AEfhzwPpWpWl/4s1BzoF/JdwaPFquuyajBYWzyWkDPD5UiCQ7IgHt IgqMDlPVGudX8PeNvEOt6rq1jF4Hi0W0e0gYBXs7iJ7x7yeRtv8Aq2ia0Ay5 A8p/lXOWuWfxN0O40zw3dTX66Z/wl5RNHt9TjfT7u+la3kufIW3nCTCcQRSy NCyCRFhkLKNjY5uRqXLHXTp6X/DW/ozCato3/X9bnjnwm+G3iW88T/HC91nw X9ht/FPxRsNW0aHWLu1cXdjZ6boNp/acf2d5whEunzzQRyhJS0UW9YS2V9bm 8NavefGGz1j7RbQ6Fp+jz2awCe6M11czzQuXaMSLbBY0t1Cs8UspM7hHgUSL cT6h8WfC+k6X4nvrvxFodrZeCw58QXEt9EkWhbbdLpvtTFsQYt5I5j5m3Eci v90g1py+IrGLWbbT3vLRdQvYJLq3tmlXzp4YzGskirnLKpliDEDAMiZ+8MzK Mopcyt0X3L9PzJcl1/rUv0UDkUUhhXhP7Tv/ACP1p/14J/6Mkr3avnX9r7xD c6J490/7Nouqax5mnjd9ja3XysSPjd50sfXJxtz905xxnOr8LsXTdpHk2qf8 lp0D/sCan/6P0+vyW/4OxDjxJ+zdzt/feIufT/kFV+sGmTan4i+J+n6hNoGq aTaafpV5bu95LanzHlltGVVEUzk8QuSSAOB61+T/APwdinHiT9m7/rt4i/np VYUviSfY0n8LPyvooorrMBNo3ZwM9K6/4X/CfUfiPq8MFpbPN5jY6YHXvWZ4 F8G3fjPXorK2haV5egA9xX6P/syfALRfgN8NrTU9Xhij1CZdwBYsxLE44Gfa gBPgl+zNo/wd0mC8vba2F5tGPkDNyMY4Hua9x+HnhXVvG24WFq0VsMAOFC5+ lUvgJ8KtZ+PXj+1NzbudMDnCMQucA/8A1q/Qf4dfs46d4O0OJBZKpQAZBPJ5 oA8P+FH7JkN1axz39tHNKQSfMAY969i8Nfsy2Vs5KWNqMDqsa16l4d8KC12J FBjHH616fpHgqKw0tppIwGI79+9AHxX+0v8As8Qr4UleCCNZFQkFQq4Oa8k/ Y5/Z5W+u5nu4lkKzH7+G/nX2T8fLMahpt7AmNvlnHtXk/wCy9pw0x7nKAKJd vI56mvQpf7jU/wAcPymR9tfP9Df1D9nW18klbOFU9kA7Vw3iv9mC21JnB0+2 YHgfItfZPhbw5beItNA2gnb0J56YrlfHvg3+x71k8sLzwc57V55Z+bfxO/Zx udJineygMEitkFTtHX0BrxPxb4UtdbkfTNctYopyCIy6Ahh09D7V+pOv/DW2 16N4pbYNuHzHdzXzJ+1b+yct1pwu7O0Bkj3bWD8qOOevtQB+Rf7W37IF1oGr XV/ptpE9sTkeVtx0Ga+TL7TG0+cpKgV++R1FfrldWsaXcvh7xDHnBCRSPkDJ 9cdvrXwv+2b+zFcfC/Unu4LQpZPKQCrBgOD79OKAPmkQKkbEoTnt1IqJIN24 87VBOfWrroY2KkYI60ybPlNgjGDmgDu/+CXH/KWT9nL/ALKz4Y/9PFrV3/gp 5pCal/wVJ/abLgHZ8VvE4Gf+wvd1S/4Jcf8AKWT9nL/srPhj/wBPFrXTf8FF IPO/4Kh/tQjOMfFfxN/6drugD58/4RCNlyIwRSL4TicgLGrH2NdYbJgnBBPp XReB/h/P4ovUijhZ2OOccHNAHGeGvg9Nr1ygjtWdT2Br6G+E3/BPu48YTR79 GMgkHGQvp9a+pP2MP+CfL+JbK0vL6wxC3zEO+OhPv7V92p8L/CXwl0ILBBBH cxqBlWLYNAH5raH/AMEv9K0q0L6jodimTjEkcbZrK8RfsReFdG3LF4e0qUpn pbx/1FfeXjrUD4muvKtkklwTkBSQKq6J+yxfeKys0tqY0k4ILDP86APzN8Rf snWN07Ja6Bp8YJ4KW0a4/So7D9hR74bhocHTH+rQCv1u0T9hXToZYnmslbby fmJ7fWvQ9B/Yv0hbfaLFD0Gdx46+9AH4TeNf2SpPBd0kh0qOJHZIeFUZ81hH j/x6t/S/2HhqFmrro0Lf8BTmv0q/b6/ZTsfCXh6xuYbcIJNc0eA4kOf3mpW0 fr6NXtvwr/Yu0dtAieS0BLRjq55yB716FT/caf8Ajn+VMn7b+R+M9z+xgLGT 954fiIHX93HXQ+Gf2SNGIHneHbGQ9Dut4jj9K/X/AMS/sQ6VcsdunoTyMiQ/ 41xPiP8AYVhty0tnbhCeSA+f6155R+fvh/8AYZ8La3tT/hFtL3uOP9FiXB/K qPxB/wCCYPk2In03w1aADk7Y4vavt3Ufg/q/w8YStbP5cZBJBB/rXa/Drxxa SlYb5S8bEBg6kAflQB+KXxT/AGSdS8I3Fyp0jyBESOAv9K8p1DwC1k+1oCpB xyK/fn40/si+Gvi94bvr3TLSE3DxOdu8gkkE98V+Zv7WH7I958NbyaU2LxRi TBBYH196APi9/CqZ4jA/Lmo9R8NpBYyyJGF2KTkn0Ga7nVtJfTblkZeB27is rW/+QLef9cH/APQTQB9e/wDBOOLz/wDghr/wUPjIyJH+Gi/n4hnr4oTwxbqh yi5I9M4r7c/4Jq8/8EQv+ChP/Xf4Zf8AqRT18iLCiHIUA0Ac9/wjcBOQqfMP 7vSmt4Zic/6tOOwwM1vGxBJ+Y81c0vwxNqEwVI5Hz0AFAHKR+E0lPEIP44rb 0T4WSatIqJZs+fRea9z+Dn7J2r+PJIVSwkk3nAUMAB+tfcfwY/4JvxeGzFc6 nZxAoM4L8jjHUE0Afnv4E/ZGu9ej8yXSCIzjllXB613kH7F+n2aZm0mD1+dF /wAK/SC/+Efh/wAD2flG1EjkcAEkCvM/HHgu51oyCC0SCIfdAwDj86APiHV/ 2d/DGjs3m6XYAAf88FJ/QVy+p/C3QPtAW20O2Yf9eyY/Va+wf+Gb7vXL3dJb hgf7zj/GtnTv2XbO2gJktYt+e5OPfvQB8I6h8Dhft+60O1jU9MQxg/pUQ/Zj llOTp8aknoI1xX6DD9nuzt4QFgUKByQT/jUunfAi1kcZt1znHBI4oA+AoP2Y WKhTpsbEdMquBUN1+zNOGJXSIAB1+RK/R3/hnGzeIN5LfQNU6fs2WT24xbKc r1LdP1r0Mz/jL/BT/wDSIkQ2+/8AM/NCb9nk28WX023DenlpVeL4KwRTbZNP tcEd40r9BfHH7M1vFAxW2C/N13fX3ry/xT+z6LXIS3y3YA+//wCuvPLPmLQ/ hTo0Um640mzOOdrwIc8/SvcvgT8KPhn4iufs2qeHNCZjtG+SxiP1P3KxPEHw 2utJy3lOuDjHX+tcsdVufDeoKwMkRz6DigD69vf+CYngXx14ZF3oeg6Cd65U xW8Snv7D0r45/aK/YbvPhZ5sn9kRwxxED5UXHJ9vwr6W/Zc/bWvPBFxa2l5e O9upIw0eQQTz0FfcHi/4XeD/ANrz4aTfYVtp71otypvKtkYPt6GgD8DL3wVF YyFXgQY9RUI8NWzdIV/75FfWP7bn7HeqfA3xT/yDpIbeRnIbII4x7182OjRO VYEFTgigDCj8MW0rECKPIGegqDUvD9vb2MriABkU4O3GDXRBQGJxyar6yAdK nz/dpMaP6rfh18W9C8E+E/hX4Z1Oe/g1fxfpMUGlBNMupba4lgsftEkTXKxm CKTyY5JFSV0Z1jfYG2tj0n4X/EPRvEXxF1fQ7K88/VNJ0ee7u4fJkURRSx30 EbbmUK2ZLO4GASR5eTgMpPjlh8CG+Lvgz4Ka2PF3ifw6fAEVrrFvZ6XHYtb6 lM1ibcrc/aLaaTZ5E9zHiF4ji4c53rG6el/AH4Or8PfiP4r1qXxDr2v3niO2 uyF1BbRI9Mt0ivZY7WBbeCIGNHuZjvl8yZt3zyvgY4Ha51fZPSvgN/yVfSv+ 23/ol6+ja+cvgN/yVfSv+23/AKJevo2ujD/CzGr8QUUUV0GQUUUUAGKoeIfD GneLLGO11SwstRtobm3vEhuoEmjWeCZJ4JQrAgPHLHHIjdUdFYEFQav0UAec eD/2lNI8VfEr4geHptP1LRLb4eaxp3h+41nUprSHT9V1C9tLa6jtrXE7TF1S 9tEPmxRhpLhFjMpDbdFfjtodx8V/CnhKzM+oT+MvDuoeJ9O1G0aKXT3tLObT on/eB8lpDqcDR7FZWVJCWXCh+O0v9l7W7Dxj8YrtPHEunad8T9Zsde02TStL WDVvDN5Dpdnp8kguJpJre5BFhbSxpJaqit5iyLcI+0Xb79lU3n7SPgz4kL4+ 8b283gvw/eeHY9Cjj0s6XqkN28MlzLclrM3XnSSWtm5MVxGoNqgChXmEoraX +f3f5g0r6EvxU/ahX4aeAdY8T2ngfxh4x0TQNVmsL+fw9Jpl20NtBCz3d+sb XiSSQ20qS28kMateGaF1S2kXa7ehaR4ottc1DVLaFLpJdIuVtJ/Ot3iVmMMc wMbMAJE2SqNyEruDLncjAeX+NP2Sv+Ex+Get+C7Hxhr3gjw1rOv3mrTR+FYo Le7vLa+Mk1/Z3Fxdpcti4vLm7nM1oLWaMPEkTxmNnk9N0Xw7LpWp6vcTX9ze jU7lJo4pVjC2MawxxiGPaoJTcjyZcs26Z+Qu1VnW2m4nuclpXxR+HPxe8P8A gDxWuoeHb+31a3g8TeEbnUUSG52XUK20V3bJOFlid49RW3JCq/8ApvlHBl2n J+Gn7TVj8UvDnii78R+CPGnw70fQbFNTkuPGtjbWVtf6ZKsxW7BWaQRJtgkM lvdeTcwDb58EW9d2lpH7OGmeENC8CaH4Z1TWfC3hrwDZwaZa6VYyxzRX9jDC IorO4a5SWRolCRNvRkmzEo83Y8qScx8M/wBiq38JeAviJ4a8WeP/ABx8VNG+ JyzJrEPieDSLdys8LwXAWXTbGzkbzIWji/etIY47eFIjGibaqSXQelzo5P2h b3T/AIkeDvD2pfDvx3pcHjOxWaHV5Rp09hpt95E9w2mXXkXck0dwsVvITKsT WhOxFuGd1Q72sfGrw74c+H+k+J9SurnT9G1u40yztHuLKdJjPqNzBa2kTwlP NjZ57mFCHVdhfL7QrEYJ+BGsa/8AEfwd4r8QePdfvLnwpYBW0LTba20/w/ea kbeaCXUTEY5L3LJcSqtvJeyQIBG2xpYxLW5qvwZ0nxb8O9H8OeIzc+IbfSLr TdR8+5fyZrq9sLqC8t7mTyPLXf8AabeKUqqqjEFSuwlaNBLc5jQfi3oY/a21 jwlL4I1TRPFGpaCLq18TTw2Bt/FNhYSw+ZFHLDO90Fs59XVfLu4oRuu5WhEi mR6tfHb4+WPwS8R+Dk1Xwvr+qaZrurW+lvrtkLJ7Tw7c3VzBY2huFluEuCLi 4u44Va2hm27mMnlp89S6N+ztbaf+0TqHxHu/EfiLV76TTpdK0nSroWaad4ct 5zaNdrbeTbxzubiSxtZHNzLOVaPEXlKzKc7x9+y0vxRi8Awa9468aahZ+Cp7 O9vrcx6ZEni+7tJ7a6tbm/Mdmro8V3axzhbJrWNmLK6PEREGraX+f3/5D7nW fDr4v6d8S/FvjrRrG1v4Lj4f65HoF+9wiLHczvptjqIeHazExiK/iUlwjb0k G0qFdobX426CPiPeeE9QurTRdciu4LWwtb6/tFn1sTWkt0kltEsrSlStrfrh 0RydOumCGOMSHE+Bn7NjfBD4j/ErxH/wnHjDxQ3xM1tNeubDWI9NW10edLeK 1VLQ2tpBL5f2a3tYsTyTHFsjbt7SvJmfEz9jvSfjN8SYNb8WeIvEHiHRrTUl 1G38LXtrpkmiRK2j6hpNxbMn2Tzpre4g1Kd5EnmkPmJHsKR743lbL0/Eeh3b aj4T+CXhPTNNe48PeEtC0+2+x6dal4bC0toLe3aTyoU+VFSK3gdtq8JHCxwF U4x9K+Nb3fx6v/Al54O8W6SI9PbUtN165S0k0jXoo/sy3AgaG4kmieJ7qJCl 1DAXO9ovNRGcYuhfsdeCvhj4Aj8M/DDStM+Delf2murSx+B9HsNLW4mIjSXf F5DQMZoYxC7NGXCYKNHIkcibGi/At9M+O9944uvGHizVUk05tL0zQLhrOLR9 Bhc27TeQkNvHNK8j2sTlrqacxkuIvKR2Uv8Ar+vmS9tDA8d/tjeFvh74Q8Ia ze2mqTp4316z0XRbewe0vJbuC71ODToNUBinaP7AzXdpKZd+VjuoFKCaRYT6 X4b8RWviqwkuLWO4SOO5uLRxPbvA++GZ4X+VwCVLIxVx8rrtZSVYE+Vax+xx p+vfBTwP4DOu3/h/Qvh/rmlajpsPh62hgWex0m+jutM02c3a3TmJBb2Qlkje OWV7csrRLI0VeqeFtAk8N2M8U19c6lLPd3F0086orgSTO6xjYqjZGrLGuRu2 xruLNliX0/rbT/gjfkRzeN9Et/E6aNJqulprLCMpYtdRi6YSLM8eI87vmW2u CvHIglI+42OB8F/tQReJ/hf4h1/VPA3i/wAH6t4bvhpVx4a12TTIdQuL2SOB 7a2gnjvJLCV7n7VbJEy3WzzJhG7I6uq9VqPw2m1H4nWfiEeINbtbW2tmhl0a IwNYX7nBimlDxtKJIvn2eVJGreafMWXZF5fD6P8Ask/2T8H9d0C/8Ya/468Q avrFv4lTXvF0UE7Jqto1rJYTPbaeljB5MEtjaN5ECwCTynLsXlkdhb6/1r/w 4XLfjr40aXqXjHxh4B8XfDzxXJ4dTw7dag1/d6ba6po3iqxSOBb2CKGCaadm T7WkbQXNvE0+ZPJWZVZh6sriQAjBUjINea6p+zxeap8VPEXix/iD42+0apo0 2i6Rp3+gLp3hZJ1g86a1jS1V5pnktopN17JchG3iMRpI6N6TBAttCkaAKqDa o9AKXTz/AK/QXU4zxn8arXSfB+r6h4W0q8+JOr6Ts3aB4a1DTv7SmzdPbNg3 dzb26bJIbkN5syc2syrukXYV8MfG/S/GeleB7rTbTUZR48sBqtrBOIrS7sLL 7OszXM9vM6TBFaW2gcRo7pLdwhlVSzplax8AtX1Dw74osrD4meOtAu/ENzHN ZX9lDpU8/htFZGkgsxdWU8bRysrlvtSTsPOYRtGqxLHNof7PWm+Dr74ez6TI rTeAtHm8NR3Wqeff3tzpUsMAkhEpmUCZ57LT5XmkWUsLeRQoMpkU7/1/XQOp R+Df7XHhb456prsGhw609voviX/hF0vns99pqUp0u31VLqCSMuPscltcp5c8 mxZGwF3CSJpLMP7S1hH8V/iL4TvPD/ieyuPhzomneIZ7w28NzBrFnei8CPZx 28slw7JJYXMbRyRRuWQbFkVlYwfDP9mWH4XeLPFGr23i3xPqUvjDxc/i7UIb 5LHyg39nQ6dDYxiG3jK2sMNrbFMlpi0Cl5n3OGr2n7PGt/8AC9/ih4yn8Www ReOvDekeGdJh07SfIvPD8Vi2pSGdp5ZZo7mZptTlZT5EaIsUaskvzMXppYat fU9I1TxXpmiXul215qFjaXOtXBtNOimnWN7+YQyTmKIE5dxDDLJtXJ2RO3RS R4v+01/yPtn76cnXv+8kr1zVfBEWqeI9C1A3+rW8egec8NjbXbQWly8kflBp 0TBmCIZAkbkxhpN5QyRxPH5J+03gePLTH8OnoD7/ALySonsOnuecn/UHp0H1 H0r8cP8Ag7G/5GP9m7/rt4i/npVfshgspQDJI4/n/n6V+N//AAdjf8jH+zd/ 128Rfz0qsIfxF6G0vhPywqaws2v7uOJBlnYAVDXsH7JHwVufil8StIt1tpJo 5LqLdhSRgt7V1GB9K/8ABNv9koanrDeIdWswlnawbw7vgD5k96+g7nSpfjD8 SHt7JXl0uykFvEgGAQAoP6g12nxsjsf2X/2eLXR9MMdrq2olYHAOWxtcngnP YV6n/wAE3PgWmt+HbPWLu1YmZ3kLkEbss39aAPqH9lj9nm0+Hug2kyWixzFe SGyQSPr9a+gbDQxfxBSgwPasrQ7ZLRY4kQqMYwecV3nguzNwAGwAABzn1oAz dD8Esl6rCI49jXTePUOn+H9i8HOOfpXSWGkxQKG2jcRWR8RdPa80f5ASQewo A+bviJpzXthcl2ALLj681558CdO8pLoqxGJj0/GvYfHmhz2um3DGNvmXFeaf s7aWNRvL2HBBa4IHHTrXfT/3Kp/ih+Uyftr+ux7z8HdTYah5J3YKnj06V1Xj vwgupHzNituPasH4W+GZbPVWZgcqDg4PrXqLWC3EA5GG6g1wFHiMvg0xxs4j UkD1/wDr1xnjPwjDrMMkE8W9GBXBJA6V774o0lILJxGFUEdRXlPiuLyGICHP PA+tAH5tftt/s1tYeIr24trXZv8AmiYN0OPTNfP3ij4dWn7TnwYvLKeBZdW0 xQzKzbXPBBP/AI8K/Ub42/D2Lxz4cuUeEvMVO3rle1fmrpGoj4E/tArBcMYL PUC0E6PwMkqec+y/rQB+Vnx++Ft38LPG09nPC0QBJAPpuNcHL/qJP901+m// AAVI/ZZtNbii8T6NZF4ntlYtHllySWPTj/8AVX5nahZvZ3E9u4KshKcj8KAO 1/4Jcf8AKWT9nL/srPhj/wBPFrXUf8FEXA/4Kh/tQjJyfix4mI5xn/ibXdcv /wAEuP8AlLJ+zl/2Vnwx/wCni1rpv+CipQf8FRP2oN4PPxX8TYI7f8Ta7oA8 10axl1XUI4wpLSEDA5xX3p/wTx/ZHPiy6bUL+xYxR+WQXcYGc+9fLf7Jfwpl +JfxU0uzSKaVZZOSq54Ck/0r9cdE0qx/Zp+F1rbQBYb68iC4cgHK+340AdnP 4o0v4P8Ah+HTtPCpPGm3C5OzNcdbW+t/Em9wPMkV35cgKOtYPg/RtQ+LOvIV MsiM2WdVzjPPWvrbwd8JLTwjosaRqwfHU49OvSgDjfg98ArKwxJcwK83yjr3 r2iy8AwWMEfl26L8vrVLw3b/ANn3HPTrivTdF05dQsY2UHGOoHNAHL2fhJLh QDECBxx2rat/CS2dvu8vb9TXX6N4bCkZDZHqKPE8aWMKjGD0yKAPiH/gp7YJ B8N9LIAyfE/h7Pp/yGbOvoL4UaOk3hu3+VjujHfk8CvDP+CnMIm+F2lMCD/x Vfhwf+VqzFfRPwrC2uiWZZlztX+Qr0Kn+40/8c/ypkL438jRvvBYk58vII55 61j6l4PQMuI9vGOea9Us7aO7tASO2AfwFZep6H85JVlz0OOK88s8a8cfDKy1 XR5Ue3R9wwSTwK+evH37OslpO1xp8O0rkgK2cfhmvsHxJa+VbuORkYJP5f41 w02mh5SNhKk46cGgD5As/Gup/DjVRFK8iLGxDIQCCBik+NPw30H9pLwNP5EM cl7GC5DMVJwDXsH7QfwIj1rTp7+1VxcIjMAo78n0r5m0bx7d/CvxaNw8uJj5 cqyZAwTz3oA/N39rf4C3Pwt8azQG0aFQMjnPc/4V89eI4zBpV8uPuwuMZ6fK a/Xr/goN8C7P4qeBYvE2mxPKZIDvaIhgpBJxwPcfnX5NfFTSn0RtVhZWVohJ Gcj60AfVv/BNEZ/4Ij/8FBx2Nx8Mf/Uinr5M+zrGDzlj92vrP/gmkdv/AARF /wCChB9Lj4ZH/wAuKevkyFhPcqAoXuMdzQBd0rS2vrpEjjJZuuO1fUf7In7J lz8QdSEjWbOoK8k9Bz7153+zB8GZfHHi+y3QysjE5wv+zX6mfs8/DLT/AIR+ E/OKbZZFGS+BgAfT3oA6D4M/Abw58GvDVuZLaKO9jUsx3knrmofGfxYm1ORr XTG2q3Ujt+eK5/4n/FmS+vnt4HjK5x8p5rD8M20l5OgG4g55/lQBvpYG6iDT NuduSSMnn3plzoMMiY8vf6gmtO10VjEeTnIziriWIETBlBKdeOtAHO2/hhSz lFwegy2cVEfDwjiIbLLntXQWkL/aSm0tuyOnFLJpEqTKNjbSeuKAMWPw3DJA q7QGIH50yDwu8EvzKQO+P/1118PhtQFcrj6k1abR1NvwOfXHFAHJTwiK2wAO DxxVO31f7OCjIAR1rpL7SgqEBR1ycjpXNanozG5YoDgEk44zXoZn/GX+Cn/6 REiG33/mVdduI7+NVUAE9yMYNczqfgGG8Vm8rORjrxXR2ulTy6gQ0cm0HPrg 1v2mkrGqoR2zk155Z4l4s+AkWpafJi3DMRgZbHevCfjB+zFPa25lW0IwCSd3 T9a++bLw8s64Kbs8jjpWd4o+Etrr1iyyxtlgcj6/hQB+R3iTQ77wZqzxENF5 bccCvpP9iP8AbN1P4X+NrFJb+QWuSGXbnIII9K6L9qX9lgwXU1xBBOOMgheO n0r5Ijt7rwfrwyrIYmxyOvWgD9m/jR8HfDf7efwTfUdLgtrvUrOIs43lGBb2 OPSvxe/al+A2o/BT4k6lp93aPB5MnAyDkEA+vvX37/wTH/bSf4e+IfsF7cQP Z3bRrIJHIHGe+a9I/wCCsv7F1l8UfDn/AAnOgW89xFf28cpaDDpkKFPIHqp7 0AfjRVXWhnSp/wDd/qK1Nb0uTQ794JldCjY+Ye+KydfGdGuP9ykxo/py1vTP FVpefs3ar4Z8O+ONdsdN2x+I5dJ16C006206bSpIP9LtZ7yFbki6ltZlKQzS IlrNsKswSX1f9lXQvHFr8SPHOoeLbxjptz/aNroNk1uYHt7SI6gySPi9u1ka RJYyJQYGZFjVraAptr5x/wCCWHxA1zxh4h+JtnrGtarqljo09jDp9vd3ck0V jHuu12RKxIRcIgwoAwqjsK+gfCPxW13xPpnhO/udE1TwZd6tr2o6Xd6beuj3 It4Y9QRNxXKgSrBFJmNmQq+FkkQ7m4pxadjdSvE679jH4MeIvh78d7C91j4s /EHx5ay288C6frtnocNvC5QsJlNjp1tLvAUqA0hTEjZUnaV+zq+SLG/n0u7S 4tZ5raePO2SJyjrkEHBByODWn/wsHX/+g5rH/gZJ/jVUqqirMU4Nu59SUV8t /wDCwdf/AOg5rH/gZJ/jR/wsHX/+g5rH/gZJ/jWn1iJPsmfUleA/tz6V47k8 OpffC3wpruo/Ea28NeJIdA8Qacmh+XpF2+mubW1uG1GRZBFcXsdmwWBHQzWk BuCkKtu5n/hYOv8A/Qc1j/wMk/xpP+Fg6/8A9BzV/wDwMk/xo+sx7AqTPoP4 meENR+IHgO90nS/FGv8AgjUbxU8rWdGisZb+wIdWPlreW9zbEsAUPmQuNrtj DYYJ8K/BWpfD/wACWekav4t8QeOr+1Mnma1rkNjDfXm52YCRbK2trcbQQg2Q p8qLu3NuY/Pv/Cwdf/6Dmr/+Bkn+NL/wsHX/APoOax/4GSf40fWIi9kzpv2O tN8eaF4m1Kw1Twnr3gf4cWfhrTYfDWg6omhxvot2up62Lm1RdLlkRYo7L+x1 iXeyCFYhuMwua9Abw58UP+F5DUB4x8BD4abedBPg+7Ouk+Rtz/af9p+Rjz/n x9h/1fyZ3fva8Y/4WDr/AP0HNX/8DJP8aX/hYOv/APQc1j/wMk/xpvExvew3 Sbdz2f49X/iTTvD+nyeGrTxFf3L3XkT2+ivp6XXlzRvCJs3w8nZA8iXDDcrE W5CiY/6PN5/8OtP8WW//AAUS+JGq3ngDxBZeE9V8EeHdHsfFb3emHT9RurC6 1a4miSFLtr1eNWRVMluilre4yQPKabl/+Fg6/wD9BzV//AyT/Gj/AIWDr/8A 0HNX/wDAyT/Gl9Yj2D2TML4pfArxb46/Zk+DenQaHqXiLxR4e8deHdQ8UW+j 2droOmvqdp4htLzWdeliuvsskkJktdRkj8lW89r9ZlilzHNH7hrXhjx/e/DW W2+H2saJ4I1k67ezmbxjolx4miktmubgnbFBqNq6CRmSSLM+IoSsZiU4WPyz /hYOv/8AQc1f/wADJP8AGj/hYOv/APQc1f8A8DJP8aPrMbW+f5f5A6TZd8Yf BDxB4i/bN+D3iSfwTp914j8I2Zm8TfEqysLHTrXUbM6bqdrJo0KNdTakEa/u rW8W2dXtkWMMbh50AZv/AAUJ+Gur/tNfsr6l4fsvhLf+IvE9/eX+neH/ALe2 js/hHUPJvLaw8SiSW6xDHE/lzpJal76NLhMW4kEkaVf+Fg6//wBBzWP/AAMk /wAaT/hYOv8A/Qc1f/wMk/xp/WV2EqT7mP8AF79mW48fftL/ABT1u3+Fmo6d o+sfDrWvDuvavp9t4ea8+JdxdxaZ9hjhjuZZI7l7OK1vrdf7ZiigRrkIokgk lYd7+yb4S+Ifgb4J/Cbw3qmiz6BFoUl/HqFuG0ex+w6PD9ph0qyuLewhe1N1 5Eli0ken+RbpJbTFJREqW9xzX/Cwdf8A+g5q/wD4GSf40f8ACwdf/wCg5q// AIGSf40PExta39f0xuk3ufUo6UYr5b/4WDr/AP0HNY/8DJP8aP8AhYOv/wDQ c1j/AMDJP8aX1iIeyZ9SUV8t/wDCwdf/AOg5rH/gZJ/jR/wsHX/+g5rH/gZJ /jR9YiHsmfUlFfLf/Cwdf/6Dmsf+Bkn+NH/Cwdf/AOg5rH/gZJ/jR9YiHsmf UmKK+W/+Fg6//wBBzWP/AAMk/wAaP+Fg6/8A9BzWP/AyT/Gj6xEPZM+pKK+W /wDhYOv/APQc1j/wMk/xo/4WDr//AEHNY/8AAyT/ABo+sRD2TPqSivlv/hYO v/8AQc1j/wADJP8AGj/hYOv/APQc1j/wMk/xo+sRD2TPqSivlv8A4WDr/wD0 HNY/8DJP8awfiL8W/FHh/QLa4tNf1aKV9U063Ym6dsxy3sEUg5JHKOw9ecjB GaaxEW7CdNo+wq+b/wBuKHxJNPOPCE+i23iX+ykOnvq8Es1iZBK52yiJ0kCs AV3KSU3btr7djcN4/wDjv4w8Hat4Zt7G38UeIY9d1hdMvJLO+Zf7Jha3nlN3 JuYKY1aFEILKT5o2732Rvh3EUPxh8T+IV8V21r4kOhaktpYNqcCXTWsTWVpM yKXBOC8jsfr6AATUqpqxUINMgiiu4Pil4WTUJ7e5v08PaiLma3haCGWXztP3 skbO5RSckKXYgYBZsZr8nv8Ag7G/5GP9m7/rt4i/npVfrz4b+Hfh/wAH3r3W kaFo+l3Tx+W0tpZRQuyZBKkqoJBIBx7CvyG/4Oxv+Rj/AGbv+u3iL+elVFF+ 8ipq0T8tbO2e8uo40UsWOOK/Uz/glP8As5QeH/B8HirUIpENrbrcxmRcAEAH 096/Nn4NeF28V+O7K2VWcmVeF5P3hX7U+HbW3+DX7Kdnap8kv9lLuVuDnyVr sOc+cP2pfH178df2j9J0O3/eW7TtgR5OAPz9a/Wr9kr4aR/D74NaHapGyyfZ huyO5JP9a/Kv9h7wDJ8UP2qE1GRJZltN0ikLwPnj7/jX7E+Br42OmWUA2gxR BQDz+FAHcaTpjSzo5DDB/Ku88MwfYwDxyB+lcz4cmS5UcDj0rp4cRxhdy5HX mgDq7O9EyDOBxxVHW9YtwnlOyAnpk81Ba3phhXIBGAcnivJPj942ufDMqXNu cYcbsAn+tAHQ/Emxjl0qURDeGBxivEv2atIurTXdSeWJ1Rrv5SVPIruPA/xp sfGlkIrmaESjhl38849TVo63pHw6sWuFmhRnUyZLjqRmuyEv9lnH+9H8pE/a X9dj1nR7uCyiTJUMRjHcdK3zdCSyBzjPTHpxXzv4O+Ls3izxEFiZTCpySp4O Pxr22z1YXlogDAcc89a4yh2szia3YYwD+frXAeKdJWfJ5474+ldjqFyVYkHJ JIHsK5/XpvLjHT5f8/4UAed6ppSwFt6E+x6V+Tv/AAV9+Hc/w/1ey8QWMUiA 3RDMFyMlZD7elfrD4s1lYZDtIPt+FfFX/BUT4e/8J/8ABaaTyXZoJQ42jP8A BJ/jQB418C7u1/aI/Ztm07UPLkuERlTafmOI/Q5r8hv2t/hHN8K/i3rtl5cy pDezKpdcZG9sV+lv7DHjlvCl09g7BYxO8bKTg84FeG/8Fj/g3Fp3jG61y1hc x3paYtjIJILf1oA+Kv8Aglx/ylk/Zy/7Kz4Y/wDTxa10n/BRhN//AAVF/afH r8WPE3/p3uq5/wD4JggL/wAFav2cwBgD4s+GP/Tva10n/BQ2Lzv+Cp37Ti4z n4s+Jv8A07XdAH1n/wAEfvg6mqeM11e4ikEFjDJIXPThHxyfevdv2lfidL41 +LUOk2jI8dtIyhV5/P8ASn/8E3/CieBP2aNc1tlKOtoyhm4AJZR6e9cN+y/a N8Xf2lbyabMiWsyn5T6mgD7k/ZJ+Fa+HPBFpPIrLPKpdtw65Le3pivcRbtMm CpbPop5qn8PPDi2mlW8MaAiOPGK9EsfBjXaAqh59qAOJj0aR50ZFJz1A5xXq vw50tl0tPNDEAE8/U1H4f+HuycGRCvPpXXw6aNMt1RRhR0oAlhsjgYG0H1rg /iZeTfaREiuevQGvSkliis2uJWCog5ycAViiHSPEdx50c0buvZZAf6UAfEX/ AAUT0CaT4OaO8qOM+L/DK8j112xH9a928O6Lc6fo9sUR2UIpHHsPwrhf+Cot pDZ/BvRxD8yf8Jn4WB5/6j9h/wDXr6J8BaTY3Gg24dlDbF747CvQqf7jT/xz /KmQvjfyG/D93urElwTtA5I6VtPYK8bAgEkkj2qxpEmk2F01pFcwu7ALtDjN T6hCsE+1fSvPLPLfHmmSxq2zdg8ZCkj61y8elCPPyO2fWvZtW0KLVrdgy/N1 z+Nc9L4GSBdwXJHTjFAHkvifTBLZyR7Th1IOe2Qa+Kv24PhMNB0o6pbRyY8w bsLnsa/QPxjoC20LfKxCgjp9a+eP2qvCEPiD4dXysrNhWIxjOQrUAfMn7KXi S3+MPwd1fw1fujy2zMIgCN3Kx9ifb0r8pP25/h+fBHxD8T2xSRBFcTgBhjoT 7V+gf7E/i7/hCP2hLzSJXCxS3Kp8xx2/+tXzj/wWL8CR6F8Qdduoo2An8+UE 8ZB59KAM7/gmp/yhC/4KE/8AXf4Zf+pFPXy/4O0V9Y1KNFRm3N2Ga+of+CaY z/wRD/4KEj1n+GX/AKkU9eSfs4eEBrWtRllYorg8D2oA+zP2GPhFDpFtY3cy OiCIM7EYx8v0r6B+JPxG8qJba3ZPJRSDg5zXF/DuOPwt4EgSPaHaBeCeelYv ibUJdQ1SO3Xkty23r2oAv6DYy+JdWDgOVz1A4/GvafDHhqPTViOD0yeP/rVg fCrwxFpmkQHYSxyee9ej6TpBkfADbPpigDPXTWNySmSG/Gr9r4Vlu/4GIPHT NdTpmgRSSKrqdpFa/wBhisoCEByTxk9aAOPtfA6wMHZCNvPNRXegNczDaudp 7AZ613NxCVjwqjGe30qO20hIySM7264oA5qPQ/3agdh61MdAV0I2OGI464rq 4tHcrkQTOPXaf6Ul1p8lrGSbdxx/d60AcTceDQ5yxIGMD2qE/D6NUOQeO+K6 m9uSgHyBSuOxxVeHUTIQeNv0Ir0Mz/jL/BT/APSIkQ2+/wDM5BvBMds5cIBn tis+fRgtzz2/yK9DlhguFbcyDnueprD1fQkYmRWxjrg8GvPLMrSLEbxnpzzj rW9baOLhi5DHd+XNUtOg81twznJFdPp1qiWpyWwAOnagDzL4tfDmLXbKRWiL 7hjjtxX5z/tY/AWfwheSXUdvPsLAk7OBk+tfqzeacdQmKlGIPT8q8c/ar/Z6 i8Z/DjUDHDI0kaBuBnow9qAPzB+CV7LpXiMojbXLDaSelfr1+w78VLb40fAF vBmvNH5kUUiRbm+Zgd7Dg5/vCvyM8Q6DcfDPx2gljkQRvzuBHSvvb9lnxDL4 bsNE17T23RTqokOflHzbf6etAH5/f8FAf2fJfgn8T72IQXEcZnON68YJY+lf M+p3ZksJwQACvH5iv19/4Lh/Bq31fwPp/irTonkE00au6DK5MZPXHrX5Ba8n kRXCkYwOaTGj+pv4C/sYaZ+zzBrcmheJvEcWoeJJIJtQuNtqVLxeacRI8LBF LTOeSzDag3cHd3L/AAwu7rUbK5uvFniK7k06SSa2EkViqxStDJD5nyW65KrK 5AbK5xkHGK62ivPdRt3Z0qKOX/4QDV/+h48T/wDgPp3/AMi0f8IBq/8A0PHi f/wH07/5FrqKKXOx2OX/AOEA1f8A6HjxP/4D6d/8i0f8IBq//Q8eJ/8AwH07 /wCRa6iijnYWOX/4QDV/+h48T/8AgPp3/wAi0f8ACAav/wBDx4n/APAfTv8A 5FrqKKOdhY5f/hANX/6HjxP/AOA+nf8AyLR/wgGr/wDQ8eJ//AfTv/kWuooo 52Fjl/8AhANX/wCh48T/APgPp3/yLR/wgGr/APQ8eJ//AAH07/5FrqKKOdhY 5f8A4QDV/wDoePE//gPp3/yLR/wgGr/9Dx4n/wDAfTv/AJFrqKKOdhY43WfD Go6Dp0l1P418YyRRYLLbabZXMnJA4SOzZzyRnAOBkngE1g/28f8Aobfid/4S X/3ur1bT9On1a8S3toZZ55M7UjQuzYBJwByeAT+FeI/DbwT+0Qusato/irQP Gc2ma0txZaP4gsdP0qC70PyFnQXl5AXkjH2hRbmFYVu90nmyTR2kciWltcbs XKu5sf28f+ht+J3/AISX/wB7qP7eP/Q2/E7/AMJL/wC91edfCOX9qC6+IfxH 0nxJ8PfHt1p3hzxzrN5pl+2naZb22seHhYk6dpliwljke6aeW3k86ZViCpNH JcrIjRD6g8OeEfEuteHdPvbrwl4i0e5vLaOeaxu7YPcWTsoLQyGFpIy6HKny 3dMg7WYYJb0SYWR5J/bx/wCht+J3/hJf/e6j+3j/ANDb8Tv/AAkv/vdXs/8A wr/Xv+gJq/8A4Byf4Uf8K/17/oCav/4Byf4VHM+39fcLTueMf28f+ht+J3/h Jf8A3uo/t4/9Db8Tv/CS/wDvdXs//Cv9e/6Amr/+Acn+FH/Cv9e/6Amr/wDg HJ/hRzPt/X3Bp3PGP7eP/Q2/E7/wkv8A73Uf28f+ht+J3/hJf/e6vZ/+Ff69 /wBATV//AADk/wAKP+Ff69/0BNX/APAOT/Cjmfb+vuDTueMf28f+ht+J3/hJ f/e6j+3j/wBDb8Tv/CS/+91ez/8ACv8AXv8AoCav/wCAcn+FH/Cv9e/6Amr/ APgHJ/hRzPt/X3Bp3PH5rye30+3un8V/ExYbouIs+FQHbbgFtv8AZ+4Lk4DY wSrAElWAg/t4/wDQ2/E7/wAJL/73V2v7YXg7x/c/BbRl8E+G/FWt+JNIvNFv 5tL0mSO0vrm0i19J7uENNJDGPMtI7hdskqLIrFScNU/gLwD441SZdX1LRPGu nR6wwuG0TUrS1L6KrW8A+z7rZWDMkiSEnzphunlw7RiIR6W0v/XQdtL3OD/t 4/8AQ2/E7/wkv/vdR/bx/wCht+J3/hJf/e6u5+K3gnxVY3Pha4sfAvj/AF+a HXYAw0aZbRLCN45Y5Lm7SW4gW4tkR2zEBM28xukLPGrIfCL4d+K7XTdRtbnw d460m1067+wWC61J9uuLq3gjSJLhZVnuHdJdhcNM6zsXYyIrZzKelwaS6/1/ S/E4b+3j/wBDb8Tv/CS/+91H9vH/AKG34nf+El/97q7j4xfCzxxdaXpd9pGi +KbqHQ746nf6RpsBgvfEEUVvMYrOGWSSFI2a6+zOd8qRusTxyHy5HBxPCXg7 4j+IfGizXvg34i6Rpk1vb6mLfU4NOktrVpVtw1lmBnn8+3eCZnJd4T9qkCSz L5awON2Fla5hf28f+ht+J3/hJf8A3uqO4vrXUZbUX3iL4k39tbXUF2beXwq6 RzNDMkyBilgrY3oucMOK6v8Aaq+DXxE8Yfs5eONN8G6b4ltvFN3oGoR6S1kJ LO5a8NrKLcRz5Tym8/yju3ADHJAJI0vAnw78VWXjnxFaHwh46stJtGja1u9S f7VBqUkzy3Mz237+WZER5vK2SxwhBGqRKYlTak3a4mkchrvjK21DWCYPEvju weWFbj7FaeHGlNuhZ4wWV7J5E3NFIQJDk4OPlxUvgvxZpPg9NQLzeNtVudUu hd3FxeeGrwSM4hihAAjtUUAJEv8AD610MfhTWn+MfimO30HV7+6sdF0mV7S3 VopifP1BlGSMAnHCuQr/AHWKqSw6vTfAXiWTVrC7XSdd065kja7mjutK+22d vM7y5iljBVmAW4RikTbFNoUR8FHZ7ivY5TTfiTYa5fxWtvZ+JTNKcLu8O6gi j1ZmaEKqgZJZiAoGSQAa/I7/AIOxzt8Rfs3H0l8RH9dKr9qPhn4N8SaLJfS6 3peqRzLBqMk93LAUt5TJBM+6MY/dRAvsWNuUChAXAEjfij/wde30Oo6z+zhL bzQzxifxLGWjcOFdG0tXUkfxKwII7EEVVFe+Kb90+Kv2CPDsfiP4sWZZG+SU Hj6rX6dfth+L28NfC6Oyjxk2ixfjsAr4R/4JMeEv7b+JAcniN2x74ANfTv7e fig6h8TLHRFYqZblYSAc7u3SuwwPev8Agjz8Oo7i8vtTlBLmEDJ46sh+vav0 OstDmS4DRnKnoDXy5/wT38Af8IJ8NrZxHhpoVyNuP89K+w/Be4opcg56fTNA HSeD45IR+8zwCea3heedcFQBhq5vxPrH2HT28odhjHHOaj8Aamb+6kMmNy44 PT8KAO8vbnZCoBOcY/GvKfjRZjUdFmV8sFGeOo+U16Hf6kDdbVxgDv3rhvi5 btJ4XuWUc7GOffBoA+E9T+N1x4E+LklnC6rHG4OOTnORWv8AtHftEXiPbW6y DY8S7sE85x715h4m+Hd/4h+Mt5dsWw0oG7GeOT/Wup/aL+DlzemxZCdy2wIw vsOa76aX1Ko/70PymT9tf12Pp39knUBrHhqK64+Ydu/Ir6T0K92W8eSSTwAf SvmP9hzQZdL8ILbzvkoGOMe4r6NmvBY7RySoHNcBRreILoW8IcMBt4OO3+eK 5+/uhqNu20ndj8aseKNT/wCJDIx6bf1zXD6Frri9fJyoORz7mgCl4h8OSy3L M2SO3vXlf7Rngi31n4ZahbyjO5f6H/Gvd9Xv1ntyeA3TpXk3j6zk1DTpoG5E nGMZoA/HzwvqR8BfHW908YVTOzgEf7Rrtf8Agpvo6eK/2dYtQYfPHaFsj1EQ rjP22NCf4YftP28hBxOitnpjdIa9O/alth4s/YkmuVwfLsJO/pGtAH5U/wDB Mddn/BW/9nVfT4teGP8A072tdV+32hl/4Kr/ALTShc7vi34l5zjH/E3uq5n/ AIJpR+V/wV4/Z4XAGPi34YGB2/4m9rXX/t1SNL/wVm/aSUndj4t+JAM9savd VtQjRb/fSaXkk/zlETv0P1A+GMX/AAhn7B12wUobiD6fxR15j/wS6VZ/jJrE zgZlMZXP1r0v4guugfsOxRF23PDGQAeOTH/9evL/APgmkPO8bXU6rlSI24Pf Jrq9lgf+fk//AABf/LBe95ff/wAA/VzwQUghiORnr09q9R0DWYLdQTjP+f8A P4188eHPEk0MEYUHGMEZrpl8bzQIJMZUckbqh08J0nL/AMAX/wAmP3vL7/8A gHtl546t7GULwu7pzjNbEd+NR0lZ0GQecenWvmrRfE7+LvETBjtETY+9nNfR /gvR4x4KiwAzcn1zyalwwvScv/AV/wDJh7xQ8VPLqvg69tYiY3eM4I7H/Ir5 J0r4keJ/hJ46u7e4hnubaVxglsDj2/GvrLR5In1JkMa4BIwfxqHxD4T03UXa SW3jZwc4xnFChhr6zl/4Cv8A5MPe8v6+R8qftY+J0+K/wL0cTkQSJ448Ixv0 bAbxJpyn9DXf+PfjhN4NtJrWziaa4jTYvzYz2ryj/gpfpU3g34Q2Emlwt5be MPDL/IcYddcsSvP+8Fr374V/D+DUtLtLrUEWSeaNXYEd9ua9OpTwf1Kn+8l8 c/sLtT/vkLm5nounX18jzn9lzXPEvj/4i3WpX3nw29u6sqE7g/WvqKW8M10o 6HgHnOar6PoFloVo8cUCKG/ix0qDTEjuNYIKLjdivLcMN0nL/wABX/yRfvD/ ABD4ng0G3XcuW474qh/wm8M8HG35hWB8f4o9PsZZVxkYwAOvNeM6X4ouJS42 ttU9VPX0qlDCW1nL/wAAX/yYe95ff/wD1nxfrSXNu5HPU5rwr42sreEr5Seq N/6C1b9/4nmkAQRvgdTuzmuM+KtjPf8Ahe6T58lGPB56GqVPB9akv/AF/wDJ i97y+/8A4B+Yvg/UjoX7V9xIAyj7UrDHA70f8Fr9BEvgo6kV5n08yZ9crWd4 9iPhr9pCKNg3mfaQWOccjd/hXa/8FfIF1L9l6xvQzh00csOf9kn+lV7PA/8A Pyf/AIAv/lge95ff/wAA+e/+CZS7/wDgiX/wUFB73PwxH/lxz1m/se+GwCJW 4/ebfu+gBrZ/4JlTNc/8EV/2/wAMzN/pfwwAyc4/4qOatb9kuwW10pS/Tdn9 B6VnCnhG3zTkl0tFP7/fVvxD3v6/4Y+lLS7QWcK9o0GR07VV8B2R1zxLNI/R CAD68mq8cgWyn6ABfzPH+fxrf+C9oI76Ry5UFhjr78Vp7PA/8/J/+AL/AOWB 73l9/wDwD2nwjp/l2sSYPyjk+leg6BpX+kLkfnn0rl/C0SSQqD0I9frXf6RY +REG5O/oORij2eB/5+T/APAF/wDLA97y+/8A4BZigWGIKBwDn8asRWocB2wF HepdPjEhG8kBT+deu/Bv4BS/ECOK5eRo7Vvu8E59e9Hs8D/z8n/4Av8A5YHv eX3/APAPNvCHgO98aahHHbxlEk7hc4r2Twp+y7DYWXm3tyMr1Bi45r0zxauh /Azw60gjjeSEcZ+XPOM184eOv2krvxjrDwWiGJSdo2Nyay5MNf45W/wr/wCS H7x63L4O8PaIyxt5LEcHC8mqup+B9F1qDCxRKpzyq5rG+Fnwmm1XSF1XUZnE SoJDuGcd60PiHqyWOgyjTYlZl6bT1xVOnhOk5f8AgC/+TD3vL7/+Ac5qX7Ou n6zeMsd0Iznpsz71U1D9kFnsWaC63NsPSLvivmb40ftG+LPh5rTOLKfyhzkS bcgHHXHsa9P/AGSf+CgUfxA1ey029iEc8jrGcy5Oc4Pb2rszGnh1UXvyvyw+ yv5I2+12IhzWMb4ofDW/+HIdpCxRWAPy47GvP5/HsAYxuQHBxy9fdP7QGg6f 4p+HUs4EanaD8vTuK/J/9ofXbrwt43njh37Bk5zjPJrkjTwdveqS/wDAF/8A Jle95ff/AMA+gPDXiGK7uNqnh+OTXbWEiC1VTjLdeDivj74VfHNo75Vl3Bhx 80mfWvof4Z+P015sh9w4x83XrVezwP8Az8n/AOAL/wCWB73l9/8AwD0WxtNj qB82eT9K0tY8OR6l4euYSBh1IqDTT9ohXDEE+prptKhSW3y2ST1Ge2aPZ4L/ AJ+T/wDAF/8ALA97y+//AIB+U/8AwUN+BjeHfFf2iND5crSHITHpzXe/sF+J 49d+GP8AY0/+vtHbaS2ehZh/Ovo/9ur4OxeJdGjmxkxhxnGeuOnNfG3wSkf4 WfFGaAPtSViOCQDkDt+NHs8D/wA/J/8AgC/+WB73l9//AAD6p/a88Pt8VP2O bu2kcSSafN5gJGcYUivwy+KmmHR9W1C2bgxqePSv328AtH44+HviXS3GQbOS YDJOCAa/EH9sfwt/wivxQ1mBQf3ZOeOOp/rik6eCt/En/wCAL/5YHvdl9/8A wD+oP9iD9nTxF+0t8F4/Euu/Gb4mWl9J9kBjsLLw/HF+906zum4fTHPD3Dgc /dVepyT7F/w7xuf+i3/GD/wH8N//ACpqj/wSZ/5NXtvrp3/pk0yvp2vMhGLV 7Gjk7nzh/wAO8bn/AKLf8YP/AAH8N/8Aypo/4d43P/Rb/jB/4D+G/wD5U19H 0VXJHsLmfc+cP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP/Afw3/8AKmvo +ijkj2Dmfc+cP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP/Afw3/8AKmvo +ijkj2Dmfc+cP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP/Afw3/8AKmvo +ijkj2Dmfc+cP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP/Afw3/8AKmvo +ijkj2Dmfc+cP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP/Afw3/8AKmvo +ijkj2Dmfc+cP+HeNz/0W/4wf+A/hv8A+VNH/DvG5/6Lf8YP/Afw3/8AKmvo +ijkj2DmZ84f8O8bn/ot/wAYP/Afw3/8qaP+HeNz/wBFv+MH/gP4b/8AlTX0 fRRyR7BzM+cP+HeNz/0W/wCMH/gP4b/+VNH/AA7xuf8Aot/xg/8AAfw3/wDK mvo+ijkj2Dmfc+cP+HeNz/0W/wCMH/gP4b/+VNH/AA7xuf8Aot/xg/8AAfw3 /wDKmvo+ijkj2Dmfc+cP+HeNz/0W/wCMH/gP4b/+VNH/AA7xuf8Aot/xg/8A Afw3/wDKmvo+ijkj2Dmfc+cP+HeNz/0W/wCMH/gP4b/+VNH/AA7xuf8Aot/x g/8AAfw3/wDKmvo+ijkj2Dmfc+cP+HeNz/0W/wCMH/gP4b/+VNH/AA7xuf8A ot/xg/8AAfw3/wDKmvo+ijkj2DmZ84f8O8bn/ot/xg/8B/Df/wAqaP8Ah3jc /wDRb/jB/wCA/hv/AOVNfR9FHJHsHMz5w/4d43P/AEW/4wf+A/hv/wCVNH/D vG5/6Lf8YP8AwH8N/wDypr6Poo5I9g5mfOH/AA7xuf8Aot/xg/8AAfw3/wDK mj/h3jc/9Fv+MH/gP4b/APlTX0fRRyR7BzM+cP8Ah3jc/wDRb/jB/wCA/hv/ AOVNH/DvG5/6Lf8AGD/wH8N//Kmvo+ijkj2DmZ8xeI/2BbzRtPjmi+N3xcZm ubeAh7bw4RiSZIyf+QT1AY496/D/AP4Oh/A154V0v9mXTI7vU9dTSh4ji+1z 21vHIU3aWVDLbRRQqFXCgLGo2qOpyT/SL45/5AkP/X/Zf+lUVfzmf8HZhxr/ AOzf7yeI/wD3FUUfZqsvaJ28tH+THd8p41/wSB8deEfh3eX914m8T+HfD5Ek hT+09RhtN2UGMeYwzWz8fvjn4V8Y/tTxX0Hijw9cadBqiv8AaItRheIqJDzu DEY/Gu3/AOCHliieAdemwAyPIR9fK/xrl/GQ/tr9qa+JIOzVSv0ImavS58D/ ACz/APAl/wDImfveR+kXwE/bz+DWheCrG0uvin8NbWSKNQyTeJ7JGU+4MvBr 13Sf+CjXwMjt1B+MvwoVuuD4usBj/wAi15f8FXe18N2kZYnZHjmvX9E1CQxg deOv8qOfA/yz/wDAl/8AIh73kYnij/go/wDBX7KTF8ZfhVLgjhfF1gT1/wCu taHwf/4KJfAyK8kkuvjV8JbUHB/feMNPT8OZq0NR18JbMhcEY6A9K1vhP4oG l68FAYpLjuKfPgf5Z/8AgS/+RD3vIde/8FIPgH/aBx8cPg/gcZHjLTiOg/6b VlfEr/goz8CL7QWig+NvwikZj/B4w05iOD2E1e5XGl/atlznKkbj/Wvnj45f HOCbXBp8DAiNuSG74o58D/LP/wACX/yIe95HiV1+1z8Eo/ECyJ8W/hgyu+5n Hiixxz/21rR8V/ts/BbWp7Yn4tfDAiALG2PFFiccj/prXpnwy0q48baqtwwx GjY55/z1re8a/Die38LzyplnUb1AX0Ga64TwP1Sa5Z/FH7S7T/ukvm5l/XY5 T4Nft6/AnwvPh/jJ8KYVZTnf4tsFxn6y16Drf/BRr4Cu5C/HD4PuPbxlpx6/ 9tq8d0j4tv4K1VGlJKo20jdjAr6n+FfiS1+JvhS1v4WXLqSRnPQ46/lXHz4H +Wf/AIEv/kSve8jzPxd/wUT+Atx4WdY/jh8IZHbGAvjHTiw59pq8xsv+Cg3w ZiuyD8YfhcsYJOf+EssAP/RtfRfxp1Caw0s2yqzLjkjtzXkkWsNDJ5QG0A5J Pb/OKfPgf5Z/+BL/AORD3vIyB/wUT+CMKDd8YvhWx/7GzT//AI7XPa//AMFD fgtKpKfFn4ZSHplfFVif/atdvq2riRAOCDxxziuR8SyvfwMmOc44FLnwP8s/ /Al/8iHveR+YP/BTn47eDfiT8Y7C+0HxZ4Z1a3SGPdJZ6pBOgIkJxlWI9K7j WP2gPAPif9hu80uTxz4OGrLpkm2zOsWxuGbYo2iPfuzn2qn/AMFJ9Bay+Idr KRy1qGzjH8Rrvfhf/pf/AAT41/ccCHSJsZ5zhFFHPgf5Z/8AgS/+RD3vI/LH /gnFo10n/BXT9ny5+yzi2Pxa8MMJBGfLx/a9oM7umM1037a8Pnf8Fbf2lcdR 8XPEn/p4uq5T/gnQx/4fEfs/DJx/wt3wzx/3F7Wuw/bNcR/8Fbf2mGIyB8W/ En/p4uq5a7pN/uU0vNp/kkNX6n6S/tBSlv2OLYrxttoxj14jrj/+CQkaavqm pBwCYfKVv1rpfi2/9pfskQJwR5C/nhK5T/gkrZzaJ4l11sYQvFzn2rEZ+n+g +FIJbaNs8kD5QveupTwTHHpErYz8p/hrhfDmvPDax9B9T2r0nwbr41OL7OxI yCP0oA8n8P6a6eN2SMbArjgDr9a+l9BuTZeH7S3LcFOc/U15tbfDKaz8Vm6X bh23e9d6biOzESlgvlptz+VAALbydVbJADd/zqaVN+4EYz6dqxtZ8ZWk9wix TZcH7vHNSrqf2oIWHPY4oA8a/b006GX4P6RHIflPjvwdkkdAfE2lg/oTXsvh +ySFAUGEUYH+f89K8O/4KFWbar8FNIAcK3/Cc+EF5HHPiPTR/UV7fp12La2R AwUqoBzjn3r0Kn+40/8AHP8AKmQvjfyNi6nLgseAPSo9CjEEck7ZDFjg5/z7 Vj3/AImWGLk/KerAdP1qxYeOLCeHyEkXeBgjjr3H6155Zzn7Q+nnV/DMzg8g L0HUZFebfA/SxfrdRSxhsbVJPIPpXsXiezXxP4flt1ZT5vA49wa5XRNBh8AW MshYF5B26HFAFK/8CQ29077gBz1XgVy/xAtLaDRLgDYxKEEenyml8T+OnvLm ZUZsEke9cT461KW70G5IP8JHJ9jQB+VP7UEmf2x3ii27UKsAP95x/QV0v/BU K4N1+x0uWz5WksP/ABxqwfjVoj3H7YU0pC8bO/T53rS/4Ke36R/snzw9xphA x/uNQB4l/wAEvef+CLP7f+f+fz4X/wDqSTV037LDBfDjZOCWOPrha5r/AIJc HH/BF39v3/r9+F//AKkk1b/7NMiQ6PwflVuPfgUAeyLqRcvGSQcYrv8A4PQY DkucEqduPrXhEni9LLxS6ZKgscjNe7fA+RNTtWkBJxgDHrzQB7d4bLRW8PXg kCvVdEiJgjbnpn9K8r0SUOkSg5KcE/nXrfhFisaCQZG0YK0AQanqbaS6sRuQ OD16c19Zfsx/FrS5/BNjGzIsoU7gW5Bz9K+cbzwwdUhxhWJGBnjNc9e+GNR8 PIxs32KoJC7sf0oA+nf2nvBaeONEnlgugONwwvvXivwg+Dkdt4g8yeQMA/Hy 15Dc/H7WNAvxa3rytG5wTmu88DfGWOQx3XmMpDYPt+tAH1H8VPEFv4a8BQWx KiPywp+m0f4VxXhTW7JtNUiRG3Dk55FcD8S/jFa+NPDcNohJfYAxPc7R71y/ hzRbqS0DRS4iA5Of/rUAem/FrwDovjLwxdTTGHcBtAC5JOP0r4j1L9nW48K+ OW1nSpHQW8/mqETuGJFfYekfCW58T6cXS7XarYxzyfyp+sfDKTRNIljmVHTb gsvPb3xXbj1aqv8ADD/0iJMNvvPA7z9qrULXw6dKvjIoAC7jLgce2K8O8aaA nxI1qWeL5i/U9cnqa9K/ag+Fb28C39mwdN+CvQjIPt7etR/s8/D+Sa18yePB 39+nSuIo+W/HnwG1Pw1cm7hDnYc5CY9v61r/AAN+K8/hbVhb3KMo3KDufGK+ /tW+GVrrOlywukZJABJGR1r5g/aW/ZlGlSJd2AiVvmPA6/pQB7T8MPGkfiPR oZoyOnY59a9B0XUQ6DkE5OOeDXxZ8FPiTc+Bp10++81VRioOeBz/APXr6f8A Cfju21OFCr/M3B6DNAFr47GPVNA2k4ba2e+a/Pz4x6Ymm/EdWhbBDjP5jmvu D41+IRJowdTjaGJ/yK+JPiBdf8JB8SlVBkhgTz6YoA+j/wBmfVvtGuajbvjE unsvv061+U3/AAVB8PLpPx010dsEnHXq1fpt+zveC2+It1EGJU2Dhs9uK/O3 /grFaKvxm1eTgkpnPf7z0mNH9N3/AASZ/wCTV7b66d/6ZNMr6dr5i/4JM/8A Jq9t9dO/9MmmV9O1NP4Qe4UUUVYgooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAx/HP/IE h/6/7L/0qir+cv8A4Ozf+Q/+zd/108R/+4qv6NPHP/IEh/6/7L/0qir+cr/g 7Qfy9c/ZvOCcSeIun/cJrH/l4WvhZl/8EK9QW68G6/bgh2YzZHr+661zjgW3 7X13bS5JbWnB/wC/zVj/APBC7x4mleNLzTS237UJVGTwSUArf/aAsJfh1+3R Mkg2q+ts3ynsZmIrYg/SzwFatDosDIFVdowPUV3ej3U+1eDjHAziuM+Dl2mt +G7ORMsPLzz3r2DwxocMkUZZVznPSgDJj006xKU4JI5JFdn8JvhpP9vZpkj8 sYIb+IfpXUeE/B9rLdRt5cWc5xt9q9H0DQ4tPjVVCg44IHSgDx39rH4wJ8HP h26RbxcvEAnltgjIPuK+JP2evDmt/F3x409w7yeZl/nYnHzdK+vv2yfA0vj/ AFaO1RA4UJweR2re/Z2/Z2t/htpyzmC281125EeT1z6UAbHwp+Gtr4L0Eeak QkByW29OB7VuR2lh4t0SWKMxNhNnK98YrP8Ai1rc+ieHbwQkoViJBBxXkf7I Hj+98UWV7JcTSv8A6QVUM2QBkiu2nC+EnLtKP4qf+RP21/XY4L9rD4BXem6f Je2CINsnO35T0PtXF/sV/HnVvh540/sbUXuXtmlCBTISqgnPQmvu/XfBEHir R2juYopA45yue3v9a+dfF/7I/wDY/j06pYRW0YLg4UYI4+mK4ij6G8YeF4vF GjuyBHZ1GCR715FrXwluLeVgqKoOcnnHX6V7l4VLW+jWyScttAIPPHP/ANas 7xxMlvGSirxk4xQB4NcfDqSL/WMpx3I/+tWFrHh2DTgXYKT716D4j1dlcjGB 7DmuD8Q3oKFi3ygH8f8AOaAPzY/4Kt3scHxA0+FANzWozgY43mun8MlfDX/B N3WJ5SQbjSJQuP8AcU/4V5t/wUm8Qx+KPjva2kJdysKLjHff6V6D+2FqafCX /gnTpGmOfLnv9JBIXgnfBG1AH5Xf8E43En/BYb9n1gQQfi54ZP8A5V7Wuw/b XVk/4KyftMuMYHxb8S/+ne6rh/8AgmdIZf8Agrp+zux6n4teGP8A072td/8A tqxhv+Cqv7TpOfl+LniXH/g3uqAP0EOonxB+zRHFkuREAMnjI21P/wAE2Lq3 0jxPrlvJgMXjxwOcZql8CUbxN8HfsoUkCI9foK4j4d+Lpvgh8YpBK7xLcygH Y3ynFAH6j+GrmGS3jGUr0jwZax2kyyKRxyeK+avhh8So9d0O1uY5Tl0Bxkf5 7V7DoHjqOCyZ9xGFP4frQB6d4j8ew6VAz/xoCBjqa4TUPiZLqLOd0igkjrXF a78QDqkrqGkK9CBx1/lVSx8RW8o2liGAyaALl7qU2kXT3/2mVliGSN2M9f8A Gur+GvxltfFwljV3EsOA2SOCfxryf4t/Eew8L+Erx2lJOzHHrkCvNv2U/GTX +p6ndCSYK8ikDdkYxQB6T/wUp+KjaF8INEtoy/mf8Jb4ZnGGxkx67YuP1UV7 p4Y+IS654VgumaQZjBY5HoPevlD9u2dPEHw20dpHDj/hKfDa4PPB1ywB/Qmv VY9f/szwj9miZgoTjBwMcV6FT/caf+Of5UyF8b+R0njP4y2098NOt5ZDLICp wcf1rO0KS50hzK11K247uXPrXzdd/EQaD8W1e5klKO56twMHrX0Fo3iyz1TS YZUkDB0BIrzyz0fw/wDE4rNHC5ZhnuQf610fiDUItfs8AnOCewNeGnxOgusp wR0wOgra0D4jN/q2Zyemc+1AGrregx2RldiuVzXA/EjWoNM0C4cnChScD6Gt rxb4wYs53tjnJzXzb+1x8a4vDHgKbEkxllYouDzkg+9AHy14nEfi/wDaQ1G8 jwRGwQZHTBJ/rXFf8FUdbEfwKa0VufsDqR6HDV6N+zL4Sn8VXmoazMA++XcS c+i/4143/wAFUblW8D3ttzmK0lH0wDQByn/BL07f+CLH7f59Lz4X/wDqSTVN +zzrYg0cYJPzdPTpVX/gmQ/l/wDBE7/goIx/huvhgf8Ay4565X9njxHut1iL 4y+OuMdKAOs8e+Ijpnip5w7jLMeG4/z1r3H9k34zW4eSC4lbgr3yO/HWvAfi 9ozC3NwBuzzn8a4/wT42vPC2rK0UkkeGyQpxigD9afCOtWuoW8bRMDnnFep+ GdVa08pmOV9OeK+Df2Zf2gpNVtrWK4aZixxkn3PvX1vpXxHt7e1DSO2M8E45 +tAHuel6/FnKk9N30qa91SKZG+4wP+eleL2fxs0q3c7rliB2HbH41Frf7W3h vw1bP9pnbKrnIAyfzYUAV/jVaxXEUsscYVoyDkDnrXlGieNNRS/NvDK5AOBh iK83+P3/AAUT0idJ4bNZ8MQAflz1/wB6uP8AgX8dJfH+vb7Z3Rncg5PXv6mg D7O8L3F1BaJLdSucAEndVzxv+0bD4a8LSWlvJIbjphf/ANdcRqMepaf4UWaW XDPFnqfT6e9eI+KtbubjX1R5HILEHJyTQB9r/sw/tLXV7ZNBdPO4eUsCW6cD 3r2nUvGi6zbOg3Osi8euDXz7+yx4ZtJfCcU+yMncAeO+BXsqyR2gKqCSOK9D M/4y/wAFP/0iJENvv/M8V+NUt2NWjt2Ba3kfHqBwa674c6NHp+jxlERWyCeB zxWb8bWVIVmK5Mb5Jx7GuZ8EfGW2swLeR2Ylsdv5V55Z7hDeLDAx6gjI9K4P x7YL4luBGwUhcgAjINdBa62dQswYwWDdOBVMW4MrMwBB6d6APnL46/BeOGNr y0SOFlUFtq4JNebeDfi5d+G50jklmAUlepNfUHxhgRtJlGAQBj9K+NNetVsr 95GGFRiwwMdzQB6F8TPjG76AWklkAdTjLZryDwLCdX1mbVJjuUMSCeeB/wDq rJ8W63P4y1eGygZhHGcHng59qn8Y+JovAugx2UJzJIgLFDjJP/6/0oA9c/Zw 1n+0fGGqXqlgi2sig/jXwN/wU+vRqXxH1GUkkvF/7M9fb/7Og/sXwFqWoSE5 kjdQe/NfAX7fmqHV/Fd8+SF8knn/AHm/xpMaP6k/+CTP/Jq9t9dO/wDTJplf TtfMX/BJn/k1e2+unf8Apk0yvp2pp/CD3CiiirEFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFAGP45/wCQJD/1/wBl/wClUVfzj/8AB2wAdX/Zwz08zxF/PSa/o48c/wDI Eh/6/wCy/wDSqKv5x/8Ag7YO3V/2cDx/rPEXX66TWP8Ay8LXws+Kv+CbnxVb 4a/HDSpDNNHFJOAcHjkqOa+8v+Cp/wAPZdK+JGgeOLSNRbauILwOowcsnmHt /tV+UXwq8Vv4a8XWV2H8topVbI9QQa/cnWdAsv2yf+CZemanahbzWPDejiVm DbWBSCNeenPFbEHVf8E//i5D43+G1sokYzwxLuycnr9a+qtA1vbEoDngetfk p/wTw+Ns3wq+I39lX85gtriPy13DIB3JgdPrX6i+CdXi1awt7mGUNHLHuyvN AHsXhLxLIt3F8xPPPNetaFefbLJW9q8J8Mn/AEmP6D69K9k8FTMNPxnORwCe 9AEmv6NaS34uZ443IxjKgnjH+FRW/jeyt18tQBgdgBz+dV/G00sds20sDt+b FeYz6hcJcEl2IzxkcGgDX+L+qSatpV3sX5PLxxjkV4/+x1p0mmaDdOSVxcs3 PGPmbivT7nVzd6PcRyDJK8ZGa4L4HSy2fh2/jjVsNMwGBxyWr0KX+41P8UPy mT9tfP8AQ9ouPiiLODYhORwSKdpfjqHXCu5dxPZgD61wUOm3FyQ0gLd8Gtfw 9pzfbgoz7kfyrzyj1iGRXRWXp7dq47x3rQMqrzjkdeK6mzQ21pvYjkdPTFcH 44b7RKSBgnP9aAOM8RDzpGcMPm59q8g+PfjWD4d+BL7Up5SiwjAwPr/9evV9 acqj/NtA7+nFfnl/wVI/aOWTSrfwnpd4BNPN/pCov8IVx1x6kUAfOvgXRLz9 pz9quNkD3ERueknICrJk+vGKvf8ABcL4t2+i3MHguwlZY9Ht0tWRDhQUj2HA z6rX0n/wSj+C9l4A+GniH4l69AsVtYmaKKWQkgt5WcgZ9RX5R/8ABQj47y/G 79ofxRqRuDNHcalcMpxgY81yO3oRQBwX/BMFg/8AwVq/ZzIOQfiz4Y/9O9rX pn7ZUYb/AIKl/tQnjK/FzxKf/KtdV5j/AMEu8f8AD2b9nLHA/wCFs+GP/Tva 16L+21qAsf8AgqR+1DnjPxb8Sn/yrXVAH39+xTfLc+H44ZT8joRg8jpXJ/t2 +F38Oa3bX9oixnc53KNpOMc8Vc/YC8Qrf28SM+QI25I/2TXV/tp+Hzr+jJlc +Qr4wPpQBzP7M/7TZsNFtLO6vX3w5Vslj3NfUWgftC20lnt+153AHkn0r8r4 9ZvfCmrTLC7RlGPPr/nNdv4d/aE1Ky2h7uT5fUDn9KAP0aj+MVvPOSs5Oe+T jNc54w+N39nSM0N24IGRhjXxzon7TU9ug8y5ZSf9n/61Q3vx1bWL4j7SGBIO APp7UAew+OfjZqXi6Z4JLmaaJ+oLtjjn+ldr+zn8RF0e5mjWTYPlLDkdM9v8 9a8c8D69YX4DzspLKcEjGK7T4TxR33ia8W2kyqkZ2/WgD0z9rz4oC4+HOnJ5 xJXxJoEmQT/DrFm39K9Ah+LsF94ZdlnO4IQcA4r5o/alt7s+F7NdzMo1zRgO mOdStcf0rstF0+7m8N3KfOpRCevHvXoVP9xp/wCOf5UyF8b+RzHxN+IL33iD zoSxKEkkZrW8CftB6lYIkElzJsXHG8muATU7OLVr2G4kDFW5zniua1/xrBpN 2zQSAAegrzyz7D8LfGKPUEMjXDZI6E81q2/xfg0q4aVrgDnJBzXwpp37Rh0o kC5YY7bcj+VU9c/aYvbwssd021gQcJ1/SgD7Y8bftG2kOmzlrsrtRvXnivjT 4/fGqT4m63FYw3EjxCUNgEgenf61w3iH44X+oWro9w2ZAf4RWX8IrGXxb49h cncEG4knGeRxQB90fsweE4PDvwgjJhRXunYggDJ+VP8AA18Pf8FQbr7RZ68A chIJx/461foP8JdNK+B1XcdluMAZ4HSvze/4KW6qrXHiSBWJ2pcD9DQBD/wT Xby/+CIH/BQpv7s3wyP/AJcU9eIfA/xKdPuITvbb5nOTx0Fe3f8ABNkbv+CH 3/BQsDqZvhl/6kU9fPHw0hkt1jK8MWzjj0/+tQB9gtpcXi7wegCK7tEOo6fn XnGo/CmeGYssWMMecDNei/B+Vk0a28xuWQDGfbpXYz+FDdKW2cNzwelAHGfB yzn8LeU3zKynGScd6+lpPiM02gmNZSrEDFeM2fht7CTO3aPUmul0y6ZAFJ+R e2aAE8W6zqjsTBcTr3O1+P8APNeeeLdM1zWo3LXFyNw6+ZkV6qY4riNt20nj FY/ilktrBljUA4PagD4m+KOm3mk3cjzSS/IcffJNbP7P/wC0B/wrnVE3TMAH 3Dkn09KvfHLTJdQaXCZ4yccd68Ul0a6gucrG3B4IPTmgD9Fof27o/FGhxWiX R+aMJ/F6AVPo/jIeJmWbfucDPf8ArXw98O/7RkvbVcuAWUHp6ivsT4GeELvV oFAVt2wHjvyKAPuL9j/4gI3h42zzbmEmMH0wK9+k1AlCRgDuc5r4O8FeJbv4 U3yFi8QJBYkZFfR/gX9oaw8QaZAst0vmFAMAfT2r0Mz/AIy/wU//AEiJENvv /M6z4lxDUdHkQhTnkA9jivDrLSWsfEgWQlcNkemM17Df+K7PVLdsSA/LwTzm vIPHGuw6Vrm5WAHtznmvPLPbvDWtolqELghV4ySatajrqiM7XBBGeO1eLaJ8 WY7UczqVH+zTfE/x8stMg+e6jzg4+X/61AG98W/G0VvZuhlG5lwOvpXyP8UP F8MEUqpLl3OD7HNanx1/aRgkvZFjuhjAGQOvH0r5+n8dyeKtbUmXKFsnP40A emfDq4S2E11KAX4IJ5P+etc147uJfEnjHYPmTco+904FXbLVY7XTsK20t2x0 +tN8ISR3HiISS7SVbPPPcUAe6axJF4F+DqwgqjMNxIHque1fml+2Bryavrl+ UfcAmOnXmvtz9pT4urbeDGSKUAJhfb7p9q/OD43+J31S+usuWLLyaTGj+vr/ AIJM/wDJq9t9dO/9MmmV9O18xf8ABJn/AJNXtvrp3/pk0yvp2pp/CD3Ciiir EFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFAGP45/5AkP/AF/2X/pVFX84/wDwdsf8hf8A Zwz08zxF/PSa/o48c/8AIEh/6/7L/wBKoq/nG/4O2f8AkLfs4/8AXTxF/PSa x/5eFr4Wfk3YTmCYFfvZBBz0xX6wf8ERf2s7e8sn+HWs3ymz1q3/ALP8p1zn f8uOB14HftX5MgkHI7c16Z+zN8a7z4N/FXRdYtLl7b7HeRSsRyBhgemDWxB+ iH7b/wCzpf8A7Mvxdmmtbd7OynnZrOQNuDgcYGSfevr7/gnt+07ZfELwDY2F 5eBtRtQ0boVIPDNjt9KonXvDv/BTj9lyKOwaC88X6VGtxGy7kduHUjsOrjt2 r4Ij1fxX+yl8WpkiE1nc6fNtmhYgrjg57j7uKAP3X8MoPNQg4HUZ+lelaBrs enwr8wAIFfFn7HH7dXhr45aVbQHVIhqAX5othByBz9elfTsOuKIh85Ixxgnm gD0m+uodesyFbcSK4i78KneV2cA8EnFS6Br5jdRuODzgn3rqTLb3sLMCpz6U Aed6jon2aCRgcArj+Vcl8EWSW0voCASs5/rXpvi20C2MnI4GSfWvKfghOi6z qEGRk3JOK7qf+51P8UPymT9tf12PVtO0IOM7QffPWtzQ/DyWJ85gwB/ECjSW jtQuSqgj1yetGt68VARD09BgVwlDdS8UxLI0at0OMDpXNeI5hdKSckNx0qlq F60UpZmwDySTXiX7Vf7bXhP9njwuZNQ1OFLyVWEce0sWIAz/ADFAGb+1t+01 o/wH8JXUl3dxxXKoTGjKSc4B9K/LX4N+BvEn7Z/7QNvZhJb+41OUsVyF8tcg H0HUiqP7Q/x48Sfth/Faa4SaafTZ3H2aBFVFxtx04Pav0P8A2V/ghoX/AATD /Z71H4p+P7eHT/EV3bLDp0cxL7XbD4ABIH+qPUUAeS/8FafjXp37Df7OWl/C rQLxLK9vbSO+vI0G5t7cckg9vSvw213VJNb1a5uZn3tPIzk/Uk/1r3//AIKO fth6t+1r8eL/AFq7vnuYVBhh+UBVUOxAHAr52oA7v/gl4hj/AOCs37OQPUfF nwx/6eLWum/4KHaq2nf8FTf2ndpIz8WPEx/8q93XNf8ABL7H/D2j9nLb0/4W z4Yx/wCDe1qX/gqDqP2D/gql+0xlgoPxW8T9T/1F7ugD6x/Yd+Mo0OW2AkAw pyMex6cV7N8e/jfBqmmrG8i7iCPu1+bPwc+Pa+Ep1J1K2t8Aj5plXsfWu38V /tO22tW6k61p0hHP/HxHn9DQB6pr11Fq2pSyqEIY447VnStHZr8oG8/iRXi6 fHm1iDY1ay6/890Of1pjfHe0dsnVLMk/9Nk/xoA9X1SSZhuQtnOSQan8N3Mg clnPfJz6V5da/G3TGiZZNW0/J7m4T/Gmf8Lv0+1Uqmr2OCc5FxGcfrQB7tF8 WZdCjIWbIx6Zr0n9mT9oaKw125lurgKX243DOf0r4q1P4zWd1EcalaHpwJl/ xrO0r41/2XI3lanAmehEy/40AfpJ8cfjnp2t+GLREuI3Ya3pEx+UnATUbZz+ imul1H9pnStN8PXYiuYxujIHymvzJn/aBn1ZPJ/tNHKMs2BMp5jPmZ/Dbmo7 v9o6a6t2jOrRYbr+9WvQqf7jT/xz/KmQvjfyPpHxR8fWm8V3cgm/du/936e1 QXnxIbWrUuJMZPHavl20+K0BnZnv7f5+5mX/ABrotL+NFjAoB1SyA75nQf1r zyz1G9v5mnDb2Cnr3FW7fWFilCq55AGeuf6V5lJ8YtJ2Mf7W04n/AK7pnr16 1j3fxmslnBXUrQ46ETIf60Ae4zatE0Izsbjqf88V1Pwj8bW2ga4JFeNTjuMk 8/SvmhfjjavCQdSs12Dj96mT+tS6T8c7O1uFYapaJjn/AFiCgD9QvAv7SCWf hCdftKLgHHyj29q/O39uH4lP4r1nXmaVXEhmXAHTOfarSftZ2ljo5hTW7FmC 9po+v518+fFn4l/8JbfXLCdZvNLfdYEck+n1oA+x/wDgmiu//giL/wAFCF9Z /hiP/Ljnrxb4TaCG2M6EBT0/Cvav+CZsqwf8ESf+CgzuyoiXHwxZmY4AA8Rz 5JNeJeAviVoejwt5us6UhY97tB2HvQB9RfDGEKbdWwVC8e3Feq2lzBHCrEru x07Cvl7wt+0j4asUQN4h0ONgBnN/EOef9quus/2rvCixHd4p8Pj0B1GH/wCK oA9r1K/iZCxZUyc1iXXiOG3bKykDtgV5DqX7VvhflR4l0Ag/3b6I/wDs1c5q v7T3h+VgF8QaIwPcXsX+NAHvJ+ISq/DqcdCMUkni5dXypYcjHtXzTc/tI6G7 Ef27pXXgi6j/AMaZB+0zpFrIfL1zTSfU3UY/rQB7N458B2ms27upUnjJBrzr XPg0gg3JGCD+vNY4/aq0lxhtZ0gf9vMZ/rVe6/ai0afK/wBr6VgD/n5jx/Og DQ8M6RF4b1SLzxs8tgfXpX0V8I/jjpfhqdFM0QJXGME+nt7V8ZeIfjpplzdF 01PTyWbd8s6Y7+9Z83x5too8pqtoGz2mQ/1oA/RHx18bNN1rTxKsyhwuBhcV 5/4a/aSfQNUUpdqAJMcg4xn6V8Uj9pGTywrapGEb1kU5/PrVKX46hbguNTts k5BEiGvQzP8AjL/BT/8ASIkQ2+/8z9SPDv7WUVxb4lvUXjI+X6e1cr8Rf2ho b2QulyAAAT8pP9K/P7SP2lkt1XdqtsuOv75BU+rftLW93Fg6nayD089a88s+ xbX9p6G3hbN2DweMf4CuE+J37VAeDEV8SSDwF4/lXyXqPx4jLYivYAD6SLiu b1/4s/b3wLpCOedwNAHt3iP44y63fsr3G5jzz/8AqrZ8LeN1huYpJJ8KD2Ff Mll41UylvtMIHfc45rqoPitCsQVr61XaP+ei9aAPqSb4rxRqNtwjk+i07w38 V4NOuJJZrpRk54Gf6V8ur8XYfKO6+tw3b96v+NMvfjNFFb+XHeQuCDkB16mg D3D42fG+PWPD0iR3LAM5P3fY18n+OPELalPId2Q3pxV7xH8SJb+IxiYMM54a uPvLxrluc49+poA/tY/4JM/8mr23107/ANMmmV9O18xf8Emf+TV7b66d/wCm TTK+nain8I3uFFFFWIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMfxz/AMgSH/r/ALL/ ANKoq/nG/wCDtn/kLfs4/wDXTxF/PSa/o58c/wDIEh/6/wCy/wDSqKv5xv8A g7Z/5C37OP8A108Rfz0msf8Al4WvhZ+SmeMU6FzFKrAkYPUU2itiD6z/AOCc H/BQPVv2VfidExnhbTZU8uUTklSMofXH8NfqJ8UfgH4O/wCChfw9i8e+EL+2 bxBPBuuLS3njKsyDaflGT0QfnX4EpI0bZU4NfRX7Hf8AwUL8Xfst6xbCwuIp bSNstHPuK9c9M+9AHtl1beNv2WvH3262tb7Tb20kI8q4jkRH6jpx6n9K+vv2 Uv8AgsjcQSrY+MksrQjaiyMjRg9c8k4rqPBn7R/wW/4KQ+E00zW9Q0rQ/E9z GCjQzxQbnXBx8x77TXiP7QX/AATO1fwFci70i2u9b0ttxW4tV84qB0JKAigD 9Qfgn+1R4S+K2jW11Ya3pciyc4S5Q456feNey2HiaN7YmO4ifALfK4xj86/A vQfCPir4W3hGk3moWcsTcxylhg9cY4r1zwx+3V8XvC0IhMMFwNuAWhck0Afq 18d/jtp3w98OXEt1d20biMkBpAT/ADr4l+AP/BS7S0+PU+k3dzYxW01+Ywxb AGWA6k188eNPin8R/wBoiJo74raxuNhEasv8z715dqv7GXiPwtrI1e2ml+0K /ng+YDk5zn9K9Cl/uNT/ABQ/KZP218/0P3U8L/FjTPE9lHPDf2zpIu4bZh/j Vfxr8dPDfhK0eXUNY0y2Ea5y9wi/oWr8btK/aN+KngPTltotpCjaMqxIAFcn 428ZeP8A4t3LDUrq+ZpDwluHOfwFeeUfX37aH/BYSy8P6Zd6f4Rm0+/ucbVY fOBz6qa+CNc8X+Nv2ufHcMl4l9qFwzYS2tY5H+8R/CM+g7V7P8Av+CY3iz4u eI7d9Q03U7DTW5lu7mNolAx6uMfrX11ZzfAf/glb4ZfUbrWtM1zxXdJ8sc91 BOIyoPRVOerj8qAE/Ye/4J7+E/2bPhtZfEb4rXY0a5hUzw6fdzRwn5W43I+1 uhr4D/4LBf8ABWbU/wBrPWDoGmXFnHoNncMY0tcqMYbHQ4/iNcf/AMFE/wDg rv4t/ao8a6jHaz2tvo8hAhjtgyKo2gcDOK+I57iS6lLyMXY9zQAksrTSFmJZ mOSTTaKO9AHd/wDBL2Pyv+CtH7Oa5zj4s+GP/Tva1/Uh8Wf+DcH9jL45fFTx N428U/B06p4m8Yarda3q95/wlmuQfa7y5meaeXy47xUTdI7NtRVUZwAAAK/l x/4Jhf8AKWz9nT/srPhj/wBO9rX9t9AH5/8A/ELf+wp/0Q5v/Cz8Qf8AydR/ xC3/ALCn/RDm/wDCz8Qf/J1foBRQB+f/APxC3/sKf9EOb/ws/EH/AMnUf8Qt /wCwp/0Q5v8Aws/EH/ydX6AUUAfn/wD8Qt/7Cn/RDm/8LPxB/wDJ1H/ELf8A sKf9EOb/AMLPxB/8nV+gFFAH5/8A/ELf+wp/0Q5v/Cz8Qf8AydR/xC3/ALCn /RDm/wDCz8Qf/J1foBRQB8BW/wDwa9fsMWchaP4IsjFShP8AwmWv9GBBH/H9 3BNM/wCIW/8AYUP/ADQ5v/Cz8Qf/ACdX6AUU+aVuVvQD8/8A/iFv/YU/6Ic3 /hZ+IP8A5Oo/4hb/ANhT/ohzf+Fn4g/+Tq/QCikB+f8A/wAQt/7Cn/RDm/8A Cz8Qf/J1H/ELf+wp/wBEOb/ws/EH/wAnV+gFFAH5/wD/ABC3/sKf9EOb/wAL PxB/8nUf8Qt/7Cn/AEQ5v/Cz8Qf/ACdX6AUUAfn/AP8AELf+wp/0Q5v/AAs/ EH/ydR/xC3/sKf8ARDm/8LPxB/8AJ1foBRQB8ffDv/ggl+yf8KPgV8RfhpoH wrNh4J+LH9m/8JXpv/CTaxL/AGr/AGdcNc2f717syxeXMzN+6dN2cNuHFcB/ xC3/ALCn/RDm/wDCz8Qf/J1foBRQB+f/APxC3/sKf9EOb/ws/EH/AMnUf8Qt /wCwp/0Q5v8Aws/EH/ydX6AUUAfn/wD8Qt/7Cn/RDm/8LPxB/wDJ1H/ELf8A sKf9EOb/AMLPxB/8nV+gFFAH5/8A/ELf+wp/0Q5v/Cz8Qf8AydR/xC3/ALCn /RDm/wDCz8Qf/J1foBRQB+f/APxC3/sKf9EOb/ws/EH/AMnUf8Qt/wCwp/0Q 5v8Aws/EH/ydX6AUUAfn/wD8Qt/7Cn/RDm/8LPxB/wDJ1H/ELf8AsKf9EOb/ AMLPxB/8nV+gFFAH5/n/AINdf2FpFAPwPOF6f8Vn4g4/8nqP+IW/9hT/AKIc 3/hZ+IP/AJOr9AKKbk3rJ3A/P/8A4hb/ANhT/ohzf+Fn4g/+TqP+IW/9hT/o hzf+Fn4g/wDk6v0AopAfn/8A8Qt/7Cn/AEQ5v/Cz8Qf/ACdR/wAQt/7Cn/RD m/8ACz8Qf/J1foBRQB+f/wDxC3/sKf8ARDm/8LPxB/8AJ1H/ABC3/sKf9EOb /wALPxB/8nV+gFFAH5//APELf+wp/wBEOb/ws/EH/wAnUf8AELf+wp/0Q5v/ AAs/EH/ydX6AUUAfn/8A8Qt/7Cn/AEQ5v/Cz8Qf/ACdQP+DXD9hQf80OP/hZ +IP/AJOr9AKKGB8xf8Emf+TV7b66d/6ZNMr6dr5i/wCCTP8AyavbfXTv/TJp lfTtRT+Eb3CiiirEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGP45/5AkP8A1/2X/pVF X843/B2z/wAhb9nH/rp4i/npNf0c+Of+QJD/ANf9l/6VRV/P9/wc9fsr/FX9 pWb4GP8ADH4beP8A4hP4fOunUT4Z8O3er/2cZf7M8rzvIjfy9/lybd2N3lvj O01j/wAvC18LPxcor1rTP+CZn7VT3i/af2bfj4IgDnHw71cHOOP+Xf1rU/4d j/tM/wDRt/x//wDDeav/API9bEHiNFe3f8Ox/wBpn/o2/wCP/wD4bzV//kej /h2P+0z/ANG3/H//AMN5q/8A8j0AeW+CviHqvgDWIr3TbqWCWI7hhyOfwr7C /Zq/4LP+O/hJD9jv/wCz9UtDtG27Rnxj6nvXh/8Aw7H/AGmf+jb/AI//APhv NX/+R6P+HY/7TP8A0bf8f/8Aw3mr/wDyPQB+hXhX/gqR8LfjBCk3imw07T72 UYla3Plj07kdgK77Sfir8APEIDxeKIIiTkBrqLI/Nq/LqP8A4Jn/ALTkX3f2 cv2gF+nw91gf+29aFn/wTz/amsyNv7O37QYAGAB8PtYGP/IFAH6v6F4k+B8T hl8a2in2vYB/7NWpcfEH4ISW+2Txva5Xji+iwR/31X5LD9gn9qsD/k3j9ob0 /wCRB1n/AOMUN+wZ+1W6kN+z1+0Mf+5A1g5/8gV305L6lUjfXmh+Uyftp/10 P1A1/wCK/wCz5orGSXxVbTkdcXcRz+TVx+v/APBSb4K/B0SDREsdTmh+75hV wx/Amvzok/4J7ftST8v+zx+0Oc9R/wAK+1gj/wBEVVn/AOCbf7TMuSf2cP2g mYnPzfD3V+f/ACXrgKPp79oH/gvH448daJc6RoNppGl2k3G+3hZXABz1Br4l +Jnxx1/4t6u17q99LcSHI/1jED8CTiu5b/gmp+0u3H/DN37QG0jn/i3mr5/9 J6cn/BM/9pZUAH7N3x/AH/VPdW/+MUAeLsxZsk5JpK9q/wCHaH7S/wD0bf8A H/8A8N7q3/xig/8ABNH9pcf823/H/wD8N7q3/wAYoA8Vor2sf8E0P2lu/wCz h8fR/wB091b/AOMU9v8Agmf+0oI8D9nL4/Zznn4eat/8YoA43/gl+gj/AOCt P7OajoPiz4Y/9O9rX9uFfx9/8E5f+Ca37Rvg3/gp18A/EOtfs/8Axu0bQdJ+ Jvh3UL/Ub/wLqlta2NtFqls8s0sjwBY40RWZnYgKoJJAFf2CUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUmB8xf8ABJn/AJNXtvrp 3/pk0yvp2vmL/gkz/wAmr23107/0yaZX07U0/hG9woooqxBRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQBj+Of+QJD/wBf9l/6VRV5z+yp11//ALd//atejeOf+QJD/wBf 9l/6VRV5z+yp11//ALd//atYv+IWvhZ69RRRWxAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRSYHzF/wSZ/5NXtvrp3/AKZNMr6dr5i/4JM/8mr23107 /wBMmmV9O1NP4RvcKKKKsQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAY/jn/AJAkP/X/ AGX/AKVRV5z+yp11/wD7d/8A2rXo3jn/AJAkP/X/AGX/AKVRV5z+yp11/wD7 d/8A2rWL/iFr4WevUUUVsQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UmB8tfsT3Mn7N3wK0zQNYl05dXa3snv7SZ7iOTTriLT7S1kgYpC6sVa3OWU4 5wMgZPrn/DRNn/e0j/wJuv8A5Fr5C/bp8QfFOz8ewab8O9JtvI1DX2/tPWGm jaXTolldtqRPgEyMsabiGAV5MhSFavN/2n/FHxj/AOFo+AZPB15qmk6Ba3Wh zeI9KsNKF3cul5qsVvPG7SWc0F1EsQkEn2a8s5bOMS3Lm5VooxwfWH7X2K7J 36a30v301XS6O94S2HWIbVm2rX10trbs76PrZn6C/wDDRNn/AHtI/wDAm6/+ Rarar+1Fo+hWqT3174fsoHmit1knvbiNTLLIsUSAtbAbnkdEUdWZlAySBX5c 6l+1L+0d8PP2cPA1w3hO58YePofCD6n4kSXwBewpd382k6xc2kMZguv3c8N3 pkEF1F5IEjajbeUYDMkY7nxFf/E2Z/Hdp481Ow1HRtN+IPga38PNa+H302OW P7Tob3c0UjTSeZA93JOFRi7xMkitLICgj25p9/62MHTSP0W/4aJs/wC9pH/g Tdf/ACLR/wANE2f97SP/AAJuv/kWvDKKy9tMfs4nuf8Aw0TZ/wB7SP8AwJuv /kWj/homz/vaR/4E3X/yLXhlFHtph7OJ7n/w0TZ/3tI/8Cbr/wCRaP8Ahomz /vaR/wCBN1/8i14ZRR7aYezie5/8NE2f97SP/Am6/wDkWj/homz/AL2kf+BN 1/8AIteGUUe2mHs4nuf/AA0TZ/3tI/8AAm6/+RaP+GibP+9pH/gTdf8AyLXh lFHtph7OJ7n/AMNE2f8Ae0j/AMCbr/5Fo/4aJs/72kf+BN1/8i14ZRR7aYez ie5/8NE2f97SP/Am6/8AkWj/AIaJs/72kf8AgTdf/IteGUUe2mHs4nuf/DRN n/e0j/wJuv8A5Fo/4aJs/wC9pH/gTdf/ACLXhlFHtph7OJ7n/wANE2f97SP/ AAJuv/kWj/homz/vaR/4E3X/AMi14ZRR7aYezie5/wDDRNn/AHtI/wDAm6/+ RaP+GibP+9pH/gTdf/IteGUUe2mHs4nuf/DRNn/e0j/wJuv/AJFo/wCGibP+ 9pH/AIE3X/yLXhlFHtph7OJ7n/w0TZ/3tI/8Cbr/AORaP+GibP8AvaR/4E3X /wAi14ZRR7aYezie5/8ADRNn/e0j/wACbr/5Fo/4aJs/72kf+BN1/wDIteGU Ue2mHs4nuf8Aw0TZ/wB7SP8AwJuv/kWj/homz/vaR/4E3X/yLXhlFHtph7OJ 7n/w0TZ/3tI/8Cbr/wCRaP8Ahomz/vaR/wCBN1/8i14ZRR7aYezie5/8NE2f 97SP/Am6/wDkWj/homz/AL2kf+BN1/8AIteGUUe2mHs4nteo/G7T9fjggluN LghW5gmd0luZGAjlSTAU265J2461S/ZT6a9yM/6PkA5x/ra8hruvgR8ZPCHw 1udXt/Enirw34fuLpIJYYtS1OC1eVMygsodgSM5GR3FXSm5S1JnFKOh9BUVw P/DVvwt/6KV4A/8AChtP/jlH/DVvwt/6KV4A/wDChtP/AI5XXdGB31FcD/w1 b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf +ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ile AP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8A wobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT /wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCO Uf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8A DVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvw t/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6K V4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/ 8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0 /wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDj lF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B 31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31Fc D/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1 b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf +ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ile AP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8A wobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT /wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCO Uf8ADVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8A DVvwt/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvw t/6KV4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6K V4A/8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/ 8KG0/wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0 /wDjlF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDj lF0B31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B 31FcD/w1b8Lf+ileAP8AwobT/wCOUf8ADVvwt/6KV4A/8KG0/wDjlF0B31Fc D/w1b8Lf+ileAP8AwobT/wCOU1/2sfhamM/EnwDg/wDUw2n/AMcobA+UPjtr Xwy+FPi6yfxv4u8f2uteL1k1GOPT11W5ikYrFLLtW3n2xqv2hBjai84UYHF7 QfB/wx8V3V5b6V428VapcacEN1Faa7czvbb923eFvyVztbGeu0+hqh8WvCHi G6/absvENxYaZf8Ag6z8N2Vl5IkJv4pvs4leaNAn3T+6TbvySn3SOvi3xr/Y ftf2k/h5eadbrqmg2/iHxImragbj7ITeQW9nOtvHc2N3Y3Ntdwi6eNvs88S7 TtnD74Y0b5SrjsZSziOBkoypThzqUVL3bacspW5OZv7Kd7anpyjSqYaNShBx cW4zcpL3nvFwiteW2jbvr2PowfBfwYcf8Tv4g8nH/IRvf/k2sjx18IfhzpXh mfVdXv8Ax5qFl4faPWvs8l5dTBpLORLmMhHvtjMskSMA3ykgZ4r420L/AIJ3 /GbwB8Ntft/C3iDwx4f8U+Jr221d57Hxb4htdP0q8tfC1nY2wtbaNhD9nXVY pmaGeGWJ7JLaNo28qKODr9Q/Ys8T+H/Ev/CwfFXia/1q/wDD/hv4g28p/wCE p1SVCup6pFdaZEtu7LC0UNgjwyRsoQSR2+1ZPJikj95bpX/q1/8AgHLbS/8A W59YeJPGWj+DYYZNX1bTtKS4fy4mvLqOBZWxnapcgE47Ck0Lxpo3ickabq+m agwzxa3ccxOP90mvFv8AgoF8I/FXxf8Ahvpll4T0yfU7+2uZJXSKS3jbbs27 Q80kaKWBIyW/LqOU/wCCf3wf8S/C/wAV36+I/A2teGV/st0hvLuawlill86H 92Db3MrAlQ7ZYBcIeckA/V4TI8rqZPLHVcWo11e1O2rttr5nhYjM8bDHxwtP DuVN2vO+1/8AI+j/APhYOgr8RdO8IvrGnx+JtXhluLPTnnVZp0jUsxGeBlVd lBILCOQgEIxEnhHxtpHj3TZLzR7+G/t4Z3tpTGcNDKhw0bqRlWHHBAPIPQiv gf8A4KHeBfHXgL9p6/8AFmh6bqM0Os2kFzpOo2lswtoLuOKOBoridYZpLeRR GHWRVG9ZCnY19Bf8E4vhn4j8D/COO78R32s3NzdWcFsVv0eM3E4muru5uwjq rKZJ76SPkZZLaJizZzX55QzaFSX1aUJKqpyTVnb2ajdT5rJe87LlTb3fR2+7 zHIKuHUMXTqRlQnTjK91ze1btKna7furVtpfjr9FY+tY1n8RvD2o+L5/D1tr 2j3Ov2olM+mRXsT3kPlJaySboQd67EvbNmyPlW7gJwJULbNYth8NvDmmeMbr xFBoGjQ6/eGUzalHZxpdy+altHJulC723x2VmjZPzLaQA/6tMemn3PELPjX4 keEPhT4X/tXxZrGp6XbtN5MZttLa93nYz8BG3Z2o5+7j5evOKPBvxY+HnxAu dOi0jxJrN4dW0yPV7RhohRJrWRQyPuaQAMVOdhIcAHKjBx5V+3ba60fgTLe6 LD5p0q5+03hSDz54bcwTRmWKIgiR0aRHKEHcqOME4B+Ov+CfWia/8WfjpcyQ +J21qy0i5tr2MxtILawgF2tzccCGONJpJESJI0AAhlnHTOPma3EFXD5hWwdW k2uWDpNKXvSc4qab+FKMW5atbLdH1NPh2GIyulmGHqK6nJVU5RXLFRk4tJ6t uSiut7vqfqF9j0H/AKCWs/8Agti/+P0fY9B/6CWs/wDgti/+P18zaR+xTbLP rV/e2ukS+K/EXiHUJtV8VabdXGkeJNU0K4v3urfTjqdtsvIRaq1tEqJMUePT oU+RJNsWbefs3fFu/vfCWtS+NpINdtBo9xrsMHiK/wDsF3JbTaWLoR26xpAy yWx15ceTGsslzaO6B4Ymtvp7o+WSvsfVf2PQf+glrP8A4LYv/j9H2PQf+glr P/gti/8Aj9fJfhP9nf4opYLqlt4m1j7RFe2Oo6Fb3/i7UkksEmSwOrR3cUkU 0U/m51DyYLiOeO1Kw+T5XmYtuv8AgP8As6eKPhbrtjcat4h1DUU0trq0t5E8 VaxdwXGnSXF3IkVxbahNdebcKTaP9p84TZZ40eO3iWGapJK5Cd1c+hfseg/9 BLWf/BbF/wDH6DY6COupayD6HTYxj/yPXwboX/BOP4kfD34D+GtG8NeMNPHi 3R/DEOkLq7a3qmny2M1rpl61nCk1sV+02sWr3IkEdzE8RtZHja3kaOM19a/C HwPcfDrwDZ6RcNalrdpTi3yY9rSFgBkA9+eKbUUrpjd+ax3v2PQf+glrP/gt i/8Aj9H2PQf+glrP/gti/wDj9fJHx2/Yl8WeI38Uah4G1q00nxBdajf3Xhy4 fWL7TR4cjm02ebbC1mEJFxr7w3V1FcC4hniRQ8bmG3ji9+tNV8Vf20iXei+H 4dNM7I08WtSyTiIG62sIjaqpcqlkSu8AG4nG4+QhuIVrD6mv8MfEmn/Enx/4 10OManAPCmsw6RbyrBHI+oF9OtL1pChkURYN1sC7nz5e7I3bR1eoeGtK0sIZ 73WUV8hW/s6IgkdRkT9R3ryP9mnURpHxr+LN03KweNbdz/4INHrpdE8Y3usa fI1zZXaTX+r3EyoyEJDHI8jl2Y4wAdgHHO7jpXh47NK1DMaWEhTvCe7s9N+u y2Omlh4yoyqN6o677HoP/QS1n/wWxf8Ax+j7HoP/AEEtZ/8ABbF/8frxb9sb R9X8SfBaHS9EfxHHc6j4n8O2t22hXN1a3y2D65YJelZrZlmjT7IZy7o67Y95 LAA48ssv2OviXpWieMZX8favf6nc3slz4Uik8a6zFDpCwaZbx6XHO5Z/OWG6 W4W4EkUgvVmE9wJXQQ17sbPV/wBbf5/gc1j68+x6D/0EtZ/8FsX/AMfo+x6D /wBBLWf/AAWxf/H6+JPgT+y1431T4UvNpWseLPCMOteKtTuEW68Ranp11L4b vvEt9f3MM1nKjNDezWcqMl5+41CN7koZoPIKye7/ALPX7Otr8Btd8b3Vpea/ cxeJNStp7f8AtTxTqmuSJBDY28I3fbJZPLl8xZtzIS0iiLe7bVVBpL+vT/g/ cH9f1/XU9kNjoIx/xMtZ5H/QNi/+P0fY9B/6CWs/+C2L/wCP18ffDr9jH4iW GveGdT8ReNNXmurCwm1LV5bPxvrPl6x4iePRT9qaAGOL7EstrqarZFfsqJOm 2BRI8cfdfsBfsx6/+y7+z9omieMdcm1zxba2EGmXstvqt3caVFb2hkisorS1 k2w2yx2vkxuYYYmmaPfKZX/eGmlrr/X9fmLXTz/Db+vkfQ/2PQf+glrP/gti /wDj9H2PQf8AoJaz/wCC2L/4/Xy1qn7KPjaf4nR69D4h1qGAaPq4vrc/EPXV ttW1Oe4tlt3W13eTZwizjuh+5BFvLebo43aBJW9V8BeHPEngDRYNE0/SdPud J064eC2uNS8Y3+qX8tt50O2WWa6geV5TG9y2x5XCtDEgkKyF4Z0tcDpdO8da RffGnXfCjNqcVpomh6drBvzbIXma7nvovK8rzMAILLdv3nd5mNo25PS+KIdI 8GabcXmpp45tbOzRpZ528KzGOGNRlnZg5AUDknoBXhmua1H4a+PnxW1KV1ij sPh/otw0jMFVAlzr7Ek+nFfM37SXxs8L+JfjF4p8VRfEL4THQoNQ1a70jQ0v NUna9a9SaN7qUT2hSK72tbSKsbiMSxRguqRrjz8djHQ+FLp/w/mePmeYVcO1 7NX/AK3P0D8L6z4U8Z+G9P1fTdY1m507VLaO7tZf7LRPNikQOjbWmDDKsDgg EZ5Aq/8AY9B/6CWs/wDgti/+P18S/DH9knVfjN4b8BeNv7Ys4dF1L4a+D9OO lytI8V9JZS3N/vlUDCPHPLYyQyguR5dyjKFk3Vs/AX9ir4hfCT9jCz8KSeML p/ihY6WtpBqFr4l1CPR7WWxjuE0eOCDHlQwRKLNZ0S2C3Ihf7Qlzubf3U2pQ 5nvoevFt281+iPsH7HoP/QS1n/wWxf8Ax+g2OghQf7S1gg9P+JbF/wDH6+TP FP7H/wARNP8AE1mPDPj7WI/CpuotJudN1Txdrc93Z6K9zHJdvb3hmeSS+kT7 QoluBJLEtxCltcWf2bzJ6P7Ov7AGt/C39rDU/iTr/iG+1TfPf/2fDF4p1eUR 276fodnaRXEMkghupIk0+6WSeYPJcEWsz5kVRBaS/r5f18vQHf8Ar+v6+8+w fseg/wDQS1n/AMFsX/x+k+xaBn/kI6xz/wBQ2Ln/AMj18haL/wAE+9W8Bal4 l1Xwp4pm0TVviB41n1nxL9iisdJF1pUlzqEj2gubKyhvZpXju1VZprhpraQC SGZdjJN0Wofsr+MdZ8cQzzeIdZtdKmhvb/UhF411Wb+0dW+0RJaMtrKrQwWX 2T7X5trGfJL3agRu1vDcqRs7X0/4b+kN9T6bFloB/wCYjrH/AILYv/j9Ilpo D5/4mOsjHY6bF/8AH64P4t6d4l1H4O+JbDwfqVrpHi+70a7t9EvrhVaGyvmg cW8zBo5AVSXYxzG4IByrdDzXww+H/if4K+C5NB0bTNP1Sxt7q7ns5dX8Y6hf 3kgllu5YxNcXME0zEn7KCWkk2iaYDcIIxMX0DWyO61vxnpGlfGrw54SQ6lLD rmj6nq0t41vGj232SaxiEaxeYQ+83pJJddvldG3fL0V7J4Y01Y2uNY1K3WWR YkMlhCod2OFUZn5YngDqTXk3iYk/td+B88H/AIQ7xFn/AMDNDrzH42/s8/Fr 4qftS6BrMHiqx03wh4atLq50q2QFVtdQlimgjuHjKHfJHFIcFnYfvDtRPmLZ Yip7OjKqldpKy6ttpWXT1bskjqwGHjXxMaNSahF3vJ7RSV9er7JJNt2Pq37D oH/QR1j/AMFsX/x+j7DoH/QR1j/wWxf/AB+vnL9p79k/Vf2jPiH4T1Q6pa6X Y6T4a1zRdSsvMkli1Qak+mrLayIFUSW729vdqWJVkka3kCMUKjziP/gnh4i8 da/4pPxFj+GHjjw94r1fSdUj0e60eV4dJl0y7SGKfFwZo57m50g/Z5pEjtwv 2aKPZIskkg0hZq70Oaej01PtT7DoH/QR1j/wWxf/AB+j7DoH/QR1j/wWxf8A x+vj/wARfsP+N9P/AGc9A8DeAdZ8E+DD4Y8Z6z4t0+KTS5bzSSjanf3uk2X2 WNoAiQNc2k+5SUhm0+JRFPGTXdfsr/sny/s6fFr4na6kthPZ/EO+XWHeFYIJ Vu2vdRmlR4oLaGNlEVxbYuJDNcysZFkkKQwAPT+vkJn0N9h0D/oI6x/4LYv/ AI/R9h0D/oI6x/4LYv8A4/XzY37EugR6L8V7Cy8MeFdAj+Nj3Ol+JJdEjhsZ YNMa1uoY5lxatFdXbSzyzMZ48g3siNJMkEavhar+x54ouP2VG+Fum2Hw90y3 0iz1OPw7qFhEmmLp180l+2m6oLa2tI4oL2B/sE7vbCNftM91LEkXkRLKlb8h vQ+sDY6AFz/aWsYPT/iWxjP/AJHo+w6B/wBBHWP/AAWxf/H6+X/2m/2a4f2l PiimjXnh3Qb2Jkh1Mah4j0RdY0O6ih0/VrW3t5LYnbLLa3t/Fc+VM0PmJMfK kZon8vp/hn8BNT8BePpvGS6J4a03xBqGkaZo+oxWmpzSy615YtluLzUL9rdZ dQnt40eO1M0IYKkpMkYvHW3FZiaa2PU/HnjXQ/BfirwVpqyatdL4u1mXSnlN nHGbNU069vPMCiRvMJNoI9uV/wBaWydu1t9pfC6zPGda1DzI8b0FjCWXJAGR 5+eSQPxrxL4iXut33j34PNr2n6Vpt2PF7lYrDUZL6M58M6o0mXeGEjbMZkX5 fmREc7C5jT2O5ttO8RfDdNG1PUdTubRBdQXehxLLFbXJluZ5VaZmIinhKTIS i85TDFuY6tLTa+3bq99e2/foTez1f9fI0RZaAf8AmI6x/wCC2L/4/Smw0EY/ 4mOscj/oGxf/AB+vn79pX9jmw/aa+Lvw61jWZbFdF8CreXP2aXStO1F7i6ea xeFRHfWlxGkey3mDSw+VcLlBHIAz14mP+Cb/AI/8S/F74S+Lda8VKNa8Grqe pa1q6+J9Uubq61e80mxiM8CfulW0+320rmwRorUwhFESrI0KTCz30/r+vvKk mtv61S/r0Puz7DoH/QR1j/wWxf8Ax+j7DoH/AEEdY/8ABbF/8frxL4N/BDW/ BWsiLVtU1ebRtG1CW90S2fxbqWqzRLLY2ySR3M9wVkulW5fUWRLhplVXt3UR lI44N741/CG1+ND6LpeuaJ4f8UeDmupG17RdZQTWdzGInaGUwNG8dy8dwkOI pdqDe0obzIYlKdho9P8AsOgdP7S1fI6j+zYsj/yPR9h0D/oI6x/4LYv/AI/X zN8a/wBnvxR8R7bwGtjaRaRJZeINKuNVsLPW3utJsbaz1DTdXMxMsccjbH0r 7JAkUYCf2gzlFUybOe8H/sReMdB13UBd+JZpfDusat4gub+x07xXrekzLa6h 4ifVYPIazniigljjuZ/MkETTyNEkQukt5XRHoI+uhY6B/wBBLVx/3DYv/j9H 2HQP+gjrH/gti/8Aj9fJfhf9jX4hX2hWkOvfEDxA+p3F/wCIn1XUB4r1S5kt 4tQF8lrJawq1vaCe2jOm7VktXt42imaCG3kzLJV/Y/8A2WPG37P2ueJ/G66T 4ci1/wCKN3qut+INGurq2tn0yee+vLyxt/tlnZGS6Ma3CwzNdPc+WWY2zrEh indle1w6XPef2hfil/wqyXwNp/hiwHiPXPHfim28MWkGqT/2Ta27zwzyCZ5o 1uXwDCBtERJ35zxg+f8Aif8Aa107wV4n1bRNZ+KH7G2k6zoNzLZ6nYXvx2S3 utOnicxyxTRPpgaN0kBRlYAqwIIB4q58dpr2f4rfs7m/t7W2nHxf00KlvcNO hjEN+I2LMiEM0YRmXBCsxUM4Advyo/ak/ZW8daD+1p+1H4gt/wBk/wCOXiTU Nf8AGninT9Om03wdq1zF4rg1PUdQ/wCJlFei0aOC3htym1Yy3nNNCyhgXeOa 8nTw7qwg5y0slu7tL0Vt23ZWNMJSjWxMaNSahF3vJ7JJN7bu+ySTd2ftL4S+ HPxm8feF9N1zQ9D+COtaLrNrFfafqFh8Rr65tb63lQPFNFKmjFZI3RlZXUkM CCCQa0V+BXx8P/MofCH/AML7UP8A5S1414P/AOCV+rftYfs/fsFXPi6G38P6 T8FPhpBbeItG1nQ7K51Rb+XStHSKA2epWN1AjJJZzLKXSOeI48tlbJHlX7OX 7Bn7S3w78V6T8L7D4keM7XU/A3hTStO1DUbbV/EmneDLaKw/4QdrSyszNBFa SSyxWHiZHksY5Tsu2W5ZGl8hOyFKEnZqxySqSVrO59d/8KJ+Pn/QofCH/wAL 7UP/AJS0f8KJ+Pn/AEKHwh/8L7UP/lLXH+Ef2D/2ir+48SnxV8VrhrHxDq+m u1jp3jjXEa20r/hLNd1HULaC5jWGS2mOk32l28U0IVz/AGf9n3QRbJa938e/ sy+IPE91+zi0OuSaufg94lXV9av9bu/N1HWIl8N6vpXmF4oUSW5e4v4ZHOyF CPNICkLGRUYdV1/piqVJR210/pHnH/Cifj5/0KHwh/8AC+1D/wCUtH/Cifj5 /wBCh8If/C+1D/5S19f0Uexh2H7SR8gf8KJ+Pn/QofCH/wAL7UP/AJS0f8KJ +Pn/AEKHwh/8L7UP/lLX1/RR7GHYOeR8maZ8Ivib4es9Qv8Axpo3gTSdLtbc NFJoviW61S4mmMsaBDHLp9sqptZ2Lh2OVUbCGLLW2j3r6N+PH/JKtU+sP/o5 K+cq5qqUZWRrTd1qG0e9G0e9FFZXZpYNo96No96KKLsLBtHvRtHvRRRdhYNo 96No96KKLsLBtHvRtHvRRRdhYNo96No96KKLsLBtHvRtHvRRRdhYNo96No96 KKLsLBtHvRtHvRXIftB/DWb4zfALxx4Qt5beCfxVoF/o8ck6q0UbXFtJCGYM jqVBfkFHGOqt0ppisdftHvUfjSx1LRdE0240q3sdQub+IztFd3T2iRoJJYzh 1ilJOYwcFQMMeeOflzT/APgmhovw/wBU8N3WgWHhi5vfB8mhR6VrUuk6bpOs WNtb63eahqEMElhYxQW0c0V2Y2SGFBcJ50UhjaY3C/X3iX/kD+Hv+vB//Sq4 qhM4Aan407+HvDA4zzr8+P8A0iqpe+LfF1g04fw7oDG3RJHEer3cpwzlBgLY kk5B4GSAMkY5rF+Bn7Pl18HfiL8QNen17+1l8d6iNTa38m4Q2cgknCjdJcSi TFu1tHvZRIPI2K62kdlZWO94wiu9E8a2esPpd1r+hvb/AGG+sbadkuYQS3+k QgEAuu4cEEHGCB94fNcV5ticuwDxWEhzNNX0btF7u0dbLd2T0ucuMrTpUvaQ V39/5Bp3iXxZq1t51vofhd49xU5124UqR1BBsgQfYgGrH9peM/8AoAeFv/Cg n/8AkKsP4UfEnV/iR498QXl5o9tpVm6xqltaQ+RbWrp8uwIeS7A5Y9yvOOBV /wDaS+G9/wDGT9nbx94Q0ua1t9R8U+HNR0i1kumZYI5bi2khRnKhmCBnBO1W OBwD0o4V4gWcZbDHKNruS2avyyauk9Una6T11sx4HEe3oqp6l3+0vGf/AEAP C3/hQT//ACFSf2l4024Og+Fj/wBx+f2/6cq43WP2fP8Ai9WveNdP0jRrG+1Z 9Kt5l0y/n0e51yOC5tZXvNSubdN9zJBFCYre2cNGY1mieURX0qw+I+Ev+CYa eGh4b2af4ZP/AAjviBPEsHm2WiXO65f+zvOSXGhp57L5Nzsu18q/Plx5uh9o b7N9KkdLufRnib4i634Lhhk1i18CaUlw/lxNeeKJIFkbGcAvaAE47VL4W+Iu p+Kb6KO2i8EXcDuBK9j4le5dEz8xVBajccZIG4fUdR51/wAFAvhH4q+L/wAN 9MsvCemT6nfW1zJK6RSQRttKbdoeaSNFLAkZLf4jlP8Agn/8H/Evww8WX6+I /A+teGl/sx0hvLuawkill86H92Db3MrAlQ7ZKhcIRnOAfr8JkuV1MnljquLU a6vanbV2218zwMRmWMhj44anh3Km7Xnfa/8AkfRnif4b+HfFutTX2q6Boup3 sm1HuLuximlZUVUQFmUkhVVVHPAUAcCs/wD4Un4M/wChR8Mf+CuD/wCJoor5 U9wP+FJ+DP8AoUfDH/grg/8AiaST4I+Cyhz4R8Lng/8AMKg9D/s0UUMDrKKK K5jUKKKKBsKKKKBGF4j+HmgeLtUW41bQ9H1O4jgWJJbuyjmdUBY7QWBIGSTj 3NUf+FJ+DP8AoUfDH/grg/8AiaKK3j8KID/hSfgz/oUfDH/grg/+Jo/4Un4M /wChR8Mf+CuD/wCJoopsSD/hSfgz/oUfDH/grg/+Jo/4Un4M/wChR8Mf+CuD /wCJoopoA/4Un4M/6FHwx/4K4P8A4mj/AIUn4M/6FHwx/wCCuD/4miikAf8A Ck/Bn/Qo+GP/AAVwf/E0f8KT8Gf9Cj4Y/wDBXB/8TRRQwHD4K+DYopol8JeG Via6e4KDS4ApkaOJGfG37xSONSepEajoow3/AIUn4M/6FHwx/wCCuD/4miih gH/Ck/Bn/Qo+GP8AwVwf/E0f8KT8Gf8AQo+GP/BXB/8AE0UUMXUP+FJ+DP8A oUfDH/grg/8AiaP+FJ+DP+hR8Mf+CuD/AOJoooGH/Ck/Bn/Qo+GP/BXB/wDE 0f8ACk/Bn/Qo+GP/AAVwf/E0UU+gMP8AhSfgz/oUfDH/AIK4P/iaP+FJ+DP+ hR8Mf+CuD/4miikhIbb/AAW8HQyXCp4T8Mot1JH5wGlwATeWsvl7vl52ebLt z08x8Y3HLv8AhSfgz/oUfDH/AIK4P/iaKKmW5Etw/wCFJ+DP+hR8Mf8Agrg/ +Jo/4Un4M/6FHwx/4K4P/iaKKpFoP+FJ+DP+hR8Mf+CuD/4mj/hSfgz/AKFH wx/4K4P/AImiihjD/hSfgz/oUfDH/grg/wDiaP8AhSfgz/oUfDH/AIK4P/ia KKBIP+FJ+DP+hR8Mf+CuD/4mj/hSfgz/AKFHwx/4K4P/AImiihjCH4LeDba4 kkj8JeGY5JLd7d2XS4AzRl43KE7eVLxxtjpmND1UYP8AhSfgz/oUfDH/AIK4 P/iaKKAD/hSfgz/oUfDH/grg/wDiaP8AhSfgz/oUfDH/AIK4P/iaKKAD/hSf gz/oUfDH/grg/wDiaP8AhSfgz/oUfDH/AIK4P/iaKKAD/hSfgz/oUfDH/grg /wDiaP8AhSfgz/oUfDH/AIK4P/iaKKAD/hSfgz/oUfDH/grg/wDiaP8AhSfg z/oUfDH/AIK4P/iaKKAGT/AjwPcGN5PBvhR3t38yJm0m3JjYqyllOzg7WYZH ZiO9P/4Un4M/6FHwx/4K4P8A4miigQf8KT8Gf9Cj4Y/8FcH/AMTR/wAKT8Gf 9Cj4Y/8ABXB/8TRRQHUP+FJ+DP8AoUfDH/grg/8AiaP+FJ+DP+hR8Mf+CuD/ AOJoooGH/Ck/Bn/Qo+GP/BXB/wDE0f8ACk/Bn/Qo+GP/AAVwf/E0UUgD/hSf gz/oUfDH/grg/wDiaP8AhSfgz/oUfDH/AIK4P/iaKKYE2mfCPwno2r2l/Z+G PD1pfWUhkt7iHToY5YGZWjZkYLlSUd1JB5V2HQmupwPQUUU+ggwPQUYHoKKK QwwPQUYHoKKKADA9BRgegoooAMD0FGB6CiigCS3meFJVR2RZU2OAcB13A4Pq MgH6gVXoorGRUQoooqSgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACp3me5g2yM0iwjZGGOQi43YHoMknHqT60UVUSZEeweg/KjYPQflRRV9CQ2 D0H5UbB6D8qKKiHwgg2D0H5UbB6D8qKKtjYbB6D8qbKoCHgdD/I0UUgZ/9k --0-2071618827-1298462259=:64948-- ========================================================================Date: Wed, 23 Feb 2011 12:54:14 +0000 Reply-To: Ciara Greene <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ciara Greene <[log in to unmask]> Subject: Multivariate Bayes Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi all, I'm trying to use the multivariate bayes analysis implemented in SPM8 to compare neural coding models in my data. I have two questions I hope you can help me with: 1. I'm a bit confused by the terminology for the model priors. In the Friston et al. NeuroImage paper where this method is described, the various priors were listed as spatial, smooth, singular and support, while in the SPM GUI the options are compact, sparse, smooth and support. I can't find a description of these new labels anywhere, so I'm not sure if the priors have simply been renamed or if these represent new models that weren't in the old version. What, for example, does a 'compact' model reflect? 2. Is it possible to compare models across subjects using random effects? I know that the Bayesian model selection facility in DCM allows for this, but I don't know how to do it in Multivariate Bayes analysis. The BMS button in the GUI allows comparison of different models, but presumably that uses fixed effects, and isn't suitable for group analysis. I haven't been able to find any options to change the inference method. Thanks for your help, Ciara ========================================================================Date: Wed, 23 Feb 2011 14:25:03 +0100 Reply-To: Torben Ellegaard Lund <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Torben Ellegaard Lund <[log in to unmask]> Subject: Re: SPM Digest - 21 Feb 2011 to 22 Feb 2011 (#2011-56) Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-type: multipart/alternative; boundary="Boundary_(ID_NJMB/RhTT/0+yoWGjAWH2A)" Message-ID: <[log in to unmask]> --Boundary_(ID_NJMB/RhTT/0+yoWGjAWH2A) Content-type: text/plain; CHARSET=US-ASCII Content-transfer-encoding: quoted-printable Dear Richard If you scan a gel phantom you will be convinced that there is low frequency drift coming from the scanner it self. This is due to a lot of things but probably mainly due to heating induced by mechanical vibrations. I would pick the cut-off frequency based on your particular scanner, (most recent scanners that I have seen have little non-white noise faster than the default 128s period used in SPM). Regarding aliasing of cardiac and respiratory noise it is in no way guaranteed to end up at low frequencies. In fact for typical heart and respiration rates and TR values around 2s the variation i e.g. heart rate is so large that there will be cardiac noise at all frequencies, not just the low ones. you can read more here: Foerster BU, Tomasi D, Caparelli EC. Magnetic field shift due to mechanical vibration in functional magnetic resonance imaging. Magn Reson Med. 2005 Nov;54(5):1261-7. PubMed PMID: 16215962; PubMed Central PMCID: PMC2408718. Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. Non-white noise in fMRI: does modelling have an impact? Neuroimage. 2006 Jan 1;29(1):54-66. Epub 2005 Aug 11. PubMed PMID: 16099175. Smith AM, Lewis BK, Ruttimann UE, Ye FQ, Sinnwell TM, Yang Y, Duyn JH, Frank JA. Investigation of low frequency drift in fMRI signal. Neuroimage. 1999 May;9(5):526-33. PubMed PMID: 10329292. https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;651c37f7.00 Best Torben Torben Ellegaard Lund Associate Professor, PhD The Danish National Research Foundation's Center of Functionally Integrative Neuroscience (CFIN) Aarhus University Aarhus University Hospital Building 10G, 5th floor, room 31 Noerrebrogade 44 8000 Aarhus C Denmark Phone: +4589494380 Fax: +4589494400 http://www.cfin.au.dk [log in to unmask] Den Uge:8 23/02/2011 kl. 01.00 skrev SPM automatic digest system: > Fra: Richard Binney <[log in to unmask]> > Dato: Uge:8 22. feb 2011 12.48.38 CET > Emne: acceptable high pass filtering > > > Dear all, > > Something has recently been said to me that has made me think, and I want to get my head around this a bit more..... > > At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant? > > What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal? > > Thanks in advance for your help, > > Richard --Boundary_(ID_NJMB/RhTT/0+yoWGjAWH2A) Content-type: text/html; CHARSET=US-ASCII Content-transfer-encoding: quoted-printable Dear Richard

If you scan a gel phantom you will be convinced that there is low frequency drift coming from the scanner it self. This is due to a lot of things but probably mainly due to heating induced by mechanical vibrations. I would pick the cut-off frequency based on your particular scanner, (most recent scanners that I have seen have little non-white noise faster than the default 128s period used in SPM). Regarding aliasing of cardiac and respiratory noise it is in no way guaranteed to end up at low frequencies. In fact for typical heart and respiration rates and TR values around 2s the variation i e.g. heart rate is so large that there will be cardiac noise at all frequencies, not just the low ones.

you can read more here:

Foerster BU, Tomasi D, Caparelli EC. Magnetic field shift due to mechanical

vibration in functional magnetic resonance imaging. Magn Reson Med. 2005
Nov;54(5):1261-7. PubMed PMID: 16215962; PubMed Central PMCID: PMC2408718.

Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. Non-white noise in fMRI:

does modelling have an impact? Neuroimage. 2006 Jan 1;29(1):54-66. Epub 2005 Aug

11. PubMed PMID: 16099175.

Smith AM, Lewis BK, Ruttimann UE, Ye FQ, Sinnwell TM, Yang Y, Duyn JH, Frank
JA. Investigation of low frequency drift in fMRI signal. Neuroimage. 1999
May;9(5):526-33. PubMed PMID: 10329292.

https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;651c37f7.00

Best

Torben

Torben Ellegaard Lund
Associate Professor, PhD
The Danish National Research Foundation's Center of Functionally Integrative Neuroscience (CFIN)
Aarhus University
Aarhus University Hospital
Building 10G, 5th floor, room 31
Noerrebrogade 44
8000 Aarhus C
Denmark
Phone: +4589494380
Fax: +4589494400
http://www.cfin.au.dk
[log in to unmask]

Den Uge:8 23/02/2011 kl. 01.00 skrev SPM automatic digest system:

Fra: Richard Binney <[log in to unmask]>
Dato: Uge:8 22. feb 2011 12.48.38 CET
Emne: acceptable high pass filtering

Dear all,

Something has recently been said to me that has made me think, and I want to get my head around this a bit more.....

At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?

What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal?

Thanks in advance for your help,

Richard

--Boundary_(ID_NJMB/RhTT/0+yoWGjAWH2A)-- ========================================================================Date: Wed, 23 Feb 2011 13:25:54 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Batch issue Comments: To: Marine Lazouret <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> I'd guess that you have a batch that was created with one version of SPM8 (which is in /Applications/MATLAB_R2008a/spm8 ), and you are trying to run it on a different computer where /Applications/MATLAB_R2008a/spm8/tpm/grey.nii does not exist. Best regards, -John On 23 February 2011 09:10, Marine Lazouret <[log in to unmask]> wrote: > Hello, > I made a batch for spatial pre processing and it stops every time during > segmentation with this message: > Running 'Segment' > Failed 'Segment' > Error using ==> spm_vol>subfunc at 111 > File "/Applications/MATLAB_R2008a/spm8/tpm/grey.nii" does not exist. > In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc" at line > 111. > In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc1" at line > 84. > In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc2" at line > 70. > In file "/Applications/spm8/spm_vol.m" (v2237), function "spm_vol" at line > 54. > In file "/Applications/spm8/spm_preproc.m" (v1143), function "spm_preproc" > at line 66. > In file "/Applications/spm8/config/spm_run_preproc.m" (v2312), function > "spm_run_preproc" at line 19. > No executable modules, but still unresolved dependencies or incomplete > module inputs. > The following modules did not run: > Failed: Segment > Skipped: Normalise: Write > Skipped: Smooth > If i treat my images step by step, nothing goes wrong and I can get to the > end of it. Does any one have an idea where this problem could be coming > from? > -- > Marine > ========================================================================Date: Wed, 23 Feb 2011 09:55:14 -0500 Reply-To: justine dupont <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: justine dupont <[log in to unmask]> Subject: Segmentation anomalies Content-Type: multipart/mixed; boundary="_5f5f7fd2-b3cc-4461-9fb1-7f8576aaf374_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_5f5f7fd2-b3cc-4461-9fb1-7f8576aaf374_ Content-Type: multipart/alternative; boundary="_5b2db070-3df8-4055-97e2-8684dd3b694f_" --_5b2db070-3df8-4055-97e2-8684dd3b694f_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear experts I recently saw something very puzzling with a VBM analysis. I have 40 T1 scans. They all have the same dimensions and voxel size and the raw images all look consistent and normal. Segmentation (with normalisation and modulation) works fine for 38 but for one the GM result is an empty brain (top row of attached image) and for the second the result is a kind of 'half brain' (bottom row of attached image). What causes this and can it be fixed? Many thanks Justine --_5b2db070-3df8-4055-97e2-8684dd3b694f_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear experts

I recently saw something very puzzling with a VBM analysis. I have 40 T1 scans. They all have the same dimensions and voxel size and the raw images all look consistent and normal. Segmentation (with normalisation and modulation) works fine for 38 but for one the GM result is an empty brain (top row of attached image) and for the second the result is a kind of 'half brain' (bottom row of attached image).

What causes this and can it be fixed?

Many thanks

Justine

--_5b2db070-3df8-4055-97e2-8684dd3b694f_-- --_5f5f7fd2-b3cc-4461-9fb1-7f8576aaf374_ Content-Type: image/png Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="weirdbrains.png" iVBORw0KGgoAAAANSUhEUgAAAfEAAALYCAMAAABbpzssAAAABGdBTUEAAK/INwWK6QAAABl0RVh0 U29mdHdhcmUAQWRvYmUgSW1hZ2VSZWFkeXHJZTwAAAIiUExURQAA/zQ0NDw8PDAwMDg4OCwsLAQE BCgoKEFBQUVFRSQkJElJSSAgIE1NTRwcHAgICFFRURgYGAwMDFVVVRQUFBAQEFlZWbrH9YCAgF1d XWFhYWVlZWlpaUBAQG1tbXFxccDAwIqKinV1dYaGho6OjoKCgpKSkpqamnl5eX19fZaWlqKiop6e nr6+vqampra2tqqqqrq6uq6urrKyss/Pz8PDw6CgoJubm5+fn7GxsZ2dna+vr5WVlZeXl7Ozs6ur q7y8vLi4uLW1taioqK2traSkpJSUlJmZmcvLy9PT08fHx4mJibe3t6mpqbu7u7m5uaysrKenp6Wl pZOTk6Ojo5iYmLS0tL+/v729vbCwsKGhoZycnIyMjJCQkIGBgYWFhdfX15GRkYSEhIODg4eHh42N jYuLi4iIiI+Pj+Pj49vb29/f3+fn5/Pz8+vr6+/v7/f39/v7+/ekKUY3OnQdDbdFHZowF+uJKvPG JCNGUAMEBCpYZctUISRPXSYgCxgtMxZYaVIgFOx0J1UfGnQcDlUeEj2u080/IQMCAhEIBkZARAYL DBgmKUYrLSpXZfiaKYomEjeQqxFGUvaNKPWhKfeSKedqJeRXJIMuGUidttVVJPeOKWE9GOWoJB8Z CEEvMRAIBgEBAfaQKdljJDyoytlIIkYqE/WHJ/m+JQECAgcMDUafuXsqGaQ3GgIBAYclEfiXKR8U DdTQyMzMzAAAAP///+ansxUAAKaiSURBVHja7L2HYxxJduaZ4TJcRmakPWVmTZVYIJpAd6vVmpHO ezvSee+0utVKOu3MaaXb837Pe++99/64qf/vvhcFkCAJEAXQTBc6g2w0wAJQVfGL9973wrzIXq7t +9WytQu+78R/sPbJEyf+h2+2n1uJf89sfCW+El/bEyP+8zfbSnwlvrYnrNx+sBL/fsXxn1uJr8TX 9hSJL8vLl/TfSvx7Y+PLkoCvxL8/Xv0AfCW+2vjavidx/A/X9qTbqtXX7Ozlz93efuPQzlP7rUP7 Y6n9PYd2ltpvH9rvpvZXH9qzQ/t7U/v1Q/ud1J4f2h9P7Y8c2m+m9sWh/c2p/ZWH9ucf2pep/QWH 9rem9lcd2ovU/qxD+7tT+3MP7evU/uxD+zMP7e9K7atD+1tS+ysO7W9K7ZtD+xtT+8sO7a9N7dtD +4sO7W9I7S8/tB+m9ucd2t+Z2p9zaD9K7S88tL8ttb/00H7x0P6a1P6SQ/vrU/ulQ/vrUvuLD+3v SO0XDu1vT+3nH9Cy10hX4t8P4j943V4TX260lfgpEL8Zqd+QYj9467Gfv2Ul5RXxf+O/+k+PJf7y 5d3EX768l/jLlyvxDyT+8kZ4fuOLtx57H/H/7p/9x/7zf+1AnL73N9KP/Mb5y5dE/OXL3/ot+vpA nIC/fJmI499++7fTt/7u79LHG8iBm/7lTuIvX67EPxLxKyu9lfhbSu418X/i3/+3/9f/+F9KxPHP P/cSxH8D/4E4kOMDQf9jCXkCfkU88f5twk3ACfdr5AQcNv5ytfHPRfwPr4jfTMGzP32zvSb+v/2b /80/+C/+O//Lv3JFHP94g3hqdxEH8pvEr/w6Qb8i/uu/Djt/+Tu/g2/9nef0m56njwcbxye/+Zv0 YSX+QcQPmxavid+cZruD+D/97/0f/9a/8O/+R//B//SGVz8Qvzbw1179BvHXXv0O4uTW6WGycMC+ /vvH098/QtB/k6CvxD+QePrsFfEbU+m3E/9T//L/+J/96//QP/Jf/Pf/yf+9XAWEl6/iOHx6iuO3 2vgZ0b7Fxl979UScvHoy79uIJ+Qr8Y9J/H4b/9//yX/8//m//ql/9L/8//7P5NUJebLx30jEz+8m ngz8ljj+2qtfE4eZ30X8N1ev/gjiyxXWW4i/EcfT970bx5flH/4f/vn/eVn+uf/2Da9+k/g7Xv0l Qb9Fq78mnrT6WzZ+iONXxFMcf7na+AcTf1O5vaHV/wy0W4n/h//A/0v/+1ePyccT8jtnYG5kZ+/k 48R7nYH5+Pn41YTLg4j/1//MA+bcHj0DsxL/SMRvzIf/4I0v3nrsPcTXWdZTIv6DO9tbj91BfF1J ecIrKSvxlfjanvaOiJX4SnxtK/G1rcTXthJf21Mhvrznh5fP+EKXj/Itn//1LydD/DAvv9z2iq+n 7G999M4H3vj5I3pqefPb7ngdD+jc5ervctxoTm9/ebXqdPWOr3rlCMrLyRF/T0/f/GR5z7t9yLhf 7vmXO17HcvxTXxM/aogsN57hJvHj7fo7Rfwwco8hnsbz2ya5vP4ty+0Pvvqxd3v5uu+X1x8Ovbrc 3m/XvX3Ltyyvftf1i7l6Pa9+91vf/urP1W99z4t8g/jLV85uufn6l1cPvu+FHefSPnVoee2a3uvV r9/R272xLK895PIe53kNfnmPf73GtNxlwsv7nunmE716qa9/6C7iL9/58M6vf2UVbxJ/1Ssvl5sv /d0OujkivguG/ioEHmHjb4fLGwP9vcSvdcDdxK+M5Y5OeZv4XQHk9QBcXj6S+HKHJLkxtt+08beG y20ddKMHvhOu/SHE74xxd3TUckO7Le/6vJtd+NpZHkH85XLHN9x8qXcTv20IvRoqy606YXlbANxO /H3q9TtCfHmIjb8TiJZleSN03k78pl98d7S9jnDXEeYRcXx5JaBvvJg74/hbxF/e0OG3jKYbWmd5 W47d1CHLLcnH8kaw/w7E8e/FDMzynXmC71h+thJ/rJd8uRJf29OYZV3bSnxtK/G1nTbxn1vbU263 EF8LYD3t6l4r8ZX42ikr8bWtxNe2El/bSnxtK/G1rcTXthJf20p8bSvxta3E17YSX9tKfG0r8ZX4 SnwlvraV+NpW4mtbia9tJb62lfjaVuJrW4mvbSW+tpX42lbia1uJr20lvhJfia/E17YSX9tKfG0r 8bWtxNe2El/bSnxtK/G1rcTXthJf20p8bSvxta3EV+Ir8ZX42ikr8bWtxNe2El/bSnxtK/G1rcTX thJf20p8bSvxta3E17YSX9tKfG0r8ZX4SnwlvraV+NpW4mtbia9tJb62lfjaVuJrW4mvbSW+tpX4 2j4O8eVTtk/+BA96JX/4PXu/H4V49rPogexnR/yk3+9KfCW+El+Jr8RX4ivxlfhKfCW+El+Jr8RX 4ivxlfhKfCW+Ev+OEc/7rs7qrq/yz9gDeV71WVkW2rmiKOq+Wol/HuJVVxRlXXdZV9fo+bLPP3EP AHQNzrqo6zrrug6jre7R8OWjqK/EH0K86gC7LpmUJpNGald3ZXkU9Mf1QN6XDuOro+a0Uhk3wW/n GIK3yvVNjQerlfinIw64tTJhnC/OL7KLzXY7xFbB9pzLP34P5A3R7jrYt3IsbLeX+2327OyLr158 df7ND188u9h6WTZVXx7rZk6VeJ7/rIh3Ze9sGObL7dawKivksH/+4ovN4EXdF0X+cXugqguXaAvj h+3l2X4Uwsqsrpul6ZelEcP2iy/P52FQXdMB/CclnqNl+WcnTk9aVVXT4MPjsT+aeF90Heeb871J vZtnDX2s+dnmbONtURbdx+uBvKd4UWjGQ3u+n4Kr66sxX/XoAITxpkEX6Pb82bjxooOh15+KOARj Qy17WK9/KHE8LZ43ayBYSLvgDVf5ZyVelXWvzX7vU8/mS1NlJJvoRXRu/mIvBVR09ZF6oHe6dIy1 u7P9ZJv8MAYaKLgmwzvv0P09dQdShbwpprOZJzuvPgFxwo0eJyGRUZ8fz/zDiKfnRcsId00N1B/J /HHE+7IruB/UVa+So4HDQQdUOf1TPz4LUtdF/TF6IIcac0qFaRb91bMd/nR4Uvrk0O/03HgsX9R4 MXG4BNV9dOLoduhUanUiXn0m4ok3qdVE/IC8flg2/GHEq6Iu5OhzMm28axpwdZkhMyvqKm/qDmaY q7MNU3XRfGgP9LoodbzYC5JjNJw62HSptCuRopVZWVBz+Kon47+C7+b9IPBw/jGJw6eQaNQpWejJ q38W4nkKI31q8OpNc+XYD8yb/HMQR5BUZufzpUpWjYSYcuKs6BFegLtDTo7Bl8sLr+uy+aAeqFRR CvZ8SAECeTjGWFfQhAu6nWBnrjggx5dl0TSHDsrz0m9H2HnRfTzi8CmlVkIoVyeHmuWpfWLi+UGq UaMBll193R+oJ9f+6YnXZa2HTUnyGU9Ho82Rrsrq/jAvgqHYd/RCqnkD5PUH9EApSi13Z03yax1+ c+lSO6Tk8K0ZmfcBOiEvO9IS6JKqZ/tJFxB7H4l4Dn/utCDgNJ7zlCh9cuWWX9FO3iRfrp70Sjom 6P3DrfzBxIu6dK3F88Ku84bGGTyd0mXWFYp6pCTeOWIMOr+IurwD+VHpnyjU5guDz/A8MDGwLsEd bxNRBJ+VfUYTPuTTgTvZeVk3SUxUlbzcwcd0dzqZBxGHeymSgdOIPkTPT5+PH7z5QS7kNycBkqFf m/qD9dtDiRe1Yxu/gGrZgW2ttUITSmWQV4Jx5cgYigppEyJvHHVxu2s9ogd6oc3ZjJ/G+Glq+JEr 0B31voKxwbEgjpRdmRx9muPFB/QAuqhayt1lZBD5xQcTR+QqHb1J8O6u3egnJ34lz5ubYiG7MSFw Hd6rKv+UxGlKdVMmeQZLoqlOwZgg286oTzhnRQcaWne0vNLldmbFrZL9/h4ohW6/ZOhfmDTsGMHD 6eRF8JWmp4DTzvAKirpO5qeAO4X3niI5TLsKZxteds59IHF4MqcODr17bVGfmvhVQta8ETqyt31+ GhIPm455IPHC8WcNuqDvSbJVCJSKSckkExk+4cawEs6dof8htqquylUL+3tMD5Ra8GdQ2xCDadUE kDG4aHgV5E1IQdUYZiWRpgCLlqy/cGV5kO14tbtLq4pCVx9CHMPb0bDGi7jpQT8x8fww5fKW/Wa3 hfmHJQ0PJF4Wcr+QS8d/FFT7AvZmLOdGZkIYay0riYvkDG63gjte3A6Z1IN7IAfv3QTefYk4AfpK MYGxJcBaQ7CAgasVy5KD6Rw7jAVdk4qk1dOaVnkAvv9yy3Wpq0cTJ94Yywwe5U1Z/KmJHzWZfxXR q+YBrv1BxLtCjDnNt+UI4MnLlehi40Gao/ONDZZpdI9ihksYIxxBs7AdPnloDxSKb+YK/Y2BU5Rk 0kWJX8wE4Kq6c0IgRLNEnKkr4nA0imZrQFzrsqL5kmpRlxeeOVU9knhOow0DTSMNyL+La2eHiP4Q K38I8cYVg0m+GrocNofECEalvLfeykxxyX3gAly0MJxLGGe1QL6xSevmgT0g+LwDs0qlpTjYMWPO EVS4EFWSvTNVSJYhooB4qSiy0ICAr6dE3WmtKZ1oSMBdBKH1I4k3+NV4K7p8Z+n9u7Ramph/AuI5 pBOZ+JJT4GRw3OhZREkbgucCyk1IE7w0AkLKGk52UfdLvzQj/OrDeqAW7a6Bg8Cvryn9g0NnsiTi OtEmDy8K2HhyNB0QAzhMUQjK1NK0jHZdmo1Zys3ZwGT3KOIVgBsj1S17LT4d8fzBxA9zzPlHJ14W bHBLmvYCXG6YglJXhQ4+wInT9JfjHgaPmMutQeSj/BiafZFBvePX398DxXDZAbgoaOfDgTFTmqjq IqHVnAsnZVbTa9FSKtqVAeIaoiEZOQmMksRNvtRyg6TxMcTzXktrDYWn5XMRf+9EXva4H3sk8abg CiYOEQ05DldnOLw3xexANs4zwRFIfQzeMkbdpCGiyqIE6moUTj+kBwqxRwbYwEydOPAGzZLhCTir HWQzfLshsZCV5HMl/rqSzB5pG9I4kuuEXRf5Qn/K3aCcfjjxvCklAhaDt1g+F/H3++fsvUPlYxMv WBkt1BtS4WTjFt6OEjEeQoSRZwwGbkIL/tbgs6SiiQBCuN8y1x/fA41qGe1bZB1hhO4n+0UE52Te 4AlNCKEAM0/DjMYeRANSMwr3Ium8oj7Mu7uDYx8vvLgtYbiHeNVBlXqu62b5TMRvy8g+4Ek/jHiv C2/hG51AhIZxG7JjMj8f0YLNHAPvtg2xjaAOCngM3yjwM3rgSh/fA0p+SSNdu7pQDIlf8ukQDfAo kncOpBnJRDxFhiArOJk5K+saopomgPCitBNpDq5w1IlLibTc3YX8TuJ5XzAMXVHeOnX9KYinSZf3 rY18TuLofxY0giJNsFgYnvXw5fhfBGX0u8ngW3kYBnzZ0ggwhEFriXi79GYntTuyB3LBnhV9BZ1Y YrggfiBqF8i4HCFHQJaU+RU6+fLMcAgJCie0oKI1HHlyCDRFAx0BEeeSpFHPLjA864cRb0phAoDf vm3uExBPyxTvXfT+nMTrku0i7BTpbukEBJoP5M2J9wClLjiIe9+OQ0vQQdySKWpBAq9f9Dgq0RzX A6XYh7ypel1rRIaCfLnWGDtIDTS+RvQm3434DvPPyNAh7ZBx00Ag8Ya/TKbpWMrVkKalDpTPtvA3 DyIOn453JHVXLZ+HeF519614f07i6GyhSqRN+Fg4CeCEOwFvuWbGZtJ4mPgwJjOPcIck7QCc/Drc I8LhUT1QieGcQlmB1ICkAAYNQgJlZcjIoQaNQWJIEprid2agJpA/EX1J3p0ehPVjABymZGlZL+U7 /tnIWP4Q4mTi/nad/kmIQ7h0921x+KzElRoV3CNiqUtbDGHhkZAPwwSfPrQZ9yPhPiCPHmEeIJCl oeuXXsyDcEf1gGbneqlg4p2A86Z1byecIG+BwEDOHEwNLJniuMnwLJxbaHhDEg7IyfNziWRCpGkb 0o4l5Wj59CzeHsnvIJ73DkJd6rv2v3904rSD7r7V7s9IPIcnJ6+IsKoo1YULjwcDb0d8mIeYmTBN 0zCO7SvkEnEwWERZ/JwfjTimByq52UK2LY5mVTXtOlGQbzTjwhUtWWoYO5Q50QXpLM3wGkkfkLBh KGjS7kKRiFDk22nGtcEIWvrd1qgHEK9qxb2908Q/OvGDasuX7wrxSvuJ/COSYEnG5pg9AAfeEaSn kPl53hDxCf82gjjkFM26Ivx2NFKmN/v7rh5w8kzTlgZaFEESTWAZJ51gDK2WqrSsIQkrzQFlFsHD 4o+3Ej7FSPwAXIsi8ebShDg0nypSiiZ3gVXHE29Khl9+p4l/fOLNERuSPyPxTsyc3KNWFEFZSsoG UmlADOQDsjN4+Wka8Wci3z4Eg7geYCRI0BCV1Tjq/IgekHGbdks6Ot1UikQc9orsn9OULrwL9IEh /wGzt5kNFD3wNf6N5ko4LepAxFH6Rmq+oOAvesTIpZpn2R9NHE4d49W4O8+3fGTiaa/WvXMon5E4 3j6JbaS8UDNkvLDw4dAUH6ZpHrJhmqeRl34m5ONIvh1xnmOEsGLJpbxg9f09kIfnbkm6TdCKdAHV RraKMK1IjCV1Bpnm4zRv5nHM4Nop+/fk3GksyoN3xyukL6DsJQR7oWmf2lKeM300cTh1/GZ+96ap j27jx8yZfUbiOkyKTJUZdC7MCYotWTL8N7S7necxg0/3rjwQh28n5GCO1FwSain2b7j1uxbg5dSQ 0KKMi+AqRGNHy6QKsh9eG8I8gjUFEAyzTTaPnl6Sp5cFI/cSP0cyjiMGc1bo9CPCwWvAWLfsNrd+ F3EB4vLuc4tP/aShCqVQlCublImTgZM7B9UQkEeVImStTEtWkcJ6+u9g5kEKTSKfjRtxbw/k6rxN e5Zo9T1NpkEGCLJTBd6c5ng284b+TpAKw5RtJ5i6l+TnwdsbKz3N/cDPew+NKRHi8WO6hBKul3K8 bUHlDuKlsMGz7vtLvLXorUYJC96RJHpCPrQI4TrtH84Oi5S2nZKBt+hyivSk4BjiJyT2/gjiZsvI vTVCFGm9RjKNUYZ8HDAt48Nmk3h7TjO4tFpqx3nab7bbzYCRaBN0Y8kdQEMwRcHd0LChQ+dNvmfi WOIpG7eiy7+vxNnARAmnTsCDt+SsfUvJGP3xcL4uQ4hnFsF8JgsfPE3IhDQXA19bLjUTwxHENadV beTvCNC00E47YGjFnWxVjsm+RwwiSrVp/0XWtmHcboj4ZrQ+hXMId8h1TkJOcXOY7tWQ61237KzM jyauSDz031fiuRiV6pa0VkHKKQnkMCZlPpG1h5DNoaX4jRg7T/OIng5EPLaBUyDvJZ/uJ45vS3K6 d7RUA9tG7u/glWlFQ85k4JOldTTI9Aj+c7bfTBsScfNmO9GSHRm5pNkfeokKws1Q44XOG1fnY2TN 0XFcI+/X31viixiUqpaOdhjSrnSayeQWrIdEHMxDJmHzcLFQ7ES8Rf/DCGP0XCq4h16o8QjinUpd 3NG6CLAxZOQKTwqDN3MyZC7SkAsYWdtpzEbKGeJms8NjhuzaeiQSNk3MQOWx5Awkr11T9V0VJlkf S7yjDawnSjz/GFp9VGJpmkJrTTPXDpKZ1Po4Xnn2ts0oWyPgG7JxJOim0CmZIkQOXkId4dUXd1DT AF5qmjmFa6UJdGsYrHiziQRcmjDMSa/5DAkUXlHYUjNw51ILit0+ZeXhMNfLMUylps3WeuOLo4nT MQh3ksTfn+gdSbwcNEl1EHfU5yolaONAcBPzYcgiTb6RT6fMqaW5bnhYmkGBgBZLBeLuCK1+CLS1 MCztQlZELC2T7MB0IkmG3xcx0qxtx5gNrceA4km7kVELzWyK5VDrNO0ffcAwkIIz2mugtt4dG8fp HIQumkcRv2UL0mcl/t5tjkcSr0cQz52kHcK0mkH7VpNNw6IpnA9jNtGiykTBFtotmoL5ZGPWg77L K8VDcW8PVPywvtUxYoasSqRpc5ipJCveIAOgsTbOG2PHIQ4ZhIRHsj5gPIycKyGVJAHHpQ9DPKz2 ALnB6+2apne7oI8nTovyjyJOE6Zv/+DnJP7+2btjbTyAeOXITdM8qyyZAfEUtjcgDvPOKAOHjW92 EFFDa4UJGAKRxLoULocKb++38d5fEVdJcRlNxLmgqfK42QHrPNDOqnGefUsmnEFAQFELPuxmSTMu kBgALjlVf6KswtD8AVQ750Xe9908HE+8pqTz4V49VZF4d537MxJHJvq+bRVHanWHcLlUkMgmeWsB tx4Pug1xm6ZX2wx2Ti59C+bkdKHbYpp2s7RzPc8d8yq/rwfKWaTv6ZGYSYrgKmluRtse1Ljd7Xeb tOViO3CD0QTlNqTJFkJKy+iFI5cgknhLyp2m3EMw0Ow6r7pm06rjiOcgTsj7B87AEG/aefNORcHP SbyhnTQfSLwSTDt85JDetDaOyGpp2QwmDYtOkRvmRvoZf+YdfG20Fsk7JB1Nc9IJEVrzvJd4NR+W W6qCFkahDyWM3EOOceFoPdzQsriHZoyO06Jd1gaj5egpbENnQWUoZOE2wJVTIk6DgeYIjSLiS5GP xxOnci+Fqx82r04TPenwpcJP5j8j4lVXlt2HEl+YpAlKLSk0B3LZhjY8kVFvZnK2lCiNhJ9SqHkz gDjSt2HwtFAtnK7pqKi5twea64OgKk2OI7dSdMjJHE6yMXIuWkjbzq1GlBjaDCHDjCZwWjjXhQnI DDwJiLTbEk9uk5II+BmIx66P7bFenVaraRv0g9bOYF0UWAydz3grIHw+4lT37u3x9gjiBSwINi5N mlL3tNMoDIn4nCa8ZhCft/gUifGWiLfoaAg4S8cYlBMO8pvdH8drcVX7z9GMFyyajg3zpL0pntfI vYyjbNwKRysoeNIZoQVPL+gg+wAHHpAaGNp2mVbUyD2kBM8iKPXdOB6r1Wn1Evju7rrsNgvvCqic NCv59v64z0ecZhJ0+cHEG6aKvCiQi6f4TXOo7dhOKY5vd5QqZxuy8N2OPgL5PLYjqap0ugBeru/E G8WXbj8S4DZXlf96it1S0M8bpAfMJMct8QogC2C6BDUilGB8UWqA/FA60hYjj0ZCuQUvffT4q2h/ M4Ob6KCgi7k9lng6v02bMu5Cnt1m4U5QqkA7sYR7APGjj41lr7O/u8rQ5H2BjOU9pTGO3hEhXFPS liMEUlg3zaq3pNHnpNzQ7xmZ+oa+graiaRjkbJbmZGk5hLGyE6a/pweQ/e2vfGHuRForo1ldQq0Z dSUMp5DMwdMjqESYdxpmmw1N9Aan2shG08LHW4hL5lvIegsdgOzCG1pL6d1FPNarH2o7k2u7A3l2 WwynpXzaTU8VLB7g1aumO65UV3YtMvpDJZhb0rCchp2uPziO90G5ik6g0NYT6GSabBtHCxaWtBpQ 0/o4Gfgm4h/ZmNZLA2S6Jis3zNUsNvcR5+109Q7ykrayyYNrT/uZaL0cv4o2pTvYPK2Vtjyj2VUQ jyEKLeB7fAwG+T9tqA8taTZJrh2DplyavimG6B5APG9qDf1/u+7N3lXpV8Wg6PBV8bZ2ej/xzlE9 gvw44lSkhCrK1fQs7/xQQ5u+3SOVW/bp2vKpn+Bhr+QPv2fv934bd3TEbIEKIgVsES5hvyatnLYx TIjk85zx3WYH4YbvGEnRcztNQ9pxChUlVaVVvHfOzW3Nq7FZFlS7j5ZBDuvddPAET8lgdYLMW3mj RdZuDrN8LeTcGCAtaMpngDanfVkRuRstl0rH6gVecCl2Y310HE8VN6h6XHf/KaTroop1Mr7ulqMl 77fxGh4pcP2OZ09Fzer61a9LT0rBhjbi087Ld6yZSpgI3VUfOOdW6a1ToqsZi4c9yyM8uo+U7QYb xAzBNmRi3iNPG0IcKBtGIgW3Tt8MBJZpZPRG9/f0QGXH11vKq1Q2SlwtzdIeF5pU4ULZ7SSo4jby sSzQNC9pN8bCRDN8YRw8nglSn3K0YHmACHC0ZR3ciu3cH0s8xdYmleu/i3h+I4Qnyn0qpdilEk0P mWUFcajg1riuv3kMnJRBgusORRFfE08FtiSd6VVvZoF5X+IHXP+hs6wNrUkpnUP0ImIGOoUwjT6O h+1lLNBiZWZHiPaBzhNfbU1IW90idTxnHZIzHbr7iFfytZGDkathYS4ZOE85Gs3xChg3JCT+syqb 0xrqPE1CtDQlNMWZ2xacmeGHzRvBG6dIhlGOeTE0RxOnCoHduwW2XhO/lsr5wRI7UlNX5N9VVO/X 6r3w4zxFeaN8GAnHlOzRNCf0Y0KeHfqFFnmSjVvKCZqbY4RK2Lr6vro399s47w1UW1cyyxmMteWm pelNmjePgdGCCrKzHbKyWcaWptdJ2mE8tG3aXM50Q2fAxPvn3PJ4UbNWFtVVvQMqXlX1qVhiKtMo Sir7WtUEG78MmVOg7Gw3b5EjctpvRSMRTgZSL3hSetB9GHtcHywc+nOyx+6BIdV2VZHzuvWvSmkd 6qtfEU911ejKDqp3dlVjMX/gamleazry4Wn/LXhRLR8qaQbekYwGCSYVHjo8KR6rU9kTOr2hqFxm emH0ME0h0Axv8+Hr48IqhAfV0/whcp0opR88jBuKPAQ10Mx6NpNs3rLYbjbj6O00IJQP7WFjeS6Q qBblPT3QsDy/FHD+qeAjUkL0I1wD1e/qr/o57YxKRSKpIEHKzmjKbzP7oghpwX4I6DsfjZLykFdw kX6UfHA982OJJ81LJZ5Sh6aKx7SyUqaarJQ/Z1c5dH4dw9MDV1V4Hrw+XnVFOqBN80xFT76iLikl 8S1tFQTx8kD8MBhKncpHFocy0LQAQBbRp1qWt+n3RxAvHadj2XAzAtofya7kUBrWRJiUUYEypHRr xmYjfICFRxum1tK+8mhh4n0hOPfFETsiFj3BdpOV5LQDgzEtGXc330OF18JkqUrGxmw7zTSxi0CO EUC7HIchDEQ8kgUIetGiSuUT8Fe7jV6O9uq07YnTMeW6S6IMdkWJV9ccCmod6qu/qqPXHcz/zvK8 9xashytTVBAPfprkQ0lPx5LshV9VLgGvDrWXazqmIXV5JRxoaFChFLqgqL6/BvdxxGsPS5OsoGrn 4BjAAY7TMAqUTCMlnzYZbVnYjul4Eo1VBHma+kI4NSIHNd5adUwPdOhUncNk8ipdw1I7Kp3/hn6V u92O6l7rNtukzW+bedww55Rup3GAtvQtJBs5JXSaOzgHREfN2rE/mniau0rnY5OZd+n+JVoTuyZ+ cKSvimXeU2D/3llWSudplz6DFqP1tyKVqGRpR4JLxQOviEPNwctGOtiehEPpBKftfSzVLb2/Mu+R xIOuaf6FPqVz4nQQDBItgKc0BZ+380VGk18br2gY4DuGgTZNeNhIX1eOUc0vVhzTA+hpZClodSmp Mltflh3TVGv11XcUxm/gc5yOGGZpMSe5dSopMEXagkUi3aZioUrlCA7w0XnuRHERl6OJp/lpSXvt qJzUQbWX9bXvps4/WHUy8UPwzvMP2fWUbvVyNKiaA/FD5Xjy19fpQp4R4kJ5COfAChp7Sd1RJorw SVXmq6PuJLqfeBekp+pdCK8NzWszqq2UzmUhk3SOtpGTctuOAIHs20aaYcWY4LymV+CEFQwddhTx SnuW9zXXNMwZgzDtNOtvbttD3zihpNM8282H9GyeR3SRpI30yMLpdLulQlSCN0tTNgt6TDM3z+oB xFN1TnKzVGmoA9FDRczrScHsivKhQPK9JfSOqRiQp2HVpTB+iNDd9Z0oV/o9q5JPb/GOIyuvtBrt PWlDsvHuiHm7I3dEROkkfA5515IhjpYd7ee2Eu+5UYXw05xN6PWgdVUpjhSOlquQQB8mNRH7kT3O RxHHOzKChi4CmuDSHWRr9eZ2vabgrOaly9L8SwI+i8LSafaWNsnTakaR1qmXqqQNLTRf2l767gHE D8jTPLk41OB9syju4VKiKxu/t2bicXVg0rTPQakf0B+qMb6utk0XOSgzbraJ+OFiB0Xu1spDJfDl IxFfrIsMfoMsFkl+XSOfgPymk9l9j1RJmTZjJu0rLZYOMT7Q9gVuroRSJ5kR1rpjesBJpNtd3RR4 NoFo3NC1HLKr3nwvNdUJETpLtNMhtK0pbDrsGsMVcdq43FSw8EZTKSB2vmXLQ4gfii6nOlNkPm9X 6bwujpmM/N61r6Ore10V0r6aBsjfmQToS+FnpKQhwbhaoaViV5pU+scjjvQqIGdUVxefEPg6lTmj K+UM5UqZhshRXmOcwSYpBqprF5MrxevBB5nf3wON30lewJdR8OrwbpqUlovCdW+qNxheV2ftIYxP 4zQHHYfDFB8VlQsKOl/lleoqKiCJdObyYps/jPjVlpaU/79jP9lhHrY6aLePRvzu3bBXT9qAOALo aHSXv5qYo90/ZX3cfQrHVgUxqm5Z07vDPiCM/oXVuxZQG0u1WZTLmNIiomvLQtjLMUBsd5omJvrO Se32g2tNdX8PqDHGokvSretLuuqo66oeUrt4U69DCgpdZ3pAZpbOpcytMJBssPGYJtsQ6HVeuTov PEc6K8zzjV8eSDwlTeWhde8QT/tkqoMzvrdG5kfbEUFF5qBdWn5dVzBPOq84FvjRNm5VFKwojKDy ZgCCp1OXLPCmcrKrjREgzqOGfKoF72oOQD3tJ4WA44wzi8R655t7eyC3wSu6/FSXGCdUHRIhQ0Ej a/XmjDOyEWvqzImQNsnT5kpBdQXTbHqw3iI/gD5HdBCcSV2I/WbDHk48rUX3zS0z5TeIH1Mj86MR JyOPtE3/FXHyqXQ5c3P8HcHH2HigG1DgwQWCdaHp1qGqrhWDvCotK6qSZUy6vmfQbjwE2UF4Mqg3 2hgXxjhvjBo2sb+3B7oWioQKXFNmpVN5J3JZ4M5ohun1yppkWtoGxIWJUzrcOMKoDU1XGIwzb5Rs 6F4upTznoij0s/OpewTxQ43b/N1N/6nzj7+K6OPteqr6giZgRHG1QHa4q/komf6gHRGRCbr8RJtg lBDRQpMJKLhSlfAysmz6zBVQFZ75RKyG5EZPQ/oYa+NgnN5f7EN9715W4V3XUbVXoXiq1oTMqOw7 vJ2q7hwEenW4OVFTCSnpsoI20vF5gl+fGIK1hFxUyuCn4H1o5ckKL6Uqiri7jNVjiL/P3KrmQTWu P9I+t7R2RrN/bxDvHzL2jjmhgO5FUC24l3CSYt5OAYoq73LagsS165oMblzX0Svhm7IAdmEk3EBH NRctj61Usi3v3dlIhxJ4KsTH6QQ47Y5n/WE6HVYG0c11RbGhhhIXTmdQ7KpQ5Nan0SunuPVwO1Rj vcz7hgSGCeTU9fZ8y5ePTHz5bLdYvr3riRbir6Vk2nxT1s1Hrq9eKiqpCy1G2/+lHcbtF5flUlXa 08Egutsxqwu4fR58G0qIrtojXS/xANVgpr2IJgzc3bezER6ZSSVokxttU6efFIqUMM1/dFAQTrRe qr6jq4qhorOCqoYUWzr9RvVJDjU9SWhputeDdjnSVgqn3fZi0h+d+APaR93Len1pzqsvuq772BX1 KwAU+VK5dDiXD/txvNjZqohcOaYbRO2M1rcql4oy624gveRt2dSQ8p4ujh7nTbyHeF7YkO5coU2s h4JsaV6TFol0ibS3qUm8I/+kA06tzvus7wPivJ+pUMHQItoL2lRYlEXe1QLqfW9pDU25cLFvuydD /Dpvv+ZfvXvO7cOVm6Wz1J1T1jDaZMrD/vx8ix72zy4s4qduMqQNdEHKxG0rC17QwSArahc6CXBg Hidb3LNaKi1V4UuVWwC5pztKaWE7JydWNEj1qHw2JPJipjlOU+syyFQP2YB0fDO1I6P7yWkiGnlZ zaMZgmCB5k/0sLu01VMivrwuqJ4fkx0+nDhsrIL5wL4DHC/Srv3lxbOdGqZN9AXVtc962KfSw7AT AzI56xVHSNWKl3QVliHVbt8fx3NnfUuTszFwWrSguzlUnWRbTqGLKgwdwlXjaD/b0MasyytuCu03 M62NK0pLqeJynTd2ssa0Z8qkcr77/YVZni7x/BPcfkWOVTsSRqmAH4g/v3wxD5dnYxyopr7NvGtH o0Pc29k+m2ygWXEjB3GojYsPg33/rqeO0cHjdN5cFTBuB+ukSU5IM9q/R5cb5ddbggoX6Mxqxsqq GaQWlKCNo4OCodstVdMpxHv86I6l6GD351vx9Ih/0vvH6X5Q6KloDPMROe7YXp4/n+dn+xgCb+3Q Zno7pSPb8+D3L9pNVIgAYSvdJm0pNZK3/L35eKPg+KmMiwRfWpEulOFUz01oif/RZr0bSXFtgxnn zIsmF5MrLNWlGFVacSq15WFjmGPPn0UqL+mK9uvnO/3EiOcPvMvwocSXIFyqo2OlCOdUenmzfRE2 baCy21RwKbOENsIxt8PlD8++pRL2Lkx6tG1o02Hwkb9vljUv0h6LQkhHl5g3VPwHXoJKuSHpQnyg 4fL6F1TG27jNzr+cqEhwqVKlSEG8y5KuaWLasQsP66abHNQOr7R+WsTzqmk+6Y3zuYEXlV5RWTs9 sDiE8fxLH6Whw13RTxdZHNoQWtvGYX7+w6DshZWFNm2wPpXmMGFm7+uBnuM3WVP0dVrnp1rqotNd Q5kn80Vdp51qr99igWcesu38QiJD5GWgI1GsLmhSSCKBK+R4uR3bkI62ltuzfVs9MeL3XLDx4Tbe C6OoRFsQ8LEyxnl49iJaTif6jA1xzvgIW49Ix318sfNnz5+dW3zjxNqruqlmeO/6eGHwyzhDCrZQ HtY1JbTicjX5guTjsFya31hk8zxmQYVfGTQfhaE4Lki2Kblp47zbXGxpUyAVfCzYxW7PlpX4w4gv ipZhvQ8tct6WijXuL4LhG8vo3G70GYvp/5GN+6++/rK93M5zkDa0ae0Sf3bz+3ogp3Nih531dLU5 ybRrwteSlLYzI0ErOxKAVa7hE7K69z8alQqtoGJysi46HQ2fthOservb+ZY2W9V2dznXT4z4ktZq Pynxqqb7cEJske9swnaK6GLDNuepEEMwWUEFW/G4Pt9/+8WLr5/P7XTuoPVCWq/m2837K+pjcMTy lSbpiuLqJub81YJ8VRd0sRbdR03QO4ywrGo4bWwpBxWmcbJlXbNC7c438+58v38+DYEu+yjC+YWt nhrxh9xc+TjiSweZno7Dc7EZhyTaPP9yF+gEShBZycirBz9cPHvx4otnXw5hbiWoTJ7MPOzY+3ug 01crvrS1nA4u67JPO3HTv0Gk9NAQI40fw43TTVNSxYA813T6oA9azuPk6871Fbvctbvzs8vdMznT ckzN97udWJ4a8ePvtHs88cB9sDwGy8RlayPtV6eTYCEdCcucObj1aXr+7Yuvn23b7TwGHqW1dA7p Mt7XA5VLt4anLKzwh8O1eTqMU+UdZx5aX2tW0NqwiZIvNeRDtlSMvFsXtILLCUVd5qpRYthe7M62 53ZK9/nI1t+p1E+Z+OPaQ+4mjlS+HuobaXL4dg4k2DbRWjptFEOmTGwR5eG+v/z2xdnzL853wI+Q GvDvcRiPvNOQ9nHS5L3uUuDO605xZgRtloThnm9bNkCBD+OkGm1jRmfTdFdo7xzVegfxSuviYrud ns1ncwBxpjsxnZtqJf4I4iKaQHMsQO7aH284j+1Aod0CdJtJP7QYAzbOX784v3z27Hk7TJ61o7Uj ZBs/sgd0ma5bEqJMWzrplMDoUxUQKt897r+elR9jbKeouIlZ0bN0j6opSpJuunaFNNNue7m9OD+P myCUqvX+YnbLSvwRxOvB0PFxKDFrxfx7s0lTLjwS8RH5OGzZt9KPX+838/YytpttYAOc8DD4XXFk DzQORKdBO+B2heY+JfSRxCFtZwp2uhz4EMNmI+GtM7rsnCbahCv81E6qlmPYzO3FOUL3TiGR0Ezx zd70K/HHEG8MpVkxDiFQmbavfwGellQbifE2m+cZDyE5ny7pMiQDcKMrU6EtjITqyB5I56WN2Y11 KQrTGgodngp8QUHQbA89+Z7bsZ1aVvrM2snVna4LXZppQEIu4zzv5nG/32zlcMlp+XS737J8Jf4Y 4hUVegptqhoQvRW/sKeLLNOcmjVZO1BxiGjaS0T6lgaHt1UlU2nUKI/tAYTtliqD+Uq4PngAp3Sf gokdApUBoGfct3DsU+t0FpSt61rXHXNsHAZZsiH4zXaazjZnbLuXjnM5XkxuWYk/hnguEZJBGzJ9 nOHP5S+PgCzoyspoM2fNMNrJjltvQuAjAritGioM1bajOLYHSghAChuuqhsFcUj+nK5hMDDvQGdW 4V9Gv4ljHCfdZEawnlZRK0aL5KMv+SC0ZG077GYdLwYfmNnudivxR8ZxyLIBodrzZgwDa8UfPA/o +BbxfLRZXuuIQL/dDHR8O1ChTtmVeGxq/fHEO5nqLIpGi87bdPUSXdKRrrymIiP4mhzM2M5+tE22 8aYulKxrKRzd31Gysmlqbli8nOpw2cZWy4uzuViJP4Z4JTZ0wAcCzVbStzz4r34M5zrsNmHchqzJ Gzr3Nc7RD3SJLRR62TED2YbgK/KjiVNFfNH1qqIlbjqXTEqBZvNooSYOlkoLze0UxiGU2ddxQ4Wx yppJBxuPuqxVn5eQ81vl5m3LWwGdN5Qr8aOJ568XLyoLZzoQ1dDUwwwPO/zRrWn3SMrjps2QPZth 2o4IsGOqsxRsBWsLyMXb0dRH9kBf0N10Os/hqFPxZIR0OixKF9nRX6orhJewoXsUo8uCp7oj2uHH HJxAq3rHXNeV1rRxv2mRyjuz24V+Jf4YGy/JchFFw+C7igpZR/4juPHYzm0YY5ZXlU33HNKQoJVT P1UNcnZ8OvhRH9kDlaYKrN2S5x3dVUmnDdJVZjS7k8rrwqnQhM5gTFB9hl8uOCsL1xkVxrYVPTfS FaXzF2cX2ygFF2ZzEfKV+MOJ56WhWS2qox99WfZUEMBsNzQNN8GFyyyHV0efe5j3hiq6DUY0HVUN QPz1l+zIalcCKsDTFkgH607lsunSOmsHS3P0dLAsYR+GOu/7JpOzLJUqldCa8XYYpW5KquDRK6Tk k5FcMYt0fCX+GBtXI1Vcbakg6qS7WlJ+bCgxI3c7MHj1PsyhTXOqtFDOfayV4TxQza0LeSTxDpHa y37Je22pEk5S6mhATXqdrkKFl/GhrZuq7LIuqpLO+gqqRjW2A3xC6com78r97mJMxQ3C9oKvxB9D XO5h3pQZQ40LEJdI0qbnozHw2nESdNBVzSMVJ7KB5JW0WqXq9i2+Idn4MXcTN0J6XnYIETVtjCQX QRUQwhBa2knT0rXnEBNh0HQcLGtaUzAh6W4kppCQWz6fnZ1fDCJdmMGljGyIs1xW4o/w6uacFjAi HRUdbFFQ1TSz+4amSMjPugwaT5Bbj0ExH4y//LIdhGppurudLmV1ZA80QrGGbqsthTAcHiTdfUET +j7SiBti2mnTeioHlCETcyJs2nmKDdKzNig+X+63zydhJx6o8J+Il7NYiT/Gxs2eJk+HESrYUKm2 jWd0jYWh/Lg1Nb2Y2saNafnzH3no7N1+JxlZGMK4ioc7UqojeiA/bOapKpUqnNFeStrELmGzBwEH NwI1sbd502RVbZ3atAZpeN8NSBJ0vgvTtBnGsImpFDub9qNaiT+GePF8O4Uo4F3niaqI7lqK5Iyq cASrKzpPvRQiDDbMF1zTQbNnTAnLW+9Hu3HH90B1SAnpnKAUjqqqUUSnOzB42kmHJ0RmNs4Fldiq aXW+o9qUfUEX8YjeIikLnKq+cyrvzsPF4FbijyHenD+HMvNy2Fw+30banNrqYJmdgUA2eXoxTWfg A5jWBds+fz7Kcph3ftzP2+u7EY7ogVqXfe56SP+cCSqLLyhJM4ZHuhQlUC390FpYeQfiSyNb6HGh C1HXSM8GXuhhUtqz/bSlW/ak3Nx2keFK/Aji/Xi229KK6Nn5bs8sHTmQrbQW+ZgX+dXp2l5ZjkQ8 6PbMSKmm3XazP2vxc92xPdADr+qgvauu7ywEP9XsTvfkIE/H35g2StJVW7bDMGvcwIwQVAaopzv3 eKkVVFvNhnFDp1Kk2GxNvRJ/lI2b/W673++ffbEfRy4M/PYEOT0H2/LqQBz+WODL4fJyH5lgdEXZ 3E7R7t/dO3x3GQ5JBQOkl2nbU+Ug1CYLERcCYnqIdCoN5AF3Dowq0/bBFApOnalagjitiBddUXI4 d5uuaJl2fCX+KOI5319s9ufPzvaT5xdIfRBeA2NIvgcJcZ1R+dx8oVKs8/k3VKJDl2qY/LBj2922 Pr4H0s0TSnJRp+KcBbK7LdOCVseptj6X3nua+BuC7pEgNNwXqQwxKxilj65ualf21RwHQ7e0sLjl q1d/JPHt+X5+/uxsbtPxf0GXpHBmBt+6ZqkySTtRKzPYAXKOzgeKzTTasfV2P787zXl3DxwqRlOJ 0qKj+XBJN+0MBjqO7qSmLVSRLrn2gZdVVuQ5a51UwtO3DEPbqlDQvqm43U50TRoTcP+rcnsU8Upc 7Det3O8muu7MUpU0Owk3hBCLJW8yBjdcddKHkC6gLjZ7WsyatzJud+pBPZBXVZ9TdXFBpeGrCKnO jKVLEWhJBZ/R7thAZ9EyVSxuZqbdb6bdWIY2jmx7ubVxCtNuY/E6jJ037Ur8cTauN2fzXg7b1sCy kRXTotYYzWQ8lXDMVFnnVWNYGzzd1U47WeZh3E4CEfcRPZBr6Zig/ep6ttJvZjh1urOBNtcMqVSf 1HVWF50aISZa6Lu6pzl96S9Gcv52t4lUHYi1+6hX4o8i7qbn89mz7UBltGE83gZpZDQx6iZviqzs dF/1tDHJzheBB/jUfbvftWy+bbP4/T1QQYMx8usV3Yxg02wPnW316WiEaamIflZXvZi8Fbru6rLh Qxt5f3Y5GgZhCWlHZ9eH7bzOwDyS+LzbTPsBUpl2E9P1v06KwdpRlF3XZX0pVFXQcUGqaQ+vy6Z5 uNhzv52qR/RA02upZVk0S07HYHh6RmRnGFAxnYKJQ+kgHvpyHNIOZuc6RjdsNmweMFg4GwfYOGf7 7chW4o8hvjTD5ThdjCTSaU4dObLmw5nvPKONhVnfl7ovoLGZDXTnlOJTGy/GYpz8I3ogd45qtpW1 qJZOKJohpysOaaOjT/ci6boqdVY0TS+jYqLQBesEiFstqHAQBqNpUxXdYbMT69rZo4gv4tzKYaa6 P4actnCh3V/KdEckQmqTO9iaookQWuSUKgwyjmMxDeoxxItaA2TdS1VR2V0mkGsRdbruhktlyiYv y3Q9TSmYo8rzutS2jd6JWhcFEnQdqIw/m3eTXok/jng1z1IKurROcsuD1GO8PANvq11Js6xNSYU1 wURIWuZsA49t0ENoHtMDBbJspiDMtKrU4bpbSffG0JV5rFG2rytNC3ZL1zhZMEeUHURF1PD0Oe1v ddEyIXk7bNW66+lxxDs5IdeluukSoo1Pu3m83PGBLnMpqqzKc7o5GKKaFj0YHyI+xFbPonpMD1SF Khhct9A6p5NJAnoRuRbNqvRNKW3ttC6yVPmplI6q/gXmZDu2erocqRR/UXO6FMxM88jXnY2PI96w IEgFQ48jQZZmvNjMlxcbM3vX9VSntJR0EX1ojWYxjJwJPrey1fmjeqBjzJV9yXS5lIwqRtFlVnTr regVNB1ThtOcW14VbnDCT20bXRF81G673dKhUucHSAEdwhRW4o8jnis7kxiSiOFGKgsjP/9q4MpY V+VZ3iA3a0mzxe1IdzIKuoyoLmKdf1gPdCTd8Lwl3XXDnGAdi7SbqXU5XSxYC9sKhJAgXdMhSVAN huKAFB5JA16AH/fjoNZzZ48ivmgbqFg6s4KuFbfIvS63ZRxMa/MGImoRjLaeyjhfjnRHDt2Npl2r qo/UA01Vd3SDiqsRzBHOVUPioXAm0I2tfY+c3IJ4p6e5tVQAxkhWxiAxNrqV+KNsvEAv02VqtCIt lB0u9xv4UxVm1lTZUuWcCoXQ5ab7qVVKEnIhB/HRe0ApPvDgSS7mwCqRf/dUy7/rZPSyLkq8SuW0 ktwhV4thKOt8Jf4YG3/AixFa0g1IqpRj8Yl7QHAToO/KunCdtt4UTtWObkLSojReRYs/TdetxD8t 8Z9FD3RdqY3xumxKxiDqlaILun3a6d7U1Ur8yRG/wR5iUikq8s0KZaP0nBf1SvwJE3/VaGI23VZv VdmsxL8HxF81qsW/Ev8+EV+zs5X4SvwDiS8/ix5YfnbET/r93kF8bU+2rcRX4ivxlfjavgfEP49e +u6079H7PWj+z5+dnTTxk36/2Z++2VbiK/GV+Ep8Jb4SX4mvxFfiK/GV+Ep8Jb4SX4mvxFfiK/GV +Ep8Jb4SX4mvxFfiK/GV+IcRpwId+Ur8e0A877u6VCyzwXLO2eEa6ZX4EyWed3SX7LTZ77bZxeX2 Yn+52U2tF+VK/GnugWE2tkOcZrqgJJupunkwwxSjt7LMV+LfYeKP2eeWl3Y0rfAmSN01VZXlV/+s qIyECaPp8pX4EyLe8Gm2hnuuyyp/sweartBShM2gqpX4kyFehnFoPRevL8N43QOQ7LXknCpy5yvx J0JctXRjyhuF2t7sgXQZpZl0tRJ/EsSrYYxG1O/rgbxR0Zh4Z33MkyWeVw1alQqLVVV17BTEaROv +GxM6PN7xnxJRfDdkyIO3F1NNWDrPmv6Dq1vqvzJE8959NpfpV/5ncTRO1wEUzwh4lVfO8F9oPpx WV04rV0J5vkTJ17ZFpqtem9cA23kaVUVRLzjkpLTI55XvWNh2p99+eXzi8ln6cZFznTZHWPmp0xc +EHad4DnWdWVdX2j1kou6qbxYmDdkyBe9aXy8/mLH/7SL/zit1/sh4zb2EakK64mz57nT5Z47Sdv uuqNQqdVURSZVkqLG3KuclJVPfNG5qdPPK86J+Pl17/0yz/96S//wrdf7NqM093Y3ghNjj2Jufxp EpdxlD6vixpvsLqefmNSZIIJJuWrfIxqIos6L+Qgy5Mnnje18PPZL/7q7/2JP/jJr3z7xcXoMwZd Gqxhuqg7iLi6O9yw++SId3ETpGk0K17PnFd0iUZG1+VwWacpmAaZS1Mq56qcBSlOnnhTsvbixS/9 5O//k3/y93/1m2e7wYpM09XMnDNV1DWpOBLw+RMkzseNGW3BiPi1I6tsMApxjartl4dZt6roXJys 7nLlozl14lUhx7Nvfwzgf+Inv/zDyylIXWalU0IIxpQuyoIKFFP9/6dH3LUXXrZCqbpQddPXDUmW ku52z2QQAkKmooFem0GMFxdTUVYj2/j6tIlXZdh/9Ws/+YM/+OmPf/GrZ1svCshzysfrstCMCaUV 9220zN2dqJ0scTNtTfSxruDIyr6vKC9ppPWSZxzu23rbwPhr5r3Znn152Zd5NGEUJ0286sTuqx// 5Pd/71d/9OIMDl2VPZxbRpNvlJ8TcmbbcaCS/k+PeNj7fQt5hlCNN9yld9jZ4BnieOQQM6bHQHBC 2P35fjP0ZcW8GGV/wsTzXocXv/aT3//7fvzDsy3dfVyn/DvNsjZI2Rhd6eFB3LPi6REvxrjbGxvx znJSsB2celXKaKTJeGuljaaDTdQls9MoRdl3paBLZfUJE69Ks/uln/7eT3/lq7PRpwmXpF6yw0RT jYwUxG07tEbVT4+4HcwY5ZbeGDxbtTRL1cCKPTc+43BrZjCuqvrGacV0U/WUxfKZje9O0Z0M8bwT w/Nf/QmAX05cFV1/NcGWXc3DFRpN8RDh1LvqaRHPlyKEILmXFTxa7xDFm6osOz7SVfCZiZHzwage Dr8sKFXBkABxDINWupMlTib+zR/96a8BuL05o5pdJ+p4r4WGlDGi7J8Y8SWXo64ZjwwgXaFcj7SE ORkHj79QboOR0actUL2AqiHP3zRewsQVa06UOEXxyx/+8q98cz5ZUXavmWbXk+1dDehCcuau/P0T Iq5Z6ErubVEpzo0sHBPWGjsOwzggjtNV8yFSsgJVB12X0+pxRdveQmvEiRKvajacffPti4vRCFff WDS5Xjkixd7VTilXvwf4aRKv+IbV0nDTVcwYqUtEa2ttmOI4BZMZM1oZBowBWVZpFqbp4P4FFz6y 9u2LDU+GeCnH8y+/OJ+9TNOp7xBPSVpX0oxb9dTm1bXfm8it8U0uhNYIXrBxj7wkjKPnmfGjl34E cVW4giJeXzcgLpnccv/WjpmTId4UfLw4u9zAS+mirF8vhr8mXqW5mPcDP0niOR8h2ySI50st4LxB 21jbTq0fW8szH8E94DMljLf4X41sfMnpEsS5Daw4SeI5iA/b/WbwUmlX1m8rt2Tjfd/dtP4nQ7zj e8m89sY0OfQb1Gn0Vhk49QinzrJ2nIi48UbadDe87ntaZZFGsBk/eKrEWRinwXOh3cFzvUkc0u1q 91P+5NbHRZjgsKUxvMo7yWlDgIWAsyOID7DxcTN7Dq8eg4GNG8sF7Y9oYOIqznpUzWkSL4UJwRJw ZGE3xHp2vVECWr0mE39yOyIquZ0vrIYBM1dVjIH4YGywdgx+nL2FjY8WxANtEwBxjuwtX6qOllCj 76N2pxnHayekFLRARu1t4nlDIbyu793sdoLEtY9zoKzM1bWzkpkwQJcPxNmPm+gzknDGxnFAdAd0 xL2SJqEcl7JgwlmVn6RW72mKpQRVuG6y5eYm8bzp6dG6u3d34wkSZ234QoC4qESSZnFoh2DHcY7I z0YP4nED4iTkbLJz7ltI+rqpuWTOmvHNG6FPhnjTpdb3PbIw+niDeE6bW2krRFc9PeI52/CphIde tGbQa4DaxrFt59mbOLWBZxA43geIHCRsETLeCMcIdu4kL9nlrMpTjOOkxNM2tqaprs4mvCKegAuh XHkjit+xx/H0iPdi2w6Cy6pxMl0+bjw3cZjmOXA/TSFk7TzYEBDRfYgej7MibQsSxaIE689a5k5S uRHw6tDSIZRXxHNy6VrQvqfy9XYnGhXVkyBe83YG6S4vwRHM8ZEzP4zTCHuGWA9ZmII1AbHct96w HqmM4jZwWkQRsg7CihMkDimejp0cDhzlNyqfZPD3hZI20HnLVwenScl1t213Oz3iLphdKVlVFQLp NjiCPLcUyiliA3sWhmhtiNFH0m19URcFvD+X6A66idzM6vSI09JYysHfPGNG3DN4dAYvN0QjimvG edXRsmH1FIjrVlolRa4Ryk0Ex8IY2qJv4cQDYJsMUZ02cOO/AOJal1XZ2QjNTsmc7HwsTo94VRfa pU1OV279mnhVZXBhcdpup2hee/U87ZDQtxj5Cdr4xATjXUeJOLy3LgvJTYhtIMSQbj5rh6FtAR1R XAmJgV/XwkdDtYAWJztuXX5qxPOm1OqwKJ5k2zVyWiXKCubH7W47eKnr18RLxaS4xchP0cYF/PgC te6hy5jueidJlbftMBJqmdkY8QUsnHWVDIFWU2nSlQvnlo4V3ryxfHYSxKveKXEXcSfDuNlAuKri VTpOxCVn5btGfnrEhW8LpvMS1huiYQhXtFaKdAwmDuxcZ4pBn0sp+xLAka0bfGtLxJnIc8Y8582J EYcM04pyr7eI5wfitLltiIeDZze8ujRcv7vh7eSI58wYJ/teCOJoOUSZt7SJE2IVnJmgHRFC0z72 vuTRh2Ew3Fhwx2OsXATXYuhPjngNp17UB+Kv4nieX8XxdLhUuBtTr+l4mrFSv2PlJ0e8CRKwcycZ 7eJrjaMcLHDddYh0pTbjnMXNptWFRgwPEZY/GAwGKDqgZmpxrORzd2LEDzNqdJCweku4EfG6UIwx cTUiXnkFBLsYjCjfsvKTI95trGKQYlwyZNmDESa2yMMNQ5R2pd3OQzaO01Y6aS14A/hguQFwiHZI ma5CIG9PjHiaUqMVUiL+RgnSRLyv0zbWtPklP1g+/YTmcZxao7vqtImXLVesolBNky+0TBoHxGok ZjFo5aVssxafKNg3ybd2GpCpR8RyISWHtlm0FqE+PeK0Qno4H/7WY3lW9ZR70wpqsvCUwOEHKGfb jFbVJ068kILpUjBraY7VIg2PBDwiYiNVc1r4LFhNUy74t0jT7VMbR09Kjoq1irpTdipPjnhievv+ liy/XhpvroE3TVc6JX0LPedO3caLqFhZQKlxEJR88HYYpiFSxLZWKcWmbIjOCdAOLfz9PM/jMNFp W0lTskJXwk4nZ+MNpSR37FnM8lT16TqGJ+B9DeK0miDVqcfxqvCC9QVkNzsQH+I4jxSwycyFDbzN RuMHD+DQc1q0GyAfkZFBqtORW9EIP54W8cOxMqf17dWks+WNafar7W4lYruijO7t9dNTI96UdMiA tCmgK2kHyLRpipFcduTtdj+xTMiL/X5shyClYXaAXx8DBodUMH1oN8Wn01JuaZMqzbkVXXN7Bb+b Wu4KOKKAS1sk+hMn3tdwzbmS5KOl4Ba8xzEgP7N0/GDe7rY+E+1uu9tBsrWW1lJafAdycjJxjAvR OTb2p0a8SsTLW/ewZe+so3ddOpFUlrRd5jSJZ5+ufSeJf3/e7/02DkGiRC4ElDp0iW1H2u9ikJBT gjbN82a7yeb9ZrvdbAOJ+BisHTawcWsh5NogWVcIeVKzrGmWrYFyu93E37bx5NbrItVzfK3gT9er 10woVQnmIb6N8XGcZmO99zYE76d5s5m3Gahvgd5b4KaH4mYckZujxcBFrdVwksTvugUke3N/U0KO 5Ew7RHHE87f1/cnZOAfxHsRTC8jMJmaJOYSZ3xDyKdugzQjoOgYfvPVxpkg+jEOElIdyE+PpEe/p PNkdxME4fzuUH2o+Qb+Vb+9nPjXiBdMCxCUtldH6KHJtokpohUUitt1sEvHNdm/siLQNXn2ap4Qc KZoFcaGnkyNOdwO4u4hXzdvVOdMkHfl1OndXnzZx7RHHQdymSdSRWhs9HTNsvYIznxPxedruZ8Tv zUijYU7Ex2CMhGLvmWhPaX08zaH1iEV3EM+zvn9nKu5qls6lEr1vuvVTI+6igHJDNm5oDnUgkvDW aNM0GDnCqUO5bRDFWxPh4+c2+OHKxhH6kdnpjrGYnxZxmjRl7PbiLnmT1bedS8jTvBsVLC26kybe WSXUUmotjT949XGwYaBpmLG1bpwQx6ftZmYqDviczHue8Ck9ihxe6KYQxi8n5tWbznH+znzpoeBX md0xF5fTFlchTp14w7XSS+c0Tbgk4sMAzd4O7TSFqDggZ+A8CzWGdiLPP28wJKa5JROXoliQ2fGT I17rRPyWUiG1y+7y91SkWdBeqZMmXhkEtKVMbr0l7UY2jsTc2jggMdcw9szOGy746CO0uscX0SJv C2ktRXcLE7Y+LeL5FfF3tynmFRRdRlac377j+bBX6qSJL1yJEln5QbpBiKcShdxY5OZI1qzjNhOB Cd9Sgk6nxwGbJmOj5RgmOs+ZiP0pEpdvF1vND1mYztxdVSGqjpZfutPW6ovUqqt6ujYAXj1wHkZy 65ZTTh6AvHAZErgwtBgSBsiTecOtD5Dq6ViKFOOJnTRMxCV7c6E7zbNQqWWX3VnLq+qLd1dYT454 IVXXLIqlTeqRS/Lr0VDzJrRxMDqTdKVly3m0tDNm3oB1oDNKcOvVkkvpT4043LNjoriJLqc9D2k9 VGV3FmjM+5KKSZz4Prd+0K6n8i+MW3h1Tvt2x0jVP+jUcEACntH2LuRqPPhAs7AT4nyE74d0c1QK zvrmxM6kJNWtb0p1GgOC0zjn8m7ikG7v1gE6OeJL8LqoloK2RPhobDvgA6h7y0Paq9wScQBOLY6b eTQYG0jOjCyXiorI8FOrEZGm0G5ubci7gnlkIZvNPPjszmLqIP5uqn56xOXMdZV3aVcPBLgfIoBO iNuI1p5KwWQw8dEOxgc6XL6hc+VU6s1wDf9YM7Flp0ccPvzmRtUGwKf9+fnlxW4KWVfddcNGd0up r9Mjrlqucxi51kIGLzmCuYFlBxJv0bexzcZhaikzs1QFakPKjU4ocKnoWiQ2D8XJEc/TWeKrg6Wk 0IWfLp9/+cWzy93os7uuyKDzqO/en3F6xAs7KIx2pFpUu0vCl4Mn8FrFOVLwMWZhav3Q4p+9oQPl NNvG29bQXTm9MLs3hduJEG9S2a7Dlqau1Ha8+OKbb79+frmJMrvbqddld/qnkJaOTQrvnXjTGXkZ YOMtojiXgk4cDmM2tRS3vYHdBwh5qvxkIvJx3SylGGdZnRpxQp5K85F5l7Qvedg9/+pHP/r62Y7O 2eUPceqnWAdGDLrOc0H1AqTU8OrGDJxz68nGvY9ZCx+OzCx9MbYBQZ1ThX2j3VLK/aCW0yRO5Z3S urfgYXP+4ttf/NGXF6NRdXbXxWi3mvgp1noSgymWvFDCcKbobBUdP6OzKd7Ax4c2C4NPOyECwjik HOzdIAm3vHCLDpfmFOuyEj4q61WTXrVxvHj24ptvvt4PVDs/u6NQUFeWt+2TOkHiOiCvphIIAC5M HKgahKUMLFrGEM8zaLlgpPGerolpaQUdRo7EVdWLGnZvX5VyIsQbKuJGZwo5QtV88eyLL784H9KB k1uI52mt9PZ9UidI3Bmr8qahved0Py9VhiDAdAIFsTyELBhrkawHr0ygTROI5+nMGZ0e5zvZnSLx w+a1gi6zg+PaXJyfne8n6ciGb7uLmdbGnSu6J1EHZimtFUu+lJopKvpBS2ggSqVvoOQ44xlzQsD1 UYFO2EM6W0rVAgQSFdlu2UlW205yHW8ZCQiEybzb7+bWHOZds1u+s3aaKjQ/jVpPS89nKqYLI0/E accbnVfgYaBiXlxQjQhLBw89fRlHEOdca6eXahF8x0+0vnpOy92czk+C+GYegryq65PdsuHJHbZC NE+jSmfOZgbpRtsiBJMc3hviTTINhU7XcIcsbYjCv/k21eAePZV9E5TSVWq4MOWp3oXUlMLSHuxx mufBM9flt59J6epCv13A8bSrbSuSbpSZayER1kAcqRdXdOQIEl1lgmI61eINhhsK40Yyrly+5B2/ nER3ssRpqm2eaffmlG64uu2EwiGGX5l4/kSId2ZSHV1sB7lOex7g19NcTNoFYWzGqcg2M8NoZarL TBsaOaMbq8W8O9mbcVLBJzNtd/v9xX66UQgge3cTqzscS3widVnJrU8yXYRQCqrmQ9OtgafFUzpf HDMeaZ8yfLqhw6eRU/VWVh4qs3tWnu4tlk3J/n/23nS9jRtr1y5MBRRqHq9dJDe5RY2UZFmezsH2 UeRvku50v6fWH9/z+54HJSfO4IiynU6kVHValmXZpHBjjVhrYVhtLnanu2Vquk8Sv2tB+kRhzGMk vrdtFRJncbi4USnhKhCH0w6Jz8dI50meALt0DgF6rtlSSrdtX+jTVMePmHhtUyDfHR+vc1U38aeI h8bST01af5TEe7lSNc1xyYFOSsJ7411mKQK0dhgjUSWcDZPAr02Fa8PAmIZhi0iX2u4fM3GPiGS5 PT3eDtL3n7DjgTiH/T2lG+5MOpo63GbJOSCcx+oIfipvjQRHesFjS2Va6YT9pCoUQXixa2XzuIlz UNnq4hhq/cexXb+24+Hqzk+N1n+cxBGSI7zmnaWw5JIth6kWxoZRNy5SCWcYtpUDes1iOGEE7wsT 1Wki9o+ZeOcFU27ri916kLYufxWPTxNjfkenP9q7iU3FLDncdo63CVWsPCoXkh3ikWEtFFOrvKc3 TyH/HpLttThey8d8/zhrU0E8bxfr7WZZATmPzD8m/uOIoOLTNyI9UuKIs5yhJUccohifTcwdR6pH WVKljMiUrJh+ReDW7GNIx+k6LfaPmTjjM+FYqb9cb1ZjLkPQHf0snf5h3M/+qRFv0rXinUa9YazN 6gjJWUAaJp0ZGMTlyjmWr7LGtQ6B3O6idfvHTXxfhhLWJB2YWV8NFQevRtPcvhCIY/+rMJV7/+SI 7/WwDheX1UCulBQ55wDx5ggZCXaoJKxnh7eeSE3BVsn6uEqzx058zzpmppbhvu12W07RV1GYs8/5 X0ZN81l/9wqsR0u8T1ZDQVPOYyKEaKlWruKxqItg2oWUoVEJ4BVNt1fpbszF/tETD4XM1uh0XO9O j3dbmPOo68OlxCZMuKNK/907zx4t8X2Rr4VlmF0XXoWpnSp1QjsZsWmax2o6SaQM9yUUZrNbpTJ+ /MTvLi+0Ih3WF6enF6vWRXDgeFyGXS61+p247LET3/tqFUrQ9zHPjXk0mkpjxUTce4stIEzB/Htn huMhkY8h63g/8clD88q1iw3ct1QHGe9q9mwEjf707i39ae5TslhBhMtmz5I3ZQxE2kKrZ50Hcuuc gI/e7Esj1ptcFo9iBQ4gfuemZSyPcNpkUbguh5Jf3H9r6V+OeHhPhxLf124hs5Kpt6kFC2KOqDvC F3rs+8x3WBoAl+uLQXT7J0T8Q92D55T9KP4wO6S59wrLvx7x/+/j517i+1qukoJajPVfeLQ3RRmV He95nAYR7xuVr+GzxfunRfxuWishR/sHPY+b+L5Jxtz3YV5rxrtReO101HC/fxg+bZJtmxSPZgUO Jz5t6P1+//civs+SZWWLu00PJw5WPdJlecc77s1y1f5GavWJEN//DYnvOzGMqZ9M2XRtb1R/GFrZ ZWqEM9vvZ+JPiPg+9ulyVPVPvdV3KxAXRo+bxc8vMJyJPwHitOYqr8Y8Ed5mRaYiFsW4ioP2pakf 2QrMxP9z2AoUIh/GzWqzvTg9jdbrxXK9GIE7fnQrMBP/z6ErUNZesBJiHCLeSCxs9yhXYCb+nwet APvxuujeRONM/MkQf/wrMBOfiT9x4v/5+TMTn4nPxJ8Y8f/zi2cm/tSJ/99fPjPxJ078/338QK3/ 70z8iRP/6dP/OxP/e3lu/2cmPhOfiT9F4vv9f/7D/8/E/zYyzvc0y/jfSqtPwGfis4zPxGc7PhOf ffWZ+N+T+P5vRvxR/7wfnaR8THx+nuzz8UnKTPxvQfy3T1LmZyY+P0+F+C9qYGbiT574LyobZ+Iz 8fmZic/PTHx+ZuLzMxOfn5n4/MzE52cmPj8z8fmZic/PTHx+ZuLzMxOfic/EZ+LzMxOfn5n4/MzE 52cmPj8z8fmZic/PTHx+ZuLzMxOfn5n4/MzE52cmPhOfic/E52cmPj8z8fmZic/PTHx+ZuLzMxOf n5n4/MzE52cmPj8z8fmZic/PTHx+ZuIz8Zn4THx+ZuLzMxOfn5n4/MzE52cmPj8z8fn504j/4Te+ 7f8Sz4/L8Df6eb8K8ejPWIHozyP+qH/emfhMfCY+E5+Jz8Rn4jPxmfhMfCY+E5+Jz8Rn4jPxmfhM fCY+E5+J/5nE4xL/+3NWIG7qoijqumnieCb+XyFe9oW3eExkfd3H/8UVaLrCRtb6LCsyj4/4UPfl TPwPJd4UGVec/xURPljvi+a/sQJx7w1emS+aYbcVGeW8BnO8lfK/QTz+s3Ta3fOnEI87LHldADKk rMPiU84gbllxiKh//gpAq1gLwJTsKLPGaKUt3kM9fcmaBzJ/IHEsd1k2fR81ZfzfJI6XbZqoC0/f 48Xj/zLxkpwpW16lY7UcxyiXgqq14LrXf9QKNKDN14VgZ0YkUeWcskrJdsjTVviuqT02Qf1HEceq 91hx7HMbZXUT/7eIh9eFDouMUUoF9db1ZfxfJA6mNX5u4UTWNT+amKY2udYUv67wWfOVVyAGbI8d hq1mZVrJrouDXYsh9dOGj+t8sdYWK2PLP4B4XOLHMkILoZNqiCppu/KPJx5gF9jeMsnbaLHEsxiH KpXK1k35XyLeYOG7Wru0LsuSOi7Gkkf8FbJvXZtk3A9Z1n/FFWgyA9Gui94mVZXYfkIdYc3jOIsn rccPRZonBp5k+bWJx2VfW+Hyqs3TfFhdRNtRZodL+WcSx6uGbebyYbHaXkTHfE53F9v1ok0MxC3+ LxCPC3pqeV70+67GKtRWq24fR/DbjKHjFveQQA/qWRZ/pRXorfGZ8ULk69Q2HyQuriNuub6Ehith Z7rwcn65ct6br0ucvL1KqnG5Xg1Vu7g4ic62len+WOLhVY0m7s3F6dHZZXTO5/Ly7OzodLvM4b88 PDp5MPEGGt0rHcQZCqeBcu9hvYuoLuouK3wHoQeiRATvufkaKwDeWSasG1JRxD8pO4TiETwJCD4/ IiyHYYN6j8s6b4G8iL8e8RL+gZLpuNxsLzZjVS0uLqObo1HUfyTxEvGnV8S93u6A+/rqKrq6urrG g4/nZ8frIRG+fqg5fyjxGo4TXCWsq7FdU5YdRNvSSyZxust9j0gJzH0iwCIrvnwFasg3XqWV/Y+x GQxbcByjCfXdw5ghUC/d0sBr9/FXIh53mYHtHtebC+jTNk3bzVl0e7bUdfmHEY+bLrMiqRYrivfJ 5fn11c1NRN7nIH5ze3tzfbRd5HAmHqjZH0i8gLT5KosbRkR10/QwMvAdO0RndNJpwPsOKhgBWpw5 XXTZJ8T84BXojEUEaIbW03KDZl+EICEjc8p4HX6f3X0JsSG/r7jgG1X11yCOl/STId1stpvVmEiZ Lo9JXP5hxMG7MDJtF+uL0+Ojk7MzEMcTQaPjOb+6efnq1curs92qdb5+GPKHEacJH4t9CU+RQWHT ALWVAvusiSwdJgg84rO68/AkYVqd/RTzA1egNhbR1rjK4rgxWd+EgLDO6Dl20ChRkHBolyn5Mgk+ Xh/U6+WAd6viLyUO3pmBbz7CTV7BTa4SZa2oNtHVyR9FnLva6mRcbXegffecnZ2dHEVHePD5+dXt q+cvnr+8PrlYOvMwY/4Q4rG3mV/V+6wSipm2uulhqkkcLCLLQNEaYbH4iIvrrujjRjrSiD9zBWJ4 a5l1C/wDjXd1DapBi9N64AW6LkKUyt+GdN+dkGfhO3pstzUj1/LLiFO5KteO0zO0uROID02yiq6P /ijicBpE0i6pzMn55Gh6jk9Po1O46ke06Tcvnz97FpC3QP7QzMChxCFFctHvC4SC1hjg7bHciFi0 wGuCOCJVpbVSyncUwszCefIV1e5nrgDUhrerRQl3UUBvhHwuo/0AFa9Qk3gWXERuwOJH4HDnqJHW qVAm/hLicVj9iqyrPM/ThGFwV3s5RucgXvwBxOOmMK5abk+pyyfgP8Zk0cXuNGj5cxJ/Q+RHq0r7 h9jyBxCHFCldNjoXhVECaA1zrFY7qZXtIqO0U1Lj61h8SlxRKCx8N3ho189agc76Wu4QZ2WCRHlm UvOojAcnNX30OkJ4CKm/k/TJeQvfF8z5vl5JK7IvId4g9kwh4UOVOocfVBgP37QvRBtdHq2SrPnq xKHRRTpuwHsKwyYJD8A36wixwg7IodZJHMhvzy8Wqcr68g8gDi9dDfvOCay70VJIjZ/eW8WVECaL uAucIH1Pd45HLLDCxb5pVf3rtOsBK1ArbxareF8yv5gx3zaJb5DjScojZv7wfpj67ALyOnwzbXqN SK0ZlBHF5xMvayvzdlwMVaJFSG/iteCv1iaPLk82uT34tPDgNG6XiWp5cXwGT+388iedDuLb9TJa rhAv7I4/EH/z4tXN0WZwtm6+PvHSejPGncbC4wdWUkqhYNaNdi4h5om4dBqrAtcGD9anx/fEcW6K X5ny+1egU1YvJZ314JYF/zz8Epy0gJ6eGzj7OuS66zsNX0zOO/QLdssK7kX9ucTjHsCHxWLRMqkZ dEyPiBSBsk2is7PdIA5W64cRhxGxslrvTi5DyB2IT8/x6cV6OUYjHMg1FD60+gsSh16/PF3mqnhY 9u8g4oiQluU+gVCbDA66InGsgnEO+g7GO2KOXzoXgmcB+vhT2/QCUh4n5ldx+b0r0InML6HRidmH U7oJNf03Px2K2yKyIaFL4mBM/53Z3Q8eHPMjcmGMaD6POCyqBnA46PDXLN5CR97h6CyT0dHZ8XJK 93414nEPEz6sKeBMtZz/hPzoeLddLdqoqqp2XO1A/NVE/MWrq5NNq33/1Yn7wqT7uKozJU04k5aJ NKAvUzx5qlWkhDE6STT2BLx3owVCtdraQoi4dj6rH7YC3ni52ce2nuSbvlgWqh6CgvcUY/z7UYjT 6kmwCR6/9qR/J+ZdubeVEuqziNOFykceXLRw2DxPq3hGGU5LaxWdQsgXzn/FeoAYAt6uT8+Ya7m5 Cem1AJ2q/ZSZ9Dxy0K1puz26vp2IA/nt+ekyNQ+LGg4hbm3adDnccCtN7bGILknAO0kdeKeJMBEh yyRx+NUpK2D3LMJy42GO90bWvzjbuO9wTlm3bkr4+z4Y8CDjQa6t8dSvGYP/LCp8CM9p2etsQt8h ZOzvTuqLsol1a3T5OcTLzmvYcADPocRA/K6yKg5qPdqdnZ9sW31gav2QgoPeumFzjMjrwxOYw32D Ut9tVmOVRoyEdLo+Pv+J+Muro3Uliq9NvCt0Rj8GIJ1gjhnSnDiRJ4hYGLVIFcF7d0maJ1rDmEPp a2N7enC2Nvt4NMXPkd+zAsqaVbGnHp+A3+XYsilC44GpMtA1rHpiOE6NG4ozeExLN76v7/JyXVMm 0mafQRwqViVDiMETGXz0O1vJmggQv7i8Pj9apwe66wcczvWZG7dH51e3Pz6BOaX86PhisxyrJGLK Q7nl6eXNq+cvniE+g1q/ObsYdFbGX5e4se2+07URsNQ6Q5QG6QZe4CZweusReCc5dXyaQPcwZIMi hkjarFdxPaifR+W/vwLYWsts30N9G5a3eBrRwPmuxsoYa8Ag4wpMGZm6nvR/BsteUAMHLz7zXbE3 Wv2eJf8U8eCnD4zD6Zmaj4vo7ojf3FzuxgPd9XuJw4aIcXtyffPyx2diTuQkvhrbNIKznFk9Xpxd vQzI8SBAC9bl6xIvsqHe+w7RlkL8ZQsTxHl6uCJS+wgiHn6fJhT/ifjkQ9Wm2Fvp/cErEBuR2Biq 0xL4lECd/qlgxK2BgIevgrjnQSr2Vp2F0pDM8Finq7nTQuCGmNo4lT2YOEROpC0EPJ2AFx9lOe6I X91cH63EYRb0/qKi2qQAfvvy1YcnMA/Eg+O2Xg55ZFgTotrNEVMwLwLy5y+vj1ep6b8q8djXct90 3iPWhUsOLw6yHrR5XgURQCwWKWwG/I5WHdCZrbB1SI1khTBxIdXPKiR+dwUKGIO46UETEhzQTt55 kYVcH4N9z6DB4kUtPEQGgmDteWBm6L95xM74Rm4TSKZJfs93+wRxEHDVwMxLCEN/XmVUdibanpxT reviqxCPO+tGOmTPf3wC9Fuei5I4hbyKDJWdyVenlz8KObz1o3Wu+vJrEi8KWcO0CsWUC6IdWOog 4hWDhUojDk90JITOqzYgp5yHjAUfJsgys/fOfVxy+Hsr0AtT72vG4cHnZnRmg68Gt0Uwu4v3gO2F CDCa4n4Rcr6eOxJuA/YL9wQ+GJ6el8zWGvtA4nHvEZkFpw0vwDg8/vl2iNa709PdZhRfhThezi12 Vy8nhtND6CB+fsaM+g7Il21kCuxnmy53Z+EsZRLym7PtcPjB7SHEYxaO1bCtkFPPPAv19sSbyLXM W3hu2lVtW1Uh/Qwxl2DBlKtl3NxlcefdxxHa760AjHcMx9Xb4JTd5dgm4IG3pdvG/0QkNSJBzd9Y xZM7qnYViv9CCSD/atfty0Gr8mHEobch4nCP4bTBPvwceCC+Wq8RIacHFrvdQxxGvN2eTVkVOODP JvAgfgPgwL0j8dUQWXoofiL+8vkk5M9v4U64g3MwhxDvsY77rIAs4ceHQgfIJFjwlv/Df2MrbQQv ZwjIpyMH6HqYd6gFb4T3WDEvio8Osn5nBRplFWsQILLFh2NvRmUTcDV9xuhPqUiFT60QyvClWLvM L3lgF9ieLIOsyy6upCoeRhwRd1INIS6jSv9FDWGJeHyxHJmKK76K59bZdHV8/fzN+/dv379/c4cd xG+vzqDQw7OdiPd9F4jDxbtT689fIiJPsv4rEi86aWFnIFOJhhRh4XUAHni3LXTfkIgoHeDnkP+P xpxRLBS6ZoTW7L1NfHfICniTw/NW1OR3bpsPkn1nIyDgAEwNAq2uAR4bS9GE8G/gj4WA8Q/6v7C+ Dn0TpU+MeiBxhGb4UXhWhvjvl7Xp+NOIuVf8gF35FYjHhR4uLm+fvX/79vVbMH9P7LDQt1eXp0Ad nvVqCeI1iGfJYgen/oNaDwdoqf+KxPsicxk4GJkkKiy1vLPhE/ChdalTEXwcEidz+O8M0iTNq7fw 60AcUpt/FBd/egV6oZOyYKalmDz08DnddmWCw0Zx5vtA4M8kgCZvYYKuB118WiOKxJ/zzK7Dnima pkw/rdZ/mzg89SQPOp2HJ7+gGpP4OLaJttlXIV7aZHV09er929d8JubMmV+dnW7XKzzLxYI1yyTe 9MUUtwchfwFNEHIw/tBS6gOI13ViutgwBk8S5lQBtOJ/gTc/5FKZSMuWej1vg5BTzFPEaDABUhrf 9XEJn7o4YAWMTbCPFd30qZqN4ZcNOTbsOhOsdOCNnRTRUWBaSASVDwGn/LN5AVuioJfDfyCOERx+ MgP+CeKeliuhTq+7X4h4XDaw4y0P1MyhNUe/H432tOK3L96/fv3u3TsiB/Q3cMLPduvFXSVGWNKI NepNrdsNj8+CJcfz6uZk29qvRzxmIqMseUiGkCy90+ftnYjnKdNSIB21I9W6S9tQOzAxZ1wOOKAC fW4LcYCMlypxcR1EvAj9g0aFYMyoKTAzSibcWlyHIXIuadsElpzbwIbgLeR3sSUM9gp3QeGbbN9U 0j6IeOd1+oH4z3p+piakAn5LFXJxxVfw3MpaLk7PX4L4u3cBOcX8DTyy09Vwl+NgX4SUkQ/VpCpf 7o4uJ0seiF9eDAdn1u8n3sFl86wgRlw6Ib8z4AO5Q9FLsB5G2LWxEsYl/Gr+QcynZAxPU/s9wqYD ZNyKsa+bKZtCKWdaMcvUnTuu8oG4+UDL8fRw5AFiyqPaUJmjmCQKIp9ZLJFTDA77em+dqB9E3Er+ EI5FADyi6e/kPGalF/P7kWSWSYsDhfz3Wz6yfH10/fL5m7fv3n3zzTdBzN8/uz3fLXMZSDNCpB8V 0YjETaarFZBPARpcerhui4MPTO8nzqOKbM9KecTAiMPThCq9vXu4z4XLx0U04N3RiW+Dxp+CtCnn 6qCB4bTB+B3guRW5igPx4LUVBd21TBC2NbpqJ+FuJxEfonERnqWjag+uHTx0wyjdeib4ZUi/Nzou EuUfQDye8i9DkHKe5mRFMOahZ8BOgSEtFgKELyeOSLTdnFzfvnoGIb8jDhG/hnWWfCkiZzAKv4UO UVzW1vGM7UNIzhzMSn9F4hmLl6BVNTMvUKOItfGzpvTRQkDGzHM7gHiCL0CxV5OzngZ/HWIWkEOt l8VHtQmfLCyzYx/D9eKZWBFqnbCogaRywxi0OZ0FGhjsuiicZkK7jCpoc4brKkTqyiOUlJJWHf6b Knsp1cOIJ+1iuQw5N7oiwoTKorip2QKGRYiYV4TtyL5cqyO6H0KC9QWEfCL+9s2LlyfbwWGbC0nn iUcVaR6xmZKlriqB8xaI8zjl+c3JWh7qrB9GvN9baHSeoojw4/NEFFF4cNyqybJHSTvZ9+Csh1w7 3iQWXHGLShZGFPp+rV6IPK57+GnZ1EJKH90HlZbSdlCd59RyKbR6LqMMsWkQ8lzJEKIFnQ7OFrjx luG5Y9d0su6VM+VDiKtkWK3XSzjIIbGYShPEq2cpJzYbdjg2AxTAgaXDv09cjBcnoVjxjjh0+svr i0Wq4ZbAMxpYWInXHKOUyKlnoNdPwyF5qH16eblNDnXd7ifeY8nK/d1KQtgY/UKjJcNkUQNjrAei sxCL4wsDnBo3BVBTvsTwRAQ0tb93BWyl912JyJrFkZmZ0qf4INog3SNiYI0NBKXSag6mgPKeFLsT RodCWr4stiS3m3CUQWgL5xuIfPcA4gV22Hq73ayhQ9jPOaQi6yfiMmX7WbTG11jZ+uXEw6lZMMvw 3Uj89ftnr66PaMQVW2GWU3i2XG+iEW+DpxWZQkwOy//i2RsEcvju00o9IGq4x1fPDED50IAQKhwM ApckzYP7NAa1DiFPIubgGJtNATmdS3y7D9WGTH5BoWfupxz0J4kvMphxhGZZGPgxSTgoDlPBeEqf VXPbp0ppHSEaMxrGfDnC5GWWtVYsswVwxzidelfRQPgG35g9hLjIF5uL3QWgb7b4sISzX09eusyH 1WYX7bbL1omvIePwxMbt8dn1zatJrb97++b51cmGXhsEC6D5BvA2Lk6j7SJl/RV8lWQEcQIPbv3N yXioWj8gHi96EK9DNYKCOfFWaXjs1VAtaFWnvGqVR1WeBgVIM5sG4qx7yoKPqQwbP+ELNPcST/u4 aayfeg5sSLLhHwgO29gq1sQzYBgqy4KbKJH4lgSGfLkQCm6eYECOTUJfR2WGBpenu72Nxaj9g4hX y+3u9HTKae+264GNAGz+s/BT17uj6OR4A7tivpw4fe9hvTs5v7oj/vr9i5uz7Ujg6bDaXoSs+sXF 6fFJdMxWM2pakS52V68C8Le0AeerQyufDsnAZJ5lSIIpD5dDygom3WDfxkAcyp2kIyh0evA0taFS gp4sU2Ca5VFKsVshM+X9drwMvsNUrUriYG/SEISNIc1FRwZxv2agGCV0FCzdt6WjxWayTWWQw7us vmRoKGwnyyz5RET+28QRAJF4aA0g9w2Ey7NwGf92tYAOjq4RLbeJ+HLPjSfx7eri6PLm+Ys3DMnf vrg9o9cmXLVY745DtfopeJ+dR+dnp+uRZ/bpsDm6fY5vD0m69y9uTxfuMCE/hLjxcRxmFOCFYKFF IWXIuwW5o2aHZOdRe2fEhymxzvNS4Quh04SeFDvL/Uf9SJ8knvNb7rpOsnAEyvxpNRFfDKnjqV2b w5MLxBmM2YqGFrIN5nwp+uvwLUPwJEKOrlFN56Q5nHjpZUvpCrT5/+2yQrzR93CaZL6AnxXdnB9t hq9CvGGJ+vb4/Pb5s4Dw/fOr42UKc5WOm93RCY/Hj9mIcnkdXV2fnW5XTEWsdpf07e+SdG+en21y dZCQH0i8pM+Y5gi2c6nZWcuQ+0Mz1jAAcVQF2pBxerbcHIwhseB46zxz8z2I23uJT1WI5dSBkoWo zJCkTobFYkCsAOuNMDDNFN8MKwSKTMPLahHyw11AUKaYcYMRzzIB8faKKbgma7rEfQbxoE4J/mLV OlMDeZ1pEr+Obq9PtmMivvzsjFn6fNyA+Auenb1+/ebV9W50yuAtsG/8MiDHJ+dX0c3N+dnxxWa9 3uAthPj93ZSweXZ9Osrs0JlL9xG3vmwQY00DUJhEh+9ERU7veeR+o+mO2uC7B3+agzNYGcMCdhY5 02fn6Vlm79PqsZHhTU+1qkDOHPrUtJqxKYL/KKzbosoKB8chYsie5iZn2F0XUuHVVMi98/t8eNPM kjRQyC4XDyBuE7jq4YwyuEynF2zPLzqeXIkUnjWIw7kaEvUV8upNbZN2fXr+MpjlO+LQjIi5jy+v WQQD4hTxq+j25ur85Bj7cHd8efX8LmND5u9fnq3zgyz5/cTLwvgyDnGSsVonbSXDR2h1qvWQ/2gr DRmHOV1C8TI8S6qUbrNM0yQQMJyzZkN6/XdXoNH6LmApgoPvw1k4xdyw0YXxKbZAumiLrDBaRUzD QsFIpuesl6lO+WI8xVdQ60zLhF6xTtS9SdXhxBuTTq76Zr1arWHQL9aIEzKeVRaKTQTnkPGjdSsf EgV/Mh7voDeWOxAPIh60+qpyrgqFD7c312dHtOKX59fRVPYGu358QuLw877Dw4DuxVUQ8q9CHLa0 2VPcQrqa4o03s2gnpT4Fw4sxmRKey3CqAulj2y1tOIvEKKFFFsfeux/HaHxiBfo7hzruQkEy54/w MNyEfwWOHIwbG1gHNx2sRdppAy+mYiui8RYbzDoeuYQQHvG5Cf6mKfuuV+lvN5P/NnGVry5ovjer 5XJF9ms45p7zEAojiSK6Otst6c19eb16zPKL8eLyZRBxeG7PXl7uVsOw3J5cveRh6OXRKafAnJ9z Dsyk5D8Q/+6777//HszfvXkJS27q+Gto9Yw9/3U4lkpgxYXjMQY7qu+6qulDL5jiDp+2w4gILU2J Pf2x2A1iBl/dS9P9/gpk7V0IVdJPt2HkzPQw6yOYB9JOeZYzeZW20XRAa+BXZCKRMtGJSHSonZgO VEPRjlKl7HtV/fYR+W8S70W7OT06OmKtEZFv1oj4HXO/cCeFg2sdHe1WlTQPyWZ/+uys93LYnr2E IxbUNKT89vzk6ASW/T39cBI/Zi9SRJ99aj1EMPf8zetvvvv+WzyA/u7N7ckqtfe/nYOiMyiLbDqZ hs/kclpryjXgUqXztyOIMxeIL7ZJmuRTxVs6DRFQ8OEsvAoPsbuPeH6XJmmoz6eKCoo183zsgJBU NJw8A+CIkKezM3g5Sc7wXKTSiURIuO8mnDnxtMMDeQwHy4yHE+eB9QaK9CRUDUN1TTPU9N25LcPk CJY9lerQ1u3fPx9HRN5uzm7pen9DPf3u9ZsXr16GrCsl/voEahyCvYvgsnFwxNHJyeU1z9PfTcTB /Bvo9d2ou/KrEPcdy0GZzUpZ5MR4jAXKlB9Ar/gFaHVsg+U4MhBmHwMtPR0q/L1Q/2uLmPnS/h7i af0hD8WUm6E+N1MhBIQcMbhmITrLmH1SSRul45LN3QKUNc8bEp6xKFazmrtTRlbAmdL0fVaJ+HDi etiS+NEpm0F+PDAKOTzWduZjtF60rGz2h83R+33iJbz11dHNmwCcDL//7hvWRcBMfwNH7oamm/3E 0WJcrLaM2C7Pb6ajtp+Qv391ucnvd9d/j3j0xz37P/oFHvZO/vdv9vPel2WFzxQzU52E4jYGYVk4 N5UJ2w3zoNptRM23GESogKA2tVn64YQ8FBaz3zct7tnz9kclUE/mP/PhDIf+Fw8pc+e9HgfNBBt0 d2RFUDmJNS53lPE0FyLjaQ9H0oS/ZwpT1IjHs0SWD5HxDQ0m+/RHzodI8zacaMBspfix8yFajqzi XbQJj7O+tLKxz0S1OXvxFiL7/bf/+OFf//z3P//5rx/+8Y9vv/3u3fsXL4O7frFdR1h8CDnctjAI JoTvH8T8+2/evrjepeY+vX4/8aZmA1FhQqiFH5zOks4dJwUwa+0dk9rSR2653LRplnwobkuru14F OZUH7uH22PtWwP40tC3jSIYQiPtQnc6KJ8RgZWOlFQnUuks5X50VOG2roOTx3kC8SgWAY8PZcGwH rVt4eP5lbwfXP9BzOw5mvE1pTlrO0Qt9AjBpaTVEKx4rrDbrRS6yLv4y4vums25x/OrtN98H4v/8 9//8z//8m8x/Ik4nks7SYrXZHYVBMCxkhXv/LmwTGIJ30OuLe9Mw9xMPHfoNiDvNw25WPA2tm9rL 8lx7C8990baMzlpYU7pXeCpYvKkfafK/6h5q4v7qZZ/UP9UW2gk1nb/QnhDKNWHzvNUw5ojKJA+p 82HMKwH++JQfaPCFY10rmyPgP8DSFgVcwSRtDo/ORLU+helk7w92t2aG+4KjtiBqQJ5XsOPUaevd 6XaRHJD5uG8oJEfLbG/fvPvuR+L/+z///jeQf/f6zfPb6/MTvJX1KmIAvMI7OZnqGvGETCuZfw/n 7dnNtrov13pAnVvo7uw8Zz4I5tqkhtcqHOvTB8j43Zlp8NXHBOs6xWUIq1IZpgFZiyCNIp6Je4nX qf3p+IovGtoKQ/PRj6cwpcnDTtNiYHEdPDcQrwXz7Ty0EzwXl9JOp28sl4s7bJUyq5IHEDf5agfi 2/WiSqROqsU0i4d9ntsVDxIiHlmvtkeXR5tKFV9KPARoi8tXd2r9X//857//TSn/4R/Q1YjU2FuK HRZxqhzzA6ecG8IW89uQjH97F9S9uLkYdNF8qa/O9ElGU5NBzinWIs21ko65NcgSfny6slHLY7Q0 o7FtGbbjex1kU4b6v6ws2TfQ37cCcSLKjxITReenggj/keIsC523qs4EiLPmbWzzVmSZC0U4iQsl t0LpIhBna3kcGt4KKQ8nXtp0eXFHHHoKwHdHl2EM7vnZDpY9ryhuC554nfAI40u1ekiuV8c3z0KE /Y8ffvgXoMOU/+P71wzOTi94eoJFHpjzAvIdTPn5+TX+uwqzA96HM9PnN2fr/MtlfM9a0owNpgW7 DJlLS1sQBfLcwZGq2ol4qGVOvGdikzNiVCITk1kNF64MSloYF9+7AtLdBeT8VlhqjmAFuZ+Vr8S+ WjZlYUwaTXVQVSuLulPhpD64ixJymd31sxQxi4+7fS3TwzMwpXfj5vSUxCnQBH52R/zydINdkIbF X1+cXB6tU/PFdjwIuducvXofIux/8PkBVvz7d89esU1hFeoQ7o6rxiXzvhzvd4IwLXQqPGeP2tXl 8abSX27H97XOCh9cOKhWHU7xWqdDxwJPqHIadmg5Jl2w8oaDmDNWMTgjOAzqbrJXUSRG3ivjsCE/ Jmn2cboebFP3xc9mV7GidN/As8syEbGWssJqpGFiiMvbMHaNnqOe5r5lRd90Tdz3+DHE4TLOiojV 9gK6lGZjuZ2SnGEo6uXxlnIfcXDnGjJ+vEoOmBpwwMSA2oy76+cT8m+n1Cn1NEz4drWYikWjNNQS AvmGc92OT8NsN44JYgsqc//3j3Y7gHhs6j6orZIl4IkwIpV5aKK3QlssDesgWBFRDZXnusZxcO3h XkEr+ym73yvTancvcdX7xP4Ixo5r3dUdNfqvljSGnVdRqKwD9BY2Q6ShYyZxOpT7fpjgWHIAEAx5 7fThxLHz3LBcr6f6sm3QodMUnktIHNz3JMqrgVr97HTp/P0HGAdNBUmWp0AOxc6jkbehDenymLn9 RRvaupJoauIeWPO2nRoQd8fTuDeogcqFdq8vz7mxteAuTO54gAXpDfWsvo/DhJ0cioY1MPDYRH1X IFArydbjBp71JOLetoM193luzUXuW1mHCVr7aZ4dL9/5bTOJFw/TrlhQO4jOpqG0msPGqIGyUPsM 2vi2si44aEw9gDg7DUNVPBZ3mqeHh8TPTk43gTgiNMr4yQ4BUfMViEOvm3TJ1pQ3bDp78eqK0h26 kDiKJjwgTuQtQrT1j+Vv+LBawqUSJjukzPKg/vGmdnepk77vmt4i+gHP8I/3UNw8BtdRlTsefAgT hiF5kxUfWbfeWDnW6t5uYtXtO6Ma/APllH4suzC6i0ta/kooimjqd+TRTWHpVeZEnrDfuShCX0FT etuUnCDjnT387CzM0mOleI44fHcS/OKra05dOjm6G7s0ET8+3t7rHR9IfBpEstieclshMFwN7GVW itkuxLvcztE0iiUI+To0nC4X7V0VddeUX6uykd2lycerFddsJp8EL57qykUWJQBehDZuyGgN0/vx 6zfeL6W2h6xAX+t6mpu2bzp26ndF1/2W2uyLIqJ7Hirkq8Lq0DCTTnX1ug7jRBpogroJdzSl8gG1 rFx+bl9m16C674iHc0rG6HDnglZfX5zuVvCOvw5x7mGr04EHN4j5OdOa293fNcxj/0V3QxruiLPB GKr8t9pfv5B4IX9xttyDwSSEXS3YPaJ8xIkOyhRZK+FH97+QydJnldb9IStQdo6TBXgDSgcnoIw5 jLFo+ubXeao6klU+pYRaKBHHnnUXXDepw3SJkjW4DZuZMpOYBxEn8ukGIjjt09DMkOlkDftqOTI2 CddZbEZ3SFXEgVM6S47DZBWHmiZThJGgdRiZwLqzKBSWtSTOERWcWaDvWiH3+69IvNGFsz/v02tU ZcoMaPNOqKL2wkbhmhy4x4mAAljpPfUyxLD/0ONRVcVBKxAXzkwXGzWZzTrLlv0ma5ru13avjLzE z58yclGFdhNux24UA9Q2jr0ebRjymUn5Cf/q0xP8AvKQfYHnBo1+HE5OQ8PCOER3krZID+rxO3T2 Mi8/CsPj+x97G8Mda/hxjOYBZQhHx5CJCTMqfPewq/YOmwoCB111P39bku6bNZm2NWyINTYqeFTi tdqtTVHnO76LGuZ+st1lwYj5sBWAacCWolrnYH6otKzpff0bP1fcRRleWpN4q2rBRD+Ja610VmfQ FD6X2vOmj9omiS0fRnwfbiJSATnrWUNKneOtwli/KLQFjaFX5cDqssOIsF25/NmVhfxCw+3noqnI 7G66f5gd2v8RN+PUwvai++kWIjrtSautb3k4ZTnKURURNHDN7tN26DuT5SV1A9snOBarKVigcOAK eE0Zx9YuG5hfCHlw5MpfLyuI177wYQgXPHxF2onjOTq8Vt6LU6YwM7XgvaY2MQ/pLf2wsdn9IXgJ 0po9aFUYZCbDEJzJiUoTdVj58hfeYsk7LLtMRMNUnBEO66X62ZC5r0i89PAN3F1eqRNO9NC8lfKm QwTMktEGtsfCjtt04WTSjsL7ugMzy8tUCo5H7UWSpfrAFbBsSCyarkFUUPRNzQ3f++znWh2OYeMh 477OdGhtTrNChOSLDPfv2Wwfq+Uw9oXWzLQm6adm//zetO2gYj2rvVgREWaEcEAoeyijuwn7X6EL 6UDoTe2japoMzKQDW94+44K7g2Tc2rSy4eSrzCSvCS33pcGq9vm6UolPEmhsH9Uyz0ev9CoZ1yto 1bhmOjzcYWITrfPRHLYCpeBsL2ZupkH5MZ3EEoH+z5x/XhbrJTw3VZhQN81PpjDGwOqJZp+1JU/f cg6UK3yeqPrBxKepu+FWQRX6ksPAbSZ+vQ8XYRdd/4DJDPsvfMou0uyhzoN7/jn3GR5K3CvZDQJK pOlUmJ7EPrS40flp6oQoUjekmY0UtGvrTCLz5fm5yvo+TPZoOC9VV4kSh8p44fq45A2GBcO88u6Y gUH53ZaGzO8zaQqNEEkl8OtcyLUmrIVjQTvHv8d7sWytKnyV82Nt88T0n0H8bgpImPEK1RHmg0yu VFTX3QN85K9yG3UZ2TAY1/j6of7aAz03rbpMaWeEYKLac2QTQierqwquGRRAm+c64mgkBT8HG6BS plkp6bsM4OFRVdSGMj1oBWIJx4Bj2ApO3aQh7zgetA4BYdPQi+ngB0KtZa6KGu4qxTRrm6TTqDmB YHHfZJtcqUyKVOGf8IVupW0+i/idEYVK6ZsP82D4JqKmfMiN0F/p/nGoG46t/3zeB8bjOSJiTqeA QLMz2GoDM1pdHos81a4IhUkpxU0YSLNQbkigx4tESLixvcpd19dsWjtoBTJE0mTNI7MwYJmD7CFk Xc/RTV1jWTMZGt+S3kcq3IrC4602aY3hCYoC8M4VnOrQ6KSwzhQ204P71OW1h91b+quL3v+cG+eD eum/gPeBxDvWEEGYlfZ9xvu/FqIsk4vxbpSTYCF5hP2QCS+9F8kw2NFkiREZ3qGQvNCSFSuHrEAs OePRsvsEYZkVHj9g1/GGXthQeoFGLRI2NadVLuvI6B6xl6aQp5XCllMIF4ssCVfzmLQa8oRHtpmD jMdfQPw3Fv9PIb7/5cb7g4jvNUefKsYD0LHGNIuLpDYJBxrKNAkpIhsJCdGzcli3bhz8KkX8Zrzi 3GsYHhCSBxGHV8ASJ8GWJ1Yg24zVT553YYQLU6BgGIAlzK8hRGosAsfa65xXzmuOj8H3d/w+6PQs rdgkQT2YDM7vnwLxL38OI25CG7ZTDvFWkcl2vV0nXiIwHZJUawW3yEV6GLXTqVs7LXO5GsaFzJWw jYJB92wHOog4K9RMGKjMzgb6xz0DcyZDwix9+P3Msbg0XNcQLSH/GUy1bIcqd9QDXc3+FTqz2I15 ai1ndaqkkjPxhxDfM7PBKsPK29El2WYzLs0SYgulyZLhdJTRamhlokPngkzGzRoxGox6aqSqap9A F3QHrABvRFV66mOFdGbTQLe6aTiqkxcIwA9k60EazshUlJ61XVWZ2qYt8Ib7K3mtopcJ/EpOC8Wu Q2xl0lYWM/GHEJdpyg4UhAVysVHVxWq5ES7X24rF2zxTTKI8TaG6ObJPSjdud+vtBcLw1EAXuCwT iThkBTLHcthQ7x5a/6eJrKHt34YWIAYNHO/OenknoiI/2jqrEAkMaV7Z0E0O4NpZM1QcLyBSkeFr aaVn4g8ibkOvqNSt4RDq9cV2e5rDL8/D7Rlpmgyc9YQFhtBrGHYJGd+stgjTtQqT1m2S1AesQMOs LSumOLCHgaCfrPnd3AfmUvbldHLIR7PO7ZijCQoPq95KKH5da9WOCSzPIs/dQsNxy5TNW13PxB9C vEbQHZoSeMuYXO3Wu20LOctZO8r7kEYX5dO9OE4nSS6HLY8VFxZBEq0qLL6MD1gBJTixKQzygA/W cSIIqy1USHrxy6qI48YrzojlJMFIrIWtRKpNxhs7Etr6OJM+U1XuuRtlYWwBHZHnYib+IOINQl4l ktAe7NrtbntxsQDcioN4aMerJCICqHTdrto8WVDG14sxTcMkXicHc8AK1Oxd6jveusC7hlmqTrZ+ uhgXogyyWbznoEwRuh8Qj2vfFE3vM8MAzRcIHxsBj3Eo6gR7UrSKw6Jknn5imttM/FM33HFoPnym RBeD26w2u9PdhiOdWiGS6ZazSLEhxIE4RwSt8R2b9Qqi19hUGsBp7l+BGJsj61kLgSC8z6bxPeHa HcXxL2EIUJPXoZrVa+wzF5WlFmXNeCyDHc9V7XtbdJUzZmwRPcK907QMLqlm4g8knrbOO3pHCHbS 1XZ7tjse8zxxOS8whScvIxWqLdmsAg9uux3T5Wqxyunf4W/+POH2iRXI4CqUfcN6J1DvO5umnKHc haxiZ01dxp27O5ssPf31qGB2oM7wprxqEZ+FmWAcGVOlywrvJXfcKD7F1uxn4g8i7heIytIhcU2i oNUvjo+2ixyuWsUqR/jOKhIuDHGDgdVycbHabmDi87yAFVeQ8UNuqhWsWuhZyMrji05IVfcg2ITT Kx5c9L5qle2YdIpjr1wedX1Nc885rpbH1bbgjC+Rt9V6FGGaqPb4I2ghMxN/GPF+WILv6FxpEIxX 4/J0txhYcwLf3Elo84i3HDoQZ3Q2bJfrkTWGac8p2L46jHiVUJwbJhIbL53om6LmsSi+xmRrncoc aiZ43XHMoUtR3WRNLWwByw9vHVwz2HpYl8VFhXDO8wgbIi6T1n3qIGUm/gnisVvmmpVkTb+BKC83 m5NVxSwII2Odu5TEIeKSxYVJulgsIOpC24Z5d1/9qnX7t1bALKCzhQmp47LhIZgyzLjFgXohHMx6 m1QpHfZ9GN0R6UKrLgxv9V63eaVrL6pxXLZS8WIchOwys14keWLKmfiDiO/V2CrE22lZpkJsLo6W 681ChO5wWPPKicgI50K5byoVdMB24BCePg7z/3N9yI3zTZoWce0k9HjRgTIPisie81UdwmwjlcxZ tp3fGfM4cpmSHeKwrkAAVyFYzLJBc76XYF6+YH9KlmUOb9DOxB9IvBgWVieyLWMl0zYdN4vVoEMT CFu4RRmJLNFMrOfAM7abZZIIdnMmQhvbmoNWoMaWYkbWOQht0cTCx3GvGIal1TTvaWrNSN1UEAPi QvtO69wyYmMvcVEIZtYRzhklOTqASbtUp87PxB9IPF5VsMuiKptCutSlp+sWVpKDA6o2h2xGGVWA DJMj0na4yBHNKRXHFa84qaruoBUorRRew7sX1nd9A+A1pz+6UM7EjL1T7ELAp9OEV15z72vYEx7c F3TdOIup5704vEPB6nCvQmaqVPp4Jv4w4vtuHKFWnepiHe5w2C0H6cKkCJmIroy6RuCzoHVdwiIp zrveNxA1IaqhPnwFeo5MVqyC92Xp6BuwG2jB+uyhkpRdbIClYfdh5FK2Fxn2PGV03VLhMzhytVXC WuMSpuQJfyb+GcSB3LLyKe7qhLXIi02VqMBXSgTQUVlyHhDPMfOk5UULIN4jxuKEiLzKDl+BsjaI 0yS8dF03rEBPqwXH6LU8soF2kRBjhIXsTIp07q2iR5c1WS14mOPxLnRdszgLYRqAFz4zrtIz8YcT r3Mekau9LZ0OgpePTlDEbV/CpMY98908xUx1oqzjnQ9wwTgBGaq4eBBxBXXcFY0sLJz/YbibA8Bj utBo5HkzOZsfox7xgAqHPH1dGN5QBpEXHBljlbYOLoTR8AkQTNiZ+IOJ710KZ0jEWaNTOFFqMV4M vE0hVSzxi+I4C4l1tn61SlW8XJI11sYIn7YP0Oqx5/U7quFthi4fHHuXw+Eo1Qkc9SRVVjSFMz1e 1Iq6zinkvvM8P0t4ny5sd4+YzJi7mw6hm9ws459BvGsNbHLpY15gJvQ6X7fhYstET8SNnC4lxhfl 6FjBUHL0ohY2/dWYpd8jDscLDlzZQaUn1d2sMMnKC1gMHQZIafiQ0CxxtO9NZitlMw7bLiTCBu3g 5hddV3DgY5jXzBu03Oyrfw7xPuW9hmW25+1TEPcEDnlO5jYQL6E8OYIlgRpfrB3vAecZmLdpNrjy ASuQGYRXdRw3LdjSgGiEhWESQJJX4ZI9BAlhlETEK7Usx6UqX5jC8ATVeY/4keN/oNN9mN/rZZXP 8fhnEI/ZoGlj0TmtdOLM6oLzCysZRjhx8QUMKQ9ShG7HwWZW81yjUDxPe8gKdIijdb2PG/xNEWYu c2DjpNhDXs8xY89kK150Dyst4aNpq2qPUC4R6ZBzYG+Y4utNuPReu5n4ZxH3OdyhxtWsXF+0+Q5a HAymvtMo5nRHCKGT7e4fyTjm2BZNV0P+ZJvUD1iBznLIQwaWNOdMoLkkDNuWyXRZLsJyzlT3DV90 X/DC+TxvRVnX8NAS07vEGvbKefjrvMcSWy8f5rOzz7HjapWoovc8uXarNr3UCccRM4FJrd6wXZu1 SElyKvOqhRmt+8wXzud5+RDirII30Nk1QOupKIJyHfI7vM8Kci6TarnyYQViCDh8OlU3dWERvRm8 PaN9uAkRu403RhdeVG4m/hnE00Viso7yoxbHY34UVHi4AiW8mZiXAQASgqI83xxtc5YQQ7HL3Dxo BXgCk4UbqOggymmwr5x4U84p5nDiF2kf8UilzJzkTLG+rj03XGaVCBN8M+h1wRvhsky0spuJP5h4 nY/KmqyGDOUD7GXoMoM0huPqKMxZKz2bMKGCUx6US0RYNWKzvH/QCtSwwXW57+n7wQvU01Wx0z2o rGOloCNOr9IiyHipE8HSJp91BaXdc8ygx+c8NvdOhxroRM51bp8h4/limHrCKrhUTHuwzzGbpvRE fdOHc64wHFstnILLlHsOdkvEA1egsZyXFdfTC5gp4ROeJAxWduFwZRhsIB5rjm1lxqWrDcyMFxDv Dr8VDuKtZahplvlM/HPi8SHR1haixRqngp6UTDwkmz2/iM7wSW94ZAXV7hZGQ5niG33uioevQNnH +4K3ICHSgjy7cOmJY6Wst8zlQr+PbTatQKF4qYryvqsLBHAWIcV6A5MCrV4o+H4ADmczi2fiDyW+ lxXMtsgl+0as0ClrHkoYS5WpLOLY3AbuOowq7wJPHb+s0yx1SfzQFeiMEHW357An9iRxAKRUHD3j EhFu39IwGUkrommaoyzCPbo2qzs45zDky4tBZ6Ft6bAVmIl/grh1o8WCh9uoePUcLx+yNLXK+Aih EBwoaFNOTFTYGJ0v2GGcJOrBK6C0ho9dwvmfrlnhjVeMySHbVcthLJqnd05HcdAssNWW0/d91nts hyIWD1yBmfgniPdVrl0reA4aLiEzcrr8mZ5WVEDInPDUxN7ClFZWeZH7xP3mIPvfXwEDpmmYwlxr kYRo3JE5Jz5LJmGmqkUVlTGb5324wRKv+bkrMBP/BPHYJa3NpadKD7cGcvaDCdcDZxFbeHkFeOjc hn9ntfrsFQjunzH0CAsNeWYAwEgtRGtsg+CdKVKZiMMCyy/vp56Jf4I4fLdKJxVCoan37w9bAQ5P NyLr6RJ2PKIVnFARLqxjOUa4vlR/xRWYif/nz16BuDMcPsJytrjn4G7D4gZJ8H/ECszE/3TivKQ4 JFU42Kk2ytg/dAVm4v/5C6xA/19cgZn4f/5mKzATn4nPxGfiM/GZ+Ex8Jj4Tn4nPxGfiM/GZ+Ex8 Jj4Tn4nPxGfiM/GZ+H+Z+P7PWIH9n0f8Uf+8nyA+P0/2mYnPxGfiM/H5+RsQ/+/4S3+d52/08/5Z 0dmjJh7NxGfiM/GZ+Ex8Jj4Tn4nPxGfiM/GZ+Ex8Jj4Tn4nPxGfiM/GZ+Ex8Jj4Tn4nPxGfiM/GZ +Ex8Jj4Tf8LE47JpmpJXlszEnz7xssssZ2dyTmfE+fVZUTfxTPyJEo/7jEOR81Q6I9I8al3icl5m 4LtyJv70iJeFcRzDa4TJ6rrwPjJWKV4ox+n3WTwTf1rEa5EkIuvKOP7FCvCu+L7jVWN1PBN/MsSb gpcD/2ICbvQzha+kNP1M/IkQ720iXVH+7gr0InUyK2fiT4F4bdpc9/etQMN7Be1TJh7/XYgXptVJ 87H01kXzG8SbIpXpJy8EfoTE47gsywb/j/nw9u04/jsQL9RSuqz7cLFQWfa8v8Z3+6gvsqLnjQZh eeqizBCm+6dCPC77rqvxdF2Dp++jvgnIA//46RLvTJuIvjG80CJuutrzDgUhlGkig6covM16roM1 HsjTTyJ/XMQRgNSZDZdbW1+Ae5FFxd3uPgz5oyXOK0Qbr6Gzi6K23gphwz10ooiEUSYz+GBs0fFK 6aKsdW7qp0C8qT1v88t527Zk1kHoyDA63X9A/lSJ+0p2vRW+txxyb3mtHC+pcZDliPfMKaEdLxJW MO2ZQsSu3SeQPybicZMZmQ+L1Xq9Wi6GPM3bxTLKddBmT9uO9ypXsdVZU2CTa2U1NjsvJ8oTYSNe bMcLZbERqPrKfa1E02tnm0dOHNbL6qT6kXg7LDa742iVq7p86sRNnviSV75aBd0mVcL7S5KqAnET ScdLB9NUWRo738dxoYo99L5/3MTjssuUdmnVDuMwtFVejevTy6voaJXaLn7axGtRJR7WKyuEAHHe PO1ckudVroWPVLBzeYpdQIVfALkRTSG0rR818bIrPG91m668592pw+bk5nl0c7SUWfOkicfKJbLo YhtulVRe8VpoUK4qJ0wWGY3PwkkaLxFV8OziXnZ1ZpLsMRNH+OlpprznPZn41epqc/byTfTs5nRQ Bwv5oyRe6HShirIXcNak5iV0aSIUlHqFX7JI6byt2lQ4XngHt93aOlZZ7aVS5eMlHsNNNwoRWffh qU26Pnv1Pnr37HqT+OYJE49F3ua6i2vhtIJUa6Vgvg2J506bSLiqhZVTUPjKCwi6sllTdx4WoHi8 xMuO93JmHZNt09N4tzp59Tb6/t3zk1F0T5i4T+CVqbjwXidw0aVONNQ7gTsNxz2S8G3aNjdWOGFF iFlN0ZTWaqeLx0q8hJvunCj6n9RUGYi/jn749vXtRerjJ0u81nphRQ0nxqpE8TRUp8LICoocWl7Z yCFMhZBrXl4biONbiqzz1jvhy0dKvM9EAttVfFTLVdp0c/n8dfSvH757cTSY8qkSj60bhdAF74wV 0Oa8OjZRgnKd8qpiEzkXiOe8nNxwXzgJ7AXsOUx5/TiJx7VxefUz4nEvht3Vs3fRv/71/ZvLhWie KvFaJzDREF4Jgc6dUEaAqATwIde8RjYSmiErTD3+RAWFD21vfA/iufX9oyReZnBH4Y1+VK9Z1lDq L99/F/3zX9++OV/qJ0vcphUvJAZNx+ugpbI6lRLxWDtUmk+kpauGtoXG14lLZdD70hYlr4J3qogf I/HGunYcfka8yfLt5fO334M4ZHyp+ydKPBYJLwGHOwa7xvjLKJ0mBI7HCS01ZDxph2FIRIjekkQa r7QtYsh4UVnfPUriJhkWIP6TVo97316cP3/9bfRP2PGzhejjp0m8M4mB8511FrZbe6bVtQR98B4g 5NLpSCYU8SHlZeEWfy6sBfEs9kxeuOIX5a2Pgnjcq3RcwGz96KsjPDft9vIVif/j3avj8ckS99DR wNzVGfw2baG3YbyDVFPIGakhOmurJB0qXgyvrbUmM6CexTUbF3L/CyF/RMTHXPoPB6Nln6l2fXL7 /vvoX9++vdkNonuaxDsDhHDImwIfpTYS/rl2IY9epbKFRZdRkvNXROQudQn3Azx1a7Oysd5nWVr/ PAf9SIhTq49tqrKuD6UvXWF1vrg4f/FN9MM3z84vRnfgkeljI17AKAsh+rLWNNMmAVuXppB0B7+t zaUD8XZwEvF5ivg84R8pb3zRxXTdai/w6eMj3hjXLhZjJZUv+NSFVzIfNiev3kX/ePf8fLeszGFH po+MeGw1S12yviwQdyEgB1aEqfgUbIVsKwfPDQo9gbvOgggN6U9gzbOsaBDTQtILL7vHSNzKdrFc jnnCLBOLQBRPyldHL19H3757fn10sZT+ICF/ZMT7zHpjFETc+8IiJq/yirkWDRF3wrUVPovyiTj8 OZnmaQLixujMl/sY4VnhRWcfIXEvq8V6vV6O+HGxi6Wmfzqujm/fQsZf3Jyf7KrD6iIeGfEiyzwA xqzu84XXPBZP0xSLUMFsyySXwkT5OOJTyHuCpZlceQU1WMeIaq2vYdibx2fHEY+vthcXF9vtZr1e jRULnlarzfENiH/3/vnLm8uV84e00z4y4jDI8NG73gMb3G/44nTIhQNwGHORVBJaPanGJEko/BUd N3h2UsCEeyBnfKb0zyreHgXxslPJsL44PTrBc3S8g6wvuAF2J5DxH7795u2bZy937UGH5I+LeEmn W9kY1CUkF/46IjUBbz3PU/hu2g0w61GSVHDSHXR8rqnRGZKzotlk+wLeOuAXj4x43Be6WmxPj87O r66uzi+PdtvNdnd0cnR0+fJt9M8fvv3um9dvzpayKJ8a8U7ztLuvjVLOCZEBn9FKapdgA8gEpg1m O0JwnjrglzR3pA2HDcYff7eOmWktso97Dx8B8bjPRMUixrPz21evXt6cnx0fB/jX59evXkf//ve/ fvjH9+9uLtJD1PrjIl5LUyhTZioQ97TqNqMTwzq3NKdwJwrx+BC+gKgN4OHHI5hRPCS39b5nTK5l /aiIl7VJlgB+cnn98vnzV3DTjvDpze3t1fXVq7cg/s9//ePbb25PK9s/NeKF8N5ksec5aSqyIoNZ LiwEPAlVbjldcxOlCdizhB+mXbK2uahr5uGt75oYe0RJnz0m4nHv3UAbfnZ+8+r585dXlycB+Mvb q/Prl2+if/7zn5Dxb16etubJEYfd9gjNIOMCbrnxzJ/q2mjpEjCvGIopG4kk/A4+PKJzxdQqawK1 wfao485aW3cftRf/9YmXhWjXR2eXl+dXt6+ev7q9vgT7K4j4zfXZ5dWL6Id//fDt99+8vtlVT454 bKnGy55tVwnrVo2HCXeIsem/wYGD5s585MNxGgVcCtb7AnGmtGSJo+9LENf1R2m3vzxxRGbJ8vTy /Pr66gZKnWYcxPGbq/Mzum7R9+D97u2b64v8yWn10jPxEneKCZccjrhFXAa5dojZMiEM9oMSPtLW 06Un/r6GOYecZ0ZSxo3NSg8FX3/krf/liZe1aLcn17DZV7cvXwURPzm7PMdzcrzbHV1H7969fv/m 2fOzdeKfHnF42g38GJYz5q2URqaIuSHmsNEeGj+TVSqiNldZVnd1A6eejQsOsIWTyiCa801nYPq1 fzzEG++Wp3TMKePBUz86Oju7PDs5Ob3YbHZn0Zv3b17g68eLgw5THhXxhrFVXHqGaMK1TK6mbQ57 ncIz19gO+aJFPD4uB7jldV2wF00zzWqMTCDpteWhqTVeJo+HeNyZanNyfQPik7d2iTA85GFOt+vl cnMcvXjx6hZR+sWgs+bJEc+8iXvWriHYgnxD0nM2YdEvTxLTDsIwA6PywWSCSZiECoCJN1CnUoch 6CHkH/cq/NWJN5lbBBE/v7wMxvvk+PT46OjoeLddju2w3EXTLjhd5qp4asR7+GUee16CGBuwqpyn CYsxOOkQZQnXTLtIeis1HHaX3jnwTtOKW2OMktbHbE76KbX+VyfemXx9RBG/hCq/prd2ujsF8NOL 9chmw210c3V2vLvYDompnxrxDpa4aHwWilO1ENXQDsPIU4WErrmmv6aSCPo988zK8KssYwb7CbhB jGbLDIZcdo+FeC2Gi7PgmEOVX14C+MXF7vj4dLdZVtjSwya6vjy+2KxXlbTdk5Nxhlc9wnBIt4Pz VgXg48DcasL6ByWUbKMcnrtlKQSHdfJYLc9Tplo5TEMa08EdyETxSIjHVOqXVxTxo2Nq89OL7fbi 9HS3XY0pjwg30eXRxXq5GKdJhk+LOGIz6GSjjICLnjrXkvc4tFJLoZNUQ8PnRkZaUcNTxCU7F5iL A3HaAeGUZU8qojX7SIiXNl0dX17DijMW20F7r9frzXa7WS3aRGrXrqKj3XoxDJU0Rf/kiMOOx2wg luwzqqoBT5vkuZSOhNvlcrlGPG6mYhEjRUH/LSXy0LugJcx4lsUFfHfzSIg3MOPHiL7hm+3C0fhy sViuVit4bQg7tauW0W6zwGLk2j5B4tYXMUd+TPXp4D22oSKCpY2yHZeclGGjarlcLYAcll2zsZ4F E3TfmJ/jOKCug4MnHgtx0W6OGX0fMRgbuceDZmurcGjg8jFaL4cwIOEpEY/+uOcvSfzv8/PeI+OF 8V1ccLITBJ1u2zAYyfkY8NmyajFOZj0aYdUWi4ULmRnJDgWKQPohU4P4DO6+bB6FjMedG7en8My3 lGQnOMMqmX4wyekgsFnRQEclxx/6ui+fmudm+5gnpCoQJ3I7jUSBupaLEV9ZLIZoAez4RGpF682S iCr/wDzBumQI8ArZPwriZZ0strvT3cVmNVZJaIaXjukmnv8zQMnbaGjzqc5PZd2TIw4ZD9N8OCCg ZbZNhJ0OO+7UAv7LCNoR5Bte+8Ia1kMAuHTVhJwrFW5W8DZz9eMgXiSLTfDQl0Mqjc+sktNgo9CU AXcGPy+2estElDwkBfOYiMeZBS2EWcYYEZpJxyENMu6GIdc6aPVhjAZ+gjjNhQNyljAH4Dl+Jx1W rYm9L/JHQ3zcsIJ1jWBM8LRY8fSonTIReBZLeG5w39fbzSIXBxylPCbiTQHisTUsR3YkXoXBAKCa hHBUgDeMeRQWIxWCBa6M5CqG5FUg7th9zJLWLC0eDfE1om8qdcd+FC9cxQ2NCA2hGp5NxKBtszs+ 3QzO3l+y/piI97XPur0N49vykFgJe71KU3xMpcjkOLSBeNsaK2U1wtdxQflNNVE8STcgDhmvu0dC vHbDagOk8Nugnkg8aQPuzTYUr293EbNwpyfnZ6fL1BRPinht2fwNpw2eGghCflO2HVF8Wx6SCUOZ T6Ihb4VV2BXcDjnHCeQumVo5FIcy7/fw3MRj8dxku2TeZWxTbbKisCLFFthuL3anx0fHp/gQnbCy 9er26mTb6vurWR8T8YJdBiSuBXMwidDQ28xCOB6MOp0KPeZ5BhlPlXbsV2lzRDFCVLmWIQvDKsfM 7xGdeVE+kuhMT8SHima8YEcp1PzFjsB5Sn50Ep2x0PHlq9vL3ejsUyOeFXsO7FNKp3mqObgt0dPY SilFmnoWPkRJouCVS+4BJiKVxGIJum4sXfdZTRn3On40xKnVlwi+BLR6ZnS+2FxQto/OLs8v8UTn 5+fXt89fvLw+XqWqf0rE61pk2Z7jtQ1vsssTzdkAvMZQikSzah0yrGRkPcuXtWScHrpOK8mWxNQp VjlmDWX8sWRZ4160KxjsVZs67lerXLXc7sD7hKflLIyJrq5ubp8/e/b85mRTiXsj8sdEvMtInOXH QC7TNJcakXYlw8MoLAHhJI+EdnkCT87xTE0JGHqh4MClHBpjlI/3pc+kfyR59V5U693pbhMSbnj7 cNxWF8cnZ2eXoUfh9vYmun356vmzN29e3F5eDPpJEW+8KbJ9SLMaODApg7R2CAZaUpZdLqnGoySX lZyOUUILeZUL6oQkDOYVihWSWfJYiDeqWp8eHV8sKsefEWptAHHWsl6zev35q1fRq+cvnr15//7F y/PdKJ8U8X1h2AXOAcQGvngKJc1ca04RZ6mjqJIEgXcUulHYfsSkejg5A3GXJuGIXFkSL6rssZyP m3x1enS0W7bJlEjPh9XuaKpeJ+oXLyLwfvP+7ftnr54e8czQWQ/3wijjUp4rcBhr6E4wOrQWpmkV JbJyiXbhpmIdBsTA1QNxEZpMgZozbR9LDUzp3eLi6Oh0PYaNjJ+PxCHgNN5k/SYC7/dv374lcWj1 +EnVuXmF2Ko2MNTKSqp1k+ZwyyjOPP6WroLDFiUVrHjw3Aw8Nk6JgQV3PHzAX7AxtESdmkdTy1pD re+Oj7erkbnEoW3H1QWJ39y+fPGMqKO30wPiF614Ur76vswko2mjJGdspxy5DI9MUnYTSDwC87SV Lsph0NlfzMAtrZzjVRpKc5KfsbbYN6VX1U+Tiv/qxHubLC6Oj3ebkFNdAPsH4q9eQJm/fU3ir1+/ fvvs5eWmUk8qHodaV9az+Ak+uHHQ0pB0EFdh4Es4JuGxUgT6Tkh2JzDxwlkwUhkxAYdVQFyv3ePp UGgyOWyPj453Iam6WoyL9UT85atn74k6wv9fv3v3+tntyTq1zZM6Ld0XNsw64ZgA6wQMugxhOYx6 VXEYTgoHLY1SiDO2A8/NqALSkGvTiQqXTTD/4hPzePrOylpUm2P2oByf7li0ulxfHF8GR/3NW5B+ F70Lz+tnN0dL58unRbz0WmXxvjPU52xMkTwElUpxOgQDcLhzaQSbzhlYEHSt4e5UzqgwYz3E8XEc W99+NIH5r0487ky6Oj1hn1loPFqtNiROpQ4Rf/fNN9E34Xn94up00MVTu+EuyxK775lb53UZCMkA PKfOBnv4ZZBtyDjvSoBGT6TVdNVzVjMqDv/x1njePuH1RxcO/NWJU62PFyfn56xuZCvCeiL+8vmz 9wD+3XfRd3y+ef38fFup+qkR76yz9f/P3rntxm0DYZgURVKiuKSkXRld143hPW+aY+u8g92n8K3t PJ3BB+z8lIPADYpugN5QJi+SIMmN+YlzIGf+6eHJIa9OxFEOUnONSpcG2ozkyhnyVhSulvS/6IOo x2SOLLrVsldK2rk2CWlE+E7M3qz3I3K0k66O34k/PDyyR6y760+bi1ovwsSI4/pFLnAXI8bqpxK1 a1xDgruYk3mnlJ2V5MUpWIfQE8ddLPx9zaPmT1C+07LVPiVVkN7x9s1u/w7I18fVCsTJjX9+f3N9 9/B4z+5pPd5+2a/m3PWTI97pWuvgybxHr0whOCI3To5bF2gbFkXJxhPeRlVmiPgJTAIUmBK28OQR dFuZpNS91GCKZexL2W0oeAPxPUXqlI2PxL9+/Xr/cPPx6lc64n5yxIOupO69VxZX5CCO1Ksc24xq TEaBoj6aVmpRcsuhBSQ0Eef4QrzqvbZtmZiCn6fgbbXZj8Rxxq/IqP85WvWR+OPt+w+rlk+um3iU e6rJC3sK3oqC0jJR442UsrT4EI6MTeLtDAVOmuMBBTduRlLgTr7cBBWMrvmLQVEJEIddPz/u0IqE M37YjsQ/f7m5vYvE7x/++mO3rMxicgp+MHGyFrKPnhxvoah5a/BQjqwbEl5cM8ML3Mrg3+iINzER h8ajHOiMU2r2cmJtCsTJrrfoPnuLRvHDcbvZ//5pPORk1lm06ftDc1L7eIqTccrGaNoEEweZ4bqc HDZRR8WAwF06s44XGF9Zxq4ElKije1xE/691MefpaS/77jlDu0KD6Xrz7Zb15hax+sP153fHJbdT VNuOQhFNhaA7IHYrIvMitg5jPFKNKZbGSkrM6JgXz41HkP4x1nqlnDbLRtsUFfWbiy3Z9d1YzRjV vT6Nnpzd3Xx8tz2vT7LpSc40NAjeKEMjfAKhWixsfG7LQVsZQ1uaKRtQx19DR0Dazin6SqTplt8b iVOacOf4/PLqLZqK1+s1Sp72zyn5Nfvy53573ohT5AISnVtqhZ7ZgUy0kxyDrVAnUIkKE+8oVLcs SnDHRB2rNCgPM73ykGdfUgbnU5xiGZPy9e4trmGuIPZEzD+gCOYj228v2vLEE57oNGrN21Li6d9p 6VAfQRm4rAoB020Mw9vJSBu/FZSIkxunD4SMQjvnsk9ybimS8rPYSf6MfIdjDgkodjif8ZOBp0nc S37myLD74F0HyfX4elrhjAurGY8SnZB6KnH+owAMJXRqobuzQvTJTqPW1fLyakdrs4nK+qOo24HN a2FPNOmpEg89nXJryLB7jD6JnWga6ZfkpZNM1FEOAim5xjM65l9R3tJLMyvlkOr88ai5vTx+Q47H 08Ml5qawKA4RwqSJB6+LmSNf3qs41bCAoH4RR1taSX4cLbcxaKNEPIbqFKcHbWeNGJKdOE/fOSG/ OJI932zWY0f5fFYXnNlTRL1SJx6ULshDL8KgoN441vUKQ78aIs47VEAhnuMynn3YdOPI+vUhYeKq d6K9OGzpdK8ibvx41rHB/wTwZImHhaxmGh1FvYnjEciZc4cdMEyKDjPHK0iC4IZVWvL4xs5nPw4f T4o4kJez5cXlG8LdYHCf6xZDz36Gd8LEKVAX4qzC86DXwuAaFdJdzlqG5AyNaBhzKTrTKZh0PS+l CmkThy83uIAoBH3si4HCGPqRWHgtxMNAkYy0LnaWkwcXlJB3zlnGozIAj15cdF3oPbnAppI+JE88 +KGjj5pw4/1w/KtXRDwoU85qY/pYHGMwQoEy8p5hmkJV0QkXpnML5O0LU9f/Ajwx4kF5DKqNhzu8 PuLw5s28wI05/tx1nfd+YGgvE4JLTqkYbtqUM22lfZgE8R/X6yJOyHnTSpSmUsLtFcF3zGMkrTTe B3hw39ly1mgVMvFJEKcDLIszYYzDU6FSynvWj0k3WT7i7aysKmlS2YFM/Om/d8A7KeYNQadkBaO5 KT8dLbjvO2ixF8L2IROfEHEU8ce2slgGJTUzFk8qWEbLgts+oR3IxJ9O3QG/MEJUaEFjbQuB7Vld c25Tq9/OxJ9+YgfU0OFUM7QuYDDKSWVfmXjCxL/tgPIDMtYkdyATf3plO5CJZ+KZeCaeiWfimXgm noln4pl4Jp6JZ+KZeCaeiWfimXgmnoln4pl4Jp6JZ+KZ+Csl/vSPlYln4pn4tIj/8nJl4pMn/tvL lYlnq/7/r+SJhwkSz2uyKxPPxDPxTDyvTDyvTDyvTDyvRIjnNfH1twADABq4l4fbNFVcAAAAAElF TkSuQmCC --_5f5f7fd2-b3cc-4461-9fb1-7f8576aaf374_-- ========================================================================Date: Wed, 23 Feb 2011 14:59:57 +0000 Reply-To: Nero Evero <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nero Evero <[log in to unmask]> Subject: paired t-test issues Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-gwZJ1dAgGbAA3cEDw04oc1CAA3yAQAnX3Uu27qORZYmBd" Message-ID: <[log in to unmask]> --part-gwZJ1dAgGbAA3cEDw04oc1CAA3yAQAnX3Uu27qORZYmBd Content-Type: multipart/mixed; boundary="part-vAWAFKNRvVgo4ABWzQAFS9A83UFwC9AbFBM1AiyhuhNcI" This is a multi-part message in MIME format. --part-vAWAFKNRvVgo4ABWzQAFS9A83UFwC9AbFBM1AiyhuhNcI Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Hello All, After specifying my second level analysis using a paired t-test design, I noticed in the parameter estimability my â€œbetas are not uniquely specifiedâ€. I am not sure what this means and wanted to know if I have made a mistake. At the first level analysis from individual subject models, I created two contrasts images for each subject. Each subject underwent two sessions (after different treatments for each scan, at different times points, with identical paradigms) and I wanted to test whether condition A > condition B between the two sessions. I am using spm8 and entered each pair of subject contrasts (17 total using fMRI data) into the second level and used all the defaults for a paired t-test. I have also attached the design matrix. Also, I am not sure what the contrast weights should look like to for the paired t-test design. Any help would be appreciated, also please let me know if you need more information. Thanks in advance. Nero --part-vAWAFKNRvVgo4ABWzQAFS9A83UFwC9AbFBM1AiyhuhNcI-- --part-gwZJ1dAgGbAA3cEDw04oc1CAA3yAQAnX3Uu27qORZYmBd Content-Transfer-Encoding: base64 Content-Type: IMAGE/PJPEG; name="paired t-test design matrix.jpg" /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMDAwYEBAMFBwYH BwcGBwcICQsJCAgKCAcHCg0KCgsMDAwMBwkODw0MDgsMDAz/2wBDAQICAgMDAwYDAwYMCAcI DAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAAR CAKdAd0DASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9/KKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKK/GH9lz/go/8AtY/t2+HNC+PnwT+MXwg8fave arcalqv7Jn/EnsNV0vw9DfS6czf2xLJ9s+0bfst1506QQl7jcqMnl2cwB+z1FfAHwU/4OAPh P8RP+Cn/AMbvgD4g8ReH/Cun/DjZYeH9SvIryL+2rzT7TUbrxJNc3EkS21nb2K2qxgysqv8A Z5pVllWVFj0Ne/4OOP2V9c/Zg+L3xF+HvxG0/wAdXnwk8PjWrnQnsNS0i71B5pVtbOKMXFoJ DHLey21u88cUkdubmJpdqsuQD7vor88P2LP+C2Vl+29rf7JkPhXxl8MGl+KlprWmfEPR7jQt ft9THiDTdAsdRubPRnMRtUjtpbsGV7uUo8LKIJJZA1eoeB/+C+P7IXxK8d+MvDWhfG3w/qmr +A9K1XW9USGwvzDLZ6ZG8t7LZzGDyr/y4Y5JQto8zSRRvJGHRWYAH1/RX5Y/sk/8HEtl+2p+ zx8Jdd0HXvhh4N+IXiH4waR4L8aeGdd0vX7uPTNN1a/1ZdOsbG5toDHNqk1lYxstxIVtBIsx lEG6OMfZ/j//AIKnfs+/C79snRP2fvEHxR8P6b8XfEHkLZ6DIk5xLOrPb28tyqG2guJVCmOC WVJZPOg2o3nxbwD3+ivmD/h8/wDswf8ADZP/AAoD/hb/AIf/AOFr/wBq/wBhf2T9mu/s39o7 c/Yvt3lfY/tG7915Xnb/AD/3GPO/d1z/APwSI/aj8d/tQf8ADT//AAnWu/25/wAK7/aA8V+C fD3+hW9r/Z+j2X2T7Na/uY08zZ5r/vJN0jbvmdsDAB9f0V8wf8Pn/wBmD/hsn/hQH/C3/D// AAtf+1f7C/sn7Nd/Zv7R25+xfbvK+x/aN37ryvO3+f8AuMed+7rn/Gv/AAXx/ZC+G/xTm8E+ IPjb4f0bxPZ+K73wXeWd5YX8P9nanaTRQ3C3MjQeXb26yTKou5WS2fZMVlYQylAD6/or5g/a Q/4LP/swfsiftHWfwl+I/wAX/D/hjx7d/ZN9hNbXc0On/amAh+2XUUTW1nlSsh+0yR7InSVt sbq58P8ADv8AwVz0r9nj/gpx+1d4Q+O3xM0/wx8LvCHiD4feF/Agv9OSO00u/wBZ0S7vLhJb qGHekcj2zyNNdyeTCIz88a8EA/Q+ivAPjL/wVO/Z9/Z8+KfjfwT4x+KPh/RPE/w28Kjxp4ns 5Enk/sbTDNDCjSyIjRi4kkubZY7QMbmX7TAUiYSoW8P+NP8AwXF+FfxM/wCCdXxW+Mn7PHxb +GGo3fw1u9Ms72+8aaPrq6ZpD3d7bQq15ZW1uNSMcscsiQyRQmNpkI3FYptgB930V8wftIf8 Fn/2YP2RP2jrP4S/Ef4v+H/DHj27+yb7Ca2u5odP+1MBD9suoomtrPKlZD9pkj2ROkrbY3Vz 7B8O/wBqPwJ8V/jt8RvhnoGu/b/G/wAJv7M/4SvTfsVxF/ZX9o27XNn+9eNYpfMhVm/dO+3G G2nigDv6K/OD9tXx9+0p8cf+C0Gn/s/fB39oX/hRfhm3+CsfxCupf+EE0vxP9rvF12awdcXQ WRN0bwnIl2jyOEy5auw/4J7/ALdnjP4ZwftRfDX9pjxxp/i7xX+yVdxaxrvjzTfD4sLTV/DV 9pz6pZ3MlrbgkXkUEVys0MEAVQkKo1w++VwD7vor5w+E/wDwVz/Z1+Nn7I/jH47+HviZp83w n8A3cthrviC806906OzuI4oJDCIrmGOaWRhdW6okSMZZJkjQPIdteP8Axp/4Li/Cv4mf8E6v it8ZP2ePi38MNRu/hrd6ZZ3t9400fXV0zSHu722hVryytrcakY5Y5ZEhkihMbTIRuKxTbAD7 vorwD9rn/gqd+z7+wf470fwx8W/ij4f8F+INe0q81uysLpJ5pns7WN5JJWEKP5e/ypEhV9rX MqNFAJZQUrj/AIyf8Fxf2V/2fNO0C68Z/FvT9Aj8VeCtP+IejLcaPqRk1TRL6dYLW4hRbcvJ I7tk26g3CRpJI8axxu6gH1fRXH/AH4/eDf2pfg14e+IXw+8Q6f4q8G+KrQXmmanZsTHcJkqy lWAeORHVkeNwskciOjqrqyjsKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK/AH9sX9mv4qftkaxD8N7n9h3/h W/7adl8QGnsvjt8PbK88OfD61ga6h1GTxI2qwnzLi4aOEKI7nzp43kkkjaO7Y2Vfv9RQB+WP iHS/ih8C/wDgq1+3Fpdn8APGHxFj/aa8FaPN4C1O60CLUfAWo3GleFr+F7DW7pp44reO5u1F r5MrKX8wbvLjljkb5P8A2Q/2Tvjt8d/in8UNU1H4aftAWOr/ABB/Yr1XwJap4/8AD2n+HNKt fEPnRWKaNpEFrHBZ6Zp+63LW9rOsE2xpLh02TCV/3+ooA/HH9jmDx/8AGz48/wDBL68n+CXx w8C2nwB8P+LPA/i6fxb4OudLjs7iHwdplvHd7juCWdxPviglm8syyQyKEDKRXH/8Ef8A4X/F n4Wf8FHPhr4N0j9nf4wfD/4ReDP+Ewutb0X4o6DZ67pXwovLh5FV/B/itkiubi3u2W1he3QM rJJdTZu/Ma6i/b6igD8Ef2YfhN8XLP8AYC/Y5+Ams/AT44eHvGX7Nn7T/hvUfFV7feFZW0a6 sptX8QXMt9ZXcLSJcWdsjRCa5AWFTPCVd0kVz9IfALwh47/Y4/bJ8a/BzxN+yr4g+NX/AAtn 9pW++MeheOPsNvL4N8GaPfrGyat/aE0b+RrFh5E4+ytHDIxOIZ282Hzv1eooA/CH9on4G/Hb 4of8FHPDceo/Br4weH9J8Cftf6N4mtYfCPhLT7P4cXvh65eJk8RXUlqhvNQ1iRbQy3V9O8sN os8kTtavMID9/wD/AARJ+E/ir4Uf8Ndf8JT4a8QeGv8AhJf2lfGWu6R/aunTWf8AaunT/Y/I vbfzFXzbeTa2yVMo204Jwa+36KAPwh/aJ+Bvx2+KH/BRzw3HqPwa+MHh/SfAn7X+jeJrWHwj 4S0+z+HF74euXiZPEV1JaobzUNYkW0Mt1fTvLDaLPJE7WrzCA6Hx9/Yi8f33/BIz/gpXZ6b8 IfGE3xC+JX7RV/f6NBbeFbltZ8VaNHr+k3FrNbqsXnXdmge+ljdN0YD3DKRlzX7nUUAfhD/w cEfA347fHT4p/tQeEdA+DXxguPD+s6V4M13wpffDHwlp76V4/wDskyW94/ia8RG1HULi2e5a O0sYnPlJaR3DWwij+0n3D9o3/gnV4z/bA+N//BVbwrN4T8YW1p8UvD/gK48DXogGn2nijVNK 0aS5hgtru5QW8kYv4LeCchgEEjKXiYh1/W6uA/aj+AX/AA1B8Cdd8C/8Jr8QPh3/AG59n/4q HwTrH9ka9p/lXEU/+j3Ox/L3+V5b/Kd0cki8bsgA/GD9jzSvFX/BTv8A4JH/ALbH7SHxA+Cn iD4i+N/2h9V0rTpPBPhK+m8OW2sad4dtrGK2v9GlnS5le4gma8lKk3KXE+nC3WEkvE/P+PPh T+0F+0R/wSF/bi0y9+EXxg1z+3NV8F2PgLWPGfw3g0j4reNbOyv7HzLbVorPdPqf2C3jiCX0 is0vmXTlxh4Lf9r/ANi/9kPwb+wX+zB4P+EfgCHUIfCngu0e3tGv7o3N3cvJK8888rkAGSWe WWVgirGpkIREQKi+n0AfiD/wWA+F/wAWfDn/AAUc+JV98Hf2d/jBr/if4nf8Ifa3Sz6DZ+OP hH8YbO3eMOmvW10ix6JcWkkMMKXAnLLFbPIBaC6N236//Dv4+/8ACwvjt8RvAv8AwhXxA0P/ AIV3/Zn/ABUOr6P9l0HxN9tt2n/4ldzvP2r7Pt8uf5V8uRgvzZzXf0UAfmD+2r8Y/FX7F3/B fTT/AIx/8KK/aA+LPgm/+AEfg37V8OPBc2vfZtRfxHNeeXI+6OJdsMOWXzC482I7cNkeP/tK fsB/GX9tn9h7/goP8Wpfh74g+Hvif9pL/hFdT8JeAdbjW51uTR/DFvaXMM0iWbTSJqF/Gs0Y 0941kguI1iZ2D+Yv7PUUAfiD8Hv2J/8AhM/+Ccf7bnib/hRf7YHxL/4Wd/YGnf2F8YPF39m/ Efxf/ZCRz/b4E/s39z9j+07rdfN1D7Y2nCHyU/1Mnn/jz4U/tBftEf8ABIX9uLTL34RfGDXP 7c1XwXY+AtY8Z/DeDSPit41s7K/sfMttWis90+p/YLeOIJfSKzS+ZdOXGHgt/wB/qKAPxB/4 K6/BLx3+zR+0x+3J4tv/ANm//hc3hL9p/wCFUH/CPeMrJLea2+H/APYmkxrf/wBpSTxH7Js+ zpqMW1/9In02wji3zFmtPUP+CW37Peq+I/8AgoX+yn8RtR8D6hf+FNA/Yg8M2GmeJ7jRnl07 TtZa6iDQw3bIY47w2c84KK4kMM0nGx2z9P8A7Tf/AAQ1+GH7UH7T/in4oXnjv44eD5fiNaWF h498NeEvGkuk6B8QLe0ia3SHU4VQyyRvaMbZ0iliHllioSR5JH+v/CfhPS/AXhXTNC0LTNP0 XRNFtIrDT9PsLdLa0sLeJAkUMUSAJHGiKqqigBQAAABQB8Yf8G4fwn8VfA7/AIIx/Brwt428 NeIPB/ifS/7b+2aRrenTaff2nma7qEsfmQTKsibo3RxuAyrqRwQa+36KKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooA5P4+eLNe8BfArxrrvhfTf7a8TaLoN9f6Rp/2eS5+33kVvI8EPlRkSSb5FVdiEM2cAgk V+Yv/D1/9ur/AKNq/wDMe+IP/kiv1morx8zy2viZRlRxEqVukba/efpHBHG2U5HQq0sxyijj XNpqVVyTikrWXK9nufkz/wAPX/26v+jav/Me+IP/AJIo/wCHr/7dX/RtX/mPfEH/AMkV+s1F eZ/q/jf+g6p9yPt/+IwcMf8ARLYT/wACqf5n5M/8PX/26v8Ao2r/AMx74g/+SKP+Hr/7dX/R tX/mPfEH/wAkV+s1FH+r+N/6Dqn3IP8AiMHDH/RLYT/wKp/mfkz/AMPX/wBur/o2r/zHviD/ AOSKP+Hr/wC3V/0bV/5j3xB/8kV+s1FH+r+N/wCg6p9yD/iMHDH/AES2E/8AAqn+Z+TP/D1/ 9ur/AKNq/wDMe+IP/kij/h6/+3V/0bV/5j3xB/8AJFfrNRR/q/jf+g6p9yD/AIjBwx/0S2E/ 8Cqf5n5M/wDD1/8Abq/6Nq/8x74g/wDkij/h6/8At1f9G1f+Y98Qf/JFfrNRR/q/jf8AoOqf cg/4jBwx/wBEthP/AAKp/mfkz/w9f/bq/wCjav8AzHviD/5Io/4ev/t1f9G1f+Y98Qf/ACRX 6zUUf6v43/oOqfcg/wCIwcMf9EthP/Aqn+Z+TP8Aw9f/AG6v+jav/Me+IP8A5Io/4ev/ALdX /RtX/mPfEH/yRX6zUUf6v43/AKDqn3IP+IwcMf8ARLYT/wACqf5n5M/8PX/26v8Ao2r/AMx7 4g/+SKP+Hr/7dX/RtX/mPfEH/wAkV+s1FH+r+N/6Dqn3IP8AiMHDH/RLYT/wKp/mfkz/AMPX /wBur/o2r/zHviD/AOSKP+Hr/wC3V/0bV/5j3xB/8kV+s1FH+r+N/wCg6p9yD/iMHDH/AES2 E/8AAqn+Z+TP/D1/9ur/AKNq/wDMe+IP/kij/h6/+3V/0bV/5j3xB/8AJFfrNRR/q/jf+g6p 9yD/AIjBwx/0S2E/8Cqf5n5M/wDD1/8Abq/6Nq/8x74g/wDkij/h6/8At1f9G1f+Y98Qf/JF frNRR/q/jf8AoOqfcg/4jBwx/wBEthP/AAKp/mfJn/BLv9rD46/tP/8ACc/8Lq+Gv/CvP7D+ wf2N/wAU9qGkf2h5v2nz/wDj7kfzNnlQ/cxt8znO4Y+s6KK+hwOHnQoRpVKjm1f3nu9b/hsf kHFOcYXNM0q4/BYWGFpz5bUoXcY2iou19feacnfq2FFFFdR8+FFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFc/wCI/iz4W8Hf 8JB/a/ibw/pX/CJ6Uuu639s1GGD+xtOb7Rtvbncw8m3b7JdYlfCH7NNz+7bHAeAPiL8cde+O 2t6V4g+FXgDw/wDDnTNVntLPxFH8Qbi91XV7MW6y295Fpq6WsabpHWGSKW7Ro3inKmaNYnnA PX6K5/xH8WfC3g7/AISD+1/E3h/Sv+ET0pdd1v7ZqMMH9jac32jbe3O5h5Nu32S6xK+EP2ab n922PMPDX7cNv4p/aHl+H0Xwr+OFrGPEFz4eg8U3ng2e28O3T2thJeXN0LlyHjs0dYrVLiWO OO6uLhBam5jjnlhAPb6K5/xH8WfC3g7/AISD+1/E3h/Sv+ET0pdd1v7ZqMMH9jac32jbe3O5 h5Nu32S6xK+EP2abn922OQ8NftR2/ib4yy+Dx4G+KFjGt3c2cWv3nhmeHRrh4RJhhMfnSOR7 a/RJJESMmzRiyx6hpUl+Aen0Vz/iP4s+FvB3/CQf2v4m8P6V/wAInpS67rf2zUYYP7G05vtG 29udzDybdvsl1iV8Ifs03P7tseAfCT9sL44+NP24dT+GHif9nX/hC/BNn/a99beNZfFVxfw3 +mWtxFBY3KLDpps1uL5pd62L3y3MMVvcySooEH2gA+n6K5/xH8WfC3g7/hIP7X8TeH9K/wCE T0pdd1v7ZqMMH9jac32jbe3O5h5Nu32S6xK+EP2abn922OA+Gn7ZGnfE34p3PhOLwD8YNFur HVdS0a51HWfBV9Y6VHPazTpC6Xbp5U1veQ209xBcwNJAEEMc8lvcXNrBMAev0Vz958WfC2nf 2l9o8TeH4P7G1W00K/8AM1GFPsOo3f2b7LZS5b93cTfbLPy4mw8n2uDaD5iZ4D9mr9rz/hpH x3438Pt8MPjB8P5/An2CO4vPGXhz+zLDVp7iORpYdPuBI8d59lkieKaWEtAWKPDLPDJHKwB6 /RXP3nxZ8Lad/aX2jxN4fg/sbVbTQr/zNRhT7DqN39m+y2UuW/d3E32yz8uJsPJ9rg2g+Yme A/Zo/aj8R/tEfZLjUfgZ8YPhfpOoaUmq2t/4y/sO38zf5ZS2e1tdSuLyC4KyFjHPBHs8p1co 4CMAev0Vz958WfC2nf2l9o8TeH4P7G1W00K/8zUYU+w6jd/ZvstlLlv3dxN9ss/LibDyfa4N oPmJnkPgT+1Hb/Hj4heOPDsHgb4oeFZfAl2bO4v/ABP4Zn0nTtXf7bfWobT55PkvIylklz5k O6Pyb+0JYSNJFEAen0Vz958WfC2nf2l9o8TeH4P7G1W00K/8zUYU+w6jd/ZvstlLlv3dxN9s s/LibDyfa4NoPmJngPgF+15/wvf4p+NfC0vww+MHgb/hENVvtKttX8VeHPsOleJ/sc0cU1zY TpJJut286B4pJxD9pSRzAJfs90IAD1+iufvPiz4W07+0vtHibw/B/Y2q2mhX/majCn2HUbv7 N9lspct+7uJvtln5cTYeT7XBtB8xM/MH/BODw/8Atk6d8bPi1q/7SWp+ALrwF4k1W6m8D6Jp GpxXF/4Pgh1C6SK3LQ6bbi8t57V4XS4mm89VgiDwJJNMsIB9f0VwHxM8eaF45+FmqaZo2veH 9T1DxV/avhjRoI/Fkmi/2pq0EN2s9hFf2m+5triJrS7Ej2yvcW32Wdwm6BgPH/8AgmB8DPiF 8F/hY/8AwsTw/wCINC1fU9K0rf8A278bdZ+JWqzzpDJ5/wBr+2W0NnZXAZx5n9nboJnZsYSK LIB9P0VwHxM+LP8AaPws1T/hXvibwBceN9Z/tXQvB/8AbOo79KvvENpDd5spvIbzZPJms7j7 RFDmaNLS54BjbHh//BLfwt+1rovhXULz9qPxB4P1KW88P6Gmj6fpc9vcanZX4S6l1aTUJLay tbXzHlnt4o47bzIY4bRF3zSCW7ugD6vorgPiZ8Wf7R+Fmqf8K98TeALjxvrP9q6F4P8A7Z1H fpV94htIbvNlN5DebJ5M1ncfaIoczRpaXPAMbY8v/wCCdkH7S48K6tdftEp4PtW1G00+40Sx 0/Vo9T1fTH2SrcQX8tvY2loZBEtkXNuJY3u21CSN4raS1tLYA+j6K8w+PHiPS/i58AvE/hzw 1400+z1vxxaa14Q8P3mmeKU0i7fWY7a9jlt7W+jjneC8t3tbpmaOCaS3NnM5hcwslfP/APwR o/Zi+Ov7OXw98aT/AB0v9QvdS8V3en32h2+o/FHWPG2o6JZJZJEdPvDeILWO8SVZJp7ixbyb ma6lCRwwW9tGAD7PorwD9tL4s+O/Fn7J15dfsz+JvAGtfF3WdKtvF3ga0v8AUbeew8Xada3l hPdrEd2Jbe4tZ0txOjoitqFufPg3pMvQfsVaf8cdM+FmoRfH268AXnjBtVkltH8I3Vxc2a2b wwuUZpra2I2XTXaRJ5bMlqtqks1zOs1zKAev0V4h+1Z8YfE3iL9mDULz4Aaz4P8AFHxS1vw/ /wAJX4C099dtIrTxZb20tpK7RyMkqTWcqXEETunlhhexKLuzaaO7iP8Agnj4W+PXgb9mDStF /aR8QeD/ABf8UtNu7pLvxB4bnLWms27ytJBI0QsrNLeREfyDGkbAiBZC5aRlQA9vorwD9tL4 keL/AIhfsnXg/Z48X+H7z4m+I9KtvFPg9LHWtNjm8TaTb3lhLfHT5rqC6tf39pOsEd1JBLbx y39qzlVYNXQfsVeDviz4I+FmoWnxj13/AISDxPJqsktrcf27Z6xsszDCETzbXRNGjXEizHYb V2G7JmYMI4gDr/j98d/DP7MXwa8Q/EDxndahY+FPClob/Vruz0q71SSzt1IDzGC1ilmMcYO9 3VCI41d3KojMD4KfHHRfj74VuNZ0Ky8YWNpbXbWbx+JPCeq+GrsuqI5ZbfUbe3mePEigSKhj JDKGLIwHxh/wST+N/wAZfiF8U5dL+KvxV/4T3y/CpuYZkuFg0rxNOs1qsmq6NG3hDSPtmn/P kXNrqV9BGl7ahvN+0wT19/0AZ/izxZpfgLwrqeu67qen6Lomi2kt/qGoX9wltaWFvEheWaWV yEjjRFZmdiAoBJIArP8Ah18WfC3xg0eTUfCXibw/4p0+LyN91pGow30KefawXkOXiZgPMtbm 2nTn5oriJxlXUn4A/wCCcmreO/2tNY1PwF+0X4v8P/G3wzqHhVLmbTtYtre58N+Mp4bqyc6r pVpceDtKjv8AT1k2SLc22oX8EIurMnzDPbzj7/8AAHwn8LfCj+2/+EW8NeH/AA1/wkuqz67q /wDZWnQ2f9q6jPt8+9uPLVfNuJNq75Xy7bRknAoA6CiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooA/LH/AILD/AP4R6N+0/4n1/VrT4IWfiv4o+CrRdQ8Q+JP BkWu6n4ATQ4tY1GXWlt30XU7a+kutNt7uMWt29hJcw+HGht7h3TfZ/qdX5g/8FSdT1K6/an8 NrD4f/aA8dXXwnzrsWq2HwnsdY/s+e5vHvUj8O3UngzVIptQghS2hzJe6fAPs+nrJdSXAvZ4 f0e+Fvh3XvCXgSw0zxL4j/4S7V7HzIn1hrCOxmv4hI3kvPFEfK+0eT5YleJYopJRI8cNujLB GAfmj/wWH+Afwj0b9p/xPr+rWnwQs/FfxR8FWi6h4h8SeDItd1PwAmhxaxqMutLbvoup219J dabb3cYtbt7CS5h8ONDb3Dum+z/U6vzB/wCCpOp6ldftT+G1h8P/ALQHjq6+E+ddi1Ww+E9j rH9nz3N496kfh26k8GapFNqEEKW0OZL3T4B9n09ZLqS4F7PD+j3wt8O694S8CWGmeJfEf/CX avY+ZE+sNYR2M1/EJG8l54oj5X2jyfLErxLFFJKJHjht0ZYIwD80f+Cw/wAA/hHo37T/AIn1 /VrT4IWfiv4o+CrRdQ8Q+JPBkWu6n4ATQ4tY1GXWlt30XU7a+kutNt7uMWt29hJcw+HGht7h 3TfZ/qdX5g/8FSdT1K6/an8NrD4f/aA8dXXwnzrsWq2HwnsdY/s+e5vHvUj8O3UngzVIptQg hS2hzJe6fAPs+nrJdSXAvZ4f0e+Fvh3XvCXgSw0zxL4j/wCEu1ex8yJ9YawjsZr+ISN5LzxR HyvtHk+WJXiWKKSUSPHDboywRgH5o/8ABYf4B/CPRv2n/E+v6tafBCz8V/FHwVaLqHiHxJ4M i13U/ACaHFrGoy60tu+i6nbX0l1ptvdxi1u3sJLmHw40NvcO6b7P9Tq/MH/gqTqepXX7U/ht YfD/AO0B46uvhPnXYtVsPhPY6x/Z89zePepH4dupPBmqRTahBCltDmS90+AfZ9PWS6kuBezw /o98LfDuveEvAlhpniXxH/wl2r2PmRPrDWEdjNfxCRvJeeKI+V9o8nyxK8SxRSSiR44bdGWC MA/NH/gsP8A/hHo37T/ifX9WtPghZ+K/ij4KtF1DxD4k8GRa7qfgBNDi1jUZdaW3fRdTtr6S 6023u4xa3b2ElzD4caG3uHdN9n+p1fmD/wAFSdT1K6/an8NrD4f/AGgPHV18J867Fqth8J7H WP7Pnubx71I/Dt1J4M1SKbUIIUtocyXunwD7Pp6yXUlwL2eH9Hvhb4d17wl4EsNM8S+I/wDh LtXsfMifWGsI7Ga/iEjeS88UR8r7R5PliV4liiklEjxw26MsEYB+aPxt+Afwj+GX/BUa+g06 0+CGjeIvEHxK0D4lah4nfwZFqOv6LM02gaVLoEavot1bmS8v7zTbt7iLU7G9tZPEcV40RiXf efqdX5Y/GSDWvF//AAUu0C+srb9pCfUvCt3p/gGL4g/8Kk0oazbu0a2dxf6feXHgia1OlmW6 nlubiTV7SPD6k9vbNB9lS5/T7wnbarZ+FdMh1290/UtbitIk1C7sLJ7K0urgIBLJFA8szwxs +4rG0shUEAu5G4gH5g/G34B/CP4Zf8FRr6DTrT4IaN4i8QfErQPiVqHid/BkWo6/oszTaBpU ugRq+i3VuZLy/vNNu3uItTsb21k8RxXjRGJd95+p1flj8ZINa8X/APBS7QL6ytv2kJ9S8K3e n+AYviD/AMKk0oazbu0a2dxf6feXHgia1OlmW6nlubiTV7SPD6k9vbNB9lS5/T7wnbarZ+Fd Mh1290/UtbitIk1C7sLJ7K0urgIBLJFA8szwxs+4rG0shUEAu5G4gH5g/G34B/CP4Zf8FRr6 DTrT4IaN4i8QfErQPiVqHid/BkWo6/oszTaBpUugRq+i3VuZLy/vNNu3uItTsb21k8RxXjRG Jd95+p1flj8ZINa8X/8ABS7QL6ytv2kJ9S8K3en+AYviD/wqTShrNu7RrZ3F/p95ceCJrU6W ZbqeW5uJNXtI8PqT29s0H2VLn9PvCdtqtn4V0yHXb3T9S1uK0iTULuwsnsrS6uAgEskUDyzP DGz7isbSyFQQC7kbiAfmD8bfgH8I/hl/wVGvoNOtPgho3iLxB8StA+JWoeJ38GRajr+izNNo GlS6BGr6LdW5kvL+8027e4i1OxvbWTxHFeNEYl33n6nV+WPxkg1rxf8A8FLtAvrK2/aQn1Lw rd6f4Bi+IP8AwqTShrNu7RrZ3F/p95ceCJrU6WZbqeW5uJNXtI8PqT29s0H2VLn9PvCdtqtn 4V0yHXb3T9S1uK0iTULuwsnsrS6uAgEskUDyzPDGz7isbSyFQQC7kbiAfmD8bfgH8I/hl/wV GvoNOtPgho3iLxB8StA+JWoeJ38GRajr+izNNoGlS6BGr6LdW5kvL+8027e4i1OxvbWTxHFe NEYl33n6nV+WPxkg1rxf/wAFLtAvrK2/aQn1Lwrd6f4Bi+IP/CpNKGs27tGtncX+n3lx4Imt TpZlup5bm4k1e0jw+pPb2zQfZUuf0u/4R3XtX+Fn9k6j4j+xeJ7rSvsl1r2hWEdr9nvGh2Pe WltdG6jj2yEyRxTm4VcKrmUA7gD80f2QfgH8I/gV/wAFLtC8LeCbT4IeHZfA3jXxG0eraD4M ibWfFb63H4ivLfRRK+ihLKPTEsNWtBdWGsSRgaFPZz28ck4gtP1Or8sfg3deJvi1/wAFLtf8 YeENM+OHgHVfHt3qFhYeLNd+DdpZyeVaxtNBDrbXHhGxm/sOQWEUMSJ4ie/kjbT0YW8zyvaf p94s0a58R+FdT06z1bUNBvL+0lt4NTsEge7053Qqs8SzxywmSMkMoljkjJUbkZcqQD8wf2Qf gH8I/gV/wUu0Lwt4JtPgh4dl8DeNfEbR6toPgyJtZ8VvrcfiK8t9FEr6KEso9MSw1a0F1Yax JGBoU9nPbxyTiC0/U6vyx+Dd14m+LX/BS7X/ABh4Q0z44eAdV8e3eoWFh4s134N2lnJ5VrG0 0EOttceEbGb+w5BYRQxIniJ7+SNtPRhbzPK9p+n3izRrnxH4V1PTrPVtQ0G8v7SW3g1OwSB7 vTndCqzxLPHLCZIyQyiWOSMlRuRlypAPzB/ZB+Afwj+BX/BS7QvC3gm0+CHh2XwN418RtHq2 g+DIm1nxW+tx+Iry30USvooSyj0xLDVrQXVhrEkYGhT2c9vHJOILT9Tq/LH4N3Xib4tf8FLt f8YeENM+OHgHVfHt3qFhYeLNd+DdpZyeVaxtNBDrbXHhGxm/sOQWEUMSJ4ie/kjbT0YW8zyv afp94s0a58R+FdT06z1bUNBvL+0lt4NTsEge7053Qqs8SzxywmSMkMoljkjJUbkZcqQD8wf2 QfgH8I/gV/wUu0Lwt4JtPgh4dl8DeNfEbR6toPgyJtZ8VvrcfiK8t9FEr6KEso9MSw1a0F1Y axJGBoU9nPbxyTiC0/T7xZ4T0vx74V1PQtd0zT9a0TWrSWw1DT7+3S5tL+3lQpLDLE4KSRuj MrIwIYEggg1+YPwbuvE3xa/4KXa/4w8IaZ8cPAOq+PbvULCw8Wa78G7Szk8q1jaaCHW2uPCN jN/YcgsIoYkTxE9/JG2nowt5nle0/S74pXv9k+BL/Um1LxBpcOi+XqtxJomm/wBpX9xBbSLc S20dsIJ5JvPjjeExwxNOyysISk3luoB+cP8AwRf+F/w4+GP7R2naT4JuPhBZ/wDCHfD+58CJ c+E/CqxX/wASW05tBnm8TXN9Lo1pPB51vqOlT+VFqGpWd6uqwTQyt9laef8AS7xZ4T0vx74V 1PQtd0zT9a0TWrSWw1DT7+3S5tL+3lQpLDLE4KSRujMrIwIYEggg1+aP/BLbQ/F+qftT+JNU 0qw+MHw8n8XY8WavceKPhVpuk23ieJLxBPa+IJY/CmhyDWGjv5pIPsOpahEk6XcjyzQxCO9/ R74pXv8AZPgS/wBSbUvEGlw6L5eq3Emiab/aV/cQW0i3EttHbCCeSbz443hMcMTTssrCEpN5 bqAfnD/wRf8Ahf8ADj4Y/tHadpPgm4+EFn/wh3w/ufAiXPhPwqsV/wDEltObQZ5vE1zfS6Na Twedb6jpU/lRahqVnerqsE0MrfZWnn/S7xZ4T0vx74V1PQtd0zT9a0TWrSWw1DT7+3S5tL+3 lQpLDLE4KSRujMrIwIYEggg1+aP/AAS20Pxfqn7U/iTVNKsPjB8PJ/F2PFmr3Hij4VabpNt4 niS8QT2viCWPwpocg1ho7+aSD7DqWoRJOl3I8s0MQjvf0e+KV7/ZPgS/1JtS8QaXDovl6rcS aJpv9pX9xBbSLcS20dsIJ5JvPjjeExwxNOyysISk3luoB+cP/BF/4X/Dj4Y/tHadpPgm4+EF n/wh3w/ufAiXPhPwqsV/8SW05tBnm8TXN9Lo1pPB51vqOlT+VFqGpWd6uqwTQyt9laef9Dvj vquq6J8M7q50KXUE1uO7shp8VrE7re3Bu4Vitbh0s7x4LOdysNzcrbuba3lnmBjMXmp+eP8A wS20Pxfqn7U/iTVNKsPjB8PJ/F2PFmr3Hij4VabpNt4niS8QT2viCWPwpocg1ho7+aSD7DqW oRJOl3I8s0MQjvf0u8WeE9L8e+FdT0LXdM0/WtE1q0lsNQ0+/t0ubS/t5UKSwyxOCkkbozKy MCGBIIINAH5g/wDBCjTPi4nx9vfEHjrRNQ8LXnjfwU+sePYL/wCG0vh271zxK1zZSJcy3K+D 9GhMkRn1QNC2paizG7LI0ixyTt+j3x31XVdE+Gd1c6FLqCa3Hd2Q0+K1id1vbg3cKxWtw6Wd 48FnO5WG5uVt3NtbyzzAxmLzU/OH/ghR8DNF8D/H298QQfALUPhBeXHgp7fT4L/QtV0+78N2 stzZSSaPLcyeEdHh1GQGO3DT3epandZsi0ckizXc7/p94s8J6X498K6noWu6Zp+taJrVpLYa hp9/bpc2l/byoUlhlicFJI3RmVkYEMCQQQaAPzB/4IUaZ8XE+Pt74g8daJqHha88b+Cn1jx7 Bf8Aw2l8O3eueJWubKRLmW5Xwfo0JkiM+qBoW1LUWY3ZZGkWOSdv1Or8sf8AghR8DNF8D/H2 98QQfALUPhBeXHgp7fT4L/QtV0+78N2stzZSSaPLcyeEdHh1GQGO3DT3epandZsi0ckizXc7 /qdQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFeYfthfHzVf2 a/ggfEuheHtP8U63d+INB8N6fpt/qr6VaTXGrazZaVE8tykFw8Ucb3qyMVhkJEZAXJyAD88P +Cvnwu8HftZ/Hb4gaf4v8C+IJLrwFpSaPfS2mkeGJLzVPD0lvYyQzaXeaj4fvr8axf6jrV3p 2l2MNzbw3d1o9/5V1b3EUin9Xq+UPiWP2vPHHjT4eapp/gL4IaDaeDfEEusanY2/xj1ox+JL d9K1CyWym2+G1HlrPeQXQ3LIPMso/lDbZET4+/tZ/tJfs6fCXVvGet/BP4IXWmaN5PnRWPxg 1SS4bzZo4V2h/DSKcNIpOWHAPU8EA+Uf+Cvnwu8HftZ/Hb4gaf4v8C+IJLrwFpSaPfS2mkeG JLzVPD0lvYyQzaXeaj4fvr8axf6jrV3p2l2MNzbw3d1o9/5V1b3EUin9Xq/I34l/8FsvjP44 8afDzVNP+EXhHQbTwb4gl1jU7G3+Kt8Y/Elu+lahZLZTbdFUeWs95BdDcsg8yyj+UNtkTsP+ H/XxS/6N78A/+HWu/wD5Q0AYf/BXz4XeDv2s/jt8QNP8X+BfEEl14C0pNHvpbTSPDEl5qnh6 S3sZIZtLvNR8P31+NYv9R1q707S7GG5t4bu60e/8q6t7iKRT+r1fkb8S/wDgtl8Z/HHjT4ea pp/wi8I6DaeDfEEusanY2/xVvjH4kt30rULJbKbboqjy1nvILoblkHmWUfyhtsidh/w/6+KX /RvfgH/w613/APKGgDD/AOCvnwu8HftZ/Hb4gaf4v8C+IJLrwFpSaPfS2mkeGJLzVPD0lvYy QzaXeaj4fvr8axf6jrV3p2l2MNzbw3d1o9/5V1b3EUin9Xq/I34l/wDBbL4z+OPGnw81TT/h F4R0G08G+IJdY1Oxt/irfGPxJbvpWoWS2U23RVHlrPeQXQ3LIPMso/lDbZE7D/h/18Uv+je/ AP8A4da7/wDlDQBh/wDBXz4XeDv2s/jt8QNP8X+BfEEl14C0pNHvpbTSPDEl5qnh6S3sZIZt LvNR8P31+NYv9R1q707S7GG5t4bu60e/8q6t7iKRT+r1fkb8S/8Agtl8Z/HHjT4eapp/wi8I 6DaeDfEEusanY2/xVvjH4kt30rULJbKbboqjy1nvILoblkHmWUfyhtsidh/w/wCvil/0b34B /wDDrXf/AMoaAMP4t/C7wd+0h/wUS0zx/rfgXxBpuu6B8VdI03V47fSPDEWqDUbHW5bfRlsL l/D7ave77HRrfW9QhfU7d7TRb6KeJ7m2dUH6vV+Rutf8FsvjPqnx98M+L4vhF4RtNE0Pw/q2 j3fh1PirffZNUuLy50yaC9kxooTzLZLG4jTMbHF/LhkG4Sdh/wAP+vil/wBG9+Af/DrXf/yh oAw/i38LvB37SH/BRLTPH+t+BfEGm67oHxV0jTdXjt9I8MRaoNRsdblt9GWwuX8Ptq97vsdG t9b1CF9Tt3tNFvop4nubZ1Qfq9X5G61/wWy+M+qfH3wz4vi+EXhG00TQ/D+raPd+HU+Kt99k 1S4vLnTJoL2TGihPMtksbiNMxscX8uGQbhJ2H/D/AK+KX/RvfgH/AMOtd/8AyhoAw/i38LvB 37SH/BRLTPH+t+BfEGm67oHxV0jTdXjt9I8MRaoNRsdblt9GWwuX8Ptq97vsdGt9b1CF9Tt3 tNFvop4nubZ1Qfq9X5G61/wWy+M+qfH3wz4vi+EXhG00TQ/D+raPd+HU+Kt99k1S4vLnTJoL 2TGihPMtksbiNMxscX8uGQbhJ2H/AA/6+KX/AEb34B/8Otd//KGgDD+Lfwu8HftIf8FEtM8f 634F8QabrugfFXSNN1eO30jwxFqg1Gx1uW30ZbC5fw+2r3u+x0a31vUIX1O3e00W+inie5tn VB+r1fkbrX/BbL4z6p8ffDPi+L4ReEbTRND8P6to934dT4q332TVLi8udMmgvZMaKE8y2Sxu I0zGxxfy4ZBuEnYf8P8Ar4pf9G9+Af8Aw613/wDKGgDD+Lfwu8HftIf8FEtM8f634F8Qabru gfFXSNN1eO30jwxFqg1Gx1uW30ZbC5fw+2r3u+x0a31vUIX1O3e00W+inie5tnVB+r1fkbrX /BbL4z6p8ffDPi+L4ReEbTRND8P6to934dT4q332TVLi8udMmgvZMaKE8y2SxuI0zGxxfy4Z BuEnYf8AD/r4pf8ARvfgH/w613/8oaAMP9iz4XeDviv/AMFDvhj8aH8C+IPDfiDxLquq6xJa JpHhiz1DSvEM+gzT6zDcmz8P2uo3ej6c+qyaVcX1zqJmXW1itrm1MoE1fq9X5G6L/wAFsvjP pfx98TeL5vhF4Ru9E1zw/pOj2nh1/irffZNLuLO51Oae9jzopTzLlL63jfEanFhFlnG0R9h/ w/6+KX/RvfgH/wAOtd//AChoAw/2LPhd4O+K/wDwUO+GPxofwL4g8N+IPEuq6rrElomkeGLP UNK8Qz6DNPrMNybPw/a6jd6Ppz6rJpVxfXOomZdbWK2ubUygTV+r1fkbov8AwWy+M+l/H3xN 4vm+EXhG70TXPD+k6PaeHX+Kt99k0u4s7nU5p72POilPMuUvreN8RqcWEWWcbRH2H/D/AK+K X/RvfgH/AMOtd/8AyhoAw/2LPhd4O+K//BQ74Y/Gh/AviDw34g8S6rqusSWiaR4Ys9Q0rxDP oM0+sw3Js/D9rqN3o+nPqsmlXF9c6iZl1tYra5tTKBNX6vV+Rui/8FsvjPpfx98TeL5vhF4R u9E1zw/pOj2nh1/irffZNLuLO51Oae9jzopTzLlL63jfEanFhFlnG0R9h/w/6+KX/RvfgH/w 613/APKGgDD/AGLPhd4O+K//AAUO+GPxofwL4g8N+IPEuq6rrElomkeGLPUNK8Qz6DNPrMNy bPw/a6jd6Ppz6rJpVxfXOomZdbWK2ubUygTV+l37QvxH1X4OfALxx4v0LwxqHjbW/Cvh+/1j T/DtgXF3r9xb20k0VlFsjkfzJnRY12xucuMKx4P5e6L/AMFsvjPpfx98TeL5vhF4Ru9E1zw/ pOj2nh1/irffZNLuLO51Oae9jzopTzLlL63jfEanFhFlnG0R9h/w/wCvil/0b34B/wDDrXf/ AMoaANP/AIJRfszfDr4Wft03/ibwZpmoSWmo+CtXh8N6tZaP4V0y3v8ARjrFnAb/AFC30TQ9 OEUeoT6f5+jzNc3kd7ZQX0yfZ2V4z9//ALQvxH1X4OfALxx4v0LwxqHjbW/Cvh+/1jT/AA7Y Fxd6/cW9tJNFZRbI5H8yZ0WNdsbnLjCseD+Xvw0/4LZfGfwP40+Ieqaj8IvCOv2njLxBFrGm WNx8Vb4R+G7dNK0+yayh3aKw8tp7Oe6O1Yx5l7J8pbdI/Yf8P+vil/0b34B/8Otd/wDyhoA0 /wDglF+zN8OvhZ+3Tf8AibwZpmoSWmo+CtXh8N6tZaP4V0y3v9GOsWcBv9Qt9E0PThFHqE+n +fo8zXN5He2UF9Mn2dleM/f/AO0L8R9V+DnwC8ceL9C8Mah421vwr4fv9Y0/w7YFxd6/cW9t JNFZRbI5H8yZ0WNdsbnLjCseD+Xvw0/4LZfGfwP40+Ieqaj8IvCOv2njLxBFrGmWNx8Vb4R+ G7dNK0+yayh3aKw8tp7Oe6O1Yx5l7J8pbdI/Yf8AD/r4pf8ARvfgH/w613/8oaANP/glF+zN 8OvhZ+3Tf+JvBmmahJaaj4K1eHw3q1lo/hXTLe/0Y6xZwG/1C30TQ9OEUeoT6f5+jzNc3kd7 ZQX0yfZ2V4z+h/xY8f8A/CqPhZ4m8U/2L4g8S/8ACNaVdar/AGRoVn9s1XVfIheX7NaQZXzb iTbsjjyNzsoyM5r8rfhp/wAFsvjP4H8afEPVNR+EXhHX7Txl4gi1jTLG4+Kt8I/DdumlafZN ZQ7tFYeW09nPdHasY8y9k+Utukf7C+AX7Wf7SX7Rfwl0nxnonwT+CFrpms+d5MV98YNUjuF8 qaSFtwTw06jLRsRhjwR0PAAPMP8Agltovwg8MftT+JJfhj4r8P8AxD1Dxd4VGs+INU8G+KtK 8RaV4evxeIZ7DVL2wsbS51C4kadHsdQ1qS8v7pLbVn3WzfamvPt/4seP/wDhVHws8TeKf7F8 QeJf+Ea0q61X+yNCs/tmq6r5ELy/ZrSDK+bcSbdkceRudlGRnNfOHw0H7Xngfxp8Q9U1HwF8 ENftPGXiCLWNMsbj4x60I/DdumlafZNZQ7vDbDy2ns57o7VjHmXsnylt0j+O/H3/AILG/Gf9 nT4tat4M1v4B/DG61TRvJ86Wx+LN9Jbt5sMcy7S/h9GOFkUHKjkHqOSAaX/BLbRfhB4Y/an8 SS/DHxX4f+IeoeLvCo1nxBqng3xVpXiLSvD1+LxDPYape2FjaXOoXEjTo9jqGtSXl/dJbas+ 62b7U159/wBfkb8NP+C2Xxn8D+NPiHqmo/CLwjr9p4y8QRaxpljcfFW+Efhu3TStPsmsod2i sPLaeznujtWMeZeyfKW3SP8AcX/BMz9vLVf2/fhf401vWvBVh4F1PwX4rfwxNZWWvPrUFzjT dPv1nWZ7W2YZW/CFDHwYydxzwAfSFFeQWf7d3wz1jxjpvh/StU8QeINX1HVbvRmg0Twrq2q/ 2XPbapc6TK9+1tbSLYW5vrK8iS5uzFBL9juHjkdIZHXQ0T9tb4R+J/iZ4T8HaV8R/B+reIvH VpdXnh+1sNTiul1ZLa0sb6VYpIy0bSfY9SsrpY93mSW84mRWiVnUA9PorgPBX7T3gnxf8LIf Fs3iLw/o2np4VsvGupJea5p8v9iaTdQyyxXdzNbzy2wtytvc4uI5Xt3+zTGOV1QtXn9h/wAF VP2cdY+FnxM8b6d8aPAGr+Evg/5P/CW6rpuqpe22l+dCksG1ot3n+dvEUXkeZ5s6yQJumjeN QD3+ivMPHP7a3wj+FcHiWfxf8R/B/gy08I+IIfCuqXfiTU4tGtINUl0631OO0Wa6MccsjWV1 DMBGzDBYZ3RuF8/0/wD4LA/suap8GrT4gQfHv4YSeFLq70qwe7GuwiSxuNTJFnDdQE+daSOE mdknSMwx21y8ojSCZkAPo+iuf8AfFnwt8V/7b/4RbxN4f8S/8I1qs+hav/ZWow3n9lajBt8+ yuPLZvKuI9y74nw67hkDIrgPA37fnwb+JPhzTtb0X4heH73w/quleIddtdZ8xo9Kl07Qb6Kw 1a9+1uogFvBcTxDzS4SRG82MyRAuAD1+iuA139rH4WeF/Alx4p1P4l+ANO8M2elafrs+r3Xi GzhsIdO1CR4rC9adpBGtvdSRyJDKTslZGCFiCKv+Jfj94N8KfBqL4hXHiHT7rwbdWlteWWp6 cx1GPVkujGtotmtuHe7kuXlhSCOASSTyTRJErvIikA7CivILz9vz4N2v9peV8QvD+p/2bpVp rI/syRtR/tCC6+zfZksvIV/t1xJ9u07bbWvmzn+1dM/d/wCn2nndB8KP2qvhx8d/Heu+GvBX jTw/4t1fw1pWk63qSaRdLeQ2tnqkc0unymaPMR8+GB5UVXLeU0UhASaJnAO/orxDwZ/wUi+B fizUfCujXHxb+GGi+MvF9pplxZeFbzxro8msl9RghntIBFb3UqTSSpcQlDA8scokRonkR0Zv T/AHxZ8LfFf+2/8AhFvE3h/xL/wjWqz6Fq/9lajDef2VqMG3z7K48tm8q4j3LvifDruGQMig DoKKKKACiiigAooooAK+UP8AgqL+z34B8R+GvAvxG1HwP4Pv/iFoHxK+HVhpnie40a2l1nTr dvG2kBoYbtkM0cZE84KK4BE0nHztn6vr5n/4K3+N7X4afsaweJL+O4msfD/xF8AalcJbqrSv HD4z0SRggYgFiFOASBnuOtAH0xXz7/wVN/5MP8d/9w//ANONtXn3/D7P4Vf9AD4g/wDgDZ// ACVXP/FP9trwr/wUX8B3/wAGvBOn+IdL8T+MfL+xXWuQQwWEX2aRbyTzHhllkGY7dwu2NssV BwMkAH5tUV9h/wDDkz4q/wDQwfD7/wADrz/5Frj/AI+/8EufiB+zn8JdW8Z63rHg+60zRvJ8 6Kxu7mS4bzZo4V2h4EU4aRScsOAep4IB82UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX2H+yx/wVh/4Zo+A+g+CP8AhAf7b/sT7R/p v9ufZvO824lm/wBX9nfbjzNv3jnbnjOB8eV9J/AL/glz8QP2jPhLpPjPRNY8H2umaz53kxX1 3cx3C+VNJC24JA6jLRsRhjwR0PAAPcf+H6//AFS3/wAuX/7lo/4YT/4eXf8AF7f+Eq/4Qr/h Nf8AmDf2b/aX2P7L/oX+v82Lfv8As2//AFa437ecbj59/wAOTPir/wBDB8Pv/A68/wDkWvYv hZ+214V/4J0eA7D4NeN9P8Q6r4n8HeZ9tutDghnsJftMjXkflvNLFIcR3CBt0a4YMBkYJAOe /wCHFH/VU/8Ay2v/ALqqT/gmr/wT88LfBf4m/tIXVnpHgDWPi74L+IB03w34/wBX8IQ3N/pX 2jwZoEiKCJVufs6teSh4Y7qLzFklAdPMJHe/8Ps/hV/0APiD/wCANn/8lVp/8EuvjbpX7Rfj D9pvxnolvqNrpms/FW38mK+jSO4XyvB3hiFtwRnUZaNiMMeCOh4AAuq/8Eyrm/8A2n5fHEXj nT7Xw6PGqeM9L0iPwrAup+D38qwe9t9F1NZVksY9WvbS4l1VTHLHfQ300Yjt5ZLi6uPD/g7/ AMEQde/Z70fwdqeq/Ej/AIWD/wAK7+z3cuj6B4Rj0i/8SRaXa+C/7Ls7aW71N4Le4Nx4G04z SzMYplvblALT5J4/uDxF+1j8LPCGseHNO1b4l+ANL1DxhqtzoWg2t34hs4Jtb1G2uhZ3Nlao 0gM9xFdEQSRRhnSUhGAbiuvufFml2fiqy0KbU9Pi1vUrSe/tNPe4Rbu6t4HhSeaOIne8cb3N uruAQpniBILrkA+AP2fP+CFsnwt8K+HL6+8Z6fH4y0/wV4TspZRDqOo2mmeI9CTwyyT2yG8g tX0uaXwppZnhNlFfThGAv4U8qKH2DSv+CdvjPVP2dP2i/AXi/wCLGn+KLz9oTw/FY3mvJ4RF hd2OqP4VsvD99qEkUd2YZo5jYw3SW8aQCEvLH5kilGT6f8J+Jrfxp4V0zWbOPUIbTVrSK8gj v7Cewu0SRA6rLbzok0EgBAaOVFkQ5VlVgQM/U/iz4W0TR/FOo3vibw/aaf4G3/8ACSXU2owx w+H9lrHeP9scti322s0U58wriKVHPysCQDxDxt+wfqutft52Pxj0Hxrp/hbTXtLBNV0uz0Bz qd7cW5uEuJEvRdLbiO/tjp1rdx3FlcGSHRtPaN4J7OzuLX5f+GP/AAQd+JfgSfQ7rUP2jtP8 QXngjw/4f0HwjDd+DNTfTNETR9R8KXsA+xPrrwiOU+FYzMtqLaSWbUbiRpSqQRR/o/o3izS/ Eeo6tZ6fqen395oF2thqcFvcJLJp1w0EVwsMyqSY5DBPBKEbBMc0bY2upOfB8WfC1z4E0jxT H4m8PyeGfEH2H+y9XXUYTYal9ukiisvInDeXL9oknhSLYT5rTRhNxZQQDxD4bfCb40fsl/Br RPC3gnTvhh8SmW7u5p4NR1y+8D6N4YtyY/s1hpFqlnrEy2ajzCIZrlhbk+XBsthBa2vH/Fv/ AIJS/wDCxvh14H02w8e/2H4g8BfFXW/ihZ6umieZ9u+265f6/b6Ncotwjyaempy6TNcRrKv2 n+x4iPs8pimt/r+uf0L4s+FvFGsW+nab4m8P6jqF5/aHkWtrqMM0039n3SWd/tRWJb7LdSRw TYH7qV1R9rECgD5Qk/4JF3PiD4hWPjPxJ8U9Q8QeMhd+G9Y1XUW0GC2j1XVLG98I3Gp3Jhic JFHep4L0pYoUwLSS4vnLXKSQw2+hpf7HfxU0j9nH4c/ACO28AN4C+GOleFYdL8fN4gvDq9xq Ph5bC8srifQBYeS1u+pafCstumrI7WzSFZ45Cu36/rP8WeLNL8BeFdT13XdT0/RdE0W0lv8A UNQv7hLa0sLeJC8s0srkJHGiKzM7EBQCSQBQB8YeHf8AgiH4W+Hn2X/hGfHHiC3/AOEa0rRI fDH9p2kN59j1HS/+EV8q4vfL8n7Vbv8A8IVoG63i+zP82p4nH2m3+xewfs+/sMf8Mq6xaSeC vFO7T9M+H/gn4cWcOt6b9tmXTvD11qTSSySQywB7i6tdSeIMqIkMsSylJlJgr1/xH8WfC3g7 /hIP7X8TeH9K/wCET0pdd1v7ZqMMH9jac32jbe3O5h5Nu32S6xK+EP2abn922NC28WaXeeKr 3QotT0+XW9NtIL+709LhGu7W3neZIJpIgd6RyPbXCo5ADGCUAko2AD4Q+Ef/AAQx/wCFWf8A CE/8XR+3/wDCHf8ACP8A/Mt+V9s/sr/hXv8A09Ns83/hA/8Aa2f2r/F9m/f+of8ABIb9lPxN +yb8DPFul+ItP1DRrTWPEFo/h3TNV+yf2zp2jad4f0bQrKPUTZ3NzaNeGLSA7yW07RyiRZAl s0jWkH0/4T8TW/jTwrpms2ceoQ2mrWkV5BHf2E9hdokiB1WW3nRJoJACA0cqLIhyrKrAgaFA BRRRQAUUUUAFFFFABXwf/wAF+fgfouu/sYan47nvfGCa3ouv+DrK3trfxZqtto0iHxXpoLTa XHcLYXEmJnxJNA8gIjIYGKMr94V8f/8ABeD/AJRl+Kv+xq8G/wDqWaRQB+W9fQX/AASy/wCT 8PAn/cQ/9N1zXz7XsX7AvxT0H4KftaeE/E/ie/8A7M0PTPtn2m58iSby/Msp4k+SNWc5d1HA OM56ZNAH7TV8+/8ABU3/AJMP8d/9w/8A9ONtR/w9N+A//Q9/+UXUf/jFefftT/tT+A/21fgP r3wy+GWvf8JN448TfZ/7N037FcWX2nyLiK5l/e3EccS7YYZG+ZxnbgZJAIB+XVFfQX/DrL48 f9CL/wCVrTv/AI/XH/G39i/4l/s6eFbfW/GXhv8AsfTLq7Wxim/tC1uN0zI7hNsUrsMrG5yR jjrkjIB5bRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAV+kv7Av7fXwl+Cn7JfhPwx4n8Wf2ZrmmfbPtNt/Zd7N5fmXs8qfPHCyHKOp4JxnHXI r82q9i+Fn7Avxa+NfgOw8T+GfCf9p6JqfmfZrn+1LKHzPLkaJ/kkmVxh0YcgZxnpg0AfpJ/w 9N+A/wD0Pf8A5RdR/wDjFfHn7U/7LHjz9tX48a98TfhloX/CTeCPE32f+zdS+229l9p8i3it pf3VxJHKu2aGRfmQZ25GQQT59/w6y+PH/Qi/+VrTv/j9fYf7LH7U/gP9ir4D6D8Mvibr3/CN eOPDP2j+0tN+xXF79m8+4luYv3tvHJE26GaNvlc43YOCCAAfHn/DrL48f9CL/wCVrTv/AI/X s3/BIL9kTWfhNqP7QWt6jY+IIvil4N+IElrYaFN4/wBU0/w3dSyeENCkgS8tbSWawfeLkZuH tLiWMFGAZoY1X6e/4em/Af8A6Hv/AMouo/8AxisH/gnH8U9B+NfxO/ai8T+Gb/8AtPQ9T+Kt r9mufIkh8zy/BnheJ/kkVXGHRhyBnGemDQByGjfsCfEvwH+1xq3iXwrqng/w94OvvGq67pt/ Y6tqdtrOl6XNLFqWrafe2gU2euR6hqd3r/kpeMp0capNc2skjyJbW3zh+z1/wSC+MH7Pd18L /F3jS+8AWH/CpvJur++8KXmq69q9lZ6do/gOzL6bAmnRT3FxqNv4P1Kxnt49jLba66Kbz57e b9XqKAPzB/Zz/wCCRPxl8M/CzSV17xNu+3/CrwXoN94T1PxGq6VHPpEPh/7Z4XvbeCyn+0af df2XqcLXf22aCBPEGprHpk32m5lu+/8Agf8A8E3PiP8As9/BP42WfhP4c/sweFfG/wASNV8I +IdNfw9btp+kWV5ZafpEWoRxQyaXMtp9kvrPUL7TJpI71Vub1JZrcFZVm+7/AAn4s0vx74V0 zXdC1PT9a0TWrSK/0/ULC4S5tL+3lQPFNFKhKSRujKyupIYEEEg1oUAfAD/8Ekbyf4u+B9OX wF8ID8ErTSvD1xrPhHXtWuvFlhpmrWOmtpN6IrG+sCmoeZplpolpbXz3FncWrafJMI5Eu9Qt L75g+FH/AAb7fGXw74c0/S/EfhL9mDUPD/hzSvDCW3hi2uVj0rxJqOkX3hqSa71F08OxzyXF 1b2HiRWurpr1408Qy28caRPdPcfs9RQB8ofs8eFfHH7BnwaGlR/A/UPGuq+LPEGpa5f2/wAO 9T0a5k00yGEI2qapq9xpM2sXkoB/057f7Q8cSJcvPNG17d+H3f8AwSk+KniX4J+D5Dpfwg8F /Eez8Vm48bWeia3eXHhjxr4X/s/Tln8Nx2502GPT9Pu5NA8P6aYFtpzDptgxklv5JruK7/R+ igD88PEP/BIrx/8AE+eF/Fmo/DBtE1W08Fw654M060uU8Ltb6bqPhS4vNFt7WVGQaXZpoGrv YRSKQ7+K76No7UCaa87DRPgd8TNH/Y2+EX7OP/CtvEEur/CvSvBE3/Cdf2npK+DdU1Hw22ma n9n3fbP7Wit7m5037F9o/sx3iM3m+RIiYb7fooA/ODwN/wAEQ/FXwi/sj+yPHHh/Xf8AhBdK 8OTaJ9stJrD+3NR0r/hBd1vc7fO+x28n/CA2uLhPtLr/AG3N+4P2BftvuH7F/wDwT91X9hTx VNc+HE8H6/HN8Nfh34BvL0h9Gu9buNAfUrS+1S5WOGbMh0+6tPIRpJC5tFgeSGNUmH1fRQB+ UPwc/wCDfzxV4J8d/DHxTrUPwgv/ABP4E/4RHfq6pNLfp/Y0fw4ibyJ3tBIMR+GPEqRcrhdW jHyC5uRF9H/8ER/gd4h+CXwM+I516w1CKPxL41jv9P1bUdLvdJ1HxekHh/RdNuNcvLK+hhvb a8v76wvbmdLmMyGaaVxLdRvHdz/X/hPxZpfj3wrpmu6Fqen61omtWkV/p+oWFwlzaX9vKgeK aKVCUkjdGVldSQwIIJBrQoAKKKKACiiigAooooAK+D/+C/Pw01rVP2MNT8UQfEPxhp2iabr/ AIOt7jwhb22lNo2qOfFemgTzSSWb36yAyIQIbuOPMEeUIMgk+8K+P/8AgvB/yjL8Vf8AY1eD f/Us0igD8t6KKKACvoL/AIJZf8n4eBP+4h/6brmvn2vcv+Cbfi3SvAv7aXgzVdb1PT9H0y1+ 3edeX1wlvbw7rC4VdzuQoyzKoyeSQOpoA/Zavjz/AILZ/wDJqvh//sa7f/0jvK+gv+GtPhV/ 0U34ff8AhRWf/wAcr5t/4KjeLdK/aX+AGj6D8ONT0/4ga5aeIYb+fTvDdwmrXcNuttco0zRQ F3WMPJGpcjaGkUZywyAfmVRXoP8AwyX8Vf8AomfxB/8ACdvP/jdY/jb4HeNfhppUd/4k8H+K fD9jNKLdLjUtKntInkKswQNIoBYhWOM5wpPY0ActRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX6S/sC/t9fCX4Kfsl+E/DHifxZ/ZmuaZ9s+0239l 3s3l+Zezyp88cLIco6ngnGcdcivzarsfCv7PHxA8daDb6rongbxjrOmXW7ybyx0W5uLebaxV trohU4ZWU4PBBHUUAfqr/wAPTfgP/wBD3/5RdR/+MV8eftT/ALLHjz9tX48a98TfhloX/CTe CPE32f8As3UvttvZfafIt4raX91cSRyrtmhkX5kGduRkEE/Pv/DJfxV/6Jn8Qf8Awnbz/wCN 1+kv7Avxa8K/Av8AZL8J+FfG/ibw/wCDvE+lfbPtuj65qMOn39p5l7PLH5kEzLIm6N0ddyjK upHBBoA+G/8Ah1l8eP8AoRf/ACtad/8AH69m/wCCQX7PXxB+Auo/tBalqmvfECK88GfECT7V 8NNIPh+Ww8Qzt4Q0KaLNxPD5y3DiaHZt1GCANFFv2r5xf7f/AOGtPhV/0U34ff8AhRWf/wAc rzH9g3xbpXjr42ftVaromp6frOmXXxVs/JvLG4S4t5tvgnwqrbXQlThlZTg8EEdRQB8/3P7B njiw/bQsrxfhjp/iDw34R+JU/iDwb4i1650bWdMgsNSvode1q4u7W6Rr3TbyK+1DVYtNudG8 uaea0sF1FWt7OC4n8g+AfwC/ao8F+NPhF8SPijH4w8Gy/Dm0a38QeJfF3j7Tbmy0DS4dK+Hq 63Pfsl/Ok1nfpoXiwxgrKUuru3u5UtZCLqH9bqKAPzB/Zz+Ef7ZOsfCzSbzxF4i+IF3pOtfC rwW8mkySRWOqz+RD4fOt6bLfXN/FeWfiCZY/EyRyraQZfVYHuNWR7W1jsOv/AGff2L/Gek/A z9prTPEPwq+KGnXPxXu/Duux6RqXxYHiWS7hPh/SIdQ0+3u9QuruK6vLW7ttQhZNSgjstSjW ytZ5HsCRb/ofRQB+eGk/sYfGjRfjf4d8MappnjC+8HeLrTw54k8cav4Z+Il9o2mL4gi0ZNF1 ZP7Tj1GHxE8cFrpGlTWQljvEupru9W9ZXkt7zTflD4Rf8E/P2rB8IvDHgPxH8LfjBpvw88B6 V4Kum0G1+KwN/rWp6PqXhV7l7W5PieSK2xCvilre3t49Oto1j06Vi91HafYv2+ooA+UP2ePj JqH7JHwaDfFrQPjhJrfi/wAQalqNsX0TW/HusywgwgS38WjDUrDSZGBAW1spksnCtNBBaGWW xtfn/Wv2Gfj18PvCvhnXPBPh/UNP1iHxrq2k6zpMHjc3mpxeCNRTTNQ14WuoSyW//E0vdQ0E W1jeo1tPJDqyXt/5Gpz31/B+l9FAH54eIf2cv2o/iXPDDcS+MPCHg7W7TwXFeeHrP4jzTano tguo+FGvtPTVY5o7qS8s4rXxkbu+SVZr6HVdPCzXbxpDY9BqOv8Ai3xh+wZ8NfgJ4m0T4oeI PjRpfh/wa3jm0v8Awzq93pnigWI02/1/SpdfkgOjXUl/Z299ZsJ74W9xNdGCaVRI5H3fRQB+ YOlf8E3/ANo/R/tP/CWaz/wsH7P4V8NWXij/AIqR7v8A4WJa2f8Awhn27RP9LMf2jzP7D8Y/ Jf8Ak2r/APCX8yf6dqf2f3/9ib9mL4j/ALK/xT1LXvGMXiDx/rWs/Cr4Z+E9b1qPxI2qf2pr mnTaxa6tdRfb50kjt4o7q2u5MJGZvMnkSKa6klR/r+igD8ofg5+w9+1xb+O/hj4j8U638YDq Gif8Ij/bMTfFi5awk+xx/DiPUPPs01H7Ncblh8dGXdG/2hjIzea0lozfR/8AwRH8Z+M/iB8D PiPqvjHxVqHjqWfxrHHZ+JP7fGu6N4gePw/osepXujXKSyQf2XNrCarJDDB5cduXkgNvayRS W0X2fRQAUUUUAFFFFABRRRQAV8H/APBfnRfH8/7GGp32n+JvB9r8Podf8HJqei3Hhm5n1m8u P+Er03bJDqK36QwRgmAmNrKUkRyDePMUx/eFfH//AAXg/wCUZfir/savBv8A6lmkUAflvRRR QAUUUUAFfYf/AARM/wCTqvEH/YqXH/pZZ18eV9h/8ETP+TqvEH/YqXH/AKWWdAH6i18ef8Fs /wDk1Xw//wBjXb/+kd5X2HXx5/wWz/5NV8P/APY12/8A6R3lAH5dUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV+q3/BNv9ofwB4F/Yt8GaVrfjrwf o+qWv27zrO+1m2t7iHdf3DLuR3DDKsrDI5BB6GvypooA/cf/AIa0+FX/AEU34ff+FFZ//HK/ Nv8Ab6+Eviv46ftaeLPFXgnwz4h8Y+GNV+x/YtY0PTptQsLvy7KCKTy54VaN9siOjbWOGRge QRXy5X7Df8Esv+TD/An/AHEP/Tjc0Afl1/wyX8Vf+iZ/EH/wnbz/AON19Lf8EWPDvxL+DFl8 e9S1e+sND8DeGPiBNdeIPCs/gLU7/wAV3Mq+EtDlV7WWG7DDKtbstuunzyybWVCTMnl/pjXg H7G//JxX7WP/AGVWx/8AUH8KUAfN9zpXxXsf20LLw/qUXxw8Tab4N+JU50hnl1zTtA8U6bqV 9DrU11HqNheRW8EejW2p/ZHttaS7tL+PQ4rXTxbzzXsKeYfsr/tLftcfFb4u/A/XNd074wLO +zRta03WvAlzoWkX2/Tfh4dZfUEktII4fIkk8Z3Vpcs0W+5szb28k0Mn2Of9Xqz/ABZ4T0vx 74V1PQtd0zT9a0TWrSWw1DT7+3S5tL+3lQpLDLE4KSRujMrIwIYEggg0AfmD+yP+0/8AtR6p 8AvC0NpoOn6Z4En+D/gbVNHu/DvhGaS7hsJrbw8NW1TTYLbTzpaXltDd+IjHpplmmlOm6WLf SPKWVtQ7/wDZ28ZfH+P4S/tBajod9+0h4z8V6x4g8H6j4Z0zx94f0jR9X0rQdQ0jQ21KXTmm tbLSBqlssurq1qZHtobvTofOgLzztefofRQB+WP7H/jL9pd/D/wp8A+Jr79pDwpfeO/D/hbx Dq+rS+H477WdLmOiR6PfWr3+pWuo6TaxwXGinV7i3u3tb24HiEbBHeWsllqXh/wi/aC/a48V fCLwx4O1TU/2wNG0bwppXgrVfEHie78C3MviRrq31LwrHqltao3huOSe3WPUtcMkck2p3V4d ADyEWrzpqH7fUUAfKH7PH7cfh74QfBoaz+0H8WNP8G6l4q8Qak2h3PxENl4Hj1W1hMKk6Xp9 5DZ39vZoXUeRqCS3UUjupur2A217cfIHw/8Aix+2l4W1vUvFXizwf8cLDXtX8ax+HPENm1tZ 6npnhjS9U0Dw7f6trOi21lNdw3MejHQ9XgsIpLeczTa7AjPeXxvbOf8AW6igD88PEPxw/ao8 dzw2Xh6y+KHhnwpr1p4LjtNa1Hwnpq+KNJsr3UfCkc97cQm3ktF1R4r3xgt/D9neGyTRbGX7 PaJKst90Go/trXPjT9gz4a+BtZ+I+nw/tMat4f8ABt58QPBthqcFh49S1kGm3viZYtKtil/B eDSDqcqx2cK3ScG2VZhFj7vooA/MHSv+Gyf9J/4T7/hYH/Iq+Gv+E4/sfyv+PH/ijP7T/s3+ z/8AmIf8lF3f2V/p3/Hnt/5gte//ALE178Zfh/8AFPUtR+N2pfEDV73V/hV8M47+1bTVu9K0 rxRNNrFnrENmmmweR/x8fZJ7qVTIIkmR3ljtI4BF9f0UAfkj+zh44/bG0nUfg/C9x8UPC/hT TbTwZYah4Ts/htpOn6Nb24g+G8OoQlRpYmto8a34sLpFLEIDpLiMQpZTJX1//wAEmfjv8UPj j4H+KqfF261BvGXhPxrb6ReabdaVFp6+HbiTw3oV/fabbiOJPPs7fUL2+S2uHaczW4gkFzco yTv9X1z/AMLfhP4W+B3gSw8LeCvDXh/wf4Z0vzPsekaJp0On2Fp5kjSyeXBCqxpukd3O0DLO xPJJoA6CiiigAooooAKKKKACvg//AIL8634/g/Yw1Ox0/wANeD7r4fTa/wCDn1PWrjxLcwaz Z3H/AAlem7Y4dOWweGeMkQAyNexECSQ7D5aiT7wr4/8A+C8H/KMvxV/2NXg3/wBSzSKAPy3o oooAKKKKACiiigAr7D/4Imf8nVeIP+xUuP8A0ss6+PK+w/8AgiZ/ydV4g/7FS4/9LLOgD9Ra +PP+C2f/ACar4f8A+xrt/wD0jvK+w6+PP+C2f/Jqvh//ALGu3/8ASO8oA/LqiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK/Yb/gll/yYf4E/wC4h/6cbmvx 5ooA/oIr8ef+Cpv/ACfh47/7h/8A6bravn2v2G/4JZf8mH+BP+4h/wCnG5oA/Hmvsf8A4IX/ ABH8VfDvwp8c7+90zwBp/wAItL+IFxqXiTxXq/i6bT7/AEXyvCmiSOwszYtbPbqqRM80l9Ds V5WKfuh5n6g14B+xv/ycV+1j/wBlVsf/AFB/ClAHzfc/tmfES0/bQsvhzrfxL1BLzwh8Sp9B 1Lw9oNlYWep+J7XUL6HUtNFot3plwur2djouqaUmpLZzaddaZDa391LLercWcg8w/ZX/AOC3 3xH/AGlvi78D7mzuvAGr6T4x2aRqnhzQLdpX1u8m034eT3ptpjNLLFcaRN4j165mQeYos9Ku Y5o1eNryD9Xqz/Fnhq38aeFdT0a8l1CG01a0lsp5LC/nsLtEkQozRXEDpNBIASVkidZEOGVl YAgA/NH9nP8A4LBfEe++Fmk6Tc+BvD+iw2Xwq8F+Iode1nX2u/skWpQ+H0vfEM1tc3zarfaP ZDWb2S4uZTHFH/wjlysuqSTXcn9n3/BH/BSPx/r37NX7SPim6/aA+CGoN4f8QeC4fCusz6Dc +D7LQdG8QaZoM8V/MJm1Oa0juBqc4tpr62njguoJ5LjzLZWtrb9H/CfhPS/AXhXTNC0LTNP0 XRNFtIrDT9PsLdLa0sLeJAkUMUSAJHGiKqqigBQAAABWhQB+eGk/t+/FDSfjf4d+E8fi7T9F 8U/Ea08OeKfDn/CxPC0WoeKNP0bUdGS2W0u9B0+50yWS8TVtL1ifUJ7ZpY9OS6tC0X2V7iXT PlD4Rf8ABfP4s/E74ReGNAt/jF8IB4g0zSvBWu+K/Hl14Vs4rDRornUvCtrqlrdQx64/mY/4 SQC4u2g02KJtM1GCBBKxn079vqKAPEP2BP2jtV/an+DWqeK9U1jwfrSv4gvbOym8K27y6NHb xFVWO21L7RPDrEYO7/T4BbhyTFLaWdzBc20XxB4B/wCCqHxQ8O/AL4afFG88W6f410T4keIL zwbd6rFZRT+DtO1K/ttK1ODXrGQWdheN4b0XTLXxE13HdyJdw3UFzaXNw4tmvrf9D/jp+yd8 LP2oP7K/4WZ8NPAHxE/sPzf7N/4Sfw9Z6v8A2f5uzzfJ+0Rv5e/yo923G7y0znaMd/QB+eHi H/gq78S/EU8OmeCbDwfqVprlp4LXQ/Gl54Z1OPTLlNb1Hwpax60+nPcxzLZ348RaklpatcpJ HN4V1BWuLljLHZ+waj+174z8Yf8ABJb4a/GNJ9P8N+IvHnh/wbqviLU7C1BtPCllq1xpq6xq kS3BljijsLK6vLtZLrzYYRaiSdZYkkVvq+uA/wCGTvhZ/wAL2/4Wl/wrTwB/ws3/AKG7/hHr P+3v+Pf7L/x++X5//Hv+5+//AKv5Pu8UAfAGlf8ABSD9o/WPtP8Awlmj/wDCv/tHhXw1e+KP +Kbe0/4V3a3n/CGfbtb/ANLEn2fy/wC3PGPz3/nWqf8ACIcx/wCg6n9o9/8A2Jv2tPiP4y+K epN8a9T8P+EodR+FXwz8SQ+HpNJbQ/8AhG9c12bWLS9sJWuZXmkuJb+1hhjjdkK5ghEZmEks /wBf0UAfkj+zh/wV0+Ouu6j8H9OgHwwh8G6raeDLO9s7zR9Y1LWZEvoPhvHdsNSuNWd3kL+O ZmSSeOaQDTk81p3meRfs/wD4JZftreKv24Pht4/1zxbYeH9I1Dw14rh0dNL0yCaKbQvN0HSN Tm0q+82Vy+oWF1qNzY3D7YMy2TZt7dt0S/T9cf8ABT4EeGf2fPCtxpHhm11BI767a/vrzU9V u9Y1PVLgokfnXV9eSy3VzIsUUMKvNK5SGCGJSI4o0UA7CiiigAooooAKKKKACvg//gvz8S9a 0v8AYw1PwvB8PfGGpaJqWv8Ag64uPF1vc6UujaW48V6aRBNHJeJftITGgBhtJI8zx5cASGP7 wr4//wCC8H/KMvxV/wBjV4N/9SzSKAPy3ooooAKKKKACiiigArZ8E/ETxB8NNVkv/Dmu6z4f vpojbvcabeyWkrxllYoWjIJUlVOM4yoPYVjUUAeg/wDDWnxV/wCim/EH/wAKK8/+OV9J/wDB Lnxbq37S/wAf9Y0H4j6nqPxA0S08PzX8GneJLh9WtIbhbm2RZlinLosgSSRQ4G4LIwzhjn4r r7D/AOCJn/J1XiD/ALFS4/8ASyzoA/QT/hkv4Vf9Ez+H3/hO2f8A8br5V/4K+/A7wV8NP2at Dv8Aw54P8L+H76bxNBbvcabpUFpK8ZtbtihaNQSpKqcZxlQewr7rr48/4LZ/8mq+H/8Asa7f /wBI7ygD8uqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACv1W/4Jt/ s8eAPHX7FvgzVdb8DeD9Z1S6+3edeX2i21xcTbb+4VdzuhY4VVUZPAAHQV+VNdj4V/aH+IHg XQbfStE8deMdH0y13eTZ2Os3Nvbw7mLNtRHCjLMzHA5JJ6mgD9lv+GS/hV/0TP4ff+E7Z/8A xuvzb/b6+LXiv4F/taeLPCvgnxN4h8HeGNK+x/YtH0PUZtPsLTzLKCWTy4IWWNN0ju7bVGWd ieSTXjv/AA1p8Vf+im/EH/worz/45X6S/sC/CXwr8dP2S/Cfirxv4Z8P+MfE+q/bPtusa5p0 OoX935d7PFH5k8ytI+2NERdzHCooHAAoA/Nr/hrT4q/9FN+IP/hRXn/xyvqb/ghZ8ePF/ibX PjRpmq+EPiB4qg1z4lG4vvHU2o6bPptg6eFdCVILgT3q6g8m2GNAYrWWMedEN4CyeX9wf8Ml /Cr/AKJn8Pv/AAnbP/43XkP7HPwm8KyfEL9r3wa3hnw+3hDUPiVBYXWhnToTptzbz+B/C4mh e32+W0cgdw6FSG3tkHJoA8//AOHhPxHl/aE/4RK/m8AeHdP8FfED/hFfHbw6U2uXmlRalrPl eGY54V1S2msv7T0250ryb2CLU4mur26E8FgthKr8f8H/APguLqvxZ+JnwqL/AA70/SPB3jO0 sU1pRrD3ep2Nxqlp4Ak06S2PlRxSRx3fjhLeeNwhMNs1wjh1FnL9v3P7PfgG8+Jll41m8D+D 5fGWm3c9/aa8+jWzana3E9pDZTzR3JTzUkktLa3t3cMC0MEUZJRFUchoX7BPwg+Huj28XgT4 ceAPhtq+kf2hNoOteGPCWlWl/wCGby+tUtbm+st1s8Udw8MUKMzRssiwxpIsiLsoA+X/ANnP /gtN/wALD+FmkwxfDr4ga1q8XhXwWo1zU18vSrzxD4gh8Pi2sr3UoLKKzj3N4k06RntYWm2Q anJ/Z8KQ2i3uhpP/AAU1+Ll58Avjd4tm8D/DDVrzwt418KeDPAw8FeKpfEdprL6/baAYbjzb mLTobuNDr1vcRsJrSO4DNAZLZYxfTfUHhP8AYp+EfgvwrpmjWfw58HzWmk+CovhxBJf6ZFf3 b+Go0CLo8txOHmnsyAC0MrsrnLMGYknQ8O/snfCzwho/iPTtJ+GngDS9P8YaVbaFr1raeHrO CHW9OtrU2dtZXSLGBPbxWpMEcUgZEiJRQF4oA+X/AAh/wU58ba1dWelaVovgDxtdeJv+Ea1X wz4kvrzUPBvhvUtD1rR7Y6fezv5Gp3FncahrMOtW1rbTxIALOKGWZbiexGoeIfDv/gvH8V/i J8M/AWl2Pwz+GF18UvE1p4M1K62a/rkOgR2+r3fhuG5ikml0lUhvCninSHSC2n1AW0d1LJNI 7QQ29/8Ao9rP7PfgHxH8ZdJ+I2o+B/B9/wDELQLRrDTPE9xo1tLrOnW7CUNDDdshmjjInnBR XAImk4+ds8Bo3/BMr9m7w5p2rWen/s+fBCxtNftFsNTgt/AulxR6jbrPFcLDMqwASRieCCUI 2QJIY2xuRSADoP2Vvj5qv7QfhXxHf634e0/wlqWheILnRpdDXVXvdT0sRpE6R6lGYIktrxkl WQxQPc27wyQT293dQTxSt8X/AAh/4LV+Ktc+Fnwz8c+IdA8PvZfFvVU8KaboKWM2nXOheKNZ h8P33hfRbm9W5u0nt5dM1K+vrjU4rdR5EUQNpb3amwl+wPEX7CPwz1fwJ4c8LaTpfiD4f+Gf Cf2n+y9I+HvirVvA9hb/AGiQSzboNHubWOTdIC48wNtaSQrgyOW6/wAQfs9+AfFnhU6Fqvgf wfqeiNd6hfnT7vRraa0NxfpdJfzeUyFPMuUvr1ZnxmUXlwHLCV9wB8oeMP8Agsh/ZusS+HvD vgnw/wCK/E8/9gWGmXFn4w3eG9V1HULrwnDcGLUY7N5G0+OPxpo0sF2tsz3IjvQbe3EULXHq Go/tvar4l/4J1fDX4y6Foen6VrfxZtPBqafY38z39poVx4lvdNsopJdnkvdR2j6ksrRqYDOI CgeAyeYnr+n/ALPfgHSdOtLO18D+D7a0sLTSrC2gi0a2SO3t9KnNxpcKKEwsdlOzS26DAgkY tGFYk1yH/DCPwz/4SP7Z/ZfiD+yf7K/sL/hE/wDhKtW/4Q3+zvsP2D7F/wAI99p/sn7P9m+T yvsuzPz43/NQB8f+Bv8Agt74q+Lv9kf2R4H8P6H/AMJ1pXhyHRPtl3Nf/wBh6jqv/CC7ri52 +T9st4/+E+tcW6fZnb+xJv34+3r9i+gP2Cf25/FX7Yvju6k1bwv4f8L+GdU+FXgT4j6DDaal NqF//wAT+PVWuYrqRooY18mSwEUaxo25UMrPmbyIPf8AxH8J/C3jH/hIP7X8NeH9V/4SzSl0 LW/tmnQz/wBs6cv2jbZXO5T51uv2u6xE+UH2mbj942TXfhP4W8Uaxcajqfhrw/qOoXn9n+fd XWnQzTTf2fdPeWG52Ulvst1JJPDk/upXZ02sSaAPzw+A/wDwXH8Z+LNR+FmjN8K9P1fRPEtp 4Ut7/wAR6p46CayX1ODwOJ53s7fR47WSSOXxxYHEb28cotbpgluPKib6w/4J4/tz/wDDfPw2 8S+KY/C//CLafpmq2UOlxtqX22a/06/0HSdcsricCKMW9wbXV4Ult0MyRyxSBZ5l2ufQNI/Z O+Fnh/7D9g+GngCy/sz7P9j+z+HrOL7J9n/s77P5e2MbPK/sjSdm3Gz+zLLGPs8WzP8A2Vv2 T/D37JXhXxHZaNd6hrGq+NPEFz4q8Sa1f29lbXevapcJFHJdyw2NvbWkcjRQQqxgt4hIYzLI HmkllkAPT6KKKACiiigAooooAK+D/wDgvz8cdF0L9jDU/Ak9l4wk1vWtf8HXlvc2/hPVbnRo 0HivTSVm1SO3awt5MQviOadJCTGApMsYb7wr4/8A+C8H/KMvxV/2NXg3/wBSzSKAPy3ooooA KKKKACiiigAooooAK7H4JfH3xb+zp4quNb8G6t/Y+qXVo1jLN9lhuN0LOjlNsqOoy0aHIGeO uCc8dRQB9Bf8PTfjx/0Pf/lF07/4xXsX7EvxT17/AIKL/FXUPBHxlv8A/hMfDGlaTJrlrZeR Hp/lXkc0MKS+ZarFIcR3Ey7SxU78kZAI+G6+w/8AgiZ/ydV4g/7FS4/9LLOgD7D/AOHWXwH/ AOhF/wDK1qP/AMfr5t/4KjfsX/DT9nT4AaPrfg3w3/Y+qXXiGGxlm/tC6uN0LW1y5TbLK6jL RocgZ464Jz+ilfHn/BbP/k1Xw/8A9jXb/wDpHeUAfl1RRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABX6S/sC/sC/CX41/sl+E/E/ifwn/aeuan9s+03P9qXsPmeXezxJ8kc yoMIijgDOM9cmvzar2L4Wft9fFr4KeA7Dwx4Z8Wf2ZommeZ9mtv7LspvL8yRpX+eSFnOXdjy TjOOmBQB+kn/AA6y+A//AEIv/la1H/4/Xx5+1P8AtT+PP2Kvjxr3wy+GWvf8I14I8M/Z/wCz dN+xW979m8+3iuZf3txHJK26aaRvmc43YGAAB59/w9N+PH/Q9/8AlF07/wCMV9h/ssfsseA/ 21fgPoPxN+Juhf8ACTeOPE32j+0tS+23Fl9p8i4ltov3VvJHEu2GGNflQZ25OSSSAfHn/D03 48f9D3/5RdO/+MV7r/wRW/bEk+Jvjr436V4tbxhq3jfxb8SheHUrfwZqL6Nsi8J6CirNqNva /wBnW0gS2IEcs0cjHy/lJlj3/R//AA6y+A//AEIv/la1H/4/XI/sIfAbwz4I8Sfta+ANFtdQ 0bwxJ8SorJYrDVbu2u7dLjwT4YaVortJRcxSbpXZZI5FkQkFGUqpAAsf/BR7xDffFrQfDK/D nT9FtJ/GupeD9d1XxBrl7aWmkvBq72dmge3025jivL2zaxvbe31J9PjuRrGnR2k940ztFz/w 2/4LaeAfip8TPhxpeneFPGFp4b8f2kLy63fi2jbRLi8tPCFzpkctvHLI8kdw/jLTbVpImYw3 AO5Db+ZdRe4Xn7BXwnv/AI7al8SpfCmfGGt6raa3q1yup3i22tXlnb21vYy3lqJRbXX2NbSC S1WeN1tZw9xAIp5JJX8/8F/8Eivgt8FdH0mXwB4X/srxB4R+0XXhm51/XNa8RWFheNa6Zb27 3NrPfj7Zb2/9iaM8NvJIFgbTLZ4DbyxpKoBx/wAE/wDgtr8I/iF4V0rTJdX1DW/iE/grw34l m0aw0qKxu9UutZTSVtbOK1e7mFpJLPrmjopubn7KDqIVb2b7HfvbdBpX/BSLxNrXwl/aL8S3 nwe1D4ey/s+XcSXcfjnxJaQ2l7brpFlq15JLLoy6pLDJBaXRk8uKG4Ega2Cv5kk8Vr2Hwz/4 Jl/Cf4YfCzS/CVtp/iC80/T/AArpXhOZ5NfvLb+0V02G0istUlhtpIbZdYhWwsvL1OKKO8h+ ywCKWNYo1TQ8Cf8ABOT4PfDT4S+OfAejeGdQtfBvxI8P2nhfxBo7+ItTntLuwtdIj0aJI0ku G+zyf2fDDA80HlzSiCJpHd41YAHn/h//AIKZa1Y62PCPir4KeMLL4sS3en28PgjRtb0q5u50 n0C11SedLy/nsbKWOK5bVLKMRTvJdHRNQlhR47S8+y+P+Ev+Dg7Q/GXwz8HzWXwc8YXXxJ8U WnhTU28FWnivw3dXcNhrl3pdsLzfHqBeGNX1jTxCl/FZzXJvrciOKFbyez+wPG37G3w8+Ivx 9sfiZrWj6hfeK7C0sLIZ1u/TTLpLC5uLqwa505ZhZXMlrc3U08Ek8DyQzMskbK6Iy+P+D/8A giR+zh4C065tdJ8J+MLWOe0tbKNj8RvEsklglrPpM9u1q76gXtZIn0LRwskBjkWPTbaMN5cY SgD3D9m/9oSz/aZ8CXniXTNB8QaJpMWq3emWb6uLWObUltpDFJOIYZ5JbbEyyxPbXiW95BLB LHPbwuhWvm/w3/wVj1XwvPaH4l/DbT/D9jrPiDXvB+g3/hrxQ+s2nifxBp+o6Vpdto1m95ZW GLy71C71W3RbkQIo0G6uN72Za6j9guf2JE8L+FbLSPh18T/ih8LI1u57/VbzS7nTtf1PxPcS pDGs2oX2v2epXVxJFFBHDG5lBWFUiyY4oUj0PHP7BXwn+JHgTSvDWs+FPtek6D4r1HxzpiLq d5DNpuuX8moS3GowTRyrLFcCbU72WJ0cG3leOSHynhhaMA8/+I//AAVZ8E/DLWNT0G88JfEC 78b6X/ZFo/he1g0838mp391ods2lrM92tn9otW8S6C00huBbFdSXyZ5zDcrD2GsftvaXdfsS +DPjT4e0PUNQtPiTaeGn8N6XqMyWMjXHiC5srTTY7ySPzhBGJ9Qt/PkiWcxxiVkSdlVHPDH/ AATe+CXg3TtHtdM8A6faR+H7TQrLT2W7uTJbJos9hPp7BzIXMivpWlCWQkyXUel2Mdw00drC iZ8H/BPfR7bR9I8Jx+PPiBH8IvD+lWOjaX8OVOljSNNgsbWKGyeC/Fl/bUVxbywQ3cVymoie K5hjkSRdqqADx/w7/wAFvfC3xD+y/wDCM+B/EFz/AMJLpWiTeGP7Tu4bP7ZqOqf8Ir5Vve+X 532W3T/hNdA3XEX2l/l1PEB+zW/232D9j79vez/bI8d6hZaT4N8QeHvD/wDwr/wh8QtH1PV7 i187WbPxBHqEkai3gkl8nyPsJjcyOGaXzQqeUkU0/QXn7Afwbuv7S8r4e+H9N/tLSrTRh/Zk bad/Z8Fr9m+zPZeQyfYbiP7Dp225tfKnH9k6Z+8/0C08noNH/Zc8CeF9Ya/0TQ/+EcnOlaFo Srol7caXDDp2i3VxdabZRxW8iRx28Ul1cKYkUJLFK0MokhxHQB8n/B7/AILqeEvGmo/DrQrr wB8UNXvPGFp4cS48TWel6Rp+jJcatB4YYSG2k1eW7hjjl8XaKrxqLgoblwj3CwySD6Q/Y2/b W8Lftx+DvEOv+ELDxBZ6ToOqw6dFPqsEMP8Aa0Fxpdhq1nf26xyuwt7ix1K0lRZhFOm8pLDE 6sg5/wAI/wDBLj4E+BP7E/srwN9l/wCEc/s/+zv+J1qEn2f7B/wj32T7053eX/wiugfezu+w fNu86fzdD9hD9jG3/Yr+HvibTF1PT9S1Lxl4gbxDqK6VYT6bo1g4srSwhtdOs57m6ltLOK0s LVEtzcyRxFWWEQwCG3hAPb6KKKACiiigAorn/ix8UtC+B3ws8TeNvFN//ZfhnwfpV1rer3nk yT/ZLO2heaeXy41aR9saM21FZjjABOBXmHhP9u/w94j+N+meArzw14w0HVb+7i0Ke5v47J7T TvET6MNdbw/KYLmVzeR6WTdNPEklgQpjW7abERAPb6+D/wDgvz+0L4B8OfsYan8OdR8ceD7H 4ha/r/g6/wBM8MXGs20Ws6jbr4r00tNDaM4mkjAgnJdUIAhk5+RsfeFfH/8AwXg/5Rl+Kv8A savBv/qWaRQB+W9FFFABRRRQAUUUUAFFFFABRRRQAV6T+y5+1H4g/ZJ+IF54k8OWej3t9e6e +mumpRSSxCN5IpCQI5EO7MS85xgnjoR5tRQB9h/8Ps/ir/0APh9/4A3n/wAlV2PwS+Nurf8A BWzxVcfDj4j2+n6LomiWjeJIJ/Dcb2129xE6W6ozTtOhjKXUhICBtyr8wAIPwXX2H/wRM/5O q8Qf9ipcf+llnQB9Bf8ADkz4Vf8AQwfEH/wOs/8A5Fr5d/4KTfsS+Ff2Ov8AhC/+EZ1DxDf/ APCR/bvtP9qTwy7PI+z7NnlxR4z5zZznoMY5z+stfn3/AMF1/wDmln/cW/8AbKgD8+6KKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAr70/Yv/AOCXPgD9oz9mnw14z1vWPGNrqms/ avOisbu2jt18q6mhXaHgdhlY1Jyx5J6DgfBdfSfwC/4Kj/ED9nT4S6T4M0TR/B91pmjed5Mt 9aXMlw3mzSTNuKTopw0jAYUcAdTyQD6y/wCHJnwq/wChg+IP/gdZ/wDyLXj3xT/ba8V/8E6P Hl/8GvBOn+H9V8MeDvL+xXWuQTT38v2mNbyTzHhlijOJLhwu2NcKFByck89/w+z+Kv8A0APh 9/4A3n/yVXsXws/Yl8K/8FF/Adh8ZfG+oeIdL8T+MfM+22uhzwwWEX2aRrOPy0milkGY7dC2 6RssWIwMAAHjv/D7P4q/9AD4ff8AgDef/JVei/8ABH/9vLwt8afix8dtL8WeMPh/o3xW8b/E pL+y8Jw6rDb6lf28PhHw/F51vZSytcSRhLSUl1DD9zKcgKQO9/4cmfCr/oYPiD/4HWf/AMi0 /wD4Jzfs82fwWm/au+HnhjXvEGl29r8Skt7LWAbWfUrB5/BfhmTz08yBrdpI3lLIJIXj+VQ6 OMggHY6f/wAFNPCXiH4teD/BWj+FvGGp634x8Qa7oFujz6RpqxHR9XvNLvrmMXt9A99HG9hc 3Dx6ct1cQ26xSTwwm4gWU8G/8FYPgl8Q/iZ4F8NaJ4j1C/X4hWjXGmasdIubfTIHa00G8tYL mWZEMEl1B4l0nyC6iN5rlbYuty8cEmhrP/BOvwtrvx28SeN5/FXxANt4y8V6V4y1/wAMf2nC 2g6pqOk2+mxaXJ5TQmW3+zTaXa3O62lhe4kGy5a4t0ht4vH/AIaf8ERPBP7P+j+HL3QfFPxA 8dav4C/0vSNM8T6vp9hYa3LbWvhuPS7O9uLLTBLFb2s3hHQZFlgTzS1vIZhdJLJC4B7B8M/+ ClXwn8bfCzS9evPGPh+LULrwrpXii5sNMa8v9638NpJDbWQe2hub24ZtQ05VtUt1vD/ammb7 aNr+1SXP8L/8FRvh54y+Hvxi8S2OhfFC3sfgnd29nraa14Mv/D8ly89lb3cbRf2jHbpFGqXK ebJdtbx2sY+03DQWbx3T+f8AwT/4IifDX4Q+FdKX+1dQfxXb+CvDfhi/1+w0nTLe7udR0FNJ /s/Woppree7ikil0XTpFsWuZNOY24MtpM7yySegeCv8Agml4e8IfD3xp4dufiB8UPEcfjO78 Oay9/rWoWV3qOm63oVlptrZ61FObUPNeM+j6bcyi7+0W8s1tkwiOWaKQA0I/+ClvwzW61y2n h+IC6houqxaUNKtfBOralrd5v0fStWe5XS7S3m1CK3hh1mximkuLeLyLiVYpAjSReZ5/Zf8A Bb34LXvwJ8IePDpnxggh8a/2G2m6PN8N9aTVbmLVLi1t0uIIzb+Xe28El5bLNPZSXEQe4tYo 3lmvLOK47DTf+CX/AIB8NfGXwF4q0LWPGGgaX8OvD+g+G9K8M2d1bNp32fQxqK6Y5uZYH1JJ Io9UvInMN5GLmGR4LhZ4ZZo5fD/hf/wbu/DH4SaxcatpfxP+MDeIP7K0bSLHWJU8Ofb9Mi0i 68P3GnFJE0hfO8j/AIRnTI1S5E0QX7S3l+bcyysAfZ/wU+O/hn9ofwrca74RutQ1LRIrtrWD UJtKu7K01QBEdbmylniRL2zkSRGiu7YyW8wOY5XAOPIPDf8AwVN+GGuz2n2mLxholve2mvXU U2paHLC0h0rUdK057aOAbria8uLnWbKKG0hie4Fx51lNFBfwS2a6GhfssfEf4G+BLfRvhX8U PD8F1f6rqGt+I9T8feDm16bV7y7kSQywW+l3uk2djlvNeVYLcLPLK87j7RJcTT8fqX/BG34R 3Hwl8JeD7C58YaNaeBPGtn4/0K8tNRie7sNU07SDo+jt++ikjkj06zjsUhSRGEp023a6+1tJ dG4APQPGX/BRv4PeANOvptW8T6hb3mnWmmXU+kr4d1ObWUOoz2VvbWw09LdrtrzzdS00S2ix G4txqdiZ4oluoDJ0Gv8A7Xvg3Tv2YND+Lmmz6h4i8KeLbTSLjw4thamO7199Xlt4NLgiS4MQ ikup7u1iU3DQxxmYGZ4kV3Xy/Tv+CTPw4tdY0zVrzXPiBrfiDTv+EemfWNU1lbu/1C80m60G 4a+uJHjPm3F9/wAIxoMd0xG0rYboVt5Z7mWYsv2BvFWnfCzwh8I4fiF4fT4JeA9K0PTtDtf+ EUmPjKxn0aG1fTb/APtY3/2GS4h1CztrvbJpTQSCPyZIXRmJAC0/4K8/BvX/ADP7AvfEHiP7 VpWmajof2PS2h/4SWfUP7G+y2Ft9oMXl3Df8JJ4cy115EC/2zDmYfZ737L6B+zn+3F4E/as8 d6tongpvEF/DpfhXw74yj1W70a40+w1PTtdju5bGS1a4VJJv3dm5kZU2IziIt50c8cXn9p/w SG+DegeZ/YFl4g8OfZdK0zTtD+x6o03/AAjU+n/2N9lv7b7QJfMuF/4Rvw5lbrz4G/saHMJ+ 0Xv2r0D4ZfsVeFvgdrEV14H1DxB4UhtfCvhbwTaWlrPDdQ2mj+H7q9ntbVTdRTSN50d/cW80 ju0hiKmN4ph51AHl/wANf+CzfwN8aaj4H0K88SahF4r8Z2miPHFpfhTxDf6MlxqsGkS28aam +mxQ+WTrukL5k4gKHUbUSpA8ojHuH7Pf7UfgT9qrR9e1HwDrv/CQaf4b1X+xr26WyuLeFpza 295G8DzRoLm3ltbu1niuYC8E0U8bxyOrA18//Dr/AIIqfCz4Zf8ACLfYNf8AiBN/wiP9j/Y/ tF9Zt5n9mf8ACG/Z/M22wzu/4QjSd+3bn7Re4274vJ7D/gmX+xlqv7Fnwl8U6Vq76fay+KPE EeqWeiWGpvq9p4VsLXSNN0ex0yK/ktbSW8jhtNLg2zTW8c2HCSNcSRvdTgH0fRRRQAUUUUAc f+0LrPjPw58AvHGo/DrSdP1/4hWHh+/uPDGmX7hLTUdUS2ka0glYyRARyTiNWJkjADH51+8P hD/gnTLr3gr9oDR/Bvg3w78QLvwT4N8V6vpmmXviz4LR+BYbDwXf+H9Pv7ie2lj0bS4UuP8A hKbZYfs0IDzRTzzS284hhurf7g/ax8O2fi/9lj4l6TqPiPw/4P0/VPCuqWl1r2u2FrqGlaJF JZyo95d210RbT28SkySRTkROiMrnaTX5Qfs2/B/4CfHn9pjwT8PNH/ZV+AHg/wAT/D/StXt4 9BvfBvii/wD+EpWDSbdxp2r3Os+EbSOz2yXei3b6rO1zeR74YkSQaq/mAH7PV8f/APBeD/lG X4q/7Grwb/6lmkV9Ifs9fDjVfg58AvA/hDXfE+oeNtb8K+H7DR9Q8RX4cXev3FvbRwy3su+S R/MmdGkbdI5y5yzHk/H/APwX5/Z78A+I/wBjDU/iNqPgfwff/ELQNf8AB1hpnie40a2l1nTr dvFemhoYbtkM0cZE84KK4BE0nHztkA/OeiiigAooooAKKKKACiiigAooooAKKKKACvYv2Jf2 sf8Ahjr4q6h4n/sD/hI/t+kyaX9m+3fY9m+aGXzN/lyZx5OMYH3s54wfHaKAP0E/4fr/APVL f/Ll/wDuWj/lNX/1TT/hWn/cZ/tL7d/4D+X5f2P/AG93mfw7efz7r9BP+CFH/NU/+4T/AO3t AB/w4o/6qn/5bX/3VXz7+3Z+wp/wxT/wiv8AxVX/AAk3/CTfa/8AmG/Yvs3keR/01k3bvO9s be+eP2Gr8+/+C6//ADSz/uLf+2VAH590UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfYf 7LH/AASe/wCGl/gPoPjf/hPv7F/tv7R/oX9h/afJ8q4lh/1n2hN2fL3fdGN2OcZPx5X2H+yx /wAFYf8Ahmj4D6D4I/4QH+2/7E+0f6b/AG59m87zbiWb/V/Z3248zb945254zgAHoP8Aw4o/ 6qn/AOW1/wDdVH/Ddn/DtH/iyX/CLf8ACa/8IV/zGf7S/s37Z9q/03/UeVLs2fadn+sbOzdx naD/AIfr/wDVLf8Ay5f/ALlo/wCGE/8Ah5d/xe3/AISr/hCv+E1/5g39m/2l9j+y/wChf6/z Yt+/7Nv/ANWuN+3nG4gB/wAP1/8Aqlv/AJcv/wBy16J/wSQ+O3/DS+pftKeN/wCy/wCxf7b+ KsP+hfaftPk+V4Q8Mw/6zYm7Pl7vujG7HOMnzv8A4cUf9VT/APLa/wDuqpP+Cav/AAT88LfB f4m/tIXVnpHgDWPi74L+IB03w34/1fwhDc3+lfaPBmgSIoIlW5+zq15KHhjuovMWSUB08wkA H1DZ/t3fDPWPGOm+H9K1TxB4g1fUdVu9GaDRPCurar/Zc9tqlzpMr37W1tIthbm+sryJLm7M UEv2O4eOR0hkddDRP21vhH4n+JnhPwdpXxH8H6t4i8dWl1eeH7Ww1OK6XVktrSxvpVikjLRt J9j1Kyulj3eZJbziZFaJWdfH9V/4JlXN/wDtPy+OIvHOn2vh0eNU8Z6XpEfhWBdT8Hv5Vg97 b6LqayrJYx6te2lxLqqmOWO+hvpoxHbyyXF1ceH/AAd/4Ig69+z3o/g7U9V+JH/Cwf8AhXf2 e7l0fQPCMekX/iSLS7XwX/ZdnbS3epvBb3BuPA2nGaWZjFMt7coBafJPGAfcHgr9p7wT4v8A hZD4tm8ReH9G09PCtl411JLzXNPl/sTSbqGWWK7uZreeW2FuVt7nFxHK9u/2aYxyuqFq4/wJ /wAFKPgN8VvCvjnW/CPxV8H+MdN+HN3aWGuy+H7was0VxdpG1nDAluHe7kuXlSCBLYSme4D2 8YedHiX5f/Z8/wCCFsnwt8K+HL6+8Z6fH4y0/wAFeE7KWUQ6jqNppniPQk8Msk9shvILV9Lm l8KaWZ4TZRX04RgL+FPKih9g8O/8E/vH9j8Jfi5oOvfE7wf401v4qXfh/W31jVvA9zbtZazY aRpOnXeolLHVLZ1klfSILu1NpJaSWFwwdJZTHHtAPULz/goH8D9I8Hal4k1L4s+ANG8M6Xqt pokmt6rrdvp+lXF5daXbatbRW93OyQ3Pm2F3b3CNC7qyOcHKOF4/T/8AgsD+y5qnwatPiBB8 e/hhJ4UurvSrB7sa7CJLG41MkWcN1AT51pI4SZ2SdIzDHbXLyiNIJmTj/hh/wSk/4UL8dvhz rvgTx7/YHhLwF4V0HwxLZPonma9qf9l28lo87X8dxHan+0LRNMtr1JrCbzY9HsGia3mtLKe1 8P8Ahj/wQd+JfgSfQ7rUP2jtP8QXngjw/wCH9B8Iw3fgzU30zRE0fUfCl7APsT668IjlPhWM zLai2klm1G4kaUqkEUYB+h/gD4s+Fviv/bf/AAi3ibw/4l/4RrVZ9C1f+ytRhvP7K1GDb59l ceWzeVcR7l3xPh13DIGRXAeEv2/Pg3443PpvxC8PzW0elatrst5JI0Fnbadpn2Fry9luJFWK K3EOp6dcxyuypcWt7BcwmW3dZTx/w2+E3xo/ZL+DWieFvBOnfDD4lMt3dzTwajrl94H0bwxb kx/ZrDSLVLPWJls1HmEQzXLC3J8uDZbCC1tfH2/4IlW8Pw98Nxad470/w74y0T4lQ/EuW+0f w9PDozvaWVpDY6Hb2Ut9LcW+hpc6RoFzJYC8eNjpSRwG0RbT7GAfWGu/tY/Czwv4EuPFOp/E vwBp3hmz0rT9dn1e68Q2cNhDp2oSPFYXrTtII1t7qSORIZSdkrIwQsQRV/xL8fvBvhT4NRfE K48Q6fdeDbq0tryy1PTmOox6sl0Y1tFs1tw73cly8sKQRwCSSeSaJIld5EU/MEn/AASLufEH xCsfGfiT4p6h4g8ZC78N6xquotoMFtHquqWN74RuNTuTDE4SKO9TwXpSxQpgWklxfOWuUkhh t9DS/wBjv4qaR+zj8OfgBHbeAG8BfDHSvCsOl+Pm8QXh1e41Hw8theWVxPoAsPJa3fUtPhWW 3TVkdrZpCs8chXaAewXn7fnwbtf7S8r4heH9T/s3SrTWR/Zkjaj/AGhBdfZvsyWXkK/264k+ 3adttrXzZz/aumfu/wDT7Tzug+FH7VXw4+O/jvXfDXgrxp4f8W6v4a0rSdb1JNIulvIbWz1S OaXT5TNHmI+fDA8qKrlvKaKQgJNEz/L/AId/4Ih+Fvh59l/4Rnxx4gt/+Ea0rRIfDH9p2kN5 9j1HS/8AhFfKuL3y/J+1W7/8IVoG63i+zP8ANqeJx9pt/sXsH7Pv7DH/AAyrrFpJ4K8U7tP0 z4f+CfhxZw63pv22ZdO8PXWpNJLJJDLAHuLq11J4gyoiQyxLKUmUmCgDQ8Gf8FIvgX4s1Hwr o1x8W/hhovjLxfaaZcWXhW88a6PJrJfUYIZ7SARW91Kk0kqXEJQwPLHKJEaJ5EdGb0/wB8Wf C3xX/tv/AIRbxN4f8S/8I1qs+hav/ZWow3n9lajBt8+yuPLZvKuI9y74nw67hkDIr4g+Ef8A wQx/4VZ/whP/ABdH7f8A8Id/wj//ADLflfbP7K/4V7/09Ns83/hA/wDa2f2r/F9m/f8AqH/B Ib9lPxN+yb8DPFul+ItP1DRrTWPEFo/h3TNV+yf2zp2jad4f0bQrKPUTZ3NzaNeGLSA7yW07 RyiRZAls0jWkAB9X0UUUAFFFFAHAftY/8IJ/wyx8S/8AhaX/ACTL/hFdU/4S7/j4/wCQP9jl +2/8e37/AP49/N/1P7z+582K+b/gD8J9T139qrw94m8RfCD9pDRZY7sa7eal4q1bwS+jSeIo tCOi/wDCQXMelahJefbJtMQWXkWyCwBlEgtI5AbhfoD9srSvE3i39mrxr4a8IReMIfEXi3w/ q2j2Gr+GZbSPUfDtxJpl0YL2M3F5aAyCdYo4tkykTTQlmiiEs8Xxf+wV+yv40+G/7WPhTWtW /ZV+EHw30+y+2eb4j0T4KeFfDl/p26znQeXfWXjHUbmHezCM+XZzb1kZG8tWaVAD9H6+P/8A gvB/yjL8Vf8AY1eDf/Us0ivsCvg//gvz8D9F139jDU/Hc974wTW9F1/wdZW9tb+LNVttGkQ+ K9NBabS47hbC4kxM+JJoHkBEZDAxRlQD856KKKACiiigAooooAKKKKACiiigAooooAKKKKAC vqP/AIJs/tteFf2Ov+E0/wCEn0/xDf8A/CR/Yfs39lwQy7PI+0b9/mSx4z5y4xnoc44z8uUU AfqL/wAPs/hV/wBAD4g/+ANn/wDJVeffHb/jcV/ZX/Csv+JH/wAK587+0v8AhJ/9F8/7bs8r yfs/n7sfZJN27bjcmN2Tj8+6/QT/AIIUf81T/wC4T/7e0Aeff8OTPir/ANDB8Pv/AAOvP/kW vHv2sf2JfFf7HX9gf8JPqHh+/wD+Ej+0fZv7Lnml2eR5W/f5kUeM+cuMZ6Hpxn9pq/Pv/guv /wA0s/7i3/tlQB+fdFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfSfwC/4Jc/ED9oz4S6T4z0T WPB9rpms+d5MV9d3MdwvlTSQtuCQOoy0bEYY8EdDwPmyvvT9i/8A4Kj+AP2dP2afDXgzW9H8 Y3WqaN9q86WxtLaS3bzbqaZdpedGOFkUHKjkHqOSAcd/w5M+Kv8A0MHw+/8AA68/+Ra9i+Fn 7bXhX/gnR4DsPg1430/xDqvifwd5n2260OCGewl+0yNeR+W80sUhxHcIG3RrhgwGRgnoP+H2 fwq/6AHxB/8AAGz/APkqvHvin+xL4r/4KL+PL/4y+CdQ8P6X4Y8Y+X9itdcnmgv4vs0a2cnm JDFLGMyW7ldsjZUqTg5AAPYf+H2fwq/6AHxB/wDAGz/+Sq0/+CXXxt0r9ovxh+034z0S31G1 0zWfirb+TFfRpHcL5Xg7wxC24IzqMtGxGGPBHQ8D5q/4cmfFX/oYPh9/4HXn/wAi12v/AASs /Yq1D4C+L/2ib+W51DWPiV4H+IDWum2EHjzW9N8KahLL4O0GWFLqzjJtXz9q2tcS2M0seFZQ 5hiAAPtHxF+1j8LPCGseHNO1b4l+ANL1DxhqtzoWg2t34hs4Jtb1G2uhZ3Nlao0gM9xFdEQS RRhnSUhGAbiuvufFml2fiqy0KbU9Pi1vUrSe/tNPe4Rbu6t4HhSeaOIne8cb3NuruAQpniBI Lrn5A0b9gT4l+A/2uNW8S+FdU8H+HvB1941XXdNv7HVtTttZ0vS5pYtS1bT720Cmz1yPUNTu 9f8AJS8ZTo41Sa5tZJHkS2tvnD9nr/gkF8YP2e7r4X+LvGl94AsP+FTeTdX994UvNV17V7Kz 07R/AdmX02BNOinuLjUbfwfqVjPbx7GW2110U3nz28wB+p3hPxNb+NPCumazZx6hDaataRXk Ed/YT2F2iSIHVZbedEmgkAIDRyosiHKsqsCBn6n8WfC2iaP4p1G98TeH7TT/AANv/wCEkupt Rhjh8P7LWO8f7Y5bFvttZopz5hXEUqOflYE/nD+zn/wSJ+Mvhn4WaSuveJt32/4VeC9BvvCe p+I1XSo59Ih8P/bPC97bwWU/2jT7r+y9Tha7+2zQQJ4g1NY9Mm+03Mt31/gn/gkFqsH7HP7V vw41H4b/ALOHhjVfj74fsX0w+E7F7XRrPWU8M2dm0b2rWIa3s7TWree+tZFeeQG9kl2RTBjI Afd+u/Fnwt4X1i407U/E3h/TtQs/7P8APtbrUYYZof7Quns7DcjMCv2q6jkghyP3sqMibmBF aFz4s0uz8VWWhTanp8Wt6laT39pp73CLd3VvA8KTzRxE73jje5t1dwCFM8QJBdc/GH7Q3/BL bxV8Svjt8Y/H+heJPD8Gr+I9KvdW8D/bFmj/ALJ8UPb+E/7PuLnarr9nsb7wXpl2DtmFz9vm ikt1S2/0zwD46/8ABDXx346tfDeu6B8OP2YB4wl/tqDXJtbS31W/hgvNY0O8ilk1S+8PXY1v UBa2Gq2wv7+wSeNdWVpDezxz3N4Afqdo3izS/Eeo6tZ6fqen395oF2thqcFvcJLJp1w0EVws MyqSY5DBPBKEbBMc0bY2upJrPizS/Dmo6TZ6jqen2N5r921hpkFxcJFJqNwsEtw0MKsQZJBB BPKUXJEcMjY2oxH5wfshf8EePH/wM+K3hzVPFHhj4Ia7eR+IPD/i7xB4xn1O51TUdU1Kz8Oa ZZX0s2mT6YqXV4+qWuo3ttqhvobu2m1Sef51uL+zvfpDUvBniP8Aa7/aA+DHjrVfg54g+F2o fBPxXd6qb7xlPodxf6hp1/4f1jTp7bT5NLvb4r/pU+nSTRzNAjrEjAyNEFAB7/oXxZ8LeKNY t9O03xN4f1HULz+0PItbXUYZppv7Puks7/aisS32W6kjgmwP3Urqj7WIFdBX5oaV/wAES/H/ AMPvgZ4O0fQPFng++8RaH4gsbXX0vTcwad4s8HWHh/Q9Lm0CceVKFj1qfwnoUt2TE/2WG5vL dTfRJIt/0HiH/gkV4/8AifPC/izUfhg2iaraeC4dc8GadaXKeF2t9N1HwpcXmi29rKjINLs0 0DV3sIpFId/Fd9G0dqBNNeAH3/4s8WaX4C8K6nruu6np+i6JotpLf6hqF/cJbWlhbxIXlmll chI40RWZnYgKASSAKz/EfxZ8LeDv+Eg/tfxN4f0r/hE9KXXdb+2ajDB/Y2nN9o23tzuYeTbt 9kusSvhD9mm5/dtj5A0T4HfEzR/2NvhF+zj/AMK28QS6v8K9K8ETf8J1/aekr4N1TUfDbaZq f2fd9s/taK3ubnTfsX2j+zHeIzeb5EiJhvP/AAN/wRD8VfCL+yP7I8ceH9d/4QXSvDk2ifbL Saw/tzUdK/4QXdb3O3zvsdvJ/wAIDa4uE+0uv9tzfuD9gX7aAfofbeLNLvPFV7oUWp6fLrem 2kF/d6elwjXdrbzvMkE0kQO9I5HtrhUcgBjBKASUbB4T8TW/jTwrpms2ceoQ2mrWkV5BHf2E 9hdokiB1WW3nRJoJACA0cqLIhyrKrAgfMH7F/wDwT91X9hTxVNc+HE8H6/HN8Nfh34BvL0h9 Gu9buNAfUrS+1S5WOGbMh0+6tPIRpJC5tFgeSGNUmHyh8HP+DfzxV4J8d/DHxTrUPwgv/E/g T/hEd+rqk0t+n9jR/DiJvIne0EgxH4Y8SpFyuF1aMfILm5EQB+r1FfGH/BEf4HeIfgl8DPiO desNQij8S+NY7/T9W1HS73SdR8XpB4f0XTbjXLyyvoYb22vL++sL25nS5jMhmmlcS3Ubx3c/ 2fQAUUUUAFFFFABXx/8A8F4P+UZfir/savBv/qWaRX2BXwf/AMF+fhprWqfsYan4og+IfjDT tE03X/B1vceELe20ptG1Rz4r00CeaSSze/WQGRCBDdxx5gjyhBkEgB+c9FFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAV9h/8Env2p/Af7NH/AAn3/Cb69/Yv9t/2d9i/0K4ufO8r 7V5n+pjfbjzE+9jO7jODj48ooA/Yb/h6b8B/+h7/APKLqP8A8Yr59/bs/wCNl3/CK/8ACkv+ K1/4Qr7X/bP/ADDfsf2ryPI/4+/K37/s033N2NnOMrn8+6/QT/ghR/zVP/uE/wDt7QB8+/8A DrL48f8AQi/+VrTv/j9effHb9lnx5+zR/ZX/AAm+hf2L/bfnfYv9Nt7nzvK2eZ/qZH248xPv Yzu4zg4/cevz7/4Lr/8ANLP+4t/7ZUAfn3RRRQAUUUUAFFFFABRRRQAUUUUAFexfCz9gX4tf GvwHYeJ/DPhP+09E1PzPs1z/AGpZQ+Z5cjRP8kkyuMOjDkDOM9MGvHa/SX9gX9vr4S/BT9kv wn4Y8T+LP7M1zTPtn2m2/su9m8vzL2eVPnjhZDlHU8E4zjrkUAfLn/DrL48f9CL/AOVrTv8A 4/X2H+yx+1P4D/Yq+A+g/DL4m69/wjXjjwz9o/tLTfsVxe/ZvPuJbmL97bxyRNuhmjb5XON2 Dgggeg/8PTfgP/0Pf/lF1H/4xXx5+1P+yx48/bV+PGvfE34ZaF/wk3gjxN9n/s3UvttvZfaf It4raX91cSRyrtmhkX5kGduRkEEgH2H/AMPTfgP/AND3/wCUXUf/AIxWD/wTj+Keg/Gv4nft ReJ/DN//AGnoep/FW1+zXPkSQ+Z5fgzwvE/ySKrjDow5AzjPTBr4Z/4dZfHj/oRf/K1p3/x+ vZv+CQX7PXxB+Auo/tBalqmvfECK88GfECT7V8NNIPh+Ww8Qzt4Q0KaLNxPD5y3DiaHZt1GC ANFFv2r5xcA/SCivzwuf2DPHFh+2hZXi/DHT/EHhvwj8Sp/EHg3xFr1zo2s6ZBYalfQ69rVx d2t0jXum3kV9qGqxabc6N5c081pYLqKtb2cFxP5B8A/gF+1R4L8afCL4kfFGPxh4Nl+HNo1v 4g8S+LvH2m3NloGlw6V8PV1ue/ZL+dJrO/TQvFhjBWUpdXdvdypayEXUIB+r3hPxZpfj3wrp mu6Fqen61omtWkV/p+oWFwlzaX9vKgeKaKVCUkjdGVldSQwIIJBrQr8wf2c/hH+2TrHws0m8 8ReIviBd6TrXwq8FvJpMkkVjqs/kQ+Hzremy31zfxXln4gmWPxMkcq2kGX1WB7jVke1tY7C/ 4c/YB8f+Kf2IP21vCN38O/ih4Z8U/Gzw/aap4dsNV+LVz4gW6v38H6fB/Zi6hLqTytJb6tb3 NtcTXCww3VubaNmntUEUIB+l9FfGH7U/7G3jPxP8Wv2jfGfhPR9Q1XVdd8FeBdd8GQ3etiS0 1bxZ4Y1fWtVtdP8AKnmxaWbzppCzJH9nikF3cOsiTPNMvzB8fv2KP2htT8QeDviFoPw/+OHi PxlcWniLSbqW9+L11bajp0N3rfh2aO7vbGz8UWNlbyNY2usf6Ho18llNNBYSPFZIyWliAfrd RX5g/sU/sk/tBeAvjRZal478LfGB9d8YeK/DvjTxnqsnxHgg8K39ynhXRodQaVLPVftcVxb6 rBqax2kNlLpl1CYLSWIW0djdaZ9H+LNfsv20v2kfgF4v8F6J8UIY/g/41vb/AF4+JPDOv+EL SCwvvDGu2azLa6pBaxX8gu2s4w8Uc01uJ25ijmlLgH1fRX5weIf2EfjL8I/gv8N2+HOl+IH8 QeH/AIgeO9G8Q27+KlfVbj4f6z4qudduXttQmuRP/bF7b6XpVrb3L3SyxPqkryyQSqbu00PE P7OX7UfxLnhhuJfGHhDwdrdp4LivPD1n8R5ptT0WwXUfCjX2npqsc0d1JeWcVr4yN3fJKs19 DqunhZrt40hsQD9D6K+ENR1/xb4w/YM+GvwE8TaJ8UPEHxo0vw/4NbxzaX/hnV7vTPFAsRpt /r+lS6/JAdGupL+zt76zYT3wt7ia6ME0qiRyPMNK/wCCb/7R+j/af+Es1n/hYP2fwr4asvFH /FSPd/8ACxLWz/4Qz7don+lmP7R5n9h+Mfkv/JtX/wCEv5k/07U/s4B+n1Z/hPxZpfj3wrpm u6Fqen61omtWkV/p+oWFwlzaX9vKgeKaKVCUkjdGVldSQwIIJBr5Q/Ym/Zi+I/7K/wAU9S17 xjF4g8f61rPwq+GfhPW9aj8SNqn9qa5p02sWurXUX2+dJI7eKO6truTCRmbzJ5EimupJUf5g +Dn7D37XFv47+GPiPxTrfxgOoaJ/wiP9sxN8WLlrCT7HH8OI9Q8+zTUfs1xuWHx0Zd0b/aGM jN5rSWjMAfq9RXxh/wAER/GfjP4gfAz4j6r4x8Vah46ln8axx2fiT+3xrujeIHj8P6LHqV7o 1ykskH9lzawmqyQwweXHbl5IDb2skUltF9n0AFFFFABRRRQAV8f/APBeD/lGX4q/7Grwb/6l mkV9gV8H/wDBfnRfH8/7GGp32n+JvB9r8Podf8HJqei3Hhm5n1m8uP8AhK9N2yQ6it+kMEYJ gJjaylJEcg3jzFMYB+c9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfcn/ AARj+LXhX4W/8LJ/4SfxN4f8O/b/AOzPs39qajDZ/aNn2zfs8xhu27lzjONwz1FfDdFAH7j/ APDWnwq/6Kb8Pv8AworP/wCOV8ef8FYf+Mo/+EB/4Vl/xcb+wv7R/tL/AIRj/ib/ANn+b9l8 rzvs+/y9/lSbd2N3lvjO04/Puv0E/wCCFH/NU/8AuE/+3tAHx5/wyX8Vf+iZ/EH/AMJ28/8A jdcv42+HniD4aarHYeJND1nw/fTRC4S31KyktJXjLMocLIASpKsM4xlSOxr976/Lr/gtn/yd V4f/AOxUt/8A0svKAPjyiiigAooooAKKKKACiiigArsfCv7PHxA8daDb6rongbxjrOmXW7yb yx0W5uLebaxVtrohU4ZWU4PBBHUVx1fqt/wTb/aH8AeBf2LfBmla3468H6Pqlr9u86zvtZtr e4h3X9wy7kdwwyrKwyOQQehoA/Or/hkv4q/9Ez+IP/hO3n/xuv0l/YF+LXhX4F/sl+E/Cvjf xN4f8HeJ9K+2fbdH1zUYdPv7TzL2eWPzIJmWRN0bo67lGVdSOCDXsP8Aw1p8Kv8Aopvw+/8A Cis//jlfm3+318JfFfx0/a08WeKvBPhnxD4x8Mar9j+xaxoenTahYXfl2UEUnlzwq0b7ZEdG 2scMjA8gigD9JP8AhrT4Vf8ARTfh9/4UVn/8crzH9g3xbpXjr42ftVaromp6frOmXXxVs/Jv LG4S4t5tvgnwqrbXQlThlZTg8EEdRX5j/wDDJfxV/wCiZ/EH/wAJ28/+N19Lf8EWPDvxL+DF l8e9S1e+sND8DeGPiBNdeIPCs/gLU7/xXcyr4S0OVXtZYbsMMq1uy266fPLJtZUJMyeWAfpj RX54XOlfFex/bQsvD+pRfHDxNpvg34lTnSGeXXNO0DxTpupX0OtTXUeo2F5FbwR6Nban9ke2 1pLu0v49DitdPFvPNewp5h+yv+0t+1x8Vvi78D9c13TvjAs77NG1rTda8CXOhaRfb9N+Hh1l 9QSS0gjh8iSTxndWlyzRb7mzNvbyTQyfY5wD9XqK/LH9kf8Aaf8A2o9U+AXhaG00HT9M8CT/ AAf8Dapo934d8IzSXcNhNbeHhq2qabBbaedLS8tobvxEY9NMs00p03Sxb6R5SytqG/4c/wCF 2+N/2IP21nsvHX7WA8a6j4ftPEPw/TxL4ZttM1+ySbwfp93Ha2P2XTYrcXkl+LuxubexLSW8 kAZBb3czXU4B+l9FfCH7VXjL4l6h8ffjdc+DL744QN8L/D+neJ7Tw6vh/U/7M+IE2l3Okayu maRfQWq2kUc8UN7psgWa4vbubW75Zrd7bSrKJ/APj98af2l/DfiDwd8QNO8VftYavHrdp4i0 2PTdB8Bx6fozzHW/DsdvdCxfwvf6jptmLKbVJwmq2dxeodMnhtrm6jmF1qAB+t1FfmD+xT8Z P2jvEnxosm8e63+0BZ6n4+8V+HfEmq+GG+Hz2ulaNY3HhXRrq6trPU7zTHsbXT7fUJdRtbqw luor5kskeG5/tBZ4tX+j/Fn7THh79qf9pH4Baj8EfjDp/j7w74Z8a3tv490zwLr1lq+mR2V1 4Y11rSfVmtfMkijW9tYVhEkiQvNKNySSpA0QB9X0V+YOm/Ff4+6b8LPA3xE8H3P7QGsW3iTx XqPhTWvD/iXwhqMHiHwBot/Dpeo3viCaC4so4tQ1Czh0jU5LeGCzmsor3xH/AGfZfabezhtb vr/EPxw/ao8dzw2Xh6y+KHhnwpr1p4LjtNa1Hwnpq+KNJsr3UfCkc97cQm3ktF1R4r3xgt/D 9neGyTRbGX7PaJKst8AfofRXwhqP7a1z40/YM+GvgbWfiPp8P7TGreH/AAbefEDwbYanBYeP UtZBpt74mWLSrYpfwXg0g6nKsdnCt0nBtlWYRY8w0r/hsn/Sf+E+/wCFgf8AIq+Gv+E4/sfy v+PH/ijP7T/s3+z/APmIf8lF3f2V/p3/AB57f+YLQB+n1FfIH7E178Zfh/8AFPUtR+N2pfED V73V/hV8M47+1bTVu9K0rxRNNrFnrENmmmweR/x8fZJ7qVTIIkmR3ljtI4BF8ofs4eOP2xtJ 1H4PwvcfFDwv4U0208GWGoeE7P4baTp+jW9uIPhvDqEJUaWJraPGt+LC6RSxCA6S4jEKWUyU AfrdRXyh/wAEmfjv8UPjj4H+KqfF261BvGXhPxrb6ReabdaVFp6+HbiTw3oV/fabbiOJPPs7 fUL2+S2uHaczW4gkFzcoyTv9X0AFFFFABRRRQAV8f/8ABeD/AJRl+Kv+xq8G/wDqWaRX2BXw f/wX51vx/B+xhqdjp/hrwfdfD6bX/Bz6nrVx4luYNZs7j/hK9N2xw6ctg8M8ZIgBka9iIEkh 2Hy1EgB+c9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX6Cf8EKP+ ap/9wn/29r8+6KAP6CK/Lr/gtn/ydV4f/wCxUt//AEsvK+PK/UX/AIImf8mq+IP+xruP/SOz oA/Lqiv6CK/Lr/gtn/ydV4f/AOxUt/8A0svKAPjyiiigAooooAKKKKACiiv2G/4JZf8AJh/g T/uIf+nG5oA/Hmv2G/4JZf8AJh/gT/uIf+nG5r6Cr8ef+Cpv/J+Hjv8A7h//AKbragD9hq8A /Y3/AOTiv2sf+yq2P/qD+FK/Havsf/ghf8R/FXw78KfHO/vdM8Aaf8ItL+IFxqXiTxXq/i6b T7/RfK8KaJI7CzNi1s9uqpEzzSX0OxXlYp+6HmAH6g1n+LPCel+PfCup6FrumafrWia1aS2G oaff26XNpf28qFJYZYnBSSN0ZlZGBDAkEEGvgC5/bM+Ilp+2hZfDnW/iXqCXnhD4lT6DqXh7 QbKws9T8T2uoX0OpaaLRbvTLhdXs7HRdU0pNSWzm0660yG1v7qWW9W4s5B5h+yv/AMFvviP+ 0t8Xfgfc2d14A1fSfGOzSNU8OaBbtK+t3k2m/Dye9NtMZpZYrjSJvEevXMyDzFFnpVzHNGrx teQAH6vUV+YP7Of/AAWC+I998LNJ0m58DeH9FhsvhV4L8RQ69rOvtd/ZItSh8Ppe+IZra5vm 1W+0eyGs3slxcymOKP8A4Ry5WXVJJruT+z7/AIc/b++Lnj79iD9tbxnoXxw+GHibxF8M/D9p 4q8DXuleApdNbS9LuPB+n65Ddtp9zfzTCO6E0625u95juILl3FxEBZQAH6X0V8Qfth/tgfEf 4MfGz4rto/iXf4S+GfhWw8farY2/hdhNpek6XqGm3mq25nuAEe41LTH1aG0mVnt7qW3mgRtN m0a9uL75w+P3/BZn4ofCrxB4O8aTfGD4Iad4H8U2niKzsNMsvB8Wq6dqd/ba34dsYI7LWbjx HY2mrRpFqssv2+Keyhjht783VpDcQS29gAfrdRX5g/sU/wDBW74j/tAfGiy0/W/H3wgs1+IX ivw7faV4Mj0lr7xV4c0PUvCujayttFZx38Nze27NqEsMmrCKQWk1pPcSWjWUsg0j7P8A2n/i T4z8A/tI/s3adoWt6fY+FPGvjXUtB8Tae+mCa71RF8Ma1qFsI7hnxBHHPYKzqsZkkPlYljRJ I5wD2+ivzQ1r/gpX8XPhB4V8M+KtW13T/Enh3XfGurfDX+05/CkulaZZXt0mmalDr91FKYrh dL0OwtvEwvrd5Laa0mtX0+6lnlsrjWT0HiH/AIKu/EvxFPDpngmw8H6laa5aeC10PxpeeGdT j0y5TW9R8KWsetPpz3Mcy2d+PEWpJaWrXKSRzeFdQVri5Yyx2YB+h9FfKGo/te+M/GH/AASW +GvxjSfT/DfiLx54f8G6r4i1OwtQbTwpZatcaausapEtwZY4o7Cyury7WS682GEWoknWWJJF b5w0r/gpB+0frH2n/hLNH/4V/wDaPCvhq98Uf8U29p/wru1vP+EM+3a3/pYk+z+X/bnjH57/ AM61T/hEOY/9B1P7QAfp9RXyB+xN+1p8R/GXxT1JvjXqfh/wlDqPwq+GfiSHw9JpLaH/AMI3 rmuzaxaXthK1zK80lxLf2sMMcbshXMEIjMwkln+UP2cP+Cunx113Ufg/p0A+GEPg3VbTwZZ3 tneaPrGpazIl9B8N47thqVxqzu8hfxzMySTxzSAacnmtO8zyKAfqd8LfhP4W+B3gSw8LeCvD Xh/wf4Z0vzPsekaJp0On2Fp5kjSyeXBCqxpukd3O0DLOxPJJroK+YP8Agll+2t4q/bg+G3j/ AFzxbYeH9I1Dw14rh0dNL0yCaKbQvN0HSNTm0q+82Vy+oWF1qNzY3D7YMy2TZt7dt0S/T9AB RRRQAUUUUAFfH/8AwXg/5Rl+Kv8AsavBv/qWaRX2BXwf/wAF+fiXrWl/sYan4Xg+HvjDUtE1 LX/B1xceLre50pdG0tx4r00iCaOS8S/aQmNADDaSR5njy4AkMYB+c9FFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV+ov/BEz/k1XxB/2Ndx/6R2dfl1XU+Cf jj41+GmlSWHhzxh4p8P2M0puHt9N1We0ieQqqlysbAFiFUZxnCgdhQB+79fl1/wWz/5Oq8P/ APYqW/8A6WXlfPv/AA1p8Vf+im/EH/worz/45X3p/wAEufCWlftL/ADWNe+I+maf8QNctPEM 1hBqPiS2TVruG3W2tnWFZZw7rGHkkYIDtDSMcZY5APzKor9x/wDhkv4Vf9Ez+H3/AITtn/8A G6/OP/gr78PPD/w0/aV0Ow8OaHo/h+xm8MwXD2+m2UdpE8hurtS5WMAFiFUZxnCgdhQB8qUU UUAFFFFABRRX6rf8E2/2ePAHjr9i3wZqut+BvB+s6pdfbvOvL7Rba4uJtt/cKu53QscKqqMn gADoKAPypr9hv+CWX/Jh/gT/ALiH/pxua9B/4ZL+FX/RM/h9/wCE7Z//ABuvzb/b6+LXiv4F /taeLPCvgnxN4h8HeGNK+x/YtH0PUZtPsLTzLKCWTy4IWWNN0ju7bVGWdieSTQB+steAfsb/ APJxX7WP/ZVbH/1B/Clflv8A8NafFX/opvxB/wDCivP/AI5X1N/wQs+PHi/xNrnxo0zVfCHx A8VQa58SjcX3jqbUdNn02wdPCuhKkFwJ71dQeTbDGgMVrLGPOiG8BZPLAP0hrP8AFnhq38ae FdT0a8l1CG01a0lsp5LC/nsLtEkQozRXEDpNBIASVkidZEOGVlYAj4w/4eE/EeX9oT/hEr+b wB4d0/wV8QP+EV8dvDpTa5eaVFqWs+V4ZjnhXVLaay/tPTbnSvJvYItTia6vboTwWC2Eqvx/ wf8A+C4uq/Fn4mfCov8ADvT9I8HeM7SxTWlGsPd6nY3GqWngCTTpLY+VHFJHHd+OEt543CEw 2zXCOHUWcoB9/wDhPwnpfgLwrpmhaFpmn6Lomi2kVhp+n2FultaWFvEgSKGKJAEjjRFVVRQA oAAAArQr4A/Zz/4LTf8ACw/hZpMMXw6+IGtavF4V8FqNc1NfL0q88Q+IIfD4trK91KCyis49 zeJNOkZ7WFptkGpyf2fCkNot7oD/AIKS/Gi8/Y5/a0+JcHhD4IXV58ErQX/hF9F8Z32taNrN v/wjOna5JNLcixgNzGIL5JonhWIXBfyCbdYxezAH3fRXwB+1j/wWh/4Un+3Dr/wq0dvAFpp/ w80ptW8VtrWp7tbaC0uPDOo6pcWtjHIrtbweGtV1a6iK+bPc3GlXSpb7bMi7z/i5/wAFgfiv 8Nfi1Y2z/C74Yaf4ba01iO90zU/F+uTeKLC/t9X8PadaWV1Z6doV48N5I+v2e23tY76G5S8h ngvGtxFLdgH6H1wHwL/ZO+Fn7L/9q/8ACtPhp4A+Hf8Abnlf2l/wjHh6z0j+0PK3+V532eNP M2ebJt3Z2+Y+Mbjn5P8A2Vf+Csfj/wDaL1ubV5vht4P0j4b+IvGuhaN4c1698UXNq2n6bqmg aRq9vHqSpZTImqTpq9utvEGS0luHksjdxzLZy6n9IftDfHTxV8Kf2gPgJ4c0nS/D914Z+J3i vUPD2vX13czC/sfJ8P6pqdstrCqeW2+TTyJJZJPkVQqxSGbzIAD1+ivzB+Cf/Bwr/wALi8Rj V7LTfAGo+EvFHiu28E+EdN0/XPtGq/2xrVj4cufDNrqssTSfYvN+1eJftsiW0sdq+izW8b3M 1uovPX/GH/BZD+zdYl8PeHfBPh/xX4nn/sCw0y4s/GG7w3quo6hdeE4bgxajHZvI2nxx+NNG lgu1tme5Ed6Db24iha4APqD/AIZO+Fn/AAvb/haX/CtPAH/Czf8Aobv+Ees/7e/49/sv/H75 fn/8e/7n7/8Aq/k+7xXf184aj+29qviX/gnV8NfjLoWh6fpWt/Fm08Gpp9jfzPf2mhXHiW90 2yikl2eS91HaPqSytGpgM4gKB4DJ5ifN/gb/AILe+Kvi7/ZH9keB/D+h/wDCdaV4ch0T7Zdz X/8AYeo6r/wgu64udvk/bLeP/hPrXFun2Z2/sSb9+Pt6/YgD9H6K+YP2Cf25/FX7Yvju6k1b wv4f8L+GdU+FXgT4j6DDaalNqF//AMT+PVWuYrqRooY18mSwEUaxo25UMrPmbyIPm/4D/wDB cfxn4s1H4WaM3wr0/V9E8S2nhS3v/EeqeOgmsl9Tg8Died7O30eO1kkjl8cWBxG9vHKLW6YJ bjyomAPv/wCCnwI8M/s+eFbjSPDNrqCR3121/fXmp6rd6xqeqXBRI/Our68llurmRYooYVea VykMEMSkRxRovYV4B/wTx/bn/wCG+fht4l8Ux+F/+EW0/TNVsodLjbUvts1/p1/oOk65ZXE4 EUYt7g2urwpLboZkjlikCzzLtc+/0AFFFFABRRRQAV8f/wDBeD/lGX4q/wCxq8G/+pZpFfYF fB//AAX5+OOi6F+xhqfgSey8YSa3rWv+Dry3ubfwnqtzo0aDxXppKzapHbtYW8mIXxHNOkhJ jAUmWMMAfnPRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABX6i/8ETP+TVfEH/Y13H/pHZ1+XVepfBL9tD4l/s6eFbjRPBviX+x9Murtr6WH+z7W43TM iIX3SxOwysaDAOOOmScgH7bV+XX/AAWz/wCTqvD/AP2Klv8A+ll5Xn3/AA9N+PH/AEPf/lF0 7/4xX1H+xL8LNB/4KL/CrUPG/wAZbH/hMfE+latJodre+fJp/lWccMMyReXatFGcSXEzbipY 78E4AAAPzaor9hv+HWXwH/6EX/ytaj/8fr4M/wCCo3wC8Jfs6fH/AEfRPBuk/wBj6ZdeH4b6 WH7VNcbpmublC+6V3YZWNBgHHHTJOQD5sooooAK7Hwr+0P8AEDwLoNvpWieOvGOj6Za7vJs7 HWbm3t4dzFm2ojhRlmZjgckk9TXHV+kv7Av7Avwl+Nf7JfhPxP4n8J/2nrmp/bPtNz/al7D5 nl3s8SfJHMqDCIo4AzjPXJoA+G/+GtPir/0U34g/+FFef/HK/SX9gX4S+Ffjp+yX4T8VeN/D Ph/xj4n1X7Z9t1jXNOh1C/u/LvZ4o/MnmVpH2xoiLuY4VFA4AFdB/wAOsvgP/wBCL/5WtR/+ P18eftT/ALU/jz9ir48a98Mvhlr3/CNeCPDP2f8As3TfsVve/ZvPt4rmX97cRyStummkb5nO N2BgAAAH6Cf8Ml/Cr/omfw+/8J2z/wDjdeQ/sc/CbwrJ8Qv2vfBreGfD7eENQ+JUFhdaGdOh Om3NvP4H8LiaF7fb5bRyB3DoVIbe2Qcmvhj/AIem/Hj/AKHv/wAounf/ABivdf8Agit+2JJ8 TfHXxv0rxa3jDVvG/i34lC8OpW/gzUX0bZF4T0FFWbUbe1/s62kCWxAjlmjkY+X8pMse8A+4 bn9nvwDefEyy8azeB/B8vjLTbue/tNefRrZtTtbie0hsp5o7kp5qSSWltb27uGBaGCKMkoiq OQ0L9gn4QfD3R7eLwJ8OPAHw21fSP7Qm0HWvDHhLSrS/8M3l9apa3N9ZbrZ4o7h4YoUZmjZZ FhjSRZEXZXl8f/BR7xDffFrQfDK/DnT9FtJ/GupeD9d1XxBrl7aWmkvBq72dmge3025jivL2 zaxvbe31J9PjuRrGnR2k940ztFz/AMNv+C2ngH4qfEz4caXp3hTxhaeG/H9pC8ut34to20S4 vLTwhc6ZHLbxyyPJHcP4y021aSJmMNwDuQ2/mXUQB9AeE/2KfhH4L8K6Zo1n8OfB81ppPgqL 4cQSX+mRX92/hqNAi6PLcTh5p7MgAtDK7K5yzBmJJ0PDv7J3ws8IaP4j07Sfhp4A0vT/ABhp VtoWvWtp4es4Idb062tTZ21ldIsYE9vFakwRxSBkSIlFAXivm/4J/wDBbX4R/ELwrpWmS6vq Gt/EJ/BXhvxLNo1hpUVjd6pdaymkra2cVq93MLSSWfXNHRTc3P2UHUQq3s32O/e20NY/4Kl+ I9D/AGe/2nfHV/8As/fEDR7r9nD72i6lr2hi513GjWmqy7pLa8nit/Ihu0kl2tN+4aNovPuD JZxAH0/41+E/hb4led/wkfhrw/r/ANp0q90KX+0tOhu/N0688r7ZZN5inNvP5EHmxH5JPJj3 A7FxwHiL/gnt8AvF/gTw54W1b4H/AAg1Twz4P+0/2DpF34N06ew0T7TIJbn7LA0Jjg82QB5P LC72ALZPNef/ABr/AOCit78HfjZ4y8O3HgbyPD/gb+xnvtV1nULrSpr+zutQ0uHUdZskeya1 udH0q01Iz3l2t2GtpbVop4YIpIrp/P8A4j/8FtNM+HXjHTGuPg18QF8BajpWt6ovizUvEvhT Q7bbpmqabpkm6LUNXgNri61FIZYb9rS8gnMcLWpk89bYA+kPCf7EXwX8BfEzTPGuhfCH4YaL 4y0W0isNP16w8K2NtqdhbxWgsooYrlIhLHGloq26orALCBGAEG2j4S/se+CPg1qK3lgPGGv3 kF3Hf2c/i/xnrPi2TSrhILm386zbVbq5NpI0F3cxO9v5ZkjlZXLLgDw/9nT/AIK96P8AtJfE WfR9F+F/xAbSdV8V2Og+FNc+0aXHZ+JLG50Oy1v+0/391E0GyxvYrr7EQ19Jat5sdvI9vqEF j7h8bf2jbn4O/G/4NeD18K6hq1p8WvEF/oMutpeQQ2mgvbaNf6ogkjLGaaSYWLoionlqFlZ5 UZYo5wDf8Wfs9+AfHsGpxa74H8H61HrV3Lf6gl/o1tcrf3EunHS5ZpQ6HzJH09ms2dslrcmE kxnbRp/7PfgHSdOtLO18D+D7a0sLTSrC2gi0a2SO3t9KnNxpcKKEwsdlOzS26DAgkYtGFYk1 8weFP+CwFuuneHdT8YfD7UPCtj4xu9Q0nSNNe8ni8RQa35+ix6P4av7G9tbRLLXNRTVZZltn na3jt7JrpbqezZrqLsPiP/wVZ8E/DLWNT0G88JfEC78b6X/ZFo/he1g0838mp391ods2lrM9 2tn9otW8S6C00huBbFdSXyZ5zDcrCAegf8MI/DP/AISP7Z/ZfiD+yf7K/sL/AIRP/hKtW/4Q 3+zvsP2D7F/wj32n+yfs/wBm+TyvsuzPz43/ADV3/iP4T+FvGP8AwkH9r+GvD+q/8JZpS6Fr f2zToZ/7Z05ftG2yudynzrdftd1iJ8oPtM3H7xs+Yax+29pd1+xL4M+NPh7Q9Q1C0+JNp4af w3peozJYyNceILmytNNjvJI/OEEYn1C38+SJZzHGJWRJ2VUfw/w7/wAFvfC3xD+y/wDCM+B/ EFz/AMJLpWiTeGP7Tu4bP7ZqOqf8Ir5Vve+X532W3T/hNdA3XEX2l/l1PEB+zW/20A+v9d+E /hbxRrFxqOp+GvD+o6hef2f591dadDNNN/Z9095YbnZSW+y3Ukk8OT+6ldnTaxJrA0j9k74W eH/sP2D4aeALL+zPs/2P7P4es4vsn2f+zvs/l7Yxs8r+yNJ2bcbP7MssY+zxbPP/ANj79vez /bI8d6hZaT4N8QeHvD//AAr/AMIfELR9T1e4tfO1mz8QR6hJGot4JJfJ8j7CY3Mjhml80Knl JFNP4f8AB7/gup4S8aaj8OtCuvAHxQ1e88YWnhxLjxNZ6XpGn6Mlxq0HhhhIbaTV5buGOOXx doqvGouChuXCPcLDJIAD6g/ZW/ZP8PfsleFfEdlo13qGsar408QXPirxJrV/b2Vtd69qlwkU cl3LDY29taRyNFBCrGC3iEhjMsgeaSWWT0+vIP2Nv21vC37cfg7xDr/hCw8QWek6DqsOnRT6 rBDD/a0Fxpdhq1nf26xyuwt7ix1K0lRZhFOm8pLDE6sg9foAKKKKACiiigAr4/8A+C8H/KMv xV/2NXg3/wBSzSK+wK+D/wDgvz+0L4B8OfsYan8OdR8ceD7H4ha/r/g6/wBM8MXGs20Ws6jb r4r00tNDaM4mkjAgnJdUIAhk5+RsAH5z0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFfqL/wRM/5NV8Qf9jXcf+kdnX5dV71+y5/wUQ8a/sk/ D+88N+HNL8L3tje6g+pO+pW08sokeOKMgGOZBtxEvGM5J56AAH7HV+XX/BbP/k6rw/8A9ipb /wDpZeUf8Ps/ir/0APh9/wCAN5/8lV6l8EvglpX/AAVs8K3HxH+I9xqOi65ol23huCDw26W1 o9vEiXCuyzrO5kL3UgJDhdqr8oIJIB+dVFfqL/w5M+FX/QwfEH/wOs//AJFr40/4KIfsueH/ ANkn41aX4b8OXms31jfaJFqTvqUscsokee4jIBjjQbcRLxjOSeegAB4LXsXws/b6+LXwU8B2 Hhjwz4s/szRNM8z7Nbf2XZTeX5kjSv8APJCznLux5JxnHTArx2vvT9i//glz4A/aM/Zp8NeM 9b1jxja6prP2rzorG7to7dfKupoV2h4HYZWNScseSeg4AB4b/wAPTfjx/wBD3/5RdO/+MV9h /ssfsseA/wBtX4D6D8TfiboX/CTeOPE32j+0tS+23Fl9p8i4ltov3VvJHEu2GGNflQZ25OSS Sf8ADkz4Vf8AQwfEH/wOs/8A5Frx74p/tteK/wDgnR48v/g14J0/w/qvhjwd5f2K61yCae/l +0xreSeY8MsUZxJcOF2xrhQoOTkkA+ov+HWXwH/6EX/ytaj/APH65H9hD4DeGfBHiT9rXwBo trqGjeGJPiVFZLFYard213bpceCfDDStFdpKLmKTdK7LJHIsiEgoylVI+av+H2fxV/6AHw+/ 8Abz/wCSq9F/4I//ALeXhb40/Fj47aX4s8YfD/Rvit43+JSX9l4Th1WG31K/t4fCPh+Lzrey lla4kjCWkpLqGH7mU5AUgAH1DefsFfCe/wDjtqXxKl8KZ8Ya3qtprerXK6neLba1eWdvbW9j LeWolFtdfY1tIJLVZ43W1nD3EAinkklfz/wX/wAEivgt8FdH0mXwB4X/ALK8QeEftF14Zudf 1zWvEVhYXjWumW9u9zaz34+2W9v/AGJozw28kgWBtMtngNvLGkq6Gn/8FNPCXiH4teD/AAVo /hbxhqet+MfEGu6Bbo8+kaasR0fV7zS765jF7fQPfRxvYXNw8enLdXENusUk8MJuIFlPBv8A wVg+CXxD+JngXw1oniPUL9fiFaNcaZqx0i5t9MgdrTQby1guZZkQwSXUHiXSfILqI3muVti6 3LxwSAGh8M/+CZfwn+GHws0vwlbaf4gvNP0/wrpXhOZ5NfvLb+0V02G0istUlhtpIbZdYhWw svL1OKKO8h+ywCKWNYo1TQ8Cf8E5Pg98NPhL458B6N4Z1C18G/Ejw/aeF/EGjv4i1Oe0u7C1 0iPRokjSS4b7PJ/Z8MMDzQeXNKIImkd3jVhn/DP/AIKVfCfxt8LNL1688Y+H4tQuvCuleKLm w0xry/3rfw2kkNtZB7aG5vbhm1DTlW1S3W8P9qaZvto2v7VJc/wv/wAFRvh54y+Hvxi8S2Oh fFC3sfgnd29nraa14Mv/AA/JcvPZW93G0X9ox26RRqlynmyXbW8drGPtNw0Fm8d04B2Hib9g r4T+M/EfxS1bVfCn2/UPjNpU+ieLJZtTvG+2Wc9jb2FxFAPNxZefa2dnHM1oIWm+xWrSF2gi ZOP8b/8ABJn4G/EXTtBh1fQfGFxeeHbua/t9Wj8f+IbfWbq4ln0+4M13qEd8t3eyJLpOlmJ7 qWUwjTbQRFFgjC6Go/8ABSHwJYeMdc0T+x/iAt1oH/CD/aGvvDdxpO//AIS3VG03Tdkd6IJW 8qYZufkHk/PF81xFNBHz+pf8FePhFa/FPRvC1lYfGDxD/b+lX+s6fq+hfCvxJq2laha2k1lE Z7Se3sn+3W8n26F47qzE1qU2lpl863EwB0Hwz/4Jf/Bb4P8AiPS9Q8P+HfEFnDo/9lNb6RJ4 x1q50F5dLsbSw064l0uW7axmuLaDT7Hy55YWlWSzgmD+dGsg6D4ffsky6J4j0fVvG/xK+IHx c1Dwtqq634cl8TxaPZ/8I/efYb6wklhGlWFiJfNtdQuI2W5Eyj5GQIw3Hj/hD/wVZ+D3xy+J mteFPDl54w1HUtL8QW2gWktt4R1O7tNeE9pZ3S6lZTQQSJLpaJf2nmX7FbeMXVpK8iwXtlNc +ofGD9pDQvgl478BeHtWs/EFze/ETVW0jTnsNNkuoYZVjL5lZfwJSMPKsS3Fy0a2lne3NsAe f6Z/wS4+BOj/AAT8LfD238DbfDPgrVU13RFbWtQe/sNRi0+TTra9W+ac3bXFrZyCG2laUvaL b2nkGI2tuYtDwx/wTe+CXg3TtHtdM8A6faR+H7TQrLT2W7uTJbJos9hPp7BzIXMivpWlCWQk yXUel2Mdw00drCic/wCDv+Cpvww8WaJoWqTReMNC03xFaeKLq0fVtDltbsnQdfs9AntjZHN7 9sub6+t0tbQQG4lLiMxRzlYG6Dxl/wAFG/g94A06+m1bxPqFveadaaZdT6Svh3U5tZQ6jPZW 9tbDT0t2u2vPN1LTRLaLEbi3Gp2JniiW6gMgBnwf8E99HttH0jwnH48+IEfwi8P6VY6Npfw5 U6WNI02CxtYobJ4L8WX9tRXFvLBDdxXKaiJ4rmGORJF2qo6C8/YD+Dd1/aXlfD3w/pv9paVa aMP7MjbTv7Pgtfs32Z7LyGT7DcR/YdO23Nr5U4/snTP3n+gWnk6Gv/te+DdO/Zg0P4uabPqH iLwp4ttNIuPDi2FqY7vX31eW3g0uCJLgxCKS6nu7WJTcNDHGZgZniRXdfL7T/grz8G9f8z+w L3xB4j+1aVpmo6H9j0tof+Eln1D+xvsthbfaDF5dw3/CSeHMtdeRAv8AbMOZh9nvfsoB7Bo/ 7LngTwvrDX+iaH/wjk50rQtCVdEvbjS4YdO0W6uLrTbKOK3kSOO3ikurhTEihJYpWhlEkOI6 8/8ACP8AwS4+BPgT+xP7K8DfZf8AhHP7P/s7/idahJ9n+wf8I99k+9Od3l/8IroH3s7vsHzb vOn83oP2c/24vAn7VnjvVtE8FN4gv4dL8K+HfGUeq3ejXGn2Gp6drsd3LYyWrXCpJN+7s3Mj KmxGcRFvOjnji8v+Gv8AwWb+BvjTUfA+hXniTUIvFfjO00R44tL8KeIb/RkuNVg0iW3jTU30 2KHyydd0hfMnEBQ6jaiVIHlEYAPQP2EP2Mbf9iv4e+JtMXU9P1LUvGXiBvEOorpVhPpujWDi ytLCG106znubqW0s4rSwtUS3NzJHEVZYRDAIbeH2+uA/Z7/aj8CftVaPr2o+Add/4SDT/Deq /wBjXt0tlcW8LTm1t7yN4HmjQXNvLa3drPFcwF4Jop43jkdWBrv6ACiiigAooooAK+P/APgv B/yjL8Vf9jV4N/8AUs0ivsCvj/8A4Lwf8oy/FX/Y1eDf/Us0igD8t6KKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr9Rf+CJn/JqviD/s a7j/ANI7Ovy6r6j/AGJf+Ck3/DHXwq1Dwx/whn/CR/b9Wk1T7T/a/wBj2b4YYvL2eRJnHk5z kfexjjJAP1lr8uv+C2f/ACdV4f8A+xUt/wD0svK9B/4fr/8AVLf/AC5f/uWj/hRP/D4r/i5v 9q/8K6/sL/imP7N+zf2v5/lf6T5/m74Nufte3ZsOPLzuO7AAPz7r6T+AX/BUf4gfs6fCXSfB miaP4PutM0bzvJlvrS5kuG82aSZtxSdFOGkYDCjgDqeT7j/w4o/6qn/5bX/3VR/w4o/6qn/5 bX/3VQB59/w+z+Kv/QA+H3/gDef/ACVXsXws/Yl8K/8ABRfwHYfGXxvqHiHS/E/jHzPttroc 8MFhF9mkazj8tJopZBmO3QtukbLFiMDAHPf8OKP+qp/+W1/91Uf8N2f8O0f+LJf8It/wmv8A whX/ADGf7S/s37Z9q/03/UeVLs2fadn+sbOzdxnaAD0H/hyZ8Kv+hg+IP/gdZ/8AyLT/APgn N+zzZ/Bab9q74eeGNe8QaXb2vxKS3stYBtZ9SsHn8F+GZPPTzIGt2kjeUsgkheP5VDo4yD53 /wAP1/8Aqlv/AJcv/wBy16J/wSQ+O3/DS+pftKeN/wCy/wCxf7b+KsP+hfaftPk+V4Q8Mw/6 zYm7Pl7vujG7HOMkA9B1n/gnX4W1347eJPG8/ir4gG28ZeK9K8Za/wCGP7ThbQdU1HSbfTYt Lk8poTLb/ZptLtbndbSwvcSDZctcW6Q28Xj/AMNP+CIngn9n/R/Dl7oPin4geOtX8Bf6XpGm eJ9X0+wsNbltrXw3Hpdne3Flpglit7WbwjoMiywJ5pa3kMwuklkhf7fooA+MPgn/AMERPhr8 IfCulL/auoP4rt/BXhvwxf6/YaTplvd3Oo6Cmk/2frUU01vPdxSRS6Lp0i2LXMmnMbcGW0md 5ZJPUPhJ/wAE/rP4OeDvHGk6f8U/jBez+Of7Eu5tWvtYtZNVsNT0vS7DTY9US5W2WSe4uI9M spLmO8NxbTvE6tB5M08Mvv8ARQB4BqX/AATY+HEvws8c+CdOPiDQ/DPjn4Vad8HZLOzvVk/s rQ7CHVIbb7NJOkkn2hY9WuFMkzShvKhJXIcv5f8AGz/giP4G+PvgvSNI1/4j/FCWWyu9TvdU uhB4fmj8SPf6rpWrTrdWM2lPpyRm90WxmaO1tYI55PtL3CzyXdy832fRQB8ofAf/AIJG+Ev2 etb8MHRviJ8ULnw34Vu9F1O18O3cukC0e/0rQLLQba8+1RWEeoxSPZWEKzJBdxwzh7iKSNre 4mt5Owg/ZM8bfF7WNIufjV4+8P8Aiy28M6rY6vpeneDfD+oeFrC5ltbqK9jGoQz6pfrdeXfW mnXMLx+RLE1o8Zke3urmCX3+igD5w+KX/BL/AMA/Fn4Z+FPDF/rHjC0j8D/ErU/i1od9aXVs t3Y+ILy71W9jn+eBonjtbvVpJoYpI2Qm2t1mE8fmxy5+nf8ABJn4cWusaZq15rnxA1vxBp3/ AAj0z6xqmsrd3+oXmk3Wg3DX1xI8Z824vv8AhGNBjumI2lbDdCtvLPcyzfT9FAHzBZfsDeKt O+FnhD4Rw/ELw+nwS8B6VoenaHa/8IpMfGVjPo0Nq+m3/wDaxv8A7DJcQ6hZ213tk0poJBH5 MkLozElp/wAEhvg3oHmf2BZeIPDn2XStM07Q/seqNN/wjU+n/wBjfZb+2+0CXzLhf+Eb8OZW 68+Bv7GhzCftF79q+n6KAPIPhl+xV4W+B2sRXXgfUPEHhSG18K+FvBNpaWs8N1DaaP4fur2e 1tVN1FNI3nR39xbzSO7SGIqY3imHnV4/8Ov+CKnws+GX/CLfYNf+IE3/AAiP9j/Y/tF9Zt5n 9mf8Ib9n8zbbDO7/AIQjSd+3bn7Re4274vJ+v6KAPnD/AIJl/sZar+xZ8JfFOlau+n2svijx BHqlnolhqb6vaeFbC10jTdHsdMiv5LW0lvI4bTS4Ns01vHNhwkjXEkb3U/0fRRQAUUUUAFFF FABXx/8A8F4P+UZfir/savBv/qWaRX2BXxv/AMFl/wBguy/as/Zo1LXfCXwm8L+OfjXo1/4d fQtSNjpsOvQ2dpr1neXNvbX920fkr9nF4dhmRW82ReshBAPy/orpPGX7Mvx9+H/iPwnpOr/s +eP7TUPHOqyaJokX9v8AheT7beJY3d+0WV1YhMWtjdSbnKr+6253Mqt0H/DCn7TX/Rt3j/8A 8Kbwn/8ALigDzuiuk8Zfsy/H34f+I/Cek6v+z54/tNQ8c6rJomiRf2/4Xk+23iWN3ftFldWI TFrY3Um5yq/utudzKrdB/wAMKftNf9G3eP8A/wAKbwn/APLigDzuiuk8Zfsy/H34f+I/Cek6 v+z54/tNQ8c6rJomiRf2/wCF5Ptt4ljd37RZXViExa2N1Jucqv7rbncyq3Qf8MKftNf9G3eP /wDwpvCf/wAuKAPO6K6Txl+zL8ffh/4j8J6Tq/7Pnj+01DxzqsmiaJF/b/heT7beJY3d+0WV 1YhMWtjdSbnKr+6253Mqt0H/AAwp+01/0bd4/wD/AApvCf8A8uKAPO6K6Txl+zL8ffh/4j8J 6Tq/7Pnj+01DxzqsmiaJF/b/AIXk+23iWN3ftFldWITFrY3Um5yq/utudzKrdB/wwp+01/0b d4//APCm8J//AC4oA87orpNS/Zl+PukfFPRvBNx+z54/j8T+INKv9bsLP+3/AAuftFnYzWUN 1L5g1by12SahZrtZgzedlQwVyvQf8MKftNf9G3eP/wDwpvCf/wAuKAPO6K6TUv2Zfj7pHxT0 bwTcfs+eP4/E/iDSr/W7Cz/t/wALn7RZ2M1lDdS+YNW8tdkmoWa7WYM3nZUMFcr0H/DCn7TX /Rt3j/8A8Kbwn/8ALigDzuiuk1L9mX4+6R8U9G8E3H7Pnj+PxP4g0q/1uws/7f8AC5+0WdjN ZQ3UvmDVvLXZJqFmu1mDN52VDBXK9B/wwp+01/0bd4//APCm8J//AC4oA87orpNS/Zl+Pukf FPRvBNx+z54/j8T+INKv9bsLP+3/AAuftFnYzWUN1L5g1by12SahZrtZgzedlQwVyvQf8MKf tNf9G3eP/wDwpvCf/wAuKAPO6K6TUv2Zfj7pHxT0bwTcfs+eP4/E/iDSr/W7Cz/t/wALn7RZ 2M1lDdS+YNW8tdkmoWa7WYM3nZUMFcr0H/DCn7TX/Rt3j/8A8Kbwn/8ALigDzuiuk039mX4+ 6v8AFPWfBNv+z54/k8T+H9KsNbv7P+3/AAuPs9nfTXsNrL5h1by23yafeLtViy+TlgoZC3Qf 8MKftNf9G3eP/wDwpvCf/wAuKAPO6K6TTf2Zfj7q/wAU9Z8E2/7Pnj+TxP4f0qw1u/s/7f8A C4+z2d9New2svmHVvLbfJp94u1WLL5OWChkLdB/wwp+01/0bd4//APCm8J//AC4oA87orpNN /Zl+Pur/ABT1nwTb/s+eP5PE/h/SrDW7+z/t/wALj7PZ3017Day+YdW8tt8mn3i7VYsvk5YK GQt0H/DCn7TX/Rt3j/8A8Kbwn/8ALigDzuiuk039mX4+6v8AFPWfBNv+z54/k8T+H9KsNbv7 P+3/AAuPs9nfTXsNrL5h1by23yafeLtViy+TlgoZC3Qf8MKftNf9G3eP/wDwpvCf/wAuKAPO 6K6Twb+zL8ffiB4j8WaTpH7Pnj+81DwNqseia3F/b/heP7FePY2l+sWW1YB82t9aybkLL+92 53Kyr0H/AAwp+01/0bd4/wD/AApvCf8A8uKAPO6/UX/giZ/yar4g/wCxruP/AEjs6/N7wb+z L8ffiB4j8WaTpH7Pnj+81DwNqseia3F/b/heP7FePY2l+sWW1YB82t9aybkLL+9253Kyr91/ 8E7/ABL8Zf2SfgrqnhvxJ+y18Z76+vtbl1JH03XfBksQjeC3jAJk15DuzE3GMYI56gAH3zXx 5+1P/wAFYf8Ahmj48a94I/4QH+2/7E+z/wCm/wBufZvO823im/1f2d9uPM2/eOdueM4HSeDf +CjXij4geI/Fmk6R+y7+0Beah4G1WPRNbi/tHwXH9ivHsbS/WLLeIAHza31rJuQsv73bncrK vxX+2h8Af2hf2jP2lvEvjPRP2Zvifa6ZrP2XyYr7xF4QjuF8q1hhbcE1p1GWjYjDHgjoeAAe 3/8AD9f/AKpb/wCXL/8ActH/AAwn/wAPLv8Ai9v/AAlX/CFf8Jr/AMwb+zf7S+x/Zf8AQv8A X+bFv3/Zt/8Aq1xv2843H4f8G/sy/H34geI/Fmk6R+z54/vNQ8DarHomtxf2/wCF4/sV49ja X6xZbVgHza31rJuQsv73bncrKv6KfsX/ABb+LX7Of7NPhrwZrf7K3xwutU0b7V50tjrfgqS3 bzbqaZdpfX0Y4WRQcqOQeo5IBxf/AA4o/wCqp/8Altf/AHVXon/BJD4E/wDDNGpftKeCP7V/ tv8AsT4qw/6b9m+zed5vhDwzN/q977ceZt+8c7c8ZwOh8G/8FGvFHxA8R+LNJ0j9l39oC81D wNqseia3F/aPguP7FePY2l+sWW8QAPm1vrWTchZf3u3O5WVef/Zc/ZSl8X/Gj45/G3xD8GfD /wAOfi74r8V/avAuueMtG0fW9e0SzTwrpGlq7y6deSkW5ure+zbxXsbPHI4Jj80mgD2Cz/bu +GeseMdN8P6VqniDxBq+o6rd6M0GieFdW1X+y57bVLnSZXv2traRbC3N9ZXkSXN2YoJfsdw8 cjpDI66GiftrfCPxP8TPCfg7SviP4P1bxF46tLq88P2thqcV0urJbWljfSrFJGWjaT7HqVld LHu8yS3nEyK0Ss6+P6r/AMEyrm//AGn5fHEXjnT7Xw6PGqeM9L0iPwrAup+D38qwe9t9F1NZ VksY9WvbS4l1VTHLHfQ300Yjt5ZLi6uPD/g7/wAEQde/Z70fwdqeq/Ej/hYP/Cu/s93Lo+ge EY9Iv/EkWl2vgv8AsuztpbvU3gt7g3HgbTjNLMximW9uUAtPknjAPuDwV+094J8X/CyHxbN4 i8P6Np6eFbLxrqSXmuafL/Ymk3UMssV3czW88tsLcrb3OLiOV7d/s0xjldULVx/gT/gpR8Bv it4V8c634R+Kvg/xjpvw5u7Sw12Xw/eDVmiuLtI2s4YEtw73cly8qQQJbCUz3Ae3jDzo8S/L /wCz5/wQtk+FvhXw5fX3jPT4/GWn+CvCdlLKIdR1G00zxHoSeGWSe2Q3kFq+lzS+FNLM8Jso r6cIwF/CnlRQ+4fDj9hPx34a+G3xh0TxL8VPD/jLUPi5/ZF/fX994Ct/Ju9RttB0zSNRGoWb 3D213p+orpcZktIktnjiurqKO4DNFPCAegTft+fBtdYhsIPiF4f1Ke6/sf7M2nSNfw3n9rXV ja2Pkywq8c2+TVNLZ/LZvIi1Swmm8uG7gkk5/wAa/wDBVv8AZl+G/wAU5vBPiD4//CDRvE9n 9tW8s7zxZZQ/2dLaTRQ3FvcyNJ5dvcLJMqiCVklfZMVRhDKU8/8AEH/BMjxV4n0fx3ourfFz /hJ9P1T4fweC/BOu+IdAm1Dxh4Gure1shBqiakL5IZrj+0rMapJLHaW91LdpZs11tsLRYuP+ JP8AwSR+JfifwX4Is/Dfx60/wPqXhW01XSnGjeFdTsdOs9LvdV0LUf7L0kWutQX+m2anQkgM f26crDfXMNu1pAlrBbAH0h4d/wCCgfwP8VeO/Efhiz+LPgBvEHhPxXbeBtTsJdbt4JodcuYz Jb6cgkZfNuJNsqKke4mW2uYh+9t5kj6/4j/H7wb8IfFXhjRPEviHT9H1Xxld/Y9It52Ia5ff FEGJAIjjM89rbiSQrGbi9s4AxmuoI5PlD9kr/gkn4q/ZS8W+D2sfit4futC8Lf2M7T2vguaz 8ST/AGLwzoug3Not+dRkthp98uh2s01rNZTYO1o5Eube0vLf1DUvgt8XP2mvFXhK++JMHg/4 Y2ngLxBZ69ZQeDfE0viWfVXicyMRc3OlWE2nSYjFo7QtNHc2Gp6rbSRK00M8IB2HhL9vz4N+ ONz6b8QvD81tHpWra7LeSSNBZ22naZ9ha8vZbiRViitxDqenXMcrsqXFrewXMJlt3WU9Brv7 WPws8L+BLjxTqfxL8Aad4Zs9K0/XZ9XuvENnDYQ6dqEjxWF607SCNbe6kjkSGUnZKyMELEEV 8v63/wAEk/FXj/4WeBYPFHxW8P3fxN8FfECL4gXPjbS/Bc1jc+MJ4IYZYbXVon1GV57d9Tst JuZoYZoIDBpFja28NmtraywaEn/BIu58QfEKx8Z+JPinqHiDxkLvw3rGq6i2gwW0eq6pY3vh G41O5MMThIo71PBelLFCmBaSXF85a5SSGG3APp/xL8fvBvhT4NRfEK48Q6fdeDbq0tryy1PT mOox6sl0Y1tFs1tw73cly8sKQRwCSSeSaJIld5EU8heft+fBu1/tLyviF4f1P+zdKtNZH9mS NqP9oQXX2b7Mll5Cv9uuJPt2nbba182c/wBq6Z+7/wBPtPO8f0v9jv4qaR+zj8OfgBHbeAG8 BfDHSvCsOl+Pm8QXh1e41Hw8theWVxPoAsPJa3fUtPhWW3TVkdrZpCs8chXbz/h3/giH4W+H n2X/AIRnxx4gt/8AhGtK0SHwx/adpDefY9R0v/hFfKuL3y/J+1W7/wDCFaBut4vsz/NqeJx9 pt/sQB9QfDr9qr4cfF7x3J4a8K+NPD/iPV10qDW0TTrpbmG5s5o4JVlhmTMU22G7spXWN2aK LUbCRwiXls0vH+DP+CkXwL8Waj4V0a4+Lfww0Xxl4vtNMuLLwreeNdHk1kvqMEM9pAIre6lS aSVLiEoYHljlEiNE8iOjNx/7N37A3ir9l3x3pzaJ8QvD+veD/CnhXRPBPhG08Q+FJrrW/Dej 2MemxXVql7Dfw2zfbFs7iaSRLKORriazaVp4NPtrUeP/AAj/AOCGP/CrP+EJ/wCLo/b/APhD v+Ef/wCZb8r7Z/ZX/Cvf+nptnm/8IH/tbP7V/i+zfvwD7f8AAHxZ8LfFf+2/+EW8TeH/ABL/ AMI1qs+hav8A2VqMN5/ZWowbfPsrjy2byriPcu+J8Ou4ZAyK6CvlD/gkN+yn4m/ZN+Bni3S/ EWn6ho1prHiC0fw7pmq/ZP7Z07RtO8P6NoVlHqJs7m5tGvDFpAd5Lado5RIsgS2aRrSD6voA KKKKACiiigAooooAz9Z8J6X4j1HSbzUdM0+/vNAu2v8ATJ7i3SWTTrhoJbdpoWYExyGCeeIu uCY5pFztdgdCiigDP1nwnpfiPUdJvNR0zT7+80C7a/0ye4t0lk064aCW3aaFmBMchgnniLrg mOaRc7XYHQoooAz9Z8J6X4j1HSbzUdM0+/vNAu2v9MnuLdJZNOuGglt2mhZgTHIYJ54i64Jj mkXO12B0KKKAM/WfCel+I9R0m81HTNPv7zQLtr/TJ7i3SWTTrhoJbdpoWYExyGCeeIuuCY5p FztdgdCiigDP1nwnpfiPUdJvNR0zT7+80C7a/wBMnuLdJZNOuGglt2mhZgTHIYJ54i64Jjmk XO12B0KKKAM+58J6XeeKrLXZtM0+XW9NtJ7C01B7dGu7W3neF54Y5SN6RyPbW7OgIDGCIkEo uNCiigDPufCel3niqy12bTNPl1vTbSewtNQe3Rru1t53heeGOUjekcj21uzoCAxgiJBKLjQo ooAz7nwnpd54qstdm0zT5db020nsLTUHt0a7tbed4XnhjlI3pHI9tbs6AgMYIiQSi40KK+YP gt/wVS8LfHXwJoevaR8O/jBAvjXwrH4u8GWN3oEKX/jO1MljBOtrCs7GD7PdanYQyT332a1Z bgXMU8tkkl2gB9H3PhPS7zxVZa7Npmny63ptpPYWmoPbo13a287wvPDHKRvSOR7a3Z0BAYwR EglFxoV84eFP+Clvh7xR8ZfDvw3b4f8AxQsfiLrV3qFnf+H30+yuZPDD2Q0Wedr+6t7qWyjj Fjr1herJHcSRtGWgDG9aKzkz/Hf/AAVS8LfCO1Nx40+Hfxg8Jw6h9juPD0d3oENzf+KrG41j TNJa8tbC1nmvovJn1nTGktLyC3vit0FjtZJo5YYwD6PufCel3niqy12bTNPl1vTbSewtNQe3 Rru1t53heeGOUjekcj21uzoCAxgiJBKLjQr5g0//AIKzfDi+tbpJNE+IFlq8fnWFnpd3oywT avrVvrFnod5otrM0n2aW4ttW1LTbKS5Ew09pb0GO8lS3vHtvP/GH/BYuXwl+1P4e8E3nwm+I Gm6edK1O08R2d0ujyarpniP7Z4dh0nS1ki1RrZvtK+INOVpEaSFX1rTi88KwakbQA+z7bwnp dn4qvddi0zT4tb1K0gsLvUEt0W7ureB5nghklA3vHG9zcMiEkKZ5SAC7Z0K+cPCn/BS3w94o +Mvh34bt8P8A4oWPxF1q71Czv/D76fZXMnhh7IaLPO1/dW91LZRxix16wvVkjuJI2jLQBjet FZyegfsv/tU6V+1b4VbXdF8O+MNH0S9tLXV9D1DWLBIbTxNpN2jPZ6laSxSSJ5cyIzfZ5zFe QjyzcW0Alh8wA7+28J6XZ+Kr3XYtM0+LW9StILC71BLdFu7q3geZ4IZJQN7xxvc3DIhJCmeU gAu2dCiigDPtvCel2fiq912LTNPi1vUrSCwu9QS3Rbu6t4HmeCGSUDe8cb3NwyISQpnlIALt nQoooAz7bwnpdn4qvddi0zT4tb1K0gsLvUEt0W7ureB5nghklA3vHG9zcMiEkKZ5SAC7Z0KK KAM/RvCel+HNR1a80/TNPsbzX7tb/U57e3SKTUbhYIrdZpmUAySCCCCIO2SI4Y1ztRQNCiig DP0bwnpfhzUdWvNP0zT7G81+7W/1Oe3t0ik1G4WCK3WaZlAMkggggiDtkiOGNc7UUDQoooAz 9G8J6X4c1HVrzT9M0+xvNfu1v9Tnt7dIpNRuFgit1mmZQDJIIIIIg7ZIjhjXO1FA0KKKAM/R vCel+HNR1a80/TNPsbzX7tb/AFOe3t0ik1G4WCK3WaZlAMkggggiDtkiOGNc7UUDQoooAz9G 8J6X4c1HVrzT9M0+xvNfu1v9Tnt7dIpNRuFgit1mmZQDJIIIIIg7ZIjhjXO1FA0KKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACvzw/ZA/4JleM/iZ+wZ8GrX4h+OdQtNV8PfB/SvDGi6LqXhUWk+kJ dDQ9Q1HSdfh81BqVmZ9GtbF7VY7KRrBru3uJJ5pjdJ+h9FAHyB+w3/wSu/4Y8+O1146/tzwB F9r/ALf/AOKe8E+A/wDhE9Bs/wC07fwrB/o9t9tufK2f8Iz5j/M3myahI37vZh/P/CP/AAQ6 /wCEZ+MWieJv+Ex8AQ/8I9/Z6f2lpvw6+xeKvFv2TxP4e177X4h1X+0H/tXUJf7A8uS68iHd NqFzP5fPkn7/AKKAPjD4rf8ABHjS/i/4at9L1nxVp+pWlld+MdSjstR8OJe6de3Gt+NtM8XW 8V5bvPi4s4J9LitZ4AyG7hml2yWrEY4/xh/wQ0s/GHhzw9p41r4QaFp9p/acut+HdC+Edrpn hXVpZr7w7qFoiadFeAi3F14Y0/7Wl1Ndy3cNxewpNaK1t9l+/wCigD5g/ZN/4JuWf7MPxT8O eLra78AaZPpWleIrC70TwT4BtfCWgyS6rNoDCa3toZZJI/Lj0CPebiW6llkupCJYoY4bdOw/ ZE/Y+uf2Y/FXjzWbzxJp+tXnja7hmnj0rw9BoFpdPE9w7ape28DGG41y7NwRe38SW8dyLazV baBYAG9vooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKK+N/wDgvtpVrr3/AAS38Z2N9bW97ZXviTwf BcW88YkinjbxVpCsjq2QykEggjBBxQB9kUV/Nx/wyl8Lv+ibeAf/AAnrT/43XE/Fb9mf4cad 47+GcNv8P/BMEV/4kmguUj0K1RbiMaPqUgRwEwyh40bByNyKeoBoA/p9or+bj/hlL4Xf9E28 A/8AhPWn/wAbrifit+zP8ONO8d/DOG3+H/gmCK/8STQXKR6FaotxGNH1KQI4CYZQ8aNg5G5F PUA0Af0+0V/Nx/wyl8Lv+ibeAf8AwnrT/wCN1xPxW/Zn+HGneO/hnDb/AA/8EwRX/iSaC5SP QrVFuIxo+pSBHATDKHjRsHI3Ip6gGgD+n2iv5uP+GUvhd/0TbwD/AOE9af8AxuuJ+K37M/w4 07x38M4bf4f+CYIr/wASTQXKR6FaotxGNH1KQI4CYZQ8aNg5G5FPUA0Af0+0V/Nx/wAMpfC7 /om3gH/wnrT/AON1xOvfsz/DiH9o/wAKWKfD/wAErZXPhvWp5bcaFaiKWSO60lUdl2YLKJJA CRkCRgPvHIB/T7RX83H/AAyl8Lv+ibeAf/CetP8A43XE69+zP8OIf2j/AApYp8P/AAStlc+G 9anltxoVqIpZI7rSVR2XZgsokkAJGQJGA+8cgH9PtFfzcf8ADKXwu/6Jt4B/8J60/wDjdcTr 37M/w4h/aP8AClinw/8ABK2Vz4b1qeW3GhWoilkjutJVHZdmCyiSQAkZAkYD7xyAf0+0V/Nx /wAMpfC7/om3gH/wnrT/AON1xOvfsz/DiH9o/wAKWKfD/wAErZXPhvWp5bcaFaiKWSO60lUd l2YLKJJACRkCRgPvHIB/T7RX83H/AAyl8Lv+ibeAf/CetP8A43XE69+zP8OIf2j/AApYp8P/ AAStlc+G9anltxoVqIpZI7rSVR2XZgsokkAJGQJGA+8cgH9PtFfzcf8ADKXwu/6Jt4B/8J60 /wDjdcToP7M/w4m/aP8AFdi/w/8ABL2Vt4b0WeK3OhWpiikkutWV3VdmAzCOMEgZIjUH7owA f0+0V/Nx/wAMpfC7/om3gH/wnrT/AON1xOg/sz/Dib9o/wAV2L/D/wAEvZW3hvRZ4rc6FamK KSS61ZXdV2YDMI4wSBkiNQfujAB/T7RX83H/AAyl8Lv+ibeAf/CetP8A43XE6D+zP8OJv2j/ ABXYv8P/AAS9lbeG9FnitzoVqYopJLrVld1XZgMwjjBIGSI1B+6MAH9PtFfzcf8ADKXwu/6J t4B/8J60/wDjdcToP7M/w4m/aP8AFdi/w/8ABL2Vt4b0WeK3OhWpiikkutWV3VdmAzCOMEgZ IjUH7owAf0+0V/Nx/wAMpfC7/om3gH/wnrT/AON1xOg/sz/Dib9o/wAV2L/D/wAEvZW3hvRZ 4rc6FamKKSS61ZXdV2YDMI4wSBkiNQfujAB/T7RX83H/AAyl8Lv+ibeAf/CetP8A43XE/Cn9 mf4caj47+JkNx8P/AATPFYeJIYLZJNCtXW3jOj6bIUQFMKpeR2wMDc7HqSaAP6faK/m4/wCG Uvhd/wBE28A/+E9af/G64n4U/sz/AA41Hx38TIbj4f8AgmeKw8SQwWySaFautvGdH02QogKY VS8jtgYG52PUk0Af0+0V/Nx/wyl8Lv8Aom3gH/wnrT/43XE/Cn9mf4caj47+JkNx8P8AwTPF YeJIYLZJNCtXW3jOj6bIUQFMKpeR2wMDc7HqSaAP6faK/m4/4ZS+F3/RNvAP/hPWn/xuuJ+F P7M/w41Hx38TIbj4f+CZ4rDxJDBbJJoVq628Z0fTZCiAphVLyO2BgbnY9STQB/T7RX83H/DK Xwu/6Jt4B/8ACetP/jdfpt/wbg+FNL8D/s+fHDS9F02w0jTLX4qv5NpZW6W8EO7w14fZtqIA oyzEnA5JJ6mgD9EaKz9Z8WaX4c1HSbPUdT0+xvNfu2sNMguLhIpNRuFgluGhhViDJIIIJ5Si 5IjhkbG1GI0KACiiigAorn/DvxZ8LeL/AB34j8LaT4m8P6p4n8H/AGb+3tItNRhnv9E+0xmW 2+1QKxkg82MF4/MC71BK5HNdBQAUVn+E/Fml+PfCuma7oWp6frWia1aRX+n6hYXCXNpf28qB 4popUJSSN0ZWV1JDAggkGi58TW9r4qstGePUDeX9pPeRSJYTvaKkLwo6yXAQwxyEzoUjd1kk CysissMpQA0KKKz9G8WaX4j1HVrPT9T0+/vNAu1sNTgt7hJZNOuGgiuFhmVSTHIYJ4JQjYJj mjbG11JANCiiigAorgPF37UfgTwD4707wtrWu/2b4n1vVRo2j6RPZXC3+vT+XayyvYQeX5l7 bwR3kL3FzbiSC2UTGaSIQTmPr/EvizS/BenRXms6np+k2k13bWEc95cJBG9xczx29vCGcgGS WeWKJEHLySIqgswBANCis/wn4s0vx74V0zXdC1PT9a0TWrSK/wBP1CwuEubS/t5UDxTRSoSk kboysrqSGBBBINaFABRRRQAUUUUAFfH/APwXg/5Rl+Kv+xq8G/8AqWaRX2BXl/7ZX7KWiftt fs76z8NvEWr+INC0zWbrT706hoklvHf2s1jf29/A0ZuIZov9dbR5DxMCpYYBIIAPxHrgPjF/ yUT4Uf8AY1T/APpk1Wv1G/4h6vBn/Rd/2gP++/DH/wApaz9Z/wCDcH4fa/qOk3d58cP2gJrj Q7pr2yfzvDS+TM0EsBbA0YBv3U8q4bI+bOMgEAHwRXAfGL/konwo/wCxqn/9Mmq1+o3/ABD1 eDP+i7/tAf8Affhj/wCUtZ+s/wDBuD8Ptf1HSbu8+OH7QE1xod017ZP53hpfJmaCWAtgaMA3 7qeVcNkfNnGQCAD4IrgPjF/yUT4Uf9jVP/6ZNVr9Rv8AiHq8Gf8ARd/2gP8Avvwx/wDKWs/W f+DcH4fa/qOk3d58cP2gJrjQ7pr2yfzvDS+TM0EsBbA0YBv3U8q4bI+bOMgEAHwRXAfGL/ko nwo/7Gqf/wBMmq1+o3/EPV4M/wCi7/tAf99+GP8A5S1n6z/wbg/D7X9R0m7vPjh+0BNcaHdN e2T+d4aXyZmglgLYGjAN+6nlXDZHzZxkAgA+CK4DxF/ydH4O/wCxV13/ANK9Gr9Rv+IerwZ/ 0Xf9oD/vvwx/8paz7n/g3B+H134pstak+OH7QDanp9rPZW83neGh5cM7wvKu3+xtp3NbwnJB I2cEZOQD4IrgPEX/ACdH4O/7FXXf/SvRq/Ub/iHq8Gf9F3/aA/778Mf/AClrPuf+DcH4fXfi my1qT44ftANqen2s9lbzed4aHlwzvC8q7f7G2nc1vCckEjZwRk5APgiuA8Rf8nR+Dv8AsVdd /wDSvRq/Ub/iHq8Gf9F3/aA/778Mf/KWs+5/4Nwfh9d+KbLWpPjh+0A2p6faz2VvN53hoeXD O8Lyrt/sbadzW8JyQSNnBGTkA+CK4DxF/wAnR+Dv+xV13/0r0av1G/4h6vBn/Rd/2gP++/DH /wApaz7n/g3B+H134pstak+OH7QDanp9rPZW83neGh5cM7wvKu3+xtp3NbwnJBI2cEZOQD4I rgPEX/J0fg7/ALFXXf8A0r0av1G/4h6vBn/Rd/2gP++/DH/ylrPuf+DcH4fXfimy1qT44ftA Nqen2s9lbzed4aHlwzvC8q7f7G2nc1vCckEjZwRk5APgiuA8O/8AJ0fjH/sVdC/9K9Zr9Rv+ IerwZ/0Xf9oD/vvwx/8AKWs+2/4Nwfh9aeKb3Wo/jh+0Aup6hawWVxN53ho+ZDA8zxLt/sba NrXExyACd/JOBgA+CK4Dw7/ydH4x/wCxV0L/ANK9Zr9Rv+IerwZ/0Xf9oD/vvwx/8paz7b/g 3B+H1p4pvdaj+OH7QC6nqFrBZXE3neGj5kMDzPEu3+xto2tcTHIAJ38k4GAD4IrgPDv/ACdH 4x/7FXQv/SvWa/Ub/iHq8Gf9F3/aA/778Mf/AClrPtv+DcH4fWnim91qP44ftALqeoWsFlcT ed4aPmQwPM8S7f7G2ja1xMcgAnfyTgYAPgiuA8O/8nR+Mf8AsVdC/wDSvWa/Ub/iHq8Gf9F3 /aA/778Mf/KWs+2/4Nwfh9aeKb3Wo/jh+0Aup6hawWVxN53ho+ZDA8zxLt/sbaNrXExyACd/ JOBgA+CK4Dw7/wAnR+Mf+xV0L/0r1mv1G/4h6vBn/Rd/2gP++/DH/wApaz7b/g3B+H1p4pvd aj+OH7QC6nqFrBZXE3neGj5kMDzPEu3+xto2tcTHIAJ38k4GAD4IrgPg7/yUT4r/APY1Qf8A pk0qv1G/4h6vBn/Rd/2gP++/DH/ylrP0b/g3B+H2gajq13afHD9oCK41y6W9vX87w03nTLBF AGwdGIX91BEuFwPlzjJJIB8EVwHwd/5KJ8V/+xqg/wDTJpVfqN/xD1eDP+i7/tAf99+GP/lL Wfo3/BuD8PtA1HVru0+OH7QEVxrl0t7ev53hpvOmWCKANg6MQv7qCJcLgfLnGSSQD4IrgPg7 /wAlE+K//Y1Qf+mTSq/Ub/iHq8Gf9F3/AGgP++/DH/ylrP0b/g3B+H2gajq13afHD9oCK41y 6W9vX87w03nTLBFAGwdGIX91BEuFwPlzjJJIB8EVwHwd/wCSifFf/saoP/TJpVfqN/xD1eDP +i7/ALQH/ffhj/5S1n6N/wAG4Pw+0DUdWu7T44ftARXGuXS3t6/neGm86ZYIoA2DoxC/uoIl wuB8ucZJJAPgiv0H/wCDen/kjPx3/wCyqt/6jHh6j/iHq8Gf9F3/AGgP++/DH/ylr6R/YP8A 2D/D37AHw88TeHvD/ibxh4s/4SzxA3iS/v8AxJJZPdtcNZWlkEUWltbxLGIbKHA8vOdxLHPA B4h/wVo+Ges698SfAniRNL8Qah4Zs/CutaHqEemeHdU8QQ6vPLr3hTVF0S+tdMtru7TT9Ts9 F1KyuLg2s0EcUzLIkpljt5/nD9nT/git8bfh1rfwD8Y2cfww8J+IvDl34RvPF1272y+KLWy0 zQPBenT6bHqCabczNGRpHiSB7a0vbaGZNTiMk8sTz27frdRQB8ofsCfsn3PgL9iXVNB0K78Y fCG+8VeIL290zULO3gOs2GiWtyun+HlNvqlvcpDIPDenaNbPHdW/2hQjmdVvDLJWh8Lf2F/i B4C/bhsPibqfx3+IHizw/p/hWTRLyw1W08PxTeJ5ZLhpIYrsWGkWe23sf3ksDGSWUy6hcBDa xfaU1D6fooA+MP8AgnR+zj4/0D40+KfHHirSNP8ADOiaX4g+Imj6NbT3Fzd6nrlvqPji91KK 9MFxbxJpUYRCR9kkmGpw3VnLOyiytEXxD9mj/gg/4q+Enw28EeEtU1nwBaeGdM1XSrzxBoel wTS6bqGzQfCFpql35LQxxzXF7J4f1zTpRIi+bYeLL+SSRi09lc/p9RQB+cHhf/glJ8VPCvwX /Y/8Ladpfwg0T/hSWlWeieMo9P1u8jtpvs3irwprkmo2IXTV+03F3/wjlw8scwt8T6iSZpdj PJyH7On/AAQo8f8AwG+HunaLbXvwwtbSx8Px22oaRp8tzDp3ia4+xfDoahYXYFqB9j1Wfwlr UF3K0cpaHVkkeC4aSaAfqdRQB+QP7Mv/AASC+OPjf9nvQ5bm+8P+BdP134VaNbw21zeXEOvP ePo3w7gvdE1KxudOltra3lXwfe2k7Si9Ux6mVlsplSSCX2/wP/wTh8Q+Dfjz8Lj4stNQ8Yal feNbPXta1V9avdYkttE0zwd4cic3+pyW9rHPeP4s8M+HrlWEaXF1HAziIQHUIU/Q+igD4A/4 KSfsQ+KviZ4n+IHi+z+G3gDx1D4m1XwBo1npuqPNrF/r1rB4r0S5vILiVrB5NI0cRwSm6tYz f2sqp9tENrMt4L3Pg/4JJ/FzR/FXiOXUvi3qHxMtNV8FeGtFnfxFrktraeJn0l9DN1pOpWf2 O5mls9TGl6l5lzLf3EduPEuqIumztcXU13+h9FAH5g2v/BEj4ha7+0d4X+JmqX3gDw9qGl6r 4b1W40Lwpr+s6f4bsP7Ob4dmW2s9NCi2FuF8Ma4sUciHcH0cyHdbxtZ3/A//AARw+LnhjwX8 LtK1nx5p/juX4f8AxKs/E11N4l8QS3Md4kOq+HL+TxBawxabGlpqkiaNqkZtX8+RpPE2pTy6 tPJPcfaf0vooA8Q/4J5eE9V8J/s3XD6vpmoaRL4i8a+MPFFnbX9u9rdiw1TxPqmpWLywSASw SPaXcDtDMqTRFzHIkciui+30UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAcf4a/aF8A+NPiFL4R0fxx4P1bxXDaXN/JotnrNtPqKW9teyWFxMYEcyCOK8iltncrhJo3 jYh1KjA8Nftu/Bfxp8PZfF2j/F74Yat4Uhu7mwk1qz8VWM+nJcW1lJf3EJnSUxiSKziluXQt lIY3kYBFLD4Q+Ef7O/jv46+BNZ0jwl8KfAEcOj/Gr4t+MX1/xJLb3PhjxnPcSeMPDsNlq1rE BfPcGe+tklTyZYG02zBF35xWzS/8Mf2L/i18Xv2/fCHxA+IfhD4oa94U0DxB4Yv2u/ipJ4Kl 1G2OmaR48CzQwaA/2cx29/rOkPE7IbgTTM4OyENGAfeGp/tY/CzRPHfinwte/EvwBaeJ/A2l PrviTSJvENnHf+H9OSOOV728gMnmW9usc0TmWQKgWVCThgTv6F8WfC3ijWLfTtN8TeH9R1C8 /tDyLW11GGaab+z7pLO/2orEt9lupI4JsD91K6o+1iBX5o/t+/sX/tCfH5/EvhTRfCHjC50t Lv4gX+n2OlSeENN8AEar4W8UWWnzW2Xj1w6pcXGrWn2x7sm3N5dajKD5IhkG/wDtUf8ABJjx N8Sx8WNF8AeBfB/hWPxt41vbTSdRT7JY2lp4f/4U7d+GNP8AMEAMqWdtq13LAlukbPEJZZEi 8ti5APrC/wD+CpHwC/4SP4Z6dpHxW8AeKv8Aha/iubwbol1oXibTr62/tGKxe8aOR1n/AOvW Dagd/P1KxTaPtCmuw8Nftu/Bfxp8PZfF2j/F74Yat4Uhu7mwk1qz8VWM+nJcW1lJf3EJnSUx iSKziluXQtlIY3kYBFLD88P2oP8Agn18b/2nPDGr6vH4e+MGs+J/GGfB09x8Sdd8GwX+lWMn hTxtpUV61voUcdomn2954oheR0mub6VTcBbQC3i+0+n+Av2H/HvxZ/aq+G/xK8d+Gfih4gj0 bxro9zqT/FS+8HT6jY2uk6F4wNndQ2egwrZrHDqeuWjRS+fNdGZmYw28dqk0oB9v/Gv9oXwD +zX4Vt9d+I3jjwf4A0S7u1sINQ8SazbaVaTXDI7rCss7ojSFI5GCA5IjY4wpxyH7VX7eHws/ Y38CeNNY8beMfD9pqHgbwrN4yvPDy6tZx63dacknkRyQW00sZbzropaxMxVJLiWOINuYCvL/ APgoD8FPiTrvx9+GPxG8AXHxQWPwr4f8Q+G9Rg+Hb+GBr8h1K50W4icr4iU2Bs1GlSiQo4uB I9rsVozOV+cPix/wTW+JnhL9m3xN8Kfh/wDD/wA+yu/hVdaVJ/aPiHSda0a/1VPAj+G7a5sr qe1s9RtfEBeKzsTII4dKk0tbiZhDd3LwxgH3f8GP2yvhr8eNO0ufw/418H3suvXc9vpNvbeJ NM1CTV0SfUoop7c2lxMkkc6aTqE0YDeZ5dpcCRI5LeeOLQ1v9rH4WeGf+EF/tL4l+ANP/wCF oeV/whv2nxDZxf8ACW+b5Pl/2dukH2vf9ot9vk793nxYzvXP54eE/wDgmX44+Kx8f6143+DO npL448QeGpk07xBLo1/dxaMfjF4g8T6vYTmK4miMZ0m602eeFZHjnKpGvnSRbFP2nf2L/wBo SX9m74x+EPDHhDxgsvxNtPHthaDwPJ4QS71g6l4n8T3lnD4hutZcSjS2tNVs5LZNNkFxCbzV vNEchgFAH2/o/wDwUJ+Guu+C/Geowa3p8et+B7TxLqN74ZvNY0yw1mSy0LVb3Sru+EdxcxxR Wb3dhMqXM8kUIDJ5rwneF8/8Sf8ABZ/4F/D74haJ4d8YeJtP8DS694g1Tw9BdeItd0fTo4n0 691+xubqaN737RFZ/afD8sSXDxCOSTULGNSZGnSD5g+On7AHxh+JP7NXxJ+HOk/B/T9E1W68 a/Fj4gW2u2+taYLfxkNc0zxbpWlwTYkSaPVJBrGnkCZGt0sraPfdxzKbKLQ8I/8ABPf4maR+ 1/p3iDxP8K/+Er8Bah8QL+TWLNr7SbmG40fUNV+LKyTT289yolt1tfFmjSyxYaRorqRFikki liUA/S628WaXeeKr3QotT0+XW9NtIL+709LhGu7W3neZIJpIgd6RyPbXCo5ADGCUAko2NCvl D/gkj8ONV0D4NeKPEPijxPqHjjxXc+ILjwVca7qgeXUbi38LE+GgXnkkkkaO6vNM1HVvKLEQ za5dLmVzJcTfV9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ/hrwn pfgvTpbPR9M0/SbSa7ub+SCzt0gje4uZ5Li4mKoADJLPLLK7nl5JHZiWYk6FFFAHJ/Hzwnr3 j34FeNdC8L6n/YvibWtBvrDSNQ+0SW32C8lt5Egm82MGSPZIytvQFlxkAkCvzF/4dQft1f8A Ryv/AJkLxB/8j1+s1FePmeSUMdKM60pK2nuya/I/SOCPFHNuFaFXD5dSozVRqT9rSjUd0raO Wy8j8mf+HUH7dX/Ryv8A5kLxB/8AI9H/AA6g/bq/6OV/8yF4g/8Akev1morzP9T8F/PU/wDA 2fb/APEyHE//AED4T/wmp/5H5M/8OoP26v8Ao5X/AMyF4g/+R6P+HUH7dX/Ryv8A5kLxB/8A I9frNRR/qfgv56n/AIGw/wCJkOJ/+gfCf+E1P/I/Jn/h1B+3V/0cr/5kLxB/8j0f8OoP26v+ jlf/ADIXiD/5Hr9ZqKP9T8F/PU/8DYf8TIcT/wDQPhP/AAmp/wCR+TP/AA6g/bq/6OV/8yF4 g/8Akes/xZ/wR0/bV8e+FdT0LXf2gtP1rRNatJbDUNPv/HWvXNpf28qFJYZYntikkbozKyMCGBIIINfrtRR/qfgv56n/AIGw/wCJkOJ/ +gfCf+E1P/I/Inwn/wAEdP21fAXhXTNC0L9oLT9F0TRbSKw0/T7Dx1r1taWFvEgSKGKJLYJH GiKqqigBQAAABWh/w6g/bq/6OV/8yF4g/wDkev1moo/1PwX89T/wNh/xMhxP/wBA+E/8Jqf+ R+TP/DqD9ur/AKOV/wDMheIP/kej/h1B+3V/0cr/AOZC8Qf/ACPX6zUUf6n4L+ep/wCBsP8A iZDif/oHwn/hNT/yPyZ/4dQft1f9HK/+ZC8Qf/I9H/DqD9ur/o5X/wAyF4g/+R6/Waij/U/B fz1P/A2H/EyHE/8A0D4T/wAJqf8Akfkz/wAOoP26v+jlf/MheIP/AJHo/wCHUH7dX/Ryv/mQ vEH/AMj1+s1FH+p+C/nqf+BsP+JkOJ/+gfCf+E1P/I/Jn/h1B+3V/wBHK/8AmQvEH/yPR/w6 g/bq/wCjlf8AzIXiD/5Hr9ZqKP8AU/Bfz1P/AANh/wATIcT/APQPhP8Awmp/5H5M/wDDqD9u r/o5X/zIXiD/AOR6P+HUH7dX/Ryv/mQvEH/yPX6zUUf6n4L+ep/4Gw/4mQ4n/wCgfCf+E1P/ ACPyZ/4dQft1f9HK/wDmQvEH/wAj0f8ADqD9ur/o5X/zIXiD/wCR6/Waij/U/Bfz1P8AwNh/ xMhxP/0D4T/wmp/5HyZ/wS7/AGUPjr+zB/wnP/C6viX/AMLE/tz7B/Y3/FQ6hq/9n+V9p8// AI+408vf5sP3M7vL5xtGfrOiivocDg4YWhHD022lfd3ervv8z8g4p4kxWf5pVzbGxhGpU5bq EVCPuxUVaK0WkVe27u+oUUUV1Hz4UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAfhj4a/aL+JfhP4yy+O7X4ofFCXW5f8AgoVc/B9ra88Yane6 MPCc4kD6UNLmnewWMCRtjiASxEIY3QohX0D9nn/g4H/ae/aH/Zu+IXxG8K/s0ah43tLrwVP4 j8EJofhPXhaWGqDxO+jjRprrbJHrkkdlLBqEktj9lGLa6hKRsryW/wB/+Gv+CSfwB8J/GWXx 3a+CtQl1uXxrc/Edra88Uave6MPEs4kD6wNLmunsFvAJG2TCANEQhjKFEK5+if8ABGD9mDw/ 4c8daPB8IPD8mk/ETSpdC1Wxubm7ura006S+m1FrLTo5JWXS7cX073SxWAt0SdYpVCvFGyAH 54fGT/greNU/aR+A3xF8ReF/hh8UfFfwdu/jTbPquk2Xinwrd2L6H4Yh1A2osNREZ0+8ngeK 3uormLUo4SpaGYOWRD4+/wDBwJ+0l+zh8KZdd1Gx+CHiK81b9nXw18cdMS38K6pYR2Fxq/iP S9LbT5g2rTGeOKC7nYSKYS0nlnaqqyv+h95/wSD+BOkaPpc2geAPD48T+Gf+EnvNG1LxJLqH iGG41HxDam21S71aKa7WTWftMeyOUXkzu0SCNJIgFK/KH7Nn/BvkPFXxa1TUfj1oHg9fh9c/ B/Svha/gjw/8RPFPiW0vXtdXg1Q3UF5qJhu9Ls4pbK1WDT4ZJo1BfMg+bzgD6f8A2Df2zfH/ AMXf2wv2k/gd8Rk8H6rrfwDu/DjweJPDemXOj2ms2+saYbxY2sZ7q7eKSB45FMguWEgkX5Iy hL/nBqH/AAWZ+NHxF8afsi/tJ6zfafYfD7xX4f8AjJ4pj+GXhya+0uO6t/D+lXjW9pqt69zN DqMmbWJo5xZQCGRpGWJtwC/sf8O/2XPAnwo+O3xG+JmgaH9g8b/Fn+zP+Er1L7bcS/2r/Z1u 1tZ/unkaKLy4WZf3SJuzltx5rx/wt/wRm/Zs8HeO9F1+z+HG/wD4Rr+210bRbvxBql54b0aL WY5otUt7XRprl9Ot7e5S4lWSCO3WIhhhBtXAB8YfBL/gth+1V8Uf2aodf/4ULqGpXmu+IPA0 Om+M9L+F/iiTRrXRtb0x73Vr9NHDPd6lHpUttNb+dbXaR3Ju7WRNilFuOw/Z3/4LF/Gz9tn4 xfCjwJ8IrP4Aa1q+q/Cp/ib4h1a5l1+PQfE/2fxONCubHTpZraC80z5be8mWW4s7zEjRReXI iG5l+n/C3/BGb9mzwZ8LLzwhp3w48rT7zVdI1sag/iDVJtes7zSIY4NKlttWe5OoWv2OGMRW 6wXCLDG8qIFWWUOa3/wRg/Zg8QeHPAuj3Hwg8PppPw70qLQtKsba5u7W2u9OjvodRWy1GOOV V1S3N9Al00V+LhHnaWVgzyyM4B8AfH39vb4hftQfFPwVeeOvDvwg1zwT8O/26rH4Q+HtJ/sX WbXVdPnsppDba59uh1dIpLhIZXTyJLRoGdt7KwAjHPy/8F6/2m/FP7D3wv8AHMtz8INI1f8A aD+H/wATNb0WbSPCV7HN4IvPCdvNc+aTc6jcRX32mG1mRFeKJYJZopGFykT2836f/wDDrj4E /wDQjf8ANVf+F2/8hrUP+Rv/AOgn/r//ACX/AOPf/plXz/8A8EnP+CGnhH9jz9k74e6H8YtE 8P8AxG+JvhPSvEGiTzz61qWv+G7Oz1a8na5isLC/ItbX7RaNDDcNDaxNKDMrl1kcuAfCGsf8 FNPF37CyeDPi/rPhbwf8TPixpf7EHhrX4/Fuoz6vBqN+dS8U2Vnb214HvpoZ44jdxXE8gjS4 upopSs1tHMI4vpD45/8ABZn4+/Bf9pjxD8CfJ+EGp+N9B+NXw++HH/CX/wDCMajDpU+neKtJ u7vzf7K/tNpVuLWa1+99uKTI+NkRG8/X/wDw5m/ZsuPDn9k3/wAOP7b08fD/AP4VasWs+INU 1Ty/Dgvvt8VihubmQr5N0EkgmUia38qFYpI1ijVdDT/+CSfwBs9RtNRn8Fahq+t2njXSviH/ AG3rHijV9V1m61vS4Db6dcXF/c3Ul1cR20TOsdvNI9uokfEeWYkAz/8Agln+2t4q/bM8CfFy 38bWHh+DxP8ABv4q+IPhleX+iQTWlhr39myRGO+jtZpZpLXfHOimJp58NGzCTDhE+n68Q8If 8E6/hX8O9R8S3Xhuw8YeGJfGXiDXPFWtrovjnXdNj1PVNZgSC/u5UgvERpCkaGI4xayL5luI ZMvXf/DH4H6L8I9R1C60q98YXcup2ljZTLrXizVdbjRLODyIWiS9uJkhkZOZZIwslxJ+8maW T56AOwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAor+eH4af8A CLf8Nk3P/CHf8I//AMNCf8PFNS+0f2N5P/CZf8Ibtn/tHf5X+mf2Pt+0+fn/AEbHnb+N1e4f Br/gq5+1H4y/4J1eL/ih8QfH/g/wBLofjWKXxfLBoE39v+CLC2vdabVfD9qk+mHS7HVGh07T raxtNVlupjJfobm5STUtNiUA/a6ivwx8Wf8ABST4w+Bv2qvjbqVnNp/wytPEfjX4F6P458WP 8PNM0HWfAujazoUs2p3t+1wlyLeSOcRW6/2tNfR2QuGhRtxR68P0z/gvV+1jYfs1+FviJL8V vt2oeH/gqnxHv9Om8M6OLDxNqK/E+Tw8YrwJaLMlu2musZW0lt33Qo4dWLlwD+j6iuA+Inib R9L+O3w5029+KX/CKavqv9p/2b4N+16XH/wnfl26tL+7uIXvJPsakTf6FLFt3Zm8xMKPm/4o /sVar8dv+Cu9149mj0/QdE8HeCvBc1p4iPhl5dcmuLbWvEdzPYaVrBmVLKOVPs8OowiGc3Nn eiE+QJFkIB9n0V+OOh/tbftg/Ez4LahqB8B/FCTxF4VtJ/Evh298SeAW1HX9I1S48D+Nf9DV pfDWk2/mW9/a6LCDaWkwaTUmgN7cxXgtk9/8Z/GD9s34U/tVeKvC2nJp/jxbfw/qc/hb7Z4c vLfQvE9wdCmvrZg1rpxtdPkXXA1gi6h4hhI0+3RZLea5li1O5AP0Por8sfEvw3+KHjj9kf43 65ql38cNKvJf2ivBHimy1Oz+H0UfiXxJ4fsovBsK6odK+yXheSKC1a8dYLaJ/tOnustnaqLn TEP2qtc/aj+FH7aHi/xjomn+MNL1Lxp4K8K2muW/ha0m8Q6B4fsLW+8TCT+ztUTwtd3E15Hc yaSTbvp11N5fiDUWMXkW8V7p4B+p1Fflj+2D4n/aG/aE0TQ/7U1L4oWPh3w7afDrxR4j/wCF ffD+6t7TTtUXX/D91dLb6dq+g3GpalJFG9/fpJaXBFsNNjtL7T0lR2vf1OoAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAr5w/4K5/tvar/AME4/wDgnV8TPjLoWh6f4j1vwhaWqafY38zx WjXF3e29lFJLs+d443uVlaNShkEZQPGW8xew/bP/AG1vC37D/gTwxq/iSw8Qa7qHjnxXpvgn wzomiQQyX+u6xfyFLe1jaeWG2iyqSuZLiaKMLEw37iit4f8AtZ/HH9nr9t79iXxR8Ov2kNQ1 D4AaJ48u77w9caL8SNUtfB+si6025hkF1YTSTNa3scUv2S4juLWW6tXDxq5cGSKgDy/4y/8A BVH4qfs+eO/BnhSXxr+zB8VtQ1v9oDwp8LNUuvBs14t/o2napHdpexahpJvZzpuoQ3VlMkLv eTpKofdBE0TBuQ/au/4OZ/Btv+xn8UvGfwS0fUJ/GXhXw/Z+LvCbeM9HI0bxjojeK18OXOow LbXQuI40uVlURXf2S4JeJ/KZN4Xfvf2RP2OPE8CfHXxP+1Np/iy8m+MHhnxlqPxGv/HvhuC0 1bXvD2nSR6Xo0r2tvDp6Rx2s00jQwxR3MgkLtKVC7fEP29f+CeX7CX7An7BPivxJ4s+IPxA1 PwT8RPCtnoHgrTPDvirRrrV08OP4og8QNaeHFnWNb+3F9ewXEs9093Mtsq7ZlB+cA+v/ABr/ AMF8PhB8M/gTN8R/Evh34gaD4Z8P/EC9+GPjJ7xNKFz4G1y1uIopLa5hW/Ml9+7lNyP7IF+f IhmchTGyjoP2H/8AgpPe/GD4WftVeNvikPD/AIc8M/s7fFXxb4T+2aVZXTbND0WGGb7VcR75 pJbgRvKz+SoDbQEiB4PgH7Zv/BIH9k3wD8Im8I/Fz9ojxB8MP+Flar4k1vXtW1vxh4e0a/8A iDeanqWjalqEsgu7MQJ5VxpGkYXT4rdUWNQwbzXL+/8Awv8AgT+zL8BP2cf2nrK9+Jnh/WPh l8UPiB4jvvild634vsorDQtW1dYLTUNIkuoDD9ixvijEUji4Rp1Bcsy0AZ/xB/4LifDX4Rfs j+Afjl4v8FfFDwr8N/HV3ZW76jqlppkEmjJfRW1xZzvCb7zb+Oa0uhc40pb6SOO3ullSKa3m hTz/APZ1/wCC6Fvq3xM8ceDPiH4c1DU/FY+OvjT4aeDtJ8D6FPd3dxonh20t7y61O6SSdjLJ BBNukjtt1xOXjS2tJXytc/8AHn/glb+zD8Wfg1D+z74o/ab8YWsvwa8FXFn4jsH8caD/AGzY +GlOg3SNfwT2jpp9nbJomiuslvDaqQGlmaWS6nll7D4D/wDBLP8AZl+P/iO7+KHwr+LniDxZ qFr8VfFHj2TxN4N8bWV19hv/ABBYwW+s6MlxZxkQW8tqLfDIVv7fKSQ3UTEOQDP/AGh/+Dhb wD4L8P8AxG0Lwh4Y8YSfELw3d/Erw3pD6xp1sdGfWfBmiLql29wYrwTGzlEsSxlAJHIcMsPD HP1v/g4S0f4P/s9+Bbjxn8OvEGofFfxr8FYvizpOl6Te6XBpXiXytGh1O+SDN9NeWNvEv21h JewJ5iabdrAbuZI4ps/4vf8ABJr9kTxh40+KPxdv/jlqHhzwpYeIPGdp4qgt/Geip4d8KeIP E+lQ6JrvnXE9vJNb3kge2cW9xclIbhowkSo/lNgfFH/gmz+yJ8AfEviPVfEPxY8YePLz9nP4 a6vf638K7fxPosmojRj4JsPDl/NNawQQX6yXOkWOn4c3EMYmeN4zEspBAOP8Kf8ABXr4cft4 fBNdF/aK+H/iDV/EFpqvwtbwt4V8N6Iui6uni3xBp7ahYXGk6zFrr+Xv8qW4inkk0yS2tmME rzSyOlegfBP/AIL0/sj/ALJf7P8A8MPDPw58DfEDQvhFB8P7Lxkl1pmi2y2Hg3R5vEDaDNJf JLdi7nuItVLLcNbRXckzSNMrXG5pD4/+zD+yV+xpP8NtT8Rav8Z/iB4D2/8ACp/GGgeLvH3j vwVp9/4dvNP0GWfw3HBa2+5IriPTGlglh1W0ZbhY3lh8/Y1yPQPjb/wR1/YS+HP7P/hPSfEv x2/4V34J8Z/Cq3+H3h++u/iDo1nD4j0mLxBb+J5NTtZ7uJlnuJr545JJIibcRXQWOKMNGVAP 0+8ReMtS0Tx34c0i28J+INY0/W/tP2zW7SaxWw8P+VGHj+1LNcR3LecxKR/ZoZ8MCZPKXDHg Pi3+2t4W+Dfxs0zwRqVh4gu55/7I/tbVLSCFrDw1/bOoS6Zo32rfKs0n26/hmto/ssVx5TRl 7j7PCVlboPiJ4m0fS/jt8OdNvfil/wAIpq+q/wBp/wBm+Dftelx/8J35durS/u7iF7yT7GpE 3+hSxbd2ZvMTCjj/ANobw18ErT4+/D3VfHkunr8QtYu4LPw1pSX9z9r8Tva3KSwtJpkD41OP TZ7j7Ykk8M0emGSW7DW37yYAHzf4d/4L2+G/iz4ctZfDHwq+MGk6hqH9iXelJqum6Dc/2/Ff X3hVFs7dY9bQRXE1r4u0h0luJIooftRdxI1vJbN9Qf8ABPz9oTXv2sf2HvhL8TfE+g/8I34g 8eeFdP1u/sFEYhWWe3SQywBJ59tvLnzYleQyrFJGJQkodF8f8O/s0/sbfDP4WWvjqy1LwBp3 gnwH/YmPEMnjuU6VpPlw+FZ9K825e7MQ3w6D4Vkj8xv3yRwN84u5TN7h4G0D4YfsA/ALw14Q h1zT/BHgTwzaTWGjjxJ4mlmW2t7e2uLx4Vur+d5Wjt7S3uJAhkIht7VsBIocKAen0V5hrP7b vwX8OfELSfCOo/F74YWPivX7trDTNFuPFVjFqOo3C3stg0MMDSiSSQXkE9sUVSRNDJHjejKN 9f2hfAL6d4kvB448Hm08G2k1/r841m2Meh28M93bzTXTb8QRxz2F/EzybQsllcqSGhkCgHYU V4h/w8C8A2uneBbzUX1DRrT4hfErV/hTpk+om2t411nTp9Xt2EjNNgR3E+jTxQbd0kklzar5 atIwTQ+An7eHws/aM8OeF9R0Dxj4fT/hPNV1/SvClrc6tZi58V/2NfXNneXOnIkr/a7f/Rmn WSIt+4kidgm7AAPX6K8w8Nftu/Bfxp8PZfF2j/F74Yat4Uhu7mwk1qz8VWM+nJcW1lJf3EJn SUxiSKziluXQtlIY3kYBFLDr/H/xZ8LfCj+xP+Ep8TeH/DX/AAkuqwaFpH9q6jDZ/wBq6jPu 8iyt/MZfNuJNrbIky7bTgHBoA6CiuA8RftY/CzwhrHhzTtW+JfgDS9Q8Yarc6FoNrd+IbOCb W9RtroWdzZWqNIDPcRXREEkUYZ0lIRgG4rj/AAF/wUJ+GvjGfxGdS1vT/Blp4au9Ss57rxJr GmWKzPp+o69Y3TLH9paZY0HhzUrkSSxpG1vEzqxaC6S3APb6K4/x58fvBvw11G507V/EOnpr dtaC8XRLZjeazdoYL24RbewhD3VxJJFpuoNHHDG8kosrjYrGJwND4W/Fnwt8cfAlh4p8FeJv D/jDwzqnmfY9X0TUYdQsLvy5Gik8ueFmjfbIjodpOGRgeQRQB0FFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wf 8FTP2KvFX7ZngT4R3HgrUPD8Hif4N/FXw/8AE2zsNbnmtLDXv7NklEljJdQxTSWu+Od2EqwT 4aNVMeHLp83/ALN//BIf46+B/wBsL4AfFLx58SdP8V2nw08a/ELxHe6Vqni3WPEl34b0vXNM ttP0rRtPv7+IzX0dsLbzJJZxbHM74SRgXf8AOD9nj4m+GfD37Blt4a1L4W6f4i/aI8RfB/41 v8TvG1l4ru7Xxp4TuIRq73Efi/TIod9zHO402O3k1SZis75RI3jjaX3D9kL/AIKaftBfDj40 fsu/CfQ9R+z+GU0r4M+HNG8Ff2BBL/wl/hnVvCt5Pr+v7mjN9L/Z89si/abWaO0t/s22eNz5 m4A9Q+H/APwQe/aS1v8AaHg+JfxC8W/DC+8Raj8YPhh8SNeni8TapqcmoDw3Yajb6tKjTafE IpLqe7Wa3tEVbe3jYwI0UUEQPl/iL/g1/wDj7bfsdeHPBGk+L/hBfeJ7r4VXPwy14Xeq6ja2 Gm7fHg8VW19azLYySXXmR5tpIpIrfymAdZJQdo8v/wCCJH/BRHxx8Df2KYPAlr8SNQsvhdov w1t9YudatRo1svww8Vaj8Q7rSrOyuNRuLG4t7Kzu7adLu5/tC3vJ47cT3FsoVFiP0B/wQ6/a 98Z/t3/8FSvhJ8UviFPp934y8RfslX1vqd1Z2otY717P4gXVis5jU7EkkS2WRwgWPzHfYkab UUA+v/8AgsN/wS/8f/8ABQX4rfC/XfBmr+D9MtPBXgr4j+G75NaurmGSW48ReHDpdk8QiglB jjnO6UsVKx8osjfLXyhY/wDBuJ8YIrGe9uvFfgCXUNG1X4O67pdjY+INV0v+1ZfCHh2XR9Tt X1G3tludN85riSS2u7dJpV8tCUhZtyc/oX/BYX9qDwv+2n490bSdf/4WTDefED42+DfDngu7 8NWk0MP/AAiui2uqaJHaixhgvprh55TbSLJPL5sTgKqzYlrj/gr/AMFf/wBpz4ueB/AWlW/x e1C90Txf8YPhL4Pb4gWHhzRJLtz4j8Ny3fiPRopBZNpxksL0Q7V+ztcW5kCTvIGC0AfQH7Uv /BC/46/Gz4weEotL8SfDCH4ffDH4a+JPh54RifXdYtls7XUvAsOhQW8emPBcpbxx6oLu5e4e 8urqW3uoo5JJfscCV7//AMEef+CX/j//AIJ9fFb4oa74z1fwfqdp418FfDjw3Ypot1czSRXH h3w4NLvXlEsEQEck43RFSxaPl1jb5a/PDxr/AMFZv2pfhv8AEX4l+G0+OniDVYdN8V/Gnwnp 13feGfDoudOi8F6HZazpl0nladHG1xNIZbe581HieCd/KigmEcyaH7XH/BZX4ifHK0j8FJ4q 0+70T4y/s6+J7nx14RnWw8vw7qjfDW18RxXWlQxQC/gs3N2I0lvr26E0h1CJYYhaxTOAfSFh /wAEH/ip4P8AAmgWXh7WfhBpen/CD4q+D/G3w68HWkF5BYS6dokl/PeQXWszQ3Gpw/b73V9S vY7ef+0k09nMMc0yzPKh+2j/AMEVPj7+1B+0VouqW+v/AAgs/BPgf4f+KfBPhi2s77UdHs9P g1bwPHosVrbaJHbTW1jbxat9suDItzPO1rdQxM0gsreMfJ/7IX7avxE+AXwW+Kl94d8VeD/h 14d8B+CvgZceIfEUHhKwGvjQT4He+v4LW9NlJDcapcG3jtLE60z2wkuktonilngU+oftvf8A BUf47fEX9oq3+EHw58dfEDR/EHxD+FWvf234Ti0XT9L8VeHPENt4HsvElp/ZmmxQT6jp32l5 ktT9pv72YzTanDb+RLZW12wB2Hwx/wCDen40eC/it4K1268T/DCS08N+Nfgp4kuUi1G+Mj2/ gzw5PpeqIgNmAZJZ5Va3BIDxgmRoWwp+P/2u/wDgin+0N+zF+z54D+E2jeCNQ+KHivxZ8H5v htd33hezurnQLO6/4WRb+IYNl6ISY5DBP+9OpRafaxxxSyR3c7o0A0P2A/8AgoD8QPgl8NtY s/BHj3w/beEv7V+Ang/xX8RrTw14f+3+CND1DQZl1SS61GOzEDfYbiKPTY5tWW5FquYm/fMX PQeOP+C6/wC174Z+Bvg3x9qni3/hHNX0H4AaV8Trzw/N4TsIbDxbeN8QU0GO5vFmtzcrb32k yxTkWc1srM6SwGJGCkA/f7xFqXiq28d+HLfSdG8P33hm6+0/29f3eszWt/pu2MG2+y2q2skd 15kmVk8ye38pQGXzSdgwPH/7N+heOfinonjmG88QeG/GGieRD/aeialJaf2pZxTNJ9hv7fm2 vrfbLdIi3MUjW3224ktmt5384HxE8TaPpfx2+HOm3vxS/wCEU1fVf7T/ALN8G/a9Lj/4Tvy7 dWl/d3EL3kn2NSJv9Cli27szeYmFHH/tDeCfihr3x9+HupaNfahffC7T7uA+INC0PWItF1OS 4FyhiuppJIWe9s4n8mWS2gvNPIht7pZBqq3K2KgHh/wi/wCCN/8Awzro/wANrnwF438P6L4n +FHhXwvomhzTeD9+kXGo6Xa+JrS91G8sobyGSb7dH4pv5THHcRPHcokrzXAaSNuP/wCCj3/B Ly+8ffsj/Dn4MfDbRPGHiJdE+Guo/Caz1e7i0C807S7CSLSDbXWpm9likjkF7o+mTG5sLS6m jhgv/Jt4rmSzngz/AIX/AB0/ass/gn4Ig+JOo/EC2ttc8K+DfEnjTxd4b+GobxJ4Ul1DT9dW +sLHTBa3az3EN9p2gR3UZsrmWI6zqE3l2tu1slh0H/BRz9qH9qD4G/sC+HPG+maT/wAIh43s vhVq/iLxg+haHaa5baL4ot9Os5hZSLPctFFp8cLa5c/aDKwE+l2KB7syrp+ogB+0z/wRI1n4 yfCLWPB/hH4v/wDCAaf4w1Xx9q3ixbTSNUgh8Sz+J9SF1FcXUWnavYi7uLC1H2OM3xuoJYiQ 9uFCxr2Go/8ABHjSxqsWraV4q0/SNbtvEHirxeLqHw4gXUNZ1TxlovimwuL0LOr3Mdo+hWVl Ku9JLm3XCTWhVNvn/wAEtf8Aj18J/iNY+HtL0TxhpWiXvxK1+/0rTh4ZMumeJbe/+I2vvrs2 q3bwM1jHaaC9lqGnP59kLua5KodRGLZfEPgX8Yv2rPgX/wAE7fhT4aPhj4waF/Y+leD9AE1h 4SGn6l4blTRNV+12ktvHoviKebT0t7Lw+xnWyaU3+rXCSTWflTaZYgH0/rP/AARfs2/Y2+F/ wl0j4gf2bP8AB74gaz8RvDOuf8Izax/Y9TnbXZ9Kk+xWr29oP7PvNWtbjykjS2n/ALO8r7PF DOY4+f8AhV/wQY8O/ATxP8PpPB3i77N4Z8FarFNJ4evxrj2EunWXivUvEek28UdrrFtHJcWc mpyxC41JNQR2t7eVYIj5yT5/wG8X/tDap+yj8TfHNj4a1DwB8Yvid8Svhvqk0F34Qurm0sUv tD8CWmtP9gmdZns7cf2pFNiZJIRa3AM8UkLSJ0Hhf44ftLv8UPCOlahZeMDHp/iD+wtNDeE4 xZeN7K28XanpmrahrdyLfZYSQ+G7XTtUtHhk06G8ur2UQR3kZSyiAM/9mT/giRrPww+LvhHx h8Tvi/8A8Lj1Dwlqvh7Vg2t6Rql9NqM+j6b4ktYLiSXVNX1AxXBuvEC3gNuIYIZbFTFbxtKZ E9g8O/8ABOq98N/Cz9mbwjbeOvsMP7PulaZpd3remafdadr3iGKwhsVFrFcw3qx2+n3cljH9 tsriK8iuIxGB5U0ENwnxB8QfiF+3xZfB3wxrsHj74wN4n1H4VaR4kvtO0b4X6RHbW2uS+GPF 97NYPDPplxMtwt/o3h6CePzQTPq0ypHbC6tILfsPF3xr/aG+OH7TXinwHqvgv4oX/wAPrT4l aPqlrJrHh66kXSU0f4meHktntLiLQNOtvsc+mte3v/H7q0gt7SOR54PKmM4B1/xT/wCCE3jb xn+z2Phfon7QX/CO+D4dK1HwzHaW3hnULD7ZpMmjaJo1g2o/2XrFiNU1C1tdIfdcXwnt5vt8 sf2SKCKGFPUPCH/BHjS9G/aV0b4gaz4q0/xJaaN41k8ax6PeeHEeM3A1PxzqNuA7zsFkgn8Z ROkoTKyaOjqFaYeR4B4e+PH7S/w8/Zg0GHTfDvxQt18OfB/wTa3Wm+F/BsfhtfCut3MuhR3M KWMvhnUDdyRQTalLK+lpPb2lvay2j6ZbXMcN1N5f8OvFv7Vnib9rHQPiavhL4geIPiJrHhWe LSdI8R/D0ReG5JdIs/iTZ6a9xqNxpOny6fcTTHRJWd5NNlnHia7AhhtZYra0APb/AIOf8Emv Ffwm/ab+EmkaFceMNO0T4QWkM83xAn0vw5FHfXR8AReE559KLTXt/HITaaNMlndWsdqJLTUH nlvgbCKL7/8Agp8CPDP7PnhW40jwza6gkd9dtf315qeq3esanqlwUSPzrq+vJZbq5kWKKGFX mlcpDBDEpEcUaL8IeLfjp+1ZbaPrGo+BdR+IGveEvCfhXxr4k8P33if4aiDxB8QZdNtfDdxp Vhe2i2tm9lcT6nPrlksa2dvLc6fbSGGPzmg1OP6w/Z58E/FDQfj78QtS1q+1Cx+F2oXc58P6 FrmsRa1qcdwblzLdQyRwq9lZyv50sdtPeagTDcWqxjSltmsWAPb6KKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACvmD/gsx+2t4q/4J2/8ABNn4kfGPwVYeH9U8T+D/AOzPsdrrcE09hL9p1Szs5PMS GWKQ4juHI2yLhgpORkH6fryD9vX9irwt/wAFEv2TvFfwc8bah4g0vwz4w+x/bLrRJ4YL+L7N eQXkflvNFLGMyW6A7o2ypYDBwQAfH/wk/wCC+Fn8Ov2TtT+KHxy8O7vBOmfFXV/hxafEXwcl rF4b8R2dteRQ2ut2lndX7Xk9vMrz5j006kwGmXsmQF2L6B4t/wCC6Pg7wv4E+NPjGP4RfGDU fAXwC8V6p4N8W+J1uvDFlYQ6jp8iRzRwJeazBczb2lhESpDvlaaNFUyHZXoH/BSn/glN4J/4 Kh+HNO0nxr4u+IHhjT7PSr3RLqLwxPp8P9qWd1faVfvFMbq0uCu260axkVoTG3yOpLK5WuP8 df8ABDv4a+NPhL8QfB8XjX4oaJafEr4leIfihq15YXemNdpe65pF3pGoWUXn2Mka2b2d7cIo KNMhcMJgyggA5+9/4OFvgvL8b08F6L4Y+KHiaO9u/DOmaXr1hp1jDpmsX/iTRpNX0GziW5vI rpJL2KJoleeCKGGbAuJII/3tGg/8HFP7PXjSf4Q2fh5vGGu6z8avD513QdLSztbG7R21FtJt tPk+2XMKSXlxqiS2aJbPNGhhluJpIbJGuweEP+Den4L+C/Gnh/XbXxP8UJLzw34g+H/iS2SX UbExvceDNKk0vS0cCzBMcsErNcAEF5ADG0K5U9B+yj/wQv8AhH+xp4g+Fuq+C/EnxQt9S+F3 h+88KrcvrsUcnibS59bbXUtL8wwR/u4dQdpFFr9nM0ZaC5NzA7xMAeP/ALAH/ByX4B/aU0T4 PaT8SfD+oeAfH/xpu9Qn0ezgktp9MhsG1+XSNKYStcfaLmSe5AtCsMHnCazvLmS3tbFUnPoH wW/4OAvhr8cPCvwp1nTfhh8cLa0+Nt3qcfgyN9I0y8u9btdKS5fWL2O3tL+eby7EW2HhKC6u DNEtpb3bFgnQfsyf8ELvhH+yH8WvC3i3wN4k+KGkt4OtL/RtP01NdiS0/sS51ddai0WSRIFu Xs7fUvMuEJm+0TCeWC6murZvs4LX/ghf8I4v2AvBP7NNx4k+KF58LvCF3JJdWb67FFJ4otZN XXV5LK/8qBImjN3HCVmgjhuoo42SK4jWa4EwB9X+IvGWpaJ478OaRbeE/EGsafrf2n7ZrdpN YrYeH/KjDx/almuI7lvOYlI/s0M+GBMnlLhjwH7Q37QmvfCT9oD4CeEtN0H7ZpPxU8V6homs atKI2h0yK38P6pqUUSDz1lFxNNZR7WEUsQihuQ5jdoC3f+IvBupa3478OavbeLPEGkafof2n 7ZolpDYtYeIPNjCR/ammt5LlfJYF4/s00GWJEnmrhRwH7Q3xF+HHh/8AaA+Anhvxcn2zxv4j 8V6hN4Gt4rtY5rS8t/D+qG7vni81Gkt0sZbm3ZtkqpLf22VUskiAHn/7Of7UXjbw14O/aB8X fGzxZ4A1HwF8HdVuLS18Q+FPCGoaXbXdnp+lwXerXhV7+/Nx9nupbqxaKD5o59KukYtITFDn /FH9o/4L/E74tfDc/EL4O6hqPinwzd6Zdpf+KPCdjNd/Cu/1XV20vTN7zu0okvNW09reK40o XUJNrFcNKlq0F0/P6F+3f+z14N/Yl+L2v6h4a1DSvhdo/h+7+I+o6V4hjtZ4/G+ieJbnUbyG +tknuXiePWLv+0I4bG7eC4EjCGS2t1khVuf8X+Lf2If2SfBfhrxP/wAJh4P1C08KeINDh3r8 SG1SS4uPEGqvqmnX+rG5vyNRjjnvr3WoJr8zm2jW8vLXaysxAPQPhd/wWL+GHxZ+HvhzXbDw /wDFC3vPF1ppGp6LoNx4Xl/tnVLDUbK/vUvIbaNnMkccGj62xRSZpxpMhtY7pbrT2vPnD4If 8Fy/HfxE8d/CDSNT8G50/wAdaV4bm1/W9O8PW5sNI1HVY/AL/Z4Vl1xbmW3VvFkiPcGEPC1/ YFILtbO7ab2/QPBv7H/xE8P/AAw0TwhZeD/ij4b8TXel/CPRrvw94gXxBp2mJoOia1qdrYXM y3T7Yxp8mp28y5eS6h1MwXKzW87is+//AGVv2QvhR+0d4Z8B6rpH/CF+IPh7/wAInqfhqW/8 bX+m2Gq3l40Fho+nRBr5f7QuF/4QPTmFpNHIrNYW8oWSVpmoA7Cz/wCCv/gHVoPDKad4N+KG r6l48u9PXwlpthpNtcXfiaw1PTtWv9I1WILcFILO9TRb9FN41vNbGMSXsVnBunXsP2l/+CiH hH4D/sC3f7QOj2HiDxz4Zu/Cr+KfD0WlaJqU39rRNp0l/bG4MNrLJp9vLGgD3V1EkUG8eYVO FPH/AAY0f9j/AEDw/pfxE8FeM/hhd6J4X8QT3Gma/bePVv8ATtFurPRNS3adbzNdPFb2dlpN 9qksemxlbWzhnuJo4IhukGh+yD4Y+Fngr4Wa78JbnxR4f1zSdb1W88Lad4F1jWbPUraw0eyh utDtNItrRru8Zrd7Hw5eySRyyNJNPBqsssVu4ntbcA5/4h/8FIdZPxi05fCej+T4J0n/AIQv +2bXxP4b1TRte1b/AISnxPeeG4PJiuxby6d9hmsnum+0Ws/22OVET7MNtw/qHxF/b78G/Dnx V420aXS/GGqXngi70/RppLDSS9pqGt376clrosVw7JDHeSHWNHKm5eG3YaiGWYra35tNDUPh p8G/2kPjZa+I1ufD/ijxh4K8n7TFp2us6D7LqF5Ha/2jZwTCG6+x6laagbb7ZHJ9kvLe7aDy p0lYfL/xN+O/7KnxTtZfjfL4Q8QeK/8AhLtK8Uw6hrWgaiVhs/D/AId1iytdW8WJ5N6kB+z3 Gm6JNDfWPmau8Vvppt0b7KEgAPUNS/4LC/DXS9RFhLovjCLUorSWS9guTpllHpd1BB4rku7K 4ubi8jtY5LeXwbrFvJN5xtfMNu63DwO88fn/AI0/4Lz/AA11H4Ba34h8F6T4w1zXrPw/4o1R 4tHsNM8U2mgPodtptzdvd3FjqY0+aOO11azuv3WorHIFktfPjvmjtW9A/aZ/Z1/Zo+EHgvRt G8YeDNP1CXxJaJ4FgsItQkfWdVtdc1WDRbm9ld7hLi5kS58SSmfUXd7qIa3qDLL5moTrcHgz 9kP9le68K/EEWeraf4ntNZu9Y+HHjHUdT+JWpa7dte6ymj6ZfaPdXlzfSzR3ko07RrZYWkWa MxwpEEaUhwDz/wDaE/4LfaH4Q8ca54D8A+D9Q8XfEnw3410jQLrR7TXvDeptcWEniSw0a/ud ltrG+zkD3sEEMeofZpvtF9bs0JhgvmtvcP2GP+Cl3ws/4KIf8JT/AMK21b+0v+EV+yXE3+lW dz59jeef9ivP9Gnm+z+d9muP9Eu/IvofK/0i1g3x7/H/ABj+zt+xLLa+IvGGs/EDw/a6frH2 jXbjUZPjTqdtYaV9t1jRfEMt7YkamsOm+dqVxoN55tmIdzXliQdtxGH9Q8Dfs5/Dz4Q/tP8A hrwv4J+I+oeF7zRLSbxbc/DaLxpf3jXNkIrjTbPyNNuL2SGw0OA3lyPs1paRQm4g03a8S2Sw yAHP+B/+Cm1l8f4PjXpfhHTNQ8D6l8LLtYh4g+IvhXX9B0C0sBp2l3t1qd893ZWyQSWyahM4 06SeKa4jtEk823guBcxHiz9ujx/8LPhL8Ata8ReAtQvdb+JvjW98LX+kaZolymp6tbppGu3e n3drZTSq+myXz6dYTtBqEmNPhvJo7uWM28txH2Hxevv2cfjj+yd4g8X6/wCLvAEfwi8VarY6 7qvjTSPFyaLYXeo2N5aQ2l6dZsriErcQ3VhZQpKs4cNaxRZ+UJXy/wDtJftlfsj6F8LPG3g7 xnH4g8a6T+y98QNI0axuNR+Jdtd6rP4o1WG4hjey1G/1pLxbiwW+vSbm9mtxZvY3DwSB9NZr cA9Q8Jf8FkvBt4/jvWbi21DxF4N0q7Gu6Nf6FpxiktfBkPhbwzrV94gvku5YnaO2fxDCGggR rxo5oVitJnjmYaH7RH/BWbQvhN4j8DHR9E8Qalo2t/FWX4Y3M0ujSL/wkN59h12CG20md5I7 f7Q2v6SNOb7U8axYaaQRWs9reyaFj8D/ANj/AOI+t6LoGkXvwwmvPGl2LjS9G0HxYtsviGGP QNIWTTorW1uFW60t9FtdEkn00I1lNbx2c0sDqUcniH9j39kv42aj8Q/ENwPB+sLrPiCTwn4q ks/GdwllZ6zcQanpMumGKG6ENleTHxPqaPbxrFJLdaw8zK1zIstAHoH7cn7cmi/sN+C/Duq6 r4e8YeI5fEfiDStFhh0Xw5qupxwJd6rY2E0ss1laXCRSRpe+ZFBJsku5I/Ih3SNx5/4T/b78 Q+NP2tNM0Sz0vT4fh9q3jWL4cwWl/pN7Ya+l7J4KHi9dUladkMEYgIsW06W0WZJMzNcLg2o0 P2Vfj58LPGv7J3gubxt8RfD/AIi1DxF4Vh+OF5beNL2zt7/TNOurz+2I9SaymuJzZafYXUiR 27NNMlmtpBGLmRoRK3QfAu+/Zx/ag+O2q/En4aeLvAHxE8W6H5UmpTeGPFyavZ6fdS272kWo TWVvcPax6g9pFJaLfNELprWN7cSmFTGADn9P/wCCs3w4vvE91pMmifECyuo/Fc3g+ze70ZYI dXvLfxXZ+FryS1kaTbLb219qWmySTAhWivQsfm3FveW9tz/in/gr74J8E33xG1O+sfEEuheB Ps1g9rJZ6fpmy8/4SLxLoc80uqXmoxafDbvN4emMZvGtFGYEWWe5vYrSL2DWP2CvhPr1qsV1 4U3+V/brQSLqd5HNZy6zrFvrd/cQSLKHhuG1O0truKeMrLaywRtbvDtFc/oP/BLj4E+EvDlx omi+Bv7C8P3nlx3ejaVrWoWGlX9ql9q1+NPuLSGdYJ9Pa41vUy9jIjWsiXAieJoooUjAPP8A xB/wWy+EHhL4k+O/CWpW/iCTxB4D1WDTJ9P0S50rxPf36ya9ZaE88dnpN7d3UPlXeo2eba7i t7yRZWENvNLFNFH2Fh/wVN+GEmnaleX8XjDS7Twt4f1HX/F876HLdx+CRYz6pb3Ftfta+aPM E+ha5Eslv51u0mmMomLXVgLvQ0//AIJf/BbTviTdeLB4d8QXOr3Oqza2iXfjHWruwsbybXrP xDNLa2ct21ta+bq1haXUiwRRrI0IVgUZkOhqf/BN74Ja54q07Wb7wDp99eadd61emO5u7ma0 1B9XfU3v1vbdpDDexsda1cRx3KSx241O7WFYlmcEA0P2Kv23PBP7eXws1DxV4JuN8Oi6rJom qW39oafqX2C8SGG48r7Vp1xdWM+YLm3k3W1zKq+b5blJo5Yo/X64/wCCnwP0X4A+FbjRtCvf GF/aXN2168niTxZqviW7DsiIVW41G4uJkjxGpEauIwSzBQzsT2FABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF cB8ZPgZ/wtv4i/CbxB/an9n/APCr/Fc/ifyPs3m/2n5uh6rpPkbt6+Vj+0/N34fPkbNo3717 +vgD/goHq/xx+FP7U/xB8VfCjQfEA0/WfCvgTSNX1/T9PuJ7mxs4LzxrNObHy9K1YzXCXU2k xSpHpl6yQagXaOBSLuAA7DwB/wAEibfwx8Av2gPDFz4h8H6Z4i+PXgpvAlzfeFPCU+k6RpNl HbalDZyLZXGoXc0kkA1SaJV+1pDHaWlhawRW8Vsu7P17/gkn4q17S7eaT4reH08QeHdVk8Q+ GrpfBc32Oz1G+8X6T4v1hryD+0d91by6npMUdrFFNbva2srxyy3soFxXiHiz9r39s3xP8YNT 8PeEvDXjBtb8ReCpbW2dvDF5a+F7PWZPAp1SDUdNOoeHrZbOP+3hDZBNX1meRXeeOWxjLI1t 1/hfxF+1B8Qv2p9J+yv4g8E6T8QNVtbe78VSfDu0e8tfC73nxLurKKWSW2VoLiGxTwz5cd7u NndXcH2m2d7q6troAPh5/wAEjfjB+y/dW3xDsPi5/wALn+K/hz+yBoZ8QWmqyQ6heWmj+JtI iudRn1LXLuWO387xQ17crZNEqxWEy21o004B+kPj5/wTs0v46ftF6p8SZtW0+y1u8tPAthaT PoqXF3p1v4c8VS+Ip4Y7gyK4jv3NvC6AKENpFKRMVVF+f/gz+2N+0v4p/ab/AGcYPiFouoeB dE+J13YRa7oi+FI7bTgkngC71cxi5uZmure8/tyHVoZbTbJJHDo1iZDZiXdq2h+3T8BfiP4i +MX7SGoeHU8Qatp/ifwr8LChvvD7arpS6ZY+J9Zl17TEtLUQTarb/wBmmea504Sy3Nymovbq StxbwgA5/wAH/wDBAnWdV8HeIbD4lfHnxB4+1fXtK0zQpNZktNUmvJbWHS/EWkX8kr6pq2oN 9oubHxPqDRi3aC1trpIJhayp51vN6hov/BHjS9L+I3ibxVN4q0+71vXPEGk6xaXr+HE+16Xb 2fxG1PxvPZRzGcv5dy99b2j4KjNhFOVc7YY8/wAJv8WfgJ/wT7+GWmfDXwP/AMIx5HivUrRr XRPA9nb36aH5+rS6JeSaKrWcEFxqdwmiDUIitkLddV1B3OleU9xZeAfsnfFb9sT4K+APhp4G 0TwN9mh8P/BXS7Lwx4c13Q9TFnqd5b+Cop4hdyx6YLex1D+34nspBfa5ZqLWFk+wLNLb3koB 7/8A8EjP2J/Ef7MPjvx/4h1Xw54g8KaNq/hXwl4J0jT/ABHb6HDrckHh+PUra3nuDpFxdQz5 sLnTomuJrlp5bi3uyIbS1Fnbp0HxR/4JS/8ACyPh1pHh/wD4T37P/Z/ivxd4g8+bRPtH9m/8 JBrlxq32/TF+0L9h8Qad53lWGrZl+z+bdv8AZn+0bI/APhp+0X+178XPiL8R9J8F6/4g1WHw F/wlcmjReJfAVhpVz4hls9D8E3/hvT787o49PuNQk1G+knMxgleC5v1Ftpk0cK6Zn61+1z+2 Rp3wC8M6lpVrqF4tp4g1aGbV9Q8IeJJNTleK20ybTLTVLSPwdFd3VnNLPq/myafo9hH5NpaR DU4LkSG7APq/9rb/AIJp2f7Uf7Qnhrx+viz+w59F1Xwhq93ZzaFa6l9pl8Nazc6lZCCaXEln 5keparBM8JEreba7ZEhjura++b9Y/wCCOFz+zF8JYLLRE8YfFxtOu9M0PQ4dLtYNT1ez0G30 jXtIa31BPFGvNpVxGun65fLHHZ29pbR3c6Stp9xbB7QfQHhP4qfHrUtR/apvdD0rUPFN54at JYvhdBrWnHQ9G1LVoYL7bp8Vrc2tpdmMSrYLLfNqF3Z3hn821mtFE1rb+H/CT4G/Ez9pL/go J4A8a+IvFXxgm8H+CvCvjjTvAvi3X/Auk6Tq8X2mDw1CL+5gutHiNjqD3V5rMMKtbW3nW2hW zCGWCW9e/AND4R/8ELZPB37LegaPc+M9P0P4saZd+FdYPiLRYdRtY4bjR/Bdr4WNlLdWF5p+ pXVmI/7TuIvLurMrJf4ZWj8+K46Dwn/wROsvh94+0zVNK8QeD77RLTw/FZS+GNf0bX9V0CLU ovCQ8LJPBpx15LJrM2McSPDewXl3JHJcxm/xJE0HzB+yF4V/a1/ZI/4JzeHNI8OP4w0fw74W u/D+j/2Evga30rWdBsL7wnpmq6jex/ZtD1Ce7ki8QTPYjZpF1MguNVa8aaULe6d7f/w1R+2j P8aPLtfCvh+7utQ+H/27QtI/4R3Xbfw9r2onwr9tSTdNpSHTrhvEKyWmzVdasnWyi8t9PS4k hvJQD6Qg/YW8Q6B8Avhdoml/EzUNR+IXwm8QXPinSfEviy3vfEdpcXt3balaXENxBNfC9ms4 7bVruK2jfUDNCIrPzLi58qQTc/8ACz/glbpfwY+AV/8ADnQvGOoPojeNfA3inT57+wSa7trf wxbeGLeK0lZHRJZLhPDSlp1SMIbw4iYRYf5g8EePP2l/h+/xc1nw/pnxQMvinxrqereGdTvv h9Ha6j461S28LeE7Xwzaara+XMljpeopFqAv7wJpwgmswskuiyF7Je/+Avj79qi8/wCChenL 4m1/4oXPwuvvGuq2c+kXng3TbbRrXSzdfEGG2YXaacl0I44tE8KyJI1ySx1RC7SLdwAAHQeA v+CH2i/Cmf4Fnw94q0+C0+Fnh/wroWv6c+n6rZaZ4nfQdRl1ODUI7PTtVs7dLyW/ury5d9Qj 1KMTPE6xhhObnP8AhZ/wQk074beDjoLfED+0rbSP7O0zQL3UNNvtavItFtdL1vRjYX0Wrajf WE+NO1/UPKS1s7KyjunEj2U9vmyPkH7LPwe/aU/Zkk+NsGgeIfiBbeFv+ErufFeoSXHw80v+ 29dl1Dxz4j07VtVtpobCMXtxF4etbTVY7YW1zLNcf2UsC/Y2Gm3Z+0p8ZP227zR5b/wfrfxg 0aHS/Ct5caSumfD7TLibxH5dr4/utNvL6K40yWSHULqPRvCC3FpGtt5UusND9ltpriKOIA9f /wCHGP8Axjb/AMKt/wCFo/8AFP8A/CK/Y/tH/CN/6Z/wkP8Awgn/AAg/9o7vtWz+z/7M/ffY Nnnfavn+3eV+4r6A/ZG/YY/4ZX+MXxE8Wf8ACU/29/wn3m/6L/Zv2X7Dv8T+KNf+/wCa/mY/ 4ST7P91c/YvM/wCWvlx+f/t1fDe88X/tf/B6/wBG8IeINQ8QafqukSwa1b6LdXD29nFqsUt+ lhrKzva6BstBM2oJcWqNrlnLFp8MzyoPs/gA+FH7TfhD/hsvVvHvgrw/4k8YeL/gBaGC90fU r3xZpWt65D/wk/2fTNN0y80qK2NuFuFjbTgJSiCzaY38+oXNxQB+n1Fflj42+Nv7Y3gv4y+O 9dS++OHiDwpY3fil9P8ADOneC9Jhje3iHxGXT47O5OjyzeYBoXhNoJJTcB5NWiMiXCXkMZ5/ 4j/t4/tg+Ef2FviJ4pTT/GGjy+ArTxXLp3ia98FNcajqb2uj6Te2cl7FqOm6Ii2aJdeI3+1r ptpGs2h2EOL6Rja6sAfrdRX54ap+03+09pPxa+P+n66moeG9L0q01NvDaWXgjXtfa3SPV7a2 0mXTZLLw/NbCS602Zprgtd65JBcSRzmxhhtLzTq+3/2ernxNefALwPN41s9Q07xlL4fsH160 v7y0vbu1vzbRm5jlns4obWaRZd4aS3ijhYglERCqgA7CiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooA4/Rv2e/APhz4y6t8RtP8D+D7H4ha/aLYan4nt9GtotZ1G3URBYZrtUE0kYEEACM5AE MfHyLjsKKKACs/xZ4T0vx74V1PQtd0zT9a0TWrSWw1DT7+3S5tL+3lQpLDLE4KSRujMrIwIY Eggg1oUUAc/8LfhP4W+B3gSw8LeCvDXh/wAH+GdL8z7HpGiadDp9haeZI0snlwQqsabpHdzt AyzsTySa6CiigAooooAKKKKACiiigAooooAKKKKACuP+I/7PfgH4x+KvDGu+L/A/g/xVrfgm 7+3+HdQ1jRra+u9AuN8T+daSyoz28m+GFt8ZU5ijOcqMdhRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q= --part-gwZJ1dAgGbAA3cEDw04oc1CAA3yAQAnX3Uu27qORZYmBd-- ========================================================================Date: Wed, 23 Feb 2011 16:17:06 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: Segmentation anomalies Comments: To: justine dupont <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hello Justine, this looks like the initial affine registration did not work, perhaps due to image inhomogeneity. Aligning the images nicely, for example by coregistering to a template prior to segmentation, may help, as may fiddling with the inhomogeneity parameters (bias FHWM etc.). Good luck, Marko justine dupont wrote: > Dear experts > > I recently saw something very puzzling with a VBM analysis. I have 40 T1 > scans. They all have the same dimensions and voxel size and the raw > images all look consistent and normal. Segmentation (with normalisation > and modulation) works fine for 38 but for one the GM result is an empty > brain (top row of attached image) and for the second the result is a > kind of 'half brain' (bottom row of attached image). > > What causes this and can it be fixed? > > Many thanks > > Justine > -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Wed, 23 Feb 2011 16:29:39 +0100 Reply-To: Martin Jensen Dietz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Martin Jensen Dietz <[log in to unmask]> Subject: Re: paired t-test issues Comments: To: Nero Evero <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi Nero, The betas are not unique because subject columns introduce non-orthogonality in the design matrix. This is completely normal however and the default for a paired t-test design matrix. Your contrast weights are zeros(1,17) 1 -1 zeros(1,17) -1 1 Best wishes, Martin On Feb 23, 2011, at 3:59 PM, Nero Evero wrote: > Hello All, > > After specifying my second level analysis using a paired t-test design, I noticed in the parameter estimability my “betas are not uniquely specified”. I am not sure what this means and wanted to know if I have made a mistake. At the first level analysis from individual subject models, I created two contrasts images for each subject. Each subject underwent two sessions (after different treatments for each scan, at different times points, with identical paradigms) and I wanted to test whether condition A > condition B between the two sessions. I am using spm8 and entered each pair of subject contrasts (17 total using fMRI data) into the second level and used all the defaults for a paired t-test. I have also attached the design matrix. Also, I am not sure what the contrast weights should look like to for the paired t-test design. Any help would be appreciated, also please let me know if you need more information. Thanks in advance. > > Nero > > ========================================================================Date: Wed, 23 Feb 2011 15:39:06 +0000 Reply-To: Chris Filo Gorgolewski <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chris Filo Gorgolewski <[log in to unmask]> Subject: Re: Neuroimaging Data Processing Workshop 23-24 March 2011, Edinburgh, UK Comments: To: nipy-devel <[log in to unmask]>, [log in to unmask], [log in to unmask], [log in to unmask], freesurfer <[log in to unmask]>, FSL - FMRIB's Software Library <[log in to unmask]> Comments: cc: dtc-edinburgh <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Message-ID: <[log in to unmask]> Registration is now open. https://www.epay.ed.ac.uk/browse/extra_info.asp?compid=1&catidB&modid=2&prodidE3&deptid$&prodvarid=0 Best regards, Chris Gorgolewski On 18 February 2011 17:58, Chris Filo Gorgolewski <[log in to unmask]> wrote: > Dear all, > > I am delighted to invite you to a Neuroimaging Data Processing > Workshop which will be held on 23-24th of March 2011 at University of > Edinburgh, UK. During the workshop we will cover neuroimaging data > analysis and management with reusable, scalable and reproducible > processing workflows that combine different software packages (such as > SPM, FSL, and FreeSurfer) using NiPyPE framework. The workshop will > also feature talks and demos covering data management, encapsulation > and visualization using PyXNAT and the Connectome Library and Viewer. > More details can be found at the workshop website: > http://nipype.blogspot.com > > Best regards, > Chris Gorgolewski > ========================================================================Date: Wed, 23 Feb 2011 11:51:53 -0500 Reply-To: justine dupont <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: justine dupont <[log in to unmask]> Subject: segmentation anomalies Content-Type: multipart/alternative; boundary="_0dea0aa1-ab97-4673-840d-8dde58316bbb_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_0dea0aa1-ab97-4673-840d-8dde58316bbb_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Abela, Marko, Shail, Donald and others surprisingly, given the consensus, the images were perfectly aligned. So the mystery remains...? Justine --_0dea0aa1-ab97-4673-840d-8dde58316bbb_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Abela, Marko, Shail, Donald and others

surprisingly, given the consensus, the images were perfectly aligned. So the mystery remains...?

Justine

--_0dea0aa1-ab97-4673-840d-8dde58316bbb_-- ========================================================================Date: Wed, 23 Feb 2011 12:27:44 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: Segmentation anomalies In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset=iso-8859-1 Mime-Version: 1.0 (Apple Message framework v1082) Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Justine, I think that Marko is correct in that it is worth investigating the image inhomogeneity and bias correction parameters. If the original alignment is ok, as you say, you may be able to try different bias correction settings. Alternatively, you can do a two-pass bias correction procedure, which is probably less elegant but often seems to work. To do this, run segmentation once and output the bias-corrected image, but no segmentations. Then run segmentation again on this bias-corrected image. For images with significant inhomogeneity this often works wonders. Best regards, Jonathan On Feb 23, 2011, at 10:17 AM, Marko Wilke wrote: > Hello Justine, > > this looks like the initial affine registration did not work, perhaps due to image inhomogeneity. Aligning the images nicely, for example by coregistering to a template prior to segmentation, may help, as may fiddling with the inhomogeneity parameters (bias FHWM etc.). > > Good luck, > Marko > > justine dupont wrote: >> Dear experts >> >> I recently saw something very puzzling with a VBM analysis. I have 40 T1 >> scans. They all have the same dimensions and voxel size and the raw >> images all look consistent and normal. Segmentation (with normalisation >> and modulation) works fine for 38 but for one the GM result is an empty >> brain (top row of attached image) and for the second the result is a >> kind of 'half brain' (bottom row of attached image). >> >> What causes this and can it be fixed? >> >> Many thanks >> >> Justine >> > > -- > ____________________________________________________ > PD Dr. med. Marko Wilke > Facharzt für Kinder- und Jugendmedizin > Leiter, Experimentelle Pädiatrische Neurobildgebung > Universitäts-Kinderklinik > Abt. III (Neuropädiatrie) > > > Marko Wilke, MD, PhD > Pediatrician > Head, Experimental Pediatric Neuroimaging > University Children's Hospital > Dept. III (Pediatric Neurology) > > > Hoppe-Seyler-Str. 1 > D - 72076 Tübingen, Germany > Tel. +49 7071 29-83416 > Fax +49 7071 29-5473 > [log in to unmask] > > http://www.medizin.uni-tuebingen.de/kinder/epn > ____________________________________________________ ========================================================================Date: Wed, 23 Feb 2011 20:20:38 +0000 Reply-To: Nero Evero <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nero Evero <[log in to unmask]> Subject: Re: paired t-test issues Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Thank you Martin for your quick responses. I have another question, if anyone could help. After completing the 2nd level analysis and creating my contrast, I noticed I had no significantly activated clusters compared to the series of within-subjects contrasts I created earlier. I was curious why that may be; possibly a lack of power (i.e. only 17 subjects) to detect any significant difference between the two treatments? Again, here is a review of my design for my crossover study: Session 1 --> Treatment 1 --> Scan (Visual paradigm: Active vs. Control) Session 2 --> Treatment 2 --> Scan (Visual paradigm: Active vs. Control) Also, would it be reasonable to run a one-way ANOVA for each session (as opposed to a paired t-test) and compare which regions are significantly activated. With this approach I was concerned I would not be able to infer one treatment was signifcantly different than another, would I be correct? Thanks for any comments in advance. Nero ========================================================================Date: Thu, 24 Feb 2011 03:47:59 +0000 Reply-To: Dashjamts Jargal <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Dashjamts Jargal <[log in to unmask]> Subject: Umodulated image analysis better in discrimination: possible explanation? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all, I am novice in the SPM. There are many questions regarding the interpretation of SPM analysis results modulated versus unmodulated structural images. I am studying Alzheimer subjects compared to healthy aged controls. I have used T1 WI images of same size, orientation and scanner, applied DARTEL tool as implemented in SPM8. Proportional global normalization eliminated multiple scattered areas of interest in the unmodulated date and produced better localization of the findings into MTL structures in modulated data. But still even after proportional global normalization, my globally normalized unmodulated data show larger relevant difference areas and have higher diagnostic accuracy than the globally normalized modulated. What could explain such result? Why the unmodulated demonstrated much more affected areas than the modulated? Is the following correct according to the definition of how modulation works: "perfectly registered unmodulated data should show no difference between the groups"?Does it mean that unmodulated data have some compound of registration failures? When I am adding covariates such as TIV, age and sex to both modulated and unmodulated ANCOVA with normalization, there is only slight improvement.( I think it is improvement because after that I am loosing those few voxels which were in the deep subcortical white matter.) I am looking forward to receiving your comments and thank you for your time. Jargal Dashjamts, PhD-student Kyushu University, Japan/ HSUM, Mongolia ========================================================================Date: Wed, 23 Feb 2011 16:36:13 -0800 Reply-To: Meghan Meyer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Meghan Meyer <[log in to unmask]> Subject: Re: Contrast Manager Error in SPM8 In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0023547c90239ad82a049cfc6633 Content-Type: text/plain; charset=ISO-8859-1 more info on this error: the model was not estimated bc of a problem using 'svd'.... Running 'Model estimation' SPM8: spm_spm (v3960) 16:29:05 - 23/02/2011 ======================================================================Initialising parameters : ...computingFailed 'Model estimation' Error using ==> svd Input to SVD must not contain NaN or Inf. In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126. In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225. In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439. In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33. Running 'Contrast Manager' Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s Failed 'Contrast Manager' Error using ==> spm_run_con at 37 This model has not been estimated. On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <[log in to unmask]> wrote: > Hello, > I'm running 1st level analyses in spm8, but for some of my subjects, i'm > getting the following error below, but i'm not sure why the model would not > have been estimated. the error appears for 9 of my 18 subjects. the other 9 > ran fine. Thanks! > > Running 'Contrast Manager' > Changing directory to: > /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s > Failed 'Contrast Manager' > Error using ==> spm_run_con at 37 > This model has not been estimated. > In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function > "spm_run_con" at line 37. > > The following modules did not run: > Failed: fMRI model specification > Failed: Model estimation > Failed: Contrast Manager > > -- > Meghan Meyer, M.A. > Graduate Student > Social Cognitive Neuroscience Lab > UCLA, Psychology > -- Meghan Meyer, M.A. Graduate Student Social Cognitive Neuroscience Lab UCLA, Psychology --0023547c90239ad82a049cfc6633 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable more info on this error: the model was not estimated bc of a problem using 'svd'....
Running 'Model estimation'

SPM8: spm_spm (v3960)                              16:29:05 - 23/02/2011
========================================================================
Initialising parameters                 :                   ...computingFailed 'Model estimation'
Error using ==> svd
Input to SVD must not contain NaN or Inf.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225.
In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439.
In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33.

Running 'Contrast Manager'
   Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.

On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <[log in to unmask]> wrote:

Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!

Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.

The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager

--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology

--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
--0023547c90239ad82a049cfc6633-- ========================================================================Date: Thu, 24 Feb 2011 02:12:23 +0000 Reply-To: Israr Ul Haq <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Israr Ul Haq <[log in to unmask]> Subject: fixed effects analysis across two sessions Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear Spm users, I am trying to see treatment effects on a patient, by putting his pre and post treatment sessions (all the runs separately) in one fixed effects analysis, and defining post â€“ pre contrast (t) by giving positive one to the experimental condition in the post treatment runs and negative one in the pretreatment runs. This is all being done at the â€˜specify first levelâ€™ option in spm8. It made sense and the few people I did ask seem to think this was okay too. However I came across something that has me confused and it would be great to hear an explanation. I had been including the control condition in the contrast too, so for the post treatment runs it was included as a negative one and for the pretreatment runs a positive one (just how itâ€™s supposed to be contrasted out of the experiment condition at each session level, by giving it a weightage equal and opposite to the experiment condition). I thought I was getting a reasonable result from the contrast till I fortunately or unfortunately tried a contrast without the control condition, which is positive and negative ones only for the experimental condition, post and pretreatment respectively. This now is giving me FAR less activation then the contrast with the control condition, which is opposite to what i was expecting, since I though the whole point of including the control condition was to subtract activations unrelated to the process of interest, making the result purer so to speak. Since I have a medicine background and limited statistical knowledge, it would be great if you can point out whether this is possible and adding the control condition into each run somehow increases the t statistic when the analysis is done across sessions, or if I am doing something wrong. My t contrast for the fixed effects is: -1 1 -1 1 1 -1 1 -1 . where the sequence of runs put into the model specification part is pre tx run1, pre tx run2, post tx run1 and post tx run2, and each run has an experimental and a control condition. Will extremely appreciate help in this matter. Regards Israr ========================================================================Date: Thu, 24 Feb 2011 09:35:18 +0100 Reply-To: "Kay H. Brodersen" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Kay H. Brodersen" <[log in to unmask]> Subject: Re: multivariate bayes analysis Comments: To: Ciara Greene <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; format=flowed; charset="UTF-8"; reply-type=original Content-Transfer-Encoding: 7bit Message-ID: <9C3287B48E0C415E9A5E53EF766836C4@KHB> Dear Ciara, Great to hear you found the SPM course useful. With regard to your first question: the priors in Multivariate Bayes have not been renamed. Instead, the code actually contains more priors than the GUI offers. You can see this by comparing the priors listed in spm_mvb_estimate (line 78) to those actually supported in spm_mvb_U (starting in line 40). I would suggest starting off by comparing those models that are directly listed in the GUI. Your second question relates to fixed-effects versus random-effects model comparison. SPM currently does not provide a fully automated pipeline for comparing different MVB coding hypotheses (i.e., spatial priors). However, this is easy to do with just a few Matlab commands. You would begin by running MVB separately for all subjects and coding hypotheses. Given n subjects and m models, this results in n*m different MVB.mat files. You then extract the log model evidences from these files (F) and arrange them in an n*m matrix M. (i) For fixed-effects model comparison, you would then compute group Bayes factors as sum(M,1) and select the model with the highest group Bayes factor. This analysis relies on the assumption that the same model is best in all subjects. (ii) For a random-effects model comparison, you can use spm_BMS(M, ...). This approach relaxes the assumption above and explicitly accounts for between-subjects variability. Let me know if this works. I'm copying this to the SPM mailing list so that other users of MVB may benefit from our discussion. Very best wishes Kay -- Kay Henning Brodersen Department of Computer Science Pattern Analysis and Machine Learning Group ETH Zurich Switzerland [log in to unmask] http://people.inf.ethz.ch/bkay/ From: Ciara Greene Sent: Monday, February 21, 2011 3:34 PM To: [log in to unmask] Subject: multivariate bayes analysis Hi Kay, Thanks for the interesting practical session on multivariate analysis at the SPM course last week, I really enjoyed it and I'm hoping to put it to good use. I'm trying out the multivariate bayes method with a view to comparing coding models in my data, and I had a couple of questions; I hope you don't mind answering them! Firstly, I'm a bit confused by the terminology for the model priors. In the Friston et al. NeuroImage paper, the various priors were listed as spatial, smooth, singular and support, while in the SPM GUI the options are compact, sparse, smooth and support. I can't find a description of these new labels anywhere, so I'm not sure if the priors have simply been renamed (e.g. singular becoming compact???) or if these represent new models that weren't in the old version. My second question related to comparing models across subjects. For obvious reasons, I'm not really interested in model comparison within a single subject, I want to see which model seems best in my whole sample (and by extension, in the population). I've read a bit about the new Bayesian model selection method for DCM implemented in SPM8 which allows a random effects comparsion of models across subjects, but I can't get that to work with the multivariate bayes. The BMS button in the GUI allows models to be compared to one another, but presumably that uses fixed effects, and isn't suitable for group analysis. As far as I understand, it also doesn't allow the results of the analyses to be saved out and carried forward to a second level. Do you know if it's possible to use the DCM model selection method outside DCM, or alternatively if there's another way of running a group level analysis here? Thanks in advance for any help you can provide! Ciara ========================================================================Date: Wed, 23 Feb 2011 16:11:21 -0800 Reply-To: Meghan Meyer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Meghan Meyer <[log in to unmask]> Subject: Contrast Manager Error in SPM8 MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0023547c8b43a1e6d7049cfc0d30 Content-Type: text/plain; charset=ISO-8859-1 Hello, I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks! Running 'Contrast Manager' Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s Failed 'Contrast Manager' Error using ==> spm_run_con at 37 This model has not been estimated. In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37. The following modules did not run: Failed: fMRI model specification Failed: Model estimation Failed: Contrast Manager -- Meghan Meyer, M.A. Graduate Student Social Cognitive Neuroscience Lab UCLA, Psychology --0023547c8b43a1e6d7049cfc0d30 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!

Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.

The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager

--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
--0023547c8b43a1e6d7049cfc0d30-- ========================================================================Date: Thu, 24 Feb 2011 10:25:46 +0100 Reply-To: Martin Jensen Dietz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Martin Jensen Dietz <[log in to unmask]> Subject: Re: paired t-test issues Comments: To: Nero Evero <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi Nero, ANOVA is used in the case where you wish to test hypothesis concerning >2 beta or contrast images. Using a paired t-test on 2 contrast images, as in your case, is more straight forward. Alternatively, you could subtract your contrast images in ImCalc and perform a one-sample t-test. Best wishes, Martin On Feb 23, 2011, at 9:20 PM, Nero Evero wrote: > Thank you Martin for your quick responses. I have another question, if anyone could help. After completing the 2nd level analysis and creating my contrast, I noticed I had no significantly activated clusters compared to the series of within-subjects contrasts I created earlier. I was curious why that may be; possibly a lack of power (i.e. only 17 subjects) to detect any significant difference between the two treatments? Again, here is a review of my design for my crossover study: > > Session 1 --> Treatment 1 --> Scan (Visual paradigm: Active vs. Control) > > Session 2 --> Treatment 2 --> Scan (Visual paradigm: Active vs. Control) > > Also, would it be reasonable to run a one-way ANOVA for each session (as opposed to a paired t-test) and compare which regions are significantly activated. With this approach I was concerned I would not be able to infer one treatment was signifcantly different than another, would I be correct? Thanks for any comments in advance. > > Nero ========================================================================Date: Thu, 24 Feb 2011 12:03:13 +0100 Reply-To: guillaume auzias <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: guillaume auzias <[log in to unmask]> Subject: DARTEL create template: minimum number of subjects? MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: [log in to unmask]> --0016364ee4e2e918b9049d0528d9 Content-Type: text/plain; charset=ISO-8859-1 Hello, I've not been able to find in the SPM list archives a clear answer to the following questions: Is there a minimum number of subjects required for the template creation using DARTEL? Does the registration of a pair of brains through a specifically created 2-brains template makes any sense? Thanks for any input, Guillaume --0016364ee4e2e918b9049d0528d9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello,

I've not been able to find in the SPM list archives a clear answer to the following questions:

Is there a minimum number of subjects required for the template creation using DARTEL?
Does the registration of a pair of brains through a specifically created 2-brains template makes any sense?

Thanks for any input,

Guillaume
--0016364ee4e2e918b9049d0528d9-- ========================================================================Date: Thu, 24 Feb 2011 19:16:50 +0800 Reply-To: li jian <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: li jian <[log in to unmask]> Subject: Re: MATLAB Out of Memory error Comments: To: Dennis Thompson <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e64bbe8ea02358049d055915 Content-Type: text/plain; charset=GB2312 Content-Transfer-Encoding: quoted-printable maybe you can add the virtual memory in window system. http://www.delete-computer-history.com/increase-virtual-memory.html Addtionally, you could used "free" command in matlab. 2011/2/19 Dennis Thompson <[log in to unmask]> > Window 32 bit limits you to 2 GB of memory by default . There are some > tricks to boost that to 3GB as outlined by Matlab in this link > > > http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67 > > > Here at the IRC we do all our processing on 64 bit linux computers. Have > not had a "Out of memory error" in years. > > > > On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote: > >> Hello All, >> >> We are trying to run a fixed effects analysis using SPM8 on a Windows 7 >> 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of >> 6600 scans (55/session). The analysis stops during the parameter estimation >> and gives the following erroe message: >> >> Initialising parameters : ...done >> Output images : ...initialised >> Plane 1/46 , block 1/3 : >> ...estimation??? Out of memory. Type HELP MEMORY for your options. >> >> Error in ==> spm_spm at 715 >> CY = CY + Y*Y'; >> >> Error in ==> spm_getSPM at 233 >> SPM = spm_spm(SPM); >> >> Error in ==> spm_results_ui at 277 >> [SPM,xSPM] = spm_getSPM; >> >> ??? Error while evaluating uicontrol Callback >> >> I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem >> to about 2GB), but I still received the error. Looking through the archives >> it seems like the common solution is to change to a 64-bit OS, but I wanted >> to know if there was any other possible solutions? Thanks in adavance. >> >> Nero >> > > -- Àî½¡ Jian Li, Institute of neuroinformatics, Dalian University of Technology, NO.2 Linggong Rd, Dalian, P.R.China 116024 --0016e64bbe8ea02358049d055915 Content-Type: text/html; charset=GB2312 Content-Transfer-Encoding: quoted-printable

maybe you can add the virtual memory in window system.

http://www.delete-computer-history.com/increase-virtual-memory.html

Addtionally, you could used "free" command in matlab.

2011/2/19 Dennis Thompson <[log in to unmask]>

Window 32 bit limits you to 2 GB of memory by default . There are some tricks to boost that to 3GB as outlined by Matlab in this link

http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67

Here at the IRC we do all our processing on 64 bit linux computers. Have not had a "Out of memory error" in years.

On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote:

Hello All,

We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message:

Initialising parameters : ...done
Output images : ...initialised
Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options.

Error in ==> spm_spm at 715
CY = CY + Y*Y';

Error in ==> spm_getSPM at 233
SPM = spm_spm(SPM);

Error in ==> spm_results_ui at 277
[SPM,xSPM] = spm_getSPM;

??? Error while evaluating uicontrol Callback

I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance.

Nero

--
Àî½¡
Jian Li,
Institute of neuroinformatics,
Dalian University of Technology,
NO.2 Linggong Rd,
Dalian, P.R.China
116024
--0016e64bbe8ea02358049d055915-- ========================================================================Date: Thu, 24 Feb 2011 12:41:49 +0100 Reply-To: "Kay H. Brodersen" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Kay H. Brodersen" <[log in to unmask]> Subject: Re: multivariate bayes analysis Comments: To: MS Al-Rawi <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; format=flowed; charset="iso-8859-1"; reply-type=original Content-Transfer-Encoding: 7bit Message-ID: Dear Rawi, Multivariate Bayes becomes accessible once you have loaded the results from a first-level GLM analysis. Use the 'Results' button to do that, which will make the three MVB-related buttons appear: multivariate Bayes, BMS, and p-value. For details, see slide 38 in: http://www.fil.ion.ucl.ac.uk/spm/course/slides11-zurich/Kay_Multivariate.pdf Best wishes Kay -----Original Message----- From: MS Al-Rawi Sent: Thursday, February 24, 2011 12:33 PM To: Kay H. Brodersen Cc: [log in to unmask] Subject: Re: [SPM] multivariate bayes analysis Hello Kay.... I want to give MVB a try, however, I don't see anything in the SPM_GUI->MVB. But, yet, there are mvb related functions, e.g. spm_mvb.m, etc. (SPM was updated using r4010). All the best, -Rawi ----- Original Message ---- > From: Kay H. Brodersen <[log in to unmask]> > To: [log in to unmask] > Sent: Thu, February 24, 2011 8:35:18 AM > Subject: Re: [SPM] multivariate bayes analysis > > Dear Ciara, > > Great to hear you found the SPM course useful. With regard to your first > question: the priors in Multivariate Bayes have not been renamed. > Instead, > the code actually contains more priors than the GUI offers. You can see > this > by comparing the priors listed in spm_mvb_estimate (line 78) to those > actually supported in spm_mvb_U (starting in line 40). I would suggest > starting off by comparing those models that are directly listed in the > GUI. > > Your second question relates to fixed-effects versus random-effects model > comparison. SPM currently does not provide a fully automated pipeline for > comparing different MVB coding hypotheses (i.e., spatial priors). > However, > this is easy to do with just a few Matlab commands. You would begin by > running MVB separately for all subjects and coding hypotheses. Given n > subjects and m models, this results in n*m different MVB.mat files. You > then > extract the log model evidences from these files (F) and arrange them in > an > n*m matrix M. > > (i) For fixed-effects model comparison, you would then compute group > Bayes > factors as sum(M,1) and select the model with the highest group Bayes > factor. This analysis relies on the assumption that the same model is > best > in all subjects. > > (ii) For a random-effects model comparison, you can use spm_BMS(M, ...). > This approach relaxes the assumption above and explicitly accounts for > between-subjects variability. > > Let me know if this works. I'm copying this to the SPM mailing list so > that > other users of MVB may benefit from our discussion. > > Very best wishes > Kay > > -- > Kay Henning Brodersen > > Department of Computer Science > Pattern Analysis and Machine Learning Group > ETH Zurich > Switzerland > > [log in to unmask] > http://people.inf.ethz.ch/bkay/ > > > > > From: Ciara Greene > Sent: Monday, February 21, 2011 3:34 PM > To: [log in to unmask] > Subject: multivariate bayes analysis > > Hi Kay, > > Thanks for the interesting practical session on multivariate analysis at > the > SPM course last week, I really enjoyed it and I'm hoping to put it to > good > use. I'm trying out the multivariate bayes method with a view to > comparing > coding models in my data, and I had a couple of questions; I hope you > don't > mind answering them! > > Firstly, I'm a bit confused by the terminology for the model priors. In > the > Friston et al. NeuroImage paper, the various priors were listed as > spatial, > smooth, singular and support, while in the SPM GUI the options are > compact, > sparse, smooth and support. I can't find a description of these new > labels > anywhere, so I'm not sure if the priors have simply been renamed (e.g. > singular becoming compact???) or if these represent new models that > weren't > in the old version. > > My second question related to comparing models across subjects. For > obvious > reasons, I'm not really interested in model comparison within a single > subject, I want to see which model seems best in my whole sample (and by > extension, in the population). I've read a bit about the new Bayesian > model > selection method for DCM implemented in SPM8 which allows a random > effects > comparsion of models across subjects, but I can't get that to work with > the > multivariate bayes. The BMS button in the GUI allows models to be > compared > to one another, but presumably that uses fixed effects, and isn't > suitable > for group analysis. As far as I understand, it also doesn't allow the > results of the analyses to be saved out and carried forward to a second > level. Do you know if it's possible to use the DCM model selection method > outside DCM, or alternatively if there's another way of running a group > level analysis here? > > Thanks in advance for any help you can provide! > > Ciara ========================================================================Date: Thu, 24 Feb 2011 11:49:25 +0000 Reply-To: Paula Banca <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Paula Banca <[log in to unmask]> Subject: Question regarding DCM MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Hello! From what I read, the best method to study connectivity (when we are dealing with fMRI data) is the DCM (dynamic causal model) which runs in SPM. However, this has been a problem for me because I have all my functional data analysed by BrainVoyager and I can't use the files from BV in SPM. Does any of you know if there is any possiblity to export data from BrainVoyager into SPM, namely the design matrix and the time series, which are the two ingredients that DCM requires? Thanks! Best regards, Paula Banca ========================================================================Date: Thu, 24 Feb 2011 12:14:22 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: Umodulated image analysis better in discrimination: possible explanation? Comments: To: Dashjamts Jargal <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> > I am novice in the SPM. There are many questions regarding the interpretation of SPM analysis results modulated versus unmodulated structural images. I am studying Alzheimer subjects compared to healthy aged controls. I have used T1 WI images of same size, orientation and scanner, applied DARTEL tool as implemented in SPM8. Proportional global normalization eliminated multiple scattered areas of interest in the unmodulated date and produced better localization of the findings into MTL structures in modulated data. But still even after proportional global normalization, my globally normalized unmodulated data show larger relevant difference areas and have higher diagnostic accuracy than the globally normalized modulated. What could explain such result? I don't know, but there are a variety of possible explanations.: 1) In those areas, maybe Jacobian scaling is capturing more of the within group variability, relative to the difference between the populations. The t stat is proportional to the difference between the groups, divided by the square root of the within-group variance. 2) Enlarged ventricles etc may result in systematic displacements of structures, making fully accurate alignment of some regions more difficult. Even a tiny systematic shift can result in pretty significant t statistics. 3) The frequencies encoded by the deformation are slightly lower than those encoded by the segmentation,so more detailed volumes are being normalised (ie divided by) some volume measure that extends over a larger region. With Dartel, information is lower frequency along the direction in which the brain deforms (See eg Ashburner & Friston, NeuroImage, in press). 4) Random statistical fluke. 5) Something else. Differences between anatomies can be described using features other than local volumetric changes. For example, local shearing may result in the same volume, but relatively different lengths or areas. > Why the unmodulated demonstrated much more affected areas than the modulated? I'm not sure, but it is not clear to me what "unmodulated" features actually represent quantitatively. Jacobian scaled features have a quantitative interpretation. > Is the following correct according to the definition of how modulation works: "perfectly registered unmodulated data should show no difference between the groups"? If grey matter tissue classes were all exactly aligned with each other, then they would be identical with each other. In practice though, such perfect registration is impossible to achieve because image registration has to be regularised. If images are aligned using a particular form of registration approach (LDDMM or Geodesic Shooting), the residual difference between a warped scan and the template, scaled by the Jacobian determinant of the transform, can have a mechanistic interpretation. It's a bit hard to explain without resorting to the underlying maths, but the following is an example of where it has been used: http://www.ncbi.nlm.nih.gov/pubmed/20879441?dopt=Abstract http://www.rrmind.research.va.gov/RRMINDRESEARCH/docs/Joshi_Workshop_2010.pdf > Does it mean that unmodulated data have some compound of registration failures? Not necessarily. Image registration simply can not be 100% exact. > When I am adding covariates such as TIV, age and sex to both modulated and unmodulated ANCOVA with normalization, there is only slight improvement.( I think it is improvement because after that I am loosing those few voxels which were in the deep subcortical white matter.) I am looking forward to receiving your comments and thank you for your time. I don't have a good answer here. Maybe someone else out there could comment. Best regards, -John ========================================================================Date: Thu, 24 Feb 2011 12:18:14 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: DARTEL create template: minimum number of subjects? Comments: To: guillaume auzias <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Pairwise registration of brains this way makes perfect sense. This is what was done for the evaluations of various registration algorithms by Klein et al (as some collaborators objected to group-wise registration). Best regards, -John On 24 February 2011 11:03, guillaume auzias <[log in to unmask]> wrote: > Hello, > > I've not been able to find in the SPM list archives a clear answer to the > following questions: > > Is there a minimum number of subjects required for the template creation > using DARTEL? > Does the registration of a pair of brains through a specifically created > 2-brains template makes any sense? > > Thanks for any input, > > Guillaume > ========================================================================Date: Thu, 24 Feb 2011 09:05:43 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: fixed effects analysis across two sessions Comments: To: Israr Ul Haq <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The control condition helps for a number of reasons: (1) it allows you to ignore baseline shifts due to the drug (e.g. Drug increases or decreases general neural activity; (2) it makes it a within-subject comparison which almost always has more power than between subjects(e.g. If A increases by 5 in one subject and 2 in another, then you will have large variability; however if B increases by 4 and 1, respectively, there is low variability for A-B; (3) without knowing your design, it's hard to interpret the implicit baseline and whether it is tied to any cognitive state or tied to statistics. The latter occurs when you have all your data occurring in your two conditions. If this is the case, then the baseline is completely arbitrary and would easily explain your results. On Wednesday, February 23, 2011, Israr Ul Haq <[log in to unmask]> wrote: > Dear Spm users, > > I am trying to see treatment effects on a patient, by putting his pre and post treatment sessions (all the runs separately) in one fixed effects analysis, and defining post – pre contrast (t) by giving positive one to the experimental condition in the post treatment runs and negative one in the pretreatment runs. This is all being done at the ‘specify first level’ option in spm8. It made sense and the few people I did ask seem to think this was okay too. However I came across something that has me confused and it would be great to hear an explanation. I had been including the control condition in the contrast too, so for the post treatment runs it was included as a negative one and for the pretreatment runs a positive one (just how it’s supposed to be contrasted out of the experiment condition at each session level, by giving it a weightage equal and opposite to the experiment condition). > I thought I was getting a reasonable result from the contrast till I fortunately or unfortunately tried a contrast without the control condition, which is positive and negative ones only for the experimental condition, post and pretreatment respectively. This now is giving me FAR less activation then the contrast with the control condition, which is opposite to what i was expecting, since I though the whole point of including the control condition was to subtract activations unrelated to the process of interest, making the result purer so to speak. Since I have a medicine background and limited statistical knowledge, it would be great if you can point out whether this is possible and adding the control condition into each run somehow increases the t statistic when the analysis is done across sessions, or if I am doing something wrong. My t contrast for the fixed effects is: > -1 1 -1 1 1 -1 1 -1 . where the sequence of runs put into the model specification part is pre tx run1, pre tx run2, post tx run1 and post tx run2, and each run has an experimental and a control condition. Will extremely appreciate help in this matter. > > Regards > Israr > -- Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. ========================================================================Date: Thu, 24 Feb 2011 15:03:56 +0000 Reply-To: Simon Vandekar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Simon Vandekar <[log in to unmask]> Subject: DARTEL Flow fields problem? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi John and SPMers, I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual: 1. Slice timing 2. Realign: Estimate and Reslice 3. Coregister the structural to functional images 4. Registration looks good. 5. Segment the anatomicals the SPM5 routine 6. Initial Import- use the *seg_sn files 7. Run Dartel with dependency on the gray and white matter images output by import step 8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced). The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box. What am I doing incorrectly here? Thanks in advance, Simon ========================================================================Date: Thu, 24 Feb 2011 15:43:34 +0000 Reply-To: Alexa Morcom <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexa Morcom <[log in to unmask]> Subject: PhD studentships in Neuroinformatics and Computational Neuroscience, Edinburgh MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----------=_1298562215-12437-60" Content-Transfer-Encoding: binary Message-ID: <[log in to unmask]> This is a multi-part message in MIME format... ------------=_1298562215-12437-60 Received: from mail-qy0-f181.google.com (mail-qy0-f181.google.com [209.85.216.181]) (authenticated user=malexa mech=PLAIN bits=0) by lmtp1.ucs.ed.ac.uk (8.13.8/8.13.7) with ESMTP id p1OFhYEx008082 (version=TLSv1/SSLv3 cipher=RC4-SHA bits8 verify=NOT) for <[log in to unmask]>; Thu, 24 Feb 2011 15:43:35 GMT Received: by qyg14 with SMTP id 14so573273qyg.12 for <[log in to unmask]>; Thu, 24 Feb 2011 07:43:34 -0800 (PST) MIME-Version: 1.0 Received: by 10.229.87.13 with SMTP id u13mr827545qcl.55.1298562214194; Thu, 24 Feb 2011 07:43:34 -0800 (PST) Received: by 10.229.241.147 with HTTP; Thu, 24 Feb 2011 07:43:34 -0800 (PST) Date: Thu, 24 Feb 2011 15:43:34 +0000 Message-ID: <[log in to unmask]> Subject: PhD studentships in Neuroinformatics and Computational Neuroscience, Edinburgh From: Alexa Morcom <[log in to unmask]> To: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> Content-Type: multipart/alternative; boundaryX-Edinburgh-Scanned: at lmtp1.ucs.ed.ac.uk with MIMEDefang 2.52, Sophie, Sophos Anti-Virus X-Scanned-By: MIMEDefang 2.52 on 129.215.149.64 --0016364ee0347ef4ab049d091383 Content-Type: text/plain; charset=ISO-8859-1 Content-Disposition: inline Dear all - please see below for full particulars and contact information. Alexa _________________________________________________ PhD studentships in Neuroinformatics and Computational Neuroscience, Edinburgh 2011-2012 applications for fully-funded PhD studentships at the University of Edinburgh Doctoral Training Centre (DTC) in Neuroinformatics and Computational Neuroscience are now being considered. The DTC is a world-class centre for research at the interface between neuroscience and the engineering, computational, and physical sciences. Our four-year programme is ideal for students with strong computational and analytical skills who want to employ cutting-edge methodology to advance research in neuroscience and related fields, or to apply ideas from neuroscience to computational problems. The first year consists of courses in neuroscience and informatics, as well as lab projects. This is followed by a three-year PhD project done in collaboration with one of the many departments and institutes affiliated with the DTC. Current DTC PhD topics fall into five main areas: * Computational neuroscience: Using analytical and computational models, potentially supplemented with experiments, to gain quantitative understanding of the nervous system. Many projects focus on the development and function of sensory and motor systems in animals, including neural coding, learning, and memory. * Biomedical imaging algorithms and tools: Using advanced data analysis techniques, such as machine learning and Bayesian approaches, for imaging-based diagnosis and research. * Cognitive science: Studying human cognitive processes and analysing them in computational terms. * Neuromorphic engineering: Using insights from neuroscience to help build better hardware, such as neuromorphic VLSI circuits and robots that perform robustly under natural conditions. * Software systems and applications: Using discoveries from neuroscience to develop software that can handle real-world data, such as video, audio, or speech. Other related areas of research may also be considered. Edinburgh has a large, world-class research community in these areas and leads the UK in creating a coherent programme in neuroinformatics and computational neuroscience. Edinburgh has often been voted 'best place to live in Britain', and has many exciting cultural and student activities. Students with a strong background in computer science, mathematics, physics, or engineering are particularly encouraged to apply. Highly motivated students with other backgrounds will also be considered. 15 full studentships (including stipend of 14,082-16,870 UK pounds/year) are available to permanent UK residents or other EU citizens who have been residing in the UK for the past three years (e.g. for education); see the web site (below) for full details. Other applicants can be accepted if they provide their own funding, typically via a scholarship from their country of origin. Further information and application forms can be obtained from: http://www.anc.ed.ac.uk/dtc For full consideration for entry in September 2011, the deadline for complete applications is March 31st, 2011. -- Dr. Alexa Morcom RCUK Academic Fellow Centre for Cognitive & Neural Systems/ Centre for Cognitive Ageing & Cognitive Epidemiology Psychology, University of Edinburgh http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336 --0016364ee0347ef4ab049d091383 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Content-Disposition: inline Dear all - please see below for full particulars and contact information.

Alexa

_________________________________________________

PhD studentships in Neuroinformatics and Computational Neuroscience, Edinburgh

2011-2012 applications for fully-funded PhD studentships at the
University of Edinburgh Doctoral Training Centre (DTC) in
Neuroinformatics and Computational Neuroscience are now being
considered. The DTC is a world-class centre for research at the
interface between neuroscience and the engineering, computational, and
physical sciences.

Our four-year programme is ideal for students with strong
computational and analytical skills who want to employ cutting-edge
methodology to advance research in neuroscience and related fields, or
to apply ideas from neuroscience to computational problems. The first
year consists of courses in neuroscience and informatics, as well as
lab projects. This is followed by a three-year PhD project done in
collaboration with one of the many departments and institutes
affiliated with the DTC.

Current DTC PhD topics fall into five main areas:

* Computational neuroscience: Using analytical and computational
models, potentially supplemented with experiments, to gain
quantitative understanding of the nervous system. Many projects
focus on the development and function of sensory and motor systems
in animals, including neural coding, learning, and memory.

* Biomedical imaging algorithms and tools: Using advanced data
analysis techniques, such as machine learning and Bayesian
approaches, for imaging-based diagnosis and research.

* Cognitive science: Studying human cognitive processes and analysing
them in computational terms.

* Neuromorphic engineering: Using insights from neuroscience to help
build better hardware, such as neuromorphic VLSI circuits and robots
that perform robustly under natural conditions.

* Software systems and applications: Using discoveries from
neuroscience to develop software that can handle real-world data,
such as video, audio, or speech.

Other related areas of research may also be considered. Edinburgh has
a large, world-class research community in these areas and leads the
UK in creating a coherent programme in neuroinformatics and
computational neuroscience. Edinburgh has often been voted 'best place
to live in Britain', and has many exciting cultural and student
activities.

Students with a strong background in computer science, mathematics,
physics, or engineering are particularly encouraged to apply. Highly
motivated students with other backgrounds will also be considered.

15 full studentships (including stipend of 14,082-16,870 UK
pounds/year) are available to permanent UK residents or other EU
citizens who have been residing in the UK for the past three years
(e.g. for education); see the web site (below) for full details.
Other applicants can be accepted if they provide their own funding,
typically via a scholarship from their country of origin.

Further information and application forms can be obtained from:
http://www.anc.ed.ac.uk/dtc

For full consideration for entry in September 2011, the deadline for
complete applications is March 31st, 2011.

--
Dr. Alexa Morcom
RCUK Academic Fellow
Centre for Cognitive & Neural Systems/ Centre for Cognitive Ageing & Cognitive Epidemiology
Psychology, University of Edinburgh
http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html

The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336

--0016364ee0347ef4ab049d091383-- ------------=_1298562215-12437-60 Content-Type: text/plain Content-Disposition: inline Content-Transfer-Encoding: 7bit MIME-Version: 1.0 X-Mailer: MIME-tools 5.420 (Entity 5.420) Content-Description: Edinburgh University charitable status The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ------------=_1298562215-12437-60-- ========================================================================Date: Thu, 24 Feb 2011 15:47:10 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: DARTEL Flow fields problem? Comments: To: Simon Vandekar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure why this would be the case. Do the template*.nii files seem to be OK? Best regards, John On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote: > Hi John and SPMers, > > I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual: > > 1. Slice timing > 2. Realign: Estimate and Reslice > 3. Coregister the structural to functional images > 4. Registration looks good. > 5. Segment the anatomicals the SPM5 routine > 6. Initial Import- use the *seg_sn files > 7. Run Dartel with dependency on the gray and white matter images output by import step > 8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced). > > The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box. > > What am I doing incorrectly here? > > Thanks in advance, > Simon > ========================================================================Date: Thu, 24 Feb 2011 17:14:25 +0100 Reply-To: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> Subject: Re: Negative values in DCM.A Comments: To: Maria Dauvermann <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a719d2a53a049d0981e7 Message-ID: <[log in to unmask]> --20cf3054a719d2a53a049d0981e7 Content-Type: text/plain; charset=ISO-8859-1 Hello Maria, as far as I understand this, connection parameters in DCM are change rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0). As stated in Friston 2003, connection parameters are always relative to parameters of self connections (which have to be negative to ensure a stable system). Regards, Rainer On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann < [log in to unmask]> wrote: > Hello, > > I had a look at the values in DCM.A after I have estimated the DCMs. > > What does a negative value in the intrinsic connection mean? How do I > interprete this result? > > Thanks for your help. > > BW, Maria > --20cf3054a719d2a53a049d0981e7 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello Maria,

as far as I understand this, connection parameters in DCM are change rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0).

As stated in Friston 2003, connection parameters are always relative to parameters of self connections (which have to be negative to ensure a stable system).

Regards,
Rainer

On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <[log in to unmask]> wrote:

Hello,

I had a look at the values in DCM.A after I have estimated the DCMs.

What does a negative value in the intrinsic connection mean? How do I interprete this result?

Thanks for your help.

BW, Maria

--20cf3054a719d2a53a049d0981e7-- ========================================================================Date: Thu, 24 Feb 2011 11:27:25 -0500 Reply-To: simon vandekar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: simon vandekar <[log in to unmask]> Subject: Re: DARTEL Flow fields problem? Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundarye6ba4fc3661fad66049d09bbb3 Message-ID: <[log in to unmask]> --90e6ba4fc3661fad66049d09bbb3 Content-Type: multipart/alternative; boundarye6ba4fc3661fad59049d09bbb1 --90e6ba4fc3661fad59049d09bbb1 Content-Type: text/plain; charset=ISO-8859-1 Thanks for the reply John, As far as I can tell the template*.nii files look good, they are gray matter images. There is also a u_rc1*_Template.nii in the same folder as my template*.nii files, I've attached the image for that. Is that what my flow fields are suppose to look like? Thank you, Simon On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>wrote: > It is a bit suspicious if all the u_*.nii are just zero. I'm not sure > why this would be the case. Do the template*.nii files seem to be OK? > > Best regards, > John > > On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote: > > Hi John and SPMers, > > > > I am attempting to normalise my fMRI data using the steps described in > pages 440 and 441 of the spm8 manual: > > > > 1. Slice timing > > 2. Realign: Estimate and Reslice > > 3. Coregister the structural to functional images > > 4. Registration looks good. > > 5. Segment the anatomicals the SPM5 routine > > 6. Initial Import- use the *seg_sn files > > 7. Run Dartel with dependency on the gray and white matter images output > by import step > > 8. Normalise to MNI, select the template6 from dartel output, the > subject's flow field, and normalise the ra* files (slice timing, resliced). > > > > The output I get is sdra* files. When I compare the output to the mni > template it looks as if the subject's image has not been normalised. I am > suspicious of the flow field because if I look at the individual subjects' > uc1* image it is an empty black box. > > > > What am I doing incorrectly here? > > > > Thanks in advance, > > Simon > > > --90e6ba4fc3661fad59049d09bbb1 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thanks for the reply John,

As far as I can tell the template*.nii files look good, they are gray matter images. There is also a u_rc1*_Template.nii in the same folder as my template*.nii files, I've attached the image for that. Is that what my flow fields are suppose to look like?

Thank you,
Simon

On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]> wrote:

It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
why this would be the case. Do the template*.nii files seem to be OK?

Best regards,
John

On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
> Hi John and SPMers,
>
> I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual:
>
> 1. Slice timing
> 2. Realign: Estimate and Reslice
> 3. Coregister the structural to functional images
> 4. Registration looks good.
> 5. Segment the anatomicals the SPM5 routine
> 6. Initial Import- use the *seg_sn files
> 7. Run Dartel with dependency on the gray and white matter images output by import step
> 8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced).
>
> The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box.
>
> What am I doing incorrectly here?
>
> Thanks in advance,
> Simon
>

--90e6ba4fc3661fad59049d09bbb1-- --90e6ba4fc3661fad66049d09bbb3 Content-Type: image/png; name="u_rc1_template.PNG" Content-Disposition: attachment; filename="u_rc1_template.PNG" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkjvvi3g0 iVBORw0KGgoAAAANSUhEUgAAAR0AAAJ2CAYAAABisVZ/AAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAALH8SURBVHhe7X0ttGVNsSRyxIgnRiAQCAQCgUAg MAgEAoFAIBAIBOITCAQCg0AgEAgEAoFAIBAIBAKDQCAQCMQTT4wYMWLEyBF3bt7zRXfc6MiqrP1z fm5nr3VWn3v2f+2qqMjMyKzPPPW/boFugW6BK7bAZ/han/nMZ576023QfaD7wNF94BXOKOj85Cc/ eTrz89Of/vSpP7U2OPM97D338/y03E9+/OMfP93z50c/+tHTx/S51rsIALsp6ERnb9D5+EDnWh18 z3U+JsCJZ93TVivHNug8ENPay0bOPH6V6ax00mvv22BzLgNt0GnQWTaLHHg16DyuKXZtUG/QeQDQ OZOhHHXuKuhcu4NvuV4znWY6H73P5yhgOPM8M9DZMvhvdUyDToNOg87JEcQjwKhBp82r6iTR5lWb V1fx6VQ75K33a5ZzLsuJ93sXoIOZtkPnPnR+BBM5+xwjpnNrIFm5foNOg85Hb1oFEJ8NGEecv0Hn Mc2rFUA+at9mOnduXh0BCNc4x6ODzsfGcPC8RwHJynkadBp0DmFSDnRWOuIt9/1YAeeaKmR+vw06 DToNOh9ZjhWD7C3AvkGnQecU0LlFZ1695sfMcNq8+lSH0tGrD6NX1/DHHHENNa9WAeDa+zfgXBzf 1273DpnfOct5lMhVgFaDTkevqgB2F+ZVM5y8tMURLOQa5wDoVDverfZrhvMhOF77XTTo3DnbuQZg HHGNRwCdBhzPxhp07hwErs3KjgCEa5yjQecxzatb+HWa6TwAyF0DNPZeI0Dn2jNm9XrNcMaAWG3H o/Zr0GnQOSxkflSnPPo8DToNOh908mubLI92vb0s5BrH3yvTacCZm31Hg/zsfDdnOo8GALe432uA xp5rXLQX92NeNdDMgca10QwsjtreoNPm1W7zqkFn2yC/N3A8ClRm52nQadB5M6Bzb4P40e5nBhZ7 t4NNN+g8AOjApNtjAp157K2ZzqMN7nu+373AMjq+QeeBwKZBZ1zN7p4H8aPd25sGnVs4ZB/9mmey la3nRie9lSP50Qb1vd9vg84DspGzgW0rOJx13C1A594H7iPfX4NOg84HNaLPAo+t523QeRuRq2vU 17mpT+dsNvAxnH8rSBx93DVB55EZxKPd+xmMp0HnDbCoowFk5XzaKc/26TzaoH30+23QeQMAcQb7 WgGJo/dt0HlbZpWC5JsBnTMGXp/zNutiXQt0Hp0xPPL9Hw08NzGvGiDyCoFHtM3RbGZ0vjNB55EH 6lu69wadNrGmq5026Lxtk+fagPbQoHPELN7nqLGkawHPWUzn2gPr1tf75JNPnmafW9/jUeBzVfOq AaMGGEe205ng4zrh3ujVrQfW0defAckR24++5+x8DTptZk3NrLOXrmnQmZtxR4DK7BwNOp8umKcz 7JGzd59rjTGdxXaOAp1rDZqzrjMDhVtsP+tZ47x7Gc/VzKsGijWgOKu9jgSgI0DnzMGh577F4L+H ax7dxg06bWaVzKwzymI06Mydvw06H5YjOZ3pnDVjz877s5/97GnrZ3but7D9CMazBXSOnnX5fPcw wO/1Ho5s97tnOrcaoFsBJ4671T1f87oNOo/BUo4CsQadg02iPQBTPfaagHDta20FoFWmc1THP2og foznOeod7GE7p5pXZw6eKlictd+Zz3aLc28Bniro7O3oHyM4nPnMe9/H3ghWg85G388tgOHMazbo fDzmVoPOgql1FmvZe94zweDa514BnwrTWe3gZ87wfe7XwLr6bnj/N29e7QWFWxx/FFjM7v2o6/B5 qsAzAp2VDt1g8MnTD3/4w9Ln6LZaeU9u3y3g8xDm1Wzg3eP2o8Bg9mxHXadB57bmVYPO09PzitTv /8VKfNXZL/Y7ciDMBl1vn+uRtr6P2TvPmE5l9lyZsasDcrTfyvXO2PeIZ8jOccT9Vt7ZXTOdrZ2c j2swmYPJ3jaqvKeVAl4BQpFlnnXgyuA4c3CeOWjxbLe4f3fNSlvrPluA527Mq0pnnu2zd0D18XPQ mr2D2N6gs2Z2NeiMV2ENkDrcp1PpyLN9GjDmgHF0G83eiQMfneVipnRMZzTrVgbpD37wg6fsUzn+ Y91nC9uJY1YZzyrbuSvQOXog4Xw///nPn876nHXPs3s/67oZ+GwFHe34IwAYgcvWbY8COGcC6yr4 NOhsFO/xoDwLcOK8Zw3+Bp2c3awAUIPOD6dlUPf6dh6W6Rw1eM8EmLd07kp7O9bDjGdkXrnZ1QGA Asj3v//9py2fFSCKfa8BRqv3tLr/yjOcyXZuBjozn0C2vdL5s33eEgjMnuUXv/jFaSZihbHh/W0B HR4cM5D53ve+97TyGQHU6iB+5P2rAFQFnxUTq0HnRP/NDBjO3N6g48GoQedihjboFPOpHoHlxGDf +mEQ2nqO2XFnAh2fG+9Kw+g8yyF6xbOpshwHEsxsvvvd7z5VPxkjmpln12Q0s3upbF+53wr4VNnO aiSryngOi15tMa9WQeeMATYb1Gds/+Uvf/m058P35IBhjzmqz4vz8zkzU4tBB52fBwwPMAc03/nO d57w+fa3v/3kPrxPfGeAciBUGdSPuM8MiI4CnzPMrAadHUxmKyDtAZw4tkHnPTg16PgoX4OOmFvX ZjhbwUGP2wsWevyvfvWrp5UPH497mzGdYCUz+UClfZjxjJzKIQ4MWs5MB0wiM6Mcw/nWt771lH2Y AfGxapZVHNKrLKdyzqP3ye7xGmznzTCdBp2LebUCOLFvg84FiBp0LrKCjxp0Vv05K6Cz1Y9Tmb1H +8xYzSpg8P6//vWvn0af3/zmN0/48H44B5tYLtxdkSeMnNzu2ZlZjZzKynRicIwcxcpymN1885vf fMLnG9/4xlN8+DcGoBHjcY7prWyk6uTeu9+qk3yrorniVK6ynas6kqugcybY7AWZOH4FaBgMGCRm 3+O42T6//e1vn/QTxzDojBy8o/fh3kGci9tvBDpxPM7PyXvocMi9QmeOwYABFANRHcEV0AHgZMAD 8Bk5mRkEqvs54Bjdf2wLFjbbZ7a9YiaumlwzH88IfBp0Eh1Og05t6ZwGnQ+Bb4WVzACjQedKWea3 YjpbgGbGZrLtmXkUDIRZye9+97sn/fB2ZjmO0cRvfDz2YabDQkFmHhWzik0jl9/FjE+vE/tHuBOs RlcF4Cxzx3QqAxb+mmAvbFp9/etff4oPs57YDnPMDXYFk9Vw/IyVsW+p4vTOZAD43V1vFJ1zbGdr Nv6bZDpHm1YrgLMVaNTZ60wqBonf//73T3/4wx/efeLv+ASIZACD3x1Y4djYpqDjdDNZETTnF9N9 3T4AmQAaUGym6dFR1Y6HeYX9YmBg4Mx0NzqIM9BRv46aV7ie+pIwqKuRsRlIZADJ94fvo2vqNgXQ mU/qCPA5wrdzdz6dBp0P/TQzhtSgc3EkB7thptOg891XzvmPCnSqZpVqRY5UzGaMZw+7ycLZM6YT LOePf/zjuw8zHcdkKr8BmDhkzhEr9w5Ul+NC7cqUnKnFKQ88E4L1uHrYwXRixnNMZ2ZeZYpkNrVG 0Sswm8zJyoxrxjxmLEejbGr2ObajDvHsmEwWcISptaeOdMWhXGE7uxTJFdA5kuHsCXFj4FVC3Q6w 9DgAEPt0AkQy8woAU/HjOJ9OBXScCDDajO8dz6a+IAc6vA86Ck8gLmUiQIfNsfAxzMSBmSOXfTAz n0mcA9eBXyPL+4rz4nwKDiumV3aODHDA1r72ta896Yf9VSNfVQY812A7DTqSvlBlNg06F2Fig85F aNig8369rZlf582BTkUEuNWUqgCNYwMZkKmmBceyBicYjTOvMg1O5lDG73Fu3E9cf8QgWXejpiLu PRgLqHAwE8d0nICQn5Ed5PF7tEMwnTgOzIhTIoJ5gIU49jNKk8giRfg9Yzq8SgOuGfsyS5lFxjhC 5swuBS/HlsKUArv5yle+8qSf2Ib7YKYzEkBWE1w1orV1VYyPCnRmkaqKOcTA4NTACjqa28SRnBis eAEskIuBhoEa9zRTHs98RG5Qx31yKFuFfbjvTOAHcOHQdzwDTOXY7p4B4BdACr/Vn/70p3ff414B OnFtl6vF5jgAL0CBgYgH0op5FfuqT0cHF7YHcIDhxEAHGLDi2Q32UQh+5iOK6wBovvjFLz596Utf evl8+ctffvl89atftc7yiuqa26xiZm0VCjbokHnVoPNhzZ8Gne9/UNiqQadW8Cszsx4CdCpO5LPM qszxm6Uv8O+a2xT3GLM0Gl1NBryk2A6zIp6dB74z8di5jeuz7ie+cw4Wp0HMfFjOXGSzhzsQs7V4 TryTOAcYDhzkf/7zn5/+8pe/vHziO1hPMJ24pzCv9LndveIa0V4sJGSzq8J0nDiQGQ+zKDCVYDnO qRu/gQGxVsbpfjINEFIh4ng2u4JNgdV84QtfeMd0wH7YvJoxrlEdoQrTcY521V9tFQvOoldc7jZW Dn61knBlWeFZ9OoI0FmNWLnBPcqTYsWvG+A8OBzocHU1NlPUr1IFCY6GxfcVEy3blyNWbNYwWOJ3 Bp04HyJpYUrFB4AT/wfgYHvsG+8q+hGrmjW6x/vHMdFOuHZ0fAYdF+J2kSH4QFTZG8dz3hcAKsAl zJn4xKDHdwYdHtgaFXN1np1ZGNdDWDyuA5Mq/mewCcDRZFYWFTrF8r1GsRp0Pq1X06DzPmLVoPOt F5bToJPXVd4TxToVdGYspyIIPMO0GuVIgcnMlMBs1uB8HDHSOsGZE7aSx+XMQFfWQrPTsyiYZrCD gcT9x326DsU5VGpe4XyIxIWZhe+xDc8IwSKYDp4r9gG7YfEknwP9QEWFLjtdI0VOYOeiTWxSBcuA qQMnLhy5OJYd02yOzAYks7VgI2ApfH0141ze2CxlZKVi4mo+1uwZR76dhwad1YjVyKzSJMsVJTCO ZXEeR4+0JEYWJauG7bPSFzOg1Pwuvm/nP1GFMZtXMIvj2VgEyedEFI2rCMZ3hMwBRnEMACbMsfAD xQfmWgARzhvXRYdWUSEGGQv7OD0CPhr9H6AUJgxMmjBvIoKEKBJ+j2MBOhyCh79DTWm3HE/cv/NR cVkPNvuyQvUuOTWrGVQJnTvgOSN03qDzabGsBp3LssrstG3QadB5U6BTcSCzHsSZWUcznYwpODOF WcOogl/M9hVmwg7hGeOJ82VCQTAG/d+V0gBDgVkFMaDLoeLZifOp2BnuzEwFsThPMJ14t3h//DzB asB0/vrXvz7FBw7pYEGxL/qOCgnZ1HImSzAZ+GnYX+PYzec///knfILtYJ9gRTh3XA/swEUnM/eB lv5gcSKfx5konK+G583y1WaFvpRBrTCduI+7jF6tVqdzeUEjn87RoMP+HM6DyvwnI3BQ5TGDBANC 5TrOV4PjtFQG53XhO2eiO/VytDvXv8k6flWdzOdj6QDKXCD3ik00vMtoU7R3ll+GfjUCHS5RwSI/ BZvw1yBiFOASoer4fO5znyuBDptVeD7nv9MEXG7vmbYlU0xntX/w+wx0ArQYeK7l15mZVyzPWA6Z N+hc6hgrg2rQuSR8Nui8V62PgKdB570455VqJxCJnWaupEFWQMqZWnuiViOZ/0pJCpg8nM+E+1JH Mc/UalLFjM1A49iIM9eyCoJ8Pi2VASEeR5DcueMe2XlcmXmZ6fBsru+Pc7aU6aByoIoN1XzLQIlZ lGp2XCQLAjxEtDS3KVgO2E38D5OKmU7sA4YU5hWYRLAEMB1XyiNzD3Bkk9Nl2MHM7ebEi/xcnF6h q2AcUdzrDL+OVpRU9rO5tMW9MZ1R6YqRCrlB50cvEaMGna++CPQadC5Z53vC5g8JOjN/joapK7lX WSErgGeGxtgezAHn0Exr1rBwES98d34XdWA7P447V/zGTCfOA1BlzQw/18yvkIEOO0uZnbqkUVwD 5Ur5mi6jfKZ9GYXMnXOVkzjBXJjpsPP4s5/97DvWE/uAIYV/qAo6GVvnkiHMdJjd8LI87LvR5XZ0 9YusuJemRXBIfubbaabz6UoPR4COgowzN7IcKjYPuOQDm1msX5kBhvPzaMTJmWgKOs55zAI93KtS /Ipzz5lAaiaxJgeFujQapqATnXpUIznTrGS5TSqacw5XaHXCkczmFTuSodOJfbB/DGrc61bzSguf cW1pLq3B9ZWRLjHTGmUlL1ye2ArwBMCvgs9sIhv1ubs0rxp0LsXdG3S+95I7xcK6Bp33yy2rj6ea CLoSPt+acX43oFNxIGf1YRSIZgmUmimOWToQmvUQnMyHBo4GU2eqJnDOCne5sqVsZlWYjjqlHWPS NAdN0dAAAP7eyn4yHQrUujOm41Y+yLK4WYXsinhldYw5yRLsBswGoXOE0sMUw/5xPjAsnv25dlIW Mnf1g6KNWZ3sSmvg2lzXh0uZahlTt7rEUeDj2M5W385VQWdW0a4CPBWmU/HrcCEurmSn1esCeKDD YMBRnwZHzrjMBAPQaEmZSvpFJgDM/EIOdDInvwLQDHh4fz4nAxdn3iN6xdQ72pXNCvXHsBnAM7Au XTOrkcwDDwMzBi/AhSv1hUnFWd6cb+UmIVewTcWBGejg2Dgv64s4PcPVTFbgAfhkS9pUM9FHosFV E4vTQtTcatCR8pkNOj95tWhe1kEadD55mYwadC7t4D6Zb+fNgs5Ms+NqCvNKBVrIaqRPYYaWsSxo eLLUCM0Ar6z2yfV+4LxmtTRrijKWOWMpWsApG2TKoLTkKet0uNO5BEinluVQrep01PHqMsnVqYw1 s3TpFzZZ4jw4jlMfXNhYo3xZXWmOHOLZmenwfY1WiADb4RrOs3rNFcYzUyqvMp5V4DnckbzHvKqa VauFvXRgOvOjkqbh9uF7UXHiEQW4XAXACtC498D+nhgMjvGxL4Nn+SrrUZ8Oh405tcGFzLXzOn+I OpWzzGveT006jfa4SoMV0GE5BfcDtD37dGagU/XpMKi4/Czn39E2cuuDVYq3H8V2GnSeyzFk9rn6 nxp0LmbZiDk16LxfqaNB58cfLDt9Su7VrZmOMg+OYjFDqGa2834u1aNalpSdzZpMqvfIz+BEeawF WUkzYaYTMznPlBxBmjlTWb3MKRYxEwbojCIeSt2VTTlA44gjiwazRfpmqyNkztRKnWAXydKIK4sn 2bzC/XIhMo5eOZ2O1ktmU5CjsCMtlBYJc+862qQaStf3e7fm1ZFRK2decd7UEWYaR76QRaziOMz6 LrlxVDVQt3GWMoPIKNXEAaATMiqA4Tg2r1wRKPg3NP8o62AQWgbo8OBVc811WAYxfoZM+YzzZ/c9 81mMxHCZP0PNLfbv6LuA+cBgGffqsuOzpYZdjWQtLMbJohyOz3K3oGjmTPWZqbUSRq9GsK5mXjXo XOoUcwVCLfXpkisrwKPHZVqn+L1Bp7YMy2ywNeh8GM16ONDZwk6c6VE1m6rXc/qLLOM+Bv/svG55 G5XOZ4K+zJnroiiZpojTQdhRqxoZ0G2tKZM5e3EuMB12Us+cxniuUa4Xs0zcg95z1TwYsRk+x4z1 aPQTjI3bdWVFi2wFCJeawWYpm8qz9dV18b6KqTUDYDxv1cy6G6YzG6y6fcXXMWJZs+uugk5m5rl7 cLlSGoZ2OVHqzHVMx62pFdE0+JPifpzILzpOVY/B94YOtwo6mt/lTEQ2PzWvjE2M6uDg5+NjKtX2 ZmtGxXb2lbkkTs2h0jC45qNVkmNhMjkfEZa4gX+IxZBODV7x76xEsjLNzmFFvDJH8sy8mg3+Bp0f v6ppNPPpNOjUSjQ06Hz3JbdNne8z1vjQoLMKNurgVSfvVg1OJbeLr51dxzGdGSvTfKlVhfDIp6Pa oUr5i2q+jaaMBHNCyJwjKy6axLM3WBIzuGySyrK44ch2+prZ86jjWwecK82RZcHr6qFct5nXTGe2 geVuZowmM23j/qDPYbEhXzsTGB7tVHYm1tWZztEMhwe+i/TM/B4jEaBTOLslZbD8SuyfnU+jRmxK OWVvxY+TPVtVSa1mijOPZgNUHahsDsV9BOhooiMnbmqNXy0h4fw7CqocHZrdL2/XnDA9Vs0zFU9q iJ6jURjUvJInJ5ny+lpaAD4AjMEy843o/XMUDyAGYNMi9Qw6WWSs4t/RyF8lfN6g8+zsdY7XTG/T oPM6StGgczFHWPEcA75B53WeWuZkZwA6zaezx4k7Mr0yk4Wdo6usx0V8kMoQOVBudYcwV9jZnDmM Z6HvI5jOLMeqygo0YsWREjap9FkDzLEaBDodR1bCBICjM1tJE/fIDmZuG02rcPeasRvHILI24ZQN t756sByYNPFMyFqPCoVRmRAfVCzkWszBNABcYFMM5vyM6tjH87JIMu4F7MUtvzMqlcFpFVWnshN4 zhiPYzsNOs/RnAady2zVoHNpAzWvUFAMLKdBJ3fWVzQ7u0FnS9RqS+KmSxGY+XYyXY1GgNgJzGtx Yz1uV1JUy4XyOVzRd3aEOmYyYjwZWxqJCjmrngFlJPlnqT/7WOL6aoaqjy2YTuzn/EU8kJ2aWAtn udmRZ359hoovZLRP5uNhxzKHw8HWgumAYfCqE8FyuCxqVosZ9Zwc61Ind7boIM7Na7SDfcW9ccY6 11524fOshGymYapILE5hOiugk4FNpRogD+QsR2mUtpANahWmcRH2MLGypWHid5hgOiC50JdzgDsQ Gd3fLDKXpVBoftSICnPnAMjEc7mi9FxADG2OFT4zvxl+Z/OLc72YZc3MIZeacDTwMFCrBseBDi/w FyYVQIArFHI5DffsCjRs3mWFwHglUxQu42vPQGeUec/36HRKDTqfFndfVfk26Lxeiga+GzC3Bp2L uTVjOg063qH8UExntva35jJVGUWWvAnwwWDD9dmRzEXTw+RCMS5mBLyELjubOWw9CqOPHM8Zq3S/ r4bnef+4V37+aAM2Mdm0ZKbD7aBL7aCt8J6C2W1REs+k92wiMtOrMiGnWg7Q4bQGTtbMinHhd1Yh cwF1xyI0vcMpnOO8MOl0+eQw6ZRlbWU6vITNLEVk5MC/S9BR08qBjRbFcgWunB8li6BFx88W6dPf s7WuwrzigcTPgfvLNDuroDMDG46iZaZWxS/E7AZtnq097nw6nIOmSy/jfDPQqYr8KqHZTCeiUaJM fMcRI+dTGa3I6cR3biCPNEIcomeTjlc0hSnFphX7dJxOh4EwM6903SxNkch8g3fnSHY+nQadSzGx PQynQccXjWrQ+eo7HRHX52nQeda7ONah7IbrCvO2UWEs1fTwTK7mA9ckjmtlqz64+6joijK2suIk rjjtZ6xodA48B9d85nbhpFFlVIheqXkKMxi/u5ozs6zumcNyBC5bgUejZRxGZ9bDauUs7WMUMRwl oWYRK7CXMLPAasB44m+YX5zwOVpVY8bKVh3KMxY6DZlHZ+pPt0H3ge4Dx/WBZ1Chf6/+CkSaaV0q sy9rWFye04zpZKspuALo7GCO67LjmHOVcM04N69JjpldfRB633ydWQh/K/uI42Y5bJlCu3JNPfco B43zzVx4H0yH25vrzGj5CPYRrDKd2UyqSuYZE5o5mVXn5DKxs9wkVRxnGiSnQGZ25RzZwWQ010rX 0tIEU1ZU6woSoyWJqz4dVrFnzz5lOtcAHTaRGEiypVocCOE4LpYV9w76yp0+Ojk6WgwSNis4GzsG paZUjBJHM99KBQBGADPLyF8BpmzfTLzoQM1lx7uET1c+lKNPMx3RKH2Do1QuIjkDGidiXBEQjkw+ PrfWQHJRRdYuOTNOS56OVgfVestIk+DIGQNPtmLEVkeygo6LJr5J0GEhGl6iIjwam1WhbobUzoUO NSuoNQKaCkgw0GTiyRkYVbYzUHL+Gp4zc3BrW6EwO9pb/RtgM9x+rphYNU/MgU6Wg8bgtgo2Mxbk BtUMaPQ+nWCSUy+CgfD6X6Oi7lp72eW6Zcv0nMF0GnSeEw+VUjbo/PzFhAuQbNC5CCW3fFbYTYPO Hfh0KubVaJVMjjbF7I6XGrMmKD5n5DIFjZkBs4EivauYz3L9Fdazld1o27gUi5GfbJZiwnobtJsz TdTMVJaiTId9APEeHHNinw8Yzsi/w9sc03HsknPQtoDJ6BgFGs1Xc2afArtGyVzqA+uBuFgYr1jK eVVcKhWTLFb5gLqaNUCzCNye6NUpTIedszN/x8xkGIEPL62bOZDjePbDREdmuu+WAskUo+5FqPOT G9R1OPbTZM7mTLvk2iJzXmcO4JG0YKT4dkXkOZlTnZ4x+ODTyXwpToDH4rvMOZt1+BHoMMCz/2km Cqw4fbPny5zX2laj5E4uW6F1exhIWAWtPhxdL8tNnGpeNeg8AwfrRKAIbtC5FFfPEl9ZZY1SpQ06 l+il8x1lDGbFAZ0xHfUbzfxUrlZOg85zFCijjluZzig8q6CTDR5e5YCjTk6Als0iI+/9aEEylutH J1PzQ3O54nmdiVVhOmxSzc7hCrNnwkqNFHK+FI7hlA42vzRsjSJebsaPfZXVuBD6qCbxiGVqH4w2 cmZjxdTaCjoKNI7FZastuIgVBz6C5biIlZaugLnFZUm5QJcrH8tRrYdxJB8JOlpMvDJT68zPoV+m 0xWtRdWf4EorVPw7M8DIfDP4nZ/NmbKaU4b20zwoBy6aoKkKbQZ41uyASQB02E+D9ozBpnV0tO4w /60TAQaDK+rOgM9lNZw5qybiTO+zAkBsRnFWevac2SqlLt8qK/oONXL8nyV2MgCxKlkDKgAfp6p+ Mz6dbIBlfp3MB9Gg815A2KBzqYPNTIcZZoPOt94FTR4KdDINgkYOqrO6+iKcOnkUgWEWoAK3kXNv NstlRbVcWLkSxcic7DPnuqqq2czlmV1V39FmGdPhzHH33TEhZTqs74nBPGI6bNpyPhEzGgwC/p+p P0dfWNzJTIdZt1PBqyzARVZW2I0yaTAC9se45+LMbjVvmOmwNsc5kt0yNxrRehNMZw/ouI6QgQ4D zUjkpmA3y9ieJVhmqQHsI4CuZRQSzaJZCj4Z6DCYMtBlviOd4dlU5fIcXB+Ia+Tw9wCikXml0a24 toLOKBQMhS0Pqoz6YxCuRA6zqCHLKaIdVwFG91eTilMV+Hkw8Pl/Xt3TmZMaYXIAFACjaQ8MOppN ngH7LOHTRRYrZW+ziX1Zkdyg815M16BzcY436FykGQ06teVoTgMdR/0zpjMTt42EdbjOKoPJklDh O2LTxEV/YrbHtUemGN+XM0Ed01E/hDPpGPydA7XKdKIaon6C9bhSHy5cz6CThadjVmTTA7J+zPa6 xjdmZC0UjhmX83tWzCuu5Mj+nVXGgxm8ogPTtAQV8UUbzJaD0dUoXL1kFbxC9OpYjOZeZZq0mQOZ hbKj6oGqf2rQeQ5lM+g16PzhJeu+QScvDNagc1mC5qFAR6MLI58Nh2cde6omVmbX0DKjo+JhzHgC nFwoO2M9jvFoO7iSq9mM7JiO+jIyzQ4ARVe6YMYDHw+DD2fy671iNQhmZZj9tLB5zLSrRaW4dESF 6ayEzytMx7FN1h9pJjg7fh3bYQcv2EhWl5gd8axvUgEh+4ngrF5RJGfh8j01kg9Jg6j6dEbmVRV0 YAIxbYfobjVaNnJMj6JjGqpnZXTm7GXNzsihzO2g94dnzxye7j1UQGeLUxmOZYCuijKjHRR0eEDG QMHAcs7jUcRqtkxNZl5VQGcWwXRRyUwEGPfpfDrOiRy/sUmFwZ7V5MkUzcE0NKtfTSVnImkGOwsI R4LYyv2p0tsB+k3MqwadD/UkDTqXCgAaJm/Qeb2mvJY3/ahBZ4WBsGM4M3t4H53tZ3oMNZk4Z4nD vZqakJlq2f0yG3G5PKP0iFGIm+9rK9NRFsW+KlccjfU6LpSuYXQtmqZMR53HynSU3TiHZ1atrmJe uXemjuTsnVXMLeyDc8Tz8pIxM30OSwHYN7JVO8RZ+pxO4kCbWdko9UFD5arYn9WuzhJoD2M6Gegw 5V8RDCIqAkGXy+iObRr5GuVtsR+Co14uIXCWpOeypl2pxpGuJtMosR+rktXsUlEy043bJ6vMOBMP MgAF+ES7BujENXG/mgrAWhT3PaP4nPrgqg5Wct6caT2KXmXJoG4QOacyp31ojhlH4NBfnAk+iogy 4GlJEE05adD5dBXOKtPhkHSDzutoygjMHIuaJYKyCrlBJy/k5QSCEAm6xM0GnQ+jgHfNdDJnKmb2 2I7BlEn+2fnJTIeFfRxl0UxnTdhjR5sLGfJsqMCgznXNleKMbux7BNPJrsNO5SwpNAMgjmqB6cQ9 436Vwiu7ydpVIzguNKttPMoy1yRidv5nbcvsRRlvZjKwOtkdo2YT+kZmCXC0M2M9cf/cdxHlmi2F 4xJwtSbySsRqpNE51ZF8tHnFvp54MfwSAEaaXxQDh8PA6ofgdZtwvugMaGAXglTpuqvENvMxaNuw MNHlmlXNKxVEaoqFu44OwkohfA6xA4R41QwGHQwu7tgrmdZVP47L6ncRK00gRnvDbHcDmjPltTpB 9q4rUgnuB9yHXekSjdhmIKQAqeF1Z55mIfis9IarvjAC1llU8G6ZToPOJcI1YjoNOpc6RiOm06Dz g5dJlcuxNOiQIriS/BmdCCaT80NE5MWJ3GKG5ogL+4uA2qwncRL9MBFYUwFEd/6VzLmbRZV4cIyY zuq1MgFilfG4tuZ2Z0dytKljOlntmIomxFF31ebMzFY2vcF0ol1GZoszXXTwjmZ0dg47zRoHQDiZ uJLPNwp8ZBEnZmis72Gf0yxapXWkZqkPDxG9UpGepiRoPlQGOn/84x+f9BNABNMgBhLTbLAJDXty GDdMhHhBoJrRoOxbGuWHIRKn+V4aaXM+nSptz0y4Kui4ErFZMfxod/WVIWSOgejKWawWiNJO7cR6 zmSJQZxJBFZAh59llmnNA5IHW9zfLHDCIljHbEfgqOZVFtZmgGTQcc+lIsBRygNHFDXql7H0uzKv GnQu9aGZ6TTovM9cbtB5XzYY+q0GnWd67QR2IxaQ1ZPR9AOX4cwmAmbeMK/+9Kc/vfv8+c9/fsIH v8c+2D/O4ULzXOOW2Q1HHXDvGWNhIaLTzWj0iM0/nv3Ud5P9PXIkZ5n8WaXGLMte2Y+aV6zT0XBx Fj7OIiVO9wTTamS+cr/QNq44kjXPKjNH1GzE83GRsThXlpIBESiehU26LAoa+zgdUUWoh+O2Mp1M u5bpmq7CdFxHyAaCmhrqz9GBzLSYQUxD5kH9eW1ymFkMRGFqcfSFzRqcm8WD3Gk42oQByBEzVvPG NVySJK6nYWouhs52Pt8LA04V4DNgV79OZs6OAAh5WGFexXVwf5WOzZGVLDTu9FnqPNa+pKDDETpn ymS+B45iaWkOV2SLEzfxbHHuUXico2gKOhpmB+CMhKsjsOLnWTWrjvLnxD0cal416Pz+HeChPESD ziV6MvpU/BCjlJIGnff5WQ06Ym7N0iDYpzNysjqm49hDtXSDK9LlCsEHgLBmBVGyYFEzM86lZ0BX pNoXXbFU2VclR2zkQHbaoJmZpWaKZpwH02FTVfUtTuuR6T+yqFCWsV8FnXhGZ0pnZpxjOlqWIytB ylFOMBMX3mc9TsZ0uD2cbihzuKs/kJ+H3QcZ48zq5YycxzONzl0yHdf5FRRYTKUmwSifiE0fAIYm N6oCl2sKxzYHNH/961+f/va3v7184jtMOTXjXPQtS65kn5MztTIlq0ugzRz0CqxaS6hSW4jNKwYd 9j3M8tiy0KtzHGPgOrOd/VZu0oj7Y1HozKRjJy3MqwAdXoOKV9vkqoBc/TDz9eBZ2IeUDWjOsXI+ sCwvjUPwGehkE8LMjFv15eD6d2deNej8/iW036Dzo1fCyCylhMG3QeeTFyezU2sj2gU25QIllSzy CsvJHMi7QMfR3JmDszIzq3nFwjQwEC2l6WZnd5w6e11tYKfvid/AXMKE+stf/vLyCUbDH/we+3DV PTZDVGhXdUDPNEAjR/FWU2rGcnh7TBQwrzi3iaMyo4jLSN8xEkNCo1M1r0ZMx5WXYKbDqTIwqWIl BiwDwwveoT5xCExVUKqDlsEgrjdjhZmDPssD5PM5k2qUY3VXTKdBp0GnQeebL8u/NOh8mJF/E6Yz Sv50jMfN1JlCNismlZllzJBcKJ2ZDDuEwVyY1YTP5u9///u7D/txsH+cjx3DTtXrnNHBvJghaZEs LTjmUkacH8M5sLf4bpw+B+cB04n/WU/FoX7VvqjeJPPfVCa4KtNhR3Jcr5LIiNmeK/QhRSb8OQw6 +M5rT4HtgPG4ejqqFh6ZObxtppLm7VmNn5FGakX748L7h+p0Zh1hFXScZsc5RN1A0/1UMIgBzjlZ bEYp0MBkYnD5xz/+8RSff/7zn+8+8Tf2CfAB6MT5MtDRbG3NE4PJl/l0FJxHuposheGI35XlZKDD UTeXUzST/I8yrVme4UDHAWvcJ/aNPuxMDzfQVByIrPkAFDiPA3B44Ts4mGM7zLFwLnPFQGR1s2k0 i/K5e86AKMv0zwrAj7Q4GuHb6kTeHL1q0PnnCwg16Pz6XY5T+HSU6TTofP0FlBp0Xhfy2hS92gs6 mVNZHaaOns9ykVifwqbFSKkMjQ2HvgEoYDmj/4Pp4BxqXnFS6grTcaLCTN7vrnHUbxkzYtNqxnSQ 8Kp1kVw6B2tZKsGJWfRKWVmm05kxDK4KyKs+gOk4lsNsB+DDoXQkEc90MspAHMsAa1RNz8yRvDWZ s5rc6djs3YIOd1AWTTHV5ZfBYUJ0LM425pSDMGs4PQKAESYS/DT4n80s/Z5pc1Ts54pgIXKVrTXF KRGZv8qBGPxCWmjdVQDULP0RUDkBJTQ6HL1yOXLqj2IQylI8RrWCVBw5Sqnhtsvy7GZCRvZ78PIt ASAcyeJVNt13jWrBz+PqGCsYONGdA2huGy38xdG4o8BmxZcDwtCgQ2riBp3fvlNcKwA16FxSORp0 fP3oWcSK01geAnQCTTl6wOUvWe0Z6K0JdpwJzjoZjmTNdDgcyXLfgymBOXHdnqy+sFNGa/SK9T1s IqjWhxXTWQEzp3qO30YF2B3oZFGvCtNxgQHn86lk1FfTQBwDctqhzLTS6A8cs2xeZUynsoa56ni4 XCiz+Cyfimv1OGEk696izdgEW80YzxzHs2RZF5W8W9BhCs6RhqxcAjrOCHQwaCr+HZdLxZnq/D0A h9MqZqspVEBH1xJX30pWwIzBlK8zW9dK9+UsfDaj2FnMphSyzDlloxqVVJ8P++1mofTMx5flaVXS AjQUzX2OmQ7nYTHIBBDxssm8hLBb+pcjWuzfyVIb+Nlc5E7TXuLv2I9lCyMQqW6rsJsGneeETSRt ZuUvGGy0Dk+DzqXImHbqAJoGnW+8C5836Hy47Mxdm1eOhjM1zJx+7NDiMqKu0JZmdzMAgbGwlsel TDgH8MxkUQaUJZtWIk8z5sLmml6H792ZhdE+Gj3LdFNwEmOxPZcRn6VxZNFJF4mpzr6jyA6nHPAg iGNcioAyHGSOg5kE03FrswebyVb45DXMtRxutvwOa2T4vrNSrZkpeyTTWWU5dws6TuiH6nsu1Mn2 pAOaUZGqLBvdRZqyTPCZGbWy/SigyXLKHJhqYTNOMp3le/H2eDeokVyNRm3NQtdJZ1TPd1SagfsO 5zy5lRK0QiD7FKsrl2bL8PDvWkPaqYWzhM5ZQTctFrYVxLeADUeWA/juyqfToJNHj7TEhgPCjJGx z0nV2FyGo0Hnk5eAhfoNG3TeR6zeNOg4v4FzUlbzbtQZ6iJCozyp2aDPlr1ZiRBlbMflj2UpFM50 UqBhkwrX5BQB1ki5ujVOZxNMJ4s8sZ7EUfyZTkajSwoMI8dvtWIhi//ckjkzphKMZ8RaZkDmluNx +p2RqTWq3KnFwvaAx9Zj71KnkzEd543nouej41QcpqUeZsmPXDFPw9NZAinnUM1EeTOgqZTkGDEc txQPJ5Zy7WmO7FTFedgPoOOiSWzPu8gUmzejhMbMx1Id0FkhdV1mdwYws+2jJXqr95qtR5WF0tmE GYG/+rW2AsiW4xp0nqMw1QzsrK5Pg8775Z4bdL77juk06DxQ9Go1szzL6XGFnzhHRx3MWUayK4XK jmYGHQ61zzQ7Z7GbTP8zKpuRRZAcVXeRKdD2AB2dRTPpPswtvjZmzjAfZqbObMXQFUevOoIdi8mc xbrvbMVSZVSj51Cmk6UtuByorPBZpn/awlq2HHP3TGdU19dVp3OUkv0RXBaDGc4soTErFl9RArNp xUDjImejIvJZRUM1q0b1nllsCDDlnKgsGTNT/2peT4AOd8SsCp7L1VHzipd7cQM+ws4ablYA4OM4 fM2iPf7uQtkrkaks8sRAk90T/47nGJWfqEblMrP2ViZWg86nQsEGnZ+/qFUbdL79oq2ZAY0DiACK Bh1vUl1VkTzypFfqJbuCXZrBnM3C+J3LXIzKQnB0CN85y1vZDphSlj2eOY9dKgNf22lpMpbDYXC3 sgVXWIz7dAmb8RwuIjhrV2U5F+3Fhem4TOZZdbqsSBaL7wIQHDPJWI9jFXE8LxnDqzdwesIW4MmE fcF0XK6Wpk1AxcyMa2auOZNLTdpZmsgWM2nrMacznS2go2s1jVSwlW1V04jLXGjkKQYrzJFR4fgZ AGV1dSolVF36RaaY5tIWFcFhVhZ1BDwKOtEJA3QyM4nzlXgguRo1OuCd4lfTDNQ0ykwtBp0Y9KiB w4W2WE08Ax9mOJysyWHteEauNIiSF1ltZS74xSZkBkAqJxiJB6vVG0eK8K2AcxVFcoPO715lcTfo XKI7DTpff9Layg78lME16DzT6azCW1XfwSaQk9xXmMws2qXFr7LQNztkNbs6ruEEclktZnYwq/NY 2ZDWbZ4xmlk+GOd+VVgOm11qaun70bbmtBQwHRb5sYAvi9Kw2QHzRlkM/+5W03TMRH0snCuFc/DA j0EPBsLXnzmXFUTZwctmJi/Ux9cfVRZksaH6i5yGh6ssgI1wbhb77EaO5JX0iFXWc3PzqkHndy+r P7Afp0Hn4nNxESYGnIp5xQO1QednrwIFDTrPkZOV5MJsXy5ixKrjrABW5uzlcHKcU1ckAFiyo1vX +Xa1cFBSg2v56EJ+roaPspusINdKhrsL3zPTcWkQmuDJ6QxgOlkSJzuKnR9HfS3OyaqgwzVqnE/H mSMBPgC0YDdf/vKXXz68koP6d1BoK1Mh85rlWSibC9DBT8O+Kr5/DpmPUiM4RYKTXrWeFL83V0uI 39kKy3EarBnzuXumkxVpVwfnqIiRqy2sAME5WSh2FOfkCnNOYh73wUAHcMvKenI5DbceulYk5Fo+ K1UJszytmWaIQYeBNsuV4mJTiF5pVNIJD9FZY4Cy2cFmVFZ1LwMjBZ1M4xLXc6ATq3Nma1Yp6Kja OKtvnOV8aRrEaIVNV24jyzvDvtFX0XfZJOY+zUxnK9BsAR2Mo9OyzGeO5Jl51aDz55cVJsLkatB5 XyCLw9wc6WHHa4POT1+Ap0HnmRlkYdiZecXHsaOaZ1Zu4CxB1IW+VZXLcnxI8pk2Y+bXazOrcgxL zaoAEl1FlFeXwKJ/AJ4An2BIsxVDR+YXTLBMnTwq8sQ1dXm2je8wr9RxyUxHmaPWsOGwMoAlW11B GY9jOmAQugSL08yMTDdXxzirqezKi6qMgM1Lt9geO6BxvlESrJp08S7YBcCpK0cW8XLpFzPz6m6Y TiYUVIByGdDu2ErUS/0TfG6m/+qLiL85MjCrARzgx3WZ2awKEAGwuFVFGYB4TS0GjFE0LMs8Z99P ABgAOtpypcOzWRHHQaczy3Z2GhDWsgQAAGx4rXCYP/C/uGgT/CXBdFytYc7rUkcz+1rUkY2lYnDe rKi5e/b4jcHD1fh2US8e1KNVPRno4M9hK4KjjO79Zs/Cv6+YYBXgubl51aDz13fgk62T3qDz1Xc+ lwadH75ag71B59mOXNXpqAbGmWBagAg0ken8ionG0Rl1mupMrIg/uzbff7AumFrBSsA+ACK6sJ9b RTSAiE0tVhyr0zjOz+pqXbECqROc6gFmGM/laLxGSRApYSYRxwXT0SgKz/xoN2WScWwwCLCLYDAQ y4WDFx9EmuJ/dfw6346LKjnmqs7aML+0vnEwHV7qyM3mHDXiMcBMJ9rHaZYyVraFYaiJy+2emYWj BNJVxvNmmI6CDnfmmYhtVG3QVcBTP0S8NH75zqeTldiI3x3oAAx0GWOAzj//+c+nf/3rXy+f+A3g FD6gUS5WnJdXK3VrdME3hPW5cH9qXrkOmlFxBzpZhTuXmxUDEQOdQSdYDUDnS1/60tMXv/jFdx8O d7OpFQAU55pla2ttZY4iuWJfLjqUvXcFHQZajWpp/pTLws9KVcwqK8R2Fy0cRcxm5lYFCGfA8xDm VYPO316Ap0GnQccBjQZXeOJs0EkiWLMlSqIRmSaOmM7RSaN8bU5aUzPQZfPGPnDUhtMWphHYSrAS diIHw2GWE0wn/oavJ/Z163Ixu8H5AqScjyiAC0wr7geO7mg3Ni11xtNEQp7NYt8wr7JZm4911wjT BUyHEzHZeRzMBkzn85///CvGA3OMBYOVGjUzc4PNDg4ggD1zlDKrTbSqieF2nZnyGhAZsXTWRXFy qjI+l7nu2M+I8dyM6WThcfd7xR9TEeupUzqrnZyFt109Zc69ymYUNsHYrGCtRJwb0SZXcTBAwoEO m1cc7XKmE5trcRzOF//DdIt9KqCjNJ9z7dSsQMicw8MOtLSjYp8YBACJAA4OmbN/J0ys+Hzuc597 +sIXvvDyib85YTKO1RIYnK3txHxqbrhn5zA0gJoFl/Ebm/KZzMNNTm6Q8n4qz3CSEHdtBjyO3GXC RQXhkanVoPNsu6pvxzEeLco+Ah+u3eP0JtypGnQ+LG3RoPNel8aTVYNO9JVnWkz/Xv0VG10jMe3L bMy9TEdnisy8GkXLlEE5Dc9spQmNdLHSmtkXazHwPe4Z9xDXgbAPM2Q4czmSlTEdmEka7XJ/g9HA EQ0TDfvG9WDeBfNiR/JoxuWoCJsS8LdpEa+Kw5GZDosDYSaxCJCzwsPUAuuJ7868YrYzYzoa1cK9 o8/xBMaMlQulHVEQzaWfxD1kTMcxc2ahPBmqeeWikpmplTGe2TvOzKybg07Fn8MvQ30LDhQzM2iW LMqDCfvOHHexPRqXQ6/4HveK4zV8DoUyACDMHgcY8Rv7ZmbfcY5///vfryJgMMfCtIJ5FyAIIMZz aJjbtaXzX6AwO6tes9CyitQ4lKxL9Tq1MAMQJ2uyT8dVAsxEgzwgYx8MMn53mJw4d4/z9rRNMjfC aDLOpCEAnjiWE415GSZWHuO+4zg8i+ZsjZa4qZpZDTqf1vhh34NqFhp0Lk7kBp0fvEwSTincoPNa fMjtVDGZGYgejumos9KZVzMvueobVky+CsNh80pFZjGzsGAsnodnS5S6YMEgTCAWCWoUirU8MMfw W7Ag/BZMB9/jHKxqhnnHq3pmZnTFfI5nw1rmPMvO/BfMeEblH7RgevwN0wumVfyP37SwF0y3mN2d EE4LjmEf3D+zVDZpNHPbuR1U/MqMxEWbXJoIM2Y2tZyURNscz8KrUowy3VX0WYlmZYxnM+hwmFgf aDQwqwN8FrkC+PAAdwKzLH/F5cNU7m0FdNSR7EKtoMZZHZ4AgqygF0yjAA8GHQabAByOWIUvh0Pt bs1y1AtS08qFbWcMEqDjnO6ZrwLX0ZworTnjQMf5fWBSadU9Tv7MVNI8aWAfPAv7/TiaydEhHWAu 3J31OwbqTFcDlqHjMQMpRBIBME7wuFpmY9W306DzbH6xE3gGPA06lyVFlC26NmSm06Dzut3UxHf9 rkGHQ1nP09eI6ejslzlwt2hzlAFlkSItYaA5MBxKd9Gr2eyj4JOZITzjONalzkCtdKgLALKmB87m YDyZCcY6H+hyOGcL5whGxRErNltXJPhqNgTTmYnVuC2ZJcR12QyYMR1dmmZUxCvOy9EoJ61gpqfO f5hW2ZrvPJs7fwaPkVlfY8bMS9do7tfIgRtsxJlUXPkwq7k8Kyi2ynScKPSi6ZqEzBt0XiexNuhc 2qNB56flZZRnDNsxnQYdigxV/Toz86Xiy3EzZ4Ag+3RUUxHXZREgrwDBtWigKmUbfUR91S+R2as8 a7PnX52BI8EhOy41fYJ1PRo+ZwUyp0zwwnzRBmgfneGzmXrkIIWGRZmOvjt1MCtD4NAuF9oCi9EV Hly2NmtPuORDprdxyyuzJof7ESt+nenNLIXbscJ0uC/gXjk1hB3j0Q5gJE4+Er9he+ybpYPMFvJb 0eysqpPvjulkRbicDoFnXBynBdDdqpkRMgYAcV1kjpg5UGCTIAsfMr3lF8fpEVkuCw+OLGcLoMMp DzC5GIQ43UFBx5lXGYhmgMOgEm0foKPvzk0aamLh/GyeciSLB0dWgkF/18GId8lgnultom2cch3P xmJRnaT4WdhUHTmVM59PHA8Q1VIbHI1jBzMmZLQHL3K4al6tgA73bQUg168adJ6VwpkvgjsYmA5H XBg8XEStQef9Sh8ZiDfovDZV0U4NOlc0r1ZSFXTBO6y44ELPWa3hVabDIU2ud8uzLYdfmfJr+JJN hjgXh2pnTCecylruNH7jVSR4jXMu3MXmlQ56F1KGieA0J3hfwXQ0tMwmiZrTyhKcX4NZY3zHvWWm rou0sVSBnfWjRRFheqsmR+UbrJnhCYoZcRb94+dlnw/aKY7DpMaFxVSDBCboMuZZm7MaMv9omE6W Fc7gwrSY12/iKnpZ9TwMQvZrOJOKBwTT3/gO0FHpPleWywRZsK/dapashGW/lC5dw8/GAINVI1ht 7JbDibbE82U6k1mYXE2rOKeCDkv0+b1WfHnOx8G+kRmA6f0BPNj0jv6SFbFHu3Ehf06LcRFRx4qd /9MxOza/+DoA2ehPAI0wtdxihFwfmvU32eqqrlbzqMBYZtqy78z5du7evGrQ+eRldmvQeb8ipQPA Bp1vfQA8DToSUq3qdFZBh2dyXhnBOY+DIWBm4+zqLFEv0xxhEAQ4sKOPa/K6mYZXl+TVDvA9ZjCW 32NgcXY6L2PjVntgBzm3ja7emYX9R6YAmxIc4YHC2jGdLIO/+rtGFtEmmvTIZUiwzS0DFG1S6SfY x0W3uB0yBqdMmfuSKojVbGSmw6yIo3voaxyd4gRXt2KoRv+a6Xy6tHAGOqOVOnVlTbbVdWAy6OA4 zdRVh6eGPFmkyFEC9xLZpo6ZCNJ95AtFhjSDDuhtdMws4qLLzmQrd8Z+bCawryBLcxiFxrPBhgEP 0KkUSGNzh5d91u+cG6Z9g8+h19RVXLO1wrSSI5f80InJ+acUdBzYKMBzwEEjT9G3+DrufPHuXKKq RrhGSy2PVik9WiD4sOZVg86vXlTE6pPQ9djZx9Wg8xu7MCHrthp0vv+OrWsJ04/ekZyBTjZLMhvg 0qAYpDwgdS1zdrLC0coMwH1nQZa+PFDhoL9ujW7Q4qC/bF7xLIfZL9rBiR2zBfjwezxjxnRctCoz Ldm80TSOYDqOcWTviE2dbP131syoY5q1NHoNPjcDcfzO+3KqCbNibrfYX6/t2oEjapnwkaNxXHPJ 6bP02V0ly+gjuJbL0ufE14/CvJpJv10Uo+rT4Y7j6DyvqslRLWeKcEdkOs8hTRet4PtXZS/b7a46 HlNfgFLsh86npgyXxFA24yJ4qGiHwRzHM83PzKtR7pyLWPHAD9AZmUoOGJzZowDkwvDqE3LXdaYl A0N8Z7+ZO4erzMc+wDiefXwctuYSES7CU3nX7AJg0GG/D8w1mPcsAmzQkZUgGnS+/SJRb9DJzSCw DOcoZuBp0HmvXm7QGUSxtoIOC7zUNFL5OmtcRv4PzIpxPpa7g+E4Kq/0V80yOKA5ApEV+kJeVhad cToTdphnDI7THVwGebV0BZgXM1Ft6yOZDpuLrJXha47MKwahuGdmIy4niwWEHBlzLJrFpGzexLt1 qyxwioDTGilrc9dUdoW+Fu+PgxlZ2oPLW8vKlT60I3mLeeVCsZWXwiFhzq/REKkzTdS8clEKNtd4 WV8+H+4zOoSjzvw7K2sd0Cit5wjdLHrFAOrUrZyXpDlK7p1lkwO/l7hfDZmPnP9ZNGlkOvIEUzHV ONyN54rnZdBhfxZHC9Vfpe+DJyaeVGalLdSkgxzCKaAV3FzOmhY/41D6KEF2a+XALaVLrxq9atB5 XcW/QefXLz6fzI8zApzY1qDzvsQKMzeXHMtLKo+y8bMVIbLI1UcBOjrjKKVnE4epP2YmNUGYtmez ixOuOabDEvowb7AP03l2CDPoZNGKisZl5qzlGd51Ti5qpQ5llwPkIjVOnLeH6WTO8GhjRJU08sTs zwEZO2GdyayC1Znw0Dm0NYXGlbwYXYdZOdwG7PxnM8oFJ9iky8qSZgW7RoBzF6AzKuTlGnoUBcle gtL4USTLAQMDlAMdzJaYMdU5OQI47uDqG0KkKAMdFhtqeDqe2fkp1HRxzzsKqULJy5EzVkxnJoYD HefHcffjQubsixqZRAo6LHPg1UjZdKyCjotAaQi8+ozuuEou2ch/qe+a+xGbha6KgQOdTKSa/e78 UA06z45AN7Nqx2/Q+eUrPUmAXYPOr19phxxb2Tq5Od/fCgC5SY6DJLG9QScpb3EtprMyG7kZlp3N SotZrzEzcZxZxqwjzuUEd04bM8pn4k6pA2P0N1Nyp93QZVj4XkdMZ+bQB9Ph9pmZgrw9Uwrz0jmc Qb9ybt7XRcOUwWQTmkaRVkCmYqbye3U+QBTp4jQIx1QqbGeV4Yz8OacU8RqZVy5fR4FITSr+e/Ti KoNtNBi4s3HHYtDhiFkWIs1MMe5I+M5hVHQSLVA9SqzkUhoKSpVOzrOjczLGby5szNfVKKIyTddO q+bVyNxyZUo4mVWPHU0UDnAc8Iz6mrb7qD/Ptjk3AvcdFqVqciiib652TgYiK7+78/JvruLlCHB2 FWZv0Hltuii15hmwQeeSJ5aFzDOGws7gBp3XRd85FO9SKTKN0ArgjPw4W2vp3C3oVGbz0SyUOVm1 47PugWcbnsWd01EpdTaTjcoXjJjOqFLfTHbg7sVlwXPn41IKmkHv2rnqxN7LdDJTiyseZqbtzCSe MVg1rxyjHLX1zMUwC6w4dsMsWRlGM51PfT2jhl+lnLNI1sy/k3WymdCtQefitK+0r4JRg87rpYqy 8TACLyQYu5o7DDwNOlcAHedjqAyOI6IV7P/RjuSKYTkFZsZmqvtW9B8cmueZUzuyspsVfYpjPfjN 1dMZOXurfhhmN6Prx7YK4xnJLUasdsZmKoXRsn00K93V3GHn8dGgs9WXMytVetfmFQZMxVE6cy5n HXM2e6uzmJ17bkBHJ6yAzqwzZsDjjpt1/Mp2NmerbTIb0AEMynRGCuKRMHP2fl2oeQZGlT7BfU/Z iEtBmL3XLKvfvW/12VTMqQwojvbjzKJWD+lIrkayspBjlfFUB5g6ifn+tjCdWeds0Lk4nbMIYQZC W4EmY78NOj98lzyqgJZFre6a6VQdohWmc4SpNeqwe0FnBjIVip3Vu2HTaMZqMjCfOc7RNs5UcQwF v7ks84q5A/2UY5ZZf6hONDNTSt+16rdmDGdkHmfbRqthzvw2MzOoGsWqnKfCcN4E6FQiWRXQqbKa igisEq2qAM2sAysdn51zFXTYLOEUgpGOhRWyI10NzKuRfsa1tWMXo0iPRh1XwUf3z9jNiNFWgGYE LCN/TRYOr4LE0fvtYThZKZVov9IKn3t1OlWm06Dzkxc/0RHtrUynQeeXpWWPo90adC6mVoPOpytG zGa7LT4Ad8zIxncdc8Y83PYZm5ltr8oUmB2yUzhjJjMBn9uu5pXKD1bM1myS2vruV95vxki3mEsz VjOLSB3NXFbONwOcmQP5Kkxnq78hM1+O8u/MTK0MpCoCsS1AU3FGz8BmNfVEQYf9NltzmNS/o9Gr LO3EDeoV8FzRcI3MqRX/zRbAmQ3aFQDYkie1cv5KqoN7npkZmbXbYeZVg05NHNag82ExqgadPHI0 cg5vBZbRcTOw5O1vDnTO8O9UNCAjhlVxKm9lP1V2M0sWdaZJlvah+psZ68lq08R5gulw+2aiyuw5 VxS8MxasIr8Za51NBDpjzwbbaOBWgaKit6mea2W/KujM2iATBh7qSD6a6VRBp6rfqQDOrLOO0jm2 gs1KVEsH7BaGkJmbsyTNEeDEOQE6M1HlNUAnA6WR6A9tORP1VUPebvBWBn8FbHh5m8o5q/tUAWeP P6dBR/KMGnTeZ4evhr8bdN4Xfc8Gb2XwN+g896Qsb2ivTH+WBLqqVD6K8WwxryozaIUJrZpYVaaj rNGxPtbRZKK+kdguQGfknHVMomIqzvrJzNSqmsSVlBbO/FbGc4RJVRX3Hc10jmY4o8jVzZhOdMBZ Z9oKOqviQfVDHAE6Wa5WBXhinyr4zM7n2rgS/ZmlGLioUIDOKGVk5jfZ8iwrfWj0TrS9RwLAzLw6 AnSqLKfCmKr7rABO1axq0DEm1EqYfKbXOcPP06DzYSRwC8C4Yyp6qQadT1JhYMWB/CZAZ9WpvCoi m1F05xytDoLZDD7bvup45fOtaqCqznbHDs9mOqvsuMK6VvLgtjCcLWkNM7ZTZTCz/VYZTiXPagY2 APNDdToV0ZoOsurgxX5VgHD7rZhSszBr9b5noFLZXvF9rPh39piuWbusgg6bkZU2qIDpilm7Ajhb fTlbQGcGFkdsPxNwRqHyBh2TVjEDtCrQVGbZlYHWoPPa3Jq9h0rbNujkJlQGSlXTapYU+1ExnRmo zLbPOnvVh1AZFEfvU7n32fOP2B+YztH3ffT5Zv6y1ZSHGWs4gpkcfY7ZPTfoPGf9Hm0ObBlcGg6u DOLY5+hBs/V8lfvd0i44b4OOZw5HA8YR5/voQWeLg3DL4DjimMrA1X22gsRZx215hsoxATpn3fPe 887YDcqJHB21urcs8rPB5mEcyVtA5win8hYQqgy+ewedFYfsyvPeG+hUgAb7zHwQe1If7sWZvAVw qj4c3m/WloeLA7dErxp0tmWn753997S7A6MGndwxe4RJtPccDTrPqtujZtwtjGX1mJUZ/5GYTgZc K8+Lc9wD6Kywm4pJhVl7L9O5pZm1BWxWNDnaNocxndWXuXUWXunsuu8qkFT333NP9+RI3vpOqset gk6lT2XXrhw726cyOCqgU60OeE3g2QM0ewCnotEpm1ezF7hScmGriG02+Ksgsrrf7Lqz7dVB++j7 NejUdC97zaTK8R8l6KzU1jnKzDrauTwDk+r2RweT6v1XQWd1Ajt6/xWGcwbTOZvxHAE4q4mdpziS t7z4amfV/aqDubLfKqvZqsXJ7mVrGzzicTPQ2dKHjj5mFXBWojdbB3uFuRxVXrRyjyvPvAo2S2kQ W17+1oFTAZPqPg0614uMNejUzCsd+A06T0/PEq/3/yJHorIGkwOlewCdirO5CmBb9tvaBo94nAOd LZPVGcesMpwVs2rVkTxjGzMQmh2/Z/tWllN1IN8t08GA2zLI7+2YRwSPrfesoHMGeGw551bAGWWW by1zsQcQzjx2D9hUVcj8HkoJn1te9tbO26BzPZNo7zvi4xt05jWSzwSOPed+M6CzVZ18dDTrFuzn yMF8i3NtmWQCdLYcd9YxexjOFvMqBu6egX/LY/eCzmpbn8Z0GnTun7EcOeDvCXRWB8FqOYvZIL0l gKxee/Ysle2r7d2gI6UzjmBGt2AnW67ZoPPjd6uduIFTGXBun9WBf8v9tz7j1nD5aYpk7sxbBsOZ 2p0jQGV0jiOe94xzHAkw7lz3wHRWZ9zZ/nsG5C2BpHLtPc+2Jd/qKo7kI8yrR/TvnAEYe895NuDE +W8JOjPwWN1+xICsDPxb7XPE862GyRt0TjCpwH72AsQZxzfojM0oBaUjBuWtAKVy3SOe765BZ2se Vjb4zjaPtp7/DLDYe85rgA2ucSums8piKvsfNSjvKaJ15DNt0eZclekcDToxELcCw1nH7QWHM46/ JuDc0ryqgMjqPmcM0FsB0FnPstqmDToHm1dngMbeczborJlUPCjOGqgV0+fofc56lo8OdO5FtbwX GM48/q2Dzp5OPzv2rIF6LbZz5v3vNa2uEjI/w7xq0JkLDxt07o/pNOhc3snp4sAzQeeW/p0zWcqe c18bbG7hSJ4xlb3br8EUcI2jzKlr3fPetr0a6Byt2RkNyrOcxY9S7/gtg84RHb5yjmsN4K1q5lve X6X9Zvtchek06MzNoT3sho9t0NluVmGw3HJQV5jPLe9vBiiV7W8OdI5WMR8FBtc4z60A51oh80qH PnKfWw7ue7z2UW37pkGnKjC8BiBc4xpvGXSO6vAr57nHgX/Le1ppu9G+DTrPmp1rAMI1rtGgs9+0 uoZe55bAsefaVwOdMzryNQbgx3SNM97R6jnPSoM4qqNvOc+eAfqWjt3SdruYzmrnq+z/MQHCNZ61 0uZn73MG6Bzd2VfP95aAY8+zrLbbbP+peXVGZ73GQPyYrnHGO1o9Z4POj572DOx7PnYGIqvbG3Se E0gfFaBWgeHM/Rt03h7orIJJdf+bgM7ZKuVHBZHV+z4TRFbPfSToVDvv2fvdM/u4xr2d1b4NOs10 DlnFoUGnmU4VpBp0HhR0VpnI2fsfBTrVjnuN/a7BJu71Gme2b4NOg85dMJ0zO/nWc98rIFzjvra2 WeW4Bp0GnQad53ILbrBcY3Df6zUq4LF1n5uBzjWTQFcdtPe8/9lm0tbz7zWvtnbgM4+7V0A4877O bE+cu0HnwZjOVlA4+7g9oHONjr7lGmcO7ns995Z2Wj2mQadB5+bm1Wqnvdb+9woMZ97XNdp2Cjqx Q3+6DboPdB84sg880b/P8B/9vVugW6Bb4OwWaNA5u4X7/N0C3QKvWqBBpztEt0C3wFVboEHnqs3d F+sW6BZo0Ok+0C3QLXDVFmjQuWpz98W6BboFGnS6D3QLdAtctQUadK7a3H2xboFugQad7gPdAt0C V22BBp2rNndfrFugW+AV6Bwpe+5ztYy++0D3AfQBhtoPQOfs7GQ+/z2XlHi0ezvzvbmkwMgyPypZ 8MyExj7367KqR72zlfNMEz7P7Lx67kcb2Pd8v2e+twadt1MPeQUsjtq3QefBSlvMgO5MsMG5G3Te DugE8zsKTKrnadBp0Fmup9Og06BTBRjfV17Hq9qn8+Ag1EznbQHCNXxQewBky7HNdB4cZNTcOhN0 Rh3sKEfyNQZZX+O2zuQGnTcEOmcCTpy7QedtsqgtbGXPMQ06DTpln06DToPOHrC5m9UgWqfz06dZ RKq6/ZGZTps8twO0I4Bk5RzNdJrp3AXTuWfQ+eSTT54qn3t+htG9rQDGEfs26DToNOg8a1VGg7IC OLFPg45fKVWBqkGnQacEOrMZbk/06p4GaxVgZvvd0zPN7mX2bo/e3qDToPPRgs4MOI7aPhv097L9 aHDJzteg06DToFP02WwFoXsBldl9NOi8ITCoRp/27ndm9GrWIbeYV7NBcOb2rQCy97gzn2nvuWfv +KjtzXTeELg9EujsHSBbjt8LGEcev+X+zz7mKFCZnadBp0HnJubV2QPInf9I0Nh7rls8/+yaM7A4 anuDToPOmwadveBw9vEzILjm9qNAZXaeBp0GnSHozDrQe2l7rXLgNQbR2UBx1vmv0Taza1Tf9579 GnQadBp0To5eVUFqBgjX2L4HTKrH3gXo7I3a9PGX/K0zHMn1jnR7plMd3Pe83zWA5dYpEQ06zXTe DNO5ZzCp3luDzrP44ozZswuyH5dZfnYRr6OYzlmDqTqYH22/s9prdt7q+96zXzOdN8R0zjKxZgW8 ogPOxIGzzr66/dFAZMv9rrbJkfvvAZXZsQ06DTolNjvvSGOfzpEDIs61ZRA/2jFHt9nK+Wbve8/2 m4NOO4GPNbXOModnnWzEdFY6e2XfRwOPvfdbaZOj95m97z3bG3TeGNMBiB8NPrNOdg3Q2Tt4H/n4 o0Fldr7Z+96zvUGnQed082rWwavbHxk09t57tY2O2m8PqMyObdBp0HkI0Nk7aN/K8UeBSuU8M/DY ur1B542CzhmRrFEnc+ZVpWPP9nkrYHHkc8za7KjtW0FldlyDToNOienMwuYNOrXi7UeAz1GgMjvP DDy2br8Z6HTU6tio1ag9j3IqX5vpHDFA3/I5ZqCxd/tWUJkd16DzhpkOA9FRwJMxnjOYztGA8cMf /vBp7+foe9p7vr3AUjl+BiKr2xt0GnTK5hWAy3WyBp3rmVcMVBXQ2LvPKqjM9m/Q+UhA50j9ztmg s3X238ti9hy/9Z73HrcXUCrHz0BkdftNQOee/Tk/+9nPnrLPPd/3lnvbY3JpRztKHLg6CPcAxZnH rj7H3v0r4LF1n1VQme3foCNMp0HnJyVzq0Fn7B/aCyKrx28FlMpxMxBZ3f5Rgs4IWI7YtoV13NMx KwwIHe6aTOdMhnLGuVcBZM/+FRDZus8quGT7v3nQOQJEjjrHPQFL9V4qABSd66jSFqMBdwYgXPOc e8Bk5ditoDI7rkGn6AA+CjCOOE91oN/Tfg06+8PsALYV4Niz7ww8tm5/SNC51mA6AiCucY5rtccR 15mBz4zpoMNmHX42yK7JSK5xrdnz7tm+FVRmxzXoDJjONQDjiGscAQbXOkeDznGMJ0BtD6jMjp2B x9btDToJ6BwBBrc4x7XAY891RsATTIe3Zx10lelcg3Xc8hozANmzfSu4ZMc16CyEum8BIluuuQcQ Vo6d3VvlXApACjozANKO7QbXLcHgmtfeAyyzY+8ReK4avap05q37zAbSI2zf+uyrx83aonK+Bp3j zK0ZcOzZ/tGCTqUTb91nNoAecfvWthgdt9oOq/cQTCeukSWZKjXfw3R+8IMfPO39KJOpnu9MBrQH XLJjG3SKYe5qh18dSI+0f7UNjgQa1z7Z+Xnfn//85y86nV/84hdP8T0+DEBZZjoPiBXzqgoQ19jv aBA6A3jinEeBzxF+nauYV0cMIHeORwKR1Xs9os1Wr9mgs86gGnR+/LQKRA8JOkcMpns/x1bQmT0X mMjo/y3nCKbD51Sm48pivAWmw2zqSAA6g/EcwXZWAcZXJHjuLPTv1V+BSDN9RmX71gH0qOxGB99s EK8wjC3mVAYwYQ65z2z/X/7yl6+Og3kFswrP4/w73AndIODBlg3ia5hNe69xBAAdDTxHgA7OsQd8 Ho7pbBnA1z6mQedSHqRBZ1+Eq0HnJ7WSB8p8jmI51wYOd72KabKyz+iZtrSbOnf5XjJWE6xFP7wv zhG/Yb9f/epX775jX5hXM9aWVR/EDFphOsEi9jKRax3/VhnPXTOdLYPnlmbVCmgcua8O1pV2mwEk A4aCDwPJr3/966f4BKjg40CJQQemlgOdGdPhfCyAzT0Ayve///0n/ewBqXsDniPMrAadQbW/VYZ0 JJCsnKtB51JjuEHHm2RHmloNOgXtzipwrOy/Agxn7zszTSqaGZxDTSswFmY68RsYTTCc3/zmN+8+ YD2O/SjTifNAp8NO5FEh95gpsT0GAdhAMIzvfe979uMYSOW3GUupnIP3mZ0v235PjGcv8LxZprMC Hiv7ng0e7AfJfCn43d0LP8uWaJW2RVyDzSQ2nxho+Ptvf/vbJ/1guzO/ADq43+jUPDtr1CMAB/vG d+z73e9+9+k73/nOy+fb3/72u+/xO8BoFSSwv4LB1vNsBaAjQOeoDPUGnStnjDfovPfZNOh86L9Z AaMV1tOgcxESnhYyX3GErpgNK4xmFM05C3hmzGa0fYX1zEwxZhKxr4tCZSZVANGI6bCzmR3JcR3Q bh1gYDKOlse+wWTi841vfOPd55vf/OYL24nPCtNh8ywAJAMGBhc9pgo8K6AT+x4FPHsZz16mE8dv NbHuFnS2goszLc4CmEpIWn0oLhqkYjvOYVLB3SzMzn4UfA9QZ18P7im7lzCf4MsBE2IQit+wXUEH 11THJzq5K3kRgxEmVYBOgA0AB2AUoDADAgAHjgFQARjUSY3zZT6kFXPuFuBzhHN5D/g06CQRrGsA TlyjwnCyQd6g06CzhQE16Ih4cK95tYfpPArQuCiQSzGYPQ/airO5ObOYZyRmPaoaxjuL3wGicBqr k5mjWi56laXH8LPgenGvYB1qRnFRc3xnVsHshx3QbJYx0+FzzBjOisl1C6az18Tam33eTIeYzmyQ HrV9L7tp0PnpSwSrQWd7usRettPmFUWxtjKdowAlO08FaGZ+HBdyHh3j0hJcflewC5dOwDOahqp5 fw5rg4XgOqrp4WeI7xEy19/YL6R6H2Z0HDJnMyPu2yWIZgJC9tHAr8OO5DiO9wEziv/VD6QsKPMn rTIc3X+LWeWO2Qo+DTrk8Lwn0FkFmhnoOKEeH8OOXBcpUo0PACIGKIOIDmCAD6cfOJOFBzuDT/Zc AB2Nhv3ud7970g9rfdAO8a6dkFBNNDxbZeCyScUDEr8HiMyAZuZI3gs4R0aztoLOHhPrzZhXW8HG KXGPYj0NOj99Cbk36LzW9DTorBfwukudzlbQuQeAqYIT7lV1RJj5mTFEqNqxg7gWjq8kTjpKrjoV Vu5i5nRhd6f7gXml6ROZFijYETM2Tp9wQQg2HbVwllM7V7LZuU0y5/TRyZ8OqI4ysbY6lreaWHfF dLZGrrYAzhFgUwWLLfs5gOH2wTNzJrgOVI4csd8E98M5TJyt7TpzdPpsIGF/Fn5xRGvk50IahAOS Le/VgRA/Jzq8iiABiLgPNt0qBeecX+wIRlM5xxHgs8XMatBZzBhv0PnFq8TJBp2L+rpB55K1X/k0 6FwZdLawl+oxrCbOagRxdIijPVAAc0oCK4FjX3bEIgUBnSxzUuqMi/2Z3TB7yJJXFey1ng5rhrhT MyWfmW4jNTarqjlTXs07NkNVmc3P5vRDuFeU3BgxkQqTqexzC7bToLMAOltZThU0tu6XmVTq7I7z s/gOkZ4//OEPT7///e9fPpoHBVOLQQfX49CzZkJnGd8Y+Opf0lQJDYdrBE3XvdIsczfjcmqEMzn1 /bJgEe0QbRbtxW0W7Ya25HZiE5YlACphYJYEMK9UNqyASnWfPeBTYTeuP6yCz0fp02nQuZQabdBp 0HHyiCr4rIKNOu5XweeUhM9rOZJXQWcrc3HHVRI31TxQU4Vn25i5WdMC80qTL9l8wAyN+4t2z0wC 7iiVRFBlNizmc2aPmldxLzPHLTuDZwW/4nxov7g3MJ1gNX/84x9ffZj1RJtysiraGEwy/ucIoTNb M1MxM7uqbGa03zWZDpvYKwC0CjbY/y5AZzW6sQo2mV9iFYRGCZtZgXN3ba5L7PwR7LvhAaa/ax1j Fdk5Ch0DSE2pUftgG4OI+mVim657NXtH+s5nAse4Jo6J9mPzyoEOgwvMrz/96U9Pf/7zn999cByA B+CD9+OeXQGIHfdHgI3KAbaAT5Xh6H4rgHN3pS1WmU6DzvtSEgiXc9qAKyPqUikadC6MJ0CmQacW udrj22mm8+ka2iMtySqzUWdqlelkqym43CRmK8w4WCjIZpdmd/M9ZaCjuhvWBrHTe6T3UX0Pa3ri ODiSORrnGJ22AUSCLnoU942ZPwYH7i/2xXmCnbCpBCcyTKoAILCbv/3tb09///vfXz7xPZhPfJjp qOM5+oBGIV07Hc1y9qRINNM5ofD6jLrz9q1AswI4mh7gVLmaT6VFsjTHykW1YiC5QYvf4rm57rAL T6uamCM12D86vCuIxQW1GAwCHAJ02HSLe2HQycqixu+sTlaltQMdLb2RFY+HaQpQChYEoAkwYlmC FiUD2Ggkks3MGNxngE0lt2xkdjXoNOi8W0tqlCLQoPN+iWNO72jQWS9x+lGBzqovhyX2Vb/OCsuJ ffcyHdV2ZA5jZkbKYkaz/KgWsYtoOeDCbyqE4+iRyxZX0wxMhzOwUf5hVFwrOnkwnWA8LjLGeiTX Nsp0WALgUjM09cHpothUZdbI74KfnxmfYzeclgGHK5fKGNVi3suGVp3JDToHM51bgI6aT6Pqfir4 c6DCv4H6B93naAr/ju8c/nVAxpqdGCQ8CTh/UQxGjtRwUfVRYqI6HwE6XCZBnYyuPo9GifTdosCX K78wcmI6sIUamScHJzWI8+rkyb/FuQECvEaX1u1xJUO2gs/ZoLMatbornc5bZToNOj948V1w6VDW djToXIrGA1Sy7PUGnROWoHmLoJMxKzUHMyYxW8COQ7tgOtCVMPtBKHjEeoI54L4yEySrAJixJGf2 qmYHjmROfdDqfipAC/bAgkBnlrHYUdMqmEkoGGbROjW30V95ph+xJLBHp83RshJHAk8zHSnAnr2k FQCq+nM48lI1s/b6dBp0fvZKTIiBx4DRoHNhgWx2NujkBb4OVSSvAM0WJ/Iq6GwBHAcyDIrZM2oy J0LGrDh2a0g5ERvrTkaSfk7+DAdtPC+HzJ3zWBXOfA4Xjne6IHVYX6rBXRzJnOXOYXct/ZmFheMc 3MYAN005wPFs1uDazKKY3WgtIsd03PtVnQ4zHZfzNGJ8KybWKsNx5m/FqfzR+XRWWM7ZoLMVcLSj ujQDzbNC9rjLBdJ8IachcZEXNXsYdFhsCEc2Z2Orw1vvK7bzfXCEB2kQMdjRyTUChiVhuBj6t771 raf4YAXPiJDFoHTZ5/HbjD3guLgf3F8ADT8vvgOkAaAOoHnS4jC+cxIz0zkCdK4FOM5JXwWhu1Ak PzrTadD57bsB2qDzXjekgsUGnR+9TAwNOiYNYtW8ykDHgSn7srLG12JYuB/WjWDm1QxzNntwHNN8 d6/MdDJ1MGt/tAaNmnXs0EZtnzheM84d0wlzCqwm2AyWCub/M6YDtsTtF99ZH8OmBDOj2I/TJPR5 szo7HAhgzQ5SS9hsdMsRx2+sKcI9sVk4M622shv1J1VMKnXsV9kN79egk+ReVYCnynAUaFzH4o7D swGDgBMSjtYKV1MGmhMWrCng8D6qHeJiYVmkbJS5rflJcR/h04lOiOcPUwkmFYMOAw1Aidcpj4HJ oMOmKuc8sVbEaYBgCka74nlHRe65D+B8s7QQXi8rnoGBMCuWvwdY3LErAOP23QI2e1jOxf/33Fno 36u/YuOsJsre6NWZPp2qIrlB51IES4GG/+bM7Qad776smdWgcwdL0Jzp06mGyHW/GdOphMQ5IuQk 8DHLuYXbWFCn2hE4O92yM5qHxeaVi+xkdYfxbHENTj9wzusAFSRDcs0ZfI9tXBZUncpgOjAhwHLi fx2c0V7MaHi2Zcemmoj8rjhaqGZr3BszR04T4XM4szXal53hYTbF/YOhhXmI7/E/p4lwnhi/E2cW 7mE8e9mNKsq3sJ0tphUIysMwnaNBpwI2mSYFkRTY9tHx2JTA9wAiXktKbX4dVAyQboBxagMXk+IK fCxFyPLBnB8pgMilZDj2E+CjAzlAJ66NDsylMBR8AcBONqHMmstwcNa6A1H1QSE6h30VcHA+ziiP e+J0kHhnWSQOgApQxbuOY7KqiDrAjwSQlXNtAZq9/pwGncQHpOaehqEd02nQ+fmLT6dB57ICaIOO N7s+GtDhqMSMLWUiQFXfgulA8MaO0m984xtP+PBMyM5SZjyYEdnxm+l+1LRys6nTm6h5lWW9M3tQ NsSRrWBFzHSCKaBcqRPcOdOSa+JwNEwTVZnpcNSPa+SoOaimIJ5FU0Cc7kgnGWiPYDpxVI41R5yF H++XI2rs8Oe+xM5mDkqsMJat+zbTKSw7MwOMI7Y70GEzhl9UdEL4cQJ0ADRf//rXn772ta+9fML+ Z1MLIMWJgRy6dE57BhodkM6H5nwd2WDjLHM2MdjMczWc1ecU58cKn3gPPHj5PnkSyKoI8iBl0AFI BOjB//TXv/71XTXA+B6fACF1gkMewCYYfFuqsGYfHsL2bDbivbPfaiRwzPLX0EbO/6N+rq3gcmTE am/U6qNjOhVQatD5+avVMV0dHK3SB79Ig863301Cqqpu0LmYW4eDzpbIlepNRuHzCmhs3afaKXj2 CQoN5hKznGM68RsiHUHFsb8TkbkIFGdeo61Ys+Pu20VnstrOLDZ0ZgV3FH5X7hpuCRrnKGam41I5 YrtjfGyOhXkIxhKshhmOspxMBsBiR2Y6WR9i5zLMKo3QMZN1eWBujGTarzMczntNqrtjOo8KOs5x rM/ifDrRqdjORwcMkwoApOYVRzdg94/AZgQ6Wfh4Bjo8wFjlqwmVmjwYHY7vR/PLdIVPVhM7wNJI HZtUztfBkb4AK7fuFZf9cLKArAC7y5VT8GGhIPtyXGXFTCqRad5c+DkDiK1m1hGA06AzWf2hynoa dC55NA06lxIeM6YTgNKgs00U+FGbVxWgcSI8p42JWSoGLVgPlKo8A0Kvo5XllD3M2CIPikzT45jO SIeS5RGx0A2CvUwPFPeFkDk6Vhwz2l/9Z8wCXOkIbSsnfNTFB9mBjjrMjlExK1OGBrOTBYOO3Wgq R1byAowjE9dVGMkK26mcb2WfLaJAjbIeJg6cDZhs+8iPU6G9VUaTie0qESG1u7XhG3QadBp05uzn TTOdGQuoAh3PipjxZrPCzEzR+iUMaDPgdtE1DalzyNwBMidlcvoGUhOQnsD6oYrDH+te4Th9Tqcp ynwcTrPC/gRmI8z+Zu81kz9k4WkuYcEFybIVMpz6XOvsaP+ZhctH/S1jPLM+unX7ESwn3sGbYzqj XKsR28kGKzMhdby68gAwt9TplplmFW3OLJTvgCvzT/AzxIBgkw+dMUCGzReNfimYwbzSc880IhkQ OUdy5nDPzGDHTvV+GNwYYAAe0OOw4lxTH7QiIioZutIbmSN2KwjAF6cAv+d8o2MbdDaUsmjQuYSj 0bEadD50onPOWIPOpX1mfqgKGPEk8GaYziybXLev+ILYzHKzo1Jo50DN1MNuptYQM1/fhe8zh+1M E6Mq5CyhUmsxaxGvuA7MK+cERtKkLkiXqbFdJ85MMdd+mcmSMQKVP3DxMc4uRwEypzLXJWgyFszv z/kGz2IpR5y3Ai7ZPncBOjP7W7fPQCIDHSdA09q36LgqhnNAwdTfDTDu2Hw+5xQf+Z44Q9yVtpi9 XAUuBhROP3D5VtkCgQAfXpET7wWgwyyKIziuELsDHRYpKtDMfF4zh382yBkUteKhVj3UchYwrzJH svqiHHg6c1JB4tr+m6MYjjOhb8Z0GnReL3msoNqg85MPlMkNOp+8WubmEXw5p4DOrCPsDZXPojEj syqT/2e6FWUpzFSUFrMTTzUZbAPjOHbsMutw98jP5OoRR5vOmI5GeBjEWM9SZTrBcrLlaGBe8X2t akk0Wqb9ZjRJzdILZlEy9m25ddy5aJc6kl3xNq2XPIvcZdGz1TY8woRy59hjVj0s6IxMqy2gE+dD R80iLtkL5I7AEQ8XrYgGx3XimjzwVbjGJg8DTQaQlY7Ag80NWjbBsuRO9ecgw5xNPoAOR/pWB0Dm lxpFFVdFnur/wTVZ5sCOZBcmd2pkrvUMM8uVL8lMKycRWAGcsxlPpa9VJsHDdDrXYDoNOpfs7xHD mTlfG3R++sEifg06r6NT2UTRoCOhcsd0MrOKy1M6ObzOeFuWDGHHoepdtDaN1pPJsq6z6NWsMzj9 jmqN2BTEBMKmoDPLMkeyapoys4ZNvyyql+mMnEZo5h/MBJSO7UZ7MIMF8+HwOZtUWW1sdpKPzKvY lkVENel2xH5WmeXK/rN+Ntru+sBuR/IWpjPrJCuRqyroxEBxPgk2taLx8GI5g5wjFCwGmxXjiudw hdcRHZr5SeLeVBg4CrlWXr6GhyGEUz8E+6LiHqKd2RflzCu+30zFnIXxeUKYyR8y5rvarxjMGYCY AWEicjlq2i+y/uBAJ4uCan5eFXhWQKS67x6wGQFtgw75dxp0fvgyy0enbNB5PwFpSN1NQpxGMtJt zQISDTqTda+2sJxsBhzNUqs+HTZT2KSBU5RXPtA1rYHQmGEyKs01cXk5FXUe4/rBbrjOML6z9sWt TsAmS6Zhmc1IHGnj6ExEYtzid7EPGB87wpm1KTNB5UCwFLAjMCQ1LbGWO7M+ML8K65nptrayHmci aqQyM7srqQ/qNNbI2Sx/65p6nVm/qmw/3LxaBZ3VjjDrWBwR0jCziuECXFxB8jgOz8H2PGazLO9G 13LiUhBo6Dg31/VFtTuuZgfwicHHoWw8u5pXbAaMhGbcIUagA3UtPw+X28T12LzKaiSzCcvtzats 8vLJupQy2qACOmreVfpKtf9pG2/xuzgTxvkMlTU50KlEsqom08p+FVCZ7dOg86w1UeBp0Pn2S/H4 Bp1LES/HdBp05mUrrhYyvzXTqaQ+uHwiXgUhnoGl+5phzCbISCTGpUhxvjg37pFXneRF7ZjpuCVR MqaTmVpZKgDuKVgMszi3nnjMrDgPZ5uziaSRtzCv+BmZxcQz8ioM+h2L4sUxukLFzKnMOqYK26kk /VaZTiWapPvABGMHPn/X4mkVlnOWTmfGYirbD2M6Z4NNNVy6CjoAoOh4aAwNkXKx9QAcrnnMa1px /WMuuq4dCJ0mXhAAhIGImRcGMg+kEejMTC0HQFn0KluryYF2Zl4p6DC4YskY93+Ym+zjchUAZzl0 e8BHo24cWdoq3JupjF1UiuUW8e7cfeh5V8yl1X0roDLbp0HnuaB3dN4GnR+81NHRBQDRKaOjAOwa dN6Xv6iyDmUe7rgGnad3/55JMv2RRK/OZDoVijyj3M4RCbCBLodNKlDaYCla1oDZTXzn8gYqf2fZ PGolI/oV23TpGY7kVURvlXYfCfIgDGQtEjMxDpOjjVcW29PUDpiL7EhmZzpYT5XpZO991mdm/UW1 WsowVsBmti+LMeM6DPAcGGH2pazHXWOVxVT2n7GY2fasL27S6VQ6f6YCnUUQZh0oi1i56JX+hnOr H6dB51JEvUHndSLtVtOq4uth8Rznl3FUDr836DxT7xXQmYEMb98DOJVsbVyLRYBZhjGvYwV2E47k WQGnYEvKeKDp0XWvonNy51tp1637agcG0MT5WC3sdDWj5E9d4ZMnh3g3zHqgWXJL/8Y2dqgf4ctZ ZTrKAlySLwsAR+aT7scmLNJBnEr7HpiOsrIZs9HthzGdame/JdhkuVcOdCoZxln5AhdpYE1MZttr KQzsdyYAZe/DdXjW2zhtE/8WABHtDdDh2ZmZE64DMItzwHkc4AOzKyJYmWDSgUdlouJnzM6RsQq8 T35n1dSEqhnk2omjkxqyr563YkJV9lkFG+7HhziSG3S+984B26BzqbHToPNhca0V5XCDzsSRfDTo VGaqldC4S4HgUHScSwVgQGanGB1J3rljMRXNTBZOEYh7YFOHfSqg3VtSRrL345iOmkAuZSRjOpw8 G8cF02FnvT4b2pijYjg3O5rj+1lMZ9TXWBjIQQbOu9KVHtR0cgx25lh2fUjZ8siUqzCVvfs8BNOp mFYzsBnZ4c58UrGa+3uW2+S0ERWnoOorXL4Smyy4D6d+ZcBR0GE7v9LGGdBw23NI3IEArw+eZccz 6OC9xb07MGfw4VwurmCYVSjkPjHrPyvb9T2wkFKlBbxO+SroOBNtVj7FXW8viGw9fhV8rurTqQyI Wado0LkUnVJHe6VtR/touzfoXMqHsNCyQccX97o66FTNqopJMAKcVVNqxHAyh7L+7sKUeN6soXm2 yEyjeM4sDcOtre3aWAFkBtYr2zWJE0wmnLqILDktDUeY0P4wr7gtMdMFW9TIXbQfK7S5nWaRyIr6 eBWcGXTAbrmGTpb9nTmYXbidWQ2f21UyUOEm7kn73Uy1vJXVuOPuEnRmL3o2II4EnFFVvmzbjL5n viAGHRX84ZwqnEOO0QjwHIDP2pC3z0LFfE+cwhCgA7D585///BSfACHkU4UZxqkKMK84b0pDvmqm Rqd2uiAGwri/LYLAWT/U7RopApCw9KECOg4Y2D/DeW8AGi70rpULXD4fD3yX5nIkyOwFnt3mVYXp zF72bMA06LzOcm7QadBBRUenmfnoQWcGOLE9A50VsFGWAqcj/z9yImN2z/afmWQcgZpFirLoEN8f J6HOzKutoK3sgU0Z1s2MzKtwKGM7J3zCQY4sc34e1r64WdOFimfsbGRaVfog78PtjXuJ+9RStVqw zVX3G0WhOLOcGY5WMGDWk+XF3ZrprAgGT2c6K87Ligmgg98BiQMc/U3NAAwSFwrWwTTzFznTiMGV BztHr/geV0Gs0nYZyDgTz617lVU4zNoywMiBDvvFOMfL+cDQfxioV02rFdBRwOFIm5Y3GVURmAkF OeQegANFO5dJ4e/I54t9cR+qXHcMJ/PtzEL2W82xin+nQefZPzDToTTo/PaFzTTofP9lwPMKEFyQ f6W6X4POhyu1lhI+R/6c2eyyahJk5g2zDmYKGWPJGI8yDl6RIWNOM1Y0cng6U4afhUVpPDNwmzML mEn6Kxom95xcs9iVEWXnMTMQ5FUF00FEDgJMZ7Jwpj1mS3W+Z8+Y9aVZH+Q2dn2Z9Vkcacty6LIV QfBsumAfVy5AHp/WZorfNbdvxnQc21Bh4YzpZGZhhQHN2M4uprMFdFbBhqM8I9NqBjgZiGTmzcxE Y8CBTwOqWSdiywCIn8mFldVW3wI6e0zS0XNqwXSOTEEWoIpkFQeqCI5NhgroVNTEmb+mEgTBAIn3 oNX9RutcMevhMDiABoXgUAzua1/72pN+AEB7QMeF6FcAx0UXZ8AzAx01o9HGpzGdBp1fvAr3Nuhc CodxvtoK02nQudQpzvw5Hw3o3Np57GZnXcBuZpatMJ3RsinsZM30PkrzNXs4y9+qmlcMbJUonz77 yFSNc2Pgs14pWET8HkxHtTns3FQRnTpHZ47kVdNqFll0Wh0co7lXYU65Ncw5qhX7sA8IzuFgMWA2 X/3qV5/c5+tf//pTfIINcWE4B9CcJsP9h0HHCRa35IXdJdPJQOdaofEsGdGFpFUIp+UZMifyDJRw HAa8Ao7z2fBgrCSNzkBnqx+nAjou+TLeLw9qgA6DEfsW2Jk6G0gzhszAl/W/VcDRayr4OFGfM7nY dwOTKcBkBjpsXrnoFa+rxkCwmqC8tTzHHvDZXNpi70s80/npgKdB532kjiN2MwAd1c5RtXGDznff MRtmOAEawXIadD40Bw/x6VSZzkryZkWPMxocqO+iDl0V6mlpBh5UFYcs719hN9HgandnVeWytArV AFWd75mJuTVax2YUnMBhXvEzakVGRHyY6aA9tJRHhe2MTPvqJOmug8hbnJ+jb+xc5gXyGHC42qQD HedEjt+cI5lTIlizg/IaXGJ3tpxNtlTxTGOUpXUo8xk5lG/GdBp0LlqFBp3vvXIkN+hcIlkNOlLE aybN5xlnxZezxw8xcnjGrJ45PDNn5WhmVcBk343zL2jyoPPfsJ6DZ5uZIzljOhnjGSmqK4wy0zox 00GbBtOJ+9dwM4eRR9J+9LNKf5rpcrTPVhi5c/6zP44Vy8wy3PLTulYaHMXMdPAbrzDCIXNVLPPy z+zUVt8St7FbxG/EemYh9sy3czjT2Qs6juVUzJcV6s8Rqzg3OouyCx7UTgez8qzsNNUwpjr72OHn ojhKYyvmlUsXqLRrBkR67IidqnkV7RagE8/hVg9lU8FlTqt55RznDuhm4DPbzpONe143eWmf4jwt zquCCFABiIv8M8hoegQfFwDFYkIAEudquTreCkCuYNioP67UYs6AZ5N5tTIQMxs5YwsjtW6DzsUk Y+EcM50Gnfdrj8/AZZXpsHgTYIdx0KCzVuDrVNBRwKkwnFECYkV57MLezHRYXcplHwPBZ6UCMgk3 K1ddkl3GarTspDrx4n5cBrFjZACfrW2cpZroYGN2we9XdTpgOvGMulghZnP8fgbTWZkYM/Ntlt3u WA/rZDj1gRM7wWAq5hEiX9FmwXScWTbyAanz+QxTa9XEuivQqfgUZsufZOFydBAFnYxesnmjNDFr ZKaeTFG31LwFCOLasxysVdDJQMappFE6QtlBBXQCUDC4NLcIoHNE9IrvrZLiMALukZxjBs5ZhIsB iNdBy6JMnNXOETCAjgoK2R/EphbMLV5VNktU3WtqVSNYDTokw2eHWoPO62JZzn/SoPPLd6VnuRyJ C6s36Fw0Optzr/ZGArLaLjPtiLIYTrYcfedsaFbORgMAXFRDMvPy72E1sxnFAZ4yHeeTcD6dUR2d 2aydaY2cU1ed6Jl5xU5TzL4ruVdZVDFjOjOTeJXxzNpM+1dmbnO/4+8usRTMhc0rZTqIgsU+vOKs pmWwnkhZ1qxfztImqmbWIUxnRLszqro1a9yBC5ddcN/5GBYKagdxYq9ZgewZPa68WPUtHQ06I8Fg NUI1YzosC4CzO6JX8WxoQw75xsDA765cAzvLK4W7FHRcx141VV3fnZmlMEXV2ZyZfC5ZUysVupyt r3zlK0/8AQiFCQvQcX2TQScDIAWfaqpEg87zUrQAoAadS3b7bMBkJTgqPp0GnYup1aDzYSRLfaI3 YTqjzGdXUIsXfAOAzNiNbs8WiMO9qAMQaJ+Vk+TIQ6aHcA47lazPNDpHzM7MFGbAo6zCiR2deaWg Ex0rmE48H2tVnJ6Eo1dZaYsVtsOsYmZesR6I2dUs8jprx+p2Tp1Bu3KwI/oQO+JhRgXL+fKXv/zy +dKXvvTueziU1UHProOM6WzR72jKDkeAmfXcNehkIXEHNK58ZvwWYAOQAvDE77GECj4MQJwxjY4d nQ+NxtnELvTLYi4XnpxFChzouBeWDY6ZTmemh5qZVxh8DCpOKTwCHbQBF6+KduOSDbFPtMUq6HBI n9XCrKGZAc+qX0dZ46hkiCu1olUYcTxHWGFiZ6ADsAnAUdABsLu+x+U2VsWDFTPLmVgNOs/g06Dz PurSoHMRW85SLkbg3aDzybuAzF2Azkys5iJWOjvwzADAYOYSi74xi1GmwwvF6b7s9wHTintGJ1Sn HmYeV7mfRV/ZImlsXmXRgEykOBsYWfRq9g4ybQ77J2ZiO2U6MbshDQLmU7SJtlH8lplXI72MMwNv yXQcSw+GPuu70Z/RX/FMwcyY6ThHsjIdMJ5wKEMPpVGsYDZaeMyxnVnkdhTFeijQ0ZfGzl9nJgV4 6IqTsepkAAxeIgCK98Ux8b8CEK/hxL4eNrUQjWHBFufUOJ8F28scGchs4ooKeZYjNFPTzhjOSIns wtMuCgPQ4WRWLmyuiuQYaDhPXGMWdctC+qs+HVxzRemtynm3bE/0WzdJqqmP/o0JOO4DbRagkIEO +3S++MUvPsUnwAdRLZepnqmhNbSeAc8sL6tB53kWiZfeoPO6NrOLZK04aht0LpHAjKWzD7JB53Uq z2niwGwWHkWtVplOMBWsqf2Xv/zlCZ/4HTMHZpT4jffR/WN77OvW5WZTC5RX1z/i2SPL9uWCVZxj NVJpVpzHM0azd3smxnNJjypgRBEvZhJOrYt2jbYAoDEwZlUfOcSvOiI1B2fO5Epbs/ma9eXoxxxt nUVZ2bzCOeNeOHqKPhWRKehxwHJgZoHpxN/YB6kRGuxwJu6bZTpbQEcparAWMBeAzl//+tdXoAOw 4X1jH/387W9/e3UcZqWYqQCCnCyKjhuDhHNjeGmR2QtlISAndDqKv8XBuRdk1GRxIXOX6OgU0yjM zmYSvge4aFQjnhfndom/XOgefclFr7ISI84EVDCqtLn6zjKhKwMQl1pRZT2b8nHv7Edk80pBB34c /j9MK4TVHeg4vw78jxWx4CyCdXfmVYPOpUoeXlyDzvt8nACgBp1LaY4GnaenZ4nX+3+xKNYs9+pI 82qV6QT7YWcxzDCwnGA3f//739994m8wHjCjuCYzHdaqQMNzBNNx5TS2ZJOPhGirzEfNqSzHayTz hxMYi+058yiOdyyDI1Z4rlGtan4+jl7puauV7DKm4wSRmvCq+zjfmXPgc6QUlRZdlrkyHZhU8T/M rTCtOOM8nMmqiRotFLiqK8tqet9UHJipYWfahswZNzOvGnRea3AadC6Fzxp0vvMSLs/EgVt9OncP Oi4E6pZD4QXs2BEXvpoMdNhfA0cxWMw//vGPp3/9618vn3/+859P8Tc+YD1xPBgSO/d0mRUkicL5 yYpltYkrtXpUkzNiOlsSELewIOc8doWuOHVEy6nGOYLp6HGsFHbPju0zv8nIp8OaoRHYjJz4M8bj cs3YPHS+MH52vi/1g0Tfco5kXqQvfDfqy4E/B6FyzuhX9TdLF6qA44rOVZYezt4B9/XpssJbzasR 6HDOla6a6XQ6HL0KwADAsOkEoPnP//zPJ3ziNwYcHBfngGM6i2ShowN4oh24Upwukuc61sog0I7v TI+Rw1XZZAZAlXB5BkS4R9UWxe8avYpOhgHG1RQ5esUCyD3iwEo7j+T5Tgyp+h8+nk0rfGeGFc/t Slg4rZYGKlinw0W8OMucncesG0OpC16wb5R/NcvBOkKn48C+QScJnzfo/PxddEl9Og06Fycwh+4b dC4Z51dnOjOTYCUNghM3w8xinQ4YTphP//Vf//Xy+V//63+9++C3YDrYN1gOzLIwyXA+qJXjGmxq ZYvxOYfiyCGb0WydAUZMh1mjK1hfXeFhpkx2QQE2bdjJDrqM5wimo0yQ1clcvAtOdZYOZM5bF9bX MLl29EopzVH4fMR8WD3NUoCR6c0RTDVR+LiQYDimw6ZWsBxe+9wtVcNMx5UurdROnoXLsyzzDHhO M6+2gI7qG1acygEeDCQAE/iCNGeLI10MOvgdwBPgg/tgzUW29nfsmxWJHw1Up3cZRUXULFUNSLZO VUVwx7lXTqyXifPg79B1r6LTusJSmViSTS3nUxoJAp0YsGJyueNmphZvZ9NI85w4MsSlcfE9i4hy 5cBsGRtOeRjVSM4qCVaKzTXoJE7lBp3fWLBzLM2J7RhIGnTeL/lTBZ4GnfpyNEtMRyXj8UKymShz BmYqTlfEK0v+dHktzEgwqDRsjwHIGqAsP0tNLdTsYdYzuz8cA/0PK1BH8vysXZV1cLLhaKXTEesZ ZZrjehpp5MgeAxRMHbfCJ0dNODUkZnvVLWUqbacdcvlgChROkVyp9eL6uzqVOSmYi5ZxsbfMgauK diw3o4vy8WoaurIGR6p0aZtRkbnqmucuclUt4nWIeTUDHWeLa4RkBDrV6oE82Lk8ADoS+w9gN8e9 AwhjEHKeFofjVWDI4MPRLTajFHxc4bDYB4OXQ84ZrXdgrqDjCkUxKDozjwEoM7UUrKugg/aFTwfv IQadW/7WrXvlMu1nqSLOHHMMZaTbGU0C2TYI+mA+ajhaTRpORQiAAYDwmlZaeD3+1iWIOTSeVTfI qleOantnSyZVzCuu7z0SCGri5zR61aDzPjm0QefD2sANOt/7QITHLKdB58OlaE4BHRV7zTQ7mWgw Mx+YPbBwD6gN1I2ZD9eOc8FE00x0JzZkRzNYTDCljFWsOJL3MJ3VlU+dI3zkWOZ0BGah8bsTwTHo RHuj7blWr6vXEttns2NWGqHqc1FRXyWcO2M/HKHTZ3T1g7hsa7Ab6G0g9vvCF77wxB/8rsmcMKl4 /XKOQrGD3mmEKotAav2nmU7nrphOBXS0czvgycLD7CdhARqbVTCt2KcDkyoc0CwwdKplztOC+aVC wiysztnVHDKf+XQysRy3VbbGu4usZfvOQMeF4Dl65PxPYV7xII/B6SaEbJFDl7Gs/oEt0aY9vp3s WCd65NA3F/jn9asCSAAw/+N//I+n+Pz3//7fn/7jP/7j5fPZz3723fbIr8rWt2L/GIstFaT5HTBA 6mKAe02sVYnCzZhOg84lWuJ8FxCeAbwbdPLVImesp0HnUtP44UGnoqkYydpnjmV1LseM7WZfLrrF nQ9MQ53HMJmCxYDdhIDw3//+98sn0ifwPYSH8Yl9cZzqeJjpuFovGb2fzdgj0HGMsMISKwJCx4Dw XMpiNBUATIeZHdN8ZjjOFFDRnBOfZQ5nBR6Nbu0Fnkx8yOkdupIIi/xgUn3+859/YTbxidken//2 3/7bU3yC+SCbPI7hGjmcT8VCS9ybY8ksXszM3ZUayVkaR4XpMAsrMZ0sjKjhTJez43J9KqDjgMeF gd35MXjYjxOAgeTQUCkDVAJo/uf//J8vn0zVDFMrzCxX/CuuVwGdvebVSu7VyLTKolkZsMezqQpY ldaIXvEA5YgiR62yzpv5CEbFomZ9U4Ef76AiHnT7aF4VAIAzutWPg1IUn/vc596ZUgCaACCYWgFK 2DdMK/hxtKA92iOeBWPQmfRq4uIdZAW8Zuu0VUFHk3tVfd+g06BjWaTqnhp0LiZeg877JWhmk0Sm izoddM50KjuzS+vW8ioRXAbDMZ3/83/+z5N+gv0ggz2iXFz8ixfvczlKOlArZpVLg+DoX2YmVX+v OJe5XV3iK7OL+B4DUZkOV8RjCu9Mq8yJPPrdZS9n/h3X7itMh+9DTSqXzsB6nNDcICIVTCecxfwJ dsMmlXMeBzNhk8oxHc52Zwe+Yzej8hbYfzWSNWKjpzKdStKeC59XnMoufJ79luVvcZ1lJIIGoCBB 9H//7//99H//7/99+fy///f/Xj7xL36PT/h7AFwRyeJlbJgZjOqrzJIJR2LLmXm1arZyOJ1BywET TCwAKe4znic6lUv4ZNNDFclM1UfgUgnLzsLcW00qBRsNRccA5TA5L1UE8AjAQcSKAcZV/0NpCoTF Xf7WKNESQJ+ppGdVBKtL0VTVyVdhOg06F99Og86lnkyDztdeWE6Dzuvo4y7zSvUaZ4HOCsvRfcFG uFQGiwPDZOJSGSgABucyWE78H4wIUS9e/iauyQmVmVN5Ft7Nctpm4spRxUC3bWaKaeY/ns/lbKHI mYteYaZjP4gWP1OW46JYju2MzCNlPiumFIv/HNPJBIG8oimbV8F4kObAZSnc4njZSrFs6jC7cuVI 2Ykd13B5Wiww3FpJUN/T1cyrBp3fveRzNehcKgdqYmSDzjdeBH4NOnfCdGazdyb319o1YDYzpS6X RR0VCONay6i3DPYTPh04oMO3Ayc1+0ayujPsVHaMZyQ/0GTM1VKkTlbAiaQu9YEXkUObsy6JHedx 71j3yhU5U42PYxMjhjOKksxSG6osZ+ZX4pQAromTpT5ocS0sgjcqsDXSzLBDGOdwGerQ9sT/yqz4 njg7nQt9reh2VrVVeBdl8yrL+nWlB5xepxLFypyZChiuwNZI2IZBxyI652xWMELFQrekTYAOcrKq oKNtlTHFmZmlIJK19573oKDDYO+ANUAn7suZbrE/R1cQ8XIpLKvgMwOdiqmVAQ7fi9McaWKnc9SO NDGqi8lKSnBBtGy9c85U1wx1t0yNAx02tSpO5QadZ4Ee+1W0QBUPiJixG3Rer3nO7CnasUHnfXH1 LEzeoDPX7LhJ4WGYDg8CZ1JVHLmY+WOAwVQI8IGZxAW4UHsntrsVKrSAWOZkdZGsWWGqjOmsMprK /s50YzU4tzUn2uozwLzK2gGMYyu7yRzQVfNptN9WpqNOXRfiVvYyYnKa1e3MuDCt3HrnSLXgWjzB cNiRnJlX2eoRWzQ7lZSIU0CHc4dcsW/nT1ANSqWEg5PzK/VnuT6+xz1hcMSgYlBh7U2cXxmAu2bF 3FkBmkp6SQYoK9fRczgRIp432oHrPavTGI5kzXuCP8eVW3CFovi3bIBWSmKsgNEe0JmZIaqryVIr NMLHmeBcEC2LjDnQCTNLayjDrOL/Z6AzA59RJGsT04nO1J9ug+4D3QeO6wOvVi//cC1zdcSNMnpn ZsEsCqOOZKfPmUWv4hwuJYHvO+6TM9FZy4Nr4hwceWHHb8V0WWEdlX1dZKhyXGUfZaQaIWPRo8ue R41kx06CvbiawbMZdMR6XFJhpkyesZ5ZGoY6lGf1ZzJ2MyqBCqaTOazBRsJcgqM4TClmOK7sKfRA wWycUpk1O6oT0vKnWWrEqmZnt3lVKXMxyzQfRa04cqVV+1jE5spM8EDhIlQ8CNXBjHvBINTwLueR ZM9eGeTX3GcmStQomgNUF/ZmHU6ADkdZspKdjsrPTJSR7J4BpZoOEfspEM1C5jPzUH1O1fvi/lUt lcGgo0DDK4HGdxYhcqh9Fj7fE8maCQUbdCjMy4mODTqvV/hs0HlfDGtWtDwGXYPO6+VpuD2moBM7 z0wspzfJHMgjUaA6jzmsDWcvFMDQxmgki2vbMNNhtjVyoKqzlGcfps3cLmq6ZSymIgo8gwFVmE6m GcKxWSoDZvhgOsFYQOE1LWC0KNxoTe3MxGLzaoXhVAp6uZl6ph/KnNtZvR9OQuWIHkes2BxCFAqi v4ztIJE0/tdCYJyQygv5udUlbsp0zgadkR9nBXRcaFd9SK7CHkdlkEeEzOl4dgYdzYx2plZ1gB8J QDMzTyNNsxIbjtVouUvNBwrQ4drAHGXRHCCnznXLp4zCzSs+nb2lLbJ8rFF4OBPTcjQVZh7OoyJA gES2NhYDkJpV8XeYXrx0jS5/E8fHbzPQ2SoFyID45kynQeen73KWtrKcBp28hvJW5/KKz2cEgNkk 5JhOg85zHZlApDOYjmpfmIGwCJD1Myzgc+LACtNhQaAz1+I+MPDR6TLPvLZLxcTawoK2AlGW61Ut mTqakbVNYrAE08n0JG6FSrAdRE9c/o9GS5xA0EWmqubWyrGzCJhud0yHHdjMbpxJpeYpzKFoS7dQ H5gOzCqYVm49dM7PYhaq2ef8TpwJXPFtOUC+CdPZAjq8giYL+DSsroI+TmZkQONlijlxEz4ddJoK 6FQAx4nmqiC0FXhmZpWbUNQHkRXA4t+jjQJ0YvBkfgjN/4nBMKP1nJukEayRQLAKOnv3GwFRZtKh P7nayrwwH4v31Lyqgo6atZl5lS1TPAOdWbH9zPxs0HmuANigky+H06Dzum0qjugMzOEjDOBp0Hkp yvny75VU8FrmlUt54JyoYDm89jivyMA5UhAOZkWqcB3W/uga5CwKDKbAURsOhSojmAkjtzKdFZYz Yk7VwZI5TTVah/uC0z2YTgwmrhnMOUKc+YySC26G1aiJW5yvklqwwmKqjFNNTqf3cWYW65m0mqJL S9DfdA10F7liRzK0O8GImE1yBIyZ50fJdDLQAaAw6ESdYwBQ/A4AYvCZ1dhhk4wVzs6nozSebXUM vJk8AKF7BZBRZ+dzz4CnMmgyxsLPp9Sfo0eZXAADD6ADOz/Ax6loMWA0GREDLQMdvRfc6ywPKwMf bbOtk8asXRWEtI1HJhUAI0ABphGDNwMNQuWIWiHxk0WAe80r9utU1Ml3bV416LyPYGkkKlNSrzKn 2eBgRuc6VIPOz94FGZyvLIt2Nei8FwmiLR7CpxMsBmYQ1hWP/3nFTWY8rsjXqLIg52q5qA3PlgwK YDiznDJkvmuZi71Mp8JwWHOj5gDPRFkKA+dHZewizqO5V3y+YDwcfWHtCBzKq5XseCkbFyHJFMGZ 03/GVrO8N+4vbEbNMteZEWaCymrESjU6YEPRzmyqNdN5LpXgylm4kHmDzvtZdpXhNOi8VtQ36LwP uUMcWPXpvCnzyimFs1Udgt2A7cR3dgTHd02TmC2hq0WnHBtx9r7WBJqtG8XZ71ldmrNC75kTWU0q nnFduHS0UJ6uexXn5oxpqFpdfV/V7GT1XTiJlB3Me5kOl9LN5By8wgf3kYzpzNImXDa5+ne2MB1e jph1N/G9mU7CdDjCpA7fMJvYqcyOZM1A57rFnIE+Wn7FrRia5WyNUiwytobfeWE+jgJVZuFRJcKZ +lVl9wwMnDfl8m5ikI+ct7rCZ1zL5RQBOHipFM14BuiMlmTZYl6N2tflBLoSKSy61GdkIGHBnyvh oWkkqELIOid2JI+0Oew85ogVt9/IxHV6KVfmYrUawF07kleYToPOJfvbRbQadC7pEBV1sLLXBp1v vfh/MsDfk5SrTPTuHMkVpgOTiusUc5jcFf9S5uPMIT6Oy3Q6aq2OR7fSBNiX/j8rAaqAwtfC7LsS 1VLzasRAssJNWbIr9DvBdNThjr+ZUbGZ5TKdM1NL/QkzpuOc/wHKWWjc1XyamVEs8mMW4BbB00Xt XFEzXk0iACALmbuUB2hzdGlipxDndh8pw9+sIjmrjaymFedhsSbHRaUy8wbHxXZnZs1qLms+E2tp AAZaWExXqFAfkjOvMl0OFxzjsh2VUhocMndZzTxQ4rtK3Ech8zg3QEdD/nFvsR2mGZtXLierkh7B mh0WbGZZ/1n7uGJlaj6p1ICrDnCu2Ujkx+yh+n0VdBCxCqBinxj70DRqqJHD1dyrh02DaNC5RKYq TuQGne+9JJU26HzlpVYOJ3c26DwPopHuQevvzkwdXQaGNTkuyuWWjOFIV2yfrQjqts/WAEeECjNr zMLsMFRHrKplK47kLIoy8/FogiZMA05bAJ3mREu+54xJgOno/ePvOA7mEK6R1dthpsOmFs/CysTU Z6Bm1AqbRFoH+4Uyh7uyEbeq5+w3l/KABE8XbXLpJJnzmOsbsapZzVrWSI0ih2/GkbyF6XCZC/WX BKBwBjnnbOF77OPKabhomZ5Pc7XgVwJIBdhioHLEgG1tmBgxEHnAuBSLGDCVIvAuquVyhTSUzcvV sj9Hw+Oce6XMLK6ja5nz4OXC7K4wVebbcXV8UWAc9+qiatGOTs80ypfjtmKfl8sKz4prqa+EBzqb kq6oGYCGy05wxMr9zgW4ABZam5qrBbJ55Zam4YiV80G5FUkrhdbuzpHcoHORiWcJlQ0633pxqDIj 4AHRoHNpnwadE80rZh5uZU5dj5zTJriEBa9vzgyHk0yD1cTxWMv873//+9M//vGPl8/f/va3p1jP HGua4xwBEjCpYqbjav2YrTDLsJkQwMNMx6VbzBiPOpedcE1XHGDKPEp5YJbgWIRGr9ikiudkdsBR GbcCZeaUzVaSQHuzKeiefQTsbAriPFykirPDncAxYzWOoUAJDKbC2d/ZkjIuK9w5fjUqhntlUzAz W2dL0CjTWSnixQ7/m4fMtzAdNnFgMrn/IyOdPzguTDJX5oJ9R9g3jmfQ+de//vUUnwAegE6AGc4X A5L9FzzY2GcSLzCLCKlvxIGNEy3yb5mIjXOiFHBcGQlQYwUdvkeOXrFpgvPzUrgOfHQQOB9DRv21 0BdMVpcHpSaiA09WabOqOFMQZ6aWE/Ppkr8AH/hpAnC+9KUvvXzYSRy/cz5VVvxMTSIWfDLoMKg7 XY47T3WNMlfd8U2YVw06P3+3WGC24kWDzkUkqKUvwCbV8Q4QbdD57kv1R2ZMFQfyKEXmTYFOsBtl M/E3GEgwlDCD4hPfYWqxqFAd0eyEhnmF88V52LzC+Zg5BSvBLKumDGYBbNdkzFmtHs4RyhYwxO8M OmrqsAgtq02MTpQ5utVJjTQIdry67GmeWWcFrEaOTZ55tcNn8ntlay5z3NXf4fc0imRxrlS2RAzX EwIbggke7OaLX/ziy0eXkmGmkyVoor0YMNi82sNwnBOZzSuXa3ZXaRCVZMmROJAjUgwwAJnwvcTn n//854sphA/MpAALVjWz2FBBR6sWcrIpjssEf5mymCX3HAJ3YW8FGpUboGwG2tSBTrx8l/PE4Wm1 51X4lfmcuIhXNiAZaDS6wkmOsZ+aAewgdTOuKqV1Vs2AxoE2+/kiuskmONo93hGuEW3ECmuYPeGr yar7OR8Pg05mXnE1wFEUSs2lin+MJ6GM4TToPDMcZJs36PzynanFAMRMp0HnUiSNI4ENOt95iXgp 623QeU5XQD1jFvytMJ1///vfT/jANMpMrbgWL00z+s65XMo+XPKgK/TFYkNe9C9LRlSG6FgP52ax nwIsIRPlqfgO+7PpwmahcySzP4RZgKvDwtQcGdZIxXBmwEruleZduWJr3JbKavS9x7vm2tmc0uEi WboKJwv6NFoXf7OZheiVigD5b11ZI9P9HOU8rjiR37x5xVUE4dMB44n/OdoUJhbMLJhdsR3HadJo lqSJWspZIigDjcuad2UzuLPHdyfwU2U3l1vQ2ZpBiBNCYxDCXOKSEtmaRw4kolOxIlkjP1pPh/1Z KoJ0pT0zXxD7nLLoGt+XEwDOKjxG27t8PfwW7YrnVXU1m1QcscpAQiNz/D4YrDisriCm61ZlwsMs j20mAhxllLt8K10eyJVAuXnIfK9Pp0HnvUnlGBRYEwZKg86lHEjWVg06l5y2SsTqTYMOZ2tr7hVA R6sIqlYnnL7s88H3YDkuksXXzFaUYKbBOT34zkmZvNQNrzrBbCk7XyYOdLlXzpxjhhTngvOTV2nQ NIMskgXVL2t2HNOBUxl6mJnAMcsXY9bjolTqPHY5bGqizkCHc+3YaRznYXbDsv9oL5hLLAJVQSib VFouIga6Y0vKXrKqf/o7H5cVR1vx41RSH94M02nQ+dD5yQPHJdSqecVlURt0xkynQefCdDKFOicu a77VXYJOxaYeVQ9kP0uW0MlrYbks82A28ONgJYk4JvblZYpH9W90ULNWBc8Yx+N8fK+c2c4zqUvK zFIOsjSIUQEq9RNx5ntGqV2nYpvcVSh0RbxwbVadj5zfiL6xAzyT2o/0Q47lVKQH7ISP/bNUCjAx VvzqgnhZWFuZDrOlbI1xJIO6hNKsIFcl1SFL6hyVV52lPrDKXoWZV12CZgY6eNla4mKW9c15Vq6a 4AigeJVQNqW042WdVTslgw7Ox1EQjoCwU9K9mCwbupJ75cyvzNRyHcgpS+FEdrlhEM4p6HCm9yzq plE89kVlURF1HleAOguTq85K25lD7TC3YHJxlr4r0KU6Gd0ntrOJNMsEr0Sk3H2MTCquLpCpjzNf jmM5KgrkCSva7Sq5Vw06v3uJjjhfQYPOL19VceRIEVcc1JyeBp3XmfezioQNOr/4xStBW8Z0XL3k zKnM+VjOvNEliLMVItgPwo5QzHZZES0GVn4eZk5gCgAaZRqcJrGlto7O5BkzYiZRWSAOMxXfEztv L7PX06s8J57dVDczEuoxg9TcqRlAV8yqkShQHfLO5I/3iX7BybOaEoC/1XGvCa+jOjtZ4qvLtner T7h0F+fIflimk1Vrcw7PLHTJtj9rWDSq5EDFFfZSkVfmu3GpCJrpDdBRKh7Hqo8DnZ+fPQaQFoRi 38lIB8Pny1IlZqYXb3fZ1QoiGODOvHF2O9Yyn+3PCZfunjjPSav3ZX4cl24yAxdnQkHFjXtUPRHn 1GnlRX2XPLG46owsKpwtATMq6q6OX651XQWdqvPY+fpual416Lxfj7xB55N3AKtCsQadSxZ3g87T u3/PJJn+eJ6+uAZJ5khUMySjuc63MxIKztbD0qgWJ26yGYYIFS9255S9jkVovg7fE77Hczn2EL+h zWKW4KxrqEJBizmhUq+ZMYKMYfHxIwbk7hlMg5MaNaVBaxQHsATTURPDMZMsosWJqsq6lHGouaYg P2M5sT17Z46ROibADmSEmp1iOtqEC4Ap+1BfjGMnlZo3MJE0gZZNsWpiZ1WbkwUdTnEkZ+BTpf4j +f7Mv5OZWhkAaQQJy884M4k7YuajYbk8R7041M5hasz0mvOEqnHovPGi0H5ZiDnazfljKiUxZuaV 5lKh47jsdC2PGc8QoMOlQ2PwOoDBe+Ls/TCN0ZajUHXVNzRqv8yUZpOKQ+IIi6u8wJkxmaKXAWFk 8rAZxeChVQldaQtXHI3z6SplQlYjVzNT+tCQeYPOb160OA0633knKGvQyQtfNehcaoDv0unsBR3V vjgZvzqVsyTKbDVP/T2Or9Budy+4tmahu/XTeSG/eE40tKsTHLMZKGq0qUsaZQ1LbOcImBYFU61K ZlplgkRn1uD+WE/CyYxgbAE68YzueTgowDWoORWFy71mjtyM0bmoE0cnM1abtYNGF7UGkCuMldUX ZsaS1TRmhsEABQdztLdb1TN+14zz0fIyR5tXd8t0Mv+CA4BMrarrTY3WrNKlgXEs7HiYUCvaIVwv QIbX3FIfEkfH4hgNrypFD3MEoBTt5ExLBR0eEFqQPM41y3eK7RyF0aJXWcQm7h0dOjo78oviewBP gI6CKJtXeDbXlvEb+kMAS+YTmIGOW1YIa5Fp34p7c+AWv2mUalR6g8PamT+GTR98x76Z/yS2A3QC WNDeWGQPNZT5PeBdZJUFrwE67t0dIg5cYToNOpeoVjCABp1fvqvGxwDeoPODV4syIujQoPM8AzD9 Zio/iqLMIikVp3LV1BqtyqlMhyMeTM/1frjsgeZSudo7zLTivjHbR3spHY0ZAG0X183yvjhKxqIz LbSlEbAsdI9zcBRKtTdasCu2AzRdnpEyHRf1jDZw7a7sy9F2ZXHuPI7pcB0jNbPYVOUMev1dmY5G IV2EyNWC5nwoLsPqUgw4r4tNt2BKvHqEFv0KVrSH6WhiZ6bNqSZ5Oia9KQ1iS+h8hfGMcnUqplbm 52FQ0vOwGabiRc0JU3DT6/Fqn0rVeUDFNgyeDHQ4YhUvEEDDHRG/scCQdVPsQ+MyFypoc++VB7dL xMSAdI5kPp9OWPx3PBeH290gzExH7iusYGfTV01uNX3ZpOLJFPcIIWDclxPjuUgW+33cOl/se2GT i306WTQMoOciolq4KzP/9tTOyULlWbLnqebVSK/ToPOhYK5B58KeG3S+/VKQXkPmDTo7zCtH82fp ERnbmTGd0SoS7ATmyBMzF2fuse6GnaPsFHWmFjsrY2A5dW4mmNQUigComGmdkhUzX2xnM4FZFOeA ZVqf7F6YhbGpB01NXBPlStkRrdofZhQMNEzpVSuCNmPTTMWTIyd1vHPWarGzn98rR7LQPllpC5ee wXlYGrFyq0TwahGIQPF699nqHMy2Yh8u1KXfM51OBmYrosCVyNVVmE6WEnEU+DjTyCWHZqUtQpjG IMFApX4VDVODckdHd0K3qNEDcIt74o7tfGIztXG0GV4w117hcCkoNIes2Rxh0FAfhzMduei885Nw XWk4gQN0WIGtPj82VTRvKavrEwOZfUtO6c7PxiVF3FJC8d4BQCxtYH+fmqUKxPHenZnO74mTPLUG Moe+IwIVS86wXwYJoOr/4chTtpbVqIxGNXJV9ek06MgaRVnn40JfDTq/egnRN+hc9FsAlwadDyNp lcJdyuI1dH6YOHAlguVmKRWvZeK9in6HZx9X2Z+z0GO2c85mZgEsl+fyD5ixY7Bi1tRVRVGhkBkV ixMroj32j82YDmh1zIBgDSMNEIMy15t21RedAJKrLYIxBNNR7Qtripwq16UWII8p2AKvIqEU3Tng +b07pqMrf3Ctapcewe8J4Bzt4QrGxXYueQE2okvMYOVOLKrHTIdXgHCRriMZjitnMStJqrl1WYG1 U3OvGnR+9wI8DTq/evHpNOi812Q16Pzog3XkD0mD2Ao6I+3OHrbDcnskhmbrWGWqZvXp6EDCbBbH Y8aLVSawZnr8z6tOYB/2IbgMd010dEyHVcFcT1er/sOByCkJ7AAHI4iZH6yM132P+wfryVI91A8G nw6zQs6u1oJUlRoxqhHhtA+nb2K/HtdcYj+Oe+/xPpjpON0ZtsfxLolY1ecAnfDRuLXKsWZ5/J/5 dLjOjkvo1DrI1QRPZpOjJM+VcraHm1eZ0GsEOlvC51sjWqPiX87BPEqj4JwdmETONIxBx6CDBf7i /xXQYQDSqAn8C6Cr0UHQ+ZzOQ7OR4TyMDgGzMNoYzxiDB/caq59izfdYlBBg5KJ87AeCADJAhzVA rMDmur5cuU7LQ0Dv4swuNrU4ksWO3VGAAQ5vZ6qrcJSBU0WD6vPhSCAGHufZxeQAkyoA5gtf+MKr T/yG7UhjQD4bO4ZZ6+PAKCt/4SoFqtbIOY+rBbtmYLNZHNig87NXStoYuA06v3pxQjfoXPoGK84b dF6bVptC5meAzix8ziraaqpEli6RqY/d72r6KMvB9mAAMEF4KWNlOmqmuEX34MB2eiAO2bMWJGY2 V0OX83VcKD0GB64Tzw+HazAbmIi87LIzTdQciTbCahDsPHZlOtmBqdnbLmGSUz141sR7iXtxinA2 k9F/1KxnloJzsw4mc7Y69TSrkvndMNMJlvP5z3/+5fO5z33u5fPZz372HfMJM4s1O2w26xroMK1m RcAca6wwncy0Wg2V3xXT2QM6Wsogi24pCI32w0Di0Cmq6LlISQxGBzoBQDBNeCVRLQzPEaAAIkjz 3T06Cu8k+awP0XXK0flGphavkArTkcV0DMgqrFPQGaUOqFk1Kv/AmfQz0GFTkNNaOP+NJxFO68B1 4l5U7zL6eyTOi20BIvDZsB8H4BOgg++xHf4ffn9ZuQpXKiPT41T9OC4F5aio1ZIj+d6YToPOJy/q ZFdGkx2Y6KzRORt0LqUsGnS+/6rfrPhzsiTPas7VYaAzS/6cqZNHkayRLH/F5MqchxyxiGvFvWhh J1X2shMWRajCLIEjWZkOryqK/cEodAULrkTIzm5OYcD9ubo38fIBRJwMGOADMOJUiTgXm1pZYqSy QB64aDPHdBxFRyfnLGpW7cYMz2Yh0/ksxcMFC5jp4H75XYLFwhfDibSctqBrhXMtoWAlSGdgjQ0z k6wAF5zHbHLFd+dUHiVxugz3rSbVKKN8BDhvEnRG/h0XYp+ZUeyT0OPZbGCzKvbjcGmYHxxuRhRo BDq8T3wPEOKVRrPwPps4TsTGQISBrmbCSn6WloBAdMdF13DtEegwK+MMaV1GF1EbDBoVBwJ0NIKE +81Ak0EHk2QAEEebWHgH0ADgMNBwES0U0orfYBbp/7qN0xy4MqCCmQoFK8vUOBmCY8NZjtVW0MkK d6kD+aGYToPOb1/yvBD2zUAgXmqDzm9eHMusNm7QeW1SNeg8mzQVp/KKqZUJDFd8QZg9tYgXTKNg Kewwzr6zSaVMJ45hpqOLA3LCKrKlMZsjXI2QNWd8xwyjPh92yLokyiw1I8vEVgEdsswx66kjmQuA wcEKVhCzOmZyLnnKRcnYr5hFFJ2ZFe2D/Xnm1dpELL4D0wETUxMJDmFOZYBZpGkOzHT0OeNZWXsV LIhrHmfFuLaaVGA9R/lxKhod5wYoFfEaOZIrPp2ZULCSl1URDm4FmyzpEYXYEckJAGHhn/poApDw 22gbAxSDTpYzxjlRXJ4D+1d9PniPK+/DmVQugxzLCrMPhkPmnBbA+UgYiDC7OLNcr6PmrpY/4faD SRrvFn2HBwBXQuQQt/qXAggYdAJoOAo1+x4g5MCVKwdyuZJKaHwUocqiVFvZzcyXM/Ln3NS8Wunk sxl31dRiIFpJHHVJkQ06l9yiBp0vloGnQef90jNX9ekcDTpbTS0HOspydLYMZsFMhzPI2Rxy2drB UPA7Ly7HbIiZTpY7xBnTbn+nn6mAt0ahMlbj6gCpSQxHMpjOqKiV6l5idgblj9kR/SWegUV+XNvH pbLMmA4XUsvuj1kPm1mcPwWzitMamPHEdphZbF5xDSTHaFwRLpeztoXpZCbVzHl810xnTx6W6/yV QbPCehRwMpNKEwUZdFg8p+I+ABDvzyFx/s5AhN85IRTJqsy21L+jRaichCBTXI8iYBxK5kp/MUux v4ZNldgvQCcAB45sp+zVWsIuSsWKaVUbuyROONi1tIkzr0bVCrn0hlZnhIkFM4tBJ/PvQBCo0SsO sSNClZlUTnG+BXC2mlaZ+rjiy3FK5MOZToPOpcxFg84PX4CnQecr75TIHEZv0Pnx8wT1PEPRv1d/ xUagk1Js97dGNLK/M1q/6lC+NtNhhqKFrBy1n+lu+BwcDtf0Dedg5rSJTKvkzBGNerFJxRoWzbRm 4aE6XuPv6Eda13dWixesiKNUcT9cRgJtyGCu72FkarEjOWM6WqMZbIIz+hFVCgBxUauM2fDvamrB 3LpH5/Fes+pumM4Zvp1ZxCoLk28xrxp0LnqPBp0vvQMe9t0AYJjZNOic6EiumFcroFNJj1gJo6/6 dNh/kPlm3LLCwUpmi/Bl6Q4ZM3EzuSt/yiFkLmimSl2XDKkpIGA6sN9ZT6LLCsdMDabD5VKZQWhK BPsLOCcqngH3F/eNtlc5AiekjpgOM8jof65WjjqYOfkTym5eUtmBiyqKeRlg3n9mXrGT3elxsuJn Tnl8pvN4FirPwuXR/jcxryr5WLcCHTWTVLDnAChzKmvxdx0crtLhqEyDFs/S3DE2y9gZrffhyj4w gEfbq6kVoAGzg4uFRcQlgAaL7fFCbKOC3twpOQcsnoHz21zkUNNHZqCD50V+HQMsHOfsGMd9Z/lY nL7BAkJeVsalRXD0CukQnFeV1UU+KnN8lkFeySbf60Ru0HkWjmWMwEVKNILk1tGK4zIG1KDzw1dr f7EyGqDXoHNZeC+rBJgt1zMqO7pScJ0njVH0apRvNfLn3D3oHJkeUTGvMnaQARCHyTMG5Ja35ZKo mTYHx6HAl5peLmmVByzU1MrUOC+Jncqa5OkSXzmxFEwIjl/USObgAWt22GkMlqPngGnFNZwhU2AV N68txkXRlCWqGZsxRWZ2XAGQmY6rUczK6sy8cuyGa+Vofesq2JyhPK44j98807kl6HAHdj6aitnF q0u6DOhMWzIyy3ggKRCxP8nlccEMdIOTr8lAx2DE11MzNECHI0Xx7lwVQVdcnaOQmuvGoktOLXEs c9bGqlvCs6m5xVUEgyVkVQHZzHQmF2eKA2gqAkCOAq5kjo/0OEeIAF2ZkdUM82Y6Yl6pKG8UBm/Q uWRzA6wadL7+LlnTlexo0HkfxbprR/IK05mlRozMKxc1mrEBZgYZAPHvzgxQppOBHO830v7w9Zhl 6X24yByXU4374NQKOKw5qsTniO8AHbeUC+tjXCGueHcuqz/Omy0PzAveaU0iVmurSeVqBSnTga+J HcqZYpkLdrnvI2ajvpsZw3E1m2eZ40c6jysRK2Y+LsP8cKazknF+Vvg80+2sJHuqwzfzFczEf9n2 LGQ+80mwnyYTy/FgdA5tdnTHOTgPDMfG87ta0c4Hg3YN0NGQPRdEV6EoRyc5TK5+NQVSfW5NTYn9 FTRdSg0LUbPUD/ZJYfDGwK+utDlaW3xlyZgt/pujweYIX84paRANOpdiW7NPg85PX2rcMDvN/EUN Ot978SmtVgBs0HkWZe1JiVjR7IwUyrMsc6dSrrKejKWMflcfUqY3qWSfs1paTQ827ZjpgBWAGWD2 j8mDncDIpeJwqk4wWFYYbczPphG4kTmr9+cihJyxz0yHzSwWQFZSPdgU4MRWJLFybWc2taCh0aVg 3N9uiZ1ZAucIcGaO44rz+OiI1UgU+NExHS345KIyLivbAcGtfsuiU5nfw4XrGYDiObiqnlvPiZeL Yf8B0+2Lnf70ci6XN5VF0hyLUfNplqnP5UMc6KCioooAKzoTF/LXioyuCPpoPa/Z/qN8tar/5pYs p0HnWVK/WsRLgedWAJOxHqcZatD5w0vdogadH7yrS3Q0y1lxImcO5LthOtWUiJVI1iwJtOJUdqyn Yn6dCVKqHRqF7bOIFTtZo+3BbrIZdrbKZXRGMJ04H0e6OEPcMRanwdHa0265Hq1NxJoddSS7lI4s /0rNKtalOBao2ensd1lhNKOoVGU1zjMUx6uanErUagl0KmUteJ9qiYvVCFbVtzMDnZGpxaHbGfCc uX2UT8RmVuYD0Xwwdl6zKA6dizuu63CZJB50OsyrLIfKyQsYODiZM5THDDQzp7yTIrBIUSNT3E+d H4eTP90zaz6ZS6rMnL5bfx8lbm4BnBU/zgrDqZhW5ZB5g84lR+uanwadP76YSw06339lMs0AyCXZ jhJvZ9UBG3Q+Xa7myFKmmam1Euk6E4xG0bPKgAQT4HwvXj0CE4rSY51oMlagTAdmMLPJLKeNI1Ns arm8M26HLJ2Bn4sjcZwxPypUNjKvMlOLy7NmgLCF4czAZSvDWWE5FWe77jPz5zwk06maWDOFcsXB vAJIR+ybgdcK6wEQaajegU60AYeTnVk8G6TRiVAjWVfe5AJqozwxNhHjuyu9oSazluJQsR+ehes8 VwZRZeZ3Ua2Zz2cVRCr7V1lNNXNcn73SXg06z3J6ZUFH+HeOAJTKORp0fvdiTjXovI5CZQDUoPMs KNvjTF6NYlUZTwV07o31VMFnJkh0DInLWcRzjxiDVhN0rEeZDgcHmG06B312f1qcbFRmg9lZxUns Zu8VdjMzsyqO5gqL2VP1b68PZ9WPsxK5Os28Wildyp2GKXLle8W306Dz63eV+KAM5mTOBp0fvSss tgo+FaaxF2Dc8ZXrjsrDVp5zi2l10+jVPYHOim9nNVm0YjLt3ecosWKWbT1jFRmTUEczQuZOBuFW 7dB2cfeh2d+ze3FOTB48lcG2us8KAGwFoJVr7AWblcTOVYZzqk7nWqBTNbG2+HlWGVJl/60ANIuO jSI7s0gP37djPbOBDvBR0MnM5Rk7dfWLnWRjBjBng83MmbwHKI46dhVAzzarGnQkRaICGnv3adB5 n1megU+DzmWxwiM+DTqDTPOz/Tqug+8FkKOPrwLSjPW47dm5szD0KPycCUWxlvneagIjZpVpQEb+ h9HAywb2ymA9AhyOPsfK/e9Jd3gT5tWWCNZKTlY2ux4NIHvPVwWgrfu5+8scyivm1d7opUtZmInN VqNRWwf4bCBvPe/Rx83uc7T9TOcxv8dSudLVNIitPp0GnUtW/FYwqR7XoLNuvswG89HgsfV8s/ts 0DmguJeG1meOytn2vSzljOOrYFI1o2bO4yxqNJp8YF5tmaCyY0YsZ4tJtXUgVyJCR5179Tx7QGZr pGqLCvmumc6WzPOjgedeol0zAMvCzrPjZjWDbw06W0yqWXZ4DLDVAb3q8znq/LPzHAE0RwFOVZvT oGPSJUYMaDaIb7W9QedH78qqNuh8sknkuNWPs9WJfKoieUuR9j1RLKdgnplSR26/FfCsXncUqh4l f87Mpr3m1YzdxPbZAJkxgBmDWN0+u95R7Kpyna37zNp0tL3yztw+pzmS94LOHqcyA9CRwHLUuVaB Yuv+s/vNUk1G2eazkPkMnLZ21MrgmA28VVCZ7T+73j1vr7TnbJ+t77JBZ9HUmg3kyvatILJ63Oxe GnTWo1gMRPcMKrN7mwFKZXuDzqcFvrKBNBuA97Z9FWBi/+ozrIAN16VZjV5t7ZSjgmFbolY8AGfs pbJ9NqDvfXsFUGb77Hm3U6Yzo8uz7av1kt3+lYzzyj7VQfmW91sFnNn75dwrLpi1p1OeCTp7c6bu HVDO0OE4ANrzfht0bmBe3RLUGnQuUZ4Ko3H7NOhcIoYNOhPT6taRrVuCDK49Y4IZI70l05lRfN2+ CggrwLN67nvbf7UtZ/u/edA5KpL1sYHPDGhG9Wqqvhw2r/Z0xJVSFdmA2DPQ3xqrOVL8t0eTsylk Xp3psv2O8Ok06HxY+7nCnhp06oK5Bp3XQssM2I+YWE736exJ/jzTqfxWWU8FaEYiwFWGcwbTmVH7 M5jOHpZ0r8dubcczBIFLaRB7mU6DzjaWUmEylRyqEQiNWOjqew9F8hGzYEV53KBTY3ANOsUiXjNz bGUmP2LfrYP/msetPuesjV1dmxkINejUgOAarOgMsME5j5hYHs68OtO/Uxm81wSTvUxmNTxeLdil AHRJ4rs908HAuMbAvtdr3DvglBI+ZzNcZXtlZl3ZpwIOZ+3ToPN6fTMIAht07oPpNOh8ugDfCqCs 7HsWsOw971Zg2nvdyvGV9q1MJLxPM53bA86ZYHOkaXU1pnNExvm1I1mVAfxI+1TAZmvk6t5AJwbJ vZo/R9/XI4EN/EFX8ek06Pzs6dYA1aBzezZyNODE+Rp0ZD1zpekrHb+6760H8yNcv9qWq2YV9m+m c11AuwbQHK1CvrpO5yymc3SlwUcAkJV7rILNVrPqnkHnrUayrgk4R4THb5IGwbPn6iBY2X9lMH4s +6603xZtToPOdRnOtcypox3HCjwP7dNp53LuK1oFnLcKOm/FqXxNhvOmQOcaZtatxYO3ZlFbwGYP 4Jyh08HMeNRAO8OBe81zHtUOq+d5E+ZVg875UawGnQ+zpa8JEGdcaxUsjtq/QWdD7tatWce1r78V cN4603lEp/JRwLH1PGcBztXFgddiOh9LVGsPyOyNWJ2pSD7avOKBdwYTOfKcW0Hi6OMadDawHAyq a7ONa16vQadWeKpBZ72dGnR2gM5bBZ8jAGevWXVWyPxMpnPPptbRbGXP+d4c6Bxd2Ks6AK/JQs64 VvU5V/bbqkJ+VPPq3ljPHmA449gzweYmuVfXEgpmg+4MILjmOVfApLpvg871BX7sAzoDOPacs0Hn ALPqLQBQFUBW9zsCcB5Bp1MZhEc6gyvnqtzTLfZp0GnQeclOXwWT6v4NOu8drBWgOHKfWwBK5ZoN OieCzr07mKvAsWe/Bp3rCgkrg/6W+1wDcG6m07mVI3k2QK/pn3HXmt3fUduPApuzo1d7VoY4YvAe wWyOuI9rnaNB5wosRwdxg86H9Y8rAHV0PR3u/NcacO46DTo/PmxZoZvV03Ed+KjZ++jzXAOAjr7n 6vkqQFLd530Y9LjVIO4FdLaG128JlHuu/eaZzi1SIqqDcrTfKhgdcc0jz1EFk+p+Hwvo7BnMj3Js g84NzKvK4G7QeW2CNeispxjcKwg16Nwp6FSA6Z73qTKY6n4NOo8POtcCm5srkm+tTr5nYDjj3qog srpfg06DzipoXbVcadahzxhkfc6fvhIVroJJdf8GnQadBp02y6yCuQoiq/s16DToNOg06HwAOqtA srJ/g06DToNOg06DzvOywvcaIbrH+1oFjb37t0/nIwCpFeZS3Vc73mc+c7w48B4H6Fu8p70gsnp8 g84bBp0qgGzZr0Hn7bCpVdDYu3+DToPOU4PO2wGQLUxsL4isHn8XoPOoKRH3HpbfAibVY5rpvB2g WgWNvfs36LxRplMFj637Nei8DdDZCyBbjm/QadBp8+ojjnZtAY29x0xBJ3boT7dB94HuA0f2gSf6 9xz47H/dAt0C3QLXa4EGneu1dV+pW6Bb4LkFGnS6G3QLdAtctQUadK7a3H2xboFugf8PMf4GEwod SrAAAAAASUVORK5CYII--90e6ba4fc3661fad66049d09bbb3-- ========================================================================Date: Thu, 24 Feb 2011 11:35:22 -0500 Reply-To: Ghazi Saidi Ladan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ghazi Saidi Ladan <[log in to unmask]> Subject: deactivation MIME-Version: 1.0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear All, could you please explain to me what it means to have a deactivation and what it means to have more deactivation in the second phase of learning than in the first? I would also appreciate some relevant references, thank you, Ladan Ladan Ghazisaidi Étudiante au Doctorat en Sciences Biomédicales Université de Montréal, Centre de recherche Institut universitaire de gériatrie de Montréal 4545 Ch. Queen Mary Montréal, Québec H3W 1W5 tél. 514 340-3540 poste 4700 e-mail: [log in to unmask] ========================================================================Date: Thu, 24 Feb 2011 12:08:38 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: deactivation Comments: To: Ghazi Saidi Ladan <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Deactivation means that you have less activity than baseline. I am not sure that I have seen studies where deactivation increases with learning. In repetition paradigms, the deactivation is less with each repetition. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 24, 2011 at 11:35 AM, Ghazi Saidi Ladan <[log in to unmask]> wrote: > Dear All, > could you please explain to me what it means to have a deactivation and what it means to have more deactivation in the second phase of learning than in the first? > I would also appreciate some relevant references, > thank you, > Ladan > > > Ladan Ghazisaidi > Étudiante au Doctorat en Sciences Biomédicales > Université de Montréal, > Centre de recherche Institut universitaire de gériatrie de Montréal > 4545 Ch. Queen Mary Montréal, Québec H3W 1W5 > tél. 514 340-3540 poste 4700 > e-mail: [log in to unmask] > ========================================================================Date: Thu, 24 Feb 2011 17:28:27 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: DARTEL Flow fields problem? Comments: To: simon vandekar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The flow field looks pretty OK to me. I thought that you were referring to something that was uniformly grey. This is the x component. The other components can be viewed by looking at u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in the file selector to 1:3). Also note that the main objective of Dartel is to align all the scans in the study together. When you normalise to MNI space, the images that are closely aligned within study are all affine transformed to MNI space. The version of MNI space is only really defined for affine aligned brain images (ie the rather blurred averages). You should note that the single subject brain that people often overlay their results on does not define MNI space. It is just the scan of a single individual that has been affine registered to MNI space. Best regards, -John On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote: > Thanks for the reply John, > > As far as I can tell the template*.nii files look good, they are gray matter > images. There is also a u_rc1*_Template.nii in the same folder as my > template*.nii files, I've attached the image for that. Is that what my flow > fields are suppose to look like? > > Thank you, > Simon > > On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]> > wrote: >> >> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure >> why this would be the case. Do the template*.nii files seem to be OK? >> >> Best regards, >> John >> >> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote: >> > Hi John and SPMers, >> > >> > I am attempting to normalise my fMRI data using the steps described in >> > pages 440 and 441 of the spm8 manual: >> > >> > 1. Slice timing >> > 2. Realign: Estimate and Reslice >> > 3. Coregister the structural to functional images >> > 4. Registration looks good. >> > 5. Segment the anatomicals the SPM5 routine >> > 6. Initial Import- use the *seg_sn files >> > 7. Run Dartel with dependency on the gray and white matter images output >> > by import step >> > 8. Normalise to MNI, select the template6 from dartel output, the >> > subject's flow field, and normalise the ra* files (slice timing, resliced). >> > >> > The output I get is sdra* files. When I compare the output to the mni >> > template it looks as if the subject's image has not been normalised. I am >> > suspicious of the flow field because if I look at the individual subjects' >> > uc1* image it is an empty black box. >> > >> > What am I doing incorrectly here? >> > >> > Thanks in advance, >> > Simon >> > > > ========================================================================Date: Thu, 24 Feb 2011 17:35:45 +0000 Reply-To: Chien-Ho Lin <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chien-Ho Lin <[log in to unmask]> Subject: script to flip MR images left to right (or vice versa) Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi, I stuides patients with stroke doing some behavior paradigm in fMRI. Patients always used their affected limb for the task so some patients used left hand some used right hand. Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side. Is there any script in spm 5 or 8 can help to accomplish this goal? I found a m file called spm_flip.m but it seems support only spm2. Your assistance are well appreciated. Thank you so much. Janice ========================================================================Date: Thu, 24 Feb 2011 18:03:09 +0000 Reply-To: Vy Dinh <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vy Dinh <[log in to unmask]> Subject: Re: Contrast Manager Error in SPM8 Comments: To: Meghan Meyer <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi Meghan, I also came across the error message "Input to SVD..." yesterday. After some debugging, we learned that our BETA values had NaNs for some voxels. If you do a search in the forum for betas and NaNs, you will come across a post that discusses how extra brain voxels in a ROI can result in NaNs. Although I encountered my error during VOI extraction (and not model estimation), I think that this may also be your problem. Did you specify an explicit mask for the model estimation? If so, it may mean that your mask may too big. I also had this issue in the past where my model estimation failed and I resolved it by using a smaller explicity mask AND downloading the 4010 version of SPM8. Hope that helps. Best, Vy T.U. Dinh Research Assistant, Neurological Sciences Rush University Medical Center Phone: (312) 563-3853 Fax: (312) 563-4660 Email: [log in to unmask] ________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf of Meghan Meyer [[log in to unmask]] Sent: Wednesday, February 23, 2011 6:36 PM To: [log in to unmask] Subject: Re: [SPM] Contrast Manager Error in SPM8 more info on this error: the model was not estimated bc of a problem using 'svd'.... Running 'Model estimation' SPM8: spm_spm (v3960) 16:29:05 - 23/02/2011 ======================================================================== Initialising parameters : ...computingFailed 'Model estimation' Error using ==> svd Input to SVD must not contain NaN or Inf. In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126. In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225. In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439. In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33. Running 'Contrast Manager' Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s Failed 'Contrast Manager' Error using ==> spm_run_con at 37 This model has not been estimated. On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <[log in to unmask]> wrote: Hello, I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks! Running 'Contrast Manager' Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s Failed 'Contrast Manager' Error using ==> spm_run_con at 37 This model has not been estimated. In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37. The following modules did not run: Failed: fMRI model specification Failed: Model estimation Failed: Contrast Manager -- Meghan Meyer, M.A. Graduate Student Social Cognitive Neuroscience Lab UCLA, Psychology -- Meghan Meyer, M.A. Graduate Student Social Cognitive Neuroscience Lab UCLA, Psychology --_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Hi Meghan,

I also came across the error message "Input to SVD..." yesterday. After some debugging, we learned that our BETA values had NaNs for some voxels. If you do a search in the forum for betas and NaNs, you will come across a post that discusses how extra brain voxels in a ROI can result in NaNs. Although I encountered my error during VOI extraction (and not model estimation), I think that this may also be your problem. Did you specify an explicit mask for the model estimation? If so, it may mean that your mask may too big. I also had this issue in the past where my model estimation failed and I resolved it by using a smaller explicity mask AND downloading the 4010 version of SPM8. Hope that helps.

Best,

Vy T.U. Dinh
Research Assistant, Neurological Sciences
Rush University Medical Center
Phone: (312) 563-3853
Fax: (312) 563-4660
Email: [log in to unmask]

From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf of Meghan Meyer [[log in to unmask]]
Sent: Wednesday, February 23, 2011 6:36 PM
To: [log in to unmask]
Subject: Re: [SPM] Contrast Manager Error in SPM8

more info on this error: the model was not estimated bc of a problem using 'svd'....
Running 'Model estimation'

SPM8: spm_spm (v3960)                              16:29:05 - 23/02/2011
========================================================================
Initialising parameters                 :                   ...computingFailed 'Model estimation'
Error using ==> svd
Input to SVD must not contain NaN or Inf.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225.
In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439.
In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33.

Running 'Contrast Manager'
   Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.

On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <[log in to unmask]> wrote:

Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!

Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.

The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager

--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology

--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology

--_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_-- ========================================================================Date: Thu, 24 Feb 2011 18:26:08 +0000 Reply-To: Meng-Chuan Lai <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Meng-Chuan Lai <[log in to unmask]> Subject: "proportional scaling" in model specification - questions for the batch itself Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear John and SPM designers/users, I have 2 questions regarding how the batch for 2nd level analysis actually works: 1. I've run a VBM analysis using factorial design. I selected at the end of batch "Global normalisation - Normalisation - proportional" since I would like to model GLM on GM regional volume relative to total GM volume (my inputs are modulated GM images). I learned that this setting will adjust individual map voxel values by dividing them by the sum (or mean?) of the individual image, is this the case? Could you please clarify how the individual-level re-scaling is actually done, in terms of what are the scaling factors (sum or mean or others) etc? 2. In the batch I also select absolute threshold masking. Could you please clarify if the individual scaling factor (mean or sum or others of the individual map) is from the original input image, or the image thresholded by this absolute threshold masking? I asked these because I would need to get the rescaled voxel values and do some post-hoc analysis, but this cannot be done directly since the rescaling is run inside the batch and no extra rescaled maps are generated. I would need to calculate it outside SPM so the clarifications will be very helpful. Thank you very much!! regards, Meng-chuan ========================================================================Date: Thu, 24 Feb 2011 19:54:52 +0000 Reply-To: Israr Ul Haq <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Israr Ul Haq <[log in to unmask]> Subject: Re: fixed effects analysis across two sessions Comments: To: Donald McLaren <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Thanks a lot, This makes sense, particularly the second explanation. although the runs1 and 2 I specified were from the same subject and the whole analysis is being done for each subject separately but I suppose in a way its the same thing and is taking into account the variability across runs. My model is actually an overt naming task, the experimental condition includes a semantic process of interest whereas the control condition has all but that. Thinking about this though, how is the direction of the change from pre to post treatment (in a voxel) taken into account? I computed a pre - post treatment contrast to check whether it will give me the same voxels (worried that the areas included both positive and negative change over the two sessions in my first contrast) and was relieved to see different voxels. However, if its not too cumbersome, can you please explain how the eventual t statistic is being computed in such a multi session analysis with two independent conditions? Heres what I understand; for each voxel, a correlation coefficient (r) is calculated for each stimulus of a specified condition, and all the r values in a run give a distribution for that condition, with a mean and standard deviation. These are then utilized in the t tests of no significant difference, where the linear t contrasts between two conditions are setup such that whether for that particular voxel, one condition had significantly different signal than the other, using the mean and sd of the r values. What i am trying to wrap my head around is how based on the weightings that we specificy (i.e +1 or -1), the t statistic is calculated for exp > control vs control > exp, since in both cases, what we are essentially inputting is the difference between the means. Intuitively it would seem that it would give a voxel as significant only if the condition weighted +1 also has the higher mean of the two conditions being contrasted. But if so, how would this apply to multiple runs and sessions, or more specifically what would be the sequence of computations in my design? Theres a plus one weighting in both pre and post treatment runs, albeit for different conditions. Thanks Israr ========================================================================Date: Thu, 24 Feb 2011 15:27:00 -0500 Reply-To: simon vandekar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: simon vandekar <[log in to unmask]> Subject: Re: DARTEL Flow fields problem? Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary¼aec53f398fec3f19049d0d0af8 Message-ID: <[log in to unmask]> --bcaec53f398fec3f19049d0d0af8 Content-Type: multipart/alternative; boundary¼aec53f398fec3f06049d0d0af6 --bcaec53f398fec3f06049d0d0af6 Content-Type: text/plain; charset=ISO-8859-1 Hi John, Thanks again for your help and sorry for all the emails. The flow fields that 'Normalise to MNI' asks for are the individual subjects' flow fields 'u_c1*.nii which look like the 3rd image selected in the attached picture. Also included in this picture are a template, and subject's functional scan after the 'normalise to MNI' step. Thank you, Simon On Thu, Feb 24, 2011 at 12:28 PM, John Ashburner <[log in to unmask]>wrote: > The flow field looks pretty OK to me. I thought that you were > referring to something that was uniformly grey. This is the x > component. The other components can be viewed by looking at > u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in > the file selector to 1:3). > > Also note that the main objective of Dartel is to align all the scans > in the study together. When you normalise to MNI space, the images > that are closely aligned within study are all affine transformed to > MNI space. The version of MNI space is only really defined for affine > aligned brain images (ie the rather blurred averages). You should > note that the single subject brain that people often overlay their > results on does not define MNI space. It is just the scan of a single > individual that has been affine registered to MNI space. > > Best regards, > -John > > On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote: > > Thanks for the reply John, > > > > As far as I can tell the template*.nii files look good, they are gray > matter > > images. There is also a u_rc1*_Template.nii in the same folder as my > > template*.nii files, I've attached the image for that. Is that what my > flow > > fields are suppose to look like? > > > > Thank you, > > Simon > > > > On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]> > > wrote: > >> > >> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure > >> why this would be the case. Do the template*.nii files seem to be OK? > >> > >> Best regards, > >> John > >> > >> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote: > >> > Hi John and SPMers, > >> > > >> > I am attempting to normalise my fMRI data using the steps described in > >> > pages 440 and 441 of the spm8 manual: > >> > > >> > 1. Slice timing > >> > 2. Realign: Estimate and Reslice > >> > 3. Coregister the structural to functional images > >> > 4. Registration looks good. > >> > 5. Segment the anatomicals the SPM5 routine > >> > 6. Initial Import- use the *seg_sn files > >> > 7. Run Dartel with dependency on the gray and white matter images > output > >> > by import step > >> > 8. Normalise to MNI, select the template6 from dartel output, the > >> > subject's flow field, and normalise the ra* files (slice timing, > resliced). > >> > > >> > The output I get is sdra* files. When I compare the output to the mni > >> > template it looks as if the subject's image has not been normalised. I > am > >> > suspicious of the flow field because if I look at the individual > subjects' > >> > uc1* image it is an empty black box. > >> > > >> > What am I doing incorrectly here? > >> > > >> > Thanks in advance, > >> > Simon > >> > > > > > > --bcaec53f398fec3f06049d0d0af6 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi John,

Thanks again for your help and sorry for all the emails. The flow fields that 'Normalise to MNI' asks for are the individual subjects' flow fields 'u_c1*.nii which look like the 3rd image selected in the attached picture. Also included in this picture are a template, and subject's functional scan after the 'normalise to MNI' step.

Thank you,
Simon

On Thu, Feb 24, 2011 at 12:28 PM, John Ashburner <[log in to unmask]> wrote:

The flow field looks pretty OK to me. I thought that you were
referring to something that was uniformly grey. This is the x
component. The other components can be viewed by looking at
u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in
the file selector to 1:3).

Also note that the main objective of Dartel is to align all the scans
in the study together. When you normalise to MNI space, the images
that are closely aligned within study are all affine transformed to
MNI space. The version of MNI space is only really defined for affine
aligned brain images (ie the rather blurred averages). You should
note that the single subject brain that people often overlay their
results on does not define MNI space. It is just the scan of a single
individual that has been affine registered to MNI space.

Best regards,
-John

On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote:
> Thanks for the reply John,
>
> As far as I can tell the template*.nii files look good, they are gray matter
> images. There is also a u_rc1*_Template.nii in the same folder as my
> template*.nii files, I've attached the image for that. Is that what my flow
> fields are suppose to look like?
>
> Thank you,
> Simon
>
> On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>
> wrote:
>>
>> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
>> why this would be the case. Do the template*.nii files seem to be OK?
>>
>> Best regards,
>> John
>>
>> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
>> > Hi John and SPMers,
>> >
>> > I am attempting to normalise my fMRI data using the steps described in
>> > pages 440 and 441 of the spm8 manual:
>> >
>> > 1. Slice timing
>> > 2. Realign: Estimate and Reslice
>> > 3. Coregister the structural to functional images
>> > 4. Registration looks good.
>> > 5. Segment the anatomicals the SPM5 routine
>> > 6. Initial Import- use the *seg_sn files
>> > 7. Run Dartel with dependency on the gray and white matter images output
>> > by import step
>> > 8. Normalise to MNI, select the template6 from dartel output, the
>> > subject's flow field, and normalise the ra* files (slice timing, resliced).
>> >
>> > The output I get is sdra* files. When I compare the output to the mni
>> > template it looks as if the subject's image has not been normalised. I am
>> > suspicious of the flow field because if I look at the individual subjects'
>> > uc1* image it is an empty black box.
>> >
>> > What am I doing incorrectly here?
>> >
>> > Thanks in advance,
>> > Simon
>> >
>
>

--bcaec53f398fec3f06049d0d0af6-- --bcaec53f398fec3f19049d0d0af8 Content-Type: image/png; name="EPI.Func.Flowfield.PNG" Content-Disposition: attachment; filename="EPI.Func.Flowfield.PNG" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkjz27yd0 iVBORw0KGgoAAAANSUhEUgAAAoIAAAL+CAYAAAA5EHWvAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAP+lSURBVHhe7b0rtDXbWp19RERERETELxAIBAKB QERgEAgEAoFAICIiEAhERAQGgUAgEAgEAoFAIBAIBAaBiEAgIhAIxBFHRERERKz/9L3T9+m7n3fc qmrOmlXVd2urfd/+1rxU9RrjHc94b+MHX/kvCkSBKBAFokAUiAJR4JEK/OCRd52bjgJRIApEgSgQ BaJAFPj6DgR/8IMffOUnGsyOgWruzL43r8s4O2MMrNr7M64x33nduRGbeN1n99R5xzEbEDwdgL8u CeAxek82evcZsz04fOricP5932d8na/lk+3UO+/92mM2IBgQ3ASiAcF3GplP+65rG71Zz2AW8bPG 3X3GV8bQWWPo3d977TEbEAwIBgRPHwPvNlp7v+/aRi8guPf5v/r99xlfAcFXj5VP+fxrj9mA4OkQ cO0BpItqjN6nGKVXX8d9xmxCw68eK1s+/z7jKzZxy/O/4nuuPWYfAYL/7t/9u6+Zn3Mm7bUHUEDw ikZr7zXfZ8wGBPeOhVe8/z7j65w15RXPJJ/Zf5bXHrO3B8EZANTXvH/iXnsAbQXB3nN5/zOIkVvT /Ngxy7Gwdg3rz2w2JPxdJd3p0YL1e3y1hu/5/GPH13uuue7E9q7vzvecPVeuPWZvCYKr8Fe9/n0T 69oDKCB4tgE64/uPHbMBwTOe4Sd/57Hj6122PAV0nzymXn1t1x6ztwHBI+DvHCC89gBaBcHV5/Qu I/6O78G9v+N7Xv8da2N25Zm/8trjEXz1YnjU56+Nr1eOmZXPDgge9fyv+DnXHrMBwcn8wZXFbC3s ee0BtAKCR2i4Ypj9tbPfv+c7tnznkd/3ns+qx+ysvjOve8V9BASvssA+xya+YpznM88Y59ces7cA wZmF5ZNe8/2Jeu0BdBUQXHn+RxjSle/z1x7x/a/9jJ8es3vu912e+IDgGQvklu98jk187Tzdon3e s+2ZXHvMXhoEj158zvi8H+ejXzJcuBoGeaW2o4l7xHdX33HE585+Ru8eZz/jqNdhzB71Wa3PGT3T Lb8PCF5lkX+GTdwyhvOeTx3D1x6zlwTBVy9C7/x8X1SvMtFXQPCden76d/37f//vv0Y/n34P7wBB ajA7H1Sz1nueDIKtMdUaiytjcPYZzb/u2ovqSpRkXpNPBaBc17fP8Npj9nIguGKgrvDagOBcj8cr PMuZaxxBoP9+5jPf/ZpPA8FZr2JAsJ5r1ZhcGVPHw8y1F9WA4BPh8Npj9lIguGKcrvLa3qJ6vIE9 boLGI7gNYAOCa7qN5sBonuv7A4IBwdF42vP7FZu453vy3uPWseO0DAi+NMdtZOhnfr+6+OL1q6GU me+oPnPkXTluoB47eVaM3swzetprZsZLbxy6XrOfN/u63vMYjdkjn+Vo/M98Fz8jIBgQHI2nPb9f sYl7vifvPXYtO0bPgOBpIDi7qH3y62YW1WMG6rGTZ8XozSzWT3vN7JikLrOvf8frZsbsUc9zZuyP visg+INucU9Cw8fYxhWbODOu85pjnst7dAwIHg6CI8OO3x+x4P2H//AfvvBzxGfhM/h5K587u6i+ ZzDPT7wVozfzPJ/4mplx93QQxP3PjP3R+MFnrP43871XeU1Pn4DgvN3rPe8Vm3iVcZPrnB0bAcEp Qz0zoEbGvLdwOoS98v8r6NvyffgcrE96XyMNZhfGGb33vGbF6M3c09NeMwOBK+N97+fx/aNxXI1Z vvdVz3BmnI6+OyDYzs0MCM4u9v3XrdjEmTGd1xzzXN6jY0DwEBDckpM3WrSu8HuAYMsr2c/TOneS rBi90SL9lN/PwtrR4/bo7+2N2ZW8xpXnPjLmo88KCAYER2No7+9XbOLe78r7z13/flr/gOBuEFwN Wxy9UJ75eVxUq3DyzOJ2lkFYMXqj+3jK7ysgG429//gf/+PXlp/W5654GVufoWO29XmveKa9sT76 voBgQPDVtnLFJr76WvL57wbFgOAuEHwyBGKh7YHgTMjtrAm/YvRGi/QVfr8aPht54RyyKtj7T//p P321fv6//+//+/Iff231mSPwnPm9j9lenu2Rz7Y11me+IyAYEHy1rVyxia++lnx+QHBmDHDMntZH cAUAZxanI1/T88Ac+T0VCOrnO0x8Uqh4xejNLNSf+JoRzM3+Xp+pji2CG4HuZ37mZ77487M/+7Nf +vNzP/dzX/z5+Z//+a/WD1+j7+Vn8nv4vXotK+O6AsGZQql3PmPfRAUEA4IzC+Oe16zYxD3fk/e+ G/Jmvi8ewU0ewVkQXFmg9r52JgS39zscCkaL6qfC4IrReycAHPlds6DXel0PANWbB1AjuDns/cIv /MKX/vziL/7iF39+6Zd+6Ys/+u/4O98DYFQ4rKCQ4352bGPM4j2j1696UY96dtWcCQgGBF8NUCs2 8dXXks+fgbcjXxMQXAbByuDPLKajhWfm9zOw516b1ntmvq/3GnzuzKL6iTC4YvSOWuBf/Tl7wc+r yX0cVfAHSKN3D/DmkPef//N//tKfX/7lX/7iz6/8yq986Y/+ju8BKPIz8fkEQ4KneglnPYQcs7Ov fycQtr4rIBgQfDUcrdjEV19LPv9IyJv5rIDgNAjOVgbvBSy+fwR9HiLr5WS1fjf6jpG3RRfV3n3P wuC7DMCs0Xs1vB31+SsQWD2nVsjXvX0AMQIfIA3ABoAD0P3qr/7q16/92q99/fqv//o3P7/xG7/x 3c9v/uZvfvHnt37rt77w89u//dvf++G/4099PT8Pn43vwffhe/H9Coa4VoDhKHRcgeBonL8aBv3z vfgqIDh/qshMbrLOu+NtzrUX1Zw1PANOd3vNtcfsW3MEZzyBR0Cgw9kq4FWJ+J5fNfrM2TzDUWh4 S87g8Yb5pyftU0GwB4EYE5rvB69b5fEDgKlXj/AH8APAVZD3X/7Lf/nCz3/9r//1u5/f+Z3f+Wr9 8HV4D4ERnwsoxPcBOgGF9BgCTnG9LRjU++6BYC9kPLuZ2QL3+tnVnAkIroPgbCug4+3NtRfVgODd IG/mfq49Zk8FwdYufisMVl4ZT8jvQZ6H7jR5nwtk6/3V7xUWW2C4AoJVdWZr0TzeOH9/MsyA4JYF /d3vmfUEzuT7uffPQ73q/WvBH2HPAe93f/d3v/jze7/3e1/4+W//7b91f/g6vA+fBzgEFAIIAZ2A QnoIAYS4XoBrDwg1nWF2s+PeuVWP08yYCAj2F6vZaMzMfPDPOt7WXHtRDQjOgNPdXnPtMfsWEBx5 AreCH943U4Xpifhagal/90rN6v/9vb3qTb7fIVFDb+5dmdFi1rNyvIH+yeQdgeDM4n3Ga2YWOr6m gj96/bS6l14/gBTDvYA+D/dqaFeBD6BGsPvv//2/f/3+7//+dz9/8Ad/8FX9/OEf/uEXfv7oj/7o ez9//Md//MUf/o6vxefg8/FdhEOAIa4LYKhQ6CHj3phdyaF9VYg4INheWGcL81bmxmth8NqLakDw bpA3cz/XHrOXBsFWMn6Vl8XcLK/CVJBrAWLVuoOf49Wanphf5WBpHtYWEHTP4BltZe4Ogg6B6lnm +CIA4pkTABX+NNQLL5yGdgl/gDLCH4GvBXkt6PuTP/mTL/z86Z/+afmD3xEO8dkEQngU4Slk+Bge QoaMWXlMD2FvzK6A4IpXe3aj0Iss8HdPDQ2/AgIrj+6xm85rL6oBwRlwuttrrj1mXw6Co5DEjAds lJeFRbrlodHEfLba0IpKr6rUthvu7WtVdbaqNTX/qgWErXyrkS6zXkHof6yR/nYC90BwdgF/1+tW PB0jLyC8vF7wocUeLPKAlw3eNoc/9/4BAhUACXvq2Wv93QHwz/7sz7748+d//udf+OH/ExLxWfgO ACE8j4BBegdZZIJ7wD1x/OJ+NVw8yhEctZZZGbszYyQg+F5vYCu0f5ydufaiGhC8G+TN3M+1x+xL QfDokHDlAaR3Rhdn5mJxgUY+llZjahWlJs6zmpIVlVUrDw/3sbKTYT/+vyfl62cqFGJRnanUrMBw ZUE9zkh/PgiugJ+HgTnGuLlgeF83FO71Y3EHoA8eNsIeIE9DvRriVa+fgl7Pu8ffufcPsEfw+4u/ +Isv/PzlX/7ldz/8N4IhoRCfQyjEtREKkUuI8UwPIeYF5gLHrY7Z2RYyPn5Xxu4IBqvnrZ5HbobK 3UvnH4+eM+/+vC26rc6d6jv23+e1F9WA4PfBaTQOZ36/f0zNwNye11x7zL4MBEcQCIMz8nr577Ho tEJ0rfAcFjNvu8E2G8yNYuI8w2IKhPQeKlS614eeH6/Q5OdryI2LKnu6YVHVHELc36gVRyv5flTl d+Rk+lSP4OpCVoWAWRCk3j8t9ODzJwA6/DHHj7BX5fIBwCoArCCvFfLFv6sH0CHwr/7qr77wQyjE 791TyLAxrkdzCDGWWVSCzRLun+FiHbOfAIKwNb3n/kQQnFlcV+fKan7ndntz7UU1IPgtUM2MwSNe s32c/QT89qdXXXvMXgIE1Uuj7Tk0R0uT8xXUmJulyfneWkNDYvQeerNe9ngDNLoHiCDAEBsrNNny A6BI2MTna9gNi6pWao56uO3xrBwxYfgZnwaCWxY17zfpY4teQIwtff4a8sUzhyeNnj+FvpXwbuXt 01Av/u5A6KHgFgg6DDoQ4nPVOwiQRe4iq4yxocH9c9xizDJUrBuXUUh4NHZHG5kt+W58z9NyBGcX 2C3zZqXqe5vNufaiGhCch8DR+JsdxzrPZ8fcymePP/PaY/YlIDjKC6yqMlveQYdATdTXKk0Htaoq k5WZVVsNekDoxWPIWJv8AuYcArTak6FAgIF/B9t2cFEFaAIwsKhqqHhLmHglzDYe0HPu8RYIrkyu o147Miat0KFWnWsoGGCOsUUvIMaAbgAA+vrcFf5GRRsKfNXfFfYcBHv/r6FheADpDdQ/1TOo4WIN GTOHUPMHMTdw/4RBjlnAIMfrrBd7BIN7x0RrLjwJBFc13DJ/Vr5j3eZce1F9OgiOxsaW8bZnkzi6 ntnf98fxtcfs4SA4C4Gj0HAFgF6lCZgCsBHQ4MHAIk0PjXppPDzH6kkseHid5kcpFGqDX/x7ywPk 3h/mXnmVJt6vYTcsquoh7HkHW7C8AoJbdk7VBDgbBLcYkyoMzCIj6M70AjwPhT9/7hxLeOaAOc/R A3ARtKpwrMKew2DP6zcLgswRdBjUvMHW33m9uA7cH4tKWF2M+YAxy1AxdNMm1FtCxVvG76zxZlg4 INg+Ym5Fyz2vnQfCay+qTwTBlXW/tfZX3QdmcuNXPNRqD1bHckDwx5Z/ZhKvhm5mvIAtDyAgkPl/ 8LaplwZwh3wnXbC5aGsrDU2UpwcEnh5tqaEeQIVMfr5+brXA08OC1xM6AYMMu2FR5dFfWFyZO0hv y6zH5X35O/1ikdXJtfr6vQBILyD7AWoPQM0DxeaCXmU8L1b3YkxpQQfgrCrQcO+bVvB6mLdVBKI5 gCPvYKtYpPIIKgRWsFjBoG6UMGbpHdQm1LNjdeQVXDXsozHUS2foFZDM2LxPfM1IjzN/P6dXQHBO p7l1+RWfNRpDvSiMp+So06c6sWtmc6nfN7q218DgtcfsoR7BI0GQCzWP6fJQHSCQ+X8ENAIgE/FH ifeaKE/PB72DPNmBXkZWgvp3EP7UKzRq28GmvvgOLKpaoaktO7SIZDQZAoL/viwY6LWDwdjSPEB6 l5kDqt5fHVN81gqAM6FYhbmWV/BIjyCBz4tGKhD867/+6y/86Gs5jjFPtJAEY5ahYj2RBOPVc1xn C8JWx++MsXfv9xOqhld0Ofu1bUC59qJ6N4/gzDiZAb/WARB6NGfr5K7eKV1qY2aBUMfezP1Vr/n+ +L32mD0MBFddw+4eVtDBYMCiwnAdc7XUAwhAYwgYixQ9c/SiuIdkFKojFGool1DI3D98D4CAgFkB QSvkptWa6iXEoqoeQlYY0zvoDX1HuVgrYbY9O8WZ0PDKtaxuIlbzTPVEEOYAIhSs6QUYU9r6pQV/ DliEKP+zqtj1vn4s1Gi1jRl5AqvegZWHUq9Fcwh5zX/zN3/zpT+EQoa5NVSMMUuvuRY/AaqPDBNv NdD6Ph3jdwfBI/Q68zN+8qyuvai+EwSPSvWZee4j2Gsd8Vod3eqnfbFnb+vAh94JYL2uGzNguLIO tnSCTVz5nE95LcfqISC4ZREfeWo0YZ+LtXoAEcKFd47hXwVAhb6qn5r3VWOozltpsA2INvwFxGnI rrXojkJu/C4MIPUQ4h4Jg/S2YJLoAstKzdn8id7z2Togt4DgSsLvTAh45G1q5ZlqLiCLQBAG1lM+ GAKuxpUXY1QgOGrdcrRHcNRL0DcovL4eCCoMYiNDaMWYZagY3mymNmC86sZl5MWeGb8zC9Ts+L4z CO7V6ZPef/VF9R0g2AaS+XDx6JmPwM+L7bS1G505etADo3tMx9Hev7TJPPTB/2TrqurAB9qcKp3K W63N2orWuhgQ7OQJHgGClccGgwG5SPQE0mODRQiQRo8NFigsVKM+ap7EX7XR8FYa3vi36t3mHiIs spV3iF4ZzcGC0dNTHnCP7N/GymJMAj3ZYdRrcDXMtgUGzwTBEQC6gYJR8hQDbi6QA8pxxY2FF4F4 g+YqDKzPu1Wpq17hs0DQr30EgoRBjlkAJ8YsQ8UAaMIgcixZAc8w8ciDPcoXHC1Wo9/f3SPYA4KR Np/6+4DgtpNi8DxnbflsBK9y2HhOn7bd8uNdFfoq8PM+vWzbxp6+/H+e3sXX6+lgComjYstR/vFI v4DgBhBseXWq/m16QggAUL2AvlAzPKuhWfdwaL5T5Z1Rr2GrlUaVxD+TlK8gWIXc1NMCo4f7YB83 9Q6qt4UnO1T922Y8Kz1v3Gjwb60aXgVSnWQzu9HqvisvIAwQ9GOvSa0yZxEIxpR7/3pFFRUMjvIE W8e+VQ2jV0LCfqzcrJfaNyweGq7gFppwzAKYoR8r4aErq4ndi70ChHvGjRvqO4LgDMDhvmde94mv CQj+NAjOPqeeLffP0HlWdVTw3L2ed4+eOj3YoYI8tmPjSV88j127c2Bz7j88BII9f3lIBPPr2e6t dTRmtTHtQeGKjt8C+MNDw3u8gTwpRCFQ87bwkAmBWHDgOVNvjSfr90J0VVPdGRjUYpBeTuCspwWL rSbmYwBpHzd6BxGmxGQgDOoCi12P9xsceVauBIJHQKBWm2NjwZYw8C7zKDhorGOq8ipX3t4e8LWq dEdtZLYWiTgEtu6hBa6t/MZe3iPHLK6ZnkHkDGK8wtBzrM5uWvaM3dECeTcQHN3vK38/8qYc9d1X X1SPDg2v6Dob0vQc69ahDczlG4V1HfpgBxz69KAHb8umfX+rAxpYvKl/6sEQBEQFQnoKNUqhnQ00 bFxpPKtlQHCw6+wt6Bx4DNup1wYPEwMFCwvztlgQol6bKul9Jl+rChE7FHq7j1lPYCv36m//9m+/ +EPPC64VRo/Xg+/w3m2EQQ0Tz3pbZos13uURnDVoft2jUHB1BrUeDwcI1IpgVgP7mNIQcA/2Zvrx +Wtaeamt4+NWPYJVcdSMR7MHfK3fccxyvPKcYhhpGHuMVeYLMrd1NV9wduyOxtSdQHB0r6u/n8nD bb1m9btWXh8Q3H5Em9vylhfQo3Lq/dNCDoZz1ePnXj+23fLDF+jtozePXj6euKUdOVikh7Qv7QPM Qs3en2AEFlwyrYp2CICqnQ1am9PWJqdaG2tojEewGYLoLejqDfTQHSCQXpte6K4KB8+AYOUJrPq+ cXH1UxtWF1iAn4Ig/k4YhNHT3EGtzmRPQ0wgTcj3nMHWIrsSYluFwS05grOLwQoIVhAI+NAx5f0m 6V1GOLhKL9jSiHkEh+8GwZkx2stlVQ08j1DHLMcrYZDFToBB9hj0sMzMUXQrY7c3ru4CgrNzZ/Z1 eyDw1Z7BgOBrQLDyArLAQ3P7NJePRRvq8WO+Hua4w1/L01dBn5/KxdZs7NGrZ7W3zmvnGens0as9 gGH3tUcv06u8Cf5sQQltSUBQ8gRXQ8Keg6AtYpi7RU+g9gXshYJbOU6t3KaWJ7B3ykKrApneyNZ7 K8+gwyD+H0ZPQ8X4PlYxe5hYj/lqNfLdGmZ7BQjOLkq93BUYr5nG4zBoWhCiXkAYIfUs62kgfI4z G4gZT7Ln5408zWfmB/byHKvKaN28MMWB4xWaYry28gW3NJs+wit4BxDcOo/0faNN+YzHdhYe91zv t8/r2t6VPaHhPdq13qsASAcM11+1mZpHrYDHkK7m8znwMWSr7bd4spcCnkMeCz79YIbeOeput7SQ TSNquBaCITaovYMbtBPH7EYnfQR3hoR5ritbxHDRZjiYVcFs4UGvzah/m+bfeXNc9/i1AM/7reni X3lZep4ghUEuog6DMHp63fw+3DMHNSaUVhNjkuruRvMFt4Lgt3kO860HZjyCW41aK4m51XJITwnh +dPcVMCzrFXmABZ4eCsArMZPb9xUnuXRv/VOGPF81NnwcC8sPFvU4gVVrbkGjThm8Xcfr9Ca+YJM Z6j6C47C/fh9QPC4go9qTlV93/yYr9ZzmoHCLfP/jiA4a1u36DV6TwsCCYD0/um56prLx+b6HtbV nD4N6/aOdfVjWL13agV/tEMalaiia/g9Xss2V/gubYIPm8SwMdaGXlGb2p7ZaMPVNy+b+whu8Qjq boQ5XAAanuvKo+K0jYeG7kYgOGrh0fPo+GePQn3V7yu4HMFgCwQxoFlJjAGtORCYqKOB7O5uNdzz g7sNhmeCoLcvYFiDp88AQJheAN08F1AhsFfVPVtgNIK/qiq9aiy9cqRcr5H0KNexBYc977YaYgVB akQDjLkL7ys2LprOULU/GsHgEflpV/YIjhb5md+3NlWtFiB+xNdeT+HMNfI1P3lW9/IIzoDgyJs3 66VyL7DmAqoXkACI0C/WEnoAta8qw7leqAGb6jl91bGurSb5GgVp2TFdj2F7AH9/93d/983P3//9 33/9wz/8wzc/+Dv+jelWao90/QSgcoOKtYFHZHrvU4z/URHJT4/X63uxDwfBXghCewXSc4NFm42i eaarnt7BKkgHtV5+YFW5OVqsq8+fgcHe57oHserZpqFhP81Bm/jyLGQOZj3RQXMePP9qNddq1iv4 ChCcyQ3UBUyrzTGeeP40xhMbjjMPEM+JqQS9nE0N07e8gZ47uvr/raPmtoDg1iKRXnVzq/0MtVEQ 9KbT3InDM4iNHRYWzxfEgjSTJ1h5BTFGtsHFd6Zuuq/07Fx4xetW7rF6bc8DqIUB3hKEwM5/Z484 Pzpw5Dnc4s29MwiuPM+et3X0OZ4H6P15mTvt56qznyrTsujZ42EK1Z/aY9fPX9fuBaO1t7I3sCu0 0wA9QN8//uM/fv2P//E/vv7pn/7pez/4N/4oHGoPVNjcKtUK66ge3IDxPxsmTmi4ExpuLebeKoYt PfAg4GbWSk49ws0HyUqoS0FsNBhfAYKt9jbqhfIcQfVCeVsZ9hjETo35gtjNYXLTgHu/tjuCIBcl bxLN86dhzHgcIOCKHkAFQO4utZJbIVALeLT1yyr06es91Ls3P1A/u5fqMMoFnEmb0I2Xg6DCIHfh 2LgwnYHGVsdp73Qc9RSujl9dKB3Qpgnw/73wFYA3+szRQj/z+xYE6skPCoB6fBf/zsrRFhDOnP26 CoMBwX9Xnpc+6w30MDDWAvUC0vlCD6AX0bF4g3l8zN/Dn9UPQ71+5CpTbzTPeGYtdi8g7TW8fgqB //zP//ylPwqGBEK8np5Cho3VNql3kF0OoA9slLeYaen/eBBcCQu7a5ohYQ5GAA2T+RkSbh3r1fIK jlp9jCCwFUrb4hFseWhaHswKBBVCCIOsjmIDXzbSbFVn9kLDPa/KaKHi71/tEWw1i9YqN81voWeZ O1oeBYhn67tLQiANBWHwFSDYyvM7om1MCwR1PKsx3jpP9DPY8khbICkIam5rVTjCzv+j03FG43dm cbwaCM5A3ug1sxDoLUJ41qv+qUd3aWhfz47V/OQK7ldg8OkgOMq7nAkf81QlegI1F1D7qXoImHnU TKPxsK4erjA6aMHtRStfv1pfaaPUG0gQJPA5CFb/j9cCBuEhhJ3H5zHChrVUc5k1hQUwyKPqGCYO CDaKB0ahCHdP0xvYygvEQ6EHRys6K5exh1tbSe6rSf4tyBzB4AxkcmGuYJAg2Fqk6Y3StjLqbWG+ IKuIq4aZq4n3MzB4NAj2CkS82SmNG6vN6QnkOAIEMhQMA6C5JTAq/FEQ9Ebfnk866wnsFXi04K+V M8Pq8d7vZ2GwN45HY9hh0tMZqqp3zGPPF0Q4ikVO2lJmlCvYChGPIONKIDgCvJnft+yueoXUiw6b 0TvjFb/zprzuMdRQsoaRe5GJcY7y/XIEZyBuNSTce958znquuvZSHR3X6qFdj2y43fH103OvaV97 67WugR4WZki4gr7/+T//5xd+/uVf/uV7P3gtgBDvBRDiM/EduHbAIO0TU1iYN+htr0Y5g3cocFrO EZwd0N6wEkaCVcLYmXDxxoDU4hA/N9i9GyMwm3FB+8LXgssRZK7Apk8UzbcaJe+zRYf2bKO3BZ7B mQV2tGj2QmoVGJ4FggAIjiPNCYQnkKeEAKDoBWRysQLgKgjuhcAZAPSK4VmPYdXrcrSBWp0jPnZb IKiVe8zJoQdbUxm8ingGBGdg0G3Tk0BwBgJ57qsDICtG2R8Of+pZrrDXCoQ8qUFDyhpKntmM9mEw IKj2euR4US+gnqikxXOAHHblQCoWw8Bce3vpWL5eOiT6RpG2tyrEmwHBKj9wFgT/9V//9Ys/gEPC ILyDiPxomJg5g62OHFW+YGvcPqpqeBUC3U2tTX6xMLBptHoDKxBchcGRh6O3UI5yELd6VloeRzbn HYXtGCJmngNd2yiM0BwHnuSg1U9VmG3kYRh5BY8CwWoX7GCg1eatzQT0gCcQAAKtWWXmFWYKgZVH UA2VV/t66xeHsMobuAKBs6+tvqcHhNX8mZ0jnucKfUYgyI0Ld914NuzjxbNAPfwyA4Oj0JmnPFwF BEdzcfR796a3esVpo+DqHFjYEf8hHLJLAYGQJ00wlKyeQvcMtsL8d11UvY/g7LrZGt89EKxCwVoR rNXAbPnCokzNBfQTu1rrXKuQzKNdmo+taSStIjUPJ3toWItEWl5BeAQVBPH/gEF6BWH7CYNVCgvT rTjW9ShXzKleOkpA8Mf951oFIr5T8Uph5gbSk8PB2FqktkJY9Xm9/IQRDPYGc89LWMFgDwS1cMRD xPS0YHJrXyRdYHsVxKPFBb/vweCrQLAHgTwxhLkuPIsau1sYNUIgdn3eZsDbDWixSGWoNDTcK/hg eyOHxCqs63k2s+A3W1QygsEe/I3eq2O3B4Ics/g8NpnGWNXwCwxtr9K9B4UrMPgEEJyFQPUC0gPI kyH0TFjYEv3B7wiHPK6LHkINHTPRns/VjxZc2YxefVGdAcGZcVzlco/CwepsAcSPCkL0wAZ6+nSu 99ZcXQe9IX8FggzNVl5B/6wqR7CqGAbkAQwZGq5AEL/He9lqppcvyAgbK4k9haWdL/j1XTeDkRPl k37PsboUGp7Z2agR976BWimspz0wsV97Bs56LDyXq/e+o+BvBhQrIJwBwV6uosIg4FkLR5jfoOcQ v7KVzCtA0MdO1XdSi4yws0VoA54nwJL2myL4MWGYbQW075Qe9acJzV4xTN0r2JsFwVayNUMyR0Oh XtdsaHs2zMzNixt/TwrHNVQnjmCsVsfPzXgFZ4pIaKeuAIIzG7KR3R31iuOJEa0zYbH48YfnwvJP /DuiNwAKhcJWGJk5oKO+kb3QJ8ZX6xl+0iI6M77wGn9+sxCoINgDfg3547ngObEpNKNubKdFLyAB cFSY6WtmD/68PZp2ZGh5Bau11EPDWjWsrWL4d4dB5gpqaJh5gvQK4nsZuWDjaaZbafEINzbeX/Cn ow8/GbNXGruHgeDIG1gdJQfDwupOPAQvP5+FwJXXvRMC1ZOnoDoCwd6kUzjBAqvJrnq+a6+VzGrR SM8reAQIjsaOFhkxJKzNxwHChECMBRaG0HAAAgmCvSKRViKzji9CUtUAtfqdewRfCYK9YpK9QKga cHz2QND7CjKvFeFh5uJgkcKmkGN1tpXMqK2M79ZnFupeS5l3QMdWEGx5hjxHTIsF9GxYPS5MT48A OOgPPLn4PcNmWCQxB/UHn8u8Qu/J1goTuy36fo5yQLCXMlO1hSHsEwKZD6hHvvG8XodA2Ihqno/W zGojWPXK1VxB33C3wNJbfbGPINvIKBBqNTE9g4RALxihI0DzmXUthY3ipoeRC0bZ6FipvIK6eVnN t3+HnWl9xzIIjhJWIU7l0SEIwuhzt4JByjwFP0ZuBe5mXzsCrBkP35bXVEn5VeJ9K4HWv5PvBXjo 8XMadqs8LXtCxKMBNBMGGXk0WmOnOjpOewVy7EAPaKXhYEKgg6DnBXq1sIfjW3mCLS9b1Sy6B4Gj 7vtbw8mzx9O1XldVI6+AoO64cQ/0XnOswshqpbtXmc54B0c5VVcAwcpjNILDnndI24WoJ1DPjmXr EAIgc8eqkyTwb/g9nxvmH35aIWQWrjFUzBAxm4irLWp5BX1RPXOhXPnu2c3xikdwBPxYV3k6CIBc 28LQ2cJTP9gXUB0vVRRuJhzcg8BesUgLBKvQslcOKwyqfddCElYQuzdQ1wG2k6GN0hQWcAn4hAc2 uGPFIxI/8f79tEfwCp7Bt4AgzxSGcYJhUG+OtoxRr0UFd9WiOwuBntw649ru7VocznwAt+CtdR0w eq2J1yogwWdpBfHI03IlEKQXUPtg6W6XFW8wbp4TqOHgyhvYygts9bnSkPBseLV1asgMDG59zWyF 8SoctmBQ81pbC4LmCmoFsZ5DrHk4rDSdAcCRZ/BKoWGCxgj++HsHAz05QttzQdvKA0j4I/T5UWE8 UQLzi6cZ4TV8bvQOaihZw8d64hHmLXsRahGbQr9D0bdRiO8vqiswduZrXwWCXhDCXGnaRWjOXEBt C4NnSS9gBX+jvODKS4h5rV6/WSDUFlMzazC9iAwvM6JDG69/jk4ccacAewsyNcjzmbHGEAY11cEj F+oZbHkEP907eDgItvK72O6DZwrzDFgOUlZ6ekl65YlZGbgOilVYdjSIt/6+8vC1XO+rIMhdjPYV pJFmyG3UU7AXlvEF6RUewVHYw49FYr9A7HY1LxCaMi+Q1cHVpGc4QM+kbDVE9tzOWQCswsMEtK2A t/K+Xo7hKgBW4W/dyMyAIEMvXkGs5xDTe9QrcBqBYTWW7gqClXdIewQCujQU7OfHwqunlaNsIaLH ifHYMP6pMEjPID7HfwiIbBPE6nC2muEzrlrL+DMMCP777wowOf7dJhIC3QvIE7roBaxyAWdsWm/9 7IGggmIvh5hrpL6m6j1IEKzagPnGX4tJ9IQRXRPcQYDPh81nFTFS1bCeMt3KIxfV8XMYvwHBH4vQ C+3pLhU7FwjM3oE8U7gFgSOPzKgK0r1wWqI+GrBbIbDnXaquZwsIcuBWifitgbvVK/gOEKx6Tuo5 wlhUOHbgScYiBfDBM2ReYBU2qCqFvUCkel5VbuCM8dRikndC4KjgZA8IeugIY28WBLlpYSoDjCzb HmHTwrZHWyuIR21JrhIanvEKtsLBmhPIMCGLQlgwwLPc1QuI5+AAyNAhjw7judF6zrnmD+Lz+ENI ZJsgFq/Ro6LPeASD1aJ6pqdv9rtnPIIrYeGqCIi50npMnJ4TzGdKT6D2J6UNG62rvbSQnkdwNVrm n1WBoIaHWwcDtPIHHQC90AS/h5MAawCjbJgD2CDRK8gNjfY+9VzBgOD/g8BefqAOXPYOxK5FW8bM gKAvZjqoHQj9d+4NbOUjeFLrVhgceQRxvZpv5bmELe+lfi7usUrEr5pLj45+6oWlXg2CDoFMJ/Bx A2OHceO9AitP4EylsIcqqurzFvz1QJHj9J0g+CpvoHsGed/cvIxCPFrghGvkMYkwtAQGwOCowGnV K3hFj2AvV7AXDuY88WIB2FoFBPf+aeEAK0e9sIhV3/rcMAf5w88EVOLvDCHj2eL7t4aJe96VWSg7 43VHgmCrPyCfs7aFUS+g5wH21skZIHTnRS8VRNen2Zz6Vj6hQyE9g+wPq1DoYeIWALLNDPMIWUiC NURPHdENqzbCZ5qDprFwbs54BEct2c4cs9PtY0bFIq22MbqgMywMo8FE/1YDaQ0NVx6NGQ9NlZc3 A4FaQLAFBh0EW55LelccQnphbH42vU8Ao6q5NNvI8GzeT/AIzoSEcb3qRQbYYmGBux67XYwXhoRh DKq8QAdBzQ2kR1BBsGr10zOSLRDcWiiyEgKuzvl8VY7gCghWHnYHwVaBE3PJtGeXj9fZ3oK9BO1e hXBrEX+nce4VVbVaxLA/oB63yIpRzBnmADJU6Dljan89V1lzkdmuCrZboZIeKPxJIIQ3hTDIimLm g7ZyBtW7+3QQrMLB2h8QmuozVi+gh4KrKJSuN7qOcr67R1DXo164t1dk0irYnAVBrSKuYFAPDtA1 QVvM+DF0LCjBesECElxnqyOHFmKy+IlpVgHBRmiYOQ0cwFzQ2T+QYWEvXa8W2ZnKxl5BiYaERzkJ R3gFZ0EQ90rvSisxt8ql6FVktk4Z+UQQnNk8IMSEMcPiIlYI4zl5mxjN/+j1Day8gQ7jM95Az2Nd AcE9fQNH790bCu5tvHTMeqpFq7jJUxl4VjYTsqtWMltAcJSYfUUQVG8gC6k0bYK9WTHvmT5BTyCA jZCgniI/z72yj3yW6hVkiNjDyvCg4Ae/x/cBQAmDbDfj1cSeM+gg6H3aRs/2ncC+N0rSCw+3gF9z AqEpnjF0Zn9ADQUr0Gn0qepiUeU2e/HmDAiuQKCPt1ZIuLVWa96g5w62ikjg/WudPMIeg/hcjHtG LzCetXBE7ZSCIJ4Z1vHemP3UCuKXF4u0QJAl7ez/VvUw6i2wVSL7aNHeCoJeUDDjHdTFsOVpUujD AOol5vZAkNVOGLBshglwYp6gV+q5V6Xqh1R5JSrDNxMG0c/qeQM9DMJcJ54lzAIR7HahaS8vcKZv 4Mgb2PM2t9IQqvxAGJSWt28Ec6u/fwX8tYpGNK91JgSEZ8axyjAjC5wQeuklZK+EhUewcDUQ1DlT NVeHh2LUQNgBgWPbF3et0lS7p15BhT33MOK54rsYVqM90mbUPLrLW8t46spoUf0E6NtjE1sgqBCo HTdgD+nxBQTqiUpadKmAz3SrFqCN1lgNKbfWz1F+dRUJq9K0KhDsRe6qMLH2iSUMujeQp47827/9 2xd+8P+AQ/QhVK8gbRXGM8axtryqohcBwSJHkK0BOJB5rBwAhXleGLw8TaQasO4VXFnkWomus1VK rd3JbLi45Q1swcUMCFYAysUVwACjrNWYNLgz1ZhbYfBIEHRPB1tfIA8GYwb3h/ECDekJZIKwV4dp gcjMKSKtMMksDHpoZRXgNJl7ZZy3cmZnUiaqcNDMd/N9OmarNAaHQ45VepdgYDlee2dlz3gFq150 Kx6bsxtK67VWGycPE7IVFz2BmitGzznmCwuqaF81BOcelaoikxX2eJbMRybseWNizYtlARu9h7gm zGHACwtI9CxXnkCiMEgQHHlYPg0IZ21ib1Os1cEaDgYAat4n1lBAtzpU+KzVC1jNRdq8nh2YAUFd 63obZL0uv7ZWaLjldPG2Mq2cQW0pw5AwAbACQXoFMe51zHNt5VGumoOvRU9XH7NTOYIrDYG1Dxw8 UgBBVjhpD7gqPFF50NTTMrNYeZUjBl4rqb0HfTPu6pl8Cd95+UI9AsHWhFAQhEHY2paj1dR11EZm 1uh5/zOvfqQ30E+gwaLBnS8XNdwz8wK9w3wVFm7lBrYM2CxE9dIXVkBwdjz3Xje65pGBXr0GfF8F gj0g5MKjYUbmtQIStMJUNy96rFPLM3gXEPT55iFhzA82iyYEQjdWBDNMSDiAfaXtgw2pYK/Xm01P YMDn4NlhMwbwaBUk0FYzF5SRCsIgT2yoWssQBLF+6KJ6pRDxrE10EJwJBysE8mx1Ta+qHCq9VA3N ge6lglQevJ4ncMYetbzRVQSu58BpeQbpJPCj52ZAkFXETGdhbizml3Y6YBUxx+1jQbA3mHVXcwQI rsKg7maOBMFRaLgXFu55BEc7ppZHkGEbhmNgcFmpBxf2ikdwdfc9a/RGIEgPMhc6nkBDbyArhaER 9NW8QG0H0KoU9t6BbsRahR8jg+a/r/IDKyjcmu86cz1VKKb1b1u9grh+37xo+Km1+BAmWOkOjwY3 L61WMjNHz90BBFuFeJoywX6sHg7Ws2R55CI32fS2YA5wozTqr+Z91rA4a2ifrWXYsgjjyHPPmBIB UAHwYw7DNrGSmODPELHbqVkQ/LQqzFmb2NoMwxZq/qeGgz3vkxDI4jl62kapGupsoQ3o2ST/3F5+ 9YqNYriZn1fl5s84cKp8wRkQZJiYoWHMC7aTYQUx7l0LR/TEEayvmnp1exBc8QZWJe8QjH3gNOlf PYKex9DzuLQW3FY+Uw8Eq0E4k68wW0JfhR190mEAObi2dmH6veplae1aRge/72ksPWv0WiBY5cOw IS6T3rELg1eBEIhJ7yFhTuDRUXKjApEe2PXyUnsetV7YV79vBeBGr51J2p6ZXw6xep/VmOVnOhT6 wkOvoB70TgOLnNCqMq8XIr46CM5AIDZJGiZkOFgjLGzHRTDDeOemCZ4RtszAwqfHb/GcVv+T8wpz Dp9FD4mOaUKChvf4Wm7ecV0ME2s1MeY4Q8TscMBQG8aX2qbRqSufEiKetYmtnpC4f3bZgNeX+YCA QBb+aDiYDoQWMLXCwh4a1tC+txIahZlbEa9e6oraiqrbgG7W/R6qAhKFQV0ftF1Mq1BEzyLmGsLO EiwcweZHC0eYh8/emNiwzoDgp4xTvY7pYpGjQBDGi4UiGMwccL6jVHjqua973pZZj2AvIXU2J1A/ Y+QRrKBBF1V371ducX7fDAhqE9eWd+Xo0HCrH1rlQdZ8UraMgQFkv0mGQDAOvEBEAXBvgcgsBK6G UUd5fC2oG8Fc9T5/T8sz4N6bllegN7+qMVuBYPVd0ASfTUDAM9azsvVIp6rxcKvgST3aV8oRnAkJ M8WGubPaOoT5gMy5huawEVjMMC8IgfSAMEGeMIiF0H8IhZhjei6rRmZotzHOvLWHwqC2DGKBEK4f 64E261Wv4CoIfopncC8I6nOGNt4iRsP+ulbMwlTPG6jznXZLI1XunPA0rpkIQ+XwGHkwq987DPZA kDAIENQf7SXItYT55Rzz+B5cM/Ne9cQRPB/2P4Wd0jHbYqZHg6Am/rMXHEGQA2OLR7DXO2004Hoh 3i2VwtUOppooLRBkXqNPvNZOj4aW4TZ6BBGCAURVJzZ8EghWTaSZBA8vAXMDGeqClgTB1nFBe9rF zBixPRDooOkgNwK/1d/PeAhGIaLe/ILR0zGr41Y9CA6C3NjhvawexjP2Fg3qFdTcsSpP8MoewRYE 4p6qAipGVjRMCFtKCISuBDMsZljg2DZD86NmYRDvh1eRXkHdmGv+oSfscxON56+NxBkiJvjDTmnh CENtDoKjtJVPac2xBQS9GlwjaNoiRiHQez+OIlS+Fuk8dG+gRzEqGGx93kyoubIVvVxGB9AKevX0 EfUIao6g9hDUTRBeU4EgvYJMv4K90kp4eAVhpxh1G4HgJ0IgrumlHsEq8Z/nxLLcfdTI1F3OKzlY o1BrD7BGeYD++5HnRe+jmnRcVLmwutu88gri39QjyDwGgqC7rnuelVd7BGeaSDP0xU0DQ14wftiN abK7g+CWY+Sq0Kh6O2agb5QPM+O18zDqzO64BYU+pqvO/L7J0XHk998revEx2wpztzZ43GUTBmlg sQHQPpjwaG/xCn66R3CmcbTaUCw4bKWE4hCeJMH5gfFKAMcCxpCwVkv+6Ec/+sLPD3/4w29aZzBE PPIMcoPFHmu6MHMB9tN9WGiC33MhBaxqXiggB6FPwqCeLoPx5cd4jcLDn+AVXAVBbGyYT8+IiD5n 7RHorWH0OVRrlhbEeSh4dq6rZ9ABbjYkPBsint3sVmu3eqR1LOrZw+r1Jvw5AHKsa5Ehvo/2ij0y MXbh0da+go8HwVZTYCY3s2J4FgR9AfVdhi9Qfs5qy7uGgdZaaEfw1wvRvgsEdSfUA0G2aYDunxAa bhUVeT6pFhUxl5StEegN9AbSVauYUXFIK/VgBQRHHr5RmLQ3FlvgX6UdVCCpea9eVVeds6x6zG62 FAR7nkH3DurigXnLsKG2kuEmhiFiguCogljbIF0RBL1K2JtGM18MmyQ2V8f8UAj0DRMgD94/wF8L BKvQMP+N/dW0gljHJz2B6oXRNA38HuOLC6mfWczju+DppBcYQKQguJIreDYMroAg100Ffox5fc7a I7ByFKgd8RxoBcGWU2Wmw4HDYG8DvGUD3UqP6dnQ1qaXmyDCoBYTarqD55VrNImV9No+iZsZ7dfr /U998zLbj/dsT+FhHsFZEASg6A5HB3YVQpodvC0Q1AFWuZcd4Hq7qp4HcQSCvhtjjhQnoS+qft3V dd4JBGkIWQ0JQ8giEZ4r7L0DvbLRJ2/vGLkeCI5gsAeAox3tjLevCntUBr76Ln2v7pD1TE7d6dI7 2PIKjjyCnlQ+o43Oaa8sZf6Ynk/rRQQzx8z1YOBTGkr3CkS4QWKrGJ4owfYhnBusGvVcPV0E4fWD 928vCGKh1LwpFocQBKtWToRHjC8upCwQIvgzVxBeMM0NbYHgTIj4zIV1FQTh9dRuCfAwsVsCnrOG /Vsh2p5dUVs34wVk8/sqX3AEhDMQ6JvGlpdxFE2p1kQND1fnz1c55RUA6lqCMa+FUtqdA2OXPQXh cJkBwbM3Kr0m6MM+gjNhDK8A5eLOViDc5Wj/I9/htHYGI4/gTE7D6iI8en2rQMTf54Cr96IgWFVq qTelup4qNAx9tcRdk1lbuVZHh4Z7eU++kGtYhGFhLHjweLB3IHTgTo+eh61nCTsEas7TSqjXQay3 URh5m2d/76Ge6hp8ga7O39TwhwIzF+xqjDoUwujpmK0WiWojprlF3BCxIo99BdVLxBZIzB1jcdFM ruDsyQ/vbijdgkDMQ+2pqUdzwn4yXYLN+Gk/uQHgQqitM+ANVBBkWBj/rl4/rRiuQmhsq6EeZUIg vfQahlOvoBaOaF4obBVzQ1k4QhjE+NKc5lkbNTpZ5tWQOAOCfM4K/HqSEvMC8Zy9s0Zvfak2h2rf 3AExe755r3vALPz1wsMtu9vigdkoSK9vJjcp3Bj3/qxayVSneOmYHR3S8OpxuPL5h3gE3RtYHYPE BGfktqyCYJV8OlsksiX/qgVcM6G8kWfQJyUnpi+qmt/Yu4erg2CVDM8m0oAChr1wn2wi7SDou7cq 9EmPl0JIK7+lF6boPYuqpcHWoiPCnFetOwh66MRDwgqBVV6l5nGx2GmUz+qblx4MjkCQHgosSN5K ptVX8O4gqHNCUyVaaTUYk3rSAucJi0QYFgYA8oeFImyb4W1jWiBIr6CnGzgIat6VNufF89ZG05jj euqI5obCJnpu6CoMriyGR712BQT5rNkuhilU0ITrJPPoW+tPKwrhOdCemz4LgZWHsOctXAHD0cbb 8/xb9sRTaapoiIOejuGqKbW+nnYY34/xq15B3cBwzOK5jkDwkzyDp4Cg57b0XMM6UEa5S54XeAQE tibZyFvY894RBnViEgS1nQ51OQoE4VE5wyM4yg/0oiL2D2S1MHQagWB1eggnbwuejshLUfBaPaFm 5AXsNVd1oOX/uzdQoVlzZXyhVq9gK5TU8ghuBUF8D0HQW8lgw0ivIPNc2VqkB4KeS/aJHsGeN5CV wpwTWj0KTWg7edQi4UBbZyiUAegcBHm+KnIHNWG+lVSPz9Dxwrwp9Qa2+nrS86wdDgAW6hVE+JO9 BfV0GdhEtVkr/U7PrCDeCoJ81myxxtxA5n96ykgVHahsmoaDOYdXIVBf30sXWYHAUZh5lIPdisi4 TW4BnzoMvPWRQ6NWwLONDJ6PF2bqmOV4HRU4HbUB2fM5LwVBTXTWKqgtIOgDrOWq9hyKLcCm72kt uFs/l4usT84eCLbCw7pgQ59W93OtxMMCUy2kszttH2yzRq/XRV+PzYLnuDpbGHq7R3BUKbwVBLd4 /WaNTa9ReQ8kvTWRh7dHIKiLvCZMM3ynxTWeL1gtJJh/PmZ1jlY7efVo6LzCa7W9COwDW4swV3Am vYGRiVHByNk5gqOwsKdKAI7YUxOJ6iyegoZVGoA2kCYIMjRMCETeIAGvSqb34hEHQY4XzT2tTi2h B1G9zVogxDNcmRtKLzDgH+NL+wr68/3UXMFZm6jVwgwL81kzj96Bv0pHqqIcnu6iToc9EHikd3DW G9jyCrZA0POkVzboLc+griW0VRy7moZVgeBonO4BuKPeOwWCPaLVXAfmOzD5tQJBGPhZEJz1BurO wqFpts1Lz4On16GhuBkY9InrCyCvvVpUR+HhCgTpsob3gEZV+xydCYKe01UdK4cNAxc9tsWA5gRB z3fTPA9OYg3HrngD+WwqQzIKHWguSi8EcQQIusdOPccEA14PAVC761f9s7Q9CBdt3bRUMKhjduQV VG31mah3XEOGCI35GNYO/jPh4Vbo5UwQHDXmxxzRwgHOB9hNeB8IgdANz0m9cnpmsD535giqJ5Ah YU0XWPEItryC9AxqAj77D3I8aYEQe58CfACDeroMxpd2PNCTZbZuXI9aOHufMwJBXrumAKg3EM/a gd+9gboxdBCs1k11nMyCYNWZoweCM97AVfibDQ1XYfNRxMV/r+kVvrHXojrduPpJIzpmV1ofvWNc zozZbrHIXhDU/lcKgqwGbYWG94Bgb+LMABwnVzVwFTarAdj7/LNA0A2qH9e11bDOGj3fMPimQftn EQSxSMDwtECwyg2sKoU1P3CUfOwQ6E1y+Z2jPxUG1ag4CPbyClv9/hhS9Q0KPUQKBw6C7KqvHfXh IdTiEd39tnJa3SPoxVqtNA33tjsI0qtNDxHDwywg6KU3XMEjOFN0RxCE3WS+LFspsXAAz9orJHsg qGeq6nFa9Kqz4pfe4tUcQW48+HlejKReZoIg7D+fN88iJvzjvrGoqid4CwiekYc1axO1WhhFcur5 ZaUw5lG1lulmrdoY6sZNC0RGENgL+/by8lsQOAK/UXrObL52ax1egUG1zVWER/MEcb/MFeSmFdGL gOCPd7KVR5BJsAwNXwkEPZevl4PYc1NXVV7+2Ud4BDWJlc0uq3NbR3mCPfBfCQ1X4eDqNBH2muTC NwJBhzD1TlTwNAuCfE6eeK/fpwtd6+8tQPWcFC8EaYUwfPfvcOYeQe3rxoIBnizhxyupZ8jbg0AP L6jRRcU9glXlfi9VwxcwzR3T5tJYILWtCMZv64ScT88RHIEg4EDng563jZwkaMyNUcsbiOeoGwA9 X5Xe4KqJLkPE1bnDWgGs881zE3WutNINOIY8V1CPGeSiWp0u47mCnxZ6mwFBbyKNUDifNcCCm2DM n3eA4FEAOAK/Xj3AChT6ettzyMzCoNvfyiuo1e/MFYSt4nqrm5cVj+AZG5bls4ZHyc1V1XCrIz4E 6zXI9MEw4xXshYZnvH+jsDANV+t7qkFZhcJ0VzwLgl4V2oNKPc9Tz23VJq1ahdfyCp4JglVoGGMA elYLH0PDGhbmxFcA9Jw6rz5TY+vfowUX1dF22s+wlbdYha1bBqpVJOKpANV4pJeIIXSeMcvTJdQz xFMlAIO60LtH0MGzqhpuVRG64fe5oqDZai7N7v1McZg9ZeQqoWFvIt07b5tzAWNH8/N8s9JKCWBx SNU7zXP8CIYVBFabFoVCb+iutk9BkIspK8Y1JQCLqm8AmBJQnVTUq9J8Z+itB4L6rLGZAfTD66l2 D0DMaBlTAFopTh5h4no5KqzcW/XbipStePB66+bs71bX9y1Q6BEdbpDpFWTlO9IaMGa53latjz61 cGRzjmDL26N5X7qzVY/gCghWoSkvg9dBv5rD1xpImh/lbm/1wMwOWH0dr7FXNUzv4ygMrdfZAkEY U3hmNfG6VzByBgh6aJh5MjSIDIX54tfyBq7mBhIEscDpd7Qg0BuTak/D6oiiFRCsjFUrpcDHCa+f BQN+2DrbhigE+skRe0LDniu4AoJ4b3XKyBNAkJtpLB7VedvsqUkPkY9TB0EN9WoaAMete62r5rve sJ3zwr3u2uqo50VRu6fVq15BzE2sg2CvlcwoteUTQVBzQTUsDBAEICv0vwoEqwKvGa9ez7M3sybO pE+NPqf1GS2tZiGwV3Ws1cPQAM+JvU+R1sAxy2MxPZ1hBIJneQYbIPj1zQ3lJxr0x8BPDqvWXfC3 RjfaRYNPHAP1mB03lP7Ee8k1fd4ci038vGeSeTK7ji8Xi/TagXjVsB+WPls1rG7mUYjYexJ5SHWU U9Aqy1ePZC/fycOQ1ff5/fQ8glot1Qoz63dyd8IqPPTl0kT7Mz2C1QkQ9EhqwjSukbkycLNznDBB Hver/Z5mvIGjvBPmlmgbDveOaO6U9+LrHVnkvQ01R3Bmd1pV13oSOMYQvcasIvU+cvQI6Z/eQqbK 59KilCrU1GsfM2olU6WAqEeQu2zm3mBcMOQyExrWHl4zea1jEMTmZt/PTH4gPIJ6wgRPEmHxAMer Fop4lbqOX4aIOW69IbTPJ8/x0zHcyr/lOG2NaQ0Le+hSC0dgu/S5Y3zx+K7eMYOtMLHrvff5zb5/ FBrW84WZQ1/lgrY8gl5wVRWLeNRM83e9yMyjTiNPXC8fT69txsZV3uVWqtasB3Dme3seyZZHUD3i mivIcYu0BoxZ2CpE4bb2wJwdZ0e+7tDQsC/uR/QRrGDQQ7Uajpod5DMD1nP6dMIwlFjl7PUmkuZE 6X1wUa2AdhYEjwoN95KvZxZVvEZzYVpnw1ZpBOysD4jVXlp4xhq+bVXiVoUVXvBQ5V3qwlo1YfbD yhUINW9QcwSr1jZe1dwzWisgSG1YvcnQIKs/tWWMNgj2PoJajThKy/AxW1UOVvOxgnO8zkPD2ltu Dwh6uOWs9jGj/oHcFGlPOVYLs5USbIFuWryqnYtVNYZ5RJzn7zkMzmxgVnOzNIWlqmhl4Qib9AKA Mb70pBEeMeiFQlcEwZmioAoEW+tNVdSlhV2+Rs44SnQd8++dga3qNa2CuNEmoxpvW69hNHYrEPQu ELhe5goitQHjFvZKNy+eijVKX/iEJugv9QjyrGHk+vgRcxig6v1S2GoVjrRK1VtewWrHMwOCDhW9 91S7tBWvIAZQz4vSKz6h5+aoYpGVpOvW7ncLCGLi8KxhjBVUY/F0ESwU6r2rQLAqEqk2ErMg2Kqi bEGgn33cA0GvGK6MmoJglQTO/EDVhU23HQT9TFktAMB1KqBWCejVnPMxuxUEeW8VCLKdyJNAUI+V IwiyirQCwarHZZXzp95AXXi1Sn621dFoMXV76KCi41m9gtpgGuMLuXP0sMA+6HnTusGsYPCTPYKE /hmPYEtrX1+qjVurrRPXxJXnyM3mKoCN4M9/r3Z8xnM3ez2z9zoCQdhYzRXkOelYr3Tz4n1PvaNB K0JwpKdv9rNe5hFkRRSLAHjWMEJ+9PTAuHGn4mFXdXe3djuzC49PmNbuphqwM7ua0cCtJiwnLe/h KBBkQ+mqfcxssci7QVDHih6ppd4QTDZopl7BngFRmNnqEey109CKylbVcK/PYRUSaaUntEK0XExV kyosqO1A/Gg5D3e4F7wFoBi3PRBcqSqkh2imaphJ2L32MVWCdtUqYdYzOGtMR6+b9QgSBBEuXAVB gpz2FNQNinoD3bb12hdVY1M3KqNwYgsE+ewJg3reNMYXT5bBBrHXC/VuIMhiEdq8mcjTDAh6oeMq HNEbPTNWZgHQ22qN1lNec2/8+u9m71NhVzdIWkDIOcTwMM9JB9dwzPJYTPViPwYEtY8gj0li5bCG /OjpYbhDKx/dW9MKI1W5Ui3PYsubNsoF8Nyb6v9bbUtmdnE6cXVR7d1HZRDwOdBQj5hrncqgJ4v0 mkq3wsMroeFWSLjqJch2Q1gE6RXkaQp+5qZ6Rdyz5otTL0ewyg90j1p1NJuHiSsQ1PYco+bSLaNV eeY47tWg0zjz2qtzX71vnOdXekh4tHATBGcqDHvPQEGz1UcQMIAxUR0z1zqtpgeDswD43S55Z24g 58wIBGkzFQQRbuL4ByyNPILewqXV169abHuw1/rdKOrRe/buracN42YWNlHPm9ajMqteqA6Dn+wR 1BNkqsbhetjCCLJncgQ1WqbewJY3zb1/CnWtUzdmwU9TEUaRndY6qtc9gtIVCFwBQYaHMS8ZHuaY rfr3asP7lc4cow3mEb8/1CM4AkH2SoJx00IAegXVMFQend7i5PCk4eBRfoEnXvsxZlXjYPX2tIoA RjCo94MB5ItqFcKsQi6EA3hYGVoBCHI33Tpi7mwQ5HjhAggYZH4UPMi4B/UKMo1AAa7nRau8ypWm mnO1BQS1YMRPVnCvoDfjVSPmi3Nl4L2ICOOhld/I8LC3AWEoWMPBCtC9zZeOUR+zs4t+Fa7HQqUn TbROFnGvdtXDVHOV+XvNE/wkEMR18dQdBUFGUAiC1fnCuqBWC7V7MRjS4uZpdYHkIuk2qGerZ8cE bRib4gN+Mb70HNequXTrtJHZjewRi+js5lifdQsEWRwEHWjv3Gb14Lvy4Ht+oDpGZsOqrfSBqkn+ DBC2Tu7Qjb2PM/1/d+L0onm9cc61pPfZVR4u7xvvIwhi7dXNi/bApAPmU72CbwNBzf3C4u6FAKx+ nPEIqietlYw+8gLqQNIH3eun1SoK8OTr3s66teD2FtVefiC1wGBkxTA8rmpAdSf9SR5B7Z3GRRDX igkEDzLugYYRk4zVwxouaYURZnbRGmJowVR1/mqrcMQ9g9UJKJ6IvwUEfUevDbBbfQ/dQ+mbl1lP qoPg7ELfep2GBgE7HMNsLOxHzFXpDQp+1d8xzu4MgtUi6J7BKiQ8Cu3OgKLOs9Wx4ACJsaB5zgRB PTOd3Q+q02VGHsF39WibqRrWbgmwed5HEHNB7d2str1UDs0p9s10L2e5B4EVCM5462ZBsJXWtQcE R6li3PD45qpVMMLcZoIg116NYiAyutJT8BUblM1nDY/OGK4KAmiINTQM483cLwx4hPzoFYThr3Y/ 6tGbnQT6Hif9Vj6BL6J6PBMWfD1zUxPuZ0Jto1wHNYQwetV96qCtDLfmV2Egsv0CjSeAqhVS+QSP oHsF2UwXk0iPXMLGgQnzPGarBcgzC1g1HjS/SsOrOg68+rbKv6uaS/facngl8cwC7V5MzQkbebDd M8SwcOt7W/OvNWZH81VhkpWNzLOhR5tNhenVxhjW5GsNDSr8ERAUFPB7eohgDFf/O8ooj9rHbPEI 9rwv1WJbeZ1Hm6aRR6XyvLeiGy3vFje0TA/goqonJNHDUuWJzkDgJ4MgD1xAFARrI8LjCoOEuCry VUXKWgWVmlJSAU8L4qqxpGlRo5zn3maltSnF9fWgzfMVZ8PM1XjvRQ0dCPVe8VkEQTwzmBeOWS1w a7WS+ZQQcdcjOAOCrRwdGmJ2T6eXByDIkB8Xd4Agdz9V0UgvxOeLTgUHPQjE4PHEaj14verBpoe2 945fGnkGdUD6otoarL4D50IKDRFOgabwphAEPcnaF9Be5V31/GfDIAx3jfIEFQR5qgK9gtwlc+Og xnE16bkXAvGJXo0HbcOiJzVwLPDoruoM1+o4r5ZnsLVQe+hCPZirEFilMrTCfb2w3xYQbEEgK++w kYFHmzmurBr1zcwsCPJ1VwJBLZgahYZnw3C6eI02GjMek5EHeVQs5N/hIOiLKvNEW70kZ1tzvAMG ZzyCDA0zh16LKWG/9fStVns0h0KfW61iSo8otMKqvbzAHnS1bO3MWNVNqdqeyiGiINjLWxxtdHsO hQqM1Ub3QFB7YMLB8clHzr0MBLEL5w5XvTzY+TDkB4PPxV2TYxUGR94F/X1rsFQPU0MnVaVltfAr APjCvyVncBUEHQKhE6vtsHukNxDh1Fa13aeCIMdLdSwhoAD3BOPI8eJth6pnr6HfmVCCwxW9gvQI 8vnjeDYe0aYewhEMOhBWMNjaPPgYrtIZep7A6kzmXj5Oz0vEOTcLgq0FijtpDQlzI4OQsI9htg9x b1/PI3hlEMRmrgWC7g3x0F5rcV+FQI+szOQH9gqIZj2CBEHMew213QkE2S2BjfR14wtbDq8oI2Zw lFTHN7a0XvEIVmPJx0/LI1iltmwFQQesygGk47EHgitexpnIkt+TziNtewWbyDELENTwcAWCvZ69 79i00LHzFhDkwdr0CrKfoBYCaNivqiJuAV9rwXII8DwHNt4dQSAX/Wrx14VfGwn3WjS0DCkG0ChE 4yEAT7AHWHMQaiNW5lXpiQy9s4bf1T6mSvRn9TCMIyYRd8oAA+aW6m6ZnmQNnWiKAMMfM8ZOQZDj g6kCemYvzuvlj44L9w6OqomrPoOtXXOvGnQGAKsilZHHugrN+Oal5633fCU9XxYbPxYGaEoDxi/A H4siDakXO82AIMYRxztezyTtM0LDo/NFtVgEtpIeQe+lyaphDelVi66O+VEoeFSIMBMW9jEwqiSv vlNznZknCJvIXFFowX6C8A73zh0e6X1UqL/1OSOPIJ+3nsDFSnGGiDEPsAnQVmvYMDF6NgJDBUHO O2/VRqgabZJHENiLtrSicW7n3BOoa52v/Z7bTbvdyjuc9QiO1t/q97g2B0GOWTzLVv/TFQ/2O4Dw 5SBIryAbZzL3C7s8hPww0Onp4SCvegv2PIO9mL8OcvcCVkeJeT7YCAQJgx4ibsFgDwRnvJ+aXK/t NqAhBiDzE+iWhoHRXkbv8giyQm42RKy5pTyRRlvJwDuEe8M9Ani5W2YeTatzfmtDMANcHB8sGIH3 jwD4b//2b1/4USCkd1DHRFVgNPIMukHT6s9RGHhLGNphYRQm5nzreQQVAisApBeQEAg7QAgE9CsE 6vFiCndeGKL5gfq6u4LgDMg7yHlUwfOqe5711me53ZoFwep9rUUVY+IOIKh2UaNmWiSHTRDXRzpL YN/V5nEj5Q2jW5svhUENKY82yiMI7BWZVIDYsruENd1UVIWgKx7B1dzrKl2ht1Hi7xiVgzMLNpEd D5jWcoQX+9Uw+FYQ1KOTMLGx88eiTje47ng09DcTKu6FhdUb6IuqH8XkIFjlCPqC73lhGobzJNqt IOgLK5vvIoTCBHt6A7l7xgD0Mzp98XxVjqAavBkYrIqM1DgypYAwyA2EG0ffKetEdk/JLAwSBOHx cxBUINQw8egIuhYMamWaVx1Xnr9Wu5pe25peYncPHNxQtkCwWow4XgmAHLfMCYTxhD1gSFgXfW0k rLDXA0F/3R09ghXAz3g1ZqIro4KrCij1c7fAoKa6+KLKzQELRrae4/rqBbXlcca/q6dSvYKeG42x DxsOGGTxCNZJegcxd6BPL2zsx8th/qlt5PNpeQV7xSFVjrGPxZ6XcMYb2Co20uutchh76TazaREj h4x7wDU9qwJBjtmeF3sUIn71uH0pCHqeIBd2LOrw8GCg0/3NcJ/CoDfBnHlAvmPwPAIPCXvzXa8U 7hWLtHLCPORXJWp72G3kXaEWHHTac00T7Nlug7kJWmFXJdi/CwS3wCBPpWFKAcYMNxD0DLpx9M2E how9F6QXttBqcnj1OC4AgoC/H/7wh9/90DPIvMFWiHh0AkkP/NSzOPs5VSPrUU7PXhBcgUCeK6ue bDxj2Ac8c+6kPbWhFRr2imFvMYIx+O7Q8ChM6X0ER6Fh2MAK0lphq1mbueIJdNtVfccIBFub+9ai ugKCo0X1zNAwx4PaQ547rPYOcwAeJaZRYZPP1BjY+8pDqF4//TshUGGQEZSqcGQvBFapCb2iC/cG 9gqNqrCwhoYdBHEvo/SI0aZmNIewxjA6h3WcKVrMcW6BoPYU7KVjqQ151dg9HASrUyOYFMuBjh0P YZAVxEyKrkJ+XhjQejCe2EwIbIWE6U3hwupHilWtY1pVotU5s1WCfmtQViBYeQHVo+ILKYyGu6NZ rdSqsnwnCGpeRKuauNV+SBtNM4+GO2YYSIwf7prVUHL3zBBytSv28JinE2hLIcAeQfBHP/rRF34I hYBEwqC3lamOpKvgjk2g1aM48i72+hX6CSIjEPR8mt4uuucRVI8ENy08+QbjlpsXDQdrXqBWCdP7 08sNrApG9N849q4Kgsyh1oW7ejYzXrmWR9ABs5V/WOWOtmzyCAj9fdoFQRdVzHWtwtyaI/iqhXR0 hGF1uoy2X6ucJpoag7lB5wmiIgqGrbAxw8eYd1w3MI64WfbTS/BcR/l2VTSh5T2eyR3UMcw8UaY/ VXnfrVB2q2/mCgj2UhyqOcN/8zFbde5gipYWjFQgeNYm5uUgqNWggBItAvBcCM0XrNzeXi5fxe97 iaSVN9Ab8Fbny2r/wJnjxaqecdpzqQrpeLGIJstqrzXoomE1ABAXU62y7PUOrE5e8EE5eyB2LzHa w8PVeaCVp5DXx4R/rSSGp5PHFeJ+uWOmcWSSNaCQaQc0lAqFVdJ1FS6B4cPzpFewAkHCoIJgtWGo YLACPoXI0XibhUAN57QqSluVcS1D3xqzNOhqILkYYaPHNkd4PnhuWOS17yV20Gwc7PmtrQ1Nr6m0 ju1PB0GMeS0W0SPmsInRTYuD4AwEug1teQJbi3hvY9DznLSAsJUniHvF+OKiCoeBVmFeFQSrELGH idlyDXNA02NYOKdh41ZkhKFj/gnbpz8EQm1ajbHgRZVVmxjPC9wCgp4i5etdFQ3sFbc4COp1z3oE Z0CwyiPUlAaMWXbv0OMRPVd/1QHD9fhVG5m3gSCrQdlcml5BDmqeK0tvjvaM0+IRDyn0wn3uMtZF U3Or3DOji7MvytVi3svTqrqRV207MIB8d+QLKiYtj5HTnoEsEGGVEhdSXURb8FcNyJUmlyMQnIHB yjuoO2SOHRhFTCiGimEY6R3EpMMPk6wJhlhI6S1UTyE9z2oQGU7hGKPh4bFzePYAQQAfvID0CI5A UFMIZsFPQdJTELQwqdWougqRtHIiewnfvVwxH7Oe6K0bGE9lwALveYFVlfCRIMg8m3c2lF4NDSsI YqPjIIixqTavFdZS8NJF9ZUg2AuxrYAgQ226qAIE2QlhT9XwqxbSWY9gDwS9uT6L5tTu0eZp2Fjz pt3Gwc5x84U1gz/4N6yxPMyBJ3t5FG3UNHqUS9r7vY8XTyvR7iF8bavCuVfU0gPBmc3LKKeWHkxu XhipewUIvipX8GUgqOcO66LurWQ070vzBR0E1YCN4K9qFaPeQPei+NnCCoa+cI+OEdvqoYHRq6oA ca860OBZgTYEQfWoaKVw6yiuXoPnmTyF2YbSnhjd8gb2cgc5blh5TsPICnQAoUKhGkmGUrDRICRi MYFeCokeTta+lkxJwHOBoQHwI2eUXsGtIKibi9lG1VXqQQV7o/ye1g6/B4mtcHJrzDJ0yI2M5rXq 2FUQxPOCt1fP5yQEegVwz/vXa4v0bhCcgUDNEWQ7EbYSgW3UFltYtH2T0srXqxZVD7V5SoQu2ls8 gr2qy17eVwUEuFaMLy6qmLMtEFxpxXE2CPbsYtVOS20fIyMEQ7bXoq2jffOoCKMj3BRrGo32KoTm eBYOgzPhYF+TR7nG7r3uNb+u8hgre9Vqct3znM/kt3rk0e9NHTYYsyzeZFN8rsu6JvNZz56I8/Ee wVERQKsalPkPurPhsXPaTqbV+0hDwFoQ0oLAEQjOegt7C7J6/1p/b3VAxwCqFlzcpy6oBMFWpbBW 1XnfwNEpH68AwS0AqJsIPaFGQZDGkOOIIWN4mjHxCIVMumYYmWDI/MIKCtU4sncbQ8QAt6NAkL0J vSCJcKjeZ1YZ74W/I0FRx6xvYrgwYOx6agNbxvAEEVYK4xmy0p3No70f4Exo2Me55t28MzS8FwRh G2dAUGFQQ2wtD0u1qa7yq1dyBEfFKq0qUA9106tMEMRYYfI9NwvuEbwKCLYgsBUR8bWTIWOmWDFv mjaPThVGRQCEzCXk3/XfuN5CY+ZQ43kwD6+1HnlYt3r2q+NhdApKq1p4Jsoxmh+zeawt2PUxi80u PLNYW/SErxEIjuzFKzcxuz2CIxCs3N3aU1BBUFvJeINp3RV4VVJVRl4lj2rrmArUWlWbvSpNXZhn QLC1kGNRbe1oONAwYNlslYupt4xpgeAsBK4mq840T61gcHQ9agTdI8iiI4VBegfxJ4CCPzCSNJSA QoIhxx3BUBOwNT0BemPsYVHEs4M3j/0EK48g8wRbOYJ+fGHVp7I6wrB1JF3PEK5A35bX6phttUlS jzaguvJoHwmCFQReGQQxv1kZD+0YwnNwa4XZNPFePYKaYlPlCb7aI6helgoscH0YX1hU2WOSIIjN gm5yrwaCWiTikbPK2+39MbEh9rCxeweZJgOvVPVDe4c1Vx0vTD3wNdaLyFqg1/MKazGIj0tveu1h 4ZHjpwetmrLi1zCCwF4ksip0wZgFu0BTTdnC87k9CI5g0Hc23lwag5bNpd1VTYNVFYH0ElvZ/qOC s9GiN+M97FVj9j6/BYsYQFW7D1Y9cVejpeowkmxeWbVX8ITqFnytuKdXQsN7AJBhMuYHalhEAZA5 gwqC+LfWjwMjIZHhFejI0Ar7WzJkgvEGL516BbWNzGyxiIaDFQS1PyU2H9iYsMijCm/0FvBqbqxs VGZeyzHb2sBUbSHYZsE3MvBmaO9AegSrHMFe9XArCVtDK+/KERzt8L2KnmOenh5sWpg7zfZaukD2 wm+64Lm3RRe/Vv6TL7wVrPXyq3qFKx5qq4qUMLa5qGIewjPKMaIgqIVAI71flV81axO1KKSybwQ8 /ll5w0dQyI2wpsloNERTZeA5ZLEJNMZ64sUjI8/eLFj5GGy1uvEG2a0Nwyg3USMSVb5s5YH0jVI1 T3wD5jCIMcvNLrTl8YiwbeyHygb3M2P3lV7AKq/1x5f/7X/6y9HE8oV+tiVIdcoIFl8ORrqpNR+m 6kPUK3UfwZ5XUerrq5McPCnfk2ir8PTo+nSxdRB00OSiqvmC2HnosXJaVeeFIjO5gSNvYGVIR8Ui s2OkCgfrzpdjhuCnXj//O3vQMcyIhVV/CImEQu6mtaWRemJgnOgVZPUwoI3NpAGD3kvQq30117QC QYdAhIJ1E9Ba2N2zozt5H38tuNO+lzMAyNf0QFALUHRHrl5BzPeWR9tB0PMEV9oheX7NFUCQlcNs KEzbyLxVj5BUuYKtPMGWZ9DDtNViqwvsCAR73pZW3hftvIIgIBgb3quDoEfHGOJldbDaKFbNaw/N ag54ZwXNH9RICLTjj6bH+FF2Xjwyk0PXgir99wr8vL9h1eew5bkehaM9RWLmGnsbpsrO+uYI3wmb yKMzmQPNbh4OgltSsV4FhptDw9oY013doypQBUE9d1jPkOXiq7kBDlszsFe9Ziac1oNBQlqVRLta 1eSLqi/E3naGoTYuqNwta1Wdl6pj9znjDRyB/xYQ9Krhmc0CQ8GaC6MAqHmAzAfEnwwDOwhivFU/ NLzMM2QlO3NsWLEJAwVDgGerIMjm0gRCPXfYK4W1P2BVIAIQxHuYg4pxgO/T501Dqa1vuIP3IgLv hege8qpgavRvPjZnQVCLR/QIMcx3tj7ys2S3gOCoAt4PWJ8Fwq3GdzSfdJNUHTeGMc0+qwqCq+Hh We9HLyneQ8+znqAWDPYKALjppXflTiCotk2rgVubWW0DVoFgK4fauysg4lEV0PlJTXpkZw/kqzE1 8vK1wK/V8Nq7hSiMzW5Cep7IGThseQU1X1A3Rxiz7O7BaB1sG+byJ4BgZZM0b3qTR3B2kfedEEGQ R+nQ2GnSKsNxvZCwA10PDP21Ho7wPMNVEGxV3M1eYy80rN5LTUwFELBnkVbVfSoIzkBg1TtwFO7g TheGji10tAKVEAi44I+CIXt10TPIVAUmUysIsrm0HjdXnTfcaiCtx9VpkQg+D7/D52PsYdxooYU2 hNUjpvys5WqBrfJlK+hb8Qbita28Vs/XUW8V5jXzXHk+NqvreqHhUeXwTG7gJ4FgFRbWc9npEcSm RL3TCv6evL4lHDYTJnYvYC/U5vAwKgLQwgS1c/j3u4Egw8HaT1dTUwhrbKyO3+kJUZpjpukRFQy6 Z7ACQT3PmPn52r+3B0orHj7dwLLJde9PBUP/npHXrvKC9wD1CBisQBB2mX1SWTByJgiOWsId6hGc aQUCg64gqIuu9jVqJa3OtrlotbzwpFcPpTGcNguCmgfl7upZOMR3+qLaqi7mogp9MGG8qg7GYwYE q/y9kQdjr0ewao/gLWK8Io65Lt5VX1sh4O88pgxQwWPK8F6GhzXUoiFnfp/uoNVLDdAmCEJ7bS5d VfsyJFwVGOlxhlV/SuYEYkwB6LQwCEalav9ATxENuHuLNDy8Cnojj7tXurfmps4R5rgyoZqhYTyz qmp4toXMrDfwE6qGq3QJ9Qi27CPA2UN3reT8Xlh4xkPoHhf9PD++rFpoewusJ+BX7UoUBGHj7hAa 1rUPY92PzIQN44+3BePc0Gp6T4/QojptscUG/N4xAbaSVcXVWcYz0OavYeP4rX96I+zW/+vpKB4h GXkmq8IUHa/V3PENTpVby9Awo3UscgLjwEFB7y7GwUx+4BEtY2bX9N0gWDUMng0NY4ASBDEQuZgx D8bDwq1Kttl/b5W9V3lVW0GwF17pVTv7ojoDgtBJ28jo8XK9HMFWS5fZQTPTPLVqlcBx4ZVxVZ4L PSKs7qVnhD2weFoIm6XyyDL8Xk+qYJiYk1BDjuodXAFB9QrqMYQaDmaT8aotkZ9eg/8HOGo4GIaJ sESDwh6IWuHM8DX7H2oOGcZbr51SKw1hNp3iLBCsKiurvlw6BkdjthcmPjo0PAuCWPyxeON50ztN D7AWjdALXBVozHg8+JqZUKD2hFQI8PyuERx6/rdvIu7oEdQeutik6rnpmOOwYfzRzS03tlgvR6Fi Ftdp+hVPJNFKYu2nyiM6ebIXm02z2f4I6mbhbfQ6nnzCJthVI2y1+x4Z4XisQtAtOKwAcDa3tgpV wyYeDYJbi5xm1nN+9ktAsPIMzux4WyBYlbLPwF+vqs4rLjX/cLZYhAnxGvrynfRsa4bZRRXfxfwc DHg2XMXO7pNBsNUs1VvD6JFKumNmeIyGQHeENFRsVlzlnQH0NDTsYeIeCPJMTjxLVpQzV9DPFFZP YKtxuZ8nzLxAjCc8X3p79SxpwC0bY+sJKqpLVfXn3paRl2/G487PnB2z7hHU0DBbLGhrED9n2Ksn HQRHlcJuSGdzA6sCuhUobBniVRBk6gwWareRvWT+FQgcLYgKgHp2rULCrGdGwRPXr+FhtalHh4ZX nt2e17YK6AiCAHx4iLi5hROE8EUY4oEB3NgSBr3hulYRVx5BprvosXQOgXpWu5/TzuupIM6PrVMw 41Go1Wuqf1P4IwxjvLMZtv5JaNajQ7kR5rowC4OtcT+aO1VKBf6NOYK4jpFHkF7BFWCbHZezn/kR IMgdLyvjWqHhFgjOlpBXic76bwqBTKrnIq7Hx7GpL6ssmdPCpH7vVzRqzaD3xVMaeuE1LR5grhVz BKtiERqJVv7UStuYlpt6VDVMj7F7Az2vhZVzmjPDBZC9rjC56PHSxYOgwZw69ZL6+cutfEEHQbZW wGTm9+LzGWpliFhPoVEvYBWG9TGlR8Xh9QqBbA0EDRha0MIYbXejLSDUY1Qtsl7t3ip46s0tbsJa IFhV1HMDo+1joC3bK2Bh5CKn5wxXoeHecYlVJd5Me49XeAT9OLHKS66bZm6W2ScTeuA5a/qMNzuv vHmjRWz29/Si8JlxcccCzMVaPTTqQXIorPK+6NX0XEeOFfYRZEGcbnSxOWAR3Ez15VavyuzCO/I4 4/cYyyxMY2sg9/RCJxZUsQ0Jz5PHXNFjGJlzxo2SRlZ0Q03o1JNHNAzNvzPaoqctqZdSj6hr/V2B ruXR8/fiO2ALCH64HmxwAazsgYh77x0CwFZffp78aHOyOhdYwNJ6H6uGMRe0WATPQJ+XRzBmwW1m PM5uQDlvDikWqULDMx5B9srCQt0rFmn17ektVK28mRaYudekBYG+0CsIamVkr6KpMnr4foJgVXWs 3k+vGvZD2bWhNHNIZkBw60DcAoIKgeyWz+RmGDqGg2EMtOEp89+gNT1zCuf0qGHyM3fSz3vE91QV xAqCzEfk92s7I44V/X4PAxMCe0Ua1VjCuGFYAdeP72ezZSaOs7pQcye18s89RlUObK/5KudVL9+V n1mBYKunIDdJrKjz8zirfM5WP7UWCLaA4GogyJ6ZGJM8S5s2ks1/teF+b3FayZfy12qSv59xTg9N 5Z0hEPZCdQQeDXF7sQvGl6e+sHjiriDIZ6kwqAWBLCSpcgYZFtaNBPMDHaIIf8wRVBjU89n1ORPW PIytsEiPImHPf6fePoU/fqfmejMKAm8mf/RMeV67Hk1Lzyo91a0NyagIBeNyy9yhFxvXgXthznpV NbySJ9hKc3HbVq3jrVSw00Cw2vHqoqvtYzzpfeT9ay1gLShTINM8Kl/gK48PKzu1jUwvPDzyDDoI tiqcuVPWhHvtI6hVl61m0nvyA7cUi2hroerIJF30ALHs5QcjwN0yPcXQEVoBNhhe1bw8FltAHxgC jCdWpNLjpL0FFQi1PQ1D0szNUk81Icg9x96LrwrBVikHHEO4L4aEsZDCYOq1swqaPRD1JAENMekZ 3dChar/kaRWtuVP16tJ52PMI6hjWdAY8Fy7uMJSsqGORE0G91z6md55w1Qvz00FQGw1jk6TN05nf xQpP5nHpmdiEqi0LV+s9+EyCHJ8ZPTfqTdJzazVU5zDoRQWaU6gwyJAbrgvjq3XWsG5yr+oR1B6R 6smnt4kwyDCxzxfYS/ee88QR3dgCHh0E/cg5/X/3FlZgSK+hwiH/rh6+6vca5uVn8/tZJAPgg21g tbOeCsUG2fRwMrStYe1V73Rv7sy2vcHr6MVmayzoTk92r6H0TA/fHgz2HDkfC4I6WHXHq0cpsVJT 22GMYHDGE+ifR7DQRZrVnQqBDoT0GupCrrlQIwB1T2cvNFxVXdI4a/dy7paxiGpFWSuHSgfIqzyC FQhqCMN3r7ox0G73WCAAFwzJomcf+/ixfQuqeFF4QajC4qPueXrVCFOEDu0n6DAKw6SNzpkgXBVi VN4wL9YgQGr4lKDERqT0ACgkuSdTex8y/4dN2bUNk/cX1DlUpUyMxu0MCHq4mSF7VrmrF5tGUttk VKFhP01kJi9wJZ3h3aFhj6Ro6oQm/Cvw66k3tJVVKGzkiestbAp/GgrG4s2QnearEjDoPVcPYS+/ jDlnmuurHkj8O2wiN7q4d24WMD4cBGfs10xY7YjXjHIEvTUQ7rECe8I4z67F67QQjsUjPl/UpmqO 4OjYudZRdCxO03OKCV/evaHq5qDeRwU+DfvS00f4AxewN6wfGcpoiOY9+vibLXiZqYweFcuwtRee E8Ys5oDmduJ68cy1gFO9gVtStGbGeyta69/39tAwPYJ6TiJDgRjg1bmHW0FwZkHTkLB6Agl9ehoE Kzu9GIAAWRWOVDmDVfEIFtcVEGwl2+sO8ZNA0E8N0WPjYLQINRgL2rqFQIPdGp4ntGaRBuBPj3fD 3/FvgEG8DjrjfUzYZX4NvabVqSPcRXNMsqpPvTBelav5pTOFFp4/R08ygI1wz8VP8xu1/Q0NPXXT PDK9Vha5cC5UENhLY6g82Q6Crfvn5oV5j+rdIJzjmbDvo+YHjkCwl+pQGcgzPYKzeYKaTqNNhzWn jGk0egSie+G80nNmodPXaCEIi6+Yo8ecLcxRPaWCMKjVp8zZ8iKIVoGBfi8BFDaR4TV8p250tUn+ rDflCMib+YwZEGTHDA/5a7EDnwufA+wCc4YZItb2WK0ziBkepieN+Xazf9LzpkUmHlKucg4116/K 91P4w7XR+6cAyNOfNBJCuNX7YfSGnspqXvRa0VSwN6py5u/xfJgbiTHLnE6PduD5VJ7sra3cRjA4 8gSeHhrWHS9Pc4BoGDh+wLp6YFbyAr0JKoGyCpVV4WCHQG8Q7IUj6gXRMHEVXqvyBCsQZAhP8w+Z Q4bBB600fIjJou7nUQjt1R7BXliYmwFvc1D1lWSIE95AnvWL0zwAfz/60Y+++VEQxOugGd7nxTQO ggwPq1eQY5IhYs1B0T5u6tWdqWJXaOLrWQGurYC4+CkkOQhqnzAPp3MOwVApuFZj8QgQdBj0caup DOoNZMd9b/6tYWFvg+Sn5Mzspq8KgtpuhGNSK8Y5LjUkpp4Q9brNejX4Hm/LxEp85tx6g2KmJ3gr EuaK9QoHuIhyQWUxCmyiVpVzowtdVgtFPqVYRO2d9katWrfwWVAPBUFuEnUD5UdyaqN82Ag9WUTP HNZuBP53LdJQIOyFllno4fDn4KfwRw8gi2E0FUaP3uPhAt4bkUDNVIVW9fNsFfPM6zhu+XzwDDFm OVcI63xGnq41ArUR6O0JB+t3n+YR1KRWiMSHqknvEJm5L2wuXQHULHT5oq0hO4ADk/e1tUfV9Jee QS8Q8KPg1CMyCmmrR7ACCnoWmftW5Vhh4mhV0id4BB0EGd5zg8Ux4C0VCDLMDdTj3RQCCYOAQ4SM 8Wx4MgeLRjC2PNG6KhzxU0aYX0MvjCYjM7FbG+TqhqO3CdAWQKwO1HxPb7DsB9JXjbe18EorrXka hV7bLAD6/VQewSrnUMcsQ96aN8PdMpPeuYlhWx+vFh4ViMzkBr77ZBF+34xBV8OsJzF5w3MtpmLe IMdmVcDhfddGrTsIbKzg1IpVhu20ep2AQU8+w4iaO+h5YlVLEP6b5oxhUVWvilddzuYGtlIEZrx7 W17T8gj2CuO0uTMB34spmAZEj6zmPVfhYd3kKkBpvh3hUM8iVm+vAqNDoXsKvcKXhW4EPoU9L35T zx/Bryrqc7hVr6AXj2hlu25IRn0KR9XRXvDCno/4fo5ZbYflzpmq5+ne3P1WR4IRbOL3LwPBVkNp 7SXISaGhD016x8PwVhizi2yv4lGrcglvBEENCTsEalEC235U7WS0kngFBr1qWN+rXiM2GSY06K7j E0FQc58UBPXAdbr6YSi0ZYy2bYEeCoKAPgLgVhCcaSWj4Wrv2ceKNAfC0ZmY+D2r0jQRn6dscPHz 3b62iVCPqoM0wyRYSGb6zs1Uufe82OoBJChWY5YebHqVmDvTOv1F+weOQHAmJHwWCPbCw5UB93xB hUFtr+Q5Ut4GRKs7q4WwWvC4yPEkG3p3FD70XG8FQQVTtjSqoG+UR0ZPEmyi2jc9Ym1LxeUWqNvy nh4IaiEQ7J5GxJiKog3jGV7lcyBcubeJtl83jFXDfH4n8+z4/NTDq1BYVesyFcBzCjV3VMeL5/t5 uNfTdCq7rBtfQi0jIZojrR5pzVcdwd3o91r9zE2SzxHooWOW98Vx6+kMM5A2s4ncA4EfAYIe+vBO 661FrOdpGbWb6YGgHgHGM2FnGwa32maot6TnGXQQ1EpOgAMbuqo30HsH6vFDo4Vza17CTJiNC5/m PekmQCGGbYTo6dDmuQRBPNMKBDVHEGDIHEGAPQtG3COooWEPt9Jw6mHw9FZzbGrowRP1vXqz1WuK z1IraP1EFE8EVwj0JtxVD0T1YGr7m1lP4Khi2L3YXnlcjVncIxcyhr29ilvHsJ4vPEpzuAMIanI3 vYIaPcF41XO36Q1xgOCzd69cq5VH1dJDT7ZgCww9yxvjkxCjp//4qTe8BoW/qnhA+8YRKGATtW+e 5litegPf6RVsgSArwqtjLT106943aqLeNa+013ZLDoTs06pFZgqDPSBswaDnGLLS1z2A9P7pKU9V NMYb/mvkaBUEvRfiCPRav3cAVE85x7FWOuuY1cb43sptBgJnc1+3gqDOie7JIqs7WQ0F9s6V9caX HJyaMwHDoc1TqyOVZhYrhS+FMj2DVfsGjkLD1ckRrByumvVWod4KCDGA/N81vMamrtpTChNPvSrc ebDH1mjxnMmv8gX21SCo6QF6ogeeF0AdYI5j3VgxDAAkBCIsjN+xOTPGDCY4ixM0505DkS2DQ8BS rwevj6FXTYbX3lW9hrp6hjBz5vC5epSUt4aoThCojpPiolwloffmEAGxtck6Ysxisdfwop4trFCu eYFHVguf6RGctaUOgy3o5+kU2li81Suu1QtOQ7be0oOeKIwjfC7HpnoDNZfWQ8OtqlL3cGkeGQsh 6E2CTeQYwfjQHKutIPiOXMEeCFbHaVZwpiFbgphDYHU2tzeX9o2EhlbpXZ7xDnpOoUJgVfRBL6Dn +rm3z1NeWv/v8Mzxzzxuz1HtgaA3ve7lr/omiV5ALYRRSOeYhf32c6FXQsJbxncPLnuexWFoeNZ4 +QV4aFj7x9Gw6SLGwalnIsJAaAWkNiAd9eVrwZ+DYOscVg8VK/wxHNyDv6phb6uYgNfqIMjQGk8Q YUNXFhIwvKaGspVXdVb7mMoj6LtiPHtdUPxcVVYMQye2jmHBCCqE8cPzfvHveD7ak4+gxarhyniq bi3DSaPplWq9vJTK4NCjy3YcbAdRHbFWecSquaThJtXSK/GZK6ih6z3edR2z3LQw7M28R4C4jlnC uBaItHoHKgSOmqJ/ukdwJV9QYdA96VokxAWc8K/NdquzZOmdq/q40QOoXg5CoLb0oCfQQ9RVWLPl BeS1efsQbx2C8cWxQW+gLqarYbNeH7YtIeDWe1og2Jq7HiHhekjvr7ZP8Vw6zan1zaKf3tSKeDDn s5c7SBh176BDqlf8+tz24yJb11xdu3rEMf7UFjPHkmOulxaxUgiiRVP0BjoEEtBx7zpmV4tDtkbp fB6sehJPBUGSv7qsNV8CixiTZvEwNB+rl5zfC7/2KjsV3rynYHV0WOtortnqUc+r4qLKhVnzyAiB GIisouMOvZVH9unFIurqH4Egi4X0nF8N3bOAh02deUIHdMOiR80YFtbFxSHQW9vowjvywniOVtV0 VfuxMeSmYVJN+tZnyPwSDflXOZfsUcaWMqzEB5BpxfMMDI6KnDBmtTpfw8FsecExS68SFwoNE6mX wL2BlVfbe3C1gGBloX5VH8EVwGjlC2rbLR2PHpql16blGWz1fPPWHxqmZbhP87wIg9y483s9t60H ghUEutcR44vzwU8S2QOBr/YKtkBwdMwmgVBtI9NUtPephlV7RVUcN+5V1tQXzRmsPINVvqBDoXsq dW67V8xtmp8b3oJlasJ8aHUaMTeVXujKG6iV6aP+lgqKfB+bZLv9Vm855oWO2a3h4C3dPPbMh48B QW8h4keM4SEwX1C9Gtost1VR3FrMegUlftKIHxmmPQM1N2rGE9nKGeSiSm8niwl4XBpDwt5QlCFO LRTRnYi322g1spwdSKuhYXoFdYL7DrgCQQCEFgvheUF3hUEP0yM0D33pRfVjfjwUWUFgqyCDxlMX QW3HoM1atQGrLrJsuaAhMOZAaWsMXkMLglp9GbUnIxOptecc9dSWMq2Q8AgCmSOo6Qua+8jTEDT3 UZtHVyCoOU4VBFehlVUIVKPXgz/93QrM7X0t76equKfnV/tdatoCxyAXxipfr4LB1hjVkNcIBL1Q xEPDHhKmt5H2TIsLmFPGRZXzVFvGzNqrFU/x3mfXSz3A71qRMi+i1KiEFnx4+kRr0+Rw1YrCaUia Hl73CvaqigmJzB2tir/0Glt566M0Mu+Q4A4jVi+rx1srhh0CV/oDEgq1RYynTWjurI7ZldNvtoZ1 t8yDapwPcwRnw8MaztCQ4KiZcK/EnT3lIDwblFZnbM627+Cip942Ll56xAxzqbxtjYe/WucVzgKq elMwgLyalJ5A7kLcg6TeQO295iHF1Qa8o3wCHUgrZw3TEKihaxWLaLd9wr/2gPQjzPA7eqS0Aavu 2LSKS/MpVS81mgQyTWLWHbq2ZKgMadWSgdV+XOw0dNIC+FbqhcJ1lS8Iw6xziOfUKhDqWG+lXGgB lm6gOGb1XFRqjznLJGr19PhReZU3cGUjc1cQpD3VVjIsrtNOC70cQU1bIABWYWEv3qiO+fJTHjRV ggBanTxRfbZCoBcYqMcY44t2bctJImdAYGujgX9vLfYjQKzCpzNQVXVsYLcORuJGNozVxNWfhEYt BvFQsEemesBTeUzpNKDdpdeS9QR6zFyvfZJ7Aau+mnC6+L9rX00AJTiE6zHnibYl45h1D/ZquPao MPGs9/stIKjGjDli6hXS8LBWxbEsHIJDfMKgdl/3A9cJhRr64r/pQdJ+lJIfecTjjrzyk+BYDRp+ pl7TqEqT14YBpJ/N5qrs6k8vkuZYqTdwFgS3FIj0cmtaIKgbCM8VVMBi3oeeMKMVr+oJrsKZmkup BTVsr8O8I1bYaejVK3E1L8U9g63xSu+M5vOo11B7rlVhUQ+duBewMgi+g9brVgOP69A5pE1rCYOa e+sbqqpdk26mOGa1+IWGUmFCvUme46QgqPlDvYVuJnTS8+7MegK/M44/vtGjvEWjz/HwsFcP9/qo ecNfTVXo5QcqrDF/z6tUudhrvpqGhiuPYOVpZHEIiw0qEORm4WgQHGl/1O9bNnGmUrTXem12TlRF m+p11NZdtF+jtjIKggRA2jbd3Ll9VXs2e/8a0vYikVbfSrYr8pYxfrJNq8l6dQKPn7LDfEFtG6PO GejwaSA4O6Y/BgQrGNS2BHzQPPdVDzXXth2jlh0EOB5j5BVD3pnfBwgHB3cYWhBQtRPh9SigKpgS TjGA9No0tEYIxC7IS/Hdo1KFC1rFIke4lWdAUL0buBbPWan64OF5sxkunzU9sPqM3RuF58EmuNBN d2srictefeeVxZ7H47k8BB7/03vmOfzMFPb0QFANJwyTziFvfaNVzqpttZkicNNrjWfAMcvelppM zZAfT0Dwces6HJkbONoFXwEEPTRMj6+CoKYmjPICe/38HAS9etcLfKrTHXogqOfLcoOgPec0xKip EgqCqzmCswvgK143A4JVgv9o0zcqmOq9X+FKN7UKguzn6m1lCH76p1aP93ICNZzfqoTV61anEb2X 1ZhnpEO93pobqH0zNedvCwjqWcIs9tM1hhGPTwHBLWP6UBD08LB7glq95DQ8rAuqVqaxKk0Plx6d s+nnV/qxSdoRXI/MqXINvJ+QVtl5CEaPtxkd88Tfw+hpHgITtr11Q9WLSSvHXgmCvdyCUU5VFery xY2VYHrMnO7wdHenx/t4NZc2XdXqVPeatirYHAIr72AFht4Hq/p/T+5uFfW0cjl7aRdVRTaNO4tH WF3nRtM3Ma32N7pZ4ZjVFjiECM0boheJY7eCQFaFeoL1Sl7gCAB7OVyfXixSzRUFQRZfeMFGq40L Q8UVBOKzmLOnGyl9foRBDU27R7J11FjVeFjzrCqP4EqobctCeOR7ZtJlVjbhW8KEVTpJKzWHhSjc kGtbmSqiwaI7jgf36rfCwb17buXEkgdox1iYRDvWKxBpnaqjOYIaCh55Bek88vCwtv2a9QjqeFsZ C6+qfH85CFbFAlVlZgWD6q5mvyAtE2/tALxtB3cH2gxSWxh4Y0xPsvbjj5j/pJVvagR1cPo5oIQZ BRlcHwaQ9ijSXbn2ZPIGvDMJxKuL6UpuTc8j6IN9lADvHqxqt1cdV+XJu5qT5lXVrQKRVli45Rmc 7X/lr3P4HOX6VGEez6VpVQe2GhBXPbf8bM5qA+NedI5ZtodRL2B1moBWPnK+twpEtua1zizon+oR nKka3hIa1hCtA2CriMNPEtENKJ+j9nJjH0G1g94jUMPBDAkrcLrnsecRbOVczTz/V7/maBDcCgru aXNvm7ad8hCxwyDTXbip03VIN9hbIFAdSFVOrEaMtChPNzmtvoGjauFWrqCmjtEjOAuCXGN6rY5u D4Je9eZeQQ0Larm85i3wrEGtatLjZGhsHBSqdh16HIz2/3FDpOcrtipAva2C5rdow029Pu/uTw+X niOJ64bR08+vqugINdo+QL1Ovqi2yte3GJaW8VwBwZZXEPegvSS1KXKrAtd7Oemz0MT2Vji45/Wr frcX/Crvo1f3VflBrX+rKgO9OpC6ch4xD1M3VdXZsNXmpeqnxTGrqQt++oQXh/jYnakqnMkJnPUG fnLVcBUunCkW0aPm1PZolIO2smoV46d6MAqhz9IjEVq5rOkHDFHrJrkFgA6BPRCcqcB8NeDNfv4s CPrnbbHLo/f4RtIrlDXFpYJBB0Cv9tccZ3pttVPFSpEEr1XbJbnt0n6V6nCZaSBdhYiZdjRTPDIC QYaGFQSrcHhrHI2eJX8/Ow5XXne4R7ACQfcKclFsgSC9BTowq+apnhdTGTvNT2G1Gpsxa/hK8x/0 6Cb3FnoSNT9Dj+jRfl7a2LV1fXgNFlV+tjdW9WRc6qPeQJ+QvWqtIwfcLAhq8YhXs+nOVEOZqmNr gdEwcNXNvlUcsgKCWyFw5H2semrNwmDVf8u9mr7jVw+755e1vOCeW+YHrPt80GewAoFVSHglJLbF 6M16Blc+e89rRyCIMesNpb1qWOcJbY//6Tl7VQGH9/TTcH7V+5WbCza19j+r3nNV+xEdM7CJulGY 9bDseQZHvHfFJlbfN2ufZ1434xVUGGROsxYF8Zn0IJC2ZwSBPU/YDAhyfI+OUdQj4zyPv+ojWIGg /hsjJHhvFRpmbivHLPTQ9J5ZiOs90yPG5sih8+Mp9+1/oy+bGXytEIdXD3uIWL1bHirWAaqd0LXP 0ayx0cR1Dm79fK36VMDTain/DC0KaL2fgKnd2NmMEwOoFU6jh9TBT13ynnvmE/Jor0ov32q042kl wqsHSxuHtpKVvYs9Q1atAhq2JRmFgXvgN5MH6M+lAsJRS4hWo9WZf9fP1jnmrZo06Z/jXDdB1aJO Lw5eV41Zhz8HiNGGpZUb2bI7I3s1m9f6CTmCulnSDbXaTS8G0jxqtS8tINNeg71QrW+q3PZ4dTq9 zb12I/ydz+dqHuPzMb60ldCWhXXL+Nj7nr0gWI2DGUBovWYWBml/teF0Kye9VeTWWmd6mjov9DyC WpSkufyMsGkuv58fvCVMXFUO47u0WEQPBOCYxT24N3TvuHrl+6c8gnvj2S33dC9M3AoVt9p09GDB DY0PboKWd1ynB4Vgp25yT3yvPqNKvvXKK0ItBlAvqX4FAnsdzWcMysqAWzF67i3mDl9DmjO702qH WoUcHcAq+JoJA8/CX+t1rTxBB1PdVc8A38xrFHq1wEW9Ot5Ytmoqq4t4a8y25sSoV+CWqvaVMeqv nfUEzm6O91zLyCOk0RQtBtJcQTbY1U1r1cNS019am2Y+Z291pJ71qurU+2gSJL3YoFV8oEVd/C4H QQ+zHa37UZ+3YhNHm+YZx8vM+jwDg4wg0DZwPaxsa8sDWIWCZ3T1jQ/XBO8zuwKCTHHRCuKtMKj5 0QRBVsDTG4hrxZhVb+CsJ3BGo1e+ZhkEZwZdNXjdC+S5Cq0qTA2DejPfCgo9p8GhwT0UNDrqLdEq Kv0Oev2qgg2/ToKhegrd3a7/jwHE721dY+VpalWheoHI0QC4xSOoO92qcEQXOt2demsWhW6FjKoq uII/95j1qoI9fYGwp8+7+rtDYeVlbPUxPBoIq/vVxVyhsDenvJccxqzCX2teaFW7wq96rStP4JFe QLVbVwNBhUF6eDVErCF/jZZUm2OFRffeacRDPbuYb+7J5fjxzbNvmqv+g1XYUb35/C4uqtqC5AoL 6ztB0OFgxSvoxSNs16JOmMq+ei5gL9o0Cy+VBxzf47njrdCw1geoV3AGBv2kkSpMrCeM6AEP8LCz ippebI7XK4xVX8enQ8OzLt4RDFZhYg1jzYKhQoH/3RdoXZBmwn+9xb4Ky1afWYW7q8+F0Rt5/UY5 Z61mxK9aUDEWVo1eKw/K8wa98KHnVWu1galy8EZeQf+eysvXgv4KUN2QVsU9I8/ljOfPoarKNdTP acGhgu8Idjlm9XWj+9vqrT7SqH46CNLGVh50jaJ4or82N9cN6OxmtJcLVnmvHRy0yK9nlys7XW0W ML6qvMBZuDjrdas2ceQV3rKJnwHCVkePah2uQsFVIQS+d4vuer3KBtzw0OvN1AZ446qq4ZUQcavZ dHUEnR/wwPPr4WVnESdsJ8Ysddmiw1nv2eUR3DKAtYq4BYNaTNICwhZgzYTnRoUCCgPV5/H3o8+Z AVt+vrZKGHmPKk/SCgQeOdi2GL0KBr26XFuijLx6FfCN4KnlFVwBwZnFbuQt1O9bAdo9Fce99jMK 4KPxyzHb8376PVW5qzPhr60LTDXWrwKC7kGv2n8oDCqIeXRB/3/091EokGOPHnz3Lrc24DO2Wb3h BEEFjiNt16s+a4tN3LKW9uZEb061wsTehmq0zqxUxI60HoGg9hGkV3AWBGe8guyj2jqHmF0T2DOV vTbhVcd8wpiHXgTBI+3VSLsjfn84CPpudmXRd7Bqee16OVs9T98oFOeLYLUQzib+V5/V+jzuJGa8 XtXO/CwI3OIR1MXNPR7VJmEEdFt/73DZCw/7eJvxCK54B6uNhV9f7z4r719Vcev6joBwNB9YITfa uKzkE73Se91LZ/iUYpFWqM8rKmchrNqIzERRKg+dQjzHo27aZ9IhemPFN3TqXbnSwvouEDwiOtdz zLRsUFW0s/f5tEAQY0pbyGirJD/IwdvHtQpHZqqIHQgVBPG97JmKdAqmThAE92pxBNitfsbLQFAv pAWDlWtak9tnwbDnmWt5k3oL64wHajaPa/azmA8z+3q9fs8HfGe10h6j1xsXLWiZba8yCo26x7EH gqM0hS2ewSqE2tqojGDX77UFgdW/z+jU8s4ymb+afzqOdaMyW72uY2PVqI1ef0WPIPRYCeX1NsSt vFdPnanCgT5eel6katNa2beWLcP4euU4GI2Trb/fYxNHjpRVPUbe9p53sHouhMDV6xhp6SCI78FY Ya44Cy/ZpohN8Xu9hI8CQXgLeeoXYFPDwsjJxWYLcwp6YcyO7vUTf/8WEHQvkA++Ue7STAjWX7MF AEcL7hG/b13XLAj2QoJH5mzMDta9Rq+XS7ri1ZoFRNevtfEY5Xt6QvUqDL4TBEfA3YPu1piHbjpm W2HtFW91NRZmx+HK664EglV4uEryn41ejADRvdOtnE73Jo82sCPbWVWOKwiuPN+zX7vXJlZRkz1z YzZMPNp8tzZxR+i9AoL0CvZ6n3qu4GpfQfUItkAQYWHkLjIsHBD88Yw9YjDkM66n4xFGL8/9es/9 ys/saiB4Za2feO2xibFnVxv3P+URXDWSeX0UiAJRIApEgSgQBaLAtRWo+4Bc+55y9VEgCkSBKBAF okAUiAITCgQEJ0TKS6JAFIgCUSAKRIEocEcFAoJ3fKq5pygQBaJAFIgCUSAKTCgQEJwQKS+JAlEg CkSBKBAFosAdFTjkiLmrVcrkevdXd91xMuSeooAqEDux3048ScPMnihwVQVOAcFRs0v+vmVERu+f baA724Ouatw8at47usaf3OP3m6fOvm9LH77RZ68Y7asO+Fx3FJhVYGU+jF47mntHN+odXc/s71eu +6jXekPpoz731Sc/zI6rvC4KfJoCh4DgkRN162c5mK1C3pGvH0Gi/l6PU1o9fWEFeP21szq3FoxP G8i5nihwtAKzsDT7utk592pg8etdua5XvbZlE4/+vtlnteV1R4+/fF4UeJcCm0Dw6Mm59/NmILB1 Mseo872es7kHFltw2AJBPy5uzykRPY/mrPZuGN81QPM9UeAsBbbAwOg9W+fb6HO3/H72WlZfV9m6 1mdUr92zOR5d6xadZt9z1jjN90aBvQosg+Boor3z9zMAyGOZZoBv62v2ACKMnh7mPWtEV7yBAcG9 0yTvf6ICswCw8roV+7jyuauvXbmO2deuREKqs25pp2gTW3arB5Wja13VaeX1T5wjued7KNAFwdGk evfve4amBWNb4W7v+2bhcGT0WucIbzmPdxYyq+caj+A9JnzuYl6BFQhYee2K3dz7ufr+le/tvXYE fLO2jzYW5xXrmfGwifh/nmOsUZnRd48iKSt6rr52fmTllVHgsxS4BQh+GgQqRI6M4lYQpKGe9Yq2 DOjs4hAQ/KyJm6t5vQKrIDD7+tk5x9f1Pnf1s6rXz24QVzbiMxtpAuDP/MzPfP3sz/7sdz+wifh/ /DuBkJ83ip4EBF8/L/IN91OgCYJbDcysUVn5/C3erxlDhNdw14k/Z9+z5XUtIHQQbBm6lRybmV1z y9PY+p6A4P0mf+6or8As2K2+bsX2tWBwy2f4e1bsxKwNdtuo9lX/DsAjAP7cz/3c18///M9//cIv /MI3P7CJ+H/8O4BQYVDt6JbN7eqzWnl95lMUuKoCJQhuMTI9o7Ll81oeL3zPFg9gyyC98t9ngLEC wSo3Zk+o5ohnExC86hTPdW9VYAUCVl67xx7OvHcV8EZRi+r3atsc8Ah5AD339hH6AHq/9Eu/9PWf //N//vrlX/7lr1/91V/9+rVf+7VvfmATf+VXfuWbf8dr8Fp6CLlh35r3vPKcVl+7dZzlfVHgbAUu B4JHQaDmpPDvM1BYvW/ms1pQeAQI6uIwuwjMLCj6moDg2VM13/9uBVZBYOX1q/Pv6I3gXvjTaIra PwU/QJ+CHz1+v/iLv/gN4AH2AIC//uu//vWbv/mbX7/1W7/1zQ9s4m/8xm98A4V4DV5PD6F7B93e jXRdeUarr333+Mz3RYGjFPhYEJzJfWvBVQ/oZkDO3++Gjjvd3p/6ntb14Pph9Ho5hauGLiB41NTI 5zxdgVUQWHn9CFhGv58N1Y6AbyZqoUUdtGU9+AO0MdQLiCP40ftHAATsAQB/+7d/++u//tf/+vU7 v/M73/zAJuLfAIWARHgGCYOaN7gaOVl5Plte+/T5kvu/rgKXAMFRaMJz/bwKzeFvFuD4Pn+9Jja3 /s73jLyFIxD0nMHRAjHjHVz5jFaO0nWHfK48CswpsAUGZt8zyvkd/Z4gOAK9mQjKTCREbaqHfNXz B68fwQ9eP0Acfgh/8PLRA/hf/st/+Qb8fvd3f/frv//3//7dD2wi/g1wCCCE15AwqDmDKyA4+1z2 vG5uVOVVUeDzFDgMBFs5fVugo9VjqpeI7NCmgEZD5aEK7lz5J3/vcOeGjiGO3p+tz2YCNHNeqlYJ WiG32ktri94z70lo+PMmb67otQrsgYKZ924p3qA9qLx0vgGejYzoRre1sa3CvPT2wdOn3j7CHj1+ 8Oyp1w+Q99/+23/7+oM/+IOvP/zDP/z64z/+468//dM//fqzP/uzb/6ETfyjP/qjb36P1+G9+CwA IWwurgXXXLWVqWzZzLM44jWvHY359CjwOgU+CgR74eBeeKKXl6LVaApuNGL4U//dAU7fr2EO7HZn fqrvUfCE0VPvofbO8lNNVJ8ZeKtes1otHI/g6yZfPvmzFTgCDlZav8y2Z/H8vFGqSi8iAvCb2SjT 06dhXi30YLGH5vt5yPf3fu/3vvH6/f7v//43kAcA/JM/+ZNvAPAv/uIvvvuBTQQQ4vd4HbyGDBMz RIzr1sKRll179TPUz//s0ZyriwJtBQ4Hwa2AgveNPIGen+IAqLkpbri2QpuGOXTnqyEPhD38h7/n n3wvroNwyFYJ9BJq/ou2s6laJmzVOSAYcxAF5hR4NUTMegQrL6C2X6lgTiMPlZevVcihdtM3vgQ/ VvkS/uj5Y8GHhnwBf/DqAQDpAYS3jwD453/+598A4F/91V999wMQxL8TBvEZ+Ex8Pq6BXsGqgtg1 ffUzDAjOzaW86rMVOB0EZ72AleFTo6VGivkoMFTaloDtCVp/4rUV1OnnYMfLHxhA/sBI9X70tXg/ rgGfC6OHa6dnsoLC6rzjUePUnjdwtQAFn5XQ8GdP5Fzd8Qq8AyJaOb26+aMHkDZQ++6xBQs3mrrh 1E2nRiZ8c+t5fG43ae9o3xjqBZwhjw+hXvwQ+ujxY9gX0MfQL8HvL//yL78Bv7/+67/+5udv/uZv vvuBTcTvAYjwFjJEjO+C3SQMYuM88gq+4xnyO44fgfnEKPAeBU4DwSoM4savZwBhzLwKjYAHw+WQ pv/f+rtCXu+ztNUBjSLCIPyBgez9sCIOnwOjRwAlELJVgnfW7zVTnfEQjiqKe58REHzPhMy3fI4C 74CIEQgyJUYhEBtgAqBvenUz69EIj2hw06thXbWdaudgswh+LPBQb5/CHzx+GvYF/OEHcEcAJAQq APLvsIn4PV6L9+Hz8PmATVwfrhv3782mW6kz73iO+I78FwWuqsDHgOAIAjHpAUiao6IGzMMT7Eml gFb9na/TP92z55+lkIddKn/Y/oA75NaffB3ex55ZMHC4H/UO6jFLowKSgOBVp2Cu+1MVeAdA9EBQ 86IZ/gUEwkZoDz7f2HJDy80sPXz6Jws6CH60eb6ZZVsX9fgh1Aswq+BPCz/o/YNnzz2A7gV0jyBB EO9lriC+F9dJryCLRj7FK/ip4zjXFQVGCpwCgjO5gNwBs1iDDUhhBAh9vkMdARh/PwtsM5+HXbHu jNkGQY0kQiTY1eoP/g0hD4AgDBzbJdDQ9Rqpbi0iiUdwNB3y+yjwEwXOBEH3BNILCC8f7B82p7B/ bqN0k8kNa7UB5u8U9FjNqzYMdor2i14+hnoZ7kX4ll4/Qp97/nrgRwhkmJgeQcKgFo7gemEjvYIY 60V1HnGr2O0VzzZzJwpcVYG3guBKPiB3wAwBA4zUAMIgEMCYjAwDhr/7D2Gt9Wf1Hu1rxb9Xr9Pv rnbJGiaBAdUfGFYYPQAhrx0GWhupAoCxCHghyZYwcUDwqtM0132GAq+ABf/MyiOIuc20GBbEsScf NsKsxtX+e7Q9tCOwdb7xJSQytKtePto4LehgUQdATHP81NOn1b49CKxyAR0AHQQZSgZowm4CSHHt uH94MnvHz3lR3Due5RljNN8ZBY5Q4DQQ7IWCCYHwBjIMgolPA0gAhNGi4eLf8SeNIv7sQZzCnkKc fpb/3T9b/5/XwiRp7qDdiHL3jD8BgjBy7JuFe/NGqjB4DIPMHMA+24x2pWgkOYJHTLd8xpUUeAc8 tECQPQGZEgNPICAQ3jDAEDegHm1gpEFtX2uDrPCnPf3U4+fhXfX0aaVv6++Eu9k/8TmaI4jvY39B 2MhWBTHbyfTCxK9+nlca27nWKKAKvA0ER95AGj7sgAE9DIUgBEBPIEMXMHIKWwAu/MBQ0BAqEFbe Pfaz8jwXevD4mfxchnX18x0S1SjT81dVy3Gniz+9VQKuB0Yexp5hYiwCWwpIRh7AgGCMQRRoK/Bq cMDnOwhyg0x7SFtIO4jNsDZjVjsDW4P/V1vYs1fq/as2rdrWpfLoKdzthUJvH8OwMAtGGB6mbUTU hEUjrXOIeSrTu8LDmUtR4KoKvBwEZ6qDtTUMDJ/mA7InFQxA1X8Kxgq7Vu4a3RBWnkL1JHqFm+e7 0Hunn8/vcEgkPFbtEnqtEmBQPR8GBhzgy/5Zet4mNKJnEAtGL0y8AoOpGr7qNM51v0KBV4NgCwIR FmZImMUh2BQyHUbtoIdm1R7SZhEQPTVl1NYFodu//du//fq7v/u7n/rBv/MHryMUEuh0s7v6d7aP 4ft4T7gPbSWjFcTQyauI9VSmd8DgK8ZgPjMKvEOBU0FQ28NoVZyGQZgHAzCCIfA+VNpmQJuQjjx4 mgBNrx2NqoY/3Bi5cVXPoSZTEyiZN6M7ajWiMHr4fxpT9s6CkYbXkjCo521iB7zSWmYWBgOC75hy +Y6rKPAuENRzg1nwoF0S4PmCJxC2ABEDPY3DPXHsv0d40o0sbBLtF/+up3rgvVrUQQj8+7//+y// ARy2QHAV/Pz1LRDEtcJu0y7y2Dl2Wmh1WdCeq698plcZ17nOKOAKvB0Eqy75Hg5mLgy8YZj0DAO3 2hDAGOpOWL2CDBczRKJVcL4jrloc0FvnwFl5CHvh4MrAwpjC6NGoAgb1XrYYvdbZxDMwGBCMgYgC P1HgldAwKhJhSBiQg00gvIEMCRPeWvl6unnl5lbzkjXqUTV4Vk8gAfAf/uEfvvDD/6eXUDexGtmo rqHyXlb/Bpvo/86oj55BzKI6hoi1Gf8ZuYKZO1Hgqgq8FARbeYHeKJr9AWn0uPsFBMLLRsPnMKVV ZwpqnivjrVu82SkNaisUojtf7W+lIWlW1vFPb6fAa+dn0ZASBCvDis/Qajk1fDxmycMhrVNIeiA4 04MwxSJXneK57q0KvAME1RuoedIMCQMCERYmBMIeVLl7HprVHDv1ElbgRa+i2ijaI4AfIVBhcAYE K5hTIG39vQJBbvRZWAfPKODYbSLWkipE/I4q4q3jLO+LAmcr8DYQbB2ZhEnLwhDs7JD3wao4eMQI gTBWBD8NrXJHqoBW5QsSBjUpWs+5hBHUUEjL6PmulzCoIWMPC2vIZQSC6h1kxRyMHxYCGj54TNk6 oaomXoXBgODZ0zDf/4kKvAoEq9xAT5NB2yykyMAeAnawKYYNw0azFbnQ6MUsCFYnfWheoINgyyOo eYIa6q08kiMYZAFdBZIsGvEcatpEbpC9u0J1xvrRz/cTx3CuKQrMKPB2EKyOTGKPLBo97n4BbZpj R4+dJyozpKrnU6pXUPP4NBzsnw0Ia+2A8e/4PQ1e5RnUvBsNuVS5N7wH9wjqThuGXc/b1NYJWjyi uTEMiazAYEBwZqrkNU9T4GhQ4OdxvgFONFVGj5FjdAR5cEiRQZEEoyNaeOZtWTRnsAKyqu9f5RGs NsW90LAXjHho2EPTaiv97wRB71fIHHB6Bdlqi15BPaJTc6iropFXFI88bX7kfu+jwEtAcBQSBqxo cYi2RkBCNHe+mPh6JmXlCVQw0/Cw5/Axf0+9df7ZhMB//Md//Pof/+N/fPeD/6cRVI8djDDhUz2D +h1VsYh6NlksoqEYQiful7t8gC1zBuExZcUcjB/DId50uncCSbVDTo7gfSZ27mS/Au8CQW0ezdxA NtBnmgwh0DeVo4rdUV5e5RFczRFkVKayxVq04sDn6TQs2lMQdO8h7Tr0QHhYYZBHdI4qiF9VPLJ/ xOUTosA5CrwcBFshYRg8AAy8gfQEAnAQAgG08ZDyXjjYcwQrEKRn0Lvje7gWIAbYcwgkELZg0D2D BMLeOZt+rqaGi7U6T6FT8wXhMcUuGOEQeAY9X3Cm6XRA8JwJl2+9jgKvBEHOPw8JM1+aBXMsEqls op/MMQoHK1RVHRJmK4a5UfXK4SNAEHYaIEho7IEgW8mwswLtIcLqXkHsRXS66T3qOV9nZOdKo8D3 FTgcBEfeQD1DmHkw2iMLk9vzArVYQyFqFgQdAqtwcwWC//RP//Q9ryBgUL116rFj49MZY6utZOgR HMEg8wV5ADv7C67mC640ke4Zy0ykKHB3BY4CBP0cDwsrCGojfd8cw4axSERTZEYhWW8jo0Vs2oVB oyP6+UyXqdrHVCDINJ3WBllb2FQeQQdBrXDW97KIDo4DegW1r6DmTldnEL/CK3j3+ZD7u68CLwVB bxWjjaO1Ko6Hp8PVz7xAFodU+YDVGZXeid4NDv9fjZ+HQNQjCAjkT88rqMUjbnSrXbfn9wAE3fC6 8cX/MwyN+0DoHLtghI2QQ6S5MaN8wVVPYCuX5r5TIncWBb5V4FUg2KoU1u4JzA1k78De5thbXLV6 Cc5sUj0C8wkgqCFlwiNBUE9i0r6CbCVTRUdeVUGceRMFrqrAy0BQIRA5gfhht3wYPG0ajVAn8t8Y /vBWK1Xj0laFnPYT9JwUhpsJY+6F89DwCAS9eKSqkFMYZAK1JnQDBGdCMvwuvBf3oR32GRLxQ9jV CDJfMCB41ama6363AkeDYFUkonYRERJtocVUGdhG2i61Fa38wJlK3VFouNVKS08ZqSI1DqVVjmDL E8h/h03UKI52ZFAQZI9Y5gpW7bWw5lQFdK+AwXePz3xfFDhKgZeAoOYFEgIBJdotnwUi8AbSE4gJ D1jqVQc7AGoIgsatVZF2Bgh6rqBW8uHaCYLew6tq5MqkbHwGk6XhMdCdcNVfUJur8vzNVSBMH8Gj plw+5yoKvBoEsTnTSmGmymBjp1ESAE8PBLmx9H6BvTYtsyA4e8RcD0p5HTNhYYaGR7DIQwMAyfQK MkKCtYV2kCDYsoFHVg9fZVznOqOAK/BSEPTG0a0cGFYJMwdGjc/seZa6C65AkMZIe3C9wyPoOTnu IQQIevVyq5kr8xMBjawi1sIRVBpiMeEh7K1j6DRPcKZ1DF4TEIzxeJoC7wJBdlDgGevIdeO5wmwb 45vYVgNprRLeAoLucRyB4ChNZ69HsAWEenoUNEKuIPOmAYKwgx4ermDwyKKRp82P3O99FChB0Ptd zcCC5r3A66QtEXiEnIY+dMdLTyCbOmtisp/qobmAbvRaO05NOJ4BwdnK4V5ouApLV1BIEByFq1m1 jPA1vpfFI4BohJC8lxZbKFT9tNQrOPNsA4L3mfC5k3kFjgRBnWeaNtNKl9Ez1nmaSHWknJ/TOwuC VVcDzV+ucgU9LNyCwMpGe3utUR/BGe+hewXVDmrDfXhdmSbjYeIji0bmR1ZeGQU+S4GXgCCrtDwv UBtHa/4LjBd3okxO9iOMtGl0lQxdJRTrbpIwqCDoeTA8TWQPCHoYpAWENNg8YL3yCmpza1yTtrBh s2kYQ280DZ01NEJDqH0FExr+rImYq/k8BV4Jgq3egawWxpxGyBMbPQVBhcEeBLaqhVsFI95PsHUk ZmWXK+9klbIzso20ldo+psoP1DxBpMjw/GGsKQwPt2wgU5U0Z/qo8PDnjeBcURSYU+BlIMhzM7Vx tHbLh6Fj2EPzAnuHmrtxqXaZaiTYRJqNSul5pNHzat2jQLC321XvJIy1gqBXSlcgSBgkGOPzEBah EUR4mFXEegg7YLBqoxCP4NxEyauep8ArQJCniRAEvbE+QBAww9OVNG2GHQ8cAD03cKZYpJW7rKeM MELj4WFtIF3lBlanmziYznoERyDIU0awlrCfINqRqQ1kVKTyCuqGeO/zft4MyR3fRYFDQFDDwr28 wFaBSNXJnjtPDT/osUUOger9IwAqCBIG1ZhWBRqEQT1ZBH+nR05BtdVHsGW81Phxd0wQbHkq+X38 fv7JsDTeh8WCMKgnjsArqKeOaI5MPIJ3mcK5j1cpsBcMqhSbCgTZWJ9NpFlAx04K7CGotktDwKO/ t/KU9X1exNbqK+iVwn7EneYYuqdwNmzNk0VmwsM8MAA2EPDM8DC6UngXBQfCo72CrxqH+dwo8GoF DgNB5r3QE6i5L+wZyLAHEqFh5GjgHAQ9/ECD4iHhylA4BDoMcidcgZd74LR/YO+YudaJJp7o3AJB 7W2ohtSLRhQGca0wyvjuqoJYzyJu9dTS/JiRZzDFIq+eivn8T1PgaBCsThOpCuhgHzUszB6CtBMj 8Jv5vYJfr8WVF49oQ3+HQM8r9Ib/Lc+lX6+DoG+sPQ2IuYIeGWF4GF5X9ld1GPR86T3P/NPGb64n Cswq8FIQpJFjWwS0OanOzoTBUOjxjvXetJlhBgdBQh/ASH/w79XpItVZw3resHvhWKihR79puHq0 g63yBWH0dKc/mys4W0GsXkFvrhoQnJ0med0TFdgDBauniSCUSfsIrxbTZjSS0QPBUbi4+v3o37z5 fVUc4j1QveNDK6LTg9UKBHuhZGgEew8QZHhYzx3mRthhEE6LgOATZ3bu2RU4BAS1Ck5PD8EEBASy LQJyNxD2QH6gnp1Z5aAwD0XDC70qYfUEViBIGGxVEBNGtX+fFmgAAvV4OebGaLi6l8+ieYp6DSMQ pDYKqHotuA58HsPDAG14XmEIuSNmO5kKBGdhMB7BGI+nKXAECGoTaQ8LYz62ThNBxAR2DHbD8/la Id0R2FVVx9qD0N/v+YK9MLDacE+foa1sdXxoeQR7lcZaeMLwMBtLY40hCGobGfUM0hbqGcRVd4SV MfC0+ZH7vY8Cu0HQjRtDwoBA5r4gTImJSW8gJiwLN5iQ3GtNQIM0ahfT8whWhSMeknVjRuBSw6ZG DdflfbJ6jVC5q9WcHc0R1HC1V+1VDaYZHsY14HvhRUBYCcANGISXQWEQhrBlABMavs+kzp0co8AK BFSvrSAQ4KHpM2oj0QIK8xe5biwS0W4HLdDT4oyq4X6vLUy14dbP878TBjUM7LZJN9BV9EQ3z1Xe Iz2CFQh6viNfQ/sH7aChn7aE9cg9gyygo1dwb/XwMaMunxIF3q/ALhCsikTYMxAGjrmBqGRlt3xW C2MCa+88zQvUqjSvQNOKuCpXhPkiHhquCkc8X9CriKsK5tYRSjPtaxQEvX0MjWPLaLfO/IRWeC8b TGsvLSZMY1fsfQWrROkeDMYj+P7JmW88V4EjQdCP3ORJS5iXjJgABDF/ATPuDRxBYOWtq3L4tIl+ ZXNbrWA8TKyfw6M5te0Wc6pHIFi1wGmBYFX0UoEgNGTRnPYTDAieO5/y7Z+rwGEg6O1iGPIAjAAE 2S2f5woDhNgupeUNdO+bJzh7eIAhglbBiOYKMuSiMFjlu3iVnIc3tC/XqG0Md/eanK0NpT0Uo7tu zaHUhtveXBo5MuylxVYy2lOLXkHtKTjyBqah9OdO4FzZ6xR4FQhq+gw3yxoxQa4bQFDtRQWC1aax VczR6pmqUQ92ItCWMK2CEG5Mq/PZtciuB4KtvEYFQYJeq/KZ/86NMAtG/NhNpsd4ePjIyuHXjcR8 chR4rQK7QZCniOiZmYRA7nTZFgGQwnOFtXdg7yBzN0QtD2GvUMMBrWo0zSOcPDxbeQC9J5bm8Ix6 eGk4GveiZw23jK4bcQdBhqgBugyNwLuAxQUgzhCxHj3nrWRGMBiP4GsnYj798xTYA4IaFq68gXra EuaoRkxgIzGXHQQ9BNxq+uybV+/KAHjz9lhskeVgyM/ytBl6//7pn/7p65//+Z+/94N/w+exuM7b bLF6eBYE/ai8qg0NbDrC6cwTRHoMw8M8dlNhEPrzHGLCYELDnzcHc0XvUaALgjCEPUBgfqDnvGCX q8fJEQQZFoaR0+bJeppIZcRaYQ+t2J1JLG71HvSQbSsM4wDoHr4WpHpyt3r+AIK6i6/+XoVzNGzN U0ZgDNlLC15B6A6vIEPEWkG82lMwIPieCZlv+RwFjgRB9lfV05ZYTEcQ5PnCCoK+cXQv4OxZwF50 BlADsCnIEeD8FKMqB5Dv+5//839+/cu//Ms3P/g7fvA7hoY1B9FzAzU/UH/nHkEHQf9/FunxhBGe v87wMFNjKq+g2sG95w5/zsjNlUSBNQU2g6DmB/pRcqwU5kki7B/IsDCABQbNd5mtPMEWKHlbGQ0V q7HwhGQNJ2hbGa3m9Uo2DwFrqKLlRaz6Znn/Qhg93XW3/q5eU/cIQgfmCWoLBQVBPAuCIBYgLRqZ aS4dEFybWHn19RXYC4K9Rvs8UYRRE02dQdGXVgtrta8WayjcuU3wFlwtEIQ3jwCHP/H/BERtpK/5 f/i9AuC//uu/fg8GKxDUTXuv6hm/WwVBbvARTod2sIFMj6m6J2h4uBUZ2fLsrz/icwdPVWAXCFZh YXoDea4wYAT5GuyWz9wX7x3I1ixqwByKFIY094Qg5PDGXaZ3u6dR0kTjXrWvw6J6AjU8o9dXtUyg QdeiFBg9NeKqg9+vv47/z+PmcD8whPC8wrsA3RUGdWesYZGA4FOnf+67p8AWGNAoStUuhkUi7Kqg x27q+etaLay9RXu5fjwVSdtcaeGdwiDAjl49evQAdPqjXj4Hv3/7t3/7+uEPf/jdD/4f78XrGBbG dagdbPUO9NzvKkewasbvm3MeNwePqhbN8bg5nr+u/QQDgrEBUeDr6xAQ1JYx6g1EyAM7XfYPBKSw f6D37euBYK9Qgoau6lFF8HKgpCdRG1OPCk2qhtSa58g+fwpnnl/oIIj7gtGjAVdD3oK+6t+Zg4P7 Qf8xhocJgswV1FYy7LA/Wz0cj2DMxdMU2AOCM95AhCoVBJFDDW8W5jBPE1GbUeUKq+3Q04/YWkpB UN/PPEH17gHkAHT6o2DIfwcA/uhHP/ruB/+vIKgVw9VhABUQ9kLDVRFeFR5mxwisMzx7HTaQpywF BJ82g3O/swpsBkFNgNbj5BQEWS3M/lh6rNworFF5wypgomfQDY6GYD2PhsaRoVv1DFYVx+ot9JYz +Cw9mo7J1lXFs7fLwfsAgnqCyQgK/fdMyMb34X6YMI1Fhf0EFQTpFSQIanf9tI+ZnTZ53RMU2AKC Ve9A5lB7s32ACfMDMVfhyefRcixeU5vhNpO2wO0HN4sOgd77j+FeevsAffTyEfTU66cA+L/+1//6 4g9e6yCoJx95LndVAbwKgprGo6k+iDhhneEJI9BVc6TZQqYqFtHm+lue/RPmRO7xngrsAkEmQGvv QIIg28ZgR8b+WIAUNpImQFUerlY+nPar0uq2qipNQdB78BEeNUQMY9LyCqo3UJO3YVj1xA/dkes1 ebWfwiNB0M815mdV4Oc66P0wTwaLCnSHR5bhYQ2RMDwSELznxM5d7VdgCwxU1cJaTEdbyeM3HQR5 tBzba1WpJ7rxrI7BrEBQC+4YjaEdQV4gwsAEQYCdgh7+7v/2v//3//7iD34HSGRoGNdEj2QLAr3T goOgN+qf8QrSfmOdYXoMbCAcEt5CKyC4f37kE+6jwCYQ1NwX5rywgTQmHICDIMiwMHZogBQYABgH wMvI++WetqphKXeezJPTELF74PT7aKj0qLiqqtgLSLzaWXflevSbeig92Vt7cAEEtYKPlXueqK3w 1zoDuTpuDiDOI+fYRobHLtErqMcstY6cS2j4PpM+dzKnwBEgyM0yvYEVCLKYjmFhFtN52xfdNLc2 iNpNQCt2fTOqqTmaK0jgI+T9n//zf76qn//7f//vd/8OUFQQ1ChFDwQVBjVPkMdujmDQw8MKgto9 gSCox805COI5xSM4Ny/yqvspsAyCzH1RA8fEZ3bJJwgiJOnnC2NyA5Ic8vw4t6plgfe+0lyUqjiD xs8r5hTYvL1B1QfQvYDV5/lnOghWHkq8B0aPVXr6p4Ogw59qwe/GdcE4ArgRYkKBjoaHHQS9uXSv aCQgeL/JnzvqK7AKgppaURWKED5YMczQsIIgj5ZzT2AvZ9g3uFosMpOiAltCryCADmAHEAQAAvh6 P3hNDwS1c0LVBkab6xMGFQR7MDgLgtW5wwqCLBgJCMYiPFWBQ0HQC0UwAfX8TOzYMNkJgtqvqiqU 8ByYFgh6dZruLqt+hWpUtUq5Ku7wdi9euKJGeNRFnx5K9whqPy+FQYc//n9LB4AgDCfyHHnuMPRn l309cg4eXIaHtWAkHsGnmoLctyuwBwRbTaT9LHYW1GGeMj8QUQjYIm+vNVtARhvFnMBqU6qgyQpi 5Aq+CgRbvQBXQNAjNl41DN1YMMI2WmgjExDM3I4Cg00vf90yel5A4GFhGDaGhdlEmmFh5KZhIvJE EUxU9g/0PLdeLhzBp/KYeWi46t2n7RdaCdfqkVQ4VGjz8GzLs1j1zBp5BLU7P+9zFQT9uDmtnGOX /eqUEYDgqHo4HsGYkqcpsBUE1UZq5AThYS0Ygd1kjiBBkOkzHhauTl9iNwQvrPPWWr2OCsxzhq2B DULRB8LD6hHEc+95BWc8gq0er1V4GB5Bbz5dVQlr2o6eLAUNtZ/qCghyI7z67PH6/BcFrqrA8hFz M6eJKAgiPEkQxGTmTreCqgoGKwjUDvgzIOhePQ/tVjl3s97IltGtehr2PIIVCPbCw+oV5PVDC3wH jCKMIc8d1uOWHAThFZw5bi4geNUpnuveqsAqDHihCDdYWjXslcOYj9ik9UCwB4HaGqtqyN9q36I2 EXaDXsEKBFdCw7Bj3snAw7sOde4VVBAchZbdK4ioE1NjWDkcENw6A/K+pyiwGwQBEi2PILvl82g5 5gfCeM2AoHezr7ret0CwdbKIhkS04tcLNtT76JCm53J6QnblkdQcQ3x/FRpeBUEvnGGlHkCQeYLQ XfMEq3OHA4JPmeq5z1UFjgRBwKC3kUFu9QoIEvp6x1AqGGqqS6t3nxbuweah8pcFI6McQRaRaNXw K0HQj/isvIQsGOGZw7B/PRDkCUssFolHcHWW5PV3UGAaBL1Bqoc49HxhAAcmH3LT2C0fSdBswMyK 4Vb/PP13Pw+TMKjd6/F5vePmNITgIVoAKSHQu+dXDVb1LE3NTdSehNWO1/saEkJZLNI6uH22ephh ap47jFwZVM7hhBG2kVEQ1HOH9bg55DZVeYLxCN5huuceVhTYAoJuJ/WMYYdBbMJaIMjIiTfDZ/GH t4Op4LDyBmoPP224D3sEW8MWMiwYUW+gVg9r+xg2lEYLGtgx7cjQs4veT1CLRTTPu+cVVMDVfrAE Qdg/eFzZOkuPl9Neqp4as/rsExpemVl57acpcAgIAir2gKCHhFdB0PsIaj6K959S7xyBlCDIXlp+ fJI2V6WxowfOTymhMao8klU7mxYIwqB6b0HVqYJoGGCCIHbGyJPRPEG0UWBouALBXp5gQPDTpm6u 59UKrMIAQsMzIEjP4AgE9ShKbQ6tEKhnDzsMsmtCC8ZoC9nXFDYF9o2No5knSABU+MPf2WtQW8dg 434ECOq1e5SlakhNIIS9h+3TozYVBL2htHoEfRO8+vxfPR7z+VHgVQrsBkE2RkWhCItFABz0CDI/ EHkbLBRxj+AKCOqB6No2RXe/PRBkcjKMixpAgJd21veGqmyq6iCohSEOndXuW6sBebII+3jxAHh6 BzUXctRgWjv5495wLVo5B+9sBYJ4fj1jyLyngOCrpmA+91MVWAWBEQh6zqCCIIrq4MVisQhTWAiD CnkzIOiVwlVYlSDInoIEQeYJwuYpBFZNpnmqCM8lVhDENWz1CGrfQy8caZ1ZzHskCDIiwkK5qo9g Zfu2hoc/dRznuqLASIElENSWCHp4Or2BIxD00HCvUpg5cB4aVhDUflkOggplekQcw8QwGlXFnO6G qx0wYBGgpjl5fmYxvs8bUXtomHmC7hFE6Bk/VT5kDwYrEMTCwoTpHggiRDIqGAkIjqZSfn83BfaA IKGPoWH/f9pPeOiRS60gyO4KrXBvBYIKjvz9TDNnt8mwPYyIMDxM75+fL8zj52ATuUFGFEOP/XwH CCoo8oQoeARXQFBPWAoI3m0m535GCiyDoB4rp41R3SOIXRjPz+SxSTQ6rePlqkpd9ZZpfiBBDEaH p4rQIGi/qerYOEIaCzfYOmHUQwsGUUGQ7VpofHhOMUHQcxMJg9zl0yPI0DQ9giMYdCDU0wSoBa5J wyN4FvAI6rmbKPLBj+fKVI2lA4KjqZTf302Bo0BQIRB/Z341vPGYk9ikoaiBdpIg2AK+2X93QGo1 dGaUAnaEjaUJg5oWg3/jmcKwg/zRjStsE23yjEew1Uew8ghWhXiaS1h5BJEaA9Dm6SKt0DBBUMPD q8//buM/9/McBQ4DQc0RxKRrgaCHRr1JKquJtW1Mq2pXc1G0V1YFggi54AewRlDrgWDVMgEgCENI jyAMHowP81IIgvhTPZK4Hhosb3CtR8w5CKqB7eUL9kBQC0YCgs+Z2LnT/QqsggBDw4CJnkeQIIhN GEEQRQ0AQdgO2AvmE89Cn7/Oe5l6uoymrdCbCDuCjTeL5giDCoDw/PmPb9Cr05oqCK16CMJOwibO gGBVUOIewRkQ7BXLrYyB/SMunxAFzlHgpSCIalWACA0cJi6Njp/QoUfOedsYegX9HN7qJI8qTEsI 5J8zIOitE/D/7hHkSR7a0d5hU0PR7hHE9bdAkDk3ni+orWv8NBb1jsIg/uEf/uE3+sMY8vB19QiO CkZ6Ry6dM1zzrVHgfQqsQABe2wNBVgzjT54ugvnHYzgxRzFfFQT3wOAKCHJzCvvB4+YIg+r5o01i xMIjF+zkMAuCmuunUHcECEJL5kjzZCWE4XmiEk9VYpPvVmrMyhh438jMN0WBYxWYAkE2keZOF5OG YWE9Z5g5gjBuPD8TIEIDV4Egc+UUBL2JdFU8AQjqnROsXjrAGdrX8IdeQT3lRFsn+MHrzBXEv8MY Akj5/RoWxudXnkff+RKGcf0wepoPSePKXXdlbB0G3SPIymFcjx+11AJBDQ8zPOJtZNQoHjsM82lR 4PMUWIGAqlBE4U//zt6rv/RLv/RdWBg9P3nOMD2C9IqNvIJ6lNwoP5CRCvUIajcDbaelEQrNWa7y tlu9VUdttTw0jGtxEPQKYvcEengY9wgt2UIGm2AWjAAENTwcEPy8eZcrer8Cm0BQC0UcBNlDkAnQ AJEZENTCkVZYWMOjGhb2XBRCIPMDFQIJazz3mAUj+GzAl+bGeJUccggBZngtewg6CDpsauEIq/Qc BL2XofYzJBBWxSMtjyBBsOqwHxB8/yTLN15TgVeAIGwnOy3AQ4XcXYSFsWHGfIXtgp3Q0OioOMQr ivleQlbVRaECQXyOpuZoJEajMVXz/REItqqWeyCoUNuDwapNDrRkwQj7qCIMDy/sCASxEW51S+iN iWuO8lx1FPj6WgJBPTez5RHUZtKohAMIcqfb8whqoYgampY3UJs5e3Wc5+z1QFDzBAFgDIV4L0E2 TdWKYRhOfpd7HT1ErG1ruAPH+7H7VePLXbjn4Xi+IPsI+hnJbDgLrWkMecIIdsVsIcPejwyVuEew aiwdj2BMxpMUOBoE6X0CiCB6AlsJSGHTfeYWj0CwqiZWr+AeEKxOW/L85N7Z73q8nG7QV0GwynlU OG79nR5IaMnwMApxGB6G7pVXUEPDAcEnzfLcKxRogiB3RJ73os1QYdAqjyALRTAB4Z6vQJDn/bKn oB4558e7Vb0DNR8ORsFzA1m4QUCDUcCPegRpcNUrSPBseeVYrYzrx/e2QJDfo8UphEEaMYIgja/m 5/j3EwTpGWyBIMPl+A41hpo0DU9EQDAGIAr0FdgCgmyxpaFgAiA2W/jB3MMcRAoNbCSP4ET0wG1E FR6eAcEqJOsFbPpdWjDix3+qrfFiPm7aFRZbudsOg1WOIEPDo3C4/15PXKFdhu2HzWeONKJUCMe3 vIJsqB8QjGV4mgIvBUFWwmFCwsipJ6wFgi1vIGCQbVO0Zx69ga1KYQAZIbAFgqxk1lw9BUL1SuqO V0EQ0FV5HgmkrWPu4BHUXbjm5fS8gjMgCM01TxCGEIsPPBHYGbNYxEMlrRNG4hF8mnl49v3OgiA3 zcyl9mPlFAQRSaE3EBtmQAoL6vaCIHupeoREbWMvR5AbYm/j5SBYdXbwtJ2qrZeHqkcgOOqj2DtW D/dMm4xeqohOwSvIo+Y8EoJnFBB89nx/8t0fDoIIPbJQREFQe2Oxjx4rh6uwsLeMcRCsjpXzkHDl DQQY0UunOXv4vCo/xnPzmJtIo6vfWYFgFSLW5tIAQT1phXk5lUfS+3UpGGtzbfYSxMKieYIwhNpl PyD45Kmfex8pcAQIOgRi0wWvFNvGAFKYR111GKhyBVsewVkQ9CPatFhEN6V6jKX3Lq06O2ieoDaV 9grm6mQQba8Fm6gNsnse0NZZzPgO2j+mxiAfU0EQz4IpMQHB0WzI7++sQAmCGhZmaFiPR2L7gyo0 rCCoLRFWQFAhUEFMwYcgqCEQrxRWECSkKZixvx/bJ6gR1PC0GzjNw+tVJ/cqiAmDBEF8N+7vSBDE tWmeIAxhQPDO0zn3dqQCrwJBhoXZcJ/pMw6CtBGzMLgFBDVvm43+R6HhyiOoxSTMW9bG0kzfaR0P 1wNBh73e/9M2s78rNv3aQkuL5Vog2GublWKRI2dYPutTFBiCIE+Z0EKRHgiyLxZyX7Db9ZYIWjHL 8LAaHubJ9ZpIc7epO80ekFWtYzQMQ2PIVjZaxUsI1NxAGly8j2EXVigT/rSVTKtwhPkwaoBbIOht ZHSHTs8qDa+eLsI8QRhDVs/hGcEr0UqcrkIk2BAkNPwp0zbX8Q4FjgJBtZfsHQjvPIvptH8goxTe ImUEg942ZnS0m36PRmiqHEC1gfQSVgUjmstN++QtvqrTQfRaNEeQnsAZEOSaoDnS9AqyjQzWJNg+ 9QpWHsGA4DtmV77jkxTogiAhUPsHsiu+Vw0DKgAXLBRBWLjqjUWDxcntxSI9EPSdZqtIRGFMPYG9 Sl5WD2vhBg2beiK546xyE7VAxZtYMxztAAqP4CwIegd/1UMTtBUE8X2snmOejIJglTitINgyip80 iHMtUeAVChwNgshLg51UG4kWJ9wsV8etqaesVTiihRO0S7MgyGgIbZCGfAl7XjXsYeHWpp3HzSmc 6bF3szmCMyCor6H9o1dQzx1ugWAaSr9iBuUzr6LA4SCo+YHsjUX40dw43YV6y4IqNMyu9cyF08nO Xn7ulWNo2EO0rd5+lUfQcxMrEHSvIKBvFQQJxD2PoIKgt42hIdTroy5sroodMcPD9Ag6CGKHHBC8 yvTNdb5SgSNBkL0DMe/YY9VTZ1reQA8RK/hptaxGKrwww494U1vMyAzTU9zb1wJBbyxdnYKkuct6 fe4ZHOUIrsCgpu7whCXkYUJvTY2pzln3tlmzYwCvy39R4KoKTIMg28a4RxCTCTABA4f8C4AGw8KY fOyNpfl4VfWwt1CpdpmEH+4wdddLCGxBGL11hEA2bqUBUo8gc/VaHkHuzD0/0RtZ69nDI48gQZBV 037usHsDaWA9JKIhIgIq8wS1jQLCI2yl4H21ZnbHVx3wue4oMKvALAR41TBggu1j2Hyf3kD2DmQT ae8d6N69mfYx+h71uPXO91UbzA2wdk3QHGkN+VanPvmRc97zVO12lcfoAMxiEd5XVSwyAkM6CthP FeFhpCoBwpHHDts3AsHZ58/XzY6rvC4KfJoCyyDIXlgMDdPAASwwwZCHxrAwJl8PBJkvqNXDCmG+ w5wFQQcwhTDtpaUGqAJB7dnH76aB6bVoAGzyRwGU/6bNpRka7oFgFRJ2ANSGsjS2CoLsp8XwsCZN BwQ/bVrmej5BgVkQGIGgegNhIzEHWS2s0RKtlG310etVDLOB88yxbq1K4VYP1+pkEW2xpV0OWmcQ e/GIQqvmRPKIOfd8+r3PFI3gc2GDER5GqhJyMwmCcGBoI/2qbdbsGIhH8BNmbK5hqwKbQVArhtkX C/kXMHLaG8tBUMMcmi9YhWVXQVC9ggqDCmZHgWC189bP1u/0v6+AIDXQymW2sKFntAoPMX8S18SC ESw+DA8TBKuCkXgEt06nvO9OCsxCwCwIVk2k9Ug5psvovJ6BH53/PY+g5iDqJrzVNsvPF9acQaaw tE5CQg9U9wxWx4J6UQz+X88arhpHV0UkWjDnDa1p/wKCd5qduZcjFdgFgvAk4YfeQLjd1RvIJOiq LYLmhGglcZWnQgPU8whqWNbBSwFNGzvTI8jdsfbz09A0Iax3rF3VtNW/V5u58rt7HsGWJ1B31hoK 1spCB0GEh5GziecDry2PmgsIHjmd8ll3UmALCHpTaXicYCPZOxA2EhsytoyBHfDIiENN7/89N7gF ggqBsA2aF1g18ddNeCs07Kcw8XhO/VOBkEV3VY/ByiPYC4trsSH1gX3mj34HC+Zg/9QjOAoNe6eE 0Xi409jPvTxLgZ8CwepoOea8MD8Qxk2PluNOF5NMvYHaOoZesKoyzj2DrRYGqyBYgZjmzbRA0I96 04IRLxapztBsAeAKCLrx1Qph9lDsQaDnTxIEmScYEHzWRM/driswWvj19+y3OgJB2EgeKYc5ydZV rNpVmNE84BbsqPfLi0UqW+ffp62yvEhPN6JeQayFIgwL6zntOKsdP/g3wqBuqHHdni9IGMTmuFUg o97ASpM9IMjjAdM+Zn2u5B3XVuAQEESYkS0RtFM+DJ174LyhaFVJ7FXEHhalR8zbx4wATK9Fw7Pa UJoJ0zRu+JP5MTCafqJJqzpv5A10j6AWy6jRZd8uPzlEjah6AnVnTUhlh31WziF8r0fNJUfw2pM4 V/8aBY4CQT1bGMV08EyxdyBtj6bGaEcAB8MKFLVXX5W77LaO+dDccGuYtyrSG1UNa34gwI8Q6DCI z+5FV2ZBUL2BqgdPqPI2Y+oRZLEIIyGaIxgQfM08yqd+vgK7QBAGDhOKp4lop3wYuhkQdK8cdnxq FPWYI/cIarf6Xmi2qp6rvld3x9jFcjerRSNudEdtGlrfXYWGPSyuZ3xWO3+tXq5ybfjdCoLMEwwI fv7kzBWeq8AqCNIrqH1XARoEQWyWkZaBDRmjJYAybayvMONgU0HhrEfQN9xqa7RDQa8noJ9BzJAy i0NoMwmAP/zhD7+DQtjSalOtrV5WQJBrBDfQfhqKthlzEORZ69gAI8rFCu+A4LnzLd9+ngKbQBAV w2wbAxAEVLBTPg9Q55m+fsh5D8porKoegx4a1fYA9Hy1cgH9Owlv3saGFcswWLqrhYHjbrZKeF6F QQ2Pa46gGjUaXTVorVCK9+Ty62ERDTvsI0+Q/bTYRgGhfjxXLFwpFjlvQuabP0eBrSCI0CIqUJlK o5tleOMxD7lJJghqFMTP9e15CHsgqNEXbxfjx1lWlb7eu1VzFfV6vfepgiBgkD+6scZne09YejN7 oWH3BiqcaiRFuzwQBBGSp91j/1SCoFYMJzT8OXMwV/IeBXaDIEBCj5XDbhel+jzFYxYEvZULYbAX CvEwyAwIetK0JmozNxDgB2OmBoy7WQczTc6u8gVnPIIOvq2k58oD6KF2/X9+NyuHHQS1nxYhMCD4 nomXb/l8BVZAEK/VPEGCIOYVC0WwWUbVPuwjKlkxP9XOqVfLPYM9b2ArNOwgqN9VNa73Kl8eq+mn FjmMee9ThocVAukdpGcQG2vcoxeO8Ii5qrsEoVkLXVSzHghCc0RDAILIaQ8Ifv78yxW+T4FDQJD5 gTByLRBkfl4P1lphYt2J9vJhZkFQm0ircYQhgTGEsYLh+tGPfvTNj+5k1XipV1K9cq1GrlXyNpun KgxW9+sQ2ANAh0FWVOtRS95YNSD4vkmXb7qGAmeAIMGmFRZutUmpiti8XYyGoAGCGtJlQQfzof2s YG9RpfbKcw21aERhUItHAIIKmhrt0PYx9GRqQeEsCDKXnH0EAYI8Yg4gCJvH6AfAnUeqsmBy9flf Y1TnKqPATyuwCQTZEoHnC+uxcvA6MT+QPQRniji0kEN3g1UD1VZ13AgyFTT1jE22jYFxojcQAPi/ /tf/+uYHxow7WYZLqp1syxvXa1/jzVP9fvVe3fs4A4N6/B3PHEblMJ4ZjSKLRdgsPKHhmIoo8PXj fnY/WPrpeQQZNUFaBuYhwpWYvw5UmhKiFbDaQF5bxiig6TnDmjPMdjGaF6gFHtriRcPBLIyr7I7m HCpgAu5oR7mhbsGgfpeCbA8Eq7Qh5lK7R5CVyYhOwUEB7REF0UKRGVs3Ow4yZ6LAVRXYDYJwsxME ARh7QLDqN+g7Qt05ai7cTKsY9zj6GZsjEITB9BYIrZ24hmV7FcSeD6MNVL0aeAb8/DV6HQRB7IwR pgoIXnXa5rrfocAsADAs3AJBttdCMV0PBHutYxz+qpOEKhCseqTC6wdbpj8MC9NLVx3jyU0lbQrt qRe8MOzMTbWm2ahXkG25tD8rPmsVBL3dmN4D7h/56shdR8SKhSLIiZ49V312HLxjTOY7osArFDgE BFkoAhBE+JGFInqu76xXsAWDVYsUzcnrgWCVNF0laXOXDGPlHkH8Gwyb9hSsCke0f98IBvF7GD3N j/Sm0Fu8gBUMQh88F+yMA4KvmEr5zLspMAsAFQjybHaEHwmCqBiGjWTFsHsEPTeYYWA/acRP22i1 j1Kvnban4oaXIKi5gZ4D7YV4atPZloawyY21FqL02skoCGrKzwoItqqGGRaGBtgAo1AEhTqeH1gd K7faSDpnDd9t5j/vfoYgyAapNGwMH+oZwwRBAMZRIKi9r6rWKK1KYQVC3bm28g+rcImCIICQic4E QfUK9hK1RyCIayUIaj6P3+8WT2CVJ4gFiIevxyP4vMmeO15T4EgQZOuYHghWJ4i0wsDeR7SykVop TDun3joFQeYG6qlF+ExNLalObHIYhKdScwbZUkaBkDnXuqmeAUFvJk1w9rY2zOOGRrCDLBQBiBME AeioGA4Irs2JvPqeCkyDICaMwiBBEK52hDzgdgcIan4gd48zQDQb2tWCi56XsQWBakwUApk8rb2w mNviHfI1oVobPXvu4shjSRCsKqtX4K9aBNSTSC0Igtgd45mxlUJyBO85uXNX+xR4JQhizqtHkN40 LxSrzhPveQA9N5A5deyR6mcDsxsCw6laAAe7QfjTs9v5d/7OozjaC1ZPaXIPZGtD3cubVp0cBDW/ EtcALXBtDAtj84v8QIaFsZ7t7R2oY2TfaMu7o8B5ChwKggAMgiCNhFfQroSI/SSQvRBYFYjwJBEH Qe5gW+dm6mkjHiLWtjaax1gBMYxe5cXkTrsHhD0ArNraIGmalcMBwfMmXb75GgpsBUE9kpOhYXoE sVlmD8EeCNIT2APBVuTAuyIALmnnFAQZEtYCOGxmNb2FPUgBf7hutgVTGNSNLO5JW3K1jrDrpdiw kwJD4KNj5aoTRegNxHUyLIxcdraNYZssPKu91cIJDV9jPucq2wrsAkH0x1KPoIPgVpBbhcVROLiq cHNvoO+U9WSRKqlaTxupmj6rke5VEzsIumYtEPRFwHMLNXGcnkkFQYRJYBjx/OIRjImIAj+twKtB EHO41TbKq4S9OtgLQ6rUEi3iqBpIuw0DdPK0Jg0JE/paIOh5g3owgDaeZr9BVvf6UXC8xwoECcaa R1mdwkJbjPvAPeCaeb46iuNg6xgWpkdQQXDlmftrM4eiwFUVOAQEARWohoML3gtFZnrqbQU/78vn xpBwxB2qhix8p6rtFNhfy/+sgNCriDXXpToP2D11MHo0urPtb/gZFfz1impg0Fk5TBBEOwXdJaeh 9FWncq77aAVWoEArhmc8gpjzmMdVqJMwozCoVcK9nqJqG7xHqp8pTK+c5gVWxSG9sLDmDbpnENdC 24v7bFVFu81UEHRQ9nxAPYJUN+TQl/0DUSTCTS/Dwp4fyNNEVp55QPDoGZfPO0uBjwDBLTmErRxA hT8NLeiO0hugamNVhT+eu+mNV9l3C/9OEGSCsnfh90asfkwcQbDSoLrHWQjUxYKArCBI4xgQPGvq 5Xs/XYFZKGADYnqWtoDgqHXMXhDUFBgAoBdq0C65HapyBCv4qzaxsDva/ktPJHHI1UroFgj6MXy0 vYRBpujgs3CN7JKANBh4AxHBUm9gdazc7DOvXvfp4znXFwVaChwGgnC/q0fQCyCqXLnKWzjjHfQ+ Vtx5ek6J5pZU3egd9CoIHMFgVThSNX9tVf9p1bB7Bo8CQeb8BARjCKLAvAKzULACgrCTCFe6R1Db oFSNpB0EW22l3CMIW+RpMLp5ZQ8/hprdBnmlsBaIzBTr6fVoZKZqnE/7vQKCCoP0bOJe9DQRegNR KIKIR+984dlnHhCcn0d55ecrsBsE4VFif6wWCI4qYLcUgXjYgQavOpNT/0270DMvsAoFEwBbIMj3 +JFMo+/XA+RxzTR6aogdkKv8n8rz2eovtgKClZGsjlz6/KGdK4wC+xSYhYIRCOp57GwfA4jS0LCC oObOaYSB87uVf6wtslqFceo9U3DSApGVzXivGHAlaqMpLbMgqGFhaMaKZ3wv22Qh8qFNpHsQuLV/ YIpF9s2zvPt8BT4OBFv5ct6igMcmsd1ClThc/RtDJGxr0IJBBcFWziCTrX2H3UpoVsPFszxh9PR0 Em05M+MRdPirTiZZBUGETKr+WmmVcP6EzRW8T4E9IIj5A+iABwogiA0zTxZByJLtYzRSUYWHGUng vPYiMN9ks1DDQdA9jt4vsLI7vkFv5WSP3ssOCN7appXjPAJBpvbQnhKcWSkMr6W2jIH+LBLRI+W8 Ynj2ebde976RmW+KAscqcDgIsr2AgtvII+iGpLUj1R1vVfihZ016/ohCGF83GxpeAUH1+GliM7+T +Tn8fxg93Zm3Qui8d88RfBUIjvprHTsM82lR4PMUmAWDyiPIvqsAQYQkAYLoY4d+q62zhhUE/VSR PSDohRoKgawSnoE535i6Xe8VBvaiGg6EM8UiVcsY9g3USmG07UExHHIDeaQcN7kEwSriMfvsszn+ vHmbK1pXYDcIwu3eCg3P9MIb9dnTXENvA6NnTLIijqDFkG0LCNnKoAeDLQDUI5lmvIGaqK2eRlwj jJ72IdQQTbUDr4pFKhj06mHo3Koa1vYxWk3XM5TrQy3viALXUmAGBggRXjXsIAg7iVw1gCB6eWIu Yn5rVe2omXRVAFZ5BNVOetWtehi132krHWWmfdUMEOprRgVvBMFW/8AKAvFa2jjoC50B3gBw2Dfm BjoEslp4b1gYYyX/RYGrKvBTIFidmwkgYLiQ4Q49WYTtY9BHkO1jCHCr3sAqNEwjpWdaAp6qrvWo 6CVsaf4eQ7HupSM4ViHi6mB2h0DtZu+hHf0ugqq2qcHnw374IekVDLohHbWO8UUDGvYaSnPHHBC8 6lTOdR+twAwI9uwlT2KCrcTRZmwqjfO+ed6wQ5sWUXiaR6tAZASD1UZRP6sV8tVQ7uzfZ+z9KETs IEhA1pNE+He2nhkViGhImNXdCoEBwaNnTz7vSgocAoLMfXkHCDIkDMjSJtA8AYStXar8Pc8ZdE+d egAdAvn/Dple/KFGqwWCeloJjJ529m/t0qsdteYDjRpKA8z9iDm0VMACxfyZ1q45xSJXmtK51qMU 2AqCAAzdOGN+acEI8tcQHoZXUNNdep79VoFIFXVpRQ2qKIGGhFfCt97L0EFxxpPY8gyOQFCrqgGC zA2EptAWPW3ZLgbaVz0DHQIDgkfNmnzOFRVYBkHd5SL3BSDBs4YJgvA86TnDo11ir/JMcw3ZKZ/t EOhdU7DC32e9dlpBzCPm1GPnn0tvo7eM0QawGt7RvlfqEXQQZE8vvN6PeJrZrbe8gzS09LKyrxaO uULYhCCIJqut/loa+ko+zBWneK55qwJ7QJBRFNhLzC/NE4SdRPhS28ioZ3CmEb3n9LW8gr5ZHHkV R9663oZz1mvor2vlCNI76ptr9wYyN7BVKVx5A9WuHQGBCQ1vnWV53yco0AXBVt4LdlgwbkjCxU4X uS8o00drBD1reFQwMttHkLte9QbCi8Zj4P7t3/7tCz8ELB6krqd+VLDmYWKFQH6m/knI1HOGNalb zwd148WcRHyHgyC8jPQKchGAYW+dNNLyDlZGnwsGwJznDGPHjDAV2ypoWNiNZkDwE6ZpruEMBWZB UMPDlc3E/GJ4GJsvnsKE+ag2cgRZo1y8asM966nTYrRRa6qZtJQVKFwBQfUGYj0YFYhgraoK3wKC Z8yofOenKjAEQRo23eFictG4YafL3BeAIIyb5gm2CkZmIVANJUGQ52YCBB3U3GvnoVs/0kkLTtTD iM/94Q9/+N0PQZOQyZzDVvPoFggC+raCoJ5DPBMqZq4h3gcQxI4Z3ggkUtMbiOfnINgynPEIfuo0 znW9QoEVEKxgkDYTKRfqFcT8wzzEfMS8pI0cgSDns879XkuXUSRmVMAx05FgBIUjIKzePxMahn1l xTObR1cFItV5wg6B8Qi+YvbkM6+kwCYQxORyEGSeoIKgGrnRbnbUpR7v74Gg5gh6k+eqGo8tFVog qBDIv6tHsAJBVuj5UUra2R8gSE8mPg9Gb9YjuAcEYSyRpI6wMLy3qKZDWF9bK+C50nB6xbAbyysN 8lxrFNiiwF4QRB4a5hE3zphrmHPssoD5iHnJnoKjFI9XgWAVDu5BYBW67gFhCwZHfQSr0DAjO7Cx 9AYy5QW6wikBjZnqglzNVnFIK9qx+tzTUHrL7Mp7PkmBZRDUtggMD7NHFgADxk3Dw+4RnDlFxM+y 9NCwegSrIhFv6dLz2jGPjxXIhDTA349+9KNvfhQEWYSilb6sXNOKP4dBBU4tcgEItnIEW3Dsmrby ejQsjHwkeCEQlgK0w1jCG6itFUa5NPEIftLUzbW8WoEtQFC1k2FzadpLptJgU8boCea05wk6cLUK RrZ4/io4G4WER3BYNbP3ApWRBxG/77WPoS3384RnCkQqT+BR3sDkCL56NubzX6nAEghqeBjhDnoF AYIwbtiRAQSZCO0ewREEVudaVqFhQBXATXP69Mg3euu8ukxz+FpnD7dAkKFheu9mQbD1PbxeB0FW Dc+csDKq8lMQxIKDhYfhE+R2wkOBxYnHLvVA0BfFVw7KfHYU+AQFtoBgFSLWzTM2XkjL0AgKNmk8 co4tslrHRdI+VN7BHhD2QrQOZyvAN3ptD/xa4NgDQVYJ436g22yBSAsCA4KfMNNyDWcrsAkEucMl CKLoAC55GDeU72t4WL1XLRBsHWzulcdeNay9/9jWhUUXIwjsARq8jK0cQQ3jshF05RH0MDGvh95M Xm/VPoYAt+oRdKPLghM2kkYOJ7y2gPYKBNnyogqlBATPnqr5/ncrcBQIYgPNbgvYeLG5NCMoVU/B GRCchcGVPL0R2FW/10gIj8vUk1AqGOx9TwsEtWcgbJu2i8Haw6PkeIKIHyEXj+C7Z1C+7yoK7AZB GDaEGbnLZRK0hjwIg0eDIFuytM78bVX0athWW7xoyFZBkAUp8BYyjMsilBYEepgY16JhaDay9obS NO6zHsFeuweCIHbOMJoMCwPaGRZutY0hDLaOX7rKAM91RoGtChwJguoV1J6C2koG87XlEazAahQq XgHAVli41eCaJ6I4BPL/e2Fi/q712RUI6oko3i6GuYHMd676BsYjuHUW5H1PUGAaBJn4XIWHARX0 CsLrxIapDHmMKoRHHkGGh2EA3CvIM3v1APIKAN1gOQyqp44tXrQtDRtVe/7hCATVYNILqW1r9Ig5 NkYdQSDBmoa+VW3IamEmU9MbyGOXWC3MkDCPX5pptvqEyZF7fLYCR4Bg1XWBDaYxD1lBzFYyhMEK sGa9hCMA9DZT+rktsGv9u6fbuD1sef48YqLvg01s3T/0wXoBm4a1BhDIDghaIDLrDUxo+NlzPHf/ rQJTIKjGzNvIwA3Pajj2yFKvoFa6bvUIMkRKA6b9BBUA6aXT4pCRAVNPnR8D1ytE0eq1ahfsBlDP zdSj6GD0aARHIEj9PDewB4JsrQA4R/iEVXVsIo3np7mBVbVwZSwzgaLA3RXYAoKV54neddpOAEtV Qcycati5HpC1vG0zAOgQqB7IqthNQa+1ma5gUMGuZwurTXsFgiycwVrA3EBubNkPVW2ZRzTiEbz7 bM397VFgCQTVK8icF0w+3+GiKIHVw57nR5jptYvxqmE9t5iGC0aJodaqV+DIU1eFh9n0mS1eeKwc Czvw71qIAiPWC53QeHkoRNvZ0OjxtS19WhBYGXYmlENHGk3AOU8TQWgKIKhHyvlh7KOGq3sGXd4b Ba6gwFEg6Btp7SuIOUmvIFvJKAj2QKwCQofBUZWu26aqKX4L9LwzQisS0wJBtYP6XgdB2kbcG/Od ubHVo+R6zfBfnR+YquErzOhcY0uBzSDInBeeMlIdN4eKLhq3LW1kqpM19GzOKrwwCk2McvcUBlnQ oTmI7nVUb+Cq0cV7YfQIbltOE2l5A6E3IFBPE2k1kXYITGg4BiMK/Dhc8uPJufrT8zzRSwVgIQyy 9RZgkOcPY+4yBUZhqxeGrXIIZ/L0mLriUKdRCy2802hLBXLes7WKltBu83N9I6+bY1wfABCasCm+ 9kLFusNN7cwJIqMN7urzTkutWIo7KHAICDLU4cfNwSuo5w63OuL3cuIUBjU3Tj2DM/k0VR6MVg5r 02fCIAs62KBaK5JpELVKbgSCVSI4jF6rQKQ6NaCXF+hAiTwaba+ARYfVwvQGzoSFExq+w1TPPawq sBUKWjBIENSuCyi048lMmKvMq1YQbAEX4W0mNWUmV49pMgpoDmk9+HNgVBtJO+kpMsyX1g12FSVh dIMnI8GTqlXCeoJID8aPbiIdEFydVXn9JyqwDIIMD7PVSO+UEUza0XFzXkjSCxlrT0FvvjoK0fZA UPMEEfplAYr/yd6BGsaoduLa66sVuiU0wujpAfIOvupJ7VUI8/MIlX62MPIDmUvjTaS5k27lBwYE P3Hq5ppercDRIMgQsVcQY4PG1lvwCmLuYj7Tc6bHVba8cw6DM5tjhTI/ElMBjX+vzmt3eKyuT69t FgQZDtbiEEQ3tPOBVwmfkRcYEHz1LMznv0OBzSDIxGcYNW0uzephVHNh0rK5NELEHh5ueQhHMKhV xCPQajVLbRlB3w3rjlgLRLSnlULYbMI2XwcQrDyiK4Uh+v38LHgWkEcz2zJmVGWXPoLvmI75jk9S YCsIelNp91BpwR3zq5G2gSpYRFGwedYOCbpRrTxonkfoYV73HKrt836qPAGJRXjMie55Bqswcq+Y TkPDLY8gw8FYN3hGOmwZeqBWxSHQdMYTuOeZjt77SWM31xIFVhSYBkHuZr2NjIY5tKcgk6D1yDme qdnqgD86eaQVJl6BQfcMVrkxvfCHG1U/QmkFBKEDQLAC38oT2IJaBUF8FhOqmUsD4wmvA08SYXVd FRau8gPjEVyZUnntXRQYLfy9349yBbWCmO23WDQCryBsA/P3RiBYHaHpNsztVlUsV0HgHhBkw/3K W4nv9xxB3gc3x7RjPBoTzgVAIEPC3i8wIHiXmZf7eLcCSyDoMMjwMCYkvYLaUxATlw1TscvV0C6L PhwKt8CgFpCs5On5zpi746pSr9pVM3zhoeARDPKeca8VCKo3sJUT6IngGhJmpTAWFoScWCQC74M3 kNbegb3QSjyC756a+b6zFdgDgj2voKbXsOsCcgVhL+EVhAeMR861vGdVuJb2qwrX9mBR+5qqJ7Bq zVWFh90jqN5DbY1VdU/wE6DwGthEQiAcCbRjyKUkBMKOaV5gawP7ypzA2MSzZ2i+/ygFdoHgqKcg vILeSsYhp/IOzrSY0f6EBMFRIYVCohslrUDuJV/777aEhTUncC8IKoSyUphHL2FhoTcQBpQNpLmT JshrWDgewaOmVj7n6grsBcEWDGpfQe26oK1kkCdIryC9Z7N5e1V6S9XaxXOjKwh0j2AVLWmFhtUj WLWQUXDF31khDJsIGwYIxPrB/qfwBupm1jex7+gV2BsTVx/vuf7nKlCC4Gg3q+Fhh0GEhxGCBHgg HImmn4RBVhD7yRh7YNALSCoYbIWOPUyshR+VV0+PYdoSElZPIMPBMHrsm8h/mwkL6/cTLL1ABDto DaV4pXDVNiYg+FxjkDv/vgJHgOAIBrWVDM8ghgcMudXMq8ZcnwkPt4reqiI3LQ4hAPopTdozVT2B 3samV8zCauEqUqP2Vo/DhE3kueiAY3hLeSQmwJkpLXomes8jeNRzHH1O5k8UuKoCTRAcGTCtNGUV nFcQw7BhN8fcF60gVi9eDwRnWsu0Kmt7MFe1ctFQr0KbnuThBm0UBubvvTBmBgRnq4S1QAS7aBpR bRcDQFcj2oLAgOBVp3Ku+2gFRgv/7O97p41ojrWeQcyiEXoGq6KOyhPHPD/veKDtWdSrx7ZZrZCw h25bzaVbzaEVBFu20iuDAYIazWBuM8PBVTpLQPDo0Z/Pe5ICh4GgtkQAdLAaDqFJeAUBJ6wg5lFK ClhH5gr24KwKD3sLGO3r57383Lv4CSDoBSLMqdGdNI2onys8ExZOsciTTELulQrMgt7odaNj5+gV ZH41bCaiKFVfwVa7Fg0HswUW+6B6/1MHwVGlsIaVq7Y0rdNIHAIrG8/z0BEKZnUwQFBTWvwUJG0c rV7Bd5wektBw7MMdFTAQRDf9/ESD0Rj4btjccU7knqLANwp8u+iP5kJ+H42+HSv5LwpcVYFljyB3 XVWeoB47xyOU4JVC1WpVOOJVxCsh4lFeXS+Hr1dZ3Drlo5WLuOoR1FA37sFzBKuK4coLybxAXJcm VvM8YejOxGo/U1jDwqP+gXzeqZC76hTPdW9VYOTpm/396KQR5lmzghieQUZSmF/NKApsgRa2eX6e egNxRjqPyfQz0r19TOtIOe2W4F0SWtGVqq8poxbIE8cPTz1Cr1PcI0PBWCtgE9V2aVGbRzBGeYKz z+iI120dZ3lfFDhbgZeCIMKR2k4GDUHRToZnEMO4be0tWIGgF6G0YPCTQbBqsVPBJoHSG65qgYiG VDQkHBA8e9rl+6+gwBFwwM8YhYc1VxAQpGcQA5SYX415z/zm6uQRB0HCYHU8Zg8GPRxMCORGWTft WqCnOdG0UbDVsFO4B2xakSKkTaKROgS7xZxmgKC3uWqFgwOCV5hJucZPV2AXCFbNpbVwBMUJMGqA QXioWDgCwwaD4FXEWzyChMmZ3ntstTICQXa1V+9dy1O34hH0tjjqEayaZWsOpX4Pk6uhH3SEnsgL 9Ao7rRKucgPduK60X/j0gZ3riwJ7FTgKBHsQqDYUc5Q9WWkz4SGD3cQch/cMEKU51mw6zWKSHghq 9XCr8tfbzCgAOtj5Zpw2jOBH+0Two/cP+eI88Yi5gKwKBgACBFvN7qmXA+BKD9SjnmuiJHtnWN7/ KQocBoJ65JyGiDGxtXCER89hRzg6h1j77Xn1MI2QGiOCVq+f4OgUEt/RerGI9yycBcGqNyJB0E8W aR0vp5XM3GFDRxhV6OoNV1klPOMNHDVkjdH7lCmb63iXAkcBwywIqt3kBhqdFwCDLLhDREU30bRn 9A6y8AOFIvAGqkewB4Le04+g6Gf+wu4wtAv7jb/j3/SHv2cBCAAQ1w0AxIYV98JG99reil5AgGCr q4Gfiz7yCFaFbkc919jEd83EfM+rFXgpCGqPLLQAYK4gDAF3tzAm3OFqGxiFnhZEKQx6D76V5tJV Sxj//ioUMguBCrQKfRUIVuBZtZ6BbthpQ0foydxAbbUQEHz19Mnn31mBo4DBQVDhpWrDhXnLDTQ9 g/CcYbMHkFLbydQahouR6wcYRCiY+YH4E/8/AkH3EgIuGUVhs3raHWxAYX/wA+DDv/OHAIgWOARA blYBgLgXbFrZ1QA2S6MXAMFWLmBA8M4zLvd2lgJdEFxtLF15BXn0HHa43N0y1MFziLGb9JNCRiDI tgOtopG9IFjlwmz1Bmphh3v//GSRHghqaGbUeV/PE45H8Kzple+9sgJngSA30AqDyJ8DPMF2Mtca 9lPTa2CzeAoJoA9eQUAg/twKgrSDLEoDABLwAKT4wf/j3/nD3zNlBRtVLQbBvQACAYDVsZewia3Q b+/f0z7myrMt136mArs9gq3qYVZ6Me8FRg0THwaAJ44gVABDwhBxq3ikdeRc5RGsqntnzh/uNYpu 5eqNPIKtJtIKgy0QbPXcYkgYurFxNM8ShvdAj5GD9vzh89hSKJI+gmdO0Xz3WQq8EwR1ftJm6hnu DBWz6TQiAIArPcITUAi7gXAugFD7A2pDaZ7/6z0B/Tx1fA5sL+wsPps5fgBRwB1/4KXEv/GH4Mej 4WCf4ASAfSL8eSGb5isHBM8a8fnepyrwNhDU1gh64ggMGRtNtzyDW0HQPXirQDgCver3vYIX9wbi /wmCo5AwPaBeIAJjyzwbGNmA4FOncu77aAWOAMFRtTAByEGwBYO6mYaHkLmDWkgCG8Rj6diAuqoC rs7/9SbQsDtqcwiBAFH+wAbhB2CKP/HvDP/CNmljewAgbZRGKtTTB5uous16B+MRPHoG5POeosBm EMSk08PTPddFPYI0an7iiBaOeBWx5gvOgCC9iVWIuQV0M2C4CoMtEKwgkCA4gkB8Jvtwsf8Wc25g cDXMwlwbzQ90r6Dn2fSqhdNH8CmmIPfpCrwCBKv8QIVA7bpQwSAjK/AMArLoGWS4GHaUG2ptM0Pv n5+i1LKBDAnjs5jzR5uD7wSEwtOHH/xdf/BviPq4F9A3qa2CEAdBXWtWqoVbtuuI51p9RmZQFLiq Ai8HQTVso3xBegYd5log2IIrLxxp5fbNgKA2R21BYQV/vYpnvT4Yvdb7NbeQu3Lk38CLyrALjK0X iAACZ0Cw13LB4TAVcled4rnurQrsBYaVamHfOGtah/+ddhQbawIhC/E0f9BTbmbsJV/D6mDt90cA ZI4fvps/CPvyx8O/3tC+6gmoWlUgqAcZzFQKj+zX3mcbENw6q/K+T1TgrSDInBc1YPBoqfHC7tN7 A6rHbGTMvB+fehYd5I4AwRHEVRCr9zMDgtBDz+KEXnoWp58gEhD8xKmWa7qaAnth4SgQrDyGtKXa pxWAhg0iw8Us4sAGW6t6eboH//TWL9r/D1W/2p5KQ70APkIfroM/Gv7lhrTl/as06oGgA+FMNONd LWSuNr5zvVGAChwCgjMFI9oslSeO8Pg5z3OBYToSBnutX2ZhcI83sGpMTUBVENQehuoNhB5sFVMV iDDxWsPCLY9g1X5hxpjGIxij8TQFXgGCVUpNlR9YFXkRpjTKAluK+Q8gYyEJNomsLoa9AMwBCrW6 l61f2P4FG0099YOVvygEYcUvc5HZ/BnfS+jDdfBH7ZAeDzfryZsBwRmb9W6v4NPmR+73PgrsAsFW nmDPsKlXsGo0rQ1TmeuioeIt3kHvT6hQtxUEZz2BhMBWHiCMnkMmP5vVenp6CEPCMPrQT6vv6Ams PIK98zpnjGpA8D6TPncyp8BeEGy13xrlCXq7J6/0182cb7ABhHoqCXL3CIYAOlb6apUvYJE/+Pde 02f39o1SUPReZ+zMt9677xeLzL5v9nVHPNeEhufmUF51DQUOA8GWV9DzWwgp3MnCaCG/hQ1TYYiQ l6LFI0eA4B4YbIHaTG6gAqCfGIL3Owj66SGeqM2QcCsvUGGwah8zys+Zrby7xvDOVUaB7QocAQxb w8MeDvZ569XG2qZLi/IQdWH/VkIhcv200heAyB+t+tW2VJ5+0mpNtRICbtuaeRCc9TK+wzu4faTl nVHgXAV2g+CqV5CgooUjDBHDQPHUEcJgVQ286hWsAGwlX9DP+e15A71IxEHQ31uBIL2IrBBGawiG Z5io3coL7OUHtrryz+yk4xE8d6Lm29+vwBEgWHkFRx7BFgRWp20oELp3EDZCPYQAQvywOTUrfbG5 ZLsXrfhtNX3uNaj3a5yxLT8NaXMguKWC+PtFKT/48Ub8uJ/3j9B8YxQ4RoG3g6AbKy0cYed8HrAO GGTn/JZXcKaiuALB2R6DqxDYA8EKIAmC6glkqxjNC+QRcuzJNZsXqEY7IHjMpMmnPEOBoyDBQWcr CLbAx0PF6h3UHEJCIdJK6CkkHPJP/Dt+z+bPVcuXVqi6ur4zQHB0bvqrYPAZsyJ3eUcFDgXB2fCw 98fizpWnjvBMTTabZhsEbbtShVy1KKOqHu6Fh1eKQfxzqlNARiFhvofFInw9Tw4BBPKIJm8a7XmB vVYToxNFZo1mPIJ3nP65p54C7wRBP56zdQLQqLlyFTLWzbe2nmEI2at9Z/v97fXI1RXD8NB9fc9T 14LJkRYzEHrUM8bn5L8ocFUFTgfB3gHryBdE9RrbH3glsXrRWuHiXmi21yx6Bu5WcgRbr4X9UIDl oe64byRta3FIlRc4C4G9hWWLwbzqgM91R4FZBY6ChBmPoIOgevmqkHAPgqrm/rQTmppDKCT4eTP6 Vr7f7OZx5Hlr5+19HwRnim62QulRzzggODur8rpPVOAtIOjd8iujxN2pF4+w9QFgUNvK6JnCLe/g yCs38gLOfO4eGMTn84g5QK62idGm0cjp0QPaabB7EDiqPFyt5otH8BOnb67plQocBQmzIOjwVxV2 jbxgVdjZIdPtr+cVt1q+zGwYZ4vNqC1f/32tjwPBEbQe9YwDgq+cifnsVytwCAi2CkbUAPWgRQ9X BxACBlnpxmPoAIT0DCJv0M8lnoG2UUhX8/sYWgag6Y+Hp1dA0K+RR8yxgStyIjUczMIQP0O4atnQ q+KrcnoCgq+eWvn8qytwFCRsBcFeRewKEHrKjoePe97HPfD3bSuYLcUYPw2CDo68rhUd2qHoLdf4 0++5+njP9T9XgcNBsNdcugWDHq5gt3zAoLaV0TCxFpGMCklaPfxa4V98HuFPu+67R1K9kjOhZC9s wXfAI8gWMagOrsLB0KN3hvAIAgOCz53gufPtCmyDmJ8GhB4ItiBtpi3KCgS1PIWtArKRJ20WELdp GBDcPmrzziiwrsBHgKCGKgA87JTvYWKtJubh6gS2mUKSnkdQ8/QYpvWjmOiJrFrazICgehl5lidA kOd5oqkrcwI9HNxqC+Nw7a0nZir8Zox6QsPrkyvvuLYC2yDmfSCokZitULg1t26U/7dPuzYIjtrx 9HR4dS/Ba4/2XP2TFXgJCG7xCirQaGUbu+TzHE14zHhsEo9HGh2u3soV9AITBUB8Jo9d0j8Jg9Ux eFXrGA8xsyqYXkCAIIpi0D8RLWJ4jJOHg1eKQvwUkSr3KKHhJ0/73PuMAvtg5idAOPIIrsBLtWk7 EgJ7m8Kj9Bh/Th8EHQZn7v/VYeHkCM7MqLzmUxV4Cwi2kpVnQsUME6O3FfoMsuk04AmhVD2FpArd ttrIVGFaeulYuQvQxOfzx3MUe82u9Xs1zAyQ5Ofi+gGCyINkn0D274JXdMYLWHkAA4KfOt1yXVdS YAwsc7llW0FwxlO/JVdu1Qt4lA7zn7MGgjOe0dUilvlr/ckYuNLYzrVGAVXgFBDUKuIKZLTPIFob sM8gC0gATYAnPVAdHjYAHHMHPWSsYMbcvipUS08g4I8HtQPY+PcRDPYAkJ/NA91x/QBB5EHyOCc/ P7h1+HwPAHtnk+aIuRiAKDCnwBYYaL1nFQZXIHAGhD7Fazan6RgEV72CAcG5MZ9XPVOBl4FgLzw8 ghgFxSpMzLOJEU6FZxC5g/QMEgbpHVSvncMgi0LcE4jPAvzhB9XK/KEHUmGwBZwMM/OzAar0BOJ6 GQ4GCLI6GN5Ab+ZatYGZzQNstaPYcgxUcgSfaSCefNdz0PIar+AqCO71DL4DlOb1DAg+ed7l3t+v wNtAsOqcvwKE2nga+XMIFRMIUWBBIASsae4gwVBz+1gJjH+jlw7vgaeO8AdvHfIR9YceSAdC/Q4W mDCvkPAHmCQAakEIQNAbRc96AXugN/pdcgTfP9nyjddSYB5cxjA4m9s3C2QjUJzxAPI1s995pB79 z1oHwZ5X9F15j9ca3bnaKPATBV4Kgq3WCKtQSDBS7yDgiUfSIXeQx9IB1hjKhWePUMjQMaCNP+ql Y7gWsIbPgLcRn6k/+Df8Dq/RRtf6Hfg74U/Bkh5AQCAAFmFuXD9AUI+MewcExiMYExAFxgocCT5H g2BVPTuCw5XfH3nv65/1PhCEJuvXV4P/eETlFVHgMxU4DQSrY5BGHkI9MxMhVHoG2W8QkMXcQYU1 LfYgqFWwRi8ggA+fhVxE/CCHD3/i3xiOJhBq/qDmFNIDiNcBAlkQAmgFALIoBCCoJ4WMNJgpBBl5 AwOCnzkZc1WfpcBRgNCCtplegSNQWYG7ldceee/rnxUQ/KyZkKu5uwIvB0Eauxk4Gb2GEORAiAIL hIoBhMi3A7gRCgFuCO8CxgBl+oN/ww9+z/YteC9gDZ+Dz9Mf/Bt+RzAE3PHzNYRMbyIhEu9hWxgA IDyAzAUECGoe4EiDUSuY1caxs4tDcgTvbgpyf67AOsD0Q8Qjr+DeFiezc/ldodLt+s2B4ErByDvu OTMoClxVgbeBYC9MPAs/6i2rvIOALAAhgA3hV0IboFDBUMO+/B3btxAA8Tn4PP7g/+HJI2wqENJb qF5E/B7XwM+jBxDQqgUhAEG9r1ktZs8hHR1eP7t4BASvOsVz3VsV2A4yNRC+GgR5vbNzei94Hq3P Tz5vGwienSe4dZzlfVHgbAUOA8HVqrVV4PHzMVlZzF57gCt42ugdVGgjFBLe4NGj50+BDe8hACLs jM/SH4ai+dlsZ4PP1x/Cn36eegH1rGCA4FYtVhLCV3uH+SIREDx7qub7363A0aDTA8FXFGysAuHR 97v9844Hwd6Reduv8/vA/+7xme+LAkcpcDgI9nZlLRjZCkKVh5DVxQqGADj8qIdP/87fA9YU2Ji7 hz/5o5/L/oZ8P/9kL0CHPwKgXvcqCB4Bf1vOEQ0IHjXl8jlXUeAoQNDP+RQ443W84h73f+ZrQLBl 9/Zf77dAmP+iwFUV+AgQ3BM2dhjUkLFWGRPy3MvHf68AUE8+oeeRnwkgJBT6ZzD0S5BUD6CHgVdA 8CgIDAhedbrmut+pwFGA8Ikg+Ip7O+4zA4LvHOf5rijwEhAc5cIc7RlsHWGn8KZevdbf/Ug3r+Al GPJ1+NM/S3+nINnKA5wFwdnzSGdhca9nIlMnCtxdgePApn3u8GgevuIaPv8zt4PgKCI1SnnZo83d 50Pu774KfBQI7vEMtnIIK3hTWKvO8+21cGmdj1z9u39OFQIPCN53cuXOrq3AHijovXcEf/r7V13D Z3/udhCc3TC/IjR+7dGeq3+yAm8DwZbxqybu1pzB6n2zfflmzuftNcLees09ENxT4DHyDO5djJ48 aXLvz1DgVbC0d+696ro+53MDgs+YYbnLT1Hg9iC4FdDe9b4WCM42m10B7NGRUivtJD5lAOc6osCr FHgVGM2C4Ku+//M/NyD4qjGdz40ClQIfB4JHAdhMWPao7xp533q/BwiuhjNmF5Lqc49qrJrpFAXu rsCrgGl2/r7q+z//cwOCd59bub/PUuBWIDgTBh41WN4CdbOGvfa4fX2N3r9iuEefdVT+0WcN41xN FDhegZV5t/LamTm68nn3e21A8PjRnE+MAm0FuiAIAzNjtPa8ZuZItBnP3QwEeh7grCdu9nVbdIBH 8OhmsjPXsXfxyKSKAndXYO8cab3/HfPzVdf+ns8NCN59buX+PkuBIQhy4s8Yr62veRcMKlCuhk1H 97bdQM4bve3f0T8DdcvnftYwztVEgeMV2DIvZt4zsiX4/czn3Pc18zbRtVzdtB+p4fEjMJ8YBd6j wDQI+oSZMWYrr9kSku21m6GHsBcK9us70ijMf9a80Zv/zOPBz7/7PcMz3xIFzlPgVfNtxi6+6ruv 8bnzNjEgeN78yDffR4HNIDhrUGaMnr5mdke30pRaX/sZ8KegNm/0ZjV/x+vuMwVyJ1GgVuBV82jG Jr7qu6/xufM2MSCY2RsF9ivwchC8huF5vQftbjrsH3r5hCjw2Qrcbc7mfl5r5z97NOfqokBbgZyU ndERBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiD z21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJA FIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQ BaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAK RIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgC USAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQ fOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQ BaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21H gSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgC USAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJA FIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGH KhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAg mDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiD z21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJA FIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQ BaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAK RIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgC USAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQ fOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQ BaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21H gSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgC USAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJA FIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGH KhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAg mDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiD z21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJA FIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQ BaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAK RIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgC USAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQ fOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQ BaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21H gSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgC USAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJA FIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGH KhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAg mDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiD z21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJA FIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQ BaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAK RIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgC USAgmDEQBaJAFIgCUSAKRIGHKhAQfOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKhAQ fOiDz21HgSgQBaJAFIgCUSAgmDEQBaJAFIgCUSAKRIGHKvAdCP7gBz/4yk80mB0D1XyZfW9el3F2 xhh4qI3PbUeBKBAFugoEBE8H4K9LAnhA8Mkwd58xm/UhCkSBKPB0BQKCAcFNIBoQDAie4dXb851P N/a5/ygQBaJAuXbzH/cY2Lx3DxTcx7uScbBnHFzpvfcZs1kWokAUiAJPVyAewXgE4xE8fQxcCQJx rQHBpy8cuf8oEAXuo0BA8HQIuM+iGo/g1YBu6/XeZ8zex5TnTqJAFIgC2xQICAYE4xE8fQxsBbKz 3hcQ3GZu864oEAWiwOcpEBA8HQLus6jGI3gWmL37e+8zZj/PJOeKokAUiALvVSAgGBCMR/D0MfBu kNv7fQHB95rpfFsUiAJR4HUKBARPh4D7LKrxCO4FrKu8/z5j9nWmNZ8cBaJAFLiGAgHBgGA8gqeP gasAIK8zIHgN856rjAJRIAqMFQgIng4B91lU4xG8GtBtvd77jNmxicwrokAUiAL3ViAgGBCMR/D0 MbAVyM56X0Dw3stC7i4KRIEnKRAQPB0C7rOoxiN4Fpi9+3vvM2afZOxzr1EgCkSBSoGAYEAwHsHT x8C7QW7v9wUEs5xEgSgQBe6iQEDwdAi4z6Iaj+BewLrK++8zZu9iyHMfUSAKRIGtCgQEA4LxCJ4+ Bq4CgKka3mpo874oEAWiwKcqEBA8HQLu412JR/BqQLf1eu8zZj/VMOe6okAUiALvUiAgGBCMR/D0 MbAVyM56X0DwXQY63xMFokAUeLUCAcHTIeA+i2o8gmeB2bu/9z5j9tUGNp8fBaJAFPh0BQKCAcF4 BE8fA+8Gub3fFxD8dMOe64sCUSAKzCoQEDwdAu6zqMYjuBewrvL++4zZWUOZ10WBKBAF7qpAQDAg GI/g6WPgKgCYquG7LgS5rygQBZ6rQEDwdAi4j3clHsGrAd3W673PmH2u6c+dR4EoEAW+VSAgGBCM R/D0MbAVyM56X0AwC0gUiAJR4C4KBARPh4D7LKrxCJ4FZu/+3vuM2bsY8txHFIgCUWCrAgHBgGA8 gqePgXeD3N7vCwhuNbh5XxSIAlHg0xQICJ4OAfdZVOMR3AtYV3n/fcbspxnkXE8UiAJR4N0KBAQD gvEInj4GrgKAqRp+t4HO90WBKBAFXq1AQPB0CLiPdyUewasB3dbrvc+YfbWBzedHgSgQBT5dgYBg QDAewdPHwFYgO+t9AcFPN+y5vigQBaLArAIBwdMh4D6LajyCZ4HZu7/3PmN21lDmdVEgCkSBuyoQ EAwIxiN4+hh4N8jt/b6A4F0XhNxXFIgCz1MgIHg6BNxnUY1HcC9gXeX99xmzzzP5ueMoEAWiwPcV CAgGBOMRPH0MXAUAUzWcBSQKRIEocDcFAoKnQ8B9vCvxCF4N6LZe733G7N0Meu4nCkSBKLCqQEAw IBiP4OljYCuQnfW+gOCqoc3ro0AUiAKfqkBA8HQIuM+iGo/gWWD27u+9z5j9VMOc64oCUSAKvEuB gGBAMB7B08fAu0Fu7/cFBN9loPM9USAKRIFXKxAQPB0C7rOoxiO4F7Cu8v77jNlXG9h8fhSIAlHg 0xUICAYE4xE8fQxcBQBTNfzpBj3XFwWiQBRYVSAgeDoE3Me7Eo/g1YBu6/XeZ8yuGsy8PgpEgShw NwUCggHBeARPHwNbgeys9wUE77YQ5H6iQBR4rgIBwdMh4D6LajyCZ4HZu7/3PmP2uaY/dx4FokAU +FaBgGBAMB7B08fAu0Fu7/cFBLOARIEoEAXuokBA8HQIuM+iGo/gXsC6yvvvM2bvYshzH1EgCkSB rQoEBAOC8QiePgauAoCpGt5qaPO+KBAFosCnKhAQPB0C7uNdiUfwakC39XrvM2Y/1TDnuqJAFIgC 71IgIBgQjEfw9DGwFcjOel9A8F0GOt8TBaJAFHi1AgHB0yHgPotqPIJngdm7v/c+Y/bVBjafHwWi QBT4dAUCggHBeARPHwPvBrm93xcQ/HTDnuuLAlEgCswqEBA8HQLus6jGI7gXsK7y/vuM2VlDmddF gSgQBe6qQEAwIBiP4Olj4CoAmKrhuy4Eua8oEAWeq0BA8HQIuI93JR7BqwHd1uu9z5h9runPnUeB KBAFvlUgIBgQjEfw9DGwFcjOel9AMAtIFIgCUeAuCgQET4eA+yyq8QieBWbv/t77jNm7GPLcRxSI AlFgqwIBwYBgPIKnj4F3g9ze7wsIbjW4eV8UiAJR4NMUCAieDgH3WVTjEdwLWFd5/33G7KcZ5FxP FIgCUeDdCgQEA4LxCJ4+Bq4CgKkafreBzvdFgSgQBV6tQEDwdAi4j3clHsGrAd3W673PmH21gc3n R4EoEAU+XYGAYEAwHsHTx8BWIDvrfQHBTzfsub4oEAWiwKwCAcHTIeA+i2o8gmeB2bu/9z5jdtZQ 5nVRIApEgbsqEBAMCMYjePoYeDfI7f2+gOBdF4TcVxSIAs9TICB4OgTcZ1GNR3AvYF3l/fcZs88z +bnjKBAFosD3FQgIBgTjETx9DFwFAFM1nAUkCkSBKHA3BQKCp0PAfbwr8QheDei2Xu99xuzdDHru JwpEgSiwqkBAMCAYj+DpY2ArkJ31voDgqqHN66NAFIgCn6pAQPB0CLjPohqP4Flg9u7vvc+Y/VTD nOuKAlEgCrxLgYBgQDAewdPHwLtBbu/3BQTfZaDzPVEgCkSBVysQEDwdAu6zqMYjuBewrvL++4zZ VxvYfH4UiAJR4NMVCAgGBOMRPH0MXAUAUzX86QY91xcFokAUWFUgIHg6BNzHuxKP4NWAbuv13mfM rhrMvD4KRIEocDcFAoIBwXgETx8DW4HsrPcFBO+2EOR+okAUeK4CAcHTIeA+i2o8gmeB2bu/9z5j 9rmmP3ceBaJAFPhWgYBgQDAewdPHwLtBbu/3BQSzgESBKBAF7qJAQPB0CLjPohqP4F7Ausr77zNm 72LIcx9RIApEga0KBAQDgvEInj4GrgKAqRreamjzvigQBaLApyoQEDwdAu7jXYlH8GpAt/V67zNm P9Uw57qiQBSIAu9SICAYEIxH8PQxsBXIznpfQPBdBjrfEwWiQBR4tQIBwTexj6MAABRKSURBVNMh 4D6LajyCZ4HZu7/3PmP21QY2nx8FokAU+HQFAoIBwXgETx8D7wa5vd8XEPx0w57riwJRIArMKhAQ PB0C7rOoxiO4F7Cu8v77jNlZQ5nXRYEoEAXuqkBAMCAYj+DpY+AqAJiq4bsuBLmvKBAFnquAgSB2 +vmJBqMx8N2w+d7M+dYjOHpvfh+NzhgD9Zh9runPnUeBKBAFvlUgHsHTvUH3CbMlNHw1z97W673P mM1CEAWiQBR4ugIBwYBgQsOnj4GtQHbW+wKCT184cv9RIArcR4GA4OkQcJ9FNR7Bs8Ds3d97nzF7 H1OeO4kCUSAKbFMgIBgQjEfw9DHwbpDb+30BwW3mNu+KAlEgCnyeAgHB0yHgPotqPIJ7Aesq77/P mP08k5wrigJRIAq8V4GAYEAwHsHTx8BVADDtY95rnvNtUSAKRIHXKxAQPB0C7uNdiUfwakC39Xrv M2Zfb2LzDVEgCkSBz1YgIBgQjEfw9DGwFcjOel9A8LPNeq4uCkSBKDCvQEDwdAi4z6Iaj+BZYPbu 773PmJ03lXllFIgCUeCeCgQEA4LxCJ4+Bt4Ncnu/LyB4z+UgdxUFosATFQgIng4B91lU4xHcC1hX ef99xuwTjX7uOQpEgSigCgQEA4LxCJ4+Bq4CgKkazvIRBaJAFLibAgHB0yHgPt6VeASvBnRbr/c+ Y/ZuBj33EwWiQBRYVSAgGBCMR/D0MbAVyM56X0Bw1dDm9VEgCkSBT1UgIHg6BNxnUY1H8Cwwe/f3 3mfMfqphznVFgSgQBd6lQEAwIBiP4Olj4N0gt/f7AoLvMtD5nigQBaLAqxUICJ4OAfdZVOMR3AtY V3n/fcbsqw1sPj8KRIEo8OkKBAQDgvEInj4GrgKAqRr+dIOe64sCUSAKrCoQEDwdAu7jXYlH8GpA t/V67zNmVw1mXh8FokAUuJsCAcGAYDyCp4+BrUB21vsCgndbCHI/USAKPFeBgODpEHCfRTUewbPA 7N3fe58x+1zTnzuPAlEgCnyrQEAwIBiP4Olj4N0gt/f7AoJZQKJAFIgCd1EgIHg6BNxnUY1HcC9g XeX99xmzdzHkuY8oEAWiwFYFAoIBwXgETx8DVwHAVA1vNbR5XxSIAlHgUxUICJ4OAffxrsQjeDWg 23q99xmzn2qYc11RIApEgXcpEBAMCMYjePoY2ApkZ70vIPguA53viQJRIAq8WoGA4OkQcJ9FNR7B s8Ds3d97nzH7agObz48CUSAKfLoCAcGAYDyCp4+Bd4Pc3u8LCH66Yc/1RYEoEAVmFQgIng4B91lU 4xHcC1hXef99xuysoczrokAUiAJ3VSAgGBCMR/D0MXAVAEzV8F0XgtxXFIgCz1UgIHg6BNzHuxKP 4NWAbuv13mfMPtf0586jQBSIAt8qEBAMCMYjePoY2ApkZ70vIJgFJApEgShwFwUCgqdDwH0W1XgE zwKzd3/vfcbsXQx57iMKRIEosFWBgGBAMB7B08fAu0Fu7/cFBLca3LwvCkSBKPBpCgQET4eA+yyq 8QjuBayrvP8+Y/bTDHKuJwpEgSjwbgUCggHBeARPHwNXAcBUDb/bQOf7okAUiAKvViAgeDoE3Me7 Eo/g1YBu6/XeZ8y+2sDm86NAFIgCn65AQDAgGI/g6WNgK5Cd9b6A4Kcb9lxfFIgCUWBWgYDg6RBw n0U1HsGzwOzd33ufMTtrKPO6KBAFosBdFQgIBgTjETx9DLwb5PZ+X0DwrgtC7isKRIHnKRAQPB0C 7rOoxiO4F7Cu8v77jNnnmfzccRSIAlHg+woEBAOC8QiePgauAoCpGs4CEgWiQBS4mwIBwdMh4D7e lXgErwZ0W6/3PmP2bgY99xMFokAUWFUgIBgQjEfw9DGwFcjOel9AcNXQ5vVRIApEgU9VICB4OgTc Z1GNR/AsMHv3995nzH6qYc51RYEoEAXepUBAMCAYj+DpY+DdILf3+wKC7zLQ+Z4oEAWiwKsVCAie DgH3WVTjEdwLWFd5/33G7KsNbD4/CkSBKPDpCgQEA4LxCJ4+Bq4CgKka/nSDnuuLAlEgCqwqEBA8 HQLu412JR/BqQLf1eu8zZlcNZl4fBaJAFLibAgHBgGA8gqePga1Adtb7AoJ3WwhyP1EgCjxXgYDg 6RBwn0U1HsGzwOzd33ufMftc0587jwJRIAp8q0BAMCAYj+DpY+DdILf3+wKCWUCiQBSIAndRICB4 OgTcZ1GNR3AvYF3l/fcZs3cx5LmPKBAFosBWBQKCAcF4BE8fA1cBwFQNbzW0eV8UiAJR4FMVCAie DgH38a7EI3g1oNt6vfcZs59qmHNdUSAKRIF3KRAQPB0Ety7G576vGqABwXOfSfTv6/8uo5rviQJR IApcSYHvQPBKF51rjQJRIApEgSgQBaJAFNivQEBwv4b5hCgQBaJAFIgCUSAKXFKBgOAlH1suOgpE gSgQBaJAFIgC+xUICO7XMJ8QBaJAFIgCUSAKRIFLKhAQvORjy0VHgSgQBaJAFIgCUWC/AqkaTtXw YX0E9w/HfEIUiAJRIApEgSjwTgUCgqeDYHqyvXPA57uiQBSIAlEgCkSBnygQEAwIxiMYixAFokAU iAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgC USAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR 4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAK RIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEK BAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGA YEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQD ggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAw IBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHB h07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7 EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+ 3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWi QBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYU iAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSI AlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJR IApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHg oQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApE gYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoE BAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBg QDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOC AcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAg GDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGH Tv7cdhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQ BaJAFIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7c dhSIAlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJA FIgCUeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSI AlEgCkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgC UeChCgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEg CkSBgGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeCh CgQEA4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSB gGBAMCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQE A4IBwYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBA MCAYOxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IB wYdO/tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAY OxAFokAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO /tx2FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAF okAUiAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2 FIgCUSAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkSBgGBAMCAYOxAFokAU iAJR4KEKBAQDggHBh07+3HYUiAJRIApEgYBgQDAgGDsQBaJAFIgCUeChCgQEA4IBwYdO/tx2FIgC USAKRIGAYEAwIBg7EAWiQBSIAlHgoQoEBAOCAcGHTv7cdhSIAlEgCkQBA8GvH0NBfqLBaAx8N2wy g6JAFIgCUSAKRIELKxCPYDyC8QheeALn0qNAFIgCUSAK7FEgIBgQDAjumUF5bxSIAlEgCkSBCysQ EAwIBgQvPIFz6VEgCkSBKBAF9igQEAwIBgT3zKC8NwpEgSgQBaLAhRUICAYEA4IXnsC59CgQBaJA FIgCexQICAYEA4J7ZlDeGwWiQBSIAlHgwgoEBAOCAcELT+BcehSIAlEgCkSBPQoEBAOCAcE9Myjv jQJRIApEgShwYQUCggHBgOCFJ3AuPQpEgSgQBaLAHgUCggHBgOCeGZT3RoEoEAWiQBS4sAIBwYBg QPDCEziXHgWiQBSIAlFgjwIBwYBgQHDPDMp7o0AUiAJRIApcWIGAYEAwIHjhCZxLjwJRIApEgSiw R4GAYEAwILhnBuW9USAKRIEoEAUurEBAMCAYELzwBM6lR4EoEAWiQBTYo0BAMCAYENwzg/LeKBAF okAUiAIXViAgGBAMCF54AufSo0AUiAJRIArsUSAgGBAMCO6ZQXlvFIgCUSAKRIELKxAQDAgGBC88 gXPpUSAKRIEoEAX2KBAQDAgGBPfMoLw3CkSBKBAFosCFFQgIBgQDgheewLn0KBAFokAUiAJ7FAgI BgQDgntmUN4bBaJAFIgCUeDCCgQEA4IBwQtP4Fx6FIgCUSAKRIE9CgQEA4IBwT0zKO+NAlEgCkSB KHBhBQKCAcGA4IUncC49CkSBKBAFosAeBQKCAcGA4J4ZlPdGgSgQBaJAFLiwAgHBgGBA8MITOJce BaJAFIgCUWCPAgHBgGBAcM8MynujQBSIAlEgClxYgYBgQDAgeOEJnEuPAlEgCkSBKLBHgYBgQDAg uGcG5b1RIApEgSgQBS6sQEAwIBgQvPAEzqVHgSgQBaJAFNijQEAwIBgQ3DOD8t4oEAWiQBSIAhdW ICAYEAwIXngC59KjQBSIAlEgCuxRICAYEAwI7plBeW8UiAJRIApEgQsrEBAMCAYELzyBc+lRIApE gSgQBfYoEBAMCAYE98ygvDcKRIEoEAWiwIUVCAgGBAOCF57AufQoEAWiQBSIAnsUCAgGBAOCe2ZQ 3hsFokAUiAJR4MIKBAQDggHBC0/gXHoUiAJRIApEgT0KBAQDggHBPTMo740CUSAKRIEocGEFAoIB wYDghSdwLj0KRIEoEAWiwB4FAoIBwYDgnhmU90aBKBAFokAUuLACAcGAYEDwwhM4lx4FokAUiAJR YI8CAcGAYEBwzwzKe6NAFIgCUSAKXFiBgODpIPiDTSD2g5Ov+8JjPpceBaJAFIgCUSAK/D8FvgPB KBIFokAUiAJRIApEgSjwLAUCgs963rnbKBAFokAUiAJRIAp8p0BAMIMhCkSBKBAFokAUiAIPVeD/ B+eqrtuYzECGAAAAAElFTkSuQmCC --bcaec53f398fec3f19049d0d0af8-- ========================================================================Date: Thu, 24 Feb 2011 15:42:24 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: fixed effects analysis across two sessions Comments: To: Israr Ul Haq <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Israr, Let me start of on the issue of means, sd, r, and t-statistics (basic ideas). (1) beta ~ weight of a particular IV (e.g. column) (2) sd ~ weight of overall variance based on covariance of betas (3) beta=sd(XY)/var(X); r=beta*sd(x)/sd(y) {for linear regression, for multiple linear regression and general linear models, you compute partial correlations} (4) r=sqrt(t^2/(t^2+df) OR t=beta-0/sd The beta and residual of the model form the basis of the r, not the other way around. Everything is test against a mean of 0. Since fMRI values are non-zero, a constant is included to account for the non-zero mean of the data. It is the betas (not r) that are compared statistically. Contrasts: Run1-C1 Run1-C2 Run2-C1 Run2-C2 Run3-C1 Run3-C2 Run4-C1 Run4-C2 1 -1 1 -1 0 0 0 0 tests C1>C2 for pretreatment 0 0 0 0 1 -1 1 -1 tests C1>C2 for postreatment 1 -1 1 -1 -1 1 -1 1 tests (C1>C2 for pretreatment) > (C1>C2 for postreatment) 1 means you add the beta, -1 means you subtract the beta, AND it is the sum of the added and subtracted betas that give you the value to compare against 0. T-statistic (matrix notation not included): T=Contrast*beta/(ResMS*Contrasts*covbeta*Contrasts) which can be thought of as effect-mean (effect-0) divided by the variance. Now, if you have multiple subjects, you take the Contrast*beta part of the T-statistic(con_* images) to the second level modelling. Let me know if that clarified the issue. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 24, 2011 at 2:54 PM, Israr Ul Haq <[log in to unmask]> wrote: > Thanks a lot, This makes sense, particularly the second explanation. although the runs1 and 2 I specified were from the same subject and the whole analysis is being done for each subject separately but I suppose in a way its the same thing and is taking into account the variability across runs. My model is actually an overt naming task, the experimental condition includes a semantic process of interest whereas the control condition has all but that. > > Thinking about this though, how is the direction of the change from pre to post treatment (in a voxel) taken into account? I computed a pre - post treatment contrast to check whether it will give me the same voxels (worried that the areas included both positive and negative change over the two sessions in my first contrast) and was relieved to see different voxels. However, if its not too cumbersome, can you please explain how the eventual t statistic is being computed in such a multi session analysis with two independent conditions? Heres what I understand; for each voxel, a correlation coefficient (r) is calculated for each stimulus of a specified condition, and all the r values in a run give a distribution for that condition, with a mean and standard deviation. These are then utilized in the t tests of no significant difference, where the linear t contrasts between two conditions are setup such that whether for that particular voxel, one condition had significantly different signal than the other, using the mean and sd of the r values. What i am trying to wrap my head around is how based on the weightings that we specificy (i.e +1 or -1), the t statistic is calculated for exp > control vs control > exp, since in both cases, what we are essentially inputting is the difference between the means. Intuitively it would seem that it would give a voxel as significant only if the condition weighted +1 also has the higher mean of the two conditions being contrasted. But if so, how would this apply to multiple runs and sessions, or more specifically what would be the sequence of computations in my design? Theres a plus one weighting in both pre and post treatment runs, albeit for different conditions. > > Thanks > Israr > > > > ========================================================================Date: Thu, 24 Feb 2011 20:51:15 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: DARTEL Flow fields problem? Comments: To: simon vandekar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Something has certainly gone wrong here. One possibility (which was my fault - sorry) is that you are using an early version of SPM5, which had a bug in the normalise to MNI option. This was fixed in a later set of SPM8 updates. The sequence of procedures that you described in your earlier email all look perfectly correct. You could narrow down the possible causes of the problem by using the normalise option of Dartel (not the normalise to MNI option), which should bring your fMRI and anatomical scans into alignment with the Template_6.nii. Bye for now, -John On 24 February 2011 20:27, simon vandekar <[log in to unmask]> wrote: > Hi John, > > Thanks again for your help and sorry for all the emails. The flow fields > that 'Normalise to MNI' asks for are the individual subjects' flow fields > 'u_c1*.nii which look like the 3rd image selected in the attached picture. > Also included in this picture are a template, and subject's functional scan > after the 'normalise to MNI' step. > > Thank you, > Simon > > > On Thu, Feb 24, 2011 at 12:28 PM, John Ashburner <[log in to unmask]> > wrote: >> >> The flow field looks pretty OK to me. I thought that you were >> referring to something that was uniformly grey. This is the x >> component. The other components can be viewed by looking at >> u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in >> the file selector to 1:3). >> >> Also note that the main objective of Dartel is to align all the scans >> in the study together. When you normalise to MNI space, the images >> that are closely aligned within study are all affine transformed to >> MNI space. The version of MNI space is only really defined for affine >> aligned brain images (ie the rather blurred averages). You should >> note that the single subject brain that people often overlay their >> results on does not define MNI space. It is just the scan of a single >> individual that has been affine registered to MNI space. >> >> Best regards, >> -John >> >> On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote: >> > Thanks for the reply John, >> > >> > As far as I can tell the template*.nii files look good, they are gray >> > matter >> > images. There is also a u_rc1*_Template.nii in the same folder as my >> > template*.nii files, I've attached the image for that. Is that what my >> > flow >> > fields are suppose to look like? >> > >> > Thank you, >> > Simon >> > >> > On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]> >> > wrote: >> >> >> >> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure >> >> why this would be the case. Do the template*.nii files seem to be OK? >> >> >> >> Best regards, >> >> John >> >> >> >> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote: >> >> > Hi John and SPMers, >> >> > >> >> > I am attempting to normalise my fMRI data using the steps described >> >> > in >> >> > pages 440 and 441 of the spm8 manual: >> >> > >> >> > 1. Slice timing >> >> > 2. Realign: Estimate and Reslice >> >> > 3. Coregister the structural to functional images >> >> > 4. Registration looks good. >> >> > 5. Segment the anatomicals the SPM5 routine >> >> > 6. Initial Import- use the *seg_sn files >> >> > 7. Run Dartel with dependency on the gray and white matter images >> >> > output >> >> > by import step >> >> > 8. Normalise to MNI, select the template6 from dartel output, the >> >> > subject's flow field, and normalise the ra* files (slice timing, >> >> > resliced). >> >> > >> >> > The output I get is sdra* files. When I compare the output to the mni >> >> > template it looks as if the subject's image has not been normalised. >> >> > I am >> >> > suspicious of the flow field because if I look at the individual >> >> > subjects' >> >> > uc1* image it is an empty black box. >> >> > >> >> > What am I doing incorrectly here? >> >> > >> >> > Thanks in advance, >> >> > Simon >> >> > >> > >> > > > ========================================================================Date: Thu, 24 Feb 2011 15:16:32 -0800 Reply-To: Michael T Rubens <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael T Rubens <[log in to unmask]> Subject: Re: script to flip MR images left to right (or vice versa) Comments: To: Chien-Ho Lin <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0023547c93373a30ff049d0f6945 Content-Type: text/plain; charset=UTF-8 how about this: %%% v = spm_vol('/image_path/brain.img'); x = spm_read_vols(v); x = x(size(x,1):-1:1,:,:); [path file] = fileparts(v.fname); v.fname = fullfile(path,['flipped_' file'); spm_write_vol(v,x) %%% cheers, michael On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote: > Hi, > > I stuides patients with stroke doing some behavior paradigm in fMRI. > Patients always used their affected limb for the task so some patients used > left hand some used right hand. > Now I would like to flip the left and right of the images so patients' > lesional hemisphere are on the same side. > > Is there any script in spm 5 or 8 can help to accomplish this goal? > > I found a m file called spm_flip.m but it seems support only spm2. > > Your assistance are well appreciated. > > Thank you so much. > > Janice > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --0023547c93373a30ff049d0f6945 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable how about this:

%%%

v = spm_vol('/image_path/brain.img');

x = spm_read_vols(v);

x = x(size(x,1):-1:1,:,:);

[path file] = fileparts(v.fname);

v.fname = fullfile(path,['flipped_' file');

spm_write_vol(v,x)

%%%

cheers,

michael

On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote:

Hi,

I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.

Is there any script in spm 5 or 8 can help to accomplish this goal?

I found a m file called spm_flip.m but it seems support only spm2.

Your assistance are well appreciated.

Thank you so much.

Janice

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--0023547c93373a30ff049d0f6945-- ========================================================================Date: Thu, 24 Feb 2011 17:28:03 -0600 Reply-To: Pilar Archila-Suerte <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pilar Archila-Suerte <[log in to unmask]> Subject: Order of subject files in ANOVA batch MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e64c1cf0cd62ce049d0f91a3 Content-Type: text/plain; charset=UTF-8 Dear SPM list, In setting ANOVAs in SPM, do the subject files need to be in the same order under each cell? I just ran two trial trial batches, one with the same order of subjects and one with a different order. The areas of activity are very similar but the order in which the areas show up as more or less intensive did vary. Any insight as to how SPM does this? should I stick to the same order of subjects for clarity? Thanks, Pilar A . --0016e64c1cf0cd62ce049d0f91a3 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Dear SPM list,Â

In setting ANOVAs in SPM, do the subject files need to be in the same order under each cell?

I just ran two trial trial batches, one with the same order of subjects and one with a different order. The areas of activity are very similar but the order in which the areas show up as more or less intensive did vary.

Any insight as to how SPM does this? should I stick to the same order of subjects for clarity?

Thanks,

Pilar A .

--0016e64c1cf0cd62ce049d0f91a3-- ========================================================================Date: Thu, 24 Feb 2011 18:38:16 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Order of subject files in ANOVA batch Comments: To: Pilar Archila-Suerte <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> You should use the flexible factorial for repeated measure designs with factors for subject and condition in your design. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte <[log in to unmask]> wrote: > Dear SPM list, > In setting ANOVAs in SPM, do the subject files need to be in the same order > under each cell? > I just ran two trial trial batches, one with the same order of subjects and > one with a different order. The areas of activity are very similar but the > order in which the areas show up as more or less intensive did vary. > Any insight as to how SPM does this? should I stick to the same order of > subjects for clarity? > Thanks, > Pilar A . ========================================================================Date: Thu, 24 Feb 2011 17:44:46 -0600 Reply-To: Pilar Archila-Suerte <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pilar Archila-Suerte <[log in to unmask]> Subject: Re: Order of subject files in ANOVA batch Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016367b60fa2fded8049d0fcf11 Content-Type: text/plain; charset=UTF-8 Mine is actually not a repeated measures design. I'm running a full factorial and I just want to compare activity in one group vs. another. Do the con files need to be in the same order in each cell in this case? Pilar On Thu, Feb 24, 2011 at 5:38 PM, MCLAREN, Donald <[log in to unmask]>wrote: > You should use the flexible factorial for repeated measure designs > with factors for subject and condition in your design. > > Best Regards, Donald McLaren > ================> D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte > <[log in to unmask]> wrote: > > Dear SPM list, > > In setting ANOVAs in SPM, do the subject files need to be in the same > order > > under each cell? > > I just ran two trial trial batches, one with the same order of subjects > and > > one with a different order. The areas of activity are very similar but > the > > order in which the areas show up as more or less intensive did vary. > > Any insight as to how SPM does this? should I stick to the same order of > > subjects for clarity? > > Thanks, > > Pilar A . > --0016367b60fa2fded8049d0fcf11 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Mine is actually not a repeated measures design. I'm running a full factorial and I just want to compare activity in one group vs. another. Do the con files need to be in the same order in each cell in this case?

PilarÂ

On Thu, Feb 24, 2011 at 5:38 PM, MCLAREN, Donald <[log in to unmask]> wrote:

You should use the flexible factorial for repeated measure designs
with factors for subject and condition in your design.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.

On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte
<[log in to unmask]> wrote:
> Dear SPM list,
> In setting ANOVAs in SPM, do the subject files need to be in the same order
> under each cell?
> I just ran two trial trial batches, one with the same order of subjects and
> one with a different order. The areas of activity are very similar but the
> order in which the areas show up as more or less intensive did vary.
> Any insight as to how SPM does this? should I stick to the same order of
> subjects for clarity?
> Thanks,
> Pilar A .

--0016367b60fa2fded8049d0fcf11-- ========================================================================Date: Thu, 24 Feb 2011 15:50:51 -0800 Reply-To: J S Lee <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: J S Lee <[log in to unmask]> Subject: Re: Can't find driving effects for PPI analysis Comments: To: [log in to unmask] MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_NextPart_000_98E4_01CBD43A.9CB39E80" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------=_NextPart_000_98E4_01CBD43A.9CB39E80 Content-Type: multipart/alternative; boundary="----=_NextPart_001_98E5_01CBD43A.9CB39E80" ------=_NextPart_001_98E5_01CBD43A.9CB39E80 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 7bit Hi Donald, Thank you for your reply! I am using SPM8. I looked at the SPM.xX.X values for each subject. The values in the SPM.xX.X *PSY* columns for each of my models line up perfectly across subjects. For the different models within a subject, there also seems to be correspondence for the PSY negative values (although there are slight baseline shifts between different models). The values in the PPI column, however, do not correspond so well. I don't think I would have expected the PPI values to correspond across subjects, however, because the VOI values differ from subject to subject, so the PPI should also differ as it represents an interaction? The PPI values for different models in the same subject also do not show perfect correspondence (png attached: It shows 2 subjects' SPM.xX.X PPI values over the first 80 scans. The All conds - control model and Cond 1 - control PPI values are plotted for each subject. I didn't include the Cond 2 - control, Cond 3 - control, etc. for clarity). All models are coming from the same VOI, so I think the adjustment has to be the same for all models (all extractions were adjusted for an F contrast of the effects of interest at the first-level model). Is this what you meant? The voxels are also identical (again, coming from the same VOI I think they have to be?). Thank you very much for taking the time to consider my question--even looking at the PSY and PPI columns has been useful! Jamie > >Are you using SPM8? The issue of summing was fixed in one of the later >releases of SPM5, so if you have an older version, that could explain >some of the issue. You could check to make sure that negative aspects >of the SPM.xX.X for the PPI term are the same for all subjects. You >could plot them. >Are you using the same adjustment for all models? >Are you using exactly the same voxels for all models? > >On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote: >> Dear list, >> >> I conducted a PPI analysis in an experiment with 6 conditions. To replicate >> a previous study's PPI analysis, I was interested in connectivity >> differences between 5 of the conditions compared to the control (6th) >> condition, so extracted my VOI (using an all effects of interest contrast), >> then created a PPI model with a [1 1 1 1 1 -1] weighting for the >> psychological context regressor. I get a reasonable replication of the same >> PPI effects from the previous study, so the results are sensible. >> >> However, in that previous study, there were not enough trials of each of the >> 5 conditions to realistically analyze them separately, which is why I >> collapsed across them. In this study, there are many more trials, so I was >> hoping to look at which of the 5 conditions were driving the original PPI >> results. I was given hope when the initial PPI replicated in this new study. >> However, when I create separate PPI models for each condition versus control >> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1] >> for model 2, etc.), NONE of these analyses show the same pattern as the 1 1 >> 1 1 1 -1 model does. Mostly there are no significant (or anywhere near >> significant) results, and those random speckles that do show up at low >> threshold are not in the same places. >> >> Is it theoretically possible that 5 conditions vs 1 other can produce a PPI, >> but that none of those conditions singly vs the 1 other can do that? Or must >> there be an error? I have checked the microtime onset files to make the >> context is specified correctly, and made sure everything matches up in terms >> of specifying the conditions. Everything about the models looks fine to me. >> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the >> previous study (in which the 5 vs 1 were equal in terms of number of >> trials), and I understand that when creating the context variable one does >> NOT sum the vector to zero the way one would in defining a contrast for a >> regional activation analysis. >> >> Many thanks in advance for any thoughts, >> Jamie Lee

_______________________________________________________________
Get the Free email that has everyone talking at http://www.mail2world.com
Unlimited Email Storage – POP3 – Calendar – SMS – Translator – Much More! ------=_NextPart_001_98E5_01CBD43A.9CB39E80-- ------=_NextPart_000_98E4_01CBD43A.9CB39E80 Content-Type: image/png; name="PPIvalues.png" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="PPIvalues.png" iVBORw0KGgoAAAANSUhEUgAAA9QAAAFFCAIAAACCEzIcAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAJJhSURBVHhe7Z2Ht1VFtu77z3vj3ZY2tG3imloM bWhb25wFSSIiUUCCRMlIkHjIkiXDQaKCCoqxDd1eu99933FuPsoVa6W999r7O+MMxmafir+qVTXX rFlz/u5/9SMCIiACIiACIiACIiACItAUAr9rSi2qRAREQAREQAREQAREQARE4H8lfGsSiIAIiIAI iIAIiIAIiECTCEj4bhJoVSMCIiACIiACIiACIiACEr41B0RABERABERABERABESgSQS8hO/f/foT 2SL7E3+a1GpVIwIiIAIiIAIiIAIiIAI1JJAufJvYnSB817DXarIIiIAIiIAIiIAIiIAItIBAuvBt jZLw3YLBUZUiIAIiIAIiIAIiIAKdRaAE4dvH5mT27NmDnZ/hw4e7/9VnERABERABERABEagpAQg5 nSUcqjfVEigqfLuti9OOh3uAp6vabql0ERABERABERABERABEWg/AhK+229M1CIREAEREAEREAER EIEOJVBU+Ha13dJ8d+gkUbdEQAREQAREQAREQATKIZAufEc6EwzI3Am+CCObKbOTckZPpYiACIiA CIiACIiACNSKQLrwXUV3JHxXQVVlioAIiIAIiIAIiIAItDkBCd9tPkBqngiIgAiIgAiIgAiIQOcQ kPDdOWOpnoiACIiACIiACIiACLQ5AQnfbT5Aap4IiIAIiIAIiIAIiEDnEJDw3TljqZ6IgAiIgAiI gAiIgAi0OQEJ320+QGqeCIiACIiACIiACIhA5xCQ8N05Y6meiIAIiIAIiIAIiIAItDkBCd9tPkBq ngiIgAiIgAiIgAiIQOcQkPDdOWOpnoiACIiACIiACIiACLQ5AQnfbT5Aap4IiIAIiIAIiIAIiEDn EJDw3TljqZ6IgAiIgAiIgAiIgAi0OQEJ320+QGqeCIiACIiACIiACIhA5xCQ8N05Y6meiIAIiIAI iIAIiIAItDkBCd9tPkBqngiIgAiIgAiIgAiIQOcQkPDdOWOpnoiACIiACIiACIiACLQ5AQnfbT5A ap4IiIAIiIAIiIAIiEDnEJDw3TljqZ6IgAiIgAiIgAiIgAi0OQEJ320+QGqeCIiACIiACIiACIhA 5xCQ8N05Y6meiIAIiIAIiIAIiIAItDkBCd9tPkBqngiIgAiIgAiIgAiIQOcQkPDdOWOpnoiACIiA CIiACIiACLQ5AQnfbT5Aap4IiIAIiIAIiIAIiEDnEJDw3TljqZ6IgAiIgAiIgAiIgAi0OQEJ320+ QGqeCIiACIiACIiACIhA5xCQ8N05Y6meiIAIiIAIiIAIiIAItDkBCd9tPkBqngiIgAiIgAiIgAiI QOcQkPDdOWOpnoiACIiACIiACIiACLQ5AQnfbT5Aap4IiIAIiIAIiIAIiEDnEJDw3TljqZ6IgAjk I7D92OdPTts7f8vZfNmVSwREQAREQAT8CUj49mellCIgAh1I4Odf/nPL8M3/9/l1+L34zU8d2EN1 SQREQAREoJ0ISPhup9FQW0RABJpOYMn2j03yxu+sDaebXr8qFAEREAER6C4CEr67a7zVWxEQAZeA q/aG8H3HyG3iIwIiIAIiIAKVEpDwXSleFS4CItDWBFy1tym/D539uq1brMaJgAiIgAjUnICE75oP oJovAiKQl4Cr9obO24TvkUuO5i1P+URABERABEQgnYCE73RGSiECItCRBDYdumACNy5c9p7/1j5f /XIPhPKO7K86JQIiIAIi0A4EJHy3wyioDSIgAi0g8MD4nSZww/gE1fO/6/Z92oLWqEoREAEREIHu ICDhuzvGWb0UARH4LQH49qba21TdtP+Gz2/REgEREAEREIGKCEj4rgisihUBEWhrAtRzT1r1oTX0 6+9/hs2JHH639bCpcSIgAiJQfwISvus/huqBCIhARgJUe0PahszN3APnHpTD74wslVwEREAERCAb AQnf2XgptQiIQAcQgGGJCdlUe1unKJTL4XcHjLK6IAIiIALtSUDCd3uOi1olAiJQFYHTF/5Bxyau 2hv1uc4H5fC7qgFQuSIgAiLQ3QQkfHf3+Kv3ItB9BGhbElB7Gwl8KYff3Tcp1GMREAERaB4BCd/N Y62aREAEWk6Aam9I2Ocv/RBuj6sXl8Pvlo+XGiACIiACnUdAwnfnjal6JAIiEEuAam98iEskh9+a QCIgAiIgAtURKEH4/t2vP5maOHjw4EzplVgEREAEihOAqttMSvALDXdcgXL4XRy1ShABERABEYgj kE1oDpdiYreEb80wERCB9idAe+4EtTd6IYff7T+UJbZw74kv4f1m5e7zJZapokRABEQggUBR4duK lvCtSSYCItDmBFyROkHtbb2Qw+82H82ymofDEEVWKgumyhEBEfAk0CThe/bs2TA14c+QIUPc/+qz CIiACFRNYMDLs8zg5OaXFqfW9ejA8Zb42hdWpiZWgpoSGDR46PUvvEtLJAx6TTuiZrecAIQcT6lL yUSgT2ddCgVpvkvBqEJEQAQqIuCqvRFJJ7UWOfxORdQBCWiGFBlxqQM6qC6IgAi0JwEJ3+05LmqV CIhAmQQoZsGTiWe5cvjtCaqmyRBEiTpv++A/N2raZTVbBESgTQhI+G6TgVAzREAEKiSAcPEmYPmo va0dcvhd4XjkKvrSd//cc+LLc19EeGfPWh5OQm4ZvtmmxHMz91EKl2f3rCSVXgREIAeBosK3+Rnk j2cL5GrQE5SSRRL4/Ju+Pfijz78XHxHwIfDDP38x6er6wRt90jONHH5nwlV14k2HLq754NN1+z/7 5d//KVgXL9TirQzTgwMNdXjBkpVdBERABFIJFBW+UyuITCDhOx835QKB//zn/2H3xR6M359+/reY iEAqAfiSM+H7kUm7UxO7CeTwOxOuShP/48f/sacev9/88HORuuBVkKpuk7ZHLjlq38zacLpIycor AiIgAj4EJHz7UFKaNiIAhTf34DPxcVLaqMVqSqsJUIYeu7w3U1vca5rQj2bKq8TlEoC1CR/8C1/9 lLtw17cgRe11+z6lCUrukpVRBERABDwJSPj2BKVk7UJgy+G+o2f73XX8Urs0S+1oYwLDFhw20SpH IBUoyy0v1Odt3MXOb9qRj77hg3/qs9jopMkgYNJNCxOMLC28L37zUz7DpM7nrh6KgAhUQEDCdwVQ VWRlBGDtzQ3YPkgfWRnszimY8lbv+W+z9oo+T+ZvOZs1r9KXSMB96z6Y1zKbo4nAOhC43ebhPoDJ 36kBmErslIoSARHoTgISvrtz3Ovaa9yzNJmbZt+5dWB1RaB2ZyQA7SZDGOZ4VaN9MNTnGWtW8tII 4HaH+9a9vfeLHEW7vgVhZxIogVcwYaSUo3BlEQEREAF/AhK+/VkpZYsJ8MYVJO9PvvzRNuNtR9MD prS43aq+pQToMfDu0dtzNKRg9hw1KkuYAJ93e+p7DlzISsn1LRj5HsWLAbh8mbVwpRcBERCBTAQk fGfCpcStJHDs3Le29e4//RV8nmADtv9CKG9ls1R3exPYdOiCmRNAtZmjpVCcywl0Dm7lZnENvu2p /9f/ZPN0FPAtGG4eTJJsoOF8sNzGqzQREAERCBCQ8K0pUQ8C2GtpamLSNvfjE5/mvH1Vj56rlcUI FDfaLmIyXqztyt0gQINvLgKZvA2GfQuGybrmSQFzcA2DCIiACJRLQMJ3uTxVWlUE4FXQNF409/zq H/+yb7AxV1Wryq0/AcYv9I9tGeh0EWcp9efX+h7Q4BuSN65a2lMPQxT/ljGYZbIb7yen7c0aBtW/ DUopAiIgAiQg4VuToQYEYGRiwe3wC4cnbDG/zKQGq0GH1cTyCNCLRW51Zm434eV1oqtLosE37lvz Jdz/vItW+5gJydHjp607acI3Tku6mrg6LwIiUDEBCd8VA1bxZRBATA2TvCFtQxBnkbQCx4cy6lEZ nUYA1+yK+2/OHSCz02i2qD/Hz39njz9cG3Ep8Pc2yHenVKN/nI3YbIGhUYv6qmpFQAS6goCE764Y 5rp3EqYmtvsGQlpC4U2hvO59VPurIEC5GRYFucuHg0KTyeCyMHchypibAB9/WJrR5ZG/t0F/H4Ic aIx1so48d1+UUQREQARAQMK3pkG7E3A9DIZdHPAmFjbmdu+J2td0AoiMU4ohATxgWDkITt70TnR1 hb/8+z907Y9TL/7X39tgpug5vFwLp+BdzV2dFwERqJKAhO8q6arsMgjAsaDtvpG2JTD9tL/C+UkZ tamMjiJQ1l1Jak/huLCjALV9ZxjUFgbf1lje9PDxNugafPv0FU6+7S0r+WqmT1FKIwIiIAJxBCR8 a260NYGwh8FAc6kXhybMNQdv616pcc0igMA6pcQMhyhm5eBOXrParnr6CNDgmzcsGebW57DL3+Db cCPypQ00nORoAERABESgIgISvisCq2LLIcCt94OTX8WViCCXpvy+9N0VRyjlVK9S6kygxPg4vIon mazJM4IG33RzxGvW575INwHyN/i2fsElTvEbuk1GpOpEQARqR0DCd+2GrIsaHOdhMIAAPhBM+PZ3 gNBFELu4qyVGhpdM1pJ5RAtvPN34bG3I5G0wk8G3lZ8jS0vgqFIREIH6EpDwXd+x6/yWQ7PlE0YH PgrcK1mdz0U99CPAuIaw/PbLkZSKMhncFxYvTSX4EKDBt+vbhF/iNkhyIVkNvq20rMpyn44ojQiI gAi4BCR8az60LwGeOH/0+ffJrdx1/JLJ33AD3L79UcuaS6B4YHm3vQp/2NzR66uNVmf4wNr9vQ1m Nfi2KpgLly+b32XVKAIi0A0EJHx3wyjXso8w4PbXZ/MkOlUZVksWanQuAhSX4e07VwG/yVSuKF+8 Pd1QAu9WunFtYY3GlSEZQj4ddu/5b83sG/4luwGy+igCItB8AhK+m89cNTYIQLzG5gpxGaJz2HEB PQy6Sq84dj/9/G/ux7QNFeguJ1CuoQj9YKQGSuxy7GV1H0L2uv2f2XMdeKg9vQ3ms97GPV1EUzL5 G7b+ZXVH5YiACIgACUj41mRoGQHuoLa/4hfWIxC1YTpCtTe+hGDt00QqyT758kef9ErT2QRKvyJZ 4vXNziZfVu/wQm7LQjiYJXwfpTo4ymfwbY2XiVFZg6hyREAEIglI+NbEaA0BGm5S8o784G9GwtuZ CU4JW9NV1doKAqU7B3QVorjj24o+dVed9GIUPvvy8TaYz+DbEMObeymBUbtrwNRbERABbwISvr1R KWGpBCgrQ62F4JT01R0Qwf1ddyMcD/P6hL4rtTcqrO0IlBVY3u0YY4/DLLjtOtxxDYo0+LZe4ga2 PewJNmn5DL6tfL65YcQ7jqs6JAIi0HoCEr5bPwbd2QII3LZ90pMJzDohakPdhU3XbD2z6rB5GO0T faMjsUNoQBQYhUDH4FYREJ6xx6FV7cj50z6dSjD4RiN9vA3mM/g2AjjZMM03fnHi0T5Y1BIREIHO ICDhuzPGsX692HL4ognfsD8pq/Ww9rYyIb6XVWaNyoH/6VuGbzaJAVJ4jVpeRVPLCix/cfOm/U8+ /snKFWgkLRnGLu+tos0qkwRo8I2FIoyFrv1xYhYJrYjBtxXIU45DZ7/WuIiACIhAuQQkfJfLU6V5 EaBzkp4DF7wy+CWC7jzOPYJfAfVORV94EL6h9jt/KT34dr07HN96ai7htqKI5vLH8+c2XdNvw3/9 H/z+/PXXdEIna4SqZw4NvnFEFq4r1dtgEYNvq46nHLM2nK66sypfBESg2whI+O62EW+L/lanok6w E22LnlfWCIja9I9mym+ofovInZW1tBkFlyUl7330YZO88QvltyvTN6MbXVwHTcjinBfx6CzSG1IR g2+jTs+SsOPq4nFQ10VABCohIOG7EqwqNJkADb6h3yqXFRVmJz4tueRy21l6aZQ2Hpm0m1J414bo KyWwPKRtSt74AOMTjFpZ1iylT4AOKxBnYmZCFudpNNnbYBGDbyNZuqvKDhsgdUcERKAIAQnfRegp b04C1FqFY+vkLPFyNjoI7yqzb5ilmrYbYjeEBirt8A3E0IJI65gdNtkGJPfNSBiZ0OCEIji+5EsO INeRTC3aTD+kkQbf1oUEb4PFDb6tiuISfC1oq5EiIALNJyDhu/nMu71GNxolbDfLxQGzb/+g9OVW 3cLSeDkMZt/WDEqfEMe70C8e1P8mfOcOLH/w+WdN5t5+523Qedvnz9atrcKDYQtnTntWTU+CkQbf 1uYEb4PFDb6tiuK2K+2JV60SARFoOQEJ3y0fgq5rQHUG34YSjsNL96PSzoNEPTdcnZzbvBVSI2RE WHtTAL1j5LZuCwpDnWW+jsPDCbXd3xw6SPsTsKUHaARBbOdZUeu2IbSWPcIJ0Wp5xhV2SFqW0Ewh vmvNt2o9i9R4EWhnAhK+23l0OrNtiIthO2vpBt/Gi+fR9CDemRx/7RWEbHMv2O/ZVaufH+KKjLA/ oQwKcaSDIQS6hrunpvYGmRy9hm3Jtv43Gcne11/rg/z11/ZfGKJc+rxh4QO2OQpXFh8CqQbfKCTB 22BZ5iK8tovXV59mK40IiIAIeBKQ8O0JSslKI0DNdOkG39ZEatb9Q9OX1remFwQ/aH2OBZ9aurD/ Xe7tQBhL/Ofnn6mmRZru8ZhWMLA8BG4jCRH8lx8a7hpdyxPKdni9afqAd36FPgbfRoERbV0oZRl8 25st7y7DiX7no1cPRUAEmkVAwnezSKueXwk0wSabNuUJt7U6YzQgEEA4uOdvU1f1u5qSN68Jfjhu DLpp0rn9dkm4kGnrTlp/8SHrQH+5dw9JwviE2c8tXWLfHx02BL7nFMkoK1j/9DTmPpgW3QYRdkz+ do2LyjL4tgYr1I7/wCmlCIiAPwEJ3/6slLIEAowLXak3Ep9j6xI60+oiYIr64t2vuGI35EUYfLvG J2gjrJNphtENCrzcgeX7zgruvM3oHR70sju8/7x4kZYnk5cfMZ7dc5jQzJlOg+9Us7FIb4NlGXxb l4ctOGxj3Z1eg5o57qpLBLqKgITvrhru1neWBt+V+uHm/n3hq441DDh55uKk/g9Szt55z4DvTzdC 8bmeOiBQumHncQuz4yPvwELXBKasMT5PTppoPLf86Y+w8w48LQy4s3HWUiu/qyzpm7Z2bDp00fPC NBcTV0wvy+Db+ivnNk0bd1UkAl1FQMJ3Vw136ztLg2+owKtrTYIbsuoqbWbJkLOX3/jflLyhpqV1 MpoBwRHio/3VjE9wdYzWq3RH2MwGN62u3EEov+vtJU+4Nwk3+KP58yzBrpcGmvCte3ilD+u//uff Jnnj8Cq18PBjXqLBt9Ve8P5AaheUQAREoDsJSPjuznFvTa9p8I3NFZ+rawSuctoWDlm/ulpaVXKf XfLVV1FS7J01J9ySsPEJbWEhNXbwTUHGG4K1rv8A4Xxg94P3G1KLZBn+oeUJXmyue3GNyd/5XBn6 N6zbUtKB4K7jl1L7HvY2WK7BNxpQ0HNOaheUQAREoDsJSPjuznFvTa9p8F21TIzYPev2f2byd6VS fks4br3sCA/3LJfMWhPXhoDxCZLxpiAO01vS8iZUSvELYYb8qzszeyZNun88fy4uI2x7LNmQQbO7 6g6rP8mCKc9c+Ic9tjApSS0qfLW6XINvNAA2Wryv3PH2WqnAlUAERKAsAhK+yyKpctIJ0EbTZ2dN Ly4xBTRntotX5NCwYPNyZ4fBicl/kLzvGvRegkAQNj7ZdOiCSRKw/M7dgDbPmC+wPO9ZwrYkoYOU 0Zc88ryRzB2+vs0xtqp58HBij+25LxpOHpNbwndsi5VbrsG3VX336O021rBpaRUW1SsCItBhBCR8 d9iAtnV34OHEdtZKDb4NQdWhfFoFmj7vcNsSwnRyMwLGJ67f4qyXEVvV36z15vAN59qTwP4koUYo xe3Np+e6a00gU+zDrAOUnJ7eA+HtO5xy74kv4boH6m34mYF9EWx+XG+DpRt8WwNyO88pl4xKEwER 6CQCEr47aTTbui9NNgWhiUs4+nRbY0pr3O7BQ03+G/jAq2lp+/4eMD6h67ROdZPHe6X+1thfbN+e bO3tcqaOHO7VIXxnsiz3Ga9uToMlgnFzTJPt/uDVkYptmoJMWXvCsuz+8BJd2pfrhaaI2/huHs0O 6Ds2ESiMUl1edkBP1YXmE/ASvn93+SfcPv7JPnh2YPDgwZ4plaxjCDT5EiTtQX3cJtQI8vpbbjFJ ceT41T7NDhifQHdoggsO032y1ysNr8dl8kNyevpUQ4oPqf1l4tdvf8JIyhQ4FZpngm9++DnhnjSN pih548Ow+Ycty9Nvf8Dvy7UFYr14cfXsiJJ1AAG8/jFehP+bfAd0XF1oDoF0cdkVqcPitb/A7fZH wndzRretajn1WYarVKW0HBEuPR0Gl1JdEwqhgUTfVcvtH3vW6BqfXNp/oAq7WM+WVJ2MclIm3SdD x0MFntpCeiR879o/yhQ4FVemBLDztgc2MrYlrwvjA9xlWuioJ6butSwjFjUiH5VunA03nTbQOuXI NJp1T8yzU8/rv3Xvr9rfZAISvpsMvHura6bBt1HOenmr/ceGYvTUm+/LdP3LNT4ZtfCACRM5oq+3 OaJ8FgL0iY53G58OBixPFPvQB5pPmmPnvjVJOnzQj0BRVGy7jjIPf9S4oDl301lLkOm9y6dVuT3H +xSuNG1LgNsHJiTu9cIDfds2VQ2rI4EShO8EoxQSmT17NrTd/BkyZIj7X33ueAJDhw5bteec7azD Xx3RnP6+NXuJ1Thn2ebm1Fh1LbPv+LMZSLwy4IVMdb364otr+/3e8s748z0mplz7wspMhbR/4lte Wmhde3TgeM/Wvv7cs4ZlzTX9PLPMGXCXZRn65z6fJwNenuWZUcmSCSzuOWAP7OgJUwIp//LyNBvZ m19a7P5pxOujLcvybR9Wh/fqF96z2p97ZWR1tajk9iGATYoblk2wqXOXJTcPQk4dRUC1uVUEigrf brv9TVBkdtKq8W5VvTT4hilI09oAhwm2bjaz0kp7R4Pv0aOWZK3INT6Bj0ITJnCknrWcdk5/y/DN 1i9/Xy7Egiihnl375tBBE76XXPsn1NXBfhs9gZSVLME3P/39rdv3aaC6gLfBshrjlkOLFwS8rKJ8 ldluBD758kfbO2j2venQxfAl4HZrttpTIwISvms0WDVuKg2+j3z0TTO70Umhdmjwvfaq3y/dcioH Rho3zx76lgmpmSLR5KixmVlomQCjdv96T06aaJI0fHj759p2Oc7RXY/MyFSdfxXdlhLWHXGvyrS6 hiub8PVWROyq+moHTMztefG/aNFtw9dh/aWRJKI+cYJ5+p7vMBTqTkUEigrfydcx4xotzXdFw9m2 xcLfn22Q0Cg0s5HNNzSvrnfU0c64cUAmg282iSW8/7dHTJiAqri6Bje5ZDpyyaSK3vvowyZ8f7l3 j3+DPxw3xnINGvBSJkW7fxXdlvLCVz/ZErH/9FeBvjNwUqRXdaS3jNVFD8gXNrXbRrBj+gvzbptR +MVn3gOuOjBzxwBUR3wIpAvfKCVs1R2QuTP5GUSBEr59xqaT0vDwDh4Am9mv5rtYqa539PA97L6B +Wr55YcfNl3Tz6TGe4b3mPyNYCX5Smu3XPO3NK7cQU/p2TaE1CEQwPHMhWSQ1A3jwutuBkNZI/ij i0sZFxXLde8dOVdPfNpwowQlZfFmRJaQ772uosao2KoJYCKZ5A3dDeqCtQlsTqp+wau6Uyq/3Qh4 Cd+lN1rCd+lI27nAFtpe012ULaO1/ll36+0m8OUw+GbHaXky/41ZJnx3TIxGxg/ydz/y/enThhQO TDLNDUjt9JHS//F5nec3JhONUhLzcCygwKb7yDjf7VRMwllKKS0JF5LPoqmixqjYqgnQzoTntBTH OyxkW9UkVX4CAQnfmh6VE4DjMFMbNNngGx375d//saph/F3r6zLQy5qYmNvg24b5k5UrrJwdTzxp wjdMljsjTAzv5Pnb5JDG0WFDsj4Gva+/RsuT0t3bZW1MB6SPOxzjZce4mKy8zF3pCza940MQ7wDa 6kIcAd49wJaB7cOSwfiE14egSxI9EShOQMJ3cYYdUgI0TIhbgQPW0vtDo8wmG3xbR6jGwCZdetea ViBDoOc2+LamIuCliYwwt3hg5EaTv6sY9KaRafTrl/9YX/Dr/y5BAfrc0iVZG0zLk3euv7WTTOez ciglPa1sA/Fo49x7u5UyL2wDSmlMZCG4SNCRDoKqI1bTkmnFFIj0RLOoyAhQNe2smt1CAhK+Wwi/ XaqGsMIrTfAn4MawKKWJrTL4tsZD3W7K73DkjlJ615xC9r7euOGX2+Cb7aTlycLx802e6IC42fnC EO5+8H57FYH3wKzj6NqL3/TEIgWgzgrQTR9nHkY7fugFEspvgrfBHEZNRYAob6sIMC5ywPzJ1Ygr 4E6rRqeT6pXw3Umjmacv0C1RqWOiWNiTbp5yL+dpocG3NYEeW8NeFIr0q8l51905oLjBt7X5o/nz rKg9Lw+0EY/04NbkDhasDnbeWV8kID0bB/zic44GwFjFsr949yu6c5kDILPQphb6RbecBPfebrIm eBvMcZ23CBDlbQkBmjBBYRQ2U+QRbmCWtqSpqrTuBCR8130EC7Uf3gNoy8hT+3INWGnw3arTOkr/ gRPtQuCam5kG35Dz8nn4dttLf+GwPPn7hO027jA6am6fSq6NRzf+npgZKwf673ytubh5kwnfs264 w9/FSr66OjtXpGVasntvFwgjgcNfYUWg8HJlTwps0CuqworFewiM11tioVdpv2pROO7s2jFp5OXd b374mZF3an2DqBZj0fGNlPDd8UMc20Hqckz3CY8NvIFXIhRuqy20+mit3UtxmDT4hnmx/23ChHrp 3HrpW0tt0Mt94yre5awl8PTG334ddt4mOsPyO2t1lh768o1XX2WF/P319fkKUS4Q2Hb083CgnGT3 3i63JngbRMxUe1LinK4UH0eoXckBNJrslbV4++teAuRp7hRxF4R4xtLC7azWnOml2j8gekX9bX0D KupYcrFyNdgS7KwU5msQtqjqxtmuiXTUgpci4Vl19JDawkvirb3xWXysafA9asBzxUtDCYjmaCLj vmHDaXlSa6tlvD1aR/x7QaMR+DzJTXX/c88YyafuG+Ffde7qOjJjpEuiVPfeLgqallXqTynHjV7P 8YINMZX3DO+ieIqe9MpKxosHMPuOK5NpKr3dW1aPAuWEA7YggeeXkbFcUgO8hAXcioTvHJJ0jizl jos03+XyjCjt0nf/xDFidQEgsnYAgjUtKU3lSaGBEnmcV6+sdXFbba3JB0PtVOcJOCuZTOlp8D1u +JxMGeMS/3j+nImMcFb99Fs7q7D1L6WdnoVQK5nJ6wh8exsEePv2rCiczA07KrPvfBh5mu9GEEx1 7+3WxRJ2Hb+Urw0+uXL4svQpFlsDFa6UvCMjffqUpjS5CfD9BwcpCYXUNOBOZDByzy8tWUBajfwy LO7HfVOu7JujtBxZck+tyIwSvsvlGSwNJ1m8id8Oru6wpVFHCJELlidui3lrLdm3gD8yvHjYdlLp ppjaHl6jqWN84N8YfG88ntpZzwQ772nc4Fw1Z1VzjFk9G5YjGQU1f+MZUoXhe77bltZOlNPzh4bl ydRFSR45cvSrS7LwWoj7bpzq3tuF0xxvg9RNlHVBImBngnUS8h/XzLqHJqjX7IWeiDt18hEWp2u9 dhNPOTsgZEfmckc2QYRNENnDojzLDGji48Kru0p3pgmo1SPTuBW1dopK+K6WPx9ULKx4Vlt7SwM6 b0resDAJx2qGk8Fy3V9EbqvVEo8qHdipUmLchOY3I1+NvBdYlsG3NYOWJ4dGvcHz9JoGEOF1Bf9I k/TSDfP3fOPCXFv//oRp0F97YkzBorozO2+50Y7Wx713gBWVx9U94JxmxQ8Gw3YmsPamdoa61XbQ 13TJnKTlUqpI7ZqG48ilLD64rILrBFyKi39AaQHHZXGCrHUh0iCkycJ3pHI93LxwU8MZ41pekd1L jmkg4TsHtAxZ8CRv3HJs/9zF21bvaK2radhQPjC+YWCAD3FiFp//Us7QeZDXcvtFXpSBYinD+LVB 0g8mTjHZrrjB978uXTr/7pJvjhxGt77r7bVit/W/iVpG/8DsbQDmShPYfv9Jy3ePk5MmFuzL2fdW G0nEP5LZdw6YfDYpyni693braoK3QR6wFPSLD7th184EnwNGiQxNkGz/kAO1ssQRgGmoKWh8DEQZ cKdE97XlSt5UorG/TdZ8e8q+YT23O0D52uy+SAQE97h3jJY8FxK+K8QOvcXaveePTxj/4fix+N3+ 3jYcbLXKPz+0NXwgYSMb1216GCjFdVp4W60Qd2LRXC5h/92qNuSrd91fHjLZrqDB9//7978/fGuS TcUDcxas2/WRa2CKzwu2np2wojdfI1ubC6beNrcTJnaghYcHvWxUYbRdsPGwPFnX7/dW2vu7ThQs rduyu4Z5VFp7uvd2WTXB2yBODm2aQXmRe5jc/pqdSXhHgMNEezZTtbC5m6GMLgGaLYG5zwbtRpsv i6TrfKy42ttKwG7eQuE7bA2SKvt6ar7DnUrIGJDFpfkeXNaUbedy8Fq8Y+VmE3fwCyl886aDLXF3 7RqcBOy8AwDpzrbIBmNlusYerbW3QWO4n0HD0c5zJtA2mCOv79cwKV5azOD7HydPcCriw9Ep03Am E5C/8d+zF7+vER80lSYKsKTybzn0/SYu4+6pf664lCv+8jcrbcFr04qX1lUl0A0/7C6s4/7uvV1Q TfA2iGONsEIx62DREg+2JXFWJbynLoeDWfHmS88YT/67g2Wp1LtOvr7E5cqnRc5ndhI2BE81FLFm S/gud9AjSusGV4P2cgwRx5V4eidN7Nl+ssmWfK7BCdwhJ48uNxhsMwXjzIe31conVnwFcJrL+Agt bEbWqks0+P54yWJ3KtrbIE5jAvL3uztLEEazdrNIethKmkiUOrdZC8MMwdlLkaqZd8vbC034XnT7 faUU2D2FhAPQ+rv3dinx7bpS7UaOM5bAUNKTd7KjaNxQtwdT0Xaa8CzwhLazaYdtvk3edS8mUgiO u9HoitGRuu2wDO1+k6p4DhuiBJqXYNCSUHi4m+E3hCbMNLcKmZ1UBRzmDVvX7zFx5/T0qfi1z4ff nrnj6MVmaoI9DU4IgvFKCsaZ57Za6XboP37w3hoO5OGfvSUpyzL4/vnrr3n8sv/9A5yN+HLfk49t 7Pdf23s+oBTuc/DaEhqRleaI+82gRfuffLyUjnz31bdrr2pYnvzwRZ2OVkrpfpFCaA9mtrYwHOKl 8PCN8ISKmuNtkLcL/GM5uW2ml+hU+0N6R22TxbPIELd5XuibbOnDoFR3W7fNIah5zScg4bsS5pCt cap4cNY7JvFc2rnjx08/OTFxgv0X9y+bFh/L3+CEICisj1xytAidwLZapKhS8rZDrM2sHaHB94Qh M7LmddN/sW0r5x729V9+/PHcooVUhB8ZOvjUtClT1p6wTWjjwTqFmsftN9N8+98WxbuHKarxoQhV N+/c2x+wMnfNmF9Wmd1QDi+6QTDNdEYXgNOckAK4CWOTbcn2j3OMDpeg1IADbiTzHBUpiz8BGizp PccfmlIWJyDhuzjDiBKwkcCg1oQbyNyQdZAIXiYo7uxe1tME/WK+zQwKJ9tgMoUsCVNwt9VKKGcs lNaWdVlkXYPvd9/bl7G7V5LjqiVV3Vt69mFy2t8okfeZoIx5472Vu0z4XrXX69ZR7vaUmzFH6BMo vE1Qhgq8rMbMHTnDylx974NlldkN5dDvB9ZDirZQfueweWuCt0FGQnCvsnkOEzWseMR8wv2mhjr3 rFfJkgnwRJQLo4iJQBMISPiuBDLkzr2LVpqo/emq91jH51sbCsg++5OdByup2yk0q8GJZYXIzpPf InHm3W216p76lE8b9ITowT7lNC0NDb6XXPunIgPBq5a4gYCN3z1a/a73WO/oUXwn7Fmw1uTvg2e+ blo3i1SEuZoj6DdMvU1QhvF3kdrdvNv3naXlCYx8yiq2s8txb2LwnR8Dms/gjXbSJXpfDvDPccGA JfAk8OCaLfD4+W1vimch+m+pnYOmGk1axl/DbtVMW9AaIVJTKyIg4bt8sBDy1u88TQ+DcK7MOqCD PLN4Ea1vP//o0/Krv1xiDoMTNoax3PKdrqKcNrzg6Dr5QvOqI19WyfumNJSpE+54rEiZ2Oltyu1c viH84tE7auTRV4dS/jYXKKv9XG4VaVUpeekZw985D9yb0MF5KW2wQuB0ZerN91nJHy1fXmLJHVwU baB3Hr/Eu4y5vWiHg/WUji6fax00wxafDduOH54xh88abBETWsg7M60ND1w6w7YqkHMm1RCorZqt xnQAAQnf5Q8iNByQcmyF/Wj+O4EK/vOvfx2fNt3+2jt12i8/lRzzBbLFweefPbt4MUPq+HuBYFMh c5tCMXeceW6rH5z8qnzEeUtEY/wjKeStpLR8ax95yoS5iYOm5C7UvWqJd8KwyQ1iPW76w+8PvvCc zcm9b88xRE27lpC7a8hIMwB/iQ2OvY0qXH0XqTqcd+Bj4xpi/cOPlFtyp5bGa4Uz1jeiECDUSO5A Rc2JpwuPlrY2Zmon2oZNgRoZe9bOzJwBdUzc4LrOp+20ClF+sCD7321o/2mDoyec7+EXQa/w2/v6 azAJw+9H8+c1ofFurMomuyBrQu9URZsTkPBd8gDheV6/75Njk9+y5fX7M6fDFXz/xSWuwicXLExY f3M0jg6MH3pwAnYIGJD4Rx5hdchSMM48PafiVSRHLyrKUi9l0tqrrzFhrojBt3vVMlKkhmX5pmv6 bbr2DzD7tkkLLz1IuX7/Z+1/Dku3dP5HNAhpaVSx2ftMM5gH4Ojgqw/2piZ+Y94HsjxJpeQm4AXE hyfusgUnk4eTQF00IahUVUxnUDh18ewsXoCPzppLhTduAZ2cMtn++/nmTQmFuC7wXD8wOQziPZva hGR42+elC3sSI3+RrOrGUENUF0PEqoGo/GYSkPBdMm1oOLat3WULK265xQnWZ/Yf5Vr82caNZTWC McOxnK3qd3X/x+clh9RJqJfRbvNth9xW4X+3rN4VLwcKJBz+mma3zS1Pvj992vakIgbfgauWcTe9 jg4bgor2PvyQzclDU6cjMmstlN+UhPxdv+199GED67O7f7m78SwDy8UNPcnvybBUpuXJJytXFJ+u HV8C77rdMnwLJG/cUSnS5eY4PIEPqEzedfDa9uGbDT9XfWehCxdAFscbnY/xCR1xIJgLp3pxUEUg F8yLV/0NN9yQIHPzTxtvuqHquxO0qgfngv1SdhHISkDCd1ZiKemhq4Anb1tYE7Rl0CnuX30l+OX/ /KOchx/KPHddW3bDf2Oxy9dD7jHT1p3MUQJjSfjc689Rfu4sdbE82T/zHRvKIgbfgauWePGIVGZf 3LwJFcHbN5XfiMwK4XvDwXa/hMSbwZ42AKbmN7AIC588i1zJ257oT1Ysg9lYXC7oIx+7f5QVXpYH 8dzzvP0zUlZeuecTi5GE67MFmw0Hr/ZqXZ0vKX+/8pAdXYeeOO3s3bCNHcRcSjU+oS4fPXJDjkMz UhBUq7Ifn9cIR2WPyTvX3zrjxgGT+j84aMBL+MVp7T1/mwp1Q+MheuWV6trpKmI8V4/qGqOSu5CA hO8yBx1rZdjDYFwFSMzLN18eKeRRm1WEj/NgRZevh1Dj2XLvf5WNFXFbjZP28jWplFx1sTwpxeDb vWqJ/TvuOJ4i6Y57BphAcHTyZBiII8u5L1Ik1FIGJV8hNI7y94nJ84Ttd97mL3m7MYlwiyNB/r5n eI8sTzxHk5Ll2+tP5fMtGK6Ir9aXviv5Lg3r2n7sc1sYEXAnoad472VgBzxQWOpx29LVRMD/bKrx iWuUfNuIra78ne9A0nNoKkqGdWbt9debYI3XVNtcYMWO+/049MAv2KJfTz03myok3NCoqDHcCKAv q6gKFSsCCQS6Tvj+4eOPIZHA0i75pnm+SQNbCwTQ8THms/IPrd7UuJe5uoQlxtXqQYvA9SvfCbgb Zz6rYoARItpwXauL5Ulxg29etcSpt0nSCfb3uH1oE+b4uDE2J/csXo0sbTiCfDZx/8zEEWzeng8s ngXrJixtErK4Ou8zS5fuPf7FqQ1XnITinlzcURXOi2R54jkWO3o/Ny31iEVH8vkWDFfUhMBefOWD g/m4nsI8iYI1nj5ctUQ34X82kN7H+IT2ey/POQCFNw8kC0ZA8xyjcpNR7b386uu2H4z19AL5e+if n2/sX9ddW6I/ULc7DENRi5vl5Q6ESmsHAt0lfEMccbURUGiVKIXjoLNn95lID4NxI338wImG5d+0 6cVnA4NmL7zuZlzJ/3DcGFu/cM4OW/Ac5dNfCqScTNnpdgCmipkyNidx+1uelGLwzauWRxcsNSkn wf7+ikr4z7fZnOydMB66OuRq29gTMIgy4dvfMgoHQfZQnFu6JG6yuZL32XeX4v600du3cQ/tdCFa uS5EWRSESFme+DzF8Nn35qoPDexbq0/4ZPFJg4OahqP6sxW6WqexU5ydDO7Z89rPpu2NbkY+R6nG Jz2X76hMXXsSUil9a6INxa10fJCWmGb1Hxtq71GPjUoudvqqY9jF7FHd9fe/l9gGK4qecCu1UCq9 2Sqwkwh0l/DtWuBxH7WbkcWlcLjNgrKQtqE+s+Tzr36gsF7c7JtuHEbc8Qx0gVCE824ZDtlTLVzD DWbAuaxaliY43PXBG5em/S1PaPA99daH8vXUvWr5/sYDPndMqfw+MWG8TWMc47Sz8hvn/iZ847Ta k9LuB++3HR3ezSKzuJL3+eXvbj3YuJ5rALf1XLk8B/kbx2iBQmD2fc0zK2R5kjocfZYGG/tOY/D7 2deNO9lYsqBBMH9zWM1yqDybc+aWGlT1s7Vr7Ak6taHv7gR+YYweySTZ+ASvKPeM2W4lIO6sORxk 7WUdF6QOVikJDr/XY49estrb6sJ7xROvLOFzlPCqnK9tdMbVVp5w8/VFuWpKoIuEbze6O0RtnB27 8jcVFV8fPJBjLGGct/nAZ8keBsPFQll+cNY7VvU3x47lqNfNsvOeAba64dqK+Q3A7sVgfjm8GjPm XNb7PQw1157OU9vf8oQG32+9MC7frOBVy1Mz3k7e/lk+ld/bbrnpwzcb8vfmTQfbVvnNsCyezjT7 PC1c9msWeRHZlbyhkjxw8pKhw9UFOsnZuPnw8cmT7JnFMVo4TiEOnWR5kjxpsTr1e3G9scXvjxcu whyI734cI7MOynRqh3WYQ5bvwfHJxRhkkUeCuBLA89Ude05aexJsGxKMT8zI5J3NZ6wQ053zxmey 0blPR5qZZnn/O21kxz892qdeBIkbeO9gy9Lzh6sQv8Inl2ca+gOAIsYzi5KJQLkEukX4hoKBC+Kl nTsMIg6OcTQflsKxB2eljGVx+3vbeHvdP/u+FY1wPOfXrvPPFU4JOdvWKWgL+j27itdxaIuCP3k6 Nmbhbpx5T/nG8lJScSOZF+ld6Xnb3PKE57PLlryfr++8anlmS0NzBuPR1KIQnslm0YnLwjdc90Sa q6YWVXWCHLEGoe223kH/HW5eQPL+6MK3lA6xQ+PaA18pcan62JRpfHUPvK5DMpPlScLoQ/KGQInr g4Z3/YZDrrQd+RkneHDI4zmjKFdlvaniWT6S0d4p0jcihelTcxvxqrAeJntfiTQ+YSj7YfMPGyuz 4uPMB0Z89m92C1NS7Y3t6eSZ6EOAcPPwmjHrhjtsSrx/3325PXcFSsa1V76htX8ogxaOmqqulEC3 CN/JpnVhKTys0EoeBtzeoIfBTLrzwx80HL6emF7I7Jtx++C5KWAO6PofjDttj+sdrqLbyb5/EBPs eZ6q1kpndnLh7Wx5gpsJtt/AUzvUPzko8aolXjiPfHjBhgMnralF8VVtyw3XH5vUiAOybfUOZG83 l5EUTfwDuOLw2sCGXQDhmaUwjbXim+9+4guke2+BERl7tp/k846M7us6nhRZnsTNNJO8cTS38uWR DQHo7cag8L0IRoBwwW6+591f2M5hBFPN53hDsbq7CrzpGxlXle+9R9ZtsT6mhi4PG59A8cFIC6OX HbNyGAuGy3LuMA6pS0G5CZbc3JChZz+azXvgs6PW0vjk5MwZpbSK3tPD4X79y7dQQdU5Y/FviVLW lEBXCN88gsc2meDkBGaylNGRMmzQGTfGkEu29OzjSXSmiJVQsJVi9s29Cn5Ows4B6YIQ8S8zRS5g nHl/hxK41WdbhY+qtVWPTTtbnny0dYfJHHNvvDMfH161/HTVewyS52kCRKvoEzMadlkwpkLMnXYb TX93y2TIZyTg/wfv3jwWwwrwPz//D4O/QI0a0I3Bqth0q+t2fURXoX1mY0cOW0V4X4J8KcuT8NQ1 yRuRvyBOrRvbsIZaP/QNTHXYnGBQAkbe+C8sv2k4Zw8F/ovVDENpAckhA0Gh4NokULTCm1K+xyc1 lw1xpBtWiNF8izMXQ54vrgHjE963gRXTV9/9i6+Cps6nH9gEjyupvWhagm3LN/NU9sTxbNYjuETx 1IN9M8R+M9kgxXWQzuBz+6OEDp6RpJuGsWsr+t3vWiOm5gbu2eDW9Grw4MG5O5Y1I1ZDuulFjLq4 7NhisVjvPfrZh7PnUIz2dEcI3Rg9DELuydRCCO40+86qbncr4hZ1+9/nYOEOtAECNxcLnOH6n99x m8Ee4Hm5nptfm4cNa1vLky1jp9hOM/3hgZnmkiV2r1p+d/Yj2k54mgBR+f3+rbd8MHGKPQu73u2z 0G0r5TeUjpkCDYIMVKcGFtbtBAtccN1t3cSFbPyXqlNIPJGmC1gr8MgDCN5JDsxZEF4u8LDI8iQw dU3y7ovO+KshwbqlDbOTUzv3JS9HUHVD4c2xS7ZRweK2f8Hyql/+sRJaX3DGGOhmXzzLX+fSh/MX WDPCHgbjHuorqp9p0//wwlp3enOxMttx1yDQP8p9jsWklCyz//y3hjbhsTz7vhs4dvOt+cPGWV8g cBc/mOUZWqQBWynQqigEQqH9uIV7fhnOiEIiv0wtPGvXImXZ1Kqz1pKQ3lOYZgme6Ttf+IbAzcuU cdExYITAt2EotGjQGedQjJTNEhQe2ajtyOq0BBv57mWNFn66fn2+GcOrcjBUiHP+AJ0BY/tBn+Rf ESQJ2wY8wzpwn6ju2Ne/8Qkp29byZPXfG4bX7wyfkqOnPOfBZQbGMcnkrpvK732TG5bNOJyBGi8u Rk+ORhbPkupxIlAFZDiTAPAUuNIeTb2h/MY7Kr1kYodOvoyFXRyLBuRv2p9gubDHH8dEruVJVluv 4nDarQRK3k/dN8JGYc37p0wG8g9FCbNvum9KEMF7/jygYaSx/3x1HHjZF6pZtxa+yG1f02eshV// ZdA1Ppk2YhaWXJiXWOGclhTl6fB77PI8bmSrIxMo+f31e2yw1l31+y/O+3olChQycPIW+Ehp2IyN 8bqvGddBe23Gb0LQg2Q4rtq7RmYnrkTIz55fWrKw1B7+MiB5Z5VHI8mHZdnI9lQ3pT2F6ayd7XDh G6YjFIshlISHB9IJz+WpI4RBJ/2WJATUgBEtdGPYfffNW2q14JQ/xwzYt+uYZT/59ts5svctzfPn 2cI0of/DWLLjbhrxfT2g/EuulKt85O2icF6+xlR34SkfpaA09u//8DAXbl9LKbOUQlbedKuN5vqV O3MUSJNTKOFoo5xqdepWxEg0OC1ZP7Yhf+/7VaHoL0nkaLl/FqoeMds9D2Rgn2BUIcCxItfgBLhg UhJp6h3XMAiOeNV0lwvIXtCdm7EWgmZbjdDa+p81+UOoS0pK3tc/tXR9v98DSM9td+TWPsLCBC8z GE0zO4H9CaxQfiOU/+GqNbsaBz57H3vU/6ZmJp50c4m7B8zohrWCEgd9jPMwGFeXa3zSf9AaXnPn RRrGDKYrKv8zyUwdLCUxns2Zdz5sT8HyBx/PXSbulT71+GS+cWX1HMB6oeriA46HPV97uI1ieSzx ucaU9jzeST784V9Rmvtu4ClnB+TpyFxxErbn9wGlNSXpOJV8nOybQyZ2q45T+RsB9/3ETRlubbgc z4Z1svCNXZCeTMJiMZYz6mi5E8BSwmRHODTonTSxoTKfOSOgMqfrA9xFo5ieyUzcnabHP/66oNk3 Tbpx2J3sFpD+vPzPy2hfSDVMwprFwPI9B7LF5cm3DhbM1YaWJ647vFPngiHxUvvrXrWEIo0WFJk8 armqnTXjZvL1FV72wjbQqU2qIgFDjYSvN8RVx2vHPPYJGJxg6iaYeif0AosGHDLyEf7kvZVmrAVZ c/Ufrra9EBGvquDQ/mVS8gaQhXc9ZDS2DBxWkWUIzvcgjq9dudPKhxbcXn58bmpmgkmbbPcmOvxo 2cNyaOEya4DPLedAvWsvv+72LP2NgxdOTloq80amv5/7TH0snhjOmigLfnr4eJEC0ccxtz5aUP7m fSSsY7kbQwPOcr2Plyt585QvIBBHCr6WJlkdHhDKWWwmyTjuBSBcdarQH9eeuGGNVNsHOh4Wu5MT BNoQCTBhmnWy8M1rZzgOhiBCCtBXMQqMLZF4G4aO0G5WYQ82q2V3Qz05dw6vUZrCG3913R1gwYXF Xr7nGdrEImbfblT5m55YlBwQB8IZjU88w87jXNXMThJ06uw4LeraykQhblza0PLk4sEjtm7Ou/62 HNPJvWqJ7HgF8r/y5VZH7c6O+/8y5/VZJlJgwuOcpx2iMa/cfd4mZKS7iUhufO2kNihgcJJq6p0w HAisSE+jAHVxx04z1qLlNwa0C41PXMl7zCvTKTkdOtCwOanoWsi+3kZopPVPXI5S/utNTbwCleUu mjPQtfqgrmfr+j22rfgb1djswmL7wpBF9rh9vHSxO+W4Z+3esMe+J17/2/A5lpTcWaD2Hj/g6eJq bzZg1MIDvMeMYnPov6ltyX0Zl1diylV7o488vvZUbPskc9/520HzXbwN7myMlPsj9dlhST1OuPdv YaRcHv6yG4Xvny5eoMaOjggAAtIhJRKTS7CohZdI6LZhXYc1lIUcnb8IASnNwpu3rGhN7laRdbVC 7bjQZkVd6Im9EhpXLM/T37n+Vuz3qWHP3FMzT88ntK9NVbHQNjGTnUNWYmWlb0OfJ9unNSyIZt39 WNZuus7sYXDFEMo5TiFc5ffkV2ftHfumzc+dyzfgaMjz7mbW9vunh8STyQOma3Bl4lfA4MTf1Duu kXiF3rukEeAWoKbN6LEWbnj4sW4zPsG5BPTBjEQDCLDZ3dr/JuMAP49VXwth/MK9m/aGFYqwUcEL WEGDAVp90NMlbufbM4IYTHhHxc6Sw5MdVu/rXlh9ZNw4K8rVGV289L1tWGvXH7QXORil8N5nGzr8 fnftYXoJ/PrESf+nOy4lpPkXpu/OLX9jny1uZ8j7MOWqvYvDSS3BX6z0EUxZXSma73BpLdR8Z2pM KtWuE77dA2WYwLL/0G27kjf2gGQHDjil2rVuF+Vv2HbvXbSS/8UHXNKC/iyTb8HIwdix7bAVi3iE qU9RIIEbVR5rcapnaOw6XEHCDo8ja+cZa+rlHuw3tkNAF5i1Iy1J30LLE1xCwOQMeIV/79lGULd5 QydlBUK1NyyPkZdnrP7+FtwaqYnZOOAeauNgXIGXz9x3lTx7BOEJbcZcwtFEpKAPicfEDtfiNq5w PiCQ/CzOa8DgBBc/Mpl6x1UE7+CHZs9rSGATxj8/tk/+HjNnJz0RdarxCSQ/vJYj9Az9T/OsDB9g Hn30tcY9S+gLT3z8FW/XVHQtBC9CVoUFD4+8qZmqCIcFCwQssykP36tjpBvcvLT5gKjJNvQfLHrP as9hVWwr7bujGhctXJ3OuVWraMu+/Ymn7OWBD4J/HAbPZ7BgMozsmHsaxw5rH/p7wdKYvYj8Hb60 mrVVVHvj9DjV33zWwqtOnyomlmt2gu6EayzeBpeSp2m1ZfE3O2EVqUAi+xiuK25kO9PshLZ35sGA nac4Au1dwMcnniVIG1hnsVW7WfqsUHq2ugI3P2O1dTUTRR6eg6e/zG32TUn6L3+dhMNun2Yw1B/E ER/PqdhZbTdNjTPP26s5Nh6flpeepiWWJ7hC4HqUx2deKlh2x92mIFw1Z1Wmzrpq7+/P9LnSg4hs QkC+M1bXQgm3nd57Y6rNfJhI5ThP9+wL9uzdxz7HQRBqwbsu7lTg4hqOm7BxuoIaXLz52EFBQHFj tUDyNpHFNTj57vNLfCHH7C0Y8e77736gr6SjU6Y9PXkHHhkGwOok4xOIQVgWYORGy2NX4OZnSN4X d+3mEfnBHY1gjZiW1XmOj4zzhYUOugYa3bFJVIRjwkO6gl9aXqFxT/Zd95Q2mekGCtXhdQ7GjfaA wCIRvcvkX4hPB+4wAB3fdV3NEdb5dbNXNOwkR71lRhe0fvG//OD5JGZKBmsZHAXgF5Yw+MWseOjV K8FxPtuaM0xvZBtyy9+MUJtbMcStNpO7sEwkK00caZLh8yXTuCbjkV8G2h8uPPKbsLxrImyCDUnC X2OF3ct5LEFcxxMaEylYJ/coYUA7TfgOiDXwYOB2Huq08D0YHENDI+UuyvgcEME/3dAIAm/LK6Ql HFuX+JxAtshn9h2IKu9v/EehxOfmpWeceQgu1GkVFGJKZJtcVPMtT2AQQsfzfJGDtajNqHW/uoPA r38QZutgQO2Nb/gilNtFCXXGq+558PaXVvI0HDJxFZITFN5w0ObeYDY+iGWzY+VmaNxx5wxvFL3n vzHZjkrHyCHG67QrRVHr7BqcXNqzh9HI8UJeitOb77+4dPzN8XxReX3RIbA6+HzDfWTdPZ9AzILA 55qUhGVuiOOwxYcuFvYneNuh4cf2eQ3Z0XTSlS4RPMoInJxgVuCuy857+i5iZvq1MxP3xz1+weuu jThevXIf/dGHDyxPGPXJ9DvmTHb9o081LE82927643XYuSD380U09cyzxEUVTYWQHXnQgfmAq0d0 9bPuzgEl1mtF5ZC/XXcx+azmqLGCeOBprll6x1VgJxHoKOE7INZARHYNQmgCi/XLjLxhLc1NMbwQ B0RwO1XEgb5/5Ev/iQLrF5p9J0QCChfoRpXHqud/+OjqNX3M17jdhiP4sFXohe0NRe6S+0MrK2XT LE8wG3k2bVv1Z2vXUP7GdvvJxk02D1dcfZ2nBz2DEFZ7u0618m02fZuccz0XwZtGD5/bkDAmvwW3 3yXG3OlzPbTzaOAGc/i4CbrkPYtXI5rssp3nRy05mmAEhZdSqqnAE4daBipgcMKhh6xW4lnNt6dO ubZqa3acPnrqi813NCJs19H4BIIdrEpMLxv+hfwHOQzSGKyAAsbHV2x+Rk/hm3mk5I2hgdk01CV4 KKDxxctk1pgJ7oKQegQXpwjnRoB3Bmgo8J7MbwLKb9fhCR/k3UvXoZs4S8nxakE7cnDmyZhZnkBn 39eMP1y1fnvjrioEcbxb4k+MNoUBKmtJTC4Hhul0cx6YDH97cIxrk402l6v2ZsOyyt90uppba8Ab 2zVVezdnbqgWfwIdInyHxRpYngRMsRl5cd/JS5BZw8oPrGUQQ909G2tHWAvuD9c/JVbqLZsP2oZ9 esYM/4yNRfm//s+Ld7+CdTA1Dg78yPJKfli4wZ/gkxGH8tgFA94VGeoSG23c5R6acOS4aeTf5dJT NsfyBDeAz86d7aq68Q36gn9dRfi+Jx/b2O+/EBMuUzfDam+IkibrQFucqahAYgpPc+/6G6LubR0z yboAt9/5TMnDjTl94vzBy3EiGzcfpk2FzAFreIScjLT4ghtQNAD3oSHlIGpGQLUPIYnaVjy/rsGu a3By/Ng5ioMwSCtCKZwXL/ZsOSzK8M6A1xXEVO+54U/1Mj7ZdOhCnKQFGRECX4LCFQKuSa7rR0wI S96utO0+Gu6IQ6i1xyTrD32DJNsYBBTh2ALwMML4xFVtUuoKKL9p8jFm0UEqqnE+g57muxQxf8tZ k2VhtkGH33gPQSN5MHtwfyNkPUxQ+kTbdWvxwuNzEJQVYGR67C/2Ajbg5RXvvrVoz/Q5b01cgf8+ PmL1lEcHr/7j9QEdFnRbpdQbWUhY/sZrbZxOmo4a8z3pDGMntXd1A9ptJXeC8B0n1rhjCemWwV82 PvNyYI2ACIunC2sf9DdQ8S6cuGTLPfe6aZoggu89/kUOs2/6HIViEmJxgro0/H4SKda4XwZkccaV AKjI54RGxjm827bwwUuwPAFP7DfoL9RLCbat+BN24oQuXAk6/avkisMNvNtALID8infCn7//wZUy jwwd/O4Lr/kDCau9kZe3iwq+COFomw8C5tiDg97lDIFta26DFuvdd19/d/jdKx5C+kp+M3iDGaAg iEAIo02tO0XDUjhOh3nBER8gBJOka3ByZst2ioMVObxzjzXQ5oYIvu1DiODbHri/oLcN/+lRJCXs RmjVQB0nVkjM9kBkx3AtvNgdkLz//T+/4BUoTtqOXJTwdESGSEvoGud/PjnYLZmCFx4EV/lNfcTY 8Y1b+Di6sUmV7yIpTxehWsa0p0D/8aJF9gxCMUTzCVy+xIsc1n+Im3w78rl/nG8+wLMK1n/I3JNf nb1rbOMN3AbrwORp4XNjiN0VhTdy2x+Wv3E4cGz8by56IT01EflOJFBC3AtYPpjKJQIgUILwHWcU n8B38ODBWeljpYMYhFUJh5s474OUjF9YWUSKNeHCef997eZePJ+2WGBvhp4DixdWFhrwcY956MEJ C2+8PSCCVxdOFptEVrNv7goIvYtmJwTBgXwWp0RMFcGZoHfqtKHD5putbaSUT5P6gjJZ1rlRPD3N D05++h2lbTpYjDxqD38Z6WEds8slDwnSbkPyGNSsdLA9UHvdZzk6Yby/dVNY7Y3y6XamuFtubjyj 73wSvZ71WiPszpFpb+84ejHH8bqN19k9BxnHyuYYblYk32AGk2Pvrd95WfseKYUjbrkpmCGXuKKS a3ByZv582gTnPob2mXXfnDy1ZdwUt50UwXes2JLVCbRPjSWmgZzHF0488pjemKh4tOEcBrMXvzhz wCOPXzDE+za+t9qBHXbVZtEXkLy/OXYsfOGBfPCkwC4Lhx4Y6MCrC9JAF4DDkMCJXFx/GXCglPOZ SNmLJtqrRjfuIsMRJ12s5BgI3uC0wwRanhx4/hnbhsw+kBcH1w99A19Cc0QDGH+39/7Nwznn24v2 QObeMaYRdS6wZfROmrTxv/tbC3HiBBMvGH35l18wpcnfNDFvnLT0+/3ql0acPfkJ5iRmJonlc4Ab 9/ZVsOXK3uUEigrfPu4Yw4gjhe/wjWmTsONEH9wAW/vGlY2NYk24ur6rEks28p64rRHYHkz5BL1m WLvjiuDwn+2K4OFr76XMIexqWc2+6b0Yob/Q4Dhr7IBhA7Y31yDHvXn502efQcsIYY4hysOi+aCh C1BXpKKXXiPKlSqwlGME8WsBpbHZ4HQ44ZcmB1tv+tO2W295//Zb9z78EH4PPP3k4UEvHR0+BCXg 99SUyadnTP9865Yv3t9Ky5O315/yFLXDyWCVMXb6xu/OnYeIAN0eOAOjq6/FbmrCpSt5u/rXLQ88 2Dt6FJkHrgtHTrNItTdS8pi1uGU29571/a5C1MYbXlx1eGJD9YUZm0+4/8dX3/CcB/09uWixzw1m LBEmoODZnzd23qmpb/m/OjIlFIqbt58w7MXdm6Q++3AJ8sQrizeMbrjCsGY0RPBdp9v5jAiK2GsG 9jw/cx9sSzhLUz+sW7YNLz+IbtPTv78reR/Y3YsLM4HxorQdaVsCm2+sRYETD/wXa1QqdixB1tQc Tu7DhceJX7DBwOPATsGyCDXmUz0wlhk9VtHyBKuWGUCaezucmDWuXa7ZaxvTsU07bDnCRpZP6R7H 8/11uwJ6bussbkXvXbD86OTG/nvorWnYR9xTptQBKjEB5G+8dUBZNu/GO9ff/9eNw0dvmfbO5nc3 YHXCL+zToNWy3zOLF2FNjvvFoo1fnLHA6tJdjmDNYpzDl25L7IWK6jYCLRa+9y5de/TyRp5jH2WW j5YsjtSZ4dAcChiowbhnbPnL/YzsGFB4Y+WCIA7lumnWXemqTwt+3c32BO589vkqZgl2C5zjW4+g 4/Gpwo0qj9ZGRsCBGonHl/hwcvseqIJcgSnh5iUWoLAsDpcXsD0Ie7YqEtIl0FlsdTA1Djv8gjE0 hOk9D91/4Nmnj414tciEicwLrxpYoPePf3PvkKH83Tds2IHhww+NePXY6yNz1wjy9OftSt5QybiO 59eu2bv5rw8feXU4K0q9fRup9qbkAf1ubs20OygcixF3PIOZNnDYYgqR23aezFHFsfmNMH44Tvny 5Bmf2Q6pws4iEDby3etu4OEVXquOvjrUf2j2rdpkq0FZ7k1SG2/mBBDB35/QUJGS3v65i09teh+b fWohTU6wdMe5EYuOrNzziSttb9xyDGsULrwGxBr0Akpfc7EX/oUZNIfbOg7lt39UMqi68ewEbqr4 3MUsVxcQqfyGzPfasMYbBZYOm1T5Rgr7ju04sO6wElzLk03X/oH3dN271Ovv/UvflnTPgL+O2WbZ k+3fMrXt8Ae9Ry+H+7kicy9aiYFet3Rrz213bHnkUb5Cn35/V6bCS0z8848/4SE6PKfhYt9/KfBJ +fG8d3Y/2Ac5YHRUYvtVVHcSaJLwPXv2bGi7+TNkyBD7fHTSbxRCPg9DIM3xMW/sfuA+PBjvXXf1 olv7z7jvnrFPPD584MBXX3zxnbvubBxCDX3DtoQVK7YPHTTIqv7Ly9N+//waStg3vLDkuVdGuo3E 52deGfXwwEkDXp5180uLYfBK/TcKD6Qs5b8rtvZyLRs1dGhymejI+qv+rzUJ+sg+kWjwcDfLsCFD Vo96nbgOjx+3ZvUObo3j35rFxFMfuL8B6qr/G9c1lLZrzBtW2oFxE/q/tOrRgePd6lCgFb5gzZ58 NN545pk5A+5add3V5OxK25kErBwTqbosO0a/MebyaE6f3wjAYaCGDh02fMRIfHBFlhXj5/S8cUX/ vWX0KMCPRPrakCHHxo2xlk9/9crocywW9xzINxaBXBMefdQGZcXV1/Z7dhUm26bRV7zpzVy8PlMt SyZPJ+3p4yd65r3lpYWo+vXbn3CPofh5/X/fvOXpJw+MSnlB2jFrgaFetefcG+Mne1ZdMBkWFi41 o4eN3+0cbrizDvNk5esjZ454NfXZ92nP+GHDJg0fNnvEiKUjX0Ox7495I/l3/9jGRCr4IOANFrdL t63d1bP9JDTBgahkmK5oT9x8Tu4XyLCRc0e8mgoBk9/Geuyk6amJUxNgdeJkw2duIjzTgP9N1DV1 7rLUoiITYJexzejel99mgq2X5/Ou++5hpfjrnGWbG8rvKQutVWPvecSyYyPL14BArmFDXjswrvGM Q0GGcWy8XO36yMxd3r35RiwLPdMbl8ixc00dM6GUqv0LGTdqzLa357hnaAVnb1x2vOEv6X9zcsNG jx7dnUKkep2PQJOE70DjMIntmx3TC72tHnllEC9XRW7J9iXi8dpSZTedAwpv7ItxNwjZTuSCHR4N y/a+PgbfuH+N62B4VBJywU43wew7kJFuRy2q/HUvLHfrChh5H5/zjh2J8pdBwlGsG/MSp4csJ1Aj tE2018SW8+LMD9waGdIZxuuZyJie240CvekPv4d6u7i0jUNtnDCuH/W6nSfC2BoqRqjcYGryyXsr YXay8aUX9w8fDiX38TGji6/aeA/cP/iVdSMnjn917oSR807u3B84vnR13gFva7B7Wb3rY44OjGvP b2wEzEPD0JERQ66MC7FHqr3xVzr2yToW7oAGBpFegJ69dxjm25ApW3E50qDBObdraJQ8+lBT9b7V MBc5vnJt3GQLPDgQTfCGOeuGhrc+e67NNwUkgLCLAxzN84K1UYVdMmyUSRhLQUI7S3+6aZVr9yXO LVl8ZNjghCmHfmHqwu4Z8xZ6X4jRYe04nkd8iT/Z9MYhiZ2nF5/JFZUAXUCcTb/nWNBZDZwy2QxJ yIh3QhvuSMuoTGuU1RVWfu/bf4aHGIgGhTe6BIu75BrpxtG9NHl6xtsNlfPIV90nAqbM1rXVuz+2 K0wbr+l3x9N9F3LwaybjnkgjdyjEitoxvvGQHps8Ge9RVp0pvM0/ATMee2e+NfLwlGm4TRspiBRp TGSBF0+ePbZwaeREPT579qmFC7eMn3Bu8aIPnn8OOwvOS2GCGCchfPDkYwdffO7Ya8OxI+B30/Bh Z+fMjDCRWrwody8iu6Avu5xAi4Vv0MdpMhZHXokInFri6JBLZ2Dq4/nH9ceeQSNn3JgUMWH32IlW JoqC/WvAwhv3LKGXipsEgRpHjV7e0BD3uwr7fY7lO3lN7OPw7npbUML2BoHqAlHlb39pLnsRMPLe s2D52r3nCYGnsXb7xIp1Y17S7Xe4g9jsP5zYELkWvv6262UMwg3fcHzIQOLH1RxX5gZbvErB197x sbGicJyRqE+NgVHGxMOBBo8+4DQa57m8eYm+QEvtOTFofI8urLvq90/dN8LsL919NEHytlqWPD5w 3Ttr3Pl/ctsu7i57x44OHLXHWXujKN58TRYxM+0lMNa6rPzuu92L3zMbt/Aw+uDxz8gqeSwgcFsu iOAQxH1y4ST9rkdm4FYxt08cwdNbSFx1cGID5pCHAksK/mvuTTJ1350JOSYbBG66pLC7GZBgtt52 6/Z3lsEmFVdX/eXdsFjgn7dgymOvvYpm7375JRNTIAHjPRaWvnihxXqV4MDEopLl4GbYmRGFWBdg +W0XVxLKnDRjoQ195DW7HI0JW36f37LN2oNorKho7sptCfJEQo2DBg/lQuQabe8ccCde6a2KwHsL g0NtG/eWPRfL77rfCrGb90Wm98ZJs/hSsXHzr0FJd320YeQEvBPiaDTQR9zf4M1p93Xa85FJbmq4 F7iofXRGo3mcz3gB+OTw8cga4Tgy4D44QU8X+BOOXg+99DxrwaFNvvHtcilT3Y8k0Hrhm83CZgn9 H9Yv1wrWVk8LBR+3msDWGYIOzEJgDDpqwHNb/vo3e4TwyPWF0Tn7tRUy77cXK83CO9OTD1mTly+h /M6xfCdXh3eDBLPvQHVcUO7521QsuDCPMZKw0qaRNyLtbX9vG4UPoIBihpcL8T00KCzWDcRtV2ci O4iNlovRu9OvqNu5GaAXqWTghYpOEm2wcC3y8OCBYeGA0vabw4flW/giG4PrTW7EEB59BOTvuMDs 4TLh0tjtEawjzDwDppxodqrkjTTv3tDnMWDdswPX7r1iaHvx4BGOJnY+9xoQ1d4wBwqQ4RMEQ/zU sYijGsgIYZcdxBUIdO3ttR+euqyZgwRJh8oJNcLIhEN8bv8Rn+0Zzmeevf+1tVc1An/i2hnvbAQk s8iOwKrHVXibCtwnYz5uCbngMJu34nD4hjbgKdv+8MN2Lge96ZZVOw8OGQKtG0e8iKx8ctKbEJQ/ eOxRnJjvuGdArPLvumsCr3yrtp9ePH7+qPsHrX9tPLQbeC0PXDFP6CPOZ+CoBPppKOCtF25UsnxI AwsR71/aUUBCmTArsqUvMtJ7vsYElN+0RIfLeVSEGvOtUU8MGmNzw71Lg+mBBYHCX8BKnieN2w9+ Qi/gTz34hpWDPTG38D3ntSu2oDAf6puca/aeXrPeXncji8WzzLkaEIJLfNZg2M1DM1Z3fNmqr85H eIIPtBO3v87t+mDMK9MHDXhp6J+fh8IOvz2X/Z4liOMB+Rtz23PBlMQpAskEigrfKL1cV4Pjxo1D mZC2cWJOjw04QYbUaH+K/IE/2n4vrObeZhIPfpBr7b6GrcVtI7ZSuwCFt+1/+EkoNvynccMblt/w /DBxZKzKPFOZ7FFfePY9H8d5+3bLxLV3Wy+gZzUh79XRE1GOqw3FhfSG0uJXZ3Z0BIZk1O/i+/Hj J1gDXOMTaKCxWsX14sy6DVz7LhzrU+PhxalxLvnrDb+E7k8f+PLeRx/mYoelbc/f/hoWMrB5m0s+ /iSUmXUQ8RJFH2p4B+NsaXD4rf47Uv6ObAwOQ9yu4T0NYZYxNHCIyfefJT37I68n/vPHf5LJl19d OQjCiRCkGUpjkDzMBaE70PPHjXVBMc6o3f3KNxUjM1LBb5ZOUOWiMRy7XSs22hyLqxFH0oemNay9 cQMv8CBH5rrwxbdj73qKZLb2vwkvOT4ZA9MGTOw0AP+Sf4kzynOW3jJwuS1BuILJLGdWr12zvWES BkEct8Mh4X175DCES7xIY7NHvLCd48dSnCVwyLX4ctv4cZd2bIdq+cysGTg63/PwQ6mKPdzPg8XO crzuLl3Zs/ssJ2fP/s+env5BvxfXo4WRfjNzLJiej7D/WEDFbgRMDErI+ObESVyUAnMm93PhKr8Z Rwl6Xxwt4nKzfy8C7Xlk+FybGC52E/Tx4mT9xVi7uXipGn3snTuvsR386U92/wdXk0ePHR/udepE 3ba0gRc1NqJ17jr7ww99yq/k0YcQzBOtH7/tO1nyHHqfsfj2wqVjs37jKgfMT6zfPHVS7NtO3Fhg tadLRwN1eM9RvGFiNM2b1tK77sTT4a7kB194jm4fOfEyzaiEgdCfupZACcJ3DnbJL+UsECId5e/U WCGuRGUSDx6zkYuP2PpLF3I4kov0CuLZC1f5vX/mO565/JNBOPDx9s2o8ojli87SOxUOgm0FxEG2 GXlDCRp2ZwaFKP0cuxFG4NSP2k0YhJhnq/APDny3TW44e+6dMB7qWEYxiFQyWQkonHqjPiPFfv+1 /6nHP3yzcaeHIgU2Vx+vc/5IAymhSeXiiw+RYSk89d+RbbgST/u//s+qflePntrwrYGxiIyqbYUc 2/aBbZ+Lbrwd/6UAjVzwXAYgVPhBEIczrDhrb+SlJ7LSPVi7jnFgB2IKtgs9V3brHWt3YmrFDQ0U VzzLxlF16gh+c+GL+bf8maLkjr8+FBe+LrWoNknAsCyGjq365svvaBi2duXODdddYxbtsK6BNABN P4QD960D78n4BlZbOK0KR+oNCN8WJh0vTr8p4T//jzEgbYU8+vG3d41630RASCQJ4bpaC5MO+PDu kdoS2v2X6ICPixgOFmw+4zIiAC7d3vdWnO/HDa9jJWCq2zjiAsyJy4tkwPKEln4fnvuG9nsT//y4 DSJ2wKyNObBiPU3+4MHGJoartUkoEK/Wx6ZMMyAwAsladUJ6rBvulUrUcmbn3jjjcp964bmcAeM8 WWHL85G/fWpXGhEwAm0tfKN9vFmCVQD2EsnDFrAlwHM1a2NDpfTE1L1QKiREQvafEFR+I5pu6WHq oO/38fbNqPJ2+80Uaa4a0nTeZmcS2TVGgIMU7rqCxg7NQ0xs/3EdPH7mC8ZcOD1jxkfnGwbfOOIP VwchHiYTLBY7yq777zvx5jhX4Q3hEhq+5Bgr/mMUlxJH//TpDsVtwnwoIn/DqKbvHjAO9Me+TbVi guSN1q4eP9P22oUPPGmNp7mUvc9gM3a9rVEXHjgfQEpaWVThQ5qvFrh8bBMP29LHyxphL/vcV287 FKnadx17YzdNHUrMw9XX/ZFy5K7hr5X+rKW2oYoEuFpg+z3OXlwBF2d9UBY0NLXvrGEgsLAaO3BN IlLPHRkmnd3BqzKNxFAjFBz4hvIfHhCeClZBoGCZWCK4bqQG3OERXz7f25FNNeU3zHhojQ1bwWU7 z7/yzpXTjKx9DITXQXa8dNnIwlUuxb6A5Qkjx+Ed49Lehs9vZIFlhR1MQcr0bAkersNvTmKQAYTq tLfBOLu7yGK/OHuuXM+DYYX3yZ7NbpAKz95FJnNV4JjzqRFbJX8Xoa28YQLtLnyjxXTdACVugl7N +gYNB2NV3vl6w9x5NV7fv2tEXys+CSCxLbm2cXW6dOU31lMfb99uVHlTcmBp4IUnGOBiQ03VWPCS a0BdDdmROzqdy4a5PTduE3x+NzbC+QtssQ47FoCS3vVIg00rYGQCmRKbSllLasL4QudNyRu6vdTV Nrf8jVl6qPeTNbuvWJtMf+9YcrCbRY8PNOYrR021LuCtiacTJjpA7AhctotU/mE0M6msMj0RrvIb tyzAExs8ZKDT78ylVvvg/lPhMunpGZelUrVWmIE0x4S198ZJczI1sp0TAxdvXgacLLmXMda9vSTV esRNgFNyPKrQkYfNcgI0aCtskwSvajhgxAJCk7yAFVYbwuRTkBptB7oM62a5L6JHBr9yRU6dMQfl D5t/GEtKPlbh8Doohys87gtS2R+wPMECRdU+lghqZCh/x4VdC7QTFoa7HnyA3qXo3gSvLll7dHLD Vq4DkabY/gWe3brDVXj3vj3jq3Mlu8PHw8gwxj7TXvK3//ApZSqBGgjfWGKop/GJFWzxrrCXTFz5 ITeYVBCZErz1wjjb+UpXfvcZqe89H2f2bY2kZ5Jlf7zRtkyYvCMaYsPkbtJEGJz4aHogDlK8C2xO UFRza6fzkwAiC91HSRrWMtDZwwMU1VFQeFuIafuFCN47qnFQa7kyxdrINEDhxAyOCFy41eR5DB2Q v7EbuWG0w7XghYdnwdR540LbyLueTz4FXtj/LqO0d+VGFkvRgW9HYOv6kgurvWl8j9rxuSC0yOw8 dreAO9avPhv06Q2XHTDHPHnqN1eg3PtYUI8lt6rvwP3yRSh4OJkzY10VvWhhmZR07dXFbYkrGR/c fxovIdCA4jGxeK7u2ZG/tM3yMeddp1LQZdgqgQWTmtcEU+8WEgtUjSMyW0BSQ1DB2489hqlWi/69 gwSGyLgN+4rJb5kbvluGb8Gz4LmqBOoKh9eBwG2rAURwJHaj7QTOBrlEYNmB9tpdb6H/9jnEgPES 5hWvdR6fMM5OTaG+ybeA0PPgsekzUl+zI7F//8Wl3tm/sfAuUeEdqHHWhtO2h+JIymcOBORvnyDE PsV2TxrGRHeDo7ek+61vQEu67Wnzzba5YqJnOGs3DBgOVcvt5qlzX9Lx2YG5C8stHKtestk3NRy4 tY1VAyvsT999TzuEHSs3JxheB5pKLxyQwgOnCq59dlzcYOjwGODN1WdDmX127hyYdEPPDZtFhGc7 /MogNwFai/tSTdB2W38ZHNFOY1N13i6lgPxNkRpyDHY+7O7GDWpLqpyZZvsHZ9c/2ndfEOrb+17f kjBPcHHWttvvvurz/2g/rvKb0x7Q7M4ZnECHC6SZlv8cyDp7eecMncLtLir84Anxw8mNsPNHp0y7 cOFrKznOsXdcvUfGjW9Y4Fx387CpSb7bsra8fdLT0w7UBIFWUZzCLEo+LcnUHUxU14sUXiNpkIa3 aJM/zAd5pmJbkrjP4enlt/fkBvDuRImPA+/VHB/fkFNn9jQEOChBcgChD3gIgpadMjReveybOMsT rGwNU6X9n2FAw/K3e7U30DYovO1aIVzicHFuuDfZ/1nuTdP1PHh08fJ//jPD5gtx/wQsvJ2LQFUo vF0OOA61yR+O2Rw3lK78jY2sabtYjqkVyBLpG8PzS89k4RrjvilX9s1RWo4sxYfALaEGmm9rLnVC EBN9FAy8dlbisuuCo/J7ef87yx0SKGkSzL6xvNKKo//j87Bq4Aop9wPY6tkVPc8mJZwqhJ2fhMs0 Hd780XMCliTJ//1886aqbbsDTeX1Gryo5NggQYkW2BSskz/gNN8kpx33N0ITwzo/8nIn0hzf39uw ObnmukDLKYrhfNknijvfpiJ9G3vOitRkFA7s9Y9If/r8C26cmIrfffN9X+9iHHtH1gK1Nw1OXnpm Wi1kwVRc4QTc8kEv/CrIyeZz0OdTOwQpnnHhQ0B/QWtvCn8+ZbYwDcQd6hqSL2f3+Y+6HFnM5/FJ 7dSnWxuXhrHE0YvrzGX7TYDLF9o9EF4HF9NtNYBCmjeM4yxP0GCeZtiwhuVvTLZwv0zhjVoQO5PL tbk3ibQeTCXjJnBPuo5Of/vU0dOp8CF2nzx0Aol/cxGoPAvvuPZjhaEtoo9cYeVgBkL34XP2kolb pYldWTNS/ZzwpX/eQBfCAm5Fmu8cknSOLOUOUG2Eb3eVgTyd+jAHlqRyqaE0V/l9+L2eEsvHGppg 9m3OX/ssXvrfZiv+8mVXQrHgxDDrywYdlYSvtKY6P7ED64cn7tq45RiiKx9duS4h0AaWKou1USIr n6KoWAIrXLj0yRKZBsI03uggVbuX1QIiOJSLuKLgXnKle76F190cp4Wige/Ce/8eqBpF0azT58yH Ri+pt5Nzc0BGHovj/AeeLt0T2+/OfnRF/n5n4WcnGyEAMfoBx96RDaDaG94Mi3glKtK75uTFO7M9 v+E5GTb3L9Ik12cUpm5AwoDdC4WPdr5nGSBAdUPqub8bfyA3Rjz7eA3evWEPRcM9i1c3VM6zV6wc 0+f5x990wW0GllBa29vQ0OQPZ49MmWB5wqsC6KmlD8jf4/421K2RCm9sIoiuwB7tu+zeJPLSfFZ0 fOW28j9Yvv7Cpb53g/BPn9h95vP98xuXthvGkxVYeMd1gU9i1gWnXBUSrlzTt5uniic5GUpzdwF/ ATqcMjKvyzNSkLUvA39KFb4DKvawxp3FukWFc1nV4TRstoTvDA+16yAv+RY2Txuh5kmI95uh7qik M58YanJwucpvND7B7JuhcN58tM/PyR9eWHvs7Rm2YOGeZSa1N/tEp2OQHQO4Up2fQLR95Z1Dtgrs 6dltQBDLF+5p4Q3g3OKF1jY31kZB7JmyQxfFvW3aupOZ8iYkxoaBtRKTELpJ0ylipYNoHn4nxEa4 +eYbDQti00T6H1j0/IiG5nvg6+FK6ZcmVfmNJnE5LtFiIZKDG20e0purov780NHw0UfYsXe4WFft /crz08sarPYsh8amkbfieIIBiSpV0ZDcQb6PRZ4Z0gAd99TbE1Rkq74+eMDmWOAOYjixG3nXp4N4 cGC7BakFQwCBGw+4iUTQL/AqzoE5C/Aw7urZvf7+v/aJsE8/Z4tMDoZhswdetcTdHrfBcZYnkQaW WHb2PvM079sgXpIVRYV33yrd/8bey7GED06bYTfmi883tvmTfYd6JzXs0DBS8H67e1ev6wbAYtDu XrGRATKRDFlw1dJnpMpKQ+2M5/3UsuoNlFOu5E1jJLeWOEHW0mTVfAdKDmPJIXzHSeqB5oWbGs4Y 98KQKv1XNL4RfJpWk1tRVpvvK8/zlz9SvAgbpWEZwrMNAxWG1y7xnk0Y1Inj5xh4r0Tlty2mkWbf WFJ56erWZ/pCo9PkGusX7llmVXtbp9xb8zgxwIIIjLxt4zo/gcjl+h7+5OgJCO7zNjfidJh9s92t hHK0JVPLrdR1b5Jg+1h1O+meD07ZI18A5t3xoHHbsSjCjNsdnQSnDa5pAQax6k7R0zw8/2AeBnwF fPz+Tlf+htTi49i7e9TeGB1aWkcam7qD7nPiETfcfHMLn2tZFto85DOZqHqaxZXfd8HgV+E71eiW zrLcgAaBYmEQzy0jUpuIpRXXGBpTesbML7/8DiXw8sPm6/9okc7wFpoVCDzemOBuV115mR4OJQNF JVie0Luoa2yGzWLtQ3+n/H38zTd/E93sxhsOjxljPTr61lQLCuFp0unfR+iGTy1e4i4FsGw5cLIv LgR2mW1bDkEid/96evl75aqTfZqa/CT6lFBKmoAbolKU3+58qE7znaD2NjIJVUdqrMkzX5sDNSY3 oJSxy1dI681O3KMBnz7QdSuHDbMWQnakMYDFpXd/mCtTvXG5Zj/6iq1uS+/8S1zjw6+byd209AGz b/uScvCWBx7Aen3Di6uOjm84+8M9y8gepdZuCVylaaCzWCWPLFhigT8Qfg8S9rpRb61btMGSuWuE J9t87bRHKJw3bhxd9yYJGfGnhOEIZ8zURyvc3HckZFxx9XU2i748dTa5qXEnOcxlOng7vogjE/k4 pH4Z9xyZRh9Ht8Fps+ZK8B3XsXcqUhQYqfbOPYiRKHzgJGT0mTbuFuLCse9hYxCwNwhUB+Wr52RL bqc9oWFbgkDhNEdJJRPXqsgBSl17ffoYmSY1YyABY0657cRLTiDYEBc0JoNK+MjMPk+a9g3kfnaK 12+eem62jSZMdxJmeHipccPrMCMeARifBNrvWp5A8R8JFusDehTISPmbH+bfdu/u0RPYI6S3XuOy UL5BTF5tvj56LNCkrev37Ju31G3AyVkzcY82zCff0LubhTufI0sLPIm5p7eP9NLCNO6Sxc+eX0Ym s77ErYQJGBMyBkrzbF64GVnLacm4tFj4NkZx4xdJxKxguV5gyXA/u7Jg5J2zTHVFNsAtwVV+9+4+ FLl2uHtw6hgTCM2+bRU2SvRAsnz4m1joF77eUBvgchv+CrW3z5wLrNrkz5PuOJ4BOTtAHuI4zgHG Pz069ZlMflQSHuYwybj547o3QRreaUtedAKjk/rkx41mOOPBgS/Zzvf67U/AxtdN8NGpTxpG/H+4 Oq5G6kGRAEfh4XbyyBIJzPuKz5OV2sHksTBzdqSxaPPhxMdXre87R16ynB7HImu07pja20oLG1/m bipnY7gEn6UAaeLanJA9IZc7dtQ6s7+BjPSfg0FPbm24Rpp640+RtgT4HucwJjLS6UrWaRO5uCVP m4SnzOeBYhrc2zalKaK9RtZoX9IfiJlEB0bT9b3YsJ078SW04EiG9RDvPzgDPL92nYmJ9q/bSDqe mv7UCCMZbknCnEdRdPKIZO5lelhmh4uyS372i5Bq7kpL3VPgsgcKwYEbxW4s0YPvG7xh9GTrC65n wJzGOh6YY55Ph9uGhCzQZ59b3rDqNpJE+uFbk2C4n6O61M00eed1aww/iYFdLHkQ/VvSwpSRXfD8 MusamPAU8E8Jz2zyqpLaZk8Bw2fKVTpeLRa+kwW1uJ4zspetGoBIuRCLKWRuWN/GeUoqSDycncrv hXc9FL4une+hRS7X7BvrlIng2/98my2jcMH2yKA+zYH9mmdW01tk2sAC/GHvaPq25ENYVzm0bua7 6weNWH/vX+bceKd5X4EVaSrkQIKslBKyW4/i3Jv4PPCRAJOXjIRH1DLCdN4GDptfoA0bF661P717 +91uOYEazWMxvgwovyFjUUTDn1LJZJoeyeNoRlBIg8Yj4E7qoCdIXWbtbUWlWntHjkXqzE/YD5KZ xJWc/L3PlKaxqRkjWRY3Ix1H4ssEVwzhjPiGts5xefE9w/0E/PAkD2XqQGcillpaeCu18YKTe8pw yc8sPb3gYWF12CDwmUsZdg3a1rtNgkdULrPQNwdaS+OrNXcMMMk7+WJJIDvD69j3vEy/854Bkb2G kw0GGEJIYDw1LJBGC5DCaVdtf336rZ0T+ve5FJx1wx23PzHvvTemNiTvCeO39OwzAhZ/12fSRgpG nvs4LGdQhWtngtcJszNJnQaZ2ha5kCZ8GWn2HVdjalMrldiKFI6W208Ahc+X4bz8JjJ73GrP2hNW CbfAyHojJ2EgV7gBgXa2fBxrKXwDq72m20+ytB03BvnQh3NB4U29wjs33bmpZ1/cQ+5fI1JiQYR5 nLtO4cs+PeLrrx0dPvTp4cu3jmncZbF7lmbtHa4i8nlLEIOsEP5AFrdbR9yl8BnWkxD0cQThVscL ZDB8TO1p+PlPWNPDy01CdiROcG/iZvQhE26VC8dnHWSN2//S0D/hGzes/YJBjYBNy58bHjlzrpTw 68mG6agsJfZLG5q4vvgMhOVNXY7jniOb/Ig270kmsqnJam9u7YGmBkYzbjgsWeSjkTyCkRlTp004 VxwZGpvCaIctDLTTZGh8SauJuMchQMN9Q47sZsJ4JU+buIw+ZMJpfKZNZBqaYeCvrimI+8wyo9Ew gRsJoJ2xN1V8iQ/4b+T8RwKuwFC0h2dRn0XZ5SBi1zyzIisZhtdxM6JAc+8dWRp6CkWMtQrhFNw0 fMdAp3A4jG3ROovVBi8GaF6fa6yxM/nGwrzsvucDxecxeR+Jm3V2cxR+sWBnkjpnIvcFnzmTvKEE Znik2XccjeSnw2dTUBoR6HvAW0IhcOGy4GzOnT1rxrj0jA1uqs2xz453nXZlWim4rvGK/SebNmOR vXJId1nbbWuo3bM0tXekhJG8BsUtoz65wtWZzyb0F4FXkj00h0n6U4rMywYnuDeJG77UaVBKRrPX R1FwzzduyRULpTn3Pmb795Ypc+Owu3BMXDA9qBtN046bA01N7VqmLSq8VpjuzdqPgDv05eJTL9Nk UnsH+pg6bVhL8rSJk2gTnqnIPiZUF37WqPh031cDxWKg8Y3Jjq6niPDAMaNdvU0lY2GA8YuX1SZP mziqmaYNCoGrE2TBMvjNkWCsIndAcSPIGJo/IpcMhNTwGak1w3TeVj7kRYulEm4h3f48dv8o/BWH CQmLaiA71QT4npfp8Rk2Jwly7U8XL9DNORIzyAvniXsgbFL47I2n//jKhs2zrrjzQ2JMEpjWhBXe 5JMgGyRP/riMzBW+VZk89Al/zZExnCV8ASNyufAh0xKBSpXWkYCE7wyjlvCcb5y2gEEKIYjAzG7G 0g8CJ8U+WwsXbgbaMF8HuFmP7MdGvOrqwvEZ9yyp9m658G13HNHOVJe3FS2mOD3nJbaAe5Nya0x9 aQmPBTbXjTfegGZgejzy8ETOjUV/vMWE108PH08tlvsrxG73ohi9oAS6mWnKJQigyduwtf/Fu1+h xwyfepnGR+3tNiDTxs/90j6kluMKtW5eTzgJ1UUO7h0jt9FWOK46DnqkL6NAjQGv3gHRir3A9KN7 b2hGM5FJZZhp9FNLSxBDIR+jLiyDcPudMEXxdBjDvvhlxy+5ZwKRjmiRmNYm+EzJO3KBpVvuCXc8 ZkufPdo+s5SmxkjMy/Q+Gf9x8gQV2KaStxrNH7kpbjhtrL+8wY+McFKOv7LvPjUmjIL7yCTvpi1f hBMWWI6FxSTK3dQM8oSSdjeBugrf7rPhs9ZHLhC5M0bOGbiqWHXXvTyFXNXv6sefnMYIGuG6IE/D Cd3+Jx/HxbXw0kYfYeYt8ZOVfWeaKPzwyy/NHj1vx5iJWEMPzllgCytDWiZsov6riT/bcJm2/dhv QsyCuMYkj0hyLsRZpDyBldRVvUe202fPKDGjLeg2PXCnEJbx+ObcZ1eOrSGdpy76gdsOVHHF9cWf J1P6jH4gjXUKX8LUPmF7C/TOCvFUeye3yudBTh7KhF0gruocbMPjCwdz9rAwumT4EXYDLSXETrKM Aa/ecY3Ea5I9quboMGHdCJOJBJJj2oSnis+yEKjIZFAYQIejfDOlXYahzM2nJs6BOhJTx4HPbsnh FtI54Kpr/2hIzYA+9YFyw+sgMQM4pGY0bpd27qBi3pyfBMhQCsf1IVfyRsh3yOhFHiifYUqeNv5D H7kk+ky2uLU0rvE8hcCTmPBo+yw13S1Sqve+BFosfGMquz++rf41HTNmypU7o+dTd2DyVMrfJpHY DxtpMjfcuLrJKH8HgJj1XuBL261fW3zUdhFae0eSTKYUxz+VbUJGyhM4gQ3HlInMmFqdO2ouz0gy 0Ca69VbRx8CYBmZgQo2Mnf7EK0uQ6/1V220aLOt/Z2ofrRbX1MSUeZFT2vPJiiSfaTj6XDTc0ogi lCmjdcdf7R0u3Kc6Do37DHrCCYsI/qtHYLmIayqtfnlQE86ISlMDLZmGm8dlqWQQDsYsxAIz2ZNM 8WnjPjKprY3DjowQu01QTnZU52q7kx9eWptYMteow4XD9ruxF+zacbj8yC/d8Dq//HDFQSHiCruT NqG1FuYzrkYr5OLBIx9OmcJ3iTNLl4Z75Fld5KPEZyQSTmCUA+uk59MUeCICNYbLTGin1RiZxb2A EU7jM0UTWqI/iUDE5G8JlNxBdlrS2qyV9rm2uP0OytaIK37XIzNmz9mEMAcBmduVv3HV3a3IjVvG nWPdsm3L3z+LcO53vr5t1d4+ydtVe2dtZ0XpodHhYbrdJKv6B+YurBF2L3QsWHW9Oco/OPxVG3T4 H4CGbMFr0+y/Kx55xrM0Rm/F0JuPAs+M1SVD3CXrxdA/Px+ItpNcaVeFtIxEgcsh9i6NeZvACqNM X3JucF+4lYQdc+B9DBMjOUK4a2seGXK1uqlSeskXNzTcyUNoTigcsXtttYw08mZG17eJa22S3Gx6 gIXlleu3MTmXG16HXlNwEOqPKOz8xM2LtxG6RjHh+9yihbil6l9+V6XEg2BPYiBeb1dBUGebSaDF mu9mdrWZdUEdcnhEQ8xyJWz3M8IIQ2rhfR3Eawjcs2HcssCBqfvffCEtq0bhWoCYfUV1P7CwpLke 1k3XkUh1leYumVHxcDEX/iJn/fWFxm3LiX3+DTx/zCM7JG9GIfXMWFEyOlLEXdJMkUS7KqRlHHx6 ek6eurQ4wj05hMSCxyG6mAysD3CAk/xKRsdEmQaroslTsFjGfYSgmVAUbDD67J6PX4o08raM+SRv ZDSH9/idcWOfw8HIkKXhtrnhdQ4+/6yVAPPCTEACzk9MtsY7LWPRm9gNw/TkO6mZKu3UxNTgmNm3 fkSgUgISvivEC2X25psbJ/IUu9f96U+9Y0bDUtAqxiEjpHD7K/TiOH90GwQ1JzRbmxesWbtmb6QI TmvvCruRq2jX82B1AjG07OZixTQWAXfFuRpeeabtjzxKPfHcG++0zyc3v195xVVWsPX2W60jD/x9 erKjG7ZCam9DQSEs9TU12fs+ZHFI5FgxUseZQgZvpKRmadsE8JtBm4oiat3ckret4Tb58UaNOPOe qlO+dCFSG/zlWwl4KLKidp2fwP0LYw8ZFvhFQdfCBvFZa+mG9OELGN3Qa/WxVQQkfFdLHoupaTVW XHMdohve87epEBPhiQ+6YVYMxSEXXxw72sU7/kAdbuvypptuWLvn45fnHJi69uSqvZ+Y7rPa1hcr nWJxqufB3PVQcAHVTAYPuWssnvGL7Q07b+iJsVvb4AZeuorX0uQS4JnYOjLm1kc9RTqpvW2MIHPb 22OqHtq1OOKrOGxO8H5uYU19fmhqDOHP8zXJp9gWpqFxBbTg+ZqBC4uU4P2tTdy6aE/40IMTMJSp 6gZa/mAUoO22ZyeTzYlbO52fBHxhQRAPO/XLh6gbcvECRnOsJbsBqfqYQEDCd5OmB0zKGHbR9lrE QuPmRz9TWIJhi+K2iWINwuvQNS/sBZvU7gLVmOdB6+zY5Tn3xYT6USY9q9QCCPvS07+/a4C0+ro/ FsDcFln5iojXiVff+U2cqcj2Se1NLBYDBb9QSKeOJXTbZrgMmzSceuWw+Kd6r4pHMrX9VSQwvx/4 hf13jvIhntJtdj7JG5Xy2sOIO57xUQRQzsOmAJnbVgNYfudov2UhBEOBjvzr0qXcpXVnRvcuRGe8 l3bnONal1xK+mzpSWHMpjwZU4PQXi1XYdT6IUMO2NF/ctZt53SA+Te1AxsqgBKV87Cr7MxYTkZxm LSgfXqKKF9jMEs4uXuwK38sfzHDLqpntzFTX9r8+ZJ166pHxqVvXBwMHWWJ4XUxwSZmpAfVNTBeZ Vd8VxriwrnKfxxbCx81CWjbnaMYX27ZadmjQc5tn8DhrybV/8lmR6Nhu7vL9jYPNa/oVPP6yu6fo xQ8fV3vNJgfkumSR2XddRqoD2inhu9mDGFaBU2vLW/NYjs35Ce/n4TrmwROf+2vImt2r+PqoafO8 h+TTcjoK8Pct4FNs09Jgl6XPQYz1xld/c9bRtGaUWxGvnSHCVLLxPS9oou8vDUy6J1duC9u2NBpo VW06RYUrLMHalkbWhkFipuo6q7rXVXt/f+Z01qqZHk80TQdvemJRqt0C74i/P3mWCd+wTsxduzKW RcDf7BvmWxjlqh/YsvqlctqQgITv1gxKQAVuNoKw9g44P6E6HMeaVJbUS9GLlw0q7FOvlKUOBtR1 3LcgeePQNlXJmlpmSxIcHDOOyu/e1Rta0oZyK4UlCXs0Zk5f6Ja4nw0D7raUk/o/2DH61yIweYyD vb9IOal5aflWLzOt1H6Zx2v8fvVBNt+m9FSY7CwltQFIQOuRp+4bkew40g2vs/tvf20cbF4OVOlT l9JURCDsdz+yImxqPEGqqCUqtuMJSPhu2RDjAbZQFyZEWjsCzk94jwfeUep7IsYrZdiTcvsVRkaq JQwapHAGaW/ZKOatmEbS2HoDLibzFtn6fNsff9IkicEPjYhrzamFiywNrMPfnLe79Y1ugxbwEmSl Cml6Msazk/sxbANaEU3gjUm4+/BvIdTkvKFYRO1tNfJyDl4pQTjBgojD/fiI1bQ5Cdyz9++FUpZI wL0Im1AsFWElHueW2AsVVQsCEr5bOUww3eYLNC1fXecntjTDF6EbjKOOul6qq/Pp9hAN27WVx+cO UN3ZJa3cLg5aOXFj6maskFk33BHpKxfa8bXXX2+zetgDQ+v77lQufNcUuzomNNZKNYoot3dNKA3u rulZz99um/ry4mpv9JEmgqv6XQ2Hgwk3GTgQs15sRKdCbPkmUFIVPgSo5IpzWeOqvXVfxQep0kQS kPDd4okR+Q7tOj+BmIJw9Fyv4fCkxS3OVT2sC/LdvERGng9YCSDQYXq7XETbMRO0d7RlnzxzU7iJ u0aONskb99I2fXCuHfvQojbx7bS67RxqdXuCOtJQ9czMGSZ/e+qwXbV3VkvxuDmCmzk2veFSNkE7 QAepPbc1AiFjwW/RvFO1QQJ0KRZnJCm1d5Mnze9+1xoxNWs3s7azNb3q7PDymcYsziTadX4CXXjT rmRlanymxFlvXkIF6DoTNDsTxR7LxLz5id9//iWTP8bc/3Kg9q9PnKRR+BtDZjW/be1cY9U3Ovj2 6xkCpp1ZRbaNwWXgvcSn8VR744NPep808AZrM3zQgJfivLbztLP/4/MsMUR22Zz44G1OmmS/+7VW e0M6tB+XpOeXnskCYxSZK+s4Rgq14Y5kLTY1fWZhOuNLgoTv1CGoPAGf9luGb3ZNSnAWabfgIYZS bVzdqXTV/cx08xI+CgN2JsUva1bdQZUPAvDSQ9124DLlmrvvsz9Nv+W+qn3q1W4soPDmTYYqGs/3 2Hx2X1U0qdwyofA2zTdU4Kklwxkfrb3LUnujUppdwYFmnPk+z/HefHSYPQ4Q2VMbrARNI0C/+5G3 Zuur9nZFSX72/NIzWVjy5jdZBdnA60FkyUXK9JlOWcvPnN6nEaWnkebbRQqBG2I3I++EaXdM5C2+ ZkD9Fmc6AhoBhTfEBdmZlP4MVlcgrbrnTruiU/xw1Vres1y0dEd1tde0ZPcFu4pLHXyV7dSzI8SW p8PBVJ8nDIpZotobE891+HPNMyvCihK6tcECuO2uP9sTYV5l9dM+BPiwBMy+K1V744WQplOBSKX5 /ovS3Jiv/gJ0OGVkXh/5ODymYV24FZ6gko8Taj2F3UCNcSr8QBuYjM2LS5D7BUOa77Z45Kn3ipRK 6xXYMgEopAratkYar7seYMzORK7o2mKCZmnEjhENw+6J9zxj+XCqjiieJmpMeuClKoTLLA1s07R8 NEqXjz19OLQpF+9m0W8ghJWEUPPUkUNYL1Htbc2kr9jH7h8VWLvwX16vX7V4My/Ty+bEe4SblJBG +YHj1krV3uVK3rx/HBCRAzKup0RelvAdV11YE59ao4nCqRMikCauWBdL3LFAQl5rhk97fjMcqa2v IoE032Gqcf5AIKnULrBlwpyhmy3I1gEhA/91TU2g/5aUVsXTV3WZXx45aoIF3D6c++xrVIcoQvbN 8quvO3bmi6obUNPy/QN8ZO0gA83SpWnWEmqRHn5OEFadYkdcoEeqvT2twzP1nXd1xtz6qCu6YSnj hVdc4HEDOGQqX4mbQCDS7LtStTc6heOafBruhFy4CEFcnnJ2QIh09b5xImak0Jn6ZZyMG67FR/Md qc8OC8SpArSbJeEFIBWmzyxNf3XwKSVrGgnfYWKuPxD3tIvSKlwgZeXcnumpyMf7BsVrN2gl9EMQ F9qz8WqVD4H3br7VpO2F4+d/eersuqt+b/+dOeJtn+zdmaY66zJq7GD20NlsYXxC2TpSse2qvRHe snQaCMjA90yoD1g+h8BiHbgBHEpvgwosSCDS7LtStXfBBvtkT5UXE6RhTzHUbUaThe84Av6a7/Bb imevI7l5jYhPotLTSPiORMrTLldBVbUbhNIHN7VAV4UAHQMsI9lxqMOhH4pzsJpashK0CYH3x0xq SNt3PvzuA4/Z53duurO+14WbAJbGIaU7JOElv+r8GDaBj2cVEKl5gn9yymS4AGdGqMahcjZNYRVq b1QEG5KNV19lE/6lV5dZ1e5xH9QKFNARwMGzU0rWZAKB0+aq1d5N6F0VwneyoUW4xiJtCCPyMfOQ 8B3kJuE78mGLjLlT38CWCQuKe/OSp7GQvCGFSz5rwkJcdRX/+PQzehXkh02rFc8yBXxqgI8cA9ec CD45GlZdFgjcELvp/IQabgbChFK8CrW39ejASw1vmwPvHYz/YkHjfXoc+kE6PzzoZXsoEMChOggq uQgB6oPMKX7d1d6GItI2w/PLcDJ+4xpMB5gn5GLKBFOQBGMSt/bkgQ4UEtffuPZY88KCfmTXMk05 mZ1kwlV54sBDXvfAlnG83JuX9KLYAUErK58f9alg2R13u/L3rL++UJ+2t6ylqQE+crSMZ+gdY7fm AwE3Ken8BIYoMEdx1d5f7t7lU0i+NOeWLrGZP+PGAVjAacoPEfzse6uh7eZzgQAO+apQrqoJUD1k vrbCgairboDK73gCEr7ba4gDzrDrHtgyAa57FIszvtI9PLTXuHZfazZOmkMhAzcvP/nos+5jkLnH yQE+Mhf3awaWWdPguPl6jVy4cEn5GxcxeaENlif+Iehz1P7PixfpVfOVWXtNuYCYl1vuudd9HYX+ O0fhytIcAu4ra2eovZvDTbX4E5Dw7c+qSSndmDs01uzIiNDm9RZ3/+XGu0lzq4nV/PDFl7xnuWzk lCbWXOOqkgN85OsYNa9dGKYKDgfDviBgfJKPpH+utTfdbHL2Qw9OQCTLqTc3wkvRveAnK1f4l6aU zSfgGmtJ7d18/t1Qo4TvthtlN+ZOBwS2bDu+alCzCCx6fkSfw5O7HpLLSH/kcQE+/EsIpOSdiu50 mR/w4Fa12tvgbxk03OTsJdf+ydV2b7qm35nZM+XYO/dkbmZGaIW4/1rQiWbWrro6noCE73YcYsbc sYcfq0A7tlJtEgERKJtA4KZXweIZOLN0DyoFG9bM7HBsQv13E9Te6Frv6g3hC8cfjhuDEJjN7Ljq KkKA4UhtF+4GT0FFcClvVgISvrMSa1J6xtzBY6+biE2CrmpEoNUEeM0D5iLF27L3xJcmOnS53s7k b4TArNTam+P1yw8/0OYKUvjB55/9/nSHe1gvPlfbrQT3VlKXPz7tNjSd0R4J3206jm7MHVyZb9NW qlkiIAKlEig3qBa1d7g0VmozVVgKgXdfGQ2xe1n/Oy/ukofNWs4W1+xbau9aDmF7N9pL+I7ztoiu +fh6DBOQn2+fWYEAaVBZdXZEaB8OSiMC3UPA3fKLX0Qu14ile0ZBPRUBELB3V23BmgxVEEgXvhNi bJrwnaNZEr5zQFMWERCBbiBAH0cwGinY30CgvoKlKbsIiIAIiEApBNJFZwnfpYBWISIgAiLgQ8CO vPBb0Dkg49VDBPepV2lEQAREQASaQ6AE4TvBKCWuD9J8N2d0VYsIiEDtCLjR9Yo0ftOhCybE69y8 CEblFQEREIHSCUQI3wFhOlnz7TYowQRl9uzZELj5M2TIEPe/+iwCIiACImAEHhs4zoTm6194twiT AS/PsnLuffntIuUorwiIQCoBCDmly2cqsIMJFNV8ewrfAYLSfHfwlFLXREAEihBwnXMXKYe24/LV UASj8oqACIhA6QSKCt/+enG36RK+Sx9IFSgCItAxBG4ZvtmU1rndjLpeUyDNdwwZdUQEREAEOoBA uvCNToatugMytyXwxyHh25+VUoqACHQbAVhpF4yrd/rCP6yEO0Zu6zZ66q8IiIAItDmBDBJziT2R 8F0iTBUlAiLQYQQQE8dE52nrTubrGm9tDltwOF8JyiUCIiACIlARAQnfFYFVsSIgAiKQk8C6fZ+a 8I0oOfmKQHT6UvwV5qtduURABERABBIISPjW9BABERCB9iJAo5G7R2/P1zJkNOG79/y3+UpQLhEQ AREQgYoISPiuCKyKFQEREIGcBHBd0kRn/OJz1lJcfyk5smetTulFQAREQAQyEZDwnQmXEouACIhA MwgUUV0jLr0J7g+M39mMtqoOERABERCBLAQkfGehpbQiIAIi0BQCsPY2AXrl7vNZK5y14bTlxcXN rHmVXgREQAREoGoCEr6rJqzyRUAERCAzgflbzuYWoCm44+Jm5oqVQQREQAREoGICEr4rBqziRUAE RCA7AYSlNOEbPr+z5r5+8MaCMXqy1qj0IiACIiAC/gQkfPuzUkoREAERaBIBxLY0ARqSdKYqL37z U76MmWpRYhEQAREQgdwEJHznRqeMIiACIlAhgatf7jEx+uvvf/avZtOhC7lV5v61KKUIiIAIiEBu AhK+c6NTRhEQARGokMAjk3abGA3vJf7VMDomrl3651JKERABERCBphGQ8N001KpIBERABDIQGLu8 N0eUSorssBrPUJmSioAIiIAINIuAhO9mkVY9IiACIpCFwJLtH5vwDSncMx9C6tBYBaF2PHMpmQiI gAiIQDMJSPhuJm3VJQIiIAK+BA6d/TprrBzGpb9j5DbfapROBERABESguQQkfDeXt2oTAREQAT8C bpR4vxz/S2X5sAWHPbMomQiIgAiIQJMJSPhuMnBVJwIiIAK+BG4ZvjmTx+6RS47mMBP3bY3SiYAI iIAIlEFAwncZFFWGCIiACFRAABF2TJj2vD159+jtlr73/LcVNEdFioAIiIAIlEBAwncJEFWECIiA CFRBIJPfQNdMBTcvq2iPyhQBERABEShOQMJ3cYYqQQREQAQqIcCIOQPnHkytAO7As17QTC1TCURA BERABEonIOG7dKQqUAREQATKIUDvJbAnSS0RUXVM+Ia+PDWxEoiACIiACLSKgITvVpFXvSIgAiKQ QgDWIyZP4zfVkgTacUu5bt+nIisCIiACItC2BCR8t+3QqGEiIAIi8L+8QwkteAIOiObXD96YyTWK 4IqACIiACLSEgITvlmBXpSIgAiLgRQAeu02kXrn7fEIGevh+YPxOr3KVSAREQAREoEUEJHy3CLyq FQEREAEPAvO3nPWx5KaCHHc0PUpVEhEQAREQgZYRkPDdMvSqWAREQARSCcDDtwnf8Pkdl5hpEJQn 1TQ8tUYlEAEREAERqJSAhO9K8apwERABEShE4PylH0z4hmAdVxBMTRTYshBlZRYBERCBJhKQ8N1E 2KpKBERABLIT4E3Kr7//OZyb7givfrlHau/sdJVDBERABJpNQMJ3s4mrPhEQARHIROCRSbtNsY0w OuGM9DAo996ZqCqxCIiACLSKgITvVpFXvSIgAiLgRWDs8t44qxIapUDtHakX96pAiURABERABJpI QMJ3E2GrKhEQARHIToBuBCGFB3JD221yOTwSZi9YOURABERABFpAQMJ3C6CrShEQARHwJ3Do7Ncm YQd8eEPVDYW3/Sk5BI9/XUopAiIgAiJQNQEJ31UTVvkiIAIiUIjAD//8xSRsiNpuQbM2nLbvYfZd qAJlFgEREAERaCIBCd9NhK2qREAERCAXAfgZDISOh2MTfgk/37lKVSYREAEREIEWEJDw3QLoqlIE REAEMhFAhB0TvilnK558JoBKLAIiIALtQ0DCd/uMhVoiAiIgAtEEeLESpiaWQvHkNVdEQAREoKYE JHzXdODUbBEQgS4isOnQBde8W/Hku2js1VUREIGOIyDhu+OGVB0SARHoOAIMYwmFNzqnePIdN8Lq kAiIQBcRkPDdRYOtroqACNSUAK5XmuYbv73nv6XzE8WTr+mAqtkiIALdTEDCdzePvvouAiJQGwI0 8uYHxZOvzeCpoSIgAiLgEJDwrekgAiIgAjUggBiWVH6bz2/Fk6/BsKmJIiACIhAiIOFbk0IEREAE akBg/pazrvCtePI1GDM1UQREQASiCEj41rwQAREQgRoQoIcTxZOvwWipiSIgAiIQT0DCt2aHCIiA CNSAwPlLP1DzrXjyNRgwNVEEREAEYghI+NbUEAEREIF6ELh+8MZAnMt6tFutFAEREAERcAiUIHz/ 7tefTFQHDx6cKb0Si4AIiIAIILwlhG+EmhcKERABERCB+hLIJjSH+2lit4Tv+s4AtVwEREAEREAE REAERKBpBIoK39ZQCd9NGzBVJAIiIAIiIAIiIAIiUF8CTRK+Z8+eDVMT/gwZMsT9rz6LgAiIgAiI gAiIQE0JQMipryColjefQGbh2yy8A6puab6bP3KqUQREQAREQAREQAREoHYEMgvfkT2U8F27gVeD RUAEREAEREAEREAEmk9AwnfzmatGERABERABERABERCBLiVQVPimFUomh4NyNdil003dFgEREAER EAEREIHuJlBU+M5HT8J3Pm7KJQIiIAIiIAIiIAIiUGsCEr5rPXxqvAiIgAiIgAiIgAiIQJ0ISPiu 02iprSIgAiIgAiIgAiIgArUmIOG71sOnxouACIiACIiACIiACNSJgITvOo2W2ioCIiACIiACIiAC IlBrAhK+az18arwIiIAIiIAIiIAIiECdCEj4rtNoqa0iIAIiIAIiIAIiIAK1JiDhu9bDp8aLgAiI gAiIgAiIgAjUiYCE7zqNltoqAiIgAiIgAiIgAiJQawISvms9fGq8CIiACIiACIiACIhAnQhI+K7T aKmtIiACIiACIiACIiACtSYg4bvWw6fGi4AIiIAIiIAIiIAI1ImAhO86jZbaKgIiIAIiIAIiIAIi UGsCEr5rPXxqvAiIgAiIgAiIgAiIQJ0ISPiu02iprSIgAiIgAiIgAiIgArUmIOG71sOnxouACIiA CIiACIiACNSJgITvOo2W2ioCIiACIiACIiACIlBrAhK+az18arwIiIAIiIAIiIAIiECdCEj4rtNo qa0iIAIiIAIiIAIiIAK1JiDhu9bDp8aLgAiIgAiIgAiIgAjUiYCE7zqNltoqAiIgAiIgAiIgAiJQ awISvms9fGq8CIiACIiACIiACIhAnQhI+K7TaKmtIiACIiACIiACIiACtSYg4bvWw6fGi4AIiIAI iIAIiIAI1ImAhO86jZbaKgIiIAIiIAIiIAIiUGsCEr5rPXxqvAiIgAiIgAiIgAiIQJ0ISPiu02ip rSIgAiIgAiIgAiIgArUmIOG71sOnxouACIiACIiACIiACNSJgITvOo2W2ioCIiACIiACIiACIlBr AhK+az18arwIiIAIiIAIiIAIiECdCEj4rtNoqa0iIAIiIAIiIAIiIAK1JiDhu9bDp8aLgAiIgAiI gAiIgAjUiYCE7zqNltoqAiIgAiIgAiIgAiJQawISvms9fGq8CIiACIiACIiACIhAnQhI+K7TaKmt IiACIiACIiACIiACtSYg4bvWw6fGi4AIiIAIiIAIiIAI1ImAhO86jZbaKgIiIAIiIAIiIAIiUGsC Er5rPXxqvAiIgAiIgAiIgAiIQJ0ISPiu02iprSIgAiIgAiIgAiIgArUmIOG71sOnxouACIiACIiA CIiACNSJgITvOo2W2ioCIiACIiACIiACIlBrAhK+az18arwIiIAIiIAIiIAIiECdCEj4rtNoqa0i IAIiIAIiIAIiIAK1JuAlfP/u15/Iftqf+OPJYvDgwZ4plUwEREAEREAEREAEREAEOoZAuvBtYneC 8J2DhYTvHNCURQREQAREQAREQAREoO4E0oVv66GE77qPtNovAiIgAiIgAiIgAiLQcgIlCN9ZbU7Q Z2m+Wz7waoAIiIAIiIAIiIAIiEDzCUQI35HCdJzm221xQprZs2dD4ObP0KFD3f/qswiIgAiIgAiI gAjUlACEnOYLcKqxvgSKar49he/6AlLLRUAEREAEREAEREAERKAsAkWFb1fb7aMdL6vdKkcEREAE REAEREAEREAEakcgXfiOdCYYkLkTfBHWjogaLAIiIAIiIAIiIAIiIAIVEUgXviuqWMWKgAiIgAiI gAiIgAiIQLcRkPDdbSOu/oqACIiACIiACIiACLSMgITvlqFXxSIgAiIgAiIgAiIgAt1G4P8DnWPr 8qlmjHIAAAAASUVORK5CYII ------=_NextPart_000_98E4_01CBD43A.9CB39E80-- ========================================================================Date: Fri, 25 Feb 2011 05:25:48 +0000 Reply-To: Israr Ul Haq <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Israr Ul Haq <[log in to unmask]> Subject: Re: fixed effects analysis across two sessions Comments: To: Donald McLaren <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear Donald, I remember reading about beta and the GLM and how its calculated by using the matrix inverse, when I first started out reading about it. For some reason I formed the whole concept of individual correlations for each time point of a voxel and the subsequent distribution with its mean and sd as a way of representing what actually beta is representing, the weights of columns. Perhaps because the visualization came easier. Thankyou very much for taking time to point this out. What I understand from your explanation is that contrast*beta is essentially the output of adding and subtracting the individual values of the betas, based on the 1's and the -1's in the contrast, 1 meaning the beta will be added and -1, it will be subtracted. I guess for my question about the direction of change in terms of betas, how does the t statistic differ between a certain value of contrast*beta and an equal but a negative value of the same. Since the absolute difference between the effect and the mean is the same, does it take into account whether the contrast*beta is a positive or a negative value? Thanks Israr ========================================================================Date: Fri, 25 Feb 2011 10:54:06 +0100 Reply-To: Martin Jensen Dietz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Martin Jensen Dietz <[log in to unmask]> Subject: Re: Order of subject files in ANOVA batch Comments: To: Pilar Archila-Suerte <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi Pilar, When comparing 2 independent groups use a two-sample t-test which is very straight forward Martin -- Martin Dietz [log in to unmask] On Feb 25, 2011, at 12:44 AM, Pilar Archila-Suerte wrote: Mine is actually not a repeated measures design. I'm running a full factorial and I just want to compare activity in one group vs. another. Do the con files need to be in the same order in each cell in this case? Pilar On Thu, Feb 24, 2011 at 5:38 PM, MCLAREN, Donald <[log in to unmask]> wrote: You should use the flexible factorial for repeated measure designs with factors for subject and condition in your design. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte <[log in to unmask]> wrote: > Dear SPM list, > In setting ANOVAs in SPM, do the subject files need to be in the same order > under each cell? > I just ran two trial trial batches, one with the same order of subjects and > one with a different order. The areas of activity are very similar but the > order in which the areas show up as more or less intensive did vary. > Any insight as to how SPM does this? should I stick to the same order of > subjects for clarity? > Thanks, > Pilar A . ========================================================================Date: Fri, 25 Feb 2011 11:42:31 +0100 Reply-To: "Maarten A.S. Boksem" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Maarten A.S. Boksem" <[log in to unmask]> Subject: PhD position in Decision Neuroscience MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0015174c187ee518de049d18fd88 Content-Type: text/plain; charset=ISO-8859-1 *PhD position in Decision Neuroscience at the Erasmus University Rotterdam* *and Donders Institute Nijmegen* The Erasmus Center for Neuroeconomics (ECNE) at the Rotterdam School of Management, Erasmus University and the Donders Institute for Brain, Cognition and Behaviour at Radboud University Nijmegen announce an opening for a PhD researcher to join an active group of researchers in the Center in the field of decision neuroscience/neuroeconomics/neuromarketing. This position will be jointly supervised by Prof. Ale Smidts (Erasmus), Dr. Alan Sanfey (Donders Institute) and Dr. Maarten Boksem (Erasmus and Donders). We currently seek outstanding applicants whose research interests lie at the intersection of decision-making, behavioral economics, and neuroscience and who are interested in studying the brain mechanisms that underlie decision-making. Particular interests of our group are the neural mechanisms that underlie social influences on decisions, emotion regulation and self-control involved in decision-making, and the role of risk and reward in such decisions. We are especially interested in applicants whose research can build bridges between existing strengths in consumer-behavior research at the marketing department of the Rotterdam School of Management and decision neuroscience at the Donders Institute. The Erasmus Center for Neuroeconomics is dedicated to conducting cutting-edge interdisciplinary research in decision neuroscience, and hosts the Erasmus Behavioral Lab which provides an excellent infrastructure for conducting behavioral and EEG/ERP experiments. The Donders Institute is a leading research institute in cognitive neuroscience and provides excellent resources for functional neuroimaging by means of two research-dedicated fMRI scanners, an MEG scanner, and EEG and TMS facilities. Additional facilities are available for the collection and analysis of genetic samples. The collaboration between Erasmus University and the Donders Institute provides an outstanding environment for studying the neural underpinnings of decision-making behavior, and the successful applicant will have full access to the facilities in both institutions. *Requirements for the PhD position * Successful candidates must have a relevant Masters degree, preferably with a background in cognitive neuroscience, cognitive psychology, or biological psychology. Candidates with a background in consumer behavior or economics, with proven evidence or a strong interest in developing cognitive neuroscience and imaging skills, are also invited to apply. A tailored PhD course program will be developed. The PhD position is for four years. PhDs receive a regular employment contract for a doctoral student. See the ERIM Doctoral Program for further information on the facilities of PhD students at Erasmus. Preferred starting date: September 2011. Applications, including CV, a brief summary of current and proposed research, and at least two letters of recommendation, should be submitted through the ERIM application website (http://www.erim.eur.nl). submit your application preferably before April 1, 2011. PhD applicants may be requested to provide GRE/ GMAT scores and TOEFLl /IELTS language scores. For further information on the position, visit our website erim.nl/neuroconomics or contact Prof. Ale Smidts ([log in to unmask]) or Dr. Maarten Boksem ( [log in to unmask]). -- Dr. Maarten A.S. Boksem RSM Erasmus University Burgemeester Oudlaan 50 3062 PA Rotterdam The Netherlands Donders Institute for Brain, Cognition and Behaviour Centre for Cognitive Neuroimaging P.O. Box 9101 6500 HB Nijmegen The Netherlands +31 (0) 24 36 68063 [log in to unmask] www.Boksem.nl --0015174c187ee518de049d18fd88 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

PhD position in Decision Neuroscience at the Erasmus University Rotterdam

and Donders Institute Nijmegen

The Erasmus Center for Neuroeconomics (ECNE) at the Rotterdam School of Management, Erasmus University and the Donders Institute for Brain, Cognition and Behaviour at Radboud University Nijmegen announce an opening for a PhD researcher to join an active group of researchers in the Center in the field of decision neuroscience/neuroeconomics/neuromarketing. This position will be jointly supervised by Prof. Ale Smidts (Erasmus), Dr. Alan Sanfey (Donders Institute) and Dr. Maarten Boksem (Erasmus and Donders).

We currently seek outstanding applicants whose research interests lie at the intersection of decision-making, behavioral economics, and neuroscience and who are interested in studying the brain mechanisms that underlie decision-making. Particular interests of our group are the neural mechanisms that underlie social influences on decisions, emotion regulation and self-control involved in decision-making, and the role of risk and reward in such decisions. We are especially interested in applicants whose research can build bridges between existing strengths in consumer-behavior research at the marketing department of the Rotterdam School of Management and decision neuroscience at the Donders Institute.

The Erasmus Center for Neuroeconomics is dedicated to conducting cutting-edge interdisciplinary research in decision neuroscience, and hosts the Erasmus Behavioral Lab which provides an excellent infrastructure for conducting behavioral and EEG/ERP experiments. The Donders Institute is a leading research institute in cognitive neuroscience and provides excellent resources for functional neuroimaging by means of two research-dedicated fMRI scanners, an MEG scanner, and EEG and TMS facilities. Additional facilities are available for the collection and analysis of genetic samples. The collaboration between Erasmus University and the Donders Institute provides an outstanding environment for studying the neural underpinnings of decision-making behavior, and the successful applicant will have full access to the facilities in both institutions.

Requirements for the PhD position

Successful candidates must have a relevant Masters degree, preferably with a background in cognitive neuroscience, cognitive psychology, or biological psychology. Candidates with a background in consumer behavior or economics, with proven evidence or a strong interest in developing cognitive neuroscience and imaging skills, are also invited to apply. A tailored PhD course program will be developed. The PhD position is for four years. PhDs receive a regular employment contract for a doctoral student. See the ERIM Doctoral Program for further information on the facilities of PhD students at Erasmus.

Preferred starting date: September 2011.

Applications, including CV, a brief summary of current and proposed research, and at least two letters of recommendation, should be submitted through the ERIM application website (http://www.erim.eur.nl). submit your application preferably before April 1, 2011. PhD applicants may be requested to provide GRE/ GMAT scores and TOEFLl /IELTS language scores. For further information on the position, visit our website erim.nl/neuroconomics or contact Prof. Ale Smidts ([log in to unmask]) or Dr. Maarten Boksem ([log in to unmask]).

--
Dr. Maarten A.S. Boksem

RSM
Erasmus University

Burgemeester Oudlaan 50
3062 PA Rotterdam
The Netherlands

Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
P.O. Box 9101
6500 HB Nijmegen
The Netherlands

+31 (0) 24 36 68063
[log in to unmask]
www.Boksem.nl

--0015174c187ee518de049d18fd88-- ========================================================================Date: Fri, 25 Feb 2011 22:39:16 +1100 Reply-To: Richard Morris <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Morris <[log in to unmask]> Subject: Re: script to flip MR images left to right (or vice versa) In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable I'm not sure, but I think you can use the image calculator in SPM, with the following function: f = 'Inv(i1)' On 25/02/2011, at 10:16 AM, Michael T Rubens wrote: how about this: %%% v = spm_vol('/image_path/brain.img'); x = spm_read_vols(v); x = x(size(x,1):-1:1,:,:); [path file] = fileparts(v.fname); v.fname = fullfile(path,['flipped_' file'); spm_write_vol(v,x) %%% cheers, michael On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote: Hi, I stuides patients with stroke doing some behavior paradigm in fMRI. Patients always used their affected limb for the task so some patients used left hand some used right hand. Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side. Is there any script in spm 5 or 8 can help to accomplish this goal? I found a m file called spm_flip.m but it seems support only spm2. Your assistance are well appreciated. Thank you so much. Janice -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable I'm not sure, but I think you can use the image calculator in SPM, with the following function:

f = 'Inv(i1)'

On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:

how about this:
%%%

v = spm_vol('/image_path/brain.img');
x = spm_read_vols(v);

x = x(size(x,1):-1:1,:,:);

[path file] = fileparts(v.fname);

v.fname = fullfile(path,['flipped_' file');

spm_write_vol(v,x)

%%%

cheers,
michael

On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote:
Hi,

I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.

Is there any script in spm 5 or 8 can help to accomplish this goal?

I found a m file called spm_flip.m but it seems support only spm2.

Your assistance are well appreciated.

Thank you so much.

Janice

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_-- ========================================================================Date: Fri, 25 Feb 2011 11:54:19 +0000 Reply-To: Rosa Sanchez Panchuelo <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Rosa Sanchez Panchuelo <[log in to unmask]> Subject: Re: script to flip MR images left to right (or vice versa) Comments: To: Richard Morris <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="------------040105050906060404020507" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --------------040105050906060404020507 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit You can do it easily with fsl: fslswapdim input_image.img -x outpu_image.img -x will flip the image in the RL direction. On 25/02/2011 11:39, Richard Morris wrote: > I'm not sure, but I think you can use the image calculator in SPM, > with the following function: > > f = 'Inv(i1)' > > > On 25/02/2011, at 10:16 AM, Michael T Rubens wrote: > >> how about this: >> %%% >> >> v = spm_vol('/image_path/brain.img'); >> x = spm_read_vols(v); >> >> x = x(size(x,1):-1:1,:,:); >> >> [path file] = fileparts(v.fname); >> v.fname = fullfile(path,['flipped_' file'); >> >> spm_write_vol(v,x) >> >> %%% >> >> >> cheers, >> michael >> >> On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask] >> > wrote: >> >> Hi, >> >> I stuides patients with stroke doing some behavior paradigm in fMRI. >> Patients always used their affected limb for the task so some >> patients used left hand some used right hand. >> Now I would like to flip the left and right of the images so >> patients' lesional hemisphere are on the same side. >> >> Is there any script in spm 5 or 8 can help to accomplish this goal? >> >> I found a m file called spm_flip.m but it seems support only spm2. >> >> Your assistance are well appreciated. >> >> Thank you so much. >> >> Janice >> >> >> >> >> -- >> Research Associate >> Gazzaley Lab >> Department of Neurology >> University of California, San Francisco > -- Rosa Maria Sanchez Panchuelo Post-doctoral Research fellow Sir Peter Mansfield Magnetic Resonance Centre University of Nottingham University Park Nottingham, NG7 2RD United Kingdom +44 115 84 66003 This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation. --------------040105050906060404020507 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit You can do it easily with fsl:
fslswapdim input_image.img -x outpu_image.img
-x will flip the image in the RL direction.

On 25/02/2011 11:39, Richard Morris wrote:

[log in to unmask]" type="cite">I'm not sure, but I think you can use the image calculator in SPM, with the following function:

f = 'Inv(i1)'

On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:

how about this:
%%%

v = spm_vol('/image_path/brain.img');

x = spm_read_vols(v);

x = x(size(x,1):-1:1,:,:);

[path file] = fileparts(v.fname);

v.fname = fullfile(path,['flipped_' file');

spm_write_vol(v,x)

%%%

cheers,

michael

On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote:

Hi,

I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.

Is there any script in spm 5 or 8 can help to accomplish this goal?

I found a m file called spm_flip.m but it seems support only spm2.

Your assistance are well appreciated.

Thank you so much.

Janice

--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco

--
Rosa Maria Sanchez Panchuelo
Post-doctoral Research fellow
Sir Peter Mansfield Magnetic Resonance Centre
University of Nottingham
University Park
Nottingham, NG7 2RD
United Kingdom
+44 115 84 66003

--------------040105050906060404020507-- ========================================================================Date: Fri, 25 Feb 2011 08:19:45 -0600 Reply-To: Michael Harms <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Harms <[log in to unmask]> Subject: Re: script to flip MR images left to right (or vice versa) Comments: To: Rosa Sanchez Panchuelo <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain Mime-Version: 1.0 Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> A note of caution about using fslswapdim in this manner however: If you do that, the data order and the header information stored in the sform/qform will no longer be in sync. So, what the sform/qform defines to be "left" will no longer be the subject's true left (assuming the labels in the original data were correct)! Read the following FSL page if you want all the details: http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html Best, -MH On Fri, 2011-02-25 at 11:54 +0000, Rosa Sanchez Panchuelo wrote: > You can do it easily with fsl: > fslswapdim input_image.img -x outpu_image.img > -x will flip the image in the RL direction. > > On 25/02/2011 11:39, Richard Morris wrote: > > I'm not sure, but I think you can use the image calculator in SPM, > > with the following function: > > > > > > f = 'Inv(i1)' > > > > > > > > On 25/02/2011, at 10:16 AM, Michael T Rubens wrote: > > > > > how about this: > > > %%% > > > > > > > > > v = spm_vol('/image_path/brain.img'); > > > x = spm_read_vols(v); > > > > > > > > > x = x(size(x,1):-1:1,:,:); > > > > > > > > > [path file] = fileparts(v.fname); > > > v.fname = fullfile(path,['flipped_' file'); > > > > > > > > > spm_write_vol(v,x) > > > > > > > > > %%% > > > > > > > > > > > > > > > cheers, > > > michael > > > > > > On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin > > > <[log in to unmask]> wrote: > > > Hi, > > > > > > I stuides patients with stroke doing some behavior > > > paradigm in fMRI. > > > Patients always used their affected limb for the task so > > > some patients used left hand some used right hand. > > > Now I would like to flip the left and right of the images > > > so patients' lesional hemisphere are on the same side. > > > > > > Is there any script in spm 5 or 8 can help to accomplish > > > this goal? > > > > > > I found a m file called spm_flip.m but it seems support > > > only spm2. > > > > > > Your assistance are well appreciated. > > > > > > Thank you so much. > > > > > > Janice > > > > > > > > > > > > -- > > > Research Associate > > > Gazzaley Lab > > > Department of Neurology > > > University of California, San Francisco > > > > > > > > > > -- > Rosa Maria Sanchez Panchuelo > Post-doctoral Research fellow > Sir Peter Mansfield Magnetic Resonance Centre > University of Nottingham > University Park > Nottingham, NG7 2RD > United Kingdom > +44 115 84 66003 > > > > > > This message and any attachment are intended solely for the addressee > and may contain confidential information. If you have received this > message in error, please send it back to me, and immediately delete > it. Please do not use, copy or disclose the information contained in > this message or in any attachment. Any views or opinions expressed by > the author of this email do not necessarily reflect the views of the > University of Nottingham. > > This message has been checked for viruses but the contents of an > attachment may still contain software viruses which could damage your > computer system: you are advised to perform your own checks. Email > communications with the University of Nottingham may be monitored as > permitted by UK legislation. > ========================================================================Date: Fri, 25 Feb 2011 15:23:11 +0100 Reply-To: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]> Subject: Re: Negative values in DCM.A Comments: To: Maria Dauvermann <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00504502e3d0ea2d57049d1c1166 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Maria, to clarify a little: -self connections need to be negative or the activation in this region would go to infinity (and with that the whole network would become unstable) -all connections are (defined) relative to self-connections because the self-connections are 'fixed' to mean=-1 with priors for their mean and variance such that the probability of the system becoming unstable being very small. (This leaves a renormalization parameters corresponding to a decay rate, fixed to 1s.) -connections in DCM are change rates, i.e. a12 tells you how much of the current state/activation (on neuronal level) at time t of region 2 is added to the change in region 1 (at time t). There might be influences from other regions on region 2 which have to be considered, but if if a12 is the sole connection, we can say: *** a12=0.1 means 10% of the state in region 2 is added to the state (activity) of region 1 and analog for a12=-0.1, this corresponds to a subtraction by 10% of the state in region 2 from the state in region 1. Regards, Rainer On Fri, Feb 25, 2011 at 8:48 AM, Maria Dauvermann < [log in to unmask]> wrote: > Dear Rainer, > > thank you very much for your reply. Now I understand why there are negative > values in the self connections but I also have positive values in some DCMs. > What does that mean? > > BW, Maria > > > > > On 24 February 2011 16:14, Rainer Bögle <[log in to unmask]>wrote: > >> Hello Maria, >> >> as far as I understand this, connection parameters in DCM are change >> rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0). >> >> As stated in Friston 2003, connection parameters are always relative to >> parameters of self connections (which have to be negative to ensure a stable >> system). >> >> Regards, >> Rainer >> >> >> >> On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann < >> [log in to unmask]> wrote: >> >>> Hello, >>> >>> I had a look at the values in DCM.A after I have estimated the DCMs. >>> >>> What does a negative value in the intrinsic connection mean? How do I >>> interprete this result? >>> >>> Thanks for your help. >>> >>> BW, Maria >>> >> >> > --00504502e3d0ea2d57049d1c1166 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Maria,

to clarify a little:

-self connections need to be negative or the activation in this region would go to infinity (and with that the whole network would become unstable)
-all connections are (defined) relative to self-connections because the self-connections are 'fixed' to mean=-1 with priors for their mean and variance such that the probability of the system becoming unstable being very small. (This leaves a renormalization parameters corresponding to a decay rate, fixed to 1s.)
-connections in DCM are change rates, i.e. a12 tells you how much of the current state/activation (on neuronal level) at time t of region 2 is added to the change in region 1 (at time t). There might be influences from other regions on region 2 which have to be considered, but if if a12 is the sole connection, we can say:
*** a12=0.1 means 10% of the state in region 2 is added to the state (activity) of region 1
and analog for a12=-0.1, this corresponds to a subtraction by 10% of the state in region 2 from the state in region 1.

Regards,
Rainer

On Fri, Feb 25, 2011 at 8:48 AM, Maria Dauvermann <[log in to unmask]> wrote:

Dear Rainer,

thank you very much for your reply. Now I understand why there are negative values in the self connections but I also have positive values in some DCMs. What does that mean?

BW, Maria

On 24 February 2011 16:14, Rainer Bögle <[log in to unmask]> wrote:
Hello Maria,

as far as I understand this, connection parameters in DCM are change rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0).

As stated in Friston 2003, connection parameters are always relative to parameters of self connections (which have to be negative to ensure a stable system).

Regards,
Rainer

On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <[log in to unmask]> wrote:
Hello,

I had a look at the values in DCM.A after I have estimated the DCMs.

What does a negative value in the intrinsic connection mean? How do I interprete this result?

Thanks for your help.

BW, Maria

--00504502e3d0ea2d57049d1c1166-- ========================================================================Date: Fri, 25 Feb 2011 10:13:26 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Can't find driving effects for PPI analysis Comments: To: J S Lee <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> As you can see the *PSY* negative columns do line up well together, but the positives do not. Samething with the PPI, where you have the control condition, the curves line up. Now, if we think about the concept of the the general linear model (Y=BX)... Trying to fit the all-control vector to the data is much different than fitting the condition1-control vector. I think this reiterates the point I'm making in a paper I'm about to submit on PPI. That is PPI as it is currently implemented fits these vectors above, rather than fitting a vector for condition1, condition2, .., and control. In splitting them up, one is then estimating the relationship of each component, rather than the joint relationship. My hunch is that if you were to separate them conditions, you would either eliminate the average effect OR more likely find out which one is driving the effect. When you only model 2 conditions, there is a chance that you attribute the variance of the data to the wrong factor or it ends up in the error term. Also, when you only model 2 conditions, you are only modelling the activitation effect of those 2 conditions. Modelling has shown that the individual model fit is improved when you separate the conditions. I'm adding some comments to the code for splitting the vectors and have termed the approach "a generalizable form of PPI (gPPI)" and hopefully can provide you with the code later today. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Thu, Feb 24, 2011 at 6:50 PM, J S Lee <[log in to unmask]> wrote: > Hi Donald, > Thank you for your reply! > I am using SPM8. > I looked at the SPM.xX.X values for each subject. The values in the SPM.xX.X > *PSY* columns for each of my models line up perfectly across subjects. For > the different models within a subject, there also seems to be correspondence > for the PSY negative values (although there are slight baseline shifts > between different models). The values in the PPI column, however, do not > correspond so well. I don't think I would have expected the PPI values to > correspond across subjects, however, because the VOI values differ from > subject to subject, so the PPI should also differ as it represents an > interaction? The PPI values for different models in the same subject also do > not show perfect correspondence (png attached: It shows 2 subjects' SPM.xX.X > PPI values over the first 80 scans. The All conds - control model and Cond 1 > - control PPI values are plotted for each subject. I didn't include the Cond > 2 - control, Cond 3 - control, etc. for clarity). > > All models are coming from the same VOI, so I think the adjustment has to be > the same for all models (all extractions were adjusted for an F contrast of > the effects of interest at the first-level model). Is this what you meant? > The voxels are also identical (again, coming from the same VOI I think they > have to be?). > > > Thank you very much for taking the time to consider my question--even > looking at the PSY and PPI columns has been useful! > > Jamie > >> >>Are you using SPM8? The issue of summing was fixed in one of the later >>releases of SPM5, so if you have an older version, that could explain >>some of the issue. You could check to make sure that negative aspects >>of the SPM.xX.X for the PPI term are the same for all subjects. You >>could plot them. >>Are you using the same adjustment for all models? >>Are you using exactly the same voxels for all models? > > >> >>On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote: >>> Dear list, >>> >>> I conducted a PPI analysis in an experiment with 6 conditions. To >>> replicate >>> a previous study's PPI analysis, I was interested in connectivity >>> differences between 5 of the conditions compared to the control (6th) >>> condition, so extracted my VOI (using an all effects of interest >>> contrast), >>> then created a PPI model with a [1 1 1 1 1 -1] weighting for the >>> psychological context regressor. I get a reasonable replication of the >>> same >>> PPI effects from the previous study, so the results are sensible. >>> >>> However, in that previous study, there were not enough trials of each of >>> the >>> 5 conditions to realistically analyze them separately, which is why I >>> collapsed across them. In this study, there are many more trials, so I >>> was >>> hoping to look at which of the 5 conditions were driving the original PPI >>> results. I was given hope when the initial PPI replicated in this new >>> study. >>> However, when I create separate PPI models for each condition versus >>> control >>> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 >>> -1] >>> for model 2, etc.), NONE of these analyses show the same pattern as the 1 >>> 1 >>> 1 1 1 -1 model does. Mostly there are no significant (or anywhere near >>> significant) results, and those random speckles that do show up at low >>> threshold are not in the same places. >>> >>> Is it theoretically possible that 5 conditions vs 1 other can produce a >>> PPI, >>> but that none of those conditions singly vs the 1 other can do that? Or >>> must >>> there be an error? I have checked the microtime onset files to make the >>> context is specified correctly, and made sure everything matches up in >>> terms >>> of specifying the conditions. Everything about the models looks fine to >>> me. >>> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the >>> previous study (in which the 5 vs 1 were equal in terms of number of >>> trials), and I understand that when creating the context variable one >>> does >>> NOT sum the vector to zero the way one would in defining a contrast for a >>> regional activation analysis. >>> >>> Many thanks in advance for any thoughts, >>> Jamie Lee > > _______________________________________________________________ > Get the Free email that has everyone talking at http://www.mail2world.com > Unlimited Email Storage – POP3 – Calendar – SMS – Translator – Much More! ========================================================================Date: Fri, 25 Feb 2011 15:26:24 +0000 Reply-To: "Stephen J. Fromm" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Stephen J. Fromm" <[log in to unmask]> Subject: Re: Can't find driving effects for PPI analysis Comments: To: Donald McLaren <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Donald, You wrote, "The issue of summing was fixed in one of the later releases of SPM5,..." Are you saying that it's now OK if the weights used sum to zero, when before they shouldn't? Best, S ========================================================================Date: Fri, 25 Feb 2011 16:23:05 +0000 Reply-To: "Stephen J. Fromm" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Stephen J. Fromm" <[log in to unmask]> Subject: DCM: significance and graph connectivity Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> I'm working on a DCM project. So far the best fitting model looks like this: DI--->R1 ==> R2 ==> R3 ^ ^ | | MI1 MI2 That is: * there are three regions, with intrinsic connections from R1 to R2 and R2 to R3 * there is one driving input DI acting on R1 * there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections (The experimental variables for MI1 and MI2 are identical.) At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2). Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant? TIA, S ========================================================================Date: Fri, 25 Feb 2011 11:38:07 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Can't find driving effects for PPI analysis Comments: To: "Stephen J. Fromm" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> I am saying two things: (1) You are contrasting the positive weight conditions against the negative weighted conditions in such a manner that the connectivity of the positive weights (1s) are assumed to be the same (e.g. if you have 3 conditions and a control AND contrast conditions versus control, you assume that the connectivity during the conditions is the same) and the same is true for the negatives (-1s). They should sum to 0 unless there are 2 conditions OR you think the connectivity is different between the positive and negative conditions, but assumptions like this are bad. (1b) Along those same lines, if you have 2 conditions plus fixation, then you are assuming that connectivity for yoru conditions is less than fixation in one condition and greater than fixation in the other. (2) gPPI -- model each condition as a separate interaction. Now if you have 3 conditions and fixation, you will get three PPI values - one for each condition. Then, you can compare the three conditions to each other as you do in fMRI activation analyses. Additionally, if you were to add the vectors for each condition together with the weights used in step 1, you would recover the same joint vector. The critical difference is the joint vector has imposed a relationship on your data that may or may not be true more complex designs. Hope that clarifies my comment from earlier. Let me know if it didn't. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Fri, Feb 25, 2011 at 10:26 AM, Stephen J. Fromm <[log in to unmask]> wrote: > Donald, > > You wrote, > > "The issue of summing was fixed in one of the later releases of SPM5,..." > > Are you saying that it's now OK if the weights used sum to zero, when before they shouldn't? > > Best, > > S > > > ========================================================================Date: Fri, 25 Feb 2011 16:44:41 +0000 Reply-To: Mohamed Seghier <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mohamed Seghier <[log in to unmask]> Subject: Re: DCM: significance and graph connectivity Comments: To: "Stephen J. Fromm" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hi Stephen, Are you using DCM8 or DCM10? If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)... I hope this helps, Mohamed On 25/02/2011 16:23, Stephen J. Fromm wrote: > I'm working on a DCM project. So far the best fitting model looks like this: > > > DI--->R1 ==> R2 ==> R3 > ^ ^ > | | > MI1 MI2 > > That is: > * there are three regions, with intrinsic connections from R1 to R2 and R2 to R3 > * there is one driving input DI acting on R1 > * there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections > > (The experimental variables for MI1 and MI2 are identical.) > > At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2). > > Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant? > > TIA, > > S > > ========================================================================Date: Fri, 25 Feb 2011 10:47:46 -0600 Reply-To: Darren Gitelman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Darren Gitelman <[log in to unmask]> Subject: Re: DCM: significance and graph connectivity Comments: To: Mohamed Seghier <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf30434548f1250a049d1e16a0 Message-ID: [log in to unmask]> --20cf30434548f1250a049d1e16a0 Content-Type: text/plain; charset=ISO-8859-1 Mohamed Would your response below differ depending on whether he was using DCM8 vs. DCM10? Darren On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier < [log in to unmask]> wrote: > Hi Stephen, > > Are you using DCM8 or DCM10? > If your connection R1==>R2 is not significant at the group level, then it > is possible that (1) your modulatory parameter MI1 is significant, which may > suggest that the inter-regional interaction between R1 and R2 increased > "specifically" during your context MI1; or (2) you have a large > inter-subject variability on this connection. For a similar situation, see > Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not > significant but motion modulation was significant)... > > I hope this helps, > > Mohamed > > > > On 25/02/2011 16:23, Stephen J. Fromm wrote: > >> I'm working on a DCM project. So far the best fitting model looks like >> this: >> >> >> DI--->R1 ==> R2 ==> R3 >> ^ ^ >> | | >> MI1 MI2 >> >> That is: >> * there are three regions, with intrinsic connections from R1 to R2 and R2 >> to R3 >> * there is one driving input DI acting on R1 >> * there are two modulatory connections MI1 and MI2 acting on the two >> between-regions intrinsic connections >> >> (The experimental variables for MI1 and MI2 are identical.) >> >> At the group level, the second connection (R2 ==> R3) is significant, but >> the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, >> insofar as the only way the system perturbations introduced by the DI can >> get to (R2 ==> R3) is through (R1 ==> R2). >> >> Does this reduce the credibility of the model? Or is it alone not enough >> to do that because of the possible vagaries of what is significant? >> >> TIA, >> >> S >> >> >> -- Darren Gitelman, MD Northwestern University 710 N. Lake Shore Dr., 1122 Chicago, IL 60611 Ph: (312) 908-8614 Fax: (312) 908-5073 --20cf30434548f1250a049d1e16a0 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Mohamed

Would your response below differ depending on whether he was using DCM8 vs. DCM10?

Darren

On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier <[log in to unmask]> wrote:

Hi Stephen,

Are you using DCM8 or DCM10?
If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)...

I hope this helps,

Mohamed

On 25/02/2011 16:23, Stephen J. Fromm wrote:

I'm working on a DCM project. So far the best fitting model looks like this:

DI--->R1 ==> R2 ==> R3
^ ^
| |
MI1 MI2

That is:
* there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
* there is one driving input DI acting on R1
* there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections

(The experimental variables for MI1 and MI2 are identical.)

At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).

Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?

TIA,

S

--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr., 1122
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073
--20cf30434548f1250a049d1e16a0-- ========================================================================Date: Fri, 25 Feb 2011 11:47:39 -0500 Reply-To: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]> Subject: Re: DCM: significance and graph connectivity Comments: To: Mohamed Seghier <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Dear Mohamed, Thanks for the quick, informative reply. I'm using DCM8, and probably not the newest version of SPM8 either (ie not 4010). I'll look at the example you cite from the seminal DCM paper. Best, Stephen J. Fromm, PhD Contractor, NIMH/MAP (301) 451--9265 ________________________________________ From: Mohamed Seghier [[log in to unmask]] Sent: Friday, February 25, 2011 11:44 AM To: Fromm, Stephen (NIH/NIMH) [C] Cc: [log in to unmask] Subject: Re: [SPM] DCM: significance and graph connectivity Hi Stephen, Are you using DCM8 or DCM10? If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)... I hope this helps, Mohamed On 25/02/2011 16:23, Stephen J. Fromm wrote: > I'm working on a DCM project. So far the best fitting model looks like this: > > > DI--->R1 ==> R2 ==> R3 > ^ ^ > | | > MI1 MI2 > > That is: > * there are three regions, with intrinsic connections from R1 to R2 and R2 to R3 > * there is one driving input DI acting on R1 > * there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections > > (The experimental variables for MI1 and MI2 are identical.) > > At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2). > > Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant? > > TIA, > > S > >========================================================================Date: Fri, 25 Feb 2011 16:55:34 +0000 Reply-To: Artur <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Artur <[log in to unmask]> Subject: Re: deactivation Comments: To: Ladan Ghazi Saidi <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi, It depends on the locus of "deactivation" spot and the type of learning. E.g. one may see a decrease in hippocampal BOLD signal accompanied by increase in implicit memory performance. For more details check out Russ Poldrack's and others' papers on hippocampus-caudate interactions. Email me if you want a package of papers on the topic. Best regards, Art ========================================================================Date: Fri, 25 Feb 2011 17:29:44 +0000 Reply-To: Mohamed Seghier <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mohamed Seghier <[log in to unmask]> Subject: Re: DCM: significance and graph connectivity Comments: To: Darren Gitelman <[log in to unmask]> In-Reply-To: [log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="------------080208090307080100090302" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --------------080208090307080100090302 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Hi Darren, A short response: yes it is also valid for DCM10... But.... My understanding is that, in DCM8, any "posterior" effects in intrinsic connectivity is expressed within the inter-regional connections (the off-diagonal of the A matrix, as all self-connections were fixed to "-1", although used as a scaling factor). In DCM10, the self-connections are allowed to deviate/vary from "-1" and thus inter-regional interactions that were significant in DCM8 may be different in DCM10 (i.e. as these effects can now be expressed in the self-connections). In other words, the generic model is more "flexible" in DCM10 than DCM8... I hope this makes sense... Note also that in DCM10 you have the option to mean-centre your inputs which makes the intrinsic (endogenous) connectivity parameters equal to the "average" connectivity across all conditions of your driving input(s). This is different from the previous interpretation of a "baseline" connectivity that was considered in DCM8... For more details, see Klaas's email of 22/11/2010... I hope this helps, Mohamed On 25/02/2011 16:47, Darren Gitelman wrote: > Mohamed > > Would your response below differ depending on whether he was using > DCM8 vs. DCM10? > > Darren > > On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier > <[log in to unmask] > wrote: > > Hi Stephen, > > Are you using DCM8 or DCM10? > If your connection R1==>R2 is not significant at the group level, > then it is possible that (1) your modulatory parameter MI1 is > significant, which may suggest that the inter-regional interaction > between R1 and R2 increased "specifically" during your context > MI1; or (2) you have a large inter-subject variability on this > connection. For a similar situation, see Figure 24 in Karl's 2003 > paper (Friston 2003 p1298: connection V1-to-V5 not significant but > motion modulation was significant)... > > I hope this helps, > > Mohamed > > > > On 25/02/2011 16:23, Stephen J. Fromm wrote: > > I'm working on a DCM project. So far the best fitting model > looks like this: > > > DI--->R1 ==> R2 ==> R3 > ^ ^ > | | > MI1 MI2 > > That is: > * there are three regions, with intrinsic connections from R1 > to R2 and R2 to R3 > * there is one driving input DI acting on R1 > * there are two modulatory connections MI1 and MI2 acting on > the two between-regions intrinsic connections > > (The experimental variables for MI1 and MI2 are identical.) > > At the group level, the second connection (R2 ==> R3) is > significant, but the first (R1 ==> R2) isn't. Conceptually, > this doesn't make sense, insofar as the only way the system > perturbations introduced by the DI can get to (R2 ==> R3) is > through (R1 ==> R2). > > Does this reduce the credibility of the model? Or is it alone > not enough to do that because of the possible vagaries of what > is significant? > > TIA, > > S > > > > > > -- > Darren Gitelman, MD > Northwestern University > 710 N. Lake Shore Dr., 1122 > Chicago, IL 60611 > Ph: (312) 908-8614 > Fax: (312) 908-5073 --------------080208090307080100090302 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit
Hi Darren,

A short response: yes it is also valid for DCM10... But....
My understanding is that, in DCM8, any "posterior" effects in intrinsic connectivity is expressed within the inter-regional connections (the off-diagonal of the A matrix, as all self-connections were fixed to "-1", although used as a scaling factor). In DCM10, the self-connections are allowed to deviate/vary from "-1" and thus inter-regional interactions that were significant in DCM8 may be different in DCM10 (i.e. as these effects can now be expressed in the self-connections). In other words, the generic model is more "flexible" in DCM10 than DCM8... I hope this makes sense...
Note also that in DCM10 you have the option to mean-centre your inputs which makes the intrinsic (endogenous) connectivity parameters equal to the “average” connectivity across all conditions of your driving input(s). This is different from the previous interpretation of a "baseline" connectivity that was considered in DCM8...
For more details, see Klaas's email of 22/11/2010...

I hope this helps,

Mohamed

On 25/02/2011 16:47, Darren Gitelman wrote:

[log in to unmask]" type="cite">Mohamed

Would your response below differ depending on whether he was using DCM8 vs. DCM10?

Darren

On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier <[log in to unmask]> wrote:

Hi Stephen,

Are you using DCM8 or DCM10?
If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)...

I hope this helps,

Mohamed

On 25/02/2011 16:23, Stephen J. Fromm wrote:

I'm working on a DCM project. So far the best fitting model looks like this:

DI--->R1 ==> R2 ==> R3
^ ^
| |
MI1 MI2

That is:
* there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
* there is one driving input DI acting on R1
* there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections

(The experimental variables for MI1 and MI2 are identical.)

At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).

Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?

TIA,

S

--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr., 1122
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073

--------------080208090307080100090302-- ========================================================================Date: Fri, 25 Feb 2011 20:33:16 +0100 Reply-To: SALEM BOUSSIDA <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: SALEM BOUSSIDA <[log in to unmask]> Subject: Mixed effects group analysis questions MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Dear SPMers, I have a group of 7 subjects (rats) and I would like to perform a group analysis (mixed effects). I have done the 1st level analysis (fixed effects) for each subject and I have two con_001 and con_002 images, wich are respectively the contrast image for a positive effect (activation) and for a negative effect (deactivation) of one condition (current stimulation) . Then, I have done a one-sample-t-test : I feed all the con_001 of each subject into the 2nd level analysis , then I entered a T contrast [1] and SPM displayed the thresholded T-statistic image. But when I entered a F contrast [1], I have got this error: " ??? Inf computed by model function, fitting cannot continue. Try using or tightening upper and lower bounds on coefficients. " Based on the procedure described in SPM8 manuel concerning the "face group data", It is mentionned that I have to enter a F contrast. I am confused by this and I have some questions: (1) which contrast should I enter, F or T contrast? and what is the differenece between them? (2) Is the one-sample-t-test, the appropriate one to run mixed effects analysis? (3) Do I have to run two separate "one-sample-t-test" for positive(activation) and negative-deactivation) effects? Thank you for your help. Best regards, Salem ========================================================================Date: Fri, 25 Feb 2011 17:13:46 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Mixed effects group analysis questions Comments: To: SALEM BOUSSIDA <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Salem, This isn't a mixed effects design as you only have one group and only one condition. What you want to do is to use the con_001 images in a one-sample t-test. DO NOT USE the con_002 images in the model. I suspect that might have been what happened. Your design (SPM.xX.X)hould have a size of [7 1]; load SPM.mat and then use size(SPM.xX.X) to find the answer. A t-contrast of 1 will find the regions that are activated. A t-contrast of -1 will find regions that are deactivated. An F-contrast is simply the square of the 2 t-tests and does not have a direction. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Fri, Feb 25, 2011 at 2:33 PM, SALEM BOUSSIDA <[log in to unmask]> wrote: > Dear SPMers, > > I have a group of 7 subjects (rats) and I would like to perform a group > analysis (mixed effects). > I have done the 1st level analysis (fixed effects) for each subject and I > have two con_001 and con_002 images, wich are respectively the contrast > image for a positive effect (activation) and for a negative effect > (deactivation) of one condition (current stimulation) . > Then, I have done a one-sample-t-test : I feed all the con_001 of each > subject into the 2nd level analysis , then I entered a T contrast [1] and > SPM displayed the thresholded T-statistic image. But when I entered a F > contrast [1], I have got this error: > " > > ??? Inf computed by model function, fitting cannot continue. > Try using or tightening upper and lower bounds on coefficients. > > " > > Based on the procedure described in SPM8 manuel concerning the "face group > data", It is mentionned that I have to enter a F contrast. > I am confused by this and I have some questions: > (1) which contrast should I enter, F or T contrast? and what is the > differenece between them? > (2) Is the one-sample-t-test, the appropriate one to run mixed effects > analysis? > (3) Do I have to run two separate "one-sample-t-test" for > positive(activation) and negative-deactivation) effects? > > > Thank you for your help. > > Best regards, > > Salem > ========================================================================Date: Fri, 25 Feb 2011 17:25:10 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: fixed effects analysis across two sessions Comments: To: Israr Ul Haq <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> If contrast*beta>0, then the t-statistic is positive. If contrast*beta<0, then the t-statistic is negative. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Fri, Feb 25, 2011 at 12:25 AM, Israr Ul Haq <[log in to unmask]> wrote: > Dear Donald, > > I remember reading about beta and the GLM and how its calculated by using the matrix inverse, when I first started out reading about it. For some reason I formed the whole concept of individual correlations for each time point of a voxel and the subsequent distribution with its mean and sd as a way of representing what actually beta is representing, the weights of columns. Perhaps because the visualization came easier. Thankyou very much for taking time to point this out. > > What I understand from your explanation is that contrast*beta is essentially the output of adding and subtracting the individual values of the betas, based on the 1's and the -1's in the contrast, 1 meaning the beta will be added and -1, it will be subtracted. I guess for my question about the direction of change in terms of betas, how does the t statistic differ between a certain value of contrast*beta and an equal but a negative value of the same. Since the absolute difference between the effect and the mean is the same, does it take into account whether the contrast*beta is a positive or a negative value? > > Thanks > Israr > > ========================================================================Date: Fri, 25 Feb 2011 17:47:20 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: comparing blocks with different durations MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016368e2380d64648049d231c4f Content-Type: text/plain; charset=ISO-8859-1 Hello all, I like to know if is possible compare two conditions with different durations, that is, task with duration of 40 s and control with duration of 20 s. Thanks in advance John Ochoa Universidad de Antioquia --0016368e2380d64648049d231c4f Content-Type: text/html; charset=ISO-8859-1 Hello all, I like to know if is possible compare two conditions with different durations, that is, task with duration of 40 s and control with duration of 20 s.

Thanks in advance

John Ochoa

Universidad de Antioquia

--0016368e2380d64648049d231c4f-- ========================================================================Date: Sat, 26 Feb 2011 01:02:42 +0100 Reply-To: Salem Boussida <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Salem Boussida <[log in to unmask]> Subject: Re: Mixed effects group analysis questions Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; DelSp="Yes"; format="flowed" Content-Disposition: inline Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Donald, Thank you for your help. I am still confused about some details: "MCLAREN, Donald" <[log in to unmask]> a Ã©critÂ : > Salem, > > This isn't a mixed effects design as you only have one group and only > one condition. What you want to do is to use the con_001 images in a > one-sample t-test. DO NOT USE the con_002 images in the model. I > suspect that might have been what happened. I am still confused about "mixed effects analysis"and random effects analysis. In prevoius discussions from SPMlist users and the notes from Terry Oakes page (http://psyphz.psych.wisc.edu/~oakes/spm/spm_random_effects.html), it is mentioned that "Random-Effects" analysis is also referred to as a "Mixed Effects" analysis, since it considers both within- and between-subject variance. Also, in SPM manual, the one-sample-t-test is considered as a "random effects analysis". Any comments about this? Which test (in SPM) should I use to consider both fixed and random effects? > > Your design (SPM.xX.X)hould have a size of [7 1]; load SPM.mat and > then use size(SPM.xX.X) to find the answer. The "SPM.xX.X" command gives me : SPM.xX.X = 1 1 1 1 1 1 1 Is it right? > > A t-contrast of 1 will find the regions that are activated. A > t-contrast of -1 will find regions that are deactivated. An F-contrast > is simply the square of the 2 t-tests and does not have a direction. > Thank you again for your help. Best regards, Salem > Best Regards, Donald McLaren > ================= > D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General > Hospital and Harvard Medical School > Office: (773) 406-2464 > ===================== > This e-mail contains CONFIDENTIAL INFORMATION which may contain > PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED > and which is intended only for the use of the individual or entity > named above. If the reader of the e-mail is not the intended recipient > or the employee or agent responsible for delivering it to the intended > recipient, you are hereby notified that you are in possession of > confidential and privileged information. Any unauthorized use, > disclosure, copying or the taking of any action in reliance on the > contents of this information is strictly prohibited and may be > unlawful. If you have received this e-mail unintentionally, please > immediately notify the sender via telephone at (773) 406-2464 or > email. > > > > On Fri, Feb 25, 2011 at 2:33 PM, SALEM BOUSSIDA > <[log in to unmask]> wrote: >> Dear SPMers, >> >> I have a group of 7 subjects (rats) and I would like to perform a group >> analysis (mixed effects). >> I have done the 1st level analysis (fixed effects) for each subject and Â I >> have two con_001 and con_002 images, wich are respectively the contrast >> image for a positive effect (activation) and for a negative effect >> (deactivation) of one condition (current stimulation) . >> Then, I have done a one-sample-t-test : I feed all the con_001 of each >> subject into the 2nd level analysis , then I entered a T contrast [1] Â and >> SPM displayed the thresholded T-statistic image. But when I entered a F >> contrast [1], I have got this error: >> " >> >> ??? Inf computed by model function, fitting cannot continue. >> Try using or tightening upper and lower bounds on coefficients. >> >> " >> >> Based on the procedure described in SPM8 manuel concerning the "face group >> data", It is mentionned that I have to enter a F contrast. >> I am Â confused by this and I have some questions: >> (1) which contrast should I enter, F or T contrast? and what is the >> differenece between them? >> (2) Is the one-sample-t-test, the appropriate one to run mixed effects >> analysis? >> (3) Do I have to run two separate "one-sample-t-test" for >> positive(activation) and negative-deactivation) effects? >> >> >> Thank you for your help. >> >> Best regards, >> >> Salem >> > ========================================================================Date: Fri, 25 Feb 2011 22:04:49 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Mixed effects group analysis questions Comments: To: Salem Boussida <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> On Fri, Feb 25, 2011 at 7:02 PM, Salem Boussida <[log in to unmask]> wrote: > Dear Donald, > > Thank you for your help. I am still confused about some details: > > > "MCLAREN, Donald" <[log in to unmask]> a écrit : > >> Salem, >> >> This isn't a mixed effects design as you only have one group and only >> one condition. What you want to do is to use the con_001 images in a >> one-sample t-test. DO NOT USE the con_002 images in the model. I >> suspect that might have been what happened. > > I am still confused about "mixed effects analysis"and random effects > analysis. In prevoius discussions from SPMlist users and the notes from > Terry Oakes page > (http://psyphz.psych.wisc.edu/~oakes/spm/spm_random_effects.html), it is > mentioned that "Random-Effects" analysis is also referred to as a "Mixed > Effects" analysis, since it considers both within- and between-subject > variance. Also, in SPM manual, the one-sample-t-test is considered as a > "random effects analysis". > Any comments about this? > Which test (in SPM) should I use to consider both fixed and random effects? The whole discussion of fixed effects and random effects are best left to the field of statistician. If you want the details of why it could be considered either, see Ch.12 in Human Brain Function. It is better to think of the analysis as within-subject factor and within-subject errors OR between-subject factors and between errors. In group comparison or comparing the group against zero, you have between subject errors (1 sample t-test, 2-sample t-test, ANOVA). Paired t-tests and repeated measures ANOVAs have within-subject error terms. And then, you can have models with both within-subject and between-subject errors. > > > >> >> Your design (SPM.xX.X)hould have a size of [7 1]; load SPM.mat and >> then use size(SPM.xX.X) to find the answer. > > The "SPM.xX.X" command gives me : SPM.xX.X = > 1 > 1 > 1 > 1 > 1 > 1 > 1 > Is it right? Yes. > >> >> A t-contrast of 1 will find the regions that are activated. A >> t-contrast of -1 will find regions that are deactivated. An F-contrast >> is simply the square of the 2 t-tests and does not have a direction. >> > Thank you again for your help. > > Best regards, > > Salem > > >> Best Regards, Donald McLaren >> ================= >> D.G. McLaren, Ph.D. >> Postdoctoral Research Fellow, GRECC, Bedford VA >> Research Fellow, Department of Neurology, Massachusetts General >> Hospital and Harvard Medical School >> Office: (773) 406-2464 >> ===================== >> This e-mail contains CONFIDENTIAL INFORMATION which may contain >> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED >> and which is intended only for the use of the individual or entity >> named above. If the reader of the e-mail is not the intended recipient >> or the employee or agent responsible for delivering it to the intended >> recipient, you are hereby notified that you are in possession of >> confidential and privileged information. Any unauthorized use, >> disclosure, copying or the taking of any action in reliance on the >> contents of this information is strictly prohibited and may be >> unlawful. If you have received this e-mail unintentionally, please >> immediately notify the sender via telephone at (773) 406-2464 or >> email. >> >> >> >> On Fri, Feb 25, 2011 at 2:33 PM, SALEM BOUSSIDA >> <[log in to unmask]> wrote: >>> >>> Dear SPMers, >>> >>> I have a group of 7 subjects (rats) and I would like to perform a group >>> analysis (mixed effects). >>> I have done the 1st level analysis (fixed effects) for each subject and >>> I >>> have two con_001 and con_002 images, wich are respectively the contrast >>> image for a positive effect (activation) and for a negative effect >>> (deactivation) of one condition (current stimulation) . >>> Then, I have done a one-sample-t-test : I feed all the con_001 of each >>> subject into the 2nd level analysis , then I entered a T contrast [1] >>> and >>> SPM displayed the thresholded T-statistic image. But when I entered a F >>> contrast [1], I have got this error: >>> " >>> >>> ??? Inf computed by model function, fitting cannot continue. >>> Try using or tightening upper and lower bounds on coefficients. >>> >>> " >>> >>> Based on the procedure described in SPM8 manuel concerning the "face >>> group >>> data", It is mentionned that I have to enter a F contrast. >>> I am confused by this and I have some questions: >>> (1) which contrast should I enter, F or T contrast? and what is the >>> differenece between them? >>> (2) Is the one-sample-t-test, the appropriate one to run mixed effects >>> analysis? >>> (3) Do I have to run two separate "one-sample-t-test" for >>> positive(activation) and negative-deactivation) effects? >>> >>> >>> Thank you for your help. >>> >>> Best regards, >>> >>> Salem >>> >> > > > > > ========================================================================Date: Fri, 25 Feb 2011 23:37:15 -0500 Reply-To: Jonathan Peelle <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jonathan Peelle <[log in to unmask]> Subject: Re: Mixed effects group analysis questions In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset=us-ascii Mime-Version: 1.0 (Apple Message framework v1082) Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Salem, > The whole discussion of fixed effects and random effects are best left > to the field of statistician. If you want the details of why it could > be considered either, see Ch.12 in Human Brain Function. To follow on what Donald said, a couple of further reference that may be of interest regarding mixed effects and group studies: Friston KJ, Stephan KE, Lund TE, Morcom A, Kiebel S (2005) Mixed-effects and fMRI studies. NeuroImage 24:244-252. Mumford JA, Nichols T (2009) Simple group fMRI modeling and inference. NeuroImage 47:1469-1475. Best regards, Jonathan -- Dr. Jonathan Peelle Department of Neurology University of Pennsylvania 3 West Gates 3400 Spruce Street Philadelphia, PA 19104 USA http://jonathanpeelle.net/ ========================================================================Date: Sat, 26 Feb 2011 15:25:26 +1100 Reply-To: =?ISO-8859-1?Q?Kristian_Löwe?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Kristian_Löwe?= <[log in to unmask]> Subject: spm_hrf: delay of response and resulting peak MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Dear SPM'ers, playing around with the function spm_hrf, I noticed the following: The resulting hrf peaks one second earlier than I would expect given the specified delay of response (relative to onset), i.e. p(1). E.g., issueing the following command, shows the above mentioned. figure; hold on; TR = 1; peak = 6; plot(0:32, spm_hrf(TR, [peak 16 1 1 6 0 32])); plot([peak peak],[-1 1],'--r'); grid on; xlim([-1 35]); I was under the impression, that the delay of response effectively is the peak of the hrf. Probably I'm missing something obvious here, I would be thankful for any guidance on this. Cheers, Kristian ========================================================================Date: Sat, 26 Feb 2011 19:35:04 +0100 Reply-To: Stefan =?iso-8859-1?b?S2z2cHBlbA==? <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Stefan =?iso-8859-1?b?S2z2cHBlbA==? <[log in to unmask]> Subject: PhD position in Freiburg: Clinical applications of pattern recognition methods In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; DelSp="Yes"; format="flowed" Content-Disposition: inline Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> A 3-years doctoral position (50% TV-L 13) is available from April 2011 at the Freiburg Brain Imaging Center (FBI, http://www.uniklinik-freiburg.de/fbi/live/index_en.html) in Germany. The candidate can obtain a PhD in the field of Psychology, Medicine or Biology The candidate will apply pattern recognition methods to multivariate data (e.g. imaging, neuropsychological test results) to improve our understanding of neurodegenerative diseases (http://www.uniklinik-freiburg.de/fbi/live/forschung/AutomatedDiagnosing_en.html). We offer The FBI (http://www.uniklinik-freiburg.de/fbi/live/index_en.html) combins efforts in neuroimaging research of the departments of Psychiatry/Psychotherapie, Neurology, Neuroradiology and Neurosurgery. It is closely collaborating with the Center for Data Analyses and Modelling (http://www.fdm.uni-freiburg.de/projects/dynamic-processes-in-life-sciences) and the Department for Pattern recognition methods (http://lmb.informatik.uni-freiburg.de/index.en.html). Support for MRI sequence development is provided by the Department of MR-Physics, that is also part of the FBI. Starting April 2011, we will begin a continuous education program in the field of neuroimaging. We require Applicants must hold a master degree or equivalent in medicine, psychology or a related field. Previous programming experience (e.g. Matlab) is advantageous but not required. Sound knowledge of statistics as well as good IT skills are essential. Disabled applicants are preferred if qualification is equal. Please send applications to Dr. Stefan Klöppel. For informal enquiries call +49 761 270 66400 or email [log in to unmask] . http://www.uniklinik-freiburg.de/fbi/live/members/kloeppel_en.html ========================================================================Date: Sun, 27 Feb 2011 20:16:32 +1100 Reply-To: Bill Budd <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Bill Budd <[log in to unmask]> Organization: University of Newcastle Subject: Re: script to flip MR images left to right (or vice versa) Comments: To: Michael Harms <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-transfer-encoding: 7BIT Content-type: text/plain; charset=us-ascii Message-ID: <[log in to unmask]> Another note of caution is that brains are not entirely symmetrical, structurally or functionally. So flipping MRI images may not result in a spatially homogenous data set for your analysis even though they are consistent in terms of the lesioned hemisphere. One option to address the lack of anatomical asymmetry is to flip epi images prior to normalisation and then normalise using symmetrical priors. (See Didolet et al, 2010 for an example creating a symmetrical template). Others on this list might have more detailed information but this approach may create additional problems with normalisation routines and anatomical labelling/ROI analyses and doesn't address the issue of functional asymmetries. Cheers -Bill > -----Original Message----- > From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] > On Behalf Of Michael Harms > Sent: Saturday, 26 February 2011 1:20 AM > To: [log in to unmask] > Subject: Re: [SPM] script to flip MR images left to right (or vice > versa) > > A note of caution about using fslswapdim in this manner however: > If you do that, the data order and the header information stored in the > sform/qform will no longer be in sync. So, what the sform/qform defines > to be "left" will no longer be the subject's true left (assuming the > labels in the original data were correct)! > > Read the following FSL page if you want all the details: > http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html > > Best, > -MH > > On Fri, 2011-02-25 at 11:54 +0000, Rosa Sanchez Panchuelo wrote: > > You can do it easily with fsl: > > fslswapdim input_image.img -x outpu_image.img > > -x will flip the image in the RL direction. > > > > On 25/02/2011 11:39, Richard Morris wrote: > > > I'm not sure, but I think you can use the image calculator in SPM, > > > with the following function: > > > > > > > > > f = 'Inv(i1)' > > > > > > > > > > > > On 25/02/2011, at 10:16 AM, Michael T Rubens wrote: > > > > > > > how about this: > > > > %%% > > > > > > > > > > > > v = spm_vol('/image_path/brain.img'); > > > > x = spm_read_vols(v); > > > > > > > > > > > > x = x(size(x,1):-1:1,:,:); > > > > > > > > > > > > [path file] = fileparts(v.fname); > > > > v.fname = fullfile(path,['flipped_' file'); > > > > > > > > > > > > spm_write_vol(v,x) > > > > > > > > > > > > %%% > > > > > > > > > > > > > > > > > > > > cheers, > > > > michael > > > > > > > > On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin > > > > <[log in to unmask]> wrote: > > > > Hi, > > > > > > > > I stuides patients with stroke doing some behavior > > > > paradigm in fMRI. > > > > Patients always used their affected limb for the task so > > > > some patients used left hand some used right hand. > > > > Now I would like to flip the left and right of the images > > > > so patients' lesional hemisphere are on the same side. > > > > > > > > Is there any script in spm 5 or 8 can help to accomplish > > > > this goal? > > > > > > > > I found a m file called spm_flip.m but it seems support > > > > only spm2. > > > > > > > > Your assistance are well appreciated. > > > > > > > > Thank you so much. > > > > > > > > Janice > > > > > > > > > > > > > > > > -- > > > > Research Associate > > > > Gazzaley Lab > > > > Department of Neurology > > > > University of California, San Francisco > > > > > > > > > > > > > > > > -- > > Rosa Maria Sanchez Panchuelo > > Post-doctoral Research fellow > > Sir Peter Mansfield Magnetic Resonance Centre > > University of Nottingham > > University Park > > Nottingham, NG7 2RD > > United Kingdom > > +44 115 84 66003 > > > > > > > > > > > > This message and any attachment are intended solely for the addressee > > and may contain confidential information. If you have received this > > message in error, please send it back to me, and immediately delete > > it. Please do not use, copy or disclose the information contained in > > this message or in any attachment. Any views or opinions expressed by > > the author of this email do not necessarily reflect the views of the > > University of Nottingham. > > > > This message has been checked for viruses but the contents of an > > attachment may still contain software viruses which could damage your > > computer system: you are advised to perform your own checks. Email > > communications with the University of Nottingham may be monitored as > > permitted by UK legislation. > > ========================================================================Date: Sun, 27 Feb 2011 20:12:49 +0800 Reply-To: =?UTF-8?B?6aOe6bif?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?UTF-8?B?6aOe6bif?= <[log in to unmask]> Subject: Re: Negative values in DCM.A Comments: To: Maria Dauvermann <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Maria, Once I have asked a similar question as yours. In the situation of DCM for EEG/MEG, the number in DCM.A means that the coupling strength is exp(number) times the prior coupling. As for the fmri, I'm not very clear either. I Hope this helps. Haoran. At 2011-02-24 15:58:16ï¼Œ"Maria Dauvermann" <[log in to unmask]> wrote: >Hello, > >I had a look at the values in DCM.A after I have estimated the DCMs. > >What does a negative value in the intrinsic connection mean? How do I interprete this result? > >Thanks for your help. > >BW, Maria ========================================================================Date: Sun, 27 Feb 2011 20:27:05 +0800 Reply-To: =?GBK?B?t8nE8Q==?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?GBK?B?t8nE8Q==?= <[log in to unmask]> Subject: [SPM DCM] how to specify the modulation? MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_52522_731246145.1298809625622" Message-ID: <[log in to unmask]> ------=_Part_52522_731246145.1298809625622 Content-Type: text/plain; charset=GBK Content-Transfer-Encoding: base64 SGVsbG8gYWxsLAqhoaGhV2hlbiB5b3UgYW5hbHlzaXMgeW91ciBkYXRhIHdpdGggdGhlIG1ldGhv ZCBvZiBEQ00sIGhhdmUgeW91IGV2ZXIgbWV0IHRoZSBwcm9ibGVtIGFib3V0IGhvdyB0byBzcGVj aWZ5IHRoZSBtb2R1bGF0aW9uPyBOb3cgSSBjaG9vc2UgNyBiYXNpYyBtb2RlbHMsIGFuZCB0aGVu IGFzc3VtZSBhbGwgdGhlIGNvbm5lY3Rpb25zIGluIGVhY2ggYmFzaWMgbW9kZWwgYXJlIG1vZHVs YXRlZCBieSB0aGUgZXhwZXJpbWVudGFsIG1hbmlwdWxhdGlvbi4gV2hhdCBJIHdhbnQgdG8ga25v dyBpcyB0aGF0IHdoZXRoZXIgb3Igbm90IHRoaXMgaXMgcmVhc29uYWJsZS6hoaGhoaGhoaGhoaGh oaGhoaGhoaGhoaGhoaGhSW4gYWRkaXRpb24sIGlzIGl0IHBvc3NpYmxlIHRoYXQgdGhlIGNvbm5l Y3Rpb25zIGJldHdlZW4gdHdvIHNvdXJjZXMgY291bGQgaW5jbHVkZSBmb3J3YXJkIGNvbm5lY3Rp dml0eSwgYmFja3dhcmQgY29ubmVjdGl2aXR5IGFuZCBsYXRlcmFsIGNvbm5lY3Rpdml0eSBhdCB0 aGUgc2FtZSB0aW1lPwqhoaGhQW55IGhlbHAgd2lsbCBiZSBncmF0ZWZ1bCEKCgotLQoKSGFvcmFu IExJIChNUykKQnJhaW4gSW1hZ2luZyBMYWIsClJlc2VhcmNoIENlbnRlciBmb3IgTGVhcm5pbmcg U2NpZW5jZSwKU291dGhlYXN0IFVuaXZlcnNpdHkKMiBTaSBQYWkgTG91ICwgTmFuamluZywgMjEw MDk2LCBQLlIuQ2hpbmE------=_Part_52522_731246145.1298809625622 Content-Type: text/html; charset=GBK Content-Transfer-Encoding: base64 PERJVj48L0RJVj4KPERJVj5IZWxsbyBhbGwsPC9ESVY+CjxESVY+oaGhoVdoZW4geW91IGFuYWx5 c2lzIHlvdXIgZGF0YSB3aXRoIHRoZSBtZXRob2Qgb2YgRENNLCBoYXZlIHlvdSBldmVyIG1ldCB0 aGUgcHJvYmxlbSZuYnNwO2Fib3V0IGhvdyB0byBzcGVjaWZ5IHRoZSBtb2R1bGF0aW9uPyZuYnNw O05vdyBJIGNob29zZSA3IGJhc2ljIG1vZGVscywgYW5kIHRoZW4gYXNzdW1lIGFsbCB0aGUgY29u bmVjdGlvbnMgaW4gZWFjaCBiYXNpYyBtb2RlbCZuYnNwO2FyZSBtb2R1bGF0ZWQgYnkgdGhlIGV4 cGVyaW1lbnRhbCBtYW5pcHVsYXRpb24uJm5ic3A7V2hhdCZuYnNwO0kgd2FudCB0byBrbm93IGlz IHRoYXQgd2hldGhlciBvciBub3QgdGhpcyBpcyZuYnNwO3JlYXNvbmFibGUuoaGhoaGhoaGhoaGh oaGhoaGhoaGhoaGhoaGhoUluIGFkZGl0aW9uLCBpcyBpdCBwb3NzaWJsZSB0aGF0IHRoZSBjb25u ZWN0aW9ucyBiZXR3ZWVuIHR3byBzb3VyY2VzJm5ic3A7Y291bGQmbmJzcDtpbmNsdWRlJm5ic3A7 Zm9yd2FyZCBjb25uZWN0aXZpdHksIGJhY2t3YXJkIGNvbm5lY3Rpdml0eSBhbmQgbGF0ZXJhbCBj b25uZWN0aXZpdHkgYXQgdGhlIHNhbWUgdGltZT8gPEJSPqGhoaFBbnkgaGVscCB3aWxsIGJlIGdy YXRlZnVsITxCUj48QlI+PC9ESVY+CjxESVY+LS08QlI+CjxESVY+PEZPTlQgY29sb3I9IiMwMDgw MDAiIGZhY2U9IkFyaWFsIj5IYW9yYW4gTEkgKE1TKTxCUj5CcmFpbiBJbWFnaW5nIExhYiw8QlI+ UmVzZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTPEZPTlQgY29sb3I9IiMwMDgwMDAiPmNpPC9G T05UPmVuY2UsPEJSPlNvdXRoZWFzdCBVbml2ZXJzaXR5PEJSPjIgU2kgUGFpIExvdSAsIE5hbmpp bmcsIDIxMDA5NiwgUC5SLkNoaW5hPC9GT05UPjwvRElWPjwvRElWPjxicj48YnI+PHNwYW4gdGl0 bGU9Im5ldGVhc2Vmb290ZXIiPjxzcGFuIGlkPSJuZXRlYXNlX21haWxfZm9vdGVyIj48L3NwYW4+ PC9zcGFuPg=------=_Part_52522_731246145.1298809625622-- ========================================================================Date: Sun, 27 Feb 2011 15:13:43 +0000 Reply-To: Nynke L <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nynke L <[log in to unmask]> Subject: Second level t-test: test against other value than zero Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello all, I have a particular group level analysis for which I want to use spm. However, till now I don't manage to so I hope you have suggestions for me. I use fMRI for the prediction of a certain binary outcome. Therefore, I use a different classification software package that computes prediction-accuracies for all voxels. For each subject, I have a .nii file with as values the accuracies (range between 0 and 1). So, this is a brain map just like you usually feed to the second level. What I want to test in the second level analysis is which brain areas have accuracies significantly higher than 0.5. So I want to test against the null-hypothesis that accuracies are 0.5 or lower. Now the problem is that the 1 sample t-test in spm on default tests against the null-hypothesis that the values are zero or higher.... So my first question is whether it is possible to set a custum value for the t-test to test against a manually set value? I already tried a different approach, namely subtracting 0.5 from the values in my input images (to put them in the default spm t-test). However, when I trie to do this with the image calculator of spm (with as expression i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 subtracted from each value but a range between 0.4-0.6. It seems that spm-image-calc is also doing something else with the image... So my second question is: how can I make image-calc subtract exactly 0.5 from all values in the image? Thanks in advance! Best regards, Nynke van der Laan ========================================================================Date: Sun, 27 Feb 2011 07:22:05 -0800 Reply-To: Faezeh Vedaei <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Faezeh Vedaei <[log in to unmask]> Subject: display result on template MRI MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-614849790-1298820125=:2452" Message-ID: <[log in to unmask]> --0-614849790-1298820125=:2452 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion? --0-614849790-1298820125=:2452 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?

--0-614849790-1298820125=:2452-- ========================================================================Date: Sun, 27 Feb 2011 07:25:39 -0800 Reply-To: Faezeh Vedaei <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Faezeh Vedaei <[log in to unmask]> Subject: display result on template MRI MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-2098163962-1298820339=:20497" Message-ID: <[log in to unmask]> --0-2098163962-1298820339=:20497 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion? --0-2098163962-1298820339=:20497 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?

--0-2098163962-1298820339=:20497-- ========================================================================Date: Sun, 27 Feb 2011 20:41:27 +0100 Reply-To: Leonhard Schilbach <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Leonhard Schilbach <[log in to unmask]> Subject: Call for Papers; Frontiers Special Topic: Toward a neuroscience of social interaction Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Mime-Version: 1.0 (Apple Message framework v1082) Message-ID: <[log in to unmask]> Dear colleagues, this is to draw your attention toward and invite submissions for an upcoming Frontiers in Neuroscience Special Topic entitled: "Toward a neuroscience of social interaction" Please find more details below and on the Frontiers website: http://www.frontiersin.org/Human%20Neuroscience/specialtopics/towards_a_neuroscience_of_soci/211 -Deadline for submission of abstracts: 30 Sept 2011 -Deadline for submission of full manuscripts: 31 Dec 2011 Looking forward to your contributions! Best regards, Leo Schilbach -- Towards a neuroscience of social interaction Hosted By: Ulrich Pfeiffer, University Hospital Cologne, Germany Bert Timmermans, University Hospital Cologne, Germany Kai Vogeley, University Hospital Cologne, Germany Chris Frith, Wellcome Trust Centre for Neuroimaging at University College London, United Kingdom Leonhard Schilbach, Max-Planck-Institute for Neurological Research, Germany The burgeoning field of social neuroscience has begun to illuminate the complex biological bases of human social cognitive abilities. However, in spite of being based on the premise of investigating the neural bases of interacting minds, the majority of studies have focused on studying brains in isolation using paradigms that investigate offline social cognition, i.e. social cognition from a detached observer's point of view, asking study participants to read out the mental states of others without being engaged in interaction with them. Consequently, the neural correlates of real-time social interaction have remained elusive and may —paradoxically— represent the 'dark matter' of social neuroscience. More recently, a growing number of researchers have begun to study online social cognition, i.e. social cognition from a participant's point of view, based on the assumption that there is something fundamentally different when we are actively engaged with others in real-time social interaction as compared to when we merely observe them. Whereas, for offline social cognition, interaction and feedback are merely a way of gathering data about the other person that feeds into processing algorithms 'inside’ the agent, it has been proposed that in online social cognition the knowledge of the other —at least in part— resides in the interaction dynamics ‘between’ the agents. Furthermore being a participant in an ongoing interaction may entail a commitment toward being responsive created by important differences in the motivational foundations of online and offline social cognition. In order to promote the development of the neuroscientific investigation of online social cognition, this Frontiers Special Topic aims at bringing together contributions from researchers in social neuroscience and related fields, whose work involves the study of at least two individuals and sometimes two brains, rather than single individuals and brains responding to a social context. Specifically, this special issue will adopt an interdisciplinary perspective on what it is that separates online from offline social cognition and the putative differences in the recruitment of underlying processes and mechanisms. Here, an important focal point will be to address the various roles of social interaction in contributing to and —at times— constituting our awareness of other minds. For this Special Topic, we, therefore, solicit reviews, original research articles, opinion and method papers, which address the investigation of social interaction and go beyond traditional concepts and ways of experimentation in doing so. While focusing on work in the neurosciences, this Special Topic also welcomes contributions in the form of behavioral studies, psychophysiological investigations, methodological innovations, computational approaches, developmental and patient studies. By focusing on cutting-edge research in social neuroscience and related fields, this Frontiers Special Issue will create new insights concerning the neurobiology of social interaction and holds the promise of helping social neuroscience to really go social. -- Leonhard Schilbach, MD Department of Psychiatry and Psychotherapy University of Cologne, Germany [log in to unmask] & Max-Planck-Institute for Neurological Research Cologne, Germany [log in to unmask]: Sun, 27 Feb 2011 17:17:44 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: Re: display result on template MRI Comments: To: Faezeh Vedaei <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016368e2380ab9416049d4aeebc Content-Type: text/plain; charset=ISO-8859-1 Hello Faezeh, I use xjview ( http://www.alivelearn.net/xjview8/ ) Regards On Sun, Feb 27, 2011 at 10:25 AM, Faezeh Vedaei <[log in to unmask]> wrote: > > > I am working with some data by SPM8,as a question how can I display my > brain glass result on multiple slices of a template MRI to view regions of > hyperperfusion and hypoperfusion? > > > --0016368e2380ab9416049d4aeebc Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello Faezeh, I use xjview ( http://www.alivelearn.net/xjview8/ )

Regards

On Sun, Feb 27, 2011 at 10:25 AM, Faezeh Vedaei <[log in to unmask]> wrote:

I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?

--0016368e2380ab9416049d4aeebc-- ========================================================================Date: Sun, 27 Feb 2011 17:22:30 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: display result on template MRI Comments: To: Faezeh Vedaei <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Click Overlay --> sections OR Overlay --> slices MRIcron is also useful Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Sun, Feb 27, 2011 at 10:22 AM, Faezeh Vedaei <[log in to unmask]> wrote: > I am working with some data by SPM8,as a question how can I display my brain > glass result on multiple slices of a template MRI to view regions of > hyperperfusion and hypoperfusion? > ========================================================================Date: Sun, 27 Feb 2011 14:25:32 -0800 Reply-To: Vladimir Bogdanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Bogdanov <[log in to unmask]> Subject: Re: display result on template MRI Comments: To: Faezeh Vedaei <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1112837336-1298845532=:86375" Message-ID: <[log in to unmask]> --0-1112837336-1298845532=:86375 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical. I hope this is helpful. Vladimir --- On Sun, 2/27/11, Faezeh Vedaei <[log in to unmask]> wrote: From: Faezeh Vedaei <[log in to unmask]> Subject: [SPM] display result on template MRI To: [log in to unmask] Date: Sunday, February 27, 2011, 4:25 PM I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion? --0-1112837336-1298845532=:86375 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable

When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical.

I hope this is helpful.

Vladimir

--- On Sun, 2/27/11, Faezeh Vedaei <[log in to unmask]> wrote:

From: Faezeh Vedaei <[log in to unmask]>
Subject: [SPM] display result on template MRI
To: [log in to unmask]
Date: Sunday, February 27, 2011, 4:25 PM

I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?

--0-1112837336-1298845532=:86375-- ========================================================================Date: Sun, 27 Feb 2011 19:55:57 -0500 Reply-To: Yune Lee <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Yune Lee <[log in to unmask]> Subject: normalization failure (anterior posterior mismatch) MIME-Version: 1.0 Content-Type: multipart/mixed; boundaryMessage-ID: <[log in to unmask]> --001636283ab280d810049d4d24ad Content-Type: multipart/alternative; boundary --001636283ab280d804049d4d24ab Content-Type: text/plain; charset=ISO-8859-1 Dear SPM experts, I've encountered a normalization failure, such that there is a mismatch of anterior-posterior between a template (EPI.nii) and a source image (meanbold.nii) This is clearly shown in the attached PDF file. Any help wold be greatly appreciated. Thanks in advance, YSL --001636283ab280d804049d4d24ab Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable
Dear SPM experts,
I've encountered a normalization failure, such that there is a mismatch of anterior-posterior between a template (EPI.nii) and a source image (meanbold.nii)
This is clearly shown in the attached PDF file.
Any help wold be greatly appreciated.

Thanks in advance,
YSL

--001636283ab280d804049d4d24ab-- --001636283ab280d810049d4d24ad Content-Type: application/pdf; name="normalization_failure.pdf" Content-Disposition: attachment; filename="normalization_failure.pdf" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkoofn320 JVBERi0xLjQKJcfsj6IKNSAwIG9iago8PC9MZW5ndGggNiAwIFIvRmlsdGVyIC9GbGF0ZURlY29k ZT4+CnN0cmVhbQp4nM1XXW/TMBR9z6+4b4whgu3YSYPEAwyYJo0hliI+FAmFrR1FcbqtQyAh/jt2 nNixb7tmQhXrVCW+Oz73w+fW9hWQmALRf93zTEZPTjO4WEVXUZITyHJGYsJAtiOeC6gjITiLBQ1G PbKOvkUfoIkIHKrvheKhrQPoHmcSXkyVkwnQFKbzyPilkJM4E5CxRD0YTGW0V1xWN4uqhpPltazq xUqNls3D6XczmenJfKLQhMP0ONo7ktXFDPTnKRw8Lcv5m9Ojsnz+9oCV5Xl1U5Ulmaxml5Scr8ry 9fuTg+nR25Pnx2UpZ1XzdVmf0y9EfeJmsXj4IBIkZqLlPV40s+oaflfzuXr7A2dLeblsZs2NQj05 zbtIaBob+EcKz4DGlE32P8IjVRKa7X+Cx7o2bP8zPEpjnqRq6t4nDdR2Lgwy4VwjqQLkCvmY5jFL qIZ+1lBN1XHmE6GQ6iXNEo3MY84mCpgkrhyqus2yqU3wi5vZdVu+lULZUDP4Bbn6ZvC1Wi1WMP/R nPWgV9PonVqbi4gnAhglaokoUQsNNGEErmfRfD8SsS4YhZ8K+F19Dx1Ytq8J15qgShNUJFm8xtCC azevjgrl98qbLYf/9uchWqO9qz7YVlo6YMvQClxJ8eVS+dngieR6rmbn4Sj05JfAKPm0q0OWdqR9 zJ1B55hPWGxLZQaO2pTg39ovDOwwSBRVrhgunl/lwhtvm+sv/K20W2b9j2h7ySc8a+vaq948jO6p rabBGBYuMuFpGxl6fO3NHkq+nyIDBCJALtYJ37wOmYz6qa/+0Glqo+ZoGDq8Rf+csMxUpw+2Mzj9 uwKasWPfXQu4bFENi2BVUdWL0DSCxJfGCBfbp//vFHSPUOGtoPqRzda1SQCTvcHRsZy2zYctQ7V7 LL08Ai6JcZgKu7OdY1IYdE5AZ5qHuebZ7D91qbSSx5aRjdS7sL1k4x80E2PcL3Jn2HU7ofxxcQus ALwmBbKNpEJiGunuDkT3KznVeAkh7cJvOJPppvMg0g7dNsdpH1Fo6fZQj8HKJ+CRISwgwV42nc48 HtNmiWuzW/z2ZzPlar1h5HnNEtozm81lcGhLhBA2P2mHuz644fxxZYtw0YO1KHzDKAqsm+1e7kBx H9IZNtS2E1+Ic4yuRfswsMX9IIQ8YXu5XxyMxGzY4+aTYMhnOo3jTlsXQuoS2mAZuaVZJ3ZPszkM 9jTXXLbanWXXuxouAq5xsUYNeG0KZBvLhZU11uPduO5bju3ti6bM60e9Q+D7V4CS1uL4BJ9ssAz7 Jw0s3W0sYJNrkJgNexycLol3KwvYTC+Kwc3slgBSm07elT60jL2n9U5sL9oMBr2oTGGtO8vOL2yo CLjCxRot4JUpkG0sF9bVWI9347pvOWoZ/gUD9GDJZW5kc3RyZWFtCmVuZG9iago2IDAgb2JqCjEw NDUKZW5kb2JqCjQgMCBvYmoKPDwvVHlwZS9QYWdlL01lZGlhQm94IFswIDAgNTk1IDg0Ml0KL1Jv dGF0ZSAwL1BhcmVudCAzIDAgUgovUmVzb3VyY2VzPDwvUHJvY1NldFsvUERGIC9JbWFnZUMgL1Rl eHRdCi9FeHRHU3RhdGUgMTYgMCBSCi9YT2JqZWN0IDE3IDAgUgovRm9udCAxOCAwIFIKPj4KL0Nv bnRlbnRzIDUgMCBSCj4+CmVuZG9iagozIDAgb2JqCjw8IC9UeXBlIC9QYWdlcyAvS2lkcyBbCjQg MCBSCl0gL0NvdW50IDEKPj4KZW5kb2JqCjEgMCBvYmoKPDwvVHlwZSAvQ2F0YWxvZyAvUGFnZXMg MyAwIFIKL01ldGFkYXRhIDIwIDAgUgo+PgplbmRvYmoKNyAwIG9iago8PC9UeXBlL0V4dEdTdGF0 ZQovT1BNIDE+PmVuZG9iagoxNiAwIG9iago8PC9SNwo3IDAgUj4+CmVuZG9iagoxNyAwIG9iago8 PC9SMTUKMTUgMCBSL1IxNAoxNCAwIFIvUjEzCjEzIDAgUi9SMTIKMTIgMCBSL1IxMQoxMSAwIFIv UjEwCjEwIDAgUj4+CmVuZG9iagoxNSAwIG9iago8PC9TdWJ0eXBlL0ltYWdlCi9Db2xvclNwYWNl L0RldmljZVJHQgovV2lkdGggMjE3Ci9IZWlnaHQgMTgxCi9CaXRzUGVyQ29tcG9uZW50IDgKL0Zp bHRlci9EQ1REZWNvZGUvTGVuZ3RoIDI5ODc+PnN0cmVhbQr/2P/uAA5BZG9iZQBkAAAAAAH/2wBD AA4KCw0LCQ4NDA0QDw4RFiQXFhQUFiwgIRokNC43NjMuMjI6QVNGOj1OPjIySGJJTlZYXV5dOEVm bWVabFNbXVn/2wBDAQ8QEBYTFioXFypZOzI7WVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZ WVlZWVlZWVlZWVlZWVlZWVlZWVn/wAARCAC1ANkDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAA AAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKB kaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZn aGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT 1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcI CQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAV YnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6 goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDzaiiigAooooAKKKKACiiigAooooAKKKKACilx RigBKKXFJigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKULmgBKBT xGSatQWjSHGKAKfNHNb0WjswHFOfRnU/doA5/kdqQmtmXS3UfdqFNNdjwKAMyitVtJkVc4qnJaOm cigCtQKcUI7UmCKAFxxTSKcDRQA2iiigAooooAKKKKACiiigAooooAKKKAM0AFOCE1PBAXPNWvKC 8KMmgCmsWBk1NDbtIflBq/bac8zZcHFdDYWEaAZWgDBh0uRscYFbem6aigZ5NXpIuwGBTrSNlegC eOJI2HFWCkTdhQYs80eXg0AQSWkbg4AqGGwVX6VoBMCpEA9KAM+6s1CnCiubv7NgT8tdsF3HkVFd WUcgPA5oA84MADkEVHJaqTxXR6lppWQlBWNLC8bHIoAyZYSpNQnitN13E5FVJoSDxQBXopSCDg0l ABRRRQAUUUUAFFFL0oASiinIuTQAgGanhiyckVJDAWYADmtux0snDOKAKlrZyy4CggVu2GjrkNIK 0bS2SJQABV0YC0AVRbInCrgVLtCjgU8cml256UARgZ5NTxYHanLFxzTioXpQA8nIpMZpY0LVJ5eB QA1Y91TiEKuTUcZ2mmzyt0FAEikFsCkmDYIFRw5zmrBcGgDIliZmO4Vl3lkrk4FdDc8dBVIqCMmg DkbiyZH6VUeDHUV1s8Kv2rKu7THSgDmLqAjkCqlbd1HgEEVkSrtY0AR0UUUAFFFFABRRRQADk1ct 4txFV4k3NgCuj0nTy+GIoAm0uyx8zCtpEC8AYpY4ggAAqZUzQA5OBSuxAqRYgDyaRtpOKAGo1Tq6 iq8qsF+UVW/fEjANAGqZQB1qs90obrVG5mdAAx5rPkkc8jNAHSQ3YPSrZYsAc1ysFxIgBINacN+d oB4oA1DSHnrUSzhlHPNDOR0oAsLhRShh1quJSRzS9qAJJPnFU5VAFOkmKjAqm7O55NACMCx4pr25 dcVMmF61PFIueRQBg3mmMYywFctfW5Rj616FqF0ixlV6VxmoYZicUAYJGKKfLw1MoAKKKKACnIMm m1oaXam6YqpG7PTPOPWgB9kirINwrprW7jjUKo/KqS6OykDvWna6WsYy3WgB5uSeQKYdQ2OF71op aJjpVf8As5DPvPagCxbmSbBPQ1r2WniTlqqW6quABwK0I52UbVoAsmwjK4ABNVdQhi0+23tjOO1a ED7Y8t1rD1eO4vSR0QUAczLcm9mJAwtWo4lC8jNPg0142JA4p86mFCSKAGbAei0CPJ6U+1u4/Lyw x+FPN3D680AR5dTxmphdbSA1CSRydKjuoAcEUAX45EcDFTN0wDWIJjDjNX4LoOo5oAe0XOahcnOA KtEhh1pBGpoApnIpMkjvVwwrnmopTGg4oAy7lGbrWNfQfKeK3ZZASaozqHU0AcjcJtNQVpX8eGPF ZtABRRRQAVs+F5I49TYyMq7oyq7jjJyOB71jVY09lTUbVnYKolQkk4AGRQB6Mj85NPVtzVCrIyB0 YMrDIIOQRQr4oAv7gEqENlqh8wnipoxxmgCdWI6VNDIfMAqFKcAS2elAG7G0axgse1Zeq6gkakRD 8ajebC/M1Z13LG/BNACxai8ibVXrUsqb4/nFR2flRdACatOwc0AY1xAMYWse+V4RkHpXWNECDWBq wXBXHNAFfTbwsAO9bKylk5rG0WzLTYI4zXVTafsjzigDCvMsOKitpWQc1duEA4qo6YBIFAGpBMGX rUwkxWJBOVbFacDbxzQBK8pPSq7xs3Wp8qp5pksuRxQBSeGq8qbFNWnLHrVaYZU5oA53UV5JrIbr W5qAzmsRxg0ANooooAKKKKAPQdMB/sy0/wCuKfyFW1jLHArJ8JebcaU5d2cJKVXcc4AVeB7V1FvH HH8zHJoAii05yoPrU32YR9TTp9Rwu1OBWVcX7HOTQBfeVIxxyapvdMScVmSX4HU0wX6kdaAL5lZu pqpMrl81LDKJB1qWVowOozQA62BwK04oWIzVXTymQWIrW86NRgEUAQNDheaxNQtg0mR61qTXJkkw vSkeFXQE9aAK2lQBZFIFdPcqrWY9hWRYIA2Kv3chSMrQBzl2AHIqqQNpBqe6bMpqLy80AUVXE3Ar XhTagNQJbgHJq4Pu4oAry5zTc4HNTvgVXc0AMdhVaVhg0+Q8VQnlxmgDN1Bhk1iOcsa0b+TcTisw 8mgAooooAKKKKAOh8IXhgv5InnKRPGdqM+FLkr0HrXYs5PfivNtOZU1K1Z2CqsyEsTgAZFegXEqp biRWDKwyCDkEetAEd3cpCDk81g3eojJwaq6hfl5GGaypJSxoAuS3jMetMjuWJ5NUdxo34oA34tQK JgGg6i27rWGkhp5kxQB0NvqbK33qu/2qxH3q5RJsVMs59aAOvs7wOck1ofaSwwK42xutr8nituK9 U9KANqC78p8k0681LzBgGsRpsnrQGyOtAFguXarkEW5ap28Zdq1Y02LQAxkAFRNkVOVJNNZRQBTY EmmFaueWtQyLigClKKy70qinJ5rSuX2KTXN6lcbicGgDOuZMuar0rHJzSUAFFFFABRRRQAVuvq5k 06GJMjykWPn2ArCpwbC4oAfI5diTTKQmkoAdSGjNJQAA4pd1JRQA4Nil3n1plFAFiKQ7hWzZv0ya wo+taEE2wjmgDokTfirUVqzMKoWMxfHpXQW33QaAJLe38sCrLDimqTjOKa8uOKAFPAqrKxzxUzyr jFV5emRQA0Mc0yVsAk0hkC9TWbqV+qREKRn2oAz9VvdpIBrnJpC7Ek1JdTmVzk1XoAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigCROKlDDIqvkinZoA6HTrgLjmuos7lCoya8+gnKVq2uoM AOaAO7adWUbeKry4rFtr5nHWrouBtG40AOkfYcmq1zqCouM1Xv76NRw2TXM314zsdpoA1L3VODtN Yc900pOSagZ2bqabQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAqjNSbRiiigBjCm0UUAFKKKKAFo NFFACBjmponINFFAGzY3LIucA0281CXntRRQBkS3Mjk5NQEk9aKKACiiigAooooAKKKKACiiigD/ 2QplbmRzdHJlYW0KZW5kb2JqCjE0IDAgb2JqCjw8L1N1YnR5cGUvSW1hZ2UKL0NvbG9yU3BhY2Uv RGV2aWNlUkdCCi9XaWR0aCAxODEKL0hlaWdodCAxODEKL0JpdHNQZXJDb21wb25lbnQgOAovRmls dGVyL0RDVERlY29kZS9MZW5ndGggMjg2MD4+c3RyZWFtCv/Y/+4ADkFkb2JlAGQAAAAAAf/bAEMA DgoLDQsJDg0MDRAPDhEWJBcWFBQWLCAhGiQ0Ljc2My4yMjpBU0Y6PU4+MjJIYklOVlhdXl04RWZt ZVpsU1tdWf/bAEMBDxAQFhMWKhcXKlk7MjtZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZ WVlZWVlZWVlZWVlZWVlZWVlZWf/AABEIALUAtQMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAA AAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGR oQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdo aWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU 1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJ Cgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVi ctEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqC g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl 5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APNqKKKACiiigAooooAKKKKACiilxQAlFOwKTFAC UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSgUAIBmlPFHfAqaK2kkPCmgCEU7PYVo w6RNIwGCPwrUh8NSeXubNAHM4PagA11KeHzzkcVmX2nPbSbdvWgDI/Cj8K1otLkkUHb1qf8AsN9u cUAYWKStSfSpYxnBrOkjZGwQaAGUUUUAFFFFABRRRQAUUUUAFFFFABRRSgUAKBQAWOBzTlRnOFBJ re0bRXdxJIpx70AM0jQ5Llg7D5feussdIghYBlBq9bLHFDtRQPpTQxaXj1oAmuLaKIAogFEsoEKg elF3J+721SaXKgUATLICoBqjqtvHLOnHap4+TUzQhnDNzigCosSRRKAKcg3NjHFWZ0UrwOlQRHBo Ame1jaM5QHiudv8ARllJKKM103mYXmqZP7zPagDgrzTZoHPy8VRKkda9FnhiuHKso5rD1bQti74l FAHK0VJLE0bEEYNR0AFFFFABRRRQAUUUAZoAXHFKoJIx1oA3H2rQ0q18+6C44oA3vDmjI+JZhx1F dX5MaptToKqwxmG2RV4wKBK8fXNAD3Pljii2f5iTVaWUuaWJypoAs3GWPWqpGDUzvu5qLG5qAJIV JbNX1j3LTLaHgVOxMQ4oApzRMM1VUEPWg0u4nNUpgN2RQAOx6Cq7gg1MDjrTXYEUARAgHPepoz5g KsMg1Wxl6vW4AFAHNa/o2397GOK5NwVYg16ncbZoijAHjFcJrenm3nyo4NAGNRTipFNoAKKKKACn qpI4pgqeBhux60ASRx4hJ710fhu1+YSkdqwx98R+tdtpEQisUOOcUAXd56elNcqy89aqvMQ5ppkZ qAHFfm4pwUYpgJFKWoAlDDpQpAaokyTinuhXBNAGlDOAuO9PlOUyazoGw3NON5lylADXfa1MLE02 b/WcU5EJFACE5FROac3BxUT5oAN2DUiT44qAggZNIMueKALgDEhs1la9GHj3Y6Ctq2jOzBqjrEOI G+lAHDMm5S1Vmq/gYZfeqcq7WIoAjooooAKs2yZOfSq1X7NfkJoAdH814oruLRitkg9q4qzjL34r tIxtgVfagAVN7ZNWPKULUQ474qVWGOTQAxk9KaUwKm6mmzHC+9AEKPscGrM8yNGvy4OKpRt83zUs vzjjtQBIucZFRlDvzTYpSqbW60+S5i2qoPzUATRoZJAKvybLeIZ6mq9q6xruPWobmTzn5PFADG+Z iaBHupueRU6KduRQBGYeMGnQQKOMc1OBkc0AY5FACiNk4zVXVx/ohJ9KsbyXxVTWiwtPwoA4RTm4 Ye9RXabZDUsfN2frT9RUAmgDOooooAB1q/bNiI1QHWrcDfLtoAuae+L0GuuD4iUmuKtD5V4M120C Ca2Qj0oAZvZzU6AkYpQFUdKVSScgcUASkbEFVpXLmp5ThOarcZoAbt705CDxTJn2imxSBjwaAHzR 8EiqEEbNcEnsa1kIKnd0qmsiCZwPWgCz5mWCjpQD8xpgAU5ppkCt1oAs7cirFucKAarxuCtTIc0A Wig257VTlfqFq4uSuKqywlTkUANtlcsGaqviKbbaYq9btk4rF8TsRCBQByUPNyT71LqDZJptsuWL e9NvGyxoAo0UHrRQAVPB94GoKliOKAJpCRMGHrXZaHKz26g9AK41+xrqdAlHlKM9qANY/eNWIsBM d6j8or8x5zUTSlJMCgCadSVqsy4xVglpB0qKUcfSgCld5KkiqVtMQ5BPetJ4/MjrPmt2jJIFAGpG jTJ8hNSW1huLE9RU/h1N6/vOOK1AiR+aR60Ac/dsIiVrOEhkk4qfU973TAdM0lrbEckUAW4BhKux jAFVQOcCr0GCADQBOn3aQqSuc1YRQVxUP3ZCp6UAVQDE5f2rlfEd/wCcQi84rptYuEt4DtPJFefz SGe4JPQmgCW2+WIk9arXLZJ9atH5UxWfMcsaAIqKKKACnRnDim0q9aALTfMRjpV7Tbs20wBPFUIy SuaaxPXvQB6RYSrdRDBB4q5HpofLHFcLomrtauAxyK6+HXUnQBSAaALUkAi4GKpSRnmphcFjycil LBqAKaLg4qOVAzAVNcOIyaqrcqScmgDQtpBbJxxT/tZJIJ61lS3Geh4qP7VuIxQBenjDtuoAASoV uRtwaElD8A0ASxr82avQo2eBVaEAHJ5rQinRMHFAF22s3YbjxxTZ4VUMSRketOOpbIfvYAFcrruv gBkibk0AZXiW8ZZvLDAj2Nc/HkiiWV7mTcxyamhQUALMCIRg81nuauTSY4qo3WgBlFFFABQOKKKA J4W5xUrKN3tVVWwauREOnPWgCFlKnIqxbTyxsNppJVwtQxy7X5oA6vTtRL4SQ81tlsRbhXCC7CuG Xity21hGtSGPIoAff3hyRmsxbwhjzUF5eLIxIqgZeaAN9bjch5pIZQMkmsdLkhakW4+TrQBoy3mD gGp7O5JYZNYDSEtVq3mKkc0AdlExaPIqZXEaFnOOKyrHUY0gIduay9W1dmyiHigCbV9bJykbY7cV zZd535OTUbMXYknOatWqY5oAekG0A96kf5QKlI+XNVZZcqaAK9w256r05jk02gAooooAKKKKACpo X2yCoaKANOQqyA1SmXaeKaJDjGaQ5b3oAbk+tSRswXAPFCwSH+E1MI9q88UARYJphBzUmcGmnlqA F6CnA/LUTHtQD2oAcW5qzAwPU1VK809DjpQBLNMVOAarO5brUjDcMnrUJXHrQA+IbmFaUUWFzVC3 IVhmrc1yAny0ALcz4G0VQZjQ8m45qMnNABRRRQAUUUUAFFFFABTgOaKKAJo4wTW7pmlwzYLfyooo A6eDRLUQdO3pXO6rp0UW4qT+VFFAHNSJtYgGmjrRRQA0nmlHBoooAczEmlQZNFFAG3penR3LqHY4 PtW1f+HbSK3V1zkjPSiigDlbq2WJyFNU2FFFAEZGKKKKACiiigAooooA/9kKZW5kc3RyZWFtCmVu ZG9iagoxMyAwIG9iago8PC9TdWJ0eXBlL0ltYWdlCi9Db2xvclNwYWNlL0RldmljZVJHQgovV2lk dGggMTgxCi9IZWlnaHQgMjE3Ci9CaXRzUGVyQ29tcG9uZW50IDgKL0ZpbHRlci9EQ1REZWNvZGUv TGVuZ3RoIDMwNDA+PnN0cmVhbQr/2P/uAA5BZG9iZQBkAAAAAAH/2wBDAA4KCw0LCQ4NDA0QDw4R FiQXFhQUFiwgIRokNC43NjMuMjI6QVNGOj1OPjIySGJJTlZYXV5dOEVmbWVabFNbXVn/2wBDAQ8Q EBYTFioXFypZOzI7WVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZ WVlZWVn/wAARCADZALUDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL /8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2Jy ggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo 6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQD BAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaX mJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6 /9oADAMBAAIRAxEAPwDzaiiigAooooAKKKUDJoASipmUKtMRdzUAMorVtNP80g7Qa0DoismNqjI7 YoA5qitS70r7P3Y1Ta32jqaAK9FWVtGcfKQD71KNLnPRo/zP+FAFGirzaVcKM/J+Zqq8DocEUAR0 UEYooAKKKKACiiigAooooAKKKKACiijFAD0Xc2KnkjCID3qJDt5pWcucUANyW4q7Z2pcjPFLY2bT SD5eK6SHT1RF4waAK9qjQ4ArSVHZQaWKz5zVlcR8NQBnXdmZI8tXOXqCN9uOM1108iscLWNqdkWb cozzQBBZWgnQEdK0BY+Xg5pulLJH8rLgVrvGCKAM9YN/FU7vSd2SorbjiCnipWHNAHn19atbtyKr AhuK6/XrIPFvA5rkCCj9KAEZCtNqwzhkAquetABRRRQAUUUUAFFFFAABk048UL0zRQAE8Vasrdpp lAFQRIZHCjvXa6BpirCHdecUAWdLsEhiViozirrhBxinO3ltjtSOQ1ADcY+7TJIsjJqVBTJg2fag CubYZyKnSEOOQDRhimAOaWwEhnKtwM0AMeJVbgYp46Vau4dj1UY44oAQgBs05iCOtG4beRUUgJ6U ALJCs0RVueK4vVrIwznA4rt0ztrP1KyFxGTjmgDhO+KQ1avbc28pGKrHmgBtFFFABRRRQAUUUqjJ oAeozUjJgU+BMmnzLmdUHegC7odiZ7hWI4ru1UW8CqBisfRbcW9qrkc1pyTBxzQAyWQMajDc4pjD nIpYhvegC2iEDNI+WqYgKoFMBweKAJbVAPvDNStFiTcoxRafM+KvlBuAoAy7o8c9ap7NxzWhqcXl 89s1UVgF5FAEUgwtVzJ2q2V35xVKZSj80AWoW3CpCmVIqvbtirJbpQBx/iODbISB3rnwM12+u24m iJFcaV2yMp9aAIGGDSVNIuDUJ60AFFFFABTk602nLyQKALsIxzVm1j86/X0FRouICa09Att8+40A dPCgW2VfamOmBU/yooFQStngUAQMcCrFkPm5qvtyKmtWw4oAvSoQc9qI4x1NWXTfDkVUDFTigCW0 YfbdgOK23hCSICRyK5GWZ7a884E9alu9dkkZGXPAxQBpa64RlUHPNVfKBjzVMzPflSTyOtXCCAFz QAsIGcGq18oDcVfSPCZrOuzmTFAEUJwOasZJFVguBUiSEUAMuYy6EH0rjtQg8q5PGMmu3Zs5rmNe ClgQOaAMm4j/AHW4VRNaoG+zNZTDDEUAJRRRQAU+L74plT2qbpKALzvsgHvW5oOSuRXPTjcUT3rq tItvLtt1AGoV3Ec1Isa96rrJtXmhp8Dg0APlCjpVdGxKKNzOaaOJM0AbEE2VAJpsrKHqlA7b/apr hGJ3KM0AQ36q8Qx1qnbWw25c5qQszEhs07O2PFAEkMSxMSpq2r85rOWUhsVcTkZoAsyTYTFZcjlp KmmfsKhCEtmgB64xUyxg1DsNORmXrQBI0WCcVy/iBdpziupEgPWsvXbZJIN3tQBzNo26ErVG5TbK atWfEpX3qK+GJz9aAKdFFFABVuxHzE1Uq9aD5SaAFY5uFArrtPZhZjPeuTt03XozXawR4tkA9KAI SzE4qVIieTTwgBqdMYoAjWPA4pHhzzU4IBpJHwhoAiQYPFakEkQjAkrIif5qS4mOODjFAF67jjJy nSmLbK0WaqxXO6LaetWDIViXHpQBHHAN/Sp5P3a8U602sctUV2TvIHTNAEGN7U4/IcUmcEYp/DHm gCZE3KDTxAGFIpIXg0olK96AIJYMHiqWrfJafhWkX3tVPWYC9r+FAHD2xxdH60mpD9+TSINl2R70 /UOWJoAzyaKSigAq9aH93VGrdt938KALenoZL3j1ruETy4EB9K4bTZfLus+9drBL58IOaAIpJcNT hONtRTx5NVckcZoAvebk0GTdVFGPrVmPkUAPHy0CPeDmmyEgDFSRH5OaAGxWxDcdKsyAhQtOgbr7 Cq5uSZtvNAFm3byzzTJ33PkUyRiXqN88UASfw1GXIp6sCtRSCgCVJyKez571RJwacC1AF2E/vBzV q/UNaH/drNiZhKM1cnk3QEH0oA4GdNt4x96hvmy1XL0f6S2PWqF0ctzQBVooooAKuWx4NU6s2p7U ASwEi5AFddZyFIBXKwALdqTXVRMpgXFADnlLE1EakwKUrhc0AQqDuq1F0qFcVLG3OKAHEZNSAcYp vepYhuNABCSCc0wxkPuxVnyiGFTmMbOnNAFIAlskU51BFTbQBUL8UARYwcUMuaCeaM0ARGLvQDgi pd1ROvcUAWI1DODT7t1jhbPpUED7TVDWbvCEZoAwLhw1w596oXZBfirBO4s1UpzlqAI6KKKACpYG 2vUVKpwc0AXy+JA1b2nT+agX0rmd2VFa2iSHzdue9AHRJHyM0TMFGKlk+SPNUHfe1ADlY5qVc5pI Ys81Y2gCgBoap4WwarNwafA3zUAaAkJxVhWBXBqvHg4pznaOKAHOBVSVhRJOelVy+40AKTSH2pKM 4oAa2alj+YUxvmFNjyr0AOdSjZPSuf1qUMcA962NSvEjiIB5rkppjPMee9ADukZqjIctVuZtqYqk TmgAooooAKKKKAJUbjmrNlOYpwQapA4NOyQcigDuI7lZ4evNIkXzVzen3ZVgGPFdRbzRmMEHnFAE pby1qEy5NRzS7jwaVFyuaAJA24UIcNUYbBpynLUAaEMmTU8mSKqRDGDV5cMlAFGRTzUajJqzMQM1 VVvmoAc4201fmp0nIzUcZ5oACdp4pssu1Cc1Y2qRkmsLWLnysqpoAzdVuDJIQDVKBdvJqJpDI+TT 2famBQA24fJqClY5NJQAUUUUAFFFFABSg4FJRQAqsVIINa1nqBQAE1kUUAdVDcLKQc1poQIxmuOt bsxEc1q/2qrRgZoA1Cw31MvrWKl8pPWrkd4hXrQBqpJlcVMs2BjNZ0Vwm3OaRrld33qALrMXNRN8 hFRmUBchhWfdajh8ZoA2lYNHmqxnVHIzWQdVKR4zWfLqJZic9aAN271JVQhTzXNXly00nXIqGWZn J5qHPNAEgOKazZppOaKACiiigAooooAKKKKACiiigAooooAKKKKAHq+BUyXGB96q1FAGot7iPGaj N62etZ9FAGgb+TGM1Ve4d2yTUNFAD2lLDFMoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP//ZCmVuZHN0cmVhbQplbmRvYmoK MTIgMCBvYmoKPDwvU3VidHlwZS9JbWFnZQovQ29sb3JTcGFjZS9EZXZpY2VSR0IKL1dpZHRoIDIx NwovSGVpZ2h0IDE4MQovQml0c1BlckNvbXBvbmVudCA4Ci9GaWx0ZXIvRmxhdGVEZWNvZGUKL0Rl Y29kZVBhcm1zPDwvUHJlZGljdG9yIDE1Ci9Db2x1bW5zIDIxNwovQ29sb3JzIDM+Pi9MZW5ndGgg ODkwMj4+c3RyZWFtCnic7Z1bj+S6rYU5gANUgAmQ/P8/GSAbSAPxw+kzzjDLa5EUJbuqe2Y3Hwou ly1R1KelS/nyzb7syz6HfftoB77sy/5rXyx+2WexLxa/bMK2bTs29n2/PfEvFr9sYM6f2r1EfrH4 ZakVFB72xeKXPdGG/JHdiOMXi1/2P5sF0b5Y/LJn2AKIh92F4xeLX/Zf++wsLvt32DNm/n9ae/ZM 4lOzeBFE+2Lxgq0F/3rAP+Pc5TqIoX3RGdrt0V6O85QnL1rTeRKL9oXj2T5nm2969QoWnwci2p8W yteE156vjl8s/pL2Mv7QflsWaeddfvxyRH4IVdetGedPN14sHLr9So1Py+IvylxhnVD/kiyqXfds mEKY+0etq1081+3TtsamPXd98aMk4cUs9ov57ID80jj+nixesVvGQ7MFf2qPtlaij8X6ipp8wPri k+ziYKgzXVs44LDat+LXhUJ9sfjxVkQhK0t//2wKTd9w/0cx9BmGB78Pi3U0O4JXf23uqU2dpD3D A55nH47jb85iR8k628OvuH9YqYUQZj998j73xox+TxZrLSw2iu36p75vQ+Y+j16+Mq/fhMWhZfAt QNnUSJuXvSGLQ0Y/yp5+/eLvwWLInH7aiLwpFpta2Nnw7Q6Iv/rsJ2Dxd6LQcv62HxYenJ1OP+me ISJD/ob7i+3r9oL/defWdH5pFguYdMNydexLY21XQGxiqttX7JZ0/tQsZj2sCmFB5OzG0GbHiJ1P /VpsX7HXz5B+eRaVwpq/l7HYmbsoXopdE03NMfOkby/G8ZdkMRuxhd0xbSuO1iPvL3/5S5a7GtXi f/7zn/DXphwWO03QDB0I93TslTgyi78WiPWgMONvKIqWkLcQnIIJB7RDYYdOSv8umXwZjicWPzOI 5NsUhQWO1sOOqO1YX6iOjQPNgkLFsUNkmHXT56lfF2w7/1P1DX+4N6cbTSmhzw6IiOA7f4pXhnux p7BmvSpDDmWHwkIpw4273L7FYhY/G4ihP9cpPBCkc+tMhz9l1qm2bODoSll02bRddPTLXi2Xq2m/ JIsZhTWIj8ejQNCzaJa9c9hCPSmOShIqZYZgv9f+VDhimv/P4hU9CN26kWwihrraPoV0FjlZOOwF XAtFxxSaDClUSjJLALVfBMdPzSJSWJMXmlIYgli7ejHi76f3A6gCqV8drwPKwgzQrNMfOrbw66wd qX1rysOLbYu64+Ora94UhQriMyi8UkN4bqFnyNmQSJLGLNmFsvwpWFQKfSOj8Nifdce6gRmFpljM Gp3VCWzdk4ZIhUS+vb1lGqmJZ6XrlPpGHD8jiyFAxJxCOSWENYsXKWyeQlnTWYUPIYtIpIOYEWkN HH2P+3kF2Y59IhaJj0L8CMS//vWv4SlWCuGUSoVfZ09vBrbAgr6S4B3273//OwORoDTBOsv0Oo7h AWGyH88iIkJgEX/+9UAwE8ImgkNdLL52zm2Wuji3QFMF0vl7F8hCHfEUTTDM9yKOvwaLBYUZiE7h 8RVPwXSsVMFhMS+K4nUboqksqkAeIF7HEcPVbDP9X9E+jEVUr1DkEEEEUWcwlBoVZLlQhVSEKQ+D PtsG6v2KFGrhjTgOi/mrsqjZ1VpYUxiCOCzLFBOvF8U666Y6ThFJn5h+GKspHD8pi6Rbm6wXkha+ 2/sc2Ynsg1hk3bdbiLyYb8cfJRLHixdxbDr5i7GIIJIiKoKHFtKUJQPxeZ4XOC7k2NSYYn9GQCiQ zt8zcLydxffDXsQigUiK6PwdG94jq2QaoBw63K/vorA39tFTIZ1VRNqTddbPwLHfTU+waM9UFwWd tI0oDEFUCp8thzaDY8eHKUUMf+34k3XWhGO2+qgp1IVqSuOnY1FBJAQPChFN6p0LP2/RnvrIKWVd COOsIg77RO2pF3CsM+q7NPzVnspioYg0Lnz//P79u4MY/r+ivn0GRUS70Z8FRQyPzHDUnvp920+x GRyb0tj0/OksFiCSIr4TqcPEEMRm1plN9T7Dg5tj1llnQh+mRL2P47Fh8zj2Weykcz+LmSJS5/tO HvbLKIq1It5rF0c5ZKGrtDNMeehGE8cNrtpXHIt5TI3ji1hUdNYskwdUxEwOs2EipdzkoyjFFHlX slvT5npnqJRZRghWKI27DBx92yIQmw53gpaddQ+LNYjaL9MAMVTEOsodHzJb4KCf4+0dtCU4Fi6F 0ogs7vCXjF5I4SfSxpSrhWWnXGKxOB6RIuYOEF0Oi/lKXYYprzoteK1fznLUX2fHA7UiDt2opfHA MeypMxA7/n8Mi8sg/u1vf9P+ejubpzPb7GY76IVmvRylIvHrOGYtEKlC8kIcaa3HejjOxvBmFpsg bjBNQRBRFFER/fTC+35NZFYrYl94lo8Z+hDuL5wcjhptFUcisul5jeOLWHSScIx4kJeB+OjNmqdq Iuytsq/Zzr1xF9/sqKbObnhwFoRhb+AwUXeMOM5eYLZWFv31KSyisB2EOYXvn+8gfv9h4TDRT/en 27jRM5DWfLsewTr9WevkFZIX7qxdQpjQcPh4XA2uw0c8EZOqy7JWuttYRBBDRXQQfU17Oy/fKIVu fRz748WmOvbTb1qTeD2+6CWbidQ4DtfAM2n8jCxi7+zYOYj+VbtmpDDrXjs4Dv1fVsdbtLBIv3nK Mo54Yh/HbOxoEUlrZcTU7mGReudQEXEGg5Ob+hmbymJW1I7zzajRzisgLpBXp3MRR9W5Yg28GDvW nnwYi9o7qyjSSJFA7Ax3bCSNN7J4oy2nv+XrWbero0pjff3E01mcav10Fg0TEcS///3v2DsPQSzq 4CKLWbJPsivpY1nC3lA7yimvSB2RRZzH1GvgN7J4bHyrV1IyC1kMQcx6Z38aZ5jsLI4LovhpWazH KkNN6juWSWN/4Jg1idexSB20zp2xd3ZdRFEkEOvoW8Qi7nwSiNkx/an6gnUSn8UxbOFIEuqfshiO GgtpnArCbSz6uQ+4EkyHiTRSpK65WbUhjh1ba7XPVs3Q+sOMuousk0WUCaw3sLqnJlHsO1OUaJ1F 6p23n+s470KoIPqaosGzsof5Zq2tj2MTxA8hT22KReupUdHnKI44aynmMRmRtSdFiXacuyzMoAtR /Mc//qGiqL3zldlGjWMWjuWQvcY6A+gMx7A4WYQ1EVQ+6qnDBXCk8Io03saiGy3i+JSl6J1nQbQG ix0E6yP7B0zZQnjpa8ai5Q2s2eeQyNWTmEwgM39qo1MWWSRRRBZdFI9ZSyiKCyDq/uxlUsOkPkQd 13Ashnr6FQ9b6O4zFvdo9TtTR0q844CtsRiKIk1ZClFcGCYWP3VYpJ0fQuHQiq65w6KtloWk0c5X 8Th/2lPfIo168AqLNGtxFn3WcuDoSznNuXMzoBmOtf7ZCMQi96mJ3RXT+IQs1ht9G0rjfp7E6E+q i5lUD3O3Kyw+zlcoHiCiKDqI2/kNK0PnmmWwksW7QLQXsmij5uqWIThFJMWE1DFjsbPE0/HkEova QdPi9jFSDEWxk8uNLBbkLXTTCyyGKS8MhDrpzzat8BiSxn00iSEQ3853+3d8UAVdZ5H+8UMQcbB4 fb5SHxbi2JSNLMfrQjilT/3xYpj+Anm6B8kg5et00yqNr2ARRfGYQWMHTX9Abz8eXddMf41Fi/6k Dlls6kfhan+YO8VimPi9OHbKTtIY9tQZi29yFY9msZ2vellnse6g3xEkXURRbMpMv/4yaaTiNWtr lxWQNV2c4r7OIgTRt+uyNPcPWQylcYch41AaceNw/k4W61kLddCuiJRFP16F4cHFS8H7yYa1PuvJ bDPIHLBcFze5pq7wp9hZ7FmTxj0aNRaZ3sni95//PmeDRXo7aR2s5q/hYcgi0VCnpvxlRPaDq1lP tTGLKJzV6SnydE+oi8dnZ+m7jnxRQS0Wiw7aWaTVnO38IqA6Cv1fw8PojfRa4KJQ+mm9u8Aofc13 ViOHuris1mG+TRaJyLfojsF61Fj4RkEbX9dNUhHOoA8QcbC4wbtYhpFq/pQdlrEYJkXt6tjo/FEe IlUMD1QhOkW7kchOGwiRRf91Tl3PYDrSqBkdO8f3u1AUtmhZEVk8fsVXUw1D0PwpO4xY1EDsP2cn /okU0kCicFhrF6HMQBxWTGbkz+yI9hYWCcTj01+tNVTHoW942ByLqovhDPr9E98XOQxB/VNdi0MQ sSDokr5LUCOQ9SbhfvckrFHrweGuhhtTOC6wqP6r5vmf1Mri2qixy6J20Fv0H7SuLGYTl8K/8NcO iBYRoAVxO3x7yLNTChDDxDMiQ2ms3QstA7HZXy+zqM5rT42vZc2Gj518uyzqT8iiw9dZWcSkbmdR w6elOJwp3p+lIA5JCit7//F2SD0xVIuidH0QOz3PAovkNq3d7CKNWU9d5EvRSFkMudzyiUv411+Y 4C0s1pMGzBTl8AGPrCAiNUxkBoBmjqED2UvvQz/D9mMJiB0cb2dRodT1HV1xLLLQOMyxSKvcGYsu QmGYausEcQFEem0MfvrBWgH9ARD53MGxxtp6IDZ7nsLtLODk6t6TxmE3vUsLnGMRu7mtvHj2Ozy3 qX9VRCc0bmsgYityIt3wdB0bEUZqmSztP193jwlaPqEpEu+DGHpSm/JhEYtoxGKmjlniFKXja7q+ mLEYXp5DHfRj/u6W2l23DEQ6TEEkCp1FOyuiTgyRRb3Jpi6jn6VMK4taBAo+/bTQUxemboTtc4hj 0YBrEK343yUs/ENu+cvugzZYvcuSrYOiX4cg7rCO6Ovt2Hiwm3YWDZq+R/OPP/7A4NLyoZ0JoB7g +ET3jgoLK0mrRMNeV0onqkOrWQwpxKK9nW2XUaOWsctiWP6MRXwyBPbRx4n60Jw6cBmLNYiIiOu3 KiLqogE0COJhzqJjhBkRB5tcuL6dR58kjdRZa6mtVLtMES8SWeCYmUujdtM6ZMQyhoAGLIZcbjB3 +S53oD7gKSWoi1v+AKdhROirskjNzlMmEMmxsHdGLcRPv4BUMzLgXnEkaSRdfMuf2aXxyWJV99EL XCor2uyJwh3+hsmk0Z7BolezjhepyrGGbFIaaxYpLrgHW8vj/CKj0DdPH5vyHz/s2MD73zIQDbqL PotkVOpM5Da5WizrvjpxVitYUZ+xOKiLBOIbrDIqf1Sc+1ncZGZgguMVFjU6mObhA4HoK03omKe/ gyK6YXxNiKeYqDQSi/t5+UNZJCsC1Tx+gcgOiwatt8YxGxmHG4ddZbF4IjziqA+fzQLUYTGsSGJR p1Mqiia9M7L4/pkRH7KI1yWR7hYsWi6KYYiyitTYzuKYRV4bv+JYSONtLGLxChYzXSykMQtQGBHt oGks4h4ePoQPww1F0VlE+9e//kUtm3zW9rlFfbQJi4qjH0ahDuOTVSpaRvMQR00NM8qs1kWdwRT+ M4uhKFrC4iN/U8ZD3h3UnMTULIaGGdUsGlSShxLnK84ixZEcVl3MrpFzJ+k6K60hTZnig8fr6ZmT ReWqhcGn7KgIH8ni9nMe/YBHwxe6WLM4NGcRY+FRwPI8YL3JX2rUnLUMR4pZKDx6OFJUUdzPyzpv yRV+mCCms51XiDBlAzoxtlShazgSNCQBdR/9Vk6lw4a0wiLq4vfG26zuYnGH9ResDPfQHSC1/g5P fyxYfJPpswmLxEpI4SYTlz3qo6kyNGUKV0ihxiGsOxMohzGnTE1YxKa1n6XxCOYKi0WjoZ+cM+2m H2fbZDZ9C4uhGVShu0S6OGTxLVpTNBnJ2RkaAjFj0WtLPaeUwwZsyZJWltp+nu+r552Y49ciX2eO pPGY+WUshnsWWdxgxVtfwEt9oldbJxAalGywqH3oBuMHbSFYxx4Iaspv8kcLhQI/HcQHrO1rLbqF f+FQ4oi4nVvO8YnR0C5iF4HEGiwqOow8liKsgoJF7KmtyeIRx8xFbViZNOJXP+BJLIbdNDlGI4cN plOYeBg7H56G0fCCZIpIsUafaZUUUya+MUGqzv18+Y+nH6ZsuSgWdRFCk5m34dtYDD0j110GtvOo kVjEuseeegpH97LJIvr2/efDAkIWvYIpmpgsXZJD66MkYxmLalQuTZY2wppDJzUaWsfqdli/YfA1 UwVxv10X+yyi7GGf+IhmMBdZzFYWd/l/iXxDr7RVeBZFmnUoKCC4UyvPzoq1R3NexRHjYMLEsY39 PkKppZjCcYrFt/m5i5bLhizifg8T9YYZi1e66R3Ew0Ys7j+nLxaNIvwiCZLGLMSW1KLu6chYnwyM c5gUJYvp66xI89X0CyIJmixWb9E82kfez2XR495nEXFssoge91l0JzMc/Sf3IaxgE93KYoIIHtuY wpDCMBGNAzmmPh/ma+khkZR7uBFalp1XgYoi9s4Fi5r+BIsGICqOOKHOJrBTb6K05DqxLOKoLo/z gPXx414w/0o+aAOtA2JljRJAmiaanaFUf8LUChzD+JAbUziGeWEWB3bhZWM0ZtBo2CyL6nSoPTWL j+ip3VnJ3aZYrD3czn/ToRppgDAdiygpIhbiggGkcctDVj0t4syASBNeCUeNUuZ8jeMetQoCcW9f GxGG+h4Wt9EMRq/UGuKIvoYgWk4keovquJ1n9MUDLbSYmashvhaBSOlvsrygLCqIyhNtXMExK6MW Cg1HSuFtBpp7FjdPf7C+qPuppjvdNIb+LhbDWJPDWPfUJOh+WS1gAaJFTFikIuQSNeDHeTqF52rF Z3mZNIA9n1yHKHSkEU/vdNBZvqEDvn+FRZMmTiwuzKbJy+Hzu0Ijn91VdMC3LbqLbzsLf109JlDS TnJGWURnPB2tyLo61YcCxzAFrY6wvGjYO+8gijtMaGji4qlhJ4Abl1hEHIs/3whHOy8da2j0cfBT LO7l1ddY/QolmcYEfaANK6t5k/FrxiJ2f0OGTGoaQzS8/1ALSM74RodFBbH2nLJosWiCozZ07aYL abT8eZsZiCYEKIj+uZ37HYWMUDu+Hi5Rs/HPPWkPvhOXnzBiOEKlnmTL70j0DRPiawtxLIQ2rOKw CnTWou99CWcttdt+TJdF/HWTmQFaweJDriEgIocvyAhZxD0W0UBCSKjRT/gUMmuwqLdOY8S84W3n AaI2UUyzg05teKI+n24qQSoyppOxuJf/j2dZtObRdABW3iMaMpJSbjJORxwzzygQ/jWEL2RR3c4Q xF8f8GAq9ZMywgsrw3yR71oXibzhLDgso8atYLEPCqbm6dDDkodDiz3/B+EpLLo0PuQ6iazvU5+y bYyIbtDOzP8MQZNZDj1vnNpD+JxFyg6z8NVNAnE7625do1o0qhQyT4FGdXt7PKdxdkXco2HiflZE DFrhp1EfvcYigbj9WGusH2FDOIZu0TYVaYhjFl9tBugPeXgIJPm5y3VfWqMYKDd94h41zozFukRa LxRJN33mO3ITBh8TQd/CJy/uuZZjmjezaLJIQUTWLD7O90CRQ7qtpQo/FY4wspRvJooEijqZgRiG aDsPQzEsnr6yqOXKKqWDY/Y6DE1fayFjUcke1sIHsLhJx40VrEGkwmsgbARitlGXZROND1kkYjJ/ NAsMVPgMSEpZKclA0SJk8XQrXoeR5YLxxEQKrGu4QyeNWMwOCn+lRk/jQpJG5DWsYHWLtmscm1xq cbazOmYsFmHJwm0Ri0c3jYHCw7wWm4pFuViCIwYhe3Sn5oU5Ygp++UWmiFk66rNG8jYWt3PXQ920 1i6lRk4rjjYPYs0iFkoL4p92ptYSjdSveOIG0/NCFwlHLaDBsld2qXnooac8fKdfEWG6rVZZpMiH MbFEvK35v4v+uolRi6crWBXEsBGbkFdsFLELWdQnedr5HxcSxUzFsyiFLFKs8OH1BYtahOOY8M6H 7azumWNHaihsOvkIifToKXzNubNa2J7vZJGI1AuqabxYZFfw51+HIO7nBq3RQcjCJ8tnA9yiysP0 3SgXYtHO40UbNSTL/1JXx4jF/ayOGY4IYsFiCHHoMAXnNhbtrCjUU9PwaIpFDKIJcNmnWvh6CyqO e1I8Xz6sbPI/jD7hqLpIKWRtKczCW5H6FkZylyvKSCCz3HFn1rlPgej+n1gkJpo4ElIhjtv5oe0q BnU7pu01EOlXDZOyogLf8bmOGEqjvmMGCxviSP90a/rDx4b7hj7LniYxOnakuxfqdZzMzyw4FYth YfQnZVFx3GAGgwf06/U6iBRWSpAKorMu8nk7K+JW6mIYq8f5bgdMMytLASLm0rxm/khNu1rqpn27 3ztrbId2D4sGFPp2ON4iGdDJaa0xHRyz+tMmvkvDpRZFEy+dUFujCWnEkGbqKzCdoizquXpS4Ogn 6mMn6r9P9OnOIY6Zh8PIoHtzLNq5uVuyuLOJOqrAEJGUOBapxpFQ8/rbpfsIWQyL8IAbq5f7aDoe WaRAYUmnWFR/ijdfH0YD6IKwt/P/ziG1aKF7dXDQ3s+dZtHOOG6iK8piiKNF0khZ7yL7uyicbg8f BLXMYuZnGLpNGi1GI1TczGe9DiNM37MOiaTr8dAyIh3HeqJjk6IYRu8GFi1a31Eot+Q2vFkcdxFF ixRxlkUDVkIWH+c7pDREIYUaIuooEHQti1d5yKJFFA7rjoK5JwNBZC58ZcYwqoXdyaL1cFQisyfE aUyL8FnOYh9EqtEja2SRrjaqaz3cxlMoJr7ahYljSRURLFpREbqdRdLO3IeieFh9v6lJCxnazSza OQpNIg/LHp5ZEzlksQnifhZa8txxHLJoEQd0GDU26qNpDGA/55UhHz6HsETX67rz/XiuhogozFgk XVSXasucHL/HoJNoiKPJFYFKpJ5bZJfhWD/Nez9rp0WBQxYfP6/CfMDDTx7ytAmLmlAIpeIeXlNn sMaBouh8ZNJoq9WHkaEciUV6I51/WtTPDC30cD90cbkwWeiVy9COYxRK9YEKjF/pKv+CQkWZnMc+ +rCCRS17jSay+P6J49HD/HhUKSSgkMa1GqT40HDQ+fO3OqguhvEc2gSL/cJYjqOJRoYg4tf+E07w P7FQ9mpFLHSRWHzA+qhF9R02vyIsTjbdhoE9dcbi7dJYsOh2gBjq4o0s7r6+GP68gKM1aihkEY/J sqbSUiDCDd1pSewKFrfofRkdEPFgPMDTDN//YCWLhzqGpbjIovbR/vRyB9GfqkitXeM5NPJwwGK/ SFbiaEmdZT8VvurXELJiI0yN3NCeeju/04qOLwoVHryJLuKDh45TstHbfr7z0MtC4epUHDXaMDt/ /1fYKuqQ1oYeelIVi81S6ZGZflgCn1azb2shcU8mePUnpaOs1CwOQczaFbGOz3vBt37YWavoGa+u i0X1D2sNGUIQj08dKerjPYtOpmNas//Pokk7XiueHpbJQ/E5zKgvkNlXTcQiHInFB9w0bQmITRY9 cZLGDUalqosIQfF0gGEAKTIqirvMWkIQqTEs4Kj+/I/FYTGaOFpEpG7UXwuPwz1hXAotzHwuWNyS ldGMxbCMmH7I4pZMX0IWTdQ9K1cYB5TDPovoXl07fZtmsXNAeGSYflZhHaeLnQWU2SmqW4gjbuDd +6HZiEU7P/sF3/+Auqj9JgoSvfJjj57HEAaTeo8wFzfsoNWHYVSn7H8smohTYWs46p4awS0aKZIN YzEVrFC6aB6j0rjAoie+5auMSgmiU1/RaHkdKYWkiAoizVpIEe/CMWCxLkn/gPrIqbwyy8o8FM7C lBXF8S4Wt+gPGDyXcOywOAwmShqBTtgd//uhIuqUJfRhgUVPc4XF5jH1kcsUHtYp82xcMmlEIvVq oz6L+OvjvOiNo1J3PsTRklsONJ70LkETRcxYzP70ewaLmNS3rDA1K1MkXcSub8uAFqwokQWO1mDR ztJLLPox2pOiKHpZCgTR6ktoVRGzrplY1Hh+dhYXjndb0Py1dLJulHTxIXdN4PE2YtFEd7fkQuOQ AGJRrXjC6pFISCFuFywa9PJZbO9h0WZ66mW2hie+DD41AivURWVRKeyzeBi+CglRVlG00Y3Smp2f NVTEt+iy2f28jhPG9jqLO85dwpAVSSyzaL058oJdT1NZ3M7Tl2MtEO/7VhzpE1P2bcwo7PexRFkH jUbPM9HI6C36BYi7rOCoHGb8PYXFrGDF/tvtGcgWRiQVuhiy2AHRhEWXRmXRZOZbPIbFIhn2c+kx OvQX8xu8wZ1EER2gfFUmr3TQVrPY3PMkezGINlr0Pgyv9+6wmAVQz9U/dUgXbfRIIM3uOCt7FxC+ vl1BpKzD6qCfpqpM1fTTsfh6BN2IRUveCNuURiv7mVoa8ayaxRDE7XwnF74LiC4AC0GkfrlA7RYW feNzsfiBIB5G2rYl3TS+lnqDtRjFUROnjDJpPA5DpCxhMdRjA4h94VpZpDFiASL6Q19fweIwoPfa h4N4mFJCOOptWbQcU+CouRhAT5cCUW0pjvjAPhMQ7ccKzvvxyB8SOQTRci1Ux8KDC5tgMQvlk1j8 JCDaSBrpLZzKYgdEyiiURjwyZBGnLJQvKuL+Y6biCPo2Ld/sjbUb8se+WHy2eQEzaTxwpMsaNln9 DhPHYiKL9nNsSn0unoXnvhNJI0XK8Q0e+YAIHhu6jmjngWnhtvozy6Iq62HT/7v89izavDQikahS aGEFNKWxqOyQRRomor2dr8HB3jlMPyxCyGK/Br9YHOQSFh8VazsPGR/nqw/DabXmS9VQsIgudVik vHC+8s9//tNFcRlEdUM/h1aPPr9Y/K9puRQUlEDHMXtBsQlM1APu+ftcCcdMSDx9zGiHRRwSRboq ETvlTvyRxTVR7LJo5/oImXsSiOrWByauQaBuevv5VAl6X7Zfm404kgNZzak0+s5sABfWxS6riYe9 S6NeCTYFYliEeqTYF9rDJlh8KoiHPQ/HqZRJbwiUUBrxDe7beR5jUn8Zi5jX8MkFWkB/AqqK4rsi hiCGOl1kUXxmx9epoX2j7yr74dfn2e04riVY4Eg98juCjuPjfBeVn6u9G3mluRisY3fKhVfi0FIi srjLH82Y1DC7IYjLnbU1WcxcnAL0CmQfcm4HR5LGR/QcCFXHDMcpafTT/TpZR41WE/vDxA6Lde88 Ne4kYxYNQp/5t6yRH4LUlUwzHHHU6NJ4bNBUBs+tRQWz8I0Mx+PE8GptBPEtuq80VEQqb5hdDeLs 6FBtmsVbOutXQnmx0w9xfMif1NRT07Takv/0lntqZ5FAJFHU2/kyRaTCal4XFbFTCwGLdl47LQTy ir0Mx7tYNPmPJOusH9EDxDCdYryV4Yie7OdHTr7BVTY4WMwmztoGsvJaA8TrCLp1WVxTxyujh3vP WiZSC+4BeSR/xqg6Eo5hZ40BzwaO2jV3QNwbF8bWoXsBiLbG4pRAfhIcn9FTa3+tC+D0DLECR8yr UEd9IxpZH0TfKCo0O6uI53KoByxeBHHNuWccf29PbfmiI01lVB3tPI+hqqWwE45+Sghi/XdzBqIW EA94GYhWsGjniIQeh1yG3nw4jjeyaD0cw6d9YjAtqu+MRTsTTF2zz1fojlIE1xokhUErQLwYVbIJ FjsgFi4+uyN+qjpqYRdw9A5dXfJPbPYqiibDRLo2NlzQVlHsWA3ivRQe1mKRPukwtRtZnDrxeeoY lnQKR+2pLa/gUBf9mF0mK0XXXOSSFVCPfAGINsui9XC8l8Wpc+sj72XRpOt4ROuOhGPY56pv1Dtj EXb4o+8teiaYKuJCBF4MomUsWt5B17Viua8LZbj9lNtZtJE66kXgmdRZrou+U0VRn5K4y90CV8L4 MhCtw2L4uWCzxXjG+PIZLFqOI67shM9wwjpWXTTBkVik52lr16zlDXPJfs066ydZyqKNRHEKyhvH fFdOv5j4lDqSND5+Piw+1EX6pMZ/bPgBymIxTFwu+ItBtCaL9vx/X2aPXEvhBSzaz7jRqPGRv0Ur GzViavhrCGK4jrhc3teD+G7/B9Q7wHEKZW5kc3RyZWFtCmVuZG9iagoxMSAwIG9iago8PC9TdWJ0 eXBlL0ltYWdlCi9Db2xvclNwYWNlL0RldmljZVJHQgovV2lkdGggMTgxCi9IZWlnaHQgMTgxCi9C aXRzUGVyQ29tcG9uZW50IDgKL0ZpbHRlci9GbGF0ZURlY29kZQovRGVjb2RlUGFybXM8PC9QcmVk aWN0b3IgMTUKL0NvbHVtbnMgMTgxCi9Db2xvcnMgMz4+L0xlbmd0aCA3MzU3Pj5zdHJlYW0KeJzt nWGL80iuhbXghQzMwu7//5E7MC9MYP3hhjEtZJ2jUyrb6aQv0YfGcewqSfWUJJfdzj/sIx+p5R+v VuAjby0fPj6i5MPHR5R8+PiIkg8fH1Hy4eMjSj58fETJh4+PKPnw8RElHz4+ouTDx0eUfPj4iJIP Hx9R8uHjibIsC+5c1/X7NTksHz5aQkf6vLw/Kz+YDx+zKS8/aaTPy3uy8iP5eIcxfuhw+Yi+ISI/ gI+raPgGqoYDPIx574bIW/NxckQvAaLTSBxUPcDNnPg+lLwdH+cHNbbwDbHHx5IOato5dUXzDpS8 Fx9nhtPPRT6qZpsDQFs2IKMCBXup2jms4fPkLfi4BIv4Ef+mgw9kBDH1IxkinPieHxRFfjYfafDS 34oSY3NdzHLaDgYP3MBmt4+z62YvROTFfBwjo8ogkYm0nVqohjBNcdp41YiLjSgR8m5R5JV8nIQD By+KASXeAo5r3J86wkZSCEE+ktgMJe8WRV7GxywcOmb4EN5uNwpKaq0zyzuhaAjHtZR8PyKv4eMw HDpmuERKrBE8YhRJndKYFJsyxsf9fq8QqcJVlPeJIi/g4zwcQyb84+P4f/7zn37u5tz//e9/NDXQ fmObsSmX2CbCIUCxo3HiOxH5bj6m4DhAhm8/xjLFjzTdbQ+Kbyz7RBZbM5litr+PNp0J3GhSMiTg 2xD5Vj76cCAZtudj48BpiIg4GUM+to3HiFoYqsRH4myBhGUQh7Y2HQtNiQgk7xBFvo+PWTg0GYjI NpaeVuJERz6sKD42VmyflRA1tGWFhPWgoUPJ46MVgeQdosg38dGBI01cC2OMGST+/e2335a9WDGQ GNUjHMndCGjVLLYf5a+//kqUJGJ0IHltFPkOPqbgoGGDxgwKh8khxBBC92MjVcsikGhKMKjYPqSh zpU8FZGn86HhSN9iNqmwePz1bIJwYMvU41XkQPVos7qvBMqGglPi0gwkr4oi78JHHF16pUpjhqg2 UAQNwr+UYKo87TFS4jRs1zgVIlVF8hJEnshH5TXcT8MGjRmYUDByVDI1IzuaU7hppymKPP4eDiRC 82cg8no+4gAv+1IDE0o6xvaRQ8vl7hN8LPunU9OoR0qqikRQIgy53Man8NEnw4pqw5nAhBJPsWLu TmWQSr3hKRUfaH6KIhGIDZHh1Q3q8z1R5Ho+xGzG5E0TStoQdWgV2Gf5EDo3T9T6eFMYSCIlK1zg +GE2U5FciMjFfByLHJQMfYUiJqvNlBpUMWvH8yk+rK5YV7i0QUqqQCI0PA/KlXx0Ikf0IK5quFQ5 hbaG0uSj306zhWGDtr8dGClZ4dJmZXWJzQQS/VVHLuOjEzkwpyAWj78PMuhjHLQ1KlN8VINaDUaz Ec0cViSRFS9KLlkjOYPINXwcgwMTiueUdIWCXQg4rOe4fp7SiMzyQZtNiOAaSadofVM+0BHVHlpw pJxSRY6qZSodryF8GD/SGAjOsJGp8JYo2YDANRKsSGhrursp+Q4+IhxVKepkDCNHR4Z8DOHwc4ch REQOnWJwD+YaXyOpAklUctj+AbmYj07kQNlK0epS5YDM8pFGNAWMaqYmkztF0nCcEh9rWEZ7yK9f v+gaCbZ8FSJn+dDxv4ocCQ7MKYfJ2ETzQeHAHikcVWvYMj3AT6+6s3YUiYhQ9S5B5BQfOrMMI8e/ /vWvlFOqFD5rWKdiGGaxq/igoquT2HW80H1QsoUQkWuoH8QeLcf56MOBkeP333/fwkaVU8T0Gkon eNieD+w0MSEQodlEDH+HHixHNggecGyBJFIiosjL+DgTORIct3CPnrbs0rFtOIGq5EILhVfxkXSI ieYhf/75Z4XIMIq8ho+TcHTcuok2bzhdhsFjCfUpwkHnJeVD2EL3i0yKtQhFJF73mmRiCpEL+BBw bAQ8mEA4MLN0RNhGv6J8YAgR+YWGkLhRTZU+H9p8rEU2LB6IOB9+XVMhYkejyBE+tEe2v6nm2BDZ CtIKDhrhk1SG9eEwCB5VXkvh3fZxpRLBh44cWjCEREQcFIHIN/EhzI5O3wiIkSPBcYO7bihNRGbh sAKReBgyUc3LyhtWTySxp/rWu458bOXqr78lJRoDmo9lmbN8UL9j5EA+bARHZUmTj7ifRrgUPGj8 qLKM6PQ8H5VPvPfIB0aRta5VD4SQU3xUcCyh7Pj9b3mUHdvHBS5YhjIEQhclqDAlI6kk4NCInORD +8T52CCIfDgi6/42np1DZI6PYfDAsiNFDgpHHBXarwaiORVEZqG8HkNEMNH/SggWIg8maCFyZ0+d PZGPCnaaWWJa8SrkBvfehBf0nlk7UVUrEEktd1JMPKUJwTE4olaOyBY8/IpXZ5nZqXWcD5x5Hjxi ZtmCx2NjNrNoRPp80CRYbaQ201+6M56i88UxPjCquTIYQjorIlNT6yAf1OnxmsXh8OCxhGuWZo8C kaaRqKdJRLCvKUSEAs1t3UJSDENIyjKdaxmt/wV8pMxC4Yh8+ImdvNBMK7SpPhzprxVw2DwiQyaG s4Ue4P3GEEKjyMkQ0uVDJ5cFLmj//e9/e2YRS6WziAjD0schHPQjNogFh65FkjyJD+8xrqhGPmgI +SY+0MVV8IjJhcbwppZNw7ACoBGiChsVH/qvRuR5fHjva7jWffz973//qxdVUVXh/BYfwjCHY/m7 8nDx5LJVpnQA0NrhV1O1leBgCEfsoplrUCUKK+rWsaKSqgqhWUaoWjnzFB+d4PH4a2FIOkIDA36l +aAcm4Sm0qQq8ar9SQFUSXdKj8f9kdEYQv744w9cMcMq5Ll8pOQS77NslQdd8+hLpX3TqqQwRcT2 1Db5wJ3DEqTSquqUHk/3uw6pCtlSDF1RRUO08nN8oGfjmsfGxH/+859Yefiax5Sc4YMOwzBsoJIU CGPQ0BlJhXpyeLDYH2ODA7FlmUcgubNb/xfzUZmUkkuqPDZZ9te0falU13zQfN9MK9/DR6ffyhy6 P4YQXYXoFPMsPlLlQa9c/JSmByvV6ekrW7VM2qLm9GPc1nBU+/sh5AwfqKeXIMMq1Q+2S/igvlv2 UgUPvKw9wEc/u1O1q8FoDtIwYR/QcyrF4FlpTxzytahSxdNlQ0Sm+aiSi6+JiWWPoe+aZUdTZz0S zfwyzNZV5Kh0plOOKlCdlfY8NcV0+aCZm66ZVsmFaiAG/nI4DLw8zC+d9CH0FJp31BNnxY86xXiV uu7/DaJZpR7ng96t3S5e0pVLxUflPqHxsNoQ2/jRGCKoyZBUrTA9ZahYZ2eq6tJVLq6lrnsZKm+a D12ZLmxZ7BFCbnA3n0pntlW+Fs6drfua+aUZw+jBnSgidBaZBXuPKSaVIGvxXNnFfMTkUhWnw2Wx oQf1AZVbm1WntjF1SoectlwFmyFbs5RUIWQbeLEKciUfw8p0E5pc/J6L8GOlUGfyiTJCIDIM9fTg 5MRqnOLxnaqlkk5y1HzEEsQXUn/tHyqzgv6zfND8QuPHUqyMNVO19qlIEJHL5F/afhU/hDJVyRLn Zadw6dRSVENqvgeGVKI+4kdaArnDc6mn+KDBw77qU5FcNB8HarpKN6oejV5iQlcH+3blED8ghe4Y SIY2alA6GxUf1SVuMxse5APjR7pnq5NL5bjKiXpKVeymE0VcpapSHTCUxqY6A0A/dszUG94dPgsi lkAO8kH1wOSi44eYkUO10H06p9ieXVdg+5kf+/rhnyr+dyT14j8gtDUSfwpIx/DKwMq6aicGMAuX MLr+EFPlSj6q+EH5wDhxOPyij1LkuH29eDmqsfnFQZlCJHaUfnHMZ6T/8tzwMoHaiJae5yMugWD9 cRkfGFSXYmVdPPOhY4ZgpYLDgI+oWPyZB282jmX0ke7Xe8FftvMT7+w12ZqPA5aKDeTjDs8K0fpU z9VpPtJIYH5ZQninvhjyIaRyWVRpYS/ZtT0fiZJKgYoMxG7zfnr7sQGCJy21K/igsW2Cj2qCGowE zS99PqqdQ3+hkhg80hq/96IpwU69zrgVT+FHPuJb5GJ3qdnDxlbQxJarJVRRn6YZMsdHFUJE/YFO tDpaNOeT0DAqRvmgw5lmkv9yZRQaNlzSlK34OGajjaZE2kZlUoka+bA9uwf5QDIMRsIvaId8VBrg R+qdSoZ8xGtdGj9iPhYtL/u4GFsb8oHtN62jR1JQIh/r36vs4had7dlFJX1jjo+4fYAPm4kcw9OT krMhBCW2j/MhSmon8bHCMnbfQC0iotg+NDof9D/q7DwfkRIaQo7xYe1gi8ZjO6ikM4Ev7MZxrSKt MeySEyyMRwoeqX1h1wFExLmR14qPVV7fvgUfSYPK2jTjqxOTzgu7hEkhhE4gHZlsT4a34/M1Bo+1 yFnRpZXVZ2Trbvb6ZY4PasMz+KBC5xbCIRDx3m/FD0LEpqiPhPmxu+joe7iyrYJHcuwUIsM4FD3z Gj4Qkeb1yxQlyEcFh57uMWbgD2wnt+oGKxOio3HlFBuM3sC/Qigc1ZFrrz69jA8aOXymDvnowEFj BgaPZIaYUohIvExNPaJHhHpJk42MdS8is1QurTwzRYYFZO/79fV7uGOHfBibJC/jo3KHdpaGHRtB UCwsZoguKsUMfBdX6JNga8KZoqOOevFbykf1RhBj/jzCB9o2y4dmgvaL9ldW0TaTtlETXw+lLh4K 3gfuKCammYgflWIVQImPqv5I19501h3hA51e1adpHQmNSTsRjgXeE2dACfUU1Z+ybn9HFD+Rrp8m oX4UzkWVqD7psA4QdE/k495+hP0CPqpJeYCPDhnUEVMzNZlgAEc6rBM8UuzFaNxBNglVmzaC+9Mx FR+/9u8lu5KPyrMOxxKeT34SH8ZmquDDJCIGfNDwRhUwgLX6iuqDs2tostCEbg/5SJe4xpyp+Eie HfLh9Ye/auwYH2Imuf3aniQVItGcqq9KhBPFtK7mW+Uf2gjdP+Tj/nULN76jfWW/bXgZH3Ej8eGB BG95TCHyJD7sBCKJAwEHfkydJkSoArpBKyiJ/ol8pEuYa/igASPtieunKbnc4PFggQjtlHpkDYKW VO3TxitEfJvOTitYQVWpdYhI1WNzTwUu5cPrj6fzEW9qxIvbWxDbj0FqX3SHh5nkg/qx4iMZpTul LpsKHiY9qfmgQUJ/u214kHgNHy40v8TkIlyPUxb/ojsqPtCDU3x0Oq3cp0ex8iQ1Vgx51QUNcptQ PqolsifyEfNLxQf1vugOj5+NH9gC5SNup3Wz9WvtvOPEoRrD+EFHumq5+hi1fZf4IfjAQcKOaF94 ouCDjkrVCxrlcCSgt17i7RXqTVSy0oH6cPso4NDxqQo578XH7+Hnf4YXL0PfxY/otcN8VEY9tE0P maa+/N4s9SYdqqGN0aVJkmlNCu3d+IilqK9/VBcvqXHqQeyFeg1nM22z6jF2cSseAIiNbw92rCA2 mtnUpU0+0LohE+n0dX8J8158LL3f8cC+NB8VIqIL1CH2ciseIPKW71//8nSGj8qZdAqhpaJxuj/x scWPan2MdvQdfIj5MXSc1XxQ6ocSm1pA8Gfe1/1VIkXEZvhAGyszjc2Efi/xxJRf/P7cvfeI0Fk+ FvnbpU0+dBfUbLSqI9Eu7yXGj038yOjiyMd99ItuQzMNGE1HChtn+fCcUt1/uZIPNOwWbt7G+jQN dtNxdIah0jiDk9doj2jX8hUCH8VpKps2iatMGx939uzgFKPUxqRwx8ZKopfiPf3r+UB7jPFBr1/E 5KBeo12g5dXE6vBhjHiMHxHKCo4Dw4aWom+jLf2O0iyK534HH7YfsIqP23794zwfSZN+8LCaD7Ro Cf8DkcKeA3GX//Ik+PCvFphydCYsYRWkyccCgceezUfstclHqj9+D2+mm+ID+0KzK2O042h3y+g/ /SMfGD+qrulX0aXC0sre0ndFv675gfoDrbuGD7o+NuRDzK3KZUM4qvZTRxbuMgo+6Ps8dNcC2ejG tEGN7diIva/1/VvBR+ru+/gYUuLdJScmf1GvTbkvzeNoxValbnvcXxg5OmOG36JL0V5sIbazFm86 pGdt4sGjuf7xFD6Wr6vc6v5cEw70XeUv6rtmF9QuV9hX2bcG4zVt4kNLOibagjYmb9tR9P0UF5pf fhVvUT7LhwEcOn7c9rdgOqKDDeqd3HcgkETKl/A/Mvb1sjnqytTX7ASwmoy+OTrkxCtzff/l5/GB 22I+VU4U+6l1tGBKYQPJEPrrb4fbtJehM+Ng6/rjYj6MwbHI65db4xY/eqHDE/VdBx3fkwyk2ZCS cQCOtB8Pa06h5mE6v8T1dYqIPYOP4fNBfT46ThmeReNNNSOTeKfV9GrqOcsBRo4hWFQSHzS/IB+R jKfwcez6ZTjS2i/oU2qeoISaFg9OHuxoGD9W21QqgpunWxhgzcca7itVfERlDvIRSw29PtbnY2VX cUOPIwEJDsoKNW37iCEXEcGRo7pVA5xa6/AnEEkeQD5wCfUyPqxRfNxgff0W7oVSw3Twp4xqZ1VB smLFenwkVrQyQ7XR5Ipd6o2hRG2n+BAT6el8dEyqnIIxLOlWeccYMckLTT6qs4xNALq/QzPiS1um QuPHGt7y8BQ+qAcdjiXUH/7PlX0+qgCboKQbVcvU3QKaio/kZT8leabSSsSVSlUcoSj9+BFbi5e4 qT5FOC7gY9vw4V/C8x/IR3J38kv1ETtNTaU2028zGOOgGgMf7wN8dNhtopzUow6ZlSYf9/rnkhUf aFt0QVxtFPEj+qvyTiWUj9hmfGlHkupHPKqJkvoyxkdSWOCLrqu01e8eQhcJZybxdiIf8f4LLoFE X2Hv0+/HjckF6w8/IFmFTFBKosfTytVSv/ontYm/4FEhQvmgeKGGiIgxLKi26/4HJHC0KgOTr6j5 kQ9RfDyLjwUubj3FnI8flfedj9isb6NtYoJi7ykGeDs4YPFId0Xcb/uAkbRNetL/nKAa9iUCV/Gx 1uunBqNzkI9l9PypjeKH7f3uG9iRw2H7kRAN4gQVA4ABgHoN/RDFajKoK5zjFVYz114UoX6IcGwS 4cD7c9jdBB/GZswC1y9V/KDzu9pDvU/5QNfQ9mffL0jbpHpSwTqjQuQqPio/3MOvaG98xMrU+7oX vzAxzQd1Skox2yNY8eWFqD3tXndU8TGU6H23X+d4bLmCw8KlnH+MBYefKLSd4kM0iNh5BvHkgle2 6IoL+LB9zRjjRwwhcYArv1SdVrPTWxMXL0k68WMNVWpHKq1Qt0fvQ1V1/VENGEXET/Hhj1cuVWXq fYnuunxE76RLmBhChiXqMT4sMDolHT6G4v1qPjrygCau1hzTsOouwiH4wL7sWj6ckg4fyZgmH6m7 pAltR3tNTNCODNXDToe6JT5wu1Kyai3xgZml4qOCw07yseyzDP1Jnlk+jA2DcAqe7nuSow8gkhrU 7FojLMUGo2JpQzeFrlhDZZr4wOBx762cbtLiwwCOSAmGkKVYJUv2YBcGQEw1UkUaOkf7Y4mKDeGY RQRP1O3QRiIfFA6sPC7mI3nnBmup27s0dBWCSiRHD8nARugQJvf146rQBKNIVCnNSyrJongkMjGM cLHrIR+VB67hw4oL3WVfi1S/2oQmpa8oJdW50Riqm+1ntggedAwqRCo4DBBMLQ/tOoCF7em/f618 xJ9JRDju7PeRq34P8uHbyMeyf+nK7e8V6KHQaJFiQPIInosSz0ojh1On6j0BgQjiIEW/r+y5+Y5P NLixfe96k8THWmQW9OQ0HwYTyNhIJEoWeC/P0BdDx+lIiMogH9GVtp86dKJTq9OGnx4HKY2E8CTt FK0Wsu4r08ffmFlQn+QBex4fBqMSKVm+3ut1m3nXFB6ZhpAaVsGaWqOIUKfEE0XwsK+rpDSD1/0C JW3HGCjopQ4fkcsDkeMsH9aYRojIAq8GrCxM/qIuiL5Aw7YTU9eRGAOwTvIRD0hwVHzguakdPYUq t8Te/TcSO5EjTTzc3uQIH9TUNHfjIIlbJ2IyucbIR3wOyM9Kl05VltEOEophAIhtRj428dfrpjb9 KQVNyTByxK7X8N/kFRwd8y/jI3lq2UssTum5Luk5GspH3IjvNPb93m98WRTNMk/i4w7i/96d2oxm JlBid004tr/xJp/OKdF1z+IDN5LjUmxPTtFt6qfx3IZkoQVA051kBzS6QDgInaD5SJFjW8yOP5RM 7UVQ4jGoT/XbZ95FBKJZcBznIzmIOqsy28JQJT5MIlIhmMygFsZOtwWYxAeOaOUgYTLyEcfGHwBO v7JO7aUm21e1G1VFxdK3q5R0GHrSnsGH/ja5svKFOBGP1HAY8HHbP4ySBvUwH8mKOGXjf5rgEkhq Ch2ywG/gafXQG4IMPL5q/zI+DIbQwHIxS6pT6PGVR1LXzkcMIelOYcdHaC+1yPbJJT7pudbXL2hg im1oKZV4ZEJEG4gtX8mH1Vikj8ILuJ8Og+1HAm2IOzUfaQ1XTx2BL/IRa1L/T5MOH9STOOrWoGQY XPsx4yAfNkJEHCNORH8NQ4gxI+P4+cVLfJ4Nr2LShjCH/o1TNr1JMq5ANPmgk1sPmzixOr6TUI7z YT1EqgNEazrF6EmWWoh8YJWKSgo+NLVrUZniPzDa1xW49oaOAU0+KhmGE9HRQT7oAULowXQ+dULI JpqPW/HuW5qztG6ojPMRk0vFh/aGDh6dEmRoS3Xic/mYEsEHbgzz9CZNPpZiLbWjLdXEh7CCY8hH ZcsQjmEgSfrrI0/xgT2d4UM0a6Px6PTrR6Y7yb7ovgTRbVKTU9TZxt5piE96puK0g8hUGYFnDa2o ThkWH/YqPqpeNCjDFjyERESWcCeZFhNUk6rTWHkkPmjwGPJRwdE5tyMLrKzQ9qtjJvio9lRdHm6Z YqH7jXzEROMb8Q3JVSAZUrgG8fss6c5LPMbP0m2mbX31MZTONO5nriv5OMB7fwZ3QoiPerqNTKMI IjI0LZKxwsMWd3bLFBsROzsBvyMd0Du62ZCPqrO083wkPBDq8eAURTqU+PHarhg21vC7H45FWvOY jQTXppXZGC96HPNB+7ucD9qL3o/fYpbplCM00SQyLNQc8Q5clVaGyWV2/5RMxXjdY4sP3aWQ1PdU I+ngZgix/YoW5cOva3Q5EnOKoxCf0brXT+Kg7ZVP9MFnpDmHr+ED++vIGT6Gx4vEVyWaTsUalY+i c0qVVjoD/ww47CfycbidKVBSINGIYC0SNY/hQT/6a/NwPAmLKRnq8DP4GJ7VQQT5WODHs5PmiQws OO7sF0+F7c1vv0c6Ovx/4EMkGiuiSPrrz9knzeOvBKWwcS/evxNFDMDL+Wgq8Fw+LpSq9w43IorE /ekFL7HyiNXG1Arpe0aRfr8TfGzyKkp0kNCnLKxcRT5sX38gH7NweFMHvnqq/DA+nlGRpGMSIggH tpb4OAaHNzVh2Lmzrm359XzMNtgMJCt7KgcpqfiI9ekKixwmXbw1NVuRULkckdkGX8bH+ZhxGCzM KUM+OnBQffDI18aSH8wHHaHmwVOdRizSztR7BKKCo6lJB5Sldy/+jPwAPk4ONmYNPGYY/A1YqRpJ oFgN7lCT88nlPCU/j4/DcAxPHFIiWks0aDi0JhcicoaPY+dezMeZYrN/7lQvnViSPlIaaFoZtjBs ZyivrWCm+dikGqHnBYPZs6aGRJQ+9HSMHDq5nAkD5/k4E3Xego805AdmPJVrZy122skvQ0QOVy3P Xn3Z5CfxcaBwqRx9xmW0wk36YF8v4eN8PfsyPmZDtICjnx2q2Sz6rboTcFR9UU1OXvWc+bYjV/Jx Mngc46NzfauHSpw41HZ4edzpXbB1QNXZY7RcxseZ1cwDLQzP0mFjtmCk2jZ16NApQuBr+fg/aKAn SQplbmRzdHJlYW0KZW5kb2JqCjEwIDAgb2JqCjw8L1N1YnR5cGUvSW1hZ2UKL0NvbG9yU3BhY2Uv RGV2aWNlUkdCCi9XaWR0aCAxODEKL0hlaWdodCAyMTcKL0JpdHNQZXJDb21wb25lbnQgOAovRmls dGVyL0ZsYXRlRGVjb2RlCi9EZWNvZGVQYXJtczw8L1ByZWRpY3RvciAxNQovQ29sdW1ucyAxODEK L0NvbG9ycyAzPj4vTGVuZ3RoIDg3Njg+PnN0cmVhbQp4nO2dYavjOA+F9UIGOjALO///R+7CXtjC 5sNbxlQoOkey7CS96czoQ0nTxJblJ8eyk7b/k1/YlmWJPlrX9ZWe1C3xGW1/K/638/yL2FDU9tsL 6DmwRXu8PYUPbdvhcXwxB0cZjcOxbSnGfLRHDuOj29r9rLwpHK+xK/LxXh12iLeXykt2XpOVtszz 8QvC0ew6iFyRj2ticSmvXgbQIWN6fsxvPk6xV2rMzln6YXxcrQ+O8seVc17XnlTybz64TftTkeUz ls5OFRXq8EX5eBz5+licdFbX5lr6mpWhi/JxthU9+SyH9/fKhP3mo+pDfkw9jsX2RvF9MSXTfORH /gx8JI4d4nNUCI0p7kw66Tcfh1lUO+6v7KGmMXLH1/mwe6LtSjkTdtIs5l35sG/nPmrm4mJnLkk5 eBZu4wZ9S/dM2+FZyKX56GpG3pf0U7Qk+aAl5CLhsKCUFAemCfvV+XAdRt8mXUtlIOKDliYxE0hG 8imt96Rb3D8hH0keajs+wiLpXWe0t2whDq9oKHEoJBtRpdSrUZvOQt6bj5wDt13UEgmu/qh8Z4la WIv2yLZLujlK0X5mPvJsA4UhMalJSM7Hw758+WKLcifqxn///SfAQW5Y7yHDza/Fh7vukYPb7RYh ol2L5TSLLmVhskQdpuPLwx64KAf3+51uFymZE5KI5sQuzQctGa9+R0N7qzsfGw8mUEVc+QvkEDQ0 tBx7ltumQ8m///6rWOCrPUUOReQn58Ne7k45LBO3H/bY+Pr1q5MTgeueOh91SVJIMgq4Lrc0PBTl /rQJSnaqyBvzgWU6OJxy3J7Wth9k6FtnArKBtdM+QDK6DaeIaPcrGZYSZaU74kQOd+0n5MMNCigb akqGRYd27bQ+R3g5kuhwgBKi9hh0iloSFX6s5WV+2vMfkXJQOJSPb9++KRyPbfupk43o6lnjZ3/q gtFtr1MFJxv//PMPFZLHhj2X+k/37LH34IPC4WTjAYSS4cQjGlYklmtKg9vOL988IO1gN3952MfH hwqJbjsVyceaN+aD9k23hAgOZ3/88YclA/mghed8OKSwkOQ6pscvbH6EKtLIQCEpIhI5OWFX5wPh wGzjgcUDDqccdFjBeq1W03rt6W3VRE0XvgT6qSs/1pMEkcdrLiRYNdq78lE8PoGjAaHKoXwsMJtt BToyBPjQ/MOeu2zX0+zBdm3UlY+I6AZyliPShEQpoWON1DgYZeUwPprlBAzxkcOhZLTtSDkixcIr z136tgRcR9dT3Np53grpISI/MhJnHz9MVaS9TiPyk/BRhEPJiJTDWYQFjnSOD4ra+lwsl1RCKCLK h81IVEgiFdmPyKX5KB7zKXCs22fDhvjAoWqFJ83w1UlIs1bgfbsuEiFyZ7dsJO3UOh+VIz+BD4Rj YdkowuESUo1+GwIELnF6xQvAUeQDEYmaY/dPq4jLWM9QkSvyUYTD5hztdUI5KByS8mF7EflICkxe UUU0p0EV0XnvTkTeko9I3u2w4sTjZtZJp+HQQDguKR+uEMcHIuIaJYBINMpoFZir2oGm7VxhDd62 PYpJbtfiA+OIU1kUj9HZCmXF+bBHP7oSsgTDTbKyYlVER5k8XcW2O3tXPmzgWsdbncC0YxlZ55Dt RJS2H+H4+vWrbPtSi6V8UE0ShojzORloVjbKdHMRFwHnPOuZ4WNexMfCRhanHI9++va0rnI0o2pB xYM6cAgfMoKIxGm1UmIlxKnIGsx4BTr7nfhwfSNmZKE5R64czVxoEkScGw7QJX3ItMgHbSN6noTO 8hElIm511bYU/Xk/PmzU3IRF+cClDnd6MxeUChwCPafwteSDVkH5kG3HRM2UEUS0WJeLWESoimA0 cDuqrmKnr59ivJZ0woKzFRdiDEekH5KuiakdxQdtLPofxdAi0rr/w1iybkZjQt9296NN/n5hRElX OZpZqVA4rHKoRXWhhFAtke21u58PrSsKcQJHdPFgQ5yEPDb+/vtvnPEiIu/EB3aM/Mg8UDz+/PNP m3bY47EuVNEEDokZTfiw8NH1U8fHun0mzdWFbtC4OTlcIVf966+/3FzGZSGIyHX5wLg0s3A8sKBp Rx7KZqgfEuRrERm6M+KjveL9l6g/KAoR6EnrtFiaqz6EpD7KXJ0P7BurHN+/f8fZrM1JqVH9oK/O kwiRCh/C4IjqEoBjDhHNQiwiTUXsolmC7BvwoTGicxY6skTlR4NrJCHUE8qHPcwVS+/vIyVYHX2N ouf22MKdhNhRJprLXJ0PKh7RnAXT0kpLIjjwlCE+sAp8fiznIwoCVlGhBEcZTVTpomoeim5U0Y7k I4HD8oFwtDktFXlq0UAb8UERsR/pEIO1RHy4nXkc8G0UTLtNR5kHH7juvm6NhogGsGuH8YGB0A17 n4XyoTkHRidpUgUOKfOBjZrmQ2pwRFi4K8SqSIODzmUiCclD17VT+HD9YfnQOUs+stgGdK+ACT7c fvsUjyvWPcK+hw8aOvoRIuKyEBxlhiTk0/hwrwtbSrfTFh1Z6LVbGT4jONbgOXXKh4CEaFFzfAjT jAolESI0C+nOdSP3Xs1HBMfCHu94SIi7D0fhEMaBMzrE6J6IDwE4luApHtnNh9SwoMe3jTMk5KV8 uPbYbnCZR1MOO6e9Pb9Ujd4n8uj2RzxRXnFbzuEDI0OjRw/DDbxv9+BjOgv5ND5QPBofTjxwtRRd T0JPrwbciXwIA0WP6T4IKAYRGeFDeohYP+nbVtdREvIJfLiI3+DZQU07mn5Y8VhgVYqOF9STREWc Y2LgKPJBv1+ZXKN5oKLQRZGULR/yzEL0jp2uuCcSkshwhZIZPhI4pJB50FstFPZ62yhJiW+4Lb3n /wQQQT+74aJ78niKiY+OMlZCHqMMzVK7Tp7CR3FkaWbXPB76EWUejo89cEiBD2GgSO3L2ZSVoaBF +zGY1m1bXTLEfKT3/fO4UTuADxp6XPNw4tHWxNDdCI7u+GL3RLqNEMu2P0a/c1XRD/SEvkUm3KsY PqIs1fGRS8jr+MC4u8xUkw8nHgs892Bdz+FIiCny4XYK8FEcVup8CNOSLhmWDzFZCF0LWdnPQ1BX X8EHSsgCa2KYmdoTo8txukmRz10VwRMxxDTWo15FKtLlQ0BCWnKqEvIBN/0TCTmXjyTcC9xtSaa1 iXrTDWduP+1mGvEuHxEZc4GmLrntiGP91Hpi9QPvyCAcn8mH3XZrYhYOdzdOvcQhJhponOH+uno7 twUuUwmopS7tQSS55Nyr1rU+7+g2OHSIiWYxKH5Ft8f4yGOtdoMnCHVNHdfEkuhPIN/lg7pNT5RY PKgnXT8j3xI+qG+tcHfHv75WNoTIXj5crG/bLz61OS3NTK1zRT4q16gLZVHGkaEKrOhV11XqXvLq +Gjps/6CiJ3F0CHmvn207Fw+XKwT8cDMAzNT61wu4PkQkzuZbOfXqFadw2E9pBsVD7tkLGxuhbOY D/N0al1CjucjEo8F1kz1hlx0N67LR0XPE5EQ1vFRQ+xHCZ00oHWpm+BDtojYnw9xWYhKyGq+Z5VL yLl8oOHdWsxMMfqjfLi36JvbP3dwRbpGh8IuH7hTtnw0a38OEfERZakv4oMiYpMPXVC3yYeeaOOY k7GHDysM3Ra5jaFBOrk6K/VGPidDjDyzEMeHG2LW4DeZj+cjEQ99i2tilo/l+d0W61ZdOUb5oKOG PdGhk5OUWK7e1Kb5kG0Wsm6fS7V3dF0K0jYSJyOfD+DD6gfyoffk3NXc5SN6i01yik0RqTewfozz eZSPnJLoAMoHTVFxrT1xchcfeQc4Ptzg4vjAvh9Sjkro64hEWtKNADaBhn4N/iviKD5kO4v5+4fR VZD80TI5nA/bAZpYRDMXmnwkfERvk2a4ODqLjo9Kw1OiQvJLk/Lhel3KZMgWEcdHG2Xct2McGXf2 fe48FGN8RB2gfOCyh05nJLjyqMcTfLj4qkt255AUYcOdqZP06pTT+JAfiCgfK7sXkzwx9Dl82OSj 8eFWxvR0G76cDwFQkpbk7ArwkRSFZeJH1vM1/kGfOT66b7X8Zngvxq2STc9iBvigF6jCscA9Obry ISkfUlMO2pjIMd2Jz/4kcbFl5r+BfCwfNNp0Q4AP+8QQ1Q90kobX2gwfbeNmvvdm9cPdk7PJxxI/ h5zzQb13O5EPWy9+g9L2NBbuvlGHvdJOxOhH3jo/k438LQZh3T5xaG/ErLCKiipCg6nW5wPdpYa3 XfC3G2yr7EbER+59hY/b8xcKsZupJ1GT3SnN7C/tT/CBFQlciskp1hmc5dohZi3Mco/hI0LEji/u aTG7MrawDDEBYg8fupi7bH8eGeNLy494EsOH/fvjOT6wogTNXD/WbZb6Ij6cfxEiLjl1M5dlu3Lq HIooSVynjUn40D9QRj5caWvwrx3O1RbuA/kQhgL9SPfY+Dg+PrY/AEH5kNpFWOXDRV+3cfHDPU34 bfvbHqgfkaNH8dHgsLmIbVdUC9UPG19FZJQPLJaKRMJN5NLH9ntT9EZ/stB+JB9o9pkgfCDIXbgT lCRtiDxstVs+bua5+ciZSD8cHCghdT4kRUR6lGBALB+qH3gXBg2b78o/ho+b+TaD+1UxTE6pKxHL uffoKuXDwhGtmHX1A+G4P/8Me1Q/bBXRnlw2dKflw85iuuPLAXzQKyniw92Z+2a+B4UXInVoDxzU PcuHwnGD3yrC6lx7XR8oIpYPuv7RtRyR+h4LLl0iS/LTI/mIEHGTW7yn75qEfuRw0FOotwkfVkX2 8GFtJx8YGbqzi4jjw0rIWXygvtnQC+sJHF9cT7gmJXuGlMM5KdtfD7B85PlQlw9FQeO+nw/nf32P 818Rsatk9973tp3D83wkiODkFpdNXeF5948qh3W4yMcteNKRliaQfGjcD+RDAi2RAAvkm+qHvYvr Ji/H8OEuJseHhrsx8egM/KoLKnnlbXd/4rClVvnozqdy/XCZxxl8YFvoHkTE8hGlIDi+2MElQmSA D4rIzaycfoO/Foz4mFCI7jHIR3ttf2hH+VjYt3+x1a/nQ1JEIgm5b1fJ8q9LncIHblvdtr+3b3vC lZwPLpGN8mElRP/zMBpfEj50z4v5wHahV46PdfuNB/x7QwpHPsQM84FvXXJK7+lbS0a7IXOnD/Hh wLXBwobrAUfxgTGJGiU1PrTeoSmMazh1gPNBY0T5sPrRVj5sT2AsqB+6nQQOrc5H++fD2/ZmMgY3 14+EDxt6dMxZt41RuyRQDntikoIgxJHPdrvKB91YgsmL7sSWHMhHdG7Chx34XCEuTBEfK5vf1vmY uADcKeiYmh0B81VU2xyBa6PDBxW0CBEcXG5wTx8rdhF8AR80/xAQD9clTmbWYH2syMdc6ygfWJrF 1/FhHY74kKB3+nxQRIT9gqXVD+QjIjSxSHu6Z1E+Iv2gXesuBoEOGOUjgoPGJ28d3dbaFQicwtzT 9Y9hPiwNKCFOP2z+QTXctmQ0EPUSUNuQD9vrshUPy4dT8pXNcikf6CG2Za51lROj8aWZbOF4HR92 fEn4qMBR0eFcwxM+bEO0KMqHbPVDD7CIVPgYgiNvHZ7r8I3Gl+kpbokPutHMroypkLT8AxvThaNC Rl5UwofuoaOGpHwsP1ZK1JQJx4dArCfgoM0sDk/rdgqDKSqFw5FxGB9R8nGDe/qutXlToz329IQP lDd9RAj1g4bJFqXb9jDHx7q9tSFxv9q3UZdXJJaebuVtYgkEnZeEDxsayscN7rzs4aMSOCkgUuTD FeKufhcEd7wFwj0fJGU+uioyobXrNv+4P1dR77W7uLZS3RjmQ7cdHzT5KPJRhAMLwSve+qkeukfI 9EiEw/Zr1AS1ln+sZl0BY13hA3nFJnfDokcm4wv6SX2WA/nAO3PYDXnjh+DAQvBKdeKx/Hh+XZXj tn2SnsKBDXfHKx/6FSlMUQX6lW7TJnfFA8/VVtDxJedDQPZewUcRedraSDwl4MOFOxGPyuCCcdCz XDe455OFcYaNKo4yOSU0RJfgo721yambv9jDitZVEXdd2m081wLRHVwwTMLgwCEJEUEJoW3ECEct dXu6PKljV+SD3nlxV14euDwEkYSg504/rHigEmCYpMZHs/otXIrFnvFFy3FencgH1cAJPpLG501N Pq3w4fQD4UA+aIDo4LJTQpIyk8bmZptzFT7kOcAjH3vyD9kGiwpP5eq04vHlyxc3raV8YFxo850b Gu5KFhLJRkUvqbkTtTk4v30pH2L64GYWTN3Nuc/iw85Tlu3MxbkUxShpvq3OXa9tIpNIyAQfrtW0 vRiZi/Lx/ft3mp/iZUfNtZY2HpvhjqfiEelZzodsO5LibvlYt2tliMgePlwQolOsMzq+2J8zPJ4P GiPkwz05Rq9XOZkPqx83+NqtMFIxTFGx+CoxHzZRlWCGdR4fa/wtmA/49zEX0hfxcYPvMA4ZvVKj DedenpkiZzRMGIRIRWyX0FEmat0EH12jfCT/XvgiPpInC8/jQ+B6WmBNrDttwW0aB9vqSEIUEbwj Q1v3Aj4w//gZ+EjEU7Z8uLNyPrAcjJQw5sSQQVUk4uMefJ3/Nx+nrH8kfCzPzMPOWSgfiIULUxQK aq5MxSJ6aIhGeCJKkSEfH9s/DXpvPvKD0XV7ovrjfs0hutbR8FcM9ScPtS04YC3Bt+twLhNFuNL2 olk+XIr6OXzYxQ/3a3SjfHSPtCFeYRRAPm7bNbHVDCs4C7X/dutc0t8+vG1XVpA8i0h76mJlo0yE BY1AV1Ddnsvx4RbXi+tjE5cL8mGdWbZph+NDT7GZo4VjZUOMlt+ExPHhcizbMVZC7uymzKiEuI+Q DN0/xwdicQof9uGxiI9pIXV8WMdsWvrNfLui+WCjYMloG/ozyFiLbaz+QqYr/Ga+x+v6xiIicIFS MqLgXJcP2WIh5npVwUjmL0mbR40GxfaZ+5KcEw/LhL5aOFBCXHsbIpYPeyXo6aofj86oSEiyPRGi q/PxDb45dwgf0RWDE1pMkBGOu7npWufDIhKlwK00vdmhiKzbp1OTaNO39RBV+LjDv3wM8CHQrzZM 8hl8RHCoeHQzjwk+8PK4PX+KmfKxsCwEJSTiIwlUJYC2X4f4cK1+Mz6QjBUyj0g8bIdZLNqG+2l9 SfkQw+KyfSANtUq2v+NDE1UacNyei9iL+JBtB6PYOj7or63vaTANpeVD+4xmHkuwMrFuH9eQrdLS UESIOBVRD9ftROYx1123M6Yo4MmeetDejI+51togUk1OBpfbdk5xh5UrKypi+BCmH1jj8vxBd1Qs C1xxiMGwdy06Umu/Lh+uhyba78IXXW03M8PUXxizI4vlww4ulg8aI1qdFqtVRAu1WG+yVhZFvmv0 SKeXFg5dYrdKJgYpYXDIgXzg+ke3we5TjBqNo+0nFA+tWmBNE9MOe7m7Gql+tNcuIlafVLTu8Sym GJ/up5YPvD/3AY+QyU4+MDTH8pFYEkF1AFOB2/ZfzFwnRXBggJI4ODrd860WTVv7p/NBf6s/av4u Pmx06Pr6ss3Xiq11jaR7HKaafOh2JB4re/gvik4xFA4Re+WgA/iAO7YrsuhTyoeF0o0vd1gfiyIw z4cLTcIHbUAeCNdD2GE5H7Z7bKQqcNDqkmigiiwwa3MOJHzkkaEf0Z3Ix+jzHxiKvXzczH82dPmo W3I1J3y4Erp9g3Cs8e990YBYLvVfqqwPzbp8YEXJ/uhIy4fmHC7/WNn6qezkQ2B9TMf7+vylbvml 7Piw8u4idX/eYcnhkJiPpG9sQNQBvY1n3dC7PDjFrSCS82qN8mHHF5xGUU9O4aN4/7ZrERzC+Fi2 /y3nwoRwRB2D+61OJJ5Yf9QaJa0ofbKE8oGe5GqRh1Rrucfff3GT2yP5cLGIxpc9fCRwoA/uqpXn qN/KidY5aC1UP1wc8KyID9dwdKPORw7H8ky2bEU0OcXJ7boV0Sg4u/jAFPXbju9XJt1Gr1dbi/6d scA/W3fhkB4fVpncidYlFx9X/gQfFTiE8WGTj/Z3hTi5fR0ft/Iv8yeWd5t1I79eV6OcUSyKDlgo i4clfDiv8tLybfdWC7GLgfovlm3lwy2eWpecG4fxsfQeIcNWRRb1XJEPPGUUDjwAxSPxlrqHZ1nf ukUVtwWarFNZt3i6mgRI9vAhLDp4lST68djQw2j5aEP6IbuvVHr9YY2R/zlM1kk9nvbH4XyoVNST jwQO2c9HPoXBxiSGzlE+xMBBg5VfH5FLeVd1j48C5Q5OvMJKR/nQJCNaOR0dXKTLB225G18oIhN8 UP+oY+4yxRKSlif+2OOj8aXoYXLiSXDkfER3XuQkPgT+WczOcidSkMRF6ltSeDHhwIs7OWbCQ2oT I4vEzAnjY3pmS70a+39T+9q9C0Pzg4rlHRCNfe7c/b0+4XlieTfIlHhoUXbmgnys5k6hwPVzFh/F FGQuyqOIFEvoOhNlPHXfck+K1A7xgYNLfebSdW+AD9nCYRFBCUlWQYqXbxGRqMCuVRKL/YNjEQ5J Ax75YzPTlf3m6dDgQj3cywcdYtwqarEv9yBSKb97TGWa07UIiG6yPMqHyzwSPiozF3Sy2SQfYoaY 5UeWWhliKhHvEl3xs1hX0XKl0U/xokw2EoeTcFE+7MjyeJ3LTKM9M3zIdhZTHGKKHRbxsTOyeQOx /Ho5tJDuSI/lY5QSPqx4rGbNNBlcKv7s5UMCCUlmMbfB3xIq8iExFkOURLVPy08k4zl/1OeIDxsT HVnczMXec7HiIXFIqYeTfEghS02ykMTqfEhAxpBcTRgtOVGOA/lwNWryMZp5OJ8j9yThI/G1KCFz E90X8+GuyO6RXbddZyRwoP90I6nxbqwiHolg7OUjaoOVkGSt/VZ+4pA6nfRfRMaEiuxJQYp8rMHz i9EQmVS3J/PA9s7wIWUJwbX20V8UQkcr3qMn9HXURrMQdDVJCRM+iuLRStuTeUjKirVJPmyTKhJS uR1TGVlozyl5r8xC0CUB8RADih5wCB8r3I2bzjyiPc0O4EMOykLO4IN6fqBRn0f5SODQbRsNy8fo gil1m+5pNs+HDEpINwupZB7uLcXiZXxU4BBoV50Pe6QrMBePocwj2tNsgA+6x+nHkt7Rje7IoIt7 +BAmIVFzup6MHp/zETlPfaZ8qHhoqtEVD/Thc/hIRhm3nEr7aZoPicUjAqI+zHUthyMpcI4PzS1w zYM+B0QdOIwPCYCg7XR83J7PperbZJSpJB/4lga3iEjXh4olTFfEI/JZYvGgI4vKSfKcR97SeT4S v+1ObeHEHZnE3SIfkiISub3HKBlSE48o1aAO48jSTB/yUFZW85xH7sAn8CEgIZXnhtprnQ+3M+cj 93bUogjmwyKeFcEhBT7sfdoP9t8ubk4buf1qPmSLiOUjGmXyTprmQ2qIdJsTVZ0c04UjGqm7rjrx KM5pkyacyEfSksUYlZD6cmq3hYmfqNWRw8kB3XqTgY86n/iT82F7HVfTXeZREQ+6/0V8yHauG624 W5K6DagjkgznXefrlgeXbuewJnxof0cLHvYWnTxhSlytNMHaKXzI9jeoEJHuinuelnb9rAwuE5R0 x/KKn9NwrM8nCCkcRfGoE9PsSD5kKyELrIW0H7LFLCQKTaUBkUvF8aVIScWfUQ/dHjrAuTkLHVnW reXOnMIHet+9LrX7KSKjy2WV/XN85B8lNY4qXKK4uE3hsHMWTDumM498/1l8JIkqTmRGEaFHjopc sTknKQfu120Lx7qd09KlUpzTJi6dxUfUmOgwfb0Zc6OMflpPREate9XmzTkPjly0HByqHHbxAye0 78GHgIS4RPX24xex8xXViuujPic7u/WOhnXCB1smwqHioXsSOCYcfikfko4yt+dfGt7Yfd3phLHr c7IzT5AnNCMquZLr0LTj33//HboJN+rzp/EhbJSJJGQIEWtJ418wcylW3R1TxOSkq7mJj8pxZ79h usftg/mI9uBHVkLcQGP/d+H1iFSqGJ2k5FV0s+AIDjtbydOOxNVr8SEBIqgiOMpMI9KMNnXUeSxn JxxRjXaAsHBgTlqHg+6/HB+yHWVwRaS90j/oOByRF/NRVA4rG5aPPcoR7b8uH/KUEIF1d4vIqSoy lIXsyTlomUktDg61Y+HoNuEUPvL9suVDgoFmVEXc22I46s5PXHzdiqjPNOewcNBF0jk4uk34HD6k hkjTkqKKHMtH0Yp81GuJ4Ph4/l+de15wJxzTn57Oh4wjohJyM0urrpBuw/CjnYgcVXI+rJwBRwXu z+RDAkQUgmigWcCwwKHLYjQnrTewYt1hpS2CWVz2DyvdT/NjXs2HQK6K6Uib9C7m7gwda9rGIXx0 I7gHES3cKYeFQ/8D9XA4Kgckx3wCH2IQoXxEAw2ONV2r8OH6L/J8AhFboHazdrxCYMcUu/8QOHYe 8yI+BCREemONpcRlrMU+y/vbdR49hSpWxVAzpKYcLuF4gXLkR04+P1b/NDnSpauRkNiB5mZ+rcp1 nrPR0GBPOIjrLY0GFMuHI8PKxh3+33q6gZfgY88xESKWlaYiVmMqfBTNdqEw/cBBrSJaWKyVDR1T MNu4sz+Hi2rpulG3i/Ih20vTZawJJW6s2ZMcJEruqujWRUcTHFYi5aiMKflHQ8dUjn8pH9FhqCJu uFnMX6lTRGyxXU8i/bd7nEtuG+tytNlirTYoGW5AOQSOUSYqZ72aj+RIOtagluAfZs+pCF7lYnoX nZGehESyoSi0P3inqeidfXslqmLu07mzDuNj9Eh6MBUSpCQSEldm5Ew+CtjTqT+0ZCzTpRRUOexh su2qoc6eI6Ny7mfyQY+3HWARoZRYevDcqNIuHyuML3U+rGasz/9oHh1TRvv7bfiYO16C6x5VZNkm JSoktP+6+qF9Qy9iLa0yY3JlaoFNNhIyPlE5KiVchQ88y3aG7SE3r1m2f6HdRUTDgcqx/vjLbTH6 of+XnusHhaPJBiWDrnDM9fQvxAc9HXUe10La66MvFSNJJQTHAkuG1Y/26ihxTmJp9n/dkQyrGXvg 2E9GpZwr8iExIrJdI1lg0cz2ZeKJ6yT7l9uyzT+0ai05KdCSQTdcLXruRIjemI89Z9HTsbeWbUbi dspzaKBu2O7BP2NHH5x42P9ybyVIPK2l+2UfHEeRUSntunxgOdhbyIrrS+xR7U6BoUG2mi9GtBJB okVFcMhu2dhz4kRpb8CHgJYkoESIOHM0YOe5ql1RumE725WWw0Hr6tqxZFTKfA8+XJl4TUdkUETc FS/jfDhDLASIca9z9puPgWIpB5URoVnSc7qBglEZX6K3sqODzyCjUvLl+HicVYmF67CIlcgT12F5 /7nRrVtOpBZXU45KyVfkQ2rhQESSVzRkIqk0b0uFtotkG6PlX5QPKUcnGgIS8VDbL/vdAq+pGfXy X8FHV7SHTsyLqmQMakeJ/+Flnk1GvZYr8tE9t3tWsq12BhBHlf9L8FE3Wv5+RKKSnR3bGRfXjNEa r8sH7p8Ybl5s19eM0RovwUdUSzIvnSvwQNvfna8HYsKBN+ND7XO15CeAo+jDpfnIq34lIod0587U 5MBeqHtydT4qtXdbu9P/s2c6xbOuy0ezz6Uk+ugKQq026sxO5/eLa9feiQ/89Dpw1D050OdfkY/P qo7azihfB99puy4fe2qcWLGNzpqr66exAT7UJrptfwr5Ai19ZcnvYjN8yCAir1+rqOP4m4DcJvmw dvaVfdK48NsqdgAfv+0ntv8DQCWoXQplbmRzdHJlYW0KZW5kb2JqCjE4IDAgb2JqCjw8L1I5Cjkg MCBSL1I4CjggMCBSPj4KZW5kb2JqCjkgMCBvYmoKPDwvQmFzZUZvbnQvQ291cmllci9UeXBlL0Zv bnQKL0VuY29kaW5nIDE5IDAgUi9TdWJ0eXBlL1R5cGUxPj4KZW5kb2JqCjE5IDAgb2JqCjw8L1R5 cGUvRW5jb2RpbmcvRGlmZmVyZW5jZXNbCjQ1L21pbnVzXT4+CmVuZG9iago4IDAgb2JqCjw8L0Jh c2VGb250L0NvdXJpZXItQm9sZC9UeXBlL0ZvbnQKL1N1YnR5cGUvVHlwZTE+PgplbmRvYmoKMjAg MCBvYmoKPDwvTGVuZ3RoIDEzNDk+PnN0cmVhbQo8P3hwYWNrZXQgYmVnaW49J++7vycgaWQ9J1c1 TTBNcENlaGlIenJlU3pOVGN6a2M5ZCc/Pgo8P2Fkb2JlLXhhcC1maWx0ZXJzIGVzYz0iQ1JMRiI/ Pgo8eDp4bXBtZXRhIHhtbG5zOng9J2Fkb2JlOm5zOm1ldGEvJyB4OnhtcHRrPSdYTVAgdG9vbGtp dCAyLjkuMS0xMywgZnJhbWV3b3JrIDEuNic+CjxyZGY6UkRGIHhtbG5zOnJkZj0naHR0cDovL3d3 dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIycgeG1sbnM6aVg9J2h0dHA6Ly9ucy5h ZG9iZS5jb20vaVgvMS4wLyc+CjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSdlYjljMGZmNC00 NTJkLTExZTAtMDAwMC0zNDQ2NzkwYzk5NDAnIHhtbG5zOnBkZj0naHR0cDovL25zLmFkb2JlLmNv bS9wZGYvMS4zLycgcGRmOlByb2R1Y2VyPSdBcnRpZmV4IEdob3N0c2NyaXB0IDguNTQnLz4KPHJk ZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9J2ViOWMwZmY0LTQ1MmQtMTFlMC0wMDAwLTM0NDY3OTBj OTk0MCcgeG1sbnM6eGFwPSdodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvJyB4YXA6TW9kaWZ5 RGF0ZT0nMjAxMS0wMi0yNycgeGFwOkNyZWF0ZURhdGU9JzIwMTEtMDItMjcnPjx4YXA6Q3JlYXRv clRvb2w+QXJ0aWZleCBHaG9zdHNjcmlwdCA4LjU0IFBERiBXcml0ZXI8L3hhcDpDcmVhdG9yVG9v bD48L3JkZjpEZXNjcmlwdGlvbj4KPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9J2ViOWMwZmY0 LTQ1MmQtMTFlMC0wMDAwLTM0NDY3OTBjOTk0MCcgeG1sbnM6eGFwTU09J2h0dHA6Ly9ucy5hZG9i ZS5jb20veGFwLzEuMC9tbS8nIHhhcE1NOkRvY3VtZW50SUQ9J2ViOWMwZmY0LTQ1MmQtMTFlMC0w MDAwLTM0NDY3OTBjOTk0MCcvPgo8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0nZWI5YzBmZjQt NDUyZC0xMWUwLTAwMDAtMzQ0Njc5MGM5OTQwJyB4bWxuczpkYz0naHR0cDovL3B1cmwub3JnL2Rj L2VsZW1lbnRzLzEuMS8nIGRjOmZvcm1hdD0nYXBwbGljYXRpb24vcGRmJz48ZGM6dGl0bGU+PHJk ZjpBbHQ+PHJkZjpsaSB4bWw6bGFuZz0neC1kZWZhdWx0Jz5DOlxcVXNlcnNcXFlTTFxcQXBwRGF0 YVxcTG9jYWxcXFRlbXBcXHRwZjVlMzhkODBfM2FlY180YjNhXzhhZDNfMmExMWIxMjcxMzkwLnBz PC9yZGY6bGk+PC9yZGY6QWx0PjwvZGM6dGl0bGU+PC9yZGY6RGVzY3JpcHRpb24+CjwvcmRmOlJE Rj4KPC94OnhtcG1ldGE+CiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKPD94cGFja2V0 IGVuZD0ndyc/PgplbmRzdHJlYW0KZW5kb2JqCjIgMCBvYmoKPDwvUHJvZHVjZXIoQXJ0aWZleCBH aG9zdHNjcmlwdCA4LjU0KQovQ3JlYXRpb25EYXRlKEQ6MjAxMTAyMjcxOTMzNTQpCi9Nb2REYXRl KEQ6MjAxMTAyMjcxOTMzNTQpCi9DcmVhdG9yKE1BVExBQiwgVGhlIE1hdGhXb3JrcywgSW5jLiBW ZXJzaW9uIDcuMTEuMC41ODQgXChSMjAxMGJcKS4gT3BlcmF0aW5nIFN5c3RlbTogTWljcm9zb2Z0 IFdpbmRvd3MgNy4pCi9UaXRsZShDOlxcVXNlcnNcXFlTTFxcQXBwRGF0YVxcTG9jYWxcXFRlbXBc XHRwZjVlMzhkODBfM2FlY180YjNhXzhhZDNfMmExMWIxMjcxMzkwLnBzKT4+ZW5kb2JqCnhyZWYK MCAyMQowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDEzOTMgMDAwMDAgbiAKMDAwMDAzODIxOCAw MDAwMCBuIAowMDAwMDAxMzM0IDAwMDAwIG4gCjAwMDAwMDExNTAgMDAwMDAgbiAKMDAwMDAwMDAx NSAwMDAwMCBuIAowMDAwMDAxMTMwIDAwMDAwIG4gCjAwMDAwMDE0NTggMDAwMDAgbiAKMDAwMDAz Njc1MiAwMDAwMCBuIAowMDAwMDM2NjE2IDAwMDAwIG4gCjAwMDAwMjc2MDcgMDAwMDAgbiAKMDAw MDAyMDA0OCAwMDAwMCBuIAowMDAwMDEwOTQ0IDAwMDAwIG4gCjAwMDAwMDc3NTcgMDAwMDAgbiAK MDAwMDAwNDc1MCAwMDAwMCBuIAowMDAwMDAxNjE2IDAwMDAwIG4gCjAwMDAwMDE0OTkgMDAwMDAg biAKMDAwMDAwMTUyOSAwMDAwMCBuIAowMDAwMDM2NTc3IDAwMDAwIG4gCjAwMDAwMzY2OTQgMDAw MDAgbiAKMDAwMDAzNjgxOSAwMDAwMCBuIAp0cmFpbGVyCjw8IC9TaXplIDIxIC9Sb290IDEgMCBS IC9JbmZvIDIgMCBSCi9JRCBbPDAxNEZGRUIyODAxNzBCMzVDQzU5NDk2QTRDODU2RENDPjwwMTRG RkVCMjgwMTcwQjM1Q0M1OTQ5NkE0Qzg1NkRDQz5dCj4+CnN0YXJ0eHJlZgozODUyNwolJUVPRgo--001636283ab280d810049d4d24ad-- ========================================================================Date: Sun, 27 Feb 2011 20:56:27 -0500 Reply-To: Glen Lee <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Glen Lee <[log in to unmask]> Subject: SPM8 error MIME-Version: 1.0 Content-Type: multipart/mixed; boundary cf3071c9bae64ca3049d4dfcd2 Message-ID: <[log in to unmask]> --20cf3071c9bae64ca3049d4dfcd2 Content-Type: multipart/alternative; boundary cf3071c9bae64c93049d4dfcd0 --20cf3071c9bae64c93049d4dfcd0 Content-Type: text/plain; charset=ISO-8859-1 Hello SPMers, Does anybody help me figuring out what this error message (Error using ==> < a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808 ) mean and how i can resolve this issue? I get this same error no matter what I try (e.g., realign, normalize, etc) in SPM8 (also see attached) Let me know. Thanks in advance. Glen --20cf3071c9bae64c93049d4dfcd0 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello SPMers,
Does anybody help me figuring out what this error message (Error using ==> < a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808 </a>) mean and how i can resolve this issue?
I get this same error no matter what I try (e.g., realign, normalize, etc) in SPM8 (also see attached)
Let me know. Thanks in advance.

Glen

--20cf3071c9bae64c93049d4dfcd0-- --20cf3071c9bae64ca3049d4dfcd2 Content-Type: image/png; name="error_in_SPM8.PNG" Content-Disposition: attachment; filename="error_in_SPM8.PNG" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gkoqhqmh0 iVBORw0KGgoAAAANSUhEUgAABAAAAAM5CAYAAACZz0NoAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAP+lSURBVHhe7L0JmCXFdSZa8sznkT8v79lPM7Zs y8uzLFuyvOgZgcozfuMZ+zHeJdnNIiFE08v12JatDUED2kBSIwRdQlgICUtoAwQ0EgiqC4lFgEQ1 0E033RS9Fl29d1XvVd21L33eOZEZ90bmzSVyufdm3vs3xFdVNyMjTvxxMu85f5w40TU6OkpS8A8I AAEgAASAABAAAkAACAABIAAEgAAQaF8EukAAtO/kYmRAAAgAASAABIAAEAACQAAIAAEgAAQ0AiAA oAtAAAgAASAABIAAEAACQAAIAAEgAAQ6AAEQAB0wyRgiEAACQAAIAAEgAASAABAAAkAACAABEADQ ASAABIAAEAACQAAIAAEgAASAABAAAh2AAAiADphkDBEIAAEgAASAABAAAkAACAABIAAEgAAIAOgA EAACQAAIAAEgAASAABAAAkAACACBDkAABEAHTDKGCASAABAAAkAACAABIAAEgAAQAAJAAAQAdAAI AAEgAASAABAAAkAACAABIAAEgEAHIAACoAMmGUMEAkAACAABIAAEgAAQAAJAAAgAASAAAgA6AASA ABAAAkAACAABIAAEgAAQAAJAoAMQAAHQAZOMIQIBIAAEgAAQAAJAAAgAASAABIAAEAABAB0AAkAA CAABIAAEgAAQAAJAAAgAASDQAQjEEgA93V3U1eWWSoUq3T1VWDzX3DrdPTvV9Yr6u5vcP517+ipO W0YbHYBxYYfozJ9vjgKk7at0kZ7X1g2mz9GpAN0phnytQwY9AwEgAASAABAAAkAACAABIAAEbBCI JAB29nR7HD9xtLwOmDhlFeozeurpdh3KnT3ULQ5bpXa1r8LtdXvr2whZ5jo9BR9vdb5KATLrW8HI o6LPbymmFUICASAABIAAEAACQAAIAAEg0BQEEhEA9RLVEwBCGiifXwgA/qXqIPn/jhledXW6x40a CIgo0BEIsjot/TqRCgbBoEkIHcEQ4Dx6ohiYvOg260Tcr/urcFRENUIiqm9XBoMPiUSgKpeHQHEj MUKjMPxkjFM/FB+WQBMANfxqGJqf+SMAYsevRueu2uu5c+fSOprAh3+lz0sARMknveehH4r0MvSn juDS10Lm1xslk2B+qmOvRWhUx+vRY41xZxFrTXk7ohMgAASAABAAAkAACAABINBmCCTbAlDnQMdE AIjzymH/4vCJIyV/JlkxdZwnv0Pvc3T82wr4b+0z+1e3lQNljCEuwsHmflM+f3uOE5rSMXMJE7++ SRSF3lYh+HicacHCP0cR+DjyOe05zmWwrEHjknv99/jriXxV/sJ1aK2df0UemNsTwrcAhMnncBC+ bScJ9EM5/wYB44zXu2Uian4zz4/77Jg6UN8fCIA2eydjOEAACAABIAAEgAAQAAJAoGEIxBIAZs9+ B9q7wltbbVb3VB1Yr+OWxCH2OJCuINUIAy1YgJNU7d+3OlsXIeBZoY6JHqi2VXOSbWRJMl7/LOt7 FRGiHHtjBdyCINAOcJTTXV2hjgitjyIAPBEN5lyIfL42Ix31eqajPu9AQJuaiAgdY2r9sNtuEDq/ uc2PQcqEjaVhrwc0DASAABAAAkAACAABIAAEgEA7IZCIAHAcfnOVuD4CoAqO6QBx6LbOBJDEIQ7a n27jdDsy2Dlwnsn0rKDH328jS5Lx1vvAstrsyKFW/vucbRVegsV7lxkhYEsAONsYwiMV8iIAdDSI 1QMU5OzmSQDE6kf8/Ms4Gk0AmJhl0SUrzFEJCAABIAAEgAAQAAJAAAgAgbZGIIIA8IdgMw51q5qW BIABYRInprbyXWug7v6IVdG47PB1BIMQAL4991Gr53YEgBEyrsLRE2wJkHB1ds6VDDJODtc3V9zr CZIApzVm1bjaRoRsqQgA5Rz7w+WTnCZQr1v1SSg1F+JNVukndcLmME4/6q870Syhc+DDMI/5qZIM ofOILQBt/YbG4IAAEAACQAAIAAEgAASAQI4IxBAARgK0iCR8Tmi919lTJwCYn3sSutk5weJAVXjl u5Zkz3QgzQRzNTn9Sfbqjyqs9R11TWMcVsdMQKf61HvN3aR71TkyPk/k/EsDCjONawghE5qELh4f 8xhAM9md9yhHvw44+FmN35fETwgM+xwAXkyVDsgxlMZRgM5Rk8HyBW1PkbpJ9MNxvr3t1yVxjJpf fxJJT//x8+PVobBnBgRAju9DNAUEgAAQAAJAAAgAASAABNoagYRbAJqLRbmOqGsuNmXsLVEOgDIO sFEyY+9/o5BFu0AACAABIAAEgAAQAAJAoKMQKCwB4D2eTxLg4V8ZEcA8pp81zxGEAdEL6VvGnUAA CAABIAAEgAAQAAJAAAh0IgKFJQA6cTIwZiAABIAAEAACQAAIAAEgAASAABAAAo1CoJAEwJkzZwgF GEAHoAPQAegAdAA60C460ChDDu0CASAABIAAEEiCQKEIAP+X/MLCAkmZn5+nubk5T5mdnSUUYAAd gA5AB6AD0AHoQKt1wG+jiN2ibRi/bZPESENdIAAEgAAQAAJ5I1AIAiDI8Ted/pmZGZIyNTVFk5OT KMAAOgAdgA5AB6AD0IHC6YDYKdpm0aSAJgNABORtwqI9IAAEgAAQSINAywkA8wtRs+XypWk6/OPj 43T69Gk6deoUjY2NqTI6OooCDKAD0AHoAHQAOgAdaLkOaNtE7BSxV8RukQULTQiIXRMUEZDGcMM9 QAAIAAEgAASyINBSAsDv/AtLLmF88oU5MTGhvkTF0T9x4gQdO3aMjh49SocPH66WkZERQgEG0AHo AHQAOgAdgA60SgdMu0TsFLFXxG4R+0XsGLFnxK4R+yYoGiCLEYd7gQAQAAJAAAgkRaAQBICw4uaq v3b85Ut0eHiY9u/fT3v27KGhoSF6+eWXaXBwEAUYQAegA9AB6AB0ADpQGB0Q+2T37t20b98+Onjw oFqsOH78eJUI8EcD6EWQpIYb6gMBIAAEgAAQyIJAIQgAYcQl5F/C5SR8Tr4wxfGXL9Lt27fT5s2b acOGDbRu3Tp69tln6ZlnnqmWtWvXev6uXeuht3W9kd57b62ueV/rf4d82eag6PgVVe+0XMAvm/4V fX4hH+YXOtBsHRAbRWyVF198kbZt26ZsmAMHDtCRI0dURIAsboidI/aO2D0gALKYr7gXCAABIAAE 0iLQMgJAf/HJ6r8O+xfnX8LnhDmXIl+kn/iLLqpUKg0vy5cto2VLl9LSJUtoyaVL6NLFl3Lhn/K7 LvI3f7548WKj8N+Xcl2+bym3sbwJsjYDD/TReJ0rC8bybCyVZ4PLkiVS+Blx/14mz43ovftTnqFl 8iy4ZcmSS/l5cov7maoj9yxfHvNcL+c6TvuqfiWuvs2ccRvcpiOvjMmRVY3JKFr+ZTxeLW9luU37 qFMWvYac0NVG6MA3vvENev7552n9+vWKCNixYwcdOnRILWxoAkBvBQABkNZ0xX1AAAgAASCQBYGW EACm8y+h/xIWJ1+M8gUpbLmE0d17771V5+Cpp55SbHkjysICHzHI+/JmpqdpWkrYSQMTfPqAW2Q/ nxTzRIKpKb6XWf1ZHo95/I9K+nNmocr0Jz7PWI5BdI9AlJ+qbeN4IZ1UyPvTPH4oQ99nEp4/zbKe mefxahnld/lMStK2Glpf5DL1yTliMixTc6DsC4yNe0zlghqzW9Rnci0hdlnG6+LuxV70xJFJ4Z9S Hrl/dmbWeTb8ZWZarWQ5x2/xT/nbfYamJfmVZCiX50QX9zN5xqQtadfE3dFh0V2nzM/LvNSON5O/ 5fO6U0P4+XKesSA9c8YvOqnaE1m1nNNT/O5xTxWpyuk+566sMoapSUPeWZbBWLkrll43XueC3zfO Ua3NLP4j18r2d5oj6wTfu+66KxbnPLH44Q9/WHcEb57tm201s688x2DOi9gqmlT46le/qqITN23a pLYvSlSjjgIQm0cnBQQJkMWExb1AAAgAASCQBoGWEgA69F+caUmWI1+Q8kUpzv8FF1xA8mUqX5Ji LOljdcJ+hhlU+ovef11/PjPJyQYlgzLnGzguyXuOcKLBkcM0fHCEDh0c5sI/D4zQwf3DdGD/Qdq/ 7wCX/bzHbz/nJjhABw4eooOHhmmY7zl2/ASfUHCKxpkoEDJgRpwNcd7Z8Zg3nGDbiRLHRZyl0xwZ cYpPPjgtRWcXlqRC7Jw4zhQfO8RFfjpOmvNzhh2dWXag5pVDtcDdnqnvWhxPcaq4pP03L/MjcghB cnqcxkc5C/LomPo5Lnic4iRI40KYiFziKDLpMidOwxl2pliqENFEHnG2HOdyxjGYWN40/86cEYeY HU5uS+mQOH889+PjcrrEmCoqa7PrnIqcgmH16EkhfXgMEzI+KTym0zy2UzzOsZNu4d9P8ZhPjXKb Y3z9lNSdZLJInEyeC3EeZayJ/zlztCB6pMggcWJ5DCzrlDiukmSKdURhLdizPLqIfFJEXiGwZnhc 84y91T+uNs1yj54cpWNHj9PxY1xYx49zKOuJkyfpJJcxfnZEN0+N8TN04jid5Gfo+OEROjp8iA4f PECH9u+jA3v3cNnLz9B+fq4O0gg/L0c5JPbkiZOMz2nWYcFG5oZ1lccm8zQ/J/MkDrqQbZJEa1zp tYxddED+E6ffOS5UnjOnyO/yvDnEgUNKzHI709LOaZmbE3TyuDznI3Rk5BDLw88wy7if5RMZD+wV eXn/7t79dJCf8YP8jB/iIjLLu2GUZT7NWAopIASG81y13784YkPGHeXwmw5W0Ls57n0edz2QkAoi qZr0mbwnkpQ0x9kKJt/85jerJ+TE9ZemD/MewfiJJ56obs8zr2kSPOlPeccGFWn78ccfVxiG1Wn1 50FjFbkFJ5kX0XNFbPJPsV3EhtEkgGwH2MvvF8kJILaOtIVtAO333sSIgAAQAAJlQaBlBIBO/KdX /yXhn+yXe/rppxWDrp3/OEPQfz3Jyoo4g5NskIyNsjPD/R9jp+SInCwwPMKOwTAdPHBIOf0H2Onf t8cpe/fsoz279/JPLuwo7BUygB2bA+wgHDl6jE7yl/u4OGXs1Ey5xqdyTAJWDSOVhA1sWU2dYPlG 2eEScuIEO2An+XfH8XLIALkujuw0r2RKEYdJyvS0Y5g4JIBDQNSRALJyKit3QhJwHTH6k/07Q/OM 4TT3PyFOIMs1yjKeYBwFSylHDztFnKeTx/m6OKinxambUk7x9LREXwjJI06byOKsXIs8Ki+EnAYh BAgXMZoUgSB1kojKzr+MUTnNjIfgNMkO5Tg7hGPsuCpM3YzNp9i5G2dHX4r0Kc7vCY5MOS764RJE 4rwe4TEJUSS6MnJIyrD7U36Xz0WX+OSKI8c5IzQ7zCeEZBASROZD5BcX1uafkCSycu04s0L4yHzL vAsuY4I5y3eCde84Y3xcsB5hzIcPq3LELUcPM/7KeRV9ETIl3nE9M3eGcZri+1wCgJ3/E9yG6LgY sXLslSKmhEDhZ0jJIfPORJ44/8Pi/HPyzr1M6knZz78f4ORY4lAfZtxEn08xaaGzYyuCh8fqEFKi Dw5RM8njFZ2emZ1WZJb+Jyv+81xvVuoJsWMWwUoRYfJcMO5M9JweY9lPyHM+wv0fZGKPn9t9LN/u Idq9axftqZYh/l2X3fw7J/Xi5/3QgYM8p5zUi+dTnj/REXnGRG/b/V9d1IX7PrEhAWzeyUnf80Uj ADRZGOeU6+tpnHNNAEhf+ni5qP7S9GFLAKQlA8pEAGiHP0hmkwzQBIA+ulgn+tPRAA8//DBt3LhR JSqUCEexdeS7xR8F0O7vEIwPCAABIAAEioNAywkAMST06r8k/HvwwQfpoosuqp4KkNQwjDI266IB ZIWXHUEhAJSTx06UOKrauTvMztMwO3OKCNgnRMBB2scrg8rxZzZfogD2sTOzn7/UD7ITqAgAcYhk tVgcNeWAu9sCQsLgw1RBHGBZ4RcHS5x/cTrV8ULi6LGsigRQEQHiGDskgFnE8XHCs52V82oUQHW5 3VlVln7U6jqvWkjEQZJ/ZyR5oxzZqBxRdqIVfuz4snN3hCMjDnMZ4TLMjpM4yILrsaNCYPCK+Rg7 2Wp1XFbZZbVdiAApzmq/GFXi5J5iR1Ocbyky3vFxia4QQsXOfZbxyDjFmTQJAFk5l9VnwXdUZHcJ AOXUyuc8JsFY+pUEToeZGBKndYSd22EeyyGJ/OBxCTm0n3XCWyRKhPNY7D/kRJGwDh0+LPN2ktuV KAPWCyE8hHwRRyqCClBOriIvgp3/k6K3rLNH2TEV3A8LEcH6OszElZRDXGQVW2SWVfwxJmAmVDRC /FyfYYwlAkAiGoQEkHurBInaBjOu5miCnetxIQEEx2NOBMAR3vM6wg72wX17ab9kxZZkWDoKwHWk TzKhIKvpjhPthNY74SBq1lz9lOgfjnDh8UsIvz+KZYHJnTmOGFCr/UEEgCLG7AgAIQF2v+yWwV00 pMrLtIvLbiYE9jH5d4ixPcwEi0RDjCoSgJ89ns/5RIxUkqesGHXDCABzS0AYGRAXDWAT4RX3PVAk QsCWBEhKBvgJAO2EN4oEiIoACCIXkkYDSH3tXBcxAsCWABD8dWSGScyIXstixi233EKSrHjLli3q NCMdBSB1zW0AxXjSIQUQAAJAAAh0AgItIwB0+L8YALIvTo76k2z/8oUpYYdZjMK47QBVIkAcTb3C LCuaEtasVoOFEDihHCYpR48cY+ePtwbwqu4wO/qS0Mcp4txxtAA7BEe4znG+b0xCrTk8eIodWWW0 Smiz7IkPWF0PCrN1fB9ZWXdzI/AKqTh5smqgHGFZZeV+xPmQ1VPl/IuTU90CoMP/HYdI75tWe/HF uVJycJEVaLX672yxEEyS/hOHTREAspKuVv9lFdqJolCOaAABIKunJ0+KIynGnzcKQAiAWVV4Xtip EgddQsyFAKkSABLGzgQABwkk+Mer6LxSLI50dSVd7VV3IgGERJHVdL29QhmmHOYvq7yiB0ddUkgI DFkB1iv/ighgh1AcbJMI2LeHiSGOFtm/lwkCJgGEQBIi6SiTH2O8NUD0Q28PUQRAZOSFyK4jGGSP vUQAMHEiEQBCXoiuutErVRKASQdFArhFOa1MZp1gAkKiL6ZnZCuFBXxcZ252njESMkb2wguh5eiK 6HR1K4LonkQlCI5Mpo2piAQme4QI4K0Aw0yQSRnhZ0ZICokIqa7+83zOsa5LjoLgfxK54uz9D9zC wp8KCSB6rvb466K2A8gzYGwBGJetNE4UwPGjTJiMSNQGk3fuFgDZBiBRCvt2cxmSyAVn9V+c/z38 u5A8guWIbPcRso91XgiRCZ4PibJp539xWwLkuiYD0hABcQ5+3PUiEQBaliREgM1qfRgBYEMEJCUb dGh72BYA3ecXvvg1+vvzK1ZF6gaRBGrrVc5bAG659asemeRv6Sfs87DIBJOkMOuY4xBsJfGfPslI Y6MJFG3TvPDCCyq/kdgOYvNIe+Y2gHZ+f2BsQAAIAAEgUCwEWkYAiBMhX37idOnwf8n6L1+WYkzG GXy212PJAJZBOVTs8EkReZwiIdbOXu4xdlhHedVaQtjVHmhzP7R8Jg65rByrfe7Oynt1ddcNlQ1K 9BNKAIhTI1sA3BB4WaUWZ1RWp9UKtRum7oTE60RsstLPjrGbKE0b45Fh/WK0u8m7JOw+8T9FVMy6 +DFmEhouBIraBy4OIDu9sjLNRSIYxJmWVWRZxdfh/7PsjEo4+gKvNsvedNkCoPa5y35K2Q/qkgBC BDgh14KtjfcaNhqX/DCiH+qdBzcCgZ1TTQQIKWQWGYusAkvEiNoSwCSQihhROSOEFBDHXwgCySXB kQOyaiwRAOyACzmk9cN+24XIrbdsOFEbkmhPEwHOVgAO0Zd9+kxGHeOQf5HNKceqzv8M450UPfG9 JV9DKE+h5OI5k+R6sg2FIwImOCJA9EFFhhxnZ5mL6IZEdEjuBHnWZmTlXBznpAIFTm1QIyKzJAFk Ak5FUbhEBRMBp3nrh5btOJMVR7WuqigKni8mUYaZwJKw/4MSSaGiWJjQUFEsjKeQfWobjjgvkpug /bcBaNjDyIAsBID5nrZ9t5v1QAA4ySzjSAcbosHvwPodW7MNcf5t/0ndZhEAfrnkb+38m/LK51lz C5gEgD85sDwrYtM88sgj6lQAORFAFjskmk/sDGUrpNp+Z4s66gEBIAAEgAAQqEegpQSAfHHqo/8k +Z+coStfljo6IMgQ7OrqoixFr3ab2wFUokG1P9wpktxLVqOnJTSdi4T4StIvKbIS6uwRl2RG/FOd BuAkgHJW+5299qYDHuboRxEAMlUqT4KbWEjal1Vxx9DVmddlZdRd2W+hdguJMGcmpZOwcFkZdRPT yUq1TkKnHHhx8iMS/1UdDa6kx6/DxJs5TMnmL6vTsi0hyMEQfZDEeoo4Yqd2TOUL4MgRdriPsAN+ RCIHJHqEw/+PHXPC/2W7g7Sb1z+FvT7FQvRQJWJ0khVOqJ+SL0DC4LnfTMRJAonVqQSsm7IKL8+T JMaSMH85+1qMXQn1zxGDBJLJU6XyC0iSQUVYuMkgJdeE2s4gSTYlgaNK5ijJHYXUYnJFbYFwom7E 6ZeEkHqvb5romWQyF7N21LaAuNwAjcgLIO/uohEBcQ65/3qUgx4XAeA9lSY4KWERCQDtNOeZBDDM 2fc7/zoywJYECNoW4CcATBJAbwOQPADPPfcc6WSAsq1MyHydB8CeCC7muwBSAQEgAASAQLkQaCkB IMaIfAnKnjgJjXv22WdzIQB+6Q2/R7f891+n//wT/6mOLPATAGHHAZkGrP/oK3WkGhcbRz9stSxq NS1QhfLzGRuvocoB5ERu1eMA5fcUEQaNlzS/HlgfZNuGStAlJBE73pJwT8qpMSGLZB97e4eJ5wdm QVqSZ06e9epxis4xe/lELBRkjBnECHq3BR0TmGZLQJYtYJrIbTcyIAkBEBUNYEsC2OQAMFfaw7YC aBULiwAQh1kc4R/84Afq/WnrjMfVCyIBTFmSOv/SXxQBoPLWuEcE65+aAOjr61NHAm7dulXlARjh bXKS+0jnAQABkOFFhFuBABAAAkAgMQItIwDki9EkACRDrnxBZo0A+PGf/Cn6sz/7Czp492vpE7/3 6kACIIwEcI4Uc86E1+eTa+M17PxrbQQ7K/HJzuEOmq24qIDEM4wbWoOAE7HvbG2ICp9vjXToFQhk RiDufWdDBvgJ2KxbAdppW4AZHaC3lkmyOX0KgI0jbxOBENaOLQEgjqwUcfD9bcln5vWwRIEmAWDW iXPy465rEkDLoOVM4/yH9aUjADR5YcqvCYA1a9aoRIAvvfSSOu0IBEDm1w8aAAJAAAgAgQwItJwA kC9kiQDIgwB4xSteQf/lwn+k977ul+j02tfSlvNeSz/7yh/3kADawPQanvvpofO8WwsWPbjPQwYE kQJB5IB85jF8D66hRV2LaM1B79aAuqiCM4dojU+GrlUb3RB/k1hw6p235hATDu49562hQ5bkQwZd wa1AAAgAgVAEbE4KkPeeGREQFoGltmUxSZyFEChaBEDSowL9zrtgYUMAfPTjN1ol5RPnXOr6iYak OQDEmZUSRgCY15MSAFGZ+OOcfzPpn5ZBy9lIAkDLJbJrAqC3t1cRAAMDAyTbHUEA4EUKBIAAEAAC rUSgEASAfBlmJQD+w3/8j/Sz5/4dveGC5fS5330VzfzgN2n4n36TzvuVnwskALxEgEMAVJ3+Davo FV1/Tw/uq0UD+A1VMWIP9J5HXYseov06kZ5h3FYdfEsCYGHhIK1ZxDKsOeglEOoiC0wCwBdx8MIq 6npFD70QQwa0UuHQNxAAAu2NgEkEhEVOBW0LiIoGaLctATYr82kJgKSJ+YJksT0FQPqSpHZSwrYA mNebSQDo1X/dv/lTJwW0IRHi6vgjAMz6JgHQ399fJQCG+ThZbAFo7/cgRgcEgAAQKDIChSIAhCFP swXgp875n/TqD1xHf3DJP9OFv/lrdPcf/SLN3vPbNPrh36Lb/p/XRBIAznaAGgGgDNMDvXQer9pH EQBirO5/SAiAB6sEgN+oVcZvSgIg3Ih2iAInAiAZAVBkRYRsQAAItB8CaRIFmglas0QABCWRLXNU gG0EgDi3csycTZG6WQkAWdG2Kf4IAf9RejoHQBhJEOeIm9e182/KJf37/84jEgAEQPu9tzAiIAAE gEC7I1BqAuAVP/Zj9Auvez391//+p/Q/fv9NdO45f0SX/fLP0PfPfQ1N3/Z6mrjut2gDJwN89U+9 skoCBGWf9hMApmOvr9VOHnCIAVXHOJHgxo1yFvkB6mXnvPo5h/ALmSBbAFatWlT9PGiV3x8B4CEA FImg211EiwwC4IVV/PmqF+iMrP4b8pyvCIKD1OvZVnA+rTnkZBM8tIblP6+HeuT6Kz5Lm9pd0zE+ IAAEmo5AVKJAm+SAQWRAmmMCw+5JSgio01jUSSz5FdtoAPnuuuOOO9RJNfqeoP374ugeOHDAquRB AOzcuZNsSlYCIEluAOnLlMk8BtD/eRJiIaguCICmv1bQIRAAAkAACGREoJQEwE/8zP9Br33rxXTO Tavp3K9+n/7qH95P5/7O79J5v/qL9JnX/Rw98/Zfp6mbX0+Tq36bdvz5/00X/9r/VXWO1TF/vr2l YU6+/6jAar0bN6j8AJoo2Me/V53/G9npN7YEOBEA7GQvWkMHOSLg4BohAlbRRuOoQCdM1lnZN534 VRslseALtIrvr674u46+/rtKAEg0gG8LgLp2fi0/wEHZssDOvmwRUAQAt3t+33BGFcLtQAAIAIFw BMKSBertATbbAdo5KiALASD3+kmAaz6xyjoHwLWf7AmMANBEx5NPPqkIh7BEgV/44tes+5K6/na0 Ux+WBDAsGkA+j3LcdQSA3pagV/rDPs9CAgQRAFo+bAHAmxEIAAEgAASKiEDpCIBf+N0/pP959zr6 4+8M0H/jn3/+lT56e8/tdO6f/Bld9pqfptv+4OfppYt+g6Z6mAC46fV04ILX0rfe/Cv0Y5wgUBxe cyWolgegtgVAbQnY/xBvAeiiGzc4OQD8q/1dAQSA3CMr/b0HJBKgVvQWAPk8ektASA6AjbKy7xAG jiHt5ADQUQQbdQRAHQEgxMErqOcFc5vAC9TjfqYIgPP7CO5/ER9LyAQE2hOBsIgA22iArIkBy7At IIwQkLHfc889yvENS9xnczKAWSeKfJAohzgCIGl/UScOxG0B8JMBWZz2PO8VuSQyI+gIQxAA7fke w6iAABAAAmVHoHQEgDjxr3rjH9JbvriG/uSOH9JffOm79CeL30OVV/8M3cCr//f/v79Me/7htTS1 6vUcBfAGOv6/f5M2/vGv0+/83E9aEQBO9ul99KAk5HtoP82phIC1fAAbVjGREEEAPLTfd4SguwWg Sgy4f9efChBPADhHDQYRABsdcsATAeAlAERRnWiCV9BnOd4fBEDZH13IDwTKiUDcaQFxEQFB27iy bgvIEtLfrG0BMu4HHniAjh8/Xrdqn8URDyMBmk0APPHEE8qJjlr1LyIJcOzYMbrvvvtAAJTzdQSp gQAQAAIdiUApCQAhAf7TT/00veHC5fQb576Nlv3Sz9J1v/mzdMc5v0jrF/06Hb/sdXT0+jfQpX/8 C/Srr/pp+vDvvJqufsOrrQkAMwLADPOfm9tAq17BYfVBBIAkEpQQfjkVgEmE6rGBBgGQKgLA3UKg V/xVWL+7JUDacyIAnOMCFyRawDgFQNU1/lZOv7vfHwRARz7vGDQQaDkCcVsCgrYGhB0XWJSIAO1E ZyESou6V9oXkWL9+vSpmHgC/A5+WDPC3YxIAUTkH0vZn3id9pSEAWkkISN8yhk2bNtFzzz0HAqDl bxYIAASAABAAArYIlJYAMPfK/zmv/j/8/72Gdix9LR277Ldo7GOvp3PfUNv3L3X/7tdeRf/nK3+8 uv/fmwvAWfE32/z77+6t1lWr/kaCPU0AVAmB6naBDXSjv55va4C5JUBWumrHZNXnANB5A5Rjr9td tIpWBSQBdNqpJSE8j48TjE0CiC0Ats8J6gEBIJAjAmEkgP5c3mdh2wLkc5MQKFpEQCNIAB1lIJn9 77rrLpIj5Q4fPuzZ0mZGQaSVwR9JIVsAwo5gTNtH2H1CAEhftjkRWl1PjvJbt24d3XnnnSRRAEF5 CbAFIMeXBpoCAkAACACB3BBoCwLgv/zMT9DE1znk/47X0/Str6fL/+yXvA676zz/Bz41QBuL2tAJ Mh6jPgtbiYr73JMXIMC4DTsv2+ZzMZqD6gUZ2blpDhoCAkAACOSEQNi2ABsSwOYdnnSLQFbnNu9t AdrZ1Svemzdvpt7eXrX3PKh885vfpLQlrE3/52nbT3rfN77xDSpiefDBB2njxo10+vRpOnXqVGBS QhAAOb0g0AwQAAJAAAjkikBbEACyOn7/9b9Bs0/9Nn3o/F8OdP71CrqfAAg6FUAlAjTC+M3fowxS m2umo26udjn7+82Effa/a43w35+rpqAxIAAEgECDEIjLCxAVERB2QoBJDCQlALKuppur01nJBPN+ TSyEfW/lFQ3RCFIlyxykuTdP3KPaGhsbCz2RAARAg14YaBYIAAEgAAQyIdA2BMCvvvqV9LpfcxL9 RRUbwyaKAIha6fcTANU8AMapAN6w/5rT7ycAgoiCMIIgTAMQAZDp2cDNQAAINAkBG/Iz6tjAuG0B aRzIrCRAkOOep1OaJAQ+7T79JH0EHUmYtt+4+8qSKBAEQJNeIOgGCAABIAAEEiHQNgRAnOPvjwCI Sh6VB0ngN0iFDPBvA7AJ77cN7dcGtDn7IAASPQuoDASAQIsRiCICshAAQZFfSUiBrI57IyICkjjn cQ511PVm9ZNERhAALX5Q0T0QAAJAAAiUGoHSEQBJjLawumEOflzbeeQG8EcJpCUB9H1RUQEgAEr9 bEJ4INDRCERtCwjbbmWzHSAtGdDJJIA45zZEQBInPkvdJARAUHK+8fHx0LD9PK8hAqCjX2EYPBAA AkCgsAh0JAFgOvpJ9onmERkQti3AJn+AruMnDaIcfZAAhX32IBgQAAIRCNjkBciSGyCO8I27npQQ CHKgk7YRd1SgjZOe5Ug/m/azOPZp7i0yGQACAK84IAAEgAAQKCICHU8AhJEBYUcf5UkCZEkuaJMj IEjhQAgU8TGETEAACPgRiMsL4CdCg0jUqOMC4xz8uOtZnfdWbwtISwTYkABmnTROfdJ7kpIAzYoK AAGA9xoQAAJAAAgUEQEQADMzqc9SjjIQ0xIFSSIBwhIKIiKgiI8aZAICQCALAmkiAqKStkblgYlz /uV6VgJA358nEZDUOffXT+J4J+krSbtp6qYhAMx78gz7N9sCAZDlice9QAAIAAEg0CgEQAA0iADQ BmTYsUz+z/2GahIiIGmiQBwX2KjHCe0CASDQSATSkABBCVnlfZuVAMjzlACTDMhKLCRxzLMQALZ5 AYLkSePk29yTlggAAdDIpxZtAwEgAASAQNEQKBUBEGbIRa3yFOVaEoe+WYkC40Jscf0MAQNgAB0o jw7YJFWNSyBYlO+MVshhG7kWR2DHyZ62n0bdZxPxEVcniERABEDRTF7IAwSAABAAAoJAYQkA/x58 Mdruv/9+uvrqq1GAAXQAOgAdgA5AB6ADhdCB++67T20l9JMAIABgaAMBIAAEgEARESgNASArCuL8 nzlDKMAAOgAdgA5AB6AD0IFC6IDYJiAAimjiQiYgAASAABAIQqDQBIB5XrMmABaYAZhfQAEG0AHo AHQAOgAdgA60VgfEJgEBAAMbCAABIAAEyoRAKQgAIQJkC4B8yc7Pn6GZ2QUUYAAdgA5AB6AD0AHo QEt1QGwSEABlMnshKxAAAkAACJSOAJidW6DJ6XkUYAAdgA5AB6AD0AHoQEt1QGwSEAAwpoEAEAAC QKBMCJSOAJienadTE7MowAA6AB2ADkAHoAPQgZbqgNgkIADKZPZCViAABIAAECgdATDFqx0nT82g AAPoAHQAOgAdgA5AB1qqA2KTgACAMQ0EgAAQAAJlQqB0BMDE1BwdPTmNAgygA9AB6AB0ADoAHWip DohNAgKgTGYvZAUCQAAIAIHSEQCnOdxx5NgkCjCADkAHoAPQAegAdKClOiA2CQgAGNNAAAgAASBQ JgRKRwCcGp+lg0cmQsuBw/fTRV1d1GWUsz6xmQ48ez2d1XUOffzZcXLqOL9HtZX1WpAsF32rvk9T HlPOrP0H3f/Nd3eR4GFeO/CtJdT17vs9GDWi7yRtKpmqc7iEvnlYz5s5t7U5rGLN4wjq5+lPnMPt 5TfntnMbN+a4+T5weDN9/M3umN98PT3NOEibXnzq5zSuX1wPf4cAG2ADHYAO2OqA2CQgAMpk9kJW IAAEgAAQKB0BIKv/AzuPh5YXd9xDi7reTB965FimOlF92F6zkUXaCqtne7+tPKqvf7+Eus6/x4PN red30aJ/D8crSft51H3xkU/S73ddQrfucGSSvz/E8vnx6L3yzdT1pk9SL9fT137/TfVz71wTJzpa L5LI7pfFL3OStuLq1vUlc2iM5cUd6+lDb+qi379yfeSzEdcProe/V4ANsIEOQAeCdEBsEhAAMKaB ABAAAkCgTAiAAIggE7IafLYOfFMJAL9zrZzjmrOddcx53K9ICtexN9urc4QNskdfW3T+m+scYYf0 uCSWGEoie5QsSdqxqWv2pZ19P2HTSALCRkbUgXMEHYAOtJMOmFGEelzymX+MmgCYnZ2liYkJGh8f rxb5rFKpUG9vL/X399PAwAANDQ3R8PAwjY6O0uTkJM3NzdGZM2fKZDdCViAABIAAECg5Ah1DAHid KG+UgOM86dDy/JzhKALA7PP3r/xk1TnV91z2fb1q7crlW7VPa2j5HUgzIiB4VduLixktEFg/Bzn1 ir1/RduGAPjQI15CwxmvrPzHR4YkwTRwVd4Yu+BUNSDdzzX2+nNx4uvH5Kzmh9UJc/Rr4yxOJEcS PFEXziN0ADpQFB3Qjr6fBLAhAEwSAARAyS1kiA8EgAAQaFMESkcAHOZwu5cGj4eWgZ0+x5kd+0Vf FqfIcQAvfzT8d2l34MuX0O9ftT6yj6j+zWv1sjC5sFP378gl9ddcxaHsFrLZ9htXT/rTY7z1gpoc YRiZuAg+XRfco+RWWLFzrdsy242TIe76wM71dLnrCGucTPmquHGkwJoqps78esb0KJM7qk5t/uP6 trkeNrf+ez2YGtjpel7MnTH79c9TpzoeR3dq7Tj3aqxsxoA64e8RYANsoAPQAdEBkwSQ3/16ITaJ bAHQEQBCAOgCAqBNLWcMCwgAASBQcgRKSgCc4C/h4DKw817X0RfjpVbH/Nzz+6OfMlb/3ZXXC+4N bT+s36DPQ2WRPt/0KXZKHRlDZQsZSxIZAuWq9i9OozjNAXKE4DJgyH7rBXzvl/VYvG1llVHfr/pT JI6sjsncmkkAhVAJkd3F1yED9L21sWaVzz+3jpwGluLsV2V1PtdjcRx8RzfrdbE2pmR1GoN/Vpxw f/i7CtgAG+hAOXTAJAH8cwYCoORWMMQHAkAACHQgAuUjAI5P0paXT4SWlwZdAuCx45465uee3x/7 FP2B7IFnpj+q3TTXQmWRPsVBdfsMlS1kLGlkMe95adB1Fr8SIUcILtV7H2Oc1RikLcbPN6asMpr3 qwgJRcoEz63U9WJYP76oe9PI6m/PwYXJhq8wIWFgV8Orpl9OxIdb1xiTeZ93vmrjNvvx1GmgHqfB B/eEv6OADbCBDpRDB/yr//K3f+4Os02CCIAOtJ4xZCAABIBAiREoHQFw5PgUbd11MrRseXk1ndd1 Nl3x2AlPHfPz+t+76A+uXh/ZblSfYdeiZemi8253ZOy7+mx2CB2Zw+RM03/UPU6f3nHb4iL3/sGb uLiYyd/nXXBJbhhuuf1T1fnb8vJ6uoIda+krDE8Zp//altudFXgtY9S9abCt60854O4cSt9MjvQJ UWV8bvajMPSNyRyrWTd4bJfQF7n92tgbo8NpsME94e8nYANsoAPtpQNik4AAKLEVDNGBABAAAh2I QPkIgBNTtG1oNLRs3XWfQwA8ftJTx/zcX2fr4yvZeTNCyy+8L7KPqP7Na04/Zsi6OGkbVNtbb19c DRH/g6tXVmX2y/alC937c5JJy+eM2YuTLS7+e4PassUoqF4dbu7Yw+ZW4emb9627NjBxsJi+xORA 0PUs8tXaM+e2hqXTt3uNZTiPyRLRR3POu7oc2erl9urMoq84BIhaiXrTSnpYj8fQH7l23u1efc86 Ptwf/o4BNsAGOgAd0DpwhG0SEAAdaD1jyEAACACBEiNQOgLgKH/Z7tg9igIMoAPQAegAdAA6AB1o qQ6ITQICoMRWMEQHAkAACHQgAqUjAI6dnKade8ZQgAF0ADoAHYAOQAegAy3VAbFJQAB0oPWMIQMB IAAESoxA+QiA0Wka3HsKBRhAB6AD0AHoAHQAOtBSHTjGNgkIgBJbwRAdCAABINCBCJSOADg+OkMv 7zuNAgygA9AB6AB0ADoAHWipDohNogmAyclJmpiYqJbZ2VmqVCrU29tL/f39NDAwQENDQzQ8PEyj o6Mk9efm5ujMmTMdaH5iyEAACAABINAqBEpHAJwYm6Gh/adRgAF0ADoAHYAOQAegAy3VAbFJTAJA nHpdQAC0yrRFv0AACAABIBCFQCkJgN0HxgkFGEAHoAPQAegAdAA60EodAAEAIxsIAAEgAATKhkDp CICTp2Zpz8EJFGAAHYAOQAegA9AB6EBLdUBsEkQAlM30hbxAAAgAgc5GoHQEwCh/2e49NIECDKAD 0AHoAHQAOgAdaKkOiE0CAqCzDWmMHggAASBQNgRKRwB87WtfIxRgAB2ADkAHoAPQAehAEXQABEDZ TF/ICwSAABDobARKRwB09nRh9EAACAABIAAEgECREAABUKTZgCxAAAgAASAQh0DpCAA5OgcFGEAH oAPQAegAdAA6UAQdAAEQZ2riOhAAAkAACBQJgQgCoI8qXV3UVVcq1KdGkP76Gj7zdnb2QVoS0L6c mTs/P8/XZz1FPpMv2SJ82UMGGJ3QAegAdAA6AB2ADogOgAAoklkLWYAAEAACQCAOAUQAtFlEgeyH lH8wTGGYQgegA9AB6AB0oPE6AAIgztTEdSAABIAAECgSAiAAOpAA6LqQIzuk/EsXiII2m38Y+403 9oExMIYOQAdMHQABUCSzFrIAASAABIBAHAIgANrMAbSJAOj6GDv/8t/bQQDAkIchDx2ADkAHoANZ dAAEQJypietAAAgAASBQJATaggCoz1PgzV2Q5Ys97F5/n0n7iJM57HpcP4kIAIcGABHQZiRQnI7g Opwd6AB0ADqQnw6AACiSWQtZgAAQAAJAIA6BtiEAwgaqHek8jR1p09+e/izoWlDfUu8DH3CIijjZ krSdigAAERA7B3FzhOv5GdPAElhCB6ADZdIBEABxpiauAwEgAASAQJEQ6AgCQAC3cbSzGhxJyIak BIBt25kIABABIAIQDQEdgA5AB6ADiXQABECRzFrIAgSAABAAAnEIdAQBYIbTJ3Hyk9yXpK7IkJQA sI0WyIUAABGQyPhLolOoi5VN6AB0ADrQXjoAAiDO1MR1IAAEgAAQKBICbU8AmGAniQKQuhw34N7u zSmQdf9/aQgAEAEgArASCB2ADkAHoAOROiAEwNzcHE1NTVXL5OQkzc7OUqVSod7eXurv76eBgQEa Ghqi4eFh1Z7UkfvOnDlTJLsQsgABIAAEgECbIwACIMSwcQgAtXkg8mcSUsFc9SlFBIAmACRXwbvj cxVgVau9VrUwn5hP6AB0ADoQrwMgANrcUsbwgAAQAAJthgAIgEgCIJ4EaGsC4EJ2/KW8hcvPggCA IRxvCAMjYAQdgA50mg6AAGgzyxjDAQJAAAi0OQIgABJEACQ5ui/OANIRAHpvf1TbUqepOQB8jn9a kiMOA1yHowAdgA5AB6ADZdeBIAJAtgNgC0CbW9AYHhAAAkCgpAiAALCMADAddu2QB/20dZZt2zP7 sGk7UxJAOP7Y64u9vtAB6AB0ADqQSAfCCADZ348cACW1jiE2EAACQKCNEQABYBEBYOus2zjoeqUj SQRAknwBqQgAOP6JjL2yr1ZBfqy4QgegA9CB/HQABEAbW8kYGhAAAkCgDREIJwD6KuqoOk/p7skF Asl4K6FxkgFXjJCRkREaHByktWvXKrZ8fn5eXTeLfCZfskFGSy1hX7R4SR10SQDYCOffPAUgaguA nyywkT8RAQDHH44/VvqgA9AB6AB0IJMOgADIxTREI0AACAABINAkBCIjAHb2dFN3z86qKH2VLs/f aWW0IQAkdE6KJgFaQQA0yvlvOQEAxz+TsYeVs/xWzoAlsIQOQAfKrgMgANJag7gPCAABIAAEWoFA IgKAdvZQd6VPySnkgDjI7p8k5ID8rQmD6nVe0a9FEVRI7hYCYOsNb1GfL1682Lj+1moEQFICwDZB n62hYduezap8WNSCmdwvqL+GRQC4Wf3Tym6LIerBsIcOQAegA9CBdtcBEACtMF/RJxAAAkAACKRF IBEB0NNdc/g1CaAJACUAbxswIwYcEsBx+nV9ua4jAF68/hy+vpjucrcA3POvb6Rly5bRHIf7z8zO cZlVZXpmWkUDXH31VW21cpuEZNB14wwpqy0AOgLgXBztF4cnrsN5gQ5AB6AD0IEoHQABkNYExX1A AAgAASDQCgRiCQDTSTWde1sCIIggMAmAxauNHACr3kpLlizlsP85muQjdCakcJ4AKbIVYMWKFW1F ADTCqLQhABrRL9qEgQwdgA5AB6ADnagDIABaYb6iTyAABIAAEEiLQCwBUHX6VVLA2mp+YwmAWXb6 p2h8YpLLBI2Pj9PMzAwIAItETSAAYIB3ogGOMUPvoQPQgVbpAAiAtCYo7gMCQAAIAIFWIGBPALB0 KqTfWNL3JAmU/ABGDoAogiAqAmApbwGY5XD/qalpFQUwyUQAIgDsDTsQAPZYtcpYRL+YI+gAdAA6 0D46AAKgFeYr+gQCQAAIAIG0CIQTAOYxgMbxf5IHoEv/7Tr9zjaBburpcRL+SdSATgJYTRRotNe9 akc1CaBcv/guPgbwyxc6SQXdYwCrSQDdkwCiTgGAIVUzpEAAtI9RCb3GXEIHoAPQgeLrAAiAtCYo 7gMCQAAIAIFWIBAZAdAogWyOAdTH/2kiAARA8Y0gGKqYI+gAdAA6AB3oNB0AAdAoaxHtAgEgAASA QCMQKCwBII6/kAAgAGBMdpoxifFC56ED0AHoQHl0AARAI8xTtAkEgAAQAAKNQqDwBIAmAXQEgIS4 owAD6AB0ADoAHYAOQAeKoAMgABploqJdIAAEgAAQaAQChSYAqnkAOBJAEwCNAAFtAgEgAASAABAA AkAgDQIgANKghnuAABAAAkCgVQiUggAQIkATAMcObycUYAAdgA5AB6AD0AHoQBF0AARAq0xY9AsE gAAQAAJpEAABAEIBhAp0ADoAHYAOQAegAyl1AARAGvMT9wABIAAEgECrEAABkPILvwirDpABq1/Q AegAdAA6AB1orQ6AAGiVCYt+gQAQAAJAIA0CIABAAGDVBzoAHYAOQAegA9CBlDoAAiCN+Yl7gAAQ AAJAoFUIdCQBcHTkS/Turi7qkvKuL1WNnnXX/oHzWdd5dM/ItkTG0NG7zlP3vvuu6PuOPrOC3mxR z1zRqcpryKqvJ2lP1Q1oA6tH+a8eHR35Hn3yLFfHlE79AX3ymWQ61ah5CdODsj0XnmfEfa708+w8 j63D3HwukzyjSedcv3ec95ZT4t5B0oepA42QL+27xqODIfNXfbbcd1ntntp7+553MRZnraB1Ae9x /3jTypp0rlA///dsUTAFAdAqExb9AgEgAASAQBoECksA6OP/9EkAeSYB1Abjm89ih981ErVRqT4r EQFgawD5jWbb+1AvudFaxdpwQBxyqXUOaZUw0sREEJnkEmNleS4CCQD32S0SAdDIZyjNOBv9LsjS vnLc1Ry6BFqIE286+MqBP+s8eje/u4VkS9J/krqNnEe0nfw9WyTMQACkMT9xDxAAAkAACLQKgc4m AK5dwZEAzqqRQwqcR59UjlptJcm/wmaurtUiBnjV7V21CAD/ClPNqOV+fBEAUe3XnDY3YsEiAsCU Sa8G1q1GhxjVRTKoyixLnFMWqR/aCWd9kkgR0cW7h535f7PxmdJZN+rEv/Kr26/VdyJd4vSgSowV +LkIi7SpjpmdQHlG/XMQilUQ3muvcKJ03Gda4cvPzCeVc+qUN1/7vWqEkPN8u0UTisZz7p/voGe0 +qyHzWlIhFGUrtm+C57T4xXctNwpx/7c8MPeyBeNR8i4/M95jShzCYCQiCVn3O67W37nenKvzIvW Y6UHEfP7rjuzyVrmdxRkz5dwAAHQKhMW/QIBIAAEgEAaBDqcAPgSG6vuqpFaRVpB9xgEQNWpcI19 0/nwOyLaCVBGp8/JDyMAgsmA+lVi2y0Afnk9q2QRK78wBvM1BuNW+20IADN8uTr/BnETpTv6mg6H N+WJWvGsEQDFfS7iCIB3M3kh21xMxzjyOdbbgQKw9eJXc/odPA3n0+PkGu8TNxw/aDuAJhDqVrKN EH7PGCIJAHOriSuX+w4K7Mf3LgiSL9PYI9pX2w8itmeYJJVJsvjfUY7MDtYyH1XSR+beuBb37NRt JwgkaFsfuYN3dL7v6LzxBAGQxvzEPUAACAABINAqBApNAMg2AL0VoCFbANixFwPcNB5N4978XRmu ejWJ76u7ZhjotgSAf4UubA+vNQHg23duRisg1LV5BmQuBICx8hk0/1G6E+zwxodHm/pdhuciaAuA 6Pw97+KxXlvLARD1HAdhGxalo58n//xWCRdjD35YDgB/FEZYRJHtnv4wZzqqnyinN5ex+wgA6/ec zuPApKyKfhFiRX1Wn5OltsovkQJeEnedGR2gCR7zeTKjM1LKmrcDifaa935uBNaaAJieniZdpqam SLYwVioV6u3tpf7+fhoYGKChoSEaHh6m0dFRmpycVHXOnDnTKhsQ/QIBIAAEgEAHIlAKAkBIgEYR AMqAZuNQOzzNJwDiV5dsCYC6LQNGQkMQAM0zMOMSQtpEAJirn6Zjruc4imTIgwAow3MRRgCI7E4u j9oKsWdbj0HkBWEbF5ljYu+JrgghAf3tmWSimXQ0jjgKclxit5uYCU+r25284fVhZEXQar3V2AOd 6vj3nCeyQkcwRORkkXf2m8O2q/gSBHqep1gCIF7WRjiRaLN57+i8sQ4iAIQIAAHQgVY1hgwEgAAQ KAECEQRAH1WMzNK1FakK9amBpb++htnu2dkHaUlA+8KWi7OvV//1z4YRAL4EUoFGqGcLQH2IrxgT QVsAnP2oOhu8NzTX3CqgjVP/SmXSHABpHb+8jaFOby8uCaA5T3X6YTin/vkPcmKCdCetHpjOsGrD SKxWlOcidguAse9bEwDReLj5FYw9/UkIAHN7j17p9m8DitoCELVVoW6rQUCG/9AIAN8WAE8/FlsA dGSCv32TAAgde0j7se85Y2uAJ4IhJGeJkkWiBTyJXOXv2naNOIInLBoiTtZOf8dh/F6yAgRACaxd iAgEgAAQAAJVBAobAaCz/8vPhkYAVFfIHAc9LLQ/KDzfDG01kwCahIA4IWKQVvcMRyYBDF558h6N 5U1EFpdgLDBZGZIAJjriMY2xG3cMYC1xnE8/LAmA2uqs1oea7kQ5vB7d9OmBhwAo6HNhQwDUxmhg EpZcLwjvmH3gnlV/HbaujhTlTPRugsCoVXV/SLyH2PHIWS+//4i/oGMAvc5rTOJC1oHAJIDucaZR BIBn64Mx9iAd88oZvsLuSaioIhYcgiboaMNqm0Z4v0nE+rdtVQm1wBwttWMDbWVN817APeVd5Y+a OxAAsKqBABAAAkCgTAiUggAQEiDPCAAYYe1phGFeMa/QAegAdAA60GwdAAFQJrMXsgIBIAAEgAAI AA7fb7axgP6AOXQAOgAdgA5AB9pDB0AAwJgGAkAACACBMiEAAgAEAAgQ6AB0ADoAHYAOQAdS6gAI gDKZvZAVCAABIAAEQACk/MLHyk17rNxgHjGP0AHoAHQAOpBFB0AAwJgGAkAACACBMiEAAgAEAFZ9 oAPQAegAdAA6AB1IqQMgAMpk9kJWIAAEgAAQAAGQ8gs/y2oB7sVqE3QAOgAdgA5AB9pDB0AAwJgG AkAACACBMiEAAgAEAFZ9oAPQAegAdAA6AB1IqQMgAMpk9kJWIAAEgAAQKCQBsLCwQHL0n1nyOgbw 2utuIhRg4NeBrCtx0CnoFHQAOgAdKJ8OZH33y/0gAGBMAwEgAASAQJkQKCQBIM5+IwmA1atXE0p7 YDA9PU379++n4eFhOn78OJ08edJTRkdH1d/y0yxjY2N06tQpOnLkiCKEshqB0gb+AQEgAASAQHMQ KMq7HwRAc+YbvQABIAAEgEB+CIAAABlQajKkKEYgCID8XkpoCQgAASAQh0BR3v0mATAzM0Mily6y kFGpVKi3t5f6+/tpYGCAhoaGFGEthPTk5KRa7Dhz5kzccHEdCAABIAAEgEBuCIAAAAEAAgARALm9 UNAQEAACQKAZCBSVABASQBMBIACaoQnoAwgAASAABJIiAAIABAAIABAASd8bqA8EgAAQaCkCRScA hASQ7YyIAGipmqBzIAAEgAAQCEAgnADoq1BXV5e3dPfkAqKEu83OzqrwNwmDGxkZocHBQVq7dq36 skQOgPbYn9+MPAtFMQKxBSCXVwMaAQJAAAhYIVCUd79/C4COAAABYDWNqAQEgAAQAAItQCAyAmBn Tzd19+ysitVX6fL8nVbeIhAA9957M13yOiE4/pSuvPfeulXwK/+Ur73uEro54FozHNuoPu6990r6 UyZn/vTKe8n8vZlyKXz8BJH5dwLs7r3yT9VYRP6k4ymKEVgjAHZSTzdjU+kLfDzkGepiIq32VMU9 RX1UYVxDmou7uUnX3TF3VSho1MnH3CSxVTcmvq3BWuET9Swl0Rcmbmu60sTxePrNNn/yvVPFw2Ls Yfql2zG/wxzJop9Rr064Y8lxfNnQwd0agaK8+0EAQCeBABAAAkCgbAgkIgBoZw91u9alNq60samN WG1sVa/zin7NuHUcBCEAtt7wFvX54sWLjetvbVoEQI0AYOP7T68sLQHQTKff7CsvgiSpw+8fb5wR ePr0aZqYmCD52ZxTADqZAAgmPspDALTm9Z0fPk10+D1Q5divijzTRFKco+6684owqCefBFeJKKsn 3Bx564mBsPnPcXytUbG27LUo734QAG2pXhgUEAACQKCtEUhEAMjKprkSKU6+Z2WSjTfTqHJIgJph piMKdATAi9efw9cX013uFoB7/vWNtGzZMrUFYJYz487wNgEpsl1APrv66qtyOa7NWWl2IwBe9zp6 Xdfr6JKbvVEAeTm4jXDQszrNeciUFz5ZxxJlBMoxf+L8/+7v/m4dCdC4YwA7mADo5oihrm4ygoac NfbEUQ/NfOe23rnLD59WjSW/fhUWxpeK+g6JiwIQYrouSkZkYl3sk2s+nVT16/U0XOvyG18zNbvd +yrKux8EQLtrGsYHBIAAEGg/BGIJADM01b9iYkMABBEEJgGweLWRA2DVW2nJkqXs8M/R5NQ0TUxN 0QTnCZAyOzdLK1asyJ8A4NX/my95XV0UQJCDK6HqJh6vu+TmauSA48gykXCJrvOntOJzlzC5IKH6 Tsi+vlfuu/dm55r+TIfAa+fcH2JvXo/bAuBvW/rwt6/G7JNJ910lRwIiIzzyWYb5h8lT9zm397l7 atsbFFHj4hSGYZgRqJ3/X/mVX6E3v/nNdNFFF3lIgCIRAJ6Q57qVScP50I6LcmpqumO1kunL6eG9 R/fh/NR6UXt241Zia9fVWHz7FQId3Fh52EHr0dFDFVpTdfK8MqpxuNfq5XZe2P4Qe694MVsAfG1L H/7tGNHzF4ddQoIkTB7/5+w07/Bsb2AgGoFhQL96e0s0LsFfps5cJYsA0CH7Hp0W/VLEgYO/n7iu 9eESBaaumbjlPL72MyFaN6KivPtBALROB9AzEAACQAAIpEMglgCoGlWe0Eyns8YRALPs9E/R+MQk lwkaHx9Xx+o0igAIWoX2EwDa+ffvVdfbB3QbZt6AmoNbizCoOd613APOZ7W/Vd+G8+1cr7URRQDU HOZaRINf9rr2XCfbJDTiogRsIwDi5PFjX/d3lSgJxjDICNTO/6//+q/TX/zFX9CVTMDceOONtHLl yioJUBQCwHGSjNVI1+GoOTN+AsBx0KsOjetIR+YIqKvjOtG1RlzHvyZHnVyR7xfTya1fLa0jAGzl MVd+q46YX0bTYXTeSWbUUeCKsmf1N4IACFpZ9skeP3/xL2brCIBYefzY+/5uCoa174ZovQ7BRcvY zbkMJJeG5Up94Dy7+u2/5v3bfRY8UQYxOLrffanGF68OqGGJQFHe/SAALCcM1YAAEAACQKAwCNgT ANro8YVnVh2VOsclnCCIigBYylsAJPx/anpGRQFMMhGgIgB4G0CjCABxdpVTbKxomw6uXhH3O8iO Y+04ptpx9UQFBDjW2iEOqldL6ufdkuB3oiMJACVTcGJDM8le8FjC7/MTAnFJAHX7Dkbx8oQlAYzC S/oIMgIl7P+8886jK664gr7yla/Qd77zHXr44YfpiSeeoL6+PkUCNJwAsErqFrIX2UO21RMAQZE4 4WHSjnNeFyWg+tDOdIAcgWHVYe8t7yq3P2zb6+Bmk8czjoB3Tm2VW2R1V3fNjIt144ogAAJITy8C NvMX/66PSwIYRcIGyROaBDACryBcnXaSYqjvCdO54ESR5jiqeMSF/ntvMrYK+Fb9q9EAirb2RQQE zWEcAZDPvMdrBmpEIVCUd78mANTWRXfLoj4JAMcAQoeBABAAAkCgiAjYHQNoHP+nMpzrvz3hkbWQ XTEmzfBPZUgaIb/dq3ZUkwDKaubFd/ExgF++0FnZ9B0DKGRALQfA1Q3ZAmDmBNCOq4cAqIahe/ME mI55UBRB4Op3QFtB9arh+Ma2gyAnOWwFPczpNkkL06mPkiEoGsA6AsA9sSBUHuNEg6BTAOIwjFoF Ekc/qEhCwIYTADanAHiccOP14HFS6wmAuqbD2lH+TtD+aP/nQXuck+x79oe5e518DwGQVp6g+2w/ 09Aa76BgJznY8QtKMKeatJq/+Ne+dQSAG5oeKo8/5D90C0CYrrmfR5E/sRhmwKX6fVLh7R/OSQDW p1949vX7SAvzWt3+fxvd99XJad7jNQM1khIAOvqrme9+MwIABAB0FggAASAABMqAQGQEQKMGUKhj AI1Qe3O12ooAiDmOL855rTr5PlLAuz+fV89912NzAFSPOKzPMeDPY+A/fsyfKyBsK4AtAeBJuBiQ 88B2C4AnB4KBR1wmaMn8f/LkSc8JAPJZcQiA8CPgaiuwriMUlrwsDQEQe/xdFgJAO4HOaq8VARAn j62z76vn3YfO8iSJAFAvQH3EYUBuBF8eA/+zZOu82hMAMfI0iABIhqGX7A063jAYF/+2FI27RKnE 51HwrOx7VvxrmFWJ6Mhwf8Xs+I7eDCIA4p7bRn17ol2NQFHe/f4tAEICIAIAegoEgAAQAAJFRgAE gC/RnTi2EgVgRQAYjmheEQA2pEEcARAcru9uVTC2LcTt84+6noQAiJSnAREA4vDrUnwCIC4buT8C IKB+GgIgLMqg+rbKSAC4jr9EA1kRAHHypCEArO6J2AIQ8OZ2wtPdOYjCPcFbPxkB4G3YI0+c42qF h3AeRtSI1T2WK+RRmARGHbikgEscxhEqgkU1+iwoESV/Vp+kMscIgARzjqrZESgqASDOv44EwBaA 7POMFoAAEAACQCB/BApJACwsLKhj/6TM8RYAKc4xgI3bAuBdjeejAV/Hhr6bEyBJDoCwleqw1X5v pnvOJRCwZz4sAWEtZ0B9ln9PeH9MpEIaIiALARCZw8BPCMRsmSiKEXjtdTe5T2eSYwBtnOz4HADR DqT9nvvw7PhxL56QMbsRC93utiFnK35KeawcUZ/zGrSHPyQBYV20ReiQkxEGccjJ9SwEgHe1Ombl uikYqhH5VtAtUIjZGhK+7cFoW+a20lOX9V/VUFEBFZVYMF7P7XIAxBESFqNGlQwIFOXdH5YEUG9d lG2Nvb291N/fTwMDAzQ0NETDw8MqKm2S8xuJfSNRkfgHBIAAEAACQKBZCBSeANBEQLMIAHGGqwnu jKSAtqcAZCYAQsL9JZS2mlgvwqEP2uPv/8x/CkBtzN7kg3lEAMTJ40+eGHYKQFtuAVD+qu8UAO0Q +hL0KWejukfaiALI8RSAeMco7LUUTnoEJnSzPAXAI08a5zUk3F+epcBTFvyOa9AKv++z+PmLf5Vb EwCx8viT0/kc2QZjaCYSTIOL9wjAGpFQ20YQk0BQxtfdzUdkBkTJBD07DjMQQFYEEwBZxxevCaiR BIGiEwASCYAIgCQzirpAAAgAASDQLARAAAScdR90pJ9aqTcS8pkOeVDyOv/Kvm0EQPVEgup+eccp d0gJ74kDYREA9fv86x17b54BabuWqV9HPJhHESY9BcBzbKEPN/Oan3D53D1X0p/y2KsJD9s6AsB5 zP3npXtXOwOSAPY4Idph594Hvjx8+9W9pwLEOUFxe7Cjrrth3P6M7knlSeO81mHrOIbhYfMBONTt 8693LqPnLw47LU/4nnLPcXMx8piEy46ckgB6xxeEoTEGY56jcQn+ivOfiBB1ykx9Cy7WgacHhF2L 032nlyAiK834mvXF3gn9gADohFnGGIEAEAACQKARCHQsAZAm7B33rKaiYVAUI7C2BaARj6nbZlR2 9gZ2i6aBABAAAkVDoCjv/rAtAIgAKJrGQB4gAASAABDQCJSGAJC8AHnmACiaIwt50pELRTECQQDg pQoEgAAQaB4C8u5fv3493XffffSlL32prtx2223qM/lplnvvvZc2btxIR44cIXlviwOftYhtInv5 dfZ/nALQPD1AT0AACAABIJAcARAAHNoP57u8GIAASP7Q4w4gAASAQNkRkHe/OP8HDhxQyfR0mZiY ICnHjx+no0ePs6N/lA4fPkKHDg3Tnj17adOmF+nb3/42CICyKwDkBwJAAAgAgdQIdCQBkBot3Fg4 BDqKACgc+hAICAABINAaBOTdf+utt3oc/1OnTtPo2BgfAztK+/cfZId/H+0a2kM7d+6izS++RD96 +hnOxL+FbvnCLSAAWjNt6BUIAAEgAAQKgAAIgAJMAkRIjwAIgPTY4U4gAASAQFkR0ASAXvE/fZqd /9ExOnHiJB07dpx27dpDO3a8TFu37WCnfys9+9zz9NRT/SoCoBEEgGT8xxaAsmoT5AYCQAAIdBYC kQRAj5zfbWQbr/6e8ABkyaBsZh2XM2/ljNwXrz+n2v6brnqU1q5dS3Jmruz3N4//k9/zzAHQWVPc 3qMFAdDe84vRAQEgAASCEDAJgPHxcRobO1V1/g8fPko7dr5MW7Zsp82bX6INGzbTM8+spyeffJpe eGFzwwgAsWukIAcAdBYIAAEgAASKjEBsBEBPt+/sZclEnpAA8AOgCQDZszc6OkprrzmLQAAUWU2K KxsIgOLODSQDAkAACDQKgSITAJoIkMULWdTo7e2l/v5+jkQYoKGhIRoeHla2j9hAkjxQbCL8AwJA AAgAASDQLASsCQA589h7djiLGHmWt/d886AIgDgCYI6Z9Cn+gpwYn1CM+hVXXJE5W29TsrU3a/bQ D4EAgBIAASAABDoPARAAnTfnGDEQAAJAAAjkg4AFAVDbBlBPAPRRnyGHbBkICg7wkwdxEQDCmjuO /zid4j19x48eo0nO6vvBD3wQBEA+8942rRSRAJAtL4FbZ3g7TergGSbbUt/bNrONgQABIAAEHASK SADIar5e/ZefiACAtgIBIAAEgEAREbAgAJwtAIERALIdwJcjICsBsGzZMhUSZzr/h4dHaPz0OL3/ /e8HAVBELWqhTIUlALp7aKcfF/28JPbk+6iShTxo4fygayAABIBAIxAoGgFg5i4SG0YKCIBGzDza BAJAAAgAgawIWBMA9R2JU9JNPYaXIyRBLgQAM+cqqY+s/nM238MjTADw3x94/wdAAGSd8Ta7v1QE gGCvts14n5v4KQEBEI8RagABINBJCBSZABDHHwRAJ2kjxgoEgAAQKBcC6QkAWc3kVc7qP3d1MysB sHzZcppn5nxqasqJAuDMviePn1BbAi677DIQAOXSr4ZLWzoCgOMC1OkavgfFv22getkfZWNEFoTe 03DU0QEQAAJAoLUIgABoLf7oHQgAASAABMqLQIJjAOtXLWXFv7bXmVf/K87f2nmRsOX6vdAVWuMe A7gk4Lo+BlDC6WZnZml6apqmJibVvroVK1aAACivrjVE8vIRABIEwM+F35E3CAHnuTKft/oIANVG 5D0NgRuNAgEgAAQKgUDRCQB9fDFOASiEukAIIAAEgAAQMBCIjQBoBFr+JIAjHOI/ODhIa9euVUfm mHvp5EtUf5FeffXVIAAaMSElbrOMBIDj4OvjNeu30nDCDZVbo+bf+wkAm3tKPKkQHQgAASAQg0AR CQC/7SJ/gwCAKgMBIAAEgEDRECgFASBfolJAABRNfVovT/kJAB+GxtGa4QSAzT2tnxtIAASAABBo FAIgABqFLNoFAkAACACBdkegFAQAIgDaXQ3Tj6+MBIB/C4B3Kw1HBsRGADinctS21wTdkx5T3AkE gAAQKDoCRSUAzCgARAAUXYsgHxAAAkCgMxEAAdCZ8942oy4fAeBLAhiUPDOOALC6p22mGAMBAkAA CNQhUGQCQEctggCA4gIBIAAEgEAREQABUMRZgUzWCJSOAPAfA6j+1vkA3GG72wBCtwBY3WMNISoC ASAABEqHQNEIAMltZBZNAiAHQOlUCwIDASAABNoeARAAbT/F7T3AUhEA7sp9d8/O2qSErPZLeH+t npMEsPq31T3tPe8YHRAAAp2NQNEJAE0GgADobD3F6IEAEAACRUSgsASAGUKHJIBFVJ1iyFRYAiDw CEwzs7/JAXiP0xR+QOUJMI4CdP6uHR/oP4Iz6J5izBCkAAJAAAjkjwAIgPwxRYtAAAgAASDQGQiA AOiMeW7bURaRAGhbsDEwIAAEgEBBECgiAeCHRqIAEAFQEIWBGEAACAABIFBFAAQAlKHUCIAAKPX0 QXggAASAQCoEQACkgg03AQEgAASAABAgEABQglIjAAKg1NMH4YEAEAACqRAAAZAKNtwEBIAAEAAC QCCcAPDuMXb3H7t7kLPiJmFxs7OzNDk5SaOjozQyMkKDg4O0du1aFS4XtP9fPrv66qvp2OHtmcq1 192UVXzcXyAEQAAUaDIgChAAAkCgSQiAAGgS0OgGCAABIAAE2g6ByAgAIQHqMpZ392QGAQRAZgjR gIsACACoAhAAAkCg8xAAAdB5c44RAwEgAASAQD4IWBIAfAxZgOPf0+1EBghJUIsYqJ1pHnZ9jREB sPJsI7qg660qAiDoLF1EAOQz4e3WCgiAdptRjAcIAAEgEI8ACIB4jFADCAABIAAEgEAQArEEgDp6 LCr0v6/ivc5/V/qMroKur3G2AFx/ThedvXJDbQvAqrfS8uXLaWF+nubn5miO68zMzKgyP48tAFDh egRAAEArgAAQAAKdhwAIgM6bc4wYCAABIAAE8kEglgBwtgAERwAoEdjB92wT8MsVcF1tAdh6A52z 5IG6HADLli5Vzv/M9DRNT02pPAETE5M0x58hB0A+k95OrYAAaKfZxFiAABAAAnYIgACwwwm1gAAQ AAJAAAj4EbAkACKAaxgBMKUIgCm3zM3NgwCA/tYh0D4EAJNsOtqm+rO2ncZq6nf2UDff64nA8d9o U8eqM6OSP+on6f2oDwSAABBIiAAIgISAoToQAAJAAAgAAReBxARAT7fPKUlLAKgtAOfQyg3mKQCr aPmyZRzuP6/C/2WbgJSZmVk+GeAMCACobXsSAO42Gb/j3leR7TfdpIJwbP41wrmP7dchLiJJh9g2 UAEIAAEgkAwBEADJ8EJtIAAEgAAQAAIagVACIPQYwC5NAAStWJqOQPj16ikAL15PZ/tWPf1JAM2E gNgCAMX1I9AOEQDK0Q/0oHeSSqRp612DAMADAgSAQIcgAAKgQyYawwQCQAAIAIHcEYiMAMi9N7fB uGMA5br8M51/+R0EQKNmpLztlp8ASOLkB622G59pAqDH2QqgE3jWHeXpW7H3k31BOT3qCEEhJdz+ zEShtsEK5dU4SA4EgEAREAABUIRZgAxAAAgAASBQRgRAAJRx1iBzFYHyEwA8FH1SRuxKvx0B4Nk2 4DrpVafeFyXgOPbGNgN/fRavro4kBXWP/1QJQrEFAE8kEAACTUYABECTAUd3QAAIAAEg0DYIFJYA kBX/hYUFVXQkACIA2kbvchtIWxAAgoZ/Nb261caEyo4A8K/gOw68u3XHQwCYjrzRjyIkvFt9wk/6 AAGQmzKjISAABKwRAAFgDRUqAgEgAASAABDwIFBoAgBbAKCtcQi0DQHgGai7LUCF8ZtJAO0IgLpA AuXQu+2YBID5udm/WSc2rwAIgDgdxXUgAATyRwAEQP6YokUgAASAABDoDARAAHTGPLftKNuTAKhN l3MSgHc13uvg+3MABJwaoJz4MAKgliugupffzR+g+jHvDdQiEABt+3BhYECgwAiAACjw5EA0IAAE gAAQKDQCIAAKPT0QLg6B0hMAcSvsnlX6JkUAhEUDgACIU0dcBwJAoEkIgABoEtDoBggAASAABNoO ARAAbTelnTWg0hMAFHMKQNx+/IBw/cAcAN093BP/C8gBEJ17MCRPQFXNEAHQWU8cRgsEioEACIBi zAOkAAJAAAgAgfIhAAKgfHMGiQ0Eyk8A8GD0KQDaSdfjcz+vOfQuWVCtV8sVUAvX9+UNcNuoOvlx pwAocbxt1J8CYJIWcQQB1BUIAAEgkD8CIADyxxQtAgEgAASAQGcgAAKgM+a5bUdZBgKgp5sdassj /rz78AP287vH7ul6lT5/DoAuqvTInv/a3n5P1wFbDhwH38wFoHMO1NSmro7RqEMYcPETGG2rdRgY EAACrUYABECrZwD9AwEgAASAQFkRAAFQ1pmD3AqBMhAAhZqquJwDhRIWwgABIAAEghEAAQDNAAJA AAgAASCQDoFYAqC6uqdX+Hi1MHYxM0YWOd5vdnaWJicnaXR0lEZGRmhwcJDWrl1LlUqF/Mf/6b+v vvpqOnZ4e6Zy7XU3pUMKdxUSARAACaclNqt/wvZQHQgAASDQAgTKRAD89rLbqL+/nwYGBmhoaIiG h4eV7SM20NzcnLJ5GvPPiRCT6Kyk/8yoL39emaRttaJ+2eV3MEs/f63AHH0CASBQHgQiCQDl/Bve vn6hNoMAMCEEAVAehWq2pCAA7BGvknlZH2D7LlETCAABINAQBMpEAPT29raIACBSW9BSEAB60sTu KyMB0C7yZ52/hjx8aBQIAIHSIxBOAMhKYZyjoJOXufuHzS+JKlnAK/q1/cXO3mJx6Lfe8Bb1+eLF i43rb6XlvggAjbDcgwiA0utb7gMAAZA7pGgQCAABIFB4BIpEAFxxxQqan5/n4l3JF7tFohr9EQD9 15yl7J4lS5bU2Uc14N3VX2VfcT6YHseWSuqMR+Wg8Ud49nTX550JJQDc7WRV+85PMkTYhzJGJZc7 ntpqfX3+mShFzCR/jHymjE6OGyZCfGPUYwi8HoePgUFY+1UZ4mzxwj+tEBAIAIGiIRBOAPDLMfaL hhOQiUOv/8nL0HxPOS/12gtdf5HoLQAvXn8OX19Md7lbAO751zfSkqVLaY6/SGd4i4CU2dk5VeTL 9coVV2YK/5ftA9gCUDQVzCYPCIBs+OFuIAAEgEAZESgSAfDBD17G+WhmaGJiyrFb5uY5tJ8JgYUF Wr58eR0BIFsANqw8m+2fJfSQuwXA72h77CnXmYy1yQImUpzkoPuCIzztCQA/WaDsPdNBjrEPlajG CTj6b1tfN6v8ZGG/mrip/ozx+efLfz0On7j79VSGzV8Zn1nIDASAQHEQyEYA+BlOZkn9BIDnZe6S CiYBsHi1kQNg1Vtp8aWXqvwAE5NTqkxNTasyxyTACmbZkQOgOMpTBElAABRhFiADEAACQKC5CBSJ AHj/+z7A+/mnaGzsFI1PTLLNMkNTTAgICbCUFzWCcgAIAbDkASMHgLnoIraVb7U531B8ji6w3BYQ 2G+A7edEAhgr+DH2oXb405Aaam98Fvml81j5zAgM39gc4Z38CtUSPfY6fKLub+6jhN6AABDoQASi twBEvmDl5edli+WLIg8CQBj0cWbShQCQL9UpLpIoZ8UKEAAdqKORQwYBAI0AAkAACHQeAkUiAN73 3ver1f/R0TE6fXqS7ZZpVcRukTD/PAgAWS1P5ywH6UZWBzru/nj7sLUEgKV8JnQSrRBlE3uux+ET MCdx7XfeI44RAwEg0EAEYpMAer9wfGeOmy/DkPPFk0YALFnCWwD4S1PYcwmpm57mrQAzszTPTPpV V16FCIAGKkMZmwYBUMZZg8xAAAgAgWwIFIoAUBEA0xwBcJqJgGllt0iRrYupIgAYGn8IuWwJyI8A kOh7f3uGfWdMTVjkQWRouj+CIez42QykRib5LeSry4cgDrph0MZdjwvdj7vf7unQUQjJcifYtY1a QAAItDMCsccAepKcBIT418KfePW/Inv+nW0A5hEs6p1pJFzpXrWjmgRQ6l98Fx8D+OUL1b2SBHBh 4QwtcPIc/3GASALYzqqYbmydQwD4ww2DQhLTYdiSu9T7oH6/aUtkQadAoBUIeJKQhTwLzaxjjYHf UQx2HEObU85gdoelSATABz7g5ACYnJzhLYzzbMOQY8dwqSw3EyEvotv5GECdBFBsnmUP8TGAa2p1 qk6+L0S9W5LQ9ey0niWbilH2nTe8XYe6e+fNf78/51OYfVgfPu+0b7v/X48ti/ymjSrfRab9Ku1H jc3melyduPZt5q+GY/bnya4/1AICQKBdEIglABoxUJ0DQM7AlbNwR9wkgGvXrlUZc/2OP44BbMQs tEebHUEAuE6A3zhyMiCX1IkGAdAeDyBGkQ4Bv/4HPQ/NrJNuFCnuym/FskgEwOWXX6H2+4vjb/7T pwDkcQxgvjkAUkwdbgECQAAIAIG2QaCwBIBG2CQDEAHQNnqX20A6gQDwZzuugbfTWaVIumySG/oZ GgIBkAE83FpuBJzn1r+a633Om1mnSWh6ohmyr1gWiQAQ20RsFf+/rASA95i5niZNFLoBAkAACACB dkeg8ASATAAiANpdDdOPr/0JgBROfuT5xkHhuuZnbnIk98zp8JDOeuLBG1IZs1/VJACq+0O92xyU g+QLgw2OgqhlYg7iQjxnRVfP065PYFoLV813r2167cadbYlAyH5oz7G5zaxTB7J+H3ifx9qzlWIL gPtOUs+0+h0EgEQ/ShSk5DwKIg/aUvcxKCAABIAAECgEAqUgADQJgAiAQuhMoYRofwKA4dYOvc1K f912AdeIr95rQwA45x2bu00d58R0mp129Cpm3fW4c6sDCACz/RqZUHMUPA6SgsVLQtTL6NYxnY0q OVIbS2LZC/UEQJjSIaCejYCtO6Zj3Mw6IQRA/fOoZU5BAJh9gACg4eFhtf0RBEDpnl4IDASAABBo CwRAALTFNHbuIDqCAJDprTuzOGgFLThs2CEQwox3adwfARCcHTo8AZWXDKhqY5ShH0AAeNoPIhA8 q6L1xzhpjKpcR8gqqjd3QgrZO/dxw8jzQCBs+4v5eTPrhBAAQc+j82yBADh2eLvnRKJGbQHIQ93Q BhAAAkAACAABPwIgAKATpUagYwgAzyy52wJ4Bd6zKh921FKd4+zPtlxPAHiCDcLa1TKFOStR9wVu ATAGGXRvbHu+TNJhBISNoxU35lI/NRC+pQg007m36SuEAPAGHEW9IxKeAoAIAEQAtPQBROdAAAgA ASAAAgA6UGoEOpMAqE2Zs5rtRgOEOq0xDn5ABEA9ARBx2oAv54C5lz70aKccCABvzgHGwDd+/5aB Kmp1BEAth4CV7KV+YiB8yxFoZni/TV8gADyr+f7VfZu/EQHQ8qcKAgABIAAEgEACBEAAJAALVYuH QNsTAHEr0XGOtExZqyIAotQlTu64CIC469J3lgiA4qk6JGoXBJqZ4M+mLxAAIADa5dnCOIAAEAAC QMAKARAAVjChUlERaHsCgFPxRR7153Fy7XMA2O/vVZ60J+FfvS4kDAGuOuduVIGNM+8nMoKce38C xEjnJyonQlG1HXK1BwLNPOLPpi8/qnaJQsNPBYiZpQZtARgfnyApp0+fprGxUxxmf5gOHDhE+/Yd oN2799HAwFZ68smn6YUXNtMtX7iFjhw5Qtded1Nm518iBBAB0B5PJkYBBIAAEOgUBEAAdMpMt+k4 258AEP9bkvjVZ+bXn3uc+dhTAFxCoZrlv5ZPIDjBl6M49Rn2vcREeAb+kK0DuUcA1I4sM/HwbJGo Eg/e3AmJZW/TZwnDaiIC/r35QXv1m1nHM/RyEgCTk1P0X//oj2jpkiW04oorquVzN91ED9x/Pw0O DoEAaKKKoysgAASAABAoLgKRBID3/Gx3n6zNUWQx45Uzb2dnZ9UROHIUzl0XG+d4VyqBZ+LKPTgG sLiK1CrJykAARK7gM3Bx1x1svWdyO3vVo5zr2jNVn73ff753XI4AkwQw9sv73gXePflGboIg5chK AFRJCS2Pg4U3w7+LnMqToAvnCghYgUwke6uUHf0WFgH/Mxz3d41U8+qvf4BevQx+3vOo45U3OwFg H7WUfkrl3X/rrbfSxISs/I+zPTFFleXL6etf+xp997vfrZbHH3+c1q1bVzwCYPVi57207CHasUqO WfWfvhL0/vIncHXeeUEntJh6EX6CS3r8C3+nJs75e0pjUSQcbOcnbH6bh7//KOFaz9pHKBKuzcMF PQGBciNgFQEgL6o8H3APAbBhJZ111jX85TxIa9euVV/gct3/DwRAuRWtUdKXgQBo1NjRbkoEcgpB Ttk7bgMC7Y+AbL/JYbEgCig/AVC6LQBs+5zDTv9bbtxGZ9Y4UV51kAmOHK2V5V/e9lsWWZp6r3mU bF1kXD6S9HQHHcebrO2iz4+QeZWe+udZERiisIxtnv5BMvRQGwgAgbQIpCYANHtZ4RV7z+qahSSa AHhgSX327WVLl9L8/DyXBU9LIAAsgO3AKiAAOnDSbYccmAMgLp+BbeOoBwSAQCgCTXAKgiIASrUF wCUAlj005xIA3giPwAhMgwywXUEOdTDd92PVfktKNPhOf/E7gSoKxI1qqMlac5jjrle3vrnRW0F5 a8wouGofehzm+z/sOMyoRzhqfH7sXBnTcF5h8xM1v1b2d9b5ZWyq0QdRhF4TnnW8aYEAEMgfgdQE gIjiP2bLlsmMigBYuoQJgLl5mpudUySADgYAAZD/5LdDiyAA2mEWGziGgCMK0xhpDZQQTQOBtkOg rxJxbGhOoy3SFoAVK1aERi7KIklvby/19/dzIsIBGhoa4gSFw7z9cTUt6TqHbtzGBMCOVdStj3M1 8bGIAIizu8Ku93R750jZc0lIgL4+3phW+6dWis0P5JKRv0bV5L89daKux7Uf4Hh6V+SF7DUTzSZc rY/rn4fTjAiAaIKgNiZ/vazzW13hd4z98IgeEAA5vdHQDBBoLgKZCQD/y9wmFCiKANARAHNMAggR ICTAwsIZ9eWKHADNVY4y9AYCoAyzBBmBABAAAvkiUKQtAJdz0sH5BW/UooxW7JZwAmBU5UGam2MC IGDbo0KrUQRAyAp2VxAJETZtAW0EEQCRNmGU82jRvscBz9sRTdp/SvVOS+DIfaH2dw7zGxSBEjiX eeOeEkfcBgSAQDIECkcALF+2jB1+x+l3Cv/ukgAgAJJNbifUBgHQCbOMMQIBIAAEvAgUaQuAQwAE 5y4qJAEgSWWTrPbXKZ+xuu5eq3NI5fM45zD0evL281iNrw3Trv88+mwIAZB5fn0TjggAvH6BQNsh UDgCQL4s/Wy4kOOIAGg73ctlQCAAcoERjQABIAAESoVAEAFw7TXXqCP/fvCDH1TLM888Q5s3b27o KQArrrwyxRaAFkYAKN88+PQAKyXwRyYE5lvJQADYts9dKCc8jmiwGpRRybJ/T5h9yuSyjSEAMs6v H6/UBIA+cSjh9ouk84X6QAAIJEYg9TGAZoISFYZk7LWN2wagtwBcf059EsAgAkBGBQIg8dx2xA0g ADpimjFIIAAEgIAHgSJtAZDoxLDTi9JGAOgkebUky2wvGTHflerRpr5jTl2U4q5Ltfo+7B01//GT Fc77UDvJIOjYWjNHQNx1nWOqdkymt31DFVI63nGPU/T43Ls9OWbssZO74+Yn6rqt/Z1lfjU+5lYA 07aPS1Lp3A8CIE7PcB0ItAoBqwiAvIXz5AAYHaWRkZHaMYABEQAgAPKegfZpDwRA+8wlRgIEgAAQ sEXATwCMjZ2iEydO0rFjx+nw4aO0Y+fLtGXLdl79f4k2bNhMzzyznp588ml64YXNdMsXbqEjR47Q tdfdRMcOb89cGkEA2OLQ8fXyXv3veEABABAAAp2AAAiATpjlNh5j5xAAQSsmyVYciqQGavWA96Du 9AmlVzbqo4h2OqtV1in8HbysqxurFZH3eLJYp+kj4SykOb4qYReo3mEIeFYt47P1m6uNb1n1777n qgnPQMj0gADoML31Dde/Ap3sXd/Z2GH0QAAIAAEQANCBUiPQEQSAa7D7DRzHAIo34Is4wf4jRLWM MiYJma0nBxxHI257Ubaxxjkzcdez9R54NwiABoDawU369SlWv+KeuxY8E+70gQDoYD3G0IEAEAAC QCATAiAAMsHXfjfL9owylampKdq3bx8dOnSIQz+PcQjoCU85efKk+lt+mmWUt56MjY1xqOhhumbl Z+noyLZMRdpoFG5rxNGvrAlofwetUqviQdcKPo/q3Gt29teYcq5R5zavWiPX+OcO45qq7/ssd12V /v0y+eWLut4AzNcwGdLwcTdA7tznBjJmf78474vuVTs875Lw94tgXsBnwtUtefd/4QtfoPHxcTp9 +jSNjo7R8eMn6OjRY7yt8Aht3zFIL720jTZtGqDnn99Ea9euoyee+BFt3LiJPn/L59W7H1sAAmyY 0CPk9H788kaetZ/FhhEBASAABNIhAAIgHW5td5ffuHSOYlyg+fl5dU6xWWZnZ6koRQy/3bt3KxJA cknIvk6zHD16VP0tP80iZMHx48fp4MGD9LFPrqLhAy9lKtJGYzDZSje8hQ2vZQ/at//gMpWMSZe3 3LDVuPdBWsbXlj1ozqH5mfz+FrrhBt3GMnrQne+tN7zF065fJv91b79+nXH69NQRud9yA22ddcZs yui0XZNFsI7uL2ics/TgMjNhlh4n/9wq8rn33HADvcXAT8mx1fuZyLlF169iqft0fmr8vVjHyeDD Sc2lK58rw7IHve0rDH3yRffpn3+nz2hsnDrJ5rg474nGPJslG19Vf7xyBz1bCi+/zvPz991Qna+1 Gasjde0G62PcnMm7//Of/zw7/qOK3HX2/h+h4eEROnDgEG3dtoNefHELO/ybad26jdTf/xyfDPBD zgfwQpUA0ORv1jwAyAHQdiYRBgQEgAAQaGsEQAC09fTGDy7I8Ted/pmZGZIiqy2Tk5OFK2L4DQ4O KhJAnPnh4WFPEVJAPpOf/iIrQEIcfOTaG+jA3k2ZirTRMHweWOI4k0seiO/DrbvkAT1XD9ASz73O 37XrUs/8zK1/zvX0ojHfL15/DstwDl3/orfdc65/UclUd/3F6+kc7kdfD8LmgSXeMak23DH6rwXW NeWp669+nKqNriX0gB6XxrXajjt2o13vuPxtBv9t4uTHJV4G3zOmZHRxd8dY3753XE6ftXHGYsd4 2MiVZo4b9kwU8F1U+LEq/TGfYVfXlI4Zz4UHWzud1++TWB1xddjz/ql7Z9l9z8i7/+abb1aRX0Lu jowc5kiwYXb+D9Levftp84uS/G8TO/8b6Nlnn6cf/nAtPf74UxwNsJH+7fP/pr4P/NFfaYkAEADx tgZqAAEgAASAQHEQAAFQnLlouiSm869X/GWl33T4dXjlqVOnVMi8FFlxKUoRJ37r1q20Y8cORQLs 3bvXU8TBl8/kp1n279/PhuIBevnll+mqj32ahgbXZyrSRkMx2bCSzvYc+7SYVtfNwwZaeXYXnb1y g1eW1YvZwD+bVm6QeVtNi7mdxavNOTQ/c373thH0Wf39wf0GyeneK3KdvZI2qHE4slflirrmjiG6 P984Xfy84x6l1YvFefZi42nXc58fu+C/Q++3ksH3bJlz594f1H60zHp8btt+OazkCtEBJV/EHBfo XdHQ57MM4/S8Bww9C/tcjclO553nykJHctQXeff39PQogle2gO3ff4D27NlLQ0O7mRTeRRs41P+5 557n0P/n6Ec/ekat/n//+4/zZ+voczd/Tt0jBMCR4a2e7V9pSAAQAE03X9AhEAACQAAIZEAABEAG 8Mp8q9/5l1V/CbmUlf6JiQl3T+Wo2j+vV1jE4NIlaEW9FZ+JU//CCy9wqOeLtH37dkUEmGXnzp3q b/lpFokaEOd/y5YtdPnVn6BtL/VnKtJG88a/lq45S4eYn0XXrHWjG9ZeQ2ex837xXb5oB8/nd9HF dXXMzwKuh7WroyruupidQEMO/Xncfeq6vk/69Y/F/dtTj8dm1Z9vHOqei+kufySIp62k2Pjrx9xv JYNv7kz5gvC0/cwzV47uVPXERi4rzOujbJr3TKDvWKzD5jDsc6UzcTpuXLfSEad+4LMYEKUVNSZ5 919//fWK+B0aGmKn/2X1rt+2bTu/17fR2mfE8e+np576ETv/T9EjjzxOa9Z8j7cCPEOfvemzTBjs V9u/Dh/akpkEAAFQZmsIsgMBIAAEOg8BEACdN+dqxJoAkJV/c9XfSaY0qpx+WVkRI2nPnj3KwBKH WRznIhVx4H/0ox/xKs9aDu18nkM+N3jKxo0b1d/y0yxCGmzaJCtEz9H7LvswrX/2kUxF2mgFLl++ UIzpC+nLMi+PXkVvYuP6wi/75+jLdGH1c/N3XS/mumr3TXTVoyFz/+ULvbkBPJEKQfLodh6lq97k Xpc23nQVPVrVr4hrVv15x/noVW+q4WTqsGpLjy0pNv760ffbyeDD2JQvaH4tPnP61YQR64rvHiu5 rDAv1ruhFc9jofsMe47V3LrvkLr3e5yOG9etdcR5ts08JfXvrHhdknf/tddeq8hfKS+8sIm/AzbQ +vXPc8j/Onrk0cfpe997hB5++PvU1/d9euihPrr//ofoySd/SDeuulERBx/9xA106MAAjRz0kgBJ owBAAHSoIYVhAwEgAARKigAIgJJOXFaxNQEgK/8S8i/7VyXMXxLjieMvxpGsqG/evFk50OvWrWOj 6ll65plnqkWcbvPvVvz+wx/+kL773e9Sb28vh3d+nx599FGr8thjj/F+0MfZOHyYlv/j++h7ffdk KtJGQ8Z/73vpjey8va2nhrunn563sSH9RnrvvXw9rK7n8x56W1175mcB15PIYOiHDR49b+uiN773 Xrr3vW+krrf1eDCUa/JZ3TVzzKH9+cah7nkb9fjre9pKio2/fsz9VjL45jlufoPmxvws7rrgYSOX FeYhOppQJ2z0BnVSYB3yHKvnK+jZUPMWp+PG9ZQ6op5z/Q5LoCvy7r/88ss5s/8TvML/A3rsscd5 lf9RdvrF4X+Y7rn3PrrnntV099338s/7+Kf8vpq/Jx6l6z9zvYoI+/A116vcL0ICSCSAPg0GBEBW CwP3AwEgAASAQJERiCQAnHPGfcV/GHmK0YnzKeHm4nTKavNdF9f6kDPA5br/n3wmLHvSL2Z/fTn2 p9P/mav/OuxfnH9JpCSJ9KSI0y/OscwHSkEwWL6cli1dQpcuXkyLL7mELnn3u+ndXC6++GIu7/KU d73rXeSUi+hdF11EF130Trrone+kd77zHfTOd7yD3nHhhapceOEFdOEFTnE+k+tSl+/j+9998bvp Eu5rsfS5+FK69FIpLAP/lM+UDNK/299F3Nc7uZ+L+O9LLllMS5Yuo+WROrSclsqYLpX2pfC4LqmN 6108LpHjIh6HtF0taizvpHeosbyDx8Gy8+8yNvm8WngsMh49ZhnnBeefT+efdx4tWrSI/v7v/57+ 7u/+ThX5+3y+Jm2pdtxyEf+86B38mfQhWEkdFy+pI7gKxoLBu991MV3CeAgm7xZMRGaRR917YbXv 8xadp/o6/3yWhz+/kGV8F9/7bsbs0iVLaMmSpQpvwV7aUnOp23LHJ1jI5zL/tTmQ+Zb5c+ZYZJWx nsdjW/T33vHK2GXMi1gWkcf53S0uLm9/+9/R2972Ni5vp7czRn/PbUj981nmd76Tx/vuS1jepTFz XJDnB+8y9S5fvmwZLWUdW7p0qSryjC5btjz0Pb982VJatuRSWiLPpzzvonOie6KH8oyInrnPhdLp 80Svz3f0Wt4n8j5Qz67zLDv66jzjot+XcLui7436nvnUp66jO+74Ft1++ze4fJ0eeOBBWnndSrVd QBMAcgpM0QiAnT3dVfuru2dnqNkidlrU9UbZOz1yBK3PPvTLYY6hKwfbsVFjQbtAAAgAgU5DwCoC QF7ieX7BeAgATkB11lnXqPBpWVFezk4OCIDGqaE/9F/2/EvYv6z866R49957b9UYe+qpp9RxgEUq ErWgS5xcZt12+F1v15ieniZVeP5kDoOKJys553WQ3A4TfGa2lPHx0zTO836aiR9d5G9J+jgxPkGT E5M0NTnF7U+rCJFZVfioL7fIZ1qGKZ01nO+RPsalH/5senqG5oy5Csdfjpl0jhFT7c44Y5ua5pMn pnwZwfU4pB81DucMcF30GNQ1GY8UGaMksJTjwjinxYnjx9QWl2PuEZHOsZFH1RnioydHVSTM+Gnj foUVf8afnzo1Rqe4nVPcntQT7KSug9kEY8bjFtwEE/57ckKuOTLIfaPSvxxB6R5DeYL7PH7iJJ3k a6e5nUm+d0YwnpVknLPGHPN8uFhMMM4ytzLnVT0w9EH1PenMtSMzJ/DUSeo4c7qMcZR/ShZ19btc 458nRQ6FD8vE7wPB4zgfrSbHq8nvJ6Q+n7U+NsbjlvGybsiczS/IefGdWe6+++6mjr2/vz+f/qrv 9DP8bg+eO29f/B2gn2U5FtZ9J8yw3s2IHso7iHXXfOc47yR5lp2TZBy9nvUdK+u+y1mePHVIz4t8 fynSY3mFPvzhj9EXvvAljga4l679xLX00ksv0ZUfXUl7hzbSwX2bM20DaOQWgLztr7ysi57uCvUZ jdXJ2Vehru6eag0hKsAB5IU+2gECQAAIZEMgNQGgmV35cq2xwN4vhDDRNAHgHD3lLct4RcJxFBY8 tyMCINtE67v9of/isIkDIGH/ss9fnP8LeDVHDCepG+dg53E9qWMuTrAucfeadeN+jzt32rwe15a+ Xm/w1mS3aSNQJvdYxvijGcXprznJynlVJzmMOs7wyRN0Uhy9Y0eVUyrO3xg7eKdPnVYO7fQkO3iu 0R4khyYBxOlVjr/rjJ/i+8fZSdX36KMkbX7WObRCBMgRlD4iQPcnzq0+B1x+yrjUaRXKqZXxsbPN jv5RyRQu+SyGdtEgb23ZvnULbeU9xFte2sKOwBZOHLaD9X8PR78c4kgYxoIdYmlDiINxxm2cMTvF 7Y2q9o6oNgUzcejFsZ9g532KMRBHqDoGISv43tPibPM9hw8dpP17dtPLOzlR5bZttHOHJKZ8mXYO DtHuPftpeOQInRg9RdNyBruniPPkEj4uHtKHzM08PwvVZ0Cei1kmXaQuEwCadDjBc3xMSA5O4jki R2K6WdOHOQv6IY72OcSnYRzYJ9hwMrWXGR+Razsnztwh+7B3ce6PIRravYf27edjNlnGY8dP0ikh K6ZnyfuWzucdVaZW7rnnnqaKKyR5s/6F9iURegvztCC6J0SAJiKFAFTEm1OEqKoSAExozc05zr7+ zlDfRQ0ajH9ennzyKTqPI2FWrLiKvvGNb9JHP/pRlRPmiqs/qU5/2b8n2zaAK1esUM/i7IyUeX4O F3isnGuHyRXTRlp064D6npXvW3lfCWEi3wNBix4amjACICpCwMo+29lD3ab9ZTjraabFTwj0VbrJ E7gg/YEBSAMt7gECQAAI5I5AagJAJHG+ZGpOvy1THRUBsJRDAfU59EIC6N0AIADymXvt1IvRoVf/ ZTVU9vw//fTTylhppvMvxmCcEx92XcaQ5F4bh9usE0UIZGkryb1JSInkdWVV33EsZSUv+f1+R7X+ bxunP6hOEBGQ7jN2yIUEYQdenPYRdsL3sRO+e9cuGtrFju2u3bSLHf+9+w4o5/bkyTHXaak53GH9 hkVeBNZnGU4zgXCUnfCDcjSlHFnJyTX37tlHe/jM8kPDTCqcYIdgSq+W8twYJIBfZ/Q7MowIM4m5 eWlHVmB5NVZFawhZw5ERQp6cYlyEKBES6DBnYRcyYB8fpbZHHH4mBQ4eHFa4HD3K5AeTE+PjHOHA Tk6jHLd83nLNa6UjCYDmwZu6J/+8iB3xxJNP8laDJXTLLV+gFVeuUAlgL7vyGia7nqU9uzbURQEk 2W74ocsuU9FSY/yMjI3xs35KSNFpRXrI9grJUSMRFQMD+REAdgRBuH3W0+110JU9l5YECHDuAwmA tO2n1gTcCASAABAAAkEIZCYAPIQuh3zZbBWIIgB0BIBaLVArBhJ+7oQoIgdAdiU2CQCdg0FWIyTh 34MPPqj2ajZr5T8seiCJUx/nCPnbSuJ8t3vdNNEJNiRBWqc/7L50jn+9Az/Fofiygn9kxDk3XMrw oRHHwT12gsPaZdtCvONf3foQsf0iMDyf25aIgBPsbMtqvLP1QApHFZwY8zj/Ggs/3nE66dd3/zNW F2bNy5ROxMCk2s4gUSBHWaaj7paIsTHZFsIrubza7wvKyv4yaoMWQAAUcxL988I8s9qOJDkPrrzy KrqMHXY5PeYDV3yUtm/pV1EAkgzQPA0gCQHwgQ98QBFrx47KdhnZXiNEwITayrOUcyy0igAItc/8 q//VSAC7KE7/rAt54F/cBwFQzGcDUgEBIAAEBIHCEQCSoMgxWp29iep3lwQAAZBdacUB0Jn/JWT7 BBv8ctSfZPuX1X/JqJxHWH/WNpKSAI2qH+dw2W5F0PIlrV8WAiNNuL8tUZDEKY+vyzkvOKxf9sDL VgHZdz8+EbCX3oIICIsA0J+HyTIjuQ04IkAcBgmTnpCcAWqPtHfV35YE8OuWPzImlgSoJl09o7YQ SP4HKbJnW97B+BeOQNEIgL1796rjUG2K1I36l+d2g9u+fAf9/fmVapG/5V/Y51l1LogAmOUFBTn5 RRJZvu9971MnB8jxrVte/BG9vGMd7dv9guc0gHQEgOTPkO1VEmEj26BaGwEQvkDTR5UcV+P94f8y f3V7/rEFIKta434gAASAQG4IFI4ACDoFQOxTRADkM+eCo04kJ4nTdPi/ZP3X2Gd13ltxf5LEgFm3 HYSRArYkRBYSwJaQaHY9/0q1rXNvWy/esbdfuW9UW0GEQFRfYWM3sTTrJM1BEfVMBCVcC3rD2NbL 5+1UvlaKRgCI42/7L65ungSAOP/mP/lbO//+z23lj6oXRADM8Gq8PEOL+QSTf/mXf+EjAx+h937w anrxhSc5J8czKhmgHAcoUQByHGASAuCyD36wugXg1CnJhyIJEDlqhhcvJAGhigBY9VbeMrmIbs8p B4Aef1SOgKgIzdxODwhz7PnzipEEIDgJIBMRKvogXeRBHrqCNoAAEAACnYhA6mMAzQQ06ktGMr66 YWRx2wD0FoDrz6lPAohjABurhpoAEGdFH/0nSYnWrVunCIAo573uSEj/EZExf7eCGLDp09Zx9283 CNqXnaStNFEBzXbs4/qL2hJg69wnrdcoBz5ru6kjAtzVf1sckhABUVsCorKu67cQCIDo9zEIALvv KxtnX5MCdi0mmxfZAiAEgLzPhAD45/f8M33ve9+jf/3AlbRp4xO0Y+ta2v3y89U8AEkJgBVXXKHa liSAc3MSwejIJ89PLQngW+nWBDkAHMfYX2qOctR1W/us/ii/5I54UPi/nh1P+4EJAEEA5KHvaAMI AAEgkBQBqwiApI3G1ffkAOBMuCOcfEofAwgCIA69bNc1ASD7/yUE+jDvRX755Zfp2WefzYUA+KU3 /B7d8t9/nf7zT/ynOuPFxhmPckps7td1kkQEpHHazXtMJzlNW2kiAuIc80Zft8kF0MhtAeYxhFkd 9zzvT0sCaBmykgBB2wHSbAnQWclBACRzNLO9nePvjluVN1f1w7YB6F6aGQEgfQaRAFqWPJ1/aTMs AkATAP/0z/9EDz/8MP3L+6+kFzb8gLa95M0DkJQAaOQxgPFagRpAAAgAASAABJIhAAIgGV6lr22S L5oAEPLlmWeeyUwA/PhP/hT92Z/9BR28+7X0id97dSICwOYM6KISAGmc/jLeE0Qq2BABtk5t1np5 OvJ5teUnBJK0G4SH4B2UGyAsGiWMnLLPC1D/yvM/q6V/KWYYQJEjAIIcfD9BEDX0OLIhDWyaBJAj 8HTJ2/m3IgD+6Z+or6+P3vO+FbTx+cdBAKSZTNwDBIAAEAACpUUABEBppy6d4P7oC4kAMAkAbdwH OdtRWwBe8YpX0H+58B/pva/7JTq99rW05bzX0s++8sc9JECwA3+Q1pzvC3NctdEqEaGNE+1f/VT3 HOil8zi0ctFD++uOEQxfOd9Aq17Bct64QYV6hpf99NB53PaD+2LqRbXR7Gss8yIe26KHaH/E2LSz 7x97UUiAJM51s+pGRQTEyWBLiMRtCfBHq4RFx4SRcP43DaICaojYEAAfv7bHkwDPTIbn/13qZnHK 4xz8uOtm340kACTqT5dWEAD/+E//6BIAV9CG9Y+BAEhnTuAuIAAEgAAQKCkCIABKOnFpxQ7bfqEj APzGvem0hxEA/+E//kf62XP/jt5wwXL63O++imZ+8Js0/E+/Sef9ys/FEgBnzhxSBMB5vQdTHT9o QwKkqZM+hL4cBMD+hxZFOvzmEYFBDn4RSYAwh9pxwluTJDDLlgBbAsB/ekBcksq0WwKwLaD+rWtD APgT4EW9u+PqxjnlQVsA1q9fT2bR85hqCwDvaV9gElXKmYQnROjVfzl1xl/yJgHitgAIAbBmzRqO AHAIgK0DT3uOAsQWgLQWBu4DAkAACACBMiAAAqAMs5SjjDb5F0wSII4A+Klz/ie9+gPX0R9c8s90 4W/+Gt39R79Is/f8No1++Lfotv/nNYkJgKgIhKAIgiTOfZK9+oUjADj0W8skZ0unl8+5N44AUAmt uE/bc+gLEQXAx+tNjJ/m0OITdOL4MT6O65g65eKoW44fP87HXsoZ3aOc/+IUZ+oepyk5gi/syD/O kzHJdcZPcV0+MUPyZsSt2gddzxIFkCQ3gM18mYksbfJkBK32y3OIbQDOS9mWAJDjVm2KHQEgx+Ny JJNEM/FPOd9+jrPNz/O8iKPPk8P/LzhJ6fgZFh0SHTR1ST6XvC/q+El+n8i9/n9VsoH7WJidphlp h4+ulGdh/NRpftbc54fJtTk5MpKP2QsjBbTzLwlndZGx+v/WxwNm/coDAZAVQdwPBIAAEAAC7YwA CIB2nt2AseVFALzix36MfuF1r6f/+t//lP7H77+Jzj3nj+iyX/4Z+v65r6Hp215PE9f9Fm3gZICv /qlXVkmAIAc+LAJAfS6h9KtW0SLJhMzbAjb2vIJ/rnJC8Rf10oEF3j7AdWqRCYvoof2OYXyg9zyu cyPdKKHtXTfSRjGSq+HtTjj/jRvEGXZ/v/FGeoXOuHzjRtWG18n2rezvf0htI6j2vehB2qfad+tx e0puVUSuYKd9w42m/DfSBu2cs8zyu8j3GW7j776ziw3vl+nbbzfqv/07tGt8QjmmU899xoeDE9Yv 2xyccdT+Vs6/IbvgoORwtzfMzj5PN3iyT3+G1omTzM7Cus9wvRtuMK47cqYnAKZp4vQpOs0JKZWT zefPy2p9ktXvmZlpvu80jbHjf+zoERoePkQHDx6gAwcO0L79+2jvvn38k1cd+e+DBw/RoUOHuM4I HTl8RBEEJ5kUGGVSYFR+sqM2KkTBsaN8DNhhOjw8TMNSn8thDls+euQoEwtcZ5RlZuzTRBY0Mi+A PxogaNuGX7fTEgHm66VTtwXYEgCiizZFnOJ5dqRn2aGWI+SIj8DV/+T9+dRTT/GzMc3kFesfPzen +JnR5TSTX/1r+/lZdKJdJiflOLpxddqL5HuRn6eF0GInfoKfs6effprbkL+FHJihWc5eb3RHa7mt hfk5dvz5meR+To8yeXbiOD9jR/k5OELH+edJflZGT56kU/z8nBZSjdubkHcStz/Nz7FkxZdxyLh2 7txZLeYxgP7P8/hKBgGQB4poAwgAASAABNoVARAA7TqzIeNKSgCYhr04jT/xM/8HvfatF9M5N62m c7/6ffqrf3g/nfs7v0vn/eov0mde93P0zNt/naZufj1Nrvpt2vHn/zdd/Gv/lxUBYDqkqzbKMUra uV9EvQfk7wWHAGAZnOvu34oIcP4+uIadftfZVwRAyD7/+fmNdQSAvm9+4yomAmpEQi2rukkAOKRB bZ+/c81xoN3fuY0H99UIgaD99f5V+JoTXnPWN9zA7V7Xr4zsH36Sf//Ek3RUGeBb6O63dtFb79lO J3/4KR7r2+i+nY6R75RBRRa8/dsvKydgfHyX+/eguj747bdT19u/zQQCG+zsEDz7aW7708/USAbu U5wGSdT1o0/xtbeupu3sIKvfGddP/Yid5dGnaSX/Ln2c5hXB0+Ik8E/lBHCb4hyHnWmvHHx2LE6P jbIjcYSOKOeaHXIem3LKxalQq+5TsWTAlDj/J4/z/SN0iB3/PXt2065dL/PpFoPK4dixcwcXdj44 2aWceLFr1y7avXs37d27V4UiHxJSQJWDfA74AS77+TiwfbSf6+zhurv43pe5DL3M9w3xfXvkPq57 aJgOMyFw7DjLOp4sOiBtVEAYMeInYGyJAE0GBEXSJEkUCAIg/Ivkkytvss4BIHVn2BmXyBT5KdE+ 4kDPMikgTv1jjz3Gz9gpOi4EFZejXI6o4kS6DAy8qEiCp556gn7wgx9w/Ufp+9//viqPPPqouv8H P3iCnnjyKa77Ep2QaBh5ZsVhd0kAiQUQIuBHP/whO/AzTNDxe+UkRzDIe+fwCA27z4n8HBFiTJ4D JtSkHBk+zM/yEYcoO3pckWtjTJbdcutXPRjc+qWvq/6+eNs3PJ8jAqDDDBIMFwgAASAABFqCAAiA lsDeuk6zEAC/8Lt/SP/z7nX0x98ZoP/GP//8K3309p7b6dw/+TO67DU/Tbf9wc/TSxf9Bk31MAFw 0+vpwAWvpW+9+VfoxzhBoDiNcREAOrTYqecQAIt6D1TvcyIAdILAjdTDTrgmA5x7nM9u3KgjADip nYTJ1hWXAOB61QgAXgV36tWuee87QL06ud8GIQn0Cr27sl/9LCAHAF8TgsFZ0dfFTbznP+dZJ+Lj CAMnguDT9DQ7w0ePPkHXBp0LfQ0b9NdyvWt/oIxu7UAfP76N7hGC4O6t7CxwKPxRJgz+tov+9lsv qaMfB+76W+r627tp27HjakVbkQuffIqOb72H3tr1t3T3S0dUvWFeAT+06Q76666/oW++cIge/TjX +/hjaiVdVtGdvp+gI+y8y4q6yKDk4D5HR3lV310lN7PXO06srPyP8Ur7ERqR1fp9e+kgr9YflFX6 A65zze2p1XZeWZwIIwKYRBg/NaraGR4+SPv27uakljtp+7attHXrVnppy0s08NKAKi9t2UJb+LOt 27bR9u3bFTkghMDuXUO0hx37Pfxzt5AD/NkQXxuUei+9RFs2v0gDmzbTSy++SC+x07TlpS20bavc z2TC0B6OMDhAw+z0jJ2aSLVFQIdo20YGJCEBgoiAoK0wgckyZa+3S67pn7aJAuUNp/eat+5t1/ie v/Od7yiSK89/4virSBjWeSfChAkBDuEXIk+OrjvJZNfBg/vpABeJatnHRX4e4OdIIl8OHZJnaD8T XHs4xH6XS4LtZF1lPd+zR9U9JA67Iq/YsT/JDr6QdpMS5cNbjdw5V9EG3K9a+ReygZ3/EXb6DzBx tm/3HqdIe1zk517+ey8/D3vkmdjNdfayjOpZlncFR9PI+0ERfN6tONL3FI/RjD7Igqfo6urVqz1N yA4H2eqgjwFEDoAsCONeIAAEgAAQKDsCIADKPoMJ5bchALTx7jf2xYl/1Rv/kN7yxTX0J3f8kP7i S9+lP1n8Hqq8+mfoBl79v////WXa8w+vpalVr+cogDfQ8f/9m7Txj3+dfufnftKKAJD+ao6GuwWg wQSA6fBL37EEgJwcoKIE8iEAaiH6vi0CBgHwo+NHeXXtMbqG8f/YI2zwS0j7Xi77nGRa2ikXZ13K iCqb6c6/6aK/uXOzCl8/dPAF+uZfd9Fff32jun/D1/+Kuv76m/QCG/Ryz+PXsCN/zeM0vPlO+ht2 9u944aBqW1a7h567nf6y6y/pq+t2U+/VXO/qPnYs2NDna9/7CP/90UeUoS+O+4H97IhwkdVxMfZl z72s5EvI8TTv0RfHv7r6LyH77FQc3CeOwy52HKSwEy6r7OxkSJsqVP8oRwQwmRBEAkxzmPPpsZO8 2sgrk4f2057du2jHjm20hR3/l8Tx5xXRzS9uVuVF/l3KwMAAXxcnfhvtYCJgUK3wc3TADv65Yzu9 vH0b7dzK16Xupk206fkNtJH3Vm9c/zwnDJPCf294gTZv5naYCNjORMCevQd4FfYETaRMNphnRICO Bgg6LrCRWwL8Dn8n5AmQo+QkoibPf7KfXjn/bpEoGAnVH+NonDVretU2l70c5SKRLrvZ8R5yizj3 UsTxd6JgBlX0yxbWZSG+hPDaJVEt/O44wATeCBN8R5UzrqMAeCsAk3Ozbj6BKgHAz9cov4McAoDJ BulDyAQmy/YIgSZRNYMcccPP0M7tOxS5JmVwxyDtUlEzIhNvxTEIgRF5roUs5CiB4xIlcGqcHfT6 PARpcBWipLe3FwRAGvBwDxAAAkAACHQEAiAAOmKaa4PMSgAICfCffuqn6Q0XLqffOPdttOyXfpau +82fpTvO+UVav+jX6fhlr6Oj17+BLv3jX6BffdVP04d/59V09RtebU0A1JIAxkUA6C0Bq2ijZwuA 87faDuBuD4iKAKh3+GvbAwIjANTRgU6dLs4VUN1fX7cFQBME5vYAr5Pv7MUPyg9Q2wKgQvM/8QSH 2h6kRz8mEQEfpe+xcyyOuSICuBx45GMK348zOSAr6BLGLj8f+SjX/6uv0wau//zX2OHnOn/51XUq BP7Z2/+Suv7yq7Tebed7Uvej36f9+zbSN//KuW8dOwtS98Er+e8//3fqZwdZ/X7VQ7zKLiH2u2jN h/nvDz+sHHYpQkyIsS/kgTjvR9l5d8KMeRWftwVM84qiIgGmJzis+DiHCw/ziqKsvg+yIzHIYfbs iHPbQ7Iqz7I5JMBhXjU8oVYp/avf01NMAJw6yREAHK3AK5979gwxAbA9nADgVXwhACQyYBs7RTUC QJz/HTQojguv/O/g61uZNBjYuJFeYOd/w3PP0fpnn6N1XJ595ll67rl1tGHDRtrEJMDAlm00uGs3 HZIogPGIpIJhyQbdJG1pSIC4aIAoEsA86cGMAAiKBgg+wrM+GaBJAnTCtoDNmzfTJiaJ8vwnGfYl CkD20ItOCAEgJINsi3nooYc4wuawIrqGuOxi0swsklRv925JtPeyEwnDZJaQXVJkG8zLKgrAfa5G OFyfHXAh6ZxtAA4BYEYAzLqROvKsHud+DzNheFCedZcA0NEyuySiZhs7/i9tpS1GlMx2JgRUpIxE 10iEgLwjmLh0cnEwUcnPjBABJzkKYXIqnyiAbfz8yryY/xABkKeGoi0gAASAABAoOwKRBEBfxUxQ 5v5e6cs8Zr8TetfFtX4qlUpg6Kjcc/XVV/OK4fZM5drrbsosf5kbyIMAMPfr/zmv/j/8/72Gdix9 LR277Ldo7GOvp3PfUNv3L3X/7tdeRf/nK388cguA54hBI8Ff+BYAM09ALdmezheQBwHgdYTcLQCK AOCtAgd6o5MALjIS7XGCwP2e8P/wbQASEaByAbhbASZ3fZtD8mUlfxOv4j9CH/VtA/jo92UP+0Ha 9M2/NhL7/TV9cyNHB2z4Ov2Vrv+XH6YP/0UX/cVXnlEr3oM7H6Kr3WsfXrOHHpaVfHbk1Qrinofp I55+VtD9EjrPK+b3r+B6K77LRv2gIgFURMBH5D6HlHBWIB0jXwx8Me5lj7ys4Kus+1OTrhPP+/85 AuAoEwCHOPx/Lzsse3jfvkMA8MqhrC5KeDFHEzhEwvFAAmBmhh0kTog2evIYJ+kb5vp71d5/cXy2 qW0AW5SzL9EAEhWgCo9FnIQd7PBLX0MS9q9D/9lpGpLPVfg/38PO3eYNTgTA8+z0KwJgLRMA/PP5 55kc2MSEAjs9O19mAmBYCIBkuQCiThUwCYGwelHJEoPyAthuCYg6XcMkA5I4+UnqluUdK3kvJNx8 3bp1Kst/Xv8WZBWeQ9b11hnRhVOnxjgC4CHu57hKdClFwusPukV+P2R8LlsEhCDcLdECHBUgiTCl jqz8S1TNcUl4yc/lKcnoL9EGHHkguQbkdD8pT//oR5yQkGXgyB3JsyH5OkYlCSATAZKz47CE9gvh yO0ekIgkeQdw9M7Qy0xACIGnHH4hKXXyTXH4+V7eLiTEoGwTUlsQOAJBZJia5pMEMuwDEPzF8b/v vvtUPpJ2IQDEDuvu2RmqWn47LamJFtp+X8X5TuEGd/Z0q9+j5MhL923b0TLFyRWHn21/aerpuYnE TeOsvnMrlN3CTiMp7gECQKDTELCKAJAXbZ4vfo8TumElnXXWNexMDHLW4bVUWb4cBEADtdCWANAi +JMAehx1/sL6Lz/zEzTxdQ75v+P1NH3r6+nyP/slT4Z5Xf8/8KkBtquIYfXSfp7kqMBa0j/HSa/d 6yMAAnILZD2aL+j+KQ5xH2OjW4XfuhnpdTZxtbdWraKxMc7FCf1nA18cAdkTLBEC7ETvdo/ekv3u gxKm62bjlpV2CdF1juJiw92tp+qzQyzXd/AKnjj+sp9+K6/wbZMQX44EkOf1ZVnVUxEETDboIs4A O+0HJUGYJPZTjoYkDOQQfo4AmFHbACQHAO9zdnMADMt+ZnFQxFGR1ctqkj7OCaByDXAEADsK4+xc Bzu8k5x5nPMJcJiyYLCPCQXZ+1xNBDgoq5C8us/O/aAQDEw01BIB7lE4HVRODCf3E9KBZdgrYc1M BMhWgK0SNSBEAEcDbOTtAM+vYzKAtwFseoEjBAb4+jYJrd5HI0eO02nZRx2x0p/kWlhEQFIywL8l ICgqICwvgE1ugLC8AHFbAtohR4C8HwRfIZQeeeQRdSxgbuXuu+lb3/oW3XXXXXTHHXfQ1772NfrK V75Mt932JfriF2+lW7/4BfrCrV+gW77AP3Xhv2/lIn/fcsvn6fOf/ze6+eab6eZ/u5l//7z6/NYv fpHbuI2+/OWv0Fduv52++rWv09e/8U2648476S7u05T/7ru/RXffdQfdecc36Btf+yp99StfoS/z vbdxG1/itr54yy30hX/7N/o89/Fvn7uZPvfZm+izPZ9V5aabbnL6vlnk4HpfuJXl/iKXL6n+b7vt 3+nf//3LLMeX6fbbv0rf4DHenQE/wV/mQeZD5qVdCIBIc2BnD3V394RW6enO4FBK29rpd53UpORC nCmTST638bzt0ziZba8rgkIAY+zC7GeHxMgwR7bCoB4QAAJAwIdAagJAs6+yYl9zCu1eZNoJfWBJ fYTBsqVLXafLux8QEQD56K4NAWAa9Gavfudf/33/9b9Bs0/9Nn3o/F8OdP51vSgHPsqJyOtaFgJh 4eAateLvTTronD5gljRkQ+Q9cuQXH/clx9PJ2fayaqYS7qmM+XLWvWTaPqGKrAyq7OC8QqcIA1kZ lD277ODulf3B2slnx11W6GoOP1/j1TrZey9FfS4EADvBstK/fTuHErPTLyHEg/zZy7LCp1b3nCRk TijvYeXwS5GcApJAUDv/kmTs9Gk5FcCX0V+dAsD79zl7/2FO4DcsScw4EaA48CKzWq3kdmX1XxIB SqKw8BVvTirIx6BJgrTDPHZZCT3I2yYOcDK0/dymKvy7kCdCTjiJyVw5WVaFGct+hO9zVjY5TJlJ CclNoBICcojzDiFBOAHgSy/yFgIOc97GhIiEN7/Me5z3Hxym4ydP5eb8hyUGzBoV4MfPHyUQR2L5 dTVJksB2jADI560c34pgJ3OjjrxUx/u5p26oYwDlOEA54o/D+FWRUz/4c44YGONV+5OSwZ/LKP/u HAfo1uG8HJP8TMpzNStkZ+jyu4QEcNTTHEcISP0JThjI/Y3LaSPSJm9NOCkr+SrZKJ9GYLyfvAn/ 3KMI5ehBTg6qijqS0DkxZI6jD/L4F5SUsWhbAPqvOUt9Vy5ZsiTUfopb4Q6M0NRkgOu8+7+zTQc+ rn1e9lcEgLpHEQDdFBGIUD91npVtX/SAhXy2uhBGAESNz8p+9csYQbREyhpGAMSQN7bjRz0gAASA QBoEUhMA0pmfvbRlYqMiAJYuYQKADYE59/gjtnvUPxAAaaa3/p6kBIC5ShdGAPzqq19Jr/s1J9Ff VLFxwPNy9uPasZFFnyyg9vvL2HjPf9x9SQmAOIdLGf1sdE/IEXti6LMBL0b8mArdlbPAOXxXjvIT o1+cATHwOWv3mBj9cjSY7NuVTP6yd1eiAnilWyUQdPfry8q9+kyRBBzCz/v+xfmXyADZ4y9h/qrw 75LxXhKIyf5+SSI2rPYQ83FfbPzL/mQJwZWiCAn+Wzkb6khAx8kIdN45ImBCHBUmL05IpnGRVyIa xCln+fUxZbI/OCrcvXqN2xNM5PhCRyY55UCIEolEkP3OXBibk4IRyyeYCm6Crzg0p/m+U3zfqNQX R0aiLgQ7HvN+2fssRIlEPqhEhZx/4YCEYXOkA29zSJsAMGxFPyongFxLGgngD/8Pyw8QFA2gjwvU 10w9tyEB9JsIJEC29/iZMwvsKMuzIM+UnBLA0S9MCEiZ5N8dvZDP+bliR13eEaNMsjn6LqH2zjtD SALJLSAnDMxI5n/echD7j/temOX60o88M8bzcpKJyOPqmEA+PUQfCXhYkvw5if4kz4Ds89fkgzj9 UuT4QSlTHDWTJfw/TvaiEQBCPm5YeTZ/ryyhh/gdL89FlCMbGoGZQwRAuN3WRxXt9Ctn2G6BpzoX nCDTDGfv6XbJBGOymhEBEE0Q1Mbkr9fT7SU8lL2bhgQIIwDkc15AE5LFsZsSEixxSo/rQAAIAIEI BDITAJ6QsIhQJ1OGKAJARwDIaoAQAXIG8gJbBiAA8tHjNASANtrjHPy463HOc5GvRzk8eUUBRJMB bKRzaKvaE+w60/4kbup+qSMOgDgFbOQrwkCcYRVBwPt3xSB3j+w7wk62crbV1oJhRRAIMSCRArIN QJJ2yd5+cfrlqLtq5nAx6MVJrjr4juMhDsXkpBxfxvvgeUXPymF3twOoxIDsWEyy0yIOuaxgSluS lCxZO+nqBzrT4ugIscIEhSIFBEPOmq6OWpTkhm5uA8lgPpky+3/WLQF5kQB+csCvi34CwL81IC4v QFi4PwiBfN7r4a2ccSPqatuZzCMdU/cuHjVHBSxI5ICcHCDbXlR0AL93ZIVfEZNMSgj55z4bimiQ HAO+qCn5u9H/ikoALHlgUkV2qOcjxH6KXFhpKAGQcVYCVvn9WwhaTQCE2q8hEQqpwvUj5tWTvwAR ARkVDrcDASCQBIHCEQDLly1zDQQ5Es49Fs4lAZAEMMnUBtfNQgD4twbErbKb14vs3DdatiRRAVEk gD8/gU30gE4iNiMGOjvUk2KgG9ECQg6M8oq4rJALISBHBkpEgIoMUOHyTA7obOEqm79kC5dEd2z0 u4RE0M9mOO2N6iOJU97MumlOCRD5ok4KaOSWgLD3g58M9tfL/pZDC0DAiwAIgHCNsI3cTKZTRvSA e6P0UxoCgGMXKmlW+4NAiiIAfH34ow6SYY7aQAAIAAF7BApHAASdAiDkOCIA7Cc1qmaeBID00w4k QBIHvRV1bRz9qDpBZ79X6/PKu6zcTbrRAhIp4EQISAi+E7o7OsZbCzgBn2TpzsPpj3JKG+XUp2m3 mc69bV9ptwXo9oNwCJoPc579WwKCdC3plgAdEYAIgHze62glGoHOJQCMsHK1J78+jL8hBIB/NdvM J2BMlcfhDZEvTrfj5I/aAhAVwZrb6QERkbHebRFBpIMQKTgdIE4HcB0IAIHkCKQ+BtBMsFJLEuPs ZYo7MUA7odefU79nHMcAJp/EJHeAAMgnaV/YPukggiCrA5/1/kgCwDyekFf0JXxf9gir3AKy0i9H g7XpSn8SUsDWQW9GvbSRAKZsNlEBYSSAGf4f9hwkyQsQRiQmea+hLhCIQqBoBIBOAij20rKHeAvA mloyZW0/OY6fv9QceHEe6677l9gjjpiLaz+rRpk2ouxvr1ScowT9TnfSJNJarjj5o67b2q/1GNvn QYhM0lgFVzv4zlzWn7IAAiCrHuJ+IAAEghGwigDIGzwbJ9TfJyIA8pkFG+yTrOpLXXNPqc29jQ65 t20/62p+UEK0uDaTOvNRzneSa6bDl+S+6haCJu3FT+KUN7tuM5z7pH1kIQOC8PPPt80pAebWlKhT Amy2BIAMyOc9j1a8CBSNAJAkgJKoVPKcVHMAYNKAABAAAkAACDQJARAATQK6KN00ggDQhr0eow0J oOvYOut51otz0ht5PQkBYLPyntSZ958Jb3N/sx3tovaX1DlvRv0sWwKS4BwVDeAnwqISBMpzHEcE YFtAUb4t2kcOEADtM5cYCRAAAkAACGRHAARAdgxL1YItAWAOKolDn7Runo59q9uKOykgT2IhyRaE qH6TtpOEwEBdPpXB3GJR8t9t9FeeQV3P5nkMel9EkQRJ3y+o75yg02nF/6UMAqBUZgqEBQJAAAgA gQYjAAKgwQAXrXlbAsBc1Zffv/vd75KcwoACDKAD0AHoAHSgyDog31fmv1YTAN69+u+gO7EFoGim EeQBAkAACHQUAiAAOmq6naz9Es4rew9lD+IInwM/ODhIa9eu5SQ9lbqVIoFH51+Q+ijAADoAHYAO QAeKrANCTuiTJuQ7rNUEQG9vL/X399PAwAANDQ0RcgB0mOGF4QIBIAAECoYACICCTUijxUlKAOhI ADGoimzwQTY4JNAB6AB0ADogOgACoNGWBNoHAkAACACBMiMAAqDMs5dCdhAAMJDhJEEHoAPQgXbW ARAAKYwD3AIEgAAQAAIdgwAIgI6ZamegIABg+Lez4Y+xQb+hA9ABEAAdZthguEAACAABIJAIARAA ieAqf2UQADCO4SBBB6AD0IF21gEQAOW3VTACIAAEgAAQaBwCIAAah20hW7YhAHSkgHl0FHIAwGFo Z4cBY4N+QwfaRwdAABTS/IBQQAAIAAEgUBAEIgmAvkoXeY+v4b8rfZlF9zuhd11c60dnovd3ojPR Hzu8nbKUa6+7KbP8ZW7AlgDQY0QSwPYxiuHgYC6hA9CBTtCBohEAv73stpacAqBtuO6enYFmy86e 7pqN191TV8djA+Zg+5XZdoLsQAAIAIF2QsAqAkC+JMK+QNKA4XFCN6yks866pnoU3fLlyz3H95iO qHypZ3H+5V4QAPHHAJpzCgIADkMnOAwYI/QcOtA+OlBPAJyhmdk5mpubo8WLL6V//Kd/pDVr1tB7 3ncFbVj/GG0deJqGBtfTgb2baOTgFjo6si2RreHvz7RbZFGjFccAKudenPa+SqD9ppx7w+n323nV +93BSH1wAGksXtwDBIAAECgeAqkJAM0cy5dbLUqgQjbxAZoAeGBJfYTBsqVLaX5+nsuCBy1EAOSj PIgAaB8jFw4L5hI6AB2ADtTrQNEIAH8EQP81Zym7acmSJeH2084e6uY6VfsqYIXeyioIIQD89wYR AKbDn/dCkJXsqAQEgAAQAAINQSA1ASDSOCRAzem3/YKIigBYusQhAISpFxLgzBln3CAA8pn/pASA iT0MTTgb0AHoAHQAOlB0HQgkAGbmaFZFACxuegRA0BaADSvPZvtpCT3EMsn3st9+6unuJjNyX9lb aUiASAKgjyqaZKhr27gmddL0nY/ZglaAABAAAkAgZwQyEwCekDBLpjmKANARAHNzHAUghUmAhYUz IABymngQADDei268Qz7oKHQAOpBFB/wEgNgRMzOzLdkCsLyynMIIgCUPTCqZ5HvZE6rvX/2vRgLY RVl6zAVLu0xC/D1bPfk+bwRAxUNI5GSSoBkgAASAABBoAQKFIwCWL1vGDr/j9DuFf3dJAOQAyK4h IABgWGcxrHEv9Ac6AB0oug4UigDgvEaJCQDeTFnJa8XdkgBgBsLTZ1/FG4EQlksgu1WCFoAAEAAC QKDZCBSOAAg6BUDIcWwByEc1QADAeC+68Q75oKPQAehAFh2IIgAuvbS5SQAlsXFyAkACAnwr8mlN gBACoKfbm9RPJQU0lvxly0F8hKfeJpAiMiHteHAfEAACQAAIZEYg9TGA5vEx6kuCv2R0spq4EwO0 E3r9OfVJAHEMYOY5jWwABAAM6yyGNe6F/kAHoANF14FAAsA9BaDZBIA3UfIiun1oiHQSQLGZlj3E WwDWBNtP4qR7j2K2d7QDj3H2RBXE7fH3XTfyPdWMDBAAjbXY0DoQAAJAoDEIWEUA5N11FicUxwBm m40s2Bfd6IN8cEygA9AB6AB0ICgJoMorxAmGJfN+JxwDmM1SwN1AAAgAASDQzgiAAGjn2Q0YGwgA GMdwkKAD0AHoQDvrgJ8AkK9CnVNoGecZuuyyy2jNmjX0nvddQRvWP0ZbB56mocH1dGDvJho5uIWO jmyjJIsNQf1Jn/J9KxEAvb291N/fTwMDAzTEEQDDw8Mk+E9OGkkAO8wWwXCBABAAAkCgdQiAAGgd 9i3pGQQADP92NvwxNug3dAA6EO2QL6dPfvKTIABaYoGgUyAABIAAECgCAiAAijALTZQBBACMYzhI 0AHoAHSgnXUgbkV+9erVIACaaHegKyAABIAAECgWAiAAijUfDZcGBAAM/3Y2/DE26Dd0ADoAAqDh pgQ6AAJAAAgAgRIjAAKgxJOXRnQQADCO4SBBB6AD0IF21gEQAGmsA9wDBIAAEAACnYIACIBOmWl3 nCAAYPi3s+GPsUG/oQPQARAAHWbYYLhAAAgAASCQCAEQAIngKn9lEAAwjuEgQQegA9CBdtYBEADl t1UwAiAABIAAEGgcAiAAGodtIVsGAQDDv50Nf4wN+g0dgA6AACik+QGhgAAQAAJAoCAIgAAoyEQ0 SwwQADCO4SBBB6AD0IF21gEQAM2yKNAPEAACQAAIlBGBSAKgr9JFXV2+UunLPE6/E3rXxbU+KpUK yXX/P/lMvtSPHd6eqVx73U2Z5S9zAyAAYPi3s+GPsUG/oQPQgaIRAL+97Dbq7++ngYEBGhoaouHh YRI9nZycpIeWdVH3qh0tMSs8Nl6obddHFbEDc7D9WjJIdAoEgAAQAAJ1CFhFAOzs6abunp25wedx QjespLPOuoYGBwdp7dq1VFm+HARAbkjXNwQCAMYxHCToAHQAOtDOOlAmAmBubi7Q5mmgGaCaFrvO dOqFDAjy8Xu6+fOeHuoGAdDoKUH7QAAIAIGmIZCaAFBfHswKy4p9LUqgQjbxAdoJfWBJfYTBsqVL aX5+nsuCBwREAOSjEyAAYPi3s+GPsUG/oQPQgTIQABtWnl21nfwRAFb21U52ys0Ize6eREaC9GH6 9EELPUIKqMUf6QsEQCJ8URkIAAEgUGQEUhMAVQa5q+b020YKREUALF3CBMDcPM3NzikSQO8GAAGQ jxqBAIBxDAcJOgAdgA60sw6UgQDQWwC23fiWwC0ADgkQbl/1dHeTGZip6iciAdzQfk0i+O71RAiA AMjHAEMrQAAIAIGCIJCZAPCQwn0Vq60CUQSAjgCYYxJAiAAhARYWzqgQOeQAyK41IABg+Lez4Y+x Qb+hA9CBdiEAQu0r/+p/NRLALgpTWRJsr3kjACoeQiEoB1SeW0GzWzNoAQgAASAABNIiUDgCYPmy ZezwO06/U/h3lwQAAZB2mmv3gQCAcQwHCToAHYAOtLMOtD0BwJstK4lW++tth76KN4JACIFQBx8R ANmNL7QABIAAECgQAoUjAIJOAZBtAIgAyEdrQADA8G9nwx9jg35DB6AD7U8AyAK+uz8/pWngzwGQ jgDQ2wgSRB6klBe3AQEgAASAQH4IpD4GUCepUYkAJfMfs8c6GWBcmJh2Qq8/pz4JII4BzG9yg1oC AQDjGA4SdAA6AB1oZx0oGgFQS5S8iG53jwFc7D9iWf3tONK29pVk6Pce1ZzEEfflADDyDZi2g7kV oN62AwHQWIsNrQMBIAAEGoOAVQRA3l1ncUKPHd5OWcq1192U93BK1V4W7NvZYMTY4BBBB6AD0IH2 0IGiEQC9vb3U399PAwMDNOQSADoJYKuOASyV4QJhgQAQAAJAIFcEQADkCmfxGwMBkL+B612BcVZk WulItLr/Row965ji7jevx9VNMr64tuKuJ+mrWXW1zFlkT9JGln7iMEkih7+tMLnM94F5T9LP4+4N 09kseGW5t5FYx7Xtvw4CoPi2CCQEAkAACACB1iEAAqB12LekZxAAjSEAkhqoqJ9sHrI4Jjb35kkA JGnLRrZm6oqNPDZ1omRuNT5B8qcZk007YQRD3Od+5z/s76B20oxF2k97n+1c63qN6AcEQEvMCXQK BIAAEAACJUUABEBJJy6t2CAAkjmeNs5XMwxaGzlaWScpBo2uH+VABeGUxCmNwzlJW0lxiOs763Ub eWzq2DqFcW3FXU8zXhvH3aZdm3biHH1/G0naNJ32IJ1Lil3S+nlhZNNO0jqIAEhrIeA+IAAEgAAQ 6AQEQAB0wiwbYwQB0FwCQIxq0wnwG+rmdW3Qp3ECohwAfx9mP0HXkhrbSVf2wsYXJUschqYMtpjG jVO3E9a3H0e/DP5VVX97UfMQ5YwlaSdKxiB9C8MuTr/CdDdM1jASJm7uzPbiHNYw59r/TPrnKUyX bJ6VqD6DSKm8CIAwXQ7DKEqHgsbfaKzjnsWk10EAdJhhg+ECASAABIBAIgRAACSCq/yVQQA0hgAI MqiDHMAwZ9l0MsOcozgHwt9GkDMV5OzEOeR+hynKCUvqKNiMNcxRjcLXvOZ3Xm2dyCCnN8xZjOov aow2DmDQvPv1KI3jGeXUR+lJVj0L0zdb/Q57hvw422AS9dwlnf8w4iDumQt7d8S9K6J00UZnonSo EXoZ945L6uRH1QcBUH5bBSMAAkAACACBxiEAAqBx2BayZRAAjSEAbFbgwhyKMOfR1oi3cXSi+ohz 2pMa5lGrjlE4RTlCNmP0O/ZZHQ4bBzCITIi6zz/GRjha/rmOk9GWvAhy/OOIApu+bUiVOCc6imSx cXTjSImoZ9fmObXR3zAconTE9tm1kd/m/WTznovTJ1uZk753zPogAAppfkAoIAAEgAAQKAgCIAAK MhHNEgMEQPsRAH7HIczxjfvc78iEOW9Bn4etVvqN+DhHK6kTlMaxiZI/zJkL6sfGqYvDvBkEQFbn OMyBTjq2KB0Jcwpb6ZTaEEBx+p0Uo6TPh40zbatjrcQ6i7MfdC8IgGZZFOgHCAABIAAEyogACIAy zloGmUEAlJ8A0A5snONv46BGrb4mNcptnBGbldwkTpCN02IrV5Qz1y4EQBKHNE5/krQVtiocRgoE tW3ryAa1afvMJCGA0hIrUVgk0X2bZzdJezbPkk0EQBqsk75r4uqDAMhgJOBWIAAEgAAQaHsEQAC0 /RR7BwgCoDEEQNiqcpxRbRrLcQZ9lGGdxEHV/ZgyxxnUcdeTOtlBzlyUPHHOqOn0JcE0alxhc+f/ PEjuMAfWX9dGb4KIiaAxhuEXJV+Ys2yDZxwB4NezqAgA27pheEU5plFkgv+a7ZzHkUX++QkbX9zn UfNg876Iw8X2/WTzfNs6/jZtxb1v4q6DAOgwwwbDBQJAAAgAgUQIRBIAfRUng7mnVPoSdRBU2e+E 3nVxrY9KpUJy3f9PPpMv9WOHt2cq1153U2b5y9wACID8CYA4Y7To15thkBcdg7LLV6Y5zEvWvNrJ OveNkMO2Tdt6RRxjVpmi7i8aAfDby26j/v5+GhgYoKGhIRoeHiaRf3Jykh5a1kXdq3Y03azo6a63 77p7dnrk2NnTXbP/crD9mj5IdAgEgAAQAAKBCFhFAMiXgP+LIQueHid0w0o666xraHBwkNauXUvL ly8HAZAF3Jh7QQCAAEi6itpIQx1t56OPzXIE85qvNPIG6W0dQW0cu5mXrEVtJw2GtmMpO9ZlIgDm 5uYCbZ4GmgGq6Z7uCpnLOXV2Xl+Furp7qmLIghA4gEbPCtoHAkAACDQHgdQEgGaGZcW+Zix4v1DC hqCd0AeW1DPQy5Yupfn5eS4LntsRAZCPQoAAyMfhsjWkUQ94QwegA9CB5upAGQiADSvPrtpO/ggA K/tqZw91mxGahrOexlrwEwJ9lW7yBARIf2AA0kCLe4AAEAAChUMgNQEgI3G+pGpOv22kQFQEwNIl DgEgrLiQAHo3AAiAfHQHBEBzDVEY/sAbOgAdgA40VwfKQADoLQDbbnxL4BaAOPuqp9vroKv6aUmA AOc+kABI234+5gtaAQJAAAgAgZwQyEwAeAhhDhmz2SoQRQDoCIC5OY4CkMIkwMLCGRUihxwA2Wcd BEBzDdEkSbikbpJEbjZORSPDhG36R51i6BvmAfPQSTrQLgRAqH3lX/2vRgLYRWH6LQkhD/yL+yAA sttbaAEIAAEgUFQECkcALF+2jB1+x+l3Cv/ukgAgALKrEQiA5jkCYc63//O4bOph9W0MehAAzZtv m/lAHcwHdKDxOtD2BADv3q/kuBrvD/8XS6Nuzz+2AGQ3wNACEAACQKAgCBSOAAg6BUC2ASACIB+N AQHQeONTG/jNJABs+/I7HyAImqcPcPyANXSgOTrQ/gSA46DbRFzGWg5hjj1/XjGSAAQnAWQiQkUf pIs8iJUNFYAAEAACQKAhCEQSAFHHAJrHw6jQMckY64ahxX0paSf0+nPqkwDiGMCGzHO10bIRAK9g nbIprTasg0L3zXOxTfnyjgCwdf6jthe0Gj/03xzHCDgD507QgaIRALV37yK63T0GcLH/iGXDkba1 r+qP8kvuiAeF/2uDwdN+YAJAEACNtdjQOhAAAkCgMQhYRQDk3XUWJ/TY4e2UpVx73U15D6dU7WXB vlmGo43DH1WnWXJG7e/X+/mT5ACICvVP47xHre5n2VbQanzRP5xY6AB0IEoHikYA9Pb2Un9/Pw0M DNCQSwCI/JOTkyrhsXwv4x8QAAJAAAgAgWYhAAKgWUgXpJ8iEwCmU/+rvBpyIZfPcFnDZTuXUS5n 3CK/y2dyTepIXbnHbKOZTkLYudlBMuQVAZDEwdeERBiREEdYNBNL9AXnDjoAHciiAyAACmJwQAwg AASAABAoJAIgAAo5LY0TqogEgOm0/y924j/PZRcX3lOSqMg9cq+00UwiwNahD8sNkCUJYJLwf5tt CFmMbtwLpw06AB0ogg6AAGicDYGWgQAQAAJAoPwIgAAo/xwmGkHRCADtqP8RO+1f5DKe0OkPIgmk DWlL2tTtN9IobSUBELRyH0QKxG0vaCQ+aBtOIXQAOtBMHQABkMgsQGUgAASAABDoMARAAHTYhBeF ADBX6P+ZHfUtOTj+fjJA2pS2mxENELQFICqsPmpPf1DywKjQfX8/UVEB/nZwCgAcs2Y6ZugL+tYM HQAB0GGGDYYLBIAAEAACiRAAAZAIrvJXLgIBoB1y2bN/vbunP2m4v219yRkgfZj5AZphgKIPODrQ AegAdKA1OgACoPy2CkYABIAAEAACjUMABEDjsC1ky0UhAMQhv7kBq/5hxID0pUkAGOWtMcqBO3CH DkAHmqEDIAAKaX5AKCAABIAAECgIAiAACjIRzRKj1QSAXv2XVXnbVfy86kmfzcgJ0AwDF33AkYIO QAegA8E6AAKgWRYF+gECQAAIAIEyIgACoIyzlkHmVhIA2vmWffkSmp+XY2/bjvRp5gSA8QwHCjoA HYAOtJ8OgADIYCTgViAABIAAEGh7BEAAtP0UewfYagJAMvNHJfybuPkzNPnlW1KTAxM9n6Lpr98W er/0rU8HyNvwt0moZ1Mnb7mStpeXjHHthB1/GCevTbtJkzLG9amv2/Qdlvwx7l5bGcLqxbVve2Rk VjlacX/c2ONkynp/mvaT9Jlk7sJOAUn7TNi0Fzf+Zl8HAdBhhg2GCwSAABAAAokQiCQA+ipdvLjq K5W+RB0EVfY7oXddXOujUqmQXPf/k8/kS/3Y4e2ZyrXX3ZRZ/jI30CoCQK/+y/F8YSv2k5/9NJ2Z nlbwTt3zzcQkwPjHr6Azs7Pq/um7vx56v8jQiK0ANga9TZ1mG8thDmsWOZKMM0ndJLLaHIeYZIw2 ckaRGjb3J5HHrGvjIDay/7Ry53VflrFluddW/qx92MyvDUmV5pmwuSfr+GxxtK1XNALgt5fdRv39 /TQwMEBDQ0M0PDxMMpbJyUmam5sLtHky2xk7e6jbY79VKNh666OK1MvBtsssMxoAAkAACACBpiBg FQGws6ebunt25iaQxwndsJLOOusaGhwcpLVr11Jl+XIQALkhXd9QKwmA/8VGxngIATD+kQ8SW0Ie gae+c7c1CTDxmWuIZme893/ti4H3iwwii5AAtgalTT0bI9imjk1fedfJW64k7SWpCwIgOFzbxkFM inPeOtbI9rKMLcu9tmPK2ocm4v2kj7/dKAIq7NmJki2svbB+bfFodL2iEQC9vb2tIQAsnPqe7i6q 9DBZYFG3gaYJmgYCQAAIAIEmIpCaABBSQIwAWbHXxklXVxjD7B2RdkIfWFIfYbBs6VKan5/nsuC5 CREA+WhFKwgAvdr++YjV/7F3vZ3mB3fUDXL629+KJQEmPnMtscJ47l04eYJOr/iX0HtFlryjAIKM Yr/hXntWHN0PM4TNekEre+a9QXW1sR/Uf9DKsVkvrm3TKYhyPJO0Y44xytkJuhaFof9alOxhOPpl C3Kk/I5SEC5xePjnLA7boPaCxhskm//eoDHatOXXTRvdCNPNOJ2Nmvuo5yrJvMbJkGS8cc963LzY OPtB75wokixOL6Oel6D3RtC7qdHOvU37RSMA/BEA/decpd7/S5YsCbef/Cv43T3JjAC5P8aplyhP tbhjUTdZ56gNBIAAEAACRUYgNQEgg3JIgJrTbxspEBUBsHQJEwBz8zQ3O6dIAL0bAARAPmrUKgJA juDbFZP4b+ydf0vzWwfqBjr13dWhjvxEz3V1kQMLx4/S2CWLIokDkSXvYwHDnCrTIA8z2G2M66D2 bR0Av7MR5sjYyBpHKkQ5IEHtJ5Etru0oHE3nLmr8YX0kmd8oRyvpnCXVjbD6YfMWhH+coxg1hiic osiTKOczbZtJn7ekcxOHaZSDHEaW2OiOzZzFzWGc8x6FXRy5YeOkN7JO0QkA2QKwYeXZbD8toYfc LQB++6mnu5vMwEtlbyUhAWK2AKj2NEEAAiAfAwutAAEgAARKgkBmAsBDMPdVrLYKRBEAOgJgjkkA IQKEBFhYOKO2BSAHQHatahUBcKFl1v+xd/wNLezYXh8JcP+9dQ79xKc/Vrfyf+bUGI0tOT82akDy EIhMeW4DSOIgxjkGYQZ2kMMRVDfI+I9yvvIgF+IcVVPOMCLAZixhYwtzIOPkMokBv4xRznTQtTAy w5ZYsSEwovqNwsDGuY3CNk/9DnP+0s5/HCGR17zaEkdJiIc0dYtAAMTpaiMd/Li2i0QALK8sp6Ac AEIALHnAyAFg2k91zruOlrSLsgy0FLh9c59/UI6nPLd6ZrdW0AIQAAJAAAg0CoHCEQDLly1jh99x +p3Cv7skAAiA7GrQbAJAh9l/xpIAEMd87IK/ovldg/WRAPfdWXXsJ677KLFyeOqcOX2Kxv7hIivn X/oRmUS+17zmNalzAYQ5RTbOls1Kc5BDF7caGtZuqwiAOCyinOYkK8ZZCYA4pyKJA2yzimvrTNoS GEHyJ5E5ipTyz0PcnMYRXHGy2hAmWXBJS+zYzlkap94Ws6h3TtzKfJRehr1rgsgTm/HFPU+NvF4o AoDzGiUmADhdXyXJar+VaRDRJiIArBBEJSAABIBAuyBQOAIg6BQA2QaACIB8VK5VBMCaBASAJgEW dg95HfzpKZrb+qIqZzh7svlvgTMqj/3Du6ydf+lDZCoDARC12pfUEfM7D3m2ndapCiMA4j6PcyBs nJ0kfcQ5XlFOq41Da+NU2c53kDMZd28csZRk/LbObLNxidOJOIy0vHF6k2Quk9QNI2KCxmVLnsVh EvRc28gc93w28nr5CQCi6v78lF/9ktzPXNH3hPz72wwlANwTAizzO6UUFbcBASAABIBAkxGIJACi jgHUSQDFEFDbACS8zD1yJi6MTDuh159TnwQQxwA2VgNaRQBsT0gAiIM++hf/jRMD1m8H8CN05vRp Glv+zkTOv7QvMgkB8J//839OHQHgdwj8xrJ+JkyHKGhFLWr1Lc6ZC2sv7nMbZyLt6l+Qg+BvK4x8 8GNmYmxeC3N60jif/j6CnJOwMQU5sXFOVdzcBGEQpENRY/VfS+rcBulkkH7bOPE2+m07z2HY2mCa ZF6TzHfYMxr1rMeRCEGOd9icZCUATDnjdC9snhrp0Cdtu0gEgDdR8iK6nY8B1EkABetlD/ExgGuC 7Sdx4r1zk2wLgOf+kIgC086rt91AADTWIkPrQAAIAIHWIBBJADRKpCxO6LHD2ylLufa6mxo1rFK0 mwX7pEaY1K9m2k9BAIiTLmV2/XOh2M4f2Edjyy5M7PwrgsElAF75yldmIgDS4IJ7go+zAy41XOII DmAFHYIOBOtA0QiAlhwDWAqLBEICASAABIBAKxAAAdAK1FvYZ6sIgDMZCICpO78WitjcxnWpnH8h AEQmTVB86UtfAgnAhA0citZh4F+FxVy0bi6AfbmxBwHQQiMDXQMBIAAEgEDhEQABUPgpylfAVhEA stquV/ST/Jy46dN0hvf+hzMAczS95oFUbZsRACAAym3ww2HD/EEHoANaB0AA5Gs3oDUgAASAABBo LwRAALTXfMaOBgRAjYgAAQCHAU4jdAA60H46AAIg1hRABSAABIAAEOhgBEAAdNjkt4oASJMEcOKG TxDNz1vN0PRD304cBaCTAP78z/88dVIEQJq95UnvSVq/VU5YM+SMSwhoM/YscoYlm7Pp17ZO1j5s x2dbL0ruLG1kHWccnllkC2q76O3F4ZH2OggAq69NVAICQAAIAIEORQAEQIdNfKsIgKTHAE585hqi BT7/0fi3MHKIJq7/hCrz216qm7np3u8kIgH0MYBvfOMbQQDkvP8/b8cjT4eumbKZcpv92siQtH4Y RjZ9pXW05L485LSR0aaODQZ5tZMFMxs5G9F+mjbzmN80/Wa5BwRAhxk2GC4QAAJAAAgkQgAEQCK4 yl+5VQTAZxLkAJi47qO88j/nAfvM6VN06h/f7XHwg44InP6ufSSAyCRJAM8991wQACAAGp4AMakj lbR+q5zKPOS0ccpt6thgkFc7WRxUGzkb0X6aNvOY3zT9ZrkHBED5bRWMAAgAASAABBqHAAiAxmFb yJabTQCIESdO9oWWBMDEJ64mmvM6/wvjp9n5vzhwdX9+9676SIDVd1pFAohMItuyZcsyEwBiJOvi X/X1f+6vm/S6XnUNus+8FiZHkAMUZuT7Q57Nv/3tmOPytxcma5oV5DC89HiD5sK8x/+7KUPcHGbF LgjPMIc0TM4o3IOcprRzGKQ/Ue0HzWUcnuacBf0eJUPcPIfda6vDjcIybkw2z3AcbmnnPEy/bfQ2 7B2YxZFPey8IgEKaHxAKCAABIAAECoIACICCTESzxGgVAfCr7CDviiEBJq7nsP/ZWe/K/8kTNLb0 gkiHfn7HtnoS4IF7I+8RWUQmIQBWrlyZiQAIcsj8zpDfyYwy4OOccb9RHObQ+I32KAcrrs+0fdhg E9a3rbMZ5kAnxTxIjjD5/U6czXwGkSJJ5iSrfHnMoS0BYjPvQfNjM5e2z1bUHLUKyzi98RMmcc9w o57bRmCX1plPcx8IgGZZFOgHCAABIAAEyogACIAyzloGmVtFAIij/fkIAuD05e8hmpnxOv+jJ2ls yflWq/kLL++sQ2Xy328JvVdkEZn0/v8sSQCjVnCDDGlboz1oRdV0GP0rbkmdrijZolYQ48YU5nhH OXxhjk4cCRDmDEZhk8T5S0IAxGEWRXTYOtZ+wsAcp995DKob5zz724siKGxIjzAZwsYR5ezZ4Gfz LEY9J2HjzxPLMOc6bm6yPK95vBvi5IvDLo0jn/YeEAAZjATcCgSAABAAAm2PQCQB0FephTVXv9wr fZlB8Tuhd11c66dSqZBc9/+Tz+RL/djh7ZnKtdfdlFn+MjfQCgJAjDhxtv8Xl/EQEuDU4kU0v2Nr FdqFE8do7NLzrJx/jr2n0UX/y3v/kcM0fuV7A+8XGUQWM/y/TARAUmO+UU5sEkc/zoFKQgDEESNx BEW7EQBxTlIYMRHnnPvbTeJcp9XRsD6i5jxKD6NIiqTjjyKw0rSVRTZbsinsuYr7PIyksJ3XOJ1s 9PV2IAB29nRXt5V199QT3PrLUuy0qOup7ZW+itM/23xalob0k1LAluNjIbe2ocNw6+mu2b7+OuY1 bX8XCX+L4aMKEAACBUYgkgDQcsuLNs8Xj8cJ3bCSzjrrGhocHKS1a9fS8uXLQQA0UGFaSQCIw/3F iCiAsXf+DUliP3H+T73nUmvnXwgARQJc+Nc0v2uQzhw/Sqc/+I+h94sMIsurXvUquvnmmzOF/8et imV1RvNwVm0IgKhx5OHoJ3F2opyDODxsHJS4NmzGG+ccx/VhMydpVryDHOi0BECQkxjVvg1uSecn L0e0KFjGjccGH1syJMt8xMkZR3hEETmNdv6l/ZYSALxYcYaPr53nXDbz/FNyzPT29lJ/fz8NDAzQ 0NAQDQ8Pq6Sjk5OTnPJmLtDmaZT9ZW1e7Oyhbv6eVLafSwbksP7j6b6nu0JZl5Tytk+t8YmpqAgK AYyxC7KfvcRNH1UYaxNfPzZFHWdeeKEdIAAEmotAagJAs6+yYl8L/bN7mWsn9IEl9REGy5YuVV+a 8/MLHiQQAZCPYrSKABBjR5zuP+KyJYIEGL3gL2ls2YWJnf8qCcCRAGNLwnMGSN8ig8iyZMkS5fxn Wf03DWH9HPiNZ//nYc5InMPlv6/23DH54TuOLchJ8NfP6sTa9hHWb5wTHOZEhOEcNBdxK51x1/UY 88DOxiELm5MorGxlSzrWID0Oct6i9DnNMxGky2H6HfVsFRXLKFzTzHPcPXm+G+L6CprvZjj8/j6a TQBceeUK5chPsUM/OT5O46f41Bp+J09PTdMl776EPv7xP3ftpEV0uyYAVi82bKfwVfwwx89mBdyz itzNCzndPTVDwnXwq+8P85rUcq8rp1QRAN0UEYhQb6DoCAI3Qa7HCfb37dZJQzCkwcfKfo3Dx9Yk CyEA/LeLTFHjz4MssRUZ9YAAEGh/BFITAM73g4So1Zx+W4YyKgJg6RImAObmaW5W2PMFZsadSQAB kI8ytpoAEMf7n7mciSABtDOf90/pU/oWGV7zmtdUnf88CIBWGLnt3GerVxDDsC2qXK3UBWAy2vAj JJs1v+0yl80mAC7/0IdoenqaRk+coBPHjtGxI0foyMgIjTMZcNE7L+Kvsj+nVRwB0Puh36d33O5G AKxeTQ8YEQDirAc5gHF2VZQDbDrdKhzdcPJ7mBAwHXplz3lIAFmVdusoZ9hugadqqfT1eVb3g8aX h1ObBZ8o+zUeH0ubzIoAYKz9BIzZvOCfhh2xFBHVgAAQ6DwEMhMAnneS1YvOceZnOdu7hL+N+rYA 6AiAOSYBhAgQEmBhgUPqkAMgF+1sJQGgowDEAb++BQSA9Cl9S/nIRz6S2+p/s4xz9NN8R8tcXW8X 56gRegRsmq+becxju+p3swmAyy67TK3+Hz96lI4eHqHDhw7RoYMH6fSp0/SOd7yDuv7842oLgIcA YNvnHHflW89DngQAL9ursPLaHBsOfMgKvOkQZzY4Avrwj6/VBECo/ZonPnF2sRlpEQJ6XHRA5rlC A0AACHQcAoUjAJbzfrmFBcfpdwr/7pIASAKYXT9bTQBoEkCO4Lu5iSSA9KWP/fvnf/5nrP5zeGoe DgTaAI7QAehA0XSg2QTAh4QAmJryrP4PMwkwfvo0vfMd7wwgAO6kxV1n0/Uv1nIAhDl5aVe466wF CcmvrjLHrDhnNjWM6AG3raDxFZYAEPIkakU+CT4RBIATRRu/tSIPnJKIjLpAAAi0PwKFIwCCTgGQ bQCIAMhHGYtCAMgqvDjksirfyO0A0rb0oZ3/s846C84/nH+QH9AB6EAb60CzCYDLL7+cT7Gd4RX/ MbX3X7YCHOetABLl+K53vaueAOi/hs4+e2UtCeCOVSrhXp4RAP4QdrWP3+igYacHiKkiq9oB+Qbq IwAM51flDEi4zUB1FZ2kOmqLRFQEa274RCQB9G65CF3+jwj/11EeyXHLx6JEK0AACJQVgUgCIOoY QDMBTS1JjBNuFndigHZCrz+nPgkgjgFsrCoVgQAwtwLonABRiQHT5gKQNvWef+mn3Zz/qORrUSty zQzXDks8Z7Ni2Eg5g9pO21/YfWnbs8GmHesAL0QS5KXXzSYArrrySpW8eI5JgFnOBTDN0QDi/MtW x0sXO8n+3vivd6stAPL7u+4cpg0rzzbC8zkBXMU59k87pd7wfW0r1Ry9uOv1x8jVO4k2ddJaJKaN KKvc/vGpdj2JApM5sXHjj7pua79mwSfQftakSIIkiNHh/yAA0uon7gMCnY6AVQRA3iBlcUKPHd5O Wcq1192U93BK1V4W7PMyzsx29J58ycwvx/ON57AtQNqQtnS2f0Uy+ML+y5L4L8opCiMA4uapmY5W lr6y3NtoDNJiHydXp19v5Jx3OradNP5mEwBx/WU5BrBUBgaEBQJAAAgAgVIgAAKgFNOUn5BFIwDE KP2xH/uxanK+/8XO+ue57EpBBMg9cq+0oYkFyfZvJvzL69i/ZhnTUavL/gRets6Tbb08xpilryz3 xsmetW0QAI1Zrc46L3HzjuuNmbei4RrnkK/mDPxr1qyh97zvCtqw/jHaOvA0DQ2upwN7N9HIwS10 dGRbooWGuP5AAORnw6AlIAAEgAAQyI4ACIDsGJaqhSISANp4/Mmf/Mmq4y579i/k8hkua7hs5zLq 5guQff3yu3wm16SO1NX7/MX5f9WrXkVLlizx7PdvpPNvOuN6PFEh+rq+aTiHtWETYp6kLz9xEGa8 B8kjdYNkDxuHlssvX9Df/nbD6uSBbxCmflmjxmleM++Lm4ckWPv7sMEjbFxRbZlz6r8/Sk/jdNff bpz8un7RnEnIUz7SIM4hBwFQKrMFwgIBIAAEgEDOCIAAyBnQojdXZAJADO0/+7M/o9/6rd+qEgF6 Jd/25xvf+EZaxidJ3HzzzXXOf6MM+SCnye/MxDnxNm345Y9yNoOcqSBn3NbpCnPko2Qy2466P+nY 82grjgAIwzaMfAiqnxdmcboUNq82RImtntiOJWjO/XKEzbetLjbqOUa75XP0w+YMBEDRLRHIBwSA ABAAAq1EAARAK9FvQd9FJwC0QffhD39YreCfe+65JE79z//8z9MrX/nKKjEgv8tnck3qiNO/cuXK wBX/Ru/316uk5k/T6QlbVQ1aRY5zqP2rrlFOnq1jHUVOhI0paiU7SMYoh9pGTj/GYU5x2FxEOZ1J HNQ4pzrMAQ7DWNe3HZ+tw55UT4L6D3PIw8gTv65EzWuS/uCYt49j3qy5BAHQAuMCXQIBIAAEgEBp EAABUJqpykfQshAA2lDUYftpfzbD4LRx7oIcx6jV0rD6fmIhynG0caxtnTwbBzqJc2pDCIStbEet RkeRGWFOcTMIgDCcbT9PglcUiRTUTpT+2uhHUj2Le16a8cyij/YmFUAA5GMvoBUgAASAABBoTwRA ALTnvIaOqmwEgGmo25IAzTbubRygJM6sDTEQtgIedW+cDFEOcpTTneQ+GwfU31cU+aAJkKQES9gq dhSJYbOqHoR/lPNuS5o0kwCwJVji9MnmuYjS42Y/x+ivfUgBEAAdZthguEAACAABIJAIARAAieAq f+UyEwBFNtD94c9xDnpYfb9Dm3WFOK6fuNXYqPvD5iMqKkGPx+/Q2uAXV8ckAcJwDGojTQRAUqIl SB4/oRBFZgQ503HkRxApEkYkhOEShWOY/LZzm0TXi/zsQ7biEQeaAJDvOyn6n/xeqVQISQDLb8tg BEAACAABIJAeARAA6bEr5Z0gAIpnrObtQEQ59Xn3lUd7WeTNcm8esqMN++fJZq5s6gBze8w7FSsQ AKU0TyA0EAACQAAINAkBEABNAroo3YAAaH/juWxOVBp541bUO9XxKdK4gyJBouRLowdFGi9kKc67 FQRAUSwOyAEEgAAQAAJFRCCSAOirdFXP/K4ac5W+zOPwO6F3XVzrR8LzzJA9M3RPvtSPHd6eqVx7 3U2Z5S9zAyAAimOkwmHAXEAHoAPQgfx1wJ8DQG8FaMctAH47LQcTLVcTR+Tr7tmZa5tWjfVVHPuV AdnZ061+b4kcVsKikh8BPWdx89Yy/SrJlDUMn4I/X2XQH1PGru4er0bt7KFueX9VS4Wye9/eLqwi AETIPF+cHid0w0o666xraHBwkNauXUuV5ctBADTwxQICIH9jEwY8MIUOQAegA8XRgY4hAMRI9BuO DbQf/E33dOdvlOYmvmtAK9vVdVaKRo7kNtY2bihv/6NIULX6+cnUfxOer0zyuRNdVP1RxK3x7q6T U/Bt8AsrNQGgmQtZsU/KUGgn9IEl9REGy5Yupfn5eS4LnudU7kEEQPZXFwiA4hipcBgwF9AB6AB0 IH8d6IQtAIERmq5BWbXP3CUjXVcv5Njab54+uO2e7m5Si/l1q1OOLWfaqzYrcD3dwStctvJFWkSu jEomRQC4shs3efrnsWkyxfmc6/eY9q1v7Fy/olbnmATRq6Hyu6WZpvuWOalh5dyf1/yFiWKLb9j8 SLth8q/RuhGHTxUzRwfCFhnDHLjE+mXMr8LFr8NJiTSL+8P0K+75qd5nPFDVZ9GVM0p/nOE5US+B 2Fo8v7H4WDxfkY9C1PzbyGf5nKXRH6vnw2L+LUV01dG30F5kAqCmYLUXni3TEhUBsHQJEwBz8zQ3 O6dIAJ3AFwRAElUKrwsCIH9jEwY8MIUOQAegA8XRAT8B0PAtAFddFRq5KIsky27rpf7+fhoYGKCh oSEaHh4m0ZfJyUmam5sLvNfqGz8iAkDsMc8CEhvcppPlGLnh9ptyOIwGnPpeJ9pmhS7MLhQHxuP0 iUPgWxGLki8enz520E2n3euc++Xyr8g5DpZxjzL43b/rnJ+a455o0U47QXrc/Le+P+v8xeETN/9x 86PaD5PfBp++Pg9ZIv0FYRfnV0Q5eKZ+1c+vV5cVHglIgCoZZqw0R63o+vt3SJQQwijE+eur+Eis CP3R8x+FX9TzGzc+nvzI5ytO/5g1i51/m/dLXD9Z9Cfq/ROPT5xk6gFySURvNEAgQZWAXLTpWeqk jgDQBEDUF0yYEFEEgI4AmGMSQIgAIQEWFpyjfBABYDutIABgjBfHGMdcYC6gA9CBZuqA2AoLC7KA 4NgOjSYArrryykgC4M8/XkwCINx+Y8PUwhmyMdADDXALByfOAc5uDRnGt17JNxoNckirMpnEizhh LpB1MscJ6SNlzOpx44+7Htd15P0W86MJgMCVext8AlZ58yQAPM6Vf35DVpg9hE8UgFb3R+tXJAFg kAOKiFLPYsAzGaE/mQgAq/HFaVjMdYv5t3m/xEmRhQAIfT82AJ/YXA3GeyZuzLbXC0cALF+2TH1x i9PvFP7dJQFAANhOKwiAZhqb6AvODXQAOgAdKI4OgABwQoDjIgA6mwDw2Um+CAT/Cp9n0cvGwbUx 1zqWADBWj12cwsiTtA5cHfye+bUjuMKnMMX9Pv2KIwCc1X6nH/V7X8Ce8EYRAEFkg40+W9exm//C EgANwSdOp+KuW4NfrVg4AiDoFADZBoAIgOSTG3QHtgAUx0iFw4C5gA5AB6AD+euAJgCCogDExli9 ejWtWbOG3vO+K2jD+sdo68DTNDS4ng7s3UQjB7fQ0ZFtiU4buirtFoAHlriZ6tek+4KP2QJQXZ01 E3aFOVw+Z6J+RcpZ0TRJA4+TrMKR60Oaw7cA+Pfkew3cOAIjHWC1u+ocfN8KW23l1bzH2AJghO03 KgIgy/zF4ROHbz0BkmAFOo4g8eutuWXAJ3haAiBufmNXXGMAjLs/rn+HADCeAf/zI9tB+F2lk1h2 S04Ff4KJzARAeP9x44vTr8jrlvNv836JkyOt/sQ9H1nx8UcY+bdc+bfgqC0q9QpQy0MSB0TA9dTH AOokCdXEL0ZCh7gTA7QTev059UkAcQxgillMcAsIgPyNTfNM+nb6HY5J/roCTIEpdKDxOmASAE5E oVOKdgygkwj5LbRqB69yJPznTdDm2lLeLHzGMVK1hHbepHOuQx9iv/n7CHJAwpJAOwny/MW/p957 3dz/ru+tJfGLThaXEL5qErsw+cX5qPDKqzkGZdua4b8inIudiWt8HgB/eHh9EkVvErZ08xeGiZX9 HhmiHSG/JT6mDJJbQmOtsYvTn7jr9c9HPTllUydKr6Lut2rbkwjPJ5/CUTvo/hXzeP2Jw0eNK6p/ RVBEPL9JH7gAYqf2bNXPv418USLEjT/qutXzkRkf3xwGbLnyJpH0HRPoANQ4AiDj/IbensUJPXZ4 eyJm3l//2utuatSwStFuFuxhuDbecAXGwBg6AB2ADmTTAU0AyIlCJgEgvzciAsB/7KA2BjTh0Nsb nANAFkHecuO29EkAS2F1lFPIoC0A5RwJpAYCQAAI1CNgtQUgb+CyOKEgALLNRhbsYZRmM0qBH/CD DkAHoAON1wGTAPCTAIUhADaspHO6ltBDWU4ByGYO4O4QBOJX3gAdEAACQKDcCIAAKPf8JZYeBEDj jU8Y+MAYOgAdgA60Tgf8BICQAJoIKAwBkMcxgIktANwABIAAEAACQMDyGMC8gcrihCICINtsZMEe Bm3rDFpgD+yhA9AB6ICdDmgCYI5X16WAAMhmN+DuBAiEHhGm91OHnP2eoIvIqq3uP69xtKod4Ncq 5NFvkxFABECTAW91dyAA7AxIGNrACToAHYAOlFMHhAAQp18TAJoEKFoOgMnJSSWjfC/jHxAAAkAA CACBZiEAAqBZSBekHxAA5TRo4Yhg3qAD0AHogJ0OmASAXv0XRxsEQEEMEYgBBIAAEAACLUUABEBL 4W9+5yAA7AxIGNrACToAHYAOlFMHEAHQfNsCPQIBIAAEgEB5EAABUJ65ykVSEAD5G7T1Zx13USMc B+kna7txbcRd1/2b9YLusW0n63hwf/76DEyBadl1IIgAQARALiYEGgECQAAIAIE2QAAEQBtMYpIh gADI37hvJwfY1nEPIwBMMqTsTgTkz/9ZAabAtBk6oAmA2dlZkmImA8QpAEksBtQFAkAACACBdkQA BEA7zmrEmEAA5G+AdxoBEBTx4MfAlkhohjOAPvLXeWAKTIusA34CQJMAkg+gkQSAfL/qIl/D8rv0 19vbS/39/TQwMEBDQ0M0PDysormQBLDDDDAMFwgAASBQEARAABRkIpolBgiA/A33OAIgzDnWn2uH 2h9eH/W51PVfNz8Luqavm/0E9WGzio8tAPnrUZEdKsiG+S6TDggBIKv+MzMzKgJAFxAAzbI00A8Q AAJAAAgUGQEQAEWenQbIBgIgf0M+LgdAFAEQ5EgndfxNIsE00qOIiThSImivf1jbcQRImRwHyJr/ 8wFMgWmzdcAkAIQE0EQACIAGGBVoEggAASAABEqHAAiA0k1ZNoFBAORvjMc5wEmd7bj6cdejnPc4 ssA2lB8RAPnrUbOdJPSHOWxXHdBbALTzr382mgAwv531VgBsAchms+BuIAAEgAAQyB8BEAD5Y1ro FkEA5G/0dxoBgBwA+etQuzpiGBd0pRU6EJQDQEgAEACFNk8gHBAAAkAACDQJARAATQK6KN2AAMjf IE9CAJjh/UlX8vNYvc+jjaBcAn4jPwiTVjgC6DN/fQemwLToOhCUAwAEQFGsEMgBBIAAEAACrUYA BECrZ6DJ/YMAyN94jyMA/An70jrhcfchCWD+c1t0RwfyYc6hA/U6EJQDYHp6uuERAPL9qv9hC0CT jRt0BwSAABAAAtYIgACwhqo9KoIA6ByHwYaYgPPQOfqAucZcd4oOBBEAzYgA8B8BiGMA28NuwiiA ABAAAu2GAAiAdpvRmPGAAGhfJ8C/Nz/I2EdofvvOf6c4dxgndDhOBzQBIKv+UpqVBBAEQIcZVBgu EAACQKCkCIAAKOnEpRUbBACM5zjjGdehI9AB6ECZdQAEQFoLAfcBASAABIBAJyAAAqATZtkYIwgA GPZlNuwhO/QXOgAdiNOBOALgvvvuozVr1tB73ncFbVj/GG0deJqGBtfTgb2baOTgFjo6so2OHd5u XaQ/vfrvzwOAYwA7zMjCcIEAEAACJUAABEAJJilPEUEAwHiOM55xHToCHYAOlFkHWk0AmGQACIA8 LRi0BQSAABAAAnkgAAIgDxRL1AYIABj2ZTbsITv0FzoAHYjTgVYQAAsLCyTFdP6RBLBExhFEBQJA AAh0EAIgADposmWoIABgPMcZz7gOHYEOQAfKrAOtJAD8RAAiADrMyMJwgQAQAAIlQAAEQAkmKU8R QQDAsC+zYQ/Zob/QAehAnA74CQB9GsD8/DyJQ96IHADa8ff/BAGQpwWDtoAAEAACQCAPBEAA5IFi idoAAQDjOc54xnXoCHQAOlBmHQgjAObm5hQBcOedd+aeBFAcfyEYpJgkAAiAEhlIEBUIAAEg0CEI gADokInWwwQBkL9h39XVRVLEYG6n38vsAED2/PUcmALTsuhAHAHwrksqtOx/vzfXUwBMAsAkAUAA dJiRheECASAABEqAAAiAEkxSniKCAIARXxYjHnJCV6ED0IE0OqAJgKmpKdLh//JTRwB8644vUl/v t+hHTz2U2zGAevXfHwUAAiBPCwZtAQEgAASAQB4IgADIA8UStQECAAZ1GoMa90BvoAPQgbLogEkA CAmgSyMJAIkAkPZNIkA+AwFQIgMJogIBIAAEOgSBUAJgZ093NZxZhzU7P7upZyeRvl7pS45UFif0 2OHtlKVce91NyQVuozuyYF8W4w9ywlGBDkAHoAOdqwOtIgCCogBAALSRAYWhAAEgAATaBIFIAqBb PH3+11fpIu3o93Q7BID8ExKgKQTA/CzNM7N+5YoVmZx/IQ5AAJyh2dlZmpycVHvWR0ZGaHBwkNau XatWKoQg8P+Tz8SggkHduQY15h5zDx2ADpRFB1pBAIjzryMA5KcURAC0iaWMYQABIAAE2gwBqy0A JgFgjl8RAD091O0mQevqqpAnIGCneY2jB7p71O1JV6FnpyZpdmaarrj8chAAGRUwKfZ6vkAAwPgv i/EPOaGr0IHO1gF/EsBmbAHQBIB2/jUZgAiAjEYLbgcCQAAIAIHcEchMAOgtAToiQEcNyN9mtIC+ LiRAMif0DE1PTtAMJ/C5/EMfAgGQUQWSYe90hgiAzjam4Uxh/qED0IEy6UARCAB9EgAIgIxGC24H AkAACACB3BHITAB4tgD0VahKAPhX/40ogTUcUm4fhn6GZjhcfRYEQC6TDwIAhnyZDHnICn2FDkAH kupAqwgAsWsQAZCLqYJGgAAQAAJAoIEINI4A4M0AFTfk3y9/Uid0nsP/5/iLdcUVVyACIKMyJMUe EQAwvpMa36gPnYEOQAdaqQN+AkAfBdjIUwCkbSEAdMEWgIzGCm4HAkAACACBhiHQQALASR5obgnQ o0jshHLEwAIn2LnqyqtAAGRUhcTYYwsAkh9ysshWGvPoG/hDB6ADSXSgVQTAzMyMhwSQbQDYApDR aMHtQAAIAAEgkDsCkQRA/VGAtSR/5jW1DYDD//Vxgd48AHJ0oFkqlGwLgDNmvQ89yxGAOAUgeQJG RADA8E5ieKMu9AU6AB1otQ60ggAQZ18IAJMEAAGQu82KBoEAEAACQCAHBKwiAHLox9NEllVoEADZ ZiML9q026tA/HAvoAHQAOgAdiNOBVhMAmggAAZDNXsHdQAAIAAEg0BgEQAA0BtfCtgoCAMZznPGM 69AR6AB0oMw60AoCQPb8a8cfBEBhTSAIBgSAABAAAowACIAOUwMQADDsy2zYQ3boL3QAOhCnAyAA OsywwXCBABAAAkAgEQIgABLBVf7KIABgPMcZz7gOHYEOQAfKrAMgAMpvq2AEQAAIAAEg0DgEQAA0 DttCtgwCAIZ9mQ37Zsv+7s/20p9c8yD9t499t3RF5Bb5m40Z+sM7ptU6AAKgkOYHhAICQAAIAIGC IAACoCAT0SwxQADAOG+1cV6W/v/uuvvpgi/9iL4ydIq+fXy+dEXkFvllHGXBHHLi/ZSHDoAAaJZF gX6AABAAAkCgjAiAACjjrGWQGQQADOw8DOxOaOOPP/5dun33aXrkxBwdH58uXRG5RX4ZRyfMF8aI d5vWgVYRANPT06SLJALEKQAZjBXcCgSAABAAAg1DAARAw6AtZsMgAGAkw1Gy0wEJ+5eV/2Ps/Je1 iPwyDsy53ZwDp/bACQRAMe0PSAUEgAAQAALFQAAEQDHmoWlSgABoDwMXjkrj51ERAMfm6ejp6dIW kR8EQON1Bc9jsTCOIwDedUmFlv3v99J73ncFbVj/GG0deJqGBtfTgb2baOTgFjo6so2OHd5uXYL6 QwRA08wadAQEgAAQAAIJEQABkBCwslcHAVAsQxWOQ3HnQxzn+9iBPnJ6qrRF5AcBUFwdw/PfmLmJ IwBWr15Na9asaSgBIFsB5ubmqFKpUG9vL/X399PAwAANDQ3R8PCwisqZnJxUdeR7Gf+AABAAAkAA CDQLgUgCoKe7i7q6AkqlL5F8fZUu6u7ZWb1HO6EvXn9Otf03XfUorV27Vn1ZBn0ZymfypZ6ElQ+q e+11NyWSvd0qgwBojMEJQ779cBXHefXReRoemyxtEflBALSfbuJ9Ez2nrSIApqamSIrOAwACoN0s KIwHCAABINAeCMRGAPR0V8jj7u/soe6EBIAfKr8TuvaaswgEQHMUCgQAnAE4D3Y6oAmAA6MTlLXs P7mZPna2QaYuvr/a5v4N19Mfnn09/ejkuPps/8n76Z1CvBp10vYPAsBurvFMtBdOIACaY0+gFyAA BIAAECgnAtYEwM6ebs8qvhpuX8UTIWCu8stluUdHEARFAEj4mxheYQTAmYUFmuVMupMTk+rnFVdc gQiAjHoGAqC9DF04Lo2bTyEA7j06xw65OOXpy74Tm+mj7Pz/4ac2V9v5xiXs4F9yv/p733qHAPjh iXHad8Jx/t+5ejxTn1pekR8RAI3TETx/xcS21QSAjgJABEBGgwW3AwEgAASAQEMQsCAAaqtWfgef +vo80QGyZSAoOMBPHsRFACxUHf8JOj12ik4cO65IgA9+4IMgADKqAQiAYhqscCSKNy/iON/DDvSe 4+OZyu71n6Y/fPOn6Yljp6vt7D72HXpH16X0Nf6sdl0+66J33FOrl7VvkR8EQPF0C897Y+ekCASA kAAgADIaLLgdCAABIAAEGoKABQHgbAEIjACQ7QC+HAFZCYBly5apL82J8XE6NTamnP8jIyM0fnqc 3v/+94MAyKgGIAAaa3jCsG8ffMVxvmNkltbvO5mprPvGJdT1jtWeNtbtXU/v+8Oz6X1PnaB1T32K fo9//z15l/7hp+i+vScy9WfKK/KDAGgfncT7xW4uQQBkNBRwOxAAAkAACLQ1AtYEQD0KfVTp6iYj t58iCXIhAGaZAGCH/9ToGJ08foKOHj6iCIEPvP8DIAAyqiMIADsDEoY2cBLH+ZvDM/TcnuOZyrNf EwLgHk8bz+5eT+/9wzfTe584Rs8+8Un63S75XT7rot/9yPpM/ZnyivwgAKDLnfY+AwGQ0VDA7UAA CAABINDWCKQnAGT1v7unBo4bDZCVAFi+bDnNz8/TNIfPKRKAtwCMnjhJU5NTdNlll4EAyKiOIADg DHSaM5B2vJoAeGb3ccpS1v6AHfw//CTdO3Ss2s7aoXvorV2XUA9/Zl5Xv3MkwFu/VqubpW8QAHje 0+p/me9rFQGgs//LT2wByGis4HYgAASAABBoGAIJjgH0rvaLRGaSvy6OBqhUnKR/mgSoBB0h2FWh NXyk3+zsLC0JuK6PARRHdYGJgNmZWUUGyLaAFStWgADIqAogAOAQlNmwb6bsQgB8g1fQnx46mqn8 aNdz9C9v6qI3Xv1ctZ0bLuRw/wvvVn//6PFP0Bvf9An61q4jzt+3v5vfo++mG9y/s/Qv8iMCAM98 M5+bIvQFAiCjoYDbgQAQAAJAoK0RiI0AaMTo45xQSQIoReqZRb7Ujx3enqlce91NjRhSadqMw16u +//JZ4J9EQw7yABnplk6oAiAgzP01OCRzOXJnc/Se5gE0KeidF3wrWqbTz7qEAB37Dxc/ez6C7iu 77M0coj8IADwzDTrmSlKP0UgACQKAEkAS2MaQVAgAASAQEchUGgCwE8EgADIrpsgAOAMFMVIL7oc 4jh//eA0PbFjpLRF5AcBgGe+6M9a3vKBAMhuK6AFIAAEgAAQaF8EQAC079wGjgwEAJyBvI3tdm1P HOevsQP9+Pbh0haRHwQAnvl2fUbDxqUJgJmZGZKi9+brFfnVq1fTmjVr6D3vu4I2rH+Mtg48TUOD 6+nA3k00cnALHR3ZlijSMI5w6O3tpf7+fhoYGKChoSEaHh5WEXWTk5MqSiAo8q7DTBMMFwgAASAA BJqIQGEJAEkEqCMA9HYARABk1wwQAHAGOs0ZSDtecZy/emCaHtl2qLRF5AcBgGc+7TNQ1vv8BIAm AUAAZLch0AIQAAJAAAiUH4FCEwB+EgAEQHaFAwEAZ6CsRn2z5f7jj3+X/m3bSbpjzzh976UDpSsi t8gv42g2dugP75lW6gAIgOy2AloAAkAACACB9kWgkASAOP5m0ZEAIACyKyIIABjmrTTMy9T3ok8/ QH9/6w/p37afpNv3T5WuiNwiv4yjTLhDVryjsupAEAEgUQCIAMhuQ6AFIAAEgAAQKD8ChSUA5Ita iiYChAQAAZBd4UAAwLjOalx30v1v//T99Me8FUDC6MtWRG6Rv5PmC2PF+010IIwAEHtCjhpGDoDs tgRaAAJAAAgAgfIiUFgCYHZ2VhEAmgQAAZCPkoEAgIEMJwk6AB2ADrSzDoAAyMdeQCtAAAgAASDQ nggUmgAwSQAQAPkoIAgAGP7tbPhjbNBv6AB0AARAPvYCWgECQAAIAIH2RKCwBIDs1xMCQBcJ3cMW gOxKCAIAxjEcJOgAdAA60M46AAIgu62AFoAAEAACQKB9ESgkASBh//r8Xk0EgADIRwlBAMDwb2fD H2ODfkMHoAMgAPKxF9AKEAACQAAItCcChSUApqenPSQACIB8FBAEAIxjOEjQAegAdKCddQAEQD72 AloBAkAACACB9kSgkASAOPtCAJgkgEQFYAtAdiUEAQDDv50Nf4wN+g0dgA6AAMhuK6AFIAAEgAAQ aF8ECkkAiLM/NTVVJQGECAABkI8SggCAcQwHCToAHYAOtLMOgADIx15AK0AACAABINCeCBSaADBJ ABAA+SggCAAY/u1s+GNs0G/oAHQABEA+9gJaAQJAAAgAgfZEoLAEgKz6CwGgSQAQAPkoYBgB8Mwz z1ClUiG57v8nn4lBBcMahjV0ADoAHYAOFF0HQADkYy+gFSAABIAAEGhPBApLAGALQGMUzk8AHD58 mAYHBwkEAIz6ohv1kA86Ch2ADtjoQBwBcN9991FfXx+9531X0Ib1j9G2l/ppaHA9Hdi7iUYObqGj I9vo2OHt1kX3p3MX6Z+ycCHEem9vL/X399PAwAANDQ3R8PCwItQnJyfV9sYg4r0xFgBaBQJAAAgA ASBAVEgCYHZ2trr6jwiAfNXUJADGxsbIhgAQCb773e+qKACUIAyuZFxWcLmCrrrycrpyxYeMcrn6 rFauoKuvRAEG0AHoAHQgUgeuYnx0Ue9Xec9eZfUddP/995MkEzaPE5bf5TNxyL/97W+7BMAK2vj8 4yAA8jUz0BoQAAJAAAgUHIFSEABCAmALQD6aJASAYCkrD5oAePnll2ndunWhWwB0z3IvShAG84yL rOJwWZilhTk2NLnIz4X5GTozP+stXEfqoQCDQuiAXz+D/jb1NUl9m7puHXlWrIt+voJ+mu3MTfNz mE+Z53aKWOZm+fsxoNjKqu+1re+vF9Z/Lp8z5nPVMqO+u2yKOPpSz08AyPeXJgAefvhh+pf3X0kv bPgBCIB8zAu0AgSAABAAAiVBAARASSYqLzE1ASCkyqlTp+jo0aO0e/du2rBhgzKMnnzyyby66pB2 JGfCLJcpojNT7OhP0PzMaZqbPq1+LsyO05nZCToz5xSamySaRwEGxdEBrZtxP/WcxdUzr4v+62Jz n1k/6nd5tsLKwqw8d7UyP3OK6+Zf5qbH+DkPL7NToyQlrp6+rusn/TkzeZLCim1b+n7b+ma9qP4z X2P8ZqRMn2JnfpzLVJ1T73fyw/6WyEL5flu8eLGKaPve975H//qBK2nTxidox9a1tPvl5+ngvs3Y AtAh39wYJhAAAkCgkxEoLAEgK9S6IAIgPxXVBIAYSadPn6Zjx47Rvn376MUXX6R77rlHkQD4lwQB EABw5ovjzKeZCxvHXJFXLnFlW99fT+5Pem8YCRBFANRfy9/514SCjXMvdW3qmXWSOOJ5EAD+/qRN WxkyO/kRBIZy/nMiAPR+/M9+9rNqT/5jjz1G7/3g1fTiC0/Sjm3P0N6hjXTowAAIgCRff6gLBIAA EAACpUSgFASAEAHC3sv+8ySJeYLqXnvdTaWcqLyEFgJA740cHx+nEydO0MGDB2nbtm0kIZFCADz1 1FN5ddcB7YAASON04p5ikga2Dro5f0nvsa0v9WwjAqReXERAK6IA/I5/K6ICbB35IBIgaWRA7mSA SwBMT41ligAQ51++1+T7TVb+H3nkERUN8L7LPkxbXvwRvbxjHe3b/YIiAA4fQhLADvjixhCBABAA Ah2NAAiADpt+IQAWFhaqeQAkE7EkApRtAEICfOlLX6ILLrgAJIC1XoAAgDNfTGc+y7zYOulJVvXT RBDkSQA0YltA0pV9XT9JBEGaLQKNigqwjQzIjQjIGAEgCwfa+Zfvtc997nO0Zs0a9f329NNP0weu +Cht3+I9AeDI8FacAmD9/YeKQAAIAAEgUEYECksATExMVLcAIAIgP9UyCQDZWiHbAI4fP04jIyMk yQC3bNmiSABZKdHRAEj8F5f80JcEkJOQqSSAKhmZLwGg/I0EgMCg6Dpgm7xPxmFb16gnz0XgsxHR lr4n/GeCJIJJEg4mqJs2kV7u981ywsKokiahoW4v6b1xsljJKRn8Z7nMqQg22/LEE09Uv8vE+ZfQ f1n9X7t2LT333HN02ZXX0M7tz9KeXRuq+/9BAORnb6AlIAAEgAAQKCYChSYAhATQRAC2AOSjQNqZ 11EAcl6xbAU4efKkigQ4cOCAIgFkheSOO+6oGk+aEMBPhxjxlmX891IuS6iyfAktX3YpLVt6qfop pbKMPzcL15F6KMAAOgAdgA5Y6IB6v8p7dnni76Senh61vU2c/+9///skpIAkvd24cSNdcfUnadfO dbR/zyZP+P/RkW2JthvK9sSgUwd03gHpu7+/nwYGBmhoaIiGh4dJou9kcUPqyPcy/gEBIAAEgAAQ aBYCIACahXQB+9FkgBggZjTAoUOHlJEiWwLESJLVEjGaZOVE9k/29fWpMEopYti0ssh5z1/72tfo m9/8Jn3rW99SiQxtyr333kurV6+mO++8k951SYW+dccXMxVp4wv/tgoFGEAHoAPQgSboQNS7X97v +h0v9R566CH13SXfY/J9pgmAD111LQ3ueE4lADRPAAABUECDBSIBASAABIBAbgiAAMgNyvI1pAkA CaeUCAtZjZCjAWVLgJAAe/fupcHBQRUR8MILL9D69etV2OQzzzyjjCgpsqrRyiKJnMTAe/DBB9Uq j6zw2BQhMx599FFFYiz73++lvt5vZSrSxh1f/3cUYAAdgA5AB5qgA1Hvfnm/63f8448/riLa5HtK vr/ku2zr1q1qNf7Kj6xU4f8H9m6i4QMvVRMAggAonz0DiYEAEAACQMAegVIQALINAFsA7CfVtqZ/ O4AcDWiSAHpLwJ49e1R+gB07dqioADGeXnrpJWVAtbrISo4c5yQrO5LUSRMT+qcmK+Snvzz77LP0 wx/+kN7zvivoR089lKlIG/ffdycKMIAOQAegA03Qgbh3v7zfxeFft24dPf/888rxl+8r+Q7btWuX +j67+uPXK+dfH/+n9//nRQAIuS7bxbAFwNYqQT0gAASAABBoBgIgAJqBcoH7CCMBJDmg5AU4duyY yg0gEQH79+9XUQFCCMipAbJNoNVl+/bt1YROsl1h06ZNnrJ582b1t/w0y4svvqiMQTEM33/5R2jD +scyFWnj+2u+gwIMoAPQAehAE3Qg7t0v73gpQlYLaS31JaJNvrvku0x+fuTaGzI7/3LccFgOABAA BTZ+IBoQAAJAoIMRAAHQwZMvQzcz/EtiQL0dQHICSOSFbAmQZEUnTpygo0eP0pEjR9SJAbpIMqNW FiEkxPEX515WdmRVx6bs3LlTGYNiHF7O+0C3DjydqUgbTz62BgUYQAegA9CBJuhA3Ltf3vHyXSDR a7LiL8T1vn376ODBg4rUFhLgY59cpcL+zZX/pKv/IAA63IjC8IEAEAACJUQABEAJJy1vkf0kgD4h QLYECBEg2wLkpACJChgbG1OEQFGKGHKyuiOGnqzoiFFoFjH45G/5aRYx/uTEAzEOr/rYp2locH2m Im08+/RjKMAAOgAdgA40QQds3/3yrhenX6LYhLiW+ySyTf6+ZuVnMzv/IADytkjQHhAAAkAACDQa ARAAjUa4JO2bJID8rqMB5IQA83gjTQgIKVCEItsUdFinGHn+aAQx+OQzM2pB/y6GoJACEgYq+0Cz FGljw3NPoQAD6AB0ADrQBB1I8u6XyDUp4vhLklu5V74HhACQFX9dxJlPU7AFoCSGDsQEAkAACAAB hQAIACiCB4EgIsBPBmhSoAg/JTJB7+kUh162KZhFDD75W36aRYxA2dYgq0Af/1QPjRzckqlIG5s3 9KMAA+gAdAA60AQdSPru1+98cf4lkk2+LzQBkMbpN+8BAQBDCggAASAABMqEAAiAMs1WE2X1EwFF /VsiEmQVXxx5cfDFqTeLGHvyt/w0i2xh8BuB5kpQ0t/FkBx44RkUYAAdgA5AB5qgA3m8+6+97qZU K/5+wgAEQBONE3QFBIAAEAACmREAAZAZQjTQSgSmp6dVMicJ89ehnX5HX/725ywQ518SHEpYaB5G oLTx0qZnUYABdAA6AB1ogg4U5d2PHACttADQNxAAAkAACKRBAARAGtRwT2EQKIoRCAIA5AcIIOgA dKB5OlCUdz8IgMKYAxAECAABIAAELBEAAWAJFKoVE4GiGIEgAJpn+MPJAtbQAehAUd79IACKaRtA KiAABIAAEAhHAAQAtKPUCBTFCAQBAIcETil0ADrQPB0oyrsfBECpTQgIDwSAABDoSARAAHTktLfP oItiBIIAaJ7hDycLWEMHoANFefeDAGgfewIjAQJAAAh0CgIgADplptt0nEUxAkEAwCGBUwodgA40 TweK8u4HAdCmxgWGBQSAABBoYwRAALTx5HbC0IpiBIIAaJ7hDycLWEMHoANFefeDAOgESwNjBAJA AAi0FwIgANprPjtuNEUxAkEAwCGBUwodgA40TweK8u4HAdBxZgcGDASAABAoPQIgAEo/hZ09gKIY gSAAmmf4w8kC1tAB6EBR3v0gADrbBsHogQAQAAJlRAAEQBlnDTJXESiKEQgCAA4JnFLoAHSgeTpQ lHc/CAAYJEAACAABIFA2BEAAlG3GIK8HgaIYgSAAmmf4w8kC1tAB6EBR3v0gAGCUAAEgAASAQNkQ AAFQthmDvCAANsH4hwMIHYAOdLYOgACAMQAEgAAQAAJAIB0ChSUAJicnaWJiolpmZ2fp6quvJmHb sxRZqcW/9kGgKEYgIgA62xmBM4r5hw40VweK8u5HBED72BMYCRAAAkCgUxAAAdApM92m4yyKEQgC oLnGP5wt4A0d6GwdKMq7HwRAmxoXGBYQAAJAoI0RAAHQxpPbCUMrihEIAqCznRE4o5h/6EBzdaAo 734QAJ1gaWCMQAAIAIH2QqDQBIBsA9AFWwDaS/HyGk1RjEAQAM01/uFsAW/oQGfrQFHe/SAA8vo2 RztAAAgAASDQLARAADQLafTTEASKYgSCAOhsZwTOKOYfOtBcHSjKux8EQEO+2tEoEAACQAAINBAB EAANBBdNNx6BohiBIACaa/zD2QLe0IHO1oGivPtBADT+ex49AAEgAASAQL4IgADIF0+01mQEimIE ggDobGcEzijmHzrQXB0oyrsfBECTv/TRHRAAAkAACGRGAARAZgjRQCsRKIoRCAKgucY/nC3gDR3o bB0oyrsfBEArLQD0DQSAABAAAmkQAAGQBjXcUxgEimIEggDobGcEzijmHzrQXB0oyrsfBEBhzAEI AgSAABAAApYIgACwBArViolAUYxAEADNNf7hbAFv6EBn60BR3v0gAIppG0AqIAAEgAAQCEcABAC0 o9QIFMUIBAHQ2c4InFHMP3SguTpQlHc/CIBSmxAQHggAASDQkQiAAOjIaW+fQRfFCAQB0FzjH84W 8IYOdLYOFOXdDwKgfewJjAQIAAEg0CkIgADolJlu03EWxQgEAdDZzgicUcw/dKC5OvD/t/fmT5cU 553v+5c4wjdCcflBRIMui45H93f7JyKYxhqFF3Av50fJFkKA0J1rZBuZMZzBN5DaSLLCM2EUYsAe Mep+JUsjwJa6sViEoFnfllobUm9A7w29kbeyqrIqKyu32s6p855PR5zo7nOqsjI/+VTW83zzyayx jP0IAJvUuaBZEIAABDYxAQSATdy5q9C0sTiBCADzdf4JtuCNDay2DYxl7EcAWAVPgzZCAAIQ2FwE EAA2V3+uXGvG4gQiAKx2MEIwSv9jA/O1gbGM/QgAK+d20GAIQAACS08AAWDpu3C1GzAWJxABYL7O P8EWvLGB1baBsYz9CACr7YPQeghAAALLSAABYBl7jToXBMbiBCIArHYwQjBK/2MD87WBsYz9CAA4 JBCAAAQgsGwEEACWrceob4XAWJxABID5Ov8EW/DGBlbbBsYy9iMA4JRAAAIQgMCyEUAAWLYeo74I AD9ebcefwI/+xwawAQQAnAEIQAACEIBAOwIIAO24cdZICIzFCSQDgICEoBQbwAbmZwNjGftVBsCl S5fE+fPnKx/53XQ6Fbt37xZ79+4V+/fvFwcPHhSHDh0SJ06cEOfOnRMXL14U77///kieqFQDAhCA AARWgQACwCr08iZu41icQASA+Tn+BFmwxgawgbGM/boAcOHCBSE/SghAANjEzgdNgwAEILDEBBAA lrjzqLoQY3ECEQAISAhKsQFsYH42YBv7T506Jc6ePev8nD59Wpw8eVLI444ePSrkuC0D+K6fu+66 S8hgXwkA6m8EALwUCEAAAhAYIwGPALAupmtrYq32mYr1tCXtf9+TpLtduPC/xDZL+TJdTj48ZWqc /pHfyYds1we1fODzZ/MQQACYn8NNcANrbAAbGIsN2MZ+GfxfddVV4gMf+ED6ueKKK8RHPvKRNA3/ vvvuS4UBBIDN8/ynJRCAAAQg0I4AGQDtuHHWSAggABCQjCUgoR7YIjYwPxvwZQDcdNNN4uabbxZ3 3nmnePjhh8WTTz6ZBv9kAIzkwU01IAABCEBgoQQQABaKn4t3JYAAMD+Hm+AG1tgANjAWG3CN/WoZ wG233SYeeeQR8dxzzxXBv9x4jwyArk9dzocABCAAgWUngACw7D244vVHACAgGUtAQj2wRWxgfjag xv7Dhw+Ld955J91VX33kTL+c8X/ssccqwT8CwIo7DDQfAhCAAARSAggAGMJSE0AAmJ/DTXADa2wA GxiLDaix/9ixY+L48ePpzL7+OXPmTLqPkPxb/15mCEiBoO9NAOXr/NgEcKndCSoPAQhAYGUIIACs TFdvzoYiABCQjCUgoR7YIjYwPxtQY7+c/Vdp/TK4D31k8D+UACBf/6eLALwFYHP6HbQKAhCAwLIT QABY9h5c8fojAMzP4Sa4gTU2gA2MxQbU2C/T+m1Bvwzy1Wy/Cvr1v4fIAEAAWHGHhOZDAAIQWBIC CABL0lFU004AAYCAZCwBCfXAFrGB+dmAGvtdQb5M/ZcBv/zb9ulbAJCz/VIAUB+ZCUAGAJ4LBCAA AQiMkQACwBh7hTpFE0AAmJ/DTXADa2wAGxiLDaix3xXgh74fQgBgD4DoRzcHQgACEIDAAgkgACwQ PpfuTgABgIBkLAEJ9cAWsYH52YAa+2Ug3/bzuc8/IN468nrnz1133ZXO9iMAdH+mUwIEIAABCAxP AAFgeMZcYUACYxIApDPJBwbYADaADSyPDSAADPiApmgIQAACEBglAQSAUXYLlYolMBYBoA8nkjK6 z8TBEIbYADYwbxsgAyD2ic1xEIAABCAwBgKjFADk+3Tffffd9B2+6iNT6+RDtuuDXc7M8GfzEEAA wNnvOiZwPjaEDWADXWwAAWDz+BS0BAIQgMAqEBi1ACBFAPVBAFgFc2zeRgQAHPcujjvnYj/YADbQ 1QYQAJo/uzkDAhCAAAQWR8AtAKxPxdraWvUzmfVS0/fffz/dLEfO7st3+B4+fFgcOHBA7Nu3T0yn U6EyABAAesG9qQtBAMB57+q8cz42hA1gA11sQAoA0m9hE8BN7W7QOAhAAAKbhoA3A2BjNhGT2UbR 2PXpWuX/bSkgALQlx3kmAQQAHPcujjvnYj/YADbQ1QaUAHD+/HmhPlIMkBNc2gQAAJ2iSURBVG8G kJMau3fvFnv37hX79+8XBw8eFIcOHUonP+QkiBQOpE/EHwhAAAIQgMC8CDQSAMTGTEym62ndpDgg MwTy/wopDsj/K8Gg+D15+JWZBFMhz5YPu1f/5sPp97fccov2+w3ODAD5kGQPgHmZxfJcBwEA572r 88752BA2gA10sQGbACCFAASA5fElqCkEIACBVSLQSACYTcqAX4kASgBIoSXLBvSMgUwEyIJ+dbz8 XWUAvHTv9cnvt4iv5UsAHvn4B8WOHTuSNLqL4ty77yWfZCPA5HP23Nk0te6uuz7DJoCrZJ0RbUUA wHHv4rhzLvaDDWADXW0AASDiYc0hEIAABCAwGgJBAUDfB0AP7mMFAJtAoAsAtzyq7QFw/w1i27bt 4r1EOT91+ow4efq0OHnqlDhx8qSQgd4dd9yBADAa0xlHRRAAcN67Ou+cjw1hA9hAFxtAABiHP0At IAABCEAgjkBQACiC/nRTwHI2f1AB4L3zSeB/Ogn8s+D/+PHj6dsAEADiOnWVjkIAwHHv4rhzLvaD DWADXW0AAWCVvA7aCgEIQGD5CcQLAElb05R+bUq/skmg3B9A2wPAJxD4MgC2J0sAzifp/qfPnE2z AE4lQoDMAiADYPmNbYgW2ASAU4m9nD171vk5LTNLEmFJHnf06FHxuc8/0DmzpKsDyfkEIdgANoAN LKcNmK8BVBsBsgfAEE99yoQABCAAga4E4l4DqL3+T+4DsKb+nwf92TKBiZjNsg3/ZNaA2gSw2ChQ e63g5P43ik0A5e9/9LXkNYBf+U/ZpoL5awBlYCc/78pPMvvPJoBdu3pznm8TAGTwf9VVV4kPfOAD 6eeKK64QH/nIR1Lbuu+++1JhAAFgOR1tAiT6DRvABsZmA6YAIPcsYhPAzelz0CoIQAACm4GANwNg qAbGvAZQCQDqbwSAoXpjucv1ZQDcdNNN4uabbxZ33nmnePjhh8WTTz6ZBv9kABBAjC2AoD7YJDaw vDaAALDcfgS1hwAEILBqBBAAVq3HN1l7XXsAqGUAt912m3jkkUfEc889VwT/8v3LZAAsr7NNoETf YQPYwJhswCYAyCwAlgBsMoeD5kAAAhDYJAQQADZJR65qM5QAcDh5leQ777wjZHCvPnKmX874P/bY Y5XgHwGA4GFMwQN1wR6xgeW2AQSAVfVAaDcEIACB5SSAALCc/UatcwJKADh27Fj6tgg5s69/zpw5 I86dOyfk3/r3MkNACgRsArjcjjeBE/2HDWADi7YBBABcEghAAAIQWCYCCADL1FvUtUZACQBy9l+l 9cvgPvSRwT8CAIHDkIGDno3Cv8vMnCFZDNmflM144bIBBACcEwhAAAIQWCYCCADL1FvU1SkAyKDC FvTLIF/N9qugX/+bDACc+qECOxXoPvDAA+L2228Xf/qnf8qnZwaSq+SrWA/Vl5TLOOGzAQQAnBMI QAACEFgmAggAy9Rb1NUpALiCfJn6LwN++bftgwCAYz9UcCeD0s//9V+Lv/3bvxVyjwq5IRiffhlI rpKv5Cx5D9WXlMs4ESMAyLcVyc3/1IdNAHFaIAABCEBgjAQQAMbYK9QpmoBaAuAK8EPfIwDg2A8V 3MmAVL6FQtqYDAhCy1L4Pbx0x2QkuUq+kjMCAPfyUPdyqFyVASAFAP2DABD9KOdACEAAAhCYIwEE gDnC5lL9E1ACgAwC2n4+9/kHmDk8QvAQcvKb/i4DUpn2L4MAgvvmwX0sM8lXckYA4B5ueo/2dTwC QP/PdkqEAAQgAIHhCIxSAJAO3fnz54UM7tRHquryIdv1gS2DPb1c/l0yXkYWfWwohgBA4NB1XLGd rwQAOXaZb6fg/9W3dXThIfkiAHAPD3EPx5aJADCck0rJEIAABCDQP4FRCwBSBFBCQJ8CwMGDB0Wb z09/+lOhPr/4xS/E97//ffHwww+LBx98MP184QtfqHxc35vH9fl/dU39b1lHWVdZ57F/fvnLX4om HwQAHP9YJ33ex+kCQB92Shn2NwkgADAGzPveNq+HANC/c0qJEIAABCAwHIGlEACkCNCnANAkwHQd ++tf/zoN/t988810cznbDvNj+U62QdZV1nnsn9/85jfC/MiNvmR6/9tvv52m+crUYMlcftdHUEQG AAHEEAGELgAcP35c8BmGAQIA9+8Q92+TMhEAhnNSKRkCEIAABPonsJICwKFDh0Sbjx6YyuDzi1/8 ojh79mwS/GcCwKlT1c/Jk6eS1F+59rX+21DfqWvKv0+cOCneeeed5HNcPPTQQ63XyLddWx8679ix Y8L1eeutt4T6yMBftkMF/2pXfwQAHP8mTvq8j1UCgNyoTtpwX5+3HrlZrK2taZ9rxV8++1al/Lfe elb85bVr4uZHkvvo2b8U1177l+KZY4+Im5O/n03uLVUXddy1f/lsrX7P/uW1Ys04Xm+DKlcvr682 NilH8mUJAGPBvO9v/XrmawDVRoBsAti/00qJEIAABCDQncBKCgBHjhwR5kcGk+o7GWzKNanyb9ux 8jvpoO7atSuZiZYCwOn0+Daz0VIIkGXIj+/fbcqWgf/RozKQflt8+ctf7i0AaeKcNz1WMlcz/PJv +X85cyrbLxmr1/2RAYDDv0iHP+baugCgC1pd/33skT8Wa3/8SCGQ2co7duyZVAD440eOFccde0YJ AeV38ty0vFQg0I61nG9ex1Ve1/Y1PR8BgLEg5n4c8hhTAJA2KUUABIDuTiolQAACEIBA/wQ8AsC6 mFZmmdSM01Ssp/Vo//ue999PXov1v8Q2S/nT6TR9aKr1/+rvPpcA+AQAFfzfdNNNacAZEgDk7H82 0348DbSbfmTQ/x9+53fEdOfOVAAw//25u+9Ov29arjxeBv+HDh9JZ/6XSQCQwf1VV12VpvnLgB8B AAd/SOd9qLJ1AcCX7dL0t6NflwLA163ZMz/8i2TWXo6r1/6x+GMpAHw9eTvGD/9CXHPNJ8Qnkv9n v/2F+GEyJqjrHj36Q/EX114r/uKH2nfJOddqxxXlyvPza8ty5TH//u/lsWZZaV3VOO+oc9P2m8cj ADA+DHUPx5arLwGQ9ogA0L+zSokQgAAEINAfATIA8mwAGSSr4P+3f/u3xe233x4pAJwW7xw/kQbo R48ea/yRqfrbt20T/+0f/iFdLmD++xv/83+m37cp+/CRo+LXvzm0lALABz7wASFFGCUCkAGAkx/r jI/lOCUAnDt3Thw4cKC3z8ZXPlpdAnD1Z8R3NzbExnc/I65e+6j4SvHvNfHRr+TfJ8d85zt3iqvz Y836fPczV4u1j36lqONXPromrv7Md9P/p+Wqa2x8RXxUv4ZR7sbGd8Vnrr5afOa75XVl3WQ5skxZ nz5ZyLIkX5YAMD4s8r637QEgJzLIAOjPWaUkCEAAAhDojwACQC4AqOD/t37rt8Tv/d7vib//+7+P EgDk7Pzbb7+TBuiHk9n2ph8ZJNyRiA2PP/54muZu/vuJJ55Iv29arjz+N0nw/+abv06zGJYtA0AK ADfffLO47bbb8n0WsmUWLAHA0V+ko9/k2roAsCGD8p4+b3z598Xa73+5Vt537rxaXH3nd4rvv/z7 a+L3v/yGeCMN/O8U/5L//Z033qid+8YbXxa/v/b74svJb/q/VZ3TMoqMravFnd+xl/vGG98RdyYC QPq7rKeR5aXXry8eCACMCU3uyyGOZRPA/pxSSoIABCAAgeEJIADkAoAMLK+88sokpfVaceedd4p/ /ud/jhIAjiSz7D//+S8Sh/on4tVXX2/8kQG6Cvpt/5YCgPy+Tdkvv/yqeOHHL4k3ksBj2QSAK664 Iu2HRx55RDz22GOpCIAAgKM/hPM+VJlKAJC2+/rrr/f2ee2hm8TaTQ/Vyvv2HVeJq+74dvH9Qzet iZseek289u07kiU1d4hvfev29O9vv/aatS7qeFmOXn56vbWbxEPJea+99pC4ae0qcce37eVWfnfU s08WsizJlwwAxoah7uOYchEAhndWuQIEIAABCPRHAAHAyAD4gz/4A3HfffclzvK3ogQAOdP+05/+ LHGM3xAvvfRK488vfvGrQgCw/VsKAPL7NmX/+Mf7xXPP/Sit27IJAB/5yEfSVxc+99xzZAAcwbmP ccLHdowuALz66quJiNfP55W/kwLA39XKe2U9CfCTQP3vXnlFvPLK3yWBeiIA/F3yb/n9VbeLPXs+ nf69nvxuq0t23E3ipmT/jdvXy2P066X/TgQA+XulXHXdtA7a7+rfr6yL26/K6tMXB1UOAgDjw6Lv fQSA/pxSSoIABCAAgeEJjFYAkJvo6BsBzmMTQLUM4J577hFPPvlklABw6NDhdPZ///5XxY9+9GLj z8GDPxd/+8AD4nvf+56w/fvpp59Ov29T9rPPvZBs0PVs4nC/tnQCgNwMUvYBewDg3C/auW97fSUA SBt++eWXe/vs37W1llq/ddf+tPw9n96S/7ZFbNmyJuT3+5PAf8uWT4vdL+0SW2VKfvLvPfuz4/XP /v17xKeTc8zf9+/Pz5Pnbt2alqGXK8vatTXfYHBL8vuWLeLTe7LyK3Xduqs3Bnq9JV8yABgn2t6n fZyHADC8s8oVIAABCECgPwIIANomgHKtvBIB1tfXowQAuc7+tdc2xAsvvCSeeeb5xp+f/OSgkBv9 PfPMM8L27xdffDH9vk3ZMvj/wQ+eTpzuV5dOAJBZGLwFAKe+D+d8UWXoAsBLL72UZPHwGYIBAgDj xKLucXVdBID+nFJKggAEIACB4QmMWgDQswDmkQGgXvmnRAD5d+g1gL/81Zvp7PzevT9Mg+2mHykc vP76gfTj+3fTcuXx//Zv+5JZ9O+Ll/a/vFQCgNyPQQX/sg94DSAO/qId/DbXVwLA6dOnhRTy+AzD QPIlA4Axos092tc5UgC4fPlyuuu//Eh/RX54C8DwTixXgAAEIACB5gRGLwCod+rKB6l8yHZ9YH/u 8w9Yg3r5GkBXsG/7/u233xa7du1KZ+efeuoH4okn/lX87+89NZ7P/35KfPe7Tyav/noiCTxeWioB QAb9+gcBAOe+632/iPOlACDfYvHLX/4yeU3oW4nA9wKfnhlIrpKv5Cx5L6KfuSbjkykAKBEAAaC5 U8oZEIAABCAwPIGVFACk09jmc+zYMaE+Mih99NFHk9T8Z4XcbO/553+cbrg3xs++ff8u/vt//+/p TPoyfqRjr78BQM74yQwBKdrI37p+pCiEE48T37cNSLv867/+a/Ff/+t/Td7k8aaQdsunXwaSq+Qr OSMAcA/3fQ/HlocAMLyzyhUgAAEIQKA/AispAJgzzCrN3Pa96zvpbMoZPfm6wK9+9auj/vzjP/6j +Jd/+ZfOgXLXQNs8Xwb1sR+5LEAFTzL4RwDA2Y91zhd1nLL3v/qrv0pnqGWaOp9+GUiukq9ivai+ 5rqrPR4hAPTnlFISBCAAAQgMT2AlBQA5c7yqnybLHIY+to8+6EOUIANgtZ33oYK3PmyTMppl+AzV l5TLGOGzAZsAINP/5b4A8o02u3fvTvYJ2pu8LWh/8lafg+LQoUOpaHXu3Ll0r4D3339/eG+PK0AA AhCAAARyAispAMiAjw8MdBvAwcfBxwawAWwAG2hjAwgA+NQQgAAEILBMBFZOAGjzcOccnEJsABvA BrABbAAbsNmAEgDUzv/qbQBkACyTO0xdIQABCKwOAbcAsD4Va2tr1c9k1gsZme4md/eX6W8yDe7w 4cPiwIEDYt++fWm6nNpBV70BQP7d11sAcOBw4LABbAAbwAawAWygLxsgA6AX15BCIAABCEBgTgS8 GQAbs4mYzDaKqqxP1yr/b1tHBAAcr74cL8rBlrABbAAbwAYWaQMIAG29Qc6DAAQgAIFFEGgkAIiN mZhM19N6SnFAZgjk/xVSHJD/V4JB8Xsyo19mEkyFPFsKAK/+zYfT72+55Rbt9xsqGQAynU5lAZAB gIO3SAePa2N/2AA2gA1gA74lACr1nyUAi3BnuSYEIAABCMQSaCQAzCZlwK9EACUApBdMlg3oGQOZ CJAF/ep4+bvKAHjp3uuT328RX8uXADzy8Q+KHTt2iIvJ7rnnL1xMPhfyz/nku4virrs+w/vaj+CA 4YRjA9gANoANYANjsQEyAGJdTo6DAAQgAIExEAgKAPo+AHpwHysA2AQCXQC45VFtD4D7bxDbtm1P g/6z594VZ5I9As6cPZt+zl84L+644w4EAAQAbAAbwAawAWwAGxiNDSAAjMGdpQ4QgAAEIBBLICgA FEF/uilgOZs/tABw5uw5cfrM2eRzRpw+fVqcP48AMJbZDurBzBs2gA1gA9gANpDZAAJArMvJcRCA AAQgMAYC8QJAUts0pV+b0q9sEij3B9D2APAJBL4MgO3JEoALydr/c+++l2YBnE2EgDQD4PwFMgCY 8RnNjA+OL8EPNoANYAPYAALAGFxZ6gABCEAAAk0IxL0GUHv9n9wHYE39Pw/6s2UCEzGbZRv+yawB tQlgsVGg9lrByf1vFJsAyt//6GvJawC/8p+yTQW11wCmmwDmGwGyCSCOFs42NoANYAPYADYwNhsg A6CJ28mxEIAABCCwaALeDIChKhf7GkApAKgPAgBO39icPuqDTWID2AA2gA0oAeDy5ctCfaTPIv8t JzV2794t9u7dK/bv3y8OHjwoDh06JE6cOCHOJfscSR9H+kT8gQAEIAABCMyLAAIAafWk1WMD2AA2 gA1gA9hASxuwCQBKCEAAmJc7y3UgAAEIQCCWAAJAywc+sz7M+mAD2AA2gA1gA9gASwBiXU6OgwAE IACBMRBAAEAAYNYHG8AGsAFsABvABlraAALAGNxZ6gABCEAAArEEEABaPvCZ9WHWBxvABrABbAAb wAYQAGJdTo6DAAQgAIExEEAAQABg1gcbwAawAWwAG8AGWtoAAsAY3FnqAAEIQAACsQQQAFo+8Jn1 YdYHG8AGsAFsABvABvRNAOXu/+rDWwBiXVGOgwAEIACBeRJAAEAAYNYHG8AGsAFsABvABlraAK8B nKfbyrUgAAEIQKArgZUTAD73+QcEHxhgA9gANoANYAPYQB8ZHLwGsKsryvkQgAAEIDBPAispAPxf DwnBBwbYADaADWAD2MBy2sB7770nfvWrX4lDhw6Jt99+Wxw/frzyOXHiRPp/+bf+OXnypDh16pQ4 evRoOhmAADBPl5NrQQACEIDAGAh4BYDZZE2srVk+0/VGdV+fronJbKM45/333xcXLlwQL917fVH+ 1Z/5rti3b5+YTqfp+rmLFy9WPvI7qbJ3fVjLBz4O33I6fPQb/YYNYAPYADYgbQABoJEbxsEQgAAE IACBgkAwA2A2mYpKuL8xE5OGAoDJWwkA586dS5X5fXdfIxAAcOpw7LEBbAAbwAawgRgbQADAk4UA BCAAAQi0IxAtAGzMJpVZ/PRy69NKhoA+yy9/lueoDAJbBkBIALhw/rw4d/asOHXyVKr233777WQA sHyBDA5sABvABrCBFbcBBIB2Th9nQQACEIAABCIEgHIJgBngi/X1SnaAXDJgSw4wxYNgBkCS/p8F /ifFO2+9LY4ePiLOnjkjbv3krQgAK+70xcwMcQwziNgANoANbG4bGJsAIP0a+ZGv/tM/clnj7t27 xd69e8X+/fvFwYMH030LZPajnASRyx3lefyBAAQgAAEIzItAhACQLQGwZgDI5QDGHgFdBYAdO3ak +wPowf9v3vx1mgXwZ3/2Z70JAP/wTob4s39nd5KuTr5Pjzk4HydKXu+p5HJP/e/m1+tyLk5yc94w gxk2gA1gA4u1gbEKAEoIUH8jAMzLneU6EIAABCAQSyBaAKgXuC6maxOh7e2XigS9CABJ6v/JEyfF 23L2/8hR8Ztf/1qcTnbt/eSffbI3AeDqJNCWf1wBd+j3vp0/gvjFOpN99yfl0Z/YADaADQxnAyEB 4PTp0+Jskkko/57HWwDMwB8BINYN5TgIQAACEJg3gfYCgJz9n8zK+ubZAF0FgJ07doqLSQaATPk/ lYgA77z9jnjr6LH0Qf6pT32qPwEgn3F3zfB/Npn5F0kGwMccGQJ9O3YIAMM5in33FeXRV9gANoAN LNYGfAKAfM2f9BmuvPLKmggw1GsAEQDm7b5yPQhAAAIQaEugwWsAq7P98oL6Jn9rSTbAdJpt+qdE gKntFYJrU7Enfw3gNsvv+msA5SaA7557NxUDzif/vuOOO3oTAKTzlgb5yR9zGYAKxn/2fNXBUVkB Crb++8eSY/WybGWoY2znhwSAqx8V4mdGL6vsBfNc8zq++tp+k2x818PxXazjC3/4YwPYwKrbgEsA UMH/b/3Wb4lrr71W/MEf/EFFBEAAaOsuch4EIAABCGwWAsEMgCEaam4CePjwYXHgwAGxb9++RESY ikuXLqUb4+gf+d1dd93VqwCgglxzGYAK9P8hCbqVk2UuCVBBt8ogUHsGKFEgDcK1DAIVlKsy1bXV 8T4BwFZPvT4h8cAUJ4J1ycUGncu8l0SsunNL+wnwsAFsABtw24BNAFDB/2//9m+L3/u93xN33nmn uO+++8Q999xTiAAIAEN4dZQJAQhAAALLRGC1BQDHRn9m+r8Z3JuiQBHU5/sKyIwCuaFf8X3+f1dG gTzeKwBo5docwibiQSi7Ia1L4Ho4pQQm2AA2gA1gA4u0AZsAINP+b7rppvSVwX//938v/vmf/1l8 61vfEk8++WTy0qL1VARAAFgmF5W6QgACEIDAEARWWgCQzkstdd82+235Tk+T12fK1bICfW8BW0aB eb43iFf7FSQWYHtrgevc4k0GWiZCk7roSxoW6ehxbQINbAAbwAawAd0GfBkAMtC3feSGgEMKAKaT JrMdeQvAEK4rZUIAAhCAQBcCKy8AuNbP64G2c6mA5dV9tlR5c+8As8OkgBBK4y+Cee1k1x4Aykky U/1T0SGf3XcZjV6melWiOrbNKwpx2nHasQFsABvABvq2gdBbAOTO/8ePH6+8AUB+N6QAoG8EKJ+b CABd3FPOhQAEIACBoQisvABQbAaYz5KnQW+yOaDurDgFACMzQAXpTxmbC7pm3SvXsIgJPodJZRrI ZQY28cDcY8C1bCHWKdOvF3sOx+H0YwPYADaADQxhAwgAQ7mFlAsBCEAAApudwCgFgMuXL6cbAZqf vjcBrAXFyez4z5Ie1zf/S2fN870CXGv41fH6xn9SSIjZ4K+oQ0MBQA/6TQHAlvrf9jpdzxvC8aNM AgpsABvABlbbBhAANrt7SvsgAAEIQGAoAggAeoAvZ/+19fKVGfo8dd5Mu9ffAiA3/it+NzbSs6Xj h2bxfbP2elaBaxmDKWT4lgZU6pLX3fYWBFeZOOOr7YzT//Q/NoANzNMGxi4AqOUA7AEwlPtKuRCA AAQg0JYAAkAiAEinRQXo5iy/TQRQsPVjXW8O0JcTqGvonaX2GgjuAWBZu2++ZUDtJWCu3devpwQK X13SrAfP9ebp5HEtggpsABvABrAB0wYQANq6fZwHAQhAAAKrTmC0AoBcBmB+hloCgHOJc4kNYAPY ADaADSyPDSAArLr7SvshAAEIQKAtAQSAPAMAx295HD/6ir7CBrABbGC1bUAKAM8++6x47LHHxEMP PVT7fOlLX0q/k3/rn//xP/6H+NGPfiSOHj0qPvf5B8RbR17v/JGTE/obAPR/swSgrXvKeRCAAAQg MBQBpwCwMZuItbW1+mcy61wX+XC8cOGCOHfuXPqKnsOHD4sDBw6Iffv2pe/Mtc3+y+/IAFhthw+H n/7HBrABbAAbkDYgBQAZ/L/55pupL6E+Z8+eFfLz9ttvi2PH3k4C/WPiyJGj4je/OSR+/vNfiB// +CXxT//0TwgAnT05CoAABCAAgWUl4M0AkCLAZLZRtm1jJiabQAB4+cf/LvjAABvABrABbAAbWE4b kALArl27KoH/qVOnxYmTJ8Xx4yfEr3716yTg/6X46cGfi42Nn4oXX3pZfP8HT4v9+18RX/jiFxAA ltVrpd4QgAAEINCZQKQAsC6mlsB/NskyBKRIUGYMTMV6Xi3X73u0DIB7rtOzDG6YSwYADt9yOnz0 G/2GDWAD2AA2IG1ACQBqxv/06ST4P3FSvPPOcfHWW2+Ln/705+KNN34iXn3tjSTof1X8+w+fE//6 r3vTDIChBADdI+MtAJ39UwqAAAQgAIGBCAQFgGIZgGvmf32aLRNQvyf/nyoFQFba9vuebAnAvdev ievueb5cAnD/DWLnzp3i0qVL4mLy+4Xz58X5984nD/rzyXf9LQHAgcSBxAawAWwAG8AGltcGdAHg zJkz4uTJU0Xwf+TIMfHGxk/EK6+8Ll588WXx/PMviqefflY89dQPxAsvvDiYACCDfvUHAWAgr5Vi IQABCECgM4GgAJAtAbBnAKRXTwL8yjIBs0qW39M9AF79G3H9tm/U9gDYsX17Gvy/K9f05Wv5zpw5 Ky5evNjbHgA4fcvr9NF39B02gA1gA9jAWAUAczNANgHs7KdSAAQgAAEI9EwgUgDwXHUwAeBsIQDI FD8EABw+nH5sABvABrABbMBcAjCmDAAEgJ69VIqDAAQgAIHeCTQWAGaTco1/pwyAdAnA9eKe5/W3 ANwvdu7YkS4BSNP/849cAtDnWwD2vzATW21vOKh8d4P44o+y47Y++PSm2TRQtb1Nm7qc26fTvv+F R8WntyyuX/bcemX5dowtnxB7XhjOPsbCPNR/WT2vFJ9+vDsLne+WWx+t3Xv7H7yhuCfHyMdWJ73O kqX5/xDfzf67zmyefSr7Yc1ht/Osx2bv3yHaRwZA7/4gBUIAAhCAwIoQaP4awDUlACTLAixBdLn+ 3/178RrAl+4V1xllzOM1gKYzsv/xT4gtFidwMzqAm6FNaYB442whokwWMNwgdiVBvxIiFlWXIZzq LmWmbDoKIso+bYF/Gjjn4p0SsJbBnpexzl3soM25i+pHnwDQph2cM7/sBASAFfFSaSYEIAABCPRO wJsB0PvV8gILASBZ53/ihJ4BsG8hbwFAAJif09bVQV5UoKDqvevGZMNLTXxIxYiOQW9XJmM5v4/M jFD/LmMwvYx1nrdNhfp9qPogACzP2G/awBgFAOnisARgKM+RciEAAQhAoC8CCAByVjGUAXCrzBAo X1dops9XUsKT45yzl+l16qnrphOa/b+8nl5edq1sBlqfEa0fYz8/5GhnLPRXM5b1Nc81263qHFuX tP6e69mcfn0GXv4ew8MVPJjBfFGeJ7sgPadhBkAMJ1cd3cFjdRmLa0mH4rv1werxso9M9sWsemGn 8eeo+scIIi77rttCaedWW0mEl91qmU5f92i+vEQXeVR9dcapHRTXT5Y+3Kru2erSoVqb/s//W1yh 31+aeOQbR0o7iOz3IlPCf7xvrCmWdeht62Ab2b1jH0dDSwBixomYcbh6jOo3+9KV2r03UNsr4mLB x163UBtjOA0lpsy7XASAvtxAyoEABCAAgVUjgAAQIQDoa0QzB6x0GM3/KwfMunbZElxIp0kFE3It uRlsFPsV5EGpmmVV5ZsBV6g+PgGgDBbLddx6fULigRmMB+tiEURswZbuWJpBe4iHzyk1xYSYGezC wd6SrENP9iFwrR/2Xdfk5K2jI+XdZ4N6eWVAYNpsKWSYQkqbc9Q1QzOqYfv277vhEkT6ukcLFrbA 3HIPFvendnxoxt92HwXvlWLfEvfYU+n3iONj+0LPcGlrG9bMGW0c9QkAoXGptN+SjW0c1sW7VFAq hNZmAkDd1tz3UjG+m1lDxpKzmLoFbaTFeDrvoL3P6yEArJq7SnshAAEIQKAvAggAEQKAHszrzqhr vbIZWOpOj2/G2gxmXUFVWX5147WY+ngFAG19u3X23QhGbYGmuTbbFEIqa+gD1zPrEG5fs43o7DN8 4Y3sipnMFqn/tmCmjQDgsskaszwosB3vtOsW5xS26shySQOuXACz20TGPSQyuYLrPu9RU8SQ/b31 xnJ/Az1jyGaTTQWAsF2XXKL7Pb9X3VyyjTRj+iLWdvx9UL2vzPvAKwBEjkvescZhl9m93EwAiOUh x8Iii0LbHLPW9oi6RdlIw/G0z2B8EWWNVQDQnTO5HIDXAPblrlIOBCAAAQj0RQABIEIA0FN/K46q YwdpX5BXCw60Mlzn2b4vglBtZsk1+2oTLWwp48VsprbEwDaraJ5bbIanz4JGsAldzyUA2Opu4xHj lOozk6H09XL284Yk5Tt7E0CTtynYOIXqGAoms8DaPWtunT21zRRq37U5pxAALIFnSByItU9bW21t 7/MeLQK4B+XSmHzzx8pGkHX2oT7zjQEuUS3Uzph7pcLFtRxJtwOLXXWxjaqgmS0FsG3m6M7yqC4J CWWdVOzKERw3eQvAYG2PqFuTsV1fIhYaX5b5dwSAvtxAyoEABCAAgVUjgADQWQCormt1rXHVHS09 6Kyk/7uccpsjnqevVsQJY+8AvS7K2Q7PsGYzg7Z2uM41U1Or6bV2PqXj776eazbbKl5YeMQ4t3pG guyL0O7zam14Ecyns4d5GwJvJrBxCtUxFExuCgFAs++wfVYD7lBgbK5xt90Ttj5QtpAGfamwVWaX 6Pdv6PoxokVMHWOuExLrogSAQF+0DYKra9eTQN4Y63wZAHr2iHVcihj3XMtu5iEAhNoeU7cYGwlx Co01y/Y7AsCquau0FwIQgAAE+iKAANBZAAinjNcC2WLGxwhmImbldCcvTUvWNwT0vNPanKGNnbnW U2StQYglXbwUAJqzaZKSW7YpC8BNHjEObZmi618+YA18inXW1dlM23V9e0P46rkSAkBg1tkX2IYC 49CeBC72Khtkl/bKyUwUmGW2lqR3u8SXUJ/FZgA0CejHnAEQIxqEBACzfZVxKWbci5hlDzGMaUdq Ew2zaVxLxnTbbWvHvvE0Znwc8zEIAH25gZQDAQhAAAKrRgABoIsA4Em9jgrqzAA+Yo20LLdMVa+u 4w3NnoZmi62Bq2dG0JfSHlOX0PVcDrk5S+/iEeO8FuvSt/hf5xdanuFLu22T+u8SbEIBb41ZIN2/ uE7DoMUMdGzl1Psvft25860Gjk0Rnct02t6jKY9ko0ctKySzsyuT77XN5mzZOYE6hgSC2PvCu/Qj UK+2+zG0CoItwbdrA8Jy3bx/eU1TwcB1//qycmr91OZeimm7Q/jV6zbEeBozPo75GASAVXNXaS8E IAABCPRFAAGggwBQBOPWHZ39s9+uNeuxO3MXM5CGg2lzaEOz+EXwZplJq8xCGUFFKKU9WJfA9WzO Z/0tAEYWRYuNsFSKriv9X9WjtlO3kQHgEgFCnKLEogYzzpWZ4zZBS4tzfDZUqY+xVEMFNuXSiri3 AKi+ihFEQnboC7j1DeKKNGzPjv82kU3V0axz/XWZ1TEjdN92EQDSerboi1YCgCPdX6bz2/oxJkPC nBGP6ePaLvot3wJQEZsa7qdR2Lvxutg2bwGQfRjKhGibOTDmwF/VDQGgLzeQciAAAQhAYNUIIAB0 FABKEUBf627frMoXCNl+U+td9UBB3zMgCzbq68/Nd0VXlgkEZkTra03ts53lGnj/Gn9fXfQgpFzb 6xdOzHTZEI90P4PA2nx9V/eQ42u+y7wIYFxLIVT/6O9+N96F7qtjzGyxNxBsEcy3CfIUt9BGirY+ r+yqHjFjr7+FYfePwpvwtb1Ha0GZ7e0IERkAZaCW2GIuHtjeJNH0vu0qALTpi7a2UW1bdo+7BA6r qFNb518fJ0JjTd0OkjJulcuomr0FoIkAYL2m0faqwKjG0+QZYhEzQ230jd/yOjHjYWgMHMvvCACr 5q7SXghAAAIQ6ItAUABYn2oB3mQmNmYTMV3vdnn5apwLFy6Ic+fOiRMnTojDhw+LAwcOiH379qWv zLl8+bL1c9ddd4m3jrze6fO5zz+Qrt3ls5wMmqbCpgFLjADQ4pV+fdlQTB37utaQ5TTtmyHrQtnL eX/Tb2W/+V4nC6d/F8skAFyx40ti7969Yv/+/eLgwYPi0KFDqe8jfaCLFy8K6RMN82ddTKXgnPhu Tf9IX08J85PZRtPTF378stc/A9i+/xbeAVQAAhAYNQGvAJAG/1q0rwZUBACc60U6oOksWCCoV/WT TnQotV+WFzpmyPbG1HHI6/dVdhqwLFBI6asdlMP4Nk8b8G0wushxaZ4M2lxrmQSA3bt3L0gAEGI2 aScAKM9V+n3LKABslvp37b9RRyBUDgIQWBgBtwCwMROTUKS/Pq28Lk5/SBRiQTKjX6Z3TxM9U6Rq 96t/8+H0+1tuuUX7/QYyAMhOCGZnqOUHMW8y2HWje0mBcrwXHbT66tjGMV7EOeXbFLLd8fnAABuI twHba/5ixrdVZjwmAeD22+9IZvIviUuXqjP50teRWY1mBsDeu69J/Z5t27bV/KPSG8xnf9MlYxMx m2W+VNNgPA0gHb6cmeE5myTXMSb7nQKA9BH1pW1mloHHP5RtTOuVt6ecrc98xNg/neofqJ9ex9SH TdhMjDaqNlh/D/HRGLjKL+oQ8sVjgXEcBCAAgZyAWwBIBsfgg2Z9vTJYy8FQH6eyQb0c0NWDRC0B eOne65PfbxFfy5cAPPLxD4pt27eLC0lK3Ln3zot3k897+Uemyd15x52d0v/l8gGWAMQ7pavsXNJ2 7AQbwAawgfHawJgEgFtv/VTqq5w9+27y94XEh7mUCQLJcsadO3fWBAC5BOD5e65L/J9t4pv5EgAz 0K74U3kwGfTJLK6tDJJt59kzPOMFAFMsSP09PUAO+IdpVVUQrs5L/h8b63atv4jwX3Vu6fW09pn9 Zf4e4hM6X3Wlq/+IYiAAAQh0IdBNADAVzkQlNQWAymCeiwq6AHDLo9oeAPffIG75kz8R750/L06d PpN8zorTyedM8rlw/oK4I1HZ2QNgvA4ZzjJ9gw1gA9gANjAPGxiTAPBnf/rJZD3/u8m6/lPizNlz 4t1330s/cuJiezKpYdsDQAoA276h7QGgT7pI38qYbe43FT/JLojcF8B6XYvvl2V6ajP4Af9QCQBt RI10bXyX+suLB+unZ2AYbcsqn+2vUHz8ba/x8Z3fxavnXAhAAAIRBPxLALwDrBz8qmqxuUFgbcPA BgLAyVNSADiTCABnSgHgDgSAeThWXAMHHhvABrABbGDMNjAmAeBPP/Fnyez/OXH8+MnEZzmXigHy IwUAmebfhwAgZ8vbBcs2T7BrAB06P+wfLlYAiKyfjk5mK/h84srvIT7WVI1WmzVG+PkcAgEIQKBG ILgJYPWBkyme6ay+qVDnamrXDIBt25IlAMkbAs7mD9Bz50ol/TN3foYMANZXs74cG8AGsAFsYMVt YFQCQJ4BcPLE6XQZwLvvJksYk49cBtAqAyBxscwUcrkkoD8BQGbfm+Vp/p3mKroyD7yp6RH+YTcB oGP9I+pX2w9BBviagxv6PZS6Hzo/Ll5RWQjN9k6IK5ujIACBzUwg+BrAyiYnlhT/Mv0peT3gNHtt jBwj9VewpGOmtuHK5P43ik0A5fF/9LXkNYBf+U/puTt3TtN1c/Jjvg6Q1wAyIzXmGSnqhn1iA9gA NjAfGzAFgDNnkuWCyef06dPi5MlTyav2jog33/yN+OUv3xQ/+9kvk1fwvSqeeuoH4oUXXhRf+OIX xNGjR9M9gbouK5Tnf/KT2R4Actb/wgW59v/9xH/JPtKnKf2kreKryWsA1SaA8vsd30xeA7inPKYI 8o0U9YnchK7n1/H5/LtqertKda8Gmub55p5PLv+wnj6flR+7/l855V3qr/uocpNF3X+V5fvaFvN7 6JhQ+XGBBwJAHCeOggAETAJBAWAIZGoPAPkOXPku3MP5JoD79u2by1sA1PvKy4eTvo5L/fsG8cUf zcTW5KHk2o2563vPbefL3aDnvftz13Ys2uHNdtB27/Zvq1/6KsF87V4fr9oyGQ7Zj8vaX22ZLGt7 pd3pdQ+1oy0f3b77tuvQva3XOdS+UFn8Pp/AebNwNgUAGXz/h9/5HbE9Sbm/4/bbi8/fPvCA+Mb/ /J/iwIGDgwkAn/707elsfzJvUfmj3gLQx2sA+90DYAjPjjIhAAEIQGBZCKykAGA6QNnr4OoB5Lwd 2nlfT3FY1HX7ckSbCgCqvX0E/jaGQ/Mcuvy++kUvZxnr3AeHWAGgDz5D2LWPQR917oMxZaymcGAT AKbJjvv/7R/+QTz++OPF53vf+5545plnBhUAZHaiDPbNP10FgOpr5mbL4ldSTwhAAAIQGDkBBAA5 S4cA4M10GLuD3VYA6DPTIjbQ64PlMgZey1jnvvvKx6APPn2U0aTN875ek7px7OYXBca0BGAoAWDk /iPVgwAEIACBJSWAABAjANwqMwTKZQIqcLSm8KdiQnVJQcwSgkyE0M7b8gmx54Wn042u9LRembau z1yr87Y+mC1X0NPazTKD9XC0UznTu270t6vWBs/yidgyQ+n7aer/rfUlAC5m9TreIHblnH3ts/b1 C+USEfX7f/zP7n5MU8Lzfjb7whQxYusSqpfi57OhmGAppm+jmWu2HcPktm/Yl+KE7NHWrjbnmO1a u3FW2YCuS1/p7S+WJGl8Mruo3te2dtns2raEyS5UVccO0zZt7beNV7sdS6Z8bSjr469DjI1yzOYP +vU+HtMSAASAJfWAqTYEIACBFSWAABAhAOjryzNnOFsuUFv3bQnulPNrC75r52sBZTVwK5cnKMdb iQClI14eUzrsZXCbfVf+35ae7WqnPDYNcrTAR+fgCuJ8bY8p0x3Ale0ogwuz/R5mFs7B9lnOcc36 +2d6HxWf3lJlWbDIA78mdYkRAFx9FbsEwiZamH0bukYXJrbAMsTIaTseG3aLTZot5Xag2IXqEZsZ Yu3HPPg3BUdTgFD1jhlPbPXx3fe1ftXaH3W9QBtUGb46ENivVmAf29+rsARgRf1Smg0BCEAAAgMT QACIEADsM+4WASB1du1BtnXWzggqXQ61GahlwVd2HVMQ0INxV73NutjWDutBX/Z7dY8EMyjU6xTj wMWUWaunY/Y8m4GtijJeZlbugfb1JACUGR2aiFELquLrEhIAXOvCm/RX6NiYa4TSxU2BSi/Tfl/4 GdltvM051YybunDWra/cwXsmFNnt2L7hZVRAbslaCY1vLqEodL39L4TbEBp7YsYSjllNgYAlAAN7 hxQPAQhAAAKblgACQIQAoM/e+2b0ytmsOBEg6EA7drevBOe2rIPI71zBRyoiWALe4ngtLbk2O9lA AKkEU5Yya0GcQ2DR0+dd+wHUBQ3P2x087YuxhVCwW+t3z1sM9PRp29KToAAQYUOhACpk170wN0UZ rd5BnhG2Y7elLLXeuTTGITi5eLXpK6cA4Foq4qlTaDwx7+ug7QTaH7qec7mLVm6oDiHb5PfVDP5l v7MEYNP6pTQMAhCAAAQGJjBKAUDunKs+l5P36qiPXGfX9Z298r2/9pnluLcAhFJ61ayXvm43uPb+ wWytvz0wtL2isAxcrOnZAwgA1XXAicBhu0Y+4xfT9nImvHztoq1Mva9cyxjqAkCAmXOphbsuoUAl ZBemzemp4+m/nXs+1FmHrlX53VhDbr76MnYjRJ9dm2u8bdcIBfGSj4uJ7dwYezSZNz3HtTlo3Sbj 7MYrqpkCiEsA8AlzgYyidgKA+/WarQUASxaCS1gjwF/dAD/U9zYB4HN3352+8u+JJ54oPk8//bR4 8cUXl/ItAAP7fxQPAQhAAAIrSmD0AoAUApZJALAGeo531Acd6Ij3289DAIi9RqO2NxQp0uClQwZA PXW7nPmNaV/fAkDZluoGd03rEqxXhA2FHG3b75VlFxHXiBEAnEwigmPXbHMxw97G3kIz4BFlhsQa MgDqGzzG2Eobm+WczSUmsARgRb1Wmg0BCEAAAp0JIAD0uATA5mA2mvWLmMGzZy9U05hjgkhfQFyb KbQE3qEN/mwZDZVrtijTFeT5NmaM6RObsGC2L7RWOTbQqwV8N1b3jWhSFzlr2qRefQZArdubZ7t4 +8VkYl0eUF1mE7LHGK61e8vY8K/2e4QNx3Kqi4Hh9fOu+ppLRUJr/EPLWuaxBwAZAJsrOO9zrHGV ZQoAJ0+eEu+8c1y89dbb4siRY+KNjZ+IV155PZn9f1k8//yL4umnnxVPPfUD8cILL4ovfPEL4ujR o0JmBHbNKpTn8xaAzr4oBUAAAhCAwBwJeAWA9akllXq63rl6clb/woUL4ty5c+LEiRPia39UXmc6 nRbp//pSAPnvsS8BsK2F9r2j3pUBoDvc5i7cRaq02vQuYhYyDcYbrB2uCQDGueWa8HKTssZtjyjT 5vjVdiUvUtzdbwGoMQvMKFvbp5Y35Kn6ekq8Hoi7gnL3LHr1jQC1zRXzuuqvf6wGlflbBRz1ktcN 2VDIWY/p29A1bEJFNJMW/RUSymx9HGVvyg6Stwk07yv76wz1+60SrHd+C4DfNkLZIzbbKexett8Q SKzlRb4FAAEAASA0Dpm/L70A8Ogt2Ss+d3xTvHH/JP33ZLZR869MP8x0weTvtvM2ZlmZrnI7O3Jj L2B9mrU/AaZY2Dgtqhmx/ePq3/nVe11Mc47mNZVtjonr/LhwJQgsN4GoDAA5UPV5g1cEgOfvEddc c3eyPu+A2Ldvn9i5c6d4/3K5B4AuAoxdAEgd+dqa6/g1tGWg6lsTLh/o2g7ycxAAykBACTVZm/Q0 8KZtjy3THZRpdblVzqJXOdfeXa4zC+4B4GifFoxnm8eVAZ0Z/BTvhjfeeV/PgqhvQlete70uLuFI OXt6vdT1fDzkMbZXE9rqWq7vr9t16BptmYT3ALD3l2k7Ia6uAKTWrtqrBN33RWwGgOveN8cT36sb fQG9zTZiBID6fVoVrPQ+tb2u0TYuVESOwNs1mgaFHL86QsLSCwCJ73N98hz58H2viff3ZMFqbX5l YyYmk1knL7Nv/61TZeZ5smSnRJVcDOhh/qrSgtlkKrpOiY29f2aTxC5nCUsDXipgyO8Stn3GB/M0 Ea4FgVUm0FoAUOqlnLEvg4K4wVAJAN/YVs8w2LF9u7h06ZK4ePFSLRNgKAEAp3F1nEb62t7X6Wy2 FtjCiXsCG8AGxmwDm0UA2PHNi7kAMBF6AoA1A1MTA2JnkJ0BZh4gF/5bU6FBzbA7sgxk4KiyD8q6 lj5i6HcZWOobylaCzKLuJbPiGqod+TFp3JqWVeUbdPyjrl/1YdsIDK7+8fVvlP/dtX9TbHl2iSzL 1TgEgKApcQAExkigtQAgG5MNQuWAHqtk+jIAtm/bngT/F8WF8xdSEeCylg2AAIBDOmaHdJnrJmea fbPLy9w26s64gQ1sPhsYkwBwxx13pBMW5h/5nZwk2b17t9i7d6/Yv3+/OHjwoDh06FCy/PFRsW3t enHfa4kA8Mb9yWy1ZQIlIgMg5He5fp9NqgFx6s81EQHW1yuz3+lMsTkdroJoVW7y/8oxvt9D5VsC z+qMvExdz9uYBsNxE1RFH4aunxw4jwwAv0Dg9r+79m8xw585+wgAY4zgqBMEOhDoLACYg3lMKpBP AEgzAJLA/8KFi+Ki/CT/vnQpeRXggHsA4BxuPueQPm3Wp7tudC9VgWUzlvCCFzYwvA2MSQD49O23 i0vJK4ubCQAn0n2Q5ISHTTxIyxpKADBnh/NZfH1CJ+hXWsqwCQBen9A3exxRfiUA73smuun1g8Ds B7QVcOR5Tv+7h/61ZaBY+7Jv7i05choEINCMwOgEgJ07dojLScAvg/7skwgAaSbA5cE2AcRZG95Z gzGMsQFsABvABvqygfEJAE0zABYoACRz99Mms/01v1KbXc9/qwWk8vtQcOj8vXn5fczGl82Mu34f 1xxEAOjcv0aHkwHQLLLiaAgsAYHRCQC2twCoZQAsAcB57Mt5pBxsCRvABrCB5bWBMQkAd9x5Z4sl AIsUALT13W0cVTMzQV9vr5fXVgCILV+l4Yeu07SNkdevpNmnyxkaLjNIkzz8m2z7lgD4MnB7fXtA awEgf4NACy5Nu4zjIQCBZgRavwZQ36Ck3GQl7pUzagnAvdfXNwFcxGsAcQKX1wmk7+g7bAAbwAZW zwbGJADIyYnmewD4BQC1SZ6+EV6663r+J301W+1TBqCh32Ux9WvEB7C6Dyg32JtOs9cOZlVUgZ9r k7zQ72qPKXW+Wb7m6LYMvEOusr99+dmVjQLj2cmzQ/3j+z3W/+7Sv4qPvhRAXwIQ2qQyOx8BIGRn /A6BRRGIygDou3KVPQBOnBCHDx8uXgOIALB6jhzOO32ODWAD2AA20MQGNrsA0LfftWnL63v2f9OC omEQgAAESgIIAMk70Js4HRwLL2wAG8AGsAFsYLE2gACw2q6sOQPd5hV8q02Q1kMAAqtMAAFAEwD2 3HqlJaXOvzv6/hceFZ/esia2Pvh0ZyFh/wszsTVJoZNlZf9ezZ3Z5SvpeuGZlLPWkKFuA328Fk/v UxkwtGmbbmPy/KZtGkugUrfvfu6bodpn9l3b/huqfma5Q9qGjUVxr2z5hNjzQjn+KXuV6cD6fbzK Y9q8bGCVroMAsMquK22HAAQgAIEuBBAAZFD2+CfEFrmWznBkU4ff85v8PXWCb5x1Dv7Ta2kCgAo2 bHXazE6eLdBo296mAZG6dh+Bv6pzH0GvbmNN29SW3RDn9dm3Q9QvVObY6z+kbdTGpnys8t0rWX1u ELt0cUB+ZxlnQ+z5fbGz7WPkjwDQxfXznOt8hZxaj99srftAtaRYCEAAAhDoQGDlBQAV4IdmnHfd WBcI+g4I6k52f9kFY3TgbHXqk2nTgKjPa/clANRsIhWkqkHVKvbtIto8hH302Y5sLBvGNux26M/g sN1/fWZM9cmOspZPYEAA6OD5cSoEIAABCKw0gZUWAFwBd7azrky/zwJwOcNvc1z1GS6XkGA6wamQ oO3cW0+RrTrV6eyvNmNWZCRYysh+S+r9YJ7RkB+jz9KV9cyWG6i6yGPMskOiiM1prrYvqcut9ZR1 c6mFql+tbQ1nCqvldry2Fkg17TPbrP9//M/VPlF96uvPIgtEq0tads6lvE61L81+y2yw2tdVgUKx ksfcIL6YZ71sfbC5jcSysqbXq2wbx/1RZNw42qLbY5GGrmXnKA46HyXs7f5RvuQmtdecQ/pdviTH rJtmmy57rqXoF1yry4XMMcLXX9m1yiBfz1rRbaPCIvK6sX2nZ0Ypllbeajx6vNpec0wj+F6+4HsM fYYAsNK+K42HAAQgAIEOBFZaANAdUdOBVY61DAZUgGo6rqnDnAcYNgdYOkl65oB+fBnMlOv8XWuO 1Zpvm8igBzV6MFk45nnwV/y/CGTK65YBTBlYmIFGjMOXBRBacFIEnua1tDbn9SlEAGMZRMx1C84D XLtVn9X2ctD3dSgFnlB/Fu1yLDEpbdTD1+z/vG6l3eZBvlVkamYjTVjFzCibAXtmk27bMW3FvF8L Ozfu2VT8Ulx0DuaSHIttNqlTzBhhtrmol6XOxRgSEMpirtuk70oBILA/iiMjoWlmTuwYwHGrJSQg AHTw/DgVAhCAAARWmsDKCgDmjL4t4FUBQzV4zgJc23rx4OycsSGdGQD6ZkXTjQEta2ptM33mutyK 0GEE3Ppsnj1TIG5zQ1cGRCYKZIGCa419JZOihQAw1LVtm5ZF9VmsABDqz8A6axtPvX7Kxk170AMw Xxk2e3DZSFNWNQGgJQvfPWHLvtl6Y7kGvciYcdima917OeudiSd2vvZUfP8YkWUc+fqrmhUSv1Fo 32NTTBCv843JSCCAX60Avmt/IwCstO9K4yEAAQhAoAOBFRYASufZH5iaM45VAcCXwu9zkvU0XzOg sJVZmaV0rPONWYZgnXW2pAjH7o1QpJI7ArhKsOnYwb4atJZp17EOoisI7PvaTfusfJuDIwNAzTq7 +jMghlgFI118cKV+a9+HRKeifxvaSIiVK7g2N42r2ld9xtlnp/WlGGp5TH4PazYbYpkG3rZNOi1v mYitUxnM5wJZRH8pHkW6fuQGpLF1L+uULRlxjU22PVFqSx7yN6TUBI2IDQRj732OW13RAAGgg+fH qRCAAAQgsNIEVlcA0NJTXQ57bf29toO1d9Y5d8pNJ7m6VjgJQgyH3xeElCny+b4Evj0AjDW3lUA4 MpBrKgC4lgzUg/DqHgjmfgg2BiEnf8hrd+mzkACQBZWh/nRvtBYKWp2CkCNDoWmwb5bfhJW97h4W xj4Gut2Yr5vT7UXeg8X+FmmqfCn8VZfw1IWnYAZAhzqpJRj6GBHTX1VBpNlrFPX2dhmbFNPQfVkR K/S9GBAAenlrTCz/zXocAsBK+640HgIQgAAEOhDwCgDrU0uwNl3vcLns1Pfff19cuHBBnDt3Tpw4 cUJ87Y/K60yn0/R32+euu+4Sbx15vdPnc59/IHW+9B2zXamq5rrYUMBQT88NrPduIQCYzlwlxd6x 6dY8BIAus/CV9OA2SwA6ZB/4rh2TLREMwgPBtrc/lygDoCmrGKGnYtuO7JFQcKNEvF3a6zqzAHaW ptvHZN+4jolJg7fVr7xXqqKDUwCojRP520HkcoYGu/43uW5oqYs5NlrbqcQtI0vBlXEV6kt+X93Z flvfb3YBYGM2KTZvncw2nH6X9NN8v3d22BwFzCZ1/9Csh96GtR58x6HaQrkQgAAEVo1AVAaAHMT7 fMBUBIDn7xHXXHO3OHDggNi3b5+Y7twp3r88BwFAmwn0pUFXd6ivb9jnSm9N1xrXNqWzvBPbk2Zb ChVxs8DKaTfrFJpljAneQs63N4vC2APA93aBmMCwlmrsSJ3WN2iLKTdmXbprkzb/2nj7EgB70KTt PB+5B4BrGUqTPQAqZbTIErEJQC5WZmaEy7bqKfzNZrx1oW9rngkgv8tEgSuzN2bk2TIhISfL1jAC 9hZiVaUcc4xwpcwb4keZmWTfMyDIs4exKbQfiW/sappdFBp7+H01hYHNLgAoZ7Rv/6svJ3c2mQp9 OqhWz/WpWJvMistJoQINoC/6lAMBCECgG4HWAoBSduWMfZmOW30guKqmBIBvbKsryDu2bxeXLl1K PpdrWQC9ZgAYznYlSC7WZvsDN9csmG19rmtGTX/LgDUI0Zx/24yjbXa/slO64y0AbQK+kKPd5i0A ssyYjQJD167txh7xBgLXtc03JpgzwNU+y9PWi1fzlWnsriUAhahkmdW2bVyn0sVrwoclALWLGPWZ bvMtAG3sobJ3gyObxWXfMWvSTRZmH5v95xNUbPdE5fWaMSwtgkybOpX1Lt8iouoefguAIUIENk+0 Z5hUr9tmbHJlTVUyaiJfPxi6t/l9NQP8UL+PSQC48447xKWLF8WF8xeST/L3hcuJDyPE5WQyQ/eR tu7aLw4ePCgOHTqUZj/KLMiLyXnSJ3L9cQkAvgyBKP9sYyYm2lJCPVhv41aagsD6dCIqiQvyeigA bdByDgQgAIHeCbQWAGRNsodMGfTHKtW+DIDt2xIB4OIlcfHCxVQEkA9QtRygTwEgnaGqbAKmrz+W M/X6/+2vu/KnvddnK2vvqU9mH23Brx6MWfch0B/alteibb21+s75rsFdyBHTf6++SzzhaAlQzPem m2nMRRmB15uZ9arxTd/pXu0737VtAkyoz/QZXSWEbX2wPouv+sBsm75ZXnZ+tb7eXe4jgtbSzkux rZKtYCujRQZAMbNe2GbWDpd9u8UuXRSs33ch25Fp/aZgUhOmbG/CiGRps802dTID/UrgbOwtoPeX uXbf9Yo/WZ6Nheu6ITuvCTbGciNbPWL3VmkyvnAsYoCygTEJALd96lNC1ufUydPi1Kmz4vTpd5Pg /r0kuL8kduzYKXbv3i327t0r9u/vTwAIZQiE/LPZpBqgp8drM/aNvE1LcG8VANqW36gyHAwBCEAA AiECnQWAiqCbpHzFLBXwCQAqA0A+OFMhQP4thYBEIe9bAChm4iIDzdosc8sU4FgnNiZtPWbGLfZ6 QxwXkyo8xHU3S5lNbWCztJt2dA/00gA88i0BTXnHZwAYgtbAY2bTdnB8dztbFENdADh9+nQyo35S vPXW2+Lo0WPi8OEj4rXXNpKA+1XxwgsviWef/ZHYu++H4okn/y35/4viC1/8QnLcUSH3BOq6r5A8 /5Of/KQ4d/acePutd8Q775xM6iKFgHNJJsBFsX37joUJAE7/zJz9L4TbuCxO07GU4oE5uY8AEHK/ +R0CEIDA4giMTgDYuWNHMuufzfxnnySVLhEB5N9DCAC+dFwzLde24VYqCgzlZGtvHYhxsha5tta6 jwC7ffey2/eQNhZjVxyznEGSFN/M/UD66ssYYcq5L0Sk4NpXXSlnOe031G9KADhz5kwR/B85cjRJ rz8ifv3rQ0nw/0oa7D/73I/E008/I/7t+/vE957414EFgOO5AHAmzQJYdAaAe4JmXUx7nI030/+l S1tb888SgMV5+lwZAhCAgEFgdAKAeguAXk+5PE5mDQwlAChHo5q+rqciVzfvq8y650sFfBvbhRwZ 2++Zg21feuAqbwgBQJVpvnat+v/8PeaW16J14dLk2m0YL8s5Kr26C8tlaSv17C9Y23Vjs/GjKfti +YoR0OuvtqwvNxm2Tk3bwPH92du8WUoB4Otf/7p48803xcmTJ5PA+3iSAfCWOHbsrTQL4Kc/PSg2 Nn4i3tg4IF5//Q3x6quvi5dfflX88IfPiocffrjXDIBP3XprsQTg9Olz4uzZ95L/X0izF3funGYZ APfLJWlbxVd72gMgZgmAL0Ozt7cHuAL75PuptgmAfRPARIhIsw/aZR7g0UMAAhCAQDsCrV8DqG9A kz5k5I6veRpZaBmAWgJw7/X1TQBtAoBs2jwEgHk7MFxveZ1P+o6+wwawAWxgcTYgBYCnn346FQFk Sn/sRwb/3/3ud3sVAO64/fZ0Mz+5AeDFizKDMXPIpN9SbgKYTCQ02AMgC4zNTxko+36P9c/qr/Jr Hojb0v+VO1op37oBIAJAO9edsyAAAQh0IxCVAdDtEvWzK3sAJDvhHj58uHwNYPJWAduOuAgAi3O0 cHJhjw1gA9gANjAmG5ACwK9+9at0R/23335bHD9+vPKRu+zL7+Tf+kdmC5w6dapXAUBmJ7r8FikA dNkEsG//i/IgAAEIQAACCADJbtljcmqoC/2BDWAD2AA2gA34bQABAAcWAhCAAAQg0I4AAgACAAII NoANYAPYADawVDaAANDO6eMsCEAAAhCAAAIATt9SOX3MijEzig1gA9gANoAAgAMLAQhAAAIQaEdg pQUA967/+cY7Pb2uKuaVWU0dWrkDdx+7wmc7ebffmbtNPdqc05RPX8cvU137aPOi29vVHvtgQBkE l9jA+G0AAaCd08dZEIAABCAAAQSAnoJ8n8PYtwDQZ3ldAq429WhzzqKc8WWqax+MxtDeLvbYBwPK GH/gRx/RR9IGEABwYCEAAQhAAALtCCAAIAC0zgBoEzC2OWdRDv8y1bUPRmNoLwIAwV0ftkwZm9+O EADaOX2cBQEIQAACEEAACAgA+x//hNiSvIvXTLc3AxVzOYF+vB5Y2YIs23eu8lR9ivcDa/Xfc+uV lfcGb7n10dr6/uoxSer/rf4lALXr5Sx89ei77mUfzMRW7b3Isn1mPcx+8jEpuVfLlWW42ufi4Qs4 wrah+kEuPUneFf3C02m/xfSned2+2xuqR8awff2b2iOB3eYP7Ohj+jjGBhAAcGAhAAEIQAAC7Qgg AIQEgBceFZ/ekgRmN84qwXQa1OXnpv/Wfs+CmnJdfVMBoEl5ylGqXTMXLnQRIAtEywAzEzHkd/Y9 AGzihzonDZLT4K8qjgxR9zLoLutZBo5mwGz+X+sHg4mqv95+naPZvhAPm9May0PZUpP+tAf//bW3 DP4jyjTuoyHsMSYo4BiCR2xgNWwAAaCd08dZEIAABCAAAQQAbUa5mFXPv1PBcxbMaIFzHvimM9Bq BvTxbNZWfsxAsYkA0LS89HpafXTnNwvWs3q7MhkyUcAhAGjn25zqWoDckEXTuutihmqP7TtdnDCz ICpMLNx0TrX2BXiYjJr0ZaUdEf1pv9aa6LW9EfWw2d5Q9khgtxqBHf1MP8fYAAIADiwEIAABCECg HQGvALA+zXfD14Pk6Xq7K2lnvf/+++LChQvi3Llz4sSJE+Jrf1ReZzqdCvm7+Ud+d9ddd4m3jrze 6fO5zz9QBOr6LL7P4bAHgr6gOWuPSkdvIgDUArtilt5eXhpEO3byrwSzjuDVt+a6nCEvxY+KwGDJ AKgLEG4W0XW3LMOwzsZrx0UxsdTf11chHiGntcy4cPdlLBO7ndRt0ido6AKMLaOjNcOB7DHEl98J HLGB1bGBVREApB82mW04/S7TT2vqojnLX59mGYJJgRuzSfpvXz06O4YNC1B1CtUrxK/hZRsdrvrG y01xTv3sqejuYTeqIgdDAAIrSiAqA0AOtH0O/BUB4Pl7xDXX3C0OHDgg9u3bJ3bu3Dk6AUA6lXoq tykcVNcxJ8GyEbA2FQCalFcGjBaxJhduZHBnZjEU2QqB1wDuV0sgNBHIJmyo8oaoeyjYt2Ve6MG2 mdmhxJnQfgz23/MlIRYetuCjKY/Y/vQJRX21tzVDTbRy1aWtPRLgrU6AR1/T1z4bWBUBwOubbszE ZDJzHjKbdAgoZdkq6M+D1KbiQsiv7lS/vPC+/dNQnWN/TwUKCSxh5/KfMxGjQx/FVobjIAABCBgE WgsASn2VM/alkx83kCkB4Bvb6kHrju3bxaVLl5LP5UpVF5kBUAZlcia8uu49JjhtIgA0Lc83Y1yf ja/P5DfddV1fMhC1Rt4jhkTXvccMgFAGQ6ivTIfUu4Qiot5WkSEgytic4ph+bCp4tC4zov76Uoy6 ndqzawgICQixAWxA2cAyCwB7774m9Zu2bdvm9J9CM9zWDE0lBuTBuynA6gF8qPxk2j8VANJzUgFg IjyJCHXnujKzbWQPRNQv1lt3CQC+9kX5r2YdPUKLt64uASAg3sS2n+MgAAEItCHQWgCQFzPVy1gl 1pcBsH1bIgBcvCQuXriYigBqNcDCBQAV+N9YrquvCgPaHgD5DKhvCYBz7bolVV/feM9M27b93xog Ot5mYG7WFnKwvSnyc6x7SCixBbu1WfOGSwCsXD3LIGxBbqgvY/szpi0xx8QseTDfrNBERHHZk2tP iqb2GLJXfidgxAY2pw0sswBw6NAh8fw91yVB9TbxzYsX06xHXyDrzMDsIQPA7beti6kK+tNgOG6C p3BE19cr6eyzSS4maJ7qPDIA/AJB2SbzuNmkKnik/m4bEcAlAMjvkwk0KbJkQk1DgaWNx885EIAA BHICnQWASkqYJ9VJJ+4TAFQGwMVEBJBCgBQBLl9+P31ALmoPAOVAFq9z03b8d234Jwd0FeRXg6w8 hTzfNV1PsddfP2eKB7bydBHBFjiZs9O1ndlDbwGwzOTqs8LmZm9NWDSpeyjYV/1jHhdiEjsjXvRj gEct4HZkP7hsQz8/VHdbUBM6p2l75TXalBlznu2Y0FspCOQ2ZyBHv9KvbWxgMwgA275xTlzMBQBX qrh3YmVQAaCjn2yZ5TeXECxaAHD6r44MhVbp+g6/uLavAhkBHQ2O0yEAgSYERicA7NyxIwn4s6A/ +yT/zkWAQQQAz1sAzN3xzdlb5bTU3mWevBHAmyqfzxqr9LytD1aXFYTKk9ctxAjt9WvmO+D1Nxc4 63qrzGhwp1zX14FXjzXrMUTd2woAZZCpLzWpv81Bn+E2g2SzfSEepiMb4uHLVIjpT//1jNc+BjIe 2thV1/rX+Bj2aHsFZ5tggXMIMrGBzWUDNgHg1KlT4uzZs87P6dOnxcmTJ4U87ujRo0JuCtx1Y2F5 vvRNXJsXy2WSu3fvFnv37hX79+8XBw8eFCoDYPMKAFr2QO6RyoB3aQSAJHdh2ma23+Z9+wQA4xpm 1kETZ55jIQABCDQhMDoBwPYWALkMYIgMABzCzeUQ0p/0JzaADWADq2EDNgFABv9XXXWV+MAHPpB+ rrjiCvGRj3xESL/ivvvuS4WB1RIAtLTydE1+PY0/dulmE8cy3T9AD271/QQqSwDC9QtdN1R/3xIA XwZrb28P8GTGVpdF2EQHKaTwdoCQDfA7BCDQnEDr1wDqG6yUm8RkM62hNwaoJQD3Xl/fBHCerwHE UVwNR5F+pp+xAWwAG9hcNuDLALjpppvEzTffLO68807x8MMPiyeffDIN/seSAaA2AZT+0o5vJnsA 7Ck3U1b+Uxb4mZ8ygJfBY+13c4rd84q5UPnN3cnqGbqPKNe3T6fZqwTNoLvpJtLqKqH6+36P9V/r jOP3QfBu0ligUgF+1pf1tywgAHS1Q86HAATsBKIyAPqGV9kD4MQJcfjw4eI1gAgAm8tJw+mmP7EB bAAbwAb6tgHXHgBqGcBtt90mHnnkEfHcc88Vwf+JxN8YSwaArMu5c9oeAH07WpQHAQhAAAIQcBBA AEjW0/ftmFAeTLEBbAAbwAawgeFsQAkAcgLhnXfeETKgVh850y9n/B977LFK8I8AgC8MAQhAAAIQ EAIBAAEAAQQbwAawAWwAG1gqG1ACwLFjx8Tx48fTmX39c+bMmXSGXf6tfy8zBKRAsOhNAMkAwAWH AAQgAIFFEUAAwOlbKqePGbXhZtRgC1tsABtYFhtQAoCc/Vdp/TK4D31k8D9vAaC6Vv9j4uFDh9Js BZYALMr15boQgAAEVpsAAgACAAIANoANYAPYADawVDagBAAZSNuCfhnkq9l+FfTrf5MBsNrOL62H AAQgsMoEEABw+pbK6VuW2SnqyUwqNoANYAPD2YASAFxBvkz9lwG//Nv2QQBYZdeXtkMAAhBYbQII ALkAsOvG7DUsW259tPeAeP/jnxBb1m4Qu1542lt2dtya2Pqg/7g2TuWeW6/UXhl0pfj04/1fo029 luWc/Q/e4OyXIfutDz6+uvdRfqgMef21NWwuxInfhwsWYbv52CoBwBXgh75HAFht55fWQwACEFhl AggAiQCQBXBXiq03yiA5HKg3cSb3vzATW9N3+YbLHSqQzIL/8voEZM2cYdWHLmFmqH5rYmeuY0N1 7+MaoTKwt2b2FuLJ7/DEBv5dKAFABvJtP5/7/APirSOvd/7cddddQr7e2Pwjv5OvNt69e7fYu3ev 2L9/vzh48KA4xB4Aq+x303YIQAACCyewkgKAfOjzgQE2gA1gA9gANrDaNoAAsHA/lApAAAIQgMCc CaycANDHw54yus+YwBCG2AA2gA1gA5vBBsgAmLPnyuUgAAEIQKATAa8AsD7N1sVXPtP1TheUJ8u0 uAsXLqSvwJE7+H7tj8pryHQ5VyqdfMhuBmeBNuD0YgPYADaADWADm8MGQgLAFTu+tJAlAMqHm8w2 rH7bxmxS+neTWe2Yig/Yg+/X2XmkAAhAAAIQ6IVAVAaAfEi4HiBtalERAJ6/R1xzzd3iwIEDYt++ fWLnzp0IAD2sScSx3ByOJf1IP2ID2AA2MG4bCAkAi9gDIA3uZdC+PrX6b2lwrwX9pp9XnJ87efJ4 NIA2Hi/nQAACEBgfgdYCgFKO5Yx9mSEwFTH5AUoA+Ma2eobBju3bxaVLl5LP5QoteQ4ZAON2gnBS 6R9sABvABrCBVbOBkABgZgDsvfua1G/atm2b23/amImJnoFpmaGPcikdAoB5rk0A0AP+vieCourO QRCAAAQgMAiB1gKArE0mApRBf+wDwpcBsH1bIgBcvCQunL8gLiZ/X778fpoRgACAU7lqTiXtxeax AWwAGxi/DTQVAORbAJ6/57rEf9omvnnxYurfmP7TbDIReuZ+6m+1EQG8AsC6mCqRoVa29ps8ps21 B3FbKRQCEIAABLoS6CwAVFLCIpVmnwCgMgBk8H/xwsVUBJDZAJfJAGD/A5ZGYAPYADaADWADI7MB nwCwc7pT2PYAkALAtm+cS3ycTACopOqbs/9FJkBclmXFMYz0y2SKf2WpZ3JeNQNgWhEkujqfnA8B CEAAAosjMDoBYOeOHcmsfxLwJzP/2Sf5txQAkn+zBGD8MyHMVtFH2AA2gA1gA6tkA14BINnXqLEA kCymnPY14x4pACQKROWa69NqBoJrL4HFua9cGQIQgAAE2hIYnQBgewuAFMdZAoBDuUoOJW3F3rEB bAAbWA4b6F8AkAkBxox8Wy/PIQDMJtVN/dJNAbUpf7nkIJzhqZYJtMhMaNsezoMABCAAgc4EWr8G UH99TPqQSB4yajPA0BsD1BKAe6+vbwLIawCXw+HBMaWfsAFsABvABrCB19PsRNfri6sbJW8VXz14 UKhNAKXPtOObyRKAPXb/SQbp1Vcxxwfa1tc4V7IKQmv8jd+1/Z5KzxMBoLMXTgEQgAAEFkAgKgOg 73pV9gA4cUIcPny4eA0gAgAOJQ4lNoANYAPYADawLDYQEgAW8RrAvv02yoMABCAAgc1DAAFgZJsJ LYvDQz1xzrEBbAAbwAawgXAGAALA5nGaaQkEIACBzUBgVALA008/LcgAwKHEocQGsAFsABvABpbF BsgA2AzuMG2AAAQgsDoERiEAHDlyJF0CoAsAcpmAvqaOTQBxBpfFGaSe2Co2gA1gA6tjAyEBYM+e PWLfvn1i//794mCyB4Bc9ngiWf547pz2GsDV8TtpKQQgAAEILJjAwgWAkydPCp8AoIsAjz/+eLrZ Dh8YYAPYADaADWAD2MAYbED6JrY/0n+RWY3r6+upAPDyyy+Ln/3sZwgAC3Z8uTwEIACBVSewMAHg 4sWLqfqtBICf/OQn4plnnimWAKgMAHNnXf17/p1lSSzic/nyZbEKn0uXLolV+ch7chk+Fy5cEHzm x6CtTfjuG9/YsYjxjGsu5jmyWbi7HEldAJAZjq+++qr4+c9/jgCw6p437YcABCCwYAILFQDeffdd cerUKXHs2LFUFX/++edTAeCpp56yBrUxrGwORcx5y3TMmJymzSYChIL9tsHQvM/rEiCfP39etPm8 9957YoiPHCd8HykkbobP2bNnxaI/Po5mH/j6Wrcf3Rb1+0C/18xxxDXGLdM4TV0hIH0Z6dN85zvf ET/84Q/Fa6+9Jn7xi1+Io0ePppMf8p6S94TtFYLQgwAEIAABCAxFYKECgHQST58+Ld566y3xy1/+ Urz00kvikUcesWYBmA5hDJA258SUO6ZjxiIGrJIQMO9gvuv15iUGDBH8m2W6hAAEgP7FAxdTmxCg vtP7q6kIIAWBGCFgTOMvdYGAj4AM/nft2iWeeOKJdILjjTfeEL/61a/SSQ/p+8h7BAEAG4IABCAA gXkTWJgAIJ09+fA7c+aMeOedd8Svf/3rVB3/1re+lQoA//qv/+pNbW8CajNnBSAADL8UwcwK6BqQ L+L8eYkAKugbSgzYzAKADLgXnQGgX79pNoBNBJB20FQICIkATcZ+joXAoghIH0b6Mt///vfFD37w A/Hiiy8KudTxN7/5TerzSN9H3hvy+UIGwKJ6ietCAAIQWE0CCxMApJOn9gGQu+HKjQDlMgApAjz0 0EPiox/9qFcEWM3uqrcaAQABIEZQmLcAMJQQsNkFADPoXqQg0EQAkP1izv7bRCBlF8oeuywJ4BkA gbESkMG/9GG+9KUvpUsan3322WL9v/R19DcASF8IAWCsPUm9IAABCGxOAl4BYH26JtbWjM90vTMJ +bBTAoB0HP/pT6rXuOnvXklFAKmex2QDjCUIHqwel5MNmnInYbBr9LyZYN9LArJZ+OGD/bb1Du0d wO+X5rrBYBfBg3PjNxiMEZ/MY9puDqgCpWUZAzdPPZMAdcmeP4ti/+STTxZ+y1/+/v9R+E9bPvXN 9FXHb775ZrrkUe59pNb/79m5Jib3v9HZr2pTQMXHc/p262Iq/cAefL82deQcCEAAAhDon0BUBsDG bCIms43erq4ezqkI8Np94vrr/0uaDnf8+PE0E0A+JF955ZU0A+Af//EfiweqEgQW8ffOHTvEjuKz U+zcuXM+9dqxU2zftk1s+5Ntyd/bxfbtsh7y+pk4Mp5PwiNhIjlVWWXcutR3x/btYptkINuf9kFy nVG1vUE/JP22M6m/bEPajrQv80/6f4Of7O+k/duTz46078uP/C7jkn0km852qdUvrVutflkd9Xp0 7d8x2LCtraqN6p4bQz03ZR2kPW3flthUYuN92PCyjg2+evf8vEnHnGIcUs+U6jNNHpONvX+SPH/+ JH0Opf2TnLcp7bAnu5Fr/v/t3/5NSDFg79694p/+9Epx1R3/km7+J/0b6efIzB49/X8RGQDSr9OD eikG2GL82ST5fjYTEwSA3nxgCoIABCCwaAKtBYD04ZGowtIRKLMEkvfdNmiRNcPgus+Lt99+O10n 98RnP1SU/cGPP5I+UOVuut/+9rfT9+ru2bMn/XxpxxXiih1fErt3765+vrRDXKFnMFyxo36MeY7+ //z83/3zpNw//92kLleIHV/KryF/+90/jyjvz8XvpnX4XfHnlms5654eK8+tniePT+uT/L7jiixz QrZdfp/1Q/V4dYztt6x8lX2RtG2HbGNWXlT5KZMye6PoA8X9iqSuqk7FsXYOtb7LWe24Ijvexqls c1lnWzld7KPCL2nPFYYN+fuv7KOUk3Z+Vm7JPOOY21ckP90WZLtdjKz3RsFXy77puX6uPpXf//nv Gnaj25LG2G+/2X0Q6gPnuBCyT9/97xs3LL/Jdqj7tkn79bqb/W22K+Z3vy1Y7k3JoOm4GclGt4Fi HMmvpe5txUwdq+pf/P67+hhUr3/lGknZO5I+T8dw89mQj2N6H8WMLy77jK2f7x5RdbQ+fwL3b4hf Nr4nLP5cf0ZqzzfjHp17/+g2lIwNvue7j4+Xr+v5Ja8dYR962coXkX6J9FHkpn9y3b9M/f/mp7ak AoB8/Z/0a6R/IzcA3P9fri+en2YGQJR/tZEE5bp/M5k18L6EkNfQY3rbRI/00dLJH3ktBIBGfDkY AhCAwJgJtBYAZKOyh1QZ9LfJFHj/jfvFZHJ/uhGOTL2V605lepwSAaRq/t3PXC2uun1dvPDCC+kD Vb5OR75Td9++fenn6x//oPjgx7+equ365+Mf/KD4+NfL7+Rxax/8eO0487zy//eLG9byMr7+cfHB tRvE/eoa999gvWa9LFmGDHa0cyv1lL/H/2a2aW9Sj9Q5U+1K/n/D/VmbP/7BtWod5bFa++Xv6ti9 afuM42U9PeXvvf/+kkd+PbO89P9pGVkbZR8Uxxj9ZesHWUflfNr6WJ7j6n9VXlv7MM+7/waNc1F3 d/+Fzs/apvW9bmN5f/j5KdvKGVlte5H1q96PZv/W+kXayQ33F/dnyH5D/eu+r5N6RfN13P8Rtlsd R+r3eKj91fon/egdu1y/azbiO1/y0Nhbx8BGbfb3fVG+vK6jXrWxwhhz0/Fcu3+s95vWpuz46jPh 4x90jb3V54b9+eIfX0P189pnytrz/LGMe+b4FOKXjT8aD/MZJ+uw4P5JGTmftX4+Qb6+51du6zH2 Ia+jfBHpl0j/RO74Lzf9e/XVV1P/5UP/z1NF8C/9G+nnSH9H+j1v3J9kWFqWAIT8q9lkIvTEzPT4 RiJAntqvRATj3EqGAALAmP146gYBCECgMYHOAkBFFF6fNl4qoAQAtSeATIvTRQCZMvf9//f/Sh6g T6Y76MrX6MiNAuWD9eWXXxb79+8Xu2/bIrbctjv9d/HZfZvYYu5fkP5/q9ilH9f237u21q/Zsixr /dOydomtRhtq7XTVQ7Z/664qk6TMXVu3iNt2J2XL37fcVvndWg9fOy2Mt+7K+0AvPyljLa+LvEZx TASv27Zk/eVm5P+tm32Y/O22E99/1fNv27JWY1HwieGXcNVZ7r5ta9a3BteF1S/Yv5JvyeS2Lblt yvNC9quV7bMNk0VlfFD238E+neXXxiKb7XjabxnLnPdNfh+G7qtdW9ecY5bvvrTZaVS7g/1vH4dU 2bU6GWOR//eErTG+2eqsxhdfe5zPF9/4mo9ZlT7p8ZmR1dc/PoX4eccf1XeW58Q8+ye9Vu/ctGeU 8Xw176EY+1A8pD8i/RLpn0g/RforctZf+i/XfO7pdFJDBf/q9X/S7/EJAE7/ypz9L9rRIAsz8deq GQDTiqBgy9DscyloY2+VEyAAAQhAoDcCCxcA0tSyJANA3xhQiQAyTU6ul3vmL65NH6AyfU6+Q1dm BcgHq3xrwMGDB9OlAh/67BPpv8vPV8XHPvRZ4zv9947/fuKz4kO9lZ/Ude1j4quV+sv6ub7X6v7V j1nanvwu6/exr9ba/9WPfUh89on8d7P+trJc5ad1y8vK6y374WNfzeum80nKWMvrUjmm1t56n3z2 QzYu1ePs/V8e05t9yHZY+zyin2RbjfM/+6EqP2XLKcMIfkVfKo7evgpz7Lt+1fvRfr8V9qDZSHpe yH412wn1v7UeEXxj6h91jLyW9f7Oxq60v832a/fUmnGf6dfMlknV7cheL/eY6LvPPvuhtfK+jrhn o5iocjzjaG2sMOzb/3vc+N96fImwz1D9G3GK4W6ML6Hre8efkfRPysg5rnV5hgeeX3n7Y+xD9aP0 R6RfIv0T6adIf0VOYEj/5dq/ei5N+5eTG3rwL/2eVACw7LFkpuiLygRLMnvfaLa/7jOuT6sZBNXy jePJAOjN6aYgCEAAAmMgMCoBQIkAajmA3CVXbpbzwuevE9fd83z67txjx46Jo0ePisOHDxefvXdf I665e684dOhQ5fPwH65ZvzePa/v/evkPiz9MlPg/fFivR/bd2tofioeN+unXtbdBnus/79DDf+hs 493XXCPu3mvU5Zq7C0bm73dfY+HlKn/v3eIaraxD8v962/XfkzLW/vDh9LqynVU+1T4z++LuawLt z8u09b8qq6191Php7TDrabtG6HzJe01nmLSlaG8EvxpLjy0son5x91Vi4wmDuq1KFn77DfVv9rvj /ovgG1d/v/2WdXDZsbv9cnwx7UOvU+h3aV/6vZYen9+HlbZJFrbvC35mP2htlveEHN+c5wf4mOOI NkZWbDYfX/T7PGT/MeNzxcbSttT7yTV+hOwzVL+u9hUaX0L8auNPytho/4L7J2XkGddaMww9v3I7 jLEPWQfdH5H+ifRTpL8iX/cn/Zfr//rFdNd/lfavv9GinQAgkng9X5/f0pP0CwyxAoBaRtAg86Bl fTkNAhCAAAT6I9D6NYBqk5p0I0C581+iTqu12rFpYnJ32fprBvekrwiUnx3WFP4/EY+dPJk+WG+x /n6LeDT5Tf4uP/dcZ16j+rs6ru3fZvm3PFpeOyvz0byeoevK43x1v07c87xetiq32r7K9Z+/R1xn MPL9ft11mdBSrbe7/OfvuU7rv+vELbdk/7/5/9Oue8uj4sSjt6Tfy7LVOXVOJjez78z2h/u/q300 s51Q/0mOZv+WzNR9kPLX+83Lz7QBn40ton71PrXdZ6lNyHZq923674D9xvSv9f6L5htX/5ixQ9qS y+at7be0XdpIUUbo98rYk9/D191TZ5wcJ6/vvB/ldRznyXY/eossu35vxjKpjf+6HVTamFzjnvo4 UjDJxxg1zrjG/1o7tfPM+zNoXx771MfG9JqO+sVwch0THJ88/FS/qTE764dqP9bLT46ZY/9kdTQ+ HltsytL1/KrYiMc+XNc7mfgncrZ/m9U/2Sn25K9UTF+tV/tkgXSsf1X3oZoE4sYeANp+TrqLqS8F qPt2CAD9ueOUBAEIQGB+BKIyAOZVHfPdvVIEkNkA6j3SMnVOfqSSLlPpNtvnpXuvF9ff+9LC2rXo 6y97fzbld+/114t7X5qfHY+9fsve/976f2ObWNv2jYXd223ZfmPbmtj2DbeN3nv92kLHrLbt4rz5 jTurxlr6J8pXUb6L9GP0WX/l68zLt+I6EIAABCAAAZ3AqAQAVTGbEGCKAerByt8XC4GkDYv7PqzN Qnz4vk5ltbn+qp4zdu5jr98y2s19H/6wuO+1bvfrXNv92n3iw74xQf6+tkN88+IStYm6MsbPyQZU 0E/gj9MNAQhAAAJjIzBKAcAlBJjCAP9/P908kQ8MsAFsABvABrCBcdvA2BxA6gMBCEAAAqtJYNQC wGp2Ca2GwJITcL6iSmWbNFmnuuQsqD4EIAABCEAAAhCAAARGRAABYESdQVUgAAEIQAACEIAABCAA AQhAAAJDEUAAGIos5UIAAhCAAAQgAAEIQAACEIAABEZEAAFgRJ1BVSAAAQhAAAIQgAAEIAABCEAA AkMRQAAYiizlQgACEIAABCAAAQhAAAIQgAAERkQAAWBEnUFVIAABCEAAAhCAAAQgAAEIQAACQxFA ABiKbLDcdTFdm4jZRvBADhglgbH339jrN8pOpVIQgAAEIAABCEAAAhDY1AS8AsD6VL22S/t7ut47 EPM65iXk7xNLpLwxm4i1taxutt87V3R9mpWfVEhdy7xOpe6TWXpcLCJ5rK/es0md/yDt7AxqmAJs 7Vf9oV9xUfYR6r9hqFRLrdifYXhjqN+QDMZw/zdp32zSXPDT7wHbvR/6XWe0loxP+p/g+CJf52ic 06S9sceGxv/YcoY6zjW+DHW9otyI50/bOmTM4+xxkf3jte8B+bTlqp8XOz4tyr6C93/SmMr4EevY 9AGPMiAAAQhAYFACURkAgwYSPTiZg9Uvf5956njnzob+DEwdI+0L9bCMf07KWVrfO9Hrv7cJIga1 oIELn00MPrJP4gGntRvMPkSo/4aFk9qbxkLaYxXNYus3bOvL0gfr38D936h9cvxoaLfVwED2ZbV/ Y37Xg36Tk3lv2TjWbapRq8MH9zD+hy/iPqI2vnQprO9z+7Q/S92iniUL7J+QfScDu5go8d/yfO6j O/qwj8HGp44NDN7/kqkmAA4+FnRsD6dDAAIQgEA8gdYCQBHsTvNZ8nQm3hfM1itlzTDQHjixCrrz AZs7CCpLwJwBC2LKz0/99tTB0GZMogPRzHF3sfE7B/UATs8wUAq+FChKVtU+qKr8Zv+ouuWzQbOs L9VMY7B8NQNjZmEo7smMZ9H24thmNqKcFBunedhHhV/SHnNGNOTcuc7Pvk/sKWee2WhuX5H8zGwT F6PoLBOtfX3Uz3d/6fe+LrClHLQxwG+/2RVCfVCrRyRfFWBY7//g4FE9QLajJh7q941+Lzlm3UPZ RTG/+23Bcm8OGAD6xn9TTFXHqvrHPn/MDK0i6DWfDXlf6H0UM7647DO2fl4z8j1/8hNd40uInzw9 ZbGeBdGV8Scve6H9YwFTs+8IPl6+rudXNqhoXMpMvIYannd88tlXlP109W8MOKYgsD41MkSifZ6G gyOHQwACEIDA3Am0FgCU460Hto0dcfWgDaSZhsp1/W7OcKQP1UYprdo66vRhqznIifMQl47vFwAS ZcGTBRCRAaCcGNWu5P/KSZHOYaWOhqJfCUr02RTdDD3li/X1pPblH1t5ZfCUsQsFKeYdEEpxjgkA 29qHeV7qENfsx91/ofOztmk2pdtYzbm18dMFHFvdJJ1F1s8/ntX6xZglD9mvKj00PlhrEc1XF2Wa iVflde19EGp/td5JGd6xy/W7ZiO+853O/cB7SXgEhtpYYYy5WZBU9on1fqtlaFWDmpgZXvf44R9f Q/ULP+09z598LNXHd3N8CvErRD61D435jAs8n0Plh9pvz6BzLUuw2befT5Cv7/mVnxxjH6HrhMYn 1+8hft39G63mlvvfKgA08p9CZPgdAhCAAAQWRaCzAFBRxKODYuPBM4QA4FDwm2YpODumTVsdhbkd BCPAs+114KqHw6EvHuoWx9taD187LYwLe9DL1wK75gJAGfi6BJdWDlaUfZj87QFgfP/VszPMGaWC Tww/TezJfPWpdVPJhdUvOKpVA+OKQxuy38oQ4t9LwykAaKKZSs9vap/BJqogypod5Wm/XrAuVtgu GPo9P8e31tjXbjN7IarNsQd1FADcz5+QYJJVMCbAs94/EfYZCpBjEbmP849Poevb+rV2zoL7J217 pH035ul7fuWFxdhH6Lqtnk9ps439hPRncdTzK1Sz8nfb/Y8AEM+PIyEAAQgsG4HNKwDImc8h1epe U2Nds7QRa7h7FADkModakO0UAOozgxUnIiaAjbhbYhywdg5WC/swMijK6kf0kzy4loFRn+1qIgDU HDRvX0XMXvdcv4juLZ1cc418RIClyg/1v7UePdlnTBuzAMYtHhVZMpb84mwW0L1ZW+j3av3cNu+7 zxAALAJThH2GAvAo22lykHH/hq5v2wNgbAJAM/tuBKv2Fh5bEBzz/AldNTQ++TIAugpcobqp323t rK35ZwlALE6OgwAEIDB6AptYAJDx1kBvB8i7tV5+NiNT34gtvD+C3QmICCw9M/R1B68aAJi/11Ku 86DVOvNuCiDmLE1PAVaMA9bWwQrZR42fGaRqt7etDqHz0xTc2s7seaAYwS/k4OujzyLqFzf6ZTZp C0ZC9hsnADiW4ETwjat/zFGBZT6O9tuXnGiyk3VJSvm7dd8B2yJmr2MfWAKglgi1WRwtqxqYYS7G nnx80ceikP3HjM8VG0vbUhdqXONLyD5D9YuxHN8xofGlUm8Lv9r4YxOqFtg/IfvvxC/0/MoLj7GP UD3aPp9C9hN6foXqVfzuuv+T76fa25fsmwA6xtfoi3MgBCAAAQgsgkDr1wBmyny2OU65zrvZK/ms r3nTHMlsAznzUzpood8l0Po1ImZCG/SEWX7dD459QNaDhGrZ5ixgfXlA0RdlZFTbyKhSPyONcCI3 gSse+OHydRuQs5TTZNMgWYedX9Y2UJIXzIMEfbPCmHjB336RbzA4nH00s51Q/9VFIOlcKmaV11nq /eLlF7dEITOHRdQv7kZK7cgVmBr3v35YzP2ftdu210JuNx3sM6512VG+WXRr+x0pvpUlNpbx0S4+ 5m1ts7lgINOpyevkTF6h8b+6EVu5YWZ101P/8yc4Plc2gqs+G4L25Ukh7+P5GLKv4PhUqV+Vn+q3 6vhj7o8QeA2wp/zY9jv7J2T/ITgRv7ueX+ase+mDNPMdQvbj+70tvzZLHG2ZDwpfpX+sD+1Y/yai QzgEAhCAAATmRiAqA2ButVnxC4VmCobGs+jrD92+octvyi/qNVw9Vnrs9euxqeMrypM9Mr7KljUK vfrLmjU05gZRNwhAAAIQgAAEILDiBBAAVtwAqq+Rmq04jfk1f+zcx16/+fVUf1eat+DTueahfU48 ext0vjYFQAACEIAABCAAAQgMQgABYBCsFAoBCEAAAhCAAAQgAAEIQAACEBgXAQSAcfUHtYHA8hNw vqJKrSlutpZ2+YHQAghAAAIQgAAEIAABCIyDAALAOPqBWkAAAhCAAAQgAAEIQAACEIAABAYlgAAw KF4KhwAEIAABCEAAAhCAAAQgAAEIjIMAAsA4+oFaQAACEIAABCAAAQhAAAIQgAAEBiWAADAoXgqH AAQgAAEIQAACEIAABCAAAQiMgwACwDj6gVpAAAIQgAAEIAABCEAAAhCAAAQGJYAAMCheX+HrYro2 EbONhVWAC3ciMPb+G3v9OsHnZAhAAAIQgAAEIAABCECgBQGvALA+Va/t0v6erre4jP8U8zrmJeTv E0ukvDGbiLW1rG623ztXdH2alZ9USF3LvE6l7pNZelwsInmsr96zSZ3/IO3sDGqYAmztV/2hX3FR 9hHqv2GoVEut2J9heGOo35AMxnD/N2nfbNJc8NPvAdu9H/pdZ7SWjE/6n+D4Il/naJzTpL2xx4bG /9hyhjrONb4Mdb2i3Ijnz+B1GPACwft35O0P1j9ntzD7qb2O1fX6VSkWZ34OfyAAAQhAYDUIRGUA DBpI9OBkDla//AGaOt65M6I/I1PHVftCOQTxz1H54PW9E73+e5sgYplNeTYx+Mg+iQecNn0w+xCh /huWfGpvGgtpj1U0i63fsK0vSx+sfwP3f6P2yfGjod1WA4fMSTfHn1IUsP+uB/0mJ/PesnGs21Sj VocP7mH8D1/EfURtfOlSWN/n9ml/fdetx/Kc9+8c2t9H/w82/nRlHPmslELgdNb8udq1epwPAQhA AAKLI9BaACiC3Wk+S57OxPuC2XojrRkG2oxTrMIeciBUloA5AxbEnjsgqdOdCgDaDF7kwzU5MVPX HWz8zkM9gNMzDNQMngwCSlbVPqjO8pn9o+om65e0bZb1pQoqguWrGRozC0PNPCQznkXbi2Ob2Yhy 0Gyc5mEfFX5Je8wZ0ZDz5zo/+75kntlobl+R/MxsExej6CwTrX191M93f+n3vi6wpRy0McBvv9kV Qn1Qq0ck36RgMVFBt3n/BweP6gGpk61NsMW2Xy8llF0U87vfFiz35oABum/8N8VUdayqf+zzx8zQ KgTU2uxolm2l91HM+OKyz9j6ec0owv6GHF8Wfv9GtN/Lz/V8ygaN9N4ufIP83w01Ou/447OfKPsw 69g0GyfCRylExohjGw55HA4BCEAAAiMm0FoAUI63Htg2dsTVgzjwYAuV6/rdnC1PH7qNHqLaOur0 Yaw5yIlzEZeO7xcAMoHAl5pnBvRGGrFyclS7kv8rJ0Y6h5U6ymON4KpwePTZFt1gPeWL9fWk9uWf SpBTc96ydoSCFPNeCaU4xwSAbe3DPC8NJmr24+6/0PlZ27T+1W0sip8u4NjqJukssn7+ka/WL8Ys ech+Vemh8cFai2i+uijTTLwqr2vvg1D7q/VOyvCOXa7fNRvxne8MAAbeS8IjMNTGCmPMzYKosk+s 91stQ6s6fsbMALvHD//4Gqpf2C/wPH/ysVQf383xqfv4suj719/+ID/f8yk/Oab/Q9cJjT+u30P2 0dl/CSwBSK+v7g8EgFA38zsEIACBTUWgswBQUcyjg2KNYcQMU6sHrEPhb5ql4OztNm11FOZunxHg 2fY6cNXD8UBfn2oBjREQWOvha6eFcUVQ0EQJ5Wg0FwBK4cAluAxnHyZ/ewAY33/17Axzxqngo98X WmBc4aeJPZkQMrVuKrmw+gWHympgXHF4Q/ZbGUL8e2k4BYAe7DPYxKxjquJhcZKn/XrBulhhu2Do 9/wc31pk331pZi9EtTn2oI4CgPv5ExJMsgrGBIDW+yfCPkMCRiwi93H+8cnWb43Gl2AFF3j/Buum 7rvqLL853sb0f+hSrZ4/afWM/YL0Z+0Q/oshsNoycOImNUJE+B0CEIAABMZOYPMKAHLms9Fsf8Ou ihAu4kt0zdJGrOHuUQCQyxxqDoBTAKjPDFYcmpgANgJQjIPWzgFrYR9GBkVZ/Yh+kgfXMjDqm8I1 cdALMUdVxNtXEbPXPdcvontLJ9hcIx8RYKnyQ/1vrUdP9hnTRrcAoAUBjj0CsllC9+aBod+r9XPb vO8+QwCwCEwR9jm8AGBY3yrdv8EbL/B8ys+Peb6ELhUaf3wZAF0FrFDdYu//dIxqs/6hWQU4GgIQ gAAERkJgEwsAMt4a6O0AeefVy89mZOobsYX3R7A7CRGBpWeGvr5hYDUAMH+vpVznQat1VsAUQMxZ yJ4CrBgHra0DFrKPGj9HkCYx2eoQOj9N0a3tzJ4H6hH8mgQYi6hf3BiX2aRtc8uQ/cYJAI4lOBF8 4+ofc1RgmY+j/fYlJ5rsZF2SUv5u3XfA5uR7nf/AEgC1RKht8BDIACjGnnx80ceikP3HjM8VG0vb UhfKXONLyD5D9YuxHN8xQ48vcfUb+v6Nq0XtqNDzqRAANHHN0f+hGrR9/oTsI/R8CtXLfJ6nYqHr PvUuAQr7L6G68DsEIAABCIyLQOvXAGYzT9rGSdqGO7FpZNbXvGkPqGwDOfNTOmih3yXq+jUiZkIb 9JFZfv356ghAateoBwnVss1ZwPryAHMTK9tGR5X6GWmGE7kJXPG6xXD5ug3IWcppsrxA1mHnl7UN luQFc9vQNyuMiRf87Rf5BoPD2Ucz2wn1X92Jkg68YlZ5naXeL15+cUsUMlNbRP3ibiSnY+pbYpIU HXP/WzfhjOYbV/+Yo3yz6Nb2O1KAzT07/JuYGfbhyIjypf+nY4gnkypLI27+ekP72Gy8iqzCoLpJ aezzJzg+a88tM/gP2pfHPmPrF2M7rmNC41P38SWudsPev3F1sB3lej6Zs+7lPdTMNwjZh+/3WPsI 9XGITnWTyOprQNW5tQ1JK4XG+i+hmvA7BCAAAQiMiUBUBsCYKryZ6xKaSRi67Yu+/tDtG7r8pvzm /UrHsddv6P5ZaPme7JGF1itw8dBrAK1ZQ2Nu0ArVbd7jywqhpakQgAAEIACBpSaAALDU3de98jEz BN2vQgkmgbFzH3v9ltGili4gC+1z4tzccBl7Z3PVmft3c/UnrYEABCAAAQj0SQABoE+alAUBCEAA AhCAAAQgAAEIQAACEBgpAQSAkXYM1YLA0hJwvsJK7dfQbK3t0nKg4hBYRgLcv8vYa9QZAhCAAAQg EE0AASAaFQdCAAIQgAAEIAABCEAAAhCAAASWlwACwPL2HTWHAAQgAAEIQAACEIAABCAAAQhEE0AA iEbFgRCAAAQgAAEIQAACEIAABCAAgeUlgACwvH1HzSEAAQhAAAIQgAAEIAABCEAAAtEEEACiUXEg BCAAAQhAAAIQgAAEIAABCEBgeQkgACys79bFdG0iZhsLqwAX7kRg7P039vp1gs/JEIAABCAAAQhA AAIQgEALAl4BYH2qXtul/T1db3EZ/ynmdcxLyN8nlkh5YzYRa2tZ3Wy/d67o+jQrP6mQupZ5nUrd J7P0uFhE8lhfvWeTOv9B2tkZ1DAF2Nqv+kO/4qLsI9R/w1CpllqxP8PwxlC/eTAY6hqx44vL/oaq V3S5EeNXdFnJgbNJc8FSZ7iWjI/6n+D4VnsdneP1kaqd6bNAO0aeb1yzSXtjjw09v2LLGeq4hdln z/Y3FJ+xlrvw8Wfk/bdwPhGGo8YGl9+mj4HmMcHxMeL6HAIBCEDARSAqA2DQQKIHJ22w+uUOaDow 5w9DPcZKB3ftC/VAihUAkkKTLADfO9Hrv7dxwpfZ/GcTg4/sk3jAadMHs49g/w1LPrU3jYW0xyqa kH0NW7/NUvpw9jMwocD41ejqcvxreN+l46MWgJsczXu7xjniXs/GXPcYWr8nGrU6fHAPz6/wRdxH 1MbHLoX1fW6f9td33ZaovIWNP3Povz7sd2F8AjZUPJ+TsdMmAFSFOfmsrj6/g+PjEtkwVYUABMZH oLUAUAS703yW3Jx9iWirNcPAcBhjZvidDwBzBqnpbFB+fur3pgKANgMW4ZxmCLKB3eWk+h9e9QBO zzBQCrF8uJRqeNUZrqrIpqOs6ibrl7RtlvWlelgFy6/MvGlZGIp7MmNYtL041id41I1GPQRtnGJn ALrYR4Vf0h5zRjHkfLjOz74vmWd2nttXJD8z28TFKDrLRGtfH/XzDQGx44fPfl32uSeSnxL1QmOM q49j7M9rP23Hp9j2+caviPFZP0S2oyZ+6tlX+ljgGGfD90pDsS8m+I45piELdbjv+WWKweZMYKz9 mxlmhQBcy47IssX0Pmpsn5qQEls/L7oI+xtyfGT8kVk7Whah+fwKjT8R/eftf5d/IE+KsN/Y27LN +Bxl3yE+sRV0CADm6ebz3Py9D7EktsocBwEIbH4CrQWAbAyvzr6EHDwrzggHLVSu63dztjytbyMR QFtHnT4MNAc1clAPCQD+LICIDAD1kFXtSv6vnED58K8Ef/JYrf0Vp15X+/WO8pQv1tcTeaP8Yyuv FE8ydqGHXP2hVzowrkB2KPswyzVnNEuBxy5qhM7PnDMjZVn9v+Z82fjpAk51trXk6M4CGL5+/gE0 NH6E7Dct3WWfMfx89qtVva19hflWU+objU8x7UvFR11Uaia+hWyoxsWaJaDZqG/stQmqtSDBqL8c gxMBepILERWBtqj8wHtheJ5ftbHOeGaE7N+eYVa1mZigwP189D8fQvULu0d++wvfH13HR8Yf/Zlp Pr/C/lHH8SNifI2x35CddRmf9eevWU6YT6hm+e9RvmLCuun4GHl5DoMABCBgI9BZAKhkhUYNdEY1 hhIAHAqzL120kYm0aavjAu4HmBHg2fY6cNXDkaGwPtUCAuOBY62Hr50WxoU96P2qBQbNBYAy8O1V AIiyD5N/XKBfDZz0fRzq2RlmVnXBJ4afJvZkgtzUuqlkvH31XL/ADeUNkEL2q8r22b8miqn09co1 ffbbgwBQin/KBkyxx7LHindJUKVSZTZKh/sraswzxc9KcF22KbQ8ybcWPWpcMAQGNYtXjAuOZ4mZ vRDV5tiDOgoA7udnICDI6xcTQFnv/4j7KyRgxCJyH+cfX2391mh8ZPzJM/AGGH9iOj9ifI2x39Cl uggAzvsvyj8I1SxSANDFXI+P2HAFVmTlOAwCEFhVAptXAJCzX41m+xuaQIRwEV+ia5Y2Yg13jwKA nE2tBdlOAaA+s1YLrkIBWASgGAehnQPQwj6MDIpqoB8xu1rLwKhvqtbEwS3EnFAwHLtXQc/1C3Xv YgWAgP32IgAYBCp8W9ifXlyMQBTqgNjfnQKAltFjnf03L+Buc8x9ngoq5hKx2saC9XsKAcCy2ewo BADf/WHfdLLJ+Bgyb8af6qacIV7Nfo8bX+Pue/+V2z3/LdmIFV+n4/isV9kziZKJmOHNVfvg1Kz/ OBoCENjsBDaxACCzgwd6O0Ah7Jrl1zdyCS8ByAqzP8Q6CABJmfUZuepDzfy9lnItKxYzw5o1IE3F HSoDwHcjtnUAQvZR4+cJcmx1CJ2fLgGoBTC5kBAR4DWZoVtE/UKDZ6j+IftNy4+xT9sMuSngeWZh 2tpXqP9D9pfxc+whEmEfIf7xv/vGoWxMsc3+W/cNsE1jOYJRczxKnWXj/Oo1bE57YAmAWkLSdnot kAFQyU4wMrhC9l+3j/rzpcI9bUtdiHTZb+j+CtUv3n7sR4buj67jY6h+ofaF+KzG+BOi6Pg9cnyN sd9QDdqOz6H+jxufQ7VzP6PsSwot5TnGR+/zIaJaHAIBCKw2gdavAVTpl8XGQ8qRavBKPutr3jRH LNtAzvyUDk7od9m19WtEzNQ2sAmz/Lof6XDga9eoO9nVsk2VuL48wNwEyrbRTqV+RprbRG4SVLxu MVy+bgNSxZ4mywtkHXZ+ORMDilf25bahb1YY42/72y+M9MZ6mmNX+2hmO6H+q28EKZ0fxayyEZ3e LxKUk1/cEoXSUfBtEDlE/dw3UtT44U0h9dhnJD+X/SrbDNlP6PcY+wkfYxk/ItvXYBgLHuqbRbcF 5lXnNL83PZsDusaD6iZmthnLqh3UyglkamWb7IVn4GyAQs+v6vhb3WQ1yv4tz69a+7Tnrhn8h+zT 93yIrV/QcDwHhGy/+/jI+FP1n+q+T6gPuvRvaHxVAkpZx2a+Wci+fb/H2ncXPt5Nrh1LDOz6qO/V 0rH+ZZee5FwIQGAzEojKANiMDR9jm0JK9tB1XvT1h27f0OU35RdaM913fcdev77bS3k9EohK8e/x ej0VFXoNoDXrqadrU0w3AvMeH7vVlrMhAAEIQAACy0MAAWB5+mqQmoZn2Aa57MoXOnbuY6/fyhvQ AgAsXUAW2qfFs7fBAvBySY0A4w/mAAEIQAACEBiOAALAcGwpGQIQgAAEIAABCEAAAhCAAAQgMBoC CACj6QoqAoFNQsD5CiXL66g2SZNpBgQgMBICjD8j6QiqAQEIQAACYyWAADDWnqFeEIAABCAAAQhA AAIQgAAEIACBHgkgAPQIk6IgAAEIQAACEIAABCAAAQhAAAJjJYAAMNaeoV4QgAAEIAABCEAAAhCA AAQgAIEeCSAA9AiToiAAAQhAAAIQgAAEIAABCEAAAmMlgAAw1p6hXhCAAAQgAAEIQAACEIAABCAA gR4JIAD0CLNZUetiujYRs41mZ3H0WAiMvf/GXr+u/bjZ29eVD+dDAAIQgAAEIAABCECgTsArAKxP 1Wu7tL+n671zNK9jXkL+PrFEyhuziVhby+pm+71zRdenWflJhdS1zOtU6j6ZpcfFIpLH+uo9m9T5 D9LOzqCGKcDWftUf+hUXZR+h/huGSrXUiv0ZhjeG+snatr2/Q/zG0r5QPfndTiB2/Hbd33DNCIyW T8Tzs0kfzibNBXP9GRLyIdaS57f5R41drueut3z5OkJLmU3aHHNsaHyNKWPIYxZmnz3b35CMKLtO gOdDP1Yx2P038vtrGexHr2Pt+VN7ne1U9B19R2UADOpo9/CQHKx+eQekD//c2PUYK33wal+ozowV AJJCkywAX6fWf2/jBPUzjCymlNnE4CP7JB5wWunB7CPYf8MyS+1NYyHtsYomZF/D1i+HP6ATPIL2 zQHhZr/EcPfn4snVxq85V2mh1w88PxuhkM/fhuN+1fGVY0V1fEyf31qAbtphMb4m17YJAKHylTjT sNqNsCQPtwHH13BVFmpfoer1aX+ha/H7YAR4PgyGVnS6f+dwf3WqX45trPYTev6kY/ugDw8hWgsA RbA7zWfJ05n4ZgqFNcPAeCDHzPA7O9hUUJqq8fn5aR+kAoA2AxHdOZnj4WLjN856gKNnGKjZB+mc lEpStQ+qs+hm/6i6yfolbZtlfamcnWD5SgE0szAU92TGpmh7cWwzG1EDgI1TrMLXxT4q/JL2mDM6 ocHFdX72fck8s/PcviL5mdkmLka+rBEzg8UUmFz2E3P/d7+//fYZI+4MyT/0WPbdP6ZYaM40xvD1 XT/2fN/44Kr/nkj7VKJpaAx33UMx97f3/uxp/Ff1t80QO69fU++zbC71PC/OMwS89Fr5cyo0/nr5 BK5fiHP52K1fN2TXqWOS1jMwvvuen8GLVA+QLGriu/7c0Z9Fjue8OV6aVXCO5Q4BwHZ+zV8bMED3 ja99jS/O50OEfTW+fzX/MXb88ppRhP3xfPD77zwfPBYW8XzZ1M+HiPvLe3+64ofMscueMcanTTzc xr+IGn8i+r/JY65Wz+gYs8lVqse2FgCU860HtqFgyFrNiAdkqFzX72Ywk3ZqIxFAW2ecdrYWvEY6 Bdksv08c8c1iRmQAqJtItSv5v+5kVoI/eazW/opTpat5ekd5yhfr65WUFFt5pXiSsQs5YaaNhFI4 Y4LAtvZhnmcqdlld3f0XOj9rm2ZTuo3VBlcbPz1Ars5mlRzd9bNnsJQil6yfz36yQbKsv5Vzh/s7 yj4Xyj9i4PXcP7V7wRhTovh6/RN//4T6NzPv3EE0x5cY+/SND1q9247v4furmjLedPwPPT9C15dN dM5gOB7u61Mjzd03/uYMffx8Myih9jULrlzjky5qNhN/Q2NYrd3eLIFkHLQ++7Ux1OUbRD3rfeU3 X7oQMbJkh3jG167jS+j54LXviPs7NP50Hf+yZ7Pb/kL3b/fnc0Qv8nwoIJkin/qB54M2Eo7q+eC/ v4LWH+EfzCMDwC8QuP3bTs/PAo7n+VMTQdo+P9090VkAqCgyUQ9KozIdAgTvAOFQkJpmKTjRtWmr ozD3AGcEeLa9Dlz1CDmYFu7WevjaaWFc2INevuaYNRcASsfSNZPd6gERZR8mf/sNGN9/9ewMU9Es +MTw08SezBecWjeVtNfP5bAWUYU1/UgPUEIOZshBDd6/hkPuG6jttuHvP5vD0Yh/8AmXBdA+u/WN n1F8PXXwnh8aH1S5vvFFEwVUenblmr7xISJACDuAnv6Nur+98KwzENXnR3h88Afg2XiQBhopS8s9 GfGcaSUA9MEn1P8x90fMMab4XnGeyjHVuTxOF6s813OulQ31QaB8V2AT0/TgMR0FAPf4E3g+5BWL cdCdwrBlOq/x8yUIyHcAzwdn//N88FtW1Pi5ws+HmPsywj+IGV9Cl2oVH6T+tLGfm/4ciOr/UM2q vwf3amixDC5Ug80rANicqRCNJr9HCBfxxblmaSPWOPcoAFiDFafzo6l/Rcyo3TAxAWwEoJgBoN0N HufgVKpoZFCUv0X0kzy4loFRnxlqEoDaZgvdgbBl+YcvGybCAYgKUCPuE6eDaNbPa4sR6mjP/CPM d4UFgMD40IsAYPRApX9b3N/Vm90xY+yNIGsZZr7xK7t/s3qm/163rPkLBZ+5o+LepM51X3Tk09P4 HnUPOQUAzUlzOEfZLHLsDLyDSUDEC5WPAGDZ7Liv50uUAUUexPOhFKsj+iel2log5vkg8W3a50Pw lovr/xj/P3SpdvFBQAAYJL4MPZNDv4dI1H/fxALA8Lsj1xWbTPGrb8QW3h/BOUsb2lfB45zUZ0Sq BmRb711zJGMGeGlX5ixITw5izADQ9gYPKW41fh4FzlaH0PnlzF95YxbtjeAXFYBr4oztDRbV76r2 G7KfqOu3FQDSh2PVca+ljAaCyKH5Rw23geCh4J/fP3p/RPH1VCJ0fqh/Ozl4Zr97Zknb3r+h/g3d 36H+C50fun7m4Gk2nKb7VpeRTad5hoi0E7mm3tzmt7MA4L5+qH0ZHzWLZdmMdV4ZAN7NVrNnmm32 375kSx9rLfsK2BaZOvogVH7JzyNApDZR3Uw4ZJeV3wMZAF3Glxj/xmvfnmdP7d5QtmbsAdU5w9QD M3T/dn0+R/Ujz4cME8+HfLndsj0foqy8flCkfxAzvoRq0Na/CPlPcc9Pd+2s+9poA57p76Zidt1B CCwx99Np/RrATFnXNjZSD7IGr+SrbjCSb/igNTBbO29+yhsk9Hv2kHGfHzKcmN/N8uv+g8OBqhVe n0Wulm06EfXlAfomU/qgqjOs1M9IY5EOaBmAhMvXbUDOgkyTWSx5rZ1f1jbwkBfMbUPfrDBmMw9/ +0Vu+MPZRzPbCfVfXQSSg5tiVtkoTe8XL79willpZvYsBa/9elK0Yu7/rve3uRFM1T7NG2gR/H0j RPj+qbavuglnDF/f1aPO96bgeeofaZ+u8UHd+6HxO/R7zP0Zc4yPo+/8qLK152Jt+VnKUY3r5oxI 2H5CfLKYqskmXzah2vL8iuz/mOdn7DG+WXSrY+RI0bSL8/kzxMg48m5iGlV+Hth4Mq2ya8RmKFRp hcbXPsaXoH/jsa+gfXZ8vsTajuu40P3b/fnM8yENWBz+H8+HwPi8FM+H9ndhqP9jnl++q4fGH9/v Uf5T5/jSeMZbnhOVMcq7h01EBqwFVlQGQPsu5swmBEJKVZOy2hy76Ou3qfOYzmnKb96vdGxavzGx lXUJ1T/0u9meefMfG0/qA4GlITDA+sd5tL3+atZ6EO97S8s86sg17AR4PmAZEIDAZiaAALCZezei bWGFKaIQDmlMAO5xyIbiNFS5ca3iKAhAoCmBpQvIQsufPHsbNGXD8f0S4PnQL09KgwAExkcAAWB8 fUKNIAABCEAAAhCAAAQgAAEIQAACvRNAAOgdKQVCAAKbmoDzFTBqP4p267GimS36+tEVHemB8Btp x1AtCGwCAoseXxZ9/WXvQvgtew9S/0gCCACRoDgMAhCAAAQgAAEIQAACEIAABCCwzAQQAJa596g7 BCAAAQhAAAIQgAAEIAABCEAgkgACQCQoDoMABCAAAQhAAAIQgAAEIAABCCwzAQSAZe496g4BCEAA AhCAAAQgAAEIQAACEIgkgAAQCYrDIAABCEAAAhCAAAQgAAEIQAACy0wAAWCZe4+6QwACEIAABCAA AQhAAAIQgAAEIgkgAESC4jAIQAACEIAABCAAAQhAAAIQgMAyE0AAWObeo+4QgAAEIAABCEAAAhCA AAQgAIFIAggAkaA4DAIQgAAEIAABCEAAAhCAAAQgsMwEEACWufeoOwQgAAEIQAACEIAABCAAAQhA IJIAAkAkKA6DAAQgAAEIQAACEIAABCAAAQgsMwEEgGXuPeoOAQhAAAIQgAAEIAABCEAAAhCIJIAA EAmKwyAAAQhAAAIQgAAEIAABCEAAAstMAAFgmXuPukMAAhCAAAQgAAEIQAACEIAABCIJIABEguIw CEAAAhCAAAQgAAEIQAACEIDAMhNAAFjm3qPuEIAABCAAAQhAAAIQgAAEIACBSAIIAJGgOAwCEIAA BCAAAQhAAAIQgAAEILDMBBAAlrn3qDsEIAABCEAAAhCAAAQgAAEIQCCSAAJAJCgOgwAEIAABCEAA AhCAAAQgAAEILDMBBIBl7j3qDgEIQAACEIAABCAAAQhAAAIQiCSAABAJisMgAAEIQAACEIAABCAA AQhAAALLTAABYJl7j7pDAAIQgAAEIAABCEAAAhCAAAQiCSAARILiMAhAAAIQgAAEIAABCEAAAhCA wDITQABY5t6j7hCAAAQgAAEIQAACEIAABCAAgUgCTgFgYzYRa2trls9EzDaEUL9P1yOv1OGw9Wm1 HrHX1NswkZU2/oR+71Dlyqmy/rbr91X+fMpZF1NpD5NZ9OXmxTeuQu76x/VP8/bH1YujIAABCEAA AhCAAAQgAAEIzIeAVwBQQasMkFTQPZtkAoD8IwO82GC8dXM2ZmLSIOi0XUfW0xeAh35vXfdNd2IS BLfoi/HwbVf/shu7nt/cIGaTqZiDxta8YpwBAQhAAAIQgAAEIAABCCwdgaglALoAoLcwFQBmSYBe ZAoYwYoM3vUsgobBoznzn2YkGGXMJnp2gD1YCgWgvt9d5RffawpIUd+8jr4Z8CKDYjrVsizM+uez zinDRHiZZccGswkU90SsSWft15Jy19V1qtfw8jP6b7peBsBmBohquyvTwlrnCPuo1C9pTyMxyFN/ JWCpLJeY+untD93pcf0rhJO/ySa/j5oIbqps2bbSFrP+D/VfbP1FUlphYyEo/A4BCEAAAhCAAAQg AAEILJRAZwEgDUy1jAA9kNKzBYqAq6EIkEQqzqBPBjiVwE0GuZby2woA3vJlvSzR2Pq05KF61nX9 LMgqA3LzOHn94hJ5QBgM/suLpuJLen4a/JeBX5nN4eMnAzu9LfUU+FoGSHKdJgJAyD5MHqnIEG0/ 4fr7+yf+fNcdHNO/IfvtnAGghB/FLfm/6v9Q/4Xqn7UbAWChIzgXhwAEIAABCEAAAhCAQAMCnQWA SgysB4COGUw94I2qp0sA6CEA9waAEeWr4CydaU0DLHuKuE8A8PIzgt2QkFHhqXOTQWB+oSLoC7XP FswbfREKIIN8rXtM6BkKegZEnskQZTSZ6FETIxy2ZOXa4HyfAODt3wgBqQ8BwCUahfov9HtsV3Ac BCAAAQhAAAIQgAAEIDAOAsMJAI5guHGzRywAZLP9WdCf/nvdnhXQlwBgDWrd0WeZOTFGAaCNfTgy PKwIGgTwCAA5QYMZAkDj0YoTIAABCEAAAhCAAAQgMGoCAwoAchI2Yr16CI93CYCZbt9sBl5d2hWg mynqtRl+mU6drOFPZ1hl8CTX3Ft2bGslACSVM69fW5LgYxfKALCUX22fnH2v7hdgpuBX2uVZouBq f8g+avw1ISNkNllqur/+/v6PP9+twRj2YATYQfsy+0hbyhFuvz2o188L9V+cAMASgOi+4EAIQAAC EIAABCAAAQgsmIBXANA3scs2S6uuV1cbqJXrzLMN+ar7AJivEozf1by6QVpejh5hW5YZ6D9nm5O5 rx/6Pd1/wDi/EuCnvysRwlwzLvLN0ezX19k6+RnXlwJD1B4A+nmy8HwduL4ZXHrNUPuKjQMV+0Tw 0DdirJxf36QwyDcNcN394/st6r4J1D9Yv1D7PZVo07/SVmsCUqUO8fdOuTbf8wpNT/9F1T9tPwJA lC1yEAQgAAEIQAACEIAABEZAICoDYAT1pAppvB4pAEALAhCAAAQgAAEIQAACEIAABCBgEEAAGLlJ VF+DJzca5A8EIAABCEAAAhCAAAQgAAEIQKA5AQSA5sw4AwIQgAAEIAABCEAAAhCAAAQgsHQEEACW rsuoMAQgAAEIQAACEIAABCAAAQhAoDkBBIDmzDgDAhCAAAQgAAEIQAACEIAABCCwdAQQAJauy6gw BCAAAQhAAAIQgAAEIAABCECgOQEEgObMOAMCEIAABCAAAQhAAAIQgAAEILB0BBAAlq7LqDAEIAAB CEAAAhCAAAQgAAEIQKA5AQSA5sw4AwIQgAAEIAABCEAAAhCAAAQgsHQEEACWrsuoMAQgAAEIQAAC EIAABCAAAQhAoDkBrwAwm6yJtTXLZ7re6Err0zUxmW3UztmYTYrybb83uohYF1NZ18ms2WmOo2Wd ZdtVvfS69nWNrhWN5efi3/X6Xc6v8NRtrKf+0+s2xvZ3YRdzbnovpJ+JSG+99Wl5L/d0/8bUg2Mg AAEIQAACEIAABCAAgfEQCGYAzCbTJLTW/mzMxKRhABFqrgwGuwsA8iqJCNBjALk+Teqlt1+2vcfy Q1xif++PX+wV+zmuVu8WfGv22U/VoksZ/PqSiQrmm9heEvDb7qnZJBcEolvoP3Dw9vdUT4qBAAQg AAEIQAACEIAABISIFgCsQaY+q6jNliuwsTPUzgBWD35ss/vG79N1mwCQZwasGUJGRO9LAWA2S2ZO leBhCVCrWRLVa6jfZCBWspiKPareSTCWzdQm5xUstTICfHXObTMsKvVP6lMROEL80wq051v2u1u4 cdbPrFseJOvalNP++uAfcf2k08vgvWV2isxeaKW3aUKdZKjsQw/YXfapBD/v/Tun9kfcphwCAQhA AAIQgAAEIAABCEQSiBAAyiUAtSAzCbj17AAZUNiCldAMtet3c7YyDUiKWVAZeOqzma4lAO0D1FQA SNKn1d9pQKfNwuqBVRYLJ2KBOUurgnj1ffL/lFEeQKX/To/JAn/ZxoLhwHxN7umyh0r7qrPFVf7K wtrzDS2rCNVP1iBmBtpqXz3x913fb7+Rd6hjJj94dmGrUlxJbCrtV4vQ4rJP7QK++3fw9gcbygEQ gAAEIAABCEAAAhCAQCyBCAGgDExrAoBlFrA3AcAxw6gCZRk0W+vTJE06QKkW+OsCgGMpRHFOER/b U7ErYoIMwnJwFQFgSL6ZYpFnICiRR8s+CPGPtTDPceEMAE/98nI7CQCaKNOWv/P6PfHLZunbpO3n wX5+n2RihF0ACC2/aSUA9NT+HsyMIiAAAQhAAAIQgAAEIACBnEC0AFAnZs7AG7PX2gntMgAC6/nn KQAkbUkD85mWATC4ADA0X8s9UMlg6Hc/BdsdF7KL2jmWDIvhBIA4/u7rd+dX8MmFpzRDo8F6AFm3 ab6EIC1rmohR5vkRGQatBICe9+NgxIYABCAAAQhAAAIQgAAEuhNoLwCY6+H1lGqjXqFAz/W7f/d2 GaBV19ybKexZNdqnqFdn8+tLDOobqjWYYdX52TIA5sC3Vn+tHik5x9sbqt3bnq+t36tr1I2Zb6N+ sh6VNmhLKfQ6OpcA+DIAIvn7rh/Hz30Tp0sktMyQpm+fkHUrNrHM78/6Mh5HhkqkgDdk+7sPb5QA AQhAAAIQgAAEIAABCOgEGrwGsJ6GXFnDnaQpT5M18/LVY2qSsXwVmf4qwTJoD/2eBXjmawjdm+St JTOc9VcBtgtQ1WsA9VcB1tb4e1P0zfT1rB36+v/0NW3yi3wdtr5ZoPx6aL5etrmVhI9px7faNrt9 hK+dqhTaqyqrgpDLvopNGDvyz/Ql9/WD9hsxFtUYNMgAMDMGqntWeOwzr1fM/Tl0+yMQcQgEIAAB CEAAAhCAAAQgEEkgmAEQWQ6HQQACEIAABCAAAQhAAAIQgAAEIDBiAggAI+4cqgYBCEAAAhCAAAQg AAEIQAACEOiLAAJAXyQpBwIQgAAEIAABCEAAAhCAAAQgMGICCAAj7hyqBgEIQAACEIAABCAAAQhA AAIQ6IsAAkBfJCkHAhCAAAQgAAEIQAACEIAABCAwYgIIACPuHKoGAQhAAAIQgAAEIAABCEAAAhDo iwACQF8kKQcCEIAABCAAAQhAAAIQgAAEIDBiAggAI+4cqgYBCEAAAhCAAAQgAAEIQAACEOiLAAJA XyQpBwIQgAAEIAABCEAAAhCAAAQgMGICCAAj7hyqBgEIQAACEIAABCAAAQhAAAIQ6IsAAkBfJCkH AhCAAAQgAAEIQAACEIAABCAwYgIIACPuHKoGAQhAAAIQgAAEIAABCEAAAhDoi8D/D/7vpu3uE6+o AAAAAElFTkSuQmCC --20cf3071c9bae64ca3049d4dfcd2-- ========================================================================Date: Sun, 27 Feb 2011 22:30:29 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: Second level t-test: test against other value than zero Comments: To: Nynke L <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a2f72ca08e049d4f4d70 Message-ID: [log in to unmask]> --20cf3054a2f72ca08e049d4f4d70 Content-Type: text/plain; charset=ISO-8859-1 Set the interpolation option to nearest neighbor. That should make it exact. Also, you might want to think about other transforms since the data is likely not to be normally distributed. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Sun, Feb 27, 2011 at 10:13 AM, Nynke L <[log in to unmask]>wrote: > Hello all, > > I have a particular group level analysis for which I want to use spm. > However, till now I don't manage to so I hope you have suggestions for me. > > I use fMRI for the prediction of a certain binary outcome. Therefore, I use > a different classification software package that computes > prediction-accuracies for all voxels. For each subject, I have a .nii file > with as values the accuracies (range between 0 and 1). So, this is a brain > map just like you usually feed to the second level. > > What I want to test in the second level analysis is which brain areas have > accuracies significantly higher than 0.5. So I want to test against the > null-hypothesis that accuracies are 0.5 or lower. Now the problem is that > the 1 sample t-test in spm on default tests against the null-hypothesis that > the values are zero or higher.... So my first question is whether it is > possible to set a custum value for the t-test to test against a manually set > value? > > I already tried a different approach, namely subtracting 0.5 from the > values in my input images (to put them in the default spm t-test). However, > when I trie to do this with the image calculator of spm (with as expression > i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 > subtracted from each value but a range between 0.4-0.6. It seems that > spm-image-calc is also doing something else with the image... So my second > question is: how can I make image-calc subtract exactly 0.5 from all values > in the image? > > Thanks in advance! > > Best regards, > Nynke van der Laan > --20cf3054a2f72ca08e049d4f4d70 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Set the interpolation option to nearest neighbor. That should make it exact.

Also, you might want to think about other transforms since the data is likely not to be normally distributed.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.

On Sun, Feb 27, 2011 at 10:13 AM, Nynke L <[log in to unmask]> wrote:

Hello all,

I have a particular group level analysis for which I want to use spm. However, till now I don't manage to so I hope you have suggestions for me.

I use fMRI for the prediction of a certain binary outcome. Therefore, I use a different classification software package that computes prediction-accuracies for all voxels. For each subject, I have a .nii file with as values the accuracies (range between 0 and 1). So, this is a brain map just like you usually feed to the second level.

What I want to test in the second level analysis is which brain areas have accuracies significantly higher than 0.5. So I want to test against the null-hypothesis that accuracies are 0.5 or lower. Now the problem is that the 1 sample t-test in spm on default tests against the null-hypothesis that the values are zero or higher.... So my first question is whether it is possible to set a custum value for the t-test to test against a manually set value?

I already tried a different approach, namely subtracting 0.5 from the values in my input images (to put them in the default spm t-test). However, when I trie to do this with the image calculator of spm (with as expression i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 subtracted from each value but a range between 0.4-0.6. It seems that spm-image-calc is also doing something else with the image... So my second question is: how can I make image-calc subtract exactly 0.5 from all values in the image?

Thanks in advance!

Best regards,
Nynke van der Laan

--20cf3054a2f72ca08e049d4f4d70-- ========================================================================Date: Mon, 28 Feb 2011 09:55:35 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: Re: SPM8 error Comments: To: Glen Lee <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Glen, this could be an installation problem, perhaps try using a newly-downloaded SPM, install the latest updates on top of it and install it into a directory without spaces (say, 'C:\Test\SPM8'), and then only add this directory to the Matlab path. Cheers, Marko Glen Lee wrote: > Hello SPMers, > Does anybody help me figuring out what this error message (Error using > ==> < a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808 > ) mean and how i can resolve this issue? > I get this same error no matter what I try (e.g., realign, normalize, > etc) in SPM8 (also see attached) > Let me know. Thanks in advance. > > Glen > > -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Mon, 28 Feb 2011 09:06:14 +0000 Reply-To: John Ashburner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Ashburner <[log in to unmask]> Subject: Re: normalization failure (anterior posterior mismatch) Comments: To: Yune Lee <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Before running the spatial normalisation, you'll need to ensure that the image is in approximate alignment with the templates (Check Reg button). For these images, it appears that they are either rotated by 180 degrees (about the z axis) or flipped in the anterior posterior direction. Best regards, -John On 28 February 2011 00:55, Yune Lee <[log in to unmask]> wrote: > > Dear SPM experts, > I've encountered a normalization failure, such that there is a mismatch of > anterior-posterior between a template (EPI.nii) and a source image > (meanbold.nii) > This is clearly shown in the attached PDF file. > Any help wold be greatly appreciated. > > Thanks in advance, > YSL > > > > ========================================================================Date: Mon, 28 Feb 2011 14:41:37 +0530 Reply-To: sarika cherodath <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: sarika cherodath <[log in to unmask]> Subject: question on VOI analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00248c05002ff8481b049d5412cf Content-Type: text/plain; charset=ISO-8859-1 Hi SPMers, I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change?? Thanks in advance, -- Sarika Cherodath Graduate Student National Brain Research Centre Manesar, Gurgaon -122050 India --00248c05002ff8481b049d5412cf Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hi SPMers,

I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change??

Thanks in advance,
--
Sarika Cherodath
Graduate Student
National Brain Research Centre
Manesar, Gurgaon -122050
India

--00248c05002ff8481b049d5412cf-- ========================================================================Date: Mon, 28 Feb 2011 10:13:17 +0100 Reply-To: Michels Lars <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michels Lars <[log in to unmask]> Subject: Postdoctoral Fellowship in multimodal developmental neuroimaging Comments: To: [log in to unmask] MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CBD727.BC9EDDD6" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CBD727.BC9EDDD6 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Applications are invited for an 18 month postdoctoral fellowship in the human multimodal neuroimaging project "Linking the major system markers for typical and atypical brain development: a multimodal imaging and spectroscopy study" (http://www.zihp.uzh.ch/1610.php#45) funded by the Zürich Institute of Human Physiology. This study will investigate the major physiological markers of brain development, using a combination of multimodal brain imaging (e.g., simultaneous EEG-fMRI, see Lüchinger et al., 2011, NeuroImage, in press) and MR-spectroscopy methods (i.e., GABA, see O'Gorman et al., 2011, J. of Mag. Reson. Img., in press). The initial phase of the study will establish baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and EEG frequency and power at rest in children, adolescents, and adults. Examining the interactions between these markers and the changes they demonstrate with age and hormone levels will allow to better understanding the global and regional processes underlying brain maturation. In addition, we will investigate changes in these physiological markers with (a) memory tasks (see Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder (ADHD, see Doehnert et al., Biol Psychiatry, 2010). The starting date of the position is May 2011. Our department is equipped with 64-channel fMRI-compatible EEG equipment and a 3 Tesla GE scanner, which is mainly dedicated for research questions. The successful applicant will have a PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology, Neuropsychology, or related fields. Fluency in English and the ability to work within a multidisciplinary team are essential. Applicants must be experienced at conducting fMRI and/or EEG studies -demonstrated by at least 2 first author publications in international peer-reviewed journals- and be familiar with analysis software such as SPM/Matlab, BrainVoyager and/or FSL. Experience with stimulus presentation software (such as Presentation), UNIX, and programming languages a plus. Salaries are in accordance with the Swiss National Research Foundation. APPLICATION INSTRUCTIONS: To apply, please send a curriculum vitae, a personal statement describing research interests, 3 letters of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to: Dr Lars Michels [log in to unmask] MR-Zentrum University Children's Hospital Steinwiesstrasse 75 Zürich 8032 Switzerland Reviews of applications will begin on the 1st of March and will continue until the position is filled. ------_=_NextPart_001_01CBD727.BC9EDDD6 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

Applications are invited for an 18 month postdoctoral fellowship in the human multimodal neuroimaging project “Linking the major system markers for typical and atypical brain development: a multimodal imaging and spectroscopy study” (http://www.zihp.uzh.ch/1610.php#45) funded by the Zürich Institute of Human Physiology.

This study will investigate the major physiological markers of brain development, using a combination of multimodal brain imaging (e.g., simultaneous EEG-fMRI, see Lüchinger et al., 2011, NeuroImage, in press) and MR-spectroscopy methods (i.e., GABA, see O’Gorman et al., 2011, J. of Mag. Reson. Img., in press). The initial phase of the study will establish baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and EEG frequency and power at rest in children, adolescents, and adults. Examining the interactions between these markers and the changes they demonstrate with age and hormone levels will allow to better understanding the global and regional processes underlying brain maturation. In addition, we will investigate changes in these physiological markers with (a) memory tasks (see Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder (ADHD, see Doehnert et al., Biol Psychiatry, 2010). The starting date of the position is May 2011. Our department is equipped with 64-channel fMRI-compatible EEG equipment and a 3 Tesla GE scanner, which is mainly dedicated for research questions.

The successful applicant will have a PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology, Neuropsychology, or related fields. Fluency in English and the ability to work within a multidisciplinary team are essential. Applicants must be experienced at conducting fMRI and/or EEG studies –demonstrated by at least 2 first author publications in international peer-reviewed journals– and be familiar with analysis software such as SPM/Matlab, BrainVoyager and/or FSL. Experience with stimulus presentation software (such as Presentation), UNIX, and programming languages a plus.

Salaries are in accordance with the Swiss National Research Foundation.

APPLICATION INSTRUCTIONS: To apply, please send a curriculum vitae, a personal statement describing research interests, 3 letters of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to:

Dr Lars Michels

[log in to unmask]

MR-Zentrum

University Children’s Hospital

Steinwiesstrasse 75

Zürich 8032

Switzerland

Reviews of applications will begin on the 1^st of March and will continue until the position is filled.

------_=_NextPart_001_01CBD727.BC9EDDD6-- ========================================================================Date: Mon, 28 Feb 2011 09:48:36 +0000 Reply-To: Vincent Koppelmans <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vincent Koppelmans <[log in to unmask]> Subject: Adjusting Dartel for ICV: RC volumes or C volumes Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPM experts, If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards, Vincent ========================================================================Date: Mon, 28 Feb 2011 11:43:44 +0100 Reply-To: =?ISO-8859-1?Q?Gemma_Monté?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Gemma_Monté?= <[log in to unmask]> Subject: Re: Adjusting Dartel for ICV: RC volumes or C volumes Comments: To: Vincent Koppelmans <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0015175cb1445b46e5049d555ca2 Content-Type: text/plain; charset=ISO-8859-1 Hi Vincent, I suggest you use the c*.nii files. They are the tissue classes in native space. You are right, DARTEL takes information encoded in the rc*.nii files. However, the flow fields u_rc1*.nii files are applied to the c1*.nii to create the warped files. So finally, you obtain files in the form swmc1*.nii, either in the DARTEL space or in the MNI space. You can check that the volumes of the GM Jacobian scaled images (without smoothing) by DARTEL are the same than the volume of the GM segments in native space (c1*.nii files). Hope this helps, Gemma On 28 February 2011 10:48, Vincent Koppelmans <[log in to unmask]>wrote: > Dear SPM experts, > > If you adjust for intra cranial volume in a dartel analyses on gray matter, > would it be best to have your ICV calculated from the RC files or the C > files. I have read that C files are a little bit more accurate than the RC > files. Then again, the dartel analysis uses RC files, therefore volumes > calculated from RC files would match the data best, right? Kind regards, > > Vincent > <[log in to unmask]> --0015175cb1445b46e5049d555ca2 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi Vincent,
I suggest you use the c*.nii files. They are the tissue classes in native space.

You are right, DARTEL takes information encoded in the rc*.nii files. However, the flow fields u_rc1*.nii files are applied to the c1*.nii to create the warped files. So finally, you obtain files in the form swmc1*.nii, either in the DARTEL space or in the MNI space. You can check that the volumes of the GM Jacobian scaled images (without smoothing) by DARTEL are the same than the volume of the GM segments in native space (c1*.nii files).

Hope this helps,
Gemma

On 28 February 2011 10:48, Vincent Koppelmans <[log in to unmask]> wrote:

Dear SPM experts,

If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,

Vincent

--0015175cb1445b46e5049d555ca2-- ========================================================================Date: Mon, 28 Feb 2011 11:50:23 +0100 Reply-To: michel grothe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: michel grothe <[log in to unmask]> Subject: Re: Adjusting Dartel for ICV: RC volumes or C volumes Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_90f969e0-6ae7-4793-8d3b-790a0b77c215_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_90f969e0-6ae7-4793-8d3b-790a0b77c215_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Vincent, the rc*-files are rigid-body (6-P) transformed versions of the c*-files and total tissue calculations should therefore be exactly the same between the two files. Given that modulation aims to preserve the native amount of tissue, totals calculated from mwrc*-files should also be equal to the ones calculated from c*- or rc*-files, although they might differ slightly because of interpolation issues. Do totals calculated from the different image-types differ in your case? Best regards, Michel > Date: Mon, 28 Feb 2011 09:48:36 +0000 > From: [log in to unmask] > Subject: [SPM] Adjusting Dartel for ICV: RC volumes or C volumes > To: [log in to unmask] > > Dear SPM experts, > > If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards, > > Vincent --_90f969e0-6ae7-4793-8d3b-790a0b77c215_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Vincent,

the rc*-files are rigid-body (6-P) transformed versions of the c*-files and total tissue calculations should therefore be exactly the same between the two files. Given that modulation aims to preserve the native amount of tissue, totals calculated from mwrc*-files should also be equal to the ones calculated from c*- or rc*-files, although they might differ slightly because of interpolation issues. Do totals calculated from the different image-types differ in your case?

Best regards,
Michel

> Date: Mon, 28 Feb 2011 09:48:36 +0000
> From: [log in to unmask]
> Subject: [SPM] Adjusting Dartel for ICV: RC volumes or C volumes
> To: [log in to unmask]
>
> Dear SPM experts,
>
> If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,
>
> Vincent
--_90f969e0-6ae7-4793-8d3b-790a0b77c215_-- ========================================================================Date: Mon, 28 Feb 2011 10:59:22 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: [SPM DCM] how to specify the modulation? Comments: To: =?UTF-8?B?6aOe6bif?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Haoran, 2011/2/27 é£žé¸Ÿ <[log in to unmask]>: > Hello all, > ã€€ã€€When you analysis your data with the method of DCM, have you ever met the > problemÂ about how to specify the modulation?Â Now I choose 7 basic models, > and then assume all the connections in each basic modelÂ are modulated by the > experimental manipulation.Â WhatÂ I want to know is that whether or not this > isÂ reasonable Usually one would have a hypothesis about which specific connections are affected by the experimental condition. >In addition, is it possible that the connections > between two sourcesÂ couldÂ includeÂ forward connectivity, backward > connectivity and lateral connectivity at the same time? No. Every connection can only have one type. Vladimir > ã€€ã€€Any help will be grateful! > > -- > Haoran LI (MS) > Brain Imaging Lab, > Research Center for Learning Science, > Southeast University > 2 Si Pai Lou , Nanjing, 210096, P.R.China > > ========================================================================Date: Mon, 28 Feb 2011 12:02:19 +0100 Reply-To: Marko Wilke <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marko Wilke <[log in to unmask]> Subject: F-values MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear All, at the risk of (once again) exhibiting my statistical ignorance, I wanted to ask for feedback re: an effect I see when doing F-tests. I am comparing sessions with different numbers of covariates and compute an omnibus F-test in SPM (for the covariates only) and sum the resulting F-values. As expected, I see that there seems to be an optimum number of covariates as the sum of F-values first increases, then decreases when adding more covariates. I expected this to be a function of the degrees of freedom but I cannot seem to find the piece of code where these are taken into account. Any input is appreciated. Cheers, Marko -- ____________________________________________________ PD Dr. med. Marko Wilke Facharzt für Kinder- und Jugendmedizin Leiter, Experimentelle Pädiatrische Neurobildgebung Universitäts-Kinderklinik Abt. III (Neuropädiatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 Tübingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================Date: Mon, 28 Feb 2011 14:12:21 +0000 Reply-To: SUBSCRIBE FSL Patricia Pires <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: SUBSCRIBE FSL Patricia Pires <[log in to unmask]> Subject: DTI with SPM Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello, i am new in the SPM forum and i will be glad if somebody could help me. My question is concerning to preocess DTI images with SPM. I have processed DTI images (FA; MD, RD...) with FSL. Could anyone tell me some lecture to find how process (e.g. FA images) with SPM or where to find information concernig SPM and FSL procedures softwares differences? Thank you very much, Patricia. ========================================================================Date: Mon, 28 Feb 2011 15:25:26 +0100 Reply-To: Janani Dhinakaran <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Janani Dhinakaran <[log in to unmask]> Subject: Unsubscribe me please MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3079b77ea0b2a8049d5874c7 Message-ID: <[log in to unmask]> --20cf3079b77ea0b2a8049d5874c7 Content-Type: text/plain; charset=UTF-8 Thank you. --20cf3079b77ea0b2a8049d5874c7 Content-Type: text/html; charset=UTF-8 Thank you.
--20cf3079b77ea0b2a8049d5874c7-- ========================================================================Date: Mon, 28 Feb 2011 15:29:46 +0100 Reply-To: Wolfgang Weber-Fahr <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Wolfgang Weber-Fahr <[log in to unmask]> Subject: Job Anouncement ZI-Mannheim, Germany MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> The Central Institute of Mental Health (State Foundation) is an internationally renowned research institute in the field of psychiatry and neuroscience, home of the Department of Psychiatry of the Medical Faculty Mannheim of the University of Heidelberg as well as psychiatric hospital with 255 inpatient and 52 day-hospital beds. Within several new projects funded by the European Union and the Federal Ministry of Education and Research we offer 2 Positions for PostDocs and PhD-Students in the area of rodent magnetic resonance functional-, perfusion- and diffusion tensor (DTI) imaging at 9.4 Tesla. The Central Institute of Mental Health is equipped with a latest generation 210 mm horizontalbore Bruker BioSpec animal system and additional cryogenic MR coils for 1H and 13C detection in the mouse brain as well as two 3 Tesla human whole body MR-scanners. The positions are for two years initially and located in the research group Translational Imaging. Potential applicants should have a diploma or master in physics, physical chemistry or neuroscience, or an equivalent field. The ideal candidate from the quantitative sciences has a solid background in NMR physics, signal detection and processing theory, and is familiar with the Bruker Paravision environment. Experience in other computer languages, Matlab or IDL, is desirable. We have openings in these primary fields of research: â€¢ Establishment of manganese enhanced magnetic resonance imaging (MEMRI) for a the longitudinal assessment of connectivity and neuronal activity in experimental animals. â€¢ Development of methods for the acquisition and post processing of functional BOLD imaging data with optogenetics for the identification and investigation of brain networks. â€¢ Diffusion Tensor Imaging for the investigation of structural integrity in different psychiatric mouse-models. The candidate would also collaborate on several ongoing animal studies of schizophrenia, depression, substance abuse and other psychiatric disorders in mouse and rat models. The assistance in the general supervision of the animal scanner would also be part of the position. More information can be found at http://www.zi-mannheim.de/transl_imaging.html. We offer an interesting job in a leading research hospital with a strong emphasis on MR-Imaging, salary is according to the German TV-L pay scale, including the social benefits of the public service sector. For further information please contact Dr. Wolfgang Weber-Fahr, Tel. +49 621 1703-2961, E-Mail: [log in to unmask] Homepage: www.zi-mannheim.de/start_en.html ---------------------------------------------------------- Wolfgang Weber-Fahr, Dr.rer.nat. Head RG Translational Imaging Neuroimaging Department Central Institute of Mental Health J5 68072 Mannheim Germany email:[log in to unmask] phone: ++49 621 1703 2961 ========================================================================Date: Mon, 28 Feb 2011 10:36:54 -0500 Reply-To: zao liu <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: zao liu <[log in to unmask]> Subject: regions of brain corresponding to VBM coordinates MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0015174bf21c068a29049d5973e3 Content-Type: text/plain; charset=ISO-8859-1 *Dear All,* ** *I did VBM analysis on our T1-w images and would like to know if there is a way to get the names of regions of activation in the brain from the coordinates (which comes as a report at the end of VBM, i.e voxelwise, cluster wise and set level reports). One more clarification the coordinates reported are in MNI right and are mm not voxel coordinates right.* --0015174bf21c068a29049d5973e3 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Dear All,

I did VBM analysis on our T1-w images and would like to know if there is a way to get the names of regions of activation in the brain from the coordinates (which comes as a report at the end of VBM, i.e voxelwise, cluster wise and set level reports). One more clarification the coordinates reported are in MNI right and are mm not voxel coordinates right.

--0015174bf21c068a29049d5973e3-- ========================================================================Date: Mon, 28 Feb 2011 16:35:44 +0000 Reply-To: Richard Binney <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Richard Binney <[log in to unmask]> Subject: PPI: adjust for effects of interest MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba1efe9a7601f6049d5a45df Message-ID: <[log in to unmask]> --90e6ba1efe9a7601f6049d5a45df Content-Type: text/plain; charset=ISO-8859-1 Hi Darren G/ Karl F/other PPI-ers, I can't find any posts on this and wondered if you can clear it up for me. In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting? My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?) when you have time or dispersion derivatives and/or motion regressors? I have a parametric design with two conditions (tasks) and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences? What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])? All your comments will be greatly appreciated. Richard --90e6ba1efe9a7601f6049d5a45df Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Hi Darren G/ Karl F/other PPI-ers,

I can't find any posts on this and wondered if you can clear it up for me.

In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?

My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?) when you have time or dispersion derivatives and/or motion regressors?

I have a parametric design with two conditions (tasks) and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?

What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?

All your comments will be greatly appreciated.

Richard

--90e6ba1efe9a7601f6049d5a45df-- ========================================================================Date: Mon, 28 Feb 2011 11:42:00 -0500 Reply-To: "MCLAREN, Donald" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "MCLAREN, Donald" <[log in to unmask]> Subject: Re: PPI: adjust for effects of interest Comments: To: Richard Binney <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary cf3054a4b5dbb06a049d5a5beb Message-ID: <[log in to unmask]> --20cf3054a4b5dbb06a049d5a5beb Content-Type: text/plain; charset=ISO-8859-1 Richard, I'd use an F-contrast that is one row per condition/modulator. In your case: 1 0 0 0 0 0 ... 0 1 0 0 0 0 ... 0 0 1 0 0 0 ... 0 0 0 1 0 0 ... 0 0 0 0 1 0 ... 0 0 0 0 0 1 ... The goal of the adjustment is to extract only the BOLD signal related to neural activity and eliminate the activity due to motion. Best Regards, Donald McLaren ================D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Mon, Feb 28, 2011 at 11:35 AM, Richard Binney < [log in to unmask]> wrote: > Hi Darren G/ Karl F/other PPI-ers, > > I can't find any posts on this and wondered if you can clear it up for me. > > In extracting the principal eigenvariate from your VOI, you are asked if > you want to adjust the extracted timecourse. When should you adjust and when > should you not worry? what is the impact of adjusting? > > My impression was that the raw timecourse would be extracted always. The > option to adjust suggests this is not always true. Do you use this option > (only?) when you have time or dispersion derivatives and/or motion > regressors? > > I have a parametric design with two conditions (tasks) and two parametric > modulations per condition. The design matrix therefore has 6 regressors of > interest. Motion regressors are also included. In extracting the timecourse > should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 > 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done > this? What are the consequences? > > What about if I were only interested in the parametric modulations of the > first condition in the PPI analysis? Should I adjust for the first 3 columns > only ([1 1 1 0 0 0])? > > All your comments will be greatly appreciated. > > Richard > --20cf3054a4b5dbb06a049d5a5beb Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

Richard,

I'd use an F-contrast that is one row per condition/modulator. In your case:

1 0 0 0 0 0 ...

0 1 0 0 0 0 ...

0 0 1 0 0 0 ...

0 0 0 1 0 0 ...

0 0 0 0 1 0 ...

0 0 0 0 0 1 ...

The goal of the adjustment is to extract only the BOLD signal related to neural activity and eliminate the activity due to motion.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.

On Mon, Feb 28, 2011 at 11:35 AM, Richard Binney <[log in to unmask]> wrote:

Hi Darren G/ Karl F/other PPI-ers,

I can't find any posts on this and wondered if you can clear it up for me.

In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?

My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?) when you have time or dispersion derivatives and/or motion regressors?

I have a parametric design with two conditions (tasks) and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?

What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?

All your comments will be greatly appreciated.

Richard

--20cf3054a4b5dbb06a049d5a5beb-- ========================================================================Date: Mon, 28 Feb 2011 11:31:33 -0600 Reply-To: Pilar Archila-Suerte <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pilar Archila-Suerte <[log in to unmask]> Subject: Art repair - mean signal change MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0015177fcec43a8612049d5b0ea2 Content-Type: text/plain; charset=UTF-8 SPM Users, When using the "scaling to percent signal change" option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don't (depending on which files I select). Why would ArtRepair give this answer: *Direct calls to spm_defauts are deprecated.* *Please use spm('Defaults',modality) or spm_get_defaults instead.* *Automatically estimated peak and contrast scaling.* * Normalizing by beta_0008.img* *Peak value = 4.97* *Contrast sum = 0.996* *Mean value = NaN* *(peak/contrast_sum)*100/bmean = **NaN* * * *ans =* * * * 4.9700 0.9960 ** NaN* All files follow the same path so I don't think that's the problem. Does anybody know why this is happening and how I can fix this? Thank you, Pilar A. --0015177fcec43a8612049d5b0ea2 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

SPM Users,

When using the "scaling to percent signal change" option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don't (depending on which files I select).

Why would ArtRepair give this answer:

Direct calls to spm_defauts are deprecated.

Please use spm('Defaults',modality) or spm_get_defaults instead.

Automatically estimated peak and contrast scaling.

Â Normalizing by beta_0008.img

Peak value Â Â = 4.97

Contrast sum Â = 0.996

Mean value Â Â = NaN

(peak/contrast_sum)*100/bmean Â = NaN

ans =

Â Â Â 4.9700 Â Â 0.9960 Â Â Â NaN

Â All files follow the same path so I don't think that's the problem.Â Does anybody know why this is happening and how I can fix this?

Thank you,

Pilar A.Â

--0015177fcec43a8612049d5b0ea2-- ========================================================================Date: Mon, 28 Feb 2011 12:36:59 -0500 Reply-To: Chris Watson <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chris Watson <[log in to unmask]> Subject: Re: Art repair - mean signal change Comments: To: Pilar Archila-Suerte <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset="UTF-8"; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> I think that was a bug in an older version of ArtRepair. Do you have the latest version? Pilar Archila-Suerte wrote: > SPM Users, > When using the "scaling to percent signal change" option in ArtRepair, > sometimes I get what I want from the files selected and sometimes I > don't (depending on which files I select). > > Why would ArtRepair give this answer: > > /Direct calls to spm_defauts are deprecated./ > /Please use spm('Defaults',modality) or spm_get_defaults instead./ > /Automatically estimated peak and contrast scaling./ > / Normalizing by beta_0008.img/ > /Peak value = 4.97/ > /Contrast sum = 0.996/ > /Mean value = NaN/ > /(peak/contrast_sum)*100/bmean = /*/NaN/* > / > / > /ans =/ > / > / > / 4.9700 0.9960 /*/ NaN/* > > All files follow the same path so I don't think that's the > problem. Does anybody know why this is happening and how I can fix this? > > Thank you, > Pilar A. ========================================================================Date: Mon, 28 Feb 2011 11:38:50 -0600 Reply-To: Pilar Archila-Suerte <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pilar Archila-Suerte <[log in to unmask]> Subject: Re: Art repair - mean signal change Comments: To: Chris Watson <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --00163683195045f1c4049d5b2847 Content-Type: text/plain; charset=UTF-8 I have ArtRepair v4. Isn't this the latest version? On Mon, Feb 28, 2011 at 11:36 AM, Chris Watson < [log in to unmask]> wrote: > I think that was a bug in an older version of ArtRepair. Do you have the > latest version? > > > Pilar Archila-Suerte wrote: > >> SPM Users, >> When using the "scaling to percent signal change" option in ArtRepair, >> sometimes I get what I want from the files selected and sometimes I don't >> (depending on which files I select). >> >> Why would ArtRepair give this answer: >> >> /Direct calls to spm_defauts are deprecated./ >> /Please use spm('Defaults',modality) or spm_get_defaults instead./ >> /Automatically estimated peak and contrast scaling./ >> / Normalizing by beta_0008.img/ >> /Peak value = 4.97/ >> /Contrast sum = 0.996/ >> /Mean value = NaN/ >> /(peak/contrast_sum)*100/bmean = /*/NaN/* >> / >> / >> /ans =/ >> / >> / >> / 4.9700 0.9960 /*/ NaN/* >> >> All files follow the same path so I don't think that's the problem. Does >> anybody know why this is happening and how I can fix this? >> >> Thank you, >> Pilar A. >> > --00163683195045f1c4049d5b2847 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I have ArtRepair v4. Isn't this the latest version?

On Mon, Feb 28, 2011 at 11:36 AM, Chris Watson <[log in to unmask]> wrote:

I think that was a bug in an older version of ArtRepair. Do you have the latest version?

Pilar Archila-Suerte wrote:

SPM Users,
When using the "scaling to percent signal change" option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don't (depending on which files I select).

Why would ArtRepair give this answer:

/Direct calls to spm_defauts are deprecated./
/Please use spm('Defaults',modality) or spm_get_defaults instead./
/Automatically estimated peak and contrast scaling./
/ Normalizing by beta_0008.img/
/Peak value Â Â = 4.97/
/Contrast sum Â = 0.996/
/Mean value Â Â = NaN/
/(peak/contrast_sum)*100/bmean Â = /*/NaN/*
/
/
/ans =/
/
/
/ Â Â 4.9700 Â Â 0.9960 Â Â /*/ Â NaN/*

Â All files follow the same path so I don't think that's the problem. Does anybody know why this is happening and how I can fix this?

Thank you,
Pilar A.

--00163683195045f1c4049d5b2847-- ========================================================================Date: Mon, 28 Feb 2011 12:47:00 -0500 Reply-To: Jason Steffener <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jason Steffener <[log in to unmask]> Subject: matlabbatch "decode dependencies" MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear All, Hello all. I am trying to create some provenance info from an SPM batch file. So I would like to take an SPM job file and read through it to pull out the different steps, the data and the parameters. I have no problem doing this EXCEPT where I have specified some dependencies. I am having trouble decoding what the dependencies refer to. Essentially if I have the following steps: Realign << Input data Reslice >> Output data smooth << Output data from reslice I would like the following: Step1 Realign input data: FILENAME parameters :XX Step2 Reslice input data: output from Step 1 output data: rFILENAME parameters: XX Step3 Smooth input data: rFILENAME output data: srFILENAME parameters: XX And ideas? Or if someone can point me to the code that translates the dependencies in the job file into something I can figure out, that would also be great. Thank you, Jason -- Jason Steffener, Ph.D. Department of Neurology Columbia University http://www.cogneurosci.org/steffener.html ========================================================================Date: Mon, 28 Feb 2011 14:08:11 +0100 Reply-To: Alexander Hammers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Hammers <[log in to unmask]> Subject: Re: question on VOI analysis Comments: To: sarika cherodath <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: multipart/mixed; boundary=Apple-Mail-79-616227633 Message-ID: <[log in to unmask]> --Apple-Mail-79-616227633 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Dear Sarika, How did you define the VOI(s)? If they are in MNI space as your second level implies, then you're applying one VOI to all the subjects... and this will unsurprisingly have the same volume in all. I don't know what kind of data you have - it sounds like fMRI. An approximation of individual volumetrics would be to backtransform your standard space VOIs into individual space - if you've used Unified Segmentation for your MRI spatial normalisation, then the *inv_seg_sn.mat file can accomplish this. Such a procedure's accuracy will be limited by 1) the quality and provenience of your standard space VOI (see our recent thread on single-subject vs multi-subject atlases) and 2) the low-ish number of degrees of freedom of the transformation for standard normalisation and Unified Segmentation. Point #2 should be improve-able if you use DARTEL. The gold standard for just one particular area would be to devise a protocol, check the reliability of results and ideally their veracity, and then outline those areas by hand in your subjects. Hope this helps, Best wishes, Alexander ----------------------------- Alexander Hammers, MD PhD --Apple-Mail-79-616227633 Content-Disposition: inline; filename=image.png Content-Type: image/png; name="image.png" Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAALAAAAA6CAIAAACf/kS3AAAgAElEQVR4Ae2ceZxVxbH4+2x3mXtn hhmG2cFhF8GYKLKoEX9KDD81RiCaRY1LNL9sRhOXmBhjjJo8RNREn8a4JmqIAU2UKCQmGhcERRZZ BEEWYdgZmJm7nv19+zZcBh0igx/+ePymxUOfPtXV1VXVVdXVfdHCMBTdpZsDezig76l0/93NAcmB boXo1oN9ONCtEPuwo/ulWyG6dWAfDhxahbCFcAoxqye8vMgI4YpAhME+FBzQixvupLsn+3p+iwh5 5njuv6RzwuW74wvhMS5Pl2p3+VgOaId2lxHaIvSEowsjLjQiFtvVXV4MEf9YyjoCIEtb5ErcuFSF iJ0JI0lNM3xHGJGOYHvreSFiKF9OBGW+KYzAdXTNEDp/9sJ01zrjwKFlkBdGhZ+wY7pt+oL/dMsS UUNEO6PkP7UZvigJ457li0igeVq5VC6RN/ZrInw5giXcMscUaWySjmaYBhR0l4/jwKFVCE1khWVH 83rUMzJRlrcubCvX9WXqaCG+RpNOwNVN08ll9FBE9u97NE9IyxRtdcOg3E9rXlmEJo/W7vIxHDi0 CqHrJakgKiJWxhJWKMycsKMiHhBadK1E9KxvCMM0867t4zTiCTRED639YfEsW9Ah08PXskKLpU0A TRF2zU/tD/nh3X5oFUILvFIvyOs4iTDieU48Gwo7PIgxfR8l8jRhWAlfmFukxxDe/oNT04t6phYm 7BIR+gY14Wh2er/6c3iLuGuzOwjhdGEAR2dtZmOuPWOlVvH0xoqpy5dviWpul2MIT4vSx/QCyzHe 3NEy4p6Xbc8zJfLOCzpnPbCOmNP0S42MqH1izXPbIsm82zl0d2sHDuyXpx1gDr7qC0eYQVqPPrhm +5iq5C0je/cpTQs/2VWMpogGRIREpo6e08o3JPsaETMr3BIix86KJtqTkbwVusIIw4S18cJ+ZY4Q +9mRdIbg/9+2Q2shrCDiibKol3MjsaFlkaElernwXYOV2k4oILyUR5gYsKWk7Cj8JRtEEMjwEQ1w A9eVqQtH93TfbUP8ughzlojkTdFa4hl5iQTn4Ung0GUfKjMVLlBJ1zd1Ox6ElubbUmvYr4r2TGAD ImPL0M+ylQ194eZFaNPCX8LP5an5gXRGQQYAYefe4QkNniTDc3N8OAg7EwTSvfEsbvJ9f/eWp/BF FNu9QuTLaxGAjqooJHveDtXfhzgPIby3t6aO/3eLaK+o19p2mJH8RXVEAjcvdSct3+7aO+Ju4vKB fW8b7bumtmiDOeaVJef0PXLOknVbIvqwMu++0waPrsDI2BvWRi6Zu2GOERUtO8b0a3hj844dFw8o EWJhJn3JjPTytp1GaZkb5F46s+qzFfFfLWz72eKUIP8QMcbU2P8+pd/IlzK/GVk6vIcwRPrW1/L3 Nme2Ou1l0YarBkV/fkxC09x/bzc/9/y8EXVNi1pE1m8/tiQxfXxdXz0jXO3uNbmrBiSERQgkTFNu Yf1QNz7ZOkK0ui5RcNKsqXjI8wzD0NRLQXX2AMgFotRFtRwqRSjiZbBDV/ww2MH6C7NjZm348cJN qL0dOE9vDMxH197/dhDa4fT1zoCn1l87vy0fpt/Y7IiHtn3t5VUbwnCrHx737PIvz94U2rtCNxww fctXZje/F4ZrXW/s06vFg2v9cFvoZUfOWj9hxrowCFNueM2irSf+cxPAOf4PvOTjK9t96uk21yt7 ePnrm1q9MPz1B17vJ5pf2GiH+XDG+63lf3znd6vonXllQ6A/sOXiV1dn7XDFtvyQGYuum7MxDBw3 DH80bwMdqfBHPjybDgdRUAIKHYsVjF/hdTcybANWoSMAkKpFNe6GO8R/fTJVL6rVfip6XuvpaYZn RNyErsXZGERCa+rC1vMH+eceJ+xIZmJjcO3AxL9WtxDLtLmWFYgbj2loFLvK9fSEAf1bNqbzkR7/ 2uR/0N5++wkNg0K3KdS//el+ESuqiTJPj4/uGfvJSRWhJpK6GGYlYtmcpyxxaKTbSpJ6oIlomWG0 a7FcEME8PDF/6zcG1YytJ0XhjetvnHtkr2mrNpDtEppjlKbv+Ey/SMQb3Mv7al3ff6aipEww9PF4 KYvU9RjkExWlB6DADChLsHnz5smTp9x1192zZ8+hvWgAWlpa3n777ZdeeimXy9GIAvCV7p9o+APu fGgVwiF/7AjH1EMErgdhxMSBt8ZS4xvqe9qaLhJOGG1qKl+QskORSJgZt2RXU0Vc+BWhSLZqoRmN xDxnVV6M6lXdOyd8TfNNf1SlKHFSvoiatrhreHR1c9k5M3ZVTltzxdLWwIoxjpk3CRjoG4jAIzCx RdSu6GFEbWHNawvPbCA+KeF7kNUuq+v1ry0R05WRtevZkaQwbJIk5tZIe09T5tEsT+g5QEVMh1oR EKDouIwD5m4HQESLUyg2sPS3bNly5513/vrXv54zZw5SR0uAQfAffPDBWWeddfHFF8+fP59XpT1F dSliOESVQ6sQuFykZ/lkAvRoaJAPSEUCx8+145AD4scgEgSmkxKW5ZCIJBmdMmOsBMONkXbeommx 2nbTyKUyKZEr7Cd8Q/NXtIlWspUi7cTEwGfTN77bfuKR4T9P6/H88OpYoLVGcyLW7uiup7cbwo2F cZTSrmhtzzCcXZIQbWK7JQKfU5WScGc21MO8Y6WF0S6CnqUEnHokFNESrdLbtTUbJ/FRiFgLnh7i pWxoOiieKbmCT614BOw4Do2YAaUNRUuQz3MSI02CaZqA0a4cxyHSgA+hlYvj0JVoGBH5iGYJO26n CNGD8oihHxlN/H5Z68T+pSWoRd58ba05qqKnxQ5R9NRjqcKJVFzG+RVbUtvak6LxrP7h1fP91YFo ClK+Ht+g+SSxObxascNek965/Zz6SktkI+IHK7N+LlIi4qYXN01b8/PCifsRhO9qbY5ZZsRyZadU hk+vTZ1Uo0f9qBGJ/nbtu+OGlhpB0sd0mauFmcCix9xE0tkVjbglhKXIngyrhq8LdV2qA3GFDnAX ixK20oniE4OBvIuxpFILlKBPnz7XXXcdIzQ0NPAEvqNp6eLIXQbv8ty6NIJGVBQnM5TGPleXVnLa yZbzymGNI2c1nz9r4/eOa/hbc9vdK9qeOS1pRe2U4bpa3jbj0UBwEpU1I74W071Ynx4lpzRtP3vm zslHV7V64orX1opY0g/MqpgbOPH/WrLz5L7ajHW5362yhRv926aNE6obAi+q69X/3byrTstNOKI+ tGxbC9yYceMga/S/ORPPXzTYe3x5MGtD2T9ObTQCP94e4/QrDJMB56G6yOtlWTMm/NDDXsQMVwuw 5tJyYR2Ic1DIvbb/gPiBUNVCRwOoK0fAk0BStYBFGQPAevfuffXVV9Oi1EgpinpVynRAQx4sUOfm D1ILRkvu2LFXHZEzDfWqyJV5gkKhXX2ivdiFA0k0wAyT4yo3VFalM2GUFFNTT+fZ8Y1x3b1p/o5N 21MvntbjC3XJvIhWRGNnV8nNf8CRmC8GR40hNVqbyWpOP37agO/0yT08f8l9H2y5+IS+F8Sb23VR nwgf/3yv97e6t853zNZtb57Z9+uDKx5Yks9xamGKrx3p/nXpNi2eIIawIptwDKYmRvaOvXxmXXt2 w42LUy1u6/QvNA7vwxr0/J7ivGQkzcE8CRJXDEkYo5KlfoQIVTdyrsX5CShd/Jbpa75upPPQJ29m FFIaIucFKbyNzIUUSnH66hWGUKiz0HETVPaqBV7T8/XC4S3cQ958Ur14KjUqdqdFZSnUk1e6KMzU i5Wif1Hi4FPXihrvI08fnUCyqt22bVWBFCrMSpFS/Kra1asiiDowduiEeS/0wp1h2EYTvTPgckI3 x1Rwnm2BfIbsAt02ckXU3TwvhZF9tqUAsYeUMBnG9cN04ZNDk+/IXoENmXzK0u5JAHJNobPLlX+T 3Qp3wLKAofnghn5awQAvh5C7STfMZ8PA9X2GlcNl2Vi6rmSzEwBG318sbvFCX24Qc8wIDHJXCz6P nFnopH3HB1qicsBQZAsVxQd4JdHsKdQpasM5e/bsxvre9bUNkyfdUeyoANRT9VV1JQKFRrWAvyPb aVRfi/xXYF197tXHD+kRiFBlxqA9Eonw5ro+Ok67ZVnKdjE2AHylXYVCkKh0nAowBksoKhdHhSvK 1BlniU5MRwSguXYsyJeJgO8hJ6JaGaeRMT8Xmk6UxKOPccpF/IyIsWMIYr6I+3jzIEFIYGMCNMFR J73CiG1kSoQbD4lISRrZEWE7FqnxCAlH0xM9Q65PiLK8CNhC6vQOrDAko2U5XLjCGjlsVjzNJNtk 4DMCuhE3mHmO0qysKdrpqzkxImJ5rcLKaxg7PSS0ELYfagS2RkIYwHhwUV71yDBlxQcqHdc6HKAF 1sEx6gQKVGhRBgPuUadd8ZOKYjtg1NEexWSegFHUV7ApcVChAKm+fmhc2rtU9hdDSEVhYLBDUEED 2ApIz8mora2tb7zxxrJly/ja2Nh4zDHHDB48OBaL8VXNgXkWdIh9BXdaOKXMW8TLmk7dD/2IZixa 9u7i995dv2m94ftjR48ZOvjIkooyvhph3DcCy897Jt47jpVOGaLECrjyxMigx0ob8TgmvEALu0S0 IgEzNDcg1HPZykBEiOX3s5ZRkg7CpK+zV7SiWS0SFzA01Aydy3Tr17UuWbhg3YY1377mCsZNcN7m uNy+8ULT4zoX93oCwkovFw3ao+LGN5vdaE3fhHdCeVu/ml4ldIhg4ZIoIBY/ovu2Zhkax/GmH/iK D5KKwpJ955133nrrLbYS8G306NFHHXVUeXn5Xv7sUQUlYwRMR9SCioKhDvNpVFKgsmXLtqVLly5e vBiY4447DpzgoJ0VqJhP/ROVgnp18oBENaWGht51dQ1nnHEWr2272n9w5Q8b6hrrauoxd7XVdfyp rqoZMWLUzJl/t21sL4ZLYqMu/yrUqeWkvXbSTv7ZZ2d85phjezf0qe9Z36+2qb6qqqGhorynNf68 L86Z+5ZKB2KB6SddFqbfk3a9of6I+tq6O6b8quAcMPJ56TIwmfIjBh2/gNPIv/n6W/WVvavrG+65 fTImPfQw49n1a9+t711dW1v929/+FpQznv/b6BOOr62qqK+o7FNbD434o9bQx19I5+WnwmAXB/UQ INOGufCG+d64WVvEY5vFE1vEQ6s/9dy6V3aELQXq5Lg4QE+SIL2YJJqqfK55f+111/wIRuER+AO7 +EOFiV95xVXvLl0OzOuvzqaxplftnXfcJXlVKBiYuXPn1tbW8+e112bTBj8dB8JleWrqn4cOGQbP 6VhV2QuEJ4w6cck7S9VX9fyQH+n46UDq+3UZSsvQ6xEjRqAcaOXrr7+BPj7++ON8Qm0hHQuh/EVz c/Oll176k5/8hE8YP4+MZMTE28gcDuedrD2hrVn23te/eN63vn7Z5m07bMenr+fahhlzgkRJj6ZX X1149hfO/f6Pr2hr2YYL0YgADM82PdMgiSGCvKWFSVsvcUUsTUaCjayLOzLk8YKM0bDs8cBha+/H TCMWWr7DWjN9w9JEvK5pSEXP3pqI/WXqc/+c8a9vXnb56g3r/NJIu1m4qOOLmCfKfWHnSWuyPBO+ 02NFi3XXisz1y5o5m0uK7cPL7NpEPpLb3kN4y1qSY6Zuu37uph3c0NHy8ozDMPMBTsOTN4iZsCam 3HHnySef/Nhjj8E3VjY8lJMthF88p06deuqpp/7ugQej0SirnK8Mi6gApkKjqtAFJqOUNCrbPG3a 01ddddXOnTtVr3g8jv1YvXr1l7/8ZQwGYKrQUVXAuaetC393rhDQBKEoJphQAuq+F06ccC5Z1ZKS kgsvvPAf//jHtm3bNm5q/utf//rNb36TsdGPadOm3XzzzcyLhAodIUzShBUMxftL3vvqOee9OWeu GWWf3XDfPbevWrvkg+3r1m5YPeell793/iVHlFckQvuJx6ede9HXW9JbPYPY29TwFUJjo2ImbM9u 1fN5y9WSuPl8wHm4iLioSkbkOQvNuLYXMfJJry3YldczWomNgNAXXbhO6Ez8P+OIS95dueCWm3+h OZqZMj53zNhJP5ryl6l/k6wyvNBwSoVDPuhnC9qrZqwc9velV7+x6TfzZX4yr1k/HdG47ktNq84f 9o3jK8u1TUap+eCy9slv7mglGS+PTEVAOktKU6rm1T+8ZsqUKcSAiPbcc8/905/+1LxxA3+2bd8K 0+BVjx49cCu33nrr3Xff/aEYQomwKEiUCYVRYsVT3HjjjQi7oqLi9ttv37ixecWKFeeddx6iQSj3 3HMPykFRgpOkFJRMVbr2ZPhOC6hp5/HGG3NxGbU1jfV1fYYMPmrRgncwd/C/aB4xksuWLR8yZGhN TV2vXjVz5rypEBYQ+I7npjLpAQMHV9fU1dbX3XHnFAwypl5tYjCFrV6ewH37js3nj5/QWNfUo1fD 9y79LktN4lcHSU5YWVvdp7H3pLum4ETwPnxKKQtPf6J49h6+3DjM/fe8QVVN1Q29bp/8y7Rsko6A vyf91501jUdgaXvX9B3Sf+CCN2cTmtK1sLPgmAJ348xtD8yp681Hl4nHlopHdlY8vEL/3SbovHoB mwmISYcpj73Q/Hx44p/Xise2G/cvu3lhypY+Ai+j9iE+xh8XgF8YefyoxYuW7GYRc4Fkxis4lOb1 G8ee+jn8bJ9G/GADboVeyLIoBXYf9fWNVVXVnHHQiLklnH/xxX/BXqYg/Qt48CN5uf8aMXwkSGpq avbwnG90YbCDLJ1biKJOsXsig0/WlgsELMe7fn3n0KOPkuEdl1x55z9ZF0OOGnQX0iKw140H7v8t JkHaPU0eFpMC/P53vp/PZLUgvPfue6++6oeGTkRoytyO3PeLciMaE2ZVz9onnnl65KjhSUuf/rdn Zv19JpsPTy8kj7mpTaznsWvAWoQxOTYRnZBBLP0JuRICEokngUgZjulHdC2WACyiyTsx7A1IN7k4 KXYn6XumPjlsxAhN46wrg7vR8D1u/JHVbSc/tctrJfo3Tq6umnZ8uOorg+3Ly7BSpUFbyChaVCT5 3zxWD188p35k+VbDrL95/uZ1MiNhOkSUuljbvH7y5Mks4tLS0hdmPX/0McMc12GOkkukGtjGyKMQ 0dC7fvoz0z716aNtrmIUIvfiskaGkl72EVxXtyIFC8sZh4/FnT9/HjEjJ2wnnHSi50u2W1GObMRV P/wBLSeccMKmTZvQA/pinPApChXYulr2qxBKwZieKryyoTjttNMgC3VWAW3HUU855ZSBAwdC0KxZ s1AIeilSOMKhhTmPGjVq4pcm8ImikH/0+cgjj+CSAMCigkqy4KAcoRr6o8/Tzxj32eHHS8psGdtA ixXG5+XED99wHDOtW9t+dkrvWeNqxg+tjJfwmUz27oIK7q7x+w7DmHTaUNPfHljRyfO2G6RT+RZ6 v5l0u+LVQw89VFlZCeUEWEy8yAoqNOII2GjgVsCzB/2H/2bigNEKE+iFpBOJhAraCBqUR2aFEH4Q QEyb9tQzzzxTW1urEKoohI5yTXa97FchoAOkIIQyNAC3N3bsWAjtOIyigK+AQQfqQp3GxYuX0MIe jJ9LIWMleEJO6WhBWTglAvlHS1l56Ve+8hUwMG02bBLJvnlSWj5JOWXsqWxnlWHLkqKAxkDcOvv9 Ng/Z5+/+/IBr+pfE/cAI27nlhb8ojrVXL7neoxtj4uLEvmVCM//8AWgkVGhnZkyfCbXDhg0bPXoU LcyOp2JXkWk0EioSOnzqU58aP3687NlZAQBWqy/0hSGsN0RA/ZZbbnnhBRbY3kANb4PGKHlhHuhF Hanx7Az3x7R13kfNn+kxAdQc1O3t7b169VIDQx+UFUXFqyfzSEJlI2jPZrM86WVaBh4RFnBgM2Lk 8Rwhy1/UkVXaTwEJgRKjwA5CMF6LfPmYeRzY5379+smlLu9nihKNzEXwyjYxc4NjaFu+37/misao hWAJZ0mI4OoKEi1o7W7p7lWLMDtxYE3c81O2trZN/kRx4btruMMLwFe/+lUmCH8ovCr6YYUSFePS qJI0Z5999v6ohqV0VBymLzQMGjQIE0vHHTt2fOtb32JxPvDAg+vWrQcDgSfADEedZal6gWF/yP9z e+eJKSgojCSRKguByWL/WRQPs1JDKuGZhsRzxBFHkIFRxPGVpeg6HsEwGDjjJ6RUaJkhMVanZHmO y5QAYImAio4oE0N0CnwQjRyU4sVNdq3czQg4swr+sLbZNau50XnTSeUi34YlTES5P5kwuW5J9nIP ewrH3nsH9K1wMNkBju+Fti6n9UuG8+Yt8SMuRzdS5wrmQU0WRlFBWsyLOp+YGnUajz322L0Y960h VI4OmDvNSrFQhQcffJC9PWkudOv9999n0/GLX/wC/fvud7/bt+8RgIFTiYNeaqx9sR7QW+cWQmGE dPBCFkXpHaN+SAGLigwpEE2hL9qgwCzuRmezEAoGpUBF+E6pQ+0YAki6sLdW4xa1sNMuXWq0dCtt s1mVqfNCqGe91mpj+cdX13EHizQ2iVCXVY1E2Vrs0YZ9hijEEnQuMyzUgi6uvG4TsVMpR2uFbITN MqCieinBMCleqSsAeEVLVVXVPpg7vDBxVoVaCVRgCGujoqL8L395mgs1JDkymYzKQ/z+978/88wz Fy1apNYhHRmCgeiihu6A9YCqnU5a5kGhQ0kX0aKSahgai1iBUaJibLyYogC61eR3Nwqd8Aorx7r5 4he/CEKUTCIhldRZ8V25LMAciVl1dXUMrfSjM9iDadOMaEmUZRfkOPIm4xvq67YS6mdPqi+NEjBY Ii3MSiqml9ZJUQXECx2HkWIuEI5km2V3jZ8g1/GLkVCwq7X0kqBwLsYyoJfioeIYjFLWTumE4tva tWs7Iu9YBzgalaJl+orlBbZwZUY/55xzvvSlCZs3byXx9eSTT5KESKVSF1xwAQmhAQMGwDFGpCMV RNAR5wHWO1cIKFb6BUFK3SCoqA1qMGCKo5LAgVWEwawPRK560YjdHTp0KDcE2Wtce9010ESCQYeV neuD5CzORK4uMkoyLSMLoxzgZD4WLEMSq7AZhldkFjmqkr8aiuY28ZMewge/pBwe2m3CKC8RWHX2 t3JZS0vPIUgH7J6WnLWyhS2EIzJHVUiy65oahF0q9Pzrr78+ctQIaFZSL3Tnco1MO4KggEtGJLBo 1apVHVDuU1W85VlQCGkq4IPaXKgn8dyPf/wj0lxf+9rXFixY4Lo2yoETAUwhKgprH7wH8LLPCugI r0gHL2NQZ4YQpwCKoxYrTJRPummQhmL1EyLIxUT6RPijThiZd3LZfOaFmS9IPSgkMBSbisPxypzl qyaemvbnyezJpkyRGAoclATIbvrrs+coEvZ8kjkSGSTyp5B35sgNOhUvoBYwzrXBCRkqja7FyObo jhZEuDnpZbI6cYJt+VUZPyacMq7VMo+wtJT0hk6zLn+3we+M0QXOxyA9E6Yz3PHhPrAvpq1Pe27p uU11Gr8q9gWnORU9Y2Qb/vTUNKYCzSwMRb9a4kqtoQ1Fpx3yHn74YSZOC3OnImdfmLLspQUwM5PL qr4FPu9WSMUBLlsBRtITJKgaHZcsWVIUENiKdcC6VDpXCKhXxIEX7KooRneKHWAgi/pBhVcaqaj0 Kq9cKKWv4gsV1UXhp67WxMKF71x77bX33nsv64wWNRajY2Z4Ll22uBDWFNeZUTAmGBVuNHEY4Uyf Ph0TxRAUqKWLIolXvJXUOTfE78swh3mb5KbEifUVrpZbtLatPcLZiGlo3L3z0wCQcw+jXKAqEQQ0 /LynDYklRDLhoTbiey9uzgX5wMh/b2AF6gU2UJ0xdizTIUH0yKMPITblcAtkSFkq7ilKIAyr+fLL L1OhBZo78pYWlIaZlpWVKQ5Qb2rqy3HXxInnKp1hefKJWKu+vp4TVPDwqjhGd8hg4qpRYTjwZ+cK ATqEBBaQgp2ReFWqvT/UCpKJUZSYgYQ40lnnn38+3VeuXPntb38bdQaSVwUDMK9qMeFxbrjhBp6E LFTozoiAAXzSSSdhv9va2jgZgi7Q7iajwAhV/+Mf/7h+/XpFJATTUbWDX2kDjXFdpjfldSd5Bs59 S3HeoCoRpOZnWuZudgy597FShpXkl1qMormm3657EW54x3M4F7Q4yNjhFfObp7WWZoQ5riY1ut7k X0KxdXm7+6ofXYEI+f0XCq2SKAxNgX5FCU8YC2Gs5ksuuQRWKBECo6iFeOrAEDBSITiAP0wWjaGR Opt/kDEzZSFAiAkh+lZgii3UAaY7pTjugVc6Vwj6KxIVHYwE9uJJ/0exA6wgC2RIM8iraoQdP/3p T1ELxDxjxgw2TsyKT0W6qSCwefPmEXXiDpn8N77xjeHDhwMDy8DD6JddfqkadNKkSZyzw0elQ5JO jJmuT5/2NIkvIMFGo2Kl/Fq4t6gqUFJIhudZ0MQxMn0uxOnVkVqRIvl8w/w2W4trXAtnZ2jaJrf2 SWNqZa2s/lC0xeOeKVa16OPf/uDe5RYb1Gor9+zpQ9JSOm7UNsmo925suO2Xt8ABjATTJH2kVEF5 BOpKYBg/VgjKnUwmIQ/aitGlMv5AsirgCYWvigMkHsDMmTNHS0xRqQVfH3roEbXGxowZoyDlNAtF sQKYLpXOg0pIBztooZiiRvoPFoKxAWbmas6KBaoR+thM/uEPf5g4cSJHtzNnzoQjZ53xBU4CWU/c tdm+fTsR8ksvvcRAIGFjfeutkq10Z0Rkz+ic/990002/+tWvwEAkNWHChDPOOAO0lqHv2rXr+eef nz79GeDJlCAM7MSrr74KFxiawFDhgWv5HFfsZEzAuiIlxWm1YYnBCXHBUX3ueD9YsMP77qv5e08o j2ttwi31YrYrfC7iJ0rsR1e2L0iVv7Vm+5KcneegO5I5Lt76wGePxY9XcIOKe9r86xci5wiL+J/h SAxs3LjxsssuO/LII/GYXGOJx6NsFEnGYAIlChAAAAVTSURBVOEIdJgR5LGBvPGGn3FzQJkTmMZk FRsLlEu7AgfUqrjooouee+45ADAtpB9YYNTh5C9/+UvMCTD8lAN4JSzFPYWQxi6VzhVCoYAsRbp6 7Vj/6BiQAllQyScqBcrkvHilkWMOfo5y+eWXv/LKK+l0moPyp556Chg1cyag6tdffz3cpBOqRS/w KAZRv+yyS3G4t9xyGzyiO9l7zyWSlDt+YIAllwe7b7vttkcffXT58uUkv/v376/oBDmaCjYiIwwI /oZbcRyOy0gxdL95XNWLG9cvyUeeXNay3nZ+89m+gzAhQdzQU65R2uol567ZOXOnXuZF8mFORLOX 9u95w6h+/WRyluu8CEHnskZKxEsLDeecczYJgyuuuBKdWLNmjTyz1kwrIm/LoSvwEBOIKhMMciL1 9lvz8S/vvfce18+OPvpo6JT6WwADHokqDkDliSeO/s53vnP//fdj/Likr75is7EiTG3SpNvZcypW KySqrqbfpafx85///KMdoAm+K2nt2tnKBqmpqen000+vrun1UWBaYDjbotbWNgIFfk0wbty42toa GuE/1g9xghDqWdkkVZjSiuUr6KVmyygcD+IvuHrzudPHKu8IPGLmE2AwUVks/Mi4cf8XNiHsXNYG BpcC/p49e1555VWslWQyUV1djRgaGhsHDRrIMipg07Zu3drenmps6P25M89orCzXNQs94rYFO3Zc fqVp/L9B5c+927Itkl61Pfvf7+Y/2JXbks/mncr+5cHzm/3tuWxbKje8p3/+sNgjo/pdfESyQidN bXGFJ9R0Iyu8aIr/5bYSTdJ1eHXBBRfyZPW37NjFNDnx5yPC4wjq0ksvve+++1ghtCQTpdBG48BB A2hhmkwZ/qxcuapfv37jxn2+srKC6cNGnDasQ2nwreR1AIOfqMWnP/1pDOeECePxyHBMLVq+ggpK GKKrRS6v/9BHSoJ/uw0QKZo9z046YO0hQwHJzwVxKpnKRiVR1U+2Cu2tN+cxB3jEWX5T3yMKfdhu Se9IHg8VKppQ4JkandB9Jgy9YFy+fBXuht/jcnI4dNgwx3YjBYmAR6Y6zN1JXAJ2g5Si8oCB4Jps nF/8myb/xFGC7aKmcz4dCdpzejQtoje8tPOJDU6O5ELhYjwD+RdW376g9frP1PLb0x6oppvxrYjh WtI0FWwrRqGEfy/RjwsjF9oJjWs7hZvJShgYkVwuLz2CJhMJ7JWwDUU5KT5wuMOJDyfjkn0dEkpy mjJ+lBlCtEFJmpADVqxfj6Y1ww10rrGxnikrRhcrB+cspAik7fyPCqGADv+nawv5D6OJhbvEk0s3 /n1j61onbgfR9EUNz76z4LyjjuUKGLcn+OeMyHx4Fkntg1l8/yvY2K0QUkxcyDd8j9wkq9LTdDYY LP1cVvQoEe+3tw8r5dcCwjEEG1BpgKTNPGxLt0JI0SJoLjTJLSRFuji5C6HVIVuFU+DcVl75IeiX PwfZ6xcl9OFWuhWiIFGSTsQE3PLDAMgGhC/P30h4owBct9f5ISC5GD90I/L34YexUhRCo8NNy7s8 H8SOlH35yxt56M1FS3n6QZPJfX+ZmcZ6GJyERQrWoVshuszg/20dCCBCg1/ykZPk4r+6HUzqmjxF wYkU3AkHJwT+aExhcoet3+i2EEp58QicZGEhCCzxF/JaM6cZAdu9wiF44MrNKt5EXnw+bHcYkhWH 9eSUrA/kaXgut3zIALF/4MxbbsXlDxC40kFv9IBL8VRwJDI5LBsO29IdVB62oj24iXVbiIPj22Hb q1shDlvRHtzEuhXi4Ph22PbqVojDVrQHN7H/AWFrGkuL8nFSAAAAAElFTkSuQmCC --Apple-Mail-79-616227633 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 Chair in Functional Neuroimaging Neurodis Foundation http://www.fondation-neurodis.org/ Postal Address: CERMEP – Imagerie du Vivant Hôpital Neurologique Pierre Wertheimer 59 Boulevard Pinel, 69003 Lyon, France Telephone +33-(0)4-72 68 86 34 Fax +33-(0)4-72 68 86 10 Email [log in to unmask];[log in to unmask] --------------------------------- Other affiliations: Visiting Reader; Honorary Consultant Neurologist Division of Neuroscience and Mental Health, Faculty of Medicine Imperial College London, UK --------------------------------- Honorary Reader in Neurology; Honorary Consultant Neurologist Department of Clinical and Experimental Epilepsy National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK On 28 Feb 2011, at 10:11, sarika cherodath wrote: > > Hi SPMers, > > I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change?? > > Thanks in advance, > -- > Sarika Cherodath > Graduate Student > National Brain Research Centre > Manesar, Gurgaon -122050 > India > --Apple-Mail-79-616227633-- ========================================================================Date: Mon, 28 Feb 2011 15:14:22 -0500 Reply-To: Jeffrey West <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jeffrey West <[log in to unmask]> Subject: question regarding flexible factorial model Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=__Part4569690E.0__=" Message-ID: <[log in to unmask]> This is a MIME message. If you are reading this text, you may want to consider changing to a mail reader or gateway that understands how to properly handle MIME multipart messages. --=__Part4569690E.0__Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: quoted-printable Hello. I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions. I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal) For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. The model did not run and I got the following error message: Running job #2 ----------------------------------------------------------------------- Running 'Factorial design specification' Failed 'Factorial design specification' Index exceeds matrix dimensions. In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482. The following modules did not run: Failed: Factorial design specification I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. Thank you for any suggestions. Jef Jeffrey West, M.A. Research Analyst Maryland Psychiatric Research Center Baltimore, Maryland 21228-0247 Phone: 410-402-6018 email: [log in to unmask] --=__Part4569690E.0__Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Content-Description: HTML

Hello.

I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.

I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)

For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3.

The model did not run and I got the following error message:

Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed 'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.

The following modules did not run:
Failed: Factorial design specification

I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model.

Thank you for any suggestions.

Jef

Jeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]

--=__Part4569690E.0__=-- ========================================================================Date: Tue, 1 Mar 2011 09:17:07 +1300 Reply-To: Ehsan Negahbani <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ehsan Negahbani <[log in to unmask]> Subject: Unsubscribe me please MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0022150475f729946e049d5d5d2c Content-Type: text/plain; charset=ISO-8859-1 -- Ehsan --0022150475f729946e049d5d5d2c Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

--
Ehsan
--0022150475f729946e049d5d5d2c-- ========================================================================Date: Mon, 28 Feb 2011 22:28:55 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: spm_eeg_convert2scalp Comments: To: Erick Britis Ortiz <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: [log in to unmask]> Hi Erick, SPM uses the same algorithm as Fieldtrip to generate a layout from the sensor array so it should be quite similar, just the convention for representing it is slightly different. I don't know what exactly the problem is so it's hard for me to advise you. In principle you can use the GUI functionality in Prepare or your own script to generate any layout you like and then load it as a channel template file via Prepare (this is a mat-file, there is an example for CTF in EEGTemplates). Best, Vladimir On Mon, Feb 28, 2011 at 10:22 PM, Erick Britis Ortiz <[log in to unmask]> wrote: > > Hi Vladimir, > > I have been using spm_eeg_convert2images to transform my MEG frequency > analysis results to image volumes and run statistics. The objective > being to determine lateralization in language (in children) by frequency. > > I assumed that spm_eeg_convert2scalp would use a standard 2D layout (à > la Fieldtrip), and when I checked, this is not true. This makes > lateralization studies, for instance, much more difficult, and I could > think of others. What is the advantage? > > My guess was that setting D.channels.X_plot2D and Y_plot2D to empty > would generate a default arrangement, but this also did not work. Could > you shed some light at the issue? > > Best, > Erick > > ========================================================================Date: Mon, 28 Feb 2011 23:08:58 +0000 Reply-To: Vladimir Litvak <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: spm_eeg_convert2scalp Comments: To: Erick Britis Ortiz <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> These 2D locations are created by projecting 3D locations represented in head coordinates so the differences you find are due to different head positions of your subjects in the helmet. This is by design, but you also can load a standard channel template file for all your subjects. Vladimir On Mon, Feb 28, 2011 at 10:55 PM, Erick Britis Ortiz <[log in to unmask]> wrote: > > Thank you as always for the prompt response! > > For you to know at least what I am talking about, a picture is attached > with the projected MEG sensors (colored by region) in my 22 subjects. > Left is greenish, right is reddish. > > The problem is that I see no reason for the MEG sensors not to have > simply a standard position. In the Fieldtrip plots, they have (for > example, in CTF151.lay). I will follow your advice, no matter. But this > is a bit dangerous in the general case, don't you think? As I said, I > just do not know if it is by design, else I could "fix" it. > > Best, > Erick > > On 2011-02-28 23:28, Vladimir Litvak wrote: >> Hi Erick, >> >> SPM uses the same algorithm as Fieldtrip to generate a layout from the >> sensor array so it should be quite similar, just the convention for >> representing it is slightly different. I don't know what exactly the >> problem is so it's hard for me to advise you. In principle you can use >> the GUI functionality in Prepare or your own script to generate any >> layout you like and then load it as a channel template file via >> Prepare (this is a mat-file, there is an example for CTF in >> EEGTemplates). >> >> Best, >> >> Vladimir >> >> >> >> On Mon, Feb 28, 2011 at 10:22 PM, Erick Britis Ortiz >> <[log in to unmask]> wrote: >>> >>> Hi Vladimir, >>> >>> I have been using spm_eeg_convert2images to transform my MEG frequency >>> analysis results to image volumes and run statistics. The objective >>> being to determine lateralization in language (in children) by frequency. >>> >>> I assumed that spm_eeg_convert2scalp would use a standard 2D layout (à >>> la Fieldtrip), and when I checked, this is not true. This makes >>> lateralization studies, for instance, much more difficult, and I could >>> think of others. What is the advantage? >>> >>> My guess was that setting D.channels.X_plot2D and Y_plot2D to empty >>> would generate a default arrangement, but this also did not work. Could >>> you shed some light at the issue? >>> >>> Best, >>> Erick >>> >>> >> > > ========================================================================Date: Mon, 28 Feb 2011 18:34:38 -0500 Reply-To: Pieter van de Vijver <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pieter van de Vijver <[log in to unmask]> Subject: Re: question regarding flexible factorial model Comments: To: Jeffrey West <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary¼aec51a894a8f7f93049d601f97 Message-ID: <[log in to unmask]> --bcaec51a894a8f7f93049d601f97 Content-Type: text/plain; charset=ISO-8859-1 Hi Jeff, You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled). So for a subject in group 2 with scans for all four conditions you would need: [2 1 2 2 2 3 2 4] Make sure your scans are entered in the right order! Also, main effect for subjects doesn't need to be specified, this is done automatically (this is because you chose 'Subjects' at the 'Specify Subjects or all Scans & Factors'). Good luck, Pieter On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]>wrote: > Hello. > > I have question relating to setting up a flexible factorial model with 2 > groups, each group has 4 conditions. > > I have 3 factors: 1 = subject (independent, variance equal), 2 = group > (independent, variance not equal), 3 = condition (not independent, variance > equal) > > For the subject level, I entered in 20 subjects, for each subject, I > entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 > 3 4. Once all 20 subjects were completed, I entered 3 main effects, and > interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 > 3. > > The model did not run and I got the following error message: > > *Running job #2 > ----------------------------------------------------------------------- > Running 'Factorial design specification' > Failed 'Factorial design specification' > Index exceeds matrix dimensions. > In file "C:\Documents and > Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), > function "spm_run_factorial_design" at line 482.* > > *The following modules did not run: > Failed: Factorial design specification* > ** > I feel like the error is in the subject level: either the scan or the > conditions. I do not feel like the conditions step is correct. Can anyone > please explain what I did wrong setting up my model. > > Thank you for any suggestions. > > Jef > > > Jeffrey West, M.A. > Research Analyst > Maryland Psychiatric Research Center > Baltimore, Maryland 21228-0247 > Phone: 410-402-6018 > email: [log in to unmask] > > > --bcaec51a894a8f7f93049d601f97 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi Jeff,

You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled).

So for a subject in group 2 with scans for all four conditions you would need:

[2 1
2 2
2 3

2 4]

Make sure your scans are entered in the right order!

Also, main effect for subjects doesn't need to be specified, this is done automatically (this is because you chose 'Subjects' at the 'Specify Subjects or all Scans & Factors').

Good luck,

Pieter

On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]> wrote:

Hello.

I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.

I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)

For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3.

The model did not run and I got the following error message:

Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed 'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.

The following modules did not run:
Failed: Factorial design specification

I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model.

Thank you for any suggestions.

Jef

Jeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]

--bcaec51a894a8f7f93049d601f97-- ========================================================================Date: Mon, 28 Feb 2011 18:38:06 -0500 Reply-To: Pieter van de Vijver <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Pieter van de Vijver <[log in to unmask]> Subject: Re: question regarding flexible factorial model Comments: To: Jeffrey West <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundarye6ba6e8996ee354c049d602b47 Message-ID: <[log in to unmask]> --90e6ba6e8996ee354c049d602b47 Content-Type: text/plain; charset=ISO-8859-1 Sorry, small correction. Main effect for subjects DOES need to be specified. Pieter On Mon, Feb 28, 2011 at 6:34 PM, Pieter van de Vijver <[log in to unmask]>wrote: > Hi Jeff, > > You should enter conditions for subjects in a nscans x factor matrix. The > rows are your scans (4 in your case) and the columns indicate the factors > (2, one for group and one for condition, subject is automatically > modelled). > So for a subject in group 2 with scans for all four conditions you would > need: > [2 1 > 2 2 > 2 3 > 2 4] > Make sure your scans are entered in the right order! > > Also, main effect for subjects doesn't need to be specified, this is done > automatically (this is because you chose 'Subjects' at the 'Specify Subjects > or all Scans & Factors'). > > Good luck, > > Pieter > > > On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]>wrote: > >> Hello. >> >> I have question relating to setting up a flexible factorial model with 2 >> groups, each group has 4 conditions. >> >> I have 3 factors: 1 = subject (independent, variance equal), 2 = group >> (independent, variance not equal), 3 = condition (not independent, variance >> equal) >> >> For the subject level, I entered in 20 subjects, for each subject, I >> entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 >> 3 4. Once all 20 subjects were completed, I entered 3 main effects, and >> interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 >> 3. >> >> The model did not run and I got the following error message: >> >> *Running job #2 >> ----------------------------------------------------------------------- >> Running 'Factorial design specification' >> Failed 'Factorial design specification' >> Index exceeds matrix dimensions. >> In file "C:\Documents and >> Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), >> function "spm_run_factorial_design" at line 482.* >> >> *The following modules did not run: >> Failed: Factorial design specification* >> ** >> I feel like the error is in the subject level: either the scan or the >> conditions. I do not feel like the conditions step is correct. Can anyone >> please explain what I did wrong setting up my model. >> >> Thank you for any suggestions. >> >> Jef >> >> >> Jeffrey West, M.A. >> Research Analyst >> Maryland Psychiatric Research Center >> Baltimore, Maryland 21228-0247 >> Phone: <410-402-6018>410-402-6018 >> email: [log in to unmask] >> >> >> > > --90e6ba6e8996ee354c049d602b47 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Sorry, small correction. Main effect for subjects DOES need to be specified.

Pieter

On Mon, Feb 28, 2011 at 6:34 PM, Pieter van de Vijver <[log in to unmask]> wrote:

Hi Jeff,

You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled).

So for a subject in group 2 with scans for all four conditions you would need:
[2 1
2 2
2 3
2 4]
Make sure your scans are entered in the right order!

Also, main effect for subjects doesn't need to be specified, this is done automatically (this is because you chose 'Subjects' at the 'Specify Subjects or all Scans & Factors').

Good luck,

Pieter

On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]> wrote:

Hello.

I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.

I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)

For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3.

The model did not run and I got the following error message:

Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed 'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.

The following modules did not run:
Failed: Factorial design specification

I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model.

Thank you for any suggestions.

Jef

Jeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]

--90e6ba6e8996ee354c049d602b47-- ========================================================================Date: Mon, 28 Feb 2011 18:44:34 -0500 Reply-To: John Fredy <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: John Fredy <[log in to unmask]> Subject: sparse data analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016364991e7155337049d6043e2 Content-Type: text/plain; charset=ISO-8859-1 Hello all, I have recorded 80 volumes in a block design experiment with a TR of 6 seconds where 3 seconds are silence, the machine is silence, and the other 3 seconds are used for the adquisition. In the 3 seconds of machine silence I present a stimulus, 0.5 seconds later of the begins of the silence, with an aproximated duration of 1.5 seconds What is the best strategy for processing this data? Regards John Ochoa Universidad de Antioquia --0016364991e7155337049d6043e2 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello all, I have recorded 80 volumes in a block design experiment with a TR of 6 seconds where 3 seconds are silence, the machine is silence, and the other 3 seconds are used for the adquisition. In the 3 seconds of machine silence I present a stimulus, 0.5 seconds later of the begins of the silence, with an aproximated duration of 1.5 seconds

What is the best strategy for processing this data?

Regards

John Ochoa

Universidad de Antioquia

--0016364991e7155337049d6043e2--